A Consistent Nonparametric Bootstrap Test of Exogeneity

This paper proposes a novel way of testing exogeneity of an explanatory variable without any parametric assumptions in the presence of a "conditional" instrumental variable. A testable implication is derived that if an explanatory variable is endogenous, the conditional distribution of the outcome given the endogenous variable is not independent of its instrumental variable(s). The test rejects the null hypothesis with probability one if the explanatory variable is endogenous and it detects alternatives converging to the null at a rate n..1=2:We propose a consistent nonparametric bootstrap test to implement this testable implication. We show that the proposed bootstrap test can be asymptotically justi.ed in the sense that it produces asymptotically correct size under the null of exogeneity, and it has unit power asymptotically. Our nonparametric test can be applied to the cases in which the outcome is generated by an additively non-separable structural relation or in which the outcome is discrete, which has not been studied in the literature.

On making causal claims: A review and recommendations

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity--which includes omitted variables, omitted selection, simultaneity, common methods bias, and measurement error--renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation is confounded, including fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66 % and up to 90 % of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

Nonclassical measurement error with applications to labor and development issues

Zero correlation between measurement error and model error has been assumed in existing panel data models dealing specifically with measurement error. We extend this literature and propose a simple model where one regressor is mismeasured, allowing the measurement error to correlate with model error. Zero correlation between measurement error and model error is a special case in our model where correlated measurement error equals zero. We ask two research questions. First, we wonder if the correlated measurement error can be identified in the context of panel data. Second, we wonder if classical instrumental variables in panel data need to be adjusted when correlation between measurement error and model error cannot be ignored. Under some regularity conditions the answer is yes to both questions. We then propose a two-step estimation corresponding to the two questions. The first step estimates correlated measurement error from a reverse regression; and the second step estimates usual coefficients of interest using adjusted instruments.

The effect of retirement on cognitive functioning

Cognitive impairment has emerged as a major driver of disability in old age, with profound effects on individual well-being and decision making at older ages. In the light of policies aimed at postponing retirement ages, an important question is whether continued labour supply helps to maintain high levels of cognition at older ages. We use data of older men from the US Health and Retirement Study to estimate the effect of continued labour market participation at older ages on later-life cognition. As retirement itself is likely to depend on cognitive functioning and may thus be endogenous, we use offers of early retirement windows as instruments for retirement in econometric models for later-life cognitive functioning. These offers of early retirement are legally required to be nondiscriminatory and thus, inter alia, unrelated to cognitive functioning. At the same time, these offers of early retirement options are significant predictors of retirement. Although the simple ordinary least squares estimates show a negative relationship between retirement duration and various measures of cognitive functioning, instrumental variable estimates suggest that these associations may not be causal effects. Specifically, we find no clear relationship between retirement duration and later-life cognition for white-collar workers and, if anything, a positive relationship for blue-collar workers. Copyright © 2011 John Wiley & Sons, Ltd.

Neither Private nor Public: The Effects of Communal Provision of Water on Child Health in Peru

The literature on local services has focused on the effects of privatization and, if anything, has compared the effects of private and mixed public-private systems versus public provision. However, alternative forms of provision such as cooperatives, which can be very prevalent in many developing countries, have been completely ignored. In this paper, we investigate the effects of communal water provison (Comités Vecinales and Juntas Administrativas de Servicios de Saneamiento) on child health in Peru. Using detailed survey data at the household- and child-level for the years 2006-2010, we exploit the cross-section variability to assess the differential impact of this form of provision. Despite controlling for a wide range of household and local characteristics, the municipalities served by communal organizations are more likely to have poorer health indicators, what would result in a downward bias on the absolute magnitude of the effect of cooperatives. We rely on an instrumental variable strategy to deal with this potential endogeneity problem, and use the personnel resources and the administrative urban/rural classi fication of the municipalities as instruments for the provision type. The results show a negative and signi cant effect of comunal water provision on diarrhea among under- five year old children. Keywords: water utilities, cooperatives, child health, regulation, Peru. JEL Classi fication Numbers: L33; L50; L95

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Does direct democracy reduce the size of government? New evidence from historical data, 1890-2000

Using historical data for all Swiss cantons from 1890 to 2000, we estimate the causal effect of direct democracy on government spending. The main innovation in this paper is that we use fixed effects to control for unobserved heterogeneity and instrumental variables to address the potential endogeneity of institutions. We find that the budget referendum and lower costs to launch a voter initiative are effective tools in reducing canton level spending. However, we find no evidence that the budget referendum results in more decentralized government or a larger local government. Our instrumental variable estimates suggest that a mandatory budget referendum reduces the size of canton spending between 13 and 19 percent. A 1 percent lower signature requirement for the initiative reduces canton spending by up to 2 percent.

Nonparametric bounds on the returns to language skills

This paper applies the theoretical literature on nonparametric bounds ontreatment effects to the estimation of how limited English proficiency (LEP)affects wages and employment opportunities for Hispanic workers in theUnited States. I analyze the identifying power of several weak assumptionson treatment response and selection, and stress the interactions between LEPand education, occupation and immigration status. I show that thecombination of two weak but credible assumptions provides informative upperbounds on the returns to language skills for certain subgroups of thepopulation. Adding age at arrival as a monotone instrumental variable alsoprovides informative lower bounds.

Fighting against Malaria: Prevent wars while waiting for the "miraculous" vaccine

The World Health Organization estimates that 300 million clinical cases of malaria occur annually and observed that during the 80's and part of the 90's its incidence increased. In this paper we explore the influence of refugees from civil wars on the incidence of malaria in the refugee-receiving countries. Using civil wars as an instrumental variable we show that for each 1,000 refugees there are between 2,000 and 2,700 cases of malaria in the refugee receiving country. On average 13% of the cases of malaria reported by the WHO are caused by forced migration as a consequence of civil wars.

