Foreign aid and domestic taxation: multiple sources,one conclusion

There are influential studies that argue that foreign aid displaces domestic tax revenue when it is given in the form of grants. These claims are based on data that are deeply problematic: several different sources are amalgamated into one dataset, with no apparent checks on compatibility. In this article, a variety of econometric strategies are used to overcome these issues of data quality. The resulting weight of evidence points to a modest but positive effect on the part of foreign aid generally on domestic tax revenue. Fears over a negative effect for aid grants appear unwarranted, and are accounted for by the inappropriate use of data or endogeneity issues.     

Winning a Battle but Losing the War? Third Sector and Social Policy in Latin America: The Case of Uruguay

This paper analyzes some aspects of the third sector’s involvement process in the provision of public social services. Using evidence garnered in previous research based on in-depth interviews, I offer elements toward an assessment of the consequences this process has produced not only in terms of the gains and losses it has produced for social policy, but also for the very identity and constitutive characteristics of the third sector. The evidence hereby compiled strengthens skepticism toward the hypothesis that sees a transformative potential in the role of the third sector in social policy in Latin America. This skepticism arises from issues detected by involved actors themselves and that are linked to the weakness of the sector as a whole.     

Influence of Perceived Economic Strain on the Relationship Between Caregiver BMI and Child BMI

As maternal BMI is a risk factor for higher child BMI, this exploratory study investigated perceived economic strain as a protective factor modifying this well-established relationship. Primary low-income female caregivers (n = 432) of preschoolers ages 3–5 years were interviewed using the Family Economic Strain Scale. Caregiver and child heights and weights were measured. Significant differences were found in perceived economic strain between normal versus underweight, overweight, and obese caregivers. A moderating effect approaching significance was noted such that at low levels of reported economic strain, caregiver BMI classification had no effect on child BMI z-score for normal weight, overweight, or obese caregivers. This study is the first to demonstrate that reduced perception of economic strain may act to buffer the impact of maternal overweight/obesity on child weight status. Given this biologically strong relationship, the potential for any modifiable factor to be detected is encouraging. Further collaborative research between economists and family resiliency researchers is suggested.     

Emergency Saving and Household Hardship

Households with limited income and wealth often struggle to access the financial liquidity needed to address unexpected expenses or income drops. Emergency savings can act as form of insurance against such economic shocks and reduce the risk of hardships that influence family wellbeing. Prior research has established that threshold amounts of liquid assets can reduce the risk of economic hardship. This study used a measure of self-reported emergency saving behavior to examine whether households who reported saving for emergencies were less likely to experience subsequent economic hardships in a longitudinal sample of households in disadvantaged neighborhoods from the Annie E. Casey Foundation’s Making Connections project. Results across a range of regression models suggest that households who saved for emergencies experienced slightly less overall hardship and were less likely to report several specific hardships, such as food insecurity and having a phone disconnected, three years later. This study supports the idea that small, unrestricted savings may play a protective role for low-income households.     

Assembly Line Balancing Using Genetic Algorithms with Heuristic-Generated Initial Populations and Multiple Evaluation Criteria*

We use genetic algorithms (GA) to solve the assembly line balancing (ALB) problem. Inparticular, we show how this technique can be used to generate feasible line balances, improve upon solutions obtained by other heuristics reported in the literature, and utilizeany one or more evaluation criteria that can be expressed in functional form. The procedure is demonstrated with two examples: (1) intimating the improvement of heuristic-generated ALB solutions by including them in the GA initial population, and (2) the possibility of balancing assembly lines with multiple criteria and side constraints. These examples suggest that GA can be a powerful tool in ALB. To investigate the utility of GA on single-criterion problems, an experiment is conducted that compares both the GA approach and conventional heuristics. Results indicate that the GA solutions are significantly improved over the heuristic solutions under the conditions studied. It is also found that the presence of heuristic-generated conventional solutions in the GA initial population leads to statistically preferred results.     

A Learning Curve Analysis of Expert System Use*

In this article, we study how an expert system affects novice problem solving in a financial risk analysis domain. We demonstrate that novice performance is improved after exposure to an expert system. Further, we show that novice performance continues to improve when the system is withdrawn. By comparing learning curves for people with exposure to those without, we can assess how much the system has benefitted its users. We demonstrate a quantitative methodology to measure the increment of learning due to the use of an information technology. We also explore the issue of how expertise is transferred from the system to the user.     

Modeling Demand For Unionization With Nontraditional Data Analysis Methods

Upon reviewing the extant literature on determinants of unionism, it becomes clear that many areas that have had a plethora of research attention do not converge upon singularly directional findings. This study explores a potential cause of such an apparent anomaly: nonlinearity of data. An exploratory examination of correlation coefficients among typical union determinant variables seems to show different patterns of relationships at different levels of union demand. Thus, a break from traditional linear data analysis techniques is explored in the interest of explaining more variance with typical, theoretically derived variables by using neural network analysis. Results of analyses on industry level data reveal that using neural network analysis to model union demand explained over four times as much variance as multiple regression analysis.