Credit market competition and the gender gap : evidence from local labor markets.
Date: 2017-08-08
Institutions: European Central Bank
Subjects: EU competition policy, credit policy, labour market, social inequality, wage determination

Alexander Popov, Sonia Zaharia 

Working Paper Series 

Credit market competition and the 
gender gap: 
evidence from local labor markets 

No 2086 / July 2017 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

Abstract

We exploit the exogenous variation in regional credit market contestability brought on by
banking deregulation in the United States to study the narrowing of the gender gap in local
labor markets. We …nd that deregulation reduced the gender gap in labor force participation,
as the subsequent increase in the demand for labor induced non-working women to enter the
labor force. Deregulation also reduced wage inequality as women became more likely to work
in the private sector, to enter high-paid "male" jobs, and to acquire higher education. Tests of
contiguous MSAs sharing a state border corroborate a genuine deregulation e¤ect.

JEL classi…cation: G28, J16, J22.
Keywords: Bank deregulation, gender gap, labor force participation, wage inequality.

ECB Working Paper 2086, July 20171Non-technical summary

Between 1970 and 1995, the labor force participation rate for working-age women in the United

States increased from 50 percent to 76 percent, and the ratio of female-to-male wage and salary

income increased from 0.60 to 0.73. Both developments represent a substantial and unprecedented

narrowing of the gender gap in US employment. Prominent conventional explanations for the

convergence in labor market outcomes between men and women during this period focus on the

waning social stigma surrounding married women’s market work, on the the adoption of time-

saving technologies due to technological progress in the home-durable-goods sector, and on the

introduction of oral contraception which facilitated women’s investment in their career.

In this paper, we argue that a so-far neglected factor can help explain the narrowing of the

gender gap in local U.S. labor markets, namely, the deregulaiton of the U.S. banking industry

during the 1970s, 1980s, and early 1990s. Over this period, U.S. states gradually lifted restrictions

on bank branching within the state and eliminated entry barriers to out-of-state banks, leading to

a signi…cant improvement in overall access to …nance. We hypothesize that intensi…ed competition

among …nancial intermediaries increased …rms’demand for labor, as well as women’s demand for

schooling in general and for career-oriented subjects in particular, with a material e¤ect on female

labor force participation rates and labor income.

Using data on 1.5 million individuals between 1977 and 1994 from the Current Population Survey

(CPS), we …nd that looser restrictions on the geographic expansion of banks are associated with

a lower gender gap in labor force participation and in wage income. Our di¤erence-in-di¤erences

estimates suggest that the removal of restrictions on intra-state bank branching increased the

number of weeks worked per year by up to one more for women than for men. It also increased

income from weekly wages by up to $40 more for employed females than for employed males.

Banking deregulation thus reduced the gender gap in labor force participation by up to 29 percent,

and gender inequality in wages by up to 18 percent. This result is robust to controlling for a wide

host of background characteristics that can determine labor market outcomes, such as age, race,

education, and marital status. It is also robust to controlling for state

(cid:2)

year …xed e¤ects which

subsume the impact of any US-wide trends, as well as for unobservable state-speci…c time-varying

factors that a¤ect all economic agents in a state equally. The measured contribution of improved

ECB Working Paper 2086, July 20172access to …nance to the narrowing of the gender gap in labor markets is also robust to controlling for

state-speci…c time-varying gender norms that can have a di¤erent impact on men and on women.

Finally, to alleviate concerns about omitted variable bias at the state level, we compare the e¤ect

of deregulation on male and female labor market outcomes between narrowly de…ned adjacent

geographic regions across state borders, and continue …nding a signi…cant deregulation e¤ect.

We identify four channels responsible for the narrowing of the gender gap in labor market

outcomes in the wake of bank branching deregulation. The …rst is the extensive margin of the

labor supply, whereby in deregulated states, and ceteris paribus, females are relatively less likely to

be classi…ed as "Not in the labor force". Second, in deregulated states, women are relatively more

likely to be classi…ed as "private sector employees". This helps explain the narrowing of the gender

gap in wages as those working in the private sector have on average higher wage income than public

sector employees and self-employed individuals. Third, we …nd that in deregulated states, female

private sector employees are relatively more likely to work in high-income jobs, and in particular in

high-income jobs in sectors previously dominated by male employees. This suggests that the shift

in labor demand induced by credit market competition was associated with the creation of jobs

characterized by a relatively higher pecuniary bene…t to female employees. Finally, we …nd that

following bank branching deregulation, working-age females are markedly less likely to be high-

school drop-outs and considerably more likely to have enrolled in, and even more likely to have

graduated from, college. This is possibly because changes in local labor demand induced by credit

market competition raised the return to skilled labor, a¤ecting simultaneously education and labor

choices.

The relatively equal opportunities that women nowadays enjoy in labor markets across Europe

and North America— compared with most of the rest of the world and with most of history— is one of

the principal achievements of modern western civilization. While a gender gap still persists in labor

force participation and in wage income even in industrialized economies, we show that credit market

competition can play a substantial and previously undocumented role in narrowing the gender gap

in labor markets. This …nding has clear policy implications in an European environment which is

considered "overbanked" and where cross-border bank consolidation has picked up speed since the

global …nancial crisis.

ECB Working Paper 2086, July 201731 Introduction

Between 1970 and 1995, in what was arguably the most signi…cant change in labor markets at the

time, the labor force participation rate for working-age women in the United States increased by 26

percentage points, from 50 percent to 76 percent (see Goldin, 2006). This development represents

a substantial closing of the gender gap in employment, with male labor force participation at

around 90 percent during this period. Wage inequality declined considerably, too, with the ratio

of female-to-male wage and salary income increasing from about 0.60 in 1970 to 0.73 in 1995 (see

Blau and Kahn, 2000). Prominent conventional explanations for the unprecedented convergence

in labor market outcomes between men and women during this period focus on the waning social

stigma surrounding married women’s work outside the home (Fernandez, Fogli, and Olivetti, 2004;

Goldin, 2006), on the the adoption of time-saving technologies due to technological progress in the

home-durable-goods sector (Greenwood, Seshadri, and Yorukoglu, 2005), and on the introduction

of oral contraception which facilitated women’s investment in their career (Goldin and Katz, 2002).

In this paper, we demonstrate the powerful contribution of a neglected factor in explaining the

narrowing of the gender gap in local U.S. labor markets: the signi…cant improvement in access

to …nance induced by the deregulation of the U.S. banking industry during the 1970s, 1980s, and

early 1990s. Over this period, U.S. states gradually lifted restrictions on bank branching within

the state and eliminated entry barriers to out-of-state banks. We hypothesize that by reducing the

cost of bank credit, intensi…ed competition among …nancial intermediaries increased …rms’demand

for labor, as well as women’s demand for schooling in general and for career-oriented subjects in

particular, with a material e¤ect on female labor force participation rates and labor income. We

use the state-level variation in deregulation dates as a way to measure the impact of changing credit

conditions on the gender gap. Furthermore, to alleviate concerns about omitted variable bias at

the state level, we compare the e¤ect of deregulation on male and female labor market outcomes

between narrowly de…ned adjacent geographic regions across state borders.

Using data on 1.5 million individuals between 1977 and 1994 from the Current Population

Survey (CPS), we …nd that more vigorous banking competition— that is, looser restrictions on the

geographic expansion of banks— is associated with a lower gender gap in labor force participation

and in wage income. Our di¤erence-in-di¤erences estimates suggest that the removal of restrictions

ECB Working Paper 2086, July 20174on intra-state bank branching increased the number of weeks worked per year by up to one more

for women than for men.

It also increased income from weekly wages by up to $40 more for

employed females than for employed males. Banking deregulation thus reduced the gender gap in

labor force participation by up to 29 percent, and gender inequality in wages by up to 18 percent.

This result is robust to controlling for a wide host of background characteristics that can determine

labor market outcomes, such as age, race, education, and marital status. Our main results are also

robust to including in the regressions state

(cid:2)

year …xed e¤ects which control for any unobservable

state-speci…c time-varying factors that a¤ect all economic agents in a state equally, as well as for

US-wide trends, such as the propensity of women with higher unobserved skills to enter the US

labor market during the 1970s and 1980s (Mulligan and Rubinstein, 2008).

Some developments, however, have an uneven e¤ect across genders. Changes in social norms

and cultural identities— both of which were rapidly evolving during this period— bene…t women

disproportionately and can have a signi…cant impact on the gender gap over time (Akerlof and

Kranton, 2000; Fernandez, Fogli, and Olivetti, 2004; Bertrand, Kamenica, and Pan, 2015). The

gradual decline in gender bias leads, for example, to the democratization of hiring procedures

(Goldin and Rouse, 2000). While we do not claim that progress in …nancial markets is a more

powerful determinant of the narrowing of the gender gap than progress in social norms, we need

to take into account the possibility that our results are contaminated by changes in the relative

power of women in society or within the family. In principle, this would be the case if changes in

social norms coincide with the relaxation of entry barriers to intra-state banking. We tackle this

concern by constructing three empirical proxies. The …rst one is a proxy for cultural norms which

we construct from survey responses on questions about the "proper" place of women in society. The

speci…c statement respondents are asked to evaluate is: “It is much better for everyone involved if

the man is the achiever outside the home and the woman takes care of the home and family.” We

use the time-varying regional share of those who answered "Strongly agree" or "Agree" as a proxy

for cultural norms that are averse to gender equality in labor markets. The second empirical proxy

is the time-varying share of women in each state that have at least some college education. The

third proxy is the time-varying share of women in each state who are active in the labor market.

We claim that all of these proxies capture cultural and social norms related to gender inequality.

The measured contribution of improved access to …nance to the narrowing of the gender gap in

ECB Working Paper 2086, July 20175labor markets is robust to controlling for such state-speci…c time-varying gender norms that can

have a di¤erent impact on men and on women.

We base our main analysis on an arguably exogenous shock to the cost of credit, in combination

with an empirical speci…cation that accounts for an exhaustive set of potential confounding factors

at the individual and at the state level. Even so, comparing individuals across states— with respect

to bank deregulation events— can be open to a number of econometric problems. Pro-competitive

banking reform can be induced by an expectation of future growth opportunities (unobservable to

the econometrician) that can bene…t women disproportionately, e.g., the expansion of sectors rich in

jobs requiring female-speci…c skills. This could create a spurious correlation between bank branching

deregulation and future changes in the gender gap. To address this issue, we modify the empirical

strategy used by Card and Krueger (1994), Holmes (1998), Black (1999), and Huang (2008) and

compare individual labor market outcomes for men and women across 28 contiguous Metropolitan

State Areas (MSAs) separated by state borders, in cases in which one state deregulated intrastate

branching earlier than the other. Because these MSAs are immediately adjacent neighbors, we

expect them to be similar in both observable, and more importantly, unobservable conditions, and

to follow similar economic paths in the absence of changes in bank entry barriers. Our main results

still obtain even in this considerably more restrictive speci…cation, suggesting that we capture a

genuine bank deregulation e¤ect uncontaminated by concurrent unobservable adjustments— at the

level of the state— in economic or labor market conditions that a¤ect men and women di¤erently.

We next turn our attention to the mechanisms explaining the pattern which we uncover in the

data. We identify four channels responsible for the narrowing of the gender gap in labor market

outcomes in the wake of bank branching deregulation. The …rst is the extensive margin of the

labor supply. The literature has already found that by intensifying competition among …nancial

intermediaries, banking deregulation increased the rate of new business creation. This, in turn,

resulted in the creation of more private sector jobs (Black and Strahan, 2002; Cetorelli and Strahan,

2006; Kerr and Nanda, 2009). We …nd that in deregulated states, and ceteris paribus, females are

relatively less likely to be classi…ed as "Not in the labor force". Our …ndings are supported by prior

literature which has found that the expansion of the service sector with its accompanying white-

collar jobs has greatly facilitated the transformation in female labor force participation (Goldin,

1990). In addition, this channel is particularly potent during a period when female labor supply

ECB Working Paper 2086, July 20176was more elastic than male labor supply (Goldin, 2006).