Identifying the socioeconomic determinants of crime in Spanish provinces

In this paper we study, having as theoretical reference the economic model of crime (Becker, 1968; Ehrlich, 1973), which are the socioeconomic and demographic determinants of crime in Spain paying attention on the role of provincial peculiarities. We estimate a crime equation using a panel dataset of Spanish provinces (NUTS3) for the period 1993 to 1999 employing the GMMsystem estimator. Empirical results suggest that lagged crime rate and clear-up rate are correlated to all typologies of crime rate considered. Property crimes are better explained by socioeconomic variables (GDP per capita, GDP growth rate and percentage of population with high school and university degree), while demographic factors reveal important and significant influences, in particular for crimes against the person. These results are obtained using an instrumental variable approach that takes advantage of the dynamic properties of our dataset to control for both measurement errors in crime data and joint endogeneity of the explanatory variables

On making causal claims: A review and recommendations

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity--which includes omitted variables, omitted selection, simultaneity, common methods bias, and measurement error--renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation is confounded, including fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66 % and up to 90 % of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

Identifying the socioeconomic determinants of crime in Spanish provinces

In this paper we study, having as theoretical reference the economic model of crime (Becker, 1968; Ehrlich, 1973), which are the socioeconomic and demographic determinants of crime in Spain paying attention on the role of provincial peculiarities. We estimate a crime equation using a panel dataset of Spanish provinces (NUTS3) for the period 1993 to 1999 employing the GMMsystem estimator. Empirical results suggest that lagged crime rate and clear-up rate are correlated to all typologies of crime rate considered. Property crimes are better explained by socioeconomic variables (GDP per capita, GDP growth rate and percentage of population with high school and university degree), while demographic factors reveal important and significant influences, in particular for crimes against the person. These results are obtained using an instrumental variable approach that takes advantage of the dynamic properties of our dataset to control for both measurement errors in crime data and joint endogeneity of the explanatory variables

A comparison of three methods of Mendelian randomization when the genetic instrument, the risk factor and the outcome are all binary.

The method of instrumental variable (referred to as Mendelian randomization when the instrument is a genetic variant) has been initially developed to infer on a causal effect of a risk factor on some outcome of interest in a linear model. Adapting this method to nonlinear models, however, is known to be problematic. In this paper, we consider the simple case when the genetic instrument, the risk factor, and the outcome are all binary. We compare via simulations the usual two-stages estimate of a causal odds-ratio and its adjusted version with a recently proposed estimate in the context of a clinical trial with noncompliance. In contrast to the former two, we confirm that the latter is (under some conditions) a valid estimate of a causal odds-ratio defined in the subpopulation of compliers, and we propose its use in the context of Mendelian randomization. By analogy with a clinical trial with noncompliance, compliers are those individuals for whom the presence/absence of the risk factor X is determined by the presence/absence of the genetic variant Z (i.e., for whom we would observe X = Z whatever the alleles randomly received at conception). We also recall and illustrate the huge variability of instrumental variable estimates when the instrument is weak (i.e., with a low percentage of compliers, as is typically the case with genetic instruments for which this proportion is frequently smaller than 10%) where the inter-quartile range of our simulated estimates was up to 18 times higher compared to a conventional (e.g., intention-to-treat) approach. We thus conclude that the need to find stronger instruments is probably as important as the need to develop a methodology allowing to consistently estimate a causal odds-ratio.

Population Size and Civil Conflict Risk: Is There A Causal Link?

[spa] La estimación del impacto del tamaño de la populación sobre la probabilidad de conflicto civil se complica por el sesgo de endogeneidad y las variables omitidas. Este artículo trata el problema de causalidad utilizando métodos de variables instrumentales en un panel de 37 países del África Sub-sahariana en el período 1981-2004. Encontramos que un aumento de la población en un 1% aumenta la probabilidad de conflicto civil por un 5.2%.

The chronic kidney disease outcomes and practice patterns study Brazil (CKDopps-Brazil): Design, data and methodology

Introduction: The chronic kidney disease outcomes and practice patterns study (CKDopps) is an international observational, prospective, cohort study involving patients with chronic kidney disease (CKD) stages 3-5 [estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2, with a major focus upon care during the advanced CKD period (eGFR < 30 ml/min/1.73 m2)]. During a 1-year enrollment period, each one of the 22 selected clinics will enroll up to 60 advanced CKD patients (eGFR < 30 ml/min/1.73 m2 and not dialysis-dependent) and 20 earlier stage CKD patients (eGFR between 30-59 ml/min/1.73 m2). Exclusion criteria: age < 18 years old, patients on chronic dialysis or prior kidney transplant. The study timeline include up to one year for enrollment of patients at each clinic starting in the end of 2013, followed by up to 2-3 years of patient follow-up with collection of detailed longitudinal patient-level data, annual clinic practice-level surveys, and patient surveys. Analyses will apply regression models to evaluate the contribution of patient-level and clinic practice-level factors to study outcomes, and utilize instrumental variable-type techniques when appropriate. Conclusion: Launching in 2013, CKDopps Brazil will study advanced CKD care in a random selection of nephrology clinics across Brazil to gain understanding of variation in care across the country, and as part of a multinational study to identify optimal treatment practices to slow kidney disease progression and improve outcomes during the transition period to end-stage kidney disease.