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The (Un)intended Consequences of Lowering Entry Barriers: Evidence from an Entry
Deregulation Reform in Portugal
Francesco Castellaneta
SKEMA Business School
Sophia Antipolis, France
Raffaele Conti
Catolica Lisbon School of Business and Economics
Lisbon, Portugal
Olenka Kacperczyk
London Business School
26 Sussex Place, NW1 6SA, London
June 2018
*Please do not circulate
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The (Un)intended Consequences of Lowering Entry Barriers: Evidence from an Entry
Deregulation Reform in Portugal
Abstract
Previous research has focused on the impact of institutional changes on transition to
entrepreneurship, but the effects of such policies on minority workers remain less well studied.
Focusing on minority workers (i.e., women), we propose that policies that reduce barriers to entry
are a double-edge sword. On one hand, they foster minority entrepreneurship (i.e., female-
founded ventures), because they especially benefit those who face stronger obstacles when
attempting to launch a new venture. On the other hand, they increase the pay gap between minority
and non-minority workers who stay in paid employment, as minority workers lost the support of
their colleagues leaving the company. Using employer–employee matched data from Portugal
between 1996 and 2009 and an entry deregulation reform enacted in Portugal during the same
time period, we find support for our claims. Following the enactment of entry deregulation
policies, female workers were more likely than male workers to enter entrepreneurship. But
amongst workers who remained in paid employment, these policies led to an increase in the
gender pay gap, with women experiencing a greater decline in wages than men. This negative
effect of entrepreneurial mobility on the incumbent minorities was amplified in industries with
greater ex-ante pay discrimination and for higher-skilled workers, consistent with the notion that
pay differential increased due to greater discrimination and/or productivity loss, following
entrepreneurial mobility of minority employees. More broadly, the study contributes to the
understanding of the downsides of policies that promote entrepreneurship.
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INTRODUCTION
Researchers who study entrepreneurship have paid an increasing attention to the importance of
institutions as a key force to promote entrepreneurship, or the launching of a new venture (Easley, 2016;
Eberhart, Easley and Eisenhardt, 2017; Kouriloff, 2000; Sine and Robert, 2003; Thebaud, 2015). A
central tenet in this literature is that institutional changes, stemming primarily from regulations of barriers
to entry, are effective in encouraging entrepreneurship (Easley, 2016; Eberhart et al. 2017), especially
when barriers to entry are high (Easley, 2016; Eberhart, Easley and Eisenhardt, 2017; Thebaud, 2015). It
follows that individuals who are at strongest disadvantage will benefit most from institutional changes
that promote entrepreneurship. Minority workers, including women and non-Whites, face in fact
substantial barriers at the pivotal stage of the entrepreneurial entry – due in part to cultural factors and
negative stereotypes about gender or race responsible for hindering any attempts to launch and operate a
new business (Budig 2002; Keister 2000; Thébaud 2015; Thébaud and Sharkey 2016; Waldinger et al.
1990; Thebaud, 2010; Blanchflower, Levine, and Zimmerman, 2003; Fairlie and Robb, 2007; Younkin
and Kuppuswamy, 2017). However, surprisingly little research examined the impact of regulations
designed to reduce barriers to entry on minorities; thus, whether such institutional changes benefit or hurt
minority workers remains an open question.
To examine the impact of institutional changes on entrepreneurial entry among minorities, we
propose that regulations that promote entry will have intended and unintended consequences on
minorities, creating a paradox worth exploring. On the one hand, by lowering barriers to entry, these
institutional changes will disproportionately increase the rates of entrepreneurship amongst those who are
most disadvantaged – or minorities. Entrepreneurship research has widely documented that initial barriers
to entrepreneurship are stronger amongst historically disadvantaged individuals, such as women or non-
Whites (Blanchflower, Levine, and Zimmerman, 2003; Cavalluzzo et al. 2002; Wu and Chua 2012;
Fairlie 1999; Hout and Rosen 2000). In particular, minorities face problems in securing resources for
starting a business. So a reduction in the amount of resources needed to found a firm will amplify the
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ability (and possibly also the willingness) to pursue entrepreneurship especially amongst individuals that
otherwise would not be able to access entrepreneurial opportunities. Hence, following the enactment of
policies reducing barriers to entry, minorities will enter entrepreneurship at higher rates than non-
minorities. On the other hand, such policies might unintendedly generate downsides for minorities who
stay in paid employment. As the odds of certain minority workers leaving for entrepreneurship increase,
this sudden attrition will lead to a decline in pay amongst minorities at incumbent firms, for at least two
reasons. First, consistent with the theories of tokenism, which predict that low-proportion demographic
groups face greater employer discrimination (Turco, 2012; Ely, 1994; Kanter, 1977; Merluzzi and
Sterling, 2016), the departure of minority peers into entrepreneurship will amplify employer bias against
traditionally disadvantaged groups, in part because numerical minorities have less bargaining power vis-
à-vis an employer. Second, consistent with the homophily principle (Festinger, 1954; Tajfel and Turner,
1986) and research on knowledge spillovers amongst socially proximate workers (Agarwal, Kapur,
McHale, 2008; Kerr, 2008, Kacperczyk, 2013) – which documents knowledge diffusion and learning
benefits amongst socially-proximate groups, in general, and minorities, in particular (e.g., Agarwal,
Kapur, McHale, 2008; Kerr, 2008) – productivity of minority workers might decline as the share of
minority workers decreases in the firm due to entrepreneurial mobility. In sum, we predict that policies
that reduce barriers to entry may be a double-edged sword, creating advantageous conditions for minority
workers to enter entrepreneurship as well as disadvantageous conditions for minority workers to advance
within incumbent firms.
To further explore the mechanisms we posit, we examine the heterogeneous effects of the
entrepreneurship-promoting policies on employees staying in paid employment. First, to the extent that
the predicted negative effects reflect an increase in discrimination of minority workers in incumbent
firms, we expect our results to be amplified in industries with higher discrimination ex-ante. A sudden
decline in the share of minority workers due to entrepreneurial mobility will encourage employers to rely
more heavily on negative stereotypes when such stereotypes are already more prevalent and more
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common prior to the policy enactment. Second, if our effects reflect productivity loss amongst minority
workers at incumbent firms, the relationships we hypothesize will be amplified for higher-skilled
workers, based on the premise that higher-skilled workers tend to benefit more from knowledge spillovers
(e.g., Gambardella and Giarratana, 2010). If productivity gap between minorities and non-minorities
increases following the regulation promoting entrepreneurial entry, a decline in pay will be amplified
amongst higher-skilled workers.
The relationship between policies that promote entrepreneurship and pay differentials across
demographic characteristics is difficult to address empirically because institutional changes enacted to
promote entrepreneurship might be endogenous with respect to racial and gender pay gaps. In particular,
finding a negative relationship between startup entry and pay differences along race or gender, may be
spurious if the relationship in question is driven by unobserved regional or institutional characteristics,
which can simultaneously influence the incentives to found new ventures as well as the incentives to
reduce minority pay gap. For example, regions with higher GDP per capita or liberal values might
promote entrepreneurship but also affect such pay inequality. By the same token, the relationship between
policies that promote entrepreneurship and the alleged pay gap can be subject to reverse-causality. A
more equitable distribution of pay along race and gender attributes might provide incentives for workers
to lobby for entrepreneurship-friendly policies, if higher income for some groups ease liquidity
constraints and increases the willingness to pursue entrepreneurship (Sorenson and Stuart, 2011). In short,
though empirically challenging, leveraging a research design that provides a clean causal estimate is
central to our understanding how policies design to promote entrepreneurship, by reducing barriers to
entry, affect pay differentials across minority and non-minority workers.
We address this empirical challenge by exploiting a quasi-natural experiment provided by the
staggered enactment of an important entry deregulation reform (he “On the Spot Firm” program) enacted
in Portugal from 2005 to2009. We take advantage of this natural experiment for three reasons. First, the
Portuguese reform reduced the barriers to startup entry, by decreasing bureaucratic and financial burden
on those starting new ventures. Second, to the extent that the reform increased the rates of minority-
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founded ventures, these policies increased entrepreneurial mobility amongst some minority employees,
affecting other minorities who stayed in paid employment. Because of their exogenous and staggered
nature, the effects of these institutional changes can be modeled using a difference-in-differences
methodology―with the “treatment” group composed of counties that are subject to these reforms, and the
“control” group composed of counties that are not. Finally, in the Portuguese context, it is possible to
study changes in gender pay differentials, an important facet of inequality that has have received
significant attention from scholars (Castilla, 2008, 2011; Cohen & Huffman, 2007; Elvira & Graham,
2002; Fernandez & Fernandez-Mateo, 2004; Reskin, 2000).
THEORY
Past Research
Scholars have increasingly linked entrepreneurial entry to changes in the institutional environment,
primarily stemming from regulations designed to lower barriers to entry (e.g., Djankov et al. 2002,
Klapper et al. 2006, Sine and David 2010; Kaplan et al., 2011). The core argument in this line of work is
that, when obstacles to entrepreneurship are removed from the institutional environment, entry rates as
well as growth orientation of new ventures increase (Kaplan, Piedra, and Seira, 2011); conversely, when
barriers to entry are stricter or better enforced, entry rates fall more disproportionally (Prantl, 2012). For
example, focusing on macro-level patterns of entrepreneurship in Mexico, Kaplan, Piedra, and Seira
(2011) find that institutional changes that reduce bureaucratic processes in entrepreneurial foundings,
increase the rate of new ventures in targeted industries. Similar patterns have been detected at the
individual level, with studies showing a robust empirical link between reforms designed to reduce barriers
to entry and an individual’s willingness and ability to become an entrepreneur, as well as the subsequent
performance of the new entrant (e.g., Eberhart et al. 2017, Eesley 2016, Hiatt et al. 2009). For example,
focusing on institutional changes in China and the alumni of a Chinese university, Eesley (2016) finds
that lowering barriers to growth encourages entrepreneurship by individuals endowed with high human
capital. In short, institutional changes that facilitate access to opportunities or resources to found new
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ventures act as an important force encouraging individual entry into entrepreneurship.
However, with few exceptions (Conti, Kacperczyk and Velntini, 2018), extant research has
generally neglected that changes in the institutional environment might have heterogeneous effect on the
entry rate of minority vs. non-minority employees. Indeed, significant penalties accrue to members of
disadvantaged groups, including women and racial minorities, in the context of entrepreneurship, making
their attempts to launch new ventures less successful. Key entrepreneurial outcomes show stark disparities
along racial and gender lines, including launching a new venture (Guzman and Kacperczyk 2016; Kanze
et al. Forthcoming; Thébaud 2010, 2015); assuming a leadership role in an entrepreneurial firm (Yang
and Aldrich 2014); and even successfully running a new organization (Ruef et al. 2003; Thébaud and
Sharkey 2016; Yang and Triana 2017). Yet, despite the disproportionate disadvantage that minority
individuals face when attempting to enter entrepreneurship, previous studies have not examined the
impact of regulations that lower entry barriers on minority individuals. As an attempt to fill this gap, we
propose that such institutional changes will have intended and unintended consequences for minorities:
they will foster advantageous conditions to enter entrepreneurship while also leading to disadvantaged
conditions for career advancement in paid employment.
