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On the nature of micro-entrepreneurship: evidencefrom ArgentinaGabriel V. Montes Rojas a & Lucas Siga ba Department of Economics, University of Illinois at Urbana-Champaign, 484 Wohlers Hall,1206 S. Sixth Street, Champaign, IL 61820, USAb Department of Economics, University of California, 9500 Gilman Drive, La Jolla, SanDiego, CA 92093, USAPublished online: 11 Apr 2011.

To cite this article: Gabriel V. Montes Rojas & Lucas Siga (2009) On the nature of micro-entrepreneurship: evidence fromArgentina, Applied Economics, 41:21, 2667-2680, DOI: 10.1080/00036840701335553

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Applied Economics, 2009, 41, 2667–2680

On the nature of

micro-entrepreneurship:

evidence from Argentina

Gabriel V. Montes Rojasa,* and Lucas Sigab

aDepartment of Economics, University of Illinois at Urbana-Champaign,

484 Wohlers Hall, 1206 S. Sixth Street, Champaign, IL 61820, USAbDepartment of Economics, University of California, San Diego,

9500 Gilman Drive, La Jolla, CA 92093, USA

We analyse the nature of micro-entrepreneurship in Argentina. We focus

on whether the sector resembles its counterpart in industrialized

countries, characterized by the risk-taking nature of the entrepreneurial

activity, or if it is the result of labour market distortions and disguised

unemployment, as in the dual economy hypothesis. Our results suggest

a segmentation of the micro-entrepreneur sector. Both young uneducated

and middle aged highly educated salaried workers have the highest

likelihood of becoming entrepreneurs. However, the first segment has

a high probability of becoming own-account workers, while the

probability of becoming micro-entrepreneurs with employees is strictly

increasing in both age and education. Moreover, the probability of

entrepreneur failure (as measured by the transition to the salaried sector)

has an inverted U shape, implying that both high and low skill

individuals are more likely to remain entrepreneurs.

I. Introduction

The fact that self-employment and informality rates

are considerably higher in developing countries than

in industrialized countries is usually attributed to the

existence of labour market distortions and excessive

regulations (e.g. Harris and Todaro, 1970; de Soto,

1989). In this line of research, individuals start micro-

firms only as a temporary activity while they queue

for a salaried job. Other authors (i.e. Maloney, 1999,

2004; Bhattacharaya, 2002) challenge this idea by

emphasizing the sector’s good performance. In this

view, the decision to start a micro-firm is similar to

that in the industrialized world literature that stresses

the risk taking entrepreneurial nature of the sector.

Moreover, individuals may face similar liquidity

constraints that determine a similar dynamic beha-

viour. This article focuses on whether the nature

of the micro-entrepreneur sector resembles its coun-

terpart in the industrialized world or if it should be

analysed in terms of the Harris and Todaro

paradigm.Given the scarcity of studies focusing on the

characteristics of the self employment and micro-

entrepreneur sector1 in developing countries, and the

relative importance of this sector in the economy, it is

important to understand its dynamics and composi-

tion. Moreover, this topic has very important policy

implications, since the low productivity encountered

in developing countries is usually linked to the lack of

*Corresponding author. E-mail: rmontes@uiuc.edu1We define the sector as own-account workers and owners of firms with less than 16 employees.

Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online � 2009 Taylor & Francis 2667http://www.informaworld.com

DOI: 10.1080/00036840701335553

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entrepreneurial capacity. A better understanding ofthe relation between the human capital and theentrepreneurship guides the policy makers on theadequate design of training and credit programmesfor this sector.

We use a household survey from Argentinato study how human capital affects the decisionto become an entrepreneur or a salaried worker.The panel data design of the survey allows us tostudy annual transitions between the salaried (andunemployed) and the entrepreneurial sectors, assum-ing that the process is a Markov-chain (where thelast period variables contain all the relevantinformation to analyse the stochastic nature of thetransition). Our empirical model distinguishesbetween the effects of observable and unobservablecharacteristics, using two proxy variables for thelatter. First, we use past earnings to control forthose characteristics not embodied in traditionalhuman capital variables (i.e. age, education), whichmay affect the individual decision to become self-employed. Second, we consider whether or not theindividual had lost his job in the gap betweenthe two periods analysed. Conditional on otherindividual characteristics, these two variables cap-ture the effect of unobserved ability or intrinsicpreference for the micro-entrepreneur sector.

The article is organized as follows: We includea literature review and state the main hypotheses inSection II; We discuss the empirical model inSection III; We analyse the data in Section IV; Wepresent econometric results in Section V; and finally,we summarize our conclusions in Section VI.

II. Conceptual Approach: Motivationand Hypotheses

Industrialized countries literature

The dominant view regarding the role of self-employment in industrialized countries emphasizesthe risk-taking, entrepreneurial nature of the sector.In the classic framework proposed by Lucas (1978),individuals are endowed with a given – and known –level of entrepreneurial or managerial ability.Individuals with a sufficiently high level of manage-rial ability become entrepreneurs, while the restbecome wageworkers. Jovanovic (1982) addeddynamics to the Lucas representation by assumingthat individuals have a vague idea about their

entrepreneurial skills, and they learn about them bystarting a firm.

In addition to increasing wages in the salariedsector, it is reasonable that human capitalincreases earnings in the self-employment sector.Rees and Shah (1986) argue that more educatedindividuals have lower costs of assessing businessopportunities and that human capital may bea complement to managerial ability. Evidence forindustrialized countries (see for instance Evansand Leighton, 1989 for the US, Carrasco, 1999 forSpain, Moore and Mueller, 2002 for Canada) showsthat formal education has a positive effect onthe likelihood of becoming an entrepreneur.Similarly, Bates (1990) shows that the probability ofsurvival of a small business in the US is positivelycorrelated to its owners’ education level. For Japan,Honjo (2004) shows that entrepreneur’s educationis positively correlated with the firm’s growth rate.

