On Oct 25, 2011, at 6:45 AM, shag carpet bomb wrote:
> ha! I can safely say that being unemployed for a year was the best
> time of my life. Places to go, things to do, a garden to grow. it was
> fanfuckingtastic!
I guess that's just another way in which you're so extraordinary!
NEW MEASURES OF THE COSTS OF UNEMPLOYMENT: EVIDENCE FROM THE SUBJECTIVE WELL-BEING OF 2.3 MILLION AMERICANS John F. Helliwell Haifang Huang Working Paper 16829 http://www.nber.org/papers/w16829
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Di Tella et al. (2003) expand the study to cover more macroeconomic factors. Continuing the use of life satisfaction in the Euro-Barometer surveys, these researchers regress individual life evaluations on personal as well as macroeconomic variables. The macro variables of interest include GDP, unemployment rates, inflation and the generosity of unemployment benefits. They find that both the level of and the changes in GDP have positive effects on life satisfaction; but there is some evidence of adaptation. On aggregate unemployment, they find “important psychic losses” of recession that go beyond personal losses of unemployed workers and those associated with lower income. Specifically, the national unemployment rate attracts a significantly negative coefficient in regressions that already include each respondent’s own unemployment status and changes in GDP. They attribute the economy-wide effect to the fear of unemployment among those who are in work or at home. Finally, they find that the generosity of unemployment benefits, measured as replacement rates, is positively correlated with a nation’s average satisfaction with life. The benefits do not, however, affect the satisfaction gap between employed and unemployed workers.
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Di Tella et al. (2001), Di Tella et al. (2003) and Wolfers (2003) also report a number of conclusions based on US data. All of them use the General Social Survey (GSS) that interviews about 1,500 individuals each year. Di Tella et al. (2001) and Di Tella et al. (2003) use surveys between 1972 and 1994 with about 27,000 observations; Wolfers (2003) uses 1973-1998 surveys with 37,000 observations. The GSS has a three-step happiness question “Taken all together, how would you say things are these days - would you say that you are very happy, pretty happy, or not too happy?” Di Tella et al. (2001) derive an adjusted measure of average happiness for each year, and find that it is negatively correlated with the year-to- year changes in inflation and in unemployment; a stronger correlation is found with changes in unemployment rate than with the rate of inflation. Di Tella et al. (2003) report a regression at the individual level that shows a large negative effect of personal unemployment status. Wolfers (2003) regresses individuals’ happiness on labor market conditions measured at the state-year level; the unemployment rate attracts a significantly negative coefficient.
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We use two surveys for our measures of well-being. One of them is the CDC’s Behavioral Risk Factor Surveillance System (BRFSS). The BRFSS is a state-based system of surveys collecting information on health risk behaviors, preventive health practices, and health care access.... The second survey we use is the Gallup Daily Poll, which is a well-being-oriented survey including many more measures of well-being than does the BRFSS....
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Figure 3 plots life satisfaction and the measure of mental health on log household income. Life satisfaction exhibits a positive and linear relation with log income; increases in log income steadily raise life satisfaction over the entire range. The measure of mental health also rises with log income, but the relation apparently is stronger at lower levels of income and weakens as income rises. This confirms the findings in Kahneman and Deaton (2010) about the qualitative distinction between life evaluations and emotional well-being. But we find no satiation point of income for the measure of mental health: an increase in income, even from the high level of $75,000, still improves mental health (i.e., reducing the number of days when mental health is not good).
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We find that aggregate unemployment reduces well-being even among those who are not unemployed. These spillover effects aggregate to a larger national total than do the direct effects, because they affect a larger fraction of the population.
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The primary variable of interest is the county-level unemployment rate (scaled as a fraction of the labor force). In Table 1, the regressions do not have each individual’s own unemployment status. So the coefficient on the unemployment rate captures the total effect. The coefficients are all negative and statistically significant at 1% confidence level. Table 2 controls for each respondents’ own-unemployment status (feasible only for the BRFSS). Including the personal unemployment status reduces the coefficients on the aggregate unemployment rate by about one-third, from -0.85 to -0.63 in the case of life satisfaction and from 4.7 to 3.0 in the case of negative mental health. The small reduction in the estimate implies that the major part of the total negative consequences of unemployment on subjective well-being is felt by those who are not (yet) themselves unemployed. This is not saying that unemployment matters less for those who are unemployed. The opposite is true, as the dummy variable indicating unemployment status attracts coefficients that are much bigger than those associated with the aggregate unemployment rate. It is just that the total number of unemployed is small relative to the total size of the population. Thus the spillover effects of unemployment can be, and are, greater than the direct effects.
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To summarize, local unemployment has significantly negative effects on well-being among the entire population, including those who are still employed.
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Using estimates from the BRFSS, we can break down the total impact of a 1% rise in the unemployment rate into its direct and indirect effects. The increase in unemployment reduces the populations well-being in three different ways. The direct monetary loss is the foregone income of those who become unemployed. The direct nonpecuniary cost is the further loss of subjective well-being suffered by those by those who become unemployed. The spillover costs are the well-being losses of those who are not themselves unemployed.
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First, and most importantly, we find robust evidence, consistent across measures and surveys, that unemployment has significant spillover effects on those who are not themselves unemployed. The evidence also holds up well in an instrumental variable approach when local unemployment rates are replaced with information based on external industrial trends. Furthermore, we find that unemployment hurts more when it is closer to home: county-level unemployment rates overwhelmingly dominate state-level unemployment when both are present in our estimations.... Second, we confirm the social-norm hypothesis in Clark (2003) that greater unemployment at the aggregate level narrows the well-being gap between employed and unemployed workers. The findings in Clark (2003) is based on UK surveys; ours is based on US ones. In the US, the break-even point where the gap disappears is 48%, twice the size as that in UK.