PRODUCT MARKET POLICIES, ALLOCATIVE EFFICIENCY
AND PRODUCTIVITY
A CROSS-COUNTRY ANALYSIS
Jens Arnold, Giuseppe Nicoletti and Stefano Scarpetta
OECD, Paris
CEPR, IMF, FRDB
Structural Reforms without Prejudice
Università Bocconi, Milan
12 March 2009
Roadmap
1. Motivation
2. Regulation, competition and growth: the channels
3. Measurement of product market regulations
•
Cross country differences and the evolution over time
4. How has ICT affected output and productivity growth
5. Reallocation across sectors and firms and the effects on
aggregate performance
6. How does regulation relate to allocative efficiency?
•
•
Evidence from industry-level analysis
Evidence from firm-level analysis
7. Concluding remarks
2
Motivation
¾ The long-standing process of economic
convergence has come to a halt in the past
decade
¾ Because of the acceleration in growth in some of the
affluent countries (e.g. the U.S.)…
¾ …and persistent slowdown in many EU and Japan
¾ At the same time, significant effort at policy
reform in most countries
¾ With policy convergence in many areas (macro,
trade)…
¾ …including in product market regulations
3
Growth divergence within the
OECD?
• Cross-country differences in GDP p.c. growth have
been large over the past two decades
• And many countries have diverged diverged from
leading countries more recently
• Developments in productivity growth are the
main source of differences, more specifically:
– ICT investment and contribution to productivity
– Efficiency in use of inputs and innovation (MFP) especially
in ICT-producing and ICT-using industries
– Productivity developments in market services crucial
Measuring PMR rigidities
• Three main approaches to assess pro-competitive reforms:
– Look at the outcomes − changes in mark-ups; changes in concentration
– Look at indicators of the stringency of regulations
– Surveys of firms or experts
• We focus on regulatory indicators
– Measure the degree to which policies promote or inhibit competition
– Bottom-up approach based on detailed regulatory data
• Two primary sets of indicators:
– The indicator of non-manufacturing regulation (NMR):
• Cover many non-manufacturing sectors (network industries, trade, business services)
• Uses information on state control and regulations affecting entry & conduct
• Focus here is mainly on entry barriers
– The indicators of Regulation Impact (RI)
• Derived from NMR indicators
• Cover 39 ISIC sectors in 21 OECD countries from 1975 to 2003
5
Evolution of PM rigidities
•
•
•
•
•
•
•
•
Evidence on all OECD measures suggests extensive liberalisation of PM
over past two decades
But at very different pace and depth across countries
Cross-country dispersion in PM rigidities increased precisely at the time of
the ICT shock
Convergence towards liberal markets accelerated over past decade
But many laggard countries liberalised only quite recently (e.g. continental
Europe)
In many of these countries crucial ICT-using sectors remain heavily
burdened by regulation (e.g. retail and business services)
Cross-border service trade still hampered by explicit barriers and
heterogeneous service regulations
Lack of integrated EU market for services an issue --> EU firms enjoying
less economies of scale and scope than US ones
6
Regulation and Growth: The channels
• Extensive theoretical literature on the links between
regulations , competition and productivity and output
growth (reviews by e.g. Griffith and Harrison, 04; Crafts, 06; OECD
2003).
