The impact of foreign investment regulation on firmproductivity and employment in Indonesia∗

Robert Genthner†and Krisztina Kis-Katos‡

July 18, 2017

Abstract

Using a yearly census of Indonesian manufacturing firms for 2000-2014, we inves-tigate the effects of a sector-specific investment policy reform on firm productivity,employment and wages. Hereby we exploit a protectionist foreign direct investmentreform (the so-called negative investment list), which enumerated selected sectorsat the five digit level that became only conditionally open to foreign investors. Thelist was first released in 2000 and has been repeatedly revised by the Indonesian au-thorities since. We use the changes within this regulatory framework to investigatethe effectiveness of the policy by assessing its impact on firm-level productivity andlabor market outcomes within the manufacturing sector, while controlling for firmand two digit sector-year fixed effects. We find robust evidence in favor of decliningforeign capital shares as a result of a tighter regulation of foreign direct investmentbut no evidence of production shifting out of the more heavily regulated sectors.Our results also indicate a sizable decrease of firm productivity in the years follow-ing the regulation as well as a positive correlation between restrictive regulation andemployment.

JEL Classification: F23, L51, D24, F21, L6Keywords: FDI, regulation, Indonesia, total factor productivity

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†University of Göttingen, Germany‡University of Göttingen, Germany and IZA

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1 Introduction

In the course of the last two decades developing and emerging economies liberalized theirmarkets substantially. The process of globalization has not only lead to a successivedismantling of trade barriers but has also facilitated the operation of multinational enter-prises by liberalizing the inflows of foreign direct investment (FDI). However, this processof trade and market liberalization has not progressed uniformly and also experienced nu-merous regulatory shifts and reversals. For instance, while FDI has been playing an everincreasing role in the Indonesian manufacturing sector, the Indonesian government hasalso set up a blacklist of sectors to be closed or only conditionally open to FDI. Thisso-called negative investment list (NIL) was first released in 2000 and has been repeat-edly revised since. Do the Indonesian authorities translate this protectionist policy intoaction? And if so, how does it affect the performance and behavior of domestic firms?

In Indonesia, investment liberalization started with the political takeover of Presi-dent Suharto in 1966 but halted quickly with the rising economic nationalism of the1970s. Driven by international agreements, the opening of the economy to foreign in-vestors restarted in the 1990s in a fairly exogenous fashion (Duggan et al. 2013). As partof the efforts to recover from the Asian financial crisis of 1997/1998, further reforms wereimplemented to improve the investment climate and to regain the confidence of foreigninvestors. At the same time, however, the government also released the NIL to protect se-lected domestic industries from international competition and foreign acquisitions. Thus,Indonesian trade policy has been characterized by ambiguous signals to investors fromabroad (Lindblad 2015).

Indonesia offers a particularly interesting and relevant case to study the effects of FDIregulation on firms. While still growing, it is already one of the largest economies in theworld, with a wide variety of industries that rely on an abundance of both human andnatural resources (Blalock and Gertler 2008). This makes it a promising future marketand, thus, explains its attractiveness as an FDI recipient. Moreover, the NIL offers aninteresting and largely unexplored policy intervention as its protectionist nature is in sharpcontrast to the ongoing integration of global markets and potentially reflects the interestsof domestic lobby groups. The excellent quality of Indonesian firm data, especially ascompared to that from other developing countries (Blalock and Gertler 2008), enables usto investigate the effects of FDI policies on the universe of middle and large manufacturingenterprises in an emerging economy at a granular scale.

FDI can affect firms both directly and indirectly. Among the direct effects, foreigncapital can substitute for domestic capital and relieve liquidity constraints in case ifdomestic capital is limited. Moreover, foreign ownership may enhance the productivityof domestic firms by introducing nontangible productive assets such as technological,

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managerial and marketing skills, trading contacts and reputation (Aitken and Harrison1999, Arnold and Javorcik 2009). Related literature generally finds that firms with foreignparticipation typically are more productive, more capital intensive and pay higher wages(Harrison and Rodríguez-Clare 2010).

Regarding indirect effects, spillovers from FDI may work horizontally within industriesor vertically along the value chain. While there is little evidence in favor of positivehorizontal spillovers and some studies even find negative effects once firms and sectorsare controlled for (Djankov and Hoekman 2000, Javorcik 2004), the opposite holds withrespect to vertical spillovers. Positive vertical spillover effects arise both backward to localsuppliers and forward to local customers (Javorcik 2004, Blalock and Gertler 2008).

Since precise data on FDI regulation is frequently unavailable, most studies rely onFDI flows to proxy for reforms in FDI regulation. But as investment flows themselves areinfluenced by a large number of different factors, these studies suffer from the fundamentalproblem of unobserved heterogeneity (Harrison and Rodríguez-Clare 2010). A secondapproach to measure FDI regulation uses aggregated indices on FDI openness (Topalovaand Khandelwal 2011, Duggan et al. 2013). However, these indices cannot be used tocapture differential effects of regulation across more disaggregated sectors and the resultswill crucially depend on the construction and definition of the related indices. Hence,the use of more disaggregated data on FDI regulation can help to trace the effects ofregulation at a much finer sectoral scale.

This paper exploits the variation of three revisions of the NIL that regulates sectors onfive digit sector code level (in 2007, 2010 and 2014), listing each sector that became fullyor partially closed to FDI.1 The first revision of 2007 substantially tightened the existingFDI regulation, increasing the restrictiveness towards FDI strongly in a wide range ofsectors. By contrast, the revision of 2010 only induced some minor changes. Finally, in2014 the regulation was substantially relaxed and again allowed for foreign participationin many formerly regulated sectors.

We rely on a firm panel over 15 years and link changes in the NIL at the fine-grainedlevel of five digit sectors to various firm-level outcomes in the manufacturing sector be-tween 2000 and 2014. Our main outcome variables include the share of foreign ownershipfor each firm, measures of firm productivity as well as firm employment and wages. Weconstruct a regulation indicator from legal sources that identifies whether a firm has beensubject to FDI limitations in any particular year. The panel structure of our data allowsus to investigate the time profile of regulation in a more flexible way by including lagsand leads of regulatory change. Our regressions are conditional on firm fixed effects and

1The KBLI (Klasifikasi Baku Lapangan Usaha) sector classification is published by BPS (IndonesianStatistical Office, Badan Pusat Statistik). It is equivalent to the United Nation’s International StandardIndustrial Classification of All Economic Activities (ISIC) on the four digit level, but is adjusted to reflectsome country-specific sectors in Indonesia at the five digit level.

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hence only consider within-firm variation in the main economic outcomes over time. Ad-ditionally, our preferred specifications include two digit sector-year effects that captureall average time variation due to global and national shocks that affect whole industries.By that, we only focus on the differences between regulated and unregulated firms withinthe same broad economic sector and year.

Our analysis faces the problem of endogenous protection: if firms operating in theleast productive sectors are more willing to lobby for protection against foreign entryor takeovers, a negative correlation between regulation and productivity may arise as aresult of the endogenous lobbying process (Grossman and Helpman 1994). Similarly, if thegovernment is especially interested in protecting manufacturing employment, firms witha larger labor force may be more successful at securing the entry of their main productinto the NIL. The use of a set of fixed effects reduces these concerns to a considerableextent. Firm fixed effects capture the time invariant differences in firm characteristics thatmake them to be more prone to protection, but they cannot deal with shifts in protectionpatterns. By contrast two digit industry-year effects capture the overall importance ofthe main economic sectors from a regulatory perspective and factor out general shifts inthe likelihood of regulation at the broad sectoral level. We assess the remaining scopefor endogenous regulation by considering pre-reform differences in firm productivity andemployment in the years before regulatory reform.

Our results document a robust negative relationship between regulation and foreigncapital shares: firms shed more foreign capital upon FDI regulation than their unregulatedcounterparts do. This effect seems to be anticipated by firms already one year beforethe regulation becomes actually binding, reflecting the importance of anticipation effectsgiven the relatively longer planning horizon of FDI. Furthermore, the analysis finds clearindications that FDI regulation also affects firm outcomes, both in terms of productivityand employment. Total factor productivity of regulated firms decreases relative to thatof non-regulated firms operating within the same broad economic sectors both in theimplementation year as well as two years afterwards. At the same time, FDI regulation ispositively related to employment: firms increase their staff in the years leading up to FDIregulation. This can also be interpreted as an indication that especially sectors with ahistorically rising employment share are successful at securing more restrictive regulation.

To our knowledge, ours is the first study to exploit fine grained variation in the regu-latory framework of foreign direct investment at a five digit level. By doing so, we providenew insights on the mechanisms between an investment policy reform and firm behaviorin a developing country. By investigating the effects of investment restrictiveness on adisaggregated level, we also add to the general literature investigating the effects of FDIregulation on firm employment and productivity.

This paper proceeds as follows. Chapter 2 describes the regulatory framework of the

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negative investment list in Indonesia, chapter 2.3 introduces the data sources. Chapter3 presents the estimation strategy and the methods of identification. Chapter 4 presentsour results, showing that the investment reform reduces the share of foreign capital withinthe firms. Additionally, it investigates the effects on firm productivity and labor marketoutcomes. Chapter 5 concludes.

