metadata: authors: 0: author: Saurav,Abhishek
1: author: Kuo,Ryan Chia
count: World
docna: 0: docna: The Voice of Foreign Direct Investment :
            Foreign Investor Policy Preferences and Experiences in
            Developing Countries
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Investment and Investment Climate,Common Carriers Industry,Food & Beverage Industry,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing
teratopic: International Economics and Trade,Macroeconomics and Economic Growth,Industry
abstracts: cdata!: This paper provides insights to inform
            government efforts to attract and retain foreign direct
            investment, by analyzing the results of a survey of more
            than 2,400 affiliates of multinational enterprises across 10
            middle-income countries. The paper explores corporate
            perspectives and decision-making on countries’ legal and
            regulatory environments, political risk, and investment
            promotion activities. The survey finds that a
            business-friendly policy environment is critical to
            multinational enterprises’ investment decisions, confirming
            the importance of removing regulatory barriers to foreign
            direct investment (particularly approval processes),
            lowering political risks, and having investment promotion
            agencies. The survey results also show that investors are
            heterogeneous, with affiliates’ sectors, trading behaviors,
            sizes, ages, source countries, and foreign ownership levels
            affecting their perceptions of and sensitivity to various
            policy factors. Thus, policy makers should tailor their
            policy efforts to the needs of priority investor segments.
            Notably, the analysis consistently finds variation based on
            the extent to which affiliates import their inputs,
            suggesting that this relatively understudied topic deserves
            increased research and policy attention.
repnme: repnme: The Voice of Foreign Direct Investment :
            Foreign Investor Policy Preferences and Experiences in
            Developing Countries
display_title: The Voice of Foreign Direct
            Investment : Foreign Investor Policy Preferences and
            Experiences in Developing Countries
listing_relative_url: /research/2020/10/32465671/voice-foreign-direct-investment-foreign-investor-policy-preferences-experiences-developing-countries
topicv3: Manufacturing Sectors m1326521 1281,Trade m1326924 698,Investment Promotion m1504556 2552,Investment Entry m1504647 2544,Host Country m1939429 4820
publishtoextweb_dt: 2020-10-06T00:00:00Z
historic_topic: International Economics and Trade,Macroeconomics and Economic Growth,Industry
origu: EFI-FCI-TIC-Investment Climate (ETIIC)
no_of_pages: 67
author_keys: 0: author_key: 1259349
1: author_key: 1792443
model_extractions: Policy Research Working Paper 9425 The Voice of Foreign Direct Investment Foreign Investor Policy Preferences and Experiences in Developing Countries _Abhishek Saurav Ryan Kuo_ Finance, Competitiveness and Innovation Global Practice October 2020
Policy Research Working Paper 9425

# **Abstract**

This paper provides insights to inform government efforts to attract and retain foreign direct investment, by analyzing the results of a survey of more than 2,400 affiliates of multinational enterprises across 10 middle-income countries. The paper explores corporate perspectives and decision-making on countries’ legal and regulatory environments, political risk, and investment promotion activities. The survey finds that a business-friendly policy environment is critical to multinational enterprises’ investment decisions, confirming the importance of removing regulatory barriers to foreign direct investment (particularly approval processes), lowering political risks, and having investment promotion agencies. The survey results also show that investors are heterogeneous, with affiliates’ sectors, trading behaviors, sizes, ages, source countries, and foreign ownership levels affecting their perceptions of and sensitivity to various policy factors. Thus, policy makers should tailor their policy efforts to the needs of priority investor segments. Notably, the analysis consistently finds variation based on the extent to which affiliates import their inputs, suggesting that this relatively understudied topic deserves increased research and policy attention.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at asaurav@worldbank.org and rkuo@worldbank.org.

_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
The Voice of Foreign Direct Investment: Foreign Investor Policy Preferences and Experiences in Developing Countries _Abhishek Saurav, Ryan Kuo_

**JEL classification** : International Economics, Industrial Organization, Microeconomics

**Keywords** : foreign direct investment, multinational corporation, business survey, investment climate, international business, globalization, global value chains, investor perceptions
# **1. INTRODUCTION**

Foreign direct investment (FDI) has been the largest source of external finance for many developing countries—greater than remittances, private debt and portfolio equity, and official development assistance (UNCTAD, 2019).<sup>1</sup> Higher FDI inflows can ease capital constraints, contribute to gross output (Borensztein, De Gregorio, and Lee 1998; Choe 2003; Hansen and Rand 2006), drive employment growth (Craigwell, 2006; Fu and Balasubramanyam, 2005; Harding & Javorcik, 2011), and increase aggregate productivity through positive productivity spillovers and technology transfers (Das, 1987; Fosfuri, Motta, & Ronde, 2001; Javorcik, 2004; Saurav and Kuo 2020; Wang & Blomstrom, 1992). FDI also deepens trade linkages (Freund & Pierola, 2012; Moran, 2014; Swenson, 2008): inter- and intrafirm trade conducted by foreign affiliates accounts for about three-fourths of global exports (UNCTAD 2013). Thus, the presence of foreign affiliates and FDI can be a significant driver of economic growth.

However, FDI inflows to developing countries have been decreasing since the global financial crisis in 2008–09. From a pre-crisis average of 3 percent of GDP per year, FDI inflows have contracted to less than 2 percent of GDP in recent years.<sup>2</sup> This trend reflects a mix of economic factors, including declining rates of return on FDI, changes in U.S. tax policy, increasingly asset-light forms of international production, and rising trade and investment policy uncertainty. On top of these preexisting trends, the COVID-19 pandemic will further decrease FDI flows.

To drive economic growth and resilience, developing countries are prioritizing efforts to strengthen investment competitiveness to better attract and retain FDI. To that end, policy makers need to identify the drivers of FDI and specific market entry and operational constraints that may deter foreign investors. Via a new survey covering over 2,400 foreign investors across 10 major middle-income countries (MICs), this paper responds to this need by assessing the importance of various policy factors—including FDIrelated regulations, political risk, and investment promotion—as well as studying heterogeneity across different types of MNE affiliates.

The remainder of the paper is structured as follows: Section 2 reviews the literature on policy factors that affect FDI. Section 3 describes the survey data and presents descriptive analysis of variables. Section 4 presents the descriptive statistics. Section 5 details the analytical methodology. Section 6 discusses the results. Finally, Section 7 concludes and discusses policy implications.

# **2. LITERATURE REVIEW: THE ROLE OF POLICY IN DRIVING FDI**

> 1 Recent projections show remittances exceeding FDI for low- and middle-income countries in 2019, although they are not projected to do so for the 10 surveyed MICs given their relatively higher FDI compared to remittances (Global Knowledge Partnership on Migration and Development [KNOMAD] database: https://www.knomad.org/data/remittances). Foreign bank lending is also a significant source of private external finance and represents about half of all external liabilities of emerging-market countries (Bräuning and Ivashina 2019).

> 2 Although FDI inflows as a share of GDP have declined in most of the surveyed MICs, experiences have varied. For example, from 2008 to 2018, FDI inflows to China fell sharply (from 3.7 percent to 1.5 percent) but increased in Brazil (from 3.0 percent to 4.7 percent). As discussed in the Overview, a mix of economic factors are plausibly shaping global FDI trends, including declining rates of return on FDI, changes in U.S. tax policy, increasingly assetlight forms of international production on the backs of digital technologies, and rising policy uncertainty.

2
# **_Legal and regulatory environment_**

The literature generally finds laws, regulations, and policies relating to FDI (e.g., foreign ownership restrictions, investment approval mechanisms, regulations governing operations) to be crucial for attracting new investment and retaining existing investors. Part of North’s (1990) conceptualization of institutions as “rules of the game”, the legal and regulatory environment for FDI shapes investment performance in host countries. Once investments are made, FDI is partially ‘sunk’, rendering it particularly sensitive to regulatory uncertainty (Saggi, 2002). In addition to uncertainty, a suboptimal business environment increases transaction costs, effectively constituting a tax on business activity (Buchanan, Le, & Rishi 2012).

A sizable body of research suggests that improvements in the quality of a country’s legal and regulatory environment are associated with higher FDI inflows. Mistura and Roulet (2019) find that legal restrictions on FDI decrease FDI inflows to both developed and developing countries. The quality of institutions governing the business environment—measured in terms of bureaucratic efficiency, corruption levels, and property rights protections—also has a statistically significant effect on FDI (Ali, Fiess, & MacDonald, 2010; Bénassy-Quéré, Coupet & Mayer, 2007; Bevan, Estrin & Meyer, 2004; Egger & Winner, 2005; Fung et al. 2005; Globerman & Shapiro 2002; Kinda 2010; Richards & Nwankwo, 2005; Staats and Biglaiser, 2012). Evidence from surveys of investors and foreign businesses reinforces empirical evidence that a supportive business climate is among the top priorities for foreign investors (A. T. Kearney 2019; Asiedu, 2006, Campos, Lien, & Pradhan, 1999; Gastanaga, Nugent & Pashamova, 1998; Kusek and Silva 2018; Smarzynska 1999; Wei 2000).

# **_Political risk and investor protection_**

Foreign businesses in host countries are exposed to the risk of disruption by political and government actions. Such political risks emanate from host country governments’ failure to honor explicit and implicit contracts with investors (Graham, Johnston, and Kingsley 2016). Specific sources of political risk include currency and transfer restrictions, unexpected changes in laws and regulations, expropriation by the government, breach of contract, and failure to honor guarantees (Fitzpatrick 1983; Henisz and Zelner 2010; Howell and Chaddick 1994; Graham, Johnston, and Kingsley 2016).

The empirical literature on FDI determinants in developing countries consistently shows that the effect of political risk on FDI is statistically significant and that high levels of political risk deter FDI inflows. Regulatory risk (Hebous, Kher, and Tran, 2020), poor institutional quality (Alfaro, Kalemli-Ozcan, and Volosovych 2008), corruption (Wei 2000), and poor property rights protections (Henisz 2000, 2002; Papaioannou 2009) can all be major deterrents to FDI inflows to developing countries. Conversely, countries that undertake investment reforms to lower political risks faced by foreign investors significantly improve FDI inflows (Biglaiser & DeRouen 2006; Krifa-Schneider & Matei 2010). Looking across affiliates, some research suggests that affiliate-specific attributes such as access to information and capital availability can affect the degree to which FDI is exposed to political risk (Barry and DiGiuseppe 2019; Graham, Johnston, and Kingsley 2016).

# **_Investment promotion_**

National and subnational investment promotion agencies (IPAs)—institutions with mandates to promote and facilitate investment—have proliferated over the past two decades. Some observers maintain that 3
the contributions of IPAs are likely to be more pronounced in developing countries, where investors may know less about the location, struggle to obtain reliable information, find the regulatory environments more challenging, and encounter further obstacles stemming from institutional and cultural differences between the investors’ home and host markets (Harding and Javorcik 2011, 2012).

Empirical evidence generally shows that IPAs can play a significant role in attracting and growing FDI in developing countries (Charlton and Davis 2007; Cho 2003; Crescenzi, Di Cataldo, and Giua 2019; Morisset and Andrews-Johnson 2004; Bezuidenhout & Pietersen 2015). Relatedly, Harding and Javorcik (2012) found that IPA-promoted sectors experienced 155 percent higher FDI inflows and 68 percent higher employment relative to non-targeted sectors. IPAs can also bolster the quality of FDI by supporting technology transfer, knowledge spillovers, and export growth (Freund and Moran 2017; Moran, Gorg, and Krieger-Boden 2018).

# **_Gaps in the literature_**

While the general importance of countries’ legal and regulatory environments and investment promotion activities is well-explored in the literature, gaps related to specific policy areas and heterogeneity across types of affiliates remain. For example, some researchers argue that factors influencing FDI may vary by sector, especially between manufacturing and services (Erramilli and Rao 1993; Tatoglu and Glaister 1998). Some studies also suggest that younger and older companies behave differently (Jimenez 2011). Nevertheless, heterogeneity remains an understudied topic given the scarcity of reliable data on FDI flows by sector. In addition, existing studies generally examine overall indices of policy and institutional quality and often do not study specific policy levers in detail; more granular approaches would be helpful to aid government policy design.

This paper builds upon the existing literature via a survey-based approach that distinguishes between specific policy areas. High-level survey findings are used to validate or question prevailing findings in the literature regarding the effect of countries’ legal and regulatory environments, and investment promotion on FDI attraction. In addition, by gathering data on respondent profiles, survey data allow us to examine heterogeneity in investors’ preferences and decision-making criteria across a wide variety of affiliate dimensions, including affiliate size, age, broad sector, and trading activity. Finally, by asking respondents about specific policy areas, the survey can provide insight into the areas that truly matter to investors beyond simple claims of policies’ general importance as a whole.

# **3. DATA**

The analyses in this paper draw upon the results of the 2019 Global Investment Competitiveness (GIC) Survey (survey), a survey of executives of the affiliates of multinational enterprises (MNEs) in developing countries. The survey data cover more than 2,400 foreign investors with operations in 10 middle-income countries: Brazil, China, India, Indonesia, Malaysia, Mexico, Nigeria, Thailand, Turkey, and Vietnam. The 10 countries covered by the survey account for more than half of the global population, one-quarter of global GDP, and one-fifth of global trade. From an FDI perspective, they accounted for over a third of global inflows (37 percent) and three-fourths (75 percent) of inflows to developing countries in 2018.

The survey was conducted using 30-minute phone interviews in the primary business language(s) of the host economies. The interviews were conducted between June and November 2019. The survey was administered to senior executives of foreign-owned affiliates who possess a broad understanding of 4
their companies’ business strategies, policy barriers, operational obstacles, and investments in the host economy. Information was collected on general characteristics and investments of the company, the importance and effect of global megatrends on the company’s business operations, FDI’s contribution to the host economy, and the importance of investment policy factors and operational obstacles faced by foreign-owned affiliates.

The survey was designed to generate results that are representative at the country level and comparable across countries. It targeted a statistically representative sample of existing foreign-owned affiliates across the 10 surveyed MICs. The target was to reach 125 interviews per sector (manufacturing and services). Each country sample comprises roughly 250 affiliates with at least some level of foreign equity ownership and at least five employees. The only exception is Nigeria, where, due to sampling frame limitations, the sample comprises 164 respondents (55 manufacturing and 109 services). Thus, across the 10 target countries, more than 2,400 responses were collected.

# **4. DESCRIPTIVE STATISTICS**

The GIC 2019 Survey attempted to draw a representative sample of foreign investors across the 10 surveyed emerging markets. This section outlines the profiles of the 2,424 survey respondents.

_Sector and subsector:_ Survey respondents operate across a wide range of sectors. By design, about half of surveyed affiliates were in the manufacturing sector, and about half were in services. Within each sector, the sample covers many subsectors: _Machinery and Equipment_ comprise the largest subsector within manufacturing, and _Wholesale and Retail Trade_ is the largest subsector within services in our sample. However, no one subsector accounts for more than 11 percent of the overall sample (see Table 1).

**Table 1. Respondents by sector and subsector**

|**Sector and Subsector**|**N**|**Share of total sample**|
|---|---|---|
|**MANUFACTURING**<br>|**1,190**|**49.1%**|
|Machinery and equipment<br>|151|6.2%|
|Metals and metal products|124|5.1%|
|Automobiles, other motor vehicles, and transport|116|4.8%|
|Rubber and plastic products|108|4.5%|
|Chemicals and chemical products|84|3.5%|
|Information technology and telecommunications|72|3.0%|
|Agroprocessing, food products, and beverages|71|2.9%|
|Electrical and electronic equipment and|52|2.1%|
|Textiles, apparel, and leather|51|2.1%|
|Wood products, paper, and printing|45|1.9%|
|Pharma|18|0.7%|
|Refined petroleum products, coke, and nuclear fuel<br>|8|0.3%|
|Other or unclassified manufacturing|290|12.0%|
|**SERVICES**|**1,234**|**50.9%**|
|Wholesale and retail trade|270|11.1%|
|Business services|116|4.8%|
|Logistics, transport, and storage|101|4.2%|
|Computer and software services|85|3.5%|
|Administrative and support services|59|2.4%|
|Construction|58|2.4%|

5
|**Sector and Subsector**|**N**|**Share of total sample**|
|---|---|---|
|Financial services including insurance|49|<br>2.0%|
|Utilities|32|1.3%|
|Other professional, scientific, and technological|20|0.8%|
|<br>Real estate|18|0.7%|
|Hospitality|16|0.7%|
|<br>Telecom|13|0.5%|
|Media|9|0.4%|
|Health|7|0.3%|
|Arts and recreation|6|0.2%|
|Education|5|0.2%|
|Scientific research and development services|5|0.2%|
|Water supply and waste management|3|0.1%|
|<br>Residential care and social work|2|0.1%|
|Machinery and equipment|1|0.0%|
|<br>Metals and metal products|1|0.0%|
|<br>Public administration and defense services|1|0.0%|
|Other or unclassified services|357|14.7%|

_Source_ : Computation based on the 2019 GIC Survey.

