Voter Influence and Big Policy Change: The Positive Political Economy of the New Deal
Author(s): Robert K. Fleck
Source: The Journal of Political Economy, Vol. 116, No. 1 (February 2008), pp. 1-37
Published by: The University of Chicago Press
Stable URL: http://www.jstor.org/stable/10.1086/528999 .
Accessed: 09/05/2011 14:53
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless
you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you
may use content in the JSTOR archive only for your personal, non-commercial use.
Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at .
http://www.jstor.org/action/showPublisher?publisherCode=ucpress. .
Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed
page of such transmission.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact [email protected].
The University of Chicago Press is collaborating with JSTOR to digitize, preserve and extend access to The
Journal of Political Economy.
http://www.jstor.org
Voter Influence and Big Policy Change: The
Positive Political Economy of the New Deal
Robert K. Fleck
Montana State University
What conditions cause major policy changes under representative government? This article addresses that question by providing a theoretically grounded analysis of a massive policy change: the New Deal.
It explains how the economic problems of the early 1930s initiated
changes on several dimensions of policy: federal spending, labor market regulation, and civil rights. The article concludes by considering
the broader lessons learned from the political economy of the New
Deal.
I.
Introduction
In his seminal paper on New Deal spending, Wright (1974) developed
and tested a theoretical model that predicts how a reelection-seeking
president will allocate funds: Higher per capita allocations will go to
states with swing electorates or more electoral votes per capita. Wright
found that New Deal spending patterns fit his model’s predictions and
that voting patterns changed in response to New Deal spending. These
findings provide much insight into the New Deal, but they alone cannot
explain what gave rise to the New Deal or, more generally, explain a
big policy change. Indeed, the basic electoral incentives analyzed by
Wright presumably would matter under very general circumstances—
essentially, whenever policy is set by representative governments, not
just during periods of major change.
For helpful comments, I thank Canice Prendergast, the anonymous referees, Jamie
Brown, Tony Cookson, Beth Davenport, Dino Falaschetti, Andy Hanssen, Chris Stoddard,
and seminar participants at Montana State University. I thank John Wallis for providing
much of the data used in this article.
[ Journal of Political Economy, 2008, vol. 116, no. 1]
䉷 2008 by The University of Chicago. All rights reserved. 0022-3808/2008/11601-0001$10.00
1
2
journal of political economy
My purpose in this article is to explain the origins of the New Deal:
Why did such massive policy changes take place? Using a combination
of theoretical and empirical analysis, I explain how the economic conditions of the early 1930s created a springboard for major changes in
federal spending, regulation, and civil rights. This helps to answer both
the historically important question of why the New Deal took the shape
it did and the more general question of how the institutional structure
of representative government shapes the way policy responds to changes
in the economy and to constituent interests.
My theoretical model examines several types of policy decisions. The
first is a basic divide-the-dollars decision, with implications similar to
those of Wright’s model: Swing states (relative to loyally partisan states)
obtain more federal spending because competition between political
parties gives those states more electoral weight; small-population states
obtain more federal spending per capita because the apportionment of
Senate seats and Electoral College votes gives those states more electoral
weight per capita. I then extend the basic model in two ways. The first
incorporates heterogeneity across states (or other political jurisdictions)
with respect to constituents’ marginal benefits from policy (i.e., constituents’ demand curves). The second considers one-dimensional policy
decisions (e.g., features of economic regulation and civil rights policy)
and formula-based spending. These extensions show why states with
greater electoral weight will have greater influence over many kinds of
policy (not just divide-the-dollars policy), and they also illuminate the
conditions under which policy changes will be large: when events occur
that cause the net benefits of changing policy to be concentrated among
constituencies with greater electoral weight.
Why does this matter for understanding the policy changes of the
1930s? Because the states with the most electoral weight shared other
important characteristics. New Dealers implemented what can be
thought of usefully as a bundle of policies, including labor market regulations, some improvement in the treatment of African Americans, and
numerous spending programs that served a variety of purposes (e.g.,
relief, public employment, civil works, road building, land reclamation).
My explanation of this bundle of policies centers around the fact that
the states with the most electoral weight differed systematically from
other states on several crucial dimensions: They suffered more severe
economic downturns at the onset of the Depression, had more land per
capita, and had a growing number of African American voters. Thus,
the electorates in politically influential states stood to gain from increased spending on relief, increased spending on land-oriented programs, and improved treatment of African Americans. This set the stage
for the electoral success of a coalition that could enact such policies.
Stated more precisely, my argument is that the magnitude of policy
political economy of the new deal
3
change depends on (i) shifts in the marginal net benefits that constituents receive from a given policy (essentially, shifts in constituents’ demand curves) and (ii) the extent to which the net benefits of policy
changes will accrue to constituents who have greater electoral weight.
The historical events leading up to the New Deal are consistent with a
combination of these two factors leading to a major policy change.
Consider the first factor in more detail. The Depression essentially
shifted constituents’ demand curves for more active economic policy.
As the 1932 Democratic landslide showed, voters disliked the Republicans’ approach to the economic crisis. With the country suffering from
massive unemployment, severe droughts, and dust-covered farmland,
politicians’ electoral success depended on convincing voters that their
policies would provide relief to the needy, address the unemployment
problem, and assist in the management and improvement of the nation’s
land and resources. The Democrats’ platform for dealing with the economic crisis emphasized relief programs, work projects, land and capital
improvements, and regulation. And voters widely expected better economic conditions to result from expanding these types of policies. Under such circumstances, the model predicts a policy change. So far, of
course, my argument fits the standard historical account, which emphasizes how the New Deal’s broad distribution of benefits helped to
build a broad coalition (e.g., Brady 1988).
Now consider the second factor: the extent to which the benefits of
policy changes accrue to constituents with greater electoral weight. As
this article shows, in the early 1930s, generating higher per capita spending in states with more electoral weight (as measured by Wright’s variables) could be accomplished by simply increasing the overall level of
spending and allocating funds according to criteria that voters would
have found reasonable: higher relief spending in states that were poorer
and more depressed and more land-oriented projects (roads, conservation, reclamation) in states that had more land. To demonstrate this
empirically, I consider hypothetical spending formulas. The data show
that a simple formula allocating dollars in proportion to income and
land variables could have generated state-level spending with a close
correlation to Wright’s measures of electoral weight; indeed, a simple
formula-based spending pattern could have generated a correlation
even higher than what Wright found for actual spending. These circumstances—that is, when measures of “need” are high in electorally
important states—are the conditions under which my model predicts a
major policy change.
This article makes parallel arguments with respect to other types of
policy. First, Depression-era voters—especially in states with greater electoral weight—favored increased labor market regulation. Second, the
migration of African Americans out of the South increased the number
4
journal of political economy
of African American voters, and it did so in states with greater electoral
weight; this increased the electoral value of policy favored by African
Americans. Again, these are conditions under which the model predicts
policy changes.
By showing how events preceding the New Deal set the foundation
for the huge change in policy brought by the New Deal, this article
contributes to the long line of research on critical elections, realignment, and the consequent policy changes (e.g., Key 1955; Burnham
1970; Ginsberg 1972, 1976; Sundquist 1973; Sinclair 1977, 1985; Clubb,
Flanigan, and Zingale 1980; Brady and Stewart 1982; Brady 1988; Miller
and Schofield 2003). My article’s principal contribution to this literature
is an improved understanding of why the critical election of 1932 led
to the types of changes—notably in distributive, regulatory, and civil
rights policy—enacted by New Dealers.
Of course, the article also contributes to the literature on New Deal
spending and, more generally, distributive politics. Several previous papers develop and apply theoretical models to New Deal data (e.g., Wright
1974; Fleck 1999a, 1999c, 2001a; Strömberg 2004). Many others analyze
New Deal spending (e.g., Arrington 1969; Reading 1972, 1973; Wallis
1984, 1987, 1991, 1998, 2001; Anderson and Tollison 1991; Couch and
Shughart 1998; Fleck 2001b; Bateman and Taylor 2003; Fishback, Kantor,
and Wallis 2003; Mason 2003). Another large literature develops theoretical models to analyze the conditions under which swing voters or
loyal supporters will receive higher allocations of funds (e.g., Cox and
McCubbins 1986; Dixit and Londregan 1996; Ansolabehere and Snyder
2006). Empirical work addressing questions similar to those I address,
but not about the New Deal, includes Bennett and Mayberry (1979),
Atlas et al. (1995), Levitt and Snyder (1995, 1997), Bender and Lott
(1996), Bronars and Lott (1997), Husted and Kenny (1997), Lee (1998,
2000, 2004), Lee and Oppenheimer (1999), Levitt and Poterba (1999),
Lott and Kenny (1999), Johnson and Libecap (2003), and Hoover and
Pecorino (2005).
II.
The Historical and Scholarly Controversy over the New Deal’s
Allocation of Benefits
A natural place to begin a discussion of the literature on the allocation
of New Deal benefits is with the way New Dealers often described their
programs. Schlesinger (1958), for example, captures the basic New
Dealer argument for relief:
No one knows how many there were on Inauguration Day—
at least 12 to 15 million, over a quarter of the labor force—
subsisting wanly and desperately on relief . . . . The existing
political economy of the new deal
5
system of public relief, largely improvised in the course of the
depression, was itself breaking down. Private charity had long
since become inadequate; municipal and state funds fell far
below the need . . . . Everywhere funds were running out.
Most people close to the subject had long concluded that the
only hope was a federal program. (263)
For land-related projects:
Against the backdrop of drought and dust, Roosevelt hoped
to awaken in the American people a sense of urgency about
their ultimate basis in nature. “Unlike most of the leading
Nations of the world,” he observed a trifle bitterly in 1934, “we
have so far failed to create a national policy for the development of our land and water resources.” Here plainly was a
major objective for his administration, even if it had to take
second place to the war against depression. And surely depression itself offered opportunities to promote the cause of
resource development. Most immediately, why not contribute
at once to conservation and relief by sending jobless men to
labor in the forests. (336–37)
Let me summarize the New Dealer argument in one sentence: In
response to the Great Depression, as well as the Dust Bowl and other
consequences of poor land stewardship, the New Deal established programs to provide relief for the unemployed and impoverished, spur
recovery from the Depression, and improve stewardship of the land.
Since the 1930s, critics have accused the Franklin Roosevelt administration of using distributive policy for the purpose of winning votes.
For example, Roosevelt’s adversaries argued that New Deal programs
manipulated spending and relief employment to win votes in politically
sensitive regions and that the Works Progress Administration (WPA)
temporarily provided relief jobs in order to influence close elections
(e.g., see Howard 1943). In more recent years, economic historians have
used political and economic data to test whether the New Deal favored
pivotal constituencies.
Wright’s (1974) article has been by far the most influential work on
the political economy of New Deal spending. On the basis of his analysis
of state-level, cross-sectional data on spending and politics, Wright concludes that, consistent with his theoretical model, spending tended to
be highest in states in which it produced the greatest expected electoral
gains for a vote-seeking president. Even after including a variety of
variables to control for economic factors (e.g., unemployment, the percentage fall in income during the onset of the Depression, and the
6
journal of political economy
percentage of the state’s land owned by the federal government), he
found large ceteris paribus effects of his political variables. Turning to
the effects of the New Deal on voters, Wright analyzed state-level data
on intertemporal changes in the Democratic vote share during the
1930s. His analysis provides evidence consistent with greater allocations
of spending and work project jobs leading to greater electoral support
for Democrats.
