Sustainability and Mission Drift in Microfinance

Microfinance institutions (MFIs) are constrained by double bottom-lines: meeting social obligations (the first bottom-line) and obtaining financial self-sufficiency (the second bottom-line). The proponents of the first bottom-line, however, are increasingly concerned that there is a trade-off between these two bottom-lines—i.e., getting hold of financial self-sufficiency may lead MFIs to drift away from their original social mission of serving the very poor, commonly known as mission drift in microfinance which is still a controversial issue. This study aims at addressing the concerns for mission drift in microfinance in a performance analysis framework. Chapter 1 deals with theoretical background, motivation and objectives of the topic. Then the study explores the validity of three major and related present-day concerns. Chapter 2 explores the impact of profitability on outreach-quality in MFIs, commonly known as mission drift, using a unique panel database that contains 4-9 years’ observations from 253 MFIs in 69 countries. Chapter 3 introduces factor analysis, a multivariate tool, in the process of analysing mission drift in microfinance and the exercise in this chapter demonstrates how the statistical tool of factor analysis can be utilised to examine this conjecture. In order to explore why some microfinance institutions (MFIs) perform better than others, Chapter 4 looks at factors which have an impact on several performance indicators of MFIs—profitability or sustainability, repayment status and cost indicators—based on quality-data on 353 institutions in 77 countries. The study also demonstrates whether such mission drift can be avoided while having self-sustainability. In Chapter 5 we examine the impact of capital and financing structure on the performance of microfinance institutions where estimations with instruments have been performed using a panel dataset of 782 MFIs in 92 countries for the period 2000-2007. Finally, Chapter 6 concludes the study by summarising the results from the previous chapters and suggesting some directions for future studies.

Economic Fluctuations and Crime : Temporary and Persistent Effects

Peer reviewed

Economic Fluctuations and Crime : Temporary and Persistent Effects

Peer reviewed

Le poids de l'histoire : rôle des facteurs économiques, politiques et institutionnels dans l'accumulation de dette publique

Ce mémoire s’intéresse à l’endettement des gouvernements et aux facteurs historiques qui en sont les causes. L’analyse utilise des données historiques des cinquante États américains afin d’explorer l’influence de différents facteurs d’ordre économique, politique et institutionnel sur l’accumulation de la dette publique. Alors que la littérature met de l’avant l’impact des facteurs économiques, politiques et institutionnels dans la détermination de l’endettement public, la contrainte budgétaire du gouvernement fait ressortir la relation entre le stock de dette publique courant et les déficits passés. Cette relation est au cœur de la question de recherche abordée par le mémoire : quel est le rôle des facteurs économiques, politiques et institutionnels historiques dans l’accumulation de dette publique? Comment estimer leur poids respectif? Afin de répondre à ces questions, l’analyse empirique intègre des variables explicatives économiques, politiques et institutionnelles ayant une composante historique. De plus, elle accorde une attention particulière aux facteurs institutionnels en utilisant différentes sources de données et des caractérisations plus ou moins détaillées pour modéliser les règles budgétaires et les limites d’endettement. Par ailleurs, la méthodologie empirique tient compte de la question de l’endogénéité potentielle des institutions fiscales. Les résultats de l’analyse économétrique confirment l’importance des facteurs économiques. Dans le cas des variables politiques, ils infirment la théorie selon laquelle les gouvernements divisés s’endettent davantage, mais confirment que l’appartenance politique des gouverneurs a un effet certain sur le poids de la dette publique. Ils indiquent également que l’indice historique d’alternance des partis politiques est important, l’alternance plus fréquente étant associée à une légère diminution de l’endettement. L’instabilité politique n’alimenterait donc pas nécessairement l’endettement public, ce qui suggère qu’il est possible qu’une plus forte compétition électorale puisse avoir un effet positif sur la rigueur budgétaire dans un système à deux partis politiques. De façon générale, les effets estimés des variables institutionnelles impliquent qu’elles ne sont que peu efficaces à limiter l’endettement des États.

Instantaneous us estimation of oscillating phasors with Taylor ? - Kalman-Fourier filters.

Tesis (Doctor en Ingeniería Eléctrica) UANL, 2011.

Kockázattal kiegészített Taylor-szabályok becslése Magyarországra = Estimation of risk-augmented Taylor rules for Hungary

Cikkünkben a magyar monetáris politikát vizsgáljuk olyan szempontból, hogy kamatdöntései meghozatalakor figyelembe vette-e az országkockázatot, és ha igen, hogyan. A kérdés megválaszolásához a monetáris politika elemzésének leggyakoribb eszközét használjuk: az ország monetáris politikáját leíró Taylor-szabályokat becslünk. A becslést több kockázati mérőszámmal is elvégeztük több, különféle Taylor-szabályt használva. Az érzékenységvizsgálatban az inflációhoz és a kibocsátási réshez is alkalmaztunk más, az alapspecifikációban szereplőtől eltérő mérőszámokat. Eredményeink szerint a Magyar Nemzeti Bank kamatdöntései jól leírhatók egy rugalmas, inflációs célkövető rezsimmel: a Taylor-szabályban szignifikáns szerepe van az inflációs céltól való eltérésének és - a szabályok egy része esetén - a kibocsátási résnek. Emellett a döntéshozók figyelembe vették az országkockázatot is, annak növekedésére a kamat emelésével válaszoltak. Az országkockázat Taylor-szabályba történő beillesztése a megfelelő kockázati mérőszám kiválasztása esetén jelentős mértékben képes javítani a Taylor-szabály illeszkedését. _____ The paper investigates the degree to which Hungarian monetary policy has considered country risk in its decisions and if so, how. The answer was sought through the commonest method of analysing a countrys monetary policy: Taylor rules for describing it. The estimation of the rule was prepared using several risk indicators and applying various types of Taylor rules. As a sensitivity analysis, other indicators of inflation and output gap were employed than in the base rule. This showed that the interest-rate decisions of the National Bank of Hungary can be well described by a flexible inflation targeting regime: in the Taylor rules, deviation of inflation from its target has a significant role and the output gap is also significant in one part of the rules. The decision-makers also considered country risk and responded to an increase in it by raising interest rates. Insertion of country risk into the Taylor rule could improve the models fit to an important degree when choosing an appropriate risk measure.

