UnequalHardTimes: TheInﬂuenceoftheGreatRecession ... · qualiﬁcations of the founder (Bigelow et al. 2014; Brooks et al. 2014; Shane et al. 2012; Thébaud 2015). However, a number

Citation Theacutebaud Sarah andAmanda J Sharkey 2016 ldquoUn-equal Hard Times The Influenceof the Great Recession on Gen-der Bias in Entrepreneurial Fi-nancingrdquo Sociological Science3 1-31Received June 12 2015Accepted August 21 2015Published January 6 2016Editor(s) Olav SorensonDOI 1015195v3a1Copyright ccopy 2016 The Au-thor(s) This open-access articlehas been published under a Cre-ative Commons Attribution Li-cense which allows unrestricteduse distribution and reproduc-tion in any form as long as theoriginal author and source havebeen creditedcb

Unequal Hard TimesThe Influence of the Great Recessionon Gender Bias in Entrepreneurial FinancingSarah Theacutebauda Amanda J Sharkeyb

a) University of California Santa Barbara b) University of Chicago

Abstract Prior work finds mixed evidence of gender bias in lendersrsquo willingness to approve loansto entrepreneurs during normal macroeconomic conditions However various theories predict thatgender bias is more likely to manifest when there is greater uncertainty or when decision-makersrsquochoices are under greater scrutiny from others Such conditions characterized the lending market inthe recent economic downturn This article draws on an analysis of panel data from the KauffmanFirm Survey to investigate how the Great Recession affected the gender gap in entrepreneurial accessto financing net of individual and firm-level characteristics Consistent with predictions we find thatwomen-led firms were significantly more likely than men-led firms to encounter difficulty in acquiringfunding when small-business lending contracted in 2009 and 2010 We assess the consistency of ourresults with two different theories of bias or discrimination Our findings shed light on mechanismsthat may contribute to disadvantages for women entrepreneurs and more broadly highlight howthe effects of ascribed status characteristics (eg gender) on economic decision-making may varysystematically with macroeconomic conditions

Keywords entrepreneurship gender discrimination status recession

THE Great Recession which arose from the financial crisis and housing marketcrash of 2007ndash2008 profoundly affected workers and firms As consumer

demand stagnated and financial markets stumbled job creation slowed and layoffsbecame more common Consequently unemployment in the United States climbedto levels not seen since the Great Depression (Grusky Western and Wimmer 2011)Furthermore although some initially conjectured that the recession might lead toheightened rates of entrepreneurial activity as some individuals who had beenpushed out of wage labor might opt to start their own businesses the empiricalevidence has shown the opposite Shane (2011) found that the rate of new businessformation fell 173 percent between 2007 and 2009 while the business closure rateincreased 116 percent in the same time frame Furthermore the small businessinvestment market was particularly hard hit by the recession with total investmentcontracting 18 percentage points between 2008 and 2011 (Cole 2012)

Although the impact of the Great Recession was widespread some populationswere hit harder than others One axis on which the recession was differentiallyfelt was that of gender In particular because the majority of job losses wereconcentrated in industrial sectors such as construction and manufacturing whichhave historically been dominated by white unionized males men bore the bruntof the recession when measured by job losses (Folbre 2010 Hout Levanon andCumberworth 2011) Casting into stark contrast the gender disparity in rates

1

Theacutebaud and Sharkey Unequal Hard Times

of job loss the popular press dubbed the economic downturn a ldquomancessionrdquo(Thompson 2009) A closer look however reveals a more complicated picture Forinstance although men were more likely to lose their jobs women who were laidoff experienced greater earnings losses than their men counterparts did when theyreturned to work (Cha 2014)

In contrast to research on gender disparities in the impact of the Great Recessionon workers in the wage labor market however possible gender differences inthe effects of the downturn on entrepreneurs have to the best of our knowledgenot been studied To address this gap we analyze gender differences in how therecession affected entrepreneurs focusing specifically on their ability to obtainfinancing Although the recessionary environment likely made it more difficult forall loan applicants to obtain funding did the downturn also affect the size of thenet gender gap in lending to entrepreneurs

An investigation of the possibility that the recession differentially affected menrsquosand womenrsquos access to credit is important for two key reasons First existingstudies offer mixed evidence as to whether investors and other individuals whoprovide capital tend to exhibit a bias against women entrepreneurs On the onehand several studies that rely on survey-based data find that women-led firmstend to receive less funding even after factors such as human capital industryand credit histories are taken into account (Carter and Shaw 2006 CavalluzzoCavalluzzo and Wolken 2002 Coleman and Robb 2009) Recent experimentalstudies have also found that potential investors are systematically likely to viewwomen entrepreneurs as less competent credible and investment-worthy thanequivalently qualified men counterparts (Bigelow et al 2014 Brooks et al 2014Theacutebaud 2015) Yet in contrast a sizable number of studies have found no evidenceof a net funding disadvantage for women-led firms (see eg Asiedu Freemanand Nti-Addae 2012 Blanchard Zhao and Yinger 2008 Blanchflower Levine andZimmerman 2003 Carter et al 2007 Cavalluzo and Wolken 2005 Orser Ridingand Manley 2006) One interpretation of these mixed results is that gender-baseddisadvantage in entrepreneurial investment is not a constant rather it may varydepending upon the conditions under which lenders and investors make decisionsThus an examination of how gender disparities in the ability to acquire fundingvary under differing macroeconomic conditions offers an opportunity to refineour theoretical understanding of when and where gender bias in entrepreneurshipis likely to emerge Doing so may help shed light on the puzzle of when andhow gender biases persist even in the face of increasingly meritocratic values andprocedures (Castilla 2008 Castilla and Benard 2010)

Second an examination of how the recession might (or might not) have differ-entially affected the ability of men-led vs women-led firms to obtain financingprovides an opportunity to empirically evaluate the discriminatory mechanismsthat may contribute to gender disparities in entrepreneurship In particular wedraw on statistical and status-based theories of discrimination to identify multiplereasons why investors may be more likely to rely on gender stereotypes whenmaking decisions during recessionary circumstances Furthermore we investi-gate whether investors may have been more likely to apply different standards of

sociological science | wwwsociologicalsciencecom 2 January 2016 | Volume 3


evaluation to men- and women-led firms during the recession a possibility that ispredicted by status-based but not statistical theories of discrimination

To examine the effect of the Great Recession on entrepreneursrsquo ability to accesscapital we analyze data from the Kauffman Firm Survey a panel study that containsdata on rates of financing for a sample of entrepreneurial ventures between theyears 2007 and 2011 Specifically we identify how the influence of gender on thelikelihood of gaining access to bank loans changed as the small-business lendingmarket contracted for part of this five-year time frame Our findings suggest thatwhile all firms had more difficulties in gaining financing following the recessionwomen-led firms were disproportionately more likely to be denied funding duringthis period Moreover our analyses indicate that this result cannot be attributedto increased scrutiny to all aspects of an entrepreneurrsquos loan application whereasgender became a more widely used driver of funding decisions during this periodindicators of firm performance and human capital did not We also find that in 2010women-led firms with a low credit score were particularly likely to be penalizedwhich suggests that investorsrsquo tendency to apply a gender-based double standard ofperformance may have been one mechanism contributing to the funding disparitieswe observe between men- and women-led firms during this period

Together these findings indicate that worsening macroeconomic conditionsincluding widespread market uncertainty and tightening standards among lendersmay in fact exacerbate the disadvantages in financing that women entrepreneurshave been found to experience and that such disadvantages may be motivated atleast in part by status-driven bias As such our study contextualizes the theoreticaldiscussion about the relevance of ascribed cultural statuses such as gender toeconomic decision-making

Gendered Disadvantage in Entrepreneurial Investment

Compared to men-led start-ups firms founded by women tend to acquire lessexternal debt and equity financing which is often critical for the survival andsuccess of an entrepreneurial venture (see Jennings and Brush 2013 for a review)Some of the gap can be explained by differences in human capital firm performanceand network ties For instance women entrepreneurs tend to operate in lesslucrative industries (Loscocco et al 1991 Marlow and McAdam 2010) run smallerbusinesses (Jennings and Brush 2013 Kalleberg and Leicht 1991) and have relativelyless human capital (Kim Aldrich and Keister 2006 Loscocco et al 1991) and socialcapital (Renzulli Aldrich and Moody 2000 Ruef Aldrich and Carter 2003) All ofthese factors contribute to their lower probability of gaining substantial externalinvestment

Whether the gender gap in funding persists after controlling for these factorshowever is unclear On the one hand several studies find that gender differencesremain even when these factors are taken into account (Carter and Shaw 2006Cavalluzzo et al 2002 Coleman and Robb 2009 Wu and Chua 2012) Moreoverrecent studies that use the strongest research design for ruling out unmeasureddifferences between men and women entrepreneurs (ie experimentally manipu-lating information about the gender of an entrepreneur) document that potential



investors lenders and technology licensing officers favor men-owned start-upsdespite the lack of any underlying differences in the quality of the venture or thequalifications of the founder (Bigelow et al 2014 Brooks et al 2014 Shane et al2012 Theacutebaud 2015) However a number of other studies have concluded thatwomen-led firms are not disadvantaged once the background characteristics offirms and entrepreneurs are taken into account (Asiedu et al 2012 Blanchard et al2008 Blanchflower et al 2003 Haines Orser and Riding 1999 Orser et al 2006)Furthermore when gender bias is detected the size of the penalty also tends tovary

We draw two overarching conclusions from this body of prior work Firstbecause disparate outcomes for men and women were documented several timesin experimental studies where the perceived quality of the venture was held con-stant through random assignment we presume that there is at least under someconditions and for some types of funding a tendency toward bias in favor of menentrepreneurs or against women-led firms Second because studies using observa-tional data find mixed results we believe that investorsrsquo likelihood of relying ongender stereotypes when making investment decisions may be contingent upon fea-tures of an evaluative context Drawing on prior work in economics and sociologywe theorize about and empirically evaluate the extent to which market uncertaintyand tightening lending standards constitute important contextual features thatmoderate the salience of gender in financing decisions

