Discussion Papers on Entrepreneurship, Growth and. Public Policy. Martin Carree University of Maastricht

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1 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy # 0106 Allocaton and Productvty of Tme n New Ventures of Female and Male Entrepreneurs Ingrd Verheul Erasmus Unversty Rotterdam and EIM Busness and Polcy Research Martn Carree Unversty of Maastrcht Roy Thurk Erasmus Unversty Rotterdam, EIM Busness and Polcy Research and Max Planck Insttute of Economcs Number of Pages: 21 The Papers on Entrepreneurshp, Growth and Publc Polcy are edted by the Group Entrepreneurshp, Growth and Publc Polcy, MPI Jena. For edtoral correspondence, please contact: egppapers@econ.mpg.de ISSN by the author Max Planck Insttute of Economcs Group Entrepreneurshp, Growth and Publc Polcy Kahlasche Str Jena, Germany Fax:

2 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 1 ALLOCATION AND PRODUCTIVITY OF TIME IN NEW VENTURES OF FEMALE AND MALE ENTREPRENEURS Ingrd Verheul a,c, Martn Carree b and Roy Thurk a,c,d a Erasmus Unversty Rotterdam, Erasmus School of Economcs, Department of Appled Economcs, P.O. Box 1738, 3000 DR Rotterdam, the Netherlands b Unversty of Maastrcht, Faculty of Economcs and Busness Admnstraton, P.O. Box 616, 6200 MD Maastrcht, the Netherlands c EIM Busness and Polcy Research, P.O. Box 7001, 2701 AA Zoetermeer, the Netherlands d Max Planck Insttute of Economcs, Kahlaïsche Strasse 10; D Jena, Germany VERSION: December 2008 ACCEPTED FOR PUBLICATION IN SMALL BUSINESS ECONOMICS DOCUMENT: Verheul Carree Thurk tme allocaton SBE resubmsson v28 JEL CLASSIFICATION: J22, L26, M13 KEY WORDS: tme allocaton, preferences, productvty, gender, new ventures ABSTRACT: Ths paper nvestgates tme allocaton decsons n new ventures of female and male entrepreneurs usng a model that dstngushes between effects of preferences and productvty on the number of workng hours. Usng data of 1,158 entrepreneurs we fnd that the preference for work tme n new ventures relates to start-up motvaton, propensty to take rsk and avalablty of other ncome. Productvty of work tme relates to human, fnancal and socal captal endowments and the prevalence of outsourcng actvtes. Ths study also evaluates actual proft effects one year after start-up. We fnd that on average women nvest less tme n the busness than men. Ths can be attrbuted to both a lower preference for work tme (drven by rsk averson and avalablty of other ncome) and a lower productvty per hour worked (due to lower endowments of human, socal and fnancal captal). ACKNOWLEDGEMENTS: The paper has been wrtten n the framework of the research program SCALES carred out by EIM Busness and Polcy Research (a Pantea company) and fnanced by the Dutch Mnstry of Economc Affars. Ingrd Verheul acknowledges fnancal support of the Fund Schedam Vlaardngen e.o. and the Trust Fund Rotterdam. An early verson of the present paper has been read at the Babson Kauffman Entrepreneurshp Research Conference, Strathclyde Unversty, Glasgow, Scotland, June 3-5, 2004, and the RENT XVIII Conference, Denmark, Copenhagen Busness School, November 25-26, CORRESPONDENCE: Ingrd Verheul, verheul@few.eur.nl

3 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 2 1. INTRODUCTION Human tme s one of the most fundamental resources nvested n new ventures. Tme allocaton theory explans how and why ndvduals allocate ther scarce tme to dfferent actvtes. Snce Becker s (1965) A Theory of the Allocaton of Tme a substantal amount of research has been done n ths area, mostly focusng on wage or contract labor (Juster and Stafford, 1991). Hardly any attenton has been pad to the tme allocaton decsons of the self-employed. The dstncton between wage- and selfemployment s mportant because the use of tme n these occupatons s dfferent n at least two respects. Frst, self-employed ndvduals tend to spend more tme workng n the market than wage-employed ndvduals (Ajay-Obe and Parker, 2005; Ln et al., 2000), whch may relate to greater job satsfacton and hgher work demands n self-employment (Hamlton, 2000; Ajay-Obe and Parker, 2005). Second, self-employed ndvduals usually have greater flexblty of workng hours than wage-employed ndvduals (Wales, 1973; Hyytnen and Ruuskanen, 2007). Research on tme allocatons decsons of self-employed ndvduals usually concentrates on how tme s allocated among dfferent actvtes wthn the frm rather than how t s dvded between the frm and other actvtes (McCarthy et al., 1990; Cooper et al., 1997). Studes by Lévesque and MacCrmmon (1997) and Lévesque and Schade (2005) deal wth the queston of how ndvduals dvde ther tme between lesure and work (n the new venture and wage-employment), but do not perform emprcal tests usng data of real entrepreneurs. Lévesque and MacCrmmon adopt an analytcal approach and ntroduce a framework for the optmal tme allocaton between wage- and self-employment. Lévesque and Schade nvestgate tme allocaton decsons of students n economcs and busness wthn an expermental settng. The present study ams at explanng tme allocaton decsons of entrepreneurs n new ventures by applyng tme allocaton models from wage labor research to the decson world of the entrepreneur. In addton, we take nto account several aspects mentoned n the entrepreneurshp lterature. Our model explans tme nvestments n new ventures by examnng the preference for work tme n the frm versus lesure tme, the expected productvty of work tme and other tme-consumng actvtes (assumed exogenous). The latter nclude wage-employment, famly responsbltes, schoolng and commute tme (all negatvely affectng tme nvested n the frm). Lesure tme s the tme avalable after work tme and tme spent on the other tme-consumng actvtes. Ths s the frst contrbuton of the paper: emprcally dsentanglng preference and (expected) productvty effects on tme nvestments n new ventures. Ths s an mportant dstncton because entrepreneurs allocate ther tme to the busness on the bass of ther wllngness and ablty to work n the frm. Our analyss s based on an extensve and rch data set ncludng tme allocaton decsons by entrepreneurs. The second contrbuton les n the nvestgaton of gender dfferences wth respect to tme allocaton decsons. Female entrepreneurs are more lkely to work n the frm on a part-tme bass than male entrepreneurs (Ajay-Obe and Parker, 2005; Clff, 1998; Verheul and Thurk, 2001). The part-tme nature of female entrepreneurshp has often been explaned n terms of household and famly responsbltes but has not been nvestgated n a set-up explctly dstngushng between preference and (expected) productvty effects. We am to provde nsght nto the reasons why women nvest less tme n the busness than men by attrbutng ther lower tme nvestments ether to a lower preference for work tme or a lower (expected) productvty per tme unt. These nsghts are mportant as they suggest (new) routes for stmulatng partcpaton of women n entrepreneural actvty n terms of workng hours. We fnd that female entrepreneurs, on average, nvest less tme n the busness than men, whch can largely be attrbuted to ther lower preference for work tme (explaned by avalablty of other ncome and rsk atttude) and a lower (expected) productvty per tme unt (explaned by relatvely lower endowments of human, fnancal and socal captal). The paper s structured as follows. The next secton dscusses the lterature on the preference for, and the productvty of work tme. Hypotheses are proposed for the antecedents of both preferences and (expected) productvty. Secton three descrbes the sample and varables. Secton four presents the utlty model. The results for preference, productvty and gender effects are gven n secton fve. Fnally, the summary and concluson of ths paper are presented.

