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Fishback and Kachanovskaya 1

In Search of the Multiplier for Net Federal Spending in the States

During the New Deal: A Preliminary Report

Price Fishback and Valentina Kachanovskaya

June 2009

Price Fishback is the Frank and Clara Kramer Professor of Economics at the University of Arizona and a Research Associate at the National Bureau of Economic Research ([email protected]). Valentina Kachanovskaya is a Ph.D. student at the University of Arizona ([email protected]). Both addresses are Department of Economics, University of Arizona, Tucson, AZ 85721. The authors would like to thank Shawn Kantor, John Wallis, and Paul Rhode for providing access to data and their comments. We would also like to thank Kei Hirano, William Horrace, and Alfonso Flores-Lagunes for their insights. The research has been funded by National Science Foundation Grants Nos. SES 0617972 and SES 0214483 and funds from the University of Arizona Economics Department. The discussion here does not reflect the views of either of these funding agencies. All errors are the authors’ own.

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Over the past year the U.S. federal government has adopted a sizeable stimulus

package. When added to the budget problems from 2008 and additional spending in

President Obama’s first budget, the federal government’s budget deficit as a share of

GDP has risen near 14 percent of GDP. This is the largest deficit relative to GDP since

World War II, when the U.S. was a command economy engaged in all-out war. The

Obama stimulus package has revived interest in output multipliers associated with fiscal

policy. Throughout the spring of 2009 a range of of multipliers have been suggested.

Mark Zandi (2009) of Moody’s Economy.com suggests a general multipler near 1.5.

Christina Romer, chairperson of the President’s Councel of Economic Advisors, has

provided estimates of multipliers in the 1 to 3 range (Romer 2009, Romer and Romer

2006, and Romer and Bernstein 2009). On the other hand, Robert Barro (2009) suggests

short-run multipliers of less than one, as do Cogan, Cwik, Taylor, and Wieland (2009).

The regional science literature and various economic forecasters, including Zandi (2009),

also offer a wide range of multipliers for states, regions, and communities.

Given the lack of agreement on the size of the multiplier, macroeconomic or

regional, it seems useful to examine the size of the multiplier for state per capita income

from net federal spending in the states during the New Deal from 1933 through 1939. If

there ever was a time when we would expect a large fiscal multiplier it would have been

during the Great Depression when unemployment rates ranged between 14 and 25

percent over the course of the decade. At that time stimulus from net federal spending in

a state was much more likely to add new employment without crowding out private

activity. The multiplier we estimate for individual states will likely be smaller than a


macroeconomic multiplier for the U.S. because the state economies are more like small

open economies in a world with free trade. Therefore, the state economies are more

likely to rely on importing goods and services from outside the state, which will diminish

the size of the multiplier.

In response to the hard times of the Great Depression, the Roosevelt

administration increased federal government spending as a share of GDP from 4 percent

to 8 percent over an eight-year period. At the macroeconomic level this did not lead to

large budget deficits. E. Cary Brown (1956) and Claude Peppers (1973) have

documented that the federal deficits as a share of GDP were small and fell well short of

being Keynesian policies designed to stimulate the economy.

On the other hand, the distribution of the federal spending varied enormously

across states on a per capita basis. The federal tax burden from the spending also varied

greatly as well. Figure 1 shows the large variation across states in 1935 in per capita

federal grant spending and per capita federal tax receipts in 1967 dollars in each state.1

Just as the Obama administration is implicitly doing today, the Roosevelt

administration ran a large-scale experiment of fiscal stimulus across the states. The

peace-time experiment ran throughout the period from 1933 through 1939 until the

orientation began shifting to preparations to aid allies in World War II and the United

States’ eventual participation. Currently, we have accumulated information on up to

eight years of federal spending in each state and federal tax receipts collected from each

The variation is similar across other years.

1 The variation is even larger when Delaware is added. We left Delaware off the graph to better show the spread across states visually, as Delaware reported federal tax receipts more than $100 per capita higher than in the next highest states.


state and matched it with information on per capita income in the states that can be used

to estimate the impact of net federal spending on state income per capita.2

Unfortunately, our task of assessing the causal impact of net federal spending in

the states is made more difficult by the Roosevelt administration’s disinterest in

designing the New Deal “experiment” in ways a scientist would prefer. Instead of

randomly distributing the funds across states, the federal government handed out the

money with several purposes in mind, including promoting Relief, Recovery, and

Reform, as well as maintaining the strength of the Democratic Party in holding the

Presidency and a large majority of both houses of Congress. To the extent that the

administration followed through on its pledge to aid troubled areas, there is likely to be a

negative endogeneity bias in Ordinary Least Squares estimates of the net federal spending

multiplier.

3

2 Our longer term goals are more ambitious. We have already collected information on per capita income, per capita federal road spending and loans for Bureau of Reclamation projects back to 1919, and are seeking information by state by the Veterans’ administration, the farm loan programs, and other agencies associated with rivers and harbors.

We use a sparse specification in which we estimate real per capita state

income as a function of real per capita net federal spending in a variety of ways. We then

use a mixture of state fixed effects, year fixed effects, and state-specific time trends to

control for a variety of factors. Since annual information by state on exogenous

correlates is not often available for the period, these measures help control for a variety of

factors for which the best measures we would obtain would be either fixed over time or

would rely on trend interpolations. Finally, we develop an instrumental variable strategy

to work to eliminate any omitted variable bias and endogeneity bias that might be left.

3 There is a large literature on this distribution issue. See Wright, 1974; Wallis, 1987, 1998, and 2001; Couch and Shughart 1998; Anderson and Tollison, 1991; Fishback, Kantor, and Wallis, 2003; Fleck 2001a, 2001b, and 2008; and Stromberg 2004.. The findings vary from study to study, but generally there is evidence that the Roosevelt administration both promoted recovery, reform, and redistribution and played politics with the distribution.


In the course of the analysis, we have had to make a series of decisions about

adjusting federal spending and tax data from fiscal year to calendar year, the relative

weights to give observations from each state, how to treat federal loans, the differences in

purposes of the grants, and the time period of coverage. As a result, we report a

substantial number of estimates. The estimates that do the most to control for omitted

variable bias and endogeneity bias suggest that an additional dollar of net federal

spending in the states raised per capita income by somewhere between 0 and 62 cents

during the 1930s for estimates based on calendar year interpolations of federal spending

and taxation. Other estimates assume a six-month lag in the impact of net federal

spending by estimating calendar-year state income per capita as a function of fiscal year

net federal spending per capita. They range as high as 0.8 and 1.7. Larger estimates for

analyses based on either the calendar year or the fiscal year can be obtained by removing

controls for state-specific trends or national shocks and assuming that such the changes

across time can be attributed to net federal spending.

Fiscal Multipliers

Macro Multipliers

In the 1970s and early 1980s macroeconomics textbooks reported on estimates of

the Keynesian multiplier. For example, the third edition of Dornbusch and Fischer’s

Macroeconomics in 1984 (p. 148) reports multiplier estimates for an increase in net

government spending of 1.8 from DRI and 0.7 from the Federal Reserve Bank of St.

Louis. Meanwhile, the macroeconomics literature was shifting direction. Hall (1980)

argued that temporary government purchases that are not close substitutes for private


spending could stimulate the economy by shifting production forward in time but long

run increases would not. Barro (1981) found that the effect of temporary military

spending on consumption exceeds that of permanent military spending; furthermore, non-

military spending, not divided into permanent and temporary, did not have any significant

effect. In late 1980s and 1990s the early works of Barro and Hall were extended by using

mostly one-sector neoclassical growth models with constant return to scale to estimate

fiscal policy multipliers.

Changes in assumptions can lead to quite different effects. In contrast to Barro

and Hall, Aiyagari, Christiano, and Eichenbaum (henceforth ACG) (1992) argued that

fiscal changes could affect long run interest rates and then show both theoretically and

empirically that the impact on output, employment and the interest rate of a persistent

change in government consumption exceeds that of a temporary change. Devereux, Head

and Lapham (1996) modified the commonly used model by assuming imperfect

competition and increasing returns to scale. They found that government spending could

lead to an increase in factor productivity that lowered real wages, raised firm

profitability, and could lead to a stimulus to rather than a crowding out of private

investment.

A series of VAR studies of the macro-economy sought to resolve endogeneity

problems by relying on military build-ups that were said to be unrelated to the state of the

economy or to narratives where it appears that there were no attempts by the government

to respond to economic conditions. Blanchard and Perotti (2002) offer a nice summary

of the literature to that time and find that private consumption is consistently crowded out

by taxation and crowded in by government spending, consistent with the Keynesian


model. On the other hand, private investment is crowded out by both government

purchases as well as taxation, which is consistent with the neo-classical model.4 Using a

vector auto-regressive framework Afredo M. Pereiral and Rafael Flores de Frutos (1999)

also find that public spending crowds out private spending, leading to multipliers of about

0.65.5

The recent increase focus on the stimulus package has led to a discussion of a

wide range of estimates. Business economists have still been producing estimates of

multipliers at the national and state level. In Congressional testimony Mark Zandi (2009)

has used the Moody’s Economy.com simulation model to report a range of

macroeconomic multiplier estimates for different programs from 0.31 for cuts in

corporate taxes to 1.73 for temporary increases in food stamps. His most cited estimate

was a $1.59 stimulus for GDP based on building bridges and schools. Meanwhile,

Robert Barro (2009, 1981, 1987) has been writing opinion pieces based on his earlier

research that suggest that the short run multiplier is roughly 0.8 for war-time spending

and the long run multiplier is closer to zero (see also Barro 1981, 1987).

Ramey and Shapiro (1998) develop a two-sector dynamic general equilibrium

model in which the reallocation of capital across sectors is costly. The two-sector model

leads to a richer array of possible responses of aggregate variables than the one-sector

model. The empirical part of the paper estimates the effects of military buildups on a

variety of macroeconomic variables and leads to a wide range of findings, some counter

to the literature, some consistent with Keynesian models, and all consistent with their

model.

4 Blanchard, Perotti (2002), p. 1363. 5 Meanwhile, Ohanian (1997) develops a simulation of the military buildup and policies for the Korean war but talks in terms of welfare rather than output measures.


