00074918.2011.585952

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Micro-entrepreneurship in a hostile environment:

evidence from Indonesia

Virginie Vial

To cite this article: Virginie Vial (2011) Micro-entrepreneurship in a hostile environment: evidence from Indonesia, Bulletin of Indonesian Economic Studies, 47:2, 233-262, DOI: 10.1080/00074918.2011.585952

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MICRO-ENTREPRENEURSHIP IN A HOSTILE

ENVIRONMENT: EVIDENCE FROM INDONESIA

Virginie Vial

Euromed Management, Marseille, and centre de recherche en Développement Economique et Finance Internationale, Aix-en-Provence

The contribution of micro-entrepreneurship to development has featured promi-nently in recent economic and policy debates. Using panel data from the Indonesia Family Life Survey over a long period (1993–2007) marked by an important eco-nomic crisis in 1997, this paper investigates the impact of inancial, human and so -cial capital on households’ participation in micro-entrepreneurship, while account-ing for corruption as well as institutional and infrastructure quality. Larger urban households that have greater inancial and social capital, and/or whose members have an elementary or secondary education, are more likely to participate. Cor-ruption at the local parliament and local government levels reduces the number of participants, while higher-quality formal institutions and infrastructure boost entrepreneurship. The period is marked by a rise in participation in 2000, but com-munities that experienced a loss in well-being due to the crisis were less likely to participate in micro-entrepreneurship.

INTRODUCTION

Entrepreneurship is crucial to employment, innovation and growth, and as such has been the subject of extensive research in both the economic and management literatures. The importance of entrepreneurship in economic growth can be ana-lysed through the study of businesses, particularly small ones. As Thurik and Wennekers (2004: 142) note: ‘the focus has shifted from small businesses as a social good that should be maintained at an economic cost to small businesses as a vehicle for entrepreneurship’. While this has become clear in the study of devel-oped countries, the small and often informal sector in developing countries is still viewed as an unlimited pool of labour for the large industrial sector. Thurik (2009) suggests that, in spite of advances in the theory of endogenous growth, includ-ing evidence of the increasinclud-ing importance of R&D in economic growth

(imply-ing that irms need economies of scale to survive and grow), small businesses

and entrepreneurship are still clearly important to economic activity. While my focus is on micro-entrepreneurship, I also refer to the literature on informal and household businesses, which represent two particular forms of micro-business.

Based on analysis of cross-country irm data, La Porta and Schleifer (2008) con

-irm that informal micro-businesses account on average for half of all economic

activity in developing countries, although they are less productive than formal

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sector irms. Yet there is little systematic knowledge of micro-entrepreneurship in

such economies.

The Indonesian case relects the state of micro-entrepreneurship in develop -ing countries more generally, and can therefore provide insights that may be

of relevance to them. About 90% of all Indonesian irms are micro-enterprises,

representing about half of national employment. Micro-enterprises – especially the low-technology ones – have been shown to absorb workers who become unemployed after an economic crisis (Tambunan 2007). The Indonesian micro-entrepreneur is likely to be male and educated, while the enterprise is informal, low-tech and specialised in trade and service industries, and generally does not experience noticeable growth – that is, is ‘trading on the margins’ (Rigg 2003). Basic institutions and infrastructure are important to the birth and performance of micro-enterprises, but government intervention in some instances hampers pro-ductivity improvement and innovation. Corruption is still very high in Indonesia, and infrastructure quality is low and declining. In addition, the country went through a major economic crisis in 1997–98, followed by a major shift in political regime with the demise of President Soeharto. For these reasons Indonesia repre-sents a good case for the study of the characteristics of micro-entrepreneurship in a hostile environment.1

This study beneits from an important geographical and historical span. It pro -vides evidence of the characteristics of micro-entrepreneurship in the rural and urban areas of Southeast Asia’s largest developing country over a long period, 1993–2007. It concentrates on the role of the three core assets necessary for any

individual to engage in micro-entrepreneurship – inancial capital, human capital

and social capital – while also accounting for environmental hostility as indicated by institutional and infrastructure quality, corruption and two indicators captur-ing the effects of the 1997–98 crisis.

The study uses panel data from the four waves (1993, 1997,2_{2000 and 2007)}

of the Indonesia Family Life Survey (IFLS) in 13 of the country’s 27 provinces. These provinces represent 83% of the total population.3_{I ind that larger urban} households headed by older members with greater inancial and social capital

but lower levels of educational achievement are more likely to engage in micro-entrepreneurship. A heightened perception of corruption at the local parliament and local government levels is associated with reduced participation in micro-entrepreneurship, while higher-quality formal institutions and infrastructure appear to encourage entrepreneurship.

1 In 2010 Indonesia scored 2.8 on the Transparency International Corruption Perception Index, on a scale ranging from 0 (totally corrupt) to 10 (not corrupt). It was rated 2.76 on the World Bank’s Logistic Performance Index 2010 on a scale ranging from 1 (very low quality) to 5 (high quality), and has witnessed a decline in infrastructure quality since 2007. See <http://siteresources.worldbank.org/INTTLF/Resources/515003-1276029788910/ LPI_Booklet_Indonesia.pdf> and <http://www.transparency.org/policy_research/ surveys_indices/cpi/2010/results>.

2 The 1997 survey did not pick up crisis effects. To do so, the researchers carried out a sup-plementary survey in 1998 using 25% of the sample.

3 The number of provinces increased from 27 to 33 in 2001. For consistency of results, the study uses the pre-2001 deinition of provinces, recombining new into old provinces.

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The irst section of this paper reviews the literature on factors inluencing par

-ticipation in micro-entrepreneurship, focusing on inancial, human and social

capital, and hostile environments. It also frames the motivations and objectives of the study for the Indonesian case. The second section presents the data and the empirical methodology used to test the impact of the three core assets and the environment for entrepreneurship on participation. The third section discusses micro-entrepreneurship in Indonesia between 1993 and 2007 by way of descrip-tive statistics, and the fourth presents and discusses the models’ results. The

study’s indings are summarised in the concluding section.

HOUSEHOLD CHARACTERISTICS, THE ENVIRONMENT AND PARTICIPATION IN MICRO-ENTREPRENEURSHIP

The literature on factors inluencing the existence and growth of small and micro

enterprises in developing countries is vast. One of the most comprehensive reviews of this literature was undertaken by Nichter and Goldmark (2009). The authors describe exhaustively the internal and external factors that have been found to

inluence ‘small irm growth in developing countries’. The literature agrees on the

importance of a number of characteristics associated with entrepreneurs, such as level of education, work experience, gender and the characteristics of the house-hold to which the entrepreneur belongs. Firm age, sector (formal or informal)

and access to inance are the key irm characteristics. External factors encompass social networks, value chains and inter-irm cooperation, such as the presence of

vertical and horizontal linkages or supporting markets. Finally, contextual factors such as economic, institutional and infrastructure quality also matter.