The Intended Consequences of Institutional Change
Although fostering entrepreneurship, or the act of launching a new venture, has been linked to the
creation of jobs and economic growth (Haltiwanger et al., 2012; Blanchflower, 2000; Steinmetz and
Wright, 1989), there is accumulated evidence that new ventures might be a source of inequality, putting
minorities at significant disadvantage. A frequent finding in the literature is that members of minority
groups face significant obstacles when launching or running a new venture (e.g., Thebaud, 2010; 2015;
Guzman and Kacperczyk, 2017; Yang and Aldrich, 2014; Ruef, Aldrich, and Carter, 2003). In the case of
racial minorities, for example, mounting evidence shows that Black entrepreneurs are less likely to enter
entrepreneurship and less likely to outperform, conditional on entry. Empirically, scholars have found that
Black-owned startups have lower revenues and profits, fewer employees, and higher closure rates (e.g.,
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Fairlie 1999; Hout and Rosen 2000; Keister and Moller 2000; Kim et al. 2006; Younkin Kuppuswamy
2017, 2018). These higher rates of failure might reflect the fact that Black entrepreneurs have lower odds
of receiving credit from suppliers (Freeland and Keister, 2016), banks (Blanchflower, Levine, and
Zimmerman, 2003), or venture capitalists (Fairlie and Robb, 2007), and these disparities persist even
when differences in creditworthiness or other observables, including human capital, industry, and credit
histories, are controlled for (Blanchflower, Levine, and Zimmerman, 2003; Cavalluzzo et al. 2002; Wu
and Chua 2012; Fairlie 1999; Hout and Rosen 2000), or in experimental conditions, wherein race and
gender are randomly assigned (Younkin and Kuppuswamy, 2017; 2018). Similar tendencies have been
documented in the case of female entrepreneurship. A long tradition of research has documented a stark
gender gap in entrepreneurship, with women being both underrepresented in entrepreneurship and more
likely to underperform upon entry than men (Ruef, Aldrich, and Carter, 2003; Kim, Aldrich, and Keister,
2006; Yang and Aldrich, 2014).
Two kinds of obstacles have been thought to prevent racial minorities or women from securing
the resources needed for entering and succeeding in entrepreneurship. First, the “pipeline problem,” or
disparities in human and social capital, experience levels, family background, and initial assets put
minorities at systematic disadvantage in the entrepreneurial setting (Fairlie 1999; Hout and Rosen 2000;
Keister and Moller 2000; Kim et al. 2006). For example, women and Blacks have been found to be
disadvantaged at the pivotal stage of entrepreneurial entry because they are less likely or able to possess
the capital or skills required to start a new venture (Buttner and Rosen, 1989; Bigelow et al., 2014;
Thébaud, 2015b; Thébaud and Sharkey, 2016) . Second, minorities are subject to discrimination by key
audiences, including consumers (Coyne et al. 2010; Younkin Kuppuswamy 2017, 2018), employees
(Kacperczyk et al. 2018), and investors (Blanchflower et al. 2003; Heilman and Chen 2003; Thébaud
2010; Younkin and Kuppuswamy 2017). In evaluating individuals’ competencies as entrepreneurs,
resource-holders try to reduce the uncertainty inherent in new ventures by applying evaluative standards
that are infused with persistent stereotypes and deep cultural biases against minorities (Huang and Pearce
2015; Kanze et al. Forthcoming). And because minorities are generally perceived as lower-status (Correll
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and Ridgeway 2003; Ridgeway 2011), evaluators will consider them as less qualified entrepreneurs
(Bigelow et al. 2014; Brooks et al. 2014; Buttner and Rosen 1989; Thébaud 2015). Resource holders can
similarly hold negative beliefs and stereotypes about minorities’ competences, making inferences about
how these competences can affect an individual’s ability to accumulate resources to launch new startups
and/or the “fitness” in entrepreneurial domains (Thebaud, 2010; Blanchflower, Levine, and Zimmerman,
2003; Fairlie and Robb, 2007; Younkin and Kuppuswamy, 2017). For example, women are seen as less
credible and less competent entrepreneurs in these settings (Buttner and Rosen, 1988; Thébaud, 2015b)
because resource holders use gender to infer the underlying quality in the absence of alternate evidence
(e.g., Correll, Benard, and Paik, 2007; Castilla, 2008; Benard and Correll, 2010; Castilla and Benard,
2010; Turco, 2010; Ridgeway, 2011).
Given that minorities tend to encounter substantial difficulties when securing the resources to
enter entrepreneurship, they may disproportionately benefit from institutional changes determining a
reduction in entry barriers, as this implies a reduction in the minimum amount of resources needed for
starting a new business. Hence, we expect the following:
H1: Following an institutional change that reduces barriers to entry, the rates of entrepreneurial
foundings will increase more for minorities than for non-minorities.
The Unintended Consequences of Institutional Change
But regulations that reduce barriers to entry might also have an effect on minorities who keep attachment
to paid employment. Specifically, as members of minority groups become more motivated and more
willing to transition into entrepreneurship, minorities who stay in paid employment might experience
significant career downsides, following entrepreneurial mobility of minority coworkers.
As the odds of minorities leaving paid employment in pursuit of entrepreneurship increase, pay
gap for disadvantaged workers at incumbent firms will subsequently increase for at least two reasons.
First, theories of tokenism predict that employers are more likely to discriminate against disadvantaged
groups when minorities become more underrepresented and when the numerical proportion of these
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groups declines within the firm (Turco, 2012; Ely, 1994; Kanter, 1977;). Kanter’s seminal argument
implies that minorities’ power, status, and opportunities in the firm will decrease as these minority
members become more underrepresented in the firm. Kanter initially argued that the relative number of
minorities (i.e., women) in the firm was highly consequential for the success of those minorities
individually, as well as a collective (1977: 395):
“(…) numbers, especially relative numbers, can strongly affect a person’s fate in an organization.
This is a system rather than an individual construct – located not in a person but in how many
people, like that person in significant ways, are present […] a strong case can be made for
number balancing as a worthwhile goal in itself, because, inside the organization, relative
numbers can play a large part in further outcomes, from work effectiveness and promotion
prospects to psychic distress.
Subsequent scholarship has associated greater minority underrepresentation at firm, industry, or
population level with limited career advancement outcomes for minority members in a variety of areas,
including science (Nosek et al., 2009), law (e.g., Fuchs-Epstein, 1983), or the military (Pazy and Oron,
2001). Common to these studies is the notion that, when the demographic composition in the firm shifts
towards the members of the majority group (i.e., males), minority members (i.e., females) will more likely
face discrimination, dismissal or exclusion (Konrad et al. 2008) because actions and decisions that
support other women are likely to be constrained (Duguid et al. 2012). Indeed, when the representation of
minority members falls, minorities are less motivated and less able to influence a firm’s culture or have an
impact on group decisions.
The negative consequences of a declining proportion of minority workers are likely to carry over
to the entrepreneurship context. To the extent that a disproportionate attrition occurs amongst minorities
due to lower barriers to entrepreneurship, the remaining workers will be less represented and therefore
more likely to witness a decline in attractive advancement options, as reflected in lower pay.
Beyond this, policies that promote entry into entrepreneurship may increase the pay gap between
minority and non-minority workers by triggering a productivity decline amongst minorities at incumbent
firms. Several strands of research emphasize the knowledge-sharing benefits of demographic and ethnic
homophily. It has been long established that demographically proximate individuals derive significant
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benefits from associating with others like themselves (Lazarsfeld and Merton 1954; McPherson, Smith-
Lovin and Cook 2001). This idea that “birds of a feather flock together” is rooted in the notion that, given
a choice of with whom to associate, individuals prefer an interaction with those who resemble them
(McPherson and Smith-Lovin, 1987; Kossinets and Watts, 2009; Lazarsfeld and Merton 1954; Byrne et
al. 1966, Judge and Ferris 1993, Tsui and O'reilly 1989). Within an organizational context, demographic
similarity triggers significant benefits, including trust, excitement, and perceived belonging (e.g.,
Verbrugge, 1977; Lazarsfeld and Merton, 1954, Ingram and Morris, 2007) as well as self-esteem and self-
identities from perceived group membership (Tsui et al., 1992; Hogg and Abrams 1988)– all of which
increase the attraction to a given organization and facilitate knowledge sharing post-hire. These benefits
of homophily and in-group membership are particularly likely to accrue to minority workers and social
proximity is particularly salient for traditionally underrepresented individuals. For example, minority job
candidates are attracted to organizations with a higher proportion of similar minorities, anticipating more
attractive advancement opportunities and better fit post-hire (e.g., Rivera, 2012).
Other research has similarly found that knowledge spillovers tend to be stronger amongst
minority groups, presumably because social cohesion facilitates social learning and knowledge transfer.
For example, shared ethnicity of inventors increases the probability of knowledge flows amongst
minorities, as such social proximity facilitates the diffusion of knowledge, fostering learning across the
focal individuals and communities (Agrawal, Kapur and McHale, 2008). Similarly, within knowledge
intensive industries, knowledge diffuses more easily through ethnic networks (Kerr, 2008). These studies
imply that minority workers will find it especially beneficial to work with other individuals of the same
minority, as colocation will foster the knowledge exchange. By contrast, a sudden increase in departures
from paid employment to entrepreneurship amongst minority workers will likely decrease the knowledge
production function amongst the remaining minority workers, or those staying in paid employment. As
their productivity declines, pay gap between minority and non-minority employees will widen.
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In sum, policies that reduce barriers to entry may be a double-edged sword, creating
advantageous conditions for minority workers who enter entrepreneurship as well as disadvantageous
conditions for minority workers who stay in paid employment. Hence, we expect:
H2: Following an institutional change that reduces barriers to entry, minority workers will face a
decline in pay relative to non-minorities.
Effect on the Minority Pay Gap: Mechanisms
Our argument suggests that policies that reduce barriers to entry will benefit minorities by fostering
conditions conducive to entrepreneurial entry, on one hand, while undermining career outcomes amongst
minorities who stay in paid employment, on the other hand. In what follows below, we probe the
mechanisms responsible for these effects by examining the cross-sectional heterogeneity of our claims.
Specifically, we consider whether our treatment effect is moderated by certain industry, and individual-
level characteristics.
As the first test of our claims, we consider whether our effects might be amplified in industries
subject to greater discrimination levels ex-ante, or prior to new-venture entry. With respect to
discrimination, we therefore expect our effects to be stronger in industries with higher minority pay gap
before new venture foundings. Because these industries were more likely to engage in discriminatory
behavior prior to startup entry, employers in these industries will be additionally likely to undermine the
advancement of minority workers ex post. We thus expect our main effect of higher wage differentials to
be amplified in industries with greater discrimination prior to the enactment of the institutional change.