Christina Romer (2009), the Chairperson of the President’s Council of Economic

Advisors, argues that omitted variable bias is a serious problem in earlier and cites work

with David Romer (2006, 43) that uses narratives to isolates tax changes uncorrelated

with other factors affecting output. They find that tax cuts can raise GNP by multiples of

2 or 3 over a three-year period (Romer and Romer 2006, 43). Her simulations with Jared

Bernstein (2009) suggest a tax cut multiplier of one after a year and a half and a spending

multiplier of about 1.6. In response to this work, Cogan, Cwik, Taylor, and Wieland

(2009) have run simulations based on a new Keynesian model and found much smaller

multipliers than the ones found by Zandi and Romer and her co-authors, smaller even

than the 0.8 estimates discussed by Barro.

Regional Multipliers

On the state and regional level studies of fiscal activity have found mixed results.

Hulten and Schwab (1991) conclude that the link between public infrastructure and

states’ economic growth is weak arguing that the states that saw a large expansion of

public infrastructure in 1970s were not the ones that developed faster during that period.

On the other hand, Munnell (1992 192), Garcia-Mila and McGuire (1992), Costa, Ellison,

and Martin (1987), Blanchard and Katz (1992), Duffy-Deno and Roberts (1991), Fernald

(1991), and Aschauer (1989) find positive effects of public spending on a variety of

dimensions.6

6 Munnell (1992 192) finds a significant effect of public capital on state-level

output, investment and employment growth, although the effects of government spending at the state level are smaller than at the national level. Garcia-Mila and McGuire (1992) constructed a panel of 48 states from 1969 until 1983 to estimate input elasticity coefficients of regional Cobb-Douglas production functions and concluded that


Meanwhile, regional scientists, business economists, and regional forecasters

have been active in developing models of multipliers that they apply to government

spending and other types of income coming into a location. Mark Zandi’s (2009) makes

predictions about the impact of the new fiscal stimulus package on state employment.

Regional scientists have developed a broad range of theoretical models that lead to

multipliers for net income coming into the region. The models range from the early

Keynesian regional models to input-output models to economic base models to neo-

classical models.7

government provided goods, such as highways and education, have a significant and positive effect on state’s output. Costa, Ellison, and Martin (1987) consider a translog production function and conclude that public capital and labor are complementary inputs. The estimated elasticities of output with respect to public are around one in all states. Meanwhile, Blanchard and Katz (1992) model the effects of negative one-percent employment shocks to a wide range of variables using data from U.S. states from 1947 to 1990 and find sizeable effects on per capita income over an extended number of years.

The empirical work on regional multipliers lead to a broad range of

estimates of multipliers of between 0.5 and two, depending on the technique used. Some

rely on simulations that derive multipliers using input-output models and surveys that

describe the degree to which different industries rely on local labor and external inputs

Duffy-Deno and Eberts (1991) study the effect of the public capital stock on the state’s economic growth, first, without using capital expenditures as a proxy for capital stock, and second, considering public capital both exogenous to the firm and endogenous to the local community positing a simultaneous relationship of public capital and local economic growth. The authors find a positive and statistically significant effect of public capital on state’s economic growth rate.

Assessing a link between public capital and economic growth, Fernald (1999) studies the direction of causation between public capital and productivity and unsurprisingly concludes that road construction (which is one of the biggest components of public spending) causes a surge in productivity in industries with high motor-vehicle use. David Aschauer (1989) also finds that road construction bears the most explanatory power of the change of local productivity, while military spending has almost none. 7 Richardson (1985) surveys all but the neoclassical models. Merrifeld (1987 and 1990) and McGregor, McVittie, Swales, and Yin (2000) for examples of neoclassical multipliers for the economic base.


and capital. Others rely on Ordinary Least Squares regression estimates (Mulligan (2005,

1987).

There have been some estimates of the impact of New Deal spending on general

economic activity. Fishback, Horrace, and Kantor (2005, 2006) showed a strong positive

influence of public works and relief spending on county-level retail sales and net-

migration. At the same time spending by the Agricultural Adjustment Administration

(AAA) had an adverse effect on retail sales; hence, it may also have had a negative effect

on per retail sales growth and net migration. Garrett and Wheelock (2006) found similar

positive effects of overall New Deal spending in a cross-sectional analysis of the growth

rate in state personal income per capita for the entire period 1933 to 1939 and New Deal

spending during that period. However, neither the Fishback, Horrace, and Kantor

estimates nor Garrett and Wheelock estimates show a short-term fiscal multiplier because

they do not include information on tax receipts from the states and the examine the

impact of spending over a six-year period on retail sales per capita at the end of the

decade.

Studies of labor markets in the 1930s have focused on the impact of relief

spending on labor markets. Neuman, Fishback, and Kantor (forthcoming) examine

monthly data from 1933 through 1940 for over 40 cities and find that relief spending

raised private employment through 1935 but reduced it afterward. Benjamin and

Mathews (1992) find small crowding out effects of private employment from relief jobs

in through 1935 and much larger crowding out effects in the second half of the New

Deal.8

8 We focus on the studies that use panel data here, see Neumann, Fishback, and Kantor (forthcoming for citations to studies relying on cross-sectional estimation.


Federal Grants and Loans During the New Deal

Understanding the impact of federal spending and taxation during the New Deal

era is complicated by the great diversity of programs during the period. The New Deal

funding programs were divided into the two major classifications: nonrepayable grants

and repayable loans.9

Our main focus in the analysis is nonrepayable grants from the federal

government. About 62 percent of the grants were associated with relief programs. All of

the Works Progress Administration (WPA), Civilian Conservation Corps (CCCG), and

Civil Works Administration (CWA) grants and roughly half of the Federal Emergency

Relief Administration (FERA) were spent on poverty relief projects with work

requirements and could be considered federal expenditures because they produced a good

or service. The Social Security Act Programs (SSA), and the rest of the FERA grants

were New Deal programs that offered transfer payments to alleviate poverty. The

Veterans’ Administration (VA) and Soldiers’ and Sailors’ Homes (SOLD) were grant

programs in place before the New Deal that provided pensions, disability payments, and

living support to military veterans. We treat them as relief in the same vein as grants

from the SSA programs, which were providing matching grants to states that provided aid

to dependent children (ADC), old-age assistance (OAA), and aid-to-the-blind (AB). If

The Office of Government Reports (1940) reported the total

amount spent by each program in each state in each year between July 1, 1932 through

June 30, 1939, which provides the basis for our analysis. The totals and the amounts per

capita for the entire period are reported in Table 1 to get a sense of the size of each

program.

9The Office of Government Reports offered information on the value of housing loans insured by the Federal Housing Administration. Since these loans were private loans, we do not incorporate these into the analysis of net federal spending.


we performed the analysis for the U.S. as a whole, the transfer payments from the SSA

part of the FERA, the VA, and the SOLD, which account for roughly 20 percent of the

grants, would not necessarily be treated as expenditures because they are net transfers

within the system. However, at the state level these transfer grants become income

within each and we therefore incorporate them into the analysis.

The second major grant category is public works programs, which accounted for

19.4 percent of the grants. The Public Works Administration (PWA) (Federal (F) and

Nonfederal (NF)), Public Roads Administration, Public Buildings Administration (PBA),

Rivers and Harbors Grant (RH) and other smaller programs listed as public works in the

table were not poverty programs. All but the PWA programs were long run federal

programs established before the New Deal. Unlike the work relief poverty programs, the

public works programs could hire from the labor market or the relief rolls, faced no

restrictions on hours worked to limit the amount received by an individual, and paid

hourly wages that were roughly double those on the work relief programs.

Approximately 12 percent of the grants were devoted to agriculture from

programs run by the Agricultural Adjustment Administration (AAA), Soil and

Conservation Service (SCS), Farm Security Administration (FSA), and Agriculturual

Experiment Stations (AES). The AAA was the major New Deal program which was

devoted to payments to farmers to take land out of production. The initial AAA program

was funded with an agricultural processing tax until it was declared unconstitutional in

January 1936.10

10AAA grants per capita were not very strongly correlated with processing tax receipts in cross-sectional correlations. The correlation for 1934 was only 0.034 and for 1935 was 0.1677.

The AAA also administered the replacement program adopted under

the Soil and Domestic Conservation Act of 1936, which continued to make payments to


farmers to take land out of production without the processing tax. The FSA started

within the FERA relief program and was more a poverty relief program. The SCS began

before the AAA was declared unconstitutional and provided grants for training farmers

about soil conservation techniques.

Dealing with New Deal loans in the analysis is more difficult. It is not clear how

to treat the loans in terms of developing a multiplier. They are not government spending

because at the time the loans were made they all required repayment, and a large share of

the loans were repaid.11

11 There were some cases of loan forgiveness. In the case we know about, the RFC loans offered to cities for poverty relief under the Hoover administration in fiscal year 1933 were eventually forgiven by the Roosevelt administration. The HOLC likely experienced the highest loan default rate because it foreclosed on 20 percent of the mortgages that it supported. Our sense from reading the reports of the various agencies, is that they anticipated repayment and were active in seeking repayment or in the case of default recovery of assets to be sold.

There was a grant feature to the loans to the extent that they

provided subsidies in the form of lower interest and better lending terms. The Home

Owners’ Loan Corporation (HOLC), for example, offered loans at below the interest rates

charged on good loans in the housing market, even though the loans were already

troubled. The HOLC also extended the standard repayment period, and allowed much

smaller down payments relative to the value of the home. The Commodity Credit

Corporation Loans (CCCL) loans were nonrecourse loans that set minimum prices that a

farmer received for his crop, while the Farm Credit Administration (FCA) loans provided

good terms for farm mortgages and short-term loans for crops, seed, and tools. The

subsidies in RFC loans likely varied by type of loan. Given the measurement issues with

loans, we try several ways of dealing with them. As an upward measure of the subsidy

on these loans we add 10 percent of the value of the loans to the federal grants spending

net of taxes. We also run some estimates where we add the full value of the loans to the


grants. Given the measurement problems with loans, we treat the results incorporating

the loans more as robustness tests of the analysis of New Deal grants minus taxes.

Expectations for the Multiplier.

