A Righteous Collusion€¦ · discrimination that arises as the result of consumer preference as...

A Righteous CollusionThe Prisoner’s Dilemma and the Civil Rights Act

Timothy Addams Hyde

Stanford University

Department of Economics

[email protected]

May 11, 2010

Abstract

Various attempts have been made to explain the sources of long-term discrim-inatory activity – behavior which does not seem to be profit-maximizing, and yetpersists in the face of market competition. Research interest has focused on theeffect of Title VII of the Civil Rights Act, and whether it has served to decreaseemployment discrimination or merely exposed firms to costly litigation. Follow-ing in the footsteps of Becker (1957), this paper develops a model of employmentdiscrimination that arises as the result of consumer preference as opposed to em-ployer or employee tastes. It predicts that many or all firms in a local industry willconspire to halt discriminatory activity simultaneously during periods of risingcosts to discrimination, effectively forming a type of cartel. I perform an empiri-cal test based on employment data collected in the period following the passageof the Civil Rights Act. The results provide suggestive evidence of the model’svalidity, and confirm the importance of viewing the question of discriminationthrough a regional lens.

Keywords: employment discrimination, American South, Civil Rights Act, prisoner’s dilemma

Acknowledgements: The author would like to thank Dr. Gavin Wright for absolutely stupendousadvising and encouragement, as well as Mark Tendall and Dr. Jayanta Bhattacharya for their moralsupport and valuable mentorship. Dr. Geoffrey Rothwell and Jesse Cunha were immensely helpful indeveloping this paper in its earliest stages.

Timothy Hyde Introduction 1

Introduction

Despite the major role that racial discrimination in employment has played in American

history, comprehensive study of the phenomenon by economists is still very much in its

infancy. Economic theorists have only started to consider the subject seriously in the

past fifty years, and empirical study in this area has been plagued by data limitations

and problems of econometric identification.

Two questions are at the heart of this field of inquiry: how and where does dis-

crimination exist, and to what extent have government efforts to combat discrimination

been effective? Specific attention has been focused on the problem of persistent discrim-

ination, which is hard to explain with current theories, and the question of the efficacy

of Title VII of the Civil Rights Act (1964), which outlawed employment discrimination

on the basis of race.

For several reasons, a study of consumer-based discrimination – that is, dis-

crimination resulting from the tastes of consumers as opposed to those of prejudiced

employers or wary coworkers – may help provide answers to these questions.

By understanding consumer discrimination and learning to differentiate it from

employer discrimination, we hope to be able to test the channels through which govern-

mental pressure operates and to understand under what conditions public policy is or

is not an effective remedy. A careful analysis of consumer discrimination may also help

unravel the mystery of persistence, as theory indicates that consumer discrimination is

the type of discrimination least susceptible to market forces.

Most previous models of discriminatory activity have been static in nature and

failed to take into account the strategic possibilities for firms in a competitive market-

place where discrimination is present. This paper hopes to further our understanding

of these dynamics by advancing a game-theoretical model of consumer discrimination,

building on a conception of the “multiple prisoners’ dilemma” borrowed from Schelling

(1973).

The model predicts that firms in an industry facing consumer discrimination

will try to collude to integrate their workforces, hoping to avoid the extra labor costs

associated with selective hiring practices. However, this collusion will likely fail because

each firm has enormous incentive to deviate by resuming discrimination and attracting

the prejudiced customers.


But what happens when we introduce outside pressure to end discrimination?

The model demonstrates that such outside pressure (e.g. federal enforcement of the

Civil Rights Act) will make the collusive arrangement viable by reducing incentive to

deviate. In periods of gradually rising pressure, the model predicts discontinuous change

as discriminatory firms in a given region and industry suddenly achieve a collusive Nash

equilibrium. If accurate, this framework may account for some of the massive and

sudden shifts in black employment witnessed in the South after the passage of the Civil

Rights Act.

While the theory is stylistic, anecdotal evidence suggests that the concept of

Righteous Collusion may indeed be plausible. Wright (2008), in a discussion of the bat-

tle for equal access to public accommodations, cites the example of forty-nine restau-

rants in Dallas that agreed to integrate simultaneously in July 1960 in response to rising

political and economic pressure from protesters and boycotters. Hanssen (1998), in a

recounting of the breaking of the color barrier in Major League Baseball, illustrates

similar collusive forces at work in that time period.

In order to evaluate our model, we take advantage of employment data made

publicly available by the Equal Employment Opportunity Commission. The Com-

mission’s report, entitled Job Patterns of Minorities and Women in Private Industry,

provides an incredibly rich dataset with information on the racial composition of the

workforce in a variety of job categories between 1966 and 1973. The data is provided

on an industry level, and disaggregated by metropolitan statistical area (MSA).

This fine-grained detail allows us to examine the dynamics of individual industry

sectors within a small geographical area – we can look at the insurance industry in

Atlanta, the textile industry in Baltimore, or the construction industry in Cincinnati

– as we evaluate the model. Additionally, because the data was gathered during the

crucial post-Title-VII era, we hope to leverage the model and its predictions to identify

the effect of the Civil Rights Act.

Unfortunately, the timeframe of the data proved too limited to test for the

localized and discontinuous jumps in black employment that are the hallmark of the

model. But we find econometric evidence for the “Southern convergence” hypothesis

that is advanced in the literature, as well as significant time trend effects that confirm

the findings of previous studies.

Additionally, we perform a difference-in-difference-in-difference test that reveals


that the Southern industries which by nature were most exposed to consumer discrimi-

nation integrated relatively quickly when compared to their non-exposed neighbors, but

Northern industries that were “exposed” lagged far behind their non-exposed counter-

parts. We cautiously interpret this result as evidence for the hypothesis, and more

specifically as evidence that the collusive potential of firms that were eager to halt dis-

crimination may have been unleashed by the Civil Rights Act. This evidence could also

help provide an explanation for the stunning black wage and employment gains during

this period in the South.

Timothy Hyde Background 4

Background

As the watershed events of the Civil Rights Movement in the 1960s recede into history,

economic historians have begun the task of investigating the dynamics of a period that

reshaped the American South more forcefully than any since Reconstruction. The Civil

Rights Movement, defined as the series of political, legal, and social victories stretching

from roughly the landmark Brown vs. Board of Education decision (1954) to the Voting

Rights Act (1965), was revolutionary in a real sense. We can paint a picture of two

different worlds – a pre-revolutionary south where blacks were systematically and often

overtly excluded from lunch counters, white-collar jobs, and the voting booth, and a

post-revolutionary South where such acts of exclusion are so rare as to be notable –

and ask how we got from Point A to Point B.

From an economic standpoint, it is hard to understand how the apparently

stable equilibrium of the pre-revolutionary South crumbled so quickly. If we specifically

concentrate on the story of black employment, we notice a rapid and widespread increase

in black employment across industries and sectors in the wake of the Civil Rights Act.

Title VII of the Act contained powerful new provisions that outlawed discrimination in

hiring and promotion on the basis of several protected factors, most notably race and

gender. But economists who have studied the issue have come to mixed conclusions

about the “effectiveness” of the Act, generally defined.

On at least one point there is no dispute: black gains in wages and employment

occurred at a furious pace in the 1960s and early 1970s, with much progress concentrated

in the South, where the black wage level had long lagged that of other regions (Donohue

& Heckman 1991). But there is no sturdy consensus about what role Title VII played

in that story.

Alternate explanations abound: were several long-term trends in ambient fac-

tors, like attitudes about racial differences and an increasingly tight southern labor

market, simply coming to a head at the same time? Or did the revolution build on

itself, with progress in school integration induced by Brown enabling blacks to advance

up the economic ladder? This paper draws on previous work in the economic theory of

discrimination by Becker (1957) and others and pioneering work in multiple prisoner’s

dilemmas by Schelling (1973) to make the point that equilibria that rest on discrimi-

natory tendencies may be particularly vulnerable to rapid, discontinuous change in the


face of an outside impetus like the Civil Rights Act.

As mentioned above, empirical evidence on this subject is in short supply; be-

cause of serious data limitations and identification challenges, there have been few

quantitative evaluations of discriminatory activity and little rigorous analysis of the

post-Title VII period. We have only minimal empirical understanding of how discrim-

ination operates, what drives it, and how it manages to persist in particular contexts.

Indeed, one of the most convincing defenses of the Effective Title VII hypothesis

(Heckman & Payner 1989) highlights a dramatic upturn in black employment in South

Carolina textile mills in the years immediately following the passage of the act. Because

the new regulations applied equally across the state, the analysis did not, and could

not, rest on a cross-sectional or panel analysis of shifting employment patterns. All the

authors could do was point out the dramatic and sudden nature of the change and offer

evidence against other explanations.

The Heckman-Payner study is notable for another reason: it draws attention to

an economic paradox that this paper attempts to unpack. In a discussion of possible

causal pathways for the effect of Title VII, the authors state:

Federal antidiscrimination activity [like Title VII] may have simply facilitated

the inevitable by giving employers an excuse for doing what they wanted to do

anyway (p. 144).

At first glance, it may be hard to understand this statement within an economic frame-

work. Because the Civil Rights Act represented an extra constraint on business op-

erations, it should have been at least weakly harmful to employers. In a 1991 paper,

Heckman (writing with Donohue) fleshes out this idea a bit more:

Substantial numbers of Southern employers appear to have been willing to gain

access to the supply of cheap black labor, but required the excuse of the fed-

eral pressure to defy long-standing community norms regarding employment of

blacks (p. 1605).

The central paradox of this theory is stated most succinctly by Wright (2008); 8: “Why

did Southern business require heavy-duty pressure in order to grasp a profit opportu-

nity?”


This paper attempts to use the Heckman hypothesis to answer that question,

offering a model of local business dynamics where discrimination, driven primarily by

consumer preferences, is subverted by collusive behavior. This investigation lies at

the confluence of two major lines of inquiry – what drives discrimination and by what

mechanism (if any) did Title VII impede it? – and will contribute to the literature by

attempting to identify the effects of a specific type of discrimination and suggest how

Title VII may have operated to end it.

The simple model described below attempts to explain how firms in industries

exposed to significant customer discrimination face different constraints than firms in

other types of industries where discrimination arising from employee or employer pref-

erences would be expected to dominate. The model does little to distinguish these two

groups of industries in times of stasis, when costs to discrimination are low or unchang-

ing. But, quite importantly, the model predicts that these two groups of industries

will move in starkly different ways during periods when the costs to discrimination are

rising rapidly – such as the years following the passage of the Civil Rights Act.

Timothy Hyde Employment Discrimination in Theory 7

Employment Discrimination in Theory

The Becker Model

Economists have been considering the dynamics of discrimination for over fifty years.

The seminal work in this field is Gary Becker’s 1957 compact reader The Economics

of Discrimination. In the introduction to this now-canonical work, Becker argues the

importance of exploring such a consequential social issue, and laments the fact that

economists had so far neglected its study.

Becker maps out a new vocabulary of discrimination and develops a new un-

derstanding of net-cost analysis, which encompasses both pecuniary and non-pecuniary

costs. Becker proposes the use of a “discrimination coefficient” (abbreviated DC) that

effectively allows non-pecuniary costs (like distaste for interacting with certain groups)

to be handled in a monetary framework.

Imagine an employer who is considering hiring a worker at a monetary wage rate

of π, and who must evaluate whether the added productivity of the labor will be worth

the cost of wages. She will not think of the wage rate as π but instead π(1 + di), where

di is her relevant DC. Similarly, a utility-maximizing consumer will act as though an

item with price p actually costs p(1 + dk), where dk is the consumer’s DC with respect

to whomever is selling him the product.

Becker proceeds to make the important point that πdi and pdk can be thought

of as the monetary equivalents of the non-pecuniary costs incurred by interaction with

groups considered distasteful. This allows us to consider non-pecuniary costs and in-

clude them in profit-maximization analysis.

Becker identifies three major sources of consequential discrimination in the labor

market: employers, employees, and consumers, with differentiated predictions concern-

ing each.