4 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 3 2. PREFERENCES AND PRODUCTIVITY Explanng workng hours Douglas and Shepherd (2002) dstngush between four factors that determne utlty from a job ncludng ncome, work effort, rsk nvolved and ndependence. The frst two factors are the tradtonal nput varables n utlty models n economc consumer behavor theory (Varan, 1984, p. 216). They are also appled to utlty-maxmzng entrepreneurs (Auster and Slver, 1976) where money ncome π and lesure tme L determne utlty U( π,l ). Ths wll be the bass for our model. We argue that the varaton n the number of workng hours across entrepreneurs reflects dfferences n the preference for workng hours or the expected productvty of work tme. Ths s n lne wth the argument of Douglas and Shepherd (2000) that the decson to become an entrepreneur depends upon both the ablty to become one and the atttude towards entrepreneurshp. Entrepreneural ablty encompasses the knowledge and sklls needed to be productve. An ndvdual s atttude reflects the utlty (s)he derves from a partcular job and the amount of work effort devoted to ths job. Antecedents of preference for work tme Ths secton dscusses the determnants of the preference for work tme n the newly started venture versus lesure tme. Attenton s pad to other sources of ncome, gender, age and ntrnsc motvaton. Frm-specfc factors, havng a partner and rsk atttude are ncluded n the analyss as controls and wll be dscussed n the next secton. Addtonal ncome Proft s often the man source of ncome for self-employed ndvduals. However, sometmes addtonal sources of ncome are avalable, such as partner ncome, nhertance and nterest receved on savngs. These sources are not dependent on the number of hours nvested n the frm and there may be an ncome effect reducng the number of desred workng hours. The avalablty of other ncome s lkely to reduce the preference for workng hours (Ajay-Obe and Parker, 2005). Hypothess 1 s formulated as follows: H1: The avalablty of other sources of ncome (other than that extracted from the busness) negatvely nfluences the preference for work tme. Gender of the entrepreneur The average number of workng hours per person has decreased consderably n the last hundred years (Maddson, 1982; 1987). However, pad workng hours for men have declned, whereas for women they have ncreased substantally (Kllngsworth and Heckman, 1986). Contemporary tme allocaton decsons also show gender dfferences. Employment rates are stll lower for women than for men n most OECD countres (OECD, 2002) and wthn any pad occupaton men tend to work longer hours than women (Ajay-Obe and Parker, 2005). Men are more lkely to work on a fulltme bass n selfemployment than women (OECD, 1998). The combnaton of work and famly responsbltes and flexble workng hours seems to be an mportant motve for women to engage n self-employment (Longstreth et al., 1987) 1. In the present study we control for other tme-consumng actvtes (e.g., wage-employment, famly responsbltes, schoolng, commute tme) and therefore expect no gender dfference regardng the preference for work tme n the frm. Hypothess 2 s formulated as follows: H2: When other tme-consumng actvtes are controlled for, the gender of the entrepreneur does not nfluence the preference for work tme. Age of the entrepreneur Tme allocaton decsons depend on age (Juster and Stafford, 1991). Market work of men reaches ts peak between the age of 25 and 44 years and decreases afterwards (Blnder and Wess, 1976; Hll, 1985). Lévesque and Mnnt (2006) argue that as people get older, they attach less value to future

5 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 4 earnngs,.e., whle wages ncrease wth age, the present value of the returns to entrepreneurshp declnes. Euwals (2001) presents evdence for ths negatve effect of age on desred workng hours for women on the Dutch labor market. Hypothess 3 s formulated as follows: H3: The age of the entrepreneur has a negatve effect on the preference for work tme. Intrnsc motvaton There are varous motves for startng a busness. Besdes the man reason of earnng a lvng (extrnsc motvaton), there are non-pecunary benefts to self-employment, such as beng one s own boss and the challenge (ntrnsc motvaton). Hamlton (2000) suggests that the non-pecunary benefts of self-employment are substantal. Douglas and Shepherd (2002) refer to ndependence as a key determnant of the utlty derved from a job. Smlarly, Hyytnen and Ruuskanen (2007) stress the mportance of entrepreneural ndependence for job satsfacton of the self-employed. It may be expected that entrepreneurs who are ntrnscally motvated, e.g., by a desre to be ther own boss, work longer hours than entrepreneurs who are largely extrnscally motvated. Hypothess 4 s formulated as follows: H4: The start-up motve of beng your own boss has a postve effect on the preference for work tme. Antecedents of productvty of work tme Indvduals who start new ventures usually are not able to adequately predct (the drvers of) ther productvty. So formally we only analyze the role of expected productvty n our utlty model. Assumng that ndvduals are capable of assessng the fruts of ther labor and there s at least some overlap between expected and realzed productvty we refer to the lterature lnkng human, socal and fnancal captal to actual productvty to formulate hypotheses on the determnants of expected productvty. In the remander of ths paper we wll just refer to productvty. Frm-specfc factors (frm sze, sector, nnovaton, type of frm and outsourcng) are ncluded n the analyss as controls and are dscussed n the next secton. Human captal Accordng to human captal theorsts (Becker, 1965; Mncer, 1974) knowledge ncreases the cogntve abltes of an ndvdual, resultng