2 The negative investment list in Indonesia

2.1 The economic context

Indonesia started to remove the first barriers to foreign investment already under the“New Order” regime of President Suharto. In 1973 it installed the investment coordina-tion board (BKPM, Badan Koordinasi Penanaman Modal) to deal with foreign investmentapprovals (Gammeltoft and Tarmidi 2013). However, due to its strong dependence on nat-ural resources, the Indonesian manufacturing sector was only poorly developed until theearly 1980s (Lindblad 2015). Starting in 1983, successful efforts towards industrializationincreased the importance of the manufacturing sector and made it the driving force be-hind Indonesia’s accelerating growth (Blalock and Gertler 2008). During the 1990s theIndonesian government has changed its previously investment-hostile regime by openingup the economy to investments from abroad. It quickly became “one of the most promisinghost countries [for investment], combining liberal legislation with a massive endowmentof natural resources and a huge and rapidly growing domestic market for manufacturedgoods” (Lindblad 2015, p. 225).

The Asian financial crisis of 1997 marks a break in Indonesia’s economic develop-ment. Despite immediate intervention by the International Monetary Fund (IMF), theconsequences of the rapidly depreciating Rupiah spread to the real economy. This wasaccompanied by social and political instability which destroyed much of the confidence inIndonesia as host for investment (World Trade Organization 1998). In this time, steps to-wards democratization, administrative reform and further trade liberalization were made.However, Indonesia did not immediately return to economic growth and foreign investorsremained cautious since the business and legal environment remained rather precarious.Major reforms after 2004 induced fiscal incentives to FDI, streamlined bureaucratic pro-cedures (World Trade Organization 2013) and prescribed non-discriminatory treatmentfor foreign and domestic investors. In the aftermath of these reforms, FDI inflows havemassively increased again and economic growth has recovered strongly.

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2.2 The negative investment list

Despite the ongoing liberalization, trade and investment policy in Indonesia remains“blurred by contradictory signals” (Lindblad 2015, p. 229). In the middle of the crisisin 2000, the president released the Presidential Decree 96/2000, at the core of which liesthe so-called negative investment list.2 It lists sectors that are closed or only conditionallyopen to FDI. These conditions include joint ventures between domestic and foreign enti-ties, authorization in certain regions and licensing requirements. Before 2000, no explicitlyformulated version of the NIL was available. There was a blacklist of sectors closed toforeign investment, but approval procedures lacked transparency and were completely inthe hands of the BKPM. The NIL 2000 lists sectors verbally, without recurring to detailedKBLI sector codes.

The NIL has been revised for the first time in 2007 and the new list was released withinthe Presidential Decree 77/2007. The new version replaced the old vague register from20003 and lists sectors by detailed KBLI coding on five digit level for the first time. In itstrade policy review on Indonesia the WTO highlights that a detailed NIL brings greatertransparency with respect to investment and therefore may be beneficial (World TradeOrganization 2013). However, closing or conditionally opening certain sectors to foreigninvestment is likely to be associated with wasted gains from FDI. In this sense, the revisedversion can be considered as a protectionist measure as it adds more sectors and involvesmore conditions compared to the NIL 2000. The NIL 2007 comprises manufacturing aswell as agriculture and services and introduces standardized categories of conditions forthe first time. According to these conditions, sectors may be absolutely closed to foreigninvestment, alternatively, FDI may only be allowed to small and medium sized firms, inform of partnerships, up to a certain limit of foreign capital ownership, in certain locationsor may require licensing by the ministry in charge.

The next revision of the NIL came with the Presidential Decree 36/2010, leavingregulation in some sectors unchanged but also removing some and adding other sectors tothe NIL. Finally, the latest revisions in 2014 and 2016 removed many sectors from the listand clearly decreased the extent of regulation. A comprehensive overview of all revisionsof the NIL is provided in Table A1 in the Appendix, including its representation in thesample and the shares of regulated firms in total manufacturing output.

An important characteristic of all versions of the NIL is that regulation is forwardlooking and does not apply to previously approved investments.4 Thus, the regulationrefers to new investment plans and interferes with possible future FDI inflows.

2In the following, this version of the list will be referred to as NIL 2000.3See Article 7, Presidential Decree 77/2007.4See Article 8, Presidential Decree 36/2010, article 5 of Presidential Decree 77/2007 and article 9 ofPresidential Decree 39/2014.

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2.3 Data

2.3.1 Firm data

The analysis is based on the annual manufacturing census (Survei Industri, SI), whichsurveys the universe of all registered Indonesian manufacturing firms with more than 20employees. The census has been conducted by BPS yearly since 1975 and contains a richset of information at the level of manufacturing plants, including the values main inputsand output, foreign ownership, the value of import and export flows as well as employmentand wages. We follow the literature by using the foreign capital share as a proxy of FDI.5

Matching of the NIL requires that five digit sector codes are unambiguously avail-able for all observations of each firm. We first deal with missing or incomplete6 sectorcodes which is especially relevant in the years 2001 and 2003. Whenever plausible andunambiguously possible, we impute the same code as in the year before or the next year.Observations for which sector codes are still missing or incomplete after this adjustmentare dropped from the sample. Additionally, sector codes are converted to the commonstandard of KBLI 2000 coding based on conversion tables provided by BPS. We drop allobservations with ambiguous conversion results. In the SI data, sector codes always referto the main product of a firm, which introduces some imprecision as multi-product firmsmay switch between sectors every year. We measure this behavior by additionally record-ing switches in the main product from year to year. One concern is that regulation createsincentives to switch the sector to avoid limitations to FDI. We test for the relevance ofthis channel in the robustness section.

The data is cleaned for missing values. As common in the literature, data points areinterpolated between the previous and the next year to avoid loss of too many observations,while further missing observations are dropped from the sample (Amiti and Konings2007). We exclude extreme outliers by dropping all observations for which inputs oroutput are not within the threefold of the inter-quartile range above and below the 25and 75 percentiles. We deal with extraordinary spikes in the data by also dropping allobservations with plant-level input growth (labor, intermediate inputs and capital) aswell as output growth that is outside the first and ninety-ninth percentile range of eachvariable’s distribution. These steps reduce our sample size by 12,865 observations.

We transform all input and output variables as well as wages to their natural loga-rithms, using a Box-Cox-transformation to deal with zeros. The log values not only allowfor a more intuitive interpretation of coefficients as elasticities but also make estimationsless vulnerable to remaining outliers.

5See for instance Amiti and Konings (2007), Blalock and Gertler (2008) or Arnold and Javorcik (2009),all based on the same SI data.

6We consider sector codes as incomplete if they have less than five digits, e.g., ‘151’ instead of ‘15111’.

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There are some concerns regarding the data quality of the SI. First, doubts arisewith respect of its completeness since it claims to include all medium sized and largemanufacturing firms in Indonesia. Due to the large number of firms it is at least possiblethat BPS misses some new entrants or cannot investigate cases of non-respondents. Eachof these cases potentially leads to non-random selection into the the sample and maybias our results. Blalock and Gertler (2008) as well as Arnold and Javorcik (2009) arguethat there are some financial incentives for the field agents to register new firms andverify firms which do not reply immediately since budgets are linked to the number ofreported establishments. On the other hand, this may create wrong incentives by temptingfield agents to fill in values of firms that are similar to those which do not report, toincrease the budget size. A second concern addresses potential misreporting by firms.Government law guarantees exclusive and anonymized use of information for statisticalpurposes. Firms may still be afraid, however, that reported information is leaked to taxauthorities or competitors. Therefore, some firms may intentionally report wrong data(Blalock and Gertler 2008). Furthermore, the questionnaires ask for quite detailed andspecific information on accounting and human resources. If firms do not put much effortinto the correct completion, those numbers may be falsely reported by accident. The abovearguments suggest that noise within the data is likely to be a considerable issue. As longas firm selection is not systematically correlated with firm attributes, however, estimationwill yield consistent results. Furthermore, as long as misreporting is not directly linked toFDI regulation, firm and two digit sector-year fixed effects will lead to unbiased estimates.

In our analyses all monetary values are deflated to the base year 2008 by using yearlyregional price deflators.7 For input and output values we use the average wholesale priceindex whereas wages are deflated by the consumer price index.

2.3.2 The regulation data

Our analysis relies on self-collected information from five revisions of the NIL, outlinedin the Presidential Decrees 96/2000, 77/2007, 36/2010, 39/2014 and 44/2016. Thesedecrees consist of legislative provisions in their preamble plus tables that list all appliedregulations by five digit KBLI sector. In the case of NIL 2000, we match the verballystated sector names to the corresponding KBLI sector codes. Furthermore, as the NIL2007 slightly changes in 2008 by an amendment to the existing regulation, we use thecontent of the first draft for 2007 and the amendment for the years starting with 2008.We convert the changing KBLI sector codes between the years and adjust the coding ofthe NIL 2010, NIL 2014 and NIL 2016 to the KBLI 2000 standard. The regulatory andfirm data are merged according to the five digit KBLI 2000 sector codes and the relevantyear.

7The price indices are taken from the BPS.