Employer Discrimination

Using his hypothesized discrimination coefficient, Becker shows that the marginal rev-

enue product of workers who are discriminated against is greater than that of non-

discriminated workers in purely monetary terms, but equal if we account for the dis-

crimination premium. This implies that employers hire a sub-optimal number of dis-


criminated workers, and overall pecuniary revenues are less than they could otherwise

be. Becker also shows that a wage differential for white and black workers will exist

as long the marginal discrimination coefficient is greater than zero, which will occur

as long as non-discriminatory firms make up a smaller share of the labor market than

minority workers do.

Because firms without discriminatory preferences are more profitable, Becker

shows that they should, over time, grow at the expense of discriminatory firms. This

result should hold even in a non-competitive market, because discriminatory owners

should have incentive to sell their firms to non-discriminatory owners that can produce

more efficiently.

Employee Discrimination

In his examination of employee discrimination, Becker imagines a world where certain

workers prefer not to work with minority workers, and must be paid higher wages be-

fore they will agree to do so. The implications depend on the production relationship

between majority and minority workers: are they perfect substitutes, perfect comple-

ments, or somewhere in between?

If the two labor inputs are perfect substitutes, the employers will segregate their

workforces entirely, only hiring workforces that are comprised entirely of majority work-

ers or entirely of minority workers, and no wage differential prevails. If, on the other

hand, there are complementarities, there will be a wage differential but discrimination

will be self-limiting in the sense that employers will seek out the most amenable majority

workers to compliment their minority labor. Becker postulates that these complemen-

tarities may arise if black and white workers have widely differing education levels,

and white supervisors are often paired with black laborers. As with employer discrim-

ination, competitive forces should theoretically serve to blunt the effects of employee

discrimination.

Customer Discrimination

Becker also views consumer discrimination through the lens of labor dynamics. If

consumers discriminate against firms with minority employees or employers, they act

as though goods and services offered by those firms are more costly than they are in


purely monetary terms. This has an effect similar to that of employer discrimination,

because the toxic effect of minority labor makes it less valuable to employers – regardless

of their particular tastes.

But as Becker is quick to point out, customer discrimination differs markedly

from other forms of discrimination because there are no obvious market forces that

would cause it to diminish over time. Theoretically, this should be the most persistent

type of discrimination, because it is difficult to conceive of discriminatory customers

being forced out of the market.

Alternate Models of Discrimination

While Becker’s theory is the best known and most widely cited, thoughtful critiques

and refinements of the theory have been issued in the more than fifty years since it

surfaced. A sampling of them are discussed below.

Some of the theories that differ most from Becker’s are those that propound what

Phelps (1972) called “statistical” discrimination. This type of discrimination requires

no prejudicial preferences on the part of any agents in the labor market; instead, the

theory relies on imperfect information and heterogeneity in job applicant quality. In

his groundbreaking work on the topic, Phelps (1972) offers the example of a lonely

traveler staying at a hotel in an unfamiliar town. As nightfall approaches, the question

becomes: should the traveller dine at the convenient hotel restaurant and wander out

into the town looking for what might be a better meal?

If he makes it a rule to dine outside the hotel without any prior investigation,

he is said to be discriminating against the hotel. Though there will be instances

where the hotel cuisine would have been preferable, the rule represents rational

behavior – it maximizes expected utility – if the cost of acquiring evaluations

of restaurants is sufficiently high and if the hotel restaurant is believed to be

inferior at least half the time (p. 659).

In this way, Phelps compares the lonely traveller to an employer comparing two job ap-

plicants with little means of determining cheaply which one is objectively more qualified.

If one is white and one is black, the employer might assume that the black applicant

has worse preparation, maybe because of institutional disparities in the provision of

schooling.


Arrow (1973) also contributes to the statistical discrimination literature by

pointing out – in a big departure from Becker’s theory – that this type of discrimi-

natory behavior on the part of firms may allow for a discriminatory equilibrium. If

those workers facing discrimination realize that their schooling, vocational training, or

other human-capital investments are not resulting in better employment opportunities,

they may decide to forgo some of this investment. This, in turn, may reinforce the

hiring firms’ preconceived notions about differences in average worker ability by race,

and perpetuate the cycle.

Yet another class of theories concern search costs, which create additional prob-

lems for applicants searching for work in a labor market that features discriminatory

employers. Borjas & Bronars (1989), for instance, develop a model of consumer discrim-

ination in an equilibrium search context that shows such discrimination is detrimental to

self-employed minority workers because it de-incentivizes minority workers with greater

entrepreneurial ability.

But all such models are variations on the main themes of taste (Becker) and

incomplete information (Phelps and Arrow). What this collection of models lacks,

however, is a conception of strategic behavior in the context of discrimination. Later

we will bring a game theoretical model developed by Schelling (1973) to bear on this

question in order to develop an understanding of discriminatory behavior in a dynamic

setting.

Timothy Hyde Evidence of Discrimination, Past and Present 11

Evidence of Discrimination, Past and Present

Despite Becker’s prediction that discrimination should dissipate over time, observers

have long noted labor market discrimination on the basis of race in the United States,

and evidence indicates that it persists to this day. Several different types of analyses

yield abundant evidence that, even controlling for factors like education and experience,

the color of one’s skin can have a significant effect on wages (Altonji & Blank 1999).

But other research suggests that the current level of discrimination is much lesser than

in the past, and that discriminatory activity seems to have fallen most sharply in the

decade after the Civil Rights Act.

Much of the quantitative evidence for discrimination rests on estimates of reduced-

form wage equations, which decompose wage differentials between members of different

races or genders into explained and unexplained components. A recent set of estimates

using 1995 data on the earnings of black and white males indicates that black males

earn 79% as much per hour as white males when no controls are used, and about 91%

as much per hour when a full set of educational, regional, and occupational control

variables are used (Altonji & Blank 1999).

While a 1992 study and other more recent ones came to similar conclusions

(Ehrenberg & Smith 2006; Darity & Mason 1998), there is still some disagreement about

whether this difference is really attributable to discrimination, or just a indication of

unobservable productivity differences.

Other researchers have used audit study methods to try to detect discrimination

not indirectly through labor market outcomes but directly in the hiring process itself.

One of the most high-profile results in this body of literature comes from Bertrand &

Mullainathan (2004), which created fake resumes and randomly appended what the

researchers called “African-American- or White-sounding names” before sending them

out in response to various job openings in Chicago and Boston. The authors monitored

callback rates for the different phantom job applicants and found that it was significantly

easier for applicants with white-sounding names to receive an interview offer.

After sending out 4,870 resumes, half of which had randomly been assigned

white-sounding names and half black-sounding ones, the authors found that 9.65% of

phantom white applicants received callbacks, as compared with 6.45% of phantom black

applicants. They also found that variation in resume “quality” (which the researchers


simulated by adding job skills and e-mail addresses and closing gaps in employment

history) affected callback rates for white applicants much more; in other words, returns

to resume enhancements for the phantom black applicants were significantly less.

These types of audit studies have been criticized by Heckman (1998) for mea-

suring the average and not the marginal level of discrimination, which Becker’s model

indicates is the relevant factor. But whether or not we can use this study to determine

whether a purposive black job-seeker’s wages would actually be affected by discrimi-

nation in this market, we can infer that race-based discrimination is not completely a

thing of the past.

Giuliano et al. (2006) investigate the effect of manager race on hiring decisions

by examining a longitudinal data set containing personnel information provided by an

unnamed national retail firm. While most previous studies of this nature have used

cross-sectional data and thus been unable to make convincing statements about the

causal relationship between a manager’s race and the racial composition of the workers

she hires, this paper exploits changes in the racial composition of the managerial staff

at each store over time to identify a racial-match effect on hiring.

The authors focus on “frontline” jobs, which are characterized by their entry-

level nature, high rates of turnover, and few skill requirements, and which make up over

90% of the workforce at the firm in question. They find that a black manager tends to

higher significantly more black employees than a non-black manager, and that effects

are more pronounced in the South.

Evidence on Customer Discrimination and Persistence

A few studies have tried to quantify consumer discrimination, and for data availability

reasons several have focused on attendance and television viewership of professional

sporting events. Hanssen (1998), to be discussed in more detail below, finds that atten-

dance at major league baseball games in the 1940s and ‘50s was negatively correlated

with the number of black players on the home team’s roster. Kanazawa & Funk (2001)

find a significant positive correlation between the number of white players in an NBA

game and its television viewership. The differential in viewership – which also seems to

be affected by the combined number of minutes the white players are actively playing

in the game – accounts for economically substantial variation in advertising revenue for


teams.

Another study based on survey data from about 800 firms in Boston, Detroit,

Atlanta, and Los Angeles also uncovers evidence of consumer discrimination. Holzer

& Ihlanfeldt (1998), using a difference-in-difference procedure and extensive controls

at the metropolitan area and firm level, determine that the racial composition of new

hires is responsive to the reported racial makeup of the firm’s consumer base, and that

this correspondence is much stronger in jobs that involve extensive interaction with

consumers on a daily or weekly basis.

A few studies have sought to determine the effect of market power and varying

levels of market competition on measures of discriminatory activity. The proposition

that discrimination should break down in the face of market competition is a linchpin

of the Becker theory. In one such paper, Hellerstein et al. (2002) examine an extensive

dataset of manufacturing industries broken down at the firm and plant level. They

find a correlation between female employment and profitability (a key indicator of

discrimination) that holds for plants with high levels of “product market power,” but

which disappears when looking solely at plants and firms in more competitive settings.

The authors cite this as tentative evidence of sex discrimination on the part of

employers, but note that firms with lower levels of female employees (i.e. the discrim-

inatory firms) do not experience lower growth over time and are not bought out by

other (presumably non-discriminatory) employers. This second finding is at odds with

conventional theories about employer discrimination, although the authors acknowledge

that the five-year window of their study may not allow for longer-term trends to exhibit

themselves.

Historical Evidence: The Regional Aspect

While the existence and extent of discrimination today is certainly of interest, we also

want to understand how discrimination has changed since mid-century, and how em-

ployment discrimination manifested itself – overtly and covertly – during the crucial

late-1960s period under study in this analysis.

Gottschalk (1997) provides a baseline estimate of discrimination over time, esti-

mating wage equations with gender and race dummy variables - along with educational,

regional, and age dummies – separately on 42 annual Current Population Survey (CPS)


datasets from 1963 to 1994. Below is a reproduction of the graph that highlights his

main finding: if we interpret the coefficient on the race dummy variable as a rough

measure of labor market discrimination, we notice a dramatic decrease in race-based

discrimination between 1963 and 1975, but essentially constant levels of discrimination

thereafter.

Figure 1: Residual Log Wage Differential, 1963-1994

However, Gottschalk completely neglects the regional nature of this momentous

change. Donohue & Heckman (1991) find that most of the wage gains by black workers

in this period were concentrated in the South, where black workers were in the worst

position relative to white workers.

Whatley & Wright (1994) argue that it only makes sense to consider discrimi-

nation through a regional lens, given the different nature and history of discriminatory

activity in the South and in the North. The authors characterize the South as a re-

gion where a profound and deeply entrenched human capital disparity between blacks

and whites resulted in a labor system where blacks had some access to low-skill jobs

at low-skill wages, but were almost uniformly barred from advancing to skilled work.


Additionally, the region was characterized by fleeting gains in black employment during

the World Wars which in each case subsided almost as soon as the troops came home.

The North, meanwhile, was a more amenable region – discriminatory, to be sure –

where blacks were able to make bigger, and more permanent, gains in particular sectors

(although sometimes painfully slowly).

The anecdotal evidence tells a similar story. In the decade before the Civil

Rights Act, when only 22 Northern states had anti-discrimination statutes on the books

(Collins 2003) and even these were enforced sporadically (Darity & Mason 1998), racial

discrimination in employment could be detected not only by looking at wage regressions

but by looking at storefront help-wanted signs and classified ads.

