Combined Effects: The Influence of Organizational Form and Structural Characteristics on Contract Performance in Mixed Sector Markets

Social service contracting to nongovernmental organizations is popular form of privatization across the world. Although nonprofits are preferable social service providers for legal and normative reasons, governments in the United States increasingly rely on for-profit organizations to deliver social services. This trend warrants further exploration about whether nonprofits or for-profits perform according to theoretical expectations when they exist in the same market. This study employs qualitative comparative analysis (QCA) to examine how sector-public, nonprofit, and for-profit-combines with structural variables to produce acceptable contract performance in juvenile justice programs. QCA is a discovery-oriented research tool that determines whether combinations of variables within cases produce a specific outcome and whether those combinations are consistent across cases. I find sector is not a necessary or sufficient predictor of acceptable performance on its own. Rather it combines with market factors to lead to acceptable contract performance. Combinations vary by sector, indicating that sectors behave differently in similar circumstances. The primary theoretical contribution of this paper is to provide a nuanced account of contract performance in mixed sector markets.     

International income poverty measurement: which way now?

We critically review conceptual and empirical issues surrounding the derivation of the international poverty line, expressed in PPP-adjusted dollars and linked to various rounds of the International Comparison Program (ICP). We find that there are some limitations in the current estimation of these lines, but show that statistically superior methods lead to lines that are relatively robust and confirm the $1.25 using 2005 PPPs and suggest $1.67–1.71 using 2011 PPPs (or close to the $1.90 proposed by the World Bank if we follow the World Bank’s approach of adjusting inflation rates in some countries); they also roughly confirm the current shape of the proposed ‘weakly relative’ poverty line. Using the new absolute line based on 2011 PPPs would lead to substantially lower poverty in our estimation. The extent of the decline depends on whether and how one treats China, India, and Indonesia differently from other countries in the 2005 and 2011 PPPs. More seriously, we note that the dependence on successive ICP rounds creates conceptual and empirical problems that have become worse over time so that we suggest that it would be best to consider alternatives to the current reliance on ICP rounds and the resulting PPPs. As a short-term solution we propose to fix the international poverty line in national currencies using either the 2005 or 2011 level; in the medium term, we argue for global poverty measurement based on internationally coordinated national poverty measurement.     

How Does Women’s Decision-Making Power Affect Budget Share,Nutrition and Education in Pakistan?

Understanding the effects of power distribution, particularly women’s decision making, on human development is important. This study used a set of direct measures of decision-making power from the Pakistan Social and Living Standard Measurement Survey and examined the relationship between women’s decision-making power and the food budget share, nutrition and child schooling. It found that in Pakistan, the relationship between women’s decision-making power and nutrition was not linear and varied depending on rural or urban residence. There was no clear evidence that higher women’s decision-making power would lead to better nutrition availability in Pakistan, but overall households were more likely to consume less grain and more vegetables. When women had higher decision-making power, children, particularly rural girls, were more likely to be enrolled in school.     

On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection

Wavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning.     

Testing an Economic Model of Nonprofit Growth: Analyzing the Behaviors and Decisions of Nonprofit Organizations,Private Donors,and Governments

This study draws an economic model of the growth of nonprofit organizations by analyzing the behaviors of three major actors—nonprofit organizations, private donors, and governments—in making decisions on the allocation of limited resources for nonprofit services. Since decisions made by each actor affect resource allocation, it is important to understand what drives these decisions. The model was tested using an unbalanced, 463 panel dataset collected from 28 OECD countries over a 23-year period. The results indicated that macro- and micro-economic trends and government policies framed the decision premises of the three major actors, which led them to leverage the supply and demand for goods and services and, in turn, determined how they allocated limited resources for nonprofit services. This result implies that understanding the interdependencies of all sectors of the economy is critical to comprehending the size and development of the nonprofit sector. Effective management of micro-economic policies and macro-economic stability is necessary. More important, however, is understanding how a decision in one part of the economy will have intended and unintended effects on the nonprofit sector.     

A Note on Selection and Gender Unemployment Gaps

Previous research has documented the impact of positive selection into employment on the ranking of countries by gender wage gaps. This note focuses on the impact of selection into labor force on cross-country differences in gender unemployment gaps. We construct the Manski bounds for the selection-free gender unemployment gaps in 26 EU countries and show that - without additional assumptions - the observed gender unemployment gaps carry little information about the selection-free gender differences in unemployment. Contrary to the common assumption of positive selection into labor force (similar to positive selection into employment documented in the gender wage gap research), we also point at an example of negative-selection bias. We show that labor force withdrawal of mothers on job-protected family leaves may lead to an overestimation of the selection-free gender unemployment gaps by as much as 1 p.p.