The literature reveals that businesses are generally more likely to grow if they

are young, non-family-owned irms operating in the formal sector; if they are

owned by better-educated and more experienced male entrepreneurs who

oper-ate within a social network; if they have access to inance; if they are part of a

favourable value chain;4_{and if they beneit from inter-irm cooperation in a con}

-ducive macroeconomic and institutional environment. Parallels can be drawn

as far as entrepreneurial participation is concerned: most factors that inluence irm growth affect participation, as is demonstrated, for example, by Delmar and

Davidsson (2000). This study focuses on four core sets of factors: inancial capi -tal, human capi-tal, social capital and environment (comprising institutional and infrastructure quality as well as macroeconomic conditions).

Financial capital and entrepreneurship

As the entrepreneur and the capitalist have two very different roles in the economy (Schumpeter 1934), an individual’s entrepreneurial ability – but not initial wealth – should matter for the decision to engage in entrepreneurship. Initial wealth may

matter, however, if formal and informal inancial markets are imperfect, if a size -able investment is needed (so that the entrepreneur needs entirely or partly to

4 A favourable value chain is deined as having at least one of the following three char -acteristics: ‘(a) strong and sophisticated demand; (b) sectoral characteristics that allow for MSE [micro and small enterprise] competitiveness; and (c) power structures that allow for MSEs to lourish’ (Nichter and Goldmark 2009: 8, table 1).

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self-inance the venture), if there is a need for collateral in order to be able to

borrow funds, or if the investment is risky and investors are risk-averse. In such circumstances, an agent would need to be wealthy before being able to start a business. A number of empirical studies provide evidence on the positive rela-tionship between initial individual or household capital and entrepreneurship. (For a review, see Gentry and Hubbard 2004.) Hernando de Soto’s (2001) ‘dead capital’ argument for the case of developing countries suggests that household assets generally cannot be used as collateral for loans because they are too small,

unregistered (in the case of land or houses) or too dificult for the lender to seize in

the case of default. As a result, entrepreneurs may have to rely on total or partial

self-inancing.

In 2006 the World Bank conducted a survey of 2,223 rural non-farm enterprises,

comprising 269 small and medium-sized irms and 1,954 micro-enterprises. Its

report on the Rural Investment Climate Survey shows that the bulk of investment

in rural enterprises is inanced from individual, family and other sources rather than the formal credit market. Half the irms considered credit to be a major con

-straint (World Bank 2006: 45–6). Only 24% of micro-irms wanting to undertake

additional investment were planning to apply for a formal sector loan, for which collateral was generally required. Tambunan (2007: 104, table 7) reports that over 34% of micro-enterprises surveyed in 2003 by Indonesia’s central statistics agency

(Badan Pusat Statistik, BPS) said that lack of capital was a major dificulty. In a

study of small-scale entrepreneurship in Makassar, Rigg (2003) concludes – like

many others – that lack of growth among the irms of small entrepreneurs is a

consequence of shortcomings in innovation, product quality and access to credit. In short, both the theoretical literature and the initial empirical evidence

sug-gest my irst hypothesis:

H1: Households with greater initial economic wealth are more likely to participate in micro-entrepreneurship.

Human capital and entrepreneurship

Entrepreneurship needs to combine inancial capital with human capital. Higher levels of education among owners boost irm growth. In most developed coun -tries they are a predictor of an increased likelihood of owning a business,

per-haps because better-educated individuals are more likely to identify proitable

opportunities and to engage in exploiting them. However, economic opportuni-ties and social systems may distort this simple relationship, shaping a rather com-plex interaction between education and entrepreneurship. In developing country settings in particular, the hypothesis that higher education conditions

entrepre-neurship needs to be modiied to take account of alternative opportunities. Poten

-tial entrepreneurs are motivated by the prospect of inancial success, but also by

the availability of opportunities for well-paid employment. They may therefore

undertake higher levels of education with the intention of inding a job in the

public or large-scale private sector, rather than engaging in businesses of their own. Overall, studies overwhelmingly agree that there is an inverted U-shaped relationship between education and entrepreneurship (Reynolds 1997). Depend-ing on the state of demand for educated workers, the effect of education on

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entre preneurship will be highly contingent on the availability of, and remunera-tion from, those alternative opportunities.

Indonesian evidence on the effect of education on entrepreneurship is sparse. Leinbach (2003) suggests that better-educated household heads are more likely to engage in entrepreneurship than those with less education. Using education as a control variable to explain the participation and performance of rural

entrepre-neurs, Gibson and Olivia (2010) ind that participating households are more likely

to be headed by members who have received a secondary education. On the basis of these considerations my second hypothesis becomes:

H2: Higher levels of education increase the likelihood of participation in micro-entrepreneurship, but beyond a certain level of education households are less likely to participate – that is, there is an inverted U-shaped relationship between level of educa-tion and entrepreneurship participaeduca-tion.

Social capital and entrepreneurship

Social capital can be deined as networks, norms and trust. This type of capital takes time to build, and requires mainly human, but to some extent also inan -cial, capital. Putnam (2000: 18–19) proposes that ‘the core idea of social capital theory is that social networks have value … [in that] social contracts affect the productivity of individuals and groups’. In developing countries, human capital as represented by formal education is limited and access to outside capital scarce; social capital is therefore likely to play a very important role in the decisions of households to engage in micro-ventures. In particular, social capital is essential to obtain access to most resources – such as start-up capital, operating licences and

suficient quantities of inputs – and to gain access to customers and users through

connections, word of mouth, reputation and network-embedded trust.

Miguel, Gertler and Levine (2005: 754) suggest that for the period 1985–95 ‘initial social capital does not predict subsequent industrial development across

274 Indonesian districts’. However, Spaan and Hartveld (2002: 294) ind that rural

sugarcane producers in East Java before the 1997 crisis were able to expand and diversify to become ‘agricultural innovators, leading entrepreneurs, patrons, political contenders, religious leaders, employers, and, last but not least, employ-ment brokers’ thanks to investemploy-ment strategies and social networking with state agencies and supra-local job brokers.

I therefore test a third hypothesis:

H3: Households with greater social capital are more likely to participate in micro-entrepreneurship.

