CROSS-REGION TRANSFERS IN A MONETARY UNION:EVIDENCE FROM THE US AND IMPLICATIONS FOR

EUROPE

STEVEN PENNINGS*

Abstract. Federal transfers to individuals are countercyclical, vary greatly

across US states, and are often given credit for helping stabilize regional

economies. This paper estimates the e�ects of these transfers using plau-

sibly exogenous regional variation in recent temporary stimulus packages

and earlier permanent social security increases. I �nd that states receiv-

ing larger transfers tend to grow faster contemporaneously, with greater

multipliers for permanent transfers. Results are consistent with an open-

economy New Keynesian model. Despite this, a counterfactual �transfer

union� in Europe (which currently lacks equivalent cross-country transfers)

only generates modest gains in regional stabilization, especially when com-

pared with locally-�nanced countercyclical transfers. JEL: E62, F45, F41

Date: December 2017. First draft: August 2013.*Development Research Group, The World Bank. Email: spennings@worldbank.org orsteven.pennings@nyu.edu; Address: World Bank, 1818 H St NW, Washington DC 20433USA. The views expressed here are the author's, and do not necessarily re�ect those ofthe World Bank, its Executive Directors, or the countries they represent. I would like tothank Virgiliu Midrigan, Mark Gertler, Bill Easterly, Aart Kraay, Emmanuel Farhi, GlennFollette, Hyun Oh, Pierre Bachas and seminar participants at NYU, the World Bank, FederalReserve Board, the NY Fed, the Bank of England, Midwest Macro, UNSW, Monash, ANU,Georgetown U, CIDE, JHU-SAIS and the CESifo Venice Summer Institute.

1. Introduction

Transfers to individuals from the US federal government are large, counter-

cyclical and vary greatly geographically across US states. In contrast with a

traditional view of government expenditure, transfers to individuals account

for the majority of federal expenditure, the majority of the increase in ex-

penditure around the 2009 �nancial crisis, and the bulk of earlier short-term

�scal stimulus in 2001 and 2008.1 A number of papers have found that a $1

decrease in per capita income in a US state/region over time is associated with

around a $0.20-0.40 net transfer from the federal government to the residents

of that state/region through the tax-transfer system (Feyrer and Sacerdote

2013, Sala-i-Martin and Sachs 1991, Bayoumi and Masson 1995). Despite the

size and countercyclicality of these federal transfers, to my knowledge there

are no estimates as to whether changes in transfers to individuals have a large

or small e�ect on growth in the short run in di�erent states (the �cross-region

transfer multiplier�). This is despite a large literature on other �scal multi-

pliers, which is discussed below. My �rst contribution is to �ll this gap using

several �natural experiments� in which permanent social security bene�t in-

creases and temporary stimulus payments change the allocation of transfers

across states in a way plausibly unrelated to regional business cycles.

The size of the cross-region transfer multiplier is also important for pol-

icy. In contrast with the US, variation in comparable cross-country transfers

within the European Monetary Union (EMU) is negligible � for example, the

European Commission's budget is only about 1% of EU GDP. For a quarter-

century, commentators have argued that US-style countercyclical cross-region

transfers might help Europe reduce the e�ects of negative shocks on the pe-

riphery � the shocks that recently threatened the viability of the EMU itself.2

1For example in FY2010, transfers to individuals from the federal government (Retire-ment/Disability Payments and other direct payments) were 53% ($1.7tr) of total governmentexpenditure (Consolidated Federal Funds Report 2010, Fig 2). This is much larger thangrants to state and local governments ($680bn), and more traditional government spend-ing of $870bn (salaries and procurement combined). According to Oh and Reis (2012),75% of the increase in US government spending over 2007-09 were transfers, with similarproportions for other countries. See Giambattista and Pennings (2017) for details.2For example The Economist writes �America's �scal union is so good at absorbing [re-gional] shocks that is often cited as a model for the more accident-prone euro zone� (Freeexchange, 28 November 2015). For earlier evidence see Sala-i-Martin and Sachs (1991):

1

However the e�cacy and design of such a policy depends on the size of the

cross-region transfer multiplier: if the multiplier is small, or similar levels of

stabilization can be achieved by other means, then a transfer union in Europe

might not be worth the sizable political costs.3 A second contribution of this

paper is to evaluate the ability of a counterfactual US-style transfer union to

smooth regional shocks in Europe, using a model consistent with empirical

evidence on the cross-sectional e�ects of transfers.

The cross-sectional e�ect of transfers on growth is conceptually di�erent

from cross-sectional multipliers for other types of spending estimated in the lit-

erature (Nakamura and Steinsson (2014), Clemens and Miron (2012) Chodorow-

Reich et al (2012), among others) or estimates of the marginal propensity to

consume (MPC) out of transfers (see Parker et al (2013) and Johnson et al

(2006), among others).4 My third contribution is to document these di�erences

using analytical expressions for transfer and purchases multipliers in Section 2

(which also underlies the novelty of the empirical work). I don't discuss canon-

ical closed-economy time-series multipliers here as they are distinct from the

cross-sectional multipliers that are the focus of this paper.5

Speci�cally, I show analytically that in a simpli�ed New Keynesian (NK)

model, the cross-region transfer multiplier is substantially smaller than a com-

parable purchase multiplier (this is also true in a neoclassical model). In a

NK model, transfers and purchases both increase output via greater demand

for locally-produced goods. But unlike for purchases, transfers can be spent

�Some economists...argue that [the] regional insurance scheme provided by the federal gov-ernment is one of the key reasons why the system of �xed exchange rates within the UnitedStates has survived without major problems.� (p20)3As outlined in Farhi and Werning (2017), transfers have two roles in a monetary union.The �rst is to provide consumption insurance, and the second is to help to close regionaloutput gaps (which is relevant even with complete markets). When I refer to �stabilization�,I am referring to stimulating regions which might have negative output gaps.4See Chodorow-Reich (2017) for a survey of cross-region spending multipliers. Many in-volve intergovernmental transfers (not to individuals), which are spent on local goods byregional governments. For example, Suarez and Wingender (2016) exclude most federalpayments to individuals because they do not depend on local population estimates.5For example, closed-economy time-series multipliers are sensitive to monetary policy,whereas here union-wide monetary policy is irrelevant as it is removed by time �xed ef-fects. The relation between cross-section and time-series multipliers has been discussed inFarhi and Werning (2016), Nakamura and Steinsson (2014) and Chodorow-Reich (2017). SeeGiambattista and Pennings (2017) for a discussion of closed economy transfer multipliers.

2

on goods produced elsewhere which provide no boost to local demand, which

makes the transfer multiplier more sensitive to the degree of home bias in con-

sumption (or the share of non-traded goods). Also unlike purchases, transfers

can be saved � which makes the transfer multiplier more sensitive to shock

persistence and the share of �nancially constrained households (increasing in

both). In contrast, in a neoclassical model (and the NK model in the long

run) transfers reduce labor supply and output via wealth e�ects, yielding a

negative multiplier. As such, even if there is a high MPC out of transfers,

it is possible for the transfer multiplier to be large (NK model, spent mostly

on local goods), small (NK model, spent mostly on foreign goods) or negative

(neoclassical model). Hence, estimates of the cross-region transfer multiplier

� beyond estimates of the MPC � are needed to distinguish between models

and determine whether cross-region transfers stimulate growth in the regions

receiving them.

Identi�cation strategy Transfers to individuals are typically endogenous

to state-level output growth in several ways, which frustrate e�orts to consis-

tently estimate the e�ects of transfers on growth in the short term.

The �rst identi�cation challenge is one of reverse causality : countercycli-

cal federal tax-transfer systems mean that causality also runs from regional

business cycles to federal transfers. To address this problem, my identi�cation

strategy combines a transfer policy change at the aggregate level which a�ects

all states � and so is unlikely to be driven by developments in a particular state

� with a predetermined and slow-moving cross-state allocation of the transfer

based on regulations determining eligibility and their relative importance in

di�erent states. This is a similar idea to a Bartik (1991) instrument, though

di�ers in implementation.6 As theory suggests that cross-region transfers mul-

tipliers are sensitive to shock persistence � meaning any one multiplier estimate

will be unrepresentative � I estimate both temporary and permanent transfer

multipliers. As quarterly state-level GDP data are unavailable for much of the

6Speci�c di�erences are: (i) I use aggregate policy changes (rather than general aggregatevariation in transfers), (ii) state allocations are constructed based on lagged eligibility cri-teria that are speci�c to the transfer policy (rather than the initial state share of aggregatetransfers), (iii) as a result, the exogenous component of transfers is same as the instrument,so I can estimate a reduced form (rather than use instrumental variables (IV), though Iestimate that speci�cation too as a robustness test).

3

sample, I estimate the e�ect of transfers on state-level labor income (W ×L),which crucially excludes the transfer itself. I focus on the impact multiplier

(contemporaneous e�ect), which is the easiest to identify.

For permanent transfers, I study a series of ad-hoc increases in the monthly

stipend of social security (old age pension) recipients over 1952-74. An increase

in the monthly social security stipend naturally leads to a larger increase in

transfers to states like Florida (FL) or West Virginia (WV), relative to Alaska

(which is less popular with retirees). It is hard to imagine Congress increasing

permanent social security rates in response to a recession in FL or WV, which

eases concerns of reverse causality. In fact, Romer and Romer (2016) provide

narrative evidence that this sample of social security increases were legislated

mostly in compensation for past in�ation, and never out of any desire to

stimulate the economy.7 As such, these social security increases are likely even

exogenous at the aggregate level, though I only require the weaker claim that

they are exogenous in the cross section.

For temporary transfers, I study once-o� stimulus payments in 2001 and

2008 (a �check in the mail�). While the relatively �xed dollar value of pay-

ments means these transfers are mechanically more important in poorer states,

other eligibility criteria � such as having a tax liability in 2001 � reduce the

progressivity of the transfer and means the cross-state allocation varies across

policies. It is di�cult to argue that these programs were enacted to help

speci�c states in particularly deep recessions given the bene�ts were widely

spread across states and eligibility rules were a simple function of prior-year

individual taxable income. Moreover, even if legislators wanted to target low-

growth states, it would be di�cult to do so as state growth rates exhibit little

quarterly persistence (it took around a quarter from legislation to payment).

The second identi�cation challenge is omitted variable bias (OVB), where

other variables might a�ect both transfers and regional growth. All my spec-

i�cations include state �xed e�ects (FEs) to remove state-level trends, and

time �xed e�ects to remove all aggregate variation like the US business cy-

cle, monetary policy or international shocks. As such, OVB concerns focus

7Romer and Romer (2016) speci�cally exclude social security increases legislated to stimulatethe economy, such as the social security amendments of 1961.

4

on confounding variables which vary both across states and over time. I ad-

dress OVB primarily through a research design involving many transfer policy

changes with di�erent sizes and timings (discussed below), but also through a

battery of robustness tests. The latter include robustness to time-varying inter-

actions between state FEs and other time-varying variables (such as oil prices,

US GDP growth or a quadratic time trend), excluding states or years one-

at-a-time, additional control variables, alternative speci�cations and placebo

tests.

My identi�cation assumption is that there is no other reason, apart from the

e�ects of the transfer, that (i) states with many retirees would happen to grow

faster or slower in particular quarters when social security bene�ts increase

and (ii) states with a particular income distribution have faster growth when

stimulus payments are made, and slower growth when they are withdrawn.

By including multiple transfer shocks with di�erent sizes and spacings over a

long period, any omitted variable violating this assumption would need to take

an unlikely time-varying pattern in high vs low transfer states. For example,

social security rates increased by 10% in June 1971, 20% in October 1972,

but not at all in 1973, and so the confounding variable would have to follow

a similar pattern in high-transfer states and only boost growth those speci�c

quarters. This is a much higher hurdle for potential confounding variables

than in a standard di�-in-di� study with a once-o� permanent policy change.8

Moreover, high transfer states change slowly over time: the cross-state allo-

cation for temporary transfers in 2001 only explains 12% of the variation in

2008 transfers, and the allocation of social security bene�t increases in 1952

only explains 28% of the allocation in 1972.

