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1
The Geographic Concentration of
Economic Activity Across the Eastern
United States, 1820-2010
by
David A. Latzko
Business and Economics Division
Pennsylvania State University, York Campus
1031 Edgecomb Avenue
York, PA 17403
phone: 717-771-4115
fax: 717-771-4062
e-mail: [email protected]
2
The Geographic Concentration of Economic Activity
Across the Eastern United States, 1820-2010
Abstract
The historical dynamics of the geographic distribution of economic activity across the eastern United
States between 1820 and 2010 are examined using the smallest feasible geographic entities, counties, as
units of analysis. The region first experienced increasing spatial concentration of population and
manufacturing, with inequality peaking early in the twentieth century. Population and manufacturing
have since become more dispersed. Agriculture showed the opposite pattern: initial dispersion followed
by increasing concentration. Initially, counties with a high manufacturing density also had a high
agricultural density. Eventually, agricultural production moved to outlying areas while manufacturing
remained concentrated near where it originated.
keywords: spatial distribution, geographic distribution, regional economic activity, regional
economic development, regional economic history, location of economic activity
3
The Geographic Concentration of Economic Activity Across the Eastern United
States, 1820-2010
A striking characteristic of the modern economy is the very uneven distribution of economic
activity across space. This is the result of several sometimes-contradictory influences. The neoclassical
growth model predicts convergence: the economies of low-income regions will grow faster than the
economies of high-income regions so that the income levels of poor economies will tend to approach
those of rich economies. Diminishing returns to capital means that poorer regions will have a higher
marginal product of capital and so a higher growth rate than rich regions, enabling the poor regions to
catch up in the long run. Convergence implies a reduction over time of differences in the distribution of
economic activity across geographic regions.
Evidence of convergence has been found for many national and continental areas, including U.S.
regions and states,1 European regions,
2 Japanese prefectures,
3 Canadian provinces,
4 U.S. counties,
5 and
counties of the Great Plains states.6 Niemi identified a divergence between the industrial structure of the
South and the rest of the United States during the second half of the nineteenth century, but noted a steady
convergence of both regional and state manufacturing structures during the period 1860-1970.7
Students of economic geography and urbanization emphasize agglomeration economies, the
benefits producers obtain when located near each other. These benefits, a combination of scale
economies and network externalities, can come from the supply side of the market (information
spillovers, competing suppliers, thicker labor markets with many buyers and sellers, and so on) as well as
from the demand side, for example, the home market effect. Agglomeration economies push the
geographical concentration of economic activity. Agglomeration diseconomies - congestion, higher input
prices, and product price competition - encourage the dispersal of economic activity.8
Ellison and Glaeser find that most American industries are geographically concentrated, and
Rappaport and Sachs observe that the concentration of U.S. industry on ocean and Great Lakes coasts
increased over the twentieth century.9 Wheaton and Shishido, using cross-sectional data for 38 counties,
4
find that urban concentration initially rises with per capita income but eventually spatial decentralization
sets in as economic development proceeds.10
Ades and Glaeser determine that the urban concentration of
population is related to political and trade factors.11
In France, extractive, traditional, and high tech
industries are highly localized, and Devereux, Griffith, and Simpson find a high degree of geographic
concentration in some U.K. industries in 1992.12
Regional industrial specialization in China increased
between 1988 and 2001, and its center of population has gradually moved towards the southeast over the
last two millennia.13
In a seminal paper, Williamson hypothesized that as a country develops from low-income levels,
regional income inequality increases and economic activity becomes more spatially concentrated. As
development proceeds, diminishing returns to capital and agglomeration diseconomies occur in the initial
growth region, leading to economic development elsewhere and a decline in regional income inequality.
‘The expected result is that a statistic describing regional inequality will trace out an inverted “U” over
the national growth path…’14
The broad narrative of industrial development in the United States has been traced by Klein,
Licht, Meyer, and Niemi.15
Statistical evidence on the changing spatial distribution of economic activity
was provided by Krugman and Kim, who analyzed manufacturing specialization across U.S. states and
the nine census divisions.16
Regional manufacturing specialization, measured by employment, declined
slightly between 1860 and 1890, rose substantially in the early twentieth century, to peak in the 1910’s,
flattened out during the interwar years, and fell considerably thereafter. Easterlin found a decline in the
concentration of manufacturing production among U.S. states between 1869 and 1947, and Dumais,
Ellison, and Glaeser determined that there was a slight decline in industry agglomeration levels across
U.S. states between 1972 and 1992.17
In Europe population has concentrated spatially since the
eighteenth century.18
Ireland and Portugal saw little change between 1985 and 1998 in agglomeration
levels but there was extensive geographic mobility of industries.19
The geographic concentration of
aggregate employment across Western Europe did not change over the 1975-2000 period although
manufacturing became less concentrated relative to physical space.20
5
To sum up, economic activity is very unevenly distributed across geographic space. The
theoretical and empirical literature indicates that the degree of this spatial inequality is not constant over
time. Here I examine the historical dynamics of agglomeration and economic concentration across the
United States over the very long run. Previous studies of this topic have used census regions or states as
the units of analysis. But, economic activity is not at all evenly distributed across such large geographic
units.21
Therefore, I use the smallest possible geographic entities, counties, as units of analysis to provide
a more precise empirical examination of the location of economic activity over time.
