The changing nature of work and inequality

Kunal Sen

Background and Motivation• Concerns about the evolution of income inequality within countries have become an important element of

public debate all over the world.

• In the last decade, increasing attention has been paid to the concentration of incomes at the very top of the distribution―namely among the richest 1 per cent.

• However, this focus tends to overshadow the component of earnings inequality that arguably remains most consequential for ‘the other 99 percent’ of the population.

• Labour earnings represent by far the largest source of incomes for most people and thus importantly determine their relative income position.

• Even though the relationship between earnings and income inequality is mediated by tax-based redistributive policies, there are limits to what these can achieve if labour markets become highly unequal.

What Determines Earnings Inequality?• A long-standing literature has studied the effect of

skill biased technological change (SBTC) on wage inequality in developed countries (for example, Berman et al. 1998).

• This literature finds that SBTC increases the marginal productivity of skilled labour relative to that of unskilled labour, increasing the relative demand for skilled labour, and consequently, the wage premium on skilled labour.

Recent Literature: The Tasks Approach• More recently, labour economists have taken a more nuanced view of skill-biased

technological change, suggesting that SBTC does not necessarily lead to a depressed demand for all low skilled workers, but for workers involved in routine tasks that can now be executed by computer technologies.

• These are workers who are not necessarily low skilled, and are more likely to be middle skilled.

• At the same time, there may be higher demand for workers whose tasks are complementary to computerization, which are workers working in occupations where cognitive skills are required (Acemoglu and Restrepo 2018).

• This view of technological change puts ‘occupations at the forefront of the inequality debate since the task content of work (routine nature of the job, cognitive skills required) is typically measured at the occupational level’ (Firpo et al. 2011)

• Routine biased technological change (RBTC).

Predictions of the Task Based Model

Implications for Empirical Analysis• With computerization, there will be a diminished demand for routine tasks,

whether manual or analytic.

• In contrast, there will be an increased demand for analytic non-routine tasks, with no discernible effect on non-routine manual tasks.

• This would imply a hollowing out of the middle skilled jobs as compared to low and high skilled jobs.

• A stark prediction of the task model is that with occupational structure shifts, there would be increased wage polarization in the labour market.

Wage inequality in the United States (source: Autor 2019)

Shifts in occupational structure in the United States (source: Autor 2019)

Does this literature have any relevance for developing countries? • No. Countries in the developing world remain at a distance from

the technological frontier.

• Yes. Rapid spread of ICT and out-sourcing of tasks to developing countries.

• ‘The fragmentation of goods production has been associated with the outsourcing of not just manufacturing tasks but also services tasks, the back office of many US manufacturers is now in India’ (WDR 2020).

• An Empirical Question.

UNU-WIDER Project: The Changing Nature of Work and Inequality

• In 2019, UNU-WIDER launched a project – The Changing Nature of Work and Inequality – to undertake an in-depth analyses of the trends in earnings inequalities in eleven major developing countries and emerging economies, located throughout Africa, Asia, and Latin America. Please see https://www.wider.unu.edu/project/changing-nature-work-and-inequality

Core Research Questions• What are the main drivers of trends in earnings inequality in

different countries and regions in the Global South?

• How does the nature of earnings inequality in developing countries differ from the experience of developed countries?

• Is the changing nature of work making labour markets more polarized or unequal in the Global South?

Research teams and country selection

Latin America

1. Argentina (R. Maurizio, A.P. Monsalvo)

2. Brazil (S. Firpo, F. Riva)

3. Chile (G. Zapata)

4. Peru (P. Ballon, J. Dávalos) Africa

5. Ghana (C. Gradín, S. Schotte)

6. South Africa (H. Bhorat, A. Thornton & team)

7. Tunisia (M. Marouani, M. Marshalian, P.L. Minh)

Asia

8. Bangladesh (B. Sayema & team)

9. China (C. Xing)

10. India (K. Mahajan, S. Khurana)

11. Indonesia (A. Yusuf, P.R. Halim)

Global (P. Lewandowski, A. Park, S. Schotte)WIDER (C. Gradín, S. Schotte, K. Sen)

Contributions of the Project• Each country study provides an in-depth assessment of national trends in earnings inequality, which

are assessed against changes in the supply of higher skilled workers and education premia, on the one hand, and changes in the occupational structure and the remuneration of tasks, on the other, while being mindful of broader macroeconomic trends and institutional developments.

