FAOSTAT: Investment: Government Expenditure

The dataset contains a set of financial indicators describing national expenditures on agricultural research and development (R&D). The indicators are derived primarily from responses to the financial resources section of the ASTI questionnaire, complemented with selected external macroeconomic indicators required to ensure comparability across countries and over time.The dataset includes both directly reported financial aggregates and derived indicators, reflecting different dimensions of agricultural R&D investment. Reported expenditure data are collected in current local currency units (LCU) and refer to total institutional expenditures, as provided by national respondents. Coverage of the private for-profit sector remains limited and is therefore excluded from the dataset. To support temporal analysis and international comparability, expenditure indicators are also expressed in constant local currency units, using country-specific GDP deflators sourced from FAOSTAT. In addition, selected indicators relate agricultural R&D expenditures to key structural variables, such as agricultural value added, allowing for meaningful cross-country comparisons of research intensity and resource allocation.

The Development Flows to Agriculture (DFA) republishes the OECD Creditor Reporting System (CRS) Aid Activity database, which provides comprehensive data on development flows from all donors to all recipients since the early 1970s. The use of CRS for compiling the DFA dataset capitalizes on the expertise of the OECD in this area, while minimizing respondents’ burden and avoiding duplication of efforts. FAO extracts components of Agriculture, forestry and fishing; rural development; agricultural industry; and general environment protection, and reorganizes these into a set of donor-recipient-purpose in terms of four flow types (Official Development Assistance (ODA) flows; Other Official Flows (OOF); private grants; and private sector instruments (PSI)). The objective of the DFA database is to provide readily available data to enable analysis on aid flows and the purposes they serve, with an emphasis on agriculture, its components, and environmental protection.

Credit to agriculture measures the amount of loans and advances given by the banking sector to farmers, rural households, agricultural cooperatives, or any other agri-related businesses. Each country’s central bank compiles its credit stocks by economic activities as part of its monetary and financial statistics publications via annual or quarterly reports online. Data collection has been carried out mostly through reports found on the central bank websites. Data are collected and compiled according to the International Standard Industrial Classification of All Economic Activities (ISIC Rev. 4: https://unstats.un.org/unsd/classifications/Econ/isic) with a special concentration on the credit to agriculture, forestry and fishing sector separately from the ones to manufacturing and services.

As part of the FAO Agriculture Capital Stock (ACS) database, the Statistics Division of FAO publishes country-by-country data on physical investment in agriculture, forestry and fishing as measured by the System of National Accounts (SNA) concept of Gross Fixed Capital Formation (GFCF). Additional variables included in the ACS are Net and Gross Capital Stock, Consumption of Fixed Capital, the Agriculture Investment ratio, and the Gross Fixed Capital Formation Agriculture Orientation Index. The FAO Agriculture Capital Stock Database is an analytical database: whenever available, the database integrates official National Accounts data harvested from the UNSD National Accounts Main Aggregates Database (UNSD AMA) and the OECD Annual National Accounts Database (OECD ANA). The database is further supplemented by OECD Structural Analysis database (OECD STAN) and, in a few cases, data from country’s statistics websites. If the full set of official data is not available for any specific country, imputation methods are applied to obtain estimates over the complete time series. Many data points in ACS are estimated and are flagged as such; they do not represent official submissions by Member Countries. With a view of producing internationally comparable net capital stock estimates, the Perpetual Inventory Method (PIM) with a constant geometric depreciation rate is employed to impute missing data. The Perpetual Inventory Method is a well-established economic model to calculate Net Capital Stocks (NCS) and Consumption of Fixed Capital (CFC) from time series of Gross Fixed Capital Formation (GFCF). Specifically, annual measures of the NCS are obtained from cumulating historical series on physical investment flows and deducting the part of assets that are depreciated (the Consumption of Fixed Capital that occurs in every period). In order to implement the PIM, long time series on aggregate GFCF in agriculture, forestry and fishing is required.An attempt is made to rely as much as possible on National Accounts data published by the OECD and UNSD. When country data are partially or fully missing, econometric techniques to impute missing observations are employed. Depending on the pattern of data missingness for the countries, different imputation methods are applied (from among the ARIMAX, PANEL regression, and OLS approaches) for the data series from 1995 to 2022. The values of Agriculture Capital Stock related indicators for 2023, including Agriculture Investment Ratio, Agriculture Orientation Index, Net Capital Stock, Gross Fixed Capital Formation and Consumption of Fixed Capital, are estimated using the Holt-Winters (HW) method (Cipra et al., 1995). The HW method is an exponential smoothing method for forecasting the annual values of economic variables. In this context, the HW method relies on existing (historical) values of the Agriculture Capital Stock. The predicted value is an extrapolation of the historical values to the specified target date, which extends the timeline without considering seasonality in the annual series.All data series in the database are provided both in national currencies and in US dollars as well as in current prices and constant prices with base year 2015.

The food and agricultural trade indicators dataset is based on trade, production and gross domestic product (GDP) data. Agri-food trade data are collected, processed and disseminated by FAO according to the standard International Merchandise Trade Statistics (IMTS) Methodology. The data is mainly provided by UNSD, Eurostat, and other national authorities as needed. The source data is checked for outliers, trade partner data is used for non-reporting countries or missing cells, and data on food aid is added to take total cross-border trade flows into account. The trade database includes the following variables: export quantity, export value, import quantity, and import value. It includes all food and agricultural products imported/exported annually by all countries in the world. Production data details are available at https://www.fao.org/faostat/en/#data/QCL and GDP data details are available at https://www.fao.org/faostat/en/#data/MK

The domain provides detailed data on the value of agricultural production that is calculated by the agricultural production data and the price data at farm gate. Thus, the value of production measures the agricultural production in monetary terms at the farm gate level.