Incarceration, Unemployment, and “ Eurosclerosis”

8/12/2019 Incarceration, Unemployment, and Eurosclerosis

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Incarceration, Unemployment, and Eurosclerosis!*

Felix Elwert

Harvard University

First Version: March 2000This Version: August 2002

* Direct correspondence to Felix Elwert, Department of Sociology, 622 William James Hall, Harvard University,Cambridge, MA 02138 ([email protected]). Part of this research was previously presented at the Bureau ofLabor Statistics, Washington D.C., March 1999, and circulated as a research report (2000) for UnderstandingUnemployment and Working Time: A Cross-Country Comparative Study,!funded by the Ford and RockefellerFoundations. I thank Christopher Winship, Stephen Morgan, Mariko Chang, Lawrence Bobo, David Howell,Orlando Patterson, Barry Bluestone, Constance Sorrentino, David Harding, Tobias Linzert, and Jay Gabler fordiscussions and advice. Laura Silverberg provided splendid editing assistance.


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Abstract

This paper contradicts the current consensus about the role of incarceration in U.S.-European

labor market performance differentials. Theoretically, I develop a more general model for the

causal interrelationship between incarceration and unemployment that integrates admission,

release, and employment effects. Empirically, I show that the causal effect of incarceration on

unemployment is (1) considerably smaller than previously reported; (2) not large enough to

noticeably impact U.S.-European performance differentials; and (3) potentially increasing, rather

than decreasing the official U.S. unemployment rate. I further demonstrate that the forced

inactivity of the U.S. incarcerated population does not question the reality of U.S.-European

labor market performance differentials, even when prisoners are included in labor market

measurement.

Word count: 12,498.


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INTRODUCTION

Measurement and explanation of labor market performance continue to pose significant

challenges for social research. Nowhere do these analytical difficulties surface more acutely

than in the international comparison of labor market regimes where sociologically enlightened

investigations must engage considerable variation across institutional frameworks. The debate

on high unemployment in Europe, and the role of U.S. incarceration therein, is a case in point.

European unemployment rates have exceeded U.S. rates since the early 1980s. Between

1984 and 2000, standardized unemployment rates in the fifteen European Union member states

hovered between eight and eleven percent, surpassing U.S. rates by four percentage points on

average (Figure 1). Earlier attempts to explain this disparity have focused on a collection of

institutional conditions, popularly known as Eurosclerosis.! This summary diagnosis attributes

the European predicament to labor market rigidities, misaligned incentive structures, excessive

workers' rights, and redistributive welfare state policies, while the United States is thought to

have escaped high unemployment through labor market deregulation and wage flexibility (for

reviews, see Krugman 1994; Howell 2000). Much empirical research contradicts this account

(e.g. Blank 1994; Freeman 1995; Buchele and Christiansen 1998; Marshall 1999; Howell 2000),

yet politically it has become a veritable article of faith that continues to inspire deregulatory

European labor market legislation. Public policy requires theory for guidance and legitimacy.

Conceivably, it is the absence of an alternative theory as simple and persuasive as

Eurosclerosis!that has allowed political pressures on the European welfare states to linger.

Recent years have witnessed the emergence of an alternative research agenda, which

maintains the institutional focus but concentrates on a surprising new candidate, incarceration.

The U.S. incarcerated population has quadrupled to two million inmates since 1980. Meanwhile,


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European incarceration grew at a slower pace and today remains at significantly lower levels.

The timing of the U.S. prison boom thus roughly coincides with the emergence of the U.S.-

European unemployment rate differential. If, as Jancovic (1977) first investigated, the United

States hides part of its unemployment problem behind bars, or, alternatively, if incarceration is to

be viewed as forced unemployment (Freeman 1995), then official labor force statistics, which

exclude the incarcerated population from consideration, understate U.S. unemployment and

overstate the EU-U.S. unemployment gap.

This paper assesses the evidence for incarceration-based accounts of the U.S.-European

unemployment rate differential. Analytically, the literature divides into two approaches, causal

and accounting (Western and Beckett 1999). The causalapproachis an explanatory one, asking

how the labor market would have performed had the prison system evolved differently, given

current definitions of labor market performance (Jancovic 1977; Western and Beckett 1998,

1999; Katz and Krueger 1999). Its main contention is that rising incarceration has lowered the

official unemployment rate because many inmates were unemployed prior to their incarceration.

The accountingapproach, by contrast, deals with measurement and asks how our perception of

labor market performance would change if we used different lenses #definitions, measures, and

conventions #to view a given reality of labor market and incarceration (Jencks 1992; Freeman

1995; Faux 1997; Buchele and Christiansen 1998; Western and Beckett 1998, 1999; Western,

Beckett and Harding 1998; Western and Pettit 2000). Its main concern is whether apparent

unemployment rate differentials from the official statistics are robust to alternative measures of

labor market inactivity that include the incarcerated population in the unemployment count.

Empirical results differ in magnitude but agree in direction. From a causal perspective,

Western and Beckett (1999) conclude that incarceration lowered the official male U.S.


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unemployment rate by 0.6 percentage points in 1995. At zero incarceration male unemployment

would have been at 6.2 rather than officially 5.6 percent (Western and Beckett 1999, p.1039).

Using a similar methodology, Katz and Krueger (1999, p.42-44) estimate that the increase in

incarceration between 1985 and 1998 caused the official male unemployment rate to decrease by

0.3 percentage points. Notwithstanding effects in the opposite direction due to increased

unemployment among released former inmates, rising incarceration is asserted to cause an

overall decrease of unemployment in the labor market (Western and Beckett 1999, p.1053).

Results from the accounting perspective are even stronger. In their analysis of the four

largest European countries, E-4, Buchele and Christiansen (1998, p.121) find that "the large

1988-95 prime-age male unemployment gap in favor of the US $ shrinks to an insignificant

non-employment gap $and reverses itself for 1992/93 in favour of the [E-4]$when the non-

employment rates are adjusted to include the incarcerated population." Similarly, Western and

Beckett (1999, p.1032) show that including inmates in the unemployment count raises the male

U.S. unemployment rate by a full third, or two percentage points, from 5.6 to 7.5 percent (1999,

p.1041). Their modified unemployment rate indicates that the U.S. has outperformed

unemployment in Europe only from the early 1990s, rather than from the early 1980s as

suggested by the official rates. According to these results,

On account of their theoretical and political importance, these results have received

considerable attention and distribution in the academic world and the public press (e.g.

Wacquant 1998; Justice Policy Institute 2000; Petersilia 2000; Mauer 2001; Downes 2001; Wray

2001). Methodologically, this new research agenda highlights the limitations of truncated

sampling in labor market research.1 Theoretically, it demonstrates the importance of non-

1For example, Mare and Winship (1984) show that a substantial portion of the relative decline in African Americanyouth employment in the 1960s and 70s can be explained through increased school enrollment and military service.


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economic institutions in the determination of market!outcomes, thus confirming a central tenet

of economic sociology. Incarceration appears to both contribute to a new explanation of existing

unemployment rate differentials and to cast doubt on their very reality. For that reason,

incarceration may furnish double ammunition against popular notions of Eurosclerosis and their

policy implications. To wit, it seems as if incarceration contributes to a new etiology for the

disease of high unemployment in Europe while simultaneously questioning its symptoms.

This paper questions the current consensus about the impact of incarceration on labor

market inactivity.2 In the main part of this paper I extend the theoretical agenda through a more

complete model for the causal interrelationship between incarceration and unemployment that

distinguishes between admission, release, and employment effects of incarceration. I note that

prior work has unduly focused on admission effects to the neglect of total causal effects.

Empirically, I show that the causal (admission) effect of incarceration on aggregate

unemployment is considerably lower than previously thought. Performing a sensitivity analysis

for the total causal effect, I argue that incarceration may quite possibly have increased, rather

than decreased unemployment in the labor market. Next, I critically engage the accounting

approach and show that it is unclear whether current measurement protocols hide or exaggerate

inactivity. Drawing on detailed U.S. and European data I show that incarceration does not

qualitatively change comparisons between U.S. and EU labor market performance from either

the causal or accounting perspectives. The results presented in this paper do not furnish support

Chandra (2001) shows that past research systematically overstates the decline in the racial wage gap by neglectingthe increased institutionalization and incarceration of African American men.2Dissenting voices are rare. In a commentary, Greenberg recently concluded that the main reason unemploymentdropped was not that the number of [inmates] rose $it was because the economy was expanding!(2001, p. 72).The present paper goes beyond Greenberg (2001) by formally engaging both causal and accounting perspectives,incorporating European data, and extending the theoretical agenda of incarceration effects through a revised modelof causal interrelationships.