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Based on the regulatory information, we calculate firm-specific exposure to regulation.While total closure to any investment unambiguously affects all firms, other rules dependon firm characteristics such as firm size or legal status. Firms that are regulated bythese conditions are permitted to receive FDI if they are either small or medium sizedor organized as a partnership. Further conditions may establish upper limits to foreigncapital and licensing by the respective authorities. Importantly, the preambles of thePresidential Decrees exclude already existing foreign investments from new regulation.We take this into account by offsetting regulation for those firms that exceed the laterlegal limits on the share of foreign capital already before the revision of the NIL. Finally,some of the NIL stipulations verbally narrow regulation to selected subcategories withina five digit KBLI sector, which we then aggregate back to the five digit KBLI level.8

We use the detailed conditions of the NIL and firm level information on size andforeign investment share to generate a single measure of exposure to regulation. Thedummy Regulated takes one if a firm is subject to any kind of regulation in a certainyear. The definition takes into account the aforementioned firm characteristics relevantfor the applicability of the regulation. For example, Regulated takes zero if a mediumsized firm operates in a sector that is open to small and medium sized firms but requireslicensing from large firms. For any large enterprise operating in the same sector, however,the dummy Regulated will take one since FDI is conditional on a successful licensingprocedure.

The final dataset is an unbalanced panel that consists of 18,799 firms with a total of114,581 observations (this gets reduced in some regressions due to missing values). Table1 presents summary statistics for the main variables in the years 2000, 2007 and 2014. Theshare of regulated firms in each year increases from 2000 to 2007 and declines afterwards.Despite the NIL, the overall share of foreign capital increases over time, although mostdomestic firms receive zero FDI throughout the whole time period.

Table 2 shows the means of foreign capital share, total factor productivity and employ-ment by sector and year. Regulation across sectors shows a very heterogeneous picture.Some industries are not affected by the NIL at all whereas other sectors like wood andwood products are already strictly regulated in 2000.

8This may bias results since we assume some cases of regulation which have not been applied in reality.However, the resulting measurement error is most likely to cause attenuation bias and thus our resultswill be underestimated Goldberg and Pavcnik (2016).

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3 Estimation strategy and identification

3.1 Effect of regulation on FDI

We test the effect of the NIL on FDI by estimating the equation:

FDI ijt =∑τ

τ=τ (β1,t+τ × REG ijt+τ ) + X ′itβ2

+ αi + γrt + φst + εit,(1)

where FDI ijt measures the percentage of foreign equity in total equity of firm i op-erating in five digit sector j in year t. REG ijt is the investment restriction according tocharacteristics of firm i operating in the five digit KBLI sector j in the respective yeart. Besides the contemporaneous effect of regulation, we also include up to three lagsand leads of regulation. Additionally, we include a vector of controls X it to capture time-variant firm characteristics. The final regressions are estimated with firm fixed effects (αi)to control for time-invariant differences across firms. We further include interactions ofisland-year (γrt)9 and two digit sector-year dummies (φst) to factor out changes commonto all firms within a certain industrial division or region in a particular year (Blalock andGertler 2008). The residuals εit are robustly estimated and clustered at firm level.

As Grossman and Helpman (1994) argue, trade policy is endogenous, and hence, weare likely to omit unobservable influence factors of the share of foreign capital that arealso systematically related to regulation. This would result in biased coefficients andmake causal interpretation impossible. Since regulation is a policy choice, these omittedvariables most probably include political economy factors like lobbying for protection orelectoral motives. For instance, a politically powerful firm lobbies for greater protectionfrom FDI and, at the same time, exhibits a lower foreign capital share due to its firmcharacteristics. Then, neglecting this confounding factor will lead to overestimation of thenegative impact of the NIL on the share of foreign capital. Those firm characteristics mayinclude productivity, access to international goods markets or a connection to productivelocal suppliers (cf. Harrison and Rodríguez-Clare 2010, Goldberg and Pavcnik 2016).

We address the problem of unobserved heterogeneity by adding fixed effects as well aslags and leads of regulation. For example, firm fixed effects absorb unobservable politicaleconomy factors to the extent that the latter are time-invariant (Goldberg and Pavcnik2005). Furthermore, island-year fixed effects control flexibly for current and expectedregional influence factors that may correlate with both regulation and foreign capitalshares. Note that these also implicitly cancel out common time trends and macroeconomicshocks. Finally, in our preferred specifications we also include sector-year fixed effects tocontrol for time variant incentives to lobby for protection at two digit sector level. Certain

9We distinguish between Sumatra, Java, Kalimantan, Sulawesi and the rest of smaller islands.

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industries, for instance those that exhibit low foreign market linkages, may share commoninterests concerning protectionist regulation to prevent future FDI inflows. Adding furtherlags and leads of regulation helps to capture potential timing patterns. A significant lagwould provide evidence in favor of causality running from regulation to foreign capitalshares. In contrast, a pronounced lead may indicate that regulation reacts to pre-existingshares in foreign equity. Alternatively, a significant first lead may also point towardsanticipation effects of regulation.

3.2 Total factor productivity estimation

In a next step, we estimate total factor productivity for each firm, while simultaneouslyaccounting for the correlation of the firm’s input choices with the error term (cf. Javorcik2004, Amiti and Konings 2007, Blalock and Gertler 2008).10 If a firm adjusts its choiceof inputs to unobserved productivity shocks, disregarding this adjustment will induce asevere simultaneity problem and, thus, lead to biased coefficients.11

The estimation is based on a Cobb-Douglas production function in value added termson plant level:

Yit −Mit = AitLαLit K

αKit , (2)

where the value added of firm i in year t is calculated by subtracting the value of theintermediate inputs Mit from total firm output Yit. Value added (VA) is a function ofproductivity Ait, the freely variable input factor labor Lit and quasi-fixed capital Kit.Taking natural logs results in:

ln(VA)it = α0 + αLlit + αKkit + ωit + eit, (3)

where small letters denote logs. The error term can be decomposed into two components,an unobserved productivity component ωit and the independently identically distributederror term eit. Simultaneity bias is introduced because a part of the productivity shocksis also correlated with the choice of the variable inputs, namely labor and intermediategoods.

In contrast to other studies, we apply an approach suggested by Wooldridge (2009)in order to consistently estimate total factor productivity. Total factor productivity isestimated on two digit sector level, which takes into account the varying importance ofinput factors across industries. A more disaggregated estimation is not feasible since manysectors would not include sufficient observations for the routine to work properly. Thus,

10Two standard semi-parametric methodologies of total factor productivity estimation are Olley and Pakes(1996) and Levinsohn and Petrin (2003).

11See Van Beveren (2012) for a detailed discussion of total factor productivity estimation in the literature.

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we estimate log total factor productivity (TFP) by:

ln(TFP)it = ln(VA)it − α̂0s − α̂ls lit − α̂ks kit, (4)

where subscript s emphasizes that input coefficients αl and αk are estimated for each twodigit sector separately. We report the estimated input coefficients sector-wise in table A2.A more detailed description of the approach is attached in the Appendix.

3.3 Impact of FDI regulation on productivity and labor market

outcomes

The second part of the analysis builds on the reduced form estimation:

Yijt =∑τ

τ=τ (β1,t+τ × REG ijt+τ )

+ β2 × FDI it+ X ′

itβ3 + αi + γrt + φst + εit,

(5)

where the outcome variable Yijt denotes firm productivity, firm employment or firm wagesper worker. REG ijt indicates if any FDI restriction applies to the firm. FDI ijt measuresthe direct plant-effect of foreign acquisitions. At the moment, we do not pay attentionto any kind of horizontal or vertical spillovers. Aitken and Harrison (1999), Amiti andKonings (2007) and Arnold and Javorcik (2009) show, however, that despite potentialspillover effects there is also a direct positive effect of foreign ownership on firm leveloutcomes such as productivity. The vector of controls X it includes time-variant firmcharacteristics such as firm age, government ownership and the log capital-labor ratio.Each regression is again estimated with firm fixed effects (αi) as well as the interactionsof island-year (γrt) and two digit sector-year (φst) fixed effects.

The argumentation for causality faces the same obstacles as before because of theendogeneity of trade policy. Firms with enough political influence may be able to lobbythe government for protection and thus can trigger higher regulation. If those firmshave certain characteristics which are systematically correlated with the error term, thecoefficients of foreign capital share and regulation will be biased. For example, those firmscould be less productive or less connected to global goods markets.

We add fixed effects to our regressions to control for as much of unobserved hetero-geneity as possible (Blalock and Gertler 2008). Firms that receive intermediate inputsfrom highly efficient local suppliers are more likely to attract FDI and have higher foreigncapital shares. Similarly, firms with connections to international import markets maylobby for exemptions from regulation. Including firm fixed effects will absorb at least thetime-invariant component of these confounding factors.

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Next, foreign investors may prefer to buy into firms in certain regions because localconditions like infrastructure are currently more promising or are expected to improve.Interest groups in remote areas may also lobby the government for tighter regulationor, vice versa, even try to achieve more FDI inflows as a source of capital. By addingisland-year fixed effects, we can reduce this source of bias from unobserved heterogeneity.

