Inter-Industry Effects of State Aid in Europelib.ugent.be/fulltxt/RUG01/002/376/752/RUG01... ·...

Inter-Industry Effects of State Aid in Europe Word count: 14,702

Helena Van Langenhove Student number: 01306651 Supervisor: Prof. Dr. Bruno Merlevede A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master in Economics (EW) Academic year: 2016 - 2017

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I declare that the content of this master Dissertation can be consulted and/or reproduced if

the sources are mentioned.

Helena Van Langenhove

I

Nederlandse Samenvatting

Deze masterproef onderzoekt of er spillover effecten van staatssubsidies zijn tussen sectoren in

de Europese Unie. We bekijken dit door na te gaan of staatshulp een effect heeft op de totale

factor productiviteitsgroei (TFP) van bedrijven in de industrie die staatshulp krijgt en bedrijven

die leveren aan en kopen van die industrie. Deze laatste noemen we respectievelijk forward

(BW) spillover effecten en forward (FW) spillover effecten. Hiervoor koppelen we bedrijfsdata

van 10 EU aan sector gerichte staatshulpschema’s. De BW en FW variabelen stellen we op

aan de hand van input-output tabellen.

Het onderzoek wordt uitgevoerd in 2 stappen. Ten eerste wordt TFP groei berekend. Dit

doen we op twee manieren, volgens de methode van de Levinson en Petrin (LP) en deze van

Wooldridge. In een tweede stap wordt deze productiviteitsgroei getoetst aan zowel state aid

en spillover variabelen, als controle variabelen en een reeks interactie termen.

Algemeen vinden we geen duidelijk effect van staatshulp op TFP groei. Wanneer we enkel

staatshulp, de spillover variabelen en de controle variabelen in onze specificatie opnemen, vin-

den we een positief effect van staatshulp op TFP groei. Voegen we hier echter interactietermen

aan toe dan verdwijnt dit effect. Duidelijke spillover effecten vinden we in deze masterproef

wel, meer bepaald negatieve BW spillovers en positieve FW spillovers. De leveranciers zullen

bijna altijd negatieve gevolgen hebben van staatshulp in een industrie, terwijl de sectoren die

kopen bij een industrie die staatshulp krijgt hier juist positieve gevolgen van hebben. Dit

laatste is echter niet robust voor alle schattingsmethoden.

II

Voorwoord

Ik heb de werking van de Europese Unie (EU) altijd al fascinerend gevonden. Een abstracte con-

structie die ervoor zorgt dat wij, Europeanen, onze belangrijke positie in de wereldeconomie

kunnen behouden. Desalniettemin staat de Europese Unie de laatste jaren erg onder druk.

Sommige lidstaten willen meer autonomie, met afscheuringen tot gevolg, anderen geloven dan

weer voluit in de Europese droom. De belangrijkste functie van de EU is het controleren en

regelen van de markten, om competitie te bewaren. Een onderdeel hiervan is de controle op

staatssubsidies. Net hierover gaat deze masterproef.

Dit werk was nooit goed tot zijn recht gekomen zonder de hulp van anderen. Daarom wil

ik graag enkele personen bedanken.

Allereerst mijn promoter Prof. Dr. Bruno Merlevede, waarbij ik terecht kon met zowel the-

oretische vragen als praktische toepassingen in Stata. Hij was gedurende het hele semester

bereikbaar en maakte tijd om met me na te denken over volgende stappen in het proces.

Speciale dank gaat uit naar mijn beide ouders. Niet alleen voor het nalezen van deze mas-

terproef, maar ook om me te laten opgroeien in een omgeving waar leren en kritisch denken

centraal staat, zonder hoge druk of verwachtingen. Ik kan me geen betere thuisbasis bedenken.

Daarnaast, mijn zus, vrienden en vriendinnen, voor de leuke ontspanningsmomenten, gekke

ervaringen, en om die vier jaren in Gent, de beste van mijn leven (tot nu toe) te maken.

Tot slot, mijn vriend Mohamed voor zijn eindeloze geduld en begrip, en om altijd de zon

te laten schijnen, wanneer het weer eens dondert in mijn hoofd.

Alvast veel leesplezier.

Helena Van Langenhove

Gent, 16 augustus 2017

III

Contents

1 Introduction 1

2 State Aid Policy 3

2.1 Industrial policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 State Aid Policy in the European Union . . . . . . . . . . . . . . . . . . . . . 4

3 Literature Review 7

3.1 The effects of state aid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.2 Spillover Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2.1 Inter-Industry Spillover Effects . . . . . . . . . . . . . . . . . . . . . . 9

3.2.2 Productivity Spillovers . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.2.3 Spillover Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Methodology and Data Sources 13

4.1 Research Questions and Methodology . . . . . . . . . . . . . . . . . . . . . . 13

4.2 European firm data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.3 Data on State Aid to the Manufacturing Sector . . . . . . . . . . . . . . . . . 15

4.4 Input Output tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5 Total Factor Productivity Estimation 18

5.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.2.1 TFP estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.2.2 Effect state aid on TFP growth . . . . . . . . . . . . . . . . . . . . . . 23

6 Inter-Industry Effects 30

6.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6.2.1 Baseline specification . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6.2.2 Baseline specification expanded with interaction terms . . . . . . . . . 36

6.3 Firm size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

7 Robustness 43

IV

7.1 Robustness Check 1: alternative spillover proxy . . . . . . . . . . . . . . . . . 43

7.2 Robustness Check 2: ignoring Austria . . . . . . . . . . . . . . . . . . . . . . 47

8 Alternative method 50

9 Conclusions 53

10 References I

A GBER: Individual notification tresholds VII

B NACE codes in the manufacturing industry IX

C Value Added as dependent variable X

D Estimation results XII

V

List of abbreviations

BW Backward

DG Direcctorate-General

EC European Commission

EP European Parliament

EU European Union

FDI Foreign direct investment

FW Forward

GBER General Block Exemption Regulation

GMM Generalized method of moments

HHI Herfindahl Hirschman Index

I-O Input-Output

IV Instrumental variable

LP Levinson Petrin (2003)

NACE Nomenclature statistique des Activitees economiques dans la Communaute

Europeenne

NIOT National Input-Output Table

OECD The Organization for Economic Co-operation and Development

OLS Ordinary least squares

OP Olley Pakes (1996)

R&D Research and Development

SAM State Aid Modernization

TFEU Treaty on the Functioning of the European Union

TFP Total factor productivity

VI

List of Tables

1 Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2 Estimates of the production function using levpet (gross revenue) . . . . . . . 21

3 Estimates of the production function using Wooldridge (gross revenue) . . . . 22

4 Direct effect state aid (LP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5 Direct effect state aid (Wooldridge) . . . . . . . . . . . . . . . . . . . . . . . 24

6 Summary statistics HHI and distance to frontier . . . . . . . . . . . . . . . . . 26

7 Direct effect state aid with industry-year fixed effects (LP) . . . . . . . . . . . 28

8 Direct effect state aid with industry-year fixed effects (Wooldridge) . . . . . . . 29

9 State aid and spillovers per sector . . . . . . . . . . . . . . . . . . . . . . . . 31

10 Correlation matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

11 Inter-industry effects per type of spillover proxy . . . . . . . . . . . . . . . . . 33

12 Summary statistics of the mean variables . . . . . . . . . . . . . . . . . . . . . 34

13 Baseline specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

14 Baseline specification with interaction terms (LP) . . . . . . . . . . . . . . . . 38

15 Baseline specification with interaction terms (Wooldridge) . . . . . . . . . . . 39

16 EU’s firm categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

17 Baseline specification with interaction terms per firm size . . . . . . . . . . . . 41

18 Correlation matrix 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

19 Robustness check 1 with interaction terms (LP) . . . . . . . . . . . . . . . . . 45

20 Robustness check 1 with interaction terms (Wooldridge) . . . . . . . . . . . . 46

21 Robustness check 2 with interaction terms (using LP) . . . . . . . . . . . . . . 48

22 Robustness check 2 with interaction terms (Wooldridge) . . . . . . . . . . . . 49

23 Alternative method with interaction terms . . . . . . . . . . . . . . . . . . . . 52

A1 Individual notification tresholds in millions of euro . . . . . . . . . . . . . . . . VII

B1 NACE codes at the 2-digit level . . . . . . . . . . . . . . . . . . . . . . . . . . IX

C1 Estimates of the production function using levpet (value added) . . . . . . . . X

C2 Estimates of the production function using Wooldridge (value added) . . . . . XI

D1 Inter-industry effects per type of state aid . . . . . . . . . . . . . . . . . . . . XII

D2 Baseline specification per firm size . . . . . . . . . . . . . . . . . . . . . . . . XIII

D3 Robustness 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIV

D4 Summary statistics of the mean variables (robustness check 2) . . . . . . . . . XV

VII

D5 Robustness 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVI

D6 Alternative method without interaction terms . . . . . . . . . . . . . . . . . . XVII

List of Figures

1 Backward and forward spillovers in the supply chain . . . . . . . . . . . . . . . 9

2 Number of notified cases in the manufacturing sector between 2007 and 2015 . 16

3 Fictional Input-Output Table . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Distribution of the distance to productivity frontier . . . . . . . . . . . . . . . 26

VIII

1 Introduction

Since the advent of neoliberalism in the 1980s, industrial policy has longtime been disregarded

by the European Union (EU). But as the financial crisis of 2008 accelerated deindustrialization

in Europe, the EU has been prompted to intensify its industrial policy. A report of the Euro-

pean Policy Centre (2014)1 showed that the Member States of the European Union suffered

from high unemployment numbers, low growth rates and weak competitiveness in the manu-

facturing sector. The EU now promotes industrial policy in favour of job creation and higher

competitiveness, especially in the manufacturing sector. One of the tools of the EU’s industrial

policy is allowing state aid of the Member States to domestic firms and sectors. The definition

of state aid can include several measures such as tariff reduction, loans or direct subsidies.

In 2015, almost 100 billion EUR of state aid2 was accepted by the European Commission.

However not much is known about the effectiveness of the EU’s state aid policy. Do firms

perform better after receiving state aid? Are the member states targeting the right industries?

Does state aid have indirect effects on other industries? These are all important questions to

improve effectiveness of the EU’s state aid policy.

The objective of this master dissertation is to analyze the inter-industry effects of state aid in

the EU. To measure a potential effect, total factor productivity (TFP) will be estimated. We

use TFP to measure the effect of state aid in firms because it represents a firms’ competitive-

ness and because it can easily be obtained using public available firm-level data. To capture

the indirect effects of state aid, we will search for productivity spillovers between sectors. TFP

spillovers are important for policymakers: if a policy (in this case state aid schemes to sectors)

raises the TFP of the firms in that sector, but this increase spills over to firms in other sectors,

then the impact of the policy is larger than when ignoring the spillover effects. In general, the

distinction is made between backward (to suppliers of intermediate inputs) and forward (to

customers of intermediate inputs) spillovers.

1Dheret, C., & Morosi, M. (2014). Towards a New Industrial Policy for Europe (EPC Issue Pa-

per No.78) http://www.epc.eu/documents/uploads/pub_4995_towards_a_new_industrial_policy_for_

europe.pdf2State Aid Scoreboard 2016 available on http://ec.europa.eu/competition/state_aid/scoreboard/

index_en.html

1

Based on related literature we expect no overall positive effect of state aid on TFP. All existing

studies look at TFP growth in t + 1 but perhaps the effect of state aid only appears after a

longer period. To control this, we shall look at the the cumulative effect of receiving state aid

during several years. We also add a competition index to look if the effect of state aid is larger

in a competitive environment or not and a distance to the productivity frontier variable to see

if laggard firms have faster productivity growth from state aid. To see if the effects of state

aid differ with firm size, we run our baseline specification for different groups of firm size.

The research in this master dissertation consists of two steps. First, we estimate TFP growth

based on two different methods, more specifically the TFP estimation of Levinson and Petrin

(2003) and the one of Wooldridge (2009). Secondly, we regress TFP with a fixed effect esti-

mator over state aid, the spillover variables and control variables. In order to do so, firm-level

data of firms from the 10 EU countries will be matched to state aid schemes decided between

2007 and 2015. The spillover variables are constructed with the national Input-Output tables.

The structure of the dissertation is as follows. Section 2 provides arguments in favour of

and against industrial policy and more specifically state aid. Followed by a summary of the

state aid regulation in the European Union. In section 3 some recent contributions to the

literature about state aid and inter-industry productivity spillover effects will be reviewed. The

research question and data sources will be discussed in section 4. Different methods to esti-

mate TFP and the according results can be found in section 5. In section 6 the estimation

results are presented. To robustness checks are done in section 7 and section 8 uses an alter-

native method to estimate TFP. The last section forms an overall conclusion of this master

dissertation.

2

2 State Aid Policy

2.1 Industrial policy

Adam Smith (1775) once said that markets work perfectly when the government does not inter-

fere. An invisible hand assures a perfect working market with correct prices. Any form of state

intervention through industrial policy would distort competition and make markets inefficient

(Stiglitz, 1991). Nevertheless, nowadays there are no states in which the government does not

intervene in the markets. This trend started with the developing countries, but ever since the

economic and financial crisis of 2008 the interest in industrial policy increased in developed

countries as well (Warwick, 2013). Although the concept of industrial policy is widely used

and much discussed by economists, there is no consensus about its definition. The concept

differs across nations, regions, stages of development and over time (Aiginger, 2007). A clear

description is proposed by Gual and Jodar-Rosell (2006), who define industrial policy as ”the

set of government interventions that by way of taxes (or subsidies) and regulations on domes-

tic products or factors of production, attempt to modify the allocation of domestic ressources

that results from the free operation of the market.” In this master dissertation however, the

definition of the European Commission will be used, as we shall investigate the effect on firms

in EU Member States, that are subject to the European legislation. The EU describes indus-

trial policy as ”policies that have an impact on the cost, price and innovative competitiveness

of industry and individual sectors, such as standardization or innovation policies, or sectoral

policies targeting e.g. the innovation performance of individual sectors” (Commission of the

European Communities, 2010). The difference is that the EU definition concentrates on the

effect of industrial policy on the competitiveness of firms. To capture the effect of industrial

policy and more specifically state aid, we need to measure the competitiveness of firms that

do and do not receive state aid. For this we use total factor productivity (TFP) estimation

since it represents the share of productivity that can not be explained by the input of capital

and labour and determines the competitiveness of a firm(Bos, Goderis, &Vannoorenberghe,

2014).

