EXPLORATIONS
IN ECONOMIC
HISTORY
26, 219-236
(1989)
Debt Peonage in Postbelium Georgia*
V.
FISHBACK?
of Economics,
University
PRICE
Department
of Georgia
Descriptions of the postbellum South vary. Farmers either were postharvest
debt peons, were subject to “seasonal credit peonage” to a monopolistic store,
or relied on seasonal credit from stores that faced spatial competition. Analysis
of Georgia Agricultural Department data shows that postharvest debt peonage
was not a major problem in the 1880s. Most other results are consistent with
both monopolistic and competitive views of the stores. Increases in income reduced indebtedness; and reliance on purchased supplies increased the likelihood of
indebtedness and of future reliance on purchased supplies. Past reliance on purchased supplies, however, did not affect the crop mix. ot989 Academic press, hc.
“Debt peonage” has been a controversial topic in the study of the
postbehum South. Definitions of the term vary, and scholars disagree
on the extent to which farmers were locked into debt during the late
1800s. The brief discussions of debt peonage by Wiener (1979, p. 979),
Woodward (1951, p. 181), and Daniel (1972, p. 24) imply that tenants
and small farmers were bonded to the same lender because they were
unable to pay off their debts at the end of the harvest.’ Ransom and
Sutch (1977, pp. 163-164; 1975, p. 406; and 1979) use the term debt
peonage but actually discuss a different form of lock-in mechanism, more
aptly described as “seasonal credit peonage.” Small farmers and tenants
typically paid out at the end of the harvest but rarely could avoid buying
on credit the next year. Their only source of credit was a local merchant,
who required them to plant cotton and therefore remain reliant on his
* Helpful comments and criticisms on earlier drafts of the paper were provided by Lee
Alston, Cletus Coughlin, Claudia Goldin, Robert Higgs, Robert M&&ire, Anthony O’Brien,
Dan Slesnick, Richard Sutch, John Wallis, Ron Warren, Paul Wilson, and two anonymous
referees. Ridge Ashworth, Mary Evans, and Hunter Sheridan provided research assistance.
AI1 errors are my own.
i Visiting at Department of Economics, University of Texas, Austin, TX 78712.
i Daniel notes that the ‘legal definition of debt peonage was even narrower. “‘Peonage
rested on debt, but the debtor had to be restrained for the legal definition to be fulfilled”
(1972, p. 24).
219
00144983189 $3.00
Copyright
0 1989 by Academic
Press, Inc.
AU rights of reproduction
in any form reserved.
220
PRICE V. FISHBACK
store for credit purchases. Other scholars, including Brown and Reynolds
(1973), FitzRandolph
(1980), Higgs (1977), Lebergott (1984, pp. 263-267),
Goldin (1979), and Temin (1979), argue that descriptions of debt or seasonal
credit peonage are misleading. Country stores faced more competition
and farmers and tenants had more credit options than Ransom and Sutch
suggest.
Despite all the discussion, relatively little empirical work has been
done on indebtedness and its determinants. The Georgia Department of
Agriculture published information
on changes in indebtedness after the
harvest payout during the 1880s. Although briefly discussed by Goldin
(1979) and Ransom and Sutch (1979), the data can be used more fully
to shed light on postbellum farming in Georgia. Since information is not
available on the extent of supply purchases on credit, Ransom and Sutch’s
hypothesis about seasonal credit peonage can be tested only indirectly.
The data do offer an opportunity
to directly examine the portrayal of
farmers locked into debt after the harvest payout. Debt peonage implies
little change in indebtedness from year to year, strong cross-sectional
relationships between indebtedness and prior indebtedness, and a major
impact of indebtedness on crop-mix decisions and reliance on purchased
supplies. Analysis of county-level survey data from Georgia in the 1880s
supports none of these implications,
suggesting that postharvest debt
peonage played no major role in postbellum Georgia agriculture.
I. CHANGING
INDEBTEDNESS
OF GEORGIA FARMERS,
1883 TO 1887
If postharvest debt peonage was a common problem, indebtedness
would have stayed relatively constant or possibly risen in most Georgia
counties. The extent of changes in indebtedness within counties between
1883 and 1887 is examined with evidence from the Georgia Department
of Agriculture. During the late 1870s and the 1880s the Georgia Department
of Agriculture surveyed a corps of agricultural correspondents (several
in each of Georgia’s 137 counties), who were “carefu.hy selected with
reference to their intelligence, practical experience, and sound judgment”
(Ransom and Sutch, 1977, p. 128, quoting Janes, 1875, p. 130). One
question asked correspondents
to “give the indebtedness of farmers
compared to last year, per cent.” We can infer from context that the
term refers to indebte,dness in December after farmers used harvest
earnings to repay merchants for seasonal credit.* The data are imperfect
* The context of discussion of indebtedness in the 1879 report (p. 5) suggests that
indebtedness was caused by failure of farmers to produce their own supplies. Two added
facts suggest that the debt described was after the harvest rather than the amount farmers
owed prior to the harvest. First, the crop surveys were filled out in early to mid December,
well after the end of the harvest and the payout period (credit prices were payable November
1). Second, the Department of Agriculture (April 1878, April 1879, April 1881) sporadically
DEBT PEONAGE
IN GEORGIA
221
because they are based on the impressions of the correspondents, and
indebtedness is not precisely defined. Indebtedness may refer to the
amount of indebtedness or to the number of farmers in debt. Despite
potential measurement error, the data are worth examining because they
represent the most comprehensive evidence on changing indebtedness
at that time.
