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Fortin/Lemieux – Econ 561 Lecture 4A IV. Labour Market Institutions and Wage Inequality 1. Overview 2. Effect of Unions on Wage Inequality 3. Effect of Minimum Wages

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Fortin/Lemieux – Econ 561 Lecture 4A

IV. Labour Market Institutions and Wage Inequality

1. Overview

2. Effect of Unions on Wage Inequality

3. Effect of Minimum Wages

Fortin/Lemieux – Econ 561 Lecture 4A

1. Overview

• Labour market institutions are a potential source of differences in wages across workers and in wage inequality across countries and over time.

• Further there have been important changes over time in the strength of these institutions.

• Two labour market institutions that have received considerable attention in Canada and the United States, are labour unions and the minimum wage.

• In Canada, minimum wages are set at the provincial level and generate more

variations.

• Some countries (namely the United States and the United Kingdom) experienced sizeable decline in unionization rate (declining in Canada, but not as much).

• These institutions and their close relatives are still quite important in many other

countries.

Fortin/Lemieux – Econ 561 Lecture 4A

Measures of Wage Inequality:

• Well-known measures of inequality include the Gini coefficient, the Theil (entropy) coefficient, the coefficient of variation, and the variance (or std deviation) of logs.

• The problem with these measures is that they only provide a particular aggregate measure of dispersion that doesn’t need indicates what happens where in the distribution

• Problematic as difference sources of changes in the wage distribution are likely to affect different parts of the distribution o For example, the minimum wage likely only affects the very bottom of the

distribution; unions affect more the middle of the distribution; bonuses and stock options only affect the top of the distribution.

• Because of these limitations, the modern inequality literature tends to rely on

flexible / non-parametric approaches to describe what happens to the whole distribution

Fortin/Lemieux – Econ 561 Lecture 4A

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• Let )( θθ wF= be a percentile rank of the log wage (w) distribution. The θth quantile or percentile of the distribution is defined as wθ. For example, the 50th quantile is the median w.5.

• Since the cumulative distribution F is monotonic, it can be inverted as )(1 θ−= Fw . It follows, for example, that

)10()90( 111090 −−− −= FFd is the 90-10 log wage differential between the 90th and the 10th quantile.

Similarly

)50()90( 115090 −−− −= FFd , )10()50( 111050 −−− −= FFd

represent the 90-50 and 50-10 log wage differential and are often used to describe upper end and lower end wage inequality, respectively.

Fortin/Lemieux – Econ 561 Lecture 4A

0.2

.4.6

.8D

ensi

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988

.69 1.61 2.3 3.22Log(Wage)

Men 1988 Normal Density

• To describe changes over time, plots of the percentiles of the wage distribution on the horizontal axis and the change in the log wage on the vertical axis are used (AKK, fig1.)

• Alternatively, indexes of some chosen percentiles of the log wage distribution are plotted (JMP, fig 1.)

• Another popular approach consists of plotting the density using kernel methods

-.2-.1

0.1

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Log

Wag

e D

iffer

entia

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0 .2 .4 .6 .8 1Quantile

Fortin – Econ 561 Lecture 4A

55.

56

6.5

77.

58

8.5

99.

510

Min

imum

Wag

e ($

2002

)

1997 2000 2003 2006 2009 2012Year

QC ON AL BC

A. Larger ProvincesChanges in Real Minimum Wages

55.

56

6.5

77.

58

8.5

99.

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Min

imum

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2002

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1997 2000 2003 2006 2009 2012Year

Nfld PEI NS NB MA SK

B. Smaller ProvincesChanges in Real Minimum Wages

35

0.30

0.35

0.40

0.45

0.50

0.55

1975 1980 1985 1990 1995 2000 2005 2010

Appendix Figure A1: The Ratio of Minimum Wages to Average Wages, Canada and the United States, 1975-2010

Canada United States

Figure 1A: Trends in state and federal minimum wages, and log(p10)-log(p50)

Figure 1B: Trends in state and federal minimum wages, and log(p90)-log(p50)

.5.6

.7.8

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Log(

p90)

-log(

p50)

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22.

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imum

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007

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1974 1980 1985 1990 1995 2000 2005

Real value of average state/federal mins Real value of fed. min.

log(p90)-log(p50), female log(p90)-log(p50), male

Note: Annual data on state and federal minimum wages and log percentiles. Minimum wages are in 2007 dollars.

-.8-.7

-.6-.5

-.4-.3

-.2

Log(

p10)

-log(

p50)

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1.7

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1.9

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Log

real

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imum

wag

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007

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1974 1980 1985 1990 1995 2000 2005

Real value of average state/federal mins Real value of fed. min.

log(p10)-log(p50), female log(p10)-log(p50), male

Note: Annual data on state and federal minimum wages and log percentiles. Minimum wages are in 2007 dollars.

Source: Autor, Manning, and Smith (2010)

41

Table 1 Union density and collective agreement coverage in selected OECD countries,

1980 and 1994

Union membership as a Collective agreement coverage percentage of paid workers as a percentage of paid workers

1980 1994 1980 1994 Canada 36 34 37 36 United Kingdom 50 34 70 47 United States 22 16 26 18 Other Countries Australia 48 35 88 80 Austria 56 42 98 98 Belgium 56 54 90 90 Denmark 76 76 69 69 Finland 70 81 95 95 France 18 9 85 95 Germany 36 29 91 92 Italy 49 39 85 82 Japan 31 24 28 21 Netherlands 35 26 76 81 New Zealand 56 30 67 31 Norway 57 58 75 74 Portugal 61 32 70 71 Spain 9 19 76 78 Sweden 80 91 86 89 Switzerland 31 27 53 50 Source: Organisation for Economic Co-operation and Development, Employment Outlook. Paris: OECD, July 1997.