Much of the more recent work has focused on identifying the extent
to which Wright’s political variables or other variables can explain New
Deal spending patterns (e.g., Wallis 1984, 1987, 1998, 2001; Anderson
and Tollison 1991; Fleck 2001b). Criticizing Wright (1974), Wallis (1998)
argues that small state populations may lead to high per capita spending,
and he therefore introduces a new explanatory variable, 1/population
(denoted 1/POP). Wallis claims that adding 1/POP to the analysis of
spending overturns many previous findings, including Wright’s large
estimated effects of political variables. Fleck (2001b) demonstrates that
the vast majority of cross-state variation in per capita spending can be
accounted for econometrically with a few nonpolitical variables, most
importantly, land area. Controlling for land area greatly reduces the
ceteris paribus explanatory power of Wright’s political variables and
Wallis’s 1/POP. These findings provide a starting point for the empirical
analysis that follows.
III.
Theoretical Model
This section develops a model of voters’ influence on policy. The model
rests on the simple premise that voter behavior is a function of three
things: policy, other factors that an incumbent politician can observe
prior to the upcoming election, and factors that an incumbent politician
cannot observe prior to the upcoming election. I will first present the
basic assumptions that build the framework and then discuss applications to different aspects of political decisions.
Assumptions
Assumption 1 (Political districts and policy dimensions). An incumbent politician sets policy on n dimensions in a country with d political
districts. Let x p (x 1 , … , x n) represent policy.
Assumption 2 (Benefits to each district). Each district i has, for
each policy dimension j, a marginal benefit curve (MB) and a marginal
cost curve (MC), such that MBij p aij ⫺ bij x j , MCij p mij , bij 1 0, and
aij ≥ mij ≥ 0. Let bi(x) represent the net benefits of policy to each district
i, calculated in the standard way from the areas between MB and MC.
Assumption 3 (The incumbent’s vote total). For each district i, let
political economy of the new deal
7
vi represent the incumbent’s popular vote margin (votes for the incumbent minus those for the top challenger), where vi p kibi(x) ⫹ l i ⫹ ei ;
ki , li , and ei are exogenous; ki 1 0; and after policy is set, ei is drawn
independently from a normal, zero mean distribution. The incumbent’s
vote total is V, where V p 冘i rw
i i , wi p 0 if vi ! 0, wi p 1 if vi ≥ 0, ri is
exogenous, and ri 1 0.
Assumption 4 (The incumbent’s objective). The incumbent sets x
to maximize his or her expected vote total (EV ). When setting x, the
incumbent knows the values of all exogenous variables except e.
These assumptions provide the foundation for a simple, easily extended characterization of the policy-making process. Assumption 1 allows for multidimensional policy and any number or type of political
districts (e.g., states, congressional districts, counties). Assumption 2
measures net benefits in a very standard way, analogous to total surplus
in a linear supply and demand diagram. Assumption 3 defines an election using winner-take-all district-level contests, with r indicating heterogeneity among districts with respect to their apportionment in calculating the vote total, and with voting a function of three factors: policy
(x), an exogenous variable known at the time policy is set (l), and an
exogenous variable unknown at the time policy is set (e). Assumption
4 provides a simple objective for the politician.
Applications
To begin, consider a definition that characterizes the case in which
voters have standard MB curves and value spending in their home district more than spending in other districts.
Definition (Simple distributive policy). In this case, x i indicates
funds allocated to district i (thus, in this case, n p d ). Each district
values own-district spending more than other-district spending, such that
aii p a home for all i; aij p a other for all i ( j; bij p b for all i, j; MCij p
m for all i, j; and a home 1 a other 1 m.
In this case, a home ⫺ a other indicates the premium on own-district
spending. This yields the following proposition.
Proposition 1. If the incumbent sets simple distributive policy, the
following statements hold true. Districts with a greater apportionment
(i.e., larger rk) will, compared to otherwise identical districts, receive
more funds (i.e., higher x k). If a given district k has (i) a sufficiently
small apportionment (i.e., near-zero rk), (ii) sufficiently extreme exogenous political leanings lk (either positive or negative), or (iii) sufficiently extreme voter responsiveness or unresponsiveness to policy
(i.e., extreme kk, either high or near zero), this will, ceteris paribus, lead
to x*k near the other districts’ ideal point for x k (i.e., lead to low x*k ).
(The proof is in App. A.)
8
journal of political economy
Proposition 1 captures a key part of Wright’s model, but in a more
general and more easily extended framework. With x measuring allocations of funds across states and r measuring electoral votes per capita,
the model applies directly to the case studied by Wright. The model
can also apply to an incumbent (or party leader) seeking public support
for his or her party’s candidates in district-level elections for seats in a
legislature (or for other offices), and to efforts to win support (e.g., roll
call votes) from already-elected legislators. In either of these legislaturerelated examples, the model predicts the effects of the apportionment
of seats in legislative bodies (e.g., with r measuring seats per capita and
x measuring district-level dollars per capita).
It is important to recognize why the theoretical effects of l and k on
spending can be positive or negative. Districts with extreme exogenous
leanings, whether against the incumbent (low l) or for the incumbent
(high l), will receive relatively little because, regardless of spending,
they will be virtually certain losses or virtually certain wins. Districts with
voters that are very unresponsive to policy (very low k) will have small
allocations, but districts with voters who are extremely responsive (very
high k) will also have small allocations. In plain language, greater voter
responsiveness (higher k) attracts funds when it indicates a high payoff
from using spending to win support among swing districts, but not when
it indicates that low spending is sufficient to maintain strong support.
For applying the model, a key consideration is that the literature on
New Deal spending has focused on testing a hypothesized positive marginal effect of k on spending. This implicitly assumes that greater variance in partisan vote shares (as a proxy for k) indicates a swing state
and, hence, a high payoff from using spending to win support. This
makes good sense for explaining a major policy change (i.e., the New
Deal) that won electoral support for Democrats in states in which the
Democratic Party historically had been weak.
The next step is to extend the model to incorporate between-district
differences in the economic returns to spending.
Definition (Distributive policy in the case of heterogeneous economic returns). The policy decision is the same as in the case of
simple distributive policy, except that there exists heterogeneity with
respect to MB, such that bj p f⫺1
j , which varies across policy dimensions
j.
This definition introduces an intuitively appealing functional form
for heterogeneity—where heterogeneity essentially just shifts demand
curves. Suppose, for example, that the model is applied to relief for the
unemployed and that the level of what one might call the “need” for
unemployment relief is proportional to the number of unemployed
individuals. An increase in fk from 1 to 1.1 would reflect a 10 percent
increase in the number of unemployed individuals in district k and,
political economy of the new deal
9
hence, would increase by 10 percent the quantity of relief spending at
which MB p MC.
Proposition 2. If the incumbent sets distributive policy in the case
of heterogeneous economic returns, proposition 1 holds, with the difference being that ideal points depend on f. Furthermore, the effect
of f on spending will differ between districts: Among otherwise identical
districts that are expected losses (i.e., Ew ! .5), a district with higher f
will have more than proportionally higher spending; among otherwise
identical districts that are expected wins (i.e., Ew 1 .5), a district with
higher f will have less than proportionally higher spending. (The proof
is in App. A.)
Proposition 2 parallels proposition 1 in terms of higher allocations
going to districts with larger apportionments and swing electorates (as
influenced by r, l, and k), but the important implication is the way in
which heterogeneity in f will affect policy decisions. Among districts
with relatively little marginal political influence over policy (i.e., among
almost sure losses and almost sure wins), spending will be higher in
districts with higher f, but not by much more than the other districts
prefer. Yet an increase in need can generate a particularly large increase
in spending when it occurs in a district with policy-sensitive voters who
have not already been won over by the incumbent’s party. In other
words, big policy changes occur when f (e.g., “need”) increases greatly
among the types of districts that Wright (1974, 31–33) argues politicians
have an incentive to favor.1
The next step is to apply the model to other types of policy. For
simplicity, consider choosing policy along a single dimension.
Definition. (Simple one-dimensional policy). Policy has a single
dimension, x, such that, for any district i, 0 ! ai ! 1, bi p 1, and
MC p 0.
This definition allows an easy exposition: For each district i, bi(x)
reaches a maximum when x p ai . In other words, ai indicates district
i’s ideal point.
Proposition 3. Suppose that the incumbent sets a simple onedimensional policy (x) and that policy is not at district k’s ideal point
(i.e., x* ( ak). An increase in district k’s apportionment (rk) will, ceteris
paribus, decrease Fx* ⫺ akF. For sufficiently low values of lk, a marginal
increase in lk will decrease Fx* ⫺ akF; but for sufficiently high values of
lk, a marginal increase in lk will increase Fx* ⫺ akF. For sufficiently low
values of kk, a marginal increase in kk will decrease Fx* ⫺ akF; but for
1
If, say, a 10 percent increase in fk (along with the funding it brings) moves district k
away from being an almost sure loss (and toward the electorally competitive range), then
the district will get more than a 10 percent increase in funds. Why? Because Ewk becomes
more sensitive to additional spending.
10
journal of political economy
sufficiently high values of kk, a marginal increase in kk will increase
Fx* ⫺ akF. (The proof is in App. A.)
This parallels propositions 1 and 2 (and Wright’s model) in terms of
districts with larger apportionments and swing electorates (as influenced
by r, l, and k) having more weight in the policy decision. The key new
insight is that the fundamental logic of my model (and Wright’s) applies
even for policies that, unlike the basic divide-the-dollars decision, allow
no district-specific variations in policy.
Consider, for example, how proposition 3 applies to the design of a
spending formula. In this case, the choice of x corresponds to the weight
on a given allocation criterion. For example, x could be the dollars
allocated per square mile of land in a state. The main point here is that
the same states that Wright’s (1974) logic suggests will be favored by
politicians under simple distributive policy will also tend to receive larger
allocations under formula-based spending. For example, if swing states
with high k and r also tend to have high values of some potential allocation criterion, then those states will receive relatively large allocations—because of their influence over how much to weight that potential criterion when designing the formula.2
IV.
Data
For the econometric analysis, the principal proxy for policy (x) is statelevel per capita New Deal spending from 1933 to 1939 (SPEND).3 The
mean value of the variable is $293, and the variation across states is
striking: The variable ranges from $147 to $1,131, with a standard deviation of $178. Thus, if a regression explains most of the variation across
states, it explains major differences.
To proxy for r (apportionment of electoral votes or legislative seats),
I start by using Wright’s V/POP, which is electoral votes per capita
(multiplied by 1,000). This variable reflects two key factors. First, if policy
depends on winning electoral votes, states with higher V/POP have
higher values of r. Second, as a result of the method used to allocate
electoral votes to states, V/POP equals members of Congress per capita;
thus, if policy depends on winning congressional seats or congressional
2
For an analysis of why governments often rely on formulas, see Johnson and Libecap
(2003). Also see, e.g., Lee (1998, 2000, 2004) and Lee and Oppenheimer (1999) on the
importance of formulas in allocating federal spending and on how congressional apportionment shapes debates over formulas.
3
See Apps. B and C (table C1) for variable definitions, data sources, and descriptive
statistics.
political economy of the new deal
11
4
support, states with higher V/POP will have higher values of r. I also
use a pair of variables—Senate seats per capita (SENATE/POP) and
House seats per capita (HOUSE/POP)—as an alternative to V/POP.5
In robustness tests discussed in Section V, I consider six additional
Congress-related proxies for r.
