1. 2010
Micro, Small, and
Medium Enterprises
Micro, Small, and Medium Enterprises
Around the World: How Many Are There,
and What Affects the Count?
MSME Country Indicators
Khrystyna Kushnir, Melina Laura Mirmulstein, and Rita Ramalho
T
his note provides an overview of new data on MSME (micro, small, and medium enterprise)
Country Indicators for 132 economies. There are 125 million formal MSMEs in this set of
economies, including 89 million in emerging markets. Descriptive statistical analysis is
presented on the relationship between formal MSME density (number of formally registered MSMEs
per 1,000 people) and key obstacles for MSMEs, such as access to finance and informality. This
analysis shows that formal MSMEs are more common in high-income economies, but that in lowand middle-income economies, MSME density is rising at a faster pace. Second, although there is
significant variance in the countries’ definitions of MSMEs, around a third of the countries covered
define MSMEs as having up to 250 employees. Third, formal MSMEs employ more than one-third
of the world’s labor force, but the percentage drops significantly with income level. Fourth, MSMEs
are more likely to identify access to finance as their biggest obstacle than are large firms. In fact, in
economies with a higher percentage of firms with no formal credit, MSME density is lower. Finally,
a larger informal sector is associated with lower formal MSME density. Measures of barriers to firm
entry and exit, such as the minimum capital requirement and the recovery rate in case of bankruptcy,
are also associated with lower formal MSME density.
World Bank / IFC
MSME Country Indicators
MSME Country Indicators record the number of
formally registered MSMEs across 132 economies. This
database is current as of August 2010 and expands on the
January 2007 “Micro, Small, and Medium Enterprises: A
Collection of Published Data” edition. The new data can
be found at http://www.ifc.org/msmecountryindicators
More specifically, the MSME Country Indicators
database contains information on the following:
• he total number of formal MSMEs in the economy
T
and the number of MSMEs per 1,000 people
(MSME density);
• breakdown into micro, small, and medium
A
enterprises based on the number of employees, where
such data is available, or based on other variables such
as annual sales;1
• The formal MSME share in total employment;
• he income group of the economy based on GNI per
T
capita, based on the World Bank Atlas method (from
the World Development Indicators);
• ime series data going back 20 years for some
T
economies, for the following variables: the number
of formally registered MSMEs, MSME density,
breakdown by size of MSMEs, and MSME share in
total employment; and
• stimates of the number of MSMEs in the informal
E
sector for 16 economies.
The dataset presents data originally collected by each
of the economies included in the sample. All the country
sources are listed in the database, the most common
being national statistical institutes or special government
agencies that monitor and administer programs for
MSMEs. As the data was originally collected by different
countries, there are limitations regarding the extent to

2. which the data can be standardized. Where possible,
MSMEs are defined as follows: micro enterprises: 1–9
employees; small: 10–49 employees; and medium: 50–249
employees. However, in the majority of countries, this
definition did not match the local definition, in which
cases the local definition took precedence. Only firms
with at least one employee are included.
Of the 132 economies covered, 46 economies define
MSMEs as those enterprises having up to 250 employees.
For 29 economies, variables other than total employment
are used or an MSME definition is not available (Figure
1). Among such other variables are the number of
employees differentiated by industry, annual turnover, and
investment. Not surprisingly, the overwhelming majority
of formal MSMEs globally are micro enterprises, with 83
percent of all MSMEs in this category.2
The data covers only the formal registered sector
(except for 16 economies where data is available). This
is an important limitation given that informal MSMEs,
especially in developing countries, often outnumber
formal MSMEs many times over. For example, in India
in 2007, there were fewer than 1.6 million registered
MSMEs and 26 million unregistered MSMEs, that is,
about 17 unregistered MSMEs for every registered one.
Important lessons were drawn from the MSME data
while building the MSME Country Indicators, in particular
the following:
Figure 1
Distribution of the MSME
Definition by Number of
Employees
A third of the economies (out of 132 covered)
define MSMEs as having up to 250 employees.
499
299
Number of Employees
250
200
■
East Asia and the Pacific [10]
Europe and Central Asia [14]
■
High-income: OECD members [28]
■
149
100
Latin America and the Caribbean [15]
Middle East and North Africa [9]
■ High-income: Non-OECD economies [14]
■
79
■
60
■
50
■
South Asia [3]
Sub-Saharan Africa [10]
19
0
10
20
30
40
50
Number of Economies
Source: MSME Country Indicators.
