Finance and Economics Discussion Series: 2014-79 Screen Reader version ♣

# Reconstruction multipliers*

Francesco Porcelli*
Riccardo Trezzi*

Abstract:

A law issued to allocate reconstruction grants following the 2009 Aquilano earthquake has resulted in a large and unanticipated discontinuity across municipalities with comparable damages. Using diff-in-diff analysis we estimate the local spending and the local tax multipliers - according to the composition of the stimulus - controlling for the negative supply shock generated by the event. The stimulus prevented a fall in economic activity and the multiplicative effects of tax cuts are estimated much higher than those of spending. Our results underline the importance of countercyclical fiscal interventions and suggest the most effective composition of such a stimulus.

Keywords: Natural disasters, fiscal multipliers, Mercalli scale
JEL Classification: C36, E62, H70

# 1 Introduction

The effectiveness of fiscal policy - typically summarized by a number the multiplier - has been an important driver of policy and academic debates in recent years. Although several contributions have provided estimates of the multiplier using different identification strategies, the literature is far from consensus. While virtually all earlier contributions have focused on aggregate effects, recent papers have shifted the attention to the local dimension. As Acconcia et al. point out, the shift is motivated not only by specific policy questions - such as countering area-specific recessionary shocks - but also by the opportunity to address econometric issues in identification: fiscal policy is highly endogenous to the business cycle and its effects are often anticipated by rational agents.

This paper contributes to the debate on the effects of government interventions relying on a natural experiment, the 2009 Aquilano earthquake that hit the Italian region of Abruzzo. Specifically, we estimate the output effect generated by the event, as a result of two combined shocks, the negative supply shock due to the quake, and the positive demand shock driven by reconstruction grants to the region. Our empirical strategy relies on the following two factors: (i) a quantified measure of damages reported by the 75,424 buildings classified after the quake, and (ii) the specific characteristics of the institutional arrangement of public grants providing insurance to the municipalities affected by the earthquake. With regard to the first factor, in the aftermath of the earthquake specialists from the Civil Protection Department (CPD) and the National Institute of Geophysics and Volcanology (INGV) visited the epicentral area with the goal of surveying the affected buildings.1 Relying on the reported damages, we construct a synthetic index that captures the negative supply shock generated by the event at the micro-municipal level. With regard to the second factor, as a complementary task the delegates assigned a synthetic number to the municipalities in the epicentral area reflecting the overall severity of the damages. Following a well-established practice, the rankings were based on the so-called Mercalli scale that classifies the destructive effects of an earthquake on twelve notches, ranging from instrumental (I) to catastrophic (XII).2 Once the list of affected municipalities was delivered to the national authorities, the central government enacted a law by decree establishing a qualifying Mercalli threshold for reconstruction grants. This threshold, ex ante unknown to the delegates, was fixed at level VI of the scale (the lowest level associated to marginal damages to civil structures) and resulted in a sharp discontinuity in grants across ex ante identical neighbor municipalities. The assigned grants were then used by the qualified municipalities for two purposes: to finance the increase of local spending directed towards reconstruction activities and to compensate for the loss in local tax revenues due to the shock to the tax base and the temporary suspension of tax payments in the most affected regions.

Studying the 305 municipalities in the Abruzzo region over the period 2002 to 2011 (3,050 observations in total) we estimate three things. First, we estimate the reconstruction grants multiplier - the elasticity of local output to exogenous reconstruction grants allocated from the central government to the qualified municipalities. Second, noticing that the discontinuity in local spending is identified at Mercalli VI as for grants while the discontinuity in local tax revenues is at Mercalli VII (due to an exogenous reduction in the local marginal tax rate which applied only to municipalities with severe damages), we estimate the local spending multiplier net of marginal tax rebates around the Mercalli VI cutoff and the the local tax multiplier net of variations of the tax base around the Mercalli VII cutoff. Finally, we estimate the output loss generated by the negative supply shock due to the destruction of physical capital relying on our index of damages.

In our econometric analysis we rely on two identification strategies based on a linear fixed-effects panel data model. In the first identification strategy, in order to address the grants endogeneity issue, we rely on a difference-in-differences approach regressing output over the index of damages and the interaction between a dummy which identifies the treatment and control groups (around the Mercalli VI cutoff) and the per capita grants. The second identification strategy addresses the possible endogeneity of damages using an instrumental variable approach. As a strictly exogenous instrument we employ the distance of each municipality from the epicenter which confirms the prior to be highly correlated with the recorded damages and fully satisfies the exclusion restrictions criteria.

In our findings, the direct effect of the earthquake on output is unambiguously negative. Our instrumental variables analysis shows that, on impact, the output loss from the quake averages 3.7 percentage points. Against the output effects of the negative supply shock, we document positive multiplicative effects of reconstruction grants. The estimated grants multiplier is bounded between 0.14 and 0.36according to the model. Multiplying these elasticities by the magnitude of the fiscal shock, our results suggest that public grants compensate the output fall (which is instead suffered by the control group) generated by the quake. Therefore, although grants multipliers remain well below unity in all models, our results suggest that following seismic events reconstruction grants provide public insurance. Output in uninsured regions contracts while it expands, although marginally, in qualified municipalities.3 Also, the local spending multiplier net of marginal tax rebates is virtually identical to the grants multiplier signaling, as expected, that most of the exogenous variations in grants financed reconstruction activities and translated into a sharp increase of local spending. However, the local tax multiplier is well above unity with point estimate of 2.56 even if this last estimate should be interpreted with caution given the restricted number of observations in the corresponding treatment group (above Mercalli VII).

Our findings contribute to the literature assessing the effectiveness of government interventions. A small but dynamic literature has produced estimates on local output elasticities to exogenous fiscal shocks using different instruments: dismissal of elected officials (Acconcia et al.), census revisions (Serrato and Wingender), variations in stimulus outlays mandated by the American Recovery and Reinvestment Act (ARRA) of 2009 (Chodorow-Reich et al.), or military buildups across US states (Nakamura and Steinsson). Close in spirit to our paper is a recent contribution by Corbi et al. who rely on a discontinuity in federal transfers to municipal governments in Brazil to identify the causal effect of fiscal policy on economic growth. Our estimates are lower than those reported in the aforementioned contributions. While output elasticity to fiscal shocks is predicted to be higher in downturns (Woodford), the grants and spending multipliers contained in this paper remain well below unity in all models, although the local tax multipliers are estimated well above one. Yet the size of the grants acted as a public insurance scheme preventing a fall in output. Our results underline the importance of countercyclical fiscal interventions after natural events and suggest the most effective composition of such stimuli.

The rest of the paper is organized as follows. Section 2 describes the 2009 Aquilano earthquake, the natural event at the heart of this study. Section 3 explains and discusses the empirical model. Section 4 describes the main features of our dataset. Section 5 discusses our main results. Section 6 is devoted to the discussion of our set of robustness checks. Section 7 concludes. Additional tables, charts, and complementary results are reported in A to F.

#
2 The 2009 Aquilano earthquake

At 03:32 am on April 6th 2009 a 6.3 magnitude earthquake hit the southern part of Italy. The epicenter was located 19.79 Kilometers to the west of L'Aquila, the capital city of Abruzzo region.4 Three hundred and nine people were killed and more than 1,500 were injured. The seismic event generated damages in 97 municipalities, 72 of which located in the province of L'Aquila. Following the declaration of the state of emergency by the Council of Ministers, a team of specialists from the CPD and the INGV visited the affected regions to assess the severity and extension of the damages. The procedure lasted ten days and on April 16th the list of affected municipalities and the estimate of total damages was made publicly available and sent to the central government.

During their mission the delegates had two separate tasks. First, they visited each building reporting damages (or suspected so) and ranked them following the AeDES international classification system (Baggio et al.). This system categorizes civil structures after a seismic event on six levels ranging from A (usable building) to F (unusable building and severe external risks).5 Table 1 shows the distribution of buildings across levels of damages and figure 2 plots the map of damages across municipalities in the epicentral region. Out of 75,424 buildings visited by the CPD and INGV specialists 55.2 percent were ranked at level A, 16.5 percent B, 3.4 percent C, 1.9 percent D, 20.4 percent E and the remaining 2.6 percent F with no significant differences across types of buildings.

#### Table 1: Percentage of buildings in each AeDES category.

AeDES category | Private | Public | Hospitals | Barracks | Schools | Factories | Overall
A | 55.0 | 57.5 | 51.5 | 71.0 | 52.9 | 56.6 | 55.2
B | 15.6 | 19.1 | 18.2 | 25.0 | 26.7 | 19.4 | 16.5
C | 3.3 | 4.5 | 15.2 | 3.0 | 2.4 | 4.5 | 3.4
D | 1.9 | 3.4 | 3.0 | - | 3.7 | 0.8 | 1.9
E | 21.5 | 14.3 | 12.1 | 1.0 | 12.5 | 15.7 | 20.4
F | 2.7 | 1.2 | - | - | 1.8 | 3.0 | 2.6
Total | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0

Source: Civil Protection Department, Italian Ministry of Interior (http://www.protezionecivile.gov.it).