To proxy for k (responsiveness of voters to policy) and l (the electorate’s exogenous leanings toward the incumbent), I follow Wright.
His variable SD is the standard deviation around the trend in the Democratic vote share in presidential elections, 1896–1932. Because the variable measures the propensity of a state’s electorate to switch with respect
to the party it supported in presidential elections, it proxies for k to the
extent that spending has a greater effect on election margins in states
in which voters show more variability in the party for which they vote.6
As a proxy for l, I use (as Wright did) the predicted 1932 Democratic
vote share (DEMpred). As an alternative method to measure the effects
of r, k, and l, I use Wright’s proxy for the political productivity of
spending in a state (VL32). This variable summarizes the way in which
Wright’s theoretical model predicts that V/POP, SD, and DEMpred influence spending (Wright 1974, 31–33).
Proposition 3 predicts the effects of political variables on one-dimensional policy. For analyzing New Deal spending, the key question is what
potential spending patterns could have been produced by formulas or,
more generally, by a set of programs employing formula-like allocation
criteria. The empirical issues are (i) whether formula-based or programbased spending, using proxies for economic returns (f) as allocation
criteria, could have allocated funds in a reelection-winning manner and
(ii) how pre–New Deal events changed what would have been a reelection-winning formula or set of programs. To address these issues, I examine hypothetical spending patterns, with funds allocated in proportion to proxies for economic returns (f); the hypothetical policy
decision is how many dollars to allocate in proportion to each proxy.
The historical literature (e.g., Schlesinger [1958], as quoted in Sec.
II) and the empirical literature (esp. Fleck 2001b) point to obvious
proxies for f: income and land variables. I follow Fleck’s choice of
4
The critical determinant of per capita congressional representation is that each state
has two Senate seats. Because the number of seats each state holds in the House is a whole
number, members of Congress per capita (which equals V/POP) is a piecewise decreasing
function of population. Members of Congress per capita has a .987 correlation with senators per capita, and the correlation would be one if representation in the House were
a continuous variable and were strictly proportional to states’ populations.
5
Several post-Wright papers focus on how the apportionment of congressional seats
affects spending (e.g., Bennett and Mayberry 1979; Atlas et al. 1995; Lee 1998, 2000, 2004;
Lee and Oppenheimer 1999; Hoover and Pecorino 2005).
6
The year 1896 is a logical starting point for the election data because the date marks
the major partisan realignment preceding the New Deal (e.g., Brady 1988).
12
journal of political economy
income variables (originally used by Arrington [1969]) and land variables. Each of these variables proxies for one of the New Deal’s famous
“relief, recovery, and reform” objectives. First, allocations to states with
low incomes would be in line with the New Dealers’ stated objective to
combat poverty (“relief”). Thus, I include personal income per capita
in 1932 (INCOME1932). Second, allocations to states that suffered large
declines in income would match the New Dealers’ stated objective to
alleviate the effects of the Depression (“recovery”). Thus, I use the
decline in personal income per capita from 1929 to 1932 (INCOME
FALL1929–32). Third, spending in states with more land would be in line
with the New Dealers’ stated objectives of improving the management
and use of land resources (an important aspect of “reform”). It would
also be in line with the criteria used in highway and other spending
formulas before, during, and after the New Deal (e.g., Key 1937; Fleck
2001b; Johnson and Libecap 2003). Given the literature’s emphasis on
land owned by the federal government, I divide land area into two
components: federal land area per capita (LANDfederal) and nonfederal
land area per capita (LANDnonfederal). As an alternative proxy, I use land
area per capita (LAND). It is important to note that all these income
and land variables measure pre–New Deal conditions defined in units
consistent with the theoretical model’s treatment of f. Thus, when used
as explanatory variables in a per capita spending regression, the variables
are logically consistent as potential determinants of linear MB curves.
I use a set of additional variables to test the robustness of the article’s
main results. To allow a comparison of my results to previous findings,
I use five variables from Wallis (1998). Two of the five proxy for economic hardship: 1930 unemployment and 1937 unemployment.7 One
measures land characteristics: federal land as a percentage of state area.
The last two of the five capture aspects of the state’s agricultural economy: the fraction of the population living on farms and farm value per
capita.
V.
Econometric Analysis of Spending
The model provides reason to examine ceteris paribus relationships (the
focus of the previous literature), but not just ceteris paribus relationships. Part of what follows focuses on ceteris paribus relationships, including the effects of economic variables (f) controlling for electoral
weight (r, k, l), and vice versa. This matters principally for testing
whether my proxies for f predict spending merely because they are
7
Unemployment in 1930 proxies for pre–New Deal conditions, as do my income variables. Measuring unemployment in 1937 reflects New Deal–era economic conditions but
cannot reasonably be interpreted as an exogenous determinant of spending (e.g., Darby
1976; Fleck 1999b).
political economy of the new deal
13
correlated with other variables the literature suggests as determinants
of spending. But some of the relationships of interest in this article
should not be estimated as ceteris paribus effects. Most critically, if
economic variables (f) account well for the variation in New Deal spending, how should I assess the relevance of my theoretical analysis of
spending formulas? Looking solely at the estimated effects of electoral
weight (r, k, l) controlling for the economic variables (f) would be a
mistake: The coefficients on the proxies for electoral weight would reflect their relationship to the nonformula component of spending
rather than to the formula-based component of interest. For these reasons, I use actual spending data to examine a variety of relationships:
E(xFr), E(xFk), E(xFl), E(xFr, k, l), E(xFf), and E(xFr, k, l, f). And I use
hypothetical spending patterns (x as a function of f) to assess how well
formula-based spending could have matched what Wright identified as
the New Deal’s politically productive allocation of funds.
Are the Proxies for r, k, and l Correlated with Spending? Yes
With per capita spending as a proxy for x, consider first what the data
indicate about E(xFr), E(xFk), and E(xFl). Table 1 presents simple correlations, which of course suffice to indicate the sign of the coefficient
of interest and the R 2 for bivariate regressions of x on r, k, or l. With
V/POP, Senate seats per capita, or House seats per capita as a proxy
for r, the table shows higher x in states with higher r. With SD as a
proxy for k, the table shows higher x in states with higher k. With the
predicted Democratic vote share as a proxy for l, the table shows higher
x in states with lower l. With VL32 as a proxy for the combined effects
of r, k, and l on political productivity, the table shows higher x in states
with greater political productivity. Overall, these results suggest that
higher spending occurred in states in which spending would have had
the most value for reelection. The relationships thus provide useful
information about the nature of the New Deal, even though the bivariate
relationships do not answer the question of why certain states received
more funds.
Now consider the extent to which Wright (1974) discovered a strong
relationship between spending and proxies for political variables. Table
2 examines E(xFr, k, l). Regression 1 includes SD and V/POP, the two
political variables that Wright found predicted so well. The .785 R 2 and
substantial coefficients show that an equation with just two political
variables can account for much of the heterogeneity in per capita spending. The explanatory power of these variables helped to inspire the
14
journal of political economy
TABLE 1
Correlation Matrix (N p 48)
SPEND
V/POP
SENATE/POP
HOUSE/POP
SPEND
V/POP
SENATE/POP
HOUSE/POP
SD
DEMpred
VL32
INCOME1932
INCOME FALL
LAND
LANDfederal
LANDnonfederal
1
.80010
.81915
.61134
.58400
⫺.24741
.38671
.04482
.16912
.92404
.84091
.89043
1
.98715
.87423
.27299
⫺.17592
.50060
.24873
.17014
.92077
.93222
.63780
1
.78541
.26699
⫺.18878
.50955
.22560
.15915
.90730
.89657
.68670
1
.24739
⫺.10820
.39025
.27758
.17544
.81072
.88719
.38588
SD
DEMpred
VL32
INCOME1932
SD
DEMpred
VL32
INCOME1932
INCOME FALL
LAND
LANDfederal
LANDnonfederal
1
⫺.45712
⫺.12614
⫺.02429
.29493
.41972
.34381
.50543
1
⫺.08694
⫺.52526
⫺.67090
⫺.14835
⫺.12110
⫺.17977
1
.15933
⫺.04665
.48771
.50092
.31891
1
.76583
.13282
.20800
⫺.10257
INCOME FALL
LAND
LANDfederal
LANDnonfederal
1
.12497
.15015
.02402
1
.97346
.79581
1
.63611
INCOME FALL
LAND
LANDfederal
LANDnonfederal
1
large literature that has followed Wright.8 Regression 2 decomposes
V/POP into its two components (Senate seats per capita and House
seats per capita) and shows that the explanatory power of V/POP comes
from its Senate seats component.9 Mathematically (though not semantically), this is the substance of Wallis’s (1998) finding for the effects of
1/POP.10
Regressions 3 and 4 in table 2 provide additional insight into the
8
To illustrate the magnitudes of the coefficients, consider the following. The standard
deviation of SD is 4.33, and the estimated equation indicates that increasing SD by 4.33
would increase per capita spending by $70. The standard deviation of V/POP is .00449,
and the estimated equation indicates that increasing V/POP by .00449 would increase per
capita spending by $123.
9
For each of regressions 2–5, an F-test rejects (p ≤ .01 ) using V/POP instead of its two
components.
10
Note that 1/POP is the econometric equivalent of Senate seats per capita (which is
2/POP). The decomposition of V/POP does not imply that Wright incorrectly interpreted
V/POP. Indeed, Wright (1974, 33) interprets his large positive coefficient on V/POP as
indicating that “small states” (i.e., states with few people) benefited from the pattern of
spending. He also explains that V/POP measures congressional representation and could
proxy for incentives to spend in an effort to logroll votes in Congress.
political economy of the new deal
15
TABLE 2
Explanatory Power of Political Variables (N p 48)
Dependent Variable
C
V/POP
SD
SPEND
(1)
SPEND
(2)
SPEND
(3)
SPEND
(4)
⫺36.9165
(1.12)
27,486.2
(9.63)
16.2673
(5.49)
102.275
(1.83)
74.5281
(1.27)
38.5522
(.53)
SENATE/POP
HOUSE/POP
VL32
DEMpred
16.4032
17.6687 18.1459
(6.00)
(6.20)
(6.02)
40,956.3
37,742.4
41,856.9
(7.78)
(6.64)
(7.96)
⫺18,941.8 ⫺18,971.8 ⫺21,121.8
(1.19)
(1.20)
(1.33)
540.923
(1.41)
1.08531
(1.33)
SENATE
APPROPRIATIONS
115.947
(1.73)
16.1858
(5.05)
41,862.6
(6.27)
⫺27,341.7
(1.36)
⫺.047312
(.21)
HOUSE
APPROPRIATIONS
SENATE TENURE
HOUSE TENURE
SENATE LEADERSHIP
HOUSE LEADERSHIP
R2
Adjusted R 2
SPEND
(5)
.7846
.7750
.8204
.8082
.8283
.8124
.8275
.8115
.070888
(.33)
.075258
(.55)
⫺.001794
(.05)
47.5545
(.77)
⫺4.86594
(.10)
.8268
.7858
Note.—Regressions are ordinary least squares; t-statistics are in parentheses.
explanatory power of Wright’s political variables. The key issue here is
to consider the effects of the electorate’s exogenous leanings (l). One
way to address this is to add Wright’s measure of political productivity
(VL32), which incorporates exogenous leanings in a manner corresponding to his theoretical model. As regression 3 shows, VL32 has a
positive coefficient (as hypothesized) that is statistically insignificant
(t p 1.41) and adds little explanatory power. An alternative way to address this issue is to use the predicted Democratic vote share as a proxy
for l. As regression 4 shows, the variable has a positive coefficient (indicating the opposite of favoring swing states over loyally Democratic
states) that is statistically insignificant. Furthermore, the variable adds
16
journal of political economy
little explanatory power and has no important effects on the other
coefficients.11
As a robustness test, the last regression in table 2 tests whether congressional variables add much predictive power or cause any meaningful
changes in the coefficients on the key variables.12 In short, they do not.