Hostile environments and entrepreneurship Institutional and infrastructure quality

In their study of entrepreneurship and family businesses in Lithuania, Dyer and

Mortensen (2005) observe that irms that construct social capital and family net -works are able to overcome environmental hostility and perform better than oth-ers. They describe hostile environments in transitional and emerging economies as displaying the following characteristics:

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(1) declining gross domestic product; (2) declining purchasing power, typically due to high inlation; (3) lack of skilled workers due to a poor education system, out-migration, or declining population; (4) lack of infrastructure, for example, transportation, banking, communications, and utilities; (5) corruption and lack of legal protection; (6) excessive governmental intrusion, for example, tax laws and burden some regulations; and (7) political uncertainty, social unrest, or war (Dyer and Mortensen 2005: 247).

I argue that emerging economies present some of the characteristics of hostile environments for entrepreneurship. Prevailing institutions, in the early phases of development, usually do not support small entrepreneurs. Rather, large and

settled irms tend to receive the most support from governments. In later phases

of development, political lobbying renders the removal of entrenched support

systems dificult, so that competition is still skewed to the beneit of large irms. Also, because of a combination of corruption, weak law enforcement and low irm

earnings, a large proportion of small enterprises remain unregistered – that is, they remain in the informal sector.

Baumol (1990) argues that low-quality institutions foster the development of unproductive entrepreneurship (that is, ‘unproductive political and legal activi-ties’), while higher-quality institutions favour the spread of productive entre-preneurship (that is, ‘productive market activities’). In this framework, the case of Asia is of particular interest because of the so-called ‘Asian paradox’ described,

among others, by Vial and Hanoteau (2010). The authors show that corruption

does not entirely hamper business or entrepreneurship in Asian economies, as indicated by their relatively high growth rates, but may rather ‘grease the wheel’

and – within systems characterised by excessive red tape – trigger higher irm

growth and productivity. Data from the World Bank’s Enterprise Survey for Indo-nesia in 2003 show that, relative to their medium-sized and large counterparts, a

far larger percentage of small irms give gifts to oficials in order to obtain oper -ating and import licences, construction permits, and electrical, phone and water connections, and to secure government contracts.5_{Regarding the issue of} infor-mality and fraud, the survey shows that 44% of surveyed irms admit reporting

less than 100% of their sales for tax purposes. This practice is more prevalent

among small irms (75%) and medium-sized irms (55%) than among large irms

(34%).

Using a random sample of data from the World Bank’s Rural Investment

Cli-mate Survey, von Luebke (2006) inds that the most important problems reported by micro-enterprises are, irst, uncertainty in relation to local government poli

-cies; second, the need to make unoficial payments to obtain licences; and third,

the time spent on licence administration. In their study of Indonesian rural non-farm enterprises, Gibson and Olivia (2010) show that infrastructure access and the quality of the electricity supply and roads affect household participation in, and employment and income generated by, non-farm enterprises.

5 See <http://www.enterprisesurveys.org/CustomQuery/Country.aspx?economyid=90 &year=2003&characteristic=sector&tab=0>. The survey covers 713 irms, comprising eight small irms (those with fewer than 20 employees), 335 medium-sized irms (20–99 em -ployees) and 368 large irms (100+ em-ployees). I acknowledge the limitation of the igures given that the sample contains such a small number of small irms.

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The fourth hypothesis for testing is therefore:

H4: The quality of institutions and infrastructure has a positive effect on participation in micro-entrepreneurship.

Macroeconomic environment

Liedholm and Mead (1999) suggest that in times of crisis small and medium-sized enterprises do not expand, but either maintain or cut employment, or close down altogether; large-scale companies are similarly affected. Especially if

unemploy-ment beneits are absent, this puts strong pressure on newly unemployed people to set up their own businesses. Van Diermen (2002) argues that, after the 1997 crisis, micro-irms became more numerous and smaller on average, informality

rates increased, and female engagement in entrepreneurship expanded in order to

provide households with supplementary income. He speculated that micro-irms

would become less numerous when economic growth resumed.

Tambunan (2000) investigates the performance of small enterprises in

Indo-nesia during the inancial and economic crisis of the late 1990s. He reports that ‘in 1998 some 5.4 million workers in the non-inancial sector were displaced by the crisis’ (p. 93). However, in the absence of generalised unemployment beneits, and

because most of the newly unemployed needed to sustain themselves, the author claims that about 50% of these workers were ‘re-absorbed in small-scale economic activities, mostly in the informal sector’.

On the basis of these considerations my inal hypothesis becomes:

H5: Crises trigger more participation in micro-entrepreneurship, but the effect is tran-sitory, and participation rates decline as economic growth resumes.

DATA DESCRIPTION AND METHODOLOGY

This study uses the IFLS database compiled and published by RAND Corpora-tion.6_{It is a rich source of panel data on communities, households and}

individu-als in four waves: 1993, 1997, 2000 and 2007. The four waves of the IFLS cover on average over 10,000 households, about two-thirds of which have been surveyed in all four years. Households were interviewed in 13 of the 27 provinces extant before 2001 (see footnote 3). Those 13 provinces account for 83% of the popula-tion. The paper uses aggregate individual data and focuses on the household level in order to study family-owned micro-enterprises.

In general, the empirical data used to study entrepreneurship suffer from a lack of information on the entrepreneurial qualities of the population, and on institutional and infrastructure quality. The IFLS statistics respond to these gaps by providing individual, household and community-level data for a representa-tive sample of the Indonesian population; the sample allows non-entrepreneurs to be used as a control group, while containing enough entrepreneurs to ensure statistical reliability. This reduces the selection bias that normally occurs in studies of entrepreneurship, where the empirical data ignore informal and

6 A full description of the data is provided by Frankenberg and Karoly (1995), Franken-berg and Thomas (2000), Strauss et al. (2004) and Strauss et al. (2009).

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micro entrepreneurship to focus solely on larger entrepreneurs, formally

regis-tered enterprises or irms over a certain minimum size, and do not include any

control group. In addition, the IFLS provides information on the quality of infra-structure and institutions, and on the macroeconomic environment. These data were collected during the same surveys and at the same levels, allowing for homogeneity of variables in a study that encompasses individual, household and environmental determinants of entrepreneurship participation.

Use of household data to study enterprise output, proit or productivity growth

represents an interesting but complex possibility that is beyond the scope of the current paper. Another interesting way to measure entrepreneurship is to study household entry into business activity. However, the structure of the current data makes this impossible, as the survey waves were carried out at intervals of several years and households that opened and closed a business between any two waves would go unreported. The primary dependent variable is households’ current par-ticipation in entrepreneurship. I acknowledge that the indings could be biased, as some households may have participated in entrepreneurship only during years in which there was no survey. This bias is counterbalanced, however, by the large population covered by the data. Independent variables are measured in the previ-ous survey wave in order to control for endogeneity between the dependent and independent variables.

