The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior

This paper establishes that a low dimensional vector of cognitive and noncognitive skills explains a variety of labor market and behavioral outcomes. For many dimensions of social performance cognitive and noncognitive skills are equally important. Our analysis addresses the problems of measurement error, imperfect proxies, and reverse causality that plague conventional studies of cognitive and noncognitive skills that regress earnings (and other outcomes) on proxies for skills. Noncognitive skills strongly influence schooling decisions, and also affect wages given schooling decisions. Schooling, employment, work experience and choice of occupation are affected by latent noncognitive and cognitive skills. We study a variety of correlated risky behaviors such as teenage pregnancy and marriage, smoking, marijuana use, and participation in illegal activities. The same low dimensional vector of abilities that explains schooling choices, wages, employment, work experience and choice of occupation explains these behavioral outcomes.

Understanding Instrumental Variables in Models with Essential Heterogeneity

This paper examines the properties of instrumental variables (IV) applied to models with essential heterogeneity, that is, models where responses to interventions are heterogeneous and agents adopt treatments (participate in programs) with at least partial knowledge of their idiosyncratic response. We analyze two-outcome and multiple-outcome models including ordered and unordered choice models. We allow for transition-specific and general instruments. We generalize previous analyses by developing weights for treatment effects for general instruments. We develop a simple test for the presence of essential heterogeneity. We note the asymmetry of the model of essential heterogeneity: outcomes of choices are heterogeneous in a general way; choices are not. When both choices and outcomes are permitted to be symmetrically heterogeneous, the method of IV breaks down for estimating treatment parameters.

Understanding Instrumental Variables in Models with Essential Heterogeneity

This paper examines the properties of instrumental variables (IV) applied to models with essential heterogeneity, that is, models where responses to interventions are heterogeneous and agents adopt treatments (participate in programs) with at least partial knowledge of their idiosyncratic response. We analyze two-outcome and multiple-outcome models including ordered and unordered choice models. We allow for transition-specific and general instruments. We generalize previous analyses by developing weights for treatment effects for general instruments. We develop a simple test for the presence of essential heterogeneity. We note the asymmetry of the model of essential heterogeneity: outcomes of choices are heterogeneous in a general way; choices are not. When both choices and outcomes are permitted to be symmetrically heterogeneous, the method of IV breaks down for estimating treatment parameters.

Testing the Correlated Random Coefficient Model

The recent literature on instrumental variables (IV) features models in which agents sort into treatment status on the basis of gains from treatment as well as on baseline-pretreatment levels. Components of the gains known to the agents and acted on by them may not be known by the observing economist. Such models are called correlated random coefficient models. Sorting on unobserved components of gains complicates the interpretation of what IV estimates. This paper examines testable implications of the hypothesis that agents do not sort into treatment based on gains. In it, we develop new tests to gauge the empirical relevance of the correlated random coefficient model to examine whether the additional complications associated with it are required. We examine the power of the proposed tests. We derive a new representation of the variance of the instrumental variable estimator for the correlated random coefficient model. We apply the methods in this paper to the prototypical empirical problem of estimating the return to schooling and find evidence of sorting into schooling based on unobserved components of gains.instrumental variables, testing, correlated random coefficient, power of tests based on IV

INSTRUMENTAL VARIABLES IN MODELS WITH MULTIPLE OUTCOMES: THE GENERAL UNORDERED CASE

This paper develops the method of local instrumental variables for mod- els with multiple, unordered treatments when treatment choice is determined by a nonparametric version of the multinomial choice model. Responses to interventions are permitted to be heterogeneous in a general way and agents are allowed to select a treatment (e.g. participate in a program) with at least partial knowledge of the idiosyncratic response to the treatments. We define treatment effects in a general model with multiple treatments as differences in counterfactual outcomes that would have been observed if the agent faced different choice sets. We show how versions of local instrumental variables can identify the corresponding treatment parameters. Direct application of local instrumental variables identies the marginal treatment effect of one option versus the next best alternative without requiring knowledge of any structural parameters from the choice equation or any large support assumptions. Using local instrumental variables to identify other treatment parameters requires ei- ther large support assumptions or knowledge of the latent index function of the multinomial choice model.