• To ensure the cross-country comparability of results, we harmonised national occupational codes to the international standard classification of occupations (ISCO-88).

• We also adopted a common methodological framework to investigate the main drivers that explain the trends in earnings inequality in each of the eleven countries.

• We are able to provide an unique, comparative assessment on how the nature of work is changing in eleven major developing countries and the role that these changes play in shaping societies.

Inequality trendsCountry 2000/05 -

2010/132010/13 -2015/19

ca 2000/05 ca 2010/13 ca 2015/19Year Gini Year Gini Year Gini Sample

Argentina Fall Rise 2003 0.39 2012 0.33 2019 0.34 EmployeesGhana Fall Rise 2005/06 0.57 2012/13 0.54 2016/17 0.57 Employees & non-agric. self-employedTunisia Fall Fall 2000 0.36 2010 0.32 2017 0.30 EmployeesIndia Stable Fall 2004 0.50 2011 0.50 2017 0.44 Urban workers (non-agric. sector)

Argentina

BrazilChilePeru

GhanaSouth Africa

TunisiaBangladeshChina

India

Indonesia

0.200.250.300.350.400.450.500.550.600.650.70

0.20 0.30 0.40 0.50 0.60 0.70

2010

/13

2000/05

Gini Index, 2000/05 - 2010/13

Argentina

ChilePeru

Ghana

South Africa

TunisiaBangladesh

ChinaIndia

Indonesia

0.200.250.300.350.400.450.500.550.600.650.70

0.20 0.30 0.40 0.50 0.60 0.70

2015

/19

2010/13

Gini Index, 2010/13 - 2015/19

Education

15

2003 - 2012 2012 - 2019

Primary education ↓↓ ↓↓Secondary education ↑ ↑Tertiary education ↑ ↑

Second. + University ↓ ↗

Share of workers

Education Premium

• Large increases in the level of education (secondary & tertiary) in all countries.

• This increase in supply was often not matched by increasing demand, implying a fall in the skill premium.

• Example Argentina:

Structural change• Substantial changes in employment, with:

– Increases in the share of workers in high-skilled occupations, decline in low-skilled (with a rebound in most recent years).

– Shift of workers out of agriculture, moving to manufacturing (Bangladesh, early in China) and, especially, services, and from self-employment to paid employment, in some cases.

– Shift of workers towards less routine occupations.

Measuring Routine Task Intensity (RTI)

• Routine Task Intensity (RTI) is defined as:

Trends in RTI

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Argentina Ghana India Tunisia

Country-specific RTI

2000/05 2010/13 2015/19

Argentina: Reduction in elementary occupations, rise in machine operators, sales and services workers, clerks, professionals/managers.

Ghana: Falling employment in agriculture and manufacturing, rising in services (trade, transport, hotels and restaurants), incl. high-skilled (education and public administration).

India: Rise in professionals/managers and services workers (mostly retail and tourism), fall in clerical jobs and machine operators. Elementary workers (esp. construction) increased post-2004.

Tunisia: Higher demand for elementary workers (laborer in construction and manufacturing) in the 2000s, and for skilled agricultural and protective service workers in 2010s.*

*Rise in RTI also observed in Chile, Bangladesh, China, Indonesia in 2000s.

Polarization in employment?• Weak evidence:

– U-shape: Bangladesh, China, Indonesia in 2000s; Peru, India (urban) in 2010s.

– Inverted U-shape: Brazil, Peru in 2000s; Bangladesh in 2010s.

– Positive: Ghana in 2000s.

– In many cases no significant relationship.

Change in (log) employment shareExample India:

Change in (log) mean earningsExample India:

Example Argentina:

Polarization in earnings?• Mixed evidence:

– U-shape: Brazil, Peru, Tunisia in 2000s; Argentina, South Africa, Indonesia in 2010s; Bangladesh, China over entire period.

– Inverted U-shape: Argentina, Chile, Ghana in 2000s; India in 2010s.

– Negative: Tunisia in 2010s.

What role for routinization in changes in employment and earnings?

21

• Weak evidence of the role of RTI (country-specific):

- Earnings:

• U-shaped pattern only detected in South Africa in 2010s.

