Essays on the economics of firms and of financial institutions

(1)

Essays on the Economics of Firms and of

Financial Institutions

Matteo Gatti

Thesis submitted for assessment with a view to obtaining the degree of Doctor of Economics of the European University Institute

(2)

(3)

European University Institute

Department of Economics

Essays on the Economics of Firms and of Financial Institutions

Matteo Gatti

Thesis submitted for assessment with a view to obtaining the degree of Doctor of Economics of the European University Institute

Examining Board

Prof. Evi Pappa, Universidad Carlos III Madrid, Supervisor Prof. Juan Dolado, Universidad Carlos III Madrid

Prof. Steven Ongena, University of Zurich

Prof. Saverio Simonelli, University of Naples Federico II

No part of this thesis may be copied, reproduced or transmitted without prior permission of the author

(4)

(5)

Researcher declaration to accompany the submission of written work I Matteo Gatti certify that I am the author of the work “Essays on the Economics of Firms and of Financial Institutions” I have presented for examination for the PhD thesis at the European University Institute. I also certify that this is solely my own original work, other than where I have clearly indicated, in this declaration and in the thesis, that it is the work of others. I warrant that I have obtained all the permissions required for using any material from other copyrighted publications. I certify that this work complies with the Code of Ethics in Academic Research issued by the European University Institute (IUE 332/2/10 (CA 297). The copyright of this work rests with its author. [quotation from it is permitted, provided that full acknowledgement is made.] This work may not be reproduced without my prior written consent. This authorisation does not, to the best of my knowledge, infringe the rights of any third party. Statement of inclusion of previous work (if applicable): I confirm that chapter 2 was jointly co‐authored with Tommaso Oliviero and I contributed 50% of the work. Signature and Date: Matteo Gatti 27/05/2019

(6)

(7)

(8)

(9)

Abstract

Understanding the propagation of shocks throughout the economy is crucial for the design of policies and for their evaluation. Shock propagation hinges on a number of factors, among which market structure and the level of competition play a relevant role. In this dissertation I investigate the role of competition on firms’ pricing behavior and on banks’ funding stability when hit by a shock. Moreover, I also assess the implications of greater capital requirements on banks’ liquidity provision.

The first chapter assesses how firms set their markups following a positive demand shock. I use the 2012 Emilia-Romagna earthquake as a natural experiment and I exploit within-firm variation of construction firms to identify the causal effect of interest. I find that markups decreased on average by 4 percentage points following the demand shock and that this drop is driven by higher competition due to firm-entry in the face of increasing marginal costs of labor.

The second chapter, joint with Tommaso Oliviero, studies the effect of an increase in deposit insurance limit to e100,000 per bank account, set by the EU in early 2009, on the market for deposits. To evaluate the impact of the new directive we use Italian banks as a control group as they were not affected by the change. We find that the increase of deposit insurance limit reduces banks’ cost of financing through deposits between 0.3 and 0.7 percentage points.

The third chapter studies the effect of a change in capital requirements on the market for collat-eralized debt obligations (CDOs). In a framework where securitization lowers banks’ monitoring effort over loans’ quality, I provide evidence of optimal risk-adjusted capital requirements that trade off the benefits of higher liquidity with the costs of lower monitoring incentives.

(10)

(11)

Acknowledgements

I am truly indebted to my two supervisors Evi Pappa and Juan J. Dolado for their support and guidance without which this work had not been possible. Evi’s countless suggestions and enthu-siasm enriched my work from the very early stages to where it stands now. Juanjo’s incredible energy and patience shaped my results and helped me not to loose momentum.

This dissertation has also immensely benefitted from endless discussions with Omar Bamieh and Tommaso Oliviero. I am grateful beyond measure for their invaluable help as colleagues and for their support as friends.

I would also like to thank Giacomo Calzolari, Andrea Ichino, Piero Gottardi and all professors and researchers at the EUI Economics department for providing excellent feedback in numerous in-teractions; Lucia Vigna, Sarah Simonsen and all administrative staff for their invaluable assistance; Mauro Lanati and the EUI Tennis team with whom I shared moments of great fun.

I thank my dearest and lifetime friends Anto, Carlotta, Carolina, Irene, Livia, Luca and Lucio who have been there all along.

My family has also been a source of constant encouragement. My siblings Benedetta and Filippo and my mother Giovanna have been my biggest supporters.

Two last special acknowledgements go to my father, whose example and memory have been a truly inspirational resource and to Anna, whose laughs have been an inexhaustible source of thrust along this journey. And much more.

(12)

(13)

List of Tables

1.1 Share of Operating Costs . . . 11

1.2 Summary Statistics . . . 15

1.3 Intention To Treat - Diff in Diff . . . 16

1.4 Competition - Sample Splitting HH Index . . . 19

1.5 Financial Constraints - Sample Splitting Cost of Debt . . . 20

1.6 Probability of Signing Contract . . . 22

1.7 Descriptive Statistics . . . 25

1.8 Baseline Contract-Level . . . 29

1.9 Interaction C_l . . . 36

1.10 Interaction C_l and HHI_l . . . 37

1.11 Firm Entry . . . 39

1.12 First Stage . . . 40

1.13 First Stage Interaction . . . 41

1.14 Number of Working Days G . . . 42

1.15 Distance House-to-Firm . . . 44

1.16 OLS Estimation - Firms’ Marginal Costs . . . 47

1.17 Reduced-Form Estimation . . . 48

1.18 First Stage Estimation . . . 50

1.19 OLS and IV Estimation . . . 50

1.20 Baseline Sample Split . . . 51

1.21 Intention To Treat - Diff in Diff - Unweighted . . . 53

(17)

2.1 Deposit insurance limit by Country in September 2008 . . . 65

2.2 Summary Statistics 2006-2008 . . . 67

2.3 Cross-Country Regression . . . 70

2.4 Bank-Level Analysis . . . 73

2.5 Summary Statistics - Bank level 2006-2008 . . . 74

2.6 Summary Statistics - Matched Sample 2006-2008 . . . 76

2.7 Bank-Level Analysis - Matched Sample . . . 77

2.8 Bank-Level Analysis: The Impact on Risk . . . 79

2.9 Heterogeneity on risk: Deposit Rates . . . 83

2.10 Heterogeneity on risk: Total Deposit . . . 83

2.11 Summary Statistics - Matched Sample 2006-2008 - Robustness . . . 86

2.12 Bank-Level Analysis: Matched Sample - Robustness . . . 87

2.13 Heterogeneity on risk: Deposit Rates - Robustness . . . 87

2.14 Heterogeneity on risk: Total Deposit - Robustness . . . 88

2.15 Heterogeneity on risk: Deposit Rates - Robustness II. . . 89

(18)

List of Figures

1.1 Reconstruction Subsidy Application . . . 7

1.2 Firms’ Markup . . . 13 1.3 Firms’ ROE . . . 14 1.4 Event Study . . . 18 1.5 Municipalities in Emilia-Romagna . . . 23 1.6 Identification Strategy . . . 26 1.7 Sdl Distribution . . . 27