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for popular notions of Eurosclerosis, but they indicate that incarceration does not provide

evidence against them either.

Next, I briefly detail the relevant trends in incarceration in the United States and Europe

and review the concepts underlying OECD labor market measurement.

TRENDS IN INCARCERATION

U.S. incarceration rates are high in international comparison. In 1999, more than one fifth,

or 1.9 million, of the 8.6 million people held in penal institutions worldwide were incarcerated in

U.S. prisons and jails (Walmsley, 2000). Table 1 shows the size of the incarcerated population

and incarceration rates per 100,000 residents for twenty-two countries of the western world,

Russia, and China. Incarceration rates in OECD countries ranged between 60 and 130. The

U.S., by contrast, imprisoned 680 out of every 100,000 adult residents, more than seven times

the European Union average, and second only to Russia. Due to a Russian mass amnesty in

2000 the U.S. today has the highest known incarceration rate in the world (Sentencing Project,

2001). When parolees and probationers are added to the count of prison and jail inmates, the

total number of individuals under the supervision of the U.S. criminal justice system in 1999

sums to 6.3 million, roughly 3 percent of the adult population and 5 percent of the total labor

force. Nationally, one percent of white men and close to seven percent of African American men

are incarcerated (Bureau of Justice Statistics [BJS], 2000a).

U.S. %leadership& in incarceration is a relatively recent development. Figure 2 shows time

series for the size of the incarcerated populations in the U.S. and the 15 member countries of the

European Union (EU-15). The number of inmates in U.S. prisons grew relatively slowly during

much of the twentieth century, doubling from about 92,000 to 196,000 between 1925 and 1970.


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The prison boom started only in the late 1970s. Between 1980 #the first year for which reliable

data on the size of the jail population are available # and 2000, the incarcerated population

quadrupled from one half to 2 million (BJS 2000a; Sentencing Project 2001). Over the same

period, European incarceration did not even double, rising from 212,000 to 366,000 (Council of

Europe 1984-2000). A juxtaposition of the trends in Figures 1 and 2 shows that the sharp rise in

U.S. incarceration since the late 1970s correlates with a decrease in the U.S. unemployment

rate.3

INDICATORS OF LABOR MARKET PERFORMANCE

This section discusses several measures of labor market performance used in the subsequent

theoretical and empirical discussions. Labor market performance is a multidimensional construct

that cannot be captured by any single indicator. Choosing between indicators depends on the

purpose of measurement. This is unproblematic as long as choices are carefully reasoned and

properly disclosed. The measures and arguments described in this section are those used in

official U.S. labor market statistics. They closely resemble European measurement standards, as

both are based on the same set of international guidelines proposed by the International Labor

Office and endorsed by OECD member countries (ILO 1982; OECD 1985).

The unemployment rate, UROfficial, is the most widely used among these statistics. It is

defined as the ratio of unemployment, U, to the labor force, LF, which equals the sum of

unemployment and employment,E.

URU

U E

U

LFOfficial =

+=* *100 100 (1)

3The zero-order correlation between the U.S. unemployment rate and incarceration is r = #.71 for men and r = #.76for both sexes.


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The Current Population Survey (CPS), which collects the official U.S. labor market data,

defines employment as doing any work for pay or profit during the survey&s reference week.

Payment may be monetary, or in kind, such as meals, living quarters, room and board, or

supplies received in place of cash wages! (U.S. Bureau of the Census 1998, section B1). The

official definition of employment is not tied to any minimum standards of decency, such as

earning above minimum wage, working under safe conditions, or even legality. The definition is

broad; literally, any work!for pay or profit qualifies (U.S. Bureau of the Census 1998, p. C4-2).

Individuals not employed are classified as non-employed.

Unemployment, by contrast, is a considerably more restrictive concept than employment or

non-employment. According to U.S. concepts, unemployment is defined by the triple criteria of

(1) not working, yet (2) being available for work during the reference week, and (3) having

actively looked for work over the past four weeks. Of these requirements, the active search

criterion is the strongest. Contacting an employment agency or prospective employer or placing

an ad in the newspaper count as active search, whereas reading ads or participating in job

training programs do not (U.S. Bureau of the Census 1988, p. B2-2).

The unemployment rate is meant to measure the degree to which labor supply is met by

demand. Labor supply is equated with the labor force. Unmet labor supply is equated with

unemployment. If labor market performance means market clearance, then the unemployment

rate is a direct measure for market failure.4

4The unemployment rate has several oft-neglected siblings, U-1 through U-7 (Shiskin 1976), which differ from thestandard unemployment rate in the degree to which they consider part-time employed individuals as unemployed.!For example, U-1 considers only persons who have been unemployed for thirteen or more weeks. U-7 includes theusual unemployed, persons working part-time for economic reasons (i.e. involuntarily), and discouraged workers(Sorrentino 1993). U-1 through U-7 thus differ in their underlying concept of labor supply.


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It is important to emphasize that unemployment, conceptually, is not the same as non-

employment, joblessness, or labor market inactivity. Unemployment is intended to capture

unmet labor supply and is therefore (per OECD definitions) limited to individuals that

demonstrate their willingness and availability for market work through active job search

behavior. A large fraction of those non-employed, jobless, or inactive does not fit this

description (e.g. full-time college students, homemakers, and retirees). The extent to which a

population is not employed therefore is better measured by the employment to population ratio,

EPR, (which divides the number of employed persons by the total, civilian, non-institutional

population), or its complement, the non-employment to population rate, NEPR. The (non-

)employment to population ratio separates the population into those that do any work for pay or

profit and those that don&t.

All of these measures (UR, EPR, NEPR, U-1/U-7) in one way or another capture labor

utilization. The unemployment rate captures the fraction of persons willing and available for

market work who are not taken up on their offer, while the employment to population ratio

measures what fraction of the population is contributing to GDP through paid work. As

indicators of labor utilization, these measures are evidently imperfect. Better indicators would

consider hours worked and hours desired to work. However, such more detailed statistics are

hard to measure and are rarely attempted.

The Exclusion of Inmates from Official Labor Market Measurement

Official labor market statistics in the United States and Europe are defined on the civilian

non-institutional population, rather than the total resident population. Excluded are members of

the armed forces and residents of non-institutional households, such as students living in dorms,


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residents of homes for the elderly, and inmates of mental institutions.5 Important for our

purposes, the entire incarcerated population # inmates of prisons and jails # is excluded from

official labor market measurement. The literature challenges the exclusion of prison and jail

inmates (e.g. Downes 2001, p.62), but never discusses its rationale. I therefore review the

official rationale before analyzing its effect on labor market statistics.

The OECD, building on the international standards of labor market measurement set by the

International Labor Office (ILO 1982), justifies the exclusion of the incarcerated population with

technical arguments regarding the primacy of the employment concept (OECD 1985, p.12). On

the most basic level, ILO and OECD define employment as direct contribution to GDP as

defined by the UN-OECD system of national accounts (SNA). Individuals contributing to GDP

are to be counted as employed (OECD 1985, p.13). But whereas OECD guidelines explicitly

acknowledge that inmates may receive monetary wages or payment in kind from jobs in the

prison industries, farm work, food preparation or prison maintenance, they are nonetheless not to

be counted as employed because the SNA regards expenses for feeding, housing, and paying

prisoners part of government intermediate consumption, which does not factor into GDP. In

short, because paid inmate labor, in a technical sense, does not contribute to GDP, inmates are

not counted as employed, even though they would fulfill the CPS criteria of employment.

Consequently, inmates may not be counted as unemployed either because unemployment

requires the possibility of employment (OECD 1985, p.15).

Compare the treatment of prisoners to that of monks in conventional labor market

measurement. The OECD acknowledges that members of contemplative religious orders (e.g.

