metadata: authors: 0: author: Wagle,Swarnim
count: World
docna: 0: docna: Investing across borders with heterogeneous
            firms : do FDI-specific regulations matter?
docty: Policy Research Working Paper
subtopic: Judicial System Reform,National Governance,Government Policies,Trade Policy,International Trade and Trade Rules,Trade and Services,Youth and Governance,Governance Indicators
teratopic: Private Sector Development,Macroeconomics and Economic Growth,International Economics and Trade,Finance and Financial Sector Development
abstracts: cdata!: This paper revisits the institutional
            determinants of foreign direct investment (FDI) using a
            comprehensive new data set on the regulations that govern
            FDI in more than 80 countries. It exploits the presence of
            confirmed zero investment flows between countries to
            estimate productivity cut-offs of firms that invest abroad
            profitably. This approach corrects likely biases arising
            from firm heterogeneity and country selection in a
            theoretically derived gravity-type model. The analysis finds
            inward FDI to be highly responsive to cross-country
            variation in specific institutional provisions, such as
            arbitration of disputes and legal procedures to establish
            foreign subsidiaries. The importance of FDI-specific
            provisions stands out even after controlling for the general
            quality of institutions. Statutory openness to FDI, however,
            has no association with actual inflow of investment. These
            results are found to be robust to different specifications.
repnme: repnme: Investing across borders with heterogeneous
            firms : do FDI-specific regulations matter?
display_title: Investing across borders with
            heterogeneous firms : do FDI-specific regulations matter
listing_relative_url: /research/2011/12/15547834/investing-across-borders-heterogeneous-firms-fdi-specific-regulations-matter
topicv3: Justice m1326106 840,Trade m1326924 698,Trade in Services m1326928 780,Governance Indicators m1327488 834,Trade and FDI m1504264 2556
publishtoextweb_dt: 2011-12-01T00:00:00Z
historic_topic: Private Sector Development,Macroeconomics and Economic Growth,International Economics and Trade,Finance and Financial Sector Development
no_of_pages: 89
author_keys: 0: author_key: 539927
model_extractions: WPS5914 Policy Research Working Paper 5914 Investing across Borders with Heterogeneous Firms Do FDI-specific Regulations Matter?

_Swarnim Waglé_ The World Bank Poverty Reduction and Economic Management Network International Trade Department December 2011
Policy Research Working Paper 5914

# **Abstract**

This paper revisits the institutional determinants of foreign direct investment (FDI) using a comprehensive new data set on the regulations that govern FDI in more than 80 countries. It exploits the presence of confirmed zero investment flows between countries to estimate productivity cut-offs of firms that invest abroad profitably. This approach corrects likely biases arising from firm heterogeneity and country selection in a theoretically derived gravity-type model. The analysis finds inward FDI to be highly responsive to crosscountry variation in specific institutional provisions, such as arbitration of disputes and legal procedures to establish foreign subsidiaries. The importance of FDIspecific provisions stands out even after controlling for the general quality of institutions. Statutory openness to FDI, however, has no association with actual inflow of investment. These results are found to be robust to different specifications.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at swarnim@post.harvard.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# _I N V E S T I N G A C R O S S B O R D E R S W I T H H E T E R O G E N E O U S F I R M S : D O F D I - S P E C I F I C R E G U L AT I O N S M AT T E R ?_

# S WA R N I M WA G L É<sup>1</sup>

Keywords: foreign direct investment, institutions, openness, firm heterogeneity JEL Classification: F21, F23, O24

> 1 Consultant, The World Bank. The author was a member of the team that prepared the World Bank Group report, _Investing Across Borders (IAB) 2010: Indicators of foreign direct investment regulation in 87 economies_ . I am grateful to Prema-chandra Athukorala, Olivier Cadot, Ivan Cherkashin, Bernard Hoekman, Yusaku Horiuchi, Scott McCracken, Biswo Poudel, José Guilherme Reis, Martin Richardson and Uttam Sharma for helpful comments. I thank colleagues in the IAB project, especially Peter Kusek, Kusi Hornberger and Antonia Menezes for their feedback.
# C O N T E N T S

|Table o|f Contents|ii|
|---|---|---|
|List of|Figures|iv|
|List of|Tables|iv|
|Acrony|ms|v|
|1<br>inv|esting across borders: do fdi-specific regulations mat-||
|ter|?|1|
|1.1|Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|1|
|1.2|Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|4|
|1.3|Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|8|
|1.4|Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|16|
||1.4.1<br>Investing Across Sectors<br>. . . . . . . . . . . . . . . . . . . . .|19|
||1.4.2<br>Arbitrating Commercial Disputes<br>. . . . . . . . . . . . . . . .|20|
||1.4.3<br>Starting a Foreign Business . . . . . . . . . . . . . . . . . . . .|22|
||1.4.4<br>Bilateral Investment Treaty<br>. . . . . . . . . . . . . . . . . . . .|23|
||1.4.5<br>Gravity and Other Variables<br>. . . . . . . . . . . . . . . . . . .|23|
|1.5|Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|24|
|1.6|Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|27|
||1.6.1<br>Country Selection Bias<br>. . . . . . . . . . . . . . . . . . . . . .|34|
||1.6.2<br>Firm Selection Bias<br>. . . . . . . . . . . . . . . . . . . . . . . .|38|
||1.6.3<br>Endogeneity Bias . . . . . . . . . . . . . . . . . . . . . . . . . .|39|
|1.7|Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|41|
|1.8|Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|43|
|App|endix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|45|
|1.A|Deriving the Gravity Equation for FDI with Heterogeneous Firms<br>.|45|
|1.B|Firm Productivity, Exports, and Horizontal FDI<br>. . . . . . . . . . . .|51|
|1.C|Additional Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|58|

iii
# L I S T O F F I G U R E S

|Figure1|FDI Infow into Developing Countries<br>. . . . . . . . . .|. . .<br>2|
|---|---|---|
|Figure2|Assets and Sales by US Affliates,2008<br>. . . . . . . . . .|. . .<br>9|
|Figure3|Illustration of the Impact of Restricting FDI<br>. . . . . . .|. . .<br>14|
|Figure4|Randomness of Country Samples<br>. . . . . . . . . . . . .|. . .<br>18|
|Figure5|Quality of Institutions and Arbitration Regimes . . . . .|. . .<br>21|
|Figure6|Correcting for Country and Firm Selection Biases<br>. . .|. . .<br>25|
|Figure7|FDI and Statutory Openness<br>. . . . . . . . . . . . . . . .|. . .<br>31|
|Figure8|Reported and Unreported FDI Data . . . . . . . . . . . .|. . .<br>35|
|Figure9|Sorting of Firms by Productivity . . . . . . . . . . . . . .|. . .<br>53|

# L I S T O F TA B L E S

|Table1|Main Regressions: FDI Stock in2007-2008<br>. . . . . . . . . . .|29|
|---|---|---|
|Table2|Regressions: OECD Source Countries Excluded . . . . . . . .|30|
|Table3|Addressing the Zero Problem<br>. . . . . . . . . . . . . . . . . .|58|
|Table4|General Quality of Institutions (Graft)<br>. . . . . . . . . . . . .|59|
|Table5|General Quality of Institutions (Rule of Law)<br>. . . . . . . . .|60|
|Table6|General Quality of Institutions (Regulatory Quality)<br>. . . . .|61|
|Table7|General Quality of Institutions (Government Effectiveness)<br>.|62|
|Table8|General Quality of Institutions (Political Stability)<br>. . . . . .|63|
|Table9|Dummy for Resource Rich Countries . . . . . . . . . . . . . .|64|
|Table10|Regressions: FDI Stock in2006 . . . . . . . . . . . . . . . . . .|65|
|Table11|Regressions: FDI Stock in2007 . . . . . . . . . . . . . . . . . .|66|
|Table12|Regressions: FDI Stock in2008 . . . . . . . . . . . . . . . . . .|67|
|Table13|Description and Source of Variables . . . . . . . . . . . . . . .|68|
|Table14|Summary Statistics of Variables<br>. . . . . . . . . . . . . . . . .|70|
|Table15|Share of IAB Countries,2007-08<br>. . . . . . . . . . . . . . . . .|71|
|Table16|Lists of Countries<br>. . . . . . . . . . . . . . . . . . . . . . . . .|72|
|Table17|Correlation Among Explanatory Variables . . . . . . . . . . .|73|
|Table18|Correlation Among WGI Variables<br>. . . . . . . . . . . . . . .|74|
|Table19|Construction of the FDI Openness Variable<br>. . . . . . . . . .|75|
|Table20|Construction of the FDI Arbitration Variable<br>. . . . . . . . .|76|
|Table21|Construction of the FDI Procedures Variable<br>. . . . . . . . .|77|
|Table22|Composite Country Scores of Selected FDI Regulation . . . .|78|

iv
# A C R O N Y M S

ASEAN Association of Southeast Asian Nations

- BIT Bilateral Investment Treaty

BRIC Brazil, Russia, India, and China

- CES Constant Elasticity of Substitution

COMTRADE Commodity Trade Statistics Database DWH Durbin-Wu-Hausman

- FDI Foreign Direct Investment

- GDP Gross Domestic Product

- ICRG International Country Risk Guide

- IAB Investing Across Borders

- NLS Non-linear Least Squares

- OLI Ownership-Location-Internalization

OLS Ordinary Least Squares

- PCA Principal Components Analysis

PPML Poisson Pseudo Maximum Likelihood

- US United States

- WDI World Development Indicators

WGI World Governance Indicators v
**1**

# I N V E S T I N G A C R O S S B O R D E R S : D O F D I - S P E C I F I C R E G U L AT I O N S M AT T E R ?

“Happy families are all alike; every unhappy family is unhappy in its own way.” – Lev Nikolayevich Tolstoy, _Anna Karenina_ , 1878

# 1.1 introduction

Foreign Direct Investment (FDI) remains one of the most important forms of crossborder capital flow into developing countries: in 2009, FDI inflow amounted to more than US$510 billion, exceeding inward remittance (US$307 billion) and development aid (US$91 billion).<sup>1</sup> As shown in Figure 1, however, just nine countries have accounted for about 60 percent of FDI inflow into developing countries over the past decade.<sup>2</sup> In an era when almost all countries in the world welcome FDI, allowing an average foreign equity ownership of 90 percent across all sectors (Table 22), this cross-country asymmetry deserves explanation that is beyond the obvious such as countries’ sizes and growth prospects.

In this paper, I focus on the policy and institutional determinants of FDI using a new cross-country data set, Investing Across Borders (IAB) 2010 _,_ drawn from World Bank Group (2010). The data set consists of indicators of FDI regulation that

> 1 The figures on FDI, remittances, and development aid are from UNCTAD (2011), World Bank (2010), and World Bank (2011), respectively.

> 2 With the onset of the global financial crisis in 2007, FDI inflow into developed countries fell from a peak of US$1.31 trillion in 2007 to US$602 billion in 2009, whereas the more modest fall in developing countries occurred with a lag of one year, from a peak of US$658 billion in 2008 to US$511 billion in 2009. FDI flow increased rapidly from 1990 onward reaching the first peak of US$1.4 trillion in 2000. There was a dip in 2003, which is attributed to the drop in the share prices of high-technology companies (Helpman 2011). According to UNCTAD (2011), total FDI stock globally stands at a record US$19.1 trillion as of 2010.

