Entrepreneurship as the safe option:Evidence from India∗

Geoffrey Borchhardt†

Yale UniversityOlav Sorenson‡

University of California, Los Angeles

March 2022

ABSTRACT: We examine the returns to entrepreneurship in India. We find that

entrepreneurs, on average, earn more than they would expect in paid employment.

Except among the most highly educated, that positive effect holds for every subgroup

of entrepreneurs, from the growth-oriented to the self-employed offering personal

services. Further analysis reveals that this positive effect appears to stem almost

entirely from the fact that entrepreneurs in India have more stable income streams,

they have fewer months with no income. Entrepreneurship may therefore represent

a safer option for these individuals.

∗Tehmine Baghdasaryan provided excellent research assistance. We thank Thomas Åstebro,Tristan Botelho, Natalie Carlson, Melody Chang, and Aruna Ranganathan for comments on earlierversions of the paper.

†165 Whitney Ave., New Haven, CT, 06511; geoffrey.borchhardt@yale.edu‡110 Westwood Plaza, Los Angeles, CA, 90095; olav.sorenson@anderson.ucla.edu

Introduction

Most research and public discourse views entrepreneurship as a risky undertaking.

For good reason. In the United States, more than 20% of businesses fail within

their first year (BLS, 2021). Only half last more than five years. Entrepreneurs

also have higher variance in their incomes than do those in paid employment: A

few earn much more than they could expect as employees, but the majority earns

less (e.g., Hamilton, 2000; Moskowitz and Vissing-Jørgensen, 2002; Tergiman, 2011).

The decision to become an entrepreneur has therefore often been seen as stemming

either from overconfidence or from a preference for extreme outcomes (e.g., Cooper,

Woo, and Dunkelberg, 1988; Moskowitz and Vissing-Jørgensen, 2002), or as driven

by the non-pecuniary rewards of building a company or being one’s own boss (e.g.,

Benz and Frey, 2008; Hamilton, 2000).

Whether entering self-employment represents a risky choice, however, depends

on the options available to the person. Institutional and national contexts, as well

as individual considerations, both define choice sets and influence the relative at-

tractiveness of entering entrepreneurship (Hwang and Phillips, 2020; Sørensen and

Sharkey, 2014; Thébaud, 2015). Yet, in the literature on entrepreneurial earnings,

nearly all research treats steady employment as the alternative to self-employment.

Exceptions exist. Studies of the longer-term career consequences of entrepreneur-

ship often incorporate future spells of unemployment as outcomes (Manso, 2016;

Merida and Rocha, 2021). But even these studies typically compare the outcomes

for entrepreneurs to similar others who initially held stable jobs.

In the modern era in high-income countries that assumption seems fair. Many, if

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not most, entrepreneurs could probably find consistent employment if they preferred

it. But in many contexts, and for some people, the alternative to self-employment

might not be a stable job but rather intermittent employment. In low- and lower-

middle income economies, for example, a large proportion of people do casual daily

wage work (Valenzuela, 2003). They have no employment contracts. They do not

know if, or how much, they might earn the next day, let alone over the course of a

week or a month.

When people do not have steady paid employment as an option, the calculus

for whether or not to become an entrepreneur changes. Instead of being the riskier

option, it may be the safer one. Although the earnings from their efforts remain

uncertain, self-employment ensures that individuals can at least work every day.

It may therefore provide more stable income than paid employment. Scholars of

entrepreneurship have long recognized that “subsistence” entrepreneurs might engage

in self-employment as an alternative to unemployment but entrepreneurship might

also be more attractive than precarious employment.

We focus on India as a context for examining the returns to entrepreneurship

where many do not have stable employment as an option. More than 60% of Indi-

ans in paid employment engage in casual work (ILO, 2018). We use a nationally-

representative, longitudinal survey, from 2017 through 2019, to estimate the short-

term returns to entrepreneurship. To account for systematic differences in who

chooses to become an entrepreneur, we estimate the earnings differentials using

matching estimators. We also explore heterogeneity in the returns to entrepreneurs.

We use monthly information on income to assess the stability of income streams.

2

We find that entrepreneurs report larger incomes than similar others in paid

employment. They also earn more than they did as paid employees prior to their

transition. In contrast to most high-income countries, this premium holds for nearly

every subgroup of entrepreneurs, not just those founding high-growth firms but also

those engaging in agriculture and personal services. Further exploration of this effect

reveals that it stems almost entirely from the steadier income streams of the self-

employed. Those in paid employment more frequently report months without any

income. Highly-educated entrepreneurs, who might have long-term salaried positions

as alternatives, meanwhile, appear to earn less than their peers in paid employment.

Our findings contribute to the literature in multiple ways. Most directly, we

extend research on the returns to entrepreneurship beyond higher-income countries.

To date, the research in this tradition has primarily focused on Europe and the

United States. More broadly, this shift in contexts highlights some of the assumptions

inherent in the existing research.

For those who face precarious employment, entrepreneurship might not repre-

sent a risky endeavor but rather the safest option available. That fact also has

important implications for policy. Recent discussions have lionized “high-growth”

entrepreneurship. But if low-growth – even no-growth – entrepreneurship offers an

attractive economic alternative for many then policy should encourage it as well.

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Does entrepreneurship pay?

Early research on the returns to entrepreneurship in the United States identified two

important stylized facts: (i) the median entrepreneur earns less than his or her em-

ployed counterparts, and (ii) entrepreneurs also bear more financial risk than employ-

ees (Evans and Leighton, 1989; Hamilton, 2000; Moskowitz and Vissing-Jørgensen,

2002). Analyses from other countries, such as Finland (Hyytinen, Ilmakunnas, and

Toivanen, 2013), Germany (Sorgner, Fritsch, and Kritikos, 2017) and Korea (Åste-

bro, Chen, and Thompson, 2011), have found similar patterns: negative returns to

entrepreneurship at the mean and median but with fatter right-hand tails.1 In other

words, entrepreneurs earn less on average but a select few earn substantially more

than those in paid employment (Hamilton, 2000).

Several streams of subsequent research have sought to explain why people would

become entrepreneurs if they earn less on average while also assuming more financial

risk. One set of papers, for example, has demonstrated that certain subgroups of

entrepreneurs enjoy positive returns. Most notably, those engaged in building orga-

nizations – as opposed to simply being self-employed – tend to earn more (Levine

and Rubinstein, 2017; Sarada, 2020; Sorgner et al., 2017). Those with high levels

of human capital also enjoy higher returns to becoming an entrepreneur (Sorgner

et al., 2017; Van Praag, van Witteloostuijn, and van der Sluis, 2013). These studies

therefore suggest that the negative average returns to entrepreneurship may stem

from a large fraction of individuals entering entrepreneurship not for the financial

gains but for the non-pecuniary rewards.1For an extensive review of the literature on the returns to entrepreneurship, see Åstebro (2012).

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Another set suggests that the negative average returns may stem from a type of

option value. People often do not know whether they might be a good entrepreneur

or whether their ideas have potential. If they do, they could earn a lot of money, they

could become part of that fat right-hand tail. Entry into entrepreneurship therefore

represents an experiment, a test of an idea. Consistent with this view, Manso (2016)

finds that people experiment with entrepreneurship in short spells and quickly return

to paid employment when unsuccessful, thus mitigating the long-term consequences

of any earnings penalty (see also Daly, 2015; Merida and Rocha, 2021).