• Three interrelated channels can be identified:
– The reduction of slack through enhanced
competition (e.g. Winston, 93; Vickers, 95, Blanchard, Giavazzi,03)
– Innovation and technology adoption (Porshke, 07;
Aghion, Howitt, 98)
– Reallocation of resources (Restuccia, Rogerson, 07; Hsieh,
Klenov, 06; Bartelsman et al. 08, 09)
7
Data used in the analysis
• Industry-level data:
– OECD Structural Analytical database (STAN)
– EU Klems data base, March 2007 release
• Firm-level data:
– Amadeus data base by commercial provider BvD
– Used a random sampling technique to match the sample
distribution to the population weights with respect to
• Country size
• Sector composition
• Firm size groups
8
Sources of productivity growth
–Wide heterogeneity of productivity growth across
industries
Industry-level
evidence
–ICT-intensive industries have driven aggregate
performance over past decade
–Productivity dispersion is related to PMR
–ICT-intensive sectors tend to have higher dispersion
with fatter right tail (the gazelles) driving aggregate
performance
¬
Aggregate productivity growth highest where resources flow most easily to
fast growing high productivity firms and industries
9
Regulations and productivity:
Evidence at the industry-level
• Growing evidence based on a neo-Schumpeterian model
(e.g. Aghion and Howitt, 2006):
– industry productivity depends on growth at the frontier; catch up to the
frontier
• Evidence suggests a link between regulation and productivity
• Nicoletti , Scarpetta 03; Conway et al. 06; Griffith et al. 06; Aghion,
Griffith, 06)
• Some (Nicoletti and Scarpetta, 2003; Conway et al, 2006) also found
an indirect effect whereby regulation curbs catch up process
• a recent study using EU-KLEMS found an effect in specific ICT
industries – e.g. post and telecom (Inklaar et al. 2008)
• Studies have used different data sets (OECD STAN, EUKLEMS)
and different measures of regulation
• But there is also empirical evidence on indirect channels to
10
productivity (via FDI, labour utilisation).
How well do economies allocate
resources across heterogeneous firms?
• Static view: Is resource allocation
correlated with firm productivity?
Olley-Pakes productivity_ decomposition:
_
P = (1 / N )∑ pi + ∑ (θ i − θ )( pi − P)
i
Simple Average
i
Allocative Efficiency
• Dynamic view: Do resources move
efficiently across heterogeneous firms?
ÎDo better firms grow faster?
11
Stringent regulations seem to reduce
allocative efficiency
• Anti-competitive regulation reduces the resource
allocation towards more productive firms
• Effect is highly significant in services industries,
and particularly in ICT-using industries
Business
Sector
Regulation Impact
Indicator
Country-year FE
Industry FE
N
R2
-0.36 **
(0.15)
0.016
Yes
Yes
849
0.20
Manufacturing
only
0.34
(0.64)
0.595
Yes
Yes
629
0.20
Services
only
-0.28 *
(0.15)
0.072
Yes
Yes
220
0.36
ICT using
sectors
Non-ICT using
sectors
-0.67 ***
(0.16)
0
Yes
Yes
242
0.36
Standard Errors in parentheses. *, **, *** indicate statistical significance at the 10, 5 and 1 percent levels, respectively.
Agriculture, forestry, fishing, mining are excluded, as are public administration, education and health sectors.
-0.41 *
(0.24)
0.092
Yes
Yes
607
0.18
12
Does regulation affect productivity
growth of firms?
• Adopt a neo-Schumpeterian growth framework:
Δ ln Aicst = α 1 Δ ln AFcst
Growth of
frontier
⎛ Aics ,t −1 ⎞
⎟ + α 3 regulation cst −1 + γ s + γ ct + ε icst
⎟
⎝ AFcs ,t −1 ⎠
− (1 − α 0 ) ln⎜⎜
Catch-up
Product Market
Regulation
(Setup similar to Griffith et al. 2006)
• In which sectors are firms most affected?
– Distinguish between ICT using sectors and others
• Which firms are most affected?
– Distinguish between firms that are catching up to the
technology frontier and those that are not
– Further isolate high-performing firms that are catching
up to the technology frontier
13
Firm-level regression results
1. Anti-competitive regulation hampers TFP
growth of firms in ICT-using sectors
2. Fast-growing firms in these sectors are
particularly affected
3. High-performing enterprises suffer the most (à
la Aghion et al. 04; 05 but cross-country)
4. Sensitivity analysis confirms these findings
–
–
Different MFP measures (OLS, Levinsohn Petrin, 03)
Broader set of policy & institutional factors (LM, F development)
–
Caveat: Future work will need to control for possible
endogeneity of frontier growth
14
Concluding remarks
• Differences in investment in ICT and MFP growth in both ICTproducing and, increasingly, ICT-using industries underpin
growing disparities in growth across OECD countries
• Many EU countries have been hesitant in reforming key ICT
sectors
• Delaying reforms not only reduced the scope of the creative
destruction process but also weakened the incentives for
adopting ICT capital
• In turn, slower productivity improvements in ICT-using
services propagated to other sectors through higher prices and
lower product quality
• More work is required to shed further light on the process of
resource reallocation and firm-level productivity
15
Thank you.
