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Python 3.12 — IPython

>>> import statsmodels.api as sm

>>> model = sm.OLS(y, X).fit()

>>> print(model.summary())

OLS Regression Results

=============================================

Dep. Variable: log_wage R-squared: 0.187

No. Observations: 2814 AIC: 4307.

=============================================

coef std err t P>|t| [0.025 0.975]

----------------------------------------------

const 1.052 0.073 14.380 0.000 0.909 1.196

education 0.089 0.004 20.765 0.000 0.081 0.098

experience 0.005 0.001 8.127 0.000 0.004 0.007

=============================================

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Table 1: Returns to Education — Cross-Sectional and Panel Estimates

	OLS		IV-2SLS	Panel FE
	(1)	(2)	(3)	(4)	(5)
Years of schooling	0.084***	0.076***	0.102***	0.071***	0.068***
	(0.004)	(0.004)	(0.011)	(0.005)	(0.006)
Work experience (yrs)	0.005***	0.004***	0.005***	0.006***	0.007***
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
Experience² / 100	−0.009*	−0.007	−0.010*	−0.011**	−0.013**
	(0.005)	(0.004)	(0.005)	(0.004)	(0.005)
Female		−0.251***	−0.262***		−0.274***
		(0.029)	(0.031)		(0.029)
Urban		0.141***	0.149***		0.162***
		(0.031)	(0.033)		(0.031)
Industry FE	No	Yes	Yes	No	Yes
Year FE	No	Yes	Yes	Yes	Yes
N	2,814	2,814	2,814	8,442	8,442
R²	0.234	0.284	0.219	0.198	0.241

Standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05. Dep. var.: log hourly wage. IV instruments education with parent education. Robust SE throughout.

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Table 2: OLS Estimates with Progressive Controls

	Dependent variable: log(wage)
	(1) Baseline	(2) +Demo.	(3) +Ind. FE	(4) +Region FE
Education	0.0891***	0.0854***	0.0823***	0.0792***
	(0.0063)	(0.0058)	(0.0055)	(0.0054)
Experience	0.0041**	0.0039**	0.0035**	0.0033**
	(0.0017)	(0.0016)	(0.0015)	(0.0015)
Female		−0.2964***	−0.2812***	−0.2743***
		(0.0358)	(0.0341)	(0.0338)
Married		0.0534*	0.0412	0.0389
		(0.0301)	(0.0289)	(0.0287)
Industry FE	No	No	Yes	Yes
Region FE	No	No	No	Yes
N	1,526	1,526	1,526	1,526
R²	0.159	0.213	0.241	0.258

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors in parentheses.

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Table 2: OLS Estimates with Progressive Controls

	Dependent variable: log(wage)
	(1) Baseline	(2) +Demo.	(3) +Ind. FE	(4) +Region FE
Education	0.0891***	0.0854***	0.0823***	0.0792***
	(0.0063)	(0.0058)	(0.0055)	(0.0054)
Experience	0.0041**	0.0039**	0.0035**	0.0033**
	(0.0017)	(0.0016)	(0.0015)	(0.0015)
Female		−0.2964***	−0.2812***	−0.2743***
		(0.0358)	(0.0341)	(0.0338)
Married		0.0534*	0.0412	0.0389
		(0.0301)	(0.0289)	(0.0287)
Industry FE	No	No	Yes	Yes
Region FE	No	No	No	Yes
N	1,526	1,526	1,526	1,526
R²	0.159	0.213	0.241	0.258

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors in parentheses.

Not just regressions

Summary statistics, logistic regressions, and more — all supported out of the box.

Summary statistics

Table 3: Summary Statistics

	Mean	SD	Min	Max	N
Log hourly wage	2.643	0.712	0.541	5.263	2,814
Years of schooling	13.12	2.784	6	20	2,814
Work experience	17.84	12.35	0	48	2,814
Female	0.468	0.499	0	1	2,814
Married	0.574	0.495	0	1	2,814

Data: Current Population Survey (2018–2024). Wage sample restricted to full-time workers aged 25–64.

OLS regression

Table 1: Returns to Education and Labor Market Outcomes

	OLS		IV-2SLS
	(1)	(2)	(3)	(4)
Years of schooling	0.084***	0.079***	0.102***	0.098***
	(0.004)	(0.004)	(0.011)	(0.011)
Work experience (yrs)	0.005***	0.005***	0.005***	0.005***
	(0.001)	(0.001)	(0.001)	(0.001)
Female	−0.285***	−0.269***	−0.276***	−0.262***
	(0.030)	(0.030)	(0.031)	(0.031)
Married		0.084**		0.079*
		(0.031)		(0.032)
Urban		0.154***		0.149***
		(0.032)		(0.033)
N	2,814	2,814	2,814	2,814
R²	0.234	0.256	0.219	0.239
Adj. R²	0.233	0.254	0.218	0.238

Standard errors in parentheses. Dependent variable: log hourly wage. IV columns instrument education with parent education. Robust SE in (2)–(4).
*** p<0.001, ** p<0.01, * p<0.05

