Abstract
This paper investigates the polarizing effect of FDI on regional earnings in host nations. A key hypothesis is that the link between FDI and regional inequality is mediated by regional capital–labor ratios. In the absence of regional FDI data, a method for estimating the effects of FDI on regional inequality is proposed in which national FDI is hypothesized to be a common factor for regional capital investment. Empirical analysis of regional panel data for Israel shows that regional capital stocks vary directly and heterogeneously with the stock of national FDI, and that regional earnings vary directly and homogeneously with regional capital–labor ratios. These two relationships are used to calculate the contribution of FDI to regional earnings inequality over time. We find a substantial polarizing effect. Between 1988 and 2010 the variance of log regional earnings increased from about 0.011 to 0.025. More than half of this increase may be attributed to the polarizing effects of FDI. Policy implications of these findings are discussed.
This is a preview of subscription content, access via your institution.
Notes
CBS: Local Authorities in Israel 2010 http://www1.cbs.gov.il/webpub/pub/text_page.html?publ=58&CYear=2010&CMonth=1.
CBS (Time SeriesDataBank): Balance Of PaymentsInternational Investment PositionAssets and Liabilities.
References
Ascani, A., Gagliardi, L.: Inward FDI and local innovative performance: an empirical investigation on Italian provinces. Rev. Reg. Res. 35, 29–47 (2015)
Banerjee, A., CarrionISilvestre, J.L.: Testing for Panel Cointegration Using Common Correlated Effects Estimators, Department of Economics Discussion Paper, pp. 11–16. University of Birmingham, Birmingham (2011)
Bartik, T.J.: Who Benefits from State and Local Economic Development Policies?. W.E. Upjohn Institute for Employment Research, Kalamazoo (1991)
Beenstock, M., Ben Zeev, N., Felsenstein, D.: Capital deepening and regional inequality: an empirical analysis. Ann. Reg. Sci. 47, 599–617 (2011)
Berman, E.: Sect, subsidy and sacrifice: an economist’s view of ultraorthodox Jews. Q. J. Econ. 115(3), 905–953 (2000)
Blomstrom, M., Kokko, A.: Multinational corporations and spillovers. J. Econ. Surv. 12, 247–277 (1998)
Bornschier, V., ChaseDunn, C.: Transnational Corporations and Underdevelopment. Praeger, New York (1985)
Choi, C.: Does foreign direct investment affect domestic income inequality? Appl. Econ. Lett. 13, 811–814 (2006)
Chintrakarn, P., Herzer, D., Peter Nunnenkamp P.: FDI and income inequality: evidence from a panel of U.S. states. Economic Inquiry 50, 788–801 (2012)
Cheng, L.K., Kwan, Y.K.: What are the determinants of the location of foreign direct investment? The Chinese experience. J. Int. Econ. 51, 379–400 (2000)
Coughlin, C., Segev, E.: Foreign direct investment in China: a spatial econometric study. World Econ. 23(1), 1–23 (2000)
Dickey, D.A., Fuller, W.A.: Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 1057–1072 (1981)
Driffield, N.L., Munday, M., Roberts, A.: Inward investment, transactions linkages, and productivity spillovers. Pap. Reg. Sci. 83(4), 699–722 (2004)
Driffield, N.L., Taylor, K.: Earnings spillovers, interregional effects and the impact of inward investment. Spat. Econ. Anal. 1(2), 187–205 (2006)
Feenstra, R.C., Hanson, G.H.: Foreign direct investment and relative wages: evidence from Mexico’s Maquiladoras. J. Int. Econ. 42, 371–93 (1997)
Figinia, P., Gorg, H.: Does foreign direct investment affect earnings inequality? An empirical investigation. World Econ. 34(9), 1455–1475 (2011)
Friedberg, R.M.: The impact of mass migration on the Israeli labor market. Q. J. Econ. 116(4), 1373–1408 (2001)
Fu, X.: Limited linkages from growth engines and regional disparities in China. J. Comp. Econ. 32, 148–164 (2004)
Haskel, J.E., Pereira, S.C., Slaughter, M.J.: Does inward foreign direct investment boost the productivity of domestic firms? Rev. Econ. Stat. 89(3), 482–496 (2008)
Hamilton, J.: Time Series Analysis. Princeton University Press, Princeton (1994)
Hedges, L.V., Olkin, I.: Statistical Methods for Metaanalysis. Academic Press, San Diego (1985)
Hellerstein, J.K., Neumark, D.: Sex, wages, and productivity: an empirical analysis of Israeli firmlevel data. Int. Econ. Rev. 40, 95–123 (1999)
Im, K., Pesaran, M.H., Shin, Y.: Testing for unit roots in heterogeneous panels. J. Econom. 115(1), 53–74 (2003)
Kessing, S.G., Konrad, K.A., Kotsogiannis, C.: Foreign direct investment and the dark side of decentralization. Econ. Policy 22(49), 5–70 (2007)
Khattab, N., Miaari, S.: The occupational mismatch amongst Palestinians and Jews in Israel: a new evidence from the LFS 2000–2010. Res. Soc. Stratif. Mobil. 34, 1–13 (2013)
Li, H., Maddala, G.S.: Bootstrapping cointegrated regressions. J. Econom. 80, 297–318 (1997)
Madariaga, N., Poncet, S.: FDI in Chinese cities: spillovers and impact on growth. World Econ. 30(5), 837–862 (2007)
Markusen, J.R., Venables, A.J.: Multinational firms and the new trade theory. J. Int. Econ. 46, 183–203 (1998)
Monastiriotis, V., Borrell, M.: Origin of FDI and domestic productivity spillovers: does European FDI have a ‘productivity advantage’ in the ENP countries?’ SEARCH Working Paper, 2/13 (2013). http://www.ub.edu/searchproject/wpcontent/uploads/2013/09/SEARCH_WorkingPaper_2.13.pdf
Moretti, E.: Local multipliers. Am. Econ. Rev. Pap. Proc. 100, 272–377 (2010)
Pedroni, P.: Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bull. Econ. Stat. 61, 653–670 (1999)
Pesaran, M.H.: Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74(4), 967–1012 (2006)
Pesaran, M.H.: A simple panel unit root test in the presence of crosssection dependence. J. Appl. Econom. 22, 265–312 (2007)
Phillips, P.C.B., Moon, H.: Linear regression limit theory for nonstationary panel data. Econometrica 67(5), 1011–1057 (1999)
Romer, P.: Endogenous technological change. J. Polit. Econ. 98, S71–102 (1990)
Sjöholm, F.: Productivity in Indonesia: the role of regional characteristics and direct foreign investment. Econ. Dev. Cult. Change 47, 559–584 (1999)
Sun, H.: Foreign direct investment and regional export performance in China. J. Reg. Sci. 41(2), 317–36 (2001)
Taylor, K., Driffield, N.: Earnings inequality and the role of multinationals: evidence from UK panel data. Labour Econ. 12, 223–49 (2005)
Tsai, P.L.: Foreign direct investment and income inequality: further evidence. World Dev. 23, 469–83 (1995)
Wei, K., Yao, S., Liu, A.: Foreign direct investment and regional inequality in China. Rev. Dev. Econ. 13(4), 778–91 (2009)
Wren, C., Jones, J.: FDI location across British regions and agglomerative forces: a Markov analysis. Spat. Econ. Anal. 7(2), 265–286 (2012)
Zhang, X., Zhang, K.H.: How does globalization affect regional inequality within a developing country? Evidence from China. J. Dev. Stud. 39, 47–67 (2003)
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1: Gini
The Gini counterparts to Eqs. (2), (3) and (7) are:
where \(G_{j}\) denotes the regional Gini coefficient for variable j and \(\Gamma _{ji}\) is the regional Gini correlation coefficient between variable j and variable i. Equation (1.1) assumes that the Gini correlations between k and X and \(\alpha \) and u are zero. Equation (1.3) assumes for simplicity that the variables are independent, in which case their Gini correlations are zero.
The Gini counterpart to Eq. (8) is:
As in Eq. (8) Gini varies directly with KFDI. There is a direct effect and a mitigating effect. The polarizing effect of FDI on regional wage inequality varies directly with \(\beta \), KFDI and inequality in \(\theta \), and it varies inversely with the elasticity of supply of labor as expressed by the Gini correlations between capital and labor. Equations (8) and (1.4) differ insofar as the polarizing effect of FDI does not depend on the variances of wages in the former but it varies inversely with the Gini coefficient for wages in the latter.
Appendix 2: Data
Regional aggregates are constructed using micro data for 1987–2010. Each variable is created from a different source and all data are aggregated to nine regions which form the basic spatial units of analysis (Fig. 1). Variables and their sources are as follows:

