Skip to main content

Does foreign direct investment polarize regional earnings? Some evidence from Israel


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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11


  1. CBS: Local Authorities in Israel 2010

  2. CBS (Time Series-DataBank): Balance Of Payments-International Investment Position-Assets and Liabilities.


  • Ascani, A., Gagliardi, L.: Inward FDI and local innovative performance: an empirical investigation on Italian provinces. Rev. Reg. Res. 35, 29–47 (2015)

    Article  Google Scholar 

  • Banerjee, A., Carrion-I-Silvestre, J.L.: Testing for Panel Cointegration Using Common Correlated Effects Estimators, Department of Economics Discussion Paper, pp. 11–16. University of Birmingham, Birmingham (2011)

    Google Scholar 

  • Bartik, T.J.: Who Benefits from State and Local Economic Development Policies?. W.E. Upjohn Institute for Employment Research, Kalamazoo (1991)

    Book  Google Scholar 

  • Beenstock, M., Ben Zeev, N., Felsenstein, D.: Capital deepening and regional inequality: an empirical analysis. Ann. Reg. Sci. 47, 599–617 (2011)

    Article  Google Scholar 

  • Berman, E.: Sect, subsidy and sacrifice: an economist’s view of ultra-orthodox Jews. Q. J. Econ. 115(3), 905–953 (2000)

    Article  Google Scholar 

  • Blomstrom, M., Kokko, A.: Multinational corporations and spillovers. J. Econ. Surv. 12, 247–277 (1998)

    Article  Google Scholar 

  • Bornschier, V., Chase-Dunn, C.: Transnational Corporations and Underdevelopment. Praeger, New York (1985)

    Google Scholar 

  • Choi, C.: Does foreign direct investment affect domestic income inequality? Appl. Econ. Lett. 13, 811–814 (2006)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Coughlin, C., Segev, E.: Foreign direct investment in China: a spatial econometric study. World Econ. 23(1), 1–23 (2000)

    Article  Google Scholar 

  • Dickey, D.A., Fuller, W.A.: Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 1057–1072 (1981)

    Article  Google Scholar 

  • Driffield, N.L., Munday, M., Roberts, A.: Inward investment, transactions linkages, and productivity spillovers. Pap. Reg. Sci. 83(4), 699–722 (2004)

    Google Scholar 

  • Driffield, N.L., Taylor, K.: Earnings spillovers, inter-regional effects and the impact of inward investment. Spat. Econ. Anal. 1(2), 187–205 (2006)

    Article  Google Scholar 

  • Feenstra, R.C., Hanson, G.H.: Foreign direct investment and relative wages: evidence from Mexico’s Maquiladoras. J. Int. Econ. 42, 371–93 (1997)

    Article  Google Scholar 

  • Figinia, P., Gorg, H.: Does foreign direct investment affect earnings inequality? An empirical investigation. World Econ. 34(9), 1455–1475 (2011)

    Article  Google Scholar 

  • Friedberg, R.M.: The impact of mass migration on the Israeli labor market. Q. J. Econ. 116(4), 1373–1408 (2001)

    Article  Google Scholar 

  • Fu, X.: Limited linkages from growth engines and regional disparities in China. J. Comp. Econ. 32, 148–164 (2004)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Hamilton, J.: Time Series Analysis. Princeton University Press, Princeton (1994)

    Google Scholar 

  • Hedges, L.V., Olkin, I.: Statistical Methods for Meta-analysis. Academic Press, San Diego (1985)

    Google Scholar 

  • Hellerstein, J.K., Neumark, D.: Sex, wages, and productivity: an empirical analysis of Israeli firm-level data. Int. Econ. Rev. 40, 95–123 (1999)

    Article  Google Scholar 

  • Im, K., Pesaran, M.H., Shin, Y.: Testing for unit roots in heterogeneous panels. J. Econom. 115(1), 53–74 (2003)

    Article  Google Scholar 

  • Kessing, S.G., Konrad, K.A., Kotsogiannis, C.: Foreign direct investment and the dark side of decentralization. Econ. Policy 22(49), 5–70 (2007)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Li, H., Maddala, G.S.: Bootstrapping cointegrated regressions. J. Econom. 80, 297–318 (1997)

    Article  Google Scholar 

  • Madariaga, N., Poncet, S.: FDI in Chinese cities: spillovers and impact on growth. World Econ. 30(5), 837–862 (2007)

    Article  Google Scholar 

  • Markusen, J.R., Venables, A.J.: Multinational firms and the new trade theory. J. Int. Econ. 46, 183–203 (1998)

    Article  Google Scholar 

  • 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).

