Working Paper Series Macroeconomic Policy and Financing for Development Division WP/20/09 September 2020 Zeinab Elbeltagy Zenathan Hasannudin Financing Structure, Micro and Small Enterprises’ Performance, and Woman Entrepreneurship in Indonesia
Working Paper Series Macroeconomic Policy and Financing for Development Division
WP/20/09 September 2020
Zeinab Elbeltagy Zenathan Hasannudin
Financing Structure, Micro and Small Enterprises’ Performance, and Woman Entrepreneurship in Indonesia
ContentsI. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
II. Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
III. MSEs & financing sources in Indonesia: stylized facts . . . . . . . . . . . . . . . . . . . . 5
A. Main difficulties experienced by manufacturing MSEs . . . . . . . . . . . . . . . . . . 5
B. Market structure of the Indonesian manufacturing sector . . . . . . . . . . . . . . . . 7
C. Heterogeneity of geographical attributes . . . . . . . . . . . . . . . . . . . . . . . . . 8
IV. Data and variables of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
A. Data description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
B. Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
a. Dependent variables: firm performance . . . . . . . . . . . . . . . . . . . . . 11
b. Independent variables: financing structure . . . . . . . . . . . . . . . . . . . . 11
c. Control variables: firm characteristics . . . . . . . . . . . . . . . . . . . . . . 12
V. Empirical strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
VI. Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
A. Formal financing and firm productivity . . . . . . . . . . . . . . . . . . . . . . . . . . 17
a. Fixed effect regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
b. GMM estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
B. Woman entrepreneurs, formal financing and productivity . . . . . . . . . . . . . . . . 22
C. Alternatives firm performance measures . . . . . . . . . . . . . . . . . . . . . . . . . 24
D. Robustness Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
a. Alternative productivity measures: using the LP method . . . . . . . . . . . 26
b. Excluding Java island . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
VII. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
A Data definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
A. List of variables included in the analysis . . . . . . . . . . . . . . . . . . . . . . . . . 31
B. Industry classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
B Additional Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
MPFD Working Papers WP/20/09
Financing Structure, Micro and Small Enterprises’
Performance, and Woman Entrepreneurship in
Indonesia∗
Zeinab Elbeltagy† Zenathan Hasannudin‡
October 1, 2020
Abstract
Access to finance has been found crucial in influencing firms’ real activities and economic performance.
This paper investigates the relationship between the financing structure and firm performance by explor-
ing a unique panel dataset of 59,968 Micro and Small Enterprises (MSEs) operating in the manufacturing
sector in Indonesia over the 2010-2015 period. We collected a rich set of information about source of
loans to assess the firm performance using yearly total factor productivity (TFP) and labor productivity
of each firm. We then examined whether more financing options available to women entrepreneurship
improves firm performance. Our results show that financial factors are highly decisive to firms’ TFP
and labor productivity. The MSEs which have access to external formal financing directly improves
productivity at the firm level. Moreover, the study finds a significant underperformance of firms owned
by women entrepreneurs compared to those owned by men entrepreneurs. Nevertheless, we found that
women entrepreneurs who have access to formal financing improve their firm’s performance. The effects
of finance on productivity are also linked to the firm’s ownership, education, size and age. Our results
are robust as demonstrated through the use of different approaches. These results provide support for
policymakers to alleviate credit constraints to enhance productivity of micro and small enterprises and
especially woman entrepreneurship in Indonesia.
JEL classification numbers: G21, J16, L25, L26, N65
Keywords: Total factor productivity, inclusive financing, woman entrepreneurship.
∗The views expressed in this Working Paper are those of the author(s) and should not necessarily be considered as
reflecting the views or carrying the endorsement of the United Nations. Working Papers describe research in progress by
the author(s) and are published to elicit comments and to further debate. This publication has been issued without formal
editing. For more information, please contact Hamza Ali Malik, MPFD Working Paper Series Editor and Director of the
Macroeconomic Policy and Financing for Development Division, E-mail: [email protected].†Intern from June to August 2019 at the Macroeconomic and Financing for Development Division of the United Nations
Economic and Social Commission for Asia and the Pacific (ESCAP). PhD student at Paris School of Economics and Paris
1 Pantheon Sorbonne University, E-mail: [email protected].‡Associate Economic Affairs Officer at the Macroeconomic and Financing for Development Division at ESCAP, E-mail:
[email protected]. The authors are grateful to substantive comments provided by Francois Langot, Maria Bas,
and Akiko Suwa-Eisenmann at the Paris School of Economics. The paper also benefit comments from Tientip Subhanij,
Masato Abe, and Yusuke Tateno at ESCAP. Romain Pradier and Patchara Arunsuwannakorn provide editorial assistance.
1
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
I. Introduction
The financing of Micro and Small Enterprises (MSEs) has been a source of great interest both to policy-
makers and academic researchers. This is due to the significant role of MSEs in the private sector to drive
the economy (Berrell et al., 2008). It is evident from the literature that MSEs find it challenging to meet
the standards of formal financial institutions to obtain funds, such as higher costs and the relatively high
risk of loan lending. Hence, the lack of access to formal financing provides an opportunity for informal
institutions to fill the gap.
It is well documented that financial conditions have a significant influence on the firms’ productivity.
For instance, Carlin et al. (2006) reported that a high cost of finance adversely affects firm output and
Nguimkeu (2016) revealed that the lack of access to credit negatively impacts the gross margins of retail
enterprises in Cameroon. On the other hand, the repressed financial system impedes economic growth
since financial distortions can lead to resource misallocations. Hsieh and Klenow (2009) show that lower
Total Factor Productivity (TFP) of developing countries can be explained by resource misallocation
across establishments in manufacturing in China and India. Specifically, they found that the calculated
gains of manufacturing TFP are 25-40% in China and 50-60% in India when capital and labor are
reallocated to equalize marginal products to the extent observed in the US.
Indonesia is an interesting case to study the impact of different finance structures on firm performance
across provinces for the following reasons: (a) according to data from the ministry of co-operatives in
2014, Micro, Small and Medium Enterprises (MSMEs) sector account for more than 95% of the industrial
units1, and contribute to between 58% to 61% to Gross Domestic Product (GDP) (Tambunan, 2019).
In terms of employment, based on Indonesian Statistics Agency (BPS) dataset in 2013, the 57.9 million
enterprises in the sector employ 114.1 million workers. Therefore, MSMEs, especially MSEs, play a
vital role in generating employment and promoting industrialization in the Indonesian economy; (b)
the policymakers in Indonesia emphasize the need to encourage MSMEs and provide them a favorable
treatment by offering credit and tax incentives for investments (Japhta et al., 2016).
With the onset of economic reforms, new policy initiatives led to further support to this sector by
undertaking a host of initiatives such as credit guarantee schemes (CGSs)2; reducing interest rates of
KUR from 22% to 12% (Indonesia, 2016) and promotion of women entrepreneurs to accelerate the growth
of this sector; (c) overall, Indonesia’s economic growth performance has been impressive, but economic
condition vary across provinces, with western areas generally being more developed than their eastern
counterparts.
This study improves upon the previous research in several aspects. First, most of similar studies have
focused on measuring the impact of credit and access to finance on a single outcome variable, such as
economic growth, poverty reduction, or income inequality. Thus, their analysis was limited to partial
effects only. In contrast, our study analyzes the impact of formal external finance on different microeco-
1There were 98.75% micro-enterprises, 1.15% small enterprises and 0.1% medium-sized enterprises based on asset
MSMEs definition.2It is considered as an essential alternative instrument to meet their financial needs. In 2007, the government of
Indonesia launched a non-collateral CGS, namely People’s Business Credit (KUR). KUR is a government program that
supports MSMEs in the form of credit policy for individual or groups that are productive, but do not yet have collateral
or their collateral is not enough.
2
MPFD Working Papers WP/20/09
nomics outcomes such as firm productivity, sales, employment and wages, as they are closely interrelated
in the real economy. Second, contrary to earlier studies that explore cross-sectional variation (i.e. static
effect), we analyze the nature of credit over time to trace the evolution of funding impact on the external
economy in Indonesia. Besides, this allows us to mitigate the unobserved impact in the productivity
measurement due to the similar cultural and economic characteristics, thus providing a better estima-
tion. Third, we use a unique large data set of Indonesian MSEs to analyze the gender differences in firm
performance, which contributes significantly to the existing literature on MSMEs, particularly MSEs, and
women empowerment in developing countries. Finally, there is a lack of studies showing how financing
through microcredit contributes to MSEs’ performance in the specific context of Indonesia.
We used an unbalanced panel dataset on 249,688 observations from MSEs enterprises provided by Indone-
sia’s central statistics agency (Badan Pusat Statistik, BPS). Our empirical results indicate that credit
plays a vital role within the community to enhance the productivity of MSE businesses. Furthermore,
we found a significant positive effect of MSE finance on sales and employment within the supported
firm. Our findings suggest that men entrepreneurs do perform better than women entrepreneurs. This
finding is thus consistent with the fact that woman-owned firms face a disadvantage in the market for
small-business credit. However, obtaining a formal loan decrease the gap in the performance of women
and men-owned firms. This finding suggests that inclusive financing, which targets micro and small firms
and women entrepreneurs, tends to be associated with both economic and social gains, leading to overall
progress towards sustainable development goals through gender equality and decent job.
This paper is structured as follows: section (II.) presents literature review on MSEs and the link between
finance structure and firm performance. In section (III.), we provide stylized facts on the factors affecting
access to formal financing by MSEs and the market structure of the Indonesian manufacturing sector.
Additionally, we provide the distribution of MSEs and TFP using cross-provincial data. Section (IV.)
details the methodology, data construction and descriptive statistics. In section (V.) we present our
empirical strategy, firm productivity estimation and model specifications. The key results of this study
and robustness check are presented in sections (VI.). Finally, a conclusion is provided in section (VII.).
II. Related Literature
The MSE sector is an essential component of economic growth for two main reasons. The first is their
potential to grow into a more productive unit. Chaston and Mangles (1997) conducted an empirical
study of small manufacturing firms to identify the relationships between capacities and growth. They
concluded that there is no single strategy for firm growth; hence, the likelihood of achieving growth by
giving different capacities priority depends on the development stage of the firm. A second reason is the
fact that MSEs constitute a significant share of employment in the component of economic growth, as
shown by Ayyagari et al. (2007). They presented comprehensive statistics on the contribution of SMEs
to total employment across 76 other developed and developing economies. They found that, on average,
SMEs account for close to 60% of manufacturing employment.
Moreover, it seems that firm characteristics (such as size, age and ownership) may have a more complex
relationship to growth. For instance, Niskanen and Niskanen (2007) investigated the determinants of
growth in a sample of Finnish SMEs and found that close lending relationships promote growth for all
3
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
firms. However, only the larger firms in their sample benefit from more competitive banking markets.
Their database also highlighted the fact that younger firms exhibit higher growth rates than older firms.
Looking into the MSEs’ role in the economy, access to finance is one key element to support their
growth. For instance, Brown et al. (2005) used the detailed information from the start-up data in Roma-
nia through 2001. They employed panel data techniques to evaluate a survey of 297 new small enterprises
and showed strong evidence that access to external finance induces growth in both employment and sales,
while taxes appear to constrain growth. Furthermore, different experimental studies are worth reviewing
in detail as they precisely measure the general effect of micro-credit expansion. Kaboski and Townsend
(2012) assess the short and long term impact of Thailand’s ‘Million Baht Village Fund ’ program. The
results show an increase in short-term credit, consumption, agriculture investment, and income growth,
but a decrease in overall asset growth. In another example, exploiting a natural experiment created by
the opening of 800 new branches of Banco Azteca in Mexico in 2002, Bruhn and Love (2014) evalu-
ated the impact of increased access to finance for low-income individuals on entrepreneurship activity,
employment, and income.
Several theoretical models focus on understanding the potential long-run effects of particular credit
facilities programs on development, which are illustrated by Ahlin and Jiang (2008), using the model
of Banerjee and Newman (1993)„ who were the first to model the long-term effects of micro-credit on
development. Their findings showed that an improvement in the credit market for agents can potentially
help them become entrepreneurs through two channels: (i) it creates self-employment opportunities
and (ii) facilitates the graduation from a low-income to a high-income category through savings. The
model is extended by Yusupov (2012) and his predictions also suggest that access to credit can promote
development in lower-income countries. Both of these models argue that graduation alone could not
sufficiently promote economic growth. Therefore, Yusupov endogenizes the probability of graduation
from micro-entrepreneurs as a function of the aggregate pool of entrepreneurs. Consequently, according
to their models, credit for MSEs is one of the main determinants of development.