Mechanisms of Gender Discrimination

When divergent outcomes arise for men- versus women-led firms what mecha-nisms might be responsible According to statistical theories of discrimination(Arrow 1973 Phelps 1972) individuals are rational actors who do not have anyintrinsic preference for men over women Rather they sometimes tend to evaluateindividuals differently on the basis of gender because they believe gender serves asa proxy for the statistical distribution of characteristics such as productivity whichare difficult to observe for any given individual This strategy represents a heuristicthat individuals use in the face of uncertainty about any pertinent unmeasuredqualities of individuals or their organizations Applied to the setting at hand whenlenders are faced with the problem of identifying nascent firms that they believewill be reasonable credit risks (ie likely to be at least successful enough to repaytheir loan) statistical discrimination would occur if they were to view gender asan indicator of some other hard-to-measure characteristics that are associated withthe likelihood of a firmrsquos success or ability to repay the loan For example becausewomen-owned businesses tend to be to be smaller and less profitable on averagelenders might infer that any particular women-led business would also be less likelyto repay a loan1 Although variants of this theory propose differing assumptionsabout the way in which such unmeasured qualities are distributed (eg differencesin means variances or both see Correll and Benard 2006 or England 1992 for adiscussion) they all assume that discrimination arises from an informational bias(ie evaluatorsrsquo lack of information) That is in this case the theory predicts thatif lenders had perfect information on for example a new firmrsquos likelihood of re-



paying the loan there would be no effect of gender per se on the outcome of aloan application because lenders would base their decisions on the likelihood ofrepayment rather than relying on gender

In contrast to statistical theories status-based theories of discrimination suggestthat discriminatory outcomes emerge from a cognitive bias on the part of individuals(see Correll and Benard 2006 for an in-depth discussion of the differences betweenthe two theories) In this framework lenders may rely on the widely shared culturalbelief that men generally are and ought to be more competent in the domain ofentrepreneurship than are women (Ridgeway 2011 Theacutebaud 2010 Theacutebaud 2015)For instance it is well established that individuals culturally associate successfulentrepreneurship with stereotypically masculine attributes like competitivenessaggressiveness and risk-taking (Bruni Gherardi and Poggio 2004 Buttner andRosen 1988 Gupta et al 2009) Not only are men believed to hold these types oftraits more often than women but these characteristics are also viewed as moredesirable in men than in women (Prentice and Carranza 2002) Therefore menentrepreneurs implicitly fulfill cultural stereotypes both about how they are andhow they should be in a way that women entrepreneurs cannot As a result fundersmay be more hesitant to support women-led ventures because they are more likelyto doubt that women possess the (generally ambiguous) forms of competence traitsand skills that people typically associate with entrepreneurship a notion whichhas been supported experimentally (Bigelow et al 2014 Theacutebaud 2015) as well asanecdotally (Buttner and Moore 1997 Carter and Cannon 1992)

However in addition to informing expectations of competence and abilitygender status beliefs can also inform the standards that are used to determinewhether a given performance is indicative of ability (Correll Benard and Paik2007 Foschi 1996) When status beliefs are salient women tend to have theirperformances judged by a stricter standard than men because when women performwell their performances are inconsistent with (low) expectations for their ability andare as a result more highly scrutinized (Foschi 1996 2008 Foschi Lai and Sigerson1994) Therefore in addition to predicting that women will have a harder timegaining investment than an equivalent male counterpart a status beliefs accountalso predicts that women entrepreneurs may need to establish more ldquoevidencerdquo ofability or past performance in order to have their ventures judged to be of the samequality Consistent with this account experimental studies have shown that womenneed to demonstrate more evidence of technical knowledge or innovation in orderfor their ventures to be viewed as equally worthy of investment (Theacutebaud 2015Tinkler et al 2015)

Thus although both statistical and status-based accounts predict that all elsebeing equal women-led firms will garner less investment than men-led firms theidea that status beliefs can activate the application of double standards suggeststhat the two theories differ in their predictions regarding how individual-levelinformation on pertinent characteristics such as productivity or credit-worthinesswill be interpreted Status-based theories posit that evaluatorsrsquo beliefs about menrsquosand womenrsquos differential competence will color their interpretation of a givenpiece of information leading the investor to judge evidence of the same level ofperformance (eg a similar credit score or level of profitability or cash flow) more



favorably for men-led firms than for women-led ones In contrast theories ofstatistical discrimination do not predict that different performance standards willbe applied to the same information when it is about men as opposed to aboutwomen (Correll and Benard 2006) Indeed one implication of statistical theories ofdiscrimination is that because gender is primarily used to overcome the problemspresented by a lack of better information the availability of more informationshould attenuate the observed effects of gender For example Altonji and Pierret(2001) posit that if statistical discrimination accounts for pay disparities the effectof observable factors such as education or race on wages should fall over time asharder-to-observe productivity is revealed through on-the-job performance

Overall gender might influence the evaluation of an entrepreneurrsquos requestfor funding through a variety of theoretical mechanisms Our primary aim inthis paper is to consider whether macroeconomic conditions may moderate thesediscriminatory processes We also present and discuss supplemental analyses inwhich we are able to further evaluate the possibility that status-based discriminationin particular was at play during the recession

Market Uncertainty and Stricter Lending StandardsCatalysts of the Gender Gap

How if at all do difficult macroeconomic conditions such as those associated withthe Great Recession affect gender disparities in access to credit At an abstract levelthe characteristics of the macroeconomic environment during the Great Recessionmap conceptually onto two factors that theories predict would also affect levels ofbias or discrimination uncertainty and stricter standards In particular recessionaryconditions lead to greater uncertainty about the likelihood that any small businessowner would be successful enough to pay back his or her loan In addition bankstightened their lending standards during this time frame (Office of the Comptrollerof the Currency 2014)

Both statistical and status-based theories of discrimination offer reasons forwhy these circumstances may have affected lendersrsquo propensity to rely on genderWe first consider the predictions of statistical theories of discrimination As notedabove these theories posit that beliefs about the statistical distribution of charac-teristics among women as a group (eg women-owned businesses are more likelyto fail) are used as a proxy for unavailable information about any specific woman(eg this particular business is owned by a woman and therefore is more likelyto fail) Although variants of this theory make different assumptions about thestatistical distribution of typically unobservable factors such as productivity orcreditworthiness the operation of statistical discrimination is posited to hinge onthe presence or absence of information about what is unobservable Presumablythen observed gender differences in evaluative outcomes should be amplified insettings where individual-level information on pertinent characteristics is lacking toa greater degree and conversely should be attenuated to the extent that better infor-mation is available The Great Recession did not have any effect on the availabilityof information about factors such as creditworthiness this leads to the prediction



that the Great Recession should not result in a shift in lendersrsquo tendency to rely ongender in financing decisions However even though the recession did not affectthe availability of information statistical discrimination might still become morecommon for a different reason In particular it is possible that the recession mayhave resulted in lenders placing greater emphasis on unobserved characteristicsthat they view as predictive of failure and that they associate with gender such asa perceived lack of commitment If this were the case investors would penalizewomen more heavily during the recession than they did either before or after it forreasons associated with statistical discrimination

This latter outcome that gender gaps in entrepreneurial financing would emergeandor widen during recessionary years is more directly predicted by social psy-chological theories of discrimination Individuals have been found to be especiallylikely to rely on conventional ideas and heuristics under conditions of uncertaintybecause they offer cognitive shortcuts for managing information that reduce thecomplex task of assessing probability (see eg Tversky and Kahneman 1974) Con-sistent with this idea research has shown that implicit cultural stereotypes aboutgender in particular tend to be applied most frequently under conditions of highuncertainty (Foschi 1996 Gorman 2006 Kanter 1977 Ridgeway and Correll 2004)For instance experimental studies suggest that when the quality of a performanceis ambiguous women job candidates are rated as less competent than their mencounterparts (Foddy and Smithson 1999 Foschi 2008 Heilman et al 2004 HeilmanBlock and Stathatos 1997) Similarly when the nature of work is more uncertainbecause selection criteria or standards are not clear there tend to be larger genderdifferences in promotion and pay than when such practices are clearer and moresystematic (Gorman 2006 Reskin 2001 Reskin and McBrier 2000 Ridgeway 19972011)

In particular Gorman (2006) finds that when a law firmrsquos work is characterizedby greater uncertainty the firm is less likely to promote women There is greaterwork uncertainty when outcomes are more variable and unpredictable when thereis no clear course of action for a specific problem (eg what worked in one case maynot work in another) and when success is dependent upon ldquoautonomous othersrdquowhose actions are themselves unpredictable In these situations the uncertainnature of the work increases bias because it increases evaluatorsrsquo doubts about thelinkage between an individualrsquos past performance and their ability to succeed whenit is unclear that previous strategies will work people rely less on past performanceand more on stereotypes and status beliefs which as noted above are particularlylikely to advantage men in entrepreneurial settings During an economic recessionthe question of whether an entrepreneur will succeed is especially likely to have anuncertain answer In particular the predictors of success in such an environmentmay be less clear consumer behavior is likely to change such that past predictorsof success may be less helpful for determining a winning approach to attractingcustomers in the future

In addition there is emerging evidence that increased visibility andor scrutinyfrom other individuals or institutional actors may exacerbate status-driven biasesas actors change their behaviors in ways that align with what they think outsiderswould view as acceptable For instance Jensen (2006) shows that firms that are



more accountable to institutional investors are more likely display characteristicsof status-anxiety quickly abandoning a high-status auditor whose reputation hadbeen tarnished Similarly Correll et al (2015) argue that status biases are likely toemerge in otherwise-meritocratic contexts when evaluators are making decisionswith an eye toward the preferences of interested third-party audiences They usean experimental study to demonstrate that under such conditions people tend tomake decisions on the basis of ldquothird-order inferencesrdquo about who most members ofthese audiences are likely to think is higher qualitymdashwhich in turn prompts a pref-erence for higher-status actors regardless of the decision-makerrsquos own assessmentof actual quality It is plausible that during recessionary circumstances lenders aregenerally under more pressure from superiors and other lending institutions tomake decisions that will be perceived as shrewd and well-reasoned due to the diffi-culties faced by the banking industry as a whole Indeed we will present evidencelater to show that banks in general dramatically tightened lending standards duringthis time frame Therefore they may also be more likely to default to higher-statusoptions (ie firms led by men entrepreneurs)

Taken together both statistical and status-based theories of discrimination sug-gest key reasons why women-led firms may be relatively more disadvantagedduring a period of market contraction net of performance and human capital andfirm characteristics In contrast to statistical arguments however status-basedarguments further predict that the application of double standards of performancewill be more prevalent during the recessionary period That is if status-based dis-crimination plays at least some role in explaining why women-led firms are moredisadvantaged during the recession then we should find evidence that investorswere more likely to apply different performance standards to men- and women-ledfirms during this period In this context we believe that this means that women-ledfirms will have to demonstrate stronger evidence of their creditworthiness in orderto be funded at the same rate as their men-led counterparts

