Partisanship and Fiscal Policy in Economic Unions: Carlino, Drautzburg, Inman & Zarra (2023)

What this is. The core results, the New Keynesian two-state monetary union model, and the regression discontinuity design that identifies partisan differences in state governors’ propensity to spend federal intergovernmental (IG) transfers: enough to understand what the paper found and how, without reading all 37 pages. To replicate or extend it, read the full source at doi:10.1257/aer.20210147.

TL;DR

The paper documents that the party of the state governor is a source of heterogeneity in how federal IG transfers affect the macroeconomy. Using a regression discontinuity design on close gubernatorial elections, it finds Republican governors spend 0.29 percent less (elasticity) per 1 percent increase in IG aid than Democratic governors, and instead reduce debt immediately and cut top income tax rates with a two-year lag. A calibrated New Keynesian model of a two-state monetary union with these estimated partisan fiscal rules implies an intertemporal trade-off: the IG transfer impact multiplier is 1.22 when all governors behave like Democrats but falls to 0.64 under equal partisan representation, a difference of 0.58; the long-run discounted multiplier, however, is 0.16 higher under equal partisanship (0.80 vs. 0.64) because delayed Republican tax cuts stimulate future output. The paper extends Besley and Case (2003), who documented partisan spending differences using OLS, to a causal RDD design, and adds the aggregate macroeconomic implications via a calibrated NK model. The partisan differences are only statistically significant in the post-Reagan era and grew with national polarization.

Core results

Magnitudes are as reported; preferred estimates use the robust RDD estimator (quadratic MOV polynomial, state x party and year x party fixed effects, bandwidth 10 percentage points). Locators point to the published version.

