E ect of a Fiscal Demand Shock on Firm Investmentimmamarino.com/patto_3.pdfthe shock disproportionately, perhaps consistent with fewer nancial frictions. In addition to corporate nance,

Effect of a Fiscal Demand Shock on FirmInvestment∗

Decio CovielloHEC Montreal

Immacolata MarinoCSEF & Federico II

Tommaso NanniciniBocconi

Nicola Persico†

Northwestern

August 26, 2019

Abstract

We study the impact of firm-level demand shocks on factor utilization. The shockwas caused by a decrease in government spending, and it represented a demandshock for infrastructure procurement firms. We leverage a proprietary dataset onprocurement winners before the shock to create a firm-level index of “exposure tothe demand shock,” and use this source of variation to study how individual firmsrespond to a fiscal shock. As expected, more-exposed firms lose more procurementbusiness after the shock.

Our main result is that firms reacted to the demand shock by cutting capitalrather than labor. Furthermore, more-impacted firms decrease debt, are not signifi-cantly more likely to exit; however, their total factor productivity (TFP) increasessomewhat. Finally, we find that firms are heterogeneous in how much they cut capi-tal, depending on firm size: larger firms translate a dollar of demand shock into twiceas big an investment cut, compared to smaller firms. We also find some evidence thatfirms incorporated in more-developed areas translate the shock disproportionately.

This is the first paper, to our knowledge, that can make causal statements abouthow individual firms’ investment responds to a fiscal demand shock.

JEL classification: G310, D92, H57, H72.Keywords: corporate investment activity, factor utilization, fiscal demand shock.

∗Declarations of interest: none. Earlier drafts of this paper circulated with the titles: “Direct Propa-gation of a Fiscal Shock: Evidence from Italy’s Stability Pact,” and “Firm-Level Effects of Fiscal Rules:Evidence from Italy’s Stability Pact.” Thanks to Hafedh Bouakez, Xavier Debrun, Francesco Decarolis,Lorenzo Forni, Maura Francese, Alessandro Gavazza, Luigi Guiso, Giovanna Messina, Roberto Perotti,Luigi Pistaferri, Morten Ravn, Fabiano Schivardi, Guido Tabellini, and seminar participants at the Uni-versity of Palermo, ECB, EIEF, Bank of Canada, IMF. Special thanks to Luigi Pascali for his generoushelp with the firm-level analysis. This research was undertaken, in part, thanks to funding from theCanada Research Chairs program. E-mails: [email protected]; [email protected]; [email protected]; [email protected].

†Corresponding author: Nicola Persico, Professor of Managerial Economics and Decision Sciences,Kellogg School of Management. 2211 Campus Drive, Evanston, IL 60208. Phone: (847)467-1796. Email:[email protected]

1 Introduction

What is the effect of fiscal demand shocks on corporate factor utilization? This important

question is theoretically ambiguous. Some models (Acemoglu et al. 2012, Cohen et al.

2011) assume that firms operate a static Cobb-Douglas production technology, so that all

production factors adjust to a shock in the same proportion; the direction of adjustment

can be positive or negative, depending on how precisely the shock is assumed to affect

firms. In these models, firm decisions are essentially static. A different, more “dynamic”

perspective views investment (hence capital) as the main shock-absorption channel, as in

many macro models. Empirically, we know little about how factor utilization reacts to a

fiscal demand shock due to the difficulty of identifying plausibly-exogenous fiscal shocks.

We make progress on the above question by studying the response of infrastructure pro-

curement firms to a fiscal demand shock. Our identification strategy leverages arguably-

exogenous variation in the firms’ dependence on government spending. Our main finding

is that firms react to a decrease in government spending by cutting capital spending rather

than labor. This finding supports the “macro” perspective, and is somewhat at odds with

the Cobb-Douglas based models and with Cohen and Malloy (2016). We provide a simple

theoretical model that rationalizes this finding based on the durability of capital.

We study the firm-level effects of demand shocks caused by the stricter enforcement

of a pre-existing fiscal rule for Italian municipalities. The fiscal rule (Patto di stabilita

dei comuni) required zero deficit for municipalities with population greater than 5,000.

In 2008, unexpected legislative changes significantly tightened enforcement of the fiscal

rule. We document that infrastructure spending in affected municipalities decreased by

13%-20% due to the change in law. This reduction represented a demand shock for infras-

tructure procurement firms – especially for those which, before 2009, did more business

in municipalities with population greater than 5,000. This shock is arguably more cleanly

identified than the ones the macro literature, and it does not generate general-equilibrium

confounding effects because municipal procurement is only 0.3% of GDP.

We leverage a proprietary dataset on procurement winners to create a firm-level index

of “exposure to the demand shock.” A firm is deemed to be highly exposed if a large

fraction of its pre-2009 revenues were earned through procurement in municipalities with

population above 5,000. Exposure to the demand shock does not correlate with firm-level

2

pre-trends conditional on firm and city-of-incorporation*year fixed effects, so variation

in exposure can be used to causally estimate the impact of a demand shock on firm-

level outcomes. Furthermore, we document that there is no selection into, or out of the

treatment. Hence we can, for the first time to our knowledge, make causal statement

about how individual firms’ factor utilization respond to a fiscal shock.

We find that, as expected, more-exposed firms lose more procurement business starting

in 2009, i.e., they are more impacted by the demand shock. Furthermore, we document

that firms reacted to the demand shock by cutting capital rather than labor. More-

impacted firms decrease debt, and are not significantly more likely to exit; however, their

total factor productivity (TFP) increases somewhat, which we interpret as evidence that

the firms’ production function has decreasing returns to scale. Finally, we find that firms

are heterogeneous in how much they cut capital, depending on firm size: larger firms

translate a dollar of demand shock into twice as big an investment cut, compared to

smaller firms. The small firms’ sluggish response may reflect the relative lumpiness of their

capital. We also find some evidence that firms incorporated in northern Italy translate

the shock disproportionately, perhaps consistent with fewer financial frictions.

In addition to corporate finance, our paper also contributes to the literature on fiscal

rules, because the variation in our paper comes from the tighter enforcement of a fiscal rule.

This source of variation is policy-relevant because: fiscal rules are increasingly common

both at the national and at the sub-national level; and, often, they are weakly enforced.1

Our results demonstrate empirically, for the first time to our knowledge, that enforcement

is a key determinant of a fiscal rule’s effectiveness and that it has an impact on firm

investment.

Related literature There is a large literature on the firm-level effects of political con-

nections. This literature is only tangentially related to this paper because the channels

through which political connections affect firms may be many, including regulation (this

is the case, for example, in Cohen et al. 2011), whereas we pinpoint government demand

1In 2015, 92 countries had fiscal rules, up from seven in 1990. However, fiscal rules are often weaklyenforced. Eyraud et al. (2018, p. 11) writes that “compliance with fiscal rules has been disappointing.”Specifically regarding sub-national fiscal rules, Fredriksen (2013, p. 6) reports that “Monitoring andreporting of sub-central fiscal performance is poor and sanctions are not always credible or effective.”

3

as the operative channel. Within this literature, the most germane paper is Cohen and

Malloy (2016), who compare firms who won a government contract with firms who did

not win and filed a protest. Using this source of variation in government-originated firm

sales, they find that an increase in government-originated demand causes firms to decrease

investment. This somewhat counterintuitive finding is attributed to the (lack of) private-

market discipline caused by reliance on public demand. While intriguing, we view this

evidence as not dispositive because of a concern about selection into treatment (whether

a firm files a protest in a government auction may be correlated with unobserved firm

characteristics).

Ferraz et al. (2015) study the effects of firm-level demand shocks on employment.

Identification is achieved by comparing bidders that narrowly won and lost a Brazilian

government procurement auction. While this source of identification is credible, their

data lack information on investment, which is the key variable in our paper.

Collard-Wexler (2013) studies demand fluctuations in the ready-mix concrete industry.

Demand is proxied by employment in the construction sector. The outcomes of interest

are sectoral dynamics (entry and exit) as well as costs of entry and of changing firm

size. Collard-Wexler (2013) reports that governments purchase half of all U.S. concrete,

primarily for road construction, and so his paper, like ours, studies the infrastructure

procurement sector.2 Compared to Collard-Wexler (2013), our analysis is less focused on

market structure and more focused on firm-level financial outcomes (for which we have a

rich set of outcome variables).

The theoretical literature on shock propagation across intersectoral networks is ger-

mane, to the extent that it makes assumptions on the firm’s factor utilization. Key papers

in this literature are Long and Plosser (1983), Horvath (1998), Dupor (1999), Carvalho

(2010), Acemoglu et al. (2012), Barrot and Sauvagnat (2016), and Baqaee (2017). Al-

though not logically required, most empirical papers in this literature assume that all the

firms in a sector adjust capital and labor in the same proportion after a shock. The macro

view is different: investment is more procyclical than employment, so after a shock, in-

vestment should adjust more than labor.3 Our findings support the macro view: the firms

2See Collard-Wexler (2013, p. 1009).3For Italy, Guiso et al. (2005) show that a large cross-section of Italian firms do not pass the burden

of temporary productivity shocks through to their employee’s wages.

4

in our sector react to a (plausibly exogenous) shock by adjusting capital, not labor.

Grembi et al. (2016) study the impact of an earlier (2001) Patto di Stabilita on mu-

nicipal public finance.4 Their identification strategy also relies on the 5,000 population

threshold. They ask whether the constraint created by the fiscal rule was binding (it was)

and whether municipalities chose to meet the constraint by, at the margin, cutting spend-

ing or increasing taxes (the latter). Chiades and Mengotto (2013) study later versions

of the Patto di Stabilita using the 5,000 population threshold. Their analysis suggests

that the Patto reduced municipal investment but had no effect on current expenditure,

however, their results are not grounded in a formal causal framework. Bonfatti and Forni

(2016) use the 5,000 population threshold to demonstrate that the introduction of the

Patto attenuated the political budget cycle. The key innovation in our paper is that our

dependent variables are firm-level outcomes, not municipal expenditures.

The rest of the paper is organized as follows. Section 2 describes the theoretical

framework. Section 3 describes the institutional background and the data. Section 4

shows how the legislative change generated a fiscal demand shock. Section 5 defines firm-

level exposure to demand shock and quantifies its impact on firm revenues. Section 6

contains the estimates of the effect of the demand shock on capital and labor. Section 7

presents several extensions. Section 8 concludes.

2 Conceptual framework for firm response

In Appendix A we develop a two-period model of factor utilization in response to a demand

shock. In this section, we sketch our argument.

Static version The one-period version of the model assumes that a competitive firm

(such as a municipal procurement firm) operates a Cobb Douglas technology y = kαl1−α.

Output y is sold at a price p. The prices of capital and labor are determined economy-

wide and are unaffected by the firm’s activity. Given these assumptions, we get the

standard result that capital and labor are utilized at a ratio independent of the output

price p. Since in this model a demand shock operates through changes in p, it follows that

the capital/labor ratio is unaffected by demand shocks. This feature is also present in

4Notably, investment expenditures were exempted from the Patto up to 2004.

5

the literature on shock propagation across networks, which makes sense because in this

literature firm decisions are also effectively static.

Dynamic version The model is made dynamic in a very simple way: we add a sec-

ond period and, crucially, we make capital durable. In this setting, a demand shock is

represented by output prices in the two periods, p1 and p2. The firm’s problem is:

maxk,l1,l2

p1kαl1−α

1 + p2kαl1−α

2 − rk − w (l1 + l2) ,

where k represents durable capital and lt represents period-t labor utilization.

In a steady state where demand is constant we have p1 = p2 = p. A persistent negative

demand shock relative to the steady state p can be modeled as p1 = p2 < p. It’s easy to

show that, if p1 = p2, then again the capital/labor ratio is independent of output prices.

Thus a permanent shock has no effect on the capital/labor ratio relative to the steady

state (though both capital and labor levels will decrease). But if p2 < p1, that is, the

shock in period 1 portends an even worse shock in period 2, then Appendix A shows

that the capital labor ratio drops in period 1 relative to the steady state level. This is

because when p1 contains negative information about p2 the firm anticipates the bad shock

in period 2 by reducing capital more sharply than labor. This asymmetric adjustment

reflects the fact that capital is durable whereas labor is purchased on the spot market.

Put differently: the firm uses capital in preference to labor to absorb the negative shock to

future profitability. Interestingly, in our data the drop in municipal procurement in 2008

(demand shock) indeed portended worse news: in the years following 2008, aggregate

procurement increasingly contracted in line with the assumption that p2 < p1.

Comparison of the two versions Comparing to the static version, the dynamic version

of the model better rationalizes the evidence in the sections that follow.

True, other models can be used to rationalize the evidence. But a model based solely on

a more flexible functional form (e.g. CES) for capital-labor substitutability should merely

be agnostic about whether capital or labor should adjust more to a demand shock. And a

model based solely on labor rigidities predicts that outsourced services (subcontractors),

not being subject to these rigidities, should adjust sharply and in the same direction as

capital, which is not what we find in Section 6.

6

In summary: while the goal of this paper is not to provide or test a theoretical model,

this section argues that the evidence to follow is consistent with an “expectation-driven”

adjustment by procurement firms to an autocorrelated demand shock.

3 Institutional background and data

Demand for procurement originates in part from municipalities. In Italy, municipal ad-

ministrations provide roads, schools, and municipal buildings. Municipalities are required

to outsource this provision to private contractors via public tenders. The average munici-

pality makes 2.7 tenders per year, each with an average value (per engineering estimate)

of 333,000 euros. The money for these public works is partly raised by the municipality

itself, with the balance coming from grants (from the region, the central government, and

the EU).

Municipal procurement is a very small fraction of GDP (only 0.3%).5 Thus a sectoral

demand shock is unlikely to reverberate through the economy. In our view, this feature

makes the procurement sector an ideal laboratory to study the direct effects on firms in

isolation of hard-to-measure general equilibrium network effects.

Appendix B contains information about the data sources and the variables we use.

3.1 Change in legislation that tightened enforcement of the fiscalrule

The Patto di stabilita dei comuni was (and is) a fiscal rule designed to check the growth

in municipal public spending, with a view to controlling municipal debt. It required zero

deficit, and in addition a 20% ceiling on total spending growth (current plus capital, year-

on-year). Only municipalities with population greater than 5,000 were subject to this

fiscal rule.6

In August 2008, a law was passed unexpectedly that made enforcement much stricter.

5We compute that in 2007 the average municipality devoted 8% of its spending to capital. We alsocompute that municipal spending equals roughly 2.5% of Italy’s GDP.

6The Patto did not apply to some “special status” regions (Valle d’Aosta, Trentino Alto Adige, FriuliVenezia Giulia, Sicily and Sardinia); we drop these regions from our sample. Notably, the Patto did notfeature a “golden rule” exempting investments from its purview until after 2013: see Guerra (2013, p.954). Grembi et al. (2016) study the impact of a 2001 version of the Patto.