While discussing a wide range of issues, Goldin (1979, pp. 20-22) and
Ransom and Sutch (1979, pp. 82-85) briefly described the changes in
indebtedness reported by the Georgia Department of Agriculture. Goldin
showed a large cumulative decline in indebtedness in Georgia between
1879 and 1889 based on aggregate state averages. Ransom and Sutch,
using county data on yearly changes in indebtedness for the years 1884
to 1887, emphasized that in any one year many Georgia counties experienced no change or increases in indebtedness. -4 more complete
picture of changing indebtedness is developed by combining Goldin’s
interest in cumulative declines with Ransom and Sutch’s emphasis on
county-level data.
Table 1 shows that most counties experienced cumulative declines in
indebtednessbetween 1883and 1887.The first column shows the frequency
distribution of farm indebtedness in 1887as a percentage of indebtedness
in 1883 for the 48 Georgia counties in which indebtedness was reported
in all 4 years. In 58% of the counties indebtedness in 1887was less than
80% of its 1883 level; less than 100% of the 1883 level in 79% of the
counties. Although missing data in any one year omit many counties
from the distribution in column one, the 48-county sample seems representative of all counties reporting data. In each year the means from
the 48-county sample are similar to the means for all counties reporting
data that year. Column 2 shows the frequency distribution when, following
Goldin (1979), 100% is inserted for the missing data in the counties left
out in column 1. Insertion of 100% probably leads to underestimation
of’the cumulative decline in indebtedness becausethe median indebtedness
ratio was 95%. The frequency distribution in the second Golumn still
shows declining indebtednessover time. In the median county, indebtedness
in 1887was 80.4% of the 1883level. In 48% of the counties indebtedness
in 1887 was less than 80% of its 1883 level; in 6% of the counties de
in 1887 was lower than its 1883 level.
In most Georgia counties during the mid-1880s, the debt lock-in faile
to prevent reductions in indebtedness. Part of the reason was income
grow&, as the per capita real value of corn and cotton crops in Georgia
asked questions about the extent to which supplies were purchased on time (Goldiu, 1979,
p. 19). Ef indebtedness daes refer to debt prior to harvest, the directions of change described
in section II are unaffected and the data are a more direct test of Ransom and Sutch’s
hypothesis.
222
Frequency Distributions
PRICE V. FISHBACK
TABLE 1
of Indebtedness in Georgia Counties, Ratio of Debt in 1887 to
Debt in 1883
Debt in 1887
Debt in 1883
Counties where
debt ratios reported
all 4 years
All counties
with 1.0 inserted
for missing values
Less than 0.10
0.10-0.20”
0.20-0.30
0.30-0.40
0.40-0.50
0.50-0.60
0.60-0.70
0.70-0.80
0.80-0.90
0.90-1.00
1.00-1.10
1.10-1.20
1.20-1.30
1.30-1.40
1.40-1.50
1.50-1.60
1.60-1.70
Greater than 1.70
Number of counties
0 %
6.25
0
6.25
4.17
12.50
12.50
16.67
8.33
12.50
8.33
6.25
2.08
2.08
0
0
2.08
0
48
0.76%
3.79
0.76
3.03
4.55
il.36
10.61
13.64
9.85
10.61
9.85
10.61
3.79
3.03
0.76
0.76
0.76
1.52
132
Note. Table entries are percentages of the total number of counties. Five counties are
left out of column 2 due to missing data in all 4 years.
Sources. Georgia Department of Agriculture, Supplemental Reports, for the years 1884,
1885, 1886, and 1887.
a Read as greater than or equal to 0.10 and less than 0.20.
rose 21.5 to 27.9% and 28.9 to 35.7%, respectively, between 1883 and
1887.3 The relationship between income and indebtedness at the county
level is explored more fully in the next section. It is possible that debt
peonage was present in some Georgia counties, as about one-fifth of the
counties experienced either no decline or an increase in indebtedness.
Whether the lack of decline in debt in those counties was caused by
debt peonage or other factors is exdmined with regression analysis of
the impact of previous indebtedness on current indebtedness.
3 The growth rates of Georgia real crop value per capital for corn and cotton were based
the following data: total output of cotton (U.S. Department of Agriculture, 1955, p.