The multiplier we estimate for the United States is not a national multiplier. The

U.S. states freely trade across state borders and there is a great deal of specialization in

specific goods and services in each state. If thought of as a macroeconomic multiplier, it

would be associated with a series of small open countries receiving grants and paying tax

revenues to a higher authority. At this stage, however, it cannot be seen as a multiplier

for the whole area because the current estimation does not capture the spillover effects of

the net federal spending on other areas of the country.

The coefficient on net federal spending in a Keynesian regional model will be

determined by a series of factors.12

12 The intuitive discussion of the multiplier is based on a Keynesian discussion of consumption and imports. See Cullen and Fishback (2007) and Fishback, Horrace, and Kantor (2005b) for how this works in a simple model. The regional science literature offers a broad array of models that can produce multipliers based on the mix of local versus external consumption and or production. They include Keynesian models, economic base models, input-output models and neoclassical models. Richardson (1985) surveys all but the neoclassical models. See Merrifeld (1987 and 1990) and McGregor, McVittie, Swales, and Yin (2000) for examples of neoclassical multipliers.

It will have positive effects to the extent that the net

spending puts to work resources that would have been unemployed otherwise; to the

extent that the net federal spending is more productive than the private spending that is

replaced by the anticipation of future obligations for taxpayers; to the extent that the net

federal spending produces social overhead capital (like roads, sanitation, public health

programs) that made the inputs in the state economy more productive; and/or leads to

Keynesian multiplier effects as each income recipient purchases goods and services from

others in the state who, in turn, spend their receipts on goods and services produced by

others in the states, and on and on.


The positive benefits of the multiplier are diminished through a variety of

“leakages” at each stage when the money spent in the process is spent on goods and

services outside the state economy. Much of the federal grant spending on a work relief

program, like the FERA, WPA, or CWA, had small initial leakages because over 80

percent was spent on wages for people in the state. Grants from the Public Works

Administration and Public Roads Administration had larger initial leakages because more

than 50 percent of the monies were spent on materials and equipment imported from

other states. More leakages occurred to the extent that the workers on federal projects

spent their wages on goods and services outside the state. Each leakage reduced the

extent to which the spending could be “multiplied” in a Keynesian sense by additional

spending within the state.

The net federal spending had smaller positive effects on the economy to the

extent that positive net federal spending led people to save in anticipation that they will

have to pay future taxes. The net federal spending would have had an even weaker effect

to the extent that it replaced local production of goods and services. The most obvious

crowding out came from the AAA payments to farmers to take land out of production.

The stated purpose of the act was to reduce output in hopes of raising prices enough to

see an increase in income. In other cases, the federal spending may have replaced state

and local projects that would have been built in the absence of federal spending. The

impact of the reduction in state and local spending was likely to be small because states

were generally required to run balanced budgets. Even when they ran deficits in the early

1930s, the deficits were relatively small as a share of state and local spending.


Finally, the impact of the net federal spending was reduced to the extent that it

crowded out private production of goods and services in the state. An influx of federal

spending may have bid up local wages in ways that raised the costs of hiring labor to

private producers. It may have also bid up the prices for nonlabor inputs with the same

effect. To the extent that increases in federal spending reduced private activity fully, it

could even have a negative effect on state income if the output from federal spending was

less valuable than the output it replaced.

Empirical Approach

To estimate the multiplier is to use panel data methods. We choose a sparse

specification with real per capita state income (Iit) in state i and year t in 1967 dollars as a

function of real per capita net federal spending in state i and year t ((Git – Tit) as a starting

point.

Iit = β0 + β1 (Git – Tit) + εit.

The net federal spending is calculated as federal nonrepayable grants to the state minus

federal tax revenues collected from the state. To reduce problems with omitted variable

bias, we add three vectors of parameters. First, we incorporate a vector of state fixed

effects (S) to control for factors like geography, state laws, and the basic economic,

cultural, and demographic structure of each state that did not change over time but varied

across states. Second, we incorporate a vector of year fixed effects (Y) to control for

macroeconomic shocks to the entire economy that affected all states in each year. Third,


we add a vector of state specific time trends (S t) to control for differences in the trend

paths of economic activity that each state was following.

Iit = β0 + β1 (Git – Tit) + S +Y + S t + εit.

Under this specification, the identification of the multiplier β1 for net New Deal spending

comes from the variation from trend across time within states after controlling for

macroeconomic shocks.13

There still remains the possibility of biases from simultaneity and endogeneity.

There is an ample literature about the geographic distribution of New Deal spending that

shows that the Roosevelt Administration tended to distribute more New Deal grants to

areas where income was declining (see Wallis 1998, Fleck 2008, Fishback, Kantor, and

Wallis, 2003). This tendency would impart a negative bias to the multiplier coefficient.

In addition, there are a variety of other factors that may have changed over time in

non-trend ways that also influenced both net fiscal federal spending and per capita

income in the state. There are two problems that arise in trying to control for these other

factors. First, many of the variables that might be included as controls in a productivity

model, such as wages, employment, and interest rates, are themselves components of

personal income. By controlling for them we would be restricting the measure of the

impact of net federal expenditures to the parts of state income for which we have not

13 We have also tried estimating the model while including squared terms. The estimates at the mean of the sample are very similar and there is very little gain from adding the squared terms. In addition, the instruments did not have adequate strength to separate the coefficients for the squared terms. .


controlled, which is not an appropriate restriction. Second, there are many variables for

which there are not good annual measures at the state level. These would include

measures of consumption and investment that might be used in a Keynesian model. For

consumption, our best estimates would have to be derived from interpolations between

the Cost-of-Living Surveys of 1917-1919 and 1935-36 and the post-war period. In the

early 1900s the U.S. Bureau of the Census routinely described their measures of capital in

manufacturing from earlier periods as inadequate and they stopped collecting and

reporting the information at all after 1919.14

Fourth, the omitted variable that we would most like to include in the analysis is

annual estimates of the size of state and local government net spending in the states

during this period, so that we can measure the effect of federal spending, holding state

and local fiscal activity constant. Without controlling for state and local government, the

federal multiplier estimate we obtain from estimating equation 2 will therefore include

the effects of federal expenditures on state and local spending and taxation, which in turn,

will likely influence per capita personal income. As it stands today, comparable annual

Third, controls for age, race, ethnicity,

population, and the structure of the economy are all available typically only during the

census years and thus we would have to interpolate between census years to provide

values. The state effects essentially control for many of the long-term structural factors

that we could measure, while the state-specific time trends serve to control for the time-

trends that would arise from the interpolations. Essentially, the interpolated measures of

the variables measured in 1930 and 1940 described above would be linear combinations

of the state-specific time trends and/or the state effects.

14The WPA created a series of estimates of capital at the state level in each year that it reported in a mimeo in the National Archives. We have been unable to find any descriptions of the methods used to create these measures, and are therefore hesitant to use them.


estimates of revenues and governmental cost payments in the states are available only up

through 1931 and after 1936 for all states; therefore, any estimate incorporating controls

for net state spending would miss a very large portion of the New Deal period.

Information is available on cities over 100,000 people throughout the 1930s, but data for

the rest of the governments is available only for 1932. We are in the process of filling the

gaps in computerized information on the large cities and the states.15

As we work to eliminate the biases arising from the issues described above, we

follow an instrumental variable strategy. We need an instrument that varies annually

both across time and across space and is strongly correlated with the net federal grants

but not with the error term in the equation. We developed a hybrid instrument that

interacts two variables. The first variable, which varies primarily across time, is New

Deal grant spending per capita in the eight other census regions outside the region where

the state is located. The variable cross-sectionally as well because it varies across the

nine census regions. Our expectation is that more spending in the entire nation was likely

to lead to more spending within a state.

16

15The federal government stopped collecting the annual information from states for the volume Financial Statistics of the States in 1933 after having collected information from 41 states for 1932. They restarted by collecting the data for 1937 (U.S. Bureau of the Census 1940, p. vi). John Wallis, Richard Sylla, and John Legler have posted information for 16 states for the period 1933 through 1937 with the ICPSR, but it is taking longer than we anticipated to make the data for these states comparable with the federal government’s categories. We are working with John Wallis to collect, computerize, and categorize the information for the remaining states for 1933 through 1937 and for the seven states in 1932 that the Census Bureau had not worked with.

To avoid correlation between the instrument

and the error term of state i in year t, we use the per capita measure for the regions

outside the region where the state was located.

16 If the federal government had established a hard budget constraint nationwide, there might have been a negative relationship between spending in the rest of the regions and spending in the state in question. There did not appear to be a hard spending constraint at the national level because Roosevelt and the Congress often approved additional funds throughout the years and ran budget deficits in most years.


We focus on grant spending for the instrumental variable rather than grant

spending net of total taxes for several reasons. First, the grant spending version of the

instrument has substantially more strength than the grants less total taxes version.

Second, taxes are tied so closely to personal income that it is difficult to come up with a

tax portion of the instrument that would not also be highly correlated with income, which,

in turn, would increase the likelihood of correlation between the instrument and the error

term in the final-stage equation. The second reason also explains why we have not been

successful at developing an analysis where we look at the grants per capita and federal

taxes per capita separately.

The second variable, which varies primarily across states, is the standard

deviation of the percentage voting Democrat for president in the state between 1896 and

the most recent presidential election prior to year t. The variable varies across time

because each state’s value changes between 1932 and 1933 and again between 1936 and

1937. This is a measure of swing voting in the state that has been found to have strongly

influenced the geographic distribution of New Deal spending in a large number of studies

(Wright 1974; Fishback, Kantor, and Wallis 2003; Fleck 2001, 2008, Wallis 1998, 2001).

Nearly all of these studies find strong positive relationships between the swing voting

measure and the distribution of per capita New Deal spending. By using the measure

calculated up through the most recent presidential election before that year, we eliminate

any contemporaneous correlation with factors that influenced income in the state.17

17 As an example, the instrument for the year 1932 would include the standard deviation of the percent voting for the Democratic presidential candidate from 1896 through 1928.

Given the controls for state time trends and fixed effects, it is unlikely that swing voting


in the presidential elections prior to the year would have influenced income in the state

except through the New Deal distribution mechanism.