#	Result	Locator	Magnitude
R1	Republican governors spend less per 1% increase in IG aid than Democratic governors (MPS elasticity partisan difference)	Table 2, col. 5, p. 713	$\gamma_{r,\text{inc}} = -0.290$ (SE 0.098); t approximately -3.0 with party FE
R2	Republican governors cut spending more per 1% decrease in IG aid than Democrats (asymmetric response to cuts)	Table 2, col. 5, p. 713	$\gamma_{r,\text{cut}} = 0.524$ (SE 0.239); statistically significant at 5%
R3	Dollar-for-dollar: Democratic MPS near $1.35 per $1 IG increase; Republican partisan difference -$1.58, leaving Republican total near zero	Table 4, col. 2, p. 716	Democrat: $1.346 (SE 0.600); Rep diff: -$1.576 (SE 0.892); Republican total not different from zero
R4	Republican states immediately lower debt outstanding relative to Democratic states; effect persists for at least 3 years	Figure 7, Panel A, p. 723	Debt elasticity partisan difference approximately -0.25 per 1% IG increase; statistically significant
R5	Republican top marginal income tax rates are lower, but only with a 2-year lag; no partisan tax difference on impact	Figure 7, Panel B, p. 723	Log tax-rate elasticity partisan difference approximately -1% after 2 years; zero on impact and year 1
R6	Democratic-led states show higher GDP growth per dollar of IG aid, with the gap persisting up to 3 years	Figure 8, p. 724	Partisan GDP growth elasticity (Rep - Dem): approximately -0.334% per 1% IG increase; marginally significant
R7	IG transfer impact multiplier falls by 0.58 under equal partisanship vs. all-Democratic baseline	Table 7, rows 1-2, col. 1-3, p. 732	Impact multiplier: 1.22 (all Democrats) vs 0.64 (equal partisanship); difference -0.58 (SE 0.33)
R8	Long-run PDV multiplier is 0.16 higher under equal partisanship than all-Democratic, because of delayed Republican tax cuts	Table 7, rows 1-2, col. 1-3, p. 732	Long-run multiplier: 0.64 (all Democrats) vs 0.80 (equal partisanship); difference +0.16 (SE 0.09)
R9	Predetermined covariates show no significant Democratic-Republican differences in close elections	Table 1, col. 6, p. 710	None of the robust equality t-statistics is significant at the 10% level; reported t-statistics range from -1.3 to 1.4; close-election sample has 299 Democratic and 333 Republican state-years
R10	Linear MSE RDD estimates also show lower spending responses for Republican governors on IG increases	Table 2, cols. 1 and 4, p. 713	Republican interaction: -0.260 (SE 0.104) without fixed effects and -0.272 (SE 0.075) with party-specific fixed effects
R11	Full-sample OLS estimates attenuate the partisan difference in spending responses	Table 2, cols. 3 and 6, p. 713	Republican interaction for positive IG growth: -0.093 (SE 0.018) without fixed effects and -0.068 (SE 0.021) with fixed effects
R12	The positive-aid partisan spending difference persists after excluding welfare and highway aid	Table 3, col. 2, p. 715	Republican x positive IG growth: -0.251 (SE 0.077), robust RDD
R13	Republicans cut spending more after aid decreases when the Census measure excludes welfare and highway aid	Table 3, col. 2, p. 715	Republican x negative IG growth: 0.438 (SE 0.175), robust RDD
R14	The positive-aid partisan spending difference persists with FAADS-defined nonmatching aid	Table 3, col. 4, p. 715	Republican x positive IG growth: -0.404 (SE 0.134), robust RDD, 1983-2010
R15	The FAADS estimate for partisan spending responses to aid cuts is not statistically significant	Table 3, col. 4, p. 715	Republican x negative IG growth: 0.186 (SE 0.140), robust RDD, 1983-2010
R16	Party- and direction-weighted estimates imply an aggregate flypaper effect close to one dollar per dollar of aid	Text p. 717	Weighted spending response: $1.051 per $1 change in aid; $0.53 for increases and $1.80 for cuts, compared with the simple OLS estimate of $0.81
R17	Spending responses differ by the size of the IG increase at the election cutoff	Figure 3, p. 712; text p. 713	Above the 75th percentile, IG growth averages 18.9% ($114 per resident); below it, 4.6% ($25); Democrats’ expenditure response is higher at the cutoff, while Republican confidence intervals overlap
R18	Partisan differences in spending increases extend beyond close elections, up to MOV 30 percentage points	Figure 4, panels A-B, pp. 717-718	Rolling-window partisan estimates remain negative through
R19	Candidate ideological differences do not vary significantly with election closeness	Figure 5 and text p. 719	p = 0.18 in the full sample; p = 0.99 after excluding elections with MOV above 40 percentage points
R20	High unemployment does not materially change the positive-aid partisan spending difference	Table 5, col. 2, p. 719	Republican x positive IG x unemployment-stress interaction: -0.009 (SE 0.15), not significant; baseline partisan interaction: -0.272 (SE 0.16)
R21	A recent state bond downgrade does not materially change the positive-aid partisan spending difference	Table 5, col. 4, p. 719	Republican x positive IG x downgrade interaction: 0.329 (SE 0.27), not significant; baseline partisan interaction: -0.313 (SE 0.11)
R22	Greater national polarization strengthens the Republican-Democratic spending difference for aid increases	Eq. 3, text pp. 720-721	Republican x positive IG growth x lagged polarization: -0.13 (SE 0.06); before 1980 implied difference approximately 0.01 (SE 0.08), after 1990 approximately -0.25 (SE 0.09)
R23	Polarization does not significantly change the partisan spending response to aid cuts	Eq. 3, text pp. 720-721	Republican x negative IG growth x lagged polarization: 0.04 (SE 0.06), not significant
R24	Partisan estimates do not differ significantly across US census divisions	Figure 6, p. 722	Division mean, inverse-sample-weighted, population-weighted, and pooled estimates show no significant differences
R25	The GDP response to IG cuts has the opposite sign from the increase response but is marginally insignificant	Text p. 724	Republican-Democratic cumulative GDP-growth difference is opposite in sign and similar in magnitude to the -0.334% increase estimate, but marginally insignificant
R26	Equal partisanship lowers the model’s average state-spending response to an IG shock	Figure 9, panel A, text p. 730	Spending difference relative to all-Democratic preferences: -0.65 percentage points of GDP; 90% CI (-0.04, -1.26)
R27	Equal partisanship lowers the model’s average state tax rate after an IG shock	Figure 9, panel B, text p. 730	Peak difference relative to all-Democratic preferences: -5.7%; 90% CI (-0.4%, -11.0%)
R28	Equal partisanship lowers impact state output, followed by higher output from year 3	Figure 9, panel C, text pp. 730-731	Impact output difference: -0.5 percentage points of GDP; 90% CI (-0.0%, -0.9%); output is 0.05% higher from year 3
R29	The impact multiplier remains lower with equal partisanship under OLS-based MPS estimates	Table 7, p. 732	Difference from all-Democratic benchmark: -0.26 (SE 0.07)
R30	The long-run multiplier remains higher with equal partisanship under OLS-based MPS estimates	Table 7, p. 732	Difference from all-Democratic benchmark: +0.07 (SE 0.02)
R31	Under GHH preferences, equal partisanship still lowers the impact multiplier	Table 7, p. 732	Difference from all-Democratic benchmark: -0.41 (SE 0.23)
R32	Under GHH preferences, equal partisanship raises the long-run multiplier	Table 7, p. 732	Difference from all-Democratic benchmark: +1.33 (SE 0.78)
R33	The changing Republican governor share is associated with lower simulated IG impact multipliers	Figure 10, panels A-B, text p. 733	Republican share rises from 30% in 1984 to 67%; maximum impact-multiplier difference is -0.42 in 2018 relative to 1984, with 90% CI (-0.03, -0.80)
R34	Long-run multipliers rise less over time than impact multipliers as the Republican share grows	Figure 10, panel C, text p. 733	Long-run multiplier increases by at most 0.13 from its 1984 benchmark; reported interval 0.01 to 0.18
R35	A density test finds no evidence that governors or voters sort around the close-election cutoff	Text p. 710, fn. 13, citing online Appendix Figure D1.1	McCrary test fails to reject continuity of the MOV running variable; the cited main-text note gives no statistic or p-value