Second, we also …nd that in deregulated states, women are relatively more likely to be classi…ed

as "private sector employees". This helps explain the narrowing of the gender gap in wages as those

working in the private sector have on average higher wage income than those working in the public

sector, as well as self-employed individuals.

Third, we …nd that in deregulated states, female private sector employees are relatively more

likely to work in high-income jobs, and in particular in high-income jobs in sectors previously

dominated by male employees. This suggests that the shift in labor demand induced by credit

market competition was associated with the creation of jobs characterized by a relatively higher

pecuniary bene…t to female employees. This evidence is consistent with the fact that currently,

the majority of the observed earnings gap comes from within-occupation di¤erences in earnings,

while the between-occupation gender gap has largely dissipated over time (Goldin, 2014).

It is

also consistent with the observation that between 1970 and 2000, women’s marginal returns to

experience increased substantially more than men’s (Olivetti, 2006).

The fourth channel is the acquisition of formal education. We …nd that following bank branch-

ing deregulation, working-age females are markedly less likely to be high-school drop-outs and

considerably more likely to have enrolled in, and even more likely to have graduated from, college.

This is possibly because changes in local labor demand induced by credit market competition raised

the return to skilled labor, a¤ecting simultaneously fertility, education, and labor choices (Galor

and Weil, 1996). Such changes in local labor demand may be related to skilled-based technolog-

ical change (Goldin, 1990), which in itself can intensify with the development of deeper …nancial

markets (Saint-Paul, 1992). We conclude that higher rates of schooling— likely in career-oriented

subjects— and labor market shifts explain much of the narrowing of the gender gap in labor income

in the wake of bank branching deregulation.

By now there exists a vast body of evidence on the impact of banking deregulation on various

economic outcomes, such as state business cycles (Morgan, Rime, and Strahan, 2004; Demyanyk,

Ostergaard, and Sorensen, 2007), personal bankruptcy (Dick and Lehnert, 2010), e¢ cient reallo-

cation (Acharya, Imbs, and Sturgess, 2011), trade (Michalski and Ors, 2012), education choices

(Levine and Rubinstein, 2013), income distribution (Beck, Levine, and Levkov, 2010), and racial

inequality (Levine, Levkov, and Rubinstein, 2014). In particular, the latter two papers also use

ECB Working Paper 2086, July 20177detailed individual level-data from the CPS, and they also consider labor market e¤ects, showing

that banking deregulation resulted in more inclusive labor markets and increased labor income rel-

atively more for unskilled and for Black workers. We contribute to this literature by demonstrating

the signi…cant impact of bank branching deregulation on female labor market outcomes during the

1970s, 1980s, and 1990s. To our knowledge, ours is the …rst paper to establish a causal link between

credit market competition and the narrowing of the gender gap in U.S. labor markets.

Also related to our paper is a large empirical literature that has studied extensively the impact of

improved access to …nance across U.S. states on the channels we identify. Researchers have shown

that, following bank branching deregulation, competition in non-…nancial markets intensi…ed as

the rate of new business creation picked up, increasing materially the total number of …rms in the

economy (Cetorelli and Strahan, 2006). Recent evidence also points to the fact that the demand

for higher education increased in deregulated states as private student loans from banks to students

became cheaper and more readily available (Sun and Yannelis, 2016). Our paper complements this

literature by providing evidence that the twin increase in business activity and in the demand for

higher education had a disproportionately larger e¤ect on women, helping close the gender gap in

both labor force participation and in labor income.

Our paper is also related to the literature on the e¤ect of …nancial market conditions on em-

ployment. One strand of research has studying the e¤ect of …rm debt on …rm-level employment

and wages (Lichtenberg and Siegel, 1990; Hanka, 1998; Falato and Liang, 2016). Another, more

recent line of research has attempted to gauge the e¤ect of access to external …nance on employ-

ment, broadly demonstrating that positive (negative) shocks to the cost of external …nance increase

(decrease) labor demand, with material e¤ect on equilibrium employment and wages (Benmelech,

Bergman, and Seru, 2011; Campello, Graham, and Harvey, 2010; Pagano and Pica, 2012; Bentolila,

Jansen, Jimenez, and Ruano, 2013; Chodorow-Reich, 2014; Duygan-Bump, Levkov, and Montoriol-

Garriga, 2015; Popov and Rocholl, 2015). More closely related to our paper is the study by Brown

and Matsa (2016) who show that …rms’worsening access to …nance results in a decline in the supply

of mostly skilled labor, and the study by Hochfellner, Montes, Schmalz, and Sosyura (2016) who

document that the lowest-paid workers are more likely to leave the …rm when credit constraints

tighten. Our paper contributes to this literature by demonstrating the heterogeneous impact that

(positive) shocks to the cost of external …nance have across genders in labor markets.

ECB Working Paper 2086, July 20178We also contribute to the literature which has sought to identify and quantify the gender gap

in labor market outcomes, such as the gap in labor force participation (Greenwood, Seshadri, and

Yorukoglu, 2005), the gap in wage income (Bayard, Hellerstein, Neumark, and Troske, 2003), and

the gap in hiring (Neumark, Bank, and Van Nort, 1996). While some analysts have argued that this

gap is primarily driven by male-female di¤erences in productivity and in work experience (O’Neill

and Polachek, 1993; Mulligan and Rubinstein, 2008), a more wide-ranging view is that equally

productive men and women face di¤erent job prospects and strike di¤erent wage bargains with their

employees (Card, Cardoso, and Klein, 2016). Altonji and Blank (1999) …nd that after controlling for

education, experience, personal characteristics, city of residence, occupation, industry, government

employment, and part-time status, only about 27 percent of the gender wage gap is explained by

di¤erences in observable characteristics. Our paper is even more closely related to studies that map

changes in credit market competition into market outcomes, by gender. For example, Cavalluzzo,

Cavalluzzo, and Wolken (2002) and Hertz (2011) …nd that female-owned …rms are less likely to apply

for and/or to obtain bank credit than male-owned …rms, both in the US and in an international

context. Belluci, Borisov, and Zazzaro (2010) …nd that female …rm owners in Italy face tighter

credit constraints when dealing with one individual bank. Alesina, Lotti, and Mistrulli (2013)

…nd that after controlling for entrepreneurial risk, female borrowers pay higher rates, especially

when their guarantor is a female, too. Muravyev, Schäfer, and Talavera (2012) …nd that female

…rm owners are more likely to be denied bank credit and that they tend to pay higher rates on

bank loans. Fortin (2005) …nds that the gender gap in employment and pay is higher in countries

with anti-egalitarian views. Ongena and Popov (2016) …nd that in countries with higher gender

bias, and after controlling for credit risk di¤erences between female- and male-owned …rms, female

entrepreneurs are more frequently discouraged from applying for bank credit and more likely to

rely on informal …nance, even though banks do not appear to discriminate against female loan

applicants.

The paper is organized as follows. Section 2 provides some background on banking deregulation

and discusses the validity of using this policy innovation to study labor market outcomes. Section 3

describes our data set. Section 4 explains our research design and discusses identi…cation. Section

5 presents and discusses the main results of the paper. Section 6 discusses the implications of our

…ndings and concludes.

ECB Working Paper 2086, July 201792 Institutional background

2.1 Bank deregulation: Institutional background

Banking became a highly regulated industry in the wake of the Great Depression. Most states

either prohibited branching altogether or limited it severely until the 1970s, when only 12 states

plus the District of Columbia allowed unrestricted state-wide branching. By 1994, however, another

35 states deregulated their restrictions on branching. In addition, in the early 1980s many states

began to enter regional or national reciprocal arrangements whereby their banks could be bought by

any state in the arrangement. Subsequently, in 1993 Montana became the last state to deregulate

interstate banking. Finally, in 1994, the Riegle-Neal Interstate Banking and Branching E¢ ciency

Act removed all branching restrictions in states that had not deregulated their banking markets

up to that point. Similar to Jayaratne and Strahan (1996), we focus on the removal of branch

restrictions within states (by means of mergers and acquisitions), because states usually deregulated

intra-state banking and only then moved to deregulate inter-state banking.

Table I reports deregulation events, by state and year. The extensive literature on the e¤ects

of banking deregulation …nds that deregulation was generally a positive development, leading to

greater bank e¢ ciency and competition, lower prices and higher quality, new business formation,

and higher economic growth (Jayaratne and Strahan, 1996; Black and Strahan, 2002). The fact

that most states chose to remove these barriers at di¤erent times provides us with variation in

the competitive environment facing lenders that is potentially exogenous with respect to individual

labor market outcomes. We capture the e¤ect of intra-state branching deregulation by constructing

an indicator variable equal to 1 in the years after a state permits branching by means of merger

and acquisition within its borders.

Our binary measures by de…nition are unable to capture how much the lifting of branching

restrictions a¤ected the size of the banking sector. For that reason, in robustness tests we also use

data on total in-state and out-of-state assets held by holding companies operating in each state in

each year, divided by total banking sector assets in each state-year. This is an attractive measure

as it provides us with a continuous variable capturing the magnitude of the ‡ows in banking capital.

ECB Working Paper 2086, July 2017102.2 Bank deregulation: Endogeneity

One question that has often been raised is whether banking deregulation was really an exogenous

event. Jayaratne and Strahan (1996) argue that states did not deregulate their local banking

markets in anticipation of future growth prospects. Kroszner and Strahan (1999) show that it is

mostly economic and political factors (such as the relative strength of large banks and of small

bank-dependent …rms) that explain the timing of branching deregulation across states. Still, the

exogeneity of banking deregulation to how friendly local labor market conditions are to females can

be put into question. For example, states with a lot of educated females or with a lot of already

working females, as well as states with a narrower cultural gender gap, might relax bank branching

restrictions sooner in anticipation of gains to both banks and females of improved credit conditions.

To address this potential issue, we construct three state-level measures of the relative economic

and cultural position of women in a state prior to deregulation. The …rst is the share of working-age

females who have some college education (i.e., college drop-outs, college graduates, or individuals

with a graduate or a professional degree). The second is the share of females out of all those in the

labor force (i.e., self-employed, privately employed, or employed by the government). The third

is the regional share of respondents in the General Social Survey (GSS) who answer "Strongly

disagree" or "Disagree" when confronted with the following statement: "It is much better for

everyone involved if the man is the achiever outside the home and the woman takes care of the

home and family." We de…ne the measures as of 1977, that is, in the …rst year when staggered

banking deregulation started. Figures 1–3 show the relationship between these variables and the

number of years it took the state to deregulate its local banking market after 1977. As can be

seen, states with a higher proportion of educated women, with a higher share of working women,

and with social norms that are more accepting of working women do not appear to be deregulating

earlier.1

3 Data

Our empirical analysis uses individual-level data on demographic characteristics, employment, ed-

ucation, labor market participation, and income from the Integrated Public Use Microdata Series

1 The respective correlations are 0.11, 0.11, and -0.22.

ECB Working Paper 2086, July 201711(IPUMS) (Flood et al., 2015). We are interested in yearly observations, which are available in the

March Annual Demographic Supplements from the U.S. Current Population Survey (IPUMS-CPS).

Our sample is restricted to the survey years 1977–1994, which coincide with the period when most

banking deregulations took place. We exclude the population that is not part of the labor force by

dropping individuals younger than 21 and older than 65 years. For the 18-year period considered

in our analysis, this yields a total of 1,554,571 households across the 50 states and the District of

Columbia.

We measure labor force participation by the number of weeks of work during the preceding cal-

endar year reported by the respondents. This proxy is consistent with other studies of the supply

of labor (e.g., Acemoglu, Autor, and Lyle, 2004). We are also interested in weekly labor income,

which we de…ne as the respondents’total pre-tax wage and salary income in the preceding calendar

year, divided by the number of weeks worked. Only respondents with strictly positive wage income

are considered in the regressions where the logarithm of wage income is the dependent variable,

which reduces our sample by 425,223 observations. In terms of employment, we construct …ve occu-

pational categories: Self-employed, Employed in the private sector, Employed in the public sector

(including Armed forces), Unemployed, and Not in the labor force. Furthermore, the respondents

are classi…ed into three educational categories: High-school or less (includes all persons with 0 to

12 years of schooling), College drop-outs (includes all respondents who have less than four years of

college education), and College or more (includes all respondents with at least a college degree). In

terms of demographics, we use data on gender, age, race, and marital status.

