Intermediate Macroeconomics Questions, due on April 29, 3:00pm (UTC+8)

MCGILL UNIVERSITY

Department of Economics

ECON 219-001

CURRENT ECONOMIC PROBLEMS: TOPICS

Professor Georgi Boichev

Winter 2021

Problem Set 4

Instructions: Please do NOT hand in. These questions are intended for practice only.

Question 1

Jake has three options upon graduating from a CEGEP: attending university, working upon completing CEGEP or receiving government assistance. If he attends university, he receives a lifetime labour income of $ 3 million. If he starts working upon completing a CEGEP, he receives a lifetime labour income of $ 2 million. The lifetime income he would receive from social assistance is $ 0.5 million. The cost of university education is $ 0.1 million.

Calculate the opportunity cost of attending university.

Step 1:

To determine the highest-valued alternative to attending university, we limit the set of options to working and receiving government assistance.

Opportunity cost of working = Net gain from gov. assistance – Net gain from working = 0.5 – 2 = -1.5 < 0

This result implies that the highest-valued alternative is working (upon graduating from CEGEP).

Step 2:

Opportunity cost of attending university = Net gain of working - Net gain of attending university = 2 – (3 – 0.1) = -0.9 < 0

This result implies that it is on one’s self-interest to go to university.

Question 2

Answer the following questions regarding to the design of social assistance program in Québec in 1986.

a) Briefly describe the time trend of social assistance income (monthly) represented by the black line in Figure 1.

The black line represents that social assistance income is increasing with age but there is a break in the time trend at age 30 when social assistance income rises by approximately $ 500.

A few notes:

Each blue dot represents the average social assistance income for a given age, e.g., 27.

The black line is a time-trend where the unit of analysis is age as opposed to time.

Black line smooths any fluctuations over one’s lifecycle, i.e., it depicts a general linear relationship.

b) Based on the income characteristics of two age groups, 16-24-year-olds and 35-44-year-olds, provided in Table 1, briefly explain whether it is appropriate to use one group as a treatment group and the other one as a control group.

These two age groups have very difference average labour income characteristics, which would cast doubt whether it is the generosity of social assistance income is driving this relationship.

c) Briefly explain why comparing two age groups in proximity to the vertical line (age 30) is appropriate for a natural experiment.

Using the two age groups in proximity to the vertical line (age 30) is appropriate for a natural experiment since the two groups are likely to have almost identical average characteristics, e.g. educational attainment, work experience, etc.

Question 3

Answer the following questions regarding labour income inequality based on Figure 2, panel a) Census 1980 – 2000, provided in the Appendix.

You may assume that the labour markets for skilled and unskilled workers function as described in the lecture notes. You may also use the evidence provided in the lecture notes that a large increase in skilled workers measured by the post-secondary graduation rates occurred during the examined period 1980 – 2000.

The variance is a measurement of income inequality that has a similar interpretation as the Gini coefficient, where a larger variance represents greater income inequality.

a) Briefly describe the key distinction between the terms: total variance, within-group variance, and between-group variance.

Total variance (TV) refers to labour income inequality of all people in the labour force irrespective of skill level.

Within-group variance (WGV) refers to labour income inequality among workers of the same skill level.

Between-group variance (BGV) refers to labour income inequality among workers of different skill level.

b) Briefly describe and interpret the decomposition of total variance into within-group variance and between-group variance in 1980.

TV was approximately 4.2, WGV = 4 and BGV = 0.2.

Note that in any year.

This implies that approx. 95 % of the labour income inequality is due to income inequality among male workers of the same skill level. Only about 5 % of the labour income inequality is due to income inequality resulting from a pay differential between skilled and unskilled male workers.

c) Briefly describe how the decomposition of the total variance into within-group variance and between-group variance has changed from 1980 to 2000.

Absolute:

The total variance increased from 4.2 to 6.2 as both its components increased. WGV increased from 4 to 5.8 and BGV from 0.2 to 0.4. This means that both types of labour income inequality increased, thereby contributing to an overall increase in labour income inequality.

Relative:

This implies that during the examined period the share of income inequality among male workers of the same skill level has declined to 93.5 % and that corresponding share of the income inequality resulting from a pay differential between skilled and unskilled male workers has increased from 5 to 6.5 %.

d) Briefly explain if the increase in skilled labour during the period 1980 – 2000 has been effective in reducing income inequality between skilled and unskilled workers.

The larger variance in 2000 relative 1980 implies that the income inequality between skilled and unskilled workers has in fact increased both in absolute terms (0.2 to 0.4) and in relative terms (5 % to 6.5 %).

e) Briefly explain if the increase in skilled labour during the period 1980 – 2000 has been effective in reducing income inequality among skilled workers.

The larger variance in 2000 relative 1980 implies that the income inequality among workers of the same skill level has increased in absolute terms (4 to 5.8) but decline in relative terms (95 % to 93.5 %).

Appendix

Figure 1

Source: Lemieux and Milligan (2008)

Table 1

	Age group: 16 - 24	Age group: 35 - 44
Average Labour Income (1986)	17,800	51,200

Source: Statistics Canada (2020)

Figure 2

Source: IRPP (2016)

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