7

Figure 1: Italian municipalities with and without fiscal rule

Notes: Municipalities with population measured in 2001, the most recent census year before the reform. Source:Authors’ calculation on National Institute of Statistics (ISTAT) data.

For the first time: (a) non-compliant municipalities would suffer substantive cuts in gov-

ernment transfers, and restrictions to borrowing for investment; (b) mayors and councilors

in non-compliant municipalities would receive a 30% salary cut.7

Municipalities with population exceeding 5,000 in 2001, the most recent census year

before the reform, will be considered “treated,” the others “controls.” Figure 1 shows the

distribution of treated and control municipalities.

7Law 133/2008 of August 6, 2008. Articles 77bis comma 20, and 61 comma 10.

8

3.2 The procurement sector and firm-level data

The main source of firm-level data is the AIDA database. This database contains informa-

tion on all Italian firms that are required to file a balance sheet; the requirement applies

to corporations but not to partnerships. In addition to yearly financial statements, AIDA

records the firms’ sector (e.g., construction), where the firm is incorporated, and the year

of incorporation. AIDA covers both public and privately-owned companies. We deflate

financial variables using the KLEMS deflators for output and inputs.

AIDA does not report whether a construction firm operates specifically in the pub-

lic procurement sector. Since we care about firms that operate in public procurement,

we restrict attention to the 7,743 AIDA firms which we can match to winners in the

procurement-market database described in Section 3.3 below.8

We start with a sample consisting of the financial information of 4,317 firms AIDA

firms that won at least one infrastructure tender before 2009. Before the fiscal demand

shock takes effect, corporate revenues equal 3.049 million euros on average, only part of

which originate from municipal procurement. Wages equal 395,000 euros. Fixed Assets

equal 464,000 euros. About 1% of the firms in our matched sample cease operations every

year. See Table 1 for summary statistics.

3.3 Procurement-market data

The firm-level dataset is augmented by merging in firm-level procurement-market out-

comes. The procurement-market data were obtained from a private company which alerts

procurement firms to upcoming tenders. The procurement-market data includes all the

call for tenders with valore stimato above 150,000 euros that have to be legally advertised

(Coviello and Mariniello 2014 discuss the details of advertising procurement contracts).

The average tender attracts 30 bidders. There are 30,788 distinct winners in the database.

28% of all tenders are for municipal roads. On average, 55% of winners are incorporated

in the tendering province.

8Many procurement-market winners are not found in AIDA, probably because they are partnerships.To evaluate how representative our matched data are of the entire sector, we compared the averageannual win of the companies we match (2 auctions with average value of 1,000,000 euros) with that ofthe procurement-market winners we do not match (2 auctions, 863,000 euros). We conclude that ourcorporation sample is not majorly unrepresentative of the universe of municipal procurement firms.

9

Table 1: Descriptive statistics (pre-fiscal demand shock)

Stats Mean St.Dev. p10 p50 p90 N(1) (2) (3) (4) (5) (6)

Panel A: Firm data

Revenues from Procurement (in 100,000) 3.892 10.53 0 0 10.90 16,161Capital (in 1,000) 464.0 2,464 12.41 110.7 979.2 16,161Labor (in 1,000) 395.3 1,501 33.99 190.2 775.3 16,161Fisc.Rule.Exp. 17.29 23.91 0 8.091 48.03 16,161Municipal exposure 21.44 25.60 1.477 11.21 59.26 16,161Inc. in Fisc.Rule.Mun. 0.827 0.378 0 1 1 16,161

Panel B: Municipal data

Total value of tenders (in 100,000) 9.703 59.82 0 1.878 19.29 30,075N.Tenders 2.667 7.762 0 1 6 30,075Avg. value of procurement (in 100,000) 3.338 4.702 0.665 2.126 6.835 19,232Percent Roads 27.72 35.86 0 7.549 100 19,233Number of bidders 30.09 26.01 5 23.67 63.33 9,637Winning rebate (in %) 17.35 8.211 7.960 16.01 28.61 10,697Winner from the same province 54.57 34.22 9.274 50.87 100 7,346

Notes: Value Proc. Won is the value of procurement won by a firm in a year (in 100,000 euros);

Capital are the firm total annual physical assets (in 1,000 euros); Labor are the firm total personnelcosts (in 1,000 euros); Financial variables are deflated using KLEMS deflators. Fisc.Rule.Exp.represents the exposure to the fiscal demand shock computed as the ratio between the firm’s value

won in municipalities with fiscal rule and the firm’s pre-fiscal demand shock revenues; Municipalexposure represents the ratio between the firm’s value won in municipalities (with and withoutfiscal rule) and the firm’s pre-fiscal demand shock revenues; Inc. in Fisc.Rule.Mun. equal one for

firms incorporated in municipality with fiscal rule. Source: Statistics pre-fiscal demand shock forprocurement companies that won at least one auction before 2009 and observed between 2004 and2011, for all Italian municipalities.

Bids are expressed as a percentage rebate on a valore stimato: this is an estimate of the

project’s cost which is computed by a municipal engineer based on a government-issued

price list. The average winning rebate is 17.35% of valore stimato, and the average valore

stimato is 330,000 euros. Table 1 reports descriptive statistics of the procurement market.

During our sample period, the law required competitive contests to be anonymous and

single-attribute (i.e., technical and quality components of the offers are not evaluated).

The tender would specify one of several mechanisms through which the contract could be

awarded. The choice of the particular mechanism depends on the valore stimato and on

some other technical components.

4 Fiscal demand shock

When municipal infrastructure spending is reduced, procurement firms experience a de-

mand shock. In this section we quantify the impact of the legislative change that tightened

10

enforcement of the fiscal rule, on municipal infrastructure spending. Figure 1 shows the

distribution of treated and control municipalities. We expect stricter enforcement to cause

a drop in procurement in treated municipalities only, and only after 2008.

Figure 2 depicts the value of public tenders for infrastructure. As expected, this value

drops after 2008, and it drops more sharply in municipalities that are covered by the fiscal

rule.9

Figure 2: Increased enforcement of fiscal rule is associated with drop in infrastructure spending

Notes: Infrastructure spending represents the amount of municipal spending for infrastructures and it is the average totalannual value of procurement for infrastructures procurement. The picture reports yearly averages across Italian municipali-ties. Diamonds represent fiscal rule municipalities with population above 5,000. The vertical dashed line indicates August2008 when the law was passed. Source: Authors’ calculation on procurement data, and municipal budget data from ItalianMinistry of Interior for all municipalities between 2004-2011.

9That the drop in spending can happen so quickly is consistent with the procurement law (D.Lgs163/06), whereby 3-year municipal procurement plans are revised on yearly basis. Because there is no“golden rule,” it makes sense for local administrators to cut investment expenditures: investment is notexempt from the Patto.

11

4.1 Identification: effect of legislative change on municipal in-frastructure spending

To estimate the average treatment effect of the legislative change on municipal infras-

tructure spending, we use a difference-in-difference research design. The treatment is

defined as the municipality being above the 5,000 population threshold after 2008. For

each municipal-level outcome we estimate the following econometric model:

yit = α + δF isc.RuleiXPostt + βFisc.Rulei + γPostt + εit, (1)

where yit represents infrastructure spending and δ is our main coefficient of interest,

Fisc.Rulei is an indicator variable for municipalities with population above 5,000, Postt

indicates the years after 2008.10 We then add municipal and time fixed effects. Standard

errors are clustered at municipal level. The coefficient δ represents the average impact of

the laws mandating stricter enforcement, on all treated municipalities.

Our research design relies on two key identifying assumptions. First, treated and

control municipalities must follow a parallel trend before the treatment. Second, no other

shocks exist that affect treated and control municipalities differentially in 2008. We bring

evidence in support of these assumptions in the Section 4.3.

4.2 Estimates: impact of legislative change on municipal infras-tructure spending

The coefficients of Fisc.Rule*Post in Table 2, columns 1 and 2 indicate that, regardless of

whether municipal- and time-fixed effects are included, the laws mandating stricter enforce-

ment reduced infrastructure spending by roughly 28% of the average pre-2009 spending

in the treated municipalities. We then add: time-varying municipal population and its

squared term, see column 3; annual transfers from other governments (region and state) to

the municipality, see column 4; and then both variables together and then add municipal

tax revenues, see column 5. Controlling for transfers and tax revenues to the municipality

is important because these transfers and taxes represent an important funding source for

infrastructure procurement. The estimated coefficient is robust to the inclusion of these

controls.10Because the law was passed in August, we chose to consider 2008 a “pre” year. The main estimates

are robust to considering 2008 a “post” year. See discussion in Section 6.2.

12

Table 2: Impact of the fiscal shock on infrastructure spending

Dep. Var. Total Value of Procurement

Method OLS FE FE FE FE FE-L&L(1) (2) (3) (4) (5) (6)

Fisc.Rule*Post -6.825*** -6.825*** -7.192*** -6.413*** -5.491***(1.170) (1.170) (1.818) (1.073) (1.666)

Post -0.793***(0.079)

Fisc.Rule 21.019***(2.113)

Population 0.003 -0.001(0.004) (0.004)

Population2 -0.001*** -0.001***(0.000) (0.000)

Transfers (in 100k) 0.010** 0.011***(0.004) (0.003)

Tax revenies (in 100k) -18,136.597(11,527.209)

Fisc.Rule*year2005 -1.791(1.871)

Fisc.Rule*year2006 -0.623(2.193)

Fisc.Rule*year2007 0.062(2.517)

Fisc.Rule*year2008 -1.439(2.179)

Fisc.Rule*year2009 -6.005***(2.296)

Fisc.Rule*year2010 -6.609***(2.470)

Fisc.Rule*year2011 -10.135***(2.537)

Observations 48,120 48,120 48,120 47,912 47,804 48,120Municipal FE No Yes Yes Yes Yes YesYear FE No Yes Yes Yes Yes YesMean Y treat-pre 24.01 24.01 24.01 24.01 24.01 24.01Eff.Fisc.Rule on Treated (%) -28.42 -28.42 -29.95 -26.71 -22.87

Notes: The table reports estimates of the effects of the fiscal shock on the annual total value ofmunicipal tenders for infrastructures in all Italian municipalities. In each of the rows, Fisc.Ruleis an indicator variable for municipalities with population above the 5,000 population threshold

and Post is an indication for the years after 2008. Population represents the municipal populationin 1,000 inhabitants. Transfers (in 100k) represents the transfer to the municipality by central

governments (state and region). Taxes (in 100k) represent municipal tax revenues. In Column 6, the

regression includes leads and lags denoted by the interactions terms between the Year2005-Year2011time dummies and the Fisc.Rule indicator. Mean Y treat-pre is the sample mean for treated

municipalities pre-2009. Eff.Fisc.Rule on Treated (%) is the ratio between the estimated coefficientof Fisc.Rule*Post and Mean Y treat-pre. SEs are clustered at municipal level. Significance at the

10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for all the public works tendered

between 2004 and 2011 in Italy.

4.3 Testing the identifying assumptions

The laws mandating stricter enforcement, we argue, caused the drop in infrastructure

spending measured above. This causal statement is based on on two identifying assump-

13

tions: parallel trends, and no confounders from the financial crisis. We discuss each in

turn.

Municipal Parallel Trends The estimates rely on a pre-2009 parallel-trend assump-

tion. This assumption is supported visually in Figure 2 and is formally tested by check-

ing the statistical significance of the interaction term Fisc.Rule and the year dummies

(Year2004 -Year2011 ) in a model where municipal demand for infrastructure is regressed

on: municipal and year dummies; the fiscal rule dummy; and the interaction terms. Col-

umn 7 of Table 2 reports the estimated coefficients on the leads for the years before 2009

(statistically, zero) and on the lags for the years after (negative and large).11 This evi-

dence suggests that the parallel-trend assumption is not rejected and they rule out any

anticipated effects of the policy, consistent with the parallel-trend assumption. Further,

the lack of anticipation effects suggests that any other changes in the Patto and its en-

forcement during our sample period were not effective in restraining municipal investment

expenditure.

Is the 2008 financial crisis affecting the municipal-level estimates? The 2008

crisis affected the national government, but not local government finances as much. The

reason is that municipalities raised revenues from property taxes and other utility fees

which are based on cadastral values, not market values.12 Still, it is possible that the

financial crisis did affect municipalities, and that it did so differentially, bringing greater

financial distress to larger municipalities. If that were the case, our estimates would be

confounded by the crisis. But the evidence does not support this hypothesis. Debt actually

decreased (Figure C.1 row 1), current spending increased more (Figure C.1 row 3), and

municipal tax revenue increased more (see Figure C.2, top-left panel) in municipalities

above 5,000 after 2008, which seems to go against the idea that larger municipalities

suffered more during the crisis.13

One might still be worried that our treatment (above 5,000 * after 2008) might still

11We find similar evidence when we restrict the sample to smaller municipalities (as opposed to all mu-nicipalities) and we look at the (1k-10k), (3k-7k) and (4k-6k) windows around the 5k treatment threshold;see columns 1, 3, and 5 of Table C.1.

12Central government transfers did decrease, but we control for them directly (col. 5, Table 2).13Consistent with this descriptive evidence, controlling for municipal tax revenues does not affect our

estimates (col. 5, Table 2). The same pattern emerges in panel two and three of Figure C.2, where we plotmunicipal tax revenue for municipalities with population between 1,000 and 10,000 inhabitants (right-toppicture) and 3,000 and 7,000 inhabitants (left-bottom picture).

14

be picking up a “big city after 2008” shock different from the fiscal rule. If that were the

case, then there should be nothing special about the 5,000 population threshold: “large”

municipalities should be more affected than “small” municipalities after 2008, and so our

estimates of “post 2008” should remain significant at many placebo population thresholds.

We rule out this possibility in Table C.3.14 Our evidence suggests that our effects are driven

precisely by the 5,000 population threshold, and not majorly by any heterogeneity in the

financial crisis’ impact.

Therefore, we conclude that the effect that we are picking up is due to a discontinuity

at 5k; that this discontinuity cannot be due to the crisis; and that any remaining effects

of the crisis on municipal public finances are well-controlled by controlling for central

government transfers.

4.4 Robustness

In Appendix C we report on several robustness checks.

• We restrict the sample to tighter windows around the treatment threshold (Table

C.2, columns 1 and 2). The point estimates decrease but they do not vanish. This

is interesting but not central to our analysis: the average treatment effect is what

matters for the firm-level analysis in the next section.

• We rule out sorting around the 5,000 population threshold (Figure C.3).

• We re-do Table 2 using log and inverse hyperbolic sine transformations of the de-

pendent variable (the latter helps guard against zeros in the data). The results are

robust; see Table G.1, columns 1-3.