n more productve and effcent behavor. Indeed, human captal has been found to postvely nfluence the performance of entrepreneural frms (Chandler and Hanks, 1994, 1998; Cooper et al., 1994; Pennngs et al., 1998). A dstncton can be made between general and specfc human captal (Becker, 1993). Castanas and Helfat (1991; 2001) dscrmnate between generc, ndustry-specfc and frm-specfc sklls or knowledge. An entrepreneur s educaton s lkely to enhance learnng and ncrease the problem-solvng ablty of an ndvdual wthn a gven envronment. Indeed, several studes fnd that (formally) hgher educated entrepreneurs perform better than others (Gmeno et al., 1997; Hamlton, 2000; Burke et al., 2000) 2. Hypothess 5 s formulated as follows: H5: The educaton level of the entrepreneur has a postve nfluence on the productvty of work tme. Next to the level of educaton, the type of educaton could matter for ndvdual performance. Obvously, there s a dfference between acqurng general and more specfc (job-related) knowledge and sklls. Therefore, we dstngush between two types of educaton: general knowledge acqured n secondary (hgh) school or at unversty, and vocatonal tranng (professonal educaton). We hypothesze that vocatonal tranng has a postve effect on productvty n the new venture snce such tranng may be more drectly applcable n the new small venture. Hypothess 6 s formulated as follows: H6: Vocatonal tranng has a postve nfluence on the productvty of work tme. Accordng to Cooper et al. (1994, p. 376) gender may serve as a proxy for lfe experences and access to networks and other resources that bear upon the prospects for success of ndvdual entrepreneurs. Although the level of educaton s largely smlar for female and male entrepreneurs

6 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 5 (Brley et al., 1987; Fscher et al., 1993), men tend to have hgher levels of entrepreneural experence (Kalleberg and Lecht, 1991; Fscher et al., 1993), fnancal management experence, and ndustry experence (Fscher et al., 1993; Verheul and Thurk, 2001). It has also been argued that women and men do not have equal access to fnancal and socal captal (Fscher et al., 1993; Moore and Buttner, 1997). Hence, women may be less productve than men because they had fewer opportuntes to acqure dfferent types of captal. Research has shown that women-led frms are outperformed by male-led frms n terms of profts, revenue growth and employment (Rosa et al., 1996; Carter et al., 1997). However, when controllng for the dfference n levels of human, socal and fnancal captal (as well as for venture characterstcs), we do not expect to fnd gender dfferences regardng the productvty of work tme. In general, research ndcates that gender dfferences n performance are neglgble when holdng other factors constant (Collns-Dodd et al., 2004; Xe and Shauman, 1998; Qusumbng, 1996). Hypothess 7 s formulated as follows: H7: When the levels of human, socal and fnancal captal are controlled for, the gender of the entrepreneur does not nfluence the productvty of work tme. The age of the entrepreneur may also be consdered a proxy for endowments of human captal, such as years of work or lfe experence (Gmeno et al., 1997; Cowlng and Taylor, 2001). Younger people have had less of an opportunty to buld up relevant human captal for entrepreneurshp. On the other hand, the mpact of addtonal experence s lkely to dmnsh wth an ncrease n age. Several studes show that productvty follows an nverted U-shaped relatonshp wth age (Kanazawa, 2003; Skrbekk, 2003). Wthn the area of entrepreneurshp, van Praag (1996; 2003) fnds that there s a U- shaped relatonshp of an entrepreneur s age wth the hazard of compulsory ext, mplyng an nverse U- shaped relaton wth survval. Hypothess 8 s formulated as follows: H8: The age of the entrepreneur has an nverse U-shaped relatonshp wth the productvty of work tme. Management-specfc knowledge of entrepreneurs ncreases the probablty of pursung proftable strateges and dealng adequately wth management ssues (Cooper et al., 1994). It s mportant to dstngush between management and entrepreneural experence, the latter referrng to experence wth startng and runnng a small frm. In general, entrepreneurshp studes fnd that entrepreneural experence s a relatvely mportant factor when explanng new venture performance (Stuart and Abett, 1990; Gmeno et al., 1997; Evans and Leghton, 1989; Reuber and Fscher, 1999), although Westhead and Wrght (1998) fnd a lmted effect. Hypothess 9 s formulated as follows: H9: Entrepreneural experence has a postve nfluence on the productvty of work tme. Entrepreneurs who have worked n the same ndustry n the past are lkely to have a network of relatonshps wth supplers, customers and dstrbutors, provdng them wth support and credblty (Cooper et al., 1994). Industry-specfc knowledge has proven to be mportant for new venture performance (Lee and Tsang, 2001), even n addton to work and entrepreneural experence (van Praag, 2003). Hypothess 10 s formulated as follows: H10: Industry experence has a postve nfluence on the productvty of work tme. Past work experence of the entrepreneur may be relevant for new frm performance, above and beyond ndustry experence. Accordng to Vesper (1980) entrepreneurs who run frms that are closely related to the actvtes they dd n the past have acqured relevant sklls and abltes as well as the approprate pror mental programmng. Van Praag (2003) fnds that occupatonal experence has addtonal explanatory value for new venture performance, n addton to that of ndustry, work and entrepreneural experence. Hypothess 11 s formulated as follows: H11: The extent to whch past work s related to the current actvtes of the entrepreneur has a postve nfluence on the productvty of work tme.

