Mali vs Rwanda: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing)
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) over time
- Mali
- Rwanda
How they compare
Rwanda currently reports 314,117 million SLC against 304,419 million SLC in Mali, a difference of 9,698 million SLC.
The two have swapped places 1 time across 29 shared years of data; in 1995 it was Mali ahead.
Mali ranks 33rd and Rwanda ranks 32nd of 181 countries.
Mali has averaged higher in every one of the 4 decades both report.
Head to head by decade
| Decade | Mali | Rwanda | Difference | Ahead |
|---|---|---|---|---|
| 1990s | 20,088 million SLC | 6,136 million SLC | 13,952 million SLC | Mali |
| 2000s | 43,854 million SLC | 21,390 million SLC | 22,464 million SLC | Mali |
| 2010s | 159,061 million SLC | 91,549 million SLC | 67,512 million SLC | Mali |
| 2020s | 257,504 million SLC | 226,610 million SLC | 30,894 million SLC | Mali |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher gross fixed capital formation (agriculture, forestry and fishing), Mali or Rwanda?
- Rwanda, at 314,117 million SLC against 304,419 million SLC in Mali as of 2023.
- What is the difference in gross fixed capital formation (agriculture, forestry and fishing) between Mali and Rwanda?
- 9,698 million SLC, with Rwanda ahead.
- How many years of comparable data are there for Mali and Rwanda?
- 29 years are reported by both, from 1995 to 2023.
- How do Mali and Rwanda rank globally for gross fixed capital formation (agriculture, forestry and fishing)?
- Mali ranks 33rd and Rwanda ranks 32nd 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.