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Keywords:

  • weight;
  • BMI;
  • employment;
  • disability;
  • labor supply

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. References

Objective: To determine the relationships between BMI and workforce participation and the presence of work limitations in a U.S. working-age population.

Research Methods and Procedures: We used data from the Panel Study of Income Dynamics, a nationwide prospective cohort, to estimate the effect of obesity in 1986 on employment and work limitations in 1999. Individuals were classified into the following weight categories: underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30). Using multivariable probit models, we estimated the relationships between obesity and both employment and work disability. All analyses were stratified by sex.

Results: After adjusting for baseline sociodemographic characteristics, smoking status, exercise, and self-reported health, obesity was associated with reduced employment at follow-up [men: marginal effect (ME) −4.8 percentage points (pp); p < 0.05; women: ME −5.8 pp; p < 0.10]. Among employed women, being either overweight or obese was associated with an increase in self-reported work limitations when compared with normal-weight individuals (overweight: ME +3.9 pp; p < 0.01; obese: ME +12.6 pp; p < 0.01). Among men, the relationship between obesity and work limitations was not statistically significant.

Discussion: Obesity appears to result in future productivity losses through reduced workforce participation and increased work limitations. These findings have important implications in the U.S., which is currently experiencing a rise in the prevalence of obesity.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. References

The prevalence of obesity has increased dramatically in the U.S. over the last decade (1, 2, 3), and public health authorities have recognized this as one of the most dangerous trends contributing to morbidity and mortality (4, 5). Obesity is associated with the onset of many chronic diseases including diabetes, cardiovascular disease, cancer, asthma, and depression (1, 5, 6). Moreover, obesity has been linked with increasing medical costs (7), work disability (2, 8), and lost wages (9, 10); however, its association with employment is not well described.

Obesity can affect employment in a number of ways. First, previous research indicated that obesity in adulthood is associated with increased risk of physical limitations and disability at older ages (11). Work disability might prevent those with worsening health conditions from working entirely (12, 13, 14, 15, 16), or individuals with poor health may take time off due to treatment or disease-related illness or complications (i.e., absenteeism). Second, poor health may change individuals’ perceptions of the utility of work vs. leisure by diminishing the marginal value of work and increasing the marginal value of leisure time. Consequently, individuals with poor health might withdraw from the labor force or cut their working hours. Third, obese individuals may face employment discrimination (17). Here, we examine the relationship between BMI and both employment and work limitations in a U.S. working-age population.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. References

Study Population

We used publicly available, deidentified data from two waves (i.e., 1986 and 1999) of the Panel Study of Income Dynamics (PSID).1 This nationwide, longitudinal cohort study has been described in detail elsewhere (18, 19). In brief, the PSID was designed to select a representative sample of U.S. individuals and their families. The PSID sample, originating in 1968, consisted of two independent samples: a cross-sectional national sample and a national sample of low-income families. The cross-sectional sample was drawn by the Survey Research Center (SRC). Commonly called the SRC sample, this was an equal probability sample of households from the 48 contiguous states and was designated to yield ∼3000 completed interviews. The second sample came from the Survey of Economic Opportunity, conducted by the Bureau of the Census for the Office of Economic Opportunity. The PSID core sample combines the SRC and Survey of Economic Opportunity samples. The original core sample was reduced from nearly 8500 families in 1996 to ∼6168 in 1997 to accommodate the study's 5-year funding cycle (20).

Participants were followed longitudinally and the data collected included sociodemographic characteristics, health behaviors, and employment status of both the head of the household and his or her spouse. The self-reported height and weight information was collected for the first time in 1986 and again in 1999. We restricted our study population to those who participated in both interviews in 1986 (baseline) and 1999 (follow-up) and who were of working age (i.e., age 18 years and older in 1986 and less than 65 years in 1999).

In 1986, there were 11,023 respondents, and 11,022 were age 18 years or more (Figure 1). Of these 11,022 individuals, 2815 individuals were 65 years or older in 1999, 1991 did not participate in the 1999 survey, 1554 had been dropped from the study due to the budgetary constraints previously described, 234 died, 134 had missing baseline weight or height information, and four were missing employment status information in 1999 despite completing the survey. This resulted in a final study sample of 4290 respondents (1895 men and 2395 women).