1
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1.1 introduction

3 is the problem of firm heterogeneity. The two biases are linked when zero flows are caused by the fixed cost of investing abroad, and only the more productive I build on the insight of the new trade literature that firms are heterogenous _within_ industries in terms of productivity, size, use of inputs, and wages.<sup>4</sup> This translates into distinct decisions by firms (that co-exist within a narrowly-defined industry) on whether or not to export, or undertake FDI, or just serve the domestic market (Greenaway & Kneller 2007; World Trade Organization 2008; Helpman 2011). When profits are a function of varying productivity and differing fixed costs, Helpman et al. (2004) show that there is a natural sorting of firms, with the most productive self-selecting to undertake FDI. The next tier serves the foreign market through export, and the least productive serve the domestic market only (Appendix B).<sup>5</sup> The earlier generation of “new” trade models that integrated economies of scale and monopolistic competition was a breakthrough in understanding a new source of comparative advantage (Helpman 2011). However, they addressed neither heterogeneity nor country selection. In particular, the assumption of symmetry in firm size and productivity leading to a prediction that all firms export to all countries is not supported by evidence at the firm level.<sup>6</sup>

- 4 The seminal paper of this new literature is Melitz (2003). He explains why only a fraction of firms export and why exporters are larger and more productive than non-exporters. Note, though, in the Melitz model, all firms pay the same wage. This model has generated a vast empirical literature on firm-level approach to international trade. It has also been applied to study the quantitative effects of simulated trade policy reforms (for a recent work, see Balistrery, Hillberry and Rutherford, 2011).

- 5 This is supported by data. Helpman et al. (2004) show for US manufacturing firms across 52 sectors and 38 countries that multinational enterprises had 15 percent more labor productivity than exporters in 1994; exporters were 39 percent more productive than non-exporters. Girma et al. (2004), Girma & Pisu (2005), and Arnold & Hussinger (2005) all find significant productivity differences between firms that invest abroad and those that do not. Chen & Moore (2010) find in the case of French multinational firms that those with low productivity are less likely to invest in host countries with a small market size, high production costs, or low trade costs.

- 6 The new trade models of the 1980s successfully explained the phenomenon of intra-industry trade as caused by product differentiation on the part of firms operating under economies of scale. However, firms were assumed to be symmetric. All of them traded, but only the volume depended on trade costs. In other words, trade costs affected only the intensive margin (the volume of export per firm), not the decision of whether to export in the first place.
4 investing across borders: do fdi-specific regulations matter?

The paper proceeds as follows. Section 2 describes the relevant literature on the determinants of FDI, highlighting the institutional drivers on which the paper builds. Section 3 sketches the theoretical derivation of a gravity-like model for FDI and its empirical extension. Section 4 introduces the data. Section 5 explains the econometric method used to incorporate firm heterogeneity and redress country selection bias. Section 6 shows the main results by comparing benchmark estimates with those obtained after correcting for biases. Section 7 uses alternative dependent and explanatory variables to check for robustness of results. The final section concludes.

# 1.2 related literature

Three sets of literature address why and where firms choose to serve foreign markets by setting up foreign subsidiaries, and not through export or licensing arrangements. The first (and early) batch of literature emphasized that firm **s** with ownership advantages have an incentive to become multi-national as they seek to internalize their proprietary assets (technology, brand, distributional efficiency) while exploiting location-specific advantages such as market size or access to factors of production. This is the Ownership-Location-Internalization (OLI) paradigm (Dunning 1977). OLI is seen as a “big-tent” paradigm as it has evolved since 1973 (when it was first introduced) to cover new ideas and practices in international business, including joint-ventures (alliance capitalism) and the internet.

Helpman (2011), however, views the OLI paradigm as too broad for the construction of a theory with sharp predictions. He highlights a more focused study of Internalization through three different lenses: the first is the transaction cost economics of firm boundaries; the second is the managerial incentives analysis of internalization; and the third is the property rights approach that builds on the theory of incomplete contracts. Firm-specific intangible assets are subject to market failures (such as information asymmetry) and because full rents cannot be appro-
1.2 related literature 5

priated on assets through arrangements with third parties, firms internalize the market transaction by establishing their own subsidiaries.<sup>7</sup> The second set of literature attempts to account for the long-term determinants of FDI in a general equilibrium framework. Helpman (1984) proposed the vertical model of FDI where a firm fragments production of differentiated final goods at locations that are abundant in factors used intensively in a specific phase of production. The headquarters specialize in research and development, and production occurs at locations with competitive factor costs. This model predicts that FDI occurs between countries that are differently endowed.

Markusen (1984) proposed a horizontal model where firm-level scale economies drive FDI, which explains a large share of FDI across countries with similar factor endowments. Firms produce the same product at multiple locations with the aim of serving local markets directly rather than through exports. Brainard (1997) finds that such FDI, relative to exports, is increasing with higher trade costs, decreasing with investment barriers, and decreasing with scale economies at the plant level.<sup>8</sup> Although the typology of FDI as either horizontal (market-seeking) or vertical (efficiency-seeking) is neat, most multinational firms today combine vertical and horizontal models of FDI. Markusen (1997) calls this a knowledge-capital model, in which activities are split across geography based on differing skill intensities. At the same time there are multiple production units of the same good taking advantage of non-rivalrous intra-firm assets.<sup>9</sup> Yeaple (2003) presents a model of why

> 7 Within subsidiaries, Lerner & Schoar (2005) find that investors in countries with weaker legal provisions for complex contract enforcement are more likely to insist on majority ownership and control of the board, even if such investments have lower valuation and returns. They contrast common and civil legal regimes in the degree to which complex contracts on cash flow and control can be assigned to different parties. Common law allows complex contract contingencies that allow investors to shift control rights depending on performance. Under civil law, control often has to be exercised through majority ownership. Lerner and Shoar (2005) show that this leads investors to opt for convertible preferred stock in common law countries, and common stock or debt in civil law countries.

- 8 In Brainard (1997), _all_ firms make the same choices of either exporting or undertaking FDI. Contrary to the framework of Helpman et al. (2004) exports and FDI do not co-exist in the same industry.

- 9 The vertical model suggests that trade and FDI are complements; the horizontal model suggests they are substitutes; and, loosely, the knowledge-capital model suggests that trade and FDI tend to be substitutes for similar countries and complements for those with different factor proportion.
6 investing across borders: do fdi-specific regulations matter?

firms may choose “complex integration” of both horizontal and vertical motivations for FDI.

The third set of literature takes a partial equilibrium approach by looking at exogenous and policy factors that affect the magnitude of FDI, not whether FDI takes place in the first place or not. It explores the role of exchange rates, trade protection, taxes, agglomeration, and the quality of institutions, among others, as driving the magnitude of FDI (Blonigen 2005).

The empirical part of this paper, which is in the partial equilibrium tradition, focuses on the institutional determinants of FDI. I am mainly concerned with poor economic institutions that constrain human behavior by distorting incentives. This includes weak rule of law, limits on private ownership, expropriation risks, lack of enforcement of contracts, poor provision of public goods, over-regulation, and high costs of doing business whose cumulative effect is to deter entrepreneurship. Poor institutions also contribute to an indifferent quality of public goods that discourage investment, domestic or foreign. The challenge in this literature has been to find appropriate measures of the quality of institutions.<sup>10</sup> One of the first papers to explore the effects of policy and institutional quality on FDI is Wheeler & Mody (1992). They consider a list of 13 variables to represent institutional quality (“risk”), and nine variables to represent openness, and show both risk and openness to be insignificant determinants of FDI. Wei (2000) and Wei & Shleifer (2000) find that corruption and tax rates on multinational firms affect inward FDI negatively. While firms may voluntarily choose not to invest in highly corrupt countries, all else being equal, Hines (1995) finds that legislation at source can be a deterrent: the passage of the Foreign Corrupt Practices Act of 1977 in the United States led to a decline in American investment in bribe-prone countries.

> In the international business literature, Dunning (1993) summarized four motives for FDI: market seeking, natural resource seeking, efficiency seeking, and strategic asset seeking.

> 10 In the economics literature, the primary focus is on market-creating institutions such as the protection of private property and the rule of law (Rodrik et al. 2002). _The Economist_ argues that there is no consensus on what the rule of law constitutes (The Economist 2008).
1.2 related literature 7

Stein & Daude (2002) and Daude & Stein (2007) use a broad set of “institutional” variables to find that regulatory burden is an important determinant of FDI location. They draw on a wide range of sources: i) World Governance Indicators (WGI) developed by Kaufmann et al. (2010); (ii) International Country Risk Guide (ICRG) variables on the risk of repudiation of contract, risk of expropriation, corruption, rule of law, and bureaucratic quality; (iii) La Porta et al. (1999)’s index of shareholder rights, and (iv) World Business Environment Surveys on taxes and regulations, policy instability, and corruption.

Bénassy-Quéré et al. (2007) use 2001 survey data from the French Finance Ministry to find that effective bureaucracy and low corruption, among others, attract FDI. Kinda (2010) brings to bear firm-level data to find that institutional problems, together with poor infrastructure and financing constraints discourage FDI in a sample of 77 developing countries. Mottaleb & Kalirajan (2010) use panel data from 68 low-income and lower-middle income developing countries to show that open countries that rely on trade, have large GDP, high growth rate, and are business-friendly tend to be more successful in attracting FDI.

Alfaro et al. (2008) explain the “Lucas Paradox” of inadequate capital flows from rich to poor countries. Despite rates of return being higher in countries where capital is scarce, they argue that poor countries do not receive investment from abroad because of institutional weaknesses. But there are exceptions. Fan et al. (2009) attribute record inflow of FDI into China in recent decades, in spite of the indifferent quality of the country’s institutions, to its stewardship of sustained economic growth.

While this literature confirms the salience of specific institutional variables, it suffers from limitations relating to model mis-specification and variable mismeasurement. The studies mentioned above do not address the problem of sample selection that arises when the pairing of FDI-sending and receiving countries does not occur randomly. They do not incorporate firm heterogeneity in a cross-national
8 investing across borders: do fdi-specific regulations matter?

framework. And they associate FDI inflow with _general_ quality of institutions, not quality that is _specific_ to FDI.<sup>11</sup> Bergstrand and Egger (2009) summarize the vast literature on the theoretical and empirical foundations of gravity models of trade and FDI. I am, however, not aware of any paper that addresses the three problems mentioned above in a study of the institutional determinants of bilateral FDI. As already mentioned, I use new data on regulations that are specific to FDI and the new empirical methodology that has been applied to export flows by Helpman et al. (2008).

Central to the paper’s methodology is the exploitation of the presence of zero flows between trading partners to estimate productivity cut-offs of firms that export abroad profitably. In trade, zero flows inferred from databases like the United Nations Commodity Trade Statistics Database (COMTRADE) are flawed because unreported figures are conflated with confirmed zeros. Baranga (2009) estimates that roughly 20 percent of the sample used by Helpman et al. (2008) has been misclassified. In contrast, the database – OECD (2011) – that I use for FDI clearly distinguishes between missing and zero flows between investment partners. This greatly enhances the appropriateness of the use of the new methodology in this paper.