Overall (paper’s conclusion). Partisanship of state governors alters how federal IG transfers translate into macroeconomic activity. More Democratic governors raise the short-run IG impact multiplier; more Republican governors raise the long-run discounted multiplier by channeling aid into delayed tax cuts. The resulting intertemporal trade-off means the optimal partisan composition of governors depends on whether the federal government prioritizes short-run or long-run stimulus. As national polarization increased after 1980, partisan differences in MPS widened and became statistically detectable, implying the aggregate consequences of partisanship for IG policy have grown over time.

Theory / model

The model is a two-state New Keynesian monetary union with home state size $n$ and foreign state size $1-n$. The states are symmetric except for their governors’ partisan MPS. Each state has constrained households (fraction $1-\mu$, hand-to-mouth) and unconstrained households (fraction $\mu$, with complete markets and bond holdings). Households in each state have utility (eq. 4, p. 724):

$$u(C_t, N_t, G_{st,t}) = \frac{1}{1-1/\varepsilon_C} C_t^{1-1/\varepsilon_C} - \kappa_i^N \frac{N_t^{1+1/\varepsilon_N}}{1+1/\varepsilon_N} + v(G_{st,t}) \tag{4}$$

where $C_t$ is aggregate consumption, $N_t$ labor supply, $G_{st,t}$ state government services, $\varepsilon_C$ the elasticity of intertemporal substitution, $\varepsilon_N$ the Frisch elasticity, and $\kappa_i^N$ a leisure weight that differs by household type $i \in \{c, u\}$. The budget constraint for unconstrained agents is (eq. 5, p. 725):

$$P_t C_t^u + B_t^u \leq (1 - \tau_t^f - \tau_t^{st}) W_t N_t^u + B_{t-1}^u R_{t-1}^n + Pr_t + Tr_t^u \tag{5}$$

Intermediate goods firms produce using labor only under decreasing returns to scale (eq. 6, p. 725):

$$y_{h,t}(z) = A_t \times N_t(z)^{1-\alpha} \tag{6}$$

where $\alpha \in (0,1)$ is the fixed-factor share. Firms face Calvo price stickiness: with probability $\xi$ a firm cannot reoptimize; calibrated $\xi = 0.735$ at annual frequency to match a peak defense spending multiplier of 0.8.

