We use data in the USA to study the effect of inward Foreign Direct Investment (FDI) in different sectors/industries on the state-level human capital, measured by the average years of tertiary schooling. We find that inward manufacturing FDI tends to lower the tertiary schooling in a host state while information FDI increases the tertiary schooling in a host state

Wang, Miao

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Marquette Universitye-Publications@MarquetteEconomics Faculty Research and Publications Economics, Department of1-1-2011FDI and human capital in the USA: is FDI indifferent industries created equal?Miao WangMarquette University, grace.wang@marquette.eduAccepted version. Applied Economics Letters, Vol. 18, No. 2 (2011): 163-166. DOI. © 2011 Taylor &Francis. Used with permission.                                                                Marquette Universitye-Publications@MarquetteEconomics Faculty Research and Publications/College of BusinessAdministrationThis paper is NOT THE PUBLISHED VERSION; but the author’s final, peer-reviewed manuscript.The published version may be accessed by following the link in the citation below.Applied Economics Letters, Vol. 18, No. 2, (2011): 163-166. DOI. This article is © Taylor & Francis(Routledge) and permission has been granted for this version to appear in e-Publications@Marquette. Taylor & Francis (Routledge) does not grant permission for this articleto be further copied/distributed or hosted elsewhere without the express permission fromTaylor & Francis (Routledge).FDI and human capital in the USA: is FDI in different industries created equal?Miao WangDepartment of Economics, Marquette University, Milwaukee, WIAbstractWe use data in the USA to study the effect of inward Foreign Direct Investment (FDI) in different sectors/industries on the state-level human capital, measured by the average years of tertiary schooling.We find that inward manufacturing FDI tends to lower the tertiary schooling in a host state whileinformation FDI increases the tertiary schooling in a host state.I. IntroductionThe USA has been the single largest recipient country of Foreign Direct Investment (FDI)in the world for decades. According to the Bureau of Economic Analysis (BEA), theinward FDI stock in the USA was $2384 billion in 2006, up from $430 billion in 1990.Foreign firms have a direct investment presence in every single state in the USA, andthey are often sought after by state and local governments for creating additional jobs                                                                   in their localities. For instance, the state of Alabama provided $300 million as incentivesto attract Mercedes-Benz AG in the early 1990s. In 2003 the state government ofMississippi offered an incentive package of $363 million to Nissan Motor Company foropening an automotive manufacturing plant in Canton, Mississippi (Canagaretna, 2004).The unprecedented magnitude of FDI has generated substantial work on why suchinvestment comes to the USA as well as on how foreign firms choose their locations.Curiously, the literature on the effects of inward FDI in different states/regions in theUSA is still sparse, though bidding wars often occur among state governments to ‘win’foreign firms.1Little evidence exists on the impact of FDI on local communities.Exceptions include Blonigen and Figlio (2000), Greenstone and Moretti (2003) andFord et al. (2008). Blonigen and Figlio (2000) use South Carolina country-level data anddiscover that foreign presence leads to an increase in local wages, but a decline in percapita public education expenditures. Greenstone and Moretti (2003) use country-level data to study the effect of FDI on local labour earnings, property values and publicfinance. The authors do not find any evidence indicating that local governments'subsidies to foreign firms will reduce residents' welfare. Ford et al. (2008) investigate the growth effect of inward FDI at the state level and argue that an individual stateneeds to reach a certain threshold level of college education to benefit from FDI.Complementing the existing studies, we examine the impact of sectoral FDI on the human capital level in each state in the USA, measured by average years of tertiaryschooling (college and above). Barba Navaretti and Venables (2004) find that foreignaffiliates in the USA are larger and tend to be more productive than US domestic firms.Similarly, Blomstrom and Kokko (2003) point out that multinational corporations canprovide ‘attractive employment opportunities to highly skilled graduates … which maybe an incentive for gifted students to complete tertiary training’ (p. 12). Foreign firmsmay have a different impact on communities than domestic firms. In addition, inwardFDI is likely to affect the skill-bias of labour demand and provide incentives forindividuals to receive higher education.We use annual data at the state level over the time period of 1997–2004. Interestingly,our results suggest that manufacturing FDI tends to decrease average level of humancapital in a state. In contrast, FDI in information industries leads to an increase in statehuman capital. FDI in other industries does not seem to have a significant impact onstate-level human capital.The rest of our article proceeds as follows: Section II presents data and variables. We discuss empirical results in Section III and conclude in Section IV.                                                                                          