Financing Structure, Micro and Small Enterprises ......MPFDWorkingPapersWP/20/09 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

mailto:[email protected]



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


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


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


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


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


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


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


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


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


Figure 3: Number of MSEs based on province, 2015

(1863,6620](1379,1863](883,1379][302,883]


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]


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


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


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


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


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


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


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


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


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


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


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


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


Table 4: The effect of financial structure on firm productivity – GMM estimator with robust standard

error


(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)


(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


* p<0.10, ** p<0.05, *** p<0.01

21


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


Table 5: Interaction term between formal financing and woman entrepreneur and firm 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)


(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


* 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


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


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


* 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


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


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


* 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


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


* 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


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


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


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).


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.


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.


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


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


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


Figure 6: Percentage of women entrepreneurs by firm size

05

1015

Wom

en e

ntre

pren

eurs

(%)

Micro Small


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



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