French Polynesia vs Tunisia: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing)

French Polynesia
700.86 million SLC
in 2023
Tunisia
788.52 million SLC
in 2023
French Polynesia rank
121st
Tunisia rank
119th

Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) over time

  • French Polynesia
  • Tunisia
05001.0k1.5k199520092023

How they compare

Tunisia currently reports 788.52 million SLC against 700.86 million SLC in French Polynesia, a difference of 87.66 million SLC.

That makes Tunisia's figure about 1.1 times French Polynesia's.

The two have swapped places 2 times across 29 shared years of data; in 1995 it was Tunisia ahead.

French Polynesia ranks 121st and Tunisia ranks 119th of 181 countries.

Tunisia has averaged higher in every one of the 4 decades both report.

Head to head by decade

Decade French Polynesia Tunisia Difference Ahead
1990s 216.18 million SLC 713.56 million SLC 497.38 million SLC Tunisia
2000s 595.03 million SLC 1,012 million SLC 417.29 million SLC Tunisia
2010s 614.09 million SLC 1,210 million SLC 596.38 million SLC Tunisia
2020s 676.95 million SLC 884.93 million SLC 207.98 million SLC Tunisia

Averages of every year both report within each decade.

Frequently asked questions

Which has higher gross fixed capital formation (agriculture, forestry and fishing), French Polynesia or Tunisia?
Tunisia, at 788.52 million SLC against 700.86 million SLC in French Polynesia as of 2023.
What is the difference in gross fixed capital formation (agriculture, forestry and fishing) between French Polynesia and Tunisia?
87.66 million SLC, with Tunisia ahead.
How many years of comparable data are there for French Polynesia and Tunisia?
29 years are reported by both, from 1995 to 2023.
How do French Polynesia and Tunisia rank globally for gross fixed capital formation (agriculture, forestry and fishing)?
French Polynesia ranks 121st and Tunisia ranks 119th 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, 2015 prices. Statizoid refreshes it automatically from the source and publishes the full history for both places.

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French Polynesia vs Tunisia: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing). Statizoid, drawing on Food and Agriculture Organization of the United Nations. Retrieved 05 September 2026, from https://environment.statizoid.com/compare/gross-fixed-capital-formation-agriculture-forestry-and-fishing-value-standard-local-2/french-polynesia/tunisia/

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<a href="https://environment.statizoid.com/compare/gross-fixed-capital-formation-agriculture-forestry-and-fishing-value-standard-local-2/french-polynesia/tunisia/">French Polynesia vs Tunisia: Gross Fixed Capital Formation (Agriculture, Forestry and Fishing)</a> — Statizoid

About this data

Indicator
Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) — Value Standard Local Currency, 2015 prices
Unit
million SLC
Source
Food and Agriculture Organization of the United Nations
Licence
CC BY-NC-SA 3.0 IGO (FAO)
Coverage
194 places, 5,526 data points, 1995–2023
Last refreshed

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.