Do redistributive state taxes reduce inequality?

Redistributive taxes will potentially affect inequality via two channels. First, because taxes typically take a larger income share of the rich than the poor, tax policies will have a "mechanical" effect on inequality. Second, redistributive taxes may engender a behavioral response, for example, by prompting changes in labor supply or affecting residential choices. In measuring the effect of tax policies on behavior, it is important to form an index of redistribution that measures only the mechanical policy effect of a tax, uncontaminated by any behavioral response. To do this, I calculate the redistributive effect of taxation based not upon the actual after--tax Gini and before--tax Gini in a given state and year, but based on the effect of the taxation system in every state and year on one single sample of households, drawn from the March 1990 CPS. (The March 1990 CPS was chosen on the basis that it is the midpoint of the period 1977-2002, but drawing a sample from another year makes no substantial difference to the results.) This "simulated redistribution index" reflects the mechanical policy impact of the taxation system, but not any behavioral changes that are induced by a more or less redistributive tax system. More details may be found in the Data Appendix.

The measure of redistribution used here accounts only for personal income taxes. While 1 control for sales taxes and the top rate of inheritance/estate taxes, I do not estimate their redistributive effect (and I do not control for other taxes, such as property taxes). To the extent that the redistributive effect of personal income taxes is positively correlated with the redistributive effect of other taxes, mine will be an underestimate of the true effect. To the extent that the redistributive effect of personal income taxes is negatively correlated with the redistributive effect of other taxes, mine will be an overestimate. However, it is somewhat reassuring to note that Feldstein and Wrobel (1998) found that omitting the redistributive effect of sales taxes made only a slight difference to their estimates.

Both the redistributive effect of taxation and inequality are calculated from the distribution of hourly wages among adults aged 16-55 with positive earnings. (3) The mean of the pre-tax Gini coefficient for the distribution of hourly wages is 0.36 with a standard deviation of 0.018. Within a state, the largest one-year movements observed in the data are -5 Gini points and +6 Gini points. At the 10th and 90th percentiles, the one-year movements are -2 and +2 Gini points respectively. Summary statistics are presented in Appendix Table 1.

On average, the mechanical effect of income taxes was to reduce the Gini coefficient by 0.024 (i.e., by 2.4 Gini points), with a standard deviation of 0.003. However, this standard deviation overstates the extent of within-state variation in the redistributive effect of taxation. Focusing only on one--year within--state changes, the largest increase and decrease observed in the data are -3.4 and +0.4 Gini points. The changes at the 10th and 90th percentiles are -0.2 Gini points and +0.1 Gini points respectively.

Figure 1 shows a scatter plot of pre-tax hourly wage Gini coefficients for the 50 states and the District of Columbia over the period 1977-2002. The steady upwards trend accords with the well-recognized rise in wage inequality over this period (see for example Autor, Katz, and Kearney, 2008). (4) Figure 2 depicts a scatter plot of the redistributive effect of taxation. Taxes became more redistributive in the late-1970s, less redistributive in the 1980s (due to the federal Tax Reform Act of 1986 (TRA86), followed by reductions in redistributivity in some states), and slightly more redistributive again in the 1990s.

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To get some sense of the within-state relationship between taxes and inequality, Figure 3 plots hourly wage inequality against the tax redistribution measure for the four most populous states in the United States: California, Florida, New York, and Texas. There are two reasons for choosing these states. First, they constitute a significant fraction of the U.S. population (around 30 percent). Second, using large states reduces the measurement error in estimating inequality using the CPS. It is difficult from this graph to discern any strong positive relationship between redistributivity and pre-tax inequality. The largest rises in hourly wage inequality have occurred in California and New York; in both cases these have taken place at a time when tax redistributivity was either falling or stable.

Clearly, national trends dominate the four graphs. Since the empirical specification will include year fixed effects, Figure 4 shows the results for the same four states, but this time with inequality and redistributivity expressed as the deviation from the (unweighted) state average. Again, there does not appear to be any positive relationship between inequality and redistributivity in any of these states.