Several studies of regional economic growth, such as the Higgins, Levy, and Young and Austin
and Schmidt papers cited above, examine county-level income data, but for time periods (1970-1998 and
1969-1994 respectively) too short to reveal the long-run distribution of economic activity across county-
sized units.22
The contribution of this paper lies in its consideration of a significant geographical area --
the eastern United States -- through an extended period of time -- 1820-2010.
Data and methods
Williamson’s inverted-U hypothesis posits three stages of regional development: increasing,
stable, and decreasing regional inequality. Data, then, are needed over a long period of time to test the
Williamson hypothesis. Counties are the logical unit of analysis as they are the smallest geographic
entities, together covering the entire eastern United States, for which a long time series of economic data
can be constructed. Beginning in 1820, the decennial U.S. censuses contain tabulations by county of
various economic variables.23
Later, periodic economic censuses provide county-level data.
County-level data are also appealing for the theoretical reason suggested by Beeson, DeJong, and
Troesken: ‘[C]ounty borders are attractive because they better reflect the limits of local economies than
do the borders of states, regions, or nations, which are aggregates of local economies; or cities, whose
political boundaries often exclude a portion of the local economy…’24
City-level data, by ignoring
suburban and rural locations, fail to span the entire geographic space.
6
Inequality in the distribution of economic activity in the eastern United States is measured by the
Gini coefficient, a measure of statistical dispersion in a population that has been used to measure
inequality in plant size, to quantify carbon use by bacterial soil communities, and to examine the equality
of student participation in classroom discussions.25
Most often used to summarize income inequality, the
Gini coefficient has been utilized by Amiti and Krugman to measure the geographic concentration of
industry.26
If the data are ordered by increasing size, the Gini coefficient is given by
(1) (n
i=1(2i – n – 1)xi)/n2
,
where x is an observed value, n is the number of values observed, i is the rank of values in ascending
order, and is the mean value of x. The Gini coefficient ranges from a minimum value of zero for perfect
equality, when all units have the same value of x, to a theoretical maximum of one when a single unit has
all of x and the other units have nothing. Higher Gini coefficients indicate a more unequal distribution.
This paper maps and analyses data using the county boundaries extant at the time of each
decennial census, taken from Thorndale and Dollarhide’s invaluable Map Guide to the U.S. Federal
Censuses.27
Because county boundaries have changed units of analysis are not consistent over time but,
although this inconsistency may bias the Gini coefficient downwards, it has little impact on observed
inequality. For example, when county-level census figures first become available for Florida and
Wisconsin, between 1830 and 1840 the number of counties in this analysis increased from 914 to 1,155.
Dropping these two states from the calculation reduces the Gini coefficient for 1840 from 0.64 to 0.63 for
population density; the Gini coefficient for agricultural density falls from 0.45 to 0.44 and that for
manufacturing density falls from 0.92 to 0.91.
This paper considers the counties of all states east of the Mississippi River (Alabama,
Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Maine, Maryland,
Massachusetts, Michigan, Mississippi, New Hampshire, New Jersey, New York, North Carolina, Ohio,
Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont, Virginia, West Virginia, and
Wisconsin) and the District of Columbia. The number of counties in the analysis ranges from 731 to
7
1638. Three measures of economic activity are constructed for each county: agricultural density,
manufacturing density, and population density.28
All densities are per square mile of county area.
In the long run, increases in population are strongly correlated with economic development, both
as indicators and as consequences.29
Rappaport and Sachs argue that population density captures
underlying variations in local productivity and quality of life. Consider an area with a set of attributes
such as access to navigable water, temperate weather, and rule of law that increase the productivity of
resident firms. The high productivity of firms increases the marginal products of both labor and capital,
inducing an inflow of each. In the long run, high productivity implies high population density. Also,
consider an area with a set of attributes such as waterfront activities, pleasant weather, and low crime that
increase the quality of life of local residents. The high quality of life induces an inflow of labor and, since
it is complementary, capital. In the long run, high quality of life implies high population density.