_Source country_ : Overall, nearly 80 percent of respondents report having foreign owners from the East Asia & Pacific and Europe & Central Asia regions (see Table 2).<sup>3</sup> The vast majority of respondents (87 percent) report having foreign owners from high-income countries.

**Table 2. Respondents by region of foreign owner**

|**Host**<br>**country**||**Decline**<br>**to**<br>**answer**|**East**<br>**Asia**<br>**&**<br>**Pacific**|**Europe**<br>**&**<br>**Central**<br>**Asia**|**Latin**<br>**America**<br>**&**<br>**Caribbean**|**Middle**<br>**East &**<br>**North**<br>**Africa**|**North**<br>**America**|**South**<br>**Asia**|**Sub-**<br>**Saharan**<br>**Africa**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|
|Brazil|n|0|39|147|15|2|45|2|0|250|
||%|0|15.6|58.8|6|0.8|18|0.8|0|100|
|China|n|6|142|61|3|0|36|2|0|250|
||%|2.4|56.8|24.4|1.2|0|14.4|0.8|0|100|
|India|n|2|50|124|2|6|56|3|7|250|
||%|0.8|20|49.6|0.8|2.4|22.4|1.2|2.8|100|
|Indonesia|n|0|203|38|1|2|10|3|1|258|
||%|0|78.68|14.73|0.39|0.78|3.88|1.16|0.39|100|
|Malaysia|n|0|160|54|1|4|25|5|1|250|
||%|0|64|21.6|0.4|1.6|10|2|0.4|100|
|Mexico|n|1|41|94|21|0|89|3|0|249|
||%|0.4|16.47|37.75|8.43|0|35.74|1.2|0|100|
|Nigeria|n|0|18|55|0|15|23|13|40|164|
||%|0|10.98|33.54|0|9.15|14.02|7.93|24.39|100|

3 Regions are defined according to World Bank Group classifications, as outlined here: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</u> 6
|Thailand|n|0|197|28|1|2|18|3|1|250|
|---|---|---|---|---|---|---|---|---|---|---|
||%|0|78.8|11.2|0.4|0.8|7.2|1.2|0.4|100|
|Turkey|n|0|24|195|2|9|18|1|1|250|
||%|0|9.6|78|0.8|3.6|7.2|0.4|0.4|100|
|Vietnam|n|0|218|31|0|0|3|1|0|253|
||%|0|86.17|12.25|0|0|1.19|0.4|0|100|
|**Total**|**n**|**9**|**1,092**|**827**|**46**|**40**|**323**|**36**|**51**|**2,424**|
||**%**|**0.37**|**45.05**|**34.12**|**1.9**|**1.65**|**13.33**|**1.49**|**2.1**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

**Table 3. Respondents by income classification of foreign owner country**

|**Country**||**Decline to answer**|**High income**|**Low or middle income**|**Total**|
|---|---|---|---|---|---|
|Brazil|n|0|228|22|250|
||%|0|91.2|8.8|100|
|China|n|6|229|15|250|
||%|2.4|91.6|6|100|
|India|n|2|232|16|250|
||%|0.8|92.8|6.4|100|
|Indonesia|n|0|209|49|258|
||%|0|81.01|18.99|100|
|Malaysia|n|0|217|33|250|
||%|0|86.8|13.2|100|
|Mexico|n|1|222|26|249|
||%|0.4|89.16|10.44|100|
|Nigeria|n|0|83|81|164|
||%|0|50.61|49.39|100|
|Thailand|n|0|229|21|250|
||%|0|91.6|8.4|100|
|Turkey|n|0|222|28|250|
||%|0|88.8|11.2|100|
|Vietnam|n|0|230|23|253|
||%|0|90.91|9.09|100|
|**Total**|**n**|**9**|**2,101**|**314**|**2,424**|
||**%**|**0.37**|**86.67**|**12.95**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

_Employment:_ About one-quarter of surveyed businesses have more than 250 employees. The remainder are small and medium enterprises (SMEs) with 250 or fewer employees, and roughly half of the SMEs have 100 or fewer employees (Table 4).

**Table 4. Respondents by employment size**

|**Country**|**Don't**<br>**know**|**<100**<br>**employees**|**100-250**|**251-**<br>**1,000**|**1,001-**<br>**10,000**|**>10,000**|**Total**|
|---|---|---|---|---|---|---|---|
|Brazil<br>n|1|133|47|52|16|1|250|
|%|0.4|53.2|18.8|20.8|6.4|0.4|100|

7
||%|45.6|11.6|19.6|3.6|13.6|6|100|
|---|---|---|---|---|---|---|---|---|
|Turkey|n|121|17|23|22|36|31|250|
||%|48.4|6.8|9.2|8.8|14.4|12.4|100|
|**Total**|**n**|**1,253**|**134**|**237**|**246**|**279**|**275**|**2,424**|
||**%**|**51.69**|**5.53**|**9.78**|**10.15**|**11.51**|**11.34**|**100**|

_Source_ : Computation based on the 2019 GIC Survey.

_Age:_ Respondents are generally fairly established in their respective markets. The average respondent has over 17 years of experience operating in its respective host market (Table 6). On average, respondents have over 10 years of experience across all 10 countries.

_Trading behavior:_ Respondents are active in global trade, both in terms of importing into and exporting from host countries. On average, respondents import 46 percent of their inputs, and exports account for 34 percent of their sales (Table 6). There is wide variation in average trading behavior across host countries, with average export share of sales ranging from 15 percent (Brazil) to 53 percent (Vietnam) and average import share of inputs ranging from 33 percent (China) to 58 percent (Mexico and Vietnam).

_Ownership._ On average, foreign investors hold large shares in affiliates in our sample. The average stake held by foreign investors is 87 percent across the 10 MICs, and within-country averages are above 80 percent in every country except for Nigeria (Table 6). Roughly two-thirds of respondents are 100 percent owned by foreign investors.

**Table 6. Descriptive statistics for years in country, foreign ownership, and trading behavior**

||**Years**<br>|**in coun**<br>|**try**<br>|**Foreign **<br>|**owners**<br>|**hip (%)**|**Expor**<br>|**t % of s**<br>|**ales**|**Import**<br>|**% of i**<br>|**nputs**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**Mean**|**SD**|**N**|**Mean**|**SD**|**n**|**Mean**|**SD**|**n**|**Mean**|**SD**|**n**|
|Brazil|20.9|18.5|239|92.0|21.4|250|14.8|24.6|222|42.0|33.1|221|
|China|14.6|6.5|234|96.9|12.0|250|36.0|36.2|234|33.3|33.4|212|
|India|14.9|10.2|248|90.1|23.2|250|32.7|34.9|237|35.7|32.3|221|
|Indonesia|17.5|10.5|252|84.3|25.1|258|31.5|39.1|241|43.7|35.0|241|
|Malaysia|22.0|15.2|232|84.4|29.7|250|47.1|39.6|238|47.8|31.2|224|
|Mexico|17.2|14.0|247|89.8|24.6|249|35.7|36.6|244|58.1|31.9|242|
|Nigeria|20.3|21.7|160|51.8|36.0|164|16.8|23.3|153|44.4|32.4|154|
|Vietnam|10.5|6.1|249|94.8|16.8|253|52.7|42.1|214|58.1|34.6|205|
|Thailand|20.5|12.6|234|83.6|29.4|250|37.8|36.5|226|45.5|32.3|217|
|Turkey|15.8|12.2|244|88.5|21.6|250|30.8|34.9|236|51.4|35.0|223|
|**Total**|**17.3**|**13.6**|**2,339**|**86.8**|**26.4**|**2,424**|**34.1**|**37.1**|**2,245**|**46.1**|**34.0**|**2,160**|

_Source_ : Computation based on the 2019 GIC Survey.

# **5. METHODOLOGY**

We employ simple summary statistics to assess different factors’ relative importance to foreign investment decisions. We examine and compare the distribution of responses across questions in a given thematic area (location decision-making criteria, legal and regulatory obstacles, investment protection, or investment promotion). Within each thematic area, questions are phrased in the exact same way, with 9
the same response options; only the factor in question varies. This standardization allows for comparability across factors and questions.

The data also allow us to employ regression specifications to study heterogeneity across investors with respect to individual factors. We explore variation in self-reported data on investment decision-making factors and obstacles with respect to overall sector (i.e., manufacturing versus services), exports’ share of sales, imports’ share of inputs, source country income group, size of the workforce, investment size, number of years in country, and level of foreign ownership.

As data for several questions are ordinal, we employ ordered logistic regressions for these analyses of heterogeneity. These specifications account for how question responses follow an established order. A factor that is ‘critically important’ is of greater importance than one that is that is merely ‘important’, which in turn is more important than a factor considered ‘not important at all’. At the same time, unlike OLS regressions, ordered logistic regressions do not assume that the data are cardinal. For example, it might be possible that the distance between ‘critically important’ and ‘important’ is different than the distance between ‘important’ and ‘somewhat important’ despite these jumps each being one gradation in the question scale.

Except when assessing the effect of being in the broad services sector, our preferred specification is an ordered logistic specification with all affiliate-level covariates, country fixed effects, and subsector fixed effects included (model 8 in all of our regression tables). We also employ OLS regressions using Equation

> 4 Subsector fixed effects are at the ISIC Intermediate SNA (A*38) level of aggregation. We do not insert subsector fixed effects in specifications with overall sector as a covariate due to collinearity between sector and subsector variables.

10
1 where y* is the observed value of the variable to check the robustness of the results (i.e., treating the dependent variable as cardinal and continuous), although the ordered logistic specification is our preferred specification for the reasons outlined earlier.

To ensure representativeness, analyses contained in the paper incorporate weights to account for different sample sizes across countries, different probabilities of sampling, and bias due to non-response. Design weights are included to ensure that the different strata (country-sector intersections) are given equal weight. Sampling weights are included to account for different probabilities of being sampled, weighting each observation by the inverse probability of selection. Finally, non-response weights are applied to maintain consistency between the distribution of MNE affiliates in the sampling frame and results from the sample along observable characteristics.<sup>5</sup> A key limitation of these approaches is that they are not suitable for robust causal inference. As our data are cross-sectional, self-reported, and cannot be linked to other measures of affiliate behavior and performance (e.g., affiliate-level financial statements), we cannot definitively rule out omitted variables, simultaneity, or reverse causality or derive quantitative estimates of these factors’ impact on FDI. Nevertheless, by identifying novel patterns and associations that occur systematically, we can compare our findings with predictions made in the existing literature as well as suggest directions for future research.

# **6. RESULTS**

# **Overall determinants of foreign investment decisions**

# _Overall results_

While survey respondents generally consider political and macroeconomic stability to be the most important factors informing their investment decisions, survey data also confirm that host countries’ policy stance towards foreign investors and the private sector is critical. Forty-two percent of respondents consider host countries’ legal and regulatory environments to be “critically important” to their decisions to invest in host countries. By this measure, legal and regulatory environments rank ahead of factors such as physical infrastructure and low labor and input costs and behind only political and macroeconomic stability. This finding is in line with the general consensus in the literature that the legal and regulatory environment in host countries plays a critical role in attracting FDI (e.g., Kusek and Silva 2018, Mistura and Roulet 2019). Similarly, 35 percent of respondents consider investor protections to be “critically important”. Again, this finds support in the broader literature regarding the importance of political risk in informing foreign investors’ decisions (Biglaiser & DeRouen 20106; Krifa-Schneider & Matei 2010).

> 5 As a robustness check, we have also run the analyses without weights and see little variation in results. These results without weights are not reported in this paper but can be made available upon request by contacting the authors.

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**Figure 1. Overall determinants of investment location decisions (% of respondents)**

**Question:** How important were the following factors in your parent company's decision to invest in this country?

<!-- Start of picture text --> Political stability 49% 35%<br>Macroeconomic stability 49% 36%<br>Legal & regulatory environment 42% 42%<br>Talent/skills 41% 43%<br>Low taxes 41% 35%<br>Market size 39% 43%<br>Physical infrastructure 39% 41%<br>Ability to export 38% 32%<br>Intellectual property protections 35% 35%<br>Investor protections 35% 38%<br>Low labor & input costs 33% 41%<br>Supply chain coordination 30% 44%<br>Local input sourcing 27% 42%<br>Resource endowments 18% 32%<br>Local acquisition targets 14% 36%<br>0% 20% 40% 60% 80% 100%<br>Critically important Important<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

# _Heterogeneity across investor types_

More export-intensive respondents place lower importance on host countries’ legal and regulatory environments. In the preferred ordered logistic specification, the coefficient on export percentage of sales is negative and statistically significant at the p<0.05 level. This finding is robust to the exclusion or inclusion of country and subsector fixed effects as well as alternative specifications using OLS (Table 11, Column 8). A 10-percentage point change in the portion of sales from export is associated with a 1- percentage point increase in the likelihood of considering legal and regulatory environments to be ‘critically important’ (Table 12). Although further research is required to explain this pattern, this coefficient may reflect exporters being less subject to regulations governing sales within their countries of operations (e.g., local consumer protection laws) relative to market-seeking investors.

**Table 7. Overall determinants of investment decisions: Coefficient signs and significance levels for heterogeneity analyses**

||**Legal/regulatory**<br>**environment**|**Inv. protection**<br>**guarantees**|
|---|---|---|
|**Services**|||
|**Export % of sales**|-**||
|**Share of sourcing via imports**||+***|
|**High-income source country**|+*||

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|**>250 employees**|+***|
|---|---|
|**>10M investment**||
|**Years in country**||
|**Level foreign ownership**||

*** p<0.01, ** p<0.05, * p<0.1

Affiliates with larger workforces place greater importance on host countries’ legal and regulatory environments. The regression coefficient on the dummy variable for having more than 250 employees is positive and statistically significant at the p<0.01 level in our preferred specification (Table 11, Column 8). This finding is robust to the inclusion or exclusion of country and subsector fixed effects. In terms of magnitude, analyzing average marginal effects in our ordered logistic regressions reveals that having more than 250 employees is associated with a 10-percentage point increase in the likelihood of finding the legal and regulatory environment to be ‘critically important’. Thus, we find support for findings by Aterido, Hallward-Driemeier, and Pages (2007) that regulatory enforcement increases with affiliate size, whereas smaller affiliates generally experience less consistent and laxer implementation of regulations on average.

Having foreign owners from high-income countries may also translate to placing greater importance on host countries’ legal and regulatory environments. The coefficient on the dummy for having a foreign owner from a high-income country is positive and marginally significant (p<0.10) in our preferred specification with the importance of host countries’ legal and regulatory environment as the dependent variable (Table 11, Column 8). This finding may reflect how investors from developing countries are better able to navigate challenging institutional environments given their firsthand experience with similar environments in their home economies, supporting conclusions by researchers such as Demir and Hu (2016). However, the coefficient in our analysis is only significant in specifications with both country and subsector fixed effects, so further research is required to draw more robust conclusions.

Finally, the survey results suggest that foreign affiliates which import a greater share of their inputs place greater importance on investor protection guarantees. In our preferred specification with the importance of investor protections as the dependent variable, the coefficient on share of sourcing via imports is positive and statistically significant at the p<0.01 level (Table 13, Column 8). This finding is robust to alternative specifications and the inclusion and exclusion of fixed effects. From a marginal effects perspective, a 10-percentage point increase in the portion of inputs sourced via imports is associated with a 1-percentage point increase in considering investor protection guarantees to be ‘critically important’ (Table 14). Importers may be more sensitive to political risk due to how political risks can impact imports: Relative to companies that source locally, importers are more exposed to sudden, adverse changes in import laws and regulations as well as currency restrictions which may affect their ability to engage in trade. However, definitively explaining this pattern requires further research as the existing literature does not explore the relationship between importing and political risk in great depth.

# **Legal and regulatory barriers to investment**

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# _Overall results_

To provide more detail on legal and regulatory barriers to investment, the GIC Survey 2019 asked respondents about the degree to which various legal and regulatory issues presented obstacles to their operations. Such issues covered issues ranging from cumbersome investment approval processes to restrictions on expatriate staff.

Cumbersome investment approval processes and operational restrictions are the most often cited regulatory barriers for FDI in surveyed MICs (Figure 2). Fifty-six percent of respondents list cumbersome investment approval processes as moderate or major obstacles to operations, and 44 percent cite price, technology, or product restrictions as moderate or major obstacles. The salience of these top two concerns holds across most host countries and sectors. In addition, these findings are consistent with prior work that also finds approval processes and restrictions on prices, technology, or products to be a significant obstacle for foreign affiliates (Mistura and Roulet 2019; UNCTAD 2019).

# _Heterogeneity across investor types_

Compared with manufacturing affiliates, services affiliates perceive joint venture requirements to be more significant obstacles on average, as evidenced by the positive and statistically significant (at the p<0.10 level) coefficient on the dummy of services in our preferred specification (Table 16, Column 6). Affiliates in the services sector also perceive expatriate restrictions to be more significant obstacles on average (see the positive and significant—at the p<0.10 level—coefficient in Table 18, Column 6).

