Roles of business experience in entrepreneurship and survival of small business: Evidence from Thai micro data

Transcription

1 Roles of business experience in entrepreneurship and survival of small business: Evidence from Thai micro data Kittipong Rueanthip 1 1 Faculty of Economics, University of the Thai Chamber of Commerce November 25, 2015 Abstract This paper measures entrepreneurial ability through its impact on entrepreneurship and survival of self-employment and small-sized business. Potential candidates for entrepreneurial ability are experiences related to the business ability which include: having an entrepreneur family member, work experience in a business of household s member, and work experience as an ex-business owner. Conceptually, good measures of entrepreneurial ability should have a positive impact on both business entering decision and likelihood of business survival. The Socio-Economic Survey (SES) panel data of Thailand during is utilized here. The results indicate that an individual who has higher prior work experience, measured by a number of working hours, in a family s member s business and/or his/her own business are more likely to entrepreneurship and less likely to exit after start-up. In contrast, although the individual with an entrepreneur family member but did not work in the business of family member tends to start their own business, they are not likely to survive in their own business. In addition, the results suggest that the learning-by-doing effect is found from prior working experience in household business since its impact on survival become insignificant when individuals did not help family business before entrepreneurship. Keywords: Entrepreneurship, Business survival, Business experience, Learning by doing, 1 Introduction Conceptually, it is better for a person to change occupation to entrepreneur to gain higher income if they have sufficient business human capital and capital to invest. In the US, it is found that workers who become entrepreneur are more likely to move to move to a higher wealth class (or remain at the highest wealth class) than an employee switching to another job. The existing entreprneeur group has a higher probability in moving to a higher wealth class and/or staying at the top wealth class (Quadrini, 2000). k.rueanthip@gmail.com 1

2 According to Evans and Jovanovic (1989), both entrepreneurial ability and available capital are main constraints for entrepreneurship. Under the assumption that individuals have and recognize their entrepreneurial ability, individuals should be able to survive in the business after start-up. With available data in recent years, emerging studies show that the exit rate among new entrants is high especially a few years after startup (see Bartelsman et al., 2005 and Millán et al., 2012). The reason for a low survival rate among nascent entrepreneurs could come from either overestimation of their own entrepreneurial ability or try and see strategy to observe their own ability (see Santarelli and Vivarelli, 2007). No matter which one is correct, it indicates the important role of real entrepreneurial ability on both business entry and exit. The question is What are good measures for entrepreneurial ability that make individuals decide to start a business and sustain it? Although recent studies reveal the positive impact of prior business experience on entrepreneurship and business survival, they still face data limitation to investigate the impact of prior entrepreneurial ability on entry and exit separately by using different source of data. Despite emerging research that illustrate the important role of prior related business experience, little is known and hardly distinguish about the role of learning by doing and innate ability. In addition, even small business importantly contribute to economy in developing countries; it is hard to find a study related to this issue that focuses on emerging economies. To fill the gap in the existing literature, this study measures the entrepreneurial ability using; education, family background, and prior working experience related to business activity. Good measures for entrepreneurial ability should have a positive impact on an entering decision and a negative impact on business closure. Contributions of this paper can be observed in a number of ways. First, the national representative random survey panel data of Thailand, an emerging economy with upper middle income level, is utilized here. The data covers more than 15,000 individuals from around 6,000 households from 2005 to Second, with a large and long enough dataset, the data allows us to analyze the same determinant factors prior entering decision of both entrepreneurship and the survival of same individuals. Finally, the study finds that prior business human capital obtained from working in household member s business and/or experience as a business owner are the key for both (re)entry and survive. With limited information, we cannot answer whether ex-business owners are more likely to reentry and survive because of learning by doing or innate ability. However, the positive correlation between recent work with more hours in family business and both likelihood of entry and survival provide us with some hints that the learning-by-doing effect plays some roles here. The rest of this paper is set out as follows. Section 2 briefly describes the underlying theoretical framework and summarizes the prior finding of previous literature related to entrepreneurship and the survival of small business. In section 3.1, model specification are described. We describe the data used in the analysis and explain the measurements of entry, exit and explanatory variables in section 3.2. In section 4, we present the reduced form estimation results of entrepreneurship using probit regression and business exit using logistic hazard regression with random effect. In short, the results show the important role of prior experience obtained from working in business of household member and having own business in the past on both entry and exit. Section 5 summarizes and discusses. 2

