THE DISTRIBUTION OF FARM PERFORMANCE: DRAFT TERMS OF REFERENCE

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1 THE DISTRIBUTION OF FARM PERFORMANCE: DRAFT TERMS OF REFERENCE 1. Background It has been suggested that OECD work under the PWB adopt an innovation system approach. The scoping paper of the project on innovation system (TAD/CA/APM/WP(2011)19) suggests that OECD develops a framework to analyze innovation systems would be developed and adapted to agriculture to take account of the specificities of this sector (e.g. characteristics of agricultural technologies, industrial structure, public good characteristics of some outputs of agriculture, and policy and market environment). This framework would aim to identify interactions between the different components of the system beyond R&D institutions, as well as interactions between agricultural policy and other parts of the innovation system. Measurement issues would be discussed and the project would identify indicators that could help evaluate the performance of the innovation system in agriculture. As a part of efforts to develop indicators for the innovation system, OECD explores distributional analysis of farm performance through the OECD Network for Farm-Level Analysis, following the methodology and data sources developed in the distribution of support study (TAD/CA/APM/WP(2010)38/FINAL). The scoping of this work was widely supported by delegates to the Working Party on Agricultural Policy and Markets on May 30-June 1, Innovative farmers adopt new technology and knowledge and obtain more value from the market. Farm-level analysis allows assessing the characteristics of high performing farmers that are more willing to adopt new technology and at which speed the technology is diffused among farmers over time. The analysis also can investigate the impact of agricultural policies on technological adoption and diffusion based on the analytical framework developed on the impact of different farm support policies on innovation. The linkage between agricultural support and farm performance gives an indication of the extent to which agricultural support favors the adoption of new knowledge and technologies (OECD 1994; Martin et.al., 1983) 1. The scoping paper proposes two projects on farm-level performance analysis: distributional analysis of farm performance indicators and decomposition analysis of productivity growth into technological change and technical efficiency changes. This TOR is developed for the first component of the project. 2. Objectives The objectives of the project on the distribution of farm performance are twofold: 1. OECD (1994). Technological Change and Structural Adjustment in OECD Agriculture. AGR/CA/(94)29. Martin, J. P. and J. M. Page (1983). The Impact of Subsidies on X-efficiency in LDC Countries: Theory and Empirical Test. Review of Economics and Statistics. 65 (4),

2 1. To illustrate the extent to which existing knowledge and technology are diffused among farmers and how it has evolved overtime through analyzing distributional information of farm performance indicators of different farm types. 2. To analyze the relationship between farm performance and farm characteristics (e.g., adoption of specific technology, farm size, the amount and type of agricultural support received) Definition issues Definitions of most of variables required for this analysis can follow the definition made in the distribution of support projects such as definition of farm and the selection of farm types (see Annex for the list and definition of variables) 3 However, this project constructs new variables such as farm performance indicators from the existing variables defined in the previous project and farm characteristics variables in farm survey data. Farm performance indicators Four farm performance indicators are proposed to represent farm s economic performance. 4 Since all four indicators are measured by a partial production factor (cash inputs, labour, land and capital), it is better to calculate multiple farm performance indicators. Farm performance could include non-economic performance such as its environmental performance (e.g., less use of chemical fertilizer), its social performance (e.g., better working condition, better animal welfare) or its multi-functional performance (e.g., better landscape). However, such non-economic farm performance indicators usually do not exist in the standard farm survey in member countries and establishing a comparable valuation method across countries is beyond the scope of this study Ratio of gross agricultural output and farm cash expense (Output input ratio) Gross agricultural output is defined as the gross value of agricultural sales at the farm-gate price, including Market price support (MPS). 2. Agricultural income per working unit Definition of agricultural income when constructing farm performance indicator should be farm income less total payments. Definitions of farm income and total payments should remain same as in the distribution of support project. 3. Agricultural income per UAA (Utilized Agricultural Area) 2. The relationship between farm performance and farm support does not necessarily prove the causality. In order to analyze the impact of farm support policy on farm performance, farm characteristics need to be controlled (e.g., managerial characteristics, land quality, climatic condition, non-farm activity and random events). 3. See Box 2.1 in Moreddu, C. (2011), Distribution of Support and Income in Agriculture, OECD Food, Agriculture and Fisheries Working Papers, No. 46, OECD Publishing In this analysis, both output and input indicators are expressed in value, not in quantity. 5. The scoping paper of innovation systems (TAD/CA/APM/WP(2011)19) proposes that a framework to analyze innovation system would be developed and adapted to take account of specificities of agricultural sector including public goods characteristics of the sector. 2