Besides, Buera et al. (2017) developed an essential general and partial equilibrium framework of the
macroeconomic effects of credit. They provide a quantitative evaluation of the aggregate impact of
finance, including the macroeconomic indicators such as output, capital, TFP, wages, interest rates,
and redistribution. The model implies that the introduction of typical micro-finance programs can have
such significant aggregate impacts that the redistributive effect of microfinance would be stronger in
general equilibrium than in partial equilibrium. Output, capital, and TFP are positively affected by
microfinance loans in both partial and general equilibrium, except for TFP, which is negatively affected
in partial equilibrium. Wages and interest rates inversely increase.
On a practical level, using panel data for 67 countries for the period 2001-2011, Lacalle-Calderón et al.
(2015) employed the Arellano and Bond (1991) Generalized Method of Moments (GMM) estimator to
analyze the causal transmission of micro-credit for economic growth. Accounting for country and time
effects, the authors showed that micro-credit had a positive and statistically significant impact on eco-
nomic development and that the channel works through private investment and consumption (Khandker,
2005). Using standardized data from Microfinance Information eXchange (MIX) for 2,382 Micro Finance
Institutions (MFIs) in 119 countries for the 1995-2012 period, Lopatta and Tchikov (2016) explored the
direct link between microfinance and economic growth through the value that MFI performance adds to
4
MPFD Working Papers WP/20/09
purchasing power. The authors found an indirect impact coming from an improvement in capital accumu-
lation and employment rates. In their later study, Lopatta et al. (2017) employed the Granger approach
and established a statistically significant relationship between MFIs’ social and financial indicators and
economic development.
Despite the significant increase in the share of women entrepreneurs in new start-up firms in Indonesia,
the empirical evidence of the effect of women entrepreneurship on firm performance is quite mixed.
On the one side, Sabarwal and Terrell (2008) provide a comprehensive view of the performance gap
between female and male-owned firms, where firms’ performance is measured in terms of sales and
profits. They showed that women entrepreneurship has a significantly negative impact on sales and is
less efficient in productivity compared to male-owned firms. On the opposite side, some studies (Kepler
et al. (2007); Watson (2002)) found no significant differences between male and female-owned firms on
business performance. Nonetheless, Coleman (2007) found that female-owned firms have significantly
higher sales growth than firms owned by men.
This strand of the literature has been growing steadily and primarily explores the aggregate effect of
credit on macroeconomic development indicators, such as, economic growth, productivity and financial
sector development. In sum, based on these theoretical and empirical studies, we maintain that credit
programs for MSEs could have a significant effect on output, capital, wages, interest rates, TFP and
poverty.
III. MSEs & financing sources in Indonesia: stylized facts
The financing gap in Indonesia between the amount of financing needed and the amount that loans
provides, as estimated from the IMF’s Financial Access Survey, is as large as 1, 320 trillion Rupiah
faced by Indonesia’s SMEs. Serving this kind of business is often challenging from a risk-management
standpoint due to their limited or no credit history. Consequently, there are many ambitious strategies
to solve this challenge to boost potential growth. One of the best-known institutions is the Bank Rakyat
Indonesia (BRI) Micro Business Division (hereafter referred to as the BRI Units) which are considered
as highly commercial because of their formality and reliance on deposits as their primary source of funds.
According to Charitonenko and Afwan (2003), at the end of 2001, state-owned BRI had served around
30 million clients (27.0 million savers and 2.8 million borrowers) through its 3,823 BRI Units and 240
branches. By 2001, BRI accounted for about 43.5% of the total value in outstanding loans in Indonesia.
On the other side, several non-bank financial institutions (NBFIs) are also important suppliers of micro-
credit at the district and village levels. Additionally, since 2008, the government started to increase the
use of guarantees as a way of channeling credit toward SMEs that tend to be financially constrained.
A. Main difficulties experienced by manufacturing MSEs
Several limitations hamper the performance of MSEs in Indonesia. These limitations may vary from
province to province, between different sectors, or even between individual enterprises within the same
sector. Previous studies came out with a list of common constraints to all MSEs (e.g., Roy and Wheeler
(2006); Bekele and Worku (2008); Thapa (2013); Das and Mohiuddin (2015); Oyelana and Adu (2015);
Moustafa and Santos (2016)), which include lack of capital, difficulties in marketing, government policies
5
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
or regulations which often generate an unfavorable business environment3, access to modern technologies,
skilled workers, and institutional support.
As shown by Figure (1), most of MSEs’ owners claimed that they had many difficulties in running
their businesses. These challenges are concentrated in three main areas, the difficulty in (i) getting
funds, which represented 41.5%, either for working capital or purchasing new machines; (ii) marketing
amounted at 23%; and (iii) getting raw material (21%). The difficult in getting funds, particularly from
formal sources, is caused by various factors, such as unstable types of businesses, poor credit history, and
no valuable assets as sufficient collateral. This lack of accessible capital for these enterprises threatens
their continued existence (Abe et al., 2015). This is followed by marketing challenges, which can be
due to many causes; such as expensive rental rates, difficulty in finding a strategic location, inadequate
capital for promotion, tight competition especially from goods imported from China with very low prices
(Navarro, 2006), and transportation costs which characterize a critical factor in strengthening a market
entry strategy.
Figure 1: Main difficulties experienced by manufacturing MSEs (%)
41.5
23.121.3
7.4
2.7 1.8 1.5 0.9
010
2030
4050
Perc
ent o
f Firm
Capital Marketing Rawmaterial
Others Workerskills
Fuel &energy
Transportation
Laborwages
Source: Authors’ calculation based on Indonesian Statistics Agency (BPS).
Whereas, there are different reasons in getting raw materials, namely the required raw materials are
not accessible locally, high price of raw materials (notably, the fluctuations in the value of Indonesian
Rupiah against the US dollar lead to a significant rise in the bill of imported raw materials since the
end of 1997/1998 Asian financial crisis at (Aimon and Satrianto, 2014)), and also the remote place
where selling the raw material is often causes high transportation costs. Besides, most of the enterprises
surveyed had never received support from either their government or the private sector.
3For instance, energy policies on fuel and electricity rate or import regulations on raw materials have substantial impact
on production costs in MSEs and hence on their price competitiveness and profit.
6
MPFD Working Papers WP/20/09
B. Market structure of the Indonesian manufacturing sector
According to Tijaja and Faisal (2014), the manufacturing industry has been long recognized as the
backbone of the Indonesian economy. The manufacturing sector has a major contribution to aggregate
output, reaching 24% of GDP in 2013. The MSE survey data covers firms in 24 manufacturing sectors4.
However, since the number of observations in some industries was small, we divided them into six broad
categories.
Table (1) shows the classification of the manufacturing industries. Most of these industries are labor-
intensive and require low levels of technology. However, the structure of the manufacturing sector has
changed over the 15 years. This change involves an increase in the importance of natural resource-
based industries such as food, beverages, tobacco, fertilizer, chemical and rubber, and a decrease in the
significance of labor-intensive sectors, such as textiles, leather and footwear, and wood products over
time.
Table 1: Employment and output by activity (% of total manufacturing)
ActivityEmployment Output
2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015
Food & beverages 28.90 27.39 24.95 31.70 36.01 35.30 30.28 20.08 21.8 30.8 37.1 36.11
Wood products & furniture 22.83 20.62 20.52 20.82 24.20 24.66 17.11 17.2 15.25 15.15 18.82 25.87
Textiles & leather products 22.12 20.70 20.95 20.96 19.79 20.12 21.19 25.27 24.97 21.53 17.04 18.73
Non-metal & plastic products 15.47 17.64 19.16 15.11 12.06 11.99 15.05 18.71 14.17 14.17 12.47 11.89
Metal & machinery products 5.72 7.45 7.28 6.06 3.93 3.96 11.04 12.49 11.65 13.43 8.32 3.49
Others 4.97 6.21 7.14 5.36 4.01 3.97 5.3 6.2 7.59 4.89 6.2 3.87
Source: Authors’ calculation based on Indonesian Statistics Agency (BPS).
In order to provide additional evidence, we also assessed the share of employment and output, which varies
across sectors. In Figure (2), the employment is dominated by the food and beverages industry, which
absorb a labor force of 80,274 people (32%), followed by the wood industry, wood and cork manufacturing
including furniture, of about 23%. Textiles and leather products represented 20.7% and finally non-
metallic minerals goods industry and plastic products representing 14.2%. The industries that absorb
the labor force least are metal and electrical equipment industry at 5.2% and other manufacturing at
4.8%. MSEs’ employment by provinces is still concentrated on the island of Java as an industrial district
at 39% and 23% for Sumatra island. Furthermore, MSEs’ production between (2010-2015) accounted
for 32.5% of food and beverages industry and 19.4% of wood and furniture, followed by 20% of textiles
and 14% of non-metal products.
4Two digit ISIC with codes from 10 to 33 are employed in this study as represented in Table (10).
7
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
Figure 2: Share of employment and output between (2010-2015) in MSEs
32.1 32.5
22.9
19.420.7 20.0
14.2 13.9
5.2
8.9
4.8 5.3
010
2030
40Sh
are
(%)
Food &beverages
Wood &furniture
Textiles &leather
Non-Metal &plastic
Metal &machinery
Others
Employment Output
Source: Authors’ calculation based on Indonesian Statistics Agency (BPS).
C. Heterogeneity of geographical attributes
In this part, we provide a snapshot of the distribution of MSEs using cross-provincial data from all 34
provinces in Indonesia. We follow the research of Blalock and Gertler (2008) who grouped the provinces
of Indonesia as geographical units. We thus combined the provinces into five leading island groups,
namely: Sumatra, Java, Kalimantan, Sulawesi and Outer Island.
In Figure (3), we can see that the number of MSEs is relatively centralized in the western and more
developed part of the county with darker colours. This includes the island of Java, Sumatra, the southern
and western part of Kalimantan. Meanwhile, in the eastern area of Indonesia, we could observe a lighter
color which showing a relatively small number of MSEs. For example, Maluku and Papua, which are
the least developed parts of Indonesia, respectively represents only 1.53% and 0.97% of total MSEs in
Indonesia.
The predominance of MSEs in this western part of Indonesia may be explained from various angles,
specifically from the demand side within the goods and services market context and from the supply side
in the labor market context. From the perspective of the market demand, the availability of infrastruc-
ture; for instance; road, railway, port infrastructure and financial services, which encourages people to
run their own business. Nevertheless, lack of support in the eastern part of the country has inhibited in-
vestment there. From the perspective of labor supply, population density is highly concentrated on these
islands that have abundant natural resources, providing attraction for firms to establish their business
and to access more workers. As a result, the western part of Indonesia has become a hub of economic
activity and witnessed an increasing the number of MSEs.
8
MPFD Working Papers WP/20/09
Figure 3: Number of MSEs based on province, 2015
(1863,6620](1379,1863](883,1379][302,883]
Source: Authors’ calculation based on Indonesian Statistics Agency (BPS).
Using TFP as a proxy to measure the productivity of the firms, we see in Figure (4) that a majority of
firms with high productivity reside in the islands of Java, Sumatra, and Kalimantan. This finding shows
that MSEs in these provinces have benefited from better infrastructure and demographic factors that
drive the firms to be more competitive and productive. We can also explain the different causes of high
productivity in these islands for Sumatra and Kalimantan with the fact that MSEs productivity can be
attributed mostly from the production of palm oil, cocoa, rubber, and other plantation product which
are abundant in these islands. Meanwhile, MSEs in Java islands focus more on manufacturing consumer
goods to meet demand from their dense populations.
Figure 4: Firm productivity (Ln TFP), 2015
(2.871874,2.934489](2.85756,2.871874](2.845983,2.85756][2.785084,2.845983]
Source: Authors’ calculation based on Indonesian Statistics Agency (BPS).
Interestingly, we also observe abnormality for Papua island with an unusually high productivity in the
same Figure (4). The infrastructure and demand factors are still least developed in Papua, but high
productivity can be explained by at least two factors. First, we notice that the province is a resource-
abundant island with minerals like gold and copper. This in turn provides extra funds for the local
government and major private companies to support MSEs with technical capacity building and financial
support to improve their productivity. Second, the province attracts migrants from the eastern part of
Java which are better equipped with skills and networks to establish MSEs as an entrepreneur in Papua.
9
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
We observe that these two factors contribute to the unusual high level of productivity for the MSEs.
In the analytical part of this paper, we try to avoid this heterogeneity issue with different islands char-
acteristics. As suggested by Amiti and Cameron (2012), we introduce islands times year control variable
as a fixed effect, to hold the variations between islands over time.
IV. Data and variables of interest
This section describes data and variable construction. The detailed firm-level data allows us to address
concerns of endogeneity and reverse causality to analyze the effect of financing structure on firm-level
performance. Thereby we can identify the underlying channels through which changes on the firm-level
characteristics affect their overall performance.