3 2 Previous literatures Entrepreneurship normally refers to the process of starting a business. However, the definition of entrepreneur still has no clear consensus. Some define an entrepreneur as someone who has a business and has to hire at least one worker, whereas others define the term to include self-employment. In the current study, entrepreneur refers to someone who is self-employed and/or has a firm that hires at least one worker. According to the static model of Evans and Jovanovic (1989),entrepreneurial ability and available capital are factors that determine entrepreneurial income. If the levels of these two factors are high and can generate entrepreneurial income that is higher than wage income, then an individual must engage in entrepreneurship. Having entrepreneurial ability helps an individual arrive at a decision as to whether entrepreneurship is necessary, regardless of the presence of financial constraints. Hence, with fixed amount of available investment capital, an individual with higher entrepreneurial ability is more likely to become an entrepreneur. This conclusion also implies that if individuals actually know their own entrepreneurial ability, then their entrepreneurial income is expected to remain higher than wage income, and they are likely to stay in business. If the assumption is correct, once individuals consider entrepreneurship, they are less likely to exit from business. However, exit rates are high during the first few years after start-up. 1. Such high exit rates imply that individuals may not truly know their business skills. On the one hand, high exit rates come from entry mistakes caused by overconfidence. On the other hand, try and see might be a good strategy for individuals to realize their business ability. If individuals actually have enough entrepreneurial skills, then they should obtain high enough business income and stay in business. However, if the decision is proven wrong, then they may decide to exit (see also Santarelli and Vivarelli, 2007). The fundamental question of this paper is What are good measures for entrepreneurial ability that make individuals decide to start a business and sustain it?. Lazear (2004) establishes the theoretical framework mentioned that, since entrepreneur occupation has complex production process, the balance in variety of skills (a so-called Jack-of-all trades ) is required to run the business rather than just one of few specific skills. The theory predicts that the individual who has a balanced set of skills has the highest probability of becoming an entrepreneur and is more likely to perform better and/or survive longer in the business. Lazear (2005) further suggests that having balanced skills is suitable for general business (i.e., restaurant, hotel) but not for innovative businesses. However, What is the source of those balanced skills?. Education, on the one hand, is expected to have a positive impact on entrepreneurship and survival because higher entrepreneurial skills usually develop through higher education. On the other hands, those with higher education also gain specific skills and, as a result, they tend to expect higher wage earnings. These conditions increase the opportunity costs of an entrepreneur compared with a wage worker. Previous empirical studies have shown that the impacts of formal education on entrepreneurship and survival are quite related. For example, while Evans and Jovanovic (1989), Paulson and Townsend (2004) and Livanos (2009) find positive relationship between entrepreneurship and education, Djankov et al. (2005) find that graduating on top of ones class has no impact on entrepreneurship. Poschke (2013) reports that entrepreneurship is prevalent among those with high and low levels of education. However, formal education and education in 1 some examples can be seen in Bartelsman et al. (2005), Strotmann (2006) and Millán et al. (2012) 3

4 related professions have no impact on entry; furthermore, both factors increase business longevity Block and Sandner, In case of developed country, although Rocha et al. (2015) show the positive impact of higher education on business exit in Portugal, most empirical work that apply data from developed countries either find a negative impact (i.e. Cressy, 1996; Gimeno et al., 1997; Baptista et al., 2007) or no significant impact upon exit (i.e. Taylor, 1999; Georgellis et al., 2007).In contrast, supported evidences for the assumption that higher education has a negative impact on business survival are found in case of developing countries, such as India (Nafziger and Terrell, 1996) or Zimbabwe (Nziramasanga and Lee, 2001). This finding indicates the higher opportunity cost of being an entrepreneur for educated individuals in developing countries. Meanwhile, various subjects in education and/or roles in the workplace might serve as other sources providing balanced skill sets considered useful for an entrepreneur. Students who enroll in more variety of subjects in business school are more likely to start their own business compared with their peers (Lazear, 2005). A higher variety of tasks in the workplace also increases the likelihood of entrepreneurship and above average post-entry performance (Lazear, 2005 and Wagner, 2004). Although various curricula at school and/or tasks in the workplace can be correlated with business entry and exit, our data have no such information. Thus, we focus on entrepreneurs family backgrounds and business experiences. With regard s family background, a number of empirical studies report that individuals coming from a household with already existing entrepreneurs (i.e. individual who has parental entrepreneur) are more likely to become entrepreneurs themselves ( Dunn and Holtz-Eakin, 1996; Taylor, 2001; Djankov et al., 2005; Lindquist et al., 2012 ). The possible explanations behind this observation are inheritance of business, access to more capital, having a role model, and easy access to entrepreneurial knowledge. However, role modeling and/or business experience might play greater roles compared with other factors, because business inheritance is rare and request for cheap loans from family business only has a small impact (Lindquist et al., 2012). One way to distinguish the impact of role modeling and learning is to separate those who help household business from those who only have a household member/entrepreneur. By doing so, Fairlie and Robb (2007) find that business profit and sale are correlated with having a self-employed family member, only when those individuals work in a business run by their family members. In relation to this finding, we test whether and how the number of working hours in household business determines the probability of both entrepreneurship and business survival. Prior working experience as an entrepreneur can be another source of entrepreneurial ability. In the US, around % of the total number of entrepreneurs are considered serial entrepreneurs (Fairlie, 2005). There are also substantial evidence showing that exbusiness owners tend to become serial entrepreneurs (Chen, 2013; Lafontaine and Shaw, 2014). However, the impact of previous experience on survival is inconclusive. Cressy (1996), Gimeno et al. (1997), Van Praag (2003) and Gottschalk et al. (2009) find no significant impact of entrepreneurial experience on new business survival. In contrast, Taylor (1999), Harada (2003), Baptista et al. (2007) show that entrepreneurial experience increases the longevity of a new business. Some argue that failed entrepreneurs can learn from their mistakes and perform better when business is restarted; however Gompers et al. (2006) find that only previously successful entrepreneurs survive longer in the new business. A recent study reports that the impact of learning from previous business experience on early performance in new busi- 4

