Belarus vs Peru: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing)
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) over time
- Belarus
- Peru
How they compare
Peru currently reports 10,080 million SLC against 8,292 million SLC in Belarus, a difference of 1,788 million SLC.
That makes Peru's figure about 1.2 times Belarus's.
Across all 29 years both countries report, Peru has been ahead every year.
Belarus ranks 89th and Peru ranks 86th of 181 countries.
Peru has averaged higher in every one of the 4 decades both report.
Head to head by decade
| Decade | Belarus | Peru | Difference | Ahead |
|---|---|---|---|---|
| 1990s | 1.32 million SLC | 1,243 million SLC | 1,242 million SLC | Peru |
| 2000s | 281.26 million SLC | 1,728 million SLC | 1,447 million SLC | Peru |
| 2010s | 2,620 million SLC | 4,227 million SLC | 1,606 million SLC | Peru |
| 2020s | 6,225 million SLC | 8,008 million SLC | 1,783 million SLC | Peru |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher gross fixed capital formation (agriculture, forestry and fishing), Belarus or Peru?
- Peru, at 10,080 million SLC against 8,292 million SLC in Belarus as of 2023.
- What is the difference in gross fixed capital formation (agriculture, forestry and fishing) between Belarus and Peru?
- 1,788 million SLC, with Peru ahead.
- How many years of comparable data are there for Belarus and Peru?
- 29 years are reported by both, from 1995 to 2023.
- How do Belarus and Peru rank globally for gross fixed capital formation (agriculture, forestry and fishing)?
- Belarus ranks 89th and Peru ranks 86th of 181 countries.
- Where does this data come from?
- Food and Agriculture Organization of the United Nations, published as Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Value Standard Local Currency. Statizoid refreshes it automatically from the source and publishes the full history for both places.
Individual pages
About this data
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.