WORLD DEVELOPMENT REPORT 2012 GENDER EQUALITY AND DEVELOPMENT BACKGROUND PAPER

GENDER AND RURAL NON-FARM ENTREPRENEURSHIP

Costa, Rita, and Bob Rijkers

2011

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Development Report 2012 team, the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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Gender and Rural Non-farm Entrepreneurship

Rita Costa Bob Rijkers DECWD DECMG

The World Bank The World Bank

Abstract: Using novel matched household-enterprise-community datasets from Bangladesh,

Ethiopia, Indonesia and Sri Lanka, this paper analyses gender differences in rural non-farm

entrepreneurship. Women have lower rates of non-farm entrepreneurship, except in Ethiopia.

Female-headed households which run a non-farm firm derive a larger share of their income from it,

even though female firms are smaller and less productive. Differences in output per worker are

overwhelmingly accounted for by sorting by sector and size, as well as differences in factor intensity

They are not due to, increasing returns to scale, differences in human capital or local investment

climate characteristics. By contrast, gender differences in investment and growth rates are small.

Key words: Gender, Rural, Non-farm, Firms, Bangladesh, Ethiopia, Indonesia, Sri Lanka

We would like to thank Klaus Deininger, Donald Larson, Josef Loening, Jack Molyneaux, Naotaka Sawada, and Mona Sur for their help in obtaining the data, and Reena Badiani, Mary Hallward-Driemeier, Andrew Mason and especially Carolina Sanchez Paramo for helpful discussions and critical feedback. The views expressed here are those of the authors and do not necessarily represent the views of the World Bank, its Executive Board or member countries. All errors are our own. Corresponding author: Bob Rijkers, brijkers@worldbank.org.

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Women Non-farm Entrepreneurs in Rural Areas

1 Introduction and motivation

The potentially deleterious effects of gender disparities on growth and poverty reduction have been

receiving progressively more policy attention, reflected, for instance, in the inclusion of the

promotion of gender parity amongst the millennium development goals and the 2012 World

Development Report on Gender Equity. Inequities in labor market opportunities are of particular

concern since labor earnings are the most important source of income for the poor in the vast

majority of developing countries (Lustig, 2000). Indeed women’s over-representation in poverty has

been related to their lack of labor market opportunities (see e.g. Buvinic and Gupta, 1997).

Moreover, labor market opportunities are an important determinant of women’s bargaining power

in household decision making, which has been shown to be positively correlated with household

spending on goods that benefit children.1

In developed countries documenting gender gaps in labor market participation, wage

employment and wages is a prominent way of measuring gender inequities in labor market

opportunities. A voluminous body of literature has demonstrated that such gaps are substantial,

even after controlling for women’s lower average educational attainment and labor market

experience (see e.g. Altonji and Blank, 1999, for a review of the literature). However, in developing

countries, earnings in the paid labor force are not the dominant source of income, especially not in

rural areas, where the vast majority of people are self-employed or working as “unpaid” workers in

family enterprises. In these settings, gender gaps in wage employment and wages and glass ceilings in

promotion prospects are less relevant (Mammen and Paxson, 2000).

While some studies have assessed gender differences in agricultural work (see e.g. Jamison

and Lau, 1982, Horrell and Krishnan, 2007, Udry, 1996, Goldstein and Udry, 2008) and

entrepreneurship in urban areas, gender-differences in off-farm entrepreneurship in rural areas have

not received much attention. This neglect is predominantly due to data-limitations (ILO, 2010), but

unfortunate because rural non-farm enterprises account for about 35-50% of rural income and

roughly a third of rural employment in developing countries (Haggblade et al, 2010) and because

1 See e.g.. Haddad, Hoddinott and Alderman, 1997, Katz and Chamorro 20002, Duflo 2000, Thomas, 1990, Quisumbing and Maluccio 2000, Attanasio and Lechece, 2002, Schady and Rosero, 2007).

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women account for an important share of such non-farm activity (ILO, 2010). Moreover, the sector

appears to be growing (Lanjouw and Lanjouw, 2001) and rural off-farm diversification is widely

considered a potentially promising poverty alleviation strategy as the vast majority of poor people

continue to live in rural areas.

To help redress these lacunae in the literature, this paper draws on rural Investment Climate Survey

pilots Bangladesh, Ethiopia, Sri Lanka and Indonesia, unique matched household-enterprise-

community datasets recently collected by the World Bank, guided by three questions that have not

yet been satisfactorily resolved in the existing literature:

How and why does participation in rural non-farm entrepreneurship differ between men and women? In particular,

what are the roles of human capital, household characteristics, domestic responsibilities such as

childcare, the investment climate2 and the characteristics of the partner?

Is non-farm enterprise income a more important source of income for women than for men? Are female headed

households more dependent on non-farm enterprise income than male-headed ones?

How and why does non-farm enterprise performance – in terms of productivity, investment and growth - vary by

gender? To what extent are gender differences in performance driven by i) differences in endowments

in terms of access to factor inputs and human capital ii) sorting into different activities and iii)

differences in returns, either due to gender differences in returns to human and physical capital, or

differences in returns to scale and iv) differences in constraints.

The remainder of this paper is organized as follows. Section 2 selectively reviews related

literature and discusses the country context. Section 3 briefly describes the data and also presents a

bird’s eye view of the rural non-farm sector. A more detailed explanation of how our key variables

of interest are defined can be found in the appendix. Section four examines gender differences in

activity choice at the individual-level using multivariate probit models. Whether non-farm enterprise

income constitutes a more important source of income for female-headed than for male-headed

households is investigated in section five, which presents tobit models of the contribution of non-

farm enterprise income to the household budget. Differences in performance of male and female

2 The World Bank defines the investment climate as the set of location-specific factors shaping the opportunities and incentives for firms to invest productively, create jobs and expand (World Bank, 2005, p19). De facto, any factor that affects firm performance and decision making can be considered part of the investment climate. This has led some (e.g. Easterly, 2002) to criticize the concept as being devoid of any meaning. Instead, we take the view that it is important to clearly specify which aspects of the investment climate we are considering.

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run firms are discussed in section six, which focuses both on differences in productivity, as well as

on differences in investment and growth. We summarize our findings and discuss their policy

implications in the conclusion.

2 Related Literature and Country Context

2.1 Related Literature

At the individual-level, women’s labor allocation is primarily determined by the opportunity

cost of working relative to earnings in productive employment, “unearned” income, other

household members’ characteristics (see e.g. Mammen and Paxson, 2000) and labor allocation, as

well as preferences over different types of employment, which may be dictated by cultural norms

and religious beliefs. In the Amhara region in Ethiopia, where the Ethiopian Rural Investment

Climate Survey data used in this paper were collected, the belief that the harvest will be bad if

women work on the farm is prevalent (Zwede and Associates, 2002, Bardasi et al., 2007). The

opportunity cost of working is inter alia determined by the presence of children in the household

and returns to working, which in turn depend on women’s human capital and the income-earnings

opportunities available to them. Literature from developed countries furthermore suggest that

parents’ occupation matters, as children of entrepreneurs are significantly more likely to become

entrepreneurs themselves (Parker, 2008, 2009).

Studies of gender differences in entrepreneurship in developing countries are scarce.

Existing studies are predominantly based on the World Bank’s Enterprise Surveys and typically find

that female entrepreneurship is inversely correlated with firm size;3 firms run by female

entrepreneurs are smaller in terms of employees, sales and capital stock. However, gender

differences in total factor productivity, profitability and capital-intensity become insignificant once

firm characteristics are controlled for (Bardasi and Sabbarwal, 2009), except for the very smallest

3 Gender differences in the performance of male and female entrepreneurs in developed countries are

relatively well documented, but the evidence is mixed. Some studies report evidence of female underperformance, whereas other do not find gender differences (see e.g the literature reviews in Parker, 2009 and Sabbarwal and Terrel, 2009 and the references therein).

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firms (Bruhm, 2009).4 Thus, gender differences manifest themselves primarily in terms of scale,

rather than differences in profitability, technology or capital intensity. However, Hallward-

Driemeier and Aterido (2009a) point out that the definition of a female-firms matters; using

definitions based on decision making authority, rather than (partial) participation in ownership as is

done in the studies cited above, results in substantial gender differences, even after firm and

manager characteristics have been controlled for.

The finding that women operate smaller scale firms begs the question why. One possible

explanation is that they sort into industries which a have lower optimal scale, although this only

pushes the question another step backwards. Another salient explanation is that they lack access to

finance. Evidence from developed countries on this issue is mixed.5 Furthermore, cultural norms

may militate against women being in power or engaging in certain activities. Alternatively, successful

female firms, which tend to be larger, may be more likely to be “captured” by husbands. Women

entrepreneurs could also face different constraints. However, Bardasi and Sabarwal (2009) find little

evidence for differences once firm characteristics are conditioned on.

Male firms also appear to grow faster and to invest more, although the evidence on gender

differences in dynamic performance is mixed. Nichter and Goldmark (2009) survey the literature on

small firm growth and find that female owned firms are often, but not always, grow more quickly

than male firms.. De Mel et al. (2008, 2009) and McKenzie and Woodruff (2008) report lower

investment rates and returns to capital for women than for men in Sri Lanka, Bangladesh and

Mexico. Using manufacturing census data from Ethiopia Shiferaw (2009) shows that female-

managed firms are much more likely to survive.

Since most of these studies are based on urban enterprises, it is not clear to what extent their

conclusions generalize to rural areas, where firms tend to be smaller and firm performance is

arguably more intimately intertwined with household- and farm events, and the investment climate is

radically different (see World Bank, 2004, Deininger et al., 2007, Jin and Deininger, 2009, Rijkers et

4 In addition, Sabbarwal and Terrel (2009) find some evidence that female firms in ECA have higher returns

to scale, but the differences in returns to scale between men and women are small, and, moreover could be due to a rather restrictive production function specification. For example, differences in technology between sectors are modeled as being additive in TFP, rather than as interacting with the returns to capital, labor and material inputs. 5 Some studies suggest women indeed have more difficulty accessing finance than men (e.g. Brush 1992,

Carter, 2000), while others detect no gender differences (Blanchflower et al., 2003, Storey, 2004, Cavalluzzo and Wolken, 2005).

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al., 2010). Despite their importance as a potential catalyst of growth and an absorber of growing

rural labor supply, little is known about the determinants of the performance of non-farm firms and

how these may vary with the gender of the manager. Studies based on household data often

document lower returns for women in off-farm employment (see e.g. Canagarajah et al., 2001), yet

evidence on gender differences in the relative importance of non-farm earnings for household income

is limited. Since the existing evidence on gender differences in rural non-farm entrepreneurship is

overwhelmingly based on household- and labor force surveys it does not allow us to disentangle the

importance of firm vis a vis household and community characteristics.

2.2 Country Context

The countries in this study vary radically in terms of the structure, size and composition of their

non-farm economy, and in their level of economic prosperity and gender parity. Ethiopia’s rural

economy is unusually fragmented and rural labor markets are very thin. In this context, self-

employment in non-farm enterprises is predominantly a means to supplement farm earnings, as well

as an important source of income for those lacking alternative options (Loening et al., 2008). By

comparison, in Indonesia, rural labor markets are well-developed, population density is high and

movements out of poverty are strongly correlated with non-farm entrepreneurship (McCulloch et

al., 2007, Priebe, 2009). Sri Lanka and Bangladesh’s rural non-farm sectors fall in between these two

starkly dissimilar cases (Headey et al., 2010), yet are interesting in their own right since Sri Lanka is

characterized by relatively high levels of human development relative to its GDP per capita, while

studying the non-farm sector Bangladesh is likely to be informative about the role of gender roles,

which place strong restrictions on women’s ability to engage in remunerated employment.

3 Data and descriptive statistics

3.1 Data

The World Bank’s Rural Investment Climate Pilot Surveys conducted in Bangladesh,

Ethiopia, Indonesia and Sri Lanka are the data used in this paper. These surveys are matched

household-enterprise-community surveys, that collect information on both enterprise and non-

enterprise owning households, the non-farm enterprises they operate, and the local investment

climate in the communities in which they are located. For the purposes of these surveys, a rural

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nonfarm enterprise was defined as any income generating activity (trade, production, or service) not

related to primary production of crops, livestock or fisheries undertaken either within the household

or in any nonhousing units. In addition, any value addition to primary production (i.e processing)

was considered to be a rural nonfarm activity.6

The matched nature of the data is a key advantage. The surveys constitute an improvement

over traditional household surveys by collecting very detailed information on enterprise

characteristics, inputs and outputs, the local economic environment and various dimensions of

performance. For example, by virtue of containing information on the capital stock and inputs they

enable us to estimate production functions and assess how productively labor is employed in these

off-farm firms. Conversely, they complement the existing investment climate surveys, which are

typically urban-based, focused on relatively large manufacturing firms, and lack information on the

household characteristics of firm managers. Such surveys do not permit analysis of participation

patterns, since they lack information on potential entrepreneurs.

Since the surveys were very similar, they facilitate a cross-country comparison. However,

data coverage, variable definitions, and samplings frames can vary from country to country. For

example, labor input in Sri Lanka was measured in terms of number of workers only, whereas in

Ethiopia it was measured in terms of days worked per employee, while in Indonesia and Bangladesh

we have information on hours worked. To maximize comparability across surveys, we have

converted our labor inputs measure into a full-time worker equivalent. Similarly, measures of

material inputs, capital and labor were converted into USD equivalents. As another example, the

definition of what constitutes a rural area and what constitutes a rural town varies across countries.

The Appendix discusses how we defined our key variables of interest and tried to maximize

comparability across countries in more detail.

While the sampling frames for the survey vary from country to country, they typically yield a

good representation of the rural non-farm sector. The surveys in Sri Lanka and Bangladesh are

representative of all rural areas in the country, while the Ethiopia data are representative of the rural

non-farm sector in the Amhara region.7 The Indonesia data cover six different kabupaten’s8

6 Thus, in many cases the term “activity” might have been more appropriate than the term enterprise. 7 Loening et al (2008) demonstrate that the rural non-farm sector in the Amhara region is very similar to the rural non-farm sector in other regions, although its composition is slightly skewed towards manufacturing activities, whereas in other regions, trade and services enterprises dominate.

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(“districts”) in six different provinces. In all countries but Ethiopia, relatively large firms were

oversampled to ensure they were included in the surveys. However, our survey suggests that large

off-farm firms are very rare in rural Ethiopia.9 Unfortunately, except in Bangladesh, we do not have

information on the households of the managers of such relatively large firms. For more information

on the samplings frames, the reader is referred to World Bank, 2005.

3.2 The rural investment climate

The rural investment climate is characterized by remoteness, weak infrastructure, low penetration of

commercial credit providers and localized markets (see also World Bank, 2005) and varies

substantially both across as well as within countries. Most non-farm enterprises are very small and

generate low profits, although the non-farm enterprise sector is highly heterogeneous both in

composition and performance. The characteristics of the rural business environment are also

reflected in the constraints firm managers report to be most severe. The appendix demonstrates that

both male and female managers consider a lack of markets (demand), transport, access to credit and

electricity as their most important constraints. Gender differences in self-reported constraints are

minimal, a finding which is robust to controlling for differences in firm characteristics of male and

female managed firms.