Logistic regression

Table 2: Logistic Regression — Employment Status

	(1) Logit	(2) Logit
Years of schooling	0.182***	0.173***
	(0.021)	(0.022)
Work experience (yrs)	0.034***	0.031***
	(0.005)	(0.005)
Female		−0.847***
		(0.124)
Married		0.412***
		(0.131)
N	5,000	5,000
Pseudo R²	0.089	0.124
Log-Lik.	−2,912	−2,801

Standard errors in parentheses. Dependent variable: employed (0/1).
*** p<0.001, ** p<0.01, * p<0.05

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Journal of Labor Economics · Vol. 44, No. 2 · 2026

Returns to Education and Labor Market Experience:
Evidence from the Current Population Survey

Sarah M. Chen and David A. Rodriguez

Department of Economics, University of Michigan

Abstract

This paper re-examines the returns to education and labor market experience using data from the Current Population Survey (2018–2024). We estimate Mincerian wage equations with progressively richer specifications. Our preferred specification yields a return to education of 8.2 percent per year of schooling. IV estimates using distance to college as an instrument suggest modest upward bias in OLS, consistent with positive selection.

3. Results

Table 1 reports robustness checks using alternative estimators. The IV specification instruments education with distance to the nearest college. The first-stage F-statistic of 42.1 exceeds conventional thresholds for weak instruments. The Heckman selection model addresses potential sample selection, yielding a positive and significant Mills ratio.

Table 1: Returns to Education and Labor Market Outcomes

	OLS		IV-2SLS
	(1)	(2)	(3)	(4)
Years of schooling	0.084***	0.079***	0.102***	0.098***
	(0.004)	(0.004)	(0.011)	(0.011)
Work experience (yrs)	0.005***	0.005***	0.005***	0.005***
	(0.001)	(0.001)	(0.001)	(0.001)
Female	−0.285***	−0.269***	−0.276***	−0.262***
	(0.030)	(0.030)	(0.031)	(0.031)
Married		0.084**		0.079*
		(0.031)		(0.032)
Urban		0.154***		0.149***
		(0.032)		(0.033)
N	2,814	2,814	2,814	2,814
R²	0.234	0.256	0.219	0.239
Adj. R²	0.233	0.254	0.218	0.238

Standard errors in parentheses. Dependent variable: log hourly wage. IV columns instrument education with parent education. Robust SE in (2)–(4).
*** p<0.001, ** p<0.01, * p<0.05

The quantile regression at the median yields a somewhat smaller education coefficient of 0.079, suggesting that returns are higher in the upper tail of the conditional wage distribution. Across all three estimators, the gender gap remains large and precisely estimated.

3.2 Progressive Specifications

Table 2 presents our OLS estimates with progressive covariate adjustment. Column (1) reports the baseline Mincerian specification. Columns (2)–(4) add demographic controls, industry fixed effects, and region fixed effects. The return to schooling declines modestly from 8.9 to 7.9 percent.

Table 2: Logistic Regression — Employment Status

	(1) Logit	(2) Logit
Years of schooling	0.182***	0.173***
	(0.021)	(0.022)
Work experience (yrs)	0.034***	0.031***
	(0.005)	(0.005)
Female		−0.847***
		(0.124)
Married		0.412***
		(0.131)
N	5,000	5,000
Pseudo R²	0.089	0.124
Log-Lik.	−2,912	−2,801

Standard errors in parentheses. Dependent variable: employed (0/1).
*** p<0.001, ** p<0.01, * p<0.05

The gender wage gap is substantial and persistent. The coefficient on Female implies that women earn approximately 27–30 log points less than men across all specifications. The marriage premium is positive but loses significance once we account for industry composition.

3.3 Summary Statistics

Table 3 presents descriptive statistics for the key variables in our analysis. The sample comprises 2,814 observations from the CPS. Mean years of schooling is 13.2 with substantial variation across the sample.

Table 3: Summary Statistics

	Mean	SD	Min	Max	N
Log hourly wage	2.643	0.712	0.541	5.263	2,814
Years of schooling	13.12	2.784	6	20	2,814
Work experience	17.84	12.35	0	48	2,814
Female	0.468	0.499	0	1	2,814
Married	0.574	0.495	0	1	2,814

Data: Current Population Survey (2018–2024). Wage sample restricted to full-time workers aged 25–64.

4. Discussion

Our estimates align closely with the meta-analytic findings of Card (1999) and the more recent work of Autor (2014). The finding that returns are higher for women than men is consistent with Dougherty (2005), who argues that female college graduates benefit disproportionately from access to professional occupations.

References

Autor, D. H. (2014). Skills, education, and the rise of earnings inequality. Science, 344(6186), 843–851.

Card, D. (1999). The causal effect of education on earnings. Handbook of Labor Economics, 3, 1801–1863.

Dougherty, C. (2005). Why are the returns to schooling higher for women than for men? Journal of Human Resources, 40(4), 969–988.

Mincer, J. (1974). Schooling, Experience, and Earnings. Columbia University Press.

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Publication-ready tablesfrom Python output

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See how it looks in your paper

Returns to Education and Labor Market Experience:Evidence from the Current Population Survey

Publication-ready tables
from Python output

Returns to Education and Labor Market Experience:
Evidence from the Current Population Survey