Chapter 3: Macro Trends, FDI Location Choice & Sister City Relationships
3.1 Dunning’s OLI (Location Specific) Variables:
3.1 Dunning’s OLI (Location Specific) Variables:
First, let’s review the outcome of Dunning’s variables. Although Dunning provides the basis for each variable choice, our selection was also limited by data access, and therefore has followed in a manner in which we were best able to collect indicators for each determinant. Several authors have already utilized Dunning’s work and tested the variables in their own studies. However, our variable for GOVQ (quality of government) was primarily tested in order to evaluate the relevance of a variable that would traditionally be measured at the “country” level. Since our research is exclusively concerned with local government policy, however, it was important to determine the relevance of such a variable when the project parameters have been scaled down. The measurement of government quality, air pollution (suspended particulates per. cm.
squared), was not found to be significant; however, the direction of the correlation was in agreement with our prediction. As air pollution decreases, investment levels should grow.
Although this is not surprising, it has been worthwhile to test as it has been found to be significant at the national level. According to Dunning & Lundan, the MNE is more often searching for good governance and environmentally responsible locations.24 The failure of GOVQ as a variable is likely due to the perception of a firm entering the Taiwanese market. The island is relatively small, and therefore local and national government quality is highly correlated, especially as it relates to environmental quality. The time frame of our study may have also played an important role in the outcome of this variable. Since our data on regional government is more comprehensive (beginning in 1975) than the statistics related to city-level investment, we are able to see a leveling off
24 Dunning & Lundan. (2006). Multinational Enterprises and the Global Economy.
Edward Elgar Publishing. Cheltenham. UK.
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eventually leading to a variance of less than 10% of the variance in previous decades.Our next variable, MKTS (market size), is one that is proven relevant time and again by studies specifically aimed at explaining regional FDI dispersion.25 Our measurement of market size was an annual figure indicating average family income for citizens living in each municipality. In Taiwan, despite the geographic proximity of each city, the variance of income is quite substantial. This particular variable was in-fact, significant and followed the expected sign. The exponential B output indicates that for every 100,000 New Taiwan Dollars (NTD) increase in the annual average family income of a municipality, the odds of receiving a new foreign investment project will increase by an additional 1.5%.
Apart from the variable related to sister cities in a given county; the strategic asset-seeking variables have performed the best. The Infrastructure (INFRA) variable was proven significant in the earlier work of Glickman and Woodward.26 The inclusion of such a variable can be accounted for by studies carried out by Barrell and Pain and have often followed a similar vector as our study in public sector spending on transportation.27 In the case of infrastructure spending, our model shows that for every additional $1 bil.
NTD of local government spending on transportation, the odds of investment from a
25 Coughlin, C., Terza, J. and Arromdee, V. (1991) State characteristicsand the location of foreign direct investment in the United States, The Review of Economics and
Statistics, 73(4), 675–83.
26 Glickman, N. and Woodward, D. (1988) The location of foreign direct investment in the United States: patterns and determinants, International Regional Science Review, 11(2), 137–54.
27 Barrell, R. and Pain, N. (1999): Domestic institutions, agglomerations and foreign directinvestment in Europe. European Economic Review, 43, pp. 925–934.
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multinational firm increase by 1.03%. The measure for industrial agglomeration or clustering has also been consistently relevant in the literature on regional FDI attraction.28 The measurement given was a labor registration for those working in a certain industry in each city. The expectation was that, the presence of more workers in the industry of concern would lead to a higher number of investments from those companies participating in that specific industry. Our results show this to be true, as each increase in relevant labor count of 1000 persons, the odds of investment grew by 1%.
Finally, the efficiency seeking variable in our study, AVGC, measured the average cost for rent and utilities per household. Although proven to be explanatory in previous studies, the costs associated with a specific community were not found relevant in our model.29. Despite a negative sign, as expected, the correlation is totally insignificant. This could be for two reasons. One, perhaps our model is improperly using rent and utility cost, where former studies have used tariff and labor cost indices. Second, Taipei was overwhelmingly chosen as a destination over New Taipei, Taichung and Kaohsiung, despite a significant cost increase, 571 out of 734 projects.
28 Feenstra, R.C. and G.H. Hanson (1997). Foreign Direct Investment and Relative Wages. Journal of International Economics 42 (3-4): 371-393.
29 Baldwin, R. and Okubo, T. Networked fdi: Sales and sourcing patterns of japanese foreign affiliates. The World Economy, 2013.
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Table 3: Statistical Findings where the Dependent Variable is “Choice to Invest”
Category Variable Coefficient (z-stat) Exp(B)
Location Specific
Total Number of Cases 734 Notes: *, **, and *** indicate significance at the .1, .05, and .01 confidence level respectively. Model LR value =1085.28