FDI location drivers and risks in MENA.

Van Wyk, Jay ; Lal, Anil K.

INTRODUCTION

This paper investigates the causes of FDI flows to the Middle East and North Africa (MENA). The subsequent analysis of foreign direct investment (FDI) in MENA intersects two approaches in International Business and International Economics. New Institutional Economics (NIE) investigates the locational determinants of FDI in host countries. Multinational enterprises (MNEs) have particularly targeted geographical regions for investment and operations to take advantage of regional economic integration.

Traditionally, macroeconomic factors have been regarded as the most important determinants for location decisions and investment activities by MNEs. These location factors include market potential and size, economic growth rates, income per capita, relative labor unit costs, exchange rates, inflation rates, and relative natural endowments (Cavers, 1974; Cheng & Kwan, 2000; Dunning, 1980). Primarily, following the seminal works of North (1981, 1990) and Rumelt, Schendel and Teech (1991), economists and international management scholars found macroeconomic factors may provide only a partial explanation of FDI location and that more attention should be focused on the influence of institutions on FDI decisions (Disdier & Mayer, 2004; Dunning, 2006; Hall & Jones, 1999; Henisz, 2000; Jensen, 2003; Knack & Keifer, 1995; Mauro, 1995; Mayer, 2001; Madambi & Navarra, 2002; Rodrik, Subramania & Trebbi, 2004; Sethi, Guisinger, Ford & Phelan, 2002). According to Wan and Hoskisson (2003), institutional theory extends transaction cost theory by adding the institutional dimension. In developing countries, institutions are particularly important because institutional immaturity or ineffectiveness raises transaction costs and risk levels for foreign investor (Child, et al., 2003; Mayer, 2004; Uhlenbruck, 2004). These dual determinants of FDI, macroeconomics and institutions, are now known as the New Institutional Economics approach to FDI. NIE can be defined as an expansion of macroeconomic determinants of FDI to include socio-political interactions and the evolution of institutions. Institutions, which may be seen as the rules of the game and their associated implementation mechanisms, can be both formal and informal and are developed endogenously in response to limitations in human capacity to process information (Sugden, 1986; Williamson, 2000; Zinnes, Eilat, Sachs,2001). NIE operates in terms of hierarchy, market and participation principles as manifested by the state, the private sector, and civil society (Williamson, 1975; Williamson, 2000, Picciotto, 1997). According to Lin and Nuggent (1995) NIE has been influenced by five strands of thought: theories of collective action, transaction cost economics, theories of the evolution of norms and rule-making, the economics of imperfect information, and property rights economics.

FDI flows to specific geographical regions or to host countries within geographical proximity have increased due to the larger markets created by economic integration (Buckley, Clagg, Forsans & Reilly, 2005; Lee, 2005; Mirza & Giroud, 2004). The commonalities of history, culture and geography have driven the formation of regional free trade agreements and regional political organizations. Hossain and Naser (2008), for example, indicate that the six Middle East countries which formed the Gulf Cooperation Council (GCC), historically had common religious, social and cultural identities. The GCC also serves as a political and economic policy-coordinating forum for its members. According to Abed (2003) MENA, countries share a common cultural and institutional heritage, along with common economic and social challenges. Regionalism occupies the space between the contradictory pulls of globalization and nationalism. Rugman and Verbeke (2004) showed that the operations of MNEs are more focused on regional rather than global markets. New Institutional Economics (NIE) is now increasingly regarded as a valid explanation of location factors determining FDI in regions as diverse as Latin America, Central and Eastern Europe, East Asia and Sub Saharan Africa (Grosse & Trevino, 2005; Trevino & Mixon, 2004; Manaim, 2007; Raminez, 2006; Zhang, 2001; Akinkugbe, 2005; Mengistu & Adams, 2007).

