Do More Heavily Regulated Economies Have Poor Performing New Ventures

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Do More Heavily Regulated Economies Have Poorer Performing New Ventures? Evidence fromBritain and SpainAuthor(s): Joan-Lluis Capelleras, Kevin F. Mole, Francis J. Greene and David J. StoreySource: Journal of International Business Studies, Vol. 39, No. 4, Institutions andInternational Business (Jun., 2008), pp. 688-704Published by: Palgrave Macmillan Journals

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^C Journal f International Business Studies (2008) 39, 688-704m^ ? 2008 Academy f Internationalusiness All rights eserved047-2506 $30.00www.jibs.net

Do more heavily regulated economieshave poorer performing new ventures?Evidence from Britain and Spain

Joan-Lluis Capelleras1,Kevin FMole2,Francis J reene2and David J torey2department of Business Economics, UniversitatAutonoma de Barcelona, Bellaterra, Barcelona,Spain; 2Warwick Business School, University ofWarwick, Coventry, UK

Correspondence:Joan-Lluis Capelleras, DepartmentEconomia de rEmpresa, Universitat

Autonoma de Barcelona, Bellaterra(Cerdanyola del Valles) 08193, Spain.Tel: + 34 935 814 297;Fax: + 34 935 812 555;E-mail: [email protected]

Received: 4 February 2006Revised: 16August 2007Accepted: 27 August 2007Online publication date: 29 November 2007

AbstractWe test two alternative perspectives on the start-up size and subsequentgrowth of new firms in a heavily regulated (HR) economy and a lightlyregulated (LR) economy. The firstargues that, in n HR economy, therewill befewer new firms, nd those that do startwill be largerthan those inan LReconomy, but they will grow more slowly. A second perspective is thatregulation does not influence the scale of entrepreneurship - merely itsdistributionbetween thatwhich isregistered and thatwhich isnot registered.Using parallel datasets forHR Spain and LR Britainwe find some support forboth perspectives. Specificallywe find that registered new firms inBritaindostartsmaller than inSpain and do grow faster.However, when both registeredand unregistered firms re included, these differencesdisappear.Journal f InternationalBusiness Studies (2008) 39, 688-704.doi: 10.1057/palgrave.jibs.8400340Keywords: regulation; entrepreneurship; start-up size; firmgrowth; Great Britain; Spain

INTRODUCTIONThis paper examines two views of the impact of internationaldifferences in the regulatory environment on the creation andsubsequent development of new firms. One view, associated withthe work of Djankov, La Porta, Lopez-de-Silanes, and Schleifer(2002), is that the cost burden of regulation shifts individualspreferences and tastes away from entrepreneurship, so that thetotal quantity of entrepreneurship is lowered. The second view,developed from Baumol (1990, 1993), is that the total quantity ofentrepreneurship is fixed, and the role of regulation ismerely toshift the distribution of entrepreneurship between productive andunproductive activities.These two views are used in the paper to compare the start-upsize and subsequent growth of new firms in two countries withradically different regulatory environments - Great Britain andSpain. The highly regulated (HR) economy is Spain, and the lightlyregulated (LR) economy is Great Britain. This categorisation isbased on the regulatory indicators provided by several studies(Djankov et al., 2002; Fonseca, Lopez-Garcia, & Pissarides, 2001;Kaufmann, Kray, & Zoido-Lobaton, 1999; Nicoletti & Scarpetta, 2003).We theorise that the Djankov interpretation is that new firms inan LR economy will be initially smaller, yet grow faster than new

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_Heavily regulated economics and new firms Joan-Lluisapelleras et al_*** 689

firms in an HR economy. In contrast, the Baumolinterpretation is that regulation does not influencethe quantity of entrepreneurship, but merely itsdistribution between productive

and unproductiveoutlets, where "productive" is equated with "registered". Thus the differences in start-up size andgrowth rates in the two economies may be muchsmaller or even disappear if both registered andunregistered new firms are included.The present study is important for three reasons.First,while prior research has provided insights intothe relationship between regulation and entrepreneurship, the Djankov/Baumol views have not beencontrasted in a single study. For example, Klapper,Laeven, and Rajan (2006) extended Djankov'sanalysis and provided evidence

on how entryregulation affected the creation and initial sizeof limited liability companies, whereas Sobel(2006) has sought to test the Baumol perspectivein a single country - the United States. Our study,however, explicitly compares the two views of howthe regulatory environment influences the initialsize and subsequent growth of new firms in twocountries with differing regulatory environments.Second, a weakness of prior research in this areahas been its focus upon macro rather than microdata. While there have been prior considerations ofindividual regulation frameworks (e.g., employee,tax regulations), there isno prior work ofwhich weare aware on how these rules influence individualentrepreneurs. In contrast, we make use of a datasetthat has parallel data for Spain and Great Britainand which includes both official and unofficialbusinesses. Our data identifies de novo businessesthat are legally independent (not subsidiaries orpart of larger enterprises), indigenous to the localarea, and actively operational (i.e., not "shell"businesses) for these two economies. This allowsus to examine differences in the microeconomicprofiles (e.g., gender, prior experience) of entrepreneurs and the subsequent growth of their businesses. Crucially, in seeking to contrast the Djankovand Baumol interpretations, our data enable us todelineate differences between registered/officialand unregistered/unofficial businesses based uponthemost common and simplest forms of regulation(registration for sales tax - value added tax).Third, there are potentially major political implications from our results. The Djankov findings havepersuaded governments, particularly in EU countries, to introduce legislation seeking to lower thetime taken to start a new business.1 Speculatingupon the possible impact of such legislative change

is, therefore, of real relevance to internationalpolicy-makers and the business community.The remainder of the paper is set out as follows.The next section provides the theoretical andempirical motivation underlying these contributions. We then describe the data collection procedures undertaken, and the variables used, in theanalysis. Univariate and multivariate techniquesare then employed to contrast the two views. Weconclude with an examination of the implicationsof the findings.

