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How individuals allocate their time between work and leisure has important implications regarding worker well-being. For example, more time at work means a greater return to human capital and a greater proclivity to seek more training opportunities. At the same time, hours spent at work decrease leisure and depend on one's home environment (including parental background), health, past migration, and government policies. In short, worker well-being depends on trade-offs and is influenced by public policy. These decisions entail time allocation, effort, human capital investment, health, and migration, among other choices. This volume considers worker well-being from the vantage of each of these alternatives. It contains ten chapters. The first three are on time allocation and work behavior, the next three on aspects of risk in the earnings process, the next two on aspects of migration, the next one on the impact of tax policies on poverty, and finally the last chapter on the role of labor market institutions on sectoral shifts in employment.
We analyse the dynamics of social assistance benefit (SA) receipt among working-age adults in Britain between 1991 and 2005. The decline in the annual SA receipt rate was…
We analyse the dynamics of social assistance benefit (SA) receipt among working-age adults in Britain between 1991 and 2005. The decline in the annual SA receipt rate was driven by a decline in the SA entry rate rather than by the SA exit rate (which also declined). We examine the determinants of these trends using a multivariate dynamic random effects probit model of SA receipt probabilities applied to British Household Panel Survey data. We show how the model may be used to derive year-by-year predictions of aggregate SA entry, exit and receipt rates. The analysis highlights the importance of the decline in the unemployment rate over the period and other changes in the socio-economic environment including two reforms to the income maintenance system in the 1990s and also illustrates the effects of self-selection (‘creaming’) on observed and unobserved characteristics.
We model transitions between unemployment, low-paid and high-paid employment by British men using a first order Markov model with endogenous switching that also takes into…
We model transitions between unemployment, low-paid and high-paid employment by British men using a first order Markov model with endogenous switching that also takes into account the endogeneity of initial conditions, selection into employment, and sample attrition. Our estimates indicate that all three selectivity issues are non-ignorable. We demonstrate several interrelationships between the dynamics of (un)employment and low-paid work between one year and the next, represented by forms of (cross-)state dependence. Controlling for heterogeneity, the probability of a man having a low-paid job in one year depends not only whether he had a job a year before but also whether that job was low paid. The probability of his being employed at all depends on whether he had a job the previous year.
Uses Bank of Italy’s Survey on Household Income and Wealth (SHIW) panel data for 1993 and 1995 to model transition probabilities at the bottom of the Italian wage distribution and to investigate the features and determinants of low‐wage mobility. The analysis is based on a bivariate probit model with endogenous switching which allows tackling the initial conditions problem, i.e. the potential endogeneity of the conditioning starting state. Results show the appropriateness of such a choice, the hypothesis of exogenous initial conditions being always rejected. Shows that while some factors such as education, sex and geographical location have an effect on low‐pay persistence, job‐related variables are more effective in avoiding falls into low pay from higher pay. It is also shown how raw persistence involves a considerable share of true state dependence, i.e. the experience of low pay raises, per se, the probability of future low‐pay episodes, irrespective of personal attributes.
We use OECD-PIAAC data to estimate the earnings effects of both years of education and of numerical skills. Our identification strategy exploits differential exposure to…
We use OECD-PIAAC data to estimate the earnings effects of both years of education and of numerical skills. Our identification strategy exploits differential exposure to educational reforms across birth cohorts and countries. We find that education has the strongest earnings effect. A one standard deviation increase in years of education raises earnings by almost 22 percentage points (corresponding to a return to education above 7 percentage points), which compares with a lower percentage points return to an equivalent increase in numerical skills. Our results suggest that the same set of unobservables drives the accumulation of both formal years of education and numeracy skills. OLS estimates underestimate returns to human capital, consistently with the idea that educational reforms favor the human capital acquisition of abler children from disadvantaged parental backgrounds. When we consider numerical skills alone education reforms cannot identify any significant effect of skills on wages, however, when we jointly consider schooling and skills as endogenous factors in a recursive structure we find a significant role for skills in determining wages.