metadata: authors: 0: author: Montenegro Calderon,Gabriela
1: author: Iacovone,Leonardo
2: author: Juarez,Laura
count: Mexico
docna: 0: docna: Opportunity versus necessity : understanding
            the heterogeneity of female micro-entrepreneurs
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Private Sector Economics,Industrial Economics,Labor Markets,Educational Populations,Private Sector Development Law,Education for Development (superceded),Economic Growth,Education For All,Marketing
teratopic: Gender,Macroeconomics and Economic Growth
abstracts: cdata!: Entrepreneurs that voluntarily choose to
            start a business because they are able to identify a good
            business opportunity and act on it -- opportunity
            entrepreneurs -- might be different along various dimensions
            from those who are forced to become entrepreneurs because of
            lack of other alternatives -- necessity entrepreneurs. To
            provide evidence on these differences, this paper exploits a
            unique data set covering a wide array of characteristics,
            including cognitive skills, non-cognitive skills and
            managerial practices, for a large sample of female
            entrepreneurs in Mexico. Descriptive results show that on
            average opportunity entrepreneurs have better performance
            and higher skills than necessity entrepreneurs. A
            discriminant analysis reveals that discrimination is
            difficult to achieve based on these observables, which
            suggests the existence of unobservables driving both the
            decision to become an opportunity entrepreneur and
            performance. Thus, an instrumental variables estimation is
            conducted, using state economic growth in the year the
            business was set up as an instrument for opportunity, to
            confirm that opportunity entrepreneurs have higher
            performance and better management practices.
geo_reg_key: 119228,517191
repnme: repnme: Opportunity versus necessity :
            understanding the heterogeneity of female micro-entrepreneurs
display_title: Opportunity versus necessity :
            understanding the heterogeneity of female micro-entrepreneurs
listing_relative_url: /research/2016/04/26218482/opportunity-versus-necessity-understanding-heterogeneity-female-micro-entrepreneurs
topicv3: Private Sector Development m1326060 1921,Education, Skills Development and Labor
            Market m1326135 1484,Economic Growth m1326241 471,Soft Skills m1327287 2137,Entrepreneurship m1582903 4424
publishtoextweb_dt: 2016-04-13T00:00:00Z
historic_topic: Gender,Macroeconomics and Economic Growth
no_of_pages: 16
author_keys: 0: author_key: 1514534
1: author_key: 900307
model_extractions: WPS7636 Policy Research Working Paper 7636 Opportunity versus Necessity Understanding the Heterogeneity of Female Micro-Entrepreneurs _Gabriela Calderon Leonardo Iacovone Laura Juarez_ Trade and Competitiveness Global Practice Group April 2016
Policy Research Working Paper 7636

# **Abstract**

Entrepreneurs that voluntarily choose to start a business because they are able to identify a good business opportunity and act on it—opportunity entrepreneurs—might be different along various dimensions from those who are forced to become entrepreneurs because of lack of other alternatives—necessity entrepreneurs. To provide evidence on these differences, this paper exploits a unique data set covering a wide array of characteristics, including cognitive skills, non-cognitive skills and managerial practices, for a large sample of female entrepreneurs in Mexico. Descriptive results show that on average opportunity entrepreneurs have better performance and higher skills than necessity entrepreneurs. A discriminant analysis reveals that discrimination is difficult to achieve based on these observables, which suggests the existence of unobservables driving both the decision to become an opportunity entrepreneur and performance. Thus, an instrumental variables estimation is conducted, using state economic growth in the year the business was set up as an instrument for opportunity, to confirm that opportunity entrepreneurs have higher performance and better management practices.

This paper is a product of the Trade and Competitiveness Global Practice Group. The paper has been prepared as a background research paper for the World Development Report 2016 “Digital Dividends.” It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at liacovone@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# versus the Opportunity Necessity: Understanding Heterogeneity of Female Micro-Entrepreneurs<sup>_∗_</sup>

Gabriela Calderon,<sup>_†_</sup> Leonardo Iacovone,<sup>_‡_</sup> and Laura Juarez<sup>_§_</sup> JEL Classification: D22, D24, L26, O12

## Keywords: Entrepreneurship, Opportunity vs Necessity, Skills, Firm Performance

> _∗_ We gratefully acknowledge funding from Instituto Nacional del Emprendedor (INADEM) at the Ministry of Economy in Mexico, USAID, The World Bank and the Instituto Nacional de las Mujeres (INMUJERES). We thank Paulina L ́opez and Sandra Aguilar for outstanding research assistance. The views expressed in this article are solely those of the authors and do not necessarily reflect those of Banco de Mexico or the World Bank.

