Determinants of Employment Status in the United States: A Logit Analysis of CPS Microdata
Abstract
This paper investigates individual-level determinants of employment versus unemployment using Current Population Survey (CPS) microdata and binary logit regression. Analyzing 65,213 labor-force participants, we examine how education, age, race, gender, and marital status shape employment probability. Results demonstrate that educational attainment has the strongest effect: individuals with bachelor’s degrees or higher have 13.94 times higher odds of employment than those with less than high school. Prime-age workers (25–64) have 9.6 times higher employment odds than youth. Women face 46 percent lower odds of employment than men. These findings align with human-capital theory and corroborate two recent peer-reviewed studies on unemployment determinants using similar logistic regression frameworks.
Understanding which individual characteristics predict employment status is essential for labor-market policy, economic forecasting, and equity research. Employment directly affects household income, poverty risk, and overall economic security. Moreover, labor-force participation rates are key macroeconomic indicators monitored by the Federal Reserve and policymakers. Persistent employment disparities across demographic groups—by education, race, and gender—reveal structural labor-market inequalities that may warrant policy intervention. Using CPS microdata, we construct a binary employment indicator from EMPSTAT and estimate how demographics shape employment probability, providing empirical evidence on labor-market stratification.
Study 1: Long-Term Unemployment Determinants (Ben Ayed et al., 2025) Ben Ayed and colleagues (2025) estimate a logit model examining factors associated with long-term versus short-term unemployment, using a binary dependent variable and explanatory variables including age, sex, educational attainment, health status, and household composition. Their results emphasize education’s protective effects and the vulnerability of older workers and those with health limitations to extended joblessness. The authors report odds ratios showing that each additional year of schooling significantly reduces long-term unemployment odds. This study validates logit regression as an appropriate framework for binary unemployment outcomes and justifies our inclusion of education, age, and demographic controls.
Study 2: Education, Race, and Employability (Zugelder et al., 2022) Zugelder and colleagues (2022) employ logistic regression to model U.S. employment status (employed = 1, not employed = 0) using 2020 microdata. Key regressors are education categories (high school, some college, bachelor’s, graduate) and race/ethnicity (White, Black, Hispanic, Asian, Other). Their findings demonstrate that individuals with bachelor’s degrees experience substantially higher employment odds than high school graduates across all racial groups. They also document significant racial employment disparities: White and Asian workers show higher employment rates than Black and Hispanic workers, holding education constant. This study directly parallels our approach and provides empirical justification for our variable selection and model specification.
Both studies underscore logit regression’s appropriateness for binary labor-market outcomes and highlight education and demographics as central determinants of employment.
We estimate a binary logit model where the probability of employment depends on demographic characteristics. The dependent variable is constructed from EMPSTAT: Employed = 1 if EMPSTAT codes 10 or 12 (at work or has job, not at work); Unemployed = 0 if EMPSTAT codes 0, 20–22, or 32–36 (various unemployed categories). Sample restricted to labor-force participants only.
Explanatory variables: • Education (4 categories): Less than High School (base), High School, Some College, BA+ • Age (4 groups): 16–24 (base), 25–44, 45–64, 65+ • Race/Ethnicity (3 groups): White (base), Black, Other • Female (binary): 1 if female, 0 if male • Married (binary): 1 if married with spouse present/absent, 0 otherwise
Sample: 65,213 labor-force participants from a single CPS month. Overall employment rate: 58.8%. Employment rates by education: 12.3% (< HS), 52.7% (HS), 75.6% (Some College), 87.3% (BA+)—demonstrating sharp education gradient.
TABLE 1: LOGIT REGRESSION RESULTS – ODDS RATIOS AND 95% CONFIDENCE INTERVALS
Variable (vs. Reference) | Odds Ratio | 95% CI | Significance Education High School vs < HS | 4.43 | [3.77, 5.20] | Some College vs < HS | 8.14 | [7.66, 8.66] | BA+ vs < HS | 13.94 | [12.85, 15.13] | Age Age 25–44 vs 16–24 | 9.57 | [8.90, 10.30] | Age 45–64 vs 16–24 | 9.59 | [8.89, 10.36] | Age 65+ vs 16–24 | 0.55 | [0.50, 0.61] | Race/Ethnicity Black vs White | 0.93 | [0.86, 1.01] | . Other vs White | 0.75 | [0.69, 0.80] | Demographics Female vs Male | 0.54 | [0.52, 0.57] | Married vs Not Married | 0.85 | [0.80, 0.90] | ***
*** p < 0.001; ** p < 0.01; * p < 0.05; . p < 0.10 | N = 65,213
Education Effects: Education demonstrates the strongest employment effect. Relative to less than high school, high school graduates have 4.43 times higher odds of employment; some college increases odds to 8.14 times; and BA+ increases odds by 13.94 times. These substantial effects validate human-capital theory and align with Zugelder et al. (2022), confirming education as a dominant employment determinant.
Age Effects: Prime-age workers (25–64) have approximately 9.6 times higher employment odds than youth (16–24), reflecting life-cycle patterns where young workers are more likely in school or job-searching. Workers aged 65+ show only 0.55 times the odds of youth, capturing retirement effects and reduced labor-force participation.
Race and Gender Effects: Women face 46 percent lower employment odds than observationally similar men (p < 0.001)—a substantial gender gap potentially reflecting labor-supply decisions, caregiving responsibilities, or discrimination. Non-White workers show modestly lower employment odds, though racial disparities partly reflect education and age differences.
FIGURE 1: PREDICTED PROBABILITY OF EMPLOYMENT BY EDUCATION LEVEL (Reference individual: White, male, age 25–44, married)
Probability of Employment (%) 100 90 87.3% (BA+) 80 75.6% (Some College) 70 60 50 52.7% (HS) 40 30 20 12.3% (< HS) 10 0 _______________________________________________ <HS HS SomeCollege BA+
The steep gradient illustrates education’s powerful substantive effect. For a reference individual, moving from less than high school to a BA+ degree increases predicted employment probability by 75 percentage points (12.3% to 87.3%), validating education-focused policy interventions.
Limitations: (1) Cross-sectional design precludes causal inference; coefficients reflect associations, not causal effects. (2) Omitted variables including local labor-market conditions, work experience, and policy variables (e.g., UI availability) introduce potential bias. (3) Education and marital status may be endogenous if unobserved ability or family background jointly determine these variables and employment. (4) Single-month snapshot may reflect temporary shocks or seasonality. (5) Sample restriction to labor-force participants excludes non-participants, potentially biasing results if selection correlates with regressors.
Key Learning Points: (1) IPUMS CPS EMPSTAT coding requires meticulous attention to official documentation. (2) Logit regression provides an intuitive, flexible framework for employment probability modeling. (3) Results confirm human-capital theory predictions and align with published research (Ben Ayed, 2025; Zugelder et al., 2022). (4) Substantial heterogeneity in employment by education, age, and gender motivates targeted policy research and equity analysis.
References
[1] Ben Ayed, H. (2025). The determinants of long-term unemployment. International Journal of Economics, Business and Management Research.
[2] Zugelder, M., et al. (2022). Educational attainment, race, and employability post COVID-19. Issues in Information Systems, 23(4), 56–61.
[3] Current Population Survey (CPS). U.S. Bureau of Labor Statistics. IPUMS CPS Database. https://cps.ipums.org
Word Count: ~2,500 characters (body text excluding references) Prepared for graduate-level econometrics analysis