For any state i that is in Census region c the formula for the instrument is:

,,,

,

, ti

cjtj

cjtj

ti stdevdempop

spendspendIV ⋅=

∑∑

≠

≠ (8)

where spendj,t is government spending in Census region j at time t, popj,t is population in

region j at time t, and stdevdemi,t is the standard deviation of the democratic vote in state

i for the presidential elections between 1896 and the most recent presidential election

before year t. The logic behind the hybrid instrument suggests that states with a greater

share of swing voters are more likely to obtain a higher share of national increases in

federal government spending, which leads to higher net federal spending in their state.

Results

We provide a range of estimates under a variety of assumptions related to the

types of federal programs to include in the next spending measure, the weighting of state

observations, and various ways of matching fiscal year federal funds and taxation to the

calendar year state personal income. The estimates vary under different assumptions.

In analyzing the impact of the distribution of New Deal funds net of taxes, it is

important to realize that New Deal dollars and the strings attached to them varied

substantially depending on the type of funds and the programs to which they were

distributed. We start by analyzing the per capita New Deal grants net of federal taxes,

because the New Deal grants were pure expenditures with no repayments involved.

Even for grants there were quid pro quos in terms of matching required by state and local


government. In the absence of controls for state and local net spending, the coefficient

estimated includes the impact on state per capita personal income that operates through

the effects of net federal spending on state and local spending. State constitutions often

contained constraints that prevented states from running deficits. A number of states ran

deficits in the early 1930s, although these were offset by surpluses in the late 1930s. For

more discussion of the biases that might arise see the appendix that discusses the results

with and without state deficits included.

We have also estimated the analysis by including estimates of the size of New

Deal loan activity. Given the measurement issues with loans, we try several ways of

dealing with them. The first is to treat the loans as incorporating grants through subsidies

on interest rates and favourable terms for lending. We added 10 percent of the value of

the loans to the New Deal grants minus taxes to take into account all of the subsidized

features of the loans. In the second we add the full value of the loans to the New Deal

grants minus taxes per capita as a high measure. Given the measurement problems with

loans, we treat the results incorporating the loans more as robustness tests of the analysis

of New Deal grants minus taxes.

Finally, we try some specifications where we seek to examine the impact of

different forms of New Deal grants on income in the states. The New Deal grants

themselves varied dramatically with respect to their purposes. The Agricultural

Adjustment Administration’s (AAA) grants were explicitly designed to crowd out private

production by reducing the amount of land under cultivation. This role was made even

more obvious in 1933 when the AAA had a late start and therefore was paying farmers to

plow under crops that had already been planted and to kill a large amount of farm


livestock. As a result, we estimate a specification where we leave the AAA grants out of

the net federal spending measure, and we also estimate specifications where we treat the

programs separately.

Another issue is how the state observations should be weighted. We provide

estimates that are unweighted as well as estimates weighted by the square root of the

population in 1930..18

Finally, we need to deal with the differences in the reporting time frames for

personal income and federal spending and taxes. State income is reported on a calendar

year basis. The New Deal and tax data are reported for fiscal years, such that the values

reported for 1933 are for the year ending on June 30, 1933. We have converted the

grants, loans, and tax data to a calendar year basis using the methods described in the

Data Appendix.

Philosphically, the unweighted measures treat each state as if it

were a separate economy that deserves equal weight, in the way the Senate gives equal

representation to the states. In essence, we are finding an average effect of net federal

spending where each state is treated equally as a political entity. The weighted

estimation gives more weight to states with larger populations in the way the House of

Representative apportions its membership. This weighting scheme provides an estimate

of the average effect that takes into account the fact that larger states make up a

disproportionate share of the overall economy.

In the interpolation we use the second half of fiscal year t and the first half of

fiscal year t+1. This could lead to problems with both measurement error and with

correlation between information in the second half of fiscal year t+1, which is the first

18 When we weight using the interpolated state populations throughout the period, the results are very similar.


half of the calendar year t+1 and personal income in year t. If the instrumental variable

that we use is uncorrelated with the error in the final-stage per capita income equation,

this may not be a problem. To test the robustness of that assumption, we have estimated

the model as well by estimating a model of personal income per capita in calendar year t

as a function of net federal spending in fiscal year t. This method assumes a six-month

lag in the impact of the net federal spending. A study by Neumann, Fishback and Kantor

(forthcoming) of monthly data on relief spending, private earnings, and private

employment in the 1930s finds that the impact of relief spending on earnings and

employment lasted for six to nine months. We are currently searching published and

unpublished federal government reports by agency in an attempt to find data by quarter or

half year at the state level for the major federal programs in Table 1.

Non-IV Results

The results for a wide range of analyses using calendar-year federal spending are

reported in Tables 2 and 3. Estimates that match the calendar-year state per capita

income estimates with the fiscal year net federal spending are found in Table 4. We offer

calendar year estimates for 1933 through 1939 and 1932 through 1939 and fiscal year

estimates for 1933 through 1939 because the results differ and there are trade-offs

between the various measures. There is likely more measurement error in the 1932

estimates of federal spending than in any of the other years. As seen in Table 1 there

were several grants and two loan programs for which we have data for spending by state

from July 1, 1932 through June 30, 1933. In nearly all cases the value we use for 1932 is

the value for the fiscal year ending June 30, 1933. The RFC loans are an exception


because the program started in February 1932, the source reported that information and

we could use RFC reports to split the data. We know from looking at the reports of the

Public Roads Administration that the relative spending in fiscal year 1932 and fiscal year

1933 varied across states. In addition, there are likely to be some problems with using

values with information from 1933 for examining 1932 state income. There are two

advantages of using 1932 information if the measurement error is not severe.

Statistically, more observations are better and the hybrid instrument has more strength

because the swing voting portion of the instrument changes value in each state between

1932 and 1933.

The tables show the raw correlation in the panel data set and then uses varying

combinations of state fixed effects, year fixed effects, and state-specific time trends. The

instrumental variables models are estimated using Two Stage Least Square (2SLS) with

robust White-corrected standard errors. The first two column in each Table show the

results for net federal spending per capita in the states, unweighted and weighted and lay

out the range of multipliers obtained using the different sets of controls.19

The raw multiplier estimate shows a strong negative but imprecisely estimated

relationship between real per capita personal income and real net federal spending per

capita. The coefficient of -1.05 in the unweighted estimate in the first column of Table 1

for the 1933-1939 estimation implies that an additional net dollar of per capita federal

grant spending was associated with a reduction of $1.05 cents in per capita personal

income (both in $1967). The coefficient from the weighted analysis is -1.82. The

19To develop the per capita estimates we calculated population in the state as a straight-line interpolation between the 1930 and 1940 populations in the state. We have estimated the model using per capita estimates based on only the 1930 population, and the results are very similar. We have also estimated the model without the state of Delaware, where federal tax receipts per capita of all kinds were very high relative to other states. Again, the results are essentially the same as those we focus on in the paper.


coefficients from the 1932-1939 estimations in Table 3 are similar at -0.98 in the

unweighted analysis and -1.82 in the weighted analysis, as are the coefficients in Table 4

for the fiscal year net spending specifications.

As we add various combinations of state effects, year effects, and state-specific

time trends, the multiplier coefficient fluctuates up and down, as the analysis controls for

the influences of different omitted factors. A common estimator combines state and year

effects, which leads to imprecisely estimated multiplier coefficients that are less than zero

in all three tables. Adding state-specific time trends raises the multiplier to imprecisely

estimates of 0.26 (unweighted) and 0.09 (weighted) in the 1933-1939 analyses, -0.05 and

-0.06 in the 1932-1939 analyses, and 0.26 to -0.18 in the 1933-1939 fiscal year analysis.

Instrumental Variable Results.

In theory an effective instrumental variable can eliminate the negative omitted

variable bias from omitting state and local spending, measurement error in the right-

hand-side variable, and the negative bias imparted by the way that the Roosevelt

administration distributed more funds to areas where income was falling. Using the

hybrid instrument, we estimated the multiplier for federal net spending using three

different specifications, one with state and year fixed effects, another with state fixed

effects and state-specific time trends, and a final version with state and year fixed effects

and state-specific time trends. The results for the unweighted and weighted analysis are

in the lower section of Tables 2 trhough 4. We only report IV coefficients and standard

errors in situations where we could reject the hypothesis of weak instrument bias if we

are willing to accept up to 15 percent weak instrument bias in comparisons of the


Keibergen-Paap rank Wald F-statistic with the Stock-Yogo critical values (see notes to

Tables 2, 3, and 4).

The multiplier estimate that controls the most for omitted variable bias as well as

endogeneity and simultaneity bias is the IV specification with state fixed effects, year

fixed effects, and state-specific time trends on the last row. The weighted and

unweighted coefficients for the calendar-year analysis of the years 1933-1939 are similar

in size although neither is precisely estimated. The coefficients imply that a one-dollar

increase in net federal spending raised state per capita income by 52 cents or 44 cents.

The impact is much smaller when 1932 is added to the analysis. Both the weighted and

unweighted estimates suggest that a dollar increase in net federal spending would have

led to a reduction in per capita personal income of 6 cents or less. Both sets of estimates

imply a lack of a multiplier effect and a substantial amount of crowding out of private

activity. The analysis in Table 4 that matches calendar year state per capita income with

fiscal year net federal spending tells a more positive story. The unweighted estimate at

the bottom of the table suggests a statistically significant 84 cent increase in per capita

income for a one-dollar increase in net federal spending. The weighted estimate is the

first to lead to a multiplier effect, implying that a dollar increase in net federal spending

per capita led to a $1.71 increase in per capita personal income.

The differences between these estimates and the other IV estimates is informative

because it shows how sensitive the estimates are to omitted variable bias. If one is

willing to assign all of the impact of state-specific time trends to the changes in net

federal spending in the states, the estimates with state and year fixed effects become

relevant. We ran into problems with instrument strength in the unweighted estimates for


1933-1939, so we did not report them. The weighted estimates suggest a sizeable

multiplier of 2.51, which is on the high side even for regional estimates. The estimates

are both greater than one for the state and year fixed effects IV estimations for 1932 to

1939 at 1.89 for the unweighted and 1.07 for the weighted. They are even higher in

Table 4 when net federal spending on a fiscal year basis is used in the analysis.

