Global Productivity

Page 295

GLOBAL PRODUCTIVITY

CHAPTER 5

269

learning outcomes are poor (World Bank 2018a). In learning-adjusted terms, which controls for the quality of education in addition to years of attainment, SAR and SSA lag substantially (six or more learning-adjusted years) behind advanced economies. Higherskilled and better-educated labor forces tend to adopt new technologies, including new information and communication and manufacturing technologies, more readily and more effectively (World Bank 2019b). Trade integration. LAC, SAR, and SSA could receive a productivity boost from more participation in global trade, particularly through deeper integration into global value chains (GVCs). EAP, meanwhile, faces maturing supply chains and has the challenge of maintaining the productivity gains it achieved through rapid trade integration in the 2000s. Regions deeply integrated into GVCs (EAP and ECA) may also experience weaker productivity should companies reassess the existing production networks, or even re-shore production, in the context of COVID-19 (Freund 2020; World Bank 2020a).

East Asia and Pacific Before the COVID-19 pandemic, EAP had the fastest productivity growth of the six regions, averaging 6.1 percent a year in 2013-18. Nevertheless, productivity levels remain below the EMDE average in most EAP economies. Factor reallocation toward more productive sectors, high levels of investment, and trade integration promoted above-average productivity growth. Most of these drivers are expected to become less favorable in the future, however, and the pandemic could further weaken investment and the supply chain linkages that have been an important conduit for productivity gains in the region over the past decade. A comprehensive set of reforms to liberalize services sectors, improve corporate management, level the playing field for private firms, enhance human capital, facilitate urban development, foster innovation, and build resilience against future unexpected shocks is needed to support robust productivity growth.

Evolution of regional productivity Rapid productivity growth. Labor productivity growth in EAP rose from an average of 4.3 percent a year in the 1980s to 6.3 percent in the 1990s and 8.9 percent in 2003-08 (figures 5.4 and 5.5).4 Although productivity growth in the region remained the highest of the six EMDE regions, it slowed decisively following the GFC, averaging 6.1 percent per year during 2013-18.5 The post-GFC productivity growth slowdown was also 4 Productivity data are available for 16 EAP countries: Cambodia, China, Fiji, Indonesia, Lao People’s Democratic Republic, Malaysia, Mongolia, Myanmar, Papua New Guinea, the Philippines, Samoa, the Solomon Islands, Thailand, Tonga, Vanuatu, and Vietnam. EAP averages are heavily influenced by China, which accounts for 80 percent of EAP output in 2013-18. That said, even the median productivity level in EAP is below that of the median EMDE region. 5 For studies using country-level data, see APO (2018); IMF (2006, 2017); and World Bank (2018b, 2019a). For studies using firm-level data, see Di Mauro et al. (2018); de Nicola, Kehayova, and Nguyen (2018); OECD (2016); and World Bank and DRCSC (2019). For studies of how product and labor market reforms have increased output and productivity, see Adler et al. (2017); Bouis, Duval, and Eugster (2016); Chen (2002); Nicoletti and Scarpetta (2005); and Timmer and Szirmai (2000).


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Annex 7B Marginal productivity gap

4min
pages 452-453

References

14min
pages 456-463

Annex 7A Data and methodology

6min
pages 448-451

References

13min
pages 421-428

Sectoral productivity gaps

2min
page 432

Annex 7C Firm TFP data, estimates, and methodology

5min
pages 454-455

Annex 6C Commodity-driven productivity developments: Methodology

2min
page 420

Conclusion and policy implications

2min
page 412

Drivers of productivity: Technology vs. demand shocks

2min
page 391

Annex 6A SVAR identification of technology drivers of productivity

8min
pages 413-416

PART III Technological Change and Sectoral Shifts

0
pages 383-386

Effects of demand shocks

2min
page 397

Figure 6.1 Global labor productivity surges and declines

7min
pages 388-390

Sub-Saharan Africa

2min
page 350

Figure 5.22 Factors supporting productivity growth in MNA

7min
pages 333-335

Figure 5.19 Drivers of productivity growth in LAC

9min
pages 325-328

South Asia

4min
pages 337-338

Conclusion

2min
page 363

Figure 5.13 Drivers of productivity growth in ECA

10min
pages 314-317

Middle East and North Africa

2min
page 329

Latin America and the Caribbean

2min
page 318

Figure 5.12 Drivers of productivity growth in ECA in regional comparison

5min
pages 312-313

Europe and Central Asia

2min
page 305

Figure 5.7 Drivers of productivity growth in EAP

3min
page 301

PART II Regional Dimensions of Productivity

0
pages 281-284

Sources of, and bottlenecks to, regional productivity growth

4min
pages 290-291

Figure 5.1 Evolution of regional productivity in EMDE regions

4min
pages 288-289

East Asia and Pacific

2min
page 295

References

12min
pages 274-280

Evolution of productivity across regions

2min
page 287

Annex 4F Productivity measurement: PPP vs. market exchange rates

4min
pages 268-269

Annex 4C Beta-convergence testing

2min
page 257

Figure 4.4 Convergence club memberships

2min
page 242

Annex 4D Estimating convergence clubs: Commonalities in productivity levels

7min
pages 258-260

Testing for convergence and its pace

4min
pages 236-237

Conclusion and policy implications

7min
pages 253-255

Convergence clubs

7min
pages 239-241

Annex 3B Robustness

2min
page 213

Conclusion

2min
page 204

Figure 3.8 Episodes across different types of events

4min
pages 193-194

Annex 3A Data, sources, and definitions

2min
page 206

How has productivity convergence evolved?

2min
page 231

Figure 3.4 Episodes of war

2min
page 187

What policies can mitigate the effects of adverse events?

2min
page 203

Figure 3.5 Correlations between war frequency and productivity growth

7min
pages 188-190

Figure B3.1.1 Severity of pandemics, epidemics, and climate disasters

6min
pages 179-181

Figure B3.1.3 Impact of epidemics

6min
pages 184-186

Annex 2A Partial correlations

2min
page 146

Figure 3.2 Episodes of natural disaster

4min
pages 175-176

Box 3.1 How do epidemics affect productivity?

1min
page 178

Adverse events: Literature and stylized facts

2min
page 171

Conclusion

2min
page 145

Figure 2.13 Developments in financial and government technology

2min
page 143

Figure 2.12 EMDE infrastructure and education gaps

2min
page 142

Policy priorities

4min
pages 140-141

Figure 2.11 Post-GFC slowdown of the drivers of productivity growth

10min
pages 136-139

References

12min
pages 101-108

Analyzing the effects of drivers

1min
page 128

Developments in drivers of productivity

2min
page 134

Figure 2.1 Innovation

5min
pages 114-115

Box 2.1 Review of recent firm-level total factor productivity literature

8min
pages 130-133

Summary of stylized facts

2min
page 126

Long-run drivers

4min
pages 112-113

Box 1.1 Productivity: Conceptual considerations and measurement challenges

9min
pages 85-88

Conclusion

2min
page 96

Annex 1A Cyclical and technology-driven labor productivity developments

1min
page 100

Figure B1.1.1 Labor productivity decomposition and natural capital in EMDEs

7min
pages 89-91

References

13min
pages 65-70

Key findings and policy messages

4min
pages 32-33

Future research directions

2min
page 64

Synopsis

2min
page 39

PART I Productivity: Trends and Explanations

0
pages 71-74

Evolution of productivity

2min
page 78

Sources of the slowdown in labor productivity growth after the GFC

2min
page 83

Implications of COVID-19 for productivity

11min
pages 34-38
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