Note: Name of the region [#] signifies the number of
economies from the region included in the analysis. The figure
uses data from 103 economies.3
2
• SME data are not always standardized across
M
countries and time. Data on MSMEs are gathered
by various institutions using different methods.
These institutions define MSMEs based on
differing variables and scales and sometimes
change their definitions. EUROSTAT’s Structural
Business Statistics provides the best example
of regional coordination and harmonization of
MSME data.
In order to have comparable MSME data, the following
steps could be taken:
• conomies should be surveyed using a unified
E
and standardized method;
• nstitutions in charge of gathering MSME data
I
should coordinate with each other regarding the
variables and methods used to determine the
size of the MSME sector.
• hese actions can be taken first at the regional level
T
and secondly expanded to the global level. In return,
economies would reap the benefits of a cross-country
and time-series analysis of MSMEs’ contribution to
development.
• SME data on the informal sector are scarce and
M
are not comparable across countries. This is due to
differences in the definition of the informal sector
and in estimation methods. Estimates of the informal
sector are needed in order to make a comprehensive
evaluation of the MSMEs’ contribution to economic
development. This data gap could be filled by
surveying MSMEs operating in the informal sector or
by encouraging institutions that collect MSME data
on the formal sector to also develop estimates of the
size of the informal sector.
• ime series data is not always available. However,
T
it is crucial for future evaluation of the reforms of
business regulations.
• ome institutions collect data on MSMEs only
S
in selected sectors, most often in manufacturing.
This limits the possibilities of evaluating MSMEs’
contribution to gross domestic product (GDP) or
employment.
For more details on the methodology, please refer to
“Methodology note on the MSME Country Indicators.”4
Where are MSMEs Most Common?
In the 132 economies covered, there are 125 million
formal MSMEs of which 89 million operate in emerging
markets. These results are in line with a recent study
published by IFC and McKinsey Company in 2010,
“Two Trillion and Counting,” which found that there are
between 80 and 100 million formal MSMEs in emerging
markets.

3. Figure 2
MSME Density Across the World
Europe and Central Asia [18]
6,667,715 MSMEs
High-income:
OECD members [29]
36,878,280 MSMEs
Latin America
and the Caribbean [16]
13,763,465 MSMEs
Middle East
and North Africa [7]
4,488,767 MSMEs
South Asia [3]
7,451,803 MSMEs
East Asia and the Pacific [11]
39,293,783 MSMEs
High-income:
Non-OECD
economies [19]
1,848,282 MSMEs
Sub-Saharan
Africa [13]
13,154,122 MSMEs
MSMEs per 1,000 people
1-10
11-20
21-30
31-40
41-50
51 and above
No data avilable
Sources: MSME Country Indicators.
Note: Name of region [#] signifies the number of economies from the region included in the analysis. The figure uses the most
recent data available after the year 2000. The figure use data for 116 economies.5
On average, there are 31 MSMEs per 1,000 people
across the 132 economies covered. The five countries with
the highest formal MSME density are as follows: Brunei
Figure 3
MSME Density and Income
per Capita
Economies with higher income per capita
tend to have more MSMEs per 1,000 people.
Figure 4
Median MSME Density by Region
IDN
100
PRY
CZE
ECU
JAM
MWI
The regional distribution of MSME density
is in line with income level distribution.
ISL
GRC
MUS
ITA
KORESP
NGA
60
PRT
HUN
BOL
CYP
SVN
NOR
LUX
Median MSME Density
80
MSME Density
Darussalam (122), Indonesia (100), Paraguay (95), the
Czech Republic (85), and Ecuador (84). Overall, economies
with higher income per capita tend to have more formal
MSMEs per 1,000 people (Figure 3). This result is in line
with data previously presented in the literature. Klapper
et al. (2008) find that business density (which includes
both MSMEs and large firms) is positively correlated with
JFN
ARMBH CHL LTU
ERA
HKG CHE
AUS
MF
MNE POL
URY
TUR EST
NZL AUT
THA BGR
MLD
EGY
COL
SAU ISR
BWA
SLV
GBR
WBG
JOR
MAR
BER
TJK
MKD BRA
DEU
AZE
ROM
IRL
BGD YEM
PAK
HRV
DZA KYZ
ARE
PTO
KWT
CRI
UZB
ARG
MDA
STK
SRB
RUS
PH
UKR
RWA
BLR
LAO TMP
CMR
LBN VEN OMN
KCZ
DOM
MOZ
IND
SDN
KEN
40
HND
VNM
20
0
0
6
8
10
GNI per Capita, Atlas Method (log)
12
Sources: MSME Country Indicators, World Development
Indicators.