THEORY AND PRIOR RESEARCHThe case that regulation reduces the quantity ofentrepreneurship, which we shall refer to as the"Djankov view",

assumes the imposition of regulatory costs, both at start-up and subsequently,inevitably detracts from the available pool ofwealth among entrepreneurs. This leads to fewerbusinesses being started and to slower growthamong established new firms. Perhaps one of theclearest expositions of the burdensome nature ofregulation in various countries is provided byDjankov et al. (2002). They concluded that thecountries where regulation was most burdensomewere more likely to have poorer economic outcomes. Subsequent work by the World Bank(2006) extended this analysis to 155 countries toinclude not only business set-up costs but also the"ease of doing business". Such regulatory burdenswere found to be correlated with one another, andwere taken to provide further evidence of thenegative impact of regulation. Similarly, De Soto(2000) argued that high levels of regulationshift entrepreneurs away from the official economy,and by operating beneath the radar screens ofofficialdom they are likely to have more restrictedaccess to capital, which potentially hinders theirexpansion and the spillover benefits to the rest ofthe economy.However, a second interpretation iswhat we termthe "Baumol view". It is that regulation provides aninstitutional formula or "rules of the game" thatinfluences the nature, but not the total volume, ofentrepreneurial activities undertaken. Althoughthis view is still underdeveloped in internationalbusiness research (Meyer & Peng, 2005), Baumol(1990) argued that entrepreneurship was informedby formal rules (regulations) and informal restraints(social norms and mores) in widely differingsocieties (e.g., Ancient Rome, China, Britain inthe nineteenth century). However, he argued thatthe prime effect of regulation was to alter the

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distribution of entrepreneurship outcomes betweenwhat can easily be captured and realised by society- what he called ''productive" entrepreneurship -and outcomes that were less socially tractable("unproductive" entrepreneurship). A Baumolinterpretation is that HR countries do not have less(or more) entrepreneurship than LR countries.Instead, the difference is between entrepreneurshipthat can be realised by society and activities that areharder to capture.To explore business entry and performance in HRand LR economies, we begin by using an Evans andJovanovic (1989) framework. Assume businessentry takes place when the individual's expectedutility as a business owner exceeds that fromemployment as an employee:2E(USE)>E(UE) 1)

While utility as a business owner comprises bothpecuniary and non-pecuniary benefits (Scott Morton& Podolny, 2002), for simplicity we assume itcorresponds to income (Y). Income from the business/self-employment, YSEt is assumed to dependupon entrepreneurial talent, 6, and access to capital, K:

YSE mK) (2)A second Evans and Jovanovic assumption is thataccess to capital, K, depends upon the personalwealth of an individual (W), and that all individuals, irrespective of their entrepreneurial talent,are able to borrow a fixed proportion X againstthat wealth:

K_=W(1 + /) (3)Based upon Eqs. (1) and (2) we can see that businessownership rates in country /will depend uponalternative employment opportunities, the distribution of entrepreneurial talent, and access tocapital K. We begin by assuming that, in the shortterm, governments seeking to raise business ownership are unable to influence 9.3We also assumegovernments do not wish to reduce the attractiveness of paid employment (UE), since this is themainprovider of earned income inmost economies.

Using this basic framework we now distinguishbetween Djankov and Baumolian interpretations.The simplest view of the Djankov perspectivedefines an HR economy to be one where it ismore"expensive" in terms of time and cost to start abusiness than in an LR economy.4 Since this is afixed cost, the effect is clearly to lower owners'

wealth. The effect from Eq. (3) is to lower K andhence YSe in an HR economy. This generates the"standard" result that fewer businesses will start inan HR economy than in an LR economy.The second effect of the higher fixed costs ofstarting a business in an HR compared with an LReconomy ismore relevant to the current paper. It isthat higher fixed costs discourage starting smallerbusinesses likely to generate low profits, becausethe income generated is likely to be insufficient tocover the fixed costs. Bartelsman, Scarpetta, andScivardi (2005) suggestthat thesehigher entry ostsin a number of European countries, compared withthe US and the UK, lead to disincentives for smallfirms to enter the market.

As well as being a fixed cost on start-ups, someforms of regulation may impose higher variablecosts for new and young businesses. They may alsoact as a disincentive for firms to grow beyond acertain scale. An example is value added tax (VAT),which is a sales tax paid only by (virtually all)registered businesses beyond a minimum size.5Another example of scale-based disincentives isone where employment thresholds exist (Pryor,2000), in which firms beyond the thresholdbecome responsible for payments of workers' pensions, or have to comply with other forms ofemployment legislation, whereas firms below thethreshold do not (Keter, 2004).6 The effect of suchthresholds is likely to constrain the growth of newand young firms.A second factor that may lead to slower growthrates in new and young firms inHR compared withLR economies is that, as a result of these highercosts, expected profits would be lower in the formereconomies. Since retained profits are a key influence on wealth (JV) this, from Eq. (3), has a directeffect on K and an indirect effect on A, since itmakes banks less willing to lend to new firms.However, the Baumol view yields

a differentinterpretation. Baumol states that "entrepreneursare always with us and always play some substantialrole" (1990: 894). He provides evidence that thesupply of entrepreneurship is broadly constantacross societies and over time. What differs is thatindividuals shift their entrepreneurial activitiesfrom productive to unproductive activities in response to changes in the social pay-off structure -which includes both formal rules and the informalknowledge of how these rules are enforced. Thusregulation does not influence the supply of entrepreneurship, only its distribution between whatcan be more easily captured by society (productive

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entrepreneurship) and other forms of entrepreneurship (unproductive) where there is likely to be someleakage in the benefits available to society (Aidis &Estrin, 2006; Murphy, Shleifer, & Vishny, 1991).

