DETERMINANTS OF US PRIVATE FOREIGN DIRECT ...

J. OF PUBLIC BUDGETING, ACCOUNTING & FINANCIAL MANAGEMENT, 9(2), 331-355

SUMMER 1997

DETERMINANTS OF U.S. PRIVATE FOREIGN DIRECT INVESTMENTS IN OPEC NATIONS : FROM PUBLIC AND NON-PUBLIC POLICY PERSPECTIVES Kingsley O. Olibe and C. Larry Crumbley* ABSTRACT. Previous research demonstrates that non-public policy variables (wage rate, raw material, GDP, GDP/capita, inverse of tax rate, and population) have significant influence in determining the flow of U.S. investment. Research has not, however, demonstrated that government accounting variables significantly affect Foreign Direct Investments (FDI) flow into either Organization of Petroleum Exporting Countries (OPEC) or non-OPEC countries. In light of this omission, the focus of this inquiry is on the examination of the potential influence of both government accounting and non-public variables in influencing the flow of the stock of U.S. foreign direct investment in the OPEC nations. To accomplish the objective, government accounting and non-public policy variables are employed to investigate whether they matter in determining investment flows into these countries. The results of the study suggest a direct linkage between the flow of FDI and accounting variables. INTRODUCTION

This article proposes and tests propositions related to two issues: (1) Do government financial characteristics influence U.S. foreign direct investment in OPEC countries ? and (2) Do non-government financial variables (wage rate, GDP, etc.) systematically influence U.S. foreign direct investment (FDI) in OPEC countries?(1) This study has two primary objectives. The first is to identify public policy factors that determine the flow of stocks of FDI into OPEC countries. The second objective is to determine any nonpublic policy variables that explain why FDI flows into these countries. Since the early 1980s, more countries have reversed their FDI policies with _______________ * Kingsley O. Olibe is a Ph.D. Candidate, Department of Accounting, Texas A&M University. Larry Crumbley. Ph.D., is KPMG Peat Marwick Professor, Department of Accounting, Louisiana State University. His teaching and research interests are in taxation and accounting education.

Copyright © 1997 by PrAcademics Press

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a view towards encouraging and facilitating its flow. In retrospective, determinants of the flow of foreign direct investments have been an elusive variable for empirical research. Loree and Guisinger (1995) suggest that the elusiveness of FDI determinants is predicated on the operational difficulties of the policy variables, which are hard to capture. In investigating FDI determinants, Root and Ahmed (1978) employed statutory corporate tax rate as a proxy to capture the overall effects of fiscal policies on new investors’ sentiments, ignoring the effects of tax holidays, costs allocation of tangible assets (accelerated depreciation), and other policy incentives that diminish or dilute the effects of corporate tax rate on reported earnings. Agodo (1978), for example, used growth in domestic market, income per capita, lower wage rate, etc. to proxy for U.S. (manufacturing firms) foreign direct investment in Africa. More recently in a cross-sectional study, Loree and Guisinger (1995) employed net investment incentives, net performance requirements measure, cultural distance(1) wage rate, and the inverse of host country tax rate to examine U.S. stock of FDI flow to forty countries. Contrary to intuition, their study did not find wage rates to be an insignificant determinant of FDI in 1977 and 1982. These studies suffer from misspecifications, ignoring the effects of rate of return on invested capital (ROI), government type, and the liquidity of the host government (surplus/deficit budget). A budget deficit, ceteris paribus, translates to higher tax rates for firms and individuals. The net effect is a reduction in disposable income (Y-T=Yd), which dampens consumption patterns. The problem of omitted variables limits the generalization of the research outcome. Vernon (1977) suggests that FDI flows, for instance, are usually influenced by the nationality of the entrepreneurs. The location of most units of a multinational network, however, are predicated on their functions such as market seeking, resources control, and tax havens. Some affiliates (Goldsbrough, 1979) posit that FDI flows are located by “straightforward least-cost calculations aimed at minimizing the delivered cost of the output.” While these studies are vital and valid, there exists a void in both empirical and theoretical research investigating public policy determinants of U.S. FDI flows to OPEC countries. The OPEC countries possess an abundance of a world-important natural resource--petroleum. Thus, the principal objective of this study is to examine the role of public accounting and nonpublic policy determinants of FDI in the host OPEC nations. (1) The remainder of this article is organized into six sections: section 2 describes

U.S. PRIVATE INVESTMENTS IN OPEC NATIONS

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background information, motivation, and a brief literature review; OPEC history is explained in section 3; section 4 delineates the hypotheses and model form; variable selection, and data descriptions are explained in section 5; and the final section contains a summary of the findings and conclusions. BACKGROUND