By using industrial policy, governments try to increase efficiency as well as addressing co-

ordination failures (Tunali &Fidrmuc, 2015). Gual and Jodar-Rossel (2006) review the market

failures that can be corrected to increase efficiency. These include externalities such as

3

spillover effects, asymmetric information, market power and public goods. Smaller companies

for instance have more difficulties financing themselves on the capital market because of the

asymmetry of information between the financial institution and the company.

There are also several arguments against industrial policy. The main argument is that ”gov-

ernments can not pick winners” because they have neither perfect information about the firm

nor adequate incentives (Cohen, 2006). Moreover, the efficiency of government intervention

is limited by corruption, rent-seeking and state capture (Rodrik, 2004). Nevertheless, the con-

sensus in the literature is that industrial policy is a must. The question is not whether there

has to be industrial policy or not, but how to create government interventions that stimulate

growth while at the same time not distorting competition (Aghion et al., 2011) (Rodrik, 2009).

The tools to implement industrial policy are numerous. A recent report of the European

Parliament (EP) (2015) states that ”the instruments used in industrial policy range from di-

rect and indirect support to specific firms and industries (e.g. grants, subsidies, loans and tax

credits) to support for knowledge institutions, infrastructure and skills.” In this master disser-

tation the focus will be on the direct support, more specific state aid, from Member States to

specific sectors.

2.2 State Aid Policy in the European Union

In the European Union, the European Commission (EC) has an exclusive competence on state

aid. Member states decisions on state aid have to be notified to and examined by the EC, more

specifically its Directorate-General (DG) for Competition (Groteke &Mause, 2016). The ra-

tionale for supranational state aid control are cross-border externalities, national commitment

problems and the functioning of a competitive internal market (Friederiszick, Roller, &Ver-

ouden, 2006). The legal definition of state aid, described in article 107 of the Treaty on the

Functioning of the European Union (TFEU)3(European Commission, 2016) is the following:

”Save as otherwise provided in the Treaties, any aid granted by a Member State or through

State resources in any form whatsoever which distorts or threatens to distort competition by

favouring certain undertakings or the production of certain goods shall, in so far as it affects

trade between Member States, be incompatible with the internal market.” Starting from this

3available on http://eur-lex.europa.eu/

4

definition Craig and De Burca (2015, Chapter 29) define four conditions to define state aid.

First, the measure must confer an advantage to the receiver of the aid. Secondly, it has to be

granted by a ’Member State or through State Resources’. This includes regional governments.

Thirdly, in order to fall within state aid the measure has to ”distort or threaten to distort

competition by favouring certain undertakings or the production of certain goods”. Last, it

needs to have an effect on Inter-State trade. Important to remark is that the EC does not

need to prove that trade will be distorted with certainty, but that it is sufficient to show that

trade might be affected by the state aid activities (Craig &De Burca, 2015, Chapter 29).

The EC (2013) classifies state aid in 4 categories: grants and tax exemptions, equity par-

ticipation, soft loans and tax deferrals and guarantees. The majority of state aid consists of

grants and tax exemptions4. These are the types of state aid which are fully transferred to the

recipient (firms or sectors) and include grants, interest subsidies, tax credit, reduction in social

security contributions and sale or rental of public land or property at prices below market value.

Notwithstanding the overall position against state aid in the TFEU, articles 107(2) and 107(3)

list situations wherein state aid is or is considered to be compatible with the internal mar-

kets. Examples are social purposes, regional aid and the development of certain economic

activities. The procedural rules are explained in article 108 and 109 TFEU. A Member State

has to notify the EU of each measure. The EC then decides whether the aid is compatible

with the internal European market. To simplify the procedure and to lower the administrative

burden for both governments and undertakings, the EC launched the State Aid Modernization

(SAM) plan in 2012 (Communication on State Aid Modernization (08.05.2012)). The two

most notable reforms were the de minimis Regulation and the extension of the General Block

Exemption Regulation (GBER). The first rule exempts aid amounts of up to 200 000 Euro

per undertaking over a period of three years from notifying to the EU. The GBER lists certain

categories of state aid that are compatible with the functioning of the internal market. In the

revised version of 2014 the main categories are: regional aid; aid to small and medium-sized

enterprises; aid for research, development, and innovation; environmental aid; training aid; aid

for local infrastructures; aid for broadband and aid for disadvantaged and disabled workers.

For each category there are individual notification tresholds5. Since we want to look at the

4Source: EU database of competition cases (ISEF registry of the European Commission)5The list of the individual notification tresholds can be found in Appendix A.

5

inter-industry effects in this master dissertation, we will focus on state aid to sectors as a whole.

General state aid schemes to sectors that fall under the GBER may not exceed 150 million

euros. The revised 2014 Regulation covers approximately 75 per cent of all state aid mea-

sures (Craig &De Burca, 2015, Chapter 29). Therefore, we have to keep in mind that not all

state aid is covered in this master dissertation, as only the data on notified state aid is available.

The EU thus both monitors and restricts state aid activities of its Member States. The

procedure for new state aids is an ex-ante procedure as explained in Article 108 of the TFEU.

The Member States are required to inform the EC about state aid before granting or altering it.

Next the EC considers if the state aid is compatible with the internal market. When this is not

the case the Commission initiates the procedure to investigate the distortion of competition

and states a final decision. The Commission is primarily concerned about the firms which do

not receive state aid an prohibits state aid when it may distort competition between member

states. However, according to this approach it is possible that the Commission prohibits state

aid that could actually increase welfare because it could induce a greater consumer surplus

(Garcia &Neven, 2005). In fact, consumers can profit of a subsidy war between the Member

States (Collie, 1998).

The European Commission uses industrial policy to promote growth and jobs. Its main ob-

jectives are mainstreaming industrial competitiveness, supporting innovation, skills and en-

trepreneurship and encouraging industrial investment (EU COM, 2014). In a report, the

European Policy Centre (2014) shows that state aid in the manufacturing sector is desirable

because of the gradual deindustrialisation process. The report further states that the manu-

facturing industry plays a key role in providing jobs to other industries (e.g. services). It is

therefore crucial to ensure a growing manufacturing industry. Two types of state aid can be

distinguished, namely horizontal and vertical aid. Horizontal aid supports general objectives

e.g. R&D, environment and energy saving, SME, employment, training, and risk capital. Ver-

tical state aid is awarded to specific sectors or firms. In the EU legislation vertical state aid

consists of sectoral aid and rescue and restructuring aid to individual firms in difficulties (Gual

&Jodar-Rossel, 2006). In general and in the EU horizontal state aid is more accepted than

vertical aid because it is less likely to distort trade (Holzner &Stollinger, 2016). As this master

dissertation shall look at the effect on the factor productivity on sectoral level, only vertical

aid will be considered.

6

3 Literature Review

Related literature to this master dissertation includes both studies about the effect of subsidies

and studies that search for inter-industry productivity spillover effects. First, we will discuss

some contributions that examine the effect of state aid on macroeconomic factors and more

importantly for this master dissertation on firm productivity growth. Secondly, (inter-industry)

spillover effects and spillover channels will be discussed.

3.1 The effects of state aid

Although much has been written on the justification of industrial policy and more specifi-

cally state aid, econometric research on this topic is scarce (Brouwer&Ozbugday, 2016). The

existing quantitative studies on state aid vary over a wide range: microeconomic and macroe-

conomic effects, developing and developed countries. The macroeconomic study of Fidrmuc

and Tunali (2015) looks at the effect of state aid on economic growth and investment for 27

EU6 countries over the period 1992-2011. According to their results state aid does not have

a significant positive effect on economic growth and investment. Nevertheless, they conclude

that state aid is not entirely pointless as it may positively affect social welfare by increasing the

consumer surplus due to lower prices. Holzner and Stollinger (2013) estimated an expanded

macroeconomic export function to investigate the impact of state aid on export performance

in the manufacturing sector for the EU27 over the period 1995-2011. This analysis finds a

positive effect for the EU 15, but notable not for the new EU Member States. The authors

associate these results with the state aid reforms in the new countries before and after the

enlargement. Most of the new Member States had to reduce their amounts of aid money

spent. Similarly, Aghion, Boulanger and Cohen (2011) investigated the effect of state aid on

exports in different sectors for 12 EU countries over the period 1992-2008. They also find a

positive effect, especially when firms are financially constrained.

More important for this dissertation are studies that examine the effect of state aid on the

total factor productivity (TFP) of firms. The results on this topic are however mixed. A study

on Chinese state aid for instance found a significant negative association between subsidies

and total factor productivity for Chinese enterprises between 1998 and 2007 (Aghion,

6Croatia is not incorporated because it accessed the EU only in 2013.

7

Dewatripont, Du, Harrison, &Legros, 2015). However, this negative effect becomes positive

when targeting more competitive sectors. The more intense competition is within an industry,

the higher the incentive to innovate, which is growth enhancing. Moreover, state aid is more

effective, when the state aid is not granted t a small number of firms (Aghion et al, 2015). This

master dissertation uses state aid on sectoral level so following Aghion et al., a small positive

effect of state aid on TFP can be expected. Gual and Jodar-Rossel (2006) investigated the

effectiveness of the EU’s vertical industrial policy in the manufacturing sector by estimating

the effect on total factor productivity. They used an unbalanced panel data set for 11 EU

Member States for a series from 1992-2003 and found a positive effect of vertical state aid on

productivity growth. The study that relates the most to this dissertation is a recent working

paper from the National Bank of Belgium (Konings, Sergant&Van Cayseele, 2014). Konings

et al. examined if there is a difference in TFP growth of firms in the manufacturing sector

that receive state aid and firms that do not. The authors used the Amadeus (Orbis) data

base for estimating the TFP function and matched them with all European state aid cases

granted between 2003 and 2011. According to their results state aid enhances factor produc-

tivity when firms are financially constraint due to limited cash availability. Consequently, they

conclude that firms that are lagging behind and firms in difficulties will have more TFP growth

when receiving state aid (Konings et. al., 2014). These kind of findings are certainly useful for

policymakers in order to make state aid more efficient and target the right firms and/or sectors.

None of the reviewed papers finds evidence for a positive overall effect of state aid on TFP.

However Aghion et al. (2015) obtained positive results when state aid is targeting competitive

sectors and Konings et al. (2014) found a significant positive effect when firms are financially

constraint. Following these studies we expect no overall positive direct effect of state aid on

factor productivity. This can be explained by referring to the reason why firms or sectors re-

ceive state aid. Perhaps those industries are not productive at all and state aid is only used to

keep them alive. Also, the studies discussed above only look at the effect in t+ 1, so one year

after receiving state aid, but perhaps the effect of state aid appears only after a longer period

of time. To check this, we shall use a cumulative state aid variable. As seen in the studies of

Aghion et al. (2015) and Konings et al. (2014) we also include a competition index variable

to look at the effect in more competitive sectors and a distance variable to see if laggard firms

have more productivity growth.

8

3.2 Spillover Effects

Besides the effect of state aid on the productivity growth of the supported firms, we are

interested in spillovers to other industries. In this section, first the concept of inter-industry

spillovers is defined. Next, related studies on productivity spillovers are discussed and finally

possible spillover channels of state aid are listed.

3.2.1 Inter-Industry Spillover Effects

”There is no reason to provide public support to an activity unless that activity has the poten-

tial to crowd in other, complementary investments or generate informational or technological

spillovers.” (Rodrik, 2004).

Spillover effects from state aid occur when state aid to a certain firm or sector results in

benefits for other firms or sectors. This implies that the firms that do receive state aid do not

fully internalize the value of these benefits (Javorcik, 2004). Spillover effects can be classi-

fied in horizontal (within-sector) and vertical (between-sector) spillovers. Additionally, vertical

spillovers can be split into forward and backward linkages (Havranek& Irsova, 2011). The

literature suggests that it is more likely to find spillovers between sectors than within sectors

as firms do not want to share their benefits with competitors (Kugler, 2005). Figure 1 pro-

vides a scheme of possible spillovers in the supply chain based on the scheme of Lenaerts and

Merlevede (2016).

Figure 1: Backward and forward spillovers in the supply chain

BackwardSpillover

ForwardSpillover

DownstreamCustomer

UpstreamSupplier

Firmwithstateaid

Goods

Spillovers

rawmaterials finalgoods

9

Backward linkages are formed between the firms that are supported and their suppliers. A

possible explanation for an increase in the factor productivity of suppliers is a positive demand

side effect. Firms that receive state aid are likely to need more intermediate inputs because of

an increase in efficiency and production. Consequently, there is a higher demand for the goods

that the supplier produces. However, a negative demand side effect is also possible.

Similarly, supported firms and their buyers form forward linkages. Downstream customers

can benefit from new, improved or cheaper intermediate inputs produced by the firms that

receive state aid (Javorcik, 2004). Again, negative effects can also be found. Firms or sectors

can regard the aid as a price subsidy and therefore not increase their productivity. The result

is that they sell cheap but low quality intermediate inputs, which is unfavorable to the down-

stream customers.

Blanchard and Kremer (1997) show the importance of state aid in maintaining supply chain

linkages. They analyzed the collapse of production chains in the Soviet Union in the transition

period, during which firms no longer received aid from the government. The result was an

enormous output decline in the countries of the former Soviet Union.

3.2.2 Productivity Spillovers

Productivity spillovers occur when the TFP of one company has an effect on the TFP of an-

other firm. More specific, when one firm experiences an improvement in efficiency, a technical

change and/or economies of scale, this affects the efficiency, technology and/or economy of

scale of a linked firm (Bos, Goderis, & Vannoorenberghe, 2014). This is very important for

policy makers since the impact of their decision can have a larger and more widespread effect

than expected. Studies on this topic are almost exclusively about Foreign Direct Investment

(FDI) and Research&Development (R&D) or technological spillover effects. However, these

studies give meaningful insights as they provide evidence of the existence of spillovers and

discuss methodologies to measure inter-industry productivity spillovers.

The FDI productivity spillover literature suggests that inter-industry positive externalities are

more common than intra-industry productivity gains (Kugler, 2005). This can also be hy-

pothesized for state aid because on the one hand both FDI and state aid can be seen as a

10

benefit and on the other hand firms are not likely to share benefits with horizontal competitors.

Vertical spillovers depend on the strength of the inter-industry linkages (Wang, 2010). Most

studies use input-output-based data to model the linkages between industries. Baldinger and

Egger (2016) for example use input-output data to investigate R&D productivity spillovers for

a panel of 12 OECD countries in 15 manufacturing industries between 1995 and 2005. Javorcik

(2004) examines whether domestic firms benefit from vertical linkages with foreign firms, using

detailed input-output data from Lithuania. Both studies find evidence of backward spillovers.

For Canada, Wang (2010) finds evidence of TFP growth through backward and forward FDI

spillovers.

