Personal, household and businessenvironmental determinants of

entrepreneurshipBesnik A. Krasniqi

Staffordshire University Business School, Stoke-on-Trent, UK

Abstract

Purpose – The purpose of this paper is to explore personal, household and business environmentalinfluences on entrepreneurship in Kosova.

Design/methodology/approach – The approach takes the form of a econometric investigationusing a binary choice model based on a nationally representative labour force and household surveyconducted by Riinvest Institute at the end of 2002.

Findings – The results suggest that males, those who live in urban areas, belonging to a largerfamily/household, have a higher likelihood of being involved in entrepreneurial activities, while aweak positive effect of age and no significant effect of marital status are found. Self-employed weremore likely to be found in sectors where start-up and sunk costs are expected to be lower (such asservices and trade), those sectors that experienced high growth (construction) and in the regions inwhich entrepreneurship is more developed. In contrast with previous studies, it is found that educationreceived household remittances and the presence of an additional wage earner in a household havenegative impact on entrepreneurial activities arising from country-specific features.

Research limitations/implications – These empirical findings identified determinantsinfluencing entrepreneurial activities providing basis for policy discussion aimed atentrepreneurship development in the country.

Originality/value – The paper complements rather scarce empirical evidence on determinants ofentrepreneurship from a unique transition country. It highlights the role of some transition andcountry-specific factors in entrepreneurial activity of the population, providing better insights inunderstanding entrepreneurial behaviour of people in general and in transition economies inparticular.

Keywords Entrepreneurialism, Business environment, Individual behaviour, Kosova

Paper type Research paper

IntroductionThe interest on factors influencing entrepreneurial activity of population has beengrowing among both, academics and policy makers. This growing interest wasprimarily due to a broad consensus in the academic literature that business start-upsare vital for economic growth for any economy via generation of income, employmentand innovation (Audretsch and Thurik, 2001; Blanchflower, 2000). Although,Schumpeter (1934) in many years ago highlighted the role of entrepreneur as an“agent of change” responsible for the introduction of new or improved products andprocesses contributing to economic change and competition, only in recent years therole of entrepreneurship in economic growth is acknowledged by new modern theories

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1462-6004.htm

The author would like to thank Riinvest Institute for Development Research for dataaccessibility, Iraj Hashi, Jean Mangan from Staffordshire University (UK), and the anonymousreferees for their useful comments.

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Journal of Small Business andEnterprise DevelopmentVol. 16 No. 1, 2009pp. 146-166q Emerald Group Publishing Limited1462-6004DOI 10.1108/14626000910932935

of economic growth. Although, not directly concerned with the role of entrepreneurshipRomer (1990) highlights the role of “knowledge” as a modern factor of economicgrowth. The higher entrepreneurial engagement can promote e spillover effect of theknowledge which is crucial for economic growth in today’s knowledge-based economyin which entrepreneurship and small firms are hailed as a key mechanisms throughwhich knowledge spillover takes place (Audretsch and Fritsch, 2003; Audretsch andThurik, 2001; Acs et al., 1994a). In addition, entrepreneurship has become increasinglyimportant for labour policy measures because apart from traditional labour policiessuch as continuous vocational training or re-education of the unemployed,self-employment is viewed as a mean to fight unemployment (Blanchflower, 2000;Pfeiffer and Reize, 2000). The role of self-employment and entrepreneurship hasbecome particularly important for transition economies because of their potential togenerate income and employment at the time when these economies were facing highunemployment and fall in output.

The aim of this study is to explore determining factors for choice ofself-employment and entrepreneurship[1]. First, the transition countries (especiallycountries that embarked latter on transition) still face high unemployment ratescompared to western countries and for this reason the contribution of entrepreneurshipand SMEs is considered to be even greater. Second, the entrepreneurial activity rate ofthe population is affected by past communist legacy especially reflected on the overallattitude of the population towards entrepreneurship at the beginning phase oftransition. Third, generally in transition countries with few exceptions (e.g. firstcountries that joined the EU) the environment for entrepreneurship was not friendly forbusiness start-ups. Despite the inadequate environment for development ofentrepreneurship these countries experienced rapid growth of the number ofself-employed since 1989 (Earle and Sakova, 2000). A systemic change that underlinedtransition economies and under which the entrepreneurship evolved provides aninteresting topic for examining the importance of the environment forentrepreneurship and other distinguished characteristics of the transition on thechanges of the levels of entrepreneurship. Moreover, this analysis will help to explaincross-country variation because it is important to test determining factors ofentrepreneurship in different contexts, in particular for transition economies wherestudies of this kind are still scarce. Kosova as conflict-plagued country makes a uniquecase for expanding model of entrepreneurship as an extreme environment forentrepreneurship (Solymossy, 2005) providing a good testing ground forentrepreneurship theories. It is important to note that the evidence from countrieswith different level of development and under different economic circumstances suchas Kosova in its late transition is necessary in providing more insights about thefactors influencing the entrepreneurial activity.

This article is based on a nationally representative survey Riinvest Labour Forceand Household Survey (RLFHS), conducted by Riinvest Institute for DevelopmentResearch at the end of 2002. The rest of the article is structured as follows. Next sectionreviews the existing literature and previous studies on determinants ofentrepreneurship. This is followed by discussion of the data and survey results.Third section discusses empirical model and variables. Fourth section presents anddiscusses empirical findings. In the last section, the study concludes.