Table II reports summary statistics of all individual characteristics of interest. The table shows

that the average respondent from our sample is female, white, married, and around 40 years old.

50% of the respondents are employed in the private sector, 13% in the public sector, 8% are self-

employed, and 24% are not in the labor force. The remaining 5% are unemployed. In terms of

educational attainment, the average respondent has a high-school degree or less. This is an average

for all 18 years in our sample, and varies strongly over time. In terms of employment, the average

respondent works in the private sector. The summary statistics also reveal that the respondents

work, on average, 36 weeks per year. The mean wage amounts to $17,351.

Figures 4–7 use the IPUMS data to illustrate the substantial narrowing in the female labor gap

over the sample period. While in 1977, in the majority of the U.S. states women were working on

ECB Working Paper 2086, July 201712average between 60% and 65% of the number of weeks per year that men worked (Figure 4), in

1994 they were working on average between 75% and 80% of the number of weeks per year that

men worked (Figure 5). Turning to the gender gap in wages, Figure 6 demonstrates that in most

states in 1977, female labor income was on average between 0.45 and 0.50 of male labor income.

This picture strongly contrasts with 1994 when in most states, working females’labor income was

more than 60 percent— and in 8 states it was more than 70 percent— of male labor income (Figure

7).

4 Empirical model

Our goal is to explore how competition in local banking markets a¤ects the gender gap in labor

markets. To that end, we study the relationship between intrastate branching deregulation at the

state level and two aspects of the gender gap, relative labor force participation and relative pay.

The exogenous increase in local credit market contestability allows us to explore the question of

how access to …nance and labor market outcomes are related. To analyze the two e¤ects, we specify

the following two linear probability models:

W eeksW orkedLastY eari;s;t = (cid:12)1F emalei;s;t + (cid:12)2F emalei;s;t

Deregulations;t

1

(cid:0)

(cid:2)

(1)

+(cid:12)3Xi;s;t + (cid:12)4(cid:9)s;t + "i;s;t

and

LogW eeklyW ageIncomei;s;t = (cid:12)1F emalei;s;t + (cid:12)2F emalei;s;t

Deregulations;t

1

(cid:0)

(cid:2)

(2)

+(cid:12)3Xi;s;t + (cid:12)4(cid:9)s;t + "i;s;t

The two main variables of interest are W eeksW orkedLastY eari;s;t and LogW eeklyW ageIncomei;s;t.

The former is equal to the number of weeks worked by individual i in state s in year t

1. The

(cid:0)

latter is equal to the logarithm of total wage income divided by the number of weeks worked, for

individual i in state s in year t

(cid:0)

1. F emalei;s;t is an indicator variable equal to one if individual i

ECB Working Paper 2086, July 201713in state s in year t is a woman. Deregulations;t is an indicator variable equal to one for states that

have removed barriers to intra-state branching, and zero otherwise; we drop all observations in a

state during the year of deregulation. This indicators captures changes in credit supply induced by

the intensi…cation of competition in the banking sector. Xi;s;t is a vector of individual-level covari-

ates including age, race, education, marital status, income, and occupation. We also include (cid:9)s;t,

a matrix of state

year …xed e¤ects. These control for (observable and unobservable) state-wide

(cid:2)

temporal shocks that are common to all agents in a state

year. This is enormously important as

(cid:2)

any variation at the state level in labor market conditions, labor demand, or growth opportunities

can a¤ect the estimates. We do not include the variable Deregulation on its own because its e¤ect

on the gender gap in labor markets is subsumed in (cid:9)s;t. Similar to Cetorelli and Strahan (2006),

the sample period is 1977–1994.2 Deregulation equals one throughout the sample period for the

15 states plus the District of Columbia, which removed bank branching restrictions earlier. We

employ models with robust standard errors clustered at the MSA level. Finally, all estimates are

weighted using sampling weights provided by the CPS.

Our hypothesis is that cross-state patterns of the reduction of the gender gap during the 1970s,

1980s, and 1990s re‡ect the e¤ect of competition among …nancial intermediaries on female labor

supply and skill acquisition. Our main coe¢ cient of interest is (cid:12)2 which captures the narrowing

in the gender gap in employment and wage income stemming from increasing access to credit via

lifting intra-state branching restrictions. For example, a positive coe¢ cient (cid:12)2 will imply that ceteris

paribus, and compared to an otherwise similar male, a female in a deregulated state is employed for

more weeks per year than a female in a state that has not yet lifted branching restrictions (Model

(1)) and has a higher weekly wage income than a female in a state that has not yet lifted branching

restrictions (Model (2)).

We perform our main tests by comparing populations of males and females to each other and

across state borders. While intuitive, this empirical strategy can result in biased estimates in the

presence of unobservable trends that di¤er across states at each point in time. For example, if

banking deregulation takes place in Virginia and subsequently the narrowing of the gender gap

in labor markets is faster than the national average, the econometrician will conclude that the

2 1977 marks the beginning of the period of dramatic state-level deregulation. We end the sample in 1994, when
deregulation of restrictions on the ability of banks to expand across local markets was completed with the passage of
the Riegle–Neal Interstate Banking and Branching E¢ ciency Act.

ECB Working Paper 2086, July 201714policy reform has produced a positive e¤ect. However, this may not be a good comparison: when

comparing Virginia (an early deregulator) to Arkansas (a late deregulator), it may be easy to …nd

di¤erence in labor market outcomes for males and for females simply because the labor markets of

the two states do not necessarily move in the same direction or at the same speed. While in Models

(1) and (2) we control for observable individual factors that determine labor outcomes, there may

still be unobservable ones at work that di¤er across the two states, such as innate preference for

consumption over leisure time. In the absence of good controls for those, it can be fallacious to

interpret the narrowing of the gender gap in Virginia as the e¤ect of a policy adopted in Virginia

but not in Arkansas.

To address this issue, we follow closely the approach in Huang (2008) and we compare indi-

viduals across border areas of two neighboring U.S. states where banking deregulation is di¤erent

across state borders. In such areas, factors that are di¢ cult for the econometrician to control for,

such as individual preferences, social norms, climate conditions, or the technological demands of

the local economy, are very similar. Therefore, the e¤ect of a certain policy change— bank branch-

ing deregulation, in this case— should be more precisely identi…ed by comparing di¤erential labor

market outcomes across state borders.

As the individuals in the annual CPS samples do not report their county of residence, we

perform this exercise at the level of the MSA. We determine MSAs that lie across state borders,

that are of approximately equal size, and that experienced deregulation events separated by at least

3 years, and we re-estimate Models (1) and (2) in these samples. Using state borders, 14 events of

state-level branching deregulation throughout the United States spanning our sample period can

be evaluated. Table III lists all such "treatment" and "control" MSAs.

5 Empirical evidence

In this Section, we present the results of our empirical tests. In Section 5.1, we present the main

results from tests where we study the e¤ect of bank branching deregulation on the labor gender

gap across U.S. states. In Section 5.2, we provide additional evidence from robustness tests where

we adopt di¤erent proxies for bank competition, we control for the e¤ects of evolving culture,

and we estimate the e¤ect of deregulation on narrowly de…ned economic zones across state borders.

ECB Working Paper 2086, July 201715Finally, in Section 5.3 we present evidence on the economic mechanisms which banking deregulation

activated and which accelerated the narrowing of the labor gender gap.

5.1 Main result

5.1.1 Labor force participation

Table IV shows result from our basic Model (1) where we estimate the di¤erential e¤ect on labor

force participation of banking deregulation across genders. We start with a speci…cation without

individual controls and …xed e¤ects (column (1)), and then we gradually add individual character-

istics (column (2)), state and year …xed e¤ects (column (3)), and state

year …xed e¤ects (column

(cid:2)

(4)).

In all regressions, the standard errors are adjusted to allow for heteroskedasticity of the

within-state shock.

In all variants of Model (1), the coe¢ cient on the interaction term between the Deregulation

dummy and the F emale dummy is positive and signi…cantly di¤erent from zero at the .01 statistical

level. The point estimate is remarkably stable across speci…cations. The point estimate in our

preferred speci…cation that controls for individual characteristics and for state-speci…c trends that

a¤ect both genders equally (column (4)) implies that removing intra-state branching restrictions

increased female labor force participation by 0.62 weeks more than it did for men. This e¤ect is

economically meaningful as well. In our sample, individuals work on average 35.8 weeks, so the

increase in female labor force participation corresponds to 1.7 percent increase relative to male labor

force participation. Alternatively put, and controlling for demographics, women in our sample work

on average 3 weeks per year less than men, and so bank branching deregulation thus narrows this

gap by about 21 percent.

Turning to the remaining individual characteristics, the data predictably suggest that age has

an inverse U-shaped e¤ect on working hours, with weeks worked per year peaking at around 45

years of age and then decreasing again. Married and single individuals tend to work less than those

who are divorced, separated, or widowed. Black individuals work about a week per year less than

everyone else. Those with some college education work about three weeks more, and high-school

drop-outs about three and a half weeks less, than high school graduates. Finally, self-employed,

privately employed, and publicly employed individuals work on average between 38 and 40 more

ECB Working Paper 2086, July 201716weeks that those not in the labor force. Relative to the latter, unemployed individuals worked on

average 20 more weeks last year, suggesting that their current unemployment status can date from

last year already. Overall, the model with individual controls and state

(cid:2)

year …xed e¤ects explains

a substantial 66 percent of the variation in labor force participation.

Interestingly, in the speci…cation with individual controls and with state and year …xed e¤ects,

but without state

(cid:2)

year …xed e¤ects, we …nd that banking deregulation reduces the average labor

force participation in the sample by about a third of a week. However, both the sign and magnitude

of this e¤ect are questionable as there are many potential confounding state-speci…c trends taking

place at the same time.

5.1.2 Wage gap

Table V shows the estimates of Model (2). We estimate the model for the sub-sample of individuals

in the CPS who are in the labor market, i.e., we do not want the estimated di¤erences in labor

income to be contaminated by the proportion of agents with zero labor income. Similar to Table

IV, we report estimates from regressions that are increasingly saturated with demographic controls

and with various combinations of …xed e¤ects, and we adjust the standard errors to allow for

heteroskedasticity of the within-state shock.

Once again, in all speci…cations the coe¢ cient on the interaction term between the Deregulation

dummy and the F emale dummy is positive and signi…cantly di¤erent from zero at the .01 statistical

level. The point estimate is very similar across all speci…cations that control for individual-speci…c

factors that can a¤ect wage income, such as age, race, education, marital status, and types of

occupation. The point estimates in columns (2)–(4) imply that removing intra-state branching

restrictions increased female wage income by around 11 percent more than male wage income.

Given average weekly income in our sample of $367.33, this relative increase in female wage income

corresponds to around $40.4 per week worked. Given that average male weekly wages are higher

by about $230, the evidence thus implies that controlling for demographics and for state-speci…c

trends, over the period 1977–1994 bank branching deregulation narrowed the wage income gap

between men and women by about 18 percent.

The individual controls have a similar e¤ect as in Table IV. Once again, age has an inverse

U-shaped e¤ect on wage income, peaking at around 46 years of age. Married individuals have

ECB Working Paper 2086, July 201717a signi…cantly higher, and single individuals signi…cantly lower wage income than those who are

divorced, separated, or widowed. Black individuals’ weekly wage income is on average about 13

percent lower than everyone else’s. Those with college education have a weekly wage income

higher by 30 percent, and those with at most a high school dimploma have a weekly wage income

lower by 26 percent, than college drop-outs. Finally, private sector employees and public sector

employes have a substantially higher wage income than those not in the labor force. Self-employed

individuals, whose main income is from their business rather than from wages, have a lower wage

income than both private and public sector employees. Overall, the model with individual controls

and state

(cid:2)

year …xed e¤ects explains 32 percent of the variation in weekly wage compensation.