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Figure 1. Selection of study sample.

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Analytical Variables

The primary outcomes examined were self-reported employment status (working vs. not working for pay outside of the home) and the presence of work limitation(s) because of health (i.e., any physical or nervous condition that limited the type or amount of work).

The presence of obesity at baseline was the primary exposure variable of interest. Obesity status was determined using BMI, which was calculated by dividing self-reported weight in kilograms by self-reported height in meters (squared). Individuals were classified into the following weight categories: underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30) (21). We hypothesized that baseline obesity would decrease the probability of employment and increase the likelihood of work limitations at follow-up.

Smoking status, exercise frequency, and health status at baseline were considered as potential confounders of the relationship between obesity and employment. Smoking status was classified into five categories: never smoked; former smoker; current smoker, 1 to 10 cigarettes per day; current smoker, 11 to 20 cigarettes per day; or current smoker, ≥21 cigarettes per day. Self-reported exercise was categorized into the following categories: no exercise, less than daily exercise, or daily exercise. Self-reported health status was assessed at baseline; responses of excellent, very good, or good health were grouped together, as were responses of fair or poor health.

Sociodemographic covariates included age, race, marital status, level of education, number of children under age 18 living in the home, household wealth, and baseline employment status (or baseline work limitations for work disability analysis). All baseline covariates except wealth, marital status, and the number of children under age 18 were measured in 1986. We used household wealth collected in 1989, the 1st year after 1986 in which this information was collected. Both marital status and the number of children under age 18 were measured as of 1999. Variables were selected a priori for their potential effects on labor market outcomes based on existing literature or their potential to confound the relationship between BMI and outcomes. Therefore, these variables were included in all multivariable models.

Analysis

Because outcomes were not rare, odds ratios derived from logistic models were not reflective of relative risks. Therefore, multivariable probit models were used to estimate absolute differences in cumulative incidence for the outcomes of interest (i.e., employment status and the presence of work limitations) (22). We estimated the effects of obesity in 1986 on the probability of working and the probability of work limitations in 1999 using five nested specifications. For ease of interpretation, the probit estimates were translated into derivatives of the probability of working with respect to the independent variables. Therefore, the probit model results should be interpreted as the absolute increase or decrease in the probability of working (or of reporting a work limitation) associated with one unit of change in the variable of interest, while setting all other variables to their mean. For example, our variable of interest, obesity, is included as a binary variable (i.e., obese vs. normal weight). Therefore, our results are reported as the absolute percentage change (i.e., marginal effect) for obese individuals relative to normal weight individuals in the probability of working (or reporting a work limitation), adjusting for other potential confounders. Our models controlled for sociodemographic characteristics alone (Model 1), Model 1 variables plus smoking status (Model 2), Model 2 variables plus exercise frequency (Model 3), and Model 3 variables plus self-reported health status (Model 4). Analyses with work limitation as the outcome were limited to persons working in 1999. All analyses were stratified by sex given the differences in workforce participation, job type, and job attachment between men and women (23).

Because a number of individuals (n = 1991) did not respond to the survey in 1999 but were otherwise eligible, we assessed for potential participation biases that could have affected our results. Briefly, a propensity score predicting participation was created using all the baseline covariates mentioned above. This propensity score was included as a covariate along with the other variables included in Model 5. Large differences in relationships between the models that included and excluded this propensity score would suggest that our results were unduly affected by participation bias.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. References

Baseline Characteristics

Table 1 shows the baseline characteristics of the study population stratified by BMI. Of the 4290 study individuals, 526 (12%) were obese in 1986. Obese individuals were older and were more likely to be men, African American, and single when compared with normal-weight individuals. Obese individuals also reported poorer health, less exercise, and lower educational attainment, and they were less likely to have a child living at home.

Table 1.  Baseline characteristics of the study population stratified by weight categories
 Total (N = 4290)Underweight (N = 151)Normal weight (N = 2419)Overweight (N = 1194)Obese (N = 526)
  • *

    p < 0.01 for pair-wise comparison with normal-weight individuals.