Results My key empirical �nding is that states receiving larger transfers

tended to have faster short-run growth in non-transfer labor income or GDP,

with a larger cross-region transfer multiplier for permanent than temporary

transfers. An example of this positive relationship is shown in Figure 1 for

once o� stimulus payments of $300-$600 in 2008Q2 (LHS) and $300 stimulus

payments in 2001Q3 (RHS). In my preferred speci�cation with time and state

8For temporary transfers, a confounding variable would have to boost growth in high transferstates in 2001Q3 as the stimulus is phased in and reduce growth in 2001Q4 as the stimulusis withdrawn (for example).

5

�xed e�ects, other controls and a longer sample period, I �nd that a state that

received an extra $1 transfer experienced an increase in labor income or GDP

in the quarter by $0.2-0.4, which is similar to the line slope in the simple cross-

section in Figure 1 (within one standard error). For permanent transfers, I �nd

that states which received an extra $1 in social security payments increased

their labor income by around $1.4-$2.2 contemporaneously, depending on the

speci�cation. Romer and Romer (2016) found that almost all of the permanent

social security increases were consumed, and Parker et al (2013) and Johnson

et al (2006) argue that a fraction of the temporary transfers were consumed

� both of which are broadly consistent with my results (though those papers

don't examine the e�ect of transfers on regions, GDP or non-transfer income).

AL

AK

AZ

ARCA

CO

CT

DE

FLGA

HIID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NHNJ

NM

NY

NCND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VAWA

WV

WI

WY

−.0

3−

.02

−.0

10

.01

.02

Rea

l Gro

wth

in la

bor

inco

me,

200

8Q2

.02 .03 .04 .05 .06 .07Transfer stimulus payments 2008Q2, share of labor income

Line: growth=−0.02+0.3*payments(t=2.04) Source: BEA

2008Q2 Economic Stimulus Payments

ALAK

AZ

AR

CA

COCT

DE

FLGA

HI

IDIL

IN

IAKSKY

LA

ME

MD

MA MI

MN

MS

MO

MT

NE

NV

NHNJ

NM

NY

NC

ND

OH

OK

OR

PA RISC

SD

TNTX

UT

VT

VA

WA

WV

WI

WY

−.0

2−

.01

0.0

1.0

2R

eal G

row

th in

labo

r in

com

e, 2

001Q

3

.015 .02 .025 .03 .035Transfer stimulus payments 2001Q3, share of labor income

Line: growth=−0.02+0.75*payments(t=2.17)

2001Q3 EGTRRA Stimulus Payments

Figure 1. Growth in labor (non-transfer) income and tempo-rary transfer stimulus payments 2008 (LHS) and 2001 (RHS)

My fourth contribution is to show that cross-region transfer multipliers of

this size can be quantitatively rationalized by a relatively standard multi-

region New Keynesian model featuring (i) home bias or non-traded goods

in consumption (so transfers are spent on local goods), (ii) a share of �nan-

cially constrained (hand-to-mouth) households (so temporary transfers are not

saved) and (iii) sticky prices and wages (so that short-run output is mostly

demand-determined). A standard neoclassical model, which lacks the last two

features, produces a response of labor income or GDP to the transfer smaller

than that in the data (and close to zero for temporary transfers).6

How much shallower would European regional recessions be with a US-style

transfer union? Using the NK model consistent with estimated multipliers, I

produce counterfactual simulations for a country experiencing a recession as

deep and persistent as that in Ireland over 2009-12 (Section 5). Without cross-

country transfers in Europe (as is the case currently), I match the relative fall

in output in early 2009 of around 3.8% as in the data. With countercyclical

transfers of the size that they are in the US, the fall in output is about 12%

smaller than without these transfers (i.e the fall in output is 3.3% rather than

3.8%) � quite a modest gain (the proportional reduction in general GDP

volatility is similar).

But more importantly, around two thirds of these gains are available to a

European country who is able to self-insure using similarly-sized debt-funded

countercyclical transfers. I show analytically in Section 2 that the cross-region

transfer multiplier estimated in the data can be decomposed into a self-�nanced

component (like a debt-funded transfer from a regional government) and a pure

cross-section component that is intrinsically connected to the federal �nancing.

The self-�nanced component dominates when transfers are temporary (includ-

ing at business-cycle frequencies), whereas the pure cross-section component

dominates for permanent transfers. It is the size of the pure cross-section

multiplier that determines potential gains from a transfer union in Europe (as

well as the di�erence between locally-�nanced and federally-�nanced purchase

multipliers). The relatively small �pure� cross-region transfer multiplier stems

from the fact that (i) European countries have relatively open economies and

(ii) it is only the Ricardian households who respond di�erently to a transfer

from Dublin vis-a-vis Brussels (hand-to-mouth HHs spend both), and Ricar-

dian households save the majority of transfers at business cycle frequencies.

Related Theoretical Literature In addition to the empirical papers cited

above, this paper is related to contemporaneous theoretical work by Farhi and

Werning (2016, 2017) using a similar NK model. Like most of the theoretical

literature, Farhi and Werning (2016) focus on the determinants of government

purchase multipliers (rather than transfer multipliers), but they do discuss

how multipliers change with the �nancing of these purchases (see Section 2.4

7

for a detailed comparison).9 Farhi and Werning (2017) derive welfare-optimal

cross-region transfers in a monetary union and show that, in certain circum-

stances, these transfers can be very e�ective in reducing regional output gaps

caused by asymmetric shocks. This paper argues that when the calibration of

a similar model is constrained by the size of the cross-region transfer multiplier

estimated in the data (and the size of transfers in a �scal union like the US),

then the circumstances are such that these gains are modest.10

2. Understanding Cross-region Transfer Multipliers in an

Analytical Model

This section uses two simple analytical models to illustrate the determinants

of the cross-region transfer multiplier and how it di�ers from other similar-

sounding cross-region multipliers in the literature. I show that the cross-region

transfer multiplier (as estimated in Section 3) tends to be smaller than analo-

gous cross-region purchase multipliers (like in Nakamura and Steinsson 2014),

and both multipliers can be decomposed into a self-�nanced component (like

Clemens and Miron 2012 for purchases), and as well as a common pure cross-

region transfer component (intrinsic to federal �nancing) which determines the

potential gains from a transfer union in Europe in Section 5.

The simple analytical models represent two limiting cases of a relatively

standard open-economy New Keynesian model used to rationalize the size of

estimated multiplier in Section 4 and simulate counterfactuals. The �rst is an

�ultra Keynesian� model in the limit when prices become perfectly sticky and

the second is a neoclassical model with �exible prices and where all households

are Ricardian. Quantitatively, the impact multipliers in the ultra Keynesian

model are quite close to those in the full NK model so long as �scal policy is

9Other papers discussing government purchase multipliers in a monetary union in NewKeynesian models include Gali and Monacelli (2008) and Nakamura and Steinsson (2014).10Moreover, Farhi and Werning (2017) corroborate my �nding that the majority of the sta-bilization gains from cross-region transfers are available to countries who run their own self-�nanced countercyclical transfers (though they only present the result in passing). Specif-ically, my �self-�nanced� transfers are similar to redistribution/de�cits in Table 1 of Farhiand Werning (2017). They �nd that with HtM HHs, sticky prices and an open economy, 83%(=55%/66%) of the gains from cross-region transfers are available to a country following apolicy of optimal redistribution if shocks are permanent, and 96% (=75%/78%) if shocksare transitory.

8

not too persistent (in the long run, the full NK model is almost neoclassical). I

report impact transfer and purchase multipliers in the �rst quarter of the shock

(as in the empirics): MTR ≡ ∂Yh,0/∂trY0 andMG ≡ ∂Yh,0/∂g

Yh,t respectively.

2.1. Full Model Overview (and simpli�cations). The full model is a rel-

atively standard small open economy New Keynesian model with a �xed ex-

change rate like Gali and Monacelli (2005, 2008) or Nakamura and Steinsson

(2014) and so I only sketch it brie�y here (with full model details in Online

Appendix 3, and a list of equations in Online Appendix 5).11

Consider a monetary union consisting of a small region (home), like an

individual US state or a small European country, and the rest of the monetary

union combined (foreign, denoted with ∗).12 The small region has population

n and the large region 1 − n, but in the simple analytical model I take the

limit such that the home region becomes atomistic (n→ 0) so that it cannot

a�ect the rest of the monetary union. Each region produces their own variety

of good, named h (produced by home) or f (produced in the rest of the

monetary union), using only labor, so Yh,t = Lt (there is no capital). Both

goods are perfectly traded and home consumption ct is a constant elasticity

of substitution (CES) combination of ch and cf with Armington elasticity θT .

Good h has a weight of α in the utility function of the home consumer, so in

the simple model home bias in consumption is α (in the full model home bias

is α − n). Home goods Yh are used for consumption at home (ch) or in the

rest of the monetary union (c∗h), or are used for local government purchases

gh .The relative price of the two goods (the terms of trade) is st = Pf,t/Ph,t.

In the full quantitative model Pf,t and Ph,t are sticky in the Calvo sense (and

11The main di�erence with Gali and Monacelli is in �nancial markets, where I assume(i) a share ω of Hand-to-mouth households (also known as rule-of-thumb consumers) whoare entirely cut o� from �nancial markets and (ii) where the remaining 1 − ω fraction ofhouseholds only have access to a non-contingent bond (i.e. markets are incomplete - in Galiand Monacelli (2005) �nancial markets are complete). In the full NK model I also assumewages are sticky (missing from Gali and Monacelli 2005/2008). Relative to Nakamura andSteinsson, I assume a share of Hand-to-Mouth households, separable utility, no capital,sticky wages and constant returns in labor.12Monetary policy follows a Taylor rule, but is irrelevant for the size of the cross-regionmultiplier as all aggregate variables are �di�erenced out�. I focus on shocks to the smallregion which do not a�ect the large region, which is equivalent to demeaning each periodwith a time �xed e�ect as in the empirics.

9

wages are also sticky), but in the simple model I assume that Pf,t and Ph,t are

either perfectly �xed or perfectly �exible.

There are two types of households, who maximize the discounted sum of

period utility Ui,t = lnci,t − L1+ϕi,t /(1 + ϕ). A fraction ω of these are Hand-to-

mouth, and so spend their whole income each period, which consists of labor

income (W × L), lump sum transfers from the federal or local government,

and pro�ts from �rms. A fraction 1−ω of households are Ricardian, such that

they can also borrow and lend a non-contingent bond (trading with the rest

of the monetary union). In the neoclassical analytical model, all households

are Ricardian.

Transfers (Tr) are untargeted and are received by members of the home

region in proportion to their population share (i.e. HtM HHs receive a fraction

ω). Government purchases are of home goods (gh) and are not valued by

households. In terms of funding, transfers and purchases can be either (i)

cross-region (as in a US �scal system) where they are funded by lump-taxes

on households in the rest of the monetary union, or (ii) self-�nanced, where

lump sum taxes fall on home Ricardian HHs. Due to Ricardian equivalence,

the latter is equivalent to a debt-funded transfer with future lump sum taxes

falling on Ricardian HHs.13 Both transfers and purchases are expressed in

deviations from steady state as a share of GDP (tr and gh), which usually

follows an AR(1) process with persistence ρ.

2.2. Analytical impact multipliers in a simple �ultra Keynesian� model

when prices are �xed. Impact multipliers are presented in Table 1. The

cross-region transfer multiplier (similar to what I estimate in the data in sec-

tion 3) is presented in the top RHS, and one can see immediately that is is sub-

stantially di�erent from the federally-�nanced purchase multiplier (estimated

by Nakamura and Steinsson, NS2014) in the top LHS, and the self-�nance pur-

chase multiplier (estimated by Clemens and Miron (2012), CM2012). Com-

paring multipliers: (i) purchase multipliers are larger than transfer multipliers

13As the US state or small European country represents a tiny fraction of the whole fed-eration, results are almost identical whether (i) the federal government levies taxes on allagents in the economy or (ii) the rest of the monetary union pays for the transfer. Homelump sum taxes fall on Ricardian HHs because higher income households (who pay moretax) are less likely to be liquidity constrained, and also to simplify the analysis.