Furthermore, one firm alone may not have enough political power. Firms operatingin declining sectors, labor-intensive industries, or parts of manufacturing with a highcontribution to GDP may form interest groups to enforce protectionist measures. To theextent that lobbying takes place on two digit sector level, we are able to control for theseinfluences by including sector-year fixed effects. This also accounts for varying influenceof interest groups over the sample period.

Finally, we again consider time patterns of the effect of regulation. For instance, lowerproductivity (or productivity trends) within sectors may directly motivate policymakersto voluntarily implement protectionist measures. Alternatively, firms may anticipate theprotectionist measure already some time before it enters into force. A significant coefficientof leading regulation supports both interpretations, whereas a significant coefficient oflagged regulation can be understood as a sign that investment policy in fact changes firmoutcomes.

4 Results

4.1 Pre-existing trends before regulation

We first test for pre-existing trends and present some evidence that supports our interpre-tation that regulation affects firm outcomes instead of just reflecting pre-existing trendsin the variables. Figures 1 to 3 graph estimated coefficients from fully specified firmlevel regressions that include time variant controls, firm fixed effects as well as island-yearand two digit sector-year fixed effects. Both the upper and lower graphs show that non-regulated firms do not differ much from regulated firms in terms of foreign capital shares,productivity and employment in the years before regulation was implemented.

In the upper graphs (a) we explicitly look at the time before the first and most compre-hensive revision of the NIL in 2007. Thereby, we compare firms that became regulated in2007 with non-regulated firms. The graph reports the coefficient of the differential effectof regulation in 2007 for the years 2001 to 2009 together with ninety percent confidenceintervals. In the bottom graphs (b) we consider the complete sample period and, there-fore, take into account the other revisions as well. Here our estimating equation followsclosely equation (5), using a seven year time window around the regulation year (withτ = −3 and τ = 3).

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Figure 1 shows the results with respect to FDI. In graph (a), there is no evidence infavor of any systematic difference between regulated and non-regulated firms before 2006.However, firms that are regulated in 2007 already decrease their foreign capital share in theprevious year. The differential effect between both groups of firms remains significantlynegative until 2008 and then vanishes again. Taking into account the complete sampleperiod confirms this result (graph (b)). There is no significant effect of regulation threeor two years before actual regulation happens. One year before regulation, however, thecoefficient drops. The negative effect has already vanished one year later.

Figure 2 repeats the same exercise for the differential effect of regulation on totalfactor productivity. The upper graph (a) detects no difference between firms which areregulated in 2007 and those which are not until 2004. However, productivity of firms thatbecome regulated in 2007 already starts decreasing in 2005 relative to those who stayunregulated. The effect is again strongest in 2006. In contrast to FDI, there seems tobe also a long-run effect since the yearly coefficient turns again negative in 2009. Thelower graph (b) makes this point even clearer. Taking into account the complete sampleperiod, there is no difference between regulated and non-regulated firms until regulationcomes into force. Once regulation applies, the protected firms show significantly lowerproductivity. The impact is strongest two periods after a revision of the NIL.

In contrast, figure 3 shows overall positive employment dynamics of regulated firms.When focusing at the revision in 2007 only, graph (a) does not show any difference before2004. Firms which will become regulated in 2007 start to increase their staff already from2004. The positive differential effect peaks in 2006 and remains significantly larger thanzero in the aftermath. We interpret this as evidence that firms with growing employ-ment are more likely to receive protection. Graph (b) does also support this qualitativeinterpretation, even though not all lags and leads coefficients turn out significant.

4.2 Effects on FDI

Table 3 reports the baseline results using equation 1. All specifications include firm andisland-year fixed effects and control for categories of firm age. Column 1 only adds thecontemporaneous effect of regulation. The estimated impact of any regulatory measure onthe foreign capital share of the average firm is -0.007. The effect is highly significant andimplies that becoming protected is associated with a 0.7 percentage points lower foreignequity ownership share for an average firm. This effect may not seem very substantialbut it still amounts to about 10% of the mean ownership within the sample (which isabout 7%). Column 2 and 3 add lags and leads of the regulation dummy to the regressionto disentangle potential timing patterns. Additionally to regulation in t, the first leadcomes out negative and significant. This allows for two interpretations. Either firms

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and investors have generally known or formed correct expectations about the NIL oneyear before the revisions and have already reduced FDI beforehand, or policymakers onlyprotect those sectors which that have been experiencing a decreasing share of foreignownership just in the year before the regulation enters into force. Of these two, the firstinterpretation seems somewhat more likely to us, also because upcoming FDI regulationhas been substantially discussed in the Indonesian press. A remaining concern is that weneglect all factors that influence industries as a whole. These can also include politicaleconomy factors such as industry-wide lobbying. Therefore, our preferred specificationsin column 4 include two digit sector-year interactions. To the extent that most of thepolitical bargaining process occurs at a more aggregated level of industrial structure, weare able to control for this source of unobservable heterogeneity. However, the coefficientsof interest do not change much and still suggest a negative correlation between regulationand FDI. In column 5, we finally include a dummy that takes one if a firm imports orexports any of its intermediates or final goods. To check for the relation of linkages tointernational markets and regulation, we also include an interaction term. While ourbaseline results also hold for this specification, the newly introduced variables do notshow surprising signs. Multinational firms also have stronger connection to import andexport markets. The negative impact of regulation is even stronger for firms with linkagesto foreign markets compared to those firms operating purely domestically.

Table 4 performs some further robustness checks. Column 1 includes one additionallag and lead compared to the previous specifications and replicates the results alreadypresented in figure 1. The coefficients of regulation in t + 1 and t remain quantitativelyand qualitatively the same. Surprisingly, foreign capital shares seem to increase on av-erage three years after implementation of the regulation. Thus, investors seem to regainthe interest for investment into protected sectors some years after the regulatory shock.Column 2 checks for sensitivity of the results to definitions of the dependent variable.Since the data is noisy, results may partly be driven by measurement error and wrongreporting of the share of foreign capital share. We thus use a dummy for multinationalenterprises which turns one if the foreign capital share exceeds ten percent. The mainresult for regulation in t + 1 and t are robust to this specification, even though the longrun positive coefficient already turns significant in t− 2.

Column 3 replicates our preferred specification but excludes about 4,500 firms thatleave the market over the sample period. Hereby we check for potential survival biasthat drives our results. However, our coefficients or interest do not change. One furtherdrawback of our data is that we can only observe the main product of each firm. Thus,multi-product firms potentially change their reported sector in order avoid or select them-selves into protection. In column 4, we exclude all firms that have switched their five digitsector during the sample period. This not only reduces the sample size but also slightly

15

changes the timing patterns of regulation. The pre-regulation effect for t + 1 disappearswhile the contemporaneous effect of regulation becomes even stronger. Sector switchingin fact seems to introduce some selection bias. In order to keep the sample size constant,we next include the sector switch dummy as well as an interaction term with regulationin column 5. Using the whole sample size restores the previous results. Firms that switchthe sector of their main product on average have lower foreign capital shares. However,the interaction of sector switch with regulation does not come out significantly. Thisweakens our concerns regarding selection issues for our analysis somewhat.

4.3 Effects on firm productivity

Table 5 shows our results with respect to total factor productivity (based on equation5). Again, column 1 only includes regulation in t and additionally controls for the shareof foreign capital, a public enterprise dummy and categories of firm age. Neither regula-tion nor FDI do come out significant in this specification. This is surprising since mosttrade literature finds a positive correlation between FDI and productivity (Javorcik 2004,Amiti and Konings 2007, Arnold and Javorcik 2009). However, this result confirms thatour time variant controls and the extensive fixed effects are successful at factoring out allunderlying characteristics that attract FDI and make firms more productive. In contrast,public enterprises are about 39 percent more productive compared to a similar firm ofthe private sector. Columns 2 and 3 add lags and leads of regulation to the regression.There is clear evidence that total factor productivity on average decreases in the yearsafter implementation of regulation. While column 2 reports a negative coefficient of reg-ulation in t − 1, the estimate is highly significant and negative for regulation in t − 2 incolumn 3. Receiving FDI protection is associated with a decline in productivity of aboutsix percent two years after. However, we also get a significant and positive correlationbetween regulation in t+2 and productivity. This may be a sign of a general decline inproductivity within regulated sectors. To control for political economy factors as well asother unobserved shocks that affect sectors as a whole, we add two digit sector-year fixedeffects in column 4. The significant and positive effect of regulation in t+ 2 vanishes andbecomes insignificant. In contrast, contemporaneous regulation and especially two yearlagged regulation remain significantly negative. This results supports our interpretationthat pre-trends and political economy factors are relevant at the level of two digit indus-tries and our preferred specification is able to at least partially control for endogenousregulation.

Finally, we again check for differential effects for firms that are connected to interna-tional product markets in column 5. Our coefficients of interest do not change much andstill provide evidence in favor of a negative impact of regulation on firm productivity. Wecan additionally reproduce a standard result of trade literature, namely that firms with

16

linkages on import and export markets are more productive compared to purely domesticenterprises (Melitz 2003). However, these internationally connected firms are also sub-stantially more vulnerable to regulation by the NIL. Relative to a domestically operatingregulated firm, firms that engage in exports or imports face a 14 percent decline in totalfactor productivity due to regulation.