Hence,
H3: Following an institutional change that reduces barriers to entry, a decline in pay amongst
minority workers will be amplified in industries with higher discrimination levels.
Our theory further implies that a pay decline amongst minorities will vary across the level of
individual skills, undermining only certain minority employees. Specifically, previous research has
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established that localized knowledge spillovers are particularly beneficial for high-skilled workers,
because high-skilled individuals have the absorptive capacity that allows them to better integrate and use
additional knowledge (Cohen and Levinthal, 1990; Gambardella and Giarratana, 2010; Zahra and George,
2002). Because greater knowledge production is only beneficial for those who have the skills to manage
its complexity and the absence of such skills reduces any gains from localized knowledge spillovers
(Fleming and Sorenson, 2000), it follows that any potential decline in knowledge spillovers will
undermine the productivity of higher-skilled minority groups the most. Hence, to the extent that
productivity loss amongst minority workers drives our effect, a decline in pay will be higher amongst
higher-skilled minority workers. Hence, we expect:
H4: Following an institutional change that reduces barriers to entry, a decline in pay amongst
minority workers will be amplified amongst higher-skilled workers.
EMPIRICAL SETTING AND DATA
We choose Portugal as an empirical context of our study, as it has some ideal characteristics for testing
our theoretical predictions about the effect of (an exogenous increase in) entry deregulation on entry and
on the female-male wage gap. First, we can rely on an extraordinary rich database, the Quadro de
Pessoal (QDP), which is a longitudinal data set with linked information of all Portuguese employees and
employers in Portugal. Indeed, since 1985 the Portuguese Ministry of Labor and Social Security has
collected information about all workers and firms based in Portugal. Such information refers to the
situation observed in the month where the survey is collected (March until 1993 and October from 1994
onwards) and covers each firm, each of its plants and each of its workers. Information on workers
includes gender, age, education level (schooling), type of contract of employment and earnings split into
different components (essentially base wage and bonuses). Firm level data includes the location, industry,
total number of workers, sale volumes and number of establishments.
The second reason why Portugal represents an ideal empirical context is because in 2005 the
Portuguese Government has established a particularly successful entry deregulation program, which was
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enacted in different moments in time across different Portuguese regions (“concelhos” or municipalities)
determining an entry deregulation in such regions. Specifically, we refer to the enactment of the “On the
Spot Firm” (Empresa na Hora) program, established by the Portuguese Ministry of Justice, together with
the Ministry of Finance, Economy and Labour and Social Security, with the objective of alleviating the
bureaucratic burden for starting a new firm. Such a program had a quite significant impact on the
foundings of new companies (Branstetter et al. 2014; Fernandes et al. 2015).
Third, Portugal is a countries where, despite female employment has been increasing steadily in
Portugal over the last 35 years, there are still evidences of significant gender inequality in wages (Cabral,
Vieira, Cardoso and Portela, 2005). As a matter of fact, even in the time period we consider (2000-2009)
female labor force represents a numerical minority, accounting for only 42 per cent of the overall labor
foce.
We restricted our analyses to the 2000-2009 time period, mainly for practical reasons. First of all,
before 2000 some variables we use in our analyses (e.g., employee contract type) are not fully available.
Second, after 2009, a new online procedure for the registration of new firms was implemented, such that
after this year all municipalities – even those where the “On the Spot Firm” program was already not
enacted – benefitted from a simplified process for the creation of new companies. This naturally nullifies
any across-regions variation in exogenous entry after 2009.
VARIABLE DESCRIPTION
Independent variable
Entry deregulation reform. We look for entry deregulation in a region – that is, an increase not
correlated with any other characteristic of the region – in order to address the endogeneity problem that
otherwise would confound our estimates. First, regional-level characteristics might lead to spurious
correlation between firm entry in a region and female-male wage gap in the same region. For example, it
could be that quality of firms in a region ―which is difficult to observe―is negatively correlated to both
entry (as high-quality incumbents would discourage new entrants) and discrimination towards female
15
employees: this would lead to overestimate the real effect of entry on the female-male wage gap.
Moreover, a potential correlation between entry and female worker discrimination could be subject to
reverse causation. It could be that regions where female employees are discriminated with respect to their
male counterparts experience greater entry rate, as discriminated employees tend to leave their employer
and found their own companies. To rule out these and other potential alternative explanations, it is
necessary to leverage a research design that provides exogenous shifts in entry―such exogenous shifts
would allow estimating the causal effect of entry on the gap in wage between female and male employees.
The entry deregulation is provided by the enactment of the “On the Spot Firm” initiative, which
established one-stop shops where an entrepreneur could register a company in less than an hour. In July
2005 the law that creates the “On the Spot Firm” program was issued and, in the same month, pilot one-
stop shops were launched in the municipalities of Coimbra, Aveiro, Barreiro and Mota. The program
expanded over time and, by the end of 2009, there were 164 shops dispersed across 308 municipalities
throughout the country. Notably, the staggered enactment of the “On the Spot Firm” program across
municipalities did not follow any specific criteria (e.g., the number of inhabitants in a municipality or the
inhabitants’ GDP per capita), such that, following previous studies (Branstetter et al., 2014), it might be
considered as a quasi-natural experiment. In other words, the timing of enactment of the “On the Spot
Firm” across municipalities might be seen as exogenous with respect to the municipality economic and
social characteristics.
Appendix Table A1 provides a list of all municipalities where one-stop shops were opened
between 2005 and 2009. Whereas the registration of a new company can be done in any of the one-stop
shops located across Portugal – regardless the location of the company’s headquarter – the fraction of
firms registered outside their municipality is trivially small (Branstetter et al. 2010).
It is largely accepted that the “On the Spot Firm” program enactment had a strongly positive
causal impact on entrepreneurship. Prior to 2005, to start a new business in Portugal it took between 54
and 78 days. An entrepreneur needed to visit several offices and fill out more than 20 forms and
documents, with an estimated cost of about 2000 euros (more than 13 per cent of the Portuguese annual
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GDP per capita). As a result, Portugal ranked very low (133 out of 155 countries) in the Doing Business
Ranking of the World Bank (World Bank, 2006). To address these issues, the Government decided to
enact the “On the Spot Firm” program in order to bring all the agencies supervising the creation of new
firms in a single office, so that entrepreneurs do not need to visit several public offices to get all the
documents required to start a new business. As a result, the company identification card, corporate
taxpayer number and social security number are all handed in the same day. To make the process even
more efficient, the initiative also created pre-approved list of company names to eliminate any
bottlenecks.
In 2007, immediately after the reform, the average time to set up a company through the “On the
Spot Firm” was 47 minutes. In 2007 and 2008, new business registration went up by 60 per cent
compared to 2006 and, by the end of 2010, 100000 firms were created on the one-stop program – a quite
impressive number, given that Portugal is a country of about eleven million inhabitants. Thanks to the
“On the Spot Firm” program, Portugal is now one of the easiest countries to start a new business,
(especially compared to the OECD average of 14 days), with an estimated cost of only 300 euros – see
Figure 1. Due to this program, Portugal was also considered by the World Bank as “Top reformer” in
business entry in 2005/2006. The success of the “On the Spot Firm” program was also established by
previous research. Both Branstetter et al. (2013) and Fernandes et al. (2015) find in fact that the program
has a quite relevant effect on the number of new firms.
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Insert Figure 1 about here
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Dependent variables
Entry into entrepreneurship. The QDP database allows tracking transitions of employees from
paid employment into entrepreneurship. Taking advantage of this, we measure mobility as a dummy equal
to one if: (a) the firm where an individual is working at a certain year is different than the firm the same
individual was working in the previous years; (b) the individual becomes an employer.
Wage. The monthly wage of the worker is constructed by adding up its components which are: (a)
the base pay, that is the gross amount of money paid in the reference month to employees on a regular
monthly basis for their normal hours of work; (b) tenure related payments; (c) regular payments.
Moderating Variable
Female. We construct a dummy variable equal to one for female employees – and zero for male
employees.
High (vs. low) skills. We measure individual skills by considering individual level of education
(years of schooling), based by the International Standard Classification of Education (ISCED). In
particular, we define a dummy variable “High education” equal to one for individuals with a ISCED 4/5/6
level – higher education (which corresponds to university degree), i.e., more than 12 years of schooling.
The complementart dummy variable “Low and medium education” is equal to one for individuals with
ISCED level 1 and 2 upper secondary education, i.e., up to 12 years of schooling.
High (vs. low) ex-ante discrimination industry. In order to construct this measure, we first
aggregated the observations into 30 industries as defined by Portuguese Classification of Economic
Activity (see Appendix 2 for a complete list of industries). Then, for each industry, we computed the
average wage difference between male and female employees before 2005 (the year when the “On the
Spot Firm” program was launched), in order to avoid any endogeneity issue with our independent
variable. Finally, we distinguish between industries with an above-median level of female-male wage gap
(“high discrimination industry”) and industries with a below-median level of female-male wage gap (“low
discrimination industry”).
18
Control Variables
Our regressions include additional characteristics of the worker and the firm as a covariate. In particular,
as for the worker, we include age and its square, the level of education, her or his qualification1, her or his
occupation within the company2, the type of contract (which can be short-term or, alternatively, has an
indefinite duration), and the monthly hours worked. In some specifications, we also include a fixed effect
per employee, in order to control from any individual time-invariant characteristics, and a fixed effect per
employee-employer match, in order to control for any time-invariant factor related to the same individual
as long as it stays in the same company.
At the firm level, we control for the size (measured by the number of the employees employed by
the firm). Furthermore, we also include firm fixed effects, which control for any firm time-invariant
characteristics, including the industry where the company operates and the ownership status (private,
public or foreign owned). Finally, we also add region (municipality) and year dummies, to control for
unobserved region characteristics and aggregate shocks.
Descriptive statistics of the variables and their pair-wise correlations are presented, respected, in
Table 1 and Table 2. Notably, about 42 per cent of the employees are female, which confirms the fact that
in Portugal the female participation in the labor force, despite being substantial, represents a numerical
minority during the time period of this study. As for the skill level, about 49 of the Portuguese have up to
lower secondary education, and only 11 per cent have a university degree. Finally, it is worth noting that
the group of employees affected by the “On the Spot Firm” program is equal to 33 per cent of the overall
1 The 8 levels of qualification defined in the QDP are:1 – top executives (top management);2 – intermediary
executives (middle management); 3 – supervisors, team leaders and foremen; 4 – higher-skilled professionals; 5 –
skilled professionals; 6 – semi-skilled professionals; 7 – non-skilled professionals; 8 – Apprentices, interns and
trainees. For the sake of the analysis, the skill levels were grouped into three categories: high-(levels1–4), medium-
(level5) and low-qualified workers(levels6–8). 2 Occupations are recorded in the QDP data at the six digit level in accordance with the International Standard
Classification of Occupations (ISCO) 1988. We use ISCO-88s major groups:1 – directors; 2 – intellectual and
scientific specialists; 3 – professional and technical; 4 – administrative and managerial; 5 – clerical and sales
workers; 6 – agriculture, silviculture and fishing; 7 – production and related workers;8 – equipment operators and
labourers, 9 – unqualified workers. We aggregate occupations 1 and 2 into one group and occupations 6 and 7 into
another single group.