> _†_ Non-resident research fellow at Poverty and Governance, Stanford University. Email address:gabcal@alumni.stanford.edu

> _‡_ The World Bank, 1818 H Street NW, Washington, DC. Email address: liacovone@worldbank.org

> _§_ Direcci ́on General de Investigaci ́on Econ ́omica, Banco de M ́exico, Av. 5 de mayo 18, M ́exico, D.F. Email address:ljuarezg@banxico.org.mx
# **1 Introduction**

Support programs for micro-businesses have become increasingly common in developing countries in recent years for at least two reasons: first, micro-enterprises employ a substantial fraction of individuals in these economies (about 47 percent in Mexico ); and second, despite their prevalence, the majority of these micro-enterprises tend to stay small and have low productivity. However, the impact of such programs —many of which provide business grants, training or a combination of both— has been mixed at best. This raises the question about whether these impacts depend on characteristics or attitudes of the entrepreneur, and therefore better targeting could significantly improve them. In fact, some recent evidence suggests that, even though the mean effects of business training might be small and not significant, greater returns are concentrated in high-potential entrepreneurs, who are the most likely to adopt better entrepreneurial practices and earn higher profits after training (Calderon _et al._ 2013, De Mel _et al._ 2012, Fafchamps _et al._ 2014).

In this paper, we provide some novel evidence on the heterogeneity of female microentrepreneurs in urban Mexico by comparing those who started their business out of opportunity with those who did so out of necessity. Our data come from the baseline survey for the randomized evaluation of a large business training program for female entrepreneurs, funded by the National Institute of Entrepreneur and implemented by the NGO CREA, _Mujeres Moviendo M ́exico_ . This data set is unique in that it provides detailed information on business outcomes, access to credit, cognitive and non-cognitive skills for a large sample of female micro-entrepreneurs in selected urban areas in Mexico.

For the empirical analysis, we first show mean differences in business performance, characteristics and skills between the two groups of microentrepreneurs. Then, we use discriminant analysis to find the combination of observable characteristics that best distinguishes opportunity from necessity entrepreneurs.<sup>1</sup> Finally, we estimate the effect of being an opportunity entrepreneur on the profitability of the business and the quality of management practices, using state GDP growth at the time when the business was opened as an instrument for opportunity.

Our results suggest that on average opportunity entrepreneurs have significantly higher profits, better management practices, and higher cognitive and selected non-cognitive skills. Our discrim-

> 1Discriminant analysis is a technique of species classification that has been used before to study microentrepreneurship by de Mel, McKenzie and Woodruff (2010) for Sri Lanka; and by Bruhn (2013) for Mexico.

2
inant analysis shows that management practices, some business characteristics and skills of the entrepreneur can be used to separate both groups, but the distinction based on these observables is only partially successful, which suggests the existence of unobservables determining both the decision to set up a business, based on a perceived opportunity, and firm performance. Finally, our instrumental variables results confirm that opportunity entrepreneurs have higher performance measured as profits and management practices, even after controlling for the observable skills of the entrepreneur.

The policy relevance of these findings is twofold. First, as mentioned before, identifying the observable characteristics that define opportunity entrepreneurs could potentially improve the targeting of business support programs, by concentrating them on the entrepreneurs with the highest growth potential. This by no means implies disregarding low-performing necessity entrepreneurs. On the contrary, the latter might be better served by interventions that help ease their transition to salaried employment. Second, our focus on female entrepreneurs is also of great relevance given that their businesses seem to be at a particular disadvantage when trying to scale-up. For instance, in Latin America, micro-firms led by women have been found to have an even smaller size and lower productivity, compared to those led by men (Bruhn, 2009). Thus, the evidence provided in this paper can be readily applied to those in more need of targeted support.