  • Data from survey in 1999.

  • Data from survey in 1989.

Mean age in years (standard deviation)46.3 (7.7)43.1* (6.8)45.5 (7.5)47.4* (7.8)48.1* (8.0)
Female (%)55.892.1*63.237.2*53.6*
Race (%)     
 White71.181.575.568.0*54.8*
 African American27.717.223.430.6*43.7*
 Other1.21.31.11.31.5
Single (never married, divorced, widowed) (%)24.221.223.423.031.6*
Education (%)     
 Less than high school12.08.69.713.1*20.7*
 High school37.938.436.938.940.3
 Some college23.021.923.123.022.8
 College14.117.215.113.310.1*
 Some graduate school9.37.310.89.33.6*
Smoking status (%)     
 Never smoked55.161.057.250.0*56.4
 Former smoker21.622.020.623.321.8
 Smokes 1 to 10 cigarettes per day7.35.97.27.87.3
 Smokes 11 to 20 cigarettes per day10.18.59. 711.49.0
 Smokes > 20 cigarettes per day5.92.55.37.5*5.5
Exercise status (%)     
 No regular exercise37.340.435.535.848.1*
 Exercise less than daily38.736.340.837.731.7*
 Exercise daily24.023.323.726.420.3
Poor or fair self-reported health status (%)7.86.66.07.616.9*
Wealth (standard deviation)79,254 (455,279)156,808 (1,268,178)73,329 (271,435)76,347 (477,516)92,579 (632,376)
Presence of children < 18 years old living at home (%)52.861.254.451.346.9*
Employed (%)77.270.976.880.573.4
Work limitations among employed (%)5.35.65.04.68.6*

Relationship between Weight and Employment

Among both men and women there was a statistically significant trend between baseline weight categories and employment at the time of follow-up. Among men who were underweight, normal weight, overweight, and obese in 1986, 83.3%, 88.5%, 88.8%, and 77.9% were employed in 1999, respectively (Cochran-Armitage test for trend, p < 0.01). Similarly, among women who were underweight, normal weight, overweight, and obese in 1986, 74.1%, 75.9%, 70.3%, and 63.5% were employed in 1999, respectively (Cochran-Armitage test for trend, p < 0.01). Pair-wise comparisons showed that the difference between normal-weight and obese men was statistically significant (p < 0.01), as were the differences between normal-weight and both overweight (p < 0.05) and obese women (p < 0.01).

Table 2 shows the unadjusted and adjusted relationships between weight and employment. In all models, obese individuals were less likely to be working in follow-up when compared with normal-weight individuals. In the unadjusted analysis, the absolute difference in the probability of working between obese and normal-weight individuals was −10.6 percentage points (pp) for men (p < 0.01) and −12.6 pp for women (p < 0.01)). After adjusting for baseline sociodemographic characteristics and smoking (Model 2), the marginal effect (ME) of obesity was −5.3 pp for men (p < 0.05) and −7.7 pp for women (p < 0.05). Further adjusting for exercise (Model 3) did not substantively affect these results. With the inclusion of self-reported health (Model 4), the effect of obesity on employment diminished for both men and women; however, the difference remained statistically significant for men (ME −4.8 pp; p < 0.05) but was of borderline significance for women (ME −5.8 pp; p < 0.1).

Table 2.  Unadjusted and adjusted relationships between BMI in 1986 and employment in 1999 among working-age adults
 Unadjusted (95% CI)Model 1 (95% CI)*Model 2 (95% CI)Model 3 (95% CI)Model 4 (95% CI)§Model 5 (95% CI)
  • CI, confidence interval. Partial derivatives of probability (marginal effects) with respect to weight categories are reported with standard errors in parentheses. The reference group is normal weight.

  • *

    Model 1 controls for age, race, marital status, education, employment status at baseline, household wealth, and the number of children under the age of 18 living in the home.

  • Model 2 adds smoking status to the variables listed for Model 1.

  • Model 3 adds exercise status to the variables listed for Model 2.

  • §

    Model 4 adds self-reported health status to the variables listed for Model 3.