To illustrate this, Baumol (1990) describes the rolethat taxes can play in redirecting entrepreneurialeffort. If taxes are high, this encourages entrepreneurs to find lower-cost ways of increasing theirown wealth. One option for such entrepreneurs isto break the law by trading in the same way as ifthey were registered for taxation purposes, soavoiding both the registration costs and the payment of taxes. The risk here is that of beingdetected and punished. A second option is to tradedifferently. An example is where there is a salesthreshold above which businesses are required toregister. The entrepreneur, rather than expandthe sales of a single business beyond the legalthreshold, instead creates multiple businesses,none of which individually exceeds the thresholdbut which, if aggregated, would do so. Both theseoptions are "unproductive" in the sense that,although they may maximise the private benefitsfor the individual entrepreneur, they fail to generate the full social benefits of entrepreneurship(De Soto, 1989).

According to this interpretation, new firm entryand subsequent development may be similar in HRand LR economies when all forms of entrepreneurship are taken into account.The two views of the impact of regulation on newfirms have separately been the focus of recentempirical research. On the one hand, there isevidence that costly regulations impede the settingup of businesses and slow down their subsequentgrowth. For instance, Bertrand and Kramarz (2002)found a strong relationship between increases inentry deterrence, such as rejection of expansion orentry decisions, and decreases in employmentgrowth. They used data on the official listing ofall applications and approvals in the French retailtrade sector between 1974 and 1998. More recentlyKlapper et al. (2006) used the Amadeus database,which contains information based on the Registrarof Companies, and show that costly regulationshamper the creation of new limited liabilitycompanies, especially in industries that shouldnaturally have high entry. They also find that theseregulations force new entrants to be larger, andcause incumbent firms innaturally high-entry industries to grow more slowly. Similarly, Bartelsmanet al. (2005) point out that certain fixed administrative costs at entry might explain why post-entry

growth of successful entrants ismuch higher in theUSA than in continental Europe.On the other hand, other studies seem to suggestthat the regulatory environment is not a keyinfluence upon new firm entry and subsequentdevelopment. Van Stel, Storey, and Thurik (2007)employ data from the Global EntrepreneurshipMonitor (GEM) and theWorld Bank Doing Businessand find no significant impact on nascent or youngbusiness formations of administrative considerations such as the time, the cost, or the number ofprocedures needed to start a business. Bowen andDe Clerq (2005), also using GEM data, show thatregulatory complexity isunrelated to the allocationof entrepreneurial effort devoted towards growthoriented activities. Sobel (2006) has recently shownthat a US state's institutional quality score ispositively related to the value of patents or venturecapital investments per capita as a proxy forproductive entrepreneurship.Two main conclusions can be drawn from suchprior research. First, both views on the impact ofthe regulatory environment on new firms havetheoretical and empirical support. However, thesources of data used in the various studies differ inone important respect. It is broadly correct to saythat those studies supporting the Djankov view arebased on official data using government-basedbusiness registers. In contrast, the work which failsto find a relationship between the regulatoryenvironment and new firm size and growth usesdata from other sources such as GEM.

Second, virtually all studies have used macro datato study regulation and entrepreneurship ratherthan microeconomic profiles of entrepreneurs andtheir firms.Moreover, these data are usually derivedfrom (internationally comparable) official statisticsand thus are based on samples of relatively largenew firms.The primary focus of the present study is toexamine start-up size and subsequent growth ineconomies with radically differing regulatory environments. Based on a joint consideration of theabove theoretical framework and prior empiricalfindings, we would expect that, from the Djankovview, HR economies will have new registered firmsthat would start larger, but grow more slowly, thanthose in the LR economies. In contrast, the Baumol

interpretation would be that the number ofregistered firms is an underestimate of the scaleand character of entrepreneurship. An examinationof all firms in an economy would show much lessor even zero differences between HR and LR




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economies in terms of start-up size and subsequentgrowth of new firms. Regulation therefore influences the distribution rather than the volume ofentrepreneurship.We now contrast these two perspectives by usingan original dataset that identifies de novo ventures,including both registered and unregistered firms, inHR Spain and LR Britain.

METHODOLOGYThe results of studies that have ranked individualeconomies in a regulatory league table showmarked differences between the regulatory environment in Britain and in Spain (Fonseca et al.,2001; Kaufmann et al., 1999; Nicoletti & Scarpetta,2003). Djankov et al. (2002) define regulation as thecosts in terms ofmoney and time of establishing a"standard" new business. Using thismeasure, GreatBritain was the fourth least regulated economy inthe world. In contrast, Spain was amuch more HReconomy, occupying position 55 out of 85 countries.7 Later work has shown these two measures aresignificantly positively correlated with a range ofother measures of the "ease of doing business" suchas the procedures, time and cost of businessinspections and licensing or the difficulties andcosts of hiring and firing of workers, and therigidity of hours (World Bank, 2006).8 On thesegrounds Spain is defined to be an HR economy andBritain an LR economy.DataTo compare new firms in HR Spain with LREngland9 data were collected from a survey ofde novo independent firms. A key purpose was notonly to identify new firms that appear in officialstatistics, but also to specifically include those thatare below the minimum size threshold and so areomitted from official statistics.