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Theoretical frameworkLiterature suggests that no discipline can claim monopoly over the entrepreneurship(see, Parker, 2005), which consequently led to adoption and development of variousapproaches and integration of different strands of literature (economics, psychology,sociology, economic geography, etc). Despite these developments in the field, there isstill a lack of established theory of entrepreneurship that would enable an ultimatelytestable empirical model (Grebel et al., 2003). Although factors influencingentrepreneurial activity are numerous, this section will focus only on thoseimportant for the purpose of this article such as personal characteristics, householdcharacteristics and features of environment for entrepreneurship aimed at informingempirical analysis in the present study.

Personal characteristicsThis section discusses micro factors or individual characteristics such as age, level ofhuman capital, gender, marital status (Kickul and Gundry, 2002; Brown et al., 2003;Evans and Leighton, 1989).

Age. An individual’s age is important factor in influencing decision to start-up thebusiness. According to Storey (1994) most of the business owners are within thecategory of 25-45 years. Individuals of middle age are assumed or have accumulatedmore capital and experience, so the probability to start-up the business will be greaterfor these individuals rather than for very young individuals. Older people haveaccumulated the general human capital via knowledge and experience (Borooah, 2001;Brown et al., 2003) but, alternatively, as an individual becomes older he/she may lackthe dynamism and motivation to start-up the business, which can be explained bypsychological factors (Levesque and Minniti, 2005), which are beyond the scope ofpresent study.

Human capital. Although the views about the role of human capital i.e. education,on decision to start-up a business is acknowledged the empirical evidence is stillinconclusive[2]. Education can enhance the managerial ability of an individual andhence increases the propensity to undertake entrepreneurial activity. In addition,individuals with higher levels of human capital may be in a better position to perceiveentrepreneurial opportunities in the market and therefore more likely to engage inentrepreneurial activity (Davidsson and Honig, 2003). On the other hand, the higherlevels of education may facilitate entry in the well-paid jobs and hence can decrease theprobability of an individual to start-up the business. An important recent theoreticalcontribution by Lazear (2002, 2004) suggests that entrepreneurs are “jacks of alltrades” rather than specialized experts as are generally found in wage and salary work.Thus, the effect of education on individual’s decision to engage in entrepreneurialactivity is inconclusive and cannot be a priory determined. Evans and Leighton (1989)in an earlier study found that more educated person has greater probability to enter theself-employment while authors such as de Wit (1993) and Cowling (2000) show thatthat high-level education deters entry to self-employment. In the study focusing ontransition economies Earle and Sakova (1999) identified that the self-employmentdecision is positively related to years of education.

Gender. Various studies have shown that gender has negative impact on decision tobecome entrepreneur. Noorderhaven et al. (2004) for OECD countries and Wagner andSternberg (2002) for Germany found that females are less likely to enter

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self-employment. A study based on Danish survey data conducted by Maire et al.(2004) found that women are more risk averse and in addition men put much moreemphasis on monetary gains than their women counterparts. This might explain whyfewer women make a decision to become entrepreneur. In addition, some of the studiesargue that females are more family oriented and are less keen for setting up a businessand less keen in perusing expansion related goals (Brush, 1992). Generally, females aremore likely to start-up a business in some female-specific areas like services, smallbeauty shops, etc. (Beaver, 2002).

Marital status. Marital status of an individual influences his decision to becomeentrepreneur via number of different channels. For instance, couples are in a betterposition to start-up a business than singles as result of risk sharing due to economicgain from marriage (Lee, 1999; Maire et al., 2004). The chances for married people to gointo self-employment are much larger because the greater opportunity for couples toraise finance or to have income savings prior to the business start-up decision.However, this might not be the case for transition economies where unemployment rateis very high and hence the likelihood of females being employed is usually lower. Inaddition, among other related factors related to marital status is the presence of thechildren. Individuals with younger children are generally less likely to take the riskassociated with the self-employment (Brown et al., 2003).

Household characteristicsEarle and Sakova (1999) identify that pre-transition family income plays a crucial rolein the start-up decision. This is because the families that have been in the position inthe former system have been able to accumulate wealth and henceforth had not beenfinancially constrained. Particularly, this was the case in the early transition whereloan opportunities were small or not available at all for start-ups. Aidis and Estrin(2006) support hypothesis that individuals who have higher household incomes aremore likely to be entrepreneurs which is considered to be the way of overcomingfinancial constraints. Studies from developed countries show that the individuals whobelong to households that have another wage earner in the family are more likely toinvolve in entrepreneurial activities than those who have single household wageearners. The explanation is linked to the risk-shifting phenomenon and capitalaccumulation. In transition context, the effect of additional wage earner onentrepreneurial activity may not be straightforward having considered highunemployment rates. Under condition of high unemployment, if a person is singlewage earner in family it is more difficult for him/her to risk the job which will not beeasy to find second job if not successful as an entrepreneur.

Business environmental factorsBusiness environment is crucial factor in explaining new firm formation. However, thedominant theories in entrepreneurship have sought to explain entrepreneurship as afunction of the types of people involved in entrepreneurial activity and, seemingly haveto a larger extent overlooked the role of opportunities (Mazzarol et al., 1999; Eckhardtand Shane, 2003). Therefore, further challenge in entrepreneurship literature is toidentify the environmental conditions which may vary across the geographicalboundaries trying to understand the importance of “valuable opportunities” offered bybusiness environment (Venkataraman, 1997; Begley et al., 2005). Next, we will discuss

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some important dimensions of environment and their impact on entrepreneurialactivity.