Developing nations in particular are employing the stock of FDI as a source of capital, technology, managerial know-how and other resources that are essential for sustainable and stable economic development and growth. This phenomenon was widespread during the early 1990s, as developing countries and economies in transition create incentives for FDI flows. In 1993, the flow of FDI received impetus from a number of vital developments at the international level. While differences exist in the scope and depth of the free-flow of foreign direct investments and the operations of multinational enterprises (MNEs), regional bilateral and unilateral efforts have led to a remarkably favorable government policy towards FDI flow among countries (Fatouros, 1990). Governments of various countries had adopted market distorting measures such as concessionary tax rate and other incentives designed to attract and encourage FDI. Accordingly, investment incentives are government measures designed primarily to influence the location, size or industry of an investment by affecting relative cost or potential for earnings or by altering the risks of such business (OECD, 1976). Some of the major investment incentives include tax incentives (e.g. tax holidays, accelerated depreciation of tangible assets, tax credits, lower tax rates), and other financial incentives, such as grants, preferential loan treatment, lower cost of capital, tariff concessions and structural measures, including the provision of infrastructure and quality of labor skills. Guisinger and Associates (1985) suggest that, on the whole, incentives distort resource allocation. Relative abundance of natural resources may make a country more attractive for FDI flow. In spite of these developments, there are still differences in the nature of measures taken by nations, reflecting differing political, economic and social priorities and attitudes towards FDI flows. Therefore, to gain an insight and understanding of FDI flows and the spread of MNEs, an important question is what public and nonpublic policy factors determine overseas investments?(1) The vital nature of this question may be viewed from two

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perspectives. First, the mode of entry into foreign markets is predicated on the motive underlying the FDI flows into the host country. Second, FDI flow depends upon the long-term objectives of the firm entering into the host country. Thus, any theoretical or empirical study to provide information on international investment decisions must reflect a firm’s motive to engage in cross-border economic activity. Further, government policy on equity investment must be designed with a clear understanding of the factors that determine foreign direct investment. In a global economic setting, MNEs capital allocation decisions are influenced by their capacity to generate funds for investment. The allocation of funds for foreign capital outlay depends on the expected rate of return, infrastructure, concessionary tax rates, host country cost of capital, budget surplus/deficit of the host country, host country reserves in exchangeable currency, political risk, government type, wage rates, gross domestic product (GDP) per capita, population, and balance of payment viability.(1) The economic underpinnings of these variables are discussed in the variable selection section. Motivation There is substantial literature relating to the flow of foreign direct investments (FDI). Research on FDI has largely focused on U.S. FDI flow into Europe, NAFTA Signatory nations, Asia and Pacific Rim nations, and economies in transition (Eastern Europe). In a cross-sectional study of countries, Loree and Guisinger (1995) employed Net Investment Measures, Net Performance Requirement Measures, Cultural Distance, and Tax Rate as a proxy for public policy determinants of FDI flow. This study provided evidence of the significance of tax rate and gross domestic product per capita variables in 1972, but they were insignificant in 1982 in determining U.S. FDI flow. Cultural distance, however, was significant in 1977 but insignificant in 1982, and infrastructure variables were significant in both years. With respect to FDI flows based on risk avoidance or minimization, Boatwrite and Renton (1975) suggest that oversea capital stock is a function of international differences in interest rates of long-term government bonds. Thus, FDI flows are sensitive to U.S. and foreign real cost of capital and investment demand variables. Caves (1996) employed capital asset pricing model (CAMP) to demonstrate how low risk tolerance investors “behave as market competitors set asset prices that convey claims to uncertain streams of future cash flows.”


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According to Caves, the principal concept underlying risk aversion is the diversification of the composition of the portfolio to maximize wealth and minimize risk inherent in volatile assets. Thus, FDI offers firms diversification opportunity absent in local markets. The financial-hierarchy hypothesis posits that exchange rate movements will induce a wealth effect due to the willingness of firms to acquire assets abroad. Goldsbrough (1979) conjects that the proportion of MNEs oversea borrowing depends on international interest-rate differentials, the distribution of its capital investments between countries, and the covariation of cash flows from the investment. Hymer’s (1960) FDI theory postulates that firms undertake oversea investments due to the monopolistic advantages inherent in such ventures “relative to host firms, in an imperfect market environment.” There is empirical evidence in support of the monopolistic advantage of FDI theory, indicating that MNEs possess monopolistic rents over host firms (Kim and Lyn, 1986). Agodo (1978), employing the ordinary least square (OLS) model in a study of the determinants of U.S. equity manufacturing investments, found no significant relationship between African low wage rate, but systematic support for both GDP and GDP/capita which were proxies for domestic market size and income level of the citizens. From a different perspective, Lecrew (1991) argued that FDI flows are sensitive to shifts in the locational attributes of a host country. According to Loree and Guisinger (1995), Lecrew’s model is based on the assumption that the stock of FDI in each country is at equilibrium state at the commencing period. Thus, changes in the stock of FDI flows is a function of shift in the productive resource variables (capital, labor and other input resources) of the host country. The theoretical and empirical literature reviewed above suggest that MNEs rationalize cross-border production to exploit international differences in factor prices. Thus, government incentives (quality of infrastructure, budget surplus, trade policy, mineral resources, lower cost of capital, etc.) may increase the attractiveness of FDI flow in a country. Among the empirical and theoretical studies examining U.S. FDI flow, Moxon (1975), Kirkpatrick and Yamin (1981), Lee (1986), and Clark, Sawyer and Sprinkle (1989) have analyzed FDI nonpublic policy determinants across industries. These studies found stock of FDI flow to be significantly related to a measure of labor intensity and inversely related to a measure of capital intensity.