Source: Card, Lemieux and Riddell (2004)

34

Appendix Table 2: Union coverage rate by region, 1984 and 2001. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Men Women - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

1986 2001 1984 2001 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - W S Central 0.130 W S Central 0.082 W S Central 0.089 W S Central 0.071

S Atlantic 0.164 S Atlantic 0.099 S Atlantic 0.111 S Atlantic 0.078Mountain 0.186 Mountain 0.109 Mountain 0.125 E S Central 0.084

E S Central 0.208 E S Central 0.124 E S Central 0.127 Mountain 0.091New England 0.257 New England 0.165 W N Central 0.152 W N Central 0.118W N Central 0.269 W N Central 0.179 New England 0.18 New England 0.142

Pacific 0.296 Pacific 0.191 E N Central 0.195 E N Central 0.156Alberta 0.331 Alberta 0.219 Pacific 0.221 Pacific 0.179

E N Central 0.342 E N Central 0.232 Middle Atlantic 0.258 Middle Atlantic 0.218Middle Atlantic 0.364 Middle Atlantic 0.253 Ontario 0.315 Ontario 0.266Ontario 0.431 Ontario 0.289 Alberta 0.34 Alberta 0.269Prairies 0.442 Maritimes 0.315 Maritimes 0.376 Maritimes 0.303Maritimes 0.468 Prairies 0.324 B.C. 0.389 B.C. 0.34B.C. 0.530 B.C. 0.348 Prairies 0.404 Quebec 0.377Quebec 0.538 Quebec 0.415 Quebec 0.449 Prairies 0.381 Canada 0.463 Canada 0.323 Canada 0.369 Canada 0.311United States 0.260 United States 0.166 United States 0.171 United States 0.133

Source: Lemieux (2003)

2 / Insights on Canadian Society November 2013 — Statistics Canada

the 1981 Survey of Work History (SWH) collected information only on union membership.2 In this analysis, these sources are combined to create a consistent time series from 1981 onwards on unionization rates, based on all employed workers aged 17 to 64 (see Data sources and definitions). The unionization rate is defined as the proportion of employed workers who are union members.3

Unionization rates declined among men, but remained steady among women

From 1981 to 2012, the overall unionization rate fell from 38% to 30%, a decline of 8 percentage points (Chart 1). The rate declined by 2 percentage points during the 1980s, by another 6 percentage points during the 1990s, and then remained fairly stable over the 2000s.

Long-term trends in unionization

Not all groups saw a similar decline. In 1981, men had a much higher rate than women—42% versus 31%. However, over the next three decades the rate remained fairly

stable among women but declined among men. As a result, by 2012 men had a slightly lower unionization rate than women (29% and 31%, respectively).

Chart 1 Unionization rates of employed individuals aged 17 to 64, 1981 to 2012

Note: Years without markers indicate the trends between survey years.Sources: Statistics Canada, Labour Force Survey, 1997 to 2012; Labour Market Activity Survey, 1986

to 1990; Survey of Union Membership, 1984; Survey of Work History, 1981.

25

30

35

40

45

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011

percentage

Both sexes

MenWomen

Table 1 Unionization rates by sex and age, employed individuals aged 17 to 64

1981 1984 1989 1999 2007 2012

percentageBoth sexes 37.6 37.9 35.9 30.4 29.8 29.917 to 24 26.4 19.8 18.4 12.5 13.8 14.825 to 34 39.8 39.7 34.7 25.6 27.6 28.435 to 44 42.0 46.1 42.9 34.6 31.2 31.245 to 54 41.7 44.4 44.6 41.5 38.3 35.955 to 64 41.9 44.7 41.6 36.4 36.4 36.0

Men 42.1 42.4 39.2 31.2 29.3 28.517 to 24 29.2 23.2 19.9 13.5 14.6 15.525 to 34 43.3 42.2 37.1 25.0 25.9 26.035 to 44 46.1 50.7 45.6 36.0 30.6 29.245 to 54 47.8 50.5 49.9 43.0 38.5 35.155 to 64 48.6 49.5 48.0 38.5 35.9 34.7

Women 31.4 32.5 32.1 29.5 30.4 31.317 to 24 23.1 16.3 16.8 11.3 13.0 14.225 to 34 34.7 36.6 32.0 26.4 29.5 31.035 to 44 36.3 40.3 39.9 33.2 31.8 33.445 to 54 32.9 36.2 38.2 40.0 38.1 36.755 to 64 29.9 36.9 31.7 33.8 36.8 37.3

Sources: Statistics Canada, Labour Force Survey, 1999, 2007 and 2012; Labour Market Activity Survey, 1989; Survey of Union Membership, 1984; Survey of Work History, 1981.

4 / Insights on Canadian Society November 2013 — Statistics Canada

Since 1999, the overall unionization rate has been stable at 30%, but some changes have occurred within industries (Table 2).6 Specifically:

• From 1999 to 2012, the unionization rate in goods-producing industries declined by 4 percentage points, but rose s l ight ly, by less than 1 percentage point, in services-producing industries. Thus, in 2012, the unionization rate for services-producing industries, 31%, exceeded the rate for goods-producing industries, 27%; it was the reverse in 1999—30% in services and 31% in goods.

• W i t h i n g o o d s - p r o d u c i n g industr ies , three sectors saw even larger dec l ines in unionization rates over

Long-term trends in unionization

Table 2 Unionization rates by North American Industry Classification (NAICS), employed individuals aged 17 to 64

1999 2007 2012Change

(1999 to 2012)

percentage percentage pointAll industries 30.4 29.8 29.9 -0.5Goods-producing industries 31.1 28.6 27.0 -4.1Agriculture 3.9 4.3 3.6 -0.3Forestry, fishing, mining, oil and gas 26.9 21.0 21.8 -5.1Utilities 67.8 65.8 62.5 -5.2Construction 30.2 30.8 31.5 1.2Manufacturing 31.2 27.7 24.4 -6.8Services-producing industries 30.1 30.2 30.7 0.6Trade 12.6 12.7 12.8 0.2Transportation and warehousing 42.7 41.1 40.5 -2.1Finance, insurance, real estate and leasing 7.9 9.7 8.9 0.9Professional, scientific and technical services 4.1 4.3 4.4 0.3Management, administrative and other support 10.5 12.8 15.3 4.9Educational services 69.3 67.2 68.0 -1.3Health care and social assistance 53.0 53.6 53.6 0.7Information, culture and recreation 27.6 25.8 25.0 -2.6Accommodation and food services 6.6 7.8 6.7 0.1Other services 8.7 9.4 8.9 0.2Public administration 65.3 67.9 67.5 2.3Public sector 70.8 71.3 71.4 0.6Private sector 18.4 17.1 16.4 -2.0

Source: Statistics Canada, Labour Force Survey, 1999, 2007 and 2012.

the period: manufacturing, -7 percentage points; utilities -5 percentage points; and forestry, fishing, mining, oil and gas, -5 percentage points.

• Within services-producing industries, some industries increased their unionization rate—particularly management, admin i s t ra t i ve and other support, +5 percentage points; and public administration, +2 percentage points. However, these were offset by declines in others, such as information, c u l t u r e a n d r e c r e a t i o n , -3 percentage points; and transportation and warehousing, -2 percentage points.7

Unionization rates also varied across various occupations and other job characteristics. For instance, full-time work, longer job tenure, large

firms, higher educational attainment, and better wages were all associated with higher unionization rates. Workers with higher educational attainment are also more likely to be unionized.

