“Flip the Switch: The Impact of the Rural Electrification Administration 1935-1940”
Carl Kitchens§ and Price Fishback#
November 2014
Abstract
To isolate the impact of access to electricity on local economies, we examine the impact
of the Rural Electrification Administration low-interest loans in the 1930s. The REA provided
loans to cooperatives to lay distribution lines to farms and aid in wiring homes. Consequently,
the number of rural farm homes electrified doubled in the United States within 5 years. We
develop a panel data set for the 1930s and use changes within counties over time to identify the
effect of the REA loans on a wide range of socio-economic measures. The REA loans
contributed significantly to increases in crop output and crop productivity and helped stave off
declines in overall farm output, productivity, and land values, but they had much smaller effects
on nonagricultural parts of the economy. The ex-ante subsidy from the low interest loans was
large, but after the program was completed, nearly all of the loans were fully repaid, and the
ultimate cost to the taxpayer was relatively low.
§
Assistant Professor of Economics, Florida State University and NBER. 239 Bellamy Building,
Tallahassee, FL 32306. ckitchens@fsu.edu
#
Thomas R. Brown Professor of Economics, University of Arizona and NBER. 401 McClelland
Hall: 1130 E Helen St. Tucson, AZ 85721. fishback@email.arizona.edu
1
1. Introduction
Access to electricity has long been considered a major contributor to the economic welfare of
populations. Households with electricity have better access to information and the use of a
broader range of consumer goods, while farms and firms find it enhances productivity in a
variety of ways. These anticipated benefits have led the World Bank to provide extensive loans
and grants to aid less developed countries in bringing electricity to the 1.3 billion people in the
world that live without it (IEA 2011).1 A number of recent studies have examined the effects on
economic activity of new dams and other large infrastructure projects, which include increased
access to electricity as one of the significant benefits (Duflo and Pande (2007), Kitchens (2013),
Kline and Moretti (2013), Lipscomb, Mobarak, and Bahram (2012), and Severnini (2012). Yet,
these studies cannot fully isolate the benefits of electrification because the projects often have
multiple purposes and involve large construction efforts. Most dams aid economic activity in an
area not only through electrification, but also through flood control, irrigation, and recreation
opportunities. The construction process often leads to the building of roads and railways to small
cities that develop around the dam site. Even though the construction city may not last long, the
extra business provided for nearby cities has the potential to lead to persistent effects over time.
One way to determine the impact of access to electricity is to focus on situations where
projects were almost exclusively focused on distributing electricity to a population that did not
have access. Among the most famous policies devoted specifically to the distribution of
electricity was the Rural Electrification Administration (REA). The REA was established in
1935 to provide loans to lay power lines from the existing electricity grid to rural areas where
1
Annually, the World Bank provides over $8 billion in energy related loans to projects that
including building coal-fired power plants in South Africa to expanding distribution networks in
Kenya, to building large-scale dams in Ethiopia. (World Bank 2012, 2013; Reuters 2010; Reuters
2012; Hidassee 2013).
2
electricity was not available. This was a period when incomes in many of these rural areas were
at modern developing world levels, as was the road infrastructure. In its first five years of
operation, the REA provided over $227 million in loans ($3.6 billion in 2010 dollars), primarily
to newly formed cooperative utilities, to survey areas, lay distribution lines, and wire homes2
The REA’s initial push more than doubled the number of electrified farms in the U.S. The
number of farms electrified in that period was more than the number electrified in the 53 years
between the start of central distribution of electricity and the introduction of the REA. In just 5
years, cooperative utilities had more mileage of distribution line than the 5 largest private
utilities in the country (Slattery 1940). Today cooperative utilities serve over 42 million people
in 47 states, maintain 42 percent of the nation’s distribution lines, and deliver 11 percent of all
electricity (NRECA 2013).
To measure the impact of the REA loans, we develop a new panel data set for the 1930s
with information on the REA loans, other New Deal programs, and a wide range of
socioeconomic measures: population growth and migration, agricultural output and productivity
for multiple goods, land values, the use of labor and machinery on farms, the extent of
manufacturing activity, retail consumption, earnings outside agriculture, and birth rates and
infant mortality rates.3 The impact of the REA is identified using variation within counties over
time and controls for the primary form of selection used by the REA in choosing projects,
distance from the existing electric grid in 1935. REA officials distributed the loans to projects
2
The REA built a handful of diesel electric generation plants, which accounted for about 3.7
percent of the total loans. The loans were available to any organization, private or public,
however many private companies refused to take the loans fearing there were strings attached
which may affect ongoing litigation with the federal government (REA 1960).
3
Taryn Dinkelman (2011) performed a similar study of electricity distribution in modern South
Africa but she focused on female labor supply. Simalrly, Grogan and Sadanand (2013) study the
effects of electrification in Nicaragua.
3
that were closer to the existing electric grid because they sought to insure that the loans would be
repaid, and the electricity delivery costs to more distant areas were high enough that the longer
distance projects would not succeed. Placebo tests suggest the absence of selection bias in our
analysis by showing no relationship between the distribution of REA loans and changes in
economic outcomes in the 1920s.
The REA was introduced into rural economies that in many cases had not yet recovered
to their 1929 levels. The loans contributed substantially to a rise in overall crop output, crop
output per farm, and crop output per acre. In addition, the REA loans helped stave off longer run
declines in the number of farms and overall output on farms. These positive benefits also helped
stave off declines in the value of land and buildings on farms. The effects on nonagricultural
parts of the economy were generally small, which is not a surprise given the tight focus of the
REA on electrifying farms and the disruptions in labor markets during the Great Depression and
the New Deal.
Finally, the REA was an inexpensive program to taxpayers. The subsidy to farmers and
investors who formed the cooperatives to lay the distribution lines came in the form of interest
rates on the REA loans that were 2 to 3 percentage points lower than the rates they would have
expected to pay in private markets that incorporated the anticipated risk of the loans into the
interest rates. The ex-ante size of the subsidy was in the range of 18 to 26 percent of the value of
the loans. Ex post, however, almost all loan recipients were either on schedule or ahead of
schedule on their repayments of principal on the loans (USDA 1943). As of 1960, less than 1
percent of REA loans were behind more than 30 days on payments (NRECA 1960). The REA
had much greater success at obtaining repayments than some other major projects, like the Home
Owners’ Loan Corporation (HOLC), which foreclosed on 20 percent of its loans (Fishback,
4
Rose, and Snowden 2013), and the Bureau of Reclamation, which did not charge interest on the
loans to irrigation districts to construct dams, and eventually forgave the principal payments on
the loans on many projects as well.
2. The Rural Electrification Administration
In 1930, almost every major city and town was electrified.4 In 1932, domestic service in
towns and cities accounted for 80.8 percent of all electric consumers nationwide, yet farm
service only accounted for 2.6 percent of all customers (U.S. Census 1932). The share of farms
with access was slightly higher, in 1925, 3.2 percent of farms had electricity, 10 percent by 1930.
By 1950, the landscape had changed dramatically, as over 90 percent of farms had electricity.
On May 11, 1935, Executive Order 7037 created the REA and extended the scope of the
Emergency Relief Appropriation Act to include rural electrification projects. Between 1935 and
1939, the REA handed out the equivalent of 0.3 percent of GDP in government subsidized loans
to newly formed cooperative utilities and existing private utilities to provide access to electricity
in rural areas. By 1940, over 250,000 miles of line had been constructed with REA loans. The
lines connected over 956,000 farms and thus more than doubled the number of farms receiving
service as of 1935, despite the continued problems with recovery from the Depression in the
agricultural sector (Slattery 1940).
The REA Loan Process
The REA provided twenty-five year loans at interest rates pegged to the long term U.S
Treasury bond rate for the construction of distribution lines to electrify farms. REA rates
4
The Federal Power Commission reported electric rates for over 19,000 towns and cities in 1935
with populations greater than 250 residents.
5
fluctuated from 2.69 to 3 percent at a time when most loans of this type charged 6 percent
interest (Slattery 1940). In other cases, the REA offered 5-year loans to help with the wiring of
homes (Slattery 1940). In a handful of cases, the REA extended loans to construct generating
plants (REA 1939). Upon loan approval, the REA helped line up engineers and experts to see
that the proposed project was completed satisfactorily (REA 1937). The REA also provided
training for the managers of newly formed cooperatives to help ensure that the new organizations
were run successfully so that the loans would be repaid according to schedule from power
revenues.
To obtain a loan, an organization, typically cooperatively owned, proposed a plan that
included engineering drawings and justification of the loan allotment. As a part of the plan
submitted to the REA, the organization had to be incorporated under its state’s laws, have
secured rights of way, and begun negotiations to secure a wholesale power source. The applicant
was required to have legal representation, a project engineer, and an accurate map of the project
subject to verification by the REA (REA 1937).
The typical REA project required $230,000 (3.6 million in 2010 dollars) in loans to
construct 250 miles of rural distribution lines to connect about 800 customers. Roughly 75
percent of these customers were farms. The rest were schools, churches, general stores, and
small shops.
One major hurdle to receiving a loan from the REA was securing wholesale power at a low
rate as power cost represented approximately 75 percent of the total payments made by loan
recipients (REA 1938b). There were far more applicants for loans than the funds available could
aid, and the REA sought to ensure that loans were repaid. Given that the loan would be paid
6
back with power revenues, the REA treated the securing of wholesale electricity at a low cost as
a very important component of whether or not an applicant would be approved. As the REA
noted,
“In terms of the feasibility of projects, the power cost is the largest item of cost in
connection with their operation….We have taken the position that if a proposed
wholesale rate for any project, taking into account variable conditions, is not such
as to make an otherwise good project economically feasible and self-sustaining,
REA cannot make a loan” (REA 1938b).
“Sometimes a difference of a fraction of a cent per kilowatt hour in the wholesale
rate will represent the difference between a sound and unsound project” (REA
1937, REA1938b, Slattery 1940) 5
Anecdotally, the proposed wholesale rate and retail rates were binding constraints. As early as
1935, Morris Cooke, then director of the REA, rejected loan applications if the wholesale rate
was too high (Richardson 1961). The relationship between electric rates and the allocation of
funds will inform our empirical strategy below.
The Relationship with Lenders
After the funds were allocated by Congress, it proved difficult at first to deliver funds
into the hands of private utilities, municipalities, or cooperative utilities to begin construction.
While surveys undertaken by the Federal Emergency Relief Administration (FERA) indicated
Slattery 1940 also notes “Too high rate or onerous conditions may wreck a project, the contract
must be approved by our engineers. It is not a matter for novices. Tense battles are frequently
fought over this issue. If unable to secure a wholesale rate of about 1 cent per kwh, the REA
loans money to build its own generating station, usually powered by diesel engine ... if it is
decided that the project is feasible and will pay out, construction estimates are prepared, a sum is
fixed to cover all costs and a recommendation for an allotment is made to the Administrator for
his approval and signature.” (p58-59 Rural America Lights Up)
5
7
that there was large rural demand for electricity, both farmers and states were unfamiliar and
leery of the cooperative utility’s organizational form (Richardson 1961).
