econ7570 contemporary issues in economic development ...econ7570 contemporary issues in economic...

ECON7570Contemporary Issues in Economic Development

Lecture 10

Satoshi Tanaka

University of Queensland

May 13, 2015

Satoshi Tanaka ECON7570 Contemporary Issues in Economic Development Lecture 10

ECON7570 Contemporary Issues in Economic Development

Structural Transformation and Economic Growth II

Satoshi Tanaka ECON7570 Contemporary Issues in Economic Development 2 of 42

Structural Transformation and Economic Growth

Consequence of the Rise of the Service Sector


Sectoral Share in Value Added (GDP), U.S.

Figure : Sectoral Shares in Value Added (GDP) for Selected DevelopedCountries, 1800 - 2000: Belgium, Spain, Finland, France, Japan, Korea,Netherlands, Sweden, UK, US


Agricultural Share in Value Added (GDP), Cross Countries



Manufacturing Share in Value Added (GDP), CrossCountries



Service Share in Value Added (GDP), Cross Countries



Recap: What Explains the Rise of the Service Sector?

Demand-side story

Extended Engel’s law (Kongsamut, Rebelo, and Xie (2001)):

1 Households like to spend a smaller fraction of their budget on foodas they become richer (Engel’s law)

2 More generally, the share of necessities falls and the share of luxuriesrises as individuals get richer

3 As a result, the share of agriculture falls, replaced by manufacturing(the luxury items at the time)

4 Then the share of manufacturing falls while services (that containmore luxuries) rises


Recap: What Explains the Rise of Service Sector?

Production-side story

Differential TFP Growth Across Sectors (Ngai and Pissarides (2005))

1 Productivity growth has been very fast in agriculture, which hasmade it unnecessary to employ as many workers as before

2 Similarly, manufacturing today is so productive that you need to hirefar fewer people to produce the same amount


Today’s Plan

Today, we are gonna investigate three outcomes of structural changein details

1 Female labor force participation and gender wage gap

2 The difference in hours worked between the U.S. and the Europe

3 Wage inequality



Narrowing Gender Gap in Labor Markets


Female Labor Force Participation (FLFP) Rate, U.S.The timing of the rise of FLFP roughly coincides with the timing ofthe rise of services

Figure : Female Labor Force Participation Rate, U.S., 1947 - 2013Satoshi Tanaka ECON7570 Contemporary Issues in Economic Development 12 of 42

Hourly Gender Wage Gap, U.S.

Figure : Hourly Gender Wage Gap (Male/Female), U.S., 1950 - 2005


Empirical Evidence

Olivetti and Petrongolo (2013)

The service sector share explains the half of the cross-countrydifferences in FLFP

Ngai and Petrongolo (2013)

The service sector share explains the large portion of the difference infemale’s hours worked and the gender wage gap across states in theU.S.

Regression framework for individuals’ data:

yi,t,s =β0 + β1 × female_dummy + β2 × service_sharet,s

+ β3 × service_sharet,s × female_dummy + ...

where yi,t,s is the variable of interest, i : individual, t: year, s: state.


Empirical EvidenceThe timing of the rise of FLFP roughly coincides with the timing ofthe rise of services

Figure : The Effect of Service Share on Hours and Wages, U.S., 1977-2009


Further Empirical Evidence

Ngai and Petrongolo’s result clearly indicates that:

In a state where the service share is larger, females work longer hoursand have higher wages

There is more direct evidence:

Brain (skill requirement) v.s. Brawn (physical requirement)(originally from Galor and Weil (1996))

In the recent years, there are more brain jobs available for women dueto the rise of services (Bacolod and Blum (2010), Rendall (2010))

Bacolod and Blum (2010) and Rendall (2010) used the Dictionary ofOccupational Titles to show the rise of brain-intensive occupations


1950 Occupations in the U.S. (Rendall, 2010)

Figure : 1950 U.S. Census Data for Share of Worker’s Occupation


2005 Occupations in the U.S. (Rendall, 2010)

Figure : 2005 U.S. Census Data for Share of Worker’s Occupation


Rendall’s (2010) Economic Model

Same number of men and women in the economy

Individuals are characterized by (bi , ri ):

1 bi is the amount of ability for individual i

bi is distributed according to a uniform distribution,b ∼ F = U[bl , bh]

2 ri is the amount of physical strength for individual i

ri = r f if i-person is female, ri = rm if i-person is male

r f and rm are some fixed numbers, and r f < rm



Wages are paid according to the amount of (bi , ri ):

wi = wb,t × bi + wr ,t × ri

A worker participates in the labor market if the wage for him/her ishigher than the value of staying at home, H:

wi = wb,tbi + wr ,tri > H

Assume wb,tbl + wr ,trm > H and wb,tbl + wr ,tr f < H so that allmen participate in the labor market, but some of women stay athome initially