A. Data description
We used the Micro and Small Industries Survey (IMK) data as the main data source of this analysis
provided by BPS5. This survey designed by BPS is a complete annual enumeration of micro and small
firms. Besides, the information on the ISIC level is available in the published summary form of the
survey, while the firm-level data can be obtained from BPS electronically. Furthermore, we follow the
definition of the 2016 Indonesian Economic Census to identify MSEs, which uses an employment-based
classification by which micro-enterprises employ 1-4 people, and small enterprises employ 5-19 people.
In this study, we have an unbalanced panel dataset of 59,968 firms, scattered on six years throughout the
2010-2015 period. Each firm in the survey is provided with a unique code that allow us to generate a panel
dataset through the firm’s unique identifier. On average, the panel data contains information on 249,688
observations. Industries are classified according to the 2-digit ISIC (ISIC rev.4) which yields 24 industries
represented in the sample (see Table 10 in the Appendices). This dataset of firm-level manufacturing
establishments contain detailed information including firm identification, sector classification, type of
ownership, workforce structure (number of paid, unpaid, male and female workers), wage bill of workers
and financial characteristics of MSEs. Among others, firms are also asked a range of different questions
about their production, output, value-added, capital and labor. All the monetary variables are deflated
using the consumer price index (CPI) with 2010 = 100.
A drawback of this dataset is that it does not cover all information on the loan amount and the interest
cost incurred on these loans over the years. Moreover, in undertaking the analysis of the data, it was
observed that some variables constituting the inputs and output of the production function were not
available every year for all firms. Therefore, certain steps needed to be taken to clean the data.
B. Descriptive statistics
The Table (2) reports summary statistics of the variables used in this study. The definitions of variables
used in our empirical research are also represented in Table (9).
5The Indonesian Census of Manufacturing is part of a decennial Economic Census that uses the Indonesian Standard
Industrial Classification (ISIC) for all economic activities.
10
MPFD Working Papers WP/20/09
a. Dependent variables: firm performance
We measure the performance of MSEs by using two leading indicators, namely TFP and labor produc-
tivity. The paper also checks other indicators that can help explain firm performance through sales,
employment and wages.
In this paper, we use the TFP as calculated by Ackerberg et al. (2015) estimation of the production
function. According to our descriptive analysis, we found that the average TFP of Indonesian enterprises
is 2.85, which is in line with Şeker and Saliola (2018)’s research, who conducted a cross-country analysis
of TFP performances of manufacturing firms in 69 emerging economies. Within the South East Asia
region, these numbers are slightly lower than those of Vietnam (average TFP ranged from 1.16 to 4.01)
but ahead of Thailand (1.06 to 2.78) and Malaysia (1.14 to 3.37).
Furthermore, we analyze the differences in productivity between companies of different sizes. Figure (5a)
depicts the link between formal financing and economic growth through TFP which shows clearly that
the firms with access to formal financing have higher productivity compared with the firms with access
to informal financing. While, Figure (5b) shows the kernel density estimation of log TFP by firm size.
Not surprisingly, the distribution of small firms is skewed relatively to the right for micro firms, which
demonstrates that, on average, companies of larger size show a higher productivity level.
We also calculate labor productivity as the ratio of the value of output produced and the number of
workers involved6. Ideally, a measure of firm productivity that is proxied by labor productivity is used
because it is an informative measure of firms’ unit labor cost, which is employed by entrepreneurs to
make decisions on profitable opportunities. Moreover, it indicates whether firms use and allocate resource
input most efficiently for productive uses (Hsieh and Klenow, 2010).
It is apparent that sales revenue is a good indicator for overall performance which reveals how much
a company earns and it can help firms to manage their budgets effectively7. Moreover, employment
and wage may also be an essential factor in firm performance. Most MSEs, especially micro-enterprises,
are family businesses. So, according to our dataset, the total number of workers during 2010 to 2015
includes as many as 249,688 people, comprising 82,959 paid workers (33.22%) and as many as 166,729
non-paid workers (66,78%)8. However, this percentage varies by group of the industry, which suggests
that industries with more complicated production processes are required to employ high skilled labor
from non-family members.
b. Independent variables: financing structure
From our dataset, we find evidence that internal financing sources (77%) are favored as a mean of securing
financing for MSEs in Indonesia, especially during the start-up stage. While, only 23% of the surveyed
firms have access to an external source of finance. This evidence reflects the deficient access to credit
6The more traditional approach of using value-added as the numerator is not adopted because value-added information
is not available in our database. However, the use of output is acceptable and more appropriate because the output is
measured at firm level.7We used the indirect taxes as a proxy of sales as the higher purchase of goods or services by the consumers, the higher
revenues for the firm.8For unpaid workers who work less than 1/3 applies regular working hours (one week) in a company/business is not
included as a worker.
11
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
sources for micro and small Indonesian firms. Moreover, as reported in Abe et al. (2015), small businesses
use internal funding sources at start-up stage and then they substitute it with external funding when
their business grows.
Formal financial sources comprises 40% of the sample, which includes bank loans (28%), cooperative
loans (5.5%), non-bank institutions (3.3%) and venture capital (0.5%). While around 60% of MSEs use
informal financing such as individual support (26%), family loans (14.3%) and other loans (22.2%) (see
Figure (7a) for detailed information). However, most firms indicate that they use banks as the primary
source for their operations, but some firms are not qualified to apply for a loan.
We also observe that there is no single dominant reason why firms do not borrow from formal financial
institutions. Figure (7b) demonstrates the constraints facing borrowing. One of the main reasons for
the MSEs not to apply for a bank loan is that 55% of the owners do not want to obtain a loan from the
commercial banks. The cause behind not applying for loans can be partly explained by religious factors
as mentioned by Al-Mahrouq (2003). He explains that loans from commercial banks come with interest,
which is forbidden in Islam9. Similarly, Demirgüç-Kunt et al. (2008) points out that the religious factor is
one of the internal reasons for owners not to seek an external formal type of financing. Furthermore, other
main reasons include high collateral requirements by the banks (notably in Java, Bali, and Sumatra) and
transaction costs which can be attributed to 13.5% of respondents which indicated for instance; cost of
information, cost of finance and the complexity of procedures which amounted to 12.3%, 9.8%, and 8%
respectively.
c. Control variables: firm characteristics
Having obtained our measure and objective to access structure-financing variables, it is essential to
control firm characteristics as they reflect the creditworthiness and resources of a firm that the lender
might take into account while making a decision to lend. We find that heterogeneity of firms in terms
of access to credit may arise due to firm characteristics, such as gender of the owner, firm size, firm age,
legal structure, and education of the owner.
In line with previous research, recent studies have shown that not only firm size but firm age seems to
have a significant effect on accessing bank financing (e.g., Chavis et al. (2011); Mac an Bhaird and Lucey
(2010); Huyghebaert and Van de Gucht (2007)). This means that younger firms rely more on informal
financing (e.g., loans from family and friends, internal or state-subsidized financing)9; whereas more
mature firms use more formal financing sources including bank loans, equity and retained earnings as
they already have track records, credit history and established relationships, which decrease information
asymmetries for capital providers (Berger and Udell, 2002). In our sample, there is a large variety of age
and size across the firms. The majority of surveyed firms are micro firms at 88% while around 12% are
small firms. Additionally, firms on average are 14 years old, but some firms are more than 100 years old.
On the legal front, in our sample, MSEs are categorized into four main types, such as sole proprietorship,
private limited company, limited partnership, and cooperative. The most common legal form under
MSEs is a sole proprietorship which represented 95% of the total sample. Nevertheless, we observed that
9Formal capital providers such as banks are more inclined to provide short term debt for younger firms as it is more
flexible and the contract is easier to terminate in case the firm does not develop as expected (Huyghebaert and Van de
Gucht, 2007).
12
MPFD Working Papers WP/20/09
a firm with sole proprietorship also recruits workers, most of them unpaid. While paying attention to
the gender of the firm owner, we pointed out that men own most MSEs in our data, which amounted
59% of the total sample. Nevertheless, we observe the number of firms owned by women (or women
entrepreneur), is increasing over our observation time from 2010 until 2015.
Within education categories, although education is the most effective tool for human development (Wa-
maungo, 2011) and can help to strengthen or improve the financial inclusion and financial literacy (Abdu,
2014), the majority of the MSEs in Indonesia are owned by less educated people. We observe that 59%
of the firm owners have a low level of education with no schooling or have only completed primary school
education. On the other end, only 19,5% of the owners completed high school and only 3% recorded
university degree-level education.
Interestingly, there is the relationship between education level and gender as described in Figure (8a).
This figure shows that female entrepreneurs, on average, have a lower level of education. For example,
female entrepreneurs accounted for 41% of all micro and small enterprises, but close to 27% of them
have not completed any schooling compared to only 18% of male owners who did not finish primary
school. Moreover, 16% of women entrepreneur have a senior high school degree, compared to 22%
of the male owners in the same category. Furthermore, as it can be observed in Figure (8b), well-
educated entrepreneurs have more likely to access the formal financial institution than low educated
entrepreneurs. For instance; entrepreneurs who have completed high school are more likely to have
access to formal financing at 31%, compared to entrepreneur who have not finished primary school at
12%. These differences in educational attainment suggest that more educated entrepreneurs, particularly
males, have relatively better access to formal financial institutions for financing need.
13
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
Table 2: Summary statistics
Variables Observation Mean Standard deviation Minimum Maximum
Firm characteristics
Size 249,688 1.122 0.327 1 2
Small 249,688 0.122 0.327 0 1
Micro 249,688 0.878 0.327 0 1
Firm age 249,687 14.023 11.053 0 115
Women entrepreneur 249,688 0.409 0.492 0 1
Sole proprietorship 249,688 0.950 0.219 0 1
Not finished primary school 249,688 0.216 0.412 0 1
Primary school 249,688 0.372 0.483 0 1
Junior high school 249,688 0.187 0.390 0 1
Senior high school 249,688 0.195 0.396 0 1
Diploma or higher 249,688 0.030 0.170 0 1
Number of workers
Salaried workers 249,688 1.190 2.574 0 19
Non-salaried workers 249,688 1.437 0.790 0 19
Male Workers 249,688 1.515 2.083 0 19
Female Workers 249,688 1.113 1.496 0 19
Workers 249,688 2.628 2.605 1 19
Wage of workers
Wage male(log) 69,336 16.947 1.201 9.2 23
Wage female(log) 33,513 16.258 1.195 3 22
Real wage(log) 83,257 16.930 1.273 3 23
Average wage(log) 82,803 15.991 0.951 2.3 20
Sources of capital
Fully internal capital 249,688 0.774 0.418 0 1
External capital 172,190 14.982 27.353 0 100
Formal financing 56,386 0.398 0.489 0 1
Collateral value 8,021 2.339 1.020 1 4
Reasons for not borrowing 224,433 4.453 1.891 1 6
Performance Indicators & others
TFP_Acfest(log) 215,884 2.846 0.087 2.6 3.2
Labor productivity(log) 218,902 16.488 1.322 9.4 22
Electricity(log) 122,082 12.585 1.989 1.9 21
Interest expense(Log) 18,442 14.469 1.633 3.2 21
Sales(log) 137,553 10.507 1.920 2.4 21
Consumer price index 249,688 117.007 12.143 100 132
14
MPFD Working Papers WP/20/09
V. Empirical strategy
As highlighted in the introduction, this paper studies the impact of financing structure on MSEs’ firm
performance and economic outcome at the firm level. Moreover, by looking at the firm’s characteristics,
this paper highlights the role of gender and education of the entrepreneurs, which also affects the rela-
tionship between financing structure and firm performance. Our empirical strategy follows a two-step
analysis. First, we estimate the TFP of firms, and second, the TFP is treated as a dependent variable
against a set of explanatory financial and economic variables.
In estimating the TFP, there are three different alternatives which has been adapted by this paper. The
first is Olley and Pakes (1996) (henceforth, OP estimation), where the framework uses investment level
as a proxy for unobserved productivity to control the endogeneity problem that arise because of the
correlation between the observable input levels and the unobservable productivity shocks. However, the
OP approach has a significant limitation as investments are not decided at each point in time. As a
result, such delay violates the monotonicity assumption (Eberhardt et al., 2010).
To address the monotonicity assumption, the second estimation of TFP is proposed by Levinsohn and
Petrin (2003) (henceforth, LP estimation). They suggest to overcome this issue by exploiting an interme-
diate input cost or electricity instead of investment as an alternative proxy to control a firm’s knowledge
of its efficiency. Nevertheless, one could see there is a problem with LP, which is functional dependence,
to be more specific, all variables are supposed to occur at the same time by using the unconditional
intermediate input demands; this could lead to collinearity.