Yet another set of studies has interpreted the average earnings penalty combined

with a fat right-hand tail – a set of entrepreneurs who become richer than they could

as employees – as evidence of a preference for skewness (e.g., Åstebro, Mata, and

Santos-Pinto, 2015; Moskowitz and Vissing-Jørgensen, 2002). Some individuals, for

example, may place little value on small increases in earnings but would derive a

great deal of satisfaction from a level of wealth that would meaningfully change their

lifestyles (Kahneman and Tversky, 1979).

These explanations all assume a particular context, a wealthy, largely Western,

one. But institutional environments differ immensely across countries. Sub-Saharan

Africa and Southern Asia, for example, have much higher entrepreneurship rates than

the U.S. and other OECD nations (ILO, 2021). Many entrepreneurs open informal

organizations, due to the cost and effort required for registration (Assenova and

Sorenson, 2017). Despite growing interest in entrepreneurship in these places (e.g.,

Assenova, 2020; Carlson, 2021; Delecourt and Fitzpatrick, 2021), little research has

investigated the returns to entrepreneurship outside of wealthy, Western countries.

5

Earnings as a function of options

An emerging literature on a careers perspective on entrepreneurship discusses the

decision to become an entrepreneur as a function of options (Burton, Sørensen, and

Dobrev, 2016; Sørensen and Sharkey, 2014). Individuals decide whether to enter

self-employment based on opportunities at their current employer (Kacperczyk, 2012;

Sørensen and Sharkey, 2014) as well as at other employers in the labor market (Hwang

and Phillips, 2020; Light, 1972; Thébaud, 2015).

The same logic should extend not just to entrepreneurial entry but also to en-

trepreneurial earnings. For people facing different choice sets, the returns to en-

trepreneurship likely vary depending on these options. Formerly incarcerated in-

dividuals, for example, face discrimination in hiring (Pager, 2003). To the extent

that self-employment allows these individuals to use better their abilities, it there-

fore probably also provides better remuneration than they could expect as employees

(Hwang and Phillips, 2020).

Precarious employment

Both options in, and alternatives to, self-employment differ across national and in-

stitutional contexts. We call attention to one specific feature of these environments,

the rarity of stable employment. In Europe, Japan, North America and many other

wealthy places, long-term employment arrangements have been the norm for cen-

turies (Valenzuela, 2003). Even most temporary staffing in high-income countries

occurs through organizations. But in other parts of the world many people cannot

6

count on a steady job (e.g., ILO, 2016). They rarely have employment contracts or

protections (Assenova and Sorenson, 2017). They often must search for jobs on a

daily or seasonal basis.

Even when these people find jobs, those jobs have few benefits. Even the pay may

be uncertain. Agreements are verbal. Employers sometimes renege on them. These

informal employees, moreover, rarely have recourse in the legal system (Assenova

and Sorenson, 2017; ILO, 2016).

In India, for example, the percentage of employees engaging in casual employment

never dropped below 60% between 1983 and 2012 (ILO, 2016). Nearly twice as

many people there than in the United States work part-time. In Bangladesh, the

casual employment number also hovers around 60%. By contrast, the European

countries with the highest shares of casual employment, Spain and Poland, have

rates of roughly one-third that.

This feature of employment in these countries affects the returns to entrepreneur-

ship in multiple ways. Lower- and middle-income economies, for example, have

higher shares of subsistence entrepreneurship. Scarce job opportunities and pre-

carious employment force people into self-employment and into starting (informal)

micro-enterprises with few, if any, employees. These businesses tend to have limited

prospects for growth (Schoar, 2010). They often do not have adequate capital. They

frequently engage in the production of commodities or the provision of undifferenti-

ated services, with little if any returns to scale.

These informal subsistence businesses therefore generate little in the way of prof-

its. They perform worse than similar formally-registered firms (Assenova and Soren-

7

son, 2017). They also earn less than entrepreneurs engaged in capitalizing on an

opportunity (Donovan, 2014), in doing something different. Given the prevalence

of subsistence entrepreneurship in these places, entrepreneurs in low- and middle-

income economies might earn less, on average, than they could in paid employment.

The comparatively worse performance by micro-enterprises in lower-income coun-

tries would matter little, however, if these entrepreneurs had only unemployment

or underemployment as alternatives. Self-employment might then represent an op-

portunity for a steadier income than an individual could earn in paid, but casual,

employment. These informal micro-enterprises often require little in the way of cap-

ital or resources. Entrepreneurs might produce home-made goods by hand. Those

involved in resale or retail might have a push-cart or stall but they might also simply

display goods on the ground. Or, they may provide personal services.

Importantly, these micro-enterprises, even when informal, allow those engaged in

them to avoid some of the precarity of the labor market. Although the sales of these

goods and services – and therefore business income – may vary, these entrepreneurs

can control whether and how much they work. They also need not worry about

employers withholding pay.

These benefits, however, may only appear when considering longer time frames.

On an hour-by-hour, or even day-by-day, basis, the self-employed might earn less than

their employed counterparts. Comparing hourly wages in paid employment to the

equivalent in self-employment provides little basis on which to determine which might

represent the financially-preferable option without also considering how many hours

the person can work. Over longer periods of time, the option value inherent in being

8

able to work longer and on any given day becomes apparent (cf. Manso, 2016; Merida

and Rocha, 2021). Even understanding the short-term returns to entrepreneurship

therefore requires comparing whether somebody who enters self-employment today

ends up being better off over the next few weeks or months than they would have

been if they had started from paid employment.

In settings where people primarily have precarious employment as an alterna-

tive to entrepreneurship, we expect that steadier incomes for the self-employed will

translate into positive returns to entrepreneurship. To offset the precarity of em-

ployment, the employed would need to earn much more per hour or day than the self

employed. However, given the large number of people in casual employment and the

absence of enforceable minimum wages (e.g., ILO, 2018), the competition for these

jobs probably keeps pay low in these places.

Heterogeneity across entrepreneurs

Our argument largely has in mind entrepreneurship from those not only in lower-

income economies but also in lower socioeconomic strata within these countries.

Much of the recent literature on the returns to entrepreneurship, however, has focused

on differentiating the returns across subgroups of the self-employed. The consistent

finding has been that highly-educated entrepreneurs and those employing others

earn premiums as entrepreneurs, even though the average entrepreneur experiences

an earnings penalty (e.g., Levine and Rubinstein, 2017; Sorgner et al., 2017).

We can similarly distinguish entrepreneurs in terms of who faces the most pre-

carity in their alternative employment. Different people face different choice sets.

9

Most lower-income countries have entrepreneurs in at least three meaningful cat-

egories: those who run businesses with employees and that may have potential to

expand, those engaged in farming and other forms of agricultural production, and the

self-employed producing simple goods or offering services. The last group includes

small shop owners, taxi-drivers, as well as a wide variety of, mostly informal, micro-

enterprises. Our argument regarding the importance of precarious and intermittent

employment as an alternative applies most obviously to this group.