4 Which income-earning activities do men and women engage in?

4.1 The structure of rural labor markets

Rural labor markets are thin and average educational attainment is low, although it varies

across countries, with workers in Indonesia being relatively well educated and Ethiopians having had

very little education on average. Self-employment, either in agriculture or off-farm, accounts for the

bulk of employment, as demonstrated in Tables 1A and 1B, which present descriptive statistics on

8 A kabupaten is a political subdivision of a province in Indonesia. The Indonesian term kabupaten is also sometimes translated as 'district' or 'municipality'. 9 This is not an artifact of the sampling strategy. Only three enterprises in the rural dataset employ more than 10 workers. Since these enterprises are all household-based we might miss out on fully commercial enterprises owned or managed by individuals not living in these communities. However, from the community level dataset one can infer that there are not more than a dozen firms with more than 20 employees in a radius of 1-h commuting distance from the 179 surveyed communities. It thus seems safe to conclude that there are very few large firms in rural areas.

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activity portfolios of individuals and households.10 The importance of different types of

employment, including non-farm self-employment, varies considerably across countries. Off-farm

entrepreneurship participation rates are lowest in Ethiopia and highest in Bangladesh. Running a

non-farm firm is often combined with undertaking other income earning activities. This suggest that

non-farm self-employment is often a secondary activity. This is especially interesting since

undertaking multiple activities is the exception rather than the norm since the vast majority of men

and women specialize in one activity.11 With the exception of Bangladesh, household level

participation rates are higher than individual participation rates (see table 3), yet few households rely

exclusively on non-farm enterprise income, and many engage in multiple activities.12 Taken together,

these suggest that income diversification is primarily a household, rather than an individual level

phenomenon.

Turning to gender differences, women are much less educated than men and less likely to be

the head of their households, unless they are widows or divorcees. They are much more likely not to

report any activity, although participation rates and gender differences vary dramatically across

countries (see Table 2).13 Roughly only about half of all Bangladeshi and Sri Lankese women report

at least one activity, while in Ethiopia approximately only a quarter of women did not report any

activity. In all countries but Ethiopia, the proportion of men working in non-farm enterprises is

larger than the proportion of women. However, conditional on reporting any activity, the likelihood

of reporting being engaged in off-farm self-employment is always higher for women than for men.

Gender differences in conditional participation rates are very small in Bangladesh, where very few

women report any income earning activity.

4.2 Empirical Strategy

To examine what is driving these differences in individuals’ activity choices, reduced form trivariate

probit models for engagement in farming, non-farm self employment and wage employment are

10

The phrasing of the different questions about individual’s activity portfolios varied across countries, forcing us to come up with an arbitrary categorization (see the appendix for details). In Indonesia, information on individuals’ engagement in agricultural activities was not available. 11 Of course, it is possible that individuals engage in different types of self-employment or hold multiple wage jobs in which case they would misleadingly be classified as engaging in one activity only. 12

In conjunction with the finding that very few household rely exclusively on non-farm household income and that most individuals report engaging in one type of activity only, this strongly 13

In this paper, we consider an individual out of the labor force if she has either engaged in agricultural self-employment, not held a wage job, nor worked in a non-farm enterprise over the past twelve months The participation rates documented here may deviate somewhat from those reported in official statistics partly because of sample coverage and partly because we are counting employment in home-based enterprises as labor market participation.

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estimated. The trivariate probit specification allows for simultaneity of activity choices, thus

accounting for the interdependence of activity choices, which has often been neglected in previous

studies of participation in the non-farm sector (two notable exceptions are de Janvry and Sadoulet,

2001 and Babatunde and Quaim, 2010). The estimable model is:

+

+

+

Where >0 if and 0 otherwise and the error terms , and are assumed to

follow a trivariate normal distribution. The dependent variables and are dummies

indicating whether or not the individual in question engages in non-farm enterprise activity, works

on the household farm or has worked for a wage respectively. Note that these categories are not

mutually exclusive.

The explanatory variables affect individuals’ relative returns and ability to participate in

different activities; is a vector of individual characteristics, notable age, education, marital status

and relation to the head of the household. is a vector of household characteristics, including the

size and composition of the household, and household assets, notably land holdings and whether or

not the household owns the house it inhabits, IC is a vector of investment climate proxies, notably,

whether or not the household uses electricity, lives in a rural town, distance to the nearest market,

prevailing wage rates, whether or not there is a credit institution present in the village. These

objective proxies correspond to the key subjective investment climate constraints identified by

managers of non-farm enterprises (see section 3.2 and the Appendix).14 Finally, is a vector that

captures partner’s educational attainment and household head’s father’s main occupation. The latter

variable is not available in the Ethiopian dataset.

4.3 Results

Results are presented in Table 6. That the decision to work in a non-farm firm cannot be

analyzed independently of the decision to work for a wage or on the family farm is evidenced by the

14 Since subjective investment climate indicators may be endogenous to performance, we prefer objective proxies instead.

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strong correlations between the error terms of the different equations. These correlations suggest

that working on the family farm and wage work, and wage work and working for a non-farm

enterprise are substitute activities, while the decisions to work for a family farm and to work in a

non-farm firm are less strongly interdependent, perhaps because they are easier to combine.15

Although the determinants of activity choice vary by gender and across countries, there are

some interesting commonalities. We will focus mostly on non-farm entrepreneurship, the subject of

this study. Age-participation profiles in non-farm enterprise activity are strongly concave for women,

and insignificant or even convex for men. A similar, but less pronounced gender difference in age-

participation profiles is observed for self-employment in agricultural activities. Education has a

strong impact on activity choice, but its association with off-farm entrepreneurship is weak and

especially so for women. Workers with the highest levels of education are significantly more likely to

work in a wage job and significantly less likely to work on a family farm or in a family firm and this

effect is especially strong for women.16 Presumably this reflects higher returns to education in wage

jobs.

Non-farm self-employment appears especially important for women who are the heads of

their households, as they are much more likely to work in a non-farm enterprise. It is important to

bear in mind that these are conditional associations; in absolute terms, male-headed households are

more likely to run a non-farm firm than female-headed households in all countries except in

Ethiopia.17 Ceteris paribus, male heads in Ethiopia and Indonesia are also more likely to engage in

non-farm enterprise activity than other men, yet, with the exception of Ethiopia, the difference

between male heads and other men is smaller than the difference between female heads and other

women.

The association between household ownership of agricultural assets (plough and land) and

activity also varies by gender, as ownership of such assets is negatively correlated with female- but

not with male- off-farm entrepreneurship, except in Indonesia where we see the opposite pattern.

15 Note, however, that the error terms are significantly negatively correlated in Ethiopia, suggesting they are substitutes in this context. 16 Men who have completed some form of secondary education are significantly more likely to run a non-farm firm in Bangladesh and Sri Lanka. For women, no such effect is observed; if anything, women who completed secondary education are less likely to be entrepreneurs in Ethiopia, and not significantly more likely to be entrepreneurs in any of the other countries. The propensity to be an entrepreneur seems to drop with further education Indeed, both men and women who have completed tertiary education are significantly more likely to be wage employed and much less likely to be self-employed in agriculture. These effects are particularly strong for women. 17 Female headed households are typically headed by widows.

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By contract, men’s self-employment in agriculture is always significantly positive correlated with

ownership of agricultural assets, while for women a positive conditional association is observed only

in Ethiopia and Sri Lanka.

Gender differences in the impacts of other variables are less pronounced. The association

between marriage and activity choice varies across countries and by gender but Bangladeshi women

who are married are far less likely to undertake any activity, presumably reflecting gender norms

constraining their engagement in income earning activities. Being located in a town is correlated with

an increased likelihood of working in a non-farm enterprise, both for men and women, but there are

few other investment climate variables that are consistently significant across countries. The

occupation of the father of the household head is strongly correlated with activity choice, but not

with the likelihood of running a non-farm enterprise. Individuals in households of which the head’s

father held a wage job are far less likely to work in agriculture and far more likely to hold a wage job.

Individuals whose father’s main source of income was running a non-farm firm appear less likely to

work on a farm but are not consistently much more likely to be engaged in NFE activity, which

contrast with studies of the intergenerational transmission of entrepreneurship in developed

countries (see e.g. Parker, 2009). By contrast, the impact of partner’s educational attainment varies

across countries but is generally insignificant. Last but not least, household size and composition are

not consistently significantly correlated with activity choice, ceteris paribus.

5 Contributions to Household Income

5.1. Descriptive Statistics

Non-farm enterprise earnings are an important source of income; in Indonesia female-

headed households on average derive 19% share of their pecuniary income from non-farm

enterprise activity while male-headed households derive an average of 21% of income from running

a non-farm firm as shown in Table 3 which presents descriptive household statistics.18 These

averages do not condition on participation: since female participation rates are lower, female-headed

households that run a firm typically derive a larger share of income from it. In Ethiopia, female

18 Table 3 only presents descriptive statistics for Ethiopia and Indonesia since these are the only two countries for which information on the contribution of non-farm enterprise activity to household income is available. Note that production for auto-consumption is not taken into account in these pecuniary income measures.

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headed households on average derive 19% of their household budget from non-farm enterprise

activity, while male-headed household only derive 6% of their income from it.

Graph 1 plots the contribution of non-farm enterprise income against log household

consumption per capita for male- and female-headed households, demonstrating that the correlation

between enterprise income and income rank is weak, except for male-headed household in

Indonesia. Thus, prima facie, it does not appear to be the case that non-farm enterprises either

exacerbate or diminish income inequality. Also note that female headed households have much

lower expenditure per capita on average and tend to be smaller than male headed households.

5.2 Empirical Strategy

To assess the contribution of non-farm enterprise activity to household income a Tobit model is

estimated following Gibson and Olivia (2010):

Where the dependent variable, , is the share of income pecuniary income derived from non-

farm enterprise activity, which is assumed to be latent variables that is only observed for positive

income shares (i.e. for , is a vector of household characteristics

including characteristics of the household head and expenditure quartile, 19 is a vector of

investment climate characteristics and is an independently distributed error term with zero mean

and constant variance.

5.3 Results

Table 5 presents the results which demonstrate that the relative contribution of non-farm

enterprise activity to household income is significantly lower in Indonesia if the household head is a

man. In Ethiopia, the coefficient on a male household head is negative too, but statistically

insignificant. However, in Ethiopia non-farm enterprise income’s contribution to household

19

We include these dummies to assess whether the contribution of enterprises to household income varies with income rank. Their inclusion may create an endogeneity bias since both the share of non-farm income and income rank are functions of non-farm enterprise income. However, our results are robust to exclusion of these dummies. The results of a robustness check in which we dropped these dummies are not presented to conserve space, but available from the authors upon request.

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income is significantly positively correlated with the share of female-household members, which is

larger in female-headed households. Thus, it seems safe to conclude that female headed households

which run a non-farm firm derive a larger share of income from non-farm enterprise activity in both

countries. In part this is due to their smaller size.

The importance of enterprise income increases with household size and being located in a

town, yet decreases with the ownership of agricultural assets and local wage rates. which presumably

proxy for the availability of alternative income earning opportunities. Household headed by

relatively highly educated individuals derive a smaller share of income from non-farm activity and

the share of income is concavely related to the age of the household head. The importance of non-

farm income does not appear to vary strongly with income levels, suggesting that the impact of non-

farm enterprise activity on inequality is limited.

6 Performance

6.1 Productivity

6.1.1 Female enterprises are smaller and less productive.

Female owned firms have significantly lower sales on average than male owned firms in all countries

surveyed. This is in part because they are significantly smaller and more likely to be household

enterprises than male run firms and in part because, with the exception of Indonesia, they tend to

have lower labor productivity, measured in terms of sales per worker. The stylized fact that the share

of firms with a female manager is inversely correlated with enterprise size, which has been

documented for large, urban-based manufacturers in Latin America (Bruhm, 2009), Africa

(Halward-Driemeier et al. forthcoming) and Eastern Europe (Sabbarwal and Terrel, 2008) also holds

in our sample of rural MSEs.

Gender differences in average output per worker are large, but vary dramatically across

countries. For example, the largest gender difference is found in Bangladesh where the US dollar

equivalent of mean log output per worker in female firms is on average $35, while it is $293 for male

run firms. In Indonesia, by contrast, there are no gender differences on average, with mean log

output per worker in female firms being equivalent to $1746 for women and $1737 for men. While

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female firms are on average less productive, labor productivity is highly dispersed, as shown in

graphs 2A-2D which plot the density of output per worker. This dispersion is indicative both of the

high heterogeneity of the non-farm sector, which comprises a wide variety of activities, and market

power. Most firms face very few competitors, if any.

At first sight, these differences in labor productivity do not appear driven by differences in

capital intensity. Surprisingly, male enterprises do not appear to use more capital per worker than

female ones. These findings do not necessarily indicate an absence of gender differences in access

to capital;20 it is very well possible that women sort into certain sectors, which operate on a relatively

smaller scale. Since they are larger, male firms tend to use more capital in absolute terms.

Sector sorting patterns by gender are very pronounced, but vary from country to country. In

Bangladesh for instance, 95% of all female firms are engaged in manufacturing and almost none are

in trade, while 49% of all male firms are in trade and only 24% are in services. In Ethiopia too,

women are overrepresented relative to men in manufacturing, which accounts for the bulk of non-

farm enterprise activity. By contrast, in Indonesia, only 3% of all female firms are in manufacturing

compared with 14% of all male firms. In this case, women are overrepresented in the trade sector.

These broad sector categories hide sorting into different subsectors (see the appendix). Differences

in output per worker could also be due to differences in input usage and human capital. With the

exception of Indonesia, male firms use significantly more inputs per worker and male entrepreneurs

are better educated, on average, then their female counterparts.

6.1.2 Empirical Strategy

To examine to what extent differences in output per worker are due to differences in factor

inputs and human capital, sorting into different sectors, returns to scale, we estimate Cobb-Douglas

production functions, modeling output Y, as a function of capital, , labor, , material inputs,

, and Total Factor Productivity (TFP), which is in turn modeled as a function of sector, S, firm

characteristics F, characteristics of the manager, E, and a host of investment climate characteristics,

20

Interestingly, only in Bangladesh differences female firms are significantly less likely to report having a loan than male firms.

17

IC:

. Taking-logs and adding

these and an error term, v, our most general estimating equation becomes:21

Where j subscripts denote sector specific factor coefficients, as we allow capital, labor and material

inputs coefficients to vary freely across sectors. In our empirical specification, we progressively add

explanatory variables to examine to what extent these different sets of explanatory variables account

for gender differences in output. Differences in productivity could also derive from differences in

returns to scale. If returns to scale are increasing (

, the larger size of male run

firms may account for their higher average productivity. Alternatively, differences in productivity

may stem from sector selection. Note that these mechanisms may interact if returns to scale vary

across sectors. In the appendix we also present specifications where we interact all variables used to

explain total factor productivity with a dummy for a female entrepreneur, to allow for gender

differences in the returns to human capital, experience and various investment climate proxies. The

results suggest that gender differences in the impact of these variables are generally statistically

negligible.

This approach has well-known limitations. Measurement error in explanatory variables or

omitted variables may lead to biased estimates of the productivity differentials. Despite having a rich

and detailed dataset, we cannot directly control for potentially important variables such as demand

and price differences. In principle, such endogeneity problems could be remedied by means of

instrumental variable estimation but, unfortunately, credible instruments are not available in our

data. However, Loening et al. (2008) argue that endogeneity bias is likely to be small. Using

precipitation based indicators of local agricultural performance as a proxy for unobserved local

demand, they find that while better local agricultural performance is strongly correlated with firm

sales, their parameter estimates are not very sensitive to controlling for this variable, indicating

endogeneiy bias is small. Moreover, firms do not frequently adjust factor inputs, despite facing

frequent shocks, again suggesting the impact of endogeneity bias may be limited.

