ISSN (Print) 0473-453X

Discussion Paper No. 1171

ISSN (Online) 2435-0982

GREENFIELD FOREIGNDIRECT INVESTMENT:SOCIAL LEARNING
DRIVES PERSISTENCE
Cho Yiu Ng
Chao Hung Chan
Kwok Ping Tsang
Charles Ka Yui Leung

March 2022

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Greenfield Foreign Direct Investment:
Social Learning drives PersistenceΩ
Cho Yiu Ng, Chao Hung Chan, Kwok Ping Tsang
And Charles Ka Yui Leung Θ

March, 2022
Abstract
This paper argues that the persistence of greenfield foreign direct investment (FDI) comesfrom information frictions. First, our simple social learning model shows that, throughsignaling effects, information frictions generate persistent greenfield FDI inflows. Second,
we show empirically that the autoregressive coefficient of greenfield FDI increases in value
with different proxies for information frictions, including six institutional and governanceindicators and two common language measures. We also find that greenfield FDI
persistence varies across industries. In particular, greenfield FDI by service firms is more
persistent than that by manufacturing firms. Finally, our findings suggest that better
governance, predictability, and transparency reduce information frictions and thereby
avoiding drastic and persistent ups and downs in FDI.

Keywords: Greenfield FDI, persistence, information, social learning
JEL: C23, F23, D21, D22ΩAcknowledgement: We are grateful to seminar participants at the City University of Hong Kong, National Taiwan
University, Higher Education Sprout Project Study Group, RCEA Conference, Taiwan Economic Association(TEA) meeting for helpful comments. Part of the research was conducted when Ng was visiting Virginia Techthrough a Fulbright Scholarship. The hospitality of Virginia Tech is gratefully acknowledged. Leung also
acknowledges the financial support from the City University of Hong Kong. The usual disclaimer applies.
Θ
Correspondence: Ng, City University of Hong Kong, cyngjoe@gmail.com ; Chan, Virginia Tech; Tsang, Virginia
Tech; tommychaohung@gmail.com ; Tsang, Virginia Tech, byront@vt.edu ; Leung, City University of Hong Kong,kycleung@cityu.edu.hk

Greenfield Foreign Direct Investment: Social Learning
Drives Persistence
March 17, 2022
Abstract
This paper argues that the persistence of greenfield foreign direct investment (FDI) comesfrom information frictions. First, our simple social learning model shows that, through signaling effects, information frictions generate persistent greenfield FDI inflows. Second, weshow empirically that the autoregressive coefficient of greenfield FDI increases in value withdifferent proxies for information frictions, including six institutional and governance indicators and two common language measures. We also find that greenfield FDI persistence varies
across industries. In particular, greenfield FDI by service firms is more persistent than that by
manufacturing firms. Finally, our findings suggest that better governance, predictability, andtransparency reduce information frictions and thereby avoiding drastic and persistent ups anddowns in FDI.

Keywords: Greenfield FDI, persistence, information, social learning
JEL:C23, F23, D21, D221

1.

Introduction

By its very nature, foreign direct investment (FDI) is about long-term business relationships.
As firms expand, reinvest, and reorganize, or as new partnerships are formed, and other firms
acquired, it is not surprising that FDI data is more persistent than other capital flows like portfolio
investment (e.g., Sarno and Taylor, 1999; Bluedorn et al., 2013; Eichengreen et al., 2018). But
the same argument does not apply to greenfield FDI where a firm invests in a different country
for the first time. Persistent greenfield FDI means that firms follow other domestic firms wheninvesting in a new country. What can explain such behaviors? Is greenfield FDI more persistentin some countries than others? What characteristics contribute to more persistent greenfield FDI?
This paper fills a gap in the existing literature by answering these questions.
The dynamics of greenfield FDI are highly policy-relevant. Greenfield FDI is beneficial to thehost economies in many aspects. Nocke and Yeaple (2007, 2008) show that more productive firms
tend to involve in greenfield investment. Compared with mergers and acquisitions (M&As), theother mode of foreign entry, not only greenfield FDI has a stronger impact on growth (Harms andMeon, 2018), but also a more significant influence on the innovation performance of domestic firms(Liu and Zou, 2008). While some studies suggest the optimal policy toward FDI should be tailoredto different types of FDI (e.g., Qiu and Wang, 2011; Nocke and Yeaple, 2007), the persistenceof greenfield FDI has not been singled out for analysis. This paper shows that the persistence(represented by a larger AR coefficient) of greenfield FDI is related to information frictions, andgreenfield FDI becomes more volatile when firms are less certain about the investment environmentof the destination. If the objective of the policymaker is stability, then sudden surges and stops arebad. The policymaker should aim at reducing information frictions, thereby avoiding drastic andpersistent ups and downs in greenfield FDI.
We focus on greenfield FDI, a form of first-time FDI, for two reasons. First, when firms
decide to invest in another country and induce information from other firms’ decisions, first-time
investments from other foreign firms are particularly informative. To decide whether to invest in
a particular country for the first time, there is more to learn from other foreign firms’ FDI intothat country than from investments by that country’s domestic firms. However, when foreign firms
have settled down in a particular country, their considerations will be more like those of domestic
firms, and their actions are less informative for potential investors into that country. Thus, we lookat how a firm decision to invest for the first time in a particular country depends on other foreign
firms’ first-time FDI. Our greenfield FDI, which naturally excludes subsequent investments, is theappropriate measure.
Second, although both are typical channels for first-time overseas investments, greenfield FDI
and M&As imply very different information needed. For greenfield FDI, companies are setting up

1

a new company abroad, establishing a supply chain, building a communication system, and integrating culturally. As a result, greenfield investors can learn a lot from the experience of greenfield
FDI by similar companies. M&As, on the other hand, represents a change in ownership and control
from a domestic firm to a foreign parent company. The target company is well-established, with themarket, labor, and business assets are already in place. M&A investors evaluate the potential financial benefit from combining two business entities, and that investment decision is firm-specific forboth the M&A investor and the target company. In this sense, while it may still exist, the signaling
effect of M&A investment history is not as evident and clear-cut as that of greenfield investment.
Given the high costs and high uncertainty features of greenfield FDI, it is difficult for companies
outside that country to read the investment environment through market research in an economy
with little transparency. Therefore, largely irreversible investments made by early movers become
an invaluable signal that it is safe to invest in such an economy, and they should encourage further
investment. Hence, we postulate that greenfield investment persistence results from information
frictions.
A simple statistical procedure would link the greenfield FDI persistence to information frictions. A typical approach to estimating persistence is to estimate an autoregressive (AR) model.
In its simplest form, the degree of persistence of an AR(1) process is positively correlated with thevariance of the time series.1 Figure 1 plots the relationship between the variance of real greenfield
FDI inflows and three proxies for information frictions, namely the voice and accountability index,
the control of corruption index, and the rule of law index.2 Voice and accountability measure theextent to which a country’s citizens can participate in selecting their government, freedom of expression, freedom of association, and freedom of the press. Suppressed freedom of speech limits
the flows of information. Weak control of corruption and an unsound rule of law may result inmany unspoken rules and hidden agendas. Figure 1 shows that countries with greater information
frictions tend to have a larger variance in real greenfield FDI inflows.3 Therefore, if greenfield FDI
is an AR(1) process, its persistence may be increasing in information frictions.
[Figure 1 about here]
Motivated by these stylized facts, we would build a simple social learning model to show that
information frictions lead to persistent greenfield FDI inflows through signaling effects. In themodel, heterogeneous firms sequentially make a publicly observed FDI decision. The type of each
1 Suppose y = γytt−1 + εt where εt is a purely random process with mean zero and variance σ . The variance of yt
σε2
is 1−γ 2 .
2 Greenfield FDI inflows are re-scaled by dividing the mean and de-trended so that its variance is comparable acrosseconomies. The data is from fDi Markets and the World Bank and covers 69 recipient economies from 2003 to 2018.
3 In Appendix Figure A1, we restrict the sample to economies with a variance of less than one. The negative
correlation remains.

2

firm, e.g., cost or productivity advantage, is private information. The profitability of the investment
project is determined by an unobserved state of the foreign country. Each firm receives a noisy
private signal about the state, interpreted as information collected through market research. Also,
based on the history of investment, firms form a public belief about the state. As a result, a firm’s
investment decision is determined by its type and the expected state of the foreign country, given
the private signal and the public belief. The model shows that FDI is an ARIMA(1,1,1) processwith a positive AR coefficient. The AR coefficient increases in both the variance of the signal andthe firm heterogeneity.
To test the theory, we examine the effects of information frictions on the AR persistence ofgreenfield FDI under a dynamic panel framework. By studying a sample of 454 source-destination
pairs of greenfield FDI during 2003-2018, we find that the AR persistence increases with information frictions. Also, we uncover the heterogeneity of greenfield FDI persistence across industries.
We find that service firms exhibit the highest AR persistence, followed by the retail firms, while
the lowest in the manufacturing firms. The finding is in line with our theory. The profitability ofinvestment in the manufacturing industry is mainly based on hard facts, like production costs. By
contrast, the profitability of investment in the service industry depends more on taste and cultural
factors. It is more difficult for service firms than manufacturing firms to collect reliable information through market research. Therefore, service firms tend to rely more on the investment historyof the early movers.
The remainder of the paper is organized as follows. Section 2 provides a literature review.
Section 3 outlines a simple social learning model of greenfield FDI. Section 4 describes the data
used in our empirical analysis and presents our empirical methodology. Section 5 presents theempirical results. Section 6 concludes.

2.

Related Literature

Our paper connects to three strands of literature. The first studies the dynamics of international
capital flows, for which Table 1 provides a summary.4 Looking at aggregate flows data, they
consistently find that FDI flows are ”cooler,” i.e., less volatile than other capital flows. Some studies
attempt to explain the difference in volatility. For instance, Goldstein and Razin (2006) assume that
the FDI investor can obtain refined information about the firm, while portfolio investors cannot.
But that advantage comes with the cost: a low resale price when facing a liquidity shock due tothe asymmetric information between the FDI owner and potential buyers. As a result, the tradeoff
between management efficiency and liquidity of investors results in a less volatile FDI relative to
4 More evidence can be found in Chuhan et al. (1996), Nachum (1999), WorldBank (1999), and Becker and Noone(2008), among others.

