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Marion Jansen, Chief Economist, ITC
At: The Third CEPR-Modena Conference on Growth in Mature Economies: Revisiting the Contribution of Openness 11-12 May 2015
SMEs, Trade and Sustainable
Growth
SMEs: different definitions, different weights
in the economy
There is no universally
accepted definition of Small
and Medium Sized
Enterprise.
In OECD countries, SMEs
represent: Over 95 per cent of the number
of firms
Around 60-70 per cent of
employment
… but the picture differs
across countries
2
r ist
it er d iet
r i desh
h i d h
d i
pt i
3
SMEs: Who are they and how do they
perform in global markets?
Internationalizing: market power,
bargaining power
Local SMEs and Global Policies
SME productivity and
internationalization
SME, Trade and Growth:
What matters: being connected,
meeting standards and challenging the
superstars
In LDCs, firms are born small and tend to stay small
5
Source: OCE calculations from World Bank ES dataset
*Developed countries in sample are predominantly eastern European nations
Least Developed Countries“De e ped” C u tries
0 1 2 3 4 5log10(Number of employees)
2
4
6
8
10
log
10(T
urn
ove
r /
do
llars
)
0.00 3.17 6.33 9.50 12.67 15.83 19.00
Density of firms
Number of firms: 2985
0 1 2 3 4 5log10(Number of employees)
2
4
6
8
10
log
10(T
urn
ove
r /
do
llars
)
0.0 16.7 33.3 50.0 66.7 83.3 100.0
Density of firms
Number of firms: 10737
Theory predicts it, data confirm it: exporters tend to be larger
in size than non-exporters
6
Source: OCE calculations from World Bank ES dataset
*‘ xp rter’ is defi ed s fir ith % r re f s es exp rted (direct p us i direct)
ExportersNon exporters
0 1 2 3 4 5log10(Number of employees)
2
4
6
8
10
log
10(T
urn
ove
r /
do
llars
)
0 31 62 92 123 154 185
Density of firms
Number of firms: 40734
0 1 2 3 4 5log10(Number of employees)
2
4
6
8
10
log
10(T
urn
ove
r /
do
llars
)
0.0 5.7 11.3 17.0 22.7 28.3 34.0
Density of firms
Number of firms: 8726
si e is the “ r ” i the ser ices sect r
7
Source: OCE calculations from World Bank ES dataset
ServicesManufacturing
0 1 2 3 4 5log10(Number of employees)
2
4
6
8
10
log
10(T
urn
ove
r /
do
llars
)
0.0 15.7 31.3 47.0 62.7 78.3 94.0
Density of firms
Number of firms: 26299
0 1 2 3 4 5log10(Number of employees)
2
4
6
8
10
log
10(T
urn
ove
r /
do
llars
)
0 17 34 52 69 86 103
Density of firms
Number of firms: 18440
SME Characteristics: Productivity and wages
11
• SMEs are generally less
productive than large
firms
• The gap is larger in
developing economies
Relative Productivity & Wage Gaps in Selected South American
and OECD Countries (Large firms=100)
Source: Adapted OECD-ECLAC, 2013, p. 47
0 20 40 60 80 100
France
Spain
Germany
Peru
Chile
Brazil
Argentina
Medium Firm Small Firm
Micro Firm Wage Gap - Micro
Wage Gap - Small Wage Gap - MediumFrom McDermott and Pietrobelli, ITC, forthcoming
SME Characteristics: Productivity and wages
12
• SMEs are generally less productive than large firms
• The gap is larger in developing economies
• A similar pattern is observed with wages
• Working with SMEs will be a challenge, but thereare large gains to be made
Relative Productivity & Wage Gaps in Selected South American
and OECD Countries (Large firms=100)
Source: Adapted OECD-ECLAC, 2013, p. 47
0 20 40 60 80 100
France
Spain
Germany
Peru
Chile
Brazil
Argentina
Medium Firm Small Firm
Micro Firm Wage Gap - Micro
Wage Gap - Small Wage Gap - MediumFrom McDermott and Pietrobelli, ITC, forthcoming
Trade matters for productivity
14
Firms engaging
in trade are more
productive than
firms not
engaging in trade
(no exports, no
imports), with the
exception of
offshore firms
that do not import
Fir s’ pr ducti it d i ter ti tr de status, Tunisia 2000-2010
All firms
Firms with more than one
employee
All Manufact
uring
Non
Manufact
uring
Services
Non exporting and importing firms 0.992*** 0.992*** 0.828*** 0.607*** 1.352***
(0.006) (0.006) (0.001) (0.012) (0.007)
Onshore firms exporting and non importing 0.314*** 0.031*** 0.268*** 0.471*** 0.0532***
(0.021) (0.021) (0.031) (0.053) (0.034)
Onshore firms exporting and importing 1.434*** 1.434*** 1.232*** 1.14*** 1.895***
(0.006) (0.006) (0.013) (0.016) (0.011)
Offshore firms and non importing -0.847*** -0.849*** -0.904*** -0.771*** -0.645***
(0.010) (0.010) (0.017) (0.085) (0.017)
Offshore firms and importing 0.566** 0.566*** 0.382*** 0.920*** 0.909***
(0.005) (0.005) (0.012) (0.095) (0.016)
N 336806 326572 105114 30712 190313
R2 0.200 0.200 0.213 0.18 0.298
From Bhagdadi, ITC, forthcoming
Extent of pricing to market: firm size,
efficie c d … r et p erPTM implies that firms react to shocks in the
bilateral exchange-rate by adjusting their FOB
export price in the home currency, rather than by
passing through shocks to consumer prices.
