MODELING FDI ATTRACTION: AN OPTIMIZATION APPLICATION
Etienne Musonera
Marketing Department, Wayne State University
310 Prentis
Detroit, Michigan 48202
Phone: (313) 415-2053
Fax: (313) 577-8833
and
Attila Yaprak
Marketing Department, Wayne State University
307 Prentis
Detroit, Michigan 48202
Phone: (313) 577-4842
Fax: (313) 577-5486
and
Leslie Monplaisir
Industrial Engineering Department, Wayne State University
4815 Fourth St.
Detroit, Michigan 48202
Phone: (313) 577-1645
Fax: (313) 577-8833
ABSTRACT
In this paper, we examine political, financial, and economic risk factors that influence the inflow of FDI into host countries. We develop an optimization model anchored in the eclectic theory of international production and show a numerical example of how our model can be employed for decision-making purposes. Our work is based on the location dimension of the eclectic theory and is premised on the notion that risks inherent in the institutional characteristics of a country’s environment determine the level of FDI inflows the country will receive.
Keywords: FDI, economic, financial, political risks, optimization
INTRODUCTION
FDI flows by multinational firms continue to be major engines of economic transformation in both the developed and the developing countries. FDI is typically associated with job creation and growth, technology spillovers, enhanced export performance and therefore a stronger currency, a broadened tax base, and rising incomes and consumer spending. In realization of these benefits, many governments, whether developed or developing, are now in ever-increasing competition to attract and sustain FDI inflows in a manner that is unprecedented in the history of economic development (Lall 1998). While governments race to attract FDI by offering ever more generous incentive packages and justify these actions with the expected externalities to be generated by foreign affiliates, however, research evidence on FDI benefits remains inconclusive (Smarzynska 2002). While there is a rich literature on FDI, for example, on the patterns of FDI, productivity growth (in upstream activities of the value chain) in transition economies, and efficient diffusion and utilization of learning benefits from FDI (Kathuria 2000, Narula 2003), studies on modeling FDI attraction are rare (Akaah and Yaprak 1988). In this paper, we hope to contribute to this discussion by offering an optimization model exemplifying the rational approach FDI donors might take in evaluating prospective FDI environments. In contrast to the modeling literature, which is rich in examples from the developed world, we offer an application of our model on FDI inflows into an African country, Tanzania. This should be viewed a significant contribution for several reasons. First, our model helps rationalize the decision process prospective FDI donors might experience, thereby potentially making their decisions more efficient and effective. Second, we offer insights from the FDI decision process from an African perspective, a lens that has not been used very frequently in FDI research. Finally, the freshness of our model provides opportunities for others to extend it into other contexts, potentially giving it greater universality.
Our paper is organized as follows. After presenting a discussion on the utility of risk in overseas investments, we briefly discuss optimization and optimization models. We then present our model and discuss the merits of its many components. We apply our model to FDI going into Tanzania, and present our results. We follow the interpretation of our results with a discussion of the limitations of our work and our suggestions for future research.
THE SIGNIFICANCE OF RISK IN FDI ATTRACTION
International business transactions carry political, economic, and financial risks that are above and beyond those found in domestic business transactions. The probability that adverse changes might take place in the host country’s business environment, and that these changes may adversely affect operating profits as well as the value of the firm’s assets constrains the free flow of foreign capital into a country. Examples of these changes include legal and regulatory changes, government transitions, environmental and human rights issues, currency crises, and terrorism. While most measures of country risk include a mix of political, financial and socio-economic indicators, we use the international country risk rating (ICRG) developed by Coplin and O’Leary (1994) in our study. A number of influential studies have employed this procedure with ratings that have had a high correlation to actual future equity returns from foreign investments (Harvey et al. 1996; Hall and Jones, 1999). We start from the premise that improvements in these factors, such as the political, financial, and/or economic climate of a country will enhance FDI inflows into that country.
OPTIMIZATION
Optimality is concerned with the modeling of deterministic and probabilistic applications under conditions of limited resources. In this study, we develop a mathematical model that yields an optimal value for composite risk points associated with the political, financial, and economic environment of a country. We calculate the risk points awarded to each of these risk components and total them to a composite risk score for the country. We assume that political risk contributes 50% to final composite risk, while financial and economic risks each contribute 25%. While these proportions might appear arbitrary, they represent the risk situation found in African countries fairly closely. They are also the proportions used by the PRS Group in its risk calculations (PRS, 2005).
