Econ 561 Topics in Industrial Relations

(AKA Labour Economics)Introduction

Prof. Nicole FortinProf. Thomas Lemieux

Contact Information

• Nicole Fortin• Office: Iona East Wing 113• E-mail: nicole.fortin@ubc.ca• Office Hours: Friday, 1:30-

3:30pm

• Thomas Lemieux• Office: Iona Director’ Suite 124• E-mail: thomas.lemieux@ubc.ca• Office Hours: Wednesday, 1:30-

3:30pm

Sample ResearchNew Methods in Applied Econometrics:• DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. "Labor Market Institutions and the

Distribution of Wages, 1973-1992: A Semiparametric Approach." Econometrica 64, no. 5 (1996): 1001-1044. Known as DFL, Google Cites: 2790

• Imbens, Guido W., and Thomas Lemieux. "Regression discontinuity designs: A guide to practice." Journal of Econometrics 142, no. 2 (2008): 615-635. Google Cites: 2221

• Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. "Unconditional quantile regressions." Econometrica 77, no. 3 (2009): 953-973. Known as RIF-regression, Google Cites: 729

Substantive Contributions:• Fortin, Nicole M. "Gender Role Attitudes and the Labour-Market Outcomes of Women across OECD

Countries." Oxford Review of Economic Policy 21, no. 3 (2005): 416-438. Google Cites: 383• Card, David, and Thomas Lemieux. "Can Falling Supply Explain the Rising Return to College for

Younger Men? A Cohort-Based Analysis." Quarterly Journal of Economics 116, no. 2 (2001): 705-746. Google Cites: 1039

Industrial Relations vs Labour Economics

• Industrial Relations is a wide interdisciplinary field that studies the employment relationship – Historically (circa 1920), the relationship between the employer

and its unionized workforce was its main focus– As most employment is no longer industrial, and most workers

are no longer unionized, that focus has shifted towards human resource management

– Introduced at UBC when Prof. Craig Riddell join in 1979

Industrial Relations vs Labour Economics

• The traditional focus of inquiry of labour economics is the labourmarket, i.e workers and their wages– Labour force participation, hours of work, jobs, unemployment– Wages, skills and productivity, performance pay

Some Stylized Facts

• Do the levels of labourmarket participation of women compare to that of men?

• Much progress, but large differences across countries

• Are public policies implicated?

Some Stylized Facts

• Are the levels of Labour Force Participation (LFP=#in LF/total pop) of men and women converging?

Source: Blau and Kahn (2016)

in the United States

Some Stylized Facts

• Is the unemployment rate (# unemployed/# in LF) back to pre-recession level?

Sources: Statistics Canada; U.S. Bureau of Labor Statistics. 2014 Budget. Conceptual differences raise the Canadian unemployment rate relative to the U.S. rate. In particular, Statistics Canada considers as unemployed those passively looking for work (e.g. reading want ads) as well as those who will begin work in the near future, while the U.S. Bureau of Labor Statistics does not include either group in the labour force. In addition, the Canadian methodology includes 15-year-olds (who have a higher-than-average unemployment rate), while the U.S. does not.

Unemployment Rates

Some Stylized Facts

• The more favorable unemployment rate (UR=# unemployed/ # in LF) in the US is in part due to a decline in US-LFP

Some Stylized Facts

• Is the unemployment rate back to pre-recession level?

• But varies by education level

Some Stylized Facts

• How about earnings and wages, does it pay to get a university degree?

• Well, there is increasing variances in earnings by education level

Bureau of Labor Statistics, U.S. Department of Labor, The Economics Daily, Weekly earnings by educational attainment in the second quarter of 2014 on the Internet at http://www.bls.gov/opub/ted/2014/ted_20140725.htm

Some Stylized Facts

• Is there really been wage stagnation in the US over time?

Some Stylized FactsAll-cause mortality, ages 45–54 for US White non-Hispanics (USW), US Hispanics (USH), and six comparison countries

Mortality by cause, white non-Hispanics ages 45–54.

Source: Anne Case, and Angus Deaton PNAS 2015;112:15078-15083

Some Stylized Facts

• What about wage inequality?

Inequality in Top Incomes/Wealth

15

Some Stylized Facts

• What about income and wage inequality?