# **2 Data and descriptive analysis**

Our data come from a baseline survey conducted in 2014 in eight urban areas in Mexico: Aguascalientes, Dolores Hidalgo, Irapuato, Le ́on, Mexico City, Quer ́etaro, San Juan del R ́ıo and Toluca. For the sampling design, a number of census tracks (called AGEBs by the Mexican Statistical Institute, INEGI) with relatively high concentration of commercial activity were chosen.<sup>2</sup> The questionnaire was applied only to women who are business owners or partners, i.e. who take managerial, financial and marketing decisions, and the sample included firm with no more than 5 employees or sales that are less than 4 million Mexican pesos per year (which is the definition of a

> 2These areas with higher concentration of commercial activity were identified relying on the 2009 Economic Census data.

3
micro-enterprise).<sup>3</sup> Our survey has information on a uniquely broad range of variables: sociodemographic characteristics, current and initial business characteristics, business outcomes (sales, profits and costs), managerial practices, cognitive and non-cognitive skills of the entrepreneur, access to credit, growth expectations and obstacles, among others. The sample consists of 10,275 female micro-entrepreneurs. To our knowledge, our survey is the first one measuring both cognitive and non-cognitive skills, in addition to business characteristics and outcomes, for such a large sample of micro-entrepreneurs in Mexico.

We classify the women in our sample as ”opportunity” or ”necessity” entrepreneurs according to the self-reported reason for opening their business. A respondent is classified as opportunity entrepreneur if she reports opening her business either because (i) she wanted to become independent, (ii) she had money and found a good business opportunity or (iii) she wanted to practice her profession or develop her career profile. Conversely, necessity entrepreneurs are those who started their business because they could not find a well-paid or suitable job and needed a source of income. After excluding women who started their business out of family tradition or other reasons, we are left with a sample of 8,949 entrepreneurs (21 percent classified as opportunity and 79 percent as necessity).

In Table 1, we report the mean differences between opportunity and necessity entrepreneurs in the following characteristics: (i) Business performance measures (weekly profits, sales and sales per worker); (ii) business practices, as measured by a standardized Composite Business Practices score (CBP)<sup>4</sup> ; (iii) characteristics of the business and the entrepreneur (age, proportion with at least one worker and monthly salary expenses); (iv) cognitive skills (education, standardized scores of Raven and digit span recall tests); and (v) non-cognitive skills (standardized measures of agreeableness, conscientiousness, extraversion, intellect/ imagination and neuroticism<sup>5</sup> , impulsiveness, locus of control, willingness to take risks, self-confidence, self-efficacy, optimism, self-satisfaction and trust).

> 3Fixed and semi-fixed stands on the street, and dwellings in which the woman was found selling or producing something (or had a sign indicating so) also qualified as enterprises and were included in the survey.

> 4The CBP score is an index that measures how well entrepreneurs in our sample manage their business and is constructed considering measures of marketing, keeping stock, record keeping and financial planning, following Fafchamps and Woodruff (2014). 5These personality traits are referred to as ”the big five” in the literature.

4
Regarding business performance, Table 1 shows that mean weekly profits and sales are higher for opportunity than for necessity entrepreneurs and the difference is statistically significant. Mean weekly sales per worker are also higher for opportunity entrepreneurs, but the difference is not statistically significant, probably because, as shown in the next panel, those entrepreneurs have a significantly larger number of workers. Opportunity entrepreneurs also have a significantly higher composite business practice score, compared to necessity ones. This descriptive evidence confirms that opportunity entrepreneurs have both better business performance and are manage their businesses significantly better. Opportunity entrepreneurs are 4 years younger than necessity ones on average, and their businesses are about 13 months younger. About 32 percent of opportunity entrepreneurs have at least one worker or more, whereas only 23 percent of necessity ones do. As a result, opportunity entrepreneurs have about double the salary expenses per month compared to necessity ones. All these mean differences are statistically significant.