Southern discrimination was historical, overt, and concentrated in certain sectors

and job types. In 1950s Atlanta, many blue- and white-collar jobs were simply off-

limits to blacks, including “firefighting, building inspection, truck driving, sales, and

auto repairs” (Wright 2008). And the textile industry in the Piedmont region of North

and South Carolina had been rigidly segregated and socially stratified for decades, as

Richard Rowan’s famous 1970 account of that industry attests.

Historical accounts point to some of the sources of these patterns of institution-

alized discrimination. Rowan and Timothy Minchin (2007) provide numerous stories

of employee prejudice in the textile industry. Rowan paints a picture of resentful white

mill workers drawn from generations of “poor whites” living in Appalachia who had

“been driven out of the mainstream of economic life” and who “developed deep antago-

nism towards blacks and refused to work with them as equals” in textile mills (Northrup

& Rowan 1970). Segregation and discrimination against blacks had a long legacy in

this industry by 1965.

But discrimination in the North was neither non-existent nor clandestine. Darity

& Mason (1998) highlight racially unambiguous help-wanted ads posted in four major

newspapers in January 1960 (reproduced below). The unabashedly race-selective em-

ployers solicit only white applicants for such positions as laboratory technicians, club

managers, and research assistants.

The historical record indicates that these employers were not the only ones with

exclusive hiring practices. In depression-era New York City, many employers made a

policy of refusing to hire blacks, which inspired a grassroots organization called the

National Negro Congress to stage “Don’t Buy Where You Can’t Work” campaigns in


Figure 2: Classified Ads

dozens of Northern cities, complete with picket lines and boycotts (Sugrue 2008). The

Pennsylvania’s Governor’s Commission on Industrial Race Relations issued a survey in

1952 that indicated that about 90% of employers in Pennsylvania admitted to practicing

some form of discriminatory hiring. Justifications ranged from “tradition” to “company

policy” (Collins 2003).

We know that at the time of the Civil Rights Act, the occupational distribution

of blacks in the South was clearly inferior to that of the North, and black wages lagged

far behind white wages in both regions. While we know black wages and employment

skyrocketed in the years after the Act, we have no firm basis for concluding that dis-

crimination was in any sense more “severe” in the South, nor that white Southern

consumers were more prejudiced than their Northern counterparts.

Timothy Hyde Combating Discrimination: From FEPL to EEOA 17

Combating Discrimination: From FEPL to EEOA

Understanding the reasons that most of the gains were reaped by Southern blacks

requires not only an appreciation for the different nature of discrimination in each

region, but the different legal regimes that existed before 1964 and the ways the Civil

Rights Act impacted each region differently. This discussion cuts to the crux of the

debate over the effectiveness of Title VII, which we explore below.

Fair Employment Laws before 1964

The employment discrimination provisions in Title VII of the Civil Rights Act were

hardly a novelty; by 1964, 22 non-southern states had in place fair employment laws

of various shapes and sizes (Collins 2003). New York’s law came first in 1945, and was

quickly replicated in several states across the North, Midwest, and West. Indeed, 98%

of non-Southern blacks lived in states with fair employment laws by the time federal

legislation came into effect.

Collins (2003) notes that the impact of these laws has been understudied relative

to the focus on federal legislation like the CRA. He describes the nature of the fair

employment laws, labeling them the “most significant civil rights legislation” enacted

before 1964, and depicts an enforcement regime that sounds quite similar to what was

to come under the federal legislation:

The details of the fair employment laws’ provisions varied somewhat across

states, but the prohibitions and methods of enforcement were, for the most part,

quite similar because they emulated New York’s law. The “standard package” of

enforcement powers wielded by the state fair employment agencies included the

power to receive and investigate complaints of discrimination, to eliminate any

unlawful discriminatory practices by conference and persuasion, and, if necessary,

to issue cease-and-desist orders [enforceable by court order] to non-compliant

firms and unions (p. 246).

Collins takes advantage of the staggered implementation of these laws across

states, and uses census data from the Integrated Public Use Microdata Series to cre-

ate a difference-in-difference-in-difference estimate of their effect on black employment,

wages, labor force participation, and occupational distribution. He finds an impact of


the fair employment laws that is both economically and statistically significant; the

relative wages of black men rose 7% in states that implemented fair employment laws

in the 1940s, and the relative wages of black women in the same states jumped 12%.

Collins notes that, because the universe of states in the study is fairly small,

some results are not robust when certain states are excluded. Additionally, he acknowl-

edges possible endogeneity concerns: perhaps the states that passed these laws did so in

response to adverse outcomes for blacks in the labor market, or as a political reverber-

ation of changing racial attitudes among white citizens. But his results regarding the

relative wages of women are quite strong, and provide some of the strongest available

cross-sectional evidence that such laws do have economically-measurable impacts.

But, of course, these fair employment laws were completely absent in the South.

Southern blacks who suffered from discrimination had essentially no avenue of recourse

until after 1964.

The Civil Rights Act and Title VII Enforcement

The passage of the Civil Rights Act in 1964 was a minor political miracle. It is often

considered one of the crowning achievements of President Lyndon B. Johnson’s titanic

legislative career, which spanned over two decades in Congress and eight years in the

executive branch. At the outset of the debate, Johnson’s advisers fretted over the

politics of the legislation as they tried to piece together a fragile coalition of northern

Democrats, liberals, and conservative Midwestern Republicans (Kotz 2005). Even after

the House passed the bill, Johnson and his advisers could only be sure of 58 votes for

the bill. He was still not entirely confident that he could convince enough conservatives

to sign on to the legislation to overcome the Senate’s requirement of a two-thirds vote

to halt a filibuster.

Johnson had reason to worry; until the Senate invoked cloture and ended the

filibuster of the civil rights bill in 1964, no filibuster of civil rights legislation had ever

been defeated in the Senate. Historians credit the cooperation of Senate Minority

Leader Everett Dirksen (R-IL), the influence of progressive religious groups like the

Catholic Church and the National Council of Churches, and Johnson’s careful handling

of Midwestern senators – his farm subsidy bill curried favor with legislators from the

Great Plains states in the months leading up to the battle over civil rights – in achieving


the final 71-29 vote on June 10 (Kotz 2005).

Opponents of the bill made several assertions: that the bill was an usurpation of

states’ rights, that it was unconstitutional, that it would violate the personal freedom

of employers and customers to interact with whomever they pleased. One reactionary

congressman invoked the Thirteenth Amendment, which banned slavery and involun-

tary servitude in the 1860s, to argue that doctors and other service providers should

not be forced to cater to blacks. Others made similar points about proposed provisions

that would ban discrimination in hiring (Kotz 2005).

But thanks to Johnson’s delicate legislative handling, the bill emerged from the

month-long congressional debate unscathed. In its final form, Section 703(a) stated, in

full:

It shall be an unlawful employment practice for an employer

1. to fail or refuse to hire or to discharge any individual, or otherwise to dis-

criminate against any individual with respect to his compensation, terms,

conditions, or privileges of employment, because of such individual’s race,

color, religion, sex, or national origin; or

2. to limit, segregate, or classify his employees in any way which would deprive

or tend to deprive any individual of employment opportunities or otherwise

adversely affect his status as an employee, because of such individual’s

race, color, religion, sex, or national origin.

The bill specifically exempted firms with fewer than 100 employees, although all firms

down to 25 employees were covered by a similar 1972 law. Firms as small as 50 employ-

ees were also covered if they enjoyed contractor status with the federal government.

This new set of ground rules in hiring was not a major shock to employers

in the North, many of whom had been contending with state-level fair employment

commissions for over a decade. But the Act demanded nothing short of a new world

order in many Southern cities and towns where discrimination and segregation were an

integral part of the fabric of society. And while Title VII does include an exception for

so-called “bona fide” instances where an employee’s race, gender, or religion constituted

a legitimate job qualification (such as the hiring of an actress to play the role of a female,

or the hiring of a teacher at a parochial school), Section 1604.2 of the Code of Federal


Regulations specifically states that the “preferences of coworkers, the employer, clients,

or customers” are not applicable (Neumark et al. 1996). This meant that any employers

making hiring decisions out of deference to their customers’ tastes did not have any more

legal protection than employers acting on their own prejudices.

Naturally, compliance was neither immediate nor automatic. The Act estab-

lished the Equal Employment Opportunity Commission, which was tasked with enforc-

ing the aforementioned provisions of Title VII. The Commission had few resources to

work with and few legal options at its disposal; the budget for 1966 barely topped $16

million and the Commission was only empowered to receive complaints, act as a medi-

ator in disputes, and refer cases to the Justice Department for further action (Smith &

Welch 1984; Chay 1998). One major affirmative step taken by the EEOC was to man-

date the submission of EEO−1 forms, which required employers to report the racial

and gender demographics of their employees in a variety of job categories (Smith &

Welch 1989).

Clearly, the passage of the Act represented a great victory for civil rights advo-

cates, but an uphill battle remained for those who sought to remake the South and end

widespread employer discrimination nationwide.

Timothy Hyde Was Title VII Effective? 21

Was Title VII Effective?

Is it hard to determine, econometrically, the “effect” of Title VII on the employment

status or wages of blacks for two principal reasons (Chay 1998). The first problem is

that the passage of the Civil Rights Act coincided with a moment of great change in

U.S. labor markets, as well as a period of rapidly-changing attitudes about race and

racial discrimination. Secondly, because the Civil Rights Act was a federal intervention

and occurred at the national level, scholars cannot find a satisfactory counterfactual to

compare to the post-CRA United States, as they might be able to do if some states

had been unaffected.

Given these technical obstacles, it comes as little surprise that economists who

have studied the issue of Title VII effectiveness are divided into two camps.

Those who argue that federal pressure was effective (Leonard 1984; Heckman &

Payner 1989; Donohue & Heckman 1991; Chay 1998) cite sudden and massive changes

in black employment and wages in the South in the years immediately following passage

of the Civil Rights Act, and contrast that period with others that have been far less

fruitful for blacks.

Leonard (1984) offers some of the most direct evidence that the mechanisms

of Title VII were effective. The author finds that state-industry units with a greater

volume of Title VII litigation between 1966 and 1978 and a higher concentration of

federal contractors saw the greatest increases in employment for protected populations

like blacks and women. He notes that much of the gains have been in white-collar

sectors like management, evidence that the law has worked not only to help black

workers find jobs, but also to secure better jobs. Leonard also finds that covered firms

(that is, firms with at least 100 employees) did not exhibit lower productivity in terms

of output-per-worker.

Others (Smith & Welch 1984; Smith & Welch 1989) sought to gain identification

by comparing these “covered” firms to smaller ones, under the hypothesis that an

effective Title VII enforcement regime would have led to a shift in black employment

from the uncovered to covered firms. They find strong evidence that this is the case,

especially in managerial and professional occupations. But they find no evidence that

improvements in black wages were associated with this enforcement, and contend that

long-term changes in education were driving this growth:


In 1940, the typical black male entering the work force finished the sixth grade

four grades less than those new white workers with whom he had to compete.

Today, the average new black worker is a high school graduate and trails his

white competitor by less than a year of education. And this is only half the

story. Dramatic improvements in the quality of black education increased the

ability of blacks to translate their schooling into more dollars in the job market.

In 1940, whites gained twice as much income as blacks from attending school

for another year. Today, there is little racial difference in the economic benefits

of schooling for young workers (p. 560).

Heckman & Payner (1989) and Donohue & Heckman (1991) point to massive

wage gains by blacks in the South in the five-year period following the passage of the

Act in an attempt to rebut the Smith/Welch human-capital hypothesis. In a study of

microdata from the South Carolina textile industry, Heckman & Payner (1989) point

to a 1965 jump in black employment so abrupt and so consistent across counties that,

they argue, improvements in schooling accessibility and quality cannot explain it.

Chay (1998), using an econometric strategy similar to that used by Smith &

Welch (1989), compares firms only covered by the 1972 Equal Employment Opportunity

Act (which extended Title VII restrictions to firms with as few as 25 employees) to firms

that had been covered since 1965. With this identification strategy, the author finds

that the Act had a “positive impact on the labor market status” of blacks.