Availability of finance. The problem of rising finance for business start-up is rootedin the credit rationing theory resulting from the information asymmetry in the capitalmarkets leading to adverse selection or moral hazardous problems (Stiglitz and Weiss,1981; Storey, 1994; Binks and Ennew, 1996 Chilosi, 2001; Bridge et al., 2003). Therefore,the role of availability of finance on individual’s decision to become entrepreneurlargely depends on individual’s personal wealth through savings or wealth inheritanceserving as a substitute for external finance. Recent evidence by Grilo and Irigoyen(2006) in a study covering European countries reported a negative influence of financialconstraints on people’s decision to start-up a business (see also, similar findings byEvans and Leighton, 1989; Blanchflower and Oswald, 1998; Guiso et al., 2002). Inaddition, Holtz-Eakin et al. (1994) and Dunn and Holtz-Eakin (2000) find evidence thatinheritance and parent’s assets which serve as a proxy for finance seem to be importantfactor influencing positively entrepreneurial activity. In transition context, due to theunderdevelopment of capital markets one should control for possible substitute forexternal finance. In this paper we control for remittances and additional wage earner.

Regional development and location. Formation of new businesses in specific area canattract other businesses as well because of likely cooperation and spillover effects ofknowledge and experience (Audretsch and Fritsch, 2000). Accordingly, thedevelopment of entrepreneurship in a certain geographical area might act as asignalling or as an indication of attractiveness for other potential entrants. Using dataon the location of firms in the Canadian biotechnology industry between 1992 and2000, Aharonson et al. (2004) find that location is a significant factor in new firmformation rate. Their study show that entrepreneurs locate their businesses inindustrial clusters in order to benefit from knowledge spillovers from other firmswhich belonging to the same technological specialization. Not only regionaldevelopment but in addition population density and urbanization rate have theircombined effect on entrepreneurship. Urban areas usually are associated with the highpopulation growth and density, which can support the growth of entrepreneurialactivities through the larger market opportunities i.e. increased demand for goods andservices, better entrepreneurship infrastructure and exploitation of economies of scale(Bruderl and Preisendorfer, 1998). Audretsch and Fritsch (1994) for Germany andKeeble and Walker (1994) for the UK found that population density and the populationgrowth have a positive impact on birth rates. Therefore, the creation of new firms tendsto be geographically concentrated in urban regions associated with higher degree ofurbanisation.

Sector. Some industries or sectors are more attractive for business start-ups thanothers. Expansion of the service sector in an economy tends to have a positive impacton entrepreneurship. The service sector is characterised by low initial capitalrequirements leading to lower barriers to entry and low sunk costs facilitating thebusiness start-ups. Most of the services are characterised by small average size of thefirm (EIM/ENSR, 1997). In contrast to services, in sectors where large economies ofscale exist like high tech industry, which leads to large sunk costs we would not expectto have high dominance of business owners (Brown et al., 2003). The composition of theindustry plays a crucial role in explaining cross-country variation of theentrepreneurship (Acs et al., 1994b), while other authors like Torrini (2005) refer to

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these country-differences to the changes in a public sector size. In view of that,expansion of the public sector influences distribution of the workers betweenindustries towards a public services in which self-employment is not possible tohappen. In transition economies, however, study by Earle and Sakova (2000) point outthat some of the self-employed would prefer to be employees rather than entrepreneursbecause in transition economies there are limited job opportunities so they wereconstrained or they are “forced” involuntarily to undertake entrepreneurial activity.

DataSample and data collectionThis paper is based on the data from labour force and household survey conducted byRiinvest Institute for Development Research in December 2002[3]. The main purpose ofthe survey was to provide data for investigation of the labour market in Kosova. Theunit of observation in this survey was the household, but data were collected for eachfamily member. Thus, total sample consists on 8,552 observations or individuals.

The sample was drawn randomly from the overall population of the households,which is a nationally representative survey. In our empirical analysis, we are interestedin individuals who are of working age aiming at identifying the underlying differencesbetween those individuals who are either self-employed or business owners versusemployed or unemployed within the working age range. Our data allow this type ofanalysis because the survey provides the data on personal and demographiccharacteristics of the individuals such as gender, age, education, marital status as wellas some other regional and geographical data such as whether they belong to rural orurban areas. Also for a sub-sample of employed and entrepreneurs the survey containsdata about the sector of activity classified into services, trade, construction and allother sectors. In addition, we converted some household data into individual data suchas additional wage earners in the household and household size.

In order to ensure representation of the sample by different categories over entirepopulation two stratifications were made. First, the survey sample was stratified tocover both urban and rural areas based on their participation in total population.Second, stratification is done in terms of regions to ensure the representation of allregions according to their share in overall population.

Descriptive statisticsInitial sub-sample comprises 4,628 working age adults between 18 and 65 who areeither employed or unemployed, self-employed or business owners who reside inKosova or abroad[4]. Table I shows that employed (wage workers) and unemployed

Categories Number (%)

Employed and unemployed 4,332 93.6Self-employed 221 4.8Business owner 75 1.6Total 4,628 100.0

Note: Emigrants are excluded from the sampleSource: RLFHS (2002)

Table I.Distribution of sample:

employed andunemployed,

self-employed andentrepreneurs (18-65

years)

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people are the dominant category in the sample (almost 93.5 per cent) followed byself-employment (4.6 per cent) while the last common is category of business owners(1.64 per cent).