10); total output of corn (USDA, 1907, pp. 16-18); cotton and corn prices received by
farm producers (USDA, 1927, p. 106); price deflators with base years (1910-1914 and
1899-1908 (Ransom and Sutch, 1977, pp. 258-259). Georgia population estimates (including
rural and urban persons) for 1883 and 1887 are interpolations between the 1880 and 1890
populations (U.S. Bureau of Census, 1895, p. 14).
on
DEBT PEONAGE
223
IN GEORGIA
II. DETERMINANTS
OF FARM INDEBTEDNESS,
PURCHASED,
AND CROP CHOICE
SUPPLIES
The extent to which Georgia farmers were locked into postharvest
indebtedness is examined with the equation below.
D, = a,(Y,)“‘(CORYCOTY,)“Z(D,- J3
(1)
(SP,)“4(CREDCASH,)“5e”‘,
where
D, is indebtedness of farmers in December of year t.
D,-, is indebtedness of farmers in December of the previous year.
Y, is farm income in year t.
CORYCOTY, is the ratio of corn output to cotton output at the end
of the harvest in year t.
SP, is the amount of supplies purchased by farmers in year t.
CREDCASH, is the ratio of credit to cash prices for corn and bacon
in year t.
II, is a random error term including other factors,, for which information
is unavailable, including the interest rate on debts after the harvest.4
The key tests for debt peonage concern the coefficients of the previous
year’s indebtedness and the ratio of corn output to cotton output. Without
debt peonage, just the mechanics of repaying debt imply that a farmer
in debt in year t - 1 is more likely to incur debt in year t. Postharvest
debt peonage implies a much stronger relationship. The essence of debt
peonage implies that once tenants and smaller farmers were in debt they
remained locked into debt after the harvest payout year after year. The
direct manifestation of debt peonage therefore is a strong relationship
between indebtednessin year t and indebtednessin year t - 1. If postharvest
debt peonage was important in postbellum Georgia, we should see a
positive and economically significant coefficient of lagged indebtedness
in Eq. (1).
In discussing “seasonal credit peonage,” Ransom and Sutch suggest
that the merchant’s requirement that tenants and small farmers plant
cotton rather than corn forced them to rely on the merchant for seasonal
credit. This reasoning can be extended to the discussion of postharvest
debt peonage. Greater reliance on seasonal credit ‘increases the 81ikelihood
that the farmer will be in debt after the harvest.
4 The credit-cash price ratio is not a measure of the actual interest on indebtedness
after the harvest. The interest rate for debt at the end of the year is unclear. Goldin (1979,
p. 22) and others claim that. some store owners extended the normal period of the loan
beyond the end of the harvest at no explicit interest rate. k?anspm and Sutch claim that
such extensions were made with an explicit interest rate attached.
224
PRICE V. FISHBACK
Whether peonage is present or not, indebtedness is negatively associated
with income and positively associated with supplies purchased. Southern
farmers had low enough wealth levels that they could be forced into debt
by unexpectedly low crop prices and yields, while good years allowed
them to avoid debt. The farmer could not go into debt to the store
without purchasing supplies, so that increases in supplies purchased
increase the risk of indebtedness after the harvest payout. Ideally, we
would use supplies purchased on credit instead of total supplies purchased,
because cash purchases do not necessarily put farmers at risk of going
into debt at the store. Total supplies purchased is a reasonable proxy
for supplies purchased on credit to the extent that per capita wealth
differs little across counties.5
The sign of the coefficient of the credit-cash price ratio is uncertain
a priori. Farmers were likely to try to avoid credit purchases, and therefore
incur a lower probability
of being in debt, when the credit-cash price
ratio was higher. Conversely, an increase in the credit-cash price ratio
caused by an exogenous increase in credit demand would cause the
merchant, whether monopolist or perfect competitor, to provide more
credit, increasing the potential for postharvest debt. For those who purchased on credit, higher interest rates increase the amount to be repaid
and therefore make it harder to avoid postharvest debt.6
The coefficient on lagged indebtedness offers a direct measure of the
impact of postharvest debt peonage, but it may not fully measure the
impact of prior debt on current indebtedness. If debt peonage were
present, it also might have operated through indirect mechanisms. Once
in debt, a farmer’s decisions about his crop mix and supply purchases
were subject to the demands of his creditor. The creditor might press
for a crop mix and supply purchases that keep the farmer in debt, while
allowing the creditor to capture the gains from the farmer’s efforts. The
indirect mechanisms are examined with empirical equations that show
the impact of past indebtedness on the farmers’ supply purchases and
his crop mix choices.
The extent to which farmers purchased supplies is a function of the
following factors:
5 Differences in wealth reduce the correlation between total supplies purchased and
credit purchases because wealthier farmers could purchase more supplies without increasing
supplies purchased on credit.