16
Annex
Figures, tables and details
Decomposition of GDP per capita growth
GDP per capita growth
Contributions to GDP per capita growth from trend changes in:
GDP trended per hours worked
Total hours/working-age population
Working-age population/total population
1986-1995
1995-2006
IRL
IRL
LUX
LUX
PRT
GRC
ESP
FIN
JPN
ESP
NLD
GBR
GBR
SWE
ITA
AUS
USA
NLD
BEL
CAN
AUS
PRT
FIN
USA
DNK
BEL
FRA
DNK
DEU
FRA
SWE
DEU
GRC
JPN
CAN
ITA
-2
-1
0
1
2
3
4
5
6
-2
-1
0
1
2
3
4
5
6
Catch-up and convergence in OECD income levels, 1995-2006,
relative to the United States
Convergence
zone
Gap in average growth rate (%) 1995-2006
3.5
Poland
2.5
Ireland
Hungary
Slovak Rep.
Korea
Finland
Greece
1.5
Turkey
Iceland
Czech Republic
Sweden
Spain
UK
0.5
Mexico
Norway
Canada
Australia
New Zealand
Austria
Portugal
Netherlands
Denmark
France
-0.5
Divergence zone
Belgium
Germany
Italy
Japan
-30
-20
Switzerland
-1.5
-80
-70
-60
-50
-40
-10
0
Gap in GDP per capita (%), 1994
10
Contribution of ICT capital to GDP growth, 1985-2006 and 20012006 (or closest year available)
percentage points
2001‐2006
0.8%
0.7%
0.6%
0.5%
0.4%
0.3%
0.2%
0.1%
0.0%
‐0.1%
1985‐2006
Hourly productivity growth and its
components
1985-2006
4.0%
3.5%
3.0%
Labour productivity growth
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
‐0.5%
‐1.0%
contribution of capital deepening
MFP growth
Productivity growth in total market services
Value added per person employed , percentage change at annual rate
%
4
3
2
1
0
‐1
‐2
‐3
2000‐2005
1995‐2000
Liberalisation in energy, transport and telecoms
6
5
4
3
2
Dispersion of regulation: Non-EU
OECD countries
1
Average regulation: EU countries
Dispersion of regulation: EU
Average regulation: Non-EU OECD
0
1980
1985
1990
1995
2000
2003
Burden of non-manufacturing regulation
manufacturing (norm)
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
e
Sw
n
de
e
th
Ne
rla
s
nd
k
ar
nm
De
Ire
n
la
d
s
Au
l
tra
ia
Un
d
ite
e
at
St
s
n
Fi
n
la
ite
Un
d
d
ng
Ki
do
m
Ne
w
Ze
an
al
d
na
Ca
da
itz
Sw
la
er
nd
a
Sp
in
e
re
G
ce
rtu
Po
ga
l
c
an
Fr
e
No
ay
rw
i
lg
Be
um
Ja
n
pa
an
m
er
G
y
l
Ita
y
s
Au
a
tri
1
ict producing
0,9
ict using
non-ict
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
a
Au
st
ri
re
ec
e
G
I ta
ly
Ja
pa
n
ay
No
rw
Po
r tu
ga
l
Sp
ai
n
an
y
er
m
G
Ca
na
da
iu
m
Be
lg
Fr
an
ce
Fi
nl
an
d
Au
st
ra
lia
at
es
St
Un
ite
d
er
la
nd
Sw
i tz
Ki
ng
do
m
De
nm
ar
k
Un
i te
d
Sw
ed
en
Ze
al
an
d
Ne
w
er
la
nd
s
Ne
th
Ire
la
nd
0
ICT sectors contribute increasingly to
labour productivity growth, 1990-04
3,5
90-95
3,0
2,5
96-04
2,0
1,5
1,0
0,5
0,0
ICT-producing
ICT-using
Non-ICT intensive
Residual
Portugal
Netherla
Belgium
Spain
Italy
France
Germany
Denmark
Austria
Japan
Sweden
UK
USA
Finland
Ireland
-0,5
Distribution of productivity growth
across country/industry/time
By country group
DEU ESP FRA ITA
By impact of regulation
Hi gh re gu la t io n
G BR I RL U SA
L o w re gu la ti o n
30
40
30
20
20
10
10
0
- .0 5
0
.0 5
.1
-. 05
0
0
.0 5
Productivity dispersion of industries
seems related to regulation
High regulation
High regulation
Low regulation
Low regulation
40
40
30
30
20
20
10
-.05
0
ICT Industries
0
.05
10
-.05
0
0
.05
Non-ICT Industries
Labour Productivity growth of industries, purged of country and industry means.
