DIVORCE LAW REFORMS AND DIVORCE RATES IN THE USA: AN INTERACTIVE FIXED‐EFFECTS APPROACH

This paper estimates the effects of unilateral divorce laws on divorce rates in the USA from a panel of state‐level divorce rates. We use the interactive fixed‐effects model to address the issue of endogeneity due to the association between cross‐state unobserved heterogeneity and divorce law reforms. We document that earlier studies in the literature do not fully control for unobserved heterogeneity and result in mixed empirical evidence on the effects of divorce law reforms. While reconciling these conflicting results, our results suggest that divorce law reforms have temporal positive effects on divorce rates, thus confirming the 2006 findings of Wolfers. Via simulation experiments, we assess the degree to which faulty inclusion or faulty exclusion of interactive fixed effects affects the policy effect estimators. Our results suggest that faulty inclusion only results in efficiency loss whereas faulty exclusion causes bias. Copyright © 2013 John Wiley & Sons, Ltd.     

FIRM HETEROGENEITY,PERSISTENT AND TRANSIENT TECHNICAL INEFFICIENCY: A GENERALIZED TRUE RANDOM‐EFFECTS model

This paper considers a panel data stochastic frontier model that disentangles unobserved firm effects (firm heterogeneity) from persistent (time‐invariant/long‐term) and transient (time‐varying/short‐term) technical inefficiency. The model gives us a four‐way error component model, viz., persistent and time‐varying inefficiency, random firm effects and noise. We use Bayesian methods of inference to provide robust and efficient methods of estimating inefficiency components in this four‐way error component model. Monte Carlo results are provided to validate its performance. We also present results from an empirical application that uses a large panel of US commercial banks. Copyright © 2012 John Wiley & Sons, Ltd.     

Identification Based on Difference‐in‐Differences Approaches with Multiple Treatments

This paper discusses identification based on difference‐in‐differences (DiD) approaches with multiple treatments. It shows that an appropriate adaptation of the common trend assumption underlying the DiD strategy for the comparison of two treatments restricts the possibility of effect heterogeneity for at least one of the treatments. The required assumption of effect homogeneity is likely to be violated because of non‐random assignment to treatment based on both observables and unobservables. However, this paper shows that, under certain conditions, the DiD estimate comparing two treatments identifies a lower bound in absolute values on the average treatment effect on the treated compared to the unobserved non‐treatment state, even if effect homogeneity is violated. This is possible if the treatments have ordered treatment effects, that is, in expectation, the effects of both treatments compared to no treatment have the same sign, and one treatment has a stronger effect than the other treatment on the respective recipients. Such assumptions are plausible if treatments are ordered or vary in intensity.     

Mixed Hidden Markov Models for Longitudinal Data: An Overview

In this paper we review statistical methods which combine hidden Markov models (HMMs) and random effects models in a longitudinal setting, leading to the class of so‐called mixed HMMs. This class of models has several interesting features. It deals with the dependence of a response variable on covariates, serial dependence, and unobserved heterogeneity in an HMM framework. It exploits the properties of HMMs, such as the relatively simple dependence structure and the efficient computational procedure, and allows one to handle a variety of real‐world time‐dependent data. We give details of the Expectation‐Maximization algorithm for computing the maximum likelihood estimates of model parameters and we illustrate the method with two real applications describing the relationship between patent counts and research and development expenditures, and between stock and market returns via the Capital Asset Pricing Model.     

Semi‐nonparametric competing risks analysis of recidivism

In this paper we specify a semi‐nonparametric competing risks (SNP‐CR) model of recidivism, for misdemeanors and felonies. The model is a bivariate mixed proportional hazard model with Weibull baseline hazards and common unobserved heterogeneity. The distribution of the latter is modeled semi‐nonparametrically, using orthonormal Legendre polynomials on the unit interval, and integrated out to make the two durations dependent, conditional on the covariates. The SNP‐CR model involved corresponds to a Logit model for felony arrest; hence the validity of the SNP‐CR model can be tested by testing the validity of the implied Logit model. The latter will be done by using the integrated conditional moment (ICM) test. In the first instance we have estimated and tested two versions of the SNP‐CR model, without and with fixed state effects. However, the ICM test rejects these models. Therefore, we have estimated and tested the model for each state separately. These state models are not rejected by the ICM test. Indeed, the estimation results vary substantially per state. Copyright © 2007 John Wiley & Sons, Ltd.     

When Does the Stepping‐Stone Work? Fixed‐Term Contracts Versus Temporary Agency Work in Changing Economic Conditions

This paper emphasizes differences among short‐term contracts in terms of career prospects. Using French data over the 2002–2010 period, we rely on a dynamic model with fixed effects to disentangle state dependence from unobserved heterogeneity. Although fixed‐term contracts may provide a ‘stepping‐stone’ to permanent positions, temporary agency work is hardly better than unemployment in this regard. The Great Recession of 2008 has changed the dynamics on the labor market and amplified the difference between fixed‐term contracts and temporary agency work. For both types of temporary workers, providing overtime work does not significantly increase the transition to permanent employment. Copyright © 2014 John Wiley & Sons, Ltd.     

Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models

This paper provides an approach to estimation and inference for nonlinear conditional mean panel data models, in the presence of cross‐sectional dependence. We modify Pesaran's (Econometrica, 2006, 74(4), 967–1012) common correlated effects correction to filter out the interactive unobserved multifactor structure. The estimation can be carried out using nonlinear least squares, by augmenting the set of explanatory variables with cross‐sectional averages of both linear and nonlinear terms. We propose pooled and mean group estimators, derive their asymptotic distributions, and show the consistency and asymptotic normality of the coefficients of the model. The features of the proposed estimators are investigated through extensive Monte Carlo experiments. We also present two empirical exercises. The first explores the nonlinear relationship between banks' capital ratios and riskiness. The second estimates the nonlinear effect of national savings on national investment in OECD countries depending on countries' openness.     

Bias corrections for two-step fixed effects panel data estimators

This paper introduces large-T bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These models include systems of equations with limited dependent variables and unobserved individual effects, and sample selection models with unobserved individual effects. Our two-step approach first estimates the reduced form by fixed effects procedures to obtain estimates of the time varying heterogeneity underlying the endogeneity/selection bias. We then estimate the primary equation by fixed effects including an appropriately constructed control variable from the reduced form estimates as an additional explanatory variable. The fixed effects approach in this second step captures the time invariant heterogeneity while the control variable accounts for the time varying heterogeneity. Since either or both steps might employ nonlinear fixed effects procedures it is necessary to bias adjust the estimates due to the incidental parameters problem. This problem is exacerbated by the two-step nature of the procedure. As these two-step approaches are not covered in the existing literature we derive the appropriate correction thereby extending the use of large-T bias adjustments to an important class of models. Simulation evidence indicates our approach works well in finite samples and an empirical example illustrates the applicability of our estimator.     

DOES EDUCATION REALLY IMPROVE HEALTH? A META‐ANALYSIS

While numerous studies assess the relationship between education and health, no consensus has been reached on whether education really improves health. We perform a meta‐analysis of 4866 estimates gleaned from 99 published studies that examine the health effects of education. We find that the current literature suffers from moderate publication bias towards the positive effects of education on health. After correcting for publication bias with an array of sophisticated methods, we find that the overall effect size is practically zero, indicating that education generates no discernible benefits to health. The heterogeneity analysis by Bayesian Model Averaging (BMA) and Frequentist Model Averaging (FMA) reveals that the reported estimates can be largely explained by whether the econometric models control for endogeneity of education, the types of data and the differences in health measurements. Our results also suggest that education may not be an effective policy option for promoting population health.     

CORRUPTION'S DIRECT EFFECTS ON PER‐CAPITA INCOME GROWTH: A META‐ANALYSIS

Corruption is a symptom of weak institutional quality and could have potentially adverse effects on economic growth. However, heterogeneity in reported findings makes it difficult to synthesize the evidence base with a view to test competing hypotheses and/or support evidence‐based policy and practice. To address this issue, we have extracted 327 estimates of corruption's direct effect on per‐capita GDP growth from 29 primary studies, following a peer‐reviewed and pre‐published systematic review protocol. Precision‐effect and funnel asymmetry tests indicate that corruption has a negative effect on per‐capita GDP growth after controlling for publication selection bias and within‐study dependence. However, multivariate meta‐regression analysis results indicate that the overall effect is not robust to inclusion of moderating variables through a general‐to‐specific procedure for model specification. We report that the marginal effect of corruption on per‐capita GDP growth is more adverse when the primary study estimates relate to long‐run growth, are based on low‐income‐country data only, and extracted from journal papers. The effect is less adverse in studies that use the International Country Risk Guide corruption perceptions index and in those reporting estimates from two‐stage least‐squares estimations.     

H‐likelihood Predictive Intervals for Unobservables

Inferences about unobserved random variables, such as future observations, random effects and latent variables, are of interest. In this paper, to make probability statements about unobserved random variables without assuming priors on fixed parameters, we propose the use of the confidence distribution for fixed parameters. We focus on their interval estimators and related probability statements. In random‐effect models, intervals can be formed either for future (yet‐to‐be‐realised) random effects or for realised values of random effects. The consistency of intervals for these two cases requires different regularity conditions. Via numerical studies, their finite sampling properties are investigated.     

A Markov Switching Factor‐Augmented VAR Model for Analyzing US Business Cycles and Monetary Policy

This paper develops a Markov switching factor‐augmented vector autoregression to investigate the transmission mechanisms of monetary policy for distinct stages of the US business cycle. We assume that autoregressive parameters and covariance matrices of the error terms are regime dependent, driven by an unobserved Markov indicator. Endogenously determined transition probabilities are governed by an underlying probit model that features a large set of possible predictors. The empirical findings provide evidence for differences in the transmission of monetary policy shocks that mainly stem from heterogeneity in the responses of financial market quantities.     

