The Returns to Education in the Early

20th Century:New Historical Evidence

Joseph Kaboski Trevon D. Logan

The Ohio State U The Ohio State U

and NBER

Outline

I. Overview

II. The Historical Record

III. Our Model

IV. New Historical Data

V. Empirical Results

VI. Robustness, Extensions and Implications

Overview• There is an important recent literature on the

interaction between skill, technology, and economic growth– such as the skill biased technical change literature.

• Beginning in the late 19th century, skill began to replace physical capital, raw materials, and unskilled labor, leading to high returns to education in the early 20th century.

• There is evidence that the returns to education over the 20th century were U shaped.

Overview• We do not know how much regional variation

there was in returns to education at the beginning of the 20th century.

• The historical record gives us some reasons to expect significant variation in the returns to education:– Segmented labor and capital markets– Different levels of industrial composition– Different natural resource endowments

• This paper looks at regional variation in the returns to education at the beginning of the 20th century

Main Results• We construct a two sector model which

highlights the idea that differences in initial technological endowments will lead to differences in the returns to education.

• We exploit a new data source, a 1909 Report to the Commissioner of Education, to estimate the returns to education of high school teachers in the South, Midwest, and West.

• We find evidence of significant variation in the returns to education in the early 20th century.

The Historical Record I• There was marked regional heterogeneity in

the factors (raw materials, physical capital, unskilled labor) that lead to high returns to education in the early 20th century– The South had a different capital market– Literacy rates varied by region– The extent of manufacturing varied by region– The capital intensity of agriculture varied by

region– The nature and type of natural resources varied by

region

The Historical Record II• There is also evidence that the tenure in

manufacturing firms was longer than previously thought, which could possibly lead to further investments in technology.

• While there is a literature that looks at the development of these differences in technology and industrial development by region, by the beginning of the 20th century these differences were part of the technological/capital endowment.

Supply and Demand Factors• If the returns to education reflect the supply

and demand for skill, we can think of the heterogeneity as giving us evidence that there was variation in supply and demand.

• Supply factors (literacy rate) relatively fixed• Demand factors (extent of manufacturing,

value of machinery, etc.)• While we can intuit about returns to ed. in high

demand/low supply and low demand/high supply scenarios, it is unclear what returns we should expect from low/low or high/high

Supply and Demand Factors in 1910Factor Georgia Wisc. Texas Calif.

Literacy 76.7 96.3 87.8 95.5

Man. % of LF* 64.1 96.5 91.2 50.8

Livestock Val. PC 31.0 67.9 81.8 53.6

Mach. Val. Per Cap. 8.0 22.7 14.6 15.4

Mach. Val. Per Ag. 25.7 119.4 48.1 105.3

*Percent changes from 1900 to 1910

The Model I• We construct a two-sector model to generate

predictions about the returns in education in the early 20th century.

• We assume that there are two sectors of production, a land/resource-intensive sector and a capital-intensive sector.

• Both sectors use skilled and unskilled labor and capital or land to produce output.

• Each region is a small open economy that takes the relative price of output as given.

The Model II• In the initial equilibrium, the fraction of

workers employed in the capital-intensive sector is increasing in the capital/land ratio, and the relative wage of skilled workers is decreasing in their size.

• We then consider the introduction of a new capital dependent sector that is more skill dependent than the old capital-intensive sector.

Predictions From the Model• The model yields four predictions:

1) If the productivity of the new technology is sufficiently large, the new sector displaces the old capital-intensive sector, and the new technology employs a higher fraction of high skilled workers than low skilled workers.

2) The number of high skilled employed in the new technology exceeds the number of high skilled employed in the old capital-intensive technology. The relative wage of high skilled workers increases.

3) The higher the capital/land ratio, the higher the fraction of skilled and unskilled workers employed in the new technology and the higher the relative wage of skilled workers.

4) The new technology raises the return to capital relative to land. Furthermore, the higher the ratio of skilled/unskilled labor, the larger the increase in the relative rental rate of capital.

Outline

I. Overview

II. The Historical Record

III. Our Model

IV. New Historical Data

V. Empirical Results

VI. Robustness, Extensions and Implications

The Data I• We use a 1909 report by Edward Thorndike to

estimate the returns to education of high school teachers.

• The report was the first in a five report plan to analyze secondary education in the U.S.

• This data is the earliest we know of that allows us to estimate the returns to education, and the earliest to do so by region.

• The data is culled from the summary reports of a survey given to approximately 5000 high school teachers in the US, chosen to be representative at the time.

The Data II• The data list the annual salary, education, and

years of experience by sex for high school teachers in Ohio, Wisconsin, Illinois, California, Texas, and Georgia.

• The data for OH/IL/WI was grouped together because the responses were deemed similar.

