Difference‐in‐differences when the treatment status is observed in only one period

This paper considers the difference‐in‐differences (DID) method when the data come from repeated cross‐sections and the treatment status is observed either before or after the implementation of a program. We propose a new method that point‐identifies the average treatment effect on the treated (ATT) via a DID method when there is at least one proxy variable for the latent treatment. Key assumptions are the stationarity of the propensity score conditional on the proxy and an exclusion restriction that the proxy must satisfy with respect to the change in average outcomes over time conditional on the true treatment status. We propose a generalized method of moments estimator for the ATT and we show that the associated overidentification test can be used to test our key assumptions. The method is used to evaluate JUNTOS, a Peruvian conditional cash transfer program. We find that the program significantly increased the demand for health inputs among children and women of reproductive age.     

Modelling Regime‐Specific Stock Price Volatility*

Single‐state generalized autoregressive conditional heteroscedasticity (GARCH) models identify only one mechanism governing the response of volatility to market shocks, and the conditional higher moments are constant, unless modelled explicitly. So they neither capture state‐dependent behaviour of volatility nor explain why the equity index skew persists into long‐dated options. Markov switching (MS) GARCH models specify several volatility states with endogenous conditional skewness and kurtosis; of these the simplest to estimate is normal mixture (NM) GARCH, which has constant state probabilities. We introduce a state‐dependent leverage effect to NM‐GARCH and thereby explain the observed characteristics of equity index returns and implied volatility skews, without resorting to time‐varying volatility risk premia. An empirical study on European equity indices identifies two‐state asymmetric NM‐GARCH as the best fit of the 15 models considered. During stable markets volatility behaviour is broadly similar across all indices, but the crash probability and the behaviour of returns and volatility during a crash depends on the index. The volatility mean‐reversion and leverage effects during crash markets are quite different from those in the stable regime.     

IDENTIFYING CAUSAL MECHANISMS (PRIMARILY) BASED ON INVERSE PROBABILITY WEIGHTING

This paper demonstrates the identification of causal mechanisms of a binary treatment under selection on observables, (primarily) based on inverse probability weighting; i.e. we consider the average indirect effect of the treatment, which operates through an intermediate variable (or mediator) that is situated on the causal path between the treatment and the outcome, as well as the (unmediated) direct effect. Even under random treatment assignment, subsequent selection into the mediator is generally non‐random such that causal mechanisms are only identified when controlling for confounders of the mediator and the outcome. To tackle this issue, units are weighted by the inverse of their conditional treatment propensity given the mediator and observed confounders. We show that the form and applicability of weighting depend on whether some confounders are themselves influenced by the treatment or not. A simulation study gives the intuition for these results and an empirical application to the direct and indirect health effects (through employment) of the US Job Corps program is also provided. Copyright © 2013 John Wiley & Sons, Ltd.     

Doubly Robust Estimation of Causal Effects with Multivalued Treatments: An Application to the Returns to Schooling

This paper provides doubly robust estimators for treatment effect parameters which are defined in a multivalued treatment effect framework. We apply this method to the unique dataset of the 1970 British Cohort Study (BCS70) to estimate returns to various levels of schooling. The analysis is carried out for female and male samples separately to capture possible gender differences. Average returns are estimated for the entire population, as well as conditional on having a specific educational achievement. For males, relative to no qualification, we find an average return to O‐levels of 6.3%, to A‐levels of 7.9% and to higher education of 25.4%. The estimated average returns to O‐level and A‐level relative to no qualification are insignificant for females, whereas the return to higher education is 19.9%.Copyright © 2014 John Wiley & Sons, Ltd.     

Macroeconomic effects of aggregate accounting conservatism: A cross‐country analysis

This paper examines whether aggregate conditional and unconditional conservatism are associated with economic growth. Prior studies find that conditional conservatism improves contracting efficiency, but that unconditional conservatism has either a neutral or detrimental impact on contracting. We therefore conjecture that country‐level conditional conservatism increases the efficiency of resource allocation in an economy, whereas country‐level unconditional conservatism is not similarly beneficial. Using a cross‐country sample, we construct country‐level estimates of conditional and unconditional conservatism. We find that conditional conservatism is associated with higher level of growth in Gross Domestic Product and Gross Domestic Product per Capita. By contrast, unconditional conservatism shows no or negative association. Our study contributes to the ongoing debate on the desirability of accounting conservatism and also extends the literature on the macroeconomic effects of aggregate financial reporting attributes.     

