Land Labour Productivity (Franck Cachia, Global Strategy)

Measuring Labour and Land Productivity in Agriculture

Franck Cachia - Global Strategy to improve agricultural and rural statisticsGSARS - www.gsars.org

Expert Consultation – RuLIS(FAO, Rome, 7-8 November 2016)

http://www.gsars.org/

1- Importance of labour productivity

Higher production/livelihoods

Higher wages and incomes

• SUSTAINABLE DEVELOPMENT GOALSo Indicator 2.3.1: “Volume of production per labour unit by classes of

farming/pastoral/forestry enterprise size”o Indicator 2.3.2: “Average income of small-scale food producers, by sex and

indigenous status”• MALABO DECLARATION: agricultural productivity x2 by 2025 in Africa

LABOUR IS THE MAIN INPUT IN MOST FARMS OF DEVELOPING COUNTRIES

Higher labour productivity generally

leads to

Presenter

Presentation Notes

Labour input often represents more than 50% of total costs of production, esp. in developing countries Higher production => higher marketable production and/or availability of prod for household consumption Higher labour productivity/returns to labour => more room to increase/request increases in wages => increase in incomes

2- General definitions and questions

General definition“The number of units of output(s) produced per unit of labour used in agricultural production“

General formula

𝑙𝑙 =𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑙𝑙𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑙𝑙 𝑂𝑂𝐴𝐴𝐴𝐴𝑢𝑢𝐴𝐴𝐴𝐴

𝐿𝐿𝐴𝐴𝐿𝐿𝐿𝐿𝐴𝐴𝐴𝐴 𝐴𝐴𝑖𝑖𝑢𝑢𝐴𝐴𝐴𝐴

Questions• Scope: what type of output/input should be considered• Measurement: how they should be quantified• Time: how to measure changes in labour productivity• Aggregation: producing farm-level/multi-output indicators

3- Different measurement concepts (1/2)

The measurement concept depends on the level of aggregation:

Productivity for: One commodity Several commodities

Output measured in: Physical quantitiesMonetary values

Commodity-equivalents

Labour input measured in:

Number of physical units (worker, days worked, etc.)

Examples:Tons of paddy rice produced per active worker in rice farms

Production value of cereals per active worker in cereal farms

Tons of cereals produced in wheat-equivalents per active worker in cereal farms

Productivity indicators should be quantity or volume-basedIf quantities are converted to values for aggregation, prices used should refer to a fixed period => constant-price measurements

Presenter

Presentation Notes

Labour input per commodity: easy for monocropping For multi-cropping (by far the majority, esp. in developing countries): labour input needs to be collected ax-ante by crop/activity or allocated ex-post based on allocation keys

If one commodity is considered, the change in productivity can be measured by comparing the simple indexes for the two time periods:

3- Different measurement concepts (2/2)

𝑙𝑙1 =𝑄𝑄1𝐿𝐿1

𝑙𝑙2 =𝑄𝑄2𝐿𝐿2

If several commodities are considered, the quantities can be aggregated by converting them to monetary units, using prices of a fixed reference period:

𝑙𝑙1 =∑𝑖𝑖=1𝑛𝑛 𝑢𝑢𝑖𝑖,1𝑄𝑄𝑖𝑖,1

𝐿𝐿1𝑙𝑙2 =

∑𝑖𝑖=1𝑛𝑛 𝑢𝑢𝑖𝑖,1𝑄𝑄𝑖𝑖,2𝐿𝐿2

Presenter

Presentation Notes

Quantity indexes (Laspeyres, Paasche, Fisher, etc.) are used to compute time-series of productivity indexes

Objective/context-specific

Objective of the productivity measure => What to include in the numerator and how to measure/quantify

4- Measuring agricultural output (numerator)

General guidelines

• Include all the farm outputs, including secondary/minor crops as well as output for minor season and any by-products (cf. Kelly et al. 1996 )

• Exclude output from non-agricultural activities• Include output from on-farm transformation/processing• If production values are needed, farm-gate prices should be

used (or at the first selling point)

Presenter

Presentation Notes

Exclusion of secondary crops and by-products leads to an underestimation of (labour) productivity, esp. in developing countries where multi-cropping/activities is the rule.

5- Measuring labour input (denominator) (1/5)

Scope

• Labour involved in agricultural activities or on-farm processing

• All types of workers: permanent, casual/seasonal, family, exchange, paid/non-paid, etc.

• Workers under the minimum legal working age? Important contribution in seasonal tasks such as harvesting, planting, etc.

