possibility of hamermesh’s methodology implementation on the basis(lecture)

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



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

Kyiv Institute of Economics 2



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




Theoretical basis

Profit function:

i profit of firm Wi earnings observable of individual

Di disamenities taken by individual




Theoretical basis

Assumption 1:

Pi(Ri) preferences of risk Pi(Ei) preference for full earnings




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




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.




Figure I Example of theory implementation




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.




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.




Figure II: Differences in disamenities and earning




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)).




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.





Table I: Summed up statistics of regression



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



Table II Corrected and not corrected logs of

earnings for boundary years




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.




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).




Table II Calculated income elasticities for

disamenities




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




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




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



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)




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.


possibility of hamermesh’s methodology implementation on the basis(lecture)

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