• Positive relationship in India and Bangladesh in 2000s, and Chile and Tunisia over entire period.

• Inverted-U: India in 2010s.

- Employment:

• RTI insignificant in most cases.

• U-shaped pattern in Indonesia in 2000s, India in 2000s & 2010s.

• Inverted U: Chile in 2000s.

Gini decomposition—the role of changes in occupation shares and wage gaps

0.00.10.20.30.40.50.6

ca 2

000/

05

ca 2

010/

13

ca 2

015/

19

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015/

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Argentina Ghana India Tunisia

Gin

i ind

ex

Inequality between/within occupations

Between Within

• Mixed evidence:

– Example Ghana: Differences in earnings within occupations explain the bulk of earnings inequality & this feature has intensified over time.

– Example Tunisia: About half of the overall inequality can be attributed to the differences between occupations. The decline in inequality during the 1st period is entirely driven by within-occupation inequality, while the smaller decline in the 2nd period was primarily driven by lower between-occupation inequality.

Gini decomposition—the role of changes in occupation shares and wage gaps

0.00.10.20.30.40.50.6

ca 2

000/

05

ca 2

010/

13

ca 2

015/

19

ca 2

000/

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010/

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ca 2

015/

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ca 2

000/

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ca 2

010/

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ca 2

015/

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Argentina Ghana India Tunisia

Gin

i ind

ex

Inequality between/within occupations

Between Within

-0.06

-0.04

-0.02

0.00

0.02

0.04

2005

/06

-20

12/1

320

12/1

3 -

2016

/17

2005

/06

-20

12/1

320

12/1

3 -

2016

/17

2005

/06

-20

12/1

320

12/1

3 -

2016

/17

2005

/06

-20

12/1

320

12/1

3 -

2016

/17

Argentina Ghana India Tunisia

Chan

ge in

Gin

i ind

ex

Decomposition (between-group)

Change in empl. shares Change in mean inc.

Gini decomposition—the role of changes in occupation shares and wage gaps• Narrowing gap in average earnings

across occupations in most cases. Equalizing effect of changes in remuneration of job characteristics (e.g., skills and tasks).

• Evidence on inequality effect associated with changes in employment shares across occupations more mixed. E.g., inequality increasing in Ghana, decreasing in India.

-0.06

-0.04

-0.02

0.00

0.02

0.04

2005

/06

-20

12/1

320

12/1

3 -

2016

/17

2005

/06

-20

12/1

320

12/1

3 -

2016

/17

2005

/06

-20

12/1

320

12/1

3 -

2016

/17

2005

/06

-20

12/1

320

12/1

3 -

2016

/17

Argentina Ghana India Tunisia

Chan

ge in

Gin

i ind

ex

Decomposition (between-group)

Change in empl. shares Change in mean inc.

Concentration index—ranking jobs by average earnings vs. RTI

• The two occupation rankings are highly similar, as indicated by the concentration ratios (above 70% in most cases).

• However, the relationship between RTI and average earnings has weakened in most cases over the study period, esp. during the 2010s. Relationship is non-monotonic, and the least routine occupations are not necessarily the best-paid ones (and vice versa).

80 %

75 % 78 % 73 % 80 %

46 %

84 % 90 %

88 % 94 % 93 %89 %

0.00.10.10.20.20.30.30.40.4

ca 2

000/

05

ca 2

010/

13

ca 2

015/

19

ca 2

000/

05

ca 2

010/

13

ca 2

015/

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ca 2

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Argentina Ghana India Tunisia

Conc

entr

atio

n in

dex

Gini between occupations RTI (country-specific)

Methodology: RIF Decompositions• The methodology was developed by Firpo et al. (2007, 2009, 2018) to quantify the role played by different

variables in explaining the change in inequality at different points in the earnings distribution.

• This extends the Oaxaca–Blinder decomposition since each variable’s contribution to the change in earnings is decomposed into a composition effect and a wage structure effect at each percentile of the earnings distribution rather than for average earnings.

• It allows us to quantify the extent to which changes in inequality over time can be attributed to changes in the distributions of worker characteristics and changes in the remuneration to these characteristics.

• Detailed decompositions allow us to look at the role of RTI.