The relevant literature on occupational choiceoffers some hints on what the age-entry profilelooks like.2 For example, Johnson (1978) andJovanovic (1979) argue that since young people areless likely to be risk averse, they should be overrepresented among those entering. However,Carrasco (1999) and Moore and Mueller’s (2002)research on Spain and Canada, respectively showthat the hazard of becoming self-employed hasa maximum for middle aged individuals (aged 35–45years for Spain, aged 45–54 years for Canada). Evansand Leighton’s (1989) study for the US reportsa constant entry rate by age. They suggest that thispattern is driven by liquidity constraints that make itdifficult for younger workers to borrow start-upfunds. This theory is developed by Evans andJovanovic (1989) and Blanchflower and Oswald(1998).

There is also evidence that, other things equal,some individuals may derive a larger utility fromentrepreneurship than from salaried work, thusreducing the net opportunity cost of enteringself-employment. Blanchflower and Oswald (1998)provide evidence on this for the UK, showing thatself-employed workers report higher levels of job andlife satisfaction. For the US, Hamilton (2000) findsthat nonpecuniary benefits, such as being your ownboss, explain the lower conditional earnings generallyreceived by the self-employed.

Overall, the stylized view of industrialized coun-tries pursued here predicts that, high skill individualsare more likely to start a firm and have a higherchance of survival. Self-employment and

2The literature refers to entry when individuals enter the entrepreneurial activity from other labour status. Conversely, itrefers to exit when individuals leave the sector for other labour status.

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entrepreneurial activities are considered voluntarybecause individuals self-select into the sector tomaximize their expected utility, in contrast to theidea that individuals enter because they are disad-vantaged in the salaried sector.

We emphasize that this is only a simplification ofa complex phenomenon. Many studies fordeveloped countries have also shown that pooreconomic conditions might push individuals to self-employment. In parallel with sociological studiesthose individuals are classified as misfits,’ in contrastto the ‘superstars’ concept that corresponds to ourdefinition of voluntary self-employment. For exam-ple, Carrasco (1999) and Moore and Mueller (2002)find that individuals with longer unemployment spellsor those who involuntarily left their previous salariedjobs (i.e. due to layoff) are more likely to become self-employed.

Developing countries literature

In contrast, the traditional literature on developingcountries emphasizes the precariousness of this typeof employment, which is a consequence of thesector’s low income and high mortality ratesamong other factors. This literature originates withHarris and Todaro (1970) who view the sector asa temporary place for those seeking better jobs inthe salaried sector. Harris and Todaro disaggregateurban employment into a modern sector, character-ized by high productivity growth and job benefitsand a traditional (or informal) sector.3 These modelstypically view the informal sector as being essentiallystagnant and unproductive, serving merely asa refuge for the urban unemployed as well as areceiving station for the newly arrived ruralmigrants. In this view, the micro-entrepreneurshipsector is usually associated with ‘disguised unem-ployment’. We will refer to this view as the‘dualistic’ or ‘dual’ hypothesis. Under this approach,younger and low-skilled individuals have a higherpropensity to enter in the entrepreneurial sector.Moreover, education should be a poor predictor offirm success.

In developing countries, where credit markets areknown to function poorly, we might also expect a flatage entry profile, or even one increasing with age.In fact, we can extend the financial capital constraintsargument to human capital: poor education systemsforce workers to accumulate know-how and skills inthe wage sector in order to later switch to self-employment.

Some empirical studies for developing

countries do not conform the Harris–Todaro view.Fajnzylber et al. (2006) for Mexico and

Bhattacharaya (2002) for India conclude that the

micro-entrepreneur sector should be analysed fromthe perspective of the industrialized country litera-

ture. In this view the best salaried workers are moreprone to enter the entrepreneur sector, and the data

fits the Evans and Jovanovic liquidity constraint

hypothesis. Moreover, Earle and Sakova’s (2000)analysis for transition economies states that employ-

ers should be considered in the same way, but own-account workers should not. In contrast, we

approach the matter in a sceptical way allowing

for the existence of different labour market segmentswhich may behave differently.

Contrasting views

We investigate the framework that better describes

the micro-entrepreneurial sector in Argentina bystudying the characteristics of those who decide to

enter and exit. The literature presented above showsthat the age and education patterns of entry and exit

might provide useful information about the sector’s

nature. In terms of education, for instance, ifunskilled workers have a higher propensity to

become entrepreneurs we might place the sectorunder the dualistic view. On the other hand, if high-

skill individuals are more likely to become entrepre-

neurs, we would consider that the Lucas-typerepresentation is more plausible. In terms of age,

this procedure is less clear, as multiple factorsmay determine the entry/exit pattern; in both

cases, we would expect a flat or increasing entry

pattern. Nevertheless, by analysing how the pathschange with education we can draw additional

conclusions.Furthermore, following Fajnzylber et al. (2006)

the level of remuneration earned in previous jobs

may offer insight about how we can distinguishmainstream from dualistic models. Individuals earn-

ing conditionally higher wages would be less likely

to be ‘misfits,’ or unsuited to formal work, andhence less prone to move into the self-employment

sector. However, in the presence of credit con-straints, those workers earning higher wages in the

salaried sector may also be able to accumulate

capital faster and hence, they may be more likely tobecome self-employed. In the following section, we

show that after conditioning on other human capital

3Different terminologies have been used for this portion of the labour force. Todaro (1969) called it ‘urban traditional’,Santos (1979) the ‘lower circuit’, McGee (1971) ‘protoproletariat’, Cole and Sanders (1985) ‘urban subsistence’.

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variables, past earnings also serve as a proxy forunmeasured ability.

Other personal characteristics such as gender andbeing household head provide important informationregarding the nature of entrepreneurship. The factthat women might have a different risk aversion thanmen applies to both views, and therefore gendershould be included as an additional covariate.Unfortunately most of the available studies on self-employment include only men, because of the specialbehaviour of women in the labour market.4

Additionally, household heads might be more riskaverse than other household members. However, inless developed societies, household heads might haveaccess to cheap labour from family members increas-ing the likelihood of a self-employment activity.

