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How it works

R Console

> model <- lm(mpg ~ wt + hp, data = mtcars)

> summary(model)

...

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 37.22727 1.59879 23.285 < 2e-16 ***

wt -3.87783 0.63273 -6.131 1.12e-06 ***

hp -0.03177 0.00903 -3.519 0.00145 **

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1

Multiple R-squared: 0.8268, Adjusted R-squared: 0.8148

> model2 <- lm(mpg ~ wt + hp + am + qsec, data = mtcars)

> summary(model2)

...

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 17.34951 9.31770 1.862 0.07362 .

wt -3.23883 0.84596 -3.829 0.00069 ***

hp -0.01785 0.01193 -1.495 0.14666

am 2.92950 1.39761 2.095 0.04576 *

qsec 0.82104 0.44408 1.849 0.07573 .

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1

Multiple R-squared: 0.8624, Adjusted R-squared: 0.842

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Publication-ready table

Table 1: Determinants of Fuel Efficiency — Multi-Model Analysis

	OLS		WLS	Robust
	(1)	(2)	(3)	(4)	(5)
Weight (1000 lbs)	−5.34***	−3.37**	−4.91***	−5.18***	−3.48**
	(0.559)	(0.946)	(0.612)	(0.583)	(0.991)
Horsepower		−0.018	−0.032**		−0.021
		(0.015)	(0.011)		(0.016)
Manual trans.		1.478	1.941		1.612
		(1.441)	(1.402)		(1.478)
Quarter mile (s)		0.558	0.612		0.491
		(0.539)	(0.551)		(0.562)
N	32	32	32	32	32
R²	0.753	0.856	0.768	0.749	0.854
Adj. R²	0.745	0.828	0.761	0.740	0.824

Standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05. Dep. var.: miles per gallon. WLS weighted by 1/wt. Robust uses HC3 SE. Data: mtcars (n=32).

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Every detail is customizable. Rename variables, change fonts, adjust significance stars, group columns — all without touching LaTeX.

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Supports both R convention (p<0.001/0.01/0.05) and economics convention (p<0.01/0.05/0.1). Fully customizable.

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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.

Column groups

Group columns under shared headers like "OLS" or "Logit". Perfect for multi-panel tables.

Titles & footnotes

Add a table title with auto-numbering, plus footnotes for methodology or data source notes.

AI refinement

Describe any change in plain English — "add a title", "remove the intercept", "use 3 decimal places".

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Download as PNG, PDF (cropped or A4), Word, or copy the LaTeX code directly into your paper.

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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, correlation matrices, and more — all supported out of the box.

Summary statistics

Table 3: Summary Statistics — Iris Dataset

	Mean	SD	Min	Max	N
Sepal length	5.843	0.828	4.300	7.900	150
Sepal width	3.057	0.436	2.000	4.400	150
Petal length	3.758	1.765	1.000	6.900	150
Petal width	1.199	0.762	0.100	2.500	150

Data: Fisher's iris dataset. Three species: setosa, versicolor, virginica.

Correlation matrix

Table 4: Correlation Matrix

	mpg	wt	hp	qsec
mpg	1.000
wt	−0.868***	1.000
hp	−0.776***	0.659***	1.000
qsec	0.419*	−0.175	−0.708***	1.000

Data: mtcars (n=32). *** p<0.001, ** p<0.01, * p<0.05

Logistic regression

Table 2: Logistic Regression — Transmission Type

	(1) Logit	(2) Logit
Weight (1000 lbs)	−4.024**	−6.418**
	(1.654)	(3.184)
Horsepower		−0.068
		(0.062)
Quarter mile time (s)		2.148*
		(1.115)
N	32	32
AIC	24.83	21.47

Standard errors in parentheses. Dependent variable: am (1=manual).
*** p<0.001, ** p<0.01, * p<0.05

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See how it looks in your paper

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: Determinants of Fuel Efficiency

	OLS
	(1) Baseline	(2) Core	(3) Full
Weight (1000 lbs)	−5.344***	−2.879**	−3.367**
	(0.559)	(0.905)	(0.946)
Horsepower		−0.037***	−0.018
		(0.010)	(0.015)
Manual transmission		2.084	1.478
		(1.376)	(1.441)
Quarter mile time (s)			0.558
			(0.539)
Cylinders			−0.419
			(0.607)
N	32	32	32
R²	0.753	0.840	0.856
Adj. R²	0.745	0.823	0.828

Standard errors in parentheses. Dependent variable: miles per gallon (mpg).
*** 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 — Transmission Type

	(1) Logit	(2) Logit
Weight (1000 lbs)	−4.024**	−6.418**
	(1.654)	(3.184)
Horsepower		−0.068
		(0.062)
Quarter mile time (s)		2.148*
		(1.115)
N	32	32
AIC	24.83	21.47

Standard errors in parentheses. Dependent variable: am (1=manual).
*** 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 Heterogeneous Returns

Table 3 examines heterogeneity in the returns to education by gender and age. The returns to education are higher for women (0.089) than for men (0.076), and higher for workers under 40 (0.091) than for older workers (0.071). The urban premium is robust across all subgroups.

Table 3: Summary Statistics — Iris Dataset

	Mean	SD	Min	Max	N
Sepal length	5.843	0.828	4.300	7.900	150
Sepal width	3.057	0.436	2.000	4.400	150
Petal length	3.758	1.765	1.000	6.900	150
Petal width	1.199	0.762	0.100	2.500	150

Data: Fisher's iris dataset. Three species: setosa, versicolor, virginica.

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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