Data and Methods

To shed light on these questions we examine panel data on funding outcomes froma set of entrepreneurs who sought loans from banks and other formal financialinstitutions between 2007 and 2011 Macroeconomic conditions varied significantlyover this time period due to the financial crisis enabling a test of the prediction thatthe recession amplified the effect of gender on financing for entrepreneurs

Sample

The data for our analysis come from the Kauffman Firm Survey (KFS) a panelstudy that follows a sample of 4928 businesses started in 2004 The survey includesdetailed information on the characteristics of each businessrsquo owner(s) as well as itsstrategy organization performance and financing in the prior calendar year2 Astratified random sample of the approximately 250000 new businesses3 listed inDun amp Bradstreetrsquos (DampB) database in 2004 determined the set of 32469 firms thatwere invited to participate in the first wave of the survey which was administered in



20054 Follow-up surveys were conducted annually through 2012 The compositionof the panel has changed over time as some firms have ceased operations and othershave failed to respond to the survey However the latter issue is not a major concernbecause the response rate to the survey is quite high conditional on still being inbusiness only approximately 11 percent of business owners that participated in thefirst year of the survey did not respond to follow-ups

In this article we focus on responses to a series of questions initiated in 2008about each firmrsquos experience in seeking financing from banks or other financialinstitutions Specifically business owners were asked if they had applied for anynew or renewed loans or lines of credit in the prior calendar year5 Our dataindicate that applying for a loan was a relatively uncommon event In any givenyear approximately 125 of respondents applied for a loan Men-led businesseswere significantly more likely than women-led businesses to seek out a loan orline of credit (132 vs 104 p lt 001) However as we will show later thedata indicate that this gender gap in applications is due to differences in othercharacteristics that predict loan-seeking rather than being a function of gender perse

Respondents who had sought loans were then asked about the outcome oftheir applications The response rate to this question and the prior question onloan applications is higher than 995 among those who agreed to participatein the survey in a given year Thus non-response to these particular questionsshould not be a concern In total our analytical sample includes 1134 reportsof loan application outcomes from 650 firms Approximately 317 of these firmsapplied for loans in multiple years we leverage this subsample in our analyses toconduct within-firm tests of how the recession impacted the gender gap in access tofinancing

One point to emphasize is that the questions most relevant to our analysis wereasked between 2008 and 2012 regarding lending outcomes between 2007 and 2011Given that our sample consists of businesses that began operating in 2004 thisimplies that our results pertain to firms that are between three and seven years oldBecause all firms in our analysis had survived for at least three years we can thinkof our sample as representing businesses of higher-than-average quality relativeto a pool of start-ups in their first year Whether our results generalize to olderor younger start-ups is an open question That said we note that most existingstudies of lending outcomes for small businesses rely on data from the NationalSurvey of Small Business Finances (NSSBF) These businesses may be small butthey are substantially older than firms in our sample For example Blanchflower etalrsquos (2003) study of racial and gender disparities in loan outcomes uses the NSSBFsample and reports that firms are on average 13 years old Thus to the best of ourknowledge the analyses we present here speak to gender disparities in lendingoutcomes for firms that are substantially younger than those studied previously

Dependent Variable

The dependent variable in our analysis corresponds to the possible answers tothe survey question about loan application outcomes described above Partici-



pants could report that their loan applications were ldquonever deniedrdquo ldquosometimesapproved and sometimes deniedrdquo or ldquoalways deniedrdquo We coded this as an ordinalvariable ranging from one to three with higher values corresponding to more fre-quent denial Approximately 697 percent of respondents reported that their loanapplications were never denied In contrast respondents said that their applicationswere sometimes denied 157 percent of the time or always denied 146 percent ofthe time respectively These figures are comparable to prior work in this areaBlanchflower et al (2003) for example report that approximately 29 percent ofloan applications from small businesses were denied in 1993 and 1998 As we willdiscuss subsequently these average loan application outcomes mask variation overtime and by gender

Independent Variables

The key independent variables of interest are gender and the lending environmentGender is a dummy variable coded ldquo1rdquo if the firmrsquos lead owner is a woman6

Women-led firms comprise about 202 percent of responses Examining the effectsof the Great Recession on the gender gap requires us to identify an appropriate timeframe when recessionary conditions might be most likely to affect entrepreneursAlthough the recession is technically defined as starting in December 2007 andending in June 2009 data from bank examiners indicates that the impact of thefinancial crisis on small business lending was most pronounced during 2009 and2010 Figure 1 which reproduces data from the Office of the Comptroller of theCurrencyrsquos Survey of Credit Underwriting Practices shows that less than 20 percentof banks reported a net tightening of their lending standards for small businesses in2007 and 2008 but over 60 percent of banks reported doing so in 2009 and 2010 Con-sistent with this finding Cole (2012) shows that the amount of small business loansoriginated increased between 2001 and 2008 and declined subsequently Thereforewe coded 2009 and 2010 as the relevant recessionary period for small businessesTo examine how the effects of gender changed over time we estimate interactioneffects between the recessionary period indicator and gender

Control Variables

To accurately estimate the effect of gender apart from any other factors that arecorrelated with gender as well as loan application outcomes we control for a seriesof owner and business characteristics In terms of owner characteristics we includean indicator variable for immigrant status which characterizes approximately 90percent of respondents7 In addition we control for the ownerrsquos industry experience(measured by the logged number of years the owner had previously worked in theindustry of the start-up) and the number of hours the owner worked in a typicalweek (via a series of 6 categorical variables)

We also controlled for business characteristics such as profitability assetswhether the business had previously obtained a business credit line region and in-dustry The credit risk measure consists of 5 categories corresponding to credit scorerisk classes assigned to the business by Dunn amp Bradstreet the leading providerof credit ratings Ratings are based on information from a variety of sources such


Theacutebaud and Sharkey Unequal Hard TimesFigure 1 Tightening of Lending Standards for Small Business Loans

Figure 1 Tightening of Lending Standards for Small Business Loans From Source Office of

the Comptroller of the Currency 2014 ldquoSurvey of Credit Underwriting Practicesrdquo (2014 P 35) httpwwwoccgovpublicationspublications-by-typesurvey-credit-underwriting-practices-reportpub-survey-cred-under-2013pdf

0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds

Figure 1 Tightening of Lending Standards for Small Business Loans (From Office of the Comptroller of theCurrency ldquoSurvey of Credit Underwriting Practicesrdquo (201435))

as a firmrsquos past experiences with banks public utility payment records and tradeexperiences with other businesses Higher scores indicate greater creditworthinessThe KFS data does not include continuous measures of profit revenue assets andliabilities but instead codes responses to these questions into one of nine categoriesFor the sake of parsimony we experimented with various ways of collapsing theprofitability and assets measures in our models (eg combining categories wherethe effects of two categories were not statistically distinguishable from one another)In the end we chose to represent profitability as a three-category variable and assetsas four categories In each case we used the modal response from our data as thereference category We control for industry using a series of more than 20 dummyvariables representing NAICS codes Credit score profit and assets are each laggedby one year

Table 1 includes descriptive statistics pertaining to firms in our sample In thistable we report on a variety of measures that may be helpful in getting a descriptivesense of firms in our sample We initially included all of these variables in ourregression analyses however for the sake of parsimony we removed those thatdid not both vary meaningfully by gender and that did not predict loan outcomes(because the omission of such variables cannot bias our results) Because we re-strict the control variables this way the set of control variables included in modelspredicting the choice to apply for a loan and the outcome of the loan application



Table 1 Characteristics of Firms Reporting Loan OutcomesFemale Male

Mean N of Mean N of Difference(SD) Firm-Years (SD) Firm-Years Significant

Outcome of Loan Application 229 905Never Denied (=1) 6157 7171 YesSometimes Denied (=2) 1616 1558 NoAlways Denied (=3) 2227 1271 Yes

Recession Period (2009-2010) 036(048) 229 038(049) 905 NoMinority 011(031) 229 012(032) 905 NoImmigrant 005(022) 229 010(030) 905 YesOwner CharacteristicsEducation 229 903

High School or Less 010(030) 008(027) NoSome college 035(048) 033(047) NoCollege 028(045) 028(045) NoSomeCompleted Graduate 027(044) 031(047) No

Age (Reference=35-44) 229 90518ndash24 0 001(008) No25ndash34 020(040) 013(033) Yes35ndash44 036(048) 029(045) Yes45ndash54 028(045) 037(048) Yes55ndash64 014(035) 019(039) No65ndash74 001(011) 002(015) No

Industry Experience (ln years) 181(111) 229 254(091) 905 YesPrior Entrepreneurship Experience(1=yes)

045(050) 229 049(050) 905 No

Total Hours Worked Per Week tminus1 229 905lt 20 010(030) 006(024) No20ndash35 020(040) 011(031) Yes36ndash45 021(041) 014(035) Yes46ndash55 023(042) 028(045) No56ndash65 017(038) 026(044) Yes66 or more 008(028) 016(036) Yes

Business CharacteristicsOwnership team tminus1 (1=Yes) 053(050) 229 054(050) 905 NoC-Corp tminus1 (1=yes) 005(022) 229 012(032) 903 YesIntellectual Property Holder tminus1

(1=yes)014(035) 229 021(041) 904 Yes

Ln Num Employees tminus1 117(106) 229 161(109) 903 YesProfit tminus1 229 905

$500 or less 040(049) 032(047) Yes$501ndash10000 010(033) 011(031) No$10001 or more 047(050) 057(050) Yes

Total Assets tminus1 229 905$25000 or less 019(039) 011(032) Yes$25001ndash$100000 025(043) 018(039) Yes

(Continued on next page)



Female MaleMean N of Mean N of Difference(SD) Firm-Years (SD) Firm-Years Significant

$100001ndash$1000000 042(049) 050(050) Yes$1001001 or more 014(035) 021(041) Yes

Revenue tminus1 225 890$500 or less 013(034) 008(028) Yes$501ndash100000 029(045) 014(034) Yes$100001ndash$1000000 039(049) 043(050) No$1000001 or more 019(039) 035(048) Yes

Has Business Credit Line tminus1 (1=yes) 038(049) 229 051(050) 905 YesMade Purchases Through Trade Financingtminus1 (1=yes)

046(050) 228 048(050) 902 No

Credit Score tminus1 229 905Credit Score=536ndash670 010(030) 014(034) NoCredit Score=493ndash535 041(049) 029(045) YesCredit Score=423ndash492 037(048) 041(049) NoCredit Score=376ndash422 006(024) 007(027) NoCredit Score=101ndash375 006(023) 009(028) No

Total Liabilities tminus1 229 904$500 or less 035(048) 027(044) Yes$501ndash10000 021(041) 017(037) No$10001ndash$1000000 025(043) 029(046) No$1000001 or more 019(039) 027(044) Yes

