Turnover or Cash?
Sharecropping in the US South.∗
Guilherme de Oliveira
ACLE, University of Amsterdam
February 16, 2016
Abstract
What drove sharecropping in the US South after the American Civil War (186165): turnover costs or liquidity needs? Under a sharecropping contract, employer and
employee shared the final output. That way, employees had a stake in production,
attaching them to the employer. In other words, sharecropping was a performance
contract that minimized turnover. However, no econometric analysis has verified that
claim. Moreover, sharecropping contracts postponed cash payments to employees to
the end of the season, while those same employees cultivated cotton, the only cash
crop of a financially-destroyed region. Employees only took those contracts due to the
lack of job alternatives. These theories point for a different relation of sharecropping
with labor force size, crop fertility, and wages. Using this theoretical framework, I clarify which force dominated the use of sharecropping contracts by running a panel-data
analysis for the southern counties in the period of 1880-1940. The empirical results
clearly point for liquidity needs as the main driver of sharecropping contracts: whenever employees have more job alternatives, the number of sharecropping contracts fall;
sharecropping contracts only react significantly to cotton productivity. Therefore, I
find evidence of the adoption of a performance contract due to endowments inequality.
Keywords: Performance Contracts; Sharecropping; Turnover; Liquidity.
JEL classification: J41; J43; N30; N50.
∗
I would like to thank Giuseppe Dari-Mattiacci, and Carmine Guerriero for their invaluable contribution
as my supervisors. I am glad for the comments from Daniel L. Chen, Mehmet Kutluay, the participants
at the 2015 Ronald Coase Institute Workshop - Tel Aviv, and of my fellow PhD students at the Tinbergen
Institute PhD seminar. I am also indebted to Suresh Naidu and Sok Chul Hong for the data provided.
I wish to thank the financial support by the “Fundação para a Ciência e Tecnologia” through the grant
SFRH/BD/76122/2011. Contact Details: Plantage Muidergracht 12, 1018 TV, Amsterdam, The Netherlands. Phone: +31 (0)205254162. Fax: +31 (0)205255318. E-mail: [email protected]
1
Introduction
Performance contracts - contracts whose payments vary according to productivity measures
- aim to align incentives between principals and agents. But it is not always clear which
incentives are at work. If they are created by an underlying problem affecting agents asymmetrically, performance contracts are a second-best solution. Further, they can worsen the
original problem. This paper goes back in history and finds that a particular performance
contract was driven not because it was hard to make agents to produce, but because principals and agents were stuck in a cash-strapped economy.
Thus, I present two competing theories. The first focus on the cost of replacing inputs.
The second looks instead to the problem that some goods yield more cash than others, and
that liquidity only realizes when sales occur. When the input is labor, performance contracts
align the interests of employees with those of employers because the former maximize their
remuneration by completing their production. Also performance contracts postpone liquidity
exchanges to the end of production cycles, minimizing the need to acquire cash during the
production process.
Although performance contracts solve two problems at once, the two theories compete
in predictions about the same variables. If there are more potential employees available,
it is easier to find a new employee. That is, labor turnover becomes less of a problem,
making performance contracts less attractive since they involve a loss of control in day-to-day
management of production by the employer. However, more potential employees increases
competition to find jobs where they can acquire liquidity. Thus, they take performance
contracts more often, even though they must wait until the end of the production cycle to
get some cash. That is, a larger labor pool might decrease or increase performance contracts,
depending on which transaction cost dominates.
Another factor where these theories compete is their presence across sectors. Turnover
should matter to all sectors whose interruption of their production is extremely costly. Consequently, those sectors use performance contracts more often. Liquidity should only be
relevant to groups of agents with limited access to credit and where there are few cash
sources. Therefore, performance contracts should vary according to the productivity of the
few liquid sectors.
Therefore, turnover costs take employers to offer performance contracts to keep employees
around. Liquidity depends on the willingness of employees to postpone their earnings to the
end of the production cycle. This difference in protagonists arises naturally: turnover is
a problem generated by employees leaving voluntarily; employers always wish to postpone
payments to their suppliers. Then, in each case, it is up to the other agent to close the
contract.
I go back to the US South (herein, South) between 1880 and 1940 to check which of
2
theories dominates. The American Civil War (1861-65) brought the abolition of slavery
(1863-65). Slaves offered steady farm labor along the crop season because they could not
leave at will. This characteristic made them valuable assets, meaning slaves were frequently
traded or used as collateral. Thus, abolition destroyed a solution for turnover, a production
method with minimal cash exchange along the season, and a source of liquidity. By the
late 1870s, many employers adopted a performance contract as known as sharecropping, a
contract where employer and employee split the final output of a season in a farm according
to a share agreed in the signature of the contract.
The current literature points both to turnover and liquidity to explain the emergence
of sharecropping contracts. The main cash crop of the US South, cotton, has at least two
critical periods where labor shortage severely damages output. Simultaneously, employers
struggled with the constant need for cash along the season to pay wages. Furthermore, many
attempted to postpone wages, both in an attempt to prevent turnover, and to decrease cash
outlays during the season. Sharecropping contracts prevented both: employees must stay
until the end of the season to get their pay.
However, as explained before, a larger local labor pool has opposite effects. More people
makes it easier for employers to replace wage employees, making sharecropping contracts
less attractive. But at the same time, more labor meant that there were relatively less job
opportunities other than sharecropping or wage farm labor, making employees more willingly
to accept a sharecropping contract and stay until the end of the season. This made employers
happy since they had to acquire less cash during the season to pay for labor services.
Relative to sectors, every crop has critical periods. Moreover, the more fertile land is, the
more is there to lose in case of labor shortages. Thus, higher land fertility takes employers
to resort more often to sharecropping. But not all crops are liquid. In the case of the South,
cotton was by large the only cash crop available. Tobacco and sugar were circumscribed to
a few pockets. Corn was basically a food crop for self-consumption, whereas. In fact, the
whole literature equates sharecropping to cotton production: the Midwest corn flooded and
ruled the marketplace. Thus, if liquidity drove sharecropping contracts, they would have
appeared more often in places better suited for cotton production. Figure ?? together with
figures ?? and ?? display a picture where sharecropping contracts follow cotton, not corn.
That is, they follow cash, not food.
Simultaneously, wage labor was highly mobile in the South so that wage-setting was
largely out of control of each employer. Thus, whenever wages increase, wage farm labor
becomes more expensive relative to sharecropping. Therefore, employers offer sharecropping
contracts more often. However, better wages means better alternative sources of cash for
employees, reducing their willingness to accept sharecropping.
I frame this reasoning into a theoretical model that structures an empirical exercise,
3
creating the first econometric study on sharecropping in the postbellum South. I use the
US Census from 1880 to 1940 to explain the proportion of sharecropping farms to the total
number of contracts in function of local labor force, farm wages, and crop fertility in each
county across the South. If there is a significant, negative relation with local labor force,
then turnover drives those results. In case there is a positive relation, liquidity drives the
creation of sharecropping contracts. Similarly, a positive(negative) relation with farm wages
points for a predominance of turnover(liquidity). Relative to crop fertility, if all crops have a
significant, positive relation with sharecropping, then turnover is the driving of sharecropping
contracts. But if only cotton has a significant, positive relation with sharecropping contracts,
then liquidity should be the culprit.
Indeed, the results consistently point for liquidity as the predominant explanation for the
proportion of sharecropping contracts in each county. I avoid endogeneity by using lagged
county-level variables, state-level variables, and geographical variables. Lagged variables
prevent a simultaneity bias. State-level variables assume that a county alone cannot affect
them. Geographical characteristics are not shaped by human action. The main conclusion
survives to three robustness checks.
Therefore, this problem finds the adoption of a performance contract because of an
asymmetrical access to capital. All agents lacked liquidity in a broken economy, but only
some had access to a unique, indispensable input to get cash: land. The performance contract
called sharecropping did not emerge because it was difficult to find labor. In the one hand,
it allowed employers to explore the advantage of having land to minimize their liquidity
outlays while getting cash. In the other hand, there were no alternative liquidity sources for
employees. In this sense, this paper contributes to the literature on endowment inequality.