The third TFP estimation by Ackerberg et al. (2015) (henceforth, ACF estimation), proposed the cor-
rected function approach that uses moment conditions very similar to those uses by OP and LP, but they
avoid this functional dependence problem that may arise in the LP framework. Specifically, both OP
and LP assumed that firms could instantly adjust some inputs at no cost when subject to productivity
shocks. Throughout the paper, we utilize ACF estimation as default option on estimating the total factor
productivity.
After obtaining the firm-level TFP estimates, we follow the methodology employed in the Levine et al.
(2000) model of financial development and growth to investigate the impact of credit facilitation by
formal financial institutions on firm’s performance in Indonesian’s manufacturing sector. The regression
framework consists of a panel regression of the performance of the firm (i) in period (t) on the formal
financing in the same period and a set of control variables.
15
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
The econometric specification is given by the following equation:
FPit = αi + β1 FSit + β Xit + µi + νt + εit (1)
Where FPi,t is a measure of firm performance. It can be measured by the TFP of the firm (i) at a
time (t) estimated using Ackerberg et al. (2015) and labor productivity. Note that all variables in our
regression have been transformed into a logarithmic form10. FSi,t is a financing structure of the firm (i)
at a time (t) and measured by formal external credit or fully internal capital. Formal financing is our
explanatory variable of interest; it is a dummy variable that takes on the value one if the firm relies on
external sources from banks, cooperatives, non-bank financial institutions (NBFI) and zero otherwise.
Meanwhile, fully internal capital is a dummy variable taking the value of one if the MSE’s primary
source of capital is based on the internal source of finance (such as; inheritance, savings and liquidation
of assets) and zero otherwise. According to the theory discussed previously, β1 is expected to be positive
for each of those dependent variables. Xi,t is a vector of observable characteristics of firm i in period
t that could influence the probability of obtaining a loan. Additionally, µi are firm-level fixed effects
controlling for unobserved characteristics of firm i that do not vary over time and νt is a set of year fixed
effects. Finally, εi,t can be interpreted as random shocks.
In terms of econometric methodology, we estimate Eq. (1) using a panel fixed effect model, taking
into account both firm and year fixed effects. Although a model-adjusted standard error can deal with
heteroscedasticity problems and autocorrelation, Wintoki et al. (2012) have claimed that bias relating
endogeneity still exists. To deal with this issue, this research applied the dynamic panel GMM explored
by Arellano and Bond (1991) to deal with the endogeneity issue. The GMM provides an abundance
of instrument variables, which makes it easier to achieve the conditions of valid instruments and over-
identification of estimators. Besides, the Arellano and Bond estimator is suitable for short panel data
that have small T and large N, meaning few periods and many individuals. This research used short
panel data with large companies and only six years, so the GMM method introduced by Arellano and
Bond (1991) was employed and believed to be best suited. In this case, we extend the Eq. (1) by adding
the lagged dependent variable.
We further verify the robustness of our outcomes. This research ran regressions in which industry
and year dummy variables were included to capture industry or year specific FE. Besides, alternative
measurement of dependent or independent variables were applied to retest the results.
10A natural logarithm is used for several variables to improve the goodness of fit of the regression models and overcome
simultaneity bias.
16
MPFD Working Papers WP/20/09
VI. Results and Discussion
In this part, we analyze the data based on our empirical strategy. We first produce the baseline result
while assessing the relationship between formal financing and firm productivity. Fixed effect regression
and GMM method are utilized to investigate our primary hypothesis that external financing sources
from formal entities have a positive effect on the MSEs performance. Second, we assess how women
entrepreneurs can have a distinct effect on the mentioned hypothesis. Lastly, we check with other firm
performance measures to look at the varying impact of financing structures to the firms’ performance.
A. Formal financing and firm productivity
a. Fixed effect regression
We present in Table (3) the benchmark result of our empirical model using the FE estimator to assess
the relationship between financing structures and firm productivity. Columns (1-4) report the results of
TFP obtained by Ackerberg et al. (2015) estimation of the production function as a dependent variable.
Meanwhile, columns (5-8) present the results of regression using labor productivity as a dependent
variable. For each dependent variable, we first put the financing structure between formal external
financing and fully internal financing as the only independent variables. Subsequently, we use control
variables that include firm characteristics to the equations. In this empirical model, we include firm fixed
effects and year times island fixed effect to hold variations constant for our regression.
Column (1) and (5) shows essential findings of the paper with coefficients on formal external financing
variables showing positive and statistically significant effects at 1% level in influencing productivity.
More specifically, this indicates that if the MSEs receive formal financing, they will have chance to
increase their TFP by 5,5% and labor productivity by 78,5%, compared to those firms with full internal
funding. The regression also shows that the effects of financing sources are relatively stronger for labor
productivity than TFP productivity, as the coefficient of formal financing is higher for the former.
On the other hand„ in column (2) and (6) we show that firms, which use only internal sources of
financing, have lower productivity, with negative and statistically significant coefficient at 1% level in
affecting productivity. To be more precise, firms which use full internal financing will have lower TFP
productivity by 5,0% and labor productivity by 63,1%, compared to those firms which use an external
source of formal financing. This is an important finding since the sheer majority of MSEs in Indonesia
rely only on internal funding. In other words, only a small fraction of MSEs in Indonesia could improve
their productivity by having access to formal financing institutions. Our main result also holds when
we include five firm specific variables into our regression model, which are women entrepreneurs (firm
owned by a woman), firm size, firm age, legal structure, and education of the owner. In column (3) and
(7), the coefficients for formal financing are positive and strongly significant at 1% level. Meanwhile, in
column (4) and (8), the coefficients for firms, which rely on internal financing, are negative and strongly
significant at 1% level.
Our findings show that external finance from financial institutions has a greater impact than internal
funding (retained earnings) for their business operations. This is mainly because the primary purpose of
the requested external funding is for working capital, buying equipment, machinery, and to develop the
17
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
business which in turns increases the productivity. Furthermore, MSEs with better access to finance are
hypothesized to have a higher chance of engaging and perform great productions11. In other words, larger
firms also tend to be better connected to banks or other formal sources of finance. This argument is in
line, for example, with Claessens et al. (2000) which found that the size of firms affecting the dependency
of obtaining credit from the bank in Asian countries, with larger firms are more bank-dependent.
Table 3: The effect of financial structure on firm productivity – Fixed effect estimator with robust
standard error
Dependent variableln (TFP_ACF) ln (Labor productivity)
(1) (2) (3) (4) (5) (6) (7) (8)
Formal financing 0.055*** 0.035*** 0.785*** 0.556***
(0.010) (0.005) (0.136) (0.078)
Fully internal financing -0.050*** -0.028*** -0.631*** -0.385***
(0.006) (0.003) (0.091) (0.054)
woman entrepreneur -0.051*** -0.054*** -0.681*** -0.705***
(0.014) (0.012) (0.213) (0.187)
Size (1=micro, 2=small) 0.063*** 0.078*** 0.361*** 0.585***
(0.008) (0.009) (0.115) (0.130)
Firm age(Log) 0.007** 0.003 -0.020 -0.088**
(0.003) (0.002) (0.052) (0.036)
Sole proprietorship -0.013*** -0.011*** -0.194*** -0.155***
(0.004) (0.002) (0.056) (0.031)
Primary school 0.008* 0.015*** 0.148* 0.248***
(0.005) (0.002) (0.084) (0.026)
Junior high school 0.021*** 0.034*** 0.368*** 0.551***
(0.006) (0.004) (0.109) (0.060)
Senior high school 0.029*** 0.050*** 0.486*** 0.795***
(0.006) (0.005) (0.093) (0.077)
Diploma or higher 0.036*** 0.057*** 0.570*** 0.845***
(0.004) (0.006) (0.081) (0.084)
Firm FE Yes Yes Yes Yes Yes Yes Yes Yes
Island x Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Cluster Level Industry Industry Industry Industry Industry Industry Industry Industry
Observations 40,995 198,718 40,995 198,718 41,551 201,478 40,995 198,718
R-squared 0.774 0.368 0.839 0.560 0.767 0.357 0.801 0.488
Robust standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
11Due to data availability, we could not test the hypotheses that the increase in productivity is from asset accumulation.
At the time the paper is written, IMK database is not available in BPS so we could not obtain new set data on assets.
18
MPFD Working Papers WP/20/09
Table (3) also provides us with a baseline result on how the firm-specific characteristics affecting produc-
tivity. One of the main results that we would like to highlight from the firm features is how the gender
of the owner affecting productivity. In short, we would like to understand whether a female entrepreneur
has higher productivity than their male counterpart. This table shows us that, for example, based on
(3), the coefficient estimates of a female entrepreneur is negative and statistically significant at the 1%
level. This suggests that a firm owned by women will decrease the TFP productivity by 5,1% compared
with a firm owned by men. The result still holds with greater magnitude when we use labor productivity
as the dependent variable.
This could be explained by many factors, which include: (a) lack of education or training opportunities
that make them disadvantaged in access to formal financing and financial institutions, thus negatively
affecting firm productivity; (b) the explanation could lie in Leahey (2006)’s work, who argued that male-
owned business outperformed woman-owned business because of female’s lack of industry experience and
their concentration in less profitable sectors of the economy which contributed significantly to their lower
sales and income; (c) another explanation cited in the literature (Barber and Odean, 2001, Dohmen et al.,
2011) is their higher levels of risk aversion that may lead them to restrict investment in their business and
thereby to limit the growth of their firms; (d) socio-cultural factors further limit woman entrepreneurs’
business growth, especially in rural areas where women are more burdened by household tasks and
childcare. All these concerns might well explain the underperformance of woman entrepreneurs.
We then observe other firm characteristics in affecting productivity, such as the size of the firm, age,
legal status, and education of the owner. First, one the size of the firm, this variable is one of the most
widely studied aspects of firm productivity (e.g., Bartelsman and Doms (2000) and Ahn (2001)). The
coefficient estimates of firm size are positive and statistically significant at 1% in all model specification
in Table (3). The result is indicating that small firms exerted substantial and positive impacts on firm
productivity measured by TFP and labor productivity as compared to micro-sized enterprises. This
result is in line with the studies of Leung et al. (2008) and Van Biesebroeck (2005), which argued that
smaller firms tended to be more productive than micro-enterprises and some of the differences between
small and micro firms could be due to a concentration of micro firms in less productive industries.
Our result shows varied outcomes when we look at the effect of firm age to firm productivity. Column
(3) shows that firm age coefficient is positively significant at 5% level with TFP in a model that includes
formal external financing. We lost the significance when the firm only relied on the informal financing in
column (4). To illustrate the result, studies of Musamali and Tarus (2013), Le (2012) and Kira (2013)
revealed that the number of years that business is operated has a positive impact on access to finance.
In other words, this means the older the business is, the easier it can reach out to external financial
resources, thereby increasing firm productivity. However, the story of firm age in affecting productivity
is different when we look at labor productivity as can be seen in column (7) and (8). The coefficient is
showing a negative result that indicates that as the firms matured the labor productivity actually falling
down.
Consistently, we observe that sole proprietorship is negatively related to firm productivity as its co-
efficients are significantly negative at 1% level in all regressions. This suggests that a single owner’s
enterprises have lower productivity in term of TFP and labor productivity. These findings are consis-
tent with most previous studies, for example the studies of Collins-Dodd et al. (2004) and Farace and
19
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
Mazzotta (2015). Based on the result, we argue that sole proprietorship is less likely to sustain business
growth and less innovative compared to other organizational forms such as a partnership or cooperative,
making it less attractive for the lenders. In addition, they may be viewed as riskier by lenders and
investors. Thus, they face considerable financial constraints from formal financial institutions, thereby
negatively affecting firm productivity.
Finally, in all our model we found that the education of business owners is relatively crucial to firm
productivity. The firm with the more educated owner has a higher effect to productivity in relation to
their financing structure. On this side, we suspect that low educated owners have a tendency of not
having sufficient knowledge on external financing and how to keep record their financial transaction,
which makes it hard for the formal financing institutions like banks to evaluate their financial situation.
Hence, the outcome shows that firm productivity will increase significantly when the entrepreneur is
more educated in respect to their capability in managing financial resources.
b. GMM estimator
Using the FE model with robust standard error can help to control unobserved effects as well as het-
eroscedasticity. However, the endogeneity issue, which leads to biased and inconsistent estimators, may
still exist. This is caused by the inability to ascertain if a simultaneous, reverse relation exists between
financing structure and firm performance (i.e. firm performance also affects financing structure deci-
sions). Besides, financing structure can be considered simply an indicator of the unobserved feature that
influences performance. We doubt the reliability of Difference-GMM to provide unbiased results. So, to
strengthen the research findings, system two-step GMM with the adjusted standard error is used to cope
with the endogeneity problem.