Details on unionization rates and coverage across these characteristics, as well as those discussed above, can be found in CANSIM tables 282-0220 to 282-0225. However, note that these tables provide information only from 1997 onwards, as they are based on the LFS.8

Diane Galarneau is a senior analyst and Thao Sohn is an analyst in the Labour Statistics Division of Statistics Canada. The authors would like to thank Éric Fecteau of Labour Statistics Division for his assistance.

HENRY S. FARBER and BRUCE WESTERN 46!

Figure 1

Union Membership Rate. Nonagricultural Workers, 1880-1998

.35 -

,3 -

.25 -

.2 -

.15 -

.1 -

.05 -

0 -

1880 1900 1920 1940 I960 1980 2000

percent in 1954. The record since that time has been one of steady decline to a low of13,3 percent in 1998. Freeman (1998) characterizes the early record (through the early1950s) of union growth as a series of discontinuous spurts followed by periods ofdecline. On that basis, the period since 1954 is best characterized as a long decline afterthe large spurt (or set of spurts) from the mid-1930s through the mid-1950s. Freeman'sconclusion is that, in general, unions grow in spurts and not through slow and steadyadditions to membership over long periods of time.^ These spurts originate in periodsof intense social unrest (the 1930s) and wars {World War II and Korea), But later wars(Vietnam) and periods of social activism (the 1960s) have not resulted in spurts oforganization. So, while future union growth may depend on another spurt occurring, wehave little guide to what might trigger such an episode of dramatic growth.

Union Membership and Coverage in the Private and Public Sectors. Using datafrom the CPS, we can calculate distinct union membership rates in the private and pub-lic sectors over the 1973-1998 period.'* Figure 2 verifies the well known fact that unionmembership rates in tbe private and public sectors have followed very different paths

Fortin/Lemieux – Econ 561 Lecture 4A

• Collective bargaining in many European countries (and Australia) is conducted at the industry or sectoral level, and the provisions are formally or informally extended to most of the labor force.

• Moreover, in many countries, unions exert considerable influence on political decisions (such as minimum wages) that directly affect labor market outcomes.

• Union coverage has also changed much over time. Union formation really got going

in the 1940s, increasing steadily into the 50s and 60s. • By the mid 1970s, about 30% of the workforce was a member of a union in some way

or another. This amount has declined considerably since 1981. • The U.S. has considerably lower union coverage than Canada’s. Up until the 1970s,

both Canada and the U.S. had high coverage, then the proportion of the U.S. workforce unionized fell markedly, from about 30% to 15% by 1998, and stood at 11% in 2013.

Fortin/Lemieux – Econ 561 Lecture 4A

2. Effect of Unions on Wage Inequality A. Assessing the Impact of Unions on Wages

• In trying to estimate the impact of unionization on wages and employment, we cannot simply compare wages of those in union sector to wages in non-union sector.

• As usual when trying to estimate a treatment effect, we are confronted with the

problem of selection bias. o On the one hand, these problems may be especially acute here since firms have to

pay unionized workers higher wages, they have an incentive to screen workers and choose only the more productive workers, so that the causation could go the other way around.

o On the other hand because there is more job protection in unionized firms, workers who have less alternatives in the non-union sector and may be less productive, may choose to join unionized firm.

Fortin/Lemieux – Econ 561 Lecture 4A

• The earlier approaches viewed with this problem as a case of omitted variable bias and introduced a bunch of productive characteristics X as controls in a regression of union status iUnion on log wages:

isiUi XUnionw εβββ +++= 0ln

• Using panel data to eliminate the individual fixed effect has been used as an alternative approach

itisititUit XUnionw ελβββ ++++= 0ln

)()()(lnln 1111 ititsititititUitit XXUnionUnionww εεββ −+−+−=− ++++

• This approaches identifies the union effect from job changers, actually union status changers, but we do not know why individual leave a unionized job, it could be to get a higher paying but riskier job elsewhere or it may have nothing to do with the unionized status of the firm.

• Panel data estimates can be very sensitive to measurement error in union status. Furthermore, if unions compress wages / returns to skill we would expect returns to both observables (X) and unobservables (λ) to be different in the two sectors.

Source: Lemieux (1998)

Nicole
Rectangle

Fortin/Lemieux – Econ 561 Lecture 4A

• Lemieux (1998) distinguished voluntary and involuntary job changers and implement a procedure that takes into account the compression effect of union.

o He finds unions increase the average wage of workers in Canada in 1986-87, but also that unions compress the returns to observable measures of skill and to a time-invariant unobserved measure of skill.

• The quasi-experimental framework provides yet another alternative estimation

strategy. • DiNardo and Lee (2004) use a regression discontinuity design to estimate the

impact of unionization on business survival, employment, output, productivity, and wages using multiple establishment-level data sets that represent establishments that faced organizing drives in the United States during 1984–2001.

• They analyze data on elections from the National Labor Relations Board (NLRB), on contract expirations from the Federal Mediation and Conciliation Service (FMCS), on subsequent business survival, employment, and output from a commercial database

Fortin/Lemieux – Econ 561 Lecture 4A

based on telephone listings (InfoUSA), as well as on employment, wages, output, and productivity.

• They compare the outcomes for employers where unions barely won the election (e.g., by one vote) are compared with those where the unions barely lost.

• The analysis finds smaller impacts on all outcomes that they examine; in particular

their estimates for wages are close to zero.

• Meanwhile, conventional estimates suggest that there still exists a sizable union wage premium: demographically similar union workers are paid 15 percent or more than their nonunion counterparts.

• On the other hand, there is a broad consensus that in the past three decades, union

power in the United States has been on the decline. Their findings seem to be in line with that perception especially at firms where unions barely won their election.

FIGURE IIIaRecognition, Subsequent Certification or Decertification, by Union Vote Share.

FIGURE IIIbContract Expiration Notice Filed, Prior to and Postcertification or

Decertification Election, by Union Vote ShareNote: Figure IIIa: Initial Elections that take place between 1984–1995, 21405

observations. Point estimates and standard errors (in parentheses) are from aregression of the dependent variable on a fourth-order polynomial and a certifi-cation status dummy variable. Figure IIIb: Post-: Elections take place (1984–1995), 21405 and 3785 for certification and decertification elections, respectively.Prior: Elections take place (1987–1999), 21457 and 3445 observations.

