How People Think About Distributing Aid

This paper examines how people think about aiding others in a way that can inform both theory and practice. It uses data gathered from Kiva, an online, non-profit organization that allows individuals to aid other individuals around the world, to isolate intuitions that people find broadly compelling. The central result of the paper is that people seem to give more priority to aiding those in greater need, at least below some threshold. That is, the data strongly suggest incorporating both a threshold and a prioritarian principle into the analysis of what principles for aid distribution people accept. This conclusion should be of broad interest to aid practitioners and policy makers. It may also provide important information for political philosophers interested in building, justifying, and criticizing theories about meeting needs using empirical evidence

Dynamic Responses to Oil Price Shocks: Conditional vs Unconditional (A)symmetry

The impulse-response-function-based Wald test has been gaining wide popularity among researchers seeking to formally test for (a)symmetries in dynamic responses of various macroeconomic aggregates to oil price shocks. However, because the IRF-based Wald test is conditional on the magnitude of an oil price shock, it can sometimes prove to be impractical, especially when producing contrasting evidence for shocks of different sizes. To circumvent this problem, this paper suggests considering a nonparametric IRF-density-based test in addition to the Wald. The former allows the analysis of (a)symmetries in dynamic impulse responses to positive and negative oil price shocks of a wide range of magnitudes. The test permits inference about a general tendency of (a)symmetries in impulse responses as opposed to (a)symmetries pertinent to a shock of a given size only. The examined (a)symmetry is thus unconditional of the magnitude of a shock. Importantly, the testing procedure allows accounting for the relative likelihood of observing the disturbance of a given size

Estimation of Firm-Level Productivity in the Presence of Exports: Evidence from China's Manufacturing

Motivated by the longstanding 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 IV 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 twenty eight 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 non-exports

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors in the model with a large number of cross-sectional units and a fixed number of time periods. The methodology we propose removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, the estimation of which however does not require the use of backfitting or marginal integration techniques. We derive the consistency and asymptotic normality results for the proposed kernel and sieve estimators. We also construct a consistent nonparametric test to test for spatial endogeneity in the data. A small Monte Carlo study shows that our proposed estimators and the test statistic exhibit good finite-sample performance

Endogenous Scope Economies in Microfinance Institutions

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

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors in the model with a large number of cross-sectional units and a fixed number of time periods. The methodology we propose removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, the estimation of which however does not require the use of backfitting or marginal integration techniques. We derive the consistency and asymptotic normality results for the proposed kernel and sieve estimators. We also construct a consistent nonparametric test to test for spatial endogeneity in the data. A small Monte Carlo study shows that our proposed estimators and the test statistic exhibit good finite-sample performance

Are all U.S. credit unions alike?

This paper raises concerns about the econometric approach used in the literature to estimate credit unions’ production technologies. We show that the existing studies did not recognize heterogeneity amongst credit unions’ technologies as captured by (endogenously selected) differing output mixes. Failure to account for the above leads to biased, inconsistent estimates and potentially misleading results. The estimates are also likely to be biased due to unobserved credit union specific effects that the literature broadly ignores. To address these concerns, we develop a generalized model of endogenous switching with polychotomous choice that is able to account for fixed effects in both the technology selection and the outcome equations. We use this model to estimate returns to scale for the U.S. retail credit unions from 1994 to 2011. Unlike recent studies, we find that not all credit unions enjoy increasing returns to scale. A nonnegligible number of large institutions operate at decreasing returns to scale, indicating that they should either cut back in size or switch to a more efficient technology by re-optimizing the output mix

Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models

This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in which unknown coefficients are permitted to be nonparametric functions of some contextual variables to allow for potential nonlinearities and parameter heterogeneity in the spatial relationship. Unlike other semiparametric spatial dependence models, ours permits the spatial autoregressive parameter to meaningfully vary across units and thus allows the identification of a neighborhood-specific spatial dependence measure conditional on the vector of contextual variables. We propose several (locally) nonparametric GMM estimators for our model. The developed two-stage estimators incorporate both the linear and quadratic orthogonality conditions and are capable of accommodating a variety of data generating processes, including the instance of a pure spatially autoregressive semiparametric model with no relevant regressors as well as multiple partially linear specifications. All proposed estimators are shown to be consistent and asymptotically normal. We also contribute to the literature by putting forward two test statistics to test for parameter constancy in our model. Both tests are consistent

An internally consistent approach to the estimation of market power and cost efficiency with an application to U.S. banking

We develop a novel unified econometric methodology for the formal examination of the market power – cost efficiency nexus. Our approach can meaningfully accommodate a mutually dependent relationship between the firm’s cost efficiency and marker power (as measured by the Lerner index) by explicitly modeling the simultaneous determination of the two in a system of nonlinear equations consisting of the firm’s cost frontier and the revenue-to-cost ratio equation derived from its stochastic revenue function. Our framework places no a priori restrictions on the sign of the dependence between the firm’s market power and efficiency as well as allows for different hierarchical orderings between the two, enabling us to discriminate between competing quiet life and efficient structure hypotheses. Among other benefits, our approach completely obviates the need for second-stage regressions of the cost efficiency estimates on the constructed market power measures which, while widely prevalent in the literature, suffer from multiple econometric problems as well as lack internal consistency/validity. We showcase our methodology by applying it to a panel of U.S. commercial banks in 1984–2007 using Bayesian MCMC methods