possibility of hamermesh’s methodology implementation on the basis(lecture)
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8/3/2019 Possibility of Hamermesh’s methodology implementation on the basis(lecture)
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Possibility of Hamermeshsmethodology implementation on the
basis of Ukrainian data:
Changing inequalities in markets for
workplace amenities
1Kyiv Institute of Economics
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Main question of research (Changing
inequalities in markets for workplaceamenities by D. Hamermesh, 1999,
further research):
Whether changing inequality of non-
wage returns exacerbates or
mitigates the rising wage inequality
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Theoretical basis
Utility function (Rosen, 1974):
Ui utility of individual Wi earnings observable of individual
Di disamenities taken by individual
Ei full earnings of individual
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Theoretical basis
Profit function:
i profit of firm Wi earnings observable of individual
Di disamenities taken by individual
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Theoretical basis
Assumption 1:
Pi(Ri) preferences of risk Pi(Ei) preference for full earnings
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Theoretical basis
Assumption 2:
Skill-neutral change in technology
Assumption 3:Full earnings are equal to the difference of
observed earnings and unobserved equivalent
of disamenities
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Set up of the model:
There exist two groups of individuals high-wage(skilled) low-wage (less skilled);
Disamenity is inferior good by definition;
Same preferences for two groups; Price effect outstrip income effect for low wage
workers so volume of disamenity consumed
increases;
Income effect outstrip price effect for high wage
workers so volume of disamenity consumed
decreases.
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Figure I Example of theory implementation
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Analysis
Main variables:
DUR average number of days lost per injury;
INC number of lost workdays per 100 FTE;
TOTINJ=DUR x INC;
W average wages across industry;
EVENITE number of working nights or
evenings.
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Problem:
Available dataset ULMS contain all abovementioned variables except EVENITE;
Solution:
We have data on amenities (benefits) (e.g.access to cheap vocation), so we can replicate
similar analysis using available indicators.
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Figure II: Differences in disamenities and earning
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Analysis: Part I
Across one year difference relatively to firstdecile (quartile) is taken for both earnings
(wage) variable and disamenities.
Construct graphs, which reflect relationship
between earnings and disamenities.
Run regression, which shows significance of
result seen on graphs, results put into the table
(table I(II in article)).
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Problem:
We have lower number of observations acrosstime dimension so our regression would have
up to 15 observations for quartile analysis and
up to 45 for decile analysis;Solution:
Creation of pseudo variation using cross
sectional dimension.
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Table I: Summed up statistics of regression
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Analysis: Part II
Correction of earnings on disamenity earning
equivalent (calculated in regression of earnings
on disamenity indicator);
W* corrected for disamenity logarithm of earnings;
impact of additional lost workday per 100 FTE on natural
logarithm of earnings;
Choosing of time periods for calculation of
double differences;
Calculation of double differences for earnings;
Summarizing all calculations in the table II (III).Kyiv Institute of Economics 15
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Table II Corrected and not corrected logs of
earnings for boundary years
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Analysis: Part III
Calculation of boundaries of income elasticity of
disamenities:
- General formula for
calculation of income elasticity of disamenity
d - income elasticity of disamenity
D - measure of disamenity;
E measure of earnings.
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Analysis: Part III
According to assumption:
So formulae of boundaries of income elasticity
of disamenity will be as follows:
All calculations summed up in the table III(VII).
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Table II Calculated income elasticities for
disamenities
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Implemented work
Data chosen: ULMS dataset for 1995-2004 years
(1995, 1999, 2000, 2002, 2004)
The programming of analysis for Part I:
- jointing of datasets for different years in one
panel;
- summarizing statistics;
- constructing of variables according to
Hamermeshs methodology (for deciles andquartiles);
- constricting graphs analogues to figure II of
Hamermeshs paper.Kyiv Institute of Economics 20
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0
1
2
3
4
5
6
7
8
1995 1999 2000 2002 2004
quart2
quart3
quart4
Differences in ln(earning) relatively to first quarter
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Differences in TOTINJ relatively to first quarter
-100
0
100
200
300
400
500
600
700
800
900
1995 1999 2000 2002 2004
quart2
quart3
quart4
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Problem:
The year of 2000 is outlier in earnings(numerous cases of earnings, which are
between zero and one);
Solution: The nature of outliers should be studied (they
should be corrected or dropped if this actions
would not introduce systematic errors)
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Conclusions:
Hamermeshs methodology can be implementedto a significant extend, but lack of variation in
time dimension and absence of some
indicators in ULMS data set will causemodifications or reduction.
Kyiv Institute of Economics 24
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