10

Table 1. �Ultra Keynesian� (�xed price) analytical impact mul-tipliers in a small region of a monetary union

NK Impact Multipliers A. Purchases B. Transfers Di� (A-B)

1. Cross-Region

(federal �nanced)

1 +MSFTr +

α

1− α1− β1− βρ

MSFTr +

α

1− α1− β1− βρ

1(as in NS2014) (this paper)

2. Self-�nanced1 +MSF

Tr MSFTr =

αω

1− αω

[1− 1− β

1− βρ

](as in CM2012)

Di�erence (1 - 2)α

1− α1− β1− βρ

(�pure CR transfer Multiplier�)

Notation: α home bias in cons.; ω share of HtM Households; ρ persistence of �scal transfer

or purchase, β discount factor. See Propositions 1 and 2 (Online Appendix 6)

(keeping �nancing constant), and (ii) federally �nanced (cross-region) multi-

pliers are larger than locally �nanced multipliers (keeping expenditure type

constant). These expressions for multipliers are derived in Propositions 1 and

2 in Online Appendix 6 which make use of a Lemma (also in Online Appendix

6). The Lemma shows that along the adjustment path, Yt − Y∞ follows an

AR(1) process with persistence ρ, where Y∞ is the limit to which home output

converges.

The self funded transfer multiplier is the contemporaneous e�ect on

GDP of transfers from the home Ricardian HH to both home households, with

the transfer shrinking over time at rate ρ. As European countries or US states

can �self insure� using countercyclical self-funded transfers � equivalent to local

debt-�nanced transfers � the self-funded transfer multiplier is an important

baseline used later in the paper. In Section 5 I �nd that at business cycle

frequencies, more than half of the cross-region transfer multiplier is driven by

the self-funded component, which limits the gains from �scal centralization in

reducing the volatility of regional business cycles.

The self-funded transfer multiplier is the smallest in Table 1 and is strictly

decreasing in ρ (as ρ→ 1,MSFTr → 0).

This multiplier will only be positive when the marginal propensity to con-

sume (MPC) out of transfers received by home HtM HHs (unity) is larger

than the MPC out of targeted transfers paid by home Ricardian HHs (1 −11

β)/(1− ρβ).14 This di�erence is maximized when transfers are perfectly front

loaded (ρ = 0). Second, this term is weighted by αω/(1 − αω). The numer-

ator is the share of the initial transfer spent on local goods - the targeting

of the transfer (ω) times the expenditure share on local goods (α). As such,

αω[1− (1− β)/(1− βρ)] is the marginal propensity to consume local goods

out of temporary untargeted self-funded transfers. The denominator is the

traditional Keynesian Multiplier 1/(1− αω); αω of each extra unit of income

is spent on local goods, which generates extra income, which is then spent

(and so forth). When there are no HtM households (ω → 0) or the economy is

very open (α → 0) the self-funded transfer multiplier will be zero (even with

�xed prices).

The cross-region transfer multiplier (estimated in the data) is equal

to the self-funded transfer multiplier plus the pure cross-region transfer mul-

tiplier. The latter is positive even if there are no HtM HHs (ω → 0) or

the transfer are highly persistent (ρ → 1) (a large self-funded multiplier also

increases the cross-region transfer multiplier). Pure cross-region transfers in-

volve a transfer from the rest of the monetary union to home Ricardian HHs

only. As these HHs are Ricardian, the pure cross-region transfer is consumed

because it never has to be repaid (unlike locally-�nanced transfers, or a loan).

However, the Ricardian HHs only consume the permanent component of the

transfer: (1− βρ)−1 is the present value of the transfer payments and (1− β)

is the interest on that sum. If the transfer is permanent (ρ → 1), then all of

the extra transfer income is consumed; though if it is very temporary (ρ→ 0)

then almost all of the transfer is saved and the pure CR transfer multiplier is

close to zero. This term is interacted with by α/(1−α), which takes the form

of an old-Keynesian multiplier applied to spending on locally-produced goods.

Home bias The size of the pure cross-region transfer multiplier is very

sensitive to the degree of home bias (α) � this is the fraction of extra con-

sumption spent on home goods without any change in prices (s = 0). Even if

prices are �xed and the transfer is consumed, if there is no home bias (α→ 0)

the cross-region transfer multiplier is zero (which is why the MPC is only

14The Ricardian HHs reduce consumption by the present value of transfers paid. For aone-o� transfer, this is the quarterly interest 1 − β. For permanent transfers (ρ → 1),consumption of the Ricardian HHs fall by the whole transfer.

12

partly informative about the cross-region transfer multiplier). When prices

are �xed, households want to spend a fraction α of a new transfer on home

goods and a fraction 1 − α on foreign goods (assume for now that ρ → 1,

which means that all the transfer is consumed). This creates excess demand

for home goods, which causes output to expand as it is demand-determined.

The increase in output also increases incomes, of which a fraction α must be

spend on home goods (and a fraction 1 − α on foreign goods). This process

continues until aggregate income (tr+ ∆y) has expanded su�ciently that the

original transfer (and foreign consumption) is a fraction 1 − α of the extra

aggregate income.15 In a very open economy (large 1-α), only a small increase

in aggregate income is required to accommodate the transfer. But for a very

closed economy (small 1 − α), domestic production has to expand almost in-

�nitely such that tr = (1−α)(tr+ ∆y). Graphically α/(1−α) is the slope of

the income expansion path in Online Appendix Figure 4.1.

Relation to purchase multipliers Table 1 shows that with �xed prices,

government purchases of home goods have impact multipliers exactly one unit

greater than those of transfer multipliers, given the same (federal or local)

�nancing. Whereas a $1 transfer increases home income initially by $1 as a

windfall, for a $1 purchase the home household has to produce an extra $1 of

output to sell to the government to generate that extra $1 of income.16 After

this initial di�erence, the e�ect of the extra $1 of income has identical e�ects

on GDP for a transfer as for a purchase, which is why the purchase multiplier

is always one unit greater than the transfer multiplier. A corollary is that

in proportional terms, transfer multipliers are more sensitive to persistence,

openness and the HtM HH share than purchase multipliers.17

A number of papers have found large federally-funded government purchase

multipliers at the regional level (eg Nakamura and Steinsson 2014 estimate

≈1.5), but smaller self-�nanced government purchase multipliers (Clemens and

Miron 2012 estimate � 1). Could �nancing explain the di�erence? The pure

15Another way to see this is that ∆cf=tr = (1 − α)(tr + ∆Y ), which is rearranged to∆y = [α/(1− α)] tr.16This initial increase in GDP is exactly $1 for a purchase because with �xed prices, outputis demand determined and so markups adjust so that workers supply su�cient extra laborto produce the extra $1 of output.17That is, ∀M > 0 and any variable X,

∣∣M−1∂M/∂X∣∣ > ∣∣(1 +M)−1∂(1 +M)/∂X

∣∣13

Table 2. Neoclassical model analytical impact multipliers

Neoclassical ImpactMultipliers

(A) Purchases of Home goods (B) Transfers Di�erence (A-B)

(1) Cross-Region(federal �nanced)

MCRG,NC =

[1− (1− β)

1− βρ

]1

1 + ϕα2 + ϕ(1− α2)θT− 1

1 + ϕ

1− β1− ρβ MCR

G,NC +1− β1− ρβ

[1

ϕ+ 1

](2) Self-�nanced MCR

G,NC +1

1 + ϕ

1− β1− ρβ

0

Di�erence (1 - 2) − 1

1 + ϕ

1− β1− ρβ

(�pure neoclassical CR transfer Multiplier�)

Notation: α home bias in cons.; ρ persistence of �scal transfer or purchase, β discount factor; θTconsumption demand elasticity H and F goods. See Propositions 3 and 4 (Online Appendix 6)

cross-region transfer multiplier discussed above is also the di�erence between

these two multipliers. With my default parameters (ρ = 0.935; α = 0.69)

the pure cross-region transfer impact multiplier is only around 0.30. As such,

unless the economy is extremely closed (α → 1) or �scal policy is highly

persistent (ρ→ 1) the pure cross-region multiplier will be modest and will not

be able to explain large federally-�nanced purchase multipliers.18

2.3. Analytical impact multipliers in a Neoclassical model. Table 2

lists cross-region and self-�nanced transfer and purchase multipliers in a neo-

classical model with �exible prices/wages and only Ricardian HHs. Like in the

ultra-Keynesian model, purchase multipliers are larger than transfer multipli-

ers. But now (i) transfer multipliers are either zero (as ρ→ 0) or negative (as

ρ→ 1), (ii) self-funded multipliers are larger than federally-�nanced multipli-

ers (and are trivially zero for self-�nanced transfers), and (iii) the pure neo-

classical cross-region transfer multiplier is negative and decreasing as ρ → 1.

In a neoclassical model, all temporary transfers are saved as there are no HtM

HHs. Any increase in consumption demand for local goods (eg from persis-

tent transfers), will result in an increase in prices of home goods and a shift

in expenditure towards foreign goods. Output will fall as households spend a

fraction of their extra income on greater leisure. See Propositions 3 and 4 in

Online Appendix 6, which also make use of the Lemma.

18These results are consistent with Nakamura and Steinsson (2014), who show numerically(in a similar model) that the federally-�nanced multiplier is only marginally greater thanthe locally-�nanced multiplier (though those are two-year rather than impact multipliers).Farhi and Werning (2016) and Chodorow-Reich (2017) produce similar expressions.

14

2.4. Relation to theoretical literature. To my knowledge, the decompo-

sition of cross-region transfer impact multipliers into �pure� and �self-funded�

components is new, as well as all expressions involving hand-to-mouth house-

holds (ω > 0), or self-funded transfer multipliers. A number of other ex-

pressions are related to those in Farhi and Werning (2016) in special cases.

Speci�cally, my expression for the cross-region transfer impact multiplier (top

RHS of Table 1) matches that of Farhi and Werning (2016) in the Cole-Ostfeld

case, if I set ω = 0 and they set t → 0 (p2458).19 In the long run (with

t→∞), these expressions converge to my multiplier in the neoclassical model.

Farhi and Werning's (2016) foreign �nanced purchase impact multipliers in the

Cole-Ostfeld case with AR(1) spending (p2459) correspond to my cross-region

purchase multiplier (Table 1 top LHS) when ω = 0. Chodorow-Reich (2017)

reports a similar expression for the di�erence between foreign and self-�nanced

purchase multipliers (p13).

3. Empirical Evidence

In this section, I estimate the causal e�ect of temporary transfers from the

2001 and 2008 US stimulus packages (Section 3.2) and permanent social secu-

rity transfers over 1952-74 (Section 3.3) on growth in labor income and GDP at

the state level. To my knowledge, these are the �rst estimates of cross-region

transfer multipliers, which are conceptually very di�erent from purchase mul-

tipliers in the literature, or estimates of the MPC (as argued in Section 2).

These estimates are broadly consistent with a quantitative NK model in Sec-

tion 4 and are used to inform counterfactual exercises for Europe in Section

5.

3.1. Identi�cation and speci�cation. There are two key challenges in iden-

tifying the e�ect of transfers on cross-sectional growth: reverse causality and

omitted variable bias.

19Farhi and Werning call ρFWNFA0 the annuity value of the cross-region transfer (whereρFW is the discount rate), which with my set-up and timing assumptions is (1−β)/(1−βρ)tr.They refer to home bias as 1 − αFW , whereas in this paper it is α. Farhi and Werning(2016) do very brie�y discuss purchase multipliers in the open economy with hand-to-mouthhouseholds but mostly evaluate their expressions numerically.

15

Reverse causality is a serious problem for transfer multiplier estimates

because transfers are strongly countercyclical.20 As such, a simple regression

of growth on transfers will pick up a combination of the transfer multiplier and

the countercyclical tax-transfer system, leading to downward biased multiplier

estimates (too negative).