As a second task, the delegates assigned a number to the municipalities in the epicentral region according to the severity of the damages.6 This number reflects a level of the so-called Mercalli scale which quantifies the effects of an earthquake on the Earth's surface, humans, objects of nature, and man-made structures on twelve notches ranging from I ('instrumental') to XII ('catastrophic').7 The definitions of the Mercalli levels are reported in C, while figure 1 plots the map of the earthquake, highlighting each municipality according to the Mercalli rank.8

#### Figure 1: Map of Aquilano earthquake.

Source: authors' calculation on INGV data.

#### Figure 2: Map of classified damages according to AeDES classification system.

Note: The size of the pies reflects the absolute number of buildings (in brackets) classified in each municipality. Source: CPD database.

On April 28th 2009 the central government enacted a law by decree (Decreto Legge 28 Aprile 2009, n.39) establishing a qualifying Mercalli threshold to receive reconstruction grants.9 The threshold, ex-ante unknown to the delegates, was fixed at level VI, the lowest level associated to (marginal) structural damages to civil structures (see C for details). The decision of the government - crucial for our identification strategy - resulted in a sharp discontinuity around the cut-off, with 49 municipalities at the immediate right (level VI and VI-VII) and 73 to the left (level V and V-VI). Figure 3 shows the discontinuity by plotting total grants against the Mercalli scale.

#### Figure 3: Discontinuity in grants.

Note: each dot is a bin (bandwidth of 0.2).

Number of bins determined using an F-test.

Grants are current plus capital from central government.

Source: authors' calculation on Italian MInistry of Interior data.

The average per capita grant to the left of the cut-off is 488.4 Euros while it increases to 2949.6 Euros per capita to the right. The cross-sectional standard deviation of grants is higher to the right-hand-side because the overall amount is proportional to the extension of the damages - meaning the number of buildings damaged - which is not captured by the Mercalli scale (which instead identifies the severity of the damages).10

The exogenous reconstruction grants were used by the qualified municipalities for two different purposes. On the one hand they financed post-disaster activities directed towards the reconstruction of public and private buildings. On the other hand, they compensated municipal budges for losses in revenues due to the suspension of local taxes. Figures 9 and 10 show the discontinuity in total expenditure (current and capital) and total tax revenues across municipalities according to Mercalli ranks. There exists a sharp discontinuity in local government expenditures at Mercalli VI which closely mimics the discontinuity in grants. However, the discontinuity in tax revenues is observed at Mercalli VII rather then VI with 49 municipalities on the immediate left of the cut-off (Mercalli VI and VI-VII) and 8 to the right (at or above Mercalli VII).11 The fact that the discontinuities in grants, local spending and local tax revenues are identified at different Mercalli levels allows a neat identification of respectively the grants multiplier, the local spending multiplier net of marginal tax rebates and the local tax multiplier net of variations in the tax base.

#
3 The empirical model

In our study we aim to estimate the short-run multiplicative effects of reconstruction grants on local economic activity at the municipal level following the 2009 Aquilano seismic event. We present the empirical model and our instrument in this section while we discuss our baseline results in section 5.

For each municipality let denote the real per capita value added in year , and its rate of growth defined as . Also, let denote the real per capita value of grants received by municipality in year from the central government, and Gi,t its growth rate as a ratio of lagged output, defined as . Following the recent literature (see for instance Barro and Redlick or Acconcia et al.) we estimate the grants multiplier relating the growth of per capita value added in municipality , to the correspondent change in per capita grants in the same municipality in the same year .12

Our empirical strategy is based on a linear fixed-effect panel data model with a difference-in-differences approach relying on the identical ex-ante behavior across control and treatment groups identified around the Mercalli VI cut-off. The empirical model is

|  |  | (1)

where is a municipal fixed-effect, is a time trend, Di,t is a dummy variable that takes the value of 1 for the municipalities belonging to the treatment group and 0 for the municipalities in the control group, is a vector of coefficients, is a set of control variables, and is a disturbance term. As standard in the literature, the variable Interactioni,t interacts Gi,t with Di,t, and the coefficient measures the contemporaneous one-year grants multiplier, meaning the elasticity of local output to exogenous shocks in grants. The selection criteria of the bandwidth around the discontinuity (figure 3) should satisfy the crucial assumption of ex-ante similarity between the control and the treatment group. Following Imbens and Kalyanaraman we estimate the optimal bandwidth around the discontinuity in grants which suggests to compare Mercalli V and V-VI ranked municipalities (control group) to Mercalli VI (treatment group). Figure 4 plots the variable of interest (local output) across the two groups (in order to ensure consistency in our estimates we also check the similarity of other observable characteristics, see table 14 in D). Before the event there exists a clear common trend but when the earthquake strikes output contracts less for treated municipalities than for the rest. Also, the ex-ante behavior of grants is tested and found to be identical across control and treatment group before the event.13

#### Figure 4: Per capita GDP (2004 = 100).

Source: authors' calculation on European Commission data.

The variable Damagesi,t captures the negative supply shock generated by the earthquake and the coefficient identifies the contemporaneous one-year output growth loss due to the destruction of physical capital. Our measure of capital stock loss is a weighted average of the number of buildings categorized in each AeDES level expressed as a share of the population. Formally

| (2)

where , , , , , .14 The variable Damagesi,t captures both, the severity and the extension of the damages. A higher score in the index can be determined either by a higher number of buildings reporting damages or (conditional on a given number of buildings) by a higher number of buildings reporting severe damages (categorized on a higher AeDES level).

Through the inclusion of fixed effects we capture unobserved time invariant municipal characteristics. In order to avoid collinearity issues between the variable Damagesi,t and yearly dummies we include a time trend that controls for aggregate monetary and fiscal policies affecting growth across all municipalities.15 Our choice to include a common time trend across municipalities is consistent with the evidence presented in figure 4.16 Furthermore, our difference in differences approach addresses potential endogeneity problems raised by the possibility that municipal-specific characteristics may be correlated with grants allocation criteria. For instance, it may be possible that the central government systematically allocates more resources in municipalities with output growth below the median in an attempt to boost it. Under this allocation rule the coefficient would be downwardly biased while would correctly capture the exogenous component of grants.

Another possible source of endogeneity is represented by the variable Damagesi,t. The negative supply shock captured with our index of damages might be either downwardly biased if municipalities with higher per capita income report less damages because of an ex-ante lower vulnerability of the buildings, or upwardly biased if municipalities with ex-ante higher output growth reported a higher ex-post index of damages due to the higher per capita capital stock. We address this potential issue in two ways. First, we consider the rate of growth of output as dependent variable instead of its level. Second, we instrument the variable Damagesi,t relying on the strict exogeneity and randomness of the earthquake.17 As an instrument we use the inverse of the distance of each municipality from the epicenter (variable Distancei,t).18 Given this instrument, our estimates are unbiased under three conditions. First, the distance should be a good predictor of the damages. Figure 8 provides robust evidence in support of this hypothesis. Second, the distance and the stochastic component of output growth should be uncorrelated, formally . Finally, the distance should be uncorrelated with output growth in order to satisfy the necessary exclusion restrictions criteria, formally . Preliminary regressions show that the distance is indeed highly correlated with the damages while it is not with output growth.

As regards the matrix of controls we include three variables capturing the evolution of the population: (i) total number of residents at December the 31th of each year, (ii) share of population younger than 14 years old, and (iii) share of population older than 65 years old (see D for details).

#
4 Data

Our dataset is a balanced panel of 305 municipalities over the period 2002 - 2011 for a total of 3,050 observations. All municipalities are located in the region of Abruzzo (see map in figure 11). Our choice eliminates 14 municipalities ranked at Mercalli V or V-VI in the neighborhood region of Lazio.19 However, the earthquake did not generate Mercalli ranks higher than V-VI outside Abruzzo and no municipalities qualified for reconstruction grants in Lazio. Also, our main dependent variable (GDP) is not available for municipalities in Lazio because it is estimated only for southern regions. For this reason, we prefer to restrict the attention to Abruzzo only and maximize the ex-ante similarity between control and treatment group.

As a measure of municipal economic activity we rely on four different variables. Our main dependent variable is the municipal value added (GDP) commissioned by the European Union Commission to monitor the development of the so-called European zone 3. These estimates are based on the official figures of provincial value added released by the Italian National Institute of Statistics (ISTAT) decomposed according to the sectoral composition of output at the municipal level.20 Given the possibility of measurement errors in the dependent variable we consider three alternatives. As a first alternative we employ high-resolution data on night lights intensity measured by satellites at night. These data come from the National Geophysical Data Center of the National Oceanic and Atmospheric Administration (U.S. Department of Commerce) and it has been shown (Henderson et al.) to proxy well for local economic activity.21 For our purposes we use the Average Visible, Stable Lights, and Cloud Free images taken from two satellites: F16 for the years from 2004 to 2009 and F18 for 2010 and 2011. The luminosity of each municipality is calculated by taking the average luminosity of all pixels corresponding to the surface of the municipality. Figure 12 shows the average luminosity over night in 2007 for the municipalities in our sample while figure 6 plots the remarkably high correlation between the GDP growth rates and night luminosity growth rates across all municipalities in the sample. As a final check, we collected data on other two alternative dependent variables: total declared personal income (tax base of national personal income tax) and business income provided by the Italian Ministry of Economy and Finance. The first one refers to the sum of all declared personal incomes in each municipality in each year and it offers the advantage of reducing the possibility of measurement errors due to fiscal evasion. The second one refers to the sum of all incomes generated by small and medium firms and self-employed workers, therefore offering an excellent proxy of economic activity.

