the productivity effects of joining multinational supply

The Productivity Effects of Joining MultinationalSupply Chains: Evidence from Firm-to-Firm

Linkages

Alonso Alfaro-Urena1, Isabela Manelici2 and Jose P. Vasquez∗2

1Banco Central de Costa Rica2UC Berkeley

First version: May, 2017. This version: March, 2018

Abstract

Can local firms boost their productivity by supplying to multinational firms (MNCs)? The an-swer to this question has, so far, proven elusive, as it requires data on actual firm-to-firm linkages,an empirical strategy that delivers causal estimates, and evidence on productivity (as opposed toperformance) gains. We make direct progress on the first two fronts by using an administrativedataset that records all firm-to-firm transactions within Costa Rica and two complementary eventstudy designs, where we define the event as the first time a local firm supplies to an MNC in thecountry. The baseline event study uses all such events in the economy and exploits the plausibleexogeneity of the timing of the event to the local firm. We address the concern of selection intosupplying to MNCs based on time-varying unobservables by using a Government-led programthat allows us to compare firms winning a deal with an MNC to their contenders to the samedeal. We show that local firms expand and adjust their production process after joining theirfirst MNC supply chain. More importantly, we provide evidence that they experience sizableand long-lasting productivity gains. Finally, to aggregate these firm-level productivity gains, oneneeds a credible estimate of how frequent supplying relationships actually are. We show thatlocal firms sell much less to MNCs than what was previously predicted through Input-Outputtables. This insight informs future research on the aggregate implications of MNC supply chains.

∗We are grateful to Andres Rodrıguez-Clare and Enrico Moretti for their continuous guidance and en-couragement. We also thank Alan Auerbach, David Card, Benjamin Faber, Thibault Fally, Fred Finan, CecileGaubert, Patrick Kline, Emmanuel Saez, Reed Walker, Danny Yagan and several seminar participants for valu-able comments and discussions. Special thanks to Dave Donaldson for an insightful discussion at the NBER“Firms, Networks, and Trade Conference”. We gratefully acknowledge the hospitality and support of theBanco Central de Costa Rica (BCCR) and the Economic Division of BCCR, in particular. We also thank Pro-comer, COMEX, and CINDE for sharing data and institutional knowledge. Weiss Fund for Research in Devel-opment Economics, “Private Enterprise Development in Low-Income Countries” (PEDL) Research Initiative,and the Clausen Center have jointly supported our visiting periods at BCCR, for which we are grateful. Theviews expressed herein are those of the authors and do not necessarily represent either the views of BCCR, orthose of our funding institutions. All results have been reviewed by BCCR to ensure no confidential informa-tion is disclosed.

1 Introduction

A long-standing debate has focused on the extent to which attracting foreign capital toa country can not only push the productivity frontier of receiving sectors, but also induceproductivity catch-up throughout the economy. As multinational corporations (MNCs) leadin the global productivity race and drive most of today’s foreign direct investment (FDI),countries court MNCs hoping for a short-cut to productivity upgrading.

While relationships between MNCs and local suppliers are not the only channel forproductivity gains, they are usually seen as the main candidate.1 Whether countries actuallyexperience widespread productivity gains through this channel, hinges on answers to twoquestions. First, do local firms see their productivity increase when they start supplyingto MNCs? Second, are these supplying opportunities frequent enough to justify hopes ofwidespread productivity upgrades?

Continued policy efforts to attract MNCs and connect them to local suppliers rely onthe belief that both of these answers are likely to be positive.2 Yet, credible answers to bothquestions have, so far, proven elusive. Three challenges have stood in the way. First, untilnow, firm-to-firm relationships have not been observable to researchers. Hence, one hadto rely on sector-level Input-Output (I-O) tables to proxy for the likelihood of supplying toMNCs. Little was known, however, about the within-country linkage patterns of MNCs andthe extent to which I-O based proxies were able to predict such linkages. A second challengecame from the lack of an empirical strategy that delivered causal estimates of the gains fromsupplying to MNCs. Finally, whenever productivity is the outcome of interest, one needs toaddress well-known difficulties in measuring it.

This article leverages an administrative dataset from Costa Rica that allows us to di-rectly overcome the first two challenges described above. Since 2008, this dataset tracks allannual firm-to-firm transactions within the country. As this dataset makes supply chainsfully visible, we can isolate the role of linkages to MNCs as potential drivers of productiv-ity gains. Our main contribution is to estimate the causal effects of becoming a supplier toan MNC – i.e., to answer the first question raised above. We achieve this by means of aneconomy-wide event study analysis, where we define the event as the first time a local firmsupplies to any MNC affiliate. In the process, we also shed light on the extent to whichMNCs actually source from local firms and how that compares to what is implied by I-Otables. These insights are key inputs in answering the second question above.

1In a review of both the macro and micro literatures on gains from FDI, Alfaro [2017] concludes that “FDIcan play an important role in economic growth, most likely via suppliers.” While we focus on the role ofsupplying relationships as conduits of productivity gains, productivity gains may also arise through, say,market reallocation (see Alfaro and Chen [Forthcoming]).

2A World Bank [2017] report, co-published with The World Trade Organization, the Organization for EconomicCo-operation and Development, and co-authors, discusses at length policy opportunities for both developedand developing countries to “deepen their involvement in global value chains (GVC) and move up the valuechain.” According to another World Bank report [Taglioni and Winkler, 2016], “integrating a country’s domes-tic suppliers into GVCs increases the possibility for GVC spillovers through [...] supplying to a multinationalin the country.” Adequate supplier programs are seen as a key policy tool to foster linkages to MNCs.

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For the identification of the causal effects of joining MNC supply chains, we exploit theplausible exogeneity of the timing of the event to both firms involved. We take advantageof the richness of our data and construct two samples of domestic firms: (i) the full samplethat includes both firms that eventually supply to an MNC and firms never observed as sup-plying to MNCs, and (ii) the restricted sample that only contains the firms that eventuallysupply to an MNC. Using both samples allows to confirm that our findings are driven bythe staggered timing of the event, as opposed to the contrast with “never-suppliers.” Af-ter reporting our baseline results, we defend their causal nature against threats of reversecausality and third-cause fallacies.

We first find that firms experience sizable expansions after their first-time supplying toan MNC. Four year after this event, firms’ sales and employment are 25 and 21 percent largerthan these values one year before the event. Firms’ capital and material inputs increaseby around 16 percent as well. More surprisingly, we show that firms also change featuresof their production process that are not mechanically related to their scale. In particular,we find evidence of significant changes in import behavior: firms boost the value of theirimports by 44 percent and increase the share of imports in their input costs by 3 percent. Wealso find a 2 percent increase in the average wage per worker, which is consistent with eithercompositional effects in firms’ workforce or rent-sharing effects.

Despite these changes in factors of production and sourcing being interesting in them-selves, we treat firm-level productivity as the main outcome of study. As common in admin-istrative datasets, we do not observe disaggregated data on firm-level quantities, prices, andquality. This makes productivity estimation subject to well-known challenges: (i) endogene-ity in input choice and (ii) variation in prices and/or markups. To address these concerns,our approach to measuring productivity effects proceeds in steps.

First, we show significant improvements in raw measures of firm performance, suchas profits and value added per worker. We then focus on production function estimationusing both Cobb-Douglas and translog specifications, and a Cobb-Douglas productivity in-dex. We show, using ordinary least squares (OLS) and classic control function approaches[Levinsohn and Petrin, 2003, Ackerberg, Caves, and Frazer, 2015], that firms in our baselinesample experience a robust 7 to 11 percent increase in productivity four years after the event.

A remaining concern is whether our estimated productivity gains could simply reflectincreases in prices and/or markups of supplying firms after the event. Anecdotal evidencecollected during interviews with suppliers to MNCs in Costa Rica and similar qualitativeevidence from other countries3 provide little support to this scenario. We are currently im-plementing the methodology of De Loecker and Warzynski [2012] to quantitatively assessthis possibility.

In addition, we also exploit the richness of our production network data to provideindirect evidence on productivity changes. We show that domestic suppliers increase thenumber of domestic (non-MNC) clients and their sales to them, after the event. We also find

3For example, see Javorcik [2008] and Javorcik, Keller, and Tybout [2008].

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evidence of gains in productivity to domestic clients of suppliers to MNCs. These findingsare inconsistent with increases in prices being the main driver of our results. Instead, thesefindings suggest changes in technical efficiency, quality and/or product scope, which aretreated as isomorphic in standard trade models [Melitz and Redding, 2014].

We then present four pieces of evidence to support the causal nature of our findingsand their interpretation. A first threat to causality is that MNCs are not the cause of theestimated gains in firm performance, but that MNCs select suppliers based on pre-trends inunobservables. Such trends should be able to signal the trend breaks in firm performancethat we now attribute to the first supplying deal with an MNC.4

We provide evidence against the first threat by making use of a government-led match-ing program called Encadenamientos Productivos (Productive Linkages henceforth). This pro-gram identifies input needs of MNCs, evaluates candidate firms, and proposes shortlistsof the most suitable suppliers for each need. Since all firms in a shortlist are comparablecontenders for the same deal, the program generates quasi-experimental variation in oppor-tunities to sell to MNCs. We exploit this empirical setting using a runners-up design, a laGreenstone, Hornbeck, and Moretti [2010].

While the Productive Linkages program was not designed with an academic purpose inmind, it lends itself to the implementation of a research design that is novel for this question.However, the small number of deals to which this research design can be applied and con-cerns of external validity become its main disadvantages with respect to our baseline eventstudies. Yet, we leverage on the features of this program to strengthen the causal argumentof our main empirical strategy and we view its analysis as complementary evidence.

Reassuringly, we show that (i) trends in outcomes of winning and losing firms beforethe event were not significantly different from each other, which lends support to the validityof the runners-up design, (ii) consistent with our baseline results, winning firms expand insize and experience productivity gains after the event, relative to losing firms, and (iii) thesefindings are not driven by a worsening in the outcomes of losing firms, but by an upwardbreak in the outcomes of winning firms after the event.

Another threat to causal inference is that a third event is the true catalyst of both a firstdeal with an MNC and upgrades in firms performance. A plausible confounding event isa change in firm management. We are in the process of combining our existing datasets toemployer-employee Social Security data to rule out this scenario.

We also investigate two alternative interpretations to our findings. We first compareour results to those from an event study where Government contracts act as demand shocks.The lack (or the weakness and short-term nature) of gains from starting to sell to the Govern-ment implies that there are additional features of matches to MNCs – plausibly knowledge

4We refer to trends unobservable to the researcher in administrative datasets, but observable to the MNC uponchoosing a supplier. Time-invariant differences in unobservables are taken care of by firm fixed effects, whichwe use in all specifications. The danger of MNCs choosing firms embarked on pre-trends in observables is ruledout by our event-study findings: in both samples, we do not find any evidence of differential preexisting trendsbetween matched and unmatched firms in the same sector, province and age-cohort. A firm in the process ofquality management certification would be embarked in such a time-varying unobservable trend.

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spillovers – that drive productivity gains. Second, we ask whether our results can reflectimprovements in tax compliance and reporting, as opposed to actual firm expansion andproductivity gains. To the extent that MNCs are under wider scrutiny from tax authorities,one can speculate that the first match to an MNC is a form of “sunshine as disinfectant.” Wefind no evidence of changes in tax compliance and reporting behaviors.

In sum, our results do not simply reflect firms’ response to demand shocks, price ef-fects, or improvements in tax compliance. Instead, they are suggestive of knowledge trans-fers by MNCs, that affect suppliers’ production technology and organization, input choices,and relationships with their domestic clients.

While the main goal of this paper is to estimate firm-level productivity gains fromjoining MNC supply chains, the aggregate implications of these firm-level gains depend onhow widespread such MNC supply chains are. While a complete response on these aggre-gate implications requires a general equilibrium model that accounts for, e.g., factor marketspillovers, we provide a set of novel aggregate moments that inform any such aggregation.

A direct measure of the degree to which MNCs are integrated in the production net-work of a country could not be achieved prior to the availability of firm-to-firm transactiondata. I-O tables would serve, instead, as the basis for an indirect measure, albeit research onfirm heterogeneity has shown that MNC source unlike the “average firm” in their sector.

We exploit our firm-to-firm transaction dataset to uncover striking two-sided hetero-geneities – both in the propensity of domestic firms to supply to MNCs and in that of MNCsto buy from domestic firms – that matter greatly when trying to estimate the aggregate inte-gration of MNCs. We document patterns of MNC integration based both on firm counts (i.e.,each firm contributing with its firm-level measure of linkage to MNCs) and firm size (i.e.,total number of local jobs “created” in upstream sectors by sales to downstream MNCs). Wecompute these measures using both our firm-to-firm transaction data and I-O tables.

MNCs are found to be substantially less integrated in Costa Rica’s production networkthan what I-O tables predict. Even though the precise cost of relying on I-O tables mightbe country-specific, our evidence suggests that estimates of measures of MNC integrationrelying on I-O tables may paint too optimistic a picture of their actual integration. Whilethe focus of previous literature has been on the measurement of productivity gains, theseaggregate moments are equally important ingredients towards any credible aggregation ofthe benefits of MNC supply chains.

The rest of this article is organized as follows: The following section provides a briefoverview of the literature to which we contribute. Section 3 introduces the data and the con-text of MNC presence in Costa Rica. Section 4 describes prior challenges to measuring thecausal effects of becoming an MNC supplier and our empirical approach to these challenges.Section 5 reports and discusses our main results. Section 6 addresses potential threats to ei-ther the causal nature of our findings or their interpretation. Section 7 makes the point thataggregating our micro-level estimates of productivity gains requires a credible estimate ofthe overall (upstream) integration of MNCs. Section 8 concludes.

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2 Related Literature

Our findings relate to a number of papers that span the trade and development lit-eratures. First, we contribute to a large literature that seeks to measure the productivitygains from FDI. Aitken and Harrison [1999], Javorcik [2004], and Blalock and Gertler [2009]are classic examples in this literature. Evidence from this literature is mixed, in part due toidentification concerns. A common insight of these papers is that linkages to local suppliersare likely conduits for productivity spillovers.5 We follow this insight and provide causalevidence on the impact of supplying an MNC for local firms.

By making linkages to foreign firms observable, we isolate their role in driving pro-ductivity gains, which could not be achieved beforehand on the basis of I-O proxies alone.In doing so, we relate to two papers that provide a formal theory of linkages and their im-portance: Rodrıguez-Clare [1996] and Carluccio and Fally [2013].

While neither of these papers proposes a justification for productivity gains to sup-pliers – gains that are our main empirical finding – they both propose a channel for im-provements in the domestic business of supplier sectors (through firm entry, hence expan-sion in input varieties). Under the assumption that the efficiency of domestic producersincreases with the range of available intermediate varieties, producers that adapt to thenewly-available varieties (demanded by MNCs) experience efficiency gains. If the mod-els in Rodrıguez-Clare [1996] and Carluccio and Fally [2013] were adjusted to allow for awithin-firm expansion in varieties (as opposed to an expansion through firm entry) and for anisomorphism between productivity and changes in product scope, then our findings on sup-pliers and those on their domestic clients would provide support to these adapted models.

A third departure from the FDI literature comes from our explicit focus on MNC pro-duction. Due to data limitations, MNCs’ engagement in international production is usuallymeasured by FDI flows and stocks, although FDI may no longer constitute their primaryform of internalization [UNCTAD, 2011, Giorgioni, 2017]. By studying MNCs with a sub-stantial presence in the country, we circumvent issues related to FDI statistics, such as therising use of shell companies.6

Our paper also relates to the vast literature linking industrial policy to economic devel-opment and growth. Since the seminal work of Rosenstein-Rodan [1943], Hirschman [1958]and Baldwin [1969], the merits and perils of industrial policy have been debated at length.This debate has centered on whether the government can address market failures by tar-geting subsidies or protection to particular firms or sectors. The existence of externalities isusually presented as the main theoretical justification for deviating from policy neutrality

5According to extensive meta-analyses [Havranek and Irsova, 2011, 2012, Irsova and Havranek, 2013] estimatedhorizontal and forward spillovers are negligible, whereas the average backward spillover is positive and sig-nificant. These meta-analyses found that a 10-percentage-point increase in foreign presence is on averageassociated with a 3.1 percent increase in the productivity of domestic firms in upstream sectors.

6Shell companies, or “special purpose entities (SPEs) are companies that do not have substantial economicactivity in a country but are used by companies as devices to raise capital or to hold assets and liabilities.[...]SPEs can lead to the inflation of FDI statistics and the obscuring of the ultimate source and destination of FDI”[OECD, 2017b].

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[Harrison and Rodrıguez-Clare, 2010]. By finding productivity spillovers from the inter-action between MNCs and domestic firms, we contribute to recent empirical evidence onexternalities that may affect the development process [Lane, 2016, Rotemberg, 2017, Liu,2017, Juhasz, 2018]. We also show that the strength of these gains depends indeed on thesector and skill-intensity of both buyers and sellers.

We also contribute to a nascent literature on domestic production networks. The the-ory on the aggregate implications of the network structure of an economy has quickly grownin recent years [Gabaix, 2011, Acemoglu, Carvalho, Ozdaglar, and Tahbaz-Salehi, 2012, Car-valho, 2014, Magerman, De Bruyne, Dhyne, and Van Hove, 2016, Oberfield, Forthcoming].Most, if not all, of the empirical evidence thus far characterizes the production networksof Belgium (e.g., Dhyne, Kikkawa, Mogstad, and Tintelnot [2017], Bernard, Dhyne, Mager-man, Manova, and Moxnes [2017b]) and Japan (e.g., Furusawa, Inui, Ito, and Tang [2017],Bernard, Moxnes, and Saito [Forthcoming]). To our knowledge, we are the first to bring evi-dence on the production network of a developing country and exploit a quasi-experimentalsetup in linkage formation to study the importance of (MNC) clients on firms’ outcomes.

This paper is also part of a growing literature using micro-data from developing coun-tries to examine firms’ responses to globalization (see, for example, Verhoogen [2008], Bustos[2011], Helpman, Itskhoki, Muendler, and Redding [2017], Atkin, Khandelwal, and Osman[2017], Atkin and Donaldson [2018] Atkin, Faber, and Gonzalez-Navarro [2018]). As MNCshave turned into vectors of globalization, policy-makers compete to attract MNCs as a wayto integrate their economy into Global Value Chains (GVC). While we provide direct evi-dence on the benefits of joining MNC supply chains, we also show the relatively low levelof local embeddedness of MNCs. This second finding questions the extent to which thebenefits of GVC participation actually spread throughout the economy.

As we uncover striking heterogeneities in both the sourcing behavior of MNCs (rela-tive to domestic firms) and in the propensity of domestic firms to supply MNCs, we alsospeak to a growing literature that uses microdata to document heterogeneities in firm par-ticipation in international trade [Bernard, Jensen, Redding, and Schott, 2012]. De Gortari[2017], Eaton, Kortum, and Kramarz [2017], Antras, Fort, and Tintelnot [2017], Bernard,Moxnes, and Ulltveit-Moe [2018] are recent contributions to this literature. In AppendixC.4, we show that even after conditioning on firms’ sector, size, and province, MNCs (i)produce more value added in-house, (ii) tend to purchase less inputs locally, (iii) tend to pur-chase a significantly higher percentage of their local inputs from other MNCs in the country,(iv) source on average from more sectors, and (v) source from fewer suppliers within eachsector (than their domestic counterparts).

Finally, our findings relate to work that considers the role of demand shocks on firmdynamics. Papers such as Ferraz, Finan, and Szerman [2016] and Lee [2017] benefit from aninstitutional setup, in Brazil and South Korea respectively, in which government contractsare assigned through auctions. The shock we study is one of winning a contract with anMNC, which is likely to not only act as a demand shock, but also provide opportunities for

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productivity improvement through knowledge transfers.

3 Data and Background

3.1 Administrative Microdata

In the Introduction we describe challenges in measuring the causal impact of becominga supplier to an MNC and, in general, uncovering the sourcing behavior of MNCs. Oneadministrative dataset is central to our ability to overcome these challenges. This datasettracks all firm-to-firm relationships in Costa Rica between 2008 and 2015. This informationis collected by the Ministry of Finance of Costa Rica (Ministerio de Hacienda) through thecompulsory tax form D-151. Each firm has to report all its suppliers and clients with a yearlycumulative amount of transactions above 2.5 million Costa Rican Colones (roughly $4,400).As D-151 forms also contain the yearly amount sold to or bought from each partner, thisfurther informs us on the intensity of all firm-to-firm linkages in Costa Rica. The Ministryof Finance uses this data to ensure corporate income tax compliance. To deter firms frominflating their costs or deflating their revenues, the Ministry of Finance uses the D-151 datato cross-check the consistency of mutual reporting of any two partners.7

We pursue the same logic to check on the quality of this data set. Over 90 percent of alltransactions and over 87 percent of the value of all transactions can be preserved for analysis,as they are either correctly filled in or filled in with minor mistakes that can be addressed(e.g., with misreporting of decimal points). In a companion paper, we also show that, re-assuringly, several of the stylized facts already established for the production networks ofBelgium and Japan also hold for Costa Rica’s network [Alfaro-Urena, Fuentes, Manelici, andVasquez, 2018]. See Appendix D.2 for details.

We add to this dataset three other firm-level administrative datasets spanning the 2005to 2015 period. The first dataset contains the universe of Costa Rican corporate tax returns.All registered corporations are required to submit electronically the yearly tax declarationD-101, in which they report their profit, revenue, and costs. We also add data from the CostaRican Social Security Fund (“Caja Costarricense del Seguro Social”) on the yearly firm-leveltotal wage bill, number of employees, and share of high-skill employees.8 Our third datasetcomes from Costa Rican customs. For each firm engaged in international trade we observeexports and imports, disaggregated by country of destination or origin, date of shipment,and product (prices and quantities sold of each ten-digit product).

To construct an exhaustive record of the foreign ownership of firms in Costa Rica, werely on several data sources. We combine time series information on the country and shareof foreign ownership in the samples of firms covered by Encuesta Trimestral de Balanza dePagos (the “Quarterly Balance of Payments Survey”), Encuesta Anual (the “Annual Survey”),

7See Section 6.4 for a discussion on the self-regulating property of third-party reporting.8A worker is defined as high-skilled if he/she earns more than the minimum wage paid to a worker withvocational post-high school training.

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and Estudio Economico (“Economic Study”). From CINDE, the organization responsible forthe attraction of FDI to Costa Rica, we have information on the year of entry and country ofownership of those MNCs attracted by CINDE. Finally, we contrast these data sources withdata from ORBIS.9 ORBIS also allows us to improve the grouping of foreign firms, based ontheir shared global ultimate ownership. See Appendix D.4 for details.

While we identify 2,408 firm groups (firms, hereafter)10 with some share of foreignownership, more than half are not part of an MNC group.11 Moreover, even among the 1,045MNC affiliates, more than half do not have a substantial presence in the country. Given ourinterest to measure the productivity gains of joining MNC supply chains, we focus on the516 MNCs whose median number of workers – over all years of activity in Costa Rica – isat least 100.12 These 516 firms employ over 85 percent of workers, produce over 80 percentof the revenues, and are responsible for over 91 percent of the exported value out of therespective totals for the full sample of 2,408 firms with foreign ownership.13

3.2 Institutional Background on Costa Rica and Procomer’s Program

Costa Rica is the third largest recipient of per capita FDI net inflows in Latin Americaand the Caribbean. Over 20 percent of Fortune 100 companies and many other high-techenterprises have established operations in Costa Rica in the last 30 years. Most of theseMNCs are leading actors in their respective markets: Intel, Hewlett Packard, Oracle, AbbottLaboratories or Bayer are some examples.14 The Free Trade Zone regime is considered to bethe mainstay of Costa Rica’s export and investment promotion strategy.15 Both CINDE, theorganization in charge of attracting FDI to Costa Rica and Procomer, the agency in chargeof promoting Costa Rican goods and services abroad, have received international awards

9The ORBIS database, a commercial product of Bureau van Dijk, contains administrative data on over 160million firms in over 100 developed and emerging countries. The financial and balance-sheet informationin ORBIS comes from business registers collected by the local Chambers of Commerce to fulfill legal andadministrative requirements [Kalemli-Ozcan, Sørensen, Villegas-Sanchez, Volosovych, and Yesiltas, 2015].

10For more details on firm groups, see Appendix D.1.11As customary in the literature studying MNCs – see Antras and Yeaple [2014] and Caves [2007] – we define

an MNC as “an enterprise that controls and manages production establishments/plants located in at least twocountries.” We focus on MNCs with their parent in a foreign country and affiliates in Costa Rica (as opposed toMNCs whose parent is Costa Rican). Henceforth, we refer to these MNCs’ affiliates in Costa Rica as the MNC.

12This size threshold is less restrictive than other choices in the literature. The average annual sales of MillionDollar Plants in Greenstone, Hornbeck, and Moretti [2010] are approximately 9 times larger than the averagesales of our 516 MNCs. Abebe, McMillan, and Serafinelli [2017] consider only openings of FDI plants inmanufacturing for which the plant’s labor force is at least 100 employees or constitutes at least 1 percent oftotal employment in local manufacturing in τ = 0 or τ = 1. We require from MNCs to employ a median of 100workers across all years of activity in Costa Rica.

13Most firms with foreign ownership that we exclude from our study are small (their median number of workersis 12) and serve local demand, either in service sectors (e.g., hotels) or in sectors with low domestic inputrequirements (e.g., import/export retail or real estate agencies). For further details, see Appendix D.4.

14For a record of MNCs attracted by CINDE, see Success Stories section here.15Companies that can apply for the Free Trade Zone Regime must export at least 50 percent of their services (if

service firms), be scientific research firms, be part of strategic sectors or be significant suppliers to other firmspart of a Free Trade Zone. Firms part of the Free Trade Zone regime are fully exempted from custom duties onthe imports of goods “necessary for the operation of the company” (e.g., packaging materials or spare parts),among others.

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https://www.bvdinfo.com/en-gb/home

https://www.cinde.org/en/why/success-stories

for this performance.16 For these reasons combined, Costa Rica provides an ideal setting tostudy the causal effects of becoming a supplier to an MNC.

Costa Rica has also been implementing since 2001 a type of government-led matchingprogram between MNCs and local suppliers that has become increasingly more appealingto countries – developed and developing – that aim to improve the local integration of MNCaffiliates. The Productive Linkages program has the stated objective to foster supply chain in-teractions between MNCs in Free Trade Zones and Costa Rican firms in the wider economy.From 2001 to 2016, Procomer has enabled 1,390 deals between 220 distinct (export-oriented)MNCs and 399 distinct domestic suppliers.17 Hence, in addition to being able to study theuniverse of supplying relationships between local firms and MNCs using our administrativetransaction data, we can also study deals mediated by Procomer.

There are four key features of the Productive Linkages program. First, the program aimsto increase the integration of high-tech MNCs in the domestic economy. These clients seemunattainable to most of the domestic suppliers, without the institutional support of Pro-comer. For this reason, Productive Linkages deals are somewhat exceptional. Second, Pro-comer has built a comprehensive database of the firms in Costa Rica that are suitable andwilling to supply to MNCs. This suitability is established by Procomer assessors after an in-spection of firm premises and a detailed survey that is concluded with an aggregate score.18

The third and most salient feature of this program is its matching procedure. WheneverMNCs approach Procomer with an input need, Procomer taps into its database of evalua-tions and suggests a ranking of suitable suppliers. Last, since firms were allowed to competefor as many deals as they were suitable for, the sample size is significantly reduced when werestrict to cases that allow us to study the effect of the first deal mediated by Productive Link-ages. Thus, only a small set of participant firms lends itself to a quasi-experimental researchdesign a la Greenstone, Hornbeck, and Moretti [2010]. Given these features combined, weleverage on the Productive Linkages program to strengthen the causal argument of our mainempirical strategy. Section 6.1 provides more details.

Centralizing the Procomer data and merging it with administrative micro-data in-volved two tasks: (i) summarizing and digitizing those parts of the data shared as PDFsor archived emails and (ii) carefully assigning tax IDs to firms.19 Moreover, we checked theaccuracy of Procomer’s records (e.g., the occurrence and amount of a certain match) in thefirm-to-firm transaction data and found reassuring overlaps. See Appendix D.5 for details.

16CINDE was awarded in 2017 for the third consecutive year as the “Best Investment Promotion Agency” ofLatin America and the Caribbean in a ranking compiled by the Site Selection magazine. Procomer has beengranted in 2016 the title of “Best Trade Promotion Organization from a Developing Country” by the Interna-tional Trade Centre.

17If we restrict to deals larger that 2.5 million colones (same lower limit as in our administrative transactionsdata) the average (median) deal is of approximately $81,000 ($24,000) USD.

18Surveys contain five modules: productive capacity, market capacity, cooperation, R&D capacity, and quality.While the structure of the survey evolved across time, there is considerable continuity in the themes covered.Each evaluation concludes with an aggregate score and guidance on the Procomer programs appropriate forthe firm. Productive Linkages is one option of follow-up. Between 2004 and 2015, Procomer carried out 1,149evaluations of such on 921 distinct firms.

19Most data sources referred to firms by a version of their name (e.g., legal name or abbreviation).

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http://www.intracen.org/itc/events/tpo-network/2016/awards/

http://www.intracen.org/itc/events/tpo-network/2016/awards/

4 Productivity Effects of Joining Multinational Supply Chains: Previous

Challenges and Our Empirical Strategy

4.1 Standard Approach and Its Challenges

Our main goal in this project is to estimate the causal effects for domestic firms ofbecoming a supplier to an MNC, especially on their productivity. The literature that is con-ceptually closest to this goal is the one focused on estimating vertical spillovers from FDI.This literature has evolved hand-in-hand with the availability of increasingly more disag-gregated data, from the early days of aggregate cross-country studies on FDI and growth torecent within-country studies using firm-level panel data (e.g., revenues, inputs costs, sector,region) and sector-level input-output tables. The most consistent finding – from micro-dataon firm outcomes and sector (or sector-by-region) variation in foreign ownership – has beenthat of a positive correlation between an increase in foreign presence in downstream (client)sectors and higher productivity for domestic firms in upstream (supplying) sectors.20

While identifying supplying relationships as most likely conduits of productivity up-grading has guided subsequent research, three challenges stand in the way of answering ourquestion of interest. First, as past “studies of vertical spillovers rely on industry-level prox-ies for linkages between industries, [they] are [...] unable to pinpoint the exact mechanismthrough which such spillovers take place ” [Javorcik and Spatareanu, 2009].21 Sector-levelmeasures of exposure to client sectors of varying foreign ownership are meant to reflect dif-ferences in the likelihood of actual supplying relationships. Unfortunately, they fail to bestrong predictors of such relationships. In Section 7 we find that the Backward measure usedas the key explanatory variable in this literature explains less than 1 percent of the actualstrength of linkages to MNCs.22

This striking result suggests that relying on sector-level measures of potential exposure

20The most common empirical exercise in this literature is to estimate the correlation between the productivityof domestic firms and a measure of these firms’ exposure (linkage) to foreign-owned firms in the country.Linkages are classified as either horizontal (within the same sector as that of the FDI-receiving firm) or vertical(either backward – from FDI-receiving firms to domestic suppliers – or forward - from FDI-receiving firms todomestic clients). Because such linkages could not be observed in the type of datasets available until now,they would be proxied by sector-level measures called Horizontal, Backward and Forward. As we focus onsupplying relationships, the Backward measure is usually defined as Backwardst = ∑

s’

Ys→s′Ys× Foreign Shares′t,

i.e., a weighted-average foreign-ownership in client sectors s′ in year t, with the weights being given by input-output table coefficients measuring the share of sector s sales purchased by sector s′. These input-output shareswould typically be fixed to those of an earlier year in the sample. All firms in sector s would be assigned thesame Backward measure in year t.

21Javorcik and Spatareanu [2009] is a notable exception. The authors rely on a survey of Czech firms that iden-tifies which firms supply to MNC affiliates in the Czech Republic. The main caveat of this study comes fromthe small sample of observed suppliers (40 suppliers).

22As described in detail in Section 7, we construct a firm-level analogue of the sector-level Backward measure,

Backwardf-fls =

N∑

i=1

Yls→iYls× Foreign Sharei, where l is the index of the firm in question, s is the sector of firm l, Yls→i

are the sales of firm l in sector s to firm i, Yls are the total sales of firm l, and Foreign Sharei is the foreign-shareof ownership of firm i. We run a regression over the entire sample of firms in Costa Rica of the firm-levelBackwardf-f

ls on their sector-level Backward measure and obtain an R2 of less than 1 percent.

10

to MNC clients hampers both the measurement of the treatment effect and the interpretationof the treatment actually “applied” to domestic firms. The low explanatory power of thesector-level Backward measure for actual linkages suggests that the wide range of estimatesof the coefficient on the Backward measure23 might either reflect (i) a spurious correlationbetween the Backward measure and productivity gains, or (ii) the combination of variouschannels of impact of MNCs, other than direct linkages (e.g., spillovers from direct suppliersto their own competitors in upstream sectors). We guide interested readers to Appendix C,where we explore in detail the strong assumptions behind the use of input-output proxiesof linkages to MNCs.