Especially, the threshold value of the amount of brain for woman’sparticipation decision is

b̂ft =

H − wr ,tr f

wb,t

Note that b̂ft is an decreasing function in wb,t



Female labor force participation is defined as:

LFP ft =

∫ bh

b̂ft

dF (b) =bh − b̂f

tbh − bl

As wb,t increases, b̂ft decreases, and then LFP f

t increases

Average female wage is

w̄ ft =

∫ bhb̂f

t

[wb,tb + wr ,tr f ] dF (b)

LFP ft

= 0.5 ×(wb,tbh + wr ,tr f + H

)assuming bh − bl = 1

Similarly, men’s average wage is

w̄mt = 0.5 × (wb,t (bh + bl ) + wr ,trm)


Analysis

1 As wb,t increases, b̂ft decreases, and then LFP f

t increases

2 As wb,t increases, women’s average wages increases by 0.5 × bh,while men’s wage increase by 0.5 × (bh + bl )

Note that men’s wages increase more than women’s wages

The gender wage gap doesn’t narrow!?

This is because, as wb,t increases, lower ability women enter labormarkets

The model can explain the increase LFP, but cannot explain thenarrowing gender wage gap in the data


Combining Human Capital

In order to overcome this negative selection effect, Rendall (2010)consider education

wi = wb,t × bi×e + wr ,t × ri

where e > 1

Rendall (2010) shows that if we take into account education, thegender wage gap could narrow


College Attainment Rate by Gender, 1950 - 2005, U.S.

Figure : College Attainment Rate by Gender, 1950 - 2005, U.S.


Gender Wage Gap, Model v.s. Data, 1950 - 2005, U.S.

Figure : Gender Wage Gap, Model v.s. Data, 1950 - 2005, U.S.


Female LFPR, Model v.s. Data, 1950 - 2005, U.S.

Figure : Female LFPR, Model v.s. Data, 1950 - 2005, U.S.


Gender Gap without Education, 1950 - 2005, U.S.

Figure : Female LFPR, Model v.s. Data, 1950 - 2005, U.S.


Summary for Brain v.s. Brawn

There are a couple of empirical evidence which shows the nature ofjobs is changing due to structural change

Rendall (2010) shows that:

Brain-Biased Technological Change (BBTC) can increase female LFP

With education, the model can also generate the narrowing genderwage gap



Hours Worked in the U.S. and Europe


Declining Working Hours in EuropeIn Europe, the average hours worked has significantly declined overtime

Figure : Hours Worked in the U.S. and Europe, 1955 - 2005


Rogerson (2008)

Rogerson (2008) explaines it by the difference in tax rate betweenthe U.S. and Europe, and structural changes

In Europe, the labor income tax is higher by about 15-20%, and theservice sector is much smaller

His story:

1 In Europe, because labor income tax rate is high, workers spend lesshours in labor markets

2 Instead, they do home production (cooking, cleaning etc.) more thanworkers in the U.S.

This idea is also supported by evidence that service sector in Europeis much smaller than the one in the U.S.


Sectoral Shares in the U.S.In Europe, the average hours worked has significantly declined overtime



Sectoral Shares in Europe



Summary for Hours Worked in the U.S. and Europe

Rogerson (2008) argues that the difference in hours worked can beexplained by the difference in tax rate and structural change

Important things are:

Home production and services are substitute

When labor income tax is high, people work less in the labormarkets, instead do home production activities

As a result, the service sector in Europe get smaller than the one inthe U.S.



Structural Changes and Inequality


Rise of High Skill Service


Share of Compensation in High-Skilled Service


Share of Compensation in High-Skilled Service

Buera and Kaboski (2012)

As the income level in the economy increases, people start demandinga more complicated service (medical service, legal service etc.)

To produce such a complicated service, workers need skills (lawyer,doctor)

Therefore, people’s demand in skill-intensive service promotes theinequality in the society


Skill Premium and Fraction of College-Educated Worker


Co-movement of Service Price and Skill Premium


Summary for Structural Changes and Inequality

Buera and Kaboski (2012)

The difference in pay between college and non-college workers has astrong correlation with the relative price of services

As the society get more developed, people’s demand for high-skilledservice creates more inequality

This trend is world-wide, not just in the U.S.!


Summary

Today, we studied three outcomes of structural change in details

1 Female labor force participation and gender wage gap

2 The difference in hours worked between the U.S. and the Europe

3 Wage inequality

Evidence support for the idea that structural changes affect a lot onpeople economic activities

Remember that these changes are still on going, not the ones in thepast


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