The Relationship Between Economic Growth and Income Inequality in Economies in Transition

ABSTRACT

In this study, we focus solely on economies in transition. The classification of the countries into the low-income and high-income economies in transition are based on the classification of the World Bank and on our own classification based on an income threshold that is endogenously estimated by our model. We use fixed-effects and dynamic panel technique such as system generalized method of moments (GMM) estimation in our analysis to mitigate endogeneity problem. Our empirical study uses dynamic econometric models to examine the relationship between income inequality and economic growth in the economies in transition. We find a positive relationship between income inequality and economic growth in high-income economies in transition, which is in stark contrast with the negative relationship for low-income economies in transition. We also find that inflation rate negatively and significantly affects economic growth in the low-income economies in transition, while it positively and significantly affects economic growth in the high-income economies in transition. We estimate an income threshold endogenously and reestimate both regressions according to our own threshold. Importantly, the contrasting qualitative difference (between low-income economies in transition and high-income economies in transition) in the relationship between income inequality and economic growth is robust whether we follow the World Bank’s classification of economies in transition or whether we classify them accordingly to what the data in our sample suggest.

KEYWORDS:

• economies in transition
• income inequality
• economic growth
• price of investment

JEL CODES:

• C01
• E01
• O10

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

^1. Tables 1–5 are in Appendix.

^2. The World Bank issued the first World Development Report in 1978. In that issue the World Bank started to classify the countries according to their per-capita gross national income (GNI). Their classification has changed over time. On Friday, July 1, 2016, the World Bank gave the following explanations about how it classifies the countries according to their per-capital GNI. “Each year on July 1, the analytical classification of the World’s economies based on estimates of gross national income (GNI) per capita for the previous year is revised.” As of July 1, 2016, low-income economies in transition are countries with GNI of $12,476 or less and high-income economies in transition are countries with GNI of more than $12,476 (Data Blogs World Bank 2016).

^3. For low-income transition economies, the following are the averages for our study period: 33.93 for Gini, 4.73% for growth, and 0.32 for PPPI, with 73.66% for inflation, 98% for schooling, and 92.43% for trade. For high-income transition economies, the following are the averages for our study period: 31.54 for Gini, 3.54% for growth, and 0.48 for PPPI, with 56.27% for inflation, 99.7% for schooling, and 98.29% for trade.

^4. The debate on whether inflation has a negative or positive impact on economic growth has a long history. Some economists such as Mundell (1965), Tobin (1965), Lucas (1973), Akerlof et al. (1996), and Kiley (2000) have argued that there is a positive relationship between inflation and capital accumulation, which in turn implies a positive impact of inflation on growth. However, some other economists such as Fama and Schwert (1977), Fischer (1993), Barro (1995), and Romer (1992) have argued that inflation negatively impacts economic growth. We let the data endogenously determine whether the inflation rates contribute positively or negatively to economic growth of the countries in each panel. For the impact of degree of openness of an economy on its economic growth, see Romer (1986, 1992), Grossman and Helpman (1991), Barro and Sala-i-Martin (1995), and Harberger (1998) as examples.

^5. An international dollar has the same purchasing power over GNI that a U.S. dollar has in the United States; GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.

^6. The Gini coefficient assumes a number between 0 and 1, where 0 corresponds with perfect equality (everyone has the same income) and 1 corresponds with perfect inequality (one person has all the income and everybody else has zero income).

^7. To determine whether we must use a fixed-effects or a random-effects model in our analysis, we performed the Hausman (1978) specification test. The test showed that the fixed-effects model is the model that we must use. The Hausman specification test basically determines whether the unique errors (υ_i) are uncorrelated with the other regressors in the model (see Greene, 2008, Chapter 9). The null hypothesis states that the unique errors are not correlated with the regressors. We ran a fixed-effects model, then ran a random model and saved the estimates and then performed the test. If the p-value prob.x 2 is significant (0.05), usually the x 2 test is used to test “goodness-of-fit” of data to a model), fixed-effects regression should be used. If the reverse is true, use the random-effects model (Fawaz, 2011).

^8. With this differenced GMM approach, the endogenous variable is properly instrumented with suitable lags of its own levels—other exogenous regressors and outside variables may enter the matrix of instrument in a standard way. However, with this differenced GMM Arellano–Bond estimator, the lag levels may be poor instruments for first differences in models (like ours) in which highly persistent variables are considered. Thus, we opt to make use of an augmented version—a system GMM approach—first described in Arellano et al. (1995). For details on this estimation procedure, see Blundell and Bond (1998). We applied the XTABOND2 procedure in STATA, which conducts a finite-sample correction to the two-step covariance matrix to correct for the downward bias of the SEs (Windmeijer, 2005).

^9. Tabassum and Majeed (2008) find that in the case of transition economies, there is clear evidence that inequality has a negative and significant effect on growth.

Additional information

Notes on contributors

Fadi Fawaz

Dr. Fadi Fawaz Department of Economics and Finance, Tennessee State University, Nashville, TN 37203, USA. E-mail: [email protected]

Masha Rahnama

Dr. Masha Rahnama Department of Economics, Texas Tech University, Lubbock, TX 79409, USA. E-mail: [email protected]