The outcome of the system GMM are reported in Table (4). It confirms the positive relationship between
formal financing and TFP and the negative relationship between internal credit and firm productivity.
This causation is statistically significant at the 5% and 1% levels in most models, except the coefficient
of internal capital in the labor productivity equation, where it is negative but insignificant.
The results in Table (4) also reveal that the signs of most control variables are consistent with the FE
method but slightly different in significance level. While AR(1) and AR(2) test the first-and second-order
serial correlation, the Hansen test the over-identifying restrictions. All p-values of AR(2) tests in the
table are higher than 0.10, which means that the null hypothesis of no second-order serial correlation
cannot be rejected. Similarly, the results of the Hansen J tests reveal that the null hypothesis that
instrument variables are valid or cannot be rejected.
In conclusion, a positive relationship between formal financing and firm productivity through TFP or
labor productivity is supported by all models, and as well the negative effect between the internal capital
and firm productivity. The consistency of the formal financing sign under the different methods applied
illustrates the robustness of the findings. Remarkably, the effect magnitude of access to credit from
formal financial institutions on labor productivity is considerably higher than those of TFP.
20
MPFD Working Papers WP/20/09
Table 4: The effect of financial structure on firm productivity – GMM estimator with robust standard
error
Dependent variableln (TFP_ACF) ln (Labor productivity)
(1) (2) (3) (4)
Formal financing 0.039*** 0.622***
(0.002) (0.029)
Internal Capital -0.000** -0.001
(0.000) (0.001)
L.TFP (ACF) 0.029*** 0.003
(0.010) (0.010)
L.Labor productivity 0.024** 0.006
(0.012) (0.010)
Woman entrepreneur -0.053*** -0.027*** -0.705*** -0.272***
(0.002) (0.004) (0.025) (0.064)
Size (1=micro, 2=small) 0.067*** 0.148*** 0.398*** 1.657***
(0.003) (0.031) (0.042) (0.550)
Firm age(Log) 0.006*** 0.017*** -0.041*** 0.154***
(0.001) (0.002) (0.010) (0.033)
Sole proprietorship 0.070 -0.380*** 0.996 -5.989***
(0.067) (0.127) (1.105) (2.091)
Primary school 0.010*** 0.131*** 0.193*** 2.025***
(0.002) (0.049) (0.025) (0.729)
Junior high school 0.021*** 0.039 0.356*** 0.842
(0.002) (0.051) (0.029) (0.845)
Senior high school 0.033*** 0.272*** 0.540*** 4.646***
(0.003) (0.041) (0.048) (0.607)
Diploma or higher 0.046*** 0.056 0.694*** 0.671
(0.010) (0.148) (0.161) (2.236)
Year FE Yes Yes Yes Yes
Observations 22,514 107,247 22,831 108,741
Number of included individuals 17,961 49,768 18,169 50,195
Hansen test (p-value) 0.066 0.090 0.071 0.087
AR(2) (p-value) 0.585 0.802 0.407 0.773
Robust standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
21
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
B. Woman entrepreneurs, formal financing and productivity
Interest in supporting women’s entrepreneurship has recently increased to promote economic inclusive-
ness as highlighted in the sustainable development goals of gender equality and decent work. This
interest comes from the recognition that women’s entrepreneurship, especially in rural areas, will gen-
erate economic growth and employment. In Indonesia, Tambunan (2017, 2019) has found that woman
entrepreneurs have a significant effect on poverty reduction as it improves family income.
Our dataset from BPS survey of MSEs reveals two interesting facts about women entrepreneurs in
Indonesia. First, approximately 41% of total MSEs are operated by women and mainly skewed towards
micro-sized firms. It means that the percentage of woman entrepreneurs in these enterprises tends to
decline by size. It can be clearly seen in Figure (6), that about 39.49% of firms owned by woman
entrepreneurs are micro-enterprises as against 1.44% for small enterprises. This gap in firm size could
explain the existence of a gender gap in firm performance partially. Second, the proportion of women
entrepreneurs in labor-intensive industries (e.g. food products (13.3%), textile (7.6%) and garments
(4.56%)) tends to be high. This may suggest that there is a difference of tendency in selecting jobs
between women and men. A woman could be avoiding heavy work which needs physical strength or
they are not having enough capital asset for their production, resulting in relatively less productivity
compared to her male counterpart.
This leads to our hypothesis that women who can obtain additional access to finance for their businesses
can increase their productivity. To test this hypothesis, we elaborate further the relationship between
external financing and women entrepreneurs to firm productivity. By extending the estimation equation
to include interaction term with women entrepreneur, it gives us:
FPit = αi+β1 Formal creditit+β2 Womanit+β3 Formal creditit×Womanit+β Xit+µi+νt+εit (2)
where FPit stands for performance/productivity of firm i at time t. Formal credit is the dummy
variable that takes the value 1 if the firms finance their business form formal financing and zero otherwise.
Woman is the dummy for woman entrepreneur which takes the value 1 for woman entrepreneur and
0 for male entrepreneur. We are as well interested in estimating β3 coefficient of the interaction term
Formal credit × Woman, which yield the additional effect associated with women-owned firms and
they obtain formal external credit. Where εit is the error that captures other variations, which are not
captured by the explanatory variables in the model.
Table (5) provides an insight into this result based on both FE and system-GMM panel regression.
The finding clearly suggests that there exists a significant gender differential in performance. Across all
specifications, male entrepreneurs do perform better than women entrepreneur, a result that is consistent
with our baseline regression. Nevertheless, if we focus on the interaction term – Formal credit×Woman
– the coefficient estimate is positive and significant across all model specifications. This suggests that a
woman entrepreneur who has access to formal external financing, such as a bank, will tend to enhance
her productivity. This, in turn, will reduce the gap of productivity between male and female entrepreneur
productivity.
22
MPFD Working Papers WP/20/09
Table 5: Interaction term between formal financing and woman entrepreneur and firm productivity
Dependent variableln (TFP_ACF) ln (Labor productivity)
FE FE GMM-Sys FE FE GMM-Sys
Formal financing 0.045*** 0.030*** 0.034*** 0.670*** 0.508*** 0.561***
(0.006) (0.006) (0.002) (0.086) (0.096) (0.034)
Woman entrepreneur -0.076*** -0.061*** -0.064*** -0.894*** -0.783*** -0.817***
(0.006) (0.005) (0.002) (0.086) (0.084) (0.029)
Formal financing x woman entrepreneur 0.025*** 0.024*** 0.024*** 0.243** 0.241* 0.250***
(0.008) (0.007) (0.003) (0.117) (0.122) (0.042)
Size (1=micro, 2=small) 0.064*** 0.067*** 0.361*** 0.398***
(0.005) (0.003) (0.090) (0.043)
Firm age(Log) 0.007*** 0.006*** -0.023 -0.041***
(0.002) (0.001) (0.028) (0.010)
Sole proprietorship -0.013*** 0.074 -0.195*** 1.061
(0.004) (0.068) (0.063) (1.114)
Primary school 0.008** 0.010*** 0.153** 0.192***
(0.004) (0.002) (0.065) (0.025)
Junior high school 0.021*** 0.020*** 0.371*** 0.354***
(0.005) (0.002) (0.077) (0.029)
Senior high school 0.030*** 0.033*** 0.492*** 0.542***
(0.005) (0.003) (0.087) (0.049)
Diploma or higher 0.037*** 0.047*** 0.575*** 0.704***
(0.006) (0.010) (0.106) (0.162)
L.TFP (ACF) 0.028***
(0.010)
L.Labor productivity 0.024**
(0.012)
Firm FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cluster Level Province Province – Province Province –
Observations 40,995 40,995 22,514 41,551 40,995 22,831
R-squared 0.800 0.838 – 0.784 0.799 –
Number of included individuals – – 17,961 – – 18,169
Hansen test (p-value) – – 0.052 – – 0.063
AR(2) (p-value) – – 0.539 – – 0.392
Robust standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
To be more specific, in our first specification using FE model, a woman entrepreneur who has access to
formal external financing will increase her TFP productivity by 2.4% while the labor productivity will
increase by about 25%. The result still holds when we include more firm-specific characteristics in the
FE model. Holding all other variables constant, we note that the interaction terms still have positive and
23
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
significant coefficients in affecting productivity, although for labor productivity, the significance drops to
10% level. In this model, the firm characteristics behavior in affecting productivity is also similar to our
baseline regression. Size, age, and owner education also have a positive effect on productivity while sole
proprietorship have a negative or no significant impact to productivity. Lastly, our GMM model shows
the robustness of our result by confirming the positive correlation of women entrepreneur with access to
formal external financing with higher productivity.
To sum up, our finding suggests that if woman entrepreneurs have more access to formal external financ-
ing, it will lead to an increase in their productivity. We are aware that there is little research that focuses
on this area, so the results presented in this paper is a significant contribution in giving more inclusive
access to the financial resource for women entrepreneur. We, therefore, advise three broad policies in
addressing these issues. First, policymaker and formal financial institutions should have a better credit
policy targeting female entrepreneurs to improve their productivity. If the women entrepreneurs remain
credit constrained compared to men, then it may impede the potential of the economy itself as women
entrepreneurs are likely to remain less productive. Second, a shift to the more productive sectors and less
labor-intensive sectors for women entrepreneurs can also help in increasing overall productivity. Under
this scenario, if women can access more credit to buy new equipment or supporting capital for their
production, it will help them to be more productive and expand their business.
Finally, policymakers need to embrace alternative financing channels to increase financial inclusion, for
example, broader use of digital finance or fintech. The shift towards digital financial services can benefit
MSMEs greatly from advances in mobile money, fintech services and online banking. These services will
create financial inclusion that can help small businesses to improve their access to credit, as well as boost
economic growth. This policy recommendation is in line with the previous study, such as Blancher et al.
(2019) and Creehan et al. (2018) to support the use of financial technology to increase access to finance
for MSMEs.
C. Alternatives firm performance measures
In this part, we further specify our main model with alternatives firm performance measures to replace
our efficiency indicators. We extend our main equation (1) to include other performance-related variables
to assess the impact of access to financing and how well the firm is run, which give us:
FPit = αi + β1 Formal creditit + β Xit + µi + νt + εit (3)
Where FPi,t is a measure of firm performance. It can be measured either by sales, employment and wage
paid by the firm i at time t. Formal credit is the dummy variable that takes the value 1 if the firms
finance their business form formal financing and zero otherwise. Xi,t is a vector of control variables of
firm i in period t that could affect the probability of obtaining a loan. µi are firm-level fixed effects, νt is
a set of year fixed effects, and εi,t is the error term. Table (6) displays the results of other alternatives. In
all specifications, we include control variables, firm fixed effects, and year fixed effects. Our result shows
that measuring firm performance by sales or employment rather than productivity does not markedly
alter the main findings in Table (3), with a similar or even stronger effect. This suggests that access to
external finance is an important component of firm growth.
24
MPFD Working Papers WP/20/09
We first find some evidence that formal financing can improve sales (column 1) in our data even after
controlling a set of firm-level variables. As a consequence, formal financing has led to an estimated 63.8%
increase in sales at the 1% significance level. As expected, we do indeed see that the availability of bank
loan enables firms to achieve slightly better margins as they can buy raw materials in bulk and expand
the production. Hence, their market share is affected positively.
In column (2), we then explore whether changes in employment can account for our results. The employ-
ment significantly increases about to 21%. These findings signify that firms are more likely to benefit
from economies of scale and invest more in machinery and skilled workers, which led to mode modest
job creation rates. Therefore, they may develop new products and take advantage of outsourcing, which
helps them to increase their productivity. This empirical evidence confirms that MSEs constitute a
significant job creation engine.