4.5 Summary of this section

This section has documented that the 2008 legislation change caused treated municipalities

to reduce their infrastructure procurement. The effects are concentrated in infrastructure

spending (current spending was unaffected: compare rows 2 and 3 in Figure C.1) and

14This table shows no effects at nearby placebo thresholds below (columns 1-3) and above (columns 5-7)the true threshold. Only column 4, the true threshold, shows an effect. Consistent with this interpretation,Table C.2 shows that as we narrow the window around the 5,000 threshold the coefficient stabilizes at-13%.

15

represent a well-identified fiscal shock. The next section traces the effects of this shock on

procurement firms.

5 Firm-level exposure to demand shock: definition,

and impact on revenues

The previous section documented that the 2008 legislation caused treated municipalities

to reduce their infrastructure procurement. This reduction represents a demand shock for

firms operating in the infrastructure procurement sector.

In this section we first compute a variable measuring each firm’s pre-2009 exposure to

the demand shock. We then show that more-exposed firms lose more procurement business

starting in 2009, i.e., they are more impacted by the demand shock, as anticipated. And

finally we show that more-exposed firms are not more exposed to other time-varying shocks

that may affect their outcomes (pre-trend analysis).

5.1 Measuring firm-level exposure

Firms which, before 2009, did more business in municipalities with population exceeding

5,000 should, after 2009, be considered more “exposed” to the demand shock. Accordingly,

we define a firm’s exposure to the fiscal demand shock as the value of procurement won

by a firm in municipalities with population greater than 5,000, as a percentage of the

firm’s total revenues, before 2009. This variable is only computed for the 4,317 companies

that won at least one auction before the fiscal demand shock. To construct this unique

variable, we leveraged the proprietary procurement-market data described in Section 3.3.

This variable is the source of identification in this paper.

Figure 3 plots the frequency of firms by exposure to the fiscal demand shock. The me-

dian firm’s exposure is just 8%; 21% of firm revenues comes from municipal procurement;

83% of these companies are incorporated in treated municipalities. Thus corporations that

operate in the municipal procurement sector are revenue-diversified.

We also check whether more-exposed firms are different from less-exposed firms. This is

done by regressing firm-level variables on the “exposure” variable. In general, we find that

more-exposed firms are smaller; see Table D.1. This variation in levels, while interesting,

16

Figure 3: Heterogeneity in firm exposure to the fiscal demand shock

010

2030

40%

of fir

ms

0 20 40 60 80 100Firm exposure to the fiscal rule

Notes: A firm’s exposure to the fiscal demand shock is defined as the value of procurement won by a firm inmunicipalities with population greater than 5,000, as a percentage of the firm’s total revenues, before 2009. Thesample median is 8% (vertical dashed line) and the standard deviation is 24%. Source: Authors’ calculation onpublic works data and AIDA data.

will not affect our estimated coefficient (δ in specification (2) ) because that specification

includes firm-level fixed effects.

5.2 Identification: impact on firm revenues of exposure to thedemand shock

To estimate the causal effect of the demand shock on revenues from procurement, we

estimate the following model:

yit = α + δF isc.Rule.Exp.iXPostt + βFisc.Rule.Exp.i + γPostt + µct + εit, (2)

where yit is the revenue variable in levels,15 Fisc.Rule.Exp. is the firm’s exposure to the

fiscal demand shock variable defined in Section 5.1. To control for municipal-level time

varying shocks, we introduced city of incorporation-by-year fixed effects µct. We also add

firm-specific and time fixed effects. While (2) is our preferred specification, we also run the

15In Table G.1 we report estimates of the effects of the fiscal shock on revenues from procurement,capital and labor expressed in logs, and also applying the inverse hyperbolic sine transformation discussedin Section 4.2. We find comparable evidence when we consider these alternative specifications.

17

specification that excludes µct and compare the estimates, in order to assess the possible

confounding effects of pre-trends.

Standard errors are clustered at firm level. In Table G.2 we repeat the analysis with

clustering at the city-of-incorporation level and find evidence that is comparable in mag-

nitude and statistical significance to our baseline estimates.16

Our firm-level research design relies on three key identifying assumptions. First, a

common trend in firms’ outcomes before the demand shock. Second, that differential

exposure to the 2008 crisis did not affect the estimates. Third, that more-exposed firms

did not move their business to non-treated municipalities after 2008. We provide evidence

in support of these assumptions in Section 6.2.

A caveat: exposure may be measured with error. Depending on the assumptions one

is willing to make on the measurement error (e.g., time-invariant versus time-variant)

our estimates will not be biased when the measurement error is time-invariant as the

measurement error will be washed-out by firms’ fixed effects, or, alternatively, will be

biased toward zero and will represent a lower bound of the true estimates.

5.3 Exposure to demand shock has the predicted effect on pro-curement revenues

We expect more-exposed firms to lose more procurement business starting in 2009, i.e.,

to be more impacted by the demand shock. Table 3, columns 1-3 confirms this: the

estimated coefficient δ is negative in all three specifications. Column 4 reports the year-

by-year impact of exposure: as expected, exposure is more harmful to procurement revenue

after 2008. These estimates are plotted in Figure 4 Panel 1 (top-left).

Regarding magnitudes: a one-standard-deviation increase in firm exposure to the

demand shock, when multiplied by the coefficient in Table 3 column 3, yields 23.91 ∗(−0.095) = −2.27, corresponding to a drop of 227 thousand euros in annual value of

procurement won, or 72% of the average value of municipal infrastructure procurement

won.17

16 In Table G.3 we repeat the analysis controlling for regional fiscal demand shocks by adding a time-varying control for the fraction of cities exposed to patto in the region of incorporation of the company(and its interaction term with the variable Postt) and find evidence that is comparable in magnitude andstatistical significance to our baseline estimates.

17In Columns 13-20 of Table D.6 in the appendix we also consider the effects on the following balance

18

In terms of our theoretical framework (Section 2), Figure 4 Panel 1 (top-left) indicates

that starting in 2008 revenues would be worse than in the past, and more so for more-

exposed firms. In the theoretical model, this knowledge will lead forward-looking firms to

drive their capital/labor ratio downward, and more so for more-exposed firms. The next

section documents such an effect.

5.4 Testing the identifying assumptions: parallel trends

The standard test for the common-trend assumption needs to be adapted to our setting

because our treatment variable is continuous and not binary. In Table 4 (Cols.1 and 2)

we check whether more- and less-exposed firms share the same trend in the revenues from

procurement (and other firm-level variables) before 2009 by regressing firm revenues from

procurement, capital and labor (and all other outcomes) on a linear time trend interacted

with the variable “Fiscal Rule Exposure.” Table 4 does not reject the hypothesis of no-

pretrend in revenues from procurement.

6 Effect of the demand shock on capital and labor

The previous section documents that more-exposed firms suffered more in revenue terms

from the demand shock. In this section we document how the demand shock impacted

capital and labor, first in reduced form and then in a “structural” (i.e., IV) form.

6.1 Reduced form: effect of exposure on capital and labor

According to our theoretical framework (Section 2), more-exposed firms should more-

sharply reduce their capital/ratio downward as soon as they learn about a permanent

decrease in demand. Table 3 confirms this: the estimated coefficient δ is negative for cap-

ital and non-significantly different from zero for labor in all three specifications (columns

sheets measures: “Total Revenues”, and “Inventory”. No significant impact of the fiscal demand shock isdetected on Revenues because revenues are the most lagging indicator: they are recognized only after theprocurement contract is carried out and payment is received. Typically, payments are received severalyears after the procurement contract is awarded. Proper accounting practices would require the firm tomention any newly-won procurement contract in the notes to their financial accounts, but these are notavailable in AIDA. The second-most lagging indicator is Accounts Receivable, because receivables are dueafter the work is performed, but before payment is received. In Column 19, Accounts Receivable showa negative (3.2%) effect (23.91 ∗ (2.432) = 58.15), corresponding to a drop of 58 thousand euros, thoughthis estimate is not statistically significant.

19

5-7 and 9-11). Columns 8 and 12 reports the year-by-year impact of exposure: as pre-

dicted, greater exposure leads to greater de-capitalization after 2008, but such pattern is

not observed for labor. These estimates are plotted in Figure 4 Panels 2 and 3 (top-right

and bottom-left).

Regarding magnitudes: based on Table 3, column 7, one standard deviation of exposure

to the demand shock decreases capital by 16%. This figure obtains by multiplying 23.91 ∗(−3.839) = −92, corresponding to a drop of 92 thousand euros, or 16% of average physical

assets.

These findings agree with our theoretical framework (Section 2): we find that starting

in 2008 the capital/labor ratio drops more sharply for more-exposed firms, as predicted

by the theoretical model. Overall, procurement firms are shown to react to a demand

shock by sharply adjusting capital, and not by adjusting labor. This finding is perfectly

consistent with the macro view that investment is procyclical, as well as with the findings

of Guiso et al. (2005) on Italian firms.18

18Guiso et al (2005) show that a large cross-section of Italian firms do not pass the burden of temporaryproductivity shocks through to their employee’s wages (nothing is said about the employment level).

20

Figure 4: Effect of firm exposure on revenues from procurement, capital and labor

Notes: Revenues from Procurement is the value of procurement won in a year (in 100,000 euros); Capital are the firm total annualphysical assets (in 1,000 euros); Labor are the firm total personnel costs (in 1,000 euros). Financial variables are deflated usingKLEMS deflators. Fisc.Rule.ExpXYearFE is the estimated coefficient of the interaction term Fiscal Rule Exposure and the yeardummies (Year2004 -Year2011) in a model where outcomes are regressed on: firm and year dummies; the Fiscal Rule Exposurevariable; the interaction terms; and city of incorporation-by-year fixed effects (see Columns 4, 8 and 12 of Table 3). Dashed linesare the associated 95% confidence intervals. A firm’s exposure to the fiscal demand shock (Fisc.Rule.Exp.) is defined as the valueof procurement won by a firm in municipalities with population greater than 5,000, as a percentage of the firm’s total revenues,before 2009. Vertical lines indicate August 2008 when the law was passed. Source: Authors’ calculation on public works dataand AIDA data.

21

Table 3: Reduced form estimates: effect of exposure on revenues from procurement, capital and labor

Dep.Var. Rev.Proc Rev.Proc Rev.Proc Rev.Proc Capital Capital Capital Capital Labor Labor Labor LaborMethod OLS FE FE FE-L&L OLS FE FE FE-L&L OLS FE FE FE-L&L

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Fisc.Rule.Exp.XPost -0.094*** -0.088*** -0.095*** -4.067*** -4.504*** -3.839*** 0.239 0.012 0.033(0.006) (0.006) (0.008) (0.694) (0.597) (1.121) (0.326) (0.204) (0.331)

Fisc.Rule.Exp. 0.105*** -6.656*** -5.446***(0.005) (0.609) (0.612)

Post -0.133 371.035*** 6.408(0.116) (45.677) (18.419)

Fisc.Rule.Exp.*Year2005 -0.111 2.157** 0.556(0.086) (1.072) (0.469)

Fisc.Rule.Exp.*Year2006 -0.107 1.252 0.166(0.082) (1.109) (0.498)


Fisc.Rule.Exp.*Year2008 -0.116 -1.199 0.287(0.085) (1.343) (0.587)

Fisc.Rule.Exp.*Year2009 -0.195** -3.030** 0.438(0.084) (1.321) (0.568)



Observations 27,764 27,678 20,003 20,003 27,764 27,678 20,003 20,003 27,764 27,678 20,003 20,003Mean Y 3.166 3.166 3.166 3.332 584.4 584.4 584.4 576.8 397.2 397.2 397.2 410.1St.Dev.Fisc.Rule 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91Eff.Fisc.Rule.Exp. (%) -70.65 -66.68 -71.76 -16.64 -18.43 -15.71 1.441 0.0751 0.201Company FE No Yes Yes Yes No Yes Yes Yes No Yes Yes YesYear FE No Yes Yes Yes No Yes Yes Yes No Yes Yes YesCityXYearFE No No Yes Yes No No Yes Yes No No Yes Yes

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firms revenues from procurement, capital accumulation and labor: Rev.Proc. is

the value of procurement won in a year (in 100,000 euros); Capital are the firm total annual physical assets (in 1,000 euros); Labor are the firm total personnel costs (in1,000 euros). Financial variables are deflated using KLEMS deflators. Fisc.Rule.Exp. represents the exposure to the fiscal demand shock computed as the ratio between

the firm’s value won in municipalities hit by the demand shock and the firm’s pre-demand shock revenues. In each of the rows, Post is an indication for the years after2008. Year2005-Year2011 are time dummies interacted with Fisc.Rule.Exp. When denoted with Yes estimates include Company, Year and city of incorporation-by-year fixed

effects. Mean Y is the sample mean for each dep.var. Eff.Fisc.Rule.Exp (%) is the ratio between the estimated coefficient of Fisc.Rule.Exp.*Post*St.Dev.Fisc.Rule and Mean

Y. Significance at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for procurement companies that won at least one auction before 2009 and observedbetween 2004 and 2011.

22

6.2 Testing the identifying assumptions: parallel trends

As in Section 5.4, we regress capital and labor before 2009 on a linear time trend interacted

with the variable “Fiscal Rule Exposure.” After controlling for city-of-incorporation by

year fixed effects, only capital shows a statistically significant pre-trend, see Table 4 Panel

A.19 However, this statistically significant negative pre-trend disappears if we consider the

year-by-year regressions in Table 3, Col. 8 or if we label the year 2008 a post- rather than

a pre-treatment year.20 This can be seen in two ways.

1. Visually, in Figure 4 panel 2 (top right): the pre-trend would not exist if we classified

the year-2008 diamond as a “Post” rather than, as currently, a “Pre” observation.

2. Computationally: if we re-define the dummy ”Post” as inclusive of the year 2008,

no variable (including capital) shows a significant pre-trend. See Table 4, Panel B.

This observation suggests two ways of dealing with the pre-trend. One is to re-define

the dummy “Post” to include 2008. The other, which we prefer, is to check whether the

current estimates are sensitive to the pre-trend. To check this, we use Oster (2019)’s

procedure.21 Table 5 displays estimates with no city-of-incorporation by year fixed effects

(Col.1), with city-of-incorporation by year fixed effects (Col.2), and bias-adjusted estimates

(Cols.3 and 4). Reassuringly, most of our estimates of βs are stable across specifications.

This stability suggests that firm-specific time-varying unobservable factors do not represent

a major source of bias for our results.