21 In the appendix, we also present models where we interact factor shares with the gender dummy. However, we had to

pool the data as the number of observations did not us to account for gender heterogeneity in technology by sector.

18

In interpreting the results it is also important to bear in mind that we only focus on the

direct impact of gender and the investment climate on firm productivity. However, perhaps the

most important gender and investment climate effects are indirect. For example, the investment

climate may also impact on allocative efficiency (see, e.g. Mengistae and Honorati, 2009), and

women may sort into small-scale low productivity sectors. In short, we have to be cautious when

interpreting the results from these models.

6.1.3 Results

The regressions are estimated separately for each country. Results are presented in Table 7.

The dependent variable is the log of sales in US dollars. We present five different models. The first

model only includes a gender dummy while the second model only includes a control for firm size.

Subsequent models add controls for sector (model 3), factor usage and differences in technology

across sectors (model 4) and other firm, human capital and investment climate characteristics (model

5).22

Models 1 and 2 confirm that raw performance gaps are large in Bangladesh, Ethiopia andSri

Lanka. These performance gaps are in part the result of sorting by size, (model 2), but appear to be

predominantly driven by sorting into different sectors (model 3) and material inputs usage (model 4);

once sector and factor usage are controlled for performance gaps reduce dramatically and the

explanatory power of our model shoots up, as is evidenced by the big increases in the R2s when we

add these explanatory variables. In Sri Lanka, the male-female productivity differential is entirely

explained by sorting. In Bangladesh, the gender differential reverses sign, while in Ethiopia the

productivity gap roughly halves. Parameter estimates appear reasonably well behaved, although it is

likely that the coefficients on capital are biased downwards due to measurement error. Capital- and

labor coefficients vary substantially across sectors, and within sectors across countries. The null

hypotheses of constant returns to scale is rejected only for manufacturing firms in Bangladesh and

trading firms in Indonesia, for which the parameter estimates suggest decreasing returns to scale.

Adding additional controls for human capital, firm characteristics and the investment

climate does not change results very much and adds limited explanatory power, suggesting that most

22

It would have been of interest to examine whether enterprises run by female household heads are more or less productive than other female firms. However, our data do not enable us to cleanly identify whether the enterprise manager is also the household head.

19

of the productivity differential is accounted for by sorting into different sectors and factor usage.

Gender productivity differences do not disappear altogether. The positive premium associated with

being a female manager in Bangladesh may not be well identified since we have relatively few female

firms. Alternatively, it could be due to the fact that female entrepreneurs are a highly eclectic group.

The low productivity of female-run firms in Ethiopia may in part driven by sorting into different

activities that our crude sector controls do not capture. They may also be due to gender differences

in hours worked, as we only observe differences in labor days. If women combine working in a non-

farm firm with household chores, they may work fewer hours in any given day.

The final specification furthermore suggest that household-based enterprises are on average

less productive than stand-alone firms (although the coefficient on the household enterprise dummy

is not statistically significant in Indonesia and Sri Lanka), that older firms are more productive

(although the coefficient on firm age is not statistically significant in Indonesia and Sri Lanka), and

that investment climate variables are not very important determinants of firm performance.

Moreover, it is remarkable how much the importance of different factors, such as schooling,

electricity usage, and location, varies across countries, underscoring how heterogeneous the non-

farm sector is across different countries.

Overall, then it seems that the evidence for gender differences in the returns to human

capital is limited. In addition, we find little evidence for gender asymmetries in the direct impact of

the investment climate on firm performance. Nor do we find evidence for gender differences in

productivity differentials associated with firm age and electrification. Moreover, differences in

returns to scale do not appear to explain gender productivity differentials; while male entrepreneurs

run larger firms we do not find evidence for increasing returns to scale. We do however, find strong

evidence for differences in productivity due to sorting into different types of activities, factor usage

and firm size.

6.2 Investment and Growth

6.2.1 Gender differences in dynamic performance are small

Gender differences in the dynamic performance of firms are small and much less pronounced than

the dramatic productivity differences discussed above. Investment and growth rates are low in

20

Ethiopia and Bangladesh, but substantial in Indonesia and Sri Lanka. Male managers are not

significantly more likely to invest than female ones in any country. In Sri Lanka, they are significantly

less likely to invest than female managers. Male firms also do not grow faster than female firms on

average. However, the absence of strong difference in absolute growth and investment rates may

obscure gender differences as male firms are very different from female firms. To assess whether or

not this is the case, we estimate growth and investment models which control for salient observable

characteristics correlated with growth and investment.

6.2.1 Empirical Strategy

Investment is modeled by means a probit for having invested using as explanatory variables

sector, firm-, manager- and investment climate characteristics. Again we progressively add

explanatory variables to assess to what extent firm and investment climate characteristics account for

gender differences in investment rates. Our most general reduced-form investment model is thus:

We use the same model to analyze growth, using as dependent variable the Haltiwanger growth

measure force as the dependent variable. The Haltiwanger growth measure is the change in

employment over the average firm-size for two periods: H = (Lt -Lt-1 )/[0.5*(Lt + Lt-1)], thus

bounding the growth rate between -2 and +2 and minimizing the impact of measurement error and

influential outliers. That is our estimable equation is:

where u is a normally distributed zero-mean error term.

The results of these regressions have to be interpreted cautiously. Suppose, for example, that

the presence of credit institutions is positively correlated with investment rates. This might indicate

that firms with better access to financial institutions are likely to invest more. However, it is also

possible that credit institutions locate in communities where the local economy is buoyant. Thus, we

have to be aware of potential reverse causality. In addition, it is important to emphasize that

estimated coefficients are conditional on firm survival.

6.2.2 Results

21

Results are presented in tables 8 and 9. We first condition on lagged size and sector to account for

sorting effects and subsequently add additional controls for firm characteristics, managerial human

capital and the investment climate.

Starting with the results for investment (table 8), controlling for lagged size and sector

(model 2) renders the investment differentials between male and female firms insignificant. Adding a

full set of controls (model 3) does not overturn these results, although male-firms in Indonesia are

some 38% less likely to invest, ceteris paribus, and this effect is significant at the 10, but not the 5%

level. Overall, investment is difficult to predict, as is evidenced by the rather low pseudo R2s and the

fact that relatively few explanatory variables are significant. Moreover, the determinants of

investment appear to vary substantially across countries; not a single explanatory variable is

statistically significant in each country.

The results of our growth regressions are presented in Table 9. Once we control for sorting

by sector and size firms run by Bangladeshi men grow faster than firms run by Bangladeshi women.

Yet controlling for sorting and manager, enterprise and investment climate characteristics does not

result in significant gender growth differentials in any of the other countries.

Our models for growth arguably have even less predictive power, as is indicated by the very

low R2s. However, the determinants of firm growth do not appear to vary as dramatically across

countries as the determinants of investment and productivity. While the parameter estimates are not

always statistically significant, they suggest that, overall, firms that started larger appear to have

grown less. This finding presumably partly reflects measurement error and survivor bias, as one may

expect firms that started large to be more likely to survive. In addition, manufacturing firms appear

to grow somewhat faster than firms engaged in services or trade. Firm growth also appears

positively correlated with electricity usage and negatively associated with firm age, though again

these patterns are not statistically significant in each country.

In the appendix we present a robustness check in which we allow all parameters to vary by

gender, but this does not overturn the results. Overall, the results suggest that gender differences in

investment and growth are not very large.

8 Conclusion

22

In spite of their increasing policy-prominence, relatively little is known about gender

inequities in rural non-agricultural labor market opportunities. This is unfortunate since such

employment accounts for a substantial share of rural income and employment, which exceed the

contributions of agriculture in some countries (Haggblade et al., 2007). Using novel matched

household-enterprise-community datasets from Bangladesh, Ethiopia, Indonesia and Sri Lanka we

attempt to redress this lacunae by documenting and analyzing gender differences in activity portfolio

choice, contributions of non-farm firms to household income and differences in productivity,

growth and investment of rural non-farm firms.

Women are much less likely to undertake income earning activities and gender differences in

non-farm entrepreneurship are large. Women have lower participation rates in non-farm enterprise

activities in Bangladesh, Indonesia and Sri Lanka, but not in Ethiopia. However, working in rural

non-farm firms appears to be very important for women who partake in income earning activities

and especially for those who are the heads of their households. Indeed, female-headed households

which run non-farm enterprises, despite being poorer on average, derive a larger share of their

pecuniary income from non-farm enterprise activity, even though enterprises run by women tend to

generate less revenue. This is of course in part due to the fact that they tend to be smaller.

Female firms are much smaller and much less productive on average, although gender

differences in productivity vary dramatically across countries. In Indonesia there are no significant

gender differences in output and productivity, while firms run by Bangladeshi men on average

produce more than ten times as much output per worker on average than female firms. In Sri Lanka

and Ethiopia gender differences in size and productivity are less dramatic, but large nonetheless.

Such differences in performance are overwhelmingly driven by sorting. Once differences in

size and sector are accounted for gender productivity differentials diminish. Differences in inputs

usage also provide part of the explanation. In fact, in Bangladesh, conditioning on sector, size and

inputs usage reverses gender productivity differentials. However, gender differences in productivity

do not fully disappear once we control for differences in human capital, firm characteristics and the

investment climate.

Gender differences in productivity are not due to returns to scale; while male owned firms

are much larger, we did not find evidence of increasing returns to scale. Nor are productivity

differentials driven by differences in capital intensity: capital labor ratios do not significantly vary by

23

gender in any of the countries considered. Moreover, our regressions do not support the hypothesis

that differences in human capital account for gender differences in firm performance, even though

male managers are on average much better educated than women who manage firms. Similarly,

differences in the local investment climate appear to have little direct impact on productivity

differences.

By contrast, gender differences in investment and growth rates are small, and do not obscure

underling underlying gender differences, as they are robust to controlling for a rich set of firm,

manager and investment climate characteristics.

Overall, our results demonstrate large gender disparities in rural off-farm labor market

opportunities and outcomes. While we have managed to rule out a large number of candidate

explanations, fully understanding why these gender differences arise requires additional research.

Collecting panel data would help us better understand the causal mechanisms underlying the

patterns documented in this paper and would permit a richer representation of the dynamics of rural

labor markets.

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27

Tables and Graphs

Figure 1: Income Portfolios vs Household Expenditure

1A: Ethiopia-male headed households 1B Ethiopia-female headed households

1C: Indonesia-male headed households 1B Indonesia-female headed households

-1-.

50

.51

Inco

me

Sh

are

2 4 6 8 10Log of per capita consumption

CI Farm CI NFE

NFE lpoly smooth: shincnfe

CI Wage Wage

CI Other Other

Male Headed Households

Income portfolios vs log C

0.5

11

.5

Inco

me

Sh

are

5 6 7 8 9 10Log of per capita consumption

CI Farm CI NFE

NFE lpoly smooth: shincnfe

CI Wage Wage

CI Other Other

Female Headed Households

Income portfolios vs log C

-.5

0.5

11

.5

Inco

me

Sh

are

6 8 10 12 14Log of per capita consumption

CI Farm Farm

CI NFE NFE

CI Wage Wage

CI Other Other

Male Headed Households

Income portfolios vs log C

-.5

0.5

1

Inco

me

Sh

are

6 7 8 9 10 11Log of per capita consumption

CI Farm Farm

CI NFE NFE

CI Wage Wage

CI Other Other

Female Headed Households

Income portfolios vs log C

28

Figure 2: Size Distributions

Notes: - Kernel density estimates, estimated using the epanechnikov kernel, - L measured in full time equivalent workers -the measure of labour in Sri Lanka is discrete, which explains why the size distribution appears bimodal.

0.2

.4.6

.8

kd

en

sity ln

Lft

-2 0 2 4 6Ln L

Female Firms Male Firms

Size Distribution - Bangladesh

0.2

.4.6

kd

en

sity ln

Lft

-4 -2 0 2 4Ln L

Female Firms Male Firms

Size Distribution - Ethiopia

0.2

.4.6

kd

en

sity ln

Lft

-2 0 2 4 6Ln L

Female Firms Male Firms

Size Distribution - Indonesia

0.2

.4.6

.81

kd

en

sity ln

Lft

0 2 4 6Ln L

Female Firms Male Firms

Size Distribution - Sri Lanka

29

Figure 3: Productivity Distributions

Notes: - Kernel density estimates, estimated using the epanechnikov kernel - L measured in full time equivalent workers, Y in USD equivalents.

0.1

.2.3

.4

kd

en

sity

lnY

Ld

ft

0 5 10 15Ln Y/L

Female Firms Male Firms

Productivity Distribution - Bangladesh

0.1

.2.3

.4

kd

en

sity ln

YL

dft

0 5 10Ln Y/L

Female Firms Male Firms

Productivity Distribution - Ethiopia

0.1

.2.3

kd

en

sity ln

YL

dft

0 5 10 15Ln Y/L

Female Firms Male Firms

Productivity Distribution - Indonesia

0.1

.2.3

kd

en

sity ln

YL

dft

0 5 10 15Ln Y/L

Female Firms Male Firms

Productivity Distribution - Sri Lanka

30

Table 1: The Structure of the Labor Market

Table 1A: Structure of the labor market – Bangladesh, Sri Lanka, Ethiopia

Bangladesh Ethiopia Indonesia Sri Lanka Women Men Women Men Women Men Women Men OLF/Not working (i) 68.24 % 4.85 % 25.69% 7.26% NA NA 50.74% 12.52% NFE only (ii) 5.46% 23.34% 5.56% 5.12% 8.78% 14.29% 19.09% 18.72% NFE + Agriculture (iii) 2.81% 19.24% 3.66% 4.18% NA NA 4.21% 12.00% NFE + Wage(iv) 0.21% 6.50% 0.31% 0.20% 1.10% 2.66% 1.17% 4.22% Ag only (v) 17.87% 14.51% 60.37% 70.30% NA NA 8.67% 11.01% Ag+wage (vi) 0.86% 8.05% 1.73% 7.77% NA NA 1.36% 8.17% Wage only (vii) 4.37 % 19.79% 2.68% 5.17% 23.05% 44.53% 14.17% 31.38% Wage + Ag + NFE (viii) 0.12% 3.70% 0 0 NA NA 0.39% 1.98%

Total NFE(sum ii,iii,iv, viii) 8.60% 52.78% 9.53% 9.50% 9.88% 16.35% 24.86% 36.92%

Total Ag (sum iii,v vi,, viii) 21.66% 45.50% 65.76% 82.25% NA NA 14.63% 33.16% Total wage sum(iv,vi, vii, viii) 5.56% 38.04% 4.72% 13.14% 24.45% 47.19% 17.09% 45.75% Total multiple activities (sum iii, iv, vi, viii)

4.00% 37.49% 5.70% 12.15% NA NA 7.13% 26.37%

Note statistics are weighted in Bangladesh, Ethiopia and Indonesia, but not in Sri Lanka – since weights were not available for that country. Ag=working on the family farm, Wage=wage employment (a composite category covering both agricultural and non-agricultural wage employment) NFE=non-farm enterprise activity; working in a household non-farm enterprise. The categories presented in this table were constructed on the basis of different questions (please refer to variable appendix for country specifics). To distract comparable indicators from these questions, we constructed employment categories sorting people on the basis of the activities they reported engaging in without drawing a distinction between primary and secondary employment.

