Agricultural and Rural Households Income Statistics in Countries in Less-Than-Ideal Conditions: an Insight Thinking to African Countries.

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1 Agricultural and Rural Households Income Statistics in Countries in Less-Than-Ideal Conditions: an Insight Thinking to African Countries. Naman Keita, Statistics Division, FAO Edoardo Pizzoli, National Accounts, ISTAT Abstract The authors would like to extend the developments on agricultural and rural households income statistics on countries with a limited availability of data, rely on the recent FAO - World Bank publication Tracking results in agriculture and rural development in less-than-ideal conditions. Feasible income indicators, considering ongoing data collections and estimation techniques, will be proposed, at national accounts and microeconomic levels. Possible applications to African countries will be also consider in the paper. Keywords Household Income, Africa, Monitoring and Evaluation, Agriculture and Rural Development, Poverty Indicators, 1. Introduction Agricultural and rural household income indicators produced by statistical systems are considered key indicators, among the economic indicators, to monitor and evaluate the results of development policies. Technical difficulties and availability of data are involved that retain statistical offices and agencies at national and international level to supply these typo of indicators. The authors would like to investigate the real data availability in African countries and address some technical difficulties, aware that in most of the countries current statistics are normally produced in less-than-ideal conditions. 2. Tools and Indicators for Rural Households Income Statistics A list of core indicators is suggested by at international level for monitoring and evaluation (M&E) in agriculture and rural development (ARD) programmes in less-than-ideal conditions (GDPRD at al., 2008). A subset, 16 indicators, is considered priority and thought for feeding into the international monitoring systems and for monitoring the national ARD activities. An extended subset, 70 indicators, is a reference list from which they can be extracted to evaluate a broad range of ARD activities. Agricultural and rural households income related indicators are classified among the subset suitable for M&E outcomes over as long-term time period. Indicators considered, by sector (A, B and C) and subsector are the following (in bold are indicated the priorities): A. Sector-wide indicators for ARD: 1) GDP per capita (%, annual growth) ( leading indicators); 2) Household income in rural areas from non-agricultural activity (%, annual growth); 3) Household income in rural areas from agricultural activity (%, annual growth); 4) Rural household income from forest-related activities (%, annual growth) (B. Specific indicators for subsectors of ARD; 4- Forestry); 5) Farmer income as a result of new technologies (by gender, % change); 6) Proportion of rural population below US$1 (Purchasing Power Parity) per day or below national poverty line (% change);

2 7) Rural poor as a proportion of the total poor population; C. Indicators for thematic areas related to ARD; 4. Policies and institutions: 8) Ratio of average income of the richest quintile to the poorest quintile in rural areas (%) The first four indicators can be calculated on a macro or a micro-economic approach. In the first case the core data required are from the aggregates estimated at national accounts level, while in the second case micro-data on income-expenditure are required from household surveys. An optimal mix of the two data sources can be also used (Ciaccia at al., 2009). To calculate the fifth indicator more information on technologies with respect to productsproductions and on agricultural practices are necessary. Last three indicators spotlight extreme income (poorest, richest) households in the population. To calculate them is necessary to use the data estimated from households surveys or agriculturepopulation censuses. 3. Data Availability in African Countries The African continent is subdivided in 53 independent countries and 3 Spanish territories (Ciudad Autónoma de Ceuta, Melilla and Canarias). Main economic variables on income and poverty are available for all the countries at national level, calculated in national accounts. Harmonized macro-level data can be found in World Bank database. Only some countries estimate the same variables at regional level or run a households survey with income information at micro-level. The World Bank supported a Living Standards Measurement Study (LSMS) surveys in 5 African countries: Cote d'ivoire, Ghana, Malawi, Morocco and South Africa. The data has been included in a internationally comparable database of rural household income sources: the Rural Income Generating Activities (RIGA) database. In 3 countries (Nigeria, Tanzania and Senegal) a deeper insight on data-availability and M&E indicators has been supported by the Global Donor Platform for Rural Development, together with FAO and the World Bank. For 2000, as a common reference year 1, 5 indicators have been selected from the Work Bank database: 1) per capita GDP (national GDP and population are also available); 2) inequality of income (Gini coefficient, %); 3) proportion of population below $1 (PPP) per day; 4) poverty headcount ratio at national poverty line (% of population); 5) population below the poverty line (% rural). In African continent, per capita GDP (at current US Dollars) is equal to $ with a standard deviation of 1, calculated at country level, while the median value, also weighted at country level, is $, pointing out an asymmetric distribution of income in the continent (more then 50% of the African population has an income lower than the mean. The countries variability goes from the maximum 7, $ (Republique des Seychelles) to the minimum $ (République démocratique du Congo) that is also the lowest estimated value at World level. With respect to the per capita GDP at World level the mean is 86.6% less (5, $ World), standard deviation is 96.4% less (10, World) and median level 79.4% less (1, $ World): main statistics indicate a clear lower level of per capita GDP with a lower variability between the African countries. 1 Data are for 2000 year. When the data is not available the first data of the closet year is used.