18
(Higher vulnerability
when PTM low: e.g., in
Sweden only a handful
of textile companies
that went bankrupt
during the recent
financial crisis had less
than 50 employees
(Pal et al. 2013))
Bargaining power within value chains
19
Ruffier (forthcoming)
Harvie et al (2010) show that moving up the
value chain (in 7 Asean country and China)
is notably facilitated by:
Higher labour productivity
Higher foreign ownership share
ICT as core business
Having acquired production knowledge
Initial level of supplier capacity will
determine:
the governance approach within the
chain
and will determine gains captured by
suppliers
Gains at the bottom of the chain
are not necessarily high
SMEs and regulation/standards
Meeting standards or complying with regulation is increasingly a
prerequisite to trade: but compliance is costly.
23
ITC NTM surveys
Results
24Variable Coefficient (Std. Err.)Equation log of competitiveness
Firm-level capacity (readiness to export)
Account 0.147 (0.049)
Dummy for production internationally certified 0.263** (0.041)
Dummy for license issued by foreign company 0.167*** (0.044)
Dummy for company having an Email address 0.503** (0.087)
Dummy for firm having its website 0.194** (0.049)
Credit 0.091* (0.042)
Training 0.166** (0.043)
Variable Coefficient (Std. Err.)
Equation probability of exporting
Firm-level data: determinants of productivity
Account -.233 (0.175)
Dummy for production internationally certified 0.749** (0.079)
Dummy for license issued by foreign company 0.148† (0.087)
Dummy for company having an Email address 0.695** (0.164)
Dummy for firm having its website 0.349** (0.089)
Credit 0.025 (0.080)
Training 0.247** (0.079)
I. Current efficiency
II. Connectivity
III. Capacity to
change
Trade to GDP elasticity has gone
down
Fernandes, Mattoo and
Ruta (2014):
Trade to GDP elasticity
increased in the 1990s
but then went down
again.
27
SCI val addedSCI employment
VARIABLES (1) (2) (3)(4) (5) (6)
1990s 0.00490 -0.00296 0.00896* -0.00526
2000s 0.000579 0.00491 0.00846* 0.000418
Asia 0.00671 0.0103 0.00561 -0.00790
LAC 0.00630 0.0195* -0.00133 -0.00262
Europe_Other 0.0382*** -0.00365 0.0197** -0.00906
Other 0.0125 0.0289 0.00149 0.0192***
1990s_Asia -0.00575 0.0249*
2000s_Asia -0.00916 0.0171
1990s_LAC -0.0173 0.00726
2000s_LAC -0.0261** -0.00211
1990s_Europe_Ot
her 0.0731*** 0.0602***
2000s_Europe_Ot
her 0.0219 0.0252*
1990s_Other -0.0322 -0.0326***
2000s_Other -0.0208
GDP/cap -0.00818*** -0.00386 -0.00442 -0.00581*** -0.00315* -0.00290
Constant 0.0603*** 0.0460*** 0.0469*** 0.0453*** 0.0438*** 0.0449***
Observations 217 217 217 128 128 128
R-squared 0.096 0.200 0.324 0.136 0.196 0.339
Fiorini et al., (forthcoming)
But China has already started to
move up the value chain
Kaplinski (2011), ILO (2010), recent news on the largest
global speed train producer being Chinese; Also,
simulations suggest that India will be the largest global
provider of skilled workers by 2030.
28
There is evidence that industrialized country
producers moved up the value chain when China
integrated in world markets (e.g. Montfort et al.,
2008, for Belgium)
=> But what will happen in industrialized
countries when China itself moves up the value
chain ???
29
Wh t’s h ppe i the p ic
(research) front?
Increased interest in trade support institutions
Concern about Basel financial rules
Increased demands for more
information/transparency on NTMs
The B20 Task Force on SMEs and
Entrepreneurship
TSIs and SMEs
30
Log of exports of goods and services per capita versus the log of TPO budgets per capita (Lederman, Olarreaga & Payton 2006).
Trade Support Institutions and SMEs
Increase in quantitative research in recent years, e.g.
Lederman, Olarreaga & Payton (2006)
IDB /Christian Volpe Martincus
Olivier Cadot
Fernandes and Mattoo (2014)
Two questions regarding the design of the institutions/interventions:
Nature of public/private collaboration
“Targetting”:Fernandes and Mattoo (2014) find that four year after receiving assistance exports of small firms declined by 65%, while exports of large firms were only 6% higher. However, the exports of medium-sized firms increased by 57%.
31
Concern about new global financial
rules
32
Source: Bank of Japan (2014)
x p e: ccess t fi ce b s d r e fir s i J p . … (is there
eed f r i cre sed “ ter ti e fi ci ”?
Increased demands for more
information/transparency on NTMs
Eg:
EU-wide firm level data collection on firm perceptions of NTMs
EU funded research network (PRONTO) on NTMs
… W O r de F ci it ti A ree e t, t b t ddress
procedural obstacles related to NTMs
33
B20 Task Force on SMEs and
Entrepreneurship
Four major themes:
Access to Markets
Access to Finance
Access to Talent and Entrepreneurship
Innovation
Creation of a World SME Forum?
34
Summing Up
SMEs represent significant share of economic in terms of GDP and employment
There has been an increased interest in SMEs at the policy level in recent years
Firm level data sets make it possible to analyze SME performance empirically: increased and different types of research studies
Three bottlenecks for SME growth are highlighted in literature and policy debate around SME internationalization:
Access to information (including role of TSIs)
Access to finance
Fixed costs related to (implementation of) NTMs
Numerous questions remain, including:
What is the relevance of power relationships notably within GVCs
How to deal with heterogeneity within the SME group (e.g. small vs. medium)
35