Turning to MENA specifically, there are a number of compelling reasons in evidence as to why location determinants of FDI require further investigation. Soliman (2003) points to two events that reshaped MENA countries' negative attitudes towards free trade and FDI. The debt crisis and the drain of commercial bank lending to developing countries, coupled with the failure of nationalistic import substitution policies, forced MENA countries to consider the success of the Southeast Asian model which is based upon attracting FDI and export led growth. As a consequence, some MENA countries reformed their FDI regimes. Regulatory frameworks for FDI have been improved in several countries especially in the service sector, e.g. finance, telecommunications and real estate. According to Siddiqi (2007b), such regulatory policy changes created more favorable host country environments for global investors, e.g. liberalization of banking licenses in Bahrain and of insurance legislation in Turkey; extension of foreign property and land ownership in Oman's tourist industry; reduction of corporate income taxes in Turkey, Egypt, Kuwait and Saudi Arabia; and changes in the contractual and tender conditions in Oman's extractive sector. Since joining the WTO, Saudi Arabia has embarked on an extensive liberalization of foreign ownership, taxation and privatization as well as the creation of free trade zones for foreign investors (Siddiqi, 2007a). In Morocco, the government adapted an Investment Charter in 1995 which extended foreign ownership in the manufacturing sector; removed restrictions on the repatriation of capital and dividends; introduced fiscal and other incentives for FDI; and guaranteed foreign investors against the risks of nationalization and expropriation (Bouoiyour, 2003).

Changes in the FDI regimes of MENA countries had a positive affect on inflow of investments into the region. According to the United Nations Conference on Trade and Development (UNCTAD) (2006), sixteen of the nineteen countries in the region gained from the influx of FDI. FDI in the energy sector increased fourfold over the five year period between 1995-1999 and the period 2000-2005. Inward stock of FDI in the region surged almost 200% between 1995 and 2005. UNCTAD also reported that cross-border mergers and acquisitions in the region saw a huge increase from $1018m in 2004 to $17116m in 2005. Much of these M&A activities were attributable to the liberalization of the non-energy and service sectors.

Despite these initial improvements in the MENA investment environment, many barriers to FDI still persist. Only 8.3% of FDI flows to developing countries in 2005 were earmarked for MENA, which is less than FDI flows to other regions such as Southeast Asia and Latin America. (UN, 2005) Broadly speaking, the political systems, values and ideologies that persist in many MENA countries, still hamper market economies and these nations' abilities to attract FDI (Dunning, 1993; Azzam, 2001). The countries in the region are often characterized by large public sectors with centralized governments, large and overstaffed civil services, and weak systems of accountability. Abed (2003) concludes, that by international standards, MENA countries continue to lag in the development of an economic and financial environment conducive to entrepreneurship, risk taking, and private-sector led investment and growth.

The objectives of the paper are straightforward. First, to review the literature on the drivers of FDI in MENA, in particular, and, more generally, to supplement information scarcity with empirical findings from other developing countries and regions. Second, to formulate testable hypotheses regarding the drivers of FDI in MENA.

In order to investigate the causes of FDI flows to MENA, this paper is organized in four parts. At the outset, the literature of FDI in MENA is reviewed. Supplementary findings about NIE determinants of FDI in developing countries are included to augment our understanding of location determinants of FDI. Second, data and methods are outlined. Third, findings are discussed. Finally, conclusions are drawn from the findings, limitations of the study are outlined, and suggestions for further research are offered. The managerial implications of the findings are highlighted.

LITERATURE REVIEW

The literature of FDI in MENA is still a work in progress. Generalizations of findings are inhibited by several factors including the limited number of countries included in most panel studies, few studies include the most important NIE independent variables, and qualitative rather than quantitative studies still dominate the FDI literature1.