The next panel in Table 1 shows that, compared to necessity entrepreneurs, opportunity ones also have significantly higher mean cognitive skills. They have significantly higher mean scores in the Raven and digit span recall tests, and 1.5 more years of schooling. The mean difference in schooling between groups corresponds roughly to that between having finished secondary school and proceeded to the first year of high school and not having finished secondary school.

The last panel in Table 1 shows that opportunity entrepreneurs have statistically significant higher mean scores for locus of control, impulsiveness, self-confidence, self-satisfaction, willingness to take risks, optimism and their attitude towards business growth, compared to their necessity counterparts, and the opposite is observed for extraversion, conscientiousness and selfefficacy. However, it is reasonable to think that at least three of those traits in which opportunity entrepreneurs score higher on average - namely willingness to take risks, optimism and their attitude towards business growth - could be correlated with better business outcomes.

# **3 Discriminant analysis**

To complement the descriptive analysis presented so far, we use discriminant analysis to explore whether management practices, business and entrepreneur characteristics and skills can be used to distinguish opportunity from necessity entrepreneurs; and then use the estimates to predict 5
whether a given observation belongs to each group.<sup>6</sup> Table 2 presents the results for our full sample of both necessity and opportunity entrepreneurs, and then for the opportunity group compared to the top and bottom profit quartiles of the necessity group. We vary the set of characteristics that are used to separate entrepreneurs into different species. For instance, in Panel A, for the full sample of entrepreneurs, using only measures of business performance would lead us to correctly classify 52 percent of entrepreneurs as opportunity and 63 percent as necessity. Using measures of business characteristics and skills seems to improve the classification of opportunity entrepreneurs, as measured with a larger proportion of correctly classified ones, but not necessarily so for necessity entrepreneurs. In the last row of Panel A, using all variables combined results in 60 and 63 percent of opportunity and necessity entrepreneurs to be correctly classified, respectively. This is about a 10 percentage point improvement from classifying entrepreneurs as opportunity or necessity over a random classification, i.e. one that would be obtained by simply flipping a coin, indicating that there might be other unobserved factors that explain that a women is a necessity or an opportunity entrepreneur.

The last two columns in Panel A show that, when including all the explanatory variables together, our model would classify 42 percent of the entrepreneurs as opportunity and 58 percent as necessity, whereas in our sample the proportions reported are 21 and 79 percent, respectively. This suggests that some entrepreneurs who report themselves as necessity ones, are in fact more similar to their opportunity counterparts, according to the discriminant analysis.

Panels B and C show the results when including all the opportunity entrepreneurs in our sample and only necessity entrepreneurs in the bottom and top quartiles of self-reported daily profits. The objective is to see whether high and low performing necessity entrepreneurs are in fact classified as more or less similar to opportunity ones. For instance, Panel B shows that, when all variables are combined in the discriminant analysis, about 74 percent of low-performing necessity entrepreneurs are correctly classified as being so. In comparison, in Panel C, about 49 percent of high-performing entrepreneurs are correctly classified as being necessity ones. This suggests that some high-performing necessity entrepreneurs are more similar in the characteristics we use for the discriminant analysis to opportunity ones, as would be expected. The last column shows that, although in Panel B about 57 percent of entrepreneurs overall are classified as necessity, which is

> 6For a description of this methodology, please refer to de Mel, McKenzie and Woodruff (2010).

6
not very different from the results for the overall sample in Panel A, in Panel C only 44 percent of high-performing entrepreneurs are classified as necessity ones, confirming that a higher proportion of high-performing necessity entrepreneurs in fact ”look like” their opportunity counterparts.

In conclusion, the discriminant analysis suggests that necessity and opportunity entrepreneurs differ in key characteristics and abilities, but some among those starting their business out of necessity actually resemble their more able opportunity counterparts. Therefore classifying entrepreneurs based on observable characteristics seems not to be very accurate. This suggests the existence of unobservable traits that drive both the decision to set up a business to pursue a good opportunity and the entrepreneur’s performance. Accordingly, in order to confirm our descriptive results suggesting that opportunity entrepreneurs have higher performance, we need to adopt an IV approach, which we discuss in the following section.