1.8 Opposite Effects on Markups . . . 31

1.9 Cl Distribution . . . 32

1.10 HHI_l Distribution . . . 33

1.11 Marginal Effects. . . 35

1.12 House-Firm Distance . . . 43

1.13 Identification Strategy . . . 45

1.14 Demand shock and Distance from Epicenter . . . 49

1.15 Firms’ Markup . . . 54

1.16 Total square meters on distance . . . 56

1.17 S_dl Distribution . . . 57

2.1 Banks Deposit Rates and Total Amounts . . . 68

2.2 Event Study . . . 72

2.3 Event Study After PSM. . . 78

(19)

3.2 Yields on CDS indexes. . . 109

3.3 From Basel I to Basel II. . . 112

(20)

(21)

1 Markups and Firm Entry: Evidence

From the 2012 Emilia Earthquake

Chapter Abstract

The cyclical behaviour of price markups is key for the propagation of shocks throughout the econ-omy. Yet, the empirical evidence about this issue is mixed. In this paper I provide direct evi-dence of markup counter-cyclicality, conditioning on a positive demand (government expenditure) shock. I exploit the exogenous increase in publicly subsidized housing reconstruction after the 2012 earthquake in Emilia-Romagna (Italy) as a natural experiment. I construct a granular measure of earthquake disruptiveness, which is used to identify the causal effect of interest. I find that markups decreased on average by 4 percentage points following the expansionary shock. Moreover, I show that the drop in markups is mainly driven by a reduction in prices due to firm-entry in the face of increasing marginal costs of labour.

(22)

1.1 Introduction

How do markups respond to fluctuations in aggregate economic variables is a key, yet unsettled, question in Macroeconomics. Markup cyclicality plays an important role in the propagation of shocks to the aggregate macroeconomy, making the understanding of its behaviour vital for the de-sign of policy responses. More specifically, in the context of fiscal policies, markup cyclicality affects the sign and size of fiscal multipliers as it helps explaining the evolution of industries’ competitive structures, their barriers to entry and their openness to new markets and products. Although a large number of papers has contributed already to this literature, theoretical models and empirical studies often provide opposite evidence, leaving the debate still open (Nekarda and Ramey (2013), Anderson, Rebelo, and Wong (2018) and Galeotti and Schiantarelli (1998)).

Motivated by the need of a greater evidence, this paper contributes to the current debate on markup cyclicality by exploiting a natural experiment to clearly identify markup response to a government expenditure shock. To identify the effect of an exogenous demand shock, I use the Emilia-Romagna (Italy) housing reconstruction plan - a plan sponsored by the Italian government to support housing reconstruction after the devastating 2012 earthquake - as the main laboratory for my study. Within this setting, this paper assesses the effect of the demand shock faced by con-struction firms involved in subsidized housing-reconcon-struction on price-cost markups. It provides evidence of counter-cyclical markups and it shows that the decrease in markups is driven both by a drop in prices and by a contextual increase in marginal costs. On one hand, the decrease in prices is driven by firm-entry, as firms headquartered in other regions move to Emilia-Romagna and new firms locate in the municipalities affected by the earthquake. On the other hand, it also shows that labour costs are procyclical and they increase more for firms exposed to a higher demand shock. Both results provide evidence of declining markups for firms affected by a positive demand shock and the more so, the larger is the demand shock.

Government subsidies for housing reconstruction have been substantial and did matter for a quick and timely recovery. The earthquake hit a densely populated area, causing overall estimated

(23)

damages that exceeded e13bln, out of which 4.5bln were devoted to residential housing. The re-construction plan set up by the Italian government had the twofold objective of avoiding a rapid disappearance of small towns and of their local production activities, as well as of employing local firms for reconstruction. Households were provided with the freedom to choose their most preferred construction firms, conditionally on presenting at least two competing quotes to the local govern-ment.

This setting allows me to exploit a novel dataset that collects detailed information on government-subsidized contracts for housing reconstruction. The data provides granular information for all households applying for government subsidies to the local municipality. Information includes contract-level variables such as the total value of the contract, the size of the houses destroyed by the earthquake, the number of working days for each contract and the damage level of each house. The richness of this data allows one to compute per-square-meter prices and per-square-meter working days for each reconstruction contract. I then combine this information with standard balance sheet data at the firm-level from Analisi Informatizzata delle Aziende Italiane (AIDA) dataset, which includes balance sheet data, financial variables as well as income statement. The resulting dataset matches contract-level information with characteristics at the firm level.

Direct selection of construction firms by households poses significant identification challenges on the causal effects of positive demand shocks on markups. As the reconstruction plan allows households to select preferred firms, contracts are not randomly assigned to firms and OLS es-timation would lead to selection bias. I address selection bias in two ways. I first exploit the exogenous variation in firms’ headquarters location and compare municipalities differently affected by the earthquake. Firm-level data allow me to estimate the intention-to-treat (ITT) by comparing markups for firms which headquarter is located in provinces hit by the earthquake with markups of firms located in provinces not hit by the earthquake. As balance sheet data spans for 10 years around the earthquake event, I also exploit the pre/post time variation induced by the earthquake, by nesting the ITT within a diff-in-diff setting. At the contract level instead, I exploit within-firm variation as I compare different contracts signed by the same construction within-firm. Comparing

(24)

contracts signed by the same firm eliminates selection bias issues as it removes all confounding variables that determine homeowners’ choice of one firm over the other.

Benefits of within-firm variation do not limit to removing selection-bias, but they also address marginal cost estimation issues. As marginal costs are often not directly observable, macro models have often estimated marginal costs assuming a functional form of the production function (Nekarda and Ramey (2013), Bils (1987a), Rotemberg and Woodford (1992)). Production function estima-tion allows marginal costs to be estimated as the inverse of the labour share. At the same time, the empirical literature has also employed more general markup estimation, measuring marginal costs with average costs when the amount of fixed costs over total costs is low (Domowitz, Hubbard, and Petersen (1986) and Anderson et al. (2018)). In this paper I deal with markup estimation in two different ways. Firstly, I consider a construction industry which features low fixed costs - the main input to production are raw materials and labor - and I estimate firm-level markups using average costs as a proxy for marginal costs1. Second, at the contract-level, I rely on an identification strat-egy described above, which compares contracts that have the same marginal costs, making markup estimation unnecessary. The identification strategy compares contracts signed by the same firm for houses with the same level of damage. Since marginal costs are the same, any variation in markups would then be driven by variation in prices.

The empirical findings of this paper support the countercyclical results of the theoretical litera-ture and they contradict the more recent results of the empirical side of the debate. The theoretical literature has provided supporting evidence in favour of countercyclical markups in a number of different settings. Bils (1987a) and Rotemberg and Woodford (1999) use labour input margins to estimate marginal costs in a New Keneysian framework. They show that, under the assumption of marginal costs of labour being more procyclical than average labour costs2, markups decrease because of higher labour costs. My results indeed confirm that labor costs are procyclical and that,

1_{I follow De Loecker and Eeckhout (2017), Domowitz et al. (1986) and Anderson et al. (2018) who use gross}

margins as a measure for markups. Gross margins are defined as the ratio between operating margin and sales value.

