Georgia vs Mali: Consumption of Fixed Capital (Agriculture, Forestry and Fishing)
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) over time
- Georgia
- Mali
How they compare
Mali currently reports 219.91 million USD against 188.59 million USD in Georgia, a difference of 31.32 million USD.
That makes Mali's figure about 1.2 times Georgia's.
The two have swapped places 1 time across 24 shared years of data; in 2000 it was Georgia ahead.
Georgia ranks 95th and Mali ranks 93rd of 182 countries.
Across the 3 decades both report, Georgia averaged higher in 2 and Mali in 1.
Head to head by decade
| Decade | Georgia | Mali | Difference | Ahead |
|---|---|---|---|---|
| 2000s | 118.78 million USD | 32.72 million USD | 86.05 million USD | Georgia |
| 2010s | 130.44 million USD | 102.81 million USD | 27.63 million USD | Georgia |
| 2020s | 142.23 million USD | 187.27 million USD | 45.04 million USD | Mali |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher consumption of fixed capital (agriculture, forestry and fishing), Georgia or Mali?
- Mali, at 219.91 million USD against 188.59 million USD in Georgia as of 2023.
- What is the difference in consumption of fixed capital (agriculture, forestry and fishing) between Georgia and Mali?
- 31.32 million USD, with Mali ahead.
- How many years of comparable data are there for Georgia and Mali?
- 24 years are reported by both, from 2000 to 2023.
- How do Georgia and Mali rank globally for consumption of fixed capital (agriculture, forestry and fishing)?
- Georgia ranks 95th and Mali ranks 93rd of 182 countries.
- Where does this data come from?
- Food and Agriculture Organization of the United Nations, published as Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value US$. 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.