5 ness is only apparent when the latter is within the same industry where these individuals have gained past experiences (Chen, 2013). A similar result regarding the effect of same industrial experience is also reported in another study that focused on retail business Lafontaine and Shaw (2014). However, the impact of prior business experience has to be carefully interpreted because this can be a consequence of either learning by doing or by innate ability. Some researchers believe that innate ability is more important than acquired ability. For instance, Silva (2007) finds that an individual who is innately talented can perform more tasks and is more likely to become an entrepreneur. Meanwhile, Chen (2013) and Rocha et al. (2015) show that greater success among serial entrepreneurs is determined by ones innate ability rather than lessons learned from past experiences. Although the impact of financial constraints on entrepreneurship and business exit is not the main focus of the current paper, many theoretical and empirical studies show the significant impact of financial constraints on business entry and exit. Therefore, we carefully control the initial level of wealth to ensure that our results on entrepreneurship and business exit are robust, regardless of whether variables for financial constraints are included in our estimated models. Theoretical studies show a positive relationship between entrepreneurship and financial constraints. Examples include the static model of Evans and Jovanovic (1989), the general equilibrium occupation choice model of Lloyd-Ellis and Bernhardt (2000), Giné and Townsend (2004) and the dynamic model of Buera (2009). Overall, these models predict that the wealthier individual and/or the one who has access to financial capital is more likely to become an entrepreneur. Most empirical studies find a positive relationship between initial level of wealth 2 and probability of entrepreneurship (Evan and Jovanovic, 1989; Blanchflower and Oswald, 1998; Paulson and Townsend, 2004; Block and Sandner, 2009; Schamlz et al., 2014).The positive impact of the exogenous arrival of new financial resource on entrepreneurship has also been observed in some studies. These exogenous shocks are, for instance, inheritance (Blanchflower and Oswald, 1998, Taylor, 2001) or unconditional cash transfer (Blattman et al., 2013) or housing appreciation (Fairlie and Krashinsky, 2012 and Schmalz et al., 2013). According to Fairlie and Krashinsky (2012) and Schmalz et al. (2013), once individuals already start their businesses, prior wealth before start-up might help them survive longer and create more added value. However, some studies (i.e. Taylor, 1999, Cressy, 1996 and Van Praag, 2003) find no such relationship. 3 Model, Data and Measurement 3.1 Econometric models According to the theoretical framework of Evans and Jovanovic (1989), entrepreneurship will happen when individual realize that their entrepreneurial income (Y E ) can be higher than wage income (Y W ) from being wage worker. Entrepreneurial income,(y E ), are defined as: Y E = θk α + r(z k), where θ is entrepreneurial ability, k is the amount of business capital, r is the interest rate and z is the initial wealth. Income of wage worker is Y W = w + rz, where w is wage income. Under the assumption that individuals knows about their θ, the investment decision depends on available k. The financial constraints of individual is 0 k λz, where 2 income, value of assets and/or value of housing collateral are often used to estimate wealth 5