**Figure 2. Salience of legal and regulatory obstacles (% of respondents)**

**Question:** To what degree are the following factors an obstacle for your company to operate in this country?

<!-- Start of picture text --> Investment approvals 26% 30%<br>Price, tech, or product restrictions 16% 28%<br>Foreign investment limits 16% 22%<br>Expat restrictions 13% 24%<br>JV requirements 15% 20%<br>Local sourcing requirements 12% 21%<br>R&D investment requirements 10% 21%<br>Minimum investment requirements 8% 19%<br>0% 20% 40% 60% 80% 100%<br>Major obstacle Moderate obstacle<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

_Note:_ JV = joint venture; R&D = research and development; tech = technology.

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Taken together, these findings support explanations by Tatoglu and Glaister (1998) and Erramilli and Rao (1993) that the higher skill content of services impacts FDI decision-making criteria. Affiliates in services may rely more on foreign expertise and brand equity. If foreign parents of affiliates are unable to exercise sufficient control over affiliates, they may be constrained in translating their expertise into operational changes at affiliates, affecting performance as well as brand perceptions. Similarly, being unable to import sufficient foreign talent may limit the degree to which affiliates may tap global expertise and skills. MNCs commonly rely on expatriate staff in affiliate operations for liaison, management capacity and control, and cross-cultural communications (Geng 2003; Seak and Enderwick, 2008; Selmer, 2005), and the role of such expatriates may be especially important in skill-intensive services. However, as the existing literature does not cover this topic in depth, further research is required to definitively explain this pattern in the survey data.

**Table 8. Legal and regulatory barriers: Coefficient signs and significance levels for heterogeneity analyses**

||**Investment**<br>**approvals**|**JV requirements**|**Foreign**<br>**investment limits**|**Expat restrictions**|**Local sourcing**<br>**requirements**|**R&D**<br>**requirements**|**Minimum inv.**<br>**Requirements**|**Price and product**<br>**restrictions**|
|---|---|---|---|---|---|---|---|---|
|**Services**||+*||+*|||||
|**Export % of sales**|||||||||
|**Share of sourcing via imports**||+*|+**||||||
|**High-income source country**|||||||||
|**>250 employees**|||||||-**||
|**>10M investment**|||||||||
|**Years in country**|-*||||||||
|**Level foreign ownership**|||||||||

*** p<0.01, ** p<0.05, * p<0.1

This table displays signs of coefficients for the variables in the rows for regressions with the column variables as the dependent variables. Positive signs denote that the row variable is associated with encountering more significant obstacles with respect to the column variable. Signs and significance levels are shown from ordered logistic regressions with all affiliate-level covariates (i.e., row variables) and country and ISIC A38 subsector fixed effects. Blanks denote coefficients that are not statistically significant (i.e., only signs of statistically significant coefficients are shown).

Finally, sourcing a higher share of inputs via imports is associated with experiencing more legal and regulatory obstacles on average. Higher import shares of input are associated with experiencing more serious obstacles with respect to joint venture requirements (Table 16, Column 8; significant at the p<0.10 level) as well as foreign investment limits (Table 17, significant at the p<0.05 level). These findings potentially relate to prior research that suggests that imports are associated with higher technological content (Javorcik and Spatareanu 2008). MNEs with more advanced intellectual property prefer full ownership of affiliates to prevent leakages to local partners (Saggi 2002).

# **Investment protection**

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# _Overall results_

Survey data confirm the importance of investor protections and other measures to minimize political risk in attracting and retaining FDI. To explore the effects of political risk on investments, the GIC Survey 2019 asked respondents how they would respond to hypothetical political risk scenarios. Two in three existing investors would consider withdrawing investments or cancelling planned investment in the face of political risk exposure in host countries (Figure 3). These findings are consistent with the broader literature on political risk which consistently identifies political risk and regulatory uncertainty as major concerns for foreign businesses. They are also in line with other surveys on the topic.<sup>6</sup> Risks of expropriation and government breach of contract are likely to elicit particularly negative investment reactions, in line with findings by Kusek and Silva (2018). About 50 percent and 40 percent, respectively, of investors would consider withdrawing existing or cancelling planned investments when faced with these risks (Figure 3). Sudden legal changes, currency restrictions, and delays in obtaining permits and approvals elicit somewhat less severe reactions. Such risks are more likely to cause investors to delay investments rather than cancel or withdraw investments completely.

**Figure 3. Reactions to political risks**

<!-- Start of picture text --> Question: How would the risk of the following events affect your investments in this<br>country?<br>At least one of the below 39% 27% 16% 11% 4%2%<br>Expropriation 24% 23% 13% 23% 12% 5%<br>Gov. breach of contract 17% 22% 16% 28% 13% 6%<br>Sudden, adverse change in laws 12% 16% 25% 36% 7% 4%<br>Currency restrictions 9% 13% 29% 33% 12% 5%<br>Delay in permits and approvals 6% 9% 38% 34% 9% 4%<br>0% 20% 40% 60% 80% 100%<br>Withdraw existing investment Cancel planned investment Delay planned investment<br>Consider delay/cancellation None Don't know<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

# _Heterogeneity across investor types_

Affiliates with more years of experience in a given host country are likely to be less sensitive to political risk in general. For each respondent, we recorded the most negative reaction across all hypothetical 6 For example, political risk ranks second among nine categories of possible impediments to FDI according to the MIGA-EIU Political Risk Survey 2013; political risk and uncertainty is ranked fifteenth among 69 organizational risks according to the Aon Global Risk Management Survey 2019; and political risks and regulatory uncertainty is ranked fourth among 12 risks according to the Association for Financial Professionals and Oliver Wyman Risk Survey 2019.

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political risks (i.e., we took the maximum value of responses across all questions dealing with individual political risks). In our preferred specification with this composite variable as the dependent variable, the coefficient on number of years in the country is negative and significant at the p<0.10 level and robust to alternative specifications (Table 29). This finding supports Barry and DiGiuseppe’s (2019) conclusion that affiliates with better information (i.e., those with more experience within a country) are better able to navigate political risks through local connections and superior knowledge of legal systems.

In contrast, affiliates which import a higher share of inputs are likely to be more sensitive to political risk. The coefficient for import share of sourcing is positive and significant at the p<0.10 level in our preferred specification, although it is not significant in all alternative specifications (Table 29). As discussed earlier, while further research on the topic is needed, this pattern may reflect importers’ higher exposure to political risk concerning import and currency restrictions: A greater assortment of policies may impact their operations, meaning higher political risk.

**Table 9. Reactions to political risks: Coefficient signs and significance levels for heterogeneity analyses**

||**Worst political**<br>**risk**|**Delay in**<br>**permits**|**Currency**<br>**restrictions**|**Gov. breach of**<br>**contract**|**Expropriation**|**Sudden,**<br>**adverse law**<br>**change**|
|---|---|---|---|---|---|---|
|**Services**||-*|||-*||
|**Export % of sales**|||||||
|**Share of sourcing via imports**|+*||||||
|**High-income source country**||-*|||||
|**>250 employees**||||||-**|
|**>10M investment**|||||||
|**Years in country**|-*||-*|-**|-*||
|**Level foreign ownership**|||||+**||

*** p<0.01, ** p<0.05, * p<0.1

This table displays signs of coefficients for the variables in the rows for regressions with the column variables as the dependent variables. Positive signs denote that the row variable is associated with more negative reactions if the event in the column were to occur. Signs and significance levels are shown from ordered logistic regressions with all affiliate-level covariates (i.e., row variables) and country and ISIC A38 subsector fixed effects. Blanks denote coefficients that are not statistically significant (i.e., only signs of statistically significant coefficients are shown).

Respondents also exhibit heterogeneity in their sensitivity to individual political risks. Affiliates with more years of experience in host countries would react less negatively to expropriation, government breach of contract, and currency restrictions, as evidenced by the negative and statistically significant coefficient (at least at the p<0.10 level) on years in country in our preferred specification (Table 33, Column 8). As discussed previously, this pattern may reflect more experienced affiliates’ greater knowledge and connections within host countries (Barry and DiGiuseppe 2019).

Similarly, affiliates in services would react less negatively to expropriation on average, as evidenced by the negative and statistically significant (at the p<0.10 level) in our preferred specification (Table 33, Column 6). Our findings could be driven by selection issues related to how expropriation is somewhat more common in services (i.e., the investors that choose to invest are those that are less sensitive to expropriation risks) (Hajzler 2012). Nevertheless, more research is required to come to a more robust explanation as this topic has received little research attention to date.

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In contrast, affiliates with higher foreign ownership shares would react more negatively to expropriation on average, as evidenced by the positive and significant (p<0.05) coefficient on foreign ownership share in our preferred specification (Table 33, Column 8). This finding may reflect high foreign ownership shares being associated with more advanced technologies, which may in turn make affiliates more sensitive to expropriation of their intellectual property (Saggi 2002). It may also reflect how domestic owners of affiliates which are joint ventures (i.e., only part foreign-owned) may be less likely to leave their home markets, even in the face of expropriation.

If faced with delays in permits and approvals, affiliates with foreign owners in high-income countries are likely to react more negatively on average. In our preferred specification, the coefficients on the dummy for high-income source country is negative and statistically significant at the p<0.10 level, and the results are robust to alternative specifications (Table 30). Given that such delays are generally more common in developing country contexts, this pattern may reflect how investors from developing countries are better able to navigate routine regulatory challenges in other developing economies given their experiences with their home markets (Demir and Hu 2016). That this pattern does not exist for the other political risks likely reflects the relative seriousness of risks; even developing country investors’ prior experience may be insufficient to ameliorate the impacts of more serious risks like expropriation.

Finally, affiliates in the services sector are more likely to react negatively to delays in permits and approvals. The coefficient on the dummy for services is negative and statistically significant at least at the p<0.10 level across all specifications, including our preferred ordered logistic specification with fixed effects (Table 30). This pattern may reflect the greater prevalence of subjective screening and approval requirements related to services FDI relative to manufacturing FDI, as documented by the OECD’s FDI Regulatory Restrictiveness Index and Golub (2009). In this context of greater bureaucratic discretion, delays for FDI in services may increase uncertainty regarding whether approvals will ever be granted, whereas delays for manufacturing investments may be more likely to reflect simple processing delays.

# **Investment promotion**

# _Overall results_

Our results confirm findings in the literature that IPAs play an important role in attracting and retaining FDI. Almost 90 percent of existing investors value at least one IPA service, and most IPA services are considered important or critically important by at least two-thirds of MNE affiliates in surveyed emerging markets (Figure 4). Indeed, respondents widely value both economy-wide advocacy efforts as well as project-specific support: Three quarters of investors value IPA efforts toward improving the overall business environment in the country. About seven in ten investors value IPA provision of preinvestment information such as location guides, sector and project profiles, and regulatory procedures. A similar share of investors value IPA assistance in setting up operations to fulfill registration requirements and obtain entry permits, among others. These findings are broadly consistent with 18
findings in Kusek and Silva (2018), where advocacy for improved business environment and business setup assistance ranked among the most valued IPA services.<sup>7</sup>

**Figure 4. Importance of IPA services**

<!-- Start of picture text --> How important are the following services offered by Investment Promotion Agencies to your<br>company in this country?<br>Most valued IPA service (composite) 61% 28%<br>Efforts to improve the business environment<br>35% 40%<br>in the country<br>Pre-investment information 30% 41%<br>Assistance in setting up 31% 39%<br>Assistance with operational issues and<br>29% 38%<br>grievances<br>Location marketing 29% 37%<br>Pre-investment assistance 25% 40%<br>Personalized contact/response to your<br>24% 40%<br>company<br>0% 20% 40% 60% 80% 100%<br>Critically important Important<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

# _Heterogeneity across investor types_

On average, importing a higher share of inputs is associated with placing greater importance on IPA services. When the importance of affiliates’ most-valued IPA service is used as the dependent variable, the coefficient on import share of inputs is positive and significant at the p<0.10 level in our preferred ordered logistic specification with country and subsector fixed effects (Table 41). This result is also robust to the exclusion of fixed effects and alternate specifications using OLS. Looking across specific services, we find similar results with respect to IPA assistance with business setup (Table 46, p<0.05) and with operational issues and grievances (Table 47, p<0.10). Both of these results are also robust to alternative specifications. Importers may value IPA services more insofar as IPAs help them navigate issues related to obtaining import licenses and addressing grievances concerning import processes, although further research is necessary to fully explain these results.

> 7 Assistance with resolving operational issues and grievances was the most valued IPA service in the 2017 GIC Survey, compared to the fourth-ranked service in the 2019 survey. Nevertheless, the 2019 survey continues to show a sizable majority (67 percent) valuing such services.

19
**Table 10. Importance of IPA services: Coefficient signs and significance levels for heterogeneity analyses**

||**Most valuable IPA**<br>**service**|**Opportunity**<br>**promotion**|**Personalized**<br>**contact, response**|**Pre-investment**<br>**information**|**Pre-investment**<br>**assistance**|**Business setup**<br>**assistance**|**Operational issues**<br>**assistance**|**Business env.**<br>**improvement**|
|---|---|---|---|---|---|---|---|---|
|**Services**|||||||||
|**Export % of sales**|||||||||
|**Share of sourcing via imports**|+*|||||+**|+*||
|**High-income source country**|||||-**|||-*|
|**>250 employees**|||||||||
|**>10M investment**||+*|+*|+*|||||
|**Years in country**|-**||-**||-*|||-*|
|**Level foreign ownership**|||||||||

*** p<0.01, ** p<0.05, * p<0.1

This table displays signs of coefficients for the variables in the rows for regressions with the column variables as the dependent variables. Positive signs denote that the row variable is associated with placing greater importance on the column variable. Signs and significance levels are shown from ordered logistic regressions with all affiliate-level covariates (i.e., row variables) and country and ISIC A38 subsector fixed effects. Blanks denote coefficients that are not statistically significant (i.e., only signs of statistically significant coefficients are shown).

Having fewer years of experience in host countries is also associated with placing greater importance on IPA services. In our preferred specification, with importance of the most important IPA service as the dependent variable, the coefficient on number of years in country is negative and significant at the p<0.05 level, although this result is only significant in ordered logistic specifications with country fixed effects. In terms of specific IPA services, having fewer years of experience is also associated with placing greater importance on personalized contact and response (Table 43, p<0.05), pre-investment assistance such as site visits (Table 45, p<0.10), and efforts to improve the host country’s business environment (Table 48, p<0.10). This finding likely reflects more experienced affiliates having more in-country knowledge and connections and therefore less need for IPA services.

Having foreign parents in high-income countries is associated with placing less importance on select IPA services. The coefficient on the dummy variable for having a foreign owner from a high-income country is not significant in regressions with the importance of the most important IPA service as the dependent variable. However, the coefficient on the dummy for high-income source country is negative and significant in regressions where the dependent variable is the importance of pre-investment assistance (Table 45) or assistance with operational issues and grievances (Table 47). This may reflect how investors from high-income source countries may have access to greater private (e.g., consultancies) or diplomatic resources (e.g., embassy pressure) to deal with such issues.

In contrast, large investment sizes are associated with placing greater importance on select IPA services. The coefficient on the dummy variable for having over USD 10 million in total investment is positive and significant in regressions with the importance of promotion of investment opportunities (Table 42), 20
personalized contact and responsiveness (Table 43), and pre-investment assistance (Table 45) as dependent variables. Large, systemically important affiliates may value personalized assistance tailored towards their specific needs, whereas smaller companies may not expect similar support from IPAs.

# **7. CONCLUSION: POLICY IMPLICATIONS AND DIRECTIONS FOR FUTURE RESEARCH**

The stark decline in FDI resulting from the COVID-19 pandemic means that policy makers must redouble their efforts to attract FDI in order to safeguard FDI’s role in driving jobs and economic transformation. This paper’s findings contribute to the literature informing such efforts by reaffirming the importance of various policy dimensions in creating a conducive environment for FDI and by providing suggestive evidence to inform efforts to tailor policy prescriptions to target investors.

Our survey findings confirm that countries’ policy environments are critical drivers of foreign investment:

- **_<u>Legal and regulatory barriers to FDI</u>_** : Regulatory barriers, especially investment approval processes, commonly obstruct MNE affiliates’ operations. Policy makers should thus renew their focus on making business-friendly reforms by simplifying approval processes and removing limits on foreign investment, among other areas.

- **_<u>Political risk and investor protections</u>_** : Investors are likely to delay, cancel, or even withdraw investments when faced with political risks such as expropriation and sudden changes in laws and regulations. These findings reaffirm the importance of government efforts to strengthen institutions and property rights, and financial products to manage against such risks (e.g., MIGA Breach of Contract coverage).

- **_<u>IPAs</u>_** : We find that foreign investors value a broad range IPA services spanning the entire investment lifecycle. Governments seeking to boost FDI should thus ensure that IPA services are tailored to foreign investors’ needs, including post-entry issues such as business environment advocacy and support with grievances.