5.2 Robustness

5.2.1 Alternative measures of deregulation

In Table VI, we test for whether our main results are robust to measuring the intensi…cation of

competition in local banking markets di¤erently. For a start, our dummy variable classi…es a state

as deregulated if it has lifted bank branching restrictions in a particular year. As such, it captures

the average change in labor market outcomes after deregulation, but it does not allow us to tease out

the timing of these changes. In columns (1) and (4) of Table VI, we replace the bank deregulation

dummy with the distance to deregulation event, in years. This variable ranges from -17 (the state

will deregulate 17 years from now) to 19 (the state deregulated 19 years ago).3 The point estimate

reported in column (1) implies that the number of weeks worked per year increase by about 1 more

for females, relative to males, per every eight years after deregulation. In terms of labor income

e¤ect, column (4) implies that in every post-deregulation year, the wage gap between female and

male workers declines by about 1.1 percent.

Second, competition was introduced in local banking markets in two di¤erent ways: by allowing

in-state banks to branch out within the state, by means of mergers, acquisitions, or de novo branches

(intra-state deregulation), and by allowing out-of-state banks to enter the local market via bank

holding companies (intra-state deregulation). While the second policy is more relevant for questions

related to …nancial and economic integration (see, e.g., Morgan, Rine, and Strahan, 2004; Michalski

3 In this exercise, we exclude the 13 states that lifted bank branching restrictions before 1977.

ECB Working Paper 2086, July 201718and Ors, 2012), it has been studied alongside intra-state branching deregulation in the context of

local economic outcomes, such as new business creation (Black and Strahan, 2002; Cetorelli and

Strahan, 2006). Both policy reforms represent a shift in the level of local contestability of the

market, by immediately increasing the threat of potential entry and reducing the market power

of local incumbents.

In columns (2) and (5) of Table VI, we replace the Deregulation dummy

with a dummy equal to 1 in states that have already opened their local banking market to entry

by out-of-state banks. We …nd a positive e¤ect of this type of bank deregulation on both relative

female labor force participation and relative female labor income. The magnitude of the measured

e¤ects are somewhat larger than in the case of intra-state banking deregulation (Table IV, column

(4) and Table V, column (4)), possibly because inter-state deregulation usually follows intra-state

deregulation, hence, its competitive e¤ect is augmented immediately by an already existing pro-

competitive force.

Finally, and similar to Morgan, Rine, and Strahan (2004), we replace the deregulation dummy

with the ratio of in-state banking assets held by out-of-state banks. This is a de facto rather than

a de jure variable, and it captures the degree of actual competition in local banking markets. Once

again, we …nd that more intense competition in local banking markets led to an increase in the

number of weeks per year that women work, relative to men (column (3)). We also …nd evidence

that competition led to a narrowing in the gender wage gap (column (6)), although this e¤ect is

marginally insigni…cant (p-value of 0.12).

5.2.2 Contiguous counties

In our main tests so far, we have been comparing females’labor market outcomes— relative to males’

labor market outcomes— in a deregulated state (the treatment group) relative to a regulated state

(the control group). As we already argued, this empirical strategy can produce biased estimates

in there are existing unobservable trends which di¤er across states. Economic conditions can be

di¤erent in deregulated states at the time of deregulation, and labor markets in those can already

be becoming friendlier to females for reasons unrelated to deregulation. Models (1) and (2) allow

us to estimate the average e¤ect of deregulation net of the impact of individual characteristics

that can determine propensity to work and/or equilibrium wages. However, our results can still be

contaminated by a host of unobservable factors that make the population of a regulated state a

ECB Working Paper 2086, July 201719poor control group.

To assuage such concerns, we proceed to adopt the approach in Huang (2008) and we compare

individuals in adjacent MSAs across neighboring U.S. states, one of which is deregulated while the

other is still regulated.4 The assumption is that two neighboring MSAs are really one economic

area when it comes to observable factors such as growth and to unobservable factors such as growth

opportunities or labor market conditions. Hence, any discernible di¤erences in how fast the labor

gender gap narrows can be attributed to changes in banking market conditions in one MSA but

not in the other.5

We report the estimates from these modi…ed versions of Model (1) and (2) in Table VII. As we

are only using individuals from the 14 treatment MSAs and the 14 control MSAs listed in Table

III, the number of observations is reduced to 35,749 in the regression where the dependent variable

is the number of weeks worked last year, and to 26,493 in the regression where the dependent

variable is labor compensation. Our main results obtain in this reduced sample, too. In particular,

we …nd that removing intra-state branching restrictions increased female labor force participation

by 1.1 weeks more than it did for men, in MSAs lying on the deregulated side of a state border

relative to MSAs lying on the regulated side (column (1)). The e¤ect is signi…cant at the 1 percent

statistical level, and its magnitude is numerically larger than in the full sample (Table IV, column

(4)), suggesting that unobservable factors unrelated to deregulation that reduce the gender gap do

not lead to an upward bias in our estimates. Numerically, the point estimate implies that bank

branching deregulation narrows the gender gap in labor force participation by 29 percent.

We also continue to …nd that banking deregulation contributed to the reduction of gender

inequality in wage income (column (2)). The e¤ect is signi…cant at the 5 percent statistical level,

but this time the magnitude is smaller than in the full sample (Table V, column (4)), amounting

to a 8 percent reduction in the gender gap. This suggests that there may be unobservable factors

at play that reduce the gender gap independently of deregulation, but these are mostly subsumed

in the deregulation e¤ect at the state level.

4 The approach in Huang (2008) relies on comparing contiguous counties. However, the MSA represents the lowest

level of geographical disaggregation in the IPUMS-CPS samples.

5 Arguably, this argument rests on the assumption that the cost of migration across geographic localities is non-
negligible, preventing labor markets from clearing immediately. Empirical evidence suggests that cross-state labor
migration in the United States is indeed limited, with around 1 percent of the population "moving for a job" from
one state to another annually, even during an economic peak (Demyanyk, Hryshko, Luengo-Prado, and Sorensen,
2016).

ECB Working Paper 2086, July 2017205.2.3 Social norms

Another way in which our main results can be biased is if states which deregulated earlier also

had market conditions and social norms more friendly to women. For example, it is possible that

states with a larger share of educated women, a larger share of working females, and a culture that

is more accepting of working women removed bank branching restrictions …rst. This would put

into question the main identifying assumption in the paper, i.e., that the intensi…cation of local

competition among …nancial intermediaries following bank branching deregulation was unrelated

to changes in local labor market that can a¤ect women more than men. While Figures 1–3 suggest

that labor market properties and social norms are not correlated with the timing of deregulation,

these can nevertheless have an e¤ect on the gender gap that can bias the empirical estimates. For

example, states where social norms and labor markets are more friendly to working women may

have experienced larger progress in labor markets independent of deregulation.

To account for this possibility, we now construct three additional variables. The …rst one is a

proxy capturing attitudes towards working women. To construct it, we take survey answers from

the GSS over the period 1977–1994.6 The data set contains a range of demographic and income

characteristics.

Importantly, it contains the variable "FEFAM" which is de…ned by the answer

to following question: "It is much better for everyone involved if the man is the achiever outside

the home and the woman takes care of the home and family." The answers are given on a scale

from 1 to 4. From these answers, we construct a variable which we denote as "Gender bias". The

variable is equal to 1 if the respondent answered "Strongly agree" or "Agree", and equal to 0 if the

respondent answered "Disagree" or "Strongly disagree". We next estimate the following regression

model:

GenderBiasi;r;t = (cid:12)1Xi;r;t + (cid:12)2(cid:9)r;t + "i;r;t

(3)

where Xi;r is a vector of individual-speci…c characteristics for individual i living in region r at

time t,7 and it includes age, education, gender, religion, employment status, and income. (cid:9)r;t is

a matrix of year dummies and of 8 region dummies equal to 1 if the individual lives in one of the

6 See Ongena and Popov (2016) for a similar approach.
7 Because respondents in GSS do not report their state of residence, but only 1 of 9 regions of residence, there is

arguably limited geographic heterogeneity in this variable.

ECB Working Paper 2086, July 201721following 8 regions: Mid-Atlantic, East North Central, West North Central, South Atlantic, East

South Central, West South Central, Mountain, and Paci…c. Then we take the point estimates on

each regional dummy from Model (3) to represent a region-speci…c time-varying estimate of local

gender bias, controlling for income and demographics, and we call this variable Social N orms. We

assign a value of 0 to all individuals living in states in New England (the reference region).

The second empirical proxy for social and market norms is the time-varying share of working-

age females who have some college education (i.e., college drop-puts, college degree, or a graduate

or professional degree), in each state-year. The third one is the time-varying share of females who

are in the labor force (i.e., self-employed, privately employed, or employed by the government), in

each state-year. We believe that all three empirical proxies signal social norms and labor market

attitudes that are more accepting of career-oriented women. Finally, we interact all three proxies

with the F emale dummy, and include the three interaction variables in Models (1) and (2), one

by one.8 We expect that these proxies for more inclusive social norms and labor markets will have

a positive e¤ect on female labor market participation, and a negative e¤ect on the gender gap in

wage income.

Table VIII presents the results from regressions that include these additional controls. We …nd

that in states with a lower gender bias, women work more weeks per year (column (1)). We also

…nd that in states with a higher share of working women, relative to men, women work more weeks

per year (column (2)) and receive higher wage income (column (5)). Finally, we …nd that in states

with a higher share of women with at least some college education, women on average work more

weeks per year (column (3)) and have higher wage income (column (6)). Crucially, we still …nd that

banking deregulation is associated with both higher labor force participation (columns (1)–(3)) and

with higher weekly wage income (columns (4)–(6)), for women relative to men. In all cases, the

e¤ect is signi…cant at least at the 5 percent statistical level, and is of similar magnitude to previous

estimates. Our results thus con…rm that the improvement in access to …nance brought about by

bank branching deregulation has an impact on the labor gender gap that is independent of social

norms and labor market properties that a¤ect women and men di¤erently.

8 The results of these tests are robust to …xing the values of all three proxies as of 1977, preceding the main wave

of deregulation events.

ECB Working Paper 2086, July 2017225.3 Economic mechanisms

5.3.1 Extensive margin of labor supply

We now turn to the economic mechanisms that underline the e¤ect of higher contestability in credit

markets on the labor gender gap. We start by noting that the positive e¤ect of bank branching

deregulation on labor force participation is not unexpected, given what we already know from

the literature.

In particular, two ingredients are necessary and su¢ cient to explain this result.

The …rst is a general increase in labor demand driven by expanding real activity. Jayaratne and

Strahan (1996) argue that banking deregulation increased e¢ ciency in the US banking sector,

leading to reduced corporate lending rates and a general increase in bank lending.9 Black and

Strahan (2002), Cetorelli and Strahan (2006), and Kerr and Nanda (2009) show that as a result,

banking deregulation intensi…ed new business creation in the non-…nancial sector, resulting in an

increase in the total number of business …rms— and in particular, of micro, small, and medium-

sized …rms— in the economy. As the number of enterprises in the economy increases, the demand

for labor increases, too, driving equilibrium wages up.10 The second necessary ingredient is that

women’s labor supply is more wage-elastic than men’s. There is direct or indirect evidence to

that in the literature, too. For example, Goldin (2006) and Bredemeier and Juessen (2013) claim

that as a result of technological advancements and of decreasing fertility, female labor supply at

the time when banking deregulation took place was more elastic with respect to wages than male

labor supply. Hence, an increase in the demand for labor following banking deregulation, and in

the presence of a relatively more elastic female labor supply, is su¢ cient to explain the relatively

higher increase in female labor force participation.11

We would like to understand, though, to what extent the increase in female labor force par-

ticipation is due to action on the extensive margin (i.e., non-working women joining the labor

market).12 In Table IX, we test for the di¤erential e¤ect of banking deregulation on labor market

9 Black and Strahan (2001) show that as a result of reduced monopoly rents, pro…ts and wages in the banking

industry declined in the wake of banking deregulation.

1 0 Goldin (1990) and Smith and Ward (1985) attribute approximately half of the rise in labor force participation

of married women to increases in women’s market wages.

1 1 An alternative mechanism which we do not explore, but which is plausibly at work, too, operates thorough an
increase in the female labor supply as cheaper consumer credit facilitates the adoption of home appliances, such as
washing machines, laundry machines, freezers, etc. For a conceptual argument, see Becker (1981).