REALIZED BETA GARCH: A MULTIVARIATE GARCH MODEL WITH REALIZED MEASURES OF VOLATILITY

We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model that incorporates realized measures of variances and covariances. Realized measures extract information about the current levels of volatilities and correlations from high‐frequency data, which is particularly useful for modeling financial returns during periods of rapid changes in the underlying covariance structure. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model specification of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than usually found with rolling‐window regressions based exclusively on daily returns. In the empirical part of the paper, we examine the cross‐sectional as well as the time variation of the conditional beta series during the financial crises. Copyright © 2014 John Wiley & Sons, Ltd.     

Stochastic volatility models: conditional normality versus heavy‐tailed distributions

Most of the empirical applications of the stochastic volatility (SV) model are based on the assumption that the conditional distribution of returns, given the latent volatility process, is normal. In this paper, the SV model based on a conditional normal distribution is compared with SV specifications using conditional heavy‐tailed distributions, especially Student's t‐distribution and the generalized error distribution. To estimate the SV specifications, a simulated maximum likelihood approach is applied. The results based on daily data on exchange rates and stock returns reveal that the SV model with a conditional normal distribution does not adequately account for the two following empirical facts simultaneously: the leptokurtic distribution of the returns and the low but slowly decaying autocorrelation functions of the squared returns. It is shown that these empirical facts are more adequately captured by an SV model with a conditional heavy‐tailed distribution. It also turns out that the choice of the conditional distribution has systematic effects on the parameter estimates of the volatility process. Copyright © 2000 John Wiley & Sons, Ltd.     

Modelling heterogeneity and dynamics in the volatility of individual wages

This paper presents a model for the heterogeneity and dynamics of the conditional mean and conditional variance of individual wages. A bias‐corrected likelihood approach, which reduces the estimation bias to a term of order 1/T², is used for estimation and inference. The small‐sample performance of the proposed estimator is investigated in a Monte Carlo study. The simulation results show that the bias of the maximum likelihood estimator is substantially corrected for designs calibrated to the data used in the empirical analysis, drawn from the PSID. The empirical results show that it is important to account for individual unobserved heterogeneity and dynamics in the variance, and that the latter is driven by job mobility. The model also explains the non‐normality observed in log‐wage data. Copyright © 2010 John Wiley & Sons, Ltd.     

Why so fed up and footloose in IT? Spelling out the associations between occupation and overall job satisfaction shown by WERS 2004

Analysis of the Workplace Employment Relations Survey 2004 employee data shows striking differences in levels of overall job satisfaction among occupational groups. The examination is based on the 81 Minor Occupation groupings in UK Standard Occupational Classification 2000 classification. Taking a possible specific occupational effect possibly conditioned by resonance effects, as a theoretical point of departure, multivariate analysis is used to restate apparent occupational effect as occupationally bundled individual‐level variables and workplace influences—a process seen as exchanging nominal‐level measurement (names of occupations) with theoretical variables. Although 13 minor occupational groups retain statistically significant independent influence after the statistical treatment, these effects are small. Detailed illustration and specification of bundling effects and further examination of their sources take information and communications technology and communication professionals as their point of reference.     

Good Governance and Norms of Citizenship: An Investigation into the System‐ and Individual‐Level Determinants of Attachment to Civic Norms

Norms of citizenship are seen as a precondition for a functioning polity and society. But what determines the importance citizens attach to these norms? Are individual‐level features, like education or social embeddedness, relevant? Do system‐level features like the economic situation or quality of governance matter? Our findings from a multilevel analysis indicate that, paradoxically, a political system's effectiveness and legitimacy undermine the very norms on which it depends for both effectiveness and legitimation. In well‐functioning states, citizens' attachment to civic norms declines. As for the effect of welfare policies, there is no “crowding‐out” effect in the sense that if the state provides for citizens who are less well off, solidarity among citizens was reduced. Few individual‐level characteristics that relate to the public sphere—such as social embeddedness—are found to matter, indicating that norms are perpetuated in the private sphere.     