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To test the relationship between taxes and inequality empirically across states, I use panel data from all 50 states and the District of Columbia over the period 1977-2002, and estimate the following equation.

[1] [GB.sub.t] = [alpha] + [beta][(bar.GB] - [bar.GA]).sub.st] + [Z.sub.st] + [zeta].sub.s] + [[lambda].sub.t] + [T.sub.r] +[[epsilon].sub.st]

In equation [1], ([bar.GB] - [bar.GA]) is the amount by which taxation mechanically reduces the Gini coefficient, GB is the Gini coefficient for before--tax inequality, Z are time--varying state characteristics, [zeta] is a vector of state dummies, [lambda] is a vector of year fixed effects, and T is a region--specific linear time trend.

Note that the year dummies remove most of the impact of changes in federal income taxes, leaving the effects of state income taxes. (5) This approach is preferable to estimating the redistributive effect of state taxes alone, since it allows for interaction between state and federal taxes. State fixed effects take account of time--invariant factors that may be correlated with both the dependent variable and the key independent variable, such as residents' taste for inequality or redistributive taxation. Including a linear time trend for each of the four Census regions (Midwest, Northeast, South, and West), allows for the possibility that long-run linear changes in a particular part of the United States--perhaps due to changing industrial composition--might have affected both inequality and taxation systems. The vector Z includes three other state taxes that might be correlated with state income taxes: the sales tax rate, the maximum state inheritance or estate tax rate, and an indicator for whether the state has an estate tax. It also includes three variables that might affect wage inequality: the unemployment rate, the log of real per capita personal income, and the unionization rate. (Below, I show that the results are robust to excluding these controls.) Standard errors are clustered at the state level, allowing for an arbitrary covariance structure over time within each state (Bertrand, Duflo, and Mullainathan, 2004).

The coefficient on [beta] can be interpreted as follows.

* [beta] = 0: more redistributive taxes have no impact on the pre-tax distribution of income.

* [beta]l < 0: more redistributive taxes not only have a mechanical effect of equalizing the wage distribution, but also lead the pre-tax wage distribution to become more equal.

* 0 < [beta] < 1: a tax system that has the mechanical effect of reducing the Gini by one point leads to a compensating increase in the pre-tax distribution of income of less than one Gini point, partly attenuating the equalizing effects of the tax change.

* [beta] = 1: a tax system that has the mechanical effect of reducing the Gini by one point leads to a compensating one Gini point increase in the pre-tax distribution of income, with the net result being that the post-tax distribution of wages remains unaffected by the redistributive effects of the tax.

* [beta] > 1: the pre-tax wage distribution overcompensates for the effect of more redistributive taxes, with the result that more redistributive taxes cause the post-tax wage distribution to become more unequal.

Although it is possible to come up with explanations as to why [beta] might be less than zero or greater than one, the main focus of the theoretical literature has been over whether [beta] is closer to zero or to one. (6) The empirical analysis below will, therefore, focus on the question of whether [beta] is closer to zero or to one. By ignoring the hypotheses with less theoretical support ([beta] < 0 and [beta] > 1), it is possible to construct a clearer "horserace" between the two most plausible explanations: that wages adjust to fully offset tax changes, or that wages do not adjust to offset tax changes.

It is possible that taxes may affect wages only with some lag. If this is the case, then simply regressing current inequality on current redistributivity may miss part of the adjustment process. Therefore I experiment with adding up to six lagged terms to the model. In the case of six lags, I estimate the equation

[2] [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

The six-year limit is necessarily arbitrary, but is chosen on the basis that it is the lag length used by Feldstein and Wrobel (1998), who analyze the period 1983-1989. Since tax rates are only available from 1977 onwards, all regressions are restricted to cover the same period, that is, 1983-2002. (7)