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Others such as Woodward and Rolfe (1993) employing conditional logit models examined determinants of country selection in direct investments. Their study found that the probability of country selection varied inversely with wage rates, and favorably with quality of infrastructure and duration of tax holidays, among other factors. Contractor (1991) and Nigh (1985), directed their studies on the levels of host countries locational characteristics. Although these two studies examined locational selection from differing perspectives, Nigh specifically focused on the impact of political stability, while Contractor emphasized the consequences of government policies in inducing FDI flows. In general, the two studies conceptually share the same theoretical framework that the stock of FDI flows are functions of host country locational characteristics. While these studies have offered some perspectives on the locational determinants of FDI flows, none has really addressed the determinants of U.S. FDI flows in OPEC countries, given their unique possession of natural resources needed by the industrialized world. Therefore, this inquiry focuses on the determinants of U.S. foreign direct investments in OPEC countries. Specifically, the study examines the association between public policy and nonpublic policy determinants of stock of FDI flows into the OPEC nations. History of OPEC Member Nations The union of thirteen sovereign nations into the group now known as the Organization of the Petroleum Exporting Countries (OPEC) is a natural phenomenon evolving from the geographic distribution of petroleum reserves. The OPEC came into existence due to a conference held in Baghdad, Iraq, September 9-14, 1960 (Danielsen,1982). The founding members (Saudi Arabia, Iran, Kuwait, Iraq and Venezuela) have veto power over the admission of new members, but otherwise all full members have equal rights within the organization. For membership into the cartel, the country must be a “substantial net exporter,” and the petroleum interest must be fundamentally in consonance with those of member countries. (1) The OPEC cartel derives its power from the fact that its members own and control two-thirds of the world’s known petroleum reserves, and their exploration and extraction costs are minuscule. Of the known oil reserves, OPEC effectively controls in excess of 75 percent, and currently produces over 40 percent of the crude oil consumed worldwide (Suranovic, 1994). Current estimates of the ratio of reserves to production are 79.6 years for OPEC nations while the worldwide ratio is estimated at 40 to 45 years. (1)


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Petroleum from the cartel can effectively compete favorably with alternative energy resources. OPEC members export approximately 86% of the petroleum resources entering into international trade. The modus operandi of the cartel is to substitute collective interest for individual action (Danielsen, 1992). The world petroleum market is only a part of the total resource and energy picture, but it is the largest, and by many criteria, the most important part. Petroleum is the swing fuel in energy markets, and OPEC is the swing producer. Furthermore, the prices of all other energy resources are dependent on the current and prospective price of oil (Danielson, 1982). The oil industry is by far the most important industry in the world as measured by sales volume. Appendix A shows that 20 percent of the top 44 U.S. corporations relative to sales are oil companies. Danielsen (1982), suggests that petroleum accounts for half the tonnage, two-thirds of the ton miles, and one-fourth of the value of all commodities exchanged in international markets. The U.S. is the leading energy consumer and leading oil importing country. Empirical evidence regarding U.S. FDI flow into these petroleum resource-rich countries represents a void in the accounting literature. The purpose of this study is to fill that void. This investigation contributes to the extant literature by providing evidence that governmental accounting and financial practices influence U.S. stock of FDI flow into the OPEC countries. Any empirical examination of U.S. FDI determinants requires the inclusion of non-policy variables to control for alternative explanations. Thus, this article first develops the propositions that will test the effects of both government accounting variables and well-established non-policy variables on FDI. HYPOTHESES AND MODEL DEVELOPMENT

The variables thought to influence the stock of investment flow can be broken into two broad categories, including a distinction between public and non-public policy variables. In theoretical and empirical concepts, the decomposition of these factors into these categories seems logical, given the normative desire of bureaucrats to manipulate accounting numbers that consequently influence the flow of investment. In retrospect, non-policy factors also are subject to manipulation, though their rate of change is assumed to be slower than that of public accounting and financial variables.

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It is vital to examine public accounting factors and nonpublic variables influencing the flow of U.S. FDI to these countries, and their unique characteristics relative to petroleum surpluses. Therefore, our explicit objective is to investigate through empirical testing what public and nonpublic proxies influence U.S. FDI flows into the OPEC nations. These variables relative to FDI provide an indication of what government incentives and financial characteristics induce U.S. FDI flow to offshore production activity. Specifically, the focus of this study is to identify empirically the financial, economic and political characteristics of the OPEC countries that are expected to enhance their attractiveness for U.S. FDI. Therefore, we used the model form that relates to absolute values of both government accounting and non-government accounting variables that explain United States’ FDI flows. The basic analytical approach consists of two testable propositions each of which postulates an independent proxy that simple economic logic or empirical evidence demonstrates to be relevant in explaining FDI decision. Agodo (1978), and Loree and Guisinger (1995) employed the ordinary least square (OLS) regression model in their analyses of FDI flows. Both models proved to be statistically significant. Thus, the specific statistical methodology for this research is multiple regression for one dependent variable definition, and the hypotheses to be tested are stated in the alternate form. Thus, we propose the following hypotheses: H1: Ceteris paribus, U.S. FDI flow to the OPEC countries is likely to be systematically influenced by any one of the government policy variables, such as budget surplus/deficit, foreign reserves, etc. H2: Ceteris paribus, U.S. FDI flow to the OPEC countries is likely to be systematically influenced by non-public policy variables, such as GDP, change in wage rate, GDP/capita, population, etc. Model Form, Variable Selection, and Explanations The OLS model exployed to test the explanatory power of both governmental accounting and nonpolicy variables are stated as follows: ln(FDI) = á0 + â1 BUSD + â2 RESER + â3 ln(GOCA) + â4 DESER + â5 POLSA + â6ÄCUDV + â7 ln(AVBAR) +â8 GOTYPE + åi ln(FDI) = á0 + â1 ÄWARA + â2GDPC +â3GDP +

(1)