Studies attempting to discover the main determinants of entrepreneurship par-ticipation emphasise that, while it is possible to control for individual as well as environmental characteristics, the problem of omitted variable bias remains. In particular, there is always the possibility that unobserved individual or

environ-mental characteristics inluence participation in entrepreneurship. Using longi -tudinal panel data, this problem can be dealt with by introducing an individual

effect. The use of a ixed-effects model would not allow estimation of the effect

of time-invariant or quasi-time-invariant variables, including most control vari-ables as well as the social and human capital varivari-ables of primary interest here (Plumper and Troeger 2007). I therefore introduce a random effect into the

speci-ication. This speciication is well suited to panel data that present a large number

of observations over a limited number of years. I note, however, that the use of random effects cannot control for the possible omitted variable bias.

To test for the impact of inancial, human and social capital on households’

participation in entrepreneurship, I use a panel Probit model with random effects, as follows:

Pr(p_it= 1|X) =β_i+α_jC_jit–1+α_FKFK_it–1+α_SKSK_it–1+α_kHK_kit–1

+α_INFINF_i+α_mINST_mi+α_nCRI_ni+ε_it (1)

where p_it is participation of household i in entrepreneurship in survey-year wave t (1993, 1997, 2000 or 2007); β_i is the random effect; C_jit is a set of j control variables; FK_it is a inancial capital variable; SK_it is a social capital variable; HK_kit is a set of k human capital variables; INF_i is an infrastructure quality variable; INST_mi is a set of m institutional quality variables; and CRI_ni is a set of n inancial and economic crisis indicators. To eliminate part of the endogeneity that might exist between the dependent and independent variables, I regress entrepreneur-ship participation in the current survey-wave year against the one survey-wave lag of time-varying explanatory variables. I acknowledge that this procedure does

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not entirely clean up all endogeneity, and keep this in mind when interpreting

the results. The regression coeficients give the change in the z-score for a

one-unit change in the explanatory variable, where the z-score measures the distance between the observed data and the sample mean, measured in standard devia-tion units. To simplify interpretadevia-tion, I report marginal effects – the change in the dependent variable induced by a one-unit change in the explanatory variable, or by a switch from zero to unity for dummies – with z-scores in parentheses.

The participation dummy indicates whether any household member partici-pates in entrepreneurship in the year of observation (p_it= 1). Note that households can report only a single enterprise for the survey years 1993 and 1997, but more than one for the survey years 2000 and 2007. The participation dummy therefore equals 1 whenever a household declares participation in one or more enterprises. I focus on non-farm micro-enterprises – that is, enterprises with strictly fewer

than ive employees engaged in non-farm activities – as farm businesses represent

a special type of economic activity.7

A limitation of the analysis is that the determinants of participation include determinants of both entry and survival (see especially the discussion by Evans and Leighton 1989: 529). In particular, the probability of participation depends on the underlying probability of entering. I control for this, in part, by carrying out a robustness check, introducing the previous survey-wave participation dummy, p_it–1, as an explanatory variable in variant (9) of the base model to account for the effect of prior participation on current participation.

The control variables, C_jit, are as follows.

• Age of household head and its squared value (HHage and HHage2_{), to}

cap-ture the inverted U-shaped relationship between age and entrepreneurship participation. Ages measured in years are transformed into logs so as to inter-pret each percentage point change in age as a corresponding percentage point change in the dependent variable.

• Gender, where the dummy male takes a value of 1 if the head is male, and 0 otherwise.

• Marital status, where the dummy married takes a value of 1 if the head is mar-ried, and 0 otherwise.

• Size of household in number of members and its squared value in logs (HHsize and HHsize2_{). Household members include dependants (young children and}

the elderly). Larger households have an incentive to participate in entrepre-neurship because more household members are available to help in the busi-ness. But I also hypothesise decreasing returns to household size because the

7 Data on the number of employees are not available for 1993 and 1997. Since the explana-tory variables are expressed in one survey-wave lag, I do not use entrepreneurship partici-pation data for 1993. The problem remains for the 1997 data, however. I am partially able to overcome this problem by using the data for 2000 and 2007 to identify the non-micro-enterprises in the 1997 sample and remove them from the study. (Note that the results remain unchanged even if those enterprises are kept in the sample.) I also acknowledge that a few non-micro-enterprises may remain in the sample for 1997. However, since the proportion of such enterprises is just 4.3% in both 2000 and 2007 (see table 1), the bias is minimal. Running the models over just the 2000 and 2007 survey waves does not substan-tively affect the results.

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largest households may include a high proportion of dependants, which may hamper participation.

• An urban dummy (urban) that equals 1 if the household is situated in an urban area, and 0 otherwise.

The primary inancial capital variable, FK_it, is measured as the log of house-hold and business assets per capita (HHBIZassets). As entrepreneurs include both new entrants and incumbents, I account for households’ personal as well as

busi-ness assets. I delate these using the World Development Indicators consumer price index, base 100 in 2000. I add the two types of assets, so that the prior decision

of households to allocate assets to a business does not artiicially delate house -hold assets. House-hold assets include the house occupied by the house-hold; other

houses and buildings; non-agricultural land and ishponds; livestock and poul -try; vehicles (cars, boats, bicycles, motorcycles); household appliances (radios,

tape recorders, TV sets, refrigerators, sewing machines, washing machines); sav

-ings, certiicates of deposit and stocks; receivables, jewellery, household furniture

and utensils; and other assets. Business assets include land, buildings, four-wheel vehicles, other vehicles and other non-farm equipment. As an alternative measure of overall household wealth, I perform a robustness check using the log of the household’s house size in square metres (housesize).

Following Miguel, Gertler and Levine (2005, 2006), the social capital vari-able, SK_it, is constructed as a dummy that indicates whether the household participates in an arisan, which is one of the most common social activities in Indonesia. Arisan are often referred to as ‘rotating savings and credit associa-tions’ (ROSCAs), as they involve the regular collection of savings from each member of the group, and the lending of those funds to members in rotation. There are several types of arisan, including religious, women’s and market

trad-ers’ groups, some of which have a signiicant inancial aspect. Miguel, Gertler

and Levine (2006: 291) in particular emphasise that they can be considered a ‘manifestation of social capital’. They are very relevant to the issue of entrepre-neurship, as in most cases they can provide the social networks necessary to

promote entrepreneurial activity, and in some instances to overcome inancial

and institutional failures. The variable for arisan is available for the years 1997, 2000 and 2007.