# 1.3 model

The model is adapted from the gravity-type trade model of Helpman et al. (2008) and Anderson & Van Wincoop (2003). The derivation of the model – for FDI – is detailed in Appendix A. A representative consumer prefers variety (v) and maximizes a Constant Elasticity of Substitution (CES) utility function U = [� x(v)<sup>ρ</sup> dv]<sup>1/ρ</sup> subject to aggregate expenditure (E). All varieties have a constant demand elasticity, σ = 1−1ρ<sup>>1.Afirmincountryiproducesoneunitofoutputwitha</sup>

> 11 One exception is Wei & Shleifer (2000), but they acknowledge their coding of incentives and restrictions on FDI to be coarse and limited to less than 50 countries.
<!-- Start of picture text --> 15<br>= oe “ @<br>E 12 , oe<br>éWY 9 ape e b4@ °<br>is)~— ote® °<br>x a, °<br>s 6 °%f. Oe<br>a e v-e °<br>Mefo) Pa ~~e<br>foe) a<br>° 3 “cs -<br>a Lc<br>a”<br>@<br>e- a7<br>77 e@ 3<br>0= ¢ @<br>0 3 6 9 12 15<br>Log of Assets (US$ million)<br>Source: US Bureau of Economic Analysis (www.bea.gov)<br>Note 1: Correlation coefficient is 0.97<br>Note 2: Affiliates are majority-owned US companies<br><!-- End of picture text -->
10 investing across borders: do fdi-specific regulations matter?

with productivity aij such that aL < aij < aH undertake FDI without incurring a loss. The profit condition (revenue over demand elasticity net of fixed costs) yields the cut-off productivity level as follows: The cumulative distribution function of firm productivity is Pr(a < aij) _≡_ G(aij) = <u>(aaHij)</u><sup>kk</sup> −−aaLL<sup>kk</sup> where k is the shape parameter such that k > σ − 1. The distribution of firm productivity G(a) is common across countries ex ante, but ex post G(aij) is the distribution of a in country i conditional on the firm investing in country j.

Total FDI<sup>12</sup> from i to j is �aaLij<sup>pjxjNidG(a)whereG(aij)ismultipliedbythe</sup> number of firms Ni which proxies for country i’s economic size. Investment can increase on either the extensive or the intensive margin. If demand or policy costs in country j are constant, the only way investment can increase is through an exogenous productivity shock in country i allowing an increased fraction of firms to invest abroad (Fij). If, on the other hand, productivity is constant, investment can increase only with an increase in the GDP (Yj) or a fall in the fixed and variable costs in host country j.

Substituting for pj and xj,

> 12 Technically in this model, FDI should be measured by sales of foreign-owned subsidiaries, but because of the lack of detailed data, it is proxied by FDI stock. Affiliate sales and FDI stock are highly correlated. The correlation between total assets (that is, FDI stock plus liabilities) and sales of US majority-owned foreign subsidiaries across 113 countries in 2008 was 0.83. The correlation coefficient of the values of assets and sales, expressed in natural logarithm, is 0.97 (see Figure 2).
1.3 model 11

As shown in Appendix A, The first component is common across all countries, but the second term in brackets reflects the country-specific fraction of firms that invests abroad.

Equation 1.2 can now be estimated empirically in its log-linear form in equation 1.4. The main host country (j) variables are as follows: GDP (lnYj), aggregated CES Price Index (lnPj), and factor costs relevant for FDI (lncj). Factor costs capture per unit cost of production in country j such as wages, and all policy-related costs.

The purpose of this paper is to examine how country-specific FDI-relevant policies and institutions affect the attraction of FDI in a single cross-section, so host countries are not assigned dummies. I approximate cj by quantified FDI-specific regulations, GDP per capita, tariffs, and education levels. Variables with subscript i are captured by a fixed effect for the FDI source country.

Bilateral variables specific to country pairs such as distance, colonial tie, contiguity, and shared ethnic language can either hinder or facilitate bilateral transactions. Such costs have an observed component dij and an unobserved component eij.<sup>13</sup> An important additional regressor is wij. It captures the ij component of Fij, the index for extensive margin. It is a monotonic function of the productivity cut-off, aij, and is correlated with dij because many of the same variables 13 Bilateral transaction costs τij<sup>1−σ</sup> are parametrized as D<sup>γ</sup> ij<sup>e−uij; uij∼N(0,σ2u)</sup>
12 investing across borders: do fdi-specific regulations matter?

that determine FDI flow determine the extensive margin. Although wij is unobserved, the cut-off condition in equation 1.1 implies that it can be estimated by the conditional probability of a positive investment flow from a probit (first-stage) estimation. Omission of wij would create a heterogeneity bias.

The second bias arises because of the correlation between eij and the included regressors in equation 1.4 as country pairs with zero investment flows are excluded from the sample. Only after controlling for these heterogeneity and sample selecThe discussion so far permits the specification of the benchmark regression (without correcting for biases) in equation 1.5. The dependent variable is the positive stock of bilateral FDI from country i to country j, averaged between 2007 and 2008.<sup>14</sup> Vector **X** consists of distance, and dummies for whether two countries share a border, an ethnic language, or have had a colonial relationship.

The benchmark regression also includes a dummy for whether the two countries have signed a Bilateral Investment Treaty (BIT) to assure reciprocal protection of foreign investment in each other’s territory.<sup>15</sup> This variable later serves as a valid

> 14 UNCTAD defines FDI stock as the value of the share of capital and reserves (including retained profits) attributable to the parent enterprise (total assets minus total liabilities), plus the net indebtedness of the associate or subsidiary to the parent firm. FDI flows plummeted in 2008-09 because of the global financial crisis. I therefore disregard FDI values after 2008.

> 15 Most BITs contain broad commitments to protect investments by investors of one state (“the investor”) in the territory of the other state (“the host state”), ranging from assurances of fair, equitable and non-discriminatory treatment to undertakings to observe investment contracts and other investment-related obligations. As Malik (2006) explains, these protections are accompanied by a powerful international arbitration mechanism that allows investors to bring claims directly against the host state alleging violations of these protections under international law. The ability of investors to enforce their rights directly against a state without the need of an agreement between the investor and state or the involvement of their own state is seen as one of the most far reaching innovations of BITs.
1.3 model 13

exclusion restriction in a two-step Heckman procedure to control for selection bias. The estimation equation is:

- **log** ( **FDIij** ) = **Xija** + **b1log** ( **Openness** ) **j** + **b2log** ( **Arbitration** ) **j** + **b3log** ( **Procedures** ) **j**

- + **b4** ( **Quality of Institutions** ) **j** + **b5log** ( **GDP** ) **j** + **b6log** ( **GDP per capita** ) **j** + **b7log** ( **Remoteness** ) **j** + **b8log** ( **Weighted Tariff** ) **j** + **b9log** ( **Schooling** ) **j** + �( **Source Dummy** ) **i** _∗_ **Di** + **eij** (1.5) **i**

The main explanatory variables in host country j are its openness to FDI, its start-up procedures for FDI, and its FDI-related arbitration regime. A simple theory on how they restrain FDI is illustrated in Figure 3. In Panel A, hurdles to start-up a subsidiary or resolve disputes act as cost-escalating measures that reduce the supply of the good or service (from Q to Q<sup>_′_</sup> ) as they raise the real resource cost to providers at every price level. If the supply of foreign providers is choked off at Q<sup>_′_</sup> (like a quota) there is a rent-creating effect in favor of incumbent firms.<sup>16</sup> Panel B of Figure 3 shows another effect of restricting foreign capital from Holmes & Hardin (2000). If there is no restriction at the world rental rate (R<sup>W</sup> ) foreign capital meets the demand between Q<sup>_′_</sup> and Q freely. With maximum ownership restriction of, say, 33 percent, every unit of foreign capital is matched by two units of domestic capital which needs a higher rate of return to be diverted into the sector. This leads to a derived demand (D<sup>_′_</sup> ) for domestic capital. The effect is that the rate of return is higher and less capital is used overall than in a state where there is no restriction on ownership of equity. The “tariff-equivalent” of the investment barrier is the difference between the return in the restricted sector and the return in the world market (R<sup>_′_</sup> − R<sup>W</sup> ).

> 16 Dee (2003) explains this in terms of liberalization leading to a “triangle gain” from allocative efficiency as the tax-equivalence posed by restrictions to entry are removed, and a “rectangle gain” from productivity enhancement when the high cost of starting or running a business is reduced in a manner that saves real resources to be used elsewhere.
A. Effects of procedural restrictions <!-- Start of picture text -->
| Price<br>|<br>|<br>S<br>: S<br>|:|<br>| f | D<br>Q’ Q Quantity<br><!-- End of picture text -->

B. Effects of equity restrictions <!-- Start of picture text -->
| Rate of<br>return<br>|<br>30% cap on<br>foreign equity<br>Ri|| en: ownership<br>Rw fh bo Ng<br>| i<br>Q” QQ Capital Flow Q<br><!-- End of picture text -->

Source: Holmes & Hardin (2000)
1.3 model 15

section). FDI Procedures measure the number of legal steps required before and after incorporation to start a wholly foreign-owned business. I construct the FDI Arbitration index by averaging indicators measuring i) the Ease of Arbitration Process and ii) the Extent of Judicial Assistance in resolving commercial disputes from the IAB data set. The Ease of Arbitration Process assesses whether there are restrictions on what the conflicting parties can or cannot do to resolve their dispute. The Extent of Judicial Assistance measures the role of domestic courts in assisting the process of arbitration and enforcing awards.

To control for the _general_ quality of institutions, I compute a composite indicator of five WGI developed by Kaufmann et al. (2010) with weights derived from Principal Components Analysis (PCA). The WGI variables are: i) the rule of law; ii) control of corruption; iii) regulatory quality; iv) political stability; and v) governmental effectiveness. Of the six WGI indicators, the one that I exclude is “voice and accountability” which is argued to capture citizens’ participation in selecting their government, as well as freedom of expression, freedom of association, and<sup>17</sup> The control for a general quality of institutions as measured by the WGI variables is important because I want to see whether FDI-specific regulations matter for FDI _over_ and _beyond_ the general quality of institutions. In other words, after controlling for the general quality of institutions, do FDI-specific provisions add any value?

Market size of the host country, proxied by GDP, is one of the most important determinants of horizontal FDI. Motives for vertical FDI are captured by GDP per capita (which proxies for average wage and the quality of infrastructure). Tariff

> 17 I concur with Thomas (2006) who argues that the concept of voice first articulated by Hirschman (1970) is not synonymous with accountability, freedom to select government, or other political freedoms. Nor is there a well-documented relationship between them. Overall, in terms of coverage of countries and topics, the WGI indicators are perhaps the most authoritative and widely used to assess the quality of institutions across countries over time. They are imperfect because they are a quantitative aggregation of perceptions (subjective data). For a summary of criticisms of these indicators and the response from the authors, see Kaufmann et al. (2007).
16 investing across borders: do fdi-specific regulations matter?

rates are measured by the weighted average of applied tariffs on manufactured imports. The effect of high tariffs on FDI, a priori, is ambiguous: it may encourage tariff-jumping horizontal FDI, but discourage vertical FDI that relies on repeated flows of parts and components across borders. The general skill level in the country is assessed by the average number of years of schooling undertaken by adults aged 25 and over. Other determinants of FDI that are not included as regressors are subsumed under the unobserved term, e. The description, source, and summary statistics of the variables are in Tables 13 and 14.

# 1.4 data

I average outward FDI position (stock) in US dollars for 2007 and 2008 from 30 OECD countries into 87 OECD and non-OECD countries belonging to the IAB sample (Table 16). The years 2007 and 2008 represent the latest and most stable number of observations in the OECD’s FDI database, prior to the plunge in crossborder flows in 2009 because of the global financial crisis. Two-year averages are used to smooth out annual fluctuations. For robustness, I also use un-averaged annual data for 2006, 2007, and 2008. Bilateral FDI from each source country, instead of aggregate FDI from all source countries, is used to cast the relationship in a gravity framework yielding a richer set of observations than would be the case for a cross-country regression with aggregate FDI. Source countries include only members of the OECD because of data constraints; however, they have historically accounted for the majority of global FDI outflows.<sup>18</sup> The choice of FDI stock as the dependent variable, preferred to FDI flows, needs elaboration. The model in Melitz (2003) which inspired Helpman et al. (2008) is inherently cross-section because it assumes steady state productivity levels for

> 18 FDI outflows from non-OECD countries are increasing. For example, in 2003, 12.3 percent of total FDI inflow into the 10 member countries of the Association of Southeast Asian Nations (ASEAN) was from China, India and other ASEAN countries; by 2008, the corresponding share had reached 24.8 percent (ASEAN 2010).
1.4 data

17 each year, and does not predict how firm productivity changes year to year. Because I do not have a variable time dimension in my econometric model, the effects of explanatory regressors are on an equilibrium level of FDI. This is better reflected by FDI stock because it is far less volatile on an annual basis than FDI flows. Importantly, the extensive margin of FDI measured by whether multinational firms from country i operate in country j can only be estimated by FDI stock, not flows.