6 λ is parameter, value equal or greater than 1, that determine the maximum amount of liquidity individual can obtain from their wealth. The profit maximizing level of capital, k, can simply obtain by solving first-order condition of Y E with respect to k. Individual face financial constraint when k > λz. Thus, individual can invest only up to λz which is the suboptimal level of Y E. The solution from solving for optimal level of capital indicates that business income of constrained individual depend on z and he/she selects into entrepreneur if: θ(λz) α r(λz) > w (1) while entrepreneurial income of unconstrained individual does not depend on z and he/she chooses to be entrepreneur if: θ 1/(1 θ) ( α r )1/(1 α) r( α r )1/(1 α) θ 1/(1 α) w (2) From the equation (1) and (2), it is obvious that entrepreneurial entry decision depend on θ either individual is financial constrained or not. The good representative for entrepreneurial ability should positively affect decision to entry and make new entrant survive in business. To analyze the goodness to fit of our candidates for entrepreneurial skills, their impact on entrepreneurship is investigated first. The probit model is applied to find out whether our candidates for entrepreneurial skills determine decision to entry. Our estimating equation of entrepreneurship can be written as: Y i = α i + (κ θ i ) + (γ z i ) + (β x i ) + ɛ i (3) The dependent variable is equal to 1 if the non-entrepreneur changes occupation to entrepreneur. The explanatory variables include candidates for entrepreneurial skills (year of education, having an entrepreneur as household member, prior work experience from helping family business and prior experience as business owner), household wealth estimated by wealth index and other demographic characteristic of individual and household (x i ). Using probit model to analyze the duration of firm s survival with contain many time periods might raise the problem of survivorship bias. To avoid the problem, the determinants of individual tenure in business are estimated using the hazard rate model. The duration of business survival in our data is observed in term of the time interval that an event occurs. It is grouped in discrete interval. Therefore, the discrete-time survival model is applied to investigate this dataset. In addition, to properly control for unobserved heterogeneity, the random effects discrete-time logit model is the main focus of this paper. The regression model has an assumption as previous works (Van Praag, 2003; Block and Sandner, 2009; Stirbat et al., 2013) that the cumulative distribution of all exit decisions over time is logistic.the hazard rate function takes the form: h(s X) = exp[d(s) + β X + δ i ] where h(s X) is the discrete time hazard rate over period s with t (s 1) < t < t s, D(s) is the baseline hazard function, β is the vector of the parameters to be estimated and X is the vector of covariate. The individual level error component, δ i, controls for the potential influence of unobserved individual characteristics on the hazard rate. We assume that it is normally distributed with zero mean, independent and identically distributed across 6 (4)

7 items i and independent of the covariates to make random individual effect in the model (Jenkins, 2005). Indeed, the Cox proportional hazards model, continuous-time duration model, is another commonly used model for this issue. However, the present of tied duration time in our data can cause biased coefficients and standard errors. In addition, it might raise other concerns as discussed in Hess and Persson (2012). First, unobserved heterogeneity (or frailty) is difficultly controlled, leading to parameter bias and bias in the estimated survivor function. Second, a restrictive and empirically questionable assumption about proportional hazards is applied in the Cox model. Regarding these concerns, a random effects discrete-time logit model is proper option since it can handle ties, control for unobserved heterogeneity and make no restrictive assumption about proportional hazards. 3.2 Data and Measurement Data Panel data from the Thai Socio-Economic Survey (SES) conducted by the National Statistical Office (NSO) of Thailand from 2005 to 2007, 2010, and 2012 (in five waves), are employed to construct an unbalanced panel dataset. The SES obtained accurate data from a random sample of more than 15,000 individuals coming from around 6,000 households. In each wave of the survey, this dataset contains two main parts devoted to 1) household information and 2) individual information. The first part provides general information about the following: household characteristic, household assets, and income from agricultural activities. The second part collects information about individual respondents who are older than 14 years. Such information includes general demographic, education, occupation, income, and consumption. 3 The respondents are asked about their current occupation to determine occupational transitions of each person in each wave of the survey. The Thai SES panel data have various applications (e.g., national representative survey) and can provide information on the business entry and exit of each respondent. In addition, the data contain information on the household backgrounds of the respondents. The disadvantage of the dataset, however, is the limited detail it can provide about occupation as the Survey did not ask about the type and industry of business and about the reason for business closure. The methods employed by the present study to construct the samples on entrepreneurship and business duration are explained below. First, all household members (including household head) aged between 15 and 70 years are included in the sample. Individuals who are non-entrepreneurs from 2005 and/or 2006 are selected to observe whether they became self-employed or small business entrepreneurs in 2006 and/or Non-entrepreneurs include the following: individuals with nonentrepreneurial occupations (e.g., wage workers, farmers), unemployed, and non-working individuals (e.g., housewife, waiting for season, retired). A student who will not graduate until the next school year is dropped from our sample. 4 Individuals answer the survey only in one wave are dropped from our sample because of the absence of information about occupational transition. Start-up of medium and large enterprises shares less than 5% of total entrepreneurial. Table1 shows the proportion of entrepreneurial activities of 3 See Chawanote and Barrett (2013) for further detail of the survey. 4 For example, the one who are student in both 2005 and 2006 is dropped out from the analysis of entrepreneurship during

