Graduate Labour Notes

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1

Graduate Labour Notes


Introduction


-

Study of labour markets:

wages and wage structure, employment, unemployment, policy

effects,
employer
labour practices.


-

Aspects:



-

theory:

applied microeconomics





models of
consumer behavior (la
bour supply)





theory of the firm (labour demand)





factor market models






investment theory (skill creation)





incentive theory (contracts)





bargaining models (unionized markets)





information models (matching, search, incentives)





macroe
conomic concerns and labour economics?





unemployment: theories with labour market frictions or

imperfections.






Productivity and its determinants (long
-
run macro)



-

empirical:

testing and measurement




special statistical techniques

data issues



-

Practicial i
mportance:


-

about

70% of income is generated through labour markets.


-

labour policy is an active area


-

research: a

very active field.



-

Course: an introduction and sampler.


-

learn

some theory, some econometrics and apply them
to data.






2


Background and History of the Field


-

See: Introduction to Cahuc and Zylberberg (2004)
Labor Economics
.


-

Rise of markets, industrial revolution:

-

modern labour markets.

-

working for pay: a whole new set of outcomes to explain.


-

A
dam Smith

Wealth of Nations
:


-

compensating differentials


-

labour productivity and division of labour.


-

labour theory of value.


-

Classical
economists
: 19th century


-

“iron law of wages”: explaining poverty.


-

Malthus: population and labour supply

in the long
-
run.


-

labour productivity and diminishing returns.


-

Marxist Economics:


-

Classical value theory
with

ethics: exploitation.


-

unemployment, technology, wages and crisis.


-

Neoclassical revolution: late 19
th

century


-

marginalism, resou
rce allocation.


-

income distribution and marginal productivity theory.


-

marginal utility: roots of the model of labour supply.


-

supply
-
demand model (Alfred Marshall)


-

Keynesian economics and labour markets: explaining the
Great
Depression


-

unempl
oyment:

rigid wages?

Causes?


-

J. Hicks:
Theory of Wages


-

introduced modern microeconomics to labour economics.


-

early model of wage determination with worker and employer coalition bargaining.


-

Institutionalists:


-

dominance of labour economics
: descriptive, atheoretical, empirical bent.


-

influential into the 1950s, 1960s: explaining internal markets, employer interviews,







describing corporate wage structures.



-

remnants:

dual labour market theories




theoretical models of institution
s.


-

1950s
-
1960s:


-

Human capital theory:

Gary Becker


3

-

Earnings determination:


Jacob Mincer


-

Discrimination theory:

Gary Becker





-

1970s
-
1990s:


-

More Becker: labour supply as a time allocation problem.


-

Microeconomic theories of unemploym
ent.


-

Contract theory and incentives.


-

Search and matching approaches to labour markets.


-

Influence of the computer:



-

empirical analysis and "microdata"



-

rise of empirical labour economics.



-

sample selectivity issues.


-

Ongoing debates:


-

Methodological debates:

Structuralists (theory
-
based empirical methods) vs. others:


-

coherent interpretation of results needs a theoretical framework.


-

problems: structural approaches often only tractable under strong technical

assumptions.




-

Identification issues:

Natural
experiments

Instrumental variables methods.


-

Current issues:

-

Wage inequality: trends and explanation.

( t
echnology
, globalization,
institutions (policy, unions) )
.


-

Educational outcomes, health and labour.


4


Data, Data

Sources

and Data Handling: Preliminaries


Importance of Data in Labour Economics
:




-

Description: identifying what puzzles need to be explained.



-

Observation may motivate theory.




e.g. views on unemployment: static, turnover views and da
ta availability.





Wage differences and discrimination.



-

Testing and measuring microeconomic relationships

-

Labour supply and wage determinants, labour costs and employment: how

responsive?



-

Policy
development and
evaluation:



-

Measurem
ent of policy effects.




-

Examples:

Welfare reform

(mid/late
-
1990s)
, EI

(1990s)
and labour supply.





Baker and Fortin (2000) Ontario pay equity.





Minimum wages, mandatory retirement etc.





Effects of taxes on incomes and employment.




-

Findings

may spur policy:




e.g. immigrant outcomes literature; wage inequality in the English speaking

world.



Types of labour market data
.



-

Survey Data and Administrative data




-

Administrative data: based on government program records.




e.g., unemplo
yment insurance files





payroll or income tax data.




-

Survey data: most readily available




-

questionnaire responses from a sample of the population of interest.




