FAO Global Database on Agricultural Capital Stock (ACS) and path toward a FAO Agricultural Productivity Database Marie Vander Donckt, Statistics Division, FAO FIRST MEETING OF THE OECD NETWORK ON AGRICULTURAL TOTAL FACTOR PRODUCTIVITY AND THE ENVIRONMENT 23-24 May 2017 OECD Headquarters, Paris
Introduction For FAO: a longstanding advocate of enhanced productivity, recognizing its link to food security, rural farmers incomes and poverty reduction World Food Summit 1996 Action Plan: Countries need to reverse the recent neglect of investment in agriculture and rural development and mobilize sufficient investment to support agricultural growth, sustainable food security and diversified rural development FAO Strategic Objective 2: Make agriculture, forestry and fisheries more productive and sustainable. Global Strategy Research Program on agricultural productivity For countries: agriculture productivity seen as cornerstone to address the challenge of improved food security: SDG indicators. E.g. SDG Ind.2.3.1 Volume of production per labour unit by classes of farming/pastoral/forestry enterprise size Malabo Declaration (2014) - Commitment #3 End hunger in Africa by 2025 by doubling current agricultural productivity levels and halving post-harvest loss Need for statistical information on sustainable agricultural productivity 23-24 May 2017 - OECD 2
Introduction The purpose of this presentation is to: Present recent methodological development work regarding the FAOSTAT Agriculture Capital Stock (ACS) database Take stock of data availability in FAOSTAT useful for productivity analysis Introduce the next steps forward and minimum requirements for useful agriculture productivity indicators FAO has initiated work to strengthen statistical capacities and data availability in terms of productivity and efficiency measurement 23-24 May 2017 - OECD 3
FAOSTAT Agricultural Capital Stock (ACS) database Recent methodological developments 23-24 May 2017 - OECD 4
The ACS DB within the FAOSTAT Macro-Statistics Domain Macro-Indicators Agriculture Capital Stock Basic Variables Gross Domestic Product (GDP) Gross Domestic Product per capita Basic Variables Gross Domestic Product (GDP) Gross Fixed Capital Formation (GFCF) Gross Domestic Product per capita Value Added, Agriculture, Forestry and Fishing Macro-Indicators Gross Fixed Capital Formation (GFCF) Value Added, Total Manufacturing Value Added, Agriculture, Forestry and Fishing Value Added, Manufacture of food, beverages and tobacco products) Agriculture Capital Stock Value Added, Total Manufacturing Annual Growth of GDP Associated Indicators Investment ratio (GFCF as a share of GDP) VA_AFF as a share of GDP VA_MAN as a share of GDP Value Added, Manufacture of food, beverages and tobacco products) VA_FBT as as a a share of of VA_MAN GDP VA_FBT as a share of VA_MAN Value Added, Manufacture of food and beverages Value Added, Manufacture of food and beverages Value Added, Manufacture of tobacco products Value Added, Manufacture of tobacco products Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) Gross Fixed Capital Formation (Agriculture, Forestry and Fishing) Consumption of Fixed Capital (Agriculture, Forestry and Fishing) Consumption of Fixed Capital (Agriculture, Forestry and Fishing) Net Capital Stocks (Agriculture, Forestry and Fishing) Net Capital Stocks (Agriculture, Forestry and Fishing) Gross Capital Stocks (Agriculture, Forestry and Fishing) Gross Capital Stocks (Agriculture, Forestry and Fishing) Annual Growth of GDP VA_FBT as a share of VA_AFF Associated Indicators Investment ratio (GFCF as a share of GDP) VA_AFF as a share of GDP VA_MAN as a share of GDP VA_FBT as a share of GDP VA_FBT as a share of VA_AFF Agriculture investment ratio (GFCF_AFF/VA_AFF) Agriculture investment ratio (GFCF_AFF/VA_AFF) GFCF Agriculture Orientation Index [(GFCF_AFF/VA_AFF)/(GFCF/GDP)] GFCF Agriculture Orientation Index [(GFCF_AFF/VA_AFF)/(GFCF/GDP)] 23-24 May 2017 - OECD 5
The ACS database Coverage Variables: GFCF_AFF, NCS_AFF, CFC_AFF, GCS_AFF, GFCF_AFF/VA_AFF (AIR) and GFCF_AOI Activity coverage: Agriculture, Forestry and Fishing (ISIC Rev. 3.1, AtB_01t05) Geographical coverage: 206 countries and territories covered International comparability: USD and constant 2005 USD in addition to LCU and constant 2005 LCU Time coverage: Data produced over 1970-2015 timespan, published data starts in 1990 23-24 May 2017 - OECD 6