3portfolio investment.
Albuquerque (2003) observes that FDI recipient countries are generally unable to operate theinvestment without the intangible assets of the indirect investors. In his model, because of inalienability, FDI commands a lower default premium associated with expropriation risk caused byimperfect enforcement of contracts and hence is less sensitive to changes in a country’s financing
constraints. It is in line with the findings in Lipsey (2001) and Levchenko and Mauro (2007) that
FDI is remarkably stable during crisis periods. Similar to Albuquerque (2003), several studies
show that international capital flows are subject to institutional factors. Related work includes Wei(2000), Gelos and Wei (2005), Alfaro et al. (2008), and Papaioannou (2009), who unanimously
find that low institutional quality reduces international capital inflows.
Our paper is more related to Albuquerque (2003). We consider the informational problems
caused by institutional quality and not the asymmetric information with the FDI, as greenfield FDI
is purely a new investment inflow that is irrelevant to disinvestment.
The second strand of literature looks at the impacts of informational problems on international
capital flows. Razin et al. (1998) introduce a model to study the pecking order of capital inflows.
They assume portfolio debt and equity are subject to asymmetric information distortions where
domestic investors observe firms’ productivity while foreign investors do not, and FDI investors
involved in the management of the firms can circumvent such distortions. Neumann (2003), on theother hand, assumes equity claims, even for portfolio equity, convey information on the investment,
while debts do not. Her model explains why one may prefer equity financing to debt financing indeveloping countries. Daude and Fratzscher (2008) provide empirical evidence that information
frictions and the quality of host country institutions are key determinants of the pecking orderin a sample of 77 countries. They find that FDI is substantially more sensitive to information
frictions than portfolio investment. More recently, Burchardi et al. (2019) study the effect of theancestry composition of U.S. counties on FDI. They find that increasing the number of residents
with ancestry from a given foreign country will increase the probability that at least one local firm
engages in FDI with that country. And the effect results from a reduction in information frictions.
Finally, this paper belongs to the much smaller strand of literature on greenfield FDI. Existing
evidence strongly supports the importance of greenfield FDI to host economies, and greenfield
R&D has a significant impact on international technology spillovers. In Chinese high-tech industries, Liu and Zou (2008) find that foreign greenfield R&D creates both intra-industry andinter-industry spillovers on innovation, and there exist only inter-industry spillovers from M&As.
Nocke and Yeaple (2008) present a model in which firms enter a foreign market through greenfield
investment or cross-border acquisitions. In equilibrium, firms engaging in greenfield investment
are systematically more efficient. Moreover, greenfield investment plays a vital role in transferringbusiness from high-cost into low-cost countries.
4We contribute to the literature in two directions. First, while greenfield FDI is beneficial tothe host economies in many aspects, not much is known about the persistence of different typesof FDI, and greenfield FDI has not been singled out for analysis in the literature. From a policy
perspective, it may be oversimplified to view FDI as a homogenous capital flow. Qiu and Wang(2011) show that optimum FDI policy should be tailored to different types of FDI. Our paper
reveals the persistence of greenfield FDI and its heterogeneity in persistence across industries.
Second, this paper connects information frictions with the persistence of greenfield FDI froma social learning perspective. More specifically, we consider the FDI productivity as a unit root
process, and firms are learning the productivity of the foreign countries based on a noisy signal
and other firms’ past FDI.5 We show that the persistence of FDI depends on how predictable
the productivity is (variance of the productivity shock), how precise the signal is (variance ofthe signal), and how much information they can extract from others’ FDI (similarity between theinvesting firm and other firms with earlier FDI). Also, our greenfield FDI data, which naturally
excludes subsequence investment, allows us to reveal how firms rely on the past investment of theother firms under information frictions.

3.

A Social Learning Model of Greenfield FDI

We model FDI decisions as a social learning problem in a discrete time model. At period t,
t = 1, 2, ..., a firm indexed n = t is making a greenfield FDI decision. The profitability of theinvestment project is determined by a hidden state θt , which is a unit root process.
θt = θt−1 + etθ ; etθ ∼ N(0, vθ ) i.i.d
The decision rule is to match with the hidden state, θt , which determine the profitability of the
project, and subject to a normal shock, at ∼ N(0, va ), i.i.d.6 We refer to at as the type of firm t,
which is the private information of firm t.
It = E t (θt ) + at
where E t (·) := E(·|It ) is the expectation operator conditional on the information set of firm t, It .
The investment decision, It , is publicly observed. Each firm t receives a noise private signal, st ,5 Khraiche and de Araujo (2021) show that the FDI process is persistent if firms learn the productivity of FDI via

a noisy signal and update their expectations adaptively. However, as pointed out by Muth (1960), adaptive learning isrational only when the underlying process is a random walk with noise. In this paper, we account for the persistenceof FDI in a more general setting.
6 This decision rule can be generated by the profit maximization problem that the firm make one time investment
with convex cost, and the return rate for each period depends on θt . The Appendix provides a detailed proof.

5

about θt .
st = θt + ets ; ets ∼ N(0, vs ) i.i.d.
Thus, the information set for firm t includes the history of investments ht−1 = {Ii }t−1i=1 and thesignal, st , i.e., It = (ht−1 , st ).
Since the type of a firm is private information, the public updates their belief on θt based on thehistory of investment, ht−1 , only. In the following, we present the five propositions of the model.
Proofs are provided in the Appendix.
Proposition 1 If θt |ht−1 is normal distributed, θt+1 |ht is also normal distributed.7
We define θt |ht−1 ∼ N(pt , vt ) as the public belief, since it is the belief of θt which is formed
with publicly announced investment, ht−1 , only. Proposition 1 shows that for any period t, if thebelief is normally distributed, it will be normally distributed for all the periods after t inductively.
Thus, we can summarize the belief dynamics in two variables: the mean of belief (pt ) , and thevariance of belief (vt ). Next, we show the dynamics explicitly.
Proposition 2 Suppose θt |ht−1 ∼ N(pt , vt ), the dynamics of belief and investment are determinedby the following systems:
It = γt st + (1 − γt )pt + at(1)

pt+1 = µ pt + (1 − µ)It(2)

vt+1 = (vt−1 + (γt2 vs + va )−1 )−1 + vθ

(3)

v−1
vs +vtwhere γt = −1 s −1 , µt = (vt−1 + (γt2 vs + va )−1 )−1 vt−1 , γt ∈ (0, 1), and µt ∈ (0, 1).
Solving (1) and (2) recursively, we can show that investment (It ), and the mean of publicbelief (pt ) are AR processes. However, the AR coefficients are time-varying, making the system
intractable. The following proposition simplifies the analysis by showing the global convergenceof the variance of public belief.
Proposition 3 Suppose θt |ht−1 ∼ N(pt , vt ). For any vt ∈ R, limi→∞ vt+i = v, where v is a constant.
The above proposition shows that if the belief is normally distributed, there is a steady state
for the variance of public belief. Then, we can directly impose the stable public belief variance as
7 With some abuse of notation θ |h j always refers to the belief of θ given information h j , while θ refers to theiiiactual state, which is a constant. We call the belief θi |h j with E(θi |h j ) = θi a consistent belief.

6the initial variance, i.e., θ0 |h0 ∼ N(p0 , v), h0 = 0/ where p0 is some constant. As a result, the timevarying coefficients in the above system become constants, vt = v; γt = γ; µt = µ.
It = γst + (1 − γ)pt + at

(4)

pt+1 = µ pt + (1 − µ)It

(5)
v−1

where v is the fixed point of f (v) = (v−1 + (γ 2 vs + va )−1 )−1 + vθ ; γ = −1 s −1 , µ = (v−1 + (γ 2 vs +vs +vva )−1 )−1 v−1 , γ ∈ (0, 1), and µ ∈ (0, 1).
Note that we do not restrict the initial belief to be consistent, i.e., p0 may not be equal to θ0 .
One may doubt whether the belief formed over time is reliable if the initial error of belief (i.e.,|θ0 − p0 |) is large. The following proposition shows that the belief formed will be reliable in somesense for any initial belief. Substituting (4) into (5) and solving the recursion give the following
proposition.
Proposition 4 The mean of public belief is an error correction process and is co-integrated withthe hidden state.

∆pt = −(1 − µ)γ(pt−1 − θt−1 ) + εtp

(6)

s +a
where εtp = (1 − µ)γet−1
t−1 , (1 − µ)γ ∈ (0, 1).
Note that θt is unobserved. By observing the history of investment, the public forms a reliable
belief about θt . Although the belief is not always consistent (i.e., pt is not always equated to θt ),the mean of belief (pt ) is cointegrated with the actual state (θt ), and the error is being corrected
over time. If there is no further shock on the type and the signal, the public will eventually learn
the true state.

Proposition 5 FDI is an ARIMA(1,1,1) process.

I
It = γ(θt − µθt−1 ) + (1 − γ(1 − µ))It−1 + εtI − µεt−1(7)where εtI = γets + at , and (1 − γ(1 − µ)) ∈ (0, 1). Proposition 5 provides the main equation for theempirical analysis. It shows that FDI is an ARIMA(1,1,1) process with a positive AR coefficient.
The error correction term in proposition 4 and the AR term in proposition 5 depend on the varianceof hidden state, signal, and type. In the following, we study their relations via numerical method.
Proposition 5 allows us to analyze the effect of the variance of signal and type on the AR(1)persistence. Our analysis is based on a numerical method as the fixed point theorem is needed to7pin down the variance of public belief, making a clear analytic solution not feasible. Figure 2a plots
the contour map of the AR coefficient in equation (7) (i.e., 1 − γ(1 − µ)), and the error correction
term of public belief in equation (6) (i.e., −(1 − µ)γ). We fix the unit of investment vθ = 1. The
approximation is made on the space (vs , va ) ∈ [0.1, 5]2 with 100 linear steps, corresponding to 0.1
to 5 times the variance of state.
According to Figure 2a, the AR coefficient is increasing in both the variance of signal (vs ) andthe variance of type (va ). The result is intuitive. When the signal is unreliable, the decision-makerweighs more on the investment history as a secondary information source. Also, when the type
heterogeneity is high, there are two effects. First, decisions of other firms become less informative since they may be driven by the difference in firms’ preference or by the hidden state, while
the investing firm cannot distinguish the two. Second, the decision-maker would like to consult
more information, and hence she weighs more on the investment history of the early movers. The
numerical result shows that the second force dominates, and the AR coefficient increases in thevariance of type (va ).
Figure 2b shows that the error correction of the mean of belief is stronger when both the variance of signal and the variance of type are small. As information becomes more precise, it is easierfor the public to deduce the true state from investment decisions, resulting in faster convergence ofthe mean of public belief to the true state. Thus, the belief is more reliable when the variance of
signal and the variance of type are small.
[Figure 2 about here]
To show the relationship between the variance of FDI and the variance of information frictions,
we derive the theoretical variance of the first-differenced FDI from equation (7).8 Figure 3a showsthat the variance of the first-differenced FDI is increasing in both the variances of signal and type.
Also, Figure 3b shows that the variance of the first-differenced FDI is increasing in the variance oft )) 9). The relationship is increasing in the variance of type while decreasing in
productivity ( ∂Var(∆(I∂ vθ
the variance of signal. When the signal is not informative, the variance of the first-differenced FDI
will be less related to the variance of fundamental (productivity) but more related to information
frictions.
[Figure 3 about here]
In sum, for two AR(1) processes with the same shock, the one with higher AR persistence will
have higher variance. In our model, the FDI process (the AR terms and the process of the shock of
22+µ)28Var(∆I ) = 1 + (ξ −µ)γ 2 vθ + 1 + (ξ − (1 + µ))2 + (ξ −(1+µ)ξ
t
1−ξ 2
1−ξ 2
(0, 1),Var(εtI ) = 
γ 2 vs + va . The
 Appendix provides a detailed proof.
2
9 ∂Var(∆(It )) = 1 + (ξ −µ)
γ 2.
1−ξ 2
∂ vθ







8



Var(εtI ), where ξ = 1 − γ(1 − µ), ξ ∈FDI) is endogenously determined by three fundamental shocks: (1) the TFP shock; (2) the signal
shock; and (3) the type shock. The variances of the latter two shocks capture information frictions.
We show that with the same TFP shock, higher variances of the latter two shocks imply a higher
persistence and variance of FDI.

4.

Data and Econometric Model

Our model generates a testable implication that greenfield FDI is determined by its realizations
at the previous period and the payoff relevant state of the foreign country. Also, the comparative
statics shows that the AR(1) coefficient is increasing in both information frictions in the host economy (represented by the variance of signal, vs ) and the type heterogeneity of firms (va ). This section will describe the dataset we would use and describe how one can empirically test the model’s
implications. Descriptive statistics are provided in Appendix Table A2 and A3.

4.1.