19
population, which makes our estimates quite indicative.
--------------------------------------
Insert Table 1 & Table 2 about here
--------------------------------------
METHODOLOGY
To examine the impact of the “On the Spot Firm” program on wage – and in particular on the female-
male wage gap – we use a difference-in-differences (diff-in-diffs) methodology based on the treatments
listed in table 1. The unit of analysis of our empirical analyses is the individual. Our methodology follows
Bertrand and Mullainathan’s (2003) application of the difference-in-differences methodology in the
presence of staggered treatments at regional (in our case municipality) level. Specifically, our main
specifications will take the form:
Yimt = f (βEntry_deregulationmt + γEntry_deregulationmt*Femalei+δFemalei+βCVCVt-1)
where Y is our dependent variables (transition into entrepreneurship and wage), “Entry_deregulation” is a
dummy variable equal to 1 if the individual i is working in a municipality m that has enacted the entry
deregulation “On the Spot Firm” program by year t and “Female” is a dummy equal to one for female
employees (and zero for male employees) . CV is a vector of control variables, including, as we
mentioned before, municipality, firm and year fixed effects. Errors are always clustered at the
municipality level, to address potential serial correlation concerns as highlighted by Bertrand et al.
(2004). The coefficient of interest is γ, which measures the differential effect of “On the Spot Firm”
program for female vs. male employees. For instance, H1 predicts that γ should be positive and
significant when Y is wage, meaning that the “On the Spot Firm” initiative increases the wage of female
workers vis-à-vis male workers. At the same time, H2 predicts that γ should be negative and significant
when Y is wage, meaning that the “On the Spot Firm” initiative decreases the wage of female workers
vis-à-vis male workers and so augments the gender wage gap. Whereas most of our tables are at the
20
individual level, as a robustness check we will initially present our analysis at the aggregate level – that is,
aggregating the individual data at the level of the treatment, as suggested in Bertrand and Mullainathan
(2004).
We can illustrate this methodology with an example. Suppose we want to measure the effect of
Lisbon’s 2005 enactment of the “On the Spot Firm” program on the female-male wage gap. We would
compute the difference in the female-male wage gap post 2005 versus pre 2005 for workers located in
Lisbon (a “treated municipality”). Yet, other events may have happened around 2005, potentially
influencing changes in the wage difference between female and male employees. For example, there may
have been an economy-wide boom that translates into higher salaries for women. To account for such
contemporaneous effects, we use a control group any municipality that has not launched the program until
2009) and compute the corresponding difference in the entrepreneurial rates post 2005 versus pre 2005.
Computing the difference between these two differences provides an estimate of the effect of Lisbon’s
2005 enactment of the “On the Spot Firm” program on the female-male wage gap, controlling for
contemporaneous changes in such a gap that are due to changes in broad economic conditions. The
difference between this example and our regression specification is that the latter accounts for the fact that
the implementation of the “On the Spot Firm” program is staggered over time across municipalities. It
follows that the composition of both the treatment and the control groups changes over time as more
states are progressively “treated.”
MAIN RESULTS
To begin with, we intend to show that the “On the Spot Firm” program actually determined a substantial
increase in the number of new entrants. In order to do so, we first represent with a simple time plot the
differential number of new entrants between treated municipalities (that is, states experiencing entry
deregulation) and control municipalities. Figure 2 provides suggestive evidence that before the enactment
of “On the Spot Firm” program (which is represented as time 0), treated and control municipalities
displayed similar entry patterns – which confirms that the “On the Spot Firm” program might be
21
considered as an exogenous treatment. However, following the entry deregulation reform, treated
municipalities experience an increase in the number of new firms compared with municipalities in the
control group. Such an increase occurs immediately after the change and tends to become more relevant
over time.
--------------------------------------
Insert Figure 2 about here
--------------------------------------
Table 3 shows the estimated effect of “On the Spot Firm” program on the number of entrants.
Using different functional specifications the effect stays positive and (both statistically and economically)
significant. In particular, at the municipality level, the “On the Spot Firm” program exerts a strong
positive effect on the number of new firms created in a certain municipality and year (column 1) – which
has increased by about 18 units – and the log of such number (column 2) – which suggests the increase is
equal to 6 per cent in relative terms. Such results are substantially confirmed even when we consider the
municipality-industry level of analysis (as we do in columns 3 and 4). Overall, consistent with previous
work, we find that the “On the Spot Firm” program increased the number of new entrants of about 6.7 per
cent.
--------------------------------------
Insert Table 3 about here
--------------------------------------
Figures 3 and 4 show, at the aggregate level, the differential effect of the entry deregulation
reform on the proportion of female (vs. male) employees entering into self-employment (Figure 3) and on
the female vs. male wage (Figure 4).3 The graphs show that, consistent with our theory, the “On the Spot
Firm” program enhanced female transition to entrepreneurship but, at the same time, lowered the wage of
female employees (compared to male employees) who stayed in paid employment.
3 To ensure our results are not driven by composition in the labor force, in counties enacting the “On the Spot Firm”
program, we just included employees present in the sample both before and after the program enactment.
22
--------------------------------------
Insert Figure 3 & Figure 4 about here
--------------------------------------
We first test our theory by considering the results of a series of regression at the municipality-
year level. There are several advantages of working at this aggregate level of analysis, rather than at
individual level. First, using a balanced panel data set at the level of the treatment, naturally accounts for
the correlation of errors at the municipality-year level – which explains why aggregating data might be
especially useful when adopting a diff-in-diff approach (e.g., Bertrand et al. 2004). Second, reducing the
number of observations by aggregating individual data makes more difficult to obtain statististically
significant effect just as an artifact of a large sample size, besides naturally reducing the effect of the
outliers. Finally, aggregating data at a greater level of analysis makes the estimation computationally
much easier, especially when controlling for multiple fixed effects and municipality-specific linear trends.
Results corroborate our theory. Consistent with H1, the entry deregulation reform increases the
entrepreneurship rate among female employees (Table 4, columns 1-3); yet, consistent with H2, it
decreases the wage of female employees that stay in paid employment (Table 4, columns 4-6).
Furthermore, as predicted by H3, the negative effect on wage is particularly salient in industries where the
female-male wage gap is traditionally higher (Table 5, columns 1-3) compared to less-discriminating
industries (Table 5, columns 4-6). Consistent with H4, the negative effect on wage is also more relevant
for high-skilled female employees (Table 6, columns 1-3) with respect to low-skilled female employees
(Table 6, columns 4-6). Interestingly, all the results are confirmed when we relax the parallel-path
assumption and we allow specific fixed effect for the group of female employees in treated municipality
and even different linear trends for female vs. male employees in the treated municipalities (columns 2-3
and 5-6 of Tables 4, 5 and 6).
--------------------------------------
Insert Tables 4, 5 and 6 about here
--------------------------------------
After having assessed the effect of the program at the aggregate level, we assess its effect on
23
entrepreneurship and wage at the individual level, which allows controlling for individual characteristics
and, in this sense, is likely to produce more reliable estimates of the entry deregulation reform effects on
individual likelihood of transitioning into entrepreneurship and wage. Results in Table 7 confirm that the
“On the Spot Firm” reform has increased the transition of existing employees into entrepreneurship.
According to column 1, there is a female gap in entrepreneurship , as female are 0.8 percentage points
less likely to start their own firms compared to their male counterparts. Consider now the specification
reported in column 2, where we include employee fixed effect such that we control for any change in the
composition of the labor force by just focusing on the employees that were already present before the
shock. Overall, the likelihood of transitioning into entrepreneurship increases by 0.0014 – equal to the
sum of the main (even if not significant) effect of the shock and the interacted effect with the female
dummy. Such coefficient represent a huge increase in relative terms – equal to about 30 per cent –
considering that the baseline probability of an employee becoming an entrepreneur is equal to 0.005.
Moreover, consistent with H1, such effect is mainly determined by an increase in female
entrepreneurship. Indeed, the main effect (which refers to the likelihood of male employees becoming
entrepreneurs) is slightly negative and not significant, whereas the interaction effect (which captures the
gap between female and male employees in the likelihood of transitioning into entrepreneurship) is highly
significant at the conventional level (p<0.001) and positive (β=0.0017). These findings are confirmed also
in specifications 3, where we also include employer fixed effects.
--------------------------------------
Insert Table 7 about here
--------------------------------------
According to hypothesis 2, we expect the greater likelihood of women transitioning into
entrepreneurship to be reflected into a lower wage for women staying in paid employment vis-à-vis their
male counterparts. To assess the extent to which the entry deregulation reform has actually affected the
wage of women vs. men workers, Table 8 reports the results of an individual-level regression where the
dependent variable is the log of wage. First of all, even after controlling for workers’ age, level of
24
education, qualification, type of contract and occupation (column 1), female employee’s wage is about 20
per cent less than male employees’ wage, which is a clear indication that some form of wage
discrimination exists and is quite relevant in the Portuguese labor market. Consider now column 2, which
is the specifications where we control for employee fixed effect. Some interesting findings emerge.
Consistent with H2, the entry deregulation reform has increased the wage gap between male and female
workers of about one percentage point (p<0.001). In relative terms, given that the baseline gap is about 20
per cent, this corresponds to a 5 per cent gap increase. In absolute terms, this corresponds instead to a gap
increase of about 220 euros on a yearly basis. Such result is also confirmed by the specifications in
columns 3 and 4, where we also control for firm fixed effect and for the employee-employer match fixed
effects. So we can conclude that our findings is not produced either by a change in the composition in the
labor force or in the composition of the employer, and not even by a re-allocation of the employee into
different firms.
--------------------------------------
Insert Table 8 about here
--------------------------------------
As a test of our mechanisms, we then consider how the effect of the entry deregulation initiative
on the female-male wage gap changes according to the characteristics of the industries where the
employees work. In H3, we have in fact hypothesized the negative effect on female wage should be more
salient in contexts where female have been traditionally discriminated. Consistent with this, Table 9show
that the effect of the “On the Spot Firm” program has been larger in industries where the (ex-ante)
discrimination was more salient. More in details, the wage gap increase by 0.5 per cent in low-
discrimination industries (Table 9, column 1) and by 1.2 per cent in high-discrimination industries (Table
9, column 3). That is, consistent with H3, the effect of an entry deregulation is more relevant in industries
where companies (and so possibly managers) tend to display a greater taste for discrimination. This result
holds valid even when we include fixed effect per employer (columns 2 and 5) and per employee-
employer match (3 and 6).