The six variables all have t-statistics below one, come nowhere close to
joint significance (p 1 .96 in an F-test), and jointly increase the R 2 value
by .0064 (and reduce the adjusted R 2 by .0224). Most important for this
article, adding the variables causes only small changes in the coefficients
on Wright’s political variables.
The findings discussed so far confirm that Wright discovered something important. The New Deal’s system for allocating benefits—whatever that system was—spent more in low-population and electorally variable states.
How Much of the Variation in Spending Can Proxies for f Explain? The
Vast Majority
For the reasons discussed in Section IV, I begin by using Fleck’s (2001b)
set of proxies for f. Regression 1 in table 3 is essentially the same as
Fleck’s first regression (p. 301). The coefficients match the New Deal’s
stated objectives, and the R 2 is .935. Thus, the observed variation in the
spending data could have been generated largely by allocating more to
states with lower pre–New Deal incomes, greater pre–New Deal declines
in income, more federal land, and more nonfederal land.13 These results
should immediately give pause to anyone who interprets New Deal
11
When VL32 and DEMpred are both added to regression 2, they fail to reach joint
statistical significance (p p .12 for an F-test), and the estimated coefficients and t-statistics
for all variables remain similar to those reported in regressions 2–4. As a robustness test,
I added a squared term for DEMpred to regression 4. This tests for a nonmonotonic relationship between l and spending—something that would occur if extreme loyalty to
either party led to lower spending. The squared term had a tiny t-statistic (t ! .01) and
virtually no effect on the results of interest.
12
These congressional variables, which were first suggested by Anderson and Tollison
(1991), are defined in App. B. A proper interpretation of how these variables relate to
spending must acknowledge that the direction of causality is ambiguous (e.g., if delivering
federal spending to constituents wins votes, then presumably tenure in office depends in
part on spending). Hence, I rely on these variables only for testing the robustness of my
main results.
13
The coefficients have substantial magnitudes. The estimated equation corresponds to
formula-based spending with each state receiving a total dollar allocation equal to ($147.41
# population) ⫺ (18.6 cents for every dollar of the state’s total 1932 personal income)
⫹ (43.7 cents for every dollar by which the state’s total personal income fell from 1929
to 1932) ⫹ ($584.91 # the number of square miles of federal land in the state) ⫹
($1,793.01 # the number of square miles of nonfederal land in the state).
political economy of the new deal
17
TABLE 3
Explanatory Power of Economic Variables (N p 48)
Dependent Variable
C
INCOME1932
INCOME FALL1929–32
LANDfederal
LANDnonfederal
SPEND
(1)
SPEND
(2)
SPEND
(3)
SPEND
(4)
147.409
(6.07)
⫺.185769
(2.27)
.437088
(3.17)
584.913
(8.85)
1,793.01
(10.29)
190.116
(19.65)
⫺239.419
(1.21)
.585821
(2.23)
⫺.193659
(.63)
40.3011
(.44)
⫺.038208
(.29)
.278384
(1.96)
528.295
(7.60)
1,335.54
(6.98)
1.67012
(3.44)
⫺3.78869
(.60)
18.8747
(2.11)
50.6678
(.38)
36.0970
(2.12)
.9562
.9459
556.052
(8.41)
1,905.67
(10.89)
%FEDLAND
UNEMPLOYMENT 1930
UNEMPLOYMENT 1937
FARMPOP
FARM VALUE
R2
Adjusted R 2
.9348
.9287
.9194
.9158
6.44846
(9.16)
10.1072
(.72)
4.67858
(.24)
526.712
(1.85)
96.7225
(2.86)
.7659
.7249
Note.—Regressions are ordinary least squares; t-statistics are in parentheses.
spending patterns as driven substantially by tweaking state-level allocations for electoral reasons.14
Because this article relies heavily on land variables, I will extend
Fleck’s (2001b) analysis to test the robustness of his claim that land area,
when measured appropriately for the estimated equation, has substantial
predictive power. To demonstrate the extent to which the land variables
alone can predict spending, regression 2 in table 3 includes just
LANDfederal and LANDnonfederal. And, to test whether the land variables
account for variation in spending that remains unaccounted for by other
variables, regression 3 replaces the land variables in regression 1 with
five variables from Wallis’s review of the literature. As regressions 1–3
show, the component of spending for which the land variables account
14
In view of concerns expressed by Wallis (2001), it is worth noting that the land variables
do not merely proxy for high spending in the Mountain West. Indeed, when regression
1 is reestimated excluding the Mountain West states, the estimated coefficients on the
land variables are larger than those reported in regression 1 (details available).
18
journal of political economy
both is substantial and is not something accounted for by the five previously used variables.15
As an additional robustness test, regression 4 adds those five previously
used variables to the specification in regression 1. In view of regression
4, to the extent that 1937 unemployment proxies for exogenous economic hardship and to the extent that %FEDLAND proxies for the
value of land-related projects, these two variables provide additional
support for this article’s argument that variation in New Deal spending
can be largely explained with proxies for f. Also of central importance
to this article (given its reliance on land variables) are two facts learned
from comparing regressions 3 and 4 with regressions 1 and 2: (i) the
land results are quite robust to the inclusion of the previously used
variables, and (ii) the previously used variables do not account for nearly
as much of the variation in spending.
Controlling for f, Do r, k, and l Predict Much of the Variation in
Spending? No
Table 4 turns to the examination of E(xFr, k, l, f). Regression 1 demonstrates the effects of adding income and land variables to the second
specification in table 2. Two results are clear. First, comparing regression
1 in table 4 with the regressions in table 2 shows that income and land
variables add a great deal of explanatory power. Second, comparing
regression 1 in table 4 with regression 1 in table 3 shows that the political
variables add much less to the explanatory power of income and land,
and this is true even though part of the “political” explanatory power
counted here comes from a (dubious) negative coefficient on HOUSE/
POP. As a robustness test, regression 2 (in table 4) adds five additional
variables; this neither resurrects the large effects of the political variables
nor eliminates the large effects of the land variables. Furthermore, even
though neither regression in table 4 includes all the political variables
15
Dividing land area into its two components (LANDfederal and LANDnonfederal) is not
necessary for demonstrating substantial explanatory power: Including LAND (i.e., square
miles per capita) as the sole regressor yields a coefficient of 874.59 (t p 16.39) and an
R 2 of .854, which exceeds all the R 2 values among the “political” regressions in table 2.
Including LANDfederal and LANDnonfederal (rather than just LAND) is supported by F-tests,
which reject restricting the coefficients on LANDfederal and LANDnonfederal to be equal
(p ! .00005 for regressions 1 and 2; p p .001 for regression 4, even though it controls for
%FEDLAND). As comparing regression 3 with regressions 1 and 4 shows, omitting land
area also causes the coefficients on the income variables to change signs. This is another
reason why including land area is critical for identifying the econometric relationship
between spending and proxies for f.
political economy of the new deal
19
TABLE 4
Explanatory Power of Political and Economic Variables
(N p 48)
Dependent Variable
C
SD
SENATE/POP
HOUSE/POP
INCOME1932
INCOME FALL1929–32
LANDfederal
LANDnonfederal
SPEND
(1)
SPEND
(2)
233.947
(4.45)
6.24831
(3.16)
5,530.98
(1.21)
⫺34,336.3
(2.52)
⫺.102002
(1.28)
.232801
(1.66)
753.526
(5.39)
1,328.24
(6.33)
126.244
(1.33)
4.06981
(1.65)
5,964.74
(1.29)
⫺31,282.2
(2.25)
⫺.029196
(.23)
.246728
(1.78)
715.336
(4.82)
1,097.67
(5.06)
.981599
(1.76)
⫺5.65531
(.91)
21.6722
(2.47)
44.7661
(.35)
23.6858
(1.23)
.9634
.9509
%FEDLAND
UNEMPLOYMENT 1930
UNEMPLOYMENT 1937
FARMPOP
FARM VALUE
R2
Adjusted R 2
.9521
.9438
Note.—Regressions are ordinary least squares; t-statistics are in parentheses.
in table 2, a variety of tests suggest that the table 4 regressions include
the relevant variables from table 2.16
Could Formula-Based Spending Have Allocated Funds to States with Electoral
Weight? Yes
The key question here is the following: Under formula-based spending
using proxies for officially stated New Deal criteria, could constituents
16
For each possible way to pair an empirical model from table 4 with an empirical model
(or combination of models) from table 2, I conducted encompassing tests. In no case of
a hypothesis pertaining to a table 4 model encompassing a table 2 model (or combination)
did the test come close to rejecting the hypothesis: Davidson-MacKinnon tests (e.g., Davidson and MacKinnon 1981) generate t ! .73 in each case, and nonnested F-tests generate
p 1 .47. Yet in every case of a hypothesis pertaining to a table 2 model (or combination)
encompassing a table 4 model, the hypothesis is strongly rejected: Davidson-MacKinnon
tests generate t 1 9 in each case, and nonnested F-tests generate p ! .00005.
20
journal of political economy
with more electoral weight have received more funds than constituents
with less electoral weight received? To investigate this, I consider hypothetical spending patterns that could have been generated by simple
formulas. Simplicity in these formulas is critical because complexity (specifically, allowing sufficiently many linearly independent proxies for f)
will, by mathematical necessity, allow a formula to match any x. For this
reason, I will employ a very parsimonious specification that includes
proxies related to three broad criteria: relief from poverty, recovery from
economic decline, and reform in the management of land resources.
The proxies are INCOME1932, INCOME FALL1929–32, and LAND.
The first hypothetical spending pattern based on those variables is
the fitted values from regression 1 in table 5. These fitted values correspond to formula-based spending with each state receiving a total
dollar allocation equal to ($191.53 # population) ⫺ (35.8 cents for
every dollar of the state’s total 1932 personal income) ⫹ (60.6 cents for
every dollar by which the state’s total personal income fell from 1929
to 1932) ⫹ ($878.43 # the number of square miles in the state). As
regression 2 in table 5 shows, such a formula would have allocated
spending in a manner that could be explained econometrically quite
well with the same two political variables (SD and V/POP) that can
explain so much of the actual spending patterns. Indeed, in terms of
R 2 values, the formula-based spending patterns would have had an even
closer relationship to those variables (R 2 p .849) than the real spending
patterns have (R 2 p .785). Furthermore, it is easy to generate an equally
simple formula-based spending pattern that has a still tighter link to
political variables. To see this, consider regression 3 in table 5. The
formula generating the dependent variable here corresponds to each
state receiving a total dollar allocation equal to ($156.11 # population)
⫺ (13.9 cents for every dollar of the state’s total 1932 personal income)
⫹ (49.0 cents for every dollar by which the state’s total personal income
fell from 1929 to 1932) ⫹ ($748.13 # the number of square miles in
the state).17 As regression 3 shows, this would have generated a .877
R 2 in a regression of spending on the political variables. Very clearly, a
close correlation to the political variables—even closer than Wright
found—in no way required New Dealers to deviate from a simple formula in line with their stated objectives.
