The Institutional Reforms Debate and FDI Flows to the MENA Region: The “Best” Ensemble

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World Development Vol. 40, No. 9, pp. 1798–1809, 2012 Ó 2012 Elsevier Ltd. All rights reserved. 0305-750X/$ - see front matter www.elsevier.com/locate/worlddev

http://dx.doi.org/10.1016/j.worlddev.2012.04.026

The Institutional Reforms Debate and FDI Flows to the MENA Region: The “Best” Ensemble WASSEEM MICHEL MINA * United Arab Emirates University, United Arab Emirates Summary. — This paper empirically examines the theoretical debate on the adoption of a best approach to reforming institutions identiﬁed by Rodrik (2008) in the context of property rights protection and FDI ﬂows to eight MENA countries. The ﬁrst best approach comprises strengthening domestic institutional functions only, while the second best comprises in addition entering into force bilateral investment treaties and the interaction between functions and treaties. Empirically both approaches to reducing investment expropriation risk encourage FDI ﬂows. The positive eﬀect of the second best approach depends on the success of the ﬁrst best approach, suggesting the two approaches are complementary. Ó 2012 Elsevier Ltd. All rights reserved. Key words — institutional reforms, property rights protection, FDI, MENA, ﬁrst best, second best

1. INTRODUCTION

given the globalization that has occurred in the past quartercentury and the growing importance of markets in resource allocation and the conduct of economic activity. The intensiﬁcation of globalization and development of markets have necessitated the adoption of common institutional functions and standards, similar to the adoption of a common language in communications, which facilitate trade and capital mobility. However, the adoption of an orthodox approach for institutional reform does not take into account a country’s unique circumstances and the interaction of institutions within the country. This view is supported by Rodrik (2008) who argues that institutional reforms promoted by the World Bank, the IMF or the WTO, to name three examples, presume the existence of a unique set of appropriate institutional arrangements to which it is “inherently desirable” that countries conform. He also warns that the convergence to a ﬁrst best practice does not “consider potential interactions with institutional features elsewhere in the system” and advocates instead for institutional reforms based on the theory of the second best. 3 Acting along the lines of the theory of the second best, many governments have strengthened PRP through bilateral investment treaties that act as complements or substitutes to their domestic institutional functions. A bilateral investment treaty is an international legal instrument between two contracting

Institutions are deﬁned as the set of rules governing human behavior (North, 1991). They include both formal and informal rules. Formal rules are legal in nature and include constitutions, laws, and regulations created and enforced by the government in response to individuals’ needs to organize interactions in society. Informal rules are social in nature and include traditions and customs inﬂuenced by cultures and beliefs. Institutions play an important role in supporting markets and transactions by protecting property rights, enforcing contracts, and facilitating collective action to provide physical and organizational infrastructure (Dixit, 2009). They create order, reduce uncertainty in the exchange of goods and capital, and help to determine transaction and production costs; thus, institutions determine the feasibility and proﬁtability of engaging in economic activity (North, 1991). Among the positive eﬀects of good institutions is the promotion of a country’s integration into the world economy (Rodrik, 2008). The ﬂow of capital constitutes one important integration channel. As other studies have shown, Property Rights Protection (PRP) encourages capital ﬂows and provides incentives for investment and capital exchange. In the examination of the inﬂuence of domestic institutions on capital ﬂows, empirical studies have focused mostly on domestic PRP institutional functions. 1, 2 These studies have used indicators of the quality of institutional functions, which assess a country’s actual performance against industrialized countries’ ﬁrst best performance. This assessment approach has been adopted despite diverging social and political norms between developing and emerging market economies and developed economies. This approach implies that institutional reforms should bring the performance of domestic institutional functions in developing and emerging market economies in line with that of developed countries. It may also imply that developing and emerging market economies should adopt an orthodox approach to reforming domestic institutions that is believed to achieve the ﬁrst best, a point that is discussed further below. The comparison of a country’s institutional performance against industrialized countries’ ﬁrst best is understandable

* The author would like to thank three anonymous reviewers, Paul Alagidede, Luc Christiaensen, Alisa DiCaprio, Suut Dogruel, Imed Drine, Augustin Fosu, Abdulnasser Hatemi, Magda Kandil, Jorge MartinezVazquez, Wim Naude, Jeﬀrey Nugent, Aleksander Surdej, and participants at the Nordic Conference on Development Economics (2010), the African Econometric Society’s 15th Annual Conference on Econometric Modeling for Africa (2010), the Third Summer Research Day of the Faculty of Business and Economics at the United Arab Emirates University (2010), and the Middle East Economic Association Meetings (2011) for useful comments and suggestions. The author would also like to thank Lay Poh Allonen and Ahmed Taha for excellent library services. Part of this research was conducted while the author was a UNU-WIDER visiting scholar in summer 2009. The ﬁnancial support of the Faculty of Business and Economics at the United Arab Emirates University (through Summer Research Grant Program) is gratefully acknowledged. Final revision accepted: November 9, 2011. 1798

THE INSTITUTIONAL REFORMS DEBATE AND FDI FLOWS TO THE MENA REGION: THE “BEST” ENSEMBLE

countries that establishes clear, simple, enforceable, and reciprocal rules for foreign investment protection from government expropriation. A treaty identiﬁes the circumstances under which expropriation can take place and the associated compensation standards, and it establishes dispute settlement mechanisms that facilitate foreign investment in the presence of imperfect domestic PRP institutional functions. This paper explores the policy debate underlying institutional reforms that is reﬂected implicitly in the assessment of institutional functions quality and explicitly in Rodrik’s (2008) views on second best institutions and explores that debate in the context of the Middle East and North Africa (MENA) region. In particular, this paper empirically examines the inﬂuence on Foreign Direct Investment (FDI) ﬂows to MENA countries of the second best approach for PRP, which is comprised of bilateral investment treaties, domestic institutional functions, and the interaction between them. The paper uses treaties entered into force with high-income OECD countries, which constitute about one-third of the treaties entered into force by eight high- and middle-income MENA countries, comprising Algeria, Egypt, Jordan, Lebanon, Libya, Morocco, Syria, and Tunisia. The paper uses panel data for the period of 1992–2008 and adopts two estimation methodologies to ensure robustness. The ﬁrst is the Random and Fixed-Eﬀects (RE/FE) dynamic panel regression model, which takes into account nonstationarity, endogeneity, heteroskedasticity, autocorrelation, and cross-panel correlation. The second is the dynamic panel Generalized Method of Moments (GMM) model, along the lines of Arellano and Bond (1991), which accounts for potential endogeneity. Both methodologies support the ﬁrst and second best approaches to reducing the risk of investment expropriation to encourage FDI ﬂows. The GMM methodology supports the second best approach of enhancing government stability to encourage FDI ﬂows. The paper contributes to the literature of development economics and economic policy literature in two respects. First, in examining the debate on institutional reform to strengthen PRP, the paper stresses that the issue is not whether but how to undertake institutional reform. Second, to the best of our knowledge, this paper is the ﬁrst in the empirical literature to model ﬁrst and second best approaches to institutional reforms and to apply the results in the context of international capital ﬂows. The paper proceeds as follows: Section 2 summarizes the ﬁndings of the empirical literature; Section 3 discusses the performance of domestic institutional functions, the proliferation of bilateral investment treaties, and FDI development in the MENA region; Section 4 discusses the empirical model and the testable hypotheses; Section 5 discusses the data challenges and the resulting modeling approach; Section 6 discusses the empirical issues and estimation methodology; Section 7 discusses the empirical results; and, Section 8 provides the conclusion. 2. EMPIRICAL LITERATURE: WHAT ARE THE FINDINGS? In previous work I survey three strands in the empirical capital ﬂows literature that are relevant to this paper. 4 In the ﬁrst strand, the inﬂuence of domestic institutional functions on capital ﬂows, 5 I ﬁnd that: (a) the quality of domestic institutional functions positively inﬂuences capital ﬂows; (b) better institutional function quality tilts a country’s capital structure toward equity and away from debt; (c) a country’s portfolio