For the main explanatory variables, I use the new IAB indicators of FDI regulation across 87 economies, prepared by the World Bank Group in 2010. With 21 countries from Sub-Saharan Africa, 20 from Eastern Europe and Central Asia, 14 from Latin America and Caribbean, 10 from East Asia and Pacific, five from South Asia, and five from the Middle East and North Africa, this sample of 87 countries can be considered random, and a fair representation of the actual world. In 200708, they constituted 87.1 percent of global population and 77.9 percent of global output (Table 15). The sample includes 15 high-income OECD countries.

As shown in Figure 4, in terms of market size, the mean and standard deviation of the sample of all countries<sup>19</sup> in the world and the subsample of 87 IAB countries are almost identical. However, within the IAB sample the distribution of the subgroup with zero FDI observations is remarkably different from the subgroup with non-zero observations. The sample selection bias is, therefore, likely to be more prominent within the IAB sample.

The 23 IAB indicators measure, among others, openness to FDI through equity ownership permitted in 11 sectors (11 indicators); time, procedures and regulations for starting a foreign business (three indicators); arbitrating commercial disputes (three indicators); and accessing industrial land (6 indicators). The data are compiled from detailed surveys filled out by over 2350 local experts from leading law and accounting firms, chambers of commerce, and investment promotion 19 Excludes countries with total population of less than one million in 2008.
<!-- Start of picture text --> —— All countries —-- IAB sample —— All countries —-- IAB sample<br>2 8<br>‘<br>@ 15 / 7 Sy 6<br>1 \ 4 ‘<br>0 0<br>20 22 24 26 28 30 2 4 6 8 10<br>Log of GDP Log of Distance<br>—— Non-Zero Group == Zero Group —— Non-Zero Group == Zero Group<br>A 8<br>3 r m 6<br>> 1\ /<br>Dncg 2 ~ A / \<br>5 / \ i \<br>A | _— \<br>Al J \ 2 \<br>-./ / \ --<br>0 ~, =. 0 --<br>20 22 24 26 28 30 4 6 8 10<br>Source: WDI & CEPII<br><!-- End of picture text -->
1.4 data

19 agencies. Collected during the period 2006-08, these indicators reflect regulations that prevailed before 2006 in each of the 87 countries.

In terms of the coverage of subjects, sectors, and countries, this data set on FDI regulations is the most comprehensive to date. It comprises both de jure indicators measuring laws and regulations on paper, and de facto indicators that measure the implementation of those laws. To the extent possible, I create sub-indices of primarily de facto indicators of FDI Arbitration and FDI Procedures to use in the empirical estimation. On FDI Openness, the indicators are only de jure, and these tend to do less justice to countries that are open to foreign investment in practice but have not enshrined it in written law. I expand on the main explanatory variables below.

# 1.4.1 _Investing Across Sectors_

Restriction on equity ownership across sectors is one of the most important indicators of a country’s policy attitude towards foreign investment. The index of FDI openness is created for 86 countries<sup>20</sup> by averaging the equity ownership permitted for foreign companies across 33 subsectors. These measure statutory (not de facto) restrictions to the ownership of equity by foreigners in new (greenfield) investment, and investment in existing firms through mergers and acquisitions. Sixty-six data points for each country are aggregated first into 33 subsectors, and then into 11 industries (eight of which are services, two are resources/primary, and one manufacturing). The final index shows on a scale of 0 to 100 the overall openness in a given country to ownership of firms by foreign investors (0 being least open).

20 Excluding Papua New Guinea for which equity data could not be confirmed.
20 investing across borders: do fdi-specific regulations matter?

# 1.4.2 _Arbitrating Commercial Disputes_

Most foreign companies prefer resolving disputes through arbitration over lengthy litigation in local courts. The indicators assess the strength of legal frameworks for alternative dispute resolution by combining the Ease of Arbitration Process and the Extent of Judicial Assistance indices to represent the quality of FDI-specific institutions. This index directly deals with how foreign investors prefer to resolve contractual or commercial disputes. It is also strongly correlated with the rule of law, government effectiveness, corruption, and regulatory quality pillars of the World Governance Indicators that summarize a country’s institutional regime in general (Figure 5).<sup>21</sup> Specifically, the index on the Ease of Arbitration Process scores, among others, best practice provisions for party autonomy and tribunal integrity. Autonomy assesses whether the laws allow parties to choose arbitrators or arbitral institutions, the language of proceedings, and whether foreign counsels can represent investors. Tribunal integrity measures whether there exist provisions to ensure that arbitrators remain independent and impartial and whether the proceedings remain confidential. The other index on judicial assistance evaluates whether local courts follow a “pro-arbitration” policy, whether tribunals decide the jurisdiction of disputes and whether the courts assist tribunals by requiring the appearance of witnesses and production of evidence.

What are the bases for these indicators? Investors need to be assured that their investments will not be unjustly expropriated and that in instances of disputes, there is a predictable course for resolution. An effective arbitration regime for FDI mitigates risk by providing legal security to investors (including assurance

> 21 The scatter plots in Figure 5 exclude outliers – Afghanistan and the Solomon Islands – that score zero on the Ease of Arbitration Process index and the Extent of Judicial Assistance index. Serbia is excluded from the WGI data set. This leaves 84 country observations.
<!-- Start of picture text --> Rule of Law 2009 (WGI) Government Effectiveness 2009 (WGI)<br>-15 -5 5 1.5 2.5 -1.5 -5 Re) 1.5 2.5<br>= 90 See oe 90 b ° e%e «6<br>BP os oe? fae 75 *e 8% %. af<br>~E 60 oc.ef ek,Bees 8 60 e +49 aeee 0°<br>2 oS Ct oe 9 Pe °<br>£ 45 “ e 45 e@ Se<br>‘6 e° 8 e<br>< 30 e 30 e<br>E 90 . 7 ° &e 04 ce fo oF 0 %<br>2”0 e are® _ 9 7 oe 852e 2 -275.% et e<br>S 60 o° 60 OL Ad<br>Re5 =e Jfve % e -*° oo 8 e<br>£45 b 45 ><br>5 fe a)<br>a 30 e 30 e<br>-15 -5 5 1.5 2.5 -1.5 -5 Re) 1.5 2.5<br>Control of Corruption 2009 (WGI) Regulatory Quality 2009 (WGI)<br>Source: WGI & IAB<br><!-- End of picture text -->
22 investing across borders: do fdi-specific regulations matter?

of contract enforcement rights, due process, and access to justice). It gives parties autonomy to create systems tailored to their disputes. According to World Bank Group (2010), countries that score well on these indicators have a strong arbitration legal framework, receive support from local courts for arbitration proceedings and efficient enforcement, adhere to international conventions, and provide autonomy to parties seeking to resolve their commercial disputes.

# 1.4.3 _Starting a Foreign Business_

Starting a Foreign Business indicators record the time, procedures, and regulations involved in establishing a local subsidiary of a foreign limited liability company. Here, I use the number of procedures required to establish a foreign business. This resembles a cost that affects the decision about whether and how much a firm invests in a foreign country. The steps include both pre- and post-incorporation procedures with which foreign businesses are officially required to comply. According to World Bank Group (2010), countries that score well on the Starting a Foreign Business indicators have simple and transparent establishment processes that abolish unnecessary steps (which create opportunities for rent-seeking). High scorers also treat foreign and domestic investors equally, and differences in treatment vary only by company size, legal form or commercial activity, not the nationality of shareholders. Tables 19 to 21 describe how indicators of FDI-specific regulations are constructed in the IAB data set. I separately construct composite country scores for the quality of selected FDI regulations in Table 22.<sup>22</sup>

> 22 The Investing Across Sectors score (1) averages the maximum percentage of equity permitted for foreign ownership in the 11 sectors mentioned in Table 19. The Ease of Process Index (2) and the Ease of Judicial Assistance Index (3) are obtained by normalizing the country score for those indices using the min-max rule: the score for a country is subtracted from the best-performing country, divided by the difference in scores between the best and worst-performing countries. The number is then multiplied by 100. Higher the score, better is the regime in place for resolving commercial disputes. The score for the Number of Procedures (4) is also obtained by normalizing the country score using the min-max rule. However, the final score is multiplied by, and subtracted from, 100. Higher the score, the fewer the number of pre- and post-incorporation procedural steps formally required to establish a wholly foreign-owned subsidiary.
1.4 data 23

# 1.4.4 _Bilateral Investment Treaty_

Data on bilateral investment treaties between 30 OECD source countries and 87 IAB host countries are collected from UNCTAD’s Investment Instruments Online.<sup>23</sup> Bilateral Investment Treaties (BITs) are agreements between countries for the reciprocal encouragement, promotion and protection of investments in their territories. Like preferential trade agreements, BITs, too, have proliferated dramatically in recent decades from around 400 in 1990 to over 2,500 at present.

# 1.4.5 _Gravity and Other Variables_

The gravity variables – distance, past colonial relationship, contiguity and shared languages – are obtained from CEPII (2010). Economic variables – GDP, GDP per capita, average (weighted) applied tariff on manufactured imports – are from the World Development Indicators (WDI) database (World Bank 2011). Mean years of schooling are from Barro & Lee (2010), as intrapolated in UNDP (2010). These explanatory variables available annually are averaged over the preceding five years, that is, 2002 to 2006 to minimize the possibility of simultaneity. I compute the remoteness index as the sum of all bilateral distances between a country and all its partners, weighted by the share of the partner’s GDP in total world output.

With 30 source countries and 87 host countries, there are 2610 potential observations. Gravity-related information is not available for three host countries (Kosovo, Montenegro and Serbia). Because 15 OECD countries also appear in the IAB sample of FDI recipients, another 15 observations are lost, reducing the number of observations to 2505. If FDI stock data are missing for the years 2007-08, but they were reported for the period 2002-06, I recode the missing data points as zero.

23 See UNCTAD (2010).
24 investing across borders: do fdi-specific regulations matter?

This affects 82 country pairs; 641 data points are deemed missing and dropped along with 29 negative values for FDI stock (divestitures).

Of the remaining 1835 observations, there are 724 observations that are confirmed to be zeros.<sup>24</sup> Not all of the 87 countries in the IAB sample have values for all explanatory variables. Papua New Guinea has no data on FDI Openness; Sierra Leone, Haiti and Liberia do not have data on tariffs. Further, four OECD source countries (Italy, Spain, Japan and Switzerland) do not have zero FDI in any of the remaining IAB countries (after missing values are dropped). This poses a problem for the two-step econometric methodology employed in this paper; those source countries are therefore dropped. Belgium and Mexico do not report any data for FDI stock for the years under consideration. All these reduce the number of observations used in the estimation of the main regressions to 1578 bilateral FDI stock values between 24 OECD source countries and 80 IAB host countries, including 666

# 1.5 estimation method

The estimation method proceeds in five stages. _First_ , I estimate the log-linearized gravity equation 1.5 using the Ordinary Least Squares (OLS) method, without correcting for biases. _Second_ , I correct for the selection bias in two steps. First, a probit model predicts the probability, pˆ ij, of countries having a positive FDI relationship conditional on explanatory variables that are used to estimate equation 1.5. I use the binary BIT variable as a valid exclusion restriction. This permits the computation of the inverse Mills ratio (η�ij) for inclusion as an additional regressor in the second step to control for sample selection.