Business Fails in Subsequent Years 008(027) 229 011(031) 905 NoPreviously Received Business Loan 039(049) 229 044(050) 905 NoHome-based Business tminus1 037(049) 228 024(043) 900 YesNote Last column tests whether Female-Male difference is statistically significant at p lt 005(two-tailed)

are slightly different However results are unchanged regardless of whether thefull suite of controls (ie all variables in Table 1) is included

Estimation

Deciding how to model the data at hand is not straightforward for two reasonsFirst the dependent variable consists of three ordered categories which leads us tofavor the ordinal logit model (Long 1997) At the same time we wish to rule out thepossibility that unobserved differences between men and women loan applicantsdrives our results this inclines us toward the use of fixed effects models Howevermaximum likelihood estimators for ordered logit models with fixed effects are notconsistent (Greene 2004 Johnson 2004)

As a compromise we estimated two sets of models First we present orderedlogistic regression models with random effects for firms using the xtologit com-mand in STATA 13 Ordered logistic regression is appropriate because the de-pendent variable in our analyses can take on any of three hierarchically arrangeddiscrete values We include random effects because we wish to account for thefact that we observe multiple years of data for some (but not all) firms in our



sample and because we are primarily interested in the impact of time-invariant vari-ables (ie gender) The model assumes that unit (ie firm) effects are uncorrelatedwith the predictor variables8 We also present fixed effects linear regression modelsestimated by ordinary least squares Although each of these models has its strengthsand weaknesses with respect to the KFS data we view the convergent results acrossthe two sets of models as speaking in favor of our theoretical arguments Finally inorder to account for the correlated structure of the panel data all models employrobust standard errors clustered on firms

Results

Gender Differences in Loan-Seeking Behavior

To the extent that men and women entrepreneurs may differ in their propensity toseek loans and that the tendency to seek a loan is associated with other individualor business characteristics that might impact loan approval understanding therelevant predictors of loan-seeking is important for the interpretation of the resultswe later present regarding the effects of gender and the recession on loan denialrates Therefore before turning to our main analyses of loan denial rates we firstconsider factors that may influence whether an entrepreneur chooses to apply fora loan We examined this issue by running a series of logistic regression modelspredicting the log odds of applying for a loan All models include random effectsand robust standard errors clustered on firms Results of these analyses appear inTable 2

Model 1 which includes only the 2009ndash2010 indicator and whether the appli-cant was a woman as well as industry and region dummies indicates that womenmay be less likely than men to seek out bank loans (β = minus026 p lt 010) Therecession indicator in this model is negative but non-significant suggesting thatentrepreneurs were no more or less likely to apply for loans during this period thanthey were during more favorable macroeconomic conditions Model 2 incorporatesan interaction of gender and the 2009ndash2010 indicator which is not statistically signif-icant Gender is also non-significant in this model Finally Model 3 whichincludesthe battery of control variables that are used in our main analysis of loan denial sug-gests that the observed negative effect of gender found in Model 1 can be attributedto the fact that men- and women-led businesses differ on other characteristics thatpredict applying for a loan after controlling for other relevant characteristics itappears that women may be more likely than men to apply for business loansduring more normal macroeconomic conditions (β = 025 p lt 010) To determinewhether women applied at different rates than men did during the 2009ndash2010 pe-riod we tested whether the sum of the main effect of gender and the gender-periodinteraction was statistically different from zero We were unable to reject the nullhypothesis of this test (p=088) and therefore conclude that there is no evidence thatwomen applied for loans at different rates than men did during the recession Weinterpret this overall pattern of results as relatively weak evidence of gender-baseddifferential selection with some indication that women may apply at higher ratesthan men during non-recessionary times net of other observable characteristics but



Table 2 Estimated Coefficients From Logistic Regression Models Predicting the Log Odds of Applying for aLoan

1 2 3

Female minus026lowast minus02 025lowast

(016) (017) (014)Recession Period (2009-2010) minus006 minus003 minus009

(008) (009) (009)Female times Recession Period minus017 minus023

(021) (02)Credit Score tminus1 (Reference is 493-535)

Credit Score=536-670 015(016)

Credit Score=423-492 minus021dagger

(011)Credit Score=376-422 minus030lowast

(018)Credit Score=101-375 minus022

(02)Constant minus248dagger minus249dagger minus239dagger

(067) (067) (056)

Controls No No YesLog Pseudo-likelihood minus294468 minus294431 minus266764Degrees of Freedom 25 26 46

Notes N=9031 firm-years Robust standard errors in parentheses All models include fixed effects forregion and industry Controls in Model 3 are ownerrsquos immigrant status ownerrsquos hours worked per weekwhether the owner is an intellectual property holder firm size (ln number of employees) total assets whetherthe firm had a business credit line in the prior year total liabilities and whether the firm is home-basedComplete model estimates are presented in the supplemental appendixlowast p lt 010 dagger p lt 005

no evidence that gender influenced the rate of loan application during 2009ndash2010We return to this point later and consider it in light of the findings of our mainanalyses of loan denial rates to which we now turn

The Changing Effect of Gender on Loan Denial Rates

We begin our analyses of loan denial rates by outlining the basic descriptive patternsin the data before turning to more complex statistical models Figure 2 shows theraw percentage of responses in each category of loan denial by the gender of mainownerfounder between 2007 and 2011 Panel A suggests that women-led firmsare less likely than men-led firms to be in the ldquonever deniedrdquo category and Panel Cshows that women-led firms are more likely than men-led firms to be in the ldquoalwaysdeniedrdquo category However these gender gaps fluctuate during the period of studyFor instance 75 percent of men-led firms versus 68 percent of women-led firmsfell into the ldquonever deniedrdquo category in 2007 a difference of about 7 percentagepoints By 2010 this gender gap was about three times as large at 18 percentage


Theacutebaud and Sharkey Unequal Hard TimesFigure 2 Rates of Funding Denial By Gender 2007-2011

0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11

Never denied Sometimes Denied Always Denied

Male-led Female-led

Panel B Panel CPanel A

Figure 2 Rates of Funding Denial By Gender 2007ndash2011

points (70 and 52 percent for men- and women-owned firms respectively) Thereverse pattern can be seen in the ldquoalways deniedrdquo category there is a sharperincrease in the percentage of women-led firms in this category between 2007 and2010 than there is for men-led firms Overall these descriptive patterns suggestthat the recession was associated with widening gender gaps in loan denial

To examine whether these descriptive trends persist after controlling for otherfactors associated with gender and loan application outcomes we estimated randomeffects ordered logistic regression models predicting the likelihood of loan denialResults are presented in Table 3 Model 1 which includes only a female indicatorand industry and region dummies shows that the raw effect of being a woman onloan outcomes is to increase the likelihood of denial (β = 096 p lt 005) Model 2incorporates an indicator for whether the business applied for a loan during the2009ndash2010 period The positive and significant coefficient on this variable indicatesas expected that loans were more likely to be denied during this time frame

In order to test our hypothesis that women-led firms faced disproportionatelylong odds of obtaining a loan during the recession Model 3 incorporates an interac-tion between the 2009ndash2010 recessionary period indicator and the female dummyvariable In this model the indicator for 2009ndash2010 remains positive and significantcapturing the fact that all entrepreneurs experienced greater difficulties obtainingfunding during this period The main effect of the female indicator variable whichrepresents the effect of being a women-led firm on loan outcomes under morenormal macroeconomic conditions is positive but not statistically significant Thisindicates that firms led by women did not experience different outcomes thanmen-led firms when the lending environment was relatively strong However theinteraction between the 2009ndash2010 indicator and female is positive and significantindicating that women entrepreneurs encountered more difficulties obtaining fi-nancing than did their men counterparts during those years As model 4 shows thispattern of results holds even after we include controls for other factors associatedwith both the gender of the firmrsquos owner and the likelihood of obtaining a loan9



In order to guard against the possibility of improper interpretation of interactionterms in non-linear models (Ai and Norton 2003) we also ran the models in Table 3separately for men and women and obtained results similar to those reported here(results not shown but available upon request)

Overall these results are consistent with our theoretical arguments that anygender disparities in funding for small businesses were exacerbated during the2009ndash2010 recessionary period More generally they point to the fact that thepresence of a gender gap in entrepreneurial access to financing was contingent onconditions in the lending environment Yet despite our extensive set of controls itis still possible that some factor that differs systematically between men and womenand that is correlated with lending outcomes may drive our results Note howeverthat for unobserved heterogeneity to account for our findings it must be due tosome factor that differs or manifests itself only during the recession because wefind no statistically significant gender gap in outcomes during non-recessionaryperiods once we control for other relevant predictors This would seem to greatlynarrow the set of possible confounds Nonetheless we formally examined thepossibility that unobserved heterogeneity drives our results by estimating linearregression models with fixed effects presented in Table 4 These analyses representa particularly strict test of our arguments because they net out any time-invariantwithin-person factors that might influence our results One downside of thesemodels is that they can only be run on individuals who applied for loans multipletimes This has two implications First the number of observations falls in theseanalyses meaning we have less statistical power Second we should be cautious ingeneralizing from these results because individuals who apply for loans multipletimes may systematically differ from the general pool of loan applicants whichincludes one-time applicants

We focus our discussion on Model 3 which includes the same set of controlsas did Model 4 of Table 3 with the exception of dropping any factors that remainfixed over time within firms (ie gender and immigrant status of the founder)Similar to the results presented earlier these analyses show that all firms found itmore difficult to obtain loans during the recession than in other times (β = 012p lt 005) Women-led firms however faced even greater barriers than men did inobtaining a loan during this period as evidenced by the positive and significantinteraction between the variables female and 2009ndash2010 The fact that we obtainresults similar to those reported earlier even after accounting for the possibility ofunobserved time-invariant heterogeneity through the use of fixed effects shouldincrease confidence in our conclusion that gender became a more important pre-dictor of the likelihood of being denied funding during the Great Recession Thisfinding is consistent with our theoretical premise that factors associated with wors-ening macroeconomic conditions such as widespread uncertainty and heightenedscrutiny in the lending environment may increase lendersrsquo reliance on stereotypesabout ascribed characteristics thereby exacerbating disadvantages for groups suchas women

We now consider explicitly how any possible differences in selection into loan-seeking would affect our results In our view the main possible threat to the validityof our results is that the elevated denial rates that women faced in 2009 and 2010



Table 3 Estimated Coefficients From Random Effects Ordered Logistic Regression Models Predicting theFrequency of Loan Denial

1 2 3 4

Female 096dagger 106dagger 06 044(037) (04) (047) (042)

