HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress

1. Alemayehu Seyoum Taffesse
International Food Policy Research Institute (IFPRI)
Measurement of Well Being and Development in Africa
Durban International Convention Center, South Africa,
12-14 November, 2015
Agricultural Statistics in Africa

2. Introduction
 Questions
 whether and how agricultural output is or is not
appropriately counted in African data?
 how can this be improved?
 how to account natural capital, for example, soil
degradation from over-cultivation in Agriculture?
 Approach
 Rely on assessment conducted;
 Experience working with CSA

3. State of Agricultural Statistics in Africa
Two Perspectives
Statistics offices - AfDB (2014)
 Questionnaire-based self-assessment by country
statistical agencies/offices;
 Four dimensions:
o Institutional Infrastructure Dimension (Prerequisites),
o Resources Dimension (Input),
o Statistical Methods and Practices Dimension (Throughput), and
o Availability of Statistical Information Dimension (Output).
54 African countries with varied degree of completeness for
each of the three questionnaire modules

4. State of Agricultural Statistics in Africa
Table 12: Country groupings by Composite ASCI
Source: AfDB (2014).
Group A Guinea-Bissau and Libya
Group B (17) Angola, Chad, Madagascar, Equatorial Guinea,
Comoros, Somalia, DRC, Zimbabwe, Congo Republic,
Seychelles, Guinea, Burundi, South Sudan, Sudan,
Djibouti, Gabon, and Gambia
Group C (24) Swaziland, Togo, Côte d’Ivoire, São Tomé and
Principe, Mauritania, Liberia, Algeria, Sierra Leone,
Zambia, Cabo Verde, Cameroon, Mali, Benin,
Malawi, Senegal, Tanzania, Morocco, Nigeria,
Lesotho, Kenya, Mozambique, Niger, Tunisia, and
Burkina Faso
Group D (9) Botswana, Mauritius, Uganda, Rwanda, Egypt,
Namibia, Ghana, South Africa, and Ethiopia
Group E (0) –

5. State of Agricultural Statistics in Africa
 Statistics offices - AfDB (2014)
 Average Results
o “Resources” (24.4%),
o “Statistical Methods and Practices” (41.4%),
o “Institutional Infrastructure” (57.2%); and
o “Availability of Statistical Information” (62.1%).
 Most statistical offices (34) perceive the quality and
accessibility of their data as ‘strong or very strong’ (top
two quintiles);
 Highlights the diversity across African countries:
o 16.3 (Guinea Bissau) to 66.5 (Ethiopia);
o Some poor countries do very well;

6. State of Agricultural Statistics in Africa
 World Bank and Researchers (focus more on data quality)
 Carletto, Jolliffe, and Banerjee (2015) “… agricultural data suffer
from poor quality and narrow sectoral focus” due to;
o difficult-to-measure smallholder agriculture is prevalent in poor
countries;
• Imperfect recall – general and crop-specific (early harvest,
extended harvest);
• Own-consumption – quantity and value;
• Non-standard units;
o agricultural data are collected with little coordination across
sectors;

7. State of Agricultural Statistics in Africa
 World Bank and Researchers (focus more on data quality)
 Devarajan (2013) - “Africa’s Statistical Tragedy,”
“I just said that growth has picked up since the mid-1990s and,
thanks to that growth, poverty is declining. The “statistical
tragedy” is that we cannot be sure of either of these phenomena.”
 Morten Jerven and others – series of papers on GDP and other data
including a special JDS issue in 2015:
“At both the micro and macro level, for analysis of short- or long-
term change, the absence of adequate data on economic
development continues to be a serious challenge for researchers
and policymakers.”

8. Some Observations
 National statistics offices (with support)
 Prioritize in the face of binding constraints (financial, human,
institutional …):
o Institutionalize regular surveys with government budgets and
external support as appropriate;
 biannual agricultural sample surveys (rotating panel and cost of
production sub-samples, a wage-farm-gate price component);
 quinquennial household income and expenditure surveys with
welfare monitoring complements (nutrition, mortality);
 quinquennial small in-depth validation surveys (methodology);
 Enhance the analytical responsibility and capacity of statistical
offices – beyond survey reports;
 Digital transition - CAPI, GPS;

9. Some Observations
 National statistics offices (with support) (cont’d)
 Capacity and data quality assessment mechanisms
o Robust consistency checks,
o Conduct in-depth validation surveys and studies in
collaboration with local and international partners;
o Regular, transparent, and public reviews of capacity and
methodology;
 Form and actively engage targeted advisory panels;
 Initiate and foster in-house research;
 Work with local stake-holders to further enhance autonomy;

10. Some Observations
 Local and international partners (including researchers)
 Fully and consistently recognize that the statistical system is part
of a broader institutional structure with corresponding
opportunities and challenges;
o quality of statistics is endogenous to development – symbiotic
relation;
o standards and outcomes: international best practice vs. ‘just
enough statistics’;
 Promote national statistical offices as a strategic organization
which deserve better treatment and greater autonomy;
 Constructively work towards effective common positions with
national statistical offices:
• acknowledge that incentives and preferences can and do differ;
• genuinely appreciate the work most statistical offices are
doing;

11. 11
Thank You