The English survey was conducted in 2001 inthree counties: Buckinghamshire, Shropshire andTees Valley (the former county of Cleveland plusDarlington). These three counties were specificallychosen to reflect high, medium and low firm entryrates, by the standards of official statistics10 (formore details see Greene, Mole, & Storey, 2004). Thissurvey used an identical approach to two previousstudies on new firms founded in the county ofCleveland,11 one undertaken in 1980 and one in1991 (Storey, 1982; Storey & Strange, 1993). Onekey benefit of the approach was its success insurveying new firms that were not registered for

VAT and hence never appeared in any UK officialstatistics.12The English study was replicated in 2003 in Spain.A geographical and administrative area (VallesOccidental, adjacent to Barcelona) with comparableeconomic characteristics to the English counties bynational standards was selected.13 In particular,Valles Occidental exhibits a firm density that issimilar to the Spanish national level (63.1 firms per1000 inhabitants). Moreover, the Spanish Mediterranean region, which includes Valles Occidental,has a number of start-ups per 100 existing firms(13.0) that isalmost identicalto that inSpain (13.3)for theperiod 1997-2000 (INCYDE, 2001).14Neither Britain nor Spain has a single, comprehensive and publicly available list of new firms.Existing lists of limited companies, which are

publicly available, exclude numerous small startups and so are of no value in this context (Dess &Robinson, 1984; Sapienza, Smith, & Gannon,1988). The VAT data are not publicly available,but even these exclude firms with sales below thethreshold. Our reservation about private databasesfor Great Britain is their greater likelihood ofincluding firms seeking credit, so generating sample bias (Birley,Muzyka, Dove, & Rossell, 1995). ForSpain, these databases are recognised to be evenmore imperfect.Databases appropriate for the purpose were therefore constructed by the researchers in each country.The English study compiled a list of new firms inthe same way as in the two previous studies. A listof new firms was derived through comparisons ofBritish Telecom telephone directories for 2000 withthose from 1995. Those firms in the directories for2000, but not present in 1995, were considered tobe potential new firms to the area.In the Spanish area an initial list of new firmswasderived using three sources of information: a list ofnew firms based on local tax payments; theChambers of Commerce and Industry directory;and a commercial database based on the OfficialRegister of Enterprises. A careful analysis of theselists was made, and overlaps between the threedatabases were detected. From this cross-checkingprocess a list of potential new firms, which weredefined as those founded between 1998 and 2000,was obtained.

Having then derived a list of potential new firms,identical procedures were used in England andSpain to produce the sample of new firms.Researchers contacted businesses by phone

in orderto determine whether they were wholly de novo




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independent firms. The study excluded firms thatwere "in-moves" to the area, subsidiaries, affiliates,not-for-profits and firms that were not trading.Face-to-face interviews were then conducted withnew firm founders, on the grounds that theresearcher was certain these were operationalbusinesses.

The questionnaire was initially designed inEnglish and was translated for the Spanish study.The Spanish questionnaire was tested through aseries of extended interviews. During this process itbecame clear that some questions were not applicable in the Spanish context, and these wereeliminated. In addition, some questions were askedonly of the Spanish firms, but the vast majority ofquestions were common to both countries. Thequestionnaire took around an hour to complete,and was administered at the normal place of workof the respondent. The final sample consists of 624new firms in England15 and 182 in Spain. However,only 490 English firms provided data on employment at start-up, whereas this was provided by allSpanish firms, and it is these responses that are usedin this paper.16Variables and Analysis MethodsThe key distinction made in the paper is between"registered" firms and "all" firms. This is basedupon whether the firm is registered to pay VAT. Thisdistinction has several merits. Most importantly,only VAT-registered businesses appear in officialstatistics in Britain and Spain.17 They are thereforeofficially registered businesses. Second, VAT is avery public (and common) form of registration, andis widely understood by entrepreneurs in botheconomies. This differentiates it from other formsof employment regulation that may apply only atparticular size thresholds. This may be thoughtequally important, as empirical evidence suggeststhat very many new entrepreneurs lack a broadunderstanding of finance (Nayak & Greenfield,1994). Finally, although we subsequently useemployment as ameasure of growth, entrepreneursrecognise sales as a key performance yardstick(Barkham, Gudgin, Hart, & Hanvey, 1996).We contrast VAT-registered businesses with allother businesses, which may of course be tradingquite legally. In the English sample 61.5% ofbusinesses are registered, compared with 69.8% inthe Spanish sample. The distinction is used in thepaper to compare start-up size and subsequent growthof new ventures in the two countries. Although awide variety of new venture performance measures

have been used in the literature, such as employment, sales, profitability, or more subjective measures of growth (Delmar, 1997; Weinzimmer,Nystrom, & Freeman, 1998), we favour the employment measure because, in an international study, itminimises inflation, currency and accounting problems, and yet it is broadly correlated with sales(Wiklund & Shepherd, 2005). Employment ismeasured at start-up and currently in terms of thenumber of persons working in the firm, includingthe founder(s) (Colombo, Delmastro, & Grilli,2004). We also compute both the absolute growthin employment and the percentage or relativegrowth.We then use a multivariate framework to determine the factors that have an impact on the initialsize of new firms in each country. As noted above,this variable ismeasured as the number of jobs atstart-up. It is therefore a count variable. ThePoisson regression model and negative binomialregression model are basic models for count dataanalysis. A negative binomial model is preferredto a Poisson model because the over-dispersionparameter alpha is significantly different fromzero. In addition to count data models, we alsoperformed the traditional ordinary least square(OLS) regression by using the logarithm transformation of the initial number of jobs as dependent variable. Results from this model are verysimilar to those obtained using the negativebinomial regression.18

Explanatory variables used to explain start-up sizecan be separated into two main categories. Following Storey (1994b), a distinction is made between"pre-start" and "at-start" variables, with a fullstatement of variables and their derivation beingfound in the Appendix. Pre-start variables capturethe characteristics of the entrepreneur and his orher access to resources. We include the followingpre-start variables (Almus, 2002; Cooper, GimenoGascon, & Woo, 1994; Mata, 1996): age and agesquared at venture creation (both are included toenable a non-linear relation); gender; education;prior managerial experience; prior employmentstatus; whether they used external forms of support; whether they had completed a formal business plan prior to commencing their business; andthe sources of initial capital. The second group ofvariables (at-start) is composed of elements relatingto the firm itself that reflect decisions observableimmediately the business starts to trade (Davidsson,Kirchhoff, Hatemi-J, & Gustavsson, 2002; Harhoff,Stahl, & Woywode, 1998). These are also set out in




