Global Productivity

Page 142

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CHAPTER 2

GLOBAL PRODUCTIVITY

FIGURE 2.12 EMDE infrastructure and education gaps Infrastructure needs to meet the Sustainable Development Goals are highest in SSA. While education gaps, measured as years of schooling, are closing in many regions, they remain large in SAR and SSA. The gaps with advanced economy levels are even larger after adjusting for educational quality. A. Infrastructure gaps

B. Years of schooling and learning-adjusted years of schooling Years 16 14 12 10 8 6 4 2 0

Expected years schooling Learning-adjusted years of schooling

AE

EAP

ECA

LAC

MNA

SAR

SSA

Sources: Rozenberg and Fay (2019); World Bank, Human Capital Project. Note: AE = advanced economy; EMDEs = emerging market and developing economies; EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MNA = Middle East and North Africa; SAR = South Asia; SSA = SubSaharan Africa. A. Investment and maintenance needs based on the Sustainable Development Goals as set out in Rozenberg and Fay (2019) including both new investment and maintenance of the existing capital stock. Infrastructure investment includes investment in electricity, transport, water supply and sanitation, flood protection, and irrigation. Preferred is defined as the infrastructure “pathway that limits stranded assets, has a relatively high per capita consumption due to electric mobility, and invests mostly in renewable energy and storage.” B. GDP-weighted expected years of schooling and learning-adjusted years of schooling from the World Bank’s Human Capital Project. Learning-adjusted years of schooling use harmonized cross-country test scores to adjust average years of schooling.

to encourage the use of financial technology (“fintech”) products in regions where few adults have access to traditional banking products and sources of finance (figure 2.13; IMF and World Bank 2019). Invest in human capital. Educational gaps with advanced economies are largest in SAR and SSA. Compared to advanced economies, average years of schooling are three years lower in SAR and five years lower in SSA. On adjusting for differences in the quality of education, these gaps increase to eight and nine years, respectively (figure 2.12). This suggests that public schooling reform should be a priority in these regions. Tailored interventions could be used to improve school attendance, provide student grants and prizes, support nutrition programs for early childhood development, upgrade teacher training, foster teacher accountability and incentivize performance. If EMDEs were to close half the gap in educational attainment between them and advanced economies, that could raise the annual growth rate by about 0.2 percentage points (figure 2.14). Better health also increases human capital. By 2017, average life expectancy at birth in EMDEs had risen to 70 years, from 50 years in 1960. This is striking progress, yet average EMDE life expectancy remains about 10 years below the average for advanced economies (81 years). Continued improvements in access to clean water, adequate sanitation, and health care would improve well-being substantially as well as raise


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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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