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investment is more sensitive to institutional function quality than FDI; and (d) the inﬂuence of domestic institutional functions on FDI has been examined in diﬀerent regions from a geographical perspective with the exception of the MENA region. The ﬁrst and fourth ﬁndings are of special importance and relevance to this paper; the ﬁrst ﬁnding lends major support to the ﬁrst best approach to institutional reforms typically heralded by international organizations, such as the World Bank and the IMF, and the fourth ﬁnding identiﬁes geographical gap in literature coverage, which this paper attempts to ﬁll. In the second strand in the literature, the inﬂuence of bilateral investment treaties on FDI, 6 I ﬁnd: (a) the inﬂuence of bilateral investment treaties depends on the degree of government commitment to PRP and is surprisingly not always positive; (b) domestic institutional functions can complement or substitute for bilateral investment treaties in attracting FDI; and, (c) the impact of bilateral investment treaties tends to diminish as the number of contracted treaties increases globally. Although some of these studies have explored the nature of the relationship between bilateral investment treaties and domestic institutional functions, whether as complements or substitutes, 7 the perspective of institutional reforms is explicitly lacking, with the exception of Hallward-Driemeier (2003). 8 Two policy questions motivate and underlie the hypotheses examined in this paper: should countries strengthen PRP by reforming domestic PRP institutional functions alone—a ﬁrst best approach—or, should they both reform domestic institutional functions and contract bilateral investment treaties—a second best approach? In the third strand, on the determinants of FDI in the MENA region, 9 I ﬁnd that natural resources and human capital discourage FDI ﬂows in GCC countries while institutional quality, trade openness, and infrastructure development encourage FDI ﬂows. In MENA countries I ﬁnd more generally that (a) market potential encourages FDI, (b) evidence on economic growth is inconclusive, and (c) the level and stability of institutional quality positively inﬂuence FDI. The concept that institutional quality inﬂuences FDI in the MENA region is of particular importance to this research and supports the ﬁrst best approach. 3. FDI, BILATERAL INVESTMENT TREATIES AND INSTITUTIONAL FUNCTIONS IN THE MENA REGION FDI ﬂows have varied across the MENA region as Table 1 shows. 10 Egypt has attracted the highest average level of FDI ﬂows during the period of 1990–2008, amounting to about $2.6 billion and has also accumulated the highest average level of FDI stocks, amounting to $22.6 billion. In contrast, Syria has attracted the lowest average level of less than half a billion dollars. With FDI expressed in per capita terms or relative to GDP, diﬀerent countries appear to be the primary FDI recipients during that period. Lebanon had the highest per capita averages with ﬂows and stocks amounting to about $323 and $1720 per capita, respectively. Lebanon had the highest average FDI ﬂow relative to GDP (6%), and Tunisia had the highest FDI stock (62%). The eight MENA countries have entered into force approximately 230 treaties as Table 1 shows, about one-third of which are with high-income OECD countries. Egypt entered into force the largest number of treaties (64), followed by Lebanon (36), and Morocco (35). Libya entered into force

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WORLD DEVELOPMENT Table 1. FDI, bilateral investment treaties, and other indicators in MENA countries (1990–2008) (period average) FDI ﬂows

Algeria Egypt Jordan Lebanon Libya Morocco Syria Tunisia

FDI stocks

Treaties

Other indicators

Net

Per capita

% GDP

Net

Per capita

% GDP

All

OECD

OIL

LABOR

TRADE

INFLATION

680.9 2596.0 642.8 1269.6 648.2 1146.8 352.7 841.1

21 34.2 119 322.6 105.9 39.4 18.8 86.4

0.8 2.5 4.9 6.1 1.1 2.3 1.2 3.4

4838.1 22568.9 5289.9 6842.8 1965.8 13249.9 7289.1 13903.9

152.5 313.5 1019.1 1717.7 338.7 450.8 443.8 1454.4

6.2 25.6 46.2 31.5 3.8 25.1 38 61.9

15 64 28 36 8 35 20 25

6 12 10 14 3 13 5 11

1616.3 806.8 (0.0) 0.1 1526.1 2.5 513.3 87.7

6596.7 2654.4 5464.9 10973.8 15273.4 3736.5 3011.3 6040.1

57.4 52.0 127.1 69.0 68.4 62.6 67.8 95.0

11.0 8.9 4.7 15.0 3.3 3.3 6.7 4.1

Notes: Treaties are counted as of June 2008. World Bank’s 2005 income classiﬁcation is used in classifying countries. The units for OIL, LABOR, TRADE, and INFLATION are in thousands of barrels per day, constant 2005 US$, percent of GDP, and percent, respectively. Appendix A to this paper provides data sources for the diﬀerent variables.

the least number (8) mainly due to the international embargo that had been in eﬀect until the ﬁrst half of the 2000s. Lebanon ratiﬁed the largest number of treaties with OECD countries (14), followed by Morocco (13), Egypt (12), and Tunisia (11). To understand the MENA region’s FDI performance and the proliferation of bilateral investment treaties, we need to assess the performance of domestic institutional functions at the regional and country levels. We therefore compare the domestic institutional function performance, both PRP and political, to that of (24) OECD countries using the International Country Risk Guide’s (ICRG) political risk components. 11 The period under investigation is 1990–2008 during which bilateral investment treaties proliferated in the MENA region. Table 2 shows that, contrary to common perception, the MENA countries’ average performance with respect to the risk of investment expropriation (labeled “investment proﬁle”) and law and order is not far from the OECD average performance. The average performance of the MENA countries on risk of investment expropriation amounted to approximately 80% of the OECD average performance, while law and order amounted to 72%. The performance on corruption and

bureaucracy quality was poor, however, reaching approximately 58% and 47% of the OECD averages, respectively. Comparing political to PRP institutional functions, the average performance of the former fared better than that of the latter. The MENA region showed better government stability than OECD countries, outperforming them by 12%. Performance related to ethnic tensions, external conﬂict, and internal conﬂict amounted to 92%, 90%, and 82% of the OECD averages, respectively. The worst performance, however, was on democratic accountability, amounting to approximately 46% of the OECD average. As we would like to evaluate the domestic PRP performance of Egypt, Lebanon, and Tunisia in particular, we compare the MENA countries’ performance in Table 2. Tunisia’s average performance ranks ﬁrst on law and order and bureaucracy quality, second on investment proﬁle and government stability, but ﬁfth on corruption. Compared to Tunisia, Lebanon’s performance lies on the other extreme; it performed worst with respect to corruption and government stability, second to worst on investment proﬁle, and ﬁfth on law and order and bureaucracy quality.

Table 2. Domestic institutional functions in MENA (1990–2008) (period average) Function

Regional level Max Institutional Score MENA OECD MENA–OECD ratio Country level Algeria Egypt Jordan Lebanon Libya Morocco Syria Tunisia

Property rights protection

Political

IP

C

L&O

BQ

GS

ET

IC

EC

MP

RP

DA

12 7.23 9.09 0.795

6 2.77 4.77 0.581

6 4 5.57 0.718

4 1.79 3.78 0.474

12 9.2 8.25 1.115

6 4.56 4.97 0.918

12 9.11 11.1 0.821

12 9.96 11.04 0.902

6 3.03 5.77 0.525

6 3.51 5.62 0.625

6 2.64 5.73 0.461

6.8 7 7.8 6.6 7.4 8 5.6 7.9

2.3 2.2 3.4 1.5 3 3 2.8 2.6

2.4 3.6 4 3.6 4 5 4.5 4.5

1.8 2 2.2 1.5 1.3 2 1.3 2

8.3 9.2 9.2 7.7 8.8 9.6 9.8 9.6

3.1 5.4 4.7 4.4 4.2 4.7 5.0 4.9

5.7 8.4 9.4 7.8 9.6 9.4 10.7 10.7

10.4 10.1 10.3 6.3 9.4 9.9 8.9 10.7

1.1 3.0 4.6 2.7 2.8 3.9 2.0 3.9

1.2 2.5 3.3 2.6 4.1 4.1 4.6 4.8

3.2 2.8 4.0 4.1 1.4 3.3 1.4 2.3

Notes: “IP,” “C,” “L&O,” “BQ,” “GS,” “ET,” “IC,” “EC,” “MP,” “RP” and “DA” are investment proﬁle, corruption, law and order, bureaucracy quality, government stability, ethnic tensions, internal conﬂict, external conﬂict, military in politics, religion in politics, and democratic accountability, respectively. A higher score indicates a lower risk. The list of OECD countries is based on the World Bank’s 2005 income classiﬁcation. It comprises 24 countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States. Source: Author calculations based on ICRG data.

