Wage Mobility in the United States

This paper examines the mobility of individuals through the wage and earnings distributions. This is of extreme importance since mobility has a direct implication for the way one views the vast changes in wage and earnings inequality in the United States over the last few decades. The measures of wage and earnings mobility analyzed are based on data for individuals surveyed in the National Longitudinal Survey for Youth from 1979 to 1991. We introduce summary measures of mobility computed over varying time horizons in order to examine how the effect on measured inequality as the time horizon is increased. The results suggest that mobility is predominantly within group mobility and increases most rapidly when the time horizon is extended up to four years, reducing wage inequality by 12-26%. We proceed therefore with more detailed examination of short-term (year-to-year) within group mobility, by estimating non-parametrically transition probabilities among quintiles of the distribution. We find that the staying probabilities, by quintiles, were higher at the higher quintiles throughout the period for both wages and earnings, and that mobility is declining over time. Hence, this paper suggests that while the level of wage inequality in the United States is somewhat lower once mobility is taken into account, the sharp increase in inequality during the 1980's is worse than it appears, due to falling mobility over time.

On the Number of Bootstrap Repetitions for BC_a Confidence Intervals

This paper considers the problem of choosing the number bootstrap repetitions B to use with the BC_{a} bootstrap confidence intervals introduced by Efron (1987). Because the simulated random variables are ancillary, we seek a choice of B that yields a confidence interval that is close to the ideal bootstrap confidence interval for which B = infinity. We specifiy a three-step method of choosing B that ensures that the lower and upper lengths of the confidence interval deviate from those of the ideal bootstrap confidence interval by at most a small percentage with high probability.

How Large are the Classification Errors in the Social Security Disability Award Process?

This paper presents an .audit. of the multistage application and appeal process that the U.S. Social Security Administration (SSA) uses to determine eligibility for disability benefits from the Disability Insurance (DI) and Supplemental Security Income (SSI) programs. We use a subset of individuals from the Health and Retirement Study who applied for DI or SSI benefits between 1992 and 1996, to estimate classification error rates under the hypothesis that applicants' self-reported disability status and the SSA's ultimate award decision are noisy but unbiased indicators of a latent .true disability status. indicator. We find that approximately 20% of SSI/DI applicants who are ultimately awarded benefits are not disabled, and that 60% of applicants who were denied benefits are disabled. We also construct an optimal statistical screening rule that results in significantly lower classification error rates than does SSA's current award process.Social Security Disability Insurance, Supplemental Security Income, Health and Retirement Study, Classification Errors.

How Large are the Classification Errors in the Social Security Disability Award Process?

This paper presents an audit' of the multistage application and appeal process that the U.S. Social Security Administration (SSA) uses to determine eligibility for disability benefits from the Disability Insurance (DI) and Supplemental Security Income (SSI) programs. We study a subset of individuals from the Health and Retirement Study (HRS) who applied for DI or SSI benefits between 1992 and 1996. We compare the SSA's ultimate award decision (i.e. after allowing for appeals) to the applicant's self-reported disability status. We use these data to estimate classification error rates under the hypothesis that applicants' self-reported disability status and the SSA's ultimate award decision are noisy but unbiased indicators of, a latent true disability status' indicator. We find that approximately 20% of SSI/DI applicants who are ultimately awarded benefits are not disabled, and that 60% of applicants who were denied benefits are disabled. Our analysis also yields insights into the patterns of self-selection induced by varying delays and award probabilities at various levels of the application and appeal process. We construct an optimal statistical screening rule using a subset of objective health indicators that the SSA uses in making award decisions that results in significantly lower classification error rates than does SSA's current award process.

Interfirm Mobility, Wages, and the Returns to Seniority and Experience in the U.S.