• Are the rich getting richer and the poor getting poorer?

25%

30%

35%

40%

45%

50%

1917

1922

1927

1932

1937

1942

1947

1952

1957

1962

1967

1972

1977

1982

1987

1992

1997

2002

2007

2012

Top

10%

Inco

me

Shar

e

Top 10% Pre-tax Income Share in the US, 1917-2013

Source: Piketty and Saez, 2003 updated to 2013. Series based on pre-tax cash

Some Stylized Facts

• What about income and wage inequality?

• Are the rich getting richer and the poor getting poorer?

• Actually, it is the very rich…

0%

5%

10%

15%

20%

25%

1913

1918

1923

1928

1933

1938

1943

1948

1953

1958

1963

1968

1973

1978

1983

1988

1993

1998

2003

2008

2013

Shar

e of

tota

l inc

ome

for e

ach

grou

p

Decomposing Top 10% into 3 Groups, 1913-2013

Top 1% (incomes above $392,000 in 2013)

Top 5-1% (incomes between $165,500 and $392,000)

Top 10-5% (incomes between $116,500 and $165,500)

Source: Piketty and Saez, 2003 updated to 2013. Series based on pre-tax cash

Some Stylized Facts

• What about income and wage inequality?

• Are the rich getting richer and the poor getting poorer?

Increases in top incomes in other countries too!

Source: Piketty and Saez, 2003

Some Stylized Facts

• What about income and wage inequality?

• Are the rich getting richer and the poor getting poorer?

But not all countries!

Source: Piketty and Saez, 2003

Course Description

• Cover some core topics in labour economics 1) labour supply 2) labour demand 3) wage determination

• Along some current issues/special topics4) wage differentials across groups and labour market discrimination5) income and wage inequality

Course web site : http://faculty.arts.ubc.ca/nfortin/econ561/head56117.html

Course Objectives• Inform students’ understanding of some salient labour market

institutions and public policies, such as – taxes and income support programs, – minimum wages, – immigration policies, – education policies, – anti-discrimination policies, etc.

• Train students in the empirical methods necessary to evaluate and conduct/improve on evidence-based policy analysis and labourmarket research

Some Important Quasi-Experimental Methods

• Differences-in-Differences (DID): comparison between change over time between a treated and a control group Card, D. “The Impact of the Mariel Boatlift on the Miami Labor Market," Industrial Labor Relations Review,

Vol. 43 (January 1990): 245-257

• Instrumental Variables (IV): use an exogenous variable to assign subjects to treated group Angrist J. and Alan Krueger, “Does Compulsory School Attendance Affect Schooling and Earnings?" Quarterly

Journal of Economics, Vol 106 (Nov. 1991) 979-1014

• Regression Discontinuity Design (RDD): comparison between a treated and a control group at a cut-off point Van der Klaauw, Wilbert. "Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression–

Discontinuity approach." International Economic Review 43, no. 4 (2002): 1249-1287. [Discussion Paper, 1997]

Actual Randomized Experiments are Increasingly Popular

• Randomized Lab Experiments Falk, Armin, Ernst Fehr and Christian Zehnder, “Fairness Perceptions and Reservation Wages—the

Behavioral Effects of Minimum Wage Laws,” Quarterly Journal of Economics, Vol. 121 (November 2008), 1347-1381.

• Field Experiments Bertrand, M. and S. Mullainathan, "Are Emily and Greg More Employable than Latisha and Jamal? A

Field Experiment on Labor Market Discrimination," American Economic Review, 94 (September 2004), 991-1013.

Duflo, Esther, Rema Hanna, and Stephen P. Ryan, “Incentives Work: Getting Teachers to Come to School,” American Economic Review, 102(2012): 1241-78.

Research Design

• Randomized trials form the conceptual benchmark for assessing the success or failure of studies based on quasi-natural experiments

• When an intervention (called “treatment”) is randomly assigned, the simple difference between groups with different exposure levels is due to the exposure itself and not to other uncontrolled factors.

• More formally, let 𝑌𝑌𝑖𝑖1 denote an outcome of interest if the observation unit 𝑖𝑖 is treated. If unit 𝑖𝑖 is not treated, we cannot observe this counterfactual or potential outcome.