In Figure 6, the top-left graph shows predicted probability of OECD countries i investing in country j on the y-axis. The x-axis plots the normalized fitted values

> 24 As already explained, one positive aspect of OECD’s bilateral FDI statistics is that they clearly distinguish between values that are missing and values that are confirmed to be zeros. In trade statistics, this is often not done, leading researchers to erroneously treat unreported (missing) data as zero.
<!-- Start of picture text --> 1 pesmee 8] ~ 1 5 7835 oe<br>&2z.<br>5 USAg at @uSA<br>ae}Es NuQe@FRA L 5 nu<br>2 a<br>UO )<br>RS} a)<br>3 KOR oO °<br>a . Q . KOR<br>0 eo econ Oo ( t em o°¢<br>5 0 5 0 Jl 2 3 A<br>Fitted values - H1 PDF of H1<br>1 7 % 1 oso 8 0 w e . . a<br>Dp . Dp .<br>aa<br>Q Q<br>g @F R A g @ FRA<br>Ba 5 a m5 s<br>ae) ae)<br>vo vo<br>gg<br>3 3<br>iB iB<br>00<br>0 1 2 3 0 1 2 3 4 5<br>IMR: control for sample selection Z1: control for heterogeneity<br>Source: Regressions by author using data from OECD (2011)<br>Note 1: Diamond-shaped dots are FDI to Mali and squared dots are FDI to Spain<br>Note 2: Other (gray) dots represent other country pairs<br><!-- End of picture text -->
26 investing across borders: do fdi-specific regulations matter?

tween Mali and France is 0.57. If France had not been a former colonial power, the predicted probability would have been 0.36. The second scenario would then have led to a higher inverse Mills ratio.<sup>25</sup> _Third_ , I take into account firm heterogeneity by incorporating controls derived ˆ ˆ from predicted probabilities which are first normalized, zij = Φ<sup>−1</sup> (pij). Because the latent variable that determines whether or not two countries have an FDI relationship is linked to the productivity level of the marginal firm, it can be used to control for the unobserved heterogeneity term in equation 1.4. If firm productivity is Pareto-distributed, this has the form _max_ �Z<sup>δ</sup> ij<sup>−1,0</sup> � where δ =<sup><u>k−</u></sup> σ−<sup><u>σ+</u></sup> 1<sup><u>1</u>.Thela-</sup> tent variable is unobserved, but its log value is estimated by the inverse of the unit ˆ ˆ normal of predicted probabilities, zij = Φ<sup>−1</sup> (pij). Given that E �zij|FDIij = 1� = E �zij� + E �ηij|FDIij = 1�, Helpman et al. (2008) show that control for unobserved ˆ � firm heterogeneity can be estimated by <u>z</u><sup>~~ˆ~~</sup> ij = zij + ηij. In other words, the index <u>z</u><sup>~~ˆ~~</sup> ij, shown on the bottom right graph of Figure 6 as Z1, controls for the effect of investment restrictions on the proportion of firms able to profitably invest abroad. If the host (and bilateral) country characteristics pose low barriers (cj, τij ,fij) and there is high demand in (Yj), for a given level of productivity (aij) a greater fraction of firms will find it profitable to invest.

_Fourth_ , the final regression is estimated both by Non-linear Least Squares (NLS) and OLS. The former is a parametric estimation that requires firm productivity to be Pareto-distributed. Helpman et al. (2008) suggest that estimates can also be obtained from OLS if the extensive margin is represented by a polynomial of <u>z</u><sup>~~ˆ~~</sup> ij.<sup>26</sup> Because the OLS coefficients have the same sign and are very close in magnitude to the NLS estimates, I opt for the simpler OLS method to report most of the robustness results in subsequent sections.

> 25 Note that probit also predicts a moderately high probability of two other source countries – United States (US) and Netherlands – having a direct investment relationship with Mali even though in reality they do not invest there. This is because the two countries have invested in similar-sized African economies: the Netherlands in Burkina Faso and Rwanda, and the US in Rwanda. The Netherlands also has a BIT signed with Burkina Faso and the US has one with Rwanda. 26 Note that η� = Φ<sup><u>φ(</u></sup> (<sup>z ˆ</sup> z ˆ<sup>i</sup> ij<sup><u>j)</u></sup> )<sup>;zˆij= Φ−1(pˆij); and</sup> <u>z</u><sup>~~ˆ~~</sup> ij = zˆij + η�ij
1.6 results 27

_Fifth_ , the two biases that are corrected in the preceding step are disentangled to assess the relative dominance of each. The purpose is to find whether the failure to control for firm heterogeneity biases the coefficients more than the failure to control for sample selection bias arising from zero investment flows between numerous country pairs.

All the inferences are based on standard errors that are robust and clustered by host country under the assumption that FDI from OECD countries flowing to a common host country is influenced by the latter’s characteristics that apply in common to all source countries, in addition to pair-specific characteristics. If a shock in a host country affects potential investment from all source countries, then FDI inflows are correlated. As Moulton (1990) shows, if errors within groups are correlated, but incorrectly assumed to be independent, standard errors are likely to be substantially biased downward leading to findings of statistical significance that are spurious. In this paper, clustering of standard errors by host country yields the most conservative set of inferences on the significance of coefficients, and is the chosen approach in all the regressions.<sup>27</sup>

# 1.6 results

Columns 3 and 4 in Table 1 report the main results, obtained from NLS and OLS models, respectively. When both the biases introduced by country selection and firm selection are corrected, FDI-specific institutions significantly affect the accumulation of foreign direct investment.

The coefficient of FDI Arbitration – a variable with a close relationship with a judicial regime and enforcement – is significant at the 5 percent level, whereas it was not statistically different from zero in the benchmark estimation that does not

> 27 I also cluster standard errors by _source_ country (to account for agglomeration tendencies) and by country pairs. Clustering by _source_ country makes several coefficients appear much more significant than when clustering is by host country. Note that the magnitudes of coefficients do not change irrespective of how the standard errors are clustered.
28 investing across borders: do fdi-specific regulations matter?

correct for biases (column 2). An improvement of ten percent in the standardized score for FDI Arbitration (say, from 70 to 77) increases the stock of FDI by at least 4 percent.

The coefficient on FDI Procedures is significant in the (biased) OLS estimate of column 2, but its magnitude increases in the bias-corrected estimates of columns 3 and 4. Both FDI Procedures and FDI Arbitration coefficients are highly significant after controlling for the general quality of institutions in the country. The coefficient on institutional quality is not positively significant either when it is proxied by the composite WGI indicator or when it is replaced by each of the five separate constituents of WGI in Tables 4 through 8.

Previous studies (for example, Daude & Stein 2007) found a strong association between good institutions and high FDI inflow. My results suggest that FDIspecific provisions in practice offer direct incentives for FDI in a manner over and above what is offered by good institutions in general. FDI is responsive to specific instruments such as an effective arbitration regime and less onerous business startup procedures. In the presence of sound FDI-specific provisions, the generally high quality of institutions and governance appears to add no additional attraction to FDI. From a policy maker’s perspective, this is not bad news. A minister keen on attracting FDI into her country need not be despondent that it would take decades to overhaul the rule of law or reduce high levels of corruption; she can start with piecemeal reforms in regulation and enforcement that are of direct concern to investors.

The coefficient of FDI Openness, however, is not significant in any of the regressions. This implies that openness to FDI “on paper” is not meaningful. While FDI Procedures and FDI Arbitration indices mainly consist of de facto indicators that assess the implementation of laws and not just the written text, the FDI Openness index comprises solely of de jure indicators. High FDI-receiving countries like Brazil, Russia, India, and China (BRIC), for example, have lower openness scores
1.6 results 29

Table 1: Main Regressions: FDI Stock in 2007-2008

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Probit|Benchmark|Hetero|geneity|Bias I|Bias II|
||||NLS|OLS|||
|FDI Procedures|-.060|.458**|.514**|.501**|.515**|.457**|
||(.095)|(.197)|(.200)|(.202)|(.199)|(.203)|
|FDI Arbitration|-.159**|.260|.411**|.409**|.362*|.300|
||(.064)|(.206)|(.200)|(.197)|(.195)|(.206)|
|FDI Openness|-.113|.054|.141|.127|.119|.061|
||(.107)|(.128)|(.126)|(.130)|(.124)|(.126)|
|Quality of Institutions|.390**|-.111|-.316|-.330|-.337|-.132|
||(.153)|(.203)|(.207)|(.208)|(.212)|(.207)|
|GDP|.624***|.857***|.468***|.496***|.492***|.842***|
||(.067)|(.083)|(.124)|(.134)|(.124)|(.092)|
|GDP Per Capita|-.189*|.036|.150|.141|.143|.008|
||(.109)|(.184)|(.190)|(.185)|(.187)|(.180)|
|Weighted Tariff|-.255*|-.390**|-.026|-.221|-.232|-.387*|
||(.137)|(.196)|(.020)|(.206)|(.203)|(.196)|
|School (Mean Years)|.575***|.295|-.146|-.083|-.081|.323|
||(.175)|(.305)|(.328)|(.331)|(.324)|(.302)|
|Remoteness|2.814***|2.822***|1.251*|1.466*|1.417*|2.787***|
||(.459)|(.669)|(.682)|(.798)|(.718)|(.692)|
|Distance|-1.578***|-1.382***|-.466*|-.550**|-.534**|-1.364***|
||(.168)|(.147)|(.236)|(.264)|(.225)|(.155)|
|Contiguity|.347|1.083**|1.030**|1.066**|1.053**|1.080**|
||(.615)|(.454)|(.437)|(.427)|(.430)|(.448)|
|Colony|.632*|.938***|.544*|.605*|.578*|.937***|
||(.363)|(.311)|(.291)|(.310)|(.296)|(.306)|
|Investment Treaty|.392***|.267|||||
||(.140)|(.179)|||||
|δfrom<br>�<br>~~ˆ~~<br>ω<sup>_~~∗~~_</sup><br>ij<br>�|||.682***||||
||||(.201)||||
|Inverse Mills Ratio<br>�<br>~~ˆ~~<br>η<sup>_~~∗~~_</sup><br>ij<br>�|||.177|.003||-.179|
||||(.358)|(.600)||(.378)|
|Z1<br>�<br>~~ˆ~~<br>z<sup>_∗_</sup><br>ij<br>�||||1.175***|||
|||||(.405)|||
|H1<br>�<br>Φ<sup>−1</sup>( ˆpr)<br>�|||||.663***||
||||||(.148)||
|No.|1578|912||912|912|912|
|Adj. R-sq.||.66|.68|.67|.67|.66|
|Note1: robust standard error|s (clustered by|host country) re|ported in|parenthesi|s||
|Note2: statistical signifcance|indicated as *|forp<0.1, ** for|p<0.05, a|nd *** forp|<0.01||
30 investing across borders: do fdi-specific regulations matter?