Recession Period (2009ndash2010) 091dagger 066dagger 063dagger

(024) (025) (025)Female times Recession Period 134dagger 119dagger


Credit Score=536ndash670 minus066(042)


Credit Score=376ndash422 047(045)

Credit Score=101ndash375 132dagger

(043)Cutpoint 1 351lowast 412dagger 418dagger 284

(18) (193) (201) (174)Cutpoint 2 532dagger 605dagger 616dagger 467dagger

(182) (197) (205) (177)

Controls No No No YesLog Pseudo-likelihood minus83072 minus82169 minus81859 minus78599Degrees of Freedom 24 25 26 43

Notes N=1134 firm-year reports of loan application outcomes Robust standard errors in parentheses allmodels include region and industry fixed effects Controls in model 4 are ownerrsquos immigrant status ownerrsquosindustry experience (in years) ownerrsquos hours worked per week profit total assets and has a business creditline Complete model estimates are presented in the supplemental appendixlowast p lt 010 dagger p lt 005

may stem from women entrepreneurs being of lower quality on some unobservabledimensions rather than gender bias on the part of lenders This relates to issuesof selectivity in that a lower-quality applicant pool consisting solely of womencould arise either if men are more reluctant than women to seek out loans and thatthey therefore hold themselves to higher quality standards prior to doing so or ifwomen entrepreneurs are for some reason particularly willing to reach out and seekfunding and that they hold themselves to lower standards prior to doing so Weknow of no theoretical accounts consistent with either of these stories

In addition even if gender-based differences in the propensity to seek a loanmade it such that men applicants were superior on some unobservable dimensionsthis would be consistent with our pattern of results only if a second conditionwere also met differential selection would have to manifest itself only during theyears of 2009 and 2010 when macro-economic conditions were highly uncertainThis second condition is essential for explaining our results because we do notobserve any difference in loan denial rates in years other than 2009ndash2010 Recall



Table 4 Estimated Coefficients From Fixed Effects Ordinary Least Squares Regression Models Predicting TheFrequency of Loan Denial

1 2 3



(014) (014)Credit Score tminus1 (Reference is 493ndash535)





Constant 091dagger 154dagger (011)(036) (018) (045)

Controls No No YesR2 (within) 005 007 01Degrees of Freedom 7 8 24

Notes N=801 firm-year reports of loan application outcomes Robust standard errors in parentheses allmodels include firm region and industry fixed effects Controls in Model 3 are ownerrsquos industry experience(ln years) ownerrsquos hours worked per week profit total assets and whether the firm had a business creditline in the prior year Complete model estimates are presented in the supplemental appendixlowast p lt 010 dagger p lt 005

however that this scenario is not consistent with what we reported earlier in ouranalyses predicting whether a business would apply for a loan Results of thatinvestigation indicated some possibility that women-led firms were more likelythan men-led firms to apply for loans during normal macroeconomic conditionsbut no evidence that men and women differed in their tendency to apply for a loanduring the 2009ndash2010 time frame Moreover in analyses not shown here we testedfor any evidence of a shift in the quality of loan applications made by men-ledand women-led businesses during the recession as opposed to before or after itResults of t-tests of differences in mean industry experience and firm size showthat women-led firms applying for loans were no different on these factors duringthe recession versus before or after it Likewise we used tests of proportions tocheck whether women-led firms were more likely for example to be in lower-ratedcredit categories to be less profitable or to be home-based during the recessionthan either before or after We did not find any evidence of a shift in quality onthese dimensions either Based on these examinations we conclude that differentialselection into applying for a loan during the downturn is unlikely to account for thedisproportionately elevated rate of denial that women-led businesses encounteredin 2009ndash2010

Finally to further investigate why and how the gender disparity in access tocredit arose we ran a set of models aimed at testing for evidence of status-based



discrimination In particular we estimated models in which we predicted thelikelihood of being denied a loan as a function of three-way interactions of gendereach of the categories of credit score and whether the year was 2009 or 2010 Thesethree-way interactions test whether the gender-based disparity in access to financingthat emerged during the recession varied according to the credit score of the firmAs noted above status-based theories predict that cognitive biases will cause thesame information to be viewed more negatively when it is provided by women inpart because women are held to a stricter standard of evaluation Table 5 presentsthe results of these analyses Because of the challenges of interpreting interactionterms in non-linear models we present the results of OLS regressions here althoughordered logistic regression models with random effects produce similar findingsThe leftmost column of coefficients includes the three-way interactions for 2009and the rightmost column includes the three-way interactions for 2010

The findings for 2009 are relatively uninteresting none of the three-way inter-actions are significant Thus in this year bias does not seem to manifest itself indifferential evaluation of credit scores and we conclude that double standards ofevaluation were unlikely to be at play during this period In contrast the three-way-interaction for the lowest two credit scores is positive and significant in 2010Thus in that year the gender penalty manifested itself in a very targeted way suchthat having a low credit score was more detrimental for women entrepreneurs thanfor men entrepreneurs This finding is consistent with Foschirsquos (1994 1996) workon double standards whereby status beliefs prompt evaluators to hold women tomore stringent requirements than men

Taken together these analyses suggest a rather nuanced picture of the effectsof gender in entrepreneurial lending markets Gender does not appear to havehad an effect on lending outcomes under more typical macroeconomic conditionsconsistent with some prior work in this area (Aseidu et al 2012 Blanchard et al2008 Blanchflower et al 2003) However when economic conditions worsenedwomen entrepreneurs faced greater difficulties obtaining funding than did menentrepreneurs with similar observable characteristics We also find evidence thatthis disadvantage was driven at least in 2010 by the fact that recessionary circum-stances prompted investors to penalize women with low credit scores more severelythan men with low credit scores a finding which is more consistent with status-based accounts than statistical discrimination accounts However we do not findsuch evidence in 2009 Thus we cannot fully disentangle with our data whetherstatus beliefs or statistical discrimination are the primary mechanism driving thisphenomenon indeed both mechanisms may simultaneously be at play

Robustness Checks

Increased Scrutiny of All Aspects of Loan Applications

One alternative to our interpretation of this pattern of results is that the findingsthus far are not particular to gender but rather are a manifestation of a more generalpattern of increased scrutiny to every aspect of a loan application In that case not



Table 5 Estimated Coefficients from Ordinary Least Squares Regression ModelsTesting Gender Differences in Interpretation of Credit Scores

(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)

Femaletimes2010 043dagger 022(02) (041)

Femaletimes2011 015 012(017) (017)

Female times Credit Score =493ndash535 minus014 minus019(021) (022)

Female times Credit Score=423ndash492 001 minus01(022) (022)

Female times Credit Score=376ndash422 034 005(033) (035)


2009 times Credit Score =493ndash535 026(016)

2009 times Credit Score=423ndash492 016(014)



Female times 2009 times Credit Score=493-535 minus014(062)



Female X 2009 X Credit Score=101-375 minus061(Continued on next page)



(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)

Credit Score=101ndash375 033dagger 045dagger(012) (012)




2010 times Credit Score=101ndash375 minus050lowast(028)

Female times 2010 times Credit Score=493ndash535 003(047)

Female X 2010 times Credit Score=423ndash492 004(059)

Female times 2010 times Credit Score=376ndash422 095lowast(054)

Female times 2010 times Credit Score=101ndash375 158dagger(054)

Constant 122dagger 126dagger(014) (014)

R2 018 019Degrees of Freedom 61 60Notes N=1134 firm-year reports of loan application outcomes Robuststandard errors in parentheses all models include region and industryfixed effects Controls for ownerrsquos immigrant status ownerrsquos industryexperience ownerrsquos work hours profits assets and received credit linepreviously are included but not reportedlowast p lt 010 dagger p lt 005

only gender but also other characteristics of loan applicants such as credit score orindustry experience would become stronger predictors of loan outcomes duringthe recession To determine whether this is the case we ran a series of modelsparalleling those presented earlier that examined the changing effects of genderduring 2009ndash2010 Instead of interacting gender with year indicators howeverwe interacted characteristics such as credit score industry experience profitabilitytotal assets and having previously obtained a business credit line with the 2009ndash2010 recessionary period indicator to determine whether the effects of these moreldquoobjectiverdquo characteristics of firms and their owners also increased in importance



when the small-business lending market tightened We entered each of theseinteractions into the models separately Of the 11 different variables we interactedwith the uncertainty period and entered into models only onemdashthe interaction thatincluded the second-best credit score categorymdashwas statistically significant Overallthen the empirical evidence suggests that the observed increase in the importanceof gender during the Great Recession was not part of a pattern of overall increasedscrutiny that similarly affected how lenders made use of all types of informationfound on a loan application

Discussion and Conclusion

In this paper we investigated the possibility that macroeconomic conditions moder-ate the probability of gender-based discrimination in entrepreneurship Our findingthat women-led firms were significantly more likely than men-led firms to be deniedfunding during 2009 and 2010 when the small-business lending market was mostconstrained and that lenders did not simultaneously increase their emphasis onother more objective factors leads us to conclude that recessionary conditions in-crease the unique perceived relevance of gender in the evaluation of entrepreneurialventures Moreover consistent with a status-based account of discrimination wefind that low credit ratings disproportionately disadvantaged women during 2010suggesting that investors also were more likely to apply gender-differentiated stan-dards of performance during this period Our findings contribute to scholarship inthe substantive area of gender and entrepreneurship as well as to work that relatesto discrimination and the operation of status processes in markets more generallyWe discuss each of these in turn

In terms of the literature on gender and entrepreneurship this study helps shedlight on the disadvantages that women-owned firms face in terms of funding acritical outcome that may ultimately influence firm survival and contribute to theaggregate gender imbalance in the rate of small-business ownership Our findingsare consistent with lab-based studies which suggest that status-driven cognitivebiases may be one mechanism that contributes to gender gaps in the financialand social support that entrepreneurs are likely to receive (Theacutebaud 2015 Tinkleret al 2015) although our results cannot rule out the possibility that statisticaldiscrimination may also be at play A key strength of our study relative to othershowever is that the longitudinal nature of our data enables us to exploit variationin macroeconomic conditions to better understand precisely when such biases aremore likely to in practice become salient enough in the minds of lenders that theyexert a systematic effect on their decision-making

