Asia Development Outlook 2018 How Technology Affects Jobs Comments - - PowerPoint PPT Presentation

Asia Development Outlook 2018 How Technology Affects Jobs

Comments motivated by the employment challenge in Sub-Saharan Africa

Jaime de Melo FERDI

Geneva, ILO Presentation April 17, 2018

How technology affected jobs in Asia

• Comprehensive upbeat report about employment prospects under current

technological change. Evidence-based

• Demand-side decomposition of employment gives net positive employment

change from technology-induced changes in labor demand: job creation in non- routine tasks exceeds job displacement by automation.

• Reasons for optimism:
• Decomposition shows large share of employment changes related to growth

in domestic demand (rather than trade that is slowing down)

• Evidence that technology creates new occupations and entire new

industries

• Use of robots associated with reduction in routine employment share but

increase in non-routine employment share

• Comparisons with other regions would be welcome
• Greater sector-focus as in Halward-Driemer-Nagyar 2017 WB report would help

get a handle on the transferability of Asian conclusions elsewhere (LA, SSA?)

• 1st. unbundling (cost of moving goods↓) Goods produced «here» and

consumed «there» but innovation local as cost of moving ideas is high. Asian Gang of 4 ELG via production becoming progressively more skill-intensive

• This globalisation phase established a middle class willing to pay taxes for the

proivision of public goods (Birdsall (2015))

• 2nd. unbundling (cost of moving ideas ↓-- ICT ‘revolution’ ) Concentration of

the ensuing great convergence to few countries (the I6 --China, Korea, India, Indonesia, Thailand, Poland). Control and coordination of production done «here» and actual production done «there». Unbundling via GVCs. Now quality of institutions matter for offshore implantation resulting in transfer of technological know-how. MNEs bet on preventing ‘knowledge spillovers’.

• Globalisation under ICT revolution has been cohesive (wages up ) in I6 group

(and a few more) as opposed to being divisive in old HICs

• 3rd. Unbundling (ongoing) when costs of moving labor ↓ (labor input no

longer in physical location. ADO 2018: sufficient complementarity of robots with non-routine jobs in Asia that prospects for employment positive in Asia.

• How relevant is this employment path for SSA’s employment challenge?

Aspects of success and transferability

Can SSA replicate Asia’s Performance (1)? SSA employment Challenge

• Mc Kinsey (2012): SSA to create 120 million jobs by 2020
• Pattern of employment across regions shows negligible and

stagnant shares of VA and manufacturing jobs in SSA (here)

Both Poverty and industry falling

• Decadal Poverty profiles Head count (HC) ratios show that SSA

was pulled by the I6-led ‘super commodity boom’(here)

• Poverty Reduction and GDP Growth: decadal rates show low

elasticity of poverty to growth in SSA(here)

• Early peaking of manufacturing and employment shares in SSA

(here)

Can SSA replicate Asia’s Performance (2)?

Current challenges: Labor is expensive

• On a comparative basis, labor is not cheap in SSA (here)
• PPP price level is high in SSA: Accounting for the Price Level

enigma in SSA (here)

Future challenges: Large migratory pressures

• n the horizon
• Insignificant contribution to CO2-emissions relative to other

regions (here)

• …but large projected damages by 2050 putting (here)

Concluding remarks

• Complementarity of tasks (routine, non-routine

cognitive) and imperfect substitution across categories of jobs

• ⇨ Conditions of assortative matching in

manufacturing i.e. (routine-low skill--SSA) and (high- skill cognitive--Asia) patterns emerge (Kremer O-Ring theory (1993)). To participate in 3rd. Unbundling:

• Increase human-capital to attract MNEs.
• Policies to raise share of middle-class in population

($10-50$ p.d. for a family of 4) now <2% in SSA to develop institutions that will attract GVC-related FDI.

Figures

Changing Distribution of Manufacturing and employment Across regions

Cha hangi nging ng distrib tributi ution

• n across

ss count ntries* ies*

Source: World Development Indicators database. Countries categorized by income level in 1994 Sources: ILOSTAT database, International Labour Organization (ILO); Key Indicators of the Labour Market (KILM) database, ILO; Groningen Growth and Development Centre (GGDC) 10-sector database, University of Groningen, Netherlands. HIC categorized by income level in 1994.

