the eﬀect of management practices and technology diﬀusion...

The Effect of Management Practices and Technology

Diffusion on Firm Performances: Evidence from the US

Marshall Plan in Italy

⇤

Michela Giorcelli

†

June 5, 2015

PRELIMINARY AND INCOMPLETE DRAFT. PLEASE DO NOT CIRCULATE OR QUOTE.

Abstract

This paper analyzes the effect of management practices and technology diffusion promoted

by the US Marshall Plan on the productivity of Italian firms in the aftermath of WWII. With the

Marshall Plan, the US administration promoted massive economic and financial aid to Western

European countries from 1948 to the end of the 50’s. Among other initiatives, starting in 1952,

the plan supported management and technology assistance by both offering training periods for

Italian managers and engineers in the US and by granting to firms new machinery not available

in Europe. I collected and digitized financial statements data on 6,065 Italian firms eligible

to receive US management and technology support, spanning from 1946 to 1970. Exploiting

an exogenous change in the implementation of the policy that determined an arguably random

selection of firms that eventually received the Marshall Plan assistance, I estimate that (1)

firms that received at least one form of assistance significantly increased their productivity,

(2) firms that received both types of transfers simultaneously showed an additional increase in

productivity, (3) the effects are persistent and increasing up to a decade after the implementation

of the program, and (4) firm exit rate is lower for firms that received the US assistance.

JEL CODES: L2, M2, O32, O33

KEYWORDS: Productivity, management practices, technology transfer, firm survival⇤I thank my adviser Ran Abramitzky, as well as Nicola Bianchi, Nick Bloom, Dave Donaldson, Mark Duggan,

Pascaline Dupas, Daniel Fetter, Caroline Hoxby, Melanie Morten, Petra Moser, Santiago Perez, Petra Persson, LuigiPistaferri, David Yang, Constantine Yannelis, and participants in seminars and conferences. This research was sup-ported by the Kauffman Dissertation Fellowship through the Ewing Marion Kauffman Foundation, and by the Ex-ploratory Travel and Data Grant through the Economic History Association., by the Graduate Research OpportunityAward through the Stanford University School of Humanities and Sciences, and by the Graduate Student GrantCompetition through the Stanford Europe Center.

†Department of Economics, Stanford University. Email: [email protected]. Mailing address: 579 Serra Mall,Stanford CA, 94305-6072, USA.

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This paper examines the extent to which the diffusion of management techniques and technology

affects productivity of receiving firms and how persistent such effects are over time. From 1952

to 1958, the US administration promoted the diffusion of management models and technology to

European firms by organizing training periods for European managers, engineers, and technicians

in US firms and by granting low-interest rate loans to buy new machinery not available for sale in

Europe. This study explores the long-run effects of such diffusion on the productivity of Italian

firms that received US support.

The Marshall Plan was a US aid plan to help Europe recover between 1948 and 1952. After

1952, it also promoted a specific program, the United States Technical Assistance and Productivity

(USTA&P) program, to increase productivity in Europe, by supporting management (through study

trips of European managers to US firms), and/or technology (through study trips of European

engineers to US firms and simultaneous loans to buy state-of-the-art US machinery).

The identification strategy of this paper uses the fact that the exposure to this program was

jointly determined by firm location and by the year in which the support was received. In more

detail, the USTA&P program was implemented only in five administrative sub-areas (province),

even though firms in five Italian administrative areas (regioni) had been declared eligible when the

program was announced. Moreover, firms received the support between 1952 and 1958, based on

their application date. I estimate a difference-in-differences model in which I compare the difference

in economic performances between firms that applied early in selected areas vs areas not selected

with the difference in economic performances between firms that applied late in selected areas vs

areas not selected.

To analyze this program, I collected and digitized financial statements data from 1946 to 1970

of 6,065 Italian firms eligible to receive the US management and technology support. Preliminary

results show that all firms that received either management or technology support significantly

increased their productivity, that the effects are persistent and increasing up to a decade after the

implementation of the program, and firm exit rate is between 25 and 41 percent lower for firms that

received the US support.

This paper is related to two strands of literature. First, it would contribute to the economic

history literature by being the first research that examines the firm level effect of the Marshall

Plan. While economic historians over the last 30 years have concentrated on analyzing the Marshall

Plan macroeconomic effects, concluding that its overall impact on growth was modest due to its

limited timing (?, ?), its impact on Western European industries has been neglected. However,

a microeconomic analysis would be extremely helpful in understanding the peculiar effects of the

Marshall Plan on single firms and industries. This aspect makes also possible to evaluate the quality

of the aid, in addition to their aggregate quantity. Second, it would shed new light on the long-

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term effect of knowledge and technology diffusion on firm productivity. Studying productivity is

relevant because its very large differences are still observed not only across countries (?), but also

within the same country and within the same five-digit sector (?). Moreover, these differences are

highly persistent over time. A large literature attributes the causes of such differences to differences

in technology within representative firms (?, ?). Furthermore, foreign sources of technology have

been found to be an important contributor to productivity growth for developed economies (?)

and in developing countries (?). More recently, it has been shown that differences in productivity

may be caused by variation in management practices and that consulting opportunities can cause

a positive effect on firm economic performances in the short run (?). However, there has been

no empirical evidence that examines the long-run effect of management and technology diffusion.

Detecting the long-run consequences of such transfers would be relevant also from a policy-making

perspective, since an increasing number of public policies promote management and technology

diffusion, especially in developing countries.1

The rest of the paper is organized as follows: Section 1 describes the institutional details of the

Marshall Plan. After describing the data in Section 2, I discuss the identification strategy in Section

3. Section 4 presents the main results. Finally, Section 6 concludes.

1 Institutional Details

1.1 The Marshall Plan

The Marshall Plan was a US-sponsored program designed to rehabilitate the economies of 17 Western

and Southern European countries after WWII.2 The program was announced by the Secretary of

State George C. Marshall on June 5, 1947 and formally approved with the Foreign Assistance

Act on April 3, 1948. The plan was in operation from 1948 to the end of the 50’s and promoted

financial aid as well as other economic initiatives. Its first phase, called European Recovery Program

(ERP), included financial and material help, such as credit, raw materials, groceries and other goods.

However, as soon as 1950, it became clear that the productivity gap between European and US firms

was increasing and that it could prevent European economic recovery and growth. In 1949, US

Bureau of Labor Statistics (BLS) Commissioner Ewan Clague noted that “[...] productivity levels

in the United States were more than twice those in Great Britain, and more than three times that1The World Bank and United Nations Industrial Development Organization (UNIDO), for example, have each es-tablished units to promote private sector development in developing countries and to provide technical assistance inthe formulation of SME and entrepreneurship policy. The International Labor Organization has implemented theStart and Improve Your Business Program (SIYB) to promote management techniques diffusion in new and existingbusinesses and has been implemented in more than 100 countries.