Growth entrepreneurs. In the United States and other high-income countries,

this category of entrepreneurs tends to be better educated than the rest of the self-

employed (Levine and Rubinstein, 2017). Assuming that the same pattern holds in

lower-income countries, we might expect these entrepreneurs to have more stable

and more attractive alternatives in paid employment. The alternative to founding

a firm might be permanent, salaried employment. We might then expect that these

entrepreneurs would benefit less from entrepreneurship than others.

Even within this group, however, entrepreneurship might still provide a path to

steadier incomes. Economies with high rates of casual work may not have enough

permanent positions even for the well educated. India, for example, has casual work

rates in excess of 60%. Over the last 30 years, however, enrollment in secondary

education has risen from below 40% to more than 70%, and tertiary education has

exploded from 6% to 30% (UNESCO, 2021). The expansion of large, formal employ-

ers may not have kept pace with this rapid rise in human capital, potentially leaving

many of the well educated wanting for stable jobs.

This subgroup may also experience positive returns to entrepreneurship for other

10

reasons as well. Studies from Western nations find positive returns, at the mean and

median, for entrepreneurs opening a business with high growth-potential (Levine

and Rubinstein, 2017; Sorgner et al., 2017). Businesses with growth potential tend

to be formal (registered). The entrepreneurs who found them have high levels of

human capital. These businesses also involve financial capital, whether from the

entrepreneur or external investors. All of these characteristics positively predict

firm performance. They should therefore translate into stronger returns for the

entrepreneur (Assenova and Sorenson, 2017; de Mel, McKenzie, and Woodruff, 2008;

McKenzie and Woodruff, 2017).

Agricultural entrepreneurs. Entrepreneurs in agriculture and farming represent

an important subgroup of the self-employed, one largely ignored in the extant liter-

ature. They account for a sizeable proportion of entrepreneurs in many low-income

economies. This group chiefly consists of people growing crops on small patches of

land – both for sale and for their own consumption. But it also includes a handful

of people who own and operate large farms. Many of those farming small plots have

little, if any, formal education. They therefore have poor prospects in paid employ-

ment. At first blush, self-employment would appear to provide an opportunity for

these individuals to escape intermittent employment.

Several factors, however, complicate this account. First, agricultural production

generally requires land. Those with insufficient land holdings might not have the

ability to work as much as they would want. Second, the seasonal nature of agri-

culture places a second constraint on the ability of these entrepreneurs to work on

a consistent basis. Third, farming these plots of land generally means residing in a

11

rural area. Those regions might have even fewer options for paid employment than

other areas.

At the same time, at the other end of the distribution, the larger farms have

employees. The farmers who own them manage premises and invest capital. These

enterprises therefore seem similar to the growth-business category, firms with em-

ployees and the potential to expand.

Setting and Data

We focus on India. Entrepreneurship is ubiquitous: roughly one out of every two

workers works for themselves (ILO, 2018). With more than 200 million self-employed

people, the economic consequences of this self-employment have important implica-

tions for the entire Indian economy.

Those in paid employment meanwhile often have precarious jobs. More than 60%

of them engage in casual work. India has minimum wage laws at the state level, but

these laws allow for numerous exceptions. Even when they should apply, enforcement

appears lax. More than 40% of casual workers earn less than the suggested national

minimum wage floor (ILO, 2018).

We use data from the Consumer Pyramids Household Survey (CPHS), a longi-

tudinal household survey conducted by the Centre for Monitoring the Indian Econ-

omy (CMIE). CMIE surveyors ask respondents a set of questions about their in-

come, expenditures, living conditions, and demographics. CMIE uses a regionally-

stratified sampling approach, modeled after the Indian Census, to collect nationally-

12

representative data. Because of its high quality, researchers have begun to use CPHS

data widely to study employment and income dynamics in India (e.g., Chodorow-

Reich, Gopinath, Mishra, and Narayanan, 2019; Malani and Ramachandran, 2021).

CMIE conducts three waves per household-year.2 Each household gets surveyed

every four months. Each wave includes roughly 160,000 households. We use data

from the early 2017 wave through the last wave in 2019, a total of eight waves.

CMIE only added the question that we use to sort entrepreneurs into subgroups in

2017, meaning that we could not conduct these analyses in earlier waves. We do

not include waves after 2019, meanwhile, to avoid any confounding effects of the

COVID-19 pandemic.

Since we wish to assess the returns to entrepreneurship compared to (possibly

intermittent) employment, we compare the earnings of the self-employed to those of

similar others engaged in paid employment at the time of the survey. Although we do

not include individuals who report being unemployed at the time of the survey, this

inclusion criteria does not eliminate all spells of unemployment. Many individuals

in paid employment experience short spells of unemployment between survey waves

(see also, Sundar, 2011). Our analyses include 280,066 employed and self-employed

individuals living in 162,605 households. We have five waves of data for the median

person in this subset.2CMIE teams operate on a continuously-rotating basis. A team in a region conducts one wave

for roughly four months, then they return to where they started. Surveys within a wave, therefore,may not cover exactly the same calendar months.

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Measures

Earnings. Our dependent variable is the logged total monthly income, in Indian

rupees, received by an individual. This measure captures all income from paid em-

ployment, as well as dividends and earned interest.3

For any businesses owned by a household, we also included business profits in

this measure. The CPHS records income from owned businesses at the household

level. In cases where more than one individual in a household reports being self-

employed, we divide these business profits evenly across all self-employed members

of the household.

We lag our income variable such that our estimate of the effect of self-employment

status at any point in time captures its relationship to income over the subsequent

four months. Because we do not update the self-employment status measure be-

tween waves, our estimate captures the short-term consequences of self-employment,

whether those consequences stem directly from self-employed income, from differ-

ences in the probability of not earning an income, or from the effects of entrepreneur-

ship on future prospects for paid employment.

Self-employment. We use two measures to identify spells of entrepreneurship.

For the first – all self-employed – we simply use the CPHS employment status vari-

able. Using a question similar to the U.S. Current Population Survey, this vari-

able identifies each individual as either employed, unemployed, or self-employed.

(Chodorow-Reich et al., 2019).3Our results remain robust to removing earnings from dividends and interest from this measure.

14

For the second, we use data on respondents’ occupations to distinguish between

subgroups of the self-employed. First, we identify those who are self-employed in the

agricultural sector. Although prior research has generally excluded the agricultural

sector, farming and agriculture account for a large share of the Indian economy.

Roughly one-third of the entrepreneurs in our sample fall in this category.

We then identify those entrepreneurs who have invested capital into their busi-

nesses, those more likely to have employees and opportunities to expand. We use the

CMIE’s occupational category of “businessman” – defined as “a person who owns

and runs a proprietorship concern or is a partner in a partnership concern” – to dis-

tinguish these entrepreneurs from the rest of the self-employed. Owners of limited

liability companies, for example, would fall into this group. Entrepreneurs in this

category operate from fixed premises, have invested capital, and employ others.

We allocate the rest of the self-employed to an other self-employment category.