Our results suggest that both views can be appliedto different segments of the population, implyingsegmentation in the micro-entrepreneur sector.On the one hand, young uneducated workers aremore likely to enter. Moreover, for low educationlevels, the probability of entry decreases with age,suggesting that low skill workers either achievesome degree of specialization in their jobs or cannotaccumulate enough human/financial capital to starta firm. On the other hand, workers with moreeducation also have a high propensity to enter.In addition, this segment of the population has anincreasing age-entry profile, consistent with theliquidity constraints literature.

Additional proof of segmentation is given by theexit-profile. The probability of failure (as measuredby the transition to the salaried sector) has aninverted U shape, implying that both high and lowskill workers are more likely to remain entrepreneurs.

III. An Empirical Model for HumanCapital and Entrepreneurship

Suppose that an individual can choose to be in anyof the following employment categories: micro-entrepreneur5 or salaried (denoted by e and s,respectively). Let the variable L be an indicator ofthis status. At any point in time t, the decision to bein any category is given by the comparison of the netvalue of the discounted future earnings. Denote theearnings in t as wtðLt,Xt,YtðLt�1,Lt�2, . . .Þ, a, "t,Lt

Þ.This function depends on the actual labour

status and observable human and financial capitalvariables that may be exogenous (X) or endogenous(Y). We will refer to the latter as path-dependent,indicating that it may be affected by the timespent in each sector, in line with Jovanovic‘learning-by-doing’ idea. In other words, Y dependson past L values. Additionally, a nonobservablecomponent a captures both entrepreneurial abilityand intrinsic preferences for a certain status, and "refers to a nonobservable random component.In some cases we will only use wt(Lt) to refer tothe earning function above. For simplicity assume:

wtðLt,Xt,YtðLt�1,Lt�2, . . .Þ, a, "t,LtÞ

¼ �LtXt þ �Lt

YtðLt�1,Lt�2, . . .Þ

þ a�I, ½Lt ¼ e� þ "t,Lt

ð1Þ

A main feature of the model is that returns to Xand Y vary by employment status. For example,past salaried experience may not contribute tobusiness performance. Conversely, owning capitalgoods may not affect earnings in the salaried sector.

In this model, we describe labour status decisionsby the optimal decision path. Assuming thatan individual lives T periods and has a time discountfactor �, we have

L�t� �T

t¼0¼ argmax

Ltf gEXTt¼0

�twtðLt,Xt,Yt

"

ðLt�1,Lt�2, . . .Þ, a, "t,LtÞ

�ð2Þ

At any point in time �, labour status is thesolution to

L�� ¼ argmaxLt

E

"XTt¼�

�t��wtðLt,Xt,Yt

ðLt�1,Lt�2, . . .Þ,a,"t,LtÞ

#ð3Þ

Consider the following probability:

P½L� ¼ ejX�,Y�� ¼ Phw�ðeÞ � w� sð Þ

þXTt¼�þ1

�t���wtðLtjL� ¼ eÞ

� wtðLtjL� ¼ sÞ�� 0jX�,Y�

ið4Þ

Ideally, all the parameters can be identified if wehave a sample of potential earnings in both theentrepreneurial and salaried sectors, for the indivi-dual’s entire life. Of course, we can only obtain

4 Some exceptions are Moore and Mueller (2002) and Tervo (2006) who find that men are more likely to be self-employed thanwomen.5We will use the terms self-employed and micro-entrepreneur interchangeably, to refer to those individuals who are own-account workers or owners of a small firm (less than 16 employees). Own-account workers can be considered as the most basicfirm structure.

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potential earnings in the actual labour status.In addition, given data limitations, the literaturehas only studied the determinants of a labour statuscomparing present period earnings. That is,

P½L� ¼ ejX� ,Y�� ¼ P½w�ðeÞ � w�ðsÞ � 0jX�,Y� � ð5Þ

Earle and Sakova (2000) studied the nature ofself-employment and entrepreneurship by examiningindividual employment status (unemployed, salaried,own-account worker and employer) using multi-nomial logit models (a bivariate logit in our simplemodel). A main drawback of this approach is that itdoes not take into account past labour statuses andindividual specific characteristics. Since actualemployment status depends on past decisions(through Y) which, in turn, depends on a, wewould obtain biased estimates.

Because of data restrictions, an intermediateapproach followed by Fajnzylber et al. (2006) (as inEvans and Leighton, 1989) is to study only a portionof the path, as we choose to do. Suppose thattransitions from one period to the other follow asimple time-homogeneous Markov-chain. That is,assume that labour status decisions can be analysedin a Markov-chain structure, where the last periodvariables contain all the information to fully describethe stochastic nature of the transition. Thus, we willbe able to estimate the following probability:

P½L� ¼ ejL��1,X�,Y�� ¼ P½w�ðejL��1Þ

� w�ðsjL��1Þ � 0jL��1,X� ,Y��

¼ P½ð�e � �sÞX� þ ð�e � �sÞY�ðL��1Þ

þ aþ ð"�, e � "�, sÞ � 0jL��1,X�,Y� �

ð6Þ

There may still be a correlation between Y and a.However, if we have at hand a proxy or instrumentalvariable, the identification problem can be solved.Unfortunately, employment surveys in developingcountries do not contain potential instrumentalvariables that can be used in this context. Our strategyis to use earnings in ��1 as a proxy for unmeasuredability, as well as an indicator of whether or not thetransition (if any) from one sector to the other had anintermediate step in unemployment (Lost Job). Notethat in the first case, past earnings do not pose aproblem to the parameters interpretation, as binaryoutcome models identify coefficients up to a scaleparameter.6 Moreover, if we assume that ð"�, e � "�, sÞis not serially correlated, the endogeneity bias can becorrected. In the second case, we expect to capturethose individuals with low entrepreneurial ability whowould be more likely to ‘fail’ and to move to the

other sector. The sign of these variables indicates thenature of each sector. If, conditional on other covaria-tes, those individuals who have lost their jobs (or withlower conditional wages) are more likely to becomeentrepreneurs, the dual hypothesis is the best repre-sentation of the sector’s nature. Note that both pastincome/wage and Lost Job may also be considered asendogenous, as they are certainly correlated withentrepreneurial ability. Therefore, we must interpretthe coefficients on these variables with caution.