While this debate has not yet reached a satisfactory conclusion, much of the

scholarly work in this subject has elided the regional aspect of discrimination. Exploring

differences in the way the Act impacted region may help us identify previously-unnoticed

effects and help gather some more evidence to either support or refute the federal

pressure hypothesis.

The Southern Convergence Hypothesis

Although Gottschalk (1997) demonstrates major relative gains in black wages between

1965 and 1980, he does not explore the regional dimension of this change. Donohue &

Heckman (1991) emphasize that gains during this period were largely concentrated in

the hands of Southern blacks:


The South was the region of the greatest black economic advance in the period

1960-1970, accounting for at least two-thirds of the increase in black economic

status over the decade. There is evidence of substantial desegregation of firms

in the South during the crucial 1965-1970 period. This black economic progress

following the passage of Title VII coincided with a sharp drop in the outflow of

blacks from the South, and even led to black migration into that region between

1970 and 1980 (p. 1606).

The authors go on to cite evidence from the Current Population Survey that the unad-

justed regional wage ratios between black males and all black workers converged from

around 0.5 in the South and 0.8 or above in other regions in 1954 to about 0.7 in all

regions by 1989. Additionally, they find that “nearly three-quarters of the growth in

relative mean wage income [in the 1960s] is due to changes in wages within occupations

in the South and to black relative occupational advance in the South.”

The deeply ingrained and persistent nature of discrimination in many aspects of

Southern society, in conjunction with the sudden and massive shifts for black workers

there in the aftermath of the Civil Rights Act, together provide strong but circumstan-

tial evidence that the Civil Rights Act was a principal driver of this change. Addressing

these question of persistence and regional convergence will be the principal motivation

for the model and empirical results that are to follow.

Timothy Hyde Hypothesis: The Righteous Collusion 24

Hypothesis: The Righteous Collusion

Schelling’s Theory of the Multiple Prisoner’s Dilemma

We start our description of the model with a detour into the early work of Thomas

Schelling, the Nobel laureate famous for his work on arms control, coordination games,

and housing segregation. In Schelling (1973), he generalizes the well known “prisoner’s

dilemma” problem to multiple parties. He characterizes each player as having two

choices (LEFT and RIGHT, denoted L and R in Figure 3 below).

Figure 3: Schelling Multiple Prisoner’s Dilemma (MPD)

Each player has the same preferences, which are a function of whether that given

player has chosen LEFT or RIGHT, and also what fraction of the other players have

chosen RIGHT. Consider the game between n + 1 players depicted below. Two utility

payoff curves are drawn over a horizontal axis that ranges from 0 to n, which represents

the number of other players who have chosen RIGHT (let x denote this quantity). One

payoff curve is drawn for each choice, and they can be interpreted as the respective

utility levels associated with each choice, conditional on the simultaneous choices of the

other players.

In the figure below, note that the LEFT payoff curve is always higher than the

RIGHT payoff curve, regardless of the choices of the other players. In the language of


game theory, this means that choosing LEFT is a dominant strategy; regardless of the

actions of the other players, it is better than choosing RIGHT. Therefore, we would

expect every player to choose LEFT in this game.

This is all well and good until we observe that both utility payoffs are at their

lowest when no player is choosing RIGHT (x = 0). If everyone has incentive to choose

LEFT, all players will end up with zero utility. This is analogous to the prisoner’s

dilemma in that the Nash equilibrium (i.e. the intersection of dominant strategies) leads

to a bad outcome for everyone. If everyone colluded to choose RIGHT simultaneously,

everyone could have positive utility (note the position of the R curve when x = n).

And yet, in the state of the world where everyone is choosing RIGHT, it is still a good

idea for each individual player to revert to choosing LEFT. In this way, the collusion

is emphatically not a Nash equilibrium because players have incentive to change their

strategy and, left unconstrained, will do so.

We will proceed to use this framework to analyze firms’ binary choice of whether

to engage in employment discrimination against black applicants, and show how the

intervention of Title VII can turn the previously unstable collusion into a stable Nash

equilibrium.

Hypothesis & Modeling

We imagine a world where firms can pursue one of two employment policies:

Discrimination. This entails the hiring of only the best-qualified white job applicants

at the lowest possible.

Non-discrimination. This entails the hiring of the best-qualified job applicants, re-

gardless of race.

Because the decisions of others in the industry may affect each firm’s choice, we

must consider the proportion of other firms in the industry that choose to discriminate.

We also assume that firms are operating in a relatively short-term context where market

entry is infeasible and competition is not perfect, and that all firms are of equal size.

We also allow firms to switch their choice as market conditions change. Specifically, we

conceive of the firms as each making a hiring policy selection in each period.


Modeling Employer Tastes

First, we consider the case where customers have no preferences or tastes for discrim-

ination, and will not change their consumption patterns based on the racial profile of

each firm’s workforce, but where employers do have a taste for discrimination and face

higher costs (both psychic and pecuniary) when they are unable to discriminate. This

might be highly realistic in certain industries where clientele or customers have little

to no interaction with most employees and therefore would not adjust consumptive

behavior based on hiring practices.

We can model the profit function of firm i in a given period t by

Πi(pt) = Π0 + (θi − Li −Wd)×D

where:

• Π0 is the “baseline” level of profit in the market that would be realized by every

firm in the counterfactual world where none discriminate. All firms would enjoy

profits of precisely Π0 if discrimination ceased everywhere or if no employers had

tastes for discrimination in the first place.

• θi is the extra revenue (or lower costs) that firm i derives from discriminating. It

may take several forms; perhaps bigoted supervisory employees or executives will

be willing to work for less in such a firm. We assume heterogeneity across firms,

even within a given industry and city.

• pt is the proportion of other firms in the industry that are contemporaneously

choosing to discriminate in time t. Note that the profit function is invariant

in pt under this set of assumptions.

• Li is the potential amount of fines and legal fees that firm i might face if found

in violation of laws that prohibit discrimination (if any exist). Because of the

uncertainty of enforcement in this context, L would probably best be modeled

as a probability distribution of possible payouts. We will let L represent the

risk-adjusted E[Li] in this simplified case.


• Wd is the wage differential, which equals the extra payroll costs that firm i must

support if it hires only white workers and does not take advantage of relatively

cheap and plentiful black labor.

• D is an indicator variable that equals 1 if a firm elects to discriminate, and 0

otherwise.

The decision rule that firms adopt is a simple one: discriminate if the benefits

of discrimination outweigh the costs of discrimination (θi > Li + Wd). Because we are

assuming that relative wage levels are exogenously determined by regional or national

conditions and because the behavior of other firms does not affect this calculation in

any way, firms are free to make their decisions without needing to account for the

behavior of other firms, discriminating just up until the point that wage pressure or

federal enforcement makes it too costly. In this sense, the varying levels of θ across

firms allow us to construct something resembling a demand curve for discrimination

(see Figure 4). In the figure, Πd is the profit function over p for firms that discriminate,

while Πnd is the profit function over p for firms that choose not to discriminate. Given

values for Wd and Li, which together act as the “price” of discrimination, we can see

what fraction of the market will opt to engage in discrimination.

Figure 4: Profit Functions


Before we move on to consider the tastes of consumers and how they will com-

plicate the firm’s decision analysis, note the trajectory of p∗ (i.e. the Nash equilibrium

level of discriminatory activity) as L increases, perhaps as the result of increasing en-

forcement of existing anti-discrimination statutes or the passage of new ones. The

profit function for discriminators shifts downward, which shifts the optimal level of

discriminatory activity to the left (Figure 5).

Figure 5: Profit Functions with Increasing L

Modeling Consumer Tastes

Now we assume a world that looks quite different from the one above: in this world,

employers and employees do not derive any personal benefit from discrimination. In

other words, θi = 0 across all firms. Obviously, if this were the case in the above model,

p∗ would be trivially 0. But now we further assume that the consumers come in three

flavors:

Type A Consumers will always prefer to patronize firms that engage in discrimina-

tion, if any exist. We can infer that these consumers have a taste for discrimination

and consider interaction with black employees to be costly. We shall denote their

share of the total demand pool as a.


Type B Consumers are indifferent to hiring policies, and will frequent the firm for

which price is lowest. We shall denote their share of the total demand pool as b.

Type C Consumers will always prefer to patronize firms that do not engage in dis-

crimination, if any exist. We can infer that these consumers have distaste for

discrimination and consider transactions with discriminatory firms to be costly.

We shall denote their share of the total demand pool as c.

This assumption will have profound effects on both of our profit functions. We

will make the dramatically simplifying assumption that, given no variation in hiring

policies across the industry, all n firms will get an equal share of the total profit that

is available in the market; in a non-discriminatory scenario, each firm would still reap

profits of Π0 and total profits would be nΠ0. We also make the assumption that each

consumer in the market is contributing an equal share of this business volume.

Now we can specify a modified profit function under these assumptions. The

function will be identical for all firms, because they all have identical preferences and

face identical constraints:

Π(pt) =bΠ0

n+

(cΠ0

n(1− pt)

)× (1−D) +

(aΠ0

npt

)×D

The basic principle is that discriminatory firms divide up the Type A business

while splitting the Type B business exactly as before and forfeiting Type C business.

Meanwhile, non-discriminatory firms divide up Type C business while splitting the

Type B business exactly as before and forfeiting Type A business.

Note that these functions are not continuous where pt is extreme. The internal

logic of this functional form breaks down if Type A and Type C consumers face no

variation across the industry; we make the refining assumption that Type A and C

customers will act like Type B customers (that is, not distinguish between firms and

frequent each kind equally) in this case. This implies:

Πnd(0) = Π0


Πd(0) = naΠ0 + bΠ0 − L

Πnd(1) = ncΠ0 + Wnd

Πd(1) = Π0 −Wd − LWe can note a significant feature of these profit functions by examining these special

cases. Specifically, we note that Πnd(0) > Πd(1) in all circumstances, just like the

payoff when everyone chose RIGHT was greater than the payoff when everyone chose

LEFT in Schelling’s model. In practice, this means that firms would prefer a scenario in

which non-discrimination is a universal industry policy, to one in which discrimination

is practiced by every firm. This is true even if there are no legal repercussions to

discrimination.

If we examine this new situation graphically, we see significant deviations from

the previous model. In Figure 6, we plot profit functions over p, taking into account

the discontinuities where p = 0 and p = 1. First, let’s compare the two extreme states

of the world: the scenario where all firms discriminate (represented by the red circle

on the p = 1 axis) and the extreme opposite situation where none of the firms discrim-

inate (represented by the black circle on the p = 0 axis). As noted above, this latter

arrangement is unilaterally preferable to the universal discrimination example from the

perspective of the firms; it represents a Pareto improvement from the perspective of the

firms because none is worse off if the market moves from total discrimination to total

non-discrimination.

Yet we note that p∗, which represents the Nash equilibrium level of discrimi-

natory activity, is not equal to 0. To understand why, consider the situation where

p = 1. All firms in the market have adopted discriminatory hiring practices, and all

are splitting business (and profit) equally. But the Type C consumers who vastly pre-

fer non-discriminatory firms are disgruntled; they would be much happier shopping or

dining at firms that do not discriminate. Before long, we would expect a savvy en-

trepreneur to switch employment strategies in an attempt to capture all of the business

from Type C customers. As the graph indicates, Πnd is greater than Πd at this point

of the discrimination continuum, so marginal firms will switch to a non-discriminatory

model, sacrificing their small share of the Type A customers but cashing in on Type C

trade.


Figure 6: Firm Profit Functions with Consumer Tastes

Eventually, when enough firms have switched to non-discrimination so as to

set p = p∗, it is no longer profitable to switch. At this point, the Type C market is

getting saturated; a critical assumption here is that a >> c, and before long the costs

of abandoning discrimination (losing business from Type A customers) will outweigh

the benefits of attracting an increasingly small share of Type C customers.