Greenfield FDI and macro-variable data

Our bilateral greenfield FDI data (in real term) is collected from the fDi Markets database
compiled by fDi Intelligence, a division of the Financial Times. It is the most comprehensive firmlevel database of cross-border greenfield investments available. The data includes the name of theinvesting firm and its parent company, the source and destination countries, the recipient industry sector of the FDI project, the date of investment, and the capital expenditure associated withthe project. Sources of the database include Financial Times news wire and internal information
sources, thousands of media sources, project data received from over 2,000 industry organizations
and investment agencies, and data purchased from market research and publication companies.
Since our theoretical model provides implications on country-level information frictions, we aggregate the firm-level data at the country level. It results in a sample of 454 country-pairs ofgreenfield FDI covering 69 source economies and 71 destination economies over the period 20132018 (see Appendix Table A1 for the list of economies). Also, as the recipient industry sector
is known, we can extract industry-country-pair data of 4 industries (subject to data availability),
namely manufacturing, retail, business service, and sales, marketing & support, from the wholesample for further analysis.
Several institutional and governance indicators are used to proxy information frictions in the
FDI destination economies. As introduced in Section 1, we employ the voice and accountability index(VA), the control of corruption index (CC), and the rule of law index (RL) (ranging fromapproximately -2.5 (weak) to 2.5 (strong) governance performance) from the World Bank’s Worldwide Governance Indicators database. This database covers all the economies in the fDi Markets
9database, meaning no observations are dropped after data matching. One drawback is that the voice
and accountability index measures two aspects: freedom of expression and free elections, but thelatter may be less relevant to information frictions.
To cope with this, we also employ the freedom of expression index (FE), the freedom ofacademic and cultural expression index (FACE), and the media bias index (MB) (ranging from0 (lowest score) to 1 (highest score)). The data comes from the World Bank’s Global State of
Democracy database. However, this database does not cover Hong Kong (SAR of China), and oursample reduces to 445 country pairs. Furthermore, to be compatible with some of the previous
studies,10 we employ two measures of common language as additional proxies of information
friction. They are the common official language (COL, which is the usual binary measure where
1 indicates two countries have the same official language, and 0 otherwise), and common native
language (CNL, which is the 0–1 probability that a random pair from two countries speak thesame native language). When both source and destination countries have a common language, itis easier for the firms from the source country to obtain information. The data comes from Melitz
and Toubal (2014).
We assume that the payoff relevant state is affected by macroeconomic conditions. Therefore,
in addition to the pull factors in the destination economy, we also control for the push factors fromthe source economy. A large body of literature has been devoted to identifying the determinants of
FDI.11 Among them, Blonigen and Piger (2014) employ the Bayesian Model Averaging procedure
to calculate the inclusion probabilities for a group of macro-variables in the OECD countries andsome non-OECD countries, and we mainly follow the list of variables with inclusion probabilities
over 50% in Blonigen and Piger (2014) to select our control variables. Besides, Desbordes and Wei(2017) find that the source and destination countries’ financial development significantly influences
greenfield FDI. Therefore, we also include domestic credit to the private sector as a proxy for
financial development. Subject to data availability, we employ the following macro-variables ofboth the source and destination economy (except where otherwise specified) as controls: real GDP(GDP), real GDP per capita (GDPcap), domestic credit to the private sector as a percentage ofGDP (Credit), human capital index (Skill), real capital per worker (Capital), inflation rate (In f ),exchange rate (Exrate) (between the source and destination economy), and corporate tax rate (Tax)(in the destination economy). The data comes from IMF, KPMG, Penn World Table, and World
Bank. Overall, our panel is unbalanced, but with only a small number of missing observations.
10 See, Daude and Fratzscher (2008), among others.
11 See, among others, Cheng and Kwan (2000); Carstensen and Toubal (2004); Braconier et al. (2005); Eicher et al.
(2012)

10

4.2.

Dynamic panel estimation

Fixed effects and endogeneity of regressors are crucial concerns when handling panel data. As
shown in equation (7), greenfield FDI is a dynamic variable. Also, the other regressors may not bestrictly exogenous since greenfield FDI contributes to economic growth, as discussed in Section
2. Given these concerns and a short panel data structure (where N is much larger than T), thesystem GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) isa natural choice. The system GMM estimator simultaneously estimates a differenced equation anda level equation, where lagged variables in levels instrument for the differenced equation, lagged
differences instrument for levels. It is a general estimator designed to address endogeneity issues
with (1) the lagged dependent variable, (2) independent variables that are not strictly exogenous,and (3) fixed individual effects.
Note that endogeneity could also result from omitted variable bias. In system GMM estimation,
the Hansen test is a common choice to test whether the instruments as a group are exogenous.
But it can also be viewed as a test of structural specification. For instance, omitting important
explanatory variables could move components of variation into the error term and make themcorrelated with the instruments, leading to a rejection of the null hypothesis in the Hansen test
(Roodman, 2009b). In other words, if the Hansen test fails to reject the null, it implies no important
explanatory variables are omitted.
We use a two-step system GMM estimator, which is more efficient and robust to heteroscedasticity and serial correlation, together with Windmeijer (2005) finite-sample adjustment to correct
the downward bias in the computed standard errors in two-step results. We employ the ”forward
orthogonal deviations” transformation (Arellano and Bover, 1995) instead of first-difference transformation to remove the fixed effects. The latter magnifies gaps in unbalanced panels, while theformer minimizes data loss in transforming the data in unbalanced panels (Roodman, 2009).12
Multicollinearity is likely a problem when including all six institutional and governance indicators in the same regression. To test the theory, we estimate the following six reduced form
dynamic panel data models for each institutional and governance indicator, for q = 1, 2, ..., 6:q

GFDIi, j,t =αq1 GFDIi, j,t−1 + αq2 GFDIi, j,t−2 + αq3 {Indicator j,t−1 × GFDIi, j,t−1 }
q

M

q

(8)

+ αq4 Indicator j,t−1 + ∑ βqmControlst−1 +YearFixedE f f ect + ui, j,tm=1qwhere GFDIi, j,t is the real greenfield FDI from economy i to j, i ̸= j; Indicator j with q = 1, 2, ..., 6
12 For example, if y ,t is missing, then both ∆y and ∆yii,ti,t+1 are missing in the first-difference transformation. The”forward orthogonal deviations” transformation minimizes data loss by subtracting the average of all future available
observations of a variable. It can be computed for all observations except the last for each variable.

11represents VA, CC, RL, FE, FACE, and MB, respectively, in economy j. Controls is a vector of theqsource and destination economy’s macro-variables described in Section 4.1. ui, j,t is the disturbanceterm that has two orthogonal components: the fixed effects and the idiosyncratic shocks. To checkfor serial correlation of order 1 in levels, we test for correlation of order 2 in differences via theArellano-Bond test. The year fixed effects, YearFixedE f f ect, are included to make the assumptionof no correlation across individuals in the idiosyncratic disturbances (the key assumption of the
autocorrelation test and the robust estimates of the coefficient standard errors) more likely to hold.
Here, we estimate an AR(2) instead of an AR(1) model for both economic and statistical reasons. It helps us to check whether the coefficient on GFDIi, j,t−2 is insignificant, as predicted by
the theory, and at the same time, we can account for serial correlation. According to our social
learning model, we expect αq1 ∈ (0, 1) and αq2 = 0. The model also predicts that greenfield FDI isincreasing in information frictions. So, we expect αq3 < 0 since better institutional quality implies
fewer information frictions, resulting in a lower AR persistence.
Next, we use COL and CNL to proxy information frictions. As the language measures aretime-invariant, for each measure, we split the whole sample into two sub-samples: economies withno common language (COL = 0, or CNL = 0), and economies with (a certain degree of) common
language (COL = 1, or CNL > 0). We estimate the following regressions for each measure andeach sub-sample.
Mkkk
GFDIi, j,t =αl1
GFDIi, j,t−1 + αl2
GFDIi, j,t−2 + ∑ βlm
Controlst−1 +YearFixedE f f ect + ui, j,t (9)m=1where l = nc, c; k = COL,CNL respectively represents the sub-samples of no common language
and common language according to COL and CNL. Again, we expect αl1 ∈ (0, 1) and αl2 = 0.
Also, as having common language implies fewer information frictions and a lower AR persistence,
we expect αnc1 > αc1 .
Finally, we study the industry-country-level sub-samples, namely manufacturing, retail, business service, and sales, marketing & support industry. First, we believe that the information frictions across sectors are different. For instance, the manufacturing industry’s profitability is mainly
based on ”hard facts,” like production costs. Also, as many manufacturing firms produce export
goods, their profitability is less likely to be influenced by ”soft factors” like taste, culture, andsocial norms in the host economy. By contrast, business service and sales, marketing & support industry sell intangible products related to legal service, logistics service, consulting service, staffing,marketing, etc. Their profitability depends more on those ”soft factors.” Thus, it is more difficult
for service firms than manufacturing firms to collect reliable information through market research.
In terms of our theoretical model, the variance of the private signal tends to be higher in businessservice and sales, marketing & support industry than in manufacturing industry. In other words,
12

service firms are expected to rely more on the investment history of the early movers. Moreover,as a traditional service industry, the retail industry is different from the other three industries. It
sells tangible products and provides sales services in the local market. Its profitability is subject
to both ”hard facts” and ”soft factors.” Thus, its AR persistence may be higher than that of the
manufacturing industry but lower than that of the business service and sales, marketing & support
industry.
Second, differences in the AR persistence across industries may be related to type heterogeneity. Firms within the same service industry provide a very different and customized service. They
recruit people with specific knowledge and skills in the service disciplines. In this sense, the type
heterogeneity of service firms tends to be larger than that of secondary production firms, where
the skill requirement tends to be similar. As our theoretical model predicts the AR persistence
increases in the type heterogeneity, we expect the AR persistence of the service industries (i.e.,business service, and sales, marketing & support) is higher than that of the manufacturing industry.
We estimate the following model for each industry sub-sample.
M

GFDIi, j,t =αd1 GFDIi, j,t−1 + αd2 GFDIi, j,t−2 + ∑ βdmControlst−1 +YearFixedE f f ect + ui, j,t (10)m=1where d = man, ret, bus, mkt, respectively represents the manufacturing, retail, business service,and sales, marketing & support industry. We expect αman1 < αret1 < αbus1 < αmkt1 .

5.

Empirical Results

Table 2, 3, and 4 show the results of the variables of interest in equations (8), (9), and (10),respectively. In the Appendix, Table A4 to A6 provide the estimates of all variables. As mentioned by Roodman (2009b), instrument proliferation can overfit instrumented variables and fail toremove their endogenous components. It also weakens the power of the Hansen test. Therefore,
we collapse the instruments and present the estimation results of using lag 2 to 4 for instruments(except where otherwise specified) instead of using all available lags. In the Appendix, Table A7
to A9 provide the estimation results of using lag 2 to 4 up to 14 (the maximum lag) for instruments.
In Table 2, the coefficients on GFDIi, j,t−1 are significant at 0.1% level and the coefficients on
GFDIi, j,t−2 are insignificant in all the specifications. The results indicate that greenfield FDI is an
AR(1) process. In column (1), the AR(1) coefficient is around 0.4, meaning that other things being
equal, if greenfield investment increases by 1 million USD this year, it will increase by 0.4 million USD next year. However, this result may overestimate or underestimate the AR persistence,as the comparative statics in Section 3.1 shows that the AR coefficient increases in information
frictions. In columns (2) to (7), we employ the six institutional and governance indicators to proxy13

information frictions. In column (2), the interaction term between GFDIi, j,t−1 and VA j,t−1 is only
significant at 10% level or, strictly speaking, insignificant. As mentioned earlier, we notice thatthe voice and accountability index may not be a good proxy for information frictions. In addition
to freedom of expression, it measures free elections, which may be less relevant to information
frictions. Despite this, the interaction terms between GFDIi, j,t−1 and the other indicators are atleast significant at 5% level in columns (3) to (7). The results are consistent with our theoreticalprediction. As the range of each index is different, in Figure 4, we visualize the negative relationship between the time average of each index in destination country j and its AR(1) coefficient on
greenfield FDI. Considering our model is linear, the AR coefficients of some countries are smaller
than zero in the models with the control and corruption index (Figure 4b) and the rule of law index(Figure 4c). However, for models with the other indices (Figure 4a and Figure 4d to 4f), all the AR
coefficients lie between 0 and 1.
[Table 2 and Figure 4 about here]
In terms of test statistics, the Arellano-Bond test for AR(1) confirms the presence of serial
correlation of order one, and the Arellano-Bond test for AR(2) does not reject the absence of thesecond-order serial correlation in the error term. The Hansen test, which can be viewed as a testof structural specification, does not reject the null that instruments as a group are exogenous. In
most of the cases, the p-values are considerably higher than 10%. Since we limit the lags used in
GMM-style instruments, the number of instruments (80) is relatively small compared to the sample
size (around 6000 observations and 450 cross-sections). Thus, the risk of overfitting endogenous
variables and weakening the Hansen test is low.
[Table 3 about here]
In addition to six institutional and governance indicators, we use two common language measures to proxy information frictions. Table 3 shows the estimation results. First, we split our sample
into two sub-samples according to whether the source and the destination economy have a common
official language (COL). In columns (1), the coefficient on GFDIi, j,t−1 is 0.329 and is significant
at 1% level.13 Also, the coefficient on GFDIi, j,t−2 is insignificant. Again, it shows that greenfield
FDI is an AR(1) process in economies that do not share a common official language (COL = 0).
It is consistent with our theory as information frictions could be high when two economies do notshare a common official language. Companies may rely heavily on the signals of the early movers,
resulting in a persistent greenfield FDI inflow. On the other hand, in the sub-sample where theeconomies have the same official language (column (2)), the AR coefficient becomes insignificant.
13 Here, we use a deeper lag for instruments (lag 2 to 6) since the Hansen test fails to reject the null when using lag

2 to 4 and 2 to 5. For details, see Appendix Table A8.