The REA changed the organizational structure of electric utilities and the way customers
connected to the electric grid. Given the REA’s stringent rate structure requirements, many
private utilities did not apply for loans, believing that REA rate requirements would yield
unprofitable projects (Richardson 1961). As a result, 90 percent of REA funds were loaned to
cooperative utilities, which were nonprofit member-owned corporations (REA 1939b). In many
states, it was unclear how to incorporate cooperative utilities, how they should make payments in
lieu of taxes given their non-profit status, and whether they should be regulated by state public
service commissions. To solve this issue, the REA wrote a model bill to recognize and
incorporate the cooperative utilities (Slattery 1940). By 1940, 23 states passed the model bill that
allowed the cooperatives to be exempt from regulatory authority on the basis that they were selfregulating; in other states cooperatives were organized under existing agricultural cooperative
legislation, adapted to meet the needs of electric cooperatives (REA 1960).
While the REA aided in addressing legal issues at the state level, it also aided in the
process at the local level. Many farmers were suspicious of joining cooperative organizations
because many agricultural cooperatives, such as dairies, had failed in the 1920s (Richardson
1961). To increase awareness of the REA program, officers from the REA frequently attended
local farm meetings in conjunction with the local agricultural extension agents to advertise and
encourage the formation of a rural cooperative utility. At these meetings, the agents described the
benefits of electricity on the farm, lighting, pumped water, milling, as well as the cost of
obtaining electricity. REA agents outlined the step-by-step instructions to secure a loan,
8
“Get a county map and mark on the map where the line should go to serve the farms that
want electricity. Find out what wholesale rate you can get. Send in this information and
the REA will tell you whether the project is feasible and what engineering and legal steps
to take next… The REA has a legal division that will tell you all about the state laws
which govern your organization” (REA 1936b)
Early on, meetings were often hijacked by private companies who sent representatives to
ask loaded questions during the meetings designed to raise doubts about the risks to which
individual farmers would be exposed in the event of default. In reality, farmers only risked a
five dollar membership fee (REA 1938b). Despite, the true low cost and lack of risk, the fears
instilled by the private utilities were sufficient to stall or prevent the formation of some
cooperatives (Richardson 1961).
Further challenges arose at the local level during the loan process. In order to construct
the distribution system, loan applicants had to secure easements and rights of way, often without
the power of eminent domain, which proved difficult in areas with many disjoint landowners.
The REA helped overcome this problem by issuing standard procedures to acquire easements by
including members of each locality on their board of directors (REA 1938, Richardson 1961).
This procedural innovation made it easier to track down absentee landowners and increased the
transmission of information, making it easier to construct distribution lines.
In addition to the legal innovations, REA engineers made technological innovations to
reduce the cost of distribution lines. Designs created by REA engineers allowed spans between
poles to be doubled, removed bulky hardware, and ultimately lowered the cost of lines from
$2,000 per mile to $850 per mile (REA 1938b). Coordinated efforts and assembly line
construction of distribution lines also reduced the length of time that if took to construct
distribution lines by over 60 percent (REA 1938b). These innovations and suggested
construction practices were developed by REA engineers (REA 1936c).
9
Within the cooperative organization, the REA also played significant roles. The REA
closely monitored the applicants in all phases of development. The REA helped applicants secure
wholesale power contracts and protected applicants by developing standard wholesale contracts
to eliminate unfavorable clauses that might jeopardize the applicant’s financial health (REA
1938b). To ensure the quality of project coordinators, managers, and accountants, the REA had
to approve selections and routinely offered training courses to inform cooperative employees of
sound business practices (REA 1938b). This was especially important for standardizing
accounting purposes so that the REA could track the financial health of its loans. The REA
trained cooperative accountants and managers in standard bookkeeping practices to move them
away from simple Inbox-Outbox methods (Richardson 1961). All of these measures were taken
to make the new cooperatives self-sufficient and to minimize the risk of default.
By 1938, the REA listed over 200 uses for electricity on the farm (REA 1938b).
Electricity meant increased productivity along multiple dimensions. Kilowatts pumped water for
irrigation and watered animals, allowing for more crops to be planted and increased consistent
quality. For example, the use of electricity reduced the worminess of artichokes by attracting
moths to light blue light traps (REA 1936b). Apple farmers in Virginia adopted spray irrigation,
which increased their yield and the grade of the product (REA 1936b). Electric motors could be
operated more cheaply and tended to break down less often than gas motors (REA 1936b).
Electric heaters in chicken coops increased hatch rates and the size of chickens. At the
agricultural experiment stations, electricity increased the profitability per hen by 23 cents in
Missouri and 53 cents in Alabama (REA 1938b). Lighting in pig pens reduced the number of
offspring lost to accidental trampling deaths, if one piglet was saved it completely offset the cost
of power for the year. Dairy farmers also benefitted, electricity meant cooled storage tanks and
10
automated milking machines. More milk received high grades, less milk spoiled, and it could be
collected with 50 percent less labor. Running water in the barn also led to increased milk
production by cows, which no longer had to wait for fresh water. In Indiana, electricity reduced
the length of time that plants spent in the greenhouse and increased the number of blooms on
flowers.
Electricity was also beneficial in the home. The REA estimated that for the average
family of five, over 300 hours and 350 miles of walking per year could be eliminated in the
collection of water (REA 1938b). Electric water heaters further reduced the labor associated with
heating water for cooking and baths. Electric lighting also reduced smoke and ash in the home by
replacing kerosene lighting. Electricity also introduced a variety of appliances into the home
such as electric ranges, washing machines, and electric irons. All of these goods improved the
quality of life for rural residents.
3. Theoretical Foundations
Access to electricity potentially led to changes in a wide range of activities in rural areas, it
likely changed the agricultural production process and consequently, the structure of
employment, the amenities in the home, and health conditions. We discuss the impacts of
electrification by discussing the intuition of a spatial equilibrium model, such as the one
proposed by Roback (1982).6 In a Roback (1982) model, workers choose to live and work in
either a rural (farm) or urban (industrial) location, such that a marginal worker, who gains utility
from their wage and amenities and disutility from property rent, is indifferent between the two
6
In Appendix B, we outline a more formal model by examining the impacts of electrification on
the profits of an incumbent firm, following the framework used by Greenstone, Hornbeck, and
Moretti (2010).
11
locations. Firms in each location have a location specific production process that uses land,
labor, capital, and energy.
In this framework, rural electrification can be considered both a shock to technology and
a reduction in the price of energy in the rural sector. First, suppose that electrification is only a
pure (factor neutral) technology shock. Under this assumption, agricultural land, labor, and
capital would become more productive in the rural location, resulting in increased rural demand
for all inputs, yielding higher rural wages, land values, and energy prices.7 If capital and energy
are complimentary, then we expect that there would be increased mechanization in agriculture.
Making the assumption that electrification only enters the model as a productivity shock is quite
restrictive. It is also natural to think of electrification as a simultaneous reduction in the price of
energy. Electricity represents an energy form that is cheaper than gasoline, kerosene, or wood
fuel. If the price of energy falls at the same time as the productivity shock, then it becomes more
difficult to determine how a firm would alter its use of all factors to minimize costs without
knowing the functional form of the production function, and the degree of complementarity or
substitutability between inputs. Despite these ambiguities, because of the increased productivity,
and the lower input prices, the land will become more profitable, thus the model still predicts that
land values will increase.
In addition to affecting productivity and the price of the energy input, rural electrification
also affects the household by improving the rural amenities. Electricity allows households to
adopt electric lighting and a variety of electric appliances. These factors would tend to increase
the rural labor supply, reducing the wage. This effect works in the opposite direction of the
7
These models usually assume that capital is internationally traded, therefore local shocks do not
affect the local price of capital. We follow by making the same assumption.
12
productivity effect, further muddling wage predictions. At the same time, improving amenities
further increases the demand for land, thus, the model still predicts that land prices should
increase. Setting the model aside, we might also expect that improvements in household
amenities could improve sanitary conditions, particularly if water collection transitions from
cisterns to piped water, or, by removing smoke from the home as families adopt electric
appliances in favor of coal or wood stoves. Finally, electrification could also change the structure
of the household if female labor is freed from household production.
Because of the ambiguities associated with the model, we seek to estimate the reduced
form effects of outcomes that are related to the factors of production and rural amenities. We
first test whether or not electrification led to an agricultural productivity shock by examining
several measures of agricultural output, then we estimate the reduced form effect of
electrification on measures of agricultural employment, agricultural capital holdings, land values,
and health and fertility outcomes. We also test for spillover effects in urban areas by examining
outcomes in sectors that would likely be affected if labor moved from the city to the rural
locations, such as retail and manufacturing sectors.
4. Sample Construction
Data for the empirical exercise are derived from a variety of federal publications. Outcome
and control variables come from Census publications compiled in Haines (2005) and a new
agricultural dataset collected by Fishback, Haines, and Rhode (2013). Data pertaining to the
value of REA loan contracts by county, as well as other New Deal Agency loans are from
Fishback, Horrace, and Kantor (2005), which originally came from unpublished mimeos of the
Office of Governmental Reports (1940). Information detailing the location of REA projects
comes from the Report of the REA (1939). Because REA loan information at the county level is
13
only available through 1939, the empirical exercise will focus on the period between 1935 and
1940, when the REA was most active in its loan program.8
Data about the state of the electric grid prior to the formation of the REA is important in
controlling for selection of projects based on access to the grid. The locations of the electric
transmission grid and electric generation plants in 1935 come from the Federal Power
Commission National Power Survey Interim Report Power Series No. 1 (1935) and were
digitized using ArcGIS. The 1935 grid does not include power generated from new dams that
were being built under the New Deal, like the TVA, because the projects were not generating
electricity as of 1935.
Figure 1 displays the service areas of the REA funded projects as of 1939. Most of the
projects were located in the Mid Atlantic, Southeast, Great Lakes, and Midwest; therefore, we
restrict the sample to counties in these states.9 The sample is restricted further to counties with
populations under 20,000 as of 1930 to eliminate large cities, which were not eligible for funding
under the REA guidelines. Of the approximately 3,000 counties in the United States, the
restrictions leave 1,377 counties in the sample. In Figure 4 we show the counties that comprise
our sample. Counties shaded in light grey represent control counties, while counties shaded in
black represent treatment counties. Within this set of counties, the average loan per farm varied
8
The REA continues to provide loans to cooperative utilities under the umbrella of the Rural
Utilities Service, which was organized in 1994. Because loans have been granted on an ongoing
basis since 1935, extending our analysis beyond 1940 without detailed county level loan
information would likely attenuate our estimates, as many of our control counties eventually
become treated after 1940. Indeed, rural electrification became universal by 1960.
9
The states left out of the sample are Alaska, Arizona, California, Colorado, Connecticut,
Delaware, Washington DC, Hawaii, Idaho, Maine, Maryland, Massachusetts, Montana, Nevada,
New Hampshire, New Mexico, New York, Oregon, Pennsylvania, Rhode Island, Utah, Vermont,
Washington, and Wyoming.
14
widely. In Figure 4, we show the distribution of REA loans per farm from 1935 to 1939. The
majority of counties did not receive any funding, however, conditional on obtaining loans from
the REA, the loans in the average county were $77 per farm.