Total factor productivity growth is the most used measure for productivity growth. It can

be defined as ”that part of the output that cannot be explained by the amount of inputs used

(such as capital, labor, energy, intermediate inputs)” (Bos et al., 2014). However, Bernstein

(1989) uses the reduction of production costs as a proxy for productivity gains. Most papers

use a two step approach which consists of first estimating TFP and then regressing TFP on

spillover variables. Javorcik (2004) examines inter-industry productivity spillovers by adding

spillover variables in the TFP function. Both estimation strategies are used in this master

dissertation and are discussed in section 6.

The few studies that measure the impact of industrial policy on TFP are studies on trade

liberalization, and more specifically tariff reductions. In section 2 we have mentioned that

tariff reduction is also a form of state aid. Bos et al. (2014) do not find evidence for inter-

industry productivity spillovers after trade liberalization in India. Similar, Paz (2014) observes

the productivity effect in Brazil after tariff reductions. He only finds significant positive forward

productivity spillovers, but no evidence of backward spillovers.

Based on the related literature we are not able to form a clear hypothesis of inter-industry

productivity of state aid. Although, studies on FDI do almost always find significant backward

spillovers which indicates a strong linkage between firms and their suppliers. Therefore we

tend to expect bigger and more significant backward than forward spillovers, but based on this

literature we can not form clear a hypothesis whether state aid generates positive or negative

spillover effects.

11

3.2.3 Spillover Channels

Spillover channels through which productivity spillovers can operate are numerous, we pick

those that possibly also generate productivity spillovers in the case of state aid. This includes

knowledge transfers, labour mobility and intermediate inputs.

Knowledge transfers. As stated before, state aid can increase competition within a sec-

tor and enhance innovation (Aghion et al., 2011). Those supported sectors can invest in

innovation in upstream sectors in prospect of buying cheaper and better quality intermediate

inputs (Javorcik, 2004). This direct knowledge transfer might generate backward productivity

spillovers. This hypothesis however does not hold for forward productivity spillovers as firms

do not profit from teaching downstream customers new techniques.

Labour mobility Spillovers from labour mobility occur when employees receive training or

accumulate experience in firms in the supported sector (Goerg &Strobl, 2005). When leaving,

the employee takes with him/her knowledge and experience which he or she can apply in firms

in upstream and downstream industries or he/she can use to set up his own enterprise. In

comparison with direct knowledge transfer, labour mobility can be seen as a indirect knowledge

transfer (Javorcik, 2004). Labour mobility generates both forward and backward spillover

effects. This spillover channel is also likely to induce horizontal spillovers, but we will not

search for these effects in this master dissertation.

Intermediate Inputs The spillover channel of intermediate inputs is bifold. First, the sup-

ported firms may require better quality intermediate inputs and as a result firms in upstream

industries have to increase their efficiency and upgrade the quality of their products (Paz,

2014). Secondly, due to the increase in TFP in the industries that receive state aid, a demand

effect is possible. The demand for intermediate inputs in state aid receiving industries might be

higher and this allows upstream suppliers to benefit economies of scale (Javorcik, 2004).

12

4 Methodology and Data Sources

4.1 Research Questions and Methodology

The purpose of this master dissertation is contribute to the analysis of the effect of state aid

on the TFP growth of firms who receive state aid and on firms that are linked to industries

that receive state aid. We focus upon 3 mayor research questions:

First, what are the direct effects of state aid? By direct effect we mean the effect on the

productivity growth of firm i in t+ 1, while industry j is receiving state aid in t and i operates

in j. To check if there is a delayed effect of state aid, we introduce a cumulative state aid

variable that adds state aid from previous years together. We also control for other factors.

With a concentration index and a distance to the productivity frontier we examine if state aid

is more or less effective in competitive industries and in firms that are already productive or not.

Secondly, are there inter-industry spillovers effects of state aid? We want to know whether

firm i has positive productive effects when industry j is receiving state aid, conditional on the

fact that firm i is supplying to (BW spillover) or buying from (FW spillover) industry j. The

total effect of state aid is obtained by the sum of the state aid coefficient and the spillover

coefficients. To see if spillover effects are larger when firms are lagging behind, we add some

interaction terms.

Thirdly, how does the effect of state aid differs with firm size? For this we run our specifica-

tion for each size category. A recent report of the EU about state aid (European Commission,

2017)7 states that state aid in the EU does not account for the needs of small and medium-

sized enterprises.

7Title: State aid support schemes for RDI in the EU’s international competitors in the fields of Science,

Research and Innovation.

13

We use three different data sources in this master dissertation. First, the Orbis data base, from

which we obtain firm specific data. Secondly, the EU’s State Aid Cases providing data on state

aid to specific sectors in the manufacturing sector. Last, we use the National Input-Output

tables (NIOT), which allows us to model the inter-industry linkages in each countries.

4.2 European firm data

The primary data source used, is the Orbis8 data base (Bureau van Dijk, 2017), which provides

panel data on firm-level over the whole world. We start from the EU15 countries and due to

missing values or too few data (Konings, Sergant&Van Cayseele, 2014), we end up with

data for 10 EU countries: Austria, Belgium, Finland, France, Germany, Italy, Netherlands,

Portugal, Spain and Sweden9 between 2007 and 2015. All are euro countries, except for

Sweden. The data is stated in thousands of euro. The firms that are selected have a minimum

of 20 employees and are active in the manufacturing industry. Table 1 shows the summary

statistics of the variables that are needed to estimate the total factor productivity and the

growth rate of TFP. All variables are deflated using consumer prices as deflators, obtained

from Eurostat10.

Table 1: Summary statistics

Overall State Aid = 0 State Aid = 1

No of firms 109.262 91.453 17.809

mean st. d. mean st. d. mean st. d.

log operating revenue 8.885 1.429 8.860 1.425 9.042 1.439

log tangible fixed assets 6.678 1.824 6.558 1.820 6.814 1.847

log material costs 7.975 1.802 7.925 1.798 8.289 1.794

log value added 7.900 1.197 7.908 1.203 7.844 1.157

log number of employees 3.926 0.798 3.925 0.798 3.930 0.801

TFP growth 0.043 0.237 0.0037 0.238 0.0084 0.230

8See:https://www.bvdinfo.com/our-products/company-information/international-products/

orbis9Denmark, Greece, Ireland, Luxembourg and the United Kingdom drop out.

10See:http://ec.europa.eu/eurostat/data/database

14

The first column presents the overall statistics and in the second and third column a distinction

is made between firms that have received state aid between 2007 and 2015 and firms that have

not. The average firm in this sample has 50 employees and this does not vary with state aid.

The means of the other variables are slightly higher when firms receive state aid, except for

the variable value added. TFP growth is on average positive over the whole sample but the

actual growth rate is slightly lower for firms in sectors that do not receive state aid.

4.3 Data on State Aid to the Manufacturing Sector

The EU provides data on state aid in the manufacturing sector since 2000. This only includes

vertical aid to sectors and firms. Gual & Jodar-Rossel (2006) and Holzner &Stollinger (2016)

argue that some horizontal aid categories are exclusively directed to the manufacturing sector,

but in this master dissertation we only include vertical aid. The EU categorizes the cases in

three types: ad hoc cases, individual application and schemes. We only look at whether a

sector received state aid in a particular year or not. To distinguish the sectoral state aid, we

only use schemes because they are applied to a sector as a whole. Ad hoc cases and individ-

ual applications are firm specific and not incorporated in our panel set. Again, the countries

discussed before are included. On sectoral level, only the NACE (Nomenclature statistique des

Activites economiques dans la Communaute Europeenne) codes11 that belong to the manu-

facturing sector are selected. At the two digit level this includes the codes C10 to C33.

Figure 2(a) shows all cases that had a final decision between 2007 and 2015 per sector,

including schemes, individual applications and ad hoc cases. Most state aid is granted to

sector 30, which represents the ”other transport equipment” industry. By ”other” is meant

that it differs from sector 29 ”motor vehicles, trailers and semi-trailers”, which is also the

second sector in row to receive state aid. Other industries that easily receive state aid are 10,

20 and 27, respectively ”food products”, ”chemicals and chemical products” and ”electrical

equipment”. Sector 12 never received state aid during the period which is not surprising since

it represents the tobacco industry.

11The list of the corresponding industries can be found in appendix A.

15

Figure 2: Number of notified cases in the manufacturing sector between 2007 and 2015

(a) Per sector

86

262

3011

1914

415

67

419

63

66

31

107

22

0 20 40 60Number of Cases

3332313029282726252423222120191817161514131110

(b) Per Member State

138

224

85

1253

161

534

26

4215

55

211

47

0 10 20 30 40 50Number of Cases

United KingdomSweden

SpainSloveniaSlovakiaRomaniaPortugal

PolandNetherlands

LithuaniaLatvia

ItalyHungaryGreece

GermanyFranceFinland

DenmarkCzech Republic

CyprusBulgariaBelgiumAustria

Figure 2(b) shows the distribution of state aid cases in the manufacturing sector in the EU

among the Member States. Poland is the leading country with 53 cases. Germany and Italy

follow on a distance. However, this does not imply that Poland granted the most state aid

expressed in euros. Schemes and individual applications each count for one case, but the

impact of a scheme is much larger than that of an individual application. In fact, Germany

grants the most state aid to its manufacturing sector (European Policy Centre, 2014). As a

leading country in manufacturing, it wants to sustain its position.

In the end, 84 sectoral schemes have been matched to 17.809 firms of the Orbis data base.

The majority of those firms are German, Italian, French and Spanish. We have not been able

to match schemes with the firm data of Austria, because the only schemes available were

not specified at the 2 digit NACE level. The most used aid instruments in our data set are

direct grants and tariff reductions, followed by interest subsidies and guarantees. Important

to remark is that we only use cases that have their decision date between 2007 and 2015 and

thus do not include all state aid. There are state aid cases decided between the year 2000 and

2006 that might be still going on in the period 2007-2015.

16

4.4 Input Output tables

To look at the inter-industry effect of state aid, patterns of the domestic supply chain are

needed. For this we use national Input-Output tables (I-O)12. A I-O table shows the amount

of intermediate inputs one industry uses from another industry or the same industry. The

tables are aggregated on the 2-digit13 NACE level. The extent of the inter-industry linkages

do vary a lot across countries but not across time within countries. Therefore we decided to

use the technical coefficients of the I-O tables of 2010 for each of the countries to construct

the Backward and Forward variables. Technical coefficients show how much percent of the

output from a particular sector is used as input in an other sector and how much a sector buys

inputs from other sectors for its own production.

The calculation of technical coefficients is explained by figure 3. Assume a country with a

domestic manufacturing sector that consists of 3 industries; A (apparel), B (beverages) and

C (Coke). The first row then tells us that sector A supplies 14 units of its output to its own

sector, 26 to sector B and 10 to sector C. In analogy the first column tells us that sector A

buys 14 units of her inputs from sector A, 8 from sector B and 16 from sector C. To calculate

the technical coefficients one need to sum op each row or column. For row 1 the sum of the

outputs is 50. To obtain the coefficients we divide the number of units by 50. The results are

0.28 (14/50), 0.52 (26/50) and 0.2 (10/50). The sum is of course 1. Most of the times the

coefficients are expressed in percentages, for example 52 % of the output of sector A is used

as input in sector B. The calculation of the vertical coefficients is similar. The horizontal coef-

ficients will be used to construct forward spillover effects and the vertical technical coefficients

for the backward spillovers.

Figure 3: Fictional Input-Output Table

Sector A B C

A 14 26 10

B 8 13 22

C 16 7 32

12Available at http://www.wiod.org/database/niots1613Sector 10, 11, 12; 13, 14, 15 & 31, 32 are taken together.

17

5 Total Factor Productivity Estimation

To capture the effect of state aid on the technological progress of the firm or sector, a two step

approach is needed (Konings, Sergant&Van Cayseele, 2014). First the total factor productivity

(TFP) has to be estimated. However, several methodological problems arise when estimating

TFP with traditional methods e.g. Ordinary Least Squares (OLS). Therefore solutions proposed

by Olley and Pakes (1996) (OP) and Levin and Petrin (2003) (LP) will be used. Secondly, we

regress the TFP on state aid to investigate the effect.

5.1 Methodology

To estimate TFP, one needs to depart from a production function e.g. the Cobb-Douglas

production function. The production function used in this master dissertation is 14:

yit = β0 + βkkit + βllit + ωit + ηit (1)

wherein yit is the log output of firm i at time t, kit and lit respectively are its capital and

labour input, ωit is the unobserved productivity and ηit is the error term.

Different methods can be used to estimate the unobserved productivity. When estimating

productivity with OLS two problems arise. First, OLS estimation is biased because produc-

tivity and input decisions are correlated (endogeneity/ simultaneity bias). Secondly, OLS

introduces also a selection bias because exits and entries of firms are not taken into account

(Olley&Pakes, 1996). A within or fixed effect estimator, which only uses the variation within

firms, solves the simultaneity bias but makes other problems worse (Levinson&Petrin, 2003).

An alternative estimator is an Instrumental variable (IV). An IV estimates consistent parame-

ters by instrumenting the endogenous inputs in the production function by regressors that are

correlated with these inputs, but not correlated with the unobserved productivity. This ap-

proach solves the endogeneity problem but does not address the selection bias (Van Beveren,

2007).To address both biases Olley and Pakes (1996) introduce an investment proxy in their

production function.

iit = iit(ωit, kit) (2)

14as used in Konings, Sergant&Van Cayseele, 2014

18

Invertion of the function is possible because of monotonicity. The function then becomes:

yit = βllit + φit(iit, kit) + ηit (3)

with φit(iit, kit) = β0 + βkkit + ωt(it, kt) (4)

The problem with the investment proxy of Olley and Pakes is that it does not take into

account costs of adjustments. Firms and especially those in the manufacturing sector do not

invest every period, which leads to many zero-investment observations. This causes that the

monotonicity condition does no longer hold (Levinsohn&Petrin, 2003). Levinsohn and Petrin

(2003) propose intermediate inputs as a solution. The production function is now

yit = β0 + βkkit + βllit + βmmit + ωit + ηit (5)

and the intermediate input proxy

mit = mit(ωit, kit) (6)

With mit the material costs of the firm. Alternatives are fuels and electricity but material

costs are more likely to be reported by firms. Again, inversion is possible because of mono-

tonicity.