My identi�cation strategy addresses reverse causality by studying speci�c

�natural experiments� � temporary stimulus packages and permanent social

security changes � where federal transfers across states change due to an ag-

gregate change in federal policy. These aggregate policies generate variation in

transfers across states through eligibility rules for the transfer which depend on

demographic characteristics of states or the distribution of prior-year taxable

income. For example, an increase in the monthly stipend of social security

recipients generates a larger increase in transfers to states with many retirees,

such as Florida. This research design impedes feedback from state business

cycles to the transfer in three ways.

First, as the policy change is at the aggregate level (a�ecting all states), it

is unlikely to be implemented to address a recession or boom in a particular

state. Romer and Romer (2016) show this speci�cally for my sample of social

security increases (discussed further below), and transfer stimulus payments

were widely spread across states with simple eligibility rules making it di�-

cult to target speci�c states.21 Second, my allocation of transfers is based on

last year's eligibility characteristics � like the lagged number of social secu-

rity recipients or the prior-year distribution of taxable income � which means

that shocks in states can't a�ect the size of the transfer mechanically. Fi-

nally, demographic characteristics or the distribution of taxable income are

essentially stocks rather than �ows � they are predetermined and slow moving

over the medium term. For example, the left side of Figure 2 (blue circles)

20The literature cited in the introduction �nds that a $1 fall in incomes in a particular stateleads to a 20-40c transfer to the residents of that state through the federal tax-transfersystem.21Even if legislators tried to target transfers at low-growth states it would be hard to knowwho they were given it takes almost a quarter from legislation to implementation of transferpolicies and quarterly growth is not persistent. An AR(1) panel regression (with state andtime �xed e�ects) suggests that a 1% increase in growth in quarter t only increases growth inquarter t+1 by 0.01% (for labor income) to 0.1% (GDP), with the autoregressive coe�cientbeing insigni�cant (not reported).

16

shows that across states, the size of the 1972Q4 increase in social security

payments was almost exactly proportional to the 1970Q2 increase in social

security payments (R-squared of 0.96). This means the distribution is almost

una�ected by using a lagged allocation as income or age structure does not

vary quarter-to-quarter.22

Omitted variable bias (OVB) The second concern is that there might

be other variables which a�ect both regional transfers and regional economic

growth. All speci�cations include (i) state �xed e�ects (µi) which control for

all state-level trends (like the faster growth of sun-belt states relative to rust-

belt states) and (ii) quarter �xed e�ects (γt) to remove aggregate variation

(like the US business cycle, monetary policy, expectations or international

shocks). As such, OVB concerns focus on confounding variables which vary

across states and over time. Results are also robust to a range of other controls

(which vary by state and over time), such as state-speci�c sensitivities to the

national business cycle or oil prices, state-speci�c quartic trends, population

growth, as well as to the removal of in�uential years or states and to placebo

tests (discussed further below).

However, as outlined in the introduction, OVB is primarily addressed through

a research design involving many transfer policy changes with di�erent sizes

and timings (and estimated in changes), which means that the confounding

variable would have to take an extremely unlikely time path in high transfer

states (and low transfer states). For example, temporary transfers in Missis-

sippi (MS) as a fraction of labor income increased by 2.3% in 2001Q3, decreased

by around the same amount in 2001Q4, increased by 6.5% in 2008Q2, fell 5.6%

in 2008Q3 and fell again by 0.9% in 2008Q4 (a small fraction of the 2008 pay-

ments spilled into Q3). Of the 88 quarters during 1952-74, 64 had no increase

in social security payments and 24 had an increase with heterogeneous sizes.

For states like Florida (as a fraction of labor income) this could be 0.35% in

1952Q4, 0.9% in 1970Q2 or 1.4% in 1972Q4 (Figure 2 LHS), or many smaller

changes. Moreover, the identity of high-transfer states changes over time and

across transfer policies. The RHS of Figure 2 shows that although correlated,

22For the stimulus packages, the allocation of transfers was actually made using informationfrom prior-year tax returns because contemporaneous taxable income was not available atthe time the payments were made.

17

AL

AK

AZ

AR

CACOCT

DE

FL

GA

HI

ID

IL

IN

IAKSKY

LA

ME

MD

MAMI MN

MS

MOMTNE

NV

NH

NJNM

NYNC

ND

OH

OKORPARI

SC

SD

TN

TXUT

VT

VAWA

WV

WI

WY

AL

AK

AZARCACO

CT

DE

FL

GAHIID

IL INIAKS

KYLA

ME

MD

MA

MI MNMS

MOMT

NENV

NH

NJ

NM

NY

NCND

OH

OK

ORPA

RI

SC SDTN

TXUT

VT

VA

WA

WV

WI

WY

0.0

02.0

04.0

06.0

08.0

119

70Q

2 or

195

2Q4

Incr

ease

in S

S b

enef

its

0 .002 .004 .006 .008 .01 .012 .014 .0161972Q4 Increase in SS benefits (share labor income)

1970Q2 SS increase 1970Q2 proportional allocation1952Q4 SS increase 1952Q4 proportional allocation

Notes: Increases SS benefits 1952Q4 0.23% PI; 1972Q4 0.75% PI ; 1970Q2 0.48%PI Lines are indicate allocations in 1952Q4 (solid) or 1970Q2 (dashed) proportional to 1972Q4adjusting for aggregate size. Source: BEA, Romer and Romer (2014), author’s calculations

1972Q4 vs 1952Q4 or 1970Q2, increase as share of labor income Perm. Social Security increases by State

ALAK

AZAR

CA

CO

CTDE

FL

GA

HIID

IL

IN

IA

KS KY

LA

ME

MD

MA

MIMN MS

MO

MT

NE

NV

NH

NJ NM

NY

NC

ND

OHOKORPARI

SC

SD

TN

TX

UT

VT

VAWA

WV

WIWY

.015

.02

.025

.03

.035

.04

Tra

nsfe

rs 2

001Q

3, s

hare

of l

abor

inco

me

.02 .03 .04 .05 .06 .07Transfer stimulus payments 2008Q2, share of labor income

Red Line: Tr2001Q3=0.02+0.13*payments (t=2.1) Green Line: size−adjusted slope 0.64. Source: BEA

2001Q3 EGTRRA vs 2008Q2 ESP

Figure 2. Cross-state allocation of transfers over time LHS:Social security increases (2 years or 20 years) RHS: 2001 and2008 Stimulus packages

the cross-state allocation of transfers in 2008 was quite di�erent from that in

2001, with the 2001 allocation explaining only 12% of the variation in 2008

transfers. This di�erence stems from the greater progressivity of the 2008 rel-

ative to the 2001 transfers.23 The LHS of Figure 2 (green triangles) shows

that demographic changes over a longer period (1952-72) led to a reallocation

of social security bene�ts across many states, such that the allocation in 1952

only explains 28% of the allocation in 1972.

This identi�cation strategy is good at identifying the short-term �relative

multiplier� of transfers across states. However, it is not designed to address

the medium/long run e�ects of transfers across states as the longer the lags

considered, the greater the chance of other correlated policy changes in the

intervening period. Accordingly, I concentrate on the contemporaneous e�ect,

which means I am more likely to be picking up short-run demand-side e�ects

rather than long-run supply-side e�ects.24

23Speci�cally, the 2008 transfer was refundable, with a phase-out for those with incomesabove $75,000 � which led to larger transfers to residents of low-income states like MS. Incontrast, low income earners with no net tax liability received no transfer in 2001 (and therewas no phase-out), which meant the transfer was relatively larger for lower-middle incomestates like MT. The allocations are still correlated because of the relatively �xed dollar valueof the transfer, which is proportionately more important in poorer states.24It also rules out most measures of state-level GDP before 2005, which are only availableat an annual frequency. In addition, I cannot examine the e�ect on consumption whichis unavailable at the state level. State-level quarterly seasonally adjusted data are not

18

Dependent variable and speci�cation In the main regressions, I in-

vestigate the e�ect of transfers on the growth rate of real quarterly labor

income (equivalent to the wage bill) ∆WLi,t ≡ (WLi,t − WLi,t−1)/WLi,t−1

which is available for the whole sample; or quarterly GDP from 2005 ∆Y i,t ≡(Y i,t− Yi,t−1)/Yi,t−1. All variables are de�ated by the national Quarterly Per-

sonal Consumption Expenditures (PCE) Chain-type Price Index (GDP de�a-

tors or CPI are not available by state).25 Labor income data comes from the

Bureau of Economic Analysis (BEA) under the o�cial title of �Earnings by

place of work� and mostly consists of Wage and Salary Disbursements (70%).26

It excludes income from transfers and is before income taxes. As state-level

quarterly labor income can be extremely volatile, especially for small states,

I drop outliers from all speci�cations and cluster standard errors by state.27

The measure of transfers is scaled by lagged labor income or GDP to retain

the �multiplier� interpretation of coe�cients ∆tri,t ≡ (tri,t − tri,t−1)/WLi,t−1

or ∆tri,t ≡ (tri,t − tri,t−1)/Y i,t−1 i.e. the dollar value of extra labor income

or GDP produced by an extra dollar of transfers. See Online Appendix Table

1.1 for descriptive statistics. I estimate Equation 1, with X being a vector of

controls (which vary across speci�cations):

(1) ∆WLi,t or ∆Yi,t = β∆tri,t +Xδ + γt + µi + eit

available for many alternative series such as hours worked, with heads-based employmentonly available since 1990.25When the BEA produces �real� estimates of state-level GDP it de�ates these using nationalprices for di�erent industries, weighted using state-speci�c industry weights. These quasi-GDP de�ators are di�cult to map into the model, so I use nominal variables and de�atethem by the PCE.26Earnings by place of work are sourced mostly from high-quality administrative records(BLS's Quarterly Census of Employment and Wages (CEW) program), and make up around3/4 of Personal Income before taxes. They also include non-wage payments by employerswhich contribute to labor costs such as employer contributions for pensions and social in-surance (12%) and social insurance taxes (5%)27Speci�cally, I drop observations where the quarterly growth rate of labor income growthor GDP is more than 3 SD from the mean (in either direction), which is around 1.5-1.75% ofthe sample. Results using labor income growth are fairly robust to including these outliers,though results using GDP are sensitive to extreme observations in Wyoming and Alaska.Dropping these two states (with a combined population of less than 1m) reinstates the mainresults.

19

3.2. Temporary Transfers.

3.2.1. Description of Transfer packages. 2008 Economic Stimulus Act

Around $100bn was transferred to households as one-time payments in 2008Q2-

Q3 as part of the Bush administration's Economic Stimulus Payments (ESP)

(Parker et al 2013).28 There were two main components of the package: (i)

$300 per capita payments made to those paying no net taxes but with at least

$3000 in eligible income (around $30bn, which I call the low-income rebate

component) and (ii) $600 per capita payments made to those paying net taxes

with a phase out for those earning over $75,000 (around $70bn, which I call

the middle-income tax refund component).29 Most of the e�ects of the 2008

ESP turn out to be driven by the low income component. Parker et al (2013)

exploited randomization in the timing of the ESP and found that about 12-

30% of the payments was spent on non-durable consumption in the months

they were received (50-90% including durables).

I allocate the payments for the low-income rebate component using the cross-

state allocation from BEA (2009), which is primarily based on the geographic

distribution of recipients of refundable Earned Income Tax Credits (EITCs).