25
--------------------------------------
Insert Table 9 about here
--------------------------------------
Based on our theory, a further mechanism through which the “On the Spot Firm” reform has
increased the female-male wage gap is a loss in knowledge spillovers for female workers staying in paid
employment, after their female colleague have left. This loss should be particularly salient for those
highly-educated employees able to absorb those spillovers. Consistent with H4, Tables 10 show that the
effect of the entry deregulation law has in fact been more negative for high skilled female employees (1-
3) compared to low-skilled female employees (columns 4-6). More in details, female employees in the
high-skilled employee group see a wage gap decrease of 2.9 per cent (column 1), compared to a 1 per cent
decrease for employees in the low-skilled group (column 4). This effect is robust to different
specifications where include not only employee fixed effect, but also firm fixed effects (columns 2 and 5)
and employer-employee match fixed effects (columns 3 and 6).
--------------------------------------
Insert Table 10 about here
--------------------------------------
ROBUSTNESS CHECKS
Additional tests on the mechanism. We do provide additional evidence consistent with our theory.
First of all, we want to provide some evidence that it is the actual loss of female colleagues driving the
wage decrease for those female employees that stay in paid employment. To do so, we construct a new
variables, by taking the difference between the number of female workers moving to another firm and the
number of male workers, and then normalizing this difference by the firm number of employees. We then
include this variable into our wage regression. If our theory is correct, we should expect that the
difference in the proportion of female vs. male employees leaving the firm should increase the female-
male wage gap. Table 11 suggests that this is the case. When the difference in the proportion of female vs.
male employees leaving increase by one percentage point, the wage gap increases by 2.8 per cent (Table
11, column 2).
26
--------------------------------------
Insert Table 11 about here
--------------------------------------
Second, the increase in the wage gap will probably decrease the job satisfaction of female
employees. Hence, we should expect an increase in female mobility to other firms (always as paid
employees), and in particular to new startups, where the founder is most likely to be female – which,
based on our theory, could allow female employees to be more productive and/or less discriminated.
Table 12 confirms that this is the case. After the entry deregulation reform, mobility of female employees
to other companies increases by 0.6 per cent (Table 12, column 1), and this effect is mainly driven by
mobility to startups (column 2) compared to mobility to incumbent firms (column 3).
--------------------------------------
Insert Table 12 about here
--------------------------------------
Finally, to provide further support to our theoretical mechanisms, we control that the effect of the
reform should not occur in contexts where those mechanisms are not at work. In particular, we argued
that the greater transition of female employees to entrepreneurship is detrimental to female employees
attached to paid employment, both for a discrimination and a productivity reason. If so, we should not
find any effect in firms where female employees are not (or at least less) discriminated and/or where
employee productivity is not (or at least less of) a concern for managers. Such firms are for instance state-
owned enterprises. On one side, they are required by law not to discriminate employees according to
gender. On the other side, state firms are economically supported by the state, such that market
profitability is less of a concern. We indeed find that the entry deregulation reform has a negative effect
only on the wage of female employees working in privately-owned firms (Table 13, columns 4-6). By
contrast, female employees working for state-owned companies even experience a slightly increase in
wage compared to their male counterpart (Table 13, columns 1-3). This might be due to the fact that, for
state-owned firms, laws and regulations might establish the presence of gender quotas: hence, in order not
to lose female employees, managers might improve their working conditions.
27
--------------------------------------
Insert Table 13 about here
--------------------------------------
Checking the exogeneity of the shock. Our identification strategy also assumes that the enactment
of the entry deregulation law is exogenous with respect to entrepreneurial activity in the municipality and,
most important, to the wage gap between female and male. As we said, previous studies confirmed that
the “On the Spot Firm” program was enacted across municipalities in a quasi-random fashion, meaning
that its enactment has not depended on the economic characteristics of the municipalities. However, to
confirm that this is the case, we estimate a simple linear probability model where the dependent variable
is equal to one in the year when the law is enacted (and zero otherwise). The independent variables are
instead the municipality entry-rate, the difference in entry rate between male and female employees, the
female-male wage gap, the employee average income, the overall population and the fraction of
population out of the labor force (all computed at year t-1). As shown in Table 14, all these variables
seem to be uncorrelated with the likelihood of enacting the “On the Spot Firm” program in the
municipality, suggesting that such enactment can in fact be considered as a valid quasi-natural
experiment. Most important, the entry rate and the female-male wage gap seem not be associated to the
enactment of the reform, which reinforce the validity of our identification strategy.
--------------------------------------
Insert Table 14 about here
--------------------------------------
Ruling out alternative explanations. Our entry deregulation shock might (also) affect the female-
male wage gap through other mechanisms. Some of the possible alternative explanations for our findings
have already ruled out by our controls and empirical tests. For instance, some might argue that the entry
deregulation reform has increased the female-male wage reform by inducing the best female workers to
leave the labor force. This explanation is not valid because, by including employee fixed effects, we are
considering the effect of the reform on the very same employees, net of any change in the composition in
the labor force.
28
A second possible explanation is that entry deregulation, by increasing competition, might
determine an environment that fits male employees more than female employees. Hence, lower wage
would be a direct result of the lower female productivity in a more competitive environment. To discard
this explanation, we perform an additional test by checking whether firms with a higher share of female
employees perform less well after the shock. Using different variable to measure firm performance
(including the log of firm sales, employees and the log of the ration between sale and employees) we do
not find evidence that the reform negatively affect the productivity of female employees (Table 15,
columns 1-3). Rather, the productivity of firms with a higher proportion of female employees seems to
increase after the reform.
--------------------------------------
Insert Table 15 about here
--------------------------------------
A third possible explanation is that the entry deregulation reform works as a signal of the extent
to which female employees are attached to their current job. After the decrease in entry costs, female
employees who are willing to move will take advantage of the reform and will transition to
entrepreneurship. The other female employees will instead stick to their current job. Managers, provided
with this information, could reduce the salary of female employer staying in paid employment. If this
theory is correct, we should expect the negative effect of the shock on wage to be greater for new-to-the-
labor-force female employees (for which information about their mobility preferences are unknown)
compared to incumbent employees already in the labor force (for which information about their mobility
preferences can be inferred from their past work background). Table 16 shows that this is not the case.
Indeed, the female employees already in the labor force experience a greater salary reduction (Table 6,
columns 4-6) compared to new-to-the-labor force female employees (Table 16, columns 1-3). One
possible reason is that female employees already in the labor force have fewer outside options, and so,
after losing the support of their colleagues, they get even more discriminated.
29
--------------------------------------
Insert Table 16 about here
--------------------------------------
CONCLUSIONS
Institutional changes that reduce barriers to entrepreneurship have a significant impact on the founding
rates as well as the quality of new ventures founded (Easley, 2016; Eberhart, Easley and Eisenhardt,
2017; Thebaud, 2015). Yet, whereas such policies intend to target those facing most persistent barriers to
entrepreneurship, little research has examined the impact of these regulations on minorities – even though
there is ample evidence that historically disadvantaged groups, such as non-Whites and women, are
particularly likely to face obstacles when entering entrepreneurship (Aldrich 2005, Dobrev and Barnett
2005, Reynolds et al. 2004, Ruef et al. 2003, Yang and Aldrich 2014).
To shed light on this effect, largely obscured by past research, we propose and find evidence for
the claim that institutional changes generate two different effects on minorities. On the one hand, by
lowering barriers to entry, institutional changes will disproportionately increase the rates of
entrepreneurship amongst minorities because a reduction in entry barriers will provide the stronger
incentives to those who were most disadvantaged prior to regulatory changes. Hence, following the
enactment of policies reducing barriers to entry, minorities will enter entrepreneurship at higher rates than
non-minorities. On the other hand, such policies might unintendedly generate downsides for minorities
who stay in paid employment because a sudden attrition of minority coworkers will trigger employer
discrimination and productivity loss, ultimately leading to a decline in pay amongst minorities in
incumbent firms.
Using rich employee-employer matched data from Portugal between 1996 and 2009, we find
strong support for our predictions. Importantly, in this setting, we leverage a staggered reform which
decreased barriers to entry, by making the process of founding a new venture less costly and more
accessible for aspiring entrepreneurs. Leveraging this exogenous reform as a quasi-natural experiment, we
find that, relative to males – who offer a baseline for our analysis, women were more likely to transition
30
into entrepreneurship, following the introduction of entrepreneurial reforms. At the same time, consistent
with our expectations, women who stay behind in paid employment witnessed a sharp decrease in pay
relative to men. Overall, then, we find strong support for the novel, so-far overlooked effect of
institutional changes on entrepreneurship, whereby these reform generate advantageous conditions for
minority entry into entrepreneurship, while also undermining potential advancement of minority workers
at incumbent firms.
Our research offers a number of contributions to the extant work in the field of institutions and
entrepreneurship. First, the present study extends the existing research on institutions in the context of
entrepreneurship, by documenting the overlooked impact of institutional policies on minority workers. In
this respect, our study is the first to theorize and document empirically that minority workers such as
women might simultaneously benefit and suffer from reforms to promote entry.
Second, our study contributes to ample research on discrimination and labor-market inequality,
more broadly. Scholars have long documented that unequal access to opportunities and resources is a
persistent feature of labor markets, with ample sociological evidence suggesting stark differences in
employment along gender or race (e.g., Holzer, 1996; Kirschenman and Neckerman, 1991; Moss and
Tilly 2001; Pager and Quillian, 2005); Pager and Pedulla, 2015; Sterling, 2015; Pager and Quillian, 2005;
Holzer, 1996; Kirschenman and Neckerman, 1991; Moss and Tilly 2001). A critical line of inquiry in this
literature is to understand organizational mechanisms that might be conducive to alleviating workforce
inequalities (Castilla 2011; Peterson & Saporta, 2004). We contribute to this debate, by pointing out how
a rise in female entrepreneurship might contribute to enlarge closing of the long-standing gap between
minorities and non-minorities.
Finally, our study offers direct contribution for the sociological and economic line of inquiry of
gender pay gap. A central insight in this research is that there exists a significant gap in the distribution of
resources along gender (e.g., Bjerk, 2008; Briscoe & Kellogg, 2011; Castilla, 2008, 2011; Cohen &
Huffman, 2007; Elvira & Graham, 2002; Fernandez & Fernandez-Mateo, 2004; Reskin, 2000) and that
such differences cannot be entirely attributed to observable differences in skill or productivity, or
31
differential sorting of women into lower-paying positions, occupations, or industries alone (Fernandez &
Mors, 2008; Petersen & Saporta, 2004; Petersen & Morgan, 1995). Instead, scholars have suggested that
organizational decision makers might exhibit persistent stereotypes and cultural beliefs that play a crucial
role in placing minorities at systematic disadvantage in labor markets (e.g. Bastos & Monteiro, 2011;
Bjerk, 2008; Briscoe & Kellogg, 2011; Castilla, 2008, 2011; Cohen & Huffman, 2007; Elvira & Graham,
2002; Fernandez & Fernandez-Mateo, 2004; Reskin, 2000). We contribute to this literature by identifying
conditions – startup entry by female employees – under which such persistent stereotypes might even be
reinforced and result in even greater sgender differences in pay.