2The 17 countries that received the US Marshall Plan aid were: Austria, Belgium and Luxembourg, Denmark, France,West Germany, Greece, Iceland, Ireland, Italy and Trieste, Netherlands, Norway, Portugal, Sweden, Switzerland,Turkey, and United Kingdom.

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of Belgium, France and other industrial countries of Europe”.3 Thus, the BLS organized visits for

their specialists in European factories and plants in order to understand the determinants of this

productivity gap. After visiting 35 factories in 6 industries in Great Britain and France, James

Silberman, BLS Chief of Productivity and Technology Development, pointed that inefficiency in

production management was the major problem, countering claims by Europeans that the main

issue was the war’s destruction.4 Paul G. Hoffman, C.E.O. of Studebaker Motors, recognized that

“[...] almost all European countries faced the necessity of a rapid increase in productivity. Their

factories were filled with outdated tools and they were employing old-fashioned methods”.5

To help address these issues, the US government expanded its initiative and introduced the

United States Technical Assistance and Productivity Program (hereafter, USTA&P).6 The USTA&P

started in 1952 and promoted the transfer of both US management and US technology to European

firms. Management support consisted of financing study trips of European managers to US firms.

Technology support combined two simultaneous forms: study trips for engineers and technicians

to US plants and low-interest rate loans to the same participating firms to buy state-of-the-art US

machinery, not available for sale in Europe.

1.2 The USTA&P Program in Italy

The agreement between Italy and the US for the implementation of the USTA&P program was

reached in 1951, when Italy joined the 115(k) of the Economic Cooperation Act and the National

Productivity Committee (named National Productivity Center since 1953) was established (ICA

(1958)). The Committee had the goal of “encouraging and facilitating by all appropriate means,

participation by firms, agencies and institutions in the [USTA&P] program” and was “prepared to

promote a program informing the Italian people of the purpose and intentions of the productivity

program of the Italian Government”.7 The original program for Italy was intended to have two

phases: a first one, in which management and technology support should have been offered to firms

in 5 representative areas, and a second one, in which it should have been extended to the rest of

the country. For the first phase, 5 Italian regioni were chosen as “representative areas” for Italian

macro-areas: the North-West was symbolized by Lombardia, the North-East by Veneto, the Center

by Toscana, the South by Campania, and the Islands by Sicilia (Figure 1, panel A).8 Firms interested3Memo from E. Clague to J. W. Gibson, December 27, 1948 (US National Archives and Records Administration).4Memo from D. Evans to E. Clague, June 28, 1948 (US National Archives and Records Administration).5Oral interview with Paul G. Hoffman (Truman Museum and Library, October 25, 1964) on the Internet atwww.trumanlibrary.org/oralhist/hoffmanp.htm (accessed March 24, 2015).

6The USTA&P program was formally approved with the Benton-Moody Amendment of section 115(k) of the EconomicCooperation Act, the Title I of the Foreign Assistance Act.

7Giuseppe Pella, Letter to Mission ECA-Division Technical Assistance, November 25, 1950 (Archivio Storico delloStato, fondo CIR, busta 18, accessed on December 18, 2013).

8Wally J. Hoff, "Up-to-date picture of the USTA&P program in Italy", October 10, 1950 (Archivio Storico dello Stato,fondo CIR, busta 18, accessed on December 18, 2013).

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in receiving USTA&P support had to submit a separate application for either management support

or technology support or both. To be eligible to submit the application, firms had to meet four

criteria: (1) operating in manufacturing sector; (2) having between 10 and 500 employees; (3) being

required to compile a balance sheet; (4) being located in one of the 5 representative areas. The

window for application was between January and June 1951. Applications for study trips (for both

management and technology support) had to be sent to the National Productivity Committee and

had to attach a statement covering the productivity program of the applicant, financial statements of

the previous two years as well as the number of managers or engineers/technicians that would have

been sent to the US. Firms that applied for technology support had also to submit an application for

loan. It had to be sent to the Technical Assistance Section of the Istituto Mobiliare Italiano (IMI)

and had to include a statement covering the productivity program of the applicant, the amount of

loan requested, the kind of machinery supposed to be purchased, and financial statements of the

previous two years. The applications were then reviewed by dedicated committees with two goals:

verifying the organizational structure of the applicant firm (such as its industrial accounting, material

handling, working conditions, marketing, firm lay-out, production planning) and examining that the

proposed productivity program would accomplish US productivity aims effectively and efficiently

(ICA 1958). For loans applications, there was an additional control made by the Centrobanca’s9

loan committee in order to make a final determination from a banking standpoint.

Out of the 6,035 eligible firms, 3,594 applied for at least one support, and only 30 applications

were discarded.10 However, when the program was about to start in 1952, the US reduced the

budget available for the Marshall Plan due to the increasing expenditures in Korean War.11 For this

reason, in Italy the policy was eventually implemented only in five selected province, one for each

selected regione. The province selected for the implementation represented average-sized province

with typical socio-economic conditions of their regioni :12 Monza and Brianza13 for Lombardy, Vi-

cenza for Veneto, Pisa for Tuscany, Salerno for Campania and Palermo for Sicily. In total, 812 firms

in these province received at least one form of support. Figure 1, panel B shows the representative9Centrobanca was a credit institute created in 1946 with the goal of distributing credit to small and medium-sizedfirms.

1016 applications were disregarded because they were incomplete, 11 because they asked loans for machinery availablefor sale in Europe, and 3 because their debts level was considered too high. These 30 firms are excluded from myanalysis in the rest of the paper.

11The Korean War began as a civil war between North and South Korea, but the conflict soon became internationalwhen in 1950, under the US leadership, the United Nations joined to support South Korea, and the People’s Republicof China (PRC) entered to aid North Korea.

12"In the province selection process, the richest or the poorest areas have been avoided in order to test if the USTA&Pprogram is successful on firms comparable with the average Italian firms. With this respect, Monza is the typicalprovincia of the North-West, Vicenza represents the North-Eastern economy, Pisa Center Italy, Salerno representsthe average economic level of Southern Italy, and Palermo the typical problems of Islands’ underdeveloped andovercrowded areas" (Emilio Battista, Letter to Mission ECA-Division Technical Assistance, December 21, 1951(Archivio Storico dello Stato, fondo CIR, busta 18, accessed on December 18, 2013).

13In 1952, Monza and Brianza were not autonomous provinces, but was under Milan province. However, it had avery strong local identity and eventually became an independent province in 2010.