5A small number of OECD countries include the military in their labor market statistics (Sorrentino, 2000). TheU.S. Bureau of Labor Statistics included the military for a few years until the early 1990s, but discontinued thepractice for three reasons: (1) Data on the number of military personnel stationed in the U.S. (not overseas) provedunreliable; (2) their inclusion made little difference; and (3) the BLS perceived little demand for the inclusive series(Phil Rones, BLS, personal communication, March 1999).


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Trappist or Buddhist monks) may spend most of their time in prayer or meditation!rather than

gainful employment. But even if the inmates [of monasteries] receive no cash payment of any

kind, so long as they are housed and fed they are considered to receive an income in kind which

is to be recorded in the GDP. Members of religious orders are therefore included in

employment! (OECD 1985, p.14). In other words, prisoners that work for pay may not be

counted as employed, but monks that receive nothing beside room and board in return for

meditation must be counted. #Obviously, these rules concern the outer limits of the OECD&s

GDP-based definitions, and they exhibit a certain degree of arbitrariness. It is this arbitrariness

that provides a good argument for exploring alternative measures of labor market performance

that do not exclude the incarcerated population wholesale. I will return to this point in the

analysis of accounting effects below.

In addition to this technical argument, several theoretical and practical reasons could be

considered to justify the exclusion of the incarcerated population from labor market

measurement. First, working or not, prisoners are not free participants in the labor market. As

such, their work may be regarded akin to slave labor (cf. Wacquant 2000; Buck 1994), which

could warrant their exclusion from official labor statistics on technical as well as moral grounds.6

Second, economists and governments routinely use the unemployment rate as an indicator of

inflationary tendencies. Low unemployment proxies wage pressures, which may lead to an

increase in the nominal wage level and thus inflation. If the incarcerated population were

counted with the unemployed, then the unemployment rate would signal an excess of labor

supply beyond the actual number of laborers still available in the market. An inclusion of the

6The debate on counting slave labor as free labor in the literature on the economics of slavery (cf. Fogel andEngerman 1974, pp.207-209; David and Temin 1976, pp. 209 -214, 223-230) parallels the contemporary discourseon prison labor in many ways. Note that the BLS originally excluded military personnel from the employment countbecause military service was not voluntary under the draft (National Commission 1979, p.49).


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incarcerated population in UROfficialwould thus mask inflationary potential. Third, although the

unemployment rate is neither intended nor particularly well suited to measure social and

economic hardship (Cain 1979, p.11; National Commission 1979, p.66-68), it is nonetheless

often used one. Conflating inadvertent hardship from unemployment in the market with socially

intended hardship in prison would further obscure the value of the unemployment rate as a

measure of misfortune. Finally, including the incarcerated population into UROfficial would

impair its international comparability. If nations differed significantly in incarceration rates, then

differences in national unemployment rates could no longer be interpreted as corresponding to

differences in the degree of labor market clearance because theunemployment

!associated with

people in prison is beyond the means of the labor market to eradicate.

To be sure, these arguments refer to the stated purpose of the official statistics. They do not

imply that the incarcerated population should never be considered in labor market measurement.

The design of any statistical measure has to be judged in light of the purpose of measurement,

and purposes may change.

Next, we turn to investigating the questions following from the exclusion of the incarcerated

population from measures of labor market performance.

THE CAUSAL EFFECT OF INCARCERATION ON UNEMPLOYMENT

This section investigates the causal effect of incarceration on unemployment. In order to

avoid a confusion of causal and accounting-type questions, I take the official concepts of

employment and unemployment as given in this section. Throughout, I adopt the standard

counterfactual notion of causality (Rubin 1978; Holland 1986; Sobel 1996; Winship and Morgan

1999) and ask what the size of the official unemployment rate would be had there been no


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changes in incarceration. I focus on the unemployment rate for two reasons. First, the

unemployment rate is the key measure of labor market performance and also the measure with

regard to which most of the debate on incarceration and labor market performance is led in the

U.S. Second, the unemployment rate serves as the reference point for the debate on weak labor

market performance in Europe. In order to contribute to both debates, I must relate to the same

outcome of interest.

I first delineate three theoretical mechanisms that causally link incarceration to

unemployment and derive a formal model for the total causal effect of incarceration.

Empirically, I present direct estimates for what I term the net admission effect of incarceration,

and a sensitivity analysis for the total causal effect. The results suggest that previously published

results overstate the effect on the U.S. unemployment rate by a wide margin. Repeating the

analysis with European data shows that incarceration fails to provide a new causal explanation

for the U.S.-European unemployment rate differential.

Theory: Admission, Release, and Employment Effects

The unemployment rate is linked to changes in incarceration via three mechanisms:

admission, release, and employment effects. In reality, these three mechanisms will occur

simultaneously and jointly produce the overall causal effect. In theory, each mechanism can be

described ceteris paribus.

Admission effects occur when new inmates, which, as a group, had a different

unemployment rate from the rest of the population, enter prison or jail. If newly admitted

inmates suffered from a higher level of unemployment prior to their incarceration than the

civilian, non-institutional population that remains behind, then UROfficialwill fall because official


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labor force statistics exclude the institutionalized population from consideration. Admission

effects are plausible because the unemployed, poor, and educationally or otherwise

disadvantaged # not to mention traditionally disadvantaged young minority males # face both

higher unemployment and greater odds of incarceration. The literature to date on the causal

effect of incarceration on unemployment exclusively considers admission effects.7

Release effects occur when released inmates, as a group, face a different unemployment rate

than the rest of the population. If newly released inmates suffer from a level of unemployment

higher than the rest of the civilian, non-institutional population, then UROfficialwill rise. Release

effects are plausible mainly because ex-convicts suffer severe stigmatization. Much

ethnographic and quantitative research documents the disadvantage, prejudice, and suspicion that

former inmates face in the labor market (e.g. Dale 1976; Davinedas 1983; Hagan 1993; Freeman

1992; Miller 1996; Holzer 1996; Western, Beckett, and Harding 1998; Kling 1999).

Furthermore, prisoners experience a deterioration of existing job skills, human capital, and social

capital8while in prison, and forego the acquisition of new work experience and schooling. To a

degree, the existence of release effects is even inscribed in the statues, as many states bar ex-

inmates from public employment (Travis et al 2001, p.31). Even short spells in jail may produce

measurable release effects if employers treat unexcused absences or the embarrassment of

imprisonment as grounds for dismissal (Miller 1996). Release effects will occur even if the net

growth of the prison population is zero, due to the constant replacement of current prisoners.

Since incarceration represents a large-scale social intervention, it has macro-level effects

beyond inmates& own past and future employment status, i.e. beyond admission and release

7Katz and Krueger (1999) and Western and Beckett (1999) acknowledge the existence of release effects, but do notprovide aggregate level estimates.8To be precise, inmates often face not only deterioration but also a transformation of their social capital. Tie s to thelegal world are undermined, while ties to crime are formed or cemented. Prisons are important recruiting venues forgangs and organized crime.


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effects. One macro-level, or general equilibrium, effect relates to the construction boom

generated by the demand for new correctional facilities (Hallinan 2001). Another concerns the

expansion in prison related employment, such as in security and maintenance (BJS 1997e, p.15).

If these macro-level implications of mass incarceration provide jobs for workers whom would

otherwise have remained unemployed, then the growth of the prison sector creates an

employment effect that lowers the official unemployment rate.

The total causal effect of incarceration on unemployment, understood as the tradeoff

between admission, release, and employment effects, is thus determined not simply by the level

of incarceration at any given point in time, but crucially by the volume of inmates moving into

and out of prison and jail over time, as well as by their macro societal ramifications.

A Model for the Total Causal Effect of Incarceration on Unemployment

We can formalize these mechanisms and derive a simple yet general measure for the total

effect of incarceration. The measure relates the official unemployment rate to the hypothetical

unemployment rate that would have been obtained had prisons and jails not admitted or released

any inmates (i.e. had there been no changes in incarceration). The difference between the

official and the hypothetical rate identifies the causal effect. For clarity of exposition, I first

consider only changes occurring during a single year.