We also consistently find evidence that foreign investors’ characteristics affect the degree to which they encounter policy-related obstacles and value IPA services. These differences between types of investors suggest that governments should prioritize and tailor reforms to most closely match their target segments for FDI. Notably, in addition to findings about the importance of affiliates’ export behavior and sector—which have been covered in the literature to some degree—we consistently find evidence that affiliates’ import intensity is an important factor shaping their policy experiences and preferences. To the extent that such affiliates are important (e.g., for GVC participation and introducing new technologies to host countries), governments should investigate and address why import-intensive affiliates commonly experience greater policy-related obstacles (e.g., eliminating policy obstacles related to import licensing and processing).

Beyond specific policy areas, we find that both the content and implementation of policies matter. Due to the divergence between regulatory provisions and their implementation, foreign investors must navigate the bureaucratic landscape in host economies. Thus, ‘on-paper’ policy reforms to ease entry, strengthen property rights, and ensure non-discriminatory treatment of foreign investors may not be adequate. Governments should also invest in agency staff capacity and install transparent, efficient, and 21
simple processes to break down siloes and reduce ambiguities that may give rise to bureaucratic discretion.

Finally, our findings suggest new directions for future research on the relationship between FDI and government policy levers. While this paper provides suggestive evidence regarding sources of heterogeneity in foreign investors’ policy experiences and preferences, it is insufficient to draw causal conclusions. More robust research leveraging more granular administrative data sets of FDI would thus be valuable for expanding our understanding of such heterogeneity. As such data sets are rare and more common in high-income countries, efforts to collect and publish granular FDI data (i.e., comprehensive data on MNE affiliate activities in a country) are vital. Finally, as the implications of COVID-19 become clearer and new data are released, future research to understand whether investor preferences have changed post-pandemic will also be critical.

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27
# **ANNEX A. REGRESSION TABLES**

**Table 11. Importance of legal and regulatory environment**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0368|-0.0206|||-0.0668|-0.0356|||
||(0.0450)|(0.0446)|||(0.112)|(0.117)|||
|Export % of sales|-0.00164***|-0.00151**|-0.00155**|-0.00145**|-0.00437***|-0.00364**|-0.00424***|-0.00361**|
||(0.000614)|(0.000607)|(0.000612)|(0.000606)|(0.00151)|(0.00155)|(0.00157)|(0.00161)|
|Share of sourcing via imports|0.000487|0.000541|0.000208|0.000165|0.00168|0.00172|0.00124|0.000970|
||(0.000680)|(0.000672)|(0.000692)|(0.000686)|(0.00171)|(0.00176)|(0.00177)|(0.00183)|
|High-income source country|0.0180|0.0947|0.0374|0.110*|0.0408|0.281|0.0808|0.319*|
||(0.0646)|(0.0662)|(0.0639)|(0.0648)|(0.164)|(0.182)|(0.167)|(0.182)|
|>250 employees|0.156***|0.147***|0.166***|0.156***|0.404***|0.400***|0.437***|0.431***|
||(0.0516)|(0.0516)|(0.0520)|(0.0516)|(0.132)|(0.139)|(0.138)|(0.144)|
|Cumulative inv. >US$10M|-0.0566|0.0157|-0.0690|0.00472|-0.112|0.0337|-0.146|0.00577|
||(0.0515)|(0.0511)|(0.0513)|(0.0507)|(0.127)|(0.131)|(0.130)|(0.135)|
|Years in country|-0.000308|-0.00163|-0.000248|-0.00174|-0.00144|-0.00554|-0.00136|-0.00586|
||(0.00174)|(0.00163)|(0.00174)|(0.00166)|(0.00444)|(0.00447)|(0.00462)|(0.00467)|
|Level of foreign ownership (%)|-0.00223**|-0.00102|-0.00199**|-0.000838|-0.00679***|-0.00386|-0.00624**|-0.00330|
||(0.000922)|(0.000986)|(0.000913)|(0.000966)|(0.00248)|(0.00269)|(0.00253)|(0.00273)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.017|0.069|0.041|0.091|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important were the following factors in your foreign owner company's decision to invest in this country? [Business-friendly legal and regulatory environment]". Higher numbers denote greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

28
**Table 12. Importance of legal and regulatory environment (average marginal effects)**

|VARIABLES|(1)<br>Margins -<br>Ologit|(2)<br>Margins -<br>Ologit|
|---|---|---|
|Services|-0.0163|-0.00866|
||(0.0272)|(0.0284)|
|Export % of sales|-0.00107***|-0.000885**|
||(0.000369)|(0.000378)|
|Share of sourcing via imports|0.000410|0.000418|
||(0.000417)|(0.000429)|
|High-income source country|0.00995|0.0684|
||(0.0401)|(0.0442)|
|>250 employees|0.0987***|0.0972***|
||(0.0323)|(0.0338)|
|Cumulative inv. >US$10M|-0.0273|0.00819|
||(0.0310)|(0.0319)|
|Years in country|-0.000352|-0.00135|
||(0.00108)|(0.00109)|
|Level of foreign ownership (%)|-0.00166***|-0.000939|
||(0.000607)|(0.000655)|
|Country fixed effects||X|
|Subsector fixed effects|||
|Observations|1,689|1,689|

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The numbers in the table represent average marginal effects (i.e., marginal change in probability) on the dependent variable taking the value of 'critically important' associated with a one-unit change in the row variable. The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important were the following factors in your parent company's decision to invest in this country? [Businessfriendly legal and regulatory environment]". Higher numbers denote greater importance.

Average marginal effects are not estimable in models with subsector fixed effects due to the small number of observations for select subsectors. Marginal effects fixed effects are not shown in the table.

29
# **Table 13. Importance of investor protections**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0848|-0.0697|||-0.133|-0.105|||
||(0.0534)|(0.0534)|||(0.107)|(0.110)|||
|Export % of sales|-0.00120*|-0.000401|-0.00115|-0.000406|-0.00286**|-0.000887|-0.00288**|-0.000963|
||(0.000705)|(0.000710)|(0.000710)|(0.000710)|(0.00141)|(0.00143)|(0.00145)|(0.00147)|
|Share of sourcing via imports|0.00194**|0.00170**|0.00208***|0.00183**|0.00489***|0.00433**|0.00555***|0.00493***|
||(0.000777)|(0.000810)|(0.000787)|(0.000808)|(0.00159)|(0.00171)|(0.00165)|(0.00174)|
|High-income source country|-0.0594|0.0475|-0.0543|0.0450|-0.118|0.116|-0.123|0.101|
||(0.0732)|(0.0721)|(0.0750)|(0.0743)|(0.156)|(0.158)|(0.161)|(0.166)|
|>250 employees|-0.0405|-0.0256|-0.0389|-0.0294|-0.109|-0.0635|-0.114|-0.0834|
||(0.0643)|(0.0647)|(0.0650)|(0.0653)|(0.129)|(0.133)|(0.133)|(0.139)|
|Cumulative inv. >US$10M|0.0115|0.0491|-0.00857|0.0339|0.0369|0.0922|0.0151|0.0794|
||(0.0645)|(0.0635)|(0.0631)|(0.0624)|(0.131)|(0.135)|(0.130)|(0.134)|
|Years in country|-0.00240|-0.00445*|-0.00210|-0.00418|-0.00337|-0.00817|-0.00341|-0.00847|
||(0.00255)<br>-|(0.00256)|(0.00254)|(0.00257)|(0.00525)<br>-|(0.00574)|(0.00551)|(0.00595)|
|Level of foreign ownership (%)|0.00274***|-0.00106|-0.00228**|-0.000734|0.00618***|-0.00262|-0.00552**|-0.00220|
||(0.00104)|(0.00110)|(0.00102)|(0.00108)|(0.00224)|(0.00234)|(0.00222)|(0.00237)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.016|0.056|0.042|0.081|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important were the following factors in your foreign owner company's decision to invest in this country? [Investment protection guarantees against expropriation and other risks (e.g. repatriating profits, currency transfers, discrimination, and breach of contract by the government)]". Higher numbers denote greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

30
**Table 14. Importance of investor protections (average marginal effects)**

|VARIABLES|(1)<br>Margins -<br>Ologit|(2)<br>Margins -<br>Ologit|
|---|---|---|
|Services|-0.0310|-0.0244|
||(0.0249)|(0.0256)|
|Export % of sales|-0.000664**|-0.000205|
||(0.000329)|(0.000331)|
|Share of sourcing via imports|0.00114***|0.00100**|
||(0.000371)|(0.000398)|
|High-income source country|-0.0274|0.0269|
||(0.0362)|(0.0365)|
|>250 employees|-0.0255|-0.0147|
||(0.0300)|(0.0308)|
|Cumulative inv. >US$10M|0.00858|0.0213|
||(0.0306)|(0.0312)|
|Years in country|-0.000785|-0.00189|
||(0.00122)|(0.00133)|
|Level of foreign ownership (%)|-0.00144***|-0.000607|
||(0.000522)|(0.000542)|
|Country fixed effects||X|
|Subsector fixed effects|||
|Observations|1,689|1,689|

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The numbers in the table represent average marginal effects (i.e., marginal change in probability) on the dependent variable taking the value of 'critically important' associated with a one-unit change in the row variable. The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important were the following factors in your parent company's decision to invest in this country? [Investment protection guarantees against expropriation and other risks (e.g. repatriating profits, currency transfers, discrimination, and breach of contract by the government)]". Higher numbers denote greater importance.

Average marginal effects are not estimable in models with subsector fixed effects due to the small number of observations for select subsectors.

31
**Table 15. Significance of legal and regulatory obstacles: Investment approvals**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0169|0.0134|||0.0342|0.0262|||
||(0.0630)|(0.0620)|||(0.106)|(0.110)|||
|Export % of sales|-0.000856|-0.000210|-0.00109|-0.000453|-0.00141|-0.000338|-0.00190|-0.000881|
||(0.000843)|(0.000855)|(0.000858)|(0.000859)|(0.00145)|(0.00156)|(0.00152)|(0.00162)|
|Share of sourcing via imports|0.000162|-0.000680|0.000447|-0.000487|0.000540|-0.000678|0.00111|-0.000172|
||(0.000919)|(0.000924)|(0.000948)|(0.000960)|(0.00160)|(0.00167)|(0.00170)|(0.00180)|
|High-income source country|-0.154*|-0.0639|-0.180*|-0.0756|-0.284*|-0.103|-0.342**|-0.147|
||(0.0932)|(0.0917)|(0.0927)|(0.0902)|(0.166)|(0.170)|(0.170)|(0.174)|
|>250 employees|-0.00680|-0.0261|-0.0192|-0.0414|-0.0126|-0.0544|-0.0370|-0.0838|
||(0.0798)|(0.0773)|(0.0781)|(0.0762)|(0.137)|(0.138)|(0.138)|(0.139)|
|Cumulative inv. >US$10M|0.00533|0.0241|0.00394|0.0276|0.0304|0.0452|0.0341|0.0575|
||(0.0776)|(0.0766)|(0.0771)|(0.0756)|(0.138)|(0.143)|(0.141)|(0.144)|
|Years in country|-0.00337|-0.00407*|-0.00266|-0.00359|-0.00587*|-0.00763**|-0.00497|-0.00686*|
||(0.00225)|(0.00221)|(0.00221)|(0.00221)|(0.00354)|(0.00377)|(0.00360)|(0.00389)|
|Level of foreign ownership (%)|0.000359|0.000947|0.000462|0.00148|0.000677|0.00158|0.000814|0.00245|
||(0.00137)|(0.00146)|(0.00137)|(0.00140)|(0.00233)|(0.00264)|(0.00241)|(0.00263)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.005|0.079|0.037|0.107|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Cumbersome investment approvals to start and operate a business]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

32
**Table 16. Significance of legal and regulatory obstacles: Joint venture requirements**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.115*|0.0992|||0.201*|0.181*|||
||(0.0637)|(0.0631)|||(0.107)|(0.108)|||
|Export % of sales|0.000226|-0.000229|0.000237|-0.000161|0.000421|-0.000439|0.000484|-0.000328|
||(0.000852)|(0.000854)|(0.000851)|(0.000849)|(0.00144)|(0.00148)|(0.00148)|(0.00150)|
|Share of sourcing via imports|0.00278***|0.00167*|0.00282***|0.00165*|0.00480***|0.00312*|0.00517***|0.00332*|
||(0.000932)|(0.000972)|(0.000938)|(0.000974)|(0.00157)|(0.00167)|(0.00164)|(0.00173)|
|High-income source country|-0.0650|-0.0448|-0.0637|-0.0281|-0.0988|-0.0915|-0.0920|-0.0543|
||(0.0940)|(0.0939)|(0.0941)|(0.0925)|(0.158)|(0.162)|(0.165)|(0.167)|
|>250 employees|-0.0511|-0.0296|-0.0609|-0.0453|-0.105|-0.0667|-0.107|-0.0772|
||(0.0803)|(0.0800)|(0.0814)|(0.0816)|(0.136)|(0.140)|(0.141)|(0.147)|
|Cumulative inv. >US$10M|-0.0426|-0.0909|-0.0615|-0.106|-0.0631|-0.158|-0.105|-0.195|
||(0.0764)|(0.0786)|(0.0754)|(0.0771)|(0.129)|(0.136)|(0.130)|(0.137)|
|Years in country|-0.00460*|-0.00428*|-0.00485**|-0.00471*|-0.00688|-0.00660|-0.00761|-0.00761|
||(0.00239)|(0.00245)|(0.00239)|(0.00247)|(0.00445)|(0.00456)|(0.00463)|(0.00480)|
|Level of foreign ownership (%)|-0.00189|-0.00171|-0.00189|-0.00132|-0.00346*|-0.00336|-0.00356|-0.00280|
||(0.00125)|(0.00133)|(0.00124)|(0.00131)|(0.00209)|(0.00226)|(0.00217)|(0.00234)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.018|0.047|0.048|0.077|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Requirements to foreign investors to enter into joint ventures with local company in your sector]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

33
**Table 17. Significance of legal and regulatory obstacles: Foreign investment limits**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0311|0.0344|||0.0487|0.0553|||
||(0.0664)|(0.0658)|||(0.110)|(0.110)|||
|Export % of sales|-0.000806|-0.000889|-0.000576|-0.000601|-0.00116|-0.00139|-0.000805|-0.000923|
||(0.000859)|(0.000877)|(0.000852)|(0.000865)|(0.00142)|(0.00146)|(0.00145)|(0.00148)|
|Share of sourcing via imports|0.00294***|0.00235**|0.00324***|0.00259***|0.00463***|0.00375**|0.00524***|0.00433**|
||(0.000971)|(0.00102)|(0.000967)|(0.000997)|(0.00162)|(0.00171)|(0.00168)|(0.00174)|
|High-income source country|-0.0773|0.00779|-0.0690|0.0236|-0.123|0.0101|-0.108|0.0406|
||(0.0983)|(0.0994)|(0.0977)|(0.0987)|(0.166)|(0.169)|(0.170)|(0.173)|
|>250 employees|-0.0247|-0.0183|-0.0113|-0.00667|-0.0299|-0.0187|-0.0115|-0.00273|
||(0.0803)|(0.0819)|(0.0810)|(0.0829)|(0.132)|(0.137)|(0.139)|(0.146)|
|Cumulative inv. >US$10M|-0.0335|-0.0184|-0.0522|-0.0391|-0.0937|-0.0650|-0.119|-0.0909|
||(0.0786)|(0.0787)|(0.0779)|(0.0779)|(0.132)|(0.133)|(0.135)|(0.137)|
|Years in country|0.00234|0.00137|0.00232|0.00102|0.00438|0.00231|0.00417|0.00150|
||(0.00246)|(0.00248)|(0.00248)|(0.00248)|(0.00378)|(0.00391)|(0.00397)|(0.00409)|
|Level of foreign ownership (%)|-0.00207|-0.000470|-0.00213|-0.000273|-0.00339|-0.000864|-0.00361|-0.000663|
||(0.00138)|(0.00145)|(0.00135)|(0.00140)|(0.00228)|(0.00243)|(0.00228)|(0.00238)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.013|0.027|0.044|0.061|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Limit on amount of permissible foreign investment in your sector(s)]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