Before we discuss the summary statistics for changes during the 1930s, we note initial
differences in the levels between counties that did and did not receive REA loans. Despite larger
declines in urban areas, the REA counties had more people than the nonREA counties on
average. Mean total output, measured as value of farm products, sold, traded, or used and then
deflated by the wholesale price index for farm products was higher in the counties receiving
REA loans in both 1929 and 1939, because there were more farms and output per acre was
higher in the REA counties. These two factors offset the lower value of output per farm in the
REA counties, which was driven by average acreage that was about one-fourth of the average in
the nonREA counties. The farms in REA counties tended to have lower shares of value devoted
to animal products while using a higher share of their production on their own farm. The levels
of inputs also varied some, as farms in REA counties had higher land and building values
(deflated by the wholesale price index for farm products) and lower values of farm machinery
and implements (deflated by the wholesale price index for farm machinery) despite having less
acreage. The farm operator worked fewer days off of the farm and used more family labor and
less hired labor. Because of the lower share of population in cities of 2,500 or more and greater
distance from larger urban areas, retail sales per capita, annual retail earnings, and manufacturing
annual earnings were lower in the REA counties, although the number of manufacturing
establishments were slightly higher in the REA counties. These differences in the features of
the REA and nonREA levels of activity, led us to control for the starting level of key economic
variables as well as to perform the placebo described below.
15
Table 1 shows the changes in outcomes experienced in both REA and nonREA counties
from 1929 to 1939. It is important to realize that the comparisons that follow here are made
between a peak economy in 1929 and the situation in 1939 as the economy was still recovering
from the ravages of the Great Depression. The counties that received REA loans were able to
stave off many of the negative changes wrought by the Great Depression better than the counties
that did not. The total value of farm products, the value per farm, and value per acre all fell in
both types of counties between 1929 and 1939; however the REA counties experienced smaller
declines. The value of crop output per farm actually rose for the REA counties while falling in
the nonREA counties. Meanwhile, both types of counties produced a higher value of animal
products and tended to use more of their output on their own farms with an average increase that
was larger in the REA counties. The value of land and buildings per farm deflated by farm
product prices fell by roughly the same amount in both types of counties, while the value of farm
implements and machinery per farm fell much less in the REA counties. Farm operators in both
REA and nonREA counties increased their average days worked off of the farm by nearly 30
days, but the rise was lower in the REA counties. Between 1935 and 1939 farms in both types of
counties used more hired labor and less family labor but the REA counties increased their use of
hired labor less and reduced their use of family labor less. The population grew in both the REA
and nonREA counties but grew by more people in the REA counties. The share of the rural farm
population dropped in both counties but by less in the REA counties, suggest less movement off
of the farm there. Retail sales per capita fell less between 1929 and 1939 in the REA counties.
Meanwhile, alternative annual earnings moved in opposite directions. Annual retail earnings fell
more in the REA counties but annual manufacturing earnings fell less, as did manufacturing
value added per acre and the number of manufacturing establishments. These summary statistics
16
show the associations between the REA and the various dimensions of economic activity, while
the analysis below examines whether these same relative changes are present after controlling for
a broad range of correlates and whether we can assign a causal role to the REA.
5. Empirical Strategy
When evaluating the effects of large infrastructure projects, researchers must carefully
consider the fact that the programs often have specific goals, and are therefore, not placed
randomly. In regards to the REA, we may be concerned that the administrators were either
positively or negatively selecting locations that would either be the most able to repay the loans,
or that were most likely to experience growth as a result of electrification. Thus, OLS estimates
may suffer from omitted variables bias. In the growing literature that evaluates the rollout of
electric infrastructure projects, previous studies have relied on variation in the cost of installing
infrastructure associated with differences in topography or the cost minimizing placement of
long lived generation equipment (Dinkleman 2011, Grogan and Sadanand 2013, Lipscomb,
Mobarak, Barham 2012, Kitchens 2014). Given that agricultural outcomes are often correlated
with topography, these types of geographic based instruments are unlikely to satisfy the
exclusion restriction.10 For instance, one may suspect that the land gradient (a common
10
It has been suggested that we use our distance measures as instruments (distance to the grid,
distance to generation station) instead of using them as control variables. To verify that these
variables are not valid instruments, we instrumented for REA loans per farm with both distance
measures. Using over identification tests, we find that the p-values of the Hansen J-Statistic are
below 0.1, suggesting that the distance measures do not satisfy the exclusion restriction as
suspected. In addition to this specification, we have also attempted to instrument for REA loans
per farm by using political uncertainty (standard deviation of percent voting democrat 18961928) and the presence of House of Representatives members on the House Agricultural
Committee, as suggested by Wright (1974), Wallis(1998, 2001), and Fleck (1999,2001). When
we used these two variables as instruments, the first stage F-Statistic was 1.36, thus it is
extremely weak. Finally, we have also attempted instrumenting for REA loans with state level
regulatory variables that dictated how property of cooperative utilities would be taxed, and state
level legal changes that determined how rates of cooperatives would be regulated.
17
instrument) is correlated with the adoption of the tractor, or improvements in transportation
networks, both of which likely affect the types of crops planted (affecting the value of crops),
land values, and population movements. Because of these empirical problems, we rely on the
historic narrative to address the potential endogeneity problem by directly including variables
that the REA considered when selecting which projects to fund, namely, the proximity of the
project to existing electric transmission and generation infrastructure.
Below we outline our empirical strategy, which relies on a differences-in-differences
approach to identify the reduced form effect of REA electrification on a variety of outcomes
related to agricultural productivity, the use of agricultural inputs, the structure of the rural
household, and urban markets. Following the discussion of our empirical strategy, we present
two pieces of evidence in support of our main findings. First, we present a set of placebo
regressions to evaluate whether or not pre 1930 trends affected where REA funds were
disbursed. Secondly, after presenting the results, we discuss how large selection on
unobservables would have to be to invalidate our results using a framework developed by
Altonji, Elder, and Tabor (2005), and extended to an OLS framework by Bellows and Miguel
(2009).
Empirical Specification
We examine the influence of the REA by using information on the dependent variables from
two years, one before the REA was put into effect and another just as the first round of REA
loans made between July 1, 1935 and June 30, 1939 had ended. The year prior to the REA year
is either 1929, 1930, or 1935, while the REA year is either 1939 or 1940. For simplicity in the
discussion of the empirical model we will use 1930 and 1940 to designate the two years. Had
18
REA loans been randomly assigned, the average treatment effect of a REA loan can be estimated
with the following ordinary least squares regression:
(5) lnYit = α0 + α1D40 + β1 REAit + β30 Xi,30 + β40 Xi,30 D40 + β2 ND3339it + μi + εit,
lnYit is the natural log of the dependent variable in county i and year t—either 1930 or 1940. D40
is a dummy variable with value 1 in 1940 and zero in 1930. REAit is the value of REA loans in
the given county in the specified time period. They take on the value of zero in 1930 and the
cumulative value of loans between July 1, 1935 and June 30, 1939 per farm in 1940.11 The vector
Xi,30 contains a variety of pre-treatment correlates measured prior to the introduction of the REA.
We allow the pre-treatment correlates to have differential effects in 1930 and 1940 by interacting
Xi,30 with the 1940 dummy, such that β30 measures the impact of the Xi,30 correlates in 1930 and
β40 measures the difference in the impact of the same correlates in 1940. The baseline
characteristics in a pre-treatment year are used rather than the levels of observed characteristics
in 1940 because electricity is a general purpose technology, which likely affected many
outcomes simultaneously. Therefore, for each outcome, the REA coefficient measures the sum
of all of the effects on that outcome in 1940 that might have operated through a variety of
outcome measures, while controlling for the prior levels of a variety of covariates. For instance,
when examining the effect of the REA on manufacturing earnings, some of the REA effect may
have come through its impact on the unemployment rate in 1939 or 1940. The coefficient
captures the combination of the REA’s direct effect on wages as well as its indirect effect
through changes in unemployment, while taking into account the levels of both wages and
unemployment (and any other potential outcomes of interest) at the beginning of the decade.
11
We inflate these value to constant 1967 dollars.
19
In addition, we control for grants and loans from other New Deal programs in the vector
ND3339, so that we are not ascribing effects of the REA that actually were the result of other
New Deal programs. The New Deal measures include Agricultural Adjustment Administration
(AAA) grants per farm, Farm Security Administration (FSA) loans per farm, Farm Credit
Administration (FCA) loans per farm, other non-farm New Deal grants per farm and other nonfarm New Deal loans per farm. Finally, to control for time-invariant unobservable characteristics
in the counties, a vector of county fixed effects, μi, has been added to the equation.
We estimate the model in first differences. After subtracting the equation for 1930 from the
equation for 1940, and making adjustments, the final equation for estimation is
(6) lnYi40 - lnYi30 = α1 + β1 REAi3539 + β40 Xi,30 + β2 ND3339i + εi40 - εi30.
The equation takes this final form because α0, β30 Xi,30 and μi terms are the same in both years.
Because D40 is zero for the 1930 equation and one for the 1940 equation, α1 and β40 Xi,30 is the
outcome of the first difference for the terms where D40 appears. There were no REA loans or
New Deal programs in 1930, so the first difference is the level value for the REA for 1935
through 1939 and the New Deal levels for 1933 through 1939. The model controls for potential
endogeneity arising from pretreatment differences in the counties and time-invariant features of
the counties, while also controlling for spending and loans in other New Deal programs,
however, it is possible that REA funds were directed to locations with differential pre-trends,
which we will test for below.
After these controls are in place, the narrative analysis of the REA decision process
shows that the dominant factor determining the location of REA loans was the potential cost of
distributing electricity, which was strongly correlated with the distance from the existing
20
electricity grid.12 To control for this selection process, we include two additional covariates that
strongly affected the cost of obtaining electric service, the distance to the nearest generation
station and the distance to the nearest transmission line. Greater distance along distribution lines
meant more load loss due to resistance in the transmission lines and thus greater cost of
delivering electricity. The distances were constructed using geocoded maps from reports filed by
the FPC in 1935, which report the locations of central service stations and transmission lines.
Figure 2 shows the spatial distribution of the electric grid from 1935, digitized in ArcGIS.
Diamond shaped markers represent the location of electric generation stations, while lines
represent the location of transmission lines.
The REA explicitly included these costs as a major component of their decision to grant
loans to applicants, as highlighted in the discussion of the REA loan process. Both the proximity
to central electric stations and to large transmission networks influenced the costs of delivering
electricity, and thus, the financial success of loan applicants. These costs potentially were very
large; in 1935 the Federal Power Commission reported those distribution losses ranged from 10
to 40 percent of all power sales in the country (FPC 1935). In regressions of REA loans per farm
as a function only of the two distance measures and a constant, a one standard deviation (OSD)
increase in distance from a generation plant leads to a 0.12 standard deviation decrease in REA
loans per farm. For an OSD increase in the distance from the transmission grid, REA loans per
farm falls by 0.079 standard deviations. Combined, these variables explain 3.3 percent of the
variation in REA loans per farm.
12
It should be noted that the electric transmission grid was constructed by private holding
companies, primarily in the 1920s, following the introduction of new conductive materials, in an
effort to integrate generation facilities in distant locations with major markets, and to link major
markets together. The placement of the grid was primarily concerned with minimizing the cost of
connecting these locations, and was not driven by the future potential of rural electrification.