3 Figure Per-capita GDP in African Countries Year 2000 (Source:elaboration on WB data) Seychelles Libya Gabon Mauritius Botswana South Africa Equatorial Guinea Tunisia Namibia Algeria Egypt, Arab Rep. Swaziland Morocco Cape Verde Congo, Rep. Djibouti Angola Cameroon Cote d'ivoire Zimbabwe Senegal Lesotho Mauritania Kenya Guinea Comoros Sudan Nigeria Benin Zambia Gambia, The Tanzania Uganda Central African Republic Ghana Togo Mali Madagascar Mozambique Burkina Faso Rwanda Liberia Eritrea Chad Niger Guinea-Bissau Malawi Sierra Leone Ethiopia Burundi Congo, Dem. Rep Per-capita GDP (current $) - Year 2000 Just 5 countries, belonging to the first quartile, have a per-capita GDP equal to 3, $, while 10 countries have about the 50% of Africa GDP and a per-capita GDP equal to 2, $; the remaining countries have a per capita GDP of $ and the last quartile (31 countries, 58,5% of the total) has a value of $. Last figure is lower than the median value for the continent and is 92.4% less than the mean in the first quartile. Finally, about 50% of African population (400 million people) has a a per-capita GDP of $, 67.5% less than the average. There are also significant regional differences: for instance, Sub-Saharan Africa countries have a per-capita DGP equal to $, 29.5% lower than the total continent. It can be expected that income distribution functions are generally asymmetric, with more people

4 concentrated toward lower per-capita income and with long tails on higher income. Significant different distributions in terms of parameters (means, median and standard deviation) are expected on different areas (rural and urban, for example) and socio-professional groups (agricultural households, etc.). These are examples of analysis that could be done when only national aggregated, macro-economic statistics are available. Micro-data on small social groups, as households, are necessary to understand better the differences in the dispersion and distribution of income in African population. The World Bank can calculate the inequality of income (GINI coefficient) and several poverty indexes based on country poverty assessments and country Poverty Reduction Strategies, but they are from different quality samples and population coverage in the countries. Furthermore, differences in the definition of the underlying data might still affect intertemporal and international comparability (Deininger, 2010). Estimated inequality of income is available only for 31 countries: it changes over countries, with a maximum in Namibia (71%) and a minimum in the Arab Republic of Egypt (29%). The mean value is 47% while the median value is slightly smaller (46%) that suggests an almost centred mean value for the distribution of the GINI coefficient in African countries. Concentration statistics of inequality in the distribution of income are over the levels in OECD countries.

5 Figure Inequality of Income in African Countries Year 2000 (Source:WB; 34 countries available data) Namibia Lesotho Botswana Sierra Leone Central African Republic Swaziland South Africa Zimbabwe Zambia Nigeria Niger Mali Malawi Madagascar Burkina Faso Gambia, The Guinea-Bissau Cote d'ivoire Cameroon Uganda Kenya Senegal Ghana Mozambique Morocco Guinea Tunisia Mauritania Tanzania Algeria Egypt, Arab Rep Inequality of Income (Gini Coeff. %) Considering a poverty index, there are at least 17 countries with more than 50% of the population below 1$ (PPP) per day, with 164 million poor over 492,4 million people (33.3%). In 32 countries (where the data is available) there are 247 million people below 1$ (PPP) per day over 619 million (39.9%).