Table 2: Regressions: OECD Source Countries Excluded

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Probit|Benchmark|Heterogeneity|Bias I|Bias II|
|FDI Procedures|-.049|.326**|.312*|.334**|.300*|
||(.096)|(.156)|(.158)|(.158)|(.156)|
|FDI Arbitration|-.150**|.246|.379**|.327*|.286|
||(.065)|(.196)|(.190)|(.184)|(.192)|
|FDI Openness|-.092|.072|.117|.106|.082|
||(.116)|(.125)|(.132)|(.126)|(.126)|
|Quality of Institutions|.274|-.130|-.233|-.238|-.078|
||(.171)|(.243)|(.257)|(.256)|(.238)|
|GDP|.668***|.939***|.669***|.659***|.974***|
||(.067)|(.082)|(.162)|(.149)|(.088)|
|GDP Per Capita|-.207*|.140|.210|.210|.105|
||(.117)|(.186)|(.192)|(.195)|(.182)|
|Distance|-1.832***|-1.945***|-1.283**|-1.222***|-2.026***|
||(.203)|(.190)|(.503)|(.424)|(.190)|
|Remoteness|3.823***|4.657***|3.309**|3.153***|4.702***|
||(.565)|(.723)|(1.302)|(1.177)|(.756)|
|Weighted Tariff|-.296**|-.489**|-.363|-.368|-.489**|
||(.150)|(.229)|(.225)|(.225)|(.224)|
|School (Mean Years)|.598***|.108|-.164|-.141|.170|
||(.187)|(.327)|(.394)|(.390)|(.332)|
|Contiguity|.217|2.297***|2.122**|2.143***|2.323***|
||(.609)|(.775)|(.819)|(.789)|(.787)|
|Colony|.494|1.246***|1.029***|1.018***|1.258***|
||(.386)|(.289)|(.339)|(.332)|(.301)|
|Language|.468**|.445|.228|.227|.415|
||(.234)|(.279)|(.324)|(.313)|(.283)|
|Bilateral Investment Treaty|.489***|.296||||
||(.151)|(.219)||||
|Inverse Mills Ratio<br>�<br>~~ˆ~~<br>η<sup>_~~∗~~_</sup><br>ij<br>�|||.306||.160|
||||(.693)||(.430)|
|Z1<br>�<br>~~ˆ~~<br>z<sup>_~~∗~~_</sup><br>ij<br>�|||1.204**|||
||||(.511)|||
|Z1*Z1|||-.116|||
||||(.094)|||
|H1<br>�<br>Φ<sup>−1</sup>( ˆpr)<br>�||||.448*<br>(.238)||
|No.|1220|622|622|622|622|
|Adj. R-sq.||.62|.63|.63|.62|

Note 1: robust standard errors, clustered by host country, reported in parenthesis Note 2: statistical significance indicated as * for p<0.1, ** for p<0.05, and *** for p<0.01
<!-- Start of picture text --> US FDI to 87 COUNTRIES AND 5 SECTORS<br>8 8 .08<br>e)<br>© ° 8<br>7 6 e) Q .06<br>q S<br>1S} ©,<br>é°2<br>~ e) Oo © &<br>se} 4 oO fe) o ra 04<br>2 6° gf 4<br>z |? 8 ° S<br>ae) eS,<br>eS 2 ° fe) =<br>7 axe) ca, & a<br>f°) 5o)6lCUCOOté‘ o)*‘C*Sw<br>0 6 BoSo Q<br>© O 0<br>0 25 50 75 100 0 25 50 75 100<br>Equity Allowed (%) Equity Allowed (%)<br>Source: IAB and US Bureau of Economic Analysis (www.bea.gov)<br>Note 1: 87 countries, 5 sectors; 133+ values (out of 435)<br><!-- End of picture text -->
32 investing across borders: do fdi-specific regulations matter?

than countries like Afghanistan and Haiti which receive insignificant amounts of FDI. Obviously BRIC offers conspicuous advantages like market size to investors that small, conflict-ridden economies do not. That, on paper, the poorer countries allow 100 percent ownership of equity should foreigners invest does not seem to matter when most other determinants of FDI are accounted for. This is illustrated in Figure 7 with outward _sectoral_ FDI data from the United States to 87 IAB countries.<sup>28</sup> There is no systematic relationship between countries’ openness score and actual FDI received by sector.

In the bias-corrected models of column 3 and 4 in Table 1, the elasticity of FDI with respect to distance drops dramatically, by two-third, from that in the (biased) benchmark model in column 2. The positive effect of a shared border increases, but the coefficient of common colony falls modestly in the bias-corrected models. Coefficients on remoteness and GDP are also subdued in the bias-corrected models. The coefficient of GDP per capita is not statistically significant in any of the regressions. Coefficients on the average education level of the adult population and the mean level of weighted tariff on manufactured import in host countries are also not statistically different from zero. I detail the estimation procedure and results in the rest of this section.

In Table 2, FDI-receiving OECD countries in the sample are dropped so that the FDI relationship is now between the 24 OECD source countries and 65 nonOECD host countries. Coefficients on FDI Arbitration and FDI Procedures are both statistically significant although the magnitudes are lower than in the fuller sample (Table 1). The elasticity of distance in the biased regression is much higher than in the main regression, which drops by about one-third when biases are corrected. This indicates that the inclusion of 15 OECD countries in the sample overestimated the effect of FDI-specific provisions and underestimated the hurdle posed by bilateral distance. When the proportion of developing countries in the sample grows,

> 28 The 5 sectors are mining, manufacturing, banking, insurance and telecommunications. Concordance between the sectors defined by the US Bureau of Economic Analysis and IAB is not exact. FDI stock figures from the US are averaged between 2007 and 2008.
1.6 results 33

the elasticity of distance increases, indicating that among less developed countries, traditional barriers remain important.

Column 1 of Tables 1 and 2 show probit estimates of the marginal effects of variables that affect the probability that two countries would have an FDI relationship. OECD countries are more likely to invest in countries whose market size is big, are closer in terms of bilateral distance, and with which they share a colonial and linguistic heritage. They are less likely to go to a country with high trade protection. Curiously, the propensity to invest in countries with good FDIspecific institutions is low after controlling for the quality of institutions and other country-specific characteristics. The coefficients on the implicit start-up cost for foreign businesses (measured by the number of legal procedures) and openness to FDI are not significant.

Importantly, in the probit model (column 1 in Tables 1 and 2), the coefficient of BIT is significant at the 1 percent level indicating a strong propensity for OECD countries to invest in host countries that assure protection against expropriation and provide other guarantees for foreign investors. Column 2 reports OLS estimates of an augmented gravity-type model with the same variables used in the probit regression, but without any correction for biases. The coefficient on the BIT variable is not significant, suggesting that it does not affect the volume of FDI after the decision to locate in a country has been made. In other words, it affects the propensity to invest (driven by fixed cost) but not the volume of investment (driven by variable cost). The BIT variable is, therefore, a valid exclusion restriction that is vital for identification in models aimed at correcting truncation biases.

The coefficient of FDI Procedures in the (biased) benchmark regression of column 2 is highly significant. As in the probit estimates, coefficients for GDP, tariff, distance, colonial history and contiguity are of the same sign and similar magnitude. One major difference between the probit and the biased OLS coefficients is that the coefficient for the quality of institutions is not significant. Remoteness
34 investing across borders: do fdi-specific regulations matter?

appears to be highly significant and positive, indicating that the relatively distant countries in the sample like the Solomon Islands, Chile, Argentina, Brazil, and Southern African countries are attractive destinations for FDI. This could be because these countries attract resource-based FDI (which is location-specific), or because remote country pairs invest much more in each other than an equi-distant pair elsewhere that is less remote.

As mentioned earlier, a major problem with log-linearized OLS regression (column 2) is that the sample only includes countries that have an active FDI relationship, and drops all country pairs between which the FDI flow on average between 2007 and 2008 is zero. Over 42 percent of the remaining sample drops out in this manner, which represents not only a mammoth loss of information, as shown in Figure 8, but also points to a potential cause of selection bias. After this, the dependent variable is not really bilateral FDI, but bilateral investment contingent on a relationship existing. A crucial variable left out of the model is the probability of being included in the sample, that is, having a non-zero FDI flow. If countries with active FDI relationships are not randomly selected from the population, and the probability of selection is correlated with independent variables like distance, then the gravity coefficients are no longer reliable.

# 1.6.1 _Country Selection Bias_

Column 5 in Table 1 reports results after correcting for selection bias using the standard Heckman procedure. In the first stage, the probit estimates in column 1 give the probability of an FDI relationship existing conditional on the same explanatory variables used to estimate the benchmark equation. An inverse Mills ratio is computed from the conditional probabilities and then included as a regressor in the second stage, which excludes the identifying variable (BIT). The magnitudes of a number of coefficients change modestly between the (biased) benchmark regression and the selection corrected model, but the overall sign and significance
<!-- Start of picture text --> OUTWARD FDI FROM 30 OECD COUNTRIES<br>La Reported [_] Confirmed Zeros [__] Missing<br>2008 fo<br>2007 (oo<br>2006 |<br>2005 (oT<br>2004<br>2003<br>2002<br>20010 fo<br>2000<br>0 500 1,000 1,500 2,000 2,500<br>Source: OECD (2011)<br>Note 1: 85 IAB host countries, excluding Kosovo and Montenegro<br><!-- End of picture text -->
36 investing across borders: do fdi-specific regulations matter?

are retained. The inverse Mills ratio is also not statistically significant, indicating that the bias arising from country selection is not a serious problem in the case of bilateral FDI. This means that even if countries with zero FDI flows excluded from the country sample in the benchmark regression are now included, the benchmark elasticities of the impact of barriers would not change much.

This result, however, does not undermine the case for the correction of potential selection bias. Indeed, the common practice in the extant gravity literature of making an ad hoc correction to zero flows by recoding zeros as unity, and then including the logged value of unity (zero) in the sample is flawed. This is seen in Table 3. The first column reports estimates when the zeros are simply dropped. Columns 2-4 correct for sample selection following three similar approaches: column 2 estimates the full model with maximum likelihood; column 3 reports results from the Heckman two-step model (with probit selection equation estimated with maximum likelihood, and the outcome equation by OLS); column 4 repeats the procedure of column 3 manually. The coefficients are identical, but this approach yields more conservative standard errors.<sup>29</sup> All three approaches confirm that the coefficients of the biased benchmark model do not alter much in the version with correction for sample selection. (The coefficient on the FDI Arbitration variable is, however, significant at the 10 percent level in two of the three models that correct the sample selection bias).

Now, compare the selection corrected estimates of columns 2-4 with those from an ad hoc adjustment in column 5. Coefficients on the FDI-related policy variables, the quality of government and schooling are completely different. Coefficients on the gravity variables, however, are similar in magnitude. Although the

> 29 The two-step approach is more popular and is the one used by Helpman et al. (2008). Verbeek (2004) argues that a full maximum likelihood estimation of the sample selection model is more efficient than the two-step procedure. Further, the OLS regression provides incorrect standard errors because the remaining residual is heteroskedastic and the inverse Mills ratios (lambdas) are not directly observed but estimated from the first stage regression. The two-step method will also not work if the lambdas do not vary much across observations. Verbeek argues that the full maximum likelihood estimation (MLE) offers an integrated approach to estimating the parameters. However, MLE requires a stronger assumption that the errors of the selection and outcome equations are jointly normally distributed.
1.6 results 37

R-squared is much higher in the ad hoc regression, the results show that the coefficients vary and inferences would be very different depending on whether the model ignores the zeros (as in column 1), corrects for them (as in columns 2-4), or makes an atheoretical ad hoc adjustment (column 5).