THE INSTITUTIONAL REFORMS DEBATE AND FDI FLOWS TO THE MENA REGION: THE “BEST” ENSEMBLE

One important observation emerges from the assessment of institutional function performance at the regional and country levels. Although investment expropriation risk is believed to be the main reason for signing bilateral investment treaties, there are other institutional functions, such as corruption and bureaucracy quality, which may be associated with signing bilateral investment treaties and should be considered when assessing institutional reforms. The average total PRP country performance for Lebanon, which is ranked last in the region, appears to support this observation. Finally, in looking beyond domestic institutional performance and bilateral investment treaties, we see that common characteristics of high FDI countries are their relatively low oil production and high trade openness. Oil production is moderate in Egypt, nonexistent in Lebanon, and limited in Tunisia, as Table 1 shows. On the other hand, trade openness is highest in Lebanon and Tunisia, with trade accounting for approximately 70% and 95% of GDP, respectively. 4. EMPIRICAL MODEL AND TESTABLE HYPOTHESES (a) Empirical model Gravity models have been increasingly used in empirical studies to explore bilateral FDI ﬂows, such as Bellack, Leibrecht, and Riedl (2008), Bevan and Estrin (2004), Desbordes and Vicard (2009), Frenkel, Funke, and Stadtmann (2004), Hallward-Driemeier (2003), and Wei (2000). However, in the absence of data on bilateral FDI ﬂows for MENA countries, it is infeasible to adopt a typical gravity model. We should note, however, that gravity models in essence constitute an extension of the location advantage hypothesis of Dunning’s (1981) Ownership–Location–Internalization (OLI) paradigm. The location advantage hypothesis argues that location advantages have to exist in the host market for a multinational corporation to invest in the location. These advantages include natural and human resource endowments, market size and potential, the degree of economic development, the degree of openness of the economy, macroeconomic stability, and PRP. Of the many location advantages that characterize the MENA region, natural resource endowments are of particular importance because, in the MENA region, it is believed that natural resource endowments, which include oil and natural gas reserves, attract resource-seeking FDI. Resource-rich MENA countries include Algeria and Libya, which are rich in both oil and natural gas reserves, and Egypt, which is rich in natural gas reserves. In addition some MENA countries enjoy a large population size, which is potentially a human resource endowment when combined with education and training. 12, 13 The respective population sizes of Egypt, Algeria, and Morocco are approximately 84 million, 35 million, and 32 million in 2010. We account for natural resource endowments and human capital in the empirical model. The empirical model expresses FDI as: FDI it ¼ b0 þ b1 FDI itl þ b2 INSTITFN it þ b3 BIT it þ b4 BITINSTITFN it þ b5 OILit þ b6 PRICEit þ b7 LABORit þ b8 TRADEit þ b9 INFLATION it þ b10 WFDIFLOWS it þ it

ð1Þ

Here, FDI is FDI ﬂows per capita, FDIit-l lagged dependent variable, INSTITFN domestic PRP institutional function,

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BIT bilateral investment treaty entered into force, BITINSTITFN is an interaction term between the natural logarithm of domestic PRP institutional function on the one hand and bilateral investment treaties on the other, OIL is oil production, PRICE is oil price, LABOR is labor productivity, TRADE is trade openness, INFLATION is inﬂation rate, WFDIFLOWS is world FDI ﬂows, and e is an error term. 14 The subscripts i, t, and l are country, time, and lag indicators with i = 1, . . . , N; t = 1, . . . , T; and l = 1,. . ., T 1. The model is double logarithmic except for BIT and BITINSTITFN. Appendix A to this paper provides more information on variable deﬁnition and data sources. The decision to use FDI ﬂows as opposed to FDI stock per capita is based on the presence of unit roots in the time series, as discussed further in Section 6. We expressed FDI ﬂows in per capita terms, rather than relative to GDP, in order to account for PRP inﬂuence on FDI ﬂows directly and not on FDI’s relative weight in the host country. This is similar to the strategies used by Hallward-Driemeier (2003) and Neumayer and Spess (2005). The inclusion of the lagged dependent variable, FDIit-l, in the empirical model serves two purposes. First, it accounts for the persistence in FDI ﬂows, especially when FDI is related to natural resources; natural resources require ﬂows of foreign investment over time. Second, it mitigates the likely upward bias in the inﬂuence of bilateral investment treaties and domestic PRP institutional functions on FDI. This bias likely results from the lack of bilateral FDI data and the consequent modeling of bilateral investment treaties as discussed in the next section. We expect its coeﬃcient to be positive. INSTITFN is domestic PRP institutional function. Because PRP is a multidimensional process, we model PRP using four ICRG political risk components: investment proﬁle, corruption, law and order, and bureaucracy quality. These four functions are outcomes of the legal and judicial systems and government bureaucracy and are essential to PRP. The investment proﬁle refers to the risk of investment expropriation, profits repatriation, and payment delays, which clearly inﬂuence PRP. Corruption is a threat to PRP as it enables people to assume positions of power through patronage rather than ability. Patronage threatens the rights of foreign investors because it facilitates government expropriation of investment or can cause direct conﬂicts with patrons and investors. Law and order refers to the strength and impartiality of the legal system as well as the popular observance of the law. Quality of bureaucracy refers to whether the bureaucracy continues to function without drastic changes in policy when there is a change in government leadership. Higher scores indicate better performance. 15 A positive coeﬃcient for each of these functions is expected, and an improvement in the performance of each of these functions is expected to be associated with more FDI ﬂows. In addition to the four PRP institutional functions, we also add government stability, which is a political institutional function based on the correlation between FDI, the diﬀerent domestic institutional coeﬃcients, and bilateral investment treaties. 16 Government stability refers to the ability of the government to carry out its declared program(s) and remain in oﬃce. A positive coeﬃcient of government stability is also expected. BIT is the annual number of bilateral investment treaties entered into force. We use bilateral investment treaties entered into force as opposed to signed treaties to account for the actual commitment to PRP by MENA countries. Treaties protect and promote foreign investments, compensate for losses, and create mechanisms for dispute settlements between

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WORLD DEVELOPMENT

contracting parties. Therefore, the coeﬃcient of BIT is expected to be positive. BITINSTITFN is an interaction term that models the policy interaction between domestic institutional functions and bilateral investment treaties. A positive coeﬃcient indicates that bilateral investment treaties complement domestic institutional functions; a negative coeﬃcient indicates substitution. OIL and PRICE are the oil production and price variables, respectively. OIL is measured in thousands of barrels per day. PRICE is the crude oil price measured as the price of Saudi Arabian Light 34 in US$ per barrel. Because oil production requires capital and technology, we expect OIL coeﬃcient to be positive. Similarly higher oil prices encourage the supply of oil, at least up to the limit enforced in OPEC countries; thus, we expect a positive PRICE coeﬃcient. 17 We include LABOR to account for the productivity of human capital in MENA countries. The more educated the labor force, the more productive it is. We measure labor productivity as real GDP per person employed. 18 We would expect a positive coeﬃcient reﬂecting the positive productivity inﬂuence on FDI in MENA countries. TRADE is the degree of trade openness of the economy. It is measured as the sum of exports and imports as a percentage of GDP. An open economy is conducive for FDI ﬂows; FDI ﬂows toward the tradable sector with potential foreign exchange earnings. Thus, we expect a positive coeﬃcient. INFLATION is a proxy for macroeconomic stability in the economy. It is measured by the inﬂation rate based on either the consumer price index or the GDP deﬂator. A higher inﬂation rate is an indicator of lower macroeconomic stability and real incomes. It therefore discourages market-seeking, but not necessarily resource-seeking, FDI ﬂows. A negative coeﬃcient is expected. We include WFDIFLOWS to account for the business cycle in the global economy. It is measured as world FDI inﬂows in millions of US$. MENA countries are likely to obtain more FDI ﬂows with the expansion of the world economy and the increase in global FDI ﬂows; thus, a positive coeﬃcient is expected. (b) Testable hypotheses We use the above empirical model to test whether a second best approach, as opposed to a ﬁrst best approach, to PRP institutional reform has a positive inﬂuence on attracting FDI ﬂows to MENA countries. As mentioned in the introduction, a ﬁrst best approach comprises reforms to PRP domestic institutional functions only, whereas a second best approach comprises reforms to domestic PRP institutional functions in addition to entering into force bilateral investment treaties, and the interaction between the two. (i) First hypothesis: A ﬁrst best approach to PRP institutional reform has a positive inﬂuence on FDI ﬂows to MENA countries. H0 : b2 6 0 H1 : b2 > 0 (i) Second hypothesis: A second best approach to PRP institutional reform has a positive inﬂuence on FDI ﬂows to MENA countries. H0 : b2 6 0; H1 : b2 > 0;

b3 6 0; b3 > 0;