According to the BEA's allocation, about 95% of the refundable 2008 low-

income ESP were paid out in 2008Q2 (as against 85% of the whole package).30

Eligibility for the 2008 stimulus payment was made based on 2007 income as

the level of 2008AY income was not known in 2008Q2. I allocate the middle

28The 2001 and 2008 stimulus payments are much cleaner examples of one-o� stimulus pay-ments than the components of the 2009 American Recovery and Reinvestment Act (ARRA).Although the ARRA was larger ($787bn), only a very small share was spent on comparableone-time payments ($13bn for $250 payments to social security recipients in 2009Q2), andother programs like the Making Work Pay tax credits were spread across several years ratherthan being concentrated in one quarter.29Households receiving either payment also received $300 for each eligible child, with eligi-bility rules similar to those for the Child Tax credit. The eligible income includes earnedincome and social security bene�ts (among other things).30According to the BEA (2009), for the low income component, $28bn was paid in in 2008Q2and $1.35bn in 2008Q3. This is consistent with a high percentage of recipients receivingtheir ESPs by direct deposit, which is faster than by check. IRS data show that around80% of those with refundable EITCs �led electronically. Direct deposit payments were madein the �rst half of May 2008, leaving plenty of time for output to be a�ected in 2008Q2.Combining with Parker et al 's (2013) quarterly pro�le, this suggests a middle-income payoutof around $50bn in 2008Q2 and $14bn in 2008Q3. All of the 2001 stimulus payments weremade in 2001Q3.

20

income tax rebate payments across states using IRS data on 2007 income tax

returns by state and income combined with the eligibility rules for the tax

rebate (see Online Appendix 1 for details).

2001 EGTRRA Stimulus payments In 2001Q3, the Bush administra-

tion transferred $38bn to households in a one-o� payment as part of the Eco-

nomic Growth and Tax Relief Reconciliation Act (EGTRRA). Individuals pay-

ing net taxes mostly received $300 per capita, though unlike the 2008 ESP

there was no payment for those with no tax liability, and no phase out for

those on high incomes. I allocate the $38bn in transfers across states using

IRS state-level data on individual tax returns for the 2000 tax year (see Online

Appendix 1 for details). Exploiting randomization of payment dates, Johnson

et al (2006) found that 20-40% of the payment was spent on non-durables in

the months that it was received, with a higher MPC for the poor and credit

constrained, and no response for durables.

3.2.2. Graphical evidence. The left panel of Figure 1 shows the relation

between total transfers in 2008Q2 as a share of labor income on the x-axis,

and the growth of labor income on the y-axis. States which received larger

transfers (as a share of labor income) tended to grow faster contemporaneously,

with a �relative multiplier� (line slope) of about 0.28 (t-stat=2.04). The cross-

state variation in transfers is striking: the 2008 ESP ranges from around 2.3%

of quarterly labor income in Connecticut (CT), to 6.4% of quarterly labor

income in Mississippi (MS). Two factors are at play here: �rst, the level of

per capita labor income is much lower in MS than CT and so �xed dollar

payments are mechanically more important. Second, much of the variation is

driven by the low-income rebate (those paying no net taxes), which is focused

towards poor states. Of the 4.1 percentage point gap in stimulus transfers in

2008Q2 between MS and CT, 3.4 percentage points are due to the cross-state

allocation of the low-income component.

Online Appendix Figure 2.1 (left side) shows the same stimulus package

and quarter, but for quarterly growth in GDP rather than labor income on

the y-axis (with transfers as a share of quarterly state GDP on the x-axis). As

before, there is a positive relationship between the size of the transfer received21

and quarterly GDP growth, though the relative multiplier is now larger at

around 0.8 (t-stat=2.4).

The right panel of Figure 1 examines the relationship between the 2001

EGTRRA stimulus payments and contemporaneous labor income growth in

2001Q3. One can see that states that received a larger payment (as a share

of labor income) tended to grow faster contemporaneously, though here the

relative multiplier is larger at 0.75 (t-stat=2.1).

3.2.3. Main Regression Results. Table 3 presents regressions of growth in

labor income or GDP on the size of the transfer at the state level over 2001-

08. Unlike the scatter plot, the regressions control for state and time e�ects,

and impose that the withdrawal of transfers the following quarter reverses any

earlier increase in labor income or GDP growth.

Panel A reports a parsimonious speci�cation with no other controls. Pooling

across all measures (Column 1) shows that a relative $1 increase in temporary

transfers to a state increases relative labor income in that state by $0.24,

signi�cant at the 1% level. In Column 2, I break down these transfers into

the three components discussed above. The results seem to be driven by the

2008 low-income rebate, with a relative multiplier of 0.33, signi�cant at the

1% level. The 2001 EGTRRA stimulus payments have a similar multiplier,

but have much larger standard errors (leading to a loss of signi�cance).

The 2008 mid-income tax rebate has little e�ect on labor income growth,

which might be because a larger fraction of this payment was saved, or perhaps

spent on goods produced outside the state receiving the transfer. First, this

rebate was targeted at households who were better o� who might be less

�nancially constrained. Comparing the MPC for non-durables from Parker

et al (2013) and Johnson et al (2006), the MPC is possibly larger for the

2001 payment. The 2008 mid-income payments were also twice as large as

the other one-o� payments ($600 vs $300). Hseih (2003) �nds that households

that saved large Alaska permanent fund dividends tended to spend small tax

refunds, which is consistent with evidence from Kaplan and Violante (2014)

using a model with �xed costs of reallocating liquidity. Alternatively, the larger

payments as part of the 2008 middle-income tax rebate might have been more

likely to be spent on goods produced outside the state (providing a smaller22

boost to local demand). A larger transfer can be spent on �big ticket� durable

goods like autos, as suggested in Parker et al (2013).

Columns 3-4 investigate the e�ect of transfers on quarterly state GDP

growth, which is available since 2005. Nonetheless, the results are quite simi-

lar. The pooled speci�cation (Column 3) suggests that the cross-state relative

GDP multiplier is slightly larger than the cross-state labor income multiplier

(0.36), and is signi�cant at the 5% level.31 As before, results seem to be driven

by the low-income rebate component which is signi�cant at the 1% level (Col-

umn 4), while the tax refund component is insigni�cant (as above).

Panel B of Table 3 adds two variables as extra controls: population growth

and state-speci�c sensitivities to the national business cycle. Population growth

is relevant as an surge in migration can increase state-level labor income, but

is unlikely to be driven by transfers which are available on a nation-wide basis.

Another concern is that some states might have more cyclically-sensitive in-

dustries � leading to a larger fall in output during the 2001 and 2008 recessions

� and these states might have received smaller transfers (for example, NY is a

higher income state and has highly cyclical industries like �nance). State sen-

sitivities to the national business cycle are controlled for by interacting state

dummies with US quarterly GDP growth (50 additional variables). The re-

sults with these additional controls are similar to those with the parsimonious

speci�cation in Columns 1-4.

In Panel C, I test whether there there is any evidence of anticipation e�ects

(applied to the pooled speci�cation). The Economic Stimulus Act of 2008

was enacted in February 2008 (2008Q1), a quarter before the payments were

made in 2008Q2 (the date used above). Likewise EGTRRA passed Congress

in May 2001, and was enacted in early June 2001 (both 2001Q2) - a quarter

before payments were made in 2001Q3 (the date used above). Columns 9 and

10 test whether people responded in advance by including a one-quarter lead

of pooled transfers. The lead is insigni�cant, which suggests an e�ect when

transfers were paid rather than announced.32

31The coe�cient is smaller than in the scatter plot, because of a smaller multiplier on thewithdrawal of stimulus (see Online Appendix Figure 2.1 and Table 2.1 ).32This is unsurprising as Ricardian HHs who respond to news also save temporary transfers.

23

Table 3. Temporary transfer multipliers � main results

��� ��� ��� ��� ���� ��� �� ���� ��� �� ��

�������������

����������������������� ! ��"

"��#�$�%���&���& '�� '� '�� '�� '�� '��

� ' �� � '��� � ' �� � '�� � ' �� � '��

�#��$� ' � ( '��

� ' �� � '���

� ����)���& '�� '� '�� '

�#�������*�� � ' �� � '��� � '� � � '���

� ��%�+����,�$& ( ' ( '�� '� ( '��

�*�$�����*�� � '��� � '��� � '��� � '��

� ��%���&���& '�� '��

� '��� � '���

������-�.���/0 10� 10� 10� 10� 10� 10� 10� 10� 10� 10�

2����"����3�&�����$,**4 10� 10� 10� 10� 10� 10�

������"��,#������������ 10� 10� 10� 10� 10� 10�

5)&��6�����& �7��� �7��� � � �7��� �7��� � � �7��� �

����������������������� ������������������������������������������������������������ ������������� !�"�� ����#$%������������

�������!�&�� ��� �'�&(�)�� �'�"*%!�*���������� �������������������� ��������������������������������)�(������

�8��"��&�*����,&�������������� �9�� ����:�����#& �:��!��$&

�!�)���;��'��� �( �� ��"��� �( �� �!�)���;��'��� �( �� ��"��� �( ��

3.2.4. Further robustness and threats to identi�cation. Table 4 presents

a range of robustness tests and alternative speci�cations for the main results:

pooled transfers with a parsimonious speci�cation. One of the �rst concerns

from looking at the left panel Figure 1 is that results might be driven by Mis-

sissippi (MS) as an outlier. In columns (1) and (6) of Table 4, I drop MS.

Point estimates are only marginally smaller, though standard errors increase

slightly such that the p-value increases from 0.6% to 3% for the labor income

speci�cation and from 2% to 5.4% for the GDP speci�cation. In Online Ap-

pendix Figure 2.2 I drop all states one-by-one and don't �nd any substantially

in�uential states.

Alternative controls Instead of heterogeneous state sensitivities to the

national business cycle (as in Table 3) states instead might have di�erent

sensitivities to the oil price (omitted variable bias). This would happen as

some states are oil importers (with lower prices reducing growth) whereas

others are oil producers (with higher prices increasing growth) and also as oil

prices were changing rapidly around 2008. To test this hypothesis I interact

state dummies with the contemporaneous and �rst lag of log real oil prices,

which adds 100 additional controls to the regression in columns (2) and (7)24

Table 4. Temporary transfer multipliers � robustness

��������

����� ���

������

���������

������

����������

����

������ ��������

� ���� ���

������

�!�������

������

�"��������

����

��#����

$�%������&��'

�((���������)��� #*� #*� #*� #*��

#*�! #*�! #*�� #*

�#*�#� �#*#!� �#*#"� �#*���

�#*�"� �#*��� �#*��� �#*���

+�������� #*#!

#*�"

�#*��

�#*�#�

� (������ #*�

#*�!

�#*���

�#*��

�����,-������.��/��.������)���

#* #*�!

�#*# � �#*���

������(%,����(��0�(1�/

#*� #*��

�#*�#� �#*��

�� �,�������������%%� 2� 2�

�(�������3��������,--��� 2� 2�

�����+��3�4,���� ������ 2� 2�

5�0$����(1�/�3��������,--��� 2� 2�

+����������+��������

� "�

�����6�4���+� 2� 2� 2� 2� 2� 2� 2� 2� 2� 2�

�&���7���(�� �8��" �8�!� �8�!� �8��� �8�!� � � � �

0�(1�/�9����()���&(���� (-� 0�(1�/�9����4,������:�0$�

;(���'�9(&,��������%�����/������ �,�������&:�������*��-%������##��#!�)(����&(���� (-�8������##��#!�)(��0$���(��:��7����&���)�(-�##��*�

�,�������<�$�)�(-�-������(%%��*��=(����������������� �,������������%%�*�=(����������=(��� ���� �,��������� ��(��()��(�������(�������������/��)����������

1��/��������,--���*�=(�������������!�������� ���������,--����1��/���>,���� �%(�:�(-���������*�=(������������"����������������8����1������� (���(���)(��

������%(%,����(����(1�/�����5�0$��0�������� ����1��/��������,--������������/��-�������,����8�������������/����-%����(��##"4��(����(1�)(��

����-���(��()�����*��=(�,-������������#�������,-����?�@�.@�����/��������)���.�,������/��##!���*�

of Table 4.33 Estimated coe�cients and their signi�cance are unchanged from

the main results.

