Philipp E. Otto

A Price is a Signal: On Intrinsic Motivation, Crowding-Out and Crowding-In

If a previously unpaid activity (e.g. donating blood) is paid, then we often observe that this activity is reduced. In this paper, it is hypothesized that the price offered is taken as a proxy for the “value�? of the activity. Depending on how the actor valued the activity previously, crowding-out or crowding-in is implied, an effect with or without persistence after stopping the payment. The model can be adapted to a number of similar situations, including those where a high price signals high costs instead of high values. Our “naïve�? explanation is confronted with Bènabou and Tiroles (2003) Principal-Agent model. A questionnaire study supports our basic hypothesis as well as some of the derived consequences, and contradicts Bènabou and Tiroles model.

Does Microfinance Cause or Reduce Suicides? Policy Recommendations for Reducing Borrower Stress

At a time when suicides by microfinance borrowers in Andhra Pradesh are extensively discussed, this paper makes an initial tentative exploration into the impact of microfinance on suicides. Does MF reduce or increase suicides? Data limitations imply methodological limitations and provisional conclusions. The literature review, starting with Emile Durkheim, of research on suicides brings out the importance of psychological factors, divorce, and unemployment. We investigate time series data on suicides in India and find a just significantly positive correlation with male suicide rates and slightly negative correlations (not significant) with female suicide rates – but no relation between microfinance and total suicides. Cross-sectional data of Indian states indicates a significant positive correlation of total suicides with number of SHGs loans outstanding to Banks, higher than with microfinance loans provided by MFIs, which only shows a weak correlation. Going beyond India, a global country-wise analysis indicates that there is no correlation between microfinance and male or female suicides, yet regression analysis of 31 countries (weakly) indicates that microfinance penetration among the poor is a causal factor for increased suicides. Three related questions are discussed. First, do rapid growth and sustainability aims make MFis forget the human angle? Second, is social pressure necessary to make people repay? Third, if Microfinance increases suicides, is this impact to be differentiated between women and men possibly due to changing roles? Based on these, we tentatively propose policy recommendations.

Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models

In this paper, we propose a test procedure to detect change points of multidimensional autoregressive processes. The considered process differs from typical applied spatial autoregressive processes in that it is assumed to evolve from a predefined center into every dimension. Additionally, structural breaks in the process can occur at a certain distance from the predefined center. The main aim of this paper is to detect such spatial changes. In particular, we focus on shifts in the mean and the autoregressive parameter. The proposed test procedure is based on the likelihood-ratio approach. Eventually, the goodness-of-fit values of the estimators are compared for different shifts. Moreover, the empirical distribution of the test statistic of the likelihood-ratio test is obtained via Monte Carlo simulations. We show that the generalized Gumbel distribution seems to be a suitable limiting distribution of the proposed test statistic. Finally, we discuss the detection of lung cancer in computed tomography scans and illustrate the proposed test procedure.

Microsavings and Market Saturation: The Evolution of Diversity in Saving Products

Saving behavior influences economic development in many possible ways. The paradox of thrift stresses the negative effects of increased individual savings. How does this relationship transfer to microfinance and to microsavings in particular? Based on savings data of differently developed countries, we investigate the relationship described by the triangle of the availability of saving products, savings behavior, and development. The data supports the assumption of the proposed saturation triangle that with an increased number of MFIs active in a country, the average deposit per MFI decreases. Furthermore, the savings product saturation of a country is linked to economic as well as social developments.

The Effect of Tax Privacy on Tax Compliance – An Experimental Investigation

Abstract In this paper, we use a tax compliance game with a public good to investigate the impact of public disclosure on tax evasion behavior experimentally. Three different types of tax privacy are tested, ranging from complete privacy to full disclosure. We expect two different effects: first, a contagion effect, arising when an individual observes non-compliance of other individuals and therefore reduces her own tax compliance; second, a shame effect of increased tax compliance due to the anticipated shame of being declared a tax evader. Both these effects are supported by the experimental results. However, the shame effect reduces tax evasion only in the short run. The influence of shame diminishes over the course of the experiment with subjects observing the non-compliance of other participants. Thus, our results indicate that when the contagion and the shame effect are present the latter is not strong enough to override the former in the long run. Furthermore, disclosing tax information anonymously increases tax evasion compared to providing no information on tax evasion behavior. These observations are of particular importance for tax policy because public disclosure may lead to more evasion instead of less when supporting a crowding-out of the tax morale.

Verfahren zur Überwachung räumlicher autoregressiver Prozesse mit externen Regressoren