Fiscal data come from the municipal budget accounts (certificati di conto consuntivi) released by the Italian Ministry of Interior.22 These data include disaggregated information on expenditures, revenues and grants recorded on accrual bases. All monetary variables are deflated using the regional consumer price index from .23 Demographic variables and time invariant characteristics are taken from . We also include a set of political variables collected from the Ministry of the Interior such as municipal turnover and voting patterns at regional elections, and political alignment of the local government with the central government.24 A detailed description of all variables, sources and summary statistics is reported in D. All earthquakes-related geophysical data (including Mercalli ranks) come from the Italian National Institute of Geophysics and Volcanology (2011 Italian Macroseismic Database (DBMI11));25 table 13 reports the distribution of Mercalli ranks across all years for all recorded events showing that the only major quake in the considered period is the one of interest in this paper. Finally, data on AeDES classified buildings come from the CPD.

#
5 Results

In this section, we discuss the results from our regression model. Table 2 reports the baseline results using value added as dependent variable. There are 73 municipalities in the control group (52 with Mercalli rank V and 21 with V-VI) and 43 in the treatment group (Mercalli VI). We run our baseline considering the period 2008-2011 for a total of 464 observations (116 municipalities, 4 years). The choice of the time span for the baseline is determined by the necessity of maximizing the similarity of the institutional framework given the 2008 reform of grants allocation which followed the reform in the municipal property tax. In robustness checks we extend both, the regression sample and the time dimension and show that our results are fully robust to the sample structure. The first two columns of table 2 refer to the difference in differences model (Model 1) while the remaining four columns (Model 2) show the results instrumenting the Damagesi,t with the Distancei,t (the last two columns of table 2 shows the first stage of the two-stage least squares estimation). Each model contains two separate columns which differ for the inclusions of demographic controls.

As reported in table 2, the one-year multiplier is statistically different from zero at 1 percent level in both model 1 and model 2, with point estimates respectively of 0.15and 0.36. Considering model 2 as a reference, this result suggests that an exogenous increase in reconstruction grants by 1 percent of local value added determines a contemporaneous increase in local output of 0.36 percent. The variable Damagesi,t enters in model 1 with the expected negative coefficient. Once the damages are instrumented (model 2), the coefficient is significant at 1 percent level and the magnitude is higher than model 1 signaling endogeneity (the point estimates of average around -0.32). The upward bias in model 1 is determined by the fact that the municipalities with ex-ante higher output growth reported a higher ex-post index of damages due to the higher capital stock. Moreover, given that damages and grants are highly correlated, the bias in the point estimates of affects also the estimates of which exhibit a downward bias in model 1. As a result, the estimates of the multiplier become more reliable after instrumenting the damages with the distance of each municipality from the epicenter of the quake. Given that the mean of the index of damages is 0.11 in the regression sample, the result from model 2 implies a negative effect of the quake of around 3.7 percentage points on output growth.26 Overall, given an average grants shock of 7.0 percent of local output in the regression sample, the negative supply shock generated by the quake is entirely compensated by the countercyclical reconstruction policies.

#### Table 2: Baseline results.

| Model 1 (DiD) | Model 1 (DiD) | Model 2 (DiD + IV) | Model 2 (DiD + IV) | Model 2 (first stage) | Model 2 (first stage)
Interaction | 0.145** | 0.146** | 0.367*** | 0.363*** | 0.500*** | 0.494***
Interaction, standard error | [0.061] | [0.061] | [0.096] | [0.094] | [0.178] | [0.178]
Damages, standard error | [0.046] | [0.046] | [0.086] | [0.084] |  |
Distance | - | - | - | - | 1.544*** | 1.547***
Distance, standard error |  |  |  |  | [0.429] | [0.431]
Controls |  | YES |  | YES |  | YES
Constant | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES
Observations | 464 | 464 | 464 | 464 | 464 | 464
R2 | 0.35 | 0.37 | - | - | 0.66 | 0.66
Underidentification test (Kleibergen-Paap) | - | - | - | - | 14.7 | 15.1
Weak identification test (Cragg-Donald) | - | - | - | - | 71.9 | 72.3

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

This result is in line with the official GDP estimates released by the Italian national institute of statistics (ISTAT) at the provincial level according to which output growth in 2009 in the province of L'Aquila was slightly higher than the national one despite the seismic event.27

The first stage reported in the last two columns of table 2 confirms two important priors. First, there exists a very high correlation between Distancei,t and Damagesi,t (see figure 8 for a graphical evidence). The coefficient of Distancei,t is significant at 1 percent level with point estimates around 1.55. Second, there exists a very high correlation between reconstruction grants and the index of damages. The coefficient of Interactioni,t is significant at 1 percent level with point estimates at 0.50. The goodness of fit is satisfactory in both models: the R2 is as high as 0.37 for model 2 while it increases to 0.66 in the first stage. Also, the underidentification test (Kleibergen-Paap Lagrange multiplier statistic) and the weak identification test (Cragg-Donald Wald F statistic) confirm that our instrument is indeed a valid one. Finally, we provide evidence that the grants multiplier can be interpreted as a local spending multiplier around the Mercalli VI cutoff. In table 3 we report the output elasticity estimated following the baseline and substituting the grants with local spending (current plus capital municipal expenditure). Because around the cutoff there is no discontinuity in local tax revenues as shown in figure 10, the coefficient of the variable Interactioni,t in table 3 is virtually identical to the baseline (0.33 versus 0.36) and therefore it can be interpreted as a local spending multiplier net of marginal tax rebates.

#### Table 3: Spending multiplier results.

| Model 1 (DiD) | Model 1 (DiD) | Model 2 (DiD + IV) | Model 2 (DiD + IV) | Model 2 (first stage) | Model 2 (first stage)
Interaction | 0.108* | 0.113* | 0.332*** | 0.331*** | 0.511*** | 0.509***
Interaction, standard error | [0.063] | [0.064] | [0.098] | [0.096] | [0.179] | [0.178]
Damages | -0.013 | -0.019 | -0.307*** | -0.308*** | - | -
Damages, standard error | [0.049] | [0.050] | [0.085] | [0.082] |  |
Distance | - | - | - | - | 1.559*** | 1.555***
Distance, standard error |  |  |  |  | [0.470] | [0.461]
Controls |  | YES |  | YES |  | YES
Constant | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES
Observations | 464 | 464 | 464 | 464 | 464 | 464
R2 | 0.35 | 0.37 | - | - | 0.65 | 0.65
Underidentification test (Kleibergen-Paap) | - | - | - | - | 15.3 | 15.9
Weak identification test (Cragg-Donald) | - | - | - | - | 71.4 | 71.3

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

#
6 Robustness checks and further results

We compare our baseline results against a large set of robustness checks. In this section we explain the set of checks and presents the results of these complementary regressions. For brevity, we present the checks only of the baseline while the tables showing the corresponding checks for spending are virtually identical and available upon request.

Different bandwidth. As a first additional result we allow for a different bandwidth with respect to the baseline. The results of this check are shown in table 5.28 We consider three alternative bandwidths. In the first one (first two columns of the table) we consider a more restrictive bandwidth with respect to the baseline and include only Mercalli V-VI and Mercalli VI for a total of 64 municipalities (21 in the control group and 43 in the treatment group). The multiplicative effects of exogenous grants is significative at 1 percent level with point estimates as high as 0.27 in the difference in differences model cum instrumental variables. Also the variable Damagesi,t enters significantly at 1 percent level with the expected sign and a magnitude similar to the one in the baseline. Similar results, presented in the two central columns of table 5, emerge when considering as a bandwidth all municipalities ranked between Mercalli V and Mercalli VI-VII (this choice enlarges the number of municipalities used in the regressions to 122 for a total of 486 observations). The only significant difference compared to the baseline emerges in the last three columns of table 5 where we allow in the regressions for all municipalities equal or above Mercalli V for a total of 130 municipalities.29 Given the observed discontinuity in local taxes (figure 10), in this case we introduce three extra variables in the model: an additional treatment dummy that takes the value of 1 for municipalities ranked at or above Mercalli VII, the per capita total tax revenues in each municipality and the interaction between the two. The coefficient of the interaction variable captures the output elasticity to an exogenous variation in the marginal tax rate (driven by the property tax rate) net of variations of the tax base (captured by the variable Damagesi,t). Therefore, the coefficient of the new interaction variable can be interpreted as a local tax multiplier. The estimates reported in table 5 remain in line with the baseline for the grants multiplier and for the index of damages. However, the estimated tax multiplier is well above unity with point estimate at 2.5. Although we rely on a robust identification strategy, this last result should be interpreted with caution given that only eight municipalities are included in the additional treatment group (above Mercalli VII). However, this estimate of the local tax multiplier is virtually the only empirical estimate at the local level in Italy.