b4 6 0 b4 > 0

5. DATA AND MODELING APPROACH (a) Data and issues FDI data are obtained from UNCTAD’s FDI online database. Because of the variation in FDI ﬂows per capita across countries, we take the natural logarithm as stated in the empirical model. To get around zero and negative values of FDI ﬂows, we use the same approach adopted by Blonigen and Davies (2004) and Neumayer and Spess (2005) as discussed in Kerner (2009). If the value of FDI ﬂows is zero, we add one dollar and take the natural logarithm, which results in a value of zero. If the value of FDI ﬂows is negative, we take the negative of the natural logarithm of the absolute value of FDI ﬂows. The same approach is used with INSTITFN and OIL. Data on bilateral investment treaties are also obtained from UNCTAD’s FDI online database. We select the high-income OECD partner countries for each of the MENA countries. (b) Modeling approach The lack of bilateral FDI ﬂows data has constituted an empirical challenge for this study. Unlike previous studies, which used bilateral FDI ﬂows data and adopted gravity models, this study uses aggregate-level FDI data. The unavailability of bilateral FDI data has aﬀected the modeling approach of bilateral investment treaties; thus, we must use a count of the number of treaties entered into force. This approach has its pros and cons. It is a simple and straightforward solution to the absence of bilateral FDI data. However, it may result in an upward BIT coeﬃcient bias if there are positive FDI ﬂows from countries with which treaties have been signed but not entered into force or with which there are no treaties. Second, the adopted modeling approach, which is based on the number of treaties that each country enters into force, implies that treaties are weighted equally in terms of promoting and protecting foreign investment. This assumption is not totally unrealistic from empirical and legal perspectives. Although in principle the number of bilateral investment treaties is related to the size of economy and should be weighted accordingly, the correlation coeﬃcients of GDP, population size, labor force, and natural resources are small. 19 Additionally, the number of OECD countries limits the number of treaties that MENA countries can enter into force. Further, from a legal perspective, the assumption is not unrealistic given the tendency in many cases for countries to adopt bilateral investment treaties as models with standardized articles and clauses and to broadly deﬁne investment in such a way that reﬂects the interest in protecting and encouraging foreign investment in all its forms, present and future. 20, 21 6. EMPIRICAL ISSUES AND ESTIMATION METHODOLOGY In constructing the empirical model, a number of issues should be considered: nonstationarity, endogeneity, heterogeneity, and serial correlation. 22 We address these issues by adopting RE/FE and GMM estimators. (a) Nonstationarity In the presence of nonstationarity, we could end up with spurious regressions. Therefore, to detect nonstationarity, we

THE INSTITUTIONAL REFORMS DEBATE AND FDI FLOWS TO THE MENA REGION: THE “BEST” ENSEMBLE

use a battery of panel unit root tests. The ﬁrst test is the Levin, Lin, and Chu (LLC) unit root test, which assumes identical ﬁrst-order autoregressive coeﬃcients across countries. The test involves the following regression equation: Dy it ¼ ai þ ci y it1 þ

k X

aj Dy itj þ eit

ð2Þ

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country eﬀect, li , in addition to a disturbance term mi;t . The lagged dependent variable is correlated with the country eﬀect li and thus the error term li þ mi;t . To eliminate the unobservable country eﬀect, the GMM estimator takes the ﬁrst diﬀerence: Dy i;t ¼ aDy i;t1 þ b0 DX i;t þ Dmi;t

ð4Þ

j¼1

The subscripts i and t are country and time indicators with i = 1, . . . , N and t = 1, . . . ,T. The null hypothesis H0: ci ¼ c ¼ 0; 8 i against the alternative hypothesis H1: c1 ¼ c2 ¼ ¼ cN < 0; 8i: We also use the Im, Pesaran, and Shin (IPS) W-stat and the Augmented Dickey Fuller–Fisher Chi-squared tests, which allow the ﬁrst-order autoregressive coeﬃcients to vary across countries under the alternative hypothesis H1: ci < 0; 8i. 23 (b) Endogeneity Endogeneity is deﬁned in terms of the correlation between the explanatory variables and the error term. It results in inconsistent Ordinary Least Squares (OLS) estimates. Endogeneity may result from unobservable country-speciﬁc eﬀects, simultaneity, and variable omission. In panel data, endogeneity may result from the presence of time-invariant, unobservable, country-speciﬁc eﬀects. Such unobservable eﬀects result in a correlation between the explanatory variables or lagged dependent variable and the error term. In the presence of unobservable country-speciﬁc eﬀects, one can adopt Fixed Eﬀects (FE) or Random Eﬀects (RE) estimators. If the individual country eﬀects are assumed to be a ﬁxed constant over time and can be estimated, then one can adopt the ﬁxed eﬀects estimator. Alternatively, if they are assumed to be randomly distributed, then one can adopt a RE estimator. To conﬁrm the selection of one type of model over the other, we use a Hausman speciﬁcation test. Endogeneity may also result from simultaneity, variable omission, and measurement error. Simultaneity arises from the reverse causality between FDI inﬂows on the one hand and bilateral investment treaties entered into force and domestic institutional functions on the other. In dealing with simultaneity, we conduct a Granger-causality test on the stationary time series to identify whether FDI Granger causes INSTITFN, BIT, or any of the explanatory variables. If we identify any reverse causality, we lag that explanatory variable. Lagging the explanatory variable is one way to deal with endogeneity, as Neumayer and Spess (2005) and Tobin and Rose-Ackerman (2006) have shown. Variable omission can be associated with unobservable country-speciﬁc eﬀects such as the strength of a MENA country’s relations with a bilateral investment treaty partner or with variables that can inﬂuence FDI ﬂows but cannot be included in the empirical model due to data unavailability, such as the ownership advantages of foreign corporations. In facing this and other sources of endogeneity, we adopt a dynamic panel GMM approach in estimating the empirical model given in Eqn. (1) following Arellano and Bond (1991). To explain the GMM estimator, consider the following empirical model: y i;t ¼ ay i;t1 þ b0 X i;t þ li þ mi;t

i ¼ 1; . . . ; N

t ¼ 1; . . . ; T ð3Þ

where yi,t is the dependent variable and Xi,t is the vector of explanatory variables, and the subscripts i and t denote country and time periods. The error term comprises unobservable

Although the unobservable country eﬀect is eliminated with diﬀerencing, an endogenous bias may still arise from the correlation between the lagged diﬀerence of the dependent variable and the error term. In this case we use instrumental variables. The diﬀerence GMM estimator uses the lagged levels of the explanatory variables as instruments on two conditions: that the error term of the diﬀerenced equation is not serially correlated and that the lagged levels of the explanatory variables are weakly exogenous. We write the moment conditions as follows: E½y i;ts ðmi;t mi;t1 Þ ¼ 0 for s P 2; t ¼ 3; . . . ; T

ð5Þ

E½X i;ts ðmi;t mi;t1 Þ ¼ 0 for s P 2; t ¼ 3; . . . ; T

ð6Þ

To ensure that these moment conditions are satisﬁed, we test the lack of second-order serial correlation and use a Sargan test of over-identifying restrictions to test for instrument validity. (c) Heterogeneity and serial correlation The performance of MENA countries is heterogeneous with respect to FDI inﬂows, domestic institutional functions, and the number of bilateral investment treaties entered into force, as well as the natural and human resource endowments, as discussed in Section 3. This is likely to generate heteroskedasticity in the error term. In the presence of heteroskedasticity, coeﬃcient estimates are consistent but ineﬃcient, and their standard errors will be biased and result in inference problems. We conduct a Wald test for panel heterogeneity on the empirical models containing a stationary time series, and the lagged explanatory variables that the Granger-causality tests detected were endogenous. In the presence of serial correlation, coeﬃcient estimates are consistent but ineﬃcient, and the standard errors are biased. To detect the presence of serial correlation, we conduct a test for serial correlation in the idiosyncratic errors of a linear panel-data model as demonstrated by Wooldridge (2002). Finally, we test whether the panels are correlated or independent of each other using the Breusch–Pagan LM test of independence.

7. EMPIRICAL RESULTS We report the panel unit root test results in Table 3. The results indicate that FDI is stationary in the level. We also see that not all domestic institutional functions INSTITFN are stationary in the levels. Corruption, law and order, and government stability are stationary in level. However, for investment proﬁle and bureaucracy quality, the LLC test indicates their stationarity based on a rejection of the null hypothesis of a common unit root in the panel of eight countries. IPS and ADF-Fisher tests, on the other hand, fail to reject the null hypothesis of individual unit roots. However, the three tests do indicate the stationarity of the two series in diﬀerence form. We therefore use these two variables in diﬀerence form. 24 The stationarity results of WFDIFLOWS are similar to those of the investment proﬁle and bureaucracy quality. BIT and the

1804

WORLD DEVELOPMENT Table 3. Panel unit root tests

Variable (log)

Variable form

LLC

IPS

ADF-Fisher

FDI INSTITFN Investment proﬁle

Level

11.344a

3.504a

34.016a

Stationary

Level Diﬀerence Level Level Level Diﬀerence Level Level

2.483a 7.783a 19.454a 7.742a 2.360a 2.820a .8.791a 7.185a

.890 5.505a 7.897a 6.330a .763 2.424a 5.410a 6.024a

18.254 58.647a 23.246c 65.190a 11.501 12.986a 55.388a 58.498a

Mixed Stationary Stationary Stationary Mixed Stationary Stationary Stationary

Level Level Level Level Level Level Diﬀerence Level Diﬀerence Level Diﬀerence Level Diﬀerence Level Diﬀerence Level Diﬀerence

2.660a 3.732a 2.553a 1.386c 2.461a .474 6.223a 11.534 12.421a 2.636 10.344a 1.493 6.981a 0.882 13.042a 2.061b 1.539c

2.623a 3.032a 2.762a 1.933b 2.651a 1.439 5.297a 8.559 9.539a 4.73 8.058a 2.174 5.189a 0.605 10.816a 0.599 4.052a