14

We observe the same pattern in columns (3) and (4), where the sample is divided by whether the
economies have a certain degree of common native language (CNL = 0 and CNL > 0).
[Table 4 about here]
Next, we examine the industry sub-sample, and the results are shown in Table 4. For the manufacturing industry sample, we estimate two specifications. Consistent with the other results, we
use lag 2 to 4 for instruments in column (1). However, the number of instruments (76) is quite
large relative to the sample size of 751 observations and 55 cross-sections. Also, the p-value ofthe Hansen test is very close to one, indicating that the risk of overfitting endogenous variables
is high. To lower the risk of overfitting, in column (2), we reduce the number of instruments to
62 by removing the most insignificant control variables one at a time until the number of instruments is roughly equal to the number of cross-sections. The coefficient on GFDIi, j,t−1 (0.349) issignificant at 1% level, and the p-value of the Hansen test reduces to 0.375. Overall, we find theAR persistence of service firms (business service and sales, marketing & support) is higher thanthat of manufacturing firms, especially when we compare the AR persistence of sales, marketing& support industry (0.8) with that of the manufacturing industry. These results are in line with
our predictions. As discussed in Section 4.2, the variance of private signal, the variance of type,
and the AR persistence for service firms are expected to be higher than manufacturing firms. Also,
the AR persistence of the retail industry lies between the service industries and the manufacturingindustry, as its profitability is subject to both ”hard facts” and ”soft factors.”
Finally, given that the p-value of the Hansen test tends to become inflated with an increasing
number of instruments, in Appendix Table A7 to A10, we follow Roodman (2009,b)’s suggestion
to report the results of using different lags for instruments as a robustness check. According to Table A7 and A8, there is no sign that the Hansen test is weakened at the selected lags for instruments(lag 2-4) as the p-values are not increasing after introducing more lags for instruments. However,in Table A9, the risk of weakening the Hansen test presences in the manufacturing, retail, andbusiness service sub-samples. We further reduce the number of instruments by removing the mostinsignificant control variable one at a time until half of the control variables are removed. Table
A10 shows that the coefficient on GFDIi, j,t−1 is not sensitive to the model reduction. The Hansen
tests do not reject the null in all model specifications.

6.

Conclusion and Policy Implications

While persistent inflows of FDI are usually portrayed as good and bullish news by the media,
we have pointed out in Section 3 that persistence itself, which characterizes both the ups and downsof FDI, contributes to higher volatility and is something that policymakers try to avoid. We show15

in this paper that some of the persistence of greenfield FDI comes from information frictions, andgreenfield FDI becomes more volatile when firms are less certain about the investment environmentof the destination. When firms see that other firms invest in a country, they treat it as a signal andfollow. When firms see that other firms retreat from a country, they do the same. Such ”herding”
behavior contributes to the volatility of FDI.
We have presented a list of factors that are related to information frictions. While some are notsubject to changes (e.g., language), other institutional and governance factors can be improved byreforms and other policy changes. Upholding the rule of law, promoting freedom of the press, andpreventing corruption in the government are some of the obvious policy implications of this paper.
Indeed, numerous other studies have already pointed out the economic benefits of having thosecharacteristics (e.g., Acemoglu et al. (2005)). Our paper points out another channel they contribute: better institutions and governance reduce information frictions, thereby avoiding drastic
and persistent ups and downs in FDI.
Due to the limitation of our data, both in the sample length and coverage of countries, we
can only obtain ”reduced form” estimates of the model. As a result, we cannot further pin down
the more structural parameters. In addition, with limited data, we cannot look at more complex
interactions across economies. For example, a firm in country A may learn from the greenfield
FDI in nearby country B. We look forward to follow-up studies that make use of a larger panel of
data.

16

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19

20

Figure 1: Relationship between variance of greenfield FDI and information frictions

Figure 2: Comparative statics: AR coefficient vs information frictions

21

Figure 3: Comparative statics: variance of FDI vs information frictions

22

23
Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively.

Figure 4: Effect of information frictions on the AR(1) coefficient

24

FDI flows have the lowest CV and theCV and AR persistence highest AR persistence among private
capital flows.

147 economies during
1980- 201134 emerging economies
during 1990-2015

Levchenko and Mauro (2007)

Bluedorn et al. (2013)

Eichengreen et al. (2018)

CV and AR persistence

Defining “sudden
stops”

142 economies during
1970- 2003

Lipsey (2001)

FDI flows are less volatile and more
persistent than other private capital flows.

FDI is remarkably stable during the“sudden stop” episodes.

Inflows of FDI into the crisis countries
are much more stable than that of theother financial flows.

Event study duringcrisis period

Lipsey et al. (1999)

Latin American in 1982,
Mexico in 1994, and East
Asia in 1997

The average number of reversals in net
flows of FDI is the lowest. The CV of
net FDI flows in Southeast Asia, Japan,
Europe, and Latin America is the lowest,
except the United States.

FDI flows are almost entirely permanent.

Findings

Average number ofreversals in net flows,
and CV

SampleMethodU.S. capital flows to 9
State-space
Asian and 9 Latin American
decomposition
countries during 1988-1997

Table 1: Summary of literature on FDI persistence

52 economies during
1980- 1995

Sarno and Taylor (1999)

Literature

Table

Table 2: Regressions of greenfield FDI: institutional and governance indicators
GFDII, j,t−1
GFDII, j,t−2(1)0.394***(0.118)0.065(0.063)

GFDII, j,t−1 ×VA j,t−1(2)0.35***(0.109)0.052(0.051)-0.145(0.082)

GFDII, j,t−1 ×CC j,t−1(3)0.429***(0.124)0.024(0.041)(4)
0.399***(0.12)
0.036(0.035)(5)0.991***(0.265)0.047(0.049)(6)
0.968***(0.245)0.047(0.051)-0.313*(0.13)GFDII, j,t−1 × RL j,t−1-0.353**(0.132)

GFDII, j,t−1 × FE j,t−1-0.943*(0.386)

GFDII, j,t−1 × FACE j,t−1-0.954**(0.349)GFDII, j,t−1 × MB j,t−1-0.985*(0.485)16.558(114.669)

VA j,t−1-20.597(152.152)

CC j,t−1

490.254(266.71)

RL j,t−1

866.912(526.063)

FE j,t−1

973.286*(423.027)

FACE j,t−1

128.145(634.082)

MB j,t−1
Contorls
Year dummy
Observations
Cross sections
Number of instruments
Arellano-Bond test for AR(1) p-value
Arellano-Bond test for AR(2) p-value
Hansen test of over-identifying restrictions p-value(7)0.911***(0.259)0.037(0.052)

6066
454
76
0.001
0.452
0.171

6066
454
80
0
0.353
0.4

6066
454
80
0
0.462
0.657

Yes
Yes
6066
454
80
0
0.693
0.843

5941
445
80
0
0.295
0.318

5941
445
80
0
0.297
0.318

5941
445
80
0
0.316
0.217

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

25

Table 3: Regressions of greenfield FDI: common language(1)(2)
COL = 0 COL = 1
0.329**
0.135(0.107)(0.165)0.121-0.01(0.066)(0.012)

GFDIi, j,t−1
GFDIi, j,t−2
Contorls
Year dummy
Observations
Cross sections
Number of instruments
Arellano-Bond test for AR(1) p-value
Arellano-Bond test for AR(2) p-value
Hansen test of over-identifying restrictions p-value(3)(4)
CNL = 0 CNL > 00.426***
0.163(0.132)(0.206)0.093-0.013(0.081)(0.02)
Yes
Yes

4801
352
106
0.004
0.866
0.152

1265
102
76
0.04
0.589
0.708

3571
263
76
0.007
0.519
0.25

2495
191
76
0.027
0.88
0.219

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

26

27
0.192
0.375

0.966

0.003

0.138

0.001

0.397

0.302

0.014

0.392

0.603

0.045

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

Contorls
Year dummy
Observations
Cross sections
Number of instruments
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
forAR(2) p-value
Hansen test of over-identifying
restrictions p-value

GFDIi, j,t−2

GFDIi, j,t−1

(2)
(3)
(4)
Manufacturing
Retail Business service
All controls Sub-set of controls
0.382***
0.349**
0.565**
0.672***(0.105)(127)(0.207)(0.183)-0.022-0.013-0.0140.149(0.06)(0.07)(0.069)(0.163)
Yes
Yes
751
760
1193
1016
55
55
87
75
76
56
76
76

(1)

Table 4: Regressions of greenfield FDI: industry sub-samples

0.262

0.386

0.004

1620
120
76

0.8***(0.167)-0.019(0.12)

Sales, marketing & support(5)Appendix
This appendix provides the followings:• List of economies: Table A1.
• Relationship between variance of greenfield FDI and three proxies for information frictions,
economies with a variance of less than one: Figure A1.
• Detailed proof of the social learning model.
• Descriptive statistics: Table A2 and A3.
• Detailed results: Table A4, A5 and A6.
• Robustness check of using lag 2 to 4 up to the maximum lag as instruments: Table A7, A8
and A9.
• Robustness check of reducing the number of controls in the manufacturing, retail and business sub-samples: Table A10.

28

29

* Lithuania and Slovenia are source economies only.

Algeria (DZ)
Belgium (BE)Colombia (CO)Estonia (EE)
Hungary (HU)
Japan (JP)Malaysia (MY)
Nigeria (NG)Portugal (PT)Singapore (SG)Sri Lanka (LK)UAE (AE)

Economies (abbreviation)
Angola (AO)
Argentina (AR)Australia (AU)Brazil (BR)
Bulgaria (BG)
Canada (CA)
Costa Rica (CR) Croatia (HR)Czech (CZ)
Finland (FI)France (FR)Germany (DE)
India (IN)Indonesia (ID)
Ireland (IE)
Kazakhstan (KZ) Kenya (KE)Kuwait (KW)
Mexico (MX)
Morocco (MA)
Myanmar (MM)
Norway (NO)Panama (PA)Peru (PE)
Qatar (QA)
Romania (RO)Russia (RU)
Slovakia (SK)Slovenia (SI)*South Africa (ZA)
Sweden (SE)Switzerland (CH)Thailand (TH)Ukraine (UA)United Kingdom (GB) United States (US)

Table A1: List of economies

Austria (AT)
Bahrain (BH)
Chile (CL)
China (CN)Denmark (DK)
Egypt (EG)Greece (GR)Hong Kong (HK)
Israel (IL)
Italy (IT)
Latvia (LV)
Lithuania (LT)*
Netherlands (NL) New Zealand (NZ)
Philippines (PH)Poland (PL)
Saudi Arabia (SA) Serbia (RS)South Korea (KR) Spain (ES)Tunisia (TN)Turkey (TR)Vietnam (VN)

30

Figure A1: Relationship between variance of greenfield FDI and information frictions, economies with a variance of less than one