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full in the Appendix. They comprise legal status,location and industry sector.Table 1 provides descriptive statistics for explanatory variables, distinguishing between registered(R) and all (A) firms. It highlights importantdifferences between the entrepreneurs and theirfirms in the two countries. The English firms aremore likely to be started by qualified males, who areolder and who have prior managerial experience.The English new firms are also much more likelythan registered Spanish firms to have received someform of support before the start-up, particularly fromthe public sector. In contrast, the Spanish firms aremore likely to have had a bank loan and areconsiderably more likely to be limited companies.Correlations and variance inflation factor (VIF) ofindependent variables were also calculated. Resultsshowed that all values are low, implying that

multicollinearity is not a pronounced problem.Finally, to examine patterns of venture growth,we repeat the analysis of Briiderl and Preisendorfer(2000). They classified new firms into four groupsbased on employment change since start-up:

(1) Decliners: Firms with negative employmentchange, that is, firmswith fewer jobs than whenthey began.(2) Statics: Firms with no change in the number ofjobs.(3) Slow growers: Firms with some, but only smallemployment growth.(4) Fast growers: Firms which have created at leastfive additional jobs since starting.

The growth rates in each group of firms arecompared between the two countries, and ananalysis of variance (ANOVA) is employed to testfor significant differences between the four groupsof firms in each country.

RESULTSTable 2 provides means, standard deviations, andthe range (min, max) for employment for registeredand all firms, respectively, forEngland and Spain. Itshows that, at start-up, the employment size ofregistered new firms in Spain was 3.76 workerscompared with 3.10 for England, and that thesedifferences were significant at the 5% level. However, these differences disappear when all firms areincluded.

Table 2 also shows that the registered Spanishfirmswere larger than the English registered firmsat start-up. After 4 years, however, there was nolonger any difference. The English firmshad caught

up with the Spanish firms and so, in relative andabsolute terms, had grown faster. This contrastswith the data for all firms. Here there is nosignificant difference in initial size, current size,or in the absolute change in employment sincestart-up in the English and Spanish firms. There issome evidence, albeit at the 10% level, that therelative growth rate of all English firms is fasterthan that of all Spanish firms. Overall, the evidenceshows no major differences between the twocountries when both registered and unregisteredfirms are considered.

Results of the negative binomial regression modelon start-up size are presented in Table 3.19 Ageneral-to-specific approach is used, beginningwith the inclusion of all variables and then movingto a "best" model. Including registered firms only,no variable is significant in explaining start-up sizein both the Spanish and the English sample. InEngland, other than the sectoral controls, thepositive influences on initial size are having a bankloan and being amale founder, although the lattervariable is significant at the 10% level only.Unemployment exerts a negative influence, albeitagain at the 10% level. In Spain having a businessplan prior to starting the business is characteristicof larger new firms.

However, when all firms are included there arethree variables common to the English and Spanishequations. First, the manager variable is significantin both countries, implying that entrepreneurswith prior managerial experience are likely to formfirms that start larger than individuals without suchexperience. Second, in both England and Spain,firms founded with a limited liability legal formstart larger than other firms. Third, individuals witha business plan before starting up had largerbusinesses than those starting without a plan. Thisrelationship is stronger in Spain than in England.The gender of the entrepreneur is also an influenceupon start-up size, but opposite results are found inthe two countries. Industry sector also appears toplay a significant role in determining start-up sizein the two samples, but it is only in hotels andrestaurants that new firms start on a larger scalethan other firms in both England and Spain. For theEnglish data, the dummy variable for health ispositively related to initial size, while trade andeducation are negatively related to start-up size. InSpain, larger new firms are found in constructionand manufacturing.Table 4 compares the different patterns ofemployment change identified in the previous




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Table 1 Descriptive statistics: registered firms only and all firmsEngland3 Spainb

Mean.d. Mean .d.Pre-start variablesMale R.84 0.37 0.69.46

A.76 0.43 0.69.46Age of founder R4.49 9.71 37.940.05A3.60 9.92 38.24.64Age2 of founder R 2073.17 885.2 1540.09 785.60A 1999.70 888.67 1555.18 757.61Formalqualification R.90 0.30 0.77.42A.90 0.30 0.72.45Manager R.69 0.46 0.45.50A.62 0.49 0.40.49Unemployed R.22 0.41 0.22.42A.22 0.41 0.24.43Support R.92 0.27 0.78.42A.89 0.32 0.70.46Businessplan R.55 0.50 0.48.50A.55 0.50 0.43.50Personalsavings R.77 0.42 0.76.43A.78 0.42 0.78.41Bankfinance R.29 0.45 0.42.49

A.28 0.45 0.36.48Finance from friends or relatives R.14 0.35 0.10 0.30A.17 0.38 0.09.29Finance from public organisations R.09 0.29 0.02 0.15A.14 0.35 0.02.15

At-start variablesLimited ompany R.47 0.50 0.91.29A.35 0.48 0.77.42Manufacturing R.21 0.41 0.26.44A.17 0.37 0.21.41Construction R.10 0.30 0.09.28A.08 0.28 0.10.31Trade R.14 0.35 0.32.47A.15 0.36 0.26.44Hotels and restaurants R.06 0.24 0.02 0.15A.07 0.26 0.03.16Transport R.04 0.19 0.02.15A.03 0.17 0.04.19Financial intermediation R.01 0.10 0.02 0.15A.03 0.16 0.02.15Business ctivities R 0.34 .48 0.20 0.40A.27 0.44 0.18.39Education R.02 0.15 0.02.12A.03 0.17 0.03.18Health R.01 0.08 0.00.00A.03 0.17 0.03.16Other services R.06 0.23 0.05.21A.13 0.34 0.10.30Tees valley R.39 0.49?A.50 0.50 ? ?Buckinghamshire R.34 0.47?

A.26 0.44 ? ?Shropshire R.27 0.44 ?A.24 0.43 ? ?