Table 1B: Household-level participation Rates in NFE

Household- level Participation Rates

Overall Male-headed Female-headed % of hh that are female-headed

Bangladesh 27.62% 28.61% 16.79% 8.32% Ethiopia 16.20% 12.13% 31.08% 21.46% Indonesia 23.32% 24.69% 15.23% 14.45% Sri Lanka 47.15% 48.80% 37.95% 14.78%

% of households that work exclusively rely on non-farm enterprise

Overall Male-headed Female-headed

Bangladesh 13.66% 13.93% 10.07% Ethiopia 4.86% 2.12% 15.83% Sri Lanka 13.72% 13.80% 13.25%

% of households whose only source of income is non-farm enterprise income

Overall Male-headed Female-headed

Ethiopia 4.47% 2.11% 13.11% Indonesia 2.70% 2.82% 1.99%

Note statistics are weighted in Bangladesh, Ethiopia and Indonesia, but not in Sri Lanka – since household weights were not available for that country

31

Table 2: Individual Characteristics

Indonesia Ethiopia Bangladesh Sri Lanka

Women

Women Men Women Men Women Men

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev. NFE 9.7

6% 0.30 16.

72%

0.37 9.26%

0.29 9.23%

0.29 8.52%

0.28 53.24%

0.50 25.68%

0.44 36.81%

0.48 ag

66.13%

0.47 82.59%

0.38 22.49%

0.42 46.92%

0.50 14.79%

0.36 34.22%

0.47 wage 24.

17%

0.43 47.14%

0.50 4.62%

0.21 13.29%

0.34 5.63%

0.23 38.11%

0.49 17.88%

0.38 44.96%

0.50 age 35.

64 13.1

5 35.05

12.73

32.25

12.49

32.46

12.89

35.04

12.70

34.78

12.64

36.94

12.29

37.10

12.78 primary 39.

90%

0.49 43.28%

0.50 9.26%

0.29 29.78%

0.46 29.85%

0.46 37.24%

0.48 18.69%

0.39 18.30%

0.39 secondary 34.

02%

0.47 37.78%

0.48 3.93%

0.19 6.78%

0.25 30.63%

0.46 33.50%

0.47 74.91%

0.43 77.85%

0.42 tertiary 4.4

4% 0.21 6.9

0% 0.25

1.07%

0.10 5.12%

0.22 1.62%

0.13 1.70%

0.13 head 8.4

4% 0.28 55.

44%

0.50 16.60%

0.37 63.82%

0.48 4.89%

0.22 55.71%

0.50 8.17%

0.27 55.85%

0.50 spouse 59.

52%

0.49 0.13%

0.04 62.78%

0.48 0.92%

0.10 61.02%

0.49 0.70%

0.08 56.44%

0.50 1.70%

0.13 child 26.

39%

0.44 39.05%

0.49 15.98%

0.37 27.62%

0.45 26.03%

0.44 38.81%

0.49 31.20%

0.46 40.07%

0.49 married 71.

99%

0.45 63.83%

0.48 67.91%

0.47 65.67%

0.47 79.41%

0.40 71.62%

0.45 divorcee 2.4

7% 0.16 4.7

0% 0.21 13.

07%

0.34 4.27%

0.20 1.86%

0.13 0.30%

0.06 widow 8.0

9% 0.27 0.4

0% 0.06 7.0

3% 0.26 0.7

8% 0.09 9.6

9% 0.30 0.1

7% 0.04

ln hh size 1.45

0.44 1.49

0.44 1.51

0.51 1.61

0.45 1.61

0.42 1.65

0.38 1.51

0.37 1.52

0.35 sh child 15.

97%

0.17 16.43%

0.17 27.23%

0.20 27.24%

0.20 19.43%

0.18 18.46%

0.18 12.90%

0.17 12.31%

0.17 sh elderly 5.0

3% 0.12 3.5

5% 0.10 2.6

7% 0.08 1.9

5% 0.07 4.4

6% 0.10 3.2

0% 0.08 4.0

6% 0.10 4.0

2% 0.10

ln land agric

0.27

0.45 0.27

0.46

2.67

2.43 2.59

2.36 0.24

0.33 0.24

0.33 own plough

76.35%

0.43 85.15%

0.36 own house 90.

16%

0.30 91.11%

0.28 91.78%

0.27 93.95%

0.24 92.69%

0.26 92.52%

0.26 91.54%

0.28 91.11%

0.28 electricity 95.

89%

0.20 96.13%

0.19 4.94%

0.22 3.79%

0.19 87.75%

0.33 90.02%

0.30 95.88%

0.20 95.41%

0.21 town 31.

94%

0.47 30.00%

0.46 5.78%

0.23 4.78%

0.21 11.10%

0.31 12.65%

0.33 lndistmark

et 1.14

1.02 1.10

1.03 2.04

0.81 2.08

0.78 79.87%

0.57 81.19%

0.57 1.93

0.90 1.96

0.90 bank 18.

80%

0.39 19.27%

0.39 33.57%

0.47 33.08%

0.47 24.16%

0.43 23.12%

0.42 52.10%

0.50 49.93%

0.50 localwageUSD

6.23

0.48 6.22

0.49 5.56

0.41 5.57

0.41 6.00

0.25 6.02

0.25 6.69

0.24 6.69

0.24 partner Primary

27.27%

0.45 26.35%

0.44 15.75%

0.36 3.80%

0.19 21.96%

0.41 19.89%

0.40 14.72%

0.35 12.52%

0.33 partner Secondary

16.24%

0.37 13.25%

0.34 1.36%

0.12 0.56%

0.07 17.29%

0.38 12.00%

0.33 40.99%

0.49 39.70%

0.49 partner Tertiary

3.94%

0.19 2.39%

0.15

1.73%

0.13 0.73%

0.09 1.03%

0.10 0.89%

0.09 hh head father Ag

56.39%

0.50 55.96%

0.50

64.83%

0.48 63.41%

0.48 43.64%

0.50 43.56%

0.50 hh head father NFE

9.98%

0.30 9.69%

0.30

16.65%

0.37 18.21%

0.39 26.12%

0.44 24.59%

0.43 hh head father wage

30.68%

0.46 32.10%

0.47

18.25%

0.39 18.11%

0.39 25.75%

0.44 26.96%

0.44

hh head father other

2.35%

0.15 2.74%

0.16

32

Table 3: Descriptive statistics households

Ethiopia Indonesia

Women Men Women

Women

Men Mean Std.

Dev. Mean Mean Mean Std. Dev. Mean Std. Dev.

NFE 31.08% 0.46 12.13% 0.33 15.23% 0.36

24.69% 0.43 sh inc nfe 19.16% 0.36 5.88% 0.21 18.82% 0.30

20.57% 0.34

sh inc wage 9.52% 0.27 6.10% 0.20 29.52% 0.34

38.82% 0.37 sh inc other 7.60% 0.17 3.20% 0.12 28.38% 0.29

19.16% 0.24 sh inc farm 63.72% 0.41 84.82% 0.30 23.28% 0.31

21.45% 0.31

# inc sources 1.46 0.67 1.29 0.54 2.34 0.68

2.26 0.75 only NFE inc 13.11% 0.34 2.11% 0.14 1.99% 0.14

2.82% 0.17

only agric inc 44.47% 0.50 70.51% 0.46 0.48% 0.07

1.73% 0.13 total expend pc 152.37 123.96 133.90 99.83 268.46 172.95

328.21 820.17 headage 48.80 15.92 42.59 14.80 56.12 13.11

45.38 13.81 headage2 26.34 16.28 20.33 14.37 33.20 14.62

22.50 14.03 head primary 4.47% 0.21 24.84% 0.43 39.37% 0.49

44.85% 0.50

head secondary 2.91% 0.17 2.47% 0.16 5.52% 0.23

30.27% 0.46 head tertiary 0.68% 0.08

3.79% 0.19 head married 12.70% 0.33 93.93% 0.24 8.43% 0.28

94.81% 0.22 ln hh size 0.96 0.60 1.53 0.45 0.85 0.59

1.35 0.46 sh child 18.48% 0.22 30.66% 0.20 7.93% 0.15

18.01% 0.19 sh women 49.86% 0.31 29.33% 0.14 70.06% 0.25

46.35% 0.17 sh elderly 10.79% 0.26 3.55% 0.12 18.45% 0.32

7.23% 0.18 ln land agric 0.19 0.33

0.27 0.46 own plough 33.54% 0.47 86.58% 0.34

own house 80.94% 0.39 94.77% 0.22 92.19% 0.27

89.05% 0.31 town 10.18% 0.30 3.91% 0.19 31.78% 0.47

32.07% 0.47

lndistmarket 1.83 0.95 2.09 0.75 1.14 0.98

1.09 1.04 bank 38.16% 0.49 33.67% 0.47 11.82% 0.32

21.09% 0.41 localwageUSD 0.22 0.04 0.23 0.04 6.15 0.51 6.22 0.48

Note: Statistics are weighted. Bolded coefficients numbers indicate the gender differences are statistically significant at the 5%

level.

33

Table 4A: Modeling Selection

NFE Agriculture Wage

Bangladesh Ethiopia Sri Lanka Bangladesh Ethiopia Sri Lanka Bangladesh Ethiopia Sri Lanka

women men women men women men women men women men women men women men women men women men

coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se age 0.086** -0.012 0.065*** 0.030 0.068*** 0.030 0.113*** 0.006 -0.003 -0.041 0.100*** 0.041 0.060 -0.029 0.116*** 0.051** 0.088*** 0.132***

(0.040) (0.044) (0.024) (0.028) (0.024) (0.022) (0.043) (0.043) (0.018) (0.027) (0.029) (0.027) (0.042) (0.040) (0.026) (0.024) (0.029) (0.023)

agesq -0.001*** 0.000 -0.078*** -0.046 -0.001*** -0.000 -0.002*** -0.000 -0.018 0.048 -0.001*** -0.000 -0.001 0.000 -0.168*** -0.059** -0.001*** -0.002***

(0.001) (0.001) (0.029) (0.034) (0.000) (0.000) (0.001) (0.001) (0.023) (0.033) (0.000) (0.000) (0.001) (0.000) (0.035) (0.029) (0.000) (0.000)

primary 0.116 0.261 -0.197 0.243** 0.281 0.096 0.350* -0.236 0.259* 0.018 0.342 0.057 0.154 0.146 -0.108 0.061 0.114 0.464*

(0.161) (0.192) (0.160) (0.096) (0.200) (0.292) (0.187) (0.193) (0.134) (0.107) (0.250) (0.288) (0.273) (0.195) (0.184) (0.087) (0.227) (0.254)

secondary 0.089 0.469*** -0.583** 0.313 0.145 0.576** 0.126 -0.492** -0.858*** -0.714*** 0.168 -0.186 -0.009 -0.210 1.031*** 0.315* 0.218 0.390

(0.196) (0.176) (0.285) (0.217) (0.193) (0.284) (0.237) (0.196) (0.309) (0.172) (0.246) (0.281) (0.289) (0.152) (0.249) (0.176) (0.219) (0.244)

tertiary -0.201 0.406 -0.133 -0.277 -2.551*** -1.760*** -0.059 -0.946** 1.783*** 0.960*** 1.278*** 1.395***

(0.593) (0.346) (0.376) (0.424) (0.475) (0.386) (0.556) (0.440) (0.428) (0.337) (0.365) (0.387)

head 1.016*** 0.288 0.552** 0.750*** 0.725*** 0.378 0.360 0.402 0.340* 1.681*** 1.312*** 0.634* 0.392 -0.359 -0.111 -1.229*** -0.231 0.343

(0.335) (0.257) (0.237) (0.270) (0.244) (0.292) (0.357) (0.263) (0.199) (0.213) (0.375) (0.350) (0.443) (0.271) (0.259) (0.213) (0.252) (0.276)

spouse 0.313 -0.402 -0.045 -0.761 0.531* -1.185** 0.434 -1.353*** 0.155 1.306*** 0.718* -0.781 -0.431 -0.342 -0.920*** -1.710*** -0.137 1.512***

(0.354) (0.469) (0.237) (0.519) (0.295) (0.486) (0.320) (0.493) (0.179) (0.461) (0.401) (0.498) (0.434) (0.460) (0.235) (0.395) (0.322) (0.432)

child 0.304 0.033 -0.159 -0.088 0.141 -0.110 0.022 -0.605** 0.041 1.657*** 0.521 0.119 0.353 -0.325 -0.141 -1.549*** 0.013 0.436*

(0.341) (0.201) (0.214) (0.272) (0.235) (0.262) (0.444) (0.272) (0.181) (0.161) (0.381) (0.312) (0.415) (0.230) (0.229) (0.166) (0.274) (0.252)

currently married -0.531* 0.111 -0.324 0.095

-1.062*** 0.382* 0.249 0.316

-0.687** 0.211 0.180 -0.163

(0.280) (0.195) (0.215) (0.189)

(0.304) (0.213) (0.167) (0.194)

(0.329) (0.192) (0.202) (0.156)

divorcee -0.102 -6.715*** 0.218 -0.105

-0.934 -6.293*** 0.329** 0.001

-0.769 6.339*** -0.001 0.024

(0.420) (0.482) (0.193) (0.245)

(0.655) (0.446) (0.160) (0.200)

(0.521) (0.501) (0.172) (0.203)

widowed 0.255 -0.728 -0.298 0.580*

0.067 -1.379 0.769*** -0.027

0.145 -1.837*** 0.113 -1.922***

(0.536) (0.633) (0.228) (0.345)

(0.430) (0.968) (0.202) (0.469)

(0.531) (0.446) (0.246) (0.441)

ln hh size -0.080 -0.064 -0.005 0.412*** 0.046 0.175 -0.223 0.145 0.036 -0.203 -0.064 -0.431*** -0.076 -0.175 0.059 -0.197 -0.125 -0.073

(0.213) (0.187) (0.109) (0.124) (0.129) (0.131) (0.225) (0.212) (0.107) (0.136) (0.132) (0.133) (0.285) (0.183) (0.136) (0.125) (0.129) (0.118)

sh child 0.243 0.067 0.222 -0.535** -0.486* 0.118 -0.152 0.488 -0.492** 0.428 0.601** 0.382 -1.387** -0.285 0.027 0.429* -0.902*** 0.048

(0.415) (0.423) (0.256) (0.259) (0.271) (0.273) (0.566) (0.396) (0.220) (0.282) (0.285) (0.285) (0.609) (0.405) (0.280) (0.249) (0.297) (0.258)

sh elderly 0.557 0.400 0.264 0.773 -0.782 -0.314 0.923 1.757* -0.379 1.748** 0.931* 0.266 -0.841 -0.933 -0.348 -0.756 -0.347 0.235

(0.896) (0.732) (0.567) (0.752) (0.481) (0.422) (0.773) (0.970) (0.394) (0.753) (0.498) (0.432) (1.159) (0.719) (0.637) (0.654) (0.470) (0.421)

ln land agric -0.058** -0.007 -0.270* -0.062 0.034 0.275*** 0.546*** 0.619*** -0.100** -0.102*** -0.034 -0.322***

(0.029) (0.028) (0.162) (0.117) (0.037) (0.033) (0.136) (0.123) (0.042) (0.028) (0.135) (0.119)

own plough

-0.699*** -0.551***

0.526*** 1.044***

-0.557*** -0.304**

(0.121) (0.131)

(0.098) (0.116)

(0.137) (0.129)

own house 0.182 -0.173 -0.150 0.094 0.400** 0.362** -0.048 0.825*** 0.464*** 0.004 0.394* 0.711*** -0.658* -0.098 -0.120 0.219 -0.883*** -0.231*

(0.227) (0.218) (0.146) (0.162) (0.173) (0.152) (0.280) (0.233) (0.146) (0.148) (0.204) (0.175) (0.353) (0.209) (0.144) (0.189) (0.144) (0.140)

uses electricity -0.110 0.182 0.065 -0.244* 0.234 0.342* 0.509** 0.043 -0.514** -0.593*** -0.494** -0.516** 0.431 -0.331* 0.110 -0.087 0.161 0.196