State government fiscal rules. Government consumption responds to IG transfers through the partisan MPS $\psi_{IG}$ (eq. 7, p. 725):

$$G_{st,t} = \psi_{IG}\!\left(\frac{IG_t}{P_t} - \bar{IG}\right) + G_{st,t}^x \tag{7}$$

The Republican home state uses $\psi_{IG} = 0$ (calibrated: Republican spending effect not significantly different from zero) while the Democratic foreign state uses $\psi_{IG}^* = 1.576$ (robust RDD estimate from Table 4, col. 2). State tax rates adjust smoothly to service debt and to cover expenditure net of IG revenue (eq. 8, p. 726):

$$\tau_{st,t} = \rho_\tau \tau_{st,t-1} + (1-\rho_\tau)\!\left\{\bar{\tau}_{st} + \psi_{st,b}\!\left[(R_{t-1}^n - 1)\frac{b_{st,t-1}}{\Pi_t} - (\bar{R}^n - 1)\frac{\bar{b}_{st}}{\bar{\Pi}}\right] + \psi_{st,E}\!\left[G_{st,t-1} - \bar{G}_{st} - \frac{IG_{t-1} - \bar{IG}}{P_t}\right]\right\} \tag{8}$$

Calibrated: persistence $\rho_\tau = 0.35$, debt loading $\psi_{st,b} = 0.99$, expenditure-net-of-IG loading $\psi_{st,E} = 0.85$ (Table 6, p. 729). This yields no tax change in year 1, a fall after year 2, and reversal toward zero by year 3, consistent with the micro estimates in Figure 7.

Monetary policy. The common central bank follows a Taylor rule reacting to union-wide inflation and the output gap (eq. 9, p. 726):

$$R_t^n = \left(\frac{\bar{\Pi}}{\beta}\right)^{\rho_r}\!\!\left[\!\left(\frac{\Pi_t^{agg}}{\bar{\Pi}}\right)^{\psi_{r,\pi}}\!\!\left(\frac{Y_t^{agg}}{Y_t^{flex}}\right)^{\psi_{r,y}}\right]^{1-\rho_r} \tag{9}$$

where $\Pi_t^{agg} = n\Pi_t + (1-n)\Pi_t^*$ and $Y_t^{agg} = nY_t + (1-n)Y_t^*$ are population-weighted. Calibrated: $\rho_r = 0.75$, $\psi_{r,\pi} = 1.5$, $\psi_{r,y} = 0.5$. The model builds on Nakamura and Steinsson (2014) by adding heterogeneous state fiscal rules that encode partisan differences. It is linearized around a deterministic steady state and solved numerically with Dynare (Adjemian et al. 2011).

Method

The identification design exploits the regression discontinuity in gubernatorial elections. Near a 50-50 vote outcome (margin of victory MOV = 0), the party of the winning governor is as good as randomly assigned. This yields a consistent estimate of partisan differences in MPS conditional on IG aid changes, without relying on the assumption that IG aid is itself randomly allocated. The design follows Lee, Moretti, and Butler (2004) and Ferreira and Gyourko (2009), who used close-election RDDs for US House representatives and mayors.

MOV is signed positive (negative) for a Democratic (Republican) winner. Standard errors are two-way clustered by state and year throughout. The bandwidth $\bar{m}$ on absolute MOV is chosen by cross-validated MSE minimization using linear MOV controls ($q = 1$). The preferred “robust” estimator uses quadratic MOV controls ($q = 2$) with the same bandwidth; the preferred bandwidth is 10 percentage points with fixed effects and 11 without. Internal validity is assessed via Table 1 (p. 710), which shows no significant differences in pre-determined covariates across Democratic and Republican winners in close elections. External validity is assessed in Section III: rolling-window OLS shows the same partisan pattern for margins up to 30 percentage points, and candidate ideological differences are uncorrelated with closeness.