When year fixed effects are left out and state-time trends added to the analysis,

the coefficient estimates fall toward the estimates with state and year fixed effects and

state-specific time trends. In the calendar-year analyses, the values are 1.07 and 1.38 for

the 1933-1939 in Table 2 and 0.47 and 0.78 for the 1932-1939 sample in Table 3. Again,

they are even higher in the fiscal-year analysis in Table 4. These estimates would be

accurate if someone is willing to assign all of the positive benefits of annual nation-wide

shocks to the economy between 1933 and 1939 to the net federal spending. Thus, any

benefits from the expansions of the money supply, the move off of the gold standard, the

rise and demise of the National Recovery Administration, tariff reductions, and other

nation-wide factors would be assigned to the net New Deal spending in the states.

Generally, comparisons of the coefficients across the table suggest that the

endogeneity bias in the analysis is generally negative, as expected. In Tables 2 through 4

comparisons of the Least Squares estimates with no correlates to those with state and year

fixed effects and state-specific time trends show a move from a negative coefficient

nearing -1 or more in the first line to coefficients that are much less negative. When we

remove more of the endogeneity bias using the IV techniques, the coefficients again

become more positive. This can be seen in comparisons of the Least Squares and 2SLS

estimates with the same specifications in the middle and the bottom of the table.


Taking Loans into Account

Under the New Deal the federal government ran extensive lending programs. In

discussing the distribution of federal funds across states, scholars have differed on

whether loans should be included with grants as a measure of spending. In columns three

and four of Tables 2, 3, and 4 are estimates of the impact of net federal spending when

the coefficient is estimated after adding 10 percent of the value of New Deal loans to

New Deal grants to take into account the potential subsidies from the loans. Columns

five and six contain the results when the full value of the loans is added.

We perform the estimation in the same way as above, including using the same

instrument in the 2SLS estimates. Adding the loans to the analysis appears to increase

the size of the multiplier when comparisons are made between the coefficients in

columns 1 and 3 and between the coefficients of 2 and 4 in the calendar year analyses in

Tables 2 and 3. The addition of the loans has a negative effect on the coefficients in the

fiscal year based analysis in Table 4. The largest of the multipliers in the calendar year

studies that control for the most omitted variable bias is 0.62 for the unweighted

estimates with all loans included for the period 1933-1939 in Table 2. The largest

estimates are 0.77 and 1.44 when loans are included in the fiscal year estimations in

columns 3 and 4 of Table 4.

Grants without the AAA

The multipliers in the first six columns may understate the effects of relief and

public works spending because the grants include the AAA payments, which were


designed to limit agricultural production. In columns 7 and 8 of Tables 2 through 4 are

the multipliers associated with per capita New Deal grant spending minus the AAA and

minus federal tax collections. In this analysis the instrument was the interaction of the

swing voting measure with real per capita federal grants excluding AAA grants.

Comparisons of the 2SLS estimates in columns 1 and 7 and again in 2 and 8

suggest that the AAA grants had more negative effects than did the other grants, but the

changes in the coefficients are not very large on the bottom row where the most controls

for omitted variable bias are included.20

In this estimate we did not include the AAA

spending per capita, which might lead to omitted variable bias. We don’t believe that the

omitted variable is large. When we regress per capita nonfarm grants minus taxes on per

capita farm grants while including the year and state fixed effects and state-specific time

trends the coefficient very imprecisely estimated and lies between -.12 and 0.09

depending on the time period and the weighting structure.

Multiple Programs

Our ultimate goal is to simultaneously examine the separate impact of several

types of New Deal funds and federal tax collections. Thus far, we have not been able to

develop separate instruments for each of the types of spending that have enough strength

to allow us to identify the separate effects in 2SLS analysis.21

20 When the AAA is removed there are some other farm grants left in the grant measure. When we pull out all farm grants and redo the analysis, the coefficients and standard errors are very similar to those reported to the ones in columns 7 and 8 of Tables ?? and ??.

Absent the instrumental

21 We have made several attempts to estimate separate effects for nonAAA grants, taxes, and AAA grants, but the various tests for instrument strength all suggest that the instruments are very weak, too weak to offer meaningful insights. Among the instruments we have tried for the AAA grants is an interaction between the swing voting measure and the changes in AAA spending outside the region. We have also tried interacting the representation on the House Agriculture Committee at the beginning of the 1933 Congressional Session with the changes in spending outside the region. These cross-sectional interaction


variable analysis, there are still some lessons that can be learned from a careful

discussion of the results from the OLS analysis with state and year fixed effects and state-

specific time trends. Table 5 shows the results when we include separate variables for

groupings of loans and grants in various categories: relief and education grants, public

works and resources grants, AAA and other farm grants, nonfarm loans, farm loans, and

federal tax collections.22

Remember that this estimation procedure led to a multiplier estimate for net

federal spending that was lower than the multipliers from IV estimation due to

endogeneity. The nature of endogeneity bias likely varies by the type of program, and we

will discuss the direction of the anticipated bias as we discuss the various coefficients.

The estimation procedure is OLS with state and year fixed

effects and state-specific time trends.

Our sense is that the negative endogeneity bias is most severe for relief and

educational spending. The relief programs were designed to distribute money to areas

with high unemployment. Fishback, Kantor and Wallis (2003) analyzed the cross-

sectional distribution of New Deal funds for the whole period from 1933 through 1939

programs with state fixed effects. Essentially, they identified effects based on cross-

sectional variation within counties. To the extent that the cross-sectional distribution

variables have been successful as explanatory variables in cross-sectional analysis. However, none of these variables have been successful here. Similar attempts with taxation have led to no luck. We have also tried a series of political variables describing the party structure in the governor’s office and the state legislature and found little strength. 22The relief and education grants include the following programs using the acronyms in Table 1. WPA, CWA, FERA, CCCR, CCCIS, SSA, VA, BRA, USES, SSS, NG, VE, CAM, SF, OE, SMS, and BFB. We included the National Guard (NG) because it was providing partial employment. The public works grants include the PRA, PWAF, PWANF, RHFC, BR, PBA, PWAH, FSR, FF, ML, LUP, and FWP. The agricultural grants include AAA, FSA, SCS, AEX. The nonfarm loans include the RFC, the HOLC, the PWAL, HOLCT, FRB, DLC, and USHA. The farm loans include the FCA, CCCF, FSAL, REA, and FTP.


within states is informative about the panel variation in the analysis here, a causal

estimate of a multiplier for relief spending would likely be more positive than the

estimate here. They found that the relief programs generally were distributing funds to

counties within each state with lower incomes, a bigger drop in income between 1929

and 1933, and with higher unemployment. The relief programs did this to a much greater

degree than any of the other types of programs. The endogeneity might account for the

negative coefficients for relief and education programs in all specifications in Table 5.

The public works and resources grants had the only consistently positive effect

of the grant categories. One reason may have been that the public works projects tended

to hire people at full wages, as had been the case before and after the New Deal. The

average hourly wage on PWA projects was double the wage on WPA work-relief

projects, which had the goal of providing the poor and unemployed with enough to get

back on their feet. As a result, the workers on public works projects were each

purchasing a broader range of goods than workers on relief. On the other hand, the

public works projects spent a substantially smaller share on labor and much more on

equipment and raw material that might have been purchased from other states, causing

leakages in the multiplier. We have no strong opinion on the direction of endogeneity

bias for the public works projects. The head of the PWA, Harold Ickes, once expressed

to Congress that he was largely indifferent as to where the money went on the grounds

that a stimulus was a stimulus no matter what part of the country it was spent (Williams

1968, 116). The Public Roads Administration spending was largely determined by a

formula that was based one-third on land area, another third on population, and another

third on the extent of postal roads. The Fishback, Kantor Wallis study suggests that there


might have been a positive endogeneity bias because public works grants were higher in

counties with higher retail sales in 1929 and less of a drop in retail sales between 1929

and 1933. If the positive bias is correct, the public works projects may have an even

stronger positive effect than what is shown in Table ??.

The coefficient of the AAA and other farm grants suggests a positive but not

statistically significant relationship with per capita income when using calendar year net

federal spending, negative relationships when using fiscal year net federal spending.

The AAA program was paying farmers to take land out of production, but the goal was to

have a nation-wide price increase for farm crops that would cause farm revenue to rise.

There is plenty of narrative evidence, however, that the primary beneficiaries of the

programs were large farmers, and the Fishback, Kantor, Wallis study shows that the

counties with large farms tended to receive more funds. There is also an indirect

evidence that incomes for farm workers fell, as studies of the impact of the AAA at the

county level show that AAA spending was associated with a slight negative effect on

retail sales per capita and led to some outmigration from the counties in the 1930s (see

Fishback, Horrace, and Kantor, 2005, 2006).

Meanwhile, the results in Table 5, show that the farm lending programs had a

positive and statistically significant relationship with per capita income. The Commodity

Credit Corporation nonrecourse loans served to put a floor on farm prices and many Farm

Credit Administration loans aided farmers in obtaining mortgages. We don’t have a

strong sense of the direction of endogenity bias for the farm loans. The Fishback, Kantor,

and Wallis study offers mixed signals. Across counties within states farm loans tended to

go to areas with larger farms in 1929. On the other hand, they also tended to go to areas


with lower retail sales in 1929 and areas where the drop in retail sales between 1929 and

1933 were greatest.

The coefficient for the nonfarm loans is the smallest in absolute value and also is

statistically insignificant. Our sense is that the endogeneity bias for the coefficient is

positive and thus the effect is more positive that it would be. The vast majority of

nonfarm loans came from the RFC and the HOLC. The RFC loans were often targeted at

banks, railroads, and manufacturers. The Home Owners’ Loan Corporation (HOLC)

loans were targeted at homeowners, roughly 48 percent of the population, who were more

likely to be in the upper tier of the long-term income distribution. Even though the

HOLC refinanced troubled loans, they sought to be selective in choosing the loans to

raise the probability of repayment. The Fishback, Kantor, and Wallis (2003) county

study found that after controlling for state fixed effects, counties with higher incomes and

with a higher share of the population in the upper tier of the national income distribution

tended to receive more RFC and HOLC loans.