Earnings (w) Average earnings in shekels at constant 2005 prices. Source: National Insurance Institute (NII) ‘Wages and Income from Work by Locality and Various Economic Variables’.

Capital (K) Constructed out of regional data for completed construction for plant, and national data for machinery (Beenstock et al. 2011). The physical data for plant have been converted into 2005 prices. Source: Central Bureau of Statistics (CBS)^{Footnote 1}.

FDI stock (KFDI) Published annually by the CBS ($ million)^{Footnote 2} and converted into shekels at 2005 prices.

Regional demographics Population, years of schooling compiled from micro data. Source: Labor Force Survey (LFS).

Regional investment incentives Value (in constant 2005 prices) of investment grants disbursed under Law for Encouragement of Capital Incentives to firms located in preferential areas, accumulated from 1967. Source: Investment Center (annual reports).
Appendix 3: Bootstrapping
When y and x are difference stationary and cointegrated, bootstrapping is carried out in the following steps (Li and Maddala 1997):

1.
Estimate by OLS:
where the DGPs for xand u are:

2.
Construct
where \(v*\) and \(w*\) are drawn with replacement from the EDFs for w and v.

3.
Construct

4.
Estimate by OLS:

5.
Repeat steps 2–4 10,000 times.

6.
The bootstrap estimate of b is the mean of the 10,000 estimates of \(\beta \).

7.
The bootstrap confidence interval for b is based on the percentiles of the distribution function for \(\beta \).
In the case of panel data step 4 is:
where \(v*\) and \(w*\) are drawn from the pooled EDFs and \(c_{i}\) and \(e_{i}\) are heterogeneous.
Appendix 4: Trend stationarity versus difference stationarity
Time series for \(y_{it }\)are used to estimate:
If \(\gamma _{I}\) is less than one in absolute value y\(_{i}\) is trend stationary. The nested test statistic (Dickey and Fuller 1981) is:
where ESS denotes error sums of squares. If DF \(_{i}\) exceeds its critical value the null hypothesis of difference stationarity for y\(_{i}\) is rejected with p value = pv \(_{i}\). The meta statistic (Hedges and Olkin 1985) is:
If Mexceeds its critical value the null hypothesis of difference stationarity is not rejected.
Rights and permissions
About this article
Cite this article
Beenstock, M., Felsenstein, D. & Rubin, Z. Does foreign direct investment polarize regional earnings? Some evidence from Israel. Lett Spat Resour Sci 10, 385–409 (2017). https://doi.org/10.1007/s120760170192z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s120760170192z
Keywords
 FDI
 Regional inequality
 Capital–labor ratio
 Common correlated effects estimator
 Regional earnings decomposition
JEL Classification
 C23
 R12
 R53