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â4POPU + åi

(2)

where: FDI is the normalized logarithmic dollar value of U.S. foreign direct investment flow to each of the countries under investigation. Alternatively, FDI is the aggregate composite of equity investment, reinvested earnings, and other long- and short-term capital flows received by each of the OPEC countries.(1) BUSD is the deflated value of budget surplus/deficit from each OPEC country measured at fiscal year-end. The BUSD of the central government is deflated by population. RESER is a measure of international liquidity (i.e., Special Drawing Rights, the country in foreign accounts measured at the end of the fiscal year.

gold, etc.) of

GOCA is the logarithmic value of government capital expenditure measured at of the fiscal year.

the end

DESER refers to the debit service ratio (i.e., central government cost of capital) external indebtedness of the country measured at the end of the fiscal year.

due to

POLSA is the political risk-index that measures the relative political stability of

anation.

ÄCUDV is a measure of the rate of devaluation of the host country monetary unit relative to the U.S. dollar. AVBAR is the average daily crude petroleum production measured at the end of year.(1)

the

GOTY refers to the nature of the host country government (i.e., elected or unelected government) and is employed as a dummy variable (1= elected, 0, otherwise). á and âs are the OLS regression coefficients; and åi is an error term.

The research design has two specific tasks to test empirically the effects of (1) governmental accounting and financial policies, and (2) non-public sector factors in explaining U.S. foreign direct investment flow in the OPEC countries. The economic rationale and our prior expectations about the variables in the models are as follows. Prior studies examining U.S. equity investments abroad employed the aggregate foreign direct investment, even though some components of FDI respond differently to location characteristics of the host country (Agodo, 1978). Loree and Guisinger (1995) desegregate FDI by excluding reinvested earnings. However, we argue that both reinvested earnings and inter-firm debt components of FDI should be excluded because they carry elements of sunk-costs that are unaffected by the host country factors that invariably affect initial investment flow from the

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U.S. More specifically, the primary focus of this study is on the factors that influence the component of FDI that originates from the U.S. to the OPEC countries. This component of FDI is the one more likely to face government scrutiny and incentive packages. The surrogate chosen to represent internal liquidity and financial efficacy of the host government is budget surplus/deficit. A budget surplus in the presence of zero taxation is interpreted as “good news” and budget deficit implies “bad news” by the investment community. A budget surplus is indicative of a government’s prudent financial husbandry, and a deficit is viewed otherwise. Budget deficit may be interpreted as a government’s excess expenditures that may induce potential austerity and concomitant higher taxes. A negative coefficient is predicted for BUSD, reflecting the relative preference for a budget surplus. The level of international liquidity of each country is proxied by the absolute value of that country’s foreign reserve. The higher the international solvency of a country relative to exchangeable “hard” currencies, the greater the likelihood of investment flow. Thus, a positive coefficient is predicted, reflecting earning repatriation motive of firm owners. Government capital expenditure is used as a proxy to capture particularly the level of infrastructure (e.g., roads, airports, wharf, etc.). This choice is based on previous research by Loree and Gusinger (1995), Schneider and Bruno (1985), and Agodo (1978), who found infrastructure significant in explaining FDI flow. The debt service ratio is chosen as a proxy for cost of capital by the central government of the OPEC countries. This ratio focuses on the proportion of the GDP that is used to service external debts. A high debt service ratio raises the specter of increased risk default on international loans. Therefore, the expectation is that higher (lower) debt service ratios should be negatively (positively) associated with U.S. FDI flow. Political risk-index is a surrogate for relative political and social stability of the countries under investigation. The political and social stability of a country minimizes the uncertainty of potential investors, and may induce U.S. foreign direct investment flow, though prior analyses relative to the relationship between political strife and FDI have resulted in differing perspectives. Agodo (1978) and Contractor (1991) suggest that political and social stability are essential in foreign investment decisions. Firms may give different considerations to the political, social, and economic conditions of a country prior to assets commitment. The political risk-index employed in this study is a country composite score developed by The Economist cited by


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Shapiro (1992). The formula for the composite risk-index is weighted more heavily toward political risk, which fits the study. The composite score ranges from a high of 57 for a high-risk environment to 11 for a relatively risk-free environment. Since many investors are risk-averse, the expectation is for a positive (negative) coefficient relative to risk-free (volatile) environment. International transactions are sometimes hampered by the lack of currency homogeneity. To proxy for the devaluation of a host country’s currency, we selected the rate of change in currency. Where a country maintains an overvalue exchange rate, the ability to maintain debt service is dim. Thus, a devalued currency relative to the dollar, ceteris paribus will induce FDI flow as the dollar’s purchasing power rises with devaluation. Currency devaluation will more likely improve FDI flow, hence, demand elasticity for petroleum is relatively high. The raw material variable is proxied by the logarithmic value of average daily barrels of crude petroleum production. Given that the U.S. is a net importer of oil, the expectation is a positive relationship between raw material (crude petroleum) and the flow of U.S. foreign direct investment. The proxy for elected and unelected officials is government type. No prediction is made with respect to the GOTY coefficient because most OPEC country governments are absolute monarchs. Non-public Policy Variables Labor costs are an integral component of drilling and production activity in the oil and gas industry. Therefore, high labor costs may be a structural impediment to the investment location decision of a U.S. investor, particular in a labor intensive industry. Agodo’s (1978) study of the investments of thirty-three U.S. manufacturing firms in selected African countries found that the relatively low labor African wage rate is insignificant in determining the flow of FDI. Conversely, Woodwind and Rolf (1993) found a significant negative association between wage rates and the probability of country selection for export induced FDI in the Caribbean Basin. Loree and Gusinger (1995) and Schneider and Frey (1985) both found labor wage rates to be a significant determinant of U.S. foreign direct investment flow. Although the empirical outcomes have been mixed, the economic rationale behind the association between wage rates and FDI location decision is still supported. Thus, we expect a negative (positive) relation between high (low) wage rates within a country and the level of investment flow.