A second shortcoming of previous findings is that their causal interpretation is rarelydefended. In a review of the literature, [Harrison and Rodrıguez-Clare, 2010] note that “mostresearch has not used any sort of identification strategy to tease out the direction of causality.One possibility is that foreign firms [..] form alliances with the most productive firms,” i.e.,results are driven by reverse causality. Instead of the MNC expansion driving productivitygains in upstream sectors, it might be that MNCs expand their downstream presence becauseupstream sectors are embarked on a positive trend in productivity.

Last, as long as the outcome of interest is productivity, even if the interpretation andcausality of treatment were not questioned, there would still be the remaining challenge ofmeasuring firm productivity (as opposed to firm performance). To our knowledge, most, ifnot all studies in this literature, have measured productivity as either revenue TFP or usingthe control-function methods available at the time (typically Olley and Pakes [1996]). InSection 5.3 we describe our progress on this front.

4.2 Empirical Strategy

The previous section details three main challenges faced by researchers hoping to un-cover the causal effects of joining MNC supply chains on firm productivity. Given our goalto measure these effects, we looked for a dataset enabling us to address these challenges.Ideally, such a dataset would meet three requirements: (i) enable us to bypass input-outputbased proxies of linkage and isolate the role of actual linkages as conduits for productivitygrowth, (ii) be suitable for an identification strategy teasing out the direction of causalitybetween a firm’s supplying to MNCs and its productivity, and (iii) allow us to pin down afirm’s productive efficiency, as opposed to firm performance.

Fortunately, a high-quality administrative dataset from Costa Rica meets the first tworequirements. In addition, while not fully meeting the third requirement, it allows forprogress on this front as well. This dataset tracks all transactions among all firms in the

23From a 2011 meta-analysis by Havranek and Irsova [2011], we learn that “the estimated size of [...] spillovers[from backward linkages] varies broadly. The point estimates of the economic effect of backward linkagesreported by the two best known studies, Javorcik [2004] and Blalock and Gertler [2009], differ by the orderof magnitude: Javorcik [2004] found the effect 30 times greater. Moreover, following the methodology ofJavorcik [2004] and Blalock and Gertler [2009], many other studies conducted for different countries havefound insignificant or even negative spillover effects.”

11

country, in particular tracking which domestic firms are actually supplying to MNCs. Thisallows us to bypass the binding assumptions behind the use of I-O based proxies of link-age. The length of the time series of this dataset creates the opportunity of an event-studyresearch design that clarifies the direction of causality. The event is defined as a first sup-plying relationship with an MNC in Costa Rica. While the dataset does not track prices andquantities separately, it allows us to track new suppliers to MNCs in their relationships withdomestic clients. We uncover boosts both in the sales to and number of non-MNC clients,which we interpret as indirect evidence on gains in productive efficiency.

Our empirical strategy consists of a series of event study models formalized below.We make use of three samples, with each sample implicitly proposing a different counter-factual path for first-time suppliers to an MNC. The first two samples are built around theeconomy-wide set of first-time matches between domestic firms and MNCs in Costa Rica.The first sample (“the full sample”) includes firms that are both eventually matched to anMNC between 2010 and 2015 and firms never observed as supplying to an MNC duringour entire firm-to-firm transaction dataset (2008 to 2015). The second sample (“the restrictedsample”) is a sub-sample of the full sample, restricted in two ways: (i) it only includes firmseventually supplying to an MNC, and (ii) it includes only those firms that we observe atleast two years before and after their event.24 The third sample (“the Procomer sample”) isconstructed from the Procomer program of Productive Linkages, a program matching MNCsto domestic firms. This program allows us not only to track outcomes for a sample of lo-cal firms winning a contract with an MNC client, but also the outcomes of the runner-upcompetitors to the same deal.

The full sample is the sample providing our baseline results, since its size allows usto control for a very fine set of fixed effects and study treatment effect heterogeneity. Thesecond and third samples are useful to rule out potential concerns with our baseline results.One concern is that our results are driven by the contrast to firms never supplying to MNCs.The second sample allows us to check whether our variation comes from this contrast withfirms never experiencing an event or from the differential timing of our events. While thischeck involves dividing our sample size by fifteen and estimating fixed effects off a set ofpotentially-special firms, it is important for identification. Reassuringly, our results confirmthe importance of the timing of the event as the driver of changes in firms’ outcomes. Last,our Procomer sample, while smaller in size, allows us to address potential concerns of selec-tion into supplying to MNCs based on time-varying firm-level unobservables. Even thoughthe three sample have different advantages and disadvantages, they paint a very consistentpicture. Other possible threats to our identification are discussed at the end of this section.

24This balancing is important to isolate the effect of the timing of the event from changes in the composition offirms (e.g. firms entering or exiting our sample) around the event year.

12

Basic economy-wide event study specification

Our main estimations are built on an economy-wide event study design. A domesticfirm experiences the “event” upon its first sale to an MNC in Costa Rica. Let τi denote thefirst year when we observe – in our firm-to-firm transaction dataset – a given domestic firmi as selling to an MNC. We study first sales to MNCs occurring between 2010 and 2015.25

Throughout Section 4, we implement versions of the following basic specification:

Outcomeit = αi + β′Covariatesit + Other Fixed Effects +C

∑k=C

θkDkit + εit. (1)

All event study specifications contain firm fixed effects, αi. The Outcome variable and time-varying vector of Covariates are adjusted to the exercise. To define Dk

it, C and C, some contextis needed. We first map calendar years t to their event time equivalent.26 Even if we hadstarted with a balanced panel of firms of T time periods, as long as we have heterogeneousevent dates, the sample is not balanced in event time.27 We thus need to decide how tohandle data outside of the event time window, given by C and C. We set C = −4 andC = +4, for our sample in event time to be almost fully balanced. We follow the approachof “binning up” the endpoints and define Dk

it := 1[t = τi + k] ∀k such that C < k < C,DC

it = 1[t ≥ τi + C], and DCit = 1[t ≤ τi + C], where 1[.] is an indicator function for the

expression in brackets being true.28

The sequence of θk is our object of interest. Because not all parameters are identifiedas written, we normalize θ-1 to zero to simplify the test for an effect on impact. The preciseinterpretation of the sequence θk depends on what we assume on the counterfactual pathof winning firms. We implement this event study using two economy-wide samples, witheach sample implicitly defining another counterfactual path.

We start with a sample containing domestic firms that become suppliers to an MNCafter 2010 (included) and domestic firms that are never observed as supplying to an MNC in

25This restriction is justified by our intention to measure precisely the first year in which a domestic firms sellsto an MNC. 2008 was the first year when the D-151 tax form (the base for the firm-to-firm dataset) could befiled electronically. However, as 2008 was the year of transition to a digitized filing, firms were still allowedto file the form on paper. Hence, we suspect the 2008 dataset is incomplete. As we look for the first year adomestic firm sells to an MNC, we cannot infer that a firm first observed as selling to an MNC in 2009 was notalso selling to an MNC in 2008 but had filed the form on paper in 2008. To avoid mismeasurement of the eventyear, we only consider matches to MNCs from 2010 onward for domestic firms not found to have sold to anMNC both in 2009 (the first year electronic filings were mandatory) and 2008. See Appendix D for details.

26For firm i becoming a first-time supplier to an MNC in 2010, τi=2010, event-year +1 is 2011 and -1 is 2009.27Units experiencing the event earlier have fewer pre-event observations, while units experiencing the event

later have fewer post-event observations.28For another example of binning endpoints in an event study, see McCrary [2007]. An alternative to “binning

up” endpoints is to fully saturate the model, include all event time dummies but only report the event dateswith a balanced sample. While we do not report these results for brevity, they are almost identical to thoseusing the “binning up” approach.

13

our firm-to-firm transactions dataset.29 Domestic firms are required to satisfy minimal sizerestrictions of a median number of workers of at least 3 and median total yearly revenues ofat least $25,000 (PPI-deflated to 2013 US$) across all years appearing in tax datasets.30 Wealso exclude firms that are state-owned, registered as households, NGOs, and those in thefinancial, construction, and education sectors. See Appendix D.1 for details.

Whenever we use this larger sample, Other Fixed Effects are a narrowly defined set ofover 23,000 [calendar year × province × 4-digit sector code × cohort] fixed effects. Hence,we compare outcomes for treated firms in event year k to outcomes in event year -1 of firmsthat are in the same age group, in the same narrowly defined sector and province (includingoneself). Moreover, the inclusion of never matched firms benchmarks trends in outcomesbetween the calendar years corresponding to event years k and -1 to outcomes between thesame calendar years for never matched firms. In this case, θk captures the double differencejust described. Last, note that whenever we use this larger sample, we cluster standarderrors at the 2-digit sector× province level; this accounts for possible correlations in outcomesamong firms in a certain 2-digit sector and province.31

Next, we propose a second sample, only containing firms eventually matched to anMNC. In addition to the conditions imposed on the larger sample, we also require from thissample to be balanced from event time -2 to +2. This avoids the results being driven bychanges in composition in the neighborhood of events. With this restricted sample, OtherFixed Effects refer to over 2,500 [calendar year× province× 4-digit sector code] fixed effects.As we continue to normalize the θ-1 coefficient to zero, θk then measures the mean outcomesof matched firms in event year k with respect to the mean outcomes of firms in the sameprovince and narrowly defined sector (including oneself) in the year prior to their first match(in excess of time trends and firm fixed effects). Last, whenever we rely on this sample, wecluster standard errors at the event year × province level.32

For all event study estimations, we present the point estimates of θk for both samples.This allows us to check on the assumptions of the event study design: (i) the suitability ofthe control group and (ii) the role of the differential timing of events across treated units indriving treatment effects.

The identification of the event study coefficients hinges on the assumption that bothfirms that have not yet been matched to an MNC and firms that will never be matched form acredible counterfactual, after accounting for fixed differences between firms and unobservedcalendar year × province × 4-digit sector code (× age cohort) common shocks. One may

29Hence we exclude firms that are observed as supplying an MNC either in 2008 or 2009. For such firms, wecannot identify the first year they become suppliers to an MNC. Results from mean-shift estimations includingthese firms as well (yearly coefficients cannot be estimated on firms with an unknown event year) are strikinglysimilar to those from the sample described here and are available upon request.

30These firms must also report workers and revenues in all years when they are active for tax purposes.31Adding event year clustering is impossible as event years cannot be assigned to never matched firms.32Event year clustering is recommended whenever event dates are concentrated on a few values (in our case, 2010

to 2015). While adding sectoral clustering to event year × province clustering is too exacting for this smallersample, results from such clustering are qualitatively similar and available upon request.

14

be concerned, however, that firms in the same sector, province and age-cohort that neversupply to an MNC are not plausibly comparable to firms eventually matched, even afteraccounting for firm-level time-invariant unobservables. To address this concern, we testwhether these two sets of controls are, in fact, exchangeable. For each exercise, we reestimatethe model on the restricted sample and check whether our point estimates change. Whilethe restricted sample makes us lose power, it is reassuring to find that results are not drivenby the contrast with firms never matched, but by the differential timing of treatment onsetamong the eventually matched.

We now discuss remaining threats to causal inference – in the form of reverse causalityand third-cause fallacy – and our approach in addressing them.

Reverse-causality:

• MNCs choose firms embarked on pre-trends in observables: Our results may simplyreflect MNCs choosing firms with positive pre-trends in outcomes. For both samples,we study whether the θk coefficients for k < 0 are significantly different from zero ornot. We find a lack of (differential) pre-trends in both samples. This finding suggeststhat (i) MNCs did not choose suppliers embarked on pre-existing trends of improve-ment and (ii) even if they may differ in levels, suppliers-to-be were embarked on trendsparallel to those of firms never supplying to an MNC.33 Note that we do not includefirm-specific trends in any of our regressions, only firm fixed effects.

• MNCs choose firms embarked on pre-trends in unobservables:34 We make use of aquasi-experimental setup provided by the Productive Linkages program of Procomer,the Trade Promotion Agency of Costa Rica. As described in Section 6.1, this setupprovides us with knowledge not only about domestic firms entering a deal with anMNC, but also on the domestic firms short-listed for that deal. Short-lists are basedon scores from evaluations carried out by Procomer, evaluations that assess featuresunobservable in our administrative data and that are relevant to MNCs. Given theirsimilar evaluation scores, contenders to a deal were plausibly equally-prepared forthat deal on those unobservable dimensions relevant to MNCs. Yet, only the chosensupplier experiences a trend break in performance at the moment of the event.

• MNCs choose firms with anticipated breaks in trends: If MNCs could anticipatetrend breaks in firm performance and schedule a first contract at the exact time whenthese breaks occur, then the match would be endogenous. We have gathered anecdo-tal evidence from employees of procurement departments of MNC affiliates in CostaRica, all in agreement that sourcing decisions need to be made in consultation withthe MNC headquarters. Therefore, MNC affiliates are restricted in their freedom totime their contracts to potentially anticipated breaks in supplier performance. More-

33Both models already control for calendar year × province × 4-digit sector code (× age cohort) shocks.34Time-invariant differences in unobservables are taken care by firm fixed effects

15

over, this uncertainty in whether and when the supplying contract is approved alsoprecludes the domestic firm from strategically timing its expansion and first match tothe MNC.

Third-cause fallacy:

• Contemporaneous shock to the firm driving both the match and the improvementsin firm performance.

For Costa Rican firms without a pedigree of having already supplied successfully toMNCs, the timing of the first deal with an MNC is uncertain. Goods and services sup-plied by domestic firms to MNCs are not specialized enough to offer bargaining powerto the supplier. Most MNCs are also exempted from import tariffs, as part of the FreeTrade Zone regime. Therefore, domestic firms are usually not in the position of dictat-ing the terms of a first deal. It is an unlikely scenario that suppliers can carefully plantowards a first match to the MNC and contemporaneous or immediate improvementsin firm performance. Using Social Security data, we inquire on potential changes inmanagement at the time of the event, triggering both the event itself and the subse-quent evolution of firm outcomes (work in progress, see Section 6.2).

In light of these arguments combined, we conclude that the economy-wide event studymethodology is appropriate to study the causal effects of joining MNC supply chains. Thebody of the paper presents graphical evidence for the full sample but all figures link to tablescontaining results for both economy-wide samples for completeness.

5 Productivity Effects of Joining Multinational Supply Chains: Sum-

mary Statistics and Results

This section implements our empirical design discussed above. Before that, we pro-vide a summary of some descriptive statistics of our data. Then we investigate what hap-pens to suppliers to MNCs after the event. We focus first on measures of firm size, andcharacteristics of firms’ production process. We then provide a discussion on productivitymeasurement in our context and present several direct and indirect pieces of evidence point-ing to non-mechanical firm expansion and to increases in productivity. We also explore theheterogeneity of our results on several dimensions, including sellers’ or buyers’ sectors andshare of high-skilled workers in the firm. Finally, we discuss the magnitude and patterns ofour main results.

16

5.1 Summary Statistics

Appendix B provides detailed summary statistics on the sample of firms included bothin the full sample (local firms never matched to an MNC and local firms eventually matchedto an MNC, unbalanced) and in the restricted sample (only local firms eventually matchedto an MNC, balanced).

Table B1 reports summary statistics for 2008 on the average wage bill, exports’ value,imports’ value, total sales, employment, value added, input costs and total net assets. Welearn that the raw average (median) local firm never supplying to an MNC is around 20 to 50percent smaller than the raw average (median) local firm that becomes a first-time supplierto an MNC after 2010. This finding is not a concern, as the identifying assumption behindour event study specification is that firms that become matched to an MNC are not experi-encing differential preexisting trends (not levels) compared to local firms in the same sector,province, and age group. Even in levels, the raw averages (medians) cannot be directlycompared, as the samples have different sectoral, provincial and age compositions.

Table B3 reports the most frequent countries of origin of the MNCs acting as first MNCclient for Costa Rican firms. More than a third of these MNCs are North American (i.e.,from United States and Canada). The remaining two thirds of these first MNC clients aremostly Latin American (e.g., Panama, Mexico, Colombia, El Salvador, Chile, Nicaragua,Venezuela), West European (e.g., Spain, Great Britain, Germany, Switzerland, and France),or Japanese. As reported in Table B4, around 40 percent of these first MNC clients are inManufacturing, with another 24 percent in Wholesale and Retail Trade. For the first-timesuppliers to an MNC, the four most frequent sectors are Wholesale and Retail Trade (32 to38 percent), Accommodation and Food Services (10 to 18 percent), Agriculture, Forestry andFishing (9 to 13 percent), and Manufacturing (9 to 10 percent).35

In terms of features of the event, as reported in Table B5, the average (median) first saleto an MNC in event year 0 is of $74K ($14K) for the unbalanced sample ($66K and $12K forthe balanced sample). The mean (median) duration of the relationship with the first MNCclient is of 2.07 years (2 years) for the unbalanced sample (2.24 years and 2 years for thebalanced sample). According to Table B8, the sale to the first MNC client in event year 0represents an average (median) share of 0.41 (0.24) of that firm’s sales for the unbalancedsample (0.32 and 0.15 for the balanced sample). Of firms that we can observe in their eventyear +2 (given the length of our panel, see Table B6), more than half of them are still sup-plying to MNCs an average (median) of 29 to 37 (14 to 24) percent of their sales (see TableB8).

35Note that firms involved in both Manufacturing and Retail (both MNCs and domestic firms) are sometimesassigned a Retail sector code. This means that the actual share of firms involved in Manufacturing is largerthan the one reported in this table. To address further concerns, we show in Subsection 5.4 that our results areare not driven by the Retail sector.

17

5.2 Anatomy of a Firm Joining MNC Supply Chains

5.2.1 Firms Expand after Starting to Supply to an MNC

Table 2 reports the effects of a first sale to an MNC on four outcome variables capturingfirm size: log total sales (including exports), log total number of workers, log total assets andlog input costs. Columns 1 to 4 report the event study estimates for the sample includingboth domestic firms that become first-time suppliers to an MNC and domestic firms neverobserved as supplying to an MNC, whereas columns 5 to 8 focus only on the balanced sam-ple of domestic firms becoming first-time suppliers to an MNC. Figure 1 plots the sequencesof event study estimates from Columns 1 to 4.

Reassuringly, first-time suppliers to MNCs do not exhibit preexisting differentialtrends with respect to either of the control groups implied by each sample. As all ourspecifications lack firm-specific trends, new suppliers to an MNC do not appear to beselected based on their past firm growth.

-.10

.1.2

.3.4

Coe

ffici

ents

: Sal

es

<=-4 -3 -2 -1 0 1 2 3 4<=Years Since First MNC Interaction

(a) Sales

-.10

.1.2

.3.4

Coe

ffici

ents

: Em

ploy

men

t


(b) Employment

-.10

.1.2

.3.4

Coe

ffici

ents

: Cap

ital


(c) Net Assets

-.10

.1.2

.3.4

Coe

ffici

ents

: Mat

eria

ls


(d) Input Costs

Figure 1: Event Study: Firms’ grow in size after starting to supply to an MNC

Notes: Figure 1 plots the estimated θk event study coefficients from a regression of the form given in Equation1, where the dependent variable is, in turn, log total sales (Panel 1a), log employment (Panel 1b), log net assets(Panel 1c), and log input costs (Panel 1d). θ−1, the coefficient of the year prior to a first match with an MNC, isnormalized to zero. The vertical lines reflect the 95% confidence intervals. The coefficients plotted correspondto Columns 1 to 4 in Table 2, obtained from the sample including both domestic firms that become first-timesuppliers to an MNC and domestic firms never observed as supplying to an MNC.

18

After their first match to an MNC, firms display robust and long-lasting increases insize. Firms expand from the same year of their first contract with an MNC, increasing theirsales by around 11 percent (columns 1 and 5), workforce by 3 to 7 percent (columns 2 and6), total assets by 5 to 9 percent (columns 3 and 7), and total input costs by approximately 10percent (columns 4 and 8).

Their sales improve further in the next three years, when the increase in sales reaches aplateau of around 25 percent (columns 2 and 6) higher a value than that of the control groupin event year -1. The number of workers presents a similar levelling off after year three,though this growth is less robust in the restricted sample.

When comparing only among firms ultimately matched to an MNC we find strongerincreases in sales than those from the extended sample. This suggests a slight trend of salesgrowth also among firms never supplying to an MNC. This phenomenon is reversed forworkers; weaker results for the sample of only first-time suppliers suggest that these firmswere more efficient in their labor use, particularly relative to their stronger increase in sales.

5.2.2 Firms do Not Only Grow, but Adjust Behavior after Starting to Supply to an MNC

While first-time suppliers to an MNC experience sizable expansions, one might beconcerned that this expansion is a mere mechanical effect of adding a new big client toone’s portfolio. We partially address this concern by looking at five outcomes that are non-mechanically related to firm scale: imports, the share of imports in total input costs forimporting firms, the number and the share of high-skilled workers, and the wage bill perworker of the firm. We show these results in Table 3. Columns 1 to 5 report the event studyestimates for the sample including both domestic firms that become first-time suppliers toan MNC and domestic firms never observed as supplying to an MNC, whereas columns 6to 10 focus only on the balanced sample of domestic firms becoming first-time suppliers toan MNC. Columns 1 to 5 are shown in Figure 2.

Importing firms experience a dramatic boost in the value of their imports of up to 44percent. Among importing firms, not only do they import substantially more but importsreplace 3 percent of inputs previously locally-sourced.

Firms starting to supply to an MNC are also found to adjust their workforce. Column5 reports event study estimates on firms’ average wage bill. While the MNC treatment ledto a 2 percent increase in average wages three to four years after the event (with a negativebut non-significant result for the sample restricted only to eventually-matched firms), thelack of worker-level data precludes us from distinguishing between compositional effects ofhiring better-paid employees and rent-sharing between firm owners and workers. Ideally,one would like to separate these two channels of adjustment.

While we do not observe the identity of workers before and after the match to an MNC,we know how many workers in a given year are labeled as high-skill. Column 6 shows thatthe number of high-skill employees of a firm increases by up to 12 percent four years afterthe first match to an MNC. However, the increase in the proportion of high-skill employees

19

in the firm is not statistically different from zero (Column 7).36

-.50

.51

Coe

ffici

ents

: Im

ports


(a) Value of Imports

-.05

0.0

5.1

Coe

ffici

ents

: Im

ports

Sha

re


(b) % Imports in Total Input Costs

-.05

0.0

5.1

.15

.2C

oeffi

cien

ts: H

S W

orke

rs


(c) Number of High-Skilled Workers

-.02

-.01

0.0

1.0

2C

oeffi

cien

ts: H

S S

hare


(d) % High-Skilled Workers in Total

-.02

0.0

2.0

4.0

6C

oeffi

cien

ts: W

age

per L


(e) Wage per Worker

Figure 2: Firms adjust behavior after starting to supply to an MNC

Notes: Figure 2 plots the estimated θk event study coefficients from a regression of the form given in Equation1, where the dependent variable is, in turn, log value of imports (Panel 2a), share of imports in total input costs(Panel 2b), log number of high-skilled workers (Panel 2c), share of high-skilled workers in the total numberof workers (Panel 2d), and the log wage per worker (Panel Panel 2e). θ−1, the coefficient of the year prior toa first match with an MNC, is normalized to zero. The vertical lines reflect the 95% confidence intervals. Thecoefficients plotted correspond to Columns 1 to 5 in Table 3, obtained from the sample including both domesticfirms that become first-time suppliers to an MNC and domestic firms never observed as supplying to an MNC.

Rich firm-level data allows us to study how firms react to joining their first MNC sup-ply chain. Overall, Table 3 reveals that first-time suppliers made technological and organi-

36The definition of high-skilled worker in our data is given by an indicator of individual earnings being largerthan the minimum wage paid to a worker with vocational post-high school training. This measure has thecaveat that a too wide range of skills is reduced to a single category of skill, not permitting us to analyzemore subtle changes in the within-firm skill distribution (e.g. potential substitutions between workers withvocational training and workers with college degrees). This might explain part of the little variation observedin our results.

20

zational changes – e.g., in factors of production and sourcing strategy.

5.3 Productivity Measurement

Despite findings on both firms’ expansion and changes in firms’ production process,our main outcome of interest is productivity. Increases in firm size and workforce could alsobe achieved by policy-makers through direct government transfers to domestic firms, in-stead of investing resources to attract MNCs. From a theoretical perspective, the presence ofproductivity spillovers is posed as the main justification for deviating from policy neutrality[Harrison and Rodrıguez-Clare, 2010]. If entering an MNC supply chain induces produc-tivity enhancements to domestic firms, there would be scope for industrial policy since theimplications of government transfers versus attraction of MNCs would be different. How-ever, correctly measuring firm level productivity is a challenging task.

In the words of De Loecker and Goldberg [2014], “the voluminous empirical literatureon the effects of trade on firm productivity” has mostly “been loose in its use of the termproductivity. [..] With sales and expenditure data alone, one cannot generally recover theunderlying components of firm performance or identify productivity. As always in empir-ical work, there are two ways out of such a situation: either one collects more data or onemakes additional assumptions that will allow identification.” This critique equally appliesto the literature on the effects of FDI on firm productivity.

To be specific on these challenges in our setup, firms that start to supply to MNCs maynot only experience gains in productive efficiency, but may also adjust their scale of produc-tion (input usage and, hence, input expenditure), prices and associated markups, as well asproduct and input quality. To be able however to get at improvements in productive effi-ciency in a data-driven fashion, one would need to observe not only the details of contractsbetween domestic suppliers and their new MNC client, but also detailed data on all firm-level inputs and outputs. Can domestic firms charge higher markups to MNCs? Is the MNCrequesting goods of a different quality or goods that require inputs of a different quality?

5.3.1 Our approach to measuring productivity effects

As common in most settings, we lack contract-specific (or even firm-specific) disaggre-gated details on quantities, prices, and quality.37 To make progress on this front, we proceedin steps.

The first step consists of studying raw measures of firm performance. These measuresinclude absolute values of sales, profits and value added, as well as their per-employeecounterparts. These measures provide a first check, but their improvements could also berelated to changes in capital utilization. This is particularly concerning since Figure 1c al-ready showed an increase in our measure of capital.

37Atkin, Khandelwal, and Osman [2017] is a notable exception. The authors collected very disaggregated surveydata for a set of rug producers in Egypt, which allows them to obtain very precise measures of quantity-basedtotal factor productivity and product quality.

21

In order to address the fact that changes in inputs could explain improvements in rawmeasures of performance, the second step focuses on proxies of productivity that take firminputs into account. To achieve this, we first provide a simple framework to micro-foundand interpret production function estimation in our context. We estimate our main speci-fication using OLS and we report results obtained with the control function approaches ofLevinsohn and Petrin [2003] and Ackerberg, Caves, and Frazer [2015]. We also constructa firm level productivity index following Syverson [2011] and show the robustness of ourresults across these different methods.

One remaining concern is related to firms changing their prices or markups. For exam-ple, in the extreme case in which quantities produced and inputs utilized remain constant, anincrease in prices/markups after the event would increase firms’ sales (prices×quantities)conditional on inputs. This would confound an increase in our measured productivity witha simple increase in prices. We argue in the third step that this is an unlikely threat to ourresults.

Our administrative data does not allow us to account for changes in product speci-fications or quality. It could be that even if prices/markups do not change, our observedincrease in measured productivity reflects changes in the product-mix, instead of improve-ments in physical TFP for the same goods produced. To inquire whether this is the case, werestrict our focus to firms in sectors producing homogeneous goods, according to the Rauch[1999] classification. This exercise is currently work in progress. Irrespective of its findings,standard trade models under CES preferences and monopolistic competition assumptionspredict that productivity and product quality are isomorphic in equilibrium [Melitz andRedding, 2014]. Thus, the implications of our results would remain the same.

Putting all steps together, we find several pieces of evidence pointing to increases infirm-level productivity after the event. This implies that first-time suppliers to MNCs ex-perience TFP improvements with respect to their counterfactual productivity path in theabsence of the event. Our results show that these increases in measured productivity arecoincidental in timing with the moment domestic firms start supplying to MNCs. Whilethis being a necessary condition for the event to cause an increase in productivity, it is stillnot sufficient. To strengthen the causal interpretation of these results, Section 6 rules outimportant alternative hypotheses that could explain our findings.

Step 1: Firm performance

First, we find strong and long-lasting improvements in firm performance. Table 4 pro-poses five measures of firm performance. As common throughout the paper, Columns 1 to 5report the event study estimates for the sample including both domestic firms that becomefirst-time suppliers to an MNC and domestic firms never observed as supplying to an MNC.Column 1 uses as dependent variable log firm profits and column 2 uses log value added.Since it might be unsurprising that those variables increase after a contract with a big client

22

(such as an MNC), Columns 3 and 4 show the same outcomes divided by the number ofemployees of the firm. Finally, Column 5 shows sales per employee.

-.10

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(f) Value Added per Worker

Figure 3: Firms improve their performance after starting to supply to an MNC

Notes: Figure 3 plots the estimated θk event study coefficients from a regression of the form given in Equation 1,where the dependent variable is, in turn, log sales (Panel 3a), log sales per worker (Panel 3b), log profits (Panel3c), log profits per worker (Panel 3d), log value added (Panel 3e), and log value added per worker (Panel 3f).θ−1, the coefficient of the year prior to a first match with an MNC, is normalized to zero. The vertical linesreflect the 95% confidence intervals. The coefficients plotted correspond to Columns 1 to 5 in Table 4.

All five measures exhibit a strong boost after the first match to an MNC and a levelingoff three years into being a supplier to an MNC. Columns 6 to 10 show the same outcomesfor the balanced sample of domestic firms becoming first-time suppliers to an MNC, withqualitatively similar results. Figure 3 plots the sequences of event study estimates fromcolumns 1 to 5 (in combination with Column 1 of Table 2 for the case of log sales).

While these raw measures of firm performance do not necessarily map one-to-one to

23

physical productivity, the boost in outcomes per employee rules out a mere expansion ofthe firm. The evidence presented shows that firms are expanding in ways that allow themto produce more value added and rents per employee. We refine these results and addressconcerns related to input utilization in the next step.

Step 2: Productivity estimation

We first introduce a simple framework to guide our productivity measures and results.To fix ideas, suppose that domestic firms have a Cobb-Douglas production technology:38

Yit =AitLβlit Kβk

it Mβmit ,

⇒ log(Yit) =log(Ait) + βl log(Lit) + βklog(Kit) + βmlog(Mit), (2)

where Yit stands for total revenues, Lit for the total number of workers, Kit for the valueof total net assets, and Mit for the total input costs (excluding the wage bill) of firm i incalendar year t. Ait, the total factor productivity of firm i in calendar year t, is our object ofinterest. We allow log(Ait) to have a component that depends upon firms’ interaction withMNCs

(Ait(MNC)

), in addition to a firm fixed effect

(αi), an interactive fixed effect for the

calendar year by province of firm i by 4-digit sector code of i by cohort of i(

λt×p(i)×s(i)×c(i)

)and firm-specific shocks in calendar year t

(εit),

log(Ait) = αi + λt×p(i)×s(i)×c(i) + Ait(MNC) + εit. (3)

We propose two event study specifications for Ait(MNC). First, we estimate a moreparsimonious specification that simply tests for a productivity shift assumed to occur im-mediately after the first match with an MNC and to remain constant in subsequent years.In practice, this shift in productivity estimates the average productivity in years post-eventwith respect to the average productivity in years pre-event, hence the notation θpost. We thenstudy yearly effects to learn about the dynamic path of productivity before and after a firstmatch with an MNC.

Mean productivity shift specification for Ait(MNC):

Ait(MNC) = θpost1 {t ≥ τi}

38We also use the translog functional form, which is a second-order approximation to general production func-tions. For both Cobb-Douglas and translog, we estimate the coefficients on factors of production over theentire sample of domestic firms, controlling for narrowly defined fixed effects. We also estimate sector-specificproduction function elasticities by interacting K, L, and M with 2-digit sector dummies. Results are robust tothis change.

24

Yearly effects specification for Ait(MNC):

Ait(MNC) =C

∑k=C

θkDkit (4)

Next, we replace Ait(MNC) with the two specifications from equation 4 first in equa-tion 3, then in equation 2, to obtain the two main models for estimation.

Mean productivity shift model:

log(Yit) =αi + λt×p(i)×s(i)×c(i) + βl log(Lit) + βklog(Kit) + βmlog(Mit)

+θ11 {t ≥ τi}+ εit (5)

Yearly effects model:

log(Yit) =αi + λt×p(i)×s(i)×c(i) + βl log(Lit) + βklog(Kit) + βmlog(Mit)

+C

∑k=C

θkDkit + εit.

(6)

For our baseline results, we estimate Equation 6 using OLS and assuming both Cobb-Douglas and translog specifications for the production function.39 Alternatively, we alsoconstruct a productivity index for the Cobb-Douglas production function, as proposed inSyverson [2011]. Instead of regressing sales on input usage and estimating the input coeffi-cients, this method “residualizes” log sales by subtracting firm-level inputs used, weightedby the respective 2-digit-level cost shares.40

Our baseline results are presented in Table 6. To account for the potential endogeneityof inputs choice, we report in Table 7 the results obtained using control function approachesproposed by Levinsohn and Petrin [2003] and Ackerberg, Caves, and Frazer [2015]. Ourresults are robust to using those methods as well.