(25)

consequently, markups defined as the inverse of the labour share are countercyclical. Rotemberg and Saloner (1986) develop a game-theoretic approach and show that firms reduce markups in booms rather than in recessions, as the incentives to deviate from cartels and undercut competitors are stronger when output increases. Jaimovich and Floetotto (2008) provide instead theoretical evidence that markups’ cyclicality can be explained by an increase in competition due to firm-entry. As technology shocks reduce prodction costs and eliminate barriers to entry, new firms enter the market and compete by reducing prices. My results confirm that lower prices are indeed driven by higher competition due to firm entry.

On the other hand, recent empirical papers provide greater evidence of procyclical markups. Nekarda and Ramey (2013) revisit the New Keneysian framework with a set of more general as-sumptions and new data to find that markups are procyclical conditioning on a positive demand shock. Anderson et al. (2018) use four levels of data aggregation to show that markups are mildly procyclical over time and display positive correlation with local income. Kim (2018) explores the relationship between markups and financial constraints, arguing that, in recessions, financially con-strained firms decrease markups to liquidate inventories and to increase their revenues and cash holdings. A sample splitting result in the diff-in-diff section also confirms that markups are more procyclical for financially constrained firms.

Finally, as most of funding for reconstruction is subsidized by the government, this paper also assesses the effectiveness of expansionary fiscal policy in getting out of a recession. Following Hart (1982) seminal paper, for a long time it was thought that the fiscal multiplier was strictly increasing in the monopoly degree, as pure profits are generated, stimulating households’ income and conse-quently aggregate demand. However, most of these models use Dixit and Stiglitz (1977) monopo-listic competition where each firm faces a constant-elasticity demand function, so that the markup is also constant. This assumption is not consistent with the evidence presented in Gali (1995), and Rotemberg and Woodford (1995) that support the hypothesis of counter-cyclical markups. In this paper, following the theoretical arguments made by Startz (1989), I present empirical evidence based on a natural experiment showing that markups behave counter-cyclically when pure profits

(26)

induces firms entry, interpreted as more firms per industry. As a result, fiscal policy produces an aggregate demand externality by stimulating entry, pushing the markup downwards, and therefore introducing efficiency gains in the economy.

The rest of the paper is structured as follows: Section 2 explains the setting and the data used; Section 3 presents the firm level analysis; Section 4 illustrates the contract level analysis and Section 5 concludes.

1.2 Setting and Data

This section describes the 2012 Emilia Romagna earthquake, the government-sponsored reconstruc-tion plan that followed, the Italian construcreconstruc-tion industry and the data sources used in the empirical analysis.

1.2.1 Government Subsidies for Reconstruction

In May 2012, Emilia-Romagna faced two intense earthquakes nine days one from the other. The first earthquake, on May 20th 2012, had a magnitude of 6.1 on the Richter scale and the epicenter set in the small town of Finale Emilia. The second earthquake, instead, occurred on May 29th, it had a magnitude of 5.9 and its epicenter was located in the nearby Mirandola. Both earthquakes had disruptive effects on the areas close to the epicenter as well as in the neighbouring municipali-ties, damaging and causing the collapse of many historical and modern buildings.

The overall estimated damages exceeded e13bln, of which e4.5bln were devoted to residential housing. The Italian Government and the Emilia-Romagna region immediately announced govern-ment subsidies and a reconstruction plan that had a twofold objective. On one side, it wanted to avoid a rapid disappearance of small villages and of their local production activities, which account for around 2%3 of the national GDP. On the other, it aimed at employing local firms for recon-struction, helping them to restart production and benefitting the whole local economy.

(27)

H

F

₁

F

2

G

q1

q2

(a) H gets two quotes q1 and q2

H

F

₁

F

2

G

q1

(b) G selects cheapest quote q1 Figure 1.1 – Reconstruction Subsidy Application

Note. This Graph shows the application process to obtain funding from the Governement. First Households

H obtain quotes from two firms F1 and F2. Quotes are then sent to the Government G, who approves the

cheapest one. Finally G pays F1

Government subsidies involved both residential units as well as productive units. As of July 2018, more than 10,000 buildings filed for a government subsidy, for a total of around 26,786 building units, 15,201 of which were for first homes. Out of the 10,000 requests, 8,500 have been already approved and have gone through - or are still under - reconstruction. The amount of subsidies for residential reconstruction already approved by the local government amount to approximately e4bn, most of which has been already liquidated. This paper focuses on the effects of this massive inflow of money to those firms involved in the reconstruction of residential housing.

Applications for subsidies to housing reconstruction opened in late 2012 and are still ongoing. In order to apply, a beneficiary would need to get a damage certificate from an independent technician, together with at least two quotes from two competing firms, firms F 1 and F 2 in Figure 1.1. Subsidies are capped, both on size and on the level of the damage reported. Household H would then need to submit the two quotes and the damage certificate to the local municipality as the last step of the application (figure (a)). If the subsidy gets approved, the municipality must select the cheapest quote (figure (b)), although the household always has the possibility to top up the government subsidy with private funds. Once all - or part of - the construction works are terminated, the municipality pays households directly.

(28)

1.2.2 The Italian construction industry

Summary reports on the Osservatorio Congiunturale dell’Industria delle Costruzioni4 show that Italian construction industry is characterized by small firms operating in local regional markets. The small size of Italian construction firms is mainly due to the intrinsic cyclicality of the construc-tion business and by strong capacity constraints related to labour market regulaconstruc-tions and to firms’ access to credit. Both frictions affect firms’ ability to grow larger and to expand their businesses through time. On one hand, Italian labour market regulations made firing more expensive for firms with more than 15 employees until 20155. At the same time, the crisis affecting the Italian con-struction industry has made firms’ access to credit more difficult6. As of 2011 the median Italian construction firm earnse1.7mln in revenues, it raises e200,000 of equity and it employs 9 workers7.

Moreover, the Italian construction industry is a local industry in normal times8_{. With the} excep-tion of few big players in the Italian market, construcexcep-tion firms undertake projects located in single provinces as they face important relocation costs when they operate in more than one province. Not only firms face higher transportation costs of materials, cranes and other machineries when they expand their business, but they also need to register all their active workers in the local Cassa Edile. Casse Edili are organizations that were created in the early twentieth century to guarantee basic rights to workmen involved in construction projects such as daily allowances, pension bene-fits and insurance benebene-fits. Over the years, Casse Edili have remained a strong institution in the Italian construction business and firms are required to contribute to their funding with monthly installments of 14.20% on enrolled workers’ gross salary. Business activity in different provinces can thus lead construction firms to bear high labour costs related to contributions for workers enrolled in more than one Cassa Edile.

In this context, the Emilia-Romagna earthquake represented a significant positive demand shock 4_{http://www.ance.it/docs/docDownload.aspx?id=42965}

5_{Articolo 18 dello Statuto dei Lavoratori}

6_{Oservatorio Congiunturale sull’Industria delle Costruzioni. http://www.ance.it/docs/docDownload.aspx?id=42965} 7_{AIDA balansheet data averaged over 2007-2016 period}

(29)

for the reconstruction business. The amount of buildings destroyed by the earthquake, the exten-siveness of their damage and the urgency with which homes and businesses needed to be rebuilt, made it difficult for the existing 3,000 firms located in Emilia-Romagna to satisfy the current de-mand for reconstruction. This paper shows that the consequences of the increase in dede-mand on the construction business translated into greater firms’ mobility rather than an increase in size. Labour market frictions determined by the Italian law, together with the temporary nature of the demand shock, resulted in firms moving towards the municipalities affected by the earthquake rather than in an increase in size of the ones located in Emilia-Romagna.