An essential point to recognize here is that spending based on a
hypothetical formula is indeed hypothetical. The hypothetical formulas
demonstrate how income and land variables are related to the political
variables, and one does not need actual spending data to do this analysis.
In other words, it is the income, land, and political data—independent
17
I obtained this formula by regressing the fitted values from regression 1 in table 2
on INCOME1932, INCOME FALL1929–32, and LAND.
.8910
.8835
⫺.357938
(3.72)
.606497
(3.53)
878.430
(18.47)
191.533
(6.55)
.8485
.8418
.8775
.8720
41.9511
(2.08)
26,965.2
(15.31)
8.82324
(4.83)
⫺.024274
(.001)
29,996.1
(13.27)
11.1621
(4.76)
Note.—Regressions are ordinary least squares; t-statistics are in parentheses.
R2
Adjusted R 2
FARMPOP
UNEMPLOYMENT 1930
LAND
INCOME FALL1929–32
INCOME1932
SD
V/POP
C
SPEND
(1)
Hypothetical
Spending B
(3)
Hypothetical
Spending A
(2)
5.75491
(.25)
114.081
(.35)
.0030
⫺.0413
227.120
(1.02)
SPEND
(4)
Dependent Variable
TABLE 5
Analysis of Hypothetical Spending Patterns (N p 48)
.0605
.0187
291.692
(78.29)
⫺456.818
(1.40)
.441872
(1.31)
Hypothetical
Spending C
(5)
.0418
⫺.0008
294.607
(72.20)
434.032
(1.22)
⫺.369744
(1.00)
Hypothetical
Spending D
(6)
22
journal of political economy
of actual spending data—that make it possible to devise a formula (i)
that is based on economic hardship and land and (ii) allocates funds
to states with greater electoral weight. It is extremely easy to devise such
a formula because of two simple facts, both critical to this article’s conclusions. First, the states most severely affected by the Depression, making them the places receiving the most funding based on economic
hardship, happened to be electorally variable states. Second, the states
that had the most land per capita, making them the expected places to
spend more per capita on a variety of New Deal work projects (e.g.,
roads, conservation, reclamation, and other land-related improvements), happened to be electorally variable states and tended to be
states with relatively few people. Thus, choosing arbitrary allocations
based on pre–New Deal economic hardship and land area will typically
generate a strong correspondence between spending and the political
variables.18
For interpreting my results, another critical point to recognize is that,
while myriad formulas with arbitrary weights on income and land variables could have allocated spending in a manner that would have generated Wright’s main results, it is not the case that any arbitrary set of
criteria could generate formula-based spending consistent with Wright’s
findings. To illustrate, consider as potential criteria two variables commonly used in the previous literature on New Deal spending. Regressions 4–6 in table 5 repeat the methods I used to generate regressions
1–3 but substitute 1930 unemployment and the fraction of the population living on farms for the income and land variables. As regression
4 shows, these two variables account for little of the variation in real
spending. And, as regressions 5 and 6 show, formulas based on these
two criteria—formulas generated using the same method I used for
regressions 2 and 3—would have produced spending grossly inconsistent
with Wright’s findings.19
In short, for spending (x) to have a close correlation to Wright’s
political variables (r and k), there was no need for New Dealers to tweak
spending to fit a “political” spending pattern. A very simple formula
consistent with the New Dealers’ stated objectives (f) could have gen18
This is easy to demonstrate in a systematic manner. For example, I considered a
continuum of extremely simple hypothetical distributions based on two criteria: per capita
land area and the percentage fall in personal income from 1929 to 1932. With 2 percent
of funds allocated on the basis of land and 98 percent on the income variable, the
correlation of per capita spending with SD is .60 and the correlation with V/POP is .57.
With 98 percent of funds allocated on the basis of land and 2 percent on the income
variable, the correlation with SD is .44 and the correlation with V/POP is .92.
19
It is worth noting that Wallis (1998, 160) uses predicted spending as a dependent
variable, but the implications differ from mine. Wallis’s predicted spending is a linear
transformation of 1/POP. Thus, when Wallis regresses his variable on V/POP, the high
R 2 (.975) is simply the (squared) correlation between senators per capita and electoral
votes per capita.
political economy of the new deal
23
erated spending patterns consistent with Wright’s findings. Furthermore, specifying an arbitrarily chosen set of criteria for a hypothetical
formula would only by chance generate such results. Thus, the key fact
is that income and land variables, which are theoretically justified (i.e.,
not arbitrarily chosen) and clearly in line with New Dealers’ stated objectives, can generate such results.
Did the New Deal’s Combination of Distributive Programs Allocate Dollars
Similarly to Hypothetical Formulas? Yes
I will now consider whether one can usefully view New Dealers’ decisions
as analogous to choosing weights on income and land variables in hypothetical formulas. To be clear, the relevant issue here is not the extent
to which New Dealers literally set explicit weights. It is whether the New
Deal’s individual spending programs allocated funds in line with the
income and land variables, with heterogeneity among programs in their
relationships to those variables. If so, the choice of how to allocate funds
across programs (e.g., relief programs, highway spending) would allow
a choice analogous to setting weights on the income and land variables.
Indeed, that was the case. To verify this, I regressed state-level per
capita spending by each of the New Deal’s 10 largest programs on
INCOME1932, INCOME FALL1929–32, LANDfederal, and LANDnonfederal. (I will
provide a brief discussion here; detailed results are available.) The New
Deal’s major programs with an official focus on providing immediate
relief from the effects of the Depression (the Agricultural Adjustment
Administration, the Civil Works Administration, the Federal Emergency
Relief Administration, and the WPA) all allocated more funds to states
with large income declines; the Agricultural Adjustment Administration
also targeted states with low incomes. And, as expected, the Bureau of
Public Roads, the Bureau of Reclamation, and the Civilian Conservation
Corps (the major land-oriented programs) allocated more funds to
states with more land, with Bureau of Reclamation spending strongly
tied to federal land area.20
In short, New Deal programs allocated funds in a manner similar to
the hypothetical formulas discussed earlier. The most relief-oriented
programs allocated more to economically depressed states, the most
land-oriented programs allocated more to states with more land, and
the combined effect was that higher spending occurred in states with
20
There is a .994 correlation between LAND and per capita spending by the Bureau
of Public Roads. This is unsurprising in view of the fact that highway spending was based
largely on a formula that used land area as a criterion (e.g., Fleck 2001b). For the Bureau
of Reclamation, per capita spending has a .941 correlation with LAND and a .977 correlation with LANDfederal. For the Civilian Conservation Corps, per capita spending has a
.873 correlation with LAND and a .826 correlation with LANDfederal.
24
journal of political economy
more electoral weight. Thus, the data again show that, to allocate more
funds to electorally important states, New Dealers had no need to deviate
from programs that spent funds in line with the New Deal’s stated
objectives.
The Nature of Distributive Policy
The empirical results for individual programs, combined with those for
overall spending, provide insight into the broader issue of how political
parties influence distributive policy. As Levitt and Snyder (1995, 961)
conclude from their analysis of more modern district-level data, “It appears that parties in the United States can, given enough time, target
types of voters, but they cannot easily target individual districts.” My
evidence is consistent with their conclusion. That is, scholars can view
New Deal spending more usefully as a set of programs that delivered
funds to specific types of constituents (analogous to proposition 3)
rather than as a set of 48 individually chosen state-level allocations designed to favor states with electoral weight (analogous to proposition
1).
VI.
Other Aspects of New Deal Policy
This section extends the application of the model, and in particular
proposition 3, to major changes in policy along dimensions other than
the distribution of spending. More specifically, I will examine economic
regulation and civil rights, two key elements of the political realignment
that occurred during the 1930s (e.g., Sundquist 1973; Brady 1988; Fleck
1999a).
Regulatory Policy
Under what circumstances does the model predict a change in regulatory policy? Consider a single dimension (x) that describes the overall
degree of regulatory activism. A major change in policy would result
from a major change in voters’ ideal points (a), as would occur when
new information caused a large shift in what voters perceived as MB
curves. Many voters saw the severity of the Depression as indicating the
ineffectiveness of the status quo economic policy and, consequently,
acted as though their MB curves (and ideal points a) for regulatory
activism had increased. This, combined with the fact that the Depression’s effects were severe throughout the country, meant that much of
the New Deal’s regulatory legislation passed through Congress with overwhelming majorities and very broad geographical bases of support (e.g.,
Brady 1988).
political economy of the new deal
25
There is, however, a richer story. If the evidence shows that support
for regulation was highest in swing districts (rather than loyally partisan
districts), proposition 3 can explain major regulatory change in a manner that parallels its explanation of distributive policy. On this point,
consider the bill that caused the most famous regional division: the Fair
Labor Standards Act of 1938 (FLSA). The FLSA’s national minimum
wage clearly won support in swing states and districts; congressional
opposition came principally from a “conservative coalition” of Republicans voting with Democrats from what had been previously the most
loyally Democratic parts of the country.21 In short, by increasing public
support for labor market regulation, the Depression increased ideal
points (values of a) along the pro–minimum wage dimension, and the
resulting values of a were especially high in states that, according to
the logic of Wright’s model and my proposition 3, would have the most
influence over policy. Hence, a major change in policy occurred.
The Treatment of African Americans
Why would the New Deal move policy in a direction preferred by most
African Americans? Consider civil rights policy in the context of proposition 3, with greater x indicating greater rights for African Americans.
By the beginning of the Depression, the American public had to some
extent become more aware (and more concerned) that many African
Americans remained impoverished and disenfranchised (e.g., Sitkoff
1981). The academic community, for example, had increasingly come
to view the unfortunate position of so many African Americans as resulting from discrimination. Also, the Harlem renaissance of the 1920s
helped win white supporters for the civil rights cause. These events
would, in the context of the model, shift upward the nonsouthern electorate’s MB curve (and ideal point a) for civil rights and, hence, move
policy toward greater civil rights.
Once again, however, the model suggests a richer story. In part because of poor economic conditions in the South, a substantial number
of African Americans moved out of the South prior to and during the
New Deal.22 For two reasons, this increased the influence that African
American voters had over policy. First, it increased the number of African American voters: In the South, African Americans typically could
21
Regional differences in a variety of factors (e.g., wages, industrial mix, voter turnout
among low-wage workers) may have contributed to the split among Democrats (e.g., Key
1949; Leuchtenberg 1963; Poole and Rosenthal 1991; Seltzer 1995, 2004; Fleck 2002, 2004).
Fleck (2002) provides the most direct evidence in support of my argument: Democrats
in the House divided along lines of pre–New Deal Democratic loyalty, but with greater
pre–New Deal Democratic loyalty predicting opposition to the FLSA.
22
In 1920, 15 percent of African Americans lived outside the South; in 1940, 23 percent
did (according to the 1930 Census and the 1940 Census).