While our study focuses on a specific type of fundingmdashbank loans and linesof creditmdashwe believe that our findings are instructive for understanding why theeffect of gender may vary across different types of funding For instance existingwork suggests that women have a particularly difficult time obtaining venturecapital funds (see eg Brush et al 2014) while several studies find no evidence of agender gap in the ability of women entrepreneurs to obtain loans from banks (seeeg Asiedu et al 2012 Blanchard et al 2008 Blanchflower et al 2003) Our studysuggests that gender bias in the small-business lending arena is of a contingent



nature whereas a gender gap emerged during periods of greater uncertainty andheightened scrutiny of lenders it was not present in every year of our data (iewomen had worse outcomes than men in 2009 and 2010 in our data but theyreceived similar treatment to men in other years) We speculate that differencesin the effects of gender in different types of funding markets may also have to dowith differing levels of uncertainty albeit of a different sort than was the focus ofthe present study Specifically the uncertainty in outcomes for the funder variessubstantially between venture capital and bank loans compared to banks venturecapitalists are more likely to invest at an early stage which makes it harder topredict which firms will survive versus which will fail Thus given what weknow about the role of uncertainty in the manifestation of bias it is perhaps notsurprising that women are more consistently disadvantaged in the ability to obtainventure capital than in their ability to obtain credit from banks Future work shouldinvestigate this possibility directly

Next while our primary analyses focused on evaluative factors influencingwhether banks were willing to supply loans to women-led businesses our supple-mental analyses showed that women-led firms were not disproportionately lesslikely to apply for a loan after controlling for other characteristics This leads us toconclude that that the credit gap we observe is largely driven by the behavior ofinvestors rather than the possible self-defeating behaviors of women entrepreneursIn general this result is inconsistent with the argument that ldquowomen donrsquot askrdquo(Babcock and Lashever 2003) Rather it seems that women entrepreneurs ask forfunding at rates equal to or greater than men when other relevant factors are takeninto account but that conditional on asking women are denied at higher rates thanmen during hard times This is informative in that it points more clearly to the locusof disparities in entrepreneursrsquo access to capital

More broadly our findings speak to theoretical debates about why gender biaspersists in modern economic settings that are otherwise premised on meritocraticideals Although our findings are generally consistent with the predictions of astatus-based account of discrimination we cannot rule out the possibility thatstatistical discrimination also contributes to the financing disadvantages womenentrepreneurs face Yet we think it plausible that status-driven gender discrimina-tion could become increasingly relevant in the future when we consider that keyfeatures of our empirical settingmdashthe small business lending marketmdashincreasinglycharacterize other market and organizational settings For instance in our settingexplicit discrimination on the basis of gender (or race for that matter) is not onlylegally barred but it is also inconsistent with the types of meritocratic organiza-tional cultures that many firms ostensibly seek to promote Moreover lendershave increasing amounts of information at their fingertips (Petersen and Rajan2000) While there will always be some uncertainty in the outcome of any loanthe availability of objective information and lendersrsquo power to analyze it is onlylikely to increase If gender discrimination continues to persist in these settingsits locus is thus increasingly likely to be rooted in the ways that decision-makersrely on cultural beliefs about gender to interpret such information than in the waythat the absence of such information prompts them to rely on gendered statisticalinferences



Finally our paper contributes to the literature on the intersection of status andgender in economic outcomes more generally Specifically our findings contributeto the body of work in sociology that argues that economic decision-makers aremore likely to cognitively rely on individualsrsquo or firmsrsquo categorical status charac-teristics during conditions of greater uncertainty and scrutiny However it alsocontributes to the broader scholarly dialogue regarding the relevance of social statusto economic decision-making For instance economic sociologists have long arguedthat economic decision-making can be influenced by an actorrsquos position within ahierarchical structure of social relationships (Burt 1992 Granovetter 1985 Podolny2010) And consistent with the idea that cultural norms values and practices alsoshape economic activity (DiMaggio 1994 Zelizer 2010) economic decisions havealso been shown to be influenced by an organizationrsquos membership in a salientsocial category such as an industry that is itself culturally imbued with statusvalue (Sharkey 2014) Both network-based and categorical forms of status becomerelevant to decision-makers largely because they are thought to reduce uncertaintyin economic exchange Consistent with this idea previous research finds that anorganizationrsquos network status cues information about its reliability and trustworthi-ness as an exchange partner and as such tends to form a particularly salient basisof decision-making under conditions of greater market uncertainty (Podolny 19942001) Our work extends this finding by identifying how the culturally constructedstatus associated with categories of individually ascribed traits can also be viewedas more relevant during times of market uncertainty

A final question regarding the findings presented here may be whether thegender discrimination that arose during the recession is in any sense rational10

More specifically is there any evidence that net of other factors affecting repaymentwomen entrepreneurs are any less likely to repay their loans which could informlendersrsquo beliefs about women entrepreneurs as a group and lead them to avoidlending to them Our data do not include ex-post loan repayment measures andwe are not aware of any studies that address this question with respect to youngUS-based businesses However the case of microfinance provides an interestingcounterpoint to the findings presented here Several prominent microfinance institu-tions explicitly target women under the theory that women borrowers are less riskyand are more likely to repay their loans (see eg World Bank 2007) Recent studiesexamining loan outcomes from microfinance institutions to borrowers in more than50 countries conclude that women borrowers are associated with lower portfoliorisk and fewer loan write-offs (Brenner 2012 DrsquoEspallier Guerin and Mersland2011) In a related vein some studies of peer-to-peer lending marketplaces thatare able to assess default rates show no statistically significant difference amongmen and women in the propensity to repay a loan (Pope and Snyder 2011) Overallwhile we hesitate to overgeneralize from these contexts to the one studied herewe can find little in the way of a solid basis for the belief that it would in fact bemore risky to lend to women Rather it seems more plausible that when recessionstrikes widely shared cultural beliefs about what kinds of people are and shouldbe successful entrepreneurs subtly enter into lendersrsquo minds affecting their senseof which ventures appear to be most worthy of financing



Notes

1 We do not know of any empirical research suggesting that women-led businesses areany less likely to repay loans than men-led businesses are

2 Thus the data pertain to information on firmsrsquo activities during calendar years 2004ndash2011

3 New businesses include not only start-ups but also purchases of existing businesses bya new ownership team and purchases of franchises

4 Of the 32469 firms invited to participate approximately 16156 completed the baselinesurvey The screening criteria in the baseline survey indicated that 11228 firms wereineligible which resulted in 4928 firms in the final sample of eligible businesses (Farhatand Robb 2014)

5 The specific question was ldquoDid [BUSINESS NAME] make any applications for new orrenewed loans or lines of credit in calendar year []rdquo

6 If a firm has multiple owners the primary owner is designated as the one with thelargest equity share If more than two owners had equal shares the primary owner wasidentified based on the number of hours worked (Farhat and Robb 2014)

7 Though minority andor immigrant status are also important dimensions along whichstereotypic biases may disadvantage firms in their quest for support we are not able toreliably investigate how the effects of these variables change over time with these databecause both groups comprise small and heterogeneous fractions of our sample

8 While we cannot estimate fixed effects ordered logit models and run a Hausman testto determine whether this assumption holds we have run the Hausman test for OLSmodels The test indicates that fixed effects are preferable to random effects in thissetting

9 Although the reported models only control for factors whose omission could bias ourresults (ie variables correlated with both gender and the likelihood of loan denial) weran preliminary models that included a wide range of variables that could plausibly berelated to gender or loan denial These include for example minority status educationage prior entrepreneurship experience whether the firm had an ownership team (asopposed to a solo founder) whether the firm was a C-corporation whether the firm hadintellectual property firm size (measured as the log number of employees including thefounder) revenue level whether the firm had trade financing total liabilities whetherthe firm had ever received a loan before and whether the firm failed prior to the end ofdata collection in 2011 (as an indicator of unobserved quality) Results are robust to theinclusion of these control variables

10 We note that even if such biases are rational they are also illegal

References

Ai Chunrong and Edward C Norton 2003 ldquoInteraction Terms in Logit and Probit ModelsrdquoEconomic Letters 80 123ndash129 httpdxdoiorg101016S0165-1765(03)00032-6

Altonji Joseph and Charles R Pierret 2001 ldquoEmployer Learning and Statistical Discrimi-nationrdquo Quarterly Journal of Economics 116(1) 313ndash350 httpdxdoiorg101162003355301556329

Arrow Kenneth J 1973 ldquoThe Theory of Discriminationrdquo Pp 3ndash33 in Discrimination inLabor Markets edited by Orley C Ashenfelter and Albert Rees Princeton NJ PrincetonUniversity Press



Asiedu Elizabeth James A Freeman and Akwasi Nti-Addae 2012 ldquoAccess to Credit bySmall Businesses How Relevant Are Race Ethnicity and Genderrdquo American EconomicReview 102(3) 532ndash537 httpdxdoiorg101257aer1023532

Babcock Linda and Sara Laschever 2003 Women Donrsquot Ask Negotiation and the Gender DividePrinceton NJ Princeton University Press

Bigelow Lyda S Leif Lundmark Judi McLean Parks and Robert Wuebker 2014 ldquoSkirtingthe Issues Experimental Evidence of Gender Bias in IPO Prospectus Evaluationsrdquo Journalof Management 40(6) 1732ndash1759 httpdxdoiorg1011770149206312441624

Blanchard Lloyd Bo Zhao and John Yinger 2008 ldquoDo Lenders Discriminate AgainstMinority and Women Entrepreneursrdquo Journal of Urban Economics 63467ndash497 httpdxdoiorg101016jjue200703001

Blanchflower David G Phillip Levine and David Zimmerman 2003 ldquoDiscrimination inthe Small-Business Credit Marketrdquo The Review of Economics and Statistics 85(4) 930ndash943httpdxdoiorg101162003465303772815835

Brenner Cynthia 2012 ldquoDo Women Make the Difference The Effect of Gender on Microfi-nance Repayment Ratesrdquo Georgetown Public Policy Review

Brooks Allison Wood Laura Huang Sarah Wood Kearney and Fiona Murray 2014 ldquoIn-vestors Prefer Entrepreneurial Ventures Pitched By Attractive Menrdquo Proceedings of theNational Academy of the Sciences 111(12) 4427ndash4431 httpdxdoiorg101073pnas1321202111

Bruni Attila Silvia Gherardi and Barbara Poggio 2004 ldquoDoing Gender Doing Entrepreneur-ship An Ethnographic Account of Intertwined Practicesrdquo Gender Work and Organization11(4)406ndash429 httpdxdoiorg101111j1468-0432200400240x

Brush Candidia G Patricia G Greene Lakschmi Balachandra and Amy E Davis 2014Women Entrepreneurs 2014 Bridging the Gender Gap in Venture Capital Babson Park MAArthur M Blank Center for Entrepreneurship Babson College

Burt Ronald S 1992 Structural Holes The Social Structure of Competition Cambridge HarvardUniversity Press

