Measuring Total Factor Productivity in Agriculture Yu Sheng (Eric) ABARES Concepts, Methodology and Data August 2015 APO Workshop 2015 Agricultural Productivity Measurement Tehran, IR Iran

Concepts and Interpretation

Agricultural productivity concept Agricultural productivity measures the ability of production units to convert economic, social and natural inputs into desirable outputs (OECD 2001, G20 MACs 2015). – Broad concept: including environmental and social factors – Narrow concept: pure economic perspective Economic outputs and inputs Agricultural production technology

Illustration of agricultural productivity What is it? -Ratio of output quantity to input quantity What causes it to change? - Technological change - Different management

Interpretation Technology and efficiency in production (Jorgenson et al. 2005) – Specific technology and efficiency disembodied in production costs Many types of agricultural productivity – Partial versus total factor productivity (TFP) – Productivity levels versus productivity growth – Different aggregation levels: farm, sector, industry etc.

Factors affecting technology and efficiency Input-output analysis – Increase in total output/fall in total input use – Changing output mix – Different input combinations ‘External’ factors – Development of and access to new technologies – Domestic and global economic conditions – Climate change and drought – Regulatory environment

Other interpretation… Agricultural productivity also measures – Returns to scale (Diewert and Fox 2008) – Economic cycles and capacity utilization (Dension 1972, Jorgenson and Griliches 1972) – Changes in allocation efficiency Technical efficiency (within effects) (Coelli et al. 1998) Resource reallocation (between effects) (Bailey et al. 1992) – Other external conditions (i.e. climate condition etc.)

Methodology and Challenges AP Metrics

Derivation of Agricultural TFP A General Production Function in Agriculture

Derivation of agricultural TFP Three key assumptions: 1)Homogeneous production function 2)Perfect competition 3)Constant returns to scale = partial price elasticity of input i = share of the value for input i

Definition of agricultural TFP - Changes in the “disembodied” technology applications of superior production techniques (e.g. tillage, breeding) more efficient management practice (i.e. doing things more efficiently) - Increasing returns to scale -Measurement errors (in outputs, inputs, weighting & sampling errors) - Anything not accounted for by the inputs  measure of “ignorance”

Literature Review: AP Measure MethodProsCons Growth accounting- Consistent with traditional MFP calculation - Coherent over time - No need to specify the form of the production function - High data requirements (monetary values of all outputs and inputs) - Does not account for noise in data - Assumes efficiency Econometric analysis- Allows hypothesis testing - Accounts for measurement error - Results might be sensitive to estimator used and data sample - Assumes efficiency Data envelopment analysis- Does not assume efficiency - Flexible with respect to data requirements (can use both monetary values or physical quantities for inputs and outputs) - No need to specify the form of the production function - Results might change if analysis extended to other countries Stochastic frontier analysis- Does not assume efficiency - Flexible with respect to data requirements - Allows hypothesis testing - Accounts for measurement error - Need to specify the form of the production function. - Need to specify the form of the inefficiency term. - Results might change if analysis extended to other countries 1

Regression based estimation methods Two broad types of methods - Parametric (e.g. regression) - Non-parametric (e.g. DEA etc.) growth accounting

Methodology: agricultural TFP measure Agricultural TFP is measured as the ratio of gross output to total input such that How we aggregate different outputs and inputs into the corresponding quantity/volume index matters for the final results Form of transformation function (i.e. parametric vs. non-parametric) Weights to be used (i.e. real price vs. implicit price)

Gross Accounting based Index Method “unweighted”weighted Carli (arith. m) Jevon (geo. m) Dutot (arith. m) LaspayresPaasche Other Types FisherTornqvist “Ideal” & “superlative” Translog p.f.Quadratic p.f.

Technical choices Two variants of TFP – Output =“value-added” or “gross output” Index method – Fisher index vs. Tornqvist index Direct or chained index Transitivity

Fisher index

Direct or chained index

Chained index Direct Index

A transitivity problem Farm A Farm BFarm C Farm D

A transitivity problem Farm A Farm BFarm C Farm D

A transitivity problem Farm A Farm BFarm C Farm D

Data Compilation

Output definition (3 principles)  Direct agricultural output  Products and services from inseparable secondary industrial activities  Some direct taxes excluding subsidies 70 commodities and commodity groups  Crops (60 items): crops, grains, oil seed, vegetables and melons, other crops  Livestock products (9 items): meat livestock, poultry and eggs, dairy products, wool, honey and bee wax  Other on-farm production : plant/land rental income, packing and marketing Output