H;1 = /Jo+ "2:, {JjFDl;it + ,.,Z;, + e;, j= l (I) II. Empirical Model and DataWe estimate a reduced form regression as follows: where subscripts i, j and t represent state, industry and year, respectively; H is humancapital measured by average years of tertiary schooling; FDI represents inward FDI; Z is a vector of other control variables.Human capital is calculated based on data from the monthly Current Population Survey(CPS) from the US Census Bureau. We focus on responses regarding the highestdegree/education obtained by adult individuals in the household. Possible responsesrange from less than 1st grade to doctorate degree. The census also provides an estimate of the total adult population providing each response. We then calculate the annual average years of schooling for each different state. As most state laws mandatesecondary school attendance until graduation, we focus on tertiary schooling, which isnot compulsory. In our sample, the mean of average tertiary schooling is 1.41 years.We measure FDI by employment in majority-owned non-bank foreign-affiliates insector j in state i as a share of total employment in state i. Comparing different measures of inward FDI in the USA, Graham and Krugman (1995) assert that the share ofUS work force employed by foreign firms is ‘an arguably better measure of actual foreign control of the US economy’ (p. 16). In addition, there are no publicly available data on FDI which include disaggregated information by both sector and state exceptthe employment data. Foreign affiliates employment data are obtained from Survey of Current Business, published by the BEA, and are available in eightsectors/industries:manufacturing; wholesale trade; retail trade; information; finance an d insurance; real estate and rental and leasing; professional, scientific and technical services; and other industries.We also control for income, government spending on education, poverty, metropopulation and unemployment in our analysis. Income is obtained from BEA andmeasured as logged per capita real gross state product. State/local governments' share of spending on elementary and secondary education and on higher education are fromthe Annual Survey of State and Local Government Finances and Census of Governments,provided by the US Census Bureau. Poverty is the percentage of population in the statebelow the poverty line.Metro population measures the percentage of state populationin metropolitan areas. Unemployment is the state unemployment rate.Metro,poverty and unemployment are obtained from the Bureau of Labor Statistics.                                                                                                                                                   To control for other population characteristics, we include the percentage of African-American population (Black), percentage of Hispanic population (Hispanic) andpercentage of population who are between 25 and 64 (Age). Corresponding data comefrom the Census Bureau. Our final sample covers the time span of 1997–2004.III. Empirical ResultsWe estimate the model using dynamic panel generalized methods of moments, whichcontrols for potential endogeneity in the model. Table 1 reports results of differentspecifications. The coefficients on manufacturing FDI in all tertiary schooling regressionsare negative and significant, which indicates that manufacturing FDI decreases the state human capital level. In contrast, FDI in information sector has a positive and significanteffect on tertiary schooling in all different specifications.Table 1. Generalized methods of moments results for human capital1.1 1.2 1.3 1.4Manufacturing −0.00917*** −0.07516** −0.07958* −0.07549*[0.02941] [0.02836] [0.04254] [0.04427]Wholesale −0.10178 −0.27022* −0.05623 −0.14927[0.12467] [0.14666] [0.19324] [0.20086]Retail 0.09848* 0.01634 0.09621 0.04754[0.05428] 0.07417 [0.07819] [0.06394]Information 0.51225*** 0.39259** 0.52790** 0.49055*[0.17518] [0.18127] [0.21191] [0.24754]Finance −0.28674 −0.37985 −0.29326 −0.25564[0.24229] [0.22885] [0.22939] [0.23207]Real estate −0.32594 −0.05346 −0.23465 −0.00139[0.37956] [0.33613] [0.42915] [0.43851]Professional, scientificand technical−0.07022 −0.10036 −0.10779 −0.08753[0.20902] [0.22822] [0.18633] [0.22518]Other −0.01637 −0.00281 −0.01419 −0.00556[0.04517] [0.03496] [0.04199] [0.04059]Income 0.68077*** 0.82351*** 0.77407*** 0.77458***[0.17388] [0.15358] [0.23581] [0.19699]Poverty −0.01200* −0.01617** −0.01033* −0.0101[0.00694] [0.00696] [0.00658] [0.00745]Age 2.78856 2.07031 0.01709 1.72539[2.17253] [2.01342] [0.03331] [2.77156]Black 0.41051 −0.14908 0.37675 0.04229[0.27560] [0.53012] [0.27792] [0.47196]Hispanic −0.18681 −1.35544* −0.00408 −0.7252[0.59316] [0.77602] [0.00779] [0.69715]                                                                                                                                                        Elementary and secondary education spending−0.51569 −0.26284 −0.00471 −0.38028[0.32371] [0.25796] [0.00343] [0.33561]Higher education spending0.69407* 0.91267** 0.00709** 0.65235[0.35166] [0.36953] [0.00339] [0.39488]Metro 0.60564 0.26044[0.51408] [0.48811]Unemployment 0.00237 0.00162[0.01816] [0.01713]F-stat (prob > F) 24.7(0.000) 45.16(0.000) 20.24 (0.000) 21.28(0.000)Number of observations 290 290 290 290Notes: Robust SEs in brackets.