We check for robustness of our productivity results in table 6. Like before, we replicateour results of figure 2 in column 1 by adding one additional lag and lead. The coefficientschange from insignificant before regulation to negative and significant in the year of reg-ulation and later. Column 2 changes the dependent variable to the log of value addedper worker which is often used as a proxy for labor productivity. In this setting it is alsonecessary to control for the capital-labor ratio since coefficients would be biased due toomitted variables. This specification robustly confirms our previous results and reports anegative coefficient of lagged regulation in t-2.

Column 3 again excludes exiting firms. However, the coefficients of interest do notchange compared to the estimates in the full sample and still report a negative coefficient oflagged regulation in t-2. Columns 4 and 5 test whether the results are sensitive to productswitching behavior by the firms. In column 4, all firms with a sector switch are excludedfrom the sample. Results get even more pronounced with a strongly negative impact ofregulation two periods after the revision of the NIL. In order to keep the sample sizeconstant, we also include the sector switch dummy and its interaction with regulation incolumn 5. Controlling for switching behavior does not alter our main results with regardto regulation. However, firms that change the sector of their main product exhibit onaverage a lower productivity. Our concern that selection into regulatory status may bean issue can be rejected since the interaction with regulation is clearly insignificant.

4.4 Effects on employment and wages

Our last set of results looks for effects of regulation on employment and wages at firm level.Table 7 takes the log of total firm employment as the dependent variable. All regressionscontrol for foreign capital share, public enterprises, capital-labor ratio and categories offirm age. Only including the contemporaneous impact of regulation, column 1 estimatesa positive and significant relationship between the NIL and firm employment. Addingfurther lags and leads in columns 2 and 3 reveals a clear timing pattern. None of the laggedcoefficients of regulation reaches conventional significance levels while regulation seems tocorrelate with employment already in the years before the revisions of the NIL. Onecan find two different explanations for this finding. It is either possible that policymakersespecially focus on sectors with increasing employment or, alternatively, that firms alreadyanticipate future protection against foreign acquisitions in the years before. Column 4

17

checks for the exact channel by including two digit industry-year fixed effects. To theextent that firms lobby for protection against foreign investment, one can control for thesepolitical economy factors. Furthermore, this captures employment trends across wholesectors. Including sector-year fixed effects does not change the coefficients of interestand both leads of regulation still stay significant. Like before, column 5 drops all firmswhich exit the market and during the sample period. In this specification the leadingvalues of regulation do not reach the ten percent significance level but still are close toit. In contrast, the lags of regulation remain clearly insignificant. Next, we again controlfor selection effects due to sector switching. Excluding firms with a sector switch doesnot change results considerably. Most of the effect is still captured within the leadingvalues of regulation, although regulation in t+1 loses its significance. In order to keepsample size constant, column 7 includes the sector switch dummy as well as its interactionwith regulation. On average, firms who have switched the sector of their main productemploy more workers. However, there is no evidence in favor of any interaction effectbetween switching and regulation while leading protection remains positively significant.Finally, column 8 investigates the differential effects on firms with linkages to internationalgoods markets. Firms which import or export some of their intermediate or final goodsemploy more workers on average. However, there is no evidence that employment of thesefirms is differently affected by regulation since the interaction term does not reach anysignificance. Across all specifications, one can observe that multinational firms do notdiffer from domestic firms in terms of employment. Similarly, public enterprises do notemploy significantly more workers than private owned firms.

In a last exercise, table 8 reports the impact of regulation on further labor marketoutcomes. In column 1, we add the third lag and lead of regulation to the previous spec-ification. The result for regulation in t+2 is confirmed within this specification. A bitsurprisingly, firm employment also increases two years after implementation of regulation.This may still capture long-term adjustment processes due to constraints on foreign cap-ital. Columns 2 and 3 disentangle the dependent variable into production workers andnon-production workers. The results on total employment seem to be driven by increasesin the number of production workers within firms, while there are no significant changesin the number of white collar jobs. Interestingly, public enterprises employ about 11 per-cent more white collar workers in comparison to private owned firms. Finally, columns 4and 5 disentangle the effects of regulation on wage development of production and non-production workers. Wages are reported as total salary per worker and year. None of thecoefficients of interest reaches conventional significance levels. Therefore, regulation doesnot seem to change per capita earnings in the manufacturing sector. Total adjustmentseems to take place through hiring new workers. Public enterprises are again different toprivate firms. Average wages in the public sector are higher and especially white collarworkers seem to be paid better if they are employed within a public enterprise.

18

5 Concluding Remarks

This paper contributes to the literature on the effects of FDI and especially its regulation.Despite an increasingly open FDI regime, the Indonesian government uses the instrumentof negative investment lists that restrict future foreign investment in particular industries.For our analysis, we especially exploit the revision of the NIL in 2007, 2010 and 2014 andidentify whether regulation successfully lowers the share of foreign capital within affectedfirms. Our identification strategy bases on fixed effects and lagged and leading values ofregulation to control for unobserved heterogeneity.

We find robust evidence that shows a substantial effect of FDI restrictions on FDIthat individual firms receive. Regulation is associated with a 0.7 percentage points lowerforeign capital share on average, once time variant controls and an extensive set of fixedeffects are controlled for. Analyzing the relationship between regulation by the NIL andfirm level productivity, we find evidence in favor of a causal negative influence of invest-ment regulation on different measures of productivity. FDI restrictions are associatedwith an instant 2.6 percent decrease of total factor productivity and a 4.3 percent fall inproductivity two years later, while there are no significant differences in productivity inthe years preceding the regulation. We also find a sizable relationship between regulationand labor market outcomes that does not appear necessarily causal. Instead, especiallythose firms and sectors become subject to FDI restrictions that have been experiencingemployment growth in the years preceding the regulatory reforms. Most of the labormarket effects work through firm employment, while adjustment of wages seem to play aminor role.

In ongoing work, we plan to address inter-industry linkages by combining sector-levelregulation with information on input-output linkages. Many studies show that backwardand forward spillovers have important effects on firm productivity (Amiti and Konings2007, Blalock and Gertler 2008). We believe that capturing these connections betweenindustries will shed even more light on the mechanisms at work. At this point, however,we are confident that we have robustly shown an important effect of FDI restrictions ona number of firm outcomes.

19

References

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Amiti, M. and Konings, J. (2007). Trade Liberalization, Intermediate Inputs, and Pro-ductivity: Evidence from Indonesia. American Economic Review, 97(5):1611–1638.

Arnold, J. M. and Javorcik, B. S. (2009). Gifted kids or pushy parents? Foreign directinvestment and plant productivity in Indonesia. Journal of International Economics,79(1):42–53.

Blalock, G. and Gertler, P. J. (2008). Welfare gains from Foreign Dircet Investmentthrough technology transfer to local suppliers. Journal of International Economics,74(2):402–421.

CompNet Task Force (2014). Micro-based Evidence of EU Competitiveness: The Comp-Net Database. ECB Working Paper Series, 1634.

Djankov, S. and Hoekman, B. (2000). Foreign Investment and Productivity Growth inCzech Enterprises. World Bank Economic Review, 14(1):49–64.

Duggan, V., Rahardja, S., and Varela, G. (2013). Service Sector Reform and Manufac-turing Productivity: Evidence from Indonesia. World Bank Policy Research WorkingPaper, 6349.

Gammeltoft, P. and Tarmidi, L. T. (2013). Chinese foreign direct investment in Indonesia:trends, drivers and impacts. International Journal of Technological Learning, Innova-tion and Development, 6(1,2):136–160.

Goldberg, P. K. and Pavcnik, N. (2005). Trade, Wages, and the Political Economy of TradeProtection: Evidence From the Colombian Trade Reforms. Journal of InternationalEconomics, 66(1):75–105.

Goldberg, P. K. and Pavcnik, N. (2016). The Effects of Trade Policy. NBER WorkingPaper Series, 21957. Cambridge, MA.

Grossman, G. M. and Helpman, E. (1994). Protection for Sale. American EconomicReview, 84(4):833–850.

Harrison, A. and Rodríguez-Clare, A. (2010). rade, Foreign Investment, and IndustrialPolicy for Developing Countries. Handbook of Development Economics, 5:4039–4214.

Javorcik, B. S. (2004). Does Foreign Direct Investment Increase the Productivity of Do-mestic Firms? In Search of Spillovers through Backward Linkages. American EconomicReview, 94(2):605–627.

20

Levinsohn, J. and Petrin, A. (2003). Estimating Production Functions Using Inputs toControl for Unobservables. The Review of Economic Studies, 70(2):317–341.

Lindblad, J. T. (2015). Foreign Direct Investment in Indonesia: Fifty Years of Discourse.Bulletin of Indonesian Economic Studies, 51(2):217–237.

Melitz, M. J. (2003). The Impact of Trade on Intra-Industry Reallocations and AggregateIndustry Productivity. Econometrica, 71(6):1695–1725.

Olley, G. S. and Pakes, A. (1996). The Dynamics of Productivity in the Telecommunica-tion Equipment Industry. Econometrica, 64(6):1263–1297.

Topalova, P. and Khandelwal, A. (2011). Trade Liberalization and Firm Productivity:The Case of India. The Review of Economics and Statistics, 93(3):995–1009.