7 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 6 Fnancal captal Fnancal captal can have a drect effect on productvty through the ablty to undertake more captal-ntensve or ambtous strateges and to change courses of acton. Captal-ntensve strateges are relatvely well protected from mtaton and characterzed by ncreased labor productvty. Indrectly, captal nvestments may enable tranng and more comprehensve plannng, nfluencng frm performance (Cooper et al., 1994). Most studes fnd a postve relatonshp between ntal captal nvestment and performance (Cooper and Gmeno-Gascon, 1992). Hypothess 12 s formulated as follows: H12: The amount of start-up captal has a postve nfluence on the productvty of work tme 3. Socal captal: networkng wth other entrepreneurs Socal captal refers to the access of an ndvdual to varous resources va nteracton wth members of a network (Portes, 1998; Bourdeu, 1986). Such a network may nclude relatonshps wth famly, frends and the communty but also more formal arrangements as professonal or busness networks. Interacton and communcaton wthn networks of entrepreneurs may contrbute to venture performance as t enables the exchange of valuable nformaton and other resources, ncludng customers, supplers and nformal credts (Brüderl and Presendörfer, 1998). Barr (2000) lnks networks to knowledge flows between frms, nfluencng frm productvty. Davdsson and Hong (2003) fnd a strong postve effect of membershp of a busness network on early stage frm performance. Hypothess 13 s formulated as follows: H13: Contact wth other entrepreneurs has a postve effect on the productvty of work tme. 3. SAMPLE AND VARIABLE DESCRIPTION To test the hypotheses we use data gathered through a detaled panel survey of EIM Busness and Polcy Research, commssoned by the Dutch Mnstry of Economc Affars. A representatve sample was drawn of ndependent new ventures regstered at the Chamber(s) of Commerce n the frst quarter of Only man establshments were selected. The dstrbuton of frms was representatve across sector and sze class. Agrcultural frms and companes extractng mnerals; busnesses that changed legal form or actvty; and relocated frms were excluded. The man themes covered by the survey questons nclude frm and owner characterstcs; fnance and nvestment; bottlenecks; strategy and goals; market and envronment; and realzaton and expectatons. In 1994 approxmately 12,000 frms were approached by telephone, of whch about 3,000 partcpated n the survey. These frms receved a questonnare by mal. A total of 1,938 questonnares was returned. Because frms were also followed n the subsequent year (1995) nformaton s avalable on tme allocaton and profts one year after start-up. In ,007 questonnares were sent back, resultng n a response rate of about 58 percent. In ths study we use the nformaton for 1994 to test for expected preference and productvty effects, applyng a nonlnear model. The nformaton for 1995 s used to test for real productvty effects, explanng profts n We use a sub-sample of 1,158 Dutch owners or owner-managers (843 male and 315 female) to test for preference and productvty effects on the number of workng hours n Ths sub-sample ncludes all observatons for whch nformaton s avalable on the number of workng hours n 1994 as well as the characterstcs of the busness and ts owner-(manager) relevant for the study. The sample characterstcs for the sub-sample of 1,158 entrepreneurs and the total sample of 1,938 entrepreneurs are practcally dentcal. The start-up rate for women s 27 percent for the total sample as well as the subsample. Ths s also a representatve fgure of women startng up frms durng the 1990s n the Netherlands. To test for effects on actual profts n 1995 we use a sub-sample of 561 out of the 1,007 respondents who returned the questonnare agan n 1995 for whom all relevant nformaton (.e., workng hours, sde-actvtes, determnants of productvty n the nonlnear analyss) was avalable. We nclude a number of controls. We nclude dummy varables for a venture n (busness and personal) servces as well as a busness n trade n both the preference and productvty part of the model. The base category s manufacturng and constructon frms. The number of employees (measured n fulltme equvalents) s also ncluded n both parts of the model. Although our sample manly conssts of

8 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 7 start-ups wth no or only a few employees, stll the entrepreneur wth employees s able to delegate some tasks and responsbltes (Churchll and Lews, 1983; Cooper et al., 1997). Ths mght lead to more lesure tme or ncreased productvty. For the explanaton of the preference for work tme, we nclude havng a partner and rsk atttude. Mantanng a (good) relatonshp takes tme and energy away from the busness, but at the same tme enables partners to share household tasks resultng n tme savngs. Because there s no evdence that marred men are more productve (Loh, 1996) the partner varable s ncluded only on the preference sde. We measure rsk atttude as the self-reported general wllngness of an ndvdual to take rsk (degree of rsk tolerance) 4. Because entrepreneural actvtes are more rsky n terms of ther outcome than many other actvtes, relatvely rsk averse entrepreneurs may lmt ther work effort (Khlstrom and Laffont, 1979). Nevertheless, Parker (2006, p. 353) argues that rsk-averse self-employed workers may have a need for self-nsurance and, accordngly, work longer hours 5. For the productvty of work tme we nclude the followng controls. The varable ReEntry captures whether the frm s a restart or take-over of an exstng frm as opposed to a newly started busness. We expect that restarted frms or takeovers wll be (more) productve sooner than newly started frms. A de novo frm needs to be bult up from scratch, whereas a restarted frm usually already has valuable busness relatons and knowledge. Innovatveness, outsourcng and commute tme are also ncluded as controls. Innovatve frms may be more productve than frms that do not pursue product nnovaton (Crépon et al., 1998; Klomp and van Leeuwen, 2001). Nevertheless, Tmmons (1986) argues that a hgh falure rate for nnovatons s the rule rather than the excepton. Astebro (2003) fnds that the return to nventons s relatvely low, possbly due the unrealstc optmsm of nventors. In ths study we nclude a categorcal varable ndcatng the mportance of new technology for the frms products. Outsourcng actvtes may postvely affect productvty (Betts et al., 1992; D Aven and Ravenscraft, 1994; Le and Htt, 1995; Qunn, 1992). The entrepreneur wll contract out those actvtes