Like Miguel, Gertler and Levine (2006), I use two alternative indicators of

infor-mal social capital to perform robustness checks. The irst is a dummy indicating

whether the household belongs to a community that displays an ‘ethic of mutual cooperation’ (ethic). The second is a dummy indicating whether such cooperation is undertaken by formal community groups in the village (groups) – for example, groups engaged in the construction of places of worship and public infrastruc-ture, or that organise funerals or weddings. Starting in 1997, the two variables are as reported by one or several prominent community members – that is, com-munity leaders or selected members of their staff – and I assume those values are constant for the other survey years. Using ethic or groups as alternative social capital variables will attribute social capital to more households, and control for social capital stemming from sources other than arisan.

To account for human capital, HK_kit, I introduce a set of k dummies indicat-ing the highest level of education attained by the household head: elementary (elementaryHH), junior high school (juniorHH), senior high school (seniorHH),

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adult education8_{(adultHH) or university (uniHH). The level of education of the}

household head is assumed to be representative of the general level of education of adults in the household, who are the members most likely to participate in the family enterprise. As education may have spillover effects between household members, however, and given that several members of a household may work in

the business, I test the results for robustness by using a set of ive dummy vari -ables that indicate whether at least one household member has attained each level of schooling (elementary, junior, senior, adult and uni).

The quality of infrastructure, INF_i, is measured using a dummy of village-level perceptions of the quality of three- and four-wheeled public transport services (transqual); it has a value of 1 if the respondents rate the quality as ‘adequate’ or ‘somewhat adequate’, and 0 if the response is ‘not adequate’, ‘far from adequate’, ‘no answer’ or ‘don’t know’.

The quality of institutions, INST_mi, is measured in two ways. The irst is the perceived quality of business permit issuance services (bizpermitqual), a dummy that equals 1 if the respondent rates quality as ‘adequate’ or ‘somewhat adequate’, and 0 if the response is ‘not adequate’, ‘far from adequate’, ‘no answer’ or ‘don’t know’. The second is a measure of perceived corruption at different administra-tive levels. Respondents were asked: ‘in your opinion, are there any cases of cor-ruption, collusion and nepotism (KKN) …’ in relation to several organisations, such as the village, sub-district and local governments, the local parliament, the

police, hospitals and schools. I chose to use the irst four indicators in turn in order

to account for different levels of institutional–geographical effects (corrDPRD for local parliaments, corrKABUP for local governments, corrKEC for sub-district governments and corrVIL for village governments). The respondents were com-munity leaders and selected members of their staff, and the possible responses were ‘yes’, ‘no’, ‘decline to answer’ and ‘don’t know’. I chose to work with a lower bound of perceived corruption; thus the dummy equals 1 if respondents answered ‘yes’, and 0 otherwise. I also carried out robustness checks with upper-bound treatments and found that this did not affect the results. Sets of dummies for both institutional and infrastructure quality are available for 2007, and I take those values as constant for all periods.

Finally, I account for the inancial crisis using a set of three variables, CRI_ni. First, I use year dummies, y97 for 1997 and y00 for 2000, that respectively capture

the pre- and post-crisis effect. The difference in the coeficients estimated for these

two dummies represents the crisis effect. As the crisis was over in 2000, this differ-ence will just capture the longer-lasting effect of the crisis. Second, I use an index of perceived loss or gain of wealth of communities after the crisis (degrad). I use data collected in 2000 on the subjective sense of material well-being at the com-munity level. Comcom-munity representatives (rather than household heads) were asked to respond to the following two questions.

• On a scale of 1 to 6, where 1 represents the village where the population is

poorest and 6 represents the village where the population is richest, what is the

8 This term is not deined, but presumably refers to education outside the regular school system. It is in any case of almost negligible importance.

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number that best represents your village, considering the current conditions of people in your village?

• What is the number that best represents your village at the time before the

economic crisis (end of 1997)?

By subtracting the irst variable, sw01, from the second variable, sw02, I obtain the number of rungs on this subjective wealth ladder gained or lost by the community between 1997 and 2000. I construct a dummy that equals 1 if the community rep-resentative perceives a loss of wealth after the crisis, and 0 otherwise.

DESCRIPTIVE STATISTICS

Table 1 shows the distribution of micro, small and large enterprises, and the

extent of participation among surveyed households. The number of both irms

and surveyed households increases over time, with the percentage of households running a business rising from 34% in 1993 to 43% in 2000 before declining to 38%

in 2007. The bulk of irms are micro-enterprises, that is, have strictly fewer than ive employees. I focus on these in all my calculations, excluding irms with ive

or more employees.

Table 2 presents descriptive statistics for all surveyed households as well as for the balanced panel, which includes only households surveyed in all four waves from 1993 through to 2007. Correlation matrices are reported in table A1 in the appendix. The average household size ranges from 4.6 members in 1993 to 5.3 members in 2007 (all households). About half of all households live in urban areas, with a large increase between 1993 (47%) and 2007 (54%). Most households are headed by married men in their mid to late 40s.

For the unbalanced panel (all households), the participation rate for micro-entrepreneurs ranges from 33.9% of households in 1993 to a peak of 41.3% in 2000. The statistics for the balanced panel display an increase in the participa-tion rate between 1997 and 2000 from 34.3% to 45.3%, suggesting that the crisis boosted participation, and a decrease in the rate to 41.4% in 2007 as household members presumably returned to employment. Real average total household assets declined by over 36% between 1993 and 2000, then increased by almost 10% between 2000 and 2007. The crisis seems to have had a substantial impact on assets that lasted until at least 2000. In constant prices, average household wealth for the balanced panel declined less drastically, by 18% between 1993 and 2000, then rose by almost 28% between 2000 and 2007 to exceed its 1993 level. The broad pre-crisis distribution of assets across households, visible in both the unbalanced and balanced panels, narrowed dramatically in 2000 and 2007, with the standard deviation for the balanced panel falling from Rp 272 million in 1993 to just Rp 88 million in 2007. Houses represented stickier assets, with their physi-cal size increasing steadily between 1997 and 2007 from 73.8 to 91.3 square metres (balanced panel). House size as an alternative measure of wealth dampens the effect of the high variation in the value of more liquid assets, especially for the crisis period.

Over 50% of households reported participating in arisan in 1997 and 2000, fall-ing closer to 40% in 2007 (balanced and unbalanced panels). About 88% of house-holds reported living in a community that had an ethic of mutual cooperation

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(ethic), and 76% in a community characterised by group-based mutual coopera-tion activities (groups) (balanced panel).

About 50% of household heads had completed elementary school, 11–15% junior high school, 13–23% senior high school and 0–12% university, while very few if any household heads had completed any adult education (balanced and unbalanced panels). Data on the highest educational attainment of any household member also show that a majority of households had at least one member who had completed elementary school (59.8% in 2000 and 51.5% in 2007), compared with 33.1% in 2000 and 33.6% in 2007 for junior high school, 38.4% and 40.9% for senior high school, 14.4% and 18.0% for university, and 0.2% and 1.1% for adult education (unbalanced panel). The data seem patchier for 1993 and 1997, however.