The ACS database Overall Approach to CS estimation Old FAOSTAT Methodology New FAOSTAT Methodology Estimate capital stock using the physical inventory approach, which adds up the sector s components of produced assets: machinery & equipment, livestock, orchards, land improvements Use existing country data whenever available. When unavailable, estimate capital stock using the PERPETUAL INVENTORY METHOD with declining balances Approach evaluated and abandoned due to: - Data quality issues: low response rates, incomplete data reported by countries, particularly for machinery and equipment ; - Methodological issues in the calculation of components such as land development or machinery and equipment; - Limited country coverage: only for select countries, and only on narrow agriculture sector, excluding forestry and fisheries. ACS i,t = 1 δ i ACS i,t 1 + 1 δ i 2 GFCF i,t i.e. Investments flows adds up cumulatively to build up the capital stock after adjustment for depreciation => Requires assumptions on initial capital stock and depreciation rates => Requires long time series on Gross Fixed Capital Formation in Agriculture 23-24 May 2017 - OECD 7
Constructing series on Gross Fixed Capital Formation Overall Approach Building on previous research programs held at the World Bank and at the FAO, we first compile long time series of the agricultural investment ratio (for each country). This is then used to derive series on agricultural investment flows from 1970 onwards. Central concept for imputing missing GFCF AFF is the Agriculture Investment Ratio (AIR) AIR i,t = GFCF AFF i,t VAAFF i,t, i = 1,2, 206 countries, t = 1970, 1971,, 2015 Whenever missing, AIR is estimated and used to recover GFCF applying: GFCF AFF i,t = AIR i,t VA AFF i,t with VA AFF available from FAO Macro-Indicators DB 23-24 May 2017 - OECD 8
Constructing series on Agriculture Gross Fixed Capital Formation Overall Approach Countries with partially missing series (few data missing) Time Series Approach Univariate ARMAX models Countries with partially missing series (many data missing) Panel Approach Countries with fully missing series Pooled OLS Approach Country-specific information used to fill data gaps of the time series Exploit time and crosscountry information to fill data gaps of the time series OLS regressions estimated on countries for which data are available. Resulting coefficients used for prediction purposes Three groups of countries: OECD, highand middle-income and lower-income (WB grouping) Three groups of countries: OECD, highand middle-income and lower-income (WB grouping) 23-24 May 2017 - OECD 9
Constructing Agriculture Gross Fixed Capital Formation: Countries with partially missing series (few missing data on AIR) (1) Fit univariate ARMA models with exogenous variables before selecting the best model for prediction purposes ARMAX(p,q) extends the ARMA(p,q) model by including the linear effect of r exogenous series (e.g. diff_gdp_percapita, inv_ratio_te, toi_aff, etc.) on the stationary response series y t : φ L y t = c + x t β + θ L ε t, where φ L is the AR polynomial and θ L is the MA one. Vector x t holds the values of the r exogenous, time-varying predictors at time t, with coefficients contained in the β vector. 23-24 May 2017 - OECD 10
Constructing Agriculture Gross Fixed Capital Formation: Countries with partially missing series (few AIR data missing) (2) ARMAX methodology in practice: 1. Load country-specific data and transform them appropriately (e.g. log-transformation, first difference, ) 2. For each country, build loop to test best prediction model based on minimization of BIC criteria: loop tests up to 144 model combinations defined along two broad dimensions: (i) parameter values of the ARMA(p,q) and a set of predictors (diff_log_gdp_pc_usd2005, diff_ gdp_percapita, inv_ratio_te and diff_inv_ratio_te, etc.) 3. After selection of the country-specific preferred model, conduct one-step ahead forecast 4. Graph forecasted against observed value to allow for graphical visualization of prediction 5. Store results and best model parameters values If too few observations and/or observation too erratic, no ARMAX imputation is performed. For those countries, we use panel estimation 23-24 May 2017 - OECD 11
23-24 May 2017 - OECD 12
Constructing Agriculture Gross Fixed Capital Formation: Countries with partially missing series (many missing data on AIR) Countries divided into three sets (based on World Bank classification): OECD Non-OECD high- and upper-middle income countries Non-OECD lower-middle and low- income countries Exploit the time-series and cross-section information contained in the full input dataset to model and estimate through panel regression a relationship between the AIR and selected independent variables: AIR j,t = α j + ߚ 1 ln (GDP_percapita) j,t + ߚ 2 IR j,t + + ε j, t with j = 1,2,, N; t = 1970,, 2015 where α j accounts for country-specific unobserved heterogeneity. Selection of exogenous regressors via leave-one-out and k-fold cross-validation schemes Fixed-Effects and Random-Effects specification are estimated and Hausman test is performed to decide which specification is more suited Infer AIR using the estimated regression coefficients, including the country specific effects 23-24 May 2017 - OECD 13