that are most tmeconsumng; of whch (s)he has lttle experence; or that do not belong to the core busness. Yet, the emprcal evdence that outsourcng actvtes lead to cost advantages remans lmted (Glley and Rasheed, 2000). The varable Commute captures whether the entrepreneur travels to and from the busness,.e., whether (s)he works from the home or n separate busness premses. The latter may be an ndcator of ambton ( thnk bg ), but may also be an expensve necessty, thereby negatvely nfluencng proftablty 6. Table 1 presents descrptons, means and standard devatons for all varables n the study. The number of workng hours per week (n) s categorzed from 1 to 7. The maxmum number of hours avalable per week s assumed to be 100 (.e. excludng tme for personal care) whch would correspond to a hypothetcal category code of We fx the maxmum number of N at 10 n the nonlnear analyss. The Hours varable has an average of 3.93, ndcatng an average number of workng hours of about 35 hours per week. The mean value for the Hours varable for female and male entrepreneurs s 3.28 and 4.17, respectvely 8. On average, men work longer hours than women do. Ths becomes clear when lookng at the gender dvde n the hghest categores of the Hours varable. Whereas 27 percent of all respondents s female, of those who work more than 60 hours (n=7) only 14.5 percent s female. Ths percentage s 17.6% for the hours category (n=6), and 19.5% for the hours category (n=5). Thus, the gender dfference ncreases wth the number of workng hours. Table 2 presents correlatons between the number of workng hours, the ndependent varables and other tme-consumng actvtes. We see that many varables correlate sgnfcantly wth workng hours, of whch StartCaptal (r = 0.476; p<0.01), OtherJob (r = ; p<0.01), OtherIncome (r = ; p<0.01) and Commute (r = 0.365; p<0.01) are most promnent. In general, correlatons between the explanatory varables are low Tables 1 and 2 about here

9 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 8 4. MODEL AND DATA ANALYSIS In ths study we argue that the number of workng hours depends on the preference for work tme and the productvty of work tme n the frm, whch are both agan drven by a range of factors. To explan workng hours we follow Carree and Verheul (2004) and propose a nonlnear model that ncludes parameters representng the preference for work versus lesure tme (α ) and the productvty of work tme (γ ). The model s rooted n economc consumer behavor theory where ncome (consumpton) and lesure determne utlty. As we wll show, a lnear model does not enable us to dstngush between such preference and productvty effects because both effects may underle the overall effect of a factor on tme nvestments. Therefore, we use a nonlnear analyss based on a maxmzng utlty set-up to dsentangle preference and productvty effects on the number of workng hours. Fnally, to test for the robustness of the expected productvty results from the nonlnear analyss, we examne the actual mpact of the productvty antecedents on profts one year after start-up. In other words: we compare the outcomes of the nonlnear model (a pror) wth the actual mpact of the selected varables (a posteror). Let N be the total tme avalable per week. We assume that entrepreneurs maxmze ther utlty 10 : Max n U ( π ( n), N n). Utlty (U ) s nfluenced postvely by expected proft (π ) and lesure tme ( N n ). Proft s dependent upon the number of hours worked ( n) and expected productvty. We use a α 1 α Cobb-Douglas form U = π ( N n ) γ for utlty and a proft functon π = βn and have the followng (logarthmc) equatons : (1) lnu = α lnπ + (1 α)ln( N n) (2) lnπ = φ + γ ln( n) Note that φ = ln β. We expect that 0 < α < 1 and that γ s postve. More workng hours results n hgher proft. The value of γ s unknown to the entrepreneur. Therefore the entrepreneur uses hs or her expectaton of productvty to determne the optmal value of n. The relaton between proftablty and number of hours s allowed to be non-lnear, assumng a possble nterdependence between hours and productvty (Barzel, 1973). After substtutng the proft functon nto the utlty functon, the frst-order condton s: d ln U / dn = αγ / n (1 α) /( N n) = 0. The optmal number of workng hours for entrepreneur s therefore: (3) n = α γ N 1 α + α γ, where α s the ndvdual-specfc preference for proft versus lesure tme and γ s the ndvdualspecfc hours elastcty of expected proft. The latter can be nterpreted as a productvty parameter. Both an ncrease n α and γ lead to a hgher utlty-maxmzng number of workng hours. The rato of the frst dervatves of the optmal number of workng hours to α and γ equals: n (4) n γ / = α γ α (1 α ) An entrepreneur can also spend tme outsde the busness, thereby lmtng the total tme avalable. We correct for the tme an entrepreneur spends on other actvtes, ncludng wage-employment, famly care, schoolng and commute tme 12. x refers to the presence of other (competng) tme-consumng actvtes of an ndvdual. The fnal model specfcaton s: (5) n = α γ ( N + δ x ) 1 α + α γ,

10 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 9 where δ s expected to be negatve. Our dependent varable s measured n seven categores. Ths suggests applyng a (nonlnear) order probt analyss. However, such an analyss fals to take nto account that the categores are equdstant (wth the possble excepton of the hghest category). Therefore, we also present a nonlnear least squares regresson analyss. The model predcts a postve productvty effect on the number of workng hours 13. Generally, an ncrease n wage or revenues per hour (n the case of self-employment) may lead to an ncrease or decrease of workng hours dependng upon whether the substtuton effect (whereby ndvduals substtute lesure for work when returns to work ncrease) or the ncome effect (whereby ndvduals respond to ther hgher earnngs by consumng more lesure at the expense of workng hours) domnates (Blundell and MaCurdy, 1999). In the emprcal lterature, fndngs are nconclusve (Grossman, 2003). Ajay-Obe and Parker (2005) show that n response to hgher wages both wage-employed and selfemployed ndvduals work fewer hours. However, Thornton (1998) fnds that self-employed male physcans have an upward-slopng labor supply curve. For women results appear more clear-cut. Bddle and Hamermesh (1990) fnd that hgher returns per hour lead to more market work for females. Euwals (2001) fnds that the hourly wage postvely affects desred hours for females n the Dutch labor market. A possble backward-bendng labor supply s more lkely at hgher hourly wage or productvty rates (Parker, 2006, p.352). Because