21
Placebo Testing
While our empirical strategy addresses endogeneity arising from differences in the initial
level of the outcome, it is possible that funds were directed to locations on differential pre-trends.
To test the effectiveness of our design and control variables, we estimate a set of placebo
regressions by examining changes in outcomes from 1920 to 1930, a period well before the
creation of the REA, as a function of REA loans per farm.13 Evidence of sizeable and
statistically significant relationships between the later REA loans and the changes in outcomes in
the 1920s would suggest that we have not controlled well for the REA selection process and
would suggest the presence of bias in the results for the 1930s that we estimate below. Because
of data limitations, we are only able to perform placebo regressions for some of the outcomes
measured here. Since the results consistently show the absence of a relationship between the
REA loans and the 1920s changes in outcomes that can be measured, it seems likely that the
results would be the same as for the other outcome measures.
Table 2 presents the results from the placebo regressions with and without the control
variables. Standard errors are presented in parenthesis and are clustered at the state level.14 The
clustering reduces problems with inference that might arise from correlation of unobservables
across counties within the same state. Column 1 reports the results without the inclusion of any
covariates while Column 2 adds the full set of covariates.
13
We use the dates 1920 to 1930 for the placebo because of data limitations. Ideally, we would
be able to investigate the trend up to 1935, just before the REA was established, which may help
alleviate concerns regarding the influence of the depression on the assignment of REA loans.
However, for many of our outcomes, the only pre-treatment data we observe in in 1929 or 1930,
thus we are not able to perform this more preferred test.
14
In all regressions we cluster the standard errors at the state level to allow for correlation within
states. We cluster at the state level instead of the county level because we estimate the
regressions in first differences, thus we only have a single observation per county.
22
In the estimates without controls in Column 1 of Table 2, the only statistically significant
coefficients are for population and the average value of land and buildings per acre. In both
cases, the coefficients have the opposite sign of the coefficients for those variables estimated in
Table 3 below; therefore, the implied selection bias works against the direction found in Table 3.
Once the correlates are included in the placebo regressions in column 2, none of the coefficients
are statistically significant and the magnitudes in most cases are substantially smaller than for the
coefficients estimated in the regressions in Table 3. When all of the placebo results are
considered, they appear consistent with the view that selection bias in the distribution of REA
loans is not a problem in the analysis.
The Impact of the REA
When discussing the impact of the REA, remember that population was growing in both
types of counties, but a decade of Depression and slow recovery left the economies in the
counties in the sample in worse shape than they had been in 1929. On most dimensions, except
population and crop output, the REA loans were transforming the counties where they were
received in an environment where nearly all counties had still failed to reach their 1929 levels by
1939. Thus, when we are discussing positive coefficients for the REA, in many situations we are
describing a setting where the REA counties performed better relative to the nonREA counties at
promoting recovery or staving off further declines.
The results in Table 3 show that the distribution of REA loans contributed to increases in
the value of crop output, and helped offset declines in overall agricultural output and
productivity per acre. Outside of agriculture the effects on the retail and manufacturing sectors
were generally small. Each entry in Table 3 shows the coefficients for separate regressions using
23
the value of REA loans per farm as the measure of REA access. For readability, we scale the
coefficient by 100 so that the coefficients can be interpreted as percentage points. Robust
standard errors clustered at the state level are reported in parenthesis.15 We will focus on the
results with the full set of pre-treatment controls in discussing the magnitudes of the effects since
these estimates are less likely to be affected by omitted variable and endogeneity bias.
Before discussing the impacts of the REA on agricultural, household, and urban
outcomes, we first want to highlight the fact that the REA did indeed increase rural
electrification rates. The changes highlighted in Table 1 show that electrification grew faster in
REA counties than non REA counties. However, it is likely that the REA stimulated activity
amongst private firms that sought to keep the rights to exclusive territories. This competitive
behavior was strong enough, that in some instances approved REA projects had to be abandoned,
thus, any estimate of the REA’s impact on rural electrification is likely to underestimate its true
effect. Despite these empirical problems, we find that the REA had a positive and statistically
significant impact on the farm electrification rate. For every $100 dollars in loans, the REA
increased rural electrification by 0.1985 log points. At the mean loan per farm, the REA loans
increased farm electrification rates by 0.153 log points. Between 1930 and 1940, the average
rural electrification rate in REA counties increased by 1.38 log points, thus, REA increased the
electrification rate by 11.08 percent.
The trends in the summary statistics in Table 1 show that the total value of farm output
declined between 1929 and 1939. The introduction of the REA into a county helped stave off
this decline relative to the nonREA counties through a complicated set of interactions by
15
We also calculate the Conley (1999) standard errors, allowing spatial correlation to exist
within 100 miles of the county seat. Our results are robust to this type of spatial correlation, thus
we report the standard errors clustered at the state level.
24
stimulating the value of output per farm, and the value of output per acre. The most
comprehensive measure of farm output is the real value of farm products sold, used, and traded
per farm, which we deflated by the wholesale price index for farm products.16 The authors of the
1940 Census considered this measure to be their closest approximation to gross farm income
(U.S. Bureau of the Census 1943, 869). In Table 3 the coefficient of the REA loans per farm is
0.0412, which implies that a one dollar increase in REA loans per farm raised the total value of
farm products, sold, traded, or used in the county by roughly four percent. The mean REA loan
per farm in REA counties of $77 was associated with a change in the natural log of the value of
farm output of 0.032 log points, which can be compared with a decline in the mean log value of
output of -0.052 log points in REA counties. This finding suggests that without the REA the
mean natural log of the value of output would have fallen by -0.084 log points rather than the
decline of -0.052 log points with the mean REA loan in place. Thus, the REA loan helped
prevent an additional 61.4 percent decline that would have occurred without the loan.
The positive effect of the REA loans was driven largely by an increase in the value of
farm products per farm and per acre, while having no statistically significant impact on the
number of farms or farm size. Here again, the productivity increases associated with the REA
worked to offset downward trends between 1929 and 1939. In the summary statistics for REA
counties in Table 1 both the value of farm products, sold, traded or used per farm and the same
value per acre fell by 9 and 13 percent respectively between 1929 and 1939. The REA loan
coefficients imply that the $77 average REA loan per farm was related to a rise of about 4.0 to
4.2 percent of the value of output per farm and per acre. Thus, without the REA the value of
16
The writers of the 1940 Census of Agriculture suggest that the value of farm products, sold,
used, or traded was their closest estimate of gross farm income U.S. Census Bureau, 1943,
volume III, 869. The Wholesale price index for farm products is series cc86 in Carter, et. al.
(2006, 3-175 to 3-176).
25
output per farm likely would have fallen by 13 percent, or about 44.5 percent more than the
decline that actually occurred. Similarly, the average REA loan per farm would have raised the
value of farm products per acre by 4.2 percent compared with a drop in the mean value of 13
percent with the REA loan in place; therefore, the REA loan helped prevent an additional 32.8
percent drop in the mean value of farm products per acre. The impact of the REA on the value
of farm products arise while controlling for the AAA grants per farm offered to farmers to take
land out of production, which also were considered to have raised output per acre but encouraged
larger farm sizes (Depew, Fishback, and Rhode, 2013). Meanwhile, the REA loans did not have
statistically significant relationships with the changes in the number of farms or the average
acreage per farm.
The REA loans were associated with increases in the value of output per farm across
several dimensions, the value of crop output, the value of animal products, and the value of farm
products used on the farmer’s own farm. The summary statistics in Table 1 show that the total
value of crops, the value of crops per farm, and the value per acre all rose in the REA counties,
while falling in the nonREA counties. We have two measures of the value of crops per farm.
One is based on crop output figures multiplied by estimates of county prices, and the second is
based on the farmers’ answers to the questions about the value of crops sold or traded. Both are
deflated using the wholesale price index for farm products. The coefficients in Table 3 suggest
that the introduction of the mean REA loan of $77 per farm would have accounted for 59.07
percent of the rise in mean total crop value in the REA counties, 35.68 percent of the rise in crop
value per farm, and 104.05 percent of the rise in the value of crops sold or traded on the farm.
The effect of the REA loan value per farm for the crop value sold or traded per farm is weaker
26
and accounts for only 14.51 percent of the change in means over the decade, while the
coefficient is not statistically significant.
The REA loans also were associated with increases in the real value of livestock products
sold or traded per farm and in the per farm value of farm products used on the farmers’ own
farm. A move from no loan to the mean REA loan per farm contributed 18.9 percent of the rise
in the mean value of livestock products sold or traded per farm and 9.05 percent of the rise in
mean products used by the farmer on his own farm.
The REA’s stimulus of the value of output per farm and per acre helped to offset a
decline in the value of the land per acre and the value of land and buildings per acre between
1930 and 1940 in REA counties. The Census reported estimates for both, but the agricultural
reporters were not convinced that the separate estimates for the value of buildings and the value
of land that they received were as good as the estimates of the combined value of land and
buildings because the purchase price of most farms was the combined value of both land and
buildings (U.S. Census Bureau, 1943, volume, p. 27.) The REA coefficients imply that the REA
loans led to increases in both the value of land per acre and the values of land and buildings per
acre that worked to offset the overall decline. In the absence of the average REA loan the mean
natural log of the value of land and buildings per acre would have fallen by -0.2262 log points.
This fall would have been 24 percent more than the actual 0.18 log point fall. The results for the
natural log of the value of land per acre were similar; the REA loans helped the value of land on
farms fall by only 0.22 log points percent rather than 0.266 log points.
One contributor to the positive relationship between REA loans and output per acre was
an increase in the real value of farm implements and machinery per farm. The values were
27
deflated by the Wholesale Price Index for farm machinery. The REA loans slowed a trend
toward less use of machinery. Between 1930 and 1940 the mean natural log of the value of
machinery per farm fell by 2.3 percent in the REA counties in Table 1. However, the REA loan
coefficient for machinery shows that with no REA loans the mean value of machinery per farm
would have fallen by 6.2 percent; thus, the REA loans staved off an additional decline that would
have been 171.11 percent more negative than the actual decline.
The REA stimulus to farm activity led to one significant change in the use of labor inputs
on farms. In counties receiving the average REA loan per farm, farm operators reduced the
number of days they worked off of the farm by about 3 percent, which worked against a 30
percent rise in the mean days worked off the farm between 1935 and 1939. The REA loans
possibly increased the use of family labor and hired labor on the farms, but the REA loan
coefficient was statistically insignificant in each case.
Despite the rise in the real value of output per farm, the REA loans appear to have had
little impact on population change in the county. The REA loan coefficients in the population
and net migration equations were positive, and the population coefficient implies that the mean
REA loan would have contributed 30 percent of the rise in average population in REA counties,
but none of the coefficients were statistically significant.17
The REA loans did have a positive effect on population through salutary effects on infant
mortality. Table 1 shows that the REA counties experienced a 29 percent decline in the infant
17
In the migration equation we had to shift away from using the semi-log because net migration
had a large number of negative values, and the log of a negative number is undefined. In
addition we ran a cross-sectional regression rather than a difference regression because the net
migration measure is a measure for the entire decade calculated as the change in population
minus the natural increase in population, in which the natural increase is defined as births minus
deaths. See Fishback, Horrace, and Kantor (2006) for more detail.