This group includes those engaged in producing goods by hand, those providing a

variety of personal services, and those selling without fixed premises (e.g., roadside

carts). At the other end of the socioeconomic spectrum, however, this group also

includes self-employed professionals, such as doctors and lawyers.4

4Although one might worry that self-employed professionals differ on many important dimen-sions from the others in this category, their inclusion here does not have a large effect on our results.These individuals only account for roughly 2% of this category. Once we move to the matchingestimators, moreover, we effectively compare these self-employed professionals to professionals inpaid employment. When we include an indicator variable to see whether they differ on average fromthe others in this category, they do seem to reap larger returns to entrepreneurship than the restof the category. However, separating out self-employed professionals from the rest of the categorydoes not meaningfully change the effect size of the other self-employment coefficient.

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Returns to Entrepreneurship

Table 1 reports person-wave summary statistics for the paid employees and en-

trepreneurs included in our analyses. Although only 44% of the person-wave ob-

servations in our data involve spells of self-employment, 60% of respondents enter

self-employment at least once during our observation window. A large number of

individuals therefore move in and out of self-employment.

The average spell of entrepreneurship lasts 2.9 waves (out of a maximum of

seven when disregarding the first wave in our sample to identify new spells). For

comparison, salaried employees in the sample also have mean spell length of 2.9

waves, while those paid on an hourly or daily basis have a mean of 2.6 waves.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Table 1 about here

- - - - - - - - - - - - - - - - - - - - - - - -

On average, the self-employed are a little older. The sample skews heavily toward

men, even more so among the self-employed. Employees, meanwhile, have higher

levels of education, on average, are more likely to live in cities, and more commonly

come from privileged castes. We adjust for these differences below by matching on

a number of demographic dimensions.

We begin by comparing the mean and median income of entrepreneurs to those in

paid employment. Table 2 reports raw and logged income (in rupees) by employment

status. The average entrepreneur earns about 8% more than the average employee

per month. In all subgroups of the self-employed, the median individual earns more

than the median employee, though agricultural and other entrepreneurs have lower

16

mean earnings. The self-employed, as well as every subgroup, have higher logged

earnings at both the mean and the median.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Table 2 about here

- - - - - - - - - - - - - - - - - - - - - - - -

Figure 1, the income density distribution, helps us to understand the differ-

ences between the raw and the logged central tendencies. The mass of the distri-

bution of earnings for growth entrepreneurs appears well to the right of those in

paid employment. The typical growth entrepreneur earns substantially more than

the typical employee. However, the distributions for those in agricultural and other

self-employment cross the distribution for paid employees. Most people in these sub-

groups earn a little more than most people in paid employment, but a larger share

of paid employees earn more than 28,000 rupees per month.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Figure 1 about here

- - - - - - - - - - - - - - - - - - - - - - - -

Matching-based estimates

We can see in Table 1 that those who engage in entrepreneurship differ on multiple

dimensions from those in paid employment. The average differences in earnings

between the employed and self-employed therefore may confound returns to these

characteristics with the effects of self-employment. For example, if older individuals

earn more, then the older average age of entrepreneurs might account for some of

17

the observed difference in earnings between the two groups. To adjust for these

differences, we begin by estimating the returns to entrepreneurship within sets of

similar individuals.

We use coarsened exact matching (CEM). Matching strategies estimate the av-

erage treatment effect on the treated (ATT)—that is, the effect for that part of

the population that enters self-employment. Coarsened exact matching compares

two individuals – one self-employed, the other in paid employment – who have the

same combination of observable characteristics. By design, CEM ensures balance,

that both groups have equivalent distributions on all of the characteristics used for

matching (Iacus, King, and Porro, 2012).

- - - - - - - - - - - - - - - - - - - - - - - -Insert Table 3 about here

- - - - - - - - - - - - - - - - - - - - - - - -

Table 3 reports linear regression estimates of the returns to entrepreneurship.

For comparison, the first two columns report results without matching. Both as

a whole and within each subgroup, the self-employed earn more than employees.

On average, the self-employed earn about 56% (= exp0.447) more over a period of

four months. The subgroup coefficients indicate that, much as in Western countries,

growth entrepreneurs reap the largest returns. They earn over 200% more than the

average paid employee.

In the next two columns, we begin by matching on gender, age, education, and

survey wave. For age and education, we split these continuous variables into six

groups (coarsening them).5 Adjusting for these characteristics reduces the estimated5We coarsened age into <25, 26-35, 36-45, 46-55, 56-65, and >65. We coarsened education to

18

returns to entrepreneurship by roughly 40% in every subgroup. Those in demo-

graphic groups that earned more had a higher probability of becoming entrepreneurs.

Adjusting for these characteristics therefore reduces the estimated returns associated

with self-employment.

The next six columns incorporate matching on more and more factors. Models

5 and 6 match on the state of residence and whether the person lives in an urban

or rural area. Matching on these factors generally increases the estimated returns

to entrepreneurship. However, the effect of this matching varies across subgroups.

Entrepreneurs in the agricultural sector, for example, appear to enjoy substantially

larger returns when compared to others in rural areas. Rural areas, where most

of these entrepreneurs live, presumably have less attractive opportunities for paid

employment. By contrast, matching on these factors actually reduces the estimated

returns to growth entrepreneurs. Most of those individuals reside in urban areas

with more employment options.

Models 7 and 8 add matching on caste and religion. These characteristics appear

to have relatively small effects on the estimated returns to entrepreneurship.6

Models 9 and 10 finally match on whether the individual had been employed,

self-employed, or unemployed in the previous wave. These models therefore esti-

less than lower primary, primary but less than secondary, some secondary, completed secondary(equivalent to high school degree), undergraduate degree, and post-graduate degree. We arrived atthese cut points based on the distributions of age and education, our understanding of the Indianeducation system, and the relationships of age and education to income for the employed. Figure4 explores the sensitivity of the results to these coarsening choices.

6In unreported models, we also explored whether the returns to entrepreneurship might vary bycaste. Although they still earn more as entrepreneurs than as employees, those from scheduled castesand tribes – the most disadvantaged group – appear to experience lower returns to entrepreneurshipthan other castes.

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mate the returns to “switchers”—those who either moved from paid employment (or

unemployment) to self-employment or from self-employment (or unemployment) to

paid employment.

Matching on past employment status generates much larger estimates of the

returns to entrepreneurship. We, however, interpret these larger effects with caution.

On the one hand, switchers might be even more similar to those who remained in

employment, providing a better estimate of returns.

But focusing on switchers could also lead to biased estimates of the average earn-

ings differential for at least two reasons. First, these estimates focus on differences

in initial earnings. If paid employment and entrepreneurship differ in their earnings

trajectories, as has been found in other settings, then conditioning on the prior state

would bias the results. To explore this possibility, we interact our self-employment

measure with indicators for the length of the employment spell to investigate whether

the premiums associated with being an entrepreneur change with job tenure (using

the matching approach in Models 7 and 8). Although we consistently find positive

returns to entrepreneurship, the effect sizes abate with time (see Figure 2).

- - - - - - - - - - - - - - - - - - - - - - - -Insert Figure 2 about here

- - - - - - - - - - - - - - - - - - - - - - - -

Second, spells of low-paying employment might increase the probability that a

person becomes an entrepreneur (cf. Hyytinen et al., 2013). If entrepreneurs earned

less as employees than would have been expected given their observable character-

istics, then self-employment might provide more of a boost in earnings relative to

20

that low baseline. The data support this idea as well. Table 4 reports linear prob-

ability models predicting entry into self-employment among those currently in paid

employment. Those with lower incomes more frequently become entrepreneurs. The

income coefficient remains negative, moreover, when comparing similar people and

in models with individual-level fixed effects.