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Gross Domestic Product (GDP) Per Capita is chosen as a proxy for income level for the residents of the countries under study (Agodo, 1978; Loree and Gusinger, 1995). High income level stimulates high standard of living and consumption patterns. Thus, with the stream of oil revenue, some OPEC countries experienced an enhanced GDP per capita. The expectation is for a positive and significant relationship between GDP per capita and investment flow. Gross domestic product is a surrogate for each of the countries’ domestic market size. We expect host-country market size to be influential because of the obvious fact that investment in oil drilling and production will usually require maximum plant capacity and output level to justify any investment. The larger the market, the more the probability of the flow of U.S. investment. Cost of capital is a proxy for lending rate in the host country. The capital-arbitrage hypothesis posits that multinational corporations borrow where the world funds are cheapest and invest them where expected returns are highest (Caves, 1996: 160). The expectation is that the high cost of capital should negatively correlate with FDI flow. Though, most OPEC nations do not have functioning capital markets, there is some suggestion that MNEs have utilized local borrowing as off-balance-sheet financing, to minimize the parent’s leverage ratio upon consolidation. Robbins and Stobaugh (1973: 127) noted that affiliates of multinational firms demonstrate higher aggregate proportion of current liabilities to current assets than their parents’ domestic operation. This behavior is consistent with a risk-induced reliance on local currency financing. Since most firms are profit maximizers, we conjecture that lower cost of capital should be associated with investment flows. Size variable is proxied for population. Though the population parameter is employed to compute GDP per capita, it is included for varying population size of the OPEC countries. On the basis of gross domestic product per capita alone, some OPEC countries may be considered poor and unattractive for investment decision. Yet some of these countries may have large populations so that on the average, aggregate purchasing power may be appealing. We expect a positive relationship between each country’s population and U.S. investment flow. The predicted sign for each dependent vvariable is summarized in Table 1. The negative (positive) sign indicates a negative (positive) relationship between the independent and the explanatory variable. No sign prediction is made between government type (GOTY) and FDI flow.


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SELECTION AND DATA DESCRIPTIONS TABLE 1 Summary of Coefficient Sign Predictions for Equations 1 and 2 _____________________________________________________________________ Independent Variable Predicted Sign for the Dependent Variable (FDI) --------------------------------------------------------------------------------------------BUSD

-

CUDV

-

GOTY

?

FOSER

+

DESER

-

GOCA

+

AVBAR

+

POLSA ÄWARA

+ +

GDPC

+

GDP

+

POPU

+

COTAX

-

COCA

-

FDI = the normalized logarithmic dollar value of U.S. foreign direct investment flow to the OPEC countries. BUSD = the deflated value of budget surplus/deficit from each OPEC country measured at year-end. CUDV = a measure of the rate of change of the host country monetary unit relative to the dollar. GOTY = refers to the nature of the host country government (i.e., elected or unelected government), and is employed as a dummy variable (1= elected, 0, otherwise). FOSER = a measure of international liquidity of the host country. It reflects the country’s special drawing rights in tradable currencies. DESER = the debit service ratio (i.e., central government cost of capital) due to external indebtedness of the country. GOCA = the logarithmic value of government capital expenditure on infrastructure, etc. AVBAR = the average daily crude petroleum production measured at the end of the year. POLSA = the surrogate for relative political and social stability of the OPEC nations. ÄWARA = a measure of relative change in labor costs. GDPC = measure of income level of the citizens of the OPEC nations. GDP = measure of the relative size of the host country domestic market. POPU = the total number of the citizens of the host countries. COTAX = the effective corporate tax rate on earnings of multinational firms engaged in operation in the OPEC countries. COCA = the lending rate or cost of capital in the host country.

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The sample analyzed in this study consists of OPEC members for a six year period from 1989 through 1994. The sample of the countries and the daily production of crude petroleum was obtained from the OPEC’s 1993 Annual Statistical Bulletin.(1) The key government accounting and financial data (budget surplus/deficit, international liquidity, etc.), population size, and lending rate were obtained from the 1994 International Financial Statistics(1). Currency devaluation rates, debt service ratios, changes in wage rates, GDP, and GDP per capita were obtained from U.S. Bureau of Economic Analysis (1993). The initial sample consists of 13 OPEC countries, with 78 observations. We exclude Libya, Iraq and Algeria because TABLE 2

Descriptive Statistics for Regression Variables in Equations 1 and 2 ______________________________________________________________ _ Variable