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areas and the selected province: in the rest of the paper I will refer to the representative areas as

blue areas and to the selected province as red areas. In other words, blue areas are representative

areas minus selected province.

Firms were ranked by the date in which applications were received and participated in study

trips between 1952 and 1958 according to this order. The possibility of receiving the study trip in

a given year was determined by the availability of hosting US firms as well as by the number of

participants from other countries.

In more detail, the study trips for managers, engineers and technicians consisted of a stay in the

US for a period that went from 4 to 6 months. They had to attend a three-week English course in

New York, and they spent a training period in a US plant.14 The US plants were chosen to be as

similar as possible to their firms of origin. The managerial training was based on the TWI (training

within industry) method. It included “job instruction”, in which employees were trained to perform

their tasks as quickly as they are capable with minimal waste; “job methods”, in which employees

were taught how to improve their processes using existing resources; and “job relations”, in which

personnel problems were solved in an analytical manner so that employees are focused on a stated

objective (ICA (1958)). The technology training was based on learning-by-doing system to teach

engineers and technicians how to use the new machinery. Loans associated with such machinery were

granted for a maximum period of 5 years at 5.50 percent interest rate through local Banche Popolari

(People’s Banks) and were received by firms within few weeks after the study trip. Provincial

Productivity Centers were created in each of the five province to provide consultation services in

carrying out the program (and to make sure that firms were carrying out the agreed-upon programs)

and to determine and report on the results. The follow-up period lasted three years for firms that

received management support and five years for firms that received technology support.

2 Data

To analyze the effect of the USTA&P program on the productivity of Italian firms, I collected

financial statements data for the 6,065 firms eligible to receive such support from 1946 to 1970. For

firms that applied for at least one kind of support I also collected applications’ data. Since, under my

best knowledge, there is not a systematic accounting for the USTA&P program in Italy, I collected

the data from different primary sources, by proceeding through three steps. First, I figured out

which firms were declared eligible to apply for the USTA&P support in 1950, when the program was14Managers, engineers and technicians entered the US under the nonimmigrant H-3 visa for industrial trainees.

According to the 1952 Immigration and Nationality Act, "an H-3 industrial trainee visa allows a foreign nationalto enter the United States at the invitation of an organization or individual to work and receive training at anyindustrial site. The maximum period of stay is two years. Then the alien must leave the country and may not seeka change of status, extension, or re-admission to the US until he or she resides outside of the US for a period of twoyears or longer".

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announced, and I collected their financial statements. Second, I used application data to establish

whether a firm applied for the USTA&P support and, in case it did, for which kind of support

it applied. Finally, I use the application data for determining which firms eventually received the

support. This section describes more in detail the data collection process and the sources that I

used.

2.1 Eligible Firms and Financial Statements Data

To be eligible to apply for USTA&P support, firms had to meet four criteria, as explained in

Section 1.2: (1) operating in manufacturing sector; (2) having between 10 and 500 employees; (3)

being required to compile a balance sheet; (4) being located in a representative area. To determine

which firms met these criteria in 1950, when the program was announced, I referred to the firm

registries stored at the Camere di Commercio Industria Artiginato e Agricoltura (CCIAA), which

are comparable to US Chambers of Commerce. In Italy, all firms operating in a given provincia had to

register with local CCIAA. These registries provide the 6,065 firms eligible to apply for the USTA&P

support. I then collected and digitized financial statements data for these 6,065 firms from 1946 to

1970. According to 1942 Italian Civil Code,15 firms that were required to compile financial statements

had to submit them to the courthouse closest to their headquarters. The financial statements

are now stored either at the Historical Archive of the Camera di Commercio of the provincia in

which the firm was located or at the Confindustria Historical Archive.16 Financial statements are

composed of two parts: the stato patrimoniale and the conto economico. The stato patrimoniale

is the balance sheet at December 31 and it was the only document required by 1942 Italian Civil

Code.17 Although at that time its structure was less well-defined than today, it had to contain

information on firm assets account as land, buildings, equipment, bonds, cash, and receivables, on

firm liabilities account as notes payable, accounts payable, and on owner’s equity. The Civil Code also

included the criteria according to which these entries had to be calculated.18 The conto economico

is the income statement, which reports firm revenues and expenses (for employees, suppliers, raw

materials, depreciation and amortization). Although not formally required by law, it was compiled

by all firms that submitted the balance sheet.

To check whether the eligible firms are representative of the Italian manufacturing sector, I

compare their sectorial distribution with those of all Italian firms and of all firms located in the

representative areas, as reported in the 1951 Italian Industrial Census. Table 1, Panel A shows

such comparison.19 Eligible firms represent about one percent of all Italian manufacturing firms and15Art. 2429, Codice Civile Italiano, Regio Decreto-Legge 03/16/1942, n.262.16Confindustria is the Italian manufacturer employers’ federation, founded in 1910 and located in Rome (Italy).17Art.2423, Codice Civile Italiano, Regio Decreto-Legge 03/16/1942, n.262.18Art.2423, Codice Civile Italiano, Regio Decreto-Legge 03/16/1942, n.262.19To check whether the eligible firms are representative of the Italian manufacturing sector, I compare their sectorial

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about 2.1 percent of manufacturing firms in representative areas. The distribution of firms across

15-sector Italian National Statistics Institute’s (ISTAT) classification is comparable in the three

different samples. Exceptions to that are represented by the wood sector, which is underrepresented

in eligible firms sample with respect to both the Italian sample and the representative areas sample.

Food sector is underrepresented with respect to the national records, but it is properly represented

compared to the representative areas. By contrast, leather, metal industries, chemical and rubber

sectors are overrepresented with respect to both the national and the representative areas data. This

might be explained by the fact that firms in these sectors are usually larger in size than firms in

other sectors and then more likely to meet the employees’ number requirement for being eligible.

The regional distribution of firms is described in Table 1, Panel B. At national level, 36.33 percent of

firms located in representative areas were located in Lombardia, while in other regions the percentage

is between 14.71 percent (in Toscana) and 17.86 percent (in Sicilia). With respect to eligible firms,

most of them are located in Lombardia (35.44 percent, in line with regional percentage), followed by

Veneto (20.00 percent) and Toscana (18.71 percent, both overrepresented). Sicilia (14.37 percent)

and Campania (11.48 percent) are slightly underrepresented.