(0.211) (0.194) (0.140) (0.146) (0.240)

(0.198) (0.240) (0.213) (0.204) (0.217) (0.226) (0.201) (0.412) (0.195) (0.264) (0.151) (0.221) (0.190) town 0.483*** 0.832*** 0.459** 0.582***

0.336 -0.450*** -1.653*** -0.570***

-0.244 -0.787*** -0.094 0.466**

(0.168) (0.141) (0.181) (0.190)

(0.231) (0.145) (0.179) (0.194)

(0.268) (0.161) (0.231) (0.195) lndistmarket 0.045 -0.079 -0.294*** -0.269*** -0.046 0.027 0.068 -0.198* 0.017 0.278*** -0.133** 0.167*** 0.086 -0.067 0.064 -0.004 0.092* -0.113**

(0.112) (0.097) (0.071) (0.066) (0.050) (0.050) (0.122) (0.113) (0.052) (0.062) (0.056) (0.049) (0.247) (0.096) (0.063) (0.051) (0.049) (0.046)

bank 0.052 0.376** 0.099 -0.113 0.183** 0.280*** 0.046 -0.030 0.035 -0.007 -0.265*** -0.209** -0.401 -0.226* 0.113 -0.050 0.054 -0.028

(0.180) (0.147) (0.099) (0.098) (0.085) (0.081) (0.211) (0.135) (0.070) (0.100) (0.095) (0.087) (0.253) (0.137) (0.103) (0.079) (0.088) (0.077)

localwage -0.488* -0.430* -0.128 -0.101 0.195 0.232 -0.802** -0.873*** -0.202** 0.024 -0.095 -0.440** -0.851* 0.009 -0.052 0.155* 0.118 -0.235

(0.260) (0.251) (0.102) (0.106) (0.191) (0.183) (0.329) (0.262) (0.093) (0.104) (0.219) (0.196) (0.504) (0.237) (0.101) (0.094) (0.203) (0.170)

partner primary 0.074 -0.219 0.403*** 0.050 -0.202 0.209 -0.158 -0.094 0.015 -0.034 0.052 0.007 0.736* -0.035 0.108 0.029 -0.340 0.169

(0.180) (0.217) (0.133) (0.169) (0.226) (0.197) (0.206) (0.183) (0.089) (0.177) (0.252) (0.209) (0.428) (0.242) (0.157) (0.150) (0.278) (0.197)

34

partner secondary 0.073 -0.316 0.205 -0.323 -0.052 0.279 0.122 -0.172 -0.148 0.057 0.095 -0.002 0.916** 0.228 0.109 0.685* -0.265 -0.186

(0.247) (0.203) (0.390) (0.412) (0.217) (0.182) (0.252) (0.206) (0.244) (0.252) (0.240) (0.194) (0.452) (0.207) (0.287) (0.392) (0.270) (0.183)

partner tertiary 0.217 -1.418** -0.652 0.567 -0.837 0.497 -0.443 0.205 1.260** 0.376 0.380 -0.278

(0.449) (0.679) (0.479) (0.501) (0.524) (0.559) (0.709) (0.430) (0.530) (0.507) (0.488) (0.404)

hh head father NFE 0.194 0.178 0.124 0.299*** -0.336 -0.193 -0.546*** -0.585*** 0.047 0.026 -0.017 -0.045

(0.151) (0.171) (0.103) (0.102) (0.217) (0.177) (0.133) (0.107) (0.260) (0.162) (0.112) (0.096) hh head father Wage

-0.146 -0.211 0.040 0.037 -0.558** -0.452*** -0.321*** -0.498*** 0.737*** 0.827*** 0.241** 0.241** (0.175) (0.149) (0.109) (0.102) (0.235) (0.173) (0.115) (0.105) (0.262) (0.181) (0.106) (0.096)

hh head father Other

0.053 -0.386

-0.525 -0.333

0.057 0.245

(0.299) (0.283)

(0.353) (0.352)

(0.253) (0.237) _cons -0.327 1.776 -1.293** -1.860*** -5.096** -5.556*** 1.291 2.384 0.054 -1.061** -2.349 3.415 2.052 1.634 -2.791*** -0.823* -2.863 0.097

(1.337) (1.329) (0.509) (0.566) (2.154) (2.064) (1.711) (1.531) (0.430) (0.500) (2.543) (2.220) (2.479) (1.290) (0.462) (0.457) (2.262) (1.914)

/atrho21 0.047 -0.015 -0.138*** -0.708*** 0.134** 0.053

(0.090) (0.070) (0.050) (0.069) (0.056) (0.050)

/atrho31 -0.063 -0.680*** -0.324*** -0.253*** -0.345*** -0.823***

(0.101) (0.112) (0.065) (0.048) (0.059) (0.063)

/atrho32 -0.227* -0.218** -0.189*** -0.178*** -0.021 -0.243***

(0.119) (0.102) (0.061) (0.050) (0.058) (0.051)

Observations 3,515 4,158 2,777 2,232 1,359 1,350

Logpseudolikelihood -2556690 -4676842 -2532 -1992 -1774.19 -2160.4

Wald chi2 411.88 2754.38 1171.76 886.95 355.30 681.97

35

Table 4B: Indonesia bivariate probit

NFE Work

women men women men

coef/se coef/se coef/se coef/se

age 0.123*** 0.015

0.089** 0.126***

(0.033) (0.034)

(0.041) (0.031)

agesq -0.001*** -0.000

-0.001** -0.002***

(0.000) (0.000)

(0.001) (0.000)

primary -0.326* -0.324*

0.023 0.020

(0.171) (0.186)

(0.203) (0.184)

secondary -0.072 -0.187

0.039 -0.075

(0.172) (0.195)

(0.229) (0.217)

tertiary 0.103 -0.204

1.126*** 0.351

(0.268) (0.248)

(0.274) (0.241)

head 1.218*** 0.484*

-0.225 0.997***

(0.314) (0.265)

(0.298) (0.370)

spouse -0.163 -0.233

0.580* 0.802

(0.292) (0.518)

(0.332) (0.681)

child -0.038 0.130

-0.266 0.800***

(0.235) (0.268)

(0.295) (0.300)

currently married 0.339 0.657**

0.151 -0.149

(0.215) (0.311)

(0.229) (0.236)

divorcee -0.175 -0.379

1.059** 0.297

(0.418) (0.391)

(0.422) (0.367)

widowed -0.039 1.029**

0.531 -1.040*

(0.315) (0.508)

(0.363) (0.544)

ln hh size 0.219* 0.030

-0.140 -0.099

(0.120) (0.141)

(0.152) (0.178)

sh child -0.420 -0.165

-0.826** 0.445

(0.291) (0.390)

(0.386) (0.406)

sh elderly -0.784* -0.599

0.475 0.970*

(0.477) (0.583)

(0.590) (0.565)

ln land agric 0.068 -0.196*

-0.262** -0.451***

(0.092) (0.102)

(0.122) (0.115)

own house -0.074 -0.045

0.174 0.030

(0.138) (0.137)

(0.176) (0.154)

uses electricity 0.311 0.567**

-0.540* -0.171

(0.207) (0.241)

(0.299) (0.281)

town 0.251** 0.183

0.243* 0.091

(0.120) (0.116)

(0.126) (0.128)

lndistmarket -0.126** 0.034

-0.119* -0.232***

(0.052) (0.048)

(0.062) (0.064)

bank 0.080 0.265**

0.248* -0.120

(0.107) (0.124)

(0.129) (0.135)

localwage -0.046 -0.075

-0.193 0.114

(0.128) (0.141)

(0.135) (0.164)

partner primary 0.434** 0.013

-0.404* 0.101

(0.204) (0.170)

(0.244) (0.206)

partner secondary 0.404** -0.075

-0.620** -0.094

(0.204) (0.200)

(0.258) (0.217)

partner tertiary 0.348 0.013

-0.447 0.139

(0.272) (0.274)

(0.327) (0.300)

hh head father - NFE 0.274** 0.458***

-0.244 -0.179

(0.127) (0.153)

(0.170) (0.156)

hh head father - Wage -0.145 -0.220

0.599*** 0.484***

(0.156) (0.136)

(0.145) (0.152)

_cons -3.757* -1.443

1.000 -4.160

(1.963) (2.261) (2.188) (2.702)

/athrho -0.413*** -0.726***

(0.076) (0.090)

Number of observations 3,629 3609 Log pseudolikelihood -1062811 -1432344

Wald chi2 274.74(52) 395.52 (52)

note: *** p<0.01, ** p<0.05, * p<0.1

36

Table 5: Tobit Models of Share of Pecuniary Income accounted for by non-farm enterprise

income

Ethiopia Indonesia

inctobit inctobit

coef/se coef/se

Head is Male -0.143 -0.136**

(0.166) (0.058)

sh child -0.197 0.040

(0.252) (0.072)

ln hh size 0.486*** 0.108***

(0.117) (0.028)

sh women 0.628** -0.084

(0.256) (0.061)

sh elderly -0.437 -0.116

(0.444) (0.081)

2nd

expenditure quartile -0.058 0.058*

(0.107) (0.031)

3nd

expenditure quartile -0.005 -0.040

(0.109) (0.032)

top expenditure quartile -0.111 0.028

(0.119) (0.037)

head age 0.034* 0.010*

(0.019) (0.006)

headage2 -0.046** -0.009

(0.021) (0.006)

head primary 0.213** -0.012

(0.097) (0.029)

head secondary -0.636*** -0.008

(0.232) (0.036)

head tertiary -0.235***

(0.072)

head Married -0.150 0.083

(0.155) (0.052)

ln land agric -0.116***

(0.027)

own plough -0.969***

(0.113)

own house -0.280** -0.075**

(0.132) (0.038)

town 0.444*** 0.136***

(0.169) (0.024)

lndistmarket -0.295*** -0.010

(0.054) (0.011)

bank -0.083 -0.002

(0.088) (0.030)

localwage -0.283*** -0.164***

(0.102) (0.025)

_cons -0.244 1.453***

(0.500) (0.285)

/sigma 1.125*** 0.477***

(0.060) (0.009)

Number of observations 2,475 2,641 Pseudo R2 0.1397 0.0308

note: *** p<0.01, ** p<0.05, * p<0.1

37

Table 6: Descriptive Statistics – Enterprises

Bangladesh Ethiopia Indonesia Sri Lanka

Women

Men

Women Men Women

Men

Women

Men

Variable Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE lnYd 5.42 1.26 8.16 1.48 4.52 1.78 5.45 1.83 7.74 1.48 8.10 1.66 6.35 1.54 7.27 1.61 lnLft -0.22 0.53 0.40 0.70 -0.42 0.74 -0.55 0.92 0.29 0.88 0.57 1.05 0.35 0.49 0.57 0.60 hiredL 8.47% 0.28 30.65% 0.46 1.95% 0.14 5.95% 0.24 25.27% 0.44 43.98% 0.50 33.75% 0.47 51.67% 0.50 sharepaid 4.54% 0.15 18.41% 0.30 1.46% 0.12 3.39% 0.15 22.53% 0.40 38.31% 0.46 18.75% 0.27 30.23% 0.31 1 worker 76.07% 0.43 32.26% 0.47 80.27% 0.40 72.97% 0.44 59.65% 0.49 44.27% 0.50 57.49% 0.50 37.62% 0.48 2workers 13.94% 0.35 37.72% 0.48 16.66% 0.37 21.57% 0.41 21.82% 0.41 24.46% 0.43 30.93% 0.46 38.58% 0.49 3-5 workers 9.53% 0.29 23.27% 0.42 2.69% 0.16 4.41% 0.21 15.10% 0.36 18.35% 0.39 8.69% 0.28 19.00% 0.39 5-50 workers 0.45% 0.07 6.31% 0.24 0.38% 0.06 0.40% 0.06 3.42% 0.18 12.59% 0.33 2.90% 0.17 4.54% 0.21 >50 workers 0.00% 0.00 0.44% 0.07 0.00% 0.00 0.66% 0.08 0.01% 0.01 0.33% 0.06 0.00% 0.00 0.26% 0.05 lnKd 4.98 1.35 5.32 1.74 4.58 2.68 4.14 2.68 6.64 2.55 6.91 2.79 6.72 1.69 7.25 1.71 lnYLdft 5.64 1.15 7.76 1.35 4.95 1.62 6.00 1.90 7.45 1.40 7.53 1.41 6.02 1.46 6.71 1.45 lnKLdft 5.21 1.40 4.92 1.60 4.92 2.53 4.50 2.65 6.34 2.58 6.34 2.87 6.37 1.67 6.68 1.61 lnMLdft 3.94 1.68 7.22 1.74 3.79 2.15 4.85 2.62 6.72 1.54 6.51 1.68 5.35 1.57 6.28 1.56 invest 20.05% 0.40 29.42% 0.46 16.27% 0.37 21.17% 0.41 48.34% 0.50 46.73% 0.50 59.89% 0.49 49.94% 0.50 lninv 0.75 0.97 3.10 1.63 1.89 1.66 2.81 1.77 5.81 5.27 6.75 0.19 4.53 1.55 5.53 1.60 dlnLft 0.01 0.07 0.03 0.22 5.79% 0.72 11.10% 0.53 0.08 0.29 0.10 0.40 0.01 0.09 0.00 0.11 shrank workers 0.06% 0.03 0.52% 0.07 2.53% 0.16 2.18% 0.15 1.05% 0.10 2.41% 0.15 0.31% 0.06 1.65% 0.13 growth workers 0.95% 0.10 6.38% 0.24 7.57% 0.26 10.43% 0.31 50.25% 0.50 55.02% 0.50 21.12% 0.41 13.54% 0.34 manufacturing 95.10% 0.22 27.84% 0.45 75.36% 0.43 46.34% 0.50 2.54% 0.16 13.51% 0.34 50.41% 0.50 37.76% 0.49 services 4.64% 0.21 23.62% 0.42 10.18% 0.30 12.41% 0.33 24.42% 0.43 39.58% 0.49 15.43% 0.36 23.35% 0.42 trade 0.26% 0.05 48.54% 0.50 14.47% 0.35 41.25% 0.49 73.03% 0.44 46.92% 0.50 34.16% 0.48 38.90% 0.49 hh ent 95.53% 0.21 20.61% 0.40 5.14% 0.22 3.92% 0.19 74.72% 0.44 73.50% 0.44 60.24% 0.49 34.46% 0.48 uses electricity 64.42% 0.48 75.39% 0.43 69.53% 0.46 64.11% 0.48 77.07% 0.42 73.27% 0.44 firm age 6.41 7.19 10.06 10.49 8.78 8.43 8.73 9.87 9.12 9.46 8.98 8.68 8.75 10.93 9.10 10.57 registered 2.90% 0.17 42.20% 0.49 2.30% 0.15 8.44% 0.28 2.96% 0.17 10.93% 0.31 35.38% 0.48 58.56% 0.49 months normal act 11.47 1.31 11.38 1.62 6.77 2.73 5.87 2.74 8.74 1.61 8.61 1.90

competition more20 1.47% 0.12 9.54% 0.29 9.74% 0.30 12.12% 0.33 29.51% 0.46 32.79% 0.47 competition 11to20 11.71% 0.32 12.05% 0.33 5.24% 0.22 3.99% 0.20 20.80% 0.41 8.48% 0.28 competition 5to10 30.16% 0.46 29.53% 0.46 4.99% 0.22 7.97% 0.27 19.95% 0.40 15.63% 0.36 competition less5 56.65% 0.50 48.88% 0.50 80.04% 0.40 75.92% 0.43 29.74% 0.46 43.10% 0.50 firm has loan 1.34% 0.12 10.15% 0.30 22.60% 0.42 22.26% 0.42 14.44% 0.35 13.32% 0.34 24.15% 0.43 28.58% 0.45

manager age 31.64 8.53 39.25 13.45 43.18 13.04 41.67 13.73 40.0 12.10 41.4 11.34 manager experience

3.25 5.32 5.60 7.52

manager primary 24.05% 0.43 35.69% 0.48 11.18% 0.32 42.17% 0.49 35.18% 0.48 31.55% 0.03 20.42% 0.40 11.26% 0.32 manager sec 35.87% 0.48 41.51% 0.49 2.89% 0.17 5.39% 0.23 41.66% 0.49 41.53% 0.03 76.51% 0.42 85.96% 0.35 manager tertiary 0.21% 0.05 4.09% 0.20 9.19% 0.29 16.52% 0.03 1.88% 0.14 2.51% 0.16 town 49.96% 0.50 45.72% 0.50 28.76% 0.45 21.09% 0.41 51.37% 0.50 51.40% 0.50

lndistmarket 60.51% 0.40 66.50% 0.50 1.16 1.00 1.64 0.93 0.66 0.97 0.93 1.12 1.51 0.92 1.81 1.14 bank 12.87% 0.34 42.46% 0.49 51.48% 0.50 59.89% 0.49 48.47% 0.50 44.29% 0.50 60.32% 0.49 69.53% 0.46 Localwage 6.15 0.21 6.07 0.24 5.52 0.40 5.55 0.40 6.50 0.45 6.46 0.43 6.69 0.23 6.70 0.22

Note: Statistics are weighted. The samples are confined to observations for whom the production function could be estimated. Bolded coefficients numbers indicate the gender differences are statistically significant at the 5% level.