Macroeconomic Determinants

According to Metwally (2004), higher rates of economic growth have resulted from FDI inflows in MENA. He also identified a feedback effect in the relationship between economic growth and capital inflow. A greater inflow of FDI leads to growth in exports of goods and services. The expansion of exports leads to growth in GNP, which in turn, encourages the attraction of more foreign capital. Hisarciklilar, Kayam and Kayalica (2007) showed that GDP of host economies in MENA, which they treated as an indicator of purchasing power and a proxy for domestic demand, is found to have a high significant impact on the amount of FDI stock in the economy. Demirbag, Taloglu and Glaister (2008) found that market potential in Turkey, i.e. growth rate and market size, is positively correlated with FDI acquisitions in the domestic market. Bouoiyour (2003) found that market size had a positive impact on FDI in Morocco. Economic growth, however, was a negative but insignificant determinant of FDI due to unstable segments of the Moroccan economy. Market size and economic growth as location drivers of FDI in MENA are compatible with similar findings of other developing countries (Tahir & Larimo, 2004; United Nations, 1998; Trevino, Daniels & Arbelaez, 2002; Sabi, 1988).

Hypothesis 1: Market size and growth rate are drivers of FDI inflows to MENA.

The literature indicates that exchange rate volatility is an impediment to FDI. According to Erdal and Tatoglu (2002), the lack of exchange rate stability hindered Turkey's efforts to attract a much higher volume of FDI. MNEs from source countries with a strong currency (overvalued) would tend to invest more in economies with a relatively weak currency (undervalued). While appreciation of the home currency makes export sales more expensive, companies in the home country may decide to invest, e.g. outsource production, in such host countries to reduce costs and to increase competitiveness. Exchange rate fluctuations affect FDI in two ways: (1) the appreciation of a source country's currency vis-a-vis a host country means that the source country's investment increases in value when denominated in the host country's currency (Ajami & Barniv, 1984; Dewenter, 1995); and (2) currency appreciation increases a firm's wealth position, lowers its relative cost of capital and allows it to invest more aggressively overseas (Froot & Stein, 1991).

Hypothesis 2: Depreciation of the host country's currency should lead to greater FDI.

Financing deficits on the current account of the balance of payment usually occurs through the sale of assets, by attracting inward FDI or by securing loans. (Krugman & Obestfield, 1994) In MENA countries the volatility of oil prices has a significant bearing on the current account. High oil prices brought large surpluses on the current account as a percentage of GDP. These large surpluses were spent rapidly and when oil prices fell, governments were obliged to undertake difficult fiscal adjustments. Most MENA governments resorted to excessive external borrowing to finance their inefficient public investments, resource imbalances and deficits on the current account. This boom and bust cycle meant that oil-producing countries oscillated between a surplus and a deficit on the current account. Non-oil producing MENA countries struggled even more and largely failed to contain current account deficits below five percent of GDP (Aristovnik, 2007).

Hypothesis 3 Deficits to the current account of the balance of payment will encourage/hinder FDI inflows to MENA.

Evidence shows that countries that pursue more open commercial policies tend to attract more FDI (Kapuna-Foreman, 2007; Nourzad, 2008; Saab, 2007). For example, countries located in East and South Asia have been able to attract greater inflow of foreign capital by pursuing more open commercial policies, perhaps signaling to potential investors that foreign investments are sought and encouraged in these countries (Vogiatzoglou, 2008; Wie, 2006).

Hypothesis 4: Greater openness of a country's commercial policy will encourage FDI inflows.

Institutional Determinants

The impact of institutions on FDI and MENA produced various results. The literature supports the notion that political instability deters FDI in MENA. Chan and Gemayel (2004) found that the degree of instability, i.e. political, financial and economic instability associated with investment risk, is a much more critical determinant of FDI in MENA countries than it is in other developing countries. For Turkey, Demirbag, Taloglu and Glaister (2008) found that investment entry mode is influenced by investment risk such as political and economic stability. The above results are consistent with the notion that political instability, particularly in developing countries, significantly reduces the inflow of FDI (Li & Resnick, 2003; Schneider & Frey, 1985; Globerman & Shapiro, 2002). Related studies indicated that increasing levels of political freedom and civil liberties in host countries may be beneficial in attracting FDI (Harms & Ursprung, 2002; Kolstad & Villanger, 2004; Disdier & Mayer, 2004). Jensen (2003) found that democratic countries attract more FDI than their authoritarian counterparts. However, Li and Resnick (2003) argued that democracies both promote FDI, e.g. protecting property rights and reducing transaction costs, as well as create barriers to FDI, e.g. protecting influential domestic producers against foreign competition.