For the purpose of the empirical analysis, we divide overall sample into twosub-samples. This consists of all individuals of working age population ofnon-entrepreneurs and entrepreneurs. As argued by Verheul et al. (2001, 2006)several problems associated with measurement of entrepreneurship and in particular,due to difficulty in comparisons over countries and time, self-employed and businessowners can be considered as equivalent to entrepreneurs. Non-entrepreneurs groupincludes all individuals regardless the type of employment and others who are in thelabour force but are not economically active (see Table II). The entrepreneurs’ groupincludes both individuals who declared themselves as a self-employed and those whodeclared that own a private firm and employ others (i.e. business owners) which in turnreflect total entrepreneurial activity of the population. The share of non-entrepreneursand entrepreneurs in the sample according to regions is reported in Table II.

Table III summarises the main characteristics of entrepreneurs andnon-entrepreneurs. Survey results show that there are some interesting differencesbetween the two groups. For instance, men are more likely to be self-employed orbusiness owners than women, which is an obvious fact especially in a transitioncountry context such as Kosova. In addition, the age cohort reflects the fact thatmajority of the individuals involved in entrepreneurial activities are younger than 40years which is slightly different with the cohort age of entrepreneurs (25-45) assuggested by suggested by Storey (1994). Thus, the age profile of individuals declaredas self-employed or firm owners appears to be closed to inverted “u-shaped” skewedmore to the right.

The incidence of entrepreneurial activity is slightly higher for married individualsrelative to non-married. This might be because of the risk pooling among individualsengaged in entrepreneurial activity because they can offset some income risk (Brownet al., 2003). However, this might not be the case in transition economies asunemployment rate is very high and especially female labour participation rate is verylow. Concerning level of education, we do not find much of difference expect theuniversity and post university category for the group of entrepreneurs with individualsinvolved in entrepreneurial activity being slightly more educated. We also do notobserve significant differences among entrepreneurs in terms of their secondary or

Regions Non-entrepreneurs Entrepreneurs Total Per cent in the sample

Prishtina 1,247 96 1,343 29.0Prizren 641 51 692 15.0Peja 528 31 559 12.1Mitrovica 756 42 798 17.2Gjilan 532 27 559 12.1Gjakova 236 15 251 5.4Ferizaj 392 34 426 9.2Total 4,332 296 4,628 100.0

Source: RLFHS (2002)

Table II.Distribution of thesample by regions

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post-secondary education. As suggested by literature the skills needed for exercise ofentrepreneurship are general rather than specialized.

Residential status is the main underlying difference between entrepreneurs andnon-entrepreneurs (except the gender). Individuals living in urban areas are as twicemore likely to be involved in entrepreneurial activities than individuals living in ruralareas. Other differences among entrepreneurs are observed in relation to remittancesthey received from Diaspora. Individuals who receive remittances are less than twicelikely to be entrepreneurs than individuals who do not receive remittances. This mightbe related to the way of how remittances are used, because survey findings suggestthat there are only few cases that reported that they invested remittances in business.Equally important the individuals who receive remittances might be discouraged toengage in entrepreneurship activities because they have more secured financialsupport.

Data limitationsEmpirical analysis is limited due to the data availability. First, the household surveywas not designed for the purpose of investigation of entrepreneurial activity ofpopulation although this type of surveys has been widely used for conducting theempirical analysis of entrepreneurial activity of population. Second, our survey doesnot contain information important for investigation of “parental role” models such aseducation of parents, family tradition in the business or information regarding toattitude towards risk. Second, the dataset used is cross-section, which is appropriatefor estimating determinants of entrepreneurship at one point in time. It would be more

Non-entrepreneurs Entrepreneurs

GenderFemale 98.2 1.8Male 88.7 11.3Age cohort18-24 93.2 6.825-40 92.5 7.541-65 95.8 4.2Marital statusNon-married 95.5 4.5Married 92.7 7.3Education levelNo education 97.2 2.8Primary 96.0 4.0Secondary 91.9 8.1University and post-university 90.8 9.2Urban/rural residentUrban 91.3 8.7Rural 95.6 4.4RemittancesReceived 96.3 3.7Not received 92.9 7.1

Note: Numbers are expressed as a percentage of the total number of individuals within the two groupsSource: RLFHS (2002)

Table III.Non-entrepreneurs and

entrepreneurs, byindividual characteristics

(%)

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appropriate to analyse the changes in the entrepreneurial behaviour of populationduring longer period instead of single period, hence the use of panel data. Finally, wewarn the reader that our data does not contain information on characteristics of theindividuals in the period prior to the business start-up. Therefore, results should beseen as an association of variables rather than as causal relationships.

Total Entrepreneurial Activity (TEA)Since the RLFHS is a nationally representative, then it is a crucial to calculate the totalentrepreneurial activity rate of the population. This is a first attempt to construct anentrepreneurship indicator for Kosova. TEA can be calculated in different ways. Themost cited study is Global Entrepreneurship Monitor (GEM), which defines TEA interms of actual entrepreneurship and predicted entrepreneurship (i.e. preference forself-employment). The TEA rate, as proposed in the GEM is defined as the share ofadults in the population of 18 to 64 years old who are either actively involved instarting a new business or in managing a business less than 42 months old (Reynoldset al., 2002, p. 5)[5].