Countries included are D, E, F, I, GB, IRL, USA.
Productivity dispersion across firms
ICT-producing
France
Electrical and optical equipment
Much of the increase in dispersion
comes from top performers.
Telecommunications
95th %ile
75th %ile
25th %ile
5th %ile
The figures present the distribution of labour productivity in each industry and year
between the 5th and 95th percentiles. The upper bound of the grey bar represent the 75th
percentile, the lower bound the 25th percentile and the line in the middle of each grey bar
being the median. Labour productivity is measured as value added per worker in 100
thousands of 1995 Euros. Source: Authors’ calculations from AMADEUS database.
Productivity dispersion across firms
ICT-producing
United Kingdom
Electrical and optical equipment
Telecommunications
Productivity dispersion across firms
ICT-using: retail distribution
France
United Kingdom
Wide differences in allocative
efficiency, especially in services
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
FIN
BEL
PRT
FRA
Manufacturing
ESP
ITA
SWE
GBR
Services
Contribution of resource allocation to sectoral MFP levels
(Based on Olley-Pakes productivity decomposition)
31
Different abilities of countries to channel
resources towards most productive firms
2
Growing faster
Average
ESP
FRA
ITA
GBR
1 2 3 4
1 2 3 4
1.5
1
0.5
0
1 2 3 4
‐1.5
‐2
1 2 3 4
Most productive
‐1
Least productive
Growing slower
‐0.5
‐2.5
‐3
ESP FRA ITA GBR Growth of real value added by productivity quartiles
(relative to average of country/sector/year group)
32
Regulation and MFP at firm level
Business
Sector
Leader Growth
Gap to the Leader
PMR
0.19 ***
0
-0.17 ***
0
-0.03
0.47
ICT using
sectors
0.15 ***
0
-0.16 ***
0
-0.09 **
0.02
Non-ICT
Distinguishing
using sectors catch-up firms
0.26 ***
0
-0.20 ***
0
-0.04
0.68
Catch-up firms: PMR
0.072
Non-catch-up firms: PMR
0.072
Dummy for catch-up firms
0.072
0.49 ***
0
-0.09 ***
0
0.70 ***
0
-0.27 ***
0
1
-0.14 ***
0
-0.04
0.37
0.24 ***
0
0.092
Catch-up firms close to
-0.03 **
0.03
0.00
0.87
0.13 ***
0
0.072
Catch-up firms far from
0.072
Dummy for close to frontier
0.072
Country-year FE
N
R2
Distinguishing
top performers
All regressions include fixed effects for country*year combinations and
for industries.