Given these selection effects and trajectory differences, we consider the matching

models that do not condition on the prior state, Models 7 and 8, better estimates of

the average returns to entrepreneurship than Models 9 and 10.7

- - - - - - - - - - - - - - - - - - - - - - - -Insert Table 4 about here

- - - - - - - - - - - - - - - - - - - - - - - -

To assess the robustness of the results to some of our estimation choices, we esti-

mated a series of models, matching on different sets of observable variables and with

varying degrees of coarseness for age and education.8 Figure 4 displays coefficient

plots for all of the different matching specifications tested, with the coefficients sorted

by magnitude. Across all of these specifications, we find large, positive returns to

entrepreneurship. Growth entrepreneurs consistently experience the highest returns

to entrepreneurship. The coefficients for returns to other self-employment display the7These same issues arise in estimates based on individual fixed effects. Because only those who

have spells of both paid employment and entrepreneurship contribute to the fixed effects estimates,they too effectively estimate off of switchers. For those interested, we report models with individual-level fixed effects in the Appendix.

8Aside from the six age and education groups used in CEM estimates above, we coarsen thetwo variables into four broad groups and match on them exactly, in respective specifications. Foreach level of coarseness we proceed by successively matching on more variables as in Table 3, i.e.,first we match on age, education, gender, and the survey wave, then add the state of residence andwhether the region is rural, and lastly add the respondents religion and caste.

21

most consistent effect size, while different matching specifications provide a slightly

wider range of estimates for agricultural entrepreneurs.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Figure 4 about here

- - - - - - - - - - - - - - - - - - - - - - - -

Quantile regressions

Figure 1 revealed that employees actually have fatter right-hand tails in the distri-

bution of earnings than agricultural and other entrepreneurs. Although these raw

distributions do not adjust for human capital and other factors that might influ-

ence earnings, they raise the possibility that even if the average person earns less

in employment, some range of people may earn more. We therefore estimate quan-

tile regressions to characterize the returns to entrepreneurship at the median and at

different points along the earnings distribution.

We estimate the quantile regressions using our matching estimator (using the

same approach as Models 7 and 8 from Table 3). We include the equivalent of case-

control fixed effects by manually demeaning our variables, following the approach

of Levine and Rubinstein (2017). The quantile regressions use a k:k matching ratio

instead of a weighted 1:m matching ratio to eliminate the need for incorporating

weights in the estimation.

Figure 5 plots the effect sizes of the quantile regression coefficients across the in-

come distribution. This analysis reveals two important results. First, we continue to

find evidence for positive returns to entrepreneurship across all subgroups at the me-

dian, strengthening our confidence in entrepreneurship as the financially safer option

22

for the typical person. However, the effect sizes at the median are much smaller than

at the mean. They correspond to entrepreneurship premiums of 7% for agricultural

entrepreneurs, 3% for other entrepreneurs, and 31% for growth entrepreneurs.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Figure 5 about here

- - - - - - - - - - - - - - - - - - - - - - - -

Second, rather than stemming from a fat right-hand tail, the large effect sizes

in our earlier models appear to come from the bottom of the income distribution.

We find extremely large, positive effects for all subgroups at the 10th percentile.

The corresponding effect sizes exceed the range of the y-axis in the figure. The

estimated increases in income for these individuals are 3.8 times for entrepreneurs in

the agricultural sector, 3.9 times for the other category, and 4.3 times for those in

the growth-potential category. Although these effect sizes seem enormous, keep in

mind that they apply to a small base.

These effects seem highly consistent with the idea that the benefit of entrepreneur-

ship comes in large part from offering steady employment. Those at the low end of

the income distribution probably often end up there because their intermittent em-

ployment has extended spells of unemployment between the days in which they find

work. We would therefore expect returns to steady employment to be most pro-

nounced at the lower ranges of the income distribution, among those facing the most

precarious options in paid employment.

Although we see those two results as the most important ones from the quantile

regressions, we would nevertheless call attention to three other patterns. First, the

23

returns for farmers and entrepreneurs in agriculture remain steady and positive be-

tween the 20th percentile and the median but start increasing in magnitude above

the median. This pattern may reflect the increasing size of farms or agricultural

operations at higher deciles of the income distribution, becoming more similar to

growth-potential businesses.

Second, for the self-employed in the other category, the positive returns to en-

trepreneurship largely disappear above the median. This self-employment appears

most beneficial to the poorer parts of the population, who probably have the most

limited options for stable employment.

Lastly, the subgroup of entrepreneurs opening businesses with the potential to

grow experience the largest returns to entrepreneurship, with large, positive effects

across the entire income distribution.

Exploring the mechanism

Although the differences in income between entrepreneurs and the employed appear

consistent with entrepreneurship being the safer option, particularly for those at the

lower end of the income distribution, these analyses do not provide direct evidence for

this mechanism. The CPHS does not include information on the day-to-day variation

in income that might indicate casual employment. However, it does include at least

four pieces of information that allow us to assess employment instability as the

underlying root cause of the entrepreneurship premium: month-by-month income

data, hours worked, type of paid employment, and education level.

24

Zero income months. We first examined the month-by-month income data to

assess whether entrepreneurs and employees differed in the stability of their incomes.

Let us first review how our data and estimates differ from those typically used

to estimate the returns to entrepreneurship. In most studies, surveys, such as the

National Labor Survey of Youth, and registry data record information on job spells.

How much did an individual earn on a particular job? Earnings for employees there-

fore have been tightly tied to particular jobs. The CMIE survey, by comparison, asks

respondents to report their income for each month since the previous wave. That

income does not necessarily come from a single job or the same job. Since we predict

future earnings (over the next four months) based on today’s employment status, our

estimate effectively captures the joint effects of pay per hour, hours worked per day,

and days worked. Even if the employed and self-employed earn comparable wages

per hour, we might find a large earnings differential if entrepreneurs have steadier

work hours.

We proxy for such precarious employment by investigating the probability of re-

porting an income of zero during any of the months between survey waves. This

analysis will not capture all of the income instability associated with casual employ-

ment. Many spells of unemployment may last less than a month. However, nearly

16% of our sample experiences at least one zero-income month, so such long spells

do not appear unusual.

Table 5 reports the results of a set of linear probability models predicting whether

the respondent reports an income of zero during any of the months between survey

waves. The first two models use our matching estimator, matching on age, gender,

25

education, caste, religion, state, urban or rural, and survey wave. The second two

include individual-level fixed effects.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Table 5 about here

- - - - - - - - - - - - - - - - - - - - - - - -

Although the coefficient from the first model suggests that entrepreneurs have

more zero-income months on average, when split into categories of entrepreneurship,

this result stems entirely from those involved in agriculture. Presumably, that effect

stems in large part from the seasonal nature of growing most crops. Both of the

other groups of entrepreneurs experience fewer zero-income months. In the models

with individual-level fixed effects, all of the subgroups appear to experience fewer

months without income, even entrepreneurs in the agricultural sector. Unobserved

differences across individuals in rural areas may determine both who becomes an

entrepreneur and their ability to secure semi-stable employment.