However, our data do not contain information about predicted entrepreneurship,thus our TEA analysis for Kosova is based on actual entrepreneurship rate only (i.e.individuals who already own firms or are self-employed). Moreover, measures of actualentrepreneurship are much more reliable compared to predicted entrepreneurshipwhen measuring TEA. Parker (2005) suggests that it is important for empirical rigourto avoid “asking entrepreneurs or other agents what they think will do in varioussituations” because this can be prone to self-serving bias. In addition, the actualentrepreneurship is used widely in the empirical literature due to statistical availabilityand easy in international comparisons. Results of the TEA for different regions and forKosova as a whole are summarised in Table IV.

As can be noticed 6.4 per cent of overall adult population are engaged inentrepreneurial activity either as self-employed or as a business owners. The rate ofself-employment of 6.4 is considered to be very small compared to other transitioncountries[6]. Regional comparisons of TEA suggest that there are regional differenceswith regard to entrepreneurial activity of population. Some regions like Prizren,

Regions TEA

Prishtina 7.15Prizren 7.37Peja 5.55Mitrovica 5.26Gjilan 4.83Gjakova 5.98Ferizaj 7.98Mean 6.40

Note: TEA is calculated as per cent of entrepreneurs (self-employed and business owners) divided bytotal number of population (18-65)Source: RLFHS (2002)

Table IV.Total entrepreneurialactivity, by regions (2002)

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Prishtina and Ferizaji have higher TEA compared to the TEA for Kosova (6.40) whileothers have slightly lower TEA like Peja, Gjakova, Mitrovica and Gjilani.

ModelThe main objective of the empirical analysis is to investigate the personal, householdand business environmental factors contributing to the entrepreneurship activity ofpopulation in Kosova. In consequence our econometric model is limited to the personal,demographic, family/household and regional characteristics of individuals and theirimpact on probability to own a business. Accordingly, the probit model is specified inorder estimate the probability of an individual to be involved in entrepreneurialactivities. We estimate three separate probit regressions. First Probit specification isrun for sample of entire working age population:

PðENTREPRENEURiÞ ¼ aþ b1GENDERi þ b2AGEi þ b3AGESQiþ

b4UNIVEDUCi þ b5MARRIEDi þ b6REMi þ b7RES_URBANiþ

b8HH_SIZEi þ b9TEAi þ 1i

where the dependent variable is dichotomous taking the value of 1 if individual i is anentrepreneur and 0 otherwise. Independent variables are: gender, marital status, age,age squared, education, remittances, residence (urban or rural), and TEA. Secondprobit specification is run only for the sub-sample, which excludes the unemployedindividuals allowing us to estimate the impact of different sectors on probability of aperson being an entrepreneur, which is important dimension of the entrepreneurshipenvironment. In addition to the services sector, which tends to have a positive impacton entrepreneurship we include also other two sector dummies, (trade andconstruction) which are meant to pick up some country-specific features (more onthis latter). Thus, second probit model is specified as follows:

PðENTREPRENEURiÞ ¼ aþ b1GENDERi þ b2AGEi þ b3AGESQiþ

b4UNIVEDUCi þ b5MARRIEDi þ b6REMi þ b7RES_URBANiþ

b8HH_SIZEi þ b9TEAi þ b10CONSTRUCTIONi þ b11SERVICESiþ

biTRADE12 þ 1i

Finally, we specify the third probit model which differs from two former specificationsbecause instead of the variable HH_SIZE (household size) we include the variableAWE (additional wage earner/s). The inclusion of this variable aims to investigate theimpact of the so-called risk sharing in the household among household members andits impact on entrepreneurial activity.

We will now turn to the discussion of the variables used in the empirical modelsspecified (see Table V for descriptive statistics). Table VI presents the definition usedin models for both independent and dependent variables. Independent variables aregrouped in three categories: variables related to personal and demographiccharacteristics of the respondent, variables related to family/householdcharacteristics and those related to the entrepreneurship environment conditions.First group of explanatory variables include age, gender, marital status and level ofeducation. Second group include family remittances (whether the respondent belongs

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to the household which received the remittances from other members of the familyliving abroad), household size, and dummy variable for additional wage earner in thefamily/household. Third group of the explanatory variables is related to theenvironment for entrepreneurship or factors that are considered to create a demand forentrepreneurship. These explanatory variables control for some industrycharacteristics as well as regional or geographical differences regardingentrepreneurial activities of the population (TEA), geographical location ofindividuals (dummy for respondents living in urban areas. In general, inclusion ofthese variables in empirical studies is important to assess the entrepreneurship policymeasures taken by authorities of different regions. In Kosova there is no establishedstrategy or policy measures in place for targeting entrepreneurship development.However, these variables will test some other features such as concentration ofpopulation in regions, especially around the urban areas. Industry specific factors areimportant in explaining the influencing factors of entrepreneurship. As we arguedearlier when industry is growing or when demand for goods or services is increasingrapidly then we expect more firms to enter the market. Equally important, the start-upcost and sunk cost matter when people decide to enter the entrepreneurship activity.For this purpose we constructed control dummy variables in sectors in which start-upcosts together with associated sunk cost are low. In addition to such sectors (servicesand trade) we constructed a dummy variable for construction sector. Inclusion of thisvariable is influenced by country specific factors because in that time Kosova wasgoing through the post-war emergency and because of reconstruction of the countrythe construction sector was booming at the time.