Extending the time period. As a second exercise we check whether the time choice made in our baseline is the driver of our results. In this check we progressively extend the time dimension of our panel in order to maximize the number of observations included in the regressions. The results of this check are reported in table 6.30 The first three columns consider the period from 2006 to 2011 and we report the results of model 1 (first column), model 2 (second column), and the first stage of model 2 (third column). The same logic is applied to the remaining columns of the table that show the results when considering all years included in our dataset (from 2002 to 2011). This last regression runs over 1,160 observations (116 municipalities over 10 years). Overall, the evidence emerging from table 6 largely confirms our baseline. All coefficients in model 2 (including the first stage) remain significant at 1 percent level, the magnitude of the estimates is in line with the baseline as well as the goodness of fit the models and the instrumental variables tests.

Alternative dependent variable. As a third check we employ a set of different dependent variables as a measure of economic activity. The first alternative dependent variable that we consider is the average night lights intensity over the year as recorded by satellite images. Because this variable is an indirect measure of economic activity, we do not express the growth rate of real per capita grants as a ratio of the lagged dependent variable. Rather, we express it as a ratio of its own lag . The results of these regressions are shown in table 7. The variables of interest enter with the expected sign and they are significant at 1 percent level in model 2. The magnitude of in this case cannot be interpreted as a traditional multiplier given the definition of Gi,t. However, it is possible to recover the multiplier estimating the average fiscal shock (0.47), the average change in night light intensity (0.05), and the elasticity between night lights change and output change (0.03). In our sample such multiplier is estimated at 0.35, remarkably close to the baseline result. Furthermore, the goodness of fit of the models is significantly high as well as the instrumental variables tests are all well above the critical values. The other two alternative dependent variables that we employ are total income and business income. We report the results of these regressions respectively in table 8 and 9. Despite the fact that the source of these data is not the same as for the previous regressions, we obtain very similar results compared to the baseline. The multiplier estimated using total income is 0.21 and the variable Damagesi,t is significant at 1 percent level. However, employing business income as dependent variable confirms the baseline in two dimensions (despite the lower number of observations): the level of significance of grants and the sign of the damages, although the magnitude of the latter is higher than in the baseline.31

Placebo. As third check we run a placebo experiment. Because Mercalli ranks between I and V were not assigned, we compare Mercalli 0 ranked municipalities with Mercalli V assuming the former as control group and the latter as treatment group. In this case the regression sample is composed by 227 municipalities out of which 175 in the control group and 52 in the treatment and the variable Di,t is a dummy that takes the value of 1 for Mercalli V and 0 for Mercalli 0. We report the results of these regressions in table 10.32 The evidence confirms our baseline since none of the coefficient of interests ( and ) are significant. Also, in model 1 the variable Damagesi,t enters with the wrong coefficient as does the distance in the first stage of model 2.

Errors robust to clusters. As a fourth check we allow for errors robust to geographical clusters. As is well known, inference in panel estimation can be highly misleading if there is spatial correlation within groups of observations (Bertrand et al.). Following a common approach in the literature, in this check our inference is based on standard errors robust to clusters, allowing for individual clusters (therefore maximizing the possible number of clusters). Our results are reported in table 11 and they confirm the baseline since all coefficients remain significant at 1 percent level.

#
7 Conclusion

In this paper we have contributed evidence of local fiscal multipliers. By relying on a natural event in Italy, we estimated the output effect generated by the event, as a result of two combined shocks, the negative supply shock due to the quake, and the positive demand shock driven by reconstruction grants. Using a difference-in-differences cum instrumental variables analysis we have shown non negligible output effects of negative supply shocks. In our estimates, the output loss from the quake averages 3.7 percentage points. Also, we estimated the grants multipliers as high as 0.36. Spending multipliers net of marginal tax rebates are estimated virtually identical to grants multipliers while tax multipliers net of variations in the tax base are well above unity.

The policy relevance of quantifying local fiscal multipliers is apparent. On one hand, we shed light to the extent to which fiscal tools can alleviate the output loss generated by large idyosinchratic shocks like earthquakes. On the other hand, this paper analyzes the optimality of the institutional rule used to allocate grants after the event. Regarding the first factor we showed that reconstruction grants effectively provided public insurance following the event preventing output from falling below trend. However, the multiplicative effects on economic activity are heterogenous across fiscal instruments: the grants and spending multipliers are estimated below unity while the tax multiplier is well above one suggesting the most effective instrument to stimulate growth following a natural disaster. Regarding the second factor, our study pointed out that the grants allocation rule used after the 2009 Aquilano quake based on a discontinuous scale might not be optimal since it translated into significant geographical variations in economic activity across neighbor municipalities with similar damages. In this dimension, a grants allocation based on a combination between a discontinuous variables such as the Mercalli scale and a continuos variable such as the distance from the epicenter could provide a more equitable and efficient distribution of grants.

## Bibliography

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# Appendix

#
A. Are the Mercalli ranks artificially manipulated?

Given our identification strategy, a natural concern about the Mercalli ranks is whether they effectively reflect the damages suffered by each municipality or whether they are artificially manipulated. This would happen if, for instance, the delegates assigned higher ranks to poorer municipalities or to municipalities in which the mayor belongs to the same political party of the central government. In this way reconstruction grants would be endogenous to political or economic cycles.

We test this hypothesis using a probit model estimated by maximum-likelihood. Our goal is to test whether the decision of classifying a municipality as Mercalli VI instead of V was based on the recorded damages only or was influenced by other factors (although the qualifying threshold was ex ante unknown to the delegates).33 The empirical model is

| (3)

where is a dummy variable that takes the value of 1 for Mercalli VI and zero for V or V-VI, is a matrix containing the other variables of interest (municipal output, municipal unemployment rate, and political alignment with the central government since reconstruction grants come almost exclusively from the central government), is a matrix that contains all other controls, and are vectors of coefficients, and is a disturbance term.34 We are interested in testing whether or any of the coefficients in are significantly different from zero. As a proxy of political allignment of each municipality with the central government, we take the results of the previous regional election.35

Our results are shown in table 4. We consider four models with an increasing number of regressors and controls.

#### Table 4: Probit analysis results.

| Model A | Model B | Model C | Model D
Damages | 5.327*** | 24.350*** | 5.263*** | 23.770**
Damages, p-value | [0.000] | [0.000] | [0.000] | [0.000]
GDP |  |  | 0.000 | 0.000
GDP, p-value |  |  | [0.944] | [0.549]
Unemployment |  |  | -0.028 | -0.054
Unemployment, p-value |  |  | [0.492] | [0.546]
Alignment |  |  | 0.016 | 0.011
Alignment, p-value |  |  | [0.161] | [0.603]
Controls |  | YES |  | YES
Constant | YES | YES | YES | YES
Number of observations | 116 | 116 | 116 | 116
Log likelihood | -55.39 | -25.94 | -54.07 | -25.33

P-values in brackets. *** significant at 1%, ** at 5% and * at 10%.

The only variable with some explanatory power is the index of damages.36 All other regressors, including municipal output, municipal unemployment rate and political alignment are not statistically different from zero. We take this result as an evidence that the Mercalli ranks reflect only the damages generated by the earthquake (additional evidence on the exogeneity of Mercalli ranks is provided by Porcelli and Trezzi). Finally, because the coefficients in a probit model do not provide direct measure of the partial effect, we estimate the marginal effect of the variable Damagesi keeping all other variables at their mean levels. The marginal effect is reported in figure 5. Visibly, the index of damages alone is able to increase the probability of receiving reconstruction grants to 1 for values slightly below 0.3. A yellow vertical line reports the average value of damages (0.25) at Mercalli VI which is associated with a marginal probability non statistically different from 1 indicating that the damages alone - and no other variables - can explain whether a municipality qualifies or not for reconstruction grants.

#### Figure 5: Marginal probability - Damages variable.

#
B. Results and robustness checks

#### Table 5: Different bandwidth results.

| V-VI to VI: DiD | V-VI to VI: DiD + IV | V-VI to VI: First stage | V to VI-VII: DiD | V to VI-VII: DiD + IV | V to VI-VII: First stage | Equal or higher than V: DiD | Equal or higher than V: DiD + IV | Equal or higher than V: First stage
Interaction grants | 0.139** | 0.268*** | 0.484*** | 0.156** | 0.441*** | 0.695*** | 0.108** | 0.325*** | 0.721***
Interaction grants, standard error | [0.066] | [0.076] | [0.190] | [0.062] | [0.116] | [0.179] | [0.045] | [0.084] | [0.127]
Interaction taxes | - | - | - | - | - | - | -0.673 | -2.559** | 0.061
Interaction taxes, standard error |  |  |  |  |  |  | [0.738] | [1.006] | [0.042]
Damages | -0.018 | -0.192*** | - | -0.010 | -0.315*** | - | -0.004 | -0.246*** | -
Damages, standard error | [0.051] | [0.065] |  | [0.030] | [0.086] |  | [0.030] | [0.077] |
Distance | - | - | 1.577*** | - | - | 1.175*** | - | - | 1.125***
Distance, standard error |  |  | [0.548] |  |  | [0.362] |  |  | [0.346]
Controls | YES | YES | YES | YES | YES | YES | YES | YES | YES
Constant | YES | YES | YES | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES | YES | YES | YES
Observations | 256 | 256 | 256 | 486 | 486 | 486 | 518 | 518 | 518
R2 | 0.42 | - | 0.67 | 0.38 | - | 0.71 | 0.40 | - | 0.77
Underidentification test | - | - | 15.4 |  | - | 17.5 |  | - | 20.0
Weak identification test | - | - | 33.3 |  | - | 49.5 |  | - | 49.1