28
mortality rate between 1930 and 1940. This trend was similar in many counties throughout the
U.S. The negative coefficient of -0.047 implies that the REA helped account for 12.4 percent of
the decline in mean infant mortality rates in the REA counties. The REA loans were also
associated with lower fertility rates but the effect was not statistically significant.
The effects of the REA on agriculture spilled over into increases in retail consumption
that worked against the trend toward lower retail consumption between 1929 and 1939. The
mean of the natural log of real retail sales per capita dropped by 0.426 log points in the REA
counties between 1929 and 1939 The REA had a statistically significant positive impact that
would have pushed against this trend by 0.033 log points. The rise in retail sales per capita
associated with the REA loans did not lead to higher annual earnings on retail sales. The REA
coefficient in the real annual retail earnings regression was negative and statistically significant
and contributed about 12 percent to the decline in retail annual earnings between 1929 and 1939
in the REA counties.
In manufacturing the REA loans were associated with increased numbers of firms and
possibly higher average annual earnings and value added per worker. The changes in means in
the REA counties between 1929 and 1939 suggested sharp drops in manufacturing activity. The
introduction of the REA helped to counteract these trends somewhat but only the REA
coefficient in the equation for the number of firms is statistically significant. At the mean level
of REA loans per farm the REA stimulus would have been 9.75 percent, working against the
decline of 38 percent between 1929 and 1939.
Possible Selection on Unobservables
29
While our placebo tests suggested that REA loans were not directed towards counties on the
basis of the pre-trends, there still might be a possibility that the 1920s decade was structurally
different from the 1930s decade in ways that we have not controlled for with all of the correlates
included in the analysis. If true, the placebo tests might not fully rule out selection bias. With
this in mind, we suggest an alternative way to evaluate the likelihood that our estimates are
biased by omitted variables correlated with the REA and the outcomes. Following the work of
Altonji, Elder, and Tabor (2005) (Henceforth AET) and extended by Bellows and Miguel (2009),
we suggest that if the estimates are not sensitive to the inclusion of observables, then they are
also unlikely to be sensitive to unobservables.
In an AET framework, the set of observable explanatory variables, X, is a random subset of
the full set of explanatory variables, Z. If a researcher only includes observable X variables, the
estimated coefficient, 𝛽𝐶 , will be the true parameter value, 𝛽𝑇𝑟𝑢𝑒 , plus any remaining omitted
variables bias after the inclusion of X. In the absence of covariates, X, then the estimate, 𝛽𝑁𝐶 ,
would be the true parameter value plus all omitted variable bias. If the coefficients between these
two sets of estimates differ little, it would suggest that there is very little omitted variables bias.
Furthermore, because the X’s are randomly drawn from Z, it is unlikely that the addition of other
variables from Z not contained in X would further affect the estimates. As a rule of thumb, AET
suggest that the estimates should have a sensitivity ratio greater than 1, where the sensitivity ratio
is defined as 𝛽
𝛽𝐶
𝑁𝐶 −𝛽𝐶
. Using this formulation, we do not need to worry about selection on
unobservables if the coefficients are not diminished by more than half upon the inclusion of
controls.
30
When comparing the estimates in Column 1 and Column 2 of Table 3, it is apparent that the
majority of our estimates are not sensitive to the inclusion of additional covariates.18 Combined
with the set of placebo regressions, which indicated that REA and nonREA counties were on
similar trends prior to the creation of the REA, we believe that it is plausible that REA funds
were randomly distributed after the controls put in place.
Robustness Checks
In our main specification, we restricted the sample in two ways. First, we imposed a
population threshold of 20,000, and we excluded states in the Northeast and West. In what
follows, we test the robustness of our results to these arbitrary sample restrictions. First, we relax
the population threshold by re-estimating the model for a different set of population thresholds:
40,000 and 100,000. Secondly, we relax the restriction on the set of states included. The results
from this specification are presented in Table 4. In each specification, we include the full set of
covariates and cluster the standard errors at the state level. We present our baseline results in
Column 1 as a reference for the reader.
In Column 2, we present the results when the sample includes all counties with under
40,000 in population. In Column 3, we present the results from the inclusion of locations with
less than 100,000 in population. The evidence provided makes it clear that our results are not
driven by sample selection arising from our imposed population cutoff. In Column 4 we present
the results using all counties in the United States with a population under 20,000. In general, the
18
6 of 27 outcomes have coefficients that fall in magnitude by more than 50 percent, these are
the (1) Net Migration (2) Percent Rural Farmers (3) Acres per Farm (4)Value of Farm Products
Sold, Traded, or Used per Acre 1929-1939, (5) Number of Hired Laborers per Farm (6) General
Fertility Rate
31
results have the same sign as our baseline results, however, in general the magnitudes are
attenuated, which should be expected given the scope of the program.
In addition to these changes in sample specification, there may be other lingering
concerns. First, a large portion of our sample lies in the Great Plains, which experienced a major
ecological change due to the Dustbowl. Failure to account for the changing conditions in the
plains could bias our estimates of the REA’s impact. We address this concern in three ways (i)
we include measures of the Dustbowl’s impact on erosion by including the Dustbowl severity
index utilized by Hansen and Libecap (2004) and Hornbeck (2012), (ii) we include measures of
the Palmer Drought Severity Index, annual precipitation, and annual average temperature, and
(iii) we drop all counties in the sample west of the 100th meridian. The results of these
specifications are reported in Appendix A and are qualitatively similar to our baseline
specifications, mitigating concern that the Dustbowl is influencing our main findings. A second
concern is that several of our REA cooperatives are located within the service territory of the
Tennessee Valley Authority (TVA). While Kitchens (2014) showed that the TVA had little
impact on rates charged to consumers, the TVA’s presence may affect our results if it helped
facilitate coordination with potential lenders. To address this concern, we drop all counties
located in Alabama, Mississippi, and Tennessee, because these are the states where the TVA had
electricity contracts as of 1940. The results of this specification are presented in Appendix A and
are qualitatively similar to our baseline results, alleviating concerns that we have confounded the
effect of the REA with the TVA. Finally, we have also re-estimated the model, dropping counties
that were unlikely to receive funding from the REA. These results, presented in the Appendix,
are similar to our main findings.
Discussion
32
The REA was different from many other electrification projects. First and foremost, it
was a loan program that focused on the construction of distribution lines and connecting
customers rather than direct investment in grid infrastructure and generation. It also worked
closely with loan applicants to design a system that was financially successful to minimize the
risk of default.
While it is difficult to measure the value of the in kind benefits that the REA provided
applicants through improvements in state legislation, wholesale contract negotiations,
management training, and engineering advice, it is possible to measure the interest subsidy. If
cooperatives had been able to acquire loans privately, the interest rate would have been at least 6
percent; however REA loan interest rates were set at approximately 3 percent, to be amortized
over 25 years (Slattery 1940).
The average REA loan contract was for a principal of $230,000 amortized over 25 years
at an interest rate of approximately 3 percent, with monthly payments of $1090.69. The average
farm mortgage rate was around 5 percent between 1935 and 1940, which would have led to
monthly payments of $1,344.56. Assuming that farm cooperatives or commercial entities obtain
the 5 percent interest rate, the ex ante interest subsidy at the time the REA loan was made would
have been the difference in the present value of the two streams of 300 monthly payments when
evaluated at 5 percent, or $43,389, which was 18.9 percent of the principal on the loan. The
actual ex ante subsidy was lower to the extent that cooperatives and firms could obtain lower
interest rates than for farm mortgages, but it might well have been higher if interest rates reached
Slattery’s estimates of 6 percent to the extent that the electrification projects were considered to
be riskier. This ex ante subsidy is comparable in size to the ex ante subsidies for the purchase
33
and refinance of one million nonfarm mortgages by the Home Owners’ Loan Corporation
(HOLC) found by Fishback, Rose, and Snowden (2013).
Ex post, the REA loans had a much better record of repayment than the HOLC loans. The
HOLC ended up foreclosing on 20 percent of their refinanced nonfarm home loans, although the
overall losses from the HOLC ended up at about 2 percent of the value of the loans refinanced.
In contrast, almost all REA loans from the 1935 to 1939 period were repaid, minimizing the ex
post costs to the government. As of December 31, 1942, three percent of borrowers were behind
on interest payments, 0.7 percent were behind on principal payments, while 37 percent of loans
had been repaid in full, and another 53 percent were ahead of schedule on principal repayments
(REA 1943).19 This contrasts with figures for the HOLC in which one-third or more of
borrowers were more than 3 months behind on principal and interest payments as late as 1940,
seven years after the start of that program. Given the speed at which loans were repaid, and the
relatively low default rates, the government’s primary expenditure came through its engineering
services, advising, and management training.
Conclusions
Electrification has been a central policy tool in developing countries as nations look to
invest in infrastructure. Several nations are currently seeking billions of dollars to extend and
make their infrastructure more reliable to attract firms and promote growth. We show here that
the REA’s extension of loans to cooperatives to electrify farms led to large productivity
improvements in agriculture, which in turn led to increases in land values. To identify the effect
of the REA on economic outcomes we use within county variation over time while controlling
19
As of 1960, less than 1 percent of all REA loans were more than 30 days delinquent (NRECA
1960).
34
for selection by including measures of cost associated with access to grid infrastructure, and then
performing placebo test that suggest a lack of selection bias.
We find that during the period 1930-1940, the introduction of the REA led to increases in
agricultural productivity, which led farmers to seek employment off the farm less often, ,
increased property values, and reduced infant mortality. The REA made rural life more
attractive by increasing productivity on the farm and improving amenities in the home. As
developing nations introduce electric grids to urban and rural areas, there will be a spatial impact
on the population as people move to take advantage of changes in wages and the standard of
living associated with the newly constructed infrastructure. We believe that this study sheds light
onto this policy debates to inform policy makers of ways to improve the lives of individuals
living without electricity, particularly in rural communities.
35
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39
Tables and Figures
Figure 1: Service Area of Individual REA Funded Projects 1939
Figure 2: 1935 Electric Transmission Grid in United States
40
Figure 3: Counties in Sample
Note: Counties with boundaries outlined appear in the sample. All of these counties have less
than 20,000 in population as of 1930. Counties shaded in gray are control counties. Counties
shade in black are treated counties.
Figure 4: Distribution of REA Loans Per Farm 1935-1939
41
Table 1: Summary Statistics 1930-1940
Net Migration 30-40
Percent Farms Electrified
Percent Rural Farmers 30-40
Population 30-40
Total Value of Farm Products
Sold, Traded, or Used in County
29-39
Number of Farms 29-39
Value of Farm Products Sold,
Traded or Used Per Farm 29-39
Value of Farm Products Sold,
Traded or Used per Acre 29-39
Acres per Farm, 29-39
Average Value of Crops Sold or
Traded 29-39
Average Value of Livestock
Products Sold or Traded 29-39
Average Farm Products Used on
Farmer's Own Farm 29-39
Crop Value per Farm 29-39
Crop Value per Acre 29-39
Total Crop Value 29-39
Value of Land per Acre 30-40
Value of Land and Building per
Acre 30-40
Value of Farm Implements and
Machinery per Farm 30-40
Average No. Days Worked Off
Farm by Farm Operator 35-39
Number of Hired Laborers per
Farm September 1939 minus
January 1935
Number of Family Working on
Farm per Farm September 1939
minus January 1935
Infant Mortality Rate 30-40
General Fertility Rate 30-40
Retail Average Annual Earnings
29-39
Mfg. Avg Annual Earnings 29-39
Mfg. Value Added Per Worker 2939
No. Mfg. Establishments 29-39
Retail Sales Per Capita 29-39
Number of Counties in NonMfg.