our sample conssts of recently started entrepreneurs wth no or only a few employees, t s unlkely that there are predomnantly hgh productvty levels. The ndvdual-specfc preferences ( α ) and productvty ( γ ) are nfluenced by a range of factors. Hypotheses have been formulated for effects on preferences ( H α n Fgure 1) and effects on productvty ( H γ n Fgure 1). The preference for workng n the frm rather than enjoyng lesure tme depends on the personal stuaton of the entrepreneur, ncludng other ncome avalable (e.g., partner ncome), gender, age, and start-up motvaton. The productvty of work tme s expected to be nfluenced by human, socal and fnancal captal nvested n the frm. Indvdual preferences and productvty are determned by addng up the effects: α = a0 + a1otherincome + a2gender +... and γ c + c Gender + c Age..., respectvely. = Fgure 1 about here Our model allows for dfferent types of gender effects on tme nvestments. Women may have a dfferent preference for work tme than men, leadng to a gender dfference n tme nvestments. Also, women and men may dffer regardng productvty, also leadng to dfferent tme nvestments. Below we wll call these effects ndrect because they run through preference and productvty. Fgure 1 assumes that these ndrect gender effects are caused by medaton (.e., other explanatory factors medate the relatonshp between gender and preferences or productvty). For example, t may be that women have less entrepreneural experence than men, negatvely affectng ther productvty and, accordngly, ther tme nvestments. 5. RESULTS In Table 3 we present the results of the dfferent approaches to explanng the number of workng hours n the frm (n). In the frst two columns we present the results of a lnear approxmaton to Equaton (5): n = λ 0 + λ1otherincome + λ2gender + λ3 Age +... Frst, we show the results of the OLS regresson and, subsequently, those for the ordered probt regresson. In the thrd and fourth column we present the results of the much rcher nonlnear analyss, dstngushng between the preference for work tme and the productvty of tme 14. Agan we estmate both a (nonlnear) least squares and an ordered probt regresson. We wll focus on the least squares results n our dscusson. One can determne the values of the estmated values for a a,,... and c c,,... from Table 3. The average (medan) values for 0, 1 a2 0, 1 c2 n α and γ by fllng n the estmated parameter α and γ are

11 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy (0.41) and 1.44 (1.02), respectvely. For women the average (medan) values for α and γ amount to 0.37 (0.36) and 1.22 (0.88), respectvely, whereas for men they amount to 0.43 (0.43) and 1.53 (1.07), respectvely. Hence, on average, female entrepreneurs n the sample have both a lower preference for work tme n the frm and a lower productvty of work tme than male entrepreneurs. From Equaton (4) we can derve that the effect of α on n s about sx tmes the effect of γ on n. Ths mples that the effect of a gender dfference of 0.06 found for α s approxmately equal to that of 0.31 found for γ Table 3 about here Lnear results From the frst column n Table 3 we see that the (other) tme consumng actvtes OtherJob, Schoolng and FamlyCare negatvely affect the number of workng hours, whereas Commute has a postve effect. The postve effect of Commute may relate to the fact that frms where the entrepreneur works from home (Commute=0) tend to operate on a smaller scale than those establshed n separate busness premses 15. The relatvely weak effect of FamlyCare may be explaned by the fact that famly responsbltes can also come at the expense of lesure tme (nstead of work tme). We see that gender has a sgnfcant negatve effect on the number of workng hours, whch ndcates that ceters parbus women nvest less tme n the busness than men. Surprsngly, Employees does not nfluence the number of workng hours. Dscussng the outcomes of the much rcher nonlnear model, we wll see that frm sze has a negatve effect on preferences and a postve effect on productvty, explanng the absence of an overall effect on the number of workng hours. Ths fndng underlnes the mportance of dscrmnatng between preference and productvty effects when studyng tme allocaton decsons. Nonlnear results Preference for work tme The results of the nonlnear model (estmated both as least squares and ordered probt) are presented n the thrd and fourth column of Table 3. We fnd that the more an entrepreneur s dependent on revenues from the frm for subsstence, the hgher the preference for work tme n the busness. In addton, ndvduals who start a busness to be ther own boss have a hgher preference for work tme than others. Hypotheses 1 and 4 are supported. There s no separate effect of gender n the nonlnear least squares regresson. Ths would confrm Hypothess 2. However, the order probt model shows a stronger and sgnfcant negatve effect of gender on the preference for work tme. Apparently, even when controllng for other tme-consumng actvtes, men mght more strongly prefer work tme over lesure tme than women do. The fndng that there are fewer 'extremes' among women when t comes to devotng tme to ther work compared to men s confrmed by research on workaholsm (Burke, 1999). There s no strong effect of age. Hence, Hypothess 3 s left wth lttle support. Wth respect to the controls, we see that the number of employees negatvely affects the preference for work tme. It appears that entrepreneurs hre more employees to delegate some tasks and responsbltes and, accordngly, work fewer hours themselves. Rsk-averse ndvduals have a lower preference for work tme than rsk-lovng people. New ventures are nherently rsky and therefore may be less attractve to rsk-averse ndvduals for nvestng ther tme. Productvty of work tme The varable StartCaptal has a postve effect on productvty,.e., nvestng larger sums of money n the busness leads to hgher productvty per workng hour. In addton, there are postve effects of the degree to whch current actvtes are related to past work (Smlarty) and, to some extent (at 10% level of sgnfcance), contact wth other entrepreneurs (Contacts) and ndustry experence (INDexperence). Hypotheses 10 through 13 are supported. In accordance wth Hypothess 7 we fnd no separate effect of the gender of the entrepreneur on the productvty of work tme. We fal to fnd sgnfcant effects of ether entrepreneural experence or age. Hence, there s no support for Hypotheses 8 or 9. Wth respect to the controls we fnd that exstng frms (.e., restarts or takeovers) are characterzed by a hgher