Van Beveren, I. (2012). Total Factor Productivity Estimation: A practical review. Journalof Economic Surveys, 26(1):98–128.

Wooldridge, J. M. (2009). On estimating firm-level production functions using proxyvariables to control for unobservables. Economic Letters, 104(3):112–114.

World Trade Organization (1998). Trade Policy Review Indonesia 1998 - Report by theSecretary. Geneva : WTO.

World Trade Organization (2013). Trade Policy Review Indonesia 2013 - Report by theSecretary. Geneva : WTO.

21

Figures

Figure 1: FDI

(a) Interaction of year dummies with regulation in 2007, from 2000-2009

-.02

-.01

5-.

01-.

005

0.0

05R

egul

ated

in 2

007

X y

ear

2001 2002 2003 2004 2005 2006 2007 2008 2009Year

(b) Regulation impact from t-3 to t+3

-.01

-.00

50

.005

.01

t-3 t-2 t-1 t t+1 t+2 t+3Effect of regulation in t

Note: Plotted coefficients are estimated within regressions controlling for categories of firm age as well as firm, island-year and industry-year FE. Bars around the point estimates are ninety percent confidence intervals.

22

Figure 2: ln(total factor productivity)

(a) Interaction of year dummies with regulation in 2007, from 2000-2009

-.15

-.1

-.05

0.0

5R

egul

ated

in 2

007

X y

ear

2001 2002 2003 2004 2005 2006 2007 2008 2009Year

(b) Regulation impact from t-3 to t+3

-.06

-.04

-.02

0.0

2.0

4

t-3 t-2 t-1 t t+1 t+2 t+3Effect of regulation in t

Note: Plotted coefficients are estimated within regressions controlling for categories of firm age and dummy of publicownership as well as firm, island-year and industry-year FE. Bars around the point estimates are ninety percentconfidence intervals.

23

Figure 3: ln(employment)

(a) Interaction of year dummies with regulation in 2007, from 2000-2009

0.0

2.0

4.0

6.0

8R

egul

ated

X y

ear

2001 2002 2003 2004 2005 2006 2007 2008 2009Year

(b) Regulation impact from t-3 to t+3

-.02

-.01

0.0

1.0

2

t-3 t-2 t-1 t t+1 t+2 t+3Effect of regulation in t

Note: Plotted coefficients are estimated within regressions controlling for categories of firm age, dummy of publicownership and log capital labor ratio as well as firm, island-year and industry-year FE. Bars around the point estimatesare ninety percent confidence intervals.

24

Tables

Table 1: Summary statistics of main variables in 2000, 2007 and 2014

2000 2007 2014

Regulated 0.03 0.22 0.16(0.18) (0.42) (0.37)

Foreign capital share 0.05 0.06 0.09(0.21) (0.22) (0.27)

ln(tfp) 10.69 10.59 11.52(1.57) (1.55) (1.60)

ln(Value added per worker) 9.87 9.87 10.79(1.27) (1.24) (1.33)

ln(Production wage) 12.56 12.59 13.31(1.58) (1.57) (1.46)

ln(Non-production wage) 11.53 11.38 12.04(1.95) (2.00) (1.81)

ln(L) 4.18 4.03 4.19(1.16) (1.08) (1.18)

ln(Production labor) 3.99 3.86 3.98(1.17) (1.08) (1.19)

ln(Non-production labor) 1.85 1.82 1.84(1.56) (1.38) (1.63)

Sector switch 0.16 0.20 0.13(0.36) (0.40) (0.34)

Firm exit 0.08 0.06 0.01(0.26) (0.24) (0.10)

Note: Means of main variables in respective year. Stan-dard deviations in parentheses.

25

Table 2: Summary statistics by sectors in 2000, 2007 and 2014

regulated ln(tfp) ln(L)

2000 2007 2014 2000 2007 2014 2000 2007 2014

Food products and beverages 0.01 0.29 0.17 9.41 9.49 10.17 3.92 3.85 4.00Tobacco products 0.00 0.30 0.79 8.32 8.52 10.42 4.08 3.99 4.10Textiles 0.00 0.11 0.10 10.64 10.43 11.25 4.51 4.12 4.16Wearing apparel 0.00 0.00 0.04 9.22 9.17 10.12 4.24 3.93 4.24Leather and leather products 0.00 0.00 0.00 11.04 10.81 11.74 4.65 4.03 4.30Wood and wood products, except furniture 0.35 0.31 0.30 10.10 9.84 10.66 4.36 4.04 4.20Pulp, paper and paper products 0.01 0.03 0.01 10.62 10.62 11.39 4.54 4.46 4.56Publishing, printing and recorded media 0.00 0.19 0.02 10.79 11.18 11.64 3.90 3.90 3.99Coke, refined petroleum products and nuclear fuel 0.00 0.00 0.00 10.72 11.00 11.01 3.89 4.26 4.17Chemicals and chemical products 0.09 0.35 0.30 12.40 12.59 13.31 4.40 4.45 4.52Rubber and plastics products 0.00 0.00 0.11 11.61 11.79 12.48 4.59 4.56 4.62Other non-metallic mineral products 0.00 0.70 0.11 9.47 9.52 10.36 3.66 3.66 3.83Basic metals 0.00 0.17 0.25 11.50 11.60 11.99 4.89 4.80 4.70Fabricated metal products 0.00 0.07 0.05 10.42 10.71 11.51 4.11 4.12 4.23Machinery and equipment 0.02 0.18 0.14 10.74 11.08 11.83 4.22 4.36 4.42Electrical equipment, office machinery, computers 0.00 0.00 0.00 12.37 12.26 13.40 4.87 4.97 5.18Radio, television and communication equipment 0.00 0.00 0.00 12.78 12.37 12.70 5.83 5.65 4.87Medical, precision and optical instruments 0.00 0.00 0.00 11.59 11.04 11.66 4.80 4.55 4.55Motor vehicles 0.00 0.00 0.00 11.97 12.03 13.17 4.60 4.55 4.84Other transport equipment 0.00 0.64 0.48 10.62 11.35 12.04 4.19 4.33 4.59Furniture 0.00 0.15 0.10 10.02 10.03 10.97 4.15 3.94 4.13

Total 0.03 0.22 0.16 10.07 10.05 11.00 4.18 4.03 4.19

Note: Average share of regulated firms, average log productivity and average log employment within sectors.

26

Table 3: Impact on FDI - baseline results

Dependent variable: Foreign direct investment

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

Regulated in t+2 −0.002 −0.000 −0.000(0.002) (0.002) (0.002)

Regulated in t+1 −0.005*** −0.005*** −0.005** −0.005**(0.002) (0.002) (0.002) (0.002)

Regulated −0.007*** −0.005** −0.005** −0.007*** −0.005**(0.002) (0.002) (0.002) (0.002) (0.002)

Regulated in t-1 0.001 0.001 0.002 0.002(0.002) (0.002) (0.002) (0.002)

Regulated in t-2 0.001 0.002 0.002(0.001) (0.002) (0.002)

Linkage to int. markets 0.009**(0.004)

Linkage to int. markets and regulated −0.022*(0.013)

Categories of firm age [0.016] [0.016] [0.016] [0.032] [0.034]

Sector-year interactions Yes Yes

Observations 114,581 114,581 114,581 114,581 114,581Firms 18,799 18,799 18,799 18,799 18,799R-squared 0.882 0.882 0.882 0.883 0.883

Note: Dependent variable is the share of foreign capital within each firm. All regression include firmand island-year FE. Robust standard errors are clustered on firm level and reported in parentheses,p-values of joint significance are reported in brackets. Significance at or below 1 percent (***), 5percent (**) and 10 percent (*).

27

Table 4: Impact on FDI - robustness

Dependent variable: Foreign direct investment

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

Regulated in t+3 0.001(0.002)

Regulated in t+2 −0.002 0.001 −0.001 −0.001 −0.000(0.002) (0.002) (0.002) (0.002) (0.002)

Regulated in t+1 −0.006*** −0.006*** −0.005** 0.001 −0.005**(0.002) (0.002) (0.002) (0.003) (0.002)

Regulated −0.006** −0.008*** −0.007*** −0.010** −0.007***(0.003) (0.003) (0.003) (0.005) (0.003)

Regulated in t-1 0.002 0.002 0.002 0.002 0.003(0.002) (0.002) (0.002) (0.003) (0.002)

Regulated in t-2 0.000 0.003* 0.002 0.003 0.002(0.002) (0.002) (0.002) (0.003) (0.002)

Regulated in t-3 0.004**(0.002)

Sector switch −0.003**(0.001)

Sector switch and regulated 0.002(0.004)

Categories of firm age [0.057] [0.132] [0.059] [0.037] [0.033]

Sector-year interactions Yes Yes Yes Yes YesMNE dummy no exit no switch

Observations 85,696 114,581 98,160 51,825 114,581Firms 13,539 18,799 14,232 8,939 18,799R-squared 0.885 0.879 0.881 0.892 0.883

Note: Dependent variable is the share of foreign capital within each firm, except for column (2)where it is a dummy that turns 1 if a firm has foreign capital participation of more than tenpercent. All regression include firm and island-year FE. Robust standard errors are clusteredon firm level and reported in parentheses, p-values of joint significance are reported in brackets.Significance at or below 1 percent (***), 5 percent (**) and 10 percent (*).