Finally, our findings have several implications for policy-makers. The first implication is that
policy interventions most likely to succeed in eliminating labor-market inequality, and especially reduce
gender-based differences in pay, should be directed toward the conditions that promote entrepreneurial
entry. Encouraging entrepreneurship in regions and counties may be a more effective way to foster
competitive pressures, that will reduce employer incentives to engage in costly discrimination. Second,
our findings also point to the specific areas in which the gap between minorities and non-minorities in
launching start-ups might be relieved with greater efficacy. Specifically, startups might have a
particularly beneficial effect on labor-markets, where traditionally disadvantaged minority groups might
witness greater improvement in an equitable access to employer resources. Overall, our study suggests
that promoting entrepreneurship, and therefore in the economy more broadly, must not necessarily
promote inequality, by placing minorities at disadvantage. Rather, at least in the context of labor markets,
such initiatives can lead to significant improvements in reducing persistent inequalities.
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TABLES
Table 1. Descriptive statistics
Count Mean SD Min Max
Entry deregulation 22996893.000 0.326 0.469 0.000 1.000
Wage 22996893.000 748.531 818.765 0.000 208333.328
Female 22996893.000 0.420 0.494 0.000 1.000
Female wage 9651204.000 643.292 580.013 0.000 66445.000
Male wage 13345689.000 824.637 947.673 0.000 208333.328
Entrepreneur 22996893.000 0.005 0.073 0.000 1.000
Female entrepreneur 9651204.000 0.004 0.063 0.000 1.000
Male entrepreneur 13345689.000 0.006 0.080 0.000 1.000
Mobility 22996893.000 0.149 0.357 0.000 1.000
Female mobility 9651204.000 0.145 0.352 0.000 1.000
Male mobility 13345689.000 0.153 0.360 0.000 1.000
Age 22996893.000 38.039 11.404 14.000 79.000
Low skilled 22743318.000 0.486 0.500 0.000 1.000
Mid skilled 22743318.000 0.402 0.490 0.000 1.000
High skilled 22743318.000 0.112 0.315 0.000 1.000
High qualification 22996893.000 0.257 0.437 0.000 1.000
Medium qualification 22996893.000 0.384 0.486 0.000 1.000
Low qualification 22996893.000 0.316 0.465 0.000 1.000
Hours worked (ln) 22996893.000 145.816 57.132 1.000 524.000
Long term contract 21279991.000 0.719 0.449 0.000 1.000
Workers (ln) 22996893.000 721.683 2320.315 1.000 20097.000
39
Table 2. Correlation
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.
1. Entry deregulation 1.00
2. Wage 0.10 1.00
3. Female 0.03 -0.11 1.00
4. Entrepreneur -0.01 NA* -0.02 1.00
5. Mobility 0.00 -0.03 -0.01 -0.03 1.00
6. Age 0.03 0.08 -0.08 -0.00 -0.10 1.00
7. Low skilled -0.14 -0.24 -0.08 -0.02 -0.02 0.31 1.00
8. Mid skilled 0.08 0.02 0.03 0.01 0.03 -0.26 -0.80 1.00
9. High skilled 0.10 0.35 0.07 0.01 -0.01 -0.08 -0.35 -0.29 1.00
10. High qualification 0.05 0.29 -0.07 0.11 -0.09 0.15 -0.26 -0.01 0.43 1.00
11. Medium qualification -0.04 -0.06 -0.09 -0.05 0.01 -0.02 0.09 0.03 -0.18 -0.46 1.00
12. Low qualification -0.02 -0.19 0.18 -0.05 0.05 -0.11 0.17 -0.03 -0.21 -0.40 -0.54 1.00
13. Hours worked 0.01 0.34 -0.01 -0.19 0.04 -0.12 -0.00 0.03 -0.04 -0.28 0.18 0.10 1.00
14. Workers 0.07 0.14 0.04 -0.02 0.00 -0.03 -0.13 0.11 0.03 0.02 -0.03 0.00 0.04 1.00
`* wage is defined only for paid employees
40
Table 3. Effect of the reform on the number of new firms
(1) (2) (3) (4)
VARIABLES
Startups
number
Log Startups
number
Startups
number
Log Startups
number
Entry deregulation 18.07*** 0.0674** 0.778*** 0.0491***
(3.947) (0.0223) (0.173) (0.00960)
Average wage (ln) -23.20** 0.0555 -1.190** -0.0102
(10.04) (0.185) (0.567) (0.0450)
Total population (ln) 21.04** 0.206 1.057** 0.109**
(8.183) (0.128) (0.400) (0.0402)
Inactive population (%) -22.17** -0.203+ -0.0106** -0.000828**
(10.63) (0.108) (0.00503) (0.000321)
Constant -7.235 0.775 -0.541 -0.461
(80.69) (1.649) (4.259) (0.477)
Observations 2,464 2,464 50,245 50,245
R-squared 0.197 0.136 0.034 0.024
Municipality FEs Yes Yes Yes Yes
Year FEs Yes Yes Yes Yes
Industry FEs Yes Yes
Robust standard errors in brackets *** p<0.001, ** p<0.05, * p<0.01, + p<0.1
41
Table 4: Effect of the reform of employee on transition into entrepreneurship and log wage (municipality level regressions)
(1)
Log
entrepreneurship
(2)
Log
entrepreneurship
(3)
Log
entrepreneurship
(4)
Log wage
(5)
Log wage
(6)
Log wage
Entry deregulation -0.031 0.075 -0.017 -0.022*** -0.015* -0.002
(0.070) (0.071) (0.077) (0.006) (0.007) (0.007)
Female -0.915*** -1.224*** -1.310*** -0.191*** -0.223*** -0.220***
(0.042) (0.102) (0.119) (0.007) (0.011) (0.012)
Entry
deregulation*Female
0.401*** 0.190* 0.330** -0.016+ -0.028** -0.033*
(0.064) (0.096) (0.110) (0.009) (0.010) (0.014)
Constant -4.986*** -8.176*** -8.122*** 6.355*** 7.373*** 7.366***
(0.058) (0.066) (0.080) (0.005) (0.007) (0.008)
R2 0.40 0.40 0.40 0.90 0.90 0.90
N 5,562 5,562 5,562 5,562 5,562 5,562
Year FE YES YES YES YES YES YES
Municipality FE YES YES YES YES YES YES
Treated municipality FE
times Female
YES YES YES YES
Treated municipality FE
times female linear trend
YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
42
Table 5: Effect of the reform on wage: high vs. low discrimination industries discrimination (municipality level regressions)
High discrimination industries Low discrimination industries
(1) (2) (3) (4) (5) (6)
Log Wage Log Wage Log Wage Log Wage Log Wage Log Wage
Entry deregulation -0.020* -0.011 -0.008 -0.020** -0.014* -0.004
(0.009) (0.009) (0.010) (0.006) (0.006) (0.007)
Female -0.303*** -0.324*** -0.325*** -0.157*** -0.186*** -0.184***
(0.007) (0.012) (0.013) (0.006) (0.009) (0.010)
Entry deregulation*Female -0.012 -0.029* -0.028* -0.007 -0.018* -0.021*
(0.010) (0.011) (0.014) (0.008) (0.008) (0.010)
Constant 6.427*** 6.874*** 6.872*** 6.354*** 7.288*** 7.282***
(0.008) (0.010) (0.013) (0.005) (0.006) (0.007)
R2 0.83 0.83 0.83 0.88 0.88 0.88
N 5,549 5,549 5,549 5,562 5,562 5,562
Year FE YES YES YES YES YES YES
Municipality FE YES YES YES YES YES YES
Treated municipality FE
times Female
YES YES YES YES
Treated municipality FE
times female linear trend
YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
43
Table 6: Effect of the reform on wage: high vs. low skilled employees (municipality level regressions)
High-skilled employees Low-skilled employees
(1) (2) (3) (4) (5) (6)
Log Wage Log Wage Log Wage Log Wage Log Wage Log Wage
Entry deregulation 0.018 0.034* 0.038+ -0.008 -0.010+ -0.009
(0.012) (0.014) (0.019) (0.005) (0.006) (0.006)
Female -0.166*** -0.269*** -0.260*** -0.249*** -0.267*** -0.267***
(0.011) (0.024) (0.028) (0.005) (0.009) (0.010)
Entry deregulation*Female -0.030* -0.061*** -0.075** -0.022** -0.019* -0.018+
(0.015) (0.018) (0.025) (0.007) (0.008) (0.010)
Constant 7.058*** 7.922*** 7.920*** 6.409*** 7.124*** 7.124***
(0.010) (0.017) (0.021) (0.004) (0.005) (0.006)
R2 0.62 0.63 0.63 0.69 0.69 0.69
N 5,537 5,537 5,537 11,119 11,119 11,119
Year FE YES YES YES YES YES YES
Municipality FE YES YES YES YES YES YES
Treated municipality FE
times Female
YES YES YES YES
Treated municipality FE
times female linear trend
YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
44
Table 7: Effect of the reform on transition into entrepreneurship
(1) (2) (3)
Becoming
entrepreneur
Becoming
entrepreneur
Becoming
entrepreneur
Entry deregulation 0.0002 -0.0003 -0.0006
(0.0003) (0.0003) (0.0004)
Female -0.0008***
(0.0001)
Entry deregulation*female 0.0009*** 0.0017*** 0.0026***
(0.0001) (0.0002) (0.0002)
Age 0.0008*** 0.0006*** 0.0001
(0.0000) (0.0001) (0.0001)
Age squared -0.0000*** -0.0000*** -0.0000
(0.0000) (0.0000) (0.0000)
Mid education 0.0012*** 0.0024*** 0.0005***
(0.0001) (0.0001) (0.0001)
High education -0.0019*** 0.0012** -0.0007
(0.0003) (0.0004) (0.0005)
Hours worked (ln) -0.0067*** -0.0095*** -0.0059***
(0.0002) (0.0003) (0.0001)
Mid qualification 0.0004*** 0.0016*** 0.0017***
(0.0001) (0.0002) (0.0003)
High qualification 0.0028*** 0.0121*** 0.0080***
(0.0001) (0.0009) (0.0007)
Workers (ln) -0.0011*** -0.0044*** -0.0061***
(0.0000) (0.0001) (0.0005)
R2 0.06 0.27 0.35
N 21,581,689 20,475,411 20,398,450
Occupation FE YES YES YES
Year FE YES YES YES
Municipality FE YES YES YES
Worker FE YES YES
Firm FE YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
45
Table 8: Effect of the reform on wage
(1) (2) (3) (4)
Log Wage Log Wage Log Wage Log Wage
Entry deregulation -0.009* 0.008* 0.007* 0.006*
(0.004) (0.003) (0.003) (0.003)