1 2 Because IPUMS does not report the individual’s past activity in labor markets, we cannot test for the intensive

margin (i.e., women already in the labor force now working more weeks per year).

ECB Working Paper 2086, July 201723status, across genders. Column (1) reports that in deregulated states, and relative to men, women

are substantially less likely to be classi…ed as "Not in labor force". The e¤ect is signi…cant at

the 1 percent statistical level, and the magnitude is substantial, suggesting that ceteris paribus,

deregulation reduces the probability that a female’s work status will be classi…ed as "Not in labor

force, house work" by 4.5 percentage points. Our results thus con…rm that to some extent, the

change in female identity over the past decades, from housewives to career-oriented professionals

(Goldin, 2006; Juhn and Potter, 2006) has— in the United States at least— been aided by dramatic

changes in the demand for labor brought about by increased competition in credit markets in the

wake of banking deregulation.

5.3.2 Type of labor market activity

We next turn to investigating the mechanisms underpinning the narrowing of the gender pay gap.

Recall that our main tests of the e¤ect of banking deregulation on wage income are performed on the

sub-sample of working individuals only, so the narrowing of the gender pay gap we uncover cannot

be attributed to the transition from unpaid non-marketplace activity to paid marketplace activity.

The rest of Table IX tests for the type of labor market activity that women choose in the wake of

deregulation. We focus on three types of broadly de…ned labor market activities: working in the

private sector (column (2)), working as a federal, state, or local government employee (column (3)),

and being self-employed (column (4)). We …nd that in the wake of banking deregulation, women

are substantially more likely— relative to men— to join the private sector. This e¤ect is signi…cant

at the 1 percent statistical level, and economically important, too: deregulation increases the

probability that a woman will be employed in the private sector by 5.3 percentage points, relative

to an otherwise identical man. Given that women are about 19 percentage points less likely to

work in the privat sector, this represents a 28 percent reduction in the gender gap in private sector

employment. The rest of the table demonstrates that following deregulation, women are not more

likely than men to work for the government or to become self-employed. The increased propensity

to join the labor market found in column (1) is thus con…ned to private sector activity.

Recall that the evidence in Table V suggests that those employed in the private sector have a

substantially higher wage income than self-employed individuals. This is partly because the main

source of income for the self-employed is "business income" and not "wage income", but also because

ECB Working Paper 2086, July 201724income from entrepreneurship is on average lower than income from wage employment, suggesting

that self-employment has mostly non-pecuniary bene…ts (see, e.g., Hamilton, 2000; Moskowitz

and Vissing-Jorgensen, 2002; Hurst and Lusardi, 2004).13 Our evidence suggests that the higher

proclivity of women to work in the private sector— possibly driven by an increase in the supply

of private sector jobs— and not to start their own business, is one mechanism that explains the

narrowing of the gender pay gap.

5.3.3 Choice of employment

We next analyze the choice of employment in a more narrow sense. The narrowing of the gender

pay gap can come not only from a higher proclivity to work in the private sector, where jobs are on

average better paid, but also from a higher propensity to enter better-paid jobs within the private

sector. With a less dynamic private sector, characterized by few large …rms and little job creation

and destruction, the availability of highly-paid jobs may be limited to entrenched males. Because

banking deregulation increased the rates of new business creation (Black and Strahan, 2002) and

the business churn in the private sector (Kerr and Nanda, 2009), the supply of highly-paid jobs

previously o¤-limits for most females possibly increased. Orazem and Mattila (1998) show that

much of the gender gap in pay is related to di¤erent rates of employment in male-dominated versus

female-dominated jobs.

In Table X, we put this hypothesis to the test. For a wide range of industrial occupations

reported in the IPUMS, we calculate the share of males and the average labor income in a particular

occupation in 1977. Then we create a dummy variable "Male job" equal to 1 if a particular

occupation falls in the top half of occupations in terms of share of males employed. We also create

a dummy variable "Well-paid job" equal to 1 if a particular occupation falls in the top half of

occupations in terms of average wage income. Finally, we create a dummy variable "Well-paid

male job" equal to 1 if a particular occupation falls in the top half of occupations both in terms of

share of males employed and in terms of average wage income.

The evidence suggests that on average during the period 1977–1994, women across the United

States were on average less likely than men to work in jobs which in 1977 were predominantly male

1 3 In unreported regressions, we show that total income is also signi…cantly lower for the self-employed than for

those employed in the private sector (results available upon request).

ECB Working Paper 2086, July 201725(column (1)), better-paid (column (2)), and predominantly male and better-paid (column (3)).

At the same time, women become substantially more likely than men to enter such occupations

in states that deregulated their banking markets. We also notice that traditionally-male jobs are

more likely to be populated by high-school dropouts (column (1)), suggesting that such jobs are

not education-intensive and therefore, probably not that well-paid. However, well-paid jobs are

considerably more likely to be populated by individuals with some college education (column (2)),

and so are well-paid male jobs (column (3)). The magnitude of the latter result is particularly

striking: in deregulated states, and relative to men, women are 6.8 percentage points more likely

to enter well-paid, traditionally male occupations. We conclude that a substantial portion of the

narrowing of the gender pay gap in the wake of banking deregulation derives from the propensity

of women to enter better-paid jobs that were in the past occupied mostly by men.

5.3.4 Educational attainment

Goldin (2006) notes that one of the main channels through which the gender pay gap has narrowed in

the United States in recent decades is the increasing number of women who choose to acquire higher

education,14 ultimately restoring the situation from before the Great Depression when most college

graduates were women. Obviously, higher education is a powerful factor in explaining changes in

individual-level productivity and employability.15 This is particularly true in modern industrialized

countries such as the United States, characterized by a gradual shift from low-skilled manufacturing-

dominated to high-skilled service-oriented economies, as well as by skill-biased technical change

(Acemoglu, 1998).

There is already evidence in the literature that the demand for higher education increased

during the post-banking-deregulation period. Recently, Sun and Yannelis (2016) have shown that

household credit in general and student loans in particular increased after states deregulated their

banking industry. They also …nd that lifting banking restrictions raises college enrollment by about

2.6 percentage points. Levine and Rubinstein (2013) show that banking deregulation boosted college

enrollment rates among able students from middle class families, and they argue that the functioning

1 4 The substantial increase in the share of female university students in the US sharply contrasts with the trend
in France— a country where tertiary education is generally free— where the share of female university students has
increased by only four percentage points, from around 50 to around 54 percent, between 1980 and 2010 (see Eurostat).
1 5 Laeven and Popov (2016) provide empirical evidence that young individuals with college degree or more were on

average less likely to be unemployed during the U.S. housing bust of the late 2000s.

ECB Working Paper 2086, July 201726of the …nancial system plays a powerful role in shaping the degree to which a child’s educational

choices— and hence economic opportunities— are de…ned by parental income. However, both papers

are silent about whether women’s demand for higher education increased relatively more. This is

necessary if one is to conclude that higher education is one of the channels via which credit market

competition narrowed the gender pay gap.

In Table XI, we present the estimates from a series of tests that allow us to address this

question. We start by constructing three dummy variables equal to 1 if an individual has at most

a high-school diploma, if an individual has some college education, and if an individual has at

least a college degree. We then regress these variables one by one on the full set of individual

co-variates— excluding the education dummies— as well as on the interaction between the F emale

dummy and the Deregulation dummy. We …nd that, all else equal, individuals who are relatively

young or relatively old, black, divorced, or unemployed are more likely to have at most high school

education (column (1)). At the same time, individuals who are middle-aged, married or single, non-

Black, privately or publicly employed, or self-employed are more likely to have at least a college

degree (column (3)). We also …nd that females are on average more likely to have at most a high

school degree (column (1)) and less likely to have started but not …nished college (column (2)) and

especially to have at least a college degree (column (3)).

Crucially, we …nd that in deregulated states and relative to men, women are less likely on

average to have stopped pursuing their education beyond the high-school level (column (1)). The

e¤ect is signi…cant at the 1 percent statistical level and it amounts to a 2.6 percentage points

decline in the proportion of females with at most high-school education. At the same time, we

…nd that the rates of college enrollment increased substantially among females, and relative to

males, in deregulated states (columns (2) and (3)).

Importantly, women in such states became

about 1.9 percentage points more likely to obtain a college degree (column (3)). The latter e¤ect is

particularly important in light of the theoretical arguments discussed above. It is also numerically

large, given a 10 percent average college graduation rate in the sample.

We conclude that higher college enrollment rates, and the subsequent increase in productivity

and employability, are among the root causes for the narrowing of the gender pay gap in the wake

of banking deregulation. This result is also consistent with the evidence in Franz (1985) who

shows that German women during the period in question were acquiring more higher education

ECB Working Paper 2086, July 201727and supplying more labor at later stages of their lives, even though the author does not relate this

e¤ect to changes in credit market conditions.

6 Conclusion

The relatively equal opportunities that women nowadays enjoy in labor markets across Europe and

North America— compared with most of the rest of the world and with most of history— is one

of the principal achievements of modern western civilization. While a gender gap still persists in

labor force participation and in wage income in the United States, this gap closed rapidly over

the course of the 1970s, 1980s, and 1990s. In this paper, we show that the banking deregulations

of that period, which opened local credit markets to competition from other in-state and out-of-

state banks, has played a substantial and previously undocumented role in narrowing the gender

gap in labor markets. Our results suggest that banking deregulation reduced the gender gap in

labor force participation by up to 29 percent, and gender inequality in wages by up to 18 percent.

This result is robust to employing alternative proxies for local credit market competition, and it is

robust to controlling for a wide range of observable individual-speci…c factors that can a¤ect labor

market outcomes, for state-speci…c trends that a¤ect all agents equally, and for shocks to labor

markets and to social norms that disproportionately bene…t females. Importantly, it also obtains

when we compare individuals across contiguous MSAs sharing a state border where unobservable

confounding factors tend to be similar. We argue that taken together, our tests corroborate a

genuine deregulation e¤ect on the gender gap in labor markets.

We next turn to investigating the mechanisms at play. Banking deregulation increased the

e¢ ciency of credit markets and made credit more widely available and more a¤ordable, both to

consumers and to corporates. We argue that the increase in business activity and in the demand for

labor, with female labor supply at the time being more wage-elastic than male labor supply (Goldin,

1990), explains the rise in female employment. In particular, we …nd that after deregulation, women

are substantially less likely to be classi…ed as "Not in the labor force", suggesting that banking

deregulation a¤ected female labor force participation on the extensive margin. Regarding the

gender pay gap, we identify three forces that have contributed to its reduction. Namely, we …nd

that women in deregulated states are substantially more likely to work in the private sector, to

ECB Working Paper 2086, July 201728enter well-paid traditionally male jobs, and to acquire more formal education.

We stop short of a number of important questions. For example, did a decline in taste-based

discrimination play a role in the narrowing of the gender gap? Levine, Levkov, and Rubinstein

(2014) have recently argued that banking deregulation boosted black workers’ relative wages by

facilitating the entry of new …rms (labor demand e¤ect) and by reducing the manifestation of racial

prejudices (discrimination e¤ect). While we focus on mechanisms related to labor demand and to

the demand for schooling, it is possible that taste-based discrimination against women declined

with banking-sector reforms. We leave this and other important questions for future research.

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ECB Working Paper 2086, July 201735Figure 1: Share of educated women and years to deregulation in 1977

Note: This ﬁgure shows the years remaining until intra-state banking deregulation and the share of working-age
women who have some college education (i.e. at least one year of college), calculated from averages at the state
level in 1977.

ECB Working Paper 2086, July 201736Figure 2: Share of working women and years to deregulation in 1977

Note: This ﬁgure shows the years remaining until intra-state banking deregulation and the share of women who are
in the labor force (i.e. self-employed, employed in the private sector, or employed by the government), calculated
from averages at the state level in 1977.

ECB Working Paper 2086, July 201737Figure 3: Social norms and years to deregulation in 1977

Note: This ﬁgure shows the years remaining until intra-state banking deregulation and the share of respondents
in the General Social Survey who answer ”Strongly disagree” or ”Disagree” when confronted with the following
statement: ”It is much better for everyone involved if the man is the achiever outside the home and the woman
takes care of the home and family.” The share is calculated in 1977 at the regional level.