  • Model 5 adds probability of participation to the variables listed for Model 4.

  • p < 0.01.

  • **

    p < 0.05.

  • ††

    p < 0.1.

Men (N = 1895)      
 Underweight−0.055−0.134−0.125−0.128−0.110−0.112
 (−0.278, 0.168)(−0.391, 0.124)(−0.382, 0.132)(−0.387, 0.131)(−0.355, 0.136)(−0.360, 0.135)
 Overweight0.0030.0160.0110.0120.0100.012
 (−0.030, 0.036)(−0.013, 0.045)(−0.017, 0.039)(−0.016, 0.039)(−0.018, 0.038)(−0.016, 0.041)
 Obese−0.106−0.042**−0.053**−0.054**−0.048**−0.043††
 (−0.164, −0.048)(−0.088, 0.005)(−0.101, −0.005)(−0.103, −0.005)(−0.095, 0.000)(−0.091, 0.005)
Women (N = 2395)      
 Underweight−0.019−0.045−0.041−0.042−0.041−0.042
 (−0.098, 0.061)(−0.128, 0.039)(−0.124, 0.042)(−0.125, 0.041)(−0.124, 0.042)(−0.125, 0.041)
 Overweight−0.057**−0.008−0.010−0.009−0.006−0.003
 (−0.106, −0.008)(−0.058, 0.041)(−0.060, 0.039)(−0.058, 0.041)(−0.055, 0.044)(−0.053, 0.046)
 Obese−0.126−0.068**−0.077**−0.068**−0.058††−0.053††
 (−0.187, −0.065)(−0.132, −0.004)(−0.141, −0.012)(−0.133, −0.004)(−0.122, 0.006)(−0.117, 0.011)

Relationship between Weight and Work Limitations

Among individuals employed in 1999, the likelihood of reporting a work limitation tended to increase with increasing baseline weight. Among women who were underweight, normal weight, overweight, and obese in 1986, 3.9%, 8.3%, 12.1%, and 22.4% reported work limitations in 1999, respectively (Cochran-Armitage test for trend, p < 0.01). Although a similar trend in reported work limitations was seen among men, 0% (underweight), 8.9% (normal weight), 8.0% (overweight), and 13.2% (obese), this did not reach statistical significance. In pair-wise comparisons with normal-weight women, the proportions of underweight (p < 0.01), overweight (p < 0.05), and obese women (p < 0.01) reporting work limitations were significantly different.

Table 3 presents the results of the regression analyses. Again, among employed at follow-up, women who were overweight or obese at baseline were more likely to report health-related work limitations when compared with normal-weight women. In the unadjusted analysis, the absolute difference between overweight and normal-weight women was +41 pp (p < 0.01), and between obese and normal-weight women, it was +14.3 pp (p < 0.01). Inclusion of other potential confounders (Models 2 to 4) has decreased magnitude but not the statistical significance of these relationships. Obese men were similarly more likely to report health-related work limitations (unadjusted ME +4.2 pp, p < 0.1); however, this relationship was not significant in adjusted models (Models 1 to 4) that control for baseline sociodemographic characteristics, smoking history, exercise frequency, and self-reported health.

Table 3.  Unadjusted and adjusted relationships between BMI in 1986 and the presence of self-reported work limitations in 1999 among working adults
 Unadjusted (95% CI)Model 1 (95% CI)*Model 2 (95% CI)Model 3 (95% CI)Model 4(95% CI)§Model 5(95% CI)
  • CI, confidence interval. Partial derivatives of probability (marginal effects) with respect to weight categories are reported with standard errors in parentheses. The reference group is normal weight.

  • *

    Model 1 controls for age, race, marital status, education, presence of work limitations at baseline, household wealth, and the number of children under the age of 18 living in the home.

  • Model 2 adds smoking status to the variables listed for Model 1.

  • Model 3 adds exercise status to the variables listed for Model 2.

  • §

    Model 4 adds self-reported health status to the variables listed for Model 3.

  • Model 5 adds probability of participation to the variables listed for Model 4.

  • Estimated results are not reliable given the small sample size in this category.