Note: The results of the regression are statistically significant
at the 5 percent level. The figure uses the most recent data
available after the year 2000. The figure uses data from 109
economies.6
45
40
35
30
25
20
15
10
5
0
Sub-Saharan East Asia
South Asia
Africa
the Pacific
[3]
[14]
[11]
Europe
Middle East High-Income: Latin America High-Income:
Central Asia North Africa Non-OECD the Caribbean OECD
[18]
[7]
economies
[16]
members
[19]
[29]
Source: MSME Country Indicators.
Note: Name of the region [#] signifies the number of
economies from the region included in the analysis. The figure
uses the most recent data available from 117 economies7 after
the year 2000.
3

4. income per capita. It is important to note that the analysis
presented in this note refers only to correlations and that
no causal inferences should therefore be made.
The regional distribution of MSME density is in line with
the income level distribution. Consequently, Sub-Saharan
Africa and high-income OECD economies are at opposite
ends of the spectrum with regard to MSME density
(Figure 4). Somewhat surprisingly, Latin America and
the Caribbean have more MSMEs per 1,000 people than
non-OECD high-income economies. However, once the
countries that are heavily dependent on mineral resources
(United Arab Emirates, Qatar, Oman, Kuwait, and Saudi
Arabia) are excluded from the sample, the MSME density
for non-OECD high-income economies is at a similar
level to that for Latin America and the Caribbean.
Globally, the number of MSMEs per 1,000 people
grew by 6 percent per year from 2000 to 2009 (Figure 5).
Europe and Central Asia experienced the biggest boom,
with 15 percent growth. Such a fast pace may have resulted
from the continuation of post-Soviet privatization in these
economies. Another possible contributing factor may be
the accession of the Eastern European economies to the
European Union (EU).
When considering the MSME growth rate from the
standpoint of income per capita (Figure 6), high- income
economies grew three times slower than low-income
economies and five times slower than lower-middleincome economies. This could be explained by the fact that
Figure 5
high-income economies start from a higher base, which is
why the growth rate appears slower. In fact, even when
taking into account differences in income level, economies
with lower bases grow at higher rates.8 Only low-income
economies do not follow the pattern of “higher income
– slower growth rate” when compared to middle-income
economies, which could be because the informal sector
absorbs more MSMEs in low-income economies than in
upper- and lower-middle-income countries.
In the high-income economies, MSMEs are not only
denser in the business structure, but also employ a higher
percentage of the workforce. In half of the high-income
economies covered, formal MSMEs employed at least 45
percent of the workforce, compared to only 27 percent in
low-income economies (Figure 7).
These indicators highlight the importance of MSMEs to
economic development and job creation. Formal MSMEs
employ more than one-third of the global population,
contributing around 33 percent of employment in
developing economies.
From a regional perspective (Figure 8), East Asia and
the Pacific have the highest ratio of MSME employment
to total employment. This is mainly driven by China,
where formal MSMEs account for 80 percent of total
employment. The low ratio of formal MSME employment
to total employment in South Asia could be explained by
the fact that in the three countries covered, Bangladesh,
India, and Pakistan, the informal sector is large.
Figure 6
MSME Growth by Region,
2000-2009
18
16
14
12
10
8
6
4
2
0
High-Income: Latin America Sub-Saharan High-Income:
East Asia
OECD members the Pacific
Non-OECD the Caribbean
Africa
[5]
[24]
economies
[2]
[4]
[9]
Middle East
North Africa
[2]
Europe
Central Asia
[14]
Global [60]
Source: MSME Country Indicators.
Note: Name of the region [#] signifies the number of
economies from the region included in the analysis. The figure
uses data for 60 economies. Data on economies that met the
next criteria were included in the analysis: (i) if the MSME
definition remained unchanged from 2000 to 2009; (ii) if there
were data available for both time periods of 2000–2004 and
2005–2009.
4
The MSME growth rate is three times
lower in high-income economies, than in
low-income economies.
Annual MSME Growth Rate
Annual MSME Growth Rate
Globally, MSMEs grew at a rate of 6 percent
per year from 2000 to 2009.
MSME Growth Rate by Income
Group, 2000-2009
12
10
8
6
4
2
0
High
[26]
Upper-middle
[13]
Lower-midlle
[15]
Low
[6]
Income Group
Source: MSME Country Indicators.