38

Table 7: Production Functions

Production functions

Bangladesh Ethiopia Indonesia Sri Lanka

coef se coef se coef se coef se MODEL 1: Gender only manager is male 2.744*** (0.206)

0.929*** (0.232) 0.143 (0.197) 0.917*** (0.178) Observations

observations

2,480

648

1,369

1,259

R2 0.228

0.060

0.002

0.056

Adjusted R2 0.228

0.059

0.001

0.055

MODEL 2:Gender and Size manager is male 2.196*** (0.194) 0.998*** (0.226) -0.104 (0.187) 0.641*** (0.169) lnLft 0.875*** (0.094) 0.561*** (0.128) 0.833*** (0.081) 1.200*** (0.091) Observations

observations

2,480

647

1,369

1,259

R2 0.359

0.125

0.256

0.223

Adjusted R2 0.359

0.123

0.255

0.221

MODEL 3:Gender, Size and Sector manager is male 0.998*** (0.290) 0.632*** (0.206) -0.067 (0.277) 0.514*** (0.157) lnLft 1.252*** (0.105) 0.786*** (0.125) 0.790*** (0.088) 1.308*** (0.089) services 0.463* (0.258) 0.092 (0.364) -0.439* (0.222) 0.619*** (0.196) trade 1.809*** (0.237) 1.443*** (0.261) -0.012 (0.367) 1.020*** (0.132) Observations 2,480

647

1,369

1,259

R2 0.555

0.227

0.273

0.298

Adjusted R2 0.554

0.222

0.271

0.296

MODEL 4: Gender Size and Sector (+ K and M) manager is male -0.310** (0.124) 0.459*** (0.152) 0.076 (0.092) -0.010 (0.099) manuflnKd 0.074*** (0.027) 0.073* (0.042) 0.027 (0.017) -0.005 (0.052) manuflnLft 0.229*** (0.042) 0.591*** (0.166) 0.344*** (0.121) 0.348*** (0.090) manuflnMd 0.668*** (0.029) 0.384*** (0.075) 0.599*** (0.087) 0.746*** (0.054) servlnKd 0.082*** (0.013) 0.167** (0.069) 0.048*** (0.017) 0.106** (0.042) servlnLft 0.423*** (0.047) 0.642*** (0.237) 0.256*** (0.075) 0.272 (0.167) servlnMd 0.536*** (0.022) 0.261*** (0.071) 0.563*** (0.046) 0.640*** (0.056) tradelnKd 0.012 (0.009) 0.062 (0.038) 0.014 (0.013) 0.043* (0.024) TradelnLft 0.182*** (0.038) 0.559*** (0.116) 0.142*** (0.046) 0.235* (0.122) tradelnMd 0.847*** (0.023) 0.558*** (0.106) 0.752*** (0.051) 0.863*** (0.034) Services 0.839*** (0.281) 0.511 (0.533) 0.222 (0.793) 0.116 (0.532) Trade -1.097*** (0.301) -0.715 (0.710) -1.041 (0.770) -1.256** (0.529) hh ent -0.320*** (0.047)

-0.525*** (0.160) -0.143* (0.080)

F TESTS – CRS

CRS-Manufacturing

p>F 0.2321

0.7564

0.8018

0.1868

CRS-Services

p>F 0.3598

0.7991

0.0234

0.8967

CRS-Trade

p>F 0.2149

0.2266

0.0322

0.1812

Observations

observations

2,480

537

1,369

1,058

R2 0.957

0.487

0.789

0.806

Adjusted R2 0.957

0.475

0.787

0.804

MODEL 5: Full specification manager is male -0.242** (0.109) 0.606*** (0.161) 0.058 (0.072) -0.064 (0.111) manuflnKd 0.073*** (0.026) 0.064* (0.034) 0.012 (0.019) -0.019 (0.052) manuflnLft 0.224*** (0.042) 0.413*** (0.121) 0.367*** (0.116) 0.398*** (0.094) manuflnMd 0.654*** (0.029) 0.467*** (0.073) 0.552*** (0.091) 0.733*** (0.056) servlnKd 0.073*** (0.013) 0.117* (0.063) 0.041** (0.019) 0.123*** (0.041) servlnLft 0.383*** (0.040) 0.517 (0.443) 0.243*** (0.078) 0.289 (0.180) servlnMd 0.521*** (0.019) 0.261** (0.102) 0.547*** (0.039) 0.622*** (0.063) tradelnKd 0.000 (0.012) 0.054 (0.044) 0.009 (0.014) 0.025 (0.027) TradelnLft 0.166*** (0.040) 0.634*** (0.168) 0.072* (0.042) 0.139 (0.102) tradelnMd 0.814*** (0.029) 0.505*** (0.097) 0.762*** (0.036) 0.866*** (0.033) Services 0.912*** (0.283) 0.827* (0.484) -0.124 (0.768) 0.010 (0.603) Trade -0.888*** (0.311) 0.090 (0.668) -1.540** (0.690) -1.222* (0.660) hh ent -0.275*** (0.050)

-0.255*** (0.096) -0.169* (0.090)

manager age -0.002 (0.006) -0.004 (0.026) -0.004 (0.013) 0.013 (0.011) mngrage2_100 0.160 (0.617) -0.010 (0.024) -0.001 (0.015) -0.034 (0.032) manager primary 0.012 (0.044) -0.620*** (0.185) -0.112 (0.090) 0.090 (0.140) manager secondary 0.020 (0.050) -0.082 (0.396) 0.020 (0.136) 0.164 (0.167) manager tertiary 0.089 (0.055)

0.268 (0.175) 0.092 (0.191)

Electricity Usage Usage 0.122** (0.051) 0.830** (0.342) 0.051 (0.081) -0.081 (0.129) lnfirmage 0.018 (0.016) 0.101 (0.062) 0.104*** (0.035) 0.072* (0.039) Town 0.026 (0.034) 0.436* (0.251) 0.075 (0.065)

39

Lndistmarket -0.073* (0.039) 0.014 (0.096) -0.004 (0.046) 0.020 (0.048)

Bank -0.066* (0.035) 0.227 (0.213) -0.044 (0.062) 0.035 (0.083)

Localwage 0.208** (0.087) 0.183 (0.208) 0.107 (0.066) 0.317** (0.156)

F TESTS – CRS

CRS-Manufacturing

p>F 0.0926

0.6777

0.5523 0.0941 CRS-Services

p>F 0.5143

0.8228

0.0176 0.8239

CRS-Trade

p>F 0.5389

0.3095

0.0005 0.7862 Observations 1,993

476

1,229 943

R2 0.960

0.570

0.809 0.822 Adjusted R2 0.960

0.549

0.806 0.818

note: *** p<0.01, ** p<0.05, * p<0.1

40

Table 8: Investment Probit

Investment Probit - Marginal Effects - Baseline

Bangladesh Ethiopia Indonesia Sri Lanka

coef se coef se coef se coef se

MODEL 1: Gender only manager is male 0.299 (0.217) 0.183 (0.130) -0.001 (0.133) -0.252** (0.128)

Observations observations

2,480

649

1,369

1,315

Pseudo R2 0.003

0.004

0.000

0.005

MODEL 2:Gender – initial size and sector manager is male 0.021 (0.242) 0.228 (0.150) -0.274 (0.220) -0.198 (0.150)

lnLftlag 0.164** (0.076) 0.263** (0.103) 0.187 (0.123) -0.050 (0.087)

services 0.002 (0.181) 0.157 (0.326) -0.388 (0.238) -0.343** (0.162)

trade -0.452*** (0.148) -0.002 (0.320) -0.029 (0.279) -0.106 (0.134)

hh ent -0.347* (0.193) 0.228 (0.150) -0.215 (0.249) 0.085 (0.139)

Observations observations

2,301

648

776

1,128

Pseudo R2 0.037

0.032

0.040

0.016

MODEL 3: FULL SPECIFICATION manager is male -0.181 (0.259) 0.291 (0.237) -0.378* (0.204) -0.074 (0.136)

lnLftlag 0.181* (0.093) 0.142 (0.087) 0.220* (0.117) 0.075 (0.098)

services -0.034 (0.193) 0.243 (0.304) -0.610** (0.302) -0.289* (0.168)

trade -0.436*** (0.168) -0.021 (0.276) -0.182 (0.278) -0.086 (0.139)

hh ent -0.397* (0.225)

-0.071 (0.288) 0.086 (0.139)

manager age -0.017 (0.031) -0.008 (0.043) 0.122*** (0.043) -0.000 (0.016)

mngrage2_100 1.477 (3.654) -0.000 (0.043) -0.142*** (0.048) -0.003 (0.052)

manager primary -0.172 (0.231) 0.144 (0.208) -0.457 (0.330) -0.396 (0.513)

manager secondary

-0.204 (0.209) 0.294 (0.251) 0.037 (0.284) -0.375 (0.513)

manager tertiary -0.108 (0.417)

-0.426 (0.472) -0.393 (0.634)

electricity Usage 0.325 (0.206) 1.227*** (0.243) 0.144 (0.251) 0.196* (0.110)

lnfirmage 0.039 (0.075) 0.191*** (0.070) -0.029 (0.119) -0.151*** (0.054)

town 0.055 (0.148) -0.137 (0.286) 0.384 (0.248)

lndistmarket 0.430*** (0.163) -0.155 (0.152) 0.005 (0.102) -0.024 (0.092)

bank 0.240 (0.177) -0.388 (0.273) 0.224 (0.279) 0.091 (0.152)

localwage -0.482 (0.299) -0.043 (0.278) 0.626** (0.262) -1.574*** (0.334)

Observations observations

1,855

574

697 993

Pseudo R2 0.069

0.106

0.152 0.073

note: *** p<0.01, ** p<0.05, * p<0.1

41

Table 9: Growth Models

Growth Models

Bangladesh Ethiopia Indonesia Sri Lanka

coef se coef se coef se coef se

MODEL 1: Gender only manager is male 0.013 (0.010) 0.085 (0.058) 0.008 (0.041) -0.006 (0.012)

Observations observations

2,301

725

776

1,130

R2 0.000

0.009

0.000

0.001

Adjusted R2 0.000

0.008

-0.001

-0.000

MODEL 2:Gender – initial size and sector

manager is male 0.054*** (0.013) 0.075 (0.051) 0.038 (0.034) -0.004 (0.011)

lnLftlag -0.047*** (0.011) -0.132*** (0.029) -0.030 (0.045) -0.018* (0.010)

services -0.065*** (0.022) -0.057 (0.046) 0.070 (0.053) -0.019 (0.015)

trade -0.096*** (0.020) -0.041 (0.049) 0.080 (0.055) -0.013 (0.014)

hh ent -0.059*** (0.020)

-0.082 (0.058) -0.005 (0.012)

Observations observations

2,301

724

776

1,128

R2 0.046

0.116

0.026

0.015

Adjusted R2 0.044

0.112

0.020

0.010

MODEL 3: FULL SPECIFICATION

manager is male 0.055*** (0.015) 0.077 (0.063) 0.017 (0.031) -0.001 (0.012)

lnLftlag -0.046*** (0.012) -0.159*** (0.034) -0.041 (0.043) -0.020* (0.011)

services -0.055*** (0.020) -0.071* (0.040) 0.044 (0.060) -0.026 (0.018)

trade -0.095*** (0.023) -0.085 (0.057) 0.076 (0.063) -0.018 (0.016)

hh ent -0.041** (0.019)

-0.064 (0.070) 0.003 (0.012)

manager age 0.001 (0.002) 0.013* (0.008) 0.020* (0.012) -0.003** (0.001)

mngrage2_100 -0.146 (0.212) -0.012 (0.009) -0.020* (0.011) 0.006* (0.003)

manager primary 0.004 (0.013) 0.047 (0.059) -0.016 (0.038) -0.003 (0.016)

manager secondary -0.004 (0.015) 0.151** (0.075) 0.041 (0.042) 0.010 (0.018)

manager tertiary 0.055* (0.033)

0.024 (0.075) 0.015 (0.024)

electricity usage 0.024* (0.012) 0.063 (0.088) 0.126*** (0.038) 0.005 (0.012)

lnfirmage -0.014*** (0.005) -0.017 (0.018) 0.022 (0.029) 0.000 (0.005)

town -0.018 (0.011) 0.096* (0.058) 0.031 (0.029)

lndistmarket 0.016* (0.008) -0.027 (0.022) 0.015 (0.014) -0.009 (0.006)

bank 0.007 (0.008) 0.001 (0.042) 0.030 (0.036) 0.009 (0.010)

localwage 0.038** (0.016) -0.035 (0.048) 0.046* (0.027) 0.031 (0.021)

Observations observations

1,855

636

697 993

R2 0.064

0.170

0.121 0.045

Adjusted R2 0.056

0.152

0.100 0.030

note: *** p<0.01, ** p<0.05, * p<0.1

42

Appendix A: Data construction

Individual Activity Portfolios.

Individuals labor market activity portfolio:

Employment categories were created based on the activities individuals reported engaging. In particular, individuals were categorizes as engaging in one or more of the following activities: Self employed in NFE; Self employed in agriculture, or wage work (includes both agricultural and non-agricultural wage employment. Individuals not reporting any such activities were classified as being out of the labor force (OLF); (Retired, unemployed or housework). However, the categorizations were based on different sets of questions:

In Bangladesh and Sri Lanka, people were asked about their main occupation and whether they worked on own

farm, for a wage and in a household non-farm enterprise over the past year. This means that people could

indicate up to three activities.

In Ethiopia we have information on workers’ primary and secondary income earning activities over the past

year (but not on other income earning activities, which explains why there are no individuals who reported

having engaged in wage employment, agricultural employment and non-farm enterprise activity).