6.3 Possible confounders in the reduced-form analysis

Does the 2008 financial crisis bias the estimates? A first line of defense against

this concern is that our estimates include city-of-incorporationXyear fixed effects. These

flexible fixed effects are expected to absorb a large part of any “2008 crisis effect.” Still,

a possible confounder to our “identification by differential exposure to treatment” is that

exposure to the 2008 demand shock may be correlated with exposure to the 2008 crisis

19In Table D.2 we report the estimates for all the other firm outcomes.20Recall that the law was passed in August, so part of 2008 is technically “post.” In Tables D.3 and

D.4 (for the municipal analysis) we repeat our analysis including the year 2008 as post-treatment and findevidence that is comparable in sign, magnitude and statistical significance to our main evidence.

21Oster (2019), Table 5 pg. 202.

23

Table 4: Pre-2009 trends of revenues from procurement, capital and labor ofexposed firms

Dep.Var. Rev.Proc Rev.Proc Capital Capital Labor LaborModel FE FE-HT FE FE-HT FE FE-HT

(1) (2) (3) (4) (5) (6)

Panel A: Pre-Reform time-Period: 2004-2008Fisc.Rule.Exp.*Year -0.0081 -0.0037 -1.3173*** -0.7496*** -0.1232* -0.0065

(0.006) (0.009) (0.171) (0.199) (0.067) (0.079)

Observations 15,924 11,428 15,924 11,428 15,924 11,428Mean Y 3.874 4.112 468 455.6 399.4 411St.Dev.Fisc.Rule 23.91 23.91 23.91 23.91 23.91 23.91Effect Fisc.Rule.Exp (%) -4.992 -2.161 -6.730 -3.934 -0.737 -0.0377

Panel B: Pre-Reform time-Period: 2004-2007Fisc.Rule.Exp.*Year -0.0132 -0.0018 -0.3822*** -0.1307 -0.2207*** -0.0647

(0.010) (0.015) (0.133) (0.179) (0.076) (0.093)

Observations 11,640 8,269 11,640 8,269 11,640 8,269Mean Y 3.898 4.151 440 435 398.4 410.2St.Dev.Fisc.Rule 23.91 23.91 23.91 23.91 23.91 23.91Effect Fisc.Rule.Exp (%) -8.086 -1.025 -2.077 -0.718 -1.324 -0.377Company FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesCityXYear FE No Yes No Yes No Yes

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firm outcomes: Rev.Proc. arethe revenues from procurement (in 100,000 euros); Capital are the firm total annual physical assets (in 1,000 euros); Labor

are the firm total personnel costs (in 1,000 euros). Financial variables are deflated using KLEMS deflators. Fisc.Rule.Exprepresents the exposure to the fiscal demand shock before the Patto, and it is computed as the ratio between the firm’s value

won in municipalities with fiscal rule and the firm’s pre-fiscal demand shock revenues. Fisc.Rule*Year is the interaction of

the exposure to the fiscal rule to the linear trend. All the columns include firm and time effects. Even columns include cityof incorporation-by-year fixed effects. Effect Fisc.Rule.Exp (%) is the % effect of the fiscal demand shock. The sample is

before the fiscal demand shock. SEs are clustered at firm level. Significance at the 10% (*), at the 5% (**), and at the 1%

(***). Source: In Panel A (Panel B): statistics for procurement companies that won at least one auction before 2009 andobserved between 2004 and 2008 (2007).

in the non-municipal portion of a firm’s revenues, and therefore our firm-level estimates

might be confounded by a post-2008 drop in private-sector demand. To explore this

possibility we construct a new variable, Private Account Receivables, by subtracting “Value

of procurement won” from “Accounts Receivable” (refer to Appendix B). This variable

proxies for the private portion of the firm’s order book. In Table D.5 we consider this

variable as the dependent variable as in the main econometric model of Table 3, and

we find that coefficient of the interaction term Fisc.Rule.ExpXPost is positive and often

statistically significant. This indicates that private revenue are not reduced differentially

24

Table 5: Oster (2019) test

Model Short: No City-by-Year FE Long: Yes City-by-Year FE Bias-Adjusted β∗ Bias-Adjusted β∗

Desc. β ( ˚St.Error) [R2] β ( ˜St.Error) [R2] R2Max=R2+(R2-R2) R2

Max=1.3·R2

(1) (2) (3) (4)Rev.Proc. -.088 (.006) [.32] -.095 (.008) [.43] -.097 -.12Capital -.4.5 (.6) [.92] -3.8 (1.1) [.93] -3.8 -3.5Labor .012 (.2) [.97] .033 (.33) [.97] .037 .56

Notes: In column 1(2), estimates are obtained without (with) city of incorporation-by-year fixed effects. In columns three

and four bias-adjusted estimates β∗ are computed with the following formula, and implemented in STATA with the .ADO

file PSACALC. β∗ ≈ β− δ[β− β]R2

Max−R2

R2−R2. In cols. 3 and 4, R2

Max are computed as in Cols. 3 and 4 of Table 5, pg. 202 of

Oster (2019) with R and R being the R2s from the model with and without city-year FE, respectively. δ is set to 1 under the

assumption that firm specific time-varying unobservables are at least as important as the observables to estimate treatment

effects. Rows reports estimates for: Rev.Proc., which is the value of procurement won in a year (in 100,000 euros); Capital,which are the firm total annual physical assets (in 1,000 euros); Labor which are the firm total personnel costs (in 1,000

euros). Financial variables are deflated using KLEMS deflators. Source: Statistics for procurement companies that won atleast one auction before 2009 and observed between 2004 and 2011.

for firms that are more exposed to municipal procurement, after 2008. Therefore, it seems

unlikely that our firm-level estimates are confounded by exposure to the financial crisis

through the firm’s private-sector demand.

Checking for treatment spillovers using procurement data. In this section we

explore whether we might be underestimating the magnitude of firm-level effects due to

treatment spillovers in the procurement market. We are concerned about the possibility

that firms which did business in municipalities that are subject to the fiscal demand shock

might, after 2008, start bidding more often in municipalities that were not hit by the fiscal

demand shock. If that were so, then pre-fiscal demand shock exposure, the variable we use

to capture treatment intensity, might not be a good proxy for actual treatment take-up.

Presumably, this would mean that our results under-estimate the true impact of the fiscal

demand shock on firms.

To explore the economic significance of any spillovers, we leverage the procurement

market data. Table 6 presents the estimates of estimating Equation 1 based on municipal-

level averages. Columns 1-4 confirm the large impact of the fiscal demand shock on the

demand for public works. Columns 5, 6 look at the percentage of tender value which is road

construction – a proxy for demand composition effects. The estimates suggest that shifts in

demand composition, if any, are negligible. Columns 7-10 are informative about spillover

effects. The estimates go against the notion that firms move away from municipalities

that are hit by the fiscal demand shock, because contests held by municipalities hit by

25

the fiscal demand shock tend to have slightly more bidders and slightly higher winning

rebates after 2008. Furthermore, we detect no effects on the firms’ radius of operation:

columns 11-12 show no statistically significant change in whether the winning firm is from

the same province as the tendering municipality.

Overall, we read the evidence coming from procurement market data as not supporting

the notion that there was significant selection out of treatment. We thus conclude that

downward biases in the estimated firm-level effects of the fiscal demand shock, if any, are

slight.

Table 6: Checking for treatment spillovers in the procurement market

Dep. Var. N.Tenders Avg.Value Perc.Roads N.Bidders Winning Rebate Winner from theof procurement same province

Model OLS FE OLS FE OLS FE OLS FE OLS FE OLS FE(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Fisc.Rule*Post -1.705*** -1.705*** 0.807*** 0.827*** 0.352 0.593 4.845*** 3.242** 1.349*** 1.300*** 2.038 1.847(0.182) (0.182) (0.252) (0.247) (0.987) (1.046) (1.385) (1.615) (0.334) (0.362) (1.408) (1.670)

Post -0.532*** 1.310*** -1.839*** 5.162*** 2.912*** 6.019***(0.019) (0.133) (0.713) (0.927) (0.255) (1.057)

Fisc.Rule 4.310*** 1.623*** 0.512 7.915*** 1.547*** -17.312***(0.278) (0.084) (0.555) (0.637) (0.237) (0.913)

Municipal FE NO YES NO YES NO YES NO YES NO YES NO YESYear FE NO YES NO YES NO YES NO YES NO YES NO YES

Notes: The table reports estimates of the effects of the fiscal demand shock on on municipal procurement outcomes: N.Tendersis the number of tenders in a municipality in a year; Avg.Value of procurement is the average value of tenders in a municipalityin a year computed using the engineers’ estimates of the value of the works; Roads is the fraction of roads’ tenders; N.Bidders is

the number of competitors submitting an offer; Winning-Rebate is the winning offer, which represents the percentage discountover the engineer’s estimate of the value of the works. In each of the rows, Fisc.Rule is an indicator variable for municipalitieswith population above the fiscal rule population threshold (5,000 inhabitants) and Post is an indication for the years after

2008. Odd (even) columns report OLS (FE) estimates (with municipal and year fixed effects). Mean Y treat-pre and St.Dev.Y treat-pre are the sample mean and standard deviation for treated municipalities pre-2009. SEs are clustered at municipallevel. Significance at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for all the public works tenderedbetween 2004 and 2011 in Italy.

6.4 Instrumental Variables Estimates

So far we have estimated a reduced-form model: how exposure to the demand shock

impacts procurement revenues, capital and labor. In this section we treat exposure as an

instrument for procurement revenues. If this instrument is valid, it provides exogenous

variation in procurement revenues y after 2008, that can be used to estimate the (inverse)

output elasticity δxδy

· yx, where x is either capital or labor.

The two-stage model is given by equation (2) together with:

xit = β1 + β2yit + µct + ωit, (3)

26

where xit is either capital or labor, and yit is procurement revenue for firm i in year t. The

rest of the variables are like in equation (2).

It is plausible that exposure to the demand shock (Fisc.Rule.Exp.iXPostt) is a valid

instrument; the first stage is strong (Table 3 Col. 3); the instrument does not correlate

with pre-trends in revenues, labor, and it correlates weakly (in the precise sense discussed

in Section 5.4) with capital; finally, the inclusion of firm- and city-year fixed effects makes it

plausible that the residual variation in the instrument be independent of any time-varying

unobservables affecting capital and labor.

Table 7 reports the estimation results. Labor is statistically unaffected by a (plausibly

exogenous) change in output, consistent with the reduced-form findings. Let us now turn

to the (inverse) elasticity of capital. Capital decreases by 40,400-51,000 euros for every

100,000 euros of a (plausibly exogenous) decrease in output, so δxδy

≈ 0.4-0.51. The average

output and capital in the sample equal 3M and 584k respectively, so y/x ≈ 5. Therefore,

the (inverse) output elasticity of capital equals 5 ∗ 0.4-0.51. ≈ 2-2.5.

Table 7: Instrumental Variable Estimates

Dep.Var. Capital Capital Capital Capital Labor Labor Labor LaborModel OLS-FE IV-FE OLS-FE-HT IV-FE-HT OLS-FE IV-FE OLS-FE-HT IV-FE-HT

(1) (2) (3) (4) (5) (6) (7) (8)

Rev.Proc 6.412 51.019*** 8.954 40.406*** -0.628 -0.141 -1.275 -0.351(7.686) (7.558) (12.176) (12.017) (1.855) (2.316) (2.825) (3.489)

First-Stage F-Stat 467.9 249.4 467.9 249.4Observations 27,678 27,678 20,003 20,003 27,678 27,678 20,003 20,003Mean Y 585.8 585.8 585.8 585.8 398 398 398 398Mean Rev.Proc 3.892 3.892Company Yes Yes Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes Yes Yes YesCityXYearFE No No Yes Yes No No Yes Yes

Notes: The table reports estimates of the effects of revenues from procurement (Rev.Proc.) on firms capital accumulation(Capital) and labor (Labor): Rev.Proc. is the value of procurement won in a year (in 100,000 euros); Capital are the firm

total annual physical assets (in 1,000 euros); Labor are the firm total personnel costs (in 1,000 euros). Financial variablesare deflated using KLEMS deflators. All the columns report FE estimates with firm and year fixed effects. When denoted

with Yes estimates are obtained including city of incorporation-by-year fixed effects. Odd (Even) columns report OLS (IV)

estimates (using Fisc.Rule.Exp.iXPostt as an instrument for Rev.Proc.). First-Stage F-Stat is the first stage statistics forthe relevance of the instrument. Mean Y is the sample mean for each dep.var. Significance at the 10% (*), at the 5% (**),and at the 1% (***). Source: Statistics for procurement companies that won at least one auction before 2009 and observed

between 2004 and 2011.

27

7 Additional Firm Outcomes and Heterogenous ef-

fects

7.1 Additional Outcomes

Accounts receivable and debt decrease Exposure to the fiscal rule shrinks both ac-

counts receivable and debt after the fiscal demand shock, as depicted in Figure 5. These

effects are economically and statistically significant.

Based on Table 8, a one-standard-deviation increase in firm exposure to the fiscal

rule decreases accounts receivable by 16.33% (-12.894*23.91/1888). We interpret this as

a mechanical effect reflecting the fiscal demand shock. More interesting, perhaps, is that

debt decreases: based on Table 8, a one-standard-deviation increase in firm exposure to

the fiscal rule decreases debt by approximately 10% (-15.668*23.91/3875). The drop in

debt may result from two channels: first, the firms’ diminished willingness to borrow, in

an industry where loans finance current production; second, the firms’ inability to use

accounts receivable as collateral with banks. Our research design does not allow us to

tease apart the two channels.

Other measures of human capital also respond sluggishly to the demand

shock

In the previous section we showed that exposure to the fiscal demand shock appears

to reduce physical assets accumulation but not human capital. Columns 1-4 of Table D.6

show no significant impact of exposure to the fiscal demand shock on number of workers.

This evidence further corroborates the idea that firms appear to be providing workers with

a form of insurance by not downsizing.

Construction firms rely heavily on outsourced services. These include: subcontractors

(for example, a school builder might hire a subcontractor to plant a garden); and also

professional services such as lawyers, accountants, etc. Table D.6 columns 5-8 indicates

that, like labor, outsourced services are not impacted by the fiscal demand shock. This

absence of effect is interesting because outsourced services are not covered by employment-

protection law. To the extent that outsourced services are labor-intensive, this finding

suggests that perhaps labor-protection laws are not primarily responsible for the observed

sluggishness of the labor response to the demand shock.