3 4. Agricultural income per farm net worth Farm characteristics For the second objective of analyzing the relationship between farm performance and farm characteristics, following categories of farm characteristics are defined. However, availability of data, in particular characteristics 4-6, is limited in some countries. The secretariat discusses bilaterally which farm characteristics variables to apply bilaterally. Definitions of the variables follow the box in the Annex unless it is specified in the text. 1. Farm size Farm size should include both economic size, land size and labour size. Both gross agricultural output and farm net worth should be indicators of economic size. For land size of farm, UAA (utilized agricultural area) should be used as an indicator. If the data is available, labour size should be represented by full-time equivalent work unit (e.g., Annual Work Unit in FADN database). 2. Support Support would include all payments to producers reported in farm-level data, as well as an estimate of MPS by farm as we have done in the distribution of support project. A representative indicator should be a share of total support in gross receipts (support rate). Moreover, it would be useful to assess the coverage of payments to producers in farm-level data compared to specific payment program (or categories) recorded in the PSEs or in the national database. To what extent payments could be disaggregated are discussed bilaterally between participating countries and the OECD Secretariat. 3. Farmer s off-farm activity Diversification to off-farm activity could bring innovation through interactive process with other sectors. A share of non-farm income in farm household income should indicate the intensity of farm household s off-farm activity. 4. Operator s characteristics Operator s characteristics determine farm s unobserved managerial input and therefore they are expected to have a significant impact on farm performance. Main operator s characteristics may include age, education attainment and farming experience, depending on the availability of such data. 5. Farm s geographical conditions Geographical conditions fundamentally determine farm performance. The indicators should represent to which extent the location of the farm is favourable to farming. Variables can be chosen depending on the availability of data in each country such as degree of slope and land quality. 6. Adoption of specific technology Depending on the data availability, adoption of specific production practice or technology should be included as a farm characteristic that directly represents innovative activity (e.g., adoption of specific crop/animal variety). Alternatively, net investment could be used as a proxy. 3

4 Which farms? Definition of farm remains again same as in the distribution of support project. Only individual, family farms of a minimum size would be retained in the distributional analysis, but whenever information is available on smaller farms and large corporate farms, it would be presented separately in the analysis, with appropriate grouping (e.g., all farms, non-corporate farms and corporate farms). A farm could be defined as an operation engaged in agricultural activities, organised as a sole proprietorship, partnership, or family corporation, where the operator or operator's household owns or controls the use of farm resources (capital, labour). The main principle of farm selection is to cover 90% of the agricultural production for all farms or for each farm type, as it was for the previous distribution of support project. Farm types We keep the same definition of farm type as in the distribution of support project. Using national definitions of farm types, a few main farm types could be selected such as crop, dairy, grazing livestock, non-ruminants, fruits and vegetables farms, and mixed farms. 4. Data/methodology issues Whenever possible, indicators would be calculated from individual farm level data. If individual data are not accessible, the analysis can be based on the average of pre-defined groups of farms. As far as possible, indicators from individual farm level data should be weighted by weighting factors to maintain representativeness. In addition to the yearly coverage made in the distribution of support project (2004, 2006 and 2007), the most recent year (presumably 2008 and 2009) should be added. As in the previous project on the distribution of support, MPS would be estimated for individual farms (or farm groups) using MPS as a % of the value of production (%MPS) for specific commodities from the PSE database, applied to the value of output from that commodity in farm-level data. For commodities where no specific MPS is estimated in the PSE database, an estimation would have to be made for their %MPS. It could be the average %MPS of all commodities for which MPS is calculated separately, the average %MPS of groups of commodities with similar policies (e.g. crops, livestock, fruits and vegetables) or any other assumption as long as it is explained. Regarding EU member states, MPS is currently available only at the aggregate EU levels: EU15, EU25 and EU27. It is suggested using the EU15 aggregate for countries that were members of the EU before Measures of differences/distribution Comparing averages of variables in the farm accounts between different farm performance group Construct farm performance indicators: 1) ratio of gross agricultural output and farm cash expense (Output input ratio), 2) agricultural income per working unit, 3) agricultural income per UAA, 4) agricultural income per net worth 4

5 Group the farms by quartile (Q1, Q2, Q3 and Q4) with respect to four different farm performance indicators both by all farms and by each farm type for each year. This quartile is different from the distribution of support project which was based on farm-size Farm characteristics variables: support (each type of payments and MPS), gross agricultural output, farm net worth, utilized agricultural area, annual work unit, share of support in gross receipts (support rate), share of non-farm income in farm household income, net investment and other farm characteristics (e.g., adoption of specific production practice or technology, age and education of farmers, farming experience) depending on the data availability of each country. Calculate the average of all farms, first quartile and fourth quartile of above farm characteristics variables for all farms and for specific farm type by four different performance indicator, for each year: Crop, dairy, grazing livestock, non-ruminant, fruits and vegetable farms and mixed farms Sample Table 1 Output input ratio in 2009 by domain (farm type, corporate/not corporate, etc ) Gross agricultural output Share of total support in gross receipts Share of non-farm income Etc.. e.g. Farm type 1 Farm type 2 etc Q1 Q4 All Q1 Q4 All 2 Agricultural income per working unit in 2009 domain (farm type, farm size, corporate/not corporate, etc ) Gross agricultural output Share of total support in gross receipts Share of non-farm income Etc.. e.g. Farm type 1 Farm type 2 etc Q1 Q4 All Q1 Q4 All Calculate the coefficient of variation (ratio of standard deviation to mean) of farm performance indicators for all farms and for specific farm type, for each year 5