  • **

    p < 0.1.

  • ††

    p < 0.05.

  • ††

    p < 0.01.

Men (N = 1618)      
 Underweight
 Overweight−0.009 (−0.039, 0.020)−0.007 (−0.035, 0.021)−0.006 (−0.034, 0.022)−0.006 (−0.035, 0.022)−0.005 (−0.033, 0.023)−0.004 (−0.033, 0.024)
 Obese0.0420 (−0.010, 0.094)**0.023 (−0.024, 0.069)0.024 (−0.023, 0.071)0.024 (−0.023, 0.071)0.023 (−0.023, 0.070)0.027 (−0.024, 0.079)
Women (N = 1754)      
 Underweight−0.051 (−0.099, −0.004)††−0.043 (−0.086, −0.001)††−0.045 (−0.084, −0.006)††−0.045 (−0.084, −0.005)††−0.045 (−0.083, −0.006)††−0.045 (−0.083, −0.006)††
 Overweight0.041 (−0.001, 0.083)**0.040 (−0.001, 0.081)**0.040 (0.000, 0.081)**0.040 (−0.001, 0.081)**0.039 (−0.001, 0.079)**0.038 (−0.002, 0.079)**
 Obese0.143 (0.078, 0.208)††0.135 (0.068, 0.202)††0.133 (0.066, 0.200)††0.133 (0.065, 0.200)††0.126 (0.059, 0.192)††0.119 (0.053, 0.186)††

Adjustments for Participant Participation

Although individuals who participated in follow-up differed significantly from those who did not (data not shown), inclusion of a variable in our models that accounted for these differences did not substantively affect our results (Tables 2 and 3). Adjusting for this propensity to participate did not alter the previously described relationships between obesity and either employment or work limitations (Model 5 in both tables). Moreover, these propensity scores were not significantly associated with our outcomes of interest (data not shown).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. References

Using a nationally representative sample of working-age adults in the U.S., we found that baseline obesity was associated with reduced employment at follow-up among men and women and increased work limitations among women who kept working at follow-up. Our results regarding employment as an outcome are consistent with the findings of Paraponaris et al. (24), who studied a similar age population (ages 18 to 64 years) in France. Two other prospective studies from Finland (25) and the U.S. (26) have suggested that obesity is not an independent risk factor for a long history for unemployment or for the transition from welfare to working, respectively. However, differences in the study populations between these latter two studies [i.e., young adults (age 31) (25) and both current and former female welfare recipients (26)] and our study (which represents all U.S. working-age adults) make it difficult to directly compare results.

In addition to work force participation, some researchers associate obesity with increased work disability (2, 27), whereas others argue that no such relationship exists (8, 28). Our findings support the former group in that we show that being overweight or obese may contribute to work limitations, especially in women. In fact, the apparent effect of obesity on both work force participation and work limitations was greater for women when compared with men. Although studying the underlying reasons for these differences by sex was not an explicit goal of the current study, it should be the focus of future research.

This study contributes to the literature in two important ways. First, it addresses the relationships between obesity and workforce participation and work limitations over a long period of follow-up (i.e., 13 years). Second, we adjust for the potential confounding effects of smoking, exercise, baseline health status, and attrition, which have not been accounted for in previous research (2, 8, 22, 23, 24, 25, 26).

Because smokers tend to have lower BMI (29) but greater likelihood of disability (30) when compared with non-smokers, we predicted that adjustment for smoking would increase the association between obesity and employment over adjusting for sociodemographic characteristics alone. The effect of this adjustment was seen more prominently for men as compared with women, which probably reflects greater smoking among men. In fact, men in this study population were significantly more likely to be current smokers when compared with women (33.7% vs. 29.2%, p < 0.01), and among smokers, men, on average, smoked 30% more cigarettes per day (data not shown).

Consistent with the findings of others (31), we also found that obese individuals were significantly less likely to engage in exercise. Because physical activity and fitness have also been shown to be independent predictors of morbidity (32, 33) and mortality (34, 35), we adjusted for exercise frequency as a potential predictor of future employment status. However, this adjustment did not substantively change the relationship between obesity and workforce participation.