8 non-entrepreneurs from 2005 to 2006 and from 2006 to In both years, about 5% of non-entrepreneurs transit into entrepreneurial occupations; half of them start business by being self-employed, and less than half choose to become small business owners. Table 1: Entrepreneurship during and Year Transition Size of entry Number of obs. Percent Remain non-entrepreneur 10, Change from non-entrepreneur to entrepreneur Self-employed Small-size Remain non-entrepreneur 10, Change from non-entrepreneur to entrepreneur Self-employed Small-size Source: Thai SES Panel data With regards business duration, we define this from the year they start business until the end of the survey and/or until they change occupation to non-entrepreneur. However, no information is available about their occupations in 2008 to 2009 and in 2011 and about the type and/or industry in which these new businesses operated. Hence, we just assumed that they survived in the same business if they were entrepreneurs during 2007 to 2010 and/or 2010 to The data about those who shifted to non-entrepreneurial occupations are inaccurate, although the survey asked the start date of the current occupation, i.e., those who changed occupation from entrepreneurial to other occupations in 2010 and Therefore, we use the current occupation provided by the respondents in the questionnaire (for each wave) to define the duration of those new businesses. If a person changed occupation to entrepreneur in 2006 but changed his/her career to nonentrepreneurial occupation again in 2010, then we define the business duration as two years. However, if the new entry started business in 2006 and is still in business in 2007 and 2010, but out of survey in 2012, then we define the duration of this business as four years. By using above definition, Table2 shows the percentage of survivors in the new entries in 2006 and The results suggest that the proportions of survivors in the new entries in both years have similar patterns. Approximately half of the new entries closed the business about one year after start-up, and only 20-25% of these survived for more than five to six years. The survivor functions of self-employment and small-sized business also have the same characteristics, as shown in Table3. Critical to our empirical design are prior entrepreneurial skills while controlling for prior wealth and other demographic characteristics. Potential candidates for skills measurement include education, household member entrepreneurs, working experience in household business, and business experience as an entrepreneur. Education level is measured by the highest year of education a person has achieved. Each The model also controlled for the year of education squared. Using dummy variables, household member 8

9 Table 2: Survival time (in years) by year of entry: Thailand, Year of entry Survival time Observation at Fail Out of Percentage of survivor (Year) beginning survey since the beginning % % % % % % % % % Source: Thai SES Panel data Table 3: Survival time (in years) by size of business: Thailand, Business size Survival time Observation at Fail Out of Percentage of survivor (Year) beginning survey since the beginning Self-employed % % % % % % Small-sized % % % % % % Source: Thai SES Panel data

10 entrepreneurs are measured as to whether members in the household are entrepreneurs. The dummy is equals 0 if there is no one in the household who is an entrepreneur and equals 1 otherwise. There is a potential problem of ambiguity that may arise if the household member entrepreneur variable is used to measure entrepreneurial skills. Owing to limited data, for example, Lindquist et al. (2012) had difficulty separating business experience in family business from family role model andmodel and can only interpret its positive impact as a result of role modeling. To resolve this problem in the current study, the SES panel data allow us to separate individuals who have household member entrepreneurs into two groups: 1) those who just have household member entrepreneur and 2) those who also help the households business. Thus, we can separate the impact of learning based on role model. Business skill acquired from helping household business is measured by the number of hours per week spent by those individuals who help in their households business. The last candidate for measuring business skills is ones business experience as a previous entrepreneur, for which the dummy variable equals 1 if individuals used to be business owners in 2005 and equals 0 otherwise. In terms of the role of financial constraints, the standard method employed by most studies to find the relationship between entrepreneurship and financial constraints is the use of initial wealth at a fixed period of time before non-entrepreneurs make a decision as to whether they will become entrepreneurs. The value of household assets is one common way to calculate wealth (Evan and Jovanovic, 1989; Dunn and Holtz-Eakin, 1996; Townsend, 2004; Schmalz et al., 2014). In general, household wealth comes from the value of ones land, house(s), and vehicle(s) owned. In the Thai SES panel data, only values of house(s) and vehicle(s) are available. However, the use of these values to calculate wealth might raise some concerns about accuracy because those values come from a self-report of households member. To avoid this problem, factor analysis and principal component analysis (PCA) are used to create asset indices (Zeller, 2004). This paper estimates the household wealth index which is a composite index composed of 40 assets ownership variables of each household by using methods of the factor analysis and the principal component analysis. It is expected that the good measure for wealth should be highly correlated with the consumption. The current paper estimates the household wealth index, which is a composite index composed of 40 asset ownership variables of each household, using factor analysis and PCA. A good measure for wealth should be highly correlated with consumption. The different wealth indices, constructed from the top 4, 10, 15, and 40 kinds of assets with the highest eigenvectors, are tested in correlation with household consumption. Results suggest that the wealth index constructed from 10 kinds of household assets has the highest correlation with household consumption; hence, this index is selected as a representative for household wealth in the present study. 5 The financial participation of the household (whether household savings and/or loan) may indicate the level of a households access to financial resources ( Paulson and Townsend, 2004). Therefore, we control for credit market availability in our model by measuring whether a household has savings and/or loans from formal and informal financial institution before the decision to become an entrepreneur is made. The other controlled variables in the model include age, age squared, gender, marriage status, location (region and area), and year in which individuals started their business. 5 Those 10 assets, ranked by eigenvector, are number of TVs, air-conditions, fans, mobile phones, telephones, water heaters, computers, VDO/VCD/DVD players, washing machines and number of bedrooms. 10