29.447a 30.400a 30.574a 20.825c 29.579a 13.69 52.034a 0.012 97.011a 7.426 77.353a 10.604 55.009a 15.335 104.52a 12.512 43.080a

Stationary Stationary Stationary Stationary Stationary Nonstationary Stationary Nonstationary Stationary Nonstationary Stationary Nonstationary Stationary Nonstationary Stationary Mixed Stationary

Corruption Law and order Bureaucratic quality Government stability BIT BITINSTITFN Investment proﬁle Corruption Law and order Bureaucratic quality Government stability OIL PRICE LABOR TRADE INFLATION WFDIFLOWS

Decision

Notes: LLC tests for common unit root, while IPS and ADF-Fisher test for individual unit roots. Panel unit root tests include individual intercept, respectively. BITINSTITFN is in level. a Signiﬁcance at p < 0.01 level. b Signiﬁcance at p < 0.05 level. c Signiﬁcance at p < 0.1.

interaction terms are stationary in level. The remaining explanatory variables OIL, PRICE, LABOR, TRADE, and INFLATION are stationary in diﬀerence form. We report in Table 4 the results of the Granger-causality tests assessing whether FDI Granger-causes INSTITFN, BIT, or any of the explanatory variables. The results are reported for one lag. Test statistics indicate the rejection of the null hypothesis of FDI not Granger-causing investment proﬁle, government stability, BIT, PRICE, LABOR, and INFLATION. Test statistics also indicate the rejection of the null for the interaction terms, except for that between bilateral investment treaties and corruption. Accordingly, we lag these variables twice in the empirical model. In Table 5, we report the results of heteroskedasticity, serial correlation, and panel independence. The results of the Wald test for panel heterogeneity indicate that the null hypothesis of homoskedasticity is rejected at the 1% level. The results of the Wooldridge test of serial correlation indicate the rejection of the null hypothesis of no serial correlation at the 1% level. Finally, the results of the Breusch–Pagan LM test of independence also indicate the rejection of independence at the 10% level. When the Hausman speciﬁcation tests recommend the adoption of the RE model in most speciﬁcations, we adopt the feasible Generalized Least Squares (GLS) estimation methodology. We also take into account the results of heteroskedasticity, serial correlation, and panel independence tests. When Hausman speciﬁcation tests suggest the adoption of an FE model, we report the robust estimates.

Table 4. Granger causality test results Variable INSTITFN Investment proﬁle Investment proﬁle Corruption Law and order Bureaucracy quality Bureaucratic quality Government stability BIT BITINSTITFN Investment proﬁle Corruption Law and order Bureaucratic quality Government stability OIL PRICE LABOR TRADE INFLATION WFDIFLOWS

Variable form

Test statistic

Level Diﬀerence Level Level Level Diﬀerence Level Level

0.927 0.071c 0.43 0.256 0.033b 0.572 0.035b 0.031b

Level Level Level Level Level Diﬀerence Diﬀerence Diﬀerence Diﬀerence Diﬀerence Diﬀerence

5.391b 1.008 4.974b 7.338a 4.825b 0.816 0.047b 0.014b 0.367 0.038b 0.331

Notes: Null hypothesis: FDI does not Granger-cause the explanatory variable. Granger causality test results are reported for one lag. Variable form, whether level or diﬀerence, is based on the panel unit root tests reported in Table 3. p values reported in test statistic. a Signiﬁcance at p < 0.01 level. b Signiﬁcance at p < 0.05 level. c Signiﬁcance at p < 0.1 level.

THE INSTITUTIONAL REFORMS DEBATE AND FDI FLOWS TO THE MENA REGION: THE “BEST” ENSEMBLE Table 5. Diagnostic tests Homoskedasticitya

INSTITFN Investment proﬁle Corruption Law and order Bureaucratic quality Government stability

Serial Independencec correlationb

0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000

0.088 0.093 0.08 0.086 0.037

Notes: Test statistics are reported for models containing the domestic institutional function in the ﬁrst column. a Modiﬁed Wald test for groupwise heteroskedasticity; the null hypothesis is homoskedasticity. b Wooldridge tests for autocorrelation in panel data; the null hypothesis is that no autocorrelation exists. c Breusch–Pagan LM test of independence of cross-section residuals; the null hypothesis is independence. p values reported.

(a) RE and FE estimates Table 6 reports the RE and FE estimates. Unless otherwise noted in the column heading, the RE estimates are reported.

1805

The ﬁrst lag of the dependent variable has a positive, and mostly unitary, inﬂuence on FDI ﬂows, which implies the persistence of FDI ﬂows to the region. As column 1 suggests, a 1% increase in the last period’s FDI ﬂows increases current FDI ﬂows by 0.92%. Among the diﬀerent domestic institutional functions that inﬂuence FDI ﬂows, a lessening in the risk of investment expropriation and improvement in government stability have a positive inﬂuence, while an improvement in corruption (i.e., less corruption) has a surprisingly negative inﬂuence. As the investment proﬁle is in diﬀerence form, a 1% improvement in the growth rate of the investment proﬁle results in an increase in FDI ﬂows of 0.7%. This positive inﬂuence holds regardless of the inclusion of the interaction term in the model. Government stability on the other hand has a striking but nonrobust positive inﬂuence on FDI ﬂows. With government stability in level rather in diﬀerence, an improvement in government stability by 1% results in an increase in FDI ﬂows of more than 3%. However, this positive inﬂuence becomes statistically insigniﬁcant when the interaction term is included in the model. In contrast to investment proﬁle and government stability, a decrease in corruption has a surprisingly negative and robust

Table 6. Institutional functions, bilateral investment treaties, and FDI ﬂows to nonGCC countries—RE (FGLS) and FE estimation (based on common and individual panel unit roots and Granger-causality test statistics). Dependent variable: log of FDI inﬂows per capita Variables (form) FDI (L) FDI (L2) INSTITFN (*) BIT (L2) BITINSTITFN (**) OIL (D) PRICE (L2.D) LABOR (L2.D) TRADE (D) INFLATION (L2.D) WFDIFLOWS (D) Constant Observations R-squared Wald test v2 t statistic H0: b2 6 0 H0: b2 + b3 + b4 6 0

(1) IP

(2) IP+

(3) C

(4) C/FE

(5) C+

(6) L&O

(7) L&O+

(8) BQ

(9) BQ+

(10) GS/FE

(11) GS+/FE

0.918a (0.059) 0.141b (0.056) 0.700b (0.356) 0.142a (0.055)

0.848a (0.062) 0.124b (0.059) 0.699a (0.133) 0.113b (0.052)

0.541a (0.173) 0.038 (0.145) 1.761b (0.708) 0.054 (0.115)

0.100 (0.134) 0.398a (0.136) 4.338a (1.263) 2.686a (0.542) 0.150b (0.074) 0.275 (0.185) 0.750 (0.631)

0.099 (0.135) 0.399a (0.153) 4.481a (1.256) 2.538a (0.549) 0.155b (0.076) 0.264 (0.198) 0.637a (0.138)

0.867a (0.064) 0.097 (0.063) 0.041 (0.399) 0.035 (0.205) 0.292 (0.304) 0.092 (0.139) 0.405b (0.159) 4.139a (1.323) 2.385a (0.568) 0.144c (0.077) 0.300 (0.212) 0.666a (0.143)

0.509a (0.157) 0.047 (0.141) 3.151b (1.388) 0.028 (0.128)

0.008 (0.210) 0.239 (0.343) 3.241 (2.966) 2.749 (1.943) 0.006 (0.144) 0.726 (0.560) 2.842b (1.163)

0.845a (0.068) 0.082 (0.067) 0.156 (0.435) 0.274 (0.529) 0.264 (0.347) 0.100 (0.153) 0.356b (0.160) 4.382a (1.289) 2.962a (0.558) 0.145c (0.077) 0.412c (0.220) 0.880 (0.697)

0.891a (0.063) 0.115c (0.062) 0.034 (0.388) 0.131b (0.058)

0.102 (0.135) 0.262b (0.134) 6.415a (1.138) 2.283a (0.518) 0.048 (0.070) 0.269 (0.168) 1.412a (0.220)

0.851a (0.064) 0.122b (0.062) 0.782a (0.143) 0.124b (0.054) 0.152b (0.065) 0.161 (0.151) 0.302b (0.135) 6.305a (1.240) 2.415a (0.544) 0.054 (0.072) 0.412b (0.178) 1.380a (0.232)

0.893a (0.061) 0.124b (0.059) 0.069 (0.371) 0.136b (0.056)

0.154 (0.131) 0.530a (0.136) 3.636a (1.319) 2.933a (0.545) 0.154b (0.074) 0.268 (0.182) 0.607a (0.147)

