Food and Accommodation Away From Home (FAAFH) — Agriculture, Forestry and Fishing — Taxes — Factor share by country
This domain contains data on three food value measures, namely: (1) Food At Home (FAH); (2) Food and Tobacco at Home (FTAH); (3) Food and Accommodation Away From Home (FAAFH), disaggregated by four primary factors (Operating Surplus, Labor, Taxes, Imports) and by five food value chain industries (Agriculture,...
What the numbers show
Food and Accommodation Away From Home (FAAFH) — Agriculture, Forestry and Fishing — Taxes — Factor share is currently reported for 64 countries. The highest value is 14.06 % in Israel; the lowest is -18.58 % in Greece.
The median across all reporting countries is 0.8827 %, and the mean is -0.966 %.
Over the past decade 27 countries rose and 37 fell. The largest increase was in T�rkiye (up 2,042.5%), and the largest decrease in Latvia (down 1,526.8%).
Food and Accommodation Away From Home (FAAFH) — Agriculture, Forestry: full country ranking
| # | Country | Latest | Year | 10-year change | Trend |
|---|---|---|---|---|---|
| 1 | Israel | 14.06 % | 2015 | up 29.1% | rising |
| 1 | Viet Nam | 5.45 % | 2015 | down 29.6% | falling |
| 2 | New Zealand | 9.02 % | 2015 | down 0.3% | flat |
| 3 | Iceland | 8.95 % | 2015 | down 9.4% | falling |
| 4 | Cyprus | 8.85 % | 2015 | up 106.7% | rising |
| 5 | Argentina | 8.63 % | 2015 | up 8.6% | flat |
| 6 | Croatia | 6.14 % | 2015 | up 21.5% | rising |
| 7 | Chile | 5.95 % | 2015 | down 2.5% | flat |
| 8 | South Africa | 5.91 % | 2015 | up 25.2% | rising |
| 9 | Brazil | 5.56 % | 2015 | down 6.0% | falling |
| 10 | Australia | 5.25 % | 2015 | down 0.6% | falling |
| 11 | Denmark | 5.13 % | 2015 | up 294.6% | volatile |
| 12 | Costa Rica | 5 % | 2015 | down 7.4% | flat |
| 13 | Republic of Korea | 4.72 % | 2015 | up 42.5% | rising |
| 14 | Canada | 4.43 % | 2015 | down 44.7% | volatile |
| 15 | Cambodia | 3.7 % | 2015 | up 27.3% | rising |
| 16 | Philippines | 3.54 % | 2015 | down 30.4% | falling |
| 17 | Morocco | 3.43 % | 2015 | up 9.8% | rising |
| 18 | India | 3.03 % | 2015 | up 36.3% | rising |
| 19 | Lithuania | 3.01 % | 2015 | up 256.4% | volatile |
| 20 | Switzerland | 2.55 % | 2015 | down 7.5% | falling |
| 21 | Peru | 2.52 % | 2015 | down 2.5% | rising |
| 22 | Russian Federation | 1.86 % | 2015 | down 45.5% | falling |
| 23 | Kazakhstan | 1.7 % | 2015 | down 13.9% | falling |
| 24 | Tunisia | 1.48 % | 2015 | down 47.6% | falling |
| 25 | United Kingdom of Great Britain and Northern Ireland | 1.3 % | 2015 | up 114.7% | volatile |
| 26 | Thailand | 1.3 % | 2015 | down 31.3% | falling |
| 27 | China, mainland | 1.2 % | 2015 | down 69.8% | falling |
| 28 | Sweden | 1.11 % | 2015 | up 170.0% | volatile |
| 29 | Brunei Darussalam | 1.09 % | 2015 | up 198.4% | rising |
| 30 | China, Hong Kong SAR | 1.05 % | 2015 | up 19.2% | flat |
| 31 | Mexico | 0.9608 % | 2015 | down 21.6% | falling |
| 32 | Colombia | 0.8047 % | 2015 | down 55.6% | volatile |
| 33 | T�rkiye | 0.7019 % | 2015 | up 2,042.5% | volatile |
| 34 | Indonesia | 0.6823 % | 2015 | down 69.2% | volatile |
| 35 | Netherlands (Kingdom of the) | 0.5608 % | 2015 | down 79.4% | falling |
| 36 | Ecuador | 0.5269 % | 2020 | up 148.5% | rising |
| 37 | Singapore | 0.3961 % | 2015 | up 114.0% | rising |
| 38 | Malaysia | 0.2338 % | 2015 | up 237.2% | volatile |
| 39 | Japan | -0.4092 % | 2015 | down 111.5% | volatile |
| 40 | Belgium | -0.5424 % | 2015 | down 61.9% | volatile |
| 41 | China, Taiwan Province of | -1.57 % | 2015 | up 34.1% | rising |
| 42 | Malta | -3.07 % | 2015 | down 224.2% | volatile |
| 43 | Slovakia | -3.63 % | 2015 | down 2.6% | flat |
| 44 | Portugal | -3.74 % | 2015 | up 39.5% | flat |