In Indonesia, people were asked whether they worked in a household non-farm enterprise over the past year

and whether they worked for a wage in the past twelve months. However, no questions were asked about

whether they worked on a family farm.

Education (primary, secondary, tertiary): Education categories were based on the years of schooling reported and

each countries’ education system, years of schooling were converted in dummies for each level of education. The few

people who indicated having completed “other types of education” were considered to have attended primary school.

Bangladesh Ethiopia Indonesia Sri Lanka

Primary “First to fifth grade” “other”

1 to 6 years of schooling “Adult literacy program” “Other literacy program” “Church/Mosque schooling”

“elementary” “First to fifth grade” “other”

Secondary “Sixth to ninth grade” “SSC\Equivalent” “HSC\Equivalent”

Above 6 years of schooling “Tech/Vocational training” “University/College”

“Junior High” “High School”

“Sixth to ninth grade”

Tertiary “University” Tertiary was not created given that the number of respondents who studied past high school was extremely small.

“Vocational school”, “Diploma I/II” “Diploma III” “Diploma IV/BA” “Masters – Phd”

“University” “Professional” “Technical college”

Note: the same education categories were used to define partner’s education and education of firm managers.

Occupation of father of household head: Categories of occupation were defined in a similar way to individual

occupations (self employment in agriculture, self employment in NFE and wage employment).

In Sri Lanka, the option other was available which led to the inclusion of the extra category “others”. No questions

about father’s occupation was included in the Indonesia survey.

43

Household variables

Household size: Number of individuals living in each household

Share of Children: Proportion of household members younger than 10 years old.

Share of Elderly: Proportion of household members older than 65 years of age.

Share women: Proportion of adult women in the household (10 to 65 years old).

House ownership: Dummy variable that indicates whether or not the household owns the house which it inhabits.

Agriculture land: Log of total land used by the household for farming. This might include both land that is owned and

land that is only controlled by the household. This variable is not available in Ethiopia.

Plough ownership: Dummy variable that takes the value one when the household owns a plough. Used only in

Ethiopia as a replacement for agriculture land owned.

Community Characteristics

Town: This is a dummy variable which takes the value one if the households live in a rural town or city. In the case of

Bangladesh it also includes peri-urban locations.

Daily wage of a male agricultural laborer in the community: The going wage rate to hire a male agricultural laborer

for one day (from the community questionnaire). In Indonesia it is an average of the wages paid in monsoon and non-

monsoon seasons.

Distance to market – Distance in km to the nearest market where locals usually travel to sell their products. In

Ethiopia, respondents were given the opportunity to answer the distance question in terms of distance covered or

travelling time; fortunately, virtually all respondents who answered these questions in terms of travelling time indicated

that their typical mode of transportation was walking, enabling us to impute distance by assuming that people on average

work 6 kilometers an hour.

Credit Institution – Dummy variable that takes the value one if there is any financial institution in the community. The

type of institution included in each survey was different. The following table shows which type of credit institution was

included in each country.

Bangladesh Ethiopia Indonesia Sri Lanka

NCB Private Bank NGO Bank Other

Bank Microfinance Community group Other

Local Commercial Bank Other Local Bank Foreign Bank NGO or business organization Government Program (KIK, KUT, etc) Cooperative Investment Funds Other

Private Commercial Bank Government Commercial Bank Rural Bank (Gramiya Bank) Samurdi/Janasaviya Bank Sanasa Bank Rural Development Bank IRDP/REAP/ABGEP Financial and leasing company Others

Factors of Production

44

Labor (Full-time Equivalent - FTE): Labor inputs were reported as the number of hours worked per year per worker

(only days in Ethiopia). This includes the time worked by paid and unpaid workers and the owner/manager. To make

them comparable we converted each of the labor input measure into a full-time worker equivalent (FTE). One FTE was

considered to work 8 hours per day, 25 days per month, 12 months per year.

Output: Measured by the total value of sales in the previous year.

Capital: the capital variable used is the sum of the value of all the assets used by the firm. This includes land, buildings,

equipment and machinery and vehicles plus the value of rental buildings and equipments.

Material inputs: material inputs are the sum of total expenditure on intermediate inputs (including, but not limited to,

costs associated with utilities usage, insurance and phone usage) and items to be resold. In Ethiopia it was computed as

the product of the number of material inputs bought times and the average price of each material input unit.

Firm variables

Size (one worker, 2-5 workers, more than 5 workers): Firm size based on the number of workers.

Sector (Manufacturing, Services, Trade): Sector in which the firm develops its activity. Sectors were defined

according to the following correspondence.

Bangladesh Ethiopia Indonesia Sri Lanka

Manufacturing Mining and quarrying Manufacturing Electricity, gas and water supply Construction

Food and beverages, brewing/distilling Manufacturing (excl: grain milling, food and beverages, distilling, wearing apparel) Grain milling

Mining and Excavation Manufacturing, including processing of agricultural products Electricity Gas and water Construction

Manufacturing non-agricultural goods Processing of Agriculture, hunting, fishing products Mining and quarrying Electricity, gas, water Construction

Trade Wholesale and retail trade (excluding repair of motor vehicles, motorcycles, personal and household good)

Retail trade via stalls and markets Retail (not stalls/mkts) Whole sale trade

Wholesale and retail trade

Retail or wholesale trading

Services Hotels and Restaurants Transport, storage and communications Financial intermediation Real estate, renting and business activities Public administration and defense; compulsory social security Education Health and social work Other community, social and personal service activities Activities of private households (ISIC Rev.

Hotels and restaurants Services(services, manufac. apparel/tailoring, rental, rec) Transport services Others (specialized services)

Car, motorcycle and household goods Repair shops Hotels, food and beverages Transportation, storage, and communication Finance Real estate, leasing and business services Health services Public and social services Catering

Services

45

3.1. – Top Level Category P) Exterritorial organizations and bodies Repair of motor vehicles, motorcycles and personal and households goods

Note: In Bangladesh and Sri Lanka firms were asked which percentage of their sales corresponded to

manufacturing, services or trade and they were considered to be of a given sector if more than 33% of sales was

generated in that sector during the past 12 months.

Manager experience: In Sri Lanka there manager’s age was not available so that variable was replaced by number of

years of experience as a manager.

Household enterprise: This variable identifies home based firms.

Electricity usage: Dummy variable that takes value one when the firm uses electricity.

Firm age: the age of the firm measured in years.

Registered: Dummy variable that takes the value one when the firm is registered.

Seasonality: Number of months of normal or high sales reported by the firm.

Number of competitors (more than 20, 11 to 20, 6 to 10, less than 5): Number of other firms working in the same

business in the area.

Loan: Whether the firm has a loan with any type of credit institution. The time period of loans to be reported was

different countries in each country. In Sri Lanka and Ethiopia firms reported loans obtained during the last 5 years, in

Bangladesh the period was the last 3 years and in Indonesia the last 12 months.

46

Appendix B: Constraints

When asked about the most important constraint to running their firm, both male and female

managers consider a lack of markets (demand), transport, access to credit and electricity as their

most important constraints, as demonstrated in Table B1. The importance of these constraints

varies across countries. For example, concerns about markets are particularly pressing in Bangladesh

and Ethiopia, which is characterized by high remoteness. Other constraints, such as with taxation,

registration procedures, labor regulation and the availability and regulation of land are only

considered the most important constraint by a very small minority of firms in each country, though

governance and public utilities are the most pressing constraints for a small group of firms. Gender

differences in perceived constraints and their ranking are not large in general, yet vary across

countries.

Table b2 demonstrates how important of a constraint a particular issue was, disaggregated

by gender and country. There are very few constraint that are always considered more of an issue by

either gender in each country. An exception is governance, which men always consider more of a

constraint than women (although the gender difference is not statistically significant in Indonesia),

perhaps because male firms tend to be larger. Registration, taxes and public infrastructure are

considered a significantly more severe obstacle by male managers in Bangladesh, Ethiopia and Sri

Lanka, but not in Indonesia, perhaps reflecting differences in firm size. Women consider access to

finance more of a constraint in Bangladesh, Ethiopia and Indonesia, while men consider it more of a

problem in Sri Lanka.

To assess to what extent gender differences in constraints might be driven by, or masked by,

firm characteristics, we estimate ordered probit models of the severity of a given problem,

controlling for firm size, capital stock, sector, human capital of the manager, investment climate

characteristics and gender.

Results are presented in Table B3A. The gender dummies are most often insignificant, and

their sign varies considerably across countries. Overall, no consistent gender patterns emerge.

Since subjective responses may be biased because of individuals’ latent optimism (or

pessimism), we also run a regression in which we use as the dependent variable the severity of the

constraint minus the average constraint severity reported by the individual in question (see

Hallward-Driemeier and Aterido, 2009b). This arguably eliminates individual “fixed” effects and

allows variation in reported constraint severity to be primarily identified off within-individual

variability. Of course, the drawback of this procedure is that variation across individuals is discarded.

The results are presented in table B3B, and are generally robust to this transformation of the

dependent variable, although it is noticeable that the coefficients associated with certain constraints

change. Interestingly, however, there is still no systematic pattern of gender differences across

countries.

In sum, gender differences in self-reported constraints are small both unconditional and

conditional on differences in firm, human capital, and investment climate characteristics.

47

Table B1: Most important constraint

Bangladesh Ethiopia Indonesia Sri Lanka

Men Women Men Women Men Women Men Women

Mean/se Mean/se Mean/se Mean/se Mean/se Mean/se Mean/se Mean/se electricity 16.99% 3.62% 4.50% 7.89% 16.65% 12.26% 27.02% 24.78%

0.38 0.19 0.21 0.27 0.37 0.33 0.44 0.43

public utilities 0.81% 0.00% 5.33% 10.00% 2.93% 5.13% 13.75% 11.38%

0.09 0.00 0.22 0.30 0.17 0.22 0.34 0.32

finance 10.24% 23.31% 31.74% 30.72% 25.07% 22.46% 19.15% 15.51%

0.30 0.43 0.47 0.46 0.43 0.42 0.39 0.36

transport 9.12% 11.13% 18.13% 11.89% 11.35% 18.72% 18.27% 19.13%

0.29 0.32 0.39 0.32 0.32 0.39 0.39 0.39

markets 42.93% 50.09% 35.56% 37.02% 28.37% 23.81% 14.02% 27.09%

0.50 0.50 0.48 0.48 0.45 0.43 0.35 0.45

labor 0.06% 0.00%

0.22% 0.39% 1.80% 1.01%

0.03 0.00

0.05 0.06 0.13 0.10

land 1.04% 0.00% 1.90% 1.41% 1.30% 0.97% 1.00% 0.40%

0.10 0.00 0.14 0.12 0.11 0.10 0.10 0.06

governance 8.15% 8.44% 0.12% 0.57% 9.60% 10.68% 1.39% 0.14%

0.27 0.28 0.03 0.08 0.29 0.31 0.12 0.04

registration 1.04% 0.00% 0.76% 0.00% 0.98% 2.23% 0.82% 0.18%

0.10 0.00 0.09 0.00 0.10 0.15 0.09 0.04

taxes 0.07% 0.00% 0.89% 0.00% 0.99% 3.27% 0.74% 0.27%

0.03 0.00 0.09 0.00 0.10 0.18 0.09 0.05

others 9.55% 3.40% 1.07% 0.50% 2.54% 0.07% 2.03% 0.10%

0.29 0.18 0.10 0.07 0.16 0.03 0.14 0.03

Table B2: Average rating of severity of constraints

Bangladesh Ethiopia Indonesia Sri Lanka

Men Women Men Women Men Women Men Women

Mean/se Mean/se Mean/se Mean/se Mean/se Mean/se Mean/se Mean/se

electricity 1.28 0.65 1.00 0.87 1.10 1.17 1.20 0.87

1.12 0.81 1.36 1.27 1.29 1.28 1.29 1.15

public utilities 0.06 0.00 1.06 1.21 0.79 1.03 1.01 0.62

0.37 0.06 1.35 1.33 1.18 1.30 1.24 1.01

finance 1.27 1.50 1.84 2.11 1.78 1.80 1.40 1.24

1.23 1.40 1.38 1.27 1.38 1.38 1.40 1.39

transport 1.36 1.38 1.81 1.33 1.26 1.32 1.17 1.13

1.18 0.95 1.37 1.40 1.33 1.35 1.36 1.32

markets 2.08 2.09 2.29 2.31 1.25 1.21 1.25 1.39

0.98 0.82 1.07 1.11 1.33 1.28 1.36 1.35

labor 0.03 0.02 0.47 0.15 0.49 0.50 0.20 0.11

0.23 0.16 1.04 0.63 0.99 0.99 0.72 0.56

land 0.14 0.11 0.52 0.65 0.49 0.46 0.11 0.04

0.51 0.55 1.10 1.14 0.99 0.99 0.54 0.28

governance 2.05 1.48 0.61 0.24 1.19 1.13 0.22 0.13

0.96 1.00 1.06 0.71 1.34 1.38 0.74 0.58

registration 0.11 0.00 0.22 0.03 0.61 0.62 0.17 0.08

0.38 0.06 0.71 0.24 1.07 1.09 0.66 0.49

taxes 0.02 0.00 0.47 0.10 0.65 0.68 0.15 0.07

0.18 0.05 1.04 0.45 1.12 1.11 0.62 0.44

Note: In each country managers were asked about the seriousness of different obstacles to investment. Constraints were

grouped by theme and the maximum value of among the different constraints in a given category was used. For example,

public utilities includes problems with water, telecommunication and postal services. Different country scales of severity of

were converted to the following common scale: 0=no problem, 1==a minor problem, 2=somewhat of a problem, 3=a major

problem.

48

Table B3: Ordered probits

A - Constraints-Ordered probit electricity finance transpot market public

utilities labor land governance registration taxes

coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se

Bangladesh Male 0.276* -0.317 -0.024** 0.251 0.504 0.156 -0.109 0.548 1.093* 0.618

(0.208) (0.255) (0.214) (0.273) (0.320) (0.434) (0.487) (0.223) (0.463) (0.490)

Ethiopia Male 0.237 -0.247 0.300 -0.210 -0.049 1.553*** -0.065 0.685*** 0.559** 0.794**

(0.248) (0.196) (0.220) (0.270) (0.213) (0.430) (0.236) (0.204) (0.273) (0.378)

Indonesia Male -0.122 -0.058 -0.058 -0.009 -0.196* -0.010 0.071 -0.049 -0.046 0.053

(0.131) (0.115) (0.133) (0.118) (0.115) (0.166) (0.134) (0.125) (0.131) (0.143)

Sri Lanka Male 0.264* 0.086 0.148 -0.004 0.355*** 0.162 0.214 -0.082 0.150 -0.112

(0.139) (0.143) (0.131) (0.136) (0.123) (0.196) (0.207) (0.218) (0.183) (0.182)

Controls not

presented:

size, capital stock, sector, manager’s age and education, firm age, a dummy for whether or not the firm uses electricity, and investment climate variables

B – “Demeaned” Constraints - Ordered probit

The demeaned measure of severity of the constraints is the reported value of each constraint subtracted by the average constraint

severity for each individual (average of the 10 constraint categories).