Detailed proof of the social learning model
In this section, we frame the FDI as a social learning game. Each firm makes FDI to match an
unobserved state (the optimal level of FDI), which is a discrete random walk process. Firms areheterogeneous in the sense that the optimal investment level is subject to a normal shock. We showthat FDI is an AR process, and its persistence depends on the variances of signal, of type, and of
the underlying state.
Let θt be the payoff relevant state with the law of motion θt = θt−1 + etθ : etθ ∼ N(0, vθ ). Thereis a sequence of players, n = 1, 2, .... They sequentially make the FDI decision, and player n = t is
active at time t. A signal about the underlying state st ∼ N(θt , vs ) is observed privately by player t.
Investment decisions are publicly observed. For simplicity, we suppose the decision rule is givenby
It = E t (θt ) + at
where at ∼ N(0, va ) is the type heterogeneity of players, E t (·) := E(·|st , ht−1 ) is the expectation
14 Since the type
operator of player t given a private signal and the history of FDI, ht−1 = {Ii }t−1i=1 .
of a firm is unobserved by the other players, the investment decision does not reveal the private
signal completely, although the investment is publicly observed.
We assume that given the history ht−1 , the public belief of state can be summarized as a normal
14 We may think of a represents the cost advantage of firm i (due to better technology or financing ability), and theifirms make investment once only at i = t, with convex cost. All firms are risk neutral, with a discount factor, δ .The
decision rule can be generated by the following optimization problem, with i = t:∞bmax E i { ∑ δ j θ̂t+ j Ii − (const − ai Ii + Ii2 )}
Ii
2
j=1

for θt is a unit root process, for any j > 0, E i θt+ j = E i θt
max
Ii

δ
b
E i {θ̂t }Ii − (const − ai Ii + Ii2 )1−δ2δTake b = 1, and normalize the unit of θt := 1−δδ θ̂t to absorb the constant 1−δ .

ai may also represent the profitability advantage of firm i (difference in rate of return). The decision rule can begenerated by the following optimization problem, with i = t:∞

max E i { ∑ δ j (θ̂t+ j + âi )Ii − (const − b1 Ii +
Ii

j=1

b2 2
I )}
2 i

for θt is a unit root process, for any j > 0, E i θt+ j = E i θt
max
Ii

δ
b2
(E i {θ̂t } + âi )Ii − (const − b1 Ii + Ii2 )1−δ2δTake b1 = 1, and normalize the unit of θt := 1−δδ θ̂t to absorb the constant 1−δ . Also, normalize the unit of âi byδ
defining ai := 1−δ âi − b1 .

31

process, θt |ht−1 ∼ N(pt , vt ). Next, we show that under this setting, θt+1 |ht is also a normal process.
Player t forms the private belief, θt |ht−1 , st by incorporating the signal, st , with the publicbelief, θt |ht−1 . The Bayes rule implies:−1 −1
θt |st , ht−1 ∼ N(γt st + (1 − γt )pt , (v−1s + vt ) )v−1
vs +vtwhere γt = −1 s −1 .
Now, we consider how the public belief on θt+1 are formed after It being publicly observed. It
can be done in two steps: (i) deducing the private information from investment decision of firm t
and update their belief about θt |ht ; (ii) incorporating the law of motion of θ and forming the beliefof θt+1 |ht .
(i) Forming public belief on θt after observing It : With at unobserved, the investment is anormal process, It |st , ht−1 ∼ N(γt st + (1 − γt )pt , vα ). Combining with the signal, st |θt ∼ N(θt , vs ),gives the investment conditional on the state with the signal and the type unobserved.
It |θt , ht−1 ∼ N(γt θt + (1 − γt )pt , γt2 vs + vα )Note that public belief at time t is θt |ht−1 ∼ N(pt , vt ). The Bayes updating implies
θt |It , ht−1 = θt |ht ∼ N(pt+1 , ωt )where ωt = (vt−1 + (γt2 vs + va )−1 )−1 , and pt+1 = ωt vt−1 pt + (1 − ωt vt−1 )It .
θ , the public belief on(ii) Forming θt+1 |ht : With the common knowledge that θt+1 = θt + et+1
θt+1 given the history ht is θt+1 |ht ∼ N(pt+1 , ωt + vθ ). It shows that when the public belief of state
at time t is a normal process, the public belief at time t + 1 is also a normal process. Inductively,
the public belief will always be a normal process.
Thus, given the initial value of θ0 and the public belief θ0 ∼ N(p0 , v0 ) (for consistence, we
impose p0 = θ0 .), the system can be written as:

st = θt + ets(11)

It = γt st + (1 − γt )pt + at

(12)pt+1 = ωt vt−1 pt + (1 − ωt vt−1 )It(13)

θθt+1 = θt + et+1

(14)

vt+1 = (vt−1 + (γt2 vs + va )−1 )−1 + vθ

(15)

32

v−1
vs +vtwhere γt = −1 s −1 , ωt = (vt−1 + (γt2 vs + va )−1 )−1 .
Solving (12) and (13) recursively shows that investment (It ) and the mean of public belief (pt )are AR processes. However, the AR coefficients are time-varying, making the system intractable.
In the following, we simplify the analysis by showing the global convergence of variance of publicbelief. For any vt ∈ R, limi→∞ vt+i = ν, where ν is a constant, we will directly impose the stablepublic belief variance as the initial variance v0 = ν, and show that γt becomes invariant under thiscondition.

Global convergence of variance of public belief
We show that for any initial variance of public belief, the public belief converges to a constantover time. With the definition of γt , equation (15) shows the dynamics of belief variance is
v−1
vt+1 = (vt−1 + (( −1 s −1 )2 vs + va )−1 )−1 + vθ .
vs + vt
The stable belief variance is the fixed point of
v−1
f (ν) = (ν −1 + (( −1 s
)2 vs + va )−1 )−1 + vθ−1
vs + ν
where f is a continuous function on ν ∈ [0, ∞), with f (0) = vθ and limν→∞ f (ν) = vs +va +vθ > 0.
The Brouwer fixed-point theorem guarantees the existence of a fixed point. Note that ∀ν ∈ [0, ∞) :f ′ (ν) > 0, f (0) = vθ > 0, and the existence of the fixed point makes sure the global convergenceand the uniqueness of the fixed point: ∃ν ∈ [vθ , vs + va + vθ ] : f (ν) = ν.

FDI and mean of public belief as ARIMA process
By imposing the fixed point as the initial belief variance: vt = v; γt = γ; ωt = ω , the system(11) - (15) becomes
st = θt + ets(16)

It = γst + (1 − γ)pt + at

(17)

pt+1 = ωv−1 pt + (1 − ωv−1 )It(18)

θθt+1 = θt + et+1

(19)

33

v−1

where v is the fixed point of f (v) = (v−1 + (γ 2 vs + va )−1 )−1 + vθ , and γ = −1 s −1 , ω = (v−1 +vs +v(γ 2 vs + va )−1 )−1 .
The above system yields the following AR process:∞

It = γθt + (1 − γ)(1 − µ) ∑ µ i It−1 + εtI(20)pt = (1 − µ)γθt−1 + (1 − γ(1 − µ))pt−1 + εtp

(21)

i=0

θ
θt+1 = θt + et+1

(22)

s +a
−1
where εtI = γets + at , εtp = (1 − µ)γet−1
t−1 , µ = ωv .

Equation (20) shows that after controlling for θ , FDI is essentially an AR(1) process. It can be
rewritten as:
I
It = γ(θt − µθt−1 ) + (1 − γ(1 − µ))It−1 + εtI − µεt−1(23)where εtI = γets + at , and (1 − γ(1 − µ)) ∈ (0, 1). First differencing (23) yields:∆It = (1 − γ(1 − µ))∆It−1 + εt∆I(24)

θ + γ∆es + ∆a − µγ∆es − µ∆awhere εt∆I = γetθ − γ µet−1
t
t−1 .
t
t−1

Rewriting Equation (21) yields the following error correction process:∆pt = −(1 − µ)γ(pt−1 − θt−1 ) + εtp .
Since −(1 − µ)γ < 0, the mean of public belief adjusts according to its lagged deviation from thetrue state.

Variance of FDI
Since It is nonstationary, we study the variance of the first-differenced FDI. Using the lag
operator L : xt−1 = Lxt , the first-differenced FDI can be expressed as:∆It = (1 − ξ L)−1 {(1 − µL)γetθ + (1 − (1 + µ)L + µL2 )εtI }34

where ξ = 1 − γ(1 − µ). ξ ∈ (0, 1):∞∆It = ∑ (ξ L)i {(1 − µL)γetθ + (1 − (1 + µ)L + µL2 )εtI }i=0

Since all shocks are i.i.d, all the correlation terms are gone. The variance of ∆It is∞!

Var(∆It ) = 1 + ∑ ξ 2i (ξ − µ)2 γ 2 vθ
i=0

∞

!

+ 1 + (ξ − (1 + µ))2 + ∑ ξ 2i (ξ 2 − (1 + µ)ξ + µ)2 Var(εtI )i=0(ξ − µ)2 2
γ vθ
= 1+
1−ξ2


(ξ 2 − (1 + µ)ξ + µ)22+ 1 + (ξ − (1 + µ)) +Var(εtI )21−ξ
where Var(εtI ) = γ 2 vs + va .

35

Table A2: Descriptive statistics: Pooled, between, and within
Variable

Mean

Std. Dev.

Min

Max

Observations

GFDI
Millions USD

Overall
BetweenWithin887.42

1874.02
1362.05
1288.64036.59-7513.72

59005.6
16337.79
52465.25

N = 7264
n = 454
T = 16GDP (Source)
Billions USD

Overall
BetweenWithin4184.035489.635232.421677.6121.85
33-4559.1225278.77
15763.76
15635.12