6 The predicted sign becomes more complex when changes in credit risk affect the
credit-cash price ratio. An increase in the riskiness of extending credit to farmers might
cause the merchant to increase the credit-cash price ratio, while reducing the amount of
credit. The increase in risk of repayment might also cause the farmer to increase the
amount of credit he seeks. Ransom and Sutch (1975, p. 414, note 21) suggest another
complication in signing a,. The merchant might miscalculate the target level of indebtedness
and retroactively adjust the debt downward if unforeseen events caused indebtedness to
be too high. This would cause merchants to reduce the debt if interest rates rise.
DEBT PEONAGE
IN GEORGIA
225
SP, = c,(Y,)“‘(CORYCOTY,-,)“(D,-,)“3(SP,-,)”e”’
VI
The effect of income on supplies purchased is uncertain. Farmers with
higher incomes may purchase more supplies because they consume more,
but they also may purchase fewer supplies to the extent that they produce
more of their own supplies. More emphasis on corn ‘in the crop mix of
the previous year leads to lesser reliance on purchased supplies in the
current year. If merchants locked farmers into debt, their control of the
cropping decisions forced farmers to rely on purchased supplies, leading
the coefficient on lagged indebtedness to be positive. The lagged value
of supplies purchased is included to consider the Ransom and Sutch
seasonal credit lock-in mechanism. To the extent that supplies purchased
are closely correlated with supplies purchased on credit, a strong positive
coefficient on lagged supplies purchased is consistent with Ransom and
Sutch’s view that farmers were locked into reliance on seasonal credit,
However, even without peonage, farmers would continually rely on purchased supplies if specializing in cotton was more profitable,
Prior indebtedness may have influenced crop-mix decisions, because
the creditor with a crop lien could tell the farmer what to plant. The
following equation shows the determinants of the ratio of corn output
to cotton output after the harvest.’ It reflects the combined decisions by
small farmers, tenants, merchants, and landlords about the crop mix.
CORYCOTY, = b0 E(FGCORCOT,)b’ E(CCREDCASH,+ l)b*
(II- ,)a(SP,- ,)b4evt,
(3)
where
E(FGCORCOT,) is the farmer’s expectation at the time of planting of
the ratio of farmgate prices of corn and cotton at the end of the harvest
in year t.
E(CCREDCASH,+ ,) is the farmer’s expectation at the time of planting
of the ratio of the credit price of corn to the cash price of corn in year
t + 1. The ratio is a measure of the implicit interest on credit purchases
of corn supplies.
v, is a random variable reflecting other factors not considered explicitly,
including weather, relative costs of production, and riskiness and variability
of output and prices.’
7 Ideally, the relationship between indebtedness and acreage planted would be considered.
See Wright and Kunreuther (1975), and McGuire and Higgs (1977)). Unfortunately, data
on cotton and corn acreage was unavailable for the years in which indebtedness was
available.
* See Sands (1986) mimeo for a comparison of the effectiveness of the safety-first models,
(Wright and Kunreuther, 1975) and mean-variance models (McGuire and Higgs, 1977) in
exp1alning acreage allocation in Georgia.
226
PRICE V. FISHBACK
Ransom and Sutch and Goldin each predict a negative sign for the
coefficient of lagged indebtedness, although for different reasons. Through
crop liens the merchant had control over the farmers’ cropping decisions
when the farmer was in debt to him. Ransom and Sutch (1975, 1977)
argue that merchants with local monopolies on credit forced farmers to
produce less corn and thus ensured that the farmer remained in debt or
continued to buy from him on credit. In contrast, Goldin (1979, pp. 2627) argues that even without a credit monopoly the merchant sought
cotton production from debtors and potential borrowers. The cost and
risk of reselling cotton was substantially lower than that of reselling
grains.
To the extent that supplies purchased is a reliable proxy for supplies
purchased on credit, Ransom and Sutch also would predict a negative
sign for the coefficient of lagged supplies purchased. If the farmer expected
to borrow from a monopolistic
merchant, he would have to accede to
the merchant’s demands by planting less corn and thus risk perpetual
reliance on seasonal credit with that merchant. Goldin and others emphasize
spatial competition
among stores after the harvest payout that would
weaken the stores’ control over the farmers’ crop-mix decisions when
negotiating for credit and thus the impact of lagged supplies purchased
on the crop mix.
Ransom and Sutch expect the crop mix to be highly responsive to the
relative farmgate prices of corn and cotton but not to the interest rate
implicit in the credit-cash price ratio for corn.’ They argue that small
farmers bought most of their supplies on credit (1979, p. 86) and that
merchants controlled most of the cropping decisions. The merchants
pushed farmers to produce an output ratio consistent with the farmgate
price ratio of corn and cotton, and then set credit prices to capture a
substantial portion of the farmers’ incomes above subsistence. Small
farmers were better off producing less cotton and more corn to avoid
paying higher implicit interest costs at the stores. With merchants determining the cropping decisions, Ransom and Sutch expect a strong
positive coefficient on the farmgate price ratio and an uncertain sign for
the coefficient of the credit-cash price ratio for corn.
