Why Latin America urgently needs CRA, and why CRA won't work for Latin America

Community development ; Latin America

Dynamic Factor Analysis with ARMA Factors

In this paper we present a new approach to the specification of dynamic factor models. Our model has three advantages over existing work. Firstly, it is based on a minimal-dimension state-space representation giving some gain in computational efficiency over existing methods. Secondly, it easily accommodates hypothesis tests about the order of the factor-filter. Thirdly, by allowing the factor-filter to have a common polynomial factor, ARMA-factor models may be estimated with little extra computational expense over the AR- factor case. We illustrate the use of our model with an application to business cycle analysis.

Access to financial services in Colombia : the"unbanked"in Bogota

The authors look at the depth of the financial sector in Bogota in terms of the"financial exclusion"of those, particularly poorer citizens, who operate without accounts in formal financial institutions-the unbanked. They begin with a review of the overall decline in financial intermediation from 1998 to 2003, which explains, in part, the high percentage of unbanked-61 percent in a recent household survey in Bogota. The authors next look at the banking system today, concluding that the present challenge is to increase financial intermediation overall, especially with the poor. Their analysis shows that Colombia's banks provide costly services mainly catered toward high-income clients. Existing fees and costs of checking, savings, and loan services average 5-10 percent of a monthly minimum wage, making them hard to afford for low-income clients. The authors also explore the characteristics and impacts of financial exclusion associated with lower and more uncertain incomes, lower education, and closer links to the informal sector. They cite the household survey conducted in Bogota, showing that 70 percent of the unbanked earn less than one minimum wage per month, are three times more likely to be unemployed than the banked, and have lower education levels. The unbanked save and borrow largely in the informal sector, at greater risk and greater cost. At the same time, however, high home ownership rates show that the unbanked have the capacity to build assets, demonstrating that they have"bankable"characteristics. The authors conclude with recommendations for government and for the financial sector to broaden access for the benefit of public and private sectors, and for the unbanked.Banks&Banking Reform,Public Sector Economics&Finance,Economic Theory&Research,Financial Intermediation,Settlement of Investment Disputes

The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference

Background: Wiener-Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling. New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy. Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s): The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions: The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference. Keywords: Granger causality, vector autoregressive modelling, time series analysi

Sentencing FIrst Offenders in Botswana

A Zimbabwe Law Review (ZLRev.) article on sentencing first offenders in Botswana.It is intended by this paper to explore among other things, the objectives of sentencing, the types of sentences, the determination of these types of sentences and what factors to consider in sentencing a first offender. Uncertainty is deep seated in the sentencing process.1 As put by an American judge, "a large number of persons accused of crimes are often at a loss when brought before the courts of the land, whether they would go free, be locked in or anything in between." An attempt has been made in this paper to shed some light to those who for the first time will find themselves faced with such a problem

Security of Employment in the Public Service in Botswana: A Synopsis

A ZLRev article on job security in the Public Service in Botswana from a legalistic perspective.The State has often been looked upon by individuals, private sector employers and employees alike as a model employer. The Employment Act governs employment relationships in Botswana yet the State is excluded from the purview of the Act. The State happens to be, and is likely to continue being the biggest single employer in the country. The problems that the Employment Act was geared to address were regarded as matters that could only arise between private sector employee and employer. The trend in Africa at present is that the public service is being trimmed down especially in those countries where the International Monetary Fund and the World Bank are involved, through programmes of structural adjustment. Even though Botswana is not undergoing such a process of structural adjustment, it is slowly but carefully moving in that direction though not at the behest of the International Monetary Fund and World Bank. There is a need to cut down on spending. Consequently, it is quite important to critically consider the question of security of employment in the public service. Perhaps the whole idea of trimming down the public service is to promote productivity and efficiency in the workplace and in the process get rid of those public servants who are found wanting in this respect. This research seeks to consider the security offered by the State as the model employer in this country

Antithrombotic Therapy in Patients Undergoing Coronary Artery Bypass Grafting: A Systematic Review and Network Meta-Analysis

Comprehensive evidence on the comparative effects of various oral antithrombotic agents on the prevention of saphenous vein graft failure (SVGF) for patients undergoing coronary artery bypass is lacking. A systematic review and frequentist random-effects network meta-analysis of 18 RCTs (n=3,413 patients) comparing the effect of antithrombotic agents on SVGF and clinical outcomes was performed. Based on moderate quality evidence, among the six eligible interventions, dual-antiplatelet therapy with aspirin and clopidogrel was superior to aspirin monotherapy in reducing SVGF (OR: 0.63; 95% CI: 0.41-0.97). No statistical differences were found for major bleeding, mortality, and myocardial infarction between antithrombotic agents, owing to low number of events for most comparisons. Though significant heterogeneity or incoherence was not found, the quality of network evidence for these outcomes ranged from very low to moderate. Adequately-powered multi-arm RCTs are needed to ascertain the effects of antithrombotic therapies to help clinicians and patients achieve optimal treatment decisions