Measuring Labor Productivity for Rural

Workers

Ellen McCullough, University of Georgia

Presentation to RULIS consultation8 November, FAO, Rome, Italy

Why Labor Productivity?

• 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?

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

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

Labor Data and the SDGs

𝐿𝐿𝐿𝐿 =𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑅𝑅𝑡𝑡 𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡𝑅𝑅𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡𝑅𝑅 𝐼𝐼𝑅𝑅𝐼𝐼𝑅𝑅𝑅𝑅𝑅𝑅

• 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

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

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

Per-person labor shares calculations are relatively similar across data sources

Labor Shares

However, hours based measures offer lower shares than participation based measures

Labor Shares

… agricultural workers work fewer hours per year

Employment Gaps

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

Productivity differentials much smaller after accounting for hours worked

Productivity Gaps

Productivity differentials much smaller after accounting for hours worked

Productivity Gaps

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.

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

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

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?

Additional issues

• Externalities • Child labor and

foregone education

• Gender and nutrition• Child care time• Rethinking 24 hour

recall measures of water and firewood