How do Training Programs Assign Participants to Training? Characterizing the Assignment Rules of Government Agencies for Welfare-to-Work Programs in California

A great deal of attention has been paid in the literature to estimating the impacts of training programs. Much less attention has been devoted to how training agencies assign participants to training programs, and to how these allocation decisions vary with agency resources, the initial skill levels of participants and the prevailing labor market conditions. This paper models the training assignment problem faced by welfare agencies, deriving empirical implications regarding aggregate training policies and testing these implications using data from Welfare-to-Work training programs run by California counties during the 1990s. I find that county welfare agencies do not seem to follow a simple returns-maximization model in their training assignment decisions. The results show that, as suggested by political economy models, the local political environment has a strong effect on training policies. In particular, I find that going from a Republican to a Democratic majority in a county's Board of Supervisors has a strong effect on training policies, significantly increasing the proportion of welfare recipients receiving human capital development training.Assignment to Training Rules, Welfare to Work Programs, Local Political Environment

CEO Power and Compensation in Financially Distressed Firms

We study the changes in CEO power and compensation that arise when firms go through financial distress. We use a matching estimator to identify suitable controls and estimate the causal effects of financial distress for a sample of U.S. public companies from 1992 to 2005. We document that, relative to those in control firms, the CEOs of distressed firms experience significant reductions in total compensation; the bulk of this reduction derives from the decline in value of new grants of stock options. These results hold not only for incumbent CEOs but also, surprisingly, for newly hired CEOs. Financial distress has important consequences on corporate governance, decreasing managerial influence over the board. We find that, among distressed firms, there is a significant decrease in the proportion of CEOs holding board chairmanship, and in the fractions of executives serving as directors or in the compensation committee of the board. We also show that periods of financial distress are associated with a decrease in opportunistic timing behavior of stock option awards. The results are suggestive of a link between managerial power and executive compensation.CEO compensation, financial distress, lucky grants, managerial influence, bias-corrected matching estimators

Not So Lucky Any More: CEO Compensation in Financially Distressed Firms

There is a debate on whether executive pay reflects rent extraction due to "managerial power" or is the result of arms-length bargaining in a principal-agent framework. In this paper we offer a test of the managerial power hypothesis by empirically examining the CEO compensation of U.S. public companies that were ever in financial distress between 1992 and 2005. Using a bias-corrected matching estimator that estimates the causal effects of financial distress, we find that, for the distressed firms, CEO turnover rates increase markedly and their CEOs, both incumbents and successors, experience significant reductions in total compensation. The bulk of the reduction in total compensation derives from the decline in value of stock option grants, which we argue is due to a change in the opportunistic timing of option grants. We define "lucky" grants as those with grant prices below or at the lowest stock price of the grant month, and we find that the proportion of lucky grants for financially distressed firms is higher before insolvency and lower upon and after insolvency, while the proportion for similar but solvent firms remains stable throughout the period. We interpret this evidence as consistent with a decrease in managerial power induced by a tightening in the "outrage" constraint due to the episode of financial distress.CEO compensation, CEO turnover, financial distress, lucky grants, bias-corrected matching estimators

Intergenerational transmission of welfare dependency: The effects of length of exposure

It is well documented that a positive correlation exists between receiving welfare as a child and depending on welfare as an adult. However, previous studies have not been able to explore many aspects of this relationship. This paper uses a unique administrative dataset for California, which follows welfare recipients since their teenage years until early adulthood, to study the causal effects of different lengths of welfare exposure as a child (conditional on welfare receipt) on future welfare dependency as a young adult. The econometric analyses in this paper use a recently developed method from the program evaluation literature, based on the estimation of a generalized propensity score (GPS). As in the binary-treatment case the GPS permits removing the biases associated with differences in the observed characteristics of individuals. In addition, for some analyses, family-level unobserved heterogeneity is controlled for by relying on pairs of siblings exposed to different lengths of exposure. The results show that there is no causal effect of length of exposure on future welfare dependency, nor on teenage childbearing. Conditional on teenage childbearing, there are no effects of length of exposure on adult welfare dependency either, but this dependency is almost three times larger for teenage mothers than for non-mothers. All results hold when controlling for unobserved heterogeneity. The results indicate that policies like time-limits are not likely to reduce the intergenerational correlation of welfare dependency.Welfare Dependency, Continuous Treatments

Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data

This paper assesses the e¤ectiveness of unconfoundedness-based estimators of mean e¤ects for multiple or multivalued treatments in eliminating biases arising from nonrandom treatment assignment. We evaluate these multiple treatment estimators by simultaneously equalizing average outcomes among several control groups from a randomized experiment. We study linear regression estimators as well as partial mean and weighting estimators based on the generalized propensity score (GPS). We also study the use of the GPS in assessing the comparability of individuals among the di¤erent treatment groups, and propose a strategy to determine the overlap or common support region that is less stringent than those previously used in the literature. Our results show that in the multiple treatment setting there may be treatment groups for which it is extremely di¢ cult to ?nd valid comparison groups, and that the GPS plays a signi?cant role in identifying those groups. In such situations, the estimators we consider perform poorly. However, their performance improves considerably once attention is restricted to those treatment groups with adequate overlap quality, with di¤erence-in-di¤erence estimators performing the best. Our results suggest that unconfoundedness-based estimators are a valuable econometric tool for evaluating multiple treatments, as long as the overlap quality is satisfactory.

Not So Lucky Any More: CEO Compensation in Financially Distressed Firms

There is a debate on whether executive pay reflects rent extraction due to “managerial power” or is the result of arms-length bargaining in a principal-agent framework. In this paper we offer a test of the managerial power hypothesis by empirically examining the CEO compensation of U.S. public companies that were ever in financial distress between 1992 and 2005. Using a bias-corrected matching estimator that estimates the causal effects of financial distress, we find that, for the distressed firms, CEO turnover rates increase markedly and their CEOs, both incumbents and successors, experience significant reductions in total compensation. The bulk of the reduction in total compensation derives from the decline in value of stock option grants, which we argue is due to a change in the opportunistic timing of option grants. We define “lucky” grants as those with grant prices below or at the lowest stock price of the grant month, and we find that the proportion of lucky grants for financially distressed firms is higher before insolvency and lower upon and after insolvency, while the proportion for similar but solvent firms remains stable throughout the period. We interpret this evidence as consistent with a decrease in managerial power induced by a tightening in the “outrage” constraint due to the episode of financial distress.CEO compensation, CEO turnover, financial distress, lucky grants, bias-corrected matching estimators

On the remote sensing of oceanic and atmospheric convection in the Greenland Sea by synthetic aperture radar

In this paper we discuss characteristic properties of radar signatures of oceanic and atmospheric convection features in the Greenland Sea. If the water surface is clean (no surface films or ice coverage), oceanic and atmospheric features can become visible in radar images via a modulation of the surface roughness, and their radar signatures can be very similar. For an unambiguous interpretation and for the retrieval of quantitative information on current and wind variations from radar imagery with such signatures, theoretical models of current and wind phenomena and their radar imaging mechanisms must be utilized. We demonstrate this approach with the analysis of some synthetic aperture radar (SAR) images acquired by the satellites ERS-2 and RADARSAT-1. In once case, an ERS-2 SAR image an a RADARSAT-1 ScanSAR image exhibit pronounced cell-like signatures with length scales on the order of 10-20 km and modulation depths of about 5-6 dB and 9-10 dB, respectively. Simulations with a numerical SAR imagaing model and various input current and wind fields reveal that the signatures in both images can be expained consistently by wind variations on the order of±2.5 ms, but not by surface current variations on realistic orders of magnitude. Accordingly, the observed features must be atmospheric convection cells. This is confirmed by visible typical cloud patterns in a NOAA AVHRR image of the test scenario. In another case, the presence of an oceanic convective chimney is obvious from in situ data, but no signatures of it are visible in an ERS-2 SAR image. We show by numerical simulations with an oceanic convection model and our SAR imaging model that this is consistent with theoretical predictions, since the current gradients associated with the observed chimney are not sufficiently strong to give rise to significant signatures in an ERS-2 SAR image under the given conditions. Further model results indicate that it should be generally difficult to observe oceanic convection features in the Greenland Sea with ERS-2 or RADARSAT-1 SAR, since their signatures resulting from pure wave-current interaction will be too weak to become visible in the noisy SAR images in most cases. This situation will improve with the availability of future high-resolution SARs such as RADARSAT-2 SAR in fine resolution mode (2004) and TerraSAR-X (2005) which will offer significantly reduced speckle noise fluctuations at comparable spatial resolutions and thus a much better visibility of small image variations on spatial scales on the order of a few hundred meters

Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data

This paper assesses the effectiveness of unconfoundedness-based estimators of mean effects for multiple or multivalued treatments in eliminating biases arising from nonrandom treatment assignment. We evaluate these multiple treatment estimators by simultaneously equalizing average outcomes among several control groups from a randomized experiment. We study linear regression estimators as well as partial mean and weighting estimators based on the generalized propensity score (GPS). We also study the use of the GPS in assessing the comparability of individuals among the different treatment groups, and propose a strategy to determine the overlap or common support region that is less stringent than those previously used in the literature. Our results show that in the multiple treatment setting there may be treatment groups for which it is extremely difficult to find valid comparison groups, and that the GPS plays a significant role in identifying those groups. In such situations, the estimators we consider perform poorly. However, their performance improves considerably once attention is restricted to those treatment groups with adequate overlap quality, with difference-in-difference estimators performing the best. Our results suggest that unconfoundedness-based estimators are a valuable econometric tool for evaluating multiple treatments, as long as the overlap quality is satisfactory.multiple treatments, nonexperimental estimators, generalized propensity score

Nonparametric Tests for Treatment Effect Heterogeneity

A large part of the recent literature on program evaluation has focused on estimation of the average effect of the treatment under assumptions of unconfoundedness or ignorability following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many cases however, researchers are interested in the effects of programs beyond estimates of the overall average or the average for the subpopulation of treated individuals. It may be of substantive interest to investigate whether there is any subpopulation for which a program or treatment has a nonzero average effect, or whether there is heterogeneity in the effect of the treatment. The hypothesis that the average effect of the treatment is zero for all subpopulations is also important for researchers interested in assessing assumptions concerning the selection mechanism. In this paper we develop two nonparametric tests. The first test is for the null hypothesis that the treatment has a zero average effect for any subpopulation defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, in other words, that there is no heterogeneity in average treatment effects by covariates. Sacrificing some generality by focusing on these two specific null hypotheses we derive tests that are straightforward to implement.average treatment effects, causality, unconfoundedness, treatment effect heterogeneity

The Effects of Female Labor Force Participation on Obesity

This paper assesses whether a causal relationship exists between recent increases in female labor force participation and the increased prevalence of obesity amongst women. The expansions of the Earned Income Tax Credit (EITC) in the 1980s and 1990s have been established by prior literature as having generated variation in female labor supply, particularly amongst single mothers. Here, we use this plausibly exogenous variation in female labor supply to identify the effect of labor force participation on obesity status. We use data from the National Health Interview Survey (NHIS) and replicate labor supply effects of the EITC expansions found in previous literature. This validates employing a difference-in-differences estimation strategy in the NHIS data, as has been done in several other data sets. Depending on the specification, we find that increased labor force participation can account for at most 19% of the observed change in obesity prevalence over our sample period. Our preferred specification, however, suggests that there is no causal link between increased female labor force participation and increased obesity.female labor force participation, obesity, earned income tax credit