Numerical investigations of linear least squares methods for derivative estimation

The results of a numerical investigation into the errors for least squares estimates of function gradients are presented. The underlying algorithm is obtained by constructing a least squares problem using a truncated Taylor expansion. An error bound associated with this method contains in its numerator terms related to the Taylor series remainder, while its denominator contains the smallest singular value of the least squares matrix. Perhaps for this reason the error bounds are often found to be pessimistic by several orders of magnitude. The circumstance under which these poor estimates arise is elucidated and an empirical correction of the theoretical error bounds is conjectured and investigated numerically. This is followed by an indication of how the conjecture is supported by a rigorous argument.

Bootstrap approaches for estimation and confidence intervals of long term memory processes

In this work, we investigate an alternative bootstrap approach based on a result of Ramsey [F.L. Ramsey, Characterization of the partial autocorrelation function, Ann. Statist. 2 (1974), pp. 1296-1301] and on the Durbin-Levinson algorithm to obtain a surrogate series from linear Gaussian processes with long range dependence. We compare this bootstrap method with other existing procedures in a wide Monte Carlo experiment by estimating, parametrically and semi-parametrically, the memory parameter d. We consider Gaussian and non-Gaussian processes to prove the robustness of the method to deviations from normality. The approach is also useful to estimate confidence intervals for the memory parameter d by improving the coverage level of the interval.

Arterial travel time estimation : revisiting the classical procedure

Travel time is an important network performance measure and it quantifies congestion in a manner easily understood by all transport users. In urban networks, travel time estimation is challenging due to number of reasons such as, fluctuations in traffic flow due to traffic signals, significant flow to/from mid link sinks/sources, etc. The classical analytical procedure utilizes cumulative plots at upstream and downstream locations for estimating travel time between the two locations. In this paper, we discuss about the issues and challenges with classical analytical procedure such as its vulnerability to non conservation of flow between the two locations. The complexity with respect to exit movement specific travel time is discussed. Recently, we have developed a methodology utilising classical procedure to estimate average travel time and its statistic on urban links (Bhaskar, Chung et al. 2010). Where, detector, signal and probe vehicle data is fused. In this paper we extend the methodology for route travel time estimation and test its performance using simulation. The originality is defining cumulative plots for each exit turning movement utilising historical database which is self updated after each estimation. The performance is also compared with a method solely based on probe (Probe-only). The performance of the proposed methodology has been found insensitive to different route flow, with average accuracy of more than 94% given a probe per estimation interval which is more than 5% increment in accuracy with respect to Probe-only method.

Development and validation of anthropometric prediction equations for estimation of lean body mass and appendicular lean soft tissue in Indian men and women

Lean body mass (LBM) and muscle mass remains difficult to quantify in large epidemiological studies due to non-availability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n= 2220; 36% females; age 18-79 y) representing a wide range of body mass index (14-44 kg/m2) participated in this study. Their LBM including ALST was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height and weight explained > 90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of SFTs) increased the explained variation by 5-8% in the fully adjusted models predicting LBM and ALST. More complex equations using all the above anthropometric variables could predict the DXA measured LBM and ALST accurately as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively) as well as good agreement by Bland Altman analyses. These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition.

Simulation-based density estimation for time series using covariate data

This paper proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.

Robust estimation using the Huber function with a data-dependent tuning constant

Robust estimation often relies on a dispersion function that is more slowly varying at large values than the square function. However, the choice of tuning constant in dispersion functions may impact the estimation efficiency to a great extent. For a given family of dispersion functions such as the Huber family, we suggest obtaining the "best" tuning constant from the data so that the asymptotic efficiency is maximized. This data-driven approach can automatically adjust the value of the tuning constant to provide the necessary resistance against outliers. Simulation studies show that substantial efficiency can be gained by this data-dependent approach compared with the traditional approach in which the tuning constant is fixed. We briefly illustrate the proposed method using two datasets.

Scale- and Context-Dependent Selection of Recreational Harvest Estimation Methods: The Australasian Experience

Fisheries managers are becoming increasingly aware of the need to quantify all forms of harvest, including that by recreational fishers. This need has been driven by both a growing recognition of the potential impact that noncommercial fishers can have on exploited resources and the requirement to allocate catch limits between different sectors of the wider fishing community in many jurisdictions. Marine recreational fishers are rarely required to report any of their activity, and some form of survey technique is usually required to estimate levels of recreational catch and effort. In this review, we describe and discuss studies that have attempted to estimate the nature and extent of recreational harvests of marine fishes in New Zealand and Australia over the past 20 years. We compare studies by method to show how circumstances dictate their application and to highlight recent developments that other researchers may find of use. Although there has been some convergence of approach, we suggest that context is an important consideration, and many of the techniques discussed here have been adapted to suit local conditions and to address recognized sources of bias. Much of this experience, along with novel improvements to existing approaches, have been reported only in "gray" literature because of an emphasis on providing estimates for immediate management purposes. This paper brings much of that work together for the first time, and we discuss how others might benefit from our experience.