Difference 1930-1940 in Levels
REA = 0
REA = 1
Mean
Mean
Diff-n-Diff
-6.98
-5.23
1.74
0.11
0.16
0.05
-4.19
-3.14
1.05
322.92
463.70
140.78
Difference 1930-1940 in Logs
REA = 0 REA = 1
Mean
Mean
Diff-n-Diff
-6.977
-5.234
1.742
1.072
1.380
0.308
-0.077
-0.056
0.022
0.010
0.028
0.018
-397.81
-33.27
-42.07
-54.94
355.75
-21.67
-0.193
-0.029
-0.052
-0.038
0.142
-0.009
-623388
-190493
432895
-0.223
-0.090
0.133
-1.78
61.22
-1.28
15.01
0.50
-46.21
-0.287
0.094
-0.129
0.078
0.158
-0.016
-116.17
229.64
345.80
0.003
0.252
0.248
252.95
91.36
-161.59
0.098
0.202
0.104
48.07
-221.91
-0.30
-251873
-10.64
62.69
218.56
0.97
291767
-10.50
14.62
440.47
1.27
543640
0.14
0.172
-0.098
-0.193
-0.129
-0.361
0.230
0.134
0.057
0.096
-0.217
0.058
0.232
0.250
0.225
0.144
-12.24
-11.85
0.39
-0.327
-0.182
0.145
-211.45
-32.86
178.59
-0.191
-0.023
0.168
32.82
28.25
-4.57
0.355
0.301
-0.054
0.09
0.04
-0.05
0.223
0.203
-0.020
-0.23
-13.67
-11.94
-0.25
-13.81
-9.44
-0.02
-0.14
2.50
-0.169
-0.227
-0.162
-0.170
-0.292
-0.116
-0.001
-0.065
0.046
-187.67
-358.91
-243.09
-333.63
-55.42
25.29
-0.273
-0.467
-0.330
-0.455
-0.058
0.012
-783.65
-3.29
-100.92
580
-689.52
-4.04
-74.75
759
94.13
-0.75
26.17
-0.331
-0.383
-0.492
580
-0.315
-0.376
-0.426
759
0.016
0.007
0.066
42
Sample
Number of Counties in Mfg.
Sample
278
461
278
461
Table 2: Placebo Regressions
REA = REA Loans per Farm
Y = Ln(Y30) – Ln(Y20)
Crop Value per Farm 19-29
Crop Value per Acre 19-29
Total Crop Value 19-29
Change in Farmland 1920-1930
Change in Number of Farms 1920-1930
Acres of Farm Land per Farm
Value of Implements and Machinery 1920-1930
Average Value of Implements and Machinery per
Farm
Average Value of Land and Buildings per Farm
Average Value of Land and Buildings per Acre
No Controls
Controls
1
2
-0.0004
0.0001
(0.0003)
(0.0002)
-0.0003
0.0004
(0.0003)
(0.0003)
-0.0004
0.0004
(0.0003)
(0.0003)
-0.0001
0.0000
(0.0001)
(0.0001)
-0.0001
0.0001
(0.0001)
(0.0002)
0.0000
-0.0001
(0.0002)
(0.0003)
-0.0005
0.0001
(0.0003)
(0.0001)
-0.0005
0.0000
(0.0003)
(0.0001)
-0.0005
-0.0004
(0.0003)
(0.0003)
-0.0005
*
(0.0003)
Population
-0.0002
(0.0000)
Number of Manufacturing Establishments
Average Annual Earnings Manufacturing Workers
Manufacturing Value Added per Worker
-0.0002
(0.0002)
***
0.0000
(0.0000)
-0.0004
-0.0001
(0.0003)
(0.0003)
0.0002
0.0000
(0.0001)
(0.0001)
0.0003
-0.0001
(0.0002)
(0.0002)
Note: Each entry in this table comes from a separate regression. Robust standard errors clustered at the state level
are in parentheses. New Deal control variables are for the period 1933 through 1939: AAA Spending per farm, FSA
and FCA Loans per Farm, New Deal Grants per farm and New Deal loans per farm. The other control variables are
43
from 1920 (or other year) and are the distance from the nearest generation station, the distance from the transmission
grid, population, number of farms, percent of the population illiterate, the percent black, percent foreign born,
percent of acres operated by tenants, and crop value (1920).
Table 3: Main Results Y = Ln(Y40) – Ln(Y30)
Net Migration 30-40
CHANGE IN
Percent Farms Electrified
Percent Rural Farmers 30-40
CHANGE IN LOG VALUE OF
Population 30-40
Total Value of Farm Products Sold,
Traded, or Used in County 29-39
Number of Farms 29-39
Value of Farm Products Sold, Traded
or Used Per Farm 29-39
Value of Farm Products Sold, Traded
or Used per Acre 29-39
Acres per Farm, 29-39
Average Value of Crops Sold or
Traded 29-39
Average Value of Livestock
Products Sold or Traded 29-39
Average Farm Products Used on
Farmer's Own Farm 29-39
Crop Value per Farm 29-39
OLS - No Controls
OLS - Controls
1
2.0727
(1.2957)
2
0.7825
(0.6210)
0.0906
(0.0633)
0.0084
(0.0074)
0.1985
(0.0486)
0.0031
(0.0065)
0.0141
(0.0123)
0.0111
(0.0068)
0.0713
(0.0380)
0.0184
(0.0113)
*
0.0897
(0.0416)
***
OLS
Coefficient
times $77
average
for loans
per farm
Change in
Mean of
ln(dependent
variable) in
REA
counties
3
0.602525
4
-2.784
Change
due to
REA as
percent
of
overall
change
col 3 as
% col 4
-21.65
0.152845
1.324
11.55
0.002387
-0.053
-4.49
0.008547
0.039
22.12
0.031724
-0.038
-83.08
0.008316
-0.043
-19.44
0.0412
(0.0217)
0.0108
(0.0090)
*
**
0.052
(0.0251)
**
0.04004
-0.081
-49.42
0.1032
(0.0412)
-0.0319
(0.0121)
**
0.055
(0.0259)
-0.0138
(0.0126)
**
0.04235
-0.125
-33.89
-0.010626
0.087
-12.26
0.1582
(0.0612)
**
0.036498
0.270
13.50
**
0.0474
(0.0365)
0.0308
(0.0310)
0.0496
(0.0233)
**
0.038192
0.215
17.73
0.0054
(0.0182)
0.115
0.027
(0.0104)
0.0622
**
0.02079
0.248
8.38
**
0.047894
0.154
31.09
*
44
Crop Value per Acre 29-39
Total Crop Value 29-39
Value of Land per Acre 30-40
Value of Land and Building per Acre
30-40
Value of Farm Implements and
Machinery per Farm 30-40
Average No. Days Worked Off Farm
by Farm Operator 35-39
Number of Hired Laborers per Farm
September 1939 minus January 1935
Number of Family Working on Farm
per Farm September 1939 minus
January 1935
Infant Mortality Rate 30-40
General Fertility Rate 30-40
Retail Average Annual Earnings 2939
Mfg. Avg Annual Earnings 29-39
Mfg. Value Added Per Worker 29-39
No. Mfg. Establishments 29-39
Retail Sales Per Capita 29-39
(0.0578)
0.1474
(0.0620)
0.1343
(0.0621)
0.0398
(0.0425)
**
**
0.0519
(0.0390)
(0.0284)
0.0766
(0.0337)
0.0735
(0.0330)
0.0552
(0.0223)
**
0.058982
0.067
87.63
**
0.056595
0.111
50.92
**
0.042504
-0.211
-20.12
0.0569
(0.0209)
**
0.043813
-0.175
-25.10
0.0719
(0.0375)
*
0.051
(0.0184)
**
0.03927
0.001
6796.47
-0.0505
(0.0218)
**
-0.0363
(0.0131)
**
-0.027951
0.281
-9.96
0.0128
(0.0272)
0.009856
0.213
4.63
0.0179
(0.0115)
-0.0471
(0.0240)
-0.0054
(0.0145)
0.013783
-0.177
-7.81
-0.036267
-0.305
11.89
-0.004158
-0.083
4.99
-0.039809
-0.294
13.54
0.013629
-0.408
-3.34
0.044583
-0.304
-14.66
*
0.036652
-0.281
-13.06
***
0.033187
-0.414
-8.02
-0.0285
(0.0371)
0.0354
(0.0118)
-0.0794
(0.0261)
0.0057
(0.0171)
***
-0.0586
(0.0269)
0.0064
(0.0186)
0.0056
(0.0400)
0.0856
(0.0363)
0.0677
(0.0172)
**
***
**
***
-0.0517
(0.0264)
0.0177
(0.0194)
0.0579
(0.0470)
0.0476
(0.0247)
0.0431
(0.0115)
*
*
Note: Each entry in this table comes from a separate regression. Coefficients and standard errors are scaled by 100.
Robust standard errors clustered at the state level are in parentheses. New Deal control variables are for the period
1933 through 1939: AAA Spending per farm, FSA and FCA Loans per Farm, New Deal Grants per farm, and New
Deal loans per farm. The number of farms and population are from 1930. The other control variables are from 1930
(or other year) and are the population, percent in manufacturing of gainful workers, number of farms, percent of the
population illiterate, the percent unemployed, the number of farms, the percent black, percent married, percent
divorced, percent foreign born, percent of acres operated by tenants, per capita crop value (1929), and percent
owning a radio.
