"How Does Household Production Affect Earnings Inequality?: Evidence from the American Time Use Survey"

Although income inequality has been studied extensively, relatively little attention has been paid to the role of household production. Economic theory predicts that households with less money income will produce more goods at home. Thus extended income, which includes the value of household production, should be more equally distributed than money income. We find this to be true, but not for the reason predicted by theory. Virtually all of the decline in measured inequality, when moving from money income to extended income, is due to the addition of a large constant--the average value of household production--to money income. This result is robust to alternative assumptions that one might make when estimating the value of household production.

Why Do BLS Hours Series Tell Different Stories About Trends in Hours Worked?

Hours worked is an important economic indicator. In addition to being a measure of labor utilization, average weekly hours are inputs into measures of productivity and hourly wages, which are two key economic indicators. However, the Bureau of Labor Statistics’ two hours series tell very different stories. Between 1973 and 2007 average weekly hours estimated from the BLS’s household survey (the Current Population Survey or CPS) indicate that average weekly hours of nonagricultural wage and salary workers decreased slightly from 39.5 to 39.3. In contrast, average hours estimated from the establishment survey (the Current Employment Statistics survey or CES) indicate that hours fell from 36.9 to 33.8 hours per week. Thus the discrepancy between the two surveys increased from about two-and-a-half hours per week to about five-and-a-half hours. Our goal in the current study is to reconcile the differences between the CPS and CES estimates of hours worked and to better understand what these surveys are measuring. We examine a number of possible explanations for the divergence of the two series: differences in workers covered, multiple jobholding, differences in the hours concept (hours worked vs. hours paid), possible overreporting of hours in CPS, and changes in the length of CES pay periods. We can explain most of the difference in levels, but cannot explain the divergent trends.of work, Comparison of household and establishment surveys

Wage Compression and the Division of Returns to Productivity Growth: Evidence from EOPP

This paper analyzes the relationship between wages and productivity during the early years of an employment relationship. Data from the Employment Opportunity Pilot Project show that worker productivity grows substantially during the first two years on the job, with most of the growth in productivity occurring at the very start of the job. Correcting for measurement error and the fact that expected productivity beyond the start of the job may be folded into the starting wage if wage revisions are not instantaneous, one finds that variation in productivity is only partially reflected in wages. Not only is productivity growth stemming from human capital accumulation while on the job only partially reflected in wage growth, but starting productivity differences for workers in the same job – in large part driven by differences in relevant experience - are only partially reflected in starting wage differences. Our empirical findings can be explained by a simple model of employer – worker cost sharing in which (a) the cost to a worker of locating and moving to a new job increases with the worker's stock of human capital and (b) equity norms prevent employers from paying senior workers lower wages than junior workers who are no more productive.Wages, Productivity, Compression

How to Think About Time-Use Data: What Inferences Can We Make About Long- and Short-Run Time Use from Time Diaries?

Time-use researchers are typically interested in the time use of individuals, but time use data are samples of person-days. Given day-to-day variation in how people spend their time, this distinction is analytically important. We examine the conditions necessary to make inferences about the time use of individuals from a sample of person-days. We also discuss whether and how surveys with multiple household members or multiple days are an improvement over single-diary surveys.time use, estimation, survey methods

How to Think About Time-Use Data: What Inferences Can We Make About Long- and Short-Run Time Use from Time Diaries?

Time-use researchers are typically interested in the time use of individuals, but time use data are samples of person-days. Given day-to-day variation in how people spend their time, this distinction is analytically important. We examine the conditions necessary to make inferences about the time use of individuals from a sample of person-days. We also discuss whether and how surveys with multiple household members or multiple days are an improvement over single-diary surveys.Time use, survey methods, estimation

How Does Household Production Affect Earnings Inequality? Evidence from the American Time Use Survey

Although income inequality has been studied extensively, relatively little attention has been paid to the role of household production. Economic theory predicts that households with less money income will produce more goods at home. Thus extended income, which includes the value of household production, should be more equally distributed than money income. We find this to be true, but not for the reason predicted by theory. Virtually all of the decline in measured inequality when moving from money income to extended income is due to the addition of a large constant--the average value of household production--to money income. This result is robust to alternative assumptions that one might make when estimating the value of household production.Inequality, Household Production, Time Use

How Does Household Production Affect Measured Income Inequality?

Although income inequality has been studied extensively, relatively little attention has been paid to the role of household production. Economic theory predicts that households with less money income will produce more goods at home. Thus extended income, which includes the value of household production, should be more equally distributed than money income. Previous studies have found this to be the case and have speculated that the more-equal distribution of extended income is due to the weak correlation between money income and household production income. We also find that extended income is more equally distributed than money income. The main contribution of our paper is that we identify the reason for this result. Our sensitivity analysis indicates that virtually all of the decline in measured inequality when moving from money income to extended income is due to the addition of a large constant – the average value of household production – to money income and that measured inequality is insensitive to the correlation between money and household production income. The practical importance of this result is that estimates of extended income inequality are robust to imputation procedures and that researchers can obtain accurate estimates of trends by simply using mean values of household production income.household production, inequality

How Responsive are Quits to Benefits?

It has been argued that one of the functions of fringe benefits is to reduce turnover. However, due to a lack of data, the effect on quits of the marginal dollar of benefits relative to the marginal dollar of wages is an under-researched topic. This paper uses the benefit incidence data in the 1979 Cohort of the National Longitudinal Survey of Youth (NLSY79) and the cost information in the National Compensation Survey to impute benefit costs. The value of imputed benefits is then entered as an explanatory variable in a mobility equation that is estimated using turnover information in the NLSY. We find that the quit rate is much more responsive to fringe benefits than to wages; this is even more the case with total turnover. We also find that benefit costs are correlated with training provision. Due to the high correlation of the costs of individual benefits, it is not possible to disentangle the effects of separate benefits. An interesting feature of the model that we develop for interpreting the strong negative relationship between fringe benefits and turnover is that abstracting from heterogeneity, workers must at the margin place a higher valuation on a dollar of wages than a dollar of benefits since otherwise an employer could profit by switching compensation from wages to fringes. Worker heterogeneity modifies this result and reinforces any causal relationship between fringe benefits and turnover provided that more stable workers have a greater preference for compensation in the form of fringes.Turnover, Fringe Benefits