An analysis of factors affecting access to credit in Lesotho's smallholder agricultural sector

The agricultural sector in Lesotho is underperforming mainly due to the inability of smallholders to move from traditional agriculture to a more scientific and technology-based one. Among the challenges inhibiting the ability of smallholders to make the step up is access to financial services, especially credit. The purpose of this study was to examine the factors that may influence the ability of smallholders to access finance by making use of a logistic regression model within the principle component regression framework. The results revealed that the ability of smallholders to access finance, and the potential to make the transition towards a more scientific and technology-based agriculture sector, is influenced by the level of farm and non-farm income, remittances and pension, farm size, availability of family labour, land ownership, savings and repayment ability. The results present important information in terms of guiding institutional arrangements needed to improve credit availability in Lesotho.     

Model specification testing of time series regressions

Given the specification of the lag length and functional form of a (non)linear time series regression we shall propose a test of the null hypothesis that the expectation of the error conditional on the exogenous variables, all lagged exogenous variables and all lagged dependent variables equals zero with probability 1. In the case that the data-generating process is strictly stationary this test is consistent with respect to the alternative hypothesis that the null is false. The test is also applicable for a particular class of non-stationary time series regressions, although in that case consistency with respect to all possible alternatives is no longer guaranteed. The test involved is a generalization of a test proposed in Bierens (1982b). Moreover, we also present a similar but simpler test of the hypothesis that the errors are martingale differences.     

Efficient estimation of income distribution parameters

The parameters of several families of distributions are estimated by means of minimum χ²; use is made of random samples taken from Dutch income-earning groups in 1973. The numerical search routine used, is the Complex method due to Box. The χ² function is evaluated by standard numerical integration procedures. The lognormal and the Gamma families are rejected because of a poor fit. The log t and the log Pearson IV families are introduced. This results in a considerable improvement of χ² critical levels. The generalized Gamma and the Champernowne function describe the income distribution reasonably well in some cases.     

Assessing the value of human resource certification: A call for evidence-based human resource management

We are delighted that our article on the value of human resource (HR) certification (Lengnick-Hall & Aguinis, 2012) has generated so much interest. In this brief response, we identify two key similarities across all of the articles published in this special issue (including our own). First, all commentators care deeply about the field of human resource management (HRM). Second, all commentators believe that additional empirical research is needed to assess the value of HR certification for stakeholders inside and outside of the field of HRM. We offer a concrete course of action. Specifically, we issue the following challenge to the Society for Human Resource Management (SHRM) Foundation: Issue a call for proposals for empirical research on the value of HR certification. Such a call would provide funding so that teams including both researchers and practitioners are encouraged to conduct research on this topic. Moreover, such a collaborative approach involving the SHRM Foundation, the HRM scholarly community, and the HRM practitioner community is likely to elevate the field of HRM and produce valuable knowledge about the value of HR certification for stakeholders inside and outside of the field, which in turn will also help narrow the much-lamented science–practice gap.