Hypothesis 5: Low levels of political freedom/civil liberties in MENA countries will deter FDI inflows.

The literature on the influence of other institutional determinants on FDI in MENA is sparse. Moen (2004) found that weak institutions in MENA countries hindered the inflow of FDI. Demirbag, Taloglu and Glaister (2008) found that government regulations, e.g. government policy, repatriation of profits and levels of industrial competition, were insignificant determinants of FDI in Turkey. Hisarciklilar, Kayam and Kayalica (2007) found that telephone mainlines, a proxy for infrastructure, did not have a significant effect on FDI.

The rich literature of institutional determinants of FDI in developing countries provides analytical insights for potential testing in MENA. Kapuria-Forman (2007) reported via regression results that increases in economic freedom, especially in areas such as government intervention in the economy, capital flows and property rights, improved the FDI attractiveness of developing countries. Bengoa and Sanchez-Robles (2003) found that the economic freedom imbedded in institutions was a positive determinant of FDI in 18 Latin American countries. Holmes, Feuler and O'Grady (2008) found that countries in the MENA region enjoyed less economic freedom than the world average. Regionally viewed, only Sub Saharan countries enjoyed less economic freedom than MENA. Only 17 countries in the world enjoyed high levels of economic freedom, none of them located in MENA (2). Javorcik and Spatareanu (2004) findings indicated that greater flexibility in the labor markets of host countries in Western and Eastern Europe was associated with larger FDI inflows. The literature also suggested that the protection of property rights had a positive impact on investment and growth (Knack & Keefer, 1995), but it may not be statistically significant (Ferrantino, 1993). Although the literature is relatively sparse, Campos and Kinoshita (2003) suggested that transitional economies with more efficient legal systems attracted more FDI. The findings regarding the impact of taxation policies on FDI are somewhat inconsistent. Older studies showed that unfavorable host country corporate taxation had a negative impact on FDI inflows (Grubert & Mutti, 1991; Hines & Rice, 1994; Loree & Guisinger, 1995). In developing countries, high taxes or complexity or uncertainties regarding tax laws had a negative influence on FDI inflows (Carstensen & Toubal, 2004; Edmiston, Mudd & Valev, 2003). Other studies suggested that there may be a positive relationship between FDI and taxation (Swenson, 1994) or that there was no significant relationship (Wheeler & Mody, 1992).

Hypothesis 6: Low levels of economic freedom in MENA countries will deter FDI inflows.

Okeahalam (2005) found that high levels of corruption did not deter investments in MENA. This finding contradicts others who found that corruption in host countries was an impediment to inward FDI (Hines, 1995; Mauro, 1995; Habib & Zurawick, 2002; Gastanajy, Nugent & Pashamova, 1998; Wei, 2000). However, Okeahalam and Bah (1998) found that many investors continue to invest in corrupt and poorly governed resource-rich countries, but tend to apply high discount factors and to ask for higher levels of expected returns to compensate for such high levels of corruption and political risk. Kolstad and Villanger (2004) found no significant impact of corruption on FDI in developing countries which raised the possibility that corruption may not be harmful to FDI in all contexts.

Hypothesis 7: Corruption in host countries may not deter FDI.

DATA AND METHODS

To examine the causes of Foreign Direct Investment (FDI), the following are postulated:

FDI = f(macro economic variables, institutional variables)

It is believed that economic variables that could influence FDI are market size, rate of growth of income, trade balance, degree of openness, and exchange rate. Institutional variables that could influence FDI are political rights, civil liberties, business freedom, investment freedom, trade freedom, and freedom from corruption.