First, note that incarceration will cause the official annual unemployment count to change if

the number of unemployed people admitted to prison differs from the number of inmates

released into unemployment in a given year. The official level of unemployment, UOfficial, thus

equals the hypothetical level, UHypo, that would have prevailed in the absence of changes in

incarceration, minus the number of unemployed people admitted, plus the number released into


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unemployment. To allow for employment effects, further subtract the number of newly created

prison-related jobs that offer employment to workers that would otherwise be unemployed,

U U xA zR w GOfficial Hypo= + ( ) , (2)

where A stands for the total number of admissions, and R is the total number of releases that

year, of which the fractions x and z are unemployed respectively.9 G is the change in the

number of jobs for prison guards, administrators, maintenance workers, etc, and wis the fraction

of these jobs filled with otherwise unemployed workers.

Similarly, the official size of the labor force, LFOfficial, equals a hypothetical level, LFHypo,

minus those removed from the labor force through admission to prison, qA, plus those added to

the labor force after being released,sR. Additionally, add the number of workers otherwise not

in the labor force who are now employed in new prison related jobs, t(G).

LF LF qA sR t GOfficial Hypo= + + ( ) . (3)

Solving (2) and (3) for the hypothetical quantities and dividing one by the other yields

URU

LF

U xA zR w G

LF qA sR t GHypo

Hypo

Hypo

Official

Official

= =

+ +

+

( )

( )

*

100. (4)

9For now I neglect that the same individual may be admitted to and released from prison or jail more than once in agiven year. I take up the issue of reincarceration in the sensitivity analysis below.


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URHypois the hypothetical unemployment rate that would have been obtained in the absence of

changes in incarceration.

We can rewrite equation 4 in order to facilitate comparison with the measure used by other

authors (e.g. Western and Beckett 1999; Katz and Krueger 1999). Without making additional

assumptions, rewriteA = R + I, where Istands for to the change in the size of the incarcerated

population during the reference year. Writez = x + e, which highlights that inmates released

from prison may face a different risk of unemployment than those newly admitted. Similarly,

write q = s + d, where dis the difference between the labor force participation rates of those that

are admitted and those that are released from incarceration.

Now assume that newly admitted inmates suffered from unemployment to the same extent

as did current inmates overall,x = upi, where upiis the fraction of all current inmates that were

unemployed prior to their incarceration. Likewise, assume that q = lfpi, where lfpi stands for the

fraction of current inmates that participated in the labor force prior to their incarceration. (We

can justify these assumptions with reference to the high turnover in the inmate population # a

large proportion of current inmates is newly admitted, see below.) Substituting into (4) yields

URU eR upi I w G

LF dR lfpi I t GHypoOfficial

Official

=

+ +

+ +

( ) ( )

( ) ( )*

100 . (5)

(No additional assumptions are made about size or sign of d, e, w, t, upi, or, lfpi. Note, that,

plausibly, d,e > 0 would imply that inmates released from prison and jail have a higher risk of

unemployment and a lower labor force participation rate than those admitted.)

In the formulation of equation 5, interpretation is straightforward. The first terms in

numerator and denominator describe the official unemployment rate. The second terms, eRand


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dR, determine the release effect: UOfficialgrows to the extent that more inmates are released into

unemployment than are admitted when unemployed. The third terms, upi(I) and lfpi(I),

determine what can be called the %net admission effect&of a change in thesizeof the incarcerated

population: UOfficialdrops if the incarcerated population grows and currently incarcerated inmates

had a higher risk of unemployment prior to their incarceration than the non-incarcerated rest of

the population. The fourth terms, w(G)and t(G), give the employment effect of prison-related

job creation. Subtracting UROfficial from URHypo thus identifies the overall causal effect of

changes in incarceration on the official unemployment rate in the year under consideration.

The measure derived in equation 4 is considerably more general than the measures used in

the literature. Rewritten as equation 5, the relationship between these measures is easy to

appreciate. For example, Western and Beckett (1999) use a measure for the causal effect of

incarceration on unemployment that adds inmates that had been unemployed prior to their

incarceration to the numerator of UOfficial, and all inmates to the denominator.

URU upi I

LF IZeroOfficial

Official

=

+

+

**100 . (6)

Western and Beckett use URZeroas a measure for a hypothetical unemployment rate at zero

incarceration and interpret the difference to the official unemployment rate as the causal effect of

incarceration. Their measure thus differs from the one developed here in three respects. First, it

omits release and employment effects. Therefore it captures only net admission effects and is

biased for the total effect of incarceration on unemployment. Second, it assumes that prior to


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incarceration all inmates had been in the labor force. Third, it implies an extreme long-run

perspective, since incarceration was never fully zero at any time during the twentieth century.

Katz and Krueger (1999) use an approach very similar to Western and Beckett (1999), but

focus on more realistically circumscribed time horizons.10 Allowing that part of the current

incarcerated population was not in the labor force prior to incarceration, Katz and Krueger use a

version of equation 6 to build a simple difference-in-difference-type estimator that identifies the

effect of a net change in the size of the incarcerated population between two points in time while

subtracting out the changes in unemployment that would have occurred concurrently regardless

of changes in incarceration

( ) ( )UR UR UR UROfficialt

Zerot

Officialt

Zerot2 2 1 1

, (7)

where t2is a point in time after t1.

As with Western and Beckett&s method, Katz and Krueger&s method from equation 7

corresponds to equation 5 only if release and employment effects cancel each other out. The

methods used in the literature thus at best identify partial (i.e. net admission) effects and are

biased with respect to the total effect of incarceration on unemployment. Western and Beckett

argue that rising incarceration in the United States has concealed a significant amount of

unemployment and caused the official unemployment rate to be lower than it would have been in

the absence of prison growth. Due to their shorter time horizon Katz and Krueger estimate

smaller effects, but also conclude that the prison boom has lowered unemployment. The

10Recall that the assumption needed to go from equation 4 to equation 5 was that current inmates would have thesame labor market status today as the one occupied at the time of their incarceration, had they not been incarcerated.This assumption is implicit in the causal interpretation of Western and Beckett&s and Katz and Krueger&s methods.Since this assumption is more credible the shorter the duration of incarceration, it seems advisable to prefer shorttime horizons that track the causal effect of changes in incarceration over a limited number of years.


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theoretical point made in equation 5 is that if releases add more people to the official

unemployment count than are removed from it by the growth of the incarcerated population and

the concomitant employment effect, then incarceration would increaseofficial unemployment,

rather than diminish it.11

Next I attempt first to replicate the literature&s estimates for the net admission effect

empirically, and supplement these results with a sensitivity analysis for the total effect of

incarceration on the U.S. unemployment rate. I then revisit the causal role of incarceration in the

U.S.-European unemployment rate differential.

Data

The data used in this paper are time series from 1976 to 1999, unless noted otherwise. This

period encompasses both the takeoff of U.S. prison growth and the time frame for the debate on

high unemployment in Europe.

United States. The Bureau of Labor Statistics provides labor market data on average annual

levels of employment and unemployment from the Current Population Survey (OECD 2001).

All data refer to the civilian non-institutional population aged 16 and above.12

Time series on the composition and size of the incarcerated population since 1976 were

compiled from various Bureau of Justice Statistics (BJS) publications (see footnotes to Tables

11This informal statement neglects changes in the labor force level caused by incarceration. Note, however, thatUOfficialis much smaller thanLFOfficial, so that changes to the numerator will dominate changes to the denominator.

Since d*Rand n*Gare probably small relative to the size of the official labor force, I assume here that theyapproximately cancel to zero. If there are no significant changes in the denominator of the measure beside thoseassociated with the net increase in incarceration then it is obvious that equation 7 overstates the decreasing effect of

incarceration on the unemployment rate if, approximately, eR > upi(I) + w(G). The empirical part of this papertakes into account changes to both the numerator and denominator.12Since most prisoners are adults I repeated all analyses with labor market series for individuals aged 20 and above.Differences in results are negligible (not shown).


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A.1 and A.2).13 In order to avoid double counting, inmate data refer to custody counts (of

inmates actually held in a given facility), rather than jurisdiction counts, wherever possible. Due

to some irredeemable double-counting owed to problems with racial categorizing, prison and jail

inmate-counts by sex and race do not always exactly sum to incarceration totals. But remaining

discrepancies do not appear to affect results. Data on size and composition of the jail population

before 1979 are not available and had to be estimated. Incarceration series are shown in

appendix Tables A.1 and A.2.