Empirical specifications

Baseline panel regression (eq. 1, p. 707). Changes in log state expenditure $\Delta \ln E_{s,t}$ in state $s$, fiscal year $t$, are regressed on log changes in IG aid interacted with the governor’s party lagged one year ($\text{Rep}_{s,t-1} = 1$ for Republican):

$$\Delta \ln E_{s,t} = (\gamma_{0,\text{inc}} + \gamma_{r,\text{inc}} \times \text{Rep}_{s,t-1})\Delta \ln IG_{s,t}^{\text{inc}} + (\gamma_{0,\text{cut}} + \gamma_{r,\text{cut}} \times \text{Rep}_{s,t-1})\Delta \ln IG_{s,t}^{\text{cut}} + \mu_0 + \mu_r \times \text{Rep}_{s,t-1} + \text{fixed effects} + e_{s,t} \tag{1}$$

where $\Delta \ln IG_{s,t}^{\text{inc}} = \max\{0, \Delta \ln IG_{s,t}\}$ captures IG increases and $\Delta \ln IG_{s,t}^{\text{cut}} = \min\{0, \Delta \ln IG_{s,t}\}$ captures decreases. The Democratic MPS elasticity is $\gamma_{0,\text{inc}}$ for increases and $\gamma_{0,\text{cut}}$ for decreases; the Republican partisan difference is $\gamma_{r,\text{inc}}$ (expected negative: Republicans spend less on increases) and $\gamma_{r,\text{cut}}$ (expected positive: Republicans cut more when aid falls). Fixed effects are state x party and year x party in the preferred specification.

RDD specification (eq. 2, p. 708). Equation (1) is augmented with a polynomial of order $q$ in the MOV and its interactions with IG changes, estimated on observations within bandwidth $|MOV_{s,t-1}| \leq \bar{m}$:

\Delta \ln E_{s,t} = (\gamma_{0,\text{inc}} + \gamma_{r,\text{inc}} \times \text{Rep}_{s,t-1})\Delta \ln IG_{s,t}^{\text{inc}} + (\gamma_{0,\text{cut}} + \gamma_{r,\text{cut}} \times \text{Rep}_{s,t-1})\Delta \ln IG_{s,t}^{\text{cut}} $$ $$+ \sum_{\hat{s} \in \{\text{cut},\text{inc}\}}\sum_{p=1}^{q} (\gamma_{0,\hat{s},m,p} + \gamma_{r,\hat{s},m,p} \times \text{Rep}_{s,t-1})\Delta \ln IG_{s,t}^{\hat{s}} \times MOV_{s,t-1}^p $$ $$+ \sum_{p=1}^{q} (\beta_{0,m,p} + \beta_{r,m,p} \times \text{Rep}_{s,t-1})MOV_{s,t-1}^p + \mu_0 + \mu_r \times \text{Rep}_{s,t-1} + \text{fixed effects} + e_{s,t} \tag{2}

The RDD identifies partisan differences $(\gamma_{r,\text{inc}}, \gamma_{r,\text{cut}})$ but not the Democratic baseline MPS $(\gamma_{0,\text{inc}}, \gamma_{0,\text{cut}})$. The paper benchmarks Republican MPS to zero (consistent with the near-zero robust estimate) and sets Democratic MPS to 1.576 (from dollar-level Table 4, col. 2). Headline results are R1 and R2 above (Table 2, cols. 5 and 2 respectively).

Debt and tax rate specifications. Equations (1) and (2) are re-estimated replacing $\Delta \ln E_{s,t}$ with the log level of total debt outstanding and the log of the state top marginal income tax rate, estimated at horizons 0-3 years (Figure 7, p. 723). These specifications produce R4 and R5: Republicans lower debt immediately; tax cuts arrive with a two-year lag. Results R4-R5 do not appear in a formal regression table; Figure 7 plots the RDD coefficient path with 68% and 90% pointwise confidence bands.

State GDP specification. Equation (2) with cumulative GDP growth as the dependent variable produces R6 (Figure 8, p. 724): a 0.334 percent Democratic advantage in GDP growth per 1 percent increase in IG aid, which persists for up to three years.