Consistent with the idea that people vote with their feet, the intuition is that ‘population density reveals
individuals’ preferences over local areas by aggregating the indirect contribution to utility via
productivity-driven higher wages with the direct contribution to utility via high quality of life.’ 30
Glaeser also argues that a high population density is associated with a higher standard of living.31
People acquire skills by interacting with one another, and a dense population increases these interactions.
So, a high population density results in a population with lots of human capital which then attracts more
people. However, Acemoglu, Johnson, and Robinson find that population density is not a good proxy for
economic prosperity in the recent data.32
No single indicator of agricultural or manufacturing activity is available for over the whole study
period. Tables 1 and 2 list the metrics used to compute county agricultural and manufacturing densities
(most often the value of farm products and the value of products in manufacturing) and the sources of the
data. There are a few years for which the agricultural or manufacturing measures utilized overlap. In
1950, the correlation between the value of all crops and the value of farm products sold is 0.73. The
correlation at the county level between the number of persons engaged in manufacturing in 1840 and the
total capital invested in manufacturing is 0.92, and the correlation in 1850 between the capital invested in
8
manufacturing and the value of products in manufacturing in a county is 0.95. These correlations give
confidence to the notion that the results obtained below are not due to the choice of variables. No
agricultural or manufacturing data are available for 1830; no manufacturing data are available for 1910.
Evolution of the geographic concentration/dispersion of economic activity
The Gini coefficients for the three measures of economic activity are listed in Table 3.
Population density, a proxy for local productivity and quality of life, follows an approximate inverted-U
pattern. As Figure 1 indicates, population became increasingly concentrated through the nineteenth and
into the twentieth century with the Gini coefficient reaching a maximum in 1950. Thereafter, population
slowly became more dispersed across the eastern United States. Six major counties/cities in the region,
Manhattan, Philadelphia, Baltimore, Washington, DC, Boston, and Brooklyn, experienced absolute
declines in population density of over 2,000 persons per square mile between 1950 and 2010. The
Northern Virginia counties of Arlington and Fairfax, the New York City boroughs of Queens and Staten
Island along with Nassau County (New York), Pinellas County (Florida), and DuPage County (Illinois)
have seen the largest absolute gains in population density since 1950, with each gaining at least 2,000
persons per square mile. In percentage terms, several Florida counties including Collier and Charlotte
have seen the largest growth in population density since 1920. Still, population is far more unequally
distributed across the eastern United States today than anytime before World War I.
Figures 2 plots the Gini coefficients for manufacturing density. Manufacturing, consistently the
most concentrated of the three measures of economic activity, traces out a near inverted-U shape.
Manufacturing concentrated rapidly between 1820 and 1850, then slowed to 1890 when manufacturing
inequality peaked. In 1890, counties containing Manhattan, Chicago, Philadelphia, Brooklyn, Pittsburgh,
Boston, and Cincinnati accounted for 37 percent of manufacturing production the eastern United States.
By 2010, just 6 percent of manufacturing output was generated in these counties.
The Gini coefficient for manufacturing density was essentially flat between 1850 and 1950 at an
extremely high level. After World War II, manufacturing production dispersed across the eastern half of
9
the country, with this dispersion accelerating since the late 1980’s. The seven leading manufacturing
counties in 2010, Cook County (Illinois), Wayne County (Michigan), Marion County (Indiana), Fairfield
County (Connecticut), Middlesex County (Massachusetts), Hamilton County (Ohio), and Oakland County
(Michigan) are mostly suburban and accounted for around 10 percent of the manufacturing value added in
the eastern half of the United States. Much manufacturing production has moved from the major cities to
their suburbs yet manufacturing still remains highly concentrated.
Figure 3 shows that agriculture, the least unequally distributed of the three variables over the
entire study period, reveals a pattern of slowly decreasing inequality to 1910 when agricultural production
was widely dispersed across the eastern states, followed by a more rapid increase in spatial concentration.
This pattern is consistent with estimates of productivity growth in agriculture. Total factor productivity
grew about 0.80 percent per year between 1850 and 1900, with the shift of farm output into new areas
accounting for a substantial part of the increase in productivity.33
As agricultural production grew more
concentrated in the twentieth century, marginal lands were abandoned and total factor productivity growth
accelerated, especially after World War II; productivity growth in agriculture averaged 1.9 percent a year
between 1948 and 1999.34
Consequently, agricultural production is more concentrated today than in
1820. In 1910, the top 10 agricultural producing counties accounted for nearly 4 percent of production in
the eastern United States; the top 10 in 2010 grew about 8 percent of the farm output in the region.
