This document discusses measuring labor productivity for rural workers. It notes that poverty reduction is associated with increases in returns to labor and employment levels. Accurately measuring labor productivity requires carefully tracking labor inputs, time worked, and other inputs. Estimating the marginal productivity of labor is challenging but important for a full assessment of income. The document highlights gaps in data on livestock labor, non-farm self-employment labor, and unpaid family labor outside of crop production. It emphasizes generating the best indicators possible given available data to track progress on sustainable development goals related to employment, income, and living standards.

1. Measuring Labor
Productivity for Rural
Workers
Ellen McCullough, University of Georgia
Presentation to RULIS consultation
8 November, FAO, Rome, Italy

2. Why Labor Productivity?

3. • The poor are endowed with labor
• Poverty reduction is strongly associated with
growth in returns to labor and employment
levels
• Changing patterns of labor supply are associated
with development process (structural change)
Why Labor Productivity?

4. As Seen in Research…
• Structural change
• Occupational choices and returns to labor
• Impact Evaluation
• Key outcome along the impact pathway
• Efficiency and technology
• Understanding labor as major factor of production
• Human capital
• Understanding heterogeneity in returns to labor
• Demographic transitions
• Aging of agriculture labor force, unemployed youth in cities
• Income smoothing
• Livelihood specialization and diversification
• Intervention targeting
• Understanding when labor is scarce and slack

5. Important types of labor earnings
in developing countries
• Farming self employment
• Self produced or non-traded inputs
• Substitutability hired labor
• Auto-consumed or non-traded outputs and inputs
• Farming wage employment
• Non-farming self employment
• Enterprise accounting
• Non-farming wage employment
Integrating GPS and self-reported measures of
land area in
household surveys

6. Labor Data and the SDGs
𝐿𝐿𝐿𝐿 =
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑡𝑡𝑡𝑡 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
• Types of Labor:
• Self Employment (farm)
• Self Employment (non-farm)
• Wage Employment (farm)
• Wage Employment (non-farm)
• Good “numerator” and “denominator” estimates are relevant for
all SDGs:
• Poverty, Decent work
• Hunger, Health, Education, Gender Equality, Water/Sanitation, Energy,
Infrastructure, Inequality, Sustainable cities, Sustainable
production/consumption Reduced Inequalities
Income sources
Employment

7. Concepts
• ALP (annual): Annual Output per worker
• Requires careful measure of returns to labor
• ALP (hourly): Returns per hour worked
• Requires careful measure of labor inputs
• Marginal revenue product of labor:
• Requires estimation of full production function (for farm and/or
non-farm enterprise, or the firm demanding labor)
• Input use is a selection variable, including (especially) time
• Wages may not equal the marginal revenue product of labor
Implications:
- Measure labor inputs carefully (intensive margin)
- Track other inputs, especially human and financial capital

8. Some Evidence
• Labor inputs differ across activities
• It matters if you look at returns per person per year
vs per hour worked
• Estimating marginal labor productivity is not
straightforward!!!
• But if you want a “full income” approach this is required

9. Per-person labor shares calculations are relatively similar
across data sources
Labor Shares

10. However, hours based measures offer lower shares than
participation based measures
Labor Shares

11. … agricultural workers work fewer hours per year
Employment Gaps

12. Cross-sector productivity differentials are large
… though not as large as national accounts based measures
Micro
gaps
National
Acct
Gaps
Productivity Gaps
National Account Gaps from Gollin et al, 2014

13. Productivity differentials much smaller after
accounting for hours worked
Productivity Gaps

14. Productivity differentials much smaller after
accounting for hours worked
Productivity Gaps

15. Prioritizing Data Needs
• Top down (driven by indicator list)
vs
• Bottom Up (driven by data availability)
Focus on what you can measure well
Generate the best indicators you can with the data
you have, given your tolerance for assumptions.

16. Data Coverage
• SDGs are based on changes in indicators. This requires:
• Time series within countries
• Comparability over time
• The more countries the better, but the time dimension is
needed
• Labor productivity is about people – surveys must
represent the population, not only farmers, or
“subsistence” farmers
• Build on existing survey platforms used for research and
indicator tracking
• fill gaps strategically
• use geolocation for data matching (to lighten the burden)
• Livestock and self employment labor are gaps

19. From Data to Indicators
• Need a RIGA (or RIGA-like) function!!!
• Generate indicators using scripts from a processed micro
dataset
• Make assumptions clear (e.g. hours per day)
• “Best” aggregate vs comparable aggregate (e.g. recall
ag family labor in hours or days)
• Valuing family labor and other non-purchased inputs?
• Technically need to estimate production functions, this is a
serious undertaking. Challenge of net income measures…
• Classifying employment (strict vs relaxed).
• Want to be ILO compatible
• Also want to be appropriate to context

20. Indicator List
• Focus on labor inputs (participation, time)
• Focus on labor returns (income, income shares, wages)
• Careful with overly prescriptive definitions of
employment
• Most returns to labor generated through self employment
• Underemployment disguised as self employment in farming,
services
• Is all casual labor precarious?
• “Unpaid family labor” only tracked for crop production.
• What about non-farm self employment labor? Livestock?
Other family labor?

21. Additional issues
• Externalities
• Child labor and
foregone education
• Gender and nutrition
• Child care time
• Rethinking 24 hour
recall measures of
water and firewood