28

Table 8: Effects of the fiscal demand shock on accounts receivable and debt

Dep.Var. Acc.Rec. Acc.Rec. Acc.Rec. Acc.Rec. Debt Debt Debt DebtModel OLS FE FE FE-HT OLS FE FE FE-HT

(1) (2) (3) (4) (5) (6) (7) (8)

Fisc.Rule.Exp.XPost -9.802** -10.511*** -12.894** -13.340*** -16.736*** -15.668**(4.054) (2.861) (6.415) (4.197) (3.454) (6.510)

Fisc.Rule.Exp. -23.662*** -44.820***(4.112) (5.680)

Post 992.928*** 1,542.586***(240.382) (246.725)

Fisc.Rule.Exp.*Year2005 2.269 7.679(3.847) (11.837)

Fisc.Rule.Exp.*Year2006 -1.033 2.580(3.114) (12.601)

Fisc.Rule.Exp.*Year2007 1.460 3.209(5.409) (13.318)

Fisc.Rule.Exp.*Year2008 -2.810 -4.216(4.346) (13.466)

Fisc.Rule.Exp.*Year2009 -11.822*** -14.044(4.553) (13.537)



Observations 27,764 27,678 20,003 20,003 27,764 27,678 20,003 20,003Mean Y 1888 1888 1888 2032 3875 3875 3875 4208St.Dev.Fisc.Rule 23.91 23.91 23.91 23.91 23.91 23.91Eff.Fisc.Rule.Exp. (%) -12.41 -13.31 -16.33 -8.231 -10.33 -9.667Company FE No Yes Yes Yes No Yes Yes YesYear FE No Yes Yes Yes No Yes Yes YesCityXYearFE No No Yes Yes No No Yes Yes

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firms Ac-counts Receivable and Debt. Financial variables are deflated using KLEMS deflators.Fisc.Rule.Exp.represents the exposure to the fiscal demand shock computed as the ratio between the firm’s value

won in municipalities hit by the demand shock and the firm’s pre-demand shock revenues. In eachof the rows, Post is an indication for the years after 2008. Year2005-Year2011 are time dum-mies interacted with Fisc.Rule.Exp. Columns 2,3,report FE estimates with firm and year fixed

effects. Columns 3,4, include city of incorporation-by-year fixed effects. Mean Y is the samplemean for each dep.var. Eff.Fisc.Rule.Exp (%) is the ratio between the estimated coefficient of

Fisc.Rule.Exp.*Post*St.Dev.Fisc.Rule and Mean Y. Significance at the 10% (*), at the 5% (**),

and at the 1% (***). Source: Statistics for procurement companies that won at least one auctionbefore 2009 and observed between 2004 and 2011.

Finally, Table D.6 columns 9-12 show that the fiscal demand shock causes workers to

withdraw money from their severance fund, which firms are then required to pay out. One

standard deviation of exposure to the fiscal demand shock decreases severance fund assets

by 4.6%.22 These payouts represent an additional channel through which firms provide

insurance to workers.

Overall, our findings support the hypothesis that procurement firms react to a de-

mand shock by sharply adjusting capital, and not by adjusting labor or subcontractors

(or by leaving the market). This finding is perfectly consistent with the macro view that

2223.91*(0.053) = 1.27, corresponding to a drop of 1.2 thousand euros, or 4.6% of severance funds assets

29

Figure 5: Effect of firm exposure on accounts receivable and debt

Notes: Accounts receivable are in 1,000 euros; Debt are the firm total debts (in 1,000 euros); Labor are the firm total personnelcosts (in 1,000 euros). Financial variables are deflated using KLEMS deflators. Fisc.Rule.ExpXYearFE is the estimated coefficientof the interaction term Fiscal Rule Exposure and the year dummies (Year2004 -Year2011) in a model where outcomes are regressedon: firm and year dummies; the Fiscal Rule Exposure variable; the interaction terms; and city of incorporation-by-year fixedeffects (see Columns 4, 8 of Table 8). Dashed lines are the associated 95% confidence intervals. A firm’s exposure to the fiscaldemand shock (Fisc.Rule.Exp.) is defined as the value of procurement won by a firm in municipalities with population greaterthan 5,000, as a percentage of the firm’s total revenues, before 2009. Vertical lines indicate August 2008 when the law waspassed. Source: Authors’ calculation on public works data and AIDA data.

investment is procyclical, as well as with the findings of Guiso et al. (2005) on Italian

firms.23

Firm do not exit This section explores the impact of the fiscal demand shock on

death dynamics in the infrastructure procurement sector. If we found sizable exit effects,

i.e., evidence of selection, then we would talk of a “cleansing effect of fiscal rules.”

At a purely descriptive level, we find that firm dynamics are impacted by the fiscal

demand shock in the way one would expect: firms that are more exposed to the fiscal

demand shock are more likely to exit. Figure 6 reports the Kaplan-Meier estimate of the

cumulative hazard of exiting within our post-2008 time window as a function of treatment.

The sample is split according to whether a firm’s exposure exceeds the median. The figure

suggests that treated firms tend to have a higher probability of exit.

Consistent with Figure 6, Table 9 reports point estimates of the impact of exposure

23Guiso et al (2005) show that a large cross-section of Italian firms do not pass the burden of temporaryproductivity shocks through to their employees wages (nothing is said about the employment level).

30

Figure 6: Kaplan-Meier estimate of the cumulative hazard of exit for firm “more” and “less”

exposed to the fiscal demand shock

Notes: More (Less) exposed firms are firms with exposure to the fiscal demand shockcomputed as the ratio between the firm’s value won in municipalities hit by the fiscaldemand shock and the firm’s pre-demand shock revenues above (below) the median.Source: Statistics for procurement companies that won at least one auction before 2009and observed between 2004 and 2011.

to the fiscal demand shock. Multiplying the estimate in col. 2 by a standard deviation

in exposure to Fisc.Rule yields 0.014*24=0.34, that is, a 0.34 percentage points increase

in the exit rate of corporations who operate in the municipal procurement sector. After

dividing by the 2.14% average exit rate, we get a 16% increase in the exit probability

relative to its baseline.24 This effect is 10 times smaller and not statistically different from

zero if we include (in col.3) city of incorporation-by-year fixed effects. Thus, we find no

evidence of a sizable “cleansing effect” of the fiscal rule.

TFP increases slightly In this section we explore the impact of the fiscal demand

shock on firms’ total factor productivity (TFP). We know that firms that are impacted by

the fiscal shock reduce factor utilization; if the firms’ production function has decreasing

returns to scale, we would expect TFP to increase as a result.

We compute TFP using the Olley and Pakes (1996) procedure. Table D.6 columns 21-

23 show that the fiscal demand shock causes TFP to increase. One standard deviation of

exposure to the fiscal demand shock increases TFP by approximately 1.3% (.0005*24/.9).

Results are comparable if we compute TFP using the Hsieh and Klenow (2009) proce-

24We cannot provide results on the effect of Fisc.Rule on the entry rate in Table 9 because the definitionof exposure to treatment limits the sample to corporations that existed before the fiscal demand shockhit.

31

Table 9: Exit

Dep.Var. Exit Exit ExitModel OLS FE FE-HT

(1) (2) (3)

Fisc.Rule.Exp.XPost 0.002 0.014* 0.001(0.008) (0.008) (0.009)

Fisc.Rule.Exp. 0.007*(0.003)

Post 2.682***(0.229)

Observations 27,764 27,764 27,764Company FE No Yes YesYear FE No Yes YesCityXYearFE No No YesMean Y 2.139 2.139 2.139St.Dev.Fisc.Rule 23.91 23.91 23.91Eff.Exposure (%) 1.862 16.08 0.986

Notes: The table reports estimates of the effects of the fiscaldemand shock on firm exit defined as the probability of exitin a given year. Fisc.Rule.Exp. represents the exposure to

the fiscal demand shock computed as the ratio between thefirm’s value won in municipalities hit by the fiscal demandshock and the firm’s pre-demand shock revenues. In each of

the rows, Post is an indication for the years after 2008. Col.1 (2) [3] reports OLS (FE) [FE-HT] estimates (with firm andyear fixed effects) [city of incorporation-by-year fixed effects].

Mean Y is the sample mean for each dep.var. Eff.Fisc.Rule onTreated (%) is the ratio between the estimated coefficient of

Fisc.Rule.Exp.*Post*St.Dev.Fisc.Rule and Mean Y. SEs are

clustered at firm level. Significance at the 10% (*), at the5% (**), and at the 1% (***). Source: Statistics for procure-

ment companies that won at least one auction before 2009 and

observed between 2004 and 2011.

dure.25 Thus, TFP increases if factor utilization decreases.

25Estimates available on request.

32

7.2 Heterogenous response by firms financial constraints, size,and North-South

Financial constraints In Table 10 Panel A we interact our measure of exposure with an

index of financial development for the firm’s region of incorporation.26 In Table 10 Panel

B we interact our measure of exposure with a firm-level dummy that proxies for financial

constrainedness before 2009.27 Labor is unresponsive to the demand shock independently

of financial development, regardless of how the latter is measured. For both measures

of financial constrainedness, we find hints that more-financially-constrained firms adjust

capital more as a function of exposure, but the estimates are not statistically significant

in the specification with city-year fixed effects.28 Therefore, the evidence that financial

constrainedness mediates the firms’ reponse to the demand shock is weak.

Firm size Small firms may struggle to adjust their capital because it is relatively

“lumpy,” compared to large firms where it may be correct to take a “fluid approximation”

to their capital.

In Panel C of Table 10 we interact our measure of exposure with a “large firm” dummy;

a firm is classified as “large” if average yearly revenue before 2009 exceeded the 75th

percentile. In keeping with the earlier “zero-effects” on labor, there is no differential

effect of exposure on labor adjustment by firm size. But, we find that larger firms show a

stronger effect of exposure on investment (Col. 3,4). This may simply reflect a mechanical

effect (bigger firms have proportionately bigger responses), or a more interesting effect:

that dollar-for-dollar (of demand shock), bigger firms adjust capital more. To get at the

dollar-for-dollar effect, we may compute the ratio of the coefficients in Cols. (4) and (2) in

Table 10 Panel C: for large firms we get (−17.48− 0.62)/(−0.30− 0.08) = 47.63, meaning

that every extra dollar of demand shock that a large firms receives is translated into a

26We use Guiso et al.’s (2004) “Normalized measure of financial development.” This indicator capturesthe availability of consumer credit and is shown to correlate with a variety of firm-level outcomes, includingfirm birth and firm growth.

27To measure financial constraints we follow Kaplan and Zingales (1997), we classify as “financiallyconstrained” a firm whose average cash flow before 2009, measured as earnings before taxes (EBITDA)plus average depreciation on assets divided by average physical capital, is below the median.

28We performed IV analysis similar to that in Section 6.4 but with a second endogenous variable(procurement revenue*constrained) and an additional instrument (Fisc.Rule.Exp*Post*constrained); theestimated coefficient of the interaction terms are not statistically significant for either measure of financialconstrainedness, consistent with the reduced-form estimates.

33

47 cents investment cut. By comparison, the equivalent calculation for small firms is:

(−0.62)/(−0.08) = 7.75. The difference is 40 cents. Quantitatively similar estimates can

be obtained from IV analyses.29 Regardless of the method, then, we find that most of the

investment adjustment comes from the 25% largest firms.

The small firms’ sluggish response may reflect the relative lumpiness of their capital.

North versus South In Panel D of Table 10 we interact our measure of exposure with

a “Northern firm” dummy; a firm is classified as “Northern” if the region of incorporation

is located in Italy’s northern section.30 Italy’s North is more economically and financially

developed than the South (see Guiso et al. 2004). In our preferred specification, the

earlier “zero-effects” on labor is confirmed: there is no differential effect of exposure on

labor adjustment by firm location. But, we find that firms located in the North show a

stronger effect of exposure on investment (Col. 3,4). To get at the dollar-for-dollar effect,

we may compute the ratio of the coefficients in Cols. (4) and (2) in Table 10 Panel D: we

get (−5.32 − 2.86)/(−.05 − .08) = 63, meaning that every extra dollar of demand shock

that a northern firm receives is translated into a 63 cents investment cut. By comparison,

the equivalent calculation for Southern firms is: (−2.86)/(−.08) = 35.75. The difference

is 27.17 cents. Quantitatively similar estimates can be obtained from IV analyses, but in

this case the estimates are not statistically significant.31 Overall, we interpret the evidence

as somewhat suggestive that some of the investment adjustment comes from the 35% of

firms incorporated in the North of Italy. Of course it is difficult to know precisely what

about a northern location creates a different firm response. For example, firms located in

the North tend to be larger. Regardless, the difference in propagation of the fiscal shock

by firms located in a wealthy versus a poor region is relevant for policy makers.

29We performed IV analysis similar to that in Section 6.4 but with a second endogenous variable(procurement revenue*large) and an additional instrument (Fisc.Rule.Exp*Post*large); the estimatedcoefficient of the interaction term is 41 and statistically significant. We also did a split-sample IV analysisidentical to that in Section 6.4, and again found that the estimated coefficients in the “large firms” sampleexceeded the one in the “small firms” sample by 41 cents.

30There regions are Liguria, Lombardia, Piemonte e Valle d’Aosta, and Emilia-Romagna, Friuli-VeneziaGiulia, Trentino-Alto Adige e Veneto.

31We performed IV analysis similar to that in Section 6.4 but with a second endogenous variable(procurement revenue*north) and an additional instrument (Fisc.Rule.Exp*Post*north); the estimatedcoefficient of the interaction term is 24 and comparable to the estimates above mentioned but in thisspecification the estimate is not statistically significant. We also did a split-sample IV analysis identicalto that in Section 6.4, and again found that the estimated coefficients in the “northern firms” sampleexceeded the one in the “non-northern firms” sample by 24.7 cents.