This paper also contributes to the literature on the persistence of sharecropping contracts
in farming. Essentially, both theories frame into Coase’s theory of the firm. This paper also
gives more evidence of sharecropping as way to for the principal to acquire entrepreneurial
units by Hallagan (1978). In fact, Shlomowitz (1979, 1984) refers to some extra requirements,
like extra working experience, for an employee to move from wage laborer to sharecropper
in the South. Akerlof (1976) states that if the employer just wants to supervise effort,
sharecropping suffices. Otherwise, she prefers wage labor. Both explanations presented in
the current paper give extra reasons to prefer sharecropping: whenever you need to prevent
turnover in a very important crop, or you want to satisfy the employee preferences with
nonmonetary perks. However, please note that this paper does not frame in the extensive
literature justifying sharecropping as a risk-sharing contract (Cheung, 1972; Rao, 1971;
Stiglitz, 1974): the difference between the two explanations does not depend, in any measure,
in risk-sharing.
The paper proceeds as follows: section 2 presents the historical context; section 3 consists
4
of a discussion a model framing two competing theories: turnover costs vs liquidity needs;
Section 4 sets an empirical strategy, and discusses the results from the regressions. Section
5 summarizes the main conclusions of this paper and sets a future research agenda.
2
Historical Review
Here I present the emergence of sharecropping contracts after the American Civil War and
the abolition of slavery1 . It is true that sharecropping contracts existed already before
1860 (Shlomowitz, 1984; Wright, 1986). However, their use clearly exploded in the 1870s
as a result of the massive damage caused by the war, the institutional shock brought by
abolition, and the struggle of employers and employees to adapt to a fluid labor market.
This section prepares the main building blocks for the model developed in section 3.
The abolition of slavery caused the American Civil War (1861-65), which opposed the
United States of America, usually named “Union” or “North”, and the Confederate States
of America, normally referred as “Confederacy” or “South”2 . Essentially, the South wanted
to keep its slaves, which made up half of its wealth in 1860 (Wright 2006, p. 60). Since the
election of Abraham Lincoln in 1860 threatened that goal, the South declared independence.
The North reacted and won a bloody, destructive conflict, preserving the territorial integrity
of the US: 2.4% of the 1860 population died in the conflict; more American soldiers died
in this war than in all other conflicts involving American troops combined; the war left the
South in tatters and economically destroyed.
The end of slavery3 was also at the heart of the postwar turmoil in the South. Slaves could
not leave at will. Thus, slavery minimized labor turnover. Slaves could also be sold, used
as collateral, and encouraged to work with non-pecuniary perks like extra rations. So, they
not only simultaneously supplied liquidity but also reduced the need for cash through the
farming season. Indeed, it paid off to use slaves in agriculture (Du Bois, 1935), as 80% of the
slaves worked in farming (Wright, 1978). In addition, Figure 1 points for a strong demand
for slaves: up to 1860, slave prices grew more than the aggregate price of US production
(WPI) and the price of cotton, the South’s main crop.
The federal government enforced abolition throughout the Reconstruction Era (1863-77).
This historical period had three distinct periods: Wartime (1863-65); Presidential (1865-66);
Radical (1866-77). “Wartime” consisted of the conquest of the secessionist states, liberating
1
Literature used for this section: Donald and Randall (1961), Foner (1988), Keller (1977), McPherson (2013),
and Roback (1984).
2
The South was composed by the states of Alabama, Arkansas, Florida, Georgia, Mississippi, Louisiana,
North Carolina, South Carolina, Tennessee, Texas, and Virginia.
3
First in 1863, with a presidential proclamation, and 1865, via the Thirteenth Amendment to the US Constitution.
5
slaves in each of them. “Presidential” began with the rise of president Johnson in 1865,
and promoted a quick reintegration of the South back into the US without interfering in
their internal affairs. Southern states seized the chance to control the labor supplied by the
former slaves, the freedmen, through a series of laws known as Black Codes4 . However, Radical Republicans took over the US Congress in 1866, and began an interventionist agenda
to guarantee the creation of a free labor economy in the South. This policy included military occupation, suspension of the Black Codes, and promotion of wage employment among
freedmen through the Freedmen’s Bureau. Lack of popularity, a financial crisis, and a stalemate in the presidential election of 1876 precipitated the end of the Radical Reconstruction
period5 .
Therefore, employers sought a substitute for slavery in their farms between 1866-1877.
After extensive experimentation, they decided for sharecropping (Shlomowitz, 1984)6 . Employers began by postponing the payment of wages to the end of the season to stabilize the
workforce (Roback, 1984; Shlomowitz, 1979). But employers soon noticed that employees had
to finance their consumption during the season, and desired autonomy in the management
of their working schedule (Roback, 1984; Shlomowitz, 1979; Wright, 1978). Sharecropping
satisfied both requirements: it was a performance contract where labor earned a share of
the final output in the end of the season. The employer usually sold the crop and deducted
whatever advances in consumption goods he gave in the beginning and during the season
to the employee. “Since the worker had a stake in the crop on that particular farm, he was
not likely to abandon his job” (Roback, 1984). The division of the output depended on
the quality of the land and in the amount of inputs supplied by each party7 . This diversity in contracting proves that employers and employees bargained over the details of every
contract.
Sharecropping contracts became part of a ‘tenancy ladder’: wage labor, sharecropping,
fixed-rent tenancy, and ownership of the land (Alston and Kauffman, 2001; Naidu, 2010;
Shlomowitz, 1984). A “wealthy farmer might choose to operate as a tenant or sharecropper,
but a poor man could not simply choose to become a cash tenant” (Wright 1978, p. 177)
4
There were already laws of this nature before the war but by “the mid-nineteenth century criminal prosecutions for enticing a servant had become virtually nonexistent, and civil cases were rare” (Roback, 1984).
5
“The states and the years of military withdrawal are Alabama (1874), Arkansas (1874), Florida (1876), Georgia (1874), Louisiana (1876), Mississippi (1874), North Carolina (1874), South Carolina (1876), Tennessee
(1874), Texas (1874), and Virginia (1874)” (Naidu, 2010).
6
Shlomowitz (1979) witnesses a “wide variety of payment schemes” during these early postbellum years (18651880): Standing Wages; Share of the Crop or Sharecropping; Sharing of Time; Standing Rent; Wages in
Kind; Other various explicit incentive schemes.
7
“For example, in 1876, the U.S. Department of Agriculture reported: ‘Contracts vary widely in details,
but are most generally based upon the following equivalents. Bare labor, one-fourth of cotton in rich land,
one-third in poor soils; labor and rations, one-half as a general rule, four-tenths in some very productive
lands; labor, rations, stock and supplies, two-thirds to three-fourths of the product’” Shlomowitz (1979).
6
because tenancy required the employee to supply many more inputs than in sharecropping.
Still, employees desired the lifestyle dominant in the rest of rural US: a family-run farm
(Du Bois, 1935; Wright 1978, 2006). Wage labor did not offer the last feature, and the
extreme mobility of wage laborers mostly attracted young men (Wright 1986, p.103). Further, employers struggled to pay enough to compensate the lack of autonomy in wage labor
(Shlomowitz, 1979), meaning that quickly this became a low-income option: “The South
was a low-wage region in a high-wage country” [Wright, 1986, p. 12]8 .
Clearly, there was fierce competition in all steps of the ‘tenancy ladder’. Collusion in
labor contracting failed during the Reconstruction Era, especially in the cotton sector: employers were too many and too heterogeneous, while employees could easily move to explore
deviations (Collins and Wanamaker, 2015; Wright, 1986; Shlomowitz, 1984). “There was
indeed a labor market in the South between 1870 and 1930, and it operated to reduce wage
differentials and some form of wage discrimination” [Wright 1986, p. 12] and “intercounty
mobility rates were high” [Wright, 1986, p. 65]. Both Wright (1986) and Du Bois (2007) underscore how personal markets were: news of job openings would travel mouth to mouth, and
employees would personally select each employee. To arrive to sharecropper, an employee
had to have a reliable reputation or he would not simply get credit from local shopkeepers
or merchants to buy consumption during the season (Wright, 1986). Wright (1986, p. 70)
states that “sharecroppers, renters and small farmers were not wage laborers; they were not
involved, directly at least, in the market we have been describing.” However, Shlomowitz
(1984) establishes a relation between wage levels and the capability of employers to pay a
premium for the lack of autonomy compared to sharecropping or tenancy can offer. In the
end, the choice between “wage labor, sharecropping, and rental...depended on relative wage
levels and rental rates” [Wright 1986, p. 90].