2.2 Applications’ Data

As explained in Section 1.2, in order to receive the USTA&P support, firms had to submit separate

applications for managers study trips and/or engineers/technicians study trips and loans. Appli-

cations for study trips are stored at Italian Central Archives of the State (ACS).20 Each firm that

applied for managers and/or engineers and technicians study trips has a separate folder that contains

the original application. It reports the name of the firm, the municipality in which it is located, the

application date, the number of people to be sent in a study trip, their qualification and their edu-

cation This information is available for all the firms that applied regardless they eventually received

the USTA&P support. For firms in five province that eventually received the support, the folder

also contains a report made by a committee that visited the firms just before the study trips, the

date and the length of the study trips and where it took place, and follow-up notes made for three

or five years after receiving the support.21 Loans applications are stored at the Historical Archive

of the Istituto Mobiliare Italiano (ASIMI).22 Also in this case, each firm that applied for technol-

ogy support has its specific folder containing the original application, the date of submission, the

distribution with those of all Italian firms and of all firms located in the representative areas, as reported in the1951 Italian Industrial Census. Table 1, Panel A shows such comparison.

20The Italian Central Archives of the State are the main national archives of Italy. They were created in 1875 underthe name of Royal Archives. They took their present name in 1953. They are located in Rome and are put underthe responsibility of the Ministry of Culture.

21Follow-up period lasted three years in case of management support and five years in case of technology support.22The Istituto Mobiliare Italiano (IMI) was created in 1931 (Legge 11/13/1931, n. 1398), with the goal of helping

firms facing troubles, despite their sustainable financial and economic positions. After WWII, IMI participated inthe rebuilding of Italy, above all by managing the financial resources provided through international aid.

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amount requested, and the type of machinery requested. For firms in five province that eventually

received the support, it also contains the date in which the date in which machinery was received, its

commercial value that corresponds to the value of loan granted to the firm and whether and when

the loan was repaid, as well as follow-up notes for five years after receiving the support.

After collecting and digitizing the application data, I matched it with financial statements data

using firm name and municipality. Table 2 shows the summary statistics for the full sample of

firms and for firms that applied for different kinds of supports. Firm characteristics and outcomes

suggest that firms that applied for receiving at least one support were on average bigger than firms

in the full sample, with higher level of assets, sales and productivity, and older. Firms that applied

for both supports have on average the highest level of productivity among firms that applied; they

were also older and have a higher level of sales and assets. On the contrary, firms that applied for

technology support were on average bigger. Table 3 reports the number of firms, managers and

technicians that participated in the USTA&P program in each year from 1952 to 1958. In the first

years of the program, the number of study trips is relatively modest, but increases sharply between

1954 and 1956, when most firms received the support. In the two last years of the program the

number of firms involved in the program decreased again. In total, 1,491 Italian managers and 2,469

technicians benefitted from the USTA&P program.

3 Identification Strategy

The firm exposure to the USTA&P support is jointly determined by its location (being located in

a red areas, selected for receiving the USTA&P support in 1952, vs being located in a blue area,

not selected for receiving the USTA&P support in 1952) and the date in which the application had

been submitted (that determined the order, and consequently the year, in which the support was

received). Thus, I can exploit two sources of variation: geographical variation across firms located

in red areas vs blue areas and timing variation across firms that applied early vs firms that applied

late. I treat only the combination of the two as exogenous. As discussed in section II.B, red areas

were selected in 1952, when firms from both red and blue areas had already applied in 1951. This

feature, in addition to the requirement of submitting financial statements for the years 1949 and

1950 jointly with the application, implies that all firms that received the USTA&P support had to

be located in red areas before 1950, the year in which the USTA&P policy was announced. This

rules out the possibility that firms strategically located or changed their location to red areas after

1951 in order to receive the support. With respect to the application date, firms were given a six-

month window to apply (from January to June 1951). The applications were then ranked based on

the date in which they were received and firms participated in study trips according to this order.

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The availability of study trips for Italian firms were determined by the US on a monthly basis and

depended on the demand for training from other European countries and the possibility for US firms

to receive managers, engineers and technicians. However, at the time of application, firms were not

aware of this mechanism, so they did not have ex-ante incentives to apply early instead of applying

late. Nevertheless, it could be possible that firms that applied early were systematically different

than firms that applied late. However, it could be tested, since I observe application date for firms

in both red and blue areas.

Thus, the idea for identification is to compare the difference in economic performances between

firms that applied early in red vs blue areas with the difference in economic performances between

firms that applied late in red vs blue areas. This approach allows controlling for firm unobservable

characteristics that might systematically differ across red and blue areas or across firms that applied

early and late. Furthermore, I can test directly whether firms in red and blue areas that applied

early or late were statistically indistinguishable in terms of their observed characteristics before the

implementation of USTA&P program. Table 3 reports the balancing tests of firms’ characteristics

and outcomes in 1951 for firms that applied for management support (Panel A), firms that applied

for technology support (Panel B), and firms that applied for both supports (Panel C). I consider firms

that apply early as firms that applied within the first three months (January-March) of the six-month

application window, and firms that applied late those that applied in the last three months (April-

June). In all cases, there are not statistically significant differences between observed characteristics

of firms in red areas that apply early and firms in blue areas that apply early. Similarly, firms in red

areas that applied late are very similar to firms in blue areas that apply late. Within red and within

blue areas, firms that apply early are statistically indistinguishable from firms that applied late.

Finally, the mean difference of the difference between red areas that applied early and blue areas

that applied early and the difference of red areas that applied late and blue areas that applied late

is always not significantly different from zero. This means that, before the implementation of the

program, there were no systematic differences across these four groups of firms. Finally, since I can

observe the kind of support chosen also for firms in blue areas, I can address the potential concern

regarding firm self-selection into the USTA&P support by comparing only firms that applied for the

same kind of support.

4 Effect on Productivity

4.1 Baseline Specification

To systematically examine the effect of the USTA&P support on firm productivity, I estimate

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TFPijt = �(Provincej · T imeit) + �i + �t + ✏ijt (1)

where TFPijt is the logarithm of total factor productivity of firm i located in area j in year

t ; Provincej is a “geographical” dummy that equal 1 if firm i was located in a red area selected

for receiving the USTA&P support; T imeit is a “time” dummy that equals 1 if firm i has already

received the USTA&P support in year t ; �i is a firm fixed effect; �t is a year fixed effect; and ✏ijt is an

error term. I compute total factor productivity in two different ways: using the algorithm suggested

by Ackerberg et al. (2007) and as residual from an augmented Cobb-Douglas production function.