| 45 | Saudi Arabia | -4.89 % | 2014 | down 7.4% | falling |
| 46 | France | -4.97 % | 2015 | down 215.8% | volatile |
| 47 | Germany | -5.24 % | 2015 | up 33.3% | rising |
| 48 | Czechia | -5.86 % | 2015 | up 32.4% | rising |
| 49 | Luxembourg | -5.98 % | 2015 | up 14.5% | rising |
| 50 | Estonia | -6.34 % | 2015 | down 361.4% | falling |
| 51 | Norway | -6.68 % | 2015 | up 45.5% | rising |
| 52 | Italy | -6.72 % | 2015 | down 50.7% | falling |
| 53 | Romania | -7.74 % | 2015 | down 191.0% | falling |
| 54 | Poland | -8.14 % | 2015 | down 703.9% | volatile |
| 55 | Spain | -10.19 % | 2015 | down 856.8% | falling |
| 56 | Slovenia | -10.78 % | 2015 | down 487.5% | volatile |
| 57 | Austria | -12.88 % | 2015 | up 30.1% | rising |
| 58 | Finland | -13.27 % | 2015 | up 17.5% | rising |
| 59 | Latvia | -14.32 % | 2015 | down 1,526.8% | falling |
| 60 | Hungary | -14.56 % | 2015 | down 343.6% | falling |
| 61 | Ireland | -16.24 % | 2015 | down 24.3% | falling |
| 62 | Bulgaria | -17.28 % | 2015 | down 954.8% | volatile |
| 63 | Greece | -18.58 % | 2015 | down 479.3% | falling |
Regions and income groups
Aggregates are excluded from the country ranking above so that a region can never outrank a country.
- United States of America 5.17 %
About this data
This domain contains data on three food value measures, namely: (1) Food At Home (FAH); (2) Food and Tobacco at Home (FTAH); (3) Food and Accommodation Away From Home (FAAFH), disaggregated by four primary factors (Operating Surplus, Labor, Taxes, Imports) and by five food value chain industries (Agriculture, Forestry and Fishing; Manufacture of food, beverages and tobacco products; Transportation and storage; Wholesale and retail trade; Accommodation and food service activities). The three food value measures differ for the bundle of goods and services they account for. In particular, the FAH refers to domestic expenditures of personal consumption for food consumed at home, at purchaser prices. The FTAH measure is similar in its target, but it refers to a broader set of economic goods, inclusive of tobacco, as food and tobacco expenditures are not always separable in the original data. On the other side, the FAAFH refers to domestic expenditures of personal consumption for food consumed away from home (e.g. in restaurants), at purchaser prices, and it also includes expenditures for accommodation in all the cases where the two types of expenditures were not separable in the original data. The values of industry decomposition measure the food production value-added distribution across different industries and factors involved in the agri-food value chain. All these estimates are based on Leontief Input Ouput modeling and Industry reduction method. The Food Value Chain domain aims to supply informaiton relevant for the SDG 12 - sustainable consumption and production patterns, SDG 2 - zero hunger and SDG 1 - no poverty, that constitue guiding SDGs in the FAO Strategic Framework 2022-2031 and its better production pillar. More in general they may inform national, regional and global food policy, including measures in the World Food Summit framework. Data are collected from national Supply and Use Tables (SUTs) and Industry by Industry Input Output tables (IOTs), via OECD database or NSOs. All input data are in line with the System of National Accounts (SNA) and main international classifications and standards related to environmental-economic accounting (respectively the System of Environmental-Economic Accounting for Agriculture, Forestry and Fisheries, SEEA AFF, and the International Standard Industrial Classification of All Economic Activities, ISIC).