Empirical findingsProbit regression results from three specifications discussed in the previous section aresummarised in the Table VII. First, the model estimates the probability of being anentrepreneur among individuals within adult working age including unemployedindividuals. Second, the model estimates the probability of being an entrepreneuramong individuals within the adult working age but is restricted for individuals whodeclared that either employed or entrepreneurs (unemployed are not included). Therewere two reasons for exclusion of the unemployed individuals. First, we wanted to

Variable Mean Std. dev Min Max

GENDER 0.76 0.43 0.00 1.00AGE 38.01 11.24 18.00 65.00AGESQ 1,571.10 891.74 324.00 4,225.00UNIVEDUC 0.26 0.44 0.00 1.00MARRIED 0.77 0.42 0.00 1.00REM 0.14 0.34 0.00 1.00RES_URBAN 0.58 0.49 0.00 1.00HH_SIZE 7.34 3.70 1.00 28.00TEA 0.54 0.50 0.00 1.00AWE 0.42 0.49 0.00 1.00CONSTRUCTION 0.09 0.29 0.00 1.00SERVICES 0.50 0.50 0.00 1.00TRADE 0.15 0.36 0.00 1.00

Table V.Descriptive statistics ofexplanatory variables

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ond

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Table VI.Description of variables

Environmentaldeterminants of

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157

estimate the impact of the three main sectors: construction, services and trade. Thisexercise is limited only to sample of employed and entrepreneurs. Second, we wantedto estimate the impact of education on entrepreneurial activity within the sample ofemployed and entrepreneurs. This is because of the opportunity cost of beingentrepreneur increases due to the better prospects of the educated people in the labourmarket in terms of getting a well-paid job or getting job at all, with the last one beingextremely important in transition economies characterized with limited employmentopportunities.

The results of the specification one suggest that personal characteristics such asgender, age and whether an individual lives in urban or rural areas are the mostimportant factor in explaining probability of being involved in entrepreneurial activity.Not surprisingly, gender has positive impact on entrepreneurial involvement with menbeing more likely to be entrepreneurs rather than women. Regarding age we havesomewhat mixed results. First, we included variable AGE, which enters the equationwith the positive sign, but the size of the coefficient is not very significant. In addition,we introduced squared term for variable age, which shows a negative and non-linearrelationship between age and likelihood of being entrepreneur. The small size of thiscoefficient might be due to the very young population of Kosova as whole meaning thatthe share of older people either as employed or as entrepreneurs is very small[7].

We included the dummy variable to show the effect of remittances onentrepreneurial activity. This variable has negative sign and is statisticallysignificant suggesting that those individuals who received remittances are lesslikely to be engaged in entrepreneurial activities. After data inspection on howremittances received were spent by individuals or their families, we find that only infew cases remittances were used for business purposes[8]. Another explanation of the

Specification 1 Specification 2 Specification 3Explanatory variables Coeff. p-value Coeff. p-value Coeff. p-value

GENDER 0.0886 * * * 0.000 0.0968 * * * 0.000 0.0486 * * 0.061AGE 0.0075 * * * 0.000 20.0032 0.684 20.0137 * * 0.055AGESQ 20.0001 * * * 0.000 0.0000 0.899 0.0001 0.132UNIVEDUC 20.0024 0.754 20.0538 * 0.058 20.0320 0.211MARRIED 0.0071 0.361 0.0151 0.653 0.0120 0.692REM 20.0189 * * * 0.007 20.0344 0.301 20.0198 0.492RES_URBAN 0.0340 * * * 0.000 0.0500 * * 0.044 0.0352 * 0.105TEA 0.0114 * * * 0.038 0.0562 * * 0.014 0.0335 * 0.103HH_SIZE 0.0005 0.460 0.0081 * * * 0.010CONSTRUCTION 0.1932 * * * 0.000 0.1437 * * * 0.001SERVICES 20.0788 * * * 0.007 20.0765 * * * 0.004TRADE 0.3449 * * * 0.000 0.2915 * * * 0.000AWE 20.2887 * * * 0.000Wald test 223.5 . 0.00 198.5 . 0 265.29 . 0Number of observations 4,628 1,311 1,311Pseudo R-squared 0.129 0.155 0.279

Notes: *Significant at 10 per cent; * *Significant at 5 per cent; and * * *Significant at 1 per cent.Variables RES_URBAN and TEA are just above the threshold of statistical significance so weincluded them as 10 per cent significant

Table VII.Probit estimates:determinants ofentrepreneurial activity(dep. variabledichotomous, equals 1 if aperson is self-employedor business owner and 0otherwise)

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negative impact of remittances is based on the so-called internal transfers within thefamilies. For example, if an individual belongs to family who received remittances thenit can act as discouraging for him/her to put higher efforts such as involvement inentrepreneurial activity, as they have a constant stream of income for coveringhousehold expenses. Our survey shows that majority of households who receiveremittances use them for consumption purposes.