Mean

STD

MIN

MAX

-------------------------------------------------------------------------------------------F B

1316.4 -18.27

1283.47 30.48

-401.00 -139.20

5015.00 14.70

C G

-1.05 .17

3.45 5.98

-18.56 -21.10

.42 8.70

F D G A

4967.9 19.00 11.46 2156.9

4215.41 16.55 13.44 2041.59

12.00 00 1.18 150.00

16748 68.00 51.04 8331.70

P Ä G G P

25.13 .17 6012.4 48.60 44.23

12.83 5.9 6672.43 42.4 65.91

11.00 -21.10 275.00 3.98 1.80

42.00 8.70 21175.0 144.58 203.54

C .427 .14 .20 .68 C .18 .18 .00 .75 ______________________________________________________________________


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of missing data. Based on these criteria, the final sample yielded 10 countries with 60 observations. The descriptive statistics of the variables used in the models is presented in Table 2. Clearly the international liquidity variable (FOSER) dominates in average size and dispersion. Appendix B displays Pearson Correlations for all the variables. These correlations do not suggest any collinearity problems. We also examined a more reliable indicator of collinearity, the eigenvalue and the variance inflation factor (VIF) for each explanatory variable, and no eigenvalue exceeded 5.00 in magnitude. A level of 30 for the eigenvalue and 10 for the VIF is generally an indication of a problem according to Neter, Wasserman and Kutner (1989) and Kennedy (1992). EMPIRICAL RESULTS

Cross-Sectional Results Table 3 reports equations 1, 2 and 3 coefficient estimates and statistical significance levels. The model has a high degree of explanatory power employing host government accounting and financial measures with R2 above .90. Generally, the estimated coefficients are statistically significant and, where sign predictions are made, carry expected signs. Consistent with Table 2 predictions, the DESER, FOSER, GOTY, GOCA, GDPC, GDP, and COTAX coefficients reported in Table 3 are positive and highly significant at .0001, .005, .05 and .10 levels with respect to the flow of U.S. investment. These results suggest that government accounting and non-policy variables are value-relevant in attracting U.S. investment to these OPEC countries. More specifically, government capital expenditure is highly significant with the expected positive relationship. With respect to non-public policy variables, GDP, COTAX, and AVBAR are significant and consistent with Agodo, 1978 and Loree and Guisinger (1995). The results of this study do not support the population factor as an influencer of investment flow. This lack of support appears to be inconsistent with Agodo’s 1978 outcome. The economic underpinning represented by the level of infrastructure which exists in a nation influences the flow of U.S. direct investment is demonstrated in this study. When average daily barrels of crude oil is included in the government accounting and financial model (i.e., the FDI measure), currency devaluation lacks significance. This lack of significance implies that petroleum resources is vital for U.S. FDI flow into these nations.

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A possible explanation for the lack of significance for currency devaluation (CUDV), change in wage rate (ÄWARA), and population (POPU) may be investors= indifference.(1) Consequently, if investors’ are indifferent to the host country currency devaluation, wage rate and population, then the sign of the coefficient is investment flow insensitive. A somewhat surprising result is the negative coefficient of average daily barrels of crude petroleum production which is a proxy for raw material in the government accounting model. Intuitively, prior expectation is that U.S. FDI flow is specifically to access such unique natural resources to hedge against future potential oil shock. This conjecture is supported by the highly significant level (.005) of average daily crude petroleum production in the non-public policy model, although the sign of the coefficient is not as predicted. One explanation for the negative coefficient is the incremental capital commitment associated with exploration and drilling, noting that petroleum is a depletable resource. Thus, as the oil wells are depleted, there is more likelihood for potential investment for exploration. In a separate result not reported, average daily barrels of oil in Model 1 is significant at .10 percent level in a two-tailed test. CONCLUSIONS

This study presents empirical evidence pertaining to a number of government accounting and financial variables of relative influence in determining U.S. foreign direct investment in the OPEC countries. With respect to accounting variables, the investor should be cognizant that government policies change with time. These governments are not subject to external evaluation and monitoring with respect to their budgets, making it impractical to determine whether the budget surplus/deficit is overstated (understated). One noticeable condition is a change in government corporate tax policy and earnings repatriation. Changes in these OPEC policies can either stimulate or stifle investment flow within a year or more. As with any empirical study, there are limitations. This study is limited to the determinants of U.S. equity investment in the OPEC countries. Foreign direct investment from other countries may have differing responses to the accounting and financial factors found significant here. With respect to U.S. FDI flow, we have been unable to disaggregate export-oriented investment flow and foreign direct investment specifically designed to serve host country petroleum resource exploration.

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According to Loree and Gusinger (1995), the separation of investments into export-oriented investments and foreign direct investment is vital due to host country tariff, custom, and excise policies on exports and imports. Further, host country wage rate, income level, and population size are not significantly related to investment flow. The evidence in this study suggests that the crucial factors that influence U.S. FDI flow to OPEC countries are factors that permit earnings repatriation, promote stable economic growth and development. Despite the robustness of the test results, most of the variables are based on estimates; hence, in third-world countries, data for empirical research are hard to operationalize. However, test results corroborate H1 and H2 that accounting and non-public policy variables are key determinants of investment flow. Another study is necessary to test and accentuate the relevance of government accounting variables in influencing investment flow in non-OPEC countries. Finally, the normative desire of government bureaucrats to manipulate accounting numbers and create fiscal illusion should be considered when interpreting the implications of the findings. NOTES

1.

Foreign direct investment (FDI) is an investment in which a resident of one country obtains a lasting interest in, and possess a degree of influence over the management of a business enterprise in another country. In the U.S., the criterion used to distinguish U.S. direct investment abroad (USDIA) from other types of investment is the 10 percent ownership of a foreign firm.

2.

Cultural distance may be defined as the cultural differences among nations which create potential investment uncertainty between the home and host countries. In general, culture represents the “set of attitudes and values that are common to a group of people” (Benito and Gripsrud 1991).