Female -0.197***
(0.006)
Entry deregulation*Female 0.006 -0.010*** -0.006*** -0.004**
(0.004) (0.002) (0.001) (0.001)
Age 0.029*** 0.028*** 0.025*** 0.024***
(0.002) (0.002) (0.002) (0.002)
Age squared -0.000*** -0.000*** -0.000*** -0.000***
(0.000) (0.000) (0.000) (0.000)
Mid education 0.125*** 0.006*** 0.001+ -0.001
(0.013) (0.001) (0.001) (0.001)
High education 0.396*** 0.104*** 0.072*** 0.051***
(0.011) (0.005) (0.005) (0.002)
Hours worked (ln) 0.703*** 0.828*** 0.852*** 0.820***
(0.045) (0.012) (0.008) (0.012)
Long term contract 0.099*** 0.016*** 0.021*** 0.010***
(0.010) (0.002) (0.001) (0.001)
Mid qualification 0.126*** 0.056*** 0.050*** 0.032***
(0.004) (0.002) (0.002) (0.002)
High qualification 0.384*** 0.137*** 0.124*** 0.083***
(0.006) (0.002) (0.003) (0.003)
Workers (ln) 0.063*** 0.030*** 0.033*** 0.042***
(0.001) (0.002) (0.003) (0.002)
R2 0.66 0.93 0.95 0.95
N 19,330,720 18,237,603 18,158,177 16,367,142
Occupation FE YES YES YES YES
Year FE YES YES YES YES
Municipality FE YES YES YES YES
Firm FE YES YES
Worker FE YES YES YES
Worker&Firm FE YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
46
Table 9: Effect of the reform on wage: low vs. high discrimination industries
High discrimination industries Low discrimination industries
(1) (2) (3) (4) (5) (6)
Log Wage Log Wage Log Wage Log Wage Log Wage Log Wage
Entry deregulation 0.006+ 0.004 0.004 0.008* 0.007* 0.006+
(0.003) (0.003) (0.003) (0.004) (0.004) (0.004)
Female
Entry deregulation*Female -0.012** -0.008** -0.007* -0.005*** -0.003* -0.001
(0.004) (0.003) (0.004) (0.001) (0.001) (0.001)
Age 0.025*** 0.023*** 0.022*** 0.028*** 0.026*** 0.026***
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Age squared -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Mid education 0.001 -0.000 -0.002 0.005*** 0.001 -0.000
(0.002) (0.001) (0.002) (0.001) (0.001) (0.001)
High education 0.072*** 0.053*** 0.046*** 0.100*** 0.072*** 0.052***
(0.005) (0.005) (0.004) (0.004) (0.003) (0.003)
Hours worked (ln) 0.885*** 0.880*** 0.855*** 0.793*** 0.825*** 0.803***
(0.008) (0.008) (0.014) (0.016) (0.012) (0.014)
Long term contract 0.015*** 0.015*** 0.008** 0.014*** 0.020*** 0.011***
(0.002) (0.002) (0.003) (0.002) (0.001) (0.001)
Mid qualification 0.035*** 0.033*** 0.027*** 0.061*** 0.052*** 0.033***
(0.001) (0.002) (0.002) (0.002) (0.002) (0.002)
High qualification 0.102*** 0.092*** 0.074*** 0.142*** 0.126*** 0.087***
(0.003) (0.002) (0.002) (0.004) (0.004) (0.004)
Workers (ln) 0.021*** 0.034*** 0.041*** 0.034*** 0.035*** 0.040***
(0.003) (0.004) (0.004) (0.001) (0.002) (0.002)
R2 0.95 0.96 0.96 0.93 0.94 0.95
N 5,807,876 5,788,977 5,357,150 11,961,167 11,901,419 10,873,753
Occupation FE YES YES YES YES YES YES
Year FE YES YES YES YES YES YES
Municipality FE YES YES YES YES YES YES
Firm FE YES YES YES YES
Worker FE YES YES YES YES YES YES
Worker&Firm FE YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
47
Table 10: Effect of the reform on wage: high versus low-skilled employees
High-skilled employees Low-skilled employees
(1) (2) (3) (4) (5) (6)
Log wage Log wage Log wage Log wage Log wage Log wage
Entry deregulation 0.015*** 0.012*** 0.010*** 0.007** 0.006* 0.006*
(0.002) (0.002) (0.002) (0.003) (0.003) (0.003)
Female
Entry deregulation*Female -0.029*** -0.025*** -0.019*** -0.010*** -0.006*** -0.005**
(0.003) (0.002) (0.003) (0.002) (0.001) (0.002)
Age 0.066*** 0.059*** 0.053*** 0.023*** 0.021*** 0.021***
(0.005) (0.005) (0.005) (0.001) (0.001) (0.001)
Age squared -0.001*** -0.001*** -0.001*** -0.000*** -0.000*** -0.000***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Mid education 0.009*** 0.003*** 0.002+
(0.001) (0.001) (0.001)
Hours worked (ln) 0.692*** 0.662*** 0.630*** 0.838*** 0.866*** 0.838***
(0.015) (0.019) (0.026) (0.014) (0.008) (0.010)
Long term contract 0.028*** 0.032*** 0.019*** 0.014*** 0.018*** 0.009***
(0.003) (0.002) (0.003) (0.001) (0.001) (0.001)
Mid qualification 0.104*** 0.083*** 0.060*** 0.055*** 0.049*** 0.033***
(0.003) (0.006) (0.005) (0.002) (0.002) (0.002)
High qualification 0.197*** 0.158*** 0.117*** 0.126*** 0.114*** 0.079***
(0.004) (0.007) (0.005) (0.003) (0.003) (0.003)
Workers (ln) 0.033*** 0.031*** 0.038*** 0.029*** 0.032*** 0.040***
(0.004) (0.002) (0.002) (0.002) (0.003) (0.002)
R2 0.93 0.95 0.96 0.91 0.93 0.94
N 2,011,519 1,991,595 1,849,043 16,137,100 16,059,292 14,474,108
Occupation FE YES YES YES YES YES YES
Year FE YES YES YES YES YES YES
County FE YES YES YES YES YES YES
Worker FE YES YES YES YES YES YES
Firm FE YES YES YES YES
Worker&Firm FE YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
48
Table 11: Effect on wage of the relative proportion of female vs. male leaving the firm
(1) (2) (3) (4)
Log Wage Log Wage Log Wage Log Wage
Female -0.192***
(0.006)
Female vs. male proportion of
employees leaving
0.019+ 0.019*** 0.010*** 0.012***
(0.011) (0.002) (0.003) (0.003)
Female*Female vs. male
proportion of employees leaving
-0.126*** -0.028*** -0.013*** -0.020***
(0.025) (0.005) (0.003) (0.003)
Age 0.029*** 0.027*** 0.025*** 0.024***
(0.002) (0.002) (0.002) (0.002)
Age squared -0.000*** -0.000*** -0.000*** -0.000***
(0.000) (0.000) (0.000) (0.000)
Mid education 0.125*** 0.006*** 0.001+ -0.001
(0.012) (0.001) (0.001) (0.001)
High education 0.395*** 0.103*** 0.072*** 0.051***
(0.011) (0.005) (0.005) (0.002)
Hours worked (ln) 0.702*** 0.828*** 0.852*** 0.820***
(0.045) (0.012) (0.008) (0.012)
Long term contract 0.098*** 0.016*** 0.021*** 0.010***
(0.010) (0.002) (0.001) (0.001)
Mid qualification 0.126*** 0.056*** 0.050*** 0.032***
(0.004) (0.002) (0.002) (0.002)
High qualification 0.384*** 0.137*** 0.124*** 0.083***
(0.006) (0.002) (0.003) (0.003)
Workers (ln) 0.063*** 0.030*** 0.033*** 0.041***
(0.001) (0.002) (0.003) (0.002)
R2 0.66 0.93 0.95 0.95
N 19,330,720 18,237,603 18,158,177 16,367,142
Occupation FE YES YES YES YES
Year FE YES YES YES YES
Municipality FE YES YES YES YES
Worker FE YES YES YES
Firm FE YES YES
Worker&Firm FE YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
49
Table 12: Effect of the reform on mobility to all firms, startups and incumbents
(1) (2) (3)
Mobility overall Mobility to startups Mobility to incumbents
Entry deregulation -0.000 0.002 -0.002
(0.002) (0.002) (0.002)
Female
Entry deregulation*Female 0.006*** 0.005*** 0.001
(0.001) (0.002) (0.001)
Age 0.007*** -0.002*** 0.009***
(0.001) (0.000) (0.001)
Age squared -0.000*** 0.000*** -0.000***
(0.000) (0.000) (0.000)
Mid education 0.026*** 0.003*** 0.023***
(0.001) (0.001) (0.001)
High education 0.083*** 0.013*** 0.070***
(0.002) (0.001) (0.002)
Hours worked (ln) 0.005*** 0.001*** 0.004***
(0.000) (0.000) (0.000)
Long term contract -0.158*** -0.017*** -0.141***
(0.003) (0.001) (0.003)
Mid qualification -0.012*** 0.003+ -0.015***
(0.002) (0.001) (0.001)
High qualification -0.016*** 0.003* -0.018***
(0.002) (0.001) (0.002)
Workers (ln) -0.011*** -0.029*** 0.018***
(0.003) (0.002) (0.002)
R2 0.44 0.50 0.42
N 18,800,218 18,800,218 18,800,218
Occupation FE YES YES YES
Year FE YES YES YES
Municipality FE YES YES YES
Firm FE YES YES YES
Worker FE YES YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
50
Table 13: Effect of the reform on wage in state-owned vs. privately-owned firms
State-owned firms Privately-owned firms
(1) (2) (3) (4) (5) (6)
Log wage Log wage Log wage Log wage Log wage Log wage
Entry deregulation -0.006 -0.005 -0.006 0.008* 0.007* 0.006+
(0.009) (0.009) (0.009) (0.003) (0.003) (0.003)
Female
Entry deregulation*Female 0.022+ 0.024* 0.024* -0.010*** -0.006*** -0.004**
(0.011) (0.011) (0.011) (0.002) (0.001) (0.001)
Age 0.028*** 0.028*** 0.029*** 0.028*** 0.025*** 0.024***
(0.005) (0.005) (0.005) (0.002) (0.002) (0.002)
Age squared -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Mid education -0.000 -0.001 -0.001 0.006*** 0.001+ -0.001
(0.009) (0.010) (0.010) (0.001) (0.001) (0.001)
High education 0.072*** 0.073*** 0.074*** 0.102*** 0.071*** 0.050***
(0.010) (0.011) (0.011) (0.005) (0.005) (0.002)
Hours worked (ln) 0.809*** 0.809*** 0.806*** 0.828*** 0.852*** 0.820***
(0.091) (0.091) (0.091) (0.012) (0.008) (0.012)
Long term contract 0.018 0.018 0.019 0.016*** 0.021*** 0.010***
(0.017) (0.018) (0.018) (0.002) (0.001) (0.001)
Mid qualification 0.018 0.015 0.018 0.056*** 0.050*** 0.032***
(0.011) (0.013) (0.013) (0.002) (0.002) (0.002)
High qualification 0.058*** 0.054*** 0.056*** 0.137*** 0.124*** 0.083***
(0.011) (0.012) (0.012) (0.002) (0.003) (0.003)
Workers (ln) -0.020 -0.023 -0.023 0.030*** 0.033*** 0.042***