Note: Name of the income group [#] signifies the number of
economies from the income group included in the analysis. The
figure uses data from 60 economies. Data on economies which
met the next criteria were included in the analysis: (i) if the
MSME definition remained unchanged from 2000 to 2009; (ii) if
there were available data in both time intervals of 2000-2004 and
2005-2009.

5. Key Obstacles for Firms and their Connection
to MSME Density
Upper-middle
[26}
Lower-midlle
[18]
Low
[20]
Income Group
Source: MSME Country Indicators, World Development
Indicators database.
Note: Name of the income group [#] signifies the number of
economies from the income group included in the analysis.
The figure uses the most recent data available after the year
2000. The figure uses data from 103 economies.9 The results of
the regression are statistically significant at the 5 percent level.
Figure 8
MSME Employment vs.
Total Employment
Figure 9
Electricity and access to finance are the
two most cited obstacles for businesses in
developing countries, and access to finance
affects small businesses much more than it
does medium and large businesses.
800
600
400
200
0
South Asia Middle East
Europe
Latin America High-Income: Sub-Saharan High-Income: East Asia
[3]
North Africa Central Asia the Caribbean Non-OECD
Africa
the Pacific
OECD
[6]
[16]
economies
[8]
[10]
[13]
members
[18]
[28]
MSME Employment
Total Employment
Source: MSME Country Indicators.
Note: Regions are grouped in ascending order based on the ratio
of the MSME employment to total employment. Name of the
region [#] signifies the number of economies from the region
included in the analysis. For the following economies the number
of employed by the MSMEs was calculated from the reported
percentage of the total employment: Armenia, China, Ecuador,
Ghana, Iceland, Israel, Jamaica, Nigeria, Myanmar, Malawi,
Malaysia, Pakistan, Singapore, Peru, Uzbekistan and South Africa.
The figure uses the most recent data available after the year 2000,
from 102 economies.10
16
14
12
10
8
6
4
2
0
SMall
Medium
Large
1,000
18
Overall
% of Firms Identifying Obstacle X
as the Biggest Obstacle
Number of People Employed (in millions)
In China, MSMEs provide 80 percent of the
total employment, driving East Asia and the
Pacific to be the leader in the ratio of MSME
employment to total employment.
1,200
Six Most Commonly Cited
Obstacles by Firms (out of 15)
Electricity
Access to Practices of Tax Rates
Finance the Informal
Sector
Political
Instability
SMall
Medium
Large
High
[39]
Overall
0
SMall
Medium
Large
10
Overall
20
SMall
Medium
Large
30
Overall
40
SMall
Medium
Large
Median MSME Employment
50
Overall
Formal MSMEs employ more than one third
of the world’s labor force, but the percentage
drops significantly with income level.
The World Bank Enterprise Surveys dataset was used
to identify the biggest obstacles for firms worldwide.
This dataset covers 98 countries, using the same sampling
and surveying methodology. It produces representative
estimates for the non-agriculture private sector economy
and allows for comparisons of firms of different sizes
within a country and globally. The Enterprise Survey data
covers several aspects of the business environment and
includes both objective and perception-based questions.
Among other things, Enterprise Surveys measure the
biggest obstacles for firms of all sizes from a list of 15
potential obstacles.
In the Enterprise Surveys dataset, firms are divided
into the following categories: small (5 to 9 employees),
medium (10 to 99 employees), and large (100 or
more employees). Although this categorization may
not match the country-level definitions used in the
MSME Country Indicators database, the information
presented in Enterprise Surveys can still be indicative
of the key obstacles facing small and medium-sized
firms.
SMall
Medium
Large
Median MSME Employment
(percentage of the total) by
Income Group
Overall
Figure 7
Corruption
Source: Enterprise Surveys Dataset.
Note: The data cover 98 countries. The 15 obstacles are access
to finance; access to land; business licensing and permits;
corruption; courts; crime, theft and disorder; customs
and trade regulations; electricity; inadequately educated
workforce; labor regulations; political instability; practices of
competitors in the informal sector; tax administration; tax rates
and transport.
5

6. Figure 10 MSME Density and Enterprises
Unserved by the Credit Institutions
Figure 11
The smaller the percentage of financially
unserved firms, the higher the formal MSME
density on average.
100
MSME density increases with SME lending.