3.

It is evident that the unprecedented rise in oil prices in 1973 due to the oil embargo induced U. S. foreign direct investment (FDI) flow in OPEC countries to secure supplies of petroleum resources..

4.

The worldwide coordination of resources by a single centralized management distinguishes MNEs from other firms engaged in international business activities. From a financial theory standpoint,


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MNEs have the ability to shift money and earnings among their affiliated firms by internal transfer mechanisms. 5.

Political stability measures may include the rate of changes of government, the level of violence associated with governance, the number of armed insurrections, military conflicts with other states, and religious and ethnic strife.

6.

Other OPEC member countries are Nigeria, Libya, Algeria, Qatar, Ecuador, and Gabon.

7.

This ratio is computed by dividing proven recoverable petroleum reserves for a given year by production for the same year. The interpretation of the ratio is that reserves exist for “Y” number of years if the production rate remains constant (i.e., continues at the current rate of drilling and refining). For a detailed discussion of world-wide petroleum ratio, see OPEC (1994)..

8.

The natural log of FDI was used to transform the dependent variable since the Kolmogorov goodness-of-fit-test for normality rejected the normality assumption at .05 level of significance. Besides, OLS regression results exhibit nonrandom pattern in plots of residual against predicted value when FDI (untransformed) is employed as the dependent variable. Upon transformation, the Kolmogorov test fails to reject the normality assumption at .05 level, and the residual plots show a linear pattern.

9.

To lessen departures of residual errors from normality in the estimated regression model, the log of average daily barrels of crude petroleum production is used. Other variables are not transformed because they are not highly skewed, and also some of the data have negative values. They were also not transformed in order to maintain consistency with prior studies that employ some of the variables in model 2.

10. The Annual OPEC Statistical Bulletin is published annually by the OPEC Secretariat, headquartered in Vienna Austria. The Bulletin includes all information on OPEC oil production. 11. The International Financial Statistics is a publication of the IMF, which includes information on most central government financial and nonfinancial data 12. We examined for lag structure effect on the DEVAL variable, and the result is consistent with the result reported here

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REFERENCES Agodo, O. (1978, March), “The Determinants of U.S. Private Manufacturing Investment in Africa,” Journal of International Business Studies, 9: 95107. Benito, G. R. G., and Gripsrud, G. (1991), “An Expansion of Foreign Direct Investment: Discrete Rational Location Choices or a Cultural Learning Process?” Journal of International Business Studies, 23, 3: 461-76. Boatwright, B. D., and Renton, G. A. (1975, November), “Analysis of United Kingdom Inflows and Outflows of Direct Foreign Investment,” Review of Economics Statistics, 57: 478-86. Caves, R. E. (1996), Multinational Enterprise and Economic Analysis, Cambridge: Cambridge University Press. Clark, D. P., Sawyer, W. C., and Sprinkle, R. L. (1989, May), “Determinants of Industry Participation Under Offshore Assembly Provisions in the U.S. Tariff Code,” Journal of World Trade, 23: 123-30. Contractor, F. (1993), Do Government Policies Towards Foreign Direct Investment Matter? An Empirical Investigation of the Link Between National Policies and FDI Flows (UNCTC Current Series A, No. 21), New York: United Nations. Danielsen, A. L. (1982), The Evolution of OPEC, New York: Harcourt Brace Jovanovich. Fatouros, A. A. (1990), “The Code and the Uruguay Round Negotiation on Trade in Services,” Centre Reporter, 29: 7-15. Goldsbrough, D. J. (1979, December), “The Role of Foreign Direct Investment in the External Adjustment Process,” IMF Staff Paper, 26: 725-54. Guisinger, S. and Associates (1985), Investment Incentives and Performance Requirements, New York: Praeger. Hymer, S. A. (1960), The International Operations of National Firms: A Study of Direct Foreign Investment (Ph.D. dissertation), Cambridge, MA: MIT Press.


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International Monetary Fund (1994), International Financial Statistics Yearbook, XLVII, Washington, DC: Author. Kennedy, P. (1992), A Guide to Econometrics, 3rd ed., Cambridge, MA: The MIT Press. Kim, W. S., and Lyn, E. (1986, January), “Excess Market Value, the Multinational Corporation and Tobin’s Q-ratio,” Journal of International Business Studies, 17: 119-125. Kirkpatrck, C., and Yamin, M. (1981, April), “The Determinants of Export Subsidiary Formation by U.S. Transnationals in Developing Countries: An Inter-industry Analysis,” World Development, 9: 373-82. Lecrew, D. J. (1991), “Factors Influencing Foreign Direct Investment by Transnational Corporations in Host Developing Countries: A Preliminary Report,” in P. J. Buckley and J. Clegg (Eds.), Multinational Enterprise in Less Developed Countries, New York: St. Martin’s Press. Lee, J. (1986, December), “Determinants of Offshore Production in Developing Countries,” Journal of Development Economics, 20: 1-13. Loree, D., and Guisinger, S. E. (1995, February), “Policy and Non-policy Determinants of U.S. Equity Foreign Investment,” Journal of International Business, 32: 281-299. Moxon, R. W. (1975, January), “The Motivation for Investment in Offshore Plants: The Case of the U.S. Electronics Industry,” Journal of International Business Studies, 6: 51-65. Neter, J., Wasserman, W., and Kutner, M. H. (1989), Applied Linear Regression Models, 2nd ed., Homewood, IL: Irwin. Nigh, D. (1985, January), “The Effect of Political Events on the United States Direct Foreign Investment: A Pooled Time-Series Cross-Sectional Analysis,” Journal of International Business Studies, 16: 1-17. Organization of Petroleum Exporting Countries (1993), Annual Statistical Bulletin, Vienna, Australia: Author. Root, F. R., and Ahmed, A. A. (1978, March). “The Influence of Policy Instruments on Manufacturing Direct Investment in Developing Countries,” Journal of International Business Studies, 9: 81-93.

