Nonparametric IV estimation of local average treatment effects with covariates

In this paper nonparametric instrumental variable estimation of local average treatment effects (LATE) is extended to incorporate covariates. Estimation of LATE is appealing since identification relies on much weaker assumptions than the identification of average treatment effects in other nonparametric instrumental variable models. Including covariates in the estimation of LATE is necessary when the instrumental variable itself is confounded, such that the IV assumptions are valid only conditional on covariates. Previous approaches to handle covariates in the estimation of LATE relied on parametric or semiparametric methods. In this paper, a nonparametric estimator for the estimation of LATE with covariates is suggested that is root-n asymptotically normal and efficient.     

Identification issues in the public/private wage gap,with an application to Italy

This paper reviews some of the standard assumptions that are imposed in order to estimate the average public/private wage gap and that are mainly related to the possible selection of the sector. There are two contributions to the existing public/private wage gap literature. One is a better understanding of the identified parameters: standard estimators identify a local effect (LATE), which in general cannot be generalized to the entire population, as instead is almost always done. The other is the partial identification of the population average treatment effect, with an instrumental variable. To the best of my knowledge, this is the first paper in this literature that employs bounds. The technique is applied to male workers in Italy. For compliers, LATE estimates a wage advantage from working in the public sector greater than 30%. This return is within the narrowest bounds on the population average treatment effect that are consistent even with a much smaller gap (about 15% or more).     

A structural analysis of the correlated random coefficient wage regression model

We estimate a dynamic programming model of schooling decisions in which the log wage regression function is set within a correlated random coefficient model. We show that estimates of the dynamic programming model can be used to obtain a number of treatment effects, including the local average treatment effect (LATE). However, unlike LATE parameters obtained in a standard IV framework, our LATE estimates are obtained without imposing separability between individual specific heterogeneity and schooling choices and are therefore not subject to a “monotonicity” restriction. We find that returns to schooling are characterized by a high degree of dispersion across individuals.     

Regression discontinuity designs: A guide to practice

In regression discontinuity (RD) designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a fixed threshold. These designs were first introduced in the evaluation literature by Thistlewaite and Campbell [1960. Regression-discontinuity analysis: an alternative to the ex-post Facto experiment. Journal of Educational Psychology 51, 309–317] With the exception of a few unpublished theoretical papers, these methods did not attract much attention in the economics literature until recently. Starting in the late 1990s, there has been a large number of studies in economics applying and extending RD methods. In this paper we review some of the practical and theoretical issues in implementation of RD methods.     

Matching and semi-parametric IV estimation, a distance-based measure of migration, and the wages of young men

Our paper estimates the effect of US internal migration on wage growth for young men between their first and second job. Our analysis of migration extends previous research by: (i) exploiting the distance-based measures of migration in the National Longitudinal Surveys of Youth 1979 (NLSY79); (ii) allowing the effect of migration to differ by schooling level and (iii) using propensity score matching to estimate the average treatment effect on the treated (ATET) for movers and (iv) using local average treatment effect (LATE) estimators with covariates to estimate the average treatment effect (ATE) and ATET for compliers.We believe the Conditional Independence Assumption (CIA) is reasonable for our matching estimators since the NLSY79 provides a relatively rich array of variables on which to match. Our matching methods are based on local linear, local cubic, and local linear ridge regressions. Local linear and local ridge regression matching produce relatively similar point estimates and standard errors, while local cubic regression matching badly over-fits the data and provides very noisy estimates.We use the bootstrap to calculate standard errors. Since the validity of the bootstrap has not been investigated for the matching estimators we use, and has been shown to be invalid for nearest neighbor matching estimators, we conduct a Monte Carlo study on the appropriateness of using the bootstrap to calculate standard errors for local linear regression matching. The data generating processes in our Monte Carlo study are relatively rich and calibrated to match our empirical models or to test the sensitivity of our results to the choice of parameter values. The estimated standard errors from the bootstrap are very close to those from the Monte Carlo experiments, which lends support to our using the bootstrap to calculate standard errors in our setting.From the matching estimators we find a significant positive effect of migration on the wage growth of college graduates, and a marginally significant negative effect for high school dropouts. We do not find any significant effects for other educational groups or for the overall sample. Our results are generally robust to changes in the model specification and changes in our distance-based measure of migration. We find that better data matters; if we use a measure of migration based on moving across county lines, we overstate the number of moves, while if we use a measure based on moving across state lines, we understate the number of moves. Further, using either the county or state measures leads to much less precise estimates.We also consider semi-parametric LATE estimators with covariates (Frölich 2007), using two sets of instrumental variables. We precisely estimate the proportion of compliers in our data, but because we have a small number of compliers, we cannot obtain precise LATE estimates.     