34
**Table 18. Significance of legal and regulatory obstacles: Expatriate staff restrictions**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.114*|0.112*|||0.196*|0.196*|||
||(0.0610)|(0.0593)|||(0.108)|(0.110)|||
|Export % of sales|0.000827|0.000498|0.000699|0.000419|0.00148|0.000662|0.00131|0.000693|
||(0.000810)|(0.000807)|(0.000823)|(0.000816)|(0.00145)|(0.00152)|(0.00152)|(0.00157)|
|Share of sourcing via imports|0.000964|-0.000266|0.00168*|0.000347|0.00164|-0.000633|0.00296*|0.000583|
||(0.000879)|(0.000902)|(0.000888)|(0.000901)|(0.00154)|(0.00166)|(0.00160)|(0.00171)|
|High-income source country|-0.151|-0.0326|-0.154*|-0.0317|-0.239|-0.0331|-0.253|-0.0332|
||(0.0952)|(0.0923)|(0.0935)|(0.0910)|(0.169)|(0.173)|(0.171)|(0.173)|
|>250 employees|-0.0309|0.00702|-0.0249|0.0144|-0.0405|0.0356|-0.0271|0.0605|
||(0.0761)|(0.0735)|(0.0755)|(0.0733)|(0.134)|(0.136)|(0.136)|(0.140)|
|Cumulative inv. >US$10M|0.0775|0.0650|0.0760|0.0619|0.128|0.113|0.131|0.112|
||(0.0741)|(0.0731)|(0.0727)|(0.0711)|(0.130)|(0.133)|(0.132)|(0.135)|
|Years in country|-0.000919|-0.00337|-0.000433|-0.00313|-0.000887|-0.00631|-0.000372|-0.00650|
||(0.00228)|(0.00222)|(0.00223)|(0.00219)|(0.00389)|(0.00398)|(0.00395)|(0.00404)|
|Level of foreign ownership (%)|-0.000919|0.00169|-0.000946|0.00179|-0.00144|0.00357|-0.00179|0.00354|
||(0.00127)|(0.00137)|(0.00124)|(0.00132)|(0.00229)|(0.00264)|(0.00232)|(0.00262)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.008|0.060|0.034|0.084|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Restrictions on hiring and bringing in expatriate staff]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

35
**Table 19. Significance of legal and regulatory obstacles: Local sourcing or hiring requirements**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.00614|-0.00158|||0.0197|0.00503|||
||(0.0622)|(0.0612)|||(0.110)|(0.114)|||
|Export % of sales|0.00113|0.000446|0.00107|0.000473|0.00183|0.000669|0.00171|0.000685|
||(0.000827)|(0.000810)|(0.000845)|(0.000817)|(0.00147)|(0.00155)|(0.00155)|(0.00160)|
|Share of sourcing via imports|0.00282***|0.00121|0.00292***|0.00116|0.00500***|0.00254|0.00528***|0.00248|
||(0.000872)|(0.000867)|(0.000911)|(0.000898)|(0.00157)|(0.00164)|(0.00169)|(0.00174)|
|High-income source country|-0.00927|0.0522|-0.00984|0.0601|-0.00308|0.100|-0.0103|0.107|
||(0.0960)|(0.0939)|(0.0946)|(0.0918)|(0.172)|(0.179)|(0.174)|(0.179)|
|>250 employees|-0.00175|0.0533|-0.0103|0.0434|0.0206|0.148|-0.000662|0.131|
||(0.0765)|(0.0748)|(0.0771)|(0.0758)|(0.133)|(0.137)|(0.138)|(0.145)|
|Cumulative inv. >US$10M|0.00699|-0.0412|0.000759|-0.0479|-0.000780|-0.0949|-0.00356|-0.0981|
||(0.0737)|(0.0745)|(0.0725)|(0.0719)|(0.131)|(0.138)|(0.131)|(0.138)|
|Years in country|-0.000384|-0.00128|-0.000565|-0.00160|-0.000295|-0.00259|-0.000681|-0.00330|
||(0.00248)|(0.00254)|(0.00243)|(0.00250)|(0.00448)|(0.00464)|(0.00447)|(0.00463)|
|Level of foreign ownership (%)|-0.00288**|-0.00150|-0.00283**|-0.00122|-0.00500**|-0.00283|-0.00497**|-0.00232|
||(0.00131)|(0.00138)|(0.00131)|(0.00137)|(0.00236)|(0.00265)|(0.00239)|(0.00268)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.013|0.080|0.030|0.096|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Requirements to foreign investors to use locally produced inputs or local staff]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level. Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

36
**Table 20. Significance of legal and regulatory obstacles: R&D investment requirements**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0255|-0.0314|||-0.0236|-0.0345|||
||(0.0604)|(0.0606)|||(0.112)|(0.115)|||
|Export % of sales|8.08e-05|-2.15e-05|-6.27e-06|-4.50e-05|0.000546|0.000219|0.000508|0.000298|
||(0.000768)|(0.000800)|(0.000770)|(0.000796)|(0.00141)|(0.00151)|(0.00144)|(0.00152)|
|Share of sourcing via imports|0.00206**|0.00117|0.00193**|0.00101|0.00327**|0.00187|0.00308*|0.00160|
||(0.000875)|(0.000902)|(0.000869)|(0.000892)|(0.00163)|(0.00172)|(0.00166)|(0.00174)|
|High-income source country|-0.0992|-0.0328|-0.112|-0.0366|-0.160|-0.0571|-0.174|-0.0550|
||(0.0927)|(0.0933)|(0.0917)|(0.0914)|(0.175)|(0.178)|(0.175)|(0.178)|
|>250 employees|-0.0910|-0.0683|-0.109|-0.0850|-0.134|-0.0938|-0.170|-0.126|
||(0.0704)|(0.0721)|(0.0695)|(0.0713)|(0.130)|(0.135)|(0.132)|(0.137)|
|Cumulative inv. >US$10M|-0.0377|-0.0584|-0.0435|-0.0643|-0.0850|-0.125|-0.109|-0.149|
||(0.0703)|(0.0723)|(0.0707)|(0.0723)|(0.130)|(0.137)|(0.133)|(0.140)|
|Years in country|0.000485|-0.000219|0.000707|-0.000274|0.00175|0.000597|0.00227|0.000565|
||(0.00229)|(0.00238)|(0.00221)|(0.00232)|(0.00427)|(0.00440)|(0.00416)|(0.00435)|
|Level of foreign ownership (%)|-0.00102|0.000380|-0.000844|0.000839|-0.00192|0.000188|-0.00156|0.000980|
||(0.00125)|(0.00135)|(0.00123)|(0.00129)|(0.00223)|(0.00251)|(0.00225)|(0.00246)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.009|0.028|0.037|0.056|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Requirements to foreign investors to invest in R&D]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

37
**Table 21. Significance of legal and regulatory obstacles: Minimum investment requirements**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0134|0.00797|||0.0589|0.0498|||
||(0.0570)|(0.0564)|||(0.109)|(0.112)|||
|Export % of sales|0.000207|0.000136|0.000188|0.000162|0.000456|0.000539|0.000542|0.000615|
||(0.000729)|(0.000742)|(0.000734)|(0.000743)|(0.00140)|(0.00147)|(0.00144)|(0.00152)|
|Share of sourcing via imports|0.000273|-0.000544|0.000333|-0.000616|0.000667|-0.000791|0.000708|-0.00102|
||(0.000867)|(0.000889)|(0.000878)|(0.000887)|(0.00161)|(0.00169)|(0.00167)|(0.00174)|
|High-income source country|-0.0755|-0.00265|-0.0745|0.00144|-0.106|0.0228|-0.107|0.0325|
||(0.0856)|(0.0846)|(0.0850)|(0.0837)|(0.162)|(0.162)|(0.165)|(0.165)|
|>250 employees|-0.145**|-0.113*|-0.176**|-0.145**|-0.254*|-0.203|-0.326**|-0.277**|
||(0.0681)|(0.0676)|(0.0701)|(0.0697)|(0.130)|(0.133)|(0.138)|(0.141)|
|Cumulative inv. >US$10M|-0.0699|-0.0742|-0.0756|-0.0781|-0.149|-0.168|-0.168|-0.182|
||(0.0689)|(0.0703)|(0.0695)|(0.0706)|(0.134)|(0.141)|(0.138)|(0.146)|
|Years in country|0.000270|-0.00166|0.000594|-0.00149|0.00137|-0.00268|0.00178|-0.00245|
||(0.00231)<br>|(0.00236)|(0.00220)|(0.00228)|(0.00468)<br>|(0.00480)|(0.00452)<br>|(0.00474)|
|Level of foreign ownership (%)|-<br>0.00319***|-0.00187|-0.00287**|-0.00144|-<br>0.00622***|-0.00454*|-<br>0.00571***|-0.00372|
||(0.00114)|(0.00127)|(0.00112)|(0.00122)|(0.00203)|(0.00240)|(0.00205)|(0.00232)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.017|0.051|0.043|0.077|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Minimum investment requirements]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

38
**Table 22. Significance of legal and regulatory obstacles: Pricing, technology, or product restrictions**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0173|-0.0153|||-0.0293|-0.0410|||
||(0.0648)|(0.0622)|||(0.115)|(0.114)|||
|Export % of sales|-0.000473|0.000114|-0.000257|0.000350|-0.000852|9.13e-06|-0.000343|0.000574|
||(0.000845)|(0.000834)|(0.000848)|(0.000826)|(0.00149)|(0.00153)|(0.00152)|(0.00154)|
|Share of sourcing via imports|0.00286***|0.00190**|0.00258***|0.00142|0.00488***|0.00320*|0.00459***|0.00253|
||(0.000919)|(0.000931)|(0.000929)|(0.000932)|(0.00161)|(0.00171)|(0.00166)|(0.00174)|
|High-income source country|-0.139|-0.0279|-0.125|-0.0155|-0.230|-0.0472|-0.204|-0.0162|
||(0.0889)|(0.0870)|(0.0890)|(0.0877)|(0.151)|(0.154)|(0.155)|(0.158)|
|>250 employees|-0.0207|0.0230|-0.0221|0.0172|-0.0343|0.0498|-0.0320|0.0502|
||(0.0812)|(0.0781)|(0.0810)|(0.0792)|(0.142)|(0.143)|(0.143)|(0.146)|
|Cumulative inv. >US$10M|0.0356|0.0337|0.0249|0.0245|0.0547|0.0526|0.0324|0.0300|
||(0.0770)|(0.0749)|(0.0771)|(0.0750)|(0.133)|(0.136)|(0.136)|(0.139)|
|Years in country|0.00306|0.00119|0.00312|0.000998|0.00518|0.00199|0.00511|0.00135|
||(0.00234)|(0.00225)|(0.00238)|(0.00230)|(0.00401)|(0.00399)|(0.00416)|(0.00417)|
|Level of foreign ownership (%)|-0.00149|0.000305|-0.00115|0.000774|-0.00249|0.000377|-0.00203|0.00115|
||(0.00135)|(0.00136)|(0.00134)|(0.00133)|(0.00240)|(0.00251)|(0.00245)|(0.00249)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.014|0.066|0.031|0.085|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Restrictions on setting prices, production technology, or format of products]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

39
**Table 23. Governance-related drivers of legal and regulatory obstacles: Administrative complexity**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.110*|0.0953*|||0.175|0.156|||
||(0.0582)|(0.0570)|||(0.111)|(0.115)|||
|Export % of sales|-0.00125|-0.000267|-0.00129*|-0.000309|-0.00253*|-0.000512|-0.00277*|-0.000736|
||(0.000768)|(0.000767)|(0.000779)|(0.000774)|(0.00145)|(0.00152)|(0.00153)|(0.00158)|
|Share of sourcing via imports|0.00224***|0.00101|0.00225***|0.000966|0.00417***|0.00183|0.00440***|0.00193|
||(0.000835)|(0.000829)|(0.000859)|(0.000847)|(0.00161)|(0.00169)|(0.00168)|(0.00175)|
|High-income source country|-0.106|-0.0122|-0.114|-0.00328|-0.213|-0.0324|-0.245|-0.0193|
||(0.0853)|(0.0848)|(0.0861)|(0.0841)|(0.167)|(0.177)|(0.175)|(0.180)|
|>250 employees|0.108|0.0835|0.0802|0.0596|0.179|0.133|0.130|0.0892|
||(0.0698)|(0.0693)|(0.0693)|(0.0689)|(0.129)|(0.137)|(0.132)|(0.140)|
|Cumulative inv. >US$10M|-0.000665|-0.0742|-0.0116|-0.0816|0.000262|-0.154|-0.0292|-0.181|
||(0.0702)|(0.0692)|(0.0690)|(0.0684)|(0.130)|(0.139)|(0.132)|(0.141)|
|Years in country|-0.00136|-0.00122|-0.00102|-0.000964|-0.00226|-0.00258|-0.00194|-0.00231|
||(0.00248)|(0.00233)|(0.00233)|(0.00224)|(0.00465)|(0.00483)|(0.00457)|(0.00476)|
|Level of foreign ownership (%)|-0.00244**|-0.000784|-0.00273**|-0.000805|-0.00479*|-0.00201|-0.00547**|-0.00204|
||(0.00123)|(0.00126)|(0.00120)|(0.00121)|(0.00248)|(0.00263)|(0.00249)|(0.00258)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.018|0.123|0.049|0.143|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Complexity of administrative procedures]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

40
**Table 24. Governance-related drivers of legal and regulatory obstacles: Public agency capacity**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0752|0.0659|||0.143|0.134|||
||(0.0591)|(0.0579)|||(0.108)|(0.115)|||
|Export % of sales|-0.00169**|5.49e-06|-0.00130*|0.000306|-0.00321**|-0.000151|-0.00254*|0.000329|
||(0.000767)|(0.000769)|(0.000782)|(0.000776)|(0.00139)|(0.00150)|(0.00146)|(0.00157)|
|Share of sourcing via imports|0.00182**|0.000989|0.00179**|0.000997|0.00322**|0.00194|0.00323**|0.00198|
||(0.000810)|(0.000767)|(0.000837)|(0.000791)|(0.00147)|(0.00149)|(0.00156)|(0.00158)|
|High-income source country|-0.207**|-0.0923|-0.220**|-0.101|-0.374**|-0.138|-0.402**|-0.163|
||(0.0853)|(0.0863)|(0.0862)|(0.0869)|(0.160)|(0.172)|(0.166)|(0.179)|
|>250 employees|-0.0549|-0.0492|-0.0704|-0.0678|-0.0814|-0.0757|-0.126|-0.114|
||(0.0766)|(0.0775)|(0.0762)|(0.0778)|(0.140)|(0.150)|(0.144)|(0.154)|
|Cumulative inv. >US$10M|0.111|-0.00978|0.0899|-0.0313|0.213|-0.0252|0.177|-0.0732|
||(0.0735)|(0.0724)|(0.0730)|(0.0724)|(0.136)|(0.144)|(0.138)|(0.148)|
|Years in country|-0.000680|-0.00141|-0.000227|-0.000852|-0.00127|-0.00420|-0.000194|-0.00289|
||(0.00236)|(0.00217)|(0.00227)|(0.00213)|(0.00423)|(0.00435)|(0.00429)|(0.00442)|
|Level of foreign ownership (%)|-0.00250*|-0.000383|-0.00261**|-0.000325|-0.00459*|-0.000866|-0.00492**|-0.00102|
||(0.00133)|(0.00139)|(0.00127)|(0.00131)|(0.00256)|(0.00288)|(0.00249)|(0.00280)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.024|0.139|0.053|0.163|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Capacity of public agencies]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

41
**Table 25. Governance-related drivers of legal and regulatory obstacles: Inter-agency coordination**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0536|0.0494|||0.104|0.0712|||
||(0.0581)|(0.0554)|||(0.108)|(0.111)|||
|Export % of sales|-0.00195**|-5.32e-05|-0.00189**|-6.34e-05|-0.00366**|-0.000162|-0.00344**|-6.70e-05|
||(0.000773)|(0.000762)|(0.000776)|(0.000760)|(0.00142)|(0.00149)|(0.00148)|(0.00154)|
|Share of sourcing via imports|0.000791|-0.000165|0.000763|-0.000304|0.00161|-0.000139|0.00171|-0.000287|
||(0.000846)|(0.000827)|(0.000844)|(0.000815)|(0.00157)|(0.00164)|(0.00161)|(0.00168)|
|High-income source country|-0.147*|-0.0413|-0.159*|-0.0409|-0.278*|-0.0914|-0.316*|-0.0967|
||(0.0835)|(0.0799)|(0.0847)|(0.0796)|(0.158)|(0.164)|(0.165)|(0.168)|
|>250 employees|-0.0259|-0.0210|-0.0184|-0.00840|-0.0565|-0.0847|-0.0595|-0.0707|
||(0.0709)|(0.0703)|(0.0708)|(0.0691)|(0.128)|(0.139)|(0.133)|(0.141)|
|Cumulative inv. >US$10M|0.0604|-0.0346|0.0326|-0.0607|0.136|-0.0289|0.0878|-0.0719|
||(0.0709)|(0.0683)|(0.0700)|(0.0677)|(0.131)|(0.136)|(0.135)|(0.139)|
|Years in country|-0.00198|-0.00287|-0.00153|-0.00255|-0.00377|-0.00670|-0.00337|-0.00629|
||(0.00222)|(0.00217)|(0.00219)|(0.00217)|(0.00391)|(0.00447)|(0.00407)|(0.00463)|
|Level of foreign ownership (%)|-0.00212**|-0.000415|-0.00258**|-0.000457|-0.00358*|-0.000732|-0.00464**|-0.00105|
||(0.00108)|(0.00112)|(0.00105)|(0.00107)|(0.00192)|(0.00218)|(0.00195)|(0.00217)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.017|0.139|0.053|0.164|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Coordination between public agencies]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