Goldin, Temin, Higgs, and FitzRandolph emphasize spatial competition
among stores and that farmers were less dependent on the stores for
credit. As sellers of corn and cotton, farmers were sensitive to the relative
farmgate prices of corn and cotton. Their sensitivity to the credit interest
rate depends on how much corn and other supplies they purchased on
credit. With higher credit interest rates farmers might produce relatively
more corn to reduce the risk of purchasing on credit. However, Goldin
9 Estimating the equations with price levels rather than ratios does not change the basic
results of the analysis.
DEBT PEONAGE
IN GEORGIA
227
suggeststhat most farmers were purchasing less than 20% of their supply
needs on credit, which lessens the impact of the credit interest rate on
their crop-mix choices.” The choice of crop mix is complicated further
for tenants, who were the most likely farmers to require seasonal credit.
The landlord had significant say about the tenant’s crop mix. Since the
landlord was less likely to purchase on credit, he was more likely to
respond to the farmgate prices of corn and cotton and not to credit
interest rates.
Effective modeling of crop-mix decisions requires assumptions about
formation of price expectations. Lack of data precludes development of
complicated descriptions of how farmers (and merchants) formed price
expectations. One way to model expectations is to assume that Georgia
farmers had perfect foresight and then substitute the actual prices for
the expected prices in Eq. (3). Perfect foresight seems an unreasonable
assumption given the uncertainty of price fluctuations during the period.
At the other extreme we can assume that Georgia farmers saw prices
as a random walk.” Their best guess about the upcoming price then
would be the price they most recently witnessed. When the farmer made
his cropping decisions in the winter and spring of year t, he used the
farmgate prices at the December harvest of year t- 1. His best estimate
of the store’s cash and credit prices in year t-t 1 would have been the
ones he saw in year t.
Ideally, the indebtedness and corn-cotton ratio equations wonld be
estimated with direct information on levels. Unfortunately, the Georgia
data are reported only as indebtedness in year t relative to indebtedness
in year t - 1. Given the multiplicative form of the equations, the coefficients
in Eqs. (l), (2), and (3) can be estimated by dividing each equation by
its lagged value. Assuming that the coefficients of the model do not
” Goldin (1979, pp. 19-21) and Ransom andSutch (1979, p, 82) use the same 60% figure
from the Georgia Department of Agriculture April Crop report of 1879 to come up with
quite different pictures of the extent to which farmers purchased supplies on credit. The
answer depends on how precisely the writer of the Georgia crop reports used the English
language. Ransom and Sutch interpreted the following table title in the April Crop Reports,
“per cent. of farm supplies purchased on time,” to mean the percent #of UN supplies
purchased on time. Thus they suggest that farmers purchased 60% of all their supplies on
time. Reading the commissioner’s discussion of the table, Goldin interpreted the 60% figure
using the following statement, “the amount of farm supplies being purchased by farmers
this year compared with last is reported 22 percent less, and of this amount 60 percent
are purchased on time.” She argues that the statement implies that 60% of the supplies
purchased by farmers were purchased on time. Further calculations on this basis imply
that a farmer in Georgia could have expected to purchase only 18% of his total supplies
on time.
” McGuire’,s (1980) research on farm prices suggests that price tIuctuations probably
were not a random walk, but given the lack of data a less rudimentary depiction of price
expectations is not possible.
228
PRICE V. FISHBACK
change from year to year, the equations to be estimated (assuming the
random walk formulation
of price expectations) become12
-=D*
0-1
(4)
sp,=
sp*-1
CORYCOTY,
CORYCOTY,-
(5)
(6)
I =
III. DATA AND TEST RESULTS
Equations (4) through (6) make up a system of equations with three
endogenous variables in time t: current indebtedness relative to the year
before, supplies purchased relative to the year before, and the comcotton output ratio relative to the year before. If the error terms in the
three equations are correlated, ordinary least-squares coefficient estimates
are subject to simultaneity bias. To avoid possible biases, the equations
are estimated with two-stage least-squares.
The equations are estimated with pooled data for 137 Georgia counties
for the years 188.5-1887 from the Georgia Department of Agriculture’s
Supplemental Reports. Note that the regressions use annual data, unlike
the data in Table 1, which describes cumulative changes in indebtedness
between 1883 and 1887. The data are answers to survey questions by
the corps of agricultural correspondents described in the first section.
Appendix Table 1 describes the construction of the variables from the
information reported in the Georgia reports. Missing values cut the sample
sizes to the levels seen in Tables 2 through 4. The indebtedness and
supplies-purchased equations are estimated using two sets of proxies for
the farmer’s income. In the first column of Tables 2 and 3 the corre‘* Greater indebtedness might also be associated with more blacks in the population,
lower wealth per capita, more share tenancy, smaller farm sizes, lower population densities,
and/or land that is more productive for cotton. Unfortunately, information is available
only on changes in indebtedness in the mid 1880s and not on the level of indebtedness,
and data on these other characteristics are available only for census years.