Following Carrasco (1999) and Earle and Sakova(2000), we also expand the entry model to a multi-nomial analysis disaggregating entrepreneurship intoown-account workers and entrepreneurs with employ-ees. Our conjecture is that the segmentation discussedin the previous section might be only applicable toown-account workers.7 Furthermore, we followCarrasco (1999) and restrict the sample to those whostart as unemployed, and estimate a multinomialmodel where the individual may remain unemployed,become salaried or become an entrepreneur.

We must take several data limitations intoaccount. The Argentinean household survey usedin this article does not provide business-relatedinformation; i.e. it has no information about capitalstock or time in business, which were goodpredictors of firm performance in previous studies(Fajnzylber et al., 2006). However, if we focus onmicro-entrepreneurs only (<16 employees), whichare adequately captured by standard employmentsurveys, individual characteristics are sufficientstatistics for the prediction of firm performance.The conclusions drawn from this study should berestricted to this sub-population.

Additionally, individuals’ financial situation andwealth are not available. In the liquidity constraintsliterature mentioned above, these variables play animportant role in the propensity to be an entrepre-neur: some individuals with high entrepreneurialability delay business start-up because of lack ofcredit. Therefore, estimates on the age profile will bemisleading. In our context, the age profile should notbe interpreted only as experience in a Minceriansense, but as a proxy variable for both experience andpast financial capital accumulation.

IV. Data and Descriptive Statistics

We use the Encuesta Permanente de Hogares (EPH),an urban household survey tracking individualsfor 2 years, both in May and August. The sample

6We do not pursue identification of the parameters in the earnings equation.7We are grateful to an anonymous referee for this suggestion.

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has a 25% replacement ratio each period. The surveycovers most Argentinean metropolitan areas and isthe most representative database of urban employ-ment available for this frequency. This surveycaptures the whole spectrum of micro-firms, bothformal (i.e. pay taxes, etc.) and informal.

The period analysed is 1995–20038 and we con-strain the sample to observe the same individual oneyear apart only, from May to May,9 to avoidseasonality issues. In addition we only considerindividuals in the age range 20–65, which comprisesthe active labour force (in Argentina, retirement ageis between 60 and 65, although lower for publicemployees). Moreover, the EPH provides incomeinformation only for the main job. Therefore, wecannot analyse individuals who run a business asa secondary occupation or those who own a firm butdeclare themselves to be salaried.

The sub-population of interest is composed byown-account workers and firm owners. The EPH isrepresentative for micro-entrepreneurs (firms with noor up to 16 employees), but for the sake ofcompleteness we will consider the whole entrepreneurpopulation, in which micro-entrepreneurs comprisealmost 99% (Table 2 below).

Some descriptive statistics

Table 1 presents basic statistics for the pooledEPH sample, comparing salaried and self-employedworkers. Some stylized facts emerge from the table.First, 27% of all workers are self-employed. Second,average income is similar for both groups, althoughthe latter has a higher dispersion. Third, entrepre-neurs are on average older but less educated thansalaried workers. In the case of education, the former

category has heavier relative tails, meaning that

we may encounter both very unskilled and profes-

sionals in this category.Table 2 shows the distribution of workers in each

sector by firm size. We observe that about 66% have

a size of one worker (i.e. own-account workers) while

96% have fewer than five employees. In terms of

employment, considering salaried workers employed

in micro-firms (nonpublic firms with<16 workers),

we find that roughly 67% of all workers are associated

with the micro-firms sector. Table 3 presents the

distribution of workers in each sector by industry.

In particular, we note that about 50% of the self-

employment sector can be found in three main sectors

(retail trade, construction and repair services).Finally, as it is desirable to have a sense of how

many individuals change from one status to the

other, we compute the dynamics of moving in or out

the self-employment sector. In Table 4, we present

transitions (understood as percentages of individuals

moving to a particular labour status) from three

sectors: self-employment, salaried and unemploy-

ment. For simplicity, we exclude individuals out of

the labour force. From Table 4 we observe that

about 71% of self-employed workers stay in the

sector while roughly 20% go to (and come from)

the salaried sector. The remaining 9% transition to

(and from) unemployment. The salaried sector

shows the least mobility among the three sectors �

around 85% of workers stay from one year to the

next. Finally, it is worth noting that of those

workers who begin as unemployed, the salaried

sector absorbs around 40% of them, about

twice as many individuals as the self-employed

sector absorbs. Nonetheless, if we acknowledge the

Table 1. Basic descriptive statistics

Self-employed Salaried

% of working people(self-employedþ salaried¼ 100%)

26.9% 73.1%

Age 42.27 (10.95) 37.81 (10.93)Years of schooling 9.74 (4.27) 10.49 (4.05)Hourly income (Pesos 2001,

1 Peso¼ 1 Dollar)3.75 (5.71) 3.68 (3.22)

Percentage of householdheads

64.9% 52.3%

Percentage of females 30.2% 42.1%

Notes: Pooled EPH data (1995–2003). SD in parenthesis.

Table 2. Self-employment and salaried by firm size

SizeSelf-employed (%)

Allsalaried (%)

Salaried (nonpublic sector) (%)

1 65.8 11.3 13.52–5 30.2 21.0 23.66–15 2.8 17.3 17.316–25 0.5 10.2 10.126–50 0.4 11.9 11.551–100 0.2 10.8 9.9101–500 0.1 12.1 10.2501 0.0 5.4 3.9

Total 100.0 100.0 100.0

Note: Pooled EPH data (1995–2003).

8Although longer series are available, there are serious problems identifying individuals over time for regions otherthan Gran Buenos Aires.9However, we also use information from the wave in between (August) to observe individuals who experience unemployment.See below on the construction of the variable Lost Job.

