Political origin and persistence of industrial policy in Africa

ABSTRACT

The literature on industrial policy in Africa has generally explained its political origins in terms of ruling elites’ distribution of benefits to their supporters. However, in competitive political contexts in which policies are deeply political and designed to satisfy clients, such as policies that support party donors, the problem of policy discontinuity is bound to arise because a change in ruling party is bound to alter the direction of distributional policies. The current paper uses Nigeria’s backward integration policy (BIP), an industrial policy on cement production, to sharpen the analytical distinction between the origins and persistence. Although the ruling elites’ political quest for survival explains the origin of Nigeria’s industrial policy on cement (ruling elites were in search of re-election funds and teamed up with domestic capitalists for donations, who in turn influenced the political elites to create policies in their area of business), it does not explain the continuation.     

Mental Health Outcomes of Youth In-Care: Investigating the Effect of General Strain and Self-control Theories

Child and Adolescent Social Work Journal - The adverse mental health outcomes of youth in-care have been examined from a number of disciplinary perspectives. A sociological approach emphasizes the...     

Optimizing minimum information pair-copula using genetic algorithm to select optimal basis functions

Constructing pair-copula using the minimum information approach is an appropriate and flexible way to survey the dependency structure between variables of interest. Minimum information pair-copula method approximates multivariate copula by applying some constraints between desired variables that are elicited from the data itself or experts’ judgment. In minimum information pair-copula, selecting basis constraints is a challenge. In this article, we apply genetic algorithms as a heuristic way to select basis constraints to optimize approximated pair-copula. The results gained show that our method optimizes model selection criteria and lead to better pair-copula approximation. Finally, we apply our proposed method to approximate pair-copula density in real dataset.     

Women in Academic Arena: Struggles,Strategies and Personal Choices

Gender issues have been a research concern within the education community at all levels in the last few decades as education is regarded as a critical tool in development and the most effective tool to empower women to be able to live a more productive life as they access higher education. Despite the increase in access to higher education and women being in the majority in the teaching profession; it is still a herculean task for women attain top management positions especially in African universities because of cultural factors and patriarchal system of leadership. This situation is often described as a “chilly climate” due to both overt and covert behaviours and systemic barriers that needs attention. This paper therefore highlights the narrative stories of women leaders in Tai Solarin University of Education, in Nigeria where the researcher works as told by selected women leaders of their struggles, challenges and choices in the course of duty.     

Bringing young children’s voices into programme development,randomized controlled trials and other unlikely places

Interventions aimed at improving children's lives are widespread and research evaluating these is central to policy decisions that affect their lives. Although there is an increasing move in intervention and evaluation research to include stakeholders’ perspectives this rarely extends to children's voices. As a psychologist committed to children's rights, this article explores my experiences of working at times on the fringes of my own discipline, drawing on wide-ranging resources and collaborating with other disciplines. The article reflects on the challenges and opportunities of multidisciplinary research and bringing young children's perspectives into these places where they are rarely heard.     

Bootstrap estimation of the variance of the error term in monotonic regression models

The variance of the error term in ordinary regression models and linear smoothers is usually estimated by adjusting the average squared residual for the trace of the smoothing matrix (the degrees of freedom of the predicted response). However, other types of variance estimators are needed when using monotonic regression (MR) models, which are particularly suitable for estimating response functions with pronounced thresholds. Here, we propose a simple bootstrap estimator to compensate for the over-fitting that occurs when MR models are estimated from empirical data. Furthermore, we show that, in the case of one or two predictors, the performance of this estimator can be enhanced by introducing adjustment factors that take into account the slope of the response function and characteristics of the distribution of the explanatory variables. Extensive simulations show that our estimators perform satisfactorily for a great variety of monotonic functions and error distributions.     

On a sampling scheme with inclusion probability proportional to size

A sampling scheme for selection of a sample of two units with inclusion probability proportionalto size is suggested which provides a non–negative variance estimator of the variance of Horvitz–Thompson estimator. The suggested sampling scheme is shown to perform better than many of the existing unequal probability and inclusion probability proportional to size sampling Achemes for a number of natural populations.     

Bayesian semiparametric models for nonignorable missing mechanisms in generalized linear models

Semiparametric models provide a more flexible form for modeling the relationship between the response and the explanatory variables. On the other hand in the literature of modeling for the missing variables, canonical form of the probability of the variable being missing (p) is modeled taking a fully parametric approach. Here we consider a regression spline based semiparametric approach to model the missingness mechanism of nonignorably missing covariates. In this model the relationship between the suitable canonical form of p (e.g. probit p) and the missing covariate is modeled through several splines. A Bayesian procedure is developed to efficiently estimate the parameters. A computationally advantageous prior construction is proposed for the parameters of the semiparametric part. A WinBUGS code is constructed to apply Gibbs sampling to obtain the posterior distributions. We show through an extensive Monte Carlo simulation experiment that response model coefficent estimators maintain better (when the true missingness mechanism is nonlinear) or equivalent (when the true missingness mechanism is linear) bias and efficiency properties with the use of proposed semiparametric missingness model compared to the conventional model.