Detailed Composition Effect

Detailed Earnings Structure Effect

2003-2012: INEQUALITY FALLEqualizing impact of:

COMP. Increase in share of formalityCOMP. Fall in RTI

Unequalizing impact of:EARN. Increase in gender wage gap

2012-2019: INEQUALITY INCREASEUnequalizing impact of:

EARN. Increase in formality wage gapEqualizing impact of:

COMP. Fall in RTI (less intense)

2003-12 2012-19Gini change -0.097*** 0.021***Composition -0.019*** -0.001

Earnings Str. -0.078*** 0.022***

RIF decomposition Argentina

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

Chan

ge in

ln e

arni

ngs

quantile

2005/06 - 2012/13

age sex education ethnic rti

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

Chan

ge in

ln e

arni

ngs

quantile

2012/13 - 2016/17

age sex education ethnic rti2006-13 2013-17

Gini change -0.028 0.021Composition 0.001 -0.006Earnings Str. -0.029 0.027

RIF Earnings Structure: Ghana

RIF Earnings Structure: India2004-2011 2011-2017

Change in Gini -0.001 -0.059Composition -0.001 0.002

Earnings Structure 0.000 -0.061

Detailed Earnings Structure across Quantiles (Urban Non-Agri Paid)

Change in Workforce structure not playing much role in explaining wage inequality decline

RIF Earnings Structure: Tunisia

Conclusions• Earnings inequality trends in developing countries have been quite heterogenous (no

universal trend)• Despite large transformation of the workforce, the role of compositional changes by

occupation or education do not seem to explain inequality trends• In most cases, trends in inequality (up or down) are primarily explained by changes in

the earnings structure instead• Traditional explaining factors of trends in earnings inequality (esp. changes in

education premium) remain relevant in cases studied, with weak evidence of job polarization or earnings associated with RTI playing a substantial role.

• Potential influence of other local factors like labour institutions (e.g. minimum wage).• SBTC still the key factor explaining earnings inequality in developing countries, more

than RTBC

Appendix

Key findings Argentina• Fall in inequality from 2003-2012, followed by small increase up to 2019.

– Both trends observed for inequality between- and within occupations (but inequality within occupations contributes largest share to total inequality).

• Occupational change: Non-significant pattern (neither polarization nor inv.-U).

– In 2000s: Increase in formal jobs; fall in employment shares in low-paying occupations; shift toward less routine work.

• Earnings: Equalising growth pattern (inv.-U) in 2000s, polarising pattern in 2010s.

– Decline in education premium in 2000s, followed by some increase in 2010s.

– Strong rise in minimum wage from 2003-2011, followed by fall in real terms.

Key findings Ghana• Shift towards jobs demanding higher skills and less routine tasks:↓ average RTI.

• Trend in inequality is primarily explained by changes in the earnings structure, while the composition effect is small. – 2005/06-12/13: substantial decline in the education premium (↑ level of education

across workers) ↔ inequality declines.

– 2012/13-16/17: slow-down in decline of education premium (smaller equalizing effect) + disequalizing effect of changes in the remuneration of non-routine jobs ↔inequality increases.

• Implications: development process has not implied real structural transformation. Low productivity in routine jobs can be highly disequalizing and need to be addressed. Inequality could increase further if supply does not keep pace with future higher demand for skills.

Key findings India• Wage inequality (urban): Stable from 2004-11, fell from 2011-17.

• Compositional changes in occupational structure had a small effect:

– Jobs requiring routine tasks declined (esp. 2004-11) job polarization.

• Inequality trends mainly attributable to changes in returns = earnings structure Higher earnings growth in occupations with lower initial wages post-2011:

– Returns to RTI (equalizing inv. U-shape pattern);

– But: Returns to education (disequalizing);

– Large part remains unexplained by the model. Institutional factors like changes in minimum wage may have played a role.

Key findings Tunisia• Fall in inequality from 2000-10 and 2010-17.

• Occupational change:

– Different to most other cases, RTI increased over the study period: Higher demand for elementary workers (construction and manufacturing) in the 2000s, and for skilled agricultural and protective service workers in 2010s.

• Earnings:

– Changes in average occupational earnings (esp. rise in lowest wages) are main driver of fall in inequality.

– Changing remuneration of tasks (RTI) played a role, but employment and wage policies in the public sector were key drivers.