The effects of educational choices on labor market, health, and social outcomes

Using a sequential model of educational choices, we investigate the effect of educational choices on labor market, health, and social outcomes. Unobserved endowments drive the correlations in unobservables across choice and outcome equations. We proxy these endowments with numerous measurements and account for measurement error in the proxies. For each schooling level, we estimate outcomes for labor market, health, and social outcome. This allows us to generate counter-factual outcomes for dynamic choices and a variety of policy and treatment effects. In our framework, responses to treatment vary among observationally identical persons and agents may select into the treatment on the basis of their responses. We find important effects of early cognitive and socio-emotional abilities on schooling choices, labor market outcomes, adult health, and social outcomes. Education at most levels causally produces gains on labor market, health, and social outcomes. We estimate the distribution of responses to education and find substantial heterogeneity on which agents act.

Comparing IV With Structural Models: What Simple IV Can and Cannot Identify

This paper compares the economic questions addressed by instrumental variables estimators with those addressed by structural approaches. We discuss Marschak's Maxim: estimators should be selected on the basis of their ability to answer well-posed economic problems with minimal assumptions. A key identifying assumption that allows structural methods to be more informative than IV can be tested with data and does not have to be imposed.

Testing the Correlated Random Coefficient Model

The recent literature on instrumental variables (IV) features models in which agents sort into treatment status on the basis of gains from treatment as well as on baseline-pretreatment levels. Components of the gains known to the agents and acted on by them may not be known by the observing economist. Such models are called correlated random coefficient models. Sorting on unobserved components of gains complicates the interpretation of what IV estimates. This paper examines testable implications of the hypothesis that agents do not sort into treatment based on gains. In it, we develop new tests to gauge the empirical relevance of the correlated random coefficient model to examine whether the additional complications associated with it are required. We examine the power of the proposed tests. We derive a new representation of the variance of the instrumental variable estimator for the correlated random coefficient model. We apply the methods in this paper to the prototypical empirical problem of estimating the return to schooling and find evidence of sorting into schooling based on unobserved components of gains.

Workplace interventions for cardiovascular diseases: protocol of a systematic review and meta-analysis

INTRODUCTION: Cardiovascular diseases (CVDs) are the number one cause of death globally, impacting on public and private sectors. Current traditional interventions to prevent CVDs are mainly provided in healthcare centres and even when they are effective, they are not enough to reduce the rising prevalence; therefore, additional strategies are needed. Evidence suggests that health interventions in the workplace supply numerous benefits improving cardiovascular risk factor profiles in individuals. Hence, the aim of this systematic review and meta-analysis is to collate the evidence from randomised controlled trials, cluster randomised trials and quasi-experimental studies of workplace interventions to determine their effectiveness in terms of improving cardiovascular risk factors and preventing CVDs. METHODS AND ANALYSIS: EMBASE, PsycINFO, PubMed, the Cochrane Central Register of Controlled Trials, LILACS, Scopus, Web of Science, WHO International Clinical Trials Registry Platform, ClinicalTrials.gov and ProQuest Dissertations & Theses Global will be searched to include articles on workplace interventions in adults for CVDs events, cardiometabolic risk factors or behavioural risk factors. The study selection, data extraction, risk of bias and the assessment of the quality of the body of evidence will be conducted by two reviewers working in parallel and disagreements will be resolved by consensus or consultations with a third reviewer. Data synthesis will be done by meta-analysis using random-effects models when possible, otherwise the vote counting method will be applied. Statistical heterogeneity will be assessed by a χ(2) test and I(2) statistics. The quality of the body of evidence for each outcome will be assessed by applying the Grading of Recommendations, Assessment, Development and Evaluation approach. ETHICS AND DISSEMINATION: Ethical approval is not required for this systematic review protocol. The results of the systematic review will be published in a peer-reviewed journal and will be publicly available. PROSPERO REGISTRATION NUMBER: CRD42021276161

Surface properties of poly(N-monoalkylmaleamic acid-alt-styrene) sodium salts: effect of the molecular weight and the side chain length

Abstract The surface properties of poly(N-monoalkylmaleamic acid-alt-styrene) sodium salts are studied as a function of the molecular weight and the size of the linear alkyl lateral chain of the polyelectrolyte. The experimental results are well described by the Gibbs-Szyszkowski treatment. Both the surface tension behavior and the standard free energy of adsorption depend on the polyelectrolyte side chain and on the average molecular weight, M w . An M w -dependent contribution to the free energy of adsorption ranging from −1.21 to −1.05 kJ for mole of methylene groups is found. The area covered by monomer units increases with M w and the sizes of side chains are similar to those reported in small-molecule systems. The nature of the functional group amide in the side chain has practically no effect on the surface properties as compared with the ester group in this kind of polyelectrolytes