Debt and tax horizon specifications (written out from §IV-A, p. 722). The authors re-estimate the RDD equation at horizons $h = 0,1,2,3$, replacing expenditure growth with the change in log total state debt or the log top marginal tax rate. The displayed form below makes that replacement explicit; it is not numbered in the paper:

$$\Delta_h \ln Y_{s,t} = (\gamma_{0,inc} + \gamma_{r,inc} \text{Rep}_{s,t-1})\Delta \ln \text{IG}_{s,t}^{inc} + (\gamma_{0,cut} + \gamma_{r,cut} \text{Rep}_{s,t-1})\Delta \ln \text{IG}_{s,t}^{cut} + \text{MOV polynomial and interactions} + \text{party fixed effects} + e_{s,t,h}$$

Here $Y_{s,t}$ is total state debt outstanding or the top marginal income tax rate, and $\Delta_h$ denotes the outcome response at horizon $h$ to current IG growth. The preferred debt and tax paths use quadratic MOV controls; standard errors are clustered by state and year. The sample covers 1983-2014, with the close-election bandwidth selected for each specification. These are Figure 7’s plotted coefficients, not a separately printed regression table.

Fiscal-stress interaction specification (written out from Table 5, p. 719). The paper interacts the aid-growth terms and party differences in equation (2) with two stress measures, separately. Table 5 defines the unemployment interaction variable as $IA_{s,t}=\min\{0, U_{s,t}-U_{s,median}\}$, while the accompanying text describes it as a dummy for unemployment above the state median; these definitions conflict in the PDF. The other specification uses a bond-downgrade dummy. The additional terms are:

$$\begin{aligned} \Delta \ln E_{s,t} ={}& (\gamma_{0,inc} + \gamma_{r,inc} \text{Rep}_{s,t-1})\Delta \ln \text{IG}_{s,t}^{inc} \\ &+ (\gamma_{0,cut} + \gamma_{r,cut} \text{Rep}_{s,t-1})\Delta \ln \text{IG}_{s,t}^{cut} \\ &+ (\delta_{0,inc} + \delta_{r,inc} \text{Rep}_{s,t-1})\Delta \ln \text{IG}_{s,t}^{inc} \text{IA}_{s,t} \\ &+ (\delta_{0,cut} + \delta_{r,cut} \text{Rep}_{s,t-1})\Delta \ln \text{IG}_{s,t}^{cut} \text{IA}_{s,t} \\ &+ (\lambda_0 + \lambda_r \text{Rep}_{s,t-1})\text{IA}_{s,t} \\ &+ \text{MOV polynomials and their interactions} + \text{party fixed effects} + e_{s,t}. \end{aligned}$$

These are two separate specifications, with state-by-party and year-by-party fixed effects and standard errors clustered by state and year. The unemployment sample has 630 observations, a 10 percentage-point MOV bandwidth, and 1983-2014 coverage; the downgrade sample has 676 observations, an 11-point bandwidth, and the same period (Table 5). The paper does not print a numbered equation for these interactions.

State GDP response to aid cuts (written out from §IV-B, p. 724). For the Figure 8 horizon estimates, equation (2) uses cumulative state GDP growth as the dependent variable and the positive or negative IG-growth terms as the regressors. This is the same RDD structure, with cumulative GDP growth replacing expenditure growth; the sample is 1983-2014, MOV controls are quadratic, and standard errors are clustered by state and year. The analogous aid-cut response is opposite in sign to the aid-increase response and marginally insignificant (text p. 724).

Polarization extension (eq. 3, p. 720). Interacting partisan MPS differences with a political polarization index $PPC_{t-1}$ (normalized; from Azzimonti 2018) shows that partisan MPS differences grew with polarization: the interaction is $-0.13 \times PPC_{t-1}$ (SE 0.06) for IG increases. Before 1980, $PPC_{t-1}$ averaged 1.1 standard deviations below the mean, implying near-zero partisan differences (consistent with the pre-Reagan evidence). After 1990, $PPC_{t-1}$ averaged 0.8 standard deviations above the mean, implying partisan differences of approximately $-0.25$ (standard error 0.09).