The effect sizes imply that growth entrepreneurs have 7 to 18 percentage-point

fewer months without income and that other entrepreneurs have 2.3 to 5.8 percentage-

point fewer. Given the global means, these represent a 42% to 100% reduction in

the probability of a zero-income month for the growth entrepreneurs and a 14% to

35% reduction for the other self-employed. The fixed effects model estimates a 2.3

percentage-point (14%) reduction in zero income months for entrepreneurs in the

agricultural sector.

Hours worked. We next examine how hours worked varied by employment status.

CMIE only began asking respondents about their average daily work hours in the

26

last wave of our sample, limiting us to cross-sectional comparisons.

We also believe that many respondents may have misunderstood this question.

We suspect that many individuals recall an average day during which they worked

rather than reporting the average number of hours worked over the past month.

Otherwise, the mean value in our sample, eight hours worked per day, does not line

up well with the fact that more than 40% of the Indian work force reported working

fewer than 35 hours per week in 2014 (ILO, 2016).

Table 6 reports results using regression without any control variables and using

our matching estimator, again matching on age, gender, education, caste, religion,

state, urban or rural, and survey wave. Despite the potential misreporting, the gen-

eral patterns appear consistent with those seen in the zero-income months analysis.

Entrepreneurs in the agricultural sector reported fewer hours worked – potentially

due to little work being available during the off season – while those in the other two

categories reported working more hours.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Table 6 about here

- - - - - - - - - - - - - - - - - - - - - - - -

Salaried employees. We next distinguish those in paid employment who work in

daily wage work from those in temporary or permanent salaried positions. Because

of the longer-term nature of salaried jobs, we would expect them to provide a similar

benefit in terms of offering stable – and therefore higher – earnings.

Table 7 reports the results of regression estimates of logged income, matching

again on age, gender, education, caste, religion, state, urban or rural, and survey

27

wave. As expected, those in permanent salaried positions earn much more than those

doing daily wage work. Based on the first column, for observationally-equivalent

individuals, growth entrepreneurship offers the highest expected income, followed by

permanent salaried employment, then other forms of entrepreneurship. Intermittent

employment, whether wage or salaried, offers the lowest expected income.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Table 7 about here

- - - - - - - - - - - - - - - - - - - - - - - -

In the second model, we remove all zero-income months from the analysis. This

adjustment should reduce some of the costs associated with instability. Our estimates

will still incorporate shorter spells of unemployment, for a few days or a couple of

weeks. But it eliminates longer-run spells.

Notice that eliminating these zero-income months dramatically cuts the premiums

associated with growth and other entrepreneurship by more than half (whereas it has

little effect on the premium associated with permanent salaried positions). Again,

given that these estimates still include shorter spells of unemployment, we cannot

reject the possibility that the greater stability of income streams accounts for all

of the entrepreneurial pay premium. Notably, after eliminating zero-income months,

even growth-potential entrepreneurs appear to earn no more than those in permanent

salaried positions.

Returns by education. Although the analysis of the earnings of salaried em-

ployees suggests that these jobs do indeed provide more stable and therefore more

28

desirable employment, those models do not address the issue of whether those be-

coming entrepreneurs might have had access to such jobs. As a final analysis, we

therefore estimate the returns to entrepreneurship by level of education.

Education strongly predicts who has access to a salaried position. Although

roughly 30% of employees hold salaried positions, that proportion climbs to 69% for

college graduates and to 86.9% for those with a graduate degree. Given this strong

relationship, holding a tertiary degree strongly suggests that an entrepreneur might

have held a salaried job if they had not become an entrepreneur.

Table 7 reports the results of regression estimates of logged income, matching

again on age, gender, education, caste, religion, state, urban or rural, and survey

wave. Here, however, we allow the returns to entrepreneurship to vary by education

level (by interacting the entrepreneur indicator variable with the level of education).

In the unmatched analysis (Model 1), the returns to entrepreneurship appear to

peak for those who finished secondary school but who did not complete a college

degree. However, after matching (Models 2 to 4), a clear pattern emerges: the

returns to entrepreneurship decline steadily with level of education. In fact, the

entrepreneurship premium turns into an entrepreneurship penalty for those with a

college or graduate degree, those for whom a salaried position probably represents

the alternative to entrepreneurship. These results therefore strongly suggest that the

positive returns to entrepreneurship seen on average, and for most subgroups, stems

from an inability to access salaried jobs.

- - - - - - - - - - - - - - - - - - - - - - - -Insert Table 8 about here

- - - - - - - - - - - - - - - - - - - - - - - -

29

Discussion

Nearly all prior research on the returns to entrepreneurship has been conducted using

data from wealthy, Western countries. In these places, most entrepreneurs choose

between being an entrepreneur and being a long-term employee. We examine the

returns to entrepreneurship in India, a country in which the alternative for many is

not a permanent job but intermittent employment.

Using nationally-representative survey data, we document a number of facts:

(i) Entrepreneurs in India, on average, earn more than observationally-equivalent

employees; (ii) when individuals shift between entrepreneurship and employment,

they earn more as entrepreneurs; (iii) the returns to entrepreneurship appear largest

at the bottom of the income distribution; (iv) entrepreneurs have fewer months

without income and work more hours in the average month than employees; and (v)

the highly educated – those who do have access to stable, salaried jobs – earn less

as entrepreneurs than as employees.

Taken together, these results appear highly consistent with the idea that en-

trepreneurship pays in India because it offers individuals more stable income streams.

Entrepreneurship and self-employment have long been considered risky. Most new

businesses fail. In high-income countries, self-employed individuals typically earn less

than they could expect as employees (e.g., Hamilton, 2000; Hyytinen et al., 2013;

Sorgner et al., 2017). But when individuals must choose between entrepreneurship

and intermittent employment, then entrepreneurship offers the safer option.

Our paper contributes to several streams of research. Most directly, we extend

the literature on the returns to entrepreneurship beyond high-income countries. In-

30

terestingly and importantly, the results differ across these settings. Whereas self-

employment, in particular, has typically been associated with an earnings penalty

(cf. Hamilton, 2000; Levine and Rubinstein, 2017), we find a premium.

Although these contrasting results may simply reflect differences in the setting,

our paper also calls attention to an implicit assumption in this literature: that the

alternative to entrepreneurship is a stable job. Even in the United States and other

Western countries, this assumption may not hold for all groups of people. Hwang and

Phillips (2020), for example, demonstrate that formerly-incarcerated individuals may

become entrepreneurs because they have effectively been excluded from the primary

labor market. Whether due to discrimination or other disadvantages, others may

find themselves in similar positions. If so, the earnings penalty associated with self-

employment, even in the United States, may arise as an artifact of choosing the wrong

counterfactual. Perhaps these people would face unemployment or underemployment

if they did not become their own bosses.

A similar issue arises in the evaluation of the “gig” economy. Self-employment

through platforms, such as driving for Lyft or UberEats, has often been seen as

unattractive and paying poorly (e.g., Vallas and Schor, 2020). But compared to

what? For those who might have a part-time, or even full-time, job at minimum

wage as the alternative, these gigs might still provide an attractive option. They

allow people to control their schedules. They also allow them to work more than

eight hours in a day, and more than 40 hours in a week.