Table 6 displays similar patterns to those observed for firm expansion. Firms ulti-mately matched to an MNC do not display differential pretrends in any of these measuresof productivity. Both when compared to firms never matched to an MNC or among them-selves, first-time suppliers do not appear to be selected into supplying to an MNC basedon a history of productivity growth. Results for Columns 1-3 are presented in Figure 4.41

First-time suppliers are found to experience an improvement in productivity from the same

39Putting aside the estimation of input elasticities, the consistent estimation of θk ∀k ∈ {C, ..., C} relies on thesame identification conditions discussed in Section 4.2.

40Concretely, the Cobb-Douglas productivity index dependent variable is Yist − αk,s2D × Kist − αl,s2D ×WBist −αm,s2D ×Mist, where αl,s2D=(2-digit sectoral wage bill)/(2-digit sectoral revenues), αm,s2D=(2-digit sectoral in-put costs)/(2-digit sectoral revenues), and αk,s2D = 1− αl,s2D − αm,s2D (avoiding the need to measure capitalcosts).

41The productivity increase in the first year may raise concerns. We discuss this in detail in Subsection 5.5.

25

year of their first match, an improvement which is amplified in the next four years to thepoint of reaching a plateau 8 to 11 percent higher than the one in event year -1.42

Columns 4 to 6 present results for the balanced sample of only domestic firms becom-ing first-time suppliers to an MNC. These columns offer estimates of the post-event coeffi-cients that are consistently larger than those in columns 1 to 3. This points to the existenceof weaker, yet positive trends of productivity for domestic firms never matched to an MNCthat are of the same age and in the same 4-digit sector and province as first-time suppliers.

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Figure 4: Event Study: OLS Production Function Estimation (CD+TL) and Cost-share Pro-ductivity Index (CD)

Notes: Figure 4 plots the estimated θk event study coefficients for different regressions. Panel 4a shows theestimation result of Equation 6. Panel 4b shows the results of an estimating equation analogous to Equation6 but using Translog production instead of Cobb-Douglas. Panel 4c estimates a regression of the form givenin Equation 1, where the dependent variable is a residualized Cobb-Douglas productivity index. θ−1, thecoefficient of the year prior to a first match with an MNC, is normalized to zero. The vertical lines reflect the95% confidence intervals. The coefficients plotted correspond to Columns 1 to 3 in Table 6.

As we consider the findings in Table 6 to be our baseline findings, we subject themto three additional robustness checks. First, in Table A1, we focus on the extended sampleand show that results are not driven by our choice of the event window. As we define C=-3and C=+3, instead of C=-4 and C=+4, results remain largely unchanged. Second, in TablesA2 and A3, we impose: (i) the set of events used in the estimation on the economy-widesample to be the same as the set of events used in the restrictive sample and (ii) to restrict

42Table 5 reports the counterpart results of Table 6 using the mean shift event study expressed in Equation 5.

26

the economy-wide sample to a set of balanced firms experiencing the event.43 Our findingsare unchallenged by these additional requirements.

Step 3: Prices and markup effects [Work in Progress]

Control function approaches for productivity estimation, like the one presented in Ta-ble 7, address the potential endogeneity of firm-level input choices. However, even if weassume that this bias is not a concern anymore, firm-level input or output prices could stillbe correlated to productivity shocks that are observed by the firm, but not by the econometri-cian. The common inability to observe prices and physical quantities separately would stillresult in biases.44 Changes in firm-level demand or organization could induce changes ininput or output prices that firms face. Therefore, changes in prices (or markups) could mis-guide our interpretation of changes in revenue-based TFP, and confound them with changesin productivity, even when input and output quantities do not change.

The main concern in our context is whether the increases in sales and revenue-basedTFP that we measure are mostly driven by increases in prices and/or markups of supplyingfirms after the event. While our balance sheet data does not allow us to rule out this possibil-ity directly, anecdotal evidence collected during interviews with suppliers to MNCs in CostaRica suggests that the contrary occurs in practice. Suppliers to MNCs have to lower theirmarkups to compete with imports exempted from custom duties through the Free TradeZone regime.45 This evidence is in line with previous research from other countries. Forinstance, interviews conducted by Javorcik, Keller, and Tybout [2008] with suppliers to Wal-Mart in Mexico reveal the “take-or-leave-it” bargaining style of Wal-Mart and thus the verylow profit margins suppliers must agree to when selling to this MNC. Survey evidence fromthe Czech Republic also finds that around 40 percent of suppliers were required by theirMNC customers to lower their prices from 1 to 30 percent [Javorcik, 2008].

Recent empirical research studying the productivity effects of trade liberalizationshas tried to distinguish between physical efficiency and markup effects [De Loecker andWarzynski, 2012, De Loecker, Goldberg, Khandelwal, and Pavcnik, 2016]. We are currentlyusing the De Loecker and Warzynski [2012] method to estimate markups and check whetherthey explain part of our results. This is work in progress. Finally subsection 5.3.2 also

43Since we restrict our sample containing only firms eventually matched to be fully balanced between eventyears -2 and +2, events can only occur between 2010 and 2013 and need to refer to treated firms for whichwe observe at least two years before and after the events. In the economy-wide sample, we allow for eventsto occur between 2010 and 2015 and balance is not imposed. In Table A2 we first require from the economy-wide sample to contain only events between 2010 and 2013, to keep the same years for the events as for therestrictive sample. In Table A3, we require from the events to have occurred only to firms for which we havedata for at least two years before and after the event, therefore using the exact same sample of events as for therestrictive sample.

44Moreover, even if we could observe prices and quantities of inputs and outputs separately, the fact that mostof the firms are multiproduct producers, would still require us to impose additional assumptions on how firmsuse their inputs in order to cleanly interpret productivity results [De Loecker and Goldberg, 2014].

4528 percent of the sales of MNCs triggering the first matches studied here are made by firms part of the FTZregime.

27

presents indirect evidence suggesting that increases in prices are unlikely to be the maindriver of our findings.

Step 4: Quality and product scope [Work in Progress]

Our administrative data does not allow us to account for changes in product specifi-cations or quality. It could be that, even if prices/markups do not change, our observedincrease in measured productivity reflects changes in the product-mix, instead of improve-ments in physical TFP for the same goods produced. To inquire whether this is the case, werestrict our focus to firms in sectors producing homogeneous goods, according to the Rauch[1999] classification. This exercise is currently work in progress.

Irrespective of the findings of this exercise, standard trade models under CES pref-erences and monopolistic competition assumptions predict that productivity and productquality are isomorphic in equilibrium [Melitz and Redding, 2014]. In this context, the no-tion of productivity includes all sources of heterogeneity in firms’ revenue relative to factorinputs. This encompasses differences in technical efficiency, management practices, firmorganization, and product quality.46 Thus, whether our results come from increases in tech-nical efficiency, or increases in good quality, or product scope seems unlikely to change theimplications of our findings.

5.3.2 Indirect evidence on productivity gains

In this subsection, we provide evidence that is consistent with productivity improve-ments and that does not rely on productivity estimation. To this end, we exploit the richnessof our production network data and, in particular, our ability to not only observe relation-ships between domestic firms and their MNC clients, but also all other business relation-ships of these domestic firms. Figure 5 shows that after the event, domestic suppliers in-crease the number of domestic (non-MNC) clients and their sales to them. Such behaviorwould be inconsistent with prices increasing (keeping quality constant) and demand forthose goods sloping down. Prices may have still increased, but our findings imply that in-creases in quality and/or product scope at the level of the firm should have compensatedfor such increase in prices. Given our discussion in the previous section, we interpret ourresults as coming from improvements in productivity. These pieces of evidence also supportour claim that increases in prices/markups are unlikely to be the main driver of our results.

An interesting pattern in Subfigure 5a is that firms experience a relative dip in do-mestic sales of 8 percent, in the year of their first match to an MNC. In the following yearsdomestic sales slowly increase relative to the control group and event year -1, to the point ofreaching an 11 percent relative growth. The number of domestic clients also rises gradually,arriving to 20 percent more domestic clients or 2 more clients relative to the average number

46This is true under CES preferences and monopolistic competition. Different sources of revenue heterogeneitycould have different implications in other frameworks [Melitz and Redding, 2014].

28

of domestic clients of 10.Table 9 investigates whether domestic clients of a first-time supplier to an MNC also

see their productivity improve after the event of this supplier. We measure productivity asin Table 6. To enter this estimation, domestic clients need to have never sold to an MNCthemselves and need to be purchasing at least 10 percent of their local inputs (in value) fromthe supplier triggering the event. If a domestic client has several suppliers that start sellingto an MNC, its event year is set as the event year of its most important supplier among them.

-.2-.1

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(a) Log Sales to Domestic Clients

-.2-.1

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(b) Log Number of Domestic Clients

Figure 5: Improved Performance with Domestic Clients After Starting to Supply to MNCs

Notes: The figures show the event study coefficients coming from regressions analogous toEquation 1. Panels 5a and 5b refer to regressions where the outcomes are the log sales todomestic clients and the log number of domestic clients. θ−1, the coefficient of the yearprior to a first match with an MNC, is normalized to zero. The vertical lines reflect the 95%confidence intervals. The coefficients plotted correspond to Columns 1 to 3 in Table 8.

While the statistical significance of point estimates is not robust across specifications,point estimates are positive and have a magnitude gradually rising in time. Columns 4 to6 focus only on the set of domestic clients of firms that eventually become suppliers to anMNC. For these domestic clients, we show that — four years after one of their significantsuppliers has started selling to an MNC – their productivity is 12 to 13 percent higher.

These findings relate to the models proposed by Rodrıguez-Clare [1996] and Carluccioand Fally [2013]. While neither of these papers proposes a justification for productivitygains to suppliers – gains that are our main empirical finding in the previous subsections –they both propose a channel for improvements in the domestic business of suppliers’ sectors(through firm entry in these sectors and subsequent expansion in input varieties).

Under the assumption that the efficiency of domestic producers increases with therange of available intermediate varieties, producers that adapt to the newly-available va-rieties (demanded by MNCs) experience efficiency gains. If we assume that the expansionin varieties occurs at the firm-level (as opposed to an expansion through firm entry upstream)and if we accept the isomorphism between productivity and changes in product scope, thena firm-level expansion in product scope could explain both our findings on suppliers and

29

these findings on their domestic clients.The slow increase in productivity for domestic clients could thus reflect an adjustment

period to the new products and services offered by the supplier to an MNC or, as in Car-luccio and Fally [2013], the costly adoption of the foreign technology. The dip then slowincrease in domestic sales of first-time suppliers to an MNC also suggests a process of tran-sition of domestic clients to the new varieties offered by the first-time supplier.47

5.3.3 Discussion of our productivity results

Productivity measurement faces important challenges that are well known in the liter-ature. We followed several alternative steps to address as many of the concerns as possible,given the constraints of common administrative datasets.

Putting all evidence together, we conclude that firms experience a productivity boostfrom their first supplying relationship with an MNC. Four years after the event, profitabilitymeasures such as profits and value added per worker increase by 7 and 11 percent, respec-tively, compared to the year before the event. Along the same lines, production functionestimation using OLS yields a 9 percent productivity increase using a translog productionfunction and an 11 percent using a Cobb-Douglas one. Once correcting for input endogene-ity using classic control function methods, we observe a slightly smaller but equally robustproductivity boost of around 8 percent. This last result is quantitatively very similar to theproductivity increase found using a Cobb-Douglas productivity index.

In sum, we observe productivity increases ranging between 7 and 11 percent. Reas-suringly, both direct and indirect pieces of evidence point in the same direction. Given thesimilarity and robustness of our productivity results, the rest of the analysis focuses on theOLS estimation of a translog production function.

5.4 Heterogeneity in Productivity Gains

5.4.1 Heterogeneity in Productivity Gains Based on Supplier’s Sector

We first inquire on the degree of heterogeneity in treatment effects based on charac-teristics of domestic suppliers. Then we document whether heterogeneous treatments, i.e.,matches occurring with MNCs of different characteristics, generate different responses. Evi-dence of heterogeneity in productivity gains would be of interest for public policy, especiallyfor programs or incentives that discriminate across the supplier or MNC characteristics driv-ing such heterogeneity. In addition, patterns of heterogeneity in productivity gains basedon supplier or MNC features can be suggestive of the channels behind these gains.

47The lack of firm-level prices for inputs and output limits our ability to discriminate between the pecuniaryand non-pecuniary parts of these externalities. Kee [2015] collects survey data from the garment sector inBangladesh that allows her to observe which domestic firms share suppliers with MNCs. In addition, shecollects firm-level input and output prices, which she then uses to deflate material costs and sales. Hence, theshared-supplier spillovers on domestic firms measured by Kee [2015] are likely to be non-pecuniary in nature.

30

Table 10 focuses on suppliers in the three most populated sectoral groups: manufac-turing, retail, and services. Columns 1 to 3 are based on the extended sample, and columns 4to 6 on the sample containing only firms that become eventually matched to an MNC. Eachcolumn is the result of a different regression preserving only the firms in the titled sector.

Four patterns stand out in Table 10. First, suppliers from retail - the most frequentsector both in the broader and restricted sample - either experience no gain or a weaker gainin productivity (Columns 5 and 2) upon getting matched to their first MNC. Second, firmsoperating in manufacturing and services experience comparable productivity boosts withineach sample, boosts which are significantly stronger than in retail. Third, the improvementin performance for firms in manufacturing appears as more sluggish (Columns 1 and 4)than that in services (Columns 3 and 6).48 Last, for both manufacturing and services, thestronger results obtained from the restricted sample imply productivity spillovers to firmsin the same 4-digit sector and province.

Next, we use our knowledge of the number of high-skilled workers among all workersemployed by a firm each year. We assign to each firm its mean share of high-skilled workersacross all years when the firm is active. These firm-level mean shares form an economy-widedistribution that we split in three equally-sized bins: the bottom 33 percentiles, the central 34to 67 percentiles and the top percentiles between 68 and 99. In Table 11, we then investigatewhether productivity gains are distinct across bins of firm-level mean shares of high-skill.Columns 1 to 3 run separate event studies for each bin, whereas Column 4 reports resultsfrom a pooled regression testing a difference in event study coefficients for the central bincompared to the bottom and top bins combined.

Domestic suppliers in the central bin of firm-level high-skill composition seem to expe-rience smaller and weaker productivity gains than those in the bottom and top bins. Whilethis difference fails to attain statistical significance at conventional levels, it is suggestiveof a mild U-shaped relationship between a firm’s share of skilled workers and its ability tobenefit from supplying to an MNC.

5.4.2 Heterogeneity in Productivity Gains Based on MNC’s Sector

In Table 12 we turn to the sector of MNCs triggering our events. Joining the sup-ply chain of an MNC in the retail sector is found to not improve suppliers’ performance,whereas joining the supply chain of an MNC either in manufacturing or services leads torobust boosts in productivity. This finding contrasts with evidence from Romania, where a10 percent increase in the number of foreign retail chains’ outlets is found to be associatedwith a 2.4 to 2.6 percent boost in TFP for food suppliers [Javorcik and Li, 2013].

Table 13 proposes a different split of event-triggering MNCs between those part of theFree Trade Zone (FTZ) regime and those outside of this regime. To be granted access to anFTZ, MNCs need to be export-oriented, conduct scientific research, or be part of a “strategic

48To the extent that firms in manufacturing have higher costs of adjustment to new technologies or processes,this result becomes intuitive.

31

sector” (e.g., advanced manufacturing and life sciences). Most MNCs in an FTZ meet allthree criteria. We find evidence that domestic firms who join the supply chain of an MNCin an FTZ see their productivity rise by up to 9 percent more than for those joining thesupply chain of an MNC outside of FTZs (Column 3). This finding is consistent with severalexplanations, e.g., a different sectoral composition for MNCs inside and outside FTZs orthe starker quality-upgrading imposed by export-oriented MNCs (mostly under the FTZregime). The limited number of first matches after 2010 to an MNC in an FTZ providesinsufficient power to discriminate among these explanations with confidence.

Alternatively, in Table 14, we classify MNCs into three equally-sized bins based ontheir mean share of skilled workers by the same method introduced above. Again, Columns1 to 3 run separate event studies for each bin of skill for the purchasing MNC, whereas Col-umn 4 reports results from a pooled regression testing a difference in event study coefficientsfor the central bin compared to the bottom and top bins combined. We learn that gains inproductivity for suppliers are not increasing in the skill-intensity of MNCs, but rather followand inverted U-shape.

Productivity improvements for domestic firms that start supplying to intermediately-skilled MNCs are up to 7 percent higher than for those that start supplying MNCs in thetails of the skill distribution of MNCs. Given the narrowly defined fixed effects used inthe regression in Column 4, this result is not simply driven, for instance, by a differentialselection of sectors into supplying MNCs of different skill-intensities. Note that the rangeof high-skill shares for MNCs in the bottom bin – [0.15, 0.55) – corresponds to ranges in thecenter and top bins – [0.05-0.34) and [0.34-1) – of high-skill shares for domestic suppliers.Thus, medium-ranged MNCs are comparable in high-skill shares to top-ranged domesticsuppliers. Meanwhile top-ranged MNCs, placed between [0,80-1), are more clustered at theright-tail of high-skill shares than top-ranged suppliers, located between [0.34-1].

Taken together, the evidence from Tables 11 and 14 supports the conjecture that moreskill-intensive MNCs are likely to provide stronger opportunities for productivity growth –plausibly through larger knowledge transfers. However, with a too wide gap in the skill-intensity of suppliers and MNCs, the latter might lack the capacity to absorb the knowledgetransferable by the most skill-intensive MNCs.

5.4.3 Heterogeneity in Productivity Gains Based on MNC’s Country of Ownership

Work in Progress.

5.4.4 Heterogeneity in Productivity Gains Based on Amount of First Sale to an MNC

Work in Progress.

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5.5 On the Magnitude and Timing of Our Estimated Productivity Gains

Our baseline results, summarized in Tables 5 and 6, suggest that as a Costa Rican firmbecomes a first time supplier to an MNC in Costa Rica: (i) its productivity increases by 3 to5 percent in the same year as the deal (compared to that in the year prior to the deal), thenfour years later its productivity is in the range of 8 to 17 percent higher (than in the yearprior to the first deal), and (ii) when we pool all years after a first deal, the average shift inproductivity experienced after a first deal with an MNC is in the range of 2 to 7 percent.

Is Costa Rica special? One first natural question is whether the magnitude of our resultsreflects special features of the Costa Rican economy, as opposed to basic differences in ourresearch design and the treatment effects we measure. To answer this question we replicate– to our closest ability – one of the most familiar specifications in the literature on verticalspillovers from FDI.

We inquire whether Costa Rica may be exceptional, by following the methodology inBlalock and Gertler [2008]. Our specification is closest to the one from their Table 6, Column3.49 On a panel dataset of Indonesian manufacturing establishments, the authors find thatthe productivity of domestic firms increases 9 percent as the share of foreign ownershipdownstream rises from 0 to 1. When using our Costa Rican panel of domestic firms, for thesame increase from 0 to 1 in foreign ownership downstream, the productivity of domesticfirms increases by 12 to 22 percent percent (depending on sample, see Table 21).50

While we estimate that downstream FDI is associated to a slightly larger productivitygain in Costa Rica than in Indonesia, both our estimates and those of Blalock and Gertler[2008] are lower than the average proposed by Havranek and Irsova [2011] in their extensivemeta-study. According to Havranek and Irsova [2011], a 10-percentage-point increase inforeign presence is on average associated with a 3.1 percent increase in the productivity ofdomestic firms in upstream sectors, a 1.5 to 2.5 times larger increase than the one we findfor Costa Rican firms. All in all, this evidence suggests that the magnitude of our resultsis not the fallout of Costa Rica being exceptional, but a direct consequence of our moredisaggregated data and causal empirical strategy.

49As Blalock and Gertler [2008], we estimate a translog production function with labor, capital and materials.Moreover, we also focus on the effects of Backward and Horizontal FDI. In contrast to Blalock and Gertler[2008], we include year-by-province and firm fixed effects, as opposed to year-by-province, sector-by-year andfirm fixed effects. While they calculate the Horizontal variable at the province level, we calculate this variableat the country-level. Hence, we cannot control for sector-by-year fixed effects, as this would absorb all thevariation in our Horizontal variable. Also, unlike Blalock and Gertler [2008], our translog production functiondoes not include energy consumption, as we do not have firm-level data on energy consumption.

50In Indonesia, usual increases in the share of downstream FDI are of approximately 20 percent, suggesting thatthe actual realized productivity gain is closer to 2 percent (0.2×0.087). According to Table B8, for Costa Rica,the sale to the first MNC client in event year 0 represents an average (median) share of 41 (24) percent of thatfirm’s sales for the unbalanced sample (32 and 15 for the balanced sample). Note that this share is at the firm-level, as opposed to the sector-level. These boosts in the share of sales going to one’s first MNC client lead toboosts in productivity in the range of 3 to 5 percent for the large sample event-study and around 5 percent forthe restricted sample (see Table 6).

33

On the onset of treatment effects from event year 0. One pattern that is potentially puz-zling is the onset of treatment effects from event year 0. While increases in firm size mightto some degree be mechanical (if firms do not shed domestic clients when becoming sup-pliers to an MNC), increases in productivity may be expected with delay. To shed light onthis pattern, one would ideally observe both the moment when the domestic firm starts itscollaboration with its first MNC client and the moment when the first payment is made. Un-fortunately, in the firm-to-firm transaction dataset, we cannot observe the starting date forthe collaboration. What this dataset can offer is the year of the first transaction of a domesticfirm with an MNC, which we label as event year 0. This dataset also does not record whenduring a year transactions occur, only the cumulative value transacted in a year betweentwo firms.

To make progress, we use the data from Procomer, the Trade Promotion Agency ofCosta Rica, described in Section 3.2. We first find that in the full sample of 1,983 dealsmediated by Procomer between 2001 and 2016, the dates when deals are agreed upon areevenly distributed across months. While the dates recorded by Procomer as the dates of theagreement are not necessarily those when the transaction is made, we assume there is noreason for transactions to be more concentrated in certain months of the year. Second, fromthe email archive shared with us, we found that around 65 percent of deals go from firstcontact to agreeing on the deal in the same calendar year. Another 27 percent of deals havethe date of the first contact and the sealing of the deal one calendar year apart. Jointly, thesefindings suggest that most transactions are likely to occur within a year of the first contact.

Given the information available in the firm-to-firm transaction dataset however, wecannot distinguish between the following two scenarios (or combinations thereof). In onescenario, effects in event year 0 reflect adjustment and learning in the new role as a supplierto an MNC. This adjustment and learning may be onset as soon as the collaboration starts,most likely in the preceding months to the transaction. In the other scenario, the smalleryear 0 effects are simply “partial year effects,” as in Bernard, Boler, Massari, Reyes, andTaglioni [2017a]. If the lag between the first contact and the first transaction is short, thiswould suggest fast learning in the new role of supplier to an MNC. As we cannot distinguishbetween these two scenarios, we recommend caution on the interpretation of year 0 effects.This caveat notwithstanding, a potentially-imprecise measure of the exact year 0 does notaffect the causal interpretation of our results or their general pattern of growth.

As a robustness check, instead of defining τi as the first year when we observe do-mestic firm i having a transaction with an MNC client, we define τi as the year prior to thatof the first transaction. With this definition of the event year, we are focusing on what islikely to be the year of the first contact with an MNC (for contacts known to be materializedin a transaction a year later). Table A4 shows that, with this new definition of τi, resultsare almost mechanically delayed by a year, with the first gains in productivity manifestingthemselves a year after the presumable first contact. While our preferred definition of theevent year is the year when they first transact with an MNC, we are reassured that results

34

are only changed in their timing as we shift the event year one year backwards.

On the pattern of increasing productivity gains. [Work in Progress] Our event study designdefines the event of interest to a domestic firm as that of its first supplying relationshipwith an MNC in Costa Rica. We focus on first-time supplying relationships, as they allowfor a clean interpretation of pre-trends in outcomes. This choice does not imply that futuresupplying relationships to other MNCs in the country cannot trigger additional productivitygains. We do not study explicitly the effect of these future supplying relationships as weassess that they are, at least to some extent, endogenous outcomes of the first relationship.51

Therefore, the pattern of productivity increasing with years since the first match to anMNC (e.g, see Table 6) most likely reflects a combination of a path of learning and adjust-ment from the first match, with the additive effect of more MNC matches. As of now, wedo not separate between these two possibilities. In future work, one can account for futurematches to MNCs. This would inform on whether this pattern reflects the time necessaryfor knowledge to be absorbed and reflected in productivity gains, or whether knowledge isquickly turned into productivity gains and shared lump-sum by each client.

In Section Appendix B, we bring two pieces of evidence that suggest that both channelsmay be at play. First, in Table B6 we learn that around 43 (28) percent of these domestic firmsstill supply to an MNC in event year +3 (+5). Hence, the pattern of increasing productivityis not fully explained by an increasingly stronger tie to MNCs. That said, for firms stillmatched to an MNC, +3 to +5 years after a first match with an MNC, their average numberof MNC clients has increased with time (see Table B7). More work is needed to disentanglebetween these two explanations.

6 Ruling Out Alternative Hypotheses

In this final step, we rule out alternative explanations that could explain the patternsin the data. If confirmed, these explanations could have two types of implications: eitherthey question the causal nature of our findings or their interpretation.

51Anecdotal evidence gathered by the authors during field interviews with domestic suppliers to MNCs suggeststhat the first match to an MNC is the most uncertain; once a firm has in its resume having supplied to one MNC,new supplying contracts are easier to secure. As an example, Electro Plast S.A., a domestic firm that has becomea leader in the high-precision plastics industry, has on its website a page dedicated to listing its customers:“Satisfied Clients: Electro Plast S.A. offers its clients a personalized service. We work 24 hours a day, 7 daysa week, and all of our employees are working [to] improve and guarantee the quality and compliance withour clients, assuring quality since the process begins. World class clients that support our name: ArthrocareHealthcare, CHML Innovative technologies, SMC, Hologic,” etc. (see website here).

35

http://www.electroplast.com/customers/

6.1 Can Unobservable Traits of Firms Aiming to Supply to MNCs Drive

our Results? The Productive Linkages Program

Despite the absence of observable pretrends in our event study regressions presentedabove, we might still be worried that firms willing to engage in supplying relationships withMNCs could differ in unobservable trends with respect to the rest. We address this concernthrough the quasi-experimental variation in opportunities to supply MNCs offered by theProductive Linkages program described in Section 3.2. This is a public program managed byProcomer, Costa Rica’s Trade Promotion Agency, which aims to increase the domestic valueadded for MNCs operating in Costa Rica and help domestic firms start to supply MNCslocally (i.e., export indirectly).52

Concretely, Productive Linkages reduces matching frictions between MNCs in CostaRica and domestic firms in upstream sectors, by identifying the input needs of MNCs andproposing to the MNC the most suitable local suppliers for each need. These suggestedsuppliers have been pre-screened by Procomer staff, who carry out thorough evaluations oftheir potential to join MNC supply chains, resulting in a firm score (Procomer score hence-forth). Deals mediated through the Productive Linkages program generate a set of firms win-ning these deals with MNCs (the “winners”) and a set of contenders to those same deals,that were not chosen (the “losers”). While there was no systematic archiving of the short-lists shared by Procomer with MNCs, we re-constructed them with the help of Procomerstaff, by applying the rules originally used to generate them.53

To our knowledge, the Productive Linkages program provides the first quasi-experimental variation in opportunities to join MNC supply chains across sets ofcomparable domestic competitors. Moreover, Procomer scores are based on informa-tion that is not available in common administrative datasets (see two examples in Figure9). Hence, Procomer scores provide a more credible resemblance among candidates for amatch with an MNC.54 Greenstone, Hornbeck, and Moretti [2010] addressed a similarproblem in the agglomeration spillovers literature by using reported rankings of countieswhere firms considered opening “million dollar plants” (MDP). Our argument parallelstheirs: the short-listed firms (counties) missing a deal with an MNC (an MDP) offer a valid

52At its origins in 1999, the program was supported by the Inter-American Development Bank and was knownas the “Supplier Development Project for High-Technology MNCs.” The program has since undergone a numberof changes in its organizational structure and name (of which Costa Rica Provee or “Costa Rica Supplies” was itslongest-lasting name). For more details, see Monge-Gonzalez and Rodrıguez-Alvarez [2013].

53For each deal, Procomer considered only firms that were either in the same four-digit ISIC sector or in thesame sector category of the “suppliers database” of CINDE. All candidates have to have been evaluated byProcomer and, hence, have a Procomer score. Productive Linkages only considered shortlists of up to five candi-dates. Shortlists could contain less than five candidates in cases in which (i) the scores of the last ranked firmswere much worse than those of the highest scored candidate, or (ii) there were fewer than five firms in theneeded supplying sector. In sum, for each deal, we use up to five of the highest-scoring firms satisfying thesectoral condition, as long as the difference between each firm’s score and the highest score in that shortlist isless than 20 points. For more details see Appendix D.5.

54Unobservables relevant to their appeal to MNCs – such as whether the firm has an English speaking employee,whether it has an enterprise resource planning software in place or whether it has the practice of carrying outfinancial feasibility studies for its projects – are reflected in their Procomer score. The intent to be matched withan MNC is declared by the very participation in the Productive Linkages program.

36

counterfactual to what would have happened with the winners’ performance had they notwon the deal.

Two elements bear noting. First, deals between MNCs and local firms that are medi-ated by Productive Linkages are on average larger than those unmediated. Also, the MNCclients in mediated deals tend to be more high-tech than those in unmediated deals.55 Thisis in line with Procomer’s objective to foster the domestic integration of high-tech MNCs,which are thought to be unreachable clients for most domestic suppliers, without the theinstitutional support of Productive Linkages. This makes mediated deals remarkable.

Second, despite the attractiveness of the Productive Linkages program as an ideal empir-ical setting for our research question, its small sample size represents its main disadvantagewith respect to our baseline event studies. Given the objectives of the program, it is allowedfor suitable firms to join as many shortlists as possible and thus, the same firm can winmultiple deals across time. For instance, the 1,390 deals mediated by Productive Linkages be-tween MNCs and domestic firms from 2001 to 2016 correspond to only 399 distinct winners.We also restrict the sample to those first-time deals through Productive Linkages happeningbetween 2009 and 2014, which reduces the number of events further.56 Finally, to isolate asmuch as possible the effect of having a relevant contract with a high-tech MNC, we restrictour sample to those winners for which the amount of the deal through the program is largerthan the average annual sales to foreign firms in the past. These restrictions leave us withonly 29 suitable events, corresponding to 29 winning and 81 losing firms.

We implement an event study design to estimate the impact of a first match withan MNC through the Productive Linkages program on a domestic firm’s subsequent perfor-mance. Similar to Greenstone, Hornbeck, and Moretti [2010], our event study design can bethought of as a generalized triple-difference design where different firms experience a firstmatch with an MNC in different years.

In contrast to the MDP setup, we also observe the Procomer scores behind the rankingshared with MNCs. We conjecture that firms’ scores are a key input to decision-makingfor MNCs, as MNCs do not have access to complete (and historical) administrative data asresearchers do. Reassuringly, we find that differences in the scores of winning and losingfirms are statistically insignificant.

55We observe that 81 percent of the deals through Procomer are with MNCs in Free Trade Zones (which offertax exemptions to high-tech MNCs). This is a significantly larger share than that among unmediated deals.Among winners, only 30 percent of their unmediated MNC deals are with firms in a Free Trade Zone. Wealso observe that 87 percent of the Procomer MNC deals are with buyers in manufacturing, as opposed to 56percent among the unmediated MNC deals.

56Of the 399 distinct firms winning deals through Procomer, only 146 firms experience their first deal throughProcomer between 2009 and 2014. All but 5 of the 146 firms already had experience in selling to foreign firmsbefore winning a deal through the Productive Linkages program. Restricting our sample to deals between 2009and 2014 allows us to compare these deals with past transactions to MNCs, which we could only observeduring the period of our transactions dataset. For more details see Appendix D.5.

37

6.1.1 Productive Linkages program: empirical specification and results

In order to compare winners and losers after the event, we run analogous regressionsto the ones in Equations 1 and 6, but modified to allow for the extra interaction between theevent dummies and an indicator dummy of winning the deal. As in Greenstone, Hornbeck,and Moretti [2010], we also refer to the winner and loser(s) associated to the same deal as a“case.”

Consider the outcome of a firm i, part of case c, at time t. We investigate the effect ofthe event for both winners and losers by runing the following regression:

Outcomeict = αi + γc + λ1D Sec × t +C

∑k=C

θLk Dk

ict +C

∑k=C

θDi f fk 1{Winner}ic × Dk

ict + εit, (7)

where αi is the firm fixed effect, λ1D Sec × t is the one-digit sector by time fixed effect,1{Winner}ic is an indicator function that equals 1 if firm i wins the deal for case c and zerootherwise. γc is the case fixed effect, which gives the intuitive interpretation of studyingdifferential outcomes within each case. As before, Dk

ict, ∀k are event time dummies. Ourcoefficients of interest are θL

k and θDi f fk , which are interpreted as the effect of the event on

the losers and on the difference in outcomes between winners and losers, respectively.