100 IDN
IDN
PRY
ECU
80
PRY
PRT
ISL
80
ITA
ESP
HUN
NOR
LUX
SVN
40
FIN
BOL
CHI
ARM
BELFRA
SWEBOK
TUR
GBR
LTU HND
ZAF
URY
VNM MEX
NLDBGR
COL BWA
LVA
SLV
PER
RUN
DEO NFL
BRA
HRV
20
TJK
SRB ARG
PHL
0
0
AZE
2
YEM
UZB
MDA
UKR
RWA
BFA KGZ
CMR
4
CHN
UGA
GHA
6
MOZ
8
Micro, Small and Medium Enterprises Unserved by the
Credit Institutions (weighted average (percentage of firms))
CZE
ECU
MWI
MSME Density
MSME Density
MUS
60
MSME Density and SME Lending/
GDP
JAM
PRT
GRC
MUS
ITA
60
NGA
LUX
BOL
CHL
KEN
ARM
IDN
IDN
IDN FRA
OHL
FIN AUS
POL ZAF
SWE
URY MNE AUTOGP
MEX
TUR
IDN
BGR
BWA COL EGY
USR
SLV IDN
MAR
JOR
PER
TJK AZE ROM
MK
IDN
DEU
MYS
BGD
20 YEMIDN KAZ
IDNIDN HRV
DZA
TTO
ARG SRH AWT
IDN
IDN
IDN
PHL TUS UKR
UGA
IDN
LAG IDN
IDNIDN
IDN
IDN
IDN
IDN
IDN
0 SDN AND TUN
40 HKG
0
KOR
ESP
HUN
CYP
SVN
JPN
BEL
EST
THA
CAN
DNK
GBR
NZL
LVA
NLD
CHN
.2
.4
.6
SME Lending/GDP
Source: MSME Country Indicators, IFC and McKinsey
Company 2010.
Note: The results are statistically significant at the 5 percent
level, while controlling for GNI per capita (log) and if an
outlier–Indonesia–is dropped. The figure uses data from
52 economies. Included economies: (i) covered in both
databases; (ii) data were not extrapolated; (iii) with available
GNI per capita, Atlas method.
Source: MSME Country Indicators, Financial Access 2010
(Consultative Group to Assist the Poor (CGAP)).
Note: The figure uses data from 101 economies. The results
of the regression are statistically significant at the 5 percent
level. When controlling for GNI per capita, Atlas method
(log), the results are not statistically significant at the 5
percent level. Data for some countries were estimated by the
CGAP. Included economies: (i) covered in both databases;
(ii) with available GNI per capita, Atlas method.
When presented with a list of 15 possible obstacles,
electricity and access to finance are the two most-cited by
businesses in developing countries (Figure 9).
Firms of different sizes rank obstacles differently.
Access to electricity is a significant constraint overall
and affects small, medium, and large enterprises alike.
However, more small businesses list access to finance
as their biggest obstacle than do medium enterprises,
and fewer large firms see it as their biggest obstacle.
On the other hand, political instability is more often
identified as the biggest obstacle by large firms than by
small ones.
It should be borne in mind that this information is
based on the perceptions of firms and that it is therefore
important to check if it is corroborated by objective
measures: Are MSMEs in fact more common where they
have easier access to credit? Are they more common where
the informal sector is smaller?
overdraft, but do not have one—is smaller (Figure 10).
This finding matches the firm-level data that identifies
access to finance as one of the most commonly
cited obstacles, in particular by small and medium
enterprises (SMEs).
MSME density is not only correlated with whether
or not credit is used, but how much. MSME density is
lower in economies where MSMEs have some access
to credit, but where it is not sufficient (underserved).
Furthermore, where SME lending (as a share of GDP)
increases, MSME density also increases (Figure 11).
Access to Finance
Formal MSME density is on average higher in
countries where the percentage of financially unserved
firms—that is, those that would like to have a loan or
6
Practices of Informal Sector and Corruption
Competition from the informal sector and corruption
among government officials also pose significant
challenges for firms. Objective measures of the size
of the informal sector, barriers to entry into and exit
from the formal market, and the existence of informal
payments shed light on the importance of these obstacles
to the existence of MSMEs. First, the larger the informal
sector in an economy, the lower the formal MSME
density (Figure 12). This is likely due to the fact that
most MSMEs are more likely to operate in the informal/

7. Figure 12 MSME Density and Shadow
Economy
Figure 14 MSME Density and “Closing a
Business” Recovery Rate
Where the shadow economy is larger,
there are fewer MSMEs participating in
the formal economy.