ωit = ωit(mit, kit) (7)

The production function can then be written as:

yit = βllit + φit(mit, kit) + ηit (8)

with φit(mit, kit) = β0 + βkkit + βmmit + ωit(mit, kit) (9)

The estimation process has two stages. First, with equation (8) βl can be estimated, us-

ing OLS. To estimate βk an additional assumption is needed. Both, OP and LP, assume

that the unobserved productivity is following a first order Markov process, given by ωit+1 =

E(ωit+1|ωit) + ξit+1. The error term ξit+1 is uncorrelated with ω and k in t+1. By rewriting

equation (1) for t+1 and replacing ωit+1 by the Markov process, the following equation is

obtained:

yit+1 − βllit+1 = β0 + βkkit+1 + E(ωit+1|ωit) + ξit+1 + ηit+1 (10)

= β0 + βkkit+1 + g(φt − βk − βm) + ξit+1 + ηit+1 (11)

The coefficient on capital can now be estimated by applying non lineair least squares.

19

Wooldridge (2009) proposes a one step in stead of a 2 step regression by using generalized

method of moments (GMM). The GMM setup has several advantages over the LP method

(Wooldridge, 2009). Firstly, the coefficient on the variable input labour is possibly not identi-

fied in the two-step estimation method if it also is a deterministic function of the unobserved

productivity and state variables15. In the one-step procedure of Wooldridge, he allows for iden-

tifying parameters on the variable inputs in the first step of LP and OP. Secondly, the GMM

estimation obtains easily a fully robust standard error. Thirdly, Wooldridge states that two-step

estimators are inefficient because they ignore contemporaneous correlation in the error terms

across regressions and that they do not account for serial correlation or heteroskedasticity in

the error terms. This is solved in the GMM method.

5.2 Results

5.2.1 TFP estimation

First, estimates are obtained for βl an βk on sector level using the LP method in Stata,

proposed by Levinsohn, Petrin and Poi (2003). yit in formula (1) can be both value added

and gross revenue. Merlevede and Theodorakopoulos (2017) describe the impact of value

added bias on estimated productivity effects. According to their results, estimated TFPs

using value added have, a heterogenous and dispersed distribution, while the distribution of

the TFP estimates when using gross revenue are more concentrated. To correct for value

added bias, TFP estimates based on gross revenue are used in the rest of this dissertation.

We estimated the production function for each sector separately. Table 2 shows the results

when the dependent variable is gross revenue. The coefficients obtained, when using value

added are summarized in appendix A. In sector 10, 28 and 30 only the first parameter βl is

identified. Stata reports in those cases that there is insufficient variation to identify the capital

and intermediate input (materials) coefficients separately. In table 3 the GMM method of

Wooldridge (2009) is followed. This method was not able to identify the coefficients of sector

12 and 19 because of the low number of firms in those sectors that are incorporated in the

panel set. We prefer the Levpet results because they are more consistent. The Wooldridge

method provides negative results in certain sectors, which is in fact not possible.

15This was first mentioned by Ackerberg, Caves and Frazer (2006) (ACF for short). As Wooldridge incorpo-

rates their remarks, there is no need to estimate TFP also with ACF.

20

Table 2: Estimates of the production function using levpet (gross revenue)

Sector Description βl βk

10 Food products 0.2899031 /

11 Beverages 0.3917842 0.2646806

12 Tobacco 0.3839167 02829979

13 Textiles 0.3243164 0.1742759

14 Wearing Apparel 0.3145671 0.1509017

15 Leather 0.2881103 0.0426918

16 Wood 0.3512373 8.40e-26

17 Paper and paper products 0.3137227 0.0263187

18 Printing and reproduction of recorded media 0.5723632 0.0487172

19 Coke and refined petroleum products 0.2788506 0.3700305

20 Chemicals and chemical products 0.3580201 0.1205654

21 Pharmaceutical products 0.395087 0.1205654

22 Rubber and plastic products 0.3366012 0.1461902

23 Other non-metallic mineral products 0.362862 0.2047821

24 Basic metals 0.3394425 3.64e-09

25 Fabricated metal products 0.4637694 0.1141186

26 Computer, electronic and optical products 0.4988211 0.019308

27 Electrical equipment 0.3798942 0.1125931

28 Machinery and equipment 0.443816 /

29 Motor vehicles, trailers and semi-trailers 0.3357163 0.080859

30 Other transport equipment 0.457494 0.1938011

31 Furniture 0.2784883 0.0190731

32 Other manufacturing 0.450348 /

33 Repair and installation of machinery and equipment 0.6909052 0.0864993

21

Table 3: Estimates of the production function using Wooldridge (gross revenue)


10 Food products 0.2809982 0.0577828

11 Beverages 0.3791358 0.0793551

12 Tobacco / /

13 Textiles 0.4087189 0.0699349

14 Wearing Apparel 0.4350473 0.08651

15 Leather 0.4047888 0.0378324

16 Wood 0.3437129 0.0138134



19 Coke and refined petroleum products / /

20 Chemicals and chemical products 0.3388384 -0.0038104

21 Pharmaceutical products 0.390593 -0.0577448



24 Basic metals 0.3234516 0.0479347




28 Machinery and equipment 0.4346837 0.0264117



31 Furniture 0.3452034 -0.0109878

32 Other manufacturing 0.4313946 0.0822994


22

5.2.2 Effect state aid on TFP growth

Next, we estimate the direct influence of state aid in industry j on the productivity growth

of firm i that operates in industry j. To capture this effect, we run the following regres-

sion:

ln∆TFPit+1 = β0 + β1StateAidjct + αi + αt + ηict (12)

where the dependent variable ln∆TFPit+1 is calculated as the lnTFP in t + 1 minus the

lnTFP in t. StateAidjct is a dummy variable, specific for each industry j across time t

and countries c. αi and αt respectively stand for firm and time fixed effects. Firm fixed

effects capture the notion that two observations from the same firm will be more alike

than observations from two different firms, in other words it controls for unobserved firm

heterogeneity that is constant over time (Gujarati & Porter, 2009, Chapter 16). Time fixed

effects account for common demand and supply shocks to all firms (Konings, Sergant&Van

Cayseele, 2014). Since some variables are defined on sector level while the TFP estimation

is on firm level, the standard deviations are underestimated. To control for this within-sector

correlation the reported standard errors are clustered at the 4 digit sector level (Moulton,

1990).

Table 4: Direct effect state aid (LP)

VARIABLES (1) (2) (3) (4)

StateAid -0.0154*** -0.0154*** -0.0149** 0.0101

(0.00436) (0.00435) (0.00625) (0.0126)

Competition 0.00375 -0.0261 -0.0221

(0.0340) (0.0354) (0.0352)

Distance -0.645*** -0.616***

(0.0472) (0.0471)

StateAidCum -0.00330*

(0.00176)

Constant 0.00524 0.00490 0.0815*** 0.0747***

(0.00394) (0.00480) (0.00780) (0.00766)

Firm fixed effects YES YES YES YES

Time fixed effects YES YES YES YES

Observations 272,441 272,441 272,441 241,862

R-squared 0.004 0.004 0.032 0.032

Number of firm 72,524 72,524 72,524 65,332

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

23

Table 5: Direct effect state aid (Wooldridge)

VARIABLES (1) (2) (3) (4)

StateAid -0.00835*** -0.00884*** -0.00856** 0.00551

(0.00315) (0.00316) (0.00428) (0.0111)

Competition 0.0744** 0.0550 0.0561

(0.0324) (0.0342) (0.0341)

Distance -0.416*** -0.390***

(0.0332) (0.0321)

StateAidCum -0.00282**

(0.00132)

Constant 0.0171*** 0.0105** 0.0609*** 0.0565***

(0.00361) (0.00485) (0.00611) (0.00603)



Observations 259,383 259,383 259,383 229,208

R-squared 0.012 0.012 0.028 0.029

Number of firm 68,310 68,310 68,310 61,273


*** p<0.01, ** p<0.05, * p<0.1

Specification (1) of table 4 and 5 shows the estimation results of equation (12) using respec-

tively LP and Wooldridge to estimate TFP. Important to remark is that with the Wooldridge

method, it is possible to include state aid in equation (1) to obtain both TFP and the effect

of state aid on it at once. However, the problem with this method is that the dependent

variable TFP is in levels and not in first differences in order to obtain TFP growth. Therefore

we estimate TFP with the Wooldridge method excluding the variable state aid.

lnTFPit = lnYit − b(lnLit) ∗ lnLit − b(lnKit) ∗ lnKit − b(lnMit) ∗ lnMit (13)

With these results we can calculate TFP growth (ln∆TFPit) and regress it over state aid. For

both methods, we find a highly significant negative relationship between state aid in sector j

and TFP growth in firm i (i operates in j). The coefficients in column (1) mean that state aid

is respectively associated with a 1,54 % and a 0,83 % decrease in productivity growth. The

negative relation between state aid and TFP growth in equation (12) may be due to a omitted

variable bias (OVB). An OVB occurs when one or more important factors are not incorporated

in the model, some coefficients are incorrectly over- or underestimated (Clarke, 2005).

24

Based on related literature, we control for the following factors that may influence firm pro-

ductivity: a competition index and the distance to the productivity frontier.

ln∆TFPit+1 =β0 + β1StateAidjct + Competitionjct +Distanceijct

+ StateAidCumjct + αi + αt + ηict

(14)

Competition To look if there is a greater impact of state aid in sectors where there is higher

competition as proposed by Aghion et al. (2011), we add a competition variable. To measure

the concentration within sectors a Herfindahl-Hirschman index (HHI) is constructed using the

share of output of a firm relative to the total output of the whole industry for each country

at each time. The HHI is the sum of squares of firms’ individual market shares (Miller, 1982).

A Herfindahl concentration index ranges from 0 to 10.000 points. A small number indicates

low concentration and high competition, while a high number represents high concentration

and thus less competition. In order for easier interpretation we divide our Herfindahl index by

10.000 which gives us a value between 0 and 1.

Competitionjct =∑i

(Outputijct

TotalOutputjct

)2

(15)

Distance to the productivity frontier Konings and Vandebussche (2008) find that firms

with a lower initial productivity lever experience more productivity gain from tariff protection.

This can be explained by the fact that the most productive firms in industries already operate

at competitive cost levels and therefore have less incentive to innovate and increase their pro-

ductivity under protection. Since tariff reduction is a type of state aid we would expect to find

the same growth enhancing effect of a lower initial productivity level. To control for this, we

construct a variable based on the variable used by Konings and Vandenbussche (2008) that

measures a firm’s distance to the productivity frontier. This frontier is the productivity level

of the firm with the highest TFP in the same NACE 2-digit level industry. We calculate the

productivity frontier for each industry in each country at each time separately. A value of 1

indicates that a firm is as productive as the frontier firm and a distance of 0 implies that a

firm is lagging far behind the most productive firm in its industry.

Distanceijct =

(TFPijct

MaxjctTFPjct

)(16)

25

Figure 4: Distribution of the distance to productivity frontierFrequency

0 .2 .4 .6 .8 1distance

When we plot the kernel distribution of firms as a function of their initial distance to the

productivity frontier (figure 4), we observe that the distribution is skewed to the left, meaning

that the majority of the firms are lagging far behind the productivity frontier and that there

are few efficient firms. Table 6 provides the summary statistics for respectively the HHI and

the distance to the frontier measure per sector at the NACE 2-digit level.

Table 6: Summary statistics HHI and distance to frontier

Competition Distance Competition Distance

sector mean st.dev. mean st.dev. sector mean st.dev. mean st.dev.

10 1252 600 0.0415 0.0838 22 842 424 0.1945 0.1318

11 171 80 0.1867 0.1667 23 554 312 0.0859 0.1231

12 6 3 0.3999 0.3719 24 360 189 0.1207 0.1560

13 486 345 0.1763 0.156 25 2636 1483 0.0322 0.0622

14 665. 385 0.0553 0.1243 26 645 367 0.0998 0.1342

15 682 385 .0637 0.127 27 605 338 0.1434 0.1418

16 364 182 0.1611 0.1888 28 1890 1101 0.0679 0.0868

17 295 155 0.1452 0.1875 29 311 133 0.1337 0.19794

18 402 209 0.1553 0.1667 30 159 97 0.1474 0.1973

19 25 19 0.2534 0.3116 31 434 326 0.1346 0.2112

20 495 218 0.1260 0.1420 32 450 261 0.0782 0.1258

21 127 60 0.1257 0.1911 33 560 329 0.1482 0.1406

26

Cumulative It is possible that the productivity effect of state aid appears only after years of

state aid. To control for this cumulative effect, we add the variable StateAidCumjct, which

is the sum of state aid received in that year, ranging from 0 to 9.

Looking at column (2) and (3) of table 4 and 5, one can conclude that the effect of state

aid is still negative when adding a Herfindahl index and a distance to frontier variable. Pro-

ductivity growth in firm i decreases when its own industry j receives state aid. However the

effect becomes positive although not significant when adding a variable that takes into ac-

count the cumulative effect (column 4). In specification (2) to (4) a Herfindahl concentration

index (Competition) is included. We do not find consistent estimation results. This is not

in line with the findings of Aghion et al. (2011). The distance variable is highly significant

and negative, which means that firms with a lower initial productivity level, know a faster

productivity growth. Firms that are lagging behind have more room for improvement. The

effect is stronger when we use Levpet based TFP growth. This is similar to the findings of

Konings and Vandenbussche (2008). The coefficient of the cumulative state aid variable is two

times negative and significant, implying that receiving state aid during several years does not

decrease productivity growth in firm i. This is in line with the overall negative effect of state

aid in table 4 and 5. Although state aid becomes positive in column (4) it is not significant.