After 1877, the South progressively revived the Black Codes, and approved new laws
targeting black employees. This set of laws was part of the Jim Crow Era, during which
blacks were relegated to a second-class citizen status until the 1960s. According to Roback
(1984), these laws moved the enforcement costs of sharecropping contracts on blacks from
employers to the government. However, Shlomowitz (1984) states that sharecropping was
a reaction to the suspension of the Black Codes during the Reconstruction Era, and there
is evidence of similar sharecropping contracts for whites and blacks (Alston and Kaufman,
2001). Wright (1986) goes a step further and states that employers used racial discrimination
to drive wages to whites and blacks alike. Therefore, in the line of some remarks by Naidu
(2010), those laws should have affected all options for black employees, not just sharecropping
contracts.
8
Collins and Wanamaker (2015): at “the turn of the twentieth century, real income per worker in the South
was less than half that in the rest of the United States.”
7
I want to finish saying that training was an irrelevant variable in agriculture at this time.
Human capital during slavery times consisted of slaves health and reproductive capacity
(Ruef, 2012), two factors absent in the postbellum South (Du Bois, 2007; Ruef, 2012).
Moreover, most skill acquisition for slaves occurred in the domestic service (Ruef, 2012)
whereas by “1850, nearly 80 percent all slaves were engaged directly in agriculture” (Wright
2006, p. 84). Further, Rueff (2012) presents records of 57977 slaves sold in the market: about
95% had no skill. Collins and Wanamaker (2015) also suggest that southern employees after
the war were less skilled than in the rest of the US. Figures 2, 3, and 4 point to the same
direction: with the exception of the black population in 1900, the South has worse literacy
rates than the rest of the US. In 1870, the rest of the US had more people who could write
and more people who could read9 . Finally, Wright (1986) presents trust, and deference
in the case of the black population as the crucial personal characteristics to access both
sharecropping and tenancy.
In the end, “once we know that the system emerged from a market process, we can
be reasonably sure that the outcome represented a balance of forces. Sharecropping was a
balance between the freedmen’s desire for autonomy and the employer’s interest in extracting
work effort and having labor when it was needed” [Wright 1986, p. 86]. In the next section,
I dispute the last sentence of this quote.
3
Two Competing Theories
This section begins with the derivation of the proportion of sharecropping farms in the total
number of farms (herein, sharecropping) in a county. That model frames two alternative
theories: turnover costs drove sharecropping versus liquidity needs drove sharecropping.
Each of these theories has 3 hypothesis about the relation between the sharecropping and
labor force available, crop fertility, and wages of farm labor in the county.
3.1
Model
There are two types of agents, employers and employees. Each employer has a farm that
he wishes to run for one season. To do so, he hires labor. He has two options: wage labor
and sharecropping. Wage labor allows the employer to manage the farm to raise crops m
and c. But it also means that the employer must pay a wage w ∈ (0, 1) during the season
to all the labor he hires, and he loses production with a probability λ ∈ (0, 1) due to labor
9
According to the US Census, in 1870 about 20% (7.4%) of the whites in the south (the rest of the country)
could not write, about 74.3% (49.3%) of the blacks in the south (the rest of the country) could not write,
about 40.4% (8.5%) of the total population in the south (the rest of the country) could not write, and about
44% (6.7%) of the total population in the south (the rest of the country) could not read.
8
turnover. Alternatively, the employer chooses to abdicate of management; in exchange, he
avoids turnover, and only pays the labor supplied in the end of the season, when he gives a
share s ∈ (0, 1) to the employee.
In summary, each option gives the employer the following payoffs:
1. Wage Labor - (1 − λ)(γm + γc )eb − αi
e2b
2
−w
2. Sharecropping - (1 − s)(γm + γc )ec
eb (ec ) is the effort exerted by the employer (employee) when managing the farm. αi is
the ability of the employer to manage the farm. The different employers in a certain county
are uniformly distributed in a continuum αi ∈ (0, 1). For instance, some employers manage
labor necessities better than others. The experiments during the Reconstruction Era hint
significant heterogeneity among employers in labor recruitment. The dependence of credit
access on reputation gives another intuition for αi (Du Bois, 2007; Wright, 1986). One may
also think about the ability of an employer in managing a specific farm. The coexistence
of more than one type of labor arrangement in the same plantation corroborates this view
(Shlomowitz, 1979; Wright, 1986).
A crucial difference between the two labor schemes is that an employer and an employee
agree on s, while w is a given for people in the county. These assumptions reflect the
discussion in section 2: between each employer-employee pair discussed the terms of each
sharecropping contract; wage labor was highly mobile across counties; not every wage laborer
could become a sharecropper, but many would-be sharecroppers became wage labor for the
right price.
Employees have three options. They can work as wage laborers and earn a total of w
across a season. As sharecroppers, they earn a share s of the production of the farm in the
end of the season. The last option consists of non-farm jobs, where they earn n ∈ (0, 1). I
assume n > w to account for the fact that wage labor in agriculture was a low-paid job.
Thus, an employee has the following payoffs for each option:
1. Wage Labor - w
2. Sharecropping - s(γm + γc )ec −
e2c
2
3. non-farm work - n
When working as a sharecropper, an employer has an effort cost higher than any employer
(α < 1). That accounts for the fact that sharecroppers had not the same management options
as employers had: the land did not belong to them; they had less access to credit during the
season.
9
The role of the size of the local labor force enters through the assumptions on three
∂λ
∂n
∂n
derivatives: ∂L
< 0, ∂L
< 0, and ∂w
> 0, where L stands for the labor force living in the
county. First, λ decreases with L since more local population makes it easier to replace
labor in case of turnover: it becomes easier and faster to find a new employee (Hanes, 1996).
Second, n decreases with L because there is more labor supply for the local non-farm sector.
The last derivative comes from the fact that wages and rental rates in farm and non-farm
markets reacted to each other (Wright, 1986). In this case, if wage labor becomes a better
option, the non-farm sector must pay better to attract more workers.
I assume risk-neutral agents because one ”cannot say that sharecropping arose over time
because of the desire of both parties to share risk, because almost all contracts already
provided for risk sharing from the beginning” [Wright 1986, p. 89]. That is, analogously to
Allen and Lueck (1992), I worry about a more relevant set of incentives.
I start solving the model from the employees perspective. If he chooses sharecropping,
2
c)
. In turn, sharecropping is
he exerts ec = s(γm + γc ). This effort yields a payoff of s2 (γm +γ
2
√
2n
better than his second-best option, non-farm labor, if s ≥ sn = (γm +γ
. This result conforms
c)
to section 2: shares decreased with land quality, and the farm labor markets reacted to nonfarm employment.
√
Sharecropping yields Πs = (γm + γc ) 2n − 2n to the employer. The negative sign
of the derivative to n proves the limits of using s to convince an employer to accept a
c)
,
sharecropping contract. If the employer opts for wage labor, he exerts eb = (1 − λ) (γmα+γ
i
2
+γc )
− w. Thus, an employer prefers sharecropping to wage labor
yielding Πw = (1 − λ)2 (γm2α
i
2
2
(γm +γc )
if αi ≥ αs = (1−λ)
. This threshold shows that sharecropping only happens when
2(Πs +w)
management is too costly for the employer.
Therefore, we have the following proportion of employers using sharecropping contracts:
p = 1 − αs = 1 −
(1 − λ)2 (γm + γc )2
2(Πs + w)
(1)
This is the variable under empirical scrutiny. The way it changes according to L, γm , γc ,
and w depends on the prevailing theory: turnover costs or liquidity needs. I discuss them in
the next two subsections.
3.2
Turnover Costs
Put simply, labor turnover hurts agriculture because crops have strict calendars (Hanes,
1996; Wright, 1986). There are also plenty of unexpected shocks, such as sudden rainfall
that might destroy a whole harvest in a few days. Since this is common knowledge, employers
also face the threat of labor contract renegotiation (Hanes, 1996; Roback, 1984; Shlomowitz
10
1979, 1984). Therefore, the more population living in a farm’s vicinity, the more easily that
farm replaces labor (Hanes, 1996). In that case, the less an employer needs a sharecropping
contract to avoid turnover.