The printed polarization regression gives the estimated coefficients and their standard errors in brackets (eq. 3, p. 720). It covers 1964-2014, includes fixed effects and the interactions between the MOV polynomial, aid, polarization, and party, and clusters standard errors by state and year:

$$\begin{aligned} \Delta \ln E_{s,t} ={}& (0.20_{[0.05]} + 0.02_{[0.05]} \text{PPC}_{t-1})\Delta \ln \text{IG}_{s,t}^{inc} \\ &+ (-0.15_{[0.07]} - 0.13_{[0.06]} \text{PPC}_{t-1})\text{Rep}_{s,t-1}\Delta \ln \text{IG}_{s,t}^{inc} \\ &+ (0.04_{[0.01]} - 0.01_{[0.04]} \text{PPC}_{t-1})\Delta \ln \text{IG}_{s,t}^{cut} \\ &+ (0.17_{[0.07]} + 0.04_{[0.06]} \text{PPC}_{t-1})\text{Rep}_{s,t-1}\Delta \ln \text{IG}_{s,t}^{cut} \\ &+ (\text{MOV}, \text{MOV}^2) \times \text{IG} \times \text{PPC}_{t-1} \times \text{party interactions} \\ &+ \text{fixed effects} + e_{s,t} \end{aligned} \tag{3}$$

Equation (1) uses the full 1,508 state-year panel for 1983-2014; equation (2)‘s preferred robust RDD with party-specific state and year fixed effects uses 630 close-election state-years within a 10 percentage-point bandwidth. Standard errors are clustered by state and year for both specifications (Table 2, p. 713).

Datasets used

Dataset	Role in paper	Wiki page
US Census Bureau, State and Local Government Finance (State Government Finances series)	Primary source for state expenditure, revenue, debt, and tax data; 1983-2014	no page yet
Federal Assistance Award Data System (FAADS) via National Archives / SAM	Identifies federal aid programs with matching provisions (excluded from main IG measure); 1983-2010	no page yet
Council of State Governments, The Book of the States	Gubernatorial election outcomes, party affiliation, and term data	no page yet
US Bureau of Economic Analysis, Regional Economic Accounts	State-level GDP (personal income), used for R6 state GDP growth specifications	no page yet
US Bureau of Labor Statistics / Federal Reserve Bank of St. Louis (FRED)	State unemployment rate (used in robustness checks, Table 5); macro data	FRED
S&P Global Ratings, History of US State Ratings	State bond downgrade dummies for fiscal-stress robustness checks (Table 5)	no page yet
Klarner (2013) State Partisan Balance Data (Harvard Dataverse)	State partisan composition of legislature; used in robustness checks and polarization analysis	no page yet
Bonica (2014) DIME Database on Ideology	Ideological scores for gubernatorial candidates; external validity check in Figure 5	no page yet

Sample: 48 US states, fiscal years 1983-2014. Alaska, Wyoming, and North Dakota excluded from main sample (large sovereign wealth funds). Close-election RDD subsample: elections with absolute MOV at most 10-11 percentage points.

When to read the full paper

Read the original if you are: calibrating the partisan heterogeneity in state fiscal responses for a fiscal federalism model (Tables 2 and 4 give the full set of MPS estimates by party, sign, and specification); studying the macroeconomic implications of state partisanship for federal stimulus design (Section V and Table 7 provide the NK model results and counterfactuals); assessing external validity of RDD designs for close gubernatorial elections (Section III, Figures 4-6, Appendix C); comparing IG multiplier estimates across methodologies (Chodorow-Reich (2019) surveys ARRA-era estimates and finds a headline multiplier of 1.7, higher than the estimates here partly because it implicitly assumes the ZLB constraint); or extending the model to incorporate the zero lower bound or other heterogeneous states. The replication data at doi:10.3886/E177001V1 includes all cleaned datasets and Dynare code.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(3), March 2023. This distillation was extracted by an LLM on 2026-06-25 and is not human-verified or independently reproduced. The AEA retains copyright; no CC licence was found in Crossref metadata. Reproduction of figures or tables requires AEA permission.

Carlino, Gerald, Thorsten Drautzburg, Robert Inman, and Nicholas Zarra. “Partisanship and Fiscal Policy in Economic Unions: Evidence from US States.” American Economic Review 113, no. 3 (March 2023): 701-737. DOI: 10.1257/aer.20210147. Replication data: doi:10.3886/E177001V1.