However, there are still other variables that contribute to the TFP growth of firms and/or

industries. In particular the R&D stock in an industry and the wage share of skilled labour,

which is a proxy for human capital (Wang, 2010). Since we do not have data of neither R&D

stocks in industries nor human capital, we use industry-year effects to account for this. Table

7 and 8 show the estimation results of specification (14) including industry-year fixed effects

αjt. The biggest difference is that now the HHI or concentration variable is negative and

significant in column (2) to (4). This means that firms that operate in competitive industries

will experience faster TFP growth. The results of the other variables remain constant. In the

next section we will add spillover variables to this equation.

ln∆TFPit+1 =β0 + β1StateAidjct + β2Competitionjct + β3Distanceijct

+ β4StateAidCumjct + αi + αt + αjt + ηict

(17)

27

Table 7: Direct effect state aid with industry-year fixed effects (LP)

VARIABLES (1) (2) (3) (4)

StateAid -0.00975* -0.00971* -0.0154* 0.00275

(0.00504) (0.00504) (0.00874) (0.0201)

Competition -0.0111 -0.0616* -0.0691*

(0.0278) (0.0358) (0.0366)

Distance -0.699*** -0.675***

(0.0527) (0.0533)

StateAidCum -0.000398

(0.00225)

Constant 0.0115 0.0125 0.0910 0.0975

(0.747) (1.370) (1.248)



Industry-year fixed effects YES YES YES YES

Observations 272,441 272,441 272,441 241,862

R-squared 0.015 0.015 0.045 0.046

Number of firm 72,524 72,524 72,524 65,332


*** p<0.01, ** p<0.05, * p<0.1

28

Table 8: Direct effect state aid with industry-year fixed effects (Wooldridge)

VARIABLES (1) (2) (3) (4)

StateAid -0.0123** -0.0122** -0.0158* 0.00981

(0.00543) (0.00543) (0.00829) (0.0159)

Competition -0.0111 -0.0444* -0.0422*

(0.0251) (0.0254) (0.0253)

Distance -0.450*** -0.426***

(0.0353) (0.0348)

StateAidCum -0.00172

(0.00166)

Constant 0.0205 0.0215 0.0786 0.0821

(0.825) (0.540) (1.179)




Observations 259,383 259,383 259,383 229,208

R-squared 0.026 0.026 0.044 0.046

Number of firm 68,310 68,310 68,310 61,273


*** p<0.01, ** p<0.05, * p<0.1

29

6 Inter-Industry Effects

6.1 Methodology

More important for the purpose of this master dissertation are possible inter-industry effects

of state aid. We want to know whether state aid in an industry generates spillover effects to

other industries or not. To investigate this effect, we add spillover variables to our specification.

These spillover variables consist of proxies for backward and forward spillovers at the country-

industry-year level. Both spillover proxies are based on the method of Javorcik (2004), using

IO-tables from the World Input-Output Database. For backward (BW) spillover (from the

supported firm to its upstream customer) the following proxy is used.

Backwardjct =∑k

αjckStateAidkct (18)

αjck is the share of sector j’s output supplied to sector k calculated from the 201016 input-

output matrix at the two-digit NACE level. The coefficient excludes imports as we want to

investigate effects through the domestic supply chain but includes inputs supplied by the own

sector17. The coefficients are multiplied by the dummy variable StateAidkct, representing the

presence of state aid in sector k, in country c at time t. We can check with this variable

whether firm i experiences productivity growth if the sector, sector j, which i is supplying to

receives state aid.

The Forwardjct (FW) proxy is calculated in a similar way and is defined as the share of

output in upstream sectors, produced by firms that do receive state aid.

Forwardjct =∑m

σjcmStateAidkct (19)

In the Forward variable, σjcm is the proportion of inputs purchased by firms in industry j

from industry m. Similar as with the BW variable, we now can check if i will benefit in terms

of productivity growth. when industry j is receiving state aid and j is supplying intermediate

goods to firm i. The proxies for backward and forward spillovers are time-varying and sector-

specific variables. While the coefficients taken from the IO table remain unchanged (2010 as

reference year), variations in level of state aid are observed over the period.

16We did not calculate the αjck for each year, because of no great variation in time.17We estimate the regression again, setting the diagonal on zero, as a robustness check in section 7.1.

30

As explained in section 4.3, the IO tables aggregate certain sectors but those aggregated sectors

do not receive state aid equally and/or in the same period. To adjust this we construct three

types of proxies. The first type is to allow the state aid variable to be 1 if at least one of the

sectors receives state aid. The second type gives the state aid variable value zero if at least

one sector is not receiving state aid. Both types are far from reality. The third type is a sum

of the state aid in aggregated sectors, weighted by the output shares.

Table 9: State aid and spillovers per sector

Sector AID (%) BW 1 FW 1 BW 2 FW 2 BW 3 FW3

10 Food Products 54.72 76.39 52.77 0.99 0.86 60.32 41.62

11 Beverages 1.66 78.45 55.89 0.87 0.83 60.99 43.33

12 Tobacco 0.00 59.40 49.21 0.92 0.94 49.02 40.78

13 Textiles 1.91 9.79 12.41 2.09 2.22 5.24 7.58

14 Wearing Apparel 1.66 6.87 9.29 1.47 1.82 3.68 5.68

15 Leather 1.61 8.99 11.77 1.48 2.12 4.17 6.72

16 Wood 2.01 15.95 9.17 5.67 7.19 13.25 8.63

17 Paper 0.00 13.67 5.19 2.071 2.99 11.10 4.50

18 Printing&Rec. 0.00 10.65 3.55 4.10 2.62 8.70 3.22

19 Coke& Ref. Petr. 0.01 15.75 7.83 12.34 6.09 15.15 7.66

20 Chemicals 6.39 15.12 19.07 10.78 11.17 14.10 17.81

21 Pharmaceutical prod. 0.13 14.29 14.25 6.53 7.34 11.70 12.88

22 Rubber&Plastics 0.00 14.76 11.88 6.44 10.52 12.78 11.48

23 Other non-metallic 0.01 13.56 4.74 4.91 3.54 10.94 4.41

24 Basic metals 0.01 16.89 3.19 6.22 2.32 8.59 3.00

25 Fab. Metal Products 0.00 9.89 3.29 6.46 2.47 8.62 3.09

26 Computer&Elec. 5.24 27.46 17.59 21.84 15.16 26.72 17.18

27 Electrical Eq. 17.94 32.65 29.45 30.64 28.74 32.22 29.31

28 Machinery&Eq. 0.00 9.70 6.20 6.70 5.41 9.12 6.03

29 Motor vehicles 0.08 10.90 4.62 2.88 3.66 5.00 4.27

30 Other transport eq. 3.06 56.05 31.21 54.88 30.35 55.48 30.90

31 Furniture 0.00 76.02 19.33 4.98 5.12 30.42 15.96

32 Other Manufacturing 4.97 39.99 16.04 5.52 6.75 21.74 13.80

33 Repair&Install. 0.26 24.92 14.20 17.68 12.98 21.23 13.94

Total 100.00 24.49 14.95 7.61 6.12 18.82 12.79

31

Table 9 lists the mean values of all three types. Non surprisingly type two has the smallest

values and type one the biggest. Type 3 lays in between since we use a weighted sum. Column

1 provides the sectoral distribution of firms that receive state aid. In our panel set most state

aid goes to the food product industry and the electrical equipment sector. Table 10 shows

that there is a high correlation between the state aid variable and the forward and backward

variables. This can be explained by the fact that we did not set the diagonal on zero in the

input output tables and thus we do not eliminate intra industry supplies.

Table 10: Correlation matrix

Stateaid Backward Forward

Stateaid 1.0000

Backward 0.7108 1.0000

Forward 0.6570 0.8229 1.000

Incuding these spillover proxies in equation 17, we obtain the following baseline specifica-

tion.

ln∆TFPit+1 =β0 + β1StateAidjct + β2Backwardjct + β3Forwardjct + β4Competitionjct

+ β5Distanceijct + β6StateAidCumjct + αi + αt + αjt + ηict

(20)

Table 11 presents the results of our baseline specification using the different types of spillover

proxies. The estimation results of the regression without the other factors that influence TFP

growth can be found in appendix D, table D1. The state aid variable is always positive, but

only significant when using the third type of the backward and forward spillovers.

For both methods of TFP estimation, LP and Wooldridge, we only find significant results for

negative backward spillovers when using type 1 and type 3. This means that an increase in

state aid causes a decrease in productivity growth in the downstream sectors. However, the

effect is rather small. There is no indication for forward spillovers for both TFP methods, since

the Forward variable appears to be insignificant for all types of state aid for all specifications.

In the baseline specification that will be used in the rest of this master dissertation, we only

include type three of the spillover types. As explained above, type three is likely to be the

most accurate since it uses weights for the sectors that are pooled together.

32

Table 11: Inter-industry effects per type of spillover proxy

LP LP LP W W W

StateAid 0.0210 0.0364 0.0424* 0.0220 0.0241 0.0336*

(0.0213) (0.0271) (0.0255) (0.0163) (0.0183) (0.0179)

Competition -0.0659* -0.0668* -0.0662* -0.0392 -0.0406 -0.0393

(0.0366) (0.0367) (0.0364) (0.0254) (0.0254) (0.0253)

Distance -0.677*** -0.675*** -0.676*** -0.427*** -0.426*** -0.427***

(0.0534) (0.0536) (0.0536) (0.0349) (0.0350) (0.0350)

StateAidCum -0.000193 -0.000239 -0.00104 -0.00160 -0.00165 -0.00211

(0.00223) (0.00230) (0.00225) (0.00165) (0.00171) (0.00167)

Backward 1 -0.000822*** -0.000507*

(0.000266) (0.000272)

Forward 1 5.44e-05 5.15e-06

(0.000355) (0.000250)

Backward 2 -0.000788 -0.000324

(0.000513) (0.000374)

Forward 2 -0.000336 -0.000152

(0.000411) (0.000289)

Backward 3 -0.000903*** -0.000475*

(0.000309) (0.000245)

Forward 3 -0.000352 -0.000260

(0.000365) (0.000256)

Constant 0.0955 0.0940 0.104 0.0820 0.0929 0.0838

Firm fixed effects YES YES YES YES YES YES

Time fixed effects YES YES YES YES YES YES

Industry-year FE YES YES YES YES YES YES

Observations 241,862 241,326 241,862 229,208 228,681 229,208

R-squared 0.046 0.046 0.046 0.046 0.046 0.046

Number of firm 65,332 65,237 65,332 61,273 61,180 61,273


*** p<0.01, ** p<0.05, * p<0.1

33

6.2 Results

6.2.1 Baseline specification

In this section the estimation results of equation (20) will be discussed. Table 12 provides

summary statistics of all our variables, which can clarify the interpretation of some of the

coefficients.

Table 12: Summary statistics of the mean variables

Variable Mean Std. Dev. Min Max 25% 75%

ln∆TFPLPt+1 0.0043142 0.2371506 -8.700584 8.476702 -0.059 0.067

ln∆TFPWt+1 0.0094279 0.202673 -7.686908 7.855598 -0.051 0.070

StateAid 0.131282 0.3377087 0 1 0 0

Backward 18.82215 23.98134 0 97.58496 5.135 19.111

Forward 12.79672 20.60369 0 84.88207 1.496 9.705

Competition 0.1168803 0.1189755 0.0001 0.4841 0.034 0.152

Distance 0.0953457 0.1390595 5.51e-06 1 0.015 0.117

StateAidCum 0.3105691 1.1286513 0 9 0 0

Table 13 gives the results of the fixed effects estimation of equation (20). Columns 1 to 3

show the estimation results of the LP method and 4 to 6 the Wooldridge method. In the

previous specifications, we found a negative direct effect of state aid on productivity growth.

This negative effect now disappears given all positive coefficients in the first row of table

13, although only significant in column 3 to 6. The presence of state aid in a sector is

associated with an increase in TFP growth, ranging from 2.8% to 4.24%. The crucial question

is whether state aid also generates significant backward and forward spillovers or not. We

find overall negative backward spillover effects, meaning that firm i in the supplying industry

has negative effect from state aid in industry j to which i sells intermediate goods. We only

find evidence for negative forward spillovers effects when the backward spillover variable is

not included. This means that the downstream industries do not benefit positively from state

aid in their supplying industries. In all other specifications the forward spillover variable does

not appear to be significant. The total effect of state aid in a particular sector is measured

by the sum of the coefficients of StateAid, Backward and Forward. For column (3) this

effect equals 0.03797718, which is positive but smaller than the direct effect of state aid

18Sum of 0.0424, - 0.000903 and -0.000352

34

because of the negative backward and forward spillover effects. The coefficients of the variables

Distance and Competition are in line with the theory explained in section 5.2.2. Firms that

lag behind have a faster productivity growth than firms that are already efficient. The highly

significant negative coefficient of the variable Distance confirms this theory. Also, firms in a

competitive environment experience a higher productivity growth rate. The HHI concentration

index however, is not always significant when estimating Wooldridge based TFP growth and

when significant is is only at the 10 % significance level. Receiving state aid during more than

1 year has no significant effect on TFP growth.

Table 13: Baseline specification

Levpet Wooldridge

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

StateAid 0.0350 0.0376 0.0424* 0.0281* 0.0309* 0.0336*

(0.0228) (0.0264) (0.0255) (0.0167) (0.0186) (0.0179)

Competition -0.0649* -0.0703* -0.0662* -0.0385 -0.0420* -0.0393

(0.0365) (0.0365) (0.0364) (0.0254) (0.0253) (0.0253)

Distance -0.676*** -0.676*** -0.676*** -0.427*** -0.427*** -0.427***

(0.0534) (0.0536) (0.0536) (0.0349) (0.0350) (0.0350)

StateAidCum -0.00107 -0.000648 -0.00104 -0.00214 -0.00189 -0.00211

(0.00221) (0.00230) (0.00225) (0.00165) (0.00168) (0.00167)

Backward 3 -0.00113*** -0.000903*** -0.000641*** -0.000475*

(0.000346) (0.000309) (0.000233) (0.000245)

Forward 3 -0.000886** -0.000352 -0.000538** -0.000260

(0.000362) (0.000365) (0.000237) (0.000256)

Constant 0.116 0.101 0.104 0.0814 0.0776 0.0838

(3.154) (0.373) (0.892)

Firm FE YES YES YES YES YES YES

Time FE YES YES YES YES YES YES

I-Y FE YES YES YES YES YES YES

Observations 241,862 241,862 241,862 229,208 229,208 229,208

R-squared 0.046 0.046 0.046 0.046 0.046 0.046

Nr of firm 65,332 65,332 65,332 61,273 61,273 61,273


*** p<0.01, ** p<0.05, * p<0.1

35

6.2.2 Baseline specification expanded with interaction terms

In table 14 and 15, we include some interaction terms. We start with the discussion of

the estimation results of table 14. The state aid variable is not consistent in sign and not

significant. In all of the specifications with interaction terms state aid to its own industry

does not have an effect on the TFP growth of firm i. The Backward variable is in general

negative and highly significant, which is in line with the previous results. Again, we do

not find significant results for forward spillover effects when we include both spillover types,

except for column (4) in which we include the interaction term of Distance and Forward.

In this specification we find positive forward spillover effects, meaning that firm i profits

from state aid to industry j that sells intermediate goods to i. This is possible through

spillover channels of better quality and cheaper inputs. The coefficients of Competition

and Distance remain significant and negative, which is in line with the results of our basis

model without interaction terms and related literature.

In specification (1) we include the interaction term of competition and state aid, which

appears to be not significant. Firms that receive state aid in a highly competitive envi-

ronment do not experience faster TFP growth than firms in non competitive industries.

