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Using a randomised experiment, we show that providing better information about prospective jobs to vocational trainees can improve their placement outcomes. The study setting is the vocational training programme DDU-GKY in India. We find that including in the training two information sessions about placement opportunities make trainees 17% more likely to stay in the jobs in which they are placed. We argue that this effect is likely driven by improved selection into training. As a result of the intervention, trainees that are over-optimistic about placement jobs are more likely to drop out before placement.

Analysis of the relationship between taxes and self-employment should account for the interplay between responses in self-employment and wage employment. To this end, we estimate a two-state multi-spell duration model which accounts for both observed and unobserved heterogeneity using a large longitudinal administrative dataset for Norway for 1993 to 2011. Our findings confirm theoretical predictions, and are robust to various changes to definitions and sample selections. A policy experiment simulating a flatter tax schedule in the year 2000 is found to encourage self-employment, delivering a net increase of predicted inflow into self-employment from 2.8% to 5.3%.

Using a randomised experiment, we show that providing better information about prospective jobs to vocational trainees can improve their placement outcomes. The study setting is the vocational training programme DDU-GKY in India. We find that including in the training two information sessions about placement opportunities make trainees 17% more likely to stay in the jobs in which they are placed. We argue that this effect is likely driven by improved selection into training. As a result of the intervention, trainees that are over-optimistic about placement jobs are more likely to drop out before placement.

We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identifed. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal distribution of instrument index is close to one. Such an estimator achieves a univariate nonparametric rate, which can range from a cubic to an 'almost' parametric rate. We then consider the case in which either the monotonic index restriction does not hold and/ or the set of observations with propensity score close to one is thin so that convergence occurs at most at a cubic rate. We explore the finite sample behaviour in a Monte Carlo study, and illustrate the use of our estimator using a model for count data with multiplicative unobserved heterogeneity.

We use firm-level data to investigate the impact of taxes on the international location of targets in M&A, allowing for heterogeneous responses by companies. The statutory tax rate in the target country is found to have a negative impact on the probability of an acquisition in that country. In addition, the estimated size of the effect is found to depend on whether (i) acquirer is a domestic or a multinational enterprise; (ii) the acquisition is domestic or cross-border; and (iii) the acquirer's country has a worldwide or territorial tax system.\ud

We use firm-level data to investigate the impact of taxes on the international location of targets in M&A allowing for heterogeneous responses by companies. The statutory tax rate in the target country is found to have a negative impact on the probability of an acquisition in that country. In addition, the estimated size of the effect is found to depend on whether (i) acquirer is a domestic or a multinational enterprise; (ii) the acquisition is domestic or cross-border; and (iii) the acquirer’s country has a worldwide or territorial tax system.

We examine the link between a mother’s autonomy - the freedom and ability to think, express, act and make decisions independently - and the nutritional status of her children. We design a novel statistical framework that accounts for cultural and traditional environment, to create a measure of maternal autonomy, a concept that has rarely been examined previously as a factor in children’s nutritional outcomes. Using data from the Third Round of the National Family Health Survey for India, supplemented with our qualitative survey, and accounting for “son preference” by limiting analysis to first-born children under 18 months of age, we document that maternal autonomy has a positive impact on the long-term nutritional status of rural children. \ud

We examine the link between a mother’s autonomy - the freedom and ability to think, express, act and make decisions independently - and the nutritional status of her children. We design a novel statistical framework that accounts for cultural and traditional environment, to create a measure of maternal autonomy, a concept that has rarely been examined previously as a factor in children’s nutritional outcomes. Using data from the Third Round of the National Family Health Survey for India, supplemented with our qualitative survey, and accounting for “son preference” by limiting analysis to first-born children under 18 months of age, we document that maternal autonomy has a positive impact on the long-term nutritional status of rural children. We find that one standard deviation increase in maternal autonomy score (i) is associated with a 10 percent reduction (representing 300,000 children) in the prevalence of stunting, and (ii) compensates for half of the estimated average decline in Height-for Age Zscores Indian children experience in the second six months of life. The findings underscore the importance of women’s empowerment in improving children’s nutrition during the critical first two years of life, a recognized “window of opportunity” for lifelong health and economic benefits.

In 2005, the Indian Government launched a conditional cash-incentive program to encourage institutional delivery. This paper studies the effects of the program on neonatal mortality using district-level household survey data. We model mortality using survival analysis, paying special attention to the substantial heaping present in the data. The main objective of this paper is to provide a set of sufficient conditions for identification and consistent estimation of the baseline hazard accounting for heaping and unobserved heterogeneity. Our identification strategy requires neither administrative data nor multiple measurements, but a correctly reported duration and the presence of some flat segments in the baseline hazard which includes this correctly reported duration point. We establish the asymptotic properties of the maximum likelihood estimator and provide a simple procedure to test whether the policy had (uniformly) reduced mortality. While our empirical findings do not confirm the latter, they do indicate that accounting for heaping matters for the estimation of the baseline hazard.