12 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 11 productvty than frms started from scratch. Also, frms that contract out actvtes are characterzed by a hgher (expected) productvty than frms that do not engage n outsourcng. A frm wth more employees has a hgher productvty. It seems that havng more employees enables effectve delegaton of actvtes. Note that Commute s ncluded two tmes n the model to enable us to dsentangle the negatve effect of commute tme as a competng tme-consumng actvty, and ts postve effect va productvty, possbly reflectng the ambton level of the entrepreneur. Explanng actual proftablty In our model t s assumed that entrepreneurs are aware of the nfluence of the varous factors on ther productvty. The outcomes of the nonlnear model wth respect to productvty (a pror) can be compared to the actual mpact of the selected varables (a posteror). Ths can be done usng selfreported profts as the varable to be explaned. We perform an ordered probt regresson analyss usng categorzed data on (realzed) profts one year after start-up (n 1995). Profts are regstered as a categorcal varable, consstng of eleven categores (numbered 0 to 10: (0) a loss; (1) loss nor proft; (2) proft up to fl.10,000; (3) proft of fl.10,000-25,000; (4) proft of fl.25,000-50,000; (5) proft of fl.50,000-75,000; (6) proft of 75, ,000; (7) proft of 100, ,000; (8) proft of fl.150, ,000; (9) proft of fl.250, ,000; (10) a proft of more than fl.500,000) 16. Fl. (florn) denotes the Dutch gulder whch had an average value of 0.62 US Dollar n The average value for the proft varable n the sample amounts to 2.75 wth a standard devaton of The average value for women and men amounts to 2.11 and 2.96, respectvely. Hence, on average female entrepreneurs have lower profts than ther male counterparts n Our analyss s based on Equaton 2. We nvestgate the mpact on the categorcal profts varable (used as a proxy for ln π ) of all factors ncluded n the nonlnear analyss explanng productvty (γ ). We use the number of workng hours n 1995 for n. Note that the analyss only gves approxmate productvty effects, as a proxy varable s used. Because the proft varable s categorcal wth dfferent ranges of proft across categores, we apply an ordered probt regresson. Use s made of 561 observatons for whch data s avalable on the number of workng hours and profts n Because only a subset of the entrepreneurs answered the proft queston one year after start-up, there may be some selecton bas. Hence, the results should be nterpreted as the relatonshp between hours and profts n 1995 for survvng frms. The fnal column n Table 3 presents the results for the ordered probt analyss explanng proftablty, reportng the components of γ n the proft equaton Table 3 about here Comparng the outcomes of the nonlnear model (estmatng expectatons of profts) wth those of the proft equaton (estmatng actual profts) we see that entrepreneural expectatons are not completely fulflled. Although some factors have a relatvely smlar mpact n both models, we dentfy a dfference for others 18. More specfcally, we fnd that outsourcng and start-up captal have an mpact n the nonlnear model, but dsappear n the proft equaton. Both outsourcng and captal nvestment are costly and may not have strong postve effects on profts n the short run (.e., one year after start-up). We see that the effect of commute tme dsappears n the proft model. Ths may be explaned by the fact that t s relatvely costly (or more costly than expected) to operate from a separate busness premses, negatvely affectng profts. On the other hand, we see that the negatve effect of (female) gender becomes sgnfcant n the proft model. Ths may reflect a dfference n ambtons of female and male entrepreneurs, where women are more lkely to value qualty and pursue other goals, not drectly related to fnancal performance (Rosa et al., 1996; Verheul et al., 2002). In the subsequent secton gender effects are dscussed n detal. Gender effects On average men work longer hours than women. The mean value for the Hours varable s 3.28 for women and 4.17 for men n Table 1. We have also seen that the average preference and productvty s lower for women. Ths ndcates that the lower tme nvestments of women are due to a combnaton of a

13 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 12 lower preference for work tme and a lower productvty per hour worked. How can we explan these gender dfferences? We do not fnd evdence for (drect) gender effects on both sdes of the nonlnear model,.e., when controlled for all other relevant factors we see no sgnfcant gender effects on preference or productvty 19. Ths may be an artfact of the model (multcollnearty) and the estmaton technque (least squares versus ordered probt), but t appears that the effects of gender are at least partally medated by other varables. Comparng the mean values for women and men for the explanatory varables (see Table 1) and usng ch-square statstcs, we fnd that, when compared to men, women are more lkely to have other ncome avalable and are more rsk averse, and these factors negatvely affect the preference for work tme. Furthermore, women have less ndustry experence and are less lkely nvolved n smlar actvtes as n the past; they nvest less fnancal captal and have less contact wth other entrepreneurs. These factors have a postve effect on the productvty of work tme. Hence, women work fewer hours than men due to a combnaton of a lower average preference for work tme and a lower productvty per hour worked, whch can agan be explaned by the avalablty of other ncome and the rsk averse nature of women (for preferences) and lower levels of human, socal and fnancal captal of women (for productvty). Although we do not fnd evdence for a (drect) gender effect on productvty n the nonlnear model, we fnd a negatve effect n the proft model. Indeed, also n the lnear analyss we have seen that, when controllng for all other explanatory varables and tme-consumng actvtes, there s a negatve drect effect of gender on the number of workng hours. In these cases gender may serve as a proxy for the effect of an underlyng factor that s not ncluded n the analyss. As dscussed earler, a man canddate for the drect gender effect may be the ambton level of the entrepreneur. It may also reflect the fact that women tend to start n dfferent ndustres than men. Although we correct for the dstncton between servce and non-servce frms, ths s stll a relatvely crude measure. 