Wage employment, mobility and development

IDE-JETRO, World Bank, Asashi Shimbun Joint Symposium

“Evolving Sources of Value-added: Good-jobs, Bad-jobs?”

United Nations University

Tokyo, March 19, 2015

Martin Rama

Chief Economist for South Asia

The World Bank

World Development Report 2013 The World Bank 3/13/2012

Moving jobs to center stage 2

Access the report athttps://openknowledge.worldbank.org/handle/10986/20395

Overview

4

• Jobs take people out of poverty. But not all jobs involve an employer and an employee.

• Wage employment drives mobility. Whether jobs are formal or informal seems less relevant.

• Firm dynamics matter. But we have a distorted picture of firm dynamics in developing countries.

• Cities are the engine of job creation. Employment opportunities are different along the rural-urban gradation

Overview

5

• Jobs take people out of poverty. But not all jobs involve an employer and an employee.

• Wage employment drives mobility. Whether jobs are formal or informal seems less relevant.

• Firm dynamics matter. But we have a distorted picture of firm dynamics in developing countries.

• Cities are the engine of job creation. Employment opportunities are different along the rural-urban gradation

Source: Covarrubias and others 2012 for the WDR 2013

Jobs are the main source of household income

Source: Inchauste and others 2012 for the WDR 2013

Jobs take households out of poverty

Source: Inchauste and others 2012 for the WDR 2013, Azevedo and others 2012 for the WDR 2013

Jobs account for much of the decline in extreme poverty

Jobs get better with development

But what is a job?

A job does not always come with a wage

Overview

12

• Jobs take people out of poverty. But not all jobs involve an employer and an employee.

• Wage employment drives mobility. Whether jobs are formal or informal seems less relevant.

• Firm dynamics matter. But we have a distorted picture of firm dynamics in developing countries.

• Cities are the engine of job creation. Employment opportunities are different along the rural-urban gradation

Considerable occupational mobility exists across generations in India

13

Sources: Based on India Human Development Survey (IHDS) 2004–05.

Occupational mobility is higher for younger population cohorts

14

Sources: Based on IHDS 2004–05.

Occupational mobility has increased more for the most disadvantaged population groups in India

15

Sources: Based on IHDS 2004–05.

Upward mobility in South Asian countries is similar to that in the United States and Vietnam

16

Sources: Based on Dang and Lanjouw 2014 for this report and Dang, Lanjouw, and Khandker 2014 for this report.

Upward mobility is much stronger in cities than in rural areas in India

17

Sources: Based on IHDS 2004–05.

Wage employment is associated with greater upward mobility in urban areas

18

Sources: Based on data from Dang and Lanjouw 2014 for this report and Dang, Lanjouw, and Khandker 2014 for this report

Overview

19

• Jobs take people out of poverty. But not all jobs involve an employer and an employee.

• Wage employment drives mobility. Whether jobs are formal or informal seems less relevant.

• Firm dynamics matter. But we have a distorted picture of firm dynamics in developing countries.

• Cities are the engine of job creation. Employment opportunities are different along the rural-urban gradation

Job creation and destruction happen everywhere

Source: WDR 2013 team based on Bartelsman, Haltiwanger, and Scarpetta (2009), and Shiferaw and Bedi(2010).

But enterprise surveys give us a distorted picture

Source: Li and Rama (2015).

Household surveys Enterprise surveys

Germany

Chile

The employment share of microenterprises is greater in developing countries

The dispersion of productivity across firms is much higher in developing countries

Source: Li and Rama (2015).

0 5 10 15 20 25 30

Mexico

India

El Salvador

Uruguay

Bolivia

Chile

Ecuador

Argentina

China

United States

productivity ratio between

90th percentile and 10th percentile of TFP distribution

Bigger firms pay higher wages than smaller ones and much more than microenterprises

Source: Li and Rama (2015).

02

46

freq

ue

ncy (

%)

0 .2 .4 .6 .8wage difference, relative to microenterprises (%)

Small Large

kernel = epanechnikov, bandwidth = 0.0221

wage premium, relative to microenterprise, %

esti

mat

es, %

But most jobs are created by microenterprises, among which there are very few “gazelles”

Source: Li and Rama (2015).