ECB Working Paper 2086, July 201738Figure 4: Weeks worked, women as share of men, in 1977

Note: This ﬁgure shows the state by state ratio of the average number of weeks worked by women to the average
number of weeks worked by men in 1977. The number of weeks worked is the total number of weeks worked by
the respondent in the year preceding the survey year.

Figure 5: Weeks worked, women as share of men, in 1994

Note: This ﬁgure shows the state by state ratio of the average number of weeks worked by women to the average
number of weeks worked by men in 1994. The number of weeks worked is the total number of weeks worked by
the respondent in the year preceding the survey year.

ECB Working Paper 2086, July 201739Figure 6: Wage income, women as share of men, in 1977

Note: This ﬁgure shows the state by state ratio of womens’ average wage income to mens’ average wage income in
1977. Wage income is respondent’s total pre-tax wage and salary income in the calendar year preceding the survey.

Figure 7: Wage income, women as share of men, in 1994

Note: This ﬁgure shows the state by state ratio of womens’ average wage income to mens’ average wage income in
1994. Wage income is respondent’s total pre-tax wage and salary income in the calendar year preceding the survey.

ECB Working Paper 2086, July 201740Table I: Deregulation years by state

States that deregulated
by 1972

Alaska
Arizona
California
Delaware
District of Columbia
Idaho
Maryland
Nevada
North Carolina
Rhode Island
South Carolina
South Dakota
Vermont

States deregulated
after 1972

Year of
deregulation

Alabama
Arkansas
Colorado
Connecticut
Florida
Georgia
Hawaii
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
New Hampshire
New Jersey
New Mexico
New York
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Tennessee
Texas
Utah
Virginia
Washington
West Virginia
Wisconsin
Wyoming

1981
1994
1991
1980
1988
1983
1986
1988
1989
1994
1987
1990
1988
1975
1984
1987
1994
1986
1990
1990
1985
1987
1977
1991
1976
1987
1979
1988
1985
1982
1985
1988
1981
1978
1985
1987
1990
1988

ECB Working Paper 2086, July 201741Table II: Summary statistics

Weeks worked last year
Weekly wage income
Self employed
Employed (private sector)
Employed (public sector)
Unemployed
Not in the labor force
High-school or less
College drop-out
College or more
Female
Age
White
Black
Other race
Single
Married
Divorced or widowed
Male job (1977)
Well-paid job (1977)
Well-paid male job (1977)
Social norms
Share of working women
Share of educated women

Observations Mean Median

St. dev.

Min

Max

1,614,187
1,172,870
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,614,187
1,198,463
1,198,463
1,198,463
1,614,187
1,614,187
1,614,187

35.76
367.33
0.08
0.50
0.13
0.05
0.24
0.60
0.30
0.10
0.52
39.87
0.87
0.09
0.04
0.18
0.68
0.14
0.63
0.46
0.37
0.08
0.39
0.24

52.00
288.46
0.00
1.00
0.00
0.00
0.00
1.00
0.00
0.00
1.00
38.00
1.00
0.00
0.00
0.00
1.00
0.00
1.00
0.00
0.00
0.07
0.39
0.25

21.71
346.03
0.27
0.50
0.33
0.21
0.43
0.49
0.46
0.30
0.50
12.65
0.33
0.29
0.18
0.38
0.47
0.35
0.48
0.50
0.48
0.07
0.04
0.05

0
0
0
0
0
0
0
0
0
0
0
21
0
0
0
0
0
0
0
0
0
–0.05
0.24
0.11

52
54,000
1
1
1
1
1
1
1
1
1
65
1
1
1
1
1
1
1
1
1
0.25
0.50
0.39

’Self employed’ is a dummy equal to 1 if the respondent is self-employed.

Note: This table presents summary statistics for the main variables used in the empirical tests. ’Weeks
worked last year’ is the total number of weeks worked by the respondent in the year preceding the
’Weekly wage income’ is the respondent’s total pre-tax wage and salary income in the
survey year.
preceding calendar year divided by ’weeks worked last year’. Only respondents with strictly positive
wage income are considered. The employment variables ’Self employed’, ’Employed (private sector)’,
’Employed (public sector)’, ’Unemployed’ and ’Not in the labor force’ classify the respondents according
to their employment status and, if they are in the labor force, the occupation in which they worked
’Employed
the most hours.
(private sector)’ is a dummy equal to 1 if the respondent is an employee in private industry. ’Employed
(public sector)’ is a dummy equal to 1 if the respondent is an employee in public sector. ’Unemployed’
is a dummy equal to 1 if the respondent declared himself as ’unemployed’.
’Not in the labor force’ is
a dummy equal to 1 if the respondent is not in the labor force (e.g doing housework, or being unable
to work, or going to school, etc.). The education categories reveal the respondent’s highest grade of
’High-school or less’ is a dummy equal to 1 if the respondent has
school or year of college completed.
’College drop-out’ is a
between 0 and 12 years of school and obtained at most a high-school diploma.
’College or
dummy equal to 1 if the respondent has between 1 and 4 years of college but no degree.
more’ is a dummy equal to 1 if the respondent has at least a Bachelor’s degree.
’Female’ is a dummy
equal to 1 if the respondent is a female. ’White’, ’Black’, and ’Other race’ are dummies equal to 1 if the
respondent is white, black, or other race, respectively. ’Single’ is a dummy equal to 1 if the respondent
is single.
’Divorced or widowed’ is a
dummy equal to 1 if the respondent is divorced or widowed. ’Male job (1977)’ is a dummy equal to 1 if a
particular occupation falls in the top half of occupations in terms of share of males employed. ’Well-paid
job (1977)’ is a dummy equal to 1 if a particular occupation falls in the top half of occupations in terms
of average wage income. ’Well-paid male job (1977)’ is a dummy equal to 1 if a particular occupation
falls in the top half of occupations both in terms of share of males employed and in terms of average
wage income.
’Male job (1977)’, ’Well-paid job (1977)’, and ’Well-paid male job (1977)’ are based on
job classiﬁcations from 1977. ’Social norms’ is constructed as described in Model (3). ’Share of working
women’ is the share of women who are in the labor force (i.e. self-employed, employed in the private
sector, or employed by the government). ’Share of educated women’ is the share of working-age women
who have some college education (i.e. at least one year of college). Both shares of working and educated
women are calculated from averages at the state level for 4 periods (1977-1981, 1982-1985, 1986-1990,
1991-1994). We use yearly individual-level data from IPUMS-CPS for the period 1977-1994.

’Married’ is a dummy equal to 1 if the respondent is married.

ECB Working Paper 2086, July 201742s
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ECB Working Paper 2086, July 201743Table IV: The eﬀect of intra-state banking deregulation on weeks worked

Weeks worked last year

1

2

3

4

Deregulation × female

Deregulation

Female

Age

Age squared

Married

Single

Black

High-school or less

College or more

Self employed

Employed (private sector)

Employed (public sector)

Unemployed

State FE
Year FE
State × year FE
N
Adj. R2

2.6193***
(0.2522)
–0.1933
(0.1500)
–13.1687***
(0.2010)

No
No
No
1,554,571
0.071

0.6267***
(0.0937)
–0.0212
(0.0927)
–2.9745***
(0.0780)
0.5049***
(0.0103)
–0.0056***
(0.0001)
0.1778***
(0.0376)
–0.2749***
(0.0560)
–1.3033***
(0.0592)
–1.4930***
(0.0315)
0.6913***
(0.0508)
38.8246***
(0.0809)
39.9962***
(0.0643)
38.6762***
(0.1331)
19.5864***
(0.1482)

No
No
No
1,554,571
0.661

0.6205***
(0.0938)
–0.3640***
(0.0806)
–2.9961***
(0.0783)
0.4948***
(0.0099)
–0.0055***
(0.0001)
0.1653***
(0.0379)
–0.3497***
(0.0543)
–1.3368***
(0.0577)
–1.4931***
(0.0327)
0.6330***
(0.0478)
38.7053***
(0.0817)
39.9109***
(0.0659)
38.6318***
(0.1323)
19.5745***
(0.1460)

Yes
Yes
No
1,554,571
0.662

0.6228***
(0.0939)

–2.9996***
(0.0784)
0.4955***
(0.0099)
–0.0055***
(0.0001)
0.1652***
(0.0379)
–0.3521***
(0.0544)
–1.3400***
(0.0579)
–1.4899***
(0.0328)
0.6392***
(0.0478)
38.6974***
(0.0819)
39.9022***
(0.0660)
38.6290***
(0.1324)
19.5917***
(0.1457)

Yes
Yes
Yes
1,554,571
0.662

The dependent variable is the total number of weeks worked by the respondent in the year
preceding the survey year. ’Deregulation’ is a dummy equal to 1 if the respondent is located in
a state where within-state bank branching restrictions have been lifted. ’Female’ is a dummy
equal to 1 if the respondent is a female. ’Married’ is a dummy equal to 1 if the respondent is
married. ’Single’ is a dummy equal to 1 if the respondent is single. The omitted category in
marital status is ’Divorced or widowed’. ’Black’ is a dummy equal to 1 if the respondent is
black. ’High-school or less’ is a dummy equal to 1 if the respondent has between 0 and 12 years
of school and obtained at most a high-school diploma.
’College or more’ is a dummy equal
to 1 if the respondent has at least a Bachelor’s degree. The omitted category in education
is ’College drop-out’. The variables ’Self employed’, ’Employed (private sector)’, ’Employed
(public sector)’, and ’Unemployed’ classify the respondents according to their occupational
status and, if they are in the labor force, the occupation in which they worked the most hours.
’Self employed’ is a dummy equal to 1 if the respondent is self-employed. ’Employed (private
sector)’ is a dummy equal to 1 if the respondent is an employee in private industry. ’Employed
(public sector)’ is a dummy equal to 1 if the respondent is an employee in public sector.
’Unemployed’ is a dummy equal to 1 if the respondent declared himself as ’unemployed’. The
omitted variable in occupational status is ’Not in the labor force’. We use yearly household
data from IPUMS-CPS for the period 1977-1994. Banking deregulation dates follow Amel
(1993). The year in which each state deregulated is dropped. All estimates are weighted by
sampling weights provided by the Current Population Survey. Standard errors clustered by
state-year are reported in parentheses, where *** indicates signiﬁcance at the 1% level, ** at
the 5% level, and * at the 10% level.