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relative size of each sector, the unemployedwho move to the salaried sector represent 6% ofsalaried workers while the unemployed absorbed bythe self-employed represent 13% of self-employed.

V. Econometric Analysis

Determinants of becoming self-employed

We study the determinants of entry in theentrepreneur sector using probit10 and multinomiallogit models. In Table 5, columns (1) and (2), we

present results for a probit model where thedependent variable takes the value zero if a salariedworker stays in the sector from one year to thenext, or the value one if he moves to the self-employed sector. We limit our sample to individualsstarting as salaried and ending as employed,either salaried or self-employed. The set of explana-tory variables includes education, age, gender, avariable identifying household heads, firm size, apublic sector employment dummy, last period wageand a variable identifying those individuals whobecame unemployed in between the survey period.Although, it would be desirable to capture all the

Table 3. Self-employment and salaried by industry

Industry Self-employed (%) Salaried (%) Total (%)

Primary sector 1.9 2.0 1.9Food, beverage and tobacco 2.4 3.2 3.0Textiles, textile products and footwear 2.3 2.2 2.2Chemical, refined petroleum and nuclear fuel 0.4 1.5 1.2Metal products, machinery and equipment 2.8 2.9 2.8Manufacture not elsewhere classified 3.1 2.8 2.9Electricity, gas and water supply 0.1 1.6 1.2Construction 16.7 6.1 8.9Wholesale trade 4.2 3.7 3.9Retail trade 25.5 8.0 12.7Restaurants and hotels 2.5 2.1 2.2Transportation and related services 5.6 4.6 4.9Financial intermediation 0.3 2.6 1.9Real estate and rental and leasing 7.7 3.6 4.7Public administration and military 0.1 19.0 13.9Teaching 1.5 12.1 9.3Social services and health 3.3 7.1 6.1Other social services 2.0 4.0 3.5Repair services 8.1 1.5 3.3Households with domestic services 5.5 8.9 8.0Other personal services 4.1 0.7 1.6

Total 100.0 100.0 100.0

Note: Pooled EPH data (1995–2003).

Table 4. Sector transitions

To

SE SAL U SE SAL U Total

From SE 23 873 6510 2987 71.5% 19.5% 9.0% 100%SAL 6693 84 155 5678 6.9% 87.2% 5.9% 100%U 2990 5729 5758 20.7% 39.6% 39.8% 100%

SE 71.1% 6.8% 20.7%SAL 19.9% 87.3% 39.4%U 8.9% 5.9% 39.9%

Total 100% 100% 100%

Notes: Pooled EPH data (1995–2003). SE: self-employed and firm owners, SAL: salaried, U: unemployed.

10 Logit and probit models gave identical results.

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individuals who became unemployed at any point

during the year, the survey only allows us to identify

those unemployed at the moment of the survey in

between our analysed periods (August) leaving out

those who temporarily became unemployed before or

after that survey. Besides, the EPH does not provide

any information about the reason for being unem-

ployed. In addition, the regressions include several

dummy variables (not presented in the table) for

industry, region and time. Only marginal effects on

the probability are reported.We present the results in two different specifica-

tions: Specification (2) differs from specification (1)

only in that it includes the interacted effects between

education and age in order to characterize how

different combinations of age and education affect

the probability of becoming self-employed. Note

that the freestanding coefficients of age and age

squared display reduced significance when the

interaction with education is included. However,

the interactions between age and education are

statistically significant. Thus, it is reasonable to

believe that specification (2) characterizes different

combinations of age and education more appro-

priately than specification (1).In Fig. 1, we graphically present the estimated

effects of combinations of age and education from

specification (2). In terms of education, for any age

level, there is a certain level that minimizes theprobability of a salaried worker becoming anentrepreneur (i.e., for any age level the figure isconcave). This level roughly corresponds to com-plete secondary school. Individuals with less educa-tion are more likely to start a micro-firm. In otherwords, low skill individuals may find that the risk ofstarting an entrepreneurial activity is relatively lowcompared to more educated persons. Moreover,individuals with education past secondary schoolwill find that the expected return of starting an

Table 5. Entry – probit (Dependent variable: 0^ salaried to salaried, 1^ salaried to entrepreneur)

1 2

Education �0.006*** (0.001) �0.037*** (0.011)Education squared/100 0.042*** (0.004) 0.154*** (0.053)Age 0.004*** (0.001) �0.003 (0.003)Age squared/100 �0.004*** (0.001) 0.003 (0.003)Educ*Age/1000 1.236** (0.538)Educ*Age^2/1000 �0.012* (0.006)Educ^2*Age^2/1000 0.041 (0.031)Educ^2*Age/1000 �0.045* (0.026)Log hourly wage �0.002* (0.001) �0.003* (0.001)Lost job 0.075*** (0.007) �0.074*** (0.007)Public adm. �0.033*** (0.003) �0.034*** (0.003)Female �0.030*** (0.002) �0.029*** (0.002)Head 0.003 (0.002) 0.002 (0.002)Firm Size2–5 �0.011*** (0.004) �0.011*** (0.004)6–15 �0.034*** (0.003) �0.034*** (0.003)16–25 �0.041*** (0.002) �0.041*** (0.002)26–50 �0.044*** (0.002) �0.044*** (0.002)51–100 �0.043*** (0.002) �0.043*** (0.002)101–500 �0.050*** (0.002) �0.050*** (0.002)�501 �0.046*** (0.002) �0.046*** (0.002)

Observations 71 282 71 282

Notes: SEs in parenthesis, *significant at 10%; **significant at 5%; ***significant at 1%.All specifications include time, region and industry dummies. This sample is restricted only tosalaried employees, who either remain salaried or become self-employed.

0

5

10

15

Education

20

30

40

50

60

Age

-

-0.1 %Chg.P-0.08-0.06

0

5

10

15

-0.14-0.12--

Fig. 1. Effect of age and education on the probability of

entry (probit model)

Note: Constructed using the coefficients of Table 5.