I construct detailed time series variables on inmates& labor market status prior to

incarceration and current work activities while imprisoned from several large representative BJS

surveys of inmates of federal and state prisons and local jails, listed in appendix Table A.3.

Missing values between survey years are interpolated linearly. Data on the flow of admissions

and releases are taken from various editions of the Sourcebook of Criminal Justice Statisticsand

the Correctional Populations in the United States series published by the BJS. Unfortunately,

detailed national time series on admissions and releases by sex and race are not currently

available.

Europe. When comparing the U.S. to Europe, an agreement has to be reached about which

European countries to include. The debate on high unemployment in Europe typically refers to

all fifteen member-countries of the European Union, EU-15. Some authors omit select weak

economies from the comparison. For example, Western and Beckett (1999) omit some high-

unemployment Southern European nations and add a few economically strong nations from

central/western Europe, yielding thirteen countries, E-13. Buchele and Christiansen (1998)

compare the U.S. to the four strongest European economies, E-4. This paper produces estimates

13This paper restricts attention to inmates of prisons and jails. Additionally, a small number of inmates are detainedin drunk tanks, police lock-ups, etc. Due to the short duration of their incarceration, they are excluded from thisanalysis, as are approximately 3000 prisoners in military confinement facilities (BJS, 1998, p.511).


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for both the EU-15 and the E-13 (but does not usually show both sets of results). Additionally I

compute adjustments for the EU-6, which is a group of economically strong Western European

nations for which the most thoroughly internationally standardized labor market series are

available. The countries comprising EU-15, E-13, and EU-6 are listed in appendix Table A.4.

Since the impact of incarceration on measures of labor market performance is small relative

to their absolute level, it is crucial to use comparable baseline labor statistics for the U.S. and

Europe. While labor market measurement is highly standardized in OECD countries, important

differences remain (for an excellent discussion, see Sorrentino 1993, 2000). Currently, neither

Eurostat, ILO, nor OECD provide fully standardized time series on the levels of employment and

unemployment in the U.S. and the E-13/Eu-15 reaching back to the 1970s.14 For this paper, I

resort to time series on absolute levels by sex published in the OECD&sLabour Force Statistics,

which are provided by Eurostat and the BLS to comply with standardized OECD definitions.15

To guard against spurious results due to incomplete standardization, I repeat analyses for both

sexes on more fully standardized time series for the EU-6 that were recently produced by the

BLS for the entire period of interest (BLS, 2001).16

Time series on the size of the incarcerated population in Europe by sex from 1976 were

compiled from Council of Europe (1984-2000). Values missing before 1983 for Switzerland,

Finland, The Netherlands, and Austria were imputed. Inmate surveys with information on

inmates& labor market status prior to incarceration and work activities while imprisoned

14

The EU did not adopt a common labor force survey questionnaire for all member countries before the early 1980s.Therefore, nicely comparable labor force data for Europe are generally unavailable before 1983. Comparable datafor Austria and Denmark do not become available until several years later.15Official U.S. definitions are very close to OECD concepts. Therefore, the OECD makes no additionaladjustments to U.S. labor force data, even for its series of Standardized Unemployment Rates (SUR). Note thatOECD SURs are not useful for the current project because they do not allow for the derivation of standardized dataon levels.16The BLS has adjusted its EU-6 series to U.S. concepts. They represent the most fully internationally standardizedseries of labor market statistics available today. However, they are not available by sex or race (Sorrent ino 2000;BLS 2001).


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comparable to the U.S. surveys listed in Table A.3 are not available for Europe. U.S. estimates

are used in their place.

Results for the Causal Effect of Incarceration on Unemployment

Net Admission Effects

To estimate the net admission effect in isolation, i.e. without consideration of release and

employment effects, I use a version of equation 6 (instead of assuming perfect labor force

participation prior to incarceration I estimate actual participation rates empirically). I identify

the causal net admission effect of changes in the size of the incarcerated population over time by

contrasting the difference between official and zero-incarceration unemployment rates at

different points in time according to equation 7. To maintain comparability with the results from

the literature I at first restrict attention to males.

Crucial for the unbiased estimation of the net admission effect is the parameter upi, the

percentage of inmates unemployed prior to their incarceration,17and to a lesser degree lfpi, their

labor force participation rate. Applying the official definition of unemployment reviewed above

as best possible to data from ten nationally representative inmate surveys (listed in Table A.3), 18

I produce yearly estimates for both parameters for several demographic groups. Table 4 shows

the estimates for U.S. males between 1978 and 1997. On average, 18 percent of male inmates

were unemployed and 86 percent were in the labor force prior to incarceration. Inmates of local

17Upi is not to be confused with inmates&unemployment rate prior to incarceration. The denominator of the latterincludes only inmates that participated in the labor force prior to their incarceration. Upi, on the other hand, isdefined relative to all inmates.18Perfect adherence to official definitions is impossible because the questions asked in the CPS and inmate surveysare not identical. A comparison of exact wordings suggests, however, that the items are sufficiently close. Themain difference concerns the reference periods for the questions on employment and on job search. The referenceperiods used in the inmate surveys are generally longer (see Table A.3). Thus, inmate surveys are likely tooverestimate employment among all prisoners, and to overestimate unemployment among non-employed prisoners.


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jails were more likely to be unemployed prior to incarceration than were inmates of state prisons;

African American men reported higher unemployment than white men (appendix Table A.5).

These rates are quite stable over time, but have decreased in recent years, perhaps reflecting both

a trickle-down of improvements in the economy at large and a more indiscriminate use of

imprisonment in the wake of mass-incarceration. Not surprisingly, these estimates for upiand

lfpidiffer considerably from the corresponding unemployment and labor force participation rates

in the general population; this just confirms that incarceration is highly selective.19

Using these estimates for the auxiliary parameters upiand lfpi, I produce new estimates for

URZero. Per equation 7, the difference over time in the differences between UROfficialand URZero

identifies the causal net admission effect of changes in the size of the incarcerated population on

the official unemployment rate. Table 5 contrasts UROfficialand URZerofor various demographics.

Over the twenty-year period from 1980 to 1999, which witnessed the nearly fourfold increase in

incarceration from one half to 2 million inmates, my estimates suggest that the net admission

effect of incarceration reduced the male UROfficialby 0.13 percentage points, which accounts for 5

percent of the official decline in unemployment over this period.20 The net admission effect on

the unemployment rate for both sexes was 0.08 percentage points, or less than 3 percent of the

official decline. Time series for the size of the incarcerated population by race are available only

19At the same time, my estimates also differ importantly from the parameters used in the literature. For example,Western and Beckett (1999, p.1039) find that in 1995 36 perc ent of current inmates had been unemployed prior totheir incarceration. Similarly, Katz and Krueger (1999, p.42), following Kling (1999), assume upi = .35 throughoutthe 1980s and 90s. By contrast, the present paper, using much of the same data as Western and Beckett plus some

additional inmate surveys, estimates upi at 16 percent for 1995. It appears that Western and Beckett conflateunemployment and non-employment (confirmed by Western, electronic communication, December 1998). Katz andKrueger&s estimate comes from a highly selective demographic (inmates sentenced to one to two years for federalcrimes in California), which is not representative of the national inmate population. These discrepancies explain alarge part of the differences between their results and those in the causal section of this paper. My estimates for upiconfirm doubts about Western and Beckett&s causal results voiced by Greenberg. Using older data, Greenbergpoints out that at most, 17 percent of the inmates would be counted as unemployed [prior to their incarceration]under Department of Labor criteria!in 1974 (2001, p. 71).20The difference between UROfficialand URZerowas 0.1 percentage points in 1980s and 0.23 in 1999. The differencein differences is 0.23 #0.1 = 0.13.


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for a more limited range of years. I find that the decreasing effect of rising incarceration on the

unemployment rates of white and African American men between 1983 and 1997 was 0.03 and

0.48 percentage points respectively, which translates into less than 1 percent and 5 percent of the

official decline over this period.

