Wage differentials in the presence of unobserved worker,firm, and match heterogeneity

We consider the problem of estimating and decomposing wage differentials in the presence of unobserved worker, firm, and match heterogeneity. Controlling for these unobservables corrects omitted variable bias in previous studies. It also allows us to measure the contribution of unmeasured characteristics of workers, firms, and worker-firm matches to observed wage differentials. An application to linked employer-employee data shows that decompositions of inter-industry earnings differentials and the male-female differential are misleading when unobserved heterogeneity is ignored.     

Adjustment and unobserved heterogeneity in dynamic stochastic frontier models

Stochastic frontier models with autocorrelated inefficiency have been proposed in the past as a way of addressing the issue of temporal variation in firm-level efficiency scores. They are justified using an underlying model of dynamic firm behavior. In this paper we argue that these models could have radically different implications for the expected long-run efficiency scores in the presence of unobserved heterogeneity. The possibility of accounting for unobserved heterogeneity is explored. Random- and correlated random-effects dynamic stochastic frontier models are proposed and applied to a panel of US electric utilities.     

Identification of first-price auctions with non-separable unobserved heterogeneity

We propose a novel methodology for identification of first-price auctions, when bidders’ private valuations are independent conditional on one-dimensional unobserved heterogeneity. We extend the existing literature ( and ) by allowing the unobserved heterogeneity to be non-separable from bidders’ valuations. Our central identifying assumption is that the distribution of bidder values is increasing in the state. When the state-space is finite, such monotonicity implies the full-rank condition needed for identification. Further, we extend our approach to the conditionally independent private values model of Li et al. (2000), as well as to unobserved heterogeneity settings in which the implicit reserve price or the cost of bidding varies across auctions.     

The role of observed and unobserved heterogeneity in the duration of unemployment

This paper studies the degree to which observable and unobservable worker characteristics account for the variation in the aggregate duration of unemployment. I model the distribution of unobserved worker heterogeneity as time varying to capture the interaction of latent attributes with changes in labor-market conditions. Unobserved heterogeneity is the main explanation for the duration dependence of unemployment hazards. Both cyclical and low-frequency variations in the mean duration of unemployment are mainly driven by one subgroup: workers who, for unobserved reasons, stay unemployed for a long time. In contrast, changes in the composition of observable characteristics of workers have negligible effects.     

Global public spending efficiency in Tuscan municipalities

The paper presents a Data Envelopment Analysis aimed at studying the efficiency of Tuscan municipalities' public expenditure. Five strategic functions of Tuscan municipalities are first considered carrying out a non-aggregate analysis; then the overall expenditure composition of each municipality and the global spending efficiency are analysed by a proposed composite indicator. The main determinants affecting the municipalities' efficiency were further investigated. In particular, the obtained results may be consistently included in the long-standing debate on the municipal size, proving that the bigger the municipality, the greater its level of public expenditure efficiency.     

Testing for unobserved heterogeneity in exponential and Weibull duration models

We examine the use of the likelihood ratio (LR) statistic to test for unobserved heterogeneity in duration models, based on mixtures of exponential or Weibull distributions. We consider both the uncensored and censored duration cases. The asymptotic null distribution of the LR test statistic is not the standard chi-square, as the standard regularity conditions do not hold. Instead, there is a nuisance parameter identified only under the alternative, and a null parameter value on the boundary of the parameter space, as in Cho and White (2007a). We accommodate these and provide methods delivering consistent asymptotic critical values. We conduct a number of Monte Carlo simulations, comparing the level and power of the LR test statistic to an information matrix (IM) test due to Chesher (1984) and Lagrange multiplier (LM) tests of Kiefer (1985) and Sharma (1987). Our simulations show that the LR test statistic generally outperforms the IM and LM tests. We also revisit the work of van den Berg and Ridder (1998) on unemployment durations and of Ghysels et al. (2004) on interarrival times between stock trades, and, as it turns out, affirm their original informal inferences.     

A two-step approach to account for unobserved spatial heterogeneity

A two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis. Empirical analysis in economics often faces the difficulty that the data are correlated and heterogeneous in some unknown form. Spatial econometric models have been widely used to account for dependence structures, but the problem of directly dealing with unobserved spatial heterogeneity has been largely unexplored. The problem can be serious particularly if we have no prior information justified by economic theory. In this paper we propose a two-step procedure to identify endogenously spatial regimes in the first step and to account for spatial dependence in the second step. This procedure is applied to hedonic house price analysis.     

