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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
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
PERMISSION
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)
Sector Description βl βk
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
17 Paper and paper products 0.3296281 0.0623212
18 Printing and reproduction of recorded media 0.5564649 0.0395681
19 Coke and refined petroleum products / /
20 Chemicals and chemical products 0.3388384 -0.0038104
21 Pharmaceutical products 0.390593 -0.0577448
22 Rubber and plastic products 0.3366012 0.0284002
23 Other non-metallic mineral products 0.3833969 0.0910775
24 Basic metals 0.3234516 0.0479347
25 Fabricated metal products 0.4576898 0.0862329
26 Computer, electronic and optical products 0.523701 0.0012684
27 Electrical equipment 0.3578604 0.0497897
28 Machinery and equipment 0.4346837 0.0264117
29 Motor vehicles, trailers and semi-trailers 0.3418131 0.0859523
30 Other transport equipment 0.4716094 0.1108876
31 Furniture 0.3452034 -0.0109878
32 Other manufacturing 0.4313946 0.0822994
33 Repair and installation of machinery and equipment 0.66482523 0.0574089
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)
Firm fixed effects YES YES YES YES
Time fixed effects YES YES YES YES
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
Robust standard errors in parentheses
*** 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)
Firm fixed effects YES YES YES YES
Time fixed effects YES YES YES YES
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
Robust standard errors in parentheses
*** 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)
Firm fixed effects YES YES YES YES
Time fixed effects YES YES YES YES
Industry-year fixed effects YES YES YES YES
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
Robust standard errors in parentheses
*** 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
Robust standard errors in parentheses
*** 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
Robust standard errors in parentheses
*** 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
Robust standard errors in parentheses
*** 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)
Distance x Backward -0.00514*** -0.00310*
(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)
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 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
Robust standard errors in parentheses
*** 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)
Firm FE YES YES YES YES YES YES
Time FE YES YES YES YES YES YES
Industry-year FE YES YES YES YES YES YES
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
Robust standard errors in parentheses
*** 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)
StateAid x Distance 0.0624 0.219* 0.239** 0.275**
(0.100) (0.115) (0.111) (0.117)
Distance x Backward -0.00501*** -0.00355*
(0.00168) (0.00184)
Distance x Forward -0.00577*** -0.00332**
(0.00148) (0.00167)
Constant 0.102 0.102 0.110 0.121 0.107
(0.488) (3.414)
Firm fixed effects YES YES YES YES YES
Time fixed effects YES YES YES YES YES
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
Robust standard errors in parentheses
*** 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)
StateAid x Distance -0.0771 0.0743 0.101 0.139
(0.100) (0.114) (0.114) (0.123)
Distance x Backward -0.00513*** -0.00348*
(0.00172) (0.00177)
Distance x Forward -0.00630*** -0.00399**
(0.00173) (0.00162)
Constant 0.0877 0.0872 0.0878 0.0757 0.0756
(0.553) (2.699)
Firm fixed effects YES YES YES YES YES
Time fixed effects YES YES YES YES YES
Industry-year FE YES YES YES YES YES
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
Robust standard errors in parentheses
*** 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)
StateAid x Distance 0.0433 0.223* 0.284** 0.312**
(0.1000) (0.117) (0.121) (0.126)
Distance x Backward -0.00577*** -0.00378**
(0.00176) (0.00185)
Distance x Forward -0.00755*** -0.00474**
(0.00168) (0.00183)
Constant 0.114 0.115 0.113 0.115 0.112
(1.488) (3.084)
Firm fixed effects YES YES YES YES YES
Time fixed effects YES YES YES YES YES
Industry-year FE YES YES YES YES YES
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
Robust standard errors in parentheses
*** 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)
StateAid x Distance -0.0759 0.0748 0.143 0.168
(0.100) (0.115) (0.122) (0.130)
Distance x Backward -0.00509*** -0.00306*
(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)
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 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
Robust standard errors in parentheses
*** 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)
Firm fixed effects YES YES YES YES
Time fixed effects YES YES YES YES
Industry-year fixed effects YES YES YES YES
Observations 3,824 3,824 3,824 3,824
R-squared 0.940 0.945 0.942 0.945
Robust standard errors in parentheses
*** 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)
Sector Description βl βk
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
17 Paper and paper products 0.7015672 0.0562426
18 Printing and reproduction of recorded media 0.8047647 0.0192888
19 Coke and refined petroleum products 0.6277061 0.0661753
20 Chemicals and chemical products 0.7305004 0.0829223
21 Pharmaceutical products 0.16995543 0.0126975
22 Rubber and plastic products 0.7233011 0.061545
23 Other non-metallic mineral products 0.680946 0.0970207
24 Basic metals 0.7622151 0.0845025
25 Fabricated metal products 0.7751624 0.0816771
26 Computer, electronic and optical products 0.8185177 0.0827129
27 Electrical equipment 0.7355771 0.0616884
28 Machinery and equipment 0.7841745 0.0543409
29 Motor vehicles, trailers and semi-trailers 0.6535825 0.0594599
30 Other transport equipment 0.7553786 0.1298356
31 Furniture 0.631006 0.0462751
32 Other manufacturing 0.8126098 0.0430402
33 Repair and installation of machinery and equipment 0.9041179 0.0071831
X
Table C2: Estimates of the production function using Wooldridge (value added)
Sector Description βl βk
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
17 Paper and paper products 0.5843714 0.1038046
18 Printing and reproduction of recorded media 0.57856419 0.0400324
19 Coke and refined petroleum products / /
20 Chemicals and chemical products 0.6841191 0.1089279
21 Pharmaceutical products 0.6402882 -0.016698
22 Rubber and plastic products 0.6473886 0.1014248
23 Other non-metallic mineral products 0.624828 0.1740591
24 Basic metals 0.7226044 0.1460477
25 Fabricated metal products 0.759757 0.1017164
26 Computer, electronic and optical products 0.7777843 0.0719046
27 Electrical equipment 0.6675125 0.0873406
28 Machinery and equipment 0.7380185 0.0586151
29 Motor vehicles, trailers and semi-trailers 0.6511669 0.0778194
30 Other transport equipment 0.7026286 0.11042451
31 Furniture 0.5489085 0.0997141
32 Other manufacturing 0.7353957 0.0905437
33 Repair and installation of machinery and equipment 0.8814198 0.0466658
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
Robust standard errors in parentheses
*** 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)
Firm FE YES YES YES YES YES YES
Time FE YES YES YES YES YES YES
Industry-year FE YES YES YES YES YES YES
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
Robust standard errors in parentheses
*** 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)
Firm FE YES YES YES YES YES YES
Time FE YES YES YES YES YES YES
Industry-year 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
Number of firm 65,332 65,332 65,332 61,273 61,273 61,273
Robust standard errors in parentheses
*** 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
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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)
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 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
Robust standard errors in parentheses
*** 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.
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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)
Firm fixed effects YES YES YES YES
Time fixed effects YES YES YES YES
Industry-year fixed effects YES YES YES YES
Observations 3,824 3,824 3,824 3,824
R-squared 0.940 0.940 0.940 0.940
Robust standard errors in parentheses
*** 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.
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