ECB Working Paper 2086, July 201744Table V: The eﬀect of intra-state banking deregulation on log weekly wage in-
come

Log weekly wage income

1

2

3

4

Deregulation × female

Deregulation

Female

Age

Age squared

Married

Single

Black

High-school or less

College or more

Self employed

Employed (private sector)

Employed (public sector)

Unemployed

State FE
Year FE
State × year FE
N
Adj. R2

0.1368***
(0.0094)
0.2814***
(0.0198)
–0.6684***
(0.0069)

No
No
No
1,129,348
0.128

0.1068***
(0.0081)
0.2487***
(0.0186)
–0.6137***
(0.0062)
0.0732***
(0.0010)
–0.0008***
(0.0000)
0.0127***
(0.0031)
–0.1345***
(0.0048)
–0.1302***
(0.0079)
–0.2773***
(0.0048)
0.3513***
(0.0074)
–0.0964***
(0.0291)
0.5051***
(0.0059)
0.4865***
(0.0063)
0.2867***
(0.0078)

No
No
No
1,129,348
0.261

0.1112***
(0.0083)
–0.0775***
(0.0071)
–0.6282***
(0.0062)
0.0641***
(0.0008)
–0.0007***
(0.0000)
0.0269***
(0.0026)
–0.1620***
(0.0037)
–0.1256***
(0.0054)
–0.2576***
(0.0039)
0.3025***
(0.0045)
–0.1607***
(0.0300)
0.4908***
(0.0057)
0.4948***
(0.0059)
0.2646***
(0.0071)

Yes
Yes
No
1,129,348
0.314

0.1096***
(0.0083)

–0.6271***
(0.0062)
0.0643***
(0.0008)
–0.0007***
(0.0000)
0.0268***
(0.0026)
–0.1619***
(0.0037)
–0.1262***
(0.0054)
–0.2575***
(0.0039)
0.3014***
(0.0045)
–0.1623***
(0.0300)
0.4909***
(0.0057)
0.4953***
(0.0059)
0.2644***
(0.0071)

Yes
Yes
Yes
1,129,348
0.315

The dependent variable is the natural logarithm of the weekly wage income of the respondent.
The regression sample includes only respondents with non-zero wage income and non-zero
weeks worked.
’Deregulation’ is a dummy equal to 1 if the respondent is located in a state
where within-state bank branching restrictions have been lifted. ’Female’ is a dummy equal to
1 if the respondent is a female. ’Married’ is a dummy equal to 1 if the respondent is married.
’Single’ is a dummy equal to 1 if the respondent is single. The omitted category in marital
status is ’Divorced or widowed’.
’Black’ is a dummy equal to 1 if the respondent is black.
’High-school or less’ is a dummy equal to 1 if the respondent has between 0 and 12 years
of school and obtained at most a high-school diploma.
’College or more’ is a dummy equal
to 1 if the respondent has at least a Bachelor’s degree. The omitted category in education
is ’College drop-out’. The variables ’Self employed’, ’Employed (private sector)’, ’Employed
(public sector)’, and ’Unemployed’ classify the respondents according to their occupational
status and, if they are in the labor force, the occupation in which they worked the most hours.
’Self employed’ is a dummy equal to 1 if the respondent is self-employed. ’Employed (private
sector)’ is a dummy equal to 1 if the respondent is an employee in private industry. ’Employed
(public sector)’ is a dummy equal to 1 if the respondent is an employee in public sector.
’Unemployed’ is a dummy equal to 1 if the respondent declared himself as ’unemployed’. The
omitted variable in occupational status is ’Not in the labor force’. We use yearly household
data from IPUMS-CPS for the period 1977-1994. Banking deregulation dates follow Amel
(1993). The year in which each state deregulated is dropped. All estimates are weighted by
sampling weights provided by the Current Population Survey. Standard errors clustered by
state-year are reported in parentheses, where *** indicates signiﬁcance at the 1% level, ** at
the 5% level, and * at the 10% level.

ECB Working Paper 2086, July 201745Years since deregulation × female

0.0418***
(0.0056)

Inter-state deregulation × female

Other state asset ratio× female

Table VI: Alternative measures of ﬁnancial deregulation

Weeks worked last year

Log weekly wage income

1

2

3

4

5

6

0.4518***
(0.0901)

–2.8043***
(0.0750)
0.4956***
(0.0099)
–0.0055***
(0.0001)
0.1639***
(0.0379)
–0.3532***
(0.0543)
–1.3394***
(0.0579)
–1.4887***
(0.0330)
0.6392***
(0.0477)
38.6986***
(0.0817)
39.8997***
(0.0658)
38.6238***
(0.1318)
19.5894***
(0.1456)

0.0576***
(0.0102)
–2.6912***
(0.0573)
0.4946***
(0.0099)
–0.0055***
(0.0001)
0.1646***
(0.0381)
–0.3588***
(0.0546)
–1.3374***
(0.0581)
–1.4936***
(0.0329)
0.6366***
(0.0480)
38.7002***
(0.0823)
39.9158***
(0.0660)
38.6535***
(0.1329)
19.5947***
(0.1462)

0.0072***
(0.0005)

–0.5831***
(0.0045)
0.0636***
(0.0009)
–0.0007***
(0.0000)
0.0268***
(0.0030)
–0.1636***
(0.0045)
–0.1269***
(0.0059)
–0.2532***
(0.0041)
0.3042***
(0.0048)
–0.1751***
(0.0334)
0.5003***
(0.0063)
0.4976***
(0.0065)
0.2772***
(0.0077)

0.1033***
(0.0086)

–0.6046***
(0.0057)
0.0642***
(0.0008)
–0.0007***
(0.0000)
0.0264***
(0.0026)
–0.1620***
(0.0037)
–0.1261***
(0.0054)
–0.2570***
(0.0039)
0.3014***
(0.0045)
–0.1605***
(0.0300)
0.4903***
(0.0057)
0.4948***
(0.0059)
0.2642***
(0.0070)

0.0048***
(0.0010)
–0.5622***
(0.0059)
0.0643***
(0.0008)
–0.0007***
(0.0000)
0.0272***
(0.0026)
–0.1616***
(0.0038)
–0.1258***
(0.0054)
–0.2583***
(0.0039)
0.3014***
(0.0045)
–0.1604***
(0.0301)
0.4928***
(0.0057)
0.4962***
(0.0059)
0.2665***
(0.0071)

–2.7712***
(0.0494)
0.4683***
(0.0109)
–0.0052***
(0.0001)
0.2002***
(0.0397)
–0.4399***
(0.0605)
–1.4518***
(0.0647)
–1.4626***
(0.0366)
0.5958***
(0.0510)
38.7769***
(0.0904)
40.0070***
(0.0717)
38.9700***
(0.1284)
19.5346***
(0.1612)

Yes
1,156,517
0.669

Yes
1,554,571
0.662

Yes
1,518,233
0.662

Yes
837,542
0.316

Yes
1,129,348
0.315

Yes
1,103,077
0.314

Female

Age

Age squared

Married

Single

Black

High-school or less

College or more

Self employed

Employed (private sector)

Employed (public sector)

Unemployed

State × year FE
N
Adj. R2

The dependent variables ’Weeks worked last year’ and ’Log weekly wage income’ are the total number of weeks worked by the
respondent in the year preceding the survey year, and the natural logarithm of weekly wage income of the respondent, respectively.
The ’Log weekly wage income’ regression samples include only respondents with non-zero wage income and non-zero weeks worked.
’Years since deregulation’ equals the number of years since intra-state bank branching restrictions have been lifted. The regressions
using ’Years since deregulation’ only include the sample of respondents from states that deregulated in or after 1977 since the dates
of deregulations that took place before 1977 are not available. ’Inter-state deregulation’ is a dummy equal to 1 if the respondent is
located in a state where bank branching restrictions between states have been lifted. ’Other state asset ratio’ equals total out-of-state
assets held by holding companies operating in each state in each year, divided by total banking sector assets in each state-year.
’Female’ is a dummy equal to 1 if the respondent is a female. ’Married’ is a dummy equal to 1 if the respondent is married. ’Single’
is a dummy equal to 1 if the respondent is single. The omitted category in marital status is ’Divorced or widowed’.
’Black’ is a
dummy equal to 1 if the respondent is black.
’High-school or less’ is a dummy equal to 1 if the respondent has between 0 and 12
years of school and obtained at most a high-school diploma. ’College or more’ is a dummy equal to 1 if the respondent has at least
a Bachelor’s degree. The omitted category in education is ’College drop-out’. The variables ’Self employed’, ’Employed (private
sector)’, ’Employed (public sector)’, and ’Unemployed’ classify the respondents according to their occupational status and, if they are
in the labor force, the occupation in which they worked the most hours. ’Self employed’ is a dummy equal to 1 if the respondent is
self-employed.
’Employed
(public sector)’ is a dummy equal to 1 if the respondent is an employee in public sector. ’Unemployed’ is a dummy equal to 1 if the
respondent declared himself as ’unemployed’. The omitted variable in occupational status is ’Not in the labor force’. We use yearly
household data from IPUMS-CPS for the period 1977-1994. Banking deregulation dates follow Amel (1993) and the ’Other state asset
ratio’ has been provided to us by Philip Strahan. The year in which each state deregulated is dropped. All estimates are weighted
by sampling weights provided by the Current Population Survey. Standard errors clustered by state-year are reported in parentheses,
where *** indicates signiﬁcance at the 1% level, ** at the 5% level, and * at the 10% level.

’Employed (private sector)’ is a dummy equal to 1 if the respondent is an employee in private industry.

ECB Working Paper 2086, July 201746Table VII: The eﬀect of ﬁnancial deregulation on weeks worked and weekly wage income using
contiguous MSAs

Weeks worked last year

Log weekly wage income

Deregulation × female

Female

Age

Age squared

Married

Single

Black

High-school or less

College or more

Self employed

Employed (private sector)

Employed (public sector)

Unemployed

MSA × year FE
Pair FE
N
Adj. R2

1

1.0880***
(0.2767)
–3.6839***
(0.2310)
0.4215***
(0.0517)
–0.0046***
(0.0006)
0.0524
(0.2098)
–0.3710
(0.2670)
–2.3058***
(0.3047)
–1.6744***
(0.1871)
0.4547*
(0.2686)
37.1018***
(0.3414)
39.7609***
(0.2059)
39.2728***
(0.3281)
18.9573***
(0.8599)

Yes
Yes
35,749
0.670

2

0.0482**
(0.0231)
–0.6044***
(0.0136)
0.0639***
(0.0039)
–0.0007***
(0.0000)
0.0579***
(0.0160)
–0.1552***
(0.0228)
–0.1143***
(0.0188)
–0.2497***
(0.0133)
0.2676***
(0.0140)
–0.4523***
(0.1234)
0.4657***
(0.0331)
0.4502***
(0.0366)
0.2267***
(0.0436)

Yes
Yes
26,493
0.306

The table reports the results from OLS regressions using a selected sample of 15 pairs of contiguous MSAs
derived from data oﬀered by Huang (2008). The dependent variables ’Weeks worked last year’ and ’Log weekly
wage income’ are the total number of weeks worked by the respondent in the year preceding the survey year,
and the natural logarithm of weekly wage income of the respondent, respectively. The ’Log weekly wage income’
regression samples include only respondents with non-zero wage income and non-zero weeks worked. ’Deregulation’
is a dummy equal to 1 if the respondent is located in a state where within-state bank branching restrictions have
been lifted. ’Female’ is a dummy equal to 1 if the respondent is a female. ’Married’ is a dummy equal to 1 if the
respondent is married. ’Single’ is a dummy equal to 1 if the respondent is single. The omitted category in marital
status is ’Divorced or widowed’. ’Black’ is a dummy equal to 1 if the respondent is black. ’High-school or less’ is
a dummy equal to 1 if the respondent has between 0 and 12 years of school and obtained at most a high-school
diploma. ’College or more’ is a dummy equal to 1 if the respondent has at least a Bachelor’s degree. The omitted
category in education is ’College drop-out’. The variables ’Self employed’, ’Employed (private sector)’, ’Employed
(public sector)’, and ’Unemployed’ classify the respondents according to their occupational status and, if they are
’Self employed’ is a dummy equal to
in the labor force, the occupation in which they worked the most hours.
1 if the respondent is self-employed.
’Employed (private sector)’ is a dummy equal to 1 if the respondent is an
employee in private industry. ’Employed (public sector)’ is a dummy equal to 1 if the respondent is an employee
in public sector.
’Unemployed’ is a dummy equal to 1 if the respondent declared himself as ’unemployed’. The
omitted variable in occupational status is ’Not in the labor force’. We use yearly household data from IPUMS-
CPS for the period 1977-1994. Banking deregulation dates follow Amel (1993). The year in which each state
deregulated is dropped. All estimates are weighted by sampling weights provided by the Current Population
Survey. Standard errors clustered by state-year are reported in parentheses, where *** indicates signiﬁcance at
the 1% level, ** at the 5% level, and * at the 10% level.