Instead of trying to control for speci�c omitted variables, an alternative is

to add general state-speci�c trends that could �exibly control for any number

of omitted variables. In Columns (3) and (8) of Table 4, I add a fourth-order

(quartic) state-speci�c polynomial trend to the regression. That is, I interact

a state FE with t, t2, t3, and t4, which adds 200 extra control variables to

the speci�cation (though many are collinear and dropped). These variables

can control for almost any trend at the state level, but estimates of transfer

multipliers (and their signi�cance) are almost unchanged.

Alternative Speci�cations Motivated by theory, the speci�cation in the

main results table imposes that the e�ect of withdrawing �scal transfers has

the same e�ect on growth as their imposition. Columns (4) and (9) in Table 4

33This �exible speci�cation can be in levels (if the �rst lag is zero) or growth rates (if thecoe�cient on the �rst lag is the negative of the one on the contemporaneous oil price). Theaggregate oil price is subsumed in the time �xed e�ect.

25

test whether there might be delayed e�ects by adding extra lags of transfers.34

For labor income, the coe�cient on contemporaneous transfers is slightly larger

than the baseline and lags are small and insigni�cant. For GDP, lags are large

(though insigni�cant), and cause the contemporaneous coe�cient to double

in size. However, its standard errors also double, so I cannot reject the claim

that the true size of the coe�cient is the same as estimated previously.35

One concern is that my measure of transfer payments could have classical

measurement error (resulting in attenuation bias), or that there are other

similar transfer payments that I am not capturing (leading to upward bias).

To address this concern, in Columns (5) and (10) of Table 4 I instrument

changes in BEA category �All other personal current transfer receipts� (as a

share of labor income or GDP) using the low- and middle-income 2008 ESP

transfers (as above) as instruments. The 2008 ESP are classi�ed as current

transfers by the BEA because they were at least partly refundable, unlike the

2001 EGTRRA payments which were non-refundable and so are classi�ed as

tax cuts.36 Estimated IV multipliers are 0.22 and 0.38 on labor income and

GDP respectively (signi�cant at the 1% level) and are nearly identical to those

on pooled transfers in the body of the text. This is because growth in �All

other personal current transfer receipts� in 2008Q2 is extremely well explained

by growth in ESP transfers as constructed above (see Online Appendix Figure

2.1 (right side)). The �rst stage coe�cients in the 2SLS estimation are close

to one, and the �rst stage F-stats are around 300, which provides an external

validation of my construction of the cross-state allocation of the 2008 ESPs.

34The coe�cient on the lag dependent variable is small � and has little e�ect on multiplierestimates due to the absence of persistence in GDP or labor income growth.35To explain these di�erent coe�cients, Online Appendix Table 2.1 estimates transfer mul-tipliers separately for each cross section. Remarkably, I �nd the reduction in growth inlabor income in 2008Q3 as stimulus is withdrawn is of a similar magnitude as the boost togrowth in 2008Q2 as stimulus is received (as in Figure 1). However, for other quarters andmeasures the boost to growth as stimulus is received is usually larger than the fall in growththe following quarter (explaining the results with lags), though di�erences are usually notstatistically di�erent.36Much of �All other personal current transfer receipts� are possibly endogenous to state-level business cycles, and so unsurprisingly a simple OLS regression of growth in laborincome or GDP on changes in these transfers yields insigni�cant coe�cients (not reported).See Online Appendix 1.

26

Placebo tests. Another way to test for omitted variable basis is to run

a placebo test where the timing of the transfers are counterfactually moved

backwards or forwards by up to six quarters.37 This can help to detect spurious

results if coe�cients are more signi�cant at other times, though it can also pick

up anticipation e�ects (for short leads) or delayed e�ects (for short lags). One

can see that the largest t-statistic in Online Appendix Figure 2.3 is always at

the t=0 (when the actual transfers occurred), and there is little evidence of

consistently positive (or negative) coe�cients at other times.

3.3. Permanent Transfers.

3.3.1. Description of Social Security increases. Before 1975, social secu-

rity payments (largely the old age pension) were not indexed to in�ation and

were increased by act of Congress on an ad-hoc basis (Wilcox 1989). Unlike

in the previous section, these were mostly permanent increases in transfers �

a higher monthly stipend received by the elderly and their dependents � and

so are more likely to be spent by unconstrained consumers.38 As the transfer

increases varied in size and timing � for example, a 10% increase in stipends

in June 1971, a 20% increase in October 1972 and no increase in 1973 �

they would not be subsumed into seasonal factors. From 1975 onwards, social

security payments were indexed to the CPI and generally adjusted annually,

making them much more predictable and so are excluded from my analysis.39

37Speci�cally, I estimate the speci�cation with controls in Panel B of Table 3 for pooledtemporary transfers (Columns 5 and 7), or the analogous speci�cation in Table 5 (Column4) for permanent social security transfers and move the dependent variable and sampleperiod forward or backwards by up to 6 quarters.38Romer and Romer (2016) note that these bene�t increases as a share of personal incomeare not fully permanent, but fall over time due to �in�ation and increases in real personalincome�. From the perspective of an optimizing household in the model, only in�ation wouldcause the transfer to decrease in value and cause a fraction to be saved. Over the 1952-74period studied here, PCE in�ation averaged around 3% per annum, and so one could arguethat the bene�t increases are �close� to permanent.39Wilcox (1989) examines the e�ects of these transfers over 1965-85, and �nds an insigni�-cant coe�cient on consumption in the second half of the sample. Romer and Romer (2016)extend Wilcox's sample back to 1952 and forward to 1991. If I extend my sample to 1952-91(using the parsimonious speci�cation in Table 5), the estimated permanent relative transfermultiplier is smaller (about 0.8, nor reported) and only signi�cant at the 10% level.

27

The sample of social security payment increases at the aggregate level covers

1952-74 and is taken from Table 1 of Romer and Romer (2016). An earlier ver-

sion of this paper used the aggregate permanent social security increases from

Wilcox (1989), which covered 1965-74 or 1968-74. Unsurprisingly, Wilcox's

and Romer and Romer's sample of permanent social security changes is very

similar for the period they overlap. Romer and Romer's sample also includes

a number of temporary social security increases, and I add these as controls in

the regression. As Romer and Romer's data is monthly whereas mine is quar-

terly, I spread the adjustments over two quarters if the permanent increase in

payments occurred mid-quarter.40

I allocate the increase in aggregate social security payments across states

in proportion to that state's share of social security payments a year before

(see Online Appendix 1 for details). This depends on the stock of retirees in

each state, which is both slow-moving and largely predetermined. The largest

permanent increases were in 1972Q4 (a 20% increase in bene�ts) of around

1.45% of quarterly labor income in West Virginia, Arkansas and Florida, which

have a large number of retirees as a share of the population (LHS of Figure

2). In contrast, the increase in transfers to residents of Alaska was only 0.2%

of labor income in the same quarter.

3.3.2. Main Regression Results. Table 5 shows that a permanent $1 in-

crease in transfers to the residents of a state tends to increase that states'

relative labor income by around $1.8 contemporaneously, though the multi-

plier varies across speci�cations from $1.4 to $2.2 and the standard errors are

wider than for temporary transfers (≈ 0.5). The �rst speci�cation (Column 1)

is the most parsimonious: it includes time and state �xed e�ects but no other

controls and yields a relative permanent transfer multiplier of 1.9, signi�cant

at the 1% level.

As above, one concern is that in particular periods population might be

growing faster in sunbelt states with many retirees, which would lead to a

spurious increase in labor income when social security payments might be

rising. Column 2 addresses this concern by controlling for population growth,

40For example, a permanent $1 increase on a $10 monthly payment starting on 1 June willbecome a 3.3% increase in Q2 and a ≈6.6% increase in Q3.

28

which reduces the multiplier slightly to around 1.7 (still signi�cant at the

1% level). Given that this speci�cation is almost as parsimonious as that in

Column (1), the coe�cient on population growth is large (0.5) and signi�cant,

and my model does not include population growth, I use it as a baseline and

control for population growth in the later speci�cations.

Another concern is that multipliers might be sensitive to particular years

(or states), and is not re�ective of a general relationship. In Online Appendix

Figure 2.4, I test this by dropping individual states or years one-by-one. No

individual states are in�uential, but the multiplier does move by more than

one standard error (≈ 0.5) if either 1952 or 1972 are excluded. Column (3)

reports the baseline speci�cation excluding both these in�uential years, which

has little e�ect on the multiplier as the e�ect of these years is mostly o�setting

(signi�cant at the 5% level).

Columns 4-6 add state level interaction terms to control for omitted variable

bias (as discussed above). In Column 4 I include US GDP growth X state �xed

e�ects, which controls for di�erent state level sensitivities to the aggregate

business cycle which reduces the multiplier to around 1.4 (signi�cant at the

1% level). This is the estimate I use to compare to the model in Table 6,

to be consistent with the speci�cation for temporary transfers. Column 5

adds controls for state-speci�c sensitivities to oil prices, which increase the

multiplier to around 1.9. Column 6 instead adds a quartic state-speci�c trend

to control for general time-varying state-speci�c covariates, also yielding a

multiplier of 1.9 (signi�cant at the 1% level).41

Online Appendix Figure 2.5 also reports placebo tests where the timing of

social security payment is moved counterfactually backwards or forwards. As

before, the most signi�cant coe�cient is at t=0.

Alternative speci�cations Romer and Romer (2016) note that during the

1952-74 period there were a number of temporary transfers to social security

recipients, re�ecting back payments for earlier increases in permanent social

security bene�ts. As these temporary increases tended to coincide with the in-

creases in permanent SS bene�ts - and have similar eligibility requirements - it

41It is unsurprising that quartic state-speci�c trends do not a�ect the results, as the speci-�cation is in changes rather than levels.

29

is important to include them as controls. Column (7) includes these temporary

transfers, as well as a fuller speci�cation (an extra lag and a lagged dependent

variable).42 The contemporaneous multiplier on permanent transfers is similar

to the baseline (though now signi�cant at the 5% level), and the temporary

social security increases are insigni�cant. I don't put much weight on these

estimates of the e�ects of temporary transfers � relative to those in Table 3

� because they are imprecise: the standard errors are almost seven times as

large and we can't reject that the temporary transfer multiplier is around 0.25

as in Table 3.43

In Column (8), I test for anticipation e�ects by adding the �rst and second

leads of permanent increases in social security payments, which are insignif-

icant (and negative). This is consistent with results in Wilcox (1989) and

Romer and Romer (2016) that the social security bene�ts were consumed

when they were received, not when they were announced.

In Column (9) I estimate an IV speci�cation, where all social security trans-

fers � which are potentially endogenous as state-level business cycles can in�u-

ence people's decision to retire � are instrumented using the ad-hoc permanent

increases in social security payments used above. The multiplier is around 2.2

with a contemporaneous speci�cation, signi�cant at the 1% level, which is

fairly close to the reduced-form baseline speci�cation (Column 2). This is

unsurprising because a $1 increase in my exogenous social security transfers

series increases all BEA social security payments by about 80c, with the �rst

stage F-statistic above 500. The strong �rst-stage relationship also provides

an additional validation of the cross-state allocation of transfers and Romer

and Romer's narrative at the aggregate level.

Comparison with literatureWilcox (1989) and Romer and Romer (2016)

�nd (among other things) that increases in permanent social security bene�ts

signi�cantly increase consumption at the aggregate level. This is consistent

with my �nding of large [non-transfer] income multipliers in the cross section,

42Later lags (beyond the 2nd) are usually positive but insigni�cant, turning negative for the5th (insig) and 6th lags (signi�cant) (using the same speci�cation as column 4).43The imprecision stems from the fact that these temporary social security payments weremuch smaller and less variable than those in Table 3, and also occurred at the same time aspermanent social security increases (and with the same cross-state allocation).