More broadly, our results highlight the importance of carefully considering the

options open to potential entrepreneurs. When people face precarious alternative

31

employment options, entrepreneurship can offer the safer option. Many features of

institutional and national contexts influence these options. Do most employees have

contracts? Does the state or country have a minimum wage? Do employees enjoy

other employment protections? Does the country have a social safety net? All of

these factors will influence the attractiveness of entrepreneurship relative to employ-

ment, and therefore the expected returns associated with becoming an entrepreneur.

As Hwang and Phillips (2020) demonstrate, these options also vary across in-

dividuals within countries. Gender, race, religion, and disabilities may similarly

influence employment options and consequently the attractiveness of entrepreneur-

ship (Biasi, Dahl, and Moser, 2021; Thébaud, 2015). Although it has not received

as much attention of late, an earlier literature on immigrant enclaves similarly called

attention to the fact that immigrants had unusually high rates of entrepreneurship,

in large part because they had difficulty finding good jobs (e.g., Light, 1972; Wilson

and Portes, 1980). We might therefore expect the returns to entrepreneurship to

vary along these same dimensions.

The fact that our findings suggest that almost all entrepreneurs enjoy an earning

premium raises an obvious question: Why then, does everyone in India not enter self-

employment? The majority do! More than half of working Indians are self-employed

and roughly 60% have at least one spell of self-employment in our two-year sample.

If we had lifetime data, an even higher share of Indians would have likely been

entrepreneurs at some point.

That said, the quantile regressions also seem relevant to answering this ques-

tion. For a large range of the earnings distribution, entrepreneurs and employees

32

earn similar amounts. Only the worst paid of the employees – the bottom decile –

appear able to earn so much more as entrepreneurs as to raise this question. Among

those people, other factors may come into play. For example, the poor may find it

difficult to amass even the small amounts of capital required for buying the inputs

for handmade goods or the inventory for street-side vending.

We would also emphasize that our empirical strategy estimates the average treat-

ment effect on the treated (ATT). In other words, we answer the question of whether

it paid to become self-employed for those who actually became self-employed. Our

findings do not necessarily extend to everyone.

This fact also has implications for the comparison of our results to those found

in high-income settings. Whereas entrepreneurs in the United States and Europe

have often been seen as pursuing self-employment for non-pecuniary reasons (e.g.,

Benz and Frey, 2008; Moskowitz and Vissing-Jørgensen, 2002), such as the value of

autonomy, most entrepreneurs in India may not have that luxury. A much larger

share of individuals in lower-income countries may decide whether to become an

entrepreneur based only on the prospective earnings.

In terms of policy, our results would appear to bolster calls for entrepreneurship

as a path for expanding employment and increasing incomes. As in the West, the

biggest gains come from the growth entrepreneurs. These companies provide the

highest expected incomes to their founders. They also probably create the most and

the best jobs (Haltiwanger, Jarmin, and Miranda, 2013; Henrekson and Johansson,

2010; Sorenson, Dahl, Canales, and Burton, 2021). The challenge for policymakers

has been in defining policies that target these ventures as they often do not appear

33

that different from other forms of self-employment in their earliest stages.

But maybe policy does not need to target these entrepreneurs. The fact that

entrepreneurship pays off most for those in the lowest income decile suggests that

policies to encourage entrepreneurship might prove particularly effective at elimi-

nating poverty, even if the startups amount to little more than self-employment.

Lowering barriers to entry into (formal) entrepreneurship, could help more people

get access to a stable source of income. It could provide a safer option for those in

the most precarious positions.

34

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38

Tables and Figures

Table 1: Summary statistics

Paid employees Self-employedmean sd mean sd

Age 38.58 11.58 42.44 11.67Woman 0.14 0.34 0.07 0.25High School 0.32 0.47 0.30 0.46College 0.21 0.41 0.15 0.36Urban 0.67 0.47 0.58 0.49ReligionBuddhist 0.01 0.09 0.00 0.06Christian 0.02 0.14 0.01 0.11Hindu 0.85 0.36 0.83 0.37Muslim 0.09 0.29 0.11 0.31Sikh 0.02 0.15 0.04 0.19Other 0.00 0.05 0.00 0.07

CasteIntermediate 0.08 0.27 0.12 0.32Not stated 0.01 0.12 0.01 0.11Other backwards 0.37 0.48 0.41 0.49Scheduled 0.28 0.45 0.14 0.35Scheduled tribes 0.07 0.25 0.05 0.22Upper 0.19 0.39 0.26 0.44

Log monthly income 8.34 3.09 8.79 2.60Entrepreneurial typeSE farming 0.00 0.00 0.35 0.48SE other 0.00 0.00 0.47 0.50SE capital 0.00 0.00 0.18 0.38

N 699,430 555,320

39

Table 2: Income by employment status

Self-employedAll Employed All Agricultural Other Growth

Monthly income 15,210 14,667 15,894 13,567 13,488 26,781(15,697) (15,207) (16,267) (14,956) (11,171) (23,883)[11,375] [10,000] [12,500] [10,250] [12,000] [21,750]

Logged monthly income 8.54 8.34 8.79 8.53 8.65 9.64(2.89) (3.09) (2.60) (2.71) (2.65) (2.02)[9.34] [9.21] [9.43] [9.24] [9.39] [9.99]

N 1,254,750 699,430 555,320 192,496 263,475 99,349

Note: Standard deviation in parentheses, median in square brackets.

0

.2

.4

.6

.8

Dens

ity

8 9 10 11 12Logged monthly income

EmployedSE agricultureSE otherSE growth

Figure 1: Income distribution by employment status

40

Table3:

OLS

afterCEM

predictin

glogg

edmon

thly

income

Unm

atched

Matched

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Selfem

ployed

0.44

7∗∗∗

0.27

0∗∗∗

0.40

8∗∗∗

0.43

4∗∗∗

0.72

9∗∗∗

(0.006

)(0.006

)(0.007

)(0.007

)(0.013

)SE

agric

ulture

0.18

8∗∗∗

0.10

6∗∗∗

0.40

7∗∗∗

0.43

6∗∗∗

0.76

3∗∗∗

(0.009

)(0.009

)(0.011

)(0.013

)(0.024

)SE

other

0.31

5∗∗∗

0.16

7∗∗∗

0.26

5∗∗∗

0.29

1∗∗∗

0.44

0∗∗∗

(0.007

)(0.007

)(0.007

)(0.007

)(0.013

)SE

grow

th1.30

1∗∗∗

0.86

3∗∗∗

0.79

0∗∗∗

0.82

1∗∗∗

1.44

2∗∗∗

(0.009

)(0.009

)(0.011

)(0.012

)(0.033

)Obs

1,25

4,75

01,25

4,75

01,25

4,72

11,25

4,72

11,23

1,87

31,23

1,87

31,14

1,66

61,14

1,66

667

9,55

463

8,66

9nu

mindividu

als

280,06

628

0,06

628

0,05

428

0,05

427

6,52

827

6,52

826

2,12

626

2,12

620

7,53

920

2,59

6Unm

atched

2626

6332

6332

3977

939

779

1218

63CasecontrolF

Esyes

yes

yes

yes

yes

yes

yes

yes

Mat

ched

onAge

etc.

yes

yes

yes

yes

yes

yes

yes

yes

Stateurba

nyes

yes

yes

yes

yes

yes

Religioncaste

yes

yes

yes

yes

Treatm

enthistory

t-1

t-1

Stan

dard

erro

rscl

uste

red

byin

divi

dual

∗p<

0.05

,∗∗p<

0.01

,∗∗∗

p<

0.001

41

.2.4

.6.8

11.2

Pred

icted

earn

ings d

iffere

ntial

(vs em

ploye

d)

0 1 2 3 4Waves in spell

SE agricultureSE otherSE growth

Self-employment type X length of employment spell

Figure 2: Marginal effects of employment status on logged earningsBased on OLS regressions using a matching estimator as in Models 7 and 8 from Table 3.