6. SUMMARY AND CONCLUSION In ths study we test for separate preference and productvty effects on the number of workng hours n new ventures of female and male entrepreneurs. We choose a nonlnear approach because n a smple lnear model ths separaton can not be made. Fndngs show that ndvduals have a lower preference for work tme f they have other ncome avalable; have employees; are more rsk-averse; and are not motvated to start a busness by beng one s own boss. Productvty of tme s postvely related to fnancal captal nvested; ndustry and relevant experence; contact wth other entrepreneurs; number of employees; runnng an exstng frm; havng separate busness premses; and the prevalence of outsourcng actvtes. The nonlnear nature of our preference-productvty model allows for separatng the effects, though at the expense of ncreased senstvty of the estmates. We fnd evdence for several gender effects. On average, women nvest fewer hours n the frm than men because of a lower average preference for work tme and a lower productvty per hour worked. The relatonshp between gender and both preferences and productvty s medated by some explanatory factors. The lower female preference for work tme can be explaned by the avalablty of other ncome and the rsk-averse nature of women, whlst the lower female productvty s due to lower levels of human, socal and fnancal captal and the fact that women run relatvely small frms. In the lnear and proft analyses we fnd a negatve drect effect of gender on workng hours and proft. Ths resdual effect may be attrbuted to omtted varables. Addtonal varables could be ncluded n the analyss capturng goals and ambton levels as well as the nature of famly responsbltes, to fnd out whether the effect s drven by persstng tradtonal gender roles or s n fact a conscentous choce of women. The expectatons of entrepreneurs about the factors that nfluence ther productvty do not completely concde wth ther actual mpact on profts. Dfferences between the tme allocaton and proft model may be explaned by a longer tme lag between hours nvested and proftablty, and overconfdence. Judgments of entrepreneurs n new ventures may be subject to overoptmsm (Camerer and Lovallo, 1999; Busentz and Barney, 1997). For example, the costs of outsourcng or rental of a busness premses may outwegh ther expected benefts, negatvely affectng realzed profts. There are several lmtatons to the model and varables used n ths study. Frst, the dstncton between servce, trade and manufacturng/constructon frms s relatvely crude, and probably does not fully capture ndustry dfferences. In addton, the effect of socal captal could be studed more n-depth, expandng networkng to nclude support from sgnfcant others (e.g., famly, frends). Another

14 Dscusson Papers on Entrepreneurshp, Growth and Publc Polcy 13 lmtaton s that the model optmzes tme nvested n two types of actvty: work n the busness and lesure. The model could be extended to optmze over tme nvested n a range of actvtes, though applcaton s restrcted by data avalablty. The large majorty of start-ups n the sample do not have employees. Accordngly, we draw conclusons on the preference for work tme and the productvty per tme unt manly for owner-managed new ventures. Fnally, we measure work effort n terms of the number of hours allocated to the busness, neglectng the fact that a hgh number of hours does not necessarly mply hgh work ntensty. Ths could be the case when the entrepreneur spends much tme n the busness, but does not use ths tme effcently 20. However, we do nvestgate productvty of work tme n the nonlnear model. Our analyss suggests that women nvest less tme n the busness than men due to a lower (expected) productvty and a lower preference for work tme. Ths suggests two routes for polcy makers to stmulate tme nvestments of women n the busness. The frst s to nfluence women s preferences for nvestng tme n the busness, for example, by llustratng the exctement and the challenge to run your own busness. Secondly, the productvty of women may be enhanced by creatng awareness of the mportance of relevant experence and knowledge for new venture success, advsng them to acqure more experence n a wage-job n a smlar sector or absorb relevant knowledge and learn from the experences of successful entrepreneurs. Female entrepreneurs are more lkely to ask famly members for advce than ther male counterparts (Marsden, 1987; Greve and Salaff, 2003) whch may relate to the dffculty of expandng ther networks to the male-domnated busness crcuts (Renzull et al., 2000). REFERENCES Ajay-Obe, O. and S.C. Parker, 2005, The changng nature of work among the self-employed n the 1990s: Evdence from Brtan, Journal of Labour Research 26 (3), Astebro, T., 2003, The return to ndependent nventon: Evdence of unrealstc optmsm, rsk seekng or skewness lovng? Economc Journal 113 (484), Auster, R.D. and M. Slver, 1976, Comparatve statstcs of the utlty maxmzng frm, Southern Economc Journal 42 (4), Barr, A., 2000, Socal captal and techncal nformaton flows n the Ghanaan manufacturng sector, Oxford Economc Papers 52 (3), Barzel, Y., 1973, The determnaton of daly hours and wages, Quarterly Journal of Economcs 87 (2), Becker, G.S., 1965, A theory of the allocaton of tme, Economc Journal 75 (299), Becker, G.S., 1993, Nobel lecture: the economc way of lookng at behavor, Journal of Poltcal Economy 101 (3), Betts, R., Bradley, S. and G. Hamel, 1992, Outsourcng and ndustral declne, Academy of Management Executve 6 (1), Bddle, J.E. and D.S. Hamermesh, 1990, Sleep and the allocaton of tme, Journal of Poltcal Economy 98 (5), Brley, S., Moss, C. and P. Saunders, 1987, Do women entrepreneurs requre dfferent tranng?, Amercan Journal of Small Busness 12 (1), Blnder, A. and Y. Wess, 1976, Human captal and labor supply: a synthess, Journal of Poltcal Economy 84 (3), Blundell, R. and T. MaCurdy, 1999, Labor supply: a revew of alternatve approaches, n: Ashenfelter and D. Card (eds.), Handbook of Labor Economcs, Volume 3A, Amsterdam: North-Holland, Bourdeu, P., 1986, The forms of captal, n: J.G. Rchardson (ed.), Handbook of Theory and Research for the Socology of Educaton, New York: Greenwood Press, Brüderl, J. and P. Presendörfer, 1998, Network support and the success of newly founded busnesses, Small Busness Economcs 10 (3), Burke, R.J., 1999, Workaholsm n organzatons: gender dfferences, Sex Roles 41 (5/6), Burke, A.E., FtzRoy, F.R. and M.A. Nolan, 2000, When less s more: dstngushng between entrepreneural choce and performance, Oxford Bulletn of Economcs and Statstcs 62 (5), Busentz, L.W. and J.B. Barney, 1997, Dfferences between entrepreneurs and managers n large organzatons: bases and heurstcs n strategc decson-makng, Journal of Busness Venturng 12, 9-30.