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Methods to Estimate the Impact of Stigma on Employment Outcomes Marjorie L. Baldwin W. P. Carey School of Business Arizona State University Steven C. Marcus.

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Presentation on theme: "Methods to Estimate the Impact of Stigma on Employment Outcomes Marjorie L. Baldwin W. P. Carey School of Business Arizona State University Steven C. Marcus."— Presentation transcript:

1 Methods to Estimate the Impact of Stigma on Employment Outcomes Marjorie L. Baldwin W. P. Carey School of Business Arizona State University Steven C. Marcus School of Social Policy and Practice University of Pennsylvania Jeffrey DeSimone Department of Economics University of Texas at Arlington Supported by Grant #R03 DA019860

2 Purpose Analyze the effect of discrimination on employment outcomes of persons with mental disabilities Using methods economists have applied to Women African-Americans Hispanics Persons with disabilities

3 Labor market discrimination Occurs when there are differences in average employment outcomes (wages, employment rates) for two groups of workers that cannot be explained by differences in average productivity Therefore, methods must Measure discrimination at the means for groups Control for differences in average productivity between the groups Challenge - most large national data sets are missing key elements

4 Data requirements Large nationally representative dataset Identifiers of persons with serious mental illness Measures of key labor market outcomes Wages Employment Usual hours worked Controls for productivity Education Work experience Health - functional limitations (social, emotional, cognitive, physical), co- morbid conditions Other Job characteristics – union, public sector, occupation Demographic characteristics – gender, race, region Family characteristics – marital status, family history, living arrangements

5 Methods 1 – dummy variable Using multivariate logistic regression we estimate the impact of group membership on employment outcomes – including relevant covariates to control for differences in average productivity group members are identified by a dummy variable (S i ) Example – output A significant, negative coefficient ( ) suggests group members may be subject to discrimination Limitations Assumes wage structures ( ) are identical for both groups Omitted variables Difficult to interpret

6 Methods 2 – decomposition Estimate the logit employment function separately for former substance users and controls – compute average predicted probabilities of employment for each group – express the difference as the sum or: A part attributed to differences in characteristics (productivity, demographic, family) An unexplained part atrributed, perhaps, to discrimination against former substance users Limitations The decomposition formula is not unique Omitted variables

7 Methods 2 – example output Employment rate High school graduate College graduate Predicted employment rate Control group95%25%75% Former substance use disorder 78%50%

8 Methods 2 – example output Employment rate High school graduate College graduate Predicted employment rate Control group95%25%75% Former substance use disorder 78%50% Suppose employment rates are 100% for college graduates but only 80% for high school graduates

9 Methods 2 – example output Employment rate High school graduate College graduate Predicted employment rate Control group95%25%75%95% Former substance use disorder 78%50% 90% Suppose employment rates are 100% for college graduates but only 80% for high school graduates

10 Methods 2 – example output Employment rate High school graduate College graduate Predicted employment rate Control group95%25%75%95% Former substance use disorder 78%50% 90% Suppose employment rates are 100% for college graduates but only 80% for high school graduates The difference in employment rates is 17 percentage points – 5 percentage points explained by differences in education – 12 percentage points possibly attributed to discrimination

11 Methods 3 – abuse attributable fractions Estimate the logistic employment function with both groups together and a dummy variable identifying persons with former substance use disorders Compute predicted probability of employment for each individual, then average to compute predicted employment rate for the sample Then create predicted probabilities assuming no one is a former substance abuser (S=0 for everyone) and average to obtain the predicted employment rate with no effect of substance use The abuse attributable fraction (AAF) is the difference, expressed as a fraction of Limitations Omitted variables Assumes employment structures are identical

12 Methods 3 – example output Predicted employment with no substance use disorders (S=0 for all) Predicted employment accounting for substance use disorders (S=1; 0 for controls) Abuse attributable fraction Full sample85%79%(85-79)/85=.07

13 Advantages – Disadvantages Dummy variableLogistic model is easily understood Omitted variables lead to over- estimate of discrimination Employment structures assumed identical for both groups Difficult to translate output to quantifiable measure of discrimination DecompositionEmployment structures allowed to vary across groups Can disaggregate explained part of differential Output gives a quantifiable measure of discrimination Omitted variables lead to over- estimate of discrimination Decomposition formula is not unique Abuse attributable fraction Output gives a quantifiable measure of discrimination Omitted variables lead to over- estimate of discrimination Employment structures assumed identical for both groups

14 What explains the potential discrimination? Discrimination motivated by misconception e g. persons with serious mental illness may be disadvantaged if employers generalize the high level of functional limitations in some current users to former users statistical discrimination Discrimination motivated by stigma e g. the desire to avoid close proximity (working with) members of a stigmatized group prejudice-based

15 Comments Effect of missing variables Unmeasured differences in worker characteristics correlated with productivity and differentially distributed across groups can cause us to overestimate the effect of discrimination on outcomes e g. work experience, accumulation of social capital Flexibility of models Easily adapted to other employment outcomes e. g. Wages, involuntary job loss Inclusion of functional limitation variables Should be ameliorated by job accommodations

16 Observed employment rates

17 Decompositions of employment differentials No mental disorder (n=9,675) Any mental disorder (n=1143) Psychotic disorders (n=101) Employed full year0.850.700.37 Mean predicted employment rate 0.895 0.7670.513 Differential in predicted rates - 0.1290.382 Explained- 0.1060.275 Unexplained-0.023 0.107

18 Observed mean hourly wages

19 Decompositions of wage differentials No mental disorder (n=8,203) Any mental disorder (n=847) Psychotic disorders (n=46) Mean log wage2.516 2.4412.069 Difference in log wages -.075.447 Difference in offer wages -.051.660 Explained-.036.389 Unexplained-.015.272


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