30

though my results don't imply theirs and vice versa (which emphasize the

novelty of these estimates).44 If I take advantage of Romer and Romer's ar-

gument that these social security increases were exogenous at the aggregate

level and estimate Equation 1 without time �xed e�ects, I get a hybrid cross-

region/aggregate multiplier of 0.55 (t=4.2, not reported). The much smaller

size of this coe�cient is intuitively consistent with Romer and Romer's �nding

of an insigni�cant e�ect of social security increases on measures of aggregate

output like monthly industrial production and employment (though in part

this is also due to wide SEs), and a strong negative monetary policy response.45

4. Quantitative Cross-Region Transfer Multipliers in a New

Keynesian Model and Comparison with Empirics

In this section I show that my full New Keynesian model (with Calvo-sticky

prices and wages) is broadly consistent with the empirical impact multipliers

estimated in Section 3 � more so than a plain-vanilla neoclassical model �

taking care that empirical and model-based multipliers are measured in a con-

sistent way (using national prices). I then produce quantitative estimates of

self-funded and cross-region transfer multipliers using the full NK model �

which are similar to those in the analytical model for less persistent shocks.

The size of these transfer multipliers underlie the gains from a counterfactual

transfer union in Europe in Section 5.

44Speci�cally, my results can be rationalized in a model with a larger relative increase inconsumption in states receiving larger relative SS transfers, which is consistent with anincrease or decrease in consumption at the aggregate level (for example due to di�erentmonetary policy responses, which are captured in my time �xed e�ects). As argued inthe introduction, a high MPC can lead to a positive, negative or zero cross-region transfermultiplier depending on whether the transfers are spent on local goods and whether pricesand/or wages are sticky.45Aggregate and cross-sectional multipliers are di�cult to compare rigorously (and this isbeyond the scope of this paper) given that the cross-sectional and time-series multipliershave di�erent theoretical determinants, and the hybrid multiplier is a mixture of both. Thereported result is using the baseline speci�cation (and sample) from Table 5 Column 2, withtime FEs replaced with an aggregate time trend.

31

Table 5. Permanent transfer multipliers � main results

��������

�� ���

����� ����������

������ ��

��������� ��

�� ���������

��������� � �������!"�

#$�����%�"�����&��

�'��� ���(��

�����

�)��"�����

��� *�

�+��,����,��-�

$� ����

�.�������������

-����*�

�/���0�

$��1�0������2

����1""�,�� ����� �1/ �1+� �1)) �1�/ �1/� �1/� �1+� �1')

�31�/� �31�+� �31+)� �31�.� �31�.� �31�.� �31''� �31�/�

4���� 31+/ 31�.

�31++� �31+'�

4���� �1�.

�31+3�

&��������

�1�'

�31+.�

"��� *�����

31+�

�31+3�

� ������ ��*�""� �1�

,�� �����5 �31'+�

,����""�,�� ����� 31�� 313.

�31'3� �31'��

&����4�� 313� 31�'

�31)�� �31�.�

4���$��1�0������ 313) 313'

�313�� �313��

"��������1#�1 31�+ 31'. 31�' 31�� 31�� 31'� 31'� 31�.

�31�'� �31��� �31�'� �31�)� �31�)� �31��� �31��� �31�'�

"�����-�6���&� 7�" 7�" 7�" 7�" 7�" 7�" 7�" 7�" 7�"

!"�#$����1�%�

������&�

7�"

����(���%�������*����� 7�"

"�����&��%�6������,�� * 7�"

&����"�����&"��� '�/

(����8��� � �9'3' �9'3' �9��� �9'3' �9'3' �9'3' �9��+ �9��+ �9'3'

#��:���;����6���������4������ �������/'�+��

����������������������� ������������������������������������� ����������������������������������� ��� � ��!��"�� � ��#�$��%�����&���'$�(��������� �� ��)�*�����

����������������������������� ������� ������������ � �������������� �����������*������� � ��� ���� ������!����������������+����&����,������������� ��� ��(��$�(� ��� ��

����� �������������� �������������&�������&����������-��� ������ �����������"'.,�/�"'0,����������� ������ ��������������������������&����1�� �����������

������� �������2��3�4� ��(���( ���������+��$����� ���������������� ����( ������������� ��5����6���&����.�� ������� ������� ������� �������� ������� ��4������������� ����

�� �� ����������( ������������ ����&������6�� ������������ ������������������( ���( �����!��� ��������� �����������&������0�������%������ �$������������������

�������������������������&������7�� ����������$���������%�����������

4.1. Calibration. The full model is calibrated to represent a relatively �stan-

dard� open economy New Keynesian model where the home region can rep-

resent either the average US state or a small European country like Ireland,

either of which are 2% of GDP of the whole monetary union (n = 0.02).

The multipliers estimated above do not have enough degrees of freedom to

allow me to estimate all the key parameters in the model (doing so would also

ignore other evidence from the literature on the degree of price stickiness etc).

Instead, my goal is to investigate the extent to which a reasonably standard NK

model can rationalize the multipliers estimated in the data. Most parameters

are taken from Nakamura and Steinsson (2014) who have a similar multi-region

model (see Online Appendix Table 2.1 for a full list of parameters).46

46Speci�cally, I calibrate to Nakamura and Steinsson's (2014) NK model with separablepreferences and no capital. One di�erence is that I follow Gali and Monacelli (2005) andassume constant returns to labor, rather than decreasing returns as NS do.

32

However, there are two main di�erences from Nakamura and Steinsson

(2014). First, my model includes a fraction of hand-to-mouth households,

which I calibrate to a third (ω = 1/3) based on evidence from Kaplan et al

(2014) for US, UK and Germany � which is in the middle of the range of esti-

mates in the literature (see Online Appendix Table 4.1). Second, I assume that

wages are sticky, and they are more rigid in Europe for the Irish counterfactual

than they are in the US (the only di�erence between the two calibrations). In

the US, nominal wages tend to be adjusted once a year on average, so I set

θw = 0.75 (as is standard in the literature). In Ireland, the average length

of collective bargaining agreements is 3 years (Du Caju et al 2008), so I set

θw = 0.912.47

4.2. Comparison of model and data. In an open economy model, care

needs to be taken to de�ate variables in the same way in the model and the

data, because the local prices and national prices di�er (as do the local CPI

and GDP de�ator). Most cross-region purchase multipliers in empirical papers

de�ate using a measure of national prices (eg Acconia et al 2014 among others)

as I do here.48

The true measure of �real GDP� in the home region, Yh, can be constructed

by de�ating nominal home GDP by the local GDP de�ator Ph. This is di�erent

from de�ating by the home CPI, which will yield Yh−(1−α)s (where s = Pf−Ph is the home terms of trade). In this paper, I de�ate by the national CPI, to

yieldmeasured GDP in the home region as Ymeas. = Yh−(1−n)s. An analogous

expression for measured labor income is: WLmeas. = wh + Lh − (1 − n)s. So

long as relative prices are relatively constant - such as in the NK model in

the short term - the choice of de�ator doesn't matter much. However, in the

neoclassical model (or in the NK model in the LR), transfers can lead to large

increases in Ymeas.and WLmeas. even though Yh falls, because decreases in Yh

are more than o�set by an increase in local prices (↓ s).49

47High rates of wage stickiness also help to produce recessions as persistent as they are inthe data (Section 5).48Exactly which national de�ator is used doesn't matter because of the presence of time�xed e�ects.49In the neoclassical model, markups and real product wages wh are constant, and henceWLmeas. = Ymeas. = Yh − (1 − n)s. Lower values of the Armington elasticity θT generate

33

Table 6 compares impact multipliers from the empirics with analogous mea-

sures from the NK and neoclassical models.50 For temporary transfers and

labor income, the cross-region transfer multiplier in the data is around 0.3

which is almost exactly the same as the full NK model. Empirical estimates

using GDP (0.39) are only half a standard error greater than those in the

NK model. In contrast, the non-response to a temporary transfer shock in

the neoclassical model is outside the 95% con�dence interval for labor income

and GDP. This is largely because the neoclassical model lacks hand-to-mouth

households, and as a result temporary transfers are saved and have little ef-

fect on the economy. The response of measured labor income to a permanent

transfer in the NK model is 1.21, which is only slightly below 1.39 as estimated

in the data (within 1/2 a SE). In the neoclassical model, real GDP falls by 0.5%

in response to a permanent transfer (−(1 + ϕ)−1 = 0.5 with ϕ = 1), but Ph

(st) increases (decreases) by around 1%, such that measured labor income in-

creases by 0.5%. Although this is borderline consistent with the data (right

on the 95% con�dence interval), the data is much more supportive of the NK

model and so I use it for the subsequent analysis.

4.3. Cross-region transfer multipliers in the full model. The RHS por-

tion of Table 6 presents impact multipliers as they are usually measured (de-

�ated by a local GDP de�ator) in the full NK model and neoclassical model

As foreshadowed in the results above, the New Keynesian model predicts an

increase in output initially by around 30c in the dollar in response to a tem-

porary transfer and by $1.1 in response to a permanent transfer. In contrast,

the neoclassical model predicts a small fall in output in response to a tempo-

rary transfer (most of which is saved), and a sizable fall in output by 50c in

response to a $1 permanent transfer (as households choose to �spend� some

larger movements in s and hence potentially larger increases in measured labor income orGDP. However, the fact that I am calibrating to small regions within a monetary union,rather than large countries, suggests a higher value of θT is appropriate. Nakamura andSteinsson (2014) argue that there is very little movement in the local CPI in response tolocal military purchases, and (consequently) get similar results when they de�ate by eithernational or state CPIs.50Empirical estimates are taken from Table 3 Col 5 (pooled temp tr, WL), Table 3 Col 7(pooled temp tr, GDP) and Table 4 Col 4 (perm SS tr).

34

Table 6. Cross-region transfer multipliers � Models vs Data

������� ������� �����

������������������ ������������� ���������������� ����������������

������� ���������������� ����������������

��������������� !"

!�#� !�$�%�

!�#$� !�#!�

!�$�

!���&!�'(�

!�#�

!�!�&!�(#�

!�'%

!�$%

!��%�

!�$%�

#������������� �"

���!

!

$�$$�

!�

��#�

!�'#&�$�#%�

)����*+��

�,���������"

)����*+��

�,���������"

-

-

-

-

!�!�

��$� !�'�

-

������������ ��������������� ���������������������������������������������������������� �������������������������

���������������������� ��������� !���������������������� ����������� !�"��������#�������������$��%����������

���� ���������#�������������������#�������������� !���������#������������������&�������������������������� !�����#���

����������������������� #�����������'���������������� �������������(�#��)�*��+�"�������������$��(�#��)�*��,�"������

�������$������(�#��+�*��-�"������(�$���������������.����*������������������������� ���/����������������0'��1�� !�2�

�����0'�������������������� ������/��&����3���4����5�6��������7+8�*9���������������������������������� ���������������

�������������������������������� ���"&���� ��#���4����������������� �����$���:�;���������#�������������������'1�

&�/������4������"�����&����1�&�/������4�����$���

�������������������������������

�������� !�� �������������"��������#������

������ ����� ���

����������$

,�.�������

�/��������"

*+�������

�/��������"

,�.�������

�/��������"

!�!�

$������������� !��#�"�

01������2������3�

-!��

!�#�

!�#�

-!�!�

)����*+��

�,���������"

-!�!4

-

of their extra income on extra leisure). The multipliers using the simple an-

alytical model (shown in brackets in columns B and D) are quite similar so

long as transfers are not too persistent. For one-o� transfers, the size of the

cross-region transfer multiplier is similar to the self-funded component, but as

the transfer becomes highly persistent (ρ → 1), cross-region transfer multi-

pliers more than triple in size due to the larger pure cross-region component

(self-�nanced multipliers go to zero, see Online Appendix Figure 4.3).51

5. Implications for Europe: the gains from of US-Style

Transfer Union

In this section I evaluate the extent to which US-style countercyclical cross-

region transfers could reduce the severity of a recession on the European pe-

riphery similar to that experienced by Ireland over 2009-12, as well as reducing

51Impulse responses in Online Appendix Figure 4.4 show that in the long run, prices adjustin the NK model yielding a value for output similar to that in the neoclassical model.

35

output volatility more generally. As Europe doesn't have countercyclical cross-

country transfers, the e�ect of such a policy can only be evaluated in a model.

The results are, of course, dependent on the type of model used; which is why

we needed to inform this choice using the empirical evidence on the e�ect of

cross-region transfers in the data.