Testing for time-invariant unobserved heterogeneity in generalized linear models for panel data

Recent literature on panel data emphasizes the importance of accounting for time-varying unobservable individual effects, which may stem from either omitted individual characteristics or macro-level shocks that affect each individual unit differently. In this paper, we propose a simple specification test of the null hypothesis that the individual effects are time-invariant against the alternative that they are time-varying. Our test is an application of Hausman (1978) testing procedure and can be used for any generalized linear model for panel data that admits a sufficient statistic for the individual effect. This is a wide class of models which includes the Gaussian linear model and a variety of nonlinear models typically employed for discrete or categorical outcomes. The basic idea of the test is to compare two alternative estimators of the model parameters based on two different formulations of the conditional maximum likelihood method. Our approach does not require assumptions on the distribution of unobserved heterogeneity, nor it requires the latter to be independent of the regressors in the model. We investigate the finite sample properties of the test through a set of Monte Carlo experiments. Our results show that the test performs well, with small size distortions and good power properties. We use a health economics example based on data from the Health and Retirement Study to illustrate the proposed test.     

Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity

We extend the identification results for nonparametric simultaneous equations models in Matzkin (2008) to situations where the observations on the vector of dependent variables might be limited, and where the number of exogenous unobservable variables is larger than the number of dependent variables.     

On the effect of obesity on employment in the presence of observed and unobserved confounding

The aim of this paper was to estimate the effect of obesity on the employment probability for Italian men and women accounting for both observed and unobserved confounding. We use microdata collected by the Italian National Statistical Office for the year 2009 during a multi‐scope survey of Italian households. The employment–obesity relationship is estimated after controlling for observed confounding by using probit regression and a propensity score weighting approach. To control for both observed and unobserved confounding (endogeneity), a semiparametric recursive bivariate probit model is employed instead. Our findings suggest that obesity has a significant negative effect on the employment probability and that endogeneity might not be an important issue.     

Output complexity, environmental conditions, and the efficiency of municipalities

Over the last few years, many studies have analyzed the efficiency of local governments in different countries. An accurate definition of their output bundles—i.e., the services and facilities they provide to their constituencies—is essential to this research. However, several difficulties emerge in this task. First, since in most cases the law only establishes the minimum amount of services and facilities to provide, it may well be the case that some municipalities go beyond the legal minimum and, consequently, might have an uncertain effect on efficiency when compared to other municipalities which stick to the legal minimum. Second, municipalities face very different environmental conditions, which raises some doubts about the plausibility of an unconditional analysis. This study tackles these problems by proposing an analysis in which the efficiency of municipalities is evaluated after splitting them into clusters according to various criteria (output mix, environmental conditions, level of powers). We perform our estimations using order-m frontiers, given their robustness to outliers and immunity to the curse of dimensionality. We provide an application to Spanish municipalities, and results show that both output mix and, more especially, environmental conditions, should be controlled for, since efficiency differences between municipalities in different groups are notable.     

Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity

This paper develops semiparametric Bayesian methods for inference of dynamic Tobit panel data models. Our approach requires that the conditional mean dependence of the unobserved heterogeneity on the initial conditions and the strictly exogenous variables be specified. Important quantities of economic interest such as the average partial effect and average transition probabilities can be readily obtained as a by‐product of the Markov chain Monte Carlo run. We apply our method to study female labor supply using a panel data set from the National Longitudinal Survey of Youth 1979. Copyright © 2008 John Wiley & Sons, Ltd.     

Unemployment insurance and subsequent job duration: job matching versus unobserved heterogeneity

The relationship between Unemployment Insurance (UI) benefit duration, unemployment duration and subsequent job duration is investigated using a multi‐state duration model with state specific unobserved heterogeneity. I examine two potential explanations for the negative correlation between unemployment and job spell durations; UI benefits increase job matching quality (the ‘Matching’ effect) versus unobserved heterogeneity (‘Adverse Selection’). The Matching effect is found to be weak. Although new jobs accepted within 5 weeks of benefit termination seem to have a higher dissolution rate, the negative correlation between unemployment and job duration is mostly explained by unobserved heterogeneity. Various simulations indicate that increasing the maximum benefit duration by one week will raise expected unemployment duration by 1.0 to 1.5 days but will raise expected job duration by 0.5 to 0.8 day only. Copyright © 2001 John Wiley & Sons, Ltd.     

