Computerizing industries and routinizing jobs: Explaining trends in aggregate productivity

Aggregate productivity growth in the U.S. has slowed down since the 2000s. We quantify the importance of differential productivity growth across occupations and across industries, and the rise of computers since the 1980s, for the productivity slowdown. Complementarity across occupations and industries in production shrinks the relative size of those with high productivity growth, reducing their contributions toward aggregate productivity growth, resulting in its slowdown. We find that such a force, especially the shrinkage of occupations with above-average productivity growth through \routinization," was present since the 1980s. Through the end of the 1990s, this force was countervailed by the extraordinarily high productivity growth in the computer industry, of which output became an increasingly more important input in all industries (\computerization"). It was only when the computer industry's productivity growth slowed down in the 2000s that the negative effect of routinization on aggregate productivity became apparent. We also show that the decline in the labor income share can be attributed to computerization, which substitutes labor across all industries

A step into the world of Pakistanis: oral health education for Pakistani adults in Hong Kong

Includes bibliographical references (p. 32).Questionnaire in English and Urdu.published_or_final_versio

Inequality of Fear and Self-Quarantine: Is There a Trade-off between GDP and Public Health?

We construct a quantitative model of an economy hit by a pandemic. People choose occupations and make work-from-home decisions to maximize income and minimize their fear of infection. Occupations differ by wage, infection risk, and the productivity loss when working from home. The model is calibrated to South Korea (SK) and the United Kingdom (UK) to compare SK’s intensive testing and quarantine policy against UK’s lockdown. We find that SK’s policies would have worked equally well in the UK, dramatically reducing both deaths and GDP losses. The key contrast between UK’s lockdown and SK’s policies was not in the intensity of testing, but weak restrictions on the activity of many (UK) versus strict restrictions on a targeted few (SK). Lockdowns themselves may not present a clear trade-off between GDP and public health either. A premature lifting of the lockdown raises GDP temporarily, but infections rise over time and people voluntarily choose to work from home for fear of infection, generating a W-shaped recession. Finally, we find that low-skill workers and self-employed always lose the most from both the pandemic itself and containment policies