good schools make good neighbors: human capital...

Good SchoolsMake GoodNeighbors:

Human CapitalSpillovers in Early

20th CenturyAgriculture

John Parman

Introduction

Human Capitaland Agriculture

Iowa’s Farms andSchools

Data

Results

Private Returns

Spillovers

Spillovers and SocialNetworks

Conclusions

Good Schools Make Good Neighbors:Human Capital Spillovers in Early 20th

Century Agriculture

John Parman

February 23, 2010




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Brief Overview

Public school expansion in the American Midwestoccurred at a time when human capital was becomingincreasingly important in agriculture.

The public school system in Iowa developed with afocus on improving agricultural productivity.

Estimates of the private returns to education for farmerssuggest that Iowa achieved this goal; an additional yearof high school increased a farmer’s earnings by 5%.

A farmer’s education also benefited his neighbors; anincrease in the mean education level of his neighbors byone year increased a farmer’s annual earnings by roughly3%.

The size of these spillovers depended on social groupmembership and a farmer’s own level of education.




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Outline of Presentation

Review of modern evidence of the role of education inagriculture

Brief history of Iowa agriculture and education

Construction of a dataset with education and incomefor farmers and their adjacent neighbors

Estimates of the private returns to education inagriculture

Estimates of human capital spillovers across farms

Estimates of human capital spillovers within and acrosssocial networks

Concluding remarks




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Previous studies of human capital and agriculture

6

2

3

4

5

Freq

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y

0

1

‐3 ‐2 ‐1 0 1 2 3 4 5 6

Estimated percent increase in output with an additional year of schooling




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions


The empirical evidence of the returns to education inagriculture is very mixed.

In very stable environments, the returns to formalschooling are small.

In these environments, experience appears moreeffective than schooling for improving productivity.

Schooling is more important in dynamic settings:educated farmers are more likely to seek outinformation, experiment with and adopt newtechnologies and adapt to changing market conditions.




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions


There is a small body of evidence suggesting thatdiffusion of information through social networks isimportant:

Bandiera & Rasul (2006): adoption of new cropsdepends on decisions of family and friendsConley & Udrey (2001): farmers learn fromexperimentation of members of their social networkFoster & Rosenzweig (1995): farmers learn how tosuccessfully adopt new seed varieties from neighbors’experimentationRyan & Gross (1943): hybrid seed corn diffusion in Iowabased largely on neighbors talking to each other

As with the formal schooling, these social networks willbe more important when the agricultural sector is moredynamic.




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Innovation in Iowa Agriculture

Traditional view, Cochrane (1993): mechanizationaccounted for nearly all technological advance onAmerican farms, latter half of the 19th century was nota period of innovation

New view, Olmstead & Rhode (1993, 2002, 2008):significant portion of growth in labor productivity wasfrom biological advances and settlement patterns

Tasks facing the Iowa farmer: constructing properdrainage systems, applying advances in soil science,introducing new crops, breeding experiments with corn,pest and disease control, etc.




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

A Basic Timeline of Iowa Education

1858: Iowa Agricultural College founded, center forresearch, educated rural teachers, directly educatedfarmers through short courses)

1862: Morrill Act passed, land grant institutions to“promote the liberal and practical education of theindustrial classes”

1887: Hatch Act passed, federal land grants foragricultural experiment stations

1888: Iowa’s Agricultural Experiment Station founded

1914: Smith-Lever Act passed, established cooperativeextension services

School consolidation and the introduction of highschools is occurring between 1880 and 1910




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Rural Schools at the Turn of the Century




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Experimental Plots

Excerpt from a 1903 study guide for school garden work:

Experimental Work1. Plant potatoes at different depths, from justunder the surface to four inches. Which methodgives best results?2. Use seed pieces of varying sizes, from those withno eye up to the whole potato.3. Any difference in value of cutting from stem tostem end and bud end?4. Which is better, one large piece or several smallones?5. What is the order of sprouting of the eyes?6. How [to best] prevent potato scab?