The demeaned measure of severity of the constraints is the reported value of each constraint subtracted by the

average constraint severity for each individual (average of the 10 constraint categories).

electricity finance transpot market public utilities

labor land governance registration taxes

coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se

Bangladesh Male 0.128* -0.301 -0.134** 0.059 0.000 -0.045 -0.056 0.512 -0.040* -0.071

(0.242) (0.198) (0.176) (0.270) (0.204) (0.197) (0.252) (0.191) (0.200) (0.220)

Ethiopia Male -0.391* -0.250 0.816** -0.033 -0.184 -0.215 -0.395 -0.163 -0.542* -0.168

(0.219) (0.356) (0.366) (0.271) (0.394) (0.274) (0.280) (0.279) (0.291) (0.241)

Indonesia Male -0.085 0.040 -0.012 0.039 -0.159* 0.043 0.176* 0.032 0.074 0.137

(0.116) (0.091) (0.114) (0.114) (0.085) (0.106) (0.092) (0.103) (0.096) (0.113)

Sri Lanka Male 0.073 -0.093 0.004 -0.142 0.139 -0.205* -0.176 -0.284*** -0.216* -0.276**

(0.103) (0.110) (0.094) (0.103) (0.095) (0.110) (0.124) (0.107) (0.111) (0.115)

Controls not

presented:

size, capital stock, sector, manager’s age and education, firm age, a dummy for whether or not the firm uses electricity, and investment climate variables

note: *** p<0.01, ** p<0.05, * p<0.1

49

Appendix C: Additional Robustness Checks

C1: Productivity

As an additional robustness check we examine whether there might be ender differences in the

impact of the investment climate on productivity or gender differences in the returns to human

capital and firm characteristics. To investigate this possibility we interact our total factor productivity

term with a dummy for a female entrepreneur, Taking-logs and adding these gender interactions

and an error term, v, our most general estimating equation becomes:23

Under the null hypotheses of no differences in returns, all the gender interaction terms

should be equal to zero ( . There are various alternative

hypotheses. For example, women may have lower returns to schooling. In this case we would expect

the interaction terms between the gender dummy and our proxies to be negative. Alternatively,

women may benefit less from access to finance, in which case we would expect the coefficient on

the gender*credit institution interaction to be significant.

Results are presented in Table C1. Gender interaction terms add very little explanatory

power. Moreover, with the exception of Bangladesh, where the sample of female firms is very small,

gender interaction terms are not in general statically significant, either individually or jointly.

Nonetheless, there is some evidence that female enterprises that are household based in Sri Lanka

are significantly more productive than male firms and that there is a positive correlation between

local wages and the productivity of male firms, but not that of female firms. In addition, age

productivity profiles for female entrepreneurs in Indonesia may be different from those of their

male counterparts. However some differences are to be expected with so many interactions terms

and, moreover, few differences are qualitatively similar in all countries under considerations. Overall

then, the evidence for the hypotheses that the returns to human capital and the impact of

investment climate factors varies by gender is limited.

23 In the appendix, we also present models where we interact factor shares with the gender dummy. However,

we had to pool the data as the number of observations did not us to account for gender heterogeneity in

technology by sector.

50

Table C1: Production function robustness check

Production functions – Robustness Check

Bangladesh Ethiopia Indonesia Sri Lanka

coef se coef se coef se coef se

manuflnKd 0.116*** (0.022) 0.100* (0.052) 0.021 (0.038) -0.023 (0.059) manuflnLft 0.187*** (0.027) 0.461*** (0.138) 0.368*** (0.123) 0.397*** (0.104)

manuflnMd 0.655*** (0.026) 0.436*** (0.095) 0.545*** (0.092) 0.749*** (0.057)

servlnKd 0.135*** (0.019) 0.152 (0.094) 0.068*** (0.021) 0.231*** (0.066)

servlnLft 0.340*** (0.043) 0.744* (0.435) 0.248*** (0.076) 0.173 (0.184)

servlnMd 0.501*** (0.020) 0.227** (0.105) 0.543*** (0.037) 0.593*** (0.066)

tradelnKd 0.024 (0.022) 0.059 (0.071) 0.048*** (0.015) 0.034 (0.040)

TradelnLft 0.138*** (0.038) 0.624*** (0.175) 0.047 (0.042) 0.139 (0.099)

tradelnMd 0.824*** (0.031) 0.440*** (0.115) 0.748*** (0.033) 0.865*** (0.030)

services 0.929*** (0.230) 0.904* (0.514) -0.283 (0.860) -0.539 (0.722)

trade -0.768*** (0.253) 0.283 (0.719) -1.669* (0.854) -1.208* (0.720)

hh ent -0.204*** (0.049)

-0.280*** (0.100) -0.239** (0.097)

manager age 0.001 (0.005) 0.012 (0.053) -0.027 (0.022) 0.009 (0.011)

mngrage2_100 -0.125 (0.487) -0.028 (0.048) 0.028 (0.023) -0.022 (0.030)

manager primary 0.086*** (0.033) -0.750*** (0.238) -0.172 (0.114) -0.106 (0.217)

manager secondary 0.056 (0.038) -0.780 (0.775) -0.016 (0.162) -0.091 (0.200)

manager tertiary 0.123*** (0.043)

0.204 (0.191) -0.193 (0.241)

electricity usage 0.062* (0.035) 0.566 (0.566) 0.081 (0.084) -0.165 (0.108)

lnfirmage 0.032*** (0.010) 0.012 (0.112) 0.133*** (0.045) 0.065 (0.044)

town 0.014 (0.025) 0.793** (0.372) 0.071 (0.078) lndistmarket -0.037 (0.029) 0.119 (0.171) -0.000 (0.054) 0.007 (0.051)

bank -0.032 (0.027) -0.079 (0.288) -0.035 (0.089) 0.001 (0.100)

localwage 0.081 (0.074) 0.305 (0.235) 0.099 (0.069) 0.323* (0.164)

Fem*hhe 0.155 (0.286)

0.187 (0.150) 0.318** (0.136)

Fem*mngrage -0.057 (0.049) -0.010 (0.059) 0.044 (0.033) 0.020 (0.036)

Fem*mngrage2_100

5.834 (6.368) 0.014 (0.057) -0.057 (0.035) -0.075 (0.120)

Fem*primary -0.722*** (0.208) 0.353 (0.470) 0.135 (0.187) 0.236 (0.264)

Fem*secondary -0.733** (0.319) 1.270 (0.855) -0.004 (0.218) 0.413 (0.321)

Fem*tertiary -0.771** (0.343)

0.114 (0.351) 0.653 (0.591)

Fem*Electricity Usage

0.407 (0.273) -0.102 (0.597) -0.194 (0.130) 0.236 (0.180)

Fem*lnfirmage -0.153 (0.116) 0.096 (0.147) -0.041 (0.061) 0.009 (0.072)

Fem*town 0.315* (0.170) -0.512 (0.507) -0.041 (0.133)

Fem*lndistmarket -0.244 (0.181) -0.149 (0.216) 0.013 (0.062) 0.059 (0.101)

Fem*bank -0.322** (0.136) 0.490 (0.452) 0.052 (0.145) 0.158 (0.113)

Fem*localwage 0.177** (0.081) -0.090 (0.165) -0.057 (0.050) -0.079** (0.034)

F TESTS – CRS CRS-Manufacturing

p>F 0.0859

0.9830

0.6235 0.0692

CRS-Services p>F 0.5221

0.7830

0.0349 0.9858

CRS-Trade p>F 0.6283

0.5688

0.0001 0.7171

F TESTS –Female

interactions

Schooling p>F 0.0074

0.2779

0.7016 0.5458

Age p>F 0.2079

0.8890

0.0758 0.8229

Investment

Climate

p>F 0.0033

0.7060

0.8132 0.0395

Other Firm

characteristics

p>F 0.4736

0.8029

0.0489 0.1313

All

p>F 0.0000

0.0279

0.0098 0.2597

N 1993

408

1,229 942 R2 0.966

0.534

0.817 0.827

Adjusted R2 0.966

0.495

0.812 0.821 note: *** p<0.01, ** p<0.05, * p<0.1

51

C2: Investment and Growth

Analogous robustness checks are conducted for our investment and growth models. That is, we

interact the explanatory variables with a dummy for the gender of the manager to see how their

impact varies with the gender of the manager. Thus the models become:

And

The null hypothesis is that the determinants of investment and growth do not vary with the gender

of the manager (i.e. that and

.

Results are presented in tables C2 and C3 and demonstrate that the overwhelming majority of

gender interaction terms are insignificant, and moreover, that few gender interactions consistently

have the same sign in different countries, although it is of interest to note that the interaction

between a presence of a credit institution and being managed by a female entrepreneur is

consistently negative in the investment probit, suggesting that women may benefit less from

proximity to credit providers. In addition, there appear to be gender differences in the age-growth

profiles in Bangladesh and Sri Lanka. Overall, however, gender differences are not systematic.

52

Table C2: Investment Robustness checks

Investment Robustness check

Bangladesh Ethiopia Indonesia Sri Lanka

coef se coef se coef se coef se

lnLftlag 0.205** (0.097) 0.184 (0.123) 0.251** (0.112) 0.070 (0.105) services 0.030 (0.189) 0.291 (0.376) -0.612* (0.332) -0.316* (0.187) trade -0.399** (0.175) -0.030 (0.295) -0.194 (0.329) -0.130 (0.155) hh ent -0.325 (0.230)

-0.048 (0.322) 0.041 (0.161)

manager age -0.019 (0.033) -0.042 (0.045) 0.093 (0.058) -0.006 (0.017) mngrage2_100 1.829 (3.835) 0.034 (0.045) -0.100 (0.062) 0.015 (0.053) manager primary -0.013 (0.218) 0.140 (0.221) -0.610 (0.470) 0.083 (0.653) manager secondary -0.081 (0.173) 0.308 (0.364) -0.097 (0.408) 0.153 (0.657) manager tertiary -0.033 (0.407)

-0.708 (0.610) -0.069 (0.763)

electricity usage 0.473** (0.213) 0.916*** (0.226) 0.130 (0.299) 0.198 (0.141) lnfirmage 0.000 (0.078) 0.281** (0.112) -0.161 (0.171) -0.158*** (0.058) town 0.012 (0.148) -0.340 (0.408) 0.578* (0.298)

lndistmarket 0.470*** (0.166) -0.263 (0.204) 0.060 (0.130) 0.066 (0.094) bank 0.301* (0.166) -0.212 (0.297) 0.457 (0.338) 0.136 (0.168) localwage -0.526* (0.302) 0.013 (0.278) 0.526** (0.250) -1.559*** (0.339) Fem*lnLftlag -0.844 (0.749) -0.201 (0.206) -0.035 (0.213) -0.181 (0.278) Fem* Serv -2.331 (1.748) 0.027 (0.477) -0.151 (0.709) 0.132 (0.416) Fem*trade

0.182 (0.391) -0.252 (0.704) 0.486 (0.319)

Fem*hhe -3.594 (2.259)

-0.169 (0.651) 0.140 (0.309) Fem*mngrage 0.068 (0.148) 0.117* (0.063) 0.058 (0.093) 0.067 (0.052) Fem*mngrage2_100 -12.161 (19.048) -0.115* (0.062) -0.084 (0.105) -0.179 (0.204) Fem*primary -0.945 (0.774) 0.055 (0.646) 0.804 (0.632) -1.204 (0.912) Fem*secondary -0.221 (0.842) 0.401 (0.564) 0.687 (0.636) -1.216 (0.991) Fem*tertiary

1.272 (0.935) 0.545 (1.220) Fem*Electricity Usage -1.131 (0.744) 0.778 (0.645) 0.329 (0.360) -0.012 (0.338) Fem*lnfirmage 0.708* (0.423) -0.272* (0.150) 0.371 (0.307) -0.042 (0.119) Fem*town 0.796 (0.657) 0.673 (0.588) -0.633 (0.394) Fem*lndistmarket 0.764 (0.594) 0.239 (0.176) -0.018 (0.147) -0.543*** (0.180) Fem*bank

-0.746* (0.448) -0.836* (0.492) -0.287 (0.282)

Fem*localwage 0.149 (0.379) -0.367* (0.208) -0.054 (0.178) 0.180* (0.098)

N 1,834

574

697 993

Pseudo R2 0.102

0.133

0.188 0.094

note: *** p<0.01, ** p<0.05, * p<0.1

53

Table C3: Growth Robustness Check

Growth – Robustness Check

Bangladesh Ethiopia Indonesia Sri Lanka

coef se coef se coef se coef se

lnLftlag -0.048*** (0.013) -0.141*** (0.042) -0.026 (0.051) -0.019 (0.013) serv -0.060*** (0.021) -0.113** (0.051) 0.079 (0.059) -0.025 (0.021)

trade -0.100*** (0.024) -0.099 (0.077) 0.139** (0.062) -0.014 (0.017)

hh ent -0.041** (0.020)

-0.111 (0.091) 0.004 (0.014)

manager age 0.001 (0.002) 0.013 (0.011) 0.029* (0.015) -0.004** (0.001)

mngrage2_100 -0.095 (0.223) -0.013 (0.012) -0.026* (0.015) 0.008** (0.004)

manager primary 0.005 (0.014) 0.066 (0.059) -0.010 (0.046) -0.028 (0.023)

manager secondary -0.001 (0.015) 0.156* (0.089) 0.000 (0.057) -0.019 (0.020)

manager tertiary 0.057* (0.033)

0.017 (0.094) -0.024 (0.027)

electricity usage 0.030** (0.013) 0.128 (0.095) 0.104* (0.058) 0.003 (0.013)

lnfirmage -0.015*** (0.006) -0.022 (0.024) 0.030 (0.037) 0.002 (0.006)

town -0.020* (0.012) -0.039 (0.089) 0.061 (0.044) lndistmarket 0.017* (0.009) -0.072* (0.042) 0.009 (0.020) -0.012 (0.007)

bank 0.009 (0.009) -0.038 (0.060) 0.024 (0.052) 0.006 (0.011)

localwage 0.043*** (0.016) -0.029 (0.052) 0.010 (0.033) 0.031 (0.022)

Fem*lnLftlag 0.025 (0.031) -0.036 (0.066) -0.050 (0.063) 0.000 (0.025)

Fem* Serv 0.123*** (0.046) 0.132 (0.085) -0.343** (0.172) -0.016 (0.037)

Fem*trade 0.182*** (0.066) 0.051 (0.093) -0.409** (0.166) -0.031 (0.035)

Fem*hhe 0.126*** (0.045)

0.056 (0.105) -0.009 (0.022)

Fem*mngrage 0.009 (0.006) -0.005 (0.014) -0.026 (0.018) 0.006** (0.002)

Fem*mngrage2_100

-1.191* (0.693) 0.005 (0.017) 0.020 (0.017) -0.016** (0.008)

Fem*primary 0.027 (0.039) -0.085 (0.174) -0.010 (0.066) 0.024 (0.041)

Fem*secondary -0.010 (0.032) -0.046 (0.156) 0.054 (0.083) 0.034 (0.031)

Fem*tertiary 0.077 (0.064)

0.003 (0.149) 0.068 (0.053)

Fem*Electricity Usage

-0.072*** (0.026) -0.161 (0.167) 0.058 (0.090) 0.023 (0.028)

Fem*lnfirmage -0.003 (0.014) 0.012 (0.038) 0.006 (0.071) -0.013 (0.011)

Fem*town 0.057*** (0.020) 0.245** (0.105) -0.067 (0.066) Fem*lndistmarket 0.008 (0.025) 0.076 (0.052) 0.047 (0.031) 0.013 (0.008)

Fem*bank -0.051 (0.039) 0.078 (0.098) -0.006 (0.097) 0.011 (0.024)

Fem*localwage -0.032*** (0.010) -0.031 (0.035) 0.063* (0.033) -0.004 (0.004)

N 1,855

636

697 993

R2 0.071

0.193

0.179 0.058

Adjusted R2 0.056

0.158

0.142 0.030

note: *** p<0.01, ** p<0.05, * p<0.1