Buttner E Holly and Dorothy Moore 1997 ldquoWomenrsquos Organizational Exodus to En-trepreneurship Self-reported Motivations and Correlates with Successrdquo Journal of SmallBusiness Management 35(1) 34ndash46

Buttner E Holly and Benson Rosen 1988 ldquoBank Loan Officersrsquo Perceptions of the Charac-teristics of Men Women and Successful Entrepreneurs Journal of Business Venturing3249ndash258 httpdxdoiorg1010160883-9026(88)90018-3

Carter Sara and Tom Cannon 1992 Women as Entrepreneurs A Study of Female BusinessOwners Their Motivations Experiences and Strategies for Success London UK AcademicPress

Carter Sara and Eleanor Shaw 2006 Womenrsquos Business Ownership Recent Research and PolicyDevelopments Report to the Small Business Service November

Carter Sara Eleanor Shaw Wing Lam and Fiona M Wilson 2007 ldquoGender En-trepreneurship and Bank Lending The Criteria and Processes Used by Bank LoanOfficers in Assessing Applicationsrdquo Entrepreneurship Theory and Practice 31427ndash444httpdxdoiorg101111j1540-6520200700181x

Castilla Emilio J 2008 ldquoGender Race and Meritocracy in Organizational Careersrdquo AmericanJournal of Sociology 113 (6)1479ndash1526 httpdxdoiorg101086588738

Castilla Emilio J and Stephen Benard 2010 ldquoThe Paradox of Meritocracy in OrganizationsrdquoAdministrative Science Quarterly 55 (4)543ndash576 httpdxdoiorg102189asqu2010554543



Cavalluzzo Ken Linda Cavalluzzo and John D Wolken 2002 ldquoCompetition Small BusinessFinancing and Discrimination Evidence From a New Surveyrdquo The Journal of Business75(4) 641ndash679 httpdxdoiorg101086341638

Cavalluzzo Ken and John Wolken 2005 ldquoSmall Business Loan Turndowns Personal Wealthand Discriminationrdquo Journal of Business 78(6) 2153ndash2177 httpdxdoiorg101086497045

Cha Youngjoo 2014 ldquoJob Mobility and the Great Recession Wage Consequences by Genderand Parenthoodrdquo Sociological Science May 2

Cole Rebel A 2012 ldquoHow Did the Financial Crisis Affect Small Business Lending in theUnited Statesrdquo Unpublished Working Paper Prepared for the Small Business Association

Coleman Susan and Alicia Robb 2009 ldquoA Comparison of New Firm Financing by GenderEvidence from the Kauffman Firm Survey Datardquo Small Business Economics 33397ndash411httpdxdoiorg101007s11187-009-9205-7

Correll Shelley J and Stephen Benard 2006 ldquoBiased Estimators Comparing Status andStatistical Theories of Gender Discriminationrdquo Pp 89ndash116 in Social Psychology of theWorkplace (Advances in Group Processes Vol 23) edited by Shane R Thye and Edward JLawler New York Elsevier httpdxdoiorg101016s0882-6145(06)23004-2

Correll Shelley J Stephen Benard and in Paik 2007 ldquoGetting a Job Is There a MotherhoodPenaltyrdquo American Journal of Sociology 1121297ndash1338 httpdxdoiorg101086511799

Correll Shelley J Cecilia Ridgway Ezra Zuckerman Sharon Jank and Sara Jordan-Bloch2015 ldquoItrsquos the Conventional Thought That Counts The Origins of Status Advantage inThird-Order Inferencerdquo Unpublished manuscript

DrsquoEspallier Bert and Isabelle Guerin and Roy Merslund 2011 ldquoWomen and Repayment inMicrofinance A Global Analysisrdquo World Development 39(5) 758ndash772 httpdxdoiorg101016jworlddev201010008

DiMaggio Paul J 1994 ldquoCulture and Economyrdquo Pp 27ndash57 in Handbook of Economic Sociologyedited by Neil Smelser and Richard Swedberg Princeton and N Y Princeton UniversityPress and Russell Sage Foundation

England Paula 1992 Comparable Worth Theories and Evidence New York Aldine De Gruyter

Fairlie Robert and Alicia Robb 2008 Race and Entrepreneurial Success Black- Asian- andWhite-owned Businesses in the United States Cambridge MA MIT Press

Farhat Joseph and Alicia Robb 2014 ldquoApplied Survey Data Analysis Using Stata TheKauffman Firm Survey Datardquo Kauffman Foundation

Foddy Margaret and Smithson M 1999 ldquoCan Gender Inequalities Be Eliminatedrdquo SocialPsychology Quarterly 62307ndash324 httpdxdoiorg1023072695831

Folbre Nancy 2010 ldquoThe Declining Demand for Menrdquo The New York Times December 13

Foschi Martha 1996 ldquoDouble Standards in the Evaluation of Men and Womenrdquo SocialPsychology Quarterly 59237ndash54 httpdxdoiorg1023072787021

Foschi Martha 2008 ldquoGender Performance Level and Competence Standards inTask Groupsrdquo Social Science Research 38447ndash457 httpdxdoiorg101016jssresearch200810004

Foschi Martha Larissa Lai and Kirsten Sigerson 1994 Gender and Double Standardsin the Assessment of Job Applicants Social Psychological Quarterly 57(4)326ndash339 httpdxdoiorg1023072787159



Gorman Elizabeth H 2006 ldquoWork Uncertainty and the Promotion of Professional WomenThe Case of Law Firm Partnershiprdquo Social Forces 85865ndash890 httpdxdoiorg101353sof20070004

Greene William 2004 ldquoThe Behavior of the Maximum Likelihood Estimator of LimitedDependent Variable Models in the Presence of Fixed Effectsrdquo Econometrics Journal 798ndash119 httpdxdoiorg101111j1368-423X200400123x

Grusky David B Bruce Western and Christopher Wimer 2011 ldquoThe Consequences ofthe Great Recessionrdquo Pp 3ndash20 in The Great Recession edited by David B Grusky BruceWestern and Christopher Wimer New York NY Russell Sage Foundation

Gupta Vishal K Daniel B Turban S Arzu Wasti and Arijit Sikdar 2009 ldquoThe Roleof Gender Stereotypes in Perceptions of Entrepreneurs and Intentions to Become anEntrepreneurrdquo Entrepreneurship Theory and Practice March 397ndash417 httpdxdoiorg101111j1540-6520200900296x

Granovetter Mark 1985 ldquoSocial Structure and Economic Action The Problem of Embed-dednessrdquo American Journal of Sociology 91(3) 481ndash510 httpdxdoiorg101086228311

Haines GH Jr BJ Orser and AL Riding 1999 ldquoMyths and Realities An Empirical Studyof Banks and the Gender of Small Business Clientsrdquo Canadian Journal of AdministrativeSciences 16 291ndash308 httpdxdoiorg101111j1936-44901999tb00690x

Heilman Madeline E Karyn J Block and Peter Stathatos 1997 ldquoThe Affirmative ActionStigma of Incompetence Effects of Performance Information Ambiguityrdquo Academy ofManagement Journal 40(3)6025 httpdxdoiorg102307257055

Heilman Madeline E Aaron S Wallen Daniella Fuchs and Melinda M Tamkins 2004ldquoPenalties for Success Reactions to Women Who Succeed at Male Gender-Typed TasksrdquoJournal of Applied Psychology 89(3) 416ndash427 httpdxdoiorg1010370021-9010893416

Hout Michael Asaf Levanon and Erin Cumberworth 2011 ldquoJob Loss and Unemploy-mentrdquo Pp 59ndash81 in The Great Recession edited by David B Grusky Bruce Western andChristopher Wimer New York NY Russell Sage Foundation

Jennings Jennifer E and Candidia G Brush 2013 ldquoResearch on Women EntrepreneursChallenges to (and from) the Broader Entrepreneurship Literaturerdquo The Academy of Man-agement Annals 7(1)661ndash713 httpdxdoiorg101080194165202013782190

Jensen Michael 2006 ldquoShould We Stay or Should We Go Accountability Status Anxietyand Client Defectionsrdquo Administrative Science Quarterly 51 97ndash128

Johnson Edward G 2004 ldquoIdentification in Discrete Choice Models with Fixed EffectsrdquoStanford University Unpublished PhD Thesis

Kalleberg Arne L and Kevin T Leicht 1991 ldquoGender and Organizational PerformanceDeterminants of Small Business Survival and Successrdquo Academy of Management Journal34(1) 136ndash161 httpdxdoiorg102307256305

Kanter Rosabeth Moss 1977 Men and Women of the Corporation New York Basic Books

Kim Philipp H Howard E Aldrich and Lisa A Keister 2006 ldquoAccess (Not) DeniedThe Impact of Financial Human and Cultural Capital on Entrepreneurial Entry in theUnited Statesrdquo Small Business Economics 27(1) 5ndash22 httpdxdoiorg101007s11187-006-0007-x

Long Scott 1997 Regression Models for Categorical and Limited Dependent Variables ThousandOaks CA Sage Publications



Loscocco Karyn A Joyce Robinson Richard H Hall and John K Allen 1991 ldquoGender andSmall Business Success An Inquiry into Womenrsquos Relative Disadvantagerdquo Social Forces70(1)65ndash85 httpdxdoiorg101093sf70165

Marlow Susan and Maura McAdam 2010 ldquoUnited States of Americardquo Pp 216ndash230 inInternational Research Handbook on Successful Women Entrepreneurs edited by Sandra LFielden and Marilyn J Davidson Cheltenham UK Edward Elgar

Office of the Comptroller of the Currency 2014 ldquoSurvey of Credit Underwriting Prac-ticesrdquo httpwwwoccgovpublicationspublications-by-typesurvey-credit-underwriting-practices-reportpub-survey-cred-under-2013pdf

Orser Barbara J Allan L Riding and Kathryn Manley 2006 ldquoWomen Entrepreneurs andFinancial Capitalrdquo Entrepreneurship Theory amp Practice 30 643ndash665 httpdxdoiorg101111j1540-6520200600140x

Petersen Mitchell A and Raghuram G Rajan 2000 ldquoDoes Distance Still Matter TheInformation Revolution in Small Business Lendingrdquo Journal of Finance 572533ndash2570httpdxdoiorg101111j1540-6520200600140x

Phelps Edmund S 1972 ldquoThe Statistical Theory of Racism and Sexismrdquo American EconomicReview 62 659ndash661

Podolny Joel 1994 ldquoMarket Uncertainty and the Character of Economic Exchangerdquo Admin-istrative Science Quarterly 39458ndash483 httpdxdoiorg1023072393299

Podolny Joel 2001 ldquoNetworks as the Pipes and Prisms of the Marketrdquo American Journal ofSociology 107(1)33ndash60 httpdxdoiorg101086323038