Different alternatives were tried and the best results were obtained by using the following model:

Log(FDI) = constant + [alpha]1log(GDP95) + [alpha]2log(PCG) + [alpha]3log(CB) + [alpha]4log(EXR) + [alpha]5 log(OPEN) + [alpha]6log(CORF) + [alpha]7log(BF) + [alpha]8log(PF) + error term

where

FDI: Inward FDI stock in current US$.

GDP95: 1995 GDP in current US$, proxy for market size.

PCG: Per capita GDP in current US$, proxy for rate of growth of GDP

EXR: US $ per unit of host country's currency proxy for exchange rate.

CB: Current account exports in current US$/ current account imports in current US$, proxy for current account balance.

OPEN: Merchandise trade as a percentage of GDP, proxy for openness of an economy

CORF: Freedom from Corruption: The perception of corruption in the business environment, including levels of governmental, judicial and administrative corruption. The index is based on the Corruption Perceptions Index of Transparency International (Beach and Kane, 2008).

BF: Business Freedom: The ability to create, operate and close an enterprise quickly and easily. Burdensome, redundant regulatory rules are the most harmful barriers to business freedom. (Beach and Kane, 2008)

PF: Political Freedom: Consists of a Political Rights Index and a Civil Liberties Index. Political Rights enable people to participate freely in the political process, including the right to vote freely for distinct alternatives in legitimate elections, to compete for public office, to join political parties and organizations, and to elect representatives. Civil liberties allow for the freedoms of expression and belief, associational and organizational rights, rule of law, and personal autonomy without state interference (Freedom House, 2008).

Based on the initial estimation results, it was decided to use the log--linear functional form to estimate the causes of FDI. Since logs cannot be used in case of negative numbers, the original data was modified to facilitate the use of data (3). The advantages of using log formulation is that it enables easy interpretation of the data as coefficients associated with independent variables or parameter estimates measure respective elasticities. For example, '[alpha]2' indicates that inward FDI stock changes by '[alpha]2' percent when per capita GDP changes by 1 % (or 1 % growth in the region causes '[alpha]2' percent change in inward FDI stock).

The data were obtained on an annual basis from 1995 to 2004. The data for FDI were obtained from World Investment Report 2007 (UNCTAD). The data for GDP95, PCG95, GDP, PCG, and CB were obtained from World Development Indicators 2006 (The World Bank). For PF, Freedom House data were used which ranks countries based on political rights as well as civil liberties. These rankings are measured on a one to seven scale, with one representing the highest degree of political freedom and seven the lowest degree of political freedom. For BF and CORF, we have used the Index of Business Freedom and Index of Freedom from Corruption compiled by Wall Street Journal & the Heritage Foundation. The index ranges from 0 to 100, with higher scores indicating higher freedom.

Pooled least squares regressions were used to estimate the results for countries in the Middle East & North Africa nations excluding Iraq4. In the authors' estimation, no problem of serious multi-collinearity was found between independent variables as indicated by Table 1.

RESULTS

Regression results show that our model is a good fit, as indicated by High R2 and high adjusted R2. The estimation also does not suffer from serial correlation, as evidenced by Durbin--Watson statistic. Also, the signs of the coefficients associated with independent variables are consistent with our various hypotheses. The positive signs associated with log(GDP95)--a proxy for market size--and log (PCG)--a proxy for economic growth, indicate that larger market size and higher economic growth lead to higher FDI (Hypothesis 1)However, the coefficient associated with market size is not statistically significant, thought the coefficient associated with economic growth is statistically significant at 20 %.

The coefficient associated with log (EXR) is negative, indicating that depreciation of host country's currency leads to greater FDI (Hypothesis 2). However, this coefficient is not statistically significant. We also found that lower log (CB)--a proxy for current account balance -or higher current account deficit or lower current account surplus leads to higher FDI (Hypothesis 3). This coefficient is statistically significant at 20 %. The results show that the sign associated with log(OPEN)--a proxy for commercial policy openness--is positive, confirming the hypothesis (Hypothesis 4), i.e. the more open a region's/country's commercial policy, the greater is the inflow of FDI. This coefficient is statistically significant at 5 %.