42
**Table 26. Governance-related drivers of legal and regulatory obstacles: Bureaucratic discretion**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0144|0.00242|||0.0175|0.0105|||
||(0.0590)|(0.0578)|||(0.111)|(0.116)|||
|Export % of sales|-0.00211***|-0.000516|-0.00196**|-0.000434|-0.00394***|-0.00107|-0.00370**|-0.00108|
||(0.000792)|(0.000791)|(0.000797)|(0.000791)|(0.00148)|(0.00154)|(0.00154)|(0.00161)|
|Share of sourcing via imports|0.00248***|0.00153*|0.00250***|0.00148*|0.00485***|0.00349**|0.00498***|0.00338*|
||(0.000847)|(0.000845)|(0.000874)|(0.000870)|(0.00159)|(0.00171)|(0.00170)|(0.00180)|
|High-income source country|-0.142*|-0.0518|-0.138|-0.0324|-0.266*|-0.103|-0.277*|-0.0706|
||(0.0832)|(0.0846)|(0.0838)|(0.0837)|(0.161)|(0.175)|(0.167)|(0.179)|
|>250 employees|0.0643|0.0447|0.0421|0.0256|0.0664|0.0519|0.0244|0.0152|
||(0.0686)|(0.0685)|(0.0689)|(0.0686)|(0.123)|(0.133)|(0.128)|(0.137)|
|Cumulative inv. >US$10M|-0.00621|-0.0810|-0.0231|-0.0915|0.00788|-0.163|-0.0309|-0.189|
||(0.0711)|(0.0691)|(0.0703)|(0.0691)|(0.130)|(0.137)|(0.134)|(0.141)|
|Years in country|-0.00114|-0.00148|-0.000676|-0.00102|-0.00243|-0.00400|-0.00156|-0.00324|
||(0.00212)|(0.00206)|(0.00204)|(0.00204)|(0.00360)|(0.00388)|(0.00365)|(0.00395)|
|Level of foreign ownership (%)|-0.00267**|-0.00132|-0.00303***|-0.00130|-0.00474**|-0.00256|-0.00532**|-0.00256|
||(0.00111)|(0.00117)|(0.00110)|(0.00114)|(0.00211)|(0.00235)|(0.00215)|(0.00232)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.021|0.130|0.055|0.151|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Discretion of the bureaucracy]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

43
**Table 27. Governance-related drivers of legal and regulatory obstacles: Quality of laws**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0527|0.0436|||0.101|0.0750|||
||(0.0570)|(0.0545)|||(0.109)|(0.112)|||
|Export % of sales|-0.00168**|-0.000278|-0.00161**|-0.000233|-0.00310**|-0.000475|-0.00290**|-0.000380|
||(0.000754)|(0.000744)|(0.000762)|(0.000750)|(0.00143)|(0.00150)|(0.00147)|(0.00154)|
|Share of sourcing via imports|0.00219***|0.00105|0.00194**|0.000702|0.00427***|0.00281*|0.00397**|0.00226|
||(0.000806)|(0.000780)|(0.000836)|(0.000805)|(0.00154)|(0.00159)|(0.00166)|(0.00169)|
|High-income source country|-0.0808|-0.0140|-0.0863|-0.0157|-0.177|-0.0330|-0.211|-0.0563|
||(0.0830)|(0.0823)|(0.0835)|(0.0828)|(0.160)|(0.170)|(0.166)|(0.176)|
|>250 employees|0.0369|0.0549|0.0189|0.0361|0.0628|0.118|0.0157|0.0761|
||(0.0706)|(0.0720)|(0.0717)|(0.0730)|(0.132)|(0.144)|(0.139)|(0.150)|
|Cumulative inv. >US$10M|0.177***|0.0763|0.163**|0.0656|0.339***|0.129|0.322**|0.110|
||(0.0676)|(0.0667)|(0.0673)|(0.0664)|(0.128)|(0.135)|(0.131)|(0.137)|
|Years in country|-0.00261|-0.00278|-0.00221|-0.00234|-0.00552|-0.00702|-0.00503|-0.00614|
||(0.00218)|(0.00222)|(0.00213)|(0.00218)|(0.00399)|(0.00461)|(0.00402)|(0.00466)|
|Level of foreign ownership (%)|0.000241|0.00115|0.000295|0.00140|0.000595|0.00221|0.000729|0.00265|
||(0.00116)|(0.00117)|(0.00115)|(0.00115)|(0.00215)|(0.00233)|(0.00219)|(0.00233)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.017|0.115|0.042|0.136|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Quality of laws and regulations]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

44
**Table 28. Governance-related drivers of legal and regulatory obstacles: Accessibility of laws**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0402|0.0284|||0.0785|0.0557|||
||(0.0614)|(0.0605)|||(0.111)|(0.113)|||
|Export % of sales|-0.000745|0.000439|-0.000707|0.000479|-0.00124|0.000957|-0.00116|0.000947|
||(0.000777)|(0.000795)|(0.000800)|(0.000805)|(0.00138)|(0.00148)|(0.00149)|(0.00157)|
|Share of sourcing via imports|0.000702|-1.07e-05|0.00102|0.000172|0.00112|0.000134|0.00169|0.000501|
||(0.000840)|(0.000825)|(0.000862)|(0.000845)|(0.00149)|(0.00153)|(0.00157)|(0.00162)|
|High-income source country|-0.0484|0.00565|-0.0631|-0.000746|-0.0845|0.0217|-0.116|0.00760|
||(0.0902)|(0.0905)|(0.0899)|(0.0904)|(0.164)|(0.172)|(0.169)|(0.179)|
|>250 employees|-0.0460|-0.0356|-0.0655|-0.0527|-0.0723|-0.0573|-0.111|-0.0877|
||(0.0738)|(0.0742)|(0.0734)|(0.0737)|(0.131)|(0.139)|(0.134)|(0.143)|
|Cumulative inv. >US$10M|0.0661|-0.0128|0.0546|-0.0254|0.119|-0.0239|0.0987|-0.0468|
||(0.0719)|(0.0724)|(0.0714)|(0.0716)|(0.129)|(0.135)|(0.132)|(0.138)|
|Years in country|-0.00101|-0.00226|-0.000183|-0.00146|-0.00142|-0.00470|6.18e-06|-0.00316|
||(0.00220)|(0.00221)|(0.00219)|(0.00224)|(0.00375)|(0.00399)|(0.00388)|(0.00420)|
|Level of foreign ownership (%)|-0.00177|-0.000492|-0.00184|-0.000286|-0.00296|-0.000940|-0.00326|-0.000672|
||(0.00123)|(0.00132)|(0.00117)|(0.00125)|(0.00215)|(0.00240)|(0.00209)|(0.00235)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.006|0.069|0.031|0.096|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Accessibility of laws and regulations]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

45
**Table 29. Reactions to political risks: Most significant political risk**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0987|-0.0790|||-0.138|-0.114|||
||(0.0660)|(0.0665)|||(0.109)|(0.115)|||
|Export % of sales|-0.000662|0.000246|-0.000865|3.13e-05|-0.000594|0.000490|-0.000856|0.000282|
||(0.000864)|(0.000894)|(0.000899)|(0.000921)|(0.00141)|(0.00155)|(0.00149)|(0.00162)|
|Share of sourcing via imports|0.000395|0.00146|0.000837|0.00186*|0.000466|0.00221|0.00112|0.00303*|
||(0.000923)|(0.000941)|(0.000966)|(0.000992)|(0.00154)|(0.00165)|(0.00167)|(0.00177)|
|High-income source country|-0.00739|0.00110|0.0202|0.0186|0.00294|-0.0204|0.0432|0.0133|
||(0.0912)|(0.0955)|(0.0924)|(0.0949)|(0.152)|(0.166)|(0.159)|(0.168)|
|>250 employees|0.0471|0.0205|0.0498|0.0118|0.0408|0.0166|0.0392|-0.00626|
||(0.0814)|(0.0799)|(0.0825)|(0.0804)|(0.136)|(0.138)|(0.141)|(0.142)|
|Cumulative inv. >US$10M|0.0373|0.0251|0.0692|0.0488|0.106|0.0717|0.158|0.112|
||(0.0810)|(0.0818)|(0.0839)|(0.0840)|(0.136)|(0.141)|(0.143)|(0.147)|
|Years in country|-0.00504**|-0.00518**|-0.00505**|-0.00501**|-0.00779**|-0.00739**|-0.00778**|-0.00717*|
||(0.00244)|(0.00236)|(0.00246)|(0.00241)|(0.00372)|(0.00372)|(0.00387)|(0.00389)|
|Level of foreign ownership (%)|0.000802|0.000762|0.000765|0.000689|0.00230|0.00210|0.00226|0.00204|
||(0.00130)|(0.00136)|(0.00135)|(0.00137)|(0.00212)|(0.00233)|(0.00224)|(0.00238)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.006|0.069|0.026|0.085|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is a composite ordered categorical variable constructed from responses to the following series of questions: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation." Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

46
**Table 30. Reactions to political risks: Delay in permits and approvals**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.113*|-0.0975*|||-0.251**|-0.215*|||
||(0.0585)|(0.0591)|||(0.112)|(0.115)|||
|Export % of sales|2.38e-05|0.000690|1.90e-05|0.000694|-0.00107|0.000385|-0.000915|0.000546|
||(0.000821)|(0.000840)|(0.000825)|(0.000836)|(0.00154)|(0.00162)|(0.00163)|(0.00169)|
|Share of sourcing via imports|-0.00106|-0.000431|-0.00123|-0.000603|-0.00189|-0.000861|-0.00221|-0.00123|
||(0.000887)|(0.000901)|(0.000887)|(0.000894)|(0.00167)|(0.00178)|(0.00177)|(0.00187)|
|High-income source country|-0.171**|-0.149|-0.171**|-0.159*|-0.312**|-0.261|-0.330**|-0.301*|
||(0.0861)|(0.0914)|(0.0851)|(0.0880)|(0.158)|(0.168)|(0.164)|(0.172)|
|>250 employees|-0.0233|-0.0167|-0.0106|-0.0135|0.0278|0.0384|0.0440|0.0409|
||(0.0662)|(0.0688)|(0.0664)|(0.0685)|(0.127)|(0.136)|(0.130)|(0.138)|
|Cumulative inv. >US$10M|0.118*|0.0985|0.116*|0.0892|0.227*|0.190|0.224*|0.161|
||(0.0674)|(0.0683)|(0.0669)|(0.0683)|(0.131)|(0.136)|(0.133)|(0.139)|
|Years in country|-0.00136|-0.00227|-0.00122|-0.00200|-0.00321|-0.00558|-0.00290|-0.00497|
||(0.00207)|(0.00206)|(0.00209)|(0.00210)|(0.00395)|(0.00407)|(0.00410)|(0.00426)|
|Level of foreign ownership (%)|-0.00115|-0.000640|-0.00107|-0.000573|-0.00220|-0.00116|-0.00201|-0.000854|
||(0.00106)|(0.00112)|(0.00109)|(0.00114)|(0.00193)|(0.00212)|(0.00204)|(0.00220)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.014|0.048|0.040|0.072|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Delays in obtaining necessary permits and approvals to start or operate a business]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

47
**Table 31. Reactions to political risks: Currency restrictions**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0930|-0.0680|||-0.147|-0.108|||
||(0.0647)|(0.0649)|||(0.107)|(0.112)|||
|Export % of sales|-0.000591|0.000566|-0.000315|0.000852|-0.00106|0.000924|-0.000616|0.00143|
||(0.000919)|(0.000900)|(0.000930)|(0.000899)|(0.00149)|(0.00153)|(0.00156)|(0.00158)|
|Share of sourcing via imports|0.000275|0.000886|0.000446|0.000870|0.000554|0.00130|0.000712|0.00121|
||(0.000923)|(0.000916)|(0.000921)|(0.000920)|(0.00151)|(0.00158)|(0.00154)|(0.00161)|
|High-income source country|-0.127|-0.0897|-0.102|-0.0781|-0.207|-0.129|-0.175|-0.126|
||(0.0982)|(0.104)|(0.0949)|(0.0996)|(0.156)|(0.173)|(0.154)|(0.173)|
|>250 employees|-0.0816|-0.0840|-0.0502|-0.0653|-0.108|-0.131|-0.0460|-0.0965|
||(0.0807)|(0.0820)|(0.0827)|(0.0834)|(0.136)|(0.143)|(0.141)|(0.147)|
|Cumulative inv. >US$10M|0.0352|-0.00709|0.0614|0.00735|0.0445|0.00767|0.0958|0.0284|
||(0.0778)|(0.0779)|(0.0773)|(0.0777)|(0.131)|(0.137)|(0.132)|(0.139)|
|Years in country|-0.00284|-0.00369*|-0.00340|-0.00407*|-0.00414|-0.00671*|-0.00510|-0.00716*|
||(0.00210)|(0.00204)|(0.00214)|(0.00212)|(0.00352)|(0.00368)|(0.00372)|(0.00396)|
|Level of foreign ownership (%)|0.000452|0.00142|0.000450|0.00144|0.00112|0.00300|0.00114|0.00300|
||(0.00140)|(0.00134)|(0.00141)|(0.00132)|(0.00225)|(0.00228)|(0.00224)|(0.00231)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.006|0.098|0.033|0.117|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Restrictions on your ability to transfer and convert currency]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

48
**Table 32. Reactions to political risks: Government breach of contract**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0801|-0.0550|||-0.103|-0.0667|||
||(0.0788)|(0.0797)|||(0.108)|(0.114)|||
|Export % of sales|5.66e-05|0.00103|6.50e-05|0.000962|5.49e-05|0.00152|0.000190|0.00155|
||(0.00103)|(0.00108)|(0.00105)|(0.00108)|(0.00139)|(0.00154)|(0.00144)|(0.00158)|
|Share of sourcing via imports|0.000149|0.00150|0.000365|0.00170|0.000217|0.00215|0.000533|0.00264|
||(0.00112)|(0.00112)|(0.00116)|(0.00115)|(0.00152)|(0.00159)|(0.00162)|(0.00167)|
|High-income source country|-0.0350|-0.0176|-0.0178|-0.0212|-0.0442|-0.0218|-0.0185|-0.0370|
||(0.105)|(0.104)|(0.105)|(0.104)|(0.136)|(0.142)|(0.139)|(0.145)|
|>250 employees|0.132|0.101|0.118|0.0822|0.174|0.135|0.144|0.105|
||(0.100)|(0.0990)|(0.0984)|(0.0968)|(0.140)|(0.143)|(0.139)|(0.142)|
|Cumulative inv. >US$10M|-0.0110|-0.0251|0.0126|-0.0139|-0.0248|-0.0321|0.0250|-0.0134|
||(0.0971)|(0.0965)|(0.0996)|(0.0977)|(0.138)|(0.140)|(0.145)|(0.146)|
|Years in country|-0.00499**|-0.00524**|-0.00496**|-0.00500**|-0.00618*|-0.00666**|-0.00656**|-0.00692**|
||(0.00253)|(0.00252)|(0.00251)|(0.00250)|(0.00321)|(0.00335)|(0.00326)|(0.00342)|
|Level of foreign ownership (%)|-0.000871|-0.000254|-0.000614|-0.000224|-0.00113|-0.000551|-0.000884|-0.000483|
||(0.00157)|(0.00169)|(0.00157)|(0.00165)|(0.00205)|(0.00232)|(0.00213)|(0.00234)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.005|0.069|0.028|0.091|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Breach of contract by the government]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

49
**Table 33. Reactions to political risks: Expropriation**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.174**|-0.138*|||-0.225**|-0.194*|||
||(0.0790)|(0.0795)|||(0.101)|(0.110)|||
|Export % of sales|-0.000341|0.00103|-0.000443|0.000976|-0.000135|0.00140|-0.000455|0.00130|
||(0.00110)|(0.00111)|(0.00110)|(0.00111)|(0.00144)|(0.00157)|(0.00147)|(0.00162)|
|Share of sourcing via imports|0.000128|0.00147|0.000649|0.00197*|0.000159|0.00222|0.000777|0.00284|
||(0.00118)|(0.00119)|(0.00118)|(0.00119)|(0.00157)|(0.00167)|(0.00162)|(0.00175)|
|High-income source country|-0.00884|0.0644|0.0534|0.114|-0.00789|0.0840|0.0737|0.155|
||(0.109)|(0.109)|(0.108)|(0.106)|(0.136)|(0.144)|(0.140)|(0.145)|
|>250 employees|-0.0410|-0.0864|-0.0270|-0.0972|-0.0803|-0.134|-0.0598|-0.149|
||(0.0968)|(0.0948)|(0.0970)|(0.0935)|(0.124)|(0.129)|(0.129)|(0.132)|
|Cumulative inv. >US$10M|0.129|0.104|0.159*|0.118|0.171|0.170|0.218*|0.201|
||(0.0962)|(0.0945)|(0.0964)|(0.0934)|(0.127)|(0.132)|(0.132)|(0.135)|
|Years in country|-0.00505*|-0.00475*|-0.00527*|-0.00473*|-0.00607*|-0.00596*|-0.00634*|-0.00589*|
||(0.00274)|(0.00258)|(0.00277)|(0.00262)|(0.00325)|(0.00327)|(0.00338)|(0.00337)|
|Level of foreign ownership (%)|0.00257*|0.00285*|0.00267*|0.00303**|0.00341**|0.00424**|0.00357**|0.00459**|
||(0.00144)|(0.00158)|(0.00139)|(0.00154)|(0.00167)|(0.00203)|(0.00169)|(0.00207)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.010|0.109|0.042|0.135|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Expropriation or taking of your property or assets by the government]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