These results contrast sharply with claims that prison and jail has lowered male

unemployment by between a half and one percentage point since the late 1980s!(Western and

Beckett, 1999, p.1041). Rather, according to my estimates, the growth of the incarcerated

population between 1989 and 1999 has lowered the official U.S. unemployment rate for males by

0.05, or one twentieth of a percentage point. Thus, while adding 1.5 million inmates to the

incarcerated population over the past twenty years did indeed have a measurable impact on the

official unemployment rate via net admission effects, this decreasing effect is considerably

smaller than previously believed. Less than 0.1 percentage points of the 3 percentage point drop

in the unemployment rate of both sexes over the past twenty years can be attributed to the

expansion of the criminal justice system. And these estimates still exclude release and

employment effects, which on balance can be expected to yield even smaller estimates for the

total causal effect. The issue of total causal effects is taken up next.

Sensitivity Analysis for the Total Causal Effect: Adding Employment and Release Effects

The total causal effect of incarceration on unemployment is determined by the balance of net

admission, release, and employment effects. To the best of my knowledge it has not yet been

estimated. The total causal effect is considerably more difficult to estimate than net admission

effects, for two reasons. First, employment effects are best understood as general equilibrium

effects and are thus near impossible to grasp in their entirety, short of modeling the full range of


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interactions between economy and prison system. For this reason, I settle for a more limited

conceptualization of employment effects that permits direct estimation. Second, release effects

cannot be estimated directly, as suitable national data are not available for all necessary

parameters. Therefore, I supplement partial estimates for the release effect with a sensitivity

analysis for one remaining parameter. This permits the assessment of reasonable bounds for the

total effect of incarceration. The point of reference throughout is UROfficial for both sexes in

1997.

Eventually, this analysis will suggest that the total effect of incarceration is to increase rather

than decrease the official unemployment rate. As this finding contradicts published results that

are based solely on net admission effects, I aim to produce high estimates for the employment

effect and low estimates for the release effect. Doing so strengthens the challenged results and

renders my estimates for the total causal effect conservative, i.e. more credible.

Consider employment effects first. As it would exceed the scope of this paper to consider

the full range of interactions between the prison system and the overall economy, I restrict

attention to job creation within the corrections sector (which presumably represents the lion&s

share of incarceration-induced employment effects). Per equation 5, the size of the employment

effect depends on the annual change in the number of jobs associated with the prison system,

G, the fraction of such jobs occupied by otherwise unemployed workers, w, and the fraction

occupied by workers that would otherwise not be in the labor force, t. To obtain G, note that

between 1983 and 1999 the employment count for correctional institution officers nationwide

rose twofold from 146,000 to 315,000 (Statistical Abstract of the United States 2000, tables 511


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and 669),21and that correctional officers and related security staff account for 63 percent of all

employees of correctional facilities (BJS 1997e, p.15).22 Thus, the average increase in prison

and jail related employment in the 1980s and 1990s can be estimated at approximately 16,000

new jobs per year. Although in reality not all of these jobs would be occupied by otherwise

unemployed workers, we can set w = 1 and thus use G for the upper bound of the annual

employment effect of incarceration during the prison boom.23

Next, consider the release effect. Per equation 5, it depends upon the number of people

released from prison and jail, R, and the difference in the risk of unemployment between those

entering and leaving incarceration in a given year, e. R is relatively straightforward to calculate,

although exact national release counts suitable for the present purpose are not available.

Turnover in prisons and jails is high. Between 1996 and 1997 prisons and jails grew by 96,000

inmates. Over the same period, 515,000 prisoners sentenced to more than one year were released

from state and federal prisons (BJS 2001, table 6.34).24 These 'million former inmates are

those most likely to suffer adverse treatment in the labor market because their long sentences

will prove hard to conceal from prospective employers.25Additionally, approximately 10 million

21This estimate of an approximately twofold increase in incarceration related employment is corroborated byparallel developments in expenditure. Between 1980 and 1998 state expenditure for corrections increased from $4.5to $30.6 billion ($20 to $113 per capita), at nearly double the growth rate of overall state expenditure.22Besides correctional officers employed as guards and security staff, prisons provide employment toadministrators, clerical workers, educators, maintenance, and food workers (BJS 1997e, table 19).23Note that w=1 implies t=0. If all new prison related jobs are occupied by otherwise unemployed workers (w=1),then all of them would be in the labor force had they not found a job ( t=0). Setting w=1 thus implies the largest

possible employment effect, given G. The problem of determining an upper bound for the employment effectconsequently reduces to estimating G. Here, I focus on prison related employment #arguably the most importantaspect of employment effects. This conceptualization neglects job creation in the (prison building) constructionsector, in (non-prison based) law enforcement, and support services for releasees (for the growing number of formerconvicts). This paper does not test whether these omissions would offset the impact of asserting w = 1.24This number underestimates the number of inmates released from prisons because the BJS excludes prisonerssentenced to less than one year and unsentenced prisoners (e.g. awaiting trial) from the release count. I additionallyexclude intrasystem transfers, escapees, and deaths.25The mean time served until release by federal prisoners was 18.7 months in 1999 (BJS 2001, table 6.53). Twentypercent of releasees in 1997 completed their maximum sentence (BJS 2001, table 6.60).


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inmates are released from local jails every year (BJS 1993a, table 6.35).26 Generously adjusting

their number for intrasystem transfers and repeat-jailing, we can set a lower bound for the

number of inmates released from jail at 2 million inmates annually.27 Adding releases from

prisons and jails together yieldsR = 2.5 million for 1997.

National data for the direct estimation of e (the difference in the risk of unemployment

between those entering and leaving incarceration) are not available.28 Therefore, direct estimates

for the total effect of changes in incarceration on unemployment cannot be provided. However,

we have estimates for a sufficient number of parameters (neglecting changes to the denominator

of equation 5 for now), to enable a sensitivity analysis which calculates critical values for eand

assesses a probable range for the overall causal effect of incarceration.

Plugging the results obtained above into equation 5,29one finds that if those released in 1997

suffered from only a 0.64 percentage point higher risk of unemployment than those admitted (i.e.

if e=0.0064), then the release effect would exactly offset the employment effect. Under this

scenario, the total causal effect of incarceration on unemployment would equal the net admission

effect calculated above. If e>0.012, (i.e. if those released suffered from more than a one

26Data on the number of inmates released from local jails in 1997 are unavailable. The BJS reports between 8 and10 million releases from local jails per year between 1983 and 1991 (BJS 1993a, table 6.35). On June 30, 1993,alone U.S. local jails released 18,591 adults (BJS 1995, table 2.4).27U.S. jails record at least 10 million releases per year. Let half of those be intrasystem transfers, and further allowthat the average remaining inmates are released 2.5 times per year. This leaves 2 million individuals to be releasedfrom jail and placed into the civilian non-institutional population. Clearly, these adjustments are exaggerations thatunderestimate the number of releasees affected each year, which serves to render my estimates conservative. Theseadjustments further guarantee that the average former jail inmate considered in this analysis has spent more than a

few days time in jail over the course of the year. This is an important point because about 60 percent of inmatesreleased from jail in a given week have spent less than five days behind bars (BJS 1990, table 11) and are thus lesslikely to suffer negative repercussions (Zagorsky 1996).28The use of social science survey data for the estimation of e is problematic because the number of self-identifiedex-convicts is too small, and likely to suffer from severe response bias (Harding 2001). National administrative datafor the estimation of eare unavailable.29From above, recall that in 1997 admissions removed 14,400 unemployed persons from the labor market (net

admission effect = upi(I)= 0.15*96,000=14,400). The upper bound of the employment effect (w(G)=1*(G)),leaves an additional 16,000 persons unemployment. For the lower bound of the number of releasees, useR=2.5million.


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percentage point higher risk of unemployment than those admitted), then changes in

incarceration have increased, rather than decreased the official unemployment rate.

This possibility stands in sharp contradiction to previously published results. While I stress

that there are no data to measure edirectly, there are at least two strong reasons to expect that e

exceeds these critical values. First, a one percentage point increase in the risk of unemployment

for inmates released over inmates admitted seems modest, particularly given the strong empirical

evidence for discrimination against ex-convicts. Consider for example evidence from California

where in 1991, only 21 percent of the state&s parolees had full-time jobs. Seventy percent were

not employed, and an additional 9 percent had%casual

&employment

!(Austin and Irwin 2001, p.