ECB Working Paper 2086, July 201747Table VIII: Robustness check: controlling for social norms

Deregulation × female

Social norms × female

Share of working women × female

Share of educated women × female

Female

Age

Age squared

Married

Single

Black

High-school or less

College or more

Self employed

Employed (private sector)

Employed (public sector)

Unemployed

State × year FE
N
Adj. R2

Weeks worked last year

Log weekly wage income

1

2

3

4

5

6

0.5250***
(0.1035)
–1.4805**
(0.6712)

–2.8130***
(0.1102)
0.4957***
(0.0099)
–0.0055***
(0.0001)
0.1647***
(0.0379)
–0.3519***
(0.0544)
–1.3400***
(0.0579)
–1.4893***
(0.0328)
0.6400***
(0.0478)
38.6946***
(0.0819)
39.8987***
(0.0662)
38.6257***
(0.1324)
19.5912***
(0.1456)

0.4609***
(0.0928)

0.2517**
(0.0992)

6.8878***
(0.9755)

–5.5537***
(0.3761)
0.4964***
(0.0099)
–0.0055***
(0.0001)
0.1602***
(0.0379)
–0.3539***
(0.0544)
–1.3395***
(0.0579)
–1.4861***
(0.0329)
0.6442***
(0.0477)
38.6874***
(0.0818)
39.8844***
(0.0657)
38.6135***
(0.1321)
19.5753***
(0.1455)

6.6195***
(0.8183)
–4.3670***
(0.1872)
0.4964***
(0.0099)
–0.0055***
(0.0001)
0.1596***
(0.0379)
–0.3524***
(0.0544)
–1.3386***
(0.0579)
–1.4851***
(0.0330)
0.6456***
(0.0478)
38.6888***
(0.0820)
39.8831***
(0.0658)
38.6126***
(0.1322)
19.5783***
(0.1457)

0.1061***
(0.0093)
–0.0532
(0.0592)

–0.6204***
(0.0100)
0.0643***
(0.0008)
–0.0007***
(0.0000)
0.0268***
(0.0026)
–0.1620***
(0.0037)
–0.1262***
(0.0054)
–0.2575***
(0.0039)
0.3015***
(0.0045)
–0.1624***
(0.0300)
0.4907***
(0.0057)
0.4952***
(0.0059)
0.2644***
(0.0071)

0.1007***
(0.0090)

0.0705***
(0.0083)

0.3855***
(0.0903)

–0.7705***
(0.0337)
0.0643***
(0.0008)
–0.0007***
(0.0000)
0.0264***
(0.0026)
–0.1620***
(0.0037)
–0.1261***
(0.0054)
–0.2573***
(0.0039)
0.3017***
(0.0045)
–0.1622***
(0.0300)
0.4902***
(0.0056)
0.4948***
(0.0059)
0.2638***
(0.0070)

0.7016***
(0.0772)
–0.7728***
(0.0166)
0.0643***
(0.0008)
–0.0007***
(0.0000)
0.0260***
(0.0026)
–0.1620***
(0.0037)
–0.1259***
(0.0054)
–0.2569***
(0.0039)
0.3019***
(0.0045)
–0.1614***
(0.0300)
0.4895***
(0.0057)
0.4943***
(0.0059)
0.2635***
(0.0070)

Yes
1,554,571
0.662

Yes
1,554,571
0.662

Yes
1,554,571
0.662

Yes
1,129,348
0.315

Yes
1,129,348
0.315

Yes
1,129,348
0.315

The dependent variables ’Weeks worked last year’ and ’Log weekly wage income’ are the total number of weeks worked by the respondent
in the year preceding the survey year, and the natural logarithm of the weekly wage income of the respondent, respectively. The ’Log
weekly wage income’ regression samples include only respondents with non-zero wage income and non-zero weeks worked. ’Deregulation’
is a dummy equal to 1 if the respondent is located in a state where within-state bank branching restrictions have been lifted. ’Female’
is a dummy equal to 1 if the respondent is a female. ’Social norms’ is constructed as described in Model (3). ’Share of working women’
is the share of women who are in the labor force (i.e. self-employed, employed in the private sector, or employed by the government).
’Share of educated women’ is the share of working-age women who have some college education (i.e. at least one year of college). Both
shares of working and educated women are calculated from averages at the state level for 4 periods (1977-1981, 1982-1985, 1986-1990,
1991-1994). ’Married’ is a dummy equal to 1 if the respondent is married. ’Single’ is a dummy equal to 1 if the respondent is single.
The omitted category in marital status is ’Divorced or widowed’. ’Black’ is a dummy equal to 1 if the respondent is black. ’High-school
or less’ is a dummy equal to 1 if the respondent has between 0 and 12 years of school and obtained at most a high-school diploma.
’College or more’ is a dummy equal to 1 if the respondent has at least a Bachelor’s degree. The omitted category in education is
’College drop-out’. The variables ’Self employed’, ’Employed (private sector)’, ’Employed (public sector)’, and ’Unemployed’ classify
the respondents according to their occupational status and, if they are in the labor force, the occupation in which they worked the most
hours. ’Self employed’ is a dummy equal to 1 if the respondent is self-employed. ’Employed (private sector)’ is a dummy equal to 1 if
the respondent is an employee in private industry. ’Employed (public sector)’ is a dummy equal to 1 if the respondent is an employee
in public sector.
’Unemployed’ is a dummy equal to 1 if the respondent declared himself as ’unemployed’. The omitted variable in
occupational status is ’Not in the labor force’. We use yearly household data from IPUMS-CPS for the period 1977-1994. Banking
deregulation dates follow Amel (1993). The year in which each state deregulated is dropped. All estimates are weighted by sampling
weights provided by the Current Population Survey. Standard errors clustered by state-year are reported in parentheses, where ***
indicates signiﬁcance at the 1% level, ** at the 5% level, and * at the 10% level.

ECB Working Paper 2086, July 201748d
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ECB Working Paper 2086, July 201749Table X: The eﬀect of intra-state banking deregulation on women’s participation in
male dominated jobs

Male job

Well-paid job

Well-paid male job

1

2

3

Deregulation × female

Female

Age

Age squared

Married

Single

Black

High-school or less

College or more

State × year FE
N
Pseudo R2

0.0706***
(0.0130)
–0.5504***
(0.0094)
0.0078***
(0.0011)
–0.0001***
(0.0000)
–0.0004
(0.0049)
–0.0378***
(0.0067)
–0.0819***
(0.0071)
0.0954***
(0.0068)
–0.0582***
(0.0109)

Yes
1,017,235
0.225

0.0475***
(0.0112)
–0.3200***
(0.0075)
0.0289***
(0.0010)
–0.0003***
(0.0000)
0.0591***
(0.0051)
–0.0320***
(0.0062)
–0.1344***
(0.0073)
–0.2362***
(0.0056)
0.2711***
(0.0078)

Yes
1,017,235
0.155

0.0679***
(0.0146)
–0.4120***
(0.0105)
0.0206***
(0.0011)
–0.0002***
(0.0000)
0.0319***
(0.0050)
–0.0443***
(0.0064)
–0.1407***
(0.0082)
–0.1086***
(0.0054)
0.0478***
(0.0083)

Yes
1,017,235
0.145

The table reports estimation results of marginal eﬀects from probit regressions. The dependent
variables ’Male job’, ’Well-paid job’, and ’Well-paid male job’ are based on job classiﬁcations from
1977. ’Male job’ is a dummy equal to 1 if a particular occupation falls in the top half of occupations
in terms of share of males employed. ’Well-paid job’ is a dummy equal to 1 if a particular occupation
falls in the top half of occupations in terms of average wage income. ’Well-paid male job’ is a dummy
equal to 1 if a particular occupation falls in the top half of occupations both in terms of share of
males employed and in terms of average wage income. ’Deregulation’ is a dummy equal to 1 if the
respondent is located in a state where within-state bank branching restrictions have been lifted.
’Married’ is a dummy equal to 1 if
’Female’ is a dummy equal to 1 if the respondent is a female.
the respondent is married. ’Single’ is a dummy equal to 1 if the respondent is single. The omitted
category in marital status is ’Divorced or widowed’. ’Black’ is a dummy equal to 1 if the respondent
is black. ’High-school or less’ is a dummy equal to 1 if the respondent has between 0 and 12 years of
school and obtained at most a high-school diploma. ’College or more’ is a dummy equal to 1 if the
respondent has at least a Bachelor’s degree. The omitted category in education is ’College drop-out’.
We use yearly household data from IPUMS-CPS for the period 1977-1994. Banking deregulation
dates follow Amel (1993). The year in which each state deregulated is dropped. All estimates are
weighted by sampling weights provided by the Current Population Survey. Standard errors clustered
by state-year are reported in parentheses, where *** indicates signiﬁcance at the 1% level, ** at the
5% level, and * at the 10% level.

ECB Working Paper 2086, July 201750Table XI: The eﬀect of intra-state banking deregulation on education

High-school or less

College drop-out

College or more

Deregulation × female

Female

Age

Age squared

Married

Single

Black

Self employed

Employed (private sector)

Employed (public sector)

Unemployed

State × year FE
N
Pseudo R2

1

–0.0262***
(0.0104)
0.0415***
(0.0074)
–0.0117***
(0.0016)
0.0002***
(0.0000)
–0.0494***
(0.0052)
–0.1431***
(0.0064)
0.1338***
(0.0102)
–0.1558***
(0.0119)
–0.0934***
(0.0062)
–0.3048***
(0.0062)
0.0657***
(0.0088)

Yes
1,554,571
0.071

2

0.0196***
(0.0098)
–0.0080***
(0.0069)
–0.0039***
(0.0014)
–0.0000*
(0.0000)
0.0209***
(0.0056)
0.0779***
(0.0088)
–0.0643***
(0.0108)
0.0746***
(0.0091)
0.0653***
(0.0051)
0.1185***
(0.0066)
–0.0440***
(0.0087)

Yes
1,554,571
0.039

3

0.0188***
(0.0110)
–0.0404***
(0.0084)
0.0157***
(0.0018)
–0.0002***
(0.0000)
0.0252***
(0.0075)
0.0639***
(0.0082)
–0.0545***
(0.0107)
0.0899***
(0.0105)
0.0309***
(0.0080)
0.2065***
(0.0071)
–0.0208***
(0.0123)

Yes
1,554,571
0.130

The table reports estimation results of marginal eﬀects from probit regressions. The dependent variables
are education categories that reveal the respondent’s highest grade of school or year of college completed.
’High-school or less’ is a dummy equal to 1 if the respondent has between 0 and 12 years of school and
’College drop-out’ is a dummy equal to 1 if the respondent
obtained at most a high-school diploma.
’College or more’ is a dummy equal to 1 if the
has between 1 and 4 years of college but no degree.
’Deregulation’ is a dummy equal to 1 if the respondent
respondent has at least a Bachelor’s degree.
is located in a state where within-state bank branching restrictions have been lifted.
’Female’ is a
dummy equal to 1 if the respondent is a female.
’Married’ is a dummy equal to 1 if the respondent is
married.
’Single’ is a dummy equal to 1 if the respondent is single. The omitted category in marital
status is ’Divorced or widowed’. ’Black’ is a dummy equal to 1 if the respondent is black. The variables
’Self employed’, ’Employed (private sector)’, ’Employed (public sector)’, and ’Unemployed’ classify the
respondents according to their occupational status and, if they are in the labor force, the occupation
in which they worked the most hours.
’Self employed’ is a dummy equal to 1 if the respondent is self-
employed. ’Employed (private sector)’ is a dummy equal to 1 if the respondent is an employee in private
’Employed (public sector)’ is a dummy equal to 1 if the respondent is an employee in public
industry.
sector.
’Unemployed’ is a dummy equal to 1 if the respondent declared himself as ’unemployed’. The
omitted variable in occupational status is ’Not in the labor force’. We use yearly household data from
IPUMS-CPS for the period 1977-1994. Banking deregulation dates follow Amel (1993). The year in
which each state deregulated is dropped. All estimates are weighted by sampling weights provided by the
Current Population Survey. Standard errors clustered by state-year are reported in parentheses, where
*** indicates signiﬁcance at the 1% level, ** at the 5% level, and * at the 10% level.

ECB Working Paper 2086, July 201751Acknowledgements 

We are grateful to Thorsten Beck, Allen Berger, Erik Hurst, Lawrence Katz, Ross Levine, Romain Ranciere, and participants in the 
World Bank Conference "Finance and Development: The Unfinished Agenda" for valuable comments. The opinions expressed herein 
are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem. 

Alexander Popov 
European Central Bank, Frankfurt am Main, Germany; email: alexander.popov@ecb.int 

Sonia Zaharia 
Goethe University Frankfurt, Frankfurt am Main, Germany 

© European Central Bank, 2017 

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This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

ISSN 
ISBN 

1725-2806 (pdf) 
978-92-899-2808-3 (pdf)

DOI 
EU catalogue No 

10.2866/15883 (pdf) 
QB-AR-17-098-EN-N (pdf)