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Robbins, S. M., and Stobaugh, R. B. (1973), Money in the Multinational Enterprise: A Study of Financial Policy, New York: Basic Books. Schneider, F., and Bruno, F. S. (1985), “Economic and Political Determinants of Foreign Direct Investment,” World Development, 13, 2: 161-175. Shapiro, A. C. (1992), Multinational Financial Management, 4th ed., Boston, MA: Allyn and Bacon. Suranovic S. M. (1994, July), “Import Policy Effect on the Optimal Price,” Energy Journal, 15: 123-144. U.S. Bureau of Economic Analysis, Department of Commerce (1993), U.S. Direct Investment Abroad: Operation of U.S. Parent Companies and their Affiliates, Washington, DC: Author. Vernon, R. (1977), Storm Over the Multinationals: The Real Issue, Cambridge, MA: Harvard University Press. Woodward, D., and Rolfe, R. J. (1993, January), “The Location of ExportOrientated Foreign Direct Investment in the Caribbean Basin,” Journal of International Business Studies, 24: 121-141.


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APPENDIX A

Leading Petroleum Corporations in the United States, 1990 _______________________________________________________________ _ Firm Name (Head-Office) Net Revenues Rank among (Billions of U.S. All U.S. Firms Dollars) ---------------------------------------------------------------------------------------------1 Exxon (New York) 103.1 1 2 Mobil (New York) 59.5 2 3 Texaco (Harrison, New York) 51.2 4 4 Standard Oil of California (San Francisco) 40.5 5 5 Gulf Oil (Pittsburgh) 26.5 7 6 Standard Oil of Indiana (Chicago) 26.1 9 7 Atlantic Richfield (Los Angeles) 23.7 11 8 Shell Oil (Houston) 19.8 12 9 Conoco (Stanford, Connecticut) 18.3 14 10 Philips Petroleum (Oklahoma) 13.4 16 11 Tenneco (Houston) 13.2 17 12 Sun (Radner, Pennsylvania) 12.9 18 13 Occidental Petroleum (Los Angeles) 12.5 20 14 Standard Oil (Cleveland) 11.0 23 15 Getty Oil of California (Los Angeles) 10.2 26 16 Union Oil of California (Los Angeles) 10.0 28 17 Marathon Oil (Findley, Ohio) 8.2 39 18 Ashland Oil (Russell, Kentucky) 8.1 40 19 Amerada Hess (New York) 7.9 43 _______________________________________________________________ Source: “The 500 Largest Industrial Corporations,” Fortune, May 1991.

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APPENDIX B

Correlation Matrix of Independent Variables and FDI in Equation 1 (N=60) _______________________________________________________________ _ Panel A. Equation 1 FDI BUSD CUDVAL DEBSER FORSER GOTY AVBAR --------------------------------------------------------------------------------------------------------FDI

1 -

-0.152 0.172 BUSA -0.152 1 0.172 CUDVAL 0.222 -0.107 0.081 0.253 DEBSER -0.034 0.491 0.417 0.001 FORSER 0.726 -0.292 0 0.032 GOTY -0.009 0.392 0.477 0.006 AVBAR 0.312 -0.552 0.024 0

0.222 0.081 -0.107 0.253 1 -0.468 0.001 0.317 0.022 -0.064 0.346 0.230 0.074

-0.034 0.417 0.491 0.001 - -0.468 0.001 1 -0.38 0.007 0.267 0.046 0.545 0

0.726 0 -0.292 0.032 0.317 0.022 -0.380 0.007 1 -0.248 0.059 0.590 0

-0.009 0.477 0.392 0.006 -0.064 0.346 0.267 0.046 0.059 0.590 1 -0.614 0

0.312 0.024 -0.552 0 0.230 0.074 -0.545 0 0 0 0 -0.614 1 -

Panel B. Equation 2 FDI GDP GDPC POP CORTA WARA COSCA --------------------------------------------------------------------------------------------------------FDI

1 -

GDP

0.858 0 0.858 1

0 0.074 GDPC

-0.167 0.128 -0.159 -

-0.167 0.128 0.531

0.531 0 0.583 0.140

0.165 0.131 -0.014 0

0.398 0.003 0.522 0.463

-0.146 0.162 -0.212 0

-0.159 1 -0.504 -0.482 0.138 -0.632 0.140 0 0 0.175 0 POP 0.583 -0.504 1 -0.777 0.277 0.187 0 0 0 0.115 0.028 0.101 CORTA 0.165 -0.014 -0.482 -0.177 1 -0.150 0.238 0.131 0.463 0 0.115 0.155 0.052 WARA 0.398 0.522 0.138 0.277 -0.150 1 -0.502 0.003 0 0.175 0.028 0.155 0 COSCA -0.146 -0.212 -0.632 0.187 0.238 -0.502 1 0.162 0.074 0 0.101 0.052 0 _______________________________________________________________________