1407ECONOMIC IMPACTS OF NEW UNIONIZATION

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ownloaded from

FIGURE IXaLog(Output/Hour), Pre- and Postelection, by Union Vote Share, LRD

Note: Observations: Preelection 38,854, Postelection 28,918, Postelection minusPreelection Mean 28,785. For definition of preelection and postelection periods,see note to Figure VIII.

FIGURE IXbLog(Production Hourly Wage), Pre- and Postelection,

by Union Vote Share, LRDNote: Observations: Preelection 38,870, Postelection 28,929, Postelection minus

Preelection Mean 28,790. For definition of preelection and postelection periods,see note to Figure VIII.

1419ECONOMIC IMPACTS OF NEW UNIONIZATION

Nicole
Oval

(10)) implies that an 8 percent decline in hours can be statisticallyruled out. For output, the estimates range from �0.043 to 0.011with the most precise estimate ruling out a 10 percent negativeimpact.

For output/hour—our measure of “productivity”—the esti-

TABLE IILEAST-SQUARES REGRESSION-DISCONTINUITY ESTIMATES OF UNION EFFECTS,

LRD SAMPLE

Dependentvariable

Coefficient on won election

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Contractexpiration 0.220 0.252 0.198 0.191 0.202 0.198 0.181 0.182 0.181 0.179

(0.021) (0.011) (0.016) (0.017) (0.020) (0.022) (0.020) (0.020) (0.020) (0.020)[4733] [28796] [28796] [28796] [28796] [28796] [28796] [28796] [28796] [28796]

Log(Hours) 0.009 �0.318 �0.260 �0.203 0.085 0.097 �0.024 0.015 0.018 0.028(0.087) (0.036) (0.063) (0.063) (0.080) (0.080) (0.056) (0.051) (0.050) (0.049)[4733] [28796] [28796] [28796] [28796] [28796] [28796] [28796] [28796] [28796]

Log(Output) 0.079 �0.347 �0.293 �0.254 0.067 0.080 �0.043 �0.010 �0.004 0.011(0.094) (0.042) (0.072) (0.073) (0.090) (0.091) (0.055) (0.050) (0.050) (0.049)[4730] [28785] [28785] [28785] [28785] [28785] [28785] [28785] [28785] [28785]

Log(Output/worker) 0.072 �0.028 �0.032 �0.051 �0.018 �0.016 �0.019 �0.019 �0.018 �0.015

(0.063) (0.029) (0.048) (0.048) (0.060) (0.061) (0.035) (0.034) (0.034) (0.034)[4730] [28785] [28785] [28785] [28785] [28785] [28785] [28785] [28785] [28785]

Log(Assets/worker) �0.121 0.122 0.020 �0.020 �0.059 �0.048 �0.136 �0.090 �0.064 �0.029

(0.108) (0.049) (0.082) (0.082) (0.102) (0.103) (0.104) (0.093) (0.075) (0.072)[3379] [20346] [20346] [20346] [20346] [20346] [20346] [20346] [20346] [20346]

Log(Wage) 0.015 �0.039 �0.041 �0.044 �0.005 �0.002 �0.026 �0.018 �0.018 �0.016(0.025) (0.011) (0.019) (0.020) (0.024) (0.024) (0.017) (0.016) (0.016) (0.015)[4733] [28796] [28796] [28796] [28796] [28796] [28796] [28796] [28796] [28796]

Sample�/�5% All All All All All All All All All

Polynomialterms 0 0 1 2 3 4 4 4 4 4

Dependentvariable Level Level Level Level Level Level

De-meaned

De-meaned

De-meaned

De-meaned

Include basemean? No No No No No No No Yes Yes Yes

Yeardummies No No No No No No No No Yes Yes

Industrydummies No No No No No No No No No Yes

Within-election clustered standard errors are in parentheses. Number of observations are in brackets.Each entry is the estimated coefficient on the “Union won” indicator in a least squares regression. “Basemean” is the average of the dependent variable for years strictly before the election year. “De-meaned”denotes that the dependent variable is the outcome minus the “base mean.” “�/� 5%” sample are electionswhere the union vote share is between 45 and 55 percent.

1421ECONOMIC IMPACTS OF NEW UNIONIZATION

Source: DiNardo and Lee (2004)

Nicole
Rectangle

Fortin/Lemieux – Econ 561 Lecture 4A

• More generally, economists think about unions trying to create or capture economic rents available in an industry or firm for workers in the union: unions are thought to fundamentally be organizations designed to capture rents available in an industry.

o Economic rents are profits or wages or income over and above what would be received in a competitive setting.

o These rents arise from product market imperfections or regulation or from regulation of labor in a competitive industry and act as a monopolist in the sale of labor.

• For example, prior to the 1980s, the airline industry offered an example where the

initial economic rents exist because of monopoly. The industry was highly regulated so that all airlines flying a particular route had to offer the same fare and other markets were served by monopolies or duopolies.

Fortin/Lemieux – Econ 561 Lecture 4A

• The impact of unions on the wage structure depends on the industrial relations system -- the social, political, legal, institutional and economic environment in which unions operate.

o Thus the mechanisms through which unions alter the wage structure and the magnitude of these impacts are likely to vary across countries.

• As usual, the difficult issue in assessing the impact of unions on the wage structure in

finding an appropriate counterfactual. • This can be done either (or both)

by comparing changes the wage structure over time as union density decline (DiNardo, Fortin and Lemieux, 1996)

by cross-country-comparison (DiNardo and Lemieux, 1997, Card, Riddell and Lemieux, 2003).

• Most progress has arguably been made where the non-union wage structure

provides a sensible benchmark for the wage structure in the absence of unions.

Fortin/Lemieux – Econ 561 Lecture 4A

• In countries (such as the US, Canada, and the UK) where highly decentralized firm-by-firm bargaining is the norm, the non-union wage structure can provide such an adequate counterfactual.

The fact the non-union sector is relatively large also reduces concerns about union spillover effects.

• This contrasts with countries (such Australia and many European countries), where

centralized bargaining between unions and groups of employers in an industry or region is the usual case.

• In decentralized economies, the impact of union on the wage structure depends

on which workers are covered (self-selection, screening) by how much the union alters the pay of those who are covered.

• For example, we will find that for male workers, union coverage tends to be

concentrated in the middle of the skill distribution and union wages tend to compress returns to skill differential relative to non-union wages, so for males, unions will have an equalizing effect on dispersion.

Fortin/Lemieux – Econ 561 Lecture 4A

• For female workers, union coverage is concentrated near the top of the skill distribution and there is less compression of returns to skill differential. So for females, unions tend to increase wage dispersion.

• Early studies identified two channels by which this disequalizing effect was operating:

o one was a between-sector effects, o the other was a hypothesized positive correlation between the union wage gain

and the level of wage in the absence of unions.