28

Table 5: Impact on productivity - baseline

Dependent variable: ln(productivity)

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

Regulated in t+2 0.034*** 0.023 0.024*(0.013) (0.014) (0.014)

Regulated in t+1 0.021 0.006 0.020 0.020(0.013) (0.013) (0.013) (0.013)

Regulated 0.004 0.005 0.006 −0.025* −0.014(0.015) (0.014) (0.014) (0.014) (0.014)

Regulated in t-1 −0.026* 0.003 −0.010 −0.009(0.014) (0.013) (0.014) (0.014)

Regulated in t-2 −0.061*** −0.043*** −0.043***(0.014) (0.015) (0.015)

Linkage to int. markets 0.066***(0.019)

Linkage to int. markets and regulated −0.143***(0.053)

Foreign capital share 0.040 0.041 0.041 0.037(0.038) (0.038) (0.036) (0.036)

GO = 1 if government share > 50% 0.385*** 0.385*** 0.386*** 0.379*** 0.379***(0.043) (0.043) (0.043) (0.042) (0.042)

Categories of firm age [0.074] [0.072] [0.068] [0.065] [0.064]

Sector-year interactions Yes Yes

Observations 114,581 114,581 114,581 114,581 114,581Firms 18,799 18,799 18,799 18,799 18,799R-squared 0.834 0.834 0.834 0.846 0.846

Note: Dependent variable is ln(total factor productivity) as estimated by GMM approach Wooldridge(2009). All regression include firm and island-year FE. Robust standard errors are clustered on firmlevel and reported in parentheses, p-values of joint significance are reported in brackets. Significanceat or below 1 percent (***), 5 percent (**) and 10 percent (*).

29

Table 6: Impact on productivity - robustness

Dependent variable: ln(productivity)

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

Regulated in t+3 0.018(0.016)

Regulated in t+2 0.014 0.022 0.021 0.024 0.024*(0.014) (0.014) (0.015) (0.027) (0.014)

Regulated in t+1 0.017 0.020 0.025* 0.018 0.020(0.015) (0.013) (0.014) (0.029) (0.013)

Regulated −0.027* −0.020 −0.029* −0.017 −0.030*(0.016) (0.014) (0.015) (0.028) (0.017)

Regulated in t-1 −0.017 −0.011 −0.010 0.015 −0.006(0.015) (0.014) (0.015) (0.031) (0.016)

Regulated in t-2 −0.036** −0.047*** −0.041*** −0.134*** −0.044***(0.015) (0.014) (0.015) (0.031) (0.015)

Regulated in t-3 −0.020(0.016)

Sector switch −0.059***(0.009)

Sector switch and regulated 0.010(0.026)

Foreign capital share 0.035 0.029 0.050 0.083 0.039(0.041) (0.035) (0.037) (0.058) (0.036)

GO = 1 if government share > 50% 0.240*** 0.373*** 0.368*** 0.415*** 0.380***(0.053) (0.041) (0.045) (0.070) (0.042)

ln(K/L) 0.051***(0.005)

Categories of firm age [0.149] [0.286] [0.181] [0.198] [0.066]

Sector-year interactions Yes Yes Yes Yes Yeslnvad_L no exit no switch

Observations 85,696 114,581 98,160 51,825 114,581Firms 13,539 18,799 14,232 8,939 18,799R-squared 0.844 0.761 0.836 0.855 0.846

Note: Dependent variable is ln(total factor productivity) as estimated by GMM approachWooldridge (2009), except for column (2) where it is ln(value added per worker). All regressioninclude firm and island-year FE. Robust standard errors are clustered on firm level and reportedin parentheses, p-values of joint significance are reported in brackets. Significance at or below 1percent (***), 5 percent (**) and 10 percent (*).

30

Tab

le7:

Impa

cton

employment

Dep

endent

variab

le:

ln(lab

or)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Regulated

int+

20.012*

0.013*

0.012

0.025*

0.013*

0.013*

(0.007

)(0.007

)(0.008

)(0.013

)(0.007

)(0.007

)Regulated

int+

10.016**

0.010*

0.011*

0.008

0.005

0.011*

0.011*

(0.007

)(0.005

)(0.006

)(0.006

)(0.012

)(0.006

)(0.006

)Regulated

0.020**

0.009

0.007

0.003

0.003

−0.004

0.000

0.00

2(0.008

)(0.005

)(0.005

)(0.006

)(0.006

)(0.011

)(0.007

)(0.007

)Regulated

int-1

0.009

0.004

0.005

0.009

−0.004

0.008

0.00

5(0.007

)(0.006

)(0.006

)(0.006

)(0.013

)(0.007

)(0.006

)Regulated

int-2

0.010

0.008

0.008

0.002

0.007

0.007

(0.007

)(0.007

)(0.007

)(0.016

)(0.007

)(0.007

)Sector

switch

0.008*

(0.004

)Sector

switch

andregu

lated

0.008

(0.012

)Linkage

toint.

markets

0.054***

(0.010

)Linkage

toint.

markets

andregu

lated

0.015

(0.024

)Fo

reigncapitalshare

0.014

0.015

0.015

0.018

0.015

0.023

0.018

0.016

(0.019

)(0.019

)(0.019

)(0.019

)(0.020

)(0.034

)(0.019

)(0.019

)GO

=1ifgovernmentshare>

50%

0.030

0.030

0.030

0.026

0.021

0.028

0.026

0.027

(0.025

)(0.025

)(0.025

)(0.025

)(0.028

)(0.041

)(0.025

)(0.025

)ln(K

/L)

−0.144***−0.144***−0.144***−0.144***−0.146***−0.159***−0.144***−0.144***

(0.005

)(0.005

)(0.005

)(0.005

)(0.005

)(0.008

)(0.005

)(0.005

)

Categoriesof

firm

age

[0.119

][0.115

][0.112

][0.106

][0.093

][0.810

][0.105

][0.105

]

Sector-yearinteractions

Yes

Yes

Yes

Yes

Yes

noexit

nosw

itch

Observation

s90,330

90,330

90,330

90,330

78,173

38,341

90,330

90,330

Firms

15,892

15,892

15,892

15,892

12,366

7,179

15,892

15,892

R-squ

ared

0.950

0.950

0.950

0.950

0.947

0.951

0.950

0.950

Note:

Dep

endent

variab

leisln(firm

employment).Allregression

includ

efirm

andisland

-yearFE.Rob

uststan

dard

errors

areclusteredon

firm

levelan

drepo

rted

inpa

rentheses,

p-values

ofjointsign

ificancearerepo

rted

inbrackets.Sign

ificanceat

orbe

low

1pe

rcent(***),

5pe

rcent(**)

and10

percent(*).

31

Table 8: Imapct on other labor market outcomes

Dependent variable: ln(L) ln(pL) ln(nL) ln(pwage) ln(nwage)

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

Regulated in t+3 0.004(0.008)

Regulated in t+2 0.016*** 0.015* 0.014 0.015 0.013(0.006) (0.008) (0.011) (0.015) (0.018)

Regulated in t+1 0.007 0.009 0.012 0.012 0.002(0.006) (0.007) (0.011) (0.014) (0.017)

Regulated 0.004 0.004 0.011 −0.003 0.025(0.006) (0.007) (0.011) (0.015) (0.019)

Regulated in t-1 0.009 0.011 −0.008 0.016 −0.009(0.007) (0.007) (0.011) (0.016) (0.019)

Regulated in t-2 0.016*** 0.010 0.010 0.003 0.003(0.006) (0.008) (0.011) (0.017) (0.020)

Regulated in t-3 −0.008(0.008)

Foreign capital share 0.018 0.023 −0.018 0.039 0.011(0.021) (0.021) (0.031) (0.036) (0.047)

GO = 1 if government share > 50% 0.012 0.005 0.109*** 0.073* 0.253***(0.031) (0.029) (0.039) (0.041) (0.052)

ln(K/L) −0.148*** −0.146*** −0.099*** −0.121*** −0.093***(0.006) (0.005) (0.006) (0.006) (0.007)

Categories of firm age [0.295] [0.233] [0.438] [0.459] [0.205]

Sector-year interactions Yes Yes Yes Yes Yes

Observations 67,294 90,330 90,330 90,330 90,330Firms 11,358 15,892 15,892 15,892 15,892R-squared 0.951 0.935 0.894 0.863 0.869

Note: Dependent variables are ln(firm employment), ln(firm employment of production workers),ln(firm employment of non-production workers), ln(wage of production workers per capita and year)and ln(wage of non-production workers per capita and year). All regression include firm and island-year FE. Robust standard errors are clustered on firm level and reported in parentheses, p-values ofjoint significance are reported in brackets. Significance at or below 1 percent (***), 5 percent (**)and 10 percent (*).

32

Appendix

The Wooldridge approach

Wooldridge (2009) suggests an alternative and more efficient way of estimating totalfactor productivity compared to the well-known procedures by Olley and Pakes (1996)or Levinsohn and Petrin (2003). Hereby, estimation of total factor productivity needsto account for potential simultaneity bias due to correlation of input choices with theerror term. The following explanations are closely guided by a detailed description of theapproach within the study of the CompNet Task Force (2014).