0.909a (0.060) 0.139b (0.058) 0.695c (0.370) 0.462 (0.675) 0.165 (0.332) 0.144 (0.135) 0.603a (0.152) 3.888a (1.421) 2.913a (0.570) 0.158b (0.078) 0.218 (0.195) 0.636a (0.151)

0.037 (0.207) 0.502 (0.363) 1.972 (2.844) 2.148 (1.800) 0.075 (0.157) 0.916 (0.599) 5.522b (2.704)

0.505a (0.153) 0.071 (0.136) 2.245 (1.480) 3.430 (3.842) 1.496 (1.672) 0.070 (0.217) 0.487 (0.367) 1.490 (3.034) 2.474 (1.654) 0.102 (0.165) 0.893 (0.586) 3.541 (3.059)

120

120

120

120

120

120

120

120

756.51a

687.1a

942.4a

120 0.644 11.66a

995.34a

793.6a

576.9a

803.32a

786.62a

120 0.648 9.99a

120 0.654 10.24a

1.966b

1.878b 1.835b

5.256

2.487

5.469 3.352

0.186

0.359 0.305

0.088

0.103 0.515

2.270a

1.517c 0.101

Notes: “IP,” “C,” “L&O,” “BQ,” and “GS” are investment proﬁle, corruption, law and order, bureaucracy quality, and government stability, respectively. + indicates the inclusion of BITINSTIFN in the regression model. L, L2, and D refer to the ﬁrst lag, second lag, and diﬀerence, respectively. RE (FGLS) estimation is used unless otherwise noted. Robust standard errors are in parentheses. Heteroskedasticity and within (panel speciﬁc AR1) and across correlations are accounted for. The one-sided critical t values at the 1%, 5%, and 10% levels for df = 120 are 2.358, 1.658, and 1.289, respectively. * The second lags of the diﬀerence of IP and BQ are used, while for GS the second lag is used. BIT is the annual number of bilateral investment treaties. ** The second lags of the interaction term with IP, L&O, BQ, and GS are used. a Signiﬁcance at p < 0.01 level. b Signiﬁcance at p < 0.05 level. c Signiﬁcance at p < 0.1 level.

1806

WORLD DEVELOPMENT

inﬂuence. As the RE and FE estimates show in columns 3, 4, and 5, an improvement or lessening in corruption by 1% decreases FDI ﬂows by approximately 0.7% and 1.8% and by approximately 0.8% when an interaction term is included in the empirical model. This result may suggest that less corruption is associated with better economic policies, which promote more domestic savings and investment and reduce reliance on foreign capital. The inﬂuence of law and order and bureaucracy quality is statistically insigniﬁcant. The inﬂuence of bilateral investment treaties seems mixed, however the statistically signiﬁcant inﬂuence is positive. This positive inﬂuence is observed in some of the speciﬁcations containing investment proﬁle, corruption, and law and order. Bilateral investment treaties with OECD countries can therefore be considered useful in the promotion of FDI ﬂows. The interaction between bilateral investment treaties and domestic institutional functions is statistically insigniﬁcant, with the exception of the interaction with corruption. Such interaction is positive and statistically signiﬁcant at the 5% level. This suggests that bilateral investment treaties increase FDI ﬂows per capita as corruption in MENA countries less-

ens. In other words, there is institutional complementarity between corruption and bilateral investment treaties. Finally LABOR, TRADE, and PRICE have positive and statistically signiﬁcant coeﬃcients suggesting that more labor productivity, trade openness, and higher oil prices result in more FDI ﬂows. The LABOR and TRADE coeﬃcients are the largest, suggesting that labor productivity and trade openness are the most inﬂuential FDI drivers in MENA countries. (b) GMM estimates Given the nature of the diﬀerence GMM estimator, and to examine the inﬂuence of the annual number of bilateral investment treaties, we include in the estimation the total (cumulative) number of bilateral investment treaties. Additionally, in all speciﬁcations, we fail to reject the null hypotheses of the Sargan over-identiﬁcation and serial correlation tests and conclude that instruments are not correlated with the residuals, and errors in ﬁrst diﬀerence regression exhibit no second-order serial correlation. GMM estimates are mostly similar to the RE/FE estimates. We should keep in mind that the dependent variable is now the

Table 7. Institutional functions, bilateral investment treaties, and FDI ﬂows to nonGCC countries—GMM estimation. Dependent variable: log of FDI inﬂows per capita

FDI (L.D) FDI (L2.D) INSTITFN (D) BIT (D)

(1) IP

(2) IP+

(3) C

(4) C+

(5) L&O

(6) L&O+

(7) BQ

(8) BQ+

(9) GS

(10) GS+

0.327a (0.107) 0.126 (0.149) 2.911a (0.700) 0.073 (0.058)

0.362a (0.101) 0.125 (0.175) 0.735 (0.658) 0.043 (0.067)

0.495c (0.260) 0.473b (0.187) 1.124 (1.808) 2.983c (1.738) 0.244a (0.071) 0.266 (0.473) 0.097 (0.137)

0.342a (0.083) 0.058 (0.211) 2.484 (1.548) 0.307a (0.115) 0.608a (0.201) 0.565b (0.252) 0.492b (0.215) 0.694 (1.684) 3.107b (1.391) 0.331a (0.102) 0.259 (0.444) 0.064 (0.125)

0.350a (0.101) 0.166 (0.153) 4.332b (2.027) 0.022 (0.072)

0.352b (0.165) 0.484a (0.164) 1.761 (1.286) 2.361 (1.834) 0.267b (0.104) 0.309 (0.430) 0.063 (0.126)

0.355a (0.092) 0.120 (0.179) 0.210 (0.582) 0.432a (0.098) 0.329a (0.086) 0.407a (0.126) 0.445a (0.156) 0.920 (1.456) 2.698 (1.774) 0.259b (0.110) 0.386 (0.403) 0.040 (0.125)

0.349a (0.074) 0.075 (0.230) 1.707 (1.334) 0.110 (0.088)

0.437b (0.185) 0.483a (0.174) 1.062 (1.760) 2.571 (1.768) 0.253b (0.107) 0.414 (0.477) 0.004 (0.161)

0.362a (0.103) 0.113 (0.177) 0.498 (0.614) 0.000 (0.083) 0.120b (0.060) 0.560b (0.222) 0.383b (0.158) 0.559 (2.070) 2.918 (1.857) 0.212b (0.098) 0.509 (0.498) 0.004 (0.161)

0.379a (0.093) 0.141 (0.184) 0.248 (0.623) 0.043 (0.071)

0.173 (0.173) 0.955a (0.237) 0.369 (1.485) 2.844b (1.370) 0.254b (0.100) 0.219 (0.414) 0.050 (0.123)

0.313a (0.111) 0.125 (0.135) 4.089a (0.848) 0.941a (0.236) 0.471a (0.107) 0.301b (0.152) 0.991a (0.194) 0.337 (1.224) 3.375a (1.071) 0.253b (0.113) 0.362 (0.435) 0.125 (0.113)

0.349b (0.176) 0.981a (0.361) 2.087 (1.787) 2.632c (1.487) 0.115b (0.050) 0.090 (0.379) 0.046 (0.137)

0.343a (0.104) 0.155 (0.150) 6.253a (2.402) 1.038b (0.436) 0.474b (0.196) 0.380b (0.162) 0.830b (0.367) 0.137 (2.107) 2.829b (1.395) 0.070 (0.058) 0.226 (0.428) 0.057 (0.135)

136 89.7a 143.66 0.429

136 260.28a 140.32 0.445

136 159.2a 139.8 0.437

136 60.63a 138.7 0.426

136 267.91a 140.4 0.439

136 182.8a 140.2 0.426

136 738.9a 138.55 0.38

136 638.7a 134.8 0.371

136 110.3a 134.4 0.575

136 153.4a 128.2 0.61

4.159a

4.822a 4.803a

1.117

0.811 0.998

0.398

0.361 0.550

1.280

1.605 1.521

2.137b

2.603a 2.602a

BITINSTITFN (D) OIL (D) PRICE (D) LABOR (D) TRADE (D) INFLATION (D) WFDIFLOWS (D) Constant Observations Wald test v2 Sargan test v2 Serial correlation test (p values) t statistic H0: b2 6 0 H0: b2 + b3 + b4 6 0

Notes: “IP,” “C,” “L&O,” “BQ,” and “GS” are investment proﬁle, corruption, law and order, bureaucracy quality, and government stability, respectively. + indicates the inclusion of BITINSTIFN in the regression model, respectively. One-step GMM estimator results are reported. Robust standard errors are in parentheses. L, L2, and D refer to the ﬁrst lag, second lag, and diﬀerence, respectively. BIT is the cumulative rather than the annual number of bilateral investment treaties entered into force. H0 for Sargan over-identiﬁcation test: instruments not correlated with residuals. H0 for serial correlation test: errors in ﬁrst-diﬀerence regression exhibit no second-order serial correlation. The one-sided critical t values at the 1%, 5%, and 10% levels for df = 120 are 2.358, 1.658, and 1.289, respectively. a Signiﬁcance at p < 0.01 level. b Signiﬁcance at p < 0.05 level. c Signiﬁcance at p < 0.1 level.