• Let 𝑌𝑌𝑖𝑖0 denote the outcome if the observation unit 𝑖𝑖 is not treated. If unit 𝑖𝑖is treated, we cannot observed this counterfactual or potential outcome.

Research Design

• Letting 𝐷𝐷𝑖𝑖 = 1 when an observation unit is treated and equal 0 when it is not, the observed outcome for unit 𝑖𝑖 becomes

𝑌𝑌𝑖𝑖 = 𝑌𝑌𝑖𝑖0(1 − 𝐷𝐷𝑖𝑖) + 𝑌𝑌𝑖𝑖1𝐷𝐷𝑖𝑖• It is assumed that an observed outcome depends only on treatment to which

unit 𝑖𝑖 is assigned and not on the allocation of other units. This assumption is called stable-unit-treatment-value assumption (SUTVA). [e.g. possible violation: special classes for gifted pupils removed from regular classes]

• Thus we cannot hope to observe 𝑌𝑌𝑖𝑖1- 𝑌𝑌𝑖𝑖0 for a particular unit 𝑖𝑖, which the true causal effect.

• We may observe the average difference in the potential outcomes as the average causal effect or Average Treatment Effect:

𝐴𝐴𝐴𝐴𝐴𝐴 = 𝐴𝐴[𝑌𝑌𝑖𝑖1- 𝑌𝑌𝑖𝑖0] = 𝐴𝐴[𝑌𝑌𝑖𝑖1]−𝐴𝐴[𝑌𝑌𝑖𝑖0]

Research Design

• In general, a comparison of the observation units that are treated and non-treated gives 𝐴𝐴 𝑌𝑌𝑖𝑖 𝐷𝐷𝑖𝑖 = 1 − 𝐴𝐴 𝑌𝑌𝑖𝑖 𝐷𝐷𝑖𝑖 = 0 = 𝐴𝐴[𝑌𝑌𝑖𝑖1|𝐷𝐷𝑖𝑖 = 1] − 𝐴𝐴[𝑌𝑌𝑖𝑖0|𝐷𝐷𝑖𝑖 = 0]

= 𝐴𝐴[𝑌𝑌𝑖𝑖1|𝐷𝐷𝑖𝑖 = 1] − 𝐴𝐴[𝑌𝑌𝑖𝑖0|𝐷𝐷𝑖𝑖 = 1]+ 𝐴𝐴[𝑌𝑌𝑖𝑖0|𝐷𝐷𝑖𝑖 = 1] − 𝐴𝐴[𝑌𝑌𝑖𝑖0|𝐷𝐷𝑖𝑖 = 0]= 𝐴𝐴[𝑌𝑌𝑖𝑖1 − 𝑌𝑌𝑖𝑖0|𝐷𝐷𝑖𝑖 = 1] + {𝐴𝐴[𝑌𝑌𝑖𝑖0|𝐷𝐷𝑖𝑖 = 1] − 𝐴𝐴[𝑌𝑌𝑖𝑖0|𝐷𝐷𝑖𝑖 = 0]}

where the first term is the ATET (average treatment of the treated) and the second term is the difference in the average outcomes between treated and non-treated units in the absence of treatment, it is called the selection bias

Research Design

• If the treatment is randomly assigned, the assignment to treatment is independent of potential outcomes 𝑌𝑌𝑖𝑖1,𝑌𝑌𝑖𝑖0 ⊥ 𝐷𝐷𝑖𝑖, the assignment is also said to be ignorable.

• The selection bias will vanish and the treatment-control difference to provides an unbiased estimate of the average treatment effect.

• The methods that we will see devise ways to correct for this bias:– DD uses a common trend assumption to control for changes over time not due

to treatment– IV uses an instrument to make treatment close to random– RDD uses units around a cut-off to mimic random assignment

Course Method

• A first graduate course in labour economics is primarily directed at M.A. students in the lectures given by the instructors on core subject matter

• Serve the needs of Ph.D. students through the additional work they will be required to perform

• Ph.D. students will present more recent papers to get closer to the frontier of knowledge with discussions engaging M.A. students

• All students will sharpen their skills with empirical methods in the context of 3 empirical assignments

• An essay will allow students to delve into a topic of particular interest

Course Evaluation

• The class will serve the needs of Ph.D students through the additional work (readings, presentation, essay) they will be required to perform

M.A. Ph.D.