In Table 4, I present estimates of equation (1) for three different samples. For firms that applied

for management support, firm productivity increased by 16.30 percent per year after receiving the

USTA&P support (Table 4, column 1, significant at 1 percent). Controlling for a “geographical”

dummy that indicated whether a province was selected to receive the USTA&P support, firm pro-

ductivity increased by 15.95 percent (Table 4, column 2, significant at 1 percent). To check whether

productivity is sensitive to its calculation, I estimate specification (1) using total factor productivity

calculated with the Solow’s residuals method. In this case, the growth is 15.60 percent (Table 4,

column 3, significant at 1 percent). For firms that applied for technology support, firm productivity

increased by 5.55 percent per year after receiving the USTA&P support (Table 4, column 5, signifi-

cant at 1 percent). Controlling for a “geographical” dummy that indicated whether a province was

selected to receive the support, firm productivity increased by 5.02 (Table 4, column 6, significant

at 1 percent). The results are robust to the use of the Solow’s residuals calculation: in this case, the

growth is 4.91 percent (Table 4, column 7, significant at 1 percent).

4.2 Complementarity Effect

Firms that applied for both supports received the two supports separately, based on the ranked

of each application. This means that in a given years some firms may have received management

support but not technology support or vice versa. This feature of the program allows to test whether

complementarity effects between management and technology exist. Table 5 reports the results of

this analysis. The productivity increase associated with receiving only management support is

estimated between 14.91 and 15.25 percent (Table 5, columns (1) and (2), significant at 1 percent)

and with receiving only technology support lies between 5.97 and 6.28 percent (Table 5, columns (1)

and (2)). Receiving both supports simultaneously adds an additional productivity growth between

5.87 and 6.08 percent (Table 5, columns (1) and (2)), with a total effect between 26.96 and 27.40

percent.

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4.3 Annual Treatment Effects

In addition to the average effects, I estimate the annual treatment effect to examine the timing of

the change in productivity. It could be the case that the increase in productivity was sharp in the

years immediately following the study trips, or that it took a few years before the new techniques

were implemented and the productivity increase. Moreover, the productivity effects could have

disappeared a few years after the study trips or they could have been persistent over years. The

annual treatment effects help to evaluate the extent of such variations over time. I estimate

TFPijt = �r(Provincej · Y earsPostUSTA&Pir) + �i + �t + ✏ijr (2)

where TFPijr is the logarithm of total factor productivity of firm i located in area j in year

t ; Provincej is a “geographical” dummy that equal 1 if firm i was located in a red area selected

for receiving the USTA&P support; Y earsPostUSTA&Pir is an indicator variable for each year

before/after USTA&P support; �i is a firm fixed effect; �t is a year fixed effect; and ✏ijr is an error

term. The coefficient �r measures the differential change in productivity difference across firms in

red and blue areas r years before/after the treatment. Since the minimum number of years I can

follow firms after the USTA&P support is 12 years, I limit my analysis to 12 years for all the firms

in my sample.I can follow firms that received the USTA&P support in 1958 for 12 years from 1959

to 1970, but I can follow firms that got it in 1952 for 18 years from 1953 to 1970. Annual coefficients

show that the effect of the UTSA&P support on management materialize one year after that the

support was received (Figure 2, panel A). The effects remain strong and significant for all the 11

years after the treatment included in the sample and have an increasing trend over time. For firms

the applied for technology support, the effects become significant from 3 to 11 years after receiving

the USTA&P support (Figure 2, panel B). For firms that applied for both supports, the effects of

USTA&P support are visible the year after receiving both USTA&P supports. Annual coefficients

are always significant and increasing over time (Figure 2, panel C).

5 Threats to Identification

5.1 Attrition

A possible concern to the identification strategy described in Section 3 is that USTA&P support

may have systematically reduced exit rate for firms in red areas. This would generate a differential

attrition rate between firms in red areas and firms in blue areas and thus the estimates of equation

(1) would be upward biased. As first step, I test whether the exit rate systematically changed across

firms in red areas and firms in blue areas in response to the USTA&P program. I estimate a probit

12

model

Exitijt = �(Provincej · T imeit +Ageijt + Sizeijt + TFPijt + Salesijt + ✏ijt)

where Exitijt is a dummy that equals 1 if firm i exits the market in year t ; Provincej is

a “geographical” dummy that equal 1 if firm i was located in a red area selected for receiving the

USTA&P support; T imeit is a “time” dummy that equals 1 if firm i has already received the USTA&P

support in year t ; Ageiit is age of firm i in province j in year t ; Sizeijt is firm i number of employees

firm i in province j in year t ; TFPijt is the logarithm of total factor productivity firm i in province j

in year t ; Salesijt is firm i annual sales in 2010 USD thousand values in province j in year t ; and ✏ijt

is an error term. The probability of exiting the market for firms in red areas compared with firms

in blue areas is 36.10 percent lower for firms that applied for management support (Table 6, column

1), 25.43 percent lower for firms that applied for technology support (Table 6, column 2), and 41.65

percent lower for firms that applied for both supports (Table 6, column 3). These results suggest

that the USTA&P program have lowered exit rate for firms in red areas. To address the differential

attrition issue, I estimate the Lee (2009)’s bounds. This approach consists of first identifying the

excess number of firms which are induced to exit the market in response to the USTA&P support

and then “trimming” the upper and lower tails of the productivity distribution by this number.

Estimates of Lee (2009)’s bounds are reported in Table 7. The productivity growth in response to

USTA&P support is bounded between 15.80 percent and 17.70 percent for firms that applied for

management support (Table 7, columns 1-2, significant at 1 percent), between 3.98 percent and 6.61

percent for firms that applied for technology support (Table 7, columns 3-4, significant at 1 percent),

and between 24.48 percent and 31.13 percent for firms that applied for both supports support (Table

7, columns 5-6, significant at 1 percent).

5.2 Anticipation Effects

Anticipation effects represent another possible concern to the identification of this paper. In fact,

firms in red areas that received the USTA&P support late might have either anticipated the adoption

of new management techniques before receiving the USTA&P support or postponed new investments

until the year of the support. If it was the case, the estimates of equation (1) underestimated /

overestimated the effect of USTA&P support on firm productivity. With respect to management,

the financial statements data do not allow to directly check whether the firm started implementing

new management techniques. However, before the study trip, US teams visited the Italian firms and

compiled a report, with a detailed analysis of their economic, financial and managerial situation. In

particular, they scored firm managerial organization using a scale from 1 (better managed firms) to

13

5 (worst managed firms). To test whether the firm score before the receiving USTA&P support is

significantly lower (i.e. better management) for firms that received either management support or

both supports later I estimate

Scorei =1958X

t=1953

Y eart + Technologyi + ✏i (3)

where Scorei is management score given to firm i before the study trip; Y eart is a dummy that

equals 1 if firm i received the USTA&P support in year t ; Technologyi is a dummy that equals 1 if

firm textiti applied also for technology support; and ✏ijt is an error term. None of the coefficients

on the year in which the support was received from 1953 to 1958 is significantly different from zero

(Table 8, column 1), which means that the receiving the support later is not correlated with lower

scores than average score in 1952, the first year in which USTA&P support was given.