ZusammenfassungDer vorliegende Beitrag befasst sich mit der statistischen Prozesskontrolle räumlicher autoregressiver Prozesse mit externen Regressoren. Das Ziel ist die Weiterentwicklung etablierter Methoden der zeitlichen Prozesskontrolle. Diese Ansätze werden für Anwendungen in der räumlichen Prozesskontrolle modifiziert. Wir illustrieren dieses Vorgehen anhand eines sozialstatistischen Beispiels, welches sich mit der Bevölkerungsentwicklung in den Landkreisen und Kreisfreien Städten der Bundesrepublik Deutschland befasst. Mittels Faktorenanalyse werden zunächst nicht beobachtbare Variablen basierend auf den zuvor gewählten manifesten Variablen identifiziert, denn für die nachfolgende Analyse sind voneinander unabhängige Faktoren erforderlich. Daraufhin sind anhand einer Clusteranalyse die Regionen in Gruppen einzuteilen. Mit Hilfe der gebildeten Cluster sind diejenigen Regionen, welche die Grundlage der Modellanpassung darstellen, im Zustand unter Kontrolle auszuwählen. Anhand der zuvor ermittelten Faktorwerte erfolgt eine Modellanpassung mit Hilfe der verallgemeinerten Momenten-Methode. Im Rahmen der statistischen Prozesskontrolle werden in einem weiteren Schritt multivariate Kontrollkarten basierend auf entweder exponentieller Glättung oder kumulierter Summe herangezogen, um Kreise außerhalb der Region im Zustand unter Kontrolle hinsichtlich ihres Kontrollzustandes zu beurteilen. Wir stellen verschiedene Ansätze vor, um die zu überwachenden Regionen für eine Prozesskontrolle zu sortieren. Schlussendlich möchten wir zeigen, dass die modifizierten Kontrollkarten strukturelle Veränderungen in Bezug auf ein zuvor geschätztes Modell signalisieren, ohne dass eine permanente Schätzung erforderlich ist.AbstractThis paper deals with statistical process control of spatial autoregressive models with exogenous regressors. The main purpose is the extension of conventional methods of process control in time series analysis. These approaches are modified for applications of spatial monitoring. The method is illustrated by an example of social statistics dealing with natural as well as spatial population change regarding administrative districts of Germany. Via factor analysis latent variables are identified based on manifest variables, because independent factors are needed for the following analysis. Afterwards, the considered regions are divided into groups via cluster analysis. The results of cluster analysis helps to find a specific region of one cluster that is used for in-control estimation. The previously mentioned model is fitted to factor scores using the generalized method of moments. Multivariate control charts based on either exponential smoothing or cumulative sum are used to evaluate full-sample data regarding their control situation. Accordingly, we propose different approaches to sort the regions to be monitored. Eventually, the modified charts signalize structural changes regarding the model based on in-control data without permanent re-estimation.

Bayes’sche Statistik in der Dienstleistungsforschung

ZusammenfassungDer vorliegende Aufsatz soll einen systematischen Literaturüberblick über die Verwendung von Modellen der Bayes’schen Statistik im Rahmen der Dienstleistungsforschung geben. Dabei wird vor allem darauf geachtet, sowohl eine quantitative als auch eine qualitative Übersicht zu liefern. Als Grundlage des systematischen Literaturüberblicks dienen die Zeitschriften dreier Teilbereiche des VHB-Jourqual3 mit dem Ranking A+ bis B. Einerseits wird der relative Anteil der Bayes’schen Statistik im Bereich der Dienstleistungsforschung betrachtet sowie eine zeitliche Einordung der Publikationen bezüglich der drei Teilbereiche und der Rankings gegeben. Andererseits wird im Rahmen der qualitativen Auswertung die thematische Breite der Publikationen dargestellt.AbstractIn this paper, a systematic literature review of the use of Bayesian statistics in service research is provided. In particular, we give a quantitative and qualitative overview of the literature published between 2005 and 2015. Moreover, the focus is on journals with the ranking A+, A, and B in the ranking system VHB-Jourqual3. Further, the results are analyzed with respect to the time of publications. Beside that quantitative aspect, the content and the intention of the publications is analyzed in detail. In particular, the research is classified into distinct classes by their main intention and contribution. Eventually, several papers are summarized to give an overview of the diversity of the applications of Bayesian statistics.

Simultaneous Surveillance of Means and Covariances of Spatial Models

This paper deals with the problem of statistical process control applied to multivariate spatial models. After introducing the target process that coincides with the spatial white noise, we concentrate on the out-of-control behavior taking into account both changes in means and covariances. Moreover, we propose conventional multivariate control charts either based on exponential smoothing or cumulative sums to monitor means and covariances simultaneously. Via Monte Carlo simulation the proposed control schemes are calibrated. Moreover, their out-of-control behavior is studied for specific mean shifts and scale transformation.

Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity

In this paper, we introduce a new spatial model that incorporates heteroscedastic variance depending on neighboring locations. The proposed process is regarded as the spatial equivalent to the temporal autoregressive conditional heteroscedasticity (ARCH) model. We show additionally how the introduced spatial ARCH model can be used in spatiotemporal settings. In contrast to the temporal ARCH model, in which the distribution is known given the full information set of the prior periods, the distribution is not straightforward in the spatial and spatiotemporal setting. However, it is possible to estimate the parameters of the model using the maximum-likelihood approach. Via Monte Carlo simulations, we demonstrate the performance of the estimator for a specific spatial weighting matrix. Moreover, we combine the known spatial autoregressive model with the spatial ARCH model assuming heteroscedastic errors. Eventually, the proposed autoregressive process is illustrated using an empirical example. Specifically, we model lung cancer mortality in 3108 U.S. counties and compare the introduced model with two benchmark approaches.

A Note on Efficient Simulation of Multidimensional Spatial Autoregressive Processes

ABSTRACT In applications of spatial statistics, it is necessary to compute the product of some matrix W of spatial weights and a vector y of observations. The weighting matrix often needs to be adapted to the specific problems, such that the computation of Wy cannot necessarily be done with available R-packages. Hence, this article suggests one possibility treating such issues. The proposed technique avoids the computation of the matrix product by calculating each entry of Wy separately. Initially, a specific spatial autoregressive process is introduced. The performance of the proposed program is briefly compared to a basic program using the matrix multiplication.