***, ** and * denote significance at 1, 5 and 10% levels, respectively.To be more specific, based on our estimates, a 0.85% (one standard deviation) increase in foreign affiliates' manufacturing employment as a share of total employment tends todecrease the average tertiary schooling in a state by 0.069 years (average acrossdifferent specifications). Given a sample average of 1.412 years for tertiary education,such a change is equivalent to 4.8% of the sample mean. On the other hand, a 0.156%(one standard deviation) increase in information FDI in a state increases the average years of tertiary schooling by 0.082 years, which is equivalent to 5.8% of the sample average. FDI in other sectors/industries does not have a significant impact on the humancapital level in a state.In addition, the coefficient on log income is positive and significant in all regressions.The magnitude of the income coefficient suggests an income elasticity of tertiaryeducation between 0.482 and 0.583, other things constant.3 In other words, a 1% rise inaverage individual income leads to an increase in quantity demanded for tertiaryschooling by 0.482–0.583%. The value of the income elasticity of tertiary schooling alsosuggests that tertiary schooling is a normal good.For the purpose of comparison, we also estimate secondary schooling regressions. The secondary schooling measure includes information on people who only complete 9–12grades without proceeding to college.4 The results are not reported, but available uponrequest. It is interesting to note that manufacturing FDI has a positive and significantcoefficient in secondary schooling regressions. It seems that manufacturing FDIincreases the share of state population that at most obtains a secondary education. Thisis indeed consistent with the negative coefficient on manufacturing FDI in our tertiaryschooling regressions. Income has a negative and significant coefficient in all secondaryschooling regressions. The income elasticity of secondary education (as the highest levelof education) is around –0.2. Intuitively, when state income rises, more people will                                                                                                              choose not to have secondary education as their highest level of formal education.Instead, they will continue with college education or higher, which is also shown in ourtertiary regressions.IV. ConclusionsThis article examines the effect of inward FDI on state-level human capital. Using state-level data over the period of 1997–2004, our estimates find that manufacturing FDIdecreases average years of tertiary schooling in the state receiving the investment. Incontrast, FDI in information industries increases the human capital level measured bytertiary education in the state receiving the investment.Our results help to extend the current understanding of the state-level welfare effect ofinward FDI. However, the results from our study do not necessarily indicate thatmanufacturing FDI will hurt the welfare in a state while information FDI has a netfavourable impact on the welfare in a state. Given that inward FDI can cause manychanges in a state, including changes in employment and state tax revenues, more needs to be done in the future to explore the overall effects of inward FDI.AcknowledgementsThe author is grateful to Brian Brush for his helpful comments and suggestions andacknowledges the financial support from the College of Business Administration MilesResearch Grant at Marquette University.Notes1A recent Toyota plant was sought by 25 states and finally was located in Mississippi in2007 (Arizona Daily Star, 28 February 2008).2There are seven different answers involving tertiary education to the CPS questions. Weassign number of years needed (beyond high school) to obtain the correspondinglevel of tertiary education as follows: some college but no degree (1 year oftertiary education), associate degree in college, occupational/vocationalprogram (2 years), associate degree in college, academic programme (2 years), bachelor's degree (4 years),master's degree (6 years), professional schooldegree (8 years) and doctorate degree (9 years). Suppose we have a state, whichhas 1% of its adult population receiving some college education, but no degree,1% of its adult population receiving an associate degree and so on. Then the average years of tertiary schooling in this state is calculated as 1% * 1 + 1% * 2 + … = …. The number of years assigned to each category is based on                                                                                                            conventional wisdom, and our final results are not sensitive to it. For instance,changing the time to obtain a doctorate degree (beyond high school) from 9 to 10 years or to 8 years does not affect our final estimates qualitatively.3The elasticity is calculated as βincome*(1/schooling) and evaluated at the sample mean.4The mean of the average years of secondary schooling is 1.59 years.ReferencesBarba Navaretti, G. and Venables, A. J. 2004.Multinational Firms in the World Economy, withFrank G. Barry, et al, Princeton and Oxford: Princeton University Press.Blomstrom, M. and Kokko, A. Human capital and inward FDI. CEPR Discussion Paper No.3762. 2003. LondonBlonigen, B. A. and Figlio, D. N. 2000. The effects of foreign direct investment on localcommunities. Journal of Urban Economics, 48: 338–63.Canagaretna, S. 2004. The drive to move south: the growing role of the automobile industry inthe southern states. Spectrum: The Journal of State Government, : 22–4.Ford, T. C., Rork, J. C. and Elmslie, B. T. 2008. Foreign direct investment, economic growth, andthe human capital threshold: evidence from US States. Review of InternationalEconomics, 16: 96–113.Graham, E. M. and Krugman, P. R. 1995. Foreign Direct Investment in the United States, 3rd, Washington: Institute for International Economics.Greenstone, M. and Moretti, E. Bidding for industrial plants: does winning a ‘Million DollarPlant’ increase welfare?. NBER Working Paper No. 9844, National Bureau of EconomicResearch, Inc. 2003.

FDI and human capital in the USA: is FDI in different industries created equal?

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FDI and human capital in the USA: is FDI in different industries created equal?

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