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entrepreneurial activity is higher relative to indivi-duals with secondary school. This pattern is similarto Fajnzylber et al.’s (2006) findings in Mexico.Moreover, provided that the low education segments(i.e. primary school) are much smaller in developedcountries, the upward effect on the probability ofentering (considering only secondary and collegeeducation levels) is consistent with the industrializedcountries findings (see Section II).

Regarding the effect of age on the likelihood ofbecoming self-employed, we find that individualswith no education will find the self-employmentsector less attractive as they become older. This canbe due to the fact that this group matches up withthe most unskilled salaried jobs, where workersachieve some level of specialization in their jobsthough they cannot accumulate enough capital tostart their own entrepreneurship activity. However,this pattern is different for higher levels of educationas the shape of the curve reverses. Note that forthose individuals with some college education, thehighest probability of becoming self-employed isroughly achieved within the age of 40–55, which ishighly compatible with a dynamic self-employedsector where individuals accumulate capitaland experience to later try starting their ownbusiness, and with Fajnzylber et al.’s (2006) findingsfor Mexico.

Overall, Fig. 1 shows two main important areaswhere it is more likely to find new entrepreneurs:the area with young workers with low educationlevels, and the area of high education in the 40–55 agerange. The former group is consistent with theso called dual labour market hypothesis, in whichless-educated individuals choose to risk the transitioninto a self-employed activity, given the instabilityof the salaried sector. The latter corresponds to therisk-taking entrepreneurial approach.

Consistent with the former view is the effect of thevariable Lost Job in Table 5, which captures thoseindividuals who involuntarily left the salariedsector. Those individuals rationed out of the salariedsector are more likely to choose starting a micro-firmas they may not be able to find a good salariedposition. Furthermore, the wage variable showsthe expected negative impact on the likelihood ofmoving, as, ceteris paribus, higher wages will makeindividuals less interested in leaving the salariedsector.

We also include firm size dummy variables totest whether individuals in any particular type of firmare more likely to start a micro-firm. As observed inTable 5, as the firm size increases, the probability

of going to the self-employment sector decreases.This is probably due to nonpecuniary benefits offeredby larger firms.11 Female workers are less likely totake risks to start an entrepreneurial activity, whichmight be related to inherited social standards.Finally, public administration workers are lessinclined to move to the self-employment sector asa consequence of the greater stability they have incomparison with other jobs in the private sector.

Table 6 expands the analysis of Table 5 bydistinguishing whether the entry takes place as aself-employed without employees (i.e. own-accountworker) or with employees. As before, the samplecontains all salaried workers in the first periodwho are employed one year later. We estimatea multinomial logit model in which those two typesof entry (with or without employees) are comparedto the base category (remaining salaried). Thesegmentation observed in Table 5 is only applicableto one type of entrepreneurs, own-account workers.Figures 2 and 3 show the estimated effect of ageand education on the probability of becoming anown-account worker and an entrepreneur withemployees, respectively, using the coefficients fromTable 6. Figure 4 takes the difference of the twoestimated effects. Figure 2 shows the same segmenta-tion pattern found in the bivariate model (compareFigures 1 and 2), though a unified pattern exists forentry with employees (Fig. 3).The likelihood ofbecoming self-employed with at least one employeeincreases with both education and age. We alsoobserve these differences through those variablesused as proxies for unobserved ability: both wageand Lost Job show opposite signs depending on thetype of entry, implying that the best salaried workersare more likely to start a firm with employees. Theseresults are similar to Earle and Sakova’s (2000)analysis who find a clear separation betweenown-account workers and entrepreneurs withemployees.

Finally we study entry patterns from unemploy-ment. In this case, we take all unemployed individualsin the first period and generate a dependent variabletaking a value of 0 if he remains unemployed inthe second period (base category), 1 if salaried and 2if self-employed. Again we consider two differentspecifications (with and without interaction terms),which are reported in Table 7. Unfortunately,the EPH does not contain information about theduration of unemployment that is positively asso-ciated with the likelihood of becoming self-employed. However, in this case, the variableLost Job can be used as a proxy for the

11Wage effects are directly captured by the variable Log of hourly wage.

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permanence as unemployed. That is, individualswho are unemployed between surveys are morelikely to have a longer unemployment spell.Figures 5 and 6 plot the interacted effect ofeducation and age on the probability of becomingsalaried and self-employed, respectively; Fig. 7plots the difference between the surfaces of the lastfigures.

We also observe two different segments here.First, young uneducated workers are more likely tobecome self-employed (as compared with salariedworkers, see Fig. 7), although the probability offinding a job decreases with age for both typesof employment (Figures 5 and 6). On the other hand,highly educated individuals in their middle 30s aremore likely to transition to self-employment.

Table 6. Entry – multinomial logit (Dependent variable: 0¼ salaried to salaried, 1¼ salaried to own-account, 2¼ salaried to

entrepreneur with employees)

1 2 1 2

Education �0.106*** (0.015) 0.192*** (0.045) �0.695*** (0.205) �0.974 (0.696)Education squared/100 0.721*** (0.074) �0.210 (0.189) 2.783*** (1.025) 4.086 (2.940)Age 0.089*** (0.011) 0.171*** (0.026) �0.062 (0.047) �0.090 (0.185)Age squared/100 �0.093*** (0.013) �0.141*** (0.030) 0.056 (0.055) 0.071 (0.212)Educ*Age/1000 24.358 (10.074) 41.028 (33.015)Educ*Age^2/1000 �0.234 (0.120) �0.319 (0.379)Educ^2*Age^2/1000 0.757 (0.609) 1.121 (1.601)Educ^2*Age/1000 �0.824 (0.509) �1.485 (1.394)Log hourly wage �0.080*** (0.029) 1.504*** (0.056) �0.087*** (0.029) 1.499*** (0.056)Lost job 0.903*** (0.061) �1.083*** (0.329) 0.897*** (0.061) �1.093*** (0.329)Public adm. �0.704*** (0.077) �2.515*** (0.230) �0.709*** (0.077) �2.520*** (0.231)Female �0.602*** (0.052) �0.887*** (0.115) �0.601*** (0.052) �0.887*** (0.116)Head 0.064 (0.040) �0.074 (0.104) 0.054 (0.041) �0.085 (0.104)Firm size