It is also natural to expect that the more fertile a farm is, the larger is the production
lost in a certain period of time. This point is true to almost every crop. For instance, among
the most relevant crops in the South (Shlomowitz, 1979; Wright, 1986)10 :
- Cotton - It has two labor peaks during the crop year: planting and harvesting (Roback,
1984; Wright 2006, p. 86).
- Cane Sugar - It has at least one labor peak. Britannica (2015) “says that sugarcane,
once harvested, cannot be stored because of sucrose decomposition.” Therefore, it is
crucial to minimize “the time between cutting and processing” (Britannica, 2015).
- Tobacco - At least three labor peaks: seeds preparation; transplanting, and harvesting.
“The prime requisite for successful tobacco culture is a supply of well-developed healthy
seedlings that is available at the proper time for transplanting” (Britannica, 2015).
Moreover, before harvesting tobacco “may be left in the field from a few hours to two
days to wilt” (Britannica, 2015).
Therefore, an increase in crop fertility should increase the use of sharecropping contracts.
In fact, a comparison between figure ?? with figure ?? hints a positive correlation between
sharecropping and cotton suitability. This idea is further confirmed by table 2, where we
find a significant, positive correlation between sharecropping and cotton suitability.
Finally, an increase in wages of farm laborers should increase the use of sharecropping
contracts. Elaborating, the more expensive the alternative becomes, the more employers
want to use sharecropping.
∂p
captures the effect of labor force in the use of sharecropping
Going back to my model, ∂L
∂p
contracts. In order to have ∂L < 0:
Πs + w ∂λ
2sn − 1 ∂n
<
1 − λ ∂L
2sn ∂L
(2)
Therefore, the gains of using less sharecropping contracts must outweigh the gains of
using more sharecropping contracts for the employer. More specifically, as turnover losses,
λ, decrease, the employer gains output when choosing wage labor, 1 − λ, but loses the payoff
of sharecropping, Πs , and the wages paid, w. Simultaneously, as the non-farm wages, n,
decrease, the employee gains to switch to sharecropping. Additionally, a decreases in n
10
The reader might ask about the production of rice. According to the literature reviewed, it was largely wiped
out after the abolition: too specific, geographically speaking; as discussed in subsection 3.3, cotton was the
only cash crop in the South.
11
11
increase the employer’s payoff in sharecropping by 2s2sn −1
. In conclusion, the decrease in
n
turnover costs must overcome that of diminished labor opportunities for the employees.
To have
∂p
, ∂p
∂γm ∂γc
> 0:
sn <
n
Πs + w
(3)
The share taken by the sharecropper has to be small enough. Otherwise, the employer
reaps more benefits by using farm wage labor.
Relative to w, the following condition must be met to have
√
2n
∂n
< √
∂w
2 2n − (γm + γc )
∂p
∂w
> 0:
(4)
n cannot increase a lot12 . That is, an increase w cannot improve too much the job
alternatives, otherwise sharecropping becomes also too expensive for the employer.
3.3
Liquidity Needs
An employee accepts a postponement of liquidity when he becomes a sharecropper. First,
he only earns cash in the end of the season. Second, during the season, he gets some
advancements of cash. But most of the benefits came in the form of food, access to services
such as credit and medical care (Naidu, 2010), and the chance to cultivate some of food crops
(Wright, 1978). In a nutshell, employers provided enough non-pecuniary goods and perks
to finance current consumption that kept employees working. This scheme saved employers
cash outlays during the season. Indeed, this system was so good for employers that they
often preferred to write-off debt created by the advancements than losing the sharecropper
(Wright, 1986), or they would accept to just rollover it to the next season (Ager, 2012;
Naidu, 2010). In addition, often employers would not directly provide current consumption:
merchants and local shopkeepers financed it in exchange of a portion of the final production
(Du Bois, 2007; Wright, 1986). In the end, sharecropping became a wage labor contract
with uncertain payment (Du Bois, 2007) and it was reduced to a piece-rate wage labor in
the early 1900s (Wright, 1978).
All in all, the employer moved the management of liquidity needs to the employee. Fixedwage labor implied a series of implicit advancements to the employees, since harvesting was
the only time of cash inflow (Roback, 1984). Simultaneously, cash was expensive in the
South: employers could no longer use slaves as collateral for loans, the civil war brought
massive material destruction, and it was forbidden to use land as a collateral (Wright, 1986).
∂p
n −1
Note that 2s2s
< 0 if sn > 12 . Thus, if sn are very small, ∂L
< 0 always.
n
√
12
Assuming the land is not too fertile, i.e., γm + γc < 2 2n.
11
12
Employers had problems to guarantee enough cash to pay wages through the season. In fact,
merchant and shopkeepers charged interests up to 60% (Wright, 1986), signaling the price
of cash in those days. Thus, it is natural that employers do not wish cash outflows during
the season.
So, employees were apparently in control. But they were stuck in the first two steps of
the tenancy ladder described in section 2. Only employees without many job alternatives
would accept the postponement of liquidity to the end of the season. However, there was
no land redistribution, meaning that millions of freedmen and poor whites had no financial
resources (Foner, 1988). In fact, the South was a land where rural populations could solely
improve their standard of living by acquiring land or moving to the city (Du Bois, 2007).
Thus, rural employees had to choose either wage labor or sharecropping. Figures ?? and ??
point to sharecropping following rural population density. Moreover, 2 presents a significant
positive association between those two variables. So, everything else equal, the more labor
force available in a county, the less job alternatives each employee would have. In turn, they
would accept more often sharecropping contracts because employers could not compensate
in cash the difference in autonomy in the working environment.
Equally important was the fact that cotton meant cash and vice-versa, as overwhelmingly
witnessed by the literature used in this paper: tobacco and (especially) sugar were limited
geographically; the Midwest dominated corn markets; the South was unfit for the production
of vegetables. In fact, cotton was behind slavery in the South since 75% of the slaves employed
in farming cultivated cotton (Wright, 1978). Table 2 points to an addiction for cotton.
Sharecropping has positive relation with cotton suitability, but an even larger correlation
with cotton productivity and cotton share13 . Figures ??, and ?? illustrate that point. In
contrast, figures ?? and ?? demonstrate an almost inverse relation between sharecropping
and corn production. All in all, farmers chose cotton regardless the type of land because
cotton was the only source of cash. Therefore, if liquidity was the main worry of employers
and employees alike, only the fertility of the land in terms of cotton production mattered.
Relative to farm labor wages, if they improve, it means employees can more easily earn
a living without waiting for liquidity in the end of the season. Therefore, employees are
less likely to take sharecropping contracts. Table 2 points for a negative relation between
sharecropping and wages of farm labor. So, the higher the wages are, the less sharecropping
contracts employees take.
∂p
> 0:
Translating the discussion into my model, I have the following condition for ∂L
Πs + w ∂λ
2sn − 1 ∂n
>
1 − λ ∂L
2sn ∂L
13
(5)
Cotton productivity: total production value per square kilometer; cotton share: share of the production
value of cotton divided by the sum of the production value of cotton, tobacco, sugar, rice, and corn.
13
Analogous to the previous subsection, the effect from diminished labor opportunities
must outpace that of decreased turnover.
To have
∂p
∂γc
> 0, where c stands for cotton:
sn <
n
Πs + w
(6)
The condition is the same as in subsection 3.2, but only cotton matters for farming
productivity.
Relative to w, the following condition must be met to have
√
2n
∂n
> √
∂w
2 2n − (γm + γc )
∂p
∂w
< 0:
(7)
That is, an increase w must significantly improve job alternatives, otherwise employers
just adjust the sharecropping contracts to cope with the change.
3.4
Summary of Predictions
In this subsection, I summarize the theories and respective implications into 3 pairs of
empirically testable predictions. Basically, each theory is associated to a different sign to
the same variable explaining the proportion of sharecropping contracts. Therefore, it is a
question of dominance: a set of signs points to one theory beats the other as the explanation
behind sharecropping.
Turnover was a central concern of every employer. Crops had strict production cycles,
i.e., every task had to be performed in a certain time of the year. In addition, immediate
labor availability was crucial to tackle several, potential emergencies. Therefore, employers
wanted to attach labor to land along the year. To do so, they took advantage on the
willingness of employees to autonomously manage their own work, and offer a contract that
made them to wait for a payment until the end of the season: a sharecropping contract.
Consequently, the more employees available around a certain farm, the less need there was
to use such a contract. The more fertile land was, the more potential output is at stake,
making sharecropping more desirable. Finally, the more expensive wage labor became, the
more employers use sharecropping contracts.