A break-through followed the important contribution by Freeman (1980), which first laid out the two-sector framework which incorporated “between” and “within” effects.

He found that unions compressed male wages within and across firms and

establishments, and substantially narrowed the wage differential between blue-collar and more highly paid white-collar employees within the organized sector.

o These two equalizing effects more than offset the “between-sector” effect that runs in the other direction.

Fortin/Lemieux – Econ 561 Lecture 4A

B. Impact of Unions on Wage Dispersion

• The impact of unions on the wage structure -- the way in which wages vary systematically with characteristics such as education, age, gender, or occupation has long been a topic of study among social scientists.

• For example,

o do unions widen or narrow pay differentials between the skilled and unskilled, between men and women, or between blue-collar and white-collar workers?

o Is the net effect of unions to increase or decrease overall wage inequality? • The interest for this topic has been renewed in recent years as analysts have struggled

to explain the rise in earnings inequality in several industrialized countries. • The fact that two of the countries with the largest declines in unionization -- the US

and the UK -- also experienced the biggest increases in wage inequality raises the question of whether these two phenomena are linked.

o If so, how much of the growth in earnings inequality can be attributed to the fall in union coverage?

Fortin/Lemieux – Econ 561 Lecture 4A

a. Two-Sector Model • A convenient framework for analyzing the effect of unions on wage inequality is the

potential outcomes model used in treatment effects literature. • Assume that each worker faces two potential wages:

o UiW , a log wage in the union sector, and

o NUiW a log wage in the nonunion sector.

• Letting iU denote union status, the observed wage for individual i is

NUii

Uiii WUWUW )1( −+= (1)

• Averaging across individuals would get the means of the potential wage outcomes in

the two sectors, )( U

iU WEW = and )( NU

iNU WEW =

and their corresponding variances UV and NUV .

Single Factor Analysis of Variance

Let each wage Wij denote observation j in group i.

Deviations from the grand mean W̄ can be written as

(Wij − W̄ ) = (Wij − W̄i) + (W̄i − W̄ ).

Taking the sum of squares,

SSS =∑

i

j

(Wij − W̄

)2 =∑

i

j

[(Wij − W̄i) + (W̄i − W̄ )

]2

Next, expand the squared term

SSS =∑

i

j

[(Wij − W̄i)2 + 2(Wij − W̄i)(W̄i − W̄ ) + (W̄i − W̄ )2

]

Distribute the summation operatiors

SSS =∑

i

j

(Wij − W̄i)2 +∑

i

j

2(Wij − W̄i)(W̄i − W̄ ) +∑

i

j

(W̄i − W̄ )2

Now, because deviations from the mean always sum to zero, the middle term in the above expression is equal to zero. Because j does not

index anything in the third term above, we can make the following simplications:

SSS =∑

i

j

(Wij − W̄

)2 =∑

i

j

(Wij − W̄i)2 +∑

i

ni(W̄i − W̄ )2,

where ni is the number of observation in group i and n =∑

ni is the total number of observations.

In population terms, the variance of wages across groups i is the sum of the within-group and between-group variances

V ar(Wij) = V ari[Wij] + Ei[V ar(W̄i)]

If there are only two groups, i = 2, one gets∑

i

j

(Wij − W̄

)2 =∑

j

(Wj1 − W̄1)2 +∑

j

(Wj2 − W̄2)2

+ n1(W̄1 − W̄ )2 + n2(W̄2 − W̄ )2,

Letting π = n1/n, since nσ̂2 =∑ ∑

(Wij − W̄i)2, one obtains the following estimate of the variance

Var(W ) = π Var(W1) + (1 − π) Var(W2) + π(1 − π) (W̄1 − W̄2)2.

Fortin/Lemieux – Econ 561 Lecture 4A

• From a single factor analysis of variance (ANOVA) decomposition into a within-sector and between-sector components, we can write the overall variance of wages as

2

2

2

)1()(

])[1()(

])[1()1(

WUUVUVV

WWUUVVUVV

WWUUVUUVV

NU

NUUNUUNU

NUUNUU

Δ−+Δ+=

−−+−+=

−−+−+=

(2)

• So that the effect of union on the variance of wages, relative to what would prevail if all workers were paid according to the current wage structure in the non-union sector, would be

2)1()( WUUVUVV NU Δ−+Δ=− (3)

o where the first term on the right hand side of this equation is a within-sector

effect associated with the difference in wage dispersion Δ in the two sectors

o and the second term is a between sector effect, arising because unions insert a wedge Δ between the average pay of union and nonunion workers that is always disequalizing.

Fortin/Lemieux – Econ 561 Lecture 4A

• This simple model can be used to simulate the impact of changing the proportion of

unionized workers, but it has limitations. • One problem is that the observed variances may not correspond to the potential

variances. For example, NUV may depend on the size on the union sector, if there are spill-over effects of unionism.

• In the absence of unionization we would observe )0(NUV , but in reality all we can

observed in )(UV NU , where U is the average union coverage. This imply that there

may be some bias in our estimate of the effect of union: )(UVV NU− . b. Heterogeneous Effects by Skill Levels

• The simple two-sector model does not incorporate differences in the extent of union

coverage or the size of the union wage effect across different workers.

Fortin/Lemieux – Econ 561 Lecture 4A

• Second generation studies (DiNardo, Fortin and Lemieux, 1996, DiNardo and Lemieux, 1997 and others) begin to develop a more complete framework where union rates depend on individual characteristics.

• DiNardo, Fortin and Lemieux (1996) develop a reweighing methodology know as

DFL and analogous the propensity score reweighing.

• They argue that de-unionization was a substantial factor: for men, the union density went from 0.317 in 1979 to 0.209 in 1988; for women from 0.170 in 1979 to 0.129 in 1988.

• Card, Lemieux and Riddell (2003) provide some simple evidence on differences in

unionization rate by wage rates and union/non-union wage gap by skill level, as well as changes over time.

• Given important differences in union density and union wag gap by skill groups, Card, Lemieux and Riddell (2003) perform a variance-decomposition that address these differences across skill groups.

43

Table 2

“Second Generation” Studies of the Impact of Unions on the Wage Structure -------------------------------------------------------------------------------------------------------------------------------------------------------------- Study Country Nature of data Findings -------------------------------------------------------------------------------------------------------------------------------------------------------------- DiNardo and US and 1981 and 1988 In 1981 unions reduced the variance of wages by 6 Lemieux (1997) Canada Men only. CPS per cent in the US and 10 per cent in Canada.

data (US) and LFS In 1988 unions reduced the variance of wages by 3 data (Canada) per cent in the US and 13 per cent in Canada.

