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«Abstract We explore which financial constraints matter the most in the choice of becoming an entrepreneur. We consider a randomly assigned welfare ...»

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Liquidity, Risk, and Occupational Choices∗

Milo Bianchi† Matteo Bobba‡

October 2010


We explore which financial constraints matter the most in the choice of becoming

an entrepreneur. We consider a randomly assigned welfare program in rural Mexico

and show that cash transfers significantly increase entry into entrepreneurship.

We then exploit the cross-households variation in the timing of these transfers

and find that current occupational choices are significantly more responsive to the transfers expected for the future than to those currently received. Guided by a simple occupational choice model, we conclude that the program has promoted entrepreneurship by enhancing the willingness to bear risk as opposed to simply relaxing current liquidity constraints.

Keywords: Financial constraints; entrepreneurship; insurance; liquidity.

JEL codes: O16, G20, L26.

∗ We thank Abhijit Banerjee, Francois Bourguignon, Eric Edmonds, Marc Gurgand, Sylvie Lambert, Jean-Marc Robin for useful comments and Christian Lehmann for excellent research assistantship. The financial support of R´gion Ile-de-France and of the Risk Foundation (Groupama Chair) is gratefully e acknowledged.

† University Paris Dauphine; E-mail: milo.bianchi@dauphine.fr ‡ Ph.D. Candidate, Paris School of Economics; E-mail: bobba@pse.ens.fr 1 1 Introduction Poor households face multiple financial constraints. They often lack the possibility to attain optimal levels of saving, borrowing and insurance against income shocks. Sev- eral dimensions of their lives are affected by these constraints, so much that “access to finance” is today recognized as a fundamental ingredient of economic development (see e.g. Banerjee [2003]; Karlan and Morduch [2009]).

At the same time, understanding the effects of an improved access to finance poses some serious challenges. First, such an improvement seldom occurs for random reasons, which makes it hard to empirically estimate its effects. Moreover, and perhaps even more fundamentally, one would like to open the box of “access to finance” and understand which of the various financial constraints are most binding in a given situation. This is often complicate but obviously key for the interpretation of the effects and the design of effective policies.

This paper takes a step along these lines by asking whether financial constraints matter and which financial constraints matter the most in the choice of becoming an entrepreneur. The possibility for poor households to set up their own business is recog- nized as a key aspect in the process of development (Hausmann and Rodrik [2003]; Ray [2007]; Naud´ [2010]), while at the same time being often hindered by financial constraints e (Banerjee and Duflo [2005]; Levine [2005]).

We address these questions by first exploiting a random variation in household income to show that financial constraints prevent some individuals the possibility to become entrepreneurs. We then decompose financial constraints by distinguishing in particular whether individuals refrain from becoming entrepreneurs as they lack enough liquidity to undertake some initial capital investment -what we call liquidity constraints- or rather as they lack the ability to insure their income against the risk posed by entrepreneurial returns -what we call insurance constraints. We develop a simple model to highlight how these constraints respond differently to the time profile of expected income shocks and exploit the variation in the timing of these shocks to try separating the effects of liquidity and insurance constraints.

More specifically, we exploit the welfare program Progresa/Oportunidades, which targets poor households in rural Mexico and provides cash transfers conditional on their behaviors in health and children education. While Section 2 provides a more detailed description of the program, we here stress some features which make it interesting for our exercise. First, the timing of access into Progresa has been randomized, thereby providing us a reliable control group to estimate its effects on occupational choices. Second, transfers are administered for an extended and predictable time period and, albeit 2 partly conditional on schooling behaviors, they typically represent a sizable increase in households’ wealth. Moreover, and perhaps most importantly for our purposes, their magnitude and time profile vary substantially according to households demographics; as a result, households face different (and partly exogenous) shocks to their current liquidity and to their ability to insure against future income fluctuations.

We start our empirical analysis by simply comparing households in treated and control communities; we show that living in a treated community significantly increases the probability of entering self-employment both from salaried work and from unemployment. Furthermore, after a series of test, we can rule out that the fact that transfers are conditional on sending children to school may explain our results (as for example it may induce a reallocation of labor within the household). Hence, we can interpret the treatment impacts as the result of income shocks and thus as (indirect) evidence that households face financial constraints.

In search of a better understanding of which financial constraints the program has relaxed the most, and distinguishing in particular liquidity from insurance constraints, we exploit the fact that, as mentioned, treated households face significantly different patterns of transfers. For example, families with a child in the ninth grade are entitled to a substantial amount of current transfers but very little transfers in a year (since in our sample period children stop being eligible after the ninth grade), while those with a child in the eighth grade have somewhat lower current transfers but much higher future transfers. We then ask in which household adult members are more likely to become entrepreneurs, and more generally whether this choice is more responsive to the size of transfers currently received or to the size of transfers expected for the future.

In order to guide our interpretation, we develop a simple occupational choice model in which individuals may face liquidity or insurance constraints. If wealth cannot be freely allocated across periods, due for example to borrowing or saving constraints, current and future transfers have different effects on the choice of becoming entrepreneur. The amount of transfers currently received is better suited to help incurring start-up costs and so it is more important if liquidity constraints are binding. Conversely, future transfers are better suited to provide insurance against future income drops due to business failure and so they have stronger effects if insurance constraints are binding.

We then test empirically whether the choice of becoming entrepreneur in the current period is more responsive to the size of transfers recently received or to those expected for the future. In order to do so, we first rule out that the very same household characteristics which determine the profile of transfers determine also occupational choices. We then show that the probability to become entrepreneur is significantly more responsive to the amount of transfers expected for the near future than to the amount currently received.