0.1

.2.3

.4Fr

actio

n

50 60 70 80 90 100Procomer score

Losers Winners

(a) Distribution of Procomer scores

0.2

.4.6

.8Fr

actio

n

-20 -10 0 10 20Difference in Procomer score: winners - losers

(b) Case-level differences in Procomer scores

Figure 6: Firms’ Scores from the Productive Linkages Program

Notes: Figure 6 compares the Procomer scores of winning and losing firms. Panel 6a showsa histogram of Procomer scores for winners (white bars) and losers (grey bars). Panel 6presents a histogram of differences between winners’ and losers’ scores. This differenceis constructed by subtracting the score of the winner minus the average score of the losersin the same case.

Before presenting the results related to Equation 7, Figure 6 presents the comparison ofProcomer scores between winners and losers. Subfigure 6a shows the distribution of Procomerscores of winning and losing firms. Subfigure 6b shows the distribution of the within-casedifference between the Procomer score assigned to the winner and the average score of the

38

losers. Overall, Figure 6 shows that winners and losers are ex-ante similar in terms of scores(we cannot reject the scores being equal in both groups at the 5 percent significance) and thatwinners are not always better than losers on this metric. Both elements favor the compara-bility of winners and losers and thus, the credibility of the counterfactual.

In order to assess the effect of starting to supply to an MNC through the ProductiveLinkages program, we run the specification in Equation 7 for log sales and employment. Wealso run a specification analogous to Equation 7, using log sales as an outcome and addingthe appropriate controls to interpret our results as productivity estimation using a translogproduction function. The results for these regressions are presented in Table 15. Figure 7plots the estimated event study coefficients θ

Di f fk for log sales (Subfigure 7a) and the translog

production function (Subfigure 7b) regressions. These θDi f fk coefficients are interpreted as

the average yearly difference between changes in the outcomes of winning firms versus theones of losing firms in event-year k, relative to the year before the event.

Subfigure 7a show that firms increase in size after the event. The estimated magnitudesare larger than the ones in our economy-wide samples, which could reflect the exceptionalnature of the matches mediated through Procomer. However, the evidence is qualitativelyconsistent with our findings presented in Subsection 5.2.1. Subfigure 7b shows our resultson productivity. Compared to the findings presented in Subsection 5.3, Subfigure 7b alsopaints a very consistent picture. Four years after the event, winners appear to be sixteenpercent more productive than losers. The magnitude of this point estimate is very close tothe ones in Columns 4 to 6 of Table 6, which are obtained for the restricted economy-widesample where we consider only firms that experience an event.

-.50

.51

1.5

Coe

ffici

ents

: Sal

es

<=-4 -3 -2 -1 0 1 2 3 4<=Years Since First MNC Interaction Through Procomer

Difference: winners-losers 95% conf.

(a) Total Sales

-.2-.1

0.1

.2.3

Coe

ffici

ents

: TL

Pro

duct

ivity



(b) OLS Estimation (TL)

Figure 7: Event Study: Difference in winners vs. losers

Notes: Figure 7 plots the estimated θDi f fk event study coefficients for regression Equation 7 using different

outcomes. These coefficients reflect yearly mean differences in outcome changes of winning firms versus losingfirms, relative to the year before the event. Panel 7a uses log sales as the outcome of interest. Panel 7b adds theappropriate controls to Equation 7 so that the regression can be interpreted as delivering productivity estimatescoming from a translog production function. As common, θ

Di f f−1 is normalized to zero. The coefficients plotted

correspond the results presented in Table 15.

The statistical insignificance of the coefficients θDi f fk for k < 0 shows that trends in

outcome variables among winners and losers were very similar before the event. Failing

39

to reject that their trends are equal gives support to our identifying assumption that losingfirms provide a valid counterfactual for winning firms.

Finally, Table 15 also shows that our findings are driven by an upward break in theoutcomes of winning firms after the event, instead of a decay in the outcomes of the losers.This can also be seen in Figure 10. We find that all coefficients θL

k (that can be interpretedas the difference in outcomes of losing firms in event-time k with respect to event-time −1)are statistically insignificant for both pre and post event years. This is important to rule outbusiness stealing effects that could contaminate our control group. In sum, our results in thissection confirm our findings of firm expansion and productivity effects triggered by joiningMNC supply chains.

External Validity: A critique that can be raised about any impact evaluation is theextent to which its results may generalize. As Procomer carries out full mappings of themain supplying sectors to MNCs, the program studied here has a comprehensive nationalreach. The program has also run for sixteen years, allowing us to estimate dynamic causaleffects of joining MNC supply chains.

Moreover, the type of matchmaking service offered by Procomer since 2001 is far fromunique across both developed and developing countries (e.g., the American Supplier Initia-tive in the US or the Local Content Unit in Rwanda [Steenbergen and Sutton, 2017]). A keyadvantage of our setup is the practice of evaluating suppliers prior to matches and the shar-ing of the rankings with the MNC, which gives us a plausible control group. In addition,we can directly investigate the external validity of our findings from Procomer matches, aswe can compare them – thanks to the firm-to-firm transaction dataset – with those from allother matches to MNCs occurring outside this public program. We are not aware of suchcircumstances in other countries with similar programs.

6.2 Can Changes in Management Explain Both the First Match with an

MNC and Our Estimated Gains in Firm Performance?

Work in Progress.

As described in Section 4.2, one threat to the causal interpretation of our results is thata third-cause might be the real trigger of both a first match to an MNC and the measured im-provements in firms performance. One of the most plausible such third-causes is a changein management. In the context of a management field experiment in India, Bloom, Eifert,Mahajan, McKenzie, and Roberts [2013] find that in the first year after Indian firms adoptimproved management practices their productivity is boosted by 17 percent. Thus an im-provement in management can have a dramatic impact on both the pursuit of the firm ofMNC clients and on the productivity of the firm. Using Social Security data, we investigatewhether firms that experience a first match with an MNC have also experienced in the yearprior a change in management.

40

https://www.sba.gov/about-sba/sba-newsroom/press-releases-media-advisories/sba-announces-expansion-american-supplier-initiative-matchmaking-events-be-held-throughout-2015

https://www.sba.gov/about-sba/sba-newsroom/press-releases-media-advisories/sba-announces-expansion-american-supplier-initiative-matchmaking-events-be-held-throughout-2015

6.3 Are Results Mainly Capturing Scale Effects?

MNC clients may differ from domestic clients in more than their potential for knowl-edge spillovers, but also in features of their contracts that are themselves attractive to do-mestic suppliers. Reliable payment,57 the potential for future scaling of the collaboration,transparent and efficient decision-making are attractive features of selling to an MNC client.A natural question then arises: How important is it that the demand shock comes froman MNC affiliate? Could other types of demand shocks – sharing relevant features with ademand shock from an MNC – replicate our findings?

To address these questions, we study the effects of public procurement on domesticfirms. Government public procurement accounted for approximately 15 percent of the 2014Costa Rican GDP (excluding oil revenues) [OECD, 2015]. Typically, over 90 percent of Gov-ernment purchases are carried out by five autonomous institutions: the Costa Rican Elec-tricity Institute (Instituto Costarricense de Electricidad), the National Road Council, the CostaRican Department of Social Security (Caja Costarricense de Seguro Social), the Costa Rican OilRefinery (Refinadora Costarricense de Petroleo) and the National Bank of Costa Rica [OECD,2015]. Hence, Government purchases share with MNC purchases features of reliability andscale. Moreover, once a firm is already pre-registered and pre–qualified, future contractswith the Government are more likely to occur.58

In terms of process, Government entities generally acquire their goods and servicesthrough public tenders, which are advertised in the official legal bulletin, La Gaceta, andother major newspapers. In 2010, the Government of Costa Rica created an electronic plat-form for public procurement called Mer-Link.59 Mer-Link allows for a transparent search ofboth open and closed public tenders, with a detailed description of the product or serviceprocured. All firms are evaluated in their ability to fulfill a given contract, with the details ofthe evaluation available for public consultation. This evaluation process has similar learningbenefits to the evaluations carried out by Procomer through its Productive Linkages programsand to audits carried out independently by MNCs prior to contracting a new supplier.60

Therefore, we study a new treatment, defined as a first sale to the Government. Asbefore, data constraints require such a sale to occur between 2010 and 2015. To avoid over-lapping treatments, we only preserve domestic firms that have never supplied to an MNC.We continue to use the event study methodology described at the beginning of Section 4.2,altered only in the event of interest. We repeat for the set of first-time suppliers to the Gov-

57The same argument is made in Ferraz, Finan, and Szerman [2016]: the Government is a more reliable payerthan most private parties. This reliability gives vendors security that the terms of the contract will be respected,which encourages them to make the investments necessary to fulfill the contract.

58Similar to contracting with MNCs, contracting with the Government presents a learning curve about the de-mand for one’s products. Ferraz, Finan, and Szerman [2016] show that in Brazil, winning a Governmentcontract leads to firm growth, not only because firms are more likely to win more contracts in the future, butalso because they enter more valuable auctions and access more product markets.

59To access the Mer-Link website, see here. Mer-Link coexists with another purchasing system, called Com-praRed, but Mer-Link has grown into the dominating platform.

60Javorcik [2008] argue that audits by MNCs can be invaluable to local suppliers, as they shed light on opera-tional deficiencies previously unknown to suppliers.

41

https://www.mer-link.co.cr/index.jsp

ernment all regression exercises conducted for first-time suppliers to an MNC. Those exer-cises employing the extended sample of first-time suppliers and firms never supplying toan MNC are replicated with an extended sample of first-time suppliers to the Governmentand firms never supplying to the Government.

Table 16 is the analog of Table 2, with the event and samples adapted to the currentexercise. We find that in the extended sample firms increase more modestly in size thanafter a first match with an MNC, whereas in the sample preserving only first-time suppliersto the Government the gains in size are no longer statistically different from zero. Switchingto measures of productivity, Tables 18 and 19 are the counterparts of prior Tables 5 and 6.The new tables exhibit significantly smaller and shorter-lived improvements in productivity,which are not robust across samples and definitions of the dependent variable. The samepattern emerges from the comparison of Table 17 to Table 4: alternative measures of firmperformance are either found unchanged by the event of starting to supply to the Govern-ment or found as only modestly improved in the year of the event alone.

The most striking finding in Table 17 is the one in Column 4. Public purchases arefound to substitute purchases from domestic private firms, which is consistent with a lackof improvement in firm size and performance. This effect contrasts with that displayed inColumn 4 of Table 4. For first-time suppliers to an MNC, at the beginning, sales to theMNC also crowd out sales to domestic firms, but from event year +3 onward, domestic salesexperience a boost as well.

We conclude that our estimated productivity gains from joining MNC supply chainsare unlikely to simply capture demand effects. The lack of (or the weakness and short-termnature of) gains from starting to sell to the Government suggests that there are additionalfeatures of matches to MNCs, plausibly knowledge spillovers, that explain our findings. Tostrengthen this claim, we are currently expanding this analysis by comparing the effects offirst-time supplying relationships with MNCs with similarly-sized first-time contracts withlarge domestic clients and domestic exporters.

6.4 Does Becoming a Supplier to an MNC Improve Tax Compliance?

Another alternative hypothesis is that domestic firms starting to supply to an MNCimprove their tax compliance in such a way that it could generate gains in firm productivityof the type we estimated in previous sections. To the extent that MNCs are under increasedscrutiny by tax authorities, one could speculate that a first match to an MNC is a form of“sunshine as disinfectant.”

Firms in Costa Rica have to report in the D-151 form all their transactions with cor-porate suppliers and clients of a yearly total amount above $4,400. In theory, the incentivestructure of D-151 is one that predicts few inconsistencies between reports of the buyer andsupplier of a transaction. But when tax authorities lack resources to pursue all anomalies inreporting, odds of being audited are not equally distributed across transactions and firms.The self-regulating benefit of third-party reporting is weakened for transactions or firms un-

42

der lower scrutiny. Pomeranz [2015] finds that randomly-assigned audit announcementslead to an increase in value added tax payments by suppliers of treated firms. By a similarlogic, if first-time suppliers to MNCs perceive MNCs as more prone to audits than domesticclients, these suppliers may improve the quality of their D-151 reporting.

Table 20 uses as dependent variables three measures of quality in reporting. As D-151allows for cross-checks of consistency in the buyer’s and seller’s records of each transaction,these measures are built based on D-151. We use here the raw version of D-151, as opposedto the version used for analysis in all other sections (see Appendix D.2 for details). As firmscan improve their reporting in two ways (reducing gaps in reported values for mutually-declared transactions and reducing the frequency of transactions that are not mutually de-clared), dependent variables capture both levers. Columns 1 to 3 focus on the extendedsample including firms never matched to an MNC, Columns 4 to 6 on the restricted samplecontaining only firms becoming suppliers to an MNC.

The first measure – called “Seller-diff” – is a weighted average of the percentagedifference in values reported, across all transactions in a year for which a firm is the seller.The percentage difference is computed as the (maximum value reported-minimum valuereported)/(minimum value reported). “Seller-diff” uses as weights the importance of thetransaction in that year for the seller. If buyers are persistently those reporting largeramounts than sellers, as tax evasion incentives would suggest, then “Seller-diff” capturesthe amount of under-reporting of one’s sales compared to what is reported by one’s buyers.“Buyer-diff” is analogously constructed, this time keeping only transactions for which afirm is the buyer. If incentives act as just described, then “Buyer-diff” reflects the amountof over-reporting of one’s purchases, compared to what is reported by one’s sellers.61

Columns 1, 2, 4, and 5 find no changes in either behavior.The third measure of reporting quality focuses on unreciprocated reports of a transac-

tion (i.e., transactions that can be found only in the D-151 forms of one party), as opposedto discrepancies in amounts declared between two-sided reports. “Mis-Seller” is defined as(the total number of buyers that reported a given firm as a seller, buyers that are not reportedback by the seller)/(the total number of buyers of the said selling firm). Columns 3 and 6 inTable 20 display no clear pattern of increased reporting of one’s buyers.

These findings from D-151 records confirm that domestic firms that join MNC supplychains do not improve their compliance in third party reporting. Furthermore, in previoussections we have found marked increases in a wide array of firms’ size and performancemeasures. Given that most of these measures either do not have a direct link to a firm’s taxliability or imply an opposite behavior of what would be predicted by a mere reduction in

61We claim this measure captures, to first order, the over-reporting behavior of the treated buyer – as opposed tounder-reporting behavior of the supplier to the treated buyer. The intuition is that treatment is plausibly moreefficient in improving reporting for the treated firm itself than for sellers to the treated firm. This intuition isconfirmed in Pomeranz [2015], whose estimated increase in value added payments is significantly higher fortreated firms than for their suppliers. An analogous argument applies for “Seller-diff”.

43

tax-evasive behaviors, it is unlikely that tax compliance effects could reproduce them all.62

7 On Measuring the Aggregate Integration of MNCs

While the main goal of this paper is to estimate firm-level productivity gains fromjoining MNC supply chains, the aggregate implications of these firm-level gains depend onthe extent to which the country overall is embedded in such MNC supply chains. Whilea complete response on these aggregate implications requires a general equilibrium modelthat accounts for, e.g., factor market spillovers and business stealing, we provide hereafter aset of aggregate moments that are likely to be informative in any such aggregation.

A direct measure of the degree of integration of MNCs in the production network of acountry could not be achieved prior to the availability of firm-to-firm transaction data. I-Otables would be, instead, the basis for an indirect measure,63 albeit research on firm het-erogeneity has shown that MNCs source differently from the “average firm” in their sector(as measured by their sector’s I-O table coefficients). While policy-makers and researchershad evidence to suggest that MNCs were more isolated than a similarly-sized, same-sectordomestic counterpart, such a conjecture could not be directly tested until now.64

We exploit our firm-to-firm transaction data to showcase a striking two-sided hetero-geneity – both in the propensity of domestic firms to supply to MNCs and in that of MNCsto source from domestic firms – that has large implications for the estimation of the aggre-gate level of integration of MNCs. We benchmark our aggregate measures of integrationbased on firm-to-firm transaction data to their corresponding measures based on I-O tables.

We first document patterns of integration of MNCs based on firm counts (i.e., whatis the distribution across all firms of firm-level measures of linkage to MNCs?), then basedon a measure of firm size - firm employment (i.e., how much local (indirect) employmentcan be attributable to sales to MNCs?). While in this section we emphasize the finding ofa significantly lower integration compared to that estimated from I-O tables, for a detaileddiscussion on the reasons why I-O based proxies of linkage to MNCs provide overestimatesof actual linkages, we invite readers to consult Appendix C.

62Costa Rica imposes increasing average tax rates on profits as a function of a firm’s revenue. The resulting taxschedule exhibits two notches at the US$150,000 and US$300,000 (2015, PPP) revenue thresholds where theaverage tax rate applied to profits jumps first from 10 to 20 percent, then from 20 to 30 percent. [Bachas andSoto, 2016] find two behavioral responses: (i) some firms reduce their revenue below these thresholds, as tolower the tax rate they face on their entire profit base and (ii) firms remaining above these revenue thresholdsrespond to the higher tax rate by reducing their reported profits. These authors show that costs are relativelyeasier to manipulate, compared to revenues. In our study, had costs been artificially high prior to a first dealwith an MNC, a higher scrutiny on firms dealing with MNCs would imply a lowering of potentially inflatedprior costs. The marked boost in input costs reported in Column 2 of Table 3 suggests a legitimate expansionin operations. Moreover, in light of Costa Rica’s tax system, persistent boosts in measures of performance perworker (e.g., Sales/Worker, Value Added/Worker, Profits/Worker, or Wage Bill/Worker) and of productivity(based on production function estimation and a productivity index) are implausible behavioral responses towhat may be a heightened scrutiny on one’s tax compliance.

63Or customs data, if lower local involvement were to be deduced from a higher use of imports64In Section Appendix C.4 we document five sources of difference between MNCs and domestic firms’ sourcing.

44

Given the available datasets to date, the most common way to overcome the unobserv-able nature of relationships between domestic suppliers and MNCs has relied on a variabletypically called Backward.65 Backwardj (shortened to Bs-s

j hereafter) is a summary statistic ofthe foreign presence in sectors supplied by sector j. We add the superscript s − s to em-phasize that Bs-s

j is constructed from “sector-to-sector” I-O tables.66 Bs-sj was intended to

“capture the extent of potential contacts between domestic suppliers [in sector j] and MNCcustomers” [Javorcik, 2004].67 However, Bs-s

j lives up to this expectation only under strongassumptions – discussed formally in Appendix C – on both the supplying behavior of do-mestic firms and the sourcing behavior of MNCs.

To reveal the unrealistic nature of these assumptions and their implications on theestimation of the integration of MNCs, we propose two new measures of linkages: onebased on our ability to observe the sectors firms sell to, and another based on our ability toalso observe the clients a firm actually sells to within a certain sector.

Concretely, we first replace the sector-level share of total sales of sector j made to sectork (assigned to all firms l in sector j) with a firm-level share of sales made to all firms in sectork (thus now each firm l in sector j has its firm-specific share),

Bf-slj = ∑

k

Yl j→k

Yl j×Hk, (8)

where Yl j are the total sales of firm l in sector j, Yl j→k are the sales of firm l in sector j to sectork, and Hk is a weighted-average of sector k’s foreign ownership, with the weights given byeach firm’s share of sales in the total sales of sector k. We use the superscript f-s to indicatethat the level of disaggregation for this measure is “firm-to-sector.”

We then add the last layer of information allowed by our firm-to-firm transactiondataset, i.e., the information on the actual identity of the clients of firm l in each buyingsector k. While the Bf-s

lj measure was already customized to each firm’s sectoral composition

of sales, Bf-flj makes the customization complete, by acknowledging differences across firms

in their actual clients:

Bf-flj =

N

∑i=1

Yl j→i

Ylj× Foreign Sharei, (9)

65Backwardjt = ∑k

Yj→kYk× Hkt, i.e., a weighted-average foreign-ownership in client sectors k in year t, with the

weights being given by input-output table coefficients measuring the share of sector j sales purchased by

sector k. Hkt = ∑i∈k

Yikt × Foreign Shareikt/(

∑i∈k

Yikt

). The input-output shares would typically be fixed to those

of an earlier year in the sample. All firms in sector j would be assigned the same Backward measure in year t.From this point onward, we omit the year t subscript.

66To ensure internal consistency, we construct I-O coefficients based on our firm-to-firm dataset. Concretely, weaggregate firm-to-firm transactions across firms in the supplying and buying sectors. Our procedure leadsto different coefficients from those directly available in the official I-O table of the Central Bank of Costa Rica(BCCR). The I-O table of BCCR is based on surveys to a sample of large firms and reflects a series of correctionsapplied for harmonization with international I-O tables.

67Given our interest in the effect of starting to supply MNCs on a firm’s performance, we focus on Bs-sj . The

discussion for the measure of forward linkages, Fs-sj , is analogous to the one for Bs-s

j .

45

where Yl j→i is the total amount that firm l in sector j sells to a firm i, Foreign Sharei is theforeign share of ownership of firm i, and N is the total number of firms in the economy.

0.1

.2.3

.4Fr

actio

n

0 .2 .4 .6 .8Backward index

Backward sector-to-sector Backward firm-to-sectorBackward firm-to-firm

Figure 8: Histograms of firm-level backward linkages

Notes: We construct three firm-level measures of backward forward linkages based on three levels of increasingknowledge on the transactions of a firm. The least data-intensive measure (called “Backward”) uses proxies forthe extent to which a firm sells to a given sector from I-O tables and for the degree of foreign ownership in thosesectors. Constructing “Backward firm-to-sector” requires to know to which sectors a firm sells to, with theforeign ownership of one’s clients still being approximated by the sector-level weighted-average ownership.“Backward firm-to-firm” uses the actual firm-to-firm transactions data, requiring no further assumptions onthe sectors one sells to and the foreign ownership of one’s clients.

Figure 8 plots the economy-wide density in backward linkages at these three levelsof aggregation. We find that not only the mean linkage varies depending on the level ofaggregation used, but that also the dispersion changes. We measure how costly each levelof aggregation is. We separate the variation in (Bs-s

lj − Bf-flj ) in its two main components.68

The first one is a measure of the cost of assuming that all firms sell in the same shares tothe same sectors. This accounts for 34 percent of the variation in the original difference.The second one is a measure of the cost of assuming that firms have the same propensityto sell to foreign clients within a sector and explains 62 percent of the total. The covarianceaccounts for 4 percent of the total.

From this exercise we learn the following: (i) the more disaggregated the informationused for a measure of linkage, the more striking is the heterogeneity among firms’ actuallinkages to MNCs, and (ii) even conditional on selling the same shares across purchasingsectors, firms differ greatly in the extent to which they actually sell to the MNC clients withineach purchasing sector. Broadly, one could split firms in two categories: the great majority

68We decompose the total sum of squared differences between the I-O matrix prediction Bs-slj and the actual value

Bf-flj as: ∑

j∑l∈j(Bs-s

lj − Bf-flj )

2 = ∑j

∑l∈j(Bs-s

lj − Bf-slj )

2 + ∑j

∑l∈j(Bf-s

lj − Bf-flj )

2 − 2 ∑j

∑l∈j(Bs-s

lj − Bf-slj )(Bf-s

lj − Bf-flj ).

46

have little to no sales to MNCs, while a minority have MNCs as their main, if not sole clients.

Table 1: Sectoral differences in indirect employment credited to MNCs (thousands)

Direct Indirect jobs I-O/jobs I-O B2B B2B

All MNC sectors 254.3 143.1 89.3 1.60

Top 10 exporting (MNC) sectors ↓

Electronic Components 6.3 1.9 0.4 5.01Medical Devices 13.5 4.8 1.4 3.53Wholesale and Retail 27.6 28.0 20.5 1.37Special Machinery 3.3 1.2 0.4 2.70Liquors 2.4 2.5 1.4 1.79Preserved Fruits 4.7 4.4 2.3 1.90Rubber tires 1.7 0.9 0.3 2.80Paper 3.1 1.0 1.2 0.84Plastic Products 3.0 0.7 0.7 0.94Vegetable Oils 4.2 4.7 4.8 0.98

Notes: We report our findings for the Top 10 non-agricultural exporting sectors in 2013, in descending order ofthe total value of exports of the given sector in 2013. These sectors correspond to the ISIC4 classifications: 2620(Manufacture of computers and peripheral equipment), 3250 (Manufacture of medical and dental instrumentsand supplies), 4510 (Sale of motor vehicles/Retail), 2829 (Manufacture of other special-purpose machinery),1103 (Manufacture of malt liquors and malt), 1030 (Processing and preserving of fruit and vegetables), 2211(Manufacture of rubber tires and tubes; retreading and rebuilding of rubber tires), 1709 (Manufacture of otherarticles of paper and paperboard), 2220 (Manufacture of plastics products), 1040 (Manufacture of vegetableand animal oils and fats), respectively.

As a second aggregate moment capturing the extent of (upstream) integration ofMNCs, we estimate the number of indirect jobs attributable to MNC business in Costa Rica.This statistic is a main pillar in the assessment of benefits from MNC entry by governments,including by the Costa Rican government.69

To compute how much indirect employment of degree 1 is attributable to an MNC ina given sector we weigh the number of workers of supplying firms (supplying sectors tothe sector of the MNC) by the share of sales that those firms (sectors) sell to MNCs (sell tothe sector of the MNC times the share of the sales of that MNC in its sector). For indirectemployment of degree 2 or more, we follow a similar logic, weighing the total employmentof a firm/sector by the product of the direct and indirect shares of sales. The total indirectemployment is the sum of indirect employment of up to degree 10, over all MNCs.

69Find the “Assessment of the Free Trade Zone Regime: Net Benefits for Costa Rica (2011-2015)” (Balance de lasZonas Francas: Beneficio Neto del Regimen para Costa Rica (2011-2015)) carried out by Procomer here. One ofthe main contributions to the estimated benefits is a multiplication of the number of indirect jobs credited toMNCs by the average wage in the economy. The number of indirect jobs in that study is estimated based onthe 2011 I-O table of the Central Bank of Costa Rica.

47

https://procomer.com/downloads/zonas-francas/balance_zf_2011_2015.pdf

In the “Direct jobs” Column of Table 1, we report the number of direct jobs of MNCsin Costa Rica in 2013. We first report this number for all MNCs in Costa Rica, then sepa-rately for MNCs in the Top 10 exporting sectors. Column “Indirect jobs I-O” computes theirindirect employment using sector-to-sector I-O shares. Column “Indirect jobs B2B” relies,instead, on firm-to-firm transactions.70 Column “I-O/B2B” contains the ratio of indirect jobscalculated using I-O tables over indirect jobs calculated using B2B transactions.

Table 1 shows striking differences in the two estimations of indirect employment. Theindirect employment predicted by the I-O table is an over-estimation – by 60 percent onaverage, in 2013 – of the indirect employment measured from the firm-to-firm dataset.71

Moreover, we uncover large cross-sector differences in the extent to which the I-O basedestimation of the number of indirect jobs differs from the firm-to-firm estimation. As wereport sectors in a decreasing order of importance in terms of total exporting value, we re-mark a positive correlation between sectors’ export performance and our measure of over-estimation of the number indirect jobs (i.e.,“ I-O/B2B”) credited to MNCs in those sectors.Export-oriented, high-tech sectors such as “Electronic Components,” “Medical Devices,” or“Special Machinery” are those whose actual integration is most overestimated when mea-sured based on I-O tables. This finding is relevant from an industrial policy perspectivesince it is common that Free Trade Zone subsidies depend on MNCs’ economic sector.72

In sum, we find that relying on I-O tables does not only hurt the estimation of the causaleffects of joining MNC supply chains, but also hurts the estimation of the aggregate extentto which a country is embedded in MNC supply chains. While aggregating our micro-levelestimates of productivity gains from supplying to MNCs is beyond the scope of this paper,we provide insights on moments of the aggregate (upstream) integration of MNCs that arevaluable inputs in such an effort.

8 Conclusion

Can local firms boost their productivity by supplying to multinational firms (MNCs)?The answer to this question has, so far, proven elusive, as it requires data on actual firm-to-firm linkages, an empirical strategy that delivers causal estimates, and evidence on produc-tivity (as opposed to performance) gains.

The data and methodology employed in this paper directly address the first two of thechallenges detailed above. We bypass the strong assumptions behind previous proxies of

70Again, both Columns “Indirect jobs I-O” and ‘Indirect jobs B2B” use the same transaction dataset, to avoid thatdifferences between these estimates reflect the way the I-O matrix is constructed by the Central Bank of CostaRica (their method does not rely yet on the B2B transaction dataset).

71In Table 22 we show that this pattern of over-estimation improves slightly across the years in our sample. Thatsaid, the improvement does not come from MNCs sourcing behavior in the country changing in the absolute,but from a closer similarity between the I-O estimate and the firm-to-firm estimate.

72In the Costa Rican case, a specific tax exemption requires firms to belong to what Procomer calls “StrategicSectors”. Law No. 7210 (Ley de Regimen de Zonas Francas) describes these sectors as “key for the economicdevelopment of the country”. The list includes: advanced manufacturing, medical devices, electronic compo-nents, biotechnology, pharmaceutical, aeronautics, and R&D related sectors.

48

firm-to-firm linkages, by leveraging an administrative dataset that provides a paper trail forall firm-to-firm transactions within Costa Rica. To provide causal estimates, we implementan event study analysis, where we define the event as the first time a local firm supplies toan MNC affiliate. The baseline event study uses all such events in the economy and exploitsthe plausible exogeneity of the timing of the event to the local firm.

We show that local firms expand and adjust their production process after becoming afirst-time supplier to an MNC. More importantly, we provide evidence that they experiencesizable and persistent productivity gains. In particular, we show that suppliers to MNCs donot only improve their domestic business (both in terms of sales and number of clients), butalso improve the productivity of their domestic clients. These findings are inconsistent withprice effects alone driving our results.

We then rule out threats to both the causal nature of our findings and their interpre-tation. We first address the concern of selection into supplying to MNCs based on time-varying unobservables, by using a Government-led program that allows us to compare firmsthat won a deal with an MNC to their contenders to the same deal. We are in the processof combining our current datasets to more comprehensive Social Security data to rule outchanges in management that might jointly trigger the match to an MNC and productivitygains.

Moreover, we compare our baseline results to those from an event study in which wedefine the event as a first procurement contract with the Government. Such a contract acts asa demand shock that is unlikely to be accompanied by the type of knowledge transfers thatcontracts with MNCs would. We find no (or short-term) productivity gains from becominga Government contractor, suggesting that our results do not reflect mere demand effects.We also show that our results cannot be explained by improvements in tax compliance, inresponse to potentially wider scrutiny from tax authorities of firms supplying to MNCs.

While beyond the scope of this paper, a natural next step is to aggregate our micro-level estimates of benefits from joining MNC supply chains in a framework that allows forgeneral equilibrium effects. A key ingredient in such an exercise is a credible estimate of howfrequent these supplying relationships are. We show that I-O tables greatly overestimate theextent to which local firms participate in MNC supply chains, as I-O tables are obliviousto the peculiar patterns of MNCs’ sourcing that we uncover in our firm-to-firm transactiondata. These insights inform future research on the aggregate implications of MNC supplychains.

