Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Bangladesh
Bangladesh: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 191,215 million SLC in 2023. ◆ Volatile
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Bangladesh, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
The most recent figure for consumption of fixed capital (agriculture, forestry and fishing) in Bangladesh is 191,215 million SLC, measured in 2023. That is the highest value across all 29 years on record.
That represents a change of up 10.8% on the previous year and up 126.3% over ten years.
Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in Bangladesh peaked at 191,215 million SLC in 2023 and was at its lowest, 35,794 million SLC, in 1995.
Bangladesh ranks 26th of 181 countries on this measure, in the top quarter.
The series is highly variable year to year, so single readings are best treated with caution.
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Bangladesh, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 35,794 million SLC | — |
| 1996 | 36,645 million SLC | +2.4% |
| 1997 | 37,668 million SLC | +2.8% |
| 1998 | 38,933 million SLC | +3.4% |
| 1999 | 40,689 million SLC | +4.5% |
| 2000 | 42,687 million SLC | +4.9% |
| 2001 | 44,563 million SLC | +4.4% |
| 2002 | 46,433 million SLC | +4.2% |
| 2003 | 48,426 million SLC | +4.3% |
| 2004 | 50,591 million SLC | +4.5% |
| 2005 | 52,916 million SLC | +4.6% |
| 2006 | 55,246 million SLC | +4.4% |
| 2007 | 57,748 million SLC | +4.5% |
| 2008 | 60,738 million SLC | +5.2% |
| 2009 | 64,242 million SLC | +5.8% |
| 2010 | 68,437 million SLC | +6.5% |
| 2011 | 73,322 million SLC | +7.1% |
| 2012 | 78,691 million SLC | +7.3% |
| 2013 | 84,479 million SLC | +7.4% |
| 2014 | 91,134 million SLC | +7.9% |
| 2015 | 98,942 million SLC | +8.6% |
| 2016 | 107,408 million SLC | +8.6% |
| 2017 | 116,418 million SLC | +8.4% |
| 2018 | 126,145 million SLC | +8.4% |
| 2019 | 136,488 million SLC | +8.2% |
| 2020 | 147,558 million SLC | +8.1% |
| 2021 | 159,656 million SLC | +8.2% |
| 2022 | 172,610 million SLC | +8.1% |
| 2023 | 191,215 million SLC | +10.8% |
Bangladesh compared with similar countries
- Bangladesh's 191,215 million SLC is above the median for lower middle income countries, which is 5,258 million SLC, 36.4× the median. (41 countries reporting)
- Bangladesh's 191,215 million SLC is above the median for South Asia, which is 67,711 million SLC, 2.8× the median. (6 countries reporting)
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 37,946 million SLC | 35,794 million SLC | 40,689 million SLC | 5 |
| 2000s | 52,359 million SLC | 42,687 million SLC | 64,242 million SLC | 10 |
| 2010s | 98,146 million SLC | 68,437 million SLC | 136,488 million SLC | 10 |
| 2020s | 167,760 million SLC | 147,558 million SLC | 191,215 million SLC | 4 |
Countries ranked near Bangladesh
More environment data for Bangladesh
- Historical exposure to drought — Land soil moisture anomaly -2.59 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -2.3 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.236 °C (2025)
- Temperature change 1.67 °C (2025)
- Cropland — Area, annual growth rate 0 % change on previous year (2024)
- Cropland — Area, per unit of GDP 0 1000 ha per US$ of GDP (2024)
- Cropland — Area, per capita 0.0001 1000 ha per person (2024)
- Arable land — Area, annual growth rate -1.03 % change on previous year (2024)
- Arable land — Area, per unit of GDP 0 1000 ha per US$ of GDP (2024)
Frequently asked questions
- What is consumption of fixed capital (agriculture, forestry and fishing) in Bangladesh?
- Consumption of fixed capital (agriculture, forestry and fishing) in Bangladesh was 191,215 million SLC in 2023, according to Food and Agriculture Organization of the United Nations.
- What is the highest consumption of fixed capital (agriculture, forestry and fishing) recorded in Bangladesh?
- The highest recorded value was 191,215 million SLC in 2023.
- What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in Bangladesh?
- The lowest recorded value was 35,794 million SLC in 1995.
- How does Bangladesh rank for consumption of fixed capital (agriculture, forestry and fishing)?
- Bangladesh ranks 26th out of 181 countries with data for 2023.
- Is consumption of fixed capital (agriculture, forestry and fishing) rising or falling in Bangladesh?
- Over the last ten years it is up 126.3%. The long-run trend across the full record is volatile.
- Where does this Bangladesh data come from?
- The figures come from Food and Agriculture Organization of the United Nations, published as part of Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value Standard Local Currency, 2015 prices. Statizoid updates them automatically from the source API.
Download this data
CSV · JSON — 29 observations, free to reuse under CC BY-NC-SA 3.0 IGO (FAO).
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.