Hospital competition under pay‐for‐performance: Quality,mortality, and readmissions

Health outcomes, such as mortality and readmission rates, are commonly used as indicators of hospital quality and as a basis to design pay‐for‐performance (P4P) incentive schemes. We propose a model of hospital behavior under P4P where patients differ in severity and can choose hospital based on quality. We assume that risk‐adjustment is not fully accounted for and that unobserved dimensions of severity remain. We show that the introduction of P4P which rewards lower mortality and/or readmission rates can weaken or strengthen hospitals' incentive to provide quality. Since patients with higher severity have a different probability of exercising patient choice when quality varies, this introduces a selection bias (patient composition effect) which in turn alters quality incentives. We also show that this composition effect increases with the degree of competition. Critically, readmission rates suffer from one additional source of selection bias through mortality rates since quality affects the distribution of survived patients. This implies that the scope for counterproductive effects of P4P is larger when financial rewards are linked to readmission rates rather than mortality rates.     

A New Utility‐Consistent Econometric Approach to Multivariate Count Data Modeling

In the current paper, we propose a new utility‐consistent modeling framework to explicitly link a count data model with an event‐type multinomial‐choice model. The proposed framework uses a multinomial probit kernel for the event‐type choice model and introduces unobserved heterogeneity in both the count and discrete‐choice components. Additionally, this paper establishes important new results regarding the distribution of the maximum of multivariate normally distributed variables, which form the basis to embed the multinomial probit model within a joint modeling system for multivariate count data. The model is applied to analyzing out‐of‐home non‐work episodes pursued by workers, using data from the National Household Travel Survey. Copyright © 2014 John Wiley & Sons, Ltd.     

HETEROGENEITY,EXCESS ZEROS,AND THE STRUCTURE OF COUNT DATA MODELS

This paper demonstrates that the unobserved heterogeneity commonly assumed to be the source of overdispersion in count data models has predictable implications for the probability structure of such mixture models. In particular, the common observation of excess zeros is a strict implication of unobserved heterogeneity. This result has important implications for using count model estimates for predicting certain interesting parameters. Test statistics to detect such heterogeneity-related departures from the null model are proposed and applied in a health-care utilization example, suggesting that a null Poisson model should be rejected in favour of a mixed alternative. © 1997 John Wiley & Sons, Ltd.     

DOES EDUCATION REDUCE INCOME INEQUALITY? A META‐REGRESSION ANALYSIS

This paper reexamines the effects of education on inequality through a comprehensive meta‐regression analysis of the extant empirical literature. We find that education affects the two tails of the distribution of income: Education reduces the income share of top earners and increases the share of the bottom earners. Education has been particularly effective in reducing inequality in Africa. Some of the results suggest that secondary schooling appears to have a stronger effect than primary schooling, though this finding is not always robust. The heterogeneity in reported estimates can be largely explained by differences in the specification of the econometric model and measure of inequality and education.     

Union status of young men in Britain: a decade of change

Previous empirical studies of individual union status in Britain have been cross‐sectional. In contrast, we use longitudinal data from the National Child Development Study, to estimate the determinants of male trade union membership over the period 1981–1991. As suggested by union theories, we find that it is important to control for unobserved individual heterogeneity, and our preferred model allows for correlation of individual heterogeneity with observable variables. Our estimates reveal that the observed decline in very large workplaces, and the contraction of the public sector, explain about one third of the predicted decline in union membership over the period. Copyright © 2000 John Wiley & Sons, Ltd.     

CCE in fixed‐T panels

The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor‐augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross‐section averages of the observables, and then applying ordinary least squares conditional on the first‐step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross‐section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large‐T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously.     

Do Neighbours Influence Value‐Added‐Tax Introduction? A Spatial Duration Analysis

The spatial survival models typically impose frailties, which characterize unobserved heterogeneity, to be spatially correlated. However, the spatial effect may not only exist in the unobserved errors, but it can also be present in the baseline hazards and the dependent variables. A new spatial survival model with these three possible spatial correlation structures is explored and used to investigate the implementation of value‐added tax (VAT) in 99 countries over the period 1970–2009. Estimation is performed by a Bayesian approach through the Markov chain Monte Carlo method. The estimation results suggest the presence of a significant spatial correlation among the VAT introductions of neighbouring countries.     

Can inflation data improve the real‐time reliability of output gap estimates?

Potential output plays a central role in monetary policy and short‐term macroeconomic policy making. Yet, characterizing the output gap involves a trend‐cycle decomposition, and unobserved component estimates are typically subject to a large uncertainty at the sample end. An important consequence is that output gap estimates can be quite inaccurate in real time, as recently highlighted by Orphanides and van Norden ( 2002 ), and this causes a serious problem for policy makers. For the cases of the US, EU‐11 and two EU countries, we evaluate the benefits of using inflation data for improving the accuracy of real‐time estimates. Copyright © 2004 John Wiley & Sons, Ltd.