With respect to technology, it is possible to test directly whether firms anticipated the USTA&P

support by systematically changing the investment choices in the years after 1952 but before receiving

the USTA&P support.

I estimate

Iij,t+1

Kijt= ↵1

Iijt

Kij,t�1+↵2

Yijt

Kijt+↵3Qijt+�(Provincej ·T imeit)+✓Managementi+

3X

n=1

�nIijn,53�55+3X

n=1

�nIijn,56�58+�i+�t+✏ijt

(4)

where Iij,t+1

Kijtis the investment rate of firm i located in area j in year t ; Iijt

Kij,t�1is the lagged

investment rate; Yijt

Kijtis the sales-to-capital ratio; Qijt is the Tobin’s Q; Provincej is a “geographical”

dummy that equal 1 if firm i was located in a red area selected for receiving the USTA&P support;

T imeit is a “time” dummy that equals 1 if firm i has already received the USTA&P support in year

t ; Managementi is a dummy that equals 1 if firm i had also applied for management support in year

t ; Iijn,53�55 is an indicator variable that equals 1 if firm i received the USTA&P support in year

53-55 and t is n (from 1 to 3) years before the USTA&P support; Iijn,56�58 is an indicator variable

that equals 1 if firm textiti received the USTA&P support in year 56-58 and t is n (from 1 to 3)

years before the USTA&P support; and ✏ijt is an error term. As sales-to-capital ratio and Tobin’s

Q are endogenous, I estimate equation 4 with GMM, using as instruments their second and third

lags, following ?. None of the coefficients on the year in which the support was received from 1953

to 1958 is significantly different from zero (Table 8, columns 2-3), which means that the receiving

the support later is not correlated with lower pre-USTA&P investments for the firms in red areas

with respect to the firms in blue areas.

14

6 Conclusions

In this paper, I study the effect of management and technology diffusion promoted by the Marshall

Plan on Italian firm productivity in the aftermath of WWII. I collected and digitized unique historical

financial statements data, from 1946 to 1970, of 6,065 Italian small and medium sized firms, eligible

to be enrolled in the program. Combining a “geographical” variation and a “time” variation in the

implementation of the policy, I find a significant increase in productivity in firms that received at

least one kind of support, persistent up to ten years. Firms that received both supports show an

additional increase in productivity, with respect to the sum of the two separate effects, suggesting

that management and technology may be complementary in production. Finally, firms that received

the USTA&P support significantly reduced the probability of exiting the market. As next step in my

research agenda, I will explore the mechanisms that determined the persistency of such effects over

time. In particular, I will focus on two different mechanisms: factor misallocation and innovation.

I will examine whether receiving these supports changed the factor allocation within firms towards

a more efficient allocation; what the role of complementarity effects was between the two kinds of

training; and whether management skills and new technologies determined an interaction with firm

labor force. Furthermore, I will analyze the effect of the USTA&P program on firm innovation,

by comparing patents activity of firms in red and blue areas from 1946 to 2010. I will also analyze

whether the USTA&P program affected other firms performances, such number of employees, number

of plants, exports, and inputs (imports and wages). Finally, I will disentangle the effect of technology

support on productivity determined by study trips and by loans, by using data on firms that received

loans (not related to the purchase of a particular machinery) through the ERP, but not through

the USTA&P program. This would also allow studying whether constrained or unconstrained loans

affect more productivity. Although management and technology diffusion are considered important

drivers of productivity growth, we still know little about their long-run term effects on firm economic

performances.

References

15

TABLES AND FIGURES

Table 1: Firms Operating in Manufacturing Sector

Panel A: All Italy, Representative Areas and 6,035 Firms Eligible for USTA&P Support

SECTOR ISTAT CODE ITALY % ITALY REPR. AREAS % REPR. AREAS ELIGIBLE % ELIGIBLEFood 301 69,355 11.44 27,966 9.74 589 9.76Tob 302 541 0.09 113 0.04 9 0.15Leat 303 6,352 1.05 3,695 1.29 226 3.74Text 304 36,359 6.00 18,870 6.56 532 8.82App 305 216,712 35.76 100,270 34.88 1,952 32.34Wood 306 110,744 18.27 54,735 19.04 798 13.22PA 307 1,817 0.30 1,142 0.40 57 0.94PR 308 6,996 1.15 3,630 1.26 81 1.34PH 309 5,415 0.89 2,397 0.83 47 0.78Met 310 694 0.11 458 0.16 84 1.39Mac 311 124,523 20.55 59,582 20.73 1,174 19.45Min 312 15,852 2.62 8,538 2.97 158 2.62Chem 313 5,434 0.90 3,069 1.07 228 3.78Rub 314 1,618 0.27 775 0.27 96 1.59Oth 315 3,681 0.61 2,202 0.77 4 0.07TOTAL 606,093 100 287,472 100 6,035 100

Panel B: Regions in Representative Areas and 6,035 Firms eligible for USTA&P Support

REGIONS ALL FIRMS % ALL FIRMS ELIGIBLE % ELIGIBLELombardia 104,438 36.33 2,139 35.44Veneto 45,116 15.69 1,207 20.00Toscana 42,290 14.71 1,129 18.71Campania 44,280 15.40 693 11.48Sicilia 51,348 17.86 867 14.37TOTAL 287,472 100 6,035 100

Notes. Number of firms operating in manufacturing sector according to the 1951 15-sector ISTAT classificationsystem. Columns "Italy" and "Repr. Areas" report the number of firms, respectively, in Italy and in the 5 repre-sentative areas from the 1951 Italian Industrial Census; column "eligible" reports the number of firms eligible forreceiving USTA&P support in 1950. 30 firms declared not eligible in 1951 are excluded. 1951 ISTAT classifica-tion system is: Food=Food and Kindred Products; Tob=Tobacco Products; Leat=Leather and Leather Products;Text=Textile Mill Products; App=Apparel, Other Textile Products, Furniture; Wood=Lumber and Wood Products;PA=Paper and Allied Products; PR=Printing and Publishing; PH=Photo-Phono-Film Industry; Met=Metal Indus-tries; Mac=Industrial Machinery and Equipment; Min=Non-Metallic Mineral Industry; Chem=Chemical and AlliedProducts; Rub=Rubber and Miscellaneous Plastics Products; Oth=Miscellaneous Manufacturing Industries.