Constructing Agriculture Gross Fixed Capital Formation: Countries with fully missing data on AIR Countries divided into three sets: OECD, non-oecd high- and upper-middle income countries and lower-middle and low- income countries (based on WB classification) Use the information contained in the input dataset to model and estimate through pooled OLS a relationship between the AIR and selected independent variables AIR k = α + β 1 ln (GDP_percapita) k + β 2 IR k + + ε k with k = 1,2,, K and K = N j=1 nb. obs j Best specification identified based on leave-one out and k-fold cross-validation. Infer AIR using the estimated regression coefficients for those countries for which we do not have agriculture investment data E.g. AIR j = α + β 1 ln (GDP_percapita) j + β 2 IR j + 23-24 May 2017 - OECD 14
Agriculture Investment Ratio (GFCF_AFF/VA_AFF) series : Official country data vs. imputed series 23-24 May 2017 - OECD 15
Agriculture Investment Ratio (GFCF_AFF/VA_AFF) series : Official country data vs. imputed series 23-24 May 2017 - OECD 16
Agriculture Investment Ratio (GFCF_AFF/VA_AFF) series : Official country data vs. imputed series 23-24 May 2017 - OECD 17
Agriculture Investment Ratio (GFCF_AFF/VA_AFF) series : Official country data vs. imputed series 23-24 May 2017 - OECD 18
Agriculture Investment Ratio (GFCF_AFF/VA_AFF) series : Official country data vs. imputed series 23-24 May 2017 - OECD 19
Constructing Agriculture Capital Stock Overall Approach Simplified Perpetual Inventory Method (PIM) with constant, ageindependent rate of consumption of fixed capital, i.e. depreciation rate, δ The central equation to compile net capital stock for country i in time t is NCS i,t = 1 δ i * NCS i,t 1 + 1 δ i /2 * GFCF i,t Investments flows adds up cumulatively to build up the capital stock after adjustment for depreciation Two key assumptions needed on Depreciation rate, δ i Initial net capital stock, NCS i,t0 23-24 May 2017 - OECD 20
NCS & PIM - The depreciation rate, δ In the absence of econometric estimates of geometric depreciation rates, δ is estimated with the declining balance method : δ = r/t A where r is an estimated declining-balance rate and, T A, an average service life of a cohort of assets. To fix the depreciation rate, we thus need to make ad-hoc assumptions on: The value of the declining balance parameter, r. It is fixed to 1.5 in ACS The average service life of agriculture, TA. It is fixed to be equal to 25 years The derived depreciation rate used in the FAO capital stock database is δ = 1.5/25 = 0.06 23-24 May 2017 - OECD 21
Toward a global Agricultural Productivity data base Current data availability in FAOSTAT 23-24 May 2017 - OECD 22
Toward a global AP database Current data availability in FAOSTAT National Accounts based series Value Added in Agriculture, Forestry and Fishing Gross Output in Agriculture, Forestry and Fishing [to be published in next release expected in September 2017] Gross Fixed Capital Formation in Agriculture, Forestry and Fishing Capital Stock in Agriculture, Forestry and Fishing Employment in in Agriculture, Forestry and Fishing Derived indicator - Gross value added per person employed, constant prices 23-24 May 2017 - OECD 23
Toward a global AP database Current data availability in FAOSTAT Crop sector: Areas harvested, Crop production ( harvested production ) Yield harvested production per ha for the area under cultivation. Livestock sector : Producing Animals/Slaughtered including laying animals, milk animals, beehives and slaughtered animals. Production Quantity including eggs, meat, milk, wool, hides and skins, honey and beeswax Yield (e.g. 100 milligrams per animal; number per animal; hectograms per animal; hectograms) Yield/Carcass Weight: 0.1 grams per animal (poultry); hectograms per animal (other animals) Intermediate inputs Fertilizers Pesticides 23-24 May 2017 - OECD 24
Toward a global AP database Current data availability in FAOSTAT Natural and Agri-Environmental factors: Air and climate change, Energy, Land by various typologies, Soil, Water, Emissions, Enteric Fermentation, Manure Management, Burning Biomass 23-24 May 2017 - OECD 25
Toward a global AP database Next steps and what indicators? 23-24 May 2017 - OECD 26