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

#### Table 6: Different time selection results.

| 2006 to 2011: DiD | 2006 to 2011: DiD + IV | 2006 to 2011: First stage | 2002 to 2011: DiD | 2002 to 2011: DiD + IV | 2002 to 2011: First stage
Interaction | 0.101* | 0.330*** | 0.504*** | 0.097* | 0.432*** | 0.538***
Interaction, standard error | [0.055] | [0.096] | [0.187] | [0.053] | [0.131] | [0.187]
Damages | -0.038 | -0.337*** | - | -0.046 | -0.466*** | -
Damages, standard error | [0.049] | [0.086] |  | [0.051] | [0.118] |
Distance | - | - | 1.523*** | - | - | 1.492***
Distance, standard error |  |  | [0.419] |  |  | [0.415]
Controls | YES | YES | YES | YES | YES | YES
Constant | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES
Observations | 696 | 696 | 696 | 1158 | 1158 | 1158
R2 | 0.25 | - | 0.66 | 0.19 | - | 0.66
Underidentification test | - | - | 14.7 | - | - | 13.9
Weak identification test | - | - | 124.7 | - | - | 223.7

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

#### Table 7: Night lights intensity as dependent variable.

| Model 1 (DiD) | Model 1 (DiD) | Model 2 (DiD + IV) | Model 2 (DiD + IV) | Model 2 (first stage) | Model 2 (first stage)
Interaction | 0.073*** | 0.069*** | 0.074*** | 0.072** | -0.003 | -0.003
Interaction, standard error | [0.016] | [0.016] | [0.028] | [0.029] | [0.011] | [0.011]
Damages | -0.045 | -0.060 | -1.850*** | -1.886*** | - | -
Damages, standard error | [0.071] | [0.072] | [0.489] | [0.486] |  |
Distance | - | - | - | - | 2.024*** | 2.021***
Distance, standard error |  |  |  |  | [0.501] | [0.495]
Controls |  | YES |  | YES |  | YES
Constant | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES
Observations | 464 | 464 | 464 | 464 | 464 | 464
R2 | 0.53 | 0.54 | - | - | 0.60 | 0.61
Underidentification test (Kleibergen-Paap) | - | - | - | - | 16.2 | 16.7
Weak identification test (Cragg-Donald) | - | - | - | - | 115.7 | 115.4

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

#### Table 8: Income as dependent variable results.

| Model 1 (DiD) | Model 1 (DiD) | Model 2 (DiD + IV) | Model 2 (DiD + IV) | Model 2 (first stage) | Model 2 (first stage)
Interaction | 0.130 | 0.129 | 0.225* | 0.215 | 0.757*** | 0.749***
| [0.099] | [0.098] | [0.134] | [0.133] | [0.198] | [0.200]
Damages | -0.117*** | -0.117*** | -0.208*** | -0.199*** | - | -
Damages, standard error | [0.039] | [0.040] | [0.077] | [0.077] |  |
Distance | - | - | - | - | 1.503*** | 1.508***
Distance, standard error |  |  |  |  | [0.412] | [0.422]
Controls |  | YES |  | YES |  | YES
Constant | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES
Observations | 464 | 464 | 464 | 464 | 464 | 464
R2 | 0.31 | 0.32 | - | - | 0.67 | 0.67
Underidentification test (Kleibergen-Paap) | - | - | - | - | 16.1 | 16.6
Weak identification test (Cragg-Donald) | - | - | - | - | 73.4 | 73.8

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

#### Table 9: Business income as dependent variable results.

| Model 1 (DiD) | Model 1 (DiD) | Model 2 (DiD + IV) | Model 2 (DiD + IV) | Model 2 (first stage) | Model 2 (first stage)
Interaction | 0.012** | 0.012** | 0.038* | 0.042* | 0.005*** | 0.005***
Interaction, standard error | [0.005] | [0.005] | [0.022] | [0.022] | [0.000] | [0.000]
Damages | -2.798** | -2.721** | -7.580* | -8.157** | - | -
Damages, standard error | [1.074] | [1.072] | [4.155] | [4.149] |  |
Distance | - | - | - | - | 0.274*** | 0.272***
Distance, standard error |  |  |  |  | [0.082] | [0.087]
Controls |  | YES |  | YES |  | YES
Constant | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES
Observations | 215 | 215 | 215 | 215 | 215 | 215
R2 | 0.32 | 0.32 | - | - | 0.97 | 0.97
Underidentification test (Kleibergen-Paap) | - | - | - | - | 9.2 | 8.9
Weak identification test (Cragg-Donald) | - | - | - | - | 15.1 | 14.4

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

#### Table 10: Placebo test results.

| Model 1 (DiD) | Model 1 (DiD) | Model 2 (DiD + IV) | Model 2 (DiD + IV) | Model 2 (first stage) | Model 2 (first stage)
Interaction | -0.105 | -0.089 | 1.124 | 1.112 | 0.276 | 0.275
Interaction, standard error | [0.216] | [0.214] | [1.134] | [1.104] | [0.197] | [0.198]
Damages | 0.126* | 0.116* | -2.434 | -2.393 | - | -
Damages, stanadard error | [0.066] | [0.064] | [2.157] | [2.098] |  |
Distance | - | - | - | - | -0.002 | -0.001
Distance, standard error |  |  |  |  | [0.026] | [0.025]
Controls |  | YES |  | YES |  | YES
Constant | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES
Observations | 902 | 902 | 902 | 902 | 902 | 902
R2 | 0.46 | 0.48 | - | - | 0.31 | 0.15
Underidentification test (Kleibergen-Paap) | - | - | - | - | 0.0 | 0.0
Weak identification test (Cragg-Donald) | - | - | - | - | 0.0 | 0.0

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

#### Table 11: Clusters results.

| Model 1 (DiD) | Model 1 (DiD) | Model 2 (DiD + IV) | Model 2 (DiD + IV) | Model 2 (first stage) | Model 2 (first stage)
Interaction | 0.145** | 0.146** | 0.367*** | 0.363*** | 0.500*** | 0.494***
Interaction, standard error | [0.061] | [0.061] | [0.096] | [0.095] | [0.195] | [0.194]
Damages | -0.017 | -0.023 | -0.314*** | -0.315*** | - | -
Damages, standard error | [0.046] | [0.046] | [0.091] | [0.088] |  |
Distance | - | - | - | - | 1.544*** | 1.547***
Distance, standard error |  |  |  |  | [0.486] | [0.488]
Controls |  | YES |  | YES |  | YES
Constant | YES | YES | YES | YES | YES | YES
Fixed effects | YES | YES | YES | YES | YES | YES
Gi,t and Di,t | YES | YES | YES | YES | YES | YES
Observations | 464 | 464 | 464 | 464 | 464 | 464
R2 | 0.35 | 0.37 | - | - | 0.65 | 0.66
Underidentification test (Kleibergen-Paap) | - | - | - | - | 11.4 | 11.8
Weak identification test (Cragg-Donald) | - | - | - | - | 71.9 | 72.3

Note: robust standard errors in brackets. *** indicates significance at 1% level, ** at 5% * at 10%.

#
C. Mercalli scale

The Richter scale (or simply magnitude) was invented by Charles Francis Richter at the California Institute of Technology. It quantifies the energy released during an earthquake on a base-10 logarithmic scale. For instance, an earthquake that measures 5.0 on the Richter scale has a shaking amplitude 10 times larger than one that measures 4.0, and corresponds to a 31.6 times larger release of energy. Technically, the magnitude is defined as the logarithm of the ratio of the amplitude of waves measured by a seismograph to an arbitrary small amplitude. However, before seismologists were able to measure the moment-magnitude of earthquakes, other scales were invented to categorize seismic episodes. In 1783 an Italian architect (Pompeo Schiantarelli) invented a rudimentary scale to classify the affected regions according to the severity of the damages. The scale underwent several revisions and it is now known as 'Mercalli scale', from the Italian volcanologist Giuseppe Mercalli who modified it in 1908. The scale is a narrative description of the damages defined on twelve levels ranging from 'instrumental' (I) to 'catastrophic' (XII). Here below we report the definitions of each level.