1.2.3 Data

In this section, I describe the data set construction and the data sources used.

I combine information from various sources. First, I use balance sheet information at the firm f , municipality m and time t level (f, m, t), from AIDA, a Bureau Van Dijk database comprehensive of all Italian firms that are required to file an official account. I download balance sheet information for all Italian construction companies which I identify using the NACE Rev 2 code for a total of 285,229 firms. Starting from this set of firms I then exclude those ones that do not report a valid tax code and those ones that do not report the province where the legal entity is set. I consider all companies with a NACE Rev 2 code equal to both 41, labelled as building constructors and to 43, labelled as specialized construction works. AIDA provides information on the size of the firm (total assets, equity, revenues), on its profitability (net profits, ROE, ROA) and on its financing position (liquidity, bank debt, total debt and cost of external financing). The firm level dataset contains a number of missing observations that reduce the number of firms to approximately 85,000. Moreover, not all of the firms reported in the contract dataset have their balance sheet stored in the AIDA dataset. Out of the 2,153 firms involved in residential reconstruction, only 895 also appear in the AIDA dataset.

Secondly, I use a new and extremely detailed contract-level (c, d, l, f, t) dataset, where c is the contract, d is the building’s level of damage, l is the municipality where the building is located in, f is the construction firm hired to repair the building and t is the year in which the subsidy

(30)

is approved. The dataset provides information on the value of the subsidy, the total amount of construction costs, the size of the house in square meters, the damages to the house, its geographic location and dates of payments made to the firm. This dataset has been collected by the earthquake commission of the Emilia-Romagna region and it is provided disaggregated, so that considerable effort has been put in place to match all firms with contracts and with the geographical location of firms. It is updated regularly as new subsidies are approved or payments to firms are made.

Finally, I use information at the municipality level on the intensity of the earthquakes. I download geographic locations for all of the epicenters of both May, 20th and May 29th events from the Italian Institute of Geophysics and Vulcanology. I also use information on the intensity - measured as damages to buildings - and magnitude, measured using the Richter scale of the earthquake.

1.3 Firm level evidence

In this section I estimate the effect of a positive demand shock to firms on firm-level markups. I first describe the challenges related to markup estimation and I then explain the identification strategy understudy.

1.3.1 Markup estimation and identification

Markup estimation is a big concern in empirical studies as marginal costs are often not directly observable and measurable. The literature on markups has thus proposed a number of different approaches depending on the available data at hand. In this section I refer to Anderson et al. (2018), Eichenbaum, Jaimovich, and Rebelo (2011) and De Loecker and Eeckhout (2017), who define firm-level markups as gross margins using firms’ balance sheet variables. The gross margins definition assumes that average costs are used as a proxy for marginal costs, which holds true for industries with very low fixed costs. This assumption indeed fits well with the features of the con-struction industry, which is characterized by very little fixed costs and mostly by labour and other variable costs. Table 1.1 summarizes firms’ operating costs as the share of total costs for firms located in Emilia-Romagna and for the ones located in the rest of Italy. It shows that depreciation

(31)

accounts for between 5% and 6% of total operating costs, a low share if compared to other more capital-intense industries. On the contrary, labour costs and raw materials represent 35% of total operating costs, while services account for 43% of total operating costs. Services include any renting cost related to machineries such as cranes and other equipment that small construction firms do not own, as well as subcontracting contracts with other companies.

Table 1.1 – Share of Operating Costs

Share of Tot Costs

(1) (2) (3)

Emilia-Romagna Rest of Italy Total

Raw Materials 23% 23% 23% Labour 10% 14% 14% Services 43% 37% 37% Depreciation 5% 6% 6% Other Costs 19% 21% 21% Observations 1400038

Notes: This table shows the share of each operating cost as a per-centage of the sum of all operating costs, averaged from 2007 to 2016. Column (1) reports the average on firms located in Emilia-Romagna, column (2) reports the average on firms located in other Italian regions, while column (3) reports the overall average.

I define markups µ as gross margins, following Anderson et al. (2018).

µf mt=

Total value of production_{f mt}− Total cost of production_{f mt}

Total cost of production_{f mt} (1.1)

where the total value of production is defined as sales + inventory change, while total costs of production are defined as raw material + payroll + services. Inventory change measures account for distortions in markup measurement that are due to costs for inputs used to produced output that is not sold. As inventories are priced at the lower value between the market value and the sales value, I follow Anderson et al. (2018) and Nekarda and Ramey (2013) and I include them in the gross margin numerator. These inventory changes are relevant in the construction industry as they account for unfinished construction units when the balance sheet is drawn up on December, 31st. Anderson et al. (2018) provide similar price markup estimates to assess cyclicality based on

(32)

scanner prices from supermarkets.

Markup estimation is not the only empirical challenge that needs to be seriously considered. Identification strategy needs to be carefully addressed in this institutional framework in order to measure the causal effect of the positive demand shock on markups. Consider the following equation

µf = α + βDf+ f (1.2)

where D_f is a dummy that equals 1 if firm f is involved in housing reconstruction. As D_f is not randomly assigned to firms, but homeowners can chose their preferred construction firm, estimates of β would be affected by selection bias. To avoid selection bias, I exploit the exogenous vari-ation induced by the earthquake using locvari-ation of firms’ headquarters (HQ) to determine firms’ assignment to treatment. Using firms’ HQ location as a proxy for the location of their business, I thus compare firms located in municipalities affected by the earthquake with firms located in municipalities that are not affected by the earthquake. This analysis provides Intention-To-Treat (ITT) of the demand shock on prices. The population of firms I refer to in this firm-level analysis is composed of all Italian construction firms that are required to file a balance sheet statement to the local chamber of commerce. The balance sheet of these firms is reported in AIDA and accounts for approximately 285,000 units.

Figure 1.2 plots markups, as computed in equation (1.1), for firms located in treated municipal-ities versus firms that are located in municipalmunicipal-ities that have not been affected by the earthquake. I identify firms in treated municipalities as the ones with legal headquarters in a municipality that has been affected by the earthquake. Gross margins are averaged on the two groups using total assets as relative weights. It shows that gross margins of firms in treated municipalities have a similar pattern to gross margins in non-treated ones in the years before the earthquake. Instead, they decrease in treated provinces from 2012 onwards and they never get back to pre-crisis levels. Theses patterns provide suggestive evidence of a reduction in markups following a positive demand shock, as gross margins drop for construction firms located in Emilia-Romagna after the earthquake.

(33)

Figure 1.2 – Firms’ Markup

Notes: This Figure plots the weighted average of gross margin rate for firms located in Emilia-Romagna vis-à-vis the ones located in the rest of Italy. Gross Margins are weighted by firm’s size, measured as total assets.