Manufacturing value added Manufacturing employment

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 1994 2000 2005 10 20 30 40 50 1990 2000 2010

*Source: Trouble in the Making: The Future

• f Manufacturing-led development

(back)

Poverty Headcount Ratio by Region, 1981-2011

9

Note: Sample: 101 countries. Poverty headcount ratio at 1.25$ per day (2005 PPP) Source: Cadot et al. (2016)

10 20 30 40 50 60 70 80 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Poverty Headcount East Asia & Pacific Europe & Central Asia Latin America & the Caribbean Middle East & North Africa South Asia Sub-Saharan Africa

(Back)

Poverty Reduction (HC) and GDP per capita Growth

(regional averages per period)

10

GDP per capita growth: (1980-1991) GDP per capita growth: (1991-2011) Note: Poverty line at 1.25$ per day (PPP). Sample of 101 countries ( 43 SSA). HC= head count. Source: Cadot et al. (2016). (Back)

EAP ECA LAC MENA SA SSA y = -3.5631x - 0.0406

• 15%
• 10%
• 5%

0% 5% 10% 15% 20%

• 2%
• 1%

0% 1% 2% 3% EAP ECA LAC MENA SA SSA y = -4.8361x - 0.4157

• 90%
• 80%
• 70%
• 60%
• 50%
• 40%
• 30%
• 20%
• 10%

0% 0% 2% 4% 6% 8%

Prospects for labor-intensive industrialization appear bleak

*Source: The Economist « From stuff to fluff » Can Africa reach middle class status by the development of industry?

(Back)

⇨ Prospects for SSA to replicate Asian model are dim: high population growth will translate in large pressure on the domestic labor markets and pression to migrate towards Europe.

Worldwide: R&D content of trade now accounts for half of value of trade in G&S and share of VA in trade down by 1/3

High labor costs in Sub-Saharan Africa seem to explain the lack of employment creation by the manufacturing sector

500 1000 1500 2000 2500 Zambia Tanzania Kenya Nigeria Bangladesh India GDP per capita (2005 $) Labor cost, annual

Source: Gelb et al. (2016)

AGO ETH GHA KEN MLI MOZ NGA SEN TZA UGA ZMB

2000 4000 6000 8000 5 6 7 8 9 GDP per capita (log) Other Countries Sub-Saharan Africa Fitted values Fitted values

(b) … a pattern confirmed by « regression analysis » ⇨ (a) Country comparisons show high manufacturing labor costs in selected SSA countries …

(Back)

SSA Price level enigma

(Back)

Price Levels vs GDP/head (economies with full data) Quadratic log-log estimate

Source: Gelb and Diofasi (2016) fig. 1b. Sample of 168 countries

Note: Income differences account for (2/3) [30%] of deviations (full sample) [SSA sample]…. SSA is outlier Other controls reduce gap by half to 15%… Contribution of Controls ⇒ Together, controls below reduce gap by half to 15%.

• Geographic characteristics

(Isolation, population density, size)

• Quality of institutions
• Subsidies to energy
• Oversampling of consumption

basket of HICs (proxies by income inequality) reduces gap from 30% to 25%

• 10% increase in AID/GDP

increases price level by 8%.

• Mismeasurement of GDP (60%

Ghana and 89% for Nigeria)

• Low agricultural productivity

raises price of food (25% of consumption basket—twice LA and Asia- Pacific).

CO2 emissions vs. Population shares

(regional averages)

• Bubbles proportional to total CO2 emissions (cement and fossil fuels).
• Regions below the 45 line have below-average per capita emissions.
• If converging CO2 emissions per capita, effort from North America, Europe and East Asia

Corneille, A. and J. de Melo (2016)

(Back)

Projected (2050) damages by region (no migration)

Damages measured as percentage of days with temperature outside 90th. Percentile

• f distribution of projected temperatures
• Strongest damages in SSA and SA (damage shares above 450 line)

⇒ If adaptation to climate change fails, strong migratory pressures from SA, SSA, EA ⇒ In absence of large redistribution of population across regions, climate-change related conflicts on the horizon. More details here

Source: Corneille, A. and J. de Melo (2016)

(Back)

References

Asia Development Outlook (ADO) 2018 “How Technology Affects Jobs”, ADB, Manilla Baldwin, Richard (2017) The Great Convergence: Information Technology and the New Globalisation, Harvard University Press Birdsall, N. (20115) « Dpes the Rise of the Middle Class Lock in Good Government in the the Developing World », European Journal of Development Research , 2015, vol. 27, issue 2, 217-229 Cadot, Olivier, Jaime de Melo, Patrick Plane, Laurent Wagner et Martha Tesfaye Woldemichael (2016) “Industrialisation and Structural Change : Can Sub-saharan Africa Develop without Factories », Revue d’Economie du Développement, n0. 2, 19-49. Corneille, A. and J. de Melo (2016) “Quelques défis de l’Afrique Sub-saharienne face au changement climatique” Ferdi Brève #165 Gelb, Alan ,Christian Meyer, et Viaya Ramachandran (2016) “Does Poor Mean Cheap ? A Comparative Look at Africa’s Industrial Labor Costs ”, Revue d’Economie du Développement, n0. 2, 51-92. Gelb, Alan, et Anna Diofasi (2016) “What Determines Purchasing-Power-Parity Exchange Rates », Revue d’économie du développement, n0. 2, 93-142 Hallward-Driemeier, M. and G. Navygar (2017) Trouble in the Making: The Future of Manufacturing-Led Development, Kremer, Michael “The O-ring of Theory of Development”. Quarterly Journal of Economics, 108 (3): 551–575