Looking at the positive significant effect of the interaction term of state aid and the dis-

tance to the productivity frontier, we can say that state aid has a more growth enhancing

effect in firms that are already efficient. For column (4) the net effect is (-0.00792 +

0.274*Distance)*StateAid. The closer the firm lays to the productivity frontier, the higher

this net effect. If the distance variable rises with one standard deviation (0.1390595)19 the

net effect of state aid on TFP growth rises with 0.0301. Next in column (3) to (5), we in-

clude interactions between Distance and the spillover variables. The two interaction terms

are both negatively significant, when we regress them separately in column (3) and (4). This

implies that the negative effect of state aid through forward and backward linkages is smaller

in lagged industries. The net effect of downstream state aid on TFP growth in column

(3) equals (-0.000511 + (-0.00505)*Distance)*Backward. When we compare the 25th

and the 75the percentile20 of the Distance distribution, we find the following coefficients:

- 0.00059 and -0.0011. Accordingly, upstream industries that are far from the productivity

frontier (a Distance value close to 0) will have almost a zero effect of state aid in the sectors

they are supplying to.

19Standard deviation can be found in table 12.20Table 12. 25th: 0.015 and 75th: 0.117.

36

The effect is different, considering the interaction with forward spillovers because of the positive

coefficient of the variable Forward in specification (4) and (5). In contrast to BW spillovers

it is now possible to have a positive net effect. In column (4) the net effect is (0.00115 +

(-0.00683)*Distance)*Forward. Again we compare the 25th and the 75th percentile. The

coefficients now are respectively 0.0014 and 0.00035. The positive effect is bigger when the

distance variable is close to zero. Firms that are lagging behind thus benefit the most from

positive forward spillover effects. When we include both interaction terms in column (5), the

coefficients are both negative. The total effect of state aid is now -0.00840721 and thus neg-

ative, which is in contrast to the results of table 13. The interpretation of the coefficients of

the interaction terms are analogue to them from column (3) and (4).

The estimation results in table 15 have similar interpretations. The results do not differ

much, except for the significant negative effect of cumulative state aid and a significant pos-

itive interaction between competition and state aid. This last effect means that state aid is

more effective and more growth enhancing when it is granted to sectors that are highly concen-

trated. For column 2 for instance the net effect is (0.0367 + 0.391*Competition)*StateAid.

When we compare the 25th and 75th percentile22 of the HHI distribution, we find coefficients

of 0.050 and 0.096. State aid thus has more effect on TFP growth in concentrated mar-

kets. This effect can be explained by the fact that innovation is already higher in competitive

sectors. In general there are two possible effects of market competition and innovation. On

the one hand increased competition in a industry can give the operating firms incentives to

innovate to protect their market position (i.e., an ”escape-competition effect”). On the other

hand, greater competition can reduce the incentives to innovate because the compensation

of innovating is so small (a ”Schumpeterian effect”) (Griffith, Harrison & Simpson, 2010).

In our results the HHI, that measures concentration, is always negative significant, meaning

that firms in competitive industries have higher TFP growth. This can be explained by the

”escape-competition effect”. Firms innovate to remain competitive. The firms in concentrated

industries do have less incentives to invest in R&D because they are already sure about their

market position. State aid in those low competitive industries can encourage firms to innovate

anyway. The interpretation of the backward and forward spillover variables are analogue to

table 14.

21Sum of -0.00803, -0.00104 and 0.00663.22Table 12. 25th: 0.034 and 75th: 0.152.

37

Table 14: Baseline specification with interaction terms (LP)

VARIABLES (1) (2) (3) (4) (5)

StateAid 0.0414 0.0303 0.00885 -0.00792 -0.00803

(0.0251) (0.0260) (0.0273) (0.0265) (0.0274)

Backward 3 -0.000905*** -0.000912*** -0.000511 -0.00149*** -0.00104***

(0.000307) (0.000308) (0.000351) (0.000320) (0.000400)

Forward 3 -0.000350 -0.000341 -0.000286 0.00115** 0.000663

(0.000366) (0.000369) (0.000377) (0.000501) (0.000515)

Competition -0.0671* -0.0674* -0.0671* -0.0632* -0.0645*

(0.0374) (0.0375) (0.0374) (0.0373) (0.0371)

Distance -0.676*** -0.678*** -0.629*** -0.640*** -0.622***

(0.0536) (0.0537) (0.0564) (0.0534) (0.0560)

StateAidCum -0.00116 -0.00107 -0.000227 -0.00142 -0.000768

(0.00267) (0.00262) (0.00260) (0.00272) (0.00268)

StateAid x Competition 0.0282 0.0389 0.00822 0.0814 0.0472

(0.230) (0.224) (0.220) (0.223) (0.222)

StateAid x Distance 0.0546 0.214* 0.274** 0.297**

(0.1000) (0.115) (0.119) (0.123)

Distance x Backward -0.00505*** -0.00317*

(0.00171) (0.00180)

Distance x Forward -0.00683*** -0.00444**

(0.00168) (0.00179)

Constant 0.104 0.105 0.107 0.117 0.115

(4.755) (2.039) (0.889)

Firm fixed effects YES YES YES YES YES

Time fixed effects YES YES YES YES YES

Industry-year fixed effects YES YES YES YES YES

Observations 241,862 241,862 241,862 241,862 241,862

R-squared 0.046 0.046 0.046 0.046 0.047

Number of firm 65,332 65,332 65,332 65,332 65,332


*** p<0.01, ** p<0.05, * p<0.1

38

Table 15: Baseline specification with interaction terms (Wooldridge)

VARIABLES (1) (2) (3) (4) (5)

StateAid 0.0199 0.0367* 0.0163 -0.00286 -0.00309

(0.0163) (0.0191) (0.0206) (0.0209) (0.0219)

Backward 3 -0.000512** -0.000504** -0.000103 -0.00115*** -0.000707**

(0.000244) (0.000243) (0.000288) (0.000286) (0.000313)

Forward 3 -0.000234 -0.000249 -0.000199 0.00137*** 0.000906**

(0.000256) (0.000254) (0.000258) (0.000443) (0.000409)

Competition -0.0504* -0.0499* -0.0497* -0.0462* -0.0472*

(0.0262) (0.0262) (0.0261) (0.0262) (0.0261)

Distance -0.427*** -0.424*** -0.375*** -0.383*** -0.366***

(0.0349) (0.0352) (0.0351) (0.0323) (0.0343)

StateAidCum -0.00402* -0.00418* -0.00328 -0.00474** -0.00403*

(0.00211) (0.00218) (0.00219) (0.00234) (0.00232)

StatAaid x Competition 0.406** 0.391** 0.358** 0.455** 0.415**

(0.178) (0.178) (0.175) (0.179) (0.178)

StateAid x Distance -0.0829 0.0712 0.139 0.164

(0.100) (0.115) (0.122) (0.129)


(0.00175) (0.00180)

Distance x Forward -0.00741*** -0.00514***

(0.00191) (0.00176)

Constant 0.0842 0.0839 0.0796 0.0845 0.0764

(2.569) (0.443)




Observations 229,208 229,208 229,208 229,208 229,208

R-squared 0.046 0.046 0.047 0.047 0.047

Number of firm 61,273 61,273 61,273 61,273 61,273


*** p<0.01, ** p<0.05, * p<0.1

39

6.3 Firm size

It is possible that spillover effects differ with firm size. To investigate this, we compare the

spillover effects in small, medium-sized and large firms. To categorize the firms we use the firm

size classification of the EU. The EU sets two tresholds: one about the number of employees

and one for the turnover of firms. Table 16 displays the categories and the related tresholds.

Table 16: EU’s firm categories

Company Category Staff headcount Turnover

Micro < 10 ≤ e2 m

Small < 50 ≤ e10 m

Medium-sized < 250 ≤ e50 m

Large > 250 > e50 m

We focus on the staff headcount criterion. Since the selected firms have a minimum of 20 em-

ployees, micro firms are not incorporated in our panel set. We end with three categories: small,

medium-sized and large firms. We have ran our baseline specification for each group.Table

17 shows the estimation results of the expanded specification. The results without interaction

terms can be found in appendix D, table D2, where they are briefly discussed. The results

for both the Levpet method (column 1 to 3) and the Wooldridge method (column 4 to 6)

are mostly alike so there is no need to discuss them separately. The direct effect of state aid

does not appear to be significant for any size. This result is not surprising since this was also

the case in section 6.2.2 where we introduced interaction terms to our baseline specification.

In terms of spillover effects, we find evidence for negative backward effects in small and large

firms. We can conclude that the negative backward spillovers we found before are mainly

driven by small and large enterprises. Positive forward spillovers on the other hand only occur

in large enterprises. This can be due to the fact that they have a greater absorption capacity

of new knowledge than small or medium-sized firms. The other variables do not vary much

with those of table 14 and 15. Firms that are lagging behind know a faster TFP growth.

Although, the HHI is not always significant it still has a negative sign, meaning that high

concentration in an industry reduces the TFP growth rate. The results for the interaction

terms StateAid*Distance and Backward*Distance are respectively positive and negative

significant for small firms only. Again, the interaction term between Distance and Forward

is negative, although not significant.

40

Table 17: Baseline specification with interaction terms per firm size

Levpet Wooldridge

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

VARIABLES small medium large small medium large

StateAid 0.00434 0.00825 0.0306 0.0105 0.0135 -0.0161

(0.0368) (0.0356) (0.0793) (0.0292) (0.0312) (0.0699)

Backward 3 -0.00186*** 5.97e-05 -0.00314** -0.000784* -0.000584 -0.00292**

(0.000609) (0.000627) (0.00140) (0.000420) (0.000550) (0.00117)

Forward 3 0.00121 -0.000297 0.00273* 0.000426 0.000956 0.00378**

(0.000764) (0.000824) (0.00162) (0.000534) (0.000673) (0.00155)

Competition -0.112 -0.0513 -0.0909 -0.110* -0.0438 -0.0321

(0.0854) (0.0362) (0.0929) (0.0638) (0.0333) (0.0754)

Distance -0.641*** -0.596*** -0.464*** -0.346*** -0.393*** -0.310***

(0.0752) (0.0634) (0.0819) (0.0369) (0.0603) (0.0650)

StateAidCum -0.00136 -0.000757 -0.00183 -0.00548* -0.00571** 0.00286

(0.00428) (0.00314) (0.00621) (0.00312) (0.00260) (0.00440)

StateAid x Comp -0.469 0.0928 0.688 0.370 0.508** 0.462

(0.460) (0.232) (0.587) (0.319) (0.203) (0.381)

StateAid x Dist 0.649*** 0.0776 -0.194 0.354** -0.0462 -0.130

(0.186) (0.158) (0.304) (0.147) (0.179) (0.267)

Distance x BW -0.00518* -0.00323 0.00335 -0.00547*** -0.000475 0.00309

(0.00267) (0.00277) (0.00356) (0.00180) (0.00280) (0.00269)

Distance x FW -0.00579** -0.00210 -0.0108** -0.00323 -0.00576* -0.0138***

(0.00244) (0.00316) (0.00499) (0.00199) (0.00325) (0.00494)

Constant 0.0984 0.128 0.0577 0.0951 0.0763*** 0.0724**

(12.47) (17.80) (0.0999) (0.0177) (0.0295)




Observations 133,755 90,278 17,829 125,389 86,517 17,302

R-squared 0.042 0.048 0.085 0.036 0.058 0.098

Number of firm 42,595 24,565 4,358 39,562 23,357 4,234


*** p<0.01, ** p<0.05, * p<0.1

41

6.4 Summary

In this section we searched for inter-industry effects of state aid. In general we found evidence

for negative backward spillovers and positive forward spillovers on the condition that an inter-

action between the FW variable and the distance to the productivity frontier is included. A

positive FW spillover means that firm i is benefiting from state aid in industry j and that j

supplies intermediate goods to i. The total effect of state aid (Direct effect + BW + FW) was

positive in our baseline specification but fell below zero when interaction terms were added.

To be more confident about the effect of productivity spillovers, we added other factors that

may influence firm productivity, more specifically a concentration index and a distance to the

productivity frontier measure. Only Distance is significant over all regressions. Firms that

are lagging behind have a higher TFP growth, because they have more capacity to grow. The

HHI concentration index is always negative and most of the times significant, meaning that

competition increases the growth of TFP. When we include interaction terms, we find signifi-

cant negative results for the interaction between Distance and the spillover variables, meaning

that the negative effect of state aid through backward linkages is lower in lagged industries

and that the positive forward spillover effect is larger in firms that are lagging behind. When

using Wooldridge’s TFP one also finds that the interaction term of state aid and concentra-

tion is positive and significant. Firms in concentrated industries have faster TFP growth when

they receive state aid than firms in competitive environments. This can be explained by the

fact that firms in a monopolistic environment have less incentives to innovate because they

are sure about their market position. State aid can provide a solution in motivating those

firms to innovate. When we perform our regression across different categories on firm size, we

find evidence for negative backward spillovers for both small and large firms. Positive forward

spillovers, however are mainly driven by large firms. Hier nog iets zeggen over dat artikel over

state aid en report EU. Daarin staat er dat vooral grote bedrijven profijt hebben door state

aid.

42

7 Robustness

This section describes two robustness checks. First, we construct new proxies for backward

and forward spillovers. Secondly, we run the regressions of section 6 again, but now without

Austrian firms, because we could not match any state aid scheme to those firms.

7.1 Robustness Check 1: alternative spillover proxy

We construct new spillover proxies using exact the same method as described in section 6.1,

but now we set the diagonal to zero. This is done in most of the studies on inter-industry

spillover effects (Javorcik, 2004) (Wang, 2010). The spillover proxies are constructed with the

following formulas. The difference with (17) and (18) is that sector k and m now can not be

the same as sector j.

BackwardRobjct =∑

kifk 6=j

αjckStateAidkct (21)

ForwardRobjct =∑

mifm 6=j

σjcmStateAidkct (22)

Table 18: Correlation matrix 2

Stateaid Backward Forward

Stateaid 1.0000

Backward -0.0361 1.0000

Forward -0.0364 0.5791 1.000

Table 19 shows the correlation between the variable state aid and the two new spillover proxies.

Unlike the correlations in table 10, we now have a negative correlation between StateAid and

both Backward and Forward.

43

We search for spillover effects by running our baseline specification with the 2 new spillover

proxies. We obtain estimation results with and without interaction terms. The coefficients of

the specification without interaction terms can be found in appendix D, table D3 , where the

results are also briefly discussed. Table 19 and 20 show respectively the estimation results of the

LP and Wooldridge method. As in table 14 and 15 we do not find evidence for a direct effect

of state aid when we include interaction terms. The variables Competition and Distance

have an expected negative effect on TFP growth. The Levpet method shows negative BW

spillovers, which is also nothing new but now we can not find evidence for any positive FW

spillover effect. In column (4) the coefficient is positive but not significant. As in table 14 and

15 the interaction term between StateAid and Distance is positive and significant for the

LP method, while the interaction between StateAid and Competition is positive significant

for the Wooldridge method. Overall, we can not conclude that our results are robust to

different ways of calculating the spillover proxies because the positive FW spillover effect has

disappeared.