Wage dispersion grew faster in the US relative to Canada for age/education groups with larger relative declines in unionization.

DiNardo, Fortin US 1979-1988 Shifts in unionization explained 15-20 per cent of rising and Lemieux (1996) CPS data wage inequality for men, 3 per cent for women.

Shifts in unionization explained up to one-half of the rise in the wage gap between male high school graduates

and dropouts. Machin (1997) UK 1983 GHS and About 40 per cent of the rise in the variance of log wages

1991 BHPS of men was attributable to the decline in unionization.

Bell and Pitt (1998) UK 1982-93 FES Approximately 20 per cent of the increase in the standard Supplemented with NCDS, deviation of log male wages during the 1980s was due GHS and BHPS to declining union density.

Card (2001) US 1973/74 and 1993 Unionization rates fell for less educated men and women CPS data but were stable (men) or rising (women) for college graduates. Union densities rose in the public sector. Shifts in unionization explained 10-15 per cent of the rise in male wage inequality, none of the rise for women.

Source: Card, Lemieux and Riddell (2003)

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44

Relative shifts in unionization explained one-half or more of the greater rise in male inequality in the private sector. Gosling and US and 1983 and 1998 Unionization fell faster in the UK than the US. Shifts in Lemieux (2001) UK CPS data (US), unionization explained up to one-third of the rise in male

GHS and LFS wage inequality in the UK and up to 40 per cent of the rise data (UK) in male inequality in the US. Shifts in unionization

explained very little of the rise in wage inequality for women in US or UK.

Source: Card, Lemieux and Riddell (2003)

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a. US Men

0.000.050.100.150.200.250.300.350.400.450.50

5.47 7.03 9.03 11.59 14.88 19.11 24.53 31.50 40.45 51.94

Hourly Wage (2001$, Log Scale)

Uni

on M

embe

rshi

p R

ate 1973/4

198419932001

b. US Women

0.000.050.100.150.200.250.300.350.400.450.50

5.47 7.03 9.03 11.59 14.88 19.11 24.53 31.50 40.45 51.94

Hourly Wage (2001$, Log Scale)

Uni

on M

embe

rshi

p R

ate 1973/4

198419932001

Figure 1. Unionization Rate by Wage Level, United States

Source: Card, Lemieux and Riddell (2003)

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Union Status Varies by Skill Group: Inversed U-Shape for Men, Lesser Decline in High Skill for Women
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a. US Men, 1993

1.50

1.70

1.90

2.10

2.30

2.50

2.70

2.90

3.10

1.50 1.70 1.90 2.10 2.30 2.50 2.70 2.90 3.10

Mean Non-union Wage

Mea

n U

nion

Wag

e

Solid line = 45 degreesDotted line = linear fit

b. US Women, 1993

1.50

1.70

1.90

2.10

2.30

2.50

2.70

2.90

3.10

1.50 1.70 1.90 2.10 2.30 2.50 2.70 2.90 3.10

Mean Non-union Wage

Mea

n U

nion

Wag

e

Solid line = 45 degreesDotted line = linear fit

Figure 4: Union Relative Wage Structure in the United States, 1993

Source: Card, Lemieux and Riddell (2003)

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Text Box
Union Premium Higher for Lower Skill Groups

a. Canadian Men

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

4.71 6.05 7.77 9.97 12.81 16.44 21.12 27.11 34.81

Hourly Wage (2001$, Log Scale)

Uni

on C

over

age

Rat

e 198419932001

b. Canadian Women

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

4.71 6.05 7.77 9.97 12.81 16.44 21.12 27.11 34.81

Hourly Wage (2001$, Log Scale)

Uni

on C

over

age

Rat

e 1984

1993

2001

Figure 2. Unionization Rate by Wage Level, Canada

Source: Card, Lemieux and Riddell (2003)

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Union Status Varies by Skill Group: Inversed U-Shape for both Genders

a. Canadian Men, 1991-95

1.9

2.1

2.3

2.5

2.7

2.9

3.1

3.3

1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3

Mean Non-union Wage

Mea

n U

nion

Wag

e

Solid line = 45 degreesDotted line = linear fit

b. Canadian Women, 1991-95

1.9

2.1

2.3

2.5

2.7

2.9

3.1

3.3

1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3

Mean Non-union Wage

Mea

n U

nion

Wag

e

Solid line = 45 degreesDotted line = linear fit

Figure 5: Union Relative Wage Structure in Canada, 1991-95

Source: Card, Lemieux and Riddell (2003)

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Union Premium Higher for Lower Skill Groups only for Men, more constant for Women

Table 2: Effect of Unions on Wage Structure of U.S. Workers, 1973-2001

1973/74 1984 1993 2001——————————————— ——————————————— ——————————————— ———————————————male female male female male female male female

Fraction Union Members 0.307 0.141 0.236 0.141 0.185 0.132 0.149 0.121

Mean Log Wages (2001$):Non-union Workers 2.646 2.270 2.573 2.276 2.535 2.337 2.667 2.457Union Workers 2.841 2.499 2.866 2.605 2.838 2.686 2.899 2.761

Union Gap (unadjusted) 0.196 0.230 0.293 0.329 0.304 0.349 0.233 0.305Union Gap (adjusted) 0.185 0.220 0.208 0.228 0.210 0.210 0.156 0.149

Standard Deviation Log Wages:Non-union Workers 0.553 0.442 0.563 0.467 0.594 0.515 0.601 0.538Union Workers 0.354 0.383 0.363 0.408 0.399 0.444 0.417 0.460

Union Gap -0.198 -0.059 -0.199 -0.058 -0.194 -0.071 -0.184 -0.077

Variance Decomposition:Overall Variance 0.258 0.195 0.289 0.223 0.331 0.270 0.340 0.289

Two sector modelWithin-sector effect -0.055 -0.007 -0.044 -0.007 -0.036 -0.009 -0.028 -0.009Between-sector effect 0.008 0.006 0.015 0.013 0.014 0.014 0.007 0.010Total effect -0.047 0.000 -0.028 0.006 -0.022 0.005 -0.021 0.001

Model with skill groupsWithin-sector effect -0.022 -0.006 -0.020 -0.007 -0.018 -0.009 -0.013 -0.009Between-sector effect 0.007 0.004 0.010 0.008 0.012 0.011 0.004 0.008Dispersion across groups -0.011 0.001 -0.007 0.000 -0.008 -0.003 -0.006 -0.005Total effect -0.026 0.000 -0.017 0.001 -0.014 -0.001 -0.015 -0.007

Sample Size 43,189 30,500 77,910 69,635 71,719 69,723 55,813 55,167Number of Skill Groups 180 180 343 343 244 246 245 246

Note: Samples include wage and salary workers age 16-64 with non-allocated hourly or weekly pay, and hourlywages between $2.00 and $90.00 per hour in 1989 dollars.