34

Table 10: Heterogeneous responses to the fiscal demand shock

Dep.Var. Rev.Proc Rev.Proc Capital Capital Labor LaborModel FE FE-HT FE FE-HT FE FE-HT

(1) (2) (3) (4) (5) (6)

Panel A: Financial DevelopmentFisc.Rule.Exp.XPostXFin.Dev. -0.1200*** -0.1164** -11.3696*** -8.6779 -1.5410 -1.7914

(0.038) (0.057) (3.833) (6.335) (1.370) (2.134)Fisc.Rule.Exp.XPost -0.0737*** -0.0810*** -1.3972 -2.7623 0.4051 0.2700

(0.010) (0.012) (1.180) (2.236) (0.351) (0.627)

Panel B: Financially Constrained FirmsFisc.Rule.Exp.XPostXFin.Cons.Firm -0.0033 -0.0064 -3.0313** -2.8982 0.6016 0.7403

(0.011) (0.014) (1.248) (2.009) (0.421) (0.720)Fisc.Rule.Exp.XPost -0.0876*** -0.0925*** -2.7897*** -2.5083** -0.2924 -0.2853

(0.007) (0.010) (0.728) (1.184) (0.335) (0.404)

Panel C: Large FirmsFisc.Rule.Exp.XPostXLarge -0.3135*** -0.2988*** -18.0003*** -17.4804*** -1.2875 -2.5294

(0.035) (0.046) (4.382) (6.519) (1.568) (2.612)Fisc.Rule.Exp.XPost -0.0761*** -0.0782*** -0.5939*** -0.6162 0.2369*** 0.4052*

(0.005) (0.006) (0.207) (0.614) (0.063) (0.218)

Panel D: Firms incorporated in the NorthFisc.Rule.Exp.XPostXNorth -0.0617*** -0.0548** -3.8882** -5.3280** -0.9990* -1.3762

(0.017) (0.026) (1.552) (2.587) (0.567) (0.935)Fisc.Rule.Exp.XPost -0.0793*** -0.0850*** -3.2565*** -2.8626** 0.3116* 0.2874

(0.006) (0.007) (0.692) (1.281) (0.181) (0.361)

Mean Y 3.137 3.330 607.4 604 414 431.8St.Dev.Fisc.Rule.Exp. 23.91 23.91 23.91 23.91 23.91 23.91Observations 27,623 19,966 27,623 19,966 27,623 19,966Company FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesCity-Year FE No Yes No Yes No Yes

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firms revenues from

procurement, capital and labor. Rev.Proc are the revenues from procurement (in 100,000 euros); Capital are the firm

total annual physical assets (in 1,000 euros); Labor are the firm total personnel costs (in 1,000 euros). Inputs and

outputs are deflated using KLEMS deflators. Fisc.Rule.Exp.XPostXFin.Dev. (Fisc.Rule.Exp.XPostXFin.Cons.Firm)

[Fisc.Rule.Exp.XPostXLarge.Firm] {Fisc.Rule.Exp.XPostXLarge.Firm} is a triple interaction term between the exposure

to the fiscal demand shock computed as the ratio between the firm’s value won in municipalities hit by the fiscal

demand shock and the firm’s pre-demand shock revenues, the dummy post, and the Guiso et al. (2004) indicator for

financial development of the region of incorporation of the firm (an indicator for firms above the median distribution

of financial constrain estimated using the firms cash flow before 2008) [an indicator for firms above the 75 percentile

of the distribution of size estimated using the firms total revenues before 2008] {Dummy for a firm incorporated in the

North}. Even columns include city of incorporation-by-year fixed effects. SEs are clustered at firm level. Significance

at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for procurement companies that won at least

one auction before 2009 and observed between 2004 and 2011.

8 Conclusions

We studied the effect of reduced government demand (fiscal demand shock) on corporate

economic activity. Our main finding is that firms reacted to the demand shock by cutting

capital rather than labor. More-impacted firms reduced debt, and were not significantly

35

more likely to exit; however, their total factor productivity (TFP) increased somewhat.

Finally, we found that firms were heterogeneous in how much they cut capital, depending

on firm size: larger firms translated a dollar of demand shock into twice as big an invest-

ment cut, compared to smaller firms. We also found some evidence that firms incorporated

in more-developed regions translate the shock disproportionately.

We interpreted these findings as suggestive that firms optimize intertemporally, and

thus investment is the premier channel for shock absorption; that there is no evidence

of a “cleansing effect” due to fiscal demand shocks; that the firms’ production function

exhibits decreasing returns to scale; that capital adjustment choices are more “lumpy”

for smaller firms; and, perhaps, that financial frictions interfere with the firms’ ability to

adjust their capital stock in response to a fiscal demand shock. But we acknowledge that

other interpretations are possible.

The variation in our paper comes from the tighter enforcement of a fiscal rule. This

source of variation is policy-relevant because: fiscal rules are increasingly common both

at the national and at the sub-national level.

Our identification strategy leveraged arguably-exogenous variation in the firms’ de-

pendence on municipal government spending. Furthermore, we checked that firms do not

select into or outside of treatment. Finally, the fiscal demand shock did not generate

general-equilibrium confounding effects because municipal procurement is a tiny fraction

of GDP. Due to this combination of features, our research design can, for the first time to

our knowledge, deliver causal statements about how individual firms’ investment responds

to a fiscal demand shock.

36

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38

For Online Publication Appendix

A Analytical appendix

A.1 One-period model (Acemoglu et at. 2012)

Following model by Acemoglu et al. (2012, p. 1984-87). Small competitive sector (such

as the municipal procurement sector) operates a Cobb Douglas technology

y = kαl1−α.

Firm solves:

max pkαl1−α − rk − wl,

where r and w are factor prices determined economy-wide and are unaffected by production

in the sector. Firm’s foc’s require:

p (1− α) kαl−α − w = 0

pαkα−1l1−α − r = 0

sowl

rk=

p (1− α) (k)α (l)1−α

pα (k)α (l)1−α =(1− α)

α

independent of p. Now consider a demand shock to demand that causes p to change to

p′ (this shock could be micro-founded in a demand system such as the one on page 1984

of Acemoglu et al. 2012). Then factor utilization l and k, will change, but since by

assumption w and r are unaffected by the shock, l and k must change in the exact same

proportion. Hence if p changes to p′ the resulting capital-labor ratio is unaffected:

k′

k=

l′

l.

It follows that k′−kk

= l′−ll

and thus elasticities to the demand shock (the multipliers) are

the same for capital and labor.

A.2 Two-period model

Same as above except two periods. We assume that capital is perfectly durable (no

depreciation) and that the discount factor is one. We are going to compare two scenarios:

a steady state where p1 = p2 = p,, and one where the future is worse than the present

p1 > p2.

1

In neither of these scenarios will the firm want to increase its capital in period 2, so for

simplicity in our modeling we assume that all the capital k that the firm will ever want is

purchased in period 2. The firm’s problem is now:

max p1kαl1−α

1 + p2kαl1−α

2 − rk − w (l1 + l2)

max kα(p1l

1−α1 + p2l

1−α2

)− rk − w (l1 + l2)

Focs

αkα−1(p1l

1−α1 + p2l

1−α2

)− r = 0

kα (1− α)(p1l

−α1

)− w = 0

kα (1− α)(p2l

−α2

)− w = 0

αkα(p1l

1−α1 + p2l

1−α2

)− rk = 0

kα (1− α)(p1l

1−α1

)− wl1 = 0

kα (1− α)(p2l

1−α2

)− wl2 = 0

From this we have (p1l

1−α1

)(p2l

1−α2

) =l1l2

p1p2

=

(l1l2

)α

(4)

αkα(p1l

1−α1 + p2l

1−α2

)− rk = 0

kα (1− α)(p1l

1−α1 + p2l

1−α2

)− w (l1 + l2) = 0

αkα(p1l

1−α1 + p2l

1−α2

)kα (1− α)

(p1l

1−α1 + p2l

1−α2

) =rk

w (l1 + l2)=

α

(1− α)(5)

Imagine that we start from p1 = p2 = p. Then from (4) we get l1 = l2 and then from

(5):k

l1=

2αw

r (1− α). (6)

Now the case where the future is worse than the present. If p′1 > p′2 then from (4) we get

l′1 > l′2 and then from (5):

α

(1− α)=

rk′

w (l′1 + l′2)=

rk′

wl′1

(1 +

l′2l′1

) =rk′

wl′1

(1 +

(p′2p′1

)1/α) ,

2

where the last equality follows from (4). Since p′1 > p′2 we get

k′

l′1=

(1 +

(p′2p′1

)1/α)

αw

r (1− α)<

2αw

r (1− α)=

k

l1. (7)

In other words, when the future is worse than the present the capital/labor ratio is lower

in period 1 compared to the steady state. This is because the firm anticipates the bad

shock in period 2 by adjusting capital more negatively than labor. This over-reaction on

capital is caused by the fact that capital is durable whereas labor is purchased on the spot

market. Another way of saying this is that the firm uses capital in preference to labor to

absorb the negative shock to future profitability.

3

B Variables, Descriptions, and Sources

Variable Description Source

MunicipalitiesTotal value of procurement Is the annual total value of municipal contests for public works. Information providerN.Tenders Is the number of tenders in a municipality in a year. Information providerAvg. value of procurement Is the average value of the contests tendered in a municipality in a year computed using the

engineers’ estimates of the value of the contest.Information provider

Percent Roads Is the fraction of roads’ contests Information providerWinning rebate Is the offer that won the procurement, which represents the percentage discount over the engineer’s

estimate of the value of the tander. A higher offers represents lower municipal procurement costs.Information provider

Winner from the sameprovince

Is the value won by firms from the same province of the municipality running the contest over thevalue of contests by year and municipality.

Authors’ calculation on In-formation provider data

Entry (Exit) rate, con-struction

Is defined as the number of new (cancelled) firms in year t over the number of active firms in yeart-1, in every municipality.

Authors’ calculation onOfficial Registry Datafrom Unioncamere.

Entry (Exit) rate, AIDA Is the defined as the number of new (death) firms in year t, in the AIDA database, over the numberof active firms in year t-1, in every municipality.

Authors’ calculation onAIDA data and OfficialRegistry Data from Union-camere.

Transfers Are the annual transfers to the municipality by the central governments (state and region). Italian Ministry of Inte-rior.

Tax revenues Are the annual tax revenues of the municipality. Italian Ministry of Inte-rior.

Total Current (Capital)Spending

Is the annual total current (capital) spendings of the municipality. Italian Ministry of Inte-rior. Variable:Totale spesecorrenti (conto capitale),impegni

Debt ratio % Is the ratio between the total annual municipal debt and the annual revenues*100. Open Bilanci. Vari-able:[(Debito in bilancioconsistenza finale + (debitofuori bilancio - sentenzeesecutive)] / Totale entratecorrenti (riscossioni) *100.

Population Is the municipal population. National Institute ofStatistics (ISTAT).

Firm balance-sheetExit Probability of exit in a given year obtained with the year fo last official submission of the balance-

sheet.AIDA. Variable: Anno ul-timo bilancio.

Severance Fund Is the firm’s total funds accumulated for severance pays (in 1000 euros). AIDA. Variable: Fondodi trattamento fine rapportolavoro.

Rev.Proc Is the value of procurement contests won in a year (in 100,000 euros). Authors’ calculation on In-formation provider data

Capital Total annual physical assets (in 1000 euros). AIDA. Variable: TotaleImmobilizzazioni Materiali.

Labor Total annual personnel costs (in 1000 euros). AIDA. Variable: Totalesalari e stipendi.

N.Workers Number of workers. AIDA. Variable: Dipen-denti.

O.Services Are the firm total costs for outsourced services (in 1,000 euros) AIDA. Variable: ServiziTot.Rev. Are the total annual revenues (in 1000 euros). AIDA. Variable: Ri-

cavi,vendite,e prestazioniInventory Are the firm total annual amount of non-completed works (in 1000 euros). AIDA. Variable: Totale ri-

manenze.TFP Is the firm total factor productivity computed using the Olley and Pakes (1996) procedure. Authors’ calculation on

AIDA data.Accounts receivable Are the firm annual accounts receivable AIDA. Variable: Totale

Crediti.Debt Are the firm annual debt (in 1,000 euros) AIDA. Variable: Totale

Debiti.

4

C Additional analysis of the impact of the legislative

change on municipal infrastructure spending

Table C.1: Check of common-trend and no-anticipation assumptions insmaller municipalities around the threshold

Test Common Trend Leads&Lags Common Trend Leads&Lags Common Trend Leads&LagsAssumption Assumption Assumption

Municipalities 1k-10k 1k-10k 3k-7k 3k-7k 4k-6k 4k-6k(1) (2) (3) (4) (5) (6)

Fisc.Rule*Year 0.189 0.097 -0.241(0.129) (0.172) (0.213)

LeadsFisc.Rule*2005 0.027 -0.193 -0.327

(0.428) (0.645) (0.906)Fisc.Rule*2006 0.311 0.302 0.846

(0.492) (0.756) (1.087)Fisc.Rule*2007 0.717 0.269 -0.561

(0.550) (0.752) (0.985)Fisc.Rule*2008 0.602 0.256 -1.089

(0.580) (0.788) (0.977)Fisc.Rule*2009 -0.658 -0.884 -1.343

(0.554) (0.769) (1.161)Fisc.Rule*2010 -0.818 -0.520 -0.834

(0.573) (0.858) (1.176)Fisc.Rule*2011 -2.252*** -1.023 -1.174

(0.460) (0.696) (0.803)

Observations 19,650 31,440 7,485 11,976 3,505 5,608Municipal FE Yes Yes Yes Yes Yes YesYear FE No Yes No Yes No Yes

Notes: The table reports estimates of the effects of the fiscal demand shock on the average annual total value of procurement for public works in

all Italian municipalities. In each of the rows, Fisc.Rule is an indicator variable for municipalities with population above the fiscal rule population

threshold (5,000 inhabitants) and Post is an indication for the years after 2008. In column 1 the sample is before the fiscal demand shock and the

regression includes a linear trend as a control. In column 2 p-value Leads is the p-value for the joint statistical significance of the leads effect of the

fiscal demand shock. SEs are clustered at municipal level. Significance at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for all

the public works tendered between 2004 and 2011 in Italy with population between 1,000 and 10,000 (cols. 1 and 2); 3,000 and 7,000 (cols. 3 and 4);

4,000 and 6,000 (cols. 5 and 6).

5

Figure C.1: Fiscal shock associated with drop in municipal debt, infrastructure spending (investment) but not with drop in current

spending (consumption)

Notes: Debt ratio (in %) is the ratio between the total annual municipal debt and the annual revenues*100. Infrastructure spending is the Infrastructure spending is the average totalannual value of procurement for infrastructures procurement. Current spending is the average total annual value of current spending. The pictures report yearly averages across all Italianmunicipalities. Diamonds represent fiscal rule municipalities with population above 5,000. The vertical dashed lines indicate August 2008 when the law was passed. Source: Authors’calculation on procurement data, and municipal budget data from Italian Ministry of Interior for all municipalities between 2004-2011. The Data on debt ratio is available between2007-2011.

6

Figure C.2: No crisis in municipal tax revenues

Notes: Average total annual value of municipal tax revenues. Diamonds represent fiscal rule municipalities withpopulation above 5,000 inhabitants. The vertical dashed lines indicate August 2008 when the law was passed.Source: Authors’ calculation on procurement data for all municipalities between 2004-2011, and with populationbetween 1,000 and 10,000 inhabitants (left picture) and 3,000 and 7,000 inhabitants (right picture).

In Table C.2 columns 1 and 2 we restrict to the tighter windows around the treatment

threshold: (1k-10k) and (3k-7k) windows around the 5k treatment threshold; the estimated

effect decreases to 13.25%. Restricting even closer (column 3) does not further decrease

the magnitude of the estimate, but statistical significance is lost due to the relatively small

number of municipalities (701).

Sorting around the threshold. A further question is whether the estimates are

confounded by selection out of treatment by municipalities. In Figure C.3 we test for the

possibility that municipalities sort below the 5,000 municipal threshold. The figure indi-

cates no evidence of any statistically significant jump in the distribution of the municipal

population around the 5,000 population threshold.32 Thus we find no evidence of sorting

around the threshold, and hence no selection out of treatment.

