The analysis so far suggests that falling agricultural employment and rising exports of commodities are correlated with growing market income inequality. But how exactly do employment in agriculture and trade with primary goods affect the income distribu- tion? How do they change the relative incomes of different segments of society? Who benefits and who loses? Which groups receive the economic gains associated with these commercial patterns?
Answering these questions requires looking at the different parts of the income dis- tribution. While my current dependent variable, the GINI coefficient, reflects changes in inequality, it does not reveal which parts of the income distribution experience in- come gains and which witness their income decline. The GINI coefficient is an aggregate statistic. Multiple configurations can produce the same value. In contrast, a focus on the income shares of specific groups can illuminate the precise ways through which commodity exports and agricultural employment influence inequality.
To gain additional insight into the implications of primary employment and trade with commodities for economic inequality in Central and Eastern Europe, I thus replace my dependent variable with the income share of the richest and the poorest twenty percent. My new outcome of interest is the share of national income going to the top and the bottom quintile of the income distribution.56 This focus allows me to explore the
changes in the relative economic well-being of these groups and to assess whether trade with commodities boosts the income of the poor relative to the richer fragments of the population or if it concentrates income in the hands of the already well-off.
Table 5 below shows the results from the models run against the ratio between the income shares of the top 20 and the bottom 20 percent. Calculated using both income shares, the ratio is more informative as it summarizes their co-movement. Rising values imply that the rich claim a higher proportion of national income, which means that they experience more substantial income gains than the poor or that their income shrinks less than the income of the bottom 20 percent. Falling values suggest that the poor’s income grows faster than the elites’. Tables A2.3 and A2.4 in Appendix 2 present the regressions with the top 20 and the bottom 20 income shares.7
5Additional robustness checks using the top and the bottom decile of the income distribution yielded largely similar results.
6Data come from the Global Income and Consumption Project, which collects income, consumption, and inequality data for the 1960-2015 period.
Table 3.3: The primary sector and the top / bottom quintile ratio in Eastern Europe
Model 9 Model 10 Model 11 Model 12 Model 13 b/se b/se b/se b/se b/se Agricultural -0.172* employment (0.07) Primary exports 22.487*** (4.15) Agricultural 67.279* exports (29.41) Foods exports 32.427** (8.54)
Ores and metals -8.426
exports (7.71) Industrial -0.125 0.011 0.041 -0.028 -0.024 employment (0.09) (0.10) (0.09) (0.09) (0.10) Democracy 0.751 1.250 -1.454 0.991 0.295 (4.46) (4.35) (4.49) (4.43) (4.33) Left seats 0.000 -0.000 0.001 -0.000 0.000 (0.00) (0.00) (0.00) (0.00) (0.00) Capital account 0.739*** 0.732*** 0.660*** 0.847*** 0.772*** openness (0.14) (0.16) (0.15) (0.14) (0.14) Trade -0.008 -0.024* -0.013 -0.024* -0.007 (0.01) (0.01) (0.01) (0.01) (0.01) FDI inflows -0.005 -0.008 -0.009 0.003 -0.010 (0.02) (0.03) (0.03) (0.02) (0.03) GDP growth 0.004 0.001 -0.028* 0.016 -0.005 (0.02) (0.02) (0.01) (0.02) (0.02) GDP per capita -4.237** -1.829* -0.619 -2.389* -1.739* (1.43) (0.91) (0.66) (1.04) (0.88) Inflation -0.000 -0.000 -0.000 -0.000 -0.000 (0.00) (0.00) (0.00) (0.00) (0.00) Unemployment -0.084* -0.029 -0.017 -0.049 -0.032 (0.03) (0.04) (0.04) (0.03) (0.04) Government -0.168* -0.118 -0.155 -0.105 -0.157 consumption (0.09) (0.09) (0.11) (0.09) (0.11) Remittances 27.797** 31.694** 33.795** 29.415** 29.304** (8.91) (10.52) (11.39) (9.26) (8.88) Commodity boom -0.406 -0.197 -0.313 -0.211 -0.489 (0.34) (0.35) (0.31) (0.36) (0.32) Constant 56.517** 24.442* 13.969 31.591* 26.335* (17.93) (12.54) (10.03) (12.56) (12.84) R-squared 0.165 0.181 0.179 0.183 0.136 N 182 180 180 180 180 ***p <0.001,**p <0.01,*p <0.10
Together, the three tables reveal a consistent picture. The primary sector employment and almost all of the commodity exports variables are statistically significant. The nega- tively signed coefficient returned by agricultural employment suggests that a larger sector
can boost the income of the poor, most likely by expanding the employment opportunities available to them. The aggregate primary goods measure indicates that rising exports are connected to a higher top 20 / bottom 20 ratio. This implies that the economic gains from engaging in trade with commodities likely do not go to the poorest in Central and Eastern Europe but accrue to the wealthy elites, exacerbating the income gap between the two groups. The models in tables A2 and A3 corroborate this conclusion: higher commodities exports are indeed associated with a decrease in the income share of the bottom 20 percent of the income distribution and an increase in the income share of the top 20 percent. Thus, they bring gains – either in the form of higher salaries or noticeable rents – to the richest in society. Agricultural exports and foods exports behave similarly. Ores and metals exports fail to reach statistical significance.
These models thus confirm the general logic of the main empirical analysis. A labor- absorbing agricultural sector can ameliorate income differentials. The exportation of primary goods in Central and Eastern Europe, however, has the potential to exacerbate inequality. While trade with commodities boosts the fortunes of the most well off, it decreases the share of national income going to the poorest quintile.
Two other findings are worth noting. First, both democracy and partisanship lose their statistical significance when the dependent variable is the ratio between the top and the bottom quintile’s income shares.8 Thus, these two political factors do not appear to
directly affect the proportion of income going to the richest and the poorest quintiles. This implies that the effect of democratic governments and leftist legislatures on the in- come distribution might take place primarily through their impact on the middle classes. Second, remittances are a statistically significant predictor of the top 20 / bottom 20 percent ratio in Eastern Europe. This is not surprising, given the high levels of emi- gration that the region experienced during the transition. The financial resources sent by residents living abroad, however, exacerbate income differentials, which suggests that wealthy families might be the ones receiving money from relatives who left the country.
8Tables A2.3 and A2.4 in Appendix 2 show that these variables also return statistically insignificant coefficients in the shares models.