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.     

PANEL PROBIT WITH FLEXIBLE CORRELATED EFFECTS: QUANTIFYING TECHNOLOGY SPILLOVERS IN THE PRESENCE OF LATENT HETEROGENEITY

In this paper, we introduce a Bayesian panel probit model with two flexible latent effects: first, unobserved individual heterogeneity that is allowed to vary in the population according to a nonparametric distribution; and second, a latent serially correlated common error component. In doing so, we extend the approach developed in Albert and Chib (Journal of the American Statistical Association 1993; 88 : 669–679; in Bayesian Biostatistics, Berry DA, Stangl DK (eds), Marcel Dekker: New York, 1996), and in Chib and Carlin (Statistics and Computing 1999; 9 : 17–26) by releasing restrictive parametric assumptions on the latent individual effect and eliminating potential spurious state dependence with latent time effects. The model is found to outperform more traditional approaches in an extensive series of Monte Carlo simulations. We then apply the model to the estimation of a patent equation using firm‐level data on research and development (R&D). We find a strong effect of technology spillovers on R&D but little evidence of product market spillovers, consistent with economic theory. The distribution of latent firm effects is found to have a multimodal structure featuring within‐industry firm clustering. Copyright © 2012 John Wiley & Sons, Ltd.     

Normal mixture GARCH(1,1): applications to exchange rate modelling

Some recent specifications for GARCH error processes explicitly assume a conditional variance that is generated by a mixture of normal components, albeit with some parameter restrictions. This paper analyses the general normal mixture GARCH(1,1) model which can capture time variation in both conditional skewness and kurtosis. A main focus of the paper is to provide evidence that, for modelling exchange rates, generalized two‐component normal mixture GARCH(1,1) models perform better than those with three or more components, and better than symmetric and skewed Student's t‐GARCH models. In addition to the extensive empirical results based on simulation and on historical data on three US dollar foreign exchange rates (British pound, euro and Japanese yen), we derive: expressions for the conditional and unconditional moments of all models; parameter conditions to ensure that the second and fourth conditional and unconditional moments are positive and finite; and analytic derivatives for the maximum likelihood estimation of the model parameters and standard errors of the estimates. Copyright © 2006 John Wiley & Sons, Ltd.     

CONDITIONALLY HETEROSKEDASTIC FACTOR MODELS WITH SKEWNESS AND LEVERAGE EFFECTS

Conditional heteroskedasticity, skewness and leverage effects are well‐known features of financial returns. The literature on factor models has often made assumptions that preclude the three effects to occur simultaneously. In this paper I propose a conditionally heteroskedastic factor model that takes into account the presence of both the conditional skewness and leverage effects. This model is specified in terms of conditional moment restrictions and unconditional moment conditions are proposed allowing inference by the generalized method of moments (GMM). The model is also shown to be closed under temporal aggregation. An application to daily excess returns on sectorial indices from the UK stock market provides strong evidence for dynamic conditional skewness and leverage with a sharp efficiency gain resulting from accounting for both effects. The estimated volatilitypersistence from the proposed model is lower than that estimated from models that rule out such effects. I also find that the longer the returns' horizon, the fewer conditionally heteroskedastic factors may be required for suitable modeling and the less strong is the evidence for dynamic leverage. Some of these results are in line with the main findings of Harvey and Siddique ( 1999 ) and Jondeau and Rockinger ( 2003 ), namely that accounting for conditional skewness impacts the persistence in the conditional variance of the return process. Copyright © 2012 John Wiley & Sons, Ltd.     

DO PEERS AFFECT STUDENT ACHIEVEMENT? EVIDENCE FROM CANADA USING GROUP SIZE VARIATION

We provide the first empirical application of a new approach proposed by Lee (Journal of Econometrics 2007; 140 (2), 333–374) to estimate peer effects in a linear‐in‐means model when individuals interact in groups. Assumingsufficient group size variation, this approach allows to control for correlated effects at the group level and to solve the simultaneity (reflection) problem. We clarify the intuition behind identification of peer effects in the model. We investigate peer effects in student achievement in French, Science, Mathematics and History in secondary schools in the Province of Québec (Canada). We estimate the model using conditional maximum likelihood and instrumental variables methods. We find some evidence of peer effects. The endogenous peer effect is large and significant in Mathematics but imprecisely estimated in the other subjects. Some contextual peer effects are also significant. In particular, for most subjects, the average age of peers has a negative effect on own test score. Using calibrated Monte Carlo simulations, we find that high dispersion in group sizes helps with potential issues of weak identification. Copyright © 2012 John Wiley & Sons, Ltd.     