In the following analysis a county is defined as agriculture-dense if its agricultural density is
more than two standard deviations above the mean for all counties in the eastern United States. A county
is manufacturing-dense if its manufacturing density is greater than two standard deviations above the
mean for all counties. The maps in Figures 4 through 11 highlight the agriculture-dense and
manufacturing-dense counties in 1820, 1890, 1950, and 2010.35
The appendix lists these counties for
each of those four years.
In 1820, just three counties were manufacturing-dense: New York (Manhattan, New York),
Suffolk (Boston, Massachusetts), and Philadelphia (Pennsylvania). Agricultural production
10
was scattered about the eastern United States, with pockets of concentration in the tidewater region of
Virginia, the area around Washington, DC, upstate New York, central Georgia, and northern Kentucky.
The densest agricultural county in 1820 was Philadelphia.
Figures 6 and 7 highlight the striking fact that economic activity in 1890, both agricultural and
manufacturing, was concentrated in the northeastern urban corridor. Four of the manufacturing-dense
counties, Baltimore City, Philadelphia, Kings, New York, and Hudson, New Jersey, were also agriculture
dense. Manhattan and Boston were the other two manufacturing-dense counties. Agricultural production
in 1890 was concentrated in southeastern Pennsylvania, northern New Jersey, southern and western New
York, and outside of Boston. Even in areas outside of the northeast, agricultural production was
concentrated around major industrial centers like Pittsburgh, Cleveland, Cincinnati, Chicago, New
Orleans, and Louisville. ‘A pattern of regional specialization existed with land-intensive, higher-
transport-cost products being produced closer to the major northeastern markets, while land-intensive,
lower-transport-cost-products were produced at a distance.’36
The same six counties that were manufacturing-dense in 1890 remained manufacturing dense in
1950, with Hudson County,New Jersey also agriculture dense. Figure 8 demonstrates that agricultural
production in the eastern United States had moved west. Large areas of central Indiana, northern and
central Illinois, and southeastern Wisconsin were agriculture dense. Agricultural output stayed high in the
Philadelphia/New York corridor. Pockets of agricultural density appeared in North Carolina, the
Delmarva Peninsula, and Mississippi.
Figures 10 and 11 depict the diffusion of economic activity, especially manufacturing, across the
eastern United States in 2010. 32 counties now had manufacturing densities more than two standard
deviations above the mean. The old manufacturing centers of Baltimore, Philadelphia, New York, and
Boston remained among the manufacturing dense counties, but manufacturing production also spread to
the suburban counties of these cities. Cleveland, Detroit, Milwaukee, Cincinnati, and Chicago and its
suburbs now appeared on the list of manufacturing dense areas as manufacturing production also moved
11
to the Mid West. Manufacturing production also spread south, with manufacturing dense counties in
Virginia, North Carolina, and Louisiana.
Agricultural production moved south with areas of concentration in Virginia, North Carolina,
Georgia, Florida, and Alabama. New Jersey was no longer agriculturally-dense but neighboring areas of
southeastern Pennsylvania were. No counties were simultaneously manufacturing and agriculture-dense
in 2010.
Although areas of agricultural and manufacturing concentration have shifted south and west in
the eastern United States since 1820, a quick glance at Figures 4 through 11 reveals that a few counties
have remained agriculture or manufacturing dense for nearly 200 years. All those counties identified as
manufacturing-dense in 1820 were still among the manufacturing-dense counties in 2010. And, the two
densest agricultural counties in 2010, Woodford and Fayette in Kentucky, were agriculture-dense in 1820.
Indeed, there is tremendous stability in the rank- ordering of counties by agricultural, manufacturing, and
population densities between 1820 and 2010.
I sort counties by agricultural, manufacturing, and population densities for each year and
calculate the Spearman rank order correlation coefficient, which measures the strength of the association
between a county’s rank-ordering in year x on that metric and its rank-order in year y. These correlations
are listed in Table 4. While there is little correlation between a county’s agricultural density rank in 1820
and its rank in 2010, there are strong correlations for manufacturing, 0.39, and population, 0.49, ranking
between those dates. The correlations between rank in the past and rank in 2010 are stronger for all three
density metrics when comparing rank in 2010 to rank in 1890 and rank in 1950. There is a strong
relationship between current and past economic activity: the most economically-dense counties in 2010
tend to be the counties that were the most economically-dense in 1950 and in 1890 and in 1820.