# **4 Instrumental Variables Approach**

Opportunity entrepreneurs appear to perform better than necessity ones. In order to estimate the partial effect of opening a business out of opportunity, controlling for the relevant observable characteristics of the firm and the entrepreneur, we estimate the following regression: Where _yi_ is alternatively the log of self-reported weekly profits or CBP score observed in 2014. Our key independent variable is _opportunity_ , a dummy indicating that the entrepreneur reported opening her business out of opportunity, defined as before, and _Xi_ is a row vector that includes age of entrepreneur, Raven test score, span test score, years of schooling, locus of control, risk attitude, self satisfaction and optimism which were the observable characteristics of the entrepreneur and the firm that were statistical significant among all of the variables used in our descriptive analysis. Robust standard errors are estimated for this equation.

Even after controlling for all the measures of skills that are available to us, _opportunity_ might be endogenous in equation (1), because it might be correlated with unobservable characteristics of 7
the entrepreneur, like her social networks that contribute to have higher profits and are captured in _ψi_ . To overcome this endogeneity, we use a two-stage least square (2SLS) estimation in which the first-stage equation is: _opportunityi_ = _α_ + _βGDPgrowth_ _~~t~~_ 0 _s_ + Γ<sup>_T_</sup> _Xi_ + _εi_ (2) where _GDPgrowth_ _~~t~~_ 0 _s_ , the instrument, is the GDP growth observed in state _s_ where the entrepreneur _i_ lives, in the year _t_ 0 she decides to open her business. Given that our survey measures profits and management performance in 2014, our assumption is that state GDP growth at the time when the business was set up is exogenous to profits (and management performance) various years later and only influences them through the choice of starting a business out of necessity or opportunity.<sup>7</sup> When we use a 2SLS specification we include years of opening fixed effects in both stages, in this way, we compare firms that opened in the same year and have similar characteristics.

Table 3 presents the estimation results. Panel A and B show the effect of the _opportunity_ dummy on weekly profits and the CBP score, respectively. Panel C, shows the first stage of the 2SLS presented in column 2. Column 1 shows that when estimating equation (1) by OLS, the effect of _opportunity_ on weekly profits is positive and significant at 1 percent in Panel A, and so is the effect of this same variable on the CBP score in Panel B. In column 2, controlling for the potential endogeneity of this variable yields a much larger positive coefficient for opportunity for both profits and the CBP score, significant at 10 percent for both. The estimate in Panel A implies that women who open their business out of opportunity in year _t_ 0, induced by higher economic growth in their state in that same year, have 2.6 times higher weekly profits than necessity ones; and that part of this effect can be explained by a 2 times better CBP score, i.e. by better management practices.

The partial F statistic shown in panel C has a value less than 10, but larger than 5, which means that the maximum IV bias we are tolerating is less than 20 percent (Staiger and Stock, 1997). The fact that the 2SLS estimates in column 2 are larger than the OLS ones in both panel A and B, might indicate that _opportunity_ is measured with error. An alternative explanation is that our instrument is identifying the effect of _opportunity_ for a specific subset of “compliers”, in our

> 7Poschke (2013) presents a model in which in each period, individuals decide whether to work or to run a firm. Selection of agents into entrepreneurship comes from the heterogeneity of the relevant outside option and the heterogeneity in firm’s performance.

8
case, women responding to become opportunity entrepreneurs when there is a higher state GDP growth in the year they opened their business or those who become necessity entrepreneurs when there is a low state GDP growth<sup>8</sup> ; i.e. a local average treatment effect (LATE), which might not be representative of the average population of female entrepreneurs.<sup>9</sup> The third column in Table 3 excludes the entrepreneurs who opened their businesses after 2011 from the estimation sample to address the concern that our instrument might directly affect profits observed in 2014, the year our survey was conducted, through a correlation with state GDP growth in that year. The estimate for _opportunity_ in Panel A and the first-stage coefficient of our instrument in Panel C are very similar in magnitude to our estimates in column 2, which is reassuring, even though both of them lose statistical significance, probably due to the smaller number of observations. The effect of _opportunity_ on the CBP score in column 3 is positive, larger than in column 2, and significant at 5 percent.