Regression results also support the view that individuals living in the urban areasare very likely to be engaged in entrepreneurial activity because of the underlinedreasons such as population growth in cities reflected via increased demand for goodsand services and hence more available opportunities for entrepreneurshipdevelopment. In addition, the dummy variable TEA denoting those regions withhigh total entrepreneurial activity rate of the population in a specific region isstatistically significant and has positive impact on the likelihood of an individual beinginvolved in entrepreneurial activity. It means that regions with TEA larger thanaverage of Kosova, seems to have a positive impact on individuals to be involved inentrepreneurship.

Other dummy variables such as the marital status, level of education and householdsize are not statistically significant. In contrast to other studies, the marital status inKosova has no impact on probability of being entrepreneur. Other studies underlinedearlier in the paper suggested that married people are more likely to be involved inentrepreneurial activity because of the risk sharing and more possibility for savingsetc. In the context of Kosova, the female labour participation rate is very low and theunemployment rate very high – around 49 per cent Riinvest Institute for DevelopmentResearch, 2003. Seemingly, in these circumstances there is relatively low likelihoodthat both husband and wife are employed. Thus, the risk-sharing phenomenon inKosova is probably weaker compared with other developed countries (this variablewas not significant even after controlling for sample of employed in specification 2and 3).

Education is not a significant factor in the first specification suggesting thatindividuals that hold university degree or above are not different from the rest of thesample concerning entrepreneurial involvement. This finding is in line with “jacks ofall trades” argument discussed earlier. In addition to education, we do not find thesupport for household size, which was assumed to have an effect through more capitaland risk sharing and probably more work force leading to cost reduction, etc.

From the results of the specification 2, some interesting differences can benoticed compared to specification 1. First, level of education matters and hasnegative impact on entrepreneurial activity when controlling for either individualswho are employed or entrepreneurs. In line with human capital theory, theindividuals holding university degree or above are more likely to get jobs, and inparticular well-paid jobs. As predicted by theory of occupational choice, the abilityof an individual increases with education which consequently increases his or herability to get a job (more important for country such as Kosova) and moreimportantly do get a well-paid job. Hence, as an outcome the education mightincreases the opportunity cost of starting up a business. Second, the remittances donot have an impact on entrepreneurial activity after controlling for unemployment.Finally, three sector dummies: construction, services and trade have a positive andsignificant impact on entrepreneurial activity. Individuals belonging to the

Environmentaldeterminants of

entrepreneurship

159

construction, services and trade sector are more likely to be entrepreneurs rathercompared to individuals involved in other sectors of activity. We selected thesesectors for several reasons. First, the services and trade are sectors where thestart-up capital and sunk cost are low than in other sectors (e.g. manufacturing) asunderlined earlier. Second, unlike other studies we included dummy for constructionsector for pure country-specific reasons as Kosova was going through theemergency phase due to the War which led to a “boom” in construction sector dueto the War destruction which generated an excessive demand for such goods.

In the specification 3, we excluded the variable household size and included otherimportant variable AWE (additional wage earners or employees in the household) totest whether the people living in household with additional wage earners are moreprone to conduct entrepreneurial activities or otherwise. This variable entered theequation with highly significant negative sign and with high magnitude. Unlike othersimilar studies conducted in developed economies living in a household with anotherwage earners has a negative impact on probability of being entrepreneur. Theexplanation of this variable is linked to the country specific factors such as highunemployment. Individual once have a job he or she is satisfied and do not try to risktheir employment opportunity for a business start-up, in a country with limited jobopportunities. It seems that the unemployment in the household has been the majorsignificant push factor for people to be involved in the entrepreneurial activities inorder to secure sufficient stream of income for living. When individuals weight costand benefits of leaving a job and taking risk to go into self-employment they seems toprefer more retaining a job regardless the fact that in their households there are othersource of income i.e. employed. Thus, the hypothesis that additional wage earner hasimportant positive impact on entrepreneurial activity because of risk sharingphenomenon does not hold for Kosova. Additionally, we can conclude that majority ofbusinesses were created by domination of push factors rather than form people whovoluntarily moved from employment to self-employment.

In order to have more precise estimators, we calculated marginal effects at samplemeans for continuous variables and specified at 1 for dummies. Some of the results ofmarginal factors for most important variables in the specification 2 are reportedhere[9]. This exercise shows that holding everything constant (variables in the secondmodel) males have about 15 per cent more chances being involved in entrepreneurialactivities than females. In addition, everything else being equal, individuals havinguniversity degree are slightly less than 5 per cent less likely than others in beinginvolved in entrepreneurial activity. Individuals located in the regions with higherTEA than average TEA for Kosova have about 5.6 per cent more chances of beinginvolved in entrepreneurial activities compared to their counterparts. Finally,individuals living in urban areas are 5 per cent more likely to be self-employed or owna business compared to the individuals located in the rural areas.

Concluding remarksThis article reviews theories of determinants of entrepreneurship most recently usedby many authors and adapted them for empirical investigation of the maindeterminants of entrepreneurship in Kosova. The empirical analysis in the presentstudy is based on the a nationally representative survey over whole population,sufficient for investigating the main determinants of entrepreneurial activities of

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individuals within working age population despite its limitations as result of datanon-availability.

The survey results clearly show that the underlying differences betweenentrepreneurs and non-entrepreneurs over working age adults (18-65 years). Wefound that only 6.4 per cent of the adults within the working age are involved in theentrepreneurial activity. In addition, survey results suggest that personalcharacteristics such as gender, age, level of education, urban residential location,and remittances play most important role in explaining the variations inentrepreneurial activity among individuals.