6 6. Estimated timing Pilot data delivered to the Secretariat by September 30 Discussion of the pilot results presented at 8 th meeting of OECD network for farm-level analysis meeting in November 3-4. Discussion of revised analytical results presented at 9 th meeting of OECD network for farm-level analysis meeting in the first half of Draft report to the APM in late

7 Annex List and definition of variables Number of farms in farm population (representing 90% of total agricultural sales) Number of working units (full time equivalent) Number of hectares of utilized agricultural area (UAA) Gross agricultural output (GAO) includes market receipts for sales of agricultural products and services such as custom work, income rental of land, quotas, building, machinery, etc. In the EU FADN, it includes sales and on-farm use of products (crop and livestock) and livestock, change in stocks of products (crop and livestock), change in valuation of livestock (minus purchases of livestock) and various non-exceptional products. Total support (often referred to as "support" in the text) includes total payments and market price support. Total payments include budgetary transfers to farmers from agricultural policy and insurance payments, conservation payment in United States, first and second pillar payments of the Common Agricultural Policy in the European Union. Three categories are distinguished: First pillar payments, which include direct income payments; Second pillar payments, which include payments made under the rural development policy of the Common Agricultural Policy; and Other payments, mainly from national expenditures. Market price support (MPS) includes transfers from consumers and taxpayers to agricultural producers from policy measures that create a gap between domestic market prices and border prices of a specific agricultural commodity, measured at the farm gate level. It is calculated in the OECD database of Producer Support Estimates (PSE) for main commodities covering ideally at least 70% of the total value of agricultural production. For each commodity, MPS is equal to the difference between the domestic producer price (PP) and a border price expressed at farm gate level (BP), multiplied by quantity produced (QP) MPS i = (PP i - BP i) * QP i. for commodity i MPS by commodity can be expressed as a proportion of the value of production of that commodity (VP). MPS ratio for commodity i = MPS i / VP i MPS for each individual farm is calculated by applying the MPS ratio of each commodity to corresponding farm receipts. An average MPS ratio for all PSE commodities is used for remaining commodity receipts. MPS by farm is the sum of MPS for each commodity produced on the farm, calculated using MPS ratios and individual farm receipts. Gross receipts include gross agricultural output and total payments. Farm cash expenses exclude depreciation. Include total crop, livestock, machinery expenses and general expenses such as net interest expenses, salaries, rent, insurance, phone, electricity, fuel, custom work, machinery rental, net property tax, building and fence repairs, freight and trucking, selling costs, marketing board fees, accounting, etc. Net operating income is the difference between gross receipts and farm cash expenses, also equal to farm income before depreciation. Depreciation includes national estimate of economic depreciation or capital cost allowance for tax purpose, i.e. an amount deducted from taxable income to account for annual depreciation costs at a rate specific to the depreciable capital item. Farm income is the difference between net operating income and depreciation. Agricultural income is the difference between farm income and total payments. Net investment includes the difference between purchases and sales of fixed assets, breeding livestock change of valuation less depreciation. Non-farm income includes off-farm income of farm operators and their other household members. It excludes off-farm income of households operating incorporated farms. Farm household income includes farm income and non-farm income. The relationships between income components are also shown in the diagram below: 7

8 Box 2. List and definition of variables in standard tables (cont.) Components of farm household income + Market receipts (GAO) + Budgetary payments = + Other receipts Gross receipts Net operating -- = income Cash expenses -- = Depreciation Farm income Farm income Total farm household + Gross wages and salaries + = income Disposable farm + Property income = household income + Social transfers = Off-farm income -- + Other income Taxes and mandatory contributions Following the fourth meeting of the network, standard tables were revised to include additional variables: Total farm assets includes market value at the end of calendar year of market livestock, breeding and replacement livestock, accounts receivable, input inventory, prepaid input expenses, crops for sale, farm investments, farmland and buildings owned (including farmhouse), machinery and equipment owned, production quota. Total farm liabilities includes short and long term money owned to banks, trust companies, credit unions, government agencies, money borrowed under advance payments, machinery and supply companies, private individual, shareholders, accounts payable at the end of calendar year. Farm net worth is the difference between total farm assets and total farm liabilities. Support rate is total support per gross agricultural output. 8