Simulating a deep and persistent regional recession on the Eu-

ropean Periphery (�Ireland�) The model used to study countercyclical

cross-region transfers in Europe is identical to the New Keynesian model for

the US, except that wages are more sticky in Ireland than the US. Following

Farhi et al (2014), I subject the regional economy to a shock that increases the

spread on borrowing by home consumers. This leads Ricardian households to

save more, reducing consumption demand for the home good and sending the

regional economy into recession. I calibrate the model to match the initial fall

in Irish GDP of -3.78% over 2009Q1-Q3 relative to that of the Euro Area (with

no countercyclical cross-region transfers), given a transfer union is supposed

to insure only against asymmetric shocks (Table 7 row 1). The spread shock

does a good job of generating a deep and persistent recession, as in the data

(Online Appendix Figure 4.5)52

Adding cross-region transfers In a counterfactual simulation I assume

that that the residents of the regional economy receive a lump-sum transfer

from the rest of the monetary union equal to 30% of any fall in GDP, based

on empirical evidence quoted in the introduction that US net federal transfers

are around 20-40% of the fall in the income of each state. Table 7 Column

C1 shows that this policy does modestly reduce the size of the fall in GDP

following the spread shock: instead of GDP falling by 3.76%, it falls by 3.33%

(i.e. the recession is 0.44ppt or 12% shallower).

Stabilization and the size of the cross-region transfer multiplier

The ability of the cross-region transfer to smooth output depends on the size

of the cross-region transfer multiplier. The initial fall in GDP is 3.76%, which

52The shock is an increase in spreads by 1.5ppts (annualized), with quarterly persistence of0.95. This is smaller shock than in Farhi et al (2014) because of a smaller inter-temporalelasticity of substitution in that paper (they also calibrate to Spain). The persistence of thefall in GDP (relative to the rest of the EMU) is around ρ = 0.935. The shock leads to alarge improvement in the trade balance, as observed empirically.

36

generates a cross-region transfer of about 1.1% of GDP (30% × 3.76%).53 The

fall in output has persistence of around 0.935, which from Table 6 suggests

that the cross-region transfers generated have an impact multiplier of 0.46.

One also needs to take account of later round e�ects � a smaller fall in output

also generates smaller countercyclical transfers � which reduce the e�ective

size of the multiplier to 0.4.54 As such, the countercyclical transfer boosts

output by 0.4× 1.1=0.44ppts of GDP which is essentially the same change in

output as in simulations in Table 7.

Comparison with self-�nanced transfers An alternative policy avail-

able to members of the EMU is to conduct their own self-funded countercycli-

cal transfers (equivalent to debt-funded transfers). To make this comparable

with countercyclical cross-region transfers, I also assume that this self-funded

transfer is 30% of any fall in GDP.

Column C2 of Table 7 shows that the initial fall in output is only slightly

larger with self-�nanced countercyclical transfers than with cross-region trans-

fers (-3.48% vs -3.33%). Quantitatively, self-�nanced transfers provide around

2/3 of the output smoothing bene�ts of cross-region transfers. This was fore-

shadowed in Table 6, where the impact multiplier of self-�nanced transfers

with ρ = 0.935 is around 60% that of cross-region transfers.

Why the limited stabilization bene�ts of cross-region transfers?

The analytical results in Table 1 suggest that countercyclical self-funded and

cross-region transfers have similar smoothing properties because the pure cross-

region transfer multiplier is small [α/(1− α)][(1− β)/(1− βρ)]. In turn, this

is because (i) countercyclical transfers at business-cycle frequencies are tem-

porary (ρ too low in (1− β)/(1− βρ)), and (ii) European economies are rea-

sonably open (α too small in α/(1−α)). The lack of persistence is important

because only the Ricardian HHs respond di�erently to transfers from Dublin

vis-a-vis Brussels (HtM HHs will spend both), but Ricardian HHs save tem-

porary transfers. With transfers of a similar persistence to those considered

here (ρ = 0.935), the analytical model Section 2 suggests that only 13% of

53The gains from stabilization are modest in part because of the modest size of counter-cyclical cross-region transfers themselves. Larger countercyclical cross-region transfers �for example as part of regional bank bailouts � might have larger e�ects.54Latter round e�ects scale the multiplier by (1 + 0.46× 0.3)−1 = 0.88.

37

the pure cross-region transfer is spent. With α = 0.69, the ampli�cation of

this spending (with rigid prices) is only α/(1−α) = 2.2, such that the �extra�

impact multiplier on cross-region transfers is only 0.3 above what would be

available with self-�nanced transfers. Allowing for sticky (rather than rigid)

prices reduces the gain to 0.2. As such, at business cycle frequencies more than

half of the cross-region transfer multiplier (of 0.46) is due to the self-funded

component, which explains why a model consistent with sizable cross-region

transfer multipliers in the data can still generate modest gains from a �scal

union. Of course, a permanent transfer would be spent � generating a much

larger pure cross-region transfer multiplier (Online Appendix Figure 4.3) � but

then that transfer wouldn't be countercyclical.

Extension: Output volatility Panel B performs an analogous exercise,

but for general output volatility rather than for a speci�c crisis episode. The

proportional reduction in output volatility is almost identical to that in the

crisis experiment.55

Table 7. Irish data and countercyclical transfersA. Data* B. Model** C. Model with 30% GDP Countercyclical Transfers

No Transfers C1. Cross-region Transfers C2. Self-funded Transfers

(1) Crisis Fall in GDP -3.78% -3.76% -3.33% -3.48%Di�. Rel. No Transfer: 0.44ppt (11.7%) 0.28ppt (7.5%)

(2) SD(∆lnGDP ) 1.90% 1.88% 1.66% 1.74%Di�. Rel. No Transfer: 0.22ppt (11.7%) 0.14ppt (7.5%)

* Crisis: GDP in 2009Q3 relative to 2009Q1 for Ireland (relative to the EMU as a whole).

** Driven by a annualized spread shock of 1.5% (Row 1) or 0.75% (Row 2). GDP Data

from Eurostat.

6. Conclusion

In this paper I investigate the size of the cross-region transfer multiplier

in the US, which is novel given this multiplier is conceptually di�erent from

other multipliers estimated in the literature. Despite �nding that cross-region

transfers signi�cantly boost short-run growth in the states receiving them in

the US, I �nd modest gains to regional stabilization from a counterfactual US-

style transfer union in Europe. This surprising result is in part because much

55Speci�cally, I simulate the model for 1000 quarters with business cycles driven by a randomsequence of spread shocks (but halving the shock standard deviation as the 2009-12 Irishrecession was unusually severe). In the baseline, this exercise matches the standard deviationof quarterly Irish GDP growth over 1999-2015 of 1.9%.

38

of the gains from a federal transfer system at business cycle frequencies can

be captured through local self-�nanced countercyclical �scal policy. As such,

policymakers seeking to stabilize business cycles on the European periphery

are likely better placed to �rst ensure that national governments are able to

respond countercyclically to shocks themselves, before engaging in the more

daunting task of implementing an EMU-wide transfer union.

An online appendix with supplementary material is available at

https://sites.google.com/site/stevenpennings/PenningsCrossRegionTransfersOnlineAppendix.pdf

References

[1] Acconia A., G. Corsetti and S. Simonelli (2014), �Ma�a and Public Spending: Evidence

on the Fiscal Multiplier from a Quasi-Experiment�, American Economic Review, 104(7),

pp 2185-2209

[2] Bartik, T. (1991), �Who Bene�ts from State and Local Economic Development Poli-

cies?� Kalamazoo, MI: W.E. Upjohn Institute for Employment Research.

[3] Bayoumi T. and P. Masson (1995), �Fiscal �ows in the United States and Canada:

Lessons for monetary union in Europe�, European Economic Review 39: 253-274

[4] BEA (2009): Regional Quarterly Report (Jan 2009)

http://www.bea.gov/scb/pdf/2009/01%20January/0109_regqtrlyreport.pdf

[5] Chodorow-Reich G. (2017) �Geographic Cross-Sectional Fiscal Spending Multipliers:

What Have We Learned?�, NBER Working Paper No. 23577

[6] Chodorow-Reich, G., L. Feiveson, Z. Liscow, and W. Woolston. (2012). �Does State

Fiscal Relief During Recessions Increase Employment? Evidence from the American

Recovery and Reinvestment Act.� American Economic Journal: Economic Policy 4(3):

118-145

[7] Clemens J. and S. Miron (2012), �Fiscal Policy Multipliers on Subnational Government

Spending�. American Economic Journal: Economic Policy, 4(2): 46-68.

[8] Du Caju, E. Gautier, D. Momferatou and M. Ward-Warmedinger (2008), �Institutional

Features of Wage Bargaining in 23 European Countries, the US and Japan�, European

Central Bank Working Paper No. 974

[9] Farhi E. and I. Werning (2016) �Fiscal Multipliers: Liquidity Traps and Currency

Unions�, Handbook of Macroeconomics Vol 2, pp 2417-2492

[10] Farhi E. and I. Werning (2017) �Fiscal Unions�, American Economic Review 107 (12),

pp 3788-3834

[11] Farhi E., Gopinath, G. and O. Itskhoki (2014), �Fiscal Devaluations�, Review of Eco-

nomic Studies 81, 725�76039

[12] Feyrer J. and B. Sacerdote (2013), �How Much Would US Style Fiscal Integration Bu�er

European Unemployment and Income Shocks? (A Comparative Empirical Analysis)�,

American Economic Review: Papers & Proceedings 103(3): 125�128

[13] Galí J.and T. Monacelli (2005) �Monetary Policy and Exchange Rate Volatility in a

Small Open Economy� Review of Economics and Statistics 72(3): 707-734

[14] Galí J. and T. Moncaelli (2008), �Optimal monetary and �scal policy in a currency

union�, Journal of International Economics 76: 116�132

[15] Giambattistia E. and S. Pennings (2017) �When is the government transfer multiplier

large?�, European Economic Review, Volume 100, November 2017, pp 525-543

[16] Hseih C-T, (2001) �Do Consumers React to Anticipated Income Changes? Evidence

from the Alaska Permanent Fund�, American Economic Review, 93(1), pp 397-405

[17] Johnson D., Parker J. and N. Souleles (2006), �Household Expenditure and the Income

Tax Rebates of 2001,� American Economic Review, 96(5), pp 1589-1610.

[18] Kaplan G. and G. Violante (2014) �A Model of the Consumption Response to Fiscal

Stimulus Payments�, Econometrica, 82(4), pp 1199-1239

[19] Kaplan G., G. Violante and J. Weidner (2014) �The Wealthy Hand-to-Mouth�, Brook-

ings Papers on Economic Activity, (Spring 2014), pp 77-138

[20] Nakamura E. and J. Steinsson (2014), Fiscal Stimulus in a Monetary Union: Evidence

from U.S. Regions, American Economic Review 104(3): 753-92.

[21] Oh H. and R. Reis (2012). �Targeted transfers and the �scal response to the great

recession�, Journal of Monetary Economics, 59(S): S50-S64.

[22] Parker J., N. Souleles, D. Johnson, and R. McClelland, (2013) "Consumer Spending

and the Economic Stimulus Payments of 2008", American Economic Review, 103(6),

pp 2530-53

[23] Romer, C., and D. (2016), "Transfer Payments and the Macroeconomy: The E�ects of

Social Security Bene�t Increases, 1952-1991." American Economic Journal: Macroeco-

nomics, 8(4): 1-42.

[24] Sala-i-Martin X. and J. Sachs (1991), �Fiscal Federalism and Optimal Currency Areas:

Evidence for Europe from the United States�, NBER Working Paper No. 3855

[25] Suarez J. C. and P. Wingender (2016), �Estimating Local Fiscal Multipliers�, mimeo

[26] Wilcox D. (1989), "Social Security Bene�ts, Consumption Expenditure, and the Life

Cycle Hypothesis," Journal of Political Economy, 97(2): 288-304.

40