The econometric analysis, nevertheless gives us a clearer picture of thedeterminants of entrepreneurial activities. Not surprisingly, we found that the menare more likely to be involved in entrepreneurial activity compared to women. Also, wefound a statistically significant and positive, but weak relationship between age andentrepreneurial activity suggesting that the elderly are only slightly more likely to beentrepreneurs. This relationship is not linear as our squared variable age is negativeand significant which means that the probability of being entrepreneur increases withage up to particular age suggesting that individuals of middle age are more prone toentrepreneurial activities. However, both coefficients are small in size, especially thelast one suggesting statistically significant but weak relationship.

In contrast with the expectations from previous studies, we do not find a significantrelationship between marital status and probability of being an entrepreneur.Individuals having university degree are less likely to be entrepreneurs in Kosova.Moreover, when controlled for the sample limited to employed people andentrepreneurs, we found a negative impact of university education on probability ofbeing entrepreneur. On the other hand, an individual belonging to the regions thatexperienced higher entrepreneurship rates is more likely to get involved inentrepreneurial activities than others. The significant and positive sign of sectordummies suggest that in rapidly growing industry (construction) or industriesassociated with low start-up capital requirements or sunk cost (services and trade) areimportant factors explaining likelihood of people being involved in entrepreneurialactivities compared to other individuals in other sectors. Surprisingly, our findingssuggest that individuals belonging to the household, which received remittances orhave additional wage earner/s in the family are less likely to be entrepreneurs. Some ofthese findings may suggest the unique characteristics of Kosova such as highunemployment rate may change people’s incentives for becoming self-employed orentrepreneurs. High unemployment may make them more reluctant to trade-off actualjobs they hold for a business opportunity that generally involves some risk especiallyin a country with extreme high unemployment.

This paper complements existing body of knowledge with empirical evidencefrom a very unique country, which embarked lately on transition process. By testingrelevant entrepreneurship theories this paper sheds some light on some general andcountry-specific factors on entrepreneurial activity. Furthermore, empirical findingsprovide good basis for initiating policy discussion aimed at entrepreneurshipdevelopment in country. The promotion of entrepreneurship is increasinglyimportant for a country with low economic growth and extreme unemployment (seeTable VIII).

Environmentaldeterminants of

entrepreneurship

161

Var

iab

les

12

34

56

78

910

1112

13

GE

ND

ER

1.00

AG

E0.

031.

00A

GE

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0.03

0.99

1.00

UN

IVE

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C0.

120.

080.

061.

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AR

RIE

D2

0.01

0.42

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0.02

1.00

RE

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0.02

0.00

0.02

20.

042

0.01

1.00

RE

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N2

0.01

0.04

0.03

0.15

0.02

20.

101.

00T

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0.00

0.00

20.

010.

010.

022

0.11

0.04

1.00

HH

_S

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20.

012

0.10

20.

092

0.11

0.00

0.17

20.

250.

001.

00C

ON

ST

RU

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ION

0.15

0.02

0.00

0.00

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20.

050.

010.

010.

001.

00S

ER

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ES

0.18

0.09

0.06

0.30

0.11

20.

060.

140.

012

0.10

20.

071.

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RA

DE

0.12

0.02

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20.

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00A

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0.01

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ally

low

Table VIII.Correlation matrix ofexplanatory variables

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Notes

1. Although the term self-employment and entrepreneurship slightly differ, they are usedinterchangeably in this paper to show entrepreneurial activity of population.

2. See Lee (1999) for an overview of studies.

3. For details of the sample and methodology see Labour Market and Unemployment inKosova, Riinvest, 2003, Prishtine.

4. Similar approach as use in many empirical studies (e.g. Wagner and Sternberg, 2002; Brownet al., 2003; Verheul et al., 2005)

5. This definition incorporates both nascent entrepreneurs and owner of new firms. Anindividual is considered a “nascent entrepreneur” under three conditions. First, if anindividual has taken action to create a new business in the past year. Second, if theindividual expects to share ownership of the new firm and, third, if the firm has not yet paidsalaries and wages for more than three months. A firm is considered a new firm in casesalaries and wages are paid for more than three months but less than 42 months (Reynoldset al., 2002, p. 38).

6. The self-employment rate in more advanced transition countries in 1998/1999 are as follows:Poland (22.4), in Czech Republic (14.59), Hungary (14.56) and Slovak Republic (7.8). Theshare of self-employed for these countries is defined as employers plus persons working intheir own account, as a proportion of the total workforce (see Parker, 2004). Note that thisdefinition is comparable to our calculations of self-employment.

7. According to Riinvest (2003) around 63 per cent of the overall population are under 30 yearsold.

8. This was the reason for not including them into a regression as a dummy for those peopleusing remittances for business purposes.

9. Other marginal effects for all regression exercised are available on request from author.

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Further reading

Constant, A. and Zimmerman, K. (2004), “Self-employment dynamics across the business cycle:migrants versus natives”, discussion paper no. 1386, Institute for Study of Labour-IZA,Bonn.

Statistical Office of Kosova (SOK) (2004), Business Register, Statistical Office of Kosova (SOK),Prishtine.

Corresponding authorBesnik A. Krasniqi can be contacted at: b.krasniqi@staffs.ac.uk

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