100
150
IDN
PRY
80
ISL
CZE
PRT
MLT
MUSGRC
ECU
BRN
JAM
MWI
ITA
60
ESP
NOR
LUX
KOR
HUN
SVN
JPN
CHL
KEN
HND
FRA
LTU
CHE AUGFIN BEL
POL
ZAF
SWL
MEX
IDN EST
IDN VNM
NUL SGP
BGR
NOD
CAN
EGY
SAI USR
BWA VAL
SLV
GBR
JOR
MAR
ROM
IRL
DEU
MYS BGR BRA
USA
YEM
HDPPAK
IDN
KAZ
ARE
TTO
KWT ARG
CRI
MDA
SVK
RUS
PML
CHN
RWA
UGA
LAO
CMR
OMN
DOM
BEA
IDN
IND
CHA
TUN
40
20
0
0
20
MSME Density
MSME Density
Where the recovery rate of investment
in case of bankruptcy is lower, there are
fewer formal MSMEs.
40
BOL
PER
UKR
60
80
Source: MSME Country Indicators, Buehn and Schneider
(2009).
Note: The figure uses data from 88 economies. The results of
the regression are statistically significant at the 5 percent level.
When controlling for GNI per capita, Atlas method (log), the
results are not statistically significant at the 5 percent level.
Included economies: (i) covered in both databases; (ii) with
available GNI per capita, Atlas method.
Where it is required to have more
minimum capital to start a business,
there are fewer MSMEs.
IDN
ECU
ISL
CZE
PRT
GRC
MSME Density
ITA
ESP
60
HUNNOR
LUX
SVN
BIH
PRY HNDCHE
BEL LTU
POR SVE DNK
TUR
MEX EST
AUT
BGR
NLD
LVA
40
JOR
MAR
TJK
HRV
DZA
KAZ
GTM
PRY
SRB UZB SVK
PUL
20
GHA
0
0
PRY
EZU CZE
BLZ
MUS
MWI
ITA
NGA
20
40
ISL
ESP
HUN
LUX SVN
BOL
CHL
KENBIH ARM
FRA ETU
HND
CHE
POL
IDN
MNE
URY
EST THA
IDN
MEX
IDN
BCR
IDN
COL BWA
LVA
SAU ISR
IDN
SLV
WBG
JOR
BEL IDN MAR
IDN IDN
DEU
MYS
BRA MKD
BGD YEM
IDN
PAK
IDN
IDN
GTM
ARE
TTO
GLR IDN
UZB SRA IDN KWT
SVK
RUS CHN
PHL JKR
RWA
BLR
LMO VEN
OMN UGA
KHM
NOMKOZ LBN IDN
IDN CMR
MOD TZA
SDN
0
50
0
PRT
JAM
GRC
60
KOR
NOR
CYP
JPN
EIM
AUS
SWE HKO BEL
DNK
SGP
AUT SZL
NLD CAN
GBR
IRL
USA
80
100
Source: MSME Country Indicators, Doing Business Index 2010.
Note: The results of the regression are statistically significant at
the 5 percent level, controlling for GNI per capita, Atlas method
(log). The figure uses data from 113 economies. 12
unregistered sector in countries where the informal
sector is large.
Second, in economies where it is more costly to start
or close a formal business, the density of formal MSMEs
is lower. Specifically, the minimum capital for “Starting a
Business” and the recovery rate for “Closing a Business”
are strongly correlated with MSME density (Figures 13 and
14). In other words, in economies where more minimum
capital is required to start a business and where it is harder
to recover investments in case of closure of the business,
formal MSME density is lower.
Finally, corruption is negatively associated with MSME
density, as evidenced by lower MSME density in countries
where firms are more frequently asked to make informal
payments (bribes) to government officials (Figure 15).
Figure 13 MSME Density and Minimum
Capital Requirement for
“Starting a Business”
80
IDN
Closing a Business: Recovery Rate (Cents on the dollar)
Size of the Shadow Economy (percentage of GDP), 2005
100
100
KWT
CHN
UKR
CMR
KHM LBN
50
100
150
200
Starting a Business: Minimum Capital (precentage of the income per capita)
Source: MSME Country Indicators, Doing Business Index 2010.
Note: The results of the regression are statistically significant at
the 10 percent level, controlling for GNI per capita, Atlas method
(log). The figure uses data from 47 economies. 11
7

8. Notes
Figure 15 MSME Density and Percentage
of Firms Expected to Make
Informal Payments
Where there is more corruption,
there are fewer MSMEs participating
in the formal economy.