3 In our view, these results tend to support the hypothesis that the program has been effective in promoting micro-entrepreneurship as it has relaxed insurance constraints as opposed to simply relaxing current liquidity constraints. While one may think of alternative stories whereby both current and future transfers matter (for example, future transfers may be used as collateral for moneylenders; or future investment may be needed to keep-up with the business needs), it is hard to explain that future transfers matter more based on liquidity constraints. This may suggest that financial barriers to entry into self-employment are not the most important obstacle in our setting (see McKenzie and Woodruff [2006] for similar evidence on micro-enterprises in urban Mexico). Instead, future transfers matter as they enhance the possibility to insure against future income fluctuations. In our case, this translates into some salaried individuals being willing to undertake the risky choice of setting up a business.

1.1 Related Literature This paper builds on the literature on the effects of improved access to finance on occupational choices. Under non-experimental research designs, Holtz-Eakin, Joulfaian and Rosen [1994] and Blanchflower and Oswald [1998] show that having received an inheritance increases the probability of being or remaining self-employed. In experimental settings, de Mel, McKenzie and Woodruff [2008] consider a sample of individual who already have a business in Sri Lanka and show that a random prize in cash or in kind considerably boosts their profits. More generally, a substantial literature has explored the effects of improved access to credit and to insurance (see e.g. Besley [1995] and Banerjee [2003] for reviews). In this literature, however, experimental evidence is still scarce and very recent (see Banerjee et al. [2009] and Zinman and Karlan [2009] for evidence on micro-credit in India and in the Philippines, respectively, and Gin´ and Yang [2009] for e evidence on weather insurance in Malawi). Moreover, despite liquidity and insurance constraints are often interrelated (Ray [1998]), little has been done to try separating their effects, which is the main focus of our paper. One notable exception is Dercon and Christiaensen [2007], who attempt to distinguish seasonal credit constraints from inter-temporal constraints related to risk on fertilizer adoption in rural Ethiopia.

Finally, in spite of the substantial body of research related to Progresa and its experimental design, to our knowledge no study has explicitly looked at the effects of the cash transfer on occupational choices. Related and complementary evidence is provided in Skoufias and Di Maro [2008] who study the incentive effects of Progresa on adult labor supply and in Gertler, Martinez and Rubio-Codina [2006], who show that the program increased productive investments and so long-term welfare.

42 Data

2.1 Program Description Launched in Mexico in 1997, Progresa is a large scale welfare program mainly aimed at improving health and human capital accumulation in the poorest rural communities.

It provides households with conditional cash transfers targeted to specific behaviors in nutrition, health and education. Initially, 506 rural villages were selected to be part of the program evaluation sample. Within those, 320 villages were randomly allocated to the treatment group and 186 villages to the control group. To check the effectiveness of randomization, Table 1 presents baseline summary statistics of several individual, household and village characteristics for the treatment and control groups, as well as the two-sided t-test that the difference in means is different from zero. None of the variables displays statistically significant baseline differences, hence confirming that randomization has been successful in attaining balanced treatment and control populations.

Cash transfers from Progresa are given bimonthly to the female head of eligible households and they come in two forms.1 The first is a fixed food stipend of 105 Pesos per month conditional on family members obtaining preventive medical care. The second is an educational scholarships which is provided for each child less than 18 years old and enrolled between the 3rd and the 9th grade, conditional on attending school a minimum of 85% of the time and not repeating a grade more than twice. As shown in Figure 1, these transfers vary between 105 and 375 Pesos per month per child, they increase with school grade and, in grades 7th to 9th, they are larger for girls than for boys.2 These amounts can be substantial: median benefits are 176 Pesos per month (roughly 18 USD in 1998), equivalent to about 28% of the monthly income of beneficiary families.

2.2 The Sample The evaluation surveys of Progresa consist of socioeconomic characteristics at the individual level repeatedly collected for 24,077 households, of which about 53% classified as eligible. A baseline survey was conducted in October 1997 and it has been followed by Household Evaluation Surveys collected every 6 months for a total of 5 waves after the baseline. Eligible households in treatment communities start receiving benefits in 1 The status of eligible household is based on a welfare index built on asset holdings in the baseline and it was intended to remain unchanged for the entire duration of the program. However, around 3,000 households were classified as non-poor in the baseline but were later re-classified as eligible. In order to avoid arbitrary classifications, we exclude them from our analysis (results are unchanged once we include them).

2 These figures are expressed in current Pesos as of the second semester of 1998. Transfer size has been increased over time in order to adjust for inflation.

5 March-April 1998; whereas eligible households in control communities were not incorporated until November 1999. In most of our analysis, we focus on eligible households during the experimental period: in addition to the baseline, we employ the first three waves of the follow-up surveys, from October 1998 to October 1999. Within this sample, program take-up was remarkably high: 94% of the treated households and 96% of the control households are reported receiving positive transfers within 18 months since program offering. Sample attrition is low (11%) and non response in the occupational choice somewhat larger (17%); however, none are related to the treatment assignment.

In the baseline, we have information on the main occupation of 20,770 eligible adult individuals (18 years old or more). Among them, 8% are entrepreneurs (mostly selfemployed), 39% are salaried, and the remaining 53% do not have a paid occupation (we refer to them as unemployed). The great majority (93%) of the unemployed are women and the reverse hold for salaried workers, whereas about 25% of the entrepreneurs are women.

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