RESPECTO A LOS HORARIOS DE CLASES

Chart 11: Employment Rate by Unemployment Rate Level Race 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

State Unemployment Rate

Per ce n t o f Ob ser vation s w ith N o n -Z e ro An n u al Ear n in gs 0 50,000 100,000 150,000 200,000 250,000 300,000 N u m b e r o f Peo p le

Total Number of People Ratio (Not Black) Ratio (Black)

Chart 12: Employment Rate by Unemployment Rate Level Ethnicity 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

State Unemployment Rate

Per ce n t o f Ob ser vation s w ith N o n -Z e ro An n u al Ear n in gs 0 50,000 100,000 150,000 200,000 250,000 300,000 N u m b e r o f Peo p le

𝑈 is the unemployment rate in state s at time t. 𝑋 is a vector of control variables that include gender, race, ethnicity, educational attainment, marital status, number of children, experience, occupation and industry. One reason to control for occupation and industry is that the labor market effects of disability could vary by industry and occupation. For example, Kruger and Kruse (1995) find that those with computer skills do not suffer as negative of an earnings effect after the onset of a disability.

𝑣 are state fixed effects and are year fixed effects.16,17 _{The year fixed} effects allow me to abstract away from long-term aggregate labor market trends among minorities, women, and the disabled as well as changing attitudes toward these groups. In addition, some states may experience higher or lower than average

(national average) unemployment for long periods of time and these long-term state- specific trends could be correlated with wage gaps between demographic groups or the disability wage gap as well as attitudes toward certain groups. Including state fixed effects allows me to focus on temporary variation in state-specific labor market conditions.

For the demographic comparisons, the equation is very similar:

𝐼 =   𝛽 + 𝛽 𝑀 + 𝛽 𝑈 + 𝛽 𝑈 𝑀 + 𝛽 𝑋 + 𝑣 + 𝜏 + 𝜀 (2)

16 _{These results do no make use of weights. Because of the nature of the SIPP Gold Standard File,}

Census Bureau staff generally do not recommend using the person level weights in the SIPP data and other researchers using these data also estimate unweighted regressions. As robustness checks, I also estimated the baseline results using various person level weights and the results do not qualitatively differ from those presented in Table 1.7.

17

The baseline results were also estimated using leads and lags of the local unemployment rate. This did not change the results appreciatively.

The only difference is that 𝑀 is an indicator variable for whether individual i is female (for the gender gap analysis), black (for the race gap analysis), or Hispanic (for the ethnicity gap analysis).

When using administrative earnings records, I am able to look at the years 1978 to 2010. Even though the SIPP has a complex sequential and sometimes

overlapping panel structure, there are SIPP earnings records for at least part of the year for all the years between 1984 and 2010. Therefore I also estimate equations (1) and (2) using hourly earnings variables from the SIPP.

Since there is a social insurance program for the disabled and the decline in labor force participation among the disabled is well documented in the literature, I am also interested in the disabled, women, blacks, or Hispanics responding to adverse labor  market  conditions  by  leaving  or  being  “forced”  to  leave  employment  and  I   estimate separate regressions removing years of zero annual earnings and keeping years of zero annual earnings. When keeping the zeros, I only keep the ones that are between years of non-zero annual earnings.

Because of the structure of the data, I can only observe the state an individual lives during the time he is in the SIPP. I assume that for years before the first

observation in the SIPP, the individual lives in the same state we first observe him in and for years after the last observation in the SIPP, he lives in the state we last observe him in. Similar to the assumptions made about state of residence, I also have to make the assumptions about the time invariance of other control variables that are only available from the SIPP for the analysis using administrative earnings records.

As variations of the baseline estimates using equations (1) and (2), I also estimated variations of these equations using, instead of the unemployment rate, an indicator for the unemployment rate being greater than 6% (using both annual and hourly earnings variables). This can be interpreted a looking at years where is the

local  labor  market  is  “good”  versus  years  where  local  labor  market  conditions  are   “bad.” Lastly, I also estimated equations (1) and (2) by business cycle; that is, looking at the following periods of time in isolation to see if the effects of interest change over time: 1980-1990, 1990-2001, and 2001-2007.

5. RESULTS

The simple averages of annual earnings at different levels of the state

unemployment rate (rounded to the nearest integer) are shown in Charts 1.3 through 1.7. The disability comparisons (Charts 1.3 and 1.4) both seem to show that the gap between the non-disabled and the disabled is larger at lower levels of unemployment. Of course, it is important to keep in mind that the sample sizes are skewed toward lower unemployment rates with the higher levels of unemployment more scarcely populated and the average earnings measure also more noisy at those levels of unemployment. This is shown by the bars that represent the percent of the sample in terms of people (not person-years) at each level of the unemployment rate.

Chart 1.5 shows the gender comparisons and there does not appear to be a clear effect either way looking just at the average level of annual earnings. Chart 1.6 is the race comparison, which looks similar to the gender comparison in that a simple visual comparison does not yield a clear direction of effect. Chart 1.7, however, the ethnicity comparison, looks more similar to the disability comparisons in Charts 1.3 and 1.4 than the gender and race comparisons in Charts 1.5 and 1.6 in that the earnings gaps appears to be smaller at higher levels of the unemployment rate.

Charts 1.8 through 1.12 show  the  “employment  rate”  by  disability  status,   gender, race, and ethnicity as well as the rounded level of the local unemployment rate. While the fraction of observations with positive annual earnings decreases more for women as the local unemployment rate increases than it does for men, we do not

see this same patter for the disabled, blacks, and Hispanics. Again, the data is much noisier for these groups, especially at higher levels of unemployment where the sample sizes decrease drastically.

Before discussing the regression results, I first ran some logit regressions estimating the effect of disability, gender, race, and ethnicity on the probability of having zero annual earnings and the probability of having zero annual income from labor market earnings and disability insurance payments. As mentioned before, my baseline analysis is done on a sample that includes years of zero annual earnings and a sample that does not, so it is first instructive to look at the differences in likelihood of having zero annual earnings among these groups. These results are shown in Table 1.8. Each panel is separate group comparison and within each, the first column shows the results for when an indicator variable for zero annual earnings is the dependent variable and the second column shows the results for when an indicator variable for the sum of annual earnings and disability insurance payments is zero. The results show that the disabled, especially those with work limiting disabilities, women, and Hispanics are more likely to experience years of zero earnings. The inclusion of disability insurance payments partially reduces these effects for the two disability categories, but does not for women and Hispanics, as would be expected.