26
journal of political economy
not vote, but in the North, they typically could (e.g., Key 1949). Thus,
in the context of proposition 3, migration increased a substantially
outside the South and decreased a only slightly in the South. A second,
complementary reason is that in many of the states to which African
Americans moved, their decisions about which party to support mattered
to those running for office: Unlike southern states, northern states often
had serious interparty competition, which gave northern ideal points
(a) more weight.23 Furthermore, prior to the New Deal, most African
American voters supported Republicans, but that changed during the
New Deal (e.g., Sundquist 1973). In sum, African Americans became a
consequential number of swing voters in states with electoral weight,
and this increased the electoral value of pleasing African Americans.
Walter White, secretary of the NAACP, recognized this point and used
it in his efforts to persuade Roosevelt to take a stronger stand against
lynching. As White explains, “The Secretary [Walter White] then called
the President’s attention to the tables . . . in which 17 states, with a
total electoral vote of 281, have a Negro voting population, 21 years of
age and over, sufficient to determine the outcome in a close election”
(Freidel 1965, 90; quoting White’s memoirs).
White argued that efforts to please voters in swing states mattered
more than efforts to please voters in loyal Democratic states. He explained that Democrats were unlikely to lose in southern states and that
Republicans were competing for the support of African American voters
in northern states. Other civil rights leaders and journalists made similar
arguments (Sitkoff 1978).
It is very important to note that my argument here—like my arguments regarding distributive policy and minimum wages—is not about
the relative influence that humanitarian or electoral concerns had in
New Dealers’ decisions. As Walter White’s analysis illustrates, a concern
for the Democratic Party’s electoral strength in states with electoral
weight could have generated policy changes similar to those generated
by a concern for African Americans. For understanding the policy
change, the key point is that a variety of events made an improvement
in the treatment of African Americans more electorally successful than
it otherwise would have been.
VII.
Conclusion
This article focuses on how a combination of economic and political
factors can lead to major shifts in policy. It is obvious, of course, that
23
In addition, the states with growing African American populations had more electoral
variability: There is a .33 correlation between SD and the change from 1930 to 1940 in
the percentage of a state’s population that was African American.
political economy of the new deal
27
changes in economic conditions can change policy decisions, as can
changes in the relative influence over policy held by different types of
constituents. But examining how the policy response to economic conditions depends on the way political institutions weigh constituents’
preferences leads to a richer explanation of policy changes. In particular,
the model I develop in this article predicts that changes in economic
conditions will cause greater policy changes when the constituents who
expect to gain from those policy changes have more electoral weight.
And the empirical findings I present show how historical events prior
to the New Deal created such conditions.
Thus, this article contributes to the understanding of a major change
in policy: the New Deal. As a result of the Depression and droughts,
the electorate valued increased spending on relief, roads, conservation,
and reclamation. Spending on these programs could easily be designed
to allocate more per capita to states in which the Depression was very
severe and to states with vast amounts of land. And, it so happened,
those were the states in which Wright’s model and mine predict that
spending would have had a high expected electoral value. Additional
analysis yields complementary findings. As expected during a period of
heightened public distrust in free markets, increased labor market regulation had widespread support, yet it had especially strong support
where constituents who favored regulation had great electoral weight.
Similarly, strengthening the Democratic Party’s position on civil rights
for African Americans had become more valuable electorally because
many African Americans had become swing voters in electorally influential states.
This explanation of the New Deal contributes to a more general understanding of policy change. As an illustration, consider again the
electoral weight of African Americans, but this time in the context of
current policy. In recent decades, African American voters have overwhelmingly supported the Democratic Party. And, of course, civil rights
and other issues associated with winning political support among African
Americans typically divide politicians along the liberal-conservative lines
that separate Democrats from Republicans on other major issues (e.g.,
Poole and Rosenthal 1997). What factors might increase the electoral
weight of African Americans and, therefore, cause a change in policy?
One potential factor is an increase in voter turnout among African
Americans; this would shift the ideal points of the electorate. In addition,
a variety of other potential factors—including migration, changes in the
salience of specific political issues, and demographic shifts—may increase the number of African Americans who are swing voters in electorally important states or districts. Indeed, such factors appear to have
stimulated recent efforts by Republicans, and by the Bush administration
in particular, to win support among African Americans (e.g., Economist
28
journal of political economy
2005). For example, to the extent that socially conservative positions
(e.g., opposition to gay marriage) increasingly appeal to religious African Americans, especially in swing states (and most famously in Ohio
during the 2004 presidential race), the effect will be to convert previously loyal Democrats into swing voters and give African Americans more
weight on other policy dimensions. Similarly, to the extent that younger
and wealthier African Americans (relative to older African Americans)
have less loyalty to the Democratic Party or less desire for liberal economic policies, Republican politicians will have greater incentives to
seek their votes, and, consequently, so will the Democrats.
As this article explains, the value of the theoretical model does not
hinge on politicians’ motives. For example, in the event of an increase
in the electoral weight of African Americans—whether the historical
increase in the 1930s or a potential increase in the future—the model
predicts a change in policy. Politicians who set policy in response to
electoral incentives would, of course, respond to the increased electoral
weight of African Americans. By contrast, incumbent ideologues might
ignore electoral incentives. Yet if they do, they will remain in office only
if their ideological positions are sufficiently close to their electorates’
preferences. Hence, policy will respond whether vote seekers or ideologues set policy. Understanding the New Deal in this context returns
the New Deal literature to the domain of positive political economy (in
which politicians’ motives are often irrelevant). What matters is that
New Dealers—regardless of how many of them were reelection seekers,
reelection-winning ideologues, or something in between—designed a
variety of policies that matched the way the Depression, drought, and
migration affected the type of policy that would return incumbents to
office. Hence, the policy watershed known as the New Deal.
Appendix A
Proofs of Propositions 1–3
Preliminary Notes on Notation
Throughout the theoretical analysis, Greek letters indicate exogenous variables.
Subscripts are suppressed when no ambiguity exists. Let f (e) represent the assumed normal distribution of e, with f(e) the cumulative distribution. Let x*
denote the incumbent’s optimal solution.
Proof of Proposition 1
To begin, note that for the relevant range of policy, b(x) increases monotonically
in home district spending and decreases monotonically in other district spending. This follows from the obvious fact that the incumbent will never set spending
political economy of the new deal
29
in a given district above the home district’s ideal point or below the other
districts’ ideal point.24 Also note that for any district i
Ewi p Pr (vi 1 0) p Pr [l i ⫹ kibi(x) ⫹ ei 1 0] p f [l i ⫹ kibi(x)].
For district k spending, the first-order condition is
⭸EV
p0⇒
⭸x k
冘( )
ri
i
( ) 冘( )
⭸Ewi
⭸Ew k
⭸Ewi
p 0 ⇒ rk
⫹ ri
p0
⭸x k
⭸x k
⭸x k
i(k
⇒ rk f [lk ⫹ kkb k(x)]kk(a home ⫺ bx k ⫺ m)
⫹
冘
ri f [l i ⫹ kibi(x)]ki(a other ⫺ bx k ⫺ m) p 0.
i(k
The first claim is that districts with a greater apportionment (i.e., larger rk)
will, compared with otherwise identical districts, receive more funds (i.e., higher
x k). To see why, suppose that rk 1 rh and x*k ! x*h for two otherwise identical
districts. This cannot maximize EV because we would have Ew k ! Ewh (evaluated
at x*); yet that along with rk 1 rh would imply that simply switching the funding
levels between the two districts would increase EV. Similarly, suppose that rk 1
rh and x*k p x*h for two otherwise identical districts. Again this cannot maximize
EV because ⭸Ewg /⭸x k p ⭸Ewg /⭸x h would hold (evaluated at x*) for any other
district g (i.e., neither k nor h); and with rk 1 rh and ⭸Ew k /⭸x k p ⭸Ewh /⭸x h (which
holds when x*k p x*h ), this implies that a marginal reallocation of funds from
district h to district k would increase EV. Finally, it is directly apparent from the
first-order conditions stated above that as rk r 0, maximizing EV requires an
x k such that a other ⫺ bx k ⫺ m r 0, which implies that x*k r (a other ⫺ m)b⫺1, the other
districts’ ideal point for x k.
The next claim is that sufficiently extreme exogenous political leanings lk
(either positive or negative) will, ceteris paribus, lead to x*k near the other
districts’ ideal point. Why? As lk r ⫺⬁ or lk r ⬁, we know f r 0 for district k
(given the obvious fact that x*k will be bounded below by the ideal point of the
other districts and above the ideal point of district k). And as f r 0 for district
k, the first-order conditions imply that a other ⫺ bx k ⫺ m r 0 , which in turn implies
that x*k r (a other ⫺ m)b⫺1.
The final claim is that sufficiently extreme voter responsiveness or unresponsiveness to policy (i.e., extreme kk, either high or near zero) will, ceteris
paribus, lead to x*k near the other districts’ ideal point for x k . Why? First,
when kk becomes sufficiently large (for any given set of other parameters
and policy), f falls sufficiently rapidly (with respect to kk increasing) that
rk f [lk ⫹ kkb k(x)]kk(a home ⫺ bx k ⫺ m) converges to zero. Convergence to zero
here follows from the properties of the normal distribution, and it is clear
from the partial derivative of rk f [lk ⫹ kkb k(x)]kk(a home ⫺ bx k ⫺ m) with respect
to kk, again making use of the fact that x*k is bounded by the ideal points
(details available). And given this term’s convergence to zero, the first-order
conditions directly imply x*k r (a other ⫺ m)b⫺1 . Second, as kk r 0 , the first-order
conditions require x k such that a other ⫺ bx k ⫺ m r 0, which implies x*k r
(a other ⫺ m)b⫺1.
24
If district k had no political weight (e.g., rk p 0 ), then a marginal reduction in
bk(x) would have no effect on wk . Hence, for EV to be at a maximum, xk would necessarily
be at the ideal point of districts other than k. That is, xk* p (aother ⫺ m)b⫺1.
30
journal of political economy
Proof of Proposition 2
Similarly to the case of proposition 1, the first-order condition is
⭸EV
p0
⭸x k
⇒ rk f [lk ⫹ kkb k(x)]kk(a home ⫺ f⫺1
k x k ⫺ m)
⫹
冘
ri f [l i ⫹ kibi(x)]ki(a other ⫺ f⫺1
k x k ⫺ m) p 0.
i(k
The first claim is that proposition 1 holds, with the difference being that ideal
points depend on f. The proof starts from the first-order condition stated above
and proceeds in the same manner as the proof for proposition 1, but the ideal
points for district k spending are now x k p (a home ⫺ m)fk for district k and
x k p (a other ⫺ m)fk for other districts.
Turning to the claims with respect to the way the effect of f on spending will
differ between districts, consider the first-order conditions for spending in two
districts, k and h:
rk f [lk ⫹ kkb k(x)]kk(a home ⫺ f⫺1
k x k ⫺ m)
⫹ rh f [lh ⫹ khbh(x)]kh(a other ⫺ f⫺1
k x k ⫺ m)
⫹
冘
ri f [l i ⫹ kibi(x)]ki(a other ⫺ f⫺1
k x k ⫺ m) p 0
(A1)
i(k,h
and
rk f [lk ⫹ kkb k(x)]kk(a other ⫺ f⫺1
h x h ⫺ m)
⫹ rh f [lh ⫹ khbh(x)]kh(a home ⫺ f⫺1
h x h ⫺ m)
⫹
冘
ri f [l i ⫹ kibi(x)]ki(a other ⫺ f⫺1
h x h ⫺ m) p 0.