Table 4 presents some robustness checks. Specifically, we use state GDP growth observed either the year before ( _t_ 0 _−_ 1, in column 1) or the year after ( _t_ 0 + 1, in column 2) as instruments, instead of that observed during the same year ( _t_ 0). In Panel C, the estimate for lagged state GDP growth is close to zero and insignificant, whereas the one for _t_ 0 +1 is positive, of similar magnitude compared to the one for _t_ 0 in Table 3, and significant at 1 percent. In column 2, Panels A and B show that the effects on profits and CBP score are positive, but only the latter is significant at 10 percent. Finally, column 3 includes the state GDP growth in _t_ 0 _−_ 1, _t_ 0 and _t_ 0 + 1 together as instruments. In Panel A and B, the effects of _opportunity_ on profits and the CBP score are positive and significant at 10 and 5 percent, respectively, confirming that becoming an opportunity entrepreneur, induced by our instruments, increases business profitability in part due to better management. In Panel C, only the state GDP growth in _t_ 0 and _t_ 0 +1 have a positive and significant effect on the endogenous variable, but the first-stage partial F statistic for the three instruments is low (3.8).

> 8Poschke(2013) argues that agents that have a low productivity in the labor market have lower performance as entrepreneurs. Therefore it is possible that this type of people are commonly laid off when there is low GDP growth and always have low entrepreneurial performance.

> 9If performance and the instrument are related in a linear way then it is possible that our estimates are upward biased due to a LATE. See Angrist and Krueger (1994, 2001) for a discussion of both measurement error and the LATE interpretation of the IV estimate, among other issues.

9
# **5 Conclusions**

We provide new evidence suggesting that opportunity entrepreneurs have more profitable businesses, manage them better and have higher measures of cognitive and of some non-cognitive skills, compared to necessity ones. Using a discriminant analysis, we show that combining these characteristics allows us to separate both types of entrepreneurs, although not perfectly, and identify those that even though they started their business out of necessity are more similar in observables to their opportunity counterparts. Given that the discriminant analysis only increases 10 percentage points of the prediction between necessity and opportunity entrepreneur compared to a random assignment, the characteristics of the firm and of the entrepreneur considered in the analysis cannot fully explain when an entrepreneur is either an opportunity or a necessity one. In this way, we proceed to analyze this characteristic with an instrumental variable approach, which confirms that businesses led by opportunity entrepreneurs are significantly more profitable than those led by necessity entrepreneurs, in part because of better management practices. These results are relevant for improving the targeting of business support programs. For instance, these programs could obtain larger impacts by concentrating on the entrepreneurs with the highest growth potential, whereas other types of interventions might help low-performing necessity entrepreneurs improve their prospects for salaried employment.

# **References**

- [1] Angrist, J. D. and A. B. Krueger (2001). “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments.” Journal of Economic Perspectives, 13(2):69-85.

- [2] Angrist, J. D. and A. B. Krueger (1994). “Why Do World War II Veterans Earn More than Nonveterans?” Journal of Labor Economics, Vol 12, No.1 74-97.

- [3] Bruhn, M. (2009). “Female-owned Firms in Latin America: Characteristics, Performance, and Obstacles to Growth.” World Bank Policy Research Working Paper Series 5122.

10
- [4] Bruhn, M. (2013). “A Tale of Two Species: Revisiting the Effect of Registration Reform on Informal business Owners in Mexico.” Journal of Development Economics, vol. 103(C), 275283.)

- [5] Calderon, G., J. M. Cunha and G. De Giorgi (2013). “Business Literacy and Development: Evidence From a Randomized Controlled Trial in Rural Mexico.” NBER Working Papers 19740.

- [6] De Mel, S., D. McKenzie and C. Woodruff (2010). “Who are the Microenterprise Owners? Evidence from Sri Lanka on Tokman versus De Soto,” NBER Chapters, in: International Differences in Entrepreneurship, National Bureau of Economic Research, 63-87.

- [7] De Mel, S., D. McKenzie and C. Woodruff (2012). “One-time Transfers of Cash or Capital have Long-lasting Effects on Microenterprises in Sri Lanka.” Science. vol. 335, no. 6071, 962966.