(0.014) (0.016) (0.016) (0.002) (0.003) (0.002)
R2 0.96 0.96 0.96 0.93 0.94 0.95
N 191,193 191,190 189,571 18,029,563 17,950,207 16,172,003
Occupation FE YES YES YES YES YES YES
Year FE YES YES YES YES YES YES
Municipality FE YES YES YES YES YES YES
Worker FE YES YES YES YES YES YES
Firm FE YES YES YES YES
Worker&Firm FE YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
51
Table 14: Determinants of the reform
(1) (2) (3) (4) (5) (6) (7) (8)
VARIABLES
Entry
deregulation
Entry
deregulation
Entry
deregulation
Entry
deregulation
Entry
deregulation
Entry
deregulation
Entry
deregulation
Entry
deregulation
Entry rate 0.290 -0.0652 -0.0715
(0.204) (0.234) (0.236)
Entry rate female gap 0.308 0.212 0.235
(0.738) (0.716) (0.721)
Wage female gap -0.0616 -0.0746 -0.0738
(0.0860) (0.117) (0.117)
Average income -0.0876 -0.0846 -0.0869 -0.0610 0.0374 0.0393 0.0642 0.0636
(0.0815) (0.0819) (0.0814) (0.0859) (0.0972) (0.0969) (0.102) (0.102)
Total population (ln) 0.0844 0.0847 0.0844 0.0856 0.0642 0.0643 0.0662 0.0656
(0.0563) (0.0558) (0.0565) (0.0563) (0.0525) (0.0522) (0.0522) (0.0526)
Fraction of inactive people -0.0180 -0.0179 -0.0181 -0.0193 -0.0105 -0.0107 -0.0124 -0.0120
(0.0509) (0.0508) (0.0509) (0.0506) (0.0593) (0.0591) (0.0585) (0.0587)
Constant -0.281 -0.313 -0.286 -0.440 151.0*** 151.0*** 151.5*** 151.4***
(0.735) (0.735) (0.735) (0.747) (4.348) (4.379) (4.465) (4.458)
Observations 2,253 2,253 2,253 2,253 2,253 2,253 2,253 2,253
R-squared 0.142 0.142 0.142 0.142 0.403 0.403 0.403 0.403
Number of municipality 308 308 308 308 308 308 308 308
Municipality FEs Yes Yes Yes Yes Yes Yes Yes Yes
Year FEs Yes Yes Yes Yes Yes Yes Yes Yes
Municipality-year trend FE No No No No Yes Yes Yes Yes
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
52
Table 15: Effect of the reform on firm performance according to the proportion of female
employees
(1) (2) (3)
Sales over workers (ln) Workers (ln) Sales (ln)
Entry deregulation -0.101* -0.022** -0.135**
(0.041) (0.007) (0.051)
Female employee proportion 0.055*** 0.051*** 0.133***
(0.016) (0.012) (0.024)
Entry deregulation*Female
employee proportion
-0.025 0.024*** -0.005
(0.021) (0.005) (0.022)
Firm age -0.012*** -0.002*** -0.015***
(0.001) (0.000) (0.001)
R2 0.48 0.88 0.51
N 4,058,254 4,058,254 4,058,254
Year FE YES YES YES
Municipality FE YES YES YES
Firm FE YES YES YES
Worker FE YES YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
53
Table 16: Effect of the reform on wage for employees new to the labor force vs. incumbent
employees
New to the labor force employees Incumbent employees
(1) (2) (3) (4) (5) (6)
Log wage Log wage Log wage Log wage Log wage Log wage
ntry deregulation 0.008* 0.007* 0.006+ 0.013** 0.009* 0.009*
(0.003) (0.003) (0.003) (0.005) (0.004) (0.004)
Female
Entry deregulation*Female -0.009*** -0.006*** -0.004** -0.015*** -0.011*** -0.010***
(0.002) (0.001) (0.001) (0.002) (0.002) (0.002)
Age 0.028*** 0.025*** 0.025*** 0.019*** 0.018*** 0.018***
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Age squared -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Mid education 0.006*** 0.001+ -0.000 0.007** -0.001 -0.003
(0.001) (0.001) (0.001) (0.002) (0.002) (0.002)
High education 0.104*** 0.072*** 0.050*** 0.091*** 0.060*** 0.059***
(0.005) (0.005) (0.002) (0.006) (0.005) (0.007)
Hours worked (ln) 0.829*** 0.852*** 0.820*** 0.822*** 0.843*** 0.825***
(0.012) (0.008) (0.012) (0.011) (0.011) (0.015)
Long term contract 0.016*** 0.021*** 0.010*** 0.016*** 0.012*** 0.005**
(0.002) (0.001) (0.001) (0.002) (0.002) (0.002)
Mid qualification 0.056*** 0.050*** 0.032*** 0.049*** 0.034*** 0.026***
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
High qualification 0.138*** 0.124*** 0.083*** 0.128*** 0.094*** 0.079***
(0.002) (0.003) (0.003) (0.004) (0.004) (0.005)
Workers (ln) 0.030*** 0.033*** 0.042*** 0.031*** 0.035*** 0.041***
(0.002) (0.003) (0.002) (0.002) (0.004) (0.003)
R2 0.93 0.95 0.95 0.92 0.95 0.95
N 17,551,899 17,472,452 15,750,718 685,704 659,142 616,424
Occupation FE YES YES YES YES YES YES
Year FE YES YES YES YES YES YES
Municipality FE YES YES YES YES YES YES
Worker FE YES YES YES YES YES YES
Firm FE YES YES YES YES
Worker&Firm FE YES YES
Robust standard errors in brackets
*** p<0.001, ** p<0.05, * p<0.01, + p<0.1
54
FIGURES
Figure 1. Effect of the reform on the time needed to found a new business
Figure 2: Effect of the reform on the number of new firms
02
04
060
80
Day
s
2004 2005 2006 2007 2008 2009
Year
Portugal OCDE countries
Number of days to start a business
55
Figure 3. Effect on aggregate female-male entrepreneurship gap (taking only employees before the
shock)
Figure 4. Effect on aggregate female-male wage gap
56
APPENDIX
Table A1. Reform enactment dates
Municipality Year
Aveiro 2005
Barreiro 2005
Beja 2005
Braga 2005
Bragança 2005
Coimbra 2005
Guarda 2005
Lisboa 2005
Loulé 2005
Moita 2005
Sintra 2005
Vila Nova de Gaia 2005
Viseu 2005
Angra Do Heroísmo 2006
Bombarral 2006
Cascais 2006
Castelo Branco 2006
Chaves 2006
Évora 2006
Faro 2006
Funchal 2006
Gondomar 2006
Guimarães 2006
Leiria 2006
Maia 2006
Odivelas 2006
Ponta Delgada 2006
Portalegre 2006
Portimão 2006
Porto 2006
Santarém 2006
São João Da Madeira 2006
Viana Do Castelo 2006
Vila Franca De Xira 2006
Vila Nova De Cerveira 2006
Vila Real 2006
Abrantes 2007
Águeda 2007
Alcácer Do Sal 2007
Caldas Da Rainha 2007
Celorico De Basto 2007
Covilhã 2007
Elvas 2007
Estremoz 2007
Figueira da Foz 2007
Fornos De Algodres 2007
Horta 2007
Lagos 2007
Lamego 2007
Mirandel 2007
Montemor 2007
Montemor-O-Novo 2007
Odivelas 2007
Oliveira do Bairro 2007
Pombal 2007
Santiago 2007
Seia 2007
Tomar 2007
Torres V 2007
Vila Do Conde 2007
Vila Nova de Foz Côa 2007
Vila Real De Santo António 2007
Alcobaça 2008
Alfândega da Fé 2008
Aljezur 2008
Aljustrel 2008
Almada 2008
Almeida 2008
Cantanhede 2008
Espinho 2008
Fafe 2008
Felgueiras 2008
Figueira de Castelo Rodrigo 2008
Idanha-A-Nova 2008
Ílhavo 2008
Loures 2008
Macedo De Cavaleiros 2008
Matosinhos 2008
Moimenta Da Beira 2008
Montalegre 2008
57
Mora 2008
Moura 2008
Óbidos 2008
Odemira 2008
Ovar 2008
Ponte Da Barca 2008
Ponte De Lima 2008
Ponte de Sor 2008
Santo Tirso 2008
São João Da Pesqueira 2008
Tondela 2008
Trofa 2008
Valença 2008
Valongo 2008
Vila Ver 2008
Alcanena 2009
Alenquer 2009
Arganil 2009
Armamar 2009
Arouca 2009
Arruda dos Vinhos 2009
Azambuja 2009
Barcelos 2009
Batalha 2009
Belmonte 2009
Borba 2009
Cadaval 2009
Caminha 2009
Campo Maior 2009
Cartaxo 2009
Castanheira De Pera 2009
Entronca 2009
Ferreira do Alentejo 2009
Ferreira do Zêzere 2009
Freixo de Espada à Cinta 2009
Lourinhã 2009
Mafra 2009
Mangualde 2009
Marco de Canaveses 2009
Marinha 2009
Mortágua 2009
Murça 2009
Murtosa 2009
Nazaré 2009
Nelas 2009
Oliveira do Hospital 2009
Ourique 2009
Pedrógão Grande 2009
Penafiel 2009
Peniche 2009
Póvoa de Varzim 2009
Resende 2009
Rio Maior 2009
Seixal 2009
Serpa 2009
Sobral de Monte Agraço 2009
Tavira 2009
Valpaços 2009
Vila Flor 2009
Vimioso 2009
Vouzela 2009
58
Table A2: List of industries
1 - Agriculture, Livestock, Hunting and Forestry
2 - Fishing
3 - Extraction of Energy Products
4 - Extractive Industries Excluding Extraction of Energy Products
5 - Food, Beverage and Tobacco Industries
6 - Textile Industry
7 - Leather and Leather Products Industry
8 - Industries of Madeira and Cork and their Works
9 - Pulp and Paper Industries and their Articles, Edition and Printing
10 - Manufacture of Coke, Refined Petroleum Products and Nuclear Fuel
11 - Manufacture of chemicals and synthetic or artificial fibers
12 - Manufacture of articles of rubber and plastics
13 - Manufacture of other non-metallic mineral products
14 - Basic Metallurgical Industries and Metal Products
15 - Manufacture of machinery and equipment N.E.
16 - Manufacture of Electrical and Optical Equipment
17 - Manufacture of Transportation Equipment
18 - Manufacturing Industries N.E.
19 - Production and Distribution of Electricity, Gas and Water
20 - Construction
21 - Wholesale and Retail, Repair of Motor Vehicles, Motorcycles and Personal and Household Goods
22 - Accommodation and Restaurant (Restaurants and Similar)
23 - Transport, Storage and Communications
24 - Financial Activities
25 - Real Estate Activities, Rental and Business Services
26 - Public Administration, Defense and Compulsory Social Security
27 - Education
28 - Health and Social Action
29 - Other Collective, Social and Personal Services Activities
30 - Families with Domestic Employees
