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TitleEssays on Tasks, Technology, and Trends in the Labor Market
LanguageEnglish
File Size1.2 MB
Total Pages175
Table of Contents
                            Declaration
	Abstract
	Acknowledgements
	Contents
Contents
	List of Figures
List of Figures
	List of Tables
List of Tables
1 Interpersonal-Service Tasks and the Occupational Structural Change
	1.1 Introduction
	1.2 Related Literature
	1.3 The Interpersonal-Service Task Intensity Index
	1.4 ITI and the Shifting Task Demand
	1.5 ITI and Technology
	1.6 Analytical Framework
		1.6.1 Industry Production
		1.6.2 Task Production
		1.6.3 Investment and Task Capital
		1.6.4 The Household
		1.6.5 Equilibrium
		1.6.6 Technical Change, and Predictions of the Model
	1.7 Conclusion
	1.A Tables
	1.B Figures
	1.C Data Appendix
2 Task-Based Sources of Job Polarization and Structural Change of Employment in the US
	2.1 Introduction
	2.2 Analytical Framework
		2.2.1 Production Technology
		2.2.2 Households
		2.2.3 Equilibrium
		2.2.4 Evolution of Technology
	2.3 Accounting for Job Polarization and Structural Change
		2.3.1 Data
		2.3.2 Summary: Trends in Employment, and Tasks
		2.3.3 Estimation and Results
		2.3.4 Growth Accounting of Employment Demand
	2.4 Conclusion
	2.A Tables
	2.B Figures
		2.C Data Appendix
			3 Skill-Biased Technical Change and Labor Market Polarization: The Role of Skill Heterogeneity Within Occupations
				3.1 Introduction
				3.2 Data
				3.3 Occupational Skills and Trends in Occupation Growth
				3.4 A Model of SBTC Within Occupations
				3.5 Conclusion
				3.A Tables
				3.B Figures
				3.C Occupational Employment Growth in 1990s
                        
Document Text Contents
Page 1

T H E L O N D O N S C H O O L O F E C O N O M I C S

A N D P O L I T I C A L S C I E N C E

E S S AY S O N TA S K S ,

T E C H N O L O G Y, A N D T R E N D S I N

T H E L A B O R M A R K E T

Orhun Sevinc

A thesis submitted to the Department of Economics of the Lon-
don School of Economics for the degree of Doctor of Philosophy,
London, July 2017.

Page 2

D E C L A R AT I O N

I certify that the thesis I have presented for examination for the PhD degree of the

London School of Economics and Political Science is solely my own work other than

where I have clearly indicated that it is the work of others (in which case the extent of

any work carried out jointly by me and any other person is clearly identified in it).

The copyright of this thesis rests with the author. Quotation from it is permitted,

provided that full acknowledgement is made. This thesis may not be reproduced

without my prior written consent.

I warrant that this authorisation does not, to the best of my belief, infringe the rights

of any third party.

I declare that my thesis consists of approximately 36,840 words.

Ankara, July 2017

Orhun Sevinc

2

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insignificant. Columns (5) to (7) show that task estimates are similar to regressions

with full set of fixed effects shown in the first three columns.

The second version of sector-specific technology growth is reflected in the regres-

sion model by adding time trend interactions of detailed sector category dummies.

Columns (8)-(10) reports again very similar results compared to other specifications

in Table 2.4 and those in Table 2.3.

2.3.3.3 The Role of Tasks in The Evolution of Employment Demand in 1990s,

and 2000s

I model occupation-specific technical change as a combination of linearly growing ITI

and RTI related parts. While this approach, in general, is practical to estimate the

long-run average impact of different channels of technology in the labor market (e.g.,

Katz and Murphy, 1992), there are reasons for doubting the linearity assumption.

The US economy during the sample period has been marked by different phases

of technical changes, especially in terms of the impact of computers. The literature

documents that during 1990s, and especially through the last 5 years of the decade,

labor productivity growth surged on the back of ICT intensive industries. On the

other hand, this impetus did not live long. After mid-2000s the aggregate productivity

growth as well as those sectors with high ICT use significantly regressed. A reflection

of slowing productivity growth of ICT is thought to be tracked in the relative price of

computers after early 2000s (Gordon, 2015).

In order to check the stability of the estimates in different parts of the sample, I

run conditional demand estimation by splitting the sample into two from the end of

2000. Columns (1)-(3) and (4)-(6) of Table 2.5 report the task coefficient estimates for

the two consecutive 13 years of the sample. The estimates for RTI is negative, large

and statistically significant before 2000, while they are small and insignificant for the

following sub-period. The vanishing impact of RTI on employment demand growth

is consistent with the view that the routinization operates through declining relative

price of ICT capital and consequently, faster productivity growth in occupations of

higher RTI. Therefore most of the impact of routinization on the labor market took

place during 1990s when productivity growth due to ICT was remarkable and the

relative price of ICT capital was decreasing at an increasing rate.

For ITI, however, the change in the estimated effect on employment growth between

the sub-periods is small and statistically insignificant, suggesting that the impact is

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more homogeneously distributed across time. Considering the fact that ITI reflects

the service content at the task level, the stable growth of employment towards more

interpersonal-service intensive employment seems to be in line with the continuous

growth of services in the economy.

2.3.3.4 Industry Demand Estimation

In order to quantify the full effect of task measures in occupational and industrial

employment reallocation, one needs to take into account the demand effects on sector

output in general equilibrium. Since sectors with high RTI and low ITI exhibit faster

productivity growth, relative prices in these sectors fall, affecting the demand for final

output from consumers. In particular, elasticity of substitution across sector output,

�, is a key parameter for understanding the shifts of labor demand. Inspection of

equation (2.28) suggests that if sectoral elasticity of substitution is smaller (greater)

than one, relative demand for a sector increases (decreases) following a rise in its

relative price. I estimate the parameter through the following equation:

log Yit = γ̃i + γ̃t − � log

pit
Pt


, (2.37)

which is log-transformed and market clearing imposed version of equation (2.28)

with industry output consumption weights and aggregate real income captured by

industry- and time-fixed effects γ̃i and γ̃t.

As in the labor demand estimation, I provide the estimation of equation (2.37) using

the two sector price measures of value added prices and marginal costs. Table 6

reports the output demand estimation. Column (1) uses sectoral value added price

indexes from BEA. The data go back to 1947 and column (1) suggests a postwar

elasticity of substitution of 0.52. In order to have sample period compatibility with

conditional labor demand estimation, column (2) narrows the time span to start from

1987, with estimated elasticity of 0.45. Column (3) uses the constructed marginal cost

measure, which is available starting with 1987. It suggests the elasticity parameter

as 0.49, which is remarkably close to value added price’s coefficient. These estimates

suggest that detailed industry output are poor substitutes, i.e. � < 1.

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