Source: Card, Lemieux and Riddell (2003)

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Table 3: Effects of Unions on Wage Structure of Canadian Workers, 1984-2001

1984 1991-95 2001——————————————— ——————————————— ———————————————male female male female male female

Fraction Union Workers 0.467 0.369 0.408 0.353 0.330 0.317

Mean Log Wages (2001$)Non-union Workers 2.658 2.365 2.661 2.452 2.728 2.495Union Workers 2.987 2.793 2.972 2.851 2.964 2.853

Union Gap (unadjusted) 0.330 0.428 0.311 0.398 0.236 0.358Union Gap (adjusted) 0.251 0.321 0.204 0.275 0.153 0.226

Standard Deviation Log Wages:Non-union Workers 0.528 0.446 0.514 0.465 0.501 0.463Union Workers 0.343 0.368 0.362 0.380 0.386 0.395

Union Gap -0.185 -0.078 -0.152 -0.084 -0.115 -0.068

Variance Decomposition:Overall Variance 0.231 0.218 0.233 0.227 0.229 0.224

Two sector modelWithin-sector effect -0.075 -0.023 -0.054 -0.025 -0.034 -0.019Between-sector effect 0.027 0.043 0.023 0.036 0.012 0.028Total effect -0.048 0.019 -0.031 0.011 -0.021 0.009

Model with skill groupsWithin-sector effect -0.041 -0.027 -0.033 -0.028 -0.025 -0.022Between-sector effect 0.017 0.022 0.010 0.017 0.006 0.012Dispersion across groups -0.014 0.014 -0.002 0.014 0.001 0.013Total effect -0.037 0.009 -0.025 0.002 -0.017 0.003

Sample size 17,737 15,356 17,981 18,323 24,003 23,703Number of skill groups 25 25 25 25 25 25

Note: Samples include wage and salary workers age 15-64 with allocated hourly or weekly pay (except in 1991-95), and hourly wages between $2.50 and $44.00 per hour in 2001 dollars.

Source: Card, Lemieux and Riddell (2003)

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Fortin/Lemieux – Econ 561 Lecture 4A

3. Effect of Minimum Wages • We have seen that minimum wages may increase the wages of low-skilled workers

• But whether the minimum wage affect the shape of the wage distribution is also of

interest

• These potential effects were not fully appreciated prior to the work by DiNardo, Fortin and Lemieux (DFL, 1996).

• Figures 1 in that paper shows the density of distribution of wages by gender,

particularly the piling up of women’s wages at the minimum wage threshold makes the case that the minimum wage must be important for the wage distribution, particularly for women.

• But developing a counterfactual is intrinsically difficult.

• The minimum wage, and similarly labour unions, could affect the wage distribution by: o Displacing low wage workers. o Boosting wages of workers who were below the minimum to the minimum

Fortin/Lemieux – Econ 561 Lecture 4A

o Inducing wages in the uncovered sector to either rise or to fall o Causing spillover effects on the wages of workers who are close substitutes to

minimum wage workers.

• In addition, two over-arching problems are: o The need to develop counterfactual wage densities, that is, an estimate of the

entire distribution of earnings under another minimum wage or union regime. o The question of institutional endogeneity: Should we view the minimum wage

as an exogenous force or an institution that is set (or influenced) by supply and demand?

• DFL addresses the first question but had little to say on the second.

• The stability of nominal federal minimum wages over the 1980s in the U.S. made

the second point less troublesome in that peculiar case, but more generally the role of supply and demand might certainly come into play o For example, Alberta was among the first province to raise its minimum wage

in the mid-2000s, amidst general increases in wages

Fortin/Lemieux – Econ 561 Lecture 4A

• Before discussing the methods in this paper in detail, we will need to become more familiar with Decomposition Methods.

• But we evaluate the assumptions.

• The assumptions required to form a counterfactual minimum wage estimate in DFL are quite stringent, but are conservative:

1. Minimum wages have no spillover effects on the distribution of wages above the minimum. This is a conservative assumption since any spillover effects (which are plausible slightly higher in the distribution) would augment the impact of the minimum wage.

2. The shape of the conditional density of wages at or below the minimum depends only upon the real minimum.

3. The minimum wage has no impact on employment probabilities, hence there is no need to develop counterfactual wage densities for workers who lose employment due to imposition of a binding minimum. This assumption is also conservative since removal of low wage observations from the distribution (due to job loss) would tend to further decrease inequality.

Fortin/Lemieux – Econ 561 Lecture 4A

• These assumption allow DFL to “graft” the lower tail of the earnings distribution

below the minimum wage (e.g., from 1979) directly onto another era’s wage distribution (e.g., 1989) when imposing the counterfactual minimum wage. This is not entirely satisfactory, but it is difficult to improve upon.

• The easiest way to see the results of the DFL analysis is to study Figures 6 and 7.

• Key results of DFL: • The decline in the minimum wage was important in the growth of lower tail inequality,

especially for women • The decline in unionization was a significant factor in increasing wage inequality

especially for men, and corresponds to a “declining middle” effect. • The effect of both labor market institutions explain more than a third the total change

in the standard deviation for men.

Fortin – Econ 561 Lecture 4A

0.2

.4.6

.8

Den

sity

0 1 2 3 4lwage

0.2

.4.6

.8

Den

sity

0 1 2 3 4lwage

0.5

11.5

Den

sity

0 1 2 3 4lwage

𝑓(𝑥) =1

𝑛ℎ(no. 𝑜𝑓 𝑋𝑖 in the same bin as 𝑥)

(bin=10, start=.00935174, width=.44223485)

(bin=50, start=.00935174, width=.08844697)

(bin=100, start=.00935174, width=.04422348)

Weighted Kernel Density Estimation

A kernel density estimator can be viewed as a smoothed histogram

The kernel density estimate f̂h of a univariate density f based on a random sample W1, . . . ,Wn of

size n, with weights θ1, . . . , θn (∑

i θi = 1), is calculated as follows

f̂h(w) =n∑

i=1

θi

hK

(w −Wi

h

),

where h is the bandwidth and K(·) is the kernel function.

• A critical issue in kernel density estimation is the choice of bandwidth h, which determined the

degree of “smoothing”. The Sheater and Jones’ (1991) plug-in method is a preferred method; it

has been shown (Park and Turlach, 1992) be among the best data-driven bandwidth selectors for

densities with complex structures.