Finally, Silva & Tenreyro (2006) point out that the presence of heteroskedasticity in trade data actually requires the use of _non_ log-linearized models. Their method allows the inclusion of zero flows in the sample by estimating the gravity model with original (non-log) FDI as the dependent variable. The results from their Poisson Pseudo Maximum Likelihood (PPML) method in column 6 do not show coefficients on the FDI-related policy variables to be statistically significant, but some variables have surprising coefficients. The elasticity of distance, for example, drops by about two-third as in the main bias-corrected model. An issue with the method proposed by Silva & Tenreyro (2006) in the context of FDI flows is that it does not seek to explain zeros as anything special. In my model, zeros are intimately linked to heterogeneous productivity of firms. A zero bilateral flow indicates that firms are not finding it profitable to invest abroad because, for a given level of foreign demand, either the firm productivity is low, or the fixed and variable costs induced by distance and policy are prohibitive.

Addressing the zero observation problem (regardless of whether the selection bias is serious or not) still does not give us consistent estimates when firms are heterogeneous. In earlier trade models that follow Krugman (1979), firms are symmetric and all firms export. Only their volume is constrained by trade costs, not the decision of firms of whether to export. When trade barriers are infinite, foreign varieties are still consumed, but zero quantity of each. The effect of trade costs is only on the intensive margin of trade. Helpman et al. (2008) show that the correction of selection bias is inadequate when the assumption of symmetric firms is rejected and firms are not affected by FDI barriers in an _identical_ manner.
38 investing across borders: do fdi-specific regulations matter?

When firms are heterogeneous, an additional bias needs to be controlled for. As FDI barriers go down, multinational firms face lower variable costs of investing abroad, so they increase their FDI. At the same time, firms that were not productive enough earlier to incur fixed costs are now in a position to do so, and contribute to increased FDI. Both the intensive and extensive margins of adjustment must be acknowledged to obtain an accurate picture of how barriers to FDI affect inflow. Ignoring the extensive margin misattributes the importance of specific barriers in restricting total investment flow because they conflate the impact of FDI barriers on these two separate margins of FDI, and render the coefficients inconsistent (Behar & Nelson 2009). This issue is addressed next, and is illustrated in Figure 6.

# 1.6.2 _Firm Selection Bias_

I follow the two-step methodology proposed by Helpman et al. (2008) to control for unobserved firm heterogeneity in a cross-country data set, as explained in the section on estimation strategy. Column 3 in Table 1 reports results from an NLS model that has controls for firm selection [ln(e<sup><u>δ</u></sup> <u>z</u><sup>~~ˆ~~</sup> ij − 1)] and country selection (η�ij). The coefficient of <u>w</u><sup>~~ˆ~~</sup> ij(δ) is highly significant at the one percent level indicating that there is a severe truncation bias.<sup>30</sup> The coefficient of the inverse Mills ratio, however, is insignificant. Column 4 in Table 1 is exactly the same model as in column 3, except that it is now estimated non-parametrically by OLS after dropping the Pareto assumption and the non-linearity of the unobserved heterogeneity term, which is estimated by a polynomial in <u>z</u><sup>~~ˆ~~</sup> ij (denoted in the results tables by Z1 and Z2).

In the bias-corrected estimates of columns 3 and 4, the coefficients of FDI Arbitration and FDI Procedures are highly significant even after controlling for the general quality of institutions. Compared to the biased benchmark regression 30 In the parametric estimation, I assume delta to be 0.6.
1.6 results 39

(column 2), the elasticity of distance drops dramatically from approximately -1.5 to -0.5. The coefficient of FDI Openness is not significant.

In columns 5 and 6, I decompose the country and firm heterogeneity biases to assess which of the two biases is more prominent. Column 5 reports results controlling only for heterogeneity bias and not the selection bias. In column 6, only the sample selection bias is corrected. It is evident that the coefficients in column 5 are close to those in columns 3 and 4, whereas coefficients in column 6 are close to that obtained in the benchmark regression (column 2). This indicates that an overwhelming share of the bias has arisen because of unobserved heterogeneity. While the dominating presence of zero bilateral flows between numerous country pairs potentially posed serious selection bias, I find that in actual estimation, it is the failure to control for unobserved heterogeneity that produces most of the bias in a gravity-like model of FDI.

# 1.6.3 _Endogeneity Bias_

A pertinent concern in the relationship between FDI regulations and FDI inflow is that regulations could be endogenous to inflow. Improved regulation can be a response by governments to low levels of FDI, or large foreign investors can exercise their influence to lobby governments for regulatory reform _after_ choosing to locate. Reverse causation of this nature would imply that errors are not independently and identically distributed leading to inconsistent estimates. Generally, while openness to FDI could be increased by “stroke-of-the-pen” reforms, improvement in institutional practices and provisions are attained only over the long run. In this paper, it is the more institutions-oriented de facto indicators of FDI Procedures and FDI Arbitration that are associated with high levels of FDI, not statutory openness. Further, case studies on FDI policy reforms suggest that major FDI inflows typically follow, not precede, reforms.
40 investing across borders: do fdi-specific regulations matter?

I formally test for the exogeneity of FDI regulations with three alternative instrumental variables related to land. The first index on Access to Land Information measures aspects of whether the land registry or cadastre have a publicly accessible inventory of private and public land. The second index on the Availability of Land Information scores the richness of 18 pieces of land-related information (for example, plot size, land value, address, previous contracts). And the third index captures the time taken (number of days) to lease public or private land in the host country on average (World Bank Group 2010). These instrumental variables are chosen because they are associated with the soundness of domestic institutions; but they are not a determinant of FDI in their own right because a substantial share of aggregate global FDI is in services for which access to industrial land is not as important as for FDI in manufacturing.

The tests follow a three-stage process. _First_ , I conduct the Durbin-Wu-Hausman (DWH) test for the endogeneity of each of the Openness, Arbitration, and Procedures variables. After confirming a high degree of correlation between the potentially endogenous variable and its instrument, Openness is instrumented by the Access to Land Information index, FDI Arbitration is instrumented by the Availability of Land Information index, and FDI Procedures is instrumented by the time it takes to lease private land. I also instrument for all the three variables together. In all the four cases, the p-value of the DWH test is greater than 0.15, which fails to reject the null hypothesis of exogeneity of regressors.

_Second_ , I check for the validity of the instruments in an over-identified model. None of the p-values of the Hansen’s _J_ chi-squared is less than 0.44; the null hypothesis that the overidentifying restriction is valid is not rejected. Finally, the results reject the null hypothesis of weak instruments for the Openness and Arbitration variables, but not the Procedures variable. However, when the Arbitration and Procedures variables are instrumented together in a just-identified model, the
1.7 robustness 41

F-statistic exceeds the critical value in the Stock-Yogo test, rejecting the null hypothesis of weak instruments.<sup>31</sup>

# 1.7 robustness

Three robustness checks are performed on the main bias-corrected OLS estimates reported in column 4 of Table 1. _First_ , is the high statistical significance of coefficients on FDI Procedures and FDI Arbitration robust to more disaggregated controls for the quality of institutions in lieu of a single composite index? Tables 4 through 8 replicate the main result by proxying the general quality of institutions by five separate WGI variables. These five indicators are highly correlated with each other (Table 18). To avoid multi-collinearity, each is run in a separate regression.

The results for all variables concur with the main results. The coefficients for four of the five institutional variables – control of graft, regulatory quality, government effectiveness, and political stability – are not statistically significant after FDI-specific regulations are included in the bias-corrected regressions. The only coefficient attached to an institutional variable that is not statistically insignificant is the rule of law. It is _negatively_ significant at the 5 percent level (column 3, Table 5). This is perhaps a result of a high degree of correlation between the rule of law and the average income level of the country. When the log of GDP per capita is not included in the regression, the coefficient on the rule of law index is no longer significant. In all results, FDI Arbitration and FDI Procedure coefficients retain their high level of significance at either the 5 or 10 percent levels. The elasticity of distance drops as dramatically as in the main result, although coefficients on other gravity variables – colonialism, contiguity and language – are affected only slightly.

> 31 These methods follow Cameron & Trivedi (2009) in checking for regressor endogeneity, overidentifying restrictions, and weak instruments.
42 investing across borders: do fdi-specific regulations matter?

In all models, the BIT coefficient is highly significant in the selection equation (column 1) and not in the benchmark outcome equation (columns 2), reaffirming its appropriateness as a candidate for exclusion restriction. It passes the prima facie test of a valid exclusion restriction by being shown to affect the propensity to invest, but not the volume of investment. This point is worth emphasizing because as appealing as the technique for controlling truncation bias is, its efficacy can be stymied by the lack of a convincing exclusion restriction. The main exclusion restriction in Helpman et al. (2008) is religion, which Anderson (2011) does not find convincing. Baranga (2009), too, finds problems with the way Helpman et al. (2008) have used the religion variable. Using an alternative but similar index yields a highly significant coefficient in the benchmark OLS regression, weakening the case for the variable’s validity as an exclusion restriction.

_Second_ , how do coefficients change when a dummy for natural resource-rich countries is added to test whether poor countries rich in oil, gas and minerals attract FDI in the extractive industries? To restrict the sample to just developing countries, I drop the FDI-receiving OECD countries. Host countries are assigned a dummy value of one if during 2002 and 2006 their average share of fuel, ores, and metal exports in total exports exceeded 20 percent.<sup>32</sup> In Table 9, the coefficient of the resource dummy is not statistically different from zero indicating that resourcerich developing countries are not likely to attract more FDI after controlling for FDI regulations, the quality of institutions, and other host country characteristics. The statistical significance of coefficients of the FDI Procedures and Arbitrations variables is retained.

_Third_ , do the main results stand when alternative time periods, namely the individual years of 2006, 2007, and 2008, are considered? I do not consider years prior to 2006 because the explanatory indicators of FDI regulation cover prevailing regimes during or before 2006. Table 10 reports results obtained by estimating the model using data for 2006. GDP, GDP per capita, and tariffs averaged over the 32 Fuel products are SITC Section 3; metals and ores are SITC Divisions 27, 28, and 68.
1.8 conclusion 43

preceding 5 years, from 2001 to 2005. The basic results not only stand, but the coefficient of FDI Arbitration increases substantially when the dependent variable uses FDI stock for the year 2006. A 10 percent increase in a country’s combined score in the Ease of Process and the Judicial Assistance indices increases FDI by over 5.6 percent.

Table 11 reports results obtained by estimating the model using data for 2007. Here, both the coefficients of FDI Arbitration and FDI Procedures fall to the extent that the FDI Arbitration coefficient is statistically significant only at the 10.6 percent level.<sup>33</sup> It is conjectured that host country regulations and characteristics matter less when there is a global glut in investment funds. Recall that the year 2007 recorded the highest levels of outward FDI ever in the world. Total FDI stock was valued at nearly US$18 trillion and FDI flows nearly reached the US$2 trillion mark Table 12 reports results obtained by estimating the model using data for 2008. The estimates are comparable to the main results, except for the coefficient of the quality of institutions. As in Table 5 when it was measured by the rule of law indicator, the coefficient is negative and significant, that is, an improved measure of the rule of law is associated with lower FDI. As stated earlier, this coefficient becomes insignificant when GDP per capita is dropped from the regression.

# 1.8 conclusion

Gravity models have long been a workhorse for explaining trade, investment, and migration flows between countries. They fit the data well, but until recently, they were seen as atheoretical (Anderson & Van Wincoop 2003). This paper uses a new empirical methodology to estimate the impact of FDI-specific institutions on FDI inflow in a theoretically derived gravity-like model.

> 33 It is significant at the 3 percent level if standard errors are clustered by source country, but I opt for the more conservative estimates.
44 investing across borders: do fdi-specific regulations matter?