Although we assume that the negative effect of obesity on employment is mediated through disability resulting from the accumulation of chronic conditions, we cannot rule out the possibility that preexisting conditions contributed to obesity at baseline. To account for this possibility, we adjusted for baseline self-reported health status. Albeit a proxy, self-reported health status has been associated with numerous chronic medical conditions and is predictive of mortality in working-age individuals (36, 37, 38). After adjusting for self-reported health, the relationship between obesity and employment remained statistically significant for men but was of borderline significance for women. However, because obese individuals are more likely to report fair to poor health (39), the inclusion of self-reported health status may have led to underestimating the true effect of obesity on employment. In other words, although adjusting for self-reported health may account for baseline comorbidities, it would also be expected to reduce the effect of obesity by the aforementioned correlation alone. Therefore, adjusting for self-reported health status would be expected to underestimate the true relationship between obesity and labor force participation and may explain why our results were no longer significant after adjusting for this variable.

Our study has other limitations that should be considered. First, we used self-reported height, weight, and employment status information, which may be subject to error. However, BMI calculated from self-reported height and weight has been shown to be highly correlated with actual BMI (40). Similarly, previous validation studies suggested that individuals could accurately recall their employment history in the last year (41, 42). Second, our sample is limited to the individuals who responded to the PSID surveys in 1986 and 1999. Because obese individuals were more likely to die between surveys (data not shown), the negative impact of obesity on employment may have been even greater. Third, the number of eligible individuals who did not participate in the follow-up survey in 1999 was relatively large (n = 1991). However, adjusting for the probability of participation (i.e., propensity score) did not alter the relationship between obesity and outcomes, nor was the propensity score itself associated with outcomes. Thus, it is unlikely that the associations that we observed were the result of participation bias. Fourth, the two state employment model (working vs. not working) instead of a model with multiple states (e.g., full-time, part-time, unemployed, retired, disabled, homemaker, and others) may be oversimplified. For example, there may be transitions between full-time and part-time employment attributable to obesity that we did not examine. Next, we adjusted for all potential confounding factors that we could identify a priori; however, endogeneity between labor market outcomes and obesity might still exist. Although reverse causation (i.e., a reversal in the causal direction between a risk factor and an outcome) is a hazard in any observational study, we attempted to minimize this risk by using baseline BMI and by adjusting for employment status and work limitations at baseline. Finally, we would have preferred to be able to stratify our study population further (i.e., beyond gender), such as by race or baseline employment status; however, there was insufficient power for these additional analyses.

Increased prevalence of clinically severe obesity that has quadrupled between 1986 and 2000 (43) suggests that reduced employment and increased work limitations associated with obesity might be even greater today than in the past. In addition, the greatest magnitude of increase in severe obesity was found in the 18- to 29-year-old age group among adults (44), and a continued increase in childhood and adolescent obesity was documented (45). This implies that obese adults in the working-age population today have been exposed to obesity at younger ages than obese individuals in the 1980s. For example, a 40-year-old in 2005 is likely to have been obese for more years than a 40-year-old in 1986 (baseline year in our analysis). Given that the increased exposure duration to obesity is likely to be correlated with the increased disability (2, 46), obesity today may have a greater effect on work limitations and employment in the future compared with 20 years ago.

In summary, our results suggest that obesity results in future productivity losses through both reduced workforce participation and increased work limitations. Unfortunately, this loss in productivity is likely to increase as our society becomes more obese. With as many as one-third of the adult U.S. population already obese (3), the economic implications of obesity on labor market outcomes are enormous. Reduced employment may increase the burden on tax payers due to a corresponding increase in the number of welfare recipients. Increased work limitations among workers may lead to productivity losses for employers and/or income losses for employees and their families. The policy implications of our findings are that it may be cost-offsetting for employers to sponsor weight maintenance programs and that public health interventions to prevent weight gain may have societal benefits beyond improvements in health.

Footnotes
  • 1

    Nonstandard abbreviations: PSID, Panel Study of Income Dynamics; SRC, Survey Research Center; pp, percentage point(s); ME, marginal effect.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. References
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