50
**Table 34. Reactions to political risks: Sudden, adverse change in laws and regulations**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0274|-0.00383|||-0.0505|-0.00377|||
||(0.0660)|(0.0663)|||(0.106)|(0.112)|||
|Export % of sales|-0.000489|0.000636|-0.000585|0.000525|-0.00152|0.000722|-0.00149|0.000718|
||(0.000912)|(0.000946)|(0.000937)|(0.000976)|(0.00150)|(0.00160)|(0.00159)|(0.00171)|
|Share of sourcing via imports|8.55e-05|0.000803|0.000196|0.000699|0.000430|0.00178|0.000690|0.00160|
||(0.000957)|(0.000963)|(0.00100)|(0.00101)|(0.00156)|(0.00165)|(0.00167)|(0.00174)|
|High-income source country|0.0335|0.0498|0.0546|0.0627|0.0798|0.127|0.115|0.150|
||(0.0942)|(0.102)|(0.0959)|(0.103)|(0.148)|(0.164)|(0.152)|(0.168)|
|>250 employees|-0.180**|-0.190**|-0.160**|-0.175**|-0.279**|-0.297**|-0.239*|-0.264**|
||(0.0785)|(0.0757)|(0.0764)|(0.0741)|(0.126)|(0.127)|(0.125)|(0.126)|
|Cumulative inv. >US$10M|0.123|0.106|0.153*|0.132*|0.199|0.176|0.249*|0.214|
||(0.0778)|(0.0779)|(0.0785)|(0.0791)|(0.126)|(0.132)|(0.130)|(0.135)|
|Years in country|0.000782|-1.87e-05|0.000330|-0.000445|0.00221|0.000676|0.00129|-0.000193|
||(0.00205)|(0.00202)|(0.00202)|(0.00201)|(0.00315)|(0.00327)|(0.00316)|(0.00334)|
|Level of foreign ownership (%)|-0.000535|-0.000315|-0.000683|-0.000399|-0.00127|-0.000998|-0.00127|-0.00100|
||(0.00138)|(0.00152)|(0.00137)|(0.00145)|(0.00215)|(0.00238)|(0.00224)|(0.00243)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.005|0.068|0.025|0.081|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Sudden change in the laws and regulations with a negative impact on your company]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

51
**Table 35. Governance drivers of political risk: Administrative complexity**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0889*|0.0810|||0.205*|0.200*|||
||(0.0527)|(0.0521)|||(0.108)|(0.111)|||
|Export % of sales|-0.000297|0.000538|-0.000580|0.000287|-0.000647|0.00124|-0.00137|0.000647|
||(0.000695)|(0.000720)|(0.000724)|(0.000737)|(0.00142)|(0.00153)|(0.00153)|(0.00162)|
|Share of sourcing via imports|0.00260***|0.00166**|0.00252***|0.00154**|0.00511***|0.00331**|0.00507***|0.00314*|
||(0.000735)|(0.000740)|(0.000778)|(0.000780)|(0.00149)|(0.00157)|(0.00164)|(0.00172)|
|High-income source country|-0.00299|0.0587|-0.0156|0.0590|-0.00351|0.135|-0.0362|0.137|
||(0.0780)|(0.0793)|(0.0813)|(0.0817)|(0.162)|(0.173)|(0.172)|(0.183)|
|>250 employees|-0.0476|-0.0525|-0.0644|-0.0675|-0.120|-0.130|-0.166|-0.167|
||(0.0647)|(0.0648)|(0.0653)|(0.0653)|(0.132)|(0.138)|(0.136)|(0.141)|
|Cumulative inv. >US$10M|-0.0615|-0.127*|-0.0664|-0.130**|-0.105|-0.264*|-0.106|-0.263*|
||(0.0656)|(0.0647)|(0.0655)|(0.0649)|(0.136)|(0.139)|(0.138)|(0.141)|
|Years in country|0.00189|0.00238|0.00220|0.00256|0.00266|0.00339|0.00310|0.00348|
||(0.00176)|(0.00170)|(0.00175)|(0.00171)|(0.00376)|(0.00375)|(0.00380)|(0.00385)|
|Level of foreign ownership (%)|-0.00152|-0.000720|-0.00169*|-0.000555|-0.00334|-0.00146|-0.00375*|-0.00126|
||(0.00105)|(0.00116)|(0.00101)|(0.00112)|(0.00220)|(0.00251)|(0.00220)|(0.00252)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.016|0.074|0.035|0.092|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Complexity of administrative procedures]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

52
**Table 36. Governance drivers of political risk: Public agency capacity**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.103*|0.0890|||0.211*|0.191*|||
||(0.0556)|(0.0548)|||(0.110)|(0.111)|||
|Export % of sales|-0.000232|0.000497|-0.000324|0.000370|-0.000605|0.000859|-0.000732|0.000693|
||(0.000730)|(0.000749)|(0.000749)|(0.000759)|(0.00143)|(0.00151)|(0.00151)|(0.00159)|
|Share of sourcing via imports|0.00169**|0.000878|0.00172**|0.000896|0.00321**|0.00189|0.00331**|0.00186|
||(0.000777)|(0.000770)|(0.000803)|(0.000794)|(0.00155)|(0.00158)|(0.00165)|(0.00167)|
|High-income source country|-0.207**|-0.155*|-0.227***|-0.164*|-0.423**|-0.341*|-0.482***|-0.380**|
||(0.0813)|(0.0836)|(0.0839)|(0.0843)|(0.167)|(0.177)|(0.177)|(0.184)|
|>250 employees|-0.0606|-0.0495|-0.0896|-0.0745|-0.107|-0.0775|-0.180|-0.134|
||(0.0684)|(0.0714)|(0.0688)|(0.0721)|(0.130)|(0.141)|(0.135)|(0.148)|
|Cumulative inv. >US$10M|0.0491|-0.0422|0.0451|-0.0459|0.0947|-0.0932|0.0815|-0.106|
||(0.0667)|(0.0676)|(0.0664)|(0.0669)|(0.129)|(0.136)|(0.132)|(0.140)|
|Years in country|0.000218|0.000378|0.000481|0.000600|0.000281|0.000108|0.00148|0.000980|
||(0.00233)|(0.00221)|(0.00226)|(0.00217)|(0.00472)|(0.00470)|(0.00469)|(0.00479)|
|Level of foreign ownership (%)|-0.000824|0.000246|-0.000940|0.000342|-0.00153|0.000504|-0.00143|0.000941|
||(0.00114)|(0.00121)|(0.00110)|(0.00115)|(0.00226)|(0.00248)|(0.00223)|(0.00243)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.015|0.072|0.034|0.090|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Capacity of public agencies]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

53
**Table 37. Governance drivers of political risk: Inter-agency coordination**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0851|0.0803|||0.176|0.176|||
||(0.0551)|(0.0555)|||(0.109)|(0.113)|||
|Export % of sales|-<br>0.00208***|-0.00132*|-<br>0.00197***|-0.00123|-<br>0.00409***|-0.00258*|-<br>0.00395***|-0.00251|
||(0.000708)|(0.000756)|(0.000719)|(0.000755)|(0.00139)|(0.00153)|(0.00146)|(0.00157)|
|Share of sourcing via imports|0.00109|0.000610|0.00120|0.000651|0.00188|0.00109|0.00226|0.00124|
||(0.000778)|(0.000805)|(0.000808)|(0.000834)|(0.00154)|(0.00164)|(0.00167)|(0.00176)|
|High-income source country|-0.0193|0.0167|-0.0217|0.0202|-0.0308|0.0327|-0.0477|0.0253|
||(0.0814)|(0.0798)|(0.0817)|(0.0795)|(0.167)|(0.166)|(0.172)|(0.171)|
|>250 employees|-0.0403|-0.0450|-0.0507|-0.0550|-0.0980|-0.114|-0.118|-0.137|
||(0.0682)|(0.0682)|(0.0681)|(0.0686)|(0.133)|(0.136)|(0.138)|(0.141)|
|Cumulative inv. >US$10M|-0.00134|-0.0582|-0.00520|-0.0609|-0.00210|-0.115|-0.0182|-0.126|
||(0.0679)|(0.0689)|(0.0676)|(0.0686)|(0.134)|(0.140)|(0.138)|(0.144)|
|Years in country|0.000415|0.000824|0.000526|0.000844|0.00107|0.00210|0.00114|0.00201|
||(0.00182)|(0.00182)|(0.00180)|(0.00182)|(0.00360)|(0.00360)|(0.00360)|(0.00366)|
|Level of foreign ownership (%)|-0.00143|-0.000799|-0.00147|-0.000652|-0.00294|-0.00173|-0.00309|-0.00157|
||(0.00116)|(0.00128)|(0.00112)|(0.00122)|(0.00236)|(0.00271)|(0.00234)|(0.00264)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.015|0.047|0.038|0.069|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Coordination between public agencies]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

54
**Table 38. Governance drivers of political risk: Bureaucratic discretion**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0142|0.00633|||0.0348|0.0254|||
||(0.0564)|(0.0565)|||(0.110)|(0.112)|||
|Export % of sales|-0.00146**|-0.000577|-0.00136*|-0.000498|-0.00284**|-0.000957|-0.00260*|-0.000792|
||(0.000736)|(0.000775)|(0.000756)|(0.000786)|(0.00144)|(0.00153)|(0.00151)|(0.00160)|
|Share of sourcing via imports|0.00227***|0.00181**|0.00251***|0.00204**|0.00459***|0.00392**|0.00513***|0.00446**|
||(0.000797)|(0.000817)|(0.000828)|(0.000847)|(0.00155)|(0.00163)|(0.00165)|(0.00173)|
|High-income source country|0.0329|0.0855|0.0399|0.0959|0.0750|0.203|0.0899|0.219|
||(0.0818)|(0.0822)|(0.0827)|(0.0830)|(0.164)|(0.169)|(0.169)|(0.174)|
|>250 employees|0.0600|0.0494|0.0552|0.0431|0.118|0.0932|0.102|0.0779|
||(0.0693)|(0.0703)|(0.0688)|(0.0700)|(0.133)|(0.137)|(0.136)|(0.140)|
|Cumulative inv. >US$10M|-0.0318|-0.0865|-0.0472|-0.103|-0.0502|-0.170|-0.0937|-0.212|
||(0.0687)|(0.0695)|(0.0684)|(0.0695)|(0.132)|(0.136)|(0.135)|(0.140)|
|Years in country|0.000243|6.01e-05|0.000604|0.000401|-0.000377|-0.000800|0.000600|-4.40e-06|
||(0.00197)|(0.00195)|(0.00201)|(0.00200)|(0.00411)|(0.00417)|(0.00426)|(0.00438)|
|Level of foreign ownership (%)|-0.00186|-0.000690|-0.00187*|-0.000542|-0.00413*|-0.00153|-0.00430*|-0.00150|
||(0.00114)|(0.00121)|(0.00110)|(0.00116)|(0.00233)|(0.00249)|(0.00229)|(0.00246)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.011|0.049|0.033|0.071|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Discretion of the bureaucracy]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

55
**Table 39. Governance drivers of political risk: Quality of laws**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.00696|0.0115|||0.00624|0.0213|||
||(0.0544)|(0.0533)|||(0.106)|(0.109)|||
|Export % of sales|-0.00109|6.79e-05|-0.00107|-2.41e-05|-0.00216|0.000323|-0.00215|7.57e-05|
||(0.000718)<br>0.00281**|(0.000747)<br>0.00230**|(0.000737)<br>0.00262**|(0.000760)<br>0.00209**|(0.00140)<br>0.00542**|(0.00153)<br>0.00493**|(0.00150)<br>0.00538**|(0.00162)<br>0.00463**|
|Share of sourcing via imports|*|*|*|*|*|*|*|*|
||(0.000773)|(0.000766)|(0.000798)|(0.000795)|(0.00151)|(0.00157)|(0.00162)|(0.00169)|
|High-income source country|-0.0396|0.00773|-0.00265|0.0325|-0.113|-0.0216|-0.0612|0.0150|
||(0.0824)|(0.0820)|(0.0828)|(0.0828)|(0.170)|(0.176)|(0.176)|(0.186)|
|>250 employees|-0.0432|-0.00849|-0.0335|-0.00873|-0.100|-0.0415|-0.0898|-0.0386|
||(0.0694)|(0.0703)|(0.0691)|(0.0710)|(0.138)|(0.148)|(0.143)|(0.156)|
|Cumulative inv. >US$10M|0.0855|-0.0145|0.0817|-0.0173|0.179|-0.0276|0.189|-0.0245|
||(0.0685)|(0.0684)|(0.0682)|(0.0676)|(0.137)|(0.144)|(0.141)|(0.147)|
|Years in country|0.000532|0.000970|0.000868|0.00151|0.000202|0.000403|0.000645|0.00136|
||(0.00180)|(0.00180)|(0.00179)|(0.00179)|(0.00356)|(0.00384)|(0.00375)|(0.00396)|
|Level of foreign ownership (%)|-0.00108|-0.000626|-0.000914|-0.000474|-0.00229|-0.00137|-0.00203|-0.00122|
||(0.00111)|(0.00122)|(0.00107)|(0.00115)|(0.00227)|(0.00260)|(0.00226)|(0.00255)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.014|0.074|0.048|0.105|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Quality of laws and regulations]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

56
**Table 40. Governance drivers of political risk: Accessibility of laws**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0438|0.0419|||0.0949|0.0926|||
||(0.0578)|(0.0575)|||(0.110)|(0.112)|||
|Export % of sales|-0.00154**|-0.000653|-0.00139*|-0.000622|-0.00285**|-0.00138|-0.00253*|-0.00128|
||(0.000750)|(0.000784)|(0.000765)|(0.000793)|(0.00143)|(0.00154)|(0.00152)|(0.00162)|
|Share of sourcing via imports|0.00109|0.000745|0.00141*|0.00104|0.00197|0.00154|0.00256|0.00202|
||(0.000799)|(0.000796)|(0.000820)|(0.000819)|(0.00150)|(0.00153)|(0.00159)|(0.00162)|
|High-income source country|-0.00266|0.0376|0.00948|0.0445|0.00271|0.0880|0.0158|0.0945|
||(0.0842)|(0.0868)|(0.0839)|(0.0861)|(0.164)|(0.176)|(0.167)|(0.180)|
|>250 employees|-0.00853|0.00994|-0.0178|-0.00536|-0.0112|0.0299|-0.0279|0.00214|
||(0.0712)|(0.0725)|(0.0694)|(0.0708)|(0.135)|(0.140)|(0.134)|(0.141)|
|Cumulative inv. >US$10M|-0.0410|-0.114|-0.0506|-0.124*|-0.0784|-0.229*|-0.107|-0.253*|
||(0.0693)|(0.0713)|(0.0687)|(0.0701)|(0.130)|(0.138)|(0.132)|(0.140)|
|Years in country|0.000838|0.000520|0.000853|0.000661|0.00189|0.000870|0.00207|0.00141|
||(0.00238)|(0.00231)|(0.00233)|(0.00230)|(0.00472)|(0.00475)|(0.00485)|(0.00487)|
|Level of foreign ownership (%)|-0.00309**|-0.00223*|-0.00302**|-0.00213*|-0.00608**|-0.00463*|-0.00609**|-0.00463*|
||(0.00122)|(0.00135)|(0.00118)|(0.00127)|(0.00247)|(0.00278)|(0.00243)|(0.00267)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.014|0.045|0.041|0.071|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Accessibility of laws and regulations]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