But turnover costs might have not dominated the emergence of sharecropping in the
South after the war. Indeed, employees wanted autonomy at work, but they could only
afford sharecropping contracts. Cotton was the only cash crop and employers were financially
desperate. Therefore, a larger labor force implies more competition for alternatives outside
the wage labor/sharecropping dichotomy, such as working in urban areas or moving up in the
14
tenancy ladder. This implies that rural workers would accept sharecropping contracts more
often. Only the fertility of cotton would push employers to seek more sharecroppers. And
since farm and non-farm labor markets were intertwined, higher wages made sharecropping
less desirable for employees.
The subsequent predictions refer to the case where turnover costs drive the use of sharecropping contracts:
Prediction 1: If labor force increases, then ceteris paribus the number of sharecropping contracts decreases.
Prediction 2: If crop fertility increases, then ceteris paribus the number of
sharecropping contracts increases.
Prediction 3: If the cost of wage labor increases, then ceteris paribus the number
of sharecropping contracts increases.
In case liquidity needs drive the use of sharecropping contracts:
Prediction 4: If labor force increases, then ceteris paribus the number of sharecropping contracts increases.
Prediction 5: If cotton fertility increases, then ceteris paribus the number of
sharecropping contracts increases. The other crops have no significant effect on
sharecropping contracts.
Prediction 6: If the outside options for workers improve, then ceteris paribus
the number of sharecropping contract decreases.
I compare the two forces in terms of dominance. That makes sense in light of the literature
discussed in the previous subsections. However, no empirical evidence clarifies which story
might dominate. This last conclusion takes me to develop the empirical study developed in
section 4.
4
Empirics
The current section serves to test the predictions mentioned in subsection 3.4. I describe
the database and the variables in subsection 4.1, which let me translate each prediction into
an empirical hypothesis in subsection 4.2. Then, in subsection 4.3 I discuss the empirical
results whose robustness I test in different ways in subsection 4.4.
15
4.1
Database, Variables and Endogeneity Issues
The US census is the best starting point to build a database for three reasons. First, it offers
demographic and agricultural variables that match the main concepts in section 2. However,
I use other databases to measure competition over the labor force and gauge crop fertility.
Second, it has county-level data, which translates into 1164 observations across the southern
states defined in section 214 . Finally, the US census data on land tenure spans from 1880 to
1940 with a decennial frequency. The first year is the only point after abolition left is 1870,
when the South still tested post-slavery institutions (Roback, 1984; Shlomowitz 1979, 1984).
1940 is taken as the last data point, before the mechanization of cotton and sugar production
(Britannica, 2015; Holley, 2000) and the effects of New Deal in the southern labor markets
(Wright, 1986).
Before proceeding, I mention details about states included in the database. I follow
Naidu (2010) and consider the eleven Confederate states plus Kentucky and West Virginia.
Kentucky had slavery and passed legislation restricting labor mobility after the war, just
like the Confederate states (see table ??). West Virginia was a strategic breakaway from
Virginia in the beginning of the war. Collectively, I name these 13 states “South”, whereas
I simply refer to the rest of the US as “rest of the US”15 .
The dependent variable is the proportion of farms that run with a sharecropping contract
(Sharecropping herein). The US Census provides the total number of farms as well as the
type of tenure. Studying the proportion instead of the absolute number of sharecropping
farms distinguishes changes common to all farms from those only affecting sharecropping
farms. For instance, an increase in the local labor force makes labor cheaper, which equally
affects all farms. Therefore, I must control for the total number of farms so that I only
get the effects related to sharecropping. One might ask why I do not include the number
of farms as an independent variable. That would create an extra endogeneity problem,
since the number of sharecropping farms and the total number of farms are simultaneously
determined. Additionally, the proportion of sharecropping farms is a continuous variable,
while the total number of farms is a count variable, which brings extra complications for
hypothesis testing (Cameron and Trivedi, 2005).
The three independent variables are rural population density, crop fertility, and wage
of agricultural laborers. The US census provides the rural population density (Rural Pop.
Dens. henceforth), which consists of the rural population between 15 and 65 years old divided
by the area of the county. This variable proxies the labor force size and I use population
14
15
I use 1880 borders.
I received several comments suggesting “North” in place of “rest of the US”. I do not do so because,
geographically, the term does not make sense, and politically, I include in the placebo analysis of subsection
4.4 some states that were insignificant territories of the Union during the American Civil War.
16
density in place of the absolute number to control for the county size. This variable brings
an endogeneity problem. Collins and Wanamaker (2015) and Du Bois (1935, 2007) have
plenty of evidence of labor mobility across the whole period. Thus, there is simultaneity
between rural population, the number of farms, and the composition of farms. Therefore, I
add one extra regression to the OLS estimation: an OLS regression with Rural Pop. Dens.
of the previous period. It is clear that Rural Pop. Dens. of the previous period might affect
contemporaneous Sharecropping but not the other way around. To be sure, the correlation
between contemporaneous and lagged Rural Pop. Dens. in each decade is always positive,
statistically significant, and always between 0.4 and 0.6.
Cotton, sugar, and tobacco fertility (cotton, sugar, and tobacco, respectively) capture
the potential fertility of each crop in the county. The data comes from GAEZ, which is
designed to be independent of human nature. So, I do not expect endogeneity issues with
this variable. However, it is time-invariant, making it inadequate for fixed-effect estimation.
Therefore, I use Britannica (2015) to identify key climatic data for the development of each
crop. Cotton depends on moderate average temperatures during growing and harvesting
seasons. Sugar relates positively on minimum temperature as it is a tropical crop. Tobacco
varies negatively on the maximum temperature as the leaves might wilt too much when
exposed to high temperatures and strong sunlight. I use the historical climate data from
NOAA to calculate the principal components of each crop for each decade16 . I left rice out
due to insufficient variation. These principal components are significantly correlated, with
the expected signs17 , with the crop production between 1880 and 1920.
The wage of agricultural laborers (Wage hereafter) measures the competition for the
local labor force. Please note this variable measures state-level wage of agricultural laborers.
Thus, there is no endogeneity problem as long as no county alone determines the state-level
wage of agricultural laborers. The literature agrees on a competitive rural labor market for
the entire period (Alston and Kauffman, 2001; Collins and Wanamaker, 2015; Naidu, 2010;
Roback, 1984; Shlomowitz, 1984). Moreover, recall from section 2 that farm and non-farm
labor markets mutually affected each other. Consequently, Wage directly controls for the
cost of farm labor, and indirectly gauges the job opportunities in the non-farm sector.
Finally, I add some extra control variables controlling for aspects mentioned in section
3. In all regressions, I control for urban population density, average farm area in the county,
and the proportion of blacks in the whole population. The first controls for alternative
employment in urban areas, the second because of a possible relation between the type of
tenure and the average farm area, and the third for documented over-representation of blacks
16
Principal Component Analysis: I use the first vector standing for the common variation between two variables: crop suitability and the relevant climate variable (average, minimum, or maximum temperature). I
call this common variation the fertility of a certain crop within the county along the time under scrutiny.
17
Positive for cotton and sugar; negative for tobacco. Data from the US census.
17
in sharecropping contracts18 . Notice that all these variables suffer the same endogeneity issue
as Rural Pop. Dens.. Thus, I use the same approach to circumvent that issue, that is, lagged
variables.
Now I can discuss the regression of interest. The following OLS regression is a reducedform approach of expression 1:
Sharecropping c,d = γc + β1 Rural Pop. Dens.c,d +
βcrop crop c,d + β2 Wage c,d + β3 Xc,d + c,d (8)
crop
Where c, d stands for county c in decade d, γc is the county-fixed effects term, crop
includes cotton, sugar and tobacco fertilities, and Xc,d includes urban population density,
average farm area, and the proportion of blacks, and c,d is the idiosyncratic error. The OLS
regression with lagged variables:
Sharecropping c,d = γc +β1 Rural Pop. Dens.c,d−1 +
βcrop crop c,d +β2 Wage c,d +β3 Xc,d−1 +c,d (9)
crop
4.2
Empirical Predictions
Let me now translate the prediction of subsection 3.4 into the words of subsection 4.1. When
turnover costs are the dominant driver of sharecropping contracts in the South:
Prediction 1: If labor force (Rural Pop. Dens.) increases, then ceteris paribus
the number of sharecropping contracts (Sharecropping) decreases: β1 < 0.