Correlation between agricultural and manufacturing density
In Krugman’s two-region model of the geographic concentration of manufacturing, mass
production and lower transport costs produce an increasingly concentrated manufacturing core and an
12
agricultural periphery.37
As transport costs to and from rural areas fall, manufacturers can afford to
concentrate in cities to take advantage of scale economies, intensifying the trend towards the
concentration of manufacturing in urban areas and agriculture in rural areas.38
According to Krugman’s
model, then, counties with a high manufacturing density ought to have a low agricultural density and
counties with a high agricultural density ought to have a low manufacturing density.
On the other hand, Meyer argues that agriculture and manufacturing were complements during
the initial phase of American industrial development. Eastern agriculture thrived before the Civil War as
rising farm productivity produced surplus labor for the factories and provided abundant farm products for
the growing urban population. Farms on poor soil and distant from urban markets declined while farmers
on good soil with good access to urban markets thrived.39
The implication of Meyer’s hypothesis is that
there ought to be a positive correlation between agriculture and manufacturing densities during the early
phase of industrial development.
I calculate the correlation between a county’s agricultural density and its manufacturing density
for each year. Figure 12 plots these agriculture/manufacturing density correlation coefficients.
Consistent with Meyer’s argument, there was a positive correlation between agricultural density and
manufacturing density until 1920. Since then, there has been a very weak negative or positive correlation
between the two variables. In other words, in the early stages of development, counties with a dense
manufacturing sector also had a dense agricultural sector. Most of the economic activity in the region,
both manufacturing and agricultural, took place in a handful of counties. In 1850, nine counties
comprising just 0.5 percent of the land area of the eastern United States -- New York County,
Philadelphia and Allegheny Counties (Pennsylvania), Baltimore, Essex, Middlesex, Suffolk and
Worcester Counties (Massachusetts), and Hamilton County (Ohio) -- produced 21 percent of its combined
agricultural and manufacturing output (2 percent of agricultural output and 33 percent of manufacturing
output) and contained 9 percent of the region’s population.
Technological advances in the transportation and processing of agricultural output weakened the
positive link between manufacturing and agricultural density over the second half of the nineteenth
13
century as agricultural production became more dispersed across the eastern half of the United States
while manufacturing grew increasingly concentrated. Railroad construction was most rapid between
1850 and 1875. Rail transportation was available during all seasons, which was not true of wagon or
water transportation. Manufacturers and farmers had year-round access to market. Another advantage of
railroads for farmers was their time-saving. Perishable products such as milk, fruit, and vegetables could
be marketed greater distances. The first shipment of meat in refrigerated cars was in 1869, and a
refrigerated car for transporting fruit was introduced in 1887. Southern and Pacific coast produce became
available in Pennsylvania markets, for example, beginning about 1885. 40
Commercial canning began
about 1890 and the quick freezing of vegetables started in 1931. These two developments enabled
agricultural products to be transported and stored throughout the year. The advent of good roads and
motor vehicles in the early twentieth century permitted farmers to bring their products to market
themselves. Over the nineteenth and into the twentieth century, these improvements in food processing
and distribution caused agricultural production to disperse to outlying regions while manufacturing
remained concentrated near those areas where it initially sprang up in order to continue to benefit from
agglomeration economies.
The spatial relationship between manufacturing and agricultural production completely collapsed
during the first decades of the twentieth century. It was around this same time that the current trends
towards a dispersion of manufacturing production and an increasing concentration of agricultural
production across the region began. As new methods of food processing, distribution, and storage were
developed, marginal lands were taken out of cultivation, leaving agricultural production increasingly
concentrated on the most productive farmland. In Pennsylvania, for example, the land in farms fell from
19,371,015 acres in 1900 to 14,112,841 acres in 1950, close to the acreage a century earlier.41
‘The
primary cause of this shrinkage was the abandonment of rough or infertile land.’42
Consistent with
Nerlove and Sadka’s model of a dual economy, as the relative cost of transporting agricultural goods has
fallen, it became economically feasible to cultivate rural land.43
And so, agricultural production in the
eastern United States moved further away from the northeastern urban corridor.
14
Conclusions
As the eastern half of the United States developed economically, it first experienced increasing
inequalities in the spatial distributions of population and manufacturing. Both forms of economic activity
concentrated around New York City, Philadelphia, Boston, and Baltimore. Spatial differentiation reached
a peak in the early post-World War II era. Since then, population and manufacturing production have
become more dispersed across the eastern United States. These patterns are consistent with Williamson’s
inverted-U hypothesis. Observed national development patterns replicate themselves over the long run
across smaller geographic areas, even with the smallest feasible spatial units of analysis. Aggregate
manufacturing concentration follows the same historical pattern of concentration-then-dispersal as the
micro industry-based data reported by Krugman and Kim and the rank-ordering of county manufacturing
density is persistent.44
The leading manufacturing counties in 2010 were the leaders over a century ago.