49

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Additional Results

Anatomy of a Firm Joining MNC Supply Chains

Table 2: Firms Expand after Starting to Supply to an MNC

Sales Employment Capital Materials Sales Employment Capital Materials(1) (2) (3) (4) (5) (6) (7) (8)

t ≤ −4 -0.002 -0.014 -0.036 -0.041 -0.038 0.008 -0.005 -0.157(0.020) (0.016) (0.025) (0.027) (0.048) (0.029) (0.075) (0.107)

t = −3 0.013 0.011 -0.015 -0.004 -0.013 -0.022 0.043 -0.085(0.019) (0.017) (0.028) (0.025) (0.031) (0.019) (0.049) (0.065)

t = −2 0.017 0.011 -0.021 0.015 -0.024 -0.036∗∗∗ -0.005 -0.052(0.017) (0.012) (0.024) (0.022) (0.018) (0.011) (0.026) (0.037)

t = 0 0.116∗∗∗ 0.075∗∗∗ 0.029 0.094∗∗∗ 0.112∗∗∗ 0.032∗ 0.048 0.108∗∗∗

(0.019) (0.016) (0.037) (0.024) (0.023) (0.016) (0.030) (0.033)t = 1 0.172∗∗∗ 0.121∗∗∗ 0.060∗ 0.108∗∗∗ 0.146∗∗∗ 0.044 0.038 0.157∗∗∗

(0.024) (0.020) (0.036) (0.031) (0.039) (0.028) (0.059) (0.046)t = 2 0.204∗∗∗ 0.156∗∗∗ 0.109∗∗ 0.094∗ 0.173∗∗∗ 0.044 0.039 0.131∗∗

(0.031) (0.023) (0.043) (0.056) (0.061) (0.047) (0.099) (0.058)t = 3 0.253∗∗∗ 0.201∗∗∗ 0.152∗∗∗ 0.124∗∗ 0.239∗∗ 0.063 0.076 0.162∗

(0.033) (0.026) (0.047) (0.059) (0.088) (0.068) (0.138) (0.088)t ≥ 4 0.257∗∗∗ 0.212∗∗∗ 0.159∗∗ 0.165∗ 0.257∗∗ 0.075 0.046 0.243∗

(0.048) (0.040) (0.071) (0.098) (0.117) (0.092) (0.195) (0.124)

Firm FE Yes Yes Yes Yes Yes Yes Yes YesYear-4DSect-Prov FE No No No No Yes Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes Yes No No No NoNever Matched Yes Yes Yes Yes No No No No

Mean Dep. Var. (level) 0.65 12.9 0.57 0.55 1.63 20.9 0.99 2.29SD Dep. Var. (level) 2.04 33.0 19.7 22.5 4.00 44.2 2.83 4.83

Adjusted R2 0.84 0.79 0.82 0.86 0.90 0.87 0.86 0.90# Observations 88,968 88,968 68,655 45,241 6,835 6,835 6,126 4,033# Fixed Effects 23,632 23,632 18,963 12,582 2,456 2,456 2,275 1,573# Firms 12,758 12,758 10,044 6,732 704 704 672 458

Notes: Table 2 shows the results of running specification 1 adapted to four dependent variables capturing firmsize: log total sales (including exports), log total number of workers, log net assets, and log input costs. Theevent is defined as a first time sale to an MNC. Columns 1 to 4 report event study estimates for the sampleincluding both domestic firms that become first-time suppliers to an MNC after 2010 and domestic firms neverobserved as supplying to an MNC during our entire firm-to-firm transaction dataset. Clustering of standarderrors is at the 2-digit sector by province level. Columns 5 to 8 focus only on the sample of domestic firmsbecoming first-time suppliers to an MNC after 2010 and use standard error clustering at event by provincelevel. For sales, net assets, and input costs, means (in levels) are reported in US$ millions (PPI-deflated to 2013US$). Robust standard errors in parentheses.

*** Significant at the 1 percent level** Significant at the 5 percent level* Significant at the 10 percent level

56

Table 3: Firms Adjust Their Behavior after Starting to Supply to an MNC

Wage-Bill/ HS HS Imports Imports Wage-Bill/ HS HS Imports ImportsWorker Workers Share Share Worker Workers Share Share

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

t ≤ −4 -0.004 -0.019 -0.001 -0.117 -0.003 0.030 -0.031 0.004 -0.337 -0.113∗

(0.008) (0.024) (0.007) (0.104) (0.015) (0.031) (0.051) (0.020) (0.202) (0.062)t = −3 0.003 -0.007 -0.002 0.133 0.000 0.020 -0.040 -0.003 -0.026 -0.054

(0.006) (0.015) (0.005) (0.116) (0.012) (0.021) (0.036) (0.016) (0.145) (0.035)t = −2 0.001 -0.010 -0.002 -0.018 0.001 0.007 -0.042∗ -0.001 -0.171∗ -0.013

(0.006) (0.018) (0.005) (0.069) (0.012) (0.013) (0.021) (0.008) (0.097) (0.020)

t = 0 0.011 0.054∗∗∗ 0.010 0.236∗∗∗ 0.012 -0.005 0.040 0.006 0.176 0.069∗∗

(0.007) (0.016) (0.006) (0.069) (0.013) (0.015) (0.030) (0.010) (0.185) (0.029)t = 1 0.011 0.069∗∗∗ 0.004 0.269∗∗∗ 0.016∗ -0.019 0.018 -0.009 0.400 0.134∗∗

(0.008) (0.022) (0.006) (0.065) (0.009) (0.023) (0.053) (0.016) (0.263) (0.047)t = 2 0.018∗∗ 0.088∗∗∗ -0.000 0.355∗∗∗ 0.027∗∗ -0.034 0.039 -0.008 0.469 0.189∗∗∗

(0.009) (0.022) (0.007) (0.074) (0.010) (0.034) (0.081) (0.028) (0.407) (0.066)t = 3 0.023∗∗ 0.112∗∗∗ 0.002 0.470∗∗∗ 0.050∗∗∗ -0.032 0.029 -0.014 0.656 0.257∗∗∗

(0.010) (0.029) (0.009) (0.110) (0.010) (0.042) (0.110) (0.037) (0.447) (0.081)t ≥ 4 0.020 0.125∗∗∗ 0.002 0.449∗∗∗ 0.034∗∗ -0.038 0.027 -0.025 0.664 0.291∗∗

(0.015) (0.028) (0.009) (0.154) (0.014) (0.048) (0.145) (0.050) (0.546) (0.106)

Firm FE Yes Yes Yes Yes Yes Yes Yes Yes Yes YesYear-4DSect-Prov FE No No No No No Yes Yes Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes Yes Yes No No No No NoNever Matched Yes Yes Yes Yes Yes No No No No No

Mean Dep. Var. (level) 0.006 5.05 0.22 0.33 0.47 0.006 8.55 0.28 0.61 0.45SD Dep. Var. (level) 0.004 16.7 0.30 1.07 0.35 0.004 24.3 0.31 1.35 0.34

Adjusted R2 0.84 0.82 0.74 0.79 0.77 0.85 0.85 0.72 0.83 0.82# Observations 88,968 88,968 88,968 11,342 7,859 6,835 6,835 6,835 1,379 1,058# Fixed Effects 23,632 23,632 23,632 4,123 3,188 2,456 2,456 2,456 619 511# Firms 12,758 12,758 12,758 2,033 1,630 704 704 704 186 175

Notes: Table 3 shows the results of running specification 1 adapted to a series of potential levers of adjustmentto joining MNC supply chains. The outcome variables used are (in order of the columns): wage bill dividedby the contemporaneous number of workers, number of high-skill employees, the proportion of high-skillemployees in the firm, value of imports (for importing firms), proportion of imports of total input costs (forimporting firms). Columns 1 to 5 report event study estimates for the sample including both domestic firmsthat become first-time suppliers to an MNC after 2010 and domestic firms never observed as supplying to anMNC during our entire firm-to-firm transaction dataset. Clustering of standard errors is at the 2-digit sectorby province level. Columns 6 to 10 focus only on the sample of domestic firms becoming first-time suppliersto an MNC after 2010 and use standard error clustering at event by province level. Robust standard errors inparentheses.


57

Productivity Measurement

Step 1: Firm Performance

Table 4: Firm performance

Profits VA Profits/L VA/L Sales/L Profits VA Profits/L VA/L Sales/L(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

t ≤ −4 0.002 0.003 0.014 0.015 0.012 -0.037 -0.076 -0.037 -0.075 -0.046(0.035) (0.041) (0.028) (0.035) (0.017) (0.088) (0.113) (0.090) (0.116) (0.049)

t = −3 0.016 0.035 0.003 0.020 0.001 -0.036 -0.033 -0.007 -0.008 0.009(0.023) (0.025) (0.019) (0.022) (0.024) (0.064) (0.084) (0.064) (0.082) (0.032)

t = −2 0.022 0.038∗ 0.010 0.028 0.006 -0.038 -0.047 0.001 -0.007 0.012(0.020) (0.023) (0.019) (0.023) (0.021) (0.042) (0.052) (0.042) (0.053) (0.018)

t = 0 0.115∗∗∗ 0.130∗∗∗ 0.036∗ 0.047∗∗ 0.041∗∗∗ 0.066∗∗ 0.066∗ 0.034 0.032 0.080∗∗∗

(0.022) (0.026) (0.019) (0.023) (0.015) (0.030) (0.035) (0.027) (0.035) (0.025)t = 1 0.165∗∗∗ 0.187∗∗∗ 0.042∗∗ 0.063∗∗∗ 0.050∗∗∗ 0.080 0.093 0.035 0.044 0.102∗∗∗

(0.026) (0.032) (0.018) (0.024) (0.015) (0.067) (0.078) (0.055) (0.067) (0.028)t = 2 0.212∗∗∗ 0.255∗∗∗ 0.051∗∗∗ 0.091∗∗∗ 0.048∗∗ 0.156 0.184 0.109 0.133 0.128∗∗∗

(0.031) (0.035) (0.017) (0.022) (0.022) (0.101) (0.120) (0.080) (0.101) (0.040)t = 3 0.304∗∗∗ 0.338∗∗∗ 0.098∗∗∗ 0.130∗∗∗ 0.052∗∗ 0.277∗ 0.309∗ 0.213∗∗ 0.237∗ 0.175∗∗∗

(0.032) (0.034) (0.023) (0.031) (0.024) (0.139) (0.158) (0.101) (0.124) (0.057)t ≥ 4 0.292∗∗∗ 0.335∗∗∗ 0.072∗∗∗ 0.111∗∗∗ 0.044 0.330∗ 0.364∗ 0.252∗ 0.274 0.182∗∗

(0.041) (0.040) (0.024) (0.031) (0.036) (0.180) (0.204) (0.132) (0.164) (0.077)

Firm FE Yes Yes Yes Yes Yes Yes Yes Yes Yes YesYear-4DSect-Prov FE No No No No No Yes Yes Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes Yes Yes No No No No NoNever Matched Yes Yes Yes Yes Yes No No No No No

Mean Dep. Var. (level) 0.37 0.29 0.032 0.026 0.37 0.63 0.50 0.033 0.027 0.63SD Dep. Var. (level) 1.33 1.17 0.058 0.058 1.33 1.60 1.33 0.046 0.045 1.60

Adjusted R2 0.75 0.71 0.63 0.61 0.79 0.82 0.78 0.63 0.60 0.84# Observations 88,295 88,295 88,295 88,295 88,968 6,781 6,781 6,781 6,781 6,835# Fixed Effects 23,533 23,533 23,533 23,533 23,632 2,448 2,448 2,448 2,448 2,456# Firms 12,716 12,716 12,716 12,716 12,758 704 704 704 704 704

Notes: Table 4 shows the results of running specification 1 adapted to five dependent variables capturing firmperformance: log profits, log value added, log profits per worker, log value added per worker, and log salesper worker. The event is defined as a first time sale to an MNC. Columns 1 to 5 report event study estimates forthe sample including both domestic firms that become first-time suppliers to an MNC after 2010 and domesticfirms never observed as supplying to an MNC during our entire firm-to-firm transaction dataset. Clusteringof standard errors is at the 2-digit sector by province level. Columns 6 to 10 focus only on the sample ofdomestic firms becoming first-time suppliers to an MNC after 2010 and use standard error clustering at eventby province level. For all dependent variables, means (in levels) are reported in US$ millions (PPI-deflated to2013 US$). Robust standard errors in parentheses.


58

Step 2: OLS Production Function Estimation and Cost-share Productivity Index [Syverson, 2011]

Table 5: Firm Level Productivity (Cost-share CD, OLS CD+TL): Mean Shift

CD CD TL CD CD TLIndex K,L,M K,L,M Index K,L,M K,L,M

(1) (2) (3) (4) (5) (6)

Mean Prod. Shift 0.051∗∗∗ 0.070∗∗∗ 0.053∗∗∗ 0.036∗ 0.035∗∗∗ 0.024∗∗

(0.013) (0.010) (0.008) (0.020) (0.011) (0.010)

Firm FE Yes Yes Yes Yes Yes YesYear-4DSect-Prov FE No No No Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes No No No

Adjusted R2 0.63 0.95 0.96 0.68 0.97 0.98# Observations 43,322 43,322 43,322 3,845 3,845 3,845# Firms 6,522 6,522 6,522 448 448 448

Notes: Table 5 shows the results of running the mean-shift version of specification 6 adapted to three measuresof productivity. The event is defined as a first time sale to an MNC. Columns 1 to 3 report mean shift eventstudy estimates for the sample including both domestic firms that become first-time suppliers to an MNC after2010 and domestic firms never observed as supplying to an MNC during our entire firm-to-firm transactiondataset. Clustering of standard errors is at the 2-digit sector by province level. Columns 4 to 6 focus only on thesample of domestic firms becoming first-time suppliers to an MNC after 2010 and use standard error cluster-ing at event by province level. Columns 1 and 4 use as a dependent variable a productivity index constructedunder the assumption a Cobb-Douglas production function. This method “residualizes” sales by subtractingfirm-level inputs used, weighted by the respective 2-digit-level cost shares. Concretely, the Cobb-Douglas pro-ductivity index dependent variable is Yist − αk,s2D ×Kist − αl,s2D ×WBist − αm,s2D ×Mist, where αl,s2D=(2-digitsectoral wage bill)/(2-digit sectoral revenues), αm,s2D=(2-digit sectoral input costs)/(2-digit sectoral revenues)and αk,s2D = 1− αl,s2D − αm,s2D. Columns 2 and 5 use a measure of productivity resulting from productionfunction estimation. These columns assume a Cobb-Douglas technology, with revenues (PPI-deflated to 2013US$) as the output measure and total net assets, number of workers and input costs as input measures for K,L, and M respectively. Columns 3 and 6 differ from Columns 2 and 5 in their assumption of a translog func-tional form. For both Cobb-Douglas and translog, we estimate the coefficients on factors of production overthe entire sample of domestic firms, controlling for narrowly defined fixed effects. Robust standard errors inparentheses.


59

Table 6: Firm Level Productivity (Cost-share CD, OLS CD+TL): Yearly Effects


(1) (2) (3) (4) (5) (6)

t ≤ −4 0.009 -0.003 0.002 0.004 0.009 -0.012(0.017) (0.014) (0.012) (0.058) (0.039) (0.036)

t = −3 -0.005 0.000 0.001 -0.016 0.016 -0.002(0.017) (0.013) (0.011) (0.040) (0.027) (0.029)

t = −2 0.004 0.011 0.013 -0.002 0.015 0.003(0.013) (0.010) (0.009) (0.022) (0.019) (0.019)

t = 0 0.030∗ 0.046∗∗∗ 0.035∗∗∗ 0.049∗∗ 0.053∗∗∗ 0.044∗∗∗

(0.016) (0.011) (0.009) (0.020) (0.013) (0.014)t = 1 0.058∗∗∗ 0.065∗∗∗ 0.051∗∗∗ 0.060∗ 0.066∗∗ 0.058∗∗∗

(0.019) (0.013) (0.010) (0.032) (0.026) (0.021)t = 2 0.072∗∗∗ 0.088∗∗∗ 0.071∗∗∗ 0.128∗∗∗ 0.119∗∗∗ 0.110∗∗∗

(0.022) (0.017) (0.015) (0.037) (0.036) (0.028)t = 3 0.075∗∗∗ 0.093∗∗∗ 0.075∗∗∗ 0.154∗∗∗ 0.146∗∗ 0.144∗∗∗

(0.020) (0.021) (0.017) (0.054) (0.056) (0.041)t ≥ 4 0.076∗∗ 0.113∗∗∗ 0.089∗∗∗ 0.166∗∗ 0.155∗∗ 0.170∗∗∗

(0.030) (0.023) (0.020) (0.068) (0.073) (0.054)

Firm FE Yes Yes Yes Yes Yes YesYear-4DSect-Prov FE No No No Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes No No NoNever Matched Yes Yes Yes No No No

Mean Dep. Var. (level) 0.29 0.87 0.87 0.28 2.35 2.35SD Dep. Var. (level) 0.53 2.43 2.43 0.45 4.93 4.93

Adjusted R2 0.63 0.95 0.96 0.68 0.97 0.98# Observations 43,322 43,322 43,322 3,845 3,845 3,845# Fixed Effects 12,205 12,205 12,205 1,501 1,501 1,501# Firms 6,522 6,522 6,522 448 448 448

Notes: Table 6 shows the results of running specification 6 adapted to the same three measures of productivitydefined for Table 5. The event is defined as a first time sale to an MNC. Columns 1 to 3 report event studyestimates for the sample including both domestic firms that become first-time suppliers to an MNC after 2010and domestic firms never observed as supplying to an MNC during our entire firm-to-firm transactions data.Clustering of standard errors is at the 2-digit sector by province level. Columns 4 to 6 focus only on the sampleof domestic firms becoming first-time suppliers to an MNC after 2010 and use standard error clustering atevent by province level. Means (in levels) of sales (residualized in Columns 1 and 4) are reported in US$millions (PPI-deflated to 2013 US$). Robust standard errors in parentheses.


60

Step 2: Control Function Methods

Table 7: Firm Level Productivity: Control Function Methods

OLS LP ACF OLS LP ACF(1) (2) (3) (4) (5) (6)

L 0.329∗∗∗ 0.251∗∗∗ 0.318∗∗∗ 0.323∗∗∗ 0.248∗∗∗ 0.330∗∗∗

(0.020) (0.008) (0.004) (0.026) (0.023) (0.112)K 0.082∗∗∗ 0.122∗∗∗ 0.088∗∗∗ 0.100∗∗∗ 0.123∗∗∗ 0.119∗∗∗

(0.008) (0.006) (0.009) (0.022) (0.017) (0.036)M 0.372∗∗∗ 0.427∗∗∗ 0.576∗∗∗ 0.440∗∗∗ 0.368∗∗∗ 0.522∗∗∗

(0.045) (0.009) (0.022) (0.029) (0.032) (0.067)

t ≤ −4 -0.003 0.000 0.001 0.009 -0.026 -0.006(0.014) (0.012) (0.012) (0.039) (0.036) (0.037)

t = −3 0.000 0.006 -0.000 0.016 0.002 -0.001(0.013) (0.012) (0.011) (0.027) (0.030) (0.030)

t = −2 0.011 0.015∗ 0.013 0.015 0.001 -0.000(0.010) (0.009) (0.008) (0.019) (0.019) (0.019)

t = 0 0.046∗∗∗ 0.042∗∗∗ 0.031∗∗∗ 0.053∗∗∗ 0.052∗∗∗ 0.040∗∗

(0.011) (0.011) (0.011) (0.013) (0.015) (0.015)t = 1 0.065∗∗∗ 0.041∗∗∗ 0.044∗∗∗ 0.066∗∗ 0.063∗∗ 0.049∗∗

(0.013) (0.011) (0.009) (0.026) (0.023) (0.021)t = 2 0.088∗∗∗ 0.068∗∗∗ 0.062∗∗∗ 0.119∗∗∗ 0.129∗∗∗ 0.101∗∗∗

(0.017) (0.014) (0.013) (0.036) (0.031) (0.029)t = 3 0.093∗∗∗ 0.074∗∗∗ 0.067∗∗∗ 0.146∗∗ 0.158∗∗∗ 0.136∗∗∗

(0.021) (0.016) (0.016) (0.056) (0.044) (0.040)t ≥ 4 0.113∗∗∗ 0.084∗∗∗ 0.076∗∗∗ 0.155∗∗ 0.193∗∗∗ 0.165∗∗∗

(0.023) (0.020) (0.018) (0.073) (0.059) (0.054)


# Observations 43,322 43,322 43,322 3,845 3,845 3,845# Fixed Effects 12,205 12,205 12,205 1,501 1,501 1,501# Firms 6,522 6,522 6,522 448 448 448

Notes: Table 7 shows the results of running specification 6 adapted to the same three measures of productivity.Columns 1 and 4 show our baseline OLS estimations. Columns 2 and 5 show results of production functionestimation following Levinsohn and Petrin [2003]. Columns 3 and 6 show results of production functionestimation following Ackerberg et al. [2015]. The top panel shows the coefficients of input elasticities of theCobb-Douglas production function. The bottom panel shows the event study coefficients. Columns 1 to 3report event study estimates for the sample including both domestic firms that become first-time suppliers toan MNC and domestic firms never suppliers. Clustering of standard errors is at the 2-digit sector by provincelevel. Columns 4 to 6 focus only on the sample of domestic firms becoming first-time suppliers to an MNCand use standard error clustering at event by province level.


Step 3: Markups Estimation [De Loecker and Warzynski, 2012] [Work in Progress]Step 4: Quality and Product Scope [Work in Progress]

61

Indirect Evidence on Productivity: Performance with Domestic Clients

Table 8: Performance with Domestic Clients

Sales to Number Sales to NumberDom. Clients Dom. Clients Dom. Clients Dom. Clients

(1) (2) (3) (4)

t ≤ −4 -0.017 -0.026 -0.071 -0.097(0.037) (0.028) (0.097) (0.120)

t = −3 -0.017 -0.018 -0.021 -0.067(0.029) (0.028) (0.070) (0.068)

t = −2 -0.018 -0.003 -0.035 -0.024(0.023) (0.021) (0.042) (0.034)

t = 0 -0.082∗∗ 0.039∗ -0.081 0.028(0.040) (0.022) (0.055) (0.036)

t = 1 -0.003 0.128∗∗∗ -0.041 0.125∗

(0.044) (0.023) (0.091) (0.062)t = 2 0.043 0.150∗∗∗ -0.005 0.199∗∗

(0.056) (0.031) (0.120) (0.089)t = 3 0.114∗∗ 0.189∗∗∗ 0.079 0.243∗∗

(0.045) (0.031) (0.155) (0.114)t ≥ 4 0.114∗ 0.207∗∗∗ 0.060 0.307∗∗

(0.066) (0.036) (0.199) (0.137)

Firm FE Yes Yes Yes YesYear-4DSect-Prov FE No No Yes YesYear-4DSect-Prov-Age FE Yes Yes No NoNever Matched Yes Yes No No

Mean Dep. Var. (level) 0.69 10.4 1.73 20.1SD Dep. Var. (level) 2.19 30.8 4.28 52.2

Adjusted R2 0.84 0.85 0.82 0.87# Observations 64,639 64,639 5,273 5,273# Fixed Effects 20,075 20,075 2,057 2,057# Firms 11,252 11,252 704 704

Notes: Table 8 shows the results of running specification 1 adapted to two dependent variables capturing firmperformance: log 1 + sales to domestic clients and log 1 + number of domestic clients. The event is definedas a first time sale to an MNC. Columns 1 and 2 report event study estimates for the sample including bothdomestic firms that become first-time suppliers to an MNC after 2010 and domestic firms never observed assupplying to an MNC during our entire firm-to-firm transaction dataset. Clustering of standard errors is atthe 2-digit sector by province level. Columns 3 and 4 focus only on the sample of domestic firms becomingfirst-time suppliers to an MNC after 2010 and use standard error clustering at event by province level. Robuststandard errors in parentheses.


62

Table 9: Productivity Gains to Domestic Buyers of New Suppliers to MNCs


(1) (2) (3) (4) (5) (6)

t ≤ −4 0.035 0.001 0.003 0.001 0.016 0.002(0.021) (0.017) (0.015) (0.054) (0.043) (0.040)

t = −3 0.017 -0.010 -0.005 0.005 -0.014 -0.015(0.020) (0.015) (0.013) (0.038) (0.031) (0.028)

t = −2 0.030 0.013 0.008 -0.007 -0.013 -0.011(0.023) (0.018) (0.015) (0.026) (0.021) (0.020)

t = 0 0.024∗ 0.004 0.003 0.040 0.015 0.015(0.014) (0.013) (0.013) (0.026) (0.021) (0.019)

t = 1 0.006 0.009 0.012 0.050 0.040 0.041(0.018) (0.018) (0.017) (0.035) (0.028) (0.026)

t = 2 0.019 0.016 0.023 0.078 0.042 0.040(0.018) (0.023) (0.022) (0.054) (0.042) (0.038)

t = 3 0.049∗∗ 0.020 0.030 0.144∗∗ 0.094∗ 0.086∗

(0.023) (0.023) (0.023) (0.067) (0.052) (0.047)t ≥ 4 0.016 0.031 0.034 0.134∗ 0.129∗∗ 0.120∗∗

(0.035) (0.025) (0.023) (0.079) (0.065) (0.060)



Adjusted R2 0.62 0.95 0.96 0.73 0.97 0.98# Observations 36,963 36,963 36,963 1,919 1,919 1,919# Fixed Effects 9,793 9,793 9,793 694 694 694# Firms 5,617 5,617 5,617 231 231 231

Notes: Table 9 checks whether domestic clients of a first-time supplier to an MNC also see their productivityimprove after the event of this supplier. Outcome variables are defined as in Table 5. To enter this estimation,domestic clients need to have never sold to an MNC themselves and need to be purchasing at least 10 percentof their local inputs (in value) from the supplier triggering the event. If a domestic client has several suppliersthat start selling to an MNC, its event year is set as the event year of its most important supplier among them.Columns 1 to 3 report event study estimates for the sample including both domestic clients of firms that neversupply to an MNC and domestic clients of firms that eventually become suppliers to an MNC. Clustering ofstandard errors is at the 2-digit sector by province level. Columns 4 to 6 focus only on the set of domesticclients of firms that eventually become suppliers to an MNC and use standard error clustering at event byprovince level. Robust standard errors in parentheses.


63

Heterogeneity in Productivity Gains

Table 10: Productivity Gains by Supplier Sector

Manuf Retail Serv Manuf Retail Serv(1) (2) (3) (4) (5) (6)

t ≤ −4 -0.01 -0.00 0.01 -0.12 0.02 -0.10(0.04) (0.01) (0.03) (0.13) (0.04) (0.12)

t = −3 0.05 -0.00 -0.02 -0.05 0.02 -0.09(0.04) (0.01) (0.03) (0.08) (0.03) (0.08)

t = −2 0.03 0.02∗ -0.02 -0.05 0.02 -0.04(0.03) (0.01) (0.03) (0.05) (0.02) (0.06)

t = 0 0.03 0.04∗∗∗ 0.07∗∗∗ 0.07 0.03 0.17∗∗∗

(0.03) (0.01) (0.02) (0.05) (0.02) (0.06)t = 1 0.08∗∗ 0.05∗∗∗ 0.08∗∗∗ 0.13 0.02 0.22∗∗∗

(0.03) (0.01) (0.03) (0.09) (0.03) (0.08)t = 2 0.12∗∗∗ 0.05∗∗∗ 0.11∗∗∗ 0.26∗∗ 0.03 0.33∗∗∗

(0.04) (0.01) (0.03) (0.13) (0.04) (0.11)t = 3 0.11∗∗ 0.08∗∗∗ 0.08∗ 0.31∗ 0.07 0.38∗∗∗

(0.04) (0.02) (0.05) (0.16) (0.05) (0.14)t ≥ 4 0.11∗∗ 0.07∗∗∗ 0.11∗∗ 0.33 0.05 0.46∗∗

(0.04) (0.02) (0.05) (0.22) (0.06) (0.18)

Firm FE Yes Yes Yes Yes Yes YesYear-4DSect-Prov FE No No No Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes No No No


Adjusted R2 0.94 0.97 0.93 0.97 0.98 0.95# Observations 6,096 22,715 14,557 505 2,515 979# Fixed Effects 1,881 5,803 4,538 253 868 453# Firms 830 3,168 2,533 63 267 150

Notes: The dependent variable in all columns is a measure of productivity resulting from production functionestimation. We assume a Cobb-Douglas technology, with revenues (PPI-deflated to 2013 US$) as the outputmeasure and total net assets, number of workers and input costs as input measures for K, L and M respec-tively. Each column reports event study point estimates from a separate regression in which firms preservedfor estimation are those operating in the sectoral group listed in the row heading. We keep the three mostpopulated sectoral groups: manufacturing, retail and services. Specifications 1 to 3 include never treated firmsand cluster standard errors at the level of the 2-digit sector by province. Specifications 4 to 6 focus only oneventually treated firms and implement the clustering at the event-by-province level. Robust standard errorsin parentheses.


64

Table 11: Event Study: Productivity Gains by Supplier Skill Level

Bottom Center Top Pooled DiffThird Third Third Center vs Rest

(1) (2) (3) (4)

t ≤ −4 -0.03 -0.01 0.01 -0.03(0.03) (0.02) (0.02) (0.02)

t = −3 -0.02 -0.03 0.01 -0.03(0.04) (0.03) (0.02) (0.03)

t = −2 0.01 0.03 -0.00 0.01(0.04) (0.02) (0.02) (0.03)

t = 0 0.07∗ 0.01 0.05∗∗∗ -0.02(0.04) (0.03) (0.02) (0.03)

t = 1 0.05 0.03 0.06∗∗∗ -0.02(0.04) (0.03) (0.02) (0.03)

t = 2 0.09∗∗ 0.06∗∗ 0.05∗∗ -0.02(0.04) (0.03) (0.02) (0.03)

t = 3 0.09∗∗ 0.04 0.09∗∗∗ -0.04(0.04) (0.03) (0.02) (0.03)

t ≥ 4 0.10∗∗ 0.07∗∗ 0.11∗∗∗ -0.05(0.05) (0.03) (0.03) (0.03)

Year-4DSect-Prov FE Yes Yes Yes YesFirm FE Yes Yes Yes Yes

HS Share [0-0.05) [0.05-0.34) [0.34-1) [0-1]

Adjusted R2 0.93 0.95 0.96 0.95# Observations 12,189 13,122 12,534 43,174# Firms 1,904 1,907 1,799 6,472

Notes: The dependent variable in all columns is a measure of productivity resulting from production functionestimation. We assume a Cobb-Douglas technology, with revenues (PPI-deflated to 2013 US$) as the outputmeasure and total net assets, number of workers and input costs as input measures for K, L and M respectively.Each supplying firm is assigned its mean share of high-skilled workers across all years when the firm is active.These firm-level mean shares form an economy-wide distribution that we split in three equally-sized bins: thebottom 33 percentiles, the central 34 to 67 percentiles and the top percentiles between 68 and 99. Columns 1 to3 run separate event studies for each bin, whereas Column 4 reports results from a pooled regression testing adifference in event study coefficients for the central bin compared to the bottom and top bins combined. Spec-ifications include never treated firms and cluster standard errors at the level of the 2-digit sector by province.Robust standard errors in parentheses.


65

Table 12: Event Study: Productivity Gains by MNC Sector

Manuf Retail Serv(1) (2) (3)

t ≤ −4 0.02 0.12 -0.05(0.08) (0.09) (0.11)

t = −3 -0.01 0.12∗ -0.07(0.05) (0.07) (0.07)

t = −2 0.06 0.10∗∗ -0.05(0.04) (0.05) (0.05)

t = 0 0.10∗∗ 0.02 0.13∗∗∗

(0.04) (0.05) (0.05)t = 1 0.08 0.06 0.20∗∗∗

(0.06) (0.06) (0.07)t = 2 0.16∗∗ 0.06 0.25∗∗∗

(0.08) (0.08) (0.09)t = 3 0.20∗∗ 0.10 0.30∗∗

(0.09) (0.09) (0.12)t ≥ 4 0.27∗∗ -0.03 0.43∗∗∗

(0.12) (0.12) (0.16)

Firm FE Yes Yes YesYear-4DSect-Prov FE Yes Yes Yes

Mean Dep. Var. (level) 2.81 2.15 1.73SD Dep. Var. (level) 5.73 4.71 3.45

Adjusted R2 0.98 0.98 0.97# Observations 1,445 576 929# Fixed Effects 691 276 455# Firms 174 69 113

Notes: The dependent variable in all columns is a measure of productivity resulting from production functionestimation. We assume a Cobb-Douglas technology, with revenues (PPI-deflated to 2013 US$) as the outputmeasure and total net assets, number of workers and input costs as input measures for K, L and M respectively.Each column reports event study point estimates from a separate regression in which firms preserved forestimation are those whose first MNC client operates in the sectoral group listed in the row heading. We keepthe three most populated sectoral groups: manufacturing, retail and services. As for this exercise one needsto know which MNC triggered the event of a first match, by construction, all specifications in this table focusonly on eventually treated firms and implement the clustering at the event-by-province level. Robust standarderrors in parentheses.


66

Table 13: Event Study: Productivity Gains by MNC FTZ Status

Not in FTZ In FTZ Pooled Diff(1) (2) (3)

t ≤ −4 0.05 -0.02 0.02(0.03) (0.06) (0.04)

t = −3 0.02 0.01 0.01(0.02) (0.05) (0.04)

t = −2 0.02 0.02 0.03(0.02) (0.06) (0.04)

t = 0 0.04∗∗ 0.17∗∗ 0.05(0.02) (0.07) (0.05)

t = 1 0.08∗∗∗ 0.26∗∗∗ 0.05(0.02) (0.07) (0.05)

t = 2 0.08∗∗∗ 0.20∗∗ 0.04(0.03) (0.09) (0.05)

t = 3 0.07∗ 0.28∗∗ 0.09∗

(0.04) (0.11) (0.05)t ≥ 4 0.04 0.31∗∗∗ 0.09∗∗

(0.05) (0.11) (0.05)

Year-4DSect-Prov FE Yes Yes YesFirm FE Yes Yes Yes

Adjusted R2 0.97 0.97 0.96# Observations 5,802 566 7,642# Firms 906 103 1,180

Notes: The dependent variable in all columns is a measure of productivity resulting from production functionestimation. We assume a Cobb-Douglas technology, with revenues (PPI-deflated to 2013 US$) as the outputmeasure and total net assets, number of workers and input costs as input measures for K, L and M respectively.Columns 1 and 2 report event study point estimates from separate regressions in which firms preserved forestimation are those whose first MNC client is either not part or part of a Free Trade Zone (FTZ). Column 3 is apooled regression over the two previous samples. As for this exercise one needs to know which MNC triggeredthe event of a first match, by construction, all specifications in this table focus only on eventually treated firmsand implement the clustering at the event-by-province level. Robust standard errors in parentheses.