45
Table 4: Robustness Specifications
Main Sample
<20K
Net Migration 30-40
<40K Pop
0.7825
1.1312
(0.6210)
(0.6133)
*
All States
<100K
1.1312
<20K Pop
*
(0.6133)
< 40K Pop
0.3474
0.6462
(0.4792)
(0.4994)
CHANGE IN
Percent Farms Electrified
Percent Rural Farmers 30-40
0.1985
***
0.1874
***
0.1874
***
0.1078
***
0.1135
(0.0486)
(0.0492)
(0.0492)
(0.0270)
(0.0272)
0.0031
0.0003
0.0003
0.0041
0.0017
(0.0065)
(0.0067)
(0.0067)
(0.0053)
(0.0053)
0.0111
0.0117
(0.0068)
(0.0066)
***
CHANGE IN LOG VALUE OF
Population 30-40
Total Value of Farm Products Sold,
Traded, or Used in County 29-39
Number of Farms 29-39
Value of Farm Products Sold, Traded
or Used Per Farm 29-39
0.0412
*
Average Value of Crops Sold or
Traded 29-39
Average Value of Livestock
Products Sold or Traded 29-39
**
0.0122
0.0199
(0.0132)
0.0078
0.0078
0.0128
(0.0090)
(0.0088)
(0.0088)
(0.0066)
0.052
**
0.055
0.0617
**
(0.0228)
**
0.0652
0.0617
**
(0.0228)
***
(0.0229)
0.0652
0.025
*
(0.0229)
0.0301
*
0.0332
*
(0.0164)
0.0357
-0.0114
-0.0114
-0.0179
(0.0118)
(0.0095)
(0.0086)
0.0474
0.06
0.0018
0.0143
(0.0365)
(0.0326)
(0.0266)
(0.0275)
0.0157
0.0218
(0.0110)
(0.0120)
0.0496
**
0.027
0.0622
0.0766
0.0735
(0.0330)
0.0543
(0.0326)
**
(0.0230)
**
0.0272
0.0721
***
0.0839
**
0.0802
(0.0299)
0.0272
***
0.0721
**
0.0839
**
**
(0.0305)
**
0.0802
(0.0299)
0.0142
(0.0058)
(0.0260)
(0.0305)
**
**
(0.0096)
(0.0260)
**
0.0543
(0.0230)
(0.0096)
**
*
**
**
**
(0.0164)
(0.0118)
0.06
**
(0.0135)
-0.0138
*
**
(0.0061)
(0.0130)
***
0.0133
(0.0126)
(0.0337)
Total Crop Value 29-39
0.0539
0.0111
(0.0051)
0.0108
(0.0284)
Crop Value per Acre 29-39
**
*
(0.0052)
(0.0117)
(0.0104)
Crop Value per Farm 29-39
(0.0066)
0.0094
(0.0209)
(0.0233)
Average Farm Products Used on
Farmer's Own Farm 29-39
*
(0.0209)
(0.0259)
Acres per Farm, 29-39
0.0117
(0.0217)
(0.0251)
Value of Farm Products Sold, Traded
or Used per Acre 29-39
0.0539
*
*
**
-0.0158
0.0137
(0.0053)
0.0074
0.0162
(0.0245)
(0.0243)
0.0258
0.0323
(0.0285)
(0.0273)
0.0206
0.0298
(0.0241)
(0.0237)
46
*
*
**
Value of Land per Acre 30-40
0.0552
**
(0.0223)
Value of Land and Building per Acre
30-40
0.0569
**
(0.0209)
Value of Farm Implements and
Machinery per Farm 30-40
0.051
Number of Hired Laborers per Farm
September 1939 minus January 1935
Number of Family Working on Farm
per Farm September 1939 minus
January 1935
Infant Mortality Rate 30-40
General Fertility Rate 30-40
Retail Average Annual Earnings 2939
Mfg. Avg Annual Earnings 29-39
Mfg. Value Added Per Worker 29-39
-0.0363
**
**
0.0648
-0.0382
**
**
0.0532
**
(0.0210)
***
0.0648
***
(0.0192)
***
-0.0382
0.0286
**
(0.0118)
***
0.0323
0.0264
***
0.0311
(0.0118)
(0.0118)
0.0136
0.0246
(0.0128)
(0.0149)
-0.0115
-0.0136
(0.0133)
(0.0133)
(0.0108)
(0.0101)
0.0128
-0.0014
-0.0014
0.0098
0.0044
(0.0272)
(0.0238)
(0.0238)
(0.0144)
(0.0149)
0.0179
0.0167
0.0048
0.0059
(0.0115)
(0.0097)
(0.0097)
(0.0063)
(0.0059)
-0.025
-0.025
-0.0266
-0.017
(0.0240)
(0.0163)
(0.0163)
(0.0169)
(0.0138)
-0.0054
0.0036
0.0036
0.0103
0.0133
(0.0145)
(0.0141)
(0.0141)
(0.0073)
(0.0071)
-0.0471
-0.0517
*
*
-0.0454
*
**
0.0167
-0.0454
*
**
-0.0279
**
-0.0279
(0.0264)
(0.0217)
(0.0217)
(0.0122)
(0.0110)
0.0177
0.0117
0.0117
0.0051
0.0068
(0.0194)
(0.0150)
(0.0150)
(0.0115)
(0.0098)
0.0579
0.0497
0.0497
0.0254
0.0305
(0.0392)
(0.0392)
(0.0319)
0.0354
0.0354
0.0372
(0.0222)
(0.0222)
(0.0170)
(0.0150)
0.0103
0.0185
(0.0079)
(0.0086)
0.0476
*
0.0431
(0.0115)
***
0.051
(0.0105)
***
0.051
(0.0105)
***
**
(0.0121)
(0.0131)
(0.0247)
Retail Sales Per Capita 29-39
0.0532
0.0505
(0.0228)
(0.0192)
(0.0470)
No. Mfg. Establishments 29-39
**
(0.0210)
(0.0184)
Average No. Days Worked Off Farm
by Farm Operator 35-39
0.0505
(0.0228)
**
*
**
(0.0283)
**
0.0377
Notes: See notes from Table 3
47
**
**
Appendix A: Additional Robustness Checks, Dust Bowl, TVA, Matching
1
Net Migration 30-40
2
3
4
5
6
7
2.0727
0.7825
0.6552
0.6571
0.5074
0.7825
0.6437
(1.2957)
(0.6210)
(0.6183)
(0.6078)
(0.7522)
(0.6210)
(0.6554)
0.0906
0.1985
(0.0633)
(0.0486)
CHANGE IN
Percent Farms Electrified
Percent Rural Farmers 30-40
CHANGE IN LOG VALUE
OF
Population 30-40
Total Value of Farm Products
Sold, Traded, or Used in
County 29-39
Number of Farms 29-39
Value of Farm Products Sold,
Traded or Used Per Farm 2939
(0.0492)
0.1967
***
(0.0457)
0.1964
***
(0.0552)
0.1985
***
(0.0486)
0.1897
0.0031
0.0022
0.0031
-0.0055
0.0031
-0.0049
(0.0060)
(0.0067)
(0.0067)
(0.0065)
(0.0053)
0.0141
0.0111
0.0093
0.0097
0.0041
0.0111
0.0064
(0.0123)
(0.0068)
(0.0061)
(0.0067)
(0.0059)
(0.0068)
(0.0056)
0.0713
0.0344
0.0412
(0.0380)
(0.0217)
(0.0220)
(0.0196)
(0.0202)
(0.0217)
(0.0237)
0.0184
0.0108
0.008
0.0101
-0.0055
0.0108
-0.0041
(0.0113)
(0.0090)
(0.0073)
(0.0090)
(0.0072)
(0.0090)
(0.0081)
0.0897
*
**
0.1032
-0.0319
0.1582
0.0412
0.052
*
**
(0.0251)
**
0.055
(0.0259)
**
**
0.0419
0.0498
*
**
(0.0242)
**
0.0533
(0.0251)
0.0395
0.0496
*
**
(0.0225)
**
0.054
(0.0246)
**
0.0289
0.052
(0.0206)
(0.0251)
0.0412
(0.0235)
*
0.055
(0.0259)
***
(0.0668)
(0.0065)
(0.0121)
Average Value of Crops Sold
or Traded 29-39
***
0.0084
(0.0412)
Acres per Farm, 29-39
0.1967
(0.0074)
(0.0416)
Value of Farm Products Sold,
Traded or Used per Acre 2939
***
*
**
0.0544
0.0503
**
**
(0.0236)
**
0.0475
*
(0.0253)
-0.0138
-0.0114
-0.0145
-0.0069
-0.0138
0.0069
(0.0126)
(0.0116)
(0.0140)
(0.0121)
(0.0126)
(0.0086)
0.0474
0.0467
0.0506
0.0136
0.0474
0.0414
48
(0.0612)
Average Value of Livestock
Products Sold or Traded 29-39
Average Farm Products Used
on Farmer's Own Farm 29-39
Crop Value per Farm 29-39
(0.0365)
0.0308
0.0496
(0.0310)
(0.0233)
0.0054
0.027
(0.0182)
(0.0104)
0.115
*
(0.0578)
Crop Value per Acre 29-39
0.1474
Value of Land per Acre 30-40
Value of Land and Building
per Acre 30-40
Value of Farm Implements and
Machinery per Farm 30-40
0.1343
**
Number of Hired Laborers per
Farm September 1939 minus
January 1935
Number of Family Working on
Farm per Farm September
1939 minus January 1935
(0.0330)
0.0398
0.0552
(0.0425)
(0.0223)
0.0519
0.0569
(0.0390)
(0.0209)
*
-0.0505
**
0.051
**
-0.0363
**
0.0256
**
0.0641
**
0.0761
**
0.0725
0.0546
**
0.0561
0.0515
**
-0.036
0.0804
0.0758
0.051
0.0531
**
0.0452
**
-0.0305
0.0337
***
(0.0104)
0.0415
0.0622
0.0484
(0.0337)
0.036
0.0735
0.0536
0.0477
-0.0382
0.0552
**
0.0569
**
0.051
-0.0363
0.0662
0.0594
**
0.0621
**
0.042
(0.0260)
**
0.0596
**
-0.0384
(0.0142)
(0.0131)
(0.0165)
-0.0285
0.0128
0.0126
0.0132
0.0159
0.0128
0.0028
(0.0371)
(0.0272)
(0.0278)
(0.0255)
(0.0277)
(0.0272)
(0.0279)
0.0179
0.0187
0.0165
0.0248
0.0179
0.0238
(0.0115)
(0.0115)
(0.0123)
(0.0123)
(0.0115)
(0.0126)
***
-0.0471
*
-0.0473
*
-0.0425
*
-0.0467
*
-0.0471
***
(0.0205)
(0.0118)
-0.0794
*
(0.0285)
(0.0133)
*
*
0.0402
(0.0131)
***
**
(0.0320)
**
(0.0218)
0.0354
**
(0.0322)
(0.0184)
**
0.0281
(0.0294)
**
(0.0209)
**
*
(0.0106)
(0.0223)
**
0.0423
(0.0230)
(0.0330)
**
(0.0183)
**
0.0766
(0.0279)
0.0528
**
(0.0284)
*
(0.0210)
**
0.027
(0.0108)
(0.0225)
**
0.0496
(0.0379)
(0.0233)
(0.0272)
**
(0.0164)
**
*
(0.0249)
**
(0.0199)
(0.0187)
**
**
(0.0213)
(0.0207)
**
0.0652
(0.0307)
**
(0.0222)
**
**
(0.0325)
(0.0313)
**
0.0229
0.0402
(0.0365)
(0.0216)
(0.0261)
**
(0.0320)
**
**
(0.0104)
(0.0284)
**
0.0471
(0.0322)
(0.0212)
(0.0107)
(0.0184)
(0.0118)
Infant Mortality Rate 30-40
0.0735
0.0477
(0.0359)
(0.0229)
(0.0337)
(0.0375)
Average No. Days Worked Off
Farm by Farm Operator 35-39
0.0766
(0.0621)
0.0719
**
(0.0284)
**
(0.0620)
Total Crop Value 29-39
0.0622
(0.0355)
*
**
*
-0.0443
49
General Fertility Rate 30-40
Retail Average Annual
Earnings 29-39
Mfg. Avg Annual Earnings 2939
Mfg. Value Added Per Worker
29-39
No. Mfg. Establishments 29-39
(0.0261)
(0.0240)
(0.0242)
(0.0226)
(0.0236)
(0.0240)
(0.0269)
0.0057
-0.0054
-0.0059
-0.005
0.0019
-0.0054
0.024
(0.0171)
(0.0145)
(0.0144)
(0.0142)
(0.0129)
(0.0145)
(0.0122)
-0.0302
-0.0517
-0.0586
**
*
-0.0471
*
-0.051
*
*
-0.0366
(0.0269)
(0.0264)
(0.0247)
(0.0265)
(0.0231)
(0.0264)
(0.0210)
0.0064
0.0177
0.0181
0.0149
0.0187
0.0177
0.0219
(0.0186)
(0.0194)
(0.0194)
(0.0190)
(0.0191)
(0.0194)
(0.0227)
0.0056
0.0579
0.0596
0.0594
0.065
0.0579
0.1008
(0.0400)
(0.0470)
(0.0471)
(0.0440)
(0.0484)
(0.0470)
(0.0554)
0.0856
**
(0.0363)
Retail Sales Per Capita 29-39
-0.0517
0.0677
(0.0172)
0.0476
*
(0.0247)
***
0.0431
(0.0115)
0.0472
*
(0.0249)
***
0.0438
(0.0118)
0.0465
*
(0.0258)
***
0.0449
(0.0129)
0.0495
*
(0.0253)
***
0.0263
(0.0122)
0.0476
*
(0.0247)
**
0.0431
(0.0115)
0.0494
*
*
*
*
(0.0267)
***
0.039
**
(0.0144)
See Notes to Table 3. Additionally, each specification reported in this table examines a specific sub-sample or includes additional covariates. Columns 1 and 2 replicate our
baseline results, reporting the coefficients without the inclusion of covariates and with covariates. Columns 3-5 address different concerns regarding the Dustbowl. Column 3
includes the Dustbowl measures used by Hansen and Libecap (2004), which uses a set of dummies to denote the severity of erosion. Column 4 replaces these measures with the
Palmer Drought Severity Index for 1930-32 and the average temperature and average precipitation in the county. Column 5 drops counties west of the 100 th meridian. Column 6
excludes counties located in Alabama, Mississippi, and Tennessee, which encompasses the service territory of the TVA in 1940. Finally, in Column 7 we report results from a
specification where we drop counties with a propensity score less than 0.4, where the propensity score is estimated by relating an indicator variable of the REA with the pretreatment X variables.