2–5 �0.181** (0.072) �1.591*** (0.120) �0.183** (0.072) �1.601*** (0.120)6–15 �0.793*** (0.079) �1.604*** (0.122) �0.791*** (0.079) �1.612*** (0.122)16–25 �1.153*** (0.091) �2.357*** (0.152) �1.151*** (0.091) �2.365*** (0.152)26–50 �1.254*** (0.093) �2.648*** (0.157) �1.252*** (0.093) �2.657*** (0.157)51–100 �1.237*** (0.093) �3.120*** (0.175) �1.234*** (0.093) �3.125*** (0.175)101–500 �1.583*** (0.099) �3.726*** (0.192) �1.581*** (0.099) �3.732*** (0.193)�501 �1.594*** (0.130) �4.430*** (0.353) �1.596*** (0.130) �4.434*** (0.353)

Observations 72 221 72 221 72221 72221

Notes: SEs in parenthesis. *significant at 10%; **significant at 5%; ***significant at 1%. All specifications include time,region and industry dummies. This sample is restricted only to salaried employees, who either remain salaried or becomeself-employed.

0

5

10

15

Education

20

30

40

50

60

Age

-2.5-2

-1.5

-1

%Chg.P

0

5

10

15

-

Fig. 2. Effect of age and education on the

probability of entry (multivariate logit model) – own-account

worker

Note: Constructed using the coefficients of Table 6.

0

5

10

15

Education

20

30

40

50

60

Age

-2-1

0

%Chg.P

0

5

10

15

Fig. 3. Effect of age and education on the probability of

entry (multivariate logit model) – entrepreneur with

employees

Note: Constructed using the coefficients of Table 6.

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The negative sign in the Lost Job variable indicatesthat those individuals who were not able to find a jobbetween surveys are less likely to move to anemployed position. Furthermore, the fact that theLost Job coefficient associated with self-employmentis smaller in absolute value than the coefficient inthe salaried option signals that those individuals aremore likely to become self-employed.

Determinants of leaving the self-employed sector

This section analyses the other side of entrepren-eurship � the characteristics of those individualsleaving the sector. Table 8 reports a probit modelin which the dependent variable takes the value 0when the self-employed individuals stay in the sectorand the value 1 when they switch to the salariedsector (marginal effects on the probability arereported). Again we present two different specifica-tions, in which the second one only differs in theinclusion of interacted effects between educationand age. In the first two columns, we use thesample containing all types of entrepreneurs, whilein the remaining columns, we restrict the sample tothose individuals starting as own-account workersonly and who may either remain entrepreneurs(with or without employees) or become salaried thefollowing year.12 The model restricted to entrepre-neurs starting with employees is omitted since itseffects can be inferred by comparing the full sampleand own-account workers sub-sample [that is col-umns (1) with (3) and (2) with (4)].

We observe that both samples produce similarresults in terms of human capital variables, which

suggests that the differences between these twogroups are smaller in terms of exit. Figure 8 presentsthe estimated effect of education and age on theprobability of exit (the figure for own accountworkers is almost identical and it is omitted). Weobserve that for any particular level of education(except for the lowest level where there is somedegree of ambiguity), as the individual ages, exitbecomes less likely. This can be due to two factors:First, young workers have a higher probability ofplacing themselves in the salaried sector than olderindividuals; Second, as the individual becomesolder, the expected returns in the salaried sectordecrease relative to the self-employed sector, sinceretirement age is customarily lower for salariedworkers.

Regarding education, the effect on the likelihoodof leaving the self-employed sector dramaticallychanges depending on the age cohort. We find thatthe highest propensity to leave the sector is foryounger entrepreneurs with secondary school.Individuals with higher and lower levels of educa-tion have a lower propensity to leave this sector.On the other hand, for older cohorts, educationplays a less important role. For individuals agedabove 40, there is no significant difference betweeneducated and noneducated persons in the propensityof moving out of the sector. This might be due tothe fact that older people are relatively less market-able in the salaried sector.

We observe some interesting patterns among theremaining variables. The variable Lost job hasa strong positive impact since failure in an entrepre-neurial project will discourage individuals to stay inthe sector. The income variable has a negative impacton the likelihood of leaving the sector, while femalemicro-entrepreneurs are more likely to leave thesector. Finally, firm size does not seem to playa central role for small firms, though larger firmshave strong positive coefficients. However, giventhat this group is likely to be underrepresented,13

we will not pursue this direction further.

VI. Final Comments and Conclusions

The final verdict about the nature of entrepreneurshipcannot be unambiguously obtained. If anything,we observe that one segment of the micro-entrepreneur population has the characteristicspredicted by the dual market hypothesis, although

05

10

15

Education

20

30

40

5060

Age

-10

1

2

%Chg.P

5

10

15

Fig. 4. Effect of age and education on the probability

of entry (multivariate logit model) – own-account vs.

entrepreneur with employees

Note: Constructed using the coefficients of Table 6.

12We are also grateful to an anonymous referee for pointed this specification.13 This group is sufficiently small to be accurately captured by a typical household survey.