Table 6: Alternative measures of firm performance
Dependent variable(1) (2) (3) (4)
ln (Sales) ln (Employment) ln (Real wage) ln (Average wage)
Formal financing 0.641*** 0.213*** 0.366 0.286
(0.098) (0.035) (0.256) (0.198)
Size (1=micro, 2=small) 1.148*** 1.173*** 1.105*** -0.094
(0.166) (0.086) (0.157) (0.147)
Firm age(Log) -0.016 0.012 -0.002 -0.019
(0.062) (0.012) (0.082) (0.055)
Sole proprietorship -0.675*** -0.074** -0.302** -0.160
(0.131) (0.034) (0.129) (0.114)
woman entrepreneur -0.831*** -0.378*** -0.424*** -0.341***
(0.125) (0.045) (0.135) (0.112)
Primary school 0.154 -0.014 0.084 0.095
(0.165) (0.033) (0.130) (0.101)
Junior high school 0.449** 0.007 0.254 0.206
(0.180) (0.050) (0.159) (0.124)
Senior high school 0.875*** 0.074 0.299** 0.223*
(0.193) (0.061) (0.138) (0.124)
Diploma or higher 1.323*** 0.165** 0.363** 0.208
(0.212) (0.068) (0.170) (0.140)
Firm FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Cluster Level Province Province Province Province
Observations 32,754 53,233 24,798 24,731
R− squared 0.843 0.867 0.860 0.829
Robust standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Surprisingly, when assessing the link between access to formal finance and wages, our result found no
25
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
significant impact on the real or average wage as presented in column (3) and (4). Several different
factors may cause this behavior. Firstly, the greater share of unpaid workers that represent 66, 78% of
total workers in our sample. Secondly and more importantly, this can be a risk that promoting MSEs
as a result of their substantial contribution to employment, may lead to a trade-off between the number
and quality of existing and new jobs in MSEs, particularly in developing countries with a high share of
informal micro-enterprises. This result is in line with Kersten et al. (2017) who found that improved
access to finance has a positive and significant impact on SMEs’ performance measures for instance
employment, sales and revenue, but an insignificant effect on wages.
In terms of additional controls, although they have kept more or less the similar sign and significance as
the outcomes for our baseline econometric specification. The same table (6) highlights some interesting
interpretations that reinforce the previous results (Table 3). The performance measures are more closely
related to the size, rather than the age, of enterprises. Small enterprises have higher sales and make
disproportionally high contributions to employment and real wages compared to micro-enterprises. Using
either of these measures, we found that the results remain invariant for the woman entrepreneur, which
indicates that women-owned firms do significantly worse than their male-owned counterparts, in terms
of sales, employment and wage.
D. Robustness Check
In this part, we show that the results are robust across the different specifications. First, we check
whether the previous findings are consistent with the effects of different methods of calculating firm
productivity. Second, we excluded Java island from the regression.
a. Alternative productivity measures: using the LP method
As discussed in model specification, we explored whether our findings are robust to alternative produc-
tivity measures suggested by Levinsohn and Petrin (2003). We do not use the Olley and Pakes (1996)
technique, as their method requires primary information on investment to proxy for unobserved produc-
tivity shocks, while prior information on investment is not provided in BPS data of MSEs. The regression
result is presented in Table (7).
Our robustness check using alternative TFP calculations shows that formal external financing still has
positive and significant significantly effect to productivity. Column (1) and (2) indicate that our result
holds on all specification which also includes firm characteristics. In column (3), we change our specifi-
cation by using the only year fixed effect and make cluster by the province to see if there are meaningful
variations between the model. Again, our result still holds with factors increasing productivity are com-
ing from formal financing, firm age, owned by men, not a sole proprietorship, and higher education of
the owner.
Interestingly, coefficients for our interaction variables which show the benefit of having better access to
finance for women entrepreneur to increase productivity has lost its significance in this model. Never-
theless, the GMM model, in column (4), also shows that the interaction terms result is in line with our
baseline regression, along with the most crucial variable of formal financing to productivity.
26
MPFD Working Papers WP/20/09
Table 7: The effect of formal financing on TFP measured by the LP method
Dependent variable (1) (2) (3) (4)
ln (TFP_LP) FE FE FE GMM-Sys
Formal financing 0.695*** 0.486*** 0.492*** 0.536***
(0.123) (0.074) (0.091) (0.030)
Woman entrepreneur -0.701** -0.700*** -0.730***
(0.278) (0.084) (0.029)
Formal financing x woman entrepreneur 0.206 0.204 0.222***
(0.199) (0.122) (0.041)
Size (1=micro, 2=small) 0.093 0.092 0.111***
(0.108) (0.089) (0.040)
Firm age(Log) 0.119** 0.116*** 0.098***
(0.051) (0.027) (0.009)
Sole proprietorship -0.176*** -0.177*** 0.659
(0.050) (0.061) (1.001)
Primary school 0.150* 0.155** 0.186***
(0.085) (0.067) (0.025)
Junior high school 0.368*** 0.373*** 0.344***
(0.109) (0.076) (0.028)
Senior high school 0.476*** 0.480*** 0.509***
(0.091) (0.085) (0.045)
Diploma or higher 0.541*** 0.545*** 0.609***
(0.083) (0.104) (0.147)
L.TFP (LP) 0.021*
(0.012)
Firm FE Yes Yes Yes Yes
Year FE No No Yes Yes
Island x Year FE Yes Yes No No
Cluster Level Industry Industry Province –
Observations 40,995 40,995 40,995 22,514
R-squared 0.766 0.787 0.785 –
Number of included individuals – – – 17,961
Hansen test (p-value) – – – 0.148
AR(2) (p-value) – – – 0.319
Robust standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
b. Excluding Java island
In Indonesia, our data-set from BPS shows that majority of MSEs are found in Java, the most populated
island and the center of economic (i.e., manufacturing industry, trade, construction, agriculture, and
services) and financial activities in Indonesia. The number of MSEs in Java alone represent 39% of the
27
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
total manufacturing business in Indonesia. In the baseline model, we grouped the islands into five main
island groups (Java, Sumatra, Kalimantan, Sulawesi, and the remaining islands grouped) and we utilize
fixed effect on island times year to restrict the variations between the islands. To test for the robustness,
we are taking our the samples from Java island all together to check if the result still holds, as suggested
by Amiti and Cameron (2012) when dealing with Indonesian data.
Table 8: Excluding Java island
Dependent variable(1) (2) (3)
ln (TFP_ACF) ln (TFP_LP) ln (Labor productivity)
Formal financing 0.033*** 0.572*** 0.582***
(0.004) (0.061) (0.062)
Woman entrepreneur -0.058*** -0.641*** -0.727***
(0.005) (0.080) (0.080)
Formal financing x Woman entrepreneur 0.020*** 0.147 0.185
(0.007) (0.119) (0.119)
Size (1=micro, 2=small) 0.057*** -0.052 0.224***
(0.004) (0.062) (0.062)
Firm age(Log) 0.005*** 0.082*** -0.060**
(0.002) (0.030) (0.030)
Sole proprietorship -0.015** -0.245** -0.254**
(0.006) (0.104) (0.104)
Primary school 0.006 0.112 0.109
(0.005) (0.077) (0.077)
Junior high school 0.018*** 0.323*** 0.321***
(0.005) (0.080) (0.081)
Senior high school 0.023*** 0.400*** 0.403***
(0.005) (0.082) (0.082)
Diploma or higher 0.035*** 0.525*** 0.553***
(0.009) (0.149) (0.149)
Firm FE Yes Yes Yes
Year FE Yes Yes Yes
Observations 25,662 25,662 25,662
R-squared 0.887 0.856 0.864
Robust standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Table (8) displays that our results of the robustness tests which exclude Java island are consistent with
the main findings of this study. Column (1) generally showing the same effect with our baseline that
there is a positive relationship between formal financing and firm performance as measured by TFP by
ACFEST method. Our main variables for formal funding and its interaction with woman entrepreneur
remain positive and significant at 1% level. Other variables on firm characteristics are also showing
similar result with our baseline.
28
MPFD Working Papers WP/20/09
In column (2), when we use TFP calculation based on Levpet method as the dependent variable, the
formal financing remains positive and significant in affecting productivity. However, in line with the
findings in Table (7), the coefficients are not significant for the interaction term of formal financing
with woman entrepreneur and size of the firm. Finally, column (3) also shows that main result from
the baseline remains robust, with significant coefficients on formal financing, size, age, legal status, the
gender of the owner, and owner education.
29
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
VII. Conclusion
This paper investigated the link between the finance structure and firm performance by exploiting a
dataset of 59,968 Indonesian micro and small manufacturing enterprises (MSEs) that comprise on average
249,688 observations from 2010 to 2015. The results show that 77% of firms rely on internal resources
of finance such as the entrepreneurs’ savings and his/her inheritance, whereas there are only 23% of
MSEs who have access to external sources of funding. Among this small proportion, most of them are
provided credit by banks (28%) and by individuals (26%). The most commonly reported reason for not
obtaining a loan is that there is reluctance in borrowing from the bank. Furthermore, perceptions of
insufficient collateral and complicated procedures are also well-known reasons expressed by micro and
small businesses owners’ to explain their lack of interest in applying for bank loans.
We used both fixed-effects panel regressions and system-GMM dynamic panel estimations. Our results
indicate that firm productivity is positively related to obtaining formal financing in all estimated speci-
fications. The availability of loans is an essential factor in promoting the productivity of small start-up
firms in Indonesia. As a consequence of greater credit availability, many productive firms can expand
or make technological progress and fulfill investments needs to increase their productivity beyond what
their internal funds can support. MSEs are still lagging in terms of credit growth. Our findings show that
better access to finance has a substantial impact on the growth of both MSEs’ sales and employment;
though there is no direct link between formal financing and wages. On the other hand, internal finance
is found to be relatively unimportant for firm growth.
Additionally, the paper presents new evidence on whether the gender of the owner influences firm per-
formance. Firstly, we measure the gender gaps in performance in terms of TFP, labor productivity,
sales, employment and the real wage. We find significant differences in the performance gap between
firms owned by men and women entrepreneurs even after controlling for the firm and year fixed effects
and cluster by industry. Secondly, we find that the interaction coefficient between the women-run firms
and obtaining a formal financing are significant and more efficient than those operating without access
to a formal loan. Such findings are also supported by various studies on women’s entrepreneurship in
developing economies.
Finally, various robustness tests were included to substantiate our results. We checked our findings firstly
using the LP method as a different measure for TFP. Secondly, we excluded the Java island, where the
majority of MSEs exist, from our regressions to see if our results still hold even after excluding this
island. Our robustness checks confirm the main findings of the positive relationship between credit and
firm productivity.
The conclusions of this study are relevant to policymakers in Indonesia for enhancing access to financial
services, especially for women-owned enterprises, to increase their productivity. Because it points out,
significant restrictions faced by manufacturing firms and, hence, suggests where reform efforts ought to
be centered. A more inclusive approach, including reaping the potential of digital finance to improve
access to credit, will benefit the MSEs.
30
MPFD Working Papers WP/20/09
Appendices
A Data definition
A. List of variables included in the analysis
Table 9: Variable description
Variable name Definition and Description Data Source
Dependent Variables –Measures of firm performance
Output (Y ) Output (rupiah) measured by the value of goods produced which is deflated
by the CPI for two-digit ISIC industries based in the constant year 2010
prices.
BPS
Electricity (log) Cost of the entire electricity consumption for a company/ business, such as
for lighting and running the engine. We used it to control for unobserved
productivity shocks.
BPS
Capital (log) It is calculated by the log of fixed assets (rupiah) deflated by the CPI for
two-digit ISIC industries based in the constant year 2010 prices
BPS
Revenue (log) It is the total income generated by the sale of goods or services related to the
company’s core operations deflated by CPI based on year 2010.
BPS
TFP (log) Total Factor Productivity of firm "i" in year "t". Own estimation
Labor productivity (log) The ratio of total output to the number of workers used to produce the output. Authors’ calculation
Sales (log) We used indirect taxes as a proxy to measure the sales, which means the taxes
imposed on consumers by manufacturers to the purchase of goods/services.
For instance; value-added tax, property tax, customs and export taxes, ex-
cluding the tax paid by the company for tax withholding workers’ remunera-
tion.
BPS
Employment (log) It is the sum of all male and female employed by the establishment. It includes
both paid and unpaid workers.
Authors’ calculation
Real wage (log) It includes all salaried workers, which is deflated by the CPI based on year
2010.
BPS
Independent & control Variables
Formal financing a Dummy variable that takes on the value one if the firm relies on external
sources from banks, cooperatives, non-bank financial institutions (NBFI),
venture capital companies to finance either working capital or a new invest-
ment, zero otherwise (Individual, families and others loans).
Authors’ calculation
Fully internal capital Dummy variable taking the value of one if the MSE’s initial source of capital is
based on the internal source of finance (inheritance, savings, own remittance
or/and liquidation of assets) and zero otherwise.
Authors’ calculation
Firm size (employees) A firm is defined as micro (1-4 employees) and small size (5-19 employees).
Size is a vector of dummy variables, micro and small, that takes the value one
if a firm is micro and zero otherwise.
BPS
Firm age (years) The years that have passed since the establishment began its operations. It
is calculated as the difference between the survey year and the year on which
the firm began operation.
Authors’ calculation
Gender of the owner Dummy variable taking the value of one if the MSE’s owner is female and 0
otherwise.