We found that higher political freedom--proxy log (PF)--leads to higher FDI (Hypothesis 5). Please note that lower number for political freedom represents higher degree of political freedom and thus negative sign associated with log (PF) is consistent with Hypothesis 5. However, the coefficient is not statistically significant. The results also indicate that higher business freedom proxy log (BF) leads to higher FDI (Hypothesis 6). This coefficient is significant at 20 % level. Lastly, the coefficient associated with log (CORF)--a proxy for freedom from corruption (a higher score representing less corruption) is negative, indicating that higher levels of corruption may not deter FDI (Hypothesis 7). This result is consistent with earlier finding of Okeahalam (2005) that high levels of corruption did not deter FDI in MENA. However, the coefficient is not statistically significant.

SUMMARY AND CONCLUSION

The results attest to general expectations that aspects of NIE (both macroeconomic and institutional factors) determine the flow of FDI to the MENA region. The findings show that a number of macroeconomic factors were significant divers of FDI, i.e. economic growth, current account deficit, and trade openness. On the institutional side business freedom, one form of economic freedom, was a significant factor in attracting FDI. The finding identified other factors that had a bearing on FDI attractiveness but not at a significant level. These include market size, currency depreciation, political freedom, and low sensitivity to corruption.

The results are in line with NIE that market opportunity promotes FDI and that burdensome and redundant regulations impede FDI (Grosse & Trevino, 2005; Van Wyk & Lal, 2008). Cost efficiency and seizing business opportunities are greatly facilitated by business-friendly regulatory regimes. The World Bank (2009), which tracks reform of business regulations on an annual basis, found, for example, that property registry reform in Egypt led to quicker title registration and revenue increases of nearly 40 percent. In another World Bank example, new company registration increased by 81 percent in Saudi Arabia after reductions in minimum capital requirements. In general, time series data sets of institutional factors are still rather shallow. In the future, with more data available, the drivers of and risks to investment may afford more in-depth analysis.

Potential future studies of FDI inflows to MENA may distinguish between oil-producing and non-oil producing countries. As suggested in the literature review above, corruption may be more of an impediment to investment in non-oil producing countries wherein ROI may be much lower than FDI in oil rich countries. Related to that, resource-seeking FDI is likely more lucrative than investments in other types of industries. Industry-linked investments (e.g. services) may provide a more nuanced picture of the influence of NIE factors on inward FDI than country aggregate FDI. For non-oil producing countries, horizontal FDI (e.g. access to low wages) or vertical interaction FDI (e.g. disaggregating the production of components and intermediate goods) may offer some opportunities for foreign investors. Export platform FDI in MENA also has potential where a host country serves as a production platform for exports to neighboring countries (Bloniger, 2005). In the future, attention should be given to analyzing, in a comparative manner, the reasons for the remarkable increases to FDI inflows in Brazil, Russia, India, China and South Africa (BRICS) with that of countries in the MENA region. During 1991 to 2004, there was a remarkable increase in world FDI inflows and there was also a distinct change in destination of FDI inflows, with developing countries increasing their share of world FDI inflows from 26% in 1991 to about 34% in 2004. It is interesting to note that during this time period, BRICS were able to increase their share of FDI inflows from 4% in 1991 to 10% in 2004. Such a comparative study may yield fruitful insights into why some countries are able to attract more FDI inflows as compared to others.

From a managerial perspective, two salient observations may be offered. First, the effort by MENA countries to diversify their economies to lessen oil dependency may create more future investment opportunities for MNEs. Market seeking investments will be encouraged by the importance of market size per capita and economic growth per capita as identified in this study. Efficiency-seeking investors may pay more attention to MENA given the region's closer geographical proximity to the European Union. Second, institutional reform in the region is a work-in-progress. Some reforms have reduced the risk of institutional inefficiency. However, further institutional reform will render institutional factors a stimulus for investment. More specifically, democratization of political systems will likely produce a positive spillover effect on the political-economy of the region. However, Kamrava (2004), an astute expert of MENA politics, cautions that real reform (e.g. termination of rent seeking, corporatism, and privileged patron-client relations) instead of cosmetic reforms to keep authoritarian regimes in place, will be the real test of democratization. The astute manager is advised to remain abreast of the shifting economic and business environments in the MENA region.