Agriculture, likely because the expansion of railroads and the introduction of refrigerated cars,
the building of good roads and the development of motor transportation, and the rise of commercial
canning, showed the opposite pattern: initial dispersion followed by increasing concentration. If these
trends continue, manufacturing production will become even more dispersed across the region.
Manufacturing leadership, having moved over the post-war era from the large cities to their suburbs, may
move to the exurbs and population will follow. As exurban land is converted to industrial and residential
use, agricultural production will become further concentrated in the region.
15
APPENDIX
Agriculture and Manufacturing Dense Counties
1820 Agriculture
Philadelphia, PA Calvert, MD Jones, GA Jasper, GA
Mathews, VA Windham, VT St. Lawrence, NY Prince George’s, MD
Clark, KY Elizabeth City, VA Norfolk, MA Northumberland, VA
Schoharie, NY Putnam, GA Fayette, KY Prince William, VA
Tompkins, NY Charleston, SC Mason, KY Talbot, MD
Stafford, VA Cecil, MD Gloucester, VA Woodford, KY
1820 Manufacturing
New York, NY
Suffolk, MA
Philadelphia, PA
1890 Agriculture
St. Lawrence, NY New Castle, DE Cuyahoga, OH Campbell, KY
Kings, NY Assumption, LA Baltimore City, MD Wayne, NY
Hudson, NJ Milwaukee, WI Monmouth, NJ Kane, IL
Delaware, PA Hamilton, OH Essex, NJ Fayette, KY
Montgomery, PA Onondaga, NY West Baton Rouge, LA Cayuga, NY
Philadelphia, PA Westchester, NY Genesee, NY St. James, LA
Bucks, PA Middlesex, MA Mason, KY Orleans, NY
Queens, NY Orange, NY Richmond, NY Hunterdon, NJ
Lancaster, PA Camden, NJ Baltimore, MD Erie, OH
Monroe, NY Lehigh, PA DuPage, IL Ascension, LA
Schoharie, NY Ontario, NY Niagara, NY Essex, MA
Chester, PA Union, NJ Yates, NY Seneca, NY
Mercer, NJ Hartford, CT Northampton, PA Woodford, KY
Gloucester, NJ Allegheny, PA Montgomery, OH McHenry, IL
1890 Manufacturing
New York, NY
Baltimore City, MD
Philadelphia, PA
Suffolk, MA
Kings, NY
Hudson, NJ
16
1950 Agriculture
Hudson, NJ Bourbon, KY Nassau, NY Champaign, IL
St. Lawrence, NY Boone, IL Ogle, IL Sangamon, IL
Sussex, DE Mercer, NJ Nash, NC Calumet, WI
Hartford, CT Lake, TN Woodford, KY Douglas, IL
DeKalb, IL Hunterdon, NJ Carroll, IL Green, WI
Kane, IL Whiteside, IL McLean, IL Dodge, WI
Monmouth, NJ Sunflower, MS Pitt, NC Caroline, MD
Schoharie, NY Suffolk, NY Wicomico, MD Kenosha, WI
Wilson, NC Walworth, WI McHenry, IL Carroll, IN
Fayette, KY Stephenson, IL Howard, IN Macon, IL
Gloucester, NJ Bureau, IL Rush, IN Clark, KY
Cumberland, NJ Tipton, IN Bergen, NJ Fond Du Lac, WI
Greene, NC Fulton, OH Benton, IN Boone, IN
Henry, IL Piatt, IL Coahoma, MS LaSalle, IL
Warren, IL Middlesex, NJ Seminole, FL DeWitt, IL
Union, NJ Racine, WI Northampton, VA Dane, WI
Kendall, IL Clinton, IN Stark, IL Jessamine, KY
Salem, NJ Logan, IL Lee, IL Moultrie, IL
1950 Manufacturing
New York, NY
Hudson, NJ
Kings, NY
Philadelphia, PA
Suffolk, MA
Baltimore City, MD
2010 Agriculture
Woodford, KY Ottawa, MI Wilkes, NC Adams, IN
Fayette, KY Union, NC Gordon, GA Carroll, IL
Duplin, NC McLean, KY Calumet, WI Jay, IN
Sampson, NC Banks, GA Madison, GA Hillsborough, FL
Franklin, GA Bourbon, KY Mitchell, GA Colquitt, GA
Mercer, OH Rockingham, VA Jackson, GA White, IN
Lancaster, PA St. Mary’s, MD Hendry, FL Gilmer, GA
Sussex, DE Lenoir, NC Brown, WI Huron, MI
Wayne, NC Caroline, MD Newton, IN Lagrange, IN
Hart, GA Kewaunee, WI Hall, GA Wayne, OH
Darke, OH Cullman, AL Allegan, MI Elkhart, IN
Greene, NC Hickman, KY DeKalb, IL Graves, KY
Chester, PA DeKalb, AL Page, VA Stephenson, IL
Jessamine, KY Wicomico, MD Dubois, IN
Lebanon, PA Jasper, IN Palm Beach, FL
17
2010 Manufacturing
New York, NY Philadelphia, PA Queens, NY Fairfield, CT
Union, NJ Milwaukee, WI Bergen, NJ Jefferson, KY
Rockland, NY Henrico, VA DuPage, IL Essex, MA
Essex, NJ Kings, NY Delaware, PA St. Charles, LA
Warwick, VA Baltimore City, MD Cuyahoga, OH St. John the Baptist, LA
Suffolk, MA Hamilton, OH Forsyth, NC Durham, NC
Marion, IN Hudson, NJ Montgomery, PA Middlesex, NJ
Cook, IL Wayne, MI Lake, IL Vanderburgh, IN
FOOTNOTES
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Martin, Convergence, Journal of Political Economy 100 (1992) 223-251.