R= registered (aN = 319, bN = 127).A = all (aN = 490, bN - 182).




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Table 2 Employment current and at start-up: registered firms only and all firmsVariablesEngland0 Spainb t-test

Mean s.d. Min Max Mean s.d. Min MaxStart-upsize R 3.10 3.29 1 25.76 2.77 1 16.98**A 2.89 3.46 1 44.26 2.65 1 16 1.31"Current" size R 6.83 11.06 109 6.33 4.63 1 23.67A 5.77 9.96 109 5.87 5.16 1 34.13Absolute growth R 3.73 9.97 -804 2.57 3.76 -6 19.78*A 2.88 8.84 -8 104 2.61 4.16 -6 26.39Relativegrowth R 1.72 5.29 -0.75 69.00 0.91 1.20 -0.50 6.33 2.57**A 1.35 4.63 -0.75 69.00 0.96 1.40 -0.50 9.00 1.66*R = registered (aN=319, bN = 127); A = all (aN = 490, bN = 182).Significance levels: *p


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_Heavily regulated economies and new firms Joan-Lluisapelleras et al_^2_ 697

Table 3 ContinuedVariable England Spain

Ceneralmodel Specificmodel Generalmodel Specific modelCoeff. z Coeff. z Coeff. Coeff. z

Construction R -0.016 -0.07 0.045 0.21 R 0.972 2.56** 0.846 2.50**A -0.098 -0.60 -0.084 -0.51 A 0.759 3.09*** 0.741 3.14***Trade R -0.370 -1.69* -0.368 -1.73* R 0.192 0.56 0.170 0.55A -0.443 -3.02*** -0.436 -2.95*** A 0.199 0.91 0.230 1.08Hotels and restaurants R 0.504 2.14** 0.514 2.25** R 0.497 0.93 0.373 0.81A 0.260 1.65 0.319 2.03** A 0.559 1.66* 0.617 1.85*Transport R -0.424 -1.32 -0.474 -1.53 R 0.776 1.65* 0.852 1.93*A -0.401 -1.52 -0.369 -1.39 A 0.424 1.34 0.440 1.42Financial intermediation R -0.645 -1.14 -0.708 -1.25 R 0.108 0.21 0.156 0.32A -0.562 -2.17** -0.508 -1.95* A 0.101 0.25 0.131 0.33Business activities R -0.175 -0.92 -0.117 -0.62 R 0.156 0.45 0.196 0.61

A -0.218 -1.68* -0.191 -1.48 A 0.106 0.46 0.130 0.58Education R -0.688 -1.72* -0.564 -1.42 R 0.455 0.79 0.801 1.66*A -0.592 -2.17** -0.565 -2.06** A 0.187 0.53 0.339 1.08Health R 1.343 2.89*** 1.517 3.43*** R ? ? ?A 0.725 3.57*** 1.072 5.59*** A 0.183 0.48 0.155 0.41aa R 0.233*** ? 0.251*** ? R 0.103*** ? 0.108*** ?A 0.208*** ? 0.234*** ? A 0.082*** ? 0.088*** ?PseudoR2 R 0.056.048 R 0.065.052A 0.054.065 A 0.099.092Log likelihood R -606.934 -647.970 R -251.019 -267.819A -888.473 -906.155 A -345.003 -349.546LRchi2 R 71.71***5.38*** R 34.93**9.23***

A 101.92*** 125.68*** A 75.96*** 70.91***Number of cases R 29514 R 12027A 45255 A 17980R = registered; A = all.

Significance based on LR test of a = 0.Significance levels: *p


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section, that is, decliners, statics, slow growers andfast growers. The results obtained from an ANOVAshow that these patterns are significantly differentin each countryThe table also compares the four groups in thetwo countries. With regard to the registered firms,there is significantly faster post-start growth amongthe English firms. This is entirely attributable to thecontribution made by the fast grower group. Thiscontrasts with the all firms data, which showsstriking similarities between the English and Spanish firms. For example, the proportion of firmslosing jobs (i.e., decliners) is 7% in England and 8%in Spain. Fast-growing firms are 15% of the Englishand 19% of the Spanish sample. Statics are thelargest group in the English dataset, whereas slowgrowers are the largest group in the Spanish case.20In short, there are no significant differences inemployment change between the two countries forthe four groups of all new firms.

Figures la and lb show these four patterns ofemployment change for registered firms only andall firms, respectively. The similarities between the

3 England SpainA i20-* 20 -

15 - / 15 -.2, -3 / ? sI io / | io_X1 5'/>-& Js-^_^>

t=0+4 t=0+5t ty=0:Decliners y=l:Statics y=2:Slowgrowers y=3:Fastgrowers

KEngland SpainA A20 - 20 -

15 - / 15 -* / o /I 10_ / I 10_ ySI / -?-?


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initially larger but slower-growing firms in comparison with an LR economy is supported only whenregistered firms are examined. When unregisteredfirms are included these differences either disappearor are much diminished. In other words, includingall firms leads to the Baumol perspective receivingsupport. Above all, it is the similarities rather thanthe differences between the Spanish and Englishbusinesses when all firms are included that aremoststriking. Indeed, what is remarkable about theresults highlighted in this paper is the degree ofsimilarity between initial size and growth for bothSpain and England. Given that these countries hadvery different regulatory regimes, the similaritiesimply support for the Baumol view.Hence we suggest that the Djankov view may bebetter suited to explain the influence of regulationon the creation and development of officiallyregistered firms,whereas the Baumol interpretation