BPS
Sole proprietorship Dummy variable that takes on the value 1 if the firm is organized as a sole pro-
prietorship and zero otherwise (private limited company, limited partnership,
cooperative and others).
BPS
(Continued)
31
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
Table 9 – Variable description (Continued)
Variable name Definition and Description Data Source
Education There are five categories for the owner of the firm, 1—“not finished primary
school”; 2—“primary school”; 3—“junior high school” 4—“senior high school”;
5—“diploma or higher”.
BPS
a Formal financing includes working capital purchases and fixed asset investment financed by other parties which meaning
that the employer has no contribution at all.
B. Industry classification
Table 10: Industry classification
ISIC 2-digit Two-digit industry Total Number Percentage
10 Food products 67,517 27.04
11 Beverages products 6,912 2.77
12 Tobacco products 5,845 2.34
13 Textiles 23,977 9.60
14 Wearing apparel 22,829 9.14
15 Leather and leather products 4,979 1.99
16 Wood and cork, except furniture 46,938 18.80
17 Paper and paper products 1,189 0.48
18 Publishing and printing 3,456 1.38
19 Coke and petroleum products 3 0.00
20 Chemical products 2,228 0.89
21 Pharmaceuticals products 650 0.26
22 Rubber and plastics products 2,293 0.92
23 Other non-metallic mineral products 28,506 11.42
24 Basic metals 516 0.21
25 Fabricated metal products 10,886 4.36
26 Electronic and optical products 91 0.04
27 Electrical equipment 134 0.05
28 Machinery and equipment 380 0.15
29 Motor vehicles and trailers 336 0.13
30 Other transport equipment 1,907 0.76
31 Furniture 10,352 4.15
32 Other manufacturing 7,206 2.89
33 Repair, installation of machinery and recycling 558 0.22
Total 249,688 100%
Source: Indonesian Statistics Agency database (BPS).
32
MPFD Working Papers WP/20/09
B Additional Figures
Figure 5: Kernel density of firms’ TFP (log)
(a) Firms’ TFP by type of finance
02
46
Den
sity
2.6 2.8 3 3.2Log of firms' TFP (ACF method)
Formal Informal
(b) Firms’ TFP by firm size
02
46
8D
ensi
ty2.6 2.8 3 3.2
Log of firms' TFP (ACF method)
Micro Small
Source: Authors’ calculation based on Indonesian Statistics Agency (BPS).
Figure 6: Percentage of women entrepreneurs by firm size
05
1015
Wom
en e
ntre
pren
eurs
(%)
Micro Small
Source: Authors’ calculation based on Indonesian Statistics Agency (BPS).
33
34
Figure 7: Credit structure of MSEs
(a) Different types of external finance (%)
28.225.9
22.2
14.4
5.53.3
0.5
010
2030
40Pe
rcen
t of f
irm
Bank Individual Others Family Cooperative Non-bank Venturecapital
(b) Reasons for not applying for a loan (%)
55.3
13.5 12.39.8
8.0
1.1
020
4060
Perc
ent o
f firm
Not interested Insufficientcollateral
Not knowingthe procedures
High interestrates
Complicatedprocedures
Proposalrejected
Figure 8: Educational attainment of MSEs
(a) Educational attainment by gender (%)
3.6
2.1
22.0
15.8
19.7
17.3
36.7
37.9
18.0
26.8
0 10 20 30 40Percent of firm
Diploma or higher
Senior high school
Junior high school
Primary school
Not finished primary school
Female Male
(b) Educational attainment by type of finance (%)
6.7
1.6
31.2
14.5
21.7
18.9
28.2
41.2
12.2
23.8
0 10 20 30 40 50Percent of firm
Diploma or higher
Senior high school
Junior high school
Primary school
Not finished primary school
Informal Formal
Source: Authors’ calculation based on Indonesian Statistics Agency (BPS).
MPFD Working Papers WP/20/09
References
Abdu, W. J. (2014), Education and microfinance as a combined empowerment approach for the micro-
finance clients, PhD thesis, Universitas Pendidikan Indonesia.
Abe, M., Troilo, M. and Batsaikhan, O. (2015), ‘Financing small and medium enterprises in asia and the
pacific’, Journal of Entrepreneurship and Public Policy .
Ackerberg, D. A., Caves, K. and Frazer, G. (2015), ‘Identification properties of recent production function
estimators’, Econometrica 83(6), 2411–2451.
Ahlin, C. and Jiang, N. (2008), ‘Can micro-credit bring development?’, Journal of Development Eco-
nomics 86(1), 1–21.
Ahn, S. (2001), ‘Firm dynamics and productivity growth: a review of micro evidence from oecd countries’.
Aimon, H. and Satrianto, A. (2014), ‘Prospect of soybean consumption and import in indonesia year
2015–2020’, Journal of Economic Studies 3, 1–13.
Al-Mahrouq, M. H. (2003), The small firm loan guarantee scheme in Jordan: an empirical investigation,
PhD thesis, Newcastle University.
Amiti, M. and Cameron, L. (2012), ‘Trade liberalization and the wage skill premium: Evidence from
Indonesia’, Journal of International Economics .
Arellano, M. and Bond, S. (1991), ‘Some tests of specification for panel data: Monte carlo evidence and
an application to employment equations’, The review of economic studies 58(2), 277–297.
Ayyagari, M., Demirgüç-Kunt, A. and Beck, T. (2007), Small and medium enterprises across the globe,
Springer.
Banerjee, A. V. and Newman, A. F. (1993), ‘Occupational choice and the process of development’,
Journal of political economy 101(2), 274–298.
Barber, B. M. and Odean, T. (2001), ‘Boys will be boys: Gender, overconfidence, and common stock
investment’, The quarterly journal of economics 116(1), 261–292.
Bartelsman, E. J. and Doms, M. (2000), ‘Understanding productivity: Lessons from longitudinal micro-
data’, Journal of Economic literature 38(3), 569–594.
Bekele, E. and Worku, Z. (2008), ‘Women entrepreneurship in micro, small and medium enterprises: The
case of ethiopia’, Journal of International Women’s Studies 10(2), 3–19.
Berger, A. N. and Udell, G. F. (2002), ‘Small business credit availability and relationship lending: The
importance of bank organisational structure’, The economic journal 112(477).
Berrell, M., Park, J., Wu, J., Song, J. and Zeng, C. (2008), ‘An empirical evidence of small business
financing in china’, Management Research News .
Blalock, G. and Gertler, P. J. (2008), ‘Welfare gains from foreign direct investment through technology
transfer to local suppliers’, Journal of international Economics 74(2), 402–421.
Blancher, N., Appendino, M., Bibolov, A., Fouejieu, A., Li, J., Ndoye, A., Panagiotakopoulou, A., Shi,
W. and Sydorenko, T. (2019), Financial inclusion of small and medium-sized enterprise in the middle
east and central asia, Technical report, IMF Departmental Paper Series.
Brown, J. D., Earle, J. S. and Lup, D. (2005), ‘What makes small firms grow? finance, human capital,
technical assistance, and the business environment in romania’, Economic Development and Cultural
Change 54(1), 33–70.
Bruhn, M. and Love, I. (2014), ‘The real impact of improved access to finance: Evidence from mexico’,
35
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
The Journal of Finance 69(3), 1347–1376.
Buera, F. J., Kaboski, J. P. and Shin, Y. (2017), The macroeconomics of microfinance, Technical report,
National Bureau of Economic Research.
Carlin, W., Schaffer, M. E. and Seabright, P. (2006), ‘Where are the real bottlenecks? a lagrangian
approach to identifying constraints on growth from subjective survey data’.
Charitonenko, S. and Afwan, I. (2003), ‘Commercialization of microfinance indonesia’.
Chaston, I. and Mangles, T. (1997), ‘Core capabilities as predictors of growth potential in small manu-
facturing firms’, Journal of Small Business Management 35(1), 47.
Chavis, L. W., Klapper, L. F. and Love, I. (2011), ‘The impact of the business environment on young
firm financing’, The world bank economic review 25(3), 486–507.
Claessens, S., Djankov, S. and Xu, L. C. (2000), ‘Corporate performance in the east asian financial crisis’,
The World Bank Research Observer 15(1), 23–46.
Coleman, S. (2007), ‘The role of human and financial capital in the profitability and growth of women-
owned small firms’, Journal of Small Business Management 45(3), 303–319.
Collins-Dodd, C., Gordon, I. M. and Smart, C. (2004), ‘Further evidence on the role of gender in financial
performance’, Journal of Small Business Management 42(4), 395–417.
Creehan, S. et al. (2018), ‘How digital innovation can increase small business access to finance in asia’,
Asia Focus (March).
Das, S. and Mohiuddin, K. M. (2015), ‘Motivational factors and the constraints of women entrepreneur-
ship development in bangladesh’, International Journal of Information, Business and Management
7(3), 377.
Demirgüç-Kunt, A., Honohan, P. and Beck, T. (2008), Finance for all?: Policies and Pitfalls in Expand-
ing Access., World bank.
Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J. and Wagner, G. G. (2011), ‘Individual
risk attitudes: Measurement, determinants, and behavioral consequences’, Journal of the European
Economic Association 9(3), 522–550.
Eberhardt, M., Helmers, C. et al. (2010), ‘Untested assumptions and data slicing: A critical review of
firm-level production function estimators’, Department of Economics, University of Oxford .
Farace, S. and Mazzotta, F. (2015), ‘The effect of human capital and networks on knowledge and inno-
vation in smes’, Journal of Innovation Economics Management (1), 39–71.
Hsieh, C.-T. and Klenow, P. J. (2009), ‘Misallocation and manufacturing tfp in china and india’, The
Quarterly journal of economics 124(4), 1403–1448.
Hsieh, C.-T. and Klenow, P. J. (2010), ‘Development accounting’, American Economic Journal: Macroe-
conomics 2(1), 207–23.
Huyghebaert, N. and Van de Gucht, L. M. (2007), ‘The determinants of financial structure: new insights
from business start-ups’, European Financial Management 13(1), 101–133.
Indonesia, B. (2016), ‘Financial stability review no. 27’.
Japhta, R., Murthy, P., Fahmi, Y., Marina, A. and Gupta, A. (2016), ‘Women-owned smes in indonesia:
A golden opportunity for local financial institutions’, Market Research Study. IFC, East Asia and the
Pacific .
Kaboski, J. P. and Townsend, R. M. (2012), ‘The impact of credit on village economies’, American
Economic Journal: Applied Economics 4(2), 98–133.
36
MPFD Working Papers WP/20/09
Kepler, E., Shane, S. et al. (2007), Are male and female entrepreneurs really that different?, Office of
Advocacy, US Small Business Administration Washington, DC.
Kersten, R., Harms, J., Liket, K. and Maas, K. (2017), ‘Small firms, large impact? a systematic review
of the sme finance literature’, World Development 97, 330–348.
Khandker, S. R. (2005), ‘Microfinance and poverty: Evidence using panel data from bangladesh’, The
World Bank Economic Review 19(2), 263–286.
Kira, A. R. (2013), ‘Determinants of financing constraints in east african countries’ smes’, International
Journal of Business and management 8(8), 49.
Lacalle-Calderón, M., Chasco, C., Alfonso-Gil, J. and Neira, I. (2015), ‘A comparative analysis of the
effect of aid and microfinance on growth’, Canadian Journal of Development Studies/Revue canadienne
d’études du développement 36(1), 72–88.
Le, P. N. M. (2012), ‘What determines the access to credit by smes?: A case study in vietnam’, Journal
of Management Research 4(4), 90.
Leahey, E. (2006), ‘Gender differences in productivity: Research specialization as a missing link’, Gender
& Society 20(6), 754–780.
Leung, D., Meh, C. and Terajima, Y. (2008), Firm size and productivity, Technical report, Bank of
Canada Working Paper.
Levine, R., Loayza, N. and Beck, T. (2000), ‘Financial intermediation and growth: Causality and causes’,
Journal of monetary Economics 46(1), 31–77.
Levinsohn, J. and Petrin, A. (2003), ‘Estimating production functions using inputs to control for unob-
servables’, The review of economic studies 70(2), 317–341.
Lopatta, K. and Tchikov, M. (2016), ‘Do microfinance institutions fulfil their promise? evidence from
cross-country data’, Applied Economics 48(18), 1655–1677.
Lopatta, K., Tchikov, M. et al. (2017), ‘The causal relationship of microfinance and economic develop-
ment: Evidence from transnational data’, International Journal of Financial Research 8(3), 162–171.
Mac an Bhaird, C. and Lucey, B. (2010), ‘Determinants of capital structure in irish smes’, Small business
economics 35(3), 357–375.