Around 63% of households in the balanced panel lived in a community that reported having good transport services, but only 50% in one that reported hav-ing good business permit issuance services. Community representatives per-ceived their local parliaments and governments as being more corrupt (17% and 14% respectively) than the sub-district or village-level authorities (both 5.0%).

Finally, 31% of households in the balanced panel lived in a community where the subjective sense of well-being had declined between 1997 and 2000. For the unbalanced panel, the proportion rises from 44% in 2000 to 50% in 2007.

TABLE 1 Distribution of Firms by Size and Participation of Households

Number of Firms Total

No. of observations 2,244 1,968 163 26 4,401 10,269

% of all irms 51.0 44.7 3.7 0.6 100.0

% of all households 42.9

2007

No. of observations 1,975 2,734 190 20 4,919 12,986

% of all irms 40.2 55.6 3.9 0.4 100.0

% of all households 37.9

a Data on irm size are not available for 1993 and 1997; see footnote 7.

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TABLE 2 Descriptive Summary Statisticsa

Entre-

preneur-ship

Partici-pation

Household Head Any Household Member

Male Married Age (years)

Education Education

Elemen-tary

Junior High

Senior High

Adult Uni-versity

Elemen-tary

Junior High

Senior High

Adult Uni-versity

All householdsb

1993

No. of observations 7,171 7,182 7,182 7,182 7,182 7,182 7,182 7,182 7,182 7,159 7,159 7,159 7,159 7,159 Mean 33.9 83.9 82.8 45.7 49.4 11.7 13.8 0.0 0.0 23.1 3.8 2.9 0.0 0.0 Standard deviation 0.47 0.37 0.38 14.5 0.50 0.32 0.35 0.00 0.00 0.42 0.19 0.17 0.00 0.00 1997

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M

TABLE 2 (continued) Descriptive Summary Statisticsa

Entre-No. of observations 5,803 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,798 5,798 5,798 5,798 5,798 Mean 32.8 85.6 85.7 45.4 52.2 11.2 12.6 0.0 0.0 23.6 3.5 2.8 0.0 0.0 Standard deviation 0.47 0.35 0.35 13.8 0.50 0.32 0.33 0.00 0.00 0.42 0.18 0.16 0.00 0.00 1997

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House-No. of observations 7,182 7,183 7,183 7,183 7,183 7,183 7,183 7,183 7,183 7,183 7,183 7,183 Mean 4.6 58.2 47.4 50.9 64.8 17.6 14.2 5.6 5.3 0.0 85.9 74.6 Standard deviation 2.18 320.0 0.50 0.50 0.48 0.38 0.35 0.23 0.22 0.00 0.35 0.44 1997

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M

House-No. of observations 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 Mean 4.7 45.1 43.1 50.3 63.2 17.1 14.2 5.1 5.0 0.0 87.8 75.7 Standard deviation 2.08 272.00 0.50 0.50 0.48 0.38 0.35 0.22 0.22 0.00 0.33 0.43 1997

No. of observations 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 5,811 Mean 6.9 47.2 91.3 47.9 50.3 63.2 17.1 14.2 5.1 5.0 30.5 43.8 87.8 75.7 Standard deviation 2.87 88.00 293.11 0.50 0.50 0.48 0.38 0.35 0.22 0.22 0.46 0.50 0.33 0.43

a The means for dummy variables are expressed as percentages. Data on house size and arisan are not available for 1993.

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MODEL RESULTS AND DISCUSSION

Table 3 displays the results of the models over the full sample of households. I ran base model (1) for several of the key variables, as well as nine alternative

models as robustness checks. The indings are consistent across the 10 models, with similar signs, sizes and signiicance of the estimated coeficients, supporting

the robustness of the results. The exception is model (9), which includes the previ-ous survey-wave participation dummy, p_it–1, as an explanatory variable. While

most of the coeficients have the same signs and signiicance as those in the other

models, they are generally smaller in size.

Table 4 displays the results of six of the models for two distinct samples: urban versus rural households. The following comments highlight urban and rural dif-ferences (table 4) as well as the main results (table 3). Focusing on the control

variables, I ind that households that participate in entrepreneurship are more

likely to be larger, urban households with older heads, regardless of the head’s gender or marital status. In both urban and rural areas, a larger household size or an older household head is correlated with an increased likelihood of entre-preneurship participation. The inverted U-shaped relationship between age and

entrepreneurship is signiicant only in urban areas. Interestingly, gender seems to

matter only in rural areas, where households headed by men are more likely to participate.

Urban households are more likely to run a business, presumably because of the increased range of economic opportunities triggered by higher population concentrations and agglomerations around industrial centres, and the lack of alternative subsistence activity (that is, agriculture). Larger households have more human resources, giving them an incentive to operate a business if this can constitute an additional income source for the family. However, I note that there are diminishing marginal effects to household size, suggesting that the numerous dependants in a very large household may constitute an impediment to entre-preneurship. A similar effect occurs with respect to the age of the household head. While the presence of an older head increases the probability of participation, there are decreasing marginal effects to age, perhaps because older heads are no

longer suficiently dynamic to start or continue to run a business. This result is in line with the inding in the literature that entrepreneurs are more likely to be

between 25 and 40 years old (Delmar and Davidsson 2000).

The indings validate hypothesis 1 (H1), showing that households that have greater inancial capital are more likely to participate in entre preneurship, regard -less of whether assets are measured with a narrow and sticky indicator (house size per capita) or a broad and somewhat less sticky indicator (family and busi-ness assets per capita).9_{The marginal effect estimated in model (1) shows, for}

example, that a 10% increase in wealth per capita results in a 0.9% increase in the probability of participating in entrepreneurship.