A semiparametric random effects approach to unobserved heterogeneity in count data models of organizational founding

The paper discusses a semiparametric random-effects approach to the problem of unobserved population heterogeneity in organizational research based on models for pooled cross-sectional time series count data. The analytical value of this approach rests in its ability to produce estimates of the structural parameters that do not depend on any specific assumption about the distribution of the heterogeneity components in the population. The practical value of the method proposed is illustrated in an empirical application to processes of organizational founding, and to the relation between density dependence and unobserved heterogeneity in spatially distributed organizational populations. The empirical evidence produced suggests that future studies of organizational founding at the population level will have to account for variation in observed as well as unmeasured (or unobservable) variables.     

Simple solutions to the initial conditions problem in dynamic,nonlinear panel data models with unobserved heterogeneity

I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models. Rather than attempting to obtain the joint distribution of all outcomes of the endogenous variables, I propose finding the distribution conditional on the initial value (and the observed history of strictly exogenous explanatory variables). The approach is flexible, and results in simple estimation strategies for at least three leading dynamic, nonlinear models: probit, Tobit and Poisson regression. I treat the general problem of estimating average partial effects, and show that simple estimators exist for important special cases. Copyright © 2005 John Wiley & Sons, Ltd.     

Measuring efficiency in the presence of head-to-head competition

We develop a new DEA model that measures organizational efficiency in the presence of head-to-head competition. Our model differs from existing DEA models that ignore competition (or any other form of interaction) among the organizations under analysis. The model assumes that organizations deploy inputs for the explicit purpose of increasing its own outputs while reducing the outputs of its competitors. We apply this model to the 2002, 2004, and 2006 political campaigns in New York State for the US. House of Representatives in which candidates spent money to increase the number of votes that they received and decrease the number of votes that their opponents received. We show that campaign inefficiency can alter the outcome of an election. Specifically, a loser would have won in six of the 57 races had he or she been efficient. We also show that incumbents are more likely to spend inefficiently than are challengers. Overall, inefficiency accounts for less than 5% of campaign funding but a loss of about 9% in votes received. We find evidence that campaign efficiency has increased since the passage of the Bipartisan Campaign Reform Act of 2002, known widely as the McCain-Feingold Act.     

Testing the proportional hazards assumption in the presence of unmeasured heterogeneity

This paper develops a discrete-time hazard model which accounts for unmeasured hetergeneity while allowing the coefficients of the regressors to vary over time. Sufficient conditions for nonparametric identifiability of the unmeasured heterogeneity distribution are derived. Testing for time-varying coefficients in this model is, under suitable conditions, equivalent to testing the proportionality assumption of the underlying continuous-time hazard model. Some Monte Carlo evidence is presented regarding the small-sample properties of this test. As an illustration, these tests are applied to the joblessness durations of displaced workers.     

The (mis)specification of discrete duration models with unobserved heterogeneity: A Monte Carlo study

Empirical researchers usually prefer statistical models that can be easily estimated with the help of commonly available software packages. Sequential binary models with or without normal random effects are an example of such models that can be adopted to estimate discrete duration models with unobserved heterogeneity. But an easy-to-implement estimation may incur a cost. In this paper we conduct a Monte Carlo simulation to evaluate the consequences of omitting or misspecifying the unobserved heterogeneity distribution in single-spell discrete duration models.     

Estimation of average treatment effects using panel data when treatment effect heterogeneity depends on unobserved fixed effects

This paper proposes a new panel data approach to identify and estimate the time-varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the linear fixed effects specification of potential outcome equations and uses exogenous variables that are correlated with the fixed effects. I apply the approach to study the impact of a mother's smoking during pregnancy on her child's birth weight.     

Foreign presence and efficiency in transition economies

This paper presents empirical evidence on the role of foreign presence in the performance of domestic manufacturing firms in five Central and Eastern European countries. Data Envelopment Analysis (DEA) was used to estimate a frontier for each sector with similar technology common for five transition countries in the sample − Bulgaria, Estonia, Hungary, Poland and Romania. Following Simar and Wilson (J Econom 136(1):31–64, 2007), this study applies a truncated regression and bootstrap technique in a second stage post-DEA analysis. Some evidence is found to support the hypothesis that foreign presence has an overall positive spillover effects on the performance of domestic firms.