-

Establishment and Household Survey Data




-

Establishment: employer survey
s




e.g., Survey of Employment, Payrolls and Hours




-

Household surveys e.g. Labour Force Survey,
Census,
SLID



5



-

Ma
tched data: matched

household and establishment

surveys
.




-

Aggregate and Microdata:




-

Microdata:

unit of observation is the ind
ividual





person (household) or business.





e.g., is the person employed, unemployed this week?




-

Aggregate: microdata which has been "aggregated"






(summed, averaged, etc.)






e.g., how many people are employed or unemployed this week in Cana
da?




-

Time series, cross
-
section, pooled and longitudinal data:




-

Time series: variation over time only




e.g., unemployment rate of Ontario 1976
-
20
10




-

Cross
-
section: variation between units of observation










the same point in time.








e.g., provinci
al unemployment rates, August 2010





-

Pooled Data: variation between cross sections and across time.






e.g., provinc
ial unemployment rates 1976
-
2010





-

Longitudinal
or Panel
Data: pooled microdata





-

follows individuals (
households) or firms across time.



-

Growth in data availability.


-

microdata especially.


-

Canada: "Data Liberation Initiative" (see link from website)


-

Predominance of American research: may partly reflect data availability.


-

Growth in computing
power.


-

ability to handle microdata has increased massively.




6

Major Canadian Sources of Labour Market Data:



Labour Force Survey (LFS):


-

Monthly, household survey.


-

Since 1945


-

major redesigns: 1966, 1976.


-

Big:


53,000 households.


-

Strat
ified sample not random.


-

must weight microdata

(weights based on Census long
-
form!)
.


-

What?


-

Employment, unemployment, hours, type of work.


-

Since 1997: wage and union status.


-

By personal characteristic, region.


-

Availability?


-

The Dail
y
: Labour Force
usually the
first Friday of each month.

http://www.statcan.gc.ca/subjects
-
sujets/labour
-
travail/lfs
-
epa/lfs
-
epa
-
eng.htm


-

Labour Force Informatio
n

(monthly)


-

Labour Force Historical Review

: summary statistics by characteristic back to 1976.




(a handy tool: we will use it occasionally)


-

Source data files: available back to 1976.




i.e., microdata (cross
-
sectional)

--

we will use some of th
ese on the assignments.


-

See
Guide to the Labour Force Survey

(link on website)


http://dsp
-
psd.pwgsc.gc.ca/collection_2009/statcan/71
-
543
-
G/71
-
543
-
g200
9001
-
eng.pdf




Survey of Labour and Income Dynamics (SLID)


-

Longitudinal data, household survey.


-

Started 1993

(earlier: Survey of Consumer Finances)
.


-

Sub
-
sample of the Labour Force Survey sample.


-

follow each person for 6 years.


-

new sub
-
sam
ple started every 3 years.


-

15,000 households in each wave (since 1998: always 2 overlapping waves).


-

Originally intended to study:


7


-

income dynamics


-

labour market dynamics


-

family dynamics.


-

Has become the main source of annual income data,

see

Statistics Canada
Income in Canada

Cat. 75
-
202 XIE.





Census


-

Every 5 years since 1961 (every 10 years pre
-
1961).


-

Subset receive detailed questionnaire

(long
-
form)
.



-

source of income and labour market data.



-

subset is a random sample.


-

Good data on personal characteristics: notably
location,
education, education, ethnic background


immigration status
and language.


-

Very limited information on jobs.


-

occupation, industry.


-

Microdata: Public use microdata files

-

large sampl
es


-

2%
-
3% of the population 15 yrs and over.



Survey of Employment, Payroll and Hours


-

Monthly establishment survey.


-

increasing
ly

relies on

administrative (tax) data.


-

Average wages, hours, number of employees by detailed industry

and geographi
cal location.


-

Publication:

Employment, Hours and Earnings

Cat. 72
-
002


-

Microdata unavailable.




8


United States Data


-

Current Population Survey (CPS):


-

US version of the Labour Force Survey.


-

much of the data is available free through:



-

B
ureau of Labor Statistics website (aggregate data: free!)



-

US Census Bureau: microdata (free!)


-

Panel Survey on Income Dynamics (PSID):


-

"Michigan Panel"


-

a major US longitudinal data set (since 1970s): similar to SLID.


-

National Longitudinal
Survey (NLS):


-

other long
-
standing US longitudinal data set.





Europe


-

Common for countries to have a household survey (like our Labour Force Survey).


-

Some countries rely on administrative datasets: sample is the population!



9

Using Data : Tips


-

Hamermesh (2000): some useful suggestions and cautions.


-

Which data set should be used?