- I Instrumental People: Not felt except by a very few people under exceptionally favorable circumstances.
- II Weak People: Felt by persons at rest, on upper floors or favorably placed.
- III Slight People: Felt indoors, hanging objects may swing, vibration similar to passing of light trucks,duration may be estimated, may not be recognized as an earthquake.
- IV Moderate People: Generally noticed indoors but not outside. Light sleepers may be awakened. Vibration may be likened to the passing of heavy traffic, or to the jolt of a heavy object falling or striking the building. Fittings: Doors and windows rattle. Glassware and crockery rattle. Liquids in open vessels may be slightly disturbed. Standing motorcars may rock. Structures: Walls and frames of buildings, and partitions and suspended ceilings in commercial buildings, may be heard to creak.
- V Rather Strong People: Generally felt outside, and by almost everyone indoors. Most sleepers awakened. A few people alarmed. Fittings: Small unstable objects are displaced or upset. Some glassware and crockery may be broken. Hanging pictures knock against the wall. Open doors may swing. Cupboard doors secured by magnetic catches may open. Pendulum clocks stop, start, or change rate. Structures: Some windows Type I cracked. A few earthenware toilet fixtures cracked.
- VI Strong People: Felt by all. People and animals alarmed. Many run outside. Difficulty experienced in walking steadily. Fittings: Objects fall from shelves. Pictures fall from walls. Some furniture moved on smooth floors, some unsecured free-standing fireplaces moved. Glassware and crockery broken. Very unstable furniture overturned. Small church and school bells ring. Appliances move on bench or table tops. Filing cabinets or easy glide drawers may open (or shut). Structures: Slight damage to Buildings Type I. Some stucco or cement plaster falls. Windows Type I broken. Damage to a few weak domestic chimneys, some may fall. Environment: Trees and bushes shake, or are heard to rustle. Loose material may be dislodged from sloping ground, e.g. existing slides, talus slopes, shingle slides.
- VII Very Strong People: General alarm. Difficulty experienced in standing. Noticed by motorcar drivers who may stop. Fittings: Large bells ring. Furniture moves on smooth floors, may move on carpeted floors. Substantial damage to fragile contents of buildings. Structures: Unreinforced stone and brick walls cracked. Buildings Type I cracked with some minor masonry falls. A few instances of damage to Buildings Type II. Unbraced parapets, unbraced brick gables, and architectural ornaments fall. Roofing tiles, especially ridge tiles may be dislodged. Many unreinforced domestic chimneys damaged, often falling from roof-line. Water tanks Type I burst. A few instances of damage to brick veneers and plaster or cement-based linings. Unrestrained water cylinders (water tanks Type II) may move and leak. Some windows Type II cracked. Suspended ceilings damaged. Environment: Water made turbid by stirred up mud. Small slides such as falls of sand and gravel banks, and small rock-falls from steep slopes and cuttings. Instances of settlement of unconsolidated or wet, or weak soils. Some fine cracks appear in sloping ground. A few instances of liquefaction (i.e. small water and sand ejections).
- VIII Destructive People: Alarm may approach panic. Steering of motorcars greatly affected. Structures: Buildings Type I heavily damaged, some collapse. Buildings Type II damaged, some with partial collapse. Buildings Type III damaged in some cases. A few instances of damage to Structures Type IV. Monuments and pre-1976 elevated tanks and factory stacks twisted or brought down. Some pre-1965 infill masonry panels damaged. A few post-1980 brick veneers damaged. Decayed timber piles of houses damaged. Houses not secured to foundations may move. Most unreinforced domestic chimneys damaged, some below roof-line, many brought down. Environment: Cracks appear on steep slopes and in wet ground. Small to moderate slides in roadside cuttings and unsupported excavations. Small water and sand ejections and localized lateral spreading adjacent to streams, canals, lakes, etc.
- IX Violent Structures: Many Buildings Type I destroyed. Buildings Type II heavily damaged, some collapse. Buildings Type III damaged, some with partial collapse. Structures Type IV damaged in some cases, some with flexible frames seriously damaged. Damage or permanent distortion to some Structures Type V. Houses not secured to foundations shifted off. Brick veneers fall and expose frames. Environment: Cracking of ground conspicuous. Land sliding general on steep slopes. Liquefaction effects intensified and more widespread, with large lateral spreading and flow sliding adjacent to streams, canals, lakes, etc.
- X Intense Structures: Most Buildings Type I destroyed. Many Buildings Type II destroyed. Buildings Type III heavily damaged, some collapse. Structures Type IV damaged, some with partial collapse. Structures Type V moderately damaged, but few partial collapses. A few instances of damage to Structures Type VI. Some well-built timber buildings moderately damaged (excluding damage from falling chimneys). Environment: Land sliding very widespread in susceptible terrain, with very large rock masses displaced on steep slopes. Landslide dams may be formed. Liquefaction effects widespread and severe.
- XI Extreme Structures: Most Buildings Type II destroyed. Many Buildings Type III destroyed. Structures Type IV heavily damaged, some collapse. Structures Type V damaged, some with partial collapse. Structures Type VI suffer minor damage, a few moderately damaged.
- XII Catastrophic Structures: Most Buildings Type III destroyed. Structures Type IV heavily damaged, some collapse. Structures Type V damaged, some with partial collapse. Structures Type VI suffer minor damage, a few moderately damaged.

Construction types. Buildings Type I: Buildings with low standard of workmanship, poor mortar, or constructed of weak materials like mud brick or rammed earth. Soft storey structures (e.g. shops) made of masonry, weak reinforced concrete or composite materials (e.g. some walls timber, some brick) not well tied together. Masonry buildings otherwise conforming to buildings Types I to III, but also having heavy unreinforced masonry towers. (Buildings constructed entirely of timber must be of extremely low quality to be Type I.). Buildings Type II: Buildings of ordinary workmanship, with mortar of average quality. No extreme weakness, such as inadequate bonding of the corners, but neither designed nor reinforced to resist lateral forces. Such buildings not having heavy unreinforced masonry towers. Buildings Type III: Reinforced masonry or concrete buildings of good workmanship and with sound mortar, but not formally designed to resist earthquake forces. Structures Type IV: Buildings and bridges designed and built to resist earthquakes to normal use standards, i.e. no special collapse or damage limiting measures taken (mid-1930s to c. 1970 for concrete and to c. 1980 for other materials). Structures Type V: Buildings and bridges, designed and built to normal use standards, i.e. no special damage limiting measures taken, other than code requirements, dating from since c. 1970 for concrete and c. 1980 for other materials. Structures Type VI: Structures, dating from c. 1980, with well-defined foundation behavior, which have been specially designed for minimal damage, e.g. seismically isolated emergency facilities, some structures with dangerous or high contents, or new generation low damage structures. Windows. Type I: Large display windows, especially shop windows. Type II: Ordinary sash or casement windows. Water tanks. Type I: External, stand mounted, corrugated iron tanks. Type II: Domestic hot-water cylinders unrestrained except by supply and delivery pipes.

#
D. Description of variables in the dataset

## D..1 Population controls

Population: total number of residents at December the 31th of each year. Source: Italian National Institute of Statistics (ISTAT). Migration: total net migration effect (immigrants minus emigrants). Source: ISTAT. Balance: natural balance (births minus deaths). Source: ISTAT. Population14: share of population younger than 14 years old. Source: ISTAT. Population65: share of population older than 65 years old. Source: ISTAT

## D..2 Political controls

Alignment: number of votes in favor of the center-left coalition at the regional elections as a share of total votes. Source: Ministry of Interior.

## D..3 Time invariant controls

Unemployment: number of unemployed people as a share of working labor force. Source: ISTAT. Population band. Number of residents (1 = small town, 8= large city). Source: ISTAT. Graduates: number of graduates as a share of total residents. Source: ISTAT. Unliteracy: rate of unliterary per thousand habitants. Source: ISTAT. Altimetry: average altimetry expressed on a discrete scale from 1 (high) to 5 (low). Source: ISTAT. Altimetrymax: maximum altimetry in meters. Source: ISTAT. Altimetrymin: minimum altimetry in meters. Source: ISTAT. Urbanization: degree of urbanization, measured on a discrete scale from 1 (low) to 3 (high). Source: ISTAT. Surface: geographical surface expressed in kilometers squared. Source: ISTAT. Coast: dummy variable taking the value of 1 if the municipality is on the coast. Source: ISTAT. Family: average number of people per family. Source: ISTAT. Foreigners: number of non-italian residents as a share of total population. Source: ISTAT. Commuters: number of working commuters as a share of total population. Source: ISTAT. Head: dummy variable taking the value of 1 if the municipality is the political head of a province. Source: ISTAT. Buildings19: share of buildings built before 1919. Source: ISTAT. Buildings45: share of buildings built before 1945. Source: ISTAT. Buildings61: share of buildings built before 1961. Source: ISTAT. Buildings71: share of buildings built before 1971. Source: ISTAT. Buildings81: share of buildings built before 1981. Source: ISTAT. Buildings91: share of buildings built before 1991. Source: ISTAT. Buildingspost91: share of buildings built after 1919. Source: ISTAT.