16

Table 2: Description of USTA&P Study TripsUSTA&P YEAR MANAGEMENT TECHNOLOGY BOTH

FIRMS MANAGERS FIRMS TECHNICIANS FIRMS MANAGERS TECHNICIANS1952 21 45 29 100 32 82 1861953 33 78 34 140 43 116 1941954 36 119 45 198 64 160 2041955 41 124 47 224 71 174 2651956 27 78 31 125 60 148 2531957 25 57 28 99 69 178 2431958 10 21 19 61 47 111 177TOTAL 193 522 233 947 386 969 1,522

Notes. Number of firms, managers, and technicians (including engineers) that receivedthe USTA&P support in each year from 1952 to 1958, by kind of support chosen.Sources: ACS, ASIMI.

Table 3: Balancing Tests of Firms’ Characteristics and Outcomes in 1951

Panel A: Firms that Applied for Management Support

RED AREAS BLUE AREAS DIFF. P-VALUE

APPLIED EARLY 98 321 n/a

Number of firms APPLIED LATE 95 337 n/a

DIFF. P-VALUE n/a n/a n/a

APPLIED EARLY 3.71 3.67 0.846

Plants per firm APPLIED LATE 3.65 3.69 0.808

DIFF. P-VALUE 0.727 0.675 0.509

APPLIED EARLY 48.38 49.12 0.383

Employees per firm APPLIED LATE 41.75 46.83 0.281

DIFF. P-VALUE 0.251 0.371 0.482

APPLIED EARLY 688.90 712.46 0.574

Annual sales APPLIED LATE 746.35 735.39 0.491

DIFF. P-VALUE 0.392 0.542 0.303

APPLIED EARLY 913.25 890.19 0.854

Current assets APPLIED LATE 877.19 895.15 0.768

DIFF. P-VALUE 0.691 0.732 0.598

APPLIED EARLY 10.54 11.87 0.439

Age APPLIED LATE 10.43 9.30 0.581

DIFF. P-VALUE 0.471 0.389 0.287


Log TFP APPLIED LATE 2.32 2.32 0.391

DIFF. P-VALUE 0.211 0.303 0.432


Managers per firm APPLIED LATE 2.15 1.99 0.394

DIFF. P-VALUE 0.289 0.408 0.395

17

Panel B: Firms that Applied for Technology Support

RED AREAS BLUE AREAS DIFF. P-VALUEAPPLIED EARLY 120 441 n/a

Number of firms APPLIED LATE 113 446 n/aDIFF. P-VALUE n/a n/a n/a

APPLIED EARLY 3.93 3.52 0.846Plants per firm APPLIED LATE 3.97 3.89 0.808

DIFF. P-VALUE 0.644 0.701 0.509APPLIED EARLY 44.73 46.04 0.383

Employees per firm APPLIED LATE 47.67 44.61 0.281DIFF. P-VALUE 0.278 0.341 0.482

APPLIED EARLY 838.93 852.48 0.574Annual sales APPLIED LATE 836.33 866.76 0.491


Current assets APPLIED LATE 981.33 982.66 0.768DIFF. P-VALUE 0.321 0.453 0.598

APPLIED EARLY 14.52 12.91 0.439Age APPLIED LATE 11.76 13.51 0.581


Log TFP APPLIED LATE 2.41 2.41 0.391DIFF. P-VALUE 0.324 0.356 0.432

APPLIED EARLY 3.95 3.54 0.291Technicians per firm APPLIED LATE 3.41 3.64 0.394


Loan per firm APPLIED LATE 221.53 220.92 0.371DIFF. P-VALUE 0.456 0.671 0.333

18

Panel C: Firms that Applied for Both Supports

RED AREAS BLUE AREAS DIFF. P-VALUEAPPLIED EARLY 185 621 n/a

Number of firms APPLIED LATE 201 596 n/aDIFF. P-VALUE n/a n/a n/a

APPLIED EARLY 3.70 3.31 0.624Plants per firm APPLIED LATE 3.42 4.07 0.576


Employees per firm APPLIED LATE 44.09 43.94 0.296DIFF. P-VALUE 0.188 0.252 0.274

APPLIED EARLY 773.15 792.19 0.387Annual sales APPLIED LATE 813.84 819.85 0.451

DIFF. P-VALUE 0.345 0.436 0.403APPLIED EARLY 976.10 1,023.49 0.437

Current assets APPLIED LATE 991.76 961.18 0.493DIFF. P-VALUE 0.421 0.389 0.477

APPLIED EARLY 15.63 13.21 0.439Age APPLIED LATE 12.91 16.19 0.581


Log TFP APPLIED LATE 2.55 2.56 0.279DIFF. P-VALUE 0.342 0.208 0.346

APPLIED EARLY 3.23 2.78 0.271Managers per firm APPLIED LATE 2.54 3.02 0.218


Technicians per firm APPLIED LATE 3.57 3.79 0.589DIFF. P-VALUE 0.211 0.303 0.432

APPLIED EARLY 323.41 267.32 0.781Loan per firm APPLIED LATE 243.71 243.14 0.653

DIFF. P-VALUE 0.612 0.541 0.509Notes. Balancing tests for 851 firms that applied for management support (Panel A), 1,140 firms that applied fortechnology support (Panel B), and 1,603 firms that applied for both supports (Panel C). Red Areas refers to firms inred areas that were selected to receive the USTA&P support; Blue Areas refers to firms in blue areas that were notselected to receive the USTA&P support; Applied Early refers to firms that applied for USTA&P support betweenJanuary and March 1951; Applied Late refers to firms that applied for USTA&P support between April and June1951; Diff. p-Value reports the p-value of the mean difference. Number of firms is the total number of firms; Plants

per firm is the total number of plants per firm; Employees per firm is the total number of employees per firm;Annual sales (USD k) per firm and Current assets (USD k) per firm are 1951 sales and assets; Age reports the ageof the plant; Log TFP reports firm total factor productivity computed using the Ackerberg et a. (2007) algorithm;Managers per firm is the number of managers for which study trips had been requested in the application; Technicians

per firm is the number of engineers/technicians for which study trips had been requested in the application; Loan

per firm (USD k) is the amount of loan requested in the application. Annual sales (USD k) per firm, Current

assets (USD k) per firm, and Loan per firm (USD k) are originally reported in 1951 Lira and have been convertedfirst to 2010 euro, revaluated at Lira 1=30.8858 euro and then to 2010 USD, exchanged at 0.785 euro=USD 1.Sources: ACS, ASIMI, CSC.

