دانلود رایگان ترجمه مقاله نابرابری و رشد: شواهد غیرخطی از پنل داده های ناهمگون (نشریه SSRN 2014)

نابرابری و رشد: شواهد غیرخطی از پنل داده های ناهمگون

Inequality and Growth: Nonlinear Evidence from Heterogeneous Panel Data

1 Introduction

Income inequality has been a major concern in development and welfare economics for more than a century. Kuznets (1955) suggests an influential hypothesis that income inequality should follow an inverse-U shape along the development process. Since then, assessing the relationship between economic growth and income inequality has long been an active topic. However, the effects of income inequality on economic growth are still controversial that requires both theoretical and empirical dedication (Aghion et al. (1999), Benabou (1996b), Barro (2000)).

First, Kuznets (1955) and Kaldor (1957) argue that there is a trade-off be- tween reducing income inequality and promoting economic growth. Individual saving rates increase with the level of income. A redistribution of resources from the rich to the poor tends to diminish an aggregate rate of saving in an economy. In this case, a higher level of income inequality would enhance economic growth at least in a transitional sense. In recent studies, Saint-Paul and Verdier (1993), Benabou (1996a), and Galor and Tsiddon (1997a, 1997b) find that income inequality will increase economic growth via human capital strengthening and externality.

Contrary to the argument that supports the positive effects of income inequality, Alesina and Rodrik (1994) and Persson and Tabellini (1994) argue that income inequality could encourage the redistribution through elections, in which redistribution policy could restrict economic growth by reducing an incentive to invest.

These two arguments have been formally tested in many previous studies, but the results are somewhat incoherent. In the early 1990s, the negative relationship between income inequality and economic growth was proven in Alesina and Rodrik (1994) and Persson and Tabellini (1994), which was before the important data collection of Deininger and Squire (1996, 1998). 1 Their data set has a panel structure with several consecutive measures of income inequality for each country. Researchers are able to overcome some biases from country-specific error terms and measurement errors by using a panel data set. For example, Forbes (2000) uses a fixed effects estimation, and provides some interesting findings; in the medium and short-run, an increase in the level of income inequality in a country exhibits a positive and significant relationship with subsequent economic growth rates. 2 In contrast, Barro (2000) uses a three-stage least squares (3SLS) estimator which treats the country-specific error term as a random variable. The results find no relationship between income inequality and economic growth. However, he divides his sample into poor and rich countries, and finds a negative relationship between income inequality and economic growth in a sample of poor countries, but a positive relationship in a sample of rich countries.

Banerjee and Duflo (2003) argue that the lack of consistency in the results is due to the fact that empirical studies estimate a linear model, whereas the true relationship is not linear; the growth rate is an inverted U-shaped function of net changes in income inequality. They further show how this nonlinearity can explain different findings in previous studies. In regard to the nonlinear relationship between income inequality and economic growth, Lin et al. (2009) employ Caner and Hansen (2004)’s instrumental variables threshold regressions approach, and suggest that the effects of income inequality on economic growth are negative and remain strongly significant below the threshold level of development. Above the threshold, the effects of income inequality become highly positive.

This paper explores nonlinear impacts of income inequality on economic growth using a panel smooth transition regression (PSTR) model. Our study is in line with Lin et al. (2009) in a sense that we also estimate the threshold endogenously in the income inequality-economic growth relationship. Contrary to Lin et al. (2009), we control the potential endogeneity problem by employing the panel data structure instead of the cross-sectional data structure, and using lagged explanatory variables as in Forbes (2000). Another distinctive feature is the use of the ‘Standardized World Income Inequality Database (SWIID)’. 3 By using a custom missing-data algorithm, SWIID overcomes the limitations of existing inequality data sets, it thus provides greater coverage across countries and over time. Then we use imputed Gini coefficients for 77 countries in our sample, and for the period 1980-2007. 4

Our results suggest that while income inequality hinders economic growth in most of the countries, it accelerates economic growth only in a country where the level of income inequality is very low. The point above which the estimated time-varying coefficient turns from positive to negative is found to be the Gini index of 24.5. Furthermore, the results reveal that the negative effects of income inequality on economic growth tend to be more severe in developing countries whose inequality is relatively higher.

The rest of the paper is structured as follows. Section 2 introduces the PSTR model. Section 3 describes the data used in this study, and presents the empirical results estimated from the PSTR model. Section 4 supplements the results from the nonlinear model with those of the linear model, considering additional explanatory variables, the possible endogeneity problem, and the Granger causality test. Section 5 concludes.