Measurement of labour input

• Several options

• With varying levels of complexity…

• And data collection requirements


Method 1 The number of workers active on the holding

Questions/issues/challenges: who is considered active ?• Threshold: one day per week ? Per month ?• Age limits• Inclusion of family labour

Limitations• It does not inform about the quantity of labour effectively used

on the farm (e.g. casual/seasonal/family labour)• It generally leads to an underestimation of labour productivity

Advantages• Straightforward, little data requirements

Presenter

Presentation Notes

Overestimation of labour, underestimation of productivity


Method 2 The number of days worked on the holding

Questions/issues/challenges:• How many hours per day to be considered?• Reporting recall issues, especially for permanent/family labour

Limitations• It does not inform about the quantity of labour effectively used

on the farm (3 hours per day ≠ 6 hours per day)• It generally leads to an underestimation of labour productivity

Advantages• More precise than Method 1


Method 3 The number of hours actually worked on the holding

Questions/issues/challenges:• How many hours per day?• Unit not common for farmers, issue of piece-rate workers• Reporting recall issues

Limitations• Heavy data collection burden

Advantages• Gold Standard: Adequately measures the amount of labour

effectively used on the holding• More precise than Methods 1 and 2

Presenter

Presentation Notes

Data collection process: nb months - nb of weeks/month – nb of days/week – nb of hours/day Data has to be collected in multiple ways to minimize the recall bias => heavy data collection burden for enumerators and respondents


Method 4 The number of full-time equivalents

Questions/issues/challenges:• What is a full-time equivalent (FTE) ?• FTE vary across countries• Issue of piece-rate workers• Reporting recall issuesLimitations• Heavy data collection burden• International comparisons may be biased because FTEs differAdvantages• Unit (FTE) more adapted to farmers’ practices• Adequately measures the amount of labour effectively used on

the holding if FTE are well-defined

Presenter

Presentation Notes

As one full time equivalent differ across countries, using country-specific FTEs may bias the comparisons Using standard FTEs (e.g. 8 hours per day, but how many days ?) is better, but FTE needs to be well defined and this would only be relevant for international comparisons

6- Addressing differences in labour types (1/4)

Why providing data by labour types matters ?• Productivity of a skilled worker ≠ unskilled• Productivity of an adult ≠ child• Productivity of men ≠ women etc.

Why quality differences should be considered in productivity measurement ?• To assess the contribution of each category of worker• To explain differences in productivity (labour and total)• To measure total productivity: aggregation of inputs require the

use of input-specific weights/wages


Main criteria : • Age

• Sex

• Family labour vs. Hired labour

• Full-time vs. Part-time

• Permanent vs. Casual/Seasonal

• Educational levels


An example of classification matrix (USDA)


The result is a state-by-year panel dataset with :• Annual hours worked• Hourly compensation• By sex, age, education, and employment class

The dataset is used to:• Construct indexes for each State and the aggregate farm sector• Adjust indexes for quality change: labour hours having higher

marginal productivity are given higher weights in forming the index of labour input

Gold standard ?• High data collection burden

An example of classification matrix (USDA)

7- The productivity of labour depends on other inputs (1/2)

7- The productivity of labour depends on other inputs (2/2)

• Labour productivity = Partial productivity indicator

• Its contribution to total farm productivity depends on the use of other factors such as land, capital and intermediate inputs

• For example, improvements in labour productivity can be related to:

o Increased mechanization because machines often require less labour to cultivate a larger area

o Changes in farming practices: chemical vs. mechanical pest control etc.

• Implications: the difference in labour productivity between developed and developing countries is partially explained by the wider use of machinery in the former group

8- A few words on land productivity (1/2)

General definition“The number of units of output(s) produced per unit of land area“

General formula

𝑙𝑙 =𝐶𝐶𝐴𝐴𝐿𝐿𝑢𝑢 𝑂𝑂𝐴𝐴𝐴𝐴𝑢𝑢𝐴𝐴𝐴𝐴𝐿𝐿𝐴𝐴𝑖𝑖𝐿𝐿 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴

Questions• Scope:

o What area concept should be consideredo What output/crops

• Land quality characteristics

9- A few words on land productivity (2/2)

Land Area• Farm area -> Cultivated area -> Planted area -> Harvested area• Planted area -> Effective yield or land productivity• Cultivated area -> Land productivity of the cropping system (e.g.

rotations including fallow land)• Harvested area -> Closer to theoretical/biological yield

Land quality• Soil and land quality characteristics impact yields• Information on the main characteristics of the land/soils need to

be collected, to:o Understand/explain differences in yieldso Impute land values/costs (e.g. hedonic regressions)

• Elements attached to the land and contributing to increase its productivity such as irrigation, terracing, drainage, etc.) are considered as capital

• Jorgenson D. W, Ho, M.S., & Samuels, J. D. 2014. Long-term Estimates of U.S. Productivity and Growth, Prepared for Presentation at Third World KLEMS Conference, Growth and Stagnation in the World Economy, Tokyo, May 19-20, 2014

• Kelly, V., Hopkins, J., Reardon, T., & Crawford, E. 1996. Improving the Measurement and Analysis of African Agricultural Productivity Promoting Complementarities Between Micro and Macro Data. Technical Paper No. 27. Office of Sustainable Development. Bureau for Africa. USAID publication. Washington D.C.

• OECD. 2001b. Measuring Productivity, Measurement of Aggregate and Industry-level Productivity Growth. OECD Manual, Paris

• Shumway, C. R., Fraumeni, B. M., Fulginiti, L. E., Samuels, J. D., & StefanouS.E. 2015. Measurement of U.S. Agricultural Productivity: A 2014 Review of Current Statistics and Proposals for Change, Working Paper Series WP 2015-12, School of Economic Science, Washington State University

10 - References