- [8] Fafchamps, M., and C. Woodruff (2014). “Identifying Gazelles: Expert Panels vs. Surveys as a Means to Identify Firms with Rapid Growth Potential” Competitive Advantage in the Global Economy (CAGE). mimeo.

- [9] Fafchamps, M., D. McKenzie, S. Quinn, and C. Woodruff (2014). “Microenterprise Growth and the Flypaper Effect: Evidence from a Randomized Experiment in Ghana. ” Journal of Development Economics, 106, 211-226.

- [10] Poschke, M. (2013).“Who Becomes an Entrepreneur? Labor Market Prospects and Occupational Choice.” Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), 693-710.

- [11] Staiger, D. and J. Stock (1997). “Instrumental Variables Regression with Weak Instruments.” Econometrica, Vol. 65, No.3, 557-586.

11
Table 1: Mean Difference between Opportunity and Necessity Entrepreneurs (full sample)

||Opportunity groupmean|Necessity groupmean|Difference|
|---|---|---|---|
|**Measures ofperformance**||||
|Weekly profts (self reported)|1937.635|1349.177|588.4576<sup>_∗∗∗_</sup>|
|Weekly sales (self reported)|4507.258|3540.586|966.6726<sup>_∗∗∗_</sup>|
|Weekly sales/workers|4395.353|4302.254|93.09963|
|Composite Business Practice score(standarized)|.3060325|-.0842768|.3903093<sup>_∗∗∗_</sup>|
|**Business characteristics**||||
|Age of entrepreneur|41.81658|45.92807|-4.111494<sup>_∗∗∗_</sup>|
|Age of business in months|90.59086|103.6411|-13.05026<sup>_∗∗∗_</sup>|
|<br>Proportion with one worker or more|.3185|.2278|.0907<sup>_∗∗∗_</sup>|
|Costs:monthlysalaryexpenses|1025.357|498.751|526.6064<sup>_∗∗∗_</sup>|
|**Cognitive skills**||||
|Total score of raven test (standarized)|.0827833|-.0211648|.1039482<sup>_∗∗∗_</sup>|
|<br>Total score of digit span test (standarized)|.1459293|-.0328984|.1788277<sup>_∗∗∗_</sup>|
|Years of schooling|9.789416|8.251668|1.537748<sup>_∗∗∗_</sup>|
|**Non-cognitive skills**||||
|Extraversion (standarized)|-.0365413|.0155396|-.0520809<sup>_∗∗_</sup>|
|Agreeableness (standarized)|-.0093652|.0075027|-.0168679|
|Conscientousness (standarized)|-.069074|.0121551|-.0812292<sup>_∗∗∗_</sup>|
|Neuroticism (standarized)|-.0246642|.0130341|-.0376982|
|Intellect/imagination (standarized)|.0074094|-.0046432|.0120526|
|<br>Self effcacy (standarized)|-.0957561|.0110521|-.1068082<sup>_∗∗∗_</sup>|
|<br>Locus of control (standarized)|.0954217|-.0202922|.1157139<sup>_∗∗∗_</sup>|
|Impulsiveness (standarized)|.0480202|-.0170598|.06508<sup>_∗∗_</sup>|
|Self confdence (standarized)|.1074716|-.0390423|.1465139<sup>_∗∗∗_</sup>|
|Attitude towards risk (standarized)|.0975181|-.0298163|.1273345<sup>_∗∗∗_</sup>|
|Self satisfaction (standarized)|.1163573|-.0443902|.1607475<sup>_∗∗∗_</sup>|
|<br>Optimism (standarized)|.138387|-.0400507|.1784377<sup>_∗∗∗_</sup>|
|Attitude towards trust (standarized)|.0137271|-.0011845|.0149116|
|Attitude towardsgrowth(standarized)|.0621355|-.0119533|.0740888<sup>_∗∗∗_</sup>|
|Observations|8949|||