0

20

40

60

80

[0-5] [6-9] [10-49] [50-199] [>=200]

per

cen

t

firm employment size

shares in job creation, % shares in job destruction, %

shares in employment, %

0

20

40

60

80

[0-5] [6-9] [10-49] [50-199] [>=200]

per

cent

firm employment size

shares in job creation, % shares in job destruction, %shares in employment, %

Job creation and destruction based

on enterprise surveys

Job creation and destruction re-

weighted by household surveys

Chile 2000-06

Overview

26

• Jobs take people out of poverty. But not all jobs involve an employer and an employee.

• Wage employment drives mobility. Whether jobs are formal or informal seems less relevant.

• Firm dynamics matter. But we have a distorted picture of firm dynamics in developing countries.

• Cities are the engine of job creation. Employment opportunities are different along the rural-urban gradation

Job opportunities vary along the rural-urban gradation

27Source: Chatterjee, Murgai and Rama (work in progress)

Farming jobs fall sharply at around 5,000 inhabitants

Jobs in factories and

offices pick up much later

Employment shares(usual status for women aged 15 and above, 2011-12)

Productivity varies along the rural-urban gradation

28Source: Based on Li and Rama (work in progress).

0.5

11

.52

2.5

Den

sity

6.5 6.75 7 7.25 7.5 7.75 8 8.25 8.5 8.75 9DistrictXsize fixed effect, nominal consumption

Rural, 0-4999

Rural, 5000+

Urban, 0-1000000

Urban,1000000+

Household weights

“Big rural” and “small urban” are almost indistinguishable The best performers

are in “small urban”

Less productive locations

More productive

locations

Top performers cluster well-defined spots

Source: Based on Li and Rama (work in progress).

Bottom performer(bottom 5%)

Average performer

Top performer(top 5%)

No data

30Source: Based on South Asia Spatial Database

City size and city governance matter for performanceExample: share of employment in manufacturing

But city size and governance are correlated, so the analysis needs to take them into account together

Determinants of light intensity per capita

31Source: Based on South Asia Spatial Database

0.5

1

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d c

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50,0

00-1

00,0

00

Administrative and size category

Base category: Administrative: Nagar Panchayat; Size: Less than 50,000

Log. Light intensity per capita

32Source: Based on South Asia Spatial Database

-.2

0.2

.4.6

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Administrative arrangement and size category

Base category: Administrative: Nagar Panchayat; Size: Less than 50,000

log of district mean of per capita expediture in urban areasDeterminants of household expenditure per capita

33Source: Based on South Asia Spatial Database

-10

01

02

03

0

Estim

ate

d c

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Sta

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apita

l

Mun

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illion

100,

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50,0

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00,0

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Administrative and size category

Base category: Administrative: Nagar Panchayat; Size: Less than 50,000

Share of wage employmentThe rural-urban gradation and wage employment

Not all top urban performers are equally inclusive

District - Meerut

District - Agra

District - Lucknow

District - Bhopal

District - Nagpur District - Faridabad

Delhi

District - Jaipur

District - Kolkata

District - Mumbai Suburban

District - Pune

District - Bangalore

District - Chennai

District - Ludhiana

District - Kanpur NagarDistrict - Varanasi

District - Patna

District - Haora

District - Indore

District - NashikDistrict - Hyderabad

District - Ludhiana

District - Ahmadabad

District - Vadodara

District - Surat

District - Thane

.1.1

5.2

.25

.3.3

5M

LD

7.6 7.8 8 8.2 8.4Location Premium

Less productive and less inclusive More productive but less inclusive

Less productive but more inclusive More productive and more inclusive

nominal consumption based

Source: Based on Li and Rama (work in progress).

Cities spread their prosperity to nearby locations7

.57

.75

8

8.2

58

.58

.75

0 0-25 25-50 50-75 75-100 100-150 150-200Distance to a mega city (km)

Rural, 0-4999 Rural, 5000+

Urban, 0-1000000

Delhi – Faiderabad - Jaipur

Source: Based on Li and Rama (work in progress).

But not all cities do so to the same extent

Bangalore7

.57

.75

8

8.2

58

.58

.75

0 0-25 25-50 50-75 75-100 100-150 150-200Distance to a mega city (km)

Rural, 0-4999 Rural, 5000+

Urban, 0-1000000

Source: Based on Li and Rama (work in progress).