Prediction 2: If crop fertility (Crop) increases, then ceteris paribus the number
of sharecropping contracts (Sharecropping) increases: βcotton > 0, βsugar > 0, and
βtobacco < 0. It is negative for tobacco because we use a principal component
variable combining a time-invariant variable, suitability, and a time-variant variable, maximum temperature, and the higher the average temperature, the lower
fertility is.
Prediction 3: If the cost of wage labor (Wage) increases, then ceteris paribus
the number of sharecropping (Sharecropping) contracts increases: β2 > 0.
When liquidity needs are the dominant driving-force of sharecropping:
Prediction 4: If labor force (Rural Pop. Dens.) increases, then ceteris paribus
the number of sharecropping contracts (Sharecropping) increases: β1 > 0.
18
Nationwide, in 1900 about 37% (20%) of the colored (white) farm managers were sharecroppers. Also
nationwide, in 1920, about 72% (67)% of the colored (white) tenants were sharecroppers.
18
Prediction 5: If cotton fertility (Cotton) increases, then ceteris paribus the
number of sharecropping contracts (Sharecropping) increases. The other crops
have no significant effect on the number of sharecropping contracts: βcotton > 0,
while βsugar and βtobacco are insignificant.
Prediction 6: If the outside options for workers (Wage) improve, then ceteris
paribus the number of sharecropping contract (Sharecropping) decreases:: β2 < 0.
4.3
Results and Discussion
Tables 3 to 5 present the results for pooled, fixed effects, and fixed effects with lagged
variables estimations. I use county-fixed effects and time dummies: the former control
time-invariant aspects, such as institutions or social capital; the latter control for common
shocks specific for the decade, such as the Great Depression (Wright, 1986), the Boll Weevil
epidemic (Wright, 1986), or the post-World War I great migration (Collins and Wanamaker,
2015). At last, I cluster the standard errors at the county-level in order to control for spatial
correlation.
Starting by table 3, time dummies are important. For instance, the significance and the
sign of Rural Pop. Dens., and of Wage completely change in all regressions. This suggests
decade-specific shocks affected rural labor relations in the South. Equally interesting, the
moment I control for more variables, the sign of Rural Pop. Dens. stays positive for any
specification. This finding points towards the liquidity need theory: when there is more
labor force available, there are less options and employees accept to postpone their wages to
the end of the season, that is, they sharecroppers more easily. The crop fertilities also point
to the liquidity theory: only Cotton has a positive, significant effect. That is, only increases
in the potential yield of cotton increases the use of sharecropping contracts. One notices
that Sugar has a negative coefficient. That is consistent with Britannica (2015), Hanes
(1996) and Shlomowitz (1984): the processing stage of sugarcane into raw sugar depends
on relatively expensive machinery, which only few farmers could afford. Therefore, given
harvested sugarcane cannot be stored, sugar benefited from coordination in that stage of
production. Sharecropping is a too decentralized system for that (Hanes, 1996; Shlomowitz,
1984).
However, the pooled estimator depends on exogeneity to time-invariant components of
the error. That is, it assumes a non-systematic relation between each variable under analysis
and components such as latitude, altitude, institutions, or social capital. This shortcoming
justifies the use of a fixed-effect estimator to control for those variables in each county. So I
turn to table 4. Again, time dummies change significantly most of the coefficients. There is a
decrease of the coefficients of Rural Pop. Dens. and of Wage. Still the signs keep pointing for
19
employees taking sharecropping contracts when the alternatives get worse. More crucially,
Cotton becomes the only significant crop in regressions (7) and (8). Therefore, these signs
point for an economy where sharecropping contracts were made to minimize employers’ cash
outlays.
However, the use of fixed-effects does not prevent endogeneity with the variable Rural
Pop. Dens.. Therefore, I run a fixed-effects regression using the lagged value of Rural
Pop. Dens. in table 5. Notice a shift in the sign of Rural Pop. Dens. in regressions
(6) and (8) at the same time Tobacco becomes significant with the sign consistent with
the idea that sharecropping contracts resulted from the will of employers to prevent labor
turnover. But remind that tobacco was more restricted geographically speaking than cotton.
Simultaneously, the coefficient of Cotton increases, while the coefficient of Wage remains
almost the same. The marginal effects of Cotton and Wage are quite large: an increase of
one standard deviation in cotton fertility (wages of farm labor) increased (decreased) the
proportion of sharecropping contracts about 0.64 (-0.11).
In summary, there is more evidence in favor of predictions 4-6 than of predictions 1-3.
Intuitively, it seems that sharecropping contracts happened mostly because of the lack of job
alternatives than of problems with labor turnover. Therefore, this empirical study breaks
down the views on the development of sharecropping in the South displayed in sections 2 and
3. In the 80 years following the American Civil War, sharecropping contracts were nothing
more than relational contracts that made wages variable, minimizing and postponing liquidity outlays for financially-strained employers. Employees accepted those contracts because
cotton was virtually the only cash source in the countryside.
4.4
Robustness Checks
In this subsection I run some robustness checks. First I split the sample according to the
distance of each state to the rest of the US. Then, I check whether the Black Codes/Jim
Crow era legislation competes with the control variables used so far. Finally, I run a placebo
regression on the rest of the US.
4.4.1
Border States
Wright (1986) displays extensive evidence proving that the South had a separate labor market, with markedly lower wages in almost any sector than the rest of the country until the
Great Depression. At the same time, Collins and Wanamaker (2015) witnesses emigration
to the rest of the US. Therefore, it is a natural step to wonder whether turnover becomes
more important for southern states bordering other states: the closer to places with higher
wages in farming and industry, the more easily employees switch to those jobs.
20
Table 6 displays a regression of states that border non-southern states, and another
regression of states that only border other southern states. In fact, although not significant,
Rural Pop. Dens. has a negative sign in the border states. It is also interesting to notice that
the fertility of all crops becomes significant at the border states19 . In addition, the coefficients
of Cotton and Wage are much larger in border states. All coefficients except Wage point for
a stronger influence of turnover in the use of sharecropping contracts: employees simply have
more job alternatives at the border. Inspecting expression 7, the contradictory variation of
Wage might come from a higher non-farm wages, which lowers the required responsiveness of
non-farm wages to farm wages. In other words, better opportunities in the non-farm sector
at the border makes sharecropping a worse option for employers, taking them to accept wage
labor more often.
4.4.2
Black Codes and Jim Crow’s laws
Roback (1984) defends the need for legislation to enforce sharecropping contracts. That is,
sharecropping only worked if employees could not simply runaway. Figure 6 summarizes the
evolution of the enactment of those laws20
- Enticement and Contract Enforcement laws - The former “made it a crime for an
employer to ‘entice’ a laborer who had a contract with another employer”, whereas the
later guaranteed that “a laborer who signed a contract and then abandoned his job
could be arrested for a criminal offense.” Basically, these laws reduced competition to
the beginning of the season, when employers and employees could sign new contracts.
- Vagrancy laws - These “essentially made it a crime to be unemployed or out of the
labor force”, increasing the search cost for better jobs.
- Emigrant-agent laws - These laws prevented agents hiring workers across states, especially those moving workers out of the South.
- Convict-lease system - Penalty for transgressors of contract enforcement and vagrancy
laws. The government would lease prisoners to private entities, relinquishing any monitoring of day-to-day use of those prisoners.
The effect of every piece of legislation is ambiguous. I expect Enticement and Contract
Enforcement laws to reinforce sharecropping as an anti-turnover contract, making it more
desirable for employers. But this legislation also makes sharecropping a less attractive option
for employees. Vagrancy laws and Emigrant-agent laws diminish the job alternatives for
19
20
It is a bit odd in the case of sugar: the main cane sugar production areas were in non-border states.
The quotes in the following descriptions are from Roback (1984).
21
employees, making them to accept sharecropping more often. Yet these laws also hinder labor
mobility in general, depressing wages, which makes wage labor a more attractive option for
employers. Finally, the existence of a convict-lease system not only has the same potential
effects as Enticement and Contract Enforcement laws (in the end, it increases punishments
for employers and employees who break contracts), but it also creates a cheap labor source,
making sharecropping less desirable for the employers.