Figure 1.3 shows firms’ return on equity (ROE) for treated and control provinces. ROE for firms in treated municipalities raises in the years after the earthquake, suggesting that the increase in construction work helped Emilia-Romagna firms recovering from the downward trend in produc-tivity. It also shows that, in spite of the decline in markups, the demand shock was high enough that it boosted construction firms’ profitability.

In addition to these two graphs, I also compare firms located in municipalities assigned to treat-ment and to control groups on other balance sheet variables. Table 1.2 compares firms on balance sheet variables averaged in the years before 2012. Firms located in treated provinces show similar markups and profitability to the non-treated ones. At the same time they are also more leveraged, more exposed to banks and they also have bigger revenues than the control group. All other differ-ences in the remaining variables are statistically significant different among the two groups. This however, does not invalidate the empirical identification adopted as location in a given municipality

(34)

Figure 1.3 – Firms’ ROE

Notes: This Figure plots ROE for firms located in Emilia-Romagna vis-à-vis the ones located in the rest of Italy. ROEs are weighted by firm’s size, measured as total assets.

is orthogonal to firms’ characteristics.

1.3.2 Difference-in-differences

Evidence presented in Figure 1.2 shows that, following the earthquake, gross margin decreased in treated municipalities, while it did not vary for firms assigned to the control group. I test formally this result in a difference-in-differences (diff-in-diff) set-up, where I compare firms located in treated versus control municipalities before and after the earthquake. The identifying assumption behind the diff-in-diff is that firms located in treated municipalities would have kept gross margins equal to the ones in the control group, had the treatment been absent. Absence of pre-trends is formally checked and confirmed by an event study.

(35)

Table 1.2 – Summary Statistics

(1) (2)

Emilia-Romagna Rest of Italy

Gross Margins .1358212 .1287287 Revenues 2274.621 1841.499 Cost of Debt 7.035542 7.674332 Total Assets 3442.163 2810.357 ROE 10.16379 11.53092 EBITDA 148.4466 146.5433 Working Capital 576.1273 585.5012 Number of Workers 8.69428 7.739714 Leverage 6.177751 5.175905 Bank Debt 36.01382 32.67766

Total Production Value 2177.097 1797.674

Observations 6434 70192

Notes: This table shows firms’ balance sheet variables averaged in the pre-period 2007-2011. Column (1) reports variables of firms located in Emilia-Romagna, while column (2) reports variables of firms located in other Italian regions.

random allocation of treatment on different municipalities and I estimate the following equation

µf mt = αf + λt+ βTm+ γpostt+ δ(Tm× postt) + θXf + ζ(Tm× t) + f mt (1.3)

where µ_{f mt} is the gross margin of firm f headquartered in municipalities m, at time t. T_m is a dummy variable that equals one if the municipality where the firm is located is hit by the earthquake and zero otherwise, and postt is a dummy that equals 1 in the post period. Xf are ex-ante firms’

balance sheet characteristics, while δ captures the interaction term and represents the parameter of interest. Firms’ fixed effects are dropped when firms’ balance sheet characteristics are added into the model.

Table 1.3 provides estimation results. Column (1) reports OLS coefficients without firm and year fixed effect, while columns (2) to (3) include them first separately and consider them then jointly in column (4). Results show that gross margins decrease in the post period, but they decrease more for firms located in treated provinces. The estimated coefficient on the interaction term shows that gross margins decrease by approximately 4.3 percentage points more in firms located in treated

(36)

Table 1.3 – Intention To Treat - Diff in Diff (1) (2) (3) (4) (5) (6) µ µ µ µ µ µ Post -0.0204∗∗∗ -0.0205∗∗∗ -0.0393∗∗∗ -0.0066 -0.0471∗∗∗ -0.0053 (0.0025) (0.0019) (0.0047) (0.0040) (0.0057) (0.0041) T 0.0181∗∗∗ 0.0182∗∗∗ 0.0000 0.0000 (0.0047) (0.0041) (.) (.) T × Post -0.0432∗∗∗ -0.0430∗∗∗ -0.0431∗∗∗ -0.0430∗∗∗ -0.0400∗∗∗ -0.0173∗∗ (0.0067) (0.0056) (0.0060) (0.0048) (0.0067) (0.0084) Cost of Debt -0.0030∗∗∗ (0.0003) Log(Total Assets) -0.0000 (0.0000) ROA 0.0066∗∗∗ (0.0002) Log(N. Employees) -0.0002∗∗∗ (0.0001) Leverage 0.0002∗∗∗ (0.0000)

Log Tot Liquidity -0.0050∗∗∗

(0.0007)

Log Tot Inventories 0.0081∗∗∗

(0.0006) Bank Debt/Rev 0.0006∗∗∗ (0.0000) T × Year -0.0053∗∗∗ (0.0016) Observations 855384 855384 855384 855384 359456 855384

Firms Fixed Effects _X _X _X

Time Fixed Effects _X _X _X

Time Trends _X _X

Notes: The table presents difference-in-differences results from estimation of equation (1.3). Values associated to T×Post present the estimated values of δ. Results in column (1) are pooled OLS estimates. Results in columns (2) to (4) include firm fixed effects, time effects and both. Results in column (5) replace firms’ fixed effects with firms’ variables. Column (6) includes the linear time trend to relax the parallel trend assumption. The sample of firms considered is the whole set of firms in AIDA. The number of observations is lower in column (5) as firms’ balance sheet variables were not available for all firms in the dataset. Standard errors in parentheses are double clustered at the province and year level.

(37)

provinces than the ones located in other provinces. Column (5) removes firms’ fixed effects and includes firms controls variable X_f averaged in the pre-period. This exercise serves both as a ro-bustness on the significance of the estimated effect and it also shows correlations between markups and other firm-level variables. Gross margins are lower for less profitable firms and for firms with a greater number of employees. They are also lower for firms with greater liquidity and for firms that have smaller inventories. Finally, column (6) includes a linear time trend component, to allow for differences in parallel trends in the pre-period. Results still hold in sign, although the size of the coefficient drops by half of its value. As a further robustness, in appendix A I reduce the size of the control group to only neighbouring regions to Emilia-Romagna, that is Lombardia, Veneto, Liguria, Toscana and Marche. The reduction in markups is still present, although smaller in size and equal to 2.3 percentage points.

Figure 1.4 tests for the parallel trend assumption of the diff-in-diff. It checks for the presence of pre-trends in the pre-period by estimating the following equation.

µf mt = αf+ λt+ 2010 X τ =2007 γτTm1(t = τ ) + 2016 X τ =2012 γτTm1(t = τ ) + f mt (1.4)

Equation 2.2 estimates the causal effect of interest for years different from 2011. Figure 1.4 reports estimation differences in the causal effect estimated when the post dummy equals 1 for years different from 2011. It shows pre-trends are absent as the drop in coefficients βτ is significant

for the years after the earthquake. This result validates the parallel trend assumption and allows for a causal interpretation of the ITT/diff-in-diff exercise. Results at the firm level are also confirmed by regressions at the municipality level in Appendix A.