THE INSTITUTIONAL REFORMS DEBATE AND FDI FLOWS TO THE MENA REGION: THE “BEST” ENSEMBLE

growth rate of FDI ﬂows. The lagged growth rate has a positive inﬂuence on the current growth rate, suggesting the persistence of growth rates. The risk of investment expropriation and government stability positively inﬂuence FDI ﬂows, as Table 7 shows. An improvement in the growth rate of investment proﬁle by 1%, results in a tripling or quadrupling of the growth rate of FDI ﬂows, with an even higher magnitude for government stability. The positive inﬂuence of bilateral investment treaties is observed in some of the speciﬁcations containing investment proﬁle, law and order, bureaucracy quality, and government stability. The coeﬃcients of the interaction terms between bilateral investment treaties and domestic institutional functions are negative in all speciﬁcations. Thus, the dynamic panel GMM estimator suggests that bilateral investment treaties are substitutes for domestic institutional functions in MENA countries. Similar to most OIL estimates obtained in the RE/FE model, OIL coeﬃcients are positive, but are statistically signiﬁcant and with much higher magnitude. PRICE coeﬃcients are also much higher than the RE/FE estimates. TRADE coeﬃcients are similarly positive and statistically signiﬁcant. Unlike the RE/FE estimates, LABOR coeﬃcients are statistically insigniﬁcant in all speciﬁcations. Also INFLATION coeﬃcients are negative and suggest that the growth in inﬂation rate discourages the growth rate in FDI ﬂows. (c) Institutional reform hypotheses The two hypotheses regarding a ﬁrst best or a second best approach to PRP institutional reforms can now be tested based on the estimates obtained under both methodologies in Tables 6 and 7. Both methodologies reject the null hypothesis that a ﬁrst best approach to reducing the risk of investment expropriation and government stability has no inﬂuence on FDI ﬂows to MENA countries; the strengthening of these two domestic PRP institutional functions has a positive inﬂuence on FDI ﬂows. Both methodologies also reject the null hypothesis that a second best approach to reducing the risk of investment expropriation has no inﬂuence; PRP can be strengthened by entering into force bilateral investment treaties with OECD countries in addition to increasing investor protection domestically. The GMM estimates reject the null hypothesis that a second best approach to enhancing government stability has no inﬂuence on FDI ﬂows. FE estimates do not reject this null hypothesis.

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8. CONCLUSION AND POLICY IMPLICATIONS In this research we explore how MENA countries can approach institutional reforms that strengthen PRP. A ﬁrst best approach is to strengthen their domestic institutional functions to approach the performance of industrialized countries. Alternatively, countries may elect to sign and enter into force bilateral investment treaties in tandem with improving their institutional functions—a second best approach. We examine this institutional reform debate in the context of FDI ﬂows to MENA countries. The results conﬁrm that reducing the risk of investment expropriation and improving government stability have a positive inﬂuence on FDI ﬂows to MENA countries. The results also conﬁrm the joint positive inﬂuence of domestically reducing the risk of investment expropriation and entering into force bilateral investment treaties with OECD countries. However, the interaction between domestic institutional strengthening and entering into force bilateral investment treaties to strengthen PRP has a negative inﬂuence on FDI ﬂows. The policy implications of the results of this paper are important. First, to continue attracting FDI ﬂows, MENA countries need to protect investors and enhance government stability. Government expropriation of foreign investment creates uncertainty about the return on investment and deters investment. Similarly, government instability generates uncertainty about the economic and political principles of the country, which essentially govern foreign investments. Second, when the attempt to strengthen domestic institutional functions is fraught with challenges and/or MENA governments wish to speed reforms and send strong signals about their commitment to PRP, it is beneﬁcial to sign and enter into force bilateral investment treaties. However, the inﬂuence of bilateral investment treaties is not as strong as that of domestic institutional strengthening. The adoption of a second best approach to strengthening PRP encourages FDI ﬂows, but its success or positive inﬂuence is dependent on the success of the ﬁrst best approach. The reform of domestic institutional functions is inevitable for the success of bilateral investment treaties. Finally, the question of whether MENA countries should adopt a ﬁrst or a second best approach to institutional reforms is a mere theoretical question. The empirical results indicate that the success of the second best approach is dependent on the success of the ﬁrst best approach, thus the two bests become an ensemble.

NOTES 1. By PRP institutional functions we refer to the outcomes of domestic institutions, mainly the legal and judicial systems and the government bureaucracy, which inﬂuence the PRP process. Examples of these functions are the issuance of laws, the enforcement of laws, contracts and order, the restriction of government’s power to expropriate and extract rents, and the control of corruption. 2. See, for example, Alfaro, Kalemli-Ozcan, and Volosovych (2008), Asiedu (2006), Busse and Hefeker (2007), Daude and Fratzscher (2008), Daude and Stein (2007), Du, Lu, and Tao (2008), Faria and Mauro (2009), Kraay and Nehru (2006), Lane (2004), Mina (2006), Mina and Martinez-Vazquez (2006), Mishra and Daly (2007), Naude and Krugell (2007), and Wei (2000). 3. Using Ghana and Vietnam as examples, Rodrik (2008) argues that despite the presence of commercial laws, courts are highly ineﬃcient,

costly to use, and potentially corruptible. In response, ﬁrms resort to relational contracting as an alternative, building long-term relationships with each other and sustaining cooperation through repeated interaction. Long term relational contracting is regarded as (better) informal substitute to the (weak) formal PRP institutions. 4. For the sake of brevity, we focus on the main messages of the literature and leave the literature survey to Mina (2010, 2011). 5. Findings are based on the surveys of Alfaro et al. (2008), Asiedu (2006), Busse and Hefeker (2007), Daude and Fratzscher (2008), Daude and Stein (2007), Du et al. (2008), Faria and Mauro (2009), Kraay and Nehru (2006), Lane (2004), Mina (2006), Mina and Martinez-Vazquez (2006), Naude and Krugell (2007), Mishra and Daly (2007), and Wei (2000).

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WORLD DEVELOPMENT

6. Findings are based on the surveys of Desbordes and Vicard (2009), Egger and Pfaﬀermayr (2004), Egger and Merlo (2007), HallwardDriemeier (2003), Kerner (2009), Mina (2009), Neumayer and Spess (2005), Tobin and Rose-Ackerman (2006), and UNCTAD (1998).

26% in 2003, amounting to about two-thirds of the average for the other two regions.

7. This relationship has been examined in Desbordes and Vicard (2009), Hallward-Driemeier (2003), Mina (2009), and Neumayer and Spess (2005).

14. We should note that because of a high correlation coeﬃcient of approximately 0.9 between oil production and domestic market size for the sample countries, as measured by either nominal or real GDP, we decided to omit the latter.

8. Hallward-Driemeier (2003) ﬁnds that bilateral investment treaties are more eﬀective in higher institutional quality settings and where institutions are already being strengthened. She argues that, “This undermines a central rational for some of the less developed countries that enter into these agreements hoping to bypass the need to strengthen property rights and institutions more generally. Put diﬀerently, if host countries are committed to trying to attract more FDI, BITs have not provided a short-cut from the need to implement broader reforms of domestic institutions” (italics added; pp. 21–22). 9. Findings are based on Chan and Gemayel (2004), Hisarciklilar, Kayam, and Kayalica (2007), Kamaly (2002), Onyeiwu (2003), and Mina (2007). 10. A table that shows the growth in the (5-year) average level of FDI in 1980–2008 is available from the author. The growth in FDI over time obliges the empirical methodology to consider its nonstationarity, as discussed further in Section 5. 11. A higher score indicates a lower risk. More information on the political risk components is provided in Section 4.

13. In addition, large population size drives market-seeking FDI.

15. The maximum score for each of these indices is provided in Table 2. 16. Correlation coeﬃcients are available from the author. 17. Mina (2007) discusses the possibility of negative oil price inﬂuence on FDI ﬂows in GCC countries. 18. Bellack et al. (2008) use unit labor costs and labor productivity in examining the inﬂuence of labor costs on FDI ﬂows to Central and Eastern European countries. 19. The correlation coeﬃcients are 0.071 for real GDP, 0.254 for population, 0.288 for labor force, 0.152 for oil production, and 0.407 for oil revenues relative to GDP. 20. For more discussion of this issue, see OECD (2006). 21. See Legum (2005). 22. See Baltagi (2005) for a discussion of these issues.

12. World Bank (2008) argues that the average level of education, and thus the level of human capital, is low in MENA relative to East Asia and Latin America. The average gross enrollment rate in secondary education amounted to 75% in 2003 in MENA, compared to 78% and 90% for East Asia and Latin America. Similarly, the average gross enrollment rate in higher education in MENA was only

23. For a discussion of panel unit root tests, see Barbieri (2009) and Lee and Chang (2009). 24. Based on the mixed results, we also included these two variables in level form in our estimation. The results are available by request.