A note on individual choice models of migration

In this paper individual probabilistic choice models are developed for the decision to migrate and the choice among alternative destinations. The models are developed to investigate how characteristics of decision makers and of alternative destinations affect choice. Several migration models in the literature (e.g., mover-stayer models) are shown to be special cases, their behavioral assumptions are made explicit, and a framework for testing these assumptions is provided.     

Ecological Fallacy and Covariates: New Insights based on Multilevel Modelling of Individual Data

The paper provides a new and more explicit formulation of the assumptions needed by the ordinary ecological regression to provide unbiased estimates and clarifies why violations of these assumptions will affect any method of ecological inference. Empirical evidence is obtained by showing that estimates provided by three main ecological inference methods are heavily biased when compared with multilevel logistic regression applied to a unique set of individual data on voting behaviour. The main findings of our paper have two important implications that can be extended to all situations where the assumptions needed to apply ecological inference are violated in the data: (i) only ecological inference methods that allow one to model the effect of covariates have a chance to produce unbiased estimates, and (ii) there are certain data generating mechanisms producing a kind of bias in ecological estimates that cannot be corrected by modelling the effect of covariates.     

A generalized focused information criterion for GMM

This paper proposes a criterion for simultaneous generalized method of moments model and moment selection: the generalized focused information criterion (GFIC). Rather than attempting to identify the “true” specification, the GFIC chooses from a set of potentially misspecified moment conditions and parameter restrictions to minimize the mean squared error (MSE) of a user‐specified target parameter. The intent of the GFIC is to formalize a situation common in applied practice. An applied researcher begins with a set of fairly weak “baseline” assumptions, assumed to be correct, and must decide whether to impose any of a number of stronger, more controversial “suspect” assumptions that yield parameter restrictions, additional moment conditions, or both. Provided that the baseline assumptions identify the model, we show how to construct an asymptotically unbiased estimator of the asymptotic MSE to select over these suspect assumptions: the GFIC. We go on to provide results for postselection inference and model averaging that can be applied both to the GFIC and various alternative selection criteria. To illustrate how our criterion can be used in practice, we specialize the GFIC to the problem of selecting over exogeneity assumptions and lag lengths in a dynamic panel model, and show that it performs well in simulations. We conclude by applying the GFIC to a dynamic panel data model for the price elasticity of cigarette demand.     

Secondary products and the measurement of productivity growth

The paper considers alternative treatments of secondary products in input-output systems and analyzes their implications for the measurement of productivity growth at both the sectoral and overall level. Two standard models of secondary products are used: (1) the commodity technology model and (2) the industry technology model. It is argued that the first model correctly relates sectoral and overall levels of productivity growth; the second model, though more conventional, aggregates sectoral levels to a biased estimate of overall productivity growth. Estimates of the two measures are provided using U.S. 85-sector input-output data for 1967, 1972, and 1977. The empirical results indicate that the alternative assumptions do not lead to significantly different estimates of commodity-level and industry-level productivity growth over this period for the full economy but do for several sectors. Moreover, changes in secondary production did not contribute significantly to the decline in productivity growth over this period but secondary production was found to have a much lower rate of productivity growth than primary production.     