44

Table 19: Robustness check 1 with interaction terms (LP)

VARIABLES (1) (2 ) (3) (4) (5)

StateAid -0.000698 -0.0132 -0.0201 -0.00727 -0.0147

(0.0198) (0.0215) (0.0231) (0.0251) (0.0245)

Competition -0.0691* -0.0695* -0.0688* -0.0659* -0.0670*

(0.0378) (0.0379) (0.0376) (0.0378) (0.0376)

Distance -0.678*** -0.679*** -0.631*** -0.646*** -0.626***

(0.0535) (0.0536) (0.0561) (0.0524) (0.0555)

StateAidCum -9.22e-05 1.29e-05 0.000484 -0.000388 0.000115

(0.00271) (0.00265) (0.00262) (0.00280) (0.00270)

Backward rob3 -0.000698** -0.000703** -0.000421 -0.00107*** -0.000713*

(0.000293) (0.000292) (0.000309) (0.000308) (0.000369)

Forward rob3 -0.000737** -0.000738** -0.000732* 0.000264 -0.000156

(0.000372) (0.000373) (0.000379) (0.000429) (0.000492)

StateAid x Competition -0.0146 -0.00279 -0.0169 0.00638 -0.00754

(0.228) (0.221) (0.219) (0.223) (0.221)


(0.100) (0.115) (0.111) (0.117)


(0.00168) (0.00184)


(0.00148) (0.00167)

Constant 0.102 0.102 0.110 0.121 0.107

(0.488) (3.414)



Industry-year FE YES YES YES YES YES

Observations 241,862 241,862 241,862 241,862 241,862

R-squared 0.046 0.046 0.047 0.046 0.047

Number of firm 65,332 65,332 65,332 65,332 65,332


*** p<0.01, ** p<0.05, * p<0.1

45

Table 20: Robustness check 1 with interaction terms (Wooldridge)

VARIABLES (1) (2) (3) (4) (5)

StateAid -0.00497 0.0104 0.00428 0.0187 0.0115

(0.0148) (0.0180) (0.0193) (0.0210) (0.0205)

Competition -0.0508* -0.0504* -0.0500* -0.0472* -0.0481*

(0.0264) (0.0263) (0.0261) (0.0263) (0.0262)

Distance -0.428*** -0.425*** -0.376*** -0.389*** -0.369***

(0.0348) (0.0350) (0.0349) (0.0315) (0.0339)

StateAidCum -0.00336 -0.00351 -0.00303 -0.00411* -0.00356

(0.00213) (0.00219) (0.00218) (0.00236) (0.00229)

Backward rob3 -0.000313 -0.000307 -1.60e-05 -0.000704*** -0.000361

(0.000228) (0.000227) (0.000236) (0.000263) (0.000282)

Forward rob3 -0.000473* -0.000472* -0.000471* 0.000631 0.000228

(0.000279) (0.000278) (0.000284) (0.000418) (0.000403)

StateAid x Competition 0.378** 0.365** 0.351** 0.387** 0.370**

(0.177) (0.177) (0.175) (0.179) (0.177)


(0.100) (0.114) (0.114) (0.123)


(0.00172) (0.00177)


(0.00173) (0.00162)

Constant 0.0877 0.0872 0.0878 0.0757 0.0756

(0.553) (2.699)




Observations 229,208 229,208 229,208 229,208 229,208

R-squared 0.046 0.046 0.047 0.047 0.047

Number of firm 61,273 61,273 61,273 61,273 61,273


*** p<0.01, ** p<0.05, * p<0.1

46

7.2 Robustness Check 2: ignoring Austria

The state aid cases available of Austria were to the manufacturing sector as a whole and not to

particular industries. For that reason, we did not match any of those cases to Austrian firms.

In the baseline results the state aid variable for Austrian firms was therefore always equal to

zero. To control for this, we exclude Austrian firms from the panel data set and regress the

same equations as in table 13 to 15. The summary statistics do not differ much from the initial

statistics 23. Again we only discuss the results of the specification with interaction terms and

the discussion and the results of the baseline specification without interaction terms can be

found in appendix D, table D5. The estimation results of the LP method are presented in

table 21 and the estimation results of the Wooldridge method in table 22. It is remarkable

that in both column (1) of table 21 and column (2) of table 22 we find a significant positive

effect of state aid to TFP growth. This was not the case in the other sections. The rest

of the variables do not differ much from before. Competition and Distance have again

expected negative significant results. We find overall negative BW spillovers and positive FW

spillovers conditional on the interaction term of Distance and Forward. We can conclude

that the results are more or less the same and that our effects are robust to excluding Austrian

firms.

23See appendix D table D4.

47

Table 21: Robustness check 2 with interaction terms (using LP)

VARIABLES (1) (2) (3) (4) (5)

StateAid 0.0436* 0.0347 0.0106 -0.00726 -0.00745

(0.0246) (0.0254) (0.0270) (0.0261) (0.0272)

Competition -0.0774** -0.0777** -0.0768** -0.0726* -0.0739*

(0.0387) (0.0388) (0.0385) (0.0384) (0.0383)

Distance -0.658*** -0.660*** -0.602*** -0.615*** -0.594***

(0.0541) (0.0542) (0.0570) (0.0537) (0.0565)

StateAidCum 1.49e-05 8.56e-05 0.00107 -0.000293 0.000493

(0.00247) (0.00243) (0.00240) (0.00255) (0.00249)

Backward 3 -0.000877*** -0.000883*** -0.000428 -0.00153*** -0.000990**

(0.000306) (0.000308) (0.000354) (0.000321) (0.000410)

Forward 3 -0.000357 -0.000350 -0.000288 0.00130*** 0.000726

(0.000358) (0.000361) (0.000369) (0.000497) (0.000518)

StateAid x Competition -0.0808 -0.0724 -0.108 -0.0250 -0.0658

(0.219) (0.212) (0.208) (0.213) (0.210)


(0.1000) (0.117) (0.121) (0.126)

Distance x Backward -0.00577*** -0.00378**

(0.00176) (0.00185)


(0.00168) (0.00183)

Constant 0.114 0.115 0.113 0.115 0.112

(1.488) (3.084)




Observations 237,930 237,930 237,930 237,930 237,930

R-squared 0.053 0.053 0.054 0.054 0.054

Number of firm 64,146 64,146 64,146 64,146 64,146


*** p<0.01, ** p<0.05, * p<0.1

48

Table 22: Robustness check 2 with interaction terms (Wooldridge)

VARIABLES 1 2 3 4 5

StateAid 0.0213 0.0366* 0.0166 -0.00244 -0.00274

(0.0164) (0.0193) (0.0207) (0.0210) (0.0219)

Competition -0.0510* -0.0505* -0.0499* -0.0465* -0.0473*

(0.0265) (0.0265) (0.0264) (0.0264) (0.0264)

Distance -0.430*** -0.428*** -0.377*** -0.385*** -0.367***

(0.0364) (0.0367) (0.0369) (0.0337) (0.0359)

StateAidCum -0.00411* -0.00425* -0.00336 -0.00481** -0.00410*

(0.00211) (0.00218) (0.00219) (0.00234) (0.00231)

Backward 3 -0.000494** -0.000487** -9.33e-05 -0.00113*** -0.000698**

(0.000246) (0.000245) (0.000289) (0.000287) (0.000314)

Forward 3 -0.000236 -0.000250 -0.000200 0.00136*** 0.000908**

(0.000257) (0.000255) (0.000259) (0.000446) (0.000412)

StateAid x Comp 0.404** 0.390** 0.357** 0.453** 0.415**

(0.179) (0.179) (0.176) (0.180) (0.178)


(0.100) (0.115) (0.122) (0.130)


(0.00175) (0.00181)

Distance x Forward -0.00736*** -0.00515***

(0.00189) (0.00175)

Constant 0.0835 0.0831 0.0999 0.0792 0.0803

(2.602) (0.500) (0.143)




Observations 226,663 226,663 226,663 226,663 226,663

R-squared 0.046 0.046 0.047 0.047 0.047

Number of firm 60,520 60,520 60,520 60,520 60,520


*** p<0.01, ** p<0.05, * p<0.1

49

8 Alternative method

Alternatively Javorcik (2004) proposes a one-step procedure by adding spillover variables in the

production function. Built on his estimation theory we estimate the following regression.

lnYijct =α+ β1lnLijct + β2lnKijct + β3lnMijtc + β4StateAidjct−1

+ β5Competitionjct + β6Distanceijct + β7StateAidCumjct−1

+ β8Backwardjct−1 + β9Forwardjct−1 + αt + αc + αjt + ηijt

(23)

Yijt is defined as the operating revenue of firm i active in sector j and country c at time t.

Lijct is the number of employees of firm i and Kijct stands for capital and is measured by the

tangible fixed assets of each firm. Mijtc materials, are calculated by the cost of materials of

firm i. These four variables are all obtained from the Orbis data base and correctly deflated,

using a specific deflator for each year and each country. StateAidjct is again a dummy vari-

able, but now giving value 1 when there was state aid in sector j and country c at time t− 1

and 0 when there was not. As before, state aid is specified at the four-digit NACE level. The

Backward and Forward variables are calculated the same way as in the two-step procedure

but here also the lag is taken. Lagged spillover variables are necessary because we now will

have the level of productivity as the dependent variable and not productivity growth. The

model includes fixed effects for years, countries and industries.

We run specification (23) both with and without interaction terms. In this section only the

results with interaction terms are being displayed. The estimation results without interaction

terms can be found in the appendix, where they are discussed briefly.

The results in table 23 indicate that firms that receive state aid tend to be more produc-

tive than non-sponsored firms. And, more important for this master dissertation, we find

a positive and significant lagged forward variable, meaning that if industry j receives state

aid and supplies intermediate goods to industry i, i will positively benefit from it. This can

be explained by intermediate goods of better quality. The coefficient 0.0351 in specification

(4) implies that state aid in a supplying industry is associated with a 3.51 % increase in the

productivity level. For the other spillover variable, LagBackward, we find a negative and

significant coefficient. Suppliers do not benefit from state aid in the industry to which they

are supplying.

50

Again, we control for other factors that may influence firm productivity by adding a competi-

tion index and a distance variable. The Herfindahl index (Competition) is overall negative,

although not always significant. Keeping in mind that a HHI value of 0 means low concentra-

tion and thus a highly competitive industry, the negative coefficient indicates that the higher

the level of competition, the higher the level of productivity. This is in line with the ”escape-

competition effect” theory, which we explained in section 6.2.2. The other variable, Distance,

is positive and highly significant. This is logical since firms that will be close to the productivity

frontier, will have a higher productivity level.

The highly negative interaction term between state aid and competition shows that low com-

petition (a HHI close to 1) can undo the positive effect of state aid and even make it negative.

The net effect for specification (4) is (1.968 + (-48.12)*Competition)*StateAid. When we

compare the 25th and 75th percentile24 of the HHI distribution, we find coefficients of 0.33192

and -5.34624. State aid thus only generates positive effects in highly competitive industries.

The interaction term between StateAid and Distance is negative but only significant in spec-

ification (1). The interactions between the spillover variables and Distance are significant

when including them separately but when incorporating them both only the backward interac-

tion is significant. This is a recurring pattern through this master dissertation. The positive

coefficients mean that upstream or downstream firms that are close to the productivity frontier

will experience more positive spillover effects in the case of forward spillovers and less negative

backward spillover effect. The net effect of upstream state aid on productivity in column (3)

equals (0.0257 + 0.0287*Distance)*Forward. Again we compare the 25th and 75th per-

centile25. The coefficients are then respectively 0.0261 and 0.029. Although the difference is

small26, firms that lay further behind have a smaller positive effect of forward spillovers. The

same calculation can be made for backward spillovers. However, due to the negative coefficient

of LagBackward, a higher value of the Distance variable can only make the absolute value

of the net coefficient smaller, but can not make the effect positive.

24Table NR. 25th: 0.034 and 75th: 0.152.25Table NR. 25th: 0.015 and 75th: 0.117.26The distribution of Distance is concentrated between 0 and 0.2, see section 5.2.2.

51

Table 23: Alternative method with interaction terms

VARIABLES 1 2 3 4

StateAid 1.799*** 1.973*** 1.828*** 1.968***

(0.366) (0.358) (0.359) (0.357)

Competition -1.010 -2.813** -1.990 -2.888**

(1.197) (1.189) (1.229) (1.200)

Distance 2.809*** 2.070*** 2.482*** 2.058***

(0.173) (0.171) (0.175) (0.175)

StateAidCum - - - -

LagBackward -0.0328*** -0.0464*** -0.0326*** -0.0453***

(0.0122) (0.00932) (0.0111) (0.00936)

LagForward 0.0307*** 0.0367*** 0.0257** 0.0351***

(0.0115) (0.00864) (0.0108) (0.00880)

StateAid x Competition -48.68*** -48.72*** -46.02*** -48.12***

(9.816) (9.525) (9.631) (9.506)

StateAid x Distance 0.707** -0.428 -0.168 -0.558

(0.281) (0.408) (0.402) (0.480)

Distance x LagBackward 0.0433*** 0.0395***

(0.00961) (0.00841)

Distance x LagForward 0.0287*** 0.00732

(0.00842) (0.00860)

Constant 4.475*** 5.004*** 4.816*** 5.040***

(0.326) (0.340) (0.336) (0.344)




Observations 3,824 3,824 3,824 3,824

R-squared 0.940 0.945 0.942 0.945


*** p<0.01, ** p<0.05, * p<0.1

52

9 Conclusions

In this master dissertation we have searched for inter-industry spillover effects of state aid in

10 EU countries. The research was executed in 2 steps. First, total factor productivity (TFP)

growth was calculated. We did this in two different ways, according to the method of Levinson

and Petrin (2003) and the method of Wooldridge (2009). In a second step, this productivity

growth was tested against both state aid and spillover variables, as control variables and a set

of interaction terms. We focused upon three mayor research questions.

First, we looked at the direct effects of state aid. We constructed a state aid dummy by

matching firm level data from the Orbis databank with state aid schemes on sectoral level.

We did not find a clear effect of state aid in industry j on the TFP growth of firm i, that

operates in industry j. It is striking that even receiving state aid during a couple of years is

not beneficial to a firms TFP growth. We controlled for other factors by adding a competition

index and variable that measures the distance to the productivity frontier. The results showed

that firms have more incentives to innovate when they operate in a competitive environment.