Empirical Assignments (3) 30% (10% each) 15% (5% each)

Essay on an Economic Debate Review 30% Review and Analysis 40%

Presentation of Journal Article Discussion 5% Presentation & summary10%

Final exam 35% 35%

Essay: Review (and Analysis) of a Debate

• The “Debate Review” (M.A. students) will bring together the results of several (2 or 3) seminal papers on a contentious topic, and address how more recent research (3 or 4 papers) is attempting to close the debate. It will summarize the findings and methodologies, and discuss how the choice of methodology may affect the findings. about 10-12 pages long (2000-3000 words), excluding tables and figures

• The “Debate Review and Analysis” (Ph.D. students) will additionally discuss in more depth how the methods used to analyze the debate of interest may influence the results across various studies; indeed it could in fact take the form of a meta-analysis. about 12-15 pages long (3000-4000 words), excluding tables and figures

Reading List (see Syllabus)

• There is no single textbook that covers the material for the entire course.• Rather there are various textbooks and journal articles, listed on the

syllabus that focus on particular topics covered in the course. • Each Ph.D. student is asked to choose one (1) paper from the readings

designated by a ☺ (or alternate) for class presentation and discussion.• Required readings will be indicated in class.• Most of the readings are available in electronic form with UBC links using

Google scholar for example, others are posted on the course’s web site.

Expected Lecture Schedule

This tentative schedule will be modified to follow the progression of the class.

Subject Due Dates Instructor1. Labour SupplyWeek 1 - Jan 3 Introduction/Subject Matter N. FortinWeek 1 - Jan 5 Neo-Classical Labour Supply N. FortinWeek 2 - Jan 10 Issues in the Estimation of Labour Supply Choice of journal article for

presentation (Ph.D. students)N. Fortin

Week 2 - Jan 12 Effect of Taxes and Income Support on Labour Supply N. Fortin

Week 3 - Jan 17 Self-selection: Roy Model N. FortinWeek 3 – Jan 19 Self-selection: Immigration Essay Proposal N. Fortin2. Labour DemandWeek 4 – Jan 24 Comparative Statics of the Demand for Labour N. FortinWeek 4 – Jan 26 Effect of Minimum Wages on Employment N. FortinWeek 5 – Jan 31 Effect of Immigration on Employment and Wages Empirical Assignment 1

Expected Lecture Schedule

This tentative schedule will be modified to follow the progression of the class.

Subject Due Dates Instructor3. Wage DeterminationWeek 5 – Feb 2 Compensating wage differentials T. LemieuxWeek 6 – Feb 7 Human Capital: Investment in Education T. LemieuxWeek 6 – Feb 9 Human Capital vs. Signaling T. LemieuxWeek 7 – Feb 14 Returns to Education I T. LemieuxWeek 7 – Feb 16 Returns to Education II T. LemieuxMid-term Break – Feb 20 to Feb 24Week 8 – Feb 28 Social Returns to Education T. Lemieux4. Wage Differentials Across Groups and Labour Market Discrimination

Week 8 – March 2 Labour Market Discrimination: Theories Empirical Assignment 2 N. FortinWeek 9 – March 7 Labour Market Discrimination: Measurement N. FortinWeek 9 – March 9 Gender and the Labour Market N. FortinWeek 10 – March 14 Race and Ethnic Differentials N. Fortin

Week 10 – March 16 Immigrant Outcomes N. Fortin

Expected Lecture Schedule

This tentative schedule will be modified to follow the progression of the class.

Subject Due Dates Instructor5. Labour Market Institutions and Wage InequalityWeek 11 – March 21 Impact of Unions and Minimum Wages on Wage

Inequality T. Lemieux

Week 11 – March 23 Decomposition Methodologies I Essay Due T. Lemieux

Week 12 – March 28 Changes over Time and Cross-Country Comparisons T. Lemieux

Week 12 – March 30 Top-End Inequality T. Lemieux

Week 13 – April 4 Supply and Demand Factors T. LemieuxWeek 13 – April 6 Skill-Biased Technological Change Empirical Assignment 3 T. Lemieux