Gender role attitudes and male-female income differences in China
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Abstract By investigating deep-rooted cultural norms, this paper explores whether and how traditional gender role attitudes impact income gaps between men and women and identifies causal effects via instrumental variable and other causal inference methods. Based on data from the Chinese General Social Survey in 2013, the results show that traditional gender role attitudes have a strong negative effect to the earnings of women but have no significant effect on men’s incomes. Through Oaxaca-Blinder decomposition, this research finds that the different effects of gender role attitudes on the incomes of men and women appear to play a prominent role in causing the gender gap in earnings. In addition, gender role attitudes have an indirect and broad effect on gender income inequality through educational attainment, labor force participation, working hours, and occupational status. These results provide us with a new perspective for understanding the persistence and mechanisms of gender income stratification under educational equalization and have implications for gender equality policies.Keywords:
Gender Inequality
Educational Attainment
Instrumental variable
Instrumental variable (IV) analysis is not only able to contribute to causal inference, but also able to carry out the comparative analysis of instrumental variables estimation and least squares estimation, which makes us profoundly understanding and recognizing the social mechanisms and processes. To analyze social science, instrumental variable plays an important role in solving endophytism problem. It is foremost method to deduce cause and effect based on investigate data. In this paper, after introducing principle and calculate for the tackle variable, we discussed its potential and limitation. While expounding weight feature of estimate in instrumental variable, we explained and compared differences among instrumental variables with an example of transnational higher education, then took apart the affect mechanism of instrumental variable during the evolution of transnational higher education.
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This article offers a framework to understand how endogeneity arises and how to control for it with instrumental variables to estimate causal relations with observational data. It builds on the state-of-the-art research in applied and theoretical econometrics to highlight the importance of endogeneity and review the methods that can be used to address it with instrumental variables.The article also discusses when the Heckman two-step procedure can be used, as well as the tests, methods and assumptions that researchers should check when using instrumental variables.To ease implementation of the instrumental variables techniques, the author offers the STATA commands of the exposed tests and methods. Further, an empirical example is provided along with the utilized STATA codes. In the end, this article serves as a`toolkit' allowing scholars not only to understand whether endogeneity is present in their empirical setting, but also to assess the empirical validity of their work when using instrumental variables.
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The trade-off between the efficiency of an instrumental and its exogeneity is widely recognized but little understood. This paper specifies the terms of that trade-off by analyzing the asymptotic mean squared errors associated with the instrumental variables estimator when the instrument may not be perfectly exogenous. The analysis shows that even seemingly minor misspecifications can play havoc with statistical inferences based on quasi-instrumental variable estimators. Simple rules of thumb are derived by which intuition can be applied to choices among alternative estimators based on different instrumental variables, or between instrumental and ordinary least squares estimators. The theoretical analysis is applied to an example drawn from Jacobson's (1990) and Green and Krasno's (1990) work on congressional campaign spending and is bolstered by Monte Carlo simulations that, for the most part, reproduce the patterns of errors predicted by the asymptotic results.
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Instrumental variable
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A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead, an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration, which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies.
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Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This paper presents two tools to conduct valid inference and tests in the presence of invalid instruments. First, we propose a simple and general approach to construct confidence intervals based on taking unions of well-known confidence intervals. Second, we propose a novel test for the null causal effect based on a collider bias. Our two proposals outperform traditional instrumental variable confidence intervals when invalid instruments are present and can also be used as a sensitivity analysis when there is concern that instrumental variables assumptions are violated. The new approach is applied to a Mendelian randomization study on the causal effect of low-density lipoprotein on globulin levels.
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This article estimates the economic returns to schooling as well as analyzing other explanatory factors for the French labor market. It addresses the issue of endogeneity bias and proposes two new instruments for use in the instrumental variable two-stage least squares technique. Our results show that the proposed instruments are relevant and adequate, based on evidence from the available literature. After using the proposed instruments, we find that the OLS coefficients for schooling are biased downwards. Finally, we choose between the two proposed instruments.
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Researchers regularly use instrumental variables to resolve concerns about regressor endogeneity. The existing literature has correctly emphasized that the choice of instrumental variables matter for the resulting estimates. This paper shows that researchers should also be concerned that the functional form of the instrument matters as well for the resulting estimates. For example, simply changing an instrumental variable from the level to the logarithm can change estimates directly. This article documents the problem, suggests why the problem occurs and suggests different approaches to the problem.
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Under a general structural equation framework for causal inference, we provide a definition of the causal effect of a variable X on another variable Y, and propose an approach to estimate this causal effect via the use of instrumental variables.
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This chapter presents various examples on instrumental variable models, either univariate or multivariate, as the extension of the LVAR(p,q) additive as well as interaction models previously presented. Based on our experimentation, we have recognized that it is very difficult to obtain the best set of instrumental variables for a specific model. In fact, we have two-stages of problems (TSOP) in doing data analysis based on an instrumental models, those are to defined a basic model based on a set of endogenous and exogenous variables that is needed instrumental variables, then to select the best set of the instrumental variables. For this reason, it is suggested to modified the basic model, instead of using the instrumental variable model.
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