We chose to build an optimization model with which to study FDI flows because we felt that such a model will help us frame the FDI problem much more comprehensively, will help reveal important cause-and-effect relationships, and will show us these interrelationships simultaneously. Our model, shown conceptually in Figure 1, is comprised of input and output variables.
Figure 1. The Principal Components of Our Optimization Model
The black box figure shows that composite risk has three sub-indices: political, financial, and economic. A separate index is created for each of the subcategories. The political risk index is based on 100 points, and the financial and economic risks on 50 points, respectively. The total points from the three indices are divided by two to produce the weights for inclusion in the composite country risk score. The composite scores range from zero to 100, and are broken down into three categories, low risk (80 to 100 points), moderate risk (50 to 80 points) and high risk (0 to 50 points).The minimum number of points that can be assigned to each component is zero, while the maximum number of points depends on the fixed weight that that component is given in the overall risk assessment. In every case, the lower the risk point total, the higher the risk, and visa versa. Also in every case, high risk points imply desirable properties for private investors; they indicate impartial, transparent, and trustworthy institutions (they signal low risk). Hence, we want to maximize the composite (political, economic, financial) risk point total in our model application. We now discuss each of the risk factors that we propose are important in FDI decisions, and thus, our modeling effort.
Political Risks
The political environment of the host country can be an important factor in FDI decisions, mainly because FDI involves the transfer of control and resources, and its determinants can be affected by factors that are beyond the firm’s home country boundaries (Bende-Nabende et al. 2000). Schollhammer and Nigh (1984), and Schneider and Frey (1985) have argued that the presence of a political system hospitable to foreign capital in terms of property rights and civil liberties plays a favorable role in attracting FDI. Our assessment of political risk is composed of 12 separate dimensions whose effect we compute independently and sum to a composite score (although we realize that these components can have interactive effects). These dimensions include government stability(GS), socioeconomic conditions (SC), investment profile (IP), internal conflict (IC), corruption in government (C), military in politics (MP), law and order (LO), ethnic tensions (ET), democratic accountability (DA), quality of the bureaucracy (BQ), external conflict (EC), and religion in politics (RT). Higher risk points mean that a country has a transparent, reliable, independent, fair, and equitable judiciary system. Higher risk points are also expected to increase FDI by decreasing instability, and thus investment risk. We now discuss each of these 12 dimensions in greater detail.
Financial Risks
A country’s financial condition plays an important role in attracting foreign capital (Feder and Just 1977, Cline 1984, Callier 1985).The financial risk rating provides a means of assessing a country’s ability to pay its financial and commercial obligations; the larger the proportion of debt, the greater the financial risk, for example. Studies have found that financial risk for the host country can be foretellers of FDI inflows (Callier, 1985). The five financial risk components that are typically used in assessing foreign environments are: foreign debt as a percentage of GDP, foreign debt service as a percentage of the export of goods and services (XGS), current account as a percentage of the export of goods and services (XGS), net liquidity as months of import coverage, and exchange rate stability.
Economic Risks
Economic risk is associated with the overall health of the economy of the country in which the investment is made. It deals with factors associated with traditional measures of fiscal policy, such as inflation, per capita GDP and GDP growth, and budget and current account balances. An economic risk rating provides a means of assessing a country’s economic strengths and weaknesses, and focuses attention on the institutional factors that may affect wealth creation.