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01
Table 2: Discriminant Analysis

|Variable set used<br>Opportunity<br>Necessity<br>in classifcation<br>correctlyclassifed(%)<br>correctlyclassifed(%)|Classifed as<br>opportunity (%)|Classifed as<br>necessity (%)|
|---|---|---|
|**Panel A: Full sample**|||
|Measures of performance only (1)<br>52.37<br>62.85|40.37|59.63|
|Business characteristics only (2)<br>59.18<br>56.19|46.48|53.52|
|Cognitive skills only (3)<br>59.06<br>56.45|47.17|52.83|
|Non-cognitive skills only (4)<br>57.13<br>53.53|48.78|51.22|
|All variables combined<br>60<br>62.75|42.06|57.94|
|**Panel B: Opportunity group and frstquartile of self reported daily profts of Nec**|**essity group**||
|All variables combined<br>60.00<br>74.11|43.13|56.87|
|**Panel C: Opportunity group and fourthquartile of self reported daily profts of N**|**ecessity group**||
|All variables combined<br>60.00<br>49.46|56.92|43.80|

Measures of performance (1) include Composite Business Practice score; Business characteristics (2) include: age of entrepreneur, age of business, proportion with one worker or more and monthly salary expenses; Cognitive skills (3) include raven (standarized) test score, digit span (standarized) test score and years of schooling; Non-cognitive skills (4) include (standarized) measures of agreeableness, conscientousness, extraversion, intellect/imagination, neuroticism, impulsiveness, locus of control, risk attitude, self-efficacy, optimism, self-confidence, self-satisfaction, attitude towards trust and attitude towards growth.

Table 3: OLS and 2SLS Estimations

||(1)<br>(2)<br>OLS model<br>2SLS model|(3)<br>2SLS model<br>Sample: _year ≤_2010|
|---|---|---|
|**Panel A: Week**|**ly profts(**_log_(_x_+ 1)**transformation)**||
|Opportunity|0.269<sup>_∗∗∗_</sup><br>2.609<sup>_∗_</sup><br>[0.038]<br>[1.332]|2.452<br>[1.493]|
|**Panel B: Comp**|**osite Business Practice score**||
|Opportunity|0.270<sup>_∗∗∗_</sup><br>2.067<sup>_∗_</sup>|3.538<sup>_∗∗_</sup>|
||[0.036]<br>[1.118]|[1.783]|
|**Panel C: First**|**stage**||
|Growth|-<br>1.237<sup>_∗∗∗_</sup><br>[0.467]|1.236<sup>_∗∗_</sup><br>[0.542]|
|F-statistic|-<br>7.014|5.204|
|_Prob > F_|-<br>0.008|0.023|
|Observations_†_|4549<br>4549|2728|

Note: All specifications include as controls: age of entrepreneur, Raven test score, Span test score, years of schooling, locus of control, risk attitude, self satisfaction, optimism, and fixed-effects for years when the entrepreneurs opened their own businesses. Robust standard errors in brackets;<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _†_ Observations in top 1% of (transformed) weekly profits were not included.

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Table 4: Robustness Checks

||(1)|(2)|(3)|
|---|---|---|---|
||IV:_Growtht−_1|IV:_Growtht_+1|IV: All instruments|
|**Panel A: Weekly profts(**_l_|_og_(_x_+ 1)**transfor**|**mation)**||
|Opportunity|12.482<br>[29.381]|1.218<br>[0.985]|1.368<sup>_∗_</sup><br>[0.806]|
|**Panel B: Composite Busin**|**ess Practice score**|||
|Opportunity|7.584<br>[17.986]|1.785<sup>_∗_</sup><br>[1.030]|2.138<sup>_∗∗_</sup><br>[0.881]|
|**Panel C: First stage**||||
|_Growth_|-|-|1.183<sup>_∗∗_</sup><br>[0.575]|
|_Growtht−_1|0.200|-|-0.286|
||[0.467]||[0.494]|
|_Growtht_+1|-|1.402<sup>_∗∗∗_</sup>|1.097<sup>_∗∗_</sup>|
|||[0.513]|[0.553]|
|F-statistic|0.184|7.476|3.856|
|_Prob > F_|0.668|0.006|0.009|
|Observations_†_|3531|3531|3531|
|Year of openingdummies|Yes|Yes|Yes|

Robust standard errors in brackets;<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _†_ Observations in top 1% of weekly profits ( _log_ ( _x_ + 1) transformation) were not included.

14