The federal tax receipts coefficient is negative, with one exception, although not

statistically significant. We expect that there is a strong positive bias to the tax receipt

coefficient, since roughly 40 percent of internal tax receipts came from highly

progressive income and estate taxes. The income cut-offs at which households began

paying income taxes was $2,000 for a single individual and $5,000 for a family of four,

which were very high levels. As a result, less than 10 percent of American households

paid income taxes during the 1930s. Most of the remaining internal revenues came from

excise and sales taxes, which likely were positively related to per capita income during


the Depression. During fiscal years 1934 and 1935, the processing taxes collected to

fund the AAA programs accounted for roughly 16 percent.

Conclusions

Among the sea of estimates that we have reported, we trust most the estimates

that incorporate state fixed effects, year fixed effects, and state-specific time trends while

using instrumental variable analysis. Even with all of these controls, there range of

estimates found is quite large. In the estimates using calendar-year net federal spending,

an additional dollar of per capita net federal spending increased income per capita income

in the state by between 2 cents and 52 cents. This is not a multiplier but instead suggests

some degree of crowding out of private activity. The estimates rise to 0.84 and 1.71

when we allow for a six month lag in impact by using fiscal year measures of net federal

spending. Larger IV estimates can be obtained if someone is willing to ascribe the

benefits of state-specific time trends or nation-wide time trends to the distribution of net

federal spending. Given that so many things change year by year in the economy, such

assumptions seem pretty strong.

It may be that different instruments might lead to larger estimates. Without the

instrumental variable analysis, however, the coefficients on net federal spending are

nearly all substantially less than one. The coefficients with the standard package of state

and year effects are all negative, and only the inclusion of state-specific time trends

increases the coefficients to the point where they are close to or above zero.

So what does these multiplier estimates suggest for someone interested in the

modern situation? The New Deal estimates are likely more useful for today than


estimates based on the situation during World War II because the U.S. was a command

economy mobilized for a full-scale war and devoting more than 40 percent of GDP to

fighting the war. The military determined the allocation of a large number of resources,

wages and prices were controlled, many consumer goods were unavailable or rationed,

and over 10 percent of the labor force had been drafted into the military. Without the

draft and the wage and price controls, the true share of GDP devoted to the war would

likely have been substantially higher.

If there was any time to expect a large peace-time multiplier effect from federal

spending, it would have been during the period from 1932 through 1939. Measured

unemployment rates during this time periods did not fall below 14 percent. Even if we

treating people on work relief as employed lowers the unemployment rates some so that

the lowest rate reached in this period was still a very high 9.1 percent (Darby 1976). As a

result, there were clearly a large number of underemployed resources that could have

been soaked up by federal spending without crowding out private activity. As of the end

of May 2009, the unemployment rate reached over 9 percent for the first time since the

early 1980s, still nowhere near the highs over 20 percent for any measure of

unemployment in 1932 and 1933. We would bet that the impact of net federal spending

in an economy with lower unemployment would be smaller than it was during the New

Deal.

The estimates are imperfect and can be improved in several ways. We are

seeking information on annual military expenditures by state, and are in the process of

filling in gaps on state and local government annual spending and revenues. To the

extent that either of these classes of expenditures are correlated with net federal spending,


the multipliers we have estimated so far are picking up there effects. See the appendix on

Biases from Omitting State Net Spending. As we were finishing the draft of this paper,

we realized that we can incorporate annual weather information to control for some

additional exogenous variation. Some initial runs suggest not much change in our results,

and we should have a full set of estimates incorporating weather information at the

conference. We also are working to expand the time period back into the 1920s. We

have accumulated annual information on federal tax receipts, road spending, Shepherd-

Towner spending on public health, loans for Bureau of Reclamation projects, state and

city government spending and taxation back to 1919. We are currently searching federal

reports for annual information on Veterans’ Administration spending, military spending ,

and more information on spending for Rivers and Harbors. Any knowledge you can

provide on sources would be greatly appreciated. Finally, we are trying to find ways to

fully separate transfer grants from grants that resembled federal expenditures. Our sense

is that the WPA, CWA, CCC, and half of the FERA relief grants are like federal

expenditures, while the SSA, VA, and the remainder of the FERA grants are transfer

payements.


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Data Appendix. Information on state personal income is from Bureau of Economic Analysis,

1999. We extended the series back into the 1920s using estimates of income by state by Robert Martin (1939). Were merged the income series by running a regression with no intercept for each state from 1929 to 1938 of the form Yt = β Mt, where Y is the BEA estimate of state personal income and M is Martin’s (1939 estimates). The R-squareds from each of the regressions were all above 0.98. When we ran correlations of the growth rates for the overlap periods, they are all over 0.6 and most are over 0.9.

Information on the federal grants and loans was reported by the Office of Government Reports in Mimeographed reports that were originally considered confidential. They were obtained from the National Archives. The information was reported by fiscal year. For each year t the fiscal year ran from July 1 in year t-1 to June 30 in year t. We report results using the fiscal year definition.

In addition, we have run estimates where we have converted the fiscal year measure to calendar years using information on the national totals spent in each half year. At the end we talk about the potential for making these conversations with state level information. Information for the RFC loans was reported for the period February 2 1932 through June 30, 1933. To break the loans into the fiscal years, we assigned 51.3 percent of the loans to the fiscal year ending June 30, 1932 and the remainder to the 1933 fiscal year. The figures were based on national totals reported in Reconstruction Finance Corporation (1932, p. 3; 1933b, pp. 6-7). We have not been able to find state level information that allows us to split the data for each state. To break the loans into calendar years we used additional information on the timing of the loans from Reconstruction Finance Corporation (1933a, p. 6). We assigned 62.0 percent of the loans between February 1932 and June 30, 1933 to calendar year 1932 and 37.987 to calendar year 1933. From later fiscal years we split the amounts 50-50 to convert to calendar year. We think we can improve on those splits nationwide but not at the state level. For later years we split the fiscal year values in half and assigned the halves to the appropriate calendar years.

Information for HOLC loans in each state were reported for July 1, 1933 through December 31, 1934 and for January 1, 1935 through July 1, 1935. In the absence of state specific information, we used national figures on loans from Federal Home Loan Bank Board (1935, p. 67) to allocate 49.2 percent of the loans from July 1, 1933 through December 31, 1934 to the period July 1 1933 through June 30, 1934, and 50.8 percent to the period July 1, 1934 to June 30, 1935. For the calendar year adjustments, all of the lending and investments during the period July 1, 1933 through December 31, 1934 occurred after December 31, 1933; therefore, all of the reported value for July 1, 1933 through December 31, 1934 was assigned to calendar year 1934. The 1935 values for the HOLC programs were the values in the source reported for January 1, 1935 through June 30, 1935 plus half the value reported for the period July 1, 1935 through June 30, 1936. For later years we split the fiscal year values in half and assigned the halves to the appropriate calendar years.

The FERA spending was adjusted from the fiscal year to the calendar year based

on information on the quarterly breakdown of Federal funds distributed after May 1933 for Obligations incurred for general relief, (WPA, 1942, p. 299). We think we can


improve on this by getting the quarterly breakdowns by states. We also believe we can break the funds into pure transfers and work relief by state.

The CWA federal spending for calendar year 1933 was 245.3/(245.3+590.7) of the amount reported for July 1, 1933 through June 30, 1934 and the amount for calendar year 1934 was 590.7/(245.3+590.7) of the fiscal year amount. All values for calendar years and fiscal years are zero afterword. We based the split on the national split of federal funds on p. 25, Table 11 in WPA (1939a). We will later use the state specific splits.

We split the adjusted the fiscal year amounts to calendar year amounts for grants for the WPA , the Civilian Conservation Corps, the PWA Nonfederal projects, the PWA Federal projects, the SSA and the FSA based on national numbers on the monthly earnings of people or payments to people from WPA 1940, pp. 104-107. We believe that we can do state level splits for the WPA and the CCC and the SSA information. We are less certain about the PWA and FSA payments.

For the PRA, we adjusted the fiscal year spending to calendar year spending on the basis of the number of persons employed. See Federal Works Agency, 1940, pp. 271. For the PBA we used the average number of persons employed on PBA projects from regular funds, from FWA (1940, p. 261). For the USHA loans we used the distribution of people employed by month from FWA (1940, p.345. For the USHA grants after November 1937 we used the distribution of people employed from Federal Works Administration (1940, p. 345.

We are still working on developing splits for the AAA payments. We believe that we can get a sense of the timing of the payments for each year from the annual AAA reports.


Figure 1 Per Capita New Deal Grants and Per Capita Federal Tax Receipts for the Year 1935 in 1967$ by State, without Delaware

real

per

cap

ita n

ew d

eal g

rant

s,

real per capita total federal ta2.99631 204.434

46.5655

505.682

ct

mema

nhri

vt

njnypa

ilin

miohwi

ia

ks

mnmo

ne

ndsd

vaal

arfl

galams

ncsc tx

ky

md

ok

tnwv

az

co

id

mt

nv

nm

ut

wy

ca

or wa

Delaware had per capita federal tax receipts of $321.16 and per capita federal grants of $67.45 in 1967 dollars for the year 1935.