11 Most explanatory variables and data applied to analyze the determinant factors of firm duration are time-invariant variables, which are the same variables applied to analyze the entrepreneurship determinant (i.e., prior characteristics of individuals before starting business). This criterion ensures exogenous variables and allows us to observe whether the impacts of those variables on entrepreneurship still play important roles on survival. One control variable for the starting size of business is added into the hazard regression model. By contrast, location (region and area) is defined as the starting location of business establishment instead of the location which the individual lived before being an entrepreneur. The description of dependent and independent variables used in the current study are presented in TableA1 4 Results 4.1 Entrepreneurship Table 4 shows the regression analysis results on entrepreneurship using the probit model. The entrepreneurship probit in Model (1) is estimated as the function of subsets of the individual characteristics, location, and household financial capabilities. The variables describing entrepreneurial skills, e.g., education, household member entrepreneurs, assistance in household business and experience as a previous entrepreneur, are included in the estimations carried out in Models (2) to (5), respectively. The results after excluding individuals who have business experiences are shown in Model (6). Finally, based on Model (5), Model (7) shows the estimation results of candidates for business skills without control of prior wealth. Consistent with Dunn and Holtz-Eakin (1996), Djankov et al. (2005, 2006), Chlosta et al. (2012) and Lindquist et al. (2012), the presence of household member entrepreneurs has a statistically positive impact on entrepreneurship, as shown in Models (3) to (7). Although assistance in households member business has an impact on the model, having a household member entrepreneur still has a statistically positive impact on the likelihood of starting a business. However, the coefficient value drops from 0.4 in Model (3) to 0.26 in Model (4). Results in Models (4) to (7) indicate that having a household business and helping run it are positively associated with probability of entrepreneurship. Moreover, spending more hours in helping run a family business also leads to a higher probability that an individual will become an entrepreneur. After the exclusion of the ex-business owners from the sample in Model (6), the impact of assistance in household business becomes stronger and significant at the 1% level. The result also remains the same when controlled variables for financial constraints are excluded in Model (7). In Model (5), business experience obtained from a previous entrepreneurial experience has a statistically positive impact on the likelihood of entrepreneurship at the 1% level; it also has the strongest impact compared with other candidates for entrepreneurial skills. Such result is consistent with previous studies (Chen, 2013; Lafontaine and Shaw, 2014) which find that the one who is an entrepreneur is more likely to return to business. Although initial wealth is not controlled, the results in Model (7) suggest that our candidates for business skills still play important roles in entrepreneurship. An increase in year of education seems to have an inverse u-shaped impact on the probability of starting a business; specifically, higher year of education has a positive impact, whereas higher year of education squared has negative impact on entrepreneurship at 1% significance level. This result might indicate that knowledge obtained from 11

12 basic education is important for doing business. However, high education also makes individuals more skilled in other careers, which in turn, increases the opportunity cost for being an entrepreneur. The positive correlation between prior unemployment status and likelihood of entrepreneurship in all models is in accordance with the results of previous studies (Evans and Leighton, 1990; Alba-Ramirez, 1994; Santarelli et al., 2009). In addition, this finding supports the argument that unemployment status is a push factor driving unemployed people to start a business to resolve their unemployment. The nature of starting a small-sized business or self-employment might be different, and the different entrepreneurships by firm size are estimated in Table5. Overall, most candidates for entrepreneurial skills still play an important role on business entry as self-employment and having a small-sized business. Turning our attention to role of financial constraints, we find that such constraints play an important role in becoming an entrepreneur, which is similar to the findings of other studies ( Evans and Jovanovic, 1989; Dunn and Holtz-Eakin, 1996; Paulson and Townsend, 2004; Block and Sandner, 2009). When an analysis includes self-employment and small-sized business (Table4),the impact of prior household wealth is inconsistent; moreover, its impact loses the significant level when entrepreneurial ability is not controlled. However, when more control is added for entrepreneurial skills, they render the role of prior wealth less statistically significant or even insignificant, as shown in Model (6). The role of financial constraints becomes obvious when analysis is separated by firm size. Results in Table5 indicate that prior household wealth has a positive significant impact on entrepreneurship as having a small-sized firm but not self-employment. A larger start-up size requires more initial investment capital, which leads to liquidity constraints on the one who wants to start a larger firm. This result is in the same line as that in Schmalz et al. (2013) who find that individuals who experienced house value appreciation are more likely to start larger firms. Self-employment in developing countries, such as Thailand, may require only a small amount of initial investment capital. Therefore, the present study finds no evidence of financial constraint to start a business by being self-employed. Notably, if household wealth per capita is used as a proxy for wealth, the results become statistically insignificant (results are available on request). This outcome is probably caused by the fact that about two-thirds of the initial investments made in small firms come from savings and funds from family members (Paulson and Townsend, 2004). Hence, the use of wealth in each household unit can better reflect the level of an individuals financial constraints. 4.2 Business exit Among our candidates for entrepreneurial skills, the presence of a household member entrepreneur, assistance in household member business, and previous business experience all have positive impacts on entrepreneurship as self-employment and/or running a smallsized firm. This section analyzes whether those variables also have significant impacts on business survival. The results from individual random effect logistic hazard models are presented in Table6-7. A positive (negative) coefficients value means that the impact on the hazard rate is positive (negative) or a negative (positive) impact on survival. The results in Table6 are included business duration of both self-employed and smallsized business. Model (2) shows that those who previously helped their household business for longer hours are more likely to survive in the new business. This result remains 12