Theoretical foundations for the study of sociomateriality

This paper compares two alternative theoretical foundations upon which the study of sociomateriality can be built: agential realism and critical realism. It begins by providing a brief overview of the sociomaterial perspective on organizational practices and considers why this perspective holds great appeal at this point in time. I then engage with Mutch's (2013) critique of the agential realist foundation upon which most current discussions of sociomateriality are constructed to highlight what practical problems are generated when authors attempt to map agential realism's philosophical discussion onto empirical phenomena. Next, I attempt to make explicit what Mutch leaves implicit in his paper: how building studies of sociomateriality on the theoretical foundation offered by critical realism can, potentially, overcome some of the practical problems created by a footing on agential realism. Finally, I push Mutch's arguments one step further to compare what practical consequences arise when researchers attempt to construct studies of sociomateriality on either of these two theoretical foundations. I suggest that there are important implications for what one can study, how one can study it, and how scholars can contribute to theory on technology and organizing based on the theoretical foundation they choose to build upon.     

Predictions of short-term rates and the expectations hypothesis

This paper emphasizes that traditional tests of the EH are based on two assumptions: the expectations hypothesis (EH) per se and an assumption about the expectations generating process (EGP) for the short-term rate. Arguing that conventional tests of the EH need to assume EGPs that may be significantly at odds with the true EGP, we investigate this possibility by analyzing the out-of-sample predictive performances of several models for predicting interest rates, including a few models which assume that the EH holds in its functional form that relates long- to short-term yields. Using US riskless yield data for a 1970–2016 monthly sample and testing methods that take into account the parameter uncertainty, the null hypothesis of an equal predictive accuracy of each model relative to the random walk alternative is hardly ever rejected at intermediate and long horizons. This confirms that, at least at a practical level, the main difficulty with the EH is represented by the effective prediction of short-term rates. We discuss the relevance of these findings for central banks’ use of forward guidance.     

Quantile treatment effects in the regression discontinuity design

We introduce a nonparametric estimator for local quantile treatment effects in the regression discontinuity (RD) design. The procedure uses local distribution regression to estimate the marginal distributions of the potential outcomes. We illustrate the procedure through Monte Carlo simulations and an application on the distributional effects of a universal pre-K program in Oklahoma. We find that participation in a pre-K program significantly raises the lower end and the middle of the distribution of test scores.     

The transfer of human resource management technology in Sino-U.S. cooperative ventures: Problems and solutions

Differences between Chinese and U.S. human resource management systems are described with respect to fundamental organization and work-related assumptions about people and performance, rewards, training and development, and educational background of HR practitioners. We identify key differences that present impediments to the modernization of HRM practices in Sino-U.S. cooperative ventures, and then, present a normative framework for the introduction of modem HRM practices into these ventures. We conclude with implications for both research and practice that focus primarily on integrating individuals and the enterprise, as well as integrating the enterprise with social and economic goals.     

Path Dependence or Path Creation?

We discuss the assumptions that underlie path dependence, as defined by Vergne and Durand, and then provide the outlines of an alternative perspective which we label as path creation. Path creation entertains a notion of agency that is distributed and emergent through relational processes that constitute phenomena. Viewed from this perspective, ‘initial conditions’ are not given, ‘contingencies’ are emergent contexts for action, ‘self‐reinforcing mechanisms’ are strategically manipulated, and ‘lock‐in’ is but a temporary stabilization of paths in‐the‐making. We develop these points using a narrative approach and highlight the theoretical and methodological implications of our perspective.     

Interval Estimation of Treatment Effects In Randomized Trials: When do Confidence Intervals Have Nominal Coverage?