67

Table 14: Event Study: Productivity Gains by MNC Skill Level

Bottom Center Top Pooled DiffThird Third Third Center vs Rest

(1) (2) (3) (4)

t ≤ −4 -0.02 0.02 -0.03 0.05(0.05) (0.08) (0.05) (0.03)

t = −3 0.01 -0.04 -0.02 0.00(0.04) (0.06) (0.04) (0.04)

t = −2 -0.01 0.03 -0.03 0.05(0.03) (0.05) (0.03) (0.04)

t = 0 0.05∗ 0.09∗∗ 0.03 0.01(0.03) (0.04) (0.04) (0.03)

t = 1 0.08∗∗ 0.17∗∗∗ 0.08∗ 0.03(0.04) (0.06) (0.04) (0.03)

t = 2 0.05 0.22∗∗∗ 0.09∗ 0.07∗

(0.05) (0.08) (0.05) (0.03)t = 3 0.10 0.29∗∗∗ 0.07 0.06

(0.06) (0.10) (0.06) (0.04)t ≥ 4 0.08 0.28∗∗ 0.05 0.07∗

(0.08) (0.12) (0.08) (0.04)

Year-4DSect-Prov FE Yes Yes Yes YesFirm FE Yes Yes Yes Yes

HS Share [0.15-0.55) [0.55-0.80) [0.80-1) [0.15-1]

Adjusted R2 0.98 0.95 0.97 0.97# Observations 1,274 1,854 2,044 6,844# Firms 191 310 340 1,073

Notes: The dependent variable in all columns is a measure of productivity resulting from production functionestimation. We assume a Cobb-Douglas technology, with revenues (PPI-deflated to 2013 US$) as the outputmeasure and total net assets, number of workers and input costs as input measures for K, L and M respectively.Each first MNC client is assigned its mean share of high-skilled workers across all years when the MNC isactive. These mean shares across all first MNC clients form a distribution that we split in three equally-sizedbins: the bottom 33 percentiles, the central 34 to 67 percentiles and the top percentiles between 68 and 99.Columns 1 to 3 run separate event studies for each bin, whereas Column 4 reports results from a pooledregression testing a difference in event study coefficients for the central bin compared to the bottom and topbins combined. As for this exercise one needs to know which MNC triggered the event of a first match, byconstruction, all specifications in this table focus only on eventually treated firms and implement the clusteringat the event-by-province level. Robust standard errors in parentheses. Robust standard errors in parentheses.


68

Ruling Out Alternative Hypotheses

Can Unobservable Traits of Firms Aiming to Supply to MNCs Drive our Results? TheProductive Linkages Program

Figure 9: An actual summary of an (anonymized) evaluation of a Costa Rican firm

Notes: The two figures above are anonymized summary sheets of two actual Procomer evaluations. Each sum-mary sheet is based on a survey asking detailed questions on each of the five modules appraised by Procomer:productive capacity, market capacity, cooperation, R&D capacity and quality. For example, the quality mod-ule asks whether the firm has both general quality management certificates (e.g., ISO-9001) and sector-specificcertificates (e.g., ISO-13485, quality management requirements for organizations producing medical devicesand related services). The cooperation module asks whether the firm has employees able to negotiate in thelanguage relevant to the market it targets. Each evaluation is concluded with an absolute score, a letter gradecategory based on the range of the absolute score and recommendations on which Procomer program the firmis fit to benefit from. The Productive Linkages program is one option of follow-up. The top summary sheetbelongs to a firm that seeks to initiate business relationships with MNCs in a Free Trade Zone (FTZ), withthe hope of acquiring knowledge and experience. The bottom summary sheet pertains to a firm diagnosedas having to make its processes more efficient; Procomer assesses that this boost in efficiency can be obtainedthrough stronger buying and selling relationships [..with MNCs part of the FTZ].

69

-.50

.51

1.5

Coe

ffici

ents

: Sal

es


Losers Winners 95% conf.

(a) Sales: Winners and Losers

-.50

.51

1.5

Coe

ffici

ents

: Sal

es



(b) Sales: Winners − Losers

-.50

.51

Coe

ffici

ents

: Em

ploy

men

t



(c) Employment: Winners and Losers

-.50

.51

Coe

ffici

ents

: Em

ploy

men

t



(d) Employment: Winners − Losers

-.2-.1

0.1

.2.3

Coe

ffici

ents

: TL

Pro

duct

ivity



(e) OLS TL Productivity: Winners and Losers

-.2-.1

0.1

.2.3

Coe

ffici

ents

: TL

Pro

duct

ivity



(f) OLS TL Productivity: Winners − Losers

Figure 10: Productive linkages: Results

Notes: Figure 10 plots the estimated event study coefficients for regression Equation 7 using different outcomes.Subfigures on the left plot the coefficients θL

k (dashed lines) and θLk + θ

Di f fk (solid lines) for different outcomes.

These coefficients reflect yearly mean outcome changes in losers and winners, relative to the year before event.For each Subfigure, the companion Subfigure to the right plots the coefficients θ

Di f fk coming from the same

regression. Lighter lines represent the associated 95% confidence intervals. Panels 10a and 10b use log salesas the outcome of interest. Panels 10c and 10d use log employment as outcome of interest. Panels 10e and10f add the appropriate controls to Equation 7 so that the regression can be interpreted productivity estimatescoming from a translog production function. Both θL

−1 and θDi f f−1 are normalized to zero. The coefficients

plotted correspond the results presented in Table 15.

70

Table 15: Event Study: Productive linkages Winners vs Losers

Employment Sales TL Prod(1) (2) (3)

Losers, t ≤ −4 -0.125 -0.148 -0.004(0.101) (0.143) (0.040)

Losers, t = −3 -0.081 -0.003 0.048(0.076) (0.105) (0.038)

Losers, t = −2 -0.050 -0.010 0.036(0.063) (0.095) (0.032)

Losers, t = 0 -0.061 -0.045 0.018(0.079) (0.090) (0.026)

Losers, t = 1 -0.059 -0.081 -0.010(0.070) (0.101) (0.031)

Losers, t = 2 -0.141 -0.120 0.018(0.103) (0.134) (0.034)

Losers, t = 3 -0.061 -0.075 -0.008(0.113) (0.185) (0.040)

Losers, t ≥ 4 -0.155 -0.159 -0.008(0.127) (0.219) (0.051)

Winners, t ≤ −4 -0.204 0.004 -0.007(0.136) (0.150) (0.052)

Winners, t = −3 -0.090 -0.068 0.015(0.138) (0.218) (0.057)

Winners, t = −2 -0.083 0.010 0.005(0.136) (0.151) (0.056)

Winners, t = 0 0.184 0.223 -0.063(0.137) (0.191) (0.068)

Winners, t = 1 0.119 0.365∗∗ 0.039(0.131) (0.157) (0.056)

Winners, t = 2 0.285∗∗ 0.630∗∗∗ 0.006(0.144) (0.192) (0.058)

Winners, t = 3 0.208 0.549∗∗∗ 0.073(0.172) (0.209) (0.061)

Winners, t ≥ 4 0.514∗∗∗ 0.830∗∗∗ 0.167∗∗

(0.156) (0.201) (0.070)

Mean Dep. Var. (level) 73.1 7.81 7.81SD Dep. Var. (level) 123.4 15.8 15.8

Adjusted R2 0.89 0.84 0.99# Observations 1160 1160 1160# Fixed Effects 139 139 139# Winners 29 29 29# Losers 81 81 81

Notes: Table 15 shows the results of running Equation 7 using different outcomes. Each column reports eventstudy estimates for the same regression. The upper panels present the coefficients and standard errors of θL

kfor the different event-years. These coefficients are interpreted as the yearly mean outcome changes of losers,relative to the year before event. The lower panel presents the coefficients and standard errors of θ

Di f fk for

the different event-years. These coefficients reflect yearly mean differences in outcome changes of winningfirms versus losing firms, relative to the year before the event. Column 1 uses log employment as the outcomeof interest. Column 2 uses log sales as the outcome of interest. Column 3 adds the appropriate controlsto Equation 7 so that the regression can be interpreted as delivering productivity estimates coming from atranslog production function. As common, both θL

−1 and θDi f f−1 are normalized to zero.


71

Are Results Mainly Capturing Scale Effects?

Table 16: No or Short-term Growth after Becoming a Government Contractor

Total Number of CD TL Total Number of CD TLSales Workers K, L K, L Sales Workers K, L K, L

(1) (2) (3) (4) (5) (6) (7) (8)

t ≤ −4 -0.011 -0.011 -0.006 0.003 0.078 0.012 0.080∗ 0.081∗

(0.017) (0.014) (0.014) (0.013) (0.046) (0.040) (0.045) (0.047)t = −3 0.000 -0.006 0.008 0.011 0.014 -0.007 0.039 0.041

(0.015) (0.014) (0.009) (0.009) (0.036) (0.033) (0.028) (0.031)t = −2 0.005 -0.007 0.007 0.008 -0.022 -0.024 0.001 0.001

(0.011) (0.011) (0.011) (0.010) (0.026) (0.018) (0.024) (0.025)

t = 0 0.044∗∗∗ 0.010 0.040∗∗∗ 0.040∗∗∗ 0.039∗ 0.021 0.008 0.009(0.009) (0.017) (0.009) (0.010) (0.022) (0.019) (0.021) (0.021)

t = 1 0.057∗∗∗ 0.040∗∗∗ 0.045∗∗∗ 0.046∗∗∗ -0.005 0.002 -0.019 -0.016(0.014) (0.012) (0.014) (0.014) (0.036) (0.041) (0.035) (0.034)

t = 2 0.069∗∗∗ 0.047∗∗∗ 0.046∗∗∗ 0.046∗∗∗ -0.030 -0.017 -0.037 -0.034(0.015) (0.016) (0.013) (0.013) (0.044) (0.065) (0.045) (0.044)

t = 3 0.040 0.034 0.028 0.026 -0.106 -0.027 -0.108 -0.104(0.027) (0.025) (0.024) (0.022) (0.065) (0.100) (0.075) (0.071)

t ≥ 4 0.023 0.050 0.001 -0.002 -0.121 -0.052 -0.110 -0.104(0.036) (0.037) (0.023) (0.023) (0.092) (0.126) (0.096) (0.087)

Firm FE Yes Yes Yes Yes Yes Yes Yes YesYear-4DSect-Prov FE No No No No Yes Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes Yes No No No NoNever Matched Yes Yes Yes Yes No No No No

Mean Dep. Var. (level) 1.02 17.7 1.02 1.02 3.96 38.1 3.96 3.96SD Dep. Var. (level) 4.88 55.6 4.88 4.88 17.4 143.4 17.4 17.4

Adjusted R2 0.87 0.83 0.92 0.93 0.94 0.92 0.96 0.96# Observations 116,198 116,198 116,198 116,198 5,069 5,069 5,069 5,069# Fixed Effects 27,901 27,901 27,901 27,901 1,784 1,784 1,784 1,784# Firms 15,518 15,518 15,518 15,518 495 495 495 495

Notes: Table 16 shows the results of running specification 6 adapted to two dependent variables capturingfirm size: log total sales (including exports) and log total number of workers. The event is defined as thefirst time a domestic firm sells to the Government. Columns 1 to 4 report the event study estimates for thesample including both domestic firms that become first-time contractors for the Government after 2010 anddomestic firms never observed as selling to the Government during our entire firm-to-firm transactions data.Clustering of standard errors is at the 2-digit sector by province level. Columns 5 to 8 focus only on the sampleof domestic firms that become first-time contractors for the Government after 2010 and use standard errorclustering at event by province level. As the extended sample in this table is a supraset of that in Table 19 –because it does not require that all firms report input costs (M) – Columns 3, 4 and 7, 8 repeat the exercise inColumns 2, 3 and 5, 6 from Table 19 with one modification only - assuming a 2-factor (instead of a 3-factor)production function. Means (in levels) of dependent variables are reported in US$ millions (PPI-deflated to2013 US$) for sales and in number of workers. Robust standard errors in parentheses.


72

Table 17: No or Short-term Improvements in Firm Performance after Becoming a Govern-ment Contractor

Sales/ VA/ Profits/ Sales to Number ofWorker Worker Worker Dom. clients Dom. clients

(1) (2) (3) (4) (5)

t ≤ −4 0.001 0.023 0.023 0.009 -0.011(0.014) (0.029) (0.023) (0.019) (0.020)

t = −3 0.007 -0.016 -0.008 -0.000 -0.015(0.012) (0.025) (0.019) (0.017) (0.014)

t = −2 0.012 0.037 0.037∗∗ -0.002 -0.006(0.011) (0.023) (0.016) (0.010) (0.011)

t = 0 0.033∗∗ 0.055∗ 0.046∗ -0.155∗∗∗ -0.011(0.014) (0.028) (0.024) (0.046) (0.013)

t = 1 0.018 0.035 0.034∗ -0.090∗∗∗ 0.014(0.012) (0.024) (0.020) (0.029) (0.018)

t = 2 0.022 0.009 0.011 -0.104∗∗ 0.032(0.017) (0.033) (0.029) (0.049) (0.020)

t = 3 0.007 0.013 0.006 -0.128∗∗ 0.001(0.025) (0.033) (0.025) (0.052) (0.025)

t ≥ 4 -0.027 -0.017 -0.007 -0.119∗∗ 0.005(0.023) (0.037) (0.032) (0.057) (0.030)

Firm FE Yes Yes Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes Yes YesNever Matched Yes Yes Yes Yes Yes

Mean Dep. Var. (level) 0.066 0.027 0.032 1.09 17.2SD Dep. Var. (level) 0.16 0.069 0.069 5.43 54.9

Adjusted R2 0.80 0.60 0.62 0.89 0.90# Observations 116,198 116,198 116,198 83,895 83,895# Fixed Effects 27,901 27,901 27,901 23,975 23,975# Firms 15,518 15,518 15,518 13,956 13,956

Notes: All columns report event study estimates for the sample including both domestic firms that becomefirst-time suppliers to the Government after 2010 and domestic firms never observed as supplying to the Gov-ernment during our entire firm-to-firm transactions data. Column 1 uses as dependent variable log salesper employee, column 2 uses log value added per employee, while column 3 uses log profits per employee.Columns 4 and 5 explore how the value of sales to domestic clients (excludes the Government) and the numberof domestic clients evolve after a domestic firm becomes a supplier to the Government. Clustering of standarderrors is at the 2-digit sector by province level. Robust standard errors in parentheses.


73

Table 18: No or Small Improvement in Firm Productivity after Becoming a GovernmentContractor


(1) (2) (3) (4) (5) (6)

Mean Prod. Shift -0.006 0.018∗ 0.004 0.005 0.027∗∗ 0.009(0.012) (0.010) (0.009) (0.014) (0.011) (0.008)

Firm FE Yes Yes Yes Yes Yes YesYear-4DSect-Prov FE Yes Yes Yes No No NoYear-4DSect-Prov-Age FE No No No Yes Yes Yes

Adjusted R2 0.63 0.96 0.97 0.62 0.96 0.97# Observations 65,750 65,750 65,750 60,682 60,682 60,682# Firms 9,522 9,522 9,522 8,552 8,552 8,552

Notes: Table 18 shows the results of running the mean-shift version of specification 6 adapted to three mea-sures of productivity. The event is defined as a first time sale to the Government. All columns report eventstudy mean shift estimates for the sample including both domestic firms that become first-time suppliers to theGovernment after 2010 and domestic firms never observed as supplying to the Government during our entirefirm-to-firm transactions data. Clustering of standard errors is at the 2-digit sector by province level. Columns1-3 differ from columns 4-6 in the fixed effects used. Columns 1 and 4 use as a dependent variable a productiv-ity index constructed under the assumption a Cobb-Douglas production function. This method “residualizes”sales by subtracting firm-level inputs used, weighted by the respective 2-digit-level cost shares. Concretely, theCobb-Douglas productivity index dependent variable is Yist − αk,s2D × Kist − αl,s2D ×WBist − αm,s2D × Mist,where αl,s2D=(2-digit sectoral wage bill)/(2-digit sectoral revenues), αm,s2D=(2-digit sectoral input costs)/(2-digit sectoral revenues) and αk,s2D = 1 − αl,s2D − αm,s2D. Columns 2 and 5 use a measure of productivityresulting from production function estimation. These columns assume a Cobb-Douglas technology, with rev-enues (PPI-deflated to 2013 US$) as the output measure and total net assets, number of workers and inputcosts as input measures for K, L and M respectively. Columns 3 and 6 differ from Columns 2 and 5 in theirassumption of a translog functional form. For both Cobb-Douglas and translog, we estimate the coefficients onfactors of production over the entire sample of domestic firms, controlling for narrowly defined fixed effects.Robust standard errors in parentheses.


74

Table 19: No or Short-term Productivity Gains from Becoming a Government Contractor


(1) (2) (3) (4) (5) (6)

t ≤ −4 0.020 -0.011 -0.003 0.043 0.026 0.062(0.012) (0.010) (0.009) (0.066) (0.061) (0.046)

t = −3 0.010 -0.000 0.001 0.022 0.002 0.031(0.012) (0.008) (0.007) (0.045) (0.042) (0.031)

t = −2 0.010 0.003 0.004 0.023 0.007 0.013(0.011) (0.009) (0.008) (0.018) (0.018) (0.015)

t = 0 0.022∗ 0.026∗∗∗ 0.014∗ 0.000 0.010 -0.008(0.012) (0.010) (0.008) (0.017) (0.021) (0.015)

t = 1 0.026∗ 0.031∗∗ 0.016∗ -0.006 -0.002 -0.024(0.015) (0.012) (0.009) (0.034) (0.038) (0.028)

t = 2 0.008 0.023 0.006 -0.001 -0.000 -0.040(0.019) (0.015) (0.012) (0.039) (0.051) (0.035)

t = 3 0.000 0.002 -0.011 -0.019 -0.037 -0.087∗

(0.018) (0.016) (0.012) (0.043) (0.069) (0.045)t ≥ 4 0.014 0.012 0.002 0.004 -0.012 -0.082

(0.021) (0.018) (0.015) (0.047) (0.088) (0.057)




Notes: Table 19 shows the results of running specification 6 adapted to the same three measures of productivitydefined for Table 18. The event is defined as a first time sale to the Government. Columns 1 to 4 reportevent study estimates for the sample including both domestic firms that become first-time suppliers to theGovernment after 2010 and domestic firms never observed as supplying to the Government during our entirefirm-to-firm transactions data. Clustering of standard errors is at the 2-digit sector by province level. Columns5 to 8 focus only on the sample of domestic firms becoming first-time suppliers to an MNC after 2010 and usestandard error clustering at event by province level. Means (in levels) of sales (residualized in Columns 1 and4) are reported in US$ millions (PPI-deflated to 2013 US$). Robust standard errors in parentheses.


75

Does Becoming a Supplier to an MNC Improve Tax Compliance?

Table 20: Similar Compliance in Third Party Reporting After Supplying to an MNC

Seller-Diff Buyer-Diff Mis-Seller Seller-Diff Buyer-Diff Mis-Seller(1) (2) (3) (4) (5) (6)

t ≤ −4 -0.005∗ 0.001 -0.001 0.000 -0.006 0.007(0.003) (0.003) (0.001) (0.016) (0.011) (0.009)

t = −3 -0.001 0.003 0.000 0.004 -0.003 0.007(0.002) (0.003) (0.001) (0.008) (0.008) (0.006)

t = −2 -0.000 0.002 -0.002∗ 0.002 -0.000 -0.000(0.002) (0.002) (0.001) (0.006) (0.005) (0.003)

t = 0 0.002 0.004∗ 0.000 -0.002 0.005 -0.001(0.002) (0.002) (0.001) (0.005) (0.004) (0.003)

t = 1 0.004 0.003 0.002 -0.002 0.011 -0.002(0.002) (0.003) (0.001) (0.008) (0.007) (0.006)

t = 2 0.000 0.001 0.003∗∗ -0.007 0.008 -0.006(0.003) (0.003) (0.001) (0.011) (0.010) (0.008)

t = 3 -0.001 0.002 0.003 -0.008 0.010 -0.008(0.004) (0.003) (0.002) (0.015) (0.013) (0.010)

t ≥ 4 -0.002 0.000 0.003∗ -0.008 0.012 -0.013(0.003) (0.003) (0.002) (0.020) (0.018) (0.015)




Notes: Table 20 shows the results of running specification 1 adapted to three measures of quality in third-partyreporting. “Seller-diff” is a weighted average of the percentage difference in values reported, across all trans-actions in a year for which a firm is the seller. The percentage difference is computed as the (maximum valuereported-minimum value reported)/(minimum value reported). “Seller-diff” uses as weights the importanceof the transaction in that year for the seller. “Buyer-diff” is analogously constructed, this time keeping onlytransactions for which a firm is the buyer. “Mis-Seller” is defined as (the total number of buyers that reporteda given firm as a seller, buyers that are not reported back by the seller)/(the total number of buyers of thesaid selling firm). The event is defined as a first time sale to an MNC. Columns 1 to 3 report event studyestimates for the sample including both domestic firms that become first-time suppliers to an MNC after 2010and domestic firms never observed as supplying to an MNC during our entire firm-to-firm transactions data.Clustering of standard errors is at the 2-digit sector by province level. Columns 4 to 6 focus only on the sampleof domestic firms becoming first-time suppliers to an MNC after 2010 and use standard error clustering atevent by province level. Robust standard errors in parentheses.


76

Is Costa Rica Special? Benchmarking to the Literature on Productivity

Gains from FDI

Table 21: Productivity Gains from FDI, a la Blalock and Gertler [2008]

TL TL

Backward 0.122∗∗∗ 0.218∗∗∗

(0.035) (0.052)Horizontal 0.039 0.109∗

(0.046) (0.064)

Firm FE Yes YesYear-Prov FE Yes YesMatched Prior Yes No

Adjusted R2 0.977 0.969# Observations 65,250 30,945# Fixed Effects 11,182 5,721# Firms 11,126 5,665

Notes: This Table reports the results of estimating an equation in the spirit of Equation 1 from Blalock andGertler [2008]. Our specification is closest to that used in Column 3, Table 6 in their paper. As Blalock andGertler [2008], we estimate a translog production function with labor, capital and materials. Moreover, we alsofocus on the effects of Backward and Horizontal FDI (therefore, not on Forward FDI as well). In contrast toBlalock and Gertler [2008], we include year-by-province and firm fixed effects, as opposed to year-by-province,sector-by-year and firm fixed effects. While they calculate the Horizontal variable at the province level, wecalculate this variable at the country-level.Hence, we cannot control for sector-by-year fixed effects, as thiswould absorb all the variation in our Horizontal variable. Also, in contrast to Blalock and Gertler [2008],our translog production function does not include energy consumption, as we do not have firm-level data onenergy consumption. Column 2 uses the large sample from our main analysis, which includes firms eventuallymatched to an MNC and firms never matched to an MNC. Column 1 adds to this large sample a third type offirms: those which are matched to an MNC from the first year of our panel of firm-to-firm transaction data.This later sample is the closest in spirit to that used in Blalock and Gertler [2008], therefore provides the betterreference for comparing the magnitude of the results. Blalock and Gertler [2008] estimate the coefficients onBackward and Horizontal as 0.090 (SE 4.40) and 0.009 (SE 0.88), respectively.


77

Assumptions Harm the Accuracy of Measures of MNC Integration

Table 22: Differences in indirect employment from MNCs, based on measurement choice(×1,000 jobs)

Direct IndirectI-O Matrix B2B trans.

Year Jobs Jobs Ind/Dir Jobs Ind/Dir I-O/B2B2008 234.37 166.54 0.71 85.26 0.36 1.952009 224.77 164.56 0.73 91.71 0.41 1.792010 236.03 170.71 0.72 87.71 0.37 1.952011 245.71 168.93 0.69 95.81 0.39 1.762012 253.51 165.00 0.65 92.86 0.37 1.782013 254.29 143.14 0.56 89.30 0.35 1.602014 250.14 139.24 0.56 89.13 0.36 1.562015 255.14 155.81 0.61 98.60 0.39 1.58

Notes: To compute how much indirect employment of degree 1 is generated by an MNC in a given sector weweigh the number of workers of supplying firms (supplying sectors to the sector of the MNC) by the shareof sales that those firms (sectors) sell to MNCs (sell to the sector of the MNC times the share of the sales ofthat MNC in its sector). For indirect employment of degree 2 or more, we follow a similar logic, weighingthe total employment of a firm/sector by the product of the direct and indirect shares of sales. The totalindirect employment is the sum of indirect employment of degrees up to degree 10, over all MNCs. Column“Direct Jobs” presents the number of workers directly employed by all MNCs in Costa Rica. Column “I-O Matrix Jobs” calculates indirect employment using sector-to-sector Input-Output shares constructed fromdisaggregated firm-to-firm transactions data. Column “I-O Matrix Ind/Dir” computes the ratio of the indirectnumber of jobs credited to MNCs (calculated based on the I-O matrix, as just described) to the direct numberof jobs in MNCs. Column “B2B trans. Jobs” uses firm-to-firm transactions to compute indirect employment.Both Columns “I-O Matrix Jobs” and “B2B trans. Jobs” use the same transactions dataset, as to avoid thatdifferences between their values reflect the way the I-O matrix is constructed by the Central Bank of CostaRica (their method does not rely yet on the firm-to-firm transaction dataset). These columns also use the sameset of domestic and MNC firms that we consider for the analysis of causal effects of supplying to MNCs.Column “I-O/B2B” presents the ratio of indirect employment calculated using sector-to-sector aggregationover indirect employment calculated using firm-to-firm transactions.

78

Appendices

Appendix A Additional Robustness Results

Table A1: Firm Level Productivity (Cost-share CD, OLS CD+TL): Event Window


(1) (2) (3) (4) (5) (6)

t ≤ −4 0.009 -0.003 0.002(0.017) (0.014) (0.012)

t ≤ −3 / t = −3 0.005 -0.002 0.002 -0.005 0.000 0.001(0.016) (0.013) (0.011) (0.017) (0.013) (0.011)

t = −2 0.004 0.011 0.013 0.004 0.011 0.013(0.013) (0.010) (0.009) (0.013) (0.010) (0.009)

t = 0 0.030∗ 0.046∗∗∗ 0.035∗∗∗ 0.030∗ 0.046∗∗∗ 0.035∗∗∗

(0.016) (0.011) (0.009) (0.016) (0.011) (0.009)t = 1 0.057∗∗∗ 0.066∗∗∗ 0.052∗∗∗ 0.058∗∗∗ 0.065∗∗∗ 0.051∗∗∗

(0.019) (0.013) (0.010) (0.019) (0.013) (0.010)t = 2 0.072∗∗∗ 0.088∗∗∗ 0.071∗∗∗ 0.072∗∗∗ 0.088∗∗∗ 0.071∗∗∗

(0.022) (0.017) (0.015) (0.022) (0.017) (0.015)t ≥ 3 / t = 3 0.075∗∗∗ 0.102∗∗∗ 0.082∗∗∗ 0.075∗∗∗ 0.093∗∗∗ 0.075∗∗∗

(0.023) (0.020) (0.017) (0.020) (0.021) (0.017)t ≥ 4 0.076∗∗ 0.113∗∗∗ 0.089∗∗∗

(0.030) (0.023) (0.020)

Firm FE Yes Yes Yes Yes Yes YesYear-4DSect-Prov-Age FE Yes Yes Yes Yes Yes Yes

Adjusted R2 0.63 0.95 0.96 0.63 0.95 0.96# Observations 43,322 43,322 43,322 43,322 43,322 43,322# Firms 6,522 6,522 6,522 6,522 6,522 6,522

Notes: Table A1 shows the results of running specification 6 adapted to the same three measures of productivitydefined for Table 5. The event is defined as a first time sale to an MNC. All columns report event studyestimates for the sample including both domestic firms that become first-time suppliers to an MNC after 2010and domestic firms never observed as supplying to an MNC during our entire firm-to-firm transactions data.Columns 1 to 3 differ from Columns 4 to 6 in the definition of C and C, i.e., C=-3 and C=+3 for Columns 1 to 3and C=-4 and C=+4 for Columns 4 to 6. Clustering of standard errors is at the 2-digit sector by province level.Robust standard errors in parentheses.


79

Table A2: Firm Level Productivity (Cost-share CD, OLS CD+TL): Only Events Between 2010and 2013


(1) (2) (3) (4) (5) (6)

t ≤ −4 0.027 0.002 0.007 0.004 0.009 -0.012(0.020) (0.016) (0.014) (0.058) (0.039) (0.036)

t = −3 0.008 0.010 0.011 -0.016 0.016 -0.002(0.016) (0.014) (0.012) (0.040) (0.027) (0.029)

t = −2 0.018 0.020 0.023∗∗ -0.002 0.015 0.003(0.015) (0.012) (0.011) (0.022) (0.019) (0.019)

t = 0 0.029∗ 0.040∗∗ 0.027∗∗ 0.049∗∗ 0.053∗∗∗ 0.044∗∗∗

(0.016) (0.016) (0.013) (0.020) (0.013) (0.014)t = 1 0.061∗∗∗ 0.067∗∗∗ 0.050∗∗∗ 0.060∗ 0.066∗∗ 0.058∗∗∗

(0.019) (0.014) (0.011) (0.032) (0.026) (0.021)t = 2 0.084∗∗∗ 0.094∗∗∗ 0.077∗∗∗ 0.128∗∗∗ 0.119∗∗∗ 0.110∗∗∗

(0.022) (0.018) (0.016) (0.037) (0.036) (0.028)t = 3 0.086∗∗∗ 0.097∗∗∗ 0.079∗∗∗ 0.154∗∗∗ 0.146∗∗ 0.144∗∗∗

(0.021) (0.023) (0.019) (0.054) (0.056) (0.041)t ≥ 4 0.088∗∗∗ 0.117∗∗∗ 0.093∗∗∗ 0.166∗∗ 0.155∗∗ 0.170∗∗∗

(0.029) (0.024) (0.021) (0.068) (0.073) (0.054)




Notes: Table A2 shows the results of running specification 6 adapted to the same three measures of productivitydefined for Table 5. The event is defined as a first time sale to an MNC. The main difference between this Tableand Table 6 is in the set of events considered in the estimation on the larger sample. In all tables (except thisone) the set of events for the larger sample allows for events in 2014 and 2015. However, as in the restrictivesample we expect the sample to be balanced between event years -2 to +2, we exclude the events occurring in2014 and 2015. As a robustness check, we replicate Table 6 with the only change of excluding all firms whoseevent occurred either in 2014 or 2015, to show that our results are not driven by having different compositionsof event years in the two samples. We prefer the full set of events for the larger sample (2010 to 2015) forimproved power. Columns 1 to 3 report event study estimates for the sample including both domestic firmsthat become first-time suppliers to an MNC after 2010 (but until 2013 only) and domestic firms never observedas supplying to an MNC during our entire firm-to-firm transactions data. Clustering of standard errors is atthe 2-digit sector by province level. Columns 4 to 6 focus only on the sample of domestic firms becomingfirst-time suppliers to an MNC after 2010 and use standard error clustering at event by province level. Means(in levels) of sales (residualized in Columns 1 and 4) are reported in US$ millions (PPI-deflated to 2013 US$).Robust standard errors in parentheses.


