LEAPFROGGING INTO THE UNKNOWN SOUTHEAST ASIA AND THE FUTURE OF JOBS Lukas Schlogl 12 September 2019, Bangkok University of Vienna @LukasSchlogl
Outline 1. Trends in structural transformation (ST) Tertiarization and convergence 2. Future employment challenges Technological catch up during late development 3. Conclusions University of Vienna University of Vienna University of Vienna @LukasSchlogl @LukasSchlogl @LukasSchlogl
Employment across sectors Agriculture Industry Services 100 100 100 75 75 75 50 50 50 25 25 25 0 0 0 1990 2010 2030 2050 1990 2010 2030 2050 1990 2010 2030 2050 HIC LIC LMC UMC (ILO modelled estimates. Dotted lines are projections based on the linear trend. Country aggregates are based on population-weighted averages.) University of Vienna @LukasSchlogl
Value addition across sectors Agriculture Industry Services 100 100 100 75 75 75 50 50 50 25 25 25 0 0 0 1990 2010 2030 2050 1990 2010 2030 2050 1990 2010 2030 2050 HIC LIC LMC UMC (ILO modelled estimates. Dotted lines are projections based on the linear trend. Country aggregates are based on population-weighted averages.) University of Vienna @LukasSchlogl
Structural convergence • Expansion of service-sector jobs at expense of industrial (HICs) and agricultural (DCs) jobs. Little potential left in HICs for cross-sector ST. • Industrial employment shares increasingly detached from development level. Congo, Bangladesh, Mexico, Finland have ~20%-25% employment in industry. • In OECD, industrial employment peaked in 60s/70s at ~30%. If path is followed, UMICs reach Clark turning point in 2030s, LMICs in 2040s. • Structural change fastest in LICs (Merotto, Weber, & Aterido, 2018) University of Vienna @LukasSchlogl
The trend in Southeast Asia Movement of labour across sectors following similar pattern: • Employment in agriculture contracting • Service sector expanding • Industrial employment mixed • Most of SEA still shows expanding employment in industry • Rich SEA contracting: Malaysia reached ‘Clark turning point’ in late 90s, Brunei in early 90s, Singapore earlier. Singapore service sector share now higher than UK or US. So, business as usual? University of Vienna @LukasSchlogl
Enter the Robots. University of Vienna @LukasSchlogl
Automatable work across SEA World Bank (2018) McKinsey (2017) 30,000 90,000 Singapore GNI per capita 20,000 Malaysia 60,000 Philippines Malaysia Thailand 10,000 30,000 Indonesia Cambodia Thailand 0 0 40 50 60 50 60 70 80 90 Share of the labour force susceptible to automation University of Vienna @LukasSchlogl
% of automatable jobs (estimates) WB (2016) MGI (2017) Cambodia 78% Indonesia 52% Malaysia 68% 51% Philippines 48% Singapore 44% Thailand 72% 55% University of Vienna University of Vienna @LukasSchlogl @LukasSchlogl
Industrial robot density low but growing fast 1 Human Development Index R² = 0.6 0.9 Singapore 0.8 Malaysia Thailand 0.7 Indonesia Philippines 0.6 0 200 400 600 800 Robots per 10k manufacturing workers (IFR estimate for 2016) University of Vienna @LukasSchlogl
Robot sales: prices ↓ demand ↑ 3.1 million industrial robots in operation globally by 2020 University of Vienna @LukasSchlogl
A trend towards service automation • Indonesian SOE Jasa Marga: phased in e-toll system in 2017 (GOI mandated it) • Unions feared loss of 20,000 jobs – JSMR denies layoffs • “Although we do not expect the majority of workers to leave JSMR (…), personnel expense growth should still be limited, as the A-Life [task rotation] program should reduce the need for additional recruitment in the coming years .” (Mirae Asset 2018) University of Vienna @LukasSchlogl
Meanwhile, 40 miles from Silicon Valley... Toll Gates at Oakland Bay Bridge “Gutierrez and other collectors sometimes worry that the FasTrak lanes could replace tollbooth operators altogether. Gutierrez says it’s a question that has been coming up repeatedly at union meetings over the last three months. “The union claims that if it does come to that, they’ll help us get to another job site,” Gutierrez says. But CalTrans officials say — and Gutierrez agrees — that it is just a rumor. No official proposal has been laid out or discussed.” ( Waheed 2012) University of Vienna @LukasSchlogl
Late developers, early adopters McDonald ’s e-Kiosks in Indonesia • Weak labour protection might make adoption of labour- displacing technology easier in countries where labour costs would otherwise provide little incentive for it University of Vienna @LukasSchlogl