It is easy to see that, even if L increases over time and forces the Πd curve

downward, the Nash equilibrium level of discriminatory activity will remain at the

intersection of the two curves. Because the Πnd never exceeds Π0 in this example,

profits at the Nash equilibrium will always be less than Π0, and a shift to the p = 0

line will always represent a Pareto improvement for the firms. This raises a question

that never arose in the previous model where consumer preferences were assumed to be

uniform: why don’t the firms make a multilateral agreement to cease discrimination,

and increase profits to Π0?

A Righteous Collusion

In most cases, a collusive agreement like the one described above will reduce social

welfare; firms are making an agreement to restrict trade in one form or another, usually

reducing output in order to support higher prices. But the collusion proposed above

– wherein all firms agree to stop discriminating, opening up the labor market and

increasing productive efficiency – seems to be beneficial to everyone. Intuition tells


us that any sort of non-competitive agreement must be reducing welfare in some way.

Where is the welfare loss in such an arrangement?

This analysis is overlooking the preferences of the consumers, specifically those

of Type A. If the collusive agreement forms, these consumers are denied a chance to

transact with discriminatory firms. If we are satisfied with these tastes for discrimina-

tion, there is no justification for this market intervention in the first place. But if we

consider these tastes themselves to be exerting negative externalities or to be in some

way undesirable from a social perspective, then fostering this collusive behavior might

be an efficient solution.

But as we observed with the Schelling model, this collusion is not sustainable

in its current form. Imagine that all n firms enter into a collusive agreement, and p

precipitously falls to 0. Along the p = 0 line, the Πd curve far exceeds Πnd. This makes

sense, because in such a world, any firm would have massive incentive to deviate from

the agreement and capture all the business from Type A customers who are dying to

patronize a firm willing to discriminate in hiring. For this reason, p = 0 is not a Nash

equilibrium and once a small percentage of firms deviate, the rest will face extremely low

profits along the Πnd curve unless they, too, break ranks with the non-discriminators.

Could we envision a government intervention that solves this coordination prob-

lem, allowing firms to enter into this collusive agreement that we have determined is

actually socially optimal? It seems the easiest way to help the nervous firms enforce

this tenuous non-equilibrium is by increasing L to the point where Πd = Πnd at p = 0.

Figure 7: Firm Profit Functions with Increasing L


Consider Figure 7. We see the result of an increase in L (in the form of some

new government policy or increased enforcement of existing anti-discrimination laws) in

a Πd curve that is constantly shifting downwards. This pushes the equilibrium p∗ from

an initial level of nearly 1 when the profit curve is represented by Πd0 to a continually

lower equilibrium as the profit function shifts ever downwards. Note that when the

profit function reaches Πd2, we have both a standard Nash equilibrium but also a

new equilibrium point at p = 0. Costs and risks to discriminatory hiring practices

have risen sufficiently so that we have achieved our goal of Πd = Πnd. This means

that a collusive agreement is now perfectly sustainable; once all firms have renounced

discriminatory practices, no firm has any incentive to start discriminating because L

costs are prohibitive. Thus a tipping point is reached, and p∗ plummets from some

positive value to 0.

This model, therefore, has a testable implication relating to the change in the

equilibrium level of discrimination as L increases. In our previous model where employer

tastes drove discriminatory behavior and customers did not change their behavior in

response, p∗ decreased linearly as L increased, eventually landing at 0 (see the left-hand

side of Figure 8). But in this model, we would expect a nonlinear decrease in p∗ that

accelerates before hitting a tipping point and plummeting to 0 (see the right-hand side

Figure 8: Changing p∗ with Increasing L

of Figure 8). Unlike the smooth, linear graph of p over L that we would see in the first

scenario, we would find a sharp discontinuity in any graphing under our second set of

assumptions.


However, estimating specific values for L and p or even defining them in easily

measurable terms is clearly impossible. We would need to find readily available variables

that are closely correlated with p and L in order to test the hypothesis that customer

tastes were driving discriminatory practices in the American South.

Have We Seen the Righteous Collusion Before?

One national industry for which historical racial employment data is plentiful – and

for which an objective measure of firm success is available – is Major League Baseball.

Hanssen (1998) capitalizes on this fact to study the dynamics of black employment

in this industry following the breaking of the color barrier: the appearance of Major

League Baseball’s first black player, Jackie Robinson, in 1947. The paper finds a strong

positive correlation between a team’s level of integration and its winning percentage,

and a strong negative correlation between the number of black players on a team and

fan attendance.

The major factor impelling the integration of a given team was the contempora-

neous integration of existing rivals. Indeed, the color barrier was itself the result

only of a tacit agreement among major league owners. The study of baseball

and the end of the color barrier is the story of a discriminatory arrangement

breaking down (p. 604).

The situation confronting MLB owners is fundamentally different from the one

confronting the hypothetical employers in our model. The firms are not really competing

with each other for attendance (except in the few cases where multiple teams play in

the same metropolitan area, as is the case in New York City and Chicago), and face few

consequences for breaking whatever tacit agreement existed. The Brooklyn Dodgers,

who signed Jackie Robinson in 1945, did not have to fear that their fans would flock

to Braves games in Boston instead. But the paper does provide a stark example of the

firms’ precarious balance between the costs and benefits of discrimination, and offers

one of the few empirical estimates of these costs.

Wright (2008) examines a situation which shares many more parallels with our

object of study: the struggle against segregation in public accommodations. We can

think about the decision facing department stores that are considering lunch-counter


integration in almost exactly the same way we think about the hiring firms’ decision in

our model.

The firms face significant costs to discrimination, in the form of the sorts of sit-

ins in Greensboro and Nashville that garnered national attention in 1960. Wright points

out that non-integrated firms sacrificed not only potential profits from black customers

but lost additional business as white customers fled the “disruption and turmoil” of the

boycotts and sit-ins. Profits at the Greensboro Woolworth’s were down 50% in 1960,

undoubtedly in large part due to the sit-ins, and other managers expressed concerns

about declining profits and delayed expansion plans (Wright 2008).

But the firms could not simply integrate at the first sign of protest; managers

would have to worry that any move to integrate would drive away white costumers to

establishments that had not yet done so. Indeed, the head of a Greensboro committee

formed in response to the sit-in crisis there reported that “the managers are extremely

sensitive to public reaction [and are afraid that if they integrate] they will lose a sufficient

percentage of their present patronage to the nonintegrated eating establishments in our

city” so as to become unprofitable (Wright 2008).

And indeed, as our model would predict, there were several instances of local

business leaders banding together and simultaneously integrating in the face of pro-

longed disruptive sit-ins that were increasing the costs of discrimination industry-wide:

. . . When the demonstrators showed their persistence by returning for new rounds

of protest, business and civic leaders in many cities were ready to reach accom-

modation, especially in the border states. The earliest major cities to announce

plans to desegregate public accommodations were San Antonio and Galveston,

Texas, and Baltimore, Maryland, in March and April, 1960. In Dallas, lunch

counters were desegregated in June 1960, and a committee of business and

civic leaders coordinated full downtown integration as of July 26, 1961 (Fair-

banks, For the City as a Whole, p. 238). In the Norfolk-Portsmouth area of

Virginia, four chain variety stores began serving white and black customers at

the same counters in July, 1960 (NYT: July 26, 1960). In Richmond, Virginia,

the two largest department stores desegregated their eating places in January,

1961, and a lengthy boycott ended in August of that year when agreement was

reached with seven downtown stores (p. 9-10).


Wright calls it “quasi-voluntary social change,” but there is no mistaking that

this is the Righteous Collusion. The massive Dallas desegregation – which entailed the

integration of forty-nine distinct downtown restaurants on a single July day! – and the

sequential Norfolk desegregations (lunch counters in July 1960, hotels in July 1961)

provide the best example of our model’s proposed mechanism, as well as evidence for

the city- and industry-specific idiosyncrasy of these dramatic shifts. A period of rising

costs to discrimination, spurred not in this case by federal laws but private political

pressure, provides firms with an effective enforcement mechanism for their simultaneous

switch to a non-discriminatory equilibrium.

In the next section, we turn to the EEOC data to try to find evidence of a similar

righteous collusion in the employment context. The ones we seek will not appear in

newspaper headlines but may still have had dramatic implications for how the Civil

Rights Act changed the face of the American South.

Timothy Hyde Data & Sources 37

Data & Sources

Although the model proposed above is highly stylistic and is supported by a raft of

fairly stringent assumptions, it may do a good job of representing a geographically

isolated market in a region where a broad group of majority individuals might plausibly

discriminate against minority individuals in an economic context. If we looked at local

industries in southern cities in the 1960s and 1970s, the model may not be a far cry

from reality. After the Civil Rights Act passed and Title VII litigation heated up in the

mid 1960s, the data show a fast and indisputable increase in black employment share

in a wide variety of industries, in a wide variety of job types, and spread across a wide

geographic area. This general positive relationship echoes our model in Figure 8 and

provides a basis for testing our two models against each other.

If we conceive of the L term as capturing the increasing peril of Title VII litiga-

tion as the late 1960s segued into the early 1970s and beyond, and if we assume that

a declining p would entail higher black employment share (denoted as s in Figure 9

below) in a given industry, all else equal, we could reimagine the curves from Figure 8

in real-life terms, where s0 is the full level of black employment that we would expect

to find when p = 0.

Figure 9: Changing Black Employment over Time

It is apparent that our assumptions about the source of discrimination (and thus

the nature of the binary discrimination choice that firms face) have a significant impact

on the expected change in black employment share over time. If we hypothesize that it


was truly customer tastes that drove discriminatory practices, we could find time series

data and test for a discontinuity of the sort shown above.

The Equal Employment Opportunity Report

In order to test this data, we would like to have access to firm-level data on the racial

composition of the workforce in a wide variety of job types. This would allow us to

spot episodes of collusion by noting sudden increases in black employment that are

concentrated in one industry and one city, and replicated across most or all firms in

that market. While that type of data may be available at the National Archives, it is

difficult to access and we did not pursue it for the purposes of writing this paper.

Instead, we look to the records of the Equal Employment Opportunity Commis-

sion, which was tasked with collecting employment data broken down by race (among

other duties). A series of five reports, titled Equal Employment Opportunity Report: Job

Patterns of Minorities and Women in Private Industry and published quasi-annually

between 1966 and 1975, provide a rich dataset that is disaggregated by metropolitan

statistical area (MSA), SIC industry code, occupation type, race, and gender. The

occupation classification available is tabulated below in Table 1.

Its most glaring deficiency from the point of the view of the author is the non-

availability of the dataset in an electronic form. While recent editions of the report are

available in limited form on the EEOC’s website, all editions during the relevant time

period only exist in hard copy. It took the author approximately 30 hours to enter the

data from five different annual volumes into spreadsheet software.

While a great many industries are included in the EEOC reports, we selected a

subsample that provides a broad range of industry types, from manufacturing to the

service sector; those are tabulated in Table 1. We also chose to select a subsample of

cities in order to ease the burden of data collection. In choosing the cities for analysis,

we balanced several considerations, like region, size, and availability of data across a

wide variety of industries. Because smaller cities often have complete data for a few

industries, we were forced to exclude some smaller metropolitan areas that otherwise

interested us, like Little Rock, Arkansas and Jackson, Mississippi. A final list of cities

by region is tabulated below in Table 2, and they are mapped in Figure 10. Population

characteristics of the cities are tabulated in Table 3. The cities were classified into


regions using the following selection rule:

South. Any city in a state that seceded from the Union during the Civil War.

Border. Any city in a state where slavery was legal in 1860, but which did not secede.

Washington, DC and Louisville, Kentucky are included in this category.

North. Any city in a state where slavery was illegal in 1860.