DEBT PEONAGE
229
IN GEORGIA
TABLE 2
Two-Stage Least-Squares Regression Estimates, Determinants of Farmer Indebtedness
Relative to Previous Year, Pooled Dam: Georgia Counties for the Years t = 1885-1887”
Dependent variable is indebtedness of farmer compared to previous year
Variable
Farm conditions compared with last year
(1)
-0.14*
(1.88)
Tatal cotton income relative to previous year
Total corn income relative to previous year
Supplies purchased, compared with last year
CREDCASH,/CREDCASH,-,
CORYCOTY,/CORYCOTY,-,
Dr-ID-Z
RZ
F value
Number of counties
(2)
0.63”
(5.17)
-0.07
(0.34)
-0.01
(0.05)
0.01
(0.12)
0.23
8.28
143
-0.01*
(0.03)
-0.21
(0.84)
0.57*
(4.39)
-0.20
(00.81)
0.229
(0.72)
- 0.003
(0.03)
0.17
4.74
143
Note. All variables are in log form. All F values reject the null hypothesis that ail the
coefficients are simultaneously zero at the 95% level. The results assume random-walk
expectations in Eq, (6). Assuming perfect foresight did not change the qualitative resuits.
a Student’s t statistics in parentheses.
* Significantly different from zero at the 98% level in a two-tailed test.
spondents’ estimates of “condition of farmers compared to the previous
year” are used as a general measure of the fortunes of farmers. In the
second column farm income is represented by two variables, the real
farmgate value of cotton output relative to the previous year and the
real farmgate value of corn output relative to the previous year. One
caveat before proceeding, the data may contain substantial measurement
error. As noted for the indebtedness variable in section I, the variables
in the regression equations are based on survey questions answered by
crop correspondents. Although numerical, the variables depict the correspondents’ impressions of not very precisely defined factors, like indebtedness, supplies purchased, and farm output, all relative to the previous
year.
The results do not support a view of farmers locked into postharvest
indebtedness season after season. Use of the log-log form allows us to
interpret the coefficients in the equations as elasticities. Had perpetual
indebtedness been a major problem among small farmers the elasticity
of lagged indebtedness in Table 2 would be large and positive and the
230
PRICE V. FISHBACK
TABLE 3
TwoStage Least-Squares Regression Estimates, Dete rminants of Total Supplies Pnrchased
Relative to Previous Year, Pooled Data: Georgia Counties for the Years t = 1885-1887”
Dependent variable is supplies purchased, compared to previous year
Variable
Farm conditions compared with last year
(1)
-0.02
(0.24)
Total cotton income relative to previous year
Total corn income relative to previous year
sp*-,/sp,-2
CORYCOTY,-,/CORYCOTY,-,
Dr-,/Q-z
I?
F value
Number of counties
(2)
0.28*
(3.37)
0.02
(0.20)
-0.002
(0.02)
0.09
3.61
143
-0.17
(1.30)
0.20
(1.47)
0.29”
(3.49)
0.08
(0.72)
0.01
(0.09)
0.11
3.47
143
Note. All variables are in log form. All F values reject the null hypothesis that ah the
coefficients are simultaneously zero at the 95% level. The results assume random-walk
expectations in Eq. (6). Assuming perfect foresight did not change the qualitative results.
a Absolute value of Student’s I statistics in parentheses.
* Significantly different from zero at the 90% level in a two-tailed test.
elasticity of the crop-mix variable large and negative. Instead, in Table
2 the elasticity of the crop-mix variable is either small when negative or
large and positive, although in neither case was it precisely estimated.
The elasticity of lagged indebtedness is very small at 0.01 and -0.003,
and we cannot reject the null hypothesis that the elasticity is zero.
Further, lagged indebtedness has little indirect impact on current indebtedness through supplies purchased and the crop mix. The elasticities
of lagged indebtedness in the supplies-purchased and crop-mix equations
are also small and imprecisely estimated. Summing all direct and indirect
effects using the elasticities in column 1 of each table, the total elasticity
of current indebtedness with respect to lagged indebtedness was very
small at 0.011 .13
Although postharvest indebtedness does not play a major role, the
results suggest that reliance on supplies purchased had strong impact on
indebtedness and was strongly correlated from year to year. Supplies
purchased increased the likelihood of indebtedness with an elasticity of
between 0.57 and 0.63 in Table 2. The elasticities of supplies purchased
I3 The total elasticity including direct and indirect effects is calculated as a3 + a,c, +
a&, using the coefficients in Eqs. (4) through (6).