Moustafa, G. and Santos, A. (2016), ‘Female entrepreneurship in developing countries, barriers and
motivation: Case study, egypt and brazil’.
Musamali, M. M. and Tarus, D. K. (2013), ‘Does firm profile influence financial access among small and
medium enterprises in kenya?’, Asian Economic and Financial Review 3(6), 714.
Navarro, P. (2006), ‘The economics of the “china price” ’, China Perspectives 2006(68), 13–27.
Nguimkeu, P. (2016), ‘Some effects of business environment on retail firms’, Applied Economics
48(18), 1647–1654.
Niskanen, M. and Niskanen, J. (2007), ‘The determinants of firm growth in small and micro firms-evidence
on relationship lending effects’, Available at SSRN 874927 .
Olley, G. S. and Pakes, A. (1996), ‘The dynamics of productivity in the telecommunications equipment
industry’, Econometrica 64(6), 1263.
Oyelana, A. A. and Adu, E. O. (2015), ‘Small and medium enterprises (smes) as a means of creating
employment and poverty reduction in fort beaufort, eastern cape province of south africa’, Journal of
Social Sciences 45(1), 8–15.
Roy, M.-A. and Wheeler, D. (2006), ‘A survey of micro-enterprise in urban west africa: drivers shaping
the sector’, Development in Practice 16(5), 452–464.
37
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
Sabarwal, S. and Terrell, K. (2008), ‘Does gender matter for firm performance’, Evidence from Eastern
Europe and Central Asia. World Bank Policy Research Working Paper (4705).
Şeker, M. and Saliola, F. (2018), ‘A cross-country analysis of total factor productivity using micro-level
data’, Central Bank Review 18(1), 13–27.
Tambunan, T. (2019), ‘Recent evidence of the development of micro, small and medium enterprises in
indonesia’, Journal of Global Entrepreneurship Research 9(1), 18.
Tambunan, T. T. H. (2017), ‘Women entrepreneurs in mses in indonesia: Their motivations and main
constraints’, JWEE (1-2), 56–86.
Thapa, A. (2013), Microenterprise development as a poverty-reduction strategy in nepal: A multidimen-
sional analysis of the factors determining microenterprise performance, PhD thesis, National Institute
of Development Administration.
Tijaja, J. and Faisal, M. (2014), ‘Industrial policy in indonesia: A global value chain perspective’, Asian
Development Bank Economics Working Paper Series (411).
Van Biesebroeck, J. (2005), ‘Firm size matters: Growth and productivity growth in african manufactur-
ing’, Economic Development and Cultural Change 53(3), 545–583.
Wamaungo, A. (2011), ‘Community participation in the development of nonformal education programmes
in community learning centre’, Submitted to Sekolah Pascasarjana,Universitas Pendidikan Indonesia .
Watson, J. (2002), ‘Comparing the performance of male-and female-controlled businesses: relating out-
puts to inputs’, Entrepreneurship theory and practice 26(3), 91–100.
Wintoki, M. B., Linck, J. S. and Netter, J. M. (2012), ‘Endogeneity and the dynamics of internal
corporate governance’, Journal of Financial Economics 105(3), 581–606.
Yusupov, N. (2012), ‘Microcredit and development in an occupational choice model’, Economics Letters
117(3), 820–823.
38
MPFD Working Papers WP/20/09
Recent MPFD Working Papers
www.unescap.org/publication-series/mpfd-working-papers
WP/20/08 Strengthening Financial Interlinkages among the SPECA Countries
By Roger Kronenberg, Ulukbek Usubaliev and Tientip Subhanij.
WP/20/07 Public-Private Partnership Systems and Sustainable Development in Asia and the Pacific
By Rui Almeida, Amaury Cassang, Daniel Lin and Masato Abe.
WP/20/06 Mainstreaming the Sustainable Development Goals into national planning, budgeting
and financing processes: Indonesian experience
By Alin Helimatussadiah.
WP/20/05 Government budget and the Sustainable Development Goals: the Philippine experience
By Rosario G. Manasan.
WP/20/04 Graduation of Bhutan from group of least developed countries: Potential implication
and policy imperatives
By Mohammad A. Razzaque.
WP/20/03 A Review of Access to Finance by Micro, Small and Medium Enterprises and Digital
Financial Services in Selected Asia-Pacific Least Developed Countries
By Nitin Madan.
WP/20/02 Asia-Pacific Small Island Developing States: Development challenges and
policy solutions
By Andrzej Bolesta.
WP/20/01 Nepal’s graduation from LDC: Potential implications and issues for consideration
By Mohammad A. Razzaque.
WP/19/08 Finteching remittances in Paradise: a path to sustainable development
By Hongjoo Hahm, Tientip Subhanij and Rui Almeida.
WP/19/07 Hide-and-seek: Can tax treaties reveal offshore wealth?
By Jeong-Dae Lee.
WP/19/06 Cheating the Government: Does taxpayer perception matter?
By Jeong-Dae Lee.
WP/19/05 Preparing to graduate – issues, challenges and strategies for Kiribati’s LDC graduation
By James Webb.
WP/19/04 LDC graduation: challenges and opportunities for Vanuatu
By Derek Brien.
WP/19/03 Metropolitan finances in India: the case of Mumbai City Corporation
By M. Govinda Rao.
WP/19/02 Philippine (Metro Manila) case study on municipal financing
By Justine Diokno-Sicat.
WP/19/01 Financing metropolitan government in Beijing City
By Roy Bahl and Baoyun Qiao.
WP/18/06 From school to work: does vocational education improve labour market outcomes?
An empirical analysis of Indonesia
By Dyah Pritadrajati.
WP/18/05 Public-private partnership for cross-border infrastructure development
By Mathieu Verougstraete.
39
Financing Structure, MSEs Performance, and Woman Entrepreneurship in Indonesia
WP/18/04 Tapping capital markets and institutional investors for infrastructure development
By Mathieu Verougstraete and Alper Aras.
WP/18/03 Regulation of cryptocurrencies: evidence from Asia and the Pacific
By Yasmin Winther de Araujo Consolino Almeida and Jose Antonio Perdosa-Garcia.
WP/18/02 Fostering peaceful sustainable development in the Pacific under the 2030 Agenda
By Anna Naupa and Derek Brien.
WP/18/01 Water security in Central Asia and the Caucasus: A key to peace and
sustainable development
By Zulfiya Suleimenova.
WP/17/09 Governance and development outcomes: re-assessing the two-way causality
By Steve Loris Gui-Diby and Saskia Moesle.
WP/17/08 Prospects for progressive tax reforms in Asia-Pacific
By Zheng Jian and Daniel Jeongdae Lee.
WP/17/07 Tax incentives and tax base protection in developing countries
By Joosung Jun.
WP/17/06 Issues paper on tax policy and public expenditure management in Asia and the Pacific
By Zheng Jian and Alberto Isgut.
WP/17/05 Environmental tax reforms in Asia and the Pacific
By Jacqueline Cottrell, Damian Ludewig, Matthias Runkel, Kai Schlegelmilch
and Florian Zerzawy.
WP/17/04 Metropolitan city finances in Asia and the Pacific region: issues, problems and reform
options
By Roy Bahl.
WP/17/03 Do data show divergence? Revisiting global income inequality trends
By Sudip Ranjan Basu.
WP/17/02 Estimating infrastructure financing needs in Asia-Pacific least developed countries,
landlocked developing countries and small island developing States
By Candice Branchoux, Lin Fang and Yusuke Tateno.
WP/17/01 What’s gender got to do with firm productivity? Evidence from firm level data in Asia
By Steve Loris Gui-Diby, Diana Rodriguez-Wong and S. Selsah Pasali.
WP/16/09 Complementarities between the global programmes of action and the 2030 Agenda for
Sustainable Development
By Alberto Isgut, Ran Kim, Gabriel Spaizmann, Yusuke Tateno and Naylin Oo.
WP/16/08 Productivity growth in India: Determinants and policy initiatives based on the existing
literature
By Arup Mitra.
WP/16/07 Fostering productivity in the rural and agricultural sector for inclusive growth and
sustainable development in Asia and the Pacific
By Upali Wickramasinghe.
WP/16/06 China’s productivity: past success and future challenges
By Yanqun Zhang.
WP/16/05 Obstacles to productivity in Asia and Pacific region: finance reigns
By Filipe Lage de Sousa.
40
MPFD Working Papers WP/20/09
WP/16/04 Pathways for adapting the Sustainable Development Goals to the national context:
The case of Pakistan
By Jaebeum Cho, Alberto Isgut, and Yusuke Tateno.
WP/16/03 An analytical framework for identifying optimal pathways towards sustainable line de-
velopment
By Jaebeum Cho, Alberto Isgut, and Yusuke Tateno.
WP/16/02 Asia-Pacific’s experience with national systems of TVET
By Jenny Grainger, Liz Bowen-Clewley and Sarah Maclean.
WP/16/01 Strengthening the capacities of Asia-Pacific to protect workers against unemployment
By John Carter.
WP/15/13 Polarizing world: GDP, development and beyond
By Michael Shashoua and Sudip Ranjan Basu.
WP/15/12 Financing development gaps in the countries with special needs in the Asia-Pacific region
By Mustafa K. Mujeri.
WP/15/11 Financing sustainable development – What can we learn from the Australian experience
of reform?
By Wayne Swan.
WP/15/10 Financing statistics development in Asia and the Pacific
by Statistics Division, ESCAP.
WP/15/09 Financing disaster risk reduction for sustainable development in Asia and the Pacific
by Disaster Risk Reduction Section, ICT and Disaster Risk Reduction Division, ESCAP.
WP/15/08 Climate finance in the Asia-Pacific: trends and innovative approaches
by Ilaria Carrozza.
WP/15/07 Inclusive finance in the Asia-Pacific region: trends and approaches
by Md. Ezazul Islam.
WP/15/06 Financing the social sector: regional challenges and opportunities
by Social Development Division, ESCAP.
WP/15/05 Financing small and medium sized enterprises for sustainable development: a view from
the Asia- Pacific region
by Nick Freeman.
WP/15/04 Trade finance for sustainable development in Asia and the Pacific
by Sailendra Narain.
WP/15/03 Capital market development and emergence of institutional investors in the Asia-Pacific
region
by Hans Genberg.
WP/15/02 Financing for development: infrastructure development in the Pacific Islands
by ESCAP Pacific Office.
WP/15/01 Infrastructure financing, public-private partnerships, and development in the Asia-
Pacific region
by Gilberto Llanto, Adoracion Navarro, Ma. Kristina Ortiz.
WP/14/01 G20 agenda for the world economy: Asia-Pacific perspectives
by Sudip Ranjan Basu, Alberto Isgut and Daniel Jeongdae Lee.
WP/13/01 Policies for structural transformation: an analysis of the Asia-Pacific experience
by C.P. Chandrasekhar and Jayati Ghosh.
41
MPFD Working Papers WP/20/..
READERSHIP SURVEY
The Macroeconomic Policy and Financing for Development Division of ESCAP is undertaking
an evaluation of this publication, Financing Structure, Micro and Small Enterprises’
Performance, and Woman Entrepreneurship in Indonesia, with a view to making future issues
more useful for our readers. We would appreciate it if you could complete this questionnaire and
return it, at your earliest convenience, to:
Director Macroeconomic Policy and Financing for Development Division
ESCAP, United Nations Building Rajadamnern Nok Avenue Bangkok
10200, THAILAND
3. Please give examples of how this publication has contributed to your work: ...........................................................................................................................................
...........................................................................................................................................
...........................................................................................................................................
...........................................................................................................................................
...........................................................................................................................................
4. Suggestions for improving the publication: ...........................................................................................................................................
...........................................................................................................................................
...........................................................................................................................................
...........................................................................................................................................
...........................................................................................................................................
42
MPDD Working Papers WP/16/..
About Economic and Social Commission for Asia and the Pacific (ESCAP)
The Economic and Social Commission for Asia and the Pacific (ESCAP) serves as the United Nations’ regional hub promoting cooperation among countries to achieve inclusive and sustainable development. The largest regional intergovernmental platform with 53 member States and 9 associate members, ESCAP has emerged as a strong regional think-tank offering countries sound analytical products that shed insight into the evolving economic, social and environmental dynamics of the region. The Commission’s strategic focus is to deliver on the 2030 Agenda for Sustainable Development, which it does by reinforcing and deepening regional cooperation and integration to advance connectivity, financial cooperation and market integration. ESCAP’s research and analysis coupled with its policy advisory services, capacity building and technical assistance to governments aims to support countries’ sustainable and inclusive development ambitions. WWW.UNESCAP.ORG
TWITTER.COM/UNESCAP
FACEBOOK.COM/UNESCAP
YOUTUBE.COM/UNESCAP