Atlanta

Nashville

Dallas

New Orleans

St. Louis

Chicago

Detroit

Cincinnati

Louisville Richmond

Charlotte

Jacksonville

Miami

Philadelphia

Washington

Baltimore

Chattanooga

Birmingham

Indianapolis

Augusta

Memphis

Figure 10: Cities Included in the Master Dataset

For any given combination of city, industry, job type, and year – like salespeople

in the retail industry in Birmingham in 1967, or service workers in the food stores

industry in Cincinnati in 1973 – we can construct a variable called “black share”, which

is simply the proportion of employees in a given sector that were black. Consider,

for example, the construction industry in Atlanta in 1966. There were 630 managers

reported industry-wide, and 6 were black. Thus:

Black share =6

630= 0.0095

This will be the main variable of interest in our analysis.

The dataset is not without limitations. Most noticeably, the dataset is not

comprehensive; the city- and industry-level charts published in the reports are only a


Figure 11: Excerpt from Vol. 2, p. 291 of the 1969 EEOC Report

portion of the total data available. Luckily, the selection rule used by the EEOC is

not problematic, as only MSAs and industries with extremely small concentrations of

non-white residents are excluded. Cities like Billings, Montana and Anchorage, Alaska

are absent, but they were not the target of our analysis in the first place.

But the data is incomplete in another way. The data in the reports is tabu-

lated directly from the Employer Information Report (Standard Form 100), commonly

known as the EEO−1 report. Employers with payrolls of 100 or more are required to

submit these reports every year, but most other firms are not. The question becomes:

are we missing a large and crucial segment of the population if we analyze data that

systematically excludes these firms? While certain industries may be disproportion-

ately affected, and their individual time-series profiles may be highly distorted by this

artifact of the data collection process, it likely remains reliable at the industry level for

most of the data we will examine.

As disclaimed in each report’s introduction, there are also concerns with self-

reporting bias and non-response bias. Because the reports are filled out by employers

– usually without any oversight – in a context where there may be a motivation to

fudge the numbers, it is reasonable to ask whether we can rely on this data for serious

analysis when it may be doctored. Smith & Welch (1984) investigate this problem by

comparing self-reported EEO−1 statistics to Current Population Survey data, where

workers themselves are asked to describe their job type. The authors found serious

discrepancies for managers and professionals, suggesting potential fraud, but the data

for other job categories seems consistent with CPS estimates.

Additionally, the 1969 report notes that fully a quarter of applicable firms (those


with at least hundred employees, or with at least fifty employees and significant contract

revenue from the federal government) failed to file EEO−1 reports in 1969, at least by

the official deadline. But this was the dawn of the EEOC regime, and we can hope that

this failure to report had more to do with logistical bungling and slow adoption of new

regulations than intent to conceal certain types of behavior.

The final concern is fundamental and immutable: for whatever reason, there was

no detailed report issued by the EEOC in 1968, 1971, or 1972. The absence of these

reports punctures our already-abbreviated time series and may make the detection of

many actual discontinuities impossible. Rather than interpolating the data, which may

seriously skew the econometric analysis, we will simply collect no data for these years

and proceed with data from the remaining five reports (1966, 1967, 1969, 1970, 1973)

in the relevant time period.

Timothy Hyde Methodology 42

Methodology

The first challenge is converting our share variable, which is only supported on a limited

domain, S ∈ [0, 1]. This means that any OLS regression run with S as an the depen-

dent variable will be treating the share variable as something it is not, specifically a

continuous variable. To avoid this, we transform S into S̃ by taking the logodds, where

odds =S

1− S

S̃ = ln

(S

1− S

)Because S̃ ∈ (−∞,∞), we can run a meaningful OLS regression that will not

in any event provide predicted values outside the possible range, as would have likely

happened for certain observations if we ran an OLS regression on the untransformed

S variable. However, we run into a new problem: S̃ is undefined for any observation

for which S = 0 or S = 1. We circumvent this issue by slightly adjusting any problem-

atic value of S, either adding or subtracting 0.5 to the numerator of the black share

expression, as appropriate.

However, this adjustment may seriously skew our S value in cases where the

overall number of employees in a sector is small. For instance, in a sector where 0 out

of 5 employees is black, the adjusted black share will be

0 + 0.5

5= 0.1

which is a substantial overstatement. This situation is most likely to arise in industries

where certain job types are not really applicable. For instance, the number of craftsmen,

operatives, and laborers in the insurance industry is quite small in every city in our

sample due to the nature of the industry. This issue dovetails with another problem

having to do with observations with few overall employees. In any regression where

share is the dependent variable and the observations are unweighted, all datapoints

from the smallest sectors to the largest sectors will influence the estimate equally.

Results from a regression on S̃ will be extremely sensitive to each individual per-

sonnel change in a small sector with five employees, because the addition or subtraction

of one black employee will shift the black share by 20 percentage points, and the logodds


by a commensurate amount. Meanwhile, it would take a seismic shift to achieve a 20

percentage-point change in black share in a larger sector with, say, 1000 employees.

This asymmetry is sensitivity is undesirable, as we do not want to give the smallest

sectors in our dataset the most influence, especially if this problem is concentrated in

low-population sectors (like blue collar workers in the service-oriented insurance indus-

try) for which the black share statistic is not really meaningful in the first place. There

are several possible solutions which we consider in turn.

Weighting the data so that larger sectors are given proportionately more

influence. This solution would involve devising a weighting scheme that assigned a

weight to each observation. If we make the natural assumption that influence should

be directly proportional to the size of the sector, we can weight each observation by

the total number of employees. Once this scheme is established, we replicate each

observation ki times, where−→k is the vector of weights and ki is the appropriate weight

for observation i.

However, any regression run with this swollen dataset (n = 7, 107, 784) will

produce extremely small standard errors and correspondingly massive t-statistics. This

is likely because the large number of identical observations reduces the variance is the

dataset relative to its size, and it makes it essentially impossible to distinguish actual

significant relationships from anything else. Unless we can determine a procedure to

adjust the standard errors, this method is not workable.

Changing the unit of observation from sector to individual. This strategy

is similar to the one proposed above, except we would also disaggregate the sector-

level observations into individual-level observations. Consider a sector with 25 total

employees, 11 of whom are black (S = 1125

= 0.44). The previous method would have

converted this observation into 25 identical observations with S = 0.44. Instead, we

could create 11 new observations with S = 1, representing the black employees, and 14

new observations with S = 0, representing the non-black employees. After performing

this routine on all observations, we would have a binary black share variable and could

use a logit or probit regression to estimate the parameters.

It is not clear whether we would run into the same problem with undersized

standard errors like we did using our first weighting scheme. Regardless, the coefficients

of such a binary outcome regression would be difficult to interpret. Rather than running

a regression that explains the relative level of black employment in an industry, we are


trying to specify an equation that explains whether a specific employee in a specific

sector is black. It will be harder to track black employment in a given sector over time,

which will in turn make it more difficult to detect evidence of collusion.

Adopting a selection rule that excludes small sectors entirely. It seems that

the simplest and most suitable solution is one that systematically excludes sectors that

are not of a certain threshold size, and then treats all remaining observations as equal,

despite remaining inequities. This compromise solution maintains interpretability and

legitimate standard error calculations, at the expense of leaving the remaining sectors

unweighted and unadjusted.

We also must determine what threshold is appropriate, and whether to include

a sector that is “too small” in 1966 but has grown to permissible size by 1973. Because

change over time is an important part of our investigation, and because the sample

is already so short in the time dimension, we decide to discard any sector if any of

its observations fall under the threshold value in any year. But we will leave the

problem of threshold level open until after we have designated an initial specification

for our regression, at which point we can conduct a sensitivity analysis on the threshold

parameter.

Proposed Specifications

Our first goal is to develop a regression that can adequately explain the black share

logodds variable S̃. There are all sorts of questions that we would want to ask before

predicting the black share in a particular sector:

1. What is the overall black share of the surrounding population?

2. Does this industry have a history of black employment?

3. What occupational types are we considering?

4. What is the current economic climate?

5. How active/aggressive are federal agencies in enforcing Title VII in this metropoli-

tan area? In this time period?

While much fruitful analysis could come of a rigorous attempt to specify this

equation with variables that could serve as proxies for these factors, we will not attempt


to do that here. Our first hurdle would be the lack of a good measure of enforcement

activity in a given area and year – indeed, our best measure of such enforcement might

be the changing black share variable itself. There is state-level data on litigation activity

and complaints filed furnished in the EEOC’s Annual Report, but it may be hard to

determine what proportion of claims are relevant and it is unclear whether higher levels

of complaints are to be expected in states with relatively high levels of enforcement, or

relatively low levels.

Given these limitations, and given the fact that we are less interested in what

ambient factors affect the level of S than we are in the dynamics of different industries

in different cities, we can be satisfied filling out our specification with fixed effects

(at the city-, industry-, year-, and job type-level) that will hopefully control for most

variation in black share. We will include the black share of total population explicitly

as a control variable, because it is certain to have a lot of explanatory power and can

be easily obtained. Additionally, its inclusion will allow us to interpret regional and

city-level fixed effects in a different light.

Our initial specification is:

S̃ = β0 + β1 ∗MSA + β2 ∗ CC + β3 ∗ SOUTH + β4 ∗ BORDER + β5 ∗ YEAR + βi + βt

where:

• MSA is the black share of population in the entire metropolitan area, including

both the central city and suburbs.

• CC is the black share of population in the central city of the metropolitan area.

• SOUTH and BORDER are regional dummy variables. See Table 2.

• βi is the fixed effect corresponding to industry i.

• βt is the fixed effect corresponding to occupational category t.

The results of our initial test can be found in Table 8. The threshold value

was arbitrarily set to 50 as a starting point; again, this means we excluded data from

any sector (that is, any city-industry-job type category) where the total number of

employees was less than 50 in any year in our sample. We alternately included and


excluded the YEAR variable, as well as the industry and job type fixed effects, in order

to test the specification’s robustness and sensitivity.

A few points are immediately obvious.

• As expected, the population share variable is significant regardless of what other

controls are included.

• It is difficult to achieve much explanatory power in the model without including

job type fixed effects. Compare the R2 value in Regression 2 to that in Regression

4, and note that the only difference in the specification is the inclusion of the job

type fixed effects. This makes sense, as we know black share varies enormously

between different job types through this time period.

• While the YEAR variable is not as vital and only marginally improves the adjusted

R2 when it is included, it is highly significant and carries a coefficient almost as

big as the regional dummy variables.

• On the surface, the industry fixed effects do not seem to be impacting the model

dramatically; Regressions 6 and 8 are almost identical. But the F test for joint sig-

nificance of the industry fixed effects (F(12,4324) = 62.99) yields an extraordinarily

small p-value, and several effects are statistically significant in and of themselves.

This specification seems like a good starting point, as the adjusted R2 is decently

high and the fixed effects are performing well. But this analysis rests on the assumption

that 50 is an appropriate threshold level. In order to test this more carefully, we rerun

this regression while adjusting the threshold cutoff. We started with a cutoff of 0,

rerunning the regression and using a threshold that was 10 employees higher each time

until the threshold reached 200. Reassuringly, the estimates are robust to this sort of

adjustment and coefficients were quite steady across all 21 estimations. From here, we

can proceed to the more interesting empirical questions at hand.

Timothy Hyde Results 47

Results

Based on the descriptive data contained in Tables 6 and 7, and our initial estimate of

the coefficient on the YEAR variable, we know that this seven-year period was a time

of dynamic change in the racial composition of the workforce at all levels. But in order

to capture a more nuanced view of this change over time, we run separate regressions

on the data from each year’s report. The results are listed below in Table 9.

Two important trends emerge. The first concerns the estimates for the coeffi-

cients of the SOUTH and BORDER regional dummy variables [highlighted in red]. In

the 1966 regression, both coefficients are highly significant and negative. Over time,

significance is generally decreasing and by 1973, the model does not find statistically

significant evidence of any regional effect. In the meantime, the population share vari-

ables gain explanatory power, which is consistent with a job market that is doing a

better job of taking advantage of local talent of all races.