1.3.3 Heterogeneity

The estimation results of the baseline regression showed a decrease in gross margins following a positive demand shock. Gross margins for firms in treated provinces decreased more than the ones of firms in non treated provinces, providing suggestive evidence for countercyclical markups.

(38)

Noth-−.1 0 .1 2006 2008 2010 2012 2014 2016 Year relative to 2012 γt CI 90% Firms’ Markup

Figure 1.4 – Event Study

Notes: The Figure plots the Event-Study for pre-trends in the difference-in-differences. Difference of the coefficient estimated using each year as treatment year against 2011 are tested to be different from zero. Confidence intervals reported around the estimates are at 90% level.

ing has been said yet on the mechanism behind this drop.

In this subsection I argue that the decrease in gross margins are driven by municipalities with a lower degree of competition in the construction market, and by firms that are not liquidity constrained. I run two sample splitting exercises to test for these hypothesis on a number of different subsamples.

Competition

I compute the Herfindahl-Hirschman Index (HHI) as a measure of competition for each province, using firms’ revenues in the pre-period to measure markets’ concentration. The HHI ranges from 0 to 1, where 0 denotes a perfectly competitive market, while 1 characterizes a monopoly. I then split the sample into four quartiles, according to the HHI values, ranking them from the lowest to the highest value.

I estimate the baseline equation (1.5) for the four different quartiles.

(39)

Results are shown in Table 1.4. Column (1) report the baseline estimates and column (2) only includes the first quartile of firms located in the most competitive markets. Column (3) includes the estimated results for the firms located in the municipalities that represent the half more competitive market, while column (4) only exludes the quartile with most concentrated provinces.

Table 1.4 – Competition - Sample Splitting HH Index

(1) (2) (3) (4) Whole Sample HH< 25 HH< 50 HH< 75 Post -0.0066 0.0134 -0.0675∗∗∗ -0.0479∗∗∗ (0.0040) (0.1064) (0.0055) (0.0045) T × Post -0.0430∗∗∗ 0.1435 -0.0355∗∗∗ -0.0512∗∗∗ (0.0048) (0.1230) (0.0056) (0.0040) Observations 855384 964 432052 644743

Province Fixed Effects _X _X _X _X

Time Fixed Effects _X _X _X _X

Notes: This table shows estimation results of the baseline regression for different sample’s subsets. The outcome variable is the grossmargin µ. Column (1) reports the fixed-effects estimation of equation 1.3 for the whole sample. Column (2) shows estimated results for a subset of firms located in the top 25% most competitive provinces. Column (3) restricts the sample of firms to the ones located in the top 50% most provinces, while Column (4) restricts it to the top 75%.

∗_{p < 0.10,} ∗∗ _{p < 0.05,}∗∗∗ _{p < 0.01}

The interaction coefficient δ decreases in absolute value as less competitive municipalities are left out of the sample and it suggests that the baseline results are mainly driven by firms located in the least competitive municipalities, where the HHI is highest. The contract-level section of the paper provides more evidence behind the mechanism of why markups decrease more in less competitive markets. Yet, this result represents a first evidence of firm-entry playing a role in the reduction of markups.

Financial Constraints

Competition is not the only force driving lower gross margins in the baseline. Chevalier and Scharf-stein (1996) and, more recently, Gilchrist, Schoenle, Sim, and Zakrajsek (2017) and Kim (2018)

(40)

show that markups have a countercyclical behaviour, and that part of this effect is reinforced by financially constrained firms following a negative demand shock. As financially constrained firms get most of their funding from revenues, they are willing to forego part of market share and to set markups above their competitors when they hit their financial constraint.

Construction firms have cash-in-advance constraints as they need to finance construction works for some time before getting paid. I explore the role of financial constraints in this setting by splitting the sample of firms on their cost of debt. Cost of debt is a variable included in the AIDA dataset, which provides information on how expensive it is for firms to access external debt. I compute the average cost of debt for each firm in the pre-period and I split the sample of firms in 4 quartiles, ranking them from the ones with the lowest cost of debt to the highest.

I estimate equation 1.5 for the four different quartiles, dropping out each time a fourth of the firms with the lowest cost of debt.

Table 1.5 – Financial Constraints - Sample Splitting Cost of Debt

(1) (2) (3) (4) Baseline HH> 25 HH> 50 HH> 75 Post -0.0066 -0.0281∗∗∗ -0.0455∗∗∗ -0.0277∗∗∗ (0.0040) (0.0044) (0.0040) (0.0039) T × Post -0.0430∗∗∗ -0.0397∗∗∗ -0.0320∗∗∗ -0.0199∗∗∗ (0.0048) (0.0054) (0.0057) (0.0062) Observations 855384 679638 559354 444434

Firms Fixed Effects _X _X _X _X

Fixed Effects _X _X _X _X

Notes: This table shows estimation results of the baseline regression for different sample’s subsets. The outcome variable is the grossmargin µ. Column (1) reports the fixed-effects estimation of equation 1.3 for the whole sample. Column (2) shows estimated results for a subset of firms that excludes the top 25% less financially constrained firms. Column (3) restricts the sample of firms to the top 50% less financially constrained, while Column (4) restricts it to the top 75% most financially constrained firms.

∗_{p < 0.10,} ∗∗ _{p < 0.05,}∗∗∗ _{p < 0.01}

Table 1.5 shows the estimated results for the baseline (column(1)) and for the three subgroups (columns (2)-(4)). The point estimates show that the baseline results are driven by those firms

(41)

that are less financially constrained in the pre-period. Following a positive demand shock, some of the firms hit the borrowing limit as they need to finance an increase in production. Since revenues are their main source of funding, they forego some of the market share to increase their liquidity by raising markups.

1.3.4 Selected Firms

The diff-in-diff estimation in the previous sections relies on a definition of treatment at the mu-nicipality level. Firms are defined as assigned to treatment if their headquarters are located in a municipality that is hit by an earthquake. This definition allows one to exploit the random alloca-tion of the earthquake across provinces and to compute the effect of a positive demand shock on gross margins without dropping information or incurring in a selection bias. Treated firms have been selected on observable and unobservable variables that are potentially correlated with the outcome variable.

To provide greater understanding of what are the firm characteristics on which firms have been selected into treatment, I run the following probit model, which estimates the correlation between each variable and the participation dummy M

Mf m=α + β0EIm+ β1CostDebtf m+ β2BankDeb/Revf m+ β3ROAf m+ β4N W orkf m+ (1.6)

+ β₅T otP rodV alf m+ β6AddV aluef m+ β7T otInvf m+ +β8Debt/Ebitdaf m+ f m

where M_{f m} is a dummy that equals 1 if the firm f in municipality m is treated, EI is a variable that measures earthquake intensity in municipality m , CostDebtf m is the firm’s cost of debt,

BankDeb/Revf m is the bank debt over revenues ratio, ROAf m is the firm’s ROA, N W orkf m is

the firm’s number of workers, T otP rodV al_{f m} is the firm’s total value of production, AddV alue_{f m} is the firm’s added value, T otInvf m is the total amount of inventories and Debt/Ebitdaf m is the

debt over EBITDA ratio. All variables are averaged over the pre-period.