Effects of Information Technology Capabilities on Strategic Alliances: Implications for the Resource‐Based View

This paper adopts a contingency approach to the resource‐based view (RBV) of the firm and seeks to establish boundary conditions for the value of certain information technology (IT) capabilities. We first identify inter‐organizational alliances as a specific strategy context in which IT capabilities are particularly valuable. We then consider more detailed boundary conditions that can shape the value of these capabilities within the alliance context. Our study shows that firms with better IT capabilities can derive greater value from an alliance, yet this effect also varies across different types of alliances depending on an individual alliance's characteristics. Specifically, IT capabilities are more valuable for alliances with a non‐equity governance structure, as well as those involving a high degree of interdependence between partners. We highlight the implications of our findings for opportunities to advance the RBV.     

The real problem: The deadly combination of psychologisation,scientism, and normative promotionalism takes strategic human resource management down a 30‐year dead end

This paper engages with Troth and Guest (2019) on psychology in HRM. I argue they misframe the central issue in debate. The real problem is not psychology per se but psychologisation—the drive to reduce explanation of macro‐level HRM outcomes to individual‐level psychological‐behavioural factors and individual differences. Accordingly, the most visible and harmful effects of psychologisation are in strategic HRM and the HRM‐performance literature but Troth and Guest's defence of psychology does not cover them. I use this response to re‐establish that it is psychologisation, not psychology per se, that is the critics' focal concern and describe how the three‐decade advance of psychologisation, along with scholastic scientism and normative promotionalism, have created severe theoretical and empirical problems in the high‐performance research programme and taken the strategic HRM field down a 30‐year dead‐end. Suggestions for a turn‐around are provided.     

Accounting earnings and firm valuation: the French case

Considering that the level of the association between stock returns and accounting earnings provides a measure of the extent to which earnings summarize the information which is useful for firm valuation, this paper analyses the contemporaneous association between stock returns and earnings changes or earnings level of individual French stocks and portfolios for periods of one, two and five years between 1981 and 1990. The empirical findings are as follows. (a) Stock returns are more linked to earnings changes than to earnings levels indicating that earnings provide more information about changes in firm value than about firm value. (b) Earnings prepared in accordance with the French accounting principles are not less value-relevant than those prepared in accordance with US or UK GAAP. (c) A cross-sectionally and time-aggregated data procedure provides a large increase in the explanatory power of earnings for returns which is consistent with a noise-in-earnings effect probably induced by accounting measurement and valuation principles and with a recognition lag effect due to the fact that value-relevant events are not integrated into earnings exactly when they occur. These two effects are shown to be the major causes of the low association between earnings and returns generally observed in studies based on short period data for individual stocks.     

So,What Am I Supposed to Do? A Multilevel Examination of Role Clarity

Although prior research generally holds that role clarity is affected by both individual characteristics and organizational contexts, current conceptual or empirical models do not reflect the multilevel nature of these antecedents. A more complete understanding of how role clarity emanates from different organizational levels is necessary to help prevent poor job performance and other harmful consequences of ambiguous role expectations. To address this, I begin this research by investigating the effects of internal work locus of control, general self‐efficacy, and leader–member exchange on role clarity. With respect to the cross‐level effects, I focus on the roles of a manager's control style and the organization's strategy‐making pattern. Analyses of a multi‐industry, multilevel dataset collected from 724 employees and 124 managers in 25 organizations in Finland suggest that all of the individual‐level independent variables and a deliberate strategy‐making pattern improve role clarity. However, a deliberate strategy‐making pattern negatively moderates the relationship between general self‐efficacy and role clarity. Finally, even though an outcome‐based control system causes role ambiguity among employees in most functional areas, it may be an effective driver of role clarity among employees in sales jobs.