13 unchanged even after controlling for prior household wealth and business location in Models (3) and (4), respectively. By considering only those new entrants in 2006, Model (5) also demonstrates the significant negative impact of helping household business on hazard rate. However, this value becomes statistically insignificant when the analysis uses only new entrants in 2007 in Model (6). This outcome can be attributed to the fact that the data about the new entrants in 2007 only represent longer term survival, i.e., threeand five-year intervals. By contrast, only being part of a household with entrepreneurs before entrepreneurship play no role on business survival, whether with or without control of assistance in household business. Results in Model (7) show that, among all variables, having entrepreneurial experience has the strongest negative impact on hazard rate. Moreover, this parameter is statistically significant at 1% level. Our finding is consistent with Taylor (1999); Baptista et al. (2007) and Millán et al. (2012) who all find that previous experience as business owner is an important factor in ensuring business sustainability. However, our results are contrary with those of Cressy (1996), Gimeno et al. (1997), Van Praag (2003) and Oberschachtsiek (2012) which find no impact of it on survival. However, the addition of the business experience variable into the model causes the unobserved heterogeneity problem, as significant correlation is found between error terms, as suggested from the likelihood ratio test of ρ. This outcome is caused by the unobserved factors of ex-business owners that increase the likelihood of business re-entry. In addition, these factors result in different correlations with hazard rate compared with others, which is a similar finding to that reported in Rocha et al. (2015). However, the impact of unobserved heterogeneity on the estimation is rather small because the coefficients show the same sign and no remarkable change in statistical significant level is reported. By including both firm sizes into the estimation, we find no evidence that those other proxies for entrepreneurial skills (i.e., year of education and prior unemployment status) have any significant impact on business duration. One might ask: If you have never done any business before, what is the key factor that can ensure business survival?. To answer this question, we exclude ex-business owners from the sample. Results in Model (8) show that among individuals without business experience in the last two years, helping the household business for a year before entrepreneurship significantly increases the chance of business survival. Although the significant level of the impact is at only 10% level, this result notably captures longerterm duration of the new start-up in Moreover, the negative impact on hazard rate turns into an insignificant level if the dummy for helping household business is applied instead of the variables for the number of hours (result available on request). This finding suggests that undertaking more hours of training in a household business should be considered. Results are more complicated when business duration is analyzed separately by firm size, as shown in Table7. They show that determinant factors of business survival between self-employment and having a small-sized business differ remarkably. Table 7 presents the result of self-employment survival duration in Models (1) to (4) and small-sized firm survival duration in Models (5) to (8). For each firm size, the sample of the first column is for new entrants in 2006, and the second and third columns are for the new entrants in The last column is also for the new entrants in 2007 but dropped out after from the sample having their own business in Notably, when we estimate the hazard function of each firm size separately, the problem of unobserved heterogeneity occurred when the business experience variable is added into the model is mitigated into the insignificant 13