DEBT PEONAGE
IN GEORGIA
231
TABLE 4
Two-Stage Least-Squares Regression Estimates, Determinants of Corn Output Relative
to Cotton Output, Pooled Data: Georgia Counties for the Years 1885-1887”
Dependent variable is CORYCOTY,/CORYCOTY,-,
Expectations assumption
Random walkb
FGCORCOT,_ ,/FGCORCOT,+,
Random walk
FGCORCOT,/FGCORCOT,,
Perfect foresight
CCREDCASH,/CCREDCASH,Dt-I/D,-2
spr-,/sp,-2
RZ
F value
Number of counties
Perfect foresight’
0.29*
(2.74)
,
0.07
(0.48)
-0.05
(0.70)
0.03
(0.44)
- 0.25%
(2.33)
0.09
(0.57)
- 0.05
(0.81)
0.01
(0.20)
0.06
0.05
2.26
143
1.71
144
Note. All variables are in log form. The F value for the random-walk model is statistically
significant at the 90% level; for the perfect foresight model it is not statistically significant
at the 90% level.
a Absolute value of Student’s t statistics in parentheses.
b Assumes that the farmer’s predictions of the farmgate prices of cotton and corn are
the farmgate prices from December of the previous year.
’ Assumes that the farmer accurately predicts the farmgate prices of cotton and corn
in December of year t.
* Significantly different from zero at the 90% level in a two-tailed test.
with respect to lagged supplies purchased in Table 3 were smaller at
0.28 and 0.29, These results are consistent with the expectations of both
Ransom and Sutch and their critics. Some choice between the two models
is possible to the extent that supplies purchased are a proxy ,for supplies
purchased on credit. Ransom and Sutch’s credit lock$n mechanism suggests
a negative relationship between lagged supplies purchased on credit and
the corn-cotton output ratio. In fact, the elasticity in Table 4 is positive,
although small and imprecisely estimated, which is more consistent with
the weaker relationship expected by those who emphasize ,spatial competition among stores. Ransom and Sutch (1979, p. 82) have argued,
however, that county-level data may be deceiving on this point because
larger farms within the county may have produced the corn to supply
the smaller farmers.
The elasticities of the income variables in Table 2 offer some evidence
that increases in income helped pull farmers out of debt. The elasticity
of the farm conditions variable in column 1 is negative and precisely
232
PRICE
V. FISHBACK
estimated, such that a 1% improvement in farm conditions was associated
with a 0.14% decline in indebtedness. When the income measure is
separated into corn and cotton components, the elasticities remain negative.
The corn income elasticity is larger in absolute value, but neither elasticity
is precisely estimated.
Indebtedness also declined slightly with increases in the interest rate
on credit purchases, although the elasticities are imprecisely estimated
in Table 2. It appears that when interest rates rose, reductions in indebtedness caused by a decline in the farmers’ demand for credit purchases
slightly more than offset increases caused by greater supplies by merchants
or the simple mechanics of repaying credit with higher interest rates.
Table 4 shows the effects of relative prices on the corn-cotton output
ratio, using both the random walk and the perfect-foresight formulations
of price expectations. The equations explain less than 10% of the variation
in the dependent variable. This is not surprising because many factors
are omitted that may determine the cropping decisions, including relative
costs of production, relative soil fertility, and the farmer’s expectations
of variability in output and price. Further, we have no measure of weather
fluctuations and other factors that would cause a divergence between
what the farmer expects when planting and,what is actually harvested.
The random walk formulation of the model is generally consistent with
expectations. The relative farmgate prices of cotton and corn are positively
related to the corn/cotton
output ratio. The elasticity is 0.29 and is
precisely estimated. The elasticity of the credit/cash ratio of corn prices
is relatively small and positive, but imprecisely estimated. The combination
of price-ratio elasticities are consistent with several competing descriptions
of the postbellum South. In Ransom and Sutch’s description the merchant
forced the farmers to plant in response to the farmgate prices of corn
and cotton and limited the farmer’s response to implicit interest rates.
Views that emphasize more competition among stores suggest that farmers
focused primarily on the farmgate prices because they did not purchase
large percentages of their corn needs on credit. We would also see this
result for tenants to the extent that landlords controlled the cropping
decisions.
The results from the perfect foresight model are similar to those for
the random walk model with one exception. The relative farmgate prices
have almost exactly the opposite effect. The elasticity is -0.25 and is
precisely estimated. This result seems very odd since there is no economic
agent at the time that would benefit from such an inverse relationship.
Even if store owners controlled the cropping decisions, they would not
be maximizing profits under this scenario. The odd result may stem from
the unreasonableness of assuming perfect foresight in a situation where
there was substantial price uncertainty.
DEBT
PEONAGE
IN GEORGIA
233
IV. CONCLUSION
Study of the Georgia Department of Agriculture data offers a tentative
picture of the role of postharvest indebtedness in southern agriculture
during the 1880s. The portrayal should be considered tentative because
of potential measurement error in the variables. To the extent that measurement error does not cause serious biases, the analysis suggests that
farmers were not perpetually in debt after the harvest in the 1880s. In
most Georgia counties there were substantial declines in indebtedness
between 1883 and 1887. Pooled analysis of data on Georgia counties
shows no strong direct relationships between indebtedness the previous
year and current indebtedness. Nor were there strong effects of lagged
indebtedness on supplies purchased and the crop mix.