might provide an explanation for the similaritiesbetween all new firms in HR and LR economies.These similarities are compatible with the view thatregulation appears to influence the distribution,and not the volume, of entrepreneurship.Unlike prior research, the present study alsoprovides a more nuanced micro viewpoint on theway formalised regulation plays out between twodiffering economies and between registered andunregistered businesses. In this context, our findings show that start-up size and subsequent growthare not strongly dependent on the regulatoryenvironment when both registered and unregistered firms are taken into account. These results arein linewith other recent studies that have found nosignificant relationship between entry regulationsand firm formation rates (van Stel et al., 2007) orhave shown that lowering entry barriers by the provision of public subsidies is inefficient (Santarelli &Vivarelli, 2002).On the other hand, our findings contrast withthose of prior studies that have used a macro levelof analysis and are based on official data forrelatively large new firms. For instance, Djankovet al. focused on "standardised" registered venturesthat have "between 5 and 50 employees one monthafter the commencement of operations" (2002: 7).Such a definition risks generating a distortedpicture since it excludes probably the bulk of newbusinesses.In addition, the findings point to the importanceof the background and skills of firm founders inexplaining new venture performance, irrespectiveof the regulatory environment. While regulations

shape the institutional setting faced by entrepreneurs, the results identify that they are not passiveagents.Given its political and business significance, it iscritical to enhance our further understanding ofthis topic, and this two-country comparison has tobe regarded as indicative rather than definitive. Thepaper, however, offers a range of future researchpossibilities.First, full panel and other longitudinal studies,including lifehistory data, would be a useful area ofresearch, both because itminimises sample attrition and also because it affords researchers theopportunity to look at longer-run business growth.Second, this study has made use of a commonand widely understood form of regulation, butother measures may generate different findings.The simplicity and applicability of VAT registrationis a strength for a general paper such as this.However, regulation is often sector specific, andthere is a need to test the impact of other forms ofregulation in differing settings.Third, one likely future research direction openedup by this paper is the potential to explore differingtheoretical constructs for understanding the entrepreneur/regulation nexus. Although we seemerit instimulating this paper by using the Evans andJovanovic (1989) model, there is clearly a role forfurther research that considers the learning undertaken by entrepreneurs (Jovanovic, 1982).

Finally, we are conscious that there are otherareas that require clarification. Most importantly,while England and Spain differmassively in termsof their regulatory environment fornew firms, theyalso differ in other factors that influence theestablishment and development of enterprises.These include differences in formalised regulation(e.g., interest rates, unemployment rates, socialsecurity payments, educational provision, taxregimes) but also likely differences in the socialnorms, mores and customs about entrepreneurship.The inclusion of other countries at similar levels ofeconomic development would provide greater confidence that observed similarities or differences innew firms were clearly attributable to regulatorydifferences.

In conclusion, the evidence from this paperpoints to the need for further reflection on therelationship between entrepreneurs and regulation.Regulation is often seen normatively, leading tounequivocal judgements about its undesirability.The implication here then is that regulation per selowers entrepreneurial capacity. Our evidence finds




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some support for this in terms of registered firms. Inthis respect, therefore, there are good grounds forholding that policy-makers should continue tosearch for regulatory outcomes that produceoptimal pay-offs. However, our evidence alsosuggests that the direct impact that regulation hasupon the performance of new firms may be lessthan is inferred from examining the behaviour ofregistered firms only. The cost of regulation interms of income forgone to both the entrepreneurand society as a whole is overstated by examiningonly registered firms. Policy-makers seeking abalance between the business community andothers in society are encouraged to bear this inmind.

ACKNOWLEDGEMENTSWe express our thanks to the Leverhulme Foundationfor their generous funding of the English element ofthisproject (F/00215/E). The Spanishparthas beenfinanced by the "SME Observatory" of the Institutd'Estudis Regionals iMetropolitans de Barcelona andthe Departament d'Economia de I'Empresa, Universitat Autonoma de Barcelona, and has also receivedfinancial support from the Spanish Ministry of Scienceand Technology (Project SEC2004-07242-C03-01 /ECON). The paper has benefited considerably fromimportant comments received from Elaine Romanelli,Andre van Stel, Simon Parker and Francis Chittendenas well as participants at the 4th InternationalEntrepreneurship Forum in Paris, the FSF group inHalmstad, Sweden, the 28th National Conference ofPolitical and Industrial Economists inAncona, Italy,the8th EEA Conference inMurcia, Spain, and seminars atTechnical University of Lisbon, University of Durham,University of Jonkoping and the UK Department ofTrade and Industry, London. We would also like tothank Departmental Editor Anand Swaminathan andfour anonymous reviewers for their constructive anddetailed feedback. We, of course, are responsible forthe paper.

NOTES^or example, France has reduced the number of

days taken to establish a "standard" business from 53days in 1999 to 8 days in2006. Spain has reducedfrom 82 to 47 and Italy from 62 to 13 over the sameperiod. Greece is the only EU-15 country with highnumbers of days that have not altered over this period.2We exclude unemployment for ease of exposition.For the same reason we also exclude a time dimension.In other words, current income or utilities arecompared rather than expected future utility.

3Here we use an Evans and Jovanovic (1989)framework inwhich individuals are able to estimate 6on the grounds that itprovides clearer outcomes thanthe Jovanovic (1982) interpretation, which assumesthat individuals estimate with greater accuracytheir talent through being in business. See Lotti andSantarelli (2004) for an empirical testing of thisapproach.4We will show later that there isa strong correlationbetween these two measures and a wide range ofother measures of regulation.5ln 2004 this threshold inthe UK was sales inexcessof ?58,000.6These size thresholds also vary according to thenature of the legislation. For example, inBritain, firmswith fewer than fiveworkers are exempt from parts ofthe Maternity and Parental Leave Regulations 1999,those with fewer than 21 employees are exempt fromparts of the Employment Relations Act 1999, and until2004 firms with fewer than 15 employees wereexempt from the Disability Discrimination Act 1995,while those with fewer than 50 do not have to complywith the EU Directive on Informing and ConsultingEmployees. A full review of these exemptions isprovided inKeter 2004).7lt is important to note that these differencesbetween Britain and Spain relate only to the cost ofstarting a business, and so may not influence thegrowth of a business. World Bank (2006), however,suggests that new firms in Spain face considerablymore regulatory constraints than those in Britain. Forexample, using the 2005 data, the UK is in eighthplace forOECD countries in employment regulationrigidity,whereas Spain is in last or 23rd place. Aseparate index shows that while it takes 25 weeks tofire a worker in the UK, it takes 68 in Spain. The UK isalso the OECD country that has "best" performance inareas of legal rights and corporate governance. Whilewe believe these differences in regulatory constraintson established businesses probably reflect