(A2)
i(k,h
Subtracting (A2) from (A1) and rearranging yields (details available)
⫺1
(a other ⫺ f⫺1
h x h ⫺ m) ⫺ (a other ⫺ fk x k ⫺ m) p
{rk f [lk ⫹ kkb k(x)]kk ⫺ rh f [lh ⫹ khbh(x)]kh }(a home ⫺ a other)
.
冘i ri f [l i ⫹ kibi(x)]ki
(A3)
Now consider fk 1 fh for otherwise identical districts. We know that if allocations
are in proportion to “need” f, the following equality must hold:
⫺1
(a other ⫺ f⫺1
h x h ⫺ m) ⫺ (a other ⫺ fk x k ⫺ m) p 0.
(A4)
From (A3) and (A4), we know that the f-driven proportional difference between
x k and x h will be more than the proportional difference between fk and fh if
the sign of (A5) is positive:
rk f [lk ⫹ kkb k(x)]kk ⫺ rh f [lh ⫹ khbh(x)]kh.
(A5)
Now consider otherwise identical districts that have fk 1 fh and are expected
losses (i.e., Ew k ! .5 and Ewh ! .5). If spending were higher in district k by the
same proportion that fk exceeds fh (i.e., if x k /x h p fk /fh), then (A5) would
have a positive sign. Why? Because the proportional increase in spending would
political economy of the new deal
31
lead to b k(x) 1 bh(x), and that would cause f [lk ⫹ kkb k(x)] 1 f [lh ⫹ khbh(x)], because f 1 0 for expected losses (recall that we have assumed lk p lh, rk p rh,
and kk p kh). With (A5) not equal to zero, spending cannot possibly be proportional to f. And, more specifically, x k /x h must exceed fk /fh in order for the
first-order conditions to hold (i.e., in order to increase the left-hand side of
[A3] and decrease the right-hand side, relative to the case in which x k /x h p
fk /fh).
By contrast, for otherwise identical districts that have fk 1 fh and are expected
wins (i.e., Ew k 1 .5 and Ewh 1 .5), we have f ! 0. From the logic of the previous
paragraph, this case implies that x k /x h must be less than fk /fh . That is, spending
increases less than in proportion to f.
Proof of Proposition 3
The first-order condition is
⭸EV
p0⇒
⭸x
冘( )
ri
i
⭸Ewi
p 0.
⭸x
Now to examine the influence of district k, consider
( ) 冘( )
⭸EV
⭸Ew k
⭸Ewi
p rk
⫹ ri
.
⭸x
⭸x
⭸x
i(k
For any district h, we can find bh(x) as the economic surplus indicated by the
MB curve: bh(x) p .5[ah2 ⫺ (ah ⫺ x)2 ]. This implies
冘( )
⭸EV
⭸Ewi
p rk f [lk ⫹ .5kk[ak2 ⫺ (ak ⫺ x)2 ]]kk(ak ⫺ x) ⫹ ri
.
⭸x
⭸x
i(k
The key issue now is how a change in rk, lk, or kk will affect ⭸EV/⭸x, evaluated
at an initial x*. For simplicity, consider the case in which ak 1 x* . (The case in
which ak ! x* is logically equivalent, except that policy moves in the opposite
direction.) The effect of rk is obvious: An increase in rk will increase ⭸EV/⭸x
and, hence, increase the politician’s optimal choice of x. The effect of lk can
be positive or negative. If lk ! ⫺.5kk[ak2 ⫺ (ak ⫺ x)2 ] , then the marginal effect of
lk on f is positive and, hence, the effects on ⭸EV/⭸x and x are positive. If lk 1
⫺.5kk[ak2 ⫺ (ak ⫺ x)2 ], then the marginal effect of lk on f is negative and, hence,
the effects on ⭸EV/⭸x and x are negative.
For sufficiently small values of kk, the marginal effect of kk on f [lk ⫹
.5kk[ak2 ⫺ (ak ⫺ x)2 ]]kk and, hence, on ⭸EV/⭸x and x will be positive. However, for
sufficiently large values of kk, the marginal effect of kk on f [lk ⫹ .5kk[ak2 ⫺
(ak ⫺ x)2 ]]kk and, hence, on ⭸EV/⭸x and x will be negative. These facts follow
directly from the properties of the normal distribution and can be seen easily
by examining the partial derivative (given the fact that policy is bounded by
ideal points) of f [lk ⫹ .5kk[ak2 ⫺ (ak ⫺ x)2 ]]kk with respect to kk(details available).
32
journal of political economy
Appendix B
Variables Used in the Econometric Analysis
Principal Proxy for x
SPEND: Expenditure by New Deal programs, 1933–39, per capita. I calculated
the variable as rdgspnd/(wpop#1,000), where rdgspnd and wpop are from
Wallis (1997).25 The variable SPEND in my article equals per capita national
spending in Wallis (1998), which approximates Wright’s (1974) spending variable (which Wright called SPND).
Principal Proxies for r, k, and l
V/POP: Electoral votes, per capita, multiplied by 1,000. Variable is wvpop from
Wallis (1997) and equals per capita electoral votes in Wallis (1998).
SENATE/POP: Senate seats per capita, multiplied by 1,000. Population data
are from the 1930 Census (ICPSR data tape).
HOUSE/POP: House seats per capita, multiplied by 1,000. House seats calculated from the electoral votes variable (wv) in Wallis (1997). Population data
are from the 1930 Census (ICPSR data tape).
SD: Standard deviation of the Democratic vote share in presidential elections,
1896–1932. The variable is wse32 from Wallis (1997), equals standard deviation
of the vote in Wallis (1998), and equals or approximates the variable Wright
(1974) called SD in his analysis.
DEMpred: Predicted Democratic vote share in the 1932 presidential election,
measured as a percentage of total vote. The prediction for each state is based
on fitting a linear trend (using ordinary least squares) to the state’s Democratic
vote share in presidential elections from 1896 to 1932; electoral data are taken
from Petersen (1963).
VL32: Wright’s (1974) measure of political productivity. The variable is wval232
from Wallis (1997) and equals political productivity in Wallis (1998). The variable
equals or approximates the variable Wright called VL32 in his analysis.
Principal Proxies for f
INCOME1932: Personal income in 1932, in dollars per capita (Arrington 1969).
INCOME FALL1929–32: Decline in personal income from 1929 to 1932, in dollars
per capita. Calculated from data in Arrington (1969).
LAND: Square miles of land in the state, per capita. Land data are from Rand
McNally (1992). Population data are from the 1930 Census (ICPSR data tape).
LANDfederal: Square miles of federal land in the state, per capita. Calculated
from the number of square miles in the state (Rand McNally 1992) and the
percentage of total acreage in each state owned by the United States (U.S. Senate
Committee on Appropriations 1939). Population data are from the 1930 Census
(ICPSR data tape).
LANDnonfederal: Square miles of nonfederal land in the state, per capita. Calculated from the number of square miles in the state (Rand McNally 1992) and
the percentage of total acreage in each state owned by the United States (U.S.
25
Wallis (1995) refers to his CONG3239.SD2 data set; Wallis (1997) refers to his
EFFECT.SD2 data set.
political economy of the new deal
33
Senate Committee on Appropriations 1939). Population data are from the 1930
Census (ICPSR data tape).
Additional Proxies for r (Used Only for Robustness Tests)
HOUSE APPROPRIATIONS: For each state, the length of tenure (in months)
of the House delegation’s membership on the Appropriations Committee. Calculated from the panel variable happ in Wallis (1995). My variable is the mean
value of happ over 7 years (1933–39). To the best of my knowledge, happ equals
the panel measure for House appropriations in Wallis (1998). (As App. C shows,
Wallis apparently scaled the variable to have a minimum value equal to one.)
SENATE APPROPRIATIONS: For each state, the length of tenure (in months)
of the Senate delegation’s tenure on the Appropriations Committee. Calculated
from the panel variable sapp in Wallis (1995). My variable is the mean value of
sapp over 7 years (1933–39). To the best of my knowledge, sapp equals the panel
measure of Senate appropriations in Wallis (1998). (As App. C shows, Wallis
apparently scaled the variable to have a minimum value equal to one.)
HOUSE TENURE: For each state, the length of tenure (in months) of the
House delegation. Calculated from the panel variable hten in Wallis (1995). My
variable is the mean value of hten over 7 years (1933–39). To the best of my
knowledge, hten equals the panel measure of House tenure in Wallis (1998).
SENATE TENURE: For each state, the length of tenure (in months) of the
Senate delegation. Calculated from the panel variable sten in Wallis (1995). My
variable is the mean value of sten over 7 years (1933–39). To the best of my
knowledge, sten equals the panel measure of Senate tenure in Wallis (1998).
HOUSE LEADERSHIP: A dummy variable that takes the value of one if any
member of the state’s House delegation held a leadership position (speaker of
the House or House majority leader) for at least one of the years from 1933 to
1939. I calculated this cross-sectional variable using the panel variable hldr in
Wallis (1995). To the best of my knowledge, my variable equals House leadership
in Wallis’s (1998) cross section.
SENATE LEADERSHIP: A dummy variable that takes the value of one if any
member of the state’s Senate delegation held a leadership position (president
pro tem or Senate majority leader) for at least one of the years from 1933 to
1939. I calculated this cross-sectional variable using the panel variable sldr in
Wallis (1995). To the best of my knowledge, my variable equals Senate leadership
in Wallis’s (1998) cross section.
Additional Proxies for f
UNEMPLOYMENT 1930: This variable is the sum of two variables (un30a ⫹
un30b) from Wallis (1997). The variable equals unemployment 1930 in Wallis
(1998).
UNEMPLOYMENT 1937: This variable is wun37 in Wallis (1997) and equals
unemployment 1937 in Wallis (1998).
%FEDLAND: Federally owned acreage as a percentage of total acreage in the
state. This variable is wpfdlnd from Wallis (1997) and equals percent federal
land in Wallis (1998).
FARMPOP: Per capita farm population. Variable is wfmid from Wallis (1997)
and equals farm population share in Wallis (1998).
FARM VALUE: Farm value per capita. I calculated the variable as farmtv30/
34
journal of political economy
(wpop#1,000), where farmtv30 and wpop are from Wallis (1997). Variable
equals farm value per capita in Wallis (1998).
Appendix C
TABLE C1
Descriptive Statistics (N p 48)
SPEND
V/POP
SENATE/POP
HOUSE/POP
SD
DEMpred
VL32
INCOME
INCOME FALL
LAND
LANDfederal
LANDnonfederal
SENATE APPROPRIATIONS
HOUSE APPROPRIATIONS
SENATE TENURE
HOUSE TENURE
SENATE LEADERSHIP
HOUSE LEADERSHIP
%FEDLAND
UNEMPLOYMENT 1930
UNEMPLOYMENT 1937
FARMPOP
FARM VALUE
Mean
Standard
Deviation
Minimum
Maximum
293.448
.00599
.00230
.00369
10.1750
47.8881
.04137
345.208
252.838
.08211
.03938
.04273
42.3303
68.3690
173.937
560.151
.06250
.08333
13.4547
5.74583
4.22083
.29156
.63530
178.143
.00448
.00351
.00115
4.32580
15.6462
.03583
143.391
80.4167
.18821
.14771
.05582
58.9605
101.038
97.9849
694.135
.24462
.27931
20.6331
2.24717
.89251
.16077
.54965
147.318
.00373
.00015
.00229
2.50000
31.0418
.00000
130.000
116.929
.00153
.000004
.00152
1.00000
1.00000
36.0000
1.57143
.00000
.00000
.10000
1.80000
2.40000
.02356
.05984
1,130.76
.03296
.02196
.01098
18.1000
97.4847
.15366
680.000
441.960
1.20687
.99772
.24629
222.285
560.571
384.571
3,926.85
1.00000
1.00000
82.6700
12.2000
6.40000
.66025
2.27885
References
Anderson, Gary M., and Robert D. Tollison. 1991. “Congressional Influence and
Patterns of New Deal Spending.” J. Law and Econ. 34 (April): 161–75.