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Table 7. Entry – multinomial logit (Dependent variable: 0¼ unemployed to unemployed, 1¼ unemployed to salaried,

2¼ unemployed to entrepreneur)

1 2 1 2

Education �0.055** (0.023) �0.071*** (0.025) �0.375 (0.266) �0.898*** (0.316)Education squared/100 0.259** (0.113) 0.347*** (0.129) 1.907 (1.366) 3.638** (1.652)Age 0.041*** (0.012) 0.187*** (0.015) �0.035 (0.064) �0.007 (0.072)Age squared/100 �0.084*** (0.016) �0.214*** (0.018) 0.019 (0.076) �0.022 (0.085)Educ*Age/1000 18.785 (13.857) 33.904*** (15.983)Educ*Age^2/1000 �0.249 (0.170) �0.318* (0.193)Educ^2*Age^2/1000 1.221 (0.906) 1.098 (1.041)Educ^2*Age/1000 �0.945 (0.725) �1.279 (0.850)Lost job �0.982*** (0.041) �0.930*** (0.051) �0.980*** (0.041) �0.928*** (0.051)Female 0.029 (0.046) �0.704*** (0.062) 0.024 (0.046) �0.709*** (0.063)Head 0.240*** (0.053) 0.359*** (0.063) 0.240*** (0.053) 0.352*** (0.063)

Observations 13 943 13 943 13 943 13 943

Notes: SEs in parenthesis. *significant at 10%; **significant at 5%; ***significant at 1%. All specifications include time,region and industry dummies. This sample is restricted to unemployed, who either remain unemployed, become salaried orentrepreneur.

0

5

10

15

2030

40

50

60

Age

-2.5-2-1.5-1

%Chg.P

5

10

15

Education

Fig. 5. Effect of age and education on the probability of

finding a job (multivariate logit model) – salaried

Note: Constructed using the coefficients of Table 7.

0

5

10

15

20

30

40

50

60

Age

-1

-0.5

0

0.5

%Chg.P

0

5

10

15

Education

Fig. 7. Effect of age and education on the probability of

finding a job (multivariate logit model) – salaried vs

entrepreneur

Note: Constructed using the coefficients of Table 7.

0

5

10

15

2030

40

50

60

Age

-2-1.5-1-0.50

%Chg.P

0

5

10

15

Education

Fig. 6. Effect of age and education on the

probability of finding a job (multivariate logit model) –

entrepreneur

Note: Constructed using the coefficients of Table 7.

0

5

10

15

2030

40

50

60

Age

0

0.1

0.2

0.3

%Chg.P

5

10

15

Education

Fig. 8. Effect of age and education on the probability of exit

(probit model)

Note: Constructed using the coefficients of Table 8.

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another segment matches the industrialized worldview. The former corresponds to the young, unedu-cated workers who are more likely to enter and stayin the sector. The latter refers to more educatedmiddle aged individuals with higher levels of educa-tion, whose behaviour is similar to that predicted bythe Evans and Jovanovic liquidity constraints model,in which individuals accumulate human andfinancial capital in the salaried sector before startinga micro-firm.

An economic policy which targets self-employmentshould take into account this dichotomy. In parti-cular, it should distinguish micro-firms, which serveas a shelter for workers excluded from the salariedsector, from those firms which represent genuineincome and employment growth potential. This isespecially important for policies regarding creditfor micro-firms and those targeting the poor.While self-employment can be a good mechanism toalleviate poverty, it may not equally increase pro-ductivity and employment. Therefore, if the samepolicy programme is applied uniformly to the wholesector, the economic incentives, which were designedfor a specific purpose, might work improperly.

As an example, consider the following case:After an economic recession, workers who had losttheir jobs might feel impelled to start their ownbusiness activities by means of the severance paythey receive. Generally, the new entrepreneurial

activity fails shortly after it was started.Programmes targeting those micro-firms might beineffective, provided the nature of those firms.Instead, the government aid should be used toalleviate their temporary income loss. By no meansdoes this imply that the government should abandontraining programmes for entrepreneurs; rather, itshould expand the nature of its help. On the otherhand, fired workers or those who choose to quitwho had genuine entrepreneurship ability mightbenefit more from receiving credit than fromunemployment benefits.

Acknowledgement

Financial support for this article was provided byThe Tinker Foundation. We are grateful to WernerBaer and William Maloney for their guidance and toan anonymous referee for his constructive commentsand suggestions.

References

Bates, T. (1990) Entrepreneur human capital inputsand small business longevity, Review of Economicsand Statistics, 72, 551–59.

Table 8. Exit (Dependent variable: 0¼ entrepreneur to entrepreneur, 1¼entrepreneur to salaried)

1 2 3 4

Education �0.006** (0.003) 0.138*** (0.037) �0.007*** (0.003) 0.142*** (0.045)Education squared/100 0.013 (0.013) �0.682*** (0.176) 0.027 (0.017) �0.732*** (0.226)Age �0.019*** (0.002) 0.010 (0.009) �0.018*** (0.002) 0.009 (0.010)Age squared/100 0.014*** (0.002) �0.015 (0.010) 0.014*** (0.003) �0.014 (0.012)Educ*Age/1000 �6.215*** (1.774) �6.467*** (2.197)Educ*Age^2/1000 0.063*** (0.021) 0.066*** (0.026)Educ^2*Age^2/1000 �0.303*** (0.099) �0.347*** (0.127)Educ^2*Age/1000 0.300*** (0.085) 0.334*** (0.109)Log hourly income �0.020*** (0.004) �0.020*** (0.004) �0.013*** (0.005) �0.013*** (0.005)Lost job 0.083*** (0.013) 0.082*** (0.013) 0.080*** (0.015) 0.079*** (0.015)Female 0.013 (0.008) 0.015* (0.008) 0.021** (0.011) 0.021** (0.011)Head 0.000 (0.007) 0.001 (0.007) 0.007 (0.009) 0.008 (0.009)Firm Size

2–5 �0.010 (0.006) �0.009 (0.006)6–15 0.019 (0.017) 0.020 (0.017)16–25 0.057 (0.040) 0.057 (0.040)26–50 0.070 (0.044) 0.070 (0.044)51–100 0.117*** (0.061) 0.116* (0.060)101–500 0.362*** (0.092) 0.360*** (0.092)�501 0.499** (0.200) 0.499** (0.200)

Observations 25 295 25 295 16 492 16 492

Notes: SEs in parenthesis. *significant at 10%; **significant at 5%; ***significant at 1%. All specifications include time,region and industry dummies. This sample contains entrepreneurs, who either remain entrepreneurs or become salaried.Columns (3) and (4) restrict the sample to individuals who started as own-account workers.

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