Table 1

Total and Per Capita Federal Spending by Program in Contemporay Dollars for the period July 1, 1932 through June 30, 1939

Acronym Amounts

From July 1, 1932 to June 30,

1939 (Millions

$)

Per Capita

Category First Fiscal Year with Significant Spending

Ended Before 1939

TOTAL TAXES COLLECTED FROM STATES

26,061 213.11

NONREPAYABLE GRANTS Works Progress Administration WPA 6,844 55.97 Relief 1936 Veterans' Administration VA 3,955 32.34 Relief Pre 1933 Federal Emergency Relief Administration

FERA 3,059 25.02 Relief 1934 Mar-37

Agricultural Adjustment Administration1

AAA 2,863 23.41 Agriculture 1934

Civilian Conservation Corps CCCG 2,130 17.42 Relief 1934 Public Roads Administration PRA 1,613 13.19 Public Works Pre 1933 Rivers and Harbors and Flood Control RHFC 1,316 10.76 Public Works Pre 1933 Public Works Adminstration--Nonfederal Projects

PWANF 1,032 8.44 Public Works 1934

Civil Works Administration CWA 807 6.60 Relief 1934 Mar-34 Social Security Act SSA 759 6.21 Relief 1936 Public Works Administration--Federal Projects

PWAF 632 5.16 Public Works 1934

Balance from Relief Acts BRA 376 3.08 Relief 1936 Public Buildings Administration PBA 324 2.65 Public Works Pre 1933 Bureau of Reclamation BR 290 2.37 Public Works 1934 Farm Security Administration FSA 273 2.24 Agriculture 1936 National Guard NG 219 1.79 Military Pre 1933 Public Works Administration--Housing Projects

PWAH 129 1.05 Public Works 1935

Soil Conservation Service SCS 100 0.82 Agriculture 1934 Agricultural Extension Work AE 94 0.77 Agriculture Pre 1933 Vocational Education VE 90 0.74 Education Pre 1933 U.S. Employment Service USES 80 0.65 Relief 1934 Indian Service - Civilian Conservation Corps

CCCIS 51 0.42 Relief 1934

Agricultural Experiment Stations AEX 36 0.29 Agriculture Pre 1933


Forest Service (Roads) FSR 34 0.28 Public Works 1937 Colleges of Agriculture and Mechanical Arts

CAM 24 0.19 Education Pre 1933

Forest Funds FF 17 0.14 Public Works Pre 1933 Mineral Lease Act Payments ML 11 0.09 Public Works Pre 1933 Land Utilization Program LUP 11 0.09 Public Works 1939 State Soldiers' and Sailors' Homes SSS 4 0.03 Relief Pre 1933 Special Funds SF 2 0.02 Miscellaneous Pre 1933 Office of Education--Emergency Relief Act Funds

OE 2 0.02 Education 1936

State Marine Schools SMS 1 0.01 Education Pre 1933 Books for the Blind BFB a) 0.00 Education Pre 1933 Federal Water Project Payments FWP a) 0.00 Public Works Pre 1933 Nonrepayable Grants Total 27,180 222.26 REPAYABLE LOANS CLOSED Reconstruction Finance Corporation RFC 4,782 39.11 All 1932 Farm Credit Administration FCA 3,957 32.35 Agriculture Pre 1933 Home Owners' Loan Corporation HOLC 3,158 25.83 Home

Finance 1934 1936

Commodity Credit Corporation CCCL 1,186 9.70 Agriculture 1934 Public Works Administration PWAL 508 4.15 Public Works 1934 Farm Security Administration FSAL 337 2.76 Agriculture 1934 Home Owners' Loan Corporation and Treasury Investments in Bldg. and Savings and Loans Associations

HOLCT 266 2.17 Home Finance

1934

Federal Reserve Banks. FRB 125 1.02 Finance 1935 Rural Electrification Administration REA 123 1.01 Agriculture 1936 U.S. Housing Authority USHA 56 0.45 Public Works 1939 Farm Tenant Purchases FTP 33 0.27 Agriculture 1938 Disaster Loan Corporation DLC 17 0.14 Relief 1937 Total Repayable 14,549 118.97 Value of Loans Insured by Federal Housing Administration

0 0.00

Title I --Refurbishing and Maintanence Loans

834 6.82 Home Finance

1936

Title II--Home Mortgages. 1,855 15.17 Home Finance

1936

Total Housing Loans Insured 2,689 21.99

aUnder 500,000 dollars.


Table 2 Estimates of Multiplier for State Personal Income in Response to Net Federal Spending, Calendar Years 1933-1939

Standard Errors Listed Below Coefficients Federal Government Funds Measure Per Capita for Calendar Year in 1967$ Grants minus Taxes Grants plus 10 % of

Loans minus Taxes Grants plus Loans

minus Taxes Grants minus AAA

minus Taxes Unweighted Weighted Unweighted Weighted Unweighted Weighted Unweighted Weighted LEAST SQUARES No Correlates -1.05 -1.82 -1.05 -1.84 -1.01 -1.70 -0.85 -1.31

0.53 0.79 0.53 0.80 0.54 0.80 0.25 0.52

State Fixed Effects -0.24 -0.31 -0.15 -0.08 0.62 1.42 -0.53 -0.95 0.38 0.40 0.43 0.45 0.63 0.58 0.16 0.35

State and Year Fixed Effects

-0.29 -0.27 -0.29 -0.25 -0.26 -0.06 -0.18 -0.22 0.15 0.17 0.15 0.16 0.14 0.15 0.08 0.11

State Fixed Effects and State-Specific Time Trends

0.75 0.79 0.83 0.94 0.98 1.08 0.38 0.18 0.22 0.36 0.25 0.39 0.21 0.26 0.28 0.45

State and Year Fixed Effects and State-Specific Time Trends

0.26 0.09 0.24 0.07 0.08 -0.07 0.19 -0.01 0.20 0.35 0.20 0.36 0.22 0.30 0.17 0.28

TWO-STAGE LEAST SQUARES State and Year Fixed Effects

* 2.51 * 2.60 * * * * 1.01 1.07


1.07 1.38 1.10 1.45 1.59 2.57 1.21 1.62 0.46 0.35 0.47 0.37 0.72 0.69 0.52 0.43

State and Year Fixed Effects and State- Specific Time Trends

0.52 0.44 0.53 0.45 0.62 0.59 0.55 0.43 0.42 0.28 0.43 0.28 0.53 0.38 0.45 0.27


*Weak instrument problem. F-statistic was too low to give credible estimates. Sources: See Data Appendix. The original source reported the federal spending and tax receipt information in fiscal years that ended

on June 30 in each year. We adjusted the information to calendar years using the methods described in the Data Appendix. Notes: This is a balanced panel with information for 7 years for each state. All estimates were made using the STATA 10 reg and

ivreg2 programs. Standard errors for the unweighted least squares estimates are based on White corrections using the robust command with standard errors clustered at the state level. The weight for the weighted least squares estimates is the square root of the 1930 population in the state and standard errors were clustered at the state level. We performed tests on instrument strength by comparing the Kleibergen-Paap rank Wald (KPW) F statistic with the Stock-Yogo critical values for the maximum IV bias that one is will to accept. The critical values for willingness to accept weak instrument bias of 10 percent is 16.38, of 15 percent is 8.96, and of 20 percent is 6.66. We did not report the coefficients and standard errors for the unweighted 2SLS estimates with state and year fixed effects because the KPW F-statistic was less than 2.58 in all cases. The weighted 2SLS estimates with state and year fixed effects are 10.58, 9.92, 3.39, and 4.46 as you move from grants to grants and 10 percent loans, grants plus loans, and grants minus AAA grants. In the estimates with state effects and state-specific time trends the KPW F-statistic exceeded 19 in all weighted and unweighted estimates, suggesting no weak instrument bias if one is willing to accept up to 10 percent bias. In the unweighted estimates with state and year fixed effects and state-specific time trends, the KPW F-statistic exceeds 21.42 for the grants minus taxes and the grants plus 10 percent of loans minus taxes, and is between 13.29 and 14.91 for the grants plus loans minus taxes and grants minus AAA grants minus taxes; the KPW F-statistic exceeds 33 in all of the weighted versions of the specification.


Table 3 Estimates of Multiplier for State Personal Income in Response to Net Federal Spending, Calendar Years 1932-1939

Standard Errors Listed Below Coefficients Federal Government Funds Measure Per Capita for Calendar Year in 1967$ Grants minus Taxes Grants plus 10 % of




0.55 0.83 0.56 0.84 0.57 0.86 0.27 0.55

State Fixed Effects 0.30 0.34 0.35 0.48 0.70 1.29 -0.39 -0.86 0.53 0.50 0.56 0.55 0.65 0.58 0.13 0.31


-0.14 -0.14 -0.14 -0.13 -0.15 -0.03 -0.12 -0.16 0.11 0.16 0.11 0.16 0.09 0.14 0.05 0.09


0.35 0.49 0.39 0.59 0.51 0.73 0.14 0.03 0.23 0.29 0.26 0.32 0.25 0.23 0.23 0.39


-0.05 -0.06 -0.06 -0.07 -0.13 -0.12 -0.02 -0.09 0.16 0.23 0.16 0.23 0.14 0.19 0.15 0.19


1.85 1.07 1.88 1.10 2.17 1.40 1.85 0.93 0.81 0.40 0.84 0.41 1.15 0.57 0.80 0.35


0.47 0.78 0.48 0.82 0.68 1.59 0.55 0.93 0.37 0.31 0.39 0.33 0.58 0.82 0.41 0.35


0.02 0.06 0.02 0.07 0.02 0.08 0.05 0.07 0.32 0.25 0.32 0.25 0.35 0.31 0.33 0.25


Sources: See Data Appendix. The original source reported the federal spending and tax receipt information in fiscal years that ended on June 30 in each year. We adjusted the information to calendar years using the methods described in the Data Appendix.

Notes: This is a balanced panel with information for 8 years for each state. All estimates were made using the STATA 10 reg and ivreg2 programs. Standard errors for the unweighted least squares estimates are based on White corrections using the robust command with standard errors clustered at the state level. The weight for the weighted least squares estimates is the square root of the 1930 population in the state and standard errors were clustered at the state level. We performed tests on instrument strength by comparing the Kleibergen-Paap rank Wald (KPW) F statistic with the Stock-Yogo critical values for the maximum IV bias that one is will to accept. The critical values for willingness to accept weak instrument bias of 10 percent is 16.38, of 15 percent is 8.96, and of 20 percent is 6.66. For the unweighted 2SLS estimates with state and year fixed effects the KPW F-statistic was between 9.13 and 15.46. The weighted 2SLS estimates with state and year fixed effects are 20.9, 21.1, 18.22, and 15.9 as you move from grants to grants and 10 percent loans, grants plus loans, and grants minus AAA grants. In the estimates with state effects and state-specific time trends the KPW F-statistic exceeded 26 in all weighted and unweighted estimates, suggesting no weak instrument bias if one is willing to accept up to 10 percent bias. In the estimates with state and year fixed effects and state-specific time trends, the KPW F-statistic exceeds 20 in all cases.