Ansolabehere, Stephen, and James M. Snyder Jr. 2006. “Party Control of State
Government and the Distribution of Public Expenditures.” Scandinavian J.
Econ. 108 (December): 547–69.
Arrington, Leonard J. 1969. “The New Deal in the West: A Preliminary Statistical
Inquiry.” Pacific Hist. Rev. 38 (August): 311–16.
Atlas, Cary M., Thomas W. Gilligan, Robert J. Hendershott, and Mark A. Zupan.
1995. “Slicing the Federal Government Net Spending Pie: Who Wins, Who
Loses, and Why.” A.E.R. 85 (June): 624–29.
Bateman, Fred, and Jason E. Taylor. 2003. “The New Deal at War: Alphabet
Agencies’ Expenditure Patterns, 1940–1945.” Explorations Econ. Hist. 40 (July):
251–77.
Bender, Bruce, and John R. Lott Jr. 1996. “Legislator Voting and Shirking: A
Critical Review of the Literature.” Public Choice 87 (April): 67–100.
political economy of the new deal
35
Bennett, James T., and Eddie R. Mayberry. 1979. “Federal Tax Burdens and
Grant Benefits to States: The Impact of Imperfect Representation.” Public
Choice 34 (September): 255–69.
Brady, David W. 1988. Critical Elections and Congressional Policy Making. Stanford,
CA: Stanford Univ. Press.
Brady, David W., and Joseph Stewart Jr. 1982. “Congressional Party Realignment
and the Transformations of Public Policy in Three Realignment Eras.” American J. Polit. Sci. 26 (May): 333–60.
Bronars, Stephen G., and John R. Lott Jr. 1997. “Do Campaign Donations Alter
How a Politician Votes? Or, Do Donors Support Candidates Who Value the
Same Things That They Do?” J. Law and Econ. 40 (October): 317–50.
Burnham, Walter Dean. 1970. Critical Elections and the Mainsprings of American
Politics. New York: Norton.
Clubb, Jerome M., William H. Flanigan, and Nancy H. Zingale. 1980. Partisan
Realignment. Beverly Hills, CA: Sage.
Couch, Jim F., and William F. Shughart II. 1998. The Political Economy of the New
Deal. Cheltenham, UK: Elgar.
Cox, Gary W., and Mathew D. McCubbins. 1986. “Electoral Politics as a Redistributive Game.” J. Politics 48 (May): 370–89.
Darby, Michael R. 1976. “Three-and-a-Half Million U.S. Employees Have Been
Mislaid: Or, an Explanation of Unemployment, 1934–1941.” J.P.E. 84 (February): 1–16.
Davidson, Russell, and James G. MacKinnon. 1981. “Several Tests for Model
Specification in the Presence of Alternative Hypotheses.” Econometrica 49
(May): 781–93.
Dixit, Avinash, and John Londregan. 1996. “The Determinants of Success of
Special Interests in Redistributive Politics.” J. Politics 58 (December): 1132–
55.
Economist. 2005. “Rebuilding the Party of Lincoln.” April 23, p. 36.
Fishback, Price V., Shawn Kantor, and John Joseph Wallis. 2003. “Can the New
Deal’s Three Rs Be Rehabilitated? A Program-by-Program, County-by-County
Analysis.” Explorations Econ. Hist. 40 (July): 278–307.
Fleck, Robert K. 1999a. “Electoral Incentives, Public Policy, and the New Deal
Realignment.” Southern Econ. J. 65 (January): 377–404.
———. 1999b. “The Marginal Effect of New Deal Relief Work on County-Level
Unemployment Statistics.” J. Econ. Hist. 59 (September): 659–87.
———. 1999c. “The Value of the Vote: A Model and Test of the Effects of Turnout
on Distributive Policy.” Econ. Inquiry 37 (October): 609–23.
———. 2001a. “Inter-party Competition, Intra-party Competition, and Distributive Policy: A Model and Test Using New Deal Data.” Public Choice 108 (July):
77–100.
———. 2001b. “Population, Land, Economic Conditions, and the Allocation of
New Deal Spending.” Explorations Econ. Hist. 38 (April): 296–304.
———. 2002. “Democratic Opposition to the Fair Labor Standards Act of 1938.”
J. Econ. Hist. 62 (March): 25–54.
———. 2004. “Democratic Opposition to the Fair Labor Standards Act of 1938:
Reply to Seltzer.” J. Econ. Hist. 64 (March): 231–35.
Freidel, Frank. 1965. F.D.R. and the South. Baton Rouge: Louisiana State Univ.
Press.
Ginsberg, Benjamin. 1972. “Critical Elections and the Substance of Party Conflict: 1844–1968.” Midwest J. Polit. Sci. 16 (November): 603–25.
36
journal of political economy
———. 1976. “Elections and Public Policy.” American Polit. Sci. Rev. 70 (March):
41–49.
Hoover, Gary A., and Paul Pecorino. 2005. “The Political Determinants of Federal
Expenditure at the State Level.” Public Choice 123 (April): 95–113.
Howard, Donald S. 1943. The WPA and Federal Relief Policy. New York: Sage Found.
Husted, Thomas A., and Lawrence W. Kenny. 1997. “The Effect of the Expansion
of the Voting Franchise on the Size of Government.” J.P.E. 105 (February):
54–82.
ICPSR (Inter-university Consortium for Political and Social Research). Historical,
Demographic, Economic, and Social Data: The United States, 1790–1970. Data tape.
Ann Arbor: Univ. Michigan.
Johnson, Ronald N., and Gary D. Libecap. 2003. “Transaction Costs and Coalition
Stability under Majority Rule.” Econ. Inquiry 41 (April): 193–207.
Key, V. O., Jr. 1937. The Administration of Federal Grants to States. Crawfordsville,
IN: Donnelley.
———. 1949. Southern Politics. New York: Knopf.
———. 1955. “A Theory of Critical Elections.” J. Politics 17 (February): 3–18.
Lee, Frances E. 1998. “Representation and Public Policy: The Consequences of
Senate Apportionment for the Geographic Distribution of Federal Funds.” J.
Politics 60 (February): 34–62.
———. 2000. “Senate Representation and Coalition Building in Distributive
Politics.” American Polit. Sci. Rev. 94 (March): 59–72.
———. 2004. “Bicameralism and Geographic Politics: Allocating Funds in the
House and Senate.” Legislative Studies Q. 29 (May): 185–213.
Lee, Frances E., and Bruce I. Oppenheimer. 1999. Sizing Up the Senate: The
Unequal Consequences of Equal Representation. Chicago: Univ. Chicago Press.
Leuchtenberg, William E. 1963. Franklin D. Roosevelt and the New Deal. New York:
Harper & Row.
Levitt, Steven D., and James M. Poterba. 1999. “Congressional Distributive Politics and State Economic Performance.” Public Choice 99 (April): 185–216.
Levitt, Steven D., and James M. Snyder Jr. 1995. “Political Parties and the Distribution of Federal Outlays.” American J. Polit. Sci. 39 (November): 958–80.
———. 1997. “The Impact of Federal Spending on House Election Outcomes.”
J.P.E. 105 (February): 30–53.
Lott, John R., Jr., and Lawrence W. Kenny. 1999. “Did Women’s Suffrage Change
the Size and Scope of Government?” J.P.E. 107 (December): 1163–98
Mason, Joseph R. 2003. “The Political Economy of Reconstruction Finance Corporation Assistance during the Great Depression.” Explorations Econ. Hist. 40
(April): 101–21.
Miller, Gary, and Norman Schofield. 2003. “Activists and Partisan Realignment
in the United States.” American Polit. Sci. Rev. 97 (May): 245–60.
Petersen, Svend. 1963. A Statistical History of the American Presidential Elections.
New York: Ungar.
Poole, Keith T., and Howard Rosenthal. 1991. “The Spatial Mapping of Minimum
Wage Legislation.” In Politics and Economics in the 1980s, edited by Alberto
Alesina and Geoffrey Carliner. Chicago: Univ. Chicago Press.
———. 1997. Congress: A Political-Economic History of Roll Call Voting. New York:
Oxford Univ. Press.
Rand McNally. 1992. The New Cosmopolitan World Atlas. Chicago: Rand McNally.
Reading, Donald. 1972. “A Statistical Analysis of New Deal Economic Programs
in the Forty-eight States, 1933–39.” PhD diss., Utah State Univ.
political economy of the new deal
37
———. 1973. “New Deal Activity and the States, 1933 to 1939.” J. Econ. Hist. 36
(December): 792–810.
Schlesinger, Arthur M. 1958. The Coming of the New Deal. Boston: Houghton
Mifflin.
Seltzer, Andrew J. 1995. “The Political Economy of the Fair Labor Standards Act
of 1938.” J.P.E. 103 (December): 1302–42.
———. 2004. “Democratic Opposition to the Fair Labor Standards Act: A Comment on Fleck.” J. Econ. Hist. 64 (March): 226–30.
Sinclair, Barbara. 1977. “Party Realignment and the Transformation of the Political Agenda: The House of Representatives, 1925–1938.” American Polit. Sci.
Rev. 71 (September): 940–53.
———. 1985. “Agenda, Policy, and Alignment Change from Coolidge to Reagan.” In Congress Reconsidered, 3rd ed., edited by Lawrence C. Dodd and Bruce
I. Oppenheimer. Washington, DC: CQ Press.
Sitkoff, Harvard. 1978. A New Deal for Blacks. New York: Oxford Univ. Press.
———. 1981. The Struggle for Black Equality, 1954–1980. New York: Hill and Wang.
Strömberg, David. 2004. “Radio’s Impact on Public Spending.” Q.J.E. 119 (February): 189–221.
Sundquist, James L. 1973. Dynamics of the Party System. Washington, DC: Brookings
Inst.
U.S. Senate Committee on Appropriations. 1939. Work Relief and Public Works
Appropriation Act of 1939. Hearings, 76th Con., 1st sess. Washington, DC: U.S.
Government Printing Office.
Wallis, John J. 1984. “The Birth of the Old Federalism: Financing the New Deal.”
J. Econ. Hist. 44 (March): 139–69.
———. 1987. “Employment, Politics, and Economic Recovery in the Great Depression.” Rev. Econ. and Statis. 69 (August): 516–20.
———. 1991. “The Political Economy of New Deal Fiscal Federalism.” Econ.
Inquiry 29 (July): 510–24.
———. 1998. “The Political Economy of New Deal Spending Revisited, Again:
With and without Nevada.” Explorations Econ. Hist. 35 (April): 140–70.
———. 2001. “The Political Economy of New Deal Spending, Yet Again: A Reply
to Fleck.” Explorations Econ. Hist. 38 (April): 305–14.
Wright, Gavin. 1974. “The Political Economy of New Deal Spending: An Econometric Analysis.” Rev. Econ. and Statis. 59 (February): 30–38.