Table 1.6 reports the estimates of equation (2.4). Firms located in provinces with higher earth-quake intensity are the ones that are more likely to be selected. Moreover, treated banks are the

(42)

ones with lower cost of debt, higher total value of production and greater productivity. Table 1.6 – Probability of Signing Contract

(1)

Signed Contract

Log Earthquake Intensity 0.0123∗∗∗

(0.0007)

Cost of Money -0.0001∗∗

(0.0001)

Bank Debt / Revenues -0.0000

(0.0000)

Log Numb. Employees -0.0002

(0.0002)

Gross margins -0.0006

(0.0009)

Log Total Production Value 0.0030∗∗∗

(0.0003)

Log Total Liquidity -0.0000

(0.0001)

Debt / Ebitda 0.0000

(0.0000)

Log Total Inventories -0.0000

(0.0001)

ROA 0.0001∗∗∗

(0.0000)

Observations 65408

Notes: This table shows estimation results of the probit regression 2.4. The outcome variable is a dummy variable M that equals 1 if a firm has been selected for housing reconstruction and 0 otherwise. Standard errors in parenthesis are clustered at the firm level.

∗ _{p < 0.10,}∗∗ _{p < 0.05,}∗∗∗ _{p < 0.01}

1.4 Contract-Level Analysis

Figure 1.2 and Table 1.3 show a sharp fall in markups charged by construction firms after the Emilia-Romagna earthquake. Although the drop in markups is sizable and significant, nothing can be said at this point on what are the driving forces behind this fall. Since markups are defined

(43)

as prices over marginal costs, a decrease in markups can be driven both by a decrease in prices, as well as by an increase in marginal costs. On one hand higher competition squeezes prices and markups, on the other labour and raw material prices are expected to become more expensive as the consequence of the earthquake.

To rule out different competing mechanisms, I exploit a rich and novel dataset containing infor-mation on the reconstruction contracts for housing reconstruction that have been subsidized by the local government. The use of this new dataset allows me to identify those firms that are actively involved in housing reconstruction as the ones affected by a positive demand shock. Moreover, it also allows me to determine the size of the positive demand shock faced by construction firms following the earthquake. Identification is obtained by exploiting within-firm variation of different contracts signed by the same firm for contracts located in municipalities hit by different demand shocks.

Figure 1.5 – Municipalities in Emilia-Romagna

Notes: This Figure shows municipalities in Emilia-Romagna affected by the earthquake. The darker the colour, the greater is the intensity of the earthquake, measured using an Intensity index from the Italian Earthquake and Vulcanology statistics.

(44)

of the earthquake. The darker colours represent those municipalities hit by a stronger earthquake, while gray municipalities are the ones that are not hit. The identification strategy compares con-tracts signed by the same firm for houses located in different municipalities so as to assess whether firms are able to price discriminate charging different markups depending on the demand shock they face.

Table 1.7 provides descriptive statistics for the variables contained in the contract-level dataset. Columns represent different damage levels among which houses are classified by with B/C being the smallest level of damage and E3 being the highest. Classification of houses is delegated to official technicians accredited by a municipality and need to provide independent assessment over the extent of the damage. Rows report instead the average values of the variables contained in the contract-level dataset such as the total value of the contract V , the amount of the contract value which is subsidized by the government, the number of square meters rebuilt per house Y and the number of working days spent per each contract L. Table 1.7 shows that contract values are increasing in the level of damage, suggesting that construction contracts for houses more severely damaged are more expensive as more needs to be rebuilt. A large number of square meters per contract reflects presence of condominiums which, by law, need to apply jointly to be entitled to obtain the subsidy. One should also notice how, on average, firms sign more than one contract, which will be key for identification.

1.4.1 Identification

Estimating the causal effect of a positive demand shocks on firms’ markups poses concerns on se-lection bias and on markup estimation similar to the ones raised in the firm level analysis. On one hand, OLS estimates are affected by selection bias as reconstruction contracts are not randomly assigned to selected firms. On the contrary, homeowners are given the possibility of choosing any preferred Italian construction firm, independently from its geographical location. Estimating the effect of a demand shock on markups would then need to take into account any firm-homeowner

(45)

Table 1.7 – Descriptive Statistics

Level of Damage

B/C E0 E1 E2 E3 Total

mean mean mean mean mean mean

Contract Value 76052 331747 415777 571303 621604 340807

Government Subsidy 67339 300797 377043 489633 564022 305459

Total Square Meters (Y) 429 411 335 398 357 395

Per Square Meter Price (P) 261 951 1431 1948 1984 1108

Working Days (L) 258 521 600 600 610 454

Approval Days 143 182 227 266 250 198

N. of contracts per firm 8 4 4 4 11 8

People per Unit 4 4 3 2 2 3

Richter Scale 3 3 3 3 4 3

Observations 6865

Notes: This table shows descriptive statistics of variables present in the contract-level dataset, averaged across different levels of damage. Columns present different levels of damages that houses can be classified in. B/C is the lowest level of damage, while E3 is the highest. Rows presents the different variables of the contract-level dataset.

relationship that could drive these results. Moreover, markup estimation poses the challenge of measuring marginal costs, which are not directly observable. The contract-level dataset does not provide any information on the operating costs for each contract, making markup estimation diffi-cult to implement.

I address these two empirical challenges jointly by implementing an identification strategy that exploits within-firm variation and that compares different contracts signed by the same firm. Com-paring contracts signed by the same firm allows one to remove any unobserved firm-specific factor that is correlated with markups and which explains firm’s selection. Moreover, within-firm vari-ation has the additional benefit of comparing contracts with similar marginal costs, as they are signed by the same firm. Since marginal costs for the two contracts are the same, any variation in markups will be reflected by variation in prices, making marginal cost estimation less relevant. In order to be sure to compare contracts with the same marginal costs, I refine the within-firm comparison even further by comparing contracts with the same level of damage so as to control for the type of intervention made necessary by the damage of the house. Marginal costs are equal for contracts signed by the same firm f and for houses with the same level of damage d. Figure 1.6

(46)

F

H

₁ ¯ d l1

H

2 ¯ d l2 c1 c2

Figure 1.6 – Identification Strategy

Notes: The figure provides a graphical evidence of the iden-tification strategy adopted. It compares two different con-tracts c1 and c2 signed by the same firm for two houses

located in the same municipality ¯l and for with the same level of damage ¯d.

provides a graphical explanation of the identification strategy adopted. The model compares two contracts c₁ and c₂ signed by the same firm F for two houses H₁ and H₂ with the same level of damage ¯d. The source of variation used is the different level of demand shock the firm F faces in

the two different municipalities l1 and l2 where the two houses are located.

1.4.2 Demand Shock

Measuring firms’s positive demand shock as a reaction to the earthquake poses an important tradeoff between exogeneity and precision of the shock. On one hand, more exogenous measures of demand shock provide true causal estimates of the effect under study. Measures such as the richter scale are truly exogenous as they capture the intensity of an earthquake measured on its own strength, regardless of its effect on buildings and houses. Yet, such measures lack in precision. Areas of Emilia-Romagna hit by a stronger magnitude might be less densely populated than those ones hit by a milder one, making the richter scale not necessarily a good measure for a positive demand shock faced by firms. On the contrary, measures of demand shock that include municipality-level characteristics may be correlated with markups, violating the exogeneity assumption. To address this concern, I compute a measure of demand shock defined as the sum of total square meters disrupted in a given municipality and I then check how this measure correlates with the richter

(47)

scale.