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Constructing a Dataset of Linked Neighbors

1915 Iowa state census: income and educationinformation (unique for pre-1940 period), occupation,ancestry, church affiliation, farm value

Plat maps: farm boundaries, ability to measure areasand distances

County superintendents of schools reports: publicschool locations, school characteristics

Basic approach: digitize plat maps and link geographicaldata to census records and school district data




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

1915 Iowa State Census




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Township Plat Maps




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Digitizing Plat Maps




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Linking Census Records to Maps

1 Each name from the maps is searched for in the census(search criteria are the name and township)

2 If a unique match is found, an image of the censusrecord is transcribed and the farmer’s characteristics arelinked to the farm polygon on the map

3 GIS software is then used to calculate acreage of eachfarm and distance of farm to schools and towns

4 GIS scripts are used to identify adjacent neighbors andcalculate neighbors’ average earnings and educationalattainment




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Sample Counties

Variable Chickasaw Poweshiek Ringgold State AverageNumber of farms 1,905 2,142 1,854 1,996Ave. farm size (acres) 152 160 168 161Ave. monthly wage paid farm help, summer months 30.80 33.00 28.43 32.70

Ave. monthly wage paid farm help, winter months 20.69 25.72 28.77 24.61Corn, acres 63,194 110,557 69,328 98,463Corn, bushels per acre 3 38 23 28Winter wheat, acres 179 860 13,245 5,929Winter wheat, bushels per acre 16 23 9 19Spring wheat, acres 1,607 780 6 1,495Spring wheat, bushels per acre 12 14 7 14Cattle, cows and heifers kept for milk 17,367 9,877 7,987 11,053Statistics are compiled from the 1915 Annual Iowa Yearbook of Agriculture .

Sample county agricultural statistics, 1915




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Farmer Characteristics

Variable MeanStandard Deviation

Age 46.70 11.68Annual earnings 1199.62 1175.47Farm value 17439.32 13079.76Incumbrance on farm 3357.70 5399.68Farm acreage 153.03 106.92Earnings per acre 9.80 10.77Farm value per acre 126.08 90.98Distance to nearest town (miles) 2.25 1.52Distance to nearest high school (miles) 6.51 3.28Foreign born (yes=1) 0.14 0.35Number of neighbors 7.84 3.26Number of observationsNotes: All dollar values are in 1915 dollars. Total schooling is defined as the sum of years of common school, grammar school, high school and college.

Farm owner characteristics, 1915

2410




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Farmers’ Educational Attainment

Variable MeanStandard Deviation

Total schooling 8.43 2.65Common school 7.91 2.65Grammar school 0.23 1.26High school 0.19 0.72College 0.10 0.54Total schooling in Iowa 5.77 4.46Total schooling in US 6.30 4.31Total schooling outside US 2.13 3.51Graded schooling 0.52 1.80Graded schooling in Iowa 0.41 1.53Graded schooling in US 0.44 1.63Graded schooling outside US 0.08 0.72Notes: Total schooling includes years of common school, grammar school, high school and college. Graded schooling includes years of grammar school, high school and college only.

Educational attainment of farm owners, 1915




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Geographical Distribution of Total Schooling

Distribution of total schooling for Poweshiek county, 1915

Farmer's Years ofTotal Schooling

0-5

5-8

8-10

10-13

13+

School LocationsTowns with high schools

Towns with graded schools




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Geographical Distribution of Earnings

Distribution of annual earnings for Poweshiek county, 1915

Farmer's Annual Earnings (1915 $)

0 - 200

201 - 578

579 - 852

853 - 1260

1261 - 1750

1751 - 2340

2341 - 3500

3501 - 5500

5501 - 9000

9001 - 21000

School LocationsTowns with high schools

Towns with graded schools




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Farmers and Their Neighbors

Variable CorrelationTotal years of schooling 0.1656Years of graded schooling 0.1494Annual earnings 0.2274Farm value 0.2309Age 0.0409Notes: Neighbor characteristics are measured as the mean value of the characteristic over all observed neighbors.

