Donor Requirements and Pockets of Effectiveness in Senegal's Bureaucracy

Donors increasingly value the work of statistics, project assessment and related offices in developing countries, but can they ensure these offices are able to do their work? This article assesses donors’ efforts to do so in Senegal's ministries of finance, health and agriculture in the mid‐2000s. It contends that donors' impact is greatest if they generate political incentives for governments to create ‘pockets of effectiveness’ in these areas. The health and agriculture case studies indicate that direct donor involvement, particularly if incompatible with domestic political forces, produces disappointing results, while the finance case studies suggest donors can induce political support for the work of specific offices if donor incentives coincide with domestic political imperatives.     

The social model of disability as an oppositional device

This article engages with debates about the UK Disabled People’s Movement’s ‘Big Idea’ – the social model of disability – positioning this as an ‘oppositional device’. This concept is adapted from the work of the art theorist and activist Brian Holmes, elaborated using insights from Foucault and others. The model’s primary operation is introducing contingency into the present, facilitating disabled people’s resistance-practices. We recognise, however, that the device can operate in a disciplinary manner when adopted by a machinery of government. Whilst our primary goal is to understand the character and operation of the social model, by providing a more general definition of an oppositional device as the concrete operation of technologies of power, we also propose a concept potentially useful for the analysis of the resistance-practices of activists involved in a wide variety of struggles. This concept may thus have implications for wider social and political analysis.     

The Attributes of Effective NGOs and the Leadership Values Associated with a Reputation for Organizational Effectiveness

Building on prior research characterizing organizational effectiveness as a social construction, this article identifies the perceived attributes of effective transnational nongovernmental organizations (NGOs) and the leadership values associated with higher reputations for organizational effectiveness. Results are based on an in‐depth, mixed‐method interview study of 152 NGO leaders located in the United States and representing all major sectors of organizational activity. Among the twenty‐nine attributes that leaders identified in peer organizations that they regarded as particularly effective, leaders stressed instantiation of sound principles or strategy, a grassroots approach, large organizational size and resources, being collaborative, singleness of focus, campaigning abilities, funding and fundraising prowess, global scope, and quality people. Furthermore, statistical analysis reveals that NGOs with leaders who value similarities with peer organizations, grassroots approaches, diversity of strategies, dedication, professionalism, and distributed organizational structures have significantly higher reputations for effectiveness.     

The Immigrant Paradox in Pregnancy: Explaining the First‐Generation Advantage for Latina Adolescents

The immigrant paradox is a population health pattern whereby later generation immigrant youth display less favorable outcomes when compared to first‐generation co‐ethnic peers. This study examines the role of number of sex partners in explaining the immigrant paradox in pregnancy among Latina adolescents. This secondary analysis utilized a nationally representative sample of Latinas in the National Longitudinal Study of Adolescent Health. As expected, first‐generation Latina adolescents demonstrated less risk than second‐ or third+ generation Latinas for becoming pregnant. Further, the lower number of sexual partners fully accounted for the first generation's advantage in lower rate of adolescent pregnancy. These findings have important implications for healthcare providers who work with Latina girls in pregnancy prevention.     

Handling Missing Values in Longitudinal Panel Data With Multiple Imputation

This article offers an applied review of key issues and methods for the analysis of longitudinal panel data in the presence of missing values. The authors consider the unique challenges associated with attrition (survey dropout), incomplete repeated measures, and unknown observations of time. Using simulated data based on 4 waves of the Marital Instability Over the Life Course Study (n = 2,034), they applied a fixed effect regression model and an event‐history analysis with time‐varying covariates. They then compared results for analyses with nonimputed missing data and with imputed data both in long and in wide structures. Imputation produced improved estimates in the event‐history analysis but only modest improvements in the estimates and standard errors of the fixed effects analysis. Factors responsible for differences in the value of imputation are examined, and recommendations for handling missing values in panel data are presented.     

Bayesian Variable Selection Under Collinearity

In this article, we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importance of strongly correlated covariates. The natural question is which one should be used in practice. The simulation studies suggest that posterior inclusion probabilities and Bayes factors that evaluate the importance of correlated covariates jointly are more appropriate, and some priors may be more adversely affected in such a setting. To obtain a better understanding behind the phenomenon, we study some toy examples with Zellner’s g-prior. The results show that strong collinearity may lead to a multimodal posterior distribution over models, in which joint summaries are more appropriate than marginal summaries. Thus, we recommend a routine examination of the correlation matrix and calculation of the joint inclusion probabilities for correlated covariates, in addition to marginal inclusion probabilities, for assessing the importance of covariates in Bayesian variable selection.