50
Appendix B: A Model of Farm Profitibility
Access to electricity potentially led to changes on a wide range of dimensions in rural areas. To
provide a framework for discussing these changes, we use a model developed by Roback (1982),
and used more recently by Greenstone, Hornbeck, and Moretti (2010), Moretti (2011), Hornbeck
and Keskin (2012), and Severnini (2012) to examine how electrification changed the
profitability of firms in a given location. We use the model to discuss the profitability of farms
because of the REA’s focus on providing access to electricity to farms in rural areas. In the
model, there are two locations, farms in each location have a production function with a location
specific productivity parameter, 𝐴. The farms use labor (L), capital (K), land (T), and energy
inputs (E) to produce a single internationally traded good with the price normalized to 1.20 The
farms in each location maximize the following profit function
(1) max 𝑓(𝐴, 𝐿, 𝐾, 𝑇, 𝐸) − 𝑤𝐿 − 𝑟𝐾 − 𝑞𝑇 − 𝑠𝐸.
𝐿,𝐾,𝑇,𝐸
The input prices are w for labor, r for capital, q for land, and s for energy. From the first order
conditions, farms choose the profit-maximizing levels of each input as a function of the factor
prices. For instance, 𝐸 ∗ (𝐴, 𝑤, 𝑟, 𝑞, 𝑠), is the optimal use of energy in a given location. Similarly,
the farm can solve for its optimal use of labor, 𝐿∗ (𝐴, 𝑤, 𝑟, 𝑞, 𝑠), capital, 𝐾 ∗ (𝐴, 𝑤, 𝑟, 𝑞, 𝑠), and
land, 𝑇 ∗ (𝐴, 𝑤, 𝑟, 𝑞, 𝑠). Farmers and workers are free to locate wherever they choose to maximize
their income and provide a fixed quantity of labor, capital is supplied elastically through a
20
Since the REA and cooperatives largely electrified farms, we model the profit decisions of
farms. Nonfarm firms in manufacturing, retailing, wholesaling and other activities can be
modeled in this way but likely with different production functions, while the prices for factors of
similar quality were likely to be similar across all economic activities because farms and
nonfarm firms competed for labor, land, building, and some forms of capital within the same
counties and nearby counties.
51
nationwide capital market, the supply of land in each location is fixed, and energy is supplied in
local markets with prices that may be regulated at state or regional levels.
When the REA gave farms access to electricity, it reduced the price of energy inputs (s),
which is expected to increase the productivity of farms, which in turn leads to increased demand
for labor. The demand for land is increased as farms are more productive and farmers wish to
produce more output. This would attract new farmers and farm workers moving into the areas.
The in-migration, would, in turn, require housing, which will add an additional boost to land
prices where the supply of land is upward sloping. The substitution of electricity for coal, wood,
or steam power leads to decreases in these energy prices. To hire more workers to relocate to the
area in a regular market, we would expect farm wages to rise and wages in manufacturing and
retail to rise as the employers try to prevent them from moving out.21 However, the high levels
of unemployment and the large number of people working less than full time in the 1930s would
likely dampen the wage effects (Fishback, Haines, Rhode 2012; Cole and Ohanian 2004, Taylor
2011). Capital is assumed to be traded in a national market so that local conditions do not
impact the price of capital.
The input prices depend on access to the REA. The productivity parameter, the wage, the
land price, and the electricity price are allowed to depend on access to the REA. By the mid
1920’s most states had adopted regulatory commissions, which often set state-wide upper bounds
on electric rates (Stigler and Freidland 1962). These rural locations did not previously have
access to electricity, so improved access to electricity would likely decrease local prices. To
21
We ignore the amenities effect on workers. If electricity does improve amenities, then even
more workers would be willing to relocate to REA served areas, likely leading to smaller
increases in wages in the rural location, and possibly larger wage increases in the urban location.
52
summarize all of the comparative statics, it is instructive to examine the change in short run
profits of an incumbent farm as a result of increased access to electricity via the REA.
The short run equilibrium profits for an incumbent farm can be written in terms of the
optimal level of inputs and their corresponding prices as well as the access to the REA, where we
denote access to the REA with the variable R.
(2) 𝜋 = 𝑓 (
𝐴(𝑅), 𝐿∗ (𝐴(𝑅), 𝑤(𝑅), 𝑟, 𝑞(𝑅), 𝑠(𝑅)), 𝐾 ∗ (𝐴(𝑅), 𝑤(𝑅), 𝑟, 𝑞(𝑅), 𝑠(𝑅)),
𝑇 ∗ (𝐴(𝑅), 𝑤(𝑅), 𝑟, 𝑞(𝑅), 𝑠(𝑅)), 𝐸 ∗ (𝐴(𝑅), 𝑤(𝑅), 𝑟, 𝑞(𝑅), 𝑠(𝑅))
)
− 𝑤(𝑅)𝐿∗ (𝐴(𝑅), 𝑤(𝑅), 𝑟, 𝑞(𝑅), 𝑠(𝑅)) − 𝑟𝐾 ∗ (𝐴(𝑅), 𝑤(𝑅), 𝑟, 𝑞(𝑅), 𝑠(𝑅))
− 𝑞𝑇 ∗ (𝐴(𝑅), 𝑤(𝑅), 𝑟, 𝑞(𝑅), 𝑠(𝑅)) − 𝑠𝐸 ∗ (𝐴(𝑅), 𝑤(𝑅), 𝑟, 𝑞(𝑅), 𝑠(𝑅))
To see how the short run profits of the incumbent farm change when farms gain access to the
REA, we take the total derivative of profits with respect to access to the REA loan program.
Differentiating with respect to access to the REA and collecting terms yields the following
relationship for farm profits.
(3)
𝑑𝜋
𝑑𝑓 𝑑𝐴
𝑑𝐴 𝑑𝐿 𝑑𝑓
𝑑𝐾 𝑑𝑓
𝑑𝑇 𝑑𝑓
𝑑𝐸 𝑑𝑓
= (𝑑𝐴 ∙ 𝑑𝑅) + 𝑑𝑅 {[𝑑𝐴 (𝑑𝐿 − 𝑤)] + [ 𝑑𝐴 (𝑑𝐾 − 𝑟)] + [𝑑𝐴 (𝑑𝑇 − 𝑞)] + [𝑑𝐴 (𝑑𝐸 − 𝑠)]} +
𝑑𝑅
𝑑𝑤 𝑑𝐿 𝑑𝑓
𝑑𝐾 𝑑𝑓
𝑑𝑇 𝑑𝑓
𝑑𝐸 𝑑𝑓
𝑑𝑞
𝑑𝐿 𝑑𝑓
{[ ( − 𝑤) − 𝐿∗ ] + [𝑑𝑤 (𝑑𝐾 − 𝑟)] + [𝑑𝑤 (𝑑𝑇 − 𝑞)] + [𝑑𝑤 (𝑑𝐸 − 𝑠)]} + 𝑑𝑅 {[𝑑𝑤 (𝑑𝐿 − 𝑤)] +
𝑑𝑅 𝑑𝑤 𝑑𝐿
𝑑𝐾 𝑑𝑓
𝑑𝑇 𝑑𝑓
𝑑𝐸 𝑑𝑓
𝑑𝑠
𝑑𝐿 𝑑𝑓
𝑑𝐾 𝑑𝑓
[𝑑𝑤 (𝑑𝐾 − 𝑟)] + [𝑑𝑤 (𝑑𝑇 − 𝑞) − 𝑇 ∗ ] + [𝑑𝑤 (𝑑𝐸 − 𝑠)]} + 𝑑𝑅 {[𝑑𝑤 (𝑑𝐿 − 𝑤)] + [𝑑𝑤 (𝑑𝐾 − 𝑟)] +
𝑑𝑇 𝑑𝑓
𝑑𝐸 𝑑𝑓
[𝑑𝑤 (𝑑𝑇 − 𝑞)] + [𝑑𝑤 (𝑑𝐸 − 𝑠)] − 𝐸 ∗ }
If inputs are purchased such that each input is paid its marginal product, then the previous
expression simplifies to
(4)
𝑑𝜋
𝑑𝑅
𝑑𝑓 𝑑𝐴
𝑑𝑤
𝑑𝑞
𝑑𝑠
= (𝑑𝐴 𝑑𝑅) − 𝑑𝑅 𝐿∗ − 𝑑𝑅 𝑇 ∗ − 𝑑𝑅 𝐸 ∗
Equation (4) shows that the REA affects farm profitability through four channels. Access to the
REA increases profits through increased farm productivity, lowers profits by raising wages,
53
decreases profitability by increasing land prices as farms and workers compete for land, raise
profits as a result of lower energy prices. The overall impact on the REA is ambiguous because it
is unclear if productivity gains and lower energy prices will be offset by more expensive labor
and land. Therefore, empirical analysis of a variety of measures of farm and nonfarm activity is
necessary to determine which effects are strongest.
54
55