# E. List of municipalities in control and treatment group

Prov. of Chieti - control: Chieti (V), Fara Filiorum Petri (V), Filetto (V), San Giovanni Teatino (V), Villa Santa Maria (V). Prov. of L'Aquila - control: Campo di Giove (V), Sulmona (V), Canistro (V), Cansano (V), Anversa degli Abruzzi (V), Prezza (V), Pacentro (V), Tagliacozzo (V), Magliano de' Marsi (V), Vittorito (V), Ortona dei Marsi (V), Scanno (V), Roccacasale (V), Rocca Pia (V), San Benedetto dei Marsi (V), Avezzano (V), Gioia dei Marsi (V), Lecce nei Marsi (V), Pettorano sul Gizio (V), Massa d'Albe (V), Opi (V), Introdacqua (V), Raiano (V), Pescina (V), Calascio (V), Collepietro (V-VI), Aielli (V-VI), Secinaro (V-VI), Molina Aterno (V-VI), Pratola Peligna (V-VI), Celano (V-VI), Scurcola Marsicana (V-VI), Cerchio (V-VI), San Benedetto in Perillis (V-VI), Corfinio (V-VI). Prov. of L'Aquila - treatment: Acciano (VI), Barete (VI), Barisciano (VI), Bugnara (VI), Cagnano Amiterno (VI), Campotosto (VI), Capestrano (VI), Capitignano (VI), Caporciano (VI), Castel del Monte (VI), Castelvecchio Calvisio (VI), Cocullo (VI), Collarmele (VI), Fagnano Alto (VI), Fontecchio (VI), Gagliano Aterno (VI), Montereale (VI), Navelli (VI), Ocre (VI), Ofena (VI), Ovindoli (VI), Pizzoli (VI), Rocca di Cambio (VI), Rocca di Mezzo (VI), San Pio delle Camere (VI), Scoppito (VI), Tornimparte (VI), Villa Santa Lucia degli Abruzzi (VI), Carapelle Calvisio (VI-VII), Castel di Ieri (VI-VII), Lucoli (VI-VII), Prata d'Ansidonia (VI-VII), San Demetrio ne' Vestini (VI-VII), Santo Stefano di Sessanio (VI-VII), Castelvecchio Subequo (VII), Goriano Sicoli (VII), Tione degli Abruzzi (VII), Fossa (VII-VIII), L'Aquila (VIII-IX), Poggio Picenze (VIII-IX), Sant'Eusanio Forconese (IX), Villa Sant'Angelo (IX). Prov. of Pescara - control: Bolognano (V), Catignano (V), Cepagatti (V), Civitaquana (V), Corvara (V), Farindola (V), Loreto Aprutino (V), Manoppello (V), Nocciano (V), Penne (V), Pescosansonesco (V), Rosciano (V), Scafa (V), Vicoli (V), Villa Celiera (V), Alanno (V-VI), Carpineto della Nora (V-VI), Castiglione a Casauria (V-VI), Pianella (V-VI), Pietranico (V-VI), Tocco da Casauria (V-VI). Prov. of Pescara - treatment: Brittoli (VI), Bussi sul Tirino (VI), Civitella Casanova (VI), Cugnoli (VI), Montebello di Bertona (VI), Popoli (VI), Torre de' Passeri (VI). Prov. of Teramo - control: Bisenti (V), Cellino Attanasio (V), Cortino (V), Crognaleto (V), Rocca Santa Maria (V), Teramo (V), Torricella Sicura (V), Valle Castellana (V), Basciano (V-VI), Castel Castagna (V-VI), Cermignano (V-VI), Isola del Gran Sasso d'Italia (V-VI). Prov. of Teramo - treatment: Arsita (VI), Castelli (VI), Colledara (VI), Fano Adriano (VI), Montorio al Vomano (VI), Penna Sant'Andrea (VI), Pietracamela (VI), Tossicia (VI).

#
F. Descriptive statistics and complementary figures

#### Table 12: Distribution of Mercalli ranks across provinces in 2009.

Mercalli rank | Chieti | L'Aquila | Pescara | Teramo | Total
0 | 99 | 31 | 18 | 27 | 175
V | 4 | 25 | 15 | 8 | 52
V-VI | 1 | 10 | 6 | 4 | 21
VI | 0 | 28 | 7 | 8 | 43
VI-VII | 0 | 6 | 0 | 0 | 6
VII | 0 | 3 | 0 | 0 | 3
VII-VIII | 0 | 1 | 0 | 0 | 1
VIII | 0 | 0 | 0 | 0 | 0
VIII-IX | 0 | 2 | 0 | 0 | 2
IX | 0 | 2 | 0 | 0 | 2
Total | 104 | 108 | 46 | 47 | 305

#### Figure 6: Output change - Lights intensity change.

#### Table 13: Distribution of Mercalli ranks across years.

Mercalli | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | Total
0 | 136 | 160 | 172 | 246 | 247 | 305 | 305 | 175 | 305 | 305 | 2,356
I | 3 | 96 | 82 | 58 | 48 | 0 | 0 | 0 | 0 | 0 | 287
II | 3 | 10 | 8 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 23
II-III | 4 | 6 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13
III | 10 | 10 | 4 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 29
III-IV | 29 | 16 | 12 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 60
IV | 44 | 2 | 9 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 56
IV-V | 54 | 4 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 68
V | 22 | 1 | 4 | 0 | 0 | 0 | 0 | 52 | 0 | 0 | 79
V-VI | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 22
VI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 43 | 0 | 0 | 43
VI-VII | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 6
VII | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3
VII-VIII | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1
VIII | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
VIII-IX | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2
IX | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2
Total | 305 | 305 | 305 | 305 | 305 | 305 | 305 | 305 | 305 | 305 | 3,050

Note: Mercalli ranks before 2009 refer to the following events: Subappennino Dauno (November 1st 2002, magnitude 5.72, epicenter in Molise region), Zona Ascoli Piceno (May 25th 2003, magnitude 4.30, epicenter in Marche region), Molise (June 1st 2003, magnitude 4.66, epicenter in Molise region), Monti dei Frentani (December 30th 2003, magnitude 4.63, epicenter in Molise region), Monti Tiburtini (October 5th 2004, magnitude 4.05, epicenter in Lazio region), Zona Teramo (December 9th 2004, magnitude 4.54, epicenter in Abruzzo region), Monti dei Frentani (March 1st 2005, magnitude 4.24, epicenter Marche region), Maceratese (April 12th 2005, magnitude 4.24, epicenter in Marche region), Valle del Topino (December 15th 2005, magnitude 4.69, epicenter in Marche region), Maceratese (April 10th 2006, magnitude 4.55, epicenter Marche region), and Promontorio del Gargano (May 29th 2006, magnitude 4.92, epicenter in Puglia region).

#### Table 14: Summary Statistics (regression sample)

Variable | All Mean | All St. Dev. | All Min | All Max | Control (V and V-VI) Mean | Control (V and V-VI) St. Dev. | Control (V and V-VI) Min | Control (V and V-VI) Max | Treatment (VI and VI-VII) Mean | Treatment (VI and VI-VII) St. Dev. | Treatment (VI and VI-VII) Min | Treatment (VI and VI-VII) Max
GDP | 13,274 | 6,472 | 4,915 | 77,014 | 13,342 | 5,263 | 6357 | 47,128 | 12,962 | 4,277 | 6,414 | 27,227
Total income | 13,106 | 2,237 | 7,582 | 21,454 | 13,119 | 2,354 | 7970 | 19,934 | 13,201 | 2,503 | 7,582 | 20,468
Business income | 334 | 734 | 0 | 8,252 | 352 | 609 | 6 | 32,06 | 105 | 86 | 0 | 450
Night lights intensity | 16.1 | 12.4 | 1.9 | 62.3 | 15.0 | 10.8 | 1.9 | 60.0 | 9.7 | 5.0 | 3.2 | 29.4
Grants | 534 | 1,019 | 0 | 21,300 | 439 | 727 | 0 | 14,054 | 990 | 1341 | 33 | 9,313
Local expenditure | 1,748 | 1,873 | 77 | 25,954 | 1,594 | 1,876 | 77 | 25,754 | 2,476 | 2,006 | 559 | 15,674
Local tax revenues | 1,955 | 643 | 868 | 7,149 | 1,927 | 759 | 1003 | 4,096 | 2,236 | 775 | 890 | 5,160
Population | 4,301 | 10,589 | 77 | 123,077 | 4,778 | 10,085 | 120 | 56,127 | 1,460 | 1,522 | 141 | 8,283
Pop under 14 | 12.3 | 3.0 | 1.3 | 26.5 | 12.5 | 2.8 | 1.3 | 21.3 | 10.9 | 3.2 | 2.1 | 18.7
Pop over 65 | 26.6 | 9.4 | 7.3 | 86.4 | 26.3 | 8.5 | 11.9 | 63.0 | 30.6 | 10.2 | 12.8 | 67.3
Unemployment | 10.1 | 3.8 | 0 | 28.8 | 10.5 | 3.8 | 4.5 | 23.2 | 10.2 | 3.5 | 0 | 23.0
Left | 50.4 | 13.1 | 5.6 | 91.4 | 50.0 | 12.7 | 15.6 | 91.4 | 51.5 | 12.5 | 21.8 | 79.0
Distance* | 45.9 | 21.8 | 2.5 | 95.2 | 33.2 | 10.9 | 6.7 | 70.9 | 21.7 | 10.4 | 2.7 | 40.5
Index of damages* | 0.07 | 0.20 | 0 | 1.40 | 0.02 | 0.09 | 0 | 0.64 | 0.27 | 0.27 | 0.01 | 1.06
No. of observations | 3,050 | 3,050 | 3,050 | 730 | 730 | 730 | 430 | 430 | 430 | 430 |  |

* refers to 2009 only. Other statistics availble upon request.