2. Barro and Sala-i-Martin, Convergence across states and regions.
3. R. J. Barro and X. Sala-i-Martin, Regional growth and migration, Journal of the Japanese and
International Economies 6 (1992) 312-346.
4. S. Coulombe and F. C. Lee, Convergence across Canadian provinces, 1961 to 1991, Canadian Journal
of Economics 28 (1995) 886-898.
5. M. J. Higgins, D. Levy, and A. T. Young, Growth and convergence across the United States: evidence
from county-level data, Review of Economics and Statistics 88 (2006) 671-681.
6. J. S. Austin and J. R. Schmidt, Convergence amid divergence in a region, Growth and Change 29
(1998) 67-88.
7. A. W. Niemi, Structural shifts in Southern manufacturing, 1849-1899, Business History Review 45
(1971) 79-84; A. W. Niemi, State and Regional Patterns in American Manufacturing 1860-1900,
Westport, CT, 1974.
8. G. S. Goldstein and T. J. Gronberg, Economies of scope and economies of agglomeration, Journal of
Urban Economics 16 (1984) 91-104; R. W. Helsley and W. C. Strange, Matching and agglomeration
economies in a system of cities, Regional Science and Urban Economics 20 (1990) 189-212; S. S.
Rosenthal and W. C. Strange, The determinants of agglomeration, Journal of Urban Economics 50 (2001)
191-229.
9. G. Ellison and E. L. Glaeser, Geographic concentration in U.S. manufacturing industries: a dartboard
approach, Journal of Political Economy 105 (1997) 889-927; J. Rappaport and J. D. Sachs, The United
States as a coastal nation, Journal of Economic Growth 8 (2003) 5-46.
18
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21
Table 1
Agriculture Density Metrics and Sources
Year Variable Data Source
1820 persons engaged in agriculture 1820 Census
1830 - -
1840 persons engaged in agriculture 1840 Census
1850 value of livestock, animals slaughtered, orchard
products, and produce of market gardens
1850 Census
1860 value of livestock, animals slaughtered, orchard
products, and produce of market gardens
1860 Census
1870 estimated value of all farm products 1870 Census
1880 estimated value of all farm products 1880 Census
1890 estimated value of all farm products 1890 Census
1900 estimated value of all farm products 1900 Census
1910 total value of all crops 1910 Census
1920 total value of all crops 1920 Census
1930 total value of all crops 1930 Census
1940 total value of all crops 1940 Census
1950 value of farm products sold in 1949 1952 County and City Data Book
1960 value of farms products sold in 1959 1962 County and City Data Book
1970 value of farms products sold in 1969 1972 County and City Data Book
1980 value of farm products sold in 1982 1988 County and City Data Book
1990 value of farm products sold in 1987 1994 County and City Data Book
2000 market value of agricultural products sold in 2002 2002 Census of Agriculture
2010 market value of agricultural products sold in 2007 2007 Census of Agriculture
22
Table 2.