Late developers, early adopters SEA: Rapid rise of gig economy HICs: Banning Uber… University of Vienna @LukasSchlogl
What’s the trouble? University of Vienna @LukasSchlogl
Lots of agricultural work, despite ST OECD ASEAN-5 (%, OECD, Employment share) (%, ASEAN-5 (simple ave.), Employment share) 100 100 75 75 50 50 25 25 0 0 1991 1996 2001 2006 2011 2016 1991 1996 2001 2006 2011 2016 Agriculture Industry Services Agriculture Industry Services University of Vienna University of Vienna @LukasSchlogl @LukasSchlogl
Premature deindustrialization? Manufacturing (% of value added, constant prices) 40 Korea China (1960-2011) (1960-2010) 35 Thailand (1951-2011) Malaysia 30 (1970-2011) 25 Indonesia (1960-2012) 20 15 10 Philippines 5 (1971-2012) 0 6.5 7 7.5 8 8.5 9 9.5 10 10.5 GDP per capita (2011 PPP dollars, log) Source: GGDC 10 Sector Database University of Vienna University of Vienna @LukasSchlogl @LukasSchlogl
R&D expenditure (%, R&D expenditure/GDP) 3 2 1 0 OECD (2015) Malaysia Thailand Vietnam (2013) Philippines Indonesia (2015) (2015) (2013) (2013) University of Vienna University of Vienna @LukasSchlogl @LukasSchlogl
Skills (%, Tertiary school enrolment rate) 80 70 60 50 40 30 20 10 0 OECD (2016) Thailand Malaysia Philippines Vietnam (2016) Indonesia (2015) (2016) (2017) (2016) Source: World Bank University of Vienna University of Vienna @LukasSchlogl @LukasSchlogl
Demographic dividend? HICs SE Asia Source: UN World Population Prospects University of Vienna University of Vienna @LukasSchlogl @LukasSchlogl
But: stable labour force participation 100 80 60 40 20 0 1990 1995 2000 2005 2010 2015 Indonesia Malaysia Philippines Singapore Vietnam Cambodia Thailand Source: ILO University of Vienna @LukasSchlogl
Technological catch-up or automation creep? • Gerschenkron’s (1962) ‘advantages of backwardness’: “ It makes sense for latecomers to utilise all the resources from the advanced world that they can acquire” ( Mathews 2006). T oday’s parlance: “ successful economic development involves a leapfrogging process” (Burlamaqui and Rainer Kattel 2014). Also a major theme in NSE (Lin). • Robotisation pressing in tradeable sectors where GVC suppliers face international competition and risk of Reshoring But… • Rodrik (2018, p. 14) argues that “The evidence to date, on the employment and trade fronts, is that the disadvantages [of new technologies] may have more than offset the advantages” for developing countries due to erosion of low-cost labour advantage. • Brynjolfsson and McAfee (2014, p. 184) argue that the “biggest effect of automation is likely to be on workers (…) in developing nations that currently rely on low -cost labor for their competitive advantage ”. University of Vienna @LukasSchlogl
Late Development, Early Adoption 1. Directed technological change • Labour saving innovation driven by firms innovating in a high-skill, high-cost, high-social-protection environment of HICs 2. Innovation as global public good • Innovation hard to contain in a hyper-globalised, digitally connected world (‘automation creep’) unless concentrated political resistance 3. Undirected early adoption • Selective early adoption during late development despite low-skilled, low- cost, low-social protection environment. 4. Specialisation towards comparative disadvantage? • Less inclusive development, a smaller middle class, less democracy? University of Vienna @LukasSchlogl
The near term future of ST in SEA … will be characterised by: • Continued expansion of service sector employment • MICs reaching Clark turning point, starting job deindustrialisation • More technological convergence unless labour protectionism … will raise the following questions: • Should technological adoption be commensurate to level of econ development? Can you automate prematurely? • Can the labour force be upskilled fast enough? • Does automation require a specific (social) policy context? University of Vienna @LukasSchlogl
Thank you! Forthcoming with Palgrave as OA: Disrupted Development. The Future of Industrialisation and Inequality in the Age of Automation (Schlogl & Sumner 2019) lukas.schloegl@univie.ac.at University of Vienna @LukasSchlogl
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