-

Variables available?


-

How are key variables coded/defined in the dataset?


-

Sample size: is it adequate?


-

What data sets have other researchers used? Why?


-

Know the Data:


-

Sample coverage: are there any restrictions? Is it representative or restricted to some subset

of the population?



-

Check questionnaire:

-

Is the question flawed? Is the question asked only of a subset of the sample? Could

ques
tion order affect results?



-

Be familiar with variable codings and definitions.



-

detail reported?



-

top
-
coding or bottom
-
coding?



-

missing value codes: don’t confuse them with real data!



-

What sampling methods were used?

-

random sample?
stratified sample? Are sample weights provided?

-

Data may need to be weighted for some uses.



-

Is the data concerned with current status or is it recall (retrospective) data?



-

This could be important for reliability of the data.



-
Preliminary inv
estigation of the data:



-

Generate descriptive statistics for key variables:

means, minimum and maximum values, frequencies.




-

Look for oddities, unexpected results.




-

Have you misunderstood something?




-

Have you made programming errors?




-

Are key figures in line with other sources? If not why? (
review
literature)


-

Cross
-
tabulations, means by characteristic, comparisons of descriptive statistics between

datasets.



10

-

Watch out for surprises/oddities!

-

Is there any preliminary evidence
on the issues you are examining?





-

Appropriate methods:

-

draw on economic theory: what variables should determine outcomes?

-

draw on econometric knowledge.

-

draw on examination of the literature (what techniques have others used? What

explanator
y variables have they used? Why?)



-

Sensitivity:


-

How do results change with changes to specification, estimation technique, sample used?




Methods in Empirical Labour Economics


-

Non
-
standard issues:



-

Qualitative data: dummy or other categorical

variables are common.



e.g., have a university degree or not
; male or female; construction worker or not, etc.



-

Dummy dependent variables: probit, logit, multinomial logit methods.



e.g.,

employed or not



-

Censored data: dependent variables with

limits

e.g. maximum or minimum earnings



-

Sample selection problems and Classical linear regression assumptions

(assumes random

sampling)
.



e.g. migrants are unlikely to be a random sample of the source population.



-

Natural experiment approach
es
.





-

Empirical Methods and Causality:



-

See Angrist and Krueger (1999) for a more detailed overview of several techniques we will



encounter later.




-

Angist and Krueger (2008)
Mostly Harmless Econometrics
. An interesting discussion of



curre
nt practice


advocate looking at the problem from an experimental perspective.







11



(1) Control for confounding variables.




e.g., adding control variables in a regression to isolate the effect of interest.




Estimating:

W
i

= a + b U
i

+ e
i

(e
i



error term, a, b coefficients)




-

What if U
i

depends on an observable variable, X
i
, that affect both W
i

and U
i
?




-

estimate of “b” mixes effect of U
i

on W
i

and the effect of X
i

on W
i
.




-

Estimate instead:





W
i

= a + b U
i

+ cX
i

+ e
i




-

This me
thod is used in most empirical studies in labour economics.









(2) Fixed effect methods:



-

Controlling for unobservable factors using multiple observations on the same





person.




-

Say that W
i

depends on unobservable factors specific to indiv
idual:






W
i

= a
i

+ b U
i

+ cX
i

+ e
i

(intercept differs by person i)




-

If unobservable is correlated with U
i

then again b is biased.




-

If have multiple observations on person i over time then a can be estimated as a




“fixed effect” or it could
be differenced out.




-

Common approach with panel / longitudinal data.





(3) Difference
-
in
-
differences estimates



-

Appeals to an experimental approach.






-

Exogenous event occurs.



-

Observe change (difference before and after event) in the va
riable of interest for




group affected by the event.



-

Observe how variable of interest changed in some “control group”.



-

Calculate the effect of the event as the difference
-
in
-
differences, i.e., difference




between the change for the group aff
ected and the control group.




e.g., Alberta ban
s

unions: observe how wages change after this event and compare to







how wages change in say Saskatchewan.


12




-

See minimum wage debate, US welfare reform effects, overtime effects, etc.



(4) Instrume
ntal variable techniques



-

Say U
i

is partly determined by unobservables that either affect W
i

or are correlated




with e
i

.




-

Then:

cov(U
i
,e
i
)

0


and estimate of b is biased.




-

Possible solution: find an instrumental variable (Z
i
)





cov
(Z
i

,U
i
)

0 and cov(Z
i

,e
i
) =0 and then use Z
i

as an instrument for Ui in




the wage equation regression.




-

Very popular in recent literature estimating the effect of education on wages.