19

Table 4: OLS Estimation of the Effect of the USTA&P Support on Firm ProductivityManagement Support Technology Support

TFP TFPACF ACF Solow Solow ACF ACF Solow Solow(1) (2) (3) (4) (5) (6) (7) (8)

Province·Time 0.150*** 0.150*** 0.145*** 0.142*** 0.054*** 0.052*** 0.048*** 0.047***(0.029) (0.031) (0.028) (0.032) (0.013) (0.016) (0.011) (0.013)

Province -0.012 -0.013 0.009 0.008(0.023) (0.021) (0.019) (0.013)

Firm FE Yes No Yes No Yes No Yes NoYear FE Yes Yes Yes Yes Yes Yes Yes YesR-Squared 0.564 0.521 0.601 0.587 0.624 0.608 0.632 0.602Observations 18,876 18,876 18,876 18,876 24,930 24,930 24,930 24,930Firms 851 851 851 851 1,140 1,140 1,140 1,140

Province-level clustered standard errors in parentheses. ***1%, **5%, *10% significanceNotes. OLS estimation for 851 firms that applied for management support (columns 1-4) and 1,140 firms that appliedfor technology support (columns 5-8). The dependent variable TFP is total factor productivity, calculated using theAckerberg et a. (2007) algorithm in columns (1), (2), (5), (6) and with the Solow’s residuals in columns (3), (4), (7),(8); Provincej is a "geographical" dummy that equal 1 if the firm was located in a red area selected for receiving theUSTA&P support, T imeit is a "time" dummy that equals 1 if firm i has received the USTA&P support in year t.Sources: ACS, ASIMI, CSC.

Table 5: OLS Estimation of Complementarity Effects of USTA&P on Firm ProductivityBoth Supports

TFPACF Solow(1) (2)

Province·Time·Management 0.142*** 0.139***(0.025) (0.026)

Province·Time·Technology 0.061*** 0.058***(0.009) (0.009)

Province·Time·Both 0.057*** 0.059***(0.007) (0.008)

Firm FE Yes YesYear FE Yes YesR-Squared 0.765 0.740Observations 36,428 36,428Firms 1,603 1,603Province-level clustered standard errors in parentheses. ***1%, **5%, *10% significance

Notes. OLS estimation for 1,603 firms that applied for both management and technology supports. The dependentvariable TFP is total factor productivity, calculated using the Ackerberg et a. (2007) algorithm in column (1) andwith the Solow’s residuals in column (2); Provincej is a "geographical" dummy that equal 1 if the firm was located ina red area selected for receiving the USTA&P support, T imeit is a "time" dummy that equals 1 if firm i has receivedthe USTA&P support in year t. Sources: ACS, ASIMI, CSC.

20

Table 6: Probit Estimation of Firm Exit in Response to the USTA&P SupportManagement Technology Both

(1) (2) (3)Province·Time -0.361*** -0.253*** -0.416***

(0.123) (0.088) (0.101)Age -0.013** -0.019** -0.021**

(0.007) (0.006) (0.009)Size -0.007* 0.011*** -0.012***

(0.005) (0.004) (0.002)TFP -0.059*** 0.043*** -0.060***

(0.008) (0.006) (0.017)Sales 0.079*** -0.061*** -0.074***

(0.017) (0.012) (0.021)Year FE Yes Yes YesObservations 18,876 24,930 36,428Firms 851 1,140 1,630

***1%, **5%, *10% significanceNotes. Marginal effects of probit estimation for 851 firms that applied for management support (column 1), 1,140firms that applied for technology support (column 2), and 1,603 firms that applied for both supports (column 3).The dependent variable TFP is total factor productivity, calculated using the Ackerberg et a. (2007) algorithm;Provincej , is a "geographical" dummy that equal 1 if the firm was located in a red area selected for receiving theUSTA&P support, T imeit is a "time" dummy that equals 1 if the firm i has received the USTA&P support in yeart, Ageijt is age of firm i in year t ; Sizeijt is firm i number of employees in year t ; TFPijt is the logarithm of totalfactor productivity of firm i in year t ; and Salesijt is firm i annual sales in 2010 USD thousand values in year t.Sources: ACS, ASIMI, CSC.

Table 7: Tightened Lee(2009) Bounds EstimationManagement Technology Both

(1) (2) (3)Province·TimeLower Bound 0.138*** 0.034*** 0.219**

(0.025) (0.011) (0.047)Upper Bound 0.163*** 0.069*** 0.271***

(0.023) (0.014) (0.056)***1%, **5%, *10% significance

Notes. Tightened Lee(2009) bounds estimation for 851 firms that applied for management support (column1), 1,140 firms that applied for technology support (column 2), and 1,603 firms that applied for both sup-ports (column 3). The dependent variable TFP is total factor productivity, calculated using the Ackerberget a. (2007) algorithm; Provincej , is a "geographical" dummy that equal 1 if the firm was located in ared area selected for receiving the USTA&P support, T imeit is a "time" dummy that equals 1 if the firmi has received the USTA&P support in year t. Variable used to compute the tightened bounds are Ageijt,age of firm i in year t ; Sizeijt, firm i number of employees in year t ; TFPijt, the logarithm of total fac-tor productivity of firm i in year t ; and Salesijt, firm i annual sales in 2010 USD thousand values in year t.Sources: ACS, ASIMI, CSC.

21

Table 8: Anticipation EffectsManagement Score Investment Ratio

(1) (2) (3)1953 -0.123

(0.241)1954 -0.157

(0.195)1955 0.104

(0.101)1956 -0.133

(0.112)1957 0.226

(0.199)1958 0.195

(0.121)Technology -0.504***

(0.187)Lagged Investment Rate -0.113*** -0.090***

(0.030) (0.010)Sales-to-Capital Ratio 0.447*** 0.491**

(0.212) (0.196)Sales-to-Capital Ratio Squared 0.081

(0.991)Tobin’s Q 0.091*** 0.083***

(0.024) (0.019)Tobin’s Q Squared 0.050***

(0.015)Management 0.031** 0.029**

(0.014) (0.013)I53�55,t�1 0.055 0.043

(0.056) (0.051)I53�55,t�2 0.027 0.031

(0.021) (0.033)I53�55,t�3 0.012 0.090

(0.081) (0.071)I56�58,t�1 0.148 0.011

(0.092) (0.016)I56�58,t�2 0.012 -0.038

(0.015) (0.029)I56�58,t�3 0.033 0.027

(0.026) (0.035)Mean Score Management 1952 3.861R-Squared 0.191Observations 579 61,358 61,358Firms 579 629 629

***1%, **5%, *10% significanceNotes. Coefficients in column (1) are OLS estimation of equation (3); Columns (2)-(3) are GMM estimation ofequation (4).

22

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