Correlations between a farmer and his neighbors




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Estimating the Returns to Education

Standard Mincer regression to estimate private returnsto education

Education measures: total schooling, schooling by type,schooling by location received

Personal characteristics: nativity, years in the US, age,township dummies, religion dummies, land quality(proxied by land value per acre)

Spillovers are estimated by including a measure ofneighbors’ education

Several measures of neighbors’ education are used(mean years of schooling, mean years of high school,maximum years of schooling, etc.)

Distinguish between neighbors who are within andoutside of a farmer’s social network (defined by religion,ancestry and birth cohort)




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Private Returns to Education

Measure of schooling used: All land owners Farmers onlyTotal schooling 0.017*** 0.013**

(0.005) (0.006)Graded schooling 0.022*** 0.010

(0.008) (0.010)Common school 0.010* 0.011*

(0.006) (0.006)Grammar school 0.005 -0.004

(0.014) (0.015)High school 0.046** 0.052**

(0.020) (0.022)College 0.064** 0.009

(0.025) (0.029)Numbers of observations 2410 2219Standard errors in parentheses. All regressions control for age, religion, land value per acre and township.* significant at 10%; ** significant at 5%; *** significant at 1%

Private returns to education, log annual earnings as dependent variable




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Human Capital Spillovers

Mean Maximum Mean Maximum Mean MaximumNeighbors' education 0.023*** 0.026*** 0.010 0.011** 0.038 0.028***

(0.008) (0.006) (0.011) (0.005) (0.029) (0.011)Own education:Common school 0.009 0.008 0.009 0.009 0.009 0.008

(0.006) (0.006) (0.006) (0.006) (0.006) (0.006)Grammar school -0.007 -0.008 -0.008 -0.008 -0.007 -0.008

(0.016) (0.015) (0.016) (0.016) (0.016) (0.016)High school 0.055** 0.052** 0.057*** 0.057*** 0.057*** 0.056**

(0.022) (0.022) (0.022) (0.022) (0.022) (0.022)College 0.021 0.021 0.023 0.023 0.022 0.023

(0.031) (0.031) (0.031) (0.031) (0.031) (0.031)Number of obs. 2158 2158 2158 2158 2158 2158Standard errors in parentheses. All regressions control for age, religion, land value per acre and township.* significant at 10%; ** significant at 5%; *** significant at 1%

Private returns to education and spillovers, log annual earnings as dependent variableNeighbors' education

measure:Total schooling All graded schooling High school




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Human Capital Spillovers with Interactions

Neighbors' education measure:

Mean years of high school Max years of high school

Own schooling:Common school 0.008 0.007

(0.007) (0.007)Grammar school -0.006 -0.006

(0.017) (0.017)High school 0.063*** 0.069***

(0.023) (0.024)College 0.037 0.045

(0.029) (0.029)Neighbors' schooling:Neighbors' high school 0.041* 0.029***

(0.021) (0.009)Own HS x -0.018 -0.011* Neighbors' HS (0.014) (0.006)Number of obs. 2158 2158

Private returns to education and spillovers with own-neighbor education interactions, log annual earnings as dependent variable

Standard errors in parentheses. All regressions control for age, religion, land value per acre and township.* significant at 10%; ** significant at 5%; *** significant at 1%




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Human Capital Spillovers Within and AcrossSocial Networks

Parents' place of birth for Chickasaw county, 1915

Austria

Bohemia

Canada

Connecticut

Denmark

England

Germany

Illinois

Iowa

Ireland

Maine

New York

New Hampshire

Norway

Ohio

Pennsylvania

Scotland

Sweden

Wisconsin

Other

Not Reported/Not Found




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions


Church affiliations for Chickasaw county, 1915

Baptist

Other

Catholic

Congregational

Evangelical

Lutheran

Methodist

Presbyterian

Not Reported/Not Found




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions


Social group defined by: Church affiliation Parents' birthplaces Birth cohort

Percentage of neighbors that are in social group 38.80% 21.8% 17.4%

Correlation of own education with that of similar neighbors 0.258 0.299 0.131

Correlation of own education with that of dissimilar neighbors 0.125 0.155 0.057

Correlation of own years HS/college with that of similar neighbors 0.242 0.103 0.048