The coefficients, while harder to interpret, tell a similar story. We must recall

that the −0.319 coefficient on SOUTH, for example, does not represent a constant

effect. To translate the coefficient back into terms black share, we must select a specific

initial level of black share, calculate the logodds, add the coefficient, and reconvert to

black share. If we perform this calculation for the median sales sector in the South in

1966 (S̄ = 0.0193) we find an effect of −0.00697. That is, we would expect the median

sales sector in the South to have a black share more than half a percentage point higher

in the counterfactual world where it is elsewhere in the country. This represents an

effect of approximately 36%. If we perform the calculation for the median craftsmen

sector in the South in 1966 (S̄ = 0.081), the effect is approximately 1.9 percentage

points, or 27%. Similar calculations show that the effect in the border states is also

large.

But, again, this effect seems to dissipate over time, which is consistent with the

regional convergence hypothesis advanced by Donohue & Heckman (1991). But another

type of convergence is demonstrated in this table: convergence between job types. The

job type fixed effects [highlighted in blue] tell the story – in the 1966 regression, all

job type fixed effects are hugely significant. This is no surprise, as we would expect

black workers to be overrepresented in blue collar sectors and underrepresented in white

collar ones. The magnitude of the coefficients are also enormous, at least compared to


those of the regional dummy variables.

And yet note the trend over time in the two categories listed. The coefficients

remain highly significant but decline in magnitude by more than 25% in each case.

Although fixed effects are not displayed for all job types, the pattern holds in every

case: negative coefficients and positive coefficients alike shrink in absolute magnitude

towards zero. This is evidence that job sectors in which blacks were underrepresented

(like management) are becoming more integrated, and vice-versa for blue-collar job

sectors.

These trends can be seen more directly in Tables 10 and 11. Note that demo-

graphic controls and city and industry fixed effects are included in these regressions but

excluded from the tables for readability.

Note the strongly significant coefficients on the YEAR variable in each regression

for both the North and South. The trends are mostly comparable by job type across

region, but the small differences between North and South may be instructive. The

“pivot point” for the South seems to be the Operatives category [highlighted in red].

All the “better” job types have statistically significant positive coefficients, and all the

“worse” job types have statistically significant negative coefficients. If we look at the

same coefficients for the North, it seems the “pivot point” is in the Craftsmen category

[highlighted in red]. Ultimately, this may indicate that craftsmen jobs in the South

may have represented more of an advancement for black workers than similar jobs in

the North.

Differences Across Industry Type

Up to this point our results have been mostly descriptive in nature. This analysis gives

us the lay of the land and may help us later, but now we start getting to the heart of

the hypothesis test that is central to this paper. The next thing we want to explore

is the difference between exposed and unexposed industries. This grouping we use

is unavoidably arbitrary, but relatively straightforward, and can be found in Table 4

below.

Recall that the ultimate prediction of the model is that exposed industries will

move through time in a different way than the unexposed industries because of the

potential for collusion and the need to contend with the tastes of customers. Our


initial task is to find evidence that the two types of industries moved through time

differently in any way, let alone the specific way my model predicts. In order to do that

we essentially repeat the analysis from Tables 10 and 11, but include a second trend

variable that is interacted with an exposure dummy variable set equal to 1 for exposed

industries (Table 12 and Table 13) and 0 for all others.

Inspecting the table, we see that there are few significant differences; that is,

we do not have much statistically significant evidence that exposed industries were

integrating more quickly or more slowly than the others in most sectors. But examine

the results for the Sales regression [highlighted in red]. In the South, black share in the

sales forces of unexposed industries was growing fairly rapidly. Importantly, the trend

in exposed industries was not much lower, and the difference is insignificant.

Meanwhile, consider the corresponding regression for the North [highlighted in

blue]. Here we notice a clear and significant difference between unexposed industries

(where the black share of the sales force is apparently growing over time) and the

exposed industries, where it is apparently not. We need to pool these regressions and

statistically compare the trends for each region and industry to formalize this analysis,

but we have evidence that black share in the sales sector in the North grew anemically

in the years after the Civil Rights Act, as opposed to the rapid growth in the South.

Difference-in-Difference-in-Difference Estimation

The result from the previous section gives an indication that the growth in black repre-

sentation in the sales force is significantly lower in exposed industries than in unexposed

ones. This is not surprising if we believe that consumer tastes create a powerful in-

centive for hiring managers in exposed industries to hire more white workers; indeed,

this result dovetails nicely with the difference-in-difference results found by Holzer &

Ihlanfeldt (1998), which showed that the race of workers in positions involving customer

interaction was responsive to customer demographics.

At the same time, we note that this result only holds in the North, where we

might expect consumer preferences for discrimination to be more intense. Are these

differences-in-differences really different? In order to determine that, we conduct a

difference-in-difference-in-difference (DDD) estimation procedure to test that question

formally.


The results are provided in Table 14. The highlighted DDD variables confirm

that there is indeed a significant disparity between the exposed-unexposed difference

in the North and the South. This shows that black share in exposed industries in the

South is growing essentially as fast as in unexposed industries in the South – faster than

we would expect given the huge disparity in growth rates in the North. This finding

is strong evidence that an understanding of the mechanics of consumer discrimination,

specifically, is key to assessing the impact of Title VII and adjudicating the argument

over its efficacy. But does this result affirm, or perhaps refine, the model of Righteous

Collusion proposed above?

We can think of the model as revealing an underappreciated “potential” for

certain industries in certain cities to dramatically reduce discrimination. Without a

recognition of the collusive dynamics at play between firms that must consider both

the preferences of their consumers and the actions of competing firms, we may at first

think that industries that are exposed to discriminatory consumers are the least likely

candidates for quick integration. But once sufficient federal enforcement is introduced,

such industries will be eager to end their discriminatory ways.

In a certain sense, the Civil Rights Act may have served to make consumer

preferences less relevant – in which case we would expect greater gains in the region

where consumer tastes were retarding progress in the first place. If we assume that

consumer preferences were indeed more intense in the South, then the differential in

response to the Civil Rights Act might be more easily explained.

But there is little evidence to support the view that consumers were “more”

discriminatory in one region than another; the same logic would hold if we assume that

the Fair Employment Laws in place in most Northern states (including all the states

where the Northern cities from our dataset are located) had been someone effective

in triggering the Righteous Collusion in those states before 1964. Perhaps the South

was simply the region where anti-discrimination enforcement had been weakest – or

nonexistent – and consequently the region best positioned for the Righteous Collusion

once the Civil Rights Act was passed.

Finally, the fact that the black sales employment share in exposed industries

grew just as fast as it did in unexposed industries within the South may provide another

pillar of evidence for our model. We might expect that firms in “exposed” industries

would naturally experience slower black employment growth as they contended with


a source of discrimination that their unexposed cousins need not have worried about.

But the evidence shows that such industries integrated just as fast, possibly because of

the dynamic outlined in our model.

Testing for Discontinuity

We originally hoped to formally test for the presence of discontinuity in the black share

variable over time, and determine whether exposed industries are significantly more

likely to exhibit them. Results of this kind, especially in conjunction with additional

analysis that demonstrates that these discontinuities are not correlated within industries

across cities or within cities across industries, would be the best circumstantial evidence

of the model’s validity.

However, because each time series only consists of five datapoints, the original

estimation procedure we conceived – which involved Chow tests run on individual sec-

tors – is not feasible. Pooling across job types or across industries may allow us or

others to gain statistical power and perform the test at a later date.

Timothy Hyde Implications & Further Research 52

Implications & Further Research

At the beginning of this analysis, we sought to answer two questions. What are the

economic sources of discrimination? And how did the institution of Title VII serve to

diminish discrimination, if at all?

We found that industries exposed to customer contact did evolve differently in

the sales sector in the tumultuous period after the Civil Rights Act went into effect.

The evidence of greater gains in the sales sector in the South as compared in the North

can be interpreted in support of the model, if we stipulate that the model predicts

that an intervention like Title VII will increase black share the most in highly exposed

industries with highly prejudiced customers and no history of government intervention.

Additionally, our finding that growth in black representation in the sales force

differed little between exposed and unexposed industries in the South provides evidence

that some factor – whether it was the Righteous Collusion outlined in our model or

something else – allowed exposed industries to overcome their exposure to consumer

discrimination.

But much remains to be shown, and opportunities for further research abound.

Does a similar dynamic develop labor markets where exposure to consumer tastes is a

non-issue but employees themselves are vehemently opposed to integration? To what

extent does residual taste for discrimination on the employers’ part hamper the mech-

anism of the Righteous Collusion? And can we find more direct evidence, possibly by

using firm-level employment data - that discontinuous change occurs in these sectors?

If the model is further substantiated by additional evidence, it can inform the

debate as to the impact and cost-effectiveness of the Title VII intervention. If these

highly exposed industries are indeed easier to “unlock” than unexposed ones, and par-

ticipating firms are eager to regulate each other, then we can argue that Title VII was

able to achieve great gains in these sectors with relatively little effort or need for ag-

gressive litigation. And theory and evidence from the psychology literature dating back

to the seminal work by Allport (1954) indicates that social contact, especially among

those perceived to be equals, can dramatically reduce prejudice itself. Indeed, many

civil rights leaders had long hoped that a few “strategically-placed” black workers, es-

pecially in sales or white-collar positions, could rapidly change attitudes and open the

doors for further integration (Sugrue 2008).

Timothy Hyde Implications & Further Research 53

The ultimate endogeneity of the tastes for discrimination (on the part of con-

sumers, employers, or whomever) has implications reaching far beyond the decade after

the passage of the Act. In this light, Title VII begins to look like the ideal intervention.

Minimal enforcement will end discriminatory behavior in the sectors where the psychol-

ogy evidence indicates it will do the most good in shifting the whole society towards

the end goal – a non-discriminatory marketplace.

Timothy Hyde 54

Tables

Table 1: Occupational and Industry Types Included in EEOC Reports

Occupations Industries

Officials and Managers Apparel & Allied ProductsProfessionals Apparel StoresTechnicians Construction

Sales Workers Eating & Drinking EstablishmentsOffice and Clerical Food & Kindred Products

Craftsmen Food StoresOperatives General MerchandiseLaborers Hotels & Lodgings

Service Workers InsurancePaper

RailroadTextiles

Wholesale

Table 2: Regional Classification of Included Cities

South Border North

Atlanta, GA Jacksonville, FL Baltimore, MD Chicago, ILAugusta, GA Memphis, TN Louisville, KY Cincinnati, OH

Birmingham, AL Miami, FL St. Louis, MO Detroit, MICharlotte, NC Nashville, TN Washington, DC Indianapolis, IN

Chattanooga, TN New Orleans, LA Philadelphia, PADallas, TX Richmond, VA

Timothy Hyde 55

Table 3: Characteristics of Included SMSAsCentral City 1960 Population 1970 Population Land Area (mi2)

Atlanta, GA 1,017,188 1,390,164 435Augusta, GA 216,639 253,460 58

Baltimore, MD 1,803,745 2,070,670 309.6Birmingham, AL 721,207 739,274 224.6

Charlotte, NC 316,781 409,370 105.7Chattanooga, TN 283,169 304,927 116.7

Chicago, IL 6,221,913 6,978,947 1277.2Cincinnati, OH 1,268,479 1,384,851 335.1

Dallas, TX 1,119,410 1,555,950 674.2Detroit, MI 3,762,360 4,199,931 872

Indianapolis, IN 944,475 1,109,882 381.2Jacksonville, FL 455,411 528,865 351.3Louisville, KY 725,139 826,553 210.4Memphis, TN 674,583 770,120 195.5

Miami, FL 935,047 1,267,792 256.7Nashville, TN 463,628 541,106 343.5

New Orleans, LA 907,123 1,045,809 273Philadelphia, PA 4,342,897 4,817,914 751.8Richmond, VA 436,044 518,319 144.6St. Louis, MO 2,104,669 2,383,017 460.6

Washington, DC 2,064,090 2,861,123 494.6

Table 4: Industry Exposure Classification

Exposed Unexposed

Apparel Stores Apparel ProductionFood Stores Construction

Eating & Drinking Establishments Food & Kindred ProductsGeneral Merchandise Paper & Allied Products

Insurance RailroadHotels Textiles

Wholesale

Tim

othyH

yde56

Table 5: Data Availability by City and Industry

Industry Atlanta Balt. Birm. Charlotte Chicago Cinc. Dallas Detroit Indi. Jack.Apparel Production • •

Apparel Stores • •Construction • • • • • •

Eating & Drinking • • • •Food & Kindred • •

Food Stores • • • • • • •General Merchandise • • • • • • • • •Hotels & Lodgings • •

Insurance • • • • • • • • •Paper • • •

Railroad • • • •Textiles • • •

Wholesale • • • • • • • • • •

Industry Louisville Memphis Miami Nash. New Orl. Phil. Richmond St. Louis Wash.Apparel Production • •

Apparel StoresConstruction • •

Eating & Drinking • • •Food & Kindred • • • • •

Food Stores • • • • • •General Merchandise • • • • • • • • •Hotels & Lodgings • •

Insurance • • • • • • • •Paper •

Railroad • • •Textiles •

Wholesale • • • • • • • • •

Tim

othyH

yde57

Table 6: Black Share by City and Job Type, 1966

Industry Atlanta Augusta Balt. Birm. Charl. Chicago Cinc. Dallas Detroit Indi.

