Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles

Hydrograph clustering helps to identify dynamic patterns within aquifers systems, an important foundation of characterizing groundwater systems and their influences, which is necessary to effectively manage groundwater resources. We develope an unsupervised modeling approach to characterize and cluster hydrographs on regional scale according to their dynamics. We apply feature-based clustering to improve the exploitation of heterogeneous datasets, explore the usefulness of existing features and propose new features specifically useful to describe groundwater hydrographs. The clustering itself is based on a powerful combination of Self-Organizing Maps with a modified DS2L-Algorithm, which automatically derives the cluster number but also allows to influence the level of detail of the clustering. We further develop a framework that combines these methods with ensemble modeling, internal cluster validation indices, resampling and consensus voting to finally obtain a robust clustering result and remove arbitrariness from the feature selection process. Further we propose a measure to sort hydrographs within clusters, useful for both interpretability and visualization. We test the framework with weekly data from the Upper Rhine Graben System, using more than 1800 hydrographs from a period of 30 years (1986-2016). The results show that our approach is adaptively capable of identifying homogeneous groups of hydrograph dynamics. The resulting clusters show both spatially known and unknown patterns, some of which correspond clearly to external controlling factors, such as intensive groundwater management in the northern part of the test area. This framework is easily transferable to other regions and, by adapting the describing features, also to other time series-clustering applications.

     

Do German Welfare‐to‐Work Programmes Reduce Welfare Dependency and Increase Employment?

Abstract. During the last decade, many Western economies reformed their welfare systems with the aim of activating welfare recipients by increasing welfare‐to‐work programmes (WTWP) and job‐search enforcement. We evaluate the short‐term effects of three important German WTWP implemented after a major reform in January 2005 (‘Hartz IV’), namely short training, further training with a planned duration of up to three months and public workfare programmes (‘One‐Euro‐Jobs’). Our analysis is based on a combination of a large‐scale survey and administrative data that is rich with respect to individual, household, agency level and regional information. We use this richness of the data to base the econometric evaluation on a selection‐on‐observables approach. We find that short‐term training programmes, on average, increase their participants' employment perspectives. There is also considerable effect heterogeneity across different subgroups of participants that could be exploited to improve the allocation of welfare recipients to the specific programmes and thus increase overall programme effectiveness.     

Convergence in the EU

The EU has long viewed economic and institutional convergence as important goals, but the results thus far have been decidedly mixed, and there remain several open questions: How exactly should convergence be defined? How much convergence is necessary? What steps can be taken to improve convergence in the EU, and how can success be defined? Finally, how much convergence can be achieved by improving the economic performance in underperforming regions, and how can convergence in the form of harmonisation towards lower welfare levels be avoided?     

Causality in the Social Sciences: a structural modelling framework

Quality & Quantity - There is no unified theory of causality in the sciences and in philosophy. In this paper, we focus on a particular framework, called structural causal modelling (SCM), as...     

The performance of estimators based on the propensity score

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observed covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters are important for all estimators. A conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score and different types of outcome variables is considered an important property.     

Do Long‐Term Unemployed Workers Benefit from Targeted Wage Subsidies?

We evaluate a wage subsidy program that is targeted at long‐term unemployed workers in Germany. We use an alternative identification procedure compared to empirical studies conducted so far. Exploiting the particular program regulations and large administrative data we estimate the impact of program availability using a regression discontinuity framework. Our results suggest no significant impact of the availability of the subsidy on labor market outcomes of the target group. Even though our analysis lacks some statistical power, our findings do not support the substantial positive effects obtained from matching studies. As our approach does not require observability of all drivers of selection, previous empirical studies justifying government expenditures on wage subsidies based on matching methods should be reconsidered.     

Risky choices and solidarity: disentangling different behavioural channels

Experimental Economics - We investigate whether informal support is sensitive to the extent to which individuals can influence their income risk exposure by opting into risk. In a laboratory...     

The Curse and Blessing of Training the Unemployed in a Changing Economy: The Case of East Germany After Unification

Abstract. We analyse the effects of government-sponsored training for the unemployed conducted during East German transition. For the microeconometric analysis, we use a new, large and informative administrative database that allows us to use matching methods to address potential selection bias, to study different types of programmes and to observe labour market outcomes over eight years. We find strong evidence that, on average, the training programmes under investigation increase long-term employment prospects and earnings. However, as an important exception, the longer training programmes are not helpful for their male participants. At least part of the explanation for this negative result is that caseworkers severely misjudged the structure of the future demand for skills.     

Active labour market policy in East Germany

We investigate the effects of the most important East German active labour market programmes on the labour market outcomes of their participants. The analysis is based on a large and informative individual database derived from administrative data sources. Using matching methods, we find that over a horizon of 2.5 years after the start of the programmes, they fail to increase the employment chances of their participants in the regular labour market. However, the programmes may have other effects for their participants that may be considered important in the especially difficult situation experienced in the East German labour market.