Manufacturing Density Metrics and Sources
Year Variable Data Source
1820 persons engaged in manufacturing 1820 Census
1830 - -
1840 total capital invested in manufacturing 1840 Census
1850 annual value of products in manufacturing 1850 Census
1860 annual value of products in manufacturing 1860 Census
1870 annual value of products in manufacturing 1870 Census
1880 annual value of products in manufacturing 1880 Census
1890 annual value of products in manufacturing 1890 Census
1900 annual value of products in manufacturing 1900 Census
1910 - -
1920 annual value of products in manufacturing 1920 Census
1930 annual value of products in manufacturing 1930 Census
1940 annual value of products in manufacturing 1940 Census
1950 value added by manufacture in 1947 1952 County and City Data Book
1960 value added by manufacture in 1958 1967 County and City Data Book
1970 value added by manufacture in 1972 1977 County and City Data Book
1980 value added by manufacture in 1982 1988 County and City Data Book
1990 value added by manufacture in 1987 1994 County and City Data Book
2000 value added by manufacture in 2002 2002 Economic Census
2010 value added by manufacture in 2007 2007 Economic Census
23
Table 3.
Gini Coefficients for the Density of Economic Activity
Year Population Agriculture Manufacturing
1820 0.604 0.449 0.825
1830 0.645
1840 0.640 0.453 0.917
1850 0.661 0.465 0.950
1860 0.701 0.409 0.943
1870 0.690 0.454 0.946
1880 0.708 0.441 0.960
1890 0.731 0.411 0.964
1900 0.734 0.390 0.949
1910 0.762 0.359
1920 0.819 0.381 0.962
1930 0.830 0.366 0.963
1940 0.827 0.387 0.957
1950 0.835 0.455 0.953
1960 0.834 0.441 0.937
1970 0.831 0.463 0.898
1980 0.804 0.481 0.879
1990 0.802 0.503 0.870
2000 0.796 0.523 0.811
2010 0.794 0.556 0.798
24
Table 4.
Spearman's Rank Correlations
Population
1820 1890 1950
1830 0.959
1840 0.870
1850 0.803
1860 0.722
1870 0.638
1880 0.610
1890 0.553
1900 0.506 0.977
1910 0.474 0.923
1920 0.452 0.864
1930 0.425 0.807
1940 0.404 0.779
1950 0.418 0.761
1960 0.436 0.747 0.979
1970 0.451 0.715 0.925
1980 0.452 0.684 0.895
1990 0.471 0.661 0.858
2000 0.478 0.637 0.823
2010 0.488 0.627 0.811
Agriculture
1820 1890 1950
1840 0.696
1850 0.633
1860 0.505
1870 0.404
1880 0.388
1890 0.368
1900 0.357 0.934
1910 0.346 0.873
1920 0.298 0.744
1930 0.264 0.652
1940 0.308 0.716
1950 0.318 0.696
1960 0.272 0.653 0.934
1970 0.185 0.572 0.863
1980 0.135 0.511 0.827
1990 0.121 0.497 0.793
2000 0.109 0.423 0.699
2010 0.068 0.426 0.690
25
Manufacturing
1820 1890 1950
1840 0.648
1850 0.686
1860 0.635
1870 0.562
1880 0.570
1890 0.501
1900 0.477 0.893
1920 0.352 0.751
1930 0.327 0.738
1940 0.367 0.744
1950 0.423 0.722
1960 0.425 0.714 0.880
1970 0.410 0.649 0.805
1980 0.403 0.603 0.748
1990 0.367 0.565 0.682
2000 0.359 0.569 0.701
2010 0.391 0.622 0.759
26
Figure 1. Gini Coefficients for Population Density
Figure 2. Gini Coefficients for Manufacturing Density
Figure 3. Gini Coefficients for Agricultural Density
Figure 4. Agriculture-Dense Counties in 1820
0.6
0.65
0.7
0.75
0.8
0.85
1820 1840 1860 1880 1900 1920 1940 1960 1980 2000
Year
27
Figure 2. Gini Coefficients for Manufacturing Density
0.75
0.8
0.85
0.9
0.95
1
1820 1840 1860 1880 1900 1920 1940 1960 1980 2000
Year
28
Figure 3. Gini Coefficients for Agricultural Density
0.35
0.4
0.45
0.5
0.55
0.6
1820 1840 1860 1880 1900 1920 1940 1960 1980 2000
Year
29
Figure 4. Agriculture-Dense Counties in 1820
30
Figure 5. Manufacturing-Dense Counties in 1820
31
Figure 6. Agriculture-Dense Counties in 1890
32
Figure 7. Manufacturing-Dense Counties in 1890
33
Figure 8. Agriculture-Dense Counties in 1950
34
Figure 9. Manufacturing-Dense Counties in 1950
35
Figure 10. Agriculture-Dense Counties in 2010
36
Figure 11. Manufacturing-Dense Counties in 2010
37
Figure 12. Correlation Coefficients Between Agricultural and Manufacturing Densities
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1820 1840 1860 1880 1900 1920 1940 1960 1980 2000
Year