METHODOLOGY
Our objective in this paper is to offer an optimization model that will explain the inflow of FDI into host countries. Our optimization model is based on the theoretical model we showed in Figure 1; that is, we view FDI inflows into a country as a function of the degree of political, financial, and economic risks in a country. Our model is linear; it does not account for the possible interactions among the variables we use in our model. It is consistent with the eclectic view of international production where FDI inflows are tied, at least in part, to the location advantages associated with particular environments (Dunning 1998). We now present our model and test it on Tanzanian data we received from the Political Risk Services (PRS) group database. In this context, our model is:
Maximize the composite risk score CRP = 0.5 PRP + .25 FRP + .25ERP
Subject to:
1) PRP = C + BQ + DA + ET + EC + GS + IC + IP + LO + RT + SC + MP ≤ 100
2) FRP = FD+ DS+ CAXGS + ERS+IL ≤ 50
3) ERP = BB+ CAGDP+ RGDPG+ GDPH+ IR ≤ 50
4) C, MP, LO, ET, DA, RT ≤ 6
5) GS, SC, IP, IC, EC ≤ 12
6) QB ≤ 4
10) FD, DS, ERS, IR, RGDPG, BB ≤ 10
11) IL, GDPH ≤ 5
12) CAXGS, CAGDP ≤ 15
13) PRP + FRP + ERP ≤ 200
14) All Variables ≥ 0
Table 1: Optimal Solution
Lower Limit / Current Risk Points Value Country X (Tanzania) / Upper Limit / Reduced CostComposite Risk Points / CRP / 100
Political Risk Points / PRP / 0 / 63.1 / 100
Corruption Risk / C / 0 / 3.1 / 6 / 0
Bureaucracy Quality Risk / BQ / 0 / 0.9 / 4 / 0
Democratic Accountability Risk / DA / 0 / 3.2 / 6 / 0
Ethnic Tensions Risk / ET / 0 / 3.6 / 6 / 0
External Conflict Risk / EC / 0 / 10 / 12 / 0
Government Stability Risk / GS / 0 / 8.1 / 12 / 0
Internal Conflict Risk / IC / 0 / 9 / 12 / 0
Investment Profile Risk / IP / 0 / 6.7 / 12 / 0
Law and Order Risk / LO / 0 / 4.6 / 6 / 0
Religious Tensions Risk / RT / 0 / 5.4 / 6 / 0
Socioeconomic Conditions Risk / SC / 0 / 4.5 / 12 / 0
Military in Politics Risk / MP / 0 / 4.2 / 5.99 / 0
Financial Risk Points / FRP / 0 / 26.4 / 50 / 0
Foreign Debt as % of GDP Risk / FD / 0 / 3.5 / 10 / 0
Debt Service as % Export of Goods & Services / DS / 0 / 7.2 / 10 / 0
Current Account as % of Exports Goods & Services / CAXGS / 0 / 7.7 / 15 / 0
Exchange Rate Stability Risk / ERS / 0 / 7.1 / 10 / 0
International (Net) Liquidity Risk / IL / 0 / 0.1 / 5 / 0
Economic Risk Points / ERP / 0 / 27.8 / 50
Budget Balance as % of GDP / BB / 0 / 6.1 / 10 / 0
Current Account Balance as % GDP (Goods &Services) / CAGDP / 0 / 7.7 / 15 / 0
Real GDP Growth / RGDPG / 0 / 6.6 / 10 / 0
GDP per Capita (head of population) / GDPH / 0 / 1.4 / 5 / 0
Inflation Rate / IR / 0 / 5.8 / 10 / 0
RESULTS
Our application shows that Tanzania’s political risk score is 63, financial risk score is 26 and economic risk score is 28, therefore, following our model, its composite risk score is 58.5/100. While helpful, this score is, of course, less useful in the absence of scores obtained through similar analyses in other countries with which Tanzania would compete in its FDI attraction efforts. For instance, if Uganda and Kenya were to score 40 and 70, respectively, we would conclude, with a reasonable degree of certainty that rational FDI donors would prefer Tanzania over Uganda as an investment site, but would like Kenya over Tanzania, and certainly when compared to Uganda. The reliability of our results would be enhanced with comparisons of this type, but we would need to conduct other analyses in order to develop a more complete decision situation. Among the analyses we might conduct is sensitivity analysis, typical in modeling applications. This involves examining what might happen when data values are changed; that is, how our optimal solution might be affected by the changes, within specified ranges, in the objective function coefficients and right hand side values. Since FDI donors operate in uncertain environments, taking many possibilities into consideration can be a critical element in modeling FDI inflow decisions. One of the interesting sensitivity analyses that can be conducted might involve shadow pricing, a procedure conducted typically in linear modeling contexts.
DISCUSSION AND CONCLUSION
In this paper, we developed an optimization model that can be used by decision-makers in multinational firms in making FDI decisions. We then showed an application of this model with Tanzanian data. This application showed that Tanzania was a relatively attractive country to receive FDI inflows, though we acknowledged that its relative attractiveness would require comparisons of its optimal solution to other countries with which it might be in competition to attract FDI. These countries might include Uganda and Kenya in Tanzania’s immediate vicinity, but also other African countries, such as Madagascar, Mozambique, and South Africa. We also talked about how sensitivity analysis, including shadow pricing, might help us arrive at a clearer picture of whether, and the extent to which, Tanzania might be a viable FDI option for prospective investors.