100
IDN
PRY
CZE
PRT
80
MSME Density
MUSMWI
60
ECU
JAM GRC
ESP
HUN
SVN
BOL
CHL ARM
BIH
LTU HND
ZAF
MNE POL
URY
EST
MEX
TUR
VNM
BGR
OOL GUY
LVA
BWI SLV
WBC JOR
MAI
PER
TJK
ROM
IRL
AZE
BRA
HRV
PAK
KAZ
GTM
CRI
MDA
SVK ARO
SRB
RUS
PHL
UKR
RWA
UGA
BLR
TMP
CMR
LBN COMOMN LAO
KGZ TZA
BFA MOZ
IMO
GHA
40
20
0
0
20
40
KEN
BGD
DZA
UZB
CHN
KHM
60
80
Percentage of Firms Expected to Pay Informal Payment (to Get Things Done)
Source: MSME Country Indicators, Enterprise Surveys.
Note: Figure uses data from 70 economies. The results of the
regression are statistically significant at the 5 percent level. When
controlling for GNI per capita, Atlas method (log), the results are
not statistically significant at the 5 percent level.
References
Buehn, Andreas and Schneider, Friedrich. 2009. “Shadow Economies
and Corruption All Over the World: Revised Estimates for 120
Countries.” Economics: The Open-Access, Open-Assessment
E-Journal, Vol. 1, 2007-2009 (Version 2). http://dx.doi.
org/10.5018/economics-ejournal.ja.2007-9
Consultative Group to Assist the Poor. 2010. “Financial Access
2010.” http://www.cgap.org/p/site/c/template.rc/1.26.14234/
IFC and McKinsey Company. 2010. “Two Trillion and
Counting.”
http://www.ifc.org/ifcext/media.nsf/Content
IFC_McKinsey_SMEs
IFC. 2009. “SME Banking Knowledge Guide.” http://
w w w. i f c. o r g / i f c e x t / g f m . n s f / A t t a c h m e n t s B y T i t l e /
SMEBankingGuidebook/$FILE/SMEBankingGuide2009.pdf
Klapper, Leora, Amit, Raphael, and Guillén, Mauro F. 2010.
“Entrepreneurship and Firm Formation across Countries.”
In Lerner, Josh, and Schoar, Antoinette, eds. International
Differences in Entrepreneurship. National Bureau of Economic
Research Conference Report. Chicago: University of
Chicago Press
Kushnir, Khrystyna. 2010. “How Do Economies Define
MSMEs?” IFC and the World Bank. http://www.ifc.org/
msmecountryindicators
Kushnir, Khrystyna. 2010. “Methodology Note on the MSME
Country Indicators.” IFC and the World Bank. http://www.ifc.
org/msmecountryindicators
World Bank. 2009. “Doing Business 2010.” http://www.ifc.org/
msmecountryindicators
Acknowledgments
The authors would like to acknowledge the valuable contributions
of Mohammad Amin, Roland Michelitsch, Peer Stein, and Hugh
Stevenson.
8
1. For the legal definition of the MSMEs adopted by governments,
please see the note: “How Do Economies Define MSMEs?”
2. This number was calculated using observations from 93
economies where the breakdown between micro, small, and
medium enterprises was available.
3.
Excluded economies: Algeria; Argentina; Armenia; Azerbaijan;
Belarus; Belize; Bolivia; Burkina Faso; China; Ecuador; Ethiopia;
Guyana; Hong Kong SAR, China; India; Indonesia; Korea,
Rep.; Kuwait; Kyrgyz Republic; Malaysia; Mauritius; Nicaragua;
Panama; Qatar; Singapore; Sri Lanka; Sudan; Thailand; United
Arab Emirates and South Africa on the grounds that they
apply an MSME definition that uses variables other than total
employment or that their MSME definition is not available.
4. Kushnir, Khrystyna. 2010. “Methodology Note on the MSME
Country Indicators.” IFC and the World Bank. http://www.ifc.
org/msmecountryindicators
5. Excluded economies: Ethiopia; Puerto Rico; Sri Lanka; Nepal;
Panama; Nicaragua; Sudan; Tunisia on the grounds that the
data do not cover all sectors of the economy; Albania; Bahrain;
Georgia on the grounds that data come from surveys; Belize;
Brunei Darussaiam; Guatamala; Guyana; Iran, Islamic Rep. on
the grounds that data beyond 2000 are not available.