I test these ideas in 7. I create 5 dummies variables: the variable equals 1 when the law
exists in the state in each decade under scrutiny21 . First, introducing time effects drastically
changes the significance of each law. That suggests that not all laws interacted with the
main variables. That is, most of the legislation had not interfered with the agents’ decisions
at the margin across the remaining control variables. Second, the only two relevant pieces of
legislation are the Anti-Vagrancy and Contract Enforcement laws. The former have a positive
sign, whereas the latter have a negative sign. These results suggest that Anti-Vagrancy Laws
truly reduced the job alternatives to sharecropping for employees, and Contract Enforcement
Laws made sharecropping less desirable for the employees. Finally, the main control variables
kept the same signals and significance levels from column (8) of table 5.
4.4.3
Placebo
Finally, I run the model in the last two columns of table 5 on the rest of the US22 . The
rest of the US was a much more dynamic and richer economy (Collins and Wanamaker,
2015). It also had a competitive labor market that functioned separately from the South
(Wright 1978, 1986). Moreover, family-run families traditionally dominated the countryside
(Hanes, 1996). Finally, they did not face the shock of abolition that the South had with
Emancipation. Therefore, I expect turnover to dominate liquidity needs across the states
outside of the South.
In fact, column (2) points for a stronger effect of turnover costs. Despite of the negative
coefficient, Wage has a much smaller effect on sharecropping. Every crop fertility has a
significant effect on sharecropping, although it is odd that Sugar has a significant effect,
given its geographical distribution.
21
22
I use figure 6 to build these variables.
States included: California, Connecticut, Delaware, Illinois, Indiana, Iowa, Kansas, Maine, Massachusetts,
Maryland, Michigan, Missouri, Nebraska, New Hampshire, New Jersey, New York, North Dakota, Minnesota,
Ohio, Pennsylvania, Rhode Island, South Dakota, Vermont, Wisconsin.
22
5
Conclusion
In conclusion, I find two alternative stories for the evolution of sharecropping in the South:
turnover costs and liquidity needs. The former predicts that a smaller labor force increases
turnover costs, making sharecropping more necessary. It also predicts that higher wages
increase drive up the need for sharecropping. Moreover, the more production employers lose
in case of a labor shortage, the more they want sharecropping contracts. Conversely, the
latter theory predicts that more labor force means less alternatives for each employee, taking
them to accept sharecropping contracts more often. Similarly, if any alternative improves,
employees sign sharecropping contracts less often. Finally, only potential losses in cotton
production drive up the use of sharecropping contracts.
The empirical exercise gives clear signs that liquidity was the dominant reason for the
signature of sharecropping contracts. Extra covariates and different robustness checks do
not affect this result.
Then, this paper opens a door for exciting research topics. Does liquidity management
drive vertical integration? To which extent are performance contracts the result of lack of job
alternatives and not optimization of business models? Is the eager adoption of performance
contracts by IT firms the result of global inequality driving employees to take them?
23
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53(3): 557-575.
Shlomowitz, Ralph. 1984. ”Bound” or ”Free”? Black Labor in Cotton and Sugarcane
Farming, 1865-1880. The Journal of Southern History, 50(4): 569-596.
Stiglitz, Joseph E.. 1974. Incentives and Risk Sharing in Sharecropping. The Review of
Economic Studies, 41(2): 219-255.
United States Department of Agriculture. 1942. Crops and Markets. Vol. 19, No. 6.
Washington, D.C.: Government Printing Office.
Wattenberg, Ben J.. 1976. The Statistical History of the United States. New York, NY:
Basic Books, Inc., Publishers.
25
Wright, Gavin. 1978. The Political Economy of the Cotton South. New York, NY: W.W.
Norton & Company, Inc..
Wright, Gavin. 1986. Old South, New South. New York, NY: Basic Books, Inc., Publishers.
Wright, Gavin. 2006. Slavery and American Economic Development. Baton Rouge, LA:
Louisiana State University Press.
26
Tables and Figures
Table 1: Summary of Variables
Variable
Definition and Sources
Median
Value
(Standard
Deviation)
Sharecropping:
Number of sharecropping farms relative to total population
in each county.
0.2938
(0.1775)
Source: US Census, 1880-1940.
Rural Pop. Dens.:
Labor force per square kilometer living in locations
with less than 2.500 inhabitants in each county.
Cotton:
Tobacco:
Sugar :
Source: US Census, 1870-1940.
Normalized first principal component extracted from county’s
average suitability for cotton and average annual temperature.
Sources: GAEZ and NOAA.
Normalized first principal component extracted from county’s
average suitability for tobacco and average maximum annual
temperature.
Sources: GAEZ and NOAA.
Normalized first principal component extracted from county’s
average suitability for cane sugar and average minimum annual
11.6272
(8.5352)
0.6755
(0.6045)
0.6106
(0.6335)
0.6344
(1.0409)
temperature.
Corn:
Sources: GAEZ and NOAA.
Normalized first principal component extracted from county’s
average suitability for corn, average annual temperature and
0.8125
(0.8448)
precipitation.
Sources: GAEZ and NOAA.
Wage:
Farm labor wage rates per day without board per state.
Source: USDA (1942).
Urban Population Density:
Labor force per square kilometer living in locations
with more than 2.500 inhabitants in each county.
1.0778
(0.4033)
3.7386
(26.1724)
Source: US Census, 1870-1940.
Average Farm Size:
Proportion of Black Population:
Average size of farms in square meters in a county.
Source: US Census, 1870-1940.
Proportion of black labor force relative to white labor force
in the county.
Source: US Census, 1870-1940.
27
657.0156
(454.4631)
0.2745
(0.2359)
Table 2: Most important correlations and respective p-values in 1880.
Sharecropping Rural Pop. Dens. Cotton Productivity Cotton Share
Rural Pop. Dens.
0.1900
0.2738
-0.1643
(0.0000)
(0.0000)
(0.0000)
Urban Pop. Dens.
-0.0634
0.1393
-0.0203
-0.0704
(0.0340)
(0.0000)
(0.4883)
(0.0319)
Cotton Suitability
0.0876
0.0232
0.1245
0.2192
(0.0034)
(0.4296)
(0.0000)
(0.0000)
Corn Suitability
0.0292
0.0710
0.3311
0.3155
(0.3298)
(0.0154)
(0.0000)
(0.0000)
Cotton Productivity
0.4257
0.2738
0.7371
(0.0000)
(0.0000)
(0.0000)
Corn Productivity
0.0154
0.6271
0.0415
-0.4163
(0.6072)
(0.0000)
(0.1573)
Cotton Share
0.3861
-0.1643
0.7371
(0.0000)
(0.0000)
(0.0000)
Corn Share
-0.3145
0.0541
-0.6463
-0.8383
(0.000)
(0.0991)
(0.000)
(0.0000)
Wage
-0.1724
-0.3933
-0.1640
0.0482
(0.000)
(0.0000)
(0.000)
(0.1418)
Table 3: Pooled Regressions.
Rural Pop. Dens.
(1)
0.00372∗∗∗
(0.001)
(2)
-0.00031
(0.000)
(3)
0.00425∗∗∗
(0.001)
0.10665∗∗∗
(0.012)
-0.02331∗∗∗
(0.005)
0.01409
(0.013)
No
No
No
8057
0.031
No
No
Yes
8057
0.334
No
No
No
8057
0.041
Cotton
Sugar
Tobacco
Wage
Extra Controls
Fixed Effects
Time Effects
N
R-Squared
Sharecropping
(4)
(5)
0.00015
0.00197∗∗∗
(0.001)
(0.001)
0.07397∗∗∗
0.08138∗∗∗
(0.010)
(0.011)
-0.02373∗∗∗
-0.01940∗∗∗
(0.005)
(0.005)
0.03206∗∗∗
0.02776∗∗
(0.011)
(0.012)
0.12611∗∗∗
(0.005)
No
No
No
No
Yes
No
8057
8057
0.337
0.226
(6)
0.00055
(0.001)
0.09764∗∗∗
(0.009)
-0.03826∗∗∗
(0.004)
0.01283
(0.010)
-0.27735∗∗∗
(0.014)
No
No
Yes
8057
0.3911
Note: *, **, and *** denote statistical significance at 10 %, 5 %, and 1 %, respectively.