Of course, it could be the case that households with better entrepreneurial skills manage to amass greater wealth. The concern here, however, is with the determi-nants of entrepreneurship participation. As I use the lag of wealth as an

explana-tory variable, and the marginal effect of wealth is still positive and signiicant

9 A robustness check using family assets only – that is, excluding business assets – yields similar results.

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TABLE 3 Panel Probit Model (Random Effects), Full Samplea

pit (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

HHBIZassetsit–1 0.090 0.080 0.080 0.085 0.090 0.087 0.088 0.035 0.087

housesize_it–1 0.091

arisanit–1 0.241 0.263 0.246 0.241 0.245 0.244 0.159 0.245

ethic_it–1 0.235

groupsit–1 0.199

elementaryHH_it–1 0.184 0.175 0.231 0.235 0.182 0.186 0.185 _0.092 0.186

juniorHHit–1 0.152 0.161 0.231 0.232 0.149 0.147 0.147 0.070 0.147

elementary_it–1 0.187

juniorit–1 0.174

senior_it–1 0.110

HHageit–1 6.524 7.500 6.120 6.165 6.407 6.556 6.735 6.746 2.875 6.736

HHage_it–12 –0.895 –1.017 –0.839 –0.844 –0.887 –0.898 –0.920 –0.921 –0.405 –0.920

HHsizeit–1 0.485 0.564 0.547 0.555 0.514 0.491 0.500 0.500 0.270 0.500

HHsize_it–12 _–0.092 _–0.108 _–0.108 _–0.110 _–0.108 _–0.095 _–0.098 _–0.097 _–0.062 _–0.098

urban 0.234 0.274 0.250 0.231 0.214 0.226 0.211 0.212 0.134 0.211

bizpermitqual 0.033 0.040 0.027 0.030 0.024 0.050 0.072 0.072 0.020 0.072

transqual 0.188 0.197 0.178 0.180 0.176 0.183 0.223 0.221 0.097 0.223

corrDPRD –0.221 –0.211 –0.143 –0.151 –0.212 –0.128

corrKABUP –0.182

degrad –0.185 –0.182 _–0.124 _–0.125 –0.180 –0.190 –0.229 –0.229 –0.141 –0.229

y97 –0.149 _–0.146

y00 0.192 0.207 0.216 0.217 0.363 0.190 0.181 0.181 0.257 0.181

p_it–1 1.003

Constant –14.530 –15.530 –13.870 –13.920 –14.190 –14.600 –15.010 –15.030 –6.851 –15.010 N 17,331 17,531 24,262 24,262 17,053 17,331 17,331 17,331 17,331 17,331

a Bold italics:_p_{< 0.001;}_bold:_{p< 0.01; italics: p}_{< 0.05. Chi-square tests are valid at the 0.1% level and reject the null hypothesis that at least one of the coeficients is}

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TABLE 4 Panel Probit Model (Random Effects), Urban versus Rural Householdsa

pit Urban Rural

(1) (2) (3) (4) (5) (6) (1) (2) (3) (4) (5) (6)

HHBIZassetsit–1 0.048 0.049 0.045 0.045 0.017 0.046 0.149 0.149 0.147 0.148 0.061 0.131

arisan_it–1 0.280 0.280 0.289 0.288 0.208 0.192 0.192 0.192 0.189 0.110

ethicit–1 0.213 0.244

elementaryHH_it–1 0.223 0.224 0.225 _0.223 _0.110 0.272 _0.144 _0.141 _0.145 _0.144 _0.074 0.182

juniorHHit–1 0.126 0.124 0.122 0.120 0.055 0.220 0.164 0.160 0.156 0.157 0.074 0.214

seniorHH_it–1 –0.049 –0.054 –0.058 –0.060 –0.013 0.017 0.127 0.121 0.117 0.116 0.067 0.197

uniHHit–1 –0.199 –0.202 –0.219 –0.219 –0.111 –0.077 0.175 0.164 0.151 0.149 0.148 0.258

male –0.001 –0.002 –0.006 –0.005 –0.011 –0.015 0.183 0.179 0.176 0.175 0.105 0.132

married_it–1 0.161 0.165 0.171 0.171 0.122 0.157 –0.138 –0.137 –0.132 –0.131 –0.074 –0.075

HHageit–1 6.109 6.109 6.301 6.290 2.817 6.083 6.621 6.670 6.827 6.836 2.795 5.831

HHage_it–12 –0.827 –0.826 –0.848 –0.847 _–0.391 –0.819 –0.917 –0.923 –0.942 –0.943 –0.399 –0.815

HHsizeit–1 0.492 0.495 0.511 0.512 0.237 0.580 0.420 0.430 0.435 0.434 0.300 0.452

HHsize_it–12 _–0.113 _–0.115 _–0.121 _–0.122 _–0.066 _–0.122 _–0.045 _–0.049 _–0.050 _–0.048 _–0.052 _–0.070

bizpermitqual 0.050 0.076 0.104 0.105 0.007 0.072 0.040 0.052 0.071 0.069 0.039 0.009

transqual 0.011 0.007 0.064 0.067 0.031 –0.010 0.289 0.283 0.305 0.296 0.133 0.280

corrDPRD –0.270 –0.165 –0.183 _–0.196 _–0.101 _–0.128

corrKABUP –0.237 _–0.146

degrad –0.225 –0.219 –0.263 –0.261 –0.162 _–0.160 _–0.151 _–0.166 –0.198 –0.198 _–0.118 _–0.101

y97 –0.228 _–0.060

y00 0.195 0.195 0.182 0.183 0.285 0.236 0.190 0.187 0.181 0.181 0.232 0.194

p_it–1 0.934 1.057

Constant –12.930 –12.970 –13.450 –13.430 –6.324 –13.110 –15.450 –15.560 –15.900 –15.920 –7.071 –13.850 N 8,084 8,084 8,084 8,084 8,084 11,349 9,247 9,247 9,247 9,247 9,247 12,913

a Bold italics:_p_{< 0.001;}_bold:_p_{< 0.01; italics: p}_{< 0.05. Chi-square tests are valid at the 0.1% level and reject the null hypothesis that at least one of the coeficients is}

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when I include the lag of participation in entrepreneurship as an explanator of

current participation (model 9), my results conidently point in the direction of

initial wealth having an impact on participation. When comparing urban and

rural areas, I ind that the marginal effect of initial inancial capital is about three times larger, and more signiicant, for rural households. Even considering that

median and average assets per capita are respectively 86% and 146% larger in

urban areas, the analysis shows that initial inancial capital still has a greater

impact in rural areas.

This result tends to support the mainstream argument about the relationship

between an individual’s prior inancial capital and that person’s participation in

entrepreneurship. It is also in line with the results of the Rural Investment Climate

Survey (World Bank 2006), which show that access to inance is a primary source of concern or dificulty for entrepreneurs. From the point of view of a potential lender, this is a relection of the costliness of generating sound information about the abil -ity and creditworthiness of new small entrepreneurs. There is a trade-off between incurring such costs, so that lending becomes relatively safe, and avoiding them, thus incurring a high risk of non-repayment. This means that new entrepreneurs

are unlikely to receive inancing from investors until such time as they have been

able to demonstrate their entrepreneurial ability and reliability. But overall, once the endogeneity issue is partially removed, initial wealth seems to condition entrepreneurship, which could also be interpreted as richer households being less risk-averse. This does not refute the argument that micro-entrepreneurship serves

mainly as a social security net (La Porta and Schleifer 2008), but it may reine it

by showing that it does so only for relatively wealthier households. The greater

impact of inancial capital on rural households’ participation can be interpreted in terms of the reduced access to external inancing in rural areas.