148).30 Second, my model neglects that release effects from previous years carry over into the

present, as inmates released in the past continue to be negatively affected by their history of

incarceration (Kling 1999, Western and Beckett 1999). This will further lower the critical value

of e. On the other hand, if release effects do not replenish the labor force beyond its depletion

through admissions (i.e. if d>0 in equation 5) then the labor force count will decrease and thus

raise the critical value of e. It seems improbable, however, that the latter argument should

outweigh the former two (the labor force is large compared to the unemployment count #

changes in the denominator therefore matter comparatively less).

On balance, these considerations suggest that incarceration has caused more unemployment

in the civilian non-institutional population than it has concealed.

Did Incarceration Cause the U.S.-European Unemployment Differential?

30What is more, one can expect parolees to fare better on the labor market than other releasees because paroleauthorities often require parolees to maintain gainful employment (Rhine et al 1991). Job placement programs existfor all types of ex-convicts (Travis et al 2001, 31-34), but Austin and Irwin (2001, p.147) cite research that mostprison systems are unable or unwilling to assist inmates!with their economic reintegration.


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If incarceration has indeed increased U.S. unemployment rates beyond the level they would

have reached without the U.S. prison boom, as is suggested by the preceding sensitivity analysis,

then the question whether incarceration has amplified the U.S.-European unemployment gap is

moot. If anything, the performance differential would be even larger. However, since a

sensitivity analysis cannot provide the same degree of confidence as direct estimation, let us

revisit the issue of net admission effects for which direct estimates exist.

Since the net admission effect of incarceration on the U.S. unemployment rate is smaller

than previously thought, it follows that the effect on the U.S.-European unemployment gap

should be smaller, too. Using the level of incarceration in 1976 as a point of reference, we can

ask how large the difference between U.S. and European unemployment rates for both sexes

would have been had incarceration remained at 1976 levels.31 To this end, Figure 3 contrasts the

official unemployment rate gap (Europe minus U.S., solid line) with the gap obtained when

considering rates adjusted for the net admission effect of incarceration (broken line).32 The

upshot is that incarceration did not change the width or timing of the unemployment rate

differential. The official EU-15 unemployment rate for both sexes surpassed the official U.S.

rate consistently from 1983, as does the unemployment rate adjusted for net admission effects.

The official unemployment rates of the E-13 and EU-6 consistently exceed U.S. rates from 1984,

and, again, adjusting for the net admission effect of incarceration does not change the timing or

affect the size of the unemployment differential noticeably. The same result holds for male

unemployment rates. Figure 4 shows that the male unemployment rate in EU-15 and E-13 has

31The year 1976 approximately marks the start of the prison boom and also the point in time when incarcerationtrends in Europe and the U.S. began to diverge. Whether one chooses 1976 or zero incarceration!as point ofreference in this case makes no qualitative difference (not shown).32I compute net-admission-adjusted unemployment series for Europe analogously to the U.S. series above. SinceEuropean inmate surveys are unavailable, I use the U.S. estimates for upiand lfpi. The European net admissioneffect is almost zero because European incarceration rates are much lower, and increased more slowly (results notshown).


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surpassed U.S. unemployment from 1984, whether or not we adjust for growing incarceration.

The difference in the gaps between official and adjusted rates is negligible compared to the size

of the gap (but, predictably, it is larger for men than for both sexes). In other words, the

dramatic increase in U.S. incarceration cannot explain, let alone explain away,! the marked

divergence between U.S. and European unemployment rates for males or both sexes.

To sum up the causal part of this paper, thus far I have demonstrated that the causal effect of

incarceration on UROfficial is (1) considerably smaller than previously reported if we follow the

literature in looking at net admission effects in isolation; (2) not large enough to have caused a

noticeable difference in U.S.-European unemployment rate differentials; and (3) potentially

increasing, rather than decreasing the official U.S. unemployment rate if we further consider

employment and release effects. The American penal system is not concealing the surplus

population!that so burdens European labor markets.

The Robustness of the U.S.-European Unemployment Rate Gap to Changes in

Definition

The accounting perspective deals with the robustness of existing measures of labor market

performance to changes in definition. As regards the incarcerated population, the prevailing

notion is that including prisoners in the count would increase the official unemployment rate

because, presumably, prisoners are inactive. For example, Western and Beckett (1999), in a

widely cited analysis, propose a modified measure of unemployment, which adds all inmates of

prisons and jails to both numerator and denominator of the official unemployment rate. They

consider their measure a better indicator of labor market inactivity! and labor

underutilization,! because it captures the hidden unemployment! and inactivity among able-


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bodied, working-age men! imposed by incarceration (Western and Beckett 1999, p.1039).

According to their modified measure, the male unemployment rate was 1.9 percentage points

higher in 1995 than officially stated, and European unemployment significantly overtakes U.S.

unemployment only from 1993!(pp.1042-43) rather than from 1983. Low unemployment in the

U.S. and high unemployment in Europe are thus thought to be an artifact of the exclusion of the

incarcerated population from official labor market measurement. Similar ideas can be found in

numerous places (e.g. Jencks 1992; Chiricos and Delone 1992; Freeman 1995; Faux 1997;

Buchele and Christiansen 1998; Beckett and Western 1997; Western and Beckett 1998; Western,

Beckett and Harding 1998; Western and Pettit 2000).

This paper agrees that the exploration of alternatives to the official standards of labor market

measurement is important. Labor market performance is a multidimensional construct and basic

trends should be robust to alternative operationalizations. But not all operationalizations are

equally valid. In the following, I question the particular practice of including the incarcerated

population wholesale in modified measures of unemployment and explore alternatives.

The core of my critique is that the inclusion of all inmates in the unemployment count

produces internally inconsistent indicators. Counting all inmates as unemployed means to

modify current concepts selectively, by applying a broad definition of unemployment to inmates

in prison and jail (including, for example, inmates that work for money), while applying the

narrow official definition of unemployment to the non-institutional population. This

inconsistency results in larger than appropriate differences between official and modified

statistics.

To arrive at internally consistent alternatives to official statistics, conceive of any

modification as a two-step procedure. First, broaden the survey universe to include prisons and


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jails (currently CPS, and with it the Bureau of Labor Statistics, simply do not count the

incarcerated population). Secondly, decide what definitions to apply to this new, broader, survey

population in a uniform and consistent manner.33 The first step, for our purposes, is

uncontroversial. Whereas above I reviewed arguments that rationalize the exclusion of the

incarcerated population from official labor market statistics for the purpose of measuring short

term labor supply, my intention in this section is not to propose a new official measure, but to

explore its robustness to the exclusion of the inmate population. It is the second step that

engenders some surprising results.

Modified Unemployment Rates

First, consider modified measures of unemployment. If inmates were included in the CPS

universe, and if the official definitions of employment and unemployment were applied to the

incarcerated population (disregarding the OECD&s SNA-based exclusion of inmates reviewed

above), then nearly half of all male inmates would have to be considered employed because they

work for money or payment in kind, as shown in Table 6. (Table 6 also shows that in 1996, 57

percent of male inmates worked in or outside of their confinement facility, and 34 percent

worked for money, slightly down from previous years.)34 Whether the remaining non-working

inmates ought to be counted as unemployed or non-employed is unclear because it is not known

33Questions regarding the post-release labor market fortunes of former inmates are tied to causal considerations andare irrelevant for the accounting perspective. The accounting perspective simply asks how a given state of the worldwould be portrayed by different ways to measure it, whereas the causal perspective asks how that reality wouldchange under different trends in incarceration.34Several authors acknowledge inmate work in principle, but underestimate its prevalence. For example, Westernand Beckett (1999, p. 1040) assert that only 10 percent of prisoners engage in paid and therefore do not incorporateworking inmates in their calculations employment. The numbers in Table 6 contradict this assertion. The numbersfor all inmates, male and female, are near identical to those presented in Table 6, due to the small number of femaleinmates (not shown).