Podolny Joel 2010 Status Signals A Sociological Study of Market Competition PrincetonPrinceton University Press httpdxdoiorg1015159781400837878

Pope Devin and Justin Snyder 2011 ldquoWhatrsquos in a Picture Evidence of Discrimination fromProspercomrdquo Journal of Human Resources 46(1) 53ndash92 httpdxdoiorg1015159781400837878

Prentice Deborah and Erica Carranza 2002 ldquoWhat Women and Men Should Be ShouldnrsquotBe Are Allowed To Be And Donrsquot Have To Be The Contents Of Prescriptive GenderStereotypesrdquo Psychology of Women Quarterly 26(4)269ndash281 httpdxdoiorg1011111471-6402t01-1-00066

Ridgeway Cecilia L 1997 ldquoInteraction and the Conservation of Gender Inequality Consid-ering Employmentrdquo American Sociological Review 62(2)218ndash235 httpdxdoiorg1023072657301

Ridgeway Cecilia L 2011 Framed by Gender How Gender Inequality Persists in theModern World Oxford University Press httpdxdoiorg101093acprofoso97801997557760010001

Ridgeway Cecilia L and Shelley J Correll 2004 ldquoUnpacking the Gender System A Theoreti-cal Perspective on Gender Beliefs and Social Relationsrdquo Gender and Society 18(4) 510ndash531httpdxdoiorg101093acprofoso97801997557760010001

Ruef Martin Howard E Aldrich and Nancy M Carter 2003 ldquoThe Structure of FoundingTeams Homophily Strong Ties and Isolation Among US Entrepreneursrdquo AmericanSociological Review 68(2)195ndash222 httpdxdoiorg1023071519766

Renzulli Linda A Howard Aldrich and James Moody 2000 ldquoFamily Matters GenderNetworks and Entrepreneurial Outcomesrdquo Social Forces 79(2)523ndash546 httpdxdoiorg101093sf792523

Reskin Barbara 2001 ldquoDiscrimination and Its Remediesrdquo Pp 567ndash600 in Sourcebook on LaborMarket Research Evolving Structures and Processes edited by Ivar Berg and Arne KallebergNew York NY Plenum httpdxdoiorg101007978-1-4615-1225-7_23



Reskin Barbara and Debra McBrier 2000 ldquoWhy Not Ascription Organizationsrsquo Em-ployment of Male and Female Managersrdquo American Sociological Review 65210ndash33httpdxdoiorg1023072657438

Shane Scott 2011 ldquoThe Great Recessionrsquos Effect on Entrepreneurshiprdquo Retrievedfrom Federal Reserve Bank of Cleveland website httpswwwclevelandfedorgenNewsroom20and20EventsPublicationsEconomic20Commentary2011ec2020110420the20great20recessions20effect20on20entrepreneurshipaspx

Shane Scott Sharon Dolmans Joseph Jankowski Isabelle Reymen and Georges Romme2012 ldquoWhich Inventors do Technology Licensing Officers Favor for Start-Upsrdquo Frontiersof Entrepreneurship Research 32 (18) Article 1

Sharkey Amanda 2014 ldquoCategories and Organizational Status The Role of IndustryStatus in the Response to Organizational Deviancerdquo American Journal of Sociology 119(5)1380ndash1433 httpdxdoiorg101086675385

Theacutebaud Sarah 2010 ldquoGender and Entrepreneurship as a Career Choice Do Self-Assessments of Ability Matterrdquo Social Psychology Quarterly 73(2) 2804 httpdxdoiorg1011770190272510377882

Theacutebaud Sarah 2015 ldquoStatus Beliefs and the Spirit of Capitalism Accounting for GenderBiases in Entrepreneurship and Innovationrdquo Social Forces 9461ndash86 httpdxdoiorg101093sfsov042

Thompson Derek 2010 ldquoItrsquos not Just a Recession Itrsquos a Mancessionrdquo The Atlantic July 9

Tinkler Justine E Kjersten Bunker Whittington Manwai C Ku and Andrea Davies 2015ldquoUncertainty and the Effects of Gender Technical Background and Social Capital onVenture Capital Evaluationsrdquo Social Science Research 511ndash16 httpdxdoiorg101016jssresearch201412008

Tversky Amos and Daniel Kahneman 1974 ldquoJudgment under Uncertainty Heuristics andBiasesrdquo Science 185(4157)1124ndash1131 httpdxdoiorg101126science18541571124

World Bank 2007 ldquoFinance for All Policies and Pitfalls in Expanding Accessrdquo World BankPolicy Report Washington DC World Bank

Wu Zhenyu and Jess H Chua 2012 ldquoSecond-Order Gender Effects The Case of USSmall Business Borrowing Costrdquo Entrepreneurship Theory and Practice 36 443ndash463 httpdxdoiorg101111j1540-6520201200503x

Zelizer Viviana 2010 Economic Lives How Culture Shapes the Economy Princeton NJPrinceton University Press httpdxdoiorg1015159781400836253

Acknowledgements This research was supported by a National Science Foundation Fel-lowship and the Center for the Study of Social Organization at Princeton UniversityWe thank Paul DiMaggio Heather Haveman Michael Jensen Johan Chu ElizabethPontikes Chris Yenkey seminar participants at Cornell the Kauffman FoundationPrinceton and the University of Michigan and Deputy Editor Olav Sorenson forhelpful comments and feedback

Sarah Theacutebaud Department of Sociology University of California Santa BarbaraE-mail sthebaudsocucsbedu

Amanda J Sharkey Booth School of Business University of ChicagoE-mail sharkeychicagoboothedu



of job loss the popular press dubbed the economic downturn a ldquomancessionrdquo(Thompson 2009) A closer look however reveals a more complicated picture Forinstance although men were more likely to lose their jobs women who were laidoff experienced greater earnings losses than their men counterparts did when theyreturned to work (Cha 2014)

In contrast to research on gender disparities in the impact of the Great Recessionon workers in the wage labor market however possible gender differences inthe effects of the downturn on entrepreneurs have to the best of our knowledgenot been studied To address this gap we analyze gender differences in how therecession affected entrepreneurs focusing specifically on their ability to obtainfinancing Although the recessionary environment likely made it more difficult forall loan applicants to obtain funding did the downturn also affect the size of thenet gender gap in lending to entrepreneurs

An investigation of the possibility that the recession differentially affected menrsquosand womenrsquos access to credit is important for two key reasons First existingstudies offer mixed evidence as to whether investors and other individuals whoprovide capital tend to exhibit a bias against women entrepreneurs On the onehand several studies that rely on survey-based data find that women-led firmstend to receive less funding even after factors such as human capital industryand credit histories are taken into account (Carter and Shaw 2006 CavalluzzoCavalluzzo and Wolken 2002 Coleman and Robb 2009) Recent experimentalstudies have also found that potential investors are systematically likely to viewwomen entrepreneurs as less competent credible and investment-worthy thanequivalently qualified men counterparts (Bigelow et al 2014 Brooks et al 2014Theacutebaud 2015) Yet in contrast a sizable number of studies have found no evidenceof a net funding disadvantage for women-led firms (see eg Asiedu Freemanand Nti-Addae 2012 Blanchard Zhao and Yinger 2008 Blanchflower Levine andZimmerman 2003 Carter et al 2007 Cavalluzo and Wolken 2005 Orser Ridingand Manley 2006) One interpretation of these mixed results is that gender-baseddisadvantage in entrepreneurial investment is not a constant rather it may varydepending upon the conditions under which lenders and investors make decisionsThus an examination of how gender disparities in the ability to acquire fundingvary under differing macroeconomic conditions offers an opportunity to refineour theoretical understanding of when and where gender bias in entrepreneurshipis likely to emerge Doing so may help shed light on the puzzle of when andhow gender biases persist even in the face of increasingly meritocratic values andprocedures (Castilla 2008 Castilla and Benard 2010)

Second an examination of how the recession might (or might not) have differ-entially affected the ability of men-led vs women-led firms to obtain financingprovides an opportunity to empirically evaluate the discriminatory mechanismsthat may contribute to gender disparities in entrepreneurship In particular wedraw on statistical and status-based theories of discrimination to identify multiplereasons why investors may be more likely to rely on gender stereotypes whenmaking decisions during recessionary circumstances Furthermore we investi-gate whether investors may have been more likely to apply different standards of






































Data and Methods


Sample








Dependent Variable








Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References






































































































































Data and Methods


Sample








Dependent Variable








Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References






























































































































Data and Methods


Sample








Dependent Variable








Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References
























































































































Data and Methods


Sample








Dependent Variable








Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References


















































































































Data and Methods


Sample








Dependent Variable








Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References











































































































Data and Methods


Sample








Dependent Variable








Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References





































































































Data and Methods


Sample








Dependent Variable








Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References







































































































Dependent Variable








Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References







































































































Control Variables







0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References





































































































0

10

20

30

40

50

60

70

2007 2008 2009 2010 2011 2012

Per

cen

t of

Len

der

s R

epor

tin

g T

igh

ten

ed S

tan

dar

ds













045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References










































































































045(050) 229 049(050) 905 No



(1=yes)014(035) 229 021(041) 904 Yes











046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References







































































































046(050) 228 048(050) 902 No





Estimation






Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References




































































































Results







1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References




































































































1 2 3





Credit Score=536-670 015(016)





(067) (067) (056)








0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References



































































































0

20

40

60

80

100

07 08 09 10 11 07 08 09 10 11 07 08 09 10 11


Male-led Female-led















1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References










































































































1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References




































































































1 2 3 4











(182) (197) (205) (177)








1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References




































































































1 2 3


















Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References






































































































Robustness Checks






(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References




































































































(1) (2)Female 001 013

(021) (022)Year

2008 013dagger 013dagger(006) (006)

2009 002 019dagger(012) (007)

2010 017 011(007) (012)

2011 016dagger 016dagger(008) (008)

Femaletimes2008 015 014(015) (015)

Femaletimes2009 041 023(06) (016)


Femaletimes2011 015 012(017) (017)















(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References



































































































(1) (2)(091)

Credit Score=493-535 006 009(008) (008)

Credit Score=423-492 003 007(008) (007)

Credit Score=376-422 009 013(01) (01)
































Notes











References




















































































































Notes











References













































































































Notes











References








































































































Notes











References



































































































Notes











References




















































































































































































































































































































































































Documents

UnequalHardTimes: TheInﬂuenceoftheGreatRecession ... · qualiﬁcations of the founder (Bigelow et al. 2014; Brooks et al. 2014; Shane et al. 2012; Thébaud 2015). However, a number