Combining Matching and Nonparametric Instrumental Variable Estimation: Theory and An Application to the Evaluation of Active Labour Market Policies

We show how instrumental variable and matching estimators can be combined in order to identify a broader array of treatment effects. Instrumental variable (IV) estimators are known to estimate effects only for the compliers, representing a subset of the entire population. By combining IV with matching, we can estimate the treatment effects for the always‐ and never‐takers as well. Since in many cases these groups are the (endogenous) outcome of some assignment process, such estimates also help in judging the implications of such a selection process. In our application to the effects of participation in active labour market programmes in Switzerland, we find large and lasting positive employment effects for the compliers, whereas the effects for the always‐ and never‐participants are small. In addition, the compliers have worse employment outcomes without treatment than those who participate in the programme with or without the intervention under investigation. This suggests that the earlier assignment policy of the caseworkers was inefficient in that the always‐participants were neither those unemployed who would experience the highest expected treatment effects nor those unemployed who had the largest need for assistance. Copyright © 2014 John Wiley & Sons, Ltd.     

Two-stage DEA: <Emphasis Type="Italic">caveat emptor</Emphasis>

This paper examines the wide-spread practice where data envelopment analysis (DEA) efficiency estimates are regressed on some environmental variables in a second-stage analysis. In the literature, only two statistical models have been proposed in which second-stage regressions are well-defined and meaningful. In the model considered by Simar and Wilson (J Prod Anal 13:49–78, 2007), truncated regression provides consistent estimation in the second stage, where as in the model proposed by Banker and Natarajan (Oper Res 56: 48–58, 2008a), ordinary least squares (OLS) provides consistent estimation. This paper examines, compares, and contrasts the very different assumptions underlying these two models, and makes clear that second-stage OLS estimation is consistent only under very peculiar and unusual assumptions on the data-generating process that limit its applicability. In addition, we show that in either case, bootstrap methods provide the only feasible means for inference in the second stage. We also comment on ad hoc specifications of second-stage regression equations that ignore the part of the data-generating process that yields data used to obtain the initial DEA estimates.     

Do professional forecasters behave as if they believed in the New Keynesian Phillips Curve for the euro area?

This paper finds that participants in the European Central Bank’s Survey of Professional Forecasters have submitted forecasts that are consistent with a (mostly forward-looking) empirical version of the New Keynesian Phillips Curve for the euro area. The estimation technique takes advantage of the panel nature of the Survey of Professional Forecasters’ dataset to exploit both its time series and cross-section dimensions, and to control for unobservable individual heterogeneity across forecasters. The estimation results suggest that euro-area inflation forecasts have reacted less to unemployment forecasts after the start of the financial crisis but another cost measure (energy inflation) remains significant. This finding is consistent with a flatter Phillips Curve in the euro area after 2007. However, the reasons suggested by the International Monetary Fund for this finding, namely a better anchoring of inflation expectations and increases in structural unemployment do not seem to find support in the survey data. Instead, the expectations for compensation per employee submitted by professional forecasters are consistent with the existence of downward real-wage rigidities in euro-area labour markets.     

Antecedents of Purchase Intention in the Online Context

The understanding of the determinant factors of customer purchase intention is necessary, and it is equally important to study the online purchase context, since this context is disseminated among customers. A theoretical model has been elaborated on and tested, considering the constructs of information quality, distrust, and perceived risk as antecedents of purchase intention, and aiming to analyze the relationship among these constructs in the online purchase context. A quantitative research study has been performed by means of the application of a survey. Multivariate statistics techniques have been applied for data analyses, including structural equation modeling. This study contributes to the evolution of the empirically tested concepts by providing a greater individual understanding of each construct presented in the theoretical model, as well as the relationship among them as determinants of purchase intention; the indication is that meaningful relationships were found which may impact greater profitability and, consequently, greater competition for online retailers.     

Export survival and the dynamics of experience

This paper focuses on developing countries’ pioneer exports to the OECD and obtains several important results on export dynamics, linking export experience and export survival. Using product level data at the SITC 5-digit level for 114 developing countries over the 1962–2009 period, we show that prior export experience obtained in non-OECD markets significantly increases survival of pioneer exports toward the OECD. The experience does not need to last long, as gaining experience for more than two years does not confer any additional benefit. The effect of experience depreciates rapidly with time: a break in export experience prior to entering the OECD reduces the advantage on survival. Finally, the role of prior export experience is particularly relevant for survival in the first two years upon entry into the OECD. The geographic dynamic of export experience reveals that experience is acquired in neighboring, easy to access markets before reaching more distant, richer partners and ultimately serving the OECD with a higher probability of survival.     

The impact of working conditions on sickness absence: a theoretical model and an empirical application to work schedules

This paper explores how poor working conditions impact sickness absence through their effect on health. Our contribution is two-fold. First, we develop a static theoretical model based on the concept of health capital, wherein poor working conditions are partially compensated by higher wages. According to our model, the effect of working conditions on sickness absence is ambiguous. Second, we apply our model to the case of working time arrangements and test the effect of working irregular schedules or work around the clock on sickness absence, using data from the French Labor Force Survey on a specific population (male manual workers in private sector). As heterogeneity may lead to severe bias, we use propensity score matching methods. Our estimates show that working irregular schedules has a significant impact on sickness absence. The results are more mitigated for work around the clock. In any case, the extent crucially depends on age.     

The Macroeconomic Effects of Shocks to Large Banks’ Capital

We propose a simple approach to quantifying the macroeconomic effects of shocks to large banks’ leverage. We first estimate a standard dynamic model of leverage targeting at the bank level and use it to derive an aggregate measure of the economic capital buffer of large US bank holding corporations. We then evaluate the response of key macro variables to a shock to this aggregate bank capital buffer using standard monetary VAR models. We find that shocks to the capital of large US banks explain a substantial share of the variance of credit to firms and real activity.