173137
0.12
83654
0.12
89483
0.14
83654
0.45
72979
0.39
Regulation and productivity acceleration are
negatively correlated
Annual average percentage point acceleration in labour productivity growth 1996‐2005 vs 1985‐1995
3.0
Greece
2.5
2.0
1.5
United States
Australia
1.0
Switzerland
Sweden
Canada
Ireland
0.5
0.0
United Kingdom Norway
Netherlands
Austria
France
Finland
Belgium
Japan SpainGermany
Portugal
Denmark
Italy
‐0.5
New Zealand
‐1.0
Correlation coefficient=‐0.4
t‐statistic=‐1.91
without Greece:
Correlation coefficient=‐0.74
t‐statistic=‐4.62
‐1.5
‐2.0
‐2.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Regulation in non‐manufacturing sectors, 1980‐95 average
5.5
6.0
Product market regulation and the
diffusion of ICT are negatively correlated
Average ICT investment
(% total investment),
95-05
30
Correlation coefficient = -0.71
t-statistic = -4.29
United States
Sweden
25
United Kingdom
Finland
Australia
Belgium
20
Canada
New Zealand
Denmark
Netherlands
Germany
Japan
Spain
15
France
Austria
Portugal
Italy
Greece
Norway
10
Ireland
5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
Average regulation in ETCR sectors, 93-03
ÎThis simple correlation is confirmed by multivariate regression analysis.
Regulation and the ICT share
Average
ICT invest
30
95-05
(% tota United Stat
investment)
Swede
25
United Kingd
Finlan
Austral
Belgium
20
Canad
New Zeala
Denma
Franc
Netherlan
German
15
Japan
Spain
Austri
Portuga
Italy
Greec
Norwa
Irelan
10
5
1,0
1,5
2,0
2,5
3,0
3,5
4,0
4,5
5,0
Average regulation in energy, transport, and commun
5,5
Stringent regulations seem to reduce
allocative efficiency
• Anti-competitive regulation reduces the resource
allocation towards more productive firms
• Effect is highly significant in services industries,
and particularly in ICT-using industries
Business
Sector
Regulation Impact
Indicator
Country-year FE
Industry FE
N
R2
-0.36 **
(0.15)
0.016
Yes
Yes
849
0.20
Manufacturing
only
0.34
(0.64)
0.595
Yes
Yes
629
0.20
Services
only
-0.28 *
(0.15)
0.072
Yes
Yes
220
0.36
ICT using
sectors
Non-ICT using
sectors
-0.67 ***
(0.16)
0
Yes
Yes
242
0.36
Standard Errors in parentheses. *, **, *** indicate statistical significance at the 10, 5 and 1 percent levels, respectively.
Agriculture, forestry, fishing, mining are excluded, as are public administration, education and health sectors.
-0.41 *
(0.24)
0.092
Yes
Yes
607
0.18
37
How well do economies allocate
resources across heterogeneous firms?
• Static view: Is resource allocation
correlated with firm productivity?
Olley-Pakes productivity_ decomposition:
_
P = (1 / N )∑ pi + ∑ (θ i − θ )( pi − P)
i
Simple Average
i
Allocative Efficiency
• Dynamic view: Do resources move
efficiently across heterogeneous firms?
ÎDo better firms grow faster?
38
How well do economies allocate
resources across heterogeneous firms?
• Static view: Is resource allocation
correlated with firm productivity?
Olley-Pakes productivity_ decomposition:
_
P = (1 / N )∑ pi + ∑ (θ i − θ )( pi − P)
i
Simple Average
i
Allocative Efficiency
• Dynamic view: Do resources move
efficiently across heterogeneous firms?
ÎDo better firms grow faster?
39
Does regulation affect productivity
growth of firms?
• Adopt a neo-Schumpeterian growth framework:
Δ ln Aicst = α 1 Δ ln AFcst
Growth of
frontier
⎛ Aics ,t −1 ⎞
⎟ + α 3 regulation cst −1 + γ s + γ ct + ε icst
⎟
⎝ AFcs ,t −1 ⎠
− (1 − α 0 ) ln⎜⎜
Catch-up
Product Market
Regulation
(Setup similar to Griffith et al. 2006)
• In which sectors are firms most affected?