In this paper, we follow on the seminal work of Altonji and Shakotko (1987) and Topel (1991) and reinvestigate the returns to seniority in the U.S. These papers specify a wage function, in which workers’ wages can change through two channels: (a) returns to their seniority; and (b) returns to their labor market experience. We start from the same wage equation as in previous studies, and, following our theoretical model, we explicitly include a participation-employment equation and an interfirm mobility equation. The employment and mobility decisions define the individual’s experience and seniority. Because experience and seniority are fully endogenized, we introduce into the wage equation a summary of the workers’ entire career and past jobs. The three-equation system is estimated simultaneously using the Panel Study of Income Dynamics (PSID). For all three education groups that we study, returns to seniority are quite high, even higher than what was previously obtained by Topel. On the other hand, the returns to experience appear to be similar to those previously found in the literature.wage mobility, interfirm mobility, returns to seniority, panel data, Markov Chain, Monte Carlo methods

Evaluating the Probability of Failure of a Banking Firm

We develop a dynamic model in which the probability of failure of an infinitely lived financial intermediary (bank) is determined endogenously as a function of observable state and policy variables. The bank takes into account the effect of the optimal policy (the interest on deposits, dividend payouts, risky investments) on the probability of failure, which in turn affects the bank's ability to extract deposits. With the aid of simulations we study the effect of variables such as bank size, the riskiness of the bank's investment opportunities, and reserve requirements on the bank's optimal policy and on its probability of failure. A major finding is that small banks choose policies that render them more risky than large banks. As the risks are correctly priced by depositors, rates offered by small banks incorporate substantial risk premia. Another interesting finding is that a tighter reserve requirement induces banks of all sizes to take fewer risks.

Quantile Regression Model with Unknown Censoring Point

The paper introduces an estimator for the linear censored quantile regression model when the censoring point is an unknown function of a set of regressors. The objective function minimized is convex and the minimization problem is a linear programming problem, for which there is a global minimum. The suggested procedure applies also to the special case of a fixed known censoring point. Under fairly weak conditions the estimator is shown to have n-convergence rate and is asymptotically normal. In the special case of a fixed censoring point it is asymptotically equivalent to the estimator suggested by Powell (1984, 1986a). A Monte Carlo study performed shows that the suggested estimator has very desirable small sample properties. It precisely corrects for the bias induced by censoring, even when there is a large amount of censoring, and for relatively small sample sizes. The estimator outperforms that suggested by Powell in cases where both apply.

On the Number of Bootstrap Repetitions for Bootstrap Standard Errors, Confidence Internals, and Tests

This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap standard errors, confidence intervals, and tests. For each of these problems, the paper provides a three-step method for choosing B to achieve a desired level of accuracy. Accuracy is measured by the percentage deviation of the bootstrap standard error estimate, confidence interval endpoint(s), test’s critical value, or test’s p -value based on B bootstrap simulations from the corresponding ideal bootstrap quantities for which B = ∞. Monte Carlo simulations show that the proposed methods work quite well. The results apply quite generally to parametric, semiparametric, and nonparametric models with independent and dependent data. The results apply to the standard nonparametric iid bootstrap, moving block bootstraps for time series data, parametric and semiparametric bootstraps, and bootstraps for regression models based on bootstrapping residual

On the Number of Bootstrap Repetitions for Bootstrap Standard Errors, Confidence Intervals, and Tests

This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap standard errors, confidence intervals, and tests. For each of these problems, the paper provides a three-step method for choosing B to achieve a desired level of accuracy. Accuracy is measured by the percentage deviation of the bootstrap standard error estimate, confidence interval endpoint(s), test's critical value, or test's p-value based on B bootstrap simulations from the corresponding ideal bootstrap quantities for which B = infinity. Monte Carlo simulations show that the proposed methods work quite well. The results apply quite generally to parametric, semiparametric, and nonparametric models with independent and dependent data. The results apply to the standard nonparametric iid bootstrap, moving block bootstraps for time series data, parametric and semiparametric bootstraps, and bootstraps for regression models based on bootstrapping residuals.Bootstrap, bootstrap repetitions, coefficient of excess kurtosis, confidence interval, density estimation, hypothesis test, p-value, quantile, simulation, standard error estimate