28
(7)
0.00243∗∗∗
(0.001)
0.07640∗∗∗
(0.011)
-0.02761∗∗∗
(0.005)
0.02823∗∗
(0.012)
0.13350∗∗∗
(0.006)
Yes
No
No
8057
0.220
(8)
0.00072
(0.000)
0.08846∗∗∗
(0.009)
-0.04086∗∗∗
(0.005)
0.00875
(0.010)
-0.26743∗∗∗
(0.013)
Yes
No
Yes
8057
0.410
Table 4: Fixed-Effects Regressions.
Rural Pop. Dens.
(1)
0.00488∗∗∗
(0.001)
(2)
-0.00079
(0.001)
(3)
0.00435∗∗∗
(0.001)
0.26755∗∗∗
(0.100)
0.13730∗∗∗
(0.015)
0.48712∗∗∗
(0.081)
No
Yes
No
8057
0.030
No
Yes
Yes
8057
0.335
No
Yes
No
8057
0.079
Cotton
Sugar
Tobacco
Wage
Extra Controls
Fixed Effects
Time Effects
N
R-Squared
Sharecropping
(4)
(5)
-0.00074
0.00126∗
(0.000)
(0.001)
1.01564∗∗∗
0.51418∗∗∗
(0.130)
(0.097)
0.05723∗∗∗
0.00875
(0.015)
(0.016)
0.07081
-0.01785
(0.089)
(0.079)
0.12717∗∗∗
(0.005)
No
No
Yes
Yes
Yes
No
8057
8057
0.355
0.234
(6)
0.00006
(0.001)
1.03052∗∗∗
(0.125)
-0.01281
(0.014)
-0.15620∗
(0.083)
-0.25629∗∗∗
(0.015)
No
Yes
Yes
8057
0.401
(7)
0.00160∗∗
(0.001)
0.51049∗∗∗
(0.098)
0.02153
(0.016)
0.02018
(0.087)
0.11649∗∗∗
(0.006)
Yes
Yes
No
8057
0.241
(8)
0.00050
(0.001)
0.89746∗∗∗
(0.117)
-0.01180
(0.014)
-0.11561
(0.081)
-0.25732∗∗∗
(0.015)
Yes
Yes
Yes
8057
0.419
Note: *, **, and *** denote statistical significance at 10 %, 5 %, and 1 %, respectively.
Table 5: Fixed-Effects Regressions with Lags.
Rural Pop. Dens.(-1)
(1)
0.00075∗∗∗
(0.000)
(2)
-0.00010
(0.000)
(3)
0.00069∗∗∗
(0.000)
0.22342∗
(0.124)
0.10400∗∗∗
(0.018)
0.56509∗∗∗
(0.097)
No
Yes
No
6056
0.005
No
Yes
Yes
6056
0.335
No
Yes
No
6056
0.051
Cotton
Sugar
Tobacco
Wage
Extra Controls
Fixed Effects
Time Effects
N
R-Squared
Sharecropping
(4)
(5)
-0.00009
0.00023
(0.000)
(0.000)
1.01085∗∗∗
0.47778∗∗∗
(0.154)
(0.119)
0.03814∗∗
-0.03124
(0.019)
(0.020)
0.08462
0.00115
(0.107)
(0.094)
0.13446∗∗∗
(0.006)
No
No
Yes
Yes
Yes
No
6056
6056
0.354
0.227
(6)
-0.00008
(0.000)
1.06199∗∗∗
(0.148)
-0.02603
(0.018)
-0.19300∗
(0.100)
-0.26817∗∗∗
(0.017)
No
Yes
Yes
6056
0.403
Note: *, **, and *** denote statistical significance at 10 %, 5 %, and 1 %, respectively.
29
(7)
0.00024
(0.000)
0.47491∗∗∗
(0.119)
-0.00035
(0.020)
0.10853
(0.096)
0.11970∗∗∗
(0.006)
Yes
Yes
No
5997
0.243
(8)
-0.00008
(0.000)
1.06608∗∗∗
(0.148)
-0.02680
(0.018)
-0.19883∗∗
(0.100)
-0.26732∗∗∗
(0.017)
Yes
Yes
Yes
5997
0.403
Table 6: Non-Border and Border States.
Rural Pop. Dens.(-1)
Cotton
Sugar
Tobacco
Wage
Extra Controls
Fixed Effects
Time Effects
N
R-Squared
Sharecropping
(Non-Border)
(Border)
0.00004
-0.00020
(0.000)
(0.000)
1.79875∗∗∗
0.60533∗∗
(0.252)
(0.212)
0.04007
-0.05565∗∗
(0.028)
(0.024)
0.13434
-0.68276∗∗∗
(0.163)
(0.142)
-0.29640∗∗∗
-0.22728∗∗∗
(0.035)
(0.024)
Yes
Yes
Yes
Yes
Yes
Yes
2608
3389
0.429
0.402
Note: *, **, and *** denote statistical significance at 10 %, 5 %, and 1 %, respectively.
30
Table 7: Main regressions with legislation variables.
Rural Pop. Dens.(-1)
Cotton
Sugar
Tobacco
Wage
Anti-Enticement Laws
Anti-Vagrancy Laws
Contract Enforcement Laws
Emigrant Agent Laws
Convict Leasing
Extra Controls
Fixed Effects
Time Effects
N
R-Squared
Sharecropping
(1)
(2)
0.00003
-0.00005
(0.000)
(0.000)
0.87450∗∗∗ 0.80293∗∗∗
(0.125)
(0.153)
0.00766
-0.03424∗
(0.018)
(0.018)
-0.07404
-0.16734∗
(0.096)
(0.103)
0.06955∗∗∗ -0.27686∗∗∗
(0.005)
(0.017)
0.00180
0.01908∗∗
(0.009)
(0.008)
0.06762∗∗∗ 0.04235∗∗∗
(0.006)
(0.008)
∗∗∗
-0.01964∗∗
0.02292
(0.008)
(0.008)
-0.00116
-0.01618∗∗∗
(0.006)
(0.006)
0.00131
-0.02804∗∗∗
(0.003)
(0.004)
Yes
Yes
Yes
Yes
No
Yes
5997
5997
0.312
0.409
Note: *, **, and *** denote statistical significance at 10 %, 5 %, and 1 %, respectively.
31
Table 8: The rest of the US.
Rural Pop. Dens.(-1)
Cotton
Sugar
Tobacco
Wage
Extra Controls
Fixed Effects
Time Effects
N
R-Squared
Sharecropping
(1)
(2)
0.00020∗
0.00007
(0.000)
(0.000)
0.75914∗∗∗ 1.23768∗∗∗
(0.084)
(0.087)
∗∗∗
-0.06482∗∗∗
-0.14386
(0.015)
(0.015)
-0.49961∗∗∗ -0.26706∗∗∗
(0.071)
(0.075)
∗∗∗
-0.02185∗∗
0.02628
(0.002)
(0.009)
Yes
Yes
Yes
Yes
No
Yes
5860
5860
0.139
0.226
Note: *, **, and *** denote statistical significance at 10 %, 5 %, and 1 %, respectively.
32
4
3
Index: 1830=1
2
1
0
1830
1840
1850
1860
Year
Price Upper South
Cotton Price
Price Lower South
WPI
Figure 1: Price of slaves compared with the price of cotton and the Wholesale Price Index
(WPI) (1830 Prices). Source: Evans (1962) and Wattenberg (1976).
33
15
10
5
0
1900
1910
1920
1930
decade
Illiteracy among whites: South
Illiteracy among whites: Rest
0
20
40
60
80
Figure 2: Illiteracy rate of the white population in the South and in the rest of US. Source:
US Census.
1900
1910
1920
1930
decade
Illiteracy among blacks: South
Illiteracy among blacks: Rest
Figure 3: Illiteracy rate of the black population in the South and in the rest of US. Source:
US Census.
34
.4
.3
.2
.1
0
1900
1910
1920
1930
decade
Illiteracy rates: South
Illiteracy rates: Rest
0
2
4
6
8
Figure 4: Illiteracy rate of the whole population in the South and in the rest of US. Source:
US Census.
1800
1820
1840
1860
1880
1900
1920
1940
Year
Cotton Price (base year=1880)
WPI (base year=1880)
Figure 5: Cotton price and Wholesale Price Index (WPI). Source: Wattenberg (1976).
35
Figure 6: Laws restricting labor mobility approved across the South (1865-1925). Source:
Roback (1984).
36