– Distinguish between ICT using sectors and others
• Which firms are most affected?
– Distinguish between firms that are catching up to the
technology frontier and those that are not
– Further isolate high-performing firms that are catching
up to the technology frontier
40
Regulations and productivity:
Evidence at the industry-level
• Growing evidence based on a neo-Schumpeterian model
(e.g. Aghion and Howitt, 2006):
– industry productivity depends on growth at the frontier; catch up to the
frontier
• Evidence suggests a link between regulation and productivity
• Nicoletti & Scarpetta (2003), Conway et al. (2006), Griffith et al.
(2006)
• Some (Nicoletti and Scarpetta, 2003; Conway et al, 2006) also found
an indirect effect whereby regulation curbs catch up process
• a recent study using EU-KLEMS found an effect in specific ICT
industries – post and telecom (Inklaar et al. 2008)
• Studies have used different data sets (OECD Stan, EU Klems)
and different measures of regulation
• But there is also empirical evidence on indirect channels to
productivity (via FDI, labour utilisation).
41
Industry-level studies of regulation
and productivity [1]
• Four main cross-country studies exploring effects of regulation :
– Nicoletti-Scarpetta (2003, NS), Conway-De Rosa-Nicoletti-Steiner
(2006, CDNS), Griffith-Harrison-Simpson (2006, GHS), InklaarTimmer-van Ark et al (2008, ITA)
• Broadly similar model specification:
– Prod depends on leader growth and catch-up (Aghion-Howitt, 2005),
– Regulations have direct and/or indirect effects through catch-up
(Griffith et al. 2004)
• Broadly similar focus:
– Role of policies promoting market entry and firm rivalry (AghionGriffith, 2005)
¾ All find that anti-competitive regulations can curb productivity
directly, though often only in some ICT sectors (especially limited in
ITA)
¾ Some find that regulations also curb competition through slowing
down catch-up (NS and CDNS)
42
Industry-level studies of regulation
and productivity [2]
• But results not fully comparable because of differences in
– Data and coverage:
• Labour productivity (CDNS) vs “broad” TFP (NS,GHS) vs “pure” TFP (ITA)
• OECD STAN (CDNS, NS, GHS) vs EU-KLEMS (ITA)
• Country/sector/time coverage (21/39/20 CDNS and NS; 9/12/13 GHS; not clear,
ITA)
– Control variables:
• FE: Country-industry, industry-year, year(CDNS); country, industry, time (NS);
country-year, industry-year (GHS); country, industry (ITA)
• Other: direct and indirect effects, skills (NS, CDNS); direct and indirect effects
(ITA); direct effects only (GHS)
– Measurement of policies
• Industry-level time-series regulation impact for all sectors (CDNS); aggregate
time-series (over network industries) and cross-section industry-level regulation
(for manufacturing and services sectors) (NS); aggregate time-series (over
network industries) and cross-section industry-level regulation (for services)
(ITA); industry-level EU anti-monopoly proceedings and dummies for sectors
affected by SMP (GHS)
43
Employment is lower where regulation is
anti-competitive
Employment rate
in services
70
Correlation coefficient = 0.90
United Kingdom United S tatesNorway
Netherlands
Denmark
S weden
Australia Canada New Zealand
t-statistic = 9.74
60
50
Italy
40
Luxembourg
Finland
Belgium France Germany
Austria
Korea
S pain
S lovak Republic
Hungary Greece
30
Japan
Portugal
Czech Republic
Poland
20
50
55
60
65
70
75
80
Total employment rate
Product market
regulation
3.0
Correlation = -0.81
Poland
t-statistic = -6.6
2.5
Hungary
2.0
Italy
Greece
1.5
Spain
Finland
1.0
Portugal
France
Czech republic
Netherlands
Norway
Japan
Belgium
Korea Luxembourg
Germany
New Zealand
Austria
Canada
Sweden
United States
Denmark
Australia
United Kingdom
0.5
0.0
15
20
25
30
35
40
45
Employment rate in
market services
44