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whether they would (or could) fulfill the availability and search criteria that are at the heart of the

concept of unemployment.35

Table 7 demonstrates how adjustments would play out under different assumptions

regarding the treatment of non-working inmates. If paid working male inmates are included in

the employment count, but all other inmates are regarded non-employed, then the resulting

modified male unemployment rate would be lower than the official rate (if only marginally so,

by less than 0.1 percentage points in 1999). This is shown in column 2. If working inmates are

counted as employed, and all remaining inmates as unemployed, then the new rate would be

about one percentage point higher than the official rate, as shown in column 3. The treatment of

non-working inmates thus determines whether a modified measure of unemployment #one that

considers the incarcerated population but also takes account of paid employment among

prisoners #would show that the U.S. labor market performs better or worse than indicated by the

official statistics. In any case, the results in Table 7 are at best half the 1.9 percentage point

difference for 1995, found by Western and Beckett (1999) (approximately reproduced in column

4). Adjustments of the unemployment rate for both sexes, predictably, are smaller by about half

(not shown).

35The point here is not to reify official definitions, but to apply them consistently. Recall that employment isdefined as doing any work for (either) pay (or profit)!over the past week (U.S. Bureau of the Census 1998, p. C4-2). Contrary to widespread belief (e.g. Western and Pettit 2000, p. 6), the official definition of employment is nottied to hours worked, or any minimum standards of decency, such as minimum wage, fair labor standards etc.Literally, any work!for pay or profit qualifies, and is duly counted in the non-institutional population. The same

standard should be applied to the inmate population, if the CPS universe were to be extended to include prisons andjails. None of this is to say that inmate work compares seamlessly to typical employment situations in the free labormarket. It doesn&t. (For an at times polemical discussion see Featherstone 2000; Buck 1994; Marks and Vining1986.) Yet paid prison work compares sufficiently to certain types of (mainly low prestige, low income) work, sothat it would still fall within the scope of the official employment definition. Note, for example, that inmatecompensation follows a different logic, as most income is in kind.! An important source of non-monetaryremuneration specific to prison labor is so-called good time,!which refers to time deducted from a sentence inexchange for work (BJS 1995, p.3). Inmate surveys indicate that a majority of working inmates work for goodtime!as well as other forms of in kind benefits such as cigarettes and food (calculations not shown). Averagemonetary wages for working state prisoners were only 56 cents per hour in 1991 (BJS 1993c, p.27).


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Modified Non-Employment-to-Population Ratios

Non-employment and the non-employment-to-population-ratio (NEPR) provide a more

suitable basis for an incarceration-adjusted measure of inactivity than does the unemployment

rate. While the concept of unemployment is too restrictive to allow for the inclusion of non-

working inmates without compromising the internal consistency of the adjusted measures, non-

employment naturally permits their inclusion.36 Using the NEPR as a basis for a modified

measure of inactivity thus avoids conflating inactivity (in prison and jail) with unemployment (in

the non-institutional population).

Table 8 shows results for incarceration-modified NEPRs for U.S. males. The official male

NEPR is reproduced in the first column. Between 1976 and 1999 it remained relatively stable at

around twenty-nine percent. Adding all male inmates not working for money or payment in kind

to the numerator of the official NEPR, and all inmates to its denominator, increases the official

NEPR by between 0.1 and 0.5 percentage points, as shown in column 3. If, again, one chooses

to disregard paid employment among inmates and adds all inmates to the NEPR, the official

NEPR would increase by between 0.3 and 1.2 percentage points, as shown in column 4.37

These adjusted series arguably yield a more encompassing and appropriate measures of

overall labor resource utilization, inactivity, and joblessness than the official NEPR. A

comparison with correspondingly modified European NEPRs tests whether official statistics

overstate the U.S.-European difference in resource utilization. Figure 5 shows the difference

between European (EU-6) and U.S. NEPRs for both sexes.38 The solid and broken lines

36In the non-institutional population non-employment is the broadest concept of inactivity #most students,homemakers, retirees, discouraged workers and the chronically ill count as non-employed but not as unemployed.37When similar adjustments are performed on the NEPR for both sexes, the difference is roughly halved (notshown).38The EU-6 comprise Western Europe&s strongest economies. Comparing the U.S. to the EU-6 results in aconservative test for the strength of performance differentials because it omits economies such as Portugal, Greece,


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represent the trend of this difference according to official and modified definitions, respectively.

For good measure, the modified measure charted in Figure 5 includes all inmates, working or

not, with non-employment. Positive values indicate that European non-employment exceeded

the U.S. NEPR. Figure 5 shows that EU-6 non-employment exceeded U.S. non-employment

over the entire period under consideration regardless of the forced inactivity of two million

inmates in U.S. prisons and jails. Due to mass incarceration in the U.S., the U.S.-European

incarceration-adjusted difference in non-employment is somewhat smaller (by about .5

percentage points) than the official difference. But this does not alter the qualitative conclusion

that the U.S. has maintained lower levels of labor market inactivity than Europe over at least the

past twenty-five years.

In sum, this analysis rejects the proposition that the U.S.-European unemployment rate gap

between 1983 and 1994 is an artifact of the exclusion of the incarcerated population from official

labor market statistics. Due to extensive (and oft underestimated) paid work among inmates,

incorporating the incarcerated population into the unemployment count might well point to an

even larger U.S.-European unemployment differential than indicated by the official rates, as was

shown in Table 7. Alternatively including the incarcerated population into a modified measure

of inactivity based on non-employment shows that the overall difference in inactivity between

the U.S. and Europe is somewhat smaller than indicated by the official NEPR, but still large and

growing, as was shown in Figure 5. This analysis does not support the notion that, in

contradistinction to conventional statistics, inactivity was higher in the U.S. than in Europe until

the mid 1990s due to the forced inactivity generated by sky-high American incarceration

and Spain, which suffer from the highest rates of unemployment and non-employment in Europe. Performancedifferences between the U.S. and the EU-15 or E-13 are even larger.


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Conclusion

Over the past years, incarceration has received considerable attention in the field of

comparative institutional labor market analysis (e.g. Faux 1997; Buchele and Christiansen 1998;

Western and Beckett 1998, 1999; Katz and Krueger 1999) and successfully challenged received

notions of the nature of labor market institutions (particularly Western and Beckett 1998, 1999).

Whereas standard analyses of aggregate labor market performance often confine their attention

to explicitly wage-setting institutions, such as trade unions, labor legislation, or the education

sector, the new research agenda highlights the importance of seemingly unrelated social

institutions, such as the criminal justice system, in the determination of market outcomes.

Empirical research on the relationship between incarceration and labor market performance

has produced several dramatic findings that challenge the conventional wisdom about the causes

of low unemployment in the United States and about origin and reality of U.S-European labor

market performance differentials. Specifically, the literature attacks conventional notions of

Eurosclerosis from two fronts, causal and accounting: first by contributing to an alternative,

incarceration-based causal explanation of existing U.S.-European unemployment rate

differentials; second by questioning the very reality of these differentials with reference to the

large fraction of the American population that is forced into labor market inactivity (hidden

unemployment!) through incarceration.

This paper extends the theoretical agenda by proposing a more general model of the causal

interaction between incarceration and unemployment, and on that basis engages previous

empirical results in a critical dialogue. From a causal perspective, I find that the fourfold

increase of incarceration in the United States over the past twenty five years has caused the

official unemployment rate to decrease by 0.08 percentage points through net admission effects,


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which amounts to only 3 percent of the overall decline in unemployment. I find that net

admission effects decreased the male unemployment rate over the same period by 0.13

percentage points, which amounts to 5 percent of the official decline. Previous research put the

impact of incarceration at multiples of these estimates. I further argue that even my small net

admission effect estimates overstate the total causal effect of incarceration on unemployment

because they do not capture concomitant release and employment effects. I concede that the

total causal effect of incarceration currently eludes direct estimation because the full extent of

macro societal implications of the prison boom (the employment effect) has yet to be theorized,

and because data requirements for a satisfactory estimation of release effects are not fully met.

However, I show that the formal model for the total causal effect developed in this paper enables

a sensitivity analysis, which suggests that incarceration likely has increased, rather than

decreased the official unemployment rate in the 1990s. In other words, the U.S. labor market

might have performed even better (as measured b

Documents

Incarceration, Unemployment, and “ Eurosclerosis”