The traditional estimates obtained from log-linearized models of barriers to FDI are not consistent because they do not account for all the information contained in bilateral data, especially between countries that invest zero amounts in each other (country selection problem). These models also do not acknowledge that firms are heterogeneous and only a fraction of them are in a position to invest abroad (firm selection problem). The amount of FDI between countries i and j is not just a function of low barriers to FDI, but also the fraction of firms that invest in country j from country i. Not controlling for the latter assigns exaggerated elasticities to policy costs and gravity variables by conflating the extensive and intensive margins of investment flow.

I find that FDI-specific regulations matter significantly for attracting FDI. Using a new, painstakingly prepared data set on FDI regulations across 87 countries, I show that de facto implementation of laws related to the arbitration of commercial disputes and the number of procedures required to set up wholly-owned foreign subsidiaries are strongly associated with high levels of FDI stock. These provisions proxy for the quality of FDI-specific institutions in the country, and affect FDI more directly than measures of the _general_ quality of institutions.

I also show that it is how the targeted laws and regulation are translated into practice that is important rather than what is written in statutes. This is reflected by the fact that (the coefficient on) openness to FDI, measured by the average percentage of equity permitted to be owned by foreign investors, is not a significant determinant of FDI. Further, the correction of biases dramatically reduces the salience of bilateral distance – a proxy for transaction costs and information asymmetry – as a barrier to inward foreign direct investment from OECD countries.
A P P E N D I X

# 1.a deriving the gravity equation for fdi with heterogeneous firms

_Consumption_ : A representative consumer prefers variety (v) and maximizes an isoelastic utility function U = [� x(v)<sup>ρ</sup> dv]<sup>1/ρ</sup> subject to aggregate expenditure, E = [� p(v)x(v) . Aggregate expenditures equal aggregate income (Y). The elasticity of substitution across products, σ = 1/1 − ρ, is the same across countries. When goods 1 to n are continuous, E takes the form �Ω<sup>p(v)x(v)whereΩisthe“mass”</sup> of goods. The consumer optimizes Λ = U<sup>ρ</sup> −[� p(v)x(v)dv] − E] Relative demand for two varieties is: Multiplying both sides of (1.7) by p(v1) and taking integral with respect to v1, we get: The CES price index (true cost of living index) of all varieties is as follows: <u>1</u> P = ��Ω<sup>p(v)1−σdv</sup> � 1−σ .

The Marshallian demand for a variety (v) is: 45
46 investing across borders: do fdi-specific regulations matter?

_Production_ : A country i firm produces one unit of output with a cost-minimizing combination of inputs (cia) where a measures the number of bundles of inputs used per unit of output; ci is the cost of the bundle, which is uniform across country i. A firm’s productivity is therefore given by a<sup><u>1</u>. Monopolistic competition</sup> with increasing returns implies decreasing average cost as quantity produced increases [l(x) = f + cx]. Each firm produces one distinct variety. Each country i has a continuum of firms measured by Ni. Relative size of two countries can therefore be estimated by<sup>N</sup> N<sup><u>i</u></sup> j<sup>.Thereisnostrategicinteractionamongfirms,andtheyonly</sup> charge a constant mark-up over the marginal cost to maximize profit as follows: Substituting δp<sup><u>δxi</u></sup> i<sup>= −σ(p)−σ−1EPσ−1in (1.9):</sup> _Investing across borders_ : Firms serving the foreign market through exports face higher variable costs (τ<sup>e</sup> ij<sup>> τf</sup> ij<sup>) and firms undertaking FDI face higher fixed costs</sup> (f<sup>f</sup> ij<sup>>fe</sup> ij<sup>).Productionthroughsubsidiaryincountryjbyparentfirmsincountry</sup>
47

1.A deriving the gravity equation for fdi with heterogeneous firms

i reduces transport costs, but there exist non-trivial coordination and transaction τ<sup>e</sup> ij<sup>cia</sup> costs. For exports, price in country j is piτ<sup>e</sup> ij<sup>_≡_</sup> ρ , which differs from price in country i by including bilateral trade costs. For FDI, or more precisely sales by foreign affiliates, the price in country j of products sold by firms headquartered in country i is: Note that factor cost is ci for exporters and cj for investors. Take Ej = Yj. Substituting (1.11) in (1.9), and using (1.8), we get: _Productivity threshold_ : sale by multinational firms from i are profitable in j when πij⩾ 0. This implies that the minimum productivity cut-off aij required to invest abroad is:
48 investing across borders: do fdi-specific regulations matter?

The cumulative distribution function of the productivity index a is assumed to be a truncated Pareto distribution with support [aL, aH]. Only firms with productivity aL < a < aij invest abroad. This is captured by the fraction of such firms G(aij) =<sup><u>(</u></sup> a<sup>a</sup> H<sup>ij)kk</sup> −<sup>−</sup> a<sup>a</sup> L<sup>Lkkwhere k is the shape parameter of the Pareto distribution such</sup> that k > σ − 1. Total sales generated by foreign-owned subsidiaries from i to j are �aaLij<sup>pjxjNidG(a) where G(aij) is multiplied by Ni.</sup> Substituting for pj and xj, and characterizing FDI flows Fij = �aaLij<sup>a1−σdG(a)</sup> for aij > aL, we get the gravity-type equation for FDI: Aggregate FDI sales abroad is the measure of firms Ni multiplied by the average value of FDI of a representative firm. Note that Nij = NiG(aij). Yj is the economic size of the host country. Fij can be expressed as �aaLij<sup>a1−σdG(a)=</sup> �aaLij aHka<sup>k</sup> −<sup>k−</sup> a<sup>σ</sup> L<sup>kda.</sup> Further, The first multiplicative term is common across all countries; the second term k−σ+1 is specific to i and j, and termed Wij = � aaiLj � − 1 . Now, log-linearizing � � (1.14), we can estimate FDI as in (1.16). φi captures all variables with subscript i as a fixed effect for the FDI source country. φj amalgamates country j specific variables: GDP (lnYj), factor and policy costs (lncj), and inward multi-lateral resistance (lnPj); wij captures the ij component of Fij.
1.A deriving the gravity equation for fdi with heterogeneous firms

49 _Sample selection:_ equation 1.16 is observed only for positive values of FDI, that is, FDIij = 1 if zij > 0 where zij is the latent (unobserved) variable that determines whether country pairs enter into an FDI relationship or not. This latent variable can be estimated by probit conditional on characteristics contained in the outcome equation 1.16. For identification, we need at least one variable that affects only the propensity of investing but not the actual amount of investment. In other words, this term (φij) affects fixed cost, but not the variable cost. Equation 1.17 specifies the determinants of the latent variable affecting FDI participation. Equation 1.18 calculates the fitted values of the latent variable (ˆzij) through predicted probabilities of firms from country i profitably investing in country j, where Φ is the standard normal distribution.

Note that the error terms of the selection equation 1.17 and the outcome equation 1.16 are correlated because the unobserved factors that determine FDI participation also affect the magnitude of FDI. These two error terms are jointly normally distributed. This leads to a sample selection bias because in equation 1.16, E[eij|FDIij = 1] = 0. Under the assumptions of the model, there exists a consistent � � estimator of E[eij|FDIij = 1] which is<sup>σ</sup> σ<sup>e2η</sup> ηij, where ηij is the inverse Mills ratio obtained from equation 1.18.
50 investing across borders: do fdi-specific regulations matter?

_Unobserved heterogeneity bias:_ the latent variable zij is related to the productivity of the marginal FDI-undertaking firm. If this firm does not find it profitable to undertake FDI, then no firm from country i will. From equation 1.12, the ratio of profits to fixed cost of this firm can be expressed as: σ−1 Now, define the latent variable Zij = � aaiLj � where Zij = exp(zij). From equation 1.15, Wij = (Zij)<sup>δ</sup> − 1 where δ =<sup><u>k−</u></sup> σ−<sup><u>σ+</u></sup> 1<sup><u>1</u></sup> Zij is unobserved, but E[zij] can be estimated by zˆij from equation 1.18.

The insight of Helpman et al. (2008) is to show that both the sample selection and productivity heterogeneity biases can be redressed in a two-step estimation procedure beginning with the same probit selection equation. However, to prevent the model from being under-identified, this method requires at least one other variable that enters the probit equation but not the FDI outcome equation to remove the collinearity problem between zˆij and investment barriers. Without the extra identifying variable, zˆij is merely a linear combination of the same explanatory variables used in both the selection and outcome equations.

We know that E[zij|FDIij = 1] = E[zij] + E[ηij|FDIij = 1].

So, expected value of the latent variable given that the bilateral FDI flow is ˆ � positive can be estimated by <u>z</u><sup>~~ˆ~~</sup> ij = zij + ηij, the sum of the fitted value of the latent variable and the inverse Mills ratio.

In equation 1.16, the control for firm selection bias (wij) is ln{exp[δ(z<sup>~~ˆ~~</sup> ij)] − 1} � and the control for sample selection bias is ηij. This is a parametric non-linear regression. When the Pareto assumption is relaxed, Helpman et al. (2008) show
1.B firm productivity, exports, and horizontal fdi

51 that equation 1.16 can be estimated non-parametrically in an ordinary least squares (OLS) regression where sample selection is controlled by the inverse Mills ratio (η�ij) and firm selection is controlled by a polynomial of <u>z</u><sup>~~ˆ~~</sup> ij.

Finally, to sum up the estimation procedure on a practical note, how do I obtain <u>z</u><sup>~~ˆ~~</sup> ij and η�ij? A probit model predicts probabilities of positive FDI from a regression that includes the standard gravity variables, host country FDI barriers and dummies for source countries. This includes the identifying variable – BIT – which is excluded in the second stage. From the predicted probabilities, an inverse Mills ratio (η�ij) is computed. Because the inverse Mills ratio would be undefined for predicted probabilities of 1, all probabilities > 0.9999999 are converted to equal ˆ ˆ 0.9999999. Next, the fitted values of the latent variable zij = Φ<sup>−1</sup> (pij) are obtained from the normalized predicted probabilities. This is added to the inverse Mills ˆ � ratio to obtain <u>z</u><sup>~~ˆ~~</sup> ij = zij + ηij.

# 1.b firm productivity, exports, and horizontal fdi

The model is from Helpman et al. (2004) and Helpman (2006) where firms vary by productivity which is discovered after entering the industry. Factor cost (c) is country-specific and productivity (inverse of a) is firm-specific. Fixed cost of serving the domestic market is cfD and the firm charges a marked-up price to maximize profit as follows: If a firm sells in a foreign country with the same demand elasticity (σ) but different demand function, and faces transport and transaction cost as well as fixed export cost, cfX, it makes additional profit from exports as follows:
52 investing across borders: do fdi-specific regulations matter?

Firms with productivity ΘD < Θ < Θ<sup>j</sup> X<sup>produceforthedomesticmarket.</sup> Those with productivity Θ > Θ<sup>j</sup> X<sup>export.AfirmthatundertakeshorizontalFDI</sup> builds a second plant in country j incurring fixed cost cfI and variable cost cja to reap profit as follows: For the case in which demand level is the same in two countries, B<sup>j</sup> = B, cj = c and fI > τ<sup>σ−1</sup> fX > fD, the model shows: This leads to a natural sorting of firms by productivity. If the liberalization of trade or investment reduces marginal costs, not only can firms trade or invest more, but new firms can participate in foreign trade or investment as the productivity cut-off required to do so falls. In Figure 9, the profit schedules πI and πX swivel backwards. As an example, Baldwin et al. (2003) found that 4.5 percent reduction in Canada-US tariffs increased firms’ propensity to export by 63 percent.
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