80

Table A3: Firm Level Productivity (Cost-share CD, OLS CD+TL): Only Balanced Sample


(1) (2) (3) (4) (5) (6)

t ≤ −4 0.032 -0.004 0.004 0.004 0.009 -0.012(0.020) (0.016) (0.014) (0.058) (0.039) (0.036)

t = −3 0.013 0.006 0.007 -0.016 0.016 -0.002(0.016) (0.014) (0.012) (0.040) (0.027) (0.029)

t = −2 0.028∗∗ 0.023∗∗ 0.024∗∗ -0.002 0.015 0.003(0.014) (0.012) (0.010) (0.022) (0.019) (0.019)

t = 0 0.044∗∗∗ 0.047∗∗∗ 0.031∗∗∗ 0.049∗∗ 0.053∗∗∗ 0.044∗∗∗

(0.015) (0.014) (0.011) (0.020) (0.013) (0.014)t = 1 0.058∗∗∗ 0.057∗∗∗ 0.043∗∗∗ 0.060∗ 0.066∗∗ 0.058∗∗∗

(0.018) (0.014) (0.012) (0.032) (0.026) (0.021)t = 2 0.095∗∗∗ 0.093∗∗∗ 0.074∗∗∗ 0.128∗∗∗ 0.119∗∗∗ 0.110∗∗∗

(0.026) (0.020) (0.018) (0.037) (0.036) (0.028)t = 3 0.090∗∗∗ 0.091∗∗∗ 0.076∗∗∗ 0.154∗∗∗ 0.146∗∗ 0.144∗∗∗

(0.024) (0.022) (0.019) (0.054) (0.056) (0.041)t ≥ 4 0.097∗∗∗ 0.114∗∗∗ 0.094∗∗∗ 0.166∗∗ 0.155∗∗ 0.170∗∗∗

(0.030) (0.025) (0.022) (0.068) (0.073) (0.054)




Notes: Table A3 shows the results of running specification 6 adapted to the same three measures of productivitydefined for Table 5. The event is defined as a first time sale to an MNC. As an additional robustness check tothat presented in Table A2, for the larger sample, we not only focus exclusively on the events occurring between2010 and 2013, but we require as well that the sample of treated firms to retain only the firms which we observefor at least 2 years before and after their event. While results are not challenged by this new specification, weprefer the baseline sample with all events between 2010 and 2015 for improved power. Columns 1 to 3 reportevent study estimates for the sample including both domestic firms that become first-time suppliers to anMNC after 2010 and domestic firms never observed as supplying to an MNC during our entire firm-to-firmtransactions data. Clustering of standard errors is at the 2-digit sector by province level. Columns 4 to 6 focusonly on the sample of domestic firms becoming first-time suppliers to an MNC after 2010 and use standarderror clustering at event by province level. Means (in levels) of sales (residualized in Columns 1 and 4) arereported in US$ millions (PPI-deflated to 2013 US$). Robust standard errors in parentheses.


81

Table A4: Firm Level Productivity (Cost-share CD, OLS CD+TL): Definition of Event Year


(1) (2) (3) (4) (5) (6)

t ≤ −4 -0.004 -0.022 -0.018 0.016 0.011 0.027(0.019) (0.017) (0.014) (0.050) (0.030) (0.030)

t = −3 0.024 0.002 0.001 0.011 -0.001 -0.000(0.021) (0.015) (0.014) (0.044) (0.027) (0.025)

t = −2 -0.009 -0.012 -0.012 -0.011 0.005 0.006(0.015) (0.010) (0.009) (0.026) (0.015) (0.020)

t = 0 -0.004 -0.012 -0.013 -0.001 -0.020 -0.013(0.013) (0.010) (0.009) (0.023) (0.020) (0.019)

t = 1 0.026∗ 0.035∗∗∗ 0.022∗∗ 0.045 0.030 0.021(0.015) (0.012) (0.009) (0.039) (0.023) (0.024)

t = 2 0.054∗∗∗ 0.054∗∗∗ 0.038∗∗∗ 0.052 0.039 0.025(0.020) (0.013) (0.010) (0.052) (0.033) (0.031)

t = 3 0.068∗∗ 0.077∗∗∗ 0.058∗∗∗ 0.117∗∗ 0.087∗∗ 0.066∗

(0.026) (0.018) (0.017) (0.057) (0.041) (0.037)t ≥ 4 0.072∗∗∗ 0.091∗∗∗ 0.069∗∗∗ 0.143∗ 0.111∗ 0.096∗

(0.023) (0.018) (0.016) (0.078) (0.062) (0.051)




Notes: Table A4 shows the results of running specification 6 adapted to the same three measures of productivitydefined for Table 5. There is only one difference with respect to specification 6: in this table, instead of definingτi as the first year when we observe domestic firm i having a transaction with an MNC client, we define τias the year prior to that of the first transaction. With this definition of the event year, we are focusing onwhat is likely to be the year of the first contact with an MNC (for contacts known to be materialized in atransaction a year later). Columns 1 to 3 report event study estimates for the sample including both domesticfirms that become first-time suppliers to an MNC after 2010 and domestic firms never observed as supplying toan MNC during our entire firm-to-firm transactions data. Clustering of standard errors is at the 2-digit sectorby province level. Columns 4 to 6 focus only on the sample of domestic firms becoming first-time suppliersto an MNC after 2010 and use standard error clustering at event by province level. Means (in levels) of sales(residualized in Columns 1 and 4) are reported in US$ millions (PPI-deflated to 2013 US$). Robust standarderrors in parentheses.


82

Appendix B Summary Statistics for the Event Study Sample

Table B1: Summary Statistics for the Firms in the Economy-Wide Event Study

N Mean S.D. Median

Firms never matched

Wage bill 7,194 62.6 214.2 25.4Exports 167 783.0 1,565.1 141.9Imports 1,109 207.5 881.2 45.0Total sales 7,194 524.2 1,457.2 215.7Employment 7,194 12.2 32.4 6.1Value Added 7,194 207.9 1,126.4 89.5Input costs 3,884 466.3 1,059.5 176.1Total Net Assets 5,688 363.2 1,579.4 102.3

Eventually matched firms(Unbalanced)

Wage bill 1,007 104.8 277.7 36.4Exports 49 1,012.9 2,435.1 178.1Imports 267 498.1 1,450.0 75.3Total sales 1,007 1,226.8 3,079.6 344.0Employment 1,007 19.6 47.9 7.6Value Added 1,007 346.6 1,109.3 115.5Input costs 625 1,288.6 2,990.2 347.5Total Net Assets 880 767.1 2,703.4 202.2

Eventually matched firms(Balanced)

Wage bill 602 107.3 246.5 40.5Exports 31 1,215.8 2,990.1 125.9Imports 175 502.8 1,343.8 93.7Total sales 602 1,432.4 3,114.1 440.5Employment 602 19.7 40.5 8.2Value Added 602 373.9 943.9 132.6Input costs 382 1,520.3 3,139.6 405.6Total Net Assets 541 813.1 3,124.6 229.9

Notes: With the exception of employment, the mean, standard deviation and median are in thousands of PPI-deflated 2013 US$. All values are corresponding to 2008, a year that is, by the construction of the sample, priorto all events in this sample. Some firms from the sample were not active in 2008, which causes the differencebetween the total number of firms described in this table and the total number of firms of each sample. Theupper panel presents raw summary statistics for the sample of firms never observed as supplying to an MNCduring our 2008 to 2015 firm-to-firm transaction data. The middle panel presents raw summary statistics forthe sample of firms observed as supplying for the first time to an MNC in Costa Rica sometime between 2010and 2015. The lower panel presents raw summary statistics for the sample of firms observed as supplying forthe first time to an MNC in Costa Rica sometime between 2010 and 2013 and for which we have a balancedsample, meaning that we have data for at least two years before and after the year of the event.

83

Table B2: Number of Firms Eventually Matched (Events) and MNCs Triggering the Events

Full sample (unbalanced) 2010 2011 2012 2013 2014 2015 Total

Firms (Events) 376 346 346 271 257 187 1,783MNCs (New, unique) 100 73 61 52 35 31 352MNCs (Total, unique) 191 173 156 156 146 121

Restricted sample (balanced) 2010 2011 2012 2013 Total

Firms (Events) 209 169 184 142 704MNCs (New, unique) 97 52 55 46 250MNCs (Total, unique) 135 103 103 88

Notes: The upper panel presents data for the sample of firms observed as supplying for the first time to anMNC in Costa Rica sometime between 2010 and 2015. The lower panel presents data for the sample of firmsobserved as supplying for the first time to an MNC in Costa Rica sometime between 2010 and 2013 and forwhich we have a balanced sample, meaning that we have data for at least two years before and after the yearof the event. The first line of each panel shows the number of events that occur in each calendar year, with thetotal representing the number of events that we observe in each sample. The second line of each panel presentsthe total number of new and unique MNCs that trigger an event in each calendar year in each sample, with thetotal showing the number of unique MNCs that we observe in each sample. The third line shows the numberof unique MNCs that trigger an event in each calendar year. Since MNCs may trigger events in multiple years,a total is not presented for this line.

84

Table B3: Country of Origin of the MNC Triggering the Event

Balanced Freq. Unbalanced Freq.Sample Sample

United States 82 United States 122Panama 18 Panama 26Mexico 16 Colombia 19Spain 14 Mexico 19Colombia 13 Spain 16Great Britain 11 Switzerland 13Germany 9 Great Britain 12Switzerland 8 Germany 11El Salvador 7 Canada 9Canada 7 France 9France 6 Japan 9Japan 5 Guatemala 7Chile 4 El Salvador 7Nicaragua 4 Nicaragua 6Venezuela 4 Chile 4... ... ... ...

Total 250 352

Notes: This Table presents the Top 15 most frequent countries of origin for the MNCs triggering the eventsin our two samples. Each observation represents a unique MNC in each sample. Since an MNC can triggermultiple events, the frequency of each country in the sample of unique MNCs is likely to differ from thefrequency of each country in the sample of events (triggered by these MNCs).

85

Table B4: Sectoral Composition of the Sample of Suppliers and MNCs

Full Sample Restricted Sample(Unbalanced) (Balanced)

Suppliers MNCs Suppliers MNCs

Agriculture, forestry and fishing 9.14 6.51 13.35 6.25Manufacturing 8.9 39.71 10.37 43.61Wholesale and Retail Trade 32.27 24.34 37.78 22.3Transportation and Storage 4.76 3.31 5.4 2.98Accommodation and Food Services 18.02 5.89 9.94 5.4Information and Communication 2.23 4.54 1.85 3.41Real Estate 3.58 2.02 3.27 1.7Professional, Scientific and Technical 6 2.02 6.25 2.13Administrative and Support Service 7.31 6.56 7.39 7.67Human Health and Social Work 2.87 1.29 1.56 1.42Art, Entertainment and Recreation 1.57 0.79 1.14 0.85Other Services 3.25 - 1.42 -Mining and Quarrying 0.09 - 0.28 -Construction - 1.46 - 0.85Education - 1.46 - 1.42Water Supply, Sewerage and Waste Manage - 0.11 - -

Notes: The full sample consists of firms observed as supplying for the first time to an MNC in Costa Ricasometime between 2010 and 2015. The restricted sample is the sample of firms observed as supplying forthe first time to an MNC in Costa Rica sometime between 2010 and 2013, and for which we have a balancedsample, meaning that we have data for at least two years before and after the year of the event. The Tablepresents shares of the number of firms in a given sector. In the first and third columns we focus on the sampleof firms that eventually supply to an MNC, while in the second and fourth columns we focus on their MNCclients.

86

Table B5: Characteristics of Amount and Length of Relationship with First MNC Client

Variable N Mean Median SD

Full sample (unbalanced)

First transaction with MNC (millions of US$) 1783 0.07 0.01 0.47Length of relationship with first MNC 1783 2.07 2.00 1.37Length of relationship with all MNCs 1783 2.50 2.00 1.55

Restricted sample (balanced)

First transaction with MNC (millions of US$) 704 0.07 0.01 0.69Length of relationship with first MNC 704 2.24 2.00 1.47Length of relationship with all MNCs 704 2.73 2.00 1.57

Notes: The full sample consists of firms observed as supplying for the first time to an MNC in Costa Ricasometime between 2010 and 2015. The restricted sample is the sample of firms observed as supplying forthe first time to an MNC in Costa Rica sometime between 2010 and 2013, and for which we have a balancedsample, meaning that we have data for at least two years before and after the year of the event. The firstline presents descriptive statistics of the first transaction of a firm with an MNC. The second line describethe length of relationships with the MNC that triggered the event, while the third line describes the length ofrelationships with any MNC.

87

Table B6: Number of Firms Still Matched to at Least 1 MNC Client in a Given Event Year

Calendar Year of Event Event Year

0 +1 +2 +3 +4 +5Full Sample (Unbalanced)

2010 376 297 253 209 160 1062011 346 261 187 138 892012 346 244 174 1122013 271 171 1062014 257 1722015 187

Total 1783 1145 720 459 249 106

Restricted Sample (Balanced)

2010 209 167 143 115 82 502011 169 119 81 58 322012 184 124 78 402013 142 83 46

Total 704 493 348 213 114 50

Notes: The full sample consists of firms observed as supplying for the first time to an MNC in Costa Ricasometime between 2010 and 2015. The restricted sample is the sample of firms observed as supplying forthe first time to an MNC in Costa Rica sometime between 2010 and 2013, and for which we have a balancedsample, meaning that we have data for at least two years before and after the year of the event. The secondcolumn for each panel reports the distribution, by calendar year, of our events. By construction, in event year0, all firms that become a first-time supplier to an MNC and that are part of a given sample have to appearin the calendar year row of their event year. Thus, by construction, the total number of firms in the columnof the event year 0 have to be 1,783 and, 704, respectively. In the column of event year +1, we can trace howmany of firms matched in a given calendar year are still matched with at least one MNC client one year aftertheir event. The last column describes the number of firms that still have matches with MNCs five years aftertheir first match with an MNC. As one can note, by construction, some cells are empty. For instance, we cannotobserve firms that are first matched to an MNC in 2011 (hence have event year 0 as 2011) in event year +5, asour firm-to-firm does not allow us to observe those firms in 2016.

88

Table B7: Number of MNC Clients in a Given Event Year

Event Year N Mean Median SD

Full Sample (Unbalanced)

0 1783 1.30 1.00 1.03+1 1145 1.73 1.00 1.77+2 720 2.01 1.00 2.29+3 459 2.26 1.00 2.69+4 249 2.63 1.00 3.37+5 106 3.14 2.00 4.36


0 704 1.20 1.00 1.08+1 493 1.38 1.00 1.27+2 348 1.55 1.00 1.85+3 213 1.76 1.00 2.23+4 114 2.05 1.00 2.93+5 50 3.00 2.00 5.43

Notes: The full sample consists of firms observed as supplying for the first time to an MNC in Costa Ricasometime between 2010 and 2015. The restricted sample is the sample of firms observed as supplying forthe first time to an MNC in Costa Rica sometime between 2010 and 2013, and for which we have a balancedsample, meaning that we have data for at least two years before and after the year of the event. For eachsample and and for each event year +k, we show summary statistics of the number of MNC clients amonglocal firms still matched to an MNC +k event years later.

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Table B8: Share of Total Sales Going to MNC Clients in a Given Event Year

Event Year N Mean Median SD

Full Sample (Unbalanced)

0 1783 0.41 0.24 0.39+1 1145 0.41 0.27 0.37+2 720 0.37 0.24 0.35+3 459 0.36 0.22 0.34+4 249 0.36 0.25 0.33+5 106 0.34 0.25 0.30


0 704 0.32 0.15 0.35+1 493 0.31 0.14 0.34+2 348 0.29 0.14 0.33+3 213 0.31 0.17 0.31+4 114 0.30 0.15 0.32+5 50 0.28 0.15 0.31

Notes: The full sample consists of firms observed as supplying for the first time to an MNC in Costa Ricasometime between 2010 and 2015. The restricted sample is the sample of firms observed as supplying forthe first time to an MNC in Costa Rica sometime between 2010 and 2013, and for which we have a balancedsample, meaning that we have data for at least two years before and after the year of the event. For eachsample and and for each event year +k, we show summary statistics of the share of total sales directed to MNCclients for local firms still matched to an MNC +k event years later.

90

Appendix C On the Measurement of Linkages to MNCs

We start with a formal discussion of measures of linkage to MNCs constructed fromI-O tables. We integrate the information from our firm-to-firm transaction dataset in twosteps. In doing so, we propose two new more disaggregated measures of linkage to MNCs.We find sufficient conditions under which I-O based measures can collapse to these two newfirm-level measures of linkage. We also explore how these alternative measures of linkagerelate in a population of firms where sufficient conditions for equivalence do not hold.

The goal of this formal exercise is to spell out the strict conditions under which sector-level measures of linkage to MNCs are informative about a firm’s actual linkage. In addi-tion, this exercise allows us to caution against a direct use of firm-level measures of linkage,as they may capture features of firms that are associated to firms’ potential productivitygrowth. These findings are informative to our choices of research designs.

We then show empirically (both here and in Section 7) that I-O based proxies of link-age to MNCs conceal both heterogeneities in the propensity of domestic firms to supplyMNCs and in the sourcing behavior of MNCs compared to domestic firms. Interacted, theseheterogeneities generate patterns of linkage between domestic firms and MNCs that aresubstantially different from what one would expect based on I-O tables.

Appendix C.1 Derivation of Results Comparing New and Existing Mea-

sures of Backward Linkages to MNCs

Let us first start by writing the three different measures of backward linkages: “sector-to-sector” Bs-s

lj , “firm-to-sector” Bf-slj and “firm-to-firm” Bf-f

lj . These definitions are the same asthose introduced in Section 7:

Bs-sj = ∑

k

Yj→k

Yj×Hk = ∑

kαjk ×Hk, (C10)

Bf-slj = ∑

k

Yl j→k

Yl j×Hk = ∑

kαljk ×Hk, (C11)

Bf-flj =

N

∑i=1

Yl j→iN∑i=1

Yl j→i

× FSi =N

∑i=1

Yl j→i

Ylj× FSi. (C12)

Result 1. For firm l in sector j, αljk = αjk for ∀k is a sufficient condition for Bf-slj = Bs-s

lj . To theextent that a firm l in sector j has the same sectoral decomposition for its output sales as its sector jaggregate decomposition, Bf-s

lj does not improve upon Bs-slj .

Result 1 is evident replacing αjk in Equation C10 for αljk in Equation C11. Equation C12adds the last layer of information allowed by our firm-to-firm transactions dataset (i.e., in-formation on the actual identity of the clients of firm l in each sector k). While the backward

91

linkage measure Bf-slj was already customized to each firm’s sectoral composition of sales,

the new measure we define (Bf-flj ) makes the customization complete, by acknowledging dif-

ferences across firms in their actual clients.Let us now reorganize the N firms in the economy across their main sector k and take

out the common factor of 1/Ylj.

Bf-flj =

N

∑i=1

Yl j→i

Ylj× FSi = ∑

k∑i∈k

Yl j→ik

Ylj× FSik =

1Ylj

∑k

∑i∈k

Yl j→ik × FSik (C13)

To identify a sufficient condition under which Bf-flj = Bf-s

lj , we rewrite Bf-slj by making the

formula of Hk explicit, factorizing out 1/Ylj and introducing Yl j→k into the summation overall firms i in sector k.

Bf-slj = ∑

k

Yl j→k

Yl j×Hk =

1Ylj

∑k

Yl j→k ×∑i∈k

Yik × FSik

∑i∈k

Yik=

1Ylj

∑k

∑i∈k

Yl j→k ×YikYk× FSik (C14)

We can further explore equation C14 by multiplying and dividing by Yl j→ik inside thesecond summation of the last step.

Bf-slj =

1Ylj

∑k

∑i∈k

Yl j→ik × FSik ×Yl j→k

Yl j→ik× Yik

Yk(C15)

Comparing the last expressions in equations C13 and C15, we obtain result 2.

Result 2. For firm l in sector j,Yl j→kYl j→ik

× YikYk

= 1 (orYl j→ikYl j→k

= YikYk

) for ∀i foreign and ∀k is a sufficient

condition for Bf-flj = Bf-s

lj . To the extent that knowing the contribution of firm i to sector k’s output

(YikYk

) is enough to capture the share of output of firm l in sector j sold to firm i of all the output sold

by firm l to sector k (Yl j→ikYl j→k

), then Bf-flj does not improve upon Bf-s

lj .

Another sufficiency result becomes apparent when instead of making the formula ofHk explicit in Bf-s

lj , we write Yl j→k as a summation of Yl j→ik acros all firms i in sector k. Result3 comes from the comparison of equations C13 and C16.

Bf-slj = ∑

k

Yl j→k

Yl j×Hk =

1Ylj

∑k

Yl j→k ×Hk =1

Ylj∑k

∑i∈k

Yl j→ik ×Hk (C16)

Result 3. If for ∀i and ∀k, FSik = Hk then Bf-flj = Bf-s

lj for ∀l in ∀j. To the extent that any buyingfirm i in sector k has the same foreign ownership share as the weighted-average Hk for its sector, thenBf-f

lj does not improve upon Bf-slj .

A natural question is how these three measures of linkage are related in a populationof firms for which the sufficient conditions do not hold. We proceed again in steps.

92

First, we derive a relationship between the sector-level Bs-slj that is assigned to any firm

l in sector j and its firm-specific Bf-slj , that incorporates the sectoral split of its sales. We use

properties of the variance of a product of random variables73 and the following equalities(holding by definition): ∑

k

Yj→kYj

= ∑k

αjk = 1 and ∑k

Yl j→kYl j

= ∑k

αl jk = 1.

Bs-slj = ∑

kαjk × Hk = κ ×

(1κ ∑

kαjk × Hk

)= κ ×E(αjk × Hk)

= κ ×[

E(αjk)×E(Hk) + Cov(αjk, Hk)]= κ ×

[E(αjk)× Hk + Cov(αjk, Hk)

]= κ ×

[1κ×∑

kαjk × Hk + Cov(αjk, Hk)

]= Hk + κ × Cov(αjk, Hk) (C17)

By a similar calculation (only replacing αjk with αl jk) we obtain the formula for Bf-slj , Bf-s

lj =

Hk + κ× Cov(αl jk, Hk), where κ is the total number of sectors k. Combining the formulas for

Bs-slj and Bf-s

lj from equations C17 we arrive to a direct relationship between the two measuresof linkage.

Result 4. Bs-slj = Bf-s

lj + κ × Cov(αjk − αl jk, Hk). Whenever a firm l in sector j has the tendency tounderperform in its sales to more foreign sectors k with respect to the sector-level I-O based share ofsales to these sectors (i.e., has the tendency to have αl jk < αjk for sectors k with a higher weighted-

average foreign presence, Hk) then Bs-slj overestimates Bf-s

lj .

Note: When the sufficient conditions in result 1 hold, the covariance term becomes zero and werecover the equality between Bs-s

lj and Bf-slj .

Next, we show that the sector-level Bs-slj is a weighted-average of firm-level Bf-s

lj . To thisend, we rewrite the definition of Bs-s

lj by breaking down the sales of sector j to sector k (Yj→k)into the sales of given firms l in sector j to sector k (Yl j→k).

Bs-slj = ∑

k

Yj→k

Yj×Hk = ∑

k

∑l∈j

Yl j→k

Yj×Hk = ∑

l∈j∑k

Yl j→k

Yj×Hk

= ∑l∈j

Yl j

Yj∑k

Yl j→k

Yl j×Hk = ∑

l∈jsl j ∑

k

Yl j→k

Yl j×Hk = ∑

l∈jsl j × Bf-s

lj

(C18)

Result 5. Bs-slj = ∑

l∈jsl j × Bf-s

lj . The sector-level Bs-slj is a weighted-average of Bf-s

lj across all firms l in

sector j, with the weights given by the share of output of each firm l in its sector j.

73Cov(X, Y) = E(XY)− E(X)E(Y) or E(XY) = E(X)E(Y) + Cov(X, Y). We use this property repeatedly inthis section.

93

Interested how the average of Bf-slj

(Bf-s

lj

)relates to Bs-s

lj , we use again properties of the

covariance of two random variables (here B f−slj and sl j), result 5 and the fact that ∑

l∈jsl j = 1:

E(

sl j × Bf-slj

)= E

(sl j)×E

(Bf-s

lj

)+ Cov

(sl j, Bf-s

lj

)1Nj

∑l∈j

sl j × Bf-slj =

(1Nj

∑l∈j

sl j

)×(

1Nj

∑l∈j

B f−slj

)+ Cov

(sl j, Bf-s

lj

)1Nj

∑l∈j

sl j × Bf-slj =

(1Nj× 1

)× Bf-s

lj + Cov(

sl j, Bf-slj

)Bs-s

lj = Bf-slj + Nj × Cov

(sl j, Bf-s

lj

)(C19)

Result 6. Bs-slj = Bf-s

lj + Nj × Cov(

sl j, Bf-slj

). To the extent that B f−s

lj and sl j are uncorrelated in a

sector j, Bs-slj is equal to the (un-weighted) average of Bf-s

lj . If B f−slj and sl j are positively (negatively)

correlated, then Bs-slj is larger (smaller) than the (un-weighted) average of Bf-s

lj . Small (large) firms in

sector j would be assigned an upwardly biased Bs-slj compared to their Bf-s

lj .

We now turn to describing the relationship between Bf-slj to Bf-f

lj . We use again proper-ties of the expected value and result 5, in addition to equalities true by construction, e.g.,

∑i∈k

Yl j→ikYlj

=Yl j→k

Ylj.

Bf-flj = ∑

k∑i∈k

Yl j→ik

Ylj× FSik = ∑

kNk E

(Yl j→ik

Ylj× FSik

)

= ∑k

Nk

[E

(Yl j→ik

Ylj

)E (FSik) + Cov

(Yl j→ik

Ylj, FSik

) ]= ∑

kNk ×

1Nk×

Yl j→k

Yl j× FSk + ∑

kNkCov

(Yl j→ik

Ylj, FSik

)

= ∑k

Yl j→k

Yl j× FSk + ∑

kNkCov

(Yl j→ik

Ylj, FSik

)(C20)

Note that in equation 8 Hk can be replaced by ∑i∈k

YikYk× FSik = FSk + Nk×Cov(Yik

Yk, FSik).

To arrive to the last expression in equation C21 we mainly rely on properties of covariances.

94

Bf-slj = ∑

k

Yl j→k

Yl j×Hk = ∑

k

Yl j→k

Yl j× FSk + ∑

k

Yl j→k

Yl j× Nk × Cov(

YikYk

, FSik)

= Bf-flj −∑

kNk × Cov

(Yl j→ik

Ylj, FSik

)+ ∑

k

Yl j→k

Yl j× Nk × Cov(

YikYk

, FSik)

= Bf-flj + ∑

kNk ×

[Yl j→k

Yl j× Cov

(YikYk

, FSik

)− Cov

(Yl j→ik

Ylj, FSik

) ]= Bf-f

lj + ∑k

Nk ×Yl j→k

Yl j×[Cov

(YikYk

, FSik

)− Cov

(Yl j→ik

Ylj×

Yl j

Yl j→k, FSik

) ]= Bf-f

lj + ∑k

Yl j→k

Yl j× Nk × Cov

(YikYk−

Yl j→ik

Yl j→k, FSik

)

(C21)

Result 7. Bf-slj = Bf-f

lj + ∑k

Yl j→kYl j× Nk × Cov

(YikYk− Yl j→ik

Yl j→k, FSik

). Whenever firm l in sector j has a

tendency to undersell to foreign firms i in sector k relative to their size in their sector k then B f−slj is an

overestimation of the true B f− flj . This tendency with respect to sector k is weighted by the importance

of sector k in the sales of firm l in sector j.

Rearranging this expression, we get Bf-flj = Bf-s

lj + ∑k


(Yl j→ikYl j→k

− YikYk

, FSik

). Even

conditional on the same Bf-slj , i.e., even after sharing the same level of aggregate foreign presence

across one’s known purchasing sectors, suppliers can still differ in their likelihood to actually sell tothe foreign buyers in those sectors.

Note: The sufficient condition in result 2 is apparent in this result as well. If for firm l in sector j,YikYk

=Yl j→ikYl j→k

for ∀i and ∀k then B f−slj = B f− f

lj .

Last, we aim for formulas relating directly the measure of linkage commonly used inthe literature (Bs-s

lj ) to the one that we can construct using the full information set on firm’s l

in sector j sourcing behavior, Bf-fl j . Plugging result 7 into result 4, we show how a firm-level

Bf-fl j differs from its sector-level proxy, Bs-s

lj .

Result 8. Bf-flj = Bs-s

lj + κ× Cov(Yl j→k

Yl j− Yj→k

Yj, Hk) + ∑

k


(Yl j→ikYl j→k

− YikYk

, FSik

). The

firm-specific Bf-flj of firm l in sector j has three components: (i) the prediction from I-O tables of the

backward linkage of firm l based on its sector’s j aggregate linkage behavior, (ii) firm’s l overperfor-mance (relative to its sector) in terms of selling a higher share of its output to more foreign sectors kand (iii) firm’s l overperformance in terms of directing its sector k sales disproportionately towardsforeign firms (relative to the size of these buyers i in their sector k).

95

Note 1: Whenever the sufficient conditions in results 1 and 2 hold, the covariance terms become zeroand Bf-f

lj becomes equal to Bs-slj .

Note 2: This result is an argument against a regression that starts from those now common in theliterature and that replaces Bs-s

lj with Bf-flj . Bf-f

lj ’s second and last components are likely to captureunobservable features of the firm that may explain both the overperformance in selling to MNCs andthe improvement in firm performance.

Interested how the average of Bf-flj

(Bf-f

lj

)relates to Bs-s

lj , we combine results 5 and 7.

Bs-slj = ∑

l∈j

Yl j

Yj× Bf-s

lj = ∑l∈j

Yl j

Yj× Bf-f

lj + ∑l∈j

Yl j

Yj∑k

Yl j→k

Yl j× Nk × Cov

(YikYk−

Yl j→ik

Yl j→k, FSik

)

= Nj ×(

1Nj

∑l∈j

Yl j

Yj

)×(

1Nj

∑l∈j

Bf-flj

)+ Nj × Cov

(Yl j

Yj, Bf-f

lj

)+

+ ∑l∈j

Yl j

Yj∑k

Yl j→k

Yl j× Nk × Cov

(YikYk−

Yl j→ik

Yl j→k, FSik

)

= Bf-flj + Nj × Cov

(Yl j

Yj, Bf-f

lj

)+ ∑

l∈j

Yl j

Yj∑k

Yl j→k

Yl j× Nk × Cov

(YikYk−

Yl j→ik

Yl j→k, FSik

)

= Bf-flj + Nj × Cov

(slj, Bf-f

lj

)+ ∑

l∈jslj ∑

k

Yl j→k

Yl j× Nk × Cov

(YikYk−

Yl j→ik

Yl j→k, FSik

)(C22)

Result 9. Bs-slj = ∑

l∈jsl j × Bf-f

lj + ∑l∈j

sl j ∑k


(YikYk− Yl j→ik

Yl j→k, FSik

)= Bf-f

lj + Nj × Cov(

slj, Bf-flj

)+ ∑

l∈jslj ∑

k


(YikYk− Yl j→ik

Yl j→k, FSik

)Note 1: Whenever the sufficient conditions of result 2 are satisfied, Bf-f

lj = Bf-slj for ∀ firm l in ∀ sector

j and result 9 collapses to result 6.

To the best of our knowledge, this is the first time these f − s and f − f linkage variablesare defined and compared to the standard s− s linkage variables. These are useful to spellout (i) the strong assumptions behind the use of sector-level linkage variables to proxy foractual firm-to-firm linkages, with implications, for instance, on the aggregate measure ofintegration of MNCs and (ii) the selection biases that would affect panel-type regressionsdirectly using the firm-to-firm linkage variables as an explanatory variable – these biaseslater inform our choices of research designs.

96

Appendix C.2 Evidence Against Sufficient Conditions for Equality

Among Linkage Proxies

We now bring the findings in the previous section to our firm-to-firm transaction data.We first show that the sufficient conditions under which the three measures of linkage(

Bs-slj , Bf-s

lj , Bf-flj

)are equivalent among each other do not hold in the data.

0.2

.4.6

0.2

.4.6

0 .5 1 0 .5 1

Electronic components Hospitality

IT&C Retail

Frac

tion

Deviation index

Figure C11: Sectoral heterogeneity in the extent to which αljk = αjk holds true

Notes: Result 1 states that for firm l in sector j, αljk = αjk, ∀k, is a sufficient condition for Bf-slj to equal Bs-s

lj .To evaluate this claim, we consider sectors j one-by-one and construct a firm-level measure that captures thefirm’s overall deviation from I-O predicted sectoral shares. Concretely, for each firm l in sector j we compute

∑k

(αljk − αjk

)2. If the claim in Result 1 were true, then this firm-level measure would equal zero. We find

evidence suggesting that firm-level sectoral divisions of sales differ greatly from I-O predicted divisions. Toexemplify our finding, we present in Figure C11 the histograms corresponding to four sectors: electronic com-ponents, hospitality, IT&C, and retail. We find sectoral differences in the frequency with which this conditionis not met: while in the electronic components sector this condition is more likely to be met, all other sectorshave a majority of firms diverging from I-O based sectoral divisions of sales.

97

0.1

.2.3

.4.5

Frac

tion

0 2 4 6 8 10Deviation index

Figure C12: Even for domestic firms selling to foreign firms,Yl j→ikYl j→k

= YikYk

does not hold

Notes: Result 2 states that for firm l in sector jYl j→ikYl j→k

= YikYk

, ∀i foreign and ∀k, is a sufficient condition for Bf-flj to

equal Bf-slj . We find that this sufficient condition does not hold either. Focusing on 2013, our sample contains

5,112 domestic firms selling to at least one of 2,140 firms with foreign capital. Conditional on being a supplierto at least one foreign firm (i.e., being one of these 5,112 domestic firms), there is a total of close to 11 millionpotential transactions with any of these 2,140 foreign buyers. 99.8 percent of these potential transactions do

not occur. Therefore, mechanically, for all of these combinationsYl j→ikYl j→k

< YikYk

. Out of the 5,110 transactions that

do materialize, Figure C12 shows the histogram of ∑k

(Yl j→ikYl j→k

− YikYk

)2computed for each firm l in sector j. We

observe that less than half of these cases are close to the target value of zero. Overall, this implies that thesufficiency requirement for Result 2 does not hold for the vast majority of combinations.