Placebo thresholds. The institutional design of the fiscal rule is expected to generate

a discontinuous spending behavior at the 5,000 population threshold, but not at nearby

thresholds. To check this prediction, in Table C.3 we perform a number of placebo tests for

32Results, available on request, are robust to different selections of samples around the threshold.

7

Table C.2: Impact of the fiscal shock on infrastructure spending for different windows aroundthe threshold

Dep. Var. Total Value of procurementModel FE FE FEMunicipal population 1k-10k 3k-7k 4k-6k

(1) (2) (3)

Fisc.Rule*Post -1.574*** -0.936** -0.891(0.306) (0.441) (0.613)

Municipalities 3,930 1,497 701Observations 31,440 11,976 5,608Municipal FE YES YES YESYear FE YES YES YESMean Y treat-pre 7.717 7.053 6.526Eff.Fisc.Rule on Treated (%) -20.40 -13.27 -13.65

Notes: The table reports estimates of the effects of the fiscal shock on the annual total

value of municipal tenders for infrastructures in all Italian municipalities. In each of therows, Fisc.Rule is an indicator variable for municipalities with population above the 5,000population threshold and Post is an indication for the years after 2008. Mean Y treat-pre

is the sample mean for treated municipalities pre-2009. Eff.Fisc.Rule on Treated (%) is theratio between the estimated coefficient of Fisc.Rule*Post and Mean Y treat-pre. SEs areclustered at municipal level. Significance at the 10% (*), at the 5% (**), and at the 1% (***).

Source: Statistics for all the public works tendered between 2004 and 2011 in Italy.

Figure C.3: No sorting around the fiscal-rule population threshold

0.0001

.0002

.0003

.0004

-4000 -2000 0 2000 4000

Notes: Distribution of the municipal population around the threshold in Italian mu-nicipalities with population between 3,000 and 7,000 inhabitants in 2007. Circlesrepresent the difference between the municipal population and the 5,000 threshold(vertical line). Circles are average observed values, the bold solid line is a kernelestimate (see McCrary, 2008), and the two thin lines are 95% confidence intervals.Discontinuity estimate, log difference in height, (and standard errors are -.15 (.26),respectively. Source: Statistics for all the public works tendered between 2004 and2011 in Italy with population between 3,000 and 7,000 inhabitants in 2007.

thresholds below (columns 1-3) and above (columns 5-7) the true threshold. None of these

placebo thresholds has a significant effect on spending, consistent with the assumption

8

that the discontinuity is specific to the 5k threshold.

Table C.3: Placebo test: effect only at actual threshold not at simulated thresholds

Dep. Var. Total value of procurement

Threshold 3.5k 4k 4.5k 5k 5.5k 6k 6.5kMunicipal population 3k-5k 3k-5k 3k-5k 3k-7k 5k-7k 5k-7k 5k-7k

(1) (2) (3) (4) (5) (6) (7)

Fisc.Rule*Post 0.087(0.399)

Fisc.Rule*Post 0.304(0.447)

Fisc.Rule*Post -0.225(0.615)

Fisc.Rule*Post -0.936**(0.441)




Observations 7,760 7,760 7,760 11,976 4,216 4,216 4,216Municipalities 970 970 970 1,497 527 527 527Mean Y treat-pre 4.388 4.388 4.388 5.034 6.222 6.222 6.222St.Dev. Y treat-pre 8.694 8.694 8.694 9.960 11.85 11.85 11.85Municipal FE YES YES YES YES YES YES YESYear FE YES YES YES YES YES YES YES

Notes: The table reports estimates of the effects of the fiscal rule on the average annual total value of municipal

contests for public works in all Italian municipalities with population between 3,000-7,000 inhabitants. In columns

1,2, and 3; columns 5,6, and 7 Fisc.Rule is an indicator variable for municipalities with population above the

simulated threshold indicated on top of each column. In column 4, Fisc.Rule is an indicator variable for munici-

palities with population above the 5,000 threshold. In all the regressions Post is an indication for the years after

2008. When denoted with YES regressions include municipal and year fixed-effect. Mean Y treat-pre and St.Dev.

Y treat-pre are the sample mean and standard deviation for treated municipalities pre-2009. SEs are clustered at

municipal level. Significance at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for all the

public works tendered between 2004 and 2011 in Italy with population between 3,000 and 7,000.

9

D Additional analysis of firm’s responses to the fiscal

rule

D.1 Pre-fiscal demand shock differences and dynamics of ex-posed firms

Table D.1: Correlates of exposure to the fiscal demand shock and firms out-comes

Dep.Var. Rev.Proc Capital Labor N.Workers O.Services Sev.Fund Tot.Rev. Invent. TFP Acc.Rec. Debt(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Fisc.Rule.Exp 0.100*** -5.969*** -5.010*** -0.225*** -15.794*** -0.294*** -39.726*** -17.820*** -0.001*** -21.859*** -40.436***(0.004) (1.540) (0.885) (0.055) (2.823) (0.041) (6.541) (3.663) (0.000) (6.134) (7.847)

Notes: The table reports OLS regressions of average firms outcomes before 2009 and exposure to the fiscal demand shock: Rev.Proc. are

the revenues from procurement (in 100,000 euros); Capital are the firm total annual physical assets (in 1,000 euros); Labor are the firm total

personnel costs (in 1,000 euros); O.Services are the firm total costs for outsourced services (in 1,000 euros); Sev. Fund is the firm’s total funds

accumulated for severance pays (in 1,000 euros); TFP is the total factor productivity computed following the Olley and Pakes (1996) procedure;

Acc.Rec are the accounts receivables (in 1,000 euros); Debts are the company debts (in 1,000 euros). Financial variables are deflated using

KLEMS deflators.Fisc.Rule.Exp. represents the exposure to the fiscal demand shock computed as the ratio between the firm’s value won in

municipalities hit by the demand shock and the firm’s pre-demand shock revenues. Mean Y is the sample mean for each dep.var. SEs are

clustered at the firm level. Significance at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for procurement companies

that won at least one auction before 2009 and observed between 2004 and 2009.

Table D.2: Pre-fiscal demand shock trends of exposed firms.

Dep.Var. N.Work. N.Work. O.Serv. O.Serv. Sev.Fund Sev.Fund Tot.Rev. Tot.Rev. Invent. Invent. TFP TFP Acc.rec. Acc.Rec. Debt DebtModel FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

Panel A: Time-Period: 2004-2009Fisc.Rule.Exp.*Year 0.0056 0.0181* 0.1169 0.2061 -0.0102** 0.0013 -0.4782 0.0793 -1.1083 -0.0647 0.0001* 0.0001 -1.7996*** -1.7825* -3.5787*** -2.2523

(0.007) (0.011) (0.269) (0.328) (0.005) (0.007) (0.637) (0.948) (1.056) (1.449) (0.000) (0.000) (0.549) (0.985) (1.305) (1.935)

Observations 7,274 4,375 15,924 11,428 15,279 10,875 15,924 11,428 15,707 11,257 14,140 9,866 15,924 11,428 15,924 11,428

Panel B: Time-Period: 2004-2008

Fisc.Rule.Exp.*Year -0.0010 0.0146 -0.3712 -0.1018 -0.0069 0.0099 -1.6314 -1.1299 -1.0731 0.6416 0.0001 0.0000 -1.3845*** -1.2014 -3.0346* -0.5477(0.009) (0.014) (0.332) (0.435) (0.005) (0.009) (1.041) (1.476) (1.446) (1.926) (0.000) (0.000) (0.403) (0.748) (1.563) (2.069)

Observations 4,678 2,676 11,640 8,269 11,096 7,822 11,640 8,269 11,435 8,108 10,314 7,146 11,640 8,269 11,640 8,269Company FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCityXYearFE No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firm outcomes: N.Workers are the number of

workers; Sev. Fund is the firm’s total funds accumulated for severance pays (in 1,000 euros). O.Services are the firm total costs for outsourced

services (in 1,000 euros); Tot.Rev. are the total annual revenues (in 1,000 euros); Inventory are the Residui. TFP is the total factor productivity

computed following the Olley and Pakes (1996) procedure; Acc.Rec are the accounts receivables (in 1,000 euros); Debts are the company debts

(in 1,000 euros). Financial variables are deflated using KLEMS deflators. Fisc.Rule represents the exposure to the fiscal demand shock before

the Patto, and it is computed as the ratio between the firm’s value won in municipalities with fiscal rule and the firm’s pre-fiscal demand

shock revenues. Fisc.Rule*Year is the interaction of the exposure to the fiscal rule to the linear trend. All the columns include firm and time

effects. Even columns include city of incorporation-by-year fixed effects. Effect Fisc.Rule.Exp (%) is the % effect of the fiscal demand shock.

The sample is before the fiscal demand shock. SEs are clustered at firm level. Significance at the 10% (*), at the 5% (**), and at the 1% (***).

Source: Statistics for procurement companies that won at least one auction before 2009 and observed between 2004 and 2009.

10

Table D.3: Reduced form estimates: effect of exposure on revenues fromprocurement, capital and labor, Post ≥ 2008

Dep.Var. Rev.Proc Rev.Proc Rev.Proc Rev.Proc Capital Capital Capital Capital Labor Labor Labor LaborMethod OLS FE FE FE-L&L OLS FE FE FE-L&L OLS FE FE FE-L&L

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Fisc.Rule.Exp.XPost -0.076*** -0.069*** -0.074*** -3.891*** -5.290*** -4.090*** 0.353 -0.084 0.022(0.008) (0.008) (0.010) (0.586) (0.610) (0.926) (0.333) (0.202) (0.243)

Fisc.Rule.Exp. 0.110*** -6.158*** -5.563***(0.007) (0.680) (0.618)

Post -0.031 356.877*** 12.378(0.128) (32.759) (18.817)

Fisc.Rule.Exp.*Year2005 -0.111 2.157** 0.556(0.086) (1.072) (0.469)



Fisc.Rule.Exp.*Year2008 -0.116 -1.199 0.287(0.085) (1.343) (0.587)




N.Firms 4231 4231 3133 3133 4231 4231 3133 3133 4231 4231 3133 3133Observations 27,764 27,678 20,003 20,003 27,764 27,678 20,003 20,003 27,764 27,678 20,003 20,003Mean Y 3.166 3.166 3.166 3.332 584.4 584.4 584.4 576.8 397.2 397.2 397.2 410.1St.Dev.Fisc.Rule 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91Eff.Fisc.Rule.Exp. (%) -55.82 -50.18 -53.90 -15.40 -20.94 -16.19 2.055 -0.487 0.129Company FE NO YES YES YES NO YES YES YES NO YES YES YESYear FE NO YES YES YES NO YES YES YES NO YES YES YESCityXYearFE NO NO YES YES NO NO YES YES NO NO YES YES

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firms revenues from procurement,

capital accumulation and labor: Rev.Proc. is the value of procurement won in a year (in 100,000 euros); Capital are the firmtotal annual physical assets (in 1,000 euros); Labor are the firm total personnel costs (in 1,000 euros). Financial variables

are deflated using KLEMS deflators. Fisc.Rule.Exp. represents the exposure to the fiscal demand shock computed as the

ratio between the firm’s value won in municipalities hit by the demand shock and the firm’s pre-demand shock revenues.In each of the rows, Post is an indication for the years after 2007. Year2005-Year2011 are time dummies interacted with

Fisc.Rule.Exp. Columns 2,3,4,6,7,8, and 10,11,12 report FE estimates with firm and year fixed effects. Columns 3,4,7,8, and

11,12 include city of incorporation-by-year fixed effects. Mean Y is the sample mean for each dep.var. Eff.Fisc.Rule.Exp(%) is the ratio between the estimated coefficient of Fisc.Rule.Exp.*Post*St.Dev.Fisc.Rule and Mean Y. Significance at the

10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for procurement companies that won at least one auction

before 2009 and observed between 2004 and 2011.

11

Table D.4: Impact of the fiscal shock on infrastructure spending, Post ≥ 2008

Dep. Var. Total Value of Procurement

Method OLS FE FE FE FE FE-L&L(1) (2) (3) (4) (5) (6)

Fisc.RuleXPost -5.459*** -5.459*** -5.946*** -5.044*** -4.298**(1.199) (1.199) (2.023) (1.095) (1.773)

Post -0.613***(0.072)

Fisc.Rule 21.189***(2.266)

Population 0.003 -0.000(0.004) (0.004)

Population2 -0.000*** -0.000***(0.000) (0.000)

Transfers (in 100k) 0.010** 0.011***(0.004) (0.003)

Tax revenies (in 100k) -18,223.428(11,547.430)

Fisc.Rule*year2005 -1.791(1.871)

Fisc.Rule*year2006 -0.623(2.193)

Fisc.Rule*year2007 0.062(2.517)

Fisc.Rule*year2008 -1.439(2.179)

Fisc.Rule*year2009 -6.005***(2.296)

Fisc.Rule*year2010 -6.609***(2.470)

Fisc.Rule*year2011 -10.135***

Municipalities 6013 6013 6013 6013 6013 6013Observations 48,120 48,120 48,120 47,912 47,804 48,120Municipal FE No Yes Yes Yes Yes YesYear FE No Yes Yes Yes Yes YesMean Y treat-pre 24.19 24.19 24.19 24.19 24.19 24.19Eff.Fisc.Rule on Treated (%) -22.56 -22.56 -24.58 -20.85 -17.76

Notes: The table reports estimates of the effects of the fiscal shock on the annual total value ofmunicipal tenders for infrastructures in all Italian municipalities. In each of the rows, Fisc.Ruleis an indicator variable for municipalities with population above the 5,000 population threshold

and Post is an indication for the years after 2007. Population represents the municipal populationin 1,000 inhabitants. Transfers (in 100k) represents the transfer to the municipality by central

governments (state and region). Taxes (in 100k) represent municipal tax revenues. In Column 6, the

regression includes leads and lags denoted by the interactions terms between the Year2005-Year2011time dummies and the Fisc.Rule indicator. Mean Y treat-pre is the sample mean for treated

municipalities pre-2009. Eff.Fisc.Rule on Treated (%) is the ratio between the estimated coefficient

of Fisc.Rule*Post and Mean Y treat-pre. SEs are clustered at municipal level. Significance at the10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for all the public works tendered

between 2004 and 2011 in Italy.