N = 7264
n = 454
T = 16GDP (Destination)
Billions USD

Overall
BetweenWithin3122.884860.684551.91
1717.2921.85
33-5620.28

25278.77
15763.76
14573.96

N = 7264
n = 454
T = 16

GDPcap (Source)
USD

Overall
BetweenWithin39380.14

14413.22
13227.75
5755.79

2399.824751.311912.08

101386.873810.39
67873.97

N = 7264
n = 454
T = 16

GDPcap (Destination)
USD

Overall
BetweenWithin30316.8220240.41
19447.43
5679.18

1583.322726.75
2848.77

146981.8
122937.758810.65

N = 7264
n = 454
T = 16

Capital (Source)
USD

Overall
BetweenWithin350722.8

120874119085.721407.33

23136.38
38635.62
245823.8695455.8636216.8466554.4

N = 6810
n = 454
T = 15

Capital (Destination)
USD

Overall
BetweenWithin257658.9

161175.3
159970.2
20967.13

2089.728258.21152759.9695455.8636216.8
373490.5

N = 6810
n = 454
T = 15Credit (Source)
Percentage

Overall
BetweenWithin125.16

44.73
42.19
14.59

0.19
24.89
60.93

233.21
190.34188.52N = 7109n = 454
T = 15.66

Credit (Destination)
Percentage

Overall
BetweenWithin

93.89

52.44
50.43
15.16

3.12
10.39
29.66

233.21
190.34157.26

N = 7128n = 454
T = 15.7

Skill (Source)
Index: 1 to 4

Overall
BetweenWithin

3.28

0.44
0.43
0.09

1.83
1.97
2.79

3.97
3.68
4.11

N = 6810
n = 454
T = 1536

Skill (Destination)
Index: 1 to 4

Overall
BetweenWithin

3

0.55
0.53
0.12

1.33
1.41
2.5

3.97
3.68
3.82

N = 6810
n = 454
T = 15

In f (Source)
Percentage

Overall
BetweenWithin

2.08

2.11
1.51
1.48

-2.6
0.26
-11.25

48.7
13.03
37.75

N = 7264
n = 454
T = 16

In f (Destination)
Percentage

Overall
BetweenWithin

3.72

4.09
2.91
2.88

-4.9
0.26
-11.49

98.2
22.51
79.41

N = 7264
n = 454
T = 16

ExrateBetween countries

Overall
BetweenWithin473.492831.842803.15421.750.000560.00072-6339.55

34798.37
30309.51
5256.53

N = 7264
n = 454
T = 16

Tax (Destination)
Percentage

Overall
BetweenWithin

27.88

8.7
8.04
3.27

0
0
12.88

55
55
52.88

N = 7189
n = 454
T = 15.83

VA (Destination)
Index: -2.5 to 2.5

Overall
BetweenWithin

0.42

0.99
0.98
0.12

-2.23
-1.73
-0.43

1.8
1.64
1.33

N = 7264
n = 454
T = 16

CC (Destination)
Index: -2.5 to 2.5

Overall
BetweenWithin

0.65

1.05
1.04
0.14

-1.67
-1.33
0.17

2.47
2.34
1.34

N = 7264
n = 454
T = 16RL (Destination)
Index: -2.5 to 2.5

Overall
BetweenWithin

0.67

0.95
0.94
0.12

-1.74
-1.39
0.17

2.1
1.99
1.17

N = 7264
n = 454
T = 16

FE (Destination)
Index: 0 to 1

Overall
BetweenWithin

0.72

0.21
0.2
0.05

0.09
0.28
0.47

1
0.97
0.93

N = 7120n = 445
T = 16

FACE (Destination)
Index: 0 to 1

Overall
BetweenWithin

0.69

0.2
0.19
0.06

0.11
0.26
0.4

1
0.97
0.89

N = 7120n = 445
T = 16

MB (Destination)
Index: 0 to 1

Overall
BetweenWithin

0.64

0.21
0.21
0.06

0.11
0.11
0.4

1
0.86
0.9

N = 7120n = 445
T = 1637

COL
BinaryOverall
BetweenWithin

0.21

0.41
0.41
0

0
0
0.21

1
1
0.21

N = 7168n = 448
T = 16CNL
Probability

Overall
BetweenWithin

0.08

0.21
0.21
0

0
0
0.08

0.99
0.99
0.08

N = 7168n = 448
T = 16

Note: To avoid extremely small or large values of regression coefficients, we adjust the units of some variables
before running the regressions.

38

Table A3: Descriptive statistics: percentile
Variable
GFDI
GDP (Source)
GDP (Destination)
GDPcap (Source)
GDPcap (Destination)
Capital (Source)
Capital (Destination)
Credit (Source)
Credit (Destination)
Skill (Source)
Skill (Destination)
In f (Source)
In f (Destination)
ExrateTax (Destination)
VA (Destination)
CC(Destination)
RL (Destination)
FE (Destination)
FACE (Destination)
MB (Destination)
COL
CNL

Unit
Millions USD
Billions USD
Billions USD
USD
USD
USD
USD
Percentage
Percentage
Index: 1 to 4
Index: 1 to 4
Percentage
Percentage
Ratio
Percentage
Index: -2.5 to 2.5
Index: -2.5 to 2.5
Index: -2.5 to 2.5
Index: 0 to 1
Index: 0 to 1
Index: 0 to 1
BinaryProbability

N
min
p25
p50
p75
max
7264
0
115.8
320.3
869.6359005.6
7264
21.85
548.872152.89 4112.91 25278.77
7264
21.85
390.221200.43 3094.11 25278.77
7264 2399.82 33203.64 39484.21 47007.67 101386.8
7264 1583.32 14024.96 29113.82 42738.12 146981.8
6810 23136.38 323726.9 360496.1 426617.8 695455.8
6810 2089.72 114338.2 270561.2 365100.7 695455.8
7109
0.19
90.84
122.63162.97233.21
7128
3.12
48.12
89.38
131.53233.21
6810
1.83
3.03
3.42
3.64
3.97
6810
1.33
2.56
3.09
3.47
3.97
7264
-2.6
0.9
1.9
2.7
48.7
7264
-4.9
1.5
2.6
4.6
98.2
7264 0.00056
0.78
1.76
9.53
34798.37
7189
0
21
28
33.5
55
7264
-2.23
-0.17
0.57
1.3
1.8
7264
-1.67
-0.33
0.52
1.7
2.47
7264
-1.74
-0.18
0.64
1.63
2.1
7120
0.09
0.54
0.8
0.9
1
7120
0.11
0.55
0.76
0.86
1
7120
0.11
0.57
0.7
0.79
1
7168
0
0
0
0
1
7168
0
0
0
0.01
0.99

Note: To avoid extremely small or large values of regression coefficients, we adjust the units of some variables
before running the regressions.

39

Table A4: Detailed results: institutional and governance indicators

GFDIi, j,t−1
GFDIi, j,t−2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0.394***(0.118)0.065(0.063)0.35***(0.109)0.052(0.051)-0.145(0.082)0.429***(0.124)0.024(0.041)0.399***(0.12)
0.036(0.035)0.991***(0.265)0.047(0.049)0.968***(0.245)0.047(0.051)0.911***(0.259)0.037(0.052)

GFDIi, j,t−1 ×VA j,t−1
GFDIi, j,t−1 ×CC j,t−1-0.313*(0.13)

GFDIi, j,t−1 × RL j,t−1-0.353**(0.132)

GFDIi, j,t−1 × FE j,t−1-0.943*(0.386)

GFDIi, j,t−1 × FACE j,t−1-0.954**(0.349)

GFDIi, j,t−1 × MB j,t−1-0.985*(0.485)16.558(114.669)

VA j,t−1-20.597(152.152)

CC j,t−1

490.254(266.71)

RL j,t−1

866.912(526.063)

FE j,t−1

973.286*(423.027)

FACE j,t−1
MB j,t−1
GDPi,t−1
GDPj,t−1
GDPcapi,t−1
GDPcap j,t−1
Crediti,t−1
Credit j,t−1
Skilli,t−1
Skill j,t−1
Capitali,t−1
Capital j,t−1
Tax j,t−1
In fi,t−1

3.598*(1.581)3.875*(1.654)0.373(0.676)-0.003(0.46)
0.189(1.351)-3.159*(1.265)-0.877(1.928)3.519(2.109)-0.205(0.827)-1.773(1.003)-10.986(7.468)
9.183

2.981
(1.719)4.259*(1.697)0.161(0.657)0.153(0.483)0.796(1.513)-3.113*(1.302)0.332(2.076)
3.928(2.213)-0.564(0.974)-1.1(1.161)-6.609(6.601)
6.401

3.199
(1.945)3.889*(1.889)-0.619(0.813)0.873(0.495)0.729(1.514)-2.155(1.334)
3.163(2.779)3.893(2.27)-0.527(1.071)-1.263(1.325)-3.745(6.83)
14.46240

4.616*(1.883)4.169*(1.815)-0.523(0.783)0.471(0.55)
0.578(1.724)-4.845**(1.879)2.564(2.806)2.631(2.665)-0.372(1.128)-3.557(1.826)2.261(8.471)
6.445

3.806*(1.619)4.778***(1.472)0.316(0.655)0.561(0.536)-0.004(1.552)-2.615(1.384)-0.243(2.041)-0.178(2.11)-0.053(0.924)-1.101(1.13)-4.569(6.156)
9.619

3.172*(1.575)4.499***(1.416)0.241(0.613)0.478(0.485)0.196(1.525)-2.616*(1.295)-0.31(1.989)0.66(2.021)-0.287(0.944)-1.217(1.275)-3.89(6.339)7.205

128.145(634.082)
3.874*(1.715)4.748**(1.581)0.456(0.705)0.333(0.52)-0.268(1.578)-2.674*(1.355)-0.197(2.197)0.274(2.149)0.031(0.97)
0.222(1.145)-5.284(6.857)8.936

In f j,t−1
Exratei, j,t−1
Year dummy:
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Constant
Observations
Cross sections
Number of instruments
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value(17.882)
2.114(11.122)-7.82(11.465)(18.571)
3.399(10.793)-0.38(11.65)(19.266)-1.465(11.182)-0.256(12.387)(22.083)
4.683(10.585)-10.226(13.535)(19.191)-11.02(12.007)
5.122(10.974)(18.943)-7.696(12.604)
4.991(11.809)(20.213)-12.567(12.242)8.633(14.269)

187.791***(50.77)103.347(55.354)500.972***(76.34)-11.701(92.011)-30.426(72.76)30.107(61.712)-116.544(62.854)1.443(70.276)-45.993(78.548)6.709(93.71)-29.265(99.529)-156.011(106.702)
15.595(104.216)
290.05(747.663)
6066
454
76

187.062***(47.69)119.278*(57.634)
497.31***(74.488)
41.801(97.026)-5.116(74.924)17.884(65.041)-138.593*(65.702)-35.948(74.026)-66.004(77.355)
2.331(95.676)-17.849(99.211)-189.507(100.408)
8.025(108.201)-365.064(849.307)
6066
454
80

169.093***(42.099)151.312*(65.169)
500.415***(81.036)
43.363(115.683)
22.968(94.889)
16.703(94.691)-148.038(81.684)-53.646(82.84)-82.565(93.096)-18.353(107.224)-14.398(124.627)-202.865(118.668)-0.856(133.916)-1250.988(870.017)
6066
454
80

206.204***(43.583)
207.219***(62.645)625.194***(88.075)
188.347(124.619)
183.006(102.485)
171.98(104.634)
13.995(98.877)
128.331(102.944)
103.778(116.232)
151.123(115.449)
159.679(139.436)-1.978(141.379)
229.599(162.2)-441.277(1142.329)
6066
454
80

194.628***(43.693)145.061*(56.804)
518.721***(78.352)
87.572(100.036)
47.864(77.55)45.817(68.179)-123.368(67.634)
6.161(72.82)-38.759(82.911)8.719(100.509)-11.824(104.709)-193.29(116.05)
67.951(118.549)
24.466(781.183)
5941
445
80

189.822***(46.173)
147.949**(55.614)530.397***(78.461)
94.455(95.59)57.607(74.731)50.679(66.133)-101.075(66.373)8.818(73.193)-16.001(84.857)
58.235(106.423)
19.024(112.28)-148.714(122.868)
96.157(121.527)-121.957(726.379)
5941
445
80

186.339***(44.583)137.2*(57.819)503.216***(80.247)
78.734(109.072)
37.48(83.967)
39.551(69.705)-137.5*(69.101)-35.614(78.678)-109.201(92.004)-73.164(107.32)-94.723(112.607)-260.643*(121.074)-52.618(128.959)
174.384(789.813)
5941
445
80

0.001

0

0

0

0

0

0

0.452

0.353

0.462

0.693

0.295

0.297

0.316

0.171

0.4

0.657

0.843

0.318

0.318

0.217

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

41

Table A5: Detailed results: common language

GFDIi, j,t−1
GFDIi, j,t−2
GDPi,t−1
GDPj,t−1
GDPcapi,t−1
GDPcap j,t−1
Crediti,t−1
Credit j,t−1
Skilli,t−1
Skill j,t−1
Capitali,t−1
Capital j,t−1
Tax j,t−1
In fi,t−1
In f j,t−1
Exratei, j,t−1
Year dummy:
2006
2007
2008

(1)
COL = 0(2)
COL = 1

(3)
CNL = 0(4)
CNL > 00.329**(0.107)0.121(0.066)
3.417**(1.304)3.596*(1.48)-0.274(0.807)-0.585(0.499)-0.997(1.272)-2.872*(1.26)
2.274(2.273)1.7(2.372)0.171(0.624)-1.149(0.965)-6.251(7.096)45.463(27.252)-5.18(9.69)-2.638(11.282)

0.135(0.165)-0.01(0.012)
8.165*(3.721)
11.889*(5.75)-0.058(1.692)
1.671(1.487)1.938(3.102)-0.639(3.717)-0.99(4.301)-3.213(4.145)2.554(2.542)-1.463(2.369)7.223(17.423)4.581(37.918)-8.778(16.763)493.915**(176.86)

0.426***(0.132)0.093(0.081)
3.888**(1.51)
2.932(1.653)0.022(1.225)-0.278(0.623)-1.886(1.571)-3.164*(1.511)
2.475(3.504)
3.144(3.259)-0.232(0.765)-1.56(1.262)
2.191(10.525)
32.433(31.368)-3.949(17.412)-6.803(12.802)