98

020

4060

8010

0FS

ik

0 20 40 60 80 100Hk

Figure C13: Heterogeneity in foreign ownership shares, FSik = Hk does not hold.

Notes: Result 3 shows that if for ∀i and ∀k, FSik = Hk, then Bf-flj equals Bf-s

lj , ∀l in ∀j. Figure C13 focuseson foreign firms alone and shows that there is considerable heterogeneity in foreign ownership shares. Thehorizontal axis corresponds to Hk. If the condition above had held for firms i foreign, then all markers onthe graph would have been been located along the 45 degree line. Instead, the graph shows no clear pattern,with most foreign firms having a foreign equity share close to 100 percent, irrespective of the value Hk of theirsector. For firms i domestic in a sector k containing at least one foreign firm, again this condition does not hold.

Appendix C.3 The More Disaggregated the Data Used, The More Hetero-

geneous the Proxies of Linkage to MNCs

Despite the sufficient conditions of equality among them not holding, the three mea-sures of linkage might still differ too little to make the further disaggregation worthwhile.We start by exploring this possibility graphically in Figure 8.

For further insights on the drivers of linkage heterogeneity, we first compare the dis-tribution of Bs-s

lj to that of Bf-slj . Even conditional on their sector j, local firms differ in how

their sales are distributed across sectors k of different sectoral levels of foreign presence (Hk).This finding reflects local firms’ selection into or away from selling to more foreign sectors,foreign firms’ selection of specific supplying sectors (or vendors in a given sector), or both.

We plot the distribution of firm-level difference between the “sector-to-sector” and“firm-to-sector” backward measures

(Bs-s

lj − Bf-slj

)in Figure C14. We learn that most of these

firm-level differences are positive, meaning that Bs-slj tends to overestimate Bf-s

lj . Guided byResult 4, we interpret this fact as one suggesting that domestic firms tend to underperformin their sales to more foreign sectors, with respect to what the I-O matrix would predict.

99

0.0

5.1

.15

Frac

tion

-1 -.5 0 .5 1B_s2s - B_f2s

Figure C14: “sector-to-sector” vs “firm-to-sector” backward linkages

Notes: The figure plots a histogram of the firm level differences between the “sector-to-sector” and the “firm-to-sector” measure of backward linkages. “Backward sector-to-sector” uses proxies for the extent to which a firmsells to a given sector from I-O tables and for the degree of foreign ownership in those sectors. To construct“Backward firm-to-sector” requires to know to which sectors a firm sells to, with the foreign ownership ofone’s clients still being approximated by the sector-level weighted-average ownership.

0.0

2.0

4.0

6.0

8Fr

actio

n

-1 -.5 0 .5 1B_f2s - B_f2f

Figure C15: “firm-to-sector” vs “firm-to-firm” backward linkages

Notes: The figure plots a histogram of the firm level differences between the “firm-to-sector” and the “firm-to-firm” measure of backward linkages. “Backward firm-to-sector” requires to know to which sectors a firm sellsto, with the foreign ownership of one’s clients still being approximated by the sector-level weighted-averageownership. “Backward firm-to-firm” uses the actual firm-to-firm transactions data, requiring no further as-sumptions on the sectors one sells to and the foreign ownership of one’s clients.

100

We then compare the distributions of Bf-slj and Bf-f

lj . Even conditional on the same sharessold to same sectors, local firms can differ in their actual customers within each sector. Orsymmetrically, even when comparing local firms in the same sector j with the same share oftheir sales to a sector k, these firms might not be equally preferred by an MNC i in sector k.

We define(

Bf-slj − Bf-f

lj

)as the firm-level difference between the ”firm-to-sector” and

”firm-to-firm” backward measures. We plot the density of these differences in Figure C15.We find a striking dispersion in the distribution of differences and a slightly positive mean.Hence Bf-s

lj tends to overestimate Bf-flj . In light of Result 7, this finding suggests that on aver-

age, the more foreign a firm is, the more likely it is that a domestic firm would undersell tothis foreign firm (relative to its importance in its sector).

Appendix C.4 MNCs Source Differently from Domestic Firms

In what follows, we expose the ways in which MNCs source differently from domesticfirms. As we want to avoid attributing to the MNC status what is in fact attributable toone’s size, sector or location, throughout this analysis we control for these three firm char-acteristics. The discussion is based on the results presented in Tables C1 for all sectors in theeconomy and Table C2 with special focus on manufacturing firms.74 The tables show resultsfrom regressing several firm-level outcomes on a set of year-by-4-digit sector and provincefixed effects, a second degree polynomial of log sales, and a foreign owned dummy, which isour explanatory variable of interest. These outcome variables capture what we consider tobe the most likely drivers of differentiation in sourcing between MNCs and domestic firms.

Conditional on sector, size, and province, do MNCs have a different share of value added of theirtotal sales than domestic firms? Column 1 of Table C1 shows that once conditioning on a veryfine set of fixed effects, foreign firms produce, on average, a robust 6 percent more of valueadded in-house. This difference could reflect both productivity differences between MNCsand domestic firms and a focus of MNCs on processes with a higher in-house value-added.

Conditional on sector, size, and province, do MNCs source a different percentage of their inputslocally, as opposed to importing? Column 2 of Table C1 reports on the share of inputs sourcedin Costa Rica (purchased both from domestic and foreign firms) in total inputs. On average,MNCs tend to purchase 7 percent less inputs locally (15 less, in manufacturing). This differ-ence is highly significant. The mean for the local content in inputs is 86 and 83 percent forthe full sample and manufacturing firms, respectively.

74Results for both tables are qualitatively very similar but the magnitudes are larger when we focus on manu-facturing firms.

101

Table C1: Foreign vs Domestic Buyer’s Behavior: All Sectors

VA Share Local Foreign Local Sectors Sup per Sector(1) (2) (3) (4) (5)

Foreign Dummy 0.060∗∗∗ -0.071∗∗∗ 0.034∗∗∗ 0.069∗∗∗ -0.076∗∗∗

(0.014) (0.002) (0.003) (0.007) (0.003)Year-4DSect FE Yes Yes Yes Yes YesProvince FE Yes Yes Yes Yes YesSales Polynomial Yes Yes Yes Yes Yes


Adjusted R2 0.048 0.42 0.17 0.64 0.37# Observations 101099 101099 101099 101099 101099# Fixed Effects 2161 2161 2161 2161 2161# Firms 19049 19049 19049 19049 19049

Notes: Each column in Table C1 shows the result of a different regression where each column header representsthe dependent variable. The regressions use data from 2008-2015 because this is the period where transactionsdata is available. The main explanatory variable of interests is a dummy variable which is equal to one forfirms with some foreign ownership. All regressions include the interaction of year by 4-digit fixed effects,province fixed effect and a second order polynomial in log total sales as control variables. VA Share in column1 is defined as the share of value added over a firm’s total sales. The variable Local in column 2 is defined as theshare of inputs purchased in Costa Rican territory over firm’s total inputs (purchased locally plus imported).The variable Foreign Local in column 3 is defined as the share of inputs purchased from foreign firms in CostaRican territory over total inputs purchased in Costa Rican territory. The variable Sectors in column 4 is thelog of the number of 4-digit sectors from which the firm source inputs. Finally, the variable Sup per Sector incolumn 5 is defined as the log of the average number of suppliers per sector from which the firm source inputs.


Conditional on sector, size, and province, do MNCs source a different percentage of their localinputs from domestic (as opposed to foreign-owned) firms? Column 3 of Table C1 reports on theshare of inputs purchased from MNCs established in Costa Rica over total inputs purchasedin Costa Rica. MNCs tend to purchase a significantly higher percentage of their local inputsfrom other MNCs. The difference for manufacturing firms is more than twice compared tothat from the sample that uses all sectors (8.6 vs 3.4 percent).

Conditional on sector, size, and province, does the sectoral split of MNCs’ purchases differ fromthe sectoral split of domestic firms’ purchases? Column 4 of Table C1 shows differences betweendomestic and foreign firms in the number of 4-digit sectors from which they source theirinputs locally. Foreign firms source on average from 6 percent more sectors. This differenceis significant at the 1 percent level, but economically small given that firms source fromaround six 4-digit sectors on average. We thus find that foreign firms source less of their

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inputs domestically, but at the same time they source from more economic sectors. While atfirst sight this might seem counter-intuitive, this finding is consistent with the fact presentedin Column 1. Foreign firms tend to outsource locally more of their low-value added tasksand focus on parts of the production process that are more value-added intensive.

Conditional on sector, size, and province, are purchases from a given sector differently concen-trated for MNCs than for domestic firms? Column 5 of Table C1 presents positive evidence onthis claim. Foreign firms on average source from fewer suppliers within each sector theysource from. The difference of 7 percent in the predicted average number of suppliers per4-digit sector is again statistically significant at the 1 percent confidence level. MNCs con-centrate their local purchases within economic sectors relatively more than domestic firms.

Table C2: Foreign vs Domestic Buyer’s Behavior: Manufacturing

VA Share Local Foreign Local Sectors Sup per Sector(1) (2) (3) (4) (5)

Foreign Dummy 0.073∗∗∗ -0.153∗∗∗ 0.086∗∗∗ 0.109∗∗∗ -0.063∗∗∗

(0.010) (0.005) (0.007) (0.015) (0.006)Year-4DSect FE Yes Yes Yes Yes YesProvince FE Yes Yes Yes Yes YesSales Polynomial Yes Yes Yes Yes Yes


Adjusted R2 0.090 0.52 0.15 0.75 0.41# Observations 16436 16436 16436 16436 16436# Fixed Effects 697 697 697 697 697# Firms 2710 2710 2710 2710 2710

Notes: Each column in Table C2 shows the result of a different regression where each column header representsthe dependent variable. The regressions use data of manufacturing firms from 2008-2015 because this is theperiod where transactions data is available. The main explanatory variable of interests is a dummy variablewhich is equal to one for firms with some foreign ownership. All regressions include the interaction of year by4-digit fixed effects, province fixed effect and a second order polynomial in log total sales as control variables.VA Share in column 1 is defined as the share of value added over a firm’s total sales. The variable Local incolumn 2 is defined as the share of inputs purchased in Costa Rican territory over firm’s total inputs (purchasedlocally plus imported). The variable Foreign Local in column 3 is defined as the share of inputs purchased fromforeign firms in Costa Rican territory over total inputs purchased in Costa Rican territory. The variable Sectorsin column 4 is the log of the number of 4-digit sectors from which the firm source inputs. Finally, the variableSup per Sector in column 5 is defined as the log of the average number of suppliers per sector from which thefirm source inputs.


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Appendix D Data Description and Construction

Appendix D.1 Corporate Tax Returns and Social Security Micro-data

Our first administrative dataset contains the universe of corporate tax returns of ac-tive firms over the 2005 to 2015 period. Firms are defined in our dataset as a corporationor individual75 conducting business in Costa Rica, and every firm must file a yearly taxdeclaration called D-101 (“Declaracion Jurada del Impuesto Sobre la Renta” or the “Affidavit ofIncome Tax”) to the Ministry of Finance of Costa Rica (Ministerio de Hacienda). This formcontains information on profit, revenue, cost, assets, among others. Costs are reported intoa few line items, which include, administrative cost (including wages), material inputs, cap-ital depreciation, interest payments and other costs. Currently not filing the D-101 results inpayments of fines up to $385, plus 11%-12% annual interest on one’s income tax liability.

In addition, this dataset includes variables that come from data reported to the CostaRican Social Security Fund (“Caja Costarricense del Seguro Social”) which include the num-ber of employees and wage bill for each firm, as well as the share of high-skill employees.76

We consider firms as active – hence, they appear in our datasets – if they report data to theSocial Security at some point during our period of analysis.

The information from those two primary sources is complemented in our dataset withinformation on ownership and management from the Central Bank and other sources weexamine. The Bank identifies groups of firms that have common owners using data fromthe National Registry of firms, domestic and foreign surveys, and other public and privateinformation. These groups of firms are called “grupo corporativo” or “corporate group”. A“grupo empresarial” or “firm group” is a set of firms that not only share ownership but behaveas one firm, meaning that one cannot consider them as separate business ventures.77

We add to the definition of firm groups the sets of firms that belong to the same cor-porate group and belong to the same sector. Additionally, we use information from theOrbis and Amadeus databases from Bureau Van Dijk to augment our knowledge of firmsthat are related to each other.78 There are 3947 corporate groups identified, as well as 1881firm groups. We expand our dataset to include tax returns data from firms even if they lacksocial security data if it is known that they are part of a corporate group.79

For the purposes of our empirical excercise, we collapse the data and treat firm groupsas one individual firm. We keep track of business relationships of all firms in the groupwith other parties, but keeping only one identifier for the group. We keep the characteristics

75For instance, any individual renting real estate or providing professional services must comply with this re-quirement.

76For the construction of this variable, a worker is defined as high-skilled if he/she earns more than the mini-mum wage paid to a worker with vocational post-high school training.

77Hypothetical firms A, B, and C owned by the same corporation operate in a way such that, in our data, someassets are owned by firm A, wages are paid by firm B, and firm C pays costs related to the whole operation,and all three firms behave as one for which the objective is to sell product z in Costa Rica.

78These are discussed in more detail in Appendix D.4.79The most telling behavior of a firm to identify such case is having income above a threshold –usually $1

million– but with no employees reported.

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(identifier, sector, location) of the most relevant firm in terms of sales within the group.We want to keep the universe of domestic private firms that are part of the non-

financial market economy. Therefore, we drop non-governmental organizations (NGOs),public entities (including utilities), and those observations that are registered as households.We drop data from the education sector (private owned schools and universities), and theconstruction sector,80 as well as firms related to the financial sector. We do not consider anobservation as a firm if there is less than one worker reported in all the period of observation.The aforementioned criteria let us identify 75,123 firms.

We impose minimal size restrictions for our sample in our empirical exercise. Firmshave to report both workers and sales with no gaps in the data, and we consider only firmsthat, over the years, have a median of at least three workers. Finally, we drop firms withmedian sales of less than $25,000 PPI-deflated to 2013 US$. These restrictions leave us with22,891 firms. The loss in number of observations, however, is not that significant whenwe compare with the universe of private firms that are part of the non-financial marketeconomy.

The size restrictions we impose do not seem to hamper the coverage of our variables.Even though we lose more than two thirds of the firms, Table D1 shows that we keep thosethat employ most of the labor force and represent the largest share of sales, exports, income,costs and assets. In most variables we have coverage of around 80% of the values of thevariables.

Table D1: Sample data coverage

Coverage

Wage bill 84.4%Exports 84.7%Imports 88.4%Total sales 77.7%Employment 79.9%Value Added 78.0%Input costs 81.1%Total Net Assets 72.5%

Notes: Values in millions of PPI-deflated 2013 US$. For details on the sources and construction of the variablesrelated to foreign trade, see Appendix D.3.

In the following table we present descriptive statistics of the variables in the universeof non-financial market economy dataset in contrast to the sample used in the empiricalexercises.

80Most of these firms are active for one construction project only, disappearing immediately after being activefor one or two years.

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Table D2: Descriptive statistics, sample data

N Mean S.D. Median

Non-financial market economy

Wage Bill 75,123 45.5 313.3 11.1Exports 4,093 490.4 3,016.2 24.2Imports 20,486 206.6 1,456.1 13.0Total Sales 74,840 446.2 3,498.4 106.1Employment 75,123 7.2 32.6 2.5Value Added 74,817 179.0 1,068.5 63.8Input Costs 62,415 307.5 2,895.0 26.0Total Net Assets 64,186 406.2 7,367.0 54.1

Sample

Wage Bill 22,891 121.0 559.0 35.9Exports 2,472 675.3 3,797.5 30.9Imports 7,937 449.9 2,292.3 32.4Total Sales 22,891 1,120.3 6,295.8 294.2Employment 22,891 17.9 57.1 6.9Value Added 22,891 410.6 1,895.7 153.2Input Costs 18,893 800.6 5,183.7 114.5Total Net Assets 20,142 954.8 12,944.2 148.1

Notes: With the exception of employment, the mean, s.d., and median in thousands of PPI-deflated 2013 US$.

Appendix D.2 Firm-to-firm Transactions Micro-data

Our most important dataset allows us tracks all firm-to-firm relationships in Costa Ricabetween 2008 and 2016. This data is collected by the Ministry of Finance through the taxform D-151, the “Declaracion anual resumen de clientes, proveedores y gastos especıficos”(Declaration of the yearly summary of clients, suppliers and specific expenses). This dec-laration is compulsory not only to private businesses, but to all actors in the economy (e.g.individuals providing professional services, public entities, NGOs, embassies etc.), irrespec-tive of being subject to the corporate income tax or not. A late filing of this fee is heavilypenalized, e.g. in 2016 the late filing fee could go from $7,040 to $70,400.

To help enforce taxes, each firm has to report all of its corporate suppliers and clientswith a yearly accumulated amount of transactions above 2.5 million Costa Rican colones(approximately $4,400).81 Third-party reporting, of the type D-151 ensures, is used by thetax authority to identify firms that have not complied with their filing obligations, e.g. firms

81For the sale of professional services by individuals, the threshold is less than $100.

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that have over-reported their costs or under-reported their revenues to reduce their profittax liability. The tax authority uses different communication interventions, namely emails,phone calls, or personal visits, to follow up with non-filers [Brockmeyer, Hernandez, Kettle,and Smith, 2016]. As D-151 forms contain the yearly amount sold to or bought from eachpartner, this dataset allows us not just to track buyer-supplier relationships in a given year,but also the intensity of those relationships.

A sequence of steps was followed to ensure that several coding or reporting errorswere corrected in the raw D-151 database, and that the ID’s of firms identified as buyers andsellers are coherent with the rest of our data. The steps can be summarized as follows:

1.

Correct

IDs

2.

Clasify

cases

3.

Correct

errors

4.

Final

dataset

The first step relates to the fact that the Ministry of Finance usually assigns extra char-acters to the ID’s of corporations or individuals, which need to be removed before the datacan be linked to the tax returns and social security microdata. The presence of foreign IDsrequire additional steps to ensure data quality: it is not unusual that early transactions ofa foreign firm are recorded using passport or foreigns ID numbers, whereas later on thosetransactions are recorded using a Costa Rican tax ID. BCCR tracks those changes to ensurethat the transactions are imputed to the correct tax ID when building the dataset.

The second step involves identifying different reporting inconsistencies. The ideal caseis one in which the transaction between two firms is reported by both firms, given the samedescription, and having the exact same reported amount in both filings. In such case, theduplication is taken into consideration to keep it as one observation, and there is no needto perform any additional corrections. However, inconsistencies arise when transactionsappear only once, the amount shown is different within a pair, submissions that were re-jected by the Ministry of Finance cause duplicates of correct lines, or there is a lack of data.Also, whenever individuals buy from firms, they are not required to report that purchase,so around one fifth of the reports by firms have no counterpart but cannot be classified asan error or misreporting.

The corrections that were done to the dataset are summarized herafter:

1. Whenever the transaction was reported by both parts but with amounts appearing todiffer because of a simple error in the position of a decimal point, historical data wasused to identify the correct data.

2. When a pair of transactions have one of the parts reporting an amount of zero, theamount from the transaction reporting a positive value was assumed to be correct.The same solution was used when one part of the couple fills in incorrectly one of theIDs as the value for the transaction.

3. When the difference in the amount of a pair of transactions is more than 20% or more

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than 50 millions of colones (close to $100 thousand), and one of the sides of the transac-tion showed a value of more than 500 millions of colones (close to $1 million)82 carefulmanual checks were completed using historical data to identify the correct value.

4. When a transaction appeared more times because of a resubmission (usually for cor-rections), only the most recent observation was kept.

The following tables summarize the number of transactions and the correspondingvalue of the transactions that were analyzed. For the empirical exercise we can use two setsof transactions: first, the data showing up in pairs that were either matched perfectly in theraw data or with inconsistencies that were solved by the corrections explained beforehand.The second set of transactions that we can use are the cases where transactions had no part-ner, either because there was a reason for not having it as explained above, or because thereis missing information.

Unsolved cases include those that could eventually be corrected but for which thevalue of the transaction is below our chosen threshold for manual checks. The second cat-egory of data that we cannot use are cases where transactions had no duplicate, but theyare classified as rejected by the Ministry of Finance in the revision of the tax declarationsubmissions. There is a small set of transactions that we were able to identify as duplicatesof others that are already considered in the data. Finally, the smallest set of transactionsincludes those that were excluded due to being mistakenly reported.83

Table D3: Number of cases, firm-to-firm micro-data

2008 2012 2015Type of case Count % Count % Count %

Data in pairs 535,863 41.9% 998,355 40.5% 1,383,820 42.2%No partner and accepted 493,769 38.7% 1,256,978 51.0% 1,626,907 49.6%

Subtotal of used data 1,029,632 80.6% 2,255,333 91.5% 3,010,727 91.9%

Unsolved 128,599 10.1% 202,710 8.2% 251,499 7.7%No partner and rejected 108,969 8.5% - 0.0% - 0.0%Duplicate 4,904 0.4% 5,936 0.2% 14,652 0.4%Excluded 5,414 0.4% 34 0.0% 32 0.0%

Total 1,277,518 100.0% 2,464,013 100.0% 3,276,910 100.0%

82This last criteria was required to prioritize which transactions would be manually checked. Even though onlya fourth of the transactions were checked, they represented more than 80% of the value.

83For example, the Ministry of Finance is aware that accountant firms sometimes mix up the forms of differentclient firms when submitting them to the tax authority, which are later rectified.

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Table D4: Value of transactions, firm-to-firm micro-data

2008 2012 2015Type of case Value % Value % Value %

Data in pairs 45,812 63.6% 55,489 67.5% 69,450 69.1%No partner and accepted 11,808 16.4% 16,637 20.2% 18,496 18.4%

Subtotal of used data 57,620 80.0% 72,126 87.7% 87,946 87.6%

Unsolved 7,766 10.8% 10,002 12.2% 12,324 12.3%No partner and rejected 6,145 8.5% - 0.0% - 0.0%Duplicate 170 0.2% 71 0.1% 172 0.2%Excluded 359 0.5% 1 0.0% 2 0.0%

Total 72,060 100.0% 82,200 100.0% 100,444 100.0%

Notes: Values in millions of PPI-deflated 2013 US$.

We are able to use no less than 80% of the transactions and value of the data from theraw files of the Ministry of Finance. After a few years, this number is above 90%. Moreover,we are not losing data that is either rejected, duplicate or excluded (especially during thefirst of our sample), these are reporting or dataset errors that cannot be considered realtransactions. Additionally, the transactions that are not used because they are categorizedas “unsolved” are usually less than 10% of the total. It should be noted that they the valuerepresents a slightly larger percentage because some of the mistakes involve the systemignoring the decimal point, which can overestimate the values of the transaction by one ormore orders of magnitude.

Descriptive statistics of our database show that the behavior of the Costa Rican pro-duction network is similar to those of other countries. For example, Bernard, Moxnes, andSaito [Forthcoming] document among several stylized factor for the Japanese productionnetwork that larger firms have more suppliers, and there is negative degree assortativityamong sellers and buyers. Both facts are observed in our data, as shown in Graphs D16 andD17.

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1

10

50

150

400

1000

Num

ber o

f Con

nect

ions

0.02 0.15 1 10 50 400Sales, Mill USD

#Clients #Supppliers

95% Confidence Interval

Third order polynomial. Model controls for province and 2-digit sector FE

Figure D16: Size, in-degree and out-degree

Notes: The figure shows the third degree polynomial regression of firm-level log number of clients and sup-pliers (vertical axis) on log sales (horizontal axis) after controlling for year, 4-digit sector and province fixedeffects. The two lines represent the number of clients and the number of suppliers as separate regressions,along their respective 95% confidence interval.

2050

150

400

Mea

n #C

usto

mer

s of

thos

e S

uppl

iers

1 5 50 400 3000Indegree: Number of Suppliers

95% Confidence Interval Indegree

Figure D17: Degree Assortativity for Costa Rican Suppliers

Notes: The x axis shows the number of suppliers for each Costa Rican firm and y axis shows the the averagenumber of clients of those suppliers.

Appendix D.3 Customs Micro-data

For the firms that engage in international trade we observe aggregate imports andexports, which are included in the database described in Appendix D.1. The data is reportedin customs and collected by the Ministry of Finance with the same identification used for therest of the data.

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We have additional disaggregation for the data on exports and imports. We have ac-cess to the individual reports for each foreign trade transaction. This allows us to observe thecountry of destination or origin, exact date of shipment, description of the product, pricesand quantities sold of each 10-digit product). From 2010 onward, the data is further dis-aggregated identifying the name of the individual foreign partner, in contrast to the oldercountry-level aggregation of daily shipments of a product.

Appendix D.4 Foreign Ownership Micro-data

There is no centralized reporting in Costa Rica regarding the country of origin of thecapital of firms. Our first source is the reporting that firms active under the Free Trade Zone(FTZ) regime. Costa Rica has followed a strategy of pursuing FDI investment by offeringbenefits to firms established in FTZ regimes. As summarized in OECD [2017a], the FTZregime exempts the beneficiary firm from custom duties on imports and exports, the with-holding tax (on royalties, fees, dividends), interest income, the sales tax on local purchasesof goods and services and the stamp duty.

In addition, the FTZ regime exempts profits from corporate income tax for 8 years andprovides a 50% corporate income tax reduction during the following 4 years, but differencesexist depending on the types of activities and the location of the FTZ. Profits from sales tothe domestic market are taxed under separate tax rules. Companies that may apply for theFTZ regime must be either (i) export service companies (at least 50% of services must beexported), (ii) scientific research firms (companies or organizations), (iii) “strategic compa-nies” or part of “strategic sectors” or (iv) “significant suppliers” (at least 40% of their salesare made to FTZ companies). Because of those benefits, the firms have to comply with fullreporting of the sources of capital and accounting data, which is collected by Procomer andis made available to the Central Bank for statistical purposes.

There are, however, limitations regarding the knowledge of foreign ownership outsideof the FTZ regime. The Central Bank carries out three additional surveys as sources forcomplementary information on flows and sources of capital for foreign owned firms.

1. Encuesta Trimestral de Balanza de Pagos, or the “Quarterly Balance of Payments Survey”:collects information on a sample of large firms (currently 250 to 300 firms) on theircountry of origin and percentage of foreign ownership.

2. Encuesta Anual, or the “Annual Survey”: similar to the quarterly survey, but adminis-tered on a yearly basis. It contains a sample of 50 to 100 firms.

3. Estudio Economico, or the “Economic Study”: when Costa Rica updated the systemof national accounts, the Central Bank surveyed thousands of firms. Out of those, itidentified and started tracking close to 944 firms having received foreign capital. Forthose firms, there is knowledge about the percentage of foreign ownership.

Our third source of information is Orbis, a commercial product belonging to Bureau

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Van Dijk and accessed through UC Berkeley’s subscription. We queried Orbis for all MNCs(Global Ultimate Owners in Orbis nomenclature) that have a presence (affiliate or branch)in Costa Rica, identifying the names and IDs of firms in Costa Rica and abroad, includ-ing intermediate ownership. As mentioned in Appendix D.1, Orbis allowed us to expandour knowledge of firm groups in Costa Rica. Orbis was also used to identify which of theforeign-owned firms in Costa Rica are actually part of an MNC. For foreign-firms for whichthis information was not readily available in Orbis, we carried out manual searches.

A final source of information is the Costa Rican Investment Promotion Agency(CINDE), which is a a private, non-profit organization that started its operations in 1982.CINDE has mediated the entry of more than 300 high-tech companies in Costa Rica,including Intel, Procter&Gamble, Hewlett Packard, Baxter and St. Jude Medical. CINDEshared with us data regarding the identity and date of entry of MNCs attracted to CostaRica by CINDE.

After cross-checking across all sources, we have identified 3,809 tax IDs that have par-tial or full foreign ownership. To obtain a sample comparable to that of our domestic firms,we apply the same criteria used in Appendix D.1. We exclude NGOs, governmental entities(e.g., embassies) and households, so as to focus on private firms. In addition, we drop firmsin the education, financial and construction sectors. After adding the information on firms’shared ownership, we arrive to 2,408 groups of firms (firms hereafter) with at least partialforeign ownership.

As motivated in Section 3, not all of these 2,408 are suitable for our analysis. More thanhalf of these 2,408 firms are not part of an MNC. Moreover, even among the 1,045 actualMNC affiliates, half do not have a substantial presence in the country. Given our interestin measuring the productivity gains of joining MNC supply chains, we focus on the 516 ofthose 1,045 MNCs whose median number of workers, – over all years in which the MNC isactive in Costa Rica, – is at least 100.

In Table D5 we present descriptive statistics for the sample of domestic private firmsthat are part of the non-financial market economy, as well as for firms with foreign owner-ship. We split firms with foreign ownership in two, making the distinction between (large)MNCs and the rest of foreign-owned firms. Excluded foreign-owned firms are significantlylarger than domestic firms, while (large) MNCs are themselves an order of magnitude largerthan excluded foreign-owned firms.

While restrictions on the MNC status and median number of workers might seemcostly for the number of firms kept – out to the respective totals for the full sample of 2,408firms with foreign ownership – these 516 MNCs are actually responsible for most of the for-eign activity in Costa Rica. Table D6 presents totals adding up values for all firms part ofthe non-financial market economy, domestic- and foreign-owned alike. Columns (B) and (C)present the percentage of those values that are accounted for by firms with (partial or full)foreign ownership and (large) MNCs, respectively.

The last column shows that for most of the variables, the MNCs that we use for our

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empirical exercises account for around 80% to 90% of the totals across all firms with for-eign ownership. Hence, the minimal criteria resulting in the sample of 516 MNCs are notrestrictive in terms of their coverage of the full sample of foreign-owned firms.

Table D5: Descriptive statistics

N Mean S.D. Median

Fully domestic firms

Wage Bill 75,123 45.5 313.3 11.1Exports 4,093 490.4 3,016.2 24.2Imports 20,486 206.6 1,456.1 13.0Total Sales 74,840 446.2 3,498.4 106.1Employment 75,123 7.2 32.6 2.5Value Added 74,817 179.0 1,068.5 63.8Input Costs 62,415 307.5 2,895.0 26.0Total Net Assets 64,186 406.2 7,367.0 54.1

Firms with Foreign OwnershipExcluding (Large) MNCs

Wage Bill 1,892 284.2 453.1 118.0Exports 654 1,244.8 4,245.7 95.3Imports 1,271 1,313.6 3,767.5 108.6Total Sales 1,891 3,292.0 9,675.3 799.7Employment 1,892 22.1 24.5 12.1Value Added 1,891 1,238.7 3,316.4 388.2Input Costs 1,779 2,019.8 8,061.2 184.7Total Net Assets 1,849 4,228.4 15,171.9 705.7

(Large) MNCs

Wage Bill 516 5,174.1 9,432.5 2,467.0Exports 377 22,444.3 106,003.2 3,441.4Imports 491 16,362.7 91,367.2 2,031.9Total Sales 516 49,828.4 133,652.9 16,190.2Employment 516 487.9 999.2 233.1Value Added 516 19,964.2 73,481.4 7,425.3Input Costs 499 30,322.8 82,202.9 7,547.5Total Net Assets 514 41,815.8 84,237.1 13,905.7

Notes: With the exception of employment, the mean, s.d., and median in thousands of PPI-deflated 2013 US$.

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Table D6: Sample coverage

(A) (B) (C) (C)/(B)Total All firms with (Large) MNCs (Large) MNCs/

foreign ownership Firms for. owned

Wage Bill 6,625.0 48.4% 40.3% 83.2%Exports 11,282.8 82.2% 75.0% 91.2%Imports 13,936.8 69.6% 57.6% 82.8%Total Sales 65,332.9 48.9% 39.4% 80.5%Employment 832.2 35.3% 30.3% 85.7%Value Added 26,034.3 48.6% 39.6% 81.5%Input Costs 37,915.7 49.4% 39.9% 80.8%Total Net Assets 55,382.7 52.9% 38.8% 73.3%

Notes: Employment in thousands. The rest of the variables are in millions of PPI-deflated 2013 US$.

Table D7: Most frequent countries of global ultimate ownership

Country Frequency Percent Cumulative

US 201 41.02 41.02PA 31 6.33 47.35ES 23 4.69 52.04MX 23 4.69 56.73CO 22 4.49 61.22GB 18 3.67 64.90DE 16 3.27 68.16CH 14 2.86 71.02FR 13 2.65 73.67JP 13 2.65 76.33CA 10 2.04 78.37GT 10 2.04 80.41SV 9 1.84 82.24NI 7 1.43 83.67NL 7 1.43 85.11VE 6 1.22 86.33CL 5 1.02 87.35ID 5 1.02 88.37IE 5 1.02 89.39LU 5 1.02 90.41

Notes: Table D7 reports the countries of global ultimate ownership that correspond to at least 5 of the 516MNCs in the final sample. 41 percent of MNCs are US-owned.

Appendix D.5 Procomer Micro-data

Work in Progress.

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the productivity effects of joining multinational supply

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