Correlation of own years HS/college with that of dissimilar neighbors 0.053 0.078 0.084

Correlation of own log earnings with that of similar neighbors 0.194 0.312 0.270

Correlation of own log earnings with that of dissimilar neighbors 0.254 0.248 0.207

Correlations between neighbors based on group membership




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions


Social group defined by:

Neighbors' education measure:Total

schoolingGraded

schoolingHigh school and college

Neighbors' education:Mean education within group 0.010** 0.027* 0.045

(0.004) (0.015) (0.035)Mean education outside group 0.012** 0.005 0.018

(0.006) (0.010) (0.018)Own education:Years of common school 0.008 0.009 0.008

(0.006) (0.006) (0.006)Years of grammar school -0.007 -0.008 -0.007

(0.014) (0.015) (0.015)Years of high school 0.053** 0.054** 0.053**

(0.026) (0.026) (0.026)Years of college 0.023 0.024 0.021

(0.033) (0.031) (0.032)Observations 2148 2148 2148Robust standard errors clustered by township in parentheses, all regressions control for age, nativity, years in the United States, religion, land value per acre and township. * significant at 10%; ** significant at 5%; *** significant at 1%

Private returns to education and spillovers by social group membership, log annual earnings as dependent variable

Parents' birthplaces




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions




schoolingGraded


Neighbors' education:Mean education within group 0.013*** 0.019** 0.037**

(0.003) (0.009) (0.017)Mean education outside group 0.017** 0.004 0.016

(0.007) (0.009) (0.018)Own education:Years of common school 0.008 0.008 0.008

(0.006) (0.006) (0.006)Years of grammar school -0.006 -0.009 -0.007

(0.014) (0.014) (0.014)Years of high school 0.051* 0.054** 0.052**

(0.027) (0.025) (0.026)Years of college 0.025 0.025 0.023

(0.033) (0.031) (0.032)Observations 2158 2158 2158


Birth cohort

Robust standard errors clustered by township in parentheses, all regressions control for age, nativity, years in the United States, religion, land value per acre and township. * significant at 10%; ** significant at 5%; *** significant at 1%




John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions




schoolingGraded


Neighbors' education:Mean education within group 0.005 -0.003 0.018

(0.004) (0.014) (0.034)Mean education outside group 0.016** 0.041*** 0.070***

(0.006) (0.010) (0.018)Own education:Years of common school 0.014 0.015* 0.014

(0.009) (0.009) (0.009)Years of grammar school 0.004 0.001 0.006

(0.021) (0.023) (0.021)Years of high school 0.049* 0.054** 0.050*

(0.026) (0.026) (0.027)Years of college 0.014 0.015 0.011

(0.032) (0.032) (0.033)Observations 1284 1284 1284

Church affiliation

Robust standard errors clustered by township in parentheses, all regressions control for age, nativity, years in the United States, religion, land value per acre and township. * significant at 10%; ** significant at 5%; *** significant at 1%





John Parman

Introduction



Data

Results

Private Returns

Spillovers


Conclusions

Summary of Findings

The organization of Iowa’s schools and researchinstitutions suggests that formal education was designedin part to improve agricultural productivity.

The data confirm that these efforts succeeded at theindividual level.

Returns to education were high for farmers - 5%increase in annual earnings from an additional year ofhigh school.

Spillovers were also significant - 2-7% increase in annualearnings from an additional year of high school foradjacent neighbors.

The results suggest that there were large public benefitsto public education in agricultural communities.

good schools make good neighbors: human capital...

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