scholarly journals Modelling annual earnings with unemployment: Non-random selection in female workers

2021 ◽  
Author(s):  
◽  
Oliver Robertson
Keyword(s):  
Selection Bias ◽  
Employment Status ◽  
Sample Selection ◽  
Sample Selection Bias ◽  
Data Set ◽  
Earnings Dynamics ◽  
Female Workers ◽  
Novel Approach ◽  
Zero Values ◽  
Annual Earnings

<p>Female earnings are underrepresented in the earnings and earnings dynamics literature. This underrepresentation is largely a result of the di erences in participation rates between male and female workers. Female workers tend to have more frequent changes in employment status, and more periods of unemployment than their male counterparts. These periods of unemployment result in observations with zero earnings, and common transformations such as the logarithm are not de ned for zero values. This means that any analysis of the logarithm of earnings is forced to exclude periods where an individual does not work, and cannot take into account the e ect of moving into or out of employment. The higher rate of unemployment in female workers also increases the risk of sample selection bias. If selection into employment is non-random, then estimating earnings equations based on only workers will result in biased estimates. This thesis takes a novel approach by focusing on the annual earnings of females, and in doing so introduces two methods for addressing the issues associated with zero earnings observations. First, the Inverse Hyperbolic Sine (IHS) function is introduced as an alternative to the logarithm. The IHS is de ned for zero values, allowing for the creation of descriptive statistics that take into account periods of unemployment and changes in employment status. While the IHS has many properties that are useful when working with annual earnings, this thesis also highlights a number of estimation issues that can arise when using the function that have not previously been mentioned in the literature. Second, a new correction for sample selection bias that has been proposed by Semykina and Wooldridge (2013) is used to model the annual earnings of female workers. Both the sample selection bias correction and the IHS are applied to data on prime aged females from the Survey of Families, Income, and Employment (SoFIE) data set.</p>

scholarly journals Modelling annual earnings with unemployment: Non-random selection in female workers

2021 ◽  
Author(s):  
◽  
Oliver Robertson
Keyword(s):  
Selection Bias ◽  
Employment Status ◽  
Sample Selection ◽  
Sample Selection Bias ◽  
Data Set ◽  
Earnings Dynamics ◽  
Female Workers ◽  
Novel Approach ◽  
Zero Values ◽  
Annual Earnings

<p>Female earnings are underrepresented in the earnings and earnings dynamics literature. This underrepresentation is largely a result of the di erences in participation rates between male and female workers. Female workers tend to have more frequent changes in employment status, and more periods of unemployment than their male counterparts. These periods of unemployment result in observations with zero earnings, and common transformations such as the logarithm are not de ned for zero values. This means that any analysis of the logarithm of earnings is forced to exclude periods where an individual does not work, and cannot take into account the e ect of moving into or out of employment. The higher rate of unemployment in female workers also increases the risk of sample selection bias. If selection into employment is non-random, then estimating earnings equations based on only workers will result in biased estimates. This thesis takes a novel approach by focusing on the annual earnings of females, and in doing so introduces two methods for addressing the issues associated with zero earnings observations. First, the Inverse Hyperbolic Sine (IHS) function is introduced as an alternative to the logarithm. The IHS is de ned for zero values, allowing for the creation of descriptive statistics that take into account periods of unemployment and changes in employment status. While the IHS has many properties that are useful when working with annual earnings, this thesis also highlights a number of estimation issues that can arise when using the function that have not previously been mentioned in the literature. Second, a new correction for sample selection bias that has been proposed by Semykina and Wooldridge (2013) is used to model the annual earnings of female workers. Both the sample selection bias correction and the IHS are applied to data on prime aged females from the Survey of Families, Income, and Employment (SoFIE) data set.</p>

The effects of health on the wages of Australian workers: gender differences and the impacts of macroeconomic conditions

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lixin Cai
Keyword(s):  
Gender Differences ◽  
Selection Bias ◽  
Unobserved Heterogeneity ◽  
Sample Selection ◽  
Ordinary Least Squares ◽  
Sample Selection Bias ◽  
Content Type ◽  
Macroeconomic Conditions ◽  
Female Workers

PurposeThe purpose of this study is to examine the effects of health on wages of Australian workers, with a focus on gender differences and the role of macroeconomic conditions in the effects.Design/methodology/approachThe first 15 waves of the Household, Income and Labour Dynamics in Australia survey are used to estimate a wage model that accounts for the endogeneity of health, unobserved heterogeneity and sample selection bias.FindingsThe results show that, after accounting for the endogeneity of health, unobserved heterogeneity and sample selection bias, better health increases wages for Australian male workers, but not for female workers. The results also show that accounting for the endogeneity of health, unobserved heterogeneity and potential sample selection bias is important in estimating the effects of health on wages. In particular, a simple ordinary least squares estimator would underestimate the effect of health on wages for males, while overestimate it for females, and simply addressing the endogeneity of health using instrumental variables could overestimate the effect for both genders. It is also found that the effects of health on wages fall under depressed macroeconomic conditions, perhaps due to reduced job mobility and increased presentism during a recession.Originality/valueThis study adds to the international literature on the effects of health on wages by providing empirical evidence from Australia. The model applied to estimate the effects takes advantage of a panel dataset to address the bias resulting potentially from all the sources of the endogeneity of health, unobserved heterogeneity and sample selection. The results indeed show that failing to address these issues would substantially bias the estimated effects of health on wages.

scholarly journals Heterogeneous Causal Effects and Sample Selection Bias

2015 ◽  
Vol 2 ◽  
pp. 351-369 ◽  
Author(s):  
Richard Breen ◽  
Seungsoo Choi ◽  
Anders Holm
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Causal Effects ◽  
Sample Selection Bias

Face Hallucination Based On Sample Selection Bias Correction

2012 ◽  
Vol 4 (5) ◽  
pp. 91-98
Author(s):  
Tao Lu ◽  
Ruimin Hu ◽  
Zhen Han ◽  
Junjun Jiang ◽  
Jun Chang
Keyword(s):  
Selection Bias ◽  
Bias Correction ◽  
Sample Selection ◽  
Sample Selection Bias ◽  
Face Hallucination

Diagnosing Sample-Selection Bias in Historical Heights: A Reply to Komlos and A’Hearn

2019 ◽  
Vol 79 (4) ◽  
pp. 1154-1175 ◽  
Author(s):  
Howard Bodenhorn ◽  
Timothy W. Guinnane ◽  
Thomas A. Mroz
Keyword(s):  
Economic Growth ◽  
Nineteenth Century ◽  
Selection Bias ◽  
Economic History ◽  
Sample Selection ◽  
Sample Selection Bias ◽  
Conditional Mean

Our 2017 article in this Journal stresses the pitfalls of using choice-based samples in economic history. A prominent example is the literature addressing the so-called antebellum puzzle. Heights researchers claim that Americans grew shorter in the first half of the nineteenth century, a period of robust economic growth. We argue that this result relies on choice-based samples. Without knowing the process that led to inclusion in the sample, researchers cannot properly estimate conditional mean heights. We proposed a diagnostic that can detect, but not correct for, selection bias. Komlos and A’Hearn’s interpretation of our analysis confuses diagnosis with cure. We dispute their view that selection bias has been appreciated in the heights literature.

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