I define the positive demand shock faced by firms in different markets as the sum of all square meters destroyed by the earthquake in a given municipality m and for a given damage level d.

Sdl = X c X f X t Ycdlf t

Sdl is the number of square meters that construction firms need to rebuild in municipality l for

houses with damage level d and characterizes the demand shock affecting market {dl}.

Figure 1.7 – Sdl Distribution 0 50000 100000 150000 S5l 0 20 40 60 Municipality

Intense Earthquake Mild Earthquake

(a) 0 10000 20000 30000 40000 50000 S5l 0 20 40 60 Municipality

Intense Earthquake Mild Earthquake

(b)

Notes: The figure plots the demand shock Sdl, for the highest and for the lowest level of damage across

municipalities. Panel (a) plots the distribution for the highest level of damage d = E3. Panel (b) plots the distribution for the lowest level of damage d = B/C. Different municipalities are ordered on the horizontal axis, numbers are just labels. Sdlvalues are plotted on the vertical axis, with higher bars indicating greater demand shock. Colours indicate intensity of the earthquake measured based on the richter scale. Blue colours are associated to intense earthquakes, with a magnitude greater than 5. Red colours are associated to milder earthquakes with a richter scale of less than 5.

Figure 1.7 displays the distribution of the demand shock for the highest and lowest level of damage, across all different municipalities. A level of damage 5 corresponds to damage level d = E3. Bars in panel (a) depict S_5l across different municipalities, the higher is the bar, the greater is the amount of square meters destroyed in that municipality for houses with the highest level of damage. The blue color shows instead those municipalities where the earthquake hit with a richter scale magnitude greater or equal to 5, while the red municipalities are the ones where the earthquake hit

(48)

with a magnitude lower than 5. Figure 1.7 shows that higher columns are associated to blue, while shorter columns are associated to red, suggesting that, indeed, a higher demand shock is associated to a greater richter scale damage and that the measure of demand shock computed as the sum of square meters is correlated with the true exogenous shock. Figure 1.17 in the Appendix shows the same graph, when S_5l is normalized by total population.

1.4.3 Contract Level Baseline

The identification strategy adopted allows me to capture the causal effect of a demand shock on firms’ markups by estimating the following contract-level model.

log(P_{cdlf t}) = α_df+ λt+ β log(Sdl) + γVl+ δXcdlf t+ υcdlf t (1.7)

Each observation defines the contract c, the house level of damage d, the municipality l where the house is located, the firm f that carried out construction works and the time t when the contract was signed. P_{cdlf t}is defined as the per-square-meter price computed as the ratio between the value of the contract Vcdlf t and the total size of the house Ycdlf t in square meters.

Pcdlf t=

Vcdlf t

Ycdlf t

(1.8)

I include municipality-damage fixed effects α_dl as the identification strategy compares contracts signed by the same firm and with the same level of damage. Moreover, including firm-damage fixed effects makes marginal cost estimation unnecessary as any variation in markups is driven by variation in prices. λ_tcaptures time fixed effects, while S_dlrepresents the exogenous demand shock.

Vl collects a set of municipality time-invariant controls, while Xcdlf t collects a number of contract

level characteristics.

Table 1.8 reports the estimation results of the model described in equation (1.7). The estimated elasticity of interest is negative and significant, indicating that firms charge lower markups to houses located in municipalities with a higher demand shock. Coefficients are small in size: firms charge

(49)

Table 1.8 – Baseline Contract-Level

(1) (2) (3)

Log(Price) Log(Price) Log(Price)

log(Sdl) -0.085∗∗∗ -0.052∗∗ -0.047∗∗ (0.029) (0.034) (0.027) Bank Offices 0.002 0.002 (0.002) (0.003) Population -6.93∗∗ _-6.61 (2.94) (4.45) Local Firms -0.953∗∗ -0.761∗ (0.486) (0.451) Observations 5730 5022 2793

Firm-Damage Fixed Effects _X _X _X

Time Fixed Effects _X _X _X

Contract-level Controls _X

The table presents estimates of coefficient β and γ of equation (1.7). The outcome variable is the price per square meter defined as P = V_Y. Contract-level controls include number of processing days, distance between houses and firms, number of people per-unit and share of uncovered costs. Standard errors in parentheses are doubled clustered at the municipality and at the damage level.

(50)

markups which are 0.085% smaller when they sign contracts for houses located in municipalities where the demand shock is 1% higher. Column (2) includes some time-invariant municipality characteristics. These variables include bank offices to control for the economic activity of these municipalities; the total population as a proxy of the size of the municipality; and the amount of local construction firms so as to control for the existing level of competition. Column (3) also includes contract-level controls such as the distance between the firm and the house, so as to control for different transportation costs; the amount of approval days it took the municipality to approve the subsidy; the number of people per unit and the extent of the contract that is covered by government subsidy. The number of observations decreases in column (3) as the distance between construction firms and reconstructed houses is not available for all contracts.

1.4.4 Interpretation of Results

Results reported in Table 1.8 suggest that firms are able to price discriminate, charging lower markups the greater is the amount of demand shock that they face. In order to understand the mechanism driving this result, further investigations need to be made on why construction firms decreased their markets despite having some positive market power. As firms can react to a posi-tive demand shock by increasing markups and profits, a reduction in markups suggests instead that firms were impaired to do that. Summary statistics on the market structure before and after the earthquake reveal that firm-entry played a role in affecting the structure of construction markets after the earthquake hit. 49% of contracts involving houses located in a given municipality were signed by construction firms headquartered in a different municipality. The percentage drops to 21% if we consider the share of firms active in a given municipality. The large amount of entrant firms suggests that, following a positive demand shock, competition among construction firms in-creased reducing prices and markups.

Before testing firm-entry’s role in the decrease in markups, careful interpretation should be given to the interpretation of the β coefficient in equation 1.7. If a positive demand shock is indeed responsible for an increase in firm-entry and for a subsequent contraction in markups, the reduced

Essays on the economics of firms and of financial institutions

Essays on the Economics of Firms and of

Financial Institutions

Matteo Gatti

European University Institute

Department of Economics

Essays on the Economics of Firms and of Financial Institutions

Matteo Gatti

Abstract

Acknowledgements

Contents

List of Tables

List of Figures

1 Markups and Firm Entry: Evidence

From the 2012 Emilia Earthquake

1.1 Introduction

1.2 Setting and Data

1.2.1 Government Subsidies for Reconstruction

H

F

F

G

H

F

F

G

1.2.2 The Italian construction industry

1.2.3 Data

1.3 Firm level evidence

1.3.1 Markup estimation and identification

1.3.2 Difference-in-differences

1.3.3 Heterogeneity

1.3.4 Selected Firms

1.4 Contract-Level Analysis

1.4.1 Identification

F

H

H

1.4.2 Demand Shock

1.4.3 Contract Level Baseline

1.4.4 Interpretation of Results