REFERENCES Alfaro, L., Kalemli-Ozcan, S., & Volosovych, V. (2008). Why doesn’t capital ﬂow from rich to poor countries? An empirical investigation. Review of Economics and Statistics, 90(2), 347–368. Arellano, M., & Bond, S. (1991). Some tests of speciﬁcation for panel data: Monte Carlo evidence with an application for employment equations. Review of Economic Studies, 58(2), 277–297. Asiedu, E. (2006). Foreign direct investment in Africa: The role of natural resources, market size, government policy, institutions and political instability. World Economy, 29(1), 63–77. Baltagi, B. (2005). Econometric analysis of panel data. New York: John Wiley & Sons. Barbieri, L. (2009). Panel unit root tests under cross-sectional dependence: An overview. Journal of Statistics: Advances in Theory and Applications, 1(2). Bellack, C., Leibrecht, M., & Riedl, A. (2008). Labour costs and FDI ﬂows into Central and Eastern European countries: A survey of the literature and empirical evidence. Structural Change and Economic Dynamics, 19(1), 17–37. Bevan, A. A., & Estrin, S. (2004). The determinants of foreign direct investment into European transition economies. Journal of Comparative Economics, 32(4), 775–787. Blonigen, B. A., & Davies, R. B. (2004). The eﬀects of bilateral tax treaties on US FDI activity. International Tax and Public Finance, 11(5), 601–622. Busse, M., & Hefeker, C. (2007). Political risk, institutions and foreign direct investment. European Journal of Political Economy, 23(2), 397–415.

Chan, K. K., & Gemayel, E. R. (2004). Risk instability and the pattern of foreign direct investment in the Middle East and North Africa region. IMF Working Paper, WP/04/139. Daude, C., & Fratzscher, M. (2008). The pecking order of crossborder investment. Journal of International Economics, 74(1), 94–119. Daude, C., & Stein, E. (2007). The quality of institutions and foreign direct investment. Economics and Politics, 19(3), 317–344. Desbordes, R., & Vicard, V. (2009). Foreign direct investment and bilateral investment treaties: An international political perspective. Journal of Comparative Economics, 37(3), 372–386. Dixit, A. (2009). Governance institutions and economic activity. American Economic Review, 99(1), 5–24. Du, J., Lu, Y., & Tao, Z. (2008). Economic institutions and FDI location choice: Evidence from US multinationals in China. Journal of Comparative Economics, 36(3), 412–429. Dunning, J. (1981). International production and the multinational enterprise. London: Allen & Unwin. Egger, P., & Merlo, V. (2007). The impact of bilateral investment treaties on FDI dynamics. World Economy, 30(10), 1536–1549. Egger, P., & Pfaﬀermayr, M. (2004). The impact of bilateral investment treaties on foreign direct investment. Journal of Comparative Economics, 32(4), 788–804. Faria, A., & Mauro, P. (2009). Institutions and the external capital structure of countries. Journal of International Money and Finance, 28(3), 367–391.

THE INSTITUTIONAL REFORMS DEBATE AND FDI FLOWS TO THE MENA REGION: THE “BEST” ENSEMBLE Frenkel, M., Funke, K., & Stadtmann, G. (2004). A panel analysis of bilateral FDI ﬂows to emerging economies. Economic Systems, 28(3), 281–300. Hallward-Driemeier, M. (2003). Do bilateral investment treaties attract FDI? Only a bit . . . and they could bite. World Bank Policy Research Working Paper 3121. Washington, DC. Hisarciklilar, M., Kayam, S. S., & Kayalica, O. (2007). Locational drivers of FDI in MENA countries: A spatial attempt. Munich Personal RePEc Archive (MPRA) Paper No. 2085. Kamaly, A. (2002). Evaluation of FDI ﬂows into the MENA region. Department of Economics, American University in Cairo, Mimeo. Kerner, A. (2009). Why should I believe you? The costs and consequences of bilateral investment treaties. International Studies Quarterly, 53(1), 73–102. Kraay, A., & Nehru, V. (2006). When is external debt sustainable?. World Bank Economic Review, 20(3), 341–365. Lane, P. R. (2004). Empirical perspectives on long-term external debt. Topics in Macroeconomics, 4(1), 1–21, Available from http://www.bepress.com/bejm/topics/vol4/iss1/art1. Lee, C.-C., & Chang, C.-P. (2009). FDI, ﬁnancial development, and economic growth: International evidence. Journal of Applied Economics, 12(2), 249–271. Legum, B. (2005). Deﬁning investment and investor: Who is entitled to claim? Symposium on making the most of international investment agreements: A common agenda, OECD, Paris. Mina, W. (2006). Does contract enforcement matter for international lending?. Applied Economics Letters, 13(6), 359–364. Mina, W. (2007). The location determinants of FDI in the GCC countries. Journal of Multinational Financial Management, 17(4), 336–348. Mina, W. (2009). External commitment mechanisms, institutions, and FDI in GCC countries. Journal of International Financial Markets, Institutions & Money, 19(2), 371–386. Mina, W. (2010). Institutional reforms debate and FDI ﬂows to MENA region: Does one “best” ﬁt all? International Studies Program Working Paper No. 10-34. Mina, W. M. (2011). Institutional reforms debate and FDI ﬂows to MENA region: Does one “best” ﬁt all? UNU-WIDER Working Paper No. 2011/50.

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Mina, W., & Martinez-Vazquez, J. (2006). Contract enforcement, institutional stability and the level and maturity of international lending. International Studies Program Working Paper No. 06-17. Mishra, A., & Daly, K. (2007). Eﬀect of quality of institutions on outward foreign direct investment. Journal of International Trade and Economic Development, 16(2), 231–244. Naude, W. A., & Krugell, W. F. (2007). Investigating geography and institutions as determinants of foreign direct investment in Africa using panel data. Applied Economics, 39(10–12), 1223–1233. Neumayer, E., & Spess, L. (2005). Do bilateral investment treaties increase foreign direct investment to developing countries?. World Development, 33(10), 1567–1585. North, D. C. (1991). Institutions. Journal of Economic Perspectives, 5(1), 97–112. OECD (2006). International investment perspectives 2006. OECD Publishing. http://dx.doi.org/10.1787/iip-2006-en. Onyeiwu, S. (2003). Analysis of FDI ﬂows to developing countries: Is the MENA region diﬀerent? Paper presented at ERF’s 10th Annual Conference, Marrakech, Morocco. Rodrik, D. (2008). Second-best institutions. American Economic Review: Papers & Proceedings, 98(2), 100–104. Tobin, J., & Rose-Ackerman, S. (2006). When BITs have similar bite: The political economic environment for bilateral investment treaties. Yale Law School, November. UNCTAD (1998). Bilateral investment treaties in the mid-1990s. Geneva: United Nations. Wei, S.-J. (2000). How taxing is corruption on international investors?. Review of Economics and Statistics, 82(1), 1–11. Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press. World Bank (2008). The road not traveled: Education reform in the Middle East and North Africa. Washington, DC: The World Bank.

APPENDIX A See Table 8.

Table 8. Variables, deﬁnitions, and data sources Variable FDI BIT INSTITFN

BITINSTITFN OIL PRICE LABOR

TRADE INFLATION

WFDIFLOWS

Deﬁnition FDI annual ﬂows per capita in US$ (in log form) Annual number of bilateral investment treaties entered into force Domestic PRP institutional functions. These are (a) investment proﬁle, (b) corruption, (c) law and order, and (d) bureaucracy quality (in log form) Interaction term between BIT and INSTITFN constructed as the product of INSTITFN (in log form) and BIT Oil production in thousands of barrels per day (in log form) Crude oil price measured by the price of Saudi Arabian Light 34 in US$/barrel (in log form) Real GDP per person employed (in log form)

Sum of exports and imports as a percentage of GDP (in log form) Inﬂation rate in percentage (in log form). Rate is calculated based on the consumer price index, except for Oman and UAE where it is based on GDP deﬂator World FDI inﬂows in millions of US$ (in log form)

Source UNCTAD’s FDI online database Author’s calculation based on UNCTAD’s bilateral investment treaties online database (as of June 1, 2008) ICRG political risk index

Author’s calculation Energy Information Administration Energy Information Administration Author’s calculation based on UNCTAD’s bilateral investment treaties online and World Development Indicators databases World Bank’s World Development Indicators World Bank’s World Development Indicators

Author’s calculation based on UNCTAD’s bilateral investment treaties online database

The Institutional Reforms Debate and FDI Flows to the MENA Region: The “Best” Ensemble

The Institutional Reforms Debate and FDI Flows to the MENA Region: The “Best” Ensemble

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