Output aggregation Livestock products Crops Output Meat livestock Poultry and eggsOther livestock products Grains and feed stuff Oil seedVegetables and melons Fruits and nuts PeanutCanolaSunflower seed Other outputs …

PIM method to estimate capital services Capital inputs: −Non-dwelling buildings and structures −Plant and machinery −Vehicles −Inventory, bio-mass and intangible assets Capital service of each asset is equal to rental rate multiplied by productive capital stock −Productive capital stock = weighted average of real investments −Rental rates = r*W/(1-F) Real interest rate is approximated by using the ‘ex ante’ rate −one-year government bond rate minus GDP deflator −ARIMA model is used to smooth Input: capital

Land service is equal to the land rental rate multiplying by the land stock −Rental rate = r* land price −Land stock is estimated as land areas in use Land prices can be unrelated to agricultural production −Urbanisation process −Distance to major cities Hedonic function is used to estimate land price related to agricultural production and for quality control −Box-Cox (1964) transformation −GIS for land characteristics −country and time specific dummies for other factors Input: land

Materials and Services (8 categories) −Fuel and lubricants −Electricity −Fertilizers −Chemicals and medicines −Seed and fodder −Livestock purchases −Repairs and maintenances −Plant hire Only implicit quantities are estimated − Farm expenditure − Price indexes for commodity groups Quality adjustment is made for −Fertilizer −Chemicals and medicines Input: intermediate inputs

Labour input quantity −Hired and self-employed workers −Total number of hours worked Labour wage −Compensation for rural workers (dual method) −Compensation for rural workers divided by hours worked Quality adjustment −Data from the United States accounted for workers’ education level and experience Input: labour

National Account Statistics Data Source (1)-top down Cross-country consistent statistics −FAO STAT/OECD Statistics −World KLEMS −EU Statistics Country-specific national accounts −AUSTRALIA: ABARES and ABS database −CANADA: Agriculture and Agri-food Canada and Statistics Canada: Canadian agricultural census/Statistics Canada CANSIM tables −UNITED STATES: ERS USDA, the US agricultural census/the US agricultural resource and management survey (ARMS)/The US NIPA database

Farm Census/Survey Data Data Source (2)-bottom up Data Source – Australian Agricultural and Grazing Industry Survey (AAGIS) – Australian Dairy Industry Survey (ADIS) – EU FADEN and US ARMS Coverage (Australian case is based on ANZSIC classification) – Crop specialists – mixed crop-livestock farmers – Beef specialists – Sheep specialists – Mixed beef-sheep farmers – Dairy farmers

Other technical issues Impact of climate change and soil quality – Lurking variables also applies to agriculture (PC 2005; Fox and Diewert 2008) – Accounting for output and input quality adjustment Measurement of capital – A general problem for TFP estimates at firm level – PIM and CIM methods Differences between data sources – Coverage and consistency – Mismatch of inputs and outputs Entry/Exits – interpretation of aggregate productivity – Knox Lovell How to model technological progress using agricultural TFP measure – Embodied and disembodied technology Does the distinction explain the technology paradox? – Relationship between rate of substitution and productivity – Relationship between technological progress and size

TFP and technologies New blueprints Certain types of scientific results New organisational and management practice / techniques Embodied technologyDisembodied technology Better design or more functionalities Higher quality of new vintages capital More efficient intermediate inputs

Embodied & Disembodied Tech. Embodied technology Disembodied technology

Embodied Technology & PFP New Fertilisers

Embodied Technology & PFP Outsourcing

EMG Workshop December 2014, Sydney Questions and Comments

Appendix Mathematical derivation

Derivation of TFP Three key assumptions: 1) Homogeneous production function = partial price elasticity of input i

Derivation of TFP – choice of outputs mix (given total & mix of inputs) Crop Livestock C0C0 L0L0 P0P0 P1P1 E0E0 E1E1 C1C1 L1L1 0 Production possibility frontier

Derivation of TFP -- choice of inputs mix (given total & mix of outputs) K L E0E0 E1E1 P0P0 L1L1 L0L0 K1K1 0 K0K0 P1P1 I Isoquant

Derivation of TFP Three key assumptions: 1)Homogeneous production function 2)Perfect competition = partial price elasticity of input i = share of the value for input i

Derivation of TFP Three key assumptions: 1)Homogeneous production function 2)Perfect competition 3)Constant returns to scale = partial price elasticity of input i = share of the value for input i

Derivation of TFP Three key assumptions: 1)Homogeneous production function 2)Perfect competition 3)Constant returns to scale = partial price elasticity of input i = share of the value for input i