0.163(0.206)-0.013(0.02)
5.932**(2.1)
7.33*(3.112)0.509(0.963)0.264(0.565)2.133(2.354)-0.921(2.269)-2.48(2.653)0.671(2.93)
1.833(1.508)-1.985(1.432)-17.987*(9.103)12.323(19.03)-14.406(12.196)307.556(200.25)

177.819**(57.291)
235.917***(56.502)528.735***(89.613)86.425(134.263)
61.442(123.605)
382.352*(155.799)

176.479*(70.063)149.716(80.122)555.634***(112.048)

53.945(80.164)153.948*(72.866)
365.002***(107.205)

42

2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Constant
Observations
Cross sections
Number of instruments
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value-25.352(98.367)
81.184(82.305)
64.361(65.847)-80.744(68.649)
51.57(69.349)
21.852(81.154)81.871(99.553)141.333(108.768)-30.433(109.114)
105.213(106.063)-220.14(787.426)
4801
352
106

114.011(181.891)-161.811(146.9)-75.691(194.223)-218.111(177.678)-274.405(228.189)-379.3(227.208)-257.391(266.152)-215.818(241.499)-403.223(310.832)-297.188(289.648)
133.052(1837.667)
1265
102
76

-59.577(136.521)
8.565(102.82)
86.191(94.417)-111.439(93.796)
74.917(101.184)-4.452(113.163)
47.265(137.623)
68.987(153.398)-113.027(145.378)
59.177(142.785)-756.791(1421.429)
3571
263
76

79.835(129.927)-24.225(118.386)-74.602(102.834)-171.125(103.999)-156.991(116.335)-235.726(123.492)-201.999(132.765)-208.858(153.133)-241.076(161.827)-124.186(170.107)783.016(1055.285)
2495
191
76

0.004

0.04

0.007

0.027

0.866

0.589

0.519

0.88

0.152

0.708

0.25

0.219

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

43

Table A6: Detailed results: industry sub-samples(1)(2)
Manufacturing
All controls
Sub-set of controls
GFDii, j,t−1
GFDii, j,t−2
GDPi,t−1
GDPj,t−1
GDPcapi,t−1
GDPcap j,t−1
Crediti,t−1
Credit j,t−1
Skilli,t−1
Skill j,t−1
Capitali,t−1
Capital j,t−1
Tax j,t−1
In fi,t−1
In f j,t−1
Exratei, j,t−1
Year dummy:
2006
2007
2008
2009
2010
2011
2012
2013

0.382***(0.105)-0.022(0.06)
5.54(5.184)2.067(3.443)16.049(25.282)-8.475(14.417)-9.216(9.507)0.952(3.711)-2.611(6.302)-15.52(10.206)-0.77(3.495)4.738(4.405)-18.118(26.846)-66.816(127.805)-55.064(30.297)-18.512(17.81)438.907(246.305)132.774(439.786)
568.613(397.187)
62.769(295.702)-411.668(385.848)
108.189(351.125)-168.336(328.084)
171.163(492.013)

0.349**(0.127)-0.013(0.07)
4.589(2.555)
6.611(3.666)
10.47(15.677)-3.344(6.625)-5.514(8.755)

1.319(4.22)-12.083(38.872)-88.233(95.531)-75.001(45.512)399.977(226.52)-1.215(202.087)
484.353(272.632)
56.259(356.789)-161.875(390.831)-90.257(301.311)-245.727(262.018)-299.581(379.694)

44

(3)

(4)

(5)

RetailBusiness service

Sales, marketing & support

0.565**(0.207)-0.014(0.069)0.232(0.34)
0.191(0.326)-6.227*(2.618)11.733(6.955)0.329(0.506)-1.353*(0.608)0.566(0.754)-2.18(1.353)-0.127(0.265)-0.679(0.471)
5.962*(2.858)-1.618(6.687)-8.484(6.792)-365.143(595.761)

0.672***(0.183)0.149(0.163)-0.009(0.195)0.044(0.313)
1.531(1.913)-0.617(0.891)-0.062(0.758)-0.388(0.287)0.096(0.535)-0.054(0.403)-0.641(0.457)-0.123(0.183)-0.383(1.324)-16.916*(7.754)-3.393(3.693)-127.453(149.703)

0.8***(0.167)-0.019(0.12)
0.155(0.128)0.063(0.124)-1.515(1.461)
1.242*(0.617)0.174(0.319)-0.028(0.193)0.371(0.317)-0.138(0.186)0.216(0.163)-0.139(0.083)0.729(0.878)2.774(3.873)-4.049(2.424)1.05(2.527)68.258*(34.307)79.508(51.891)
71.167*(31.801)
142.439***(43.701)
89.569*(39.188)
22.542(29.936)88.9*(37.698)
182.277***(35.716)

12.18(18.942)59.193*(28.421)22.111(46.583)-13.707(44.499)-8.354(36.396)-17.453(37.801)45.459(37.389)-52.727(43.378)

2.274(12.374)
41.262*(16.992)
9.079(15.399)-20.82(15.526)
4.543(15.323)4.543(18.778)-14.034(14.984)-15.247(16.785)

2014
2015
2016
2017
2018
Constant
Observations
Cross sections
Number of instruments
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value

114.565(585.013)-627.228(378.224)-174.583(769.648)-813.19(728.551)
66.718(411.356)
6623.767(3578.147)
751
55
76

-310.279(444.188)-806.539(462.966)-738.913(563.767)-1011.97(582.83)-452.668(419.855)
2384.821(2781.79)
760
55
56

60.731(41.093)83.248*(39.609)
98.884*(45.652)87.15(45.39)55.138(47.222)609.45(492.984)
1193
87
76

-11.672(39.809)-53.34**(18.455)-65.617*(29.713)-25.751(22.964)-0.445(26.676)366.031(276.596)
1016
75
76

-7.904(16.308)-15.739(18.111)-16.697(22.759)-5.957(22.143)-10.793(24.886)-124.91(150.404)
1620
120
76

0.001

0.003

0.014

0.045

0.004

0.138

0.192

0.302

0.603

0.386

0.966

0.375

0.397

0.392

0.262

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

45

Table A7: Robustness check: institutional and governance indicators
Instrument lag
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
VA j,t−1 × GFDIi, j,t−1
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
CC j,t−1 × GFDIi, j,t−1
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
RL j,t−1 × GFDIi, j,t−1
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
FE j,t−1 × GFDIi, j,t−1
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
FACE j,t−1 × GFDIi, j,t−1
Arellano-Bond test
for AR(1) p-value

2-4

2-5

2-6

2-7

2-8

2-9

2-10

Proxy for information frictions: VA
144
160
176
0.191
0.181
0.194*(0.098)(0.094)(0.084)
0.063
0.064
0.064
(0.059)(0.057)(0.062)-0.121-0.132-0.137*(0.071)(0.071)(0.069)

2-11

2-12

2-13

2-14

192
0.223**(0.082)0.059(0.063)-0.134(0.07)

208
0.196*(0.081)0.06(0.062)-0.18*(0.072)224
0.188**(0.072)0.064(0.062)-0.174*(0.074)239
0.15*(0.073)0.074(0.064)-0.165*(0.077)80
0.35***(0.109)0.052(0.051)-0.145(0.082)96
0.191(0.123)0.056(0.05)-0.212**(0.077)112
0.146(0.12)
0.052(0.057)-0.226**(0.088)128
0.172(0.105)0.056(0.058)-0.156*(0.075)

0

0.001

0.002

0

0

0

0

0

0

0

0

0.353

0.885

1

0.97

0.966

0.982

0.96

0.808

0.898

0.973

0.765

0.199

0.054

0.07

0.032

0.005

0.001

454

454

454

454

192
0.312***(0.074)0.051(0.062)-0.208*(0.098)208
0.308***(0.069)0.046(0.059)-0.268**(0.086)224
0.302***(0.063)0.051(0.06)-0.257**(0.089)239
0.267***(0.065)0.059(0.061)-0.25**(0.086)

0.4

0.052

0.042

0.076

0.147

454

454

454

454

80
0.429***(0.124)0.024(0.041)-0.313*(0.13)

96
0.352**(0.126)0.027(0.047)-0.344***(0.097)112
0.289*(0.115)0.027(0.053)-0.346***(0.096)128
0.274**(0.099)0.041(0.057)-0.265**(0.096)

454
454
454
Proxy for information frictions: CC
144
160
176
0.289**
0.274**
0.288***(0.095)(0.091)(0.081)
0.048
0.053
0.055
(0.057)(0.056)(0.061)-0.226*-0.219*-0.218*(0.094)(0.096)(0.096)

0

0

0

0

0

0

0

0

0

0

0

0.462

0.758

0.974

0.993

0.938

0.964

0.996

0.841

0.928

0.976

0.806

0.657

0.154

0.157

0.172

0.14

0.208

0.034

0.025

0.02

0.001

0

454

454

454

454

454

454

454

454

80
0.399***(0.12)
0.036(0.035)-0.353**(0.132)96
0.308*(0.121)
0.032(0.044)-0.386***(0.106)112
0.253*(0.115)0.034(0.054)-0.349**(0.114)1280.241*(0.099)0.047(0.056)-0.271**(0.101)

454
454
454
Proxy for information frictions: RL
144
160
176
0.247**
0.227*
0.245**(0.096)(0.09)(0.084)
0.054
0.061
0.062
(0.057)(0.057)(0.061)-0.223*-0.207*-0.194*(0.097)(0.095)(0.096)192
0.277***(0.078)0.06(0.062)-0.187*(0.095)208
0.28***(0.072)0.055(0.061)-0.254**(0.092)224
0.274***(0.067)0.059(0.062)-0.24*(0.097)239
0.249***(0.069)0.067(0.064)-0.239**(0.092)

0

0

0

0

0

0

0

0

0

0

0

0.693

0.964

0.811

0.785

0.844

0.725

0.809

0.975

0.895

0.854

0.672

0.843

0.286

0.093

0.148

0.132

0.18

0.044

0.042

0.027

0.005

0.004

454

454

454

454

454

454

454

454

80
0.991***(0.265)0.047(0.049)-0.943*(0.386)96
1.009***(0.27)
0.06(0.052)-1.22**(0.392)112
0.957***(0.297)0.056(0.055)-1.162**(0.442)128
0.734***(0.231)0.064(0.057)-0.816*(0.347)

454
454
454
Proxy for information frictions: FE
144
160
176
0.641**
0.607**
0.637***(0.205)(0.201)(0.194)
0.068
0.072
0.072
(0.059)(0.057)(0.063)-0.678*-0.624*-0.637*(0.308)(0.305)(0.296)192
0.655***(0.19)
0.07(0.063)-0.617*(0.296)208
0.779***(0.204)0.072(0.063)-0.843**(0.328)224
0.743***(0.199)0.075(0.062)-0.817*(0.32)

239
0.633**(0.2)
0.082(0.065)-0.685*(0.323)

0

0.001

0.001

0

0

0

0

0

0

0

0

0.295

0.833

0.855

0.924

0.977

0.983

0.931

0.789

0.882

0.986

0.831

0.318

0.065

0.09

0.154

0.222

0.268

0.09

0.059

0.02

0.005

0.004

445

445

445

445

445

445

445

445

80
0.968***(0.245)0.047(0.051)-0.954**(0.349)960.945***(0.258)0.063(0.051)-1.165**(0.393)112
0.866**(0.28)
0.061(0.055)-1.058*(0.429)128
0.731***(0.225)0.065(0.055)-0.835*(0.34)

192
0.654***(0.184)0.07(0.062)-0.614*(0.284)208
0.76***(0.196)0.074(0.062)-0.814**(0.314)224
0.726***(0.193)0.076(0.061)-0.783*(0.309)239
0.624***(0.195)0.083(0.064)-0.662*(0.311)

0

0.001

0.001

0

0

0

0

0

445
445
445
Proxy for information frictions: FACE
144
160
176
0.668***
0.663***
0.682***(0.198)(0.204)(0.19)
0.065
0.071
0.072
(0.056)(0.055)(0.061)-0.738*-0.733*-0.731*(0.299)(0.315)(0.297)
0