#### Figure 7: Index of damages - Mercalli ranks.

#### Figure 8: Index of damages - distance.

#### Figure 9: Discontinuity in expenditures (bandwidth 0.2).

#### Figure 10: Discontinuity in tax revenues (bandwidth 0.2).

#### Figure 11: Map of Italian regions.

#### Figure 12: Map of night lights intensity (average of 2007).

#### Footnotes

* We are extremely grateful to Giancarlo Corsetti for useful comments and invaluable support. We are also grateful to the participants of the conferences and workshops at the University of Cambridge, University of Pavia, University of Exeter, University of Rome Tor Vergata, University of Barcelona (Autonoma), New Economic School, Board of Governors of the Federal Reserve Bank, University of Ferrara, KOF Swiss Economic Institute, 2014 IAEE conference (Queen Mary University of London), 2014 CFM macroeconomics workshop (at NIESR), and the 2014 IIPF annual congress in Lugano for helpful comments. Finally, we are grateful to the Italian National Institute of Geophysics and Volcanology and to the Civil Protection Department for providing the requested data and information. We thank the University of Cambridge, the University of Exeter and the ESRC for financial support. All errors and omissions remain ours. This paper previously circulated with the title 'Shake me the money!'. This version: October 2014. Disclaimer: the views expressed in this paper are those of the authors and do not necessarily reflect those of the Board of Governors of the Federal Reserve System. Return to Text
* Business School, University of Exeter, UK. Return to Text
* Board of Governors, Federal Reserve System, USA. E-mail address: riccardo.trezzi@frb.gov. Return to Text
1. The Department of Civil Protection is a structure of the Prime Minister's Office which coordinates and directs the national service of civil protection. When a national emergency is declared, it coordinates the relief on the entire national territory. It coordinates activities in response to natural disasters, catastrophes or other events which, due to their intensity and extent, must be tackled using special means and powers. In this case, the council of ministers declares the state of emergency by issuing a law by decree and identifies the actions to be undertaken to manage the event. Return to Text
2. Contrary to the well-known Richter scale (which quantifies the moment magnitude of an earthquake meaning the energy released by the event), the Mercalli scale classifies the destructive effects of an earthquake. While every quake has only one magnitude recorded at the epicenter, the destructive effects (therefore the Mercalli ranks) vary greatly across municipalities according to a large set of factors, including the distance from the epicenter or the ex ante vulnerability of buildings. See C for details. Return to Text
3. Although Italy is one of the most seismic countries in the world, households and firms cannot insure against seismic risks since no there is no private insurance market. Return to Text
4. Abruzzo is a southern region of Italy composed by 305 municipalities grouped in 4 provinces (L'Aquila, Chieti, Teramo, and Pescara) for a total 1.3 million inhabitants. See figure 11. Return to Text
5. The six categories are defined as follows: A: usable building; B: usable building after short-term measures; C: partially unusable building; D: temporary unusable building; E: unusable building; F: unusable building and severe external risks. For details see Baggio et al. Return to Text
6. Because of the extension and severity of the damages in the epicentral region, the delegates did not distinguish between Mercalli levels below V and assigned a 0 instead (not shown in the map). Return to Text
7. As an example we report the definition of level VI (Strong):"People: Felt by all. People and animals alarmed. Many run outside. Difficulty experienced in walking steadily. Fittings: Objects fall from shelves. Pictures fall from walls. Some furniture moved on smooth floors, some unsecured free-standing fireplaces moved. Glassware and crockery broken. Very unstable furniture overturned. Small church and school bells ring. Appliances move on bench or table tops. Filing cabinets or easy glide drawers may open (or shut). Structures: Slight damage to Buildings Type I. Some stucco or cement plaster falls. Windows Type I broken. Damage to a few weak domestic chimneys, some may fall. Environment: Trees and bushes shake, or are heard to rustle. Loose material may be dislodged from sloping ground, e.g. existing slides, talus slopes, shingle slides" (source: Mercalli scale). Return to Text
8. Out of 305 municipalities in Abruzzo, 177 were ranked below V, 79 at level V (including V-VI), 41 at level VI (including VI-VII) and 8 at level VII or above. Table 12 shows the distribution across provinces. Return to Text
9. For completeness we report the original text (in Italian) from the law by decree (Decreto Legge 28 Aprile 2009, n.39): "I predetti provvedimenti hanno effetto esclusivamente nei confronti dei comuni interessati dagli eventi sismici che hanno colpito la regione Abruzzo a partire dal 6 aprile 2009 che, sulla base dei dati risultanti dai rilievi macrosismici effettuati dal Dipartimento della protezione civile, hanno risentito un'intensita' MSC uguale o superiore al sesto grado". Return to Text
10. See figure 7 for the correlation between Mercalli ranks and the index of damages. Return to Text
11. The discontinuity in tax revenues is generated by two shocks: a negative shock to the tax base (mainly buildings for the property tax) and a shock to the marginal tax rate due to the suspension of tax payments in 2009 for municipalities reporting severe damages. Return to Text
12. As a measure of grants we consider the sum of both, current and capital grants from central government given that regional government did not provide any financial support for the reconstruction. Return to Text
13. We tested the ex-ante similarity of each variable of interest by regressing the variable on a set of yearly dummies and the interaction between the year dummies and Di,t. The F-statistics on the interactions never reject the null hypothesis of similar behavior across treatment and control groups. Specifically, the p-values of the F-test are the following: output (0.4941), grants (0.9083), and local spending (0.3431). Return to Text
14. Robustness checks (not reported in this version of the paper but available on request) show that our results are insensitive to this choice. Return to Text
15. We specify a quadratic trend. This choice - instead of a linear trend - is driven by the observed U-shaped growth path during the considered period. Specifying a linear trend instead of quadratic makes little (or no difference) on the estimated parameters. Return to Text
16. The inclusion of individual-specific time trends makes no difference on the estimates. For this reason we prefer a more conservative model with a common trend across municipalities. Return to Text
17. Abruzzo is the second most seismic region in Italy after Calabria. Virtually the entire territory is classified as highly seismic by the INGV. Therefore, the ex-ante probability of an earthquake (meaning the ex-ante probability of a municipality to be relatively close to the epicenter) is uniformly distributed across points in the region. Return to Text
18. The coordinates of the epicenter were determined by the INGV and they are as follows: latitude 42.295, longitude 13.628. The distance is calculated using the municipal coordinates released by the national institute of statistics (typically the chosen point is the center of the inhabited part of the municipal surface). Return to Text
19. The municipalities are: Accumoli, Amatrice, Antrodoco, Borbona, Borgo Velino, Borgorose, Castel Sant'Angelo, Cittaducale, Cittareale, Fiamignano, Micigliano, Pescoracchiano, Petrella Salto and Posta, all located in the province of Rieti. Return to Text
20. The Italian provinces are well defined political and geographical entities similar to the US counties. In Abruzzo there are four provinces (Chieti, L'Aquila, Pescara, and Teramo) composed by an average of 76 municipalities each. Return to Text
21. The data are publicly available at: http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html Return to Text
22. Available at: http://finanzalocale.interno.it. Return to Text
23. All ISTAT data are available at: http://dati.istat.it/?lang=en. Return to Text
24. For the measure of the political orientation of each municipality, we take the results of regional election rather than the results of municipal elections because the presence of local political parties which do not allow to unambiguously identify the political orientation of the council. Instead, at regional elections voters choose among the same parties as in the general elections. Return to Text
25. Available at: http://emidius.mi.ingv.it/DBMI11/. Return to Text
26. The figure is calculated by multiplying 0.11 times 0.32. Return to Text
27. According to ISTAT output contracted by 5.5 percent in Italy in 2009. Not surprisingly, the contraction was bigger for Abruzzo region (6.6 percent) given that traditionally the output performance of southern regions is lower than the corresponding national one. However, output contracted only by 5.2 percent in the province of L'Aquila despite the seismic event. Return to Text
28. In the last two models the number of observations is 486 rather 488 and 1212 rather then 1220 because 4 municipalities are missing the 2010 and 2011 fiscal entries. The municipalities (all located in the province of L'Aquila) are: Capistrello, Carapelle Calvisio, Civitella Alfedena, and Scontrone. Return to Text
29. The total number of observations in these regressions is 518 rather then 520 because the municipality of Carapelle Calvisio did not report the budget data for 2010 and 2011. Return to Text
30. In the last three columns the number of observations is 1,158 rather than 1,160 because the 2003 and 2004 fiscal data for municipality of Secinaro are missing. Return to Text
31. The lower number of observations is due to missing values in the data from the Ministry of Interior due to privacy issues. Return to Text
32. The total number of observations in table 10 is 902 rather than 908 (= 227 times 4) since three municipalities have not reported the budgets for 2010 and 2011. The three municipalities (all located in the province of L'Aquila and all reporting a Mercalli 0 in 2009) are: Capistrello, Civitella Alfedena, and Scontrone. Return to Text
33. We assume that each building was correctly categorized following the AeDES system. This assumption is reinforced by the fact that the owner of the building is typically allowed to ask for a double check if the AeDES level is somehow controversial. Return to Text
34. The list of controls used in this regression is as follows (see D for definitions and sources): unemployment, alignment, population band, graduates, unliterary, altimetry, altimetrymax, altimetrymin, urbanization, surface, coast, family, foreigners, commuters, head, left, buildings19, buildings45, buildings61, buildings71, buildings81, buildings91, buildingspost91. Return to Text
35. We take the results of the previous regional election rather than the results of municipal elections because the huge heterogeneity of local political parties who run for mayoral elections do not allow us to identify the political orientation of the council. Instead, at regional elections voters choose among the very same parties as in the general elections. Return to Text
36. Very similar results are obtained when allowing for the distance as a regressor instead of the damages. Return to Text
♣ This version is optimized for use by screen readers. Descriptions for all mathematical expressions are provided in LaTex format. A printable pdf version is available. Return to Text