• Thorndike sent out a supplementary survey to gauge the extent of measurement error and misreporting of education and experience; he found that the first survey did not suffer from large amounts of measurement error or aggregation bias. We use the first survey.

Summary Statistics

Georgia OH/IL/WI Texas Calif.

Avg. Salary 828 848 733 1142(377) (379) (278) (316)

Avg. Schooling

12.6 12.6 12.6 13.8(1.7) (1.9) (1.9) (1.4)

Avg. Experience

8.2 9.1 9.6 8.3(5.8) (7.2) (7.1) (7.0)

Fraction Male

67% 57% 64% 34%

N 137 3141 381 658

Empirical Results I• We use this information to estimate the returns

to education for teachers in Texas, California, Georgia and Ohio/Illinois/Wisconsin.

• The results show that there is marked geographical variation in the returns to education for high school teachers. Teachers in Georgia have lower returns than teachers in the Midwest.

• As expected, returns in Texas are high and returns in California are low.

Empirical Results II

Georgia OH/IL/WI Texas Calif.

Schooling .033 .070 .071 .005(2.06) (23.33) (7.88) (0.83)

Experience .012 .048 .034 .034(0.92) (12.0) (4.86) (8.5)

Experience^2 .0004 -.0008 -.0009 -.0007(0.66) (8.0) (3.0) (7.0)

Male .64 .16 .27 .22(10.67) (16.0) (9.0) (11.0)

N 137 3141 381 658

A Caveat – Median Data• The data for OH/IL/WI is pooled, and it is not

possible to generate state-specific estimates based on individual data for these states.

• We do have data on median incomes by sex, education and experience for each state (OH, IL, WI).

• We use individual data from CA, TX, & GA to create median data for those states to see how well it tracks with their individual returns.

• Since the overall pattern from the CA, TX, & GA regressions is consistent with their individual returns, we use the median returns to estimate the returns to education for each Midwestern state.

Median Return EstimatesOhio Illinois Wisconsin

Schooling .080 .073 .034(8.89) (8.11) (3.4)

Experience .030 .026 .042(3.33) (2.89) (4.2)

Experience^2 -.0003 .0001 -.0006(1.0) (0.33) (2.0)

Male .079 .192 .274(1.68) (4.0) (5.37)

N 133 122 99

Generalizability I• Unfortunately, we only have evidence from

high school teachers. This raises the question of generalizability.

• Do the returns to education for teachers track with general returns to education overall?

• To answer this question we must turn to the present– we use the IPUMS 5% samples from the 1980, 1990, and 2000 census to see how, by state, the returns for teachers (all levels) track with overall returns to education.

Generalizability II• By state, we regress the overall return to

education for each Census year on the return for teachers and time dummies.

• While we do find a positive relationship there are two important caveats– Teachers are not distinguished by type (primary,

secondary) in the Census, so we must use the returns to all teachers.

– The dramatic changes in the qualifications of teachers in the 20th century (and lack of data) doesn’t allow us to say with certainty whether the relationship between teacher returns and all returns was strong in the first half of the century.

Robustness I• We believe that these estimates reflect the

returns to education and not simply salary variation for teachers

1) Variation in teachers’ education levels was large.2) The estimates of returns could not be inferred from

looking at average salary and schooling by state.

• We also stratify the sample by gender and experience (>5 yrs. of experience)

– We divide the sample by gender because men had more outside options other than teaching

– We divide the sample by experience because those with fewer years of teaching as less “tied” to the profession

Robusteness IIMen Less Experience

Georgia .020 .012(0.95) (0.4)

OH/IL/WI .075 .052(18.75) (1.3)

Texas .085 .071(9.44) (7.1)

California .004 .020(0.4) (2.0)

Implications• What does this finding of regional

heterogenity tell us about returns to education from 1910 to 1940?

• The returns to education were high in the beginning of the 20th century, and teachers’ returns to education do decline over time, and then begin to increase again post 1980.

• Also, the returns for teachers track well with the general pattern of overall returns.

Figure 1: Comparison of Mincerian Returns over Time

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1940 1950 1960 1970 1980 1990 2000

Year

Min

ceri

an R

etu

rn

Just Teachers ,All States ,Sex Dummy

All Workers ,All States ,Sex Dummy

Just Teachers ,Samples States ,Sex Dummy

All Workers ,Samples States ,Sex Dummy

Source: IPUMS

Conclusion• There was marked heterogeneity in the returns to

education in the early 20th century.• Regions that had a large capital endowment had

higher returns to education. • The returns that we estimate for high school

teachers track well with overall returns, and the returns to teachers also show a U shaped pattern over the 20th century.

• Endowments matter for the returns to education, and depending on the endowment, regions may experience increasing or U-shaped returns to education.

Thanks!!