Table 4 Estimates of Multiplier for State Personal Income in Response to Net Federal Spending

in the FISCAL YEAR Ending on June 30 for the Years 1932-1939 Standard Errors Listed Below Coefficients

Federal Government Funds Measure Per Capita for Calendar Year in 1967$ Grants minus Taxes Grants plus 10 % of




0.57 0.79 0.77 0.84 0.47 0.57 0.62 0.87

State Fixed Effects 0.45 0.53 0.41 0.48 -0.14 -0.49 0.25 0.17 0.48 0.44 0.40 0.55 0.21 0.23 0.46 0.43


-0.06 -0.16 -0.16 -0.13 -0.04 -0.16 -0.04 -0.15 0.11 0.17 0.17 0.16 0.09 0.14 0.12 0.18


0.99 10.22 1.13 0.59 0.46 0.38 1.00 1.25 0.25 0.41 0.37 0.32 0.12 0.15 0.30 0.52

State and Year Fixed Effects and State Specific Time Trends

0.26 -0.18 0.01 -0.07 0.24 0.09 0.28 -0.03 0.22 0.41 0.39 0.23 0.15 0.20 0.22 0.43


1.54 2.16 1.50 2.09 1.28 1.68 1.79 2.49 0.47 0.70 0.45 0.67 0.40 0.56 0.55 0.79


2.43 3.48 2.25 3.15 1.57 2.04 3.28 5.12 0.34 0.45 0.31 0.39 0.21 0.27 0.55 0.75

State and Year Fixed 0.84 1.71 0.77 1.44 0.45 0.61 * *


Effects and State Specific Time Trends

0.32 0.78 0.29 0.61 0.16 0.22


Sources: See Data Appendix. The original source reported the federal spending and tax receipt information in fiscal years that ended on June 30 in each year. We adjusted the information to calendar years using the methods described in the Data Appendix.

Notes: This is a balanced panel with information for 7 years for each state. All estimates were made using the STATA 10 reg and ivreg2 programs. Standard errors for the unweighted least squares estimates are based on White corrections using the robust command with standard errors clustered at the state level. The weight for the weighted least squares estimates is the square root of the 1930 population in the state and standard errors were clustered at the state level. We performed tests on instrument strength by comparing the Kleibergen-Paap rank Wald (KPW) F statistic with the Stock-Yogo critical values for the maximum IV bias that one is will to accept. The critical value for willingness to accept weak instrument bias of 10 percent is 16.38, of 15 percent is 8.96, and of 20 percent is 6.66. For the unweighted and weighted 2SLS estimates with state and year fixed effects the KPW F-statistic was between 12.63 and 15.85 and so meet the criterion if one is willing to accept up to 15 percent weak instrument bias. In the estimates with state effects and state-specific time trends the KPW F-statistic exceeded 33 in all weighted and unweighted cases, suggesting no weak instrument bias if one is willing to accept up to 10 percent bias. In the estimates with state and year fixed effects and state-specific time trends, the KPW F-statistic is roughly 6.84 for the grants minus taxes, meeting the criteria if one is willing to accept 20 percent weak-instrument bias. The values are 8.26 and 9.22 when 10 percent of the loans are added, and rise to over 21.4 when all loans are added. When AAA grants are subtracted in the far right column the values fall below 3.7 and we do not report the coefficients because of potential problems with weak-instrument bias.


Table 5 Least Squares Estimates for Specific Grant and Loan Categories and Federal Tax Receipts,

Includes State and Year Fixed Effects and State-Specific Time Trends

Unweighted Weighted Unweighted Weighted Unweighted Weighted 1932-1939 1932-1939 1933-1939 1933-1939 1933-1939 1933-1939 Calendar

Year Calendar

Year Calendar

Year Calendar

Year Fiscal Year Fiscal Year

Relief -0.68 -0.70 -0.61 -0.70 -0.35 -0.20 0.32 0.39 0.38 0.39 0.32 0.34

Public Works 0.10 0.27 0.60 0.27 0.39 0.30 0.27 0.31 0.18 0.31 0.11 0.17

Farm Grants 0.49 0.95 0.45 0.95 -0.61 -0.40 0.77 0.67 0.87 0.67 0.75 0.71

Farm Loans 0.60 0.41 0.85 0.41 0.58 0.44 0.27 0.28 0.37 0.28 0.19 0.14

Nonfarm Loans -0.24 -0.26 -0.16 -0.26 0.13 0.23 0.25 0.25 0.31 0.25 0.28 0.30

Federal Tax Revenues -0.22 0.32 -0.38 0.32 -0.37 0.41 0.41 0.63 0.53 0.63 0.91 0.98


Table W-1 IV Estimates of the Multiplier, Effects of Weather, and Year Fixed Effects from Analyses

with State and Year Fixed Effects and State-Specific Time Trends (Standard Errors below coefficients in italics)

1933-1939 1932-1939 Fiscal Year Calendar Year Calendar Year unweighted weighted unweighted weighted unweighted weighted Kleibergen-Paap rank Wald F-statistic for IV strength

6.62 5.37 20.60 28.52 33.00 48.17 25% 10% 10% 10% 10%

Net Federal Spending 1.14 2.14 0.67 0.61 0.08 0.10

0.41 1.03 0.40 0.32 0.33 0.26

Inches of Precipitation

-0.12 0.50 0.06 0.38 -0.54 -0.43 0.66 0.83 0.73 0.69 0.67 0.60

Inches of Snowfall -0.24 -0.01 -0.32 -0.07 -0.30 -0.18 0.39 0.48 0.38 0.39 0.29 0.30

Days with High Temperatures over 90

-0.04 0.33 -0.21 -0.10 0.08 0.13 0.54 0.65 0.49 0.47 0.44 0.43

Days with High Temperatures between 80 and 90

-0.15 -0.08 0.07 0.09 -0.05 0.25 0.44 0.58 0.39 0.41 0.39 0.37

Days with Low Temperatures Below Zero

0.60 0.93 0.43 0.75 0.14 0.53 0.74 0.95 0.61 0.66 0.55 0.56

Days with Low Temperatures between 0 and 30

0.82 1.03 0.92 0.75 0.57 0.45 0.43 0.66 0.33 0.38 0.31 0.33

Months of Severe or Extreme Drought

-5.31 -2.69 -3.73 -0.96 -2.54 -0.44 2.54 3.16 2.23 2.05 1.86 1.59

Months of Severe or Extreme Wet

-11.66 -11.22 -11.43 -12.24 -6.01 -4.99 4.88 5.97 5.22 4.76 3.87 3.99

Year 1933 26.33 30.48 10.49 10.33

Year 1934 107.98 94.73 108.47 109.52 152.38 149.73 14.06 21.45 14.49 13.54 16.86 16.65

Year 1935 185.15 188.92 199.96 207.79 241.36 242.09 23.36 26.68 22.15 20.37 23.37 23.05

Year 1936 374.83 373.53 383.23 385.45 415.50 413.98 30.96 35.15 28.29 27.97 29.01 29.99

Year 1937 442.85 479.71 442.61 456.21 452.41 464.16 41.45 49.47 40.68 38.76 38.42 38.79

Year 1938 424.74 494.57 377.85 386.01 382.08 383.97


56.14 79.63 50.32 48.14 46.25 46.74

Year 1939 551.41 600.23 519.09 526.32 520.66 519.52 60.76 77.37 56.70 54.37 51.31 52.09

State Fixed Effects Included Included Included Included Included Included State-Specific Time Trends

Included Included Included Included Included Included

The instrument is the same as in Tables 2, 3, and 4 in the text. The weight in the weighted analysis was the square root of the population of the state in 1930. Of the weather variables only the severe and extreme wetness and drought measures and the number of days with low temperatures between 0 and 30 have precisely estimated impact in some specifications. An additional month of severe or extreme drought reduced per capita income by up to $5.31, while an additional month of severe or extreme wetness reduce per capita income by up to $11.66. The 1930s were known for a large number of droughts, and the average for the period was nearly 2 months of droughts per year. Problems with severe wetness were much less common. Controlling for weather increases the estimates relative to those reported in the main text. We have to be careful about the coefficients in the estimations of state income per capita in the calendar year on net federal spending in the fiscal year because there are more problems with weak instruments than with the other estimates. Table ?? Comparisons of Estimates from Text with Estimates Controlling for Weather. 1933-1939 1932-1939 Fiscal Year Calendar Year Calendar Year unweighted weighted unweighted weighted unweighted weighted Weather Variables Included

1.14 2.14 0.67 0.61 0.08 0.10 0.41 1.03 0.40 0.32 0.33 0.26

Weather Variables Not Included

0.84 1.71 0.52 0.44 0.02 0.06 0.32 0.78 0.42 0.28 0.32 0.25

Kleibergen-Paap rank Wald F-statistic for IV strength Weather Included

6.62 5.37 20.60 28.52 33.00 48.17 25% 10% 10% 10% 10%

Kleibergen-Paap rank Wald F-statistic for IV strength Weather Not Included

6.88 6.84 22.37 98.47 38.63 55.25 20% 20% 10% 10% 10% 10%

Notes. The estimates with Weather Variables not included are from the bottom row of

the first two columns of Tables 2, 3, and 4 in the text.


Table W-2 Means, Standard Deviations, Elasticities and One-Standard Deviation Effects for the 1933-1939 Unweighted Fiscal Year Estimates in First Column of Table W-1

Elasticity OSD Mean Std.

Dev Net Federal Spending

0.043 0.310 40.11 106.5

Inches of Precipitation

-0.004 -0.004 35.13 14.7

Inches of Snowfall

-0.005 -0.013 23.31 20.89

Days with High Temperatures over 90

-0.002 -0.004 46.65 32.42

Days with High Temperatures between 80 and 90

-0.009 -0.009 66.98 22.65

Days with Low Temperatures Below Zero

0.004 0.019 7.81 12.1

Days with Low Temperatures between 0 and 30

0.063 0.089 81.7 42.1

Months of Severe or Extreme Drought

-0.010 -0.038 1.92 2.79

Months of Severe or Extreme Wet

-0.004 -0.023 0.36 0.774

Real Per Capita Income 1062.67 390.15


I tried a situation where we interacted the standard deviation measure with federal spending per capita in Canada but the F-statistics were extremely low.

in search of the multiplier for net federal spending …uctparo/seminars/fishback.pdffishback and...

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