57
**Table 41. Importance of IPA services: Most important IPA service**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.00482|0.000821|||-0.0379|-0.0177|||
||(0.0407)|(0.0393)|||(0.120)|(0.127)|||
|Export % of sales|-0.000178|-0.000248|-0.000109|-0.000194|-0.00213|-0.00131|-0.00172|-0.000963|
||(0.000543)|(0.000529)|(0.000546)|(0.000546)|(0.00162)|(0.00173)|(0.00168)|(0.00181)|
|Share of sourcing via imports|0.00116**|0.00105*|0.00106*|0.000959*|0.00385**|0.00342*|0.00370**|0.00326*|
||(0.000537)|(0.000536)|(0.000562)|(0.000556)|(0.00168)|(0.00179)|(0.00178)|(0.00187)|
|High-income source country|-0.130***|-0.0535|-0.125**|-0.0549|-0.407**|-0.156|-0.417**|-0.174|
||(0.0482)|(0.0494)|(0.0506)|(0.0508)|(0.179)|(0.198)|(0.188)|(0.201)|
|>250 employees|0.0430|0.0488|0.0365|0.0436|0.157|0.209|0.134|0.191|
||(0.0467)|(0.0464)|(0.0470)|(0.0462)|(0.148)|(0.156)|(0.151)|(0.157)|
|Cumulative inv. >US$10M|-0.0271|0.0270|-0.0332|0.0229|-0.0414|0.0659|-0.0708|0.0453|
||(0.0459)|(0.0445)|(0.0454)|(0.0445)|(0.138)|(0.145)|(0.141)|(0.148)|
|Years in country|-0.00105|-0.00237|-0.000781|-0.00232|-0.00478|-0.0103**|-0.00412|-0.0101**|
||(0.00165)|(0.00147)|(0.00168)|(0.00150)|(0.00492)|(0.00490)|(0.00498)|(0.00499)|
|Level of foreign ownership (%)|-0.00227***|-0.000340|-0.00201***|-0.000245|-0.00750***|-0.000967|-0.00708***|-0.000986|
||(0.000700)|(0.000745)|(0.000689)|(0.000741)|(0.00274)|(0.00301)|(0.00272)|(0.00301)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.018|0.091|0.035|0.105|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is a composite ordered categorical variable constructed from responses to the following series of questions: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Maximum score across all services]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

58
**Table 42. Importance of IPA services: Promotion of investment opportunities**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0163|0.0128|||0.0171|-0.00105|||
||(0.0557)|(0.0537)|||(0.113)|(0.115)|||
|Export % of sales|0.000245|8.00e-05|0.000113|6.74e-05|1.77e-05|-5.90e-05|-0.000194|-0.000111|
||(0.000739)|(0.000720)|(0.000736)|(0.000733)|(0.00149)|(0.00151)|(0.00152)|(0.00160)|
|Share of sourcing via imports|0.00120|0.000561|0.00107|0.000303|0.00238|0.000900|0.00199|0.000220|
||(0.000807)|(0.000811)|(0.000795)|(0.000792)|(0.00165)|(0.00172)|(0.00169)|(0.00175)|
|High-income source country|-0.228***|-0.118|-0.241***|-0.117|-0.488***|-0.256|-0.509***|-0.241|
||(0.0812)|(0.0797)|(0.0765)|(0.0765)|(0.173)|(0.178)|(0.171)|(0.179)|
|>250 employees|0.00367|-0.00907|-0.00389|-0.00438|-0.00665|-0.0159|-0.0200|0.00280|
||(0.0669)|(0.0668)|(0.0679)|(0.0673)|(0.135)|(0.141)|(0.140)|(0.146)|
|Cumulative inv. >US$10M|0.0432|0.103*|0.0548|0.115*|0.114|0.190|0.146|0.232*|
||(0.0637)|(0.0607)|(0.0643)|(0.0614)|(0.130)|(0.130)|(0.133)|(0.134)|
|Years in country|0.000437|-0.00143|0.000419|-0.00208|-0.000121|-0.00556|0.000395|-0.00638|
||(0.00188)|(0.00184)|(0.00189)|(0.00185)|(0.00373)|(0.00399)|(0.00379)|(0.00416)|
|Level of foreign ownership (%)|-0.00394***|-0.00119|-0.00405***|-0.00106|-0.00800***|-0.00205|-0.00843***|-0.00214|
||(0.00112)|(0.00121)|(0.00113)|(0.00118)|(0.00251)|(0.00267)|(0.00256)|(0.00276)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.027|0.092|0.049|0.116|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Promoting investment opportunities]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level. Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

59
**Table 43. Importance of IPA services: Personalized contact and responsiveness to your company**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0438|0.0263|||0.126|0.0687|||
||(0.0549)|(0.0511)|||(0.111)|(0.112)|||
|Export % of sales|0.00113|0.000774|0.000981|0.000706|0.00194|0.00131|0.00167|0.00111|
||(0.000689)|(0.000646)|(0.000702)|(0.000663)|(0.00138)|(0.00142)|(0.00144)|(0.00148)|
|Share of sourcing via imports|0.00171**|0.00113|0.00168**|0.00107|0.00341**|0.00208|0.00341**|0.00200|
||(0.000762)|(0.000726)|(0.000789)|(0.000762)|(0.00154)|(0.00156)|(0.00163)|(0.00167)|
|High-income source country|-0.0810|0.0145|-0.0998|0.00572|-0.151|0.0271|-0.194|0.0148|
||(0.0749)|(0.0717)|(0.0770)|(0.0725)|(0.153)|(0.157)|(0.160)|(0.161)|
|>250 employees|0.0745|0.0641|0.0794|0.0774|0.161|0.136|0.167|0.164|
||(0.0683)|(0.0642)|(0.0675)|(0.0645)|(0.141)|(0.141)|(0.141)|(0.145)|
|Cumulative inv. >US$10M|0.0512|0.102*|0.0455|0.0998*|0.110|0.236*|0.108|0.244*|
||(0.0653)|(0.0598)|(0.0653)|(0.0602)|(0.133)|(0.132)|(0.135)|(0.135)|
|Years in country|-0.00172|-0.00353*|-0.00130|-0.00375*|-0.00394|-0.00830**|-0.00313|-0.00867**|
||(0.00220)|(0.00198)|(0.00219)|(0.00200)|(0.00423)|(0.00415)|(0.00422)|(0.00423)|
|Level of foreign ownership (%)|-0.00163|0.00124|-0.00174|0.00119|-0.00323|0.00273|-0.00354|0.00252|
||(0.00114)|(0.00110)|(0.00113)|(0.00109)|(0.00241)|(0.00246)|(0.00242)|(0.00245)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.010|0.115|0.025|0.126|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Personalized contact and responsiveness to your company]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

60
**Table 44. Importance of IPA services: Pre-investment Information (e.g. location guide, sector and project profiles, regulatory procedures)**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0140|-0.0131|||-0.0611|-0.0649|||
||(0.0533)|(0.0518)|||(0.111)|(0.115)|||
|Export % of sales|0.000274|-0.000183|0.000405|-1.14e-05|-0.000315|-0.000897|5.68e-05|-0.000448|
||(0.000680)|(0.000653)|(0.000682)|(0.000666)|(0.00140)|(0.00142)|(0.00144)|(0.00149)|
|Share of sourcing via imports|0.000989|0.000703|0.000813|0.000599|0.00266*|0.00186|0.00243|0.00173|
||(0.000750)|(0.000774)|(0.000767)|(0.000781)|(0.00158)|(0.00170)|(0.00165)|(0.00176)|
|High-income source country|-0.152**|-0.0820|-0.156**|-0.0832|-0.342**|-0.196|-0.359**|-0.199|
||(0.0768)|(0.0766)|(0.0771)|(0.0772)|(0.169)|(0.171)|(0.173)|(0.176)|
|>250 employees|0.00645|0.0156|-0.00448|0.00779|-0.00922|0.0339|-0.0328|0.0176|
||(0.0685)|(0.0692)|(0.0694)|(0.0705)|(0.143)|(0.148)|(0.146)|(0.153)|
|Cumulative inv. >US$10M|0.0536|0.123*|0.0434|0.115*|0.148|0.260*|0.126|0.247*|
||(0.0643)|(0.0634)|(0.0646)|(0.0637)|(0.137)|(0.141)|(0.141)|(0.144)|
|Years in country|-0.000689|-0.00141|-0.000737|-0.00177|-0.00159|-0.00304|-0.00138|-0.00352|
||(0.00234)|(0.00221)|(0.00237)|(0.00221)|(0.00523)|(0.00500)|(0.00536)|(0.00507)|
|Level of foreign ownership (%)|5.49e-05|0.00134|3.65e-05|0.00132|-1.26e-05|0.00303|-6.79e-06|0.00293|
||(0.00119)|(0.00119)|(0.00118)|(0.00118)|(0.00252)|(0.00263)|(0.00256)|(0.00263)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.006|0.069|0.028|0.092|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Pre-investment Information (e.g. location's guide, sector and project profiles, regulatory procedures)]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

61
**Table 45. Importance of IPA services: Pre-investment assistance (e.g. site visits, briefings, meetings with stakeholders)**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.00137|0.00312|||-0.0216|-0.00589|||
||(0.0549)|(0.0534)|||(0.109)|(0.112)|||
|Export % of sales|0.000642|0.000140|0.000466|-3.41e-05|0.000709|0.000108|0.000299|-0.000344|
||(0.000729)|(0.000730)|(0.000733)|(0.000735)|(0.00143)|(0.00151)|(0.00150)|(0.00158)|
|Share of sourcing via imports|-5.21e-05|-0.000165|-4.67e-05|-6.85e-05|0.000214|-0.000372|0.000307|-5.45e-05|
||(0.000799)|(0.000831)|(0.000793)|(0.000820)|(0.00160)|(0.00174)|(0.00165)|(0.00177)|
|High-income source country|-0.207***|-0.142*|-0.212***|-0.156*|-0.445***|-0.318*|-0.480***|-0.372**|
||(0.0789)|(0.0793)|(0.0805)|(0.0803)|(0.163)|(0.174)|(0.173)|(0.182)|
|>250 employees|0.000996|0.0234|-0.0298|-0.00899|-0.0513|0.0141|-0.118|-0.0587|
||(0.0650)|(0.0668)|(0.0647)|(0.0662)|(0.128)|(0.138)|(0.132)|(0.142)|
|Cumulative inv. >US$10M|-0.0181|0.0485|-0.0151|0.0506|-0.0123|0.0962|0.00690|0.114|
||(0.0638)|(0.0626)|(0.0631)|(0.0620)|(0.127)|(0.129)|(0.129)|(0.131)|
|Years in country|-0.00267|-0.00400*|-0.00237|-0.00380*|-0.00493|-0.00762*|-0.00435|-0.00726*|
||(0.00218)|(0.00206)|(0.00220)|(0.00204)|(0.00402)|(0.00393)|(0.00414)|(0.00399)|
|Level of foreign ownership (%)|-0.00118|9.35e-05|-0.00112|-4.14e-05|-0.00186|0.000882|-0.00190|0.000398|
||(0.00106)|(0.00109)|(0.00105)|(0.00108)|(0.00213)|(0.00223)|(0.00216)|(0.00226)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.011|0.069|0.043|0.099|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Pre-investment assistance (e.g. site visits, briefings, meetings with stakeholders)]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

62
**Table 46. Importance of IPA services: Business setup assistance**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0315|0.0351|||0.0516|0.0828|||
||(0.0560)|(0.0547)|||(0.115)|(0.120)|||
|Export % of sales|0.00134*|0.000737|0.00145*|0.000808|0.00157|0.000977|0.00183|0.00109|
||(0.000741)|(0.000729)|(0.000747)|(0.000740)|(0.00153)|(0.00161)|(0.00159)|(0.00168)|
|Share of sourcing via imports|0.00188**|0.00192**|0.00163**|0.00166**|0.00419***|0.00426**|0.00385**|0.00390**|
||(0.000756)|(0.000763)|(0.000768)|(0.000771)|(0.00159)|(0.00171)|(0.00164)|(0.00174)|
|High-income source country|-0.194**|-0.129|-0.184**|-0.117|-0.416**|-0.269|-0.395**|-0.249|
||(0.0782)|(0.0815)|(0.0810)|(0.0818)|(0.170)|(0.187)|(0.175)|(0.189)|
|>250 employees|0.0147|-0.000197|0.0120|0.000322|0.0238|-0.000257|0.0208|0.00282|
||(0.0711)|(0.0722)|(0.0721)|(0.0733)|(0.146)|(0.156)|(0.150)|(0.161)|
|Cumulative inv. >US$10M|-0.0651|0.0269|-0.0610|0.0346|-0.111|0.0509|-0.103|0.0697|
||(0.0658)|(0.0639)|(0.0667)|(0.0648)|(0.134)|(0.137)|(0.138)|(0.142)|
|Years in country|-0.00100|-0.00196|-0.00104|-0.00228|-0.00189|-0.00401|-0.00219|-0.00477|
||(0.00251)|(0.00242)|(0.00252)|(0.00240)|(0.00510)|(0.00506)|(0.00517)|(0.00509)|
|Level of foreign ownership (%)|-0.000904|9.20e-05|-0.00106|-9.90e-05|-0.00172|0.000613|-0.00222|-6.90e-05|
||(0.00126)|(0.00128)|(0.00123)|(0.00122)|(0.00274)|(0.00295)|(0.00272)|(0.00288)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.015|0.087|0.031|0.105|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Assistance in setting up of business (e.g. registration requirements and entry permits)]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

63
**Table 47. Importance of IPA services: Assistance with operational issues and grievances**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.000110|-0.00707|||0.0105|-0.00605|||
||(0.0545)|(0.0527)|||(0.110)|(0.113)|||
|Export % of sales|0.000343|2.76e-05|0.000419|6.80e-05|0.000272|-2.10e-05|0.000503|0.000148|
||(0.000700)|(0.000674)|(0.000706)|(0.000692)|(0.00143)|(0.00145)|(0.00147)|(0.00151)|
|Share of sourcing via imports|0.00154**|0.00121|0.00159**|0.00134*|0.00346**|0.00250|0.00359**|0.00303*|
||(0.000739)|(0.000744)|(0.000756)|(0.000759)|(0.00153)|(0.00161)|(0.00160)|(0.00167)|
|High-income source country|-0.207***|-0.133*|-0.220***|-0.146*|-0.402**|-0.240|-0.443***|-0.282|
||(0.0734)|(0.0741)|(0.0753)|(0.0745)|(0.157)|(0.170)|(0.164)|(0.173)|
|>250 employees|0.0277|0.0201|0.0165|0.0113|0.0256|0.0246|0.00688|0.00868|
||(0.0653)|(0.0647)|(0.0659)|(0.0654)|(0.132)|(0.137)|(0.135)|(0.141)|
|Cumulative inv. >US$10M|-0.0604|-0.00765|-0.0643|-0.00630|-0.104|-0.0279|-0.115|-0.0221|
||(0.0628)|(0.0599)|(0.0628)|(0.0601)|(0.126)|(0.127)|(0.129)|(0.130)|
|Years in country|-0.00195|-0.00281|-0.00182|-0.00293|-0.00348|-0.00511|-0.00349|-0.00519|
||(0.00258)|(0.00243)|(0.00257)|(0.00240)|(0.00545)|(0.00526)|(0.00554)|(0.00532)|
|Level of foreign ownership (%)|-0.00201*|-0.000202|-0.00204*|-0.000351|-0.00434*|-0.000438|-0.00455*|-0.000815|
||(0.00107)|(0.00109)|(0.00106)|(0.00107)|(0.00236)|(0.00246)|(0.00239)|(0.00247)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.016|0.080|0.037|0.101|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Assistance with operational issues and grievances]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

64
**Table 48. Importance of IPA services: Efforts to improve the overall business environment in the country**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.00663|0.00226|||-0.00976|0.0308|||
||(0.0503)|(0.0493)|||(0.111)|(0.114)|||
|Export % of sales|-0.000190|-0.000414|-0.000350|-0.000552|-0.000675|-0.000505|-0.000893|-0.000669|
||(0.000673)|(0.000663)|(0.000677)|(0.000678)|(0.00149)|(0.00155)|(0.00153)|(0.00161)|
|Share of sourcing via imports|5.88e-05|-0.000103|-0.000140|-0.000289|-0.000124|-0.000689|-0.000375|-0.00101|
||(0.000711)|(0.000718)|(0.000720)|(0.000728)|(0.00159)|(0.00167)|(0.00164)|(0.00173)|
|High-income source country|-0.218***|-0.147**|-0.220***|-0.153**|-0.503***|-0.325*|-0.515***|-0.349*|
||(0.0680)|(0.0696)|(0.0705)|(0.0707)|(0.165)|(0.176)|(0.177)|(0.184)|
|>250 employees|0.00641|0.0179|0.00275|0.0115|0.0142|0.0712|0.0147|0.0614|
||(0.0611)|(0.0619)|(0.0626)|(0.0627)|(0.137)|(0.145)|(0.145)|(0.152)|
|Cumulative inv. >US$10M|-0.00130|0.0654|-0.00391|0.0646|0.0443|0.162|0.0296|0.150|
||(0.0584)|(0.0581)|(0.0578)|(0.0573)|(0.131)|(0.137)|(0.133)|(0.140)|
|Years in country|-0.00139|-0.00279|-0.00126|-0.00292*|-0.00350|-0.00734*|-0.00306|-0.00738*|
||(0.00183)|(0.00177)|(0.00183)|(0.00176)|(0.00396)|(0.00409)|(0.00393)|(0.00403)|
|Level of foreign ownership (%)|-0.00126|0.000407|-0.00110|0.000513|-0.00277|0.00110|-0.00254|0.00130|
||(0.000972)|(0.00100)|(0.000967)|(0.00101)|(0.00233)|(0.00244)|(0.00237)|(0.00252)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.013|0.066|0.035|0.089|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Efforts to improve the overall business environment in the country]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

65