6. Excluded economies: Ethiopia; Puerto Rico; Sri Lanka; Nepal;
Panama; Nicaragua and Tunisia on the grounds that data do not
cover all sectors of the economy; Albania; Bahrain and Georgia
on the grounds that data come from surveys; Netherlands
Antilles; American Samoa; Bermuda; Guam; Myanmar;
Northern Mariana Islands; Qatar and the Virgin Islands (United
States) on the grounds that the data on GNI per capita, using the
Atlas method, are not available and Belize; Brunei Darussalam;
Guatemala; Guyana and Iran, Islamic Rep. on the grounds that
data beyond 2000 is not available.
7. Excluded economies: Ethiopia; Puerto Rico; Sri Lanka; Nepal;
Panama; Nicaragua and Tunisia on the grounds that data do not
cover all sectors of the economy; Albania; Bahrain and Georgia
on the grounds that data come from surveys; Belize; Brunei
Darussalam; Guatemala; Guyana and Iran, Islamic Rep. on the
grounds that data beyond 2000 is not available.
8. This result is statistically significant at the 1 percent level.
9. Excluded economies: Ethiopia; Puerto Rico; Sri Lanka; Nepal;
Panama; Nicaragua and Tunisia on the grounds that the data
do not cover all sectors of the economy; Albania; Bahrain
and Georgia on the grounds that the data come from surveys;
Netherlands Antilles; American Samoa; Bermuda; Guam;
Myanmar; Northern Mariana Islands; Qatar and Virgin Islands
(United States) on the grounds that data on GNI per capita,
Atlas method, are not available; Belize; Brunei Darussalam;
Guatemala; Guyana and Iran, Islamic Rep. on the grounds that
data beyond 2000 are not available; Timor-Leste; Burkina Faso;
Dominican Republic; Sudan; Tanzania and Venezuela, RB on the
grounds that data on employment by MSMEs are not available.
10. Excluded economies: Burkina Faso; Dominican Republic;
Iran, Islamic Rep.; Sudan; Timor Leste; Tunisia; Tanzania and
Venezuela, RB on the grounds that data are not available; Belize;
Brunei Darussalam; Guatemala and Guyana on the grounds that
data after 2000 are not available; Bolivia; Botswana; Cameroon
and Trinidad and Tobago on the gounds that data cover
enterprises in the private sector only; Canada and Tajikistan on
the grounds that data cover enterprises with no employees; Sri
Lanka and Mauritius on the grounds that data do not cover all
sizes of MSMEs; the West Bank and Gaza on the grounds that
data include governmental and non-governmental enterprises;
Montenegro on the grounds that there are no data on total
employment; Albania; Bahrain and Georgia on the grounds that
data come from surveys; Ethiopia; Nepal; Nicaragua; Panama
and Puerto Rico on the grounds that data do not cover all
sectors of the economy.

9. 11. Excluded economies: Ethiopia; Puerto Rico; Sri Lanka; Nepal;
Panama; Nicaragua and Tunisia on the grounds that data do not
cover all sectors of the economy; Albania; Bahrain and Georgia
on the grounds that data come from surveys; Netherlands Antilles;
Bermuda; Guam; Malta; Myanmar; Northern Mariana Islands and
Virgin Islands (United States) on the grounds that they are not
covered by the Doing Business Index data; Qatar and American
Samoa on the basis that the data on GNI per capita, Atlas method
are not available. In addition, countries with minimum capital of
less than 5 percent of GNI per capita and those with minimum
capital above 200 percent of GNI per capita were also excluded to
minimize the possibility of results being driven by outliers.
12. Excluded economies: Ethiopia; Puerto Rico; Sri Lanka; Nepal;
Panama; Nicaragua and Tunisia on the grounds that data do not
cover all sectors of the economy; Albania; Bahrain and Georgia
on the grounds that data come from surveys; Netherlands Antilles;
Bermuda; Guam; Malta; Myanmar; Northern Mariana Islands and
Virgin Islands (United States) on the grounds that they are not
covered by the Doing Business Index data; Qatar and American
Samoa on the basis that the data on GNI per capita, Atlas method
are not available.
MSME Country Indicators is a joint work of Access to Finance, Global Indicators and Analysis, and Sustainable Business
Advisory. Visit MSME Country Indicators at http://www.ifc.org/msmecountryindicators. This publication carries
the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in
this note are entirely those of the authors. They do not necessarily represent the views of the International Bank for
Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the
World Bank or the governments they represent.
9