Scope economies resulting from the joint offering of loans and savings accounts (as opposed to loans only) are customarily invoked to promote the transformation of credit-only microfinance institutions (MFIs) into integrated loans-and-savings entities. To ensure robust inference, we estimate scope economies for the microfinance industry using a novel approach which, among its other advantages, accommodates inherent heterogeneity across loans-only and loans-and-savings MFIs as well as controls for endogenous self-selection of institutions into the either type. For analysis, we use a large 2004-2014 Mixmarket dataset. Unlike earlier studies, we do not find prevalent scope economies in the microfinance industry. We find that the median degree of scope economies is statistically indistinguishable from zero and that scope economies are significantly positive for less than a half of loans-and-savings MFIs. For a non-trivial 14% of institutions, the empirical evidence suggests the existence of significantly negative diseconomies of scope indicating that the separate production of loans and savings accounts actually has the potential to reduce an MFI's costs. We also find that the failure to account for endogenous selectivity dramatically overestimates the degree of scope economies resulting in the failure to detect scope diseconomies among MFIs. Thus, our findings call for caution when invoking scope economies as a blanket justification for universal expansion of the scope of financial operations by MFIs. Instead, promoting integrated loans-and-savings MFIs may be justifiable as a means to meeting the needs of the poor rather than as a way for the industry to save costs.
This paper offers a methodology to address the endogeneity of inputs in the directional technology distance function (DTDF) based formulation of banking technology which explicitly accommodates the presence of undesirable nonperforming loans -an inherent characteristic of the bank's production due to its exposure to credit risk. Specifically, we model nonperforming loans as an undesirable output in the bank's production process. Since the stochastic DTDF describing banking technology is likely to suffer from the endogeneity of inputs, we propose addressing this problem by considering a system consisting of the DTDF and the first-order conditions from the bank's cost minimization problem. The first-order conditions also allow us to identify the "cost-optimal" directional vector for the banking DTDF, thus eliminating the uncertainty associated with an ad hoc choice of the direction. We apply our cost system approach to the data on large U.S. commercial banks for the 2001-2010 period, which we estimate via Bayesian MCMC methods subject to theoretical regularity conditions. We document dramatic distortions in banks' efficiency, productivity growth and scale elasticity estimates when the endogeneity of inputs is assumed away and/or the DTDF is fitted in an arbitrary direction.
Credit unions differ in the types of financial services they offer to their members. This paper explicitly models this observed heterogeneity using a generalized model of endogenous ordered switching. Our approach captures the endogenous choice that credit unions make when adding new products to their financial services mix. The model that we consider also allows for the dependence between unobserved effects and regressors in both the selection and outcome equations and can accommodate the presence of predetermined covariates in the model. We use this model to estimate returns to scale for U.S. retail credit unions from 1996 to 2011. We document strong evidence of persistent technological heterogeneity among credit unions offering different financial service mixes, which, if ignored, can produce quite misleading results. Employing our model, we find that credit unions of all types exhibit substantial economies of scale.Keywords: Credit Unions, Correlated Effects, Ordered Choice, Panel Data, Production, Returns to Scale, Switching Regression JEL Classification: C33, C34, D24, G21 * Email : emalikov@stlawu.edu (Malikov), drestr16@eafit.edu.co (Restrepo), kkar@binghamton.edu (Kumbhakar).We would like to thank the editor, the associate editor and two anonymous referees for many insightful comments and suggestions that helped improve this article. We are also thankful to Alfonso Flores-Lagunes, Thierry Magnac, Dave Wheelock, Jeff Wooldridge and seminar participants at Syracuse University, University at Albany, Federal Reserve Bank of Dallas and the 2013 Midwest Econometrics Group Meeting at Indiana University Bloomington for many helpful comments and suggestions. Any remaining errors are our own. We also gratefully acknowledge the technical assistance from the Center for Scientific Computation APOLO at EAFIT University in setting up the cluster we used for the estimation. Restrepo-Tobón acknowledges financial support from the Colombian Administrative Department of Sciences, Technology and Innovation, Colombian Fulbright Commission and EAFIT University. The original version of this paper was circulated under the title "Are All U.S. Credit Unions Alike? A Generalized Model of Heterogeneous Technologies with Endogenous Switching and Correlated Effects".
Motivated by the long-standing interest of economists in understanding the nexus between firm productivity and export behavior, this paper develops a novel structural framework for control-function-based nonparametric identification of the gross production function and latent firm productivity in the presence of endogenous export opportunities that is robust to recent unidentification critiques of proxy estimators. We provide a workable identification strategy, whereby the firm's degree of export orientation provides the needed (excluded) relevant independent exogenous variation in endogenous freely varying inputs, thus allowing us to identify the production function. We estimate our fully nonparametric instrumental variable model using the Landweber-Fridman regularization with the unknown functions approximated via artificial neural network sieves with a sigmoid activation function, which are known for their superior performance relative to other popular sieve approximators, including the polynomial series favored in the literature. Using our methodology, we obtain robust productivity estimates for manufacturing firms from 28 industries in China during the 1999-2006 period to take a close look at China's exporter productivity puzzle, whereby exporters are found to exhibit lower productivity levels than nonexports.J Appl Econ. 2020;35:457-480.wileyonlinelibrary.com/journal/jae
This article offers a methodology to address the endogeneity of inputs in the input distance function (IDF) formulation of the production processes. We propose to tackle endogenous input ratios appearing in the normalized IDF by considering a flexible (simultaneous) system of the IDF and the first-order conditions from the firm's cost minimization problem. Our model can accommodate both technical and (input) allocative inefficiencies amongst firms. We also present the algorithm for quantifying the cost of allocative inefficiency. We showcase our costsystem-based model by applying it to study the production of Norwegian dairy farms during the 1991-2008 period. Among other things, we find both an economically and statistically significant improvement in the levels of technical efficiency among dairy farms associated with the 1997 quota scheme change, which a more conventional single-equation stochastic frontier model appears to be unable to detect.
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