  • Moretti, E.: Local multipliers. Am. Econ. Rev. Pap. Proc. 100, 272–377 (2010)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Pesaran, M.H.: A simple panel unit root test in the presence of cross-section dependence. J. Appl. Econom. 22, 265–312 (2007)

    Article  Google Scholar 

  • Phillips, P.C.B., Moon, H.: Linear regression limit theory for nonstationary panel data. Econometrica 67(5), 1011–1057 (1999)

    Article  Google Scholar 

  • Romer, P.: Endogenous technological change. J. Polit. Econ. 98, S71–102 (1990)

    Article  Google Scholar 

  • Sjöholm, F.: Productivity in Indonesia: the role of regional characteristics and direct foreign investment. Econ. Dev. Cult. Change 47, 559–584 (1999)

    Article  Google Scholar 

  • Sun, H.: Foreign direct investment and regional export performance in China. J. Reg. Sci. 41(2), 317–36 (2001)

    Article  Google Scholar 

  • Taylor, K., Driffield, N.: Earnings inequality and the role of multinationals: evidence from UK panel data. Labour Econ. 12, 223–49 (2005)

    Article  Google Scholar 

  • Tsai, P.L.: Foreign direct investment and income inequality: further evidence. World Dev. 23, 469–83 (1995)

    Article  Google Scholar 

  • Wei, K., Yao, S., Liu, A.: Foreign direct investment and regional inequality in China. Rev. Dev. Econ. 13(4), 778–91 (2009)

    Article  Google Scholar 

  • Wren, C., Jones, J.: FDI location across British regions and agglomerative forces: a Markov analysis. Spat. Econ. Anal. 7(2), 265–286 (2012)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Daniel Felsenstein.


Appendix 1: Gini

The Gini counterparts to Eqs. (2), (3) and (7) are:

$$\begin{aligned} G_{\ln w}= & {} \left[ G_\alpha ^2 +\beta ^{2}G_{\ln k}^2 +\gamma ^{2}G_X^2 +G_u^2 +\beta \gamma G_{\ln k} G_X (\Gamma _{\ln k,X} +\Gamma _{X,\ln k} )\right] ^{\frac{1}{2}} \quad \quad \end{aligned}$$
$$\begin{aligned} G_{\ln k}= & {} \left[ G_{\ln K}^2 +G_{\ln L}^2 -G_{\ln K} G_{\ln L} (\Gamma _{\ln K,\ln L} +\Gamma _{\ln L,\ln K} )\right] ^{\frac{1}{2}} \end{aligned}$$
$$\begin{aligned} G_{\ln K_t }= & {} \left[ G_\phi ^2 +(\ln { KFDI}_t )^{2}G_\theta ^2 +G_{\pi Z}^2 +G_v^2 \right] ^{\frac{1}{2}} \end{aligned}$$

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:

$$\begin{aligned} \frac{\partial G_{\ln w} }{\partial \ln \textit{KFDI}_t }=\frac{\beta ^{2}\ln \textit{KFDI}_t G_\theta ^2 \left[ 1-\frac{1}{2}(\Gamma _{\ln K,\ln L} +\Gamma _{\ln L,\ln K} )G_{\ln L} /G_{\ln K} \right] }{G_{\ln w} G_{\ln k} } \end{aligned}$$

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. 1.

    Estimate by OLS:

$$\begin{aligned} y_t =a+bx_t +u_t \end{aligned}$$

where the DGPs for xand u are:

$$\begin{aligned} \Delta x_t= & {} c+w_t \end{aligned}$$
$$\begin{aligned} u_t= & {} eu_{t-1} +v_t \end{aligned}$$
  1. 2.


$$\begin{aligned} x_t^*= & {} c+x_{t-1}^*+w_t^*\end{aligned}$$
$$\begin{aligned} u_t^*= & {} eu_{t-1}^+ +v_t^*\end{aligned}$$

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

  1. 3.


$$\begin{aligned} y_t^*=a+bx_t^*+u_t^*\end{aligned}$$
  1. 4.

    Estimate by OLS:

$$\begin{aligned} y_t^*=\alpha +\beta x_t^*+\omega _t \end{aligned}$$
  1. 5.

    Repeat steps 2–4 10,000 times.

  2. 6.

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

  3. 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:

$$\begin{aligned} y_{it}^*=\alpha _i +\beta x_{it}^*+\omega _{it} \end{aligned}$$

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:

$$\begin{aligned} y_{it}= & {} \alpha _i +\gamma _i t+\lambda _i y_{it-1} +v_{it} \end{aligned}$$
$$\begin{aligned} \Delta y_{it}= & {} \phi _i +w_{it} \end{aligned}$$

If \(\gamma _{I}\) is less than one in absolute value y\(_{i}\) is trend stationary. The nested test statistic (Dickey and Fuller 1981) is:

$$\begin{aligned} DF_i =\frac{(ESS_{wi} -ESS_{vi} )(T-3)}{2ESS_{vi} } \end{aligned}$$

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:

$$\begin{aligned} M=-2\sum _{i=1}^N {\ln pv_i } \sim \chi _{2N}^2 \end{aligned}$$

If Mexceeds its critical value the null hypothesis of difference stationarity is not rejected.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • FDI
  • Regional inequality
  • Capital–labor ratio
  • Common correlated effects estimator
  • Regional earnings decomposition

JEL Classification

  • C23
  • R12
  • R53