7 research outputs found
The impact of minimum wages on informal and formal labor market outcomes: evidence from Indonesia
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
Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes
Poverty is a multidimensional concept often comprising a monetary outcome and
other welfare dimensions such as education, subjective well-being or health,
that are measured on an ordinal scale. In applied research, multidimensional
poverty is ubiquitously assessed by studying each poverty dimension
independently in univariate regression models or by combining several poverty
dimensions into a scalar index. This inhibits a thorough analysis of the
potentially varying interdependence between the poverty dimensions. We propose
a multivariate copula generalized additive model for location, scale and shape
(copula GAMLSS or distributional copula model) to tackle this challenge. By
relating the copula parameter to covariates, we specifically examine if certain
factors determine the dependence between poverty dimensions. Furthermore,
specifying the full conditional bivariate distribution, allows us to derive
several features such as poverty risks and dependence measures coherently from
one model for different individuals. We demonstrate the approach by studying
two important poverty dimensions: income and education. Since the level of
education is measured on an ordinal scale while income is continuous, we extend
the bivariate copula GAMLSS to the case of mixed ordered-continuous outcomes.
The new model is integrated into the GJRM package in R and applied to data from
Indonesia. Particular emphasis is given to the spatial variation of the
income-education dependence and groups of individuals at risk of being
simultaneously poor in both education and income dimensions
Treatment effects beyond the mean using distributional regression: Methods and guidance.
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