Similar, the distance to the productivity frontier had a negative significant coefficient. TFP

growth is faster in firms that are lagging behind. In an interaction with the state aid variable,

we might conclude that state aid is more effective in less competitive industries and in firms

that are already efficient.

Secondly, we searched for inter-industry spillover effect of state aid. The backward (BW)

and forward (FW) spillover variables were constricted with national Input-Output tables. In

general, we found negative BW spillovers which means that suppliers did not benefit from the

fact that a particular industry to which they supply received state aid. We have found some

evidence for positive FW spillover effects conditional on the presence of the interaction term

between Distance and Forward. This result indicates that firm i is benefiting from state

aid in industry j when i is buying intermediate goods from j. A possible spillover channel

here is that i now has access to intermediate goods of improved quality. When we looked

at the interaction terms, we found that firms that are lagging behind from the productivity

frontier will have a less negative effect from BW spillovers or a larger positive FW spillover

effect.

53

Thirdly, we wanted to see if the effect of state aid differed with size. We ran our specification

for small, medium-sized and large firms. We found evidence for negative backward spillovers

for both small and large but that the positive significant forward spillovers are mainly driven

by large firms.

To see if our results are robust, we have done two checks. First, we have used a different

spillover proxy. The difference is that the diagonal of the I-O table was now set on zero. With

this specification, the positive FW spillover effects disappeared. The second robustness check

was a panel set without Austrian firms because we could not match Austrian firms to state

aid schemes. Overall, the results remained the same. In the last section we have used an

alternative method to investigate inter-industry spillover effects. Again negative backward and

positive forward spillovers have been found. In sum, our research indicates that state aid has

no clear direct effect on firms, that there are negative BW effects and that there are probably

some positive FW spillover effects.

More research can be done to better understand state aid efficiency. We recommend for further

research that all state aid should be included in the study. By this we mean all state aid cases

that are available and decided in a particular period as we did for the period 2007-2015. There

are probably state aid schemes decided in the year 2000 that still apply. Furthermore, one

could take a look at spillover effects in firms with different categories. We did this for different

firm sizes but this could also be applied to other firm characteristics as e.g. book to market

(B/M), return on assets (ROA) and investment growth. Besides spillover effects, one could

also measure the efficiency of state aid by looking at firm entry in industries. Do industries

that receive state aid attract more entrants? Or, one could analyze whether firm survival is

greater in industries that receive state aid.

54

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VI

A GBER: Individual notification tresholds

Table A1: Individual notification tresholds in millions of euro

Category Amount Per

Regional investment 100

Regional urban development 20 project

SME investment 7.5 undertaking/project

SME consultancy 2 undertaking/project

SME participation in fairs 2 undertaking/project

SMEs in ETC 2 undertaking/project

Risk finance 15 undertaking (total)

Start-ups 1-3 undertaking

Fundamental research 40 under/project

Industrial research 20 under/project

Experimental development 15 under/project

Feasibility studies for research activities 7.5 study

Investment in research infrastructure 20

Innovation clusters 7.5 cluster

Innovation aid for SMEs 5 undertaking/project

Process & organizational innovation 7.5 undertaking/project

Training 2 project

Disadvantaged workers 5 under/year

Workers with disabilities 10 under/year

Environmental investment 5 undertaking/project

Energy efficiency 10

Operating aid for green electricity 5 undertaking/project

Remediation of contaminated sites 20 undertaking/project

Energy infrastructure 50 undertaking/project

Broadband infrastructure 70 project

Culture & heritage conservation 100 project

Operating aid for culture & heritage conservation 50 under/year

Audio-visual works 50 year

VII

Sport & multifunctional infrastructure 15

Operating aid for sport infrastructure 2 infrastructure/year

Local infrastructure 10 project

VIII

B NACE codes in the manufacturing industry

Table B1: NACE codes at the 2-digit level

Sector Description

10 Food products

11 Beverages

12 Tobacco

13 Textiles

14 Wearing Apparel

15 Leather

16 Wood

17 Paper and paper products

18 Printing and reproduction of recorded media

19 Coke and refined petroleum products

20 Chemicals and chemical products

21 Pharmaceutical products

22 Rubber and plastic products

23 Other non-metallic mineral products

24 Basic metals

25 Fabricated metal products

26 Computer, electronic and optical products

27 Electrical equipment

28 Machinery and equipment

29 Motor vehicles, trailers and semi-trailers

30 Other transport equipment

31 Furniture

32 Other manufacturing

33 Repair and installation of machinery and equipment

IX

C Value Added as dependent variable

Table C1: Estimates of the production function using levpet (value added)


10 Food products 0.6506588 0.0631371

11 Beverages 0.6843236 0.1160507

12 Tobacco 0.27457022 0-0.188612

13 Textiles 0.6615582 0.1019539

14 Wearing Apparel 0.6055324 0.1032731

15 Leather 0.5935633 0.142673

16 Wood 0.7364187 0.0362512



19 Coke and refined petroleum products 0.6277061 0.0661753


21 Pharmaceutical products 0.16995543 0.0126975



24 Basic metals 0.7622151 0.0845025







31 Furniture 0.631006 0.0462751



X

Table C2: Estimates of the production function using Wooldridge (value added)


10 Food products 0.6279818 0.0869774

11 Beverages 0.722463 0.182622

12 Tobacco / /

13 Textiles 0.6824166 0.1242311

14 Wearing Apparel 0.5992846 0.1380798

15 Leather 0.6481832 0.0585233

16 Wood 0.6785883 0.0138134



19 Coke and refined petroleum products / /


21 Pharmaceutical products 0.6402882 -0.016698



24 Basic metals 0.7226044 0.1460477







31 Furniture 0.5489085 0.0997141



XI

D Estimation results

Table D1: Inter-industry effects per type of state aid

LP LP LP W W W

stateaid 0.00334 0.00422 0.00405 -0.000904 -0.00954 -0.00736

(0.00911) (0.00908) (0.00871) (0.00780) (0.00945) (0.00765)

backward 1 -0.000527** -0.000524*

(0.000233) (0.000288)

forward 1 0.000155 0.000199

(0.000213) (0.000204)

backward 2 -0.000689** -0.000464

(0.000285) (0.000355)

forward 2 5.57e-05 0.000301

(0.000245) (0.000234)

backward 3 -0.000429* -0.000259

(0.000244) (0.000242)

forward 3 -9.14e-05 6.40e-05

(0.000209) (0.000195)

Constant 0.0224 0.0111 0.0219 0.0308 0.0203 0.0271

(1.472) (0.584) (0.501)

Firm fixed effects YES YES YES

Time fixed effects YES YES YES

Industry-year FE YES YES YES

Observations 272,441 271,905 272,441 259,383 258,856 259,383

R-squared 0.015 0.015 0.015 0.026 0.026 0.026

Number of firm 72,524 72,429 72,524 68,310 68,217 68,310


*** p<0.01, ** p<0.05, * p<0.1

XII

Table D2: Baseline specification per firm size

Levpet Wooldridge

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

VARIABLES small medium large small medium large

StateAid 0.0882** 0.0216 0.00410 0.0722*** 0.0158 -0.0332

(0.0368) (0.0234) (0.0313) (0.0263) (0.0194) (0.0298)

Backward 3 -0.00162*** -9.06e-05 -0.00185 -0.000826** -0.000145 -0.00159

(0.000399) (0.000448) (0.00122) (0.000341) (0.000407) (0.00105)

Forward 3 -9.42e-05 -0.000811 -0.000151 -0.000291 -0.000340 0.000167

(0.000469) (0.000505) (0.000989) (0.000327) (0.000354) (0.000923)

Competition -0.138 -0.0470 -0.0678 -0.115* -0.0318 -0.0196

(0.0867) (0.0353) (0.0942) (0.0631) (0.0325) (0.0755)

Distance -0.719*** -0.640*** -0.508*** -0.417*** -0.438*** -0.371***

(0.0731) (0.0581) (0.0693) (0.0420) (0.0543) (0.0540)

StateAid cum -0.00276 -0.000654 0.00259 -0.00435* -0.00256 0.00558*

(0.00364) (0.00266) (0.00324) (0.00253) (0.00205) (0.00334)

Constant 0.109 0.137 0.191*** 0.106 0.125*** 0.0811***

(14.92) (0.0298) (4.297) (0.0141) (0.0272)




Observations 133,755 90,278 17,829 125,389 86,517 17,302

R-squared 0.041 0.048 0.082 0.035 0.058 0.093

Number of firm 42,595 24,565 4,358 39,562 23,357 4,234


*** p<0.01, ** p<0.05, * p<0.1

Table D2 State aid to an industry is likely to affect the small firms in that industry positively.

No results are found for medium-sized and large firms. Also, negative backward spillovers

appear to be only significant for small firms. Forward spillovers are overall not significant.

Further, competition has no significant effect on TFP growth which is contrary to the previous

results. The distance variable on the other hand is consistent with the previous results and thus

negative and highly significant over all firm size. The effect is the biggest for small firms.

XIII

Table D3: Robustness 1

Levpet Wooldridge

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

StateAid -0.00135 0.000600 -0.00120 0.00769 0.00862 0.00779

(0.0206) (0.0202) (0.0206) (0.0162) (0.0159) (0.0162)

Competition -0.0673* -0.0718* -0.0696* -0.0392 -0.0420* -0.0403

(0.0372) (0.0366) (0.0370) (0.0255) (0.0252) (0.0254)

Distance -0.677*** -0.677*** -0.678*** -0.427*** -0.428*** -0.428***

(0.0534) (0.0535) (0.0535) (0.0349) (0.0348) (0.0349)

StateAid cum -0.000245 -0.000157 -0.000156 -0.00165 -0.00160 -0.00160

(0.00226) (0.00227) (0.00226) (0.00167) (0.00167) (0.00167)

Backward rob3 -0.00107*** -0.000698** -0.000555** -0.000319

(0.000285) (0.000292) (0.000217) (0.000228)

Forward rob3 -0.00124*** -0.000737** -0.000702** -0.000473*

(0.000349

Constant 0.0983 0.0959 0.102 0.0762 0.0773 0.0876

(3.300) (3.396) (2.565)




Observations 241,862 241,862 241,862 229,208 229,208 229,208

R-squared 0.046 0.046 0.046 0.046 0.046 0.046

Number of firm 65,332 65,332 65,332 61,273 61,273 61,273


*** p<0.01, ** p<0.05, * p<0.1

Table D3 Comparing table D3 to table 13 we observe that the positive direct effect of state

aid disappears when the diagonal is set on zero. The other variables remain more or less the

same. Firms that lag behind and that operate in a competitive industry have higher TFP

growth. Again negative backward and forward spillovers are found. The absolute value of

the coefficients however are now smaller for the backward variable and bigger for the forward

variables.

XIV

Table D4: Summary statistics of the mean variables (robustness check 2)

Variable Mean Std. Dev. Min Max 25% 75%

dTFPLPt+1 0.0041782 0.2210938 -8.700584 8.476702 -0.059 0.067

dTFPWt+1 0.0094001 0.202457 -7.686908 7.855598 -0.051 0.070

StateAid 0.1364142 0.3432281 0 1 0 0

Backward 19.55796 24.14945 0 97.58496 5.135 19.111

Forward 13.29698 20.84359 0 84.88207 1.496 9.705

Competition 0.1213976 0.1196241 0.0001 0.4841 0.034 0.152

Distance 0.0932206 0.134657 5.51e-06 1 0.150 0.117

StateAidCum 0.3234662 1.31164 0 9 0 0

XV

Table D5: Robustness 2

Levpet Wooldridge

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

StateAid 0.0334 0.0360 0.0408 0.0261** 0.0323* 0.0349*

(0.0223) (0.0256) (0.0248) (0.0133) (0.0185) (0.0179)

Competition -0.0786** -0.0838** -0.0799** 0.0591* -0.0425* -0.0400

(0.0381) (0.0381) (0.0380) (0.0342) (0.0255) (0.0256)

Distance -0.658*** -0.658*** -0.658*** -0.391*** -0.430*** -0.430***

(0.0539) (0.0541) (0.0540) (0.0333) (0.0365) (0.0365)

StateAid cum -0.000376 2.85e-05 -0.000344 -0.00306** -0.00200 -0.00221

(0.00201) (0.00211) (0.00205) (0.00135) (0.00167) (0.00166)

Backward 3 -0.00111*** -0.000885*** -0.000743*** -0.000458*

(0.000339) (0.000308) (0.000239) (0.000247)

Forward 3 -0.000873** -0.000352 -0.000529** -0.000262

(0.000352) (0.000356) (0.000238) (0.000258)

Constant 0.108 0.103 0.114 0.0669*** 0.0834 0.0830

(0.753) (0.00789) (1.352)



I-Y FE YES YES YES YES YES YES

Observations 237,930 237,930 237,930 226,663 226,663 226,663

R-squared 0.053 0.053 0.053 0.029 0.046 0.046


*** p<0.01, ** p<0.05, * p<0.1

Table D5 Comparing table D5 to table 13 we observe that the positive direct effect remains

when we exclude Austrian firms. The other variables remain more or less the same. Firms

that lag behind and that operate in a competitive industry have higher TFP growth. Again

negative BW and FW spillovers are found.

XVI

Table D6: Alternative method without interaction terms

VARIABLES 1 2 3 4

LagStateAid 0.0380 0.0385 0.0375 0.0384

(0.0271) (0.0282) (0.0282) (0.0282)

Competition -2.114* -2.122* -2.072* -1.072

(1.140) (1.171) (1.177) (1.198)

Distance 2.816*** 2.818*** 2.818*** 2.824***

(0.171) (0.171) (0.171) (0.172)

LagStateAidCum - - - -

LagBackward 9.53e-06 -0.0328***

(0.000715) (0.0122)

LagForward 0.000156 0.0306***

(0.000676) (0.0115)

Constant 4.501*** 4.500*** 4.500*** 4.485***

(0.326) (0.326) (0.326) (0.326)




Observations 3,824 3,824 3,824 3,824

R-squared 0.940 0.940 0.940 0.940


*** p<0.01, ** p<0.05, * p<0.1

Table D6 With the alternative method the variable state aid is still positive but not signifi-

cant. This is in contrast with table 13. The negative significant coefficient of the competition

variable reflects that the level of TFP is 2 times lower if the concentration index raises with

one standard deviation of 0.1189755. the Distance variable is now positive and again highly

significant. This is logical because the firms that are already close to the productivity frontier

have the highest level of productivity. As with the main method we find negative BW spillover

effects and positive FW spillovers.

XVII

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