14 level. With regards the survival of self-employment, most proxies for business skills, financial participation, and demographic characteristics have no significant impact on this parameter. Interestingly, experience from helping a household business and/or having one s own business in the past have no significant impact on self-employment duration. When the one who has his/her own business drops out from the sample, as shown in Model (4), education and prior unemployment status demonstrate the significant effect of self-employment on survival. Moreover, higher year of education leads to an increase in the probability of business exit, as evidenced by cases from India (Nafziger and Terrell, 1996) and Zimbabwe (Nziramasanga and Lee, 2001). However, the likelihood of exit is significantly reduced if an individual transitions from unemployment to self-employment. The possible explanation is that income from a self-employment is extremely small and becoming self-employed becomes an option for individuals who cannot find other wageearning jobs. Individuals with higher education might find better opportunities to gain higher wage rates from wage-earning occupations, whereas those with prior unemployment status might have less opportunity cost of exiting business. In contrast, for small-sized firms, some proxies for business skills that significantly influence entrepreneurship also matter for survival of those new entries. Nascent entrepreneurs who used to help in the family business are less likely to exit after starting a business as a small-sized firm, and the impact of this factor is statistically significant and consistent, as shown in Models (5) to (8). One might argue that this impact might have resulted from the innate ability rather than from learning by doing, because individuals might choose to help a household business because he/she knows that he/she is capable of doing so. However, we suspect that such impact rather comes from learning by doing because its impact on survival becomes insignificant if ones experience from helping a household business in last two years is applied instead of experience in the last year before entrepreneurship (results are not shown). If its impact comes from innate ability, then this outcome should not depreciate in a short period of time, which is in contrast to the impact of learning by doing from experience on business owners which disappeared shortly (Parker, 2013 and Rocha et al., 2015). Therefore, the impact of prior working experience in households member business supports learning by doing rather than parental role modeling. This finding is also consistent with Fairlie and Robb (2007) who find that having parents who are entrepreneurs has no significant impact on survival; only entrepreneurs who used to work or help in their family business can earn entrepreneurial skills, which results in their higher chance of survival than other entrepreneurs who simply have a self-employed family member. Model (7) demonstrates that entrepreneurial experience has the strongest impact with the highest significance level on the survival of a small business. Some studies find that when previously successful entrepreneurs return to business, they are more likely to perform better and/or survive in business longer compared with previously failed entrepreneurs (Gompers et al., 2006). Some argue that learning by doing from past experience is important only when the industry in which the new start-up business is closely related to the past experience (Chen, 2013; Rocha et al., 2015). Although we find that a small business owner with some experience in business is more likely to survive, we cannot clearly conclude that this outcome resulted from learning by doing or selection on ability, as analyzed in some recent studies. Moreover, for those who close their business during 2005 to 2006, we do not know exactly whether they closed after a successful business, because of better opportunity in other business, or because of business failure. 14

15 In addition, no information is available about the industry in which the businesses were formed. In the current study, the impact of past experience as a business owner on survival is only a mix impact of prior entrepreneurial experience between success and failed entrepreneurs and between the same and different industries in which the past and new businesses are formed. Turning our attention to role of financial constraints, we find no evidence that those individuals with higher initial household wealth are more likely to survive longer when starting a new business. The impact remains insignificant whether the model includes both firm sizes together or separately. The probit model is used to analyze the probability of business exit at only one-year period during 2006 to 2007 for new entrant in However, prior household wealth still plays no role in survival of self-employed entrepreneurs and small-sized firms (results available on request). Our finding that initial wealth has no significant correlation with survival is similar with Cressy (1996), Taylor (2001, 2004) and Van Praag (2003). 5 Conclusion If the decision of entrepreneurship and the chance of survival of the new start-up are determined by prior entrepreneurial ability and/or skills, regardless of financial constraints possibilities, then good measure for business skills should not only increase likelihood of entry but also make a new entrepreneur less likely to exit. Using the national representative random survey of Thailand, this paper investigates the candidates for entrepreneurial skills: education, family background, and prior working experience related to business activity, through entrepreneurship and its survival of small business. Among all candidates, individuals experience working in a household member s business and/or as a previous business owner are likely to (re)enter and are the fittest to survival. Although having experience as business owner are strongly affect re-entry decision and chance of survive, the results face unobserved heterogeneity problem since there may be some unobserved factors which determine decision to re-entry. Without knowing about specific industry, we cannot tell whether innate ability or the learning-by-doing affect re-entrepreneurship. In addition, with no information of reason of closing previous business, it is hard to observe whether previously failed entrepreneur can learns from their mistakes and improve their performance when re-entry into business. Therefore, it is too early to suggest that previously failed entrepreneur should obtain financial support to start business again. However, by investigating in family background, the results seems to support that working in a business of household member is good measure for entrepreneurial skills since individuals with this experience are more likely to enter and survive in the business. It suggests that business human capital is transferred only when practice occurs via hours of participation with the business of a family member not just have family member as entrepreneur. These results are not sensitive whether prior household wealth is included or excluded. We also find that helping household business has significant negative impact on likelihood of exit only when individuals have recent help experience with the family business not too distantly before they start-up their business. Individuals who do not participate in a business of household member for more than one year turn the positive impact- of helping a business of household member - into insignificant. We suspect that this impact also comes from learning-by-doing effect. This is be- 15