Only looking at one individual firm, the error term can be split up into two componentsin theoretical terms. Let small letters denote logs, this leads to:

yt = α0 + αllt + αkkt + ωt + et, (6)

where yt defines log value added. Log labor and log capital are denoted by lt and kt,respectively. Moreover, ωt stands for unobserved productivity shocks which are also cor-related with the input choices and et is an independently identically distributed errorterm. Stated differently, for the i.i.d. component it must hold that

E(et|lt, kt,mt, lt−1, kt−1,mt−1, ..., l1, k1,m1) = 0. (7)

At the same time, assume that the dynamics of productivity shocks are restricted to

E(ωt|kt, lt−1, kt−1,mt−1, ..., l1, k1,m1) = E(ωt|ωt−1)= j(ωt−1)

= j(g(kt−1,mt−1)),

(8)

where ωt−1 = g(kt−1,mt−1). By introducing productivity innovations at, the error com-ponents turns to

ωt = j(ωt−1 + at), (9)

under the assumption that

E(at|kt, lt−1, kt−1,mt−1, ..., l1, k1,m1) = 0. (10)

Consequently, only the contemporaneous choice variables lt and mt are correlated withinnovations at, while kt and all past values of inputs are uncorrelated with at. In termsof the production function, this leads to a system of two equations which identifies the

33

coefficients of the input factors αl and αk.

yt = α0 + αllt + αkkt + j(g(kt−1,mt−1)) + ut (11)

yt = α0 + αllt + αkkt + g(kt,mt) + et, (12)

where ut = at+et and E(ut|kt, lt−1, kt−1,mt−1, ..., l1, k1,m1) = 0. Both equations (11) and(12) can be simultaneously estimated within a general method of moments frameworkusing an appropriate set of instruments. However, following the CompNet Task Force(2014) we assume that the productivity process is a random walk with drift ωt = τ +

ωt−1 + at. Furthermore, the function g(.) is assumed a polynomial of order three. Then,equation (11) becomes:

yt = (α0 + τ) + αllt + αkkt + g(kt−1,mt−1) + ut. (13)

Finally, we estimate equation (13) using a pooled instrumental variable approach.Thereby, our instrument for labor is the one period lag of labor input.

34

Table A1: Conditions of the NIL in 2000, 2007, 2010 and 2014: affected sectors and regulated firms inthe sample

Industry division closed a b c d e f h iRegulatedfirms insample

% share ofregulatedfirmswithinindustry

% share ofregulatedfirms intotaloutput

Panel A: NIL 2000

Food and beverages 3 0 0 0 0 1 0 0 0 26 0.79 0.85Wood products 0 0 0 0 2 3 0 0 0 406 34.73 2.93Pulp and paper 0 0 0 0 0 1 0 0 0 2 0.78 0.57Publishing and printing media 0 0 0 0 0 1 0 0 0 0 0 0Chemicals 2 0 2 0 0 1 0 0 0 55 9.34 0.65Machinery and equipment 1 0 0 0 0 0 0 0 0 3 1.66 0.00

Regulated firms in sample 41 0 28 0 261 162 0 0 0 492 3.44 5.00

Panel B: NIL 2007

Food and beverages 3 14 7 7 0 0 0 1 0 1118 28.90 8.44Tobacco products 0 1 3 0 0 3 0 0 0 280 30.34 9.20Textiles 0 3 1 0 0 0 0 0 0 182 10.98 0.05Wood products 0 7 5 0 0 4 0 0 0 288 30.80 1.24Pulp and paper 0 0 0 0 0 2 0 0 0 7 2.56 1.57Publishing and printing media 0 0 0 0 0 1 2 0 0 69 18.75 0.47Chemicals 3 1 1 3 0 2 1 2 0 179 34.76 2.50Rubber and plastic 0 1 0 0 0 0 0 0 0 0 0 0Other non-metallic mineral prod. 0 1 11 0 0 0 0 0 0 923 70.46 4.75Basic metals 1 0 0 0 0 1 0 0 0 18 17.14 0.82Fabricated metal products 0 4 1 0 0 0 0 0 0 29 6.94 0.19Machinery and equipment 0 0 3 0 0 0 0 0 1 32 18.10 0.06Other transport equipment 0 0 4 0 0 0 0 0 0 83 63.85 1.37Furniture 0 1 6 0 0 0 0 0 0 245 14.69 0.64

Regulated firms in sample 67 130 3150 87 0 333 103 123 1 3453 22.26 31.30

Panel C: NIL 2010

Food and beverages 3 16 9 0 0 0 0 11 0 1254 31.75 12.20Tobacco products 0 1 1 0 0 3 0 1 0 685 88.73 4.52Textiles 0 5 1 0 0 0 0 0 0 271 13.53 0.68Wearing apparel 0 1 0 0 0 0 0 0 0 26 2.17 0.39Wood products 0 7 5 0 0 5 0 0 0 313 35.45 1.23Pulp and paper 0 0 0 0 0 2 0 0 0 3 1.01 1.77Publishing and printing media 0 0 0 0 0 1 2 0 0 7 2.58 0.00Chemicals 3 1 1 2 0 3 1 3 0 201 31.96 1.50Rubber and plastic 0 3 0 0 0 1 0 3 0 39 3.95 2.22Other non-metallic mineral prod. 0 1 6 0 0 0 0 0 0 143 11.33 0.03Basic metals 0 0 0 0 0 1 0 0 0 31 19.75 0.85Fabricated metal products 0 4 2 0 0 0 0 0 0 68 12.76 0.27Machinery and equipment 0 0 3 0 0 0 0 0 1 35 18.32 0.34Other transport equipment 0 0 4 0 0 0 0 0 0 98 51.90 0.79Furniture 0 1 5 0 0 1 0 0 0 194 13.23 0.27

Regulated firms in sample 54 297 2712 84 0 515 53 469 2 3368 21.45 27.06

Panel D: NIL 2014

Food and beverages 3 16 7 4 0 0 0 11 0 702 17.31 16.08Tobacco products 0 1 1 0 0 3 0 1 0 486 79.41 3.97Textiles 0 5 1 0 0 0 0 0 0 167 9.77 0.97Wearing apparel 0 1 0 0 0 0 0 0 0 44 3.91 0.78Wood products 0 7 3 0 0 5 0 0 0 216 29.92 1.48Pulp and paper 0 0 0 0 0 2 0 0 0 4 1.43 1.56Publishing and printing media 0 0 0 0 0 1 2 0 0 7 2.34 0.22Chemicals 3 1 1 2 0 3 1 3 0 197 30.26 1.46Rubber and plastic 0 2 0 0 0 0 0 2 1 109 11.19 2.27Other non-metallic mineral prod. 0 1 6 0 0 0 0 0 0 127 10.84 0.05Basic metals 0 0 0 0 0 1 0 0 0 44 25.29 0.92Fabricated metal products 0 4 1 0 0 0 0 0 1 28 5.34 0.18Machinery and equipment 0 0 3 0 0 0 0 0 1 33 14.47 0.46Other transport equipment 0 0 4 0 0 0 0 0 1 98 47.57 0.68Furniture 0 1 5 0 0 1 0 0 0 121 9.70 0.33

Regulated firms in sample 42 297 1672 98 0 513 48 465 113 2383 16.00 31.41

Note: Panels A to D relate to the regulation of the NIL 2000, 2007, 2010 and 2014. In the center block of each panel, rows depictindustry sectors (two digit level) and columns show conditions of the NIL (from closed to i). Hence, the center block representsthe number of five digit sectors which are subject to regulation in the respective year. The edge area in each panel shows thetotal number of regulated firms in the sample for the respective year, as well as the share of regulated firms within each industryand their share in total manufacturing output.

35

Table A2: Production function coefficients by two digit sector

ln(tfp)

Sector Labor Capital Observations

Food products and beverages 15 0.567 0.149 43607Tobacco products 16 0.643 0.115 7885Textiles 17 0.546 0.081 18299Wearing apparel 18 0.794 0.093 14877Leather and leather products 19 0.690 0.027 4269Wood and wood products, except furniture 20 0.597 0.115 10527Pulp, paper and paper products 21 0.574 0.112 3088Publishing, printing and recorded media 22 0.679 0.037 4164Coke, refined petroleum products and nuclear fuel 23 0.514 0.146 361Chemicals and chemical products 24 0.444 0.059 7186Rubber and plastics products 25 0.510 0.061 10757Other non-metallic mineral products 26 0.440 0.142 15445Basic metals 27 0.538 0.131 1635Fabricated metal products 28 0.660 0.084 6303Machinery and equipment 29 0.617 0.089 2624Electrical equipment, office machinery, computers 31 0.669 0.021 1544Radio, television and communication equipment 32 0.599 0.051 1103Medical, precision and optical instruments 33 0.543 0.096 413Motor vehicles 34 0.595 0.049 1897Other transport equipment 35 0.513 0.121 2030Furniture 36 0.719 0.067 16980

Note: The production function is estimated by GMM according to Wooldridge (2009).

36