• The choice of kernel function which give more (or less) weight to observations close (or further) to

wi is also important; a common choice is the Gaussian function, but the Epanichov has also given

good results.

• When the data carry sample weights these will be θi; alternatively, they can be multiplied by usual

hours of work and normalized to add up to one to provide an “hours-weighted” density.

Source: Silverman (1986)

Figure 1a. Kernel Density Estimates of Men's Real Log Wages 1973-1992 ($1979)

1973

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1974

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1975

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1976

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1977

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1978

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1979

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1980

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1981

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1982

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1983

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1984

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1985

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1986

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1987

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1988

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1989

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1990

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1991

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1 1992

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

Figure 1b. Kernel Density Estimates of Women's Real Log Wages 1973-1992 ($1979)

1973

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1974

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1975

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1976

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1977

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25

1978

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1979

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1980

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1981

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1982

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25

1983

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1984

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1985

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1986

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1987

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25

1988

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

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1.25 1989

ln(2) ln(5) ln(10) ln(25)

0

.25

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1.25 1990

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25 1991

ln(2) ln(5) ln(10) ln(25)

0

.25

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.75

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1.25 1992

ln(2) ln(5) ln(10) ln(25)

0

.25

.5

.75

1

1.25

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Fortin/Lemieux – Econ 561 Lecture 4A

• For males, supply and demand are likely to have been quite important to the growth of earnings inequality.

• Some remarks:

o The exercise appears to have much more explanatory power for earnings inequality among women than men. This is not surprising because the minimum wage had been much more binding for women who compose a disproportionate share of the lower tail of the wage distribution.

o Because the decomposition is sequential, the choice of the ordering of factors is not

innocuous.

o The decomposition does not deal with residual wage inequality.

• Despite its limitations (such as partial equilibrium analysis, assumption of exogeneity of minimum wages), the DFL results caused economists to take the minimum wage more seriously as an explanation for rising wage inequality.

Fortin/Lemieux – Econ 561 Lecture 4A

• Several years later, David Lee’s (1999) paper offered a potentially more compelling

empirical case for its importance.

• Lee’s idea was to use cross-state differences in the effective minimum wage to study the impact of the erosion of the minimum wage on earnings inequality.

• The effective minimum wage is defined as the log difference between the nominal

value of the minimum wage and a state’s median wage.

• DFL (1994) had also included an analysis of high/low minimum wage states.

• He then examines the cross-state relationship between the decline in the minimum wage over 1979 to 1989 and the growth in lower-tail inequality.

• The key identifying assumption for his approach is that cross-state variation in the

effective minimum is not systematically related to underlying variation in the states’wage distributions (i.e., the distribution that would prevail in the absence of a minimum wage).

Source: DiNardo, Fortin and Lemieux (1994)

Copyright ©1999. All Rights Reserved.

Source: Lee (1999)

Nicole
Line

Fortin/Lemieux – Econ 561 Lecture 4A

• Figure III which plots the 10-50 and 75-50 differentials for three high wage and three low wage states makes a compelling case that the effective minimum wage construct has empirical traction. The drop in the lower tail of the distribution is much more pronounced in low wage states, but this is not so for the 75-50 differential.

• If the minimum wage has no disemployment effect on workers whose latent wages ∗are below the minimum and has no spillover effect on workers whose wages are

above the minimum, then (Figure IVa):− = ∗ − ∗ if − < ∗ − ∗ = − otherwise

• These observations suggest a simple empirical approach: a regression of the change in state level wage inequality on the change in the state’s effective minimum wage.

• But a practical problem immediately arises in estimating the following regression: − = + − +

which is that there is a potential mechanical relationship between the dependent and independent variables induced by the presence of in both expressions.

Fortin/Lemieux – Econ 561 Lecture 4A

• This relationship may induce an upward bias in estimates of , giving rise to the spurious conclusion that increases in minimum wages reduce inequality (note: the dependent variable is by construction negative).

• The actual estimating equation (with = − ) is − = + ̅ + ̅ +

• Lee estimates that more than the entire rise of lower tail earnings inequality between 1979 and 1989 was due to the falling federal minimum wage.

• Problem 1: Violation of the identifying assumption. What if the median wage is correlated with latent wage dispersion? Then, states with a higher effective median will have higher 10-50 inequality and lower 90-50 inequality.

• Problem 2: Measurement error. While this might not be a serious problem in the cross-section, it may lead to severe biases after adding state fixed effects.

• Lee’s approach (violation of the mean-variance independence assumption and

simultaneity bias due to measurement error) are discussed and taken-on in a subsequent paper by Autor, Manning and Smith (2009).

Copyright ©1999. All Rights Reserved.

Source: Lee (1999)

-1.1

-1-.9

-.8-.7

-.6-.5

Rel

ativ

e W

age

Perc

entil

e

-1.1 -1 -.9 -.8 -.7 -.6 -.5Relative Minimum Wage

45 degrees line

A. 5th Percentile

-1.1

-1-.9

-.8-.7

-.6-.5

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age

Perc

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-1.1 -1 -.9 -.8 -.7 -.6 -.5Relative Minimum Wage

B. 10th Percentile

-1-.9

-.8-.7

-.6-.5

-.4R

elat

ive

Wag

e Pe

rcen

tile

-1.1 -1 -.9 -.8 -.7 -.6 -.5Relative Minimum Wage

C. 15th Percentile

-1-.9

-.8-.7

-.6-.5

-.4R

elat

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rcen

tile

-1.1 -1 -.9 -.8 -.7 -.6 -.5Relative Minimum Wage

D. 20th Percentile

Appendix Figure A2. Relative Wage Percentiles and Minimum Wages

Source: Fortin and Lemieux (2015)

Copyright ©1999. All Rights Reserved.

Source: Lee (1999)

Nicole
Rectangle

Fortin/Lemieux – Econ 561 Lecture 4A

• AMS provided an updated assessment of the impact of the minimum wage on the wage distribution by using a longer panel (incorporating many additional years of data and including significantly more variation in state minimum wages) and an instrumental variables specification that purges estimates of simultaneity bias stemming from errors in variables.

• They estimate that 30-60% of the growth in lower tail inequality in the female wage distribution between 1979 and 1988 as measured by the differential between the log of the 50th and 10th percentiles is attributable to the decline in the real value of the minimum wage.

• While around one-third of the growth in male lower tail inequality over this period

could be attributed to the minimum wage.

• In the end, their findings are very close to those of DFL.