the situationin the period inwhich our firmswere trading, theWorld Bank (2006) studywas published after our ownsurveywas completed. It an therefore only be a proxyfor regulatory differences.8World Bank (2006: 93) shows the correlationcoefficients between the "ease of starting a business"measure and nine other regulatory indicators. For the155 countries included, the correlations ranged from0.35 to 0.55.

9Although Djankov et al. (2002) refer to GreatBritain, the regulatory data are derived forLondon, thecapital and largest city of England,

so the currentpaper will use the term "England". Great Britain

Journalof International Business Studies



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comprises England, Wales and Scotland, but notNorthern Ireland.10Using the measure of new VAT registrations per10,000 population, the rates for Tees Valley, Shropshire and Buckinghamshire in2001 were 18, 32 and52, respectively. The English average was 33. Equally

importantly, as shown by Greene et al. (2004), whileformation rates do varywith the economic cycle, TeesValley isalways low, Shropshire isalways average, andBuckinghamshire is always high, over the period1980-2001.

nThe area currently referred to as Tees Valley iscomposed primarily of the former county of Cleveland.It also includes the area of Darlington. These namechanges reflect local government reorganisation.12Storeynd Strange 1993) found hatonly50% ofthe new firms were registered for VAT. Of theunregistered 50% of firms, 16% had sales below theVAT threshold and so were not required to register,4% had sales above the limit,and the remaining 30%refused to disclose their sales.

13The population of the selected areas in bothcountries is quite similar. The population of VallesOccidental was approximately 740,000 in2001. Thenumber of inhabitants inTees Valley was 640,000 inthe same year. Buckinghamshire had 480,000 inhabitants. In Shropshire this figure was 440,000. VallesOccidental has a lower GDP per head than the threeEnglish areas, reflecting national differences (in 2001the Spanish GDP per head was $20,155 comparedwith $25,479 in the UK). The unemployment rate ishigher in the Spanish area than inthe English counties(7.1 inValles Occidental, 5.4 in Tees Valley, 1.9 inShropshire and 1.4 in Buckinghamshire, all in year2001). This broadly reflects unemployment rates ineach country (5.1 in the UK and 10.5 in Spain for theyear 2001).14Data provided by Arauzo-Carod, Liviano-Solis, &Martin-Bofarull (2005) also show that the new firmformation rate for the region is very similar to theaverage for Spain.15Given the multi-stage processes necessary toidentify this group of new firms, there is no singlestatistic that isan ideal measure of response rate. TheTees Valley results illustrate the issue. A total of 2490"new firms" were identified from the telephonedirectory, and contact was attempted with 2112. Of

these, 791 were ineligible on grounds of age, ownership or sector; 278 had ceased trading or movedoutside the area, implying an eligible population of1043. In total, 320 firms were interviewed. Oneestimate of response rate is therefore 320/1043,implying a response rate of 32%. However, a total of336 telephone lines were either disconnected orreallocated, which is likelyto be a powerful sign thatthe business has ceased. Ifthese are also assumed tobe dead businesses, and the denominator includes

only businesses known to be eligible at the time of thesurvey, then the response is320/707 or a rate of 45%.Our, highly subjective, judgement is that the realresponse rate is likely to be around 40% for TeesValley. For Buckinghamshire the response rate, comparable to the Tees Valley figure of 45% is69%, andfor Shropshire the comparable figure is75%. ForVallesOccidental (the Spanish area), the comparable estimation of the response is 182/404, implying a rate of43%.

16The reason for this discrepancy is that the Englishsurvey asked about employment 1, 3 and 5 years priorto the interview, but did not ask a specific questionabout start-up.Where the available data correspondedwith the year of start-up these data were used, butclearly it as not appropriate to use them for all cases.There were no significant differences between the 624and 490 cases in terms of sector, age or geography.For Spain a specific question on number of employeesat start-up was used.17ln the Spanish sample, the firms identified as nonregistered forVAT are those exempted from VAT orthose that are in a special scheme (or simplifiedscheme) of VAT inSpain for small firms.18For space reasons, OLS results are not presented inthe results section but are available from the authorsupon request.19The negative binomial regression is used toestimate the model because the Poisson estimation isinappropriate due to over-dispersion. As shown inTable 3, the over-dispersion parameter a issignificantlydifferent from zero.

20Bruderl and Preisendorfer (2000) find that 8% aredecliners, which is virtually the same as our result.However, they find 60% of firms are statics, 7% arefast growers, and 25% are slow growers, which doesdiffer from the findings of this paper.

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born in England and is a citizen of the UK. E-mail:[email protected] J. Greene (PhD, University of Durham) isan Associate Professor atWarwick Business Schoolspecialising in the development of new firms,graduate entrepreneurship and public policytowards entrepreneurship and small firm issues.Born in Scotland, he is a citizen of the UK. E-mail:[email protected].

David J. Storey (PhD, University ofNewcastle uponTyne) is Associate Dean (Research) at WarwickBusiness School. He is the author of Understandingthe Small Business Sector, the most frequently citedwriting on small business, small firms or SMEs.He was born in England and is a citizen of the UK.E-mail: [email protected].

Accepted by Arie Y Lewin, Editor-in-Chief, 27 August 2007. This paper has been with the authors for two revisions.


Do More Heavily Regulated Economies Have Poor Performing New Ventures

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