Table 4: LPM predicting self-employment in t+1 among those working in paid employment in wave t

(1) (2) (3) (4)Log monthly income -0.013∗∗∗ -0.014∗∗∗ -0.024∗∗∗ -0.018∗∗∗

(0.000) (0.000) (0.000) (0.000)Constant 0.220∗∗∗ 0.019∗∗∗ 0.374∗∗∗ 0.241∗∗∗

(0.001) (0.003) (0.003) (0.051)Obs 699,430 699,411 616,359 650,247num individuals 204,066 204,059 186,287 154,895Controls yes matched yesFixed effects matched-strata individualStandard errors clustered by individual∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Controls include age, gender, education, survey wave, urban status of region, state,religion, and caste. We match on six coarsened categories of age and education incolumn 3.

42

0.2

.4.6

.81

1.21.4

Effec

t size

Effect of self employment on logged monthly income

Matched estimates Unadjusted estimate

(a) All self-employed

0.2

.4.6

.81

1.21.4

Effec

t size

Matched estimates - agricultural entrepreneurs

Matched estimates Unadjusted estimate

(b) Agricultural entrepreneurs

0.2

.4.6

.81

1.21.4

Effec

t size

Matched estimates - other self-employed

Matched estimates Unadjusted estimate

(c) Other entrepreneurs

0.2

.4.6

.81

1.21.4

Effec

t size

Matched estimates - growth entrepreneurs

Matched estimates Unadjusted estimate

(d) Growth entrepreneurs

Figure 4: Coefficient plots based on CEM estimates by self-employment type (sorted by effect size)

43

0.2

.4.6

.8Es

timate

d Sho

rt Te

rm O

ption

Valu

e of E

ntrep

rene

ursh

ip

10th 20th 30th 40th 50th 60th 70th 80th 90thQuantiles

SE agriculture SE otherSE growth

Figure 5: Quantile regression coefficients by entrepreneurship statusThe first three bars are capped to facilitate interpretation of the rest of the graph. The estimated effect sizes at the10th quantile are 3.8, 3.9, and 4.3 for the respective groups. We match on age, gender, education, urban status of

region, state, religion, and caste.

Table 5: Linear probability models predicting any month with zero income

(1) (2) (3) (4)Self employed 0.051∗∗∗ -0.067∗∗∗

(0.001) (0.001)SE agriculture 0.221∗∗∗ -0.023∗∗∗

(0.002) (0.002)SE other -0.023∗∗∗ -0.058∗∗∗

(0.001) (0.001)SE growth -0.070∗∗∗ -0.180∗∗∗

(0.001) (0.002)Obs 1,141,666 1,141,666 1,214,992 1,214,992num individuals 262,126 262,126 240,327 240,327Controls matched matched yes yesFixed effects case-control case-control individual individualStandard errors clustered by individual∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

We match on age, gender, education, survey wave, urban status of region, state,religion, and caste in columns 1 and 2.

44

Table 6: OLS regressions predicting average hours worked in a day

(1) (2) (3) (4)Self employed 0.000 -0.042∗∗∗

(0.007) (0.008)SE agriculture -0.461∗∗∗ -0.526∗∗∗

(0.011) (0.015)SE other 0.143∗∗∗ 0.153∗∗∗

(0.009) (0.009)SE growth 0.533∗∗∗ 0.359∗∗∗

(0.012) (0.015)Obs 124,112 124,112 111,089 111,089Controls matched matchedFixed effects case-control case-controlRobust errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

We match on age, gender, education, survey wave, urban status of region, state,religion, and caste in columns 3 and 4.

Table 7: OLS estimates after matching predicting income (during non-zero earnings months)

Log income Excl. zero months(1) (2)

Ref.: Daily wage worksalaried - temporary 0.003 0.074∗∗∗

(0.017) (0.005)salaried 0.704∗∗∗ 0.597∗∗∗

(0.016) (0.006)SE agriculture 0.594∗∗∗ 0.510∗∗∗

(0.013) (0.005)SE other 0.507∗∗∗ 0.213∗∗∗

(0.010) (0.003)SE growth 1.160∗∗∗ 0.549∗∗∗

(0.015) (0.005)Obs 1,141,666 1,034,561num individuals 262,126 238,628Controls matched matchedFixed effects case-control case-controlStandard errors clustered by individual∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

We match on age, gender, education, survey waVe, urban status,state, religion, and caste in columns 3 and 4.

45

Table 8: OLS estimates after matching predicting income – by education

Unmatched Matched(1) (2) (3) (4)

Self-employed 0.408∗∗∗ 0.219∗∗∗ 0.327∗∗∗ 0.309∗∗∗(0.017) (0.016) (0.018) (0.019)

Ref.: SE × less than lower primarySE × less than secondary 0.083∗∗∗ 0.114∗∗∗ 0.158∗∗∗ 0.210∗∗∗

(0.020) (0.019) (0.022) (0.023)SE × some secondary 0.180∗∗∗ 0.128∗∗∗ 0.150∗∗∗ 0.197∗∗∗

(0.021) (0.020) (0.022) (0.024)SE × finished secondary 0.227∗∗∗ 0.075∗∗ 0.115∗∗∗ 0.175∗∗∗

(0.025) (0.024) (0.027) (0.029)SE × undergrad -0.108∗∗∗ -0.160∗∗∗ -0.127∗∗∗ -0.084∗∗

(0.025) (0.024) (0.026) (0.028)SE × post-grad -0.330∗∗∗ -0.299∗∗∗ -0.277∗∗∗ -0.260∗∗∗

(0.029) (0.028) (0.030) (0.031)Obs 1,254,750 1,254,721 1,231,873 1,141,666num individuals 280,066 280,054 276,528 262,126UnmatchedCase control FEs yes yes yesMatched onAge etc. yes yes yesState urban yes yesReligion caste yes

Standard errors clustered by individual∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

46

A Appendix

Table A.1: OLS with fixed effects predicting logged monthly income

(1) (2)Self employed 0.856∗∗∗

(0.009)SE agriculture 0.922∗∗∗

(0.015)SE other 0.607∗∗∗

(0.009)SE growth 1.734∗∗∗

(0.018)Obs 1,215,019 1,215,019num individuals 240,335 240,335Month FEs yes yesIndividual FEs yes yesStandard errors clustered by individual∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

47