46

0

0

Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
MB j,t−1 × GFDIi, j,t−1
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections

0.297

0.825

0.853

0.877

0.891

0.952

0.888

0.714

0.794

0.867

0.942

0.318

0.066

0.084

0.139

0.238

0.267

0.095

0.043

0.022

0.007

0.005

445

445

445

445

445

445

445

445

80
0.911***(0.259)0.037(0.052)-0.985*(0.485)960.916***(0.245)0.048(0.053)-1.318**(0.448)112
0.96***(0.259)0.041(0.055)-1.469**(0.501)128
0.769***(0.204)0.05(0.055)-1.074**(0.389)192
0.563***(0.17)
0.066(0.065)-0.526(0.296)208
0.6***(0.164)0.07(0.065)-0.613*(0.301)224
0.554***(0.166)0.075(0.064)-0.564(0.316)239
0.472**(0.158)0.083(0.066)-0.476(0.301)

0

0.004

0.005

0.001

0

0

0

0

0

0

0

0.316

0.841

0.904

0.898

0.938

0.938

0.864

0.712

0.783

0.89

0.888

0.217

0.017

0.017

0.045

0.106

0.121

0.036

0.029

0.007

0.001

0.002

445

445

445

445

445

445

445

445

445

445

445

445
445
445
Proxy for information frictions: MB
144
160
176
0.618***
0.568**
0.569***(0.182)(0.182)(0.171)
0.059
0.062
0.066
(0.061)(0.06)(0.066)-0.771*-0.664*-0.613*(0.329)(0.314)(0.289)Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

47

Table A8: Robustness check: common language
Instrument lag
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections

2-4
76
0.419**(0.132)0.097(0.081)91
106
0.332** 0.329**(0.111) (0.107)
0.126
0.121
(0.065) (0.066)

2-8
2-9
2-10
2-11
Proxy for information frictions: COL = 0
121
136
151
166
181
0.325** 0.323** 0.325** 0.344** 0.374***(0.108) (0.108) (0.115) (0.119)(0.115)
0.124
0.122
0.125
0.122
0.124
(0.068)(0.07)(0.075) (0.079)(0.086)

0.005

0.005

0.004

0.004

0.004

0.005

0.006

0.006

0.005

0.006

0.006

0.532

0.897

0.866

0.903

0.898

0.919

0.853

0.8

0.79

0.834

0.963

0.052

0.08

0.152

0.156

0.111

0.092

0.063

0.016

0.022

0.009

0.003

352

352

352

352

352

352

76
0.135
(0.165)-0.01(0.012)91
0.145(0.112)-0.014(0.013)106
0.14(0.11)-0.015(0.013)

352
352
352
352
352
Proxy for information frictions: COL = 1
121
136
151
166
181
0.167
0.146
0.138
0.156
0.153
(0.115) (0.104) (0.109) (0.105)(0.107)-0.016-0.017-0.013-0.014-0.016(0.012) (0.011) (0.012) (0.012)(0.011)196
0.155(0.105)-0.018(0.011)

211
0.147
(0.1)
-0.021(0.012)

225
0.077(0.09)-0.018(0.011)

0.04

0.011

0.012

0.01

0.009

0.011

0.008

0.008

0.008

0.008

0.008

0.589

0.567

0.564

0.655

0.588

0.538

0.61

0.619

0.637

0.632

0.38

0.708

0.469

0.511

0.898

0.994

0.999

1

1

1

1

1

102

102

102

102

102

102

91
106
0.366** 0.364**(0.117) (0.118)
0.112
0.11
(0.071) (0.075)

102
102
102
102
102
Proxy for information frictions: CNL = 0
121
136
151
166
181
0.337** 0.335** 0.326** 0.332** 0.357***(0.11)(0.11)(0.118) (0.117)(0.11)
0.125
0.119
0.119
0.128
0.132
(0.077) (0.079) (0.083) (0.082)(0.086)196
0.354***(0.111)0.13(0.085)211
0.339**(0.109)0.134(0.086)225
0.331**(0.111)0.135(0.085)

0.007

0.007

0.006

0.007

0.006

0.007

0.008

0.008

0.007

0.008

0.007

0.519

0.738

0.73

0.914

0.882

0.911

0.955

0.92

0.915

0.981

0.991

0.25

0.26

0.267

0.153

0.081

0.073

0.057

0.038

0.074

0.035

0.027

263

263

263

263

263

263

76
0.163
(0.206)-0.013(0.02)

91
0.225(0.121)-0.019(0.018)106
0.18(0.093)-0.017(0.018)

263
263
263
263
263
Proxy for information frictions: CNL > 0
121
136
151
166
181
0.196
0.166
0.162
0.153
0.151
(0.102) (0.089) (0.085) (0.083)(0.083)-0.012-0.012-0.01-0.01-0.009(0.016) (0.015) (0.015) (0.015)(0.014)196
0.141(0.08)-0.009(0.014)211
0.14(0.082)-0.01(0.014)225
0.068(0.076)-0.007(0.014)

0.027

0

0

0

0

0

0

0

0

0

0

0.88

0.939

0.918

0.944

0.833

0.801

0.756

0.741

0.706

0.71

0.411

0.219

0.1

0.008

0.003

0.012

0.018

0.036

0.06

0.2

0.568

0.834

191

191

191

191

191

191

191

191

191

191

191

76
0.426***(0.132)0.093(0.081)

2-5

2-6

2-7

2-12

2-13

2-14

196
0.368***(0.112)0.121(0.083)211
0.357***(0.112)0.123(0.084)225
0.333**(0.11)
0.134(0.081)

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

48

Table A9: Robustness check: industry sub-samples
Instrument lag
Number of instruments
GFDI {i,j,t-1}
GFDI {i,j,t-2}
Arellano-Bond test for AR(1) in first differences
Arellano-Bond test for AR(2) in first differences
Hansen test of over-identifying restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections

2-4

2-5

2-6

56
0.349**(0.127)-0.013(0.07)
0.003
0.192
0.375
55

66
0.336*(0.133)0.003(0.055)
0.002
0.188
0.775
55

76
0.3*
(0.136)-0.008(0.062)
0.003
0.221
0.981
55

2-7

2-11

2-12

2-13

2-14

86
0.292*(0.127)-0.008(0.068)
0.002
0.178
0.995
55

76
0.565**(0.207)-0.014(0.069)91
0.428**(0.15)
0.061(0.065)106
0.398**(0.141)0.07(0.065)

2-8
2-9
2-10
Manufacturing (sub-set of controls)
96
106
116
0.253*
0.235
0.214(0.117)(0.132)(0.146)
0.004
0.003
0.013
(0.054)(0.056)(0.062)
0.002
0.004
0.007
0.255
0.371
0.413
1
1
1
55
55
55
Retail
121
136
151
166
0.433**
0.38**
0.403**
0.428**(0.143)(0.143)(0.133)(0.137)
0.044
0.071
0.039
0.039
(0.059)(0.057)(0.048)(0.048)126
0.27(0.152)-0.01(0.067)
0.007
0.298
1
55

136
0.31*
(0.149)-0.025(0.061)
0.002
0.203
1
55

146
0.206
(0.13)
0.011(0.056)
0.004
0.45
1
55

155
0.188
(0.11)
0.032(0.046)
0.005
0.564
1
55

181
0.388**(0.132)0.056(0.052)196
0.381**(0.129)0.046(0.052)211
0.377**(0.121)
0.039(0.049)225
0.384**(0.137)0.053(0.052)

0.014

0.014

0.012

0.008

0.009

0.006

0.007

0.01

0.009

0.006

0.009

0.302

0.718

0.813

0.592

0.824

0.565

0.538

0.696

0.633

0.584

0.689

0.397

0.537

0.925

0.995

0.999

87

87

87

87

87

1

1

76
0.672***(0.183)0.149(0.163)91
0.69***(0.168)0.119(0.15)

106
0.663***(0.117)0.126(0.133)121
0.63***(0.072)0.13(0.105)

0.045

0.028

0.015

0.011

0.01

0.007

0.603

0.491

0.468

0.456

0.383

0.392

0.954

0.99

0.999

1

75

75

75

75

76
0.8***(0.167)-0.019(0.12)

91
0.806***(0.153)-0.018(0.117)1060.705***(0.148)0.001(0.131)121
0.667***(0.125)0.014(0.121)

0.004

0.003

0.004

0.003

0.003

0.004

0.386

0.377

0.486

0.513

0.487

0.262

0.228

0.255

0.505

120

120

120

120

1

1

1

1

87

87

87

87

181
0.516***(0.07)
0.155(0.104)196
0.437***
(0.1)
0.185
(0.102)211
0.454***(0.089)0.177(0.099)225
0.444***(0.087)
0.198*(0.089)

0.007

0.007

0.011

0.007

0.006

0.305

0.415

0.595

0.821

0.744

0.84

1

1

87
87
Business service
136
151
166
0.666*** 0.69***
0.6***(0.076)(0.075)(0.07)
0.109
0.085
0.116
(0.106)(0.103)(0.102)

1

1

1

1

75

75

75

75

181
0.57***(0.102)0.047(0.12)

196
0.554***(0.098)0.051(0.116)211
0.552***(0.099)0.055(0.115)225
0.543***(0.099)0.059(0.115)

0.003

0.002

0.002

0.002

0.002

0.593

0.602

0.677

0.699

0.714

0.738

0.64

0.867

0.988

0.999

1

1

1

120

120

120

120

120

120

120

75
75
75
Sales, marketing & support
136
151
166
0.681*** 0.621*** 0.613***(0.135)(0.128)(0.121)
0
0.025
0.031
(0.132)(0.13)(0.125)

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.

49

Table A10: Robustness check: manufacturing, retail and business service sub-samples, reducing
the number of controls
Instrument lag
Number of controls

14

13

12

2-4
11
10
Manufacturinga

Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections

9

8

7

56
0.349**(0.127)-0.013(0.07)

52
0.337*(0.136)-0.013(0.07)

48
0.268(0.152)-0.004(0.068)

0.003

0.004

0.006

0.192

0.213

0.293

0.375

0.318

0.207

55

55

55

RetailNumber of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections
Number of instruments
GFDIi, j,t−1
GFDIi, j,t−2
Arellano-Bond test
for AR(1) p-value
Arellano-Bond test
for AR(2) p-value
Hansen test of over-identifying
restrictions p-value
Cross sections

76
0.565**(0.207)-0.014(0.069)72
0.521*(0.231)-0.01(0.068)68
0.544*(0.24)-0.003(0.066)64
0.519(0.27)
0(0.074)60
0.558*(0.264)-0.016(0.077)56
0.546*(0.279)-0.024(0.081)52
0.603(0.316)-0.039(0.097)48
0.626(0.323)-0.049
(0.1)

0.014

0.02

0.018

0.032

0.021

0.03

0.039

0.037

0.302

0.328

0.324

0.4

0.318

0.334

0.339

0.313

0.397

0.389

0.458

0.345

0.239

0.281

0.263

0.171

87

87

87

87

87

87

76
0.672***(0.183)0.149(0.163)72
0.72***(0.201)0.115(0.18)

68
0.679**(0.226)0.129(0.202)

87
87
Business service
6460
0.691** 0.714***(0.236)(0.178)0.118
0.119(0.205)(0.168)

0.045

0.047

0.063

0.064

0.033

0.028

0.035

0.035

0.603

0.526

0.592

0.568

0.497

0.476

0.513

0.5

0.392

0.394

0.251

0.264

0.292

0.382

0.724

0.662

75

75

75

75

75

75

75

75

56
0.719***(0.167)0.118(0.156)52
48
0.705*** 0.7***(0.183) (0.186)
0.124
0.119
(0.16)(0.157)

Note: *, **, and *** indicate 5%, 1%, and 0.1% level of significance respectively. Standard errors in parentheses.
a The manufacturing sub-sample starts from 9 controls because the number of controls has been reduced to 9 in Table 4.

50