12

D.2 Additional balance sheet outcomes

Table D.5: Effects of the fiscal demand shock on private sector accounts receivable

Dep.Var. Priv.S.A.Rec. Priv.S.A.Rec. Priv.S.A.Rec. Priv.S.A.Rec.Model OLS FE FE-HT FE-HT

(1) (2) (3) (4)

Fisc.Rule.Exp.XPost 4.810** 2.805 6.913**(2.048) (2.073) (2.786)

Fisc.Rule.Exp. -30.100***(2.794)

Post 541.414***(108.592)

Fisc.Rule.Exp.*Year2005 15.648(15.722)







Observations 27,520 27,434 19,806 19,806Company FE No Yes Yes YesYear FE No Yes Yes YesCityXYearFE No No Yes YesMean Y 1490 1490 1490 1676St.Dev.Fisc.Rule 23.91 23.91 23.91Eff.Fisc.Rule.Exp. (%) 7.718 4.501 11.09

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firms

private sectors account receivables measured by the difference between total revenues and revenuesfrom procurement. Financial variables are deflated using KLEMS deflators.Fisc.Rule.Exp. rep-resents the exposure to the fiscal demand shock computed as the ratio between the firm’s value

won in municipalities hit by the demand shock and the firm’s pre-demand shock revenues. In eachof the rows, Post is an indication for the years after 2008. Year2005-Year2011 are time dum-

mies interacted with Fisc.Rule.Exp. Columns 2,3,report FE estimates with firm and year fixed

effects. Columns 3,4, include city of incorporation-by-year fixed effects. Mean Y is the samplemean for each dep.var. Eff.Fisc.Rule.Exp (%) is the ratio between the estimated coefficient ofFisc.Rule.Exp.*Post*St.Dev.Fisc.Rule and Mean Y. Significance at the 10% (*), at the 5% (**),

and at the 1% (***). Source: Statistics for procurement companies that won at least one auctionbefore 2009 and observed between 2004 and 2011.

13

Table D.6: Effect of the fiscal demand shock on other outcomes

Dep.Var. N.Workers N.Workers N.Workers N.Workers O.Services O.Services O.Services O.Services Sev.Fund Sev.Fund Sev.Fund Sev.Fund Tot.Rev. Tot.Rev. Tot.Rev. Tot.Rev. Inventory Inventory Inventory Inventory TFP TFP TFP TFP(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24)

Fisc.Rule.Exp.XPost 0.069* -0.016 -0.015 2.920 1.947 0.440 -0.056*** -0.078*** -0.053*** 4.824 3.231 1.432 -4.504** -5.917*** -2.432 0.001*** 0.001*** 0.0005*(0.037) (0.019) (0.032) (1.805) (1.471) (2.070) (0.015) (0.013) (0.017) (3.559) (2.375) (4.822) (1.964) (1.956) (2.629) (0.000) (0.000) (0.00025)

Fisc.Rule.Exp. -0.335*** -17.404*** -0.332*** -43.382*** -19.684*** -0.000***(0.048) (1.970) (0.032) (4.543) (2.866) (0.000)

Post -1.643 -100.628 8.323*** -155.451 527.363*** -0.238***(1.876) (88.409) (0.708) (196.851) (106.735) (0.004)

Fisc.Rule.Exp.*Year2005 0.119 0.523 0.010 -1.746 2.353 -0.0003(0.089) (1.812) (0.034) (5.288) (12.451) (0.001)



Fisc.Rule.Exp.*Year2008 0.157 2.272 -0.017 -0.049 -0.410 -0.00004(0.098) (2.413) (0.040) (6.771) (14.385) (0.001)

Fisc.Rule.Exp.*Year2009 0.124 1.702 -0.038 0.063 -2.156 0.0002(0.101) (2.415) (0.041) (6.357) (14.374) (0.001)

Fisc.Rule.Exp.*Year2010 0.192* 2.123 -0.055 -0.297 -1.166 0.0006(0.115) (2.490) (0.041) (6.728) (14.506) (0.001)

Fisc.Rule.Exp.*Year2011 0.117 1.729 -0.071* 0.051 -1.729 -0.0001(0.103) (2.482) (0.042) (6.820) (14.414) (0.001)

Observations 16,135 15,700 9,948 9,948 27,764 27,678 20,003 20,003 26,471 26,360 18,851 18,851 27,764 27,678 20,003 20,003 27,520 27,434 19,806 19,806 25,361 25,251 17,770 17,770Mean Y 24.63 24.63 24.63 26.46 1156 1156 1156 1238 27.09 27.09 27.09 27.41 3001 3001 3001 3124 1806 1806 1806 2009 0.888 0.888 0.888 0.889St.Dev.Fisc.Rule 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91Eff.Fisc.Rule.Exp. (%) 6.706 -1.568 -1.483 6.039 4.026 0.909 -4.917 -6.885 -4.645 3.844 2.575 1.141 -5.962 -7.832 -3.219 2.374 2.166 1.262Company FE No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes No Yes Yes YesYear FE No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes No Yes Yes YesCityXYearFE No No Yes Yes No No Yes Yes No No Yes Yes No No Yes Yes No No Yes Yes No No Yes Yes

The table reports estimates of the effects of exposure to the fiscal demand shock on other firms outcomes: N.Workers is the number of workers; O.Services are the firm total costs foroutsourced services (in 1,000 euros); Sev.Fund is the firm’s total funds accumulated for severance pays (in 1,000 euros); Tot.Rev. are the total annual revenues (in 1,000 euros); Inventoryare Residui. TFP is the total factor productivity computed following the Olley and Pakes (1996) procedure. Financial variables are deflated using KLEMS deflators.Fisc.Rule.Exp.

represents the exposure to the fiscal demand shock computed as the ratio between the firm’s value won in municipalities hit by the demand shock and the firm’s pre-demand shockrevenues. In each of the rows, Post is an indication for the years after 2008. Year2005-Year2011 are time dummies interacted with Fisc.Rule.Exp. Columns 2,3,4,6,7,8,10,11,12,18,19,20and 21,22,23,24 report FE estimates with firm and year fixed effects. Columns 3,4,7,8,11,12,15,16,19,20, and 23 include city of incorporation-by-year fixed effects. Mean Y is the samplemean for each dep.var. Eff.Fisc.Rule.Exp (%) is the ratio between the estimated coefficient of Fisc.Rule.Exp.*Post*St.Dev.Fisc.Rule and Mean Y. Significance at the 10% (*), at the 5%(**), and at the 1% (***). Source: Statistics for procurement companies that won at least one auction before 2009 and observed between 2004 and 2011.

14

E Additional Robustness Checks

Table G.1: Robustness: different data transformations

Municipal data Firm dataDep.Var. Tot.Val.Proc. Tot.Val.Proc. Tot.Val.Proc. Rev.Proc Rev.Proc Rev.Proc Capital Capital Capital Labor Labor LaborDep.Var.Spec Levels Logs IHS Levels Logs IHS Levels Logs IHS Levels Logs IHS

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Fisc.Rule.XPost -6.825*** -0.257*** -0.377***(1.170) (0.033) (0.027)

Fisc.Rule.ExpXPost -0.088*** -0.259*** -0.192*** -4.504*** -0.044*** -0.032*** 0.012 0.020** -0.000(0.006) (0.022) (0.008) (0.597) (0.012) (0.012) (0.204) (0.009) (0.013)

Observations 48,120 26,724 48,120 27,678 8,145 27,678 27,678 23,114 27,678 27,678 22,603 27,678Company FE YES YES YES Yes Yes Yes Yes Yes Yes Yes Yes YesYear FE YES YES YES Yes Yes Yes Yes Yes Yes Yes Yes YesMean Y treat-pre 24.01 2.383 2.628Mean Y 3.161 1.469 0.810 585.8 4.857 5.420 398 5.271 5.654St.Dev.Fisc.Rule 23.91 1.356 1.575 23.91 1.356 1.575 23.91 1.356 1.575

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on municipal spending for procurement, and

firms revenues from procurement, capital accumulation and labor: Tot.Val.Proc. is the annual total value of municipal tenders forinfrastructures in all Italian municipalities; Rev.Proc. is the value of procurement won in a year (in 100,000 euros); Capital are the firmtotal annual physical assets (in 1,000 euros); Labor are the firm total personnel costs (in 1,000 euros). These variables are expressed in

levels, in logs or in IHS depending on the specification. Financial variables are deflated using KLEMS deflators.Fisc.Rule.Exp. representsthe exposure to the fiscal demand shock computed as the ratio between the firm’s value won in municipalities hit by the demand shockand the firm’s pre-demand shock revenues, and it is expressed in levels, in logs or in IHS depending on the specification. In each of the

rows, Post is an indication for the years after 2008. In Cols.1-3 estimates are for municipal data, while in Cols.4-12 for firms. Mean Y isthe sample mean for each dep.var. Significance at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics for procurementcompanies that won at least one auction before 2009 and observed between 2004 and 2011.

15

Table G.2: Robustness: Effect of the fiscal demand shock on capital and labor. Standard errors clusteredat the city of incorporation level

Dep.Var. Rev.Proc Rev.Proc Capital Capital Labor Labor N.Workers N.Workers O.Services O.Services Sev.Fund Sev.Fund Tot.Rev. Tot.Rev. Invent. Invent. TFP TFP Acc.Rec. Acc.Rec. Debt DebtModel FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22)

Fisc.Rule.Exp.XPost -0.088*** -0.095*** -4.504*** -3.839*** 0.012 0.033 -0.016 -0.015 1.947 0.440 -0.078*** -0.053*** 3.231 1.432 -5.917*** -2.432 0.001*** 0.000* -10.511*** -12.894*** -16.736*** -15.668***(0.006) (0.007) (0.576) (0.944) (0.189) (0.161) (0.019) (0.018) (1.445) (0.993) (0.015) (0.018) (2.096) (2.696) (2.196) (2.416) (0.000) (0.000) (2.737) (4.579) (3.714) (5.184)

Observations 27,678 20,003 27,678 20,003 27,678 20,003 15,700 9,948 27,678 20,003 26,360 18,851 27,678 20,003 27,434 19,806 24,394 17,011 27,678 20,003 27,678 20,003Company FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesMean Y 3.161 3.161 585.8 585.8 398 398 24.92 24.92 1158 1158 27.17 27.17 3006 3006 1810 1810 0.887 0.887 1892 1892 3883 3883St.Dev.Fisc.Rule 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91Eff.Fisc.Rule.Exp. (%) -66.78 -71.87 -18.38 -15.67 0.0749 0.201 -1.549 -1.466 4.018 0.907 -6.864 -4.630 2.570 1.139 -7.817 -3.213 2.168 1.263 -13.28 -16.29 -10.31 -9.647

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firms outcomes: Rev.Proc. are the revenues from procurement (in 100,000 euros); Capitalare the firm total annual physical assets (in 1,000 euros); Labor are the firm total personnel costs (in 1,000 euros); O.Services are the firm total costs for outsourced services (in 1,000euros); Sev. Fund is the firm’s total funds accumulated for severance pays (in 1,000 euros). TFP is the total factor productivity computed following the Olley and Pakes (1996)

procedure. Financial variables are deflated using KLEMS deflators.Fisc.Rule.Exp. represents the exposure to the fiscal demand shock computed as the ratio between the firm’s valuewon in municipalities hit by the demand shock and the firm’s pre-demand shock revenues. In each of the rows, Post is an indication for the years after 2008. Odd (even) columns reportestimates with firm fixed effects (add city of incorporation-by-year fixed effects). Mean Y is the sample mean for each dep.var. Eff.Fisc.Rule.Exp (%) is the ratio between the estimatedcoefficient of Fisc.Rule.Exp.*Post*St.Dev.Fisc.Rule and Mean Y. SEs are clustered at the city level. Significance at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics

for procurement companies that won at least one auction before 2009 and observed between 2004 and 2011.

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Table G.3: Robustness: Effect of the fiscal demand shock on capital and labor controlling for regionalfiscal demand shocks

Dep.Var. Rev.Proc Rev.Proc Capital Capital Labor Labor N.Workers N.Workers O.Services O.Services Sev.Fund Sev.Fund Tot.Rev. Tot.Rev. Invent. Invent. TFP TFP Acc.Rec. Acc.Rec. Debt DebtModel FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT FE FE-HT

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22)

Fisc.Rule.Exp.XPost -0.089*** -0.095*** -4.467*** -3.824*** 0.014 0.039 -0.016 -0.015 1.763 0.469 -0.076*** -0.052*** 2.699 1.494 -6.076*** -2.446 0.001*** 0.000* -10.212*** -12.836** -16.812*** -15.640**(0.006) (0.008) (0.596) (1.119) (0.207) (0.330) (0.019) (0.032) (1.403) (2.064) (0.013) (0.017) (2.297) (4.818) (2.051) (2.625) (0.000) (0.000) (2.674) (6.413) (3.431) (6.509)

Observations 27,678 20,003 27,678 20,003 27,678 20,003 15,700 9,948 27,678 20,003 26,360 18,851 27,678 20,003 27,434 19,806 24,394 17,011 27,678 20,003 27,678 20,003Company FE No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No YesYear FE No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No YesMean Y 3.161 3.161 585.8 585.8 398 398 24.92 24.92 1158 1158 27.17 27.17 3006 3006 1810 1810 0.887 0.887 1892 1892 3883 3883St.Dev.Fisc.Rule 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91 23.91Eff.Fisc.Rule.Exp. (%) -67.45 -71.93 -18.23 -15.61 0.0860 0.231 -1.521 -1.433 3.637 0.967 -6.721 -4.594 2.146 1.188 -8.027 -3.231 2.156 1.267 -12.90 -16.22 -10.35 -9.631

Notes: The table reports estimates of the effects of exposure to the fiscal demand shock on firms outcomes: Rev.Proc. are the revenues from procurement (in 100,000 euros); Capital

are the firm total annual physical assets (in 1,000 euros); Labor are the firm total personnel costs (in 1,000 euros); O.Services are the firm total costs for outsourced services (in 1,000euros); Sev. Fund is the firm’s total funds accumulated for severance pays (in 1,000 euros). TFP is the total factor productivity computed following the Olley and Pakes (1996)procedure. Financial variables are deflated using KLEMS deflators.Fisc.Rule.Exp. represents the exposure to the fiscal demand shock computed as the ratio between the firm’s value

won in municipalities hit by the demand shock and the firm’s pre-demand shock revenues. In each of the rows, Post is an indication for the years after 2008. All the regressionsinclude the fraction of cities exposed to Patto in the region of incorporation of each company (and its interaction with the Post dummy). Odd (even) columns report estimates withfirm fixed effects (add city of incorporation-by-year fixed effects). Mean Y is the sample mean for each dep.var. Eff.Fisc.Rule.Exp (%) is the ratio between the estimated coefficient

of Fisc.Rule.Exp.*Post*St.Dev.Fisc.Rule and Mean Y. SEs are clustered at the city level. Significance at the 10% (*), at the 5% (**), and at the 1% (***). Source: Statistics forprocurement companies that won at least one auction before 2009 and observed between 2004 and 2011.

17

Documents

E ect of a Fiscal Demand Shock on Firm Investmentimmamarino.com/patto_3.pdfthe shock disproportionately, perhaps consistent with fewer nancial frictions. In addition to corporate nance,