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Anil K. Lal, Pittsburg State University

ENDNOTES

(1.) Tarzi (2005) identified key location variables as drivers or risks to FDI such as size of domestic market, high rate of economic growth, macroeconomic stability (low inflation rate, relative stable exchange rate), low levels of macro-political risk (instability), low levels of micro-political risks (regulations), and a well developed infrastructure. Bolinger (2005) identified partial equilibrium determinants of FDI such as exchange rates, taxes, institutions (corruption), trade protectionism and trade effects. Pajunen (2008) identified seven institutions which may influence the FDI decision, including corruption, political instability, labor regulations, justice and the judicial system in a society, political rights and civil liberties, property rights, and taxation policy.

(2.) The Economic Freedom Index measures freedoms related to operating a business, absence of trade barriers, taxation, government expenditures, price stability, free flow of investments, independence of banking sector, property rights, and freedom in the labor market.

(3.) Inward FDI stock has been used in place of inward FDI flow to take care of problems caused by some negative inward FDI flow. Thus, change in inward FDI stock is used as a proxy for FDI inflow. The same reasoning applies to use of GDP. To take care of negative numbers associated with current account balance (exports of goods--imports of goods), we have taken the ratio of current account exports to current account imports of goods and services as a proxy for current account balance (CB).

(4.) Inward FDI stock has been used in place of inward FDI flow to take care of problems caused by some negative inward FDI flow. Thus, change in inward FDI stock is used as a proxy for FDI inflow. The same reasoning applies to use of GDP. To take care of negative numbers associated with current account balance (exports of goods--imports of goods), we have taken the ratio of current account exports to current account imports of goods and services as a proxy for current account balance (CB).

Table 1: Correlation Matrix

               Log(GDP95)     Log(PCG)       Log(CB)        Log(OPEN)

Log(GDP95)      1.0000
Log(PCG)        0.0085         1.0000
Log(CB)         0.0381         0.4675         1.0000
Log(OPEN)      -0.3416         0.5471         0.2974         1.0000
Log(EXR)        0.4879        -0.0927        -0.1269        -0.1089
Log(CORF)       0.0317         0.6912         0.2901         0.5510
Log(BF)         0.0092         0.3700        -0.1269         0.5132
Log(PF)        -0.3081        -0.2529         0.2511         0.0080

               Log(EXR)       Log(CORF)      Log(BF)        Log(PF)

Log(GDP95)
Log(PCG)
Log(CB)
Log(OPEN)
Log(EXR)        1.0000
Log(CORF)      -0.0593         1.0000
Log(BF)        -0.0871        0.6183          1.0000        -0.2014
Log(PF)        -0.2759        -0.1412        -0.2014         1.0000

The best results of estimation are presented below. To take care of
the problem of autocorrelation AR(1), AR(2), and AR(3) terms were
added to the model:

Table 2. Results of Estimation

Variable               Coefficient     T-Statistic

Constant                  1.51            0.03
Log(GDP95)                0.39            0.21
Log(PCG)                0.25 ***          1.41
Log(EXR)                  -0.01           -0.09
Log(CB)                 -0 09 ***         -1.31
Log(OPEN)                0.41 *           2.17
Log(PF)                   -0.02           -0.06
Log(BF)                 0.17 ***          1.37
Log(CORF)                 -0.01           -0.28
AR(1)                     1.25            17.98
AR(2)                     -0.13           -1.17
AR(3)                    -0.12 *          -1.94
R Squared                 0.98
Adj R squared             0.97
Durbin-Watson Stat        2.02
Total Observations         132

* significant at 5% level.

** significant at 10% level.

*** significant at 20% level.