Hypothesis 2 (H2) is partially validated, in that the results show that house-holds headed by a member who has received at most an elementary or junior high school education are more likely to participate in entrepreneurship. The mar-ginal effect of elementary education is higher in urban than in rural areas. Using the full sample of households, the effect of other levels of education is not

statis-tically signiicant, in spite of the expected negative sign related to the inverted

U-shaped relationship between educational level and entrepreneurship partici-pation. Splitting the sample into urban and rural sub-samples shows, however,

that university-level education has a signiicant negative marginal effect in urban

areas only.10

Theory suggests that both households with little or no education and those with relatively high levels of education should be less likely to engage in

entre-preneurship – the irst because of a lack of basic skills necessary for business

success, such as literacy and numeracy, and the second because of greater oppor-tunities for well-paid employment. Thus the low level of education of entrepre-neurial households in this study suggests that participation may be prompted by a lack of jobs for low-skilled workers or the need for them to supplement their pay. This is especially the case in urban areas, where elementary-educated house-hold heads are more likely than their rural counterparts to participate in

entrepre-neurship, and university-educated household heads signiicantly less likely to do

10 Presumably there are very few individuals with a university education in rural areas.

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so. While the option of engaging in entrepreneurial activity may serve as a form of social security net, the low level of education of household heads probably also explains the high proportion of low-technology and low-productivity micro-businesses. Results using the level of education attained by any member of the

household conirm this, with one exception: in model (5), the role of senior high

school education plays a positive role in increasing the participation rate. This may be a sign that other, better-educated – and possibly younger – household members drive the push towards entrepreneurship. However, I cannot exclude the endogeneity issue: it could also mean that entrepreneurial households have managed to increase the general level of education in the household by

distribut-ing the beneits of entrepreneurship to the youngest members.

Social capital in its various forms plays a very signiicant and positive role

in supporting entrepreneurship. This result validates hypothesis 3 (H3). At the household level, participation in an arisan is associated with a higher probability of participation, and the marginal effect is higher in urban areas. This form of social capital provides both the network and the opportunity to obtain access to the funding that is essential to run a business. The effect of social capital measured at the village level supports this: both the mutual cooperation ethic variable and the group-based mutual cooperation groups variable are associated with higher levels of participation in entrepreneurship. The higher marginal effect of arisan participation in urban areas may indicate that this form of community partici-pation somehow compensates for the lack of non-organised community links in more anonymous urban settings: on average, 52% of urban households but only 41% of rural households participate in arisan, while 61% of urban households, but 84% of rural households, are involved in mutual cooperation activities. This is

conirmed by the similar size of the marginal effect for the ethic variable in urban and rural areas. Some endogeneity may remain, and I do not exclude the pos-sibility of the existence of a third factor driving both entrepreneurship and arisan participation over time.

The signs and statistical signiicance of the coeficients for infrastructure and institutional quality, and for the perception of corruption variables, conirm

hypothesis 4 (H4). The quality of infrastructure is correlated with households’ likelihood of participation, especially for rural areas. Better-quality transport ser-vices, in particular, could ease access to both upstream and downstream markets, overcoming the liability of rural location and thus encouraging participation in entrepreneurship.

The quality of business permit issuance seems to have no statistically signii -cant effect on participation. However, if the corruption indicators are removed

from the model, the coeficient for this variable becomes positive and signiicant

(model (10)), indicating that better-quality business-related institutions have a positive impact on entrepreneurship. Most micro-enterprises are informal and probably do not go to the trouble of obtaining permits, but the quality of the issu-ing services also probably partly captures the overall quality of local institutions.11

11 The correlation matrix in table A1 shows that the quality of business permit issuance services is strongly and negatively correlated with perceptions of corruption at the lo-cal government and lolo-cal parliament levels, with correlation coeficients of –30 and –32 respectively.

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Corruption at the local parliament and local government levels is negatively correlated with the probability of participation. Rather than ‘greasing the wheel’ for business, as it appears to do in the case of medium and large-scale

manufactur-ing, institutional corruption seems to prevent micro-enterprises from lourishing.

Micro-businesses have the opportunity to remain informal by avoiding business registration, implying, in theory, that low-level corruption should not affect them.

However, even informal businesses can attract the attention of local oficials, sim

-ply because they operate in the open. This could give oficials the opportunity to

impose informal taxation on them, thereby deterring participation. By contrast,

corruption at the sub-district and village levels does not signiicantly affect par -ticipation.

I propose two possible explanations. First, as the data were collected at the village level, it is possible that the respondents (comprising community leaders and selected members of their staff) would have been less likely to report cor-ruption, either because they themselves were corrupt or because they were in direct contact with corrupt agents. Table 2 indeed shows that for the balanced panel, 17% and 14% of households respectively resided in areas where the local parliament and local government were perceived to be corrupt, whereas just 5% of households lived in areas where the sub-district and village authorities were perceived to be corrupt. I therefore cannot exclude the possibility that corruption

at the local parliament or government level relects corruption at the sub-district

and village levels. The second possible explanation is that the lower levels of gov-ernment have very limited authority and therefore very limited power of extor-tion. I also note that the corruption variables for both local parliament and local government have a higher marginal effect on entrepreneurship participation in urban areas. Urban areas also display a higher average perception of corruption at the local parliament level, with 36% of urban households, but only 24% of rural households, reporting living in corrupt areas. (The percentages are 31% and 23% respectively for corruption at the local government level.)

Finally, the marginal effects estimated on the macroeconomic indicators

con-irm hypothesis 5 (H5). Entrepreneurship participation was more prevalent in

2000 than in either 1997 or 2007, in both urban and rural areas. As suggested by the summary statistics for the unbalanced panel in table 2, participation rates jump from 34% in 1997 to 41% in 2000, before falling back sharply to 36% in 2007. (For the balanced panel the rates are 34%, 45% and 41% respectively.) This hints at a plausible and positive but transitory effect of the crisis on participa-tion during the ensuing three-year period, supporting the argument that micro-entrepreneurship acts as a social safety net in times of crisis. This result is in line

with previous indings showing that the crisis shifted occupations towards

self-employment and work in family businesses (Thomas, Beegle and Frankenberg 2000).

Furthermore, I ind that communities that reported a decline in well-being

between 1997 and 2000 had lower participation in entrepreneurship, with a higher negative marginal effect for urban households. This points in two direc-tions. First, communities that have seen their material well-being erode have been less able to engage in entrepreneurship – which is consistent with the positive relationship between initial household wealth and participation in entrepreneur-ship. Second, entrepreneurs may have migrated to (mostly urban) areas offering