Total Pop. 22.5% 28.3% 23.0% 30.2% 23.4% 16.7% 10.7% 15.6% 17.1% 11.9%

Managers 0.85 0.10 2.05 0.55 0.33 1.05 0.83 0.42 1.38 0.46Professionals 0.42 0.73 0.61 1.26 0.86 1.07 1.17 0.54 1.72 0.75Technicians 0.98 1.36 1.51 1.56 2.14 1.89 2.17 1.38 2.08 1.08

Sales 2.26 16.63 1.16 5.94 1.25 1.89 2.57 1.31 3.55 0.91OfficeClerical 3.64 1.99 3.03 1.52 1.40 2.67 1.83 1.18 5.29 1.47

Craftsmen 11.31 4.60 6.10 7.69 7.95 7.00 2.72 7.63 4.80 4.21Operatives 25.51 18.20 21.36 29.16 18.34 14.98 7.26 20.71 10.23 7.128Laborers 50.70 35.52 46.35 45.56 55.34 39.04 18.97 31.97 26.18 11.30Service 57.14 52.83 50.59 49.63 43.34 36.27 30.61 58.62 28.66 25.47

Industry Jacks. Louisville Memphis Miami Nash. N Orl. Phil. Richm. St. L Wash.

Total Pop. 22.6% 12.0% 37.6% 14.3% 17.9% 30.9% 16.9% 25.4% 15.3% 24.4%




Tim

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Table 7: Black Share by City and Job Type, 1973

Industry Atlanta Augusta Balt. Birm. Charl. Chicago Cinc. Dallas Detroit Indi.

Total Pop. 22.2% 27.2% 24.4% 28.7% 22.8% 18.6% 11.2% 16.3% 18.9% 12.9%




Industry Jacks. Louisville Memphis Miami Nash. N Orl. Phil. Richm. St. L Wash.

Total Pop. 22.1% 12.5% 37.4% 14.1% 17.7% 31.0% 18.1% 24.8% 16.5% 24.8%




Tim

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Table 8: Initial Specification TestsVARIABLES (1) (2) (3) (4) (5) (6) (7) (8)

MSA share 2.783*** 3.362*** 2.484*** 3.065*** 3.188*** 3.815*** 2.499*** 3.116***(4.242) (5.149) (6.639) (8.397) (4.949) (5.955) (7.084) (9.104)

CC share 2.276*** 1.805*** 2.309*** 1.837*** 1.966*** 1.453*** 2.257*** 1.753***(8.002) (6.299) (14.23) (11.47) (6.940) (5.093) (14.56) (11.50)

South -0.105 -0.139* -0.114*** -0.148*** -0.120 -0.154** -0.119*** -0.152***(-1.355) (-1.805) (-2.583) (-3.444) (-1.535) (-1.998) (-2.780) (-3.696)

Border -0.262*** -0.232*** -0.238*** -0.208*** -0.140* -0.105 -0.158*** -0.123***(-3.412) (-3.040) (-5.441) (-4.883) (-1.809) (-1.365) (-3.715) (-2.994)

Year 0.0994*** 0.0994*** 0.102*** 0.100***(9.131) (16.35) (9.673) (17.79)

Industry X X X XFixed Effects

Job Type X X X XFixed Effects

Threshold 50 50 50 50 50 50 50 50Observations 4350 4350 4350 4350 4350 4350 4350 4350Adjusted R2 0.053 0.070 0.692 0.710 0.113 0.131 0.734 0.752

t-statistics in parentheses, constant term omitted from table*** p<0.01, ** p<0.05, * p<0.1

Timothy Hyde 60

Table 9: Year-by-Year EquationsYEAR: 1966 1967 1969 1970 1973Dependent Variable: logodds logodds logodds logodds logodds

Demographic Variables

MSA Share 2.315*** 2.435*** 2.563*** 3.392*** 3.550***(2.594) (2.935) (3.583) (4.934) (5.622)

CC Share 2.290*** 2.507*** 1.955*** 1.665*** 1.485***(5.180) (6.196) (6.107) (5.592) (5.976)

South -0.319*** -0.431*** -0.230*** -0.232*** 0.0411(-2.999) (-4.391) (-2.670) (-2.788) (0.538)

Border -0.227** -0.295*** -0.155* -0.191** -0.0870(-2.200) (-3.056) (-1.833) (-2.329) (-1.133)

Industry-Level Fixed Effects

Construction -0.163 -0.341 -0.145 -0.152 -0.488***(-0.691) (-1.548) (-0.744) (-0.803) (-2.754)

· · ·Textiles -0.329 -0.650*** -0.378* -0.217 -0.160

(-1.266) (-2.677) (-1.758) (-1.042) (-0.819)

Occupation-Level Fixed Effects

Laborers 2.228*** 2.184*** 1.845*** 1.811*** 1.422***(15.27) (15.99) (15.27) (15.46) (12.94)

· · ·Managers -2.053*** -2.109*** -1.825*** -1.733*** -1.466***

(-15.53) (-17.04) (-16.66) (-16.33) (-14.72)

Observations 758 758 758 758 758Adjusted R2 0.777 0.792 0.773 0.769 0.719

t-statistics in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 10: Southern Trends in Black Share by Job Type

TYPE: Managers Prof. Tech. Sales Clerical Craftsmen Operatives Laborers Service

Year 0.191*** 0.237*** 0.173*** 0.142*** 0.155*** 0.0717*** 0.0118 -0.0680** -0.0990***(8.075) (7.097) (5.509) (4.054) (7.782) (3.669) (0.663) (-2.411) (-3.232)

Constant -377.3*** -469.5*** -340.7*** -280.5*** -305.6*** -141.0*** -21.76 136.2** 197.2***(-7.127) (-5.524) (-4.067) (-7.798) (-3.661) (-0.622) (2.453) (3.269) (-8.111)

Obs 295 100 110 190 265 220 215 180 150Adj R2 0.179 0.333 0.212 0.075 0.184 0.054 -0.003 0.026 0.060


Table 11: Northern Trends in Black Share by Job Type


Year 0.0744*** 0.143*** 0.109** 0.0990* 0.0449* 0.0230 -0.0456* -0.110*** -0.179***(3.056) (3.841) (2.526) (1.966) (1.730) (0.778) (-1.860) (-3.890) (-5.427)

Constant -147.7*** -282.3*** -214.9** -195.6* -88.52* -45.24 90.75* 217.8*** 353.8***(-3.860) (-2.535) (-1.973) (-1.733) (-0.775) (1.880) (3.918) (5.449) (-3.083)

Obs 110 70 65 100 115 90 90 80 110Adj R2 0.071 0.166 0.078 0.028 0.017 -0.004 0.027 0.152 0.207


61

Table 12: Southern Trends in Black Share by Job Type, Exposed vs. Unexposed Industries


Year 0.228*** 0.200*** 0.131*** 0.169*** 0.125*** 0.0743*** 0.0265* -0.0734*** -0.130***(8.140) (4.710) (3.703) (5.359) (6.274) (3.357) (1.791) (-3.437) (-3.714)

Exposure -0.0761* 0.106 0.130** -0.0606 0.0682** -0.00811 -0.0453* 0.0279 0.0467×Year (-1.905) (1.480) (2.064) (-1.284) (2.251) (-0.207) (-1.746) (0.576) (1.088)

Constant -450.6*** -605.6*** -514.1*** -335.9*** -246.7*** -146.2*** -51.13* 147.1*** 169.1***(-8.163) (-5.309) (-5.031) (-5.406) (-6.275) (-3.353) (-1.753) (3.499) (3.467)

Observations 295 100 110 190 265 220 215 180 150Adjusted R2 0.510 0.405 0.476 0.624 0.447 0.125 0.278 0.345 0.455


Table 13: Northern Trends in Black Share by Job Type, Exposed vs. Unexposed Industries


Year 0.0730*** 0.156*** 0.0558 0.173*** 0.0515** 0.00678 -0.0306 -0.123*** -0.184***(2.803) (4.331) (1.188) (4.286) (2.120) (0.254) (-1.135) (-5.518) (-5.015)

Exposure 0.00312 -0.0317 0.138* -0.165*** -0.0137 0.0418 -0.0449 0.0437 0.0116×Year (0.0807) (-0.575) (1.820) (-2.736) (-0.392) (0.977) (-0.961) (1.092) (0.213)

Constant -145.0*** -245.8*** -381.4*** -343.3*** -101.8** -95.32 149.5* 244.9*** 363.4***(-2.830) (-2.997) (-3.262) (-4.317) (-2.130) (-1.446) (1.989) (5.557) (5.024)



62

Table 14: Trends in Black Share by Job Type, Exposed vs. Unexposed Industries, North vs. South


Year 0.148*** 0.158*** 0.0825*** 0.110*** 0.0801*** 0.0715*** 0.0530*** -0.0132 -0.0300(6.431) (4.734) (2.799) (4.304) (4.741) (3.796) (3.535) (-0.725) (-0.898)

Exposure -0.0554* -0.0210 0.0859* -0.0378 0.0129 -0.0273 -0.0543** -0.0242 -0.000554×Year (-1.696) (-0.385) (1.680) (-0.991) (0.505) (-0.829) (-2.056) (-0.612) (-0.0133)

North 0.0363 0.0230 -0.00793 0.0714* 0.0396 -0.0260 -0.112*** -0.185*** -0.236***×Year (1.068) (0.520) (-0.199) (1.958) (1.587) (-0.922) (-5.115) (-6.844) (-6.022)

Border -0.000422 0.0408 0.0713* 0.0218 0.0120 -0.0747*** -0.127*** -0.109*** -0.0819**×Year (-0.0148) (1.021) (1.918) (0.691) (0.572) (-3.201) (-6.862) (-4.863) (-2.136)

Exposure × -0.0154 0.0998 0.0786 -0.104* 0.0204 -0.0442 0.0295 0.0917* -0.0179North×Year (-0.317) (1.432) (1.205) (-1.932) (0.563) (-0.943) (0.761) (1.766) (-0.343)

Exposure × -0.00593 0.0204 -0.0114 -0.0404 0.0222 0.0367 0.00912 0.0365 -0.0203Border×Year (-0.147) (0.331) (-0.180) (-0.877) (0.693) (0.911) (0.276) (0.776) (-0.420)

Industry X X X X X X X X XFixed Effects

Occupation X X X X X X X X XFixed Effects

Constant -1.649*** -1.396*** -0.836*** -2.815*** -0.109 0.267* 1.180*** 2.243*** 2.956***(-8.584) (-3.629) (-3.317) (-13.32) (-0.769) (1.696) (9.531) (14.93) (8.676)



63

Timothy Hyde References 64

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