This paper studies the effects of minimum wages on informal and formal sector wages and employment in Indonesia between 1997 and 2007. Applying fixed-effects methods, the estimates suggest that minimum wages have a significant positive effect on formal sector wages, while there are no spillover effects on informal workers. Regarding employment, we find no statistically significant negative effects of minimum wages on the probability of being formally employed. These findings suggest that employers use adjustment channels other than employment or that effects such as a demand stimulus on a local level outweigh the possible negative employment effects.Jel codes: J08, J46

Background/aimHorse riding is a popular sport, which bears the risk of serious injuries. This study aims to assess whether individual factors influence the risk to sustain major injuries.MethodsRetrospective data were collected from all equine-related accidents at a German Level I Trauma Centre between 2004 and 2014. Logistic regression was used to identify the risk factors for major injures.Results770 patients were included (87.9% females). Falling off the horse (67.7%) and being kicked by the horse (16.5%) were the two main injury mechanisms. Men and individuals of higher age showed higher odds for all tested parameters of serious injury. Patients falling off a horse had higher odds for being treated as inpatients, whereas patients who were kicked had higher odds for a surgical therapy (OR 1.7) and intensive care unit/intermediate care unit (ICU/IMC) treatment (OR 1.2). The head was the body region most often injured (32.6%) and operated (32.9%). Patients with head injuries had the highest odds for being hospitalised (OR 6.13). Head or trunk injuries lead to the highest odds for an ICU/IMC treatment (head: OR 4.37; trunk: OR 2.47). Upper and lower limb injuries showed the highest odds for a surgical therapy (upper limb: OR 2.61; lower limb: OR 1.7).ConclusionRisk prevention programmes should include older individuals and males as target groups. Thus a rethinking of the overall risk assessment is necessary. Not only horseback riding itself, but also handling a horse bears a relevant risk for major injuries. Serious head injures remain frequent, serious and an important issue to be handled in equestrians sports.

This paper introduces distributional regression also known as generalized additive models for location, scale and shape (GAMLSS) as a modeling framework for analyzing treatment effects beyond the mean. In contrast to mean regression models, GAMLSS relate each distributional parameter to covariates. Therefore, they can be used to model the treatment effect not only on the mean but on the whole conditional distribution. Since they encompass a wide range of different distributions, GAMLSS provide a flexible framework for modeling non-normal outcomes in which additionally nonlinear and spatial effects can easily be incorporated. We elaborate on the combination of GAMLSS with program evaluation methods including randomized controlled trials, panel data techniques, difference in differences, instrumental variables, and regression discontinuity design. We provide practical guidance on the usage of GAMLSS by reanalyzing data from the Mexican Progresa program. Contrary to expectations, no significant effects of a cash transfer on the conditional consumption inequality level between treatment and control group are found.

We tackle two limitations of standard instrumental variable regression in experimental and observational studies: restricted estimation to the conditional mean of the outcome and the assumption of a linear relationship between regressors and outcome. More flexible regression approaches that solve these limitations have already been developed but have not yet been adopted in causality analysis. The paper develops an instrumental variable estimation procedure building on the framework of generalized additive models for location, scale and shape. This enables modelling all distributional parameters of potentially complex response distributions and non-linear relationships between the explanatory variables, instrument and outcome. The approach shows good performance in simulations and is applied to a study that estimates the effect of rural electrification on the employment of females and males in the South African province of KwaZulu-Natal. We find positive marginal effects for the mean for employment of females rates, negative effects for employment of males and a reduced conditional standard deviation for both, indicating homogenization in employment rates due to the electrification programme. Although none of the effects are statistically significant, the application demonstrates the potentials of using generalized additive models for location, scale and shape in instrumental variable regression for both to account for endogeneity and to estimate treatment effects beyond the mean.

This paper analyzes several modifications to improve a simple measure of vulnerability as expected poverty. Firstly, in order to model income, we apply distributional regression relating potentially each parameter of the conditional income distribution to the covariates. Secondly, we determine the vulnerability cutoff endogenously instead of defining a household as vulnerable if its probability of being poor in the next period is larger than 0.5. For this purpose, we employ the receiver operating characteristic curve that is able to consider prerequisites according to a particular targeting mechanism. Using long-term panel data from Germany, we build both mean and distributional regression models with the established 0.5 probability cutoff and our vulnerability cutoff. We find that our new cutoff considerably increases predictive performance. Placing the income regression model into the distributional regression framework does not improve predictions further but has the advantage of a coherent model where parameters are estimated simultaneously replacing the original three step estimation approach.