Other results suggest that increases in income helped bring farmers
out of debt, and that the crop mix was sensitive to the relative farmgate
prices of corn and cotton but not to the interest rate on credit. Reliance
on purchased supplies increased the chance of indebtedness and farmers
who purchased supplies in one year were more likely to purchase supplies
the following year. Generally, these results are consistent with both
Ransom and Sutch’s view of monopolistic stores and their critics’ emphasis
on competition. One result might allow a choice between the two views
if supplies purchased is an adequate proxy for supplies purchased on
credit. Contrary to what Ransom and Sutch might predict, increased
supplies purchased the previous year did not lead farmers to produce
more cotton and less corn. It is not clear how strong this result is,
hovvever, because the data are county aggregates and supplies purchased
may not be an adequate proxy for supplies purchased on credit.
The absence of perpetual indebtedness after the harvest sharpens the
focus in the debate between Ransom and Sutch and their critics. Did
the stores have a monopoly over seasonal credit or did farmers have
alternatives? Ransom and Sutch argue that generally one merchant was
the only source of credit to the farmer, To obtain credit, the farmer was
forced by the merchant to plant cotton and therefore ‘remain dependent
on seasonal credit, Scholars who emphasize spatial competition argue
that the farmer faced alternative sources of credit at the begiming of
each sqason, either from local sources or by moving to other areas. If
the farmer faced such alternatives, he would not be locked in to one
merchant unless he’was actually ‘in debt after the harvest. Since farmers
were not perpetually in debt after the harvest, the lock-in mechanism
did not exist if the farmer was able to choose among sources df credit.
The answer to the debate then rests on determining the ability of tenants
at the beginning of each season to switch accounts among local merchants
and landlords or to move to other areas.
Recent micro-level studies do not yet provide a conclusive answer.
234
PRICE
V. FISHBACK
Nickless (1987) found that one Alabama merchant’s “frequent” customers
tended to rely on seasonal credit. However, Nickless has not fully addressed
the mobility issue because she studied only one merchant in an area
where local landlords (and possibly other merchants) also offered credit.
Recent micro-level work on the Natchez district in Mississippi and Louisiana seems to support views of spatial competition.
Wayne (1983, pp.
174477) shows that several stores had easy access to the same plantations,
hence the same customers. Although most tenants dealt with only one
store during the growing season, they were able to switch stores at the
beginning of each season. On a more general level, scattered evidence
cited by Wright (1986, pp. 65, 112-l 13) suggests that. tenants were mobile
between plantations. However, more micro-level studies of the local store
and plantation labor markets in other regions are needed to effectively
settle the issue.
APPENDIX
TABLE 1
Descriptions
of Variables
Given t = 1887. Other years are the same unless otherwise specified.
“Indebtedness
of farmers compared with last year” in
1887.”
Two sets of measures. One, “condition of farmers compared
K/K-,
with last year” in 1887. Two, the ratio of cotton income
in 1887 to cotton income in 1886 and the ratio of corn
income in 1887 to corn income in 1886. Each ratio is calculated as the ratio of total product compared with an
average in 1887 to total product compared with an average
in 1886. The total product ratio is then multiplied by the
ratio of the average farmgate price December 1 in 1887 to
the average farmgate price December 1 in 1886.
“Supplies purchased, compared with last year” in 1887.
spt/spt- 1
CREDCASH,/CREDCASH,1
An unweighted sum of the ratios for pounds of bacon and
bushels of corn of the average time price payable November
1 to the average cash price during 1887. The value for
1887 is then divided by the same sum of the ratios for
1886.
CORYCOTY,/CORYCOTY,I
“Total product compared with an average” of corn in 1887
divided by “total product compared with an average” of
cotton in 1887. The ratio for 1887 is then divided by the
ratio for 1886.
O/D,-
I
DEBT PEONAGE
APPENDIX
IN GEORGIA
235
TABLE I-Continued
FGCORCOT,-JFGCORCOT,-,
FGCORCOT,-,
is the average price of corn per bushel
divided by the average price of cotton per pound December
1, 1886. FGCORCOT,-2
is the same ratio for 1885.
CCREDCASH,/CCREDCASH,_
1
Average time price of corn payable November 1 divided
by the average cash price of corn during 1887. This ratio
is then divided by the same ratio for 1886.
Source. All variables are from Georgia Department of Agriculture, Supplemerrtal Reports,
pp. 6-10; 1886, pp. 6-10; 1885, pp. 6-9; 1884, pp. 8-11; 1883, pp. 10-13; 1882, pp.
is-21 )
Note. All prices above were deflated by the Warren-Pearson wholesale price index for
ah commodities based on 1910-1914 dollars to create real prices. Ransom and Sutch (1977,
p. 258) also used this series to deflate the value of crop output. Missing values were put
in for the time and cash prices of corn and bacon in situations where the cash price was
higher than the credit price.
1887,
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