6 Figure Population below 1 $ (PPP) per day in African Countries Year 2000 (%; Source: WB; 32 countries available data) Zambia Madagascar Sierra Leone Mozambique Swaziland Lesotho Burundi Guinea-Bissau Malawi Chad Mali Niger Rwanda Gambia, The Burkina Faso Eritrea Kenya Mauritania Ethiopia Cameroon Guinea Ghana Tanzania Zimbabwe Nigeria Uganda Senegal Morocco Benin Algeria Egypt, Arab Rep. Tunisia 0,0 10,0 20,0 30,0 40,0 50,0 60,0 70,0 80,0 % If a second poverty index is considered, there are at least 13 countries (59.1%)over the 22 with data available, with more than 50% of the population below the national poverty line.

7 Figure Proportion of the population below the nation poverty line in African countries Year 2000 (%; Source: WB, 22 countries available data) Zambia Madagascar Sierra Leone Swaziland Lesotho Burundi Guinea-Bissau Malawi Mali Gambia, The Rwanda Burkina Faso Mozambique Mauritania Ethiopia Cameroon Ghana Tanzania Uganda Benin Morocco Egypt, Arab Rep. 0,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 80,00 % In rural areas, in the countries where the data is available, there are also differences: countries where more than 50% of the rural population is below the national poverty line, with a maximum in Zambia (72.9%) and countries with lower levels. In most of the countries, the percentage of poor people is higher in rural areas with respect to the total.

8 Figure Proportion of the population below the nation poverty line in rural population with respect to the total in African countries Year 2000 (%; Source: WB, 15 countries available data) Zambia Madagascar Sierra Leone Niger Guinea-Bissau Lesotho Kenya Senegal Nigeria Ghana Zimbabw e Algeria Egypt, Arab Rep. Tunisia Morocco % Total Rural 4. Some Study Cases Three African countries (Nigeria, Tanzania and Senegal) are considered among the pilot countries to validate ARD M&E indexes suggested by GDPRD at al., From the National reports, the World Bank database and National statistical office websites, the potentially measurable indicators are the following:

9 Table Indicators Availability in Pilot African Countries (GDPRD at al., 2008) Indicators Countries N. Description Nigeria Senegal Tanzania I. Whole economy and all sectors: 1 GDP per capita x x x 2a Household income (total) x x x 1b Inequality of income (Gini coefficient); x x x 1c Proportion of population below $1 (PPP) per day; x x x 1d Poverty headcount ratio at national poverty line (Proportion of total pop.) NO NO NO 8a Share of poorest quintile in national income or consumption x x NO II. Rural areas and agricultural sector: 2b Agricultural household income (total) x 2c Rural household income (total) x 2 Household income in rural areas from non-agricultural activity x x NO 3 Household income in rural areas from agricultural activity NO x 4 Rural household income from forest-related activities NO x x 5 Farmer income as a result of new technologies x 6a Proportion of rural population below US$1 (PPP) per day x x x 6b Proportion of rural population below national poverty line x x NO 7 Rural poor as a proportion of the total poor population NO NO NO 8 Ratio of average income of the richest quintile to the poorest quintile in rural areas NO NO NO National accounts and related indicators are available for all the countries. Households surveys that include income information are available in all the countries. The coverage of the population and periodicity are change in for different surveys and countries. In Nigeria there is an annual General Household Survey (GHS) that include income questions and has been done in 2006, There is also a National Integrated Surveys of Households and Infrastructure that include the National Living Standards Survey (NLSS) and Households Expenditure Survey, the Multiple Indicators Cluster Survey (MICS) and Core Welfare Indicator Questionnaire (CWIQ). Finally an initiated Rural Welfare Statistical Surveys annually planned. In Tanzania a Household Budget Survey is conducted every 5 years and a Living Standards Measurement Studies (LSMS) survey was done for In Senegal, a periodical Enquête Sénégalaise Auprès des Ménages gives information on households income. There is also an Enquête de Suivi sur la Pouverté au Senegal that is used to produce indicators on budget/consumption. Potentially, refining statically techniques and classifications, all income indicators could be calculated and income distributions estimated. 5. Conclusions To calculate household income indicators is a difficult task for statistics and international comparability in particular in countries with different levels of development, as it is in Africa, is more complicate. Anyway statistical capability and data availability is growing in many countries that, with an appropriate use of statistical tools could allow the periodical production of income indicators. Even available aggregated data, at national level, combined with partial micro-data, at household/population level, could be enough to produce concentration indicators on income; dispersion and poverty indicators and tendency indicators on a long-run time prospective.

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