In the classical theory of randomized trials (RTs), the treatment effect is defined as a mean difference of potential outcomes. To achieve nominal coverage probability for confidence intervals (CIs) on treatment effects in RTs, certain assumptions are necessary. Specifically, one must either make assumptions about the joint distribution of potential outcomes or enroll subjects in the trial by random sampling of the target population on which the treatment effect is defined. In practice, no such sampling usually takes place and assumptions about the joint distribution of potential outcomes cannot be verified based on observed data. Furthermore, the most common of these assumptions, such as treatment‐unit additivity (TUA) or independence are biologically implausible in most RTs involving human subjects. Hence, it is not usually possible to construct CIs on treatment effects with nominal coverage probability. However, for any joint distribution of potential outcomes, the standard estimator of the variance of the difference of two independent sample means produces CIs with asymptotic coverage at least at the nominal level. This interpretation of CIs as conservative bounds may not always hold in conventional regression models applied to RT data.     

High-investment HR values and firm performance among local firms and U.S. MNCs’ subsidiaries in South Asia: a comparative study

Grounded in institutional theory, this study investigates the differential adoption and internalization of high-investment human resource (HR) values by local companies and by subsidiaries of US firms located throughout South Asia; and the impact of these HR values on firms’ performance. In line with our predictions, results suggest that US subsidiaries have a greater rate of adoption of high-investment HR values compared to local South Asian firms. Contrary to our predictions, however, both types of firms are similar in the level of internalization of their respective HR values. Finally, while greater levels of high-investment HR value adoption is associated with firm performance across the board, this relationship tends to be stronger for US MNCs’ subsidiaries compared to local South Asian companies. Theoretical and practical implications for the transfer and diffusion of high-investment HR values in institutionally and culturally distant contexts are discussed.     

Specification and estimation of spatial econometric models: A discussion of alternative strategies for spatial economic modelling

The semantical insufficiency of (spatial) economic theories necessitates the making of additional assumptions — thereby introducing substantial specification uncertainty — in order to arrive at a fully specified econometric model. The traditional or current approach to econometric modelling treats specification uncertainty inadequately. This proposition is illustrated by two well-known examples from the spatial economic literature. Two alternative specification strategies for spatial economic modelling — designed to improve the current spatial econometric modelling approach — are proposed. One of these strategies is used for a specification analysis of agricultural output in Eire.     

The new urban economics: An evaluation

This paper reviews and evaluates the branch of urban economic theory labelled the “New Urban Economics.” This sub-field attempts to integrate welfare economics and urban economics within a general equilibrium framework using mathematical methods of analysis. Particular attention is given to the assumptions behind these models (one-dimensional space, monocentricity, homogeneous households, exclusive zoning, long-run equilibrium, etc.) and their implications. Extensions of the basic model—the treatment of utility, road congestion, other externalities, dynamics and optimum geography—are also examined.     

Pursuing justice against the abuse of power by the rich through social media: A case study

ABSTRACT

The purpose of this case study is to compare and contrast two recent legal cases and to derive practical implications to promote social justice in terms of income inequality. This study focuses on exploring and analyzing two of the most recent legal cases of the abuse of power by the rich in order to identify the effects of social media on these types of cases. Despite the availability of many studies and findings about the tolerance level to income inequality, the following logic is more appropriate in applying and analyzing the two cases: If people don’t know, see, or experience the abuse of the rich or income inequality, they are more tolerant of the abuse and income inequality. Based on the analysis of the two cases, the article concludes that these two cases are not the usual air rage cases; they are more about the abuse of the rich and the punishment decided by the use of technology.     

Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

We review some results on the analysis of longitudinal data or, more generally, of repeated measures via linear mixed models starting with some exploratory statistical tools that may be employed to specify a tentative model. We follow with a summary of inferential procedures under a Gaussian set‐up and then discuss different diagnostic methods focusing on residual analysis but also addressing global and local influence. Based on the interpretation of diagnostic plots related to three types of residuals (marginal, conditional and predicted random effects) as well as on other tools, we proceed to identify remedial measures for possible violations of the proposed model assumptions, ranging from fine‐tuning of the model to the use of elliptically symmetric or skew‐elliptical linear mixed models as well as of robust estimation methods. We specify many results available in the literature in a unified notation and highlight those with greater practical appeal. In each case, we discuss the availability of model diagnostics as well as of software and give general guidelines for model selection. We conclude with analyses of three practical examples and suggest further directions for research.