Buenas Dias !

1. Buenas Dias!

2. Observations and Suggestions for Improving Agricultural and Rural Statistics in Developing CountriesIsidoro P. DavidICAS III, Cancun, Mexico2-4 November 2004

3. Contents • Introduction • Some definitions: Implications on Data Analysis & Availability. • The Future of Agricultural Censuses • Outstanding Design, Measurement & Estimation Problems • Improving Statistics on the Food Poor, Undernourished, & Hungry

4. On Definitions Rural & Agricultural • Used interchangeably? • Rural relates to condition, state or geographic area (with shifting boundaries) • Agricultural relates to activities, e.g. raising crops China’s 1996 agri census was really rural census

5. Table 1a. Population and Number of Villages in 1980 and 1990 CPH, Philippines. Table 1b. Poverty Incidences, 1985 – 1997 Philippines Source: David and Maligalig (2001)

6. In Philippines, Rural – Urban dropped as strata in the new master sample for household surveys that was implemented beginning 2003.

7. Short-term (less than one year) poverty is averaged out in poverty statistics presented as annual and broken down into rural, urban, regions. Thus, dearth of statistics on transient poverty experienced by agricultural households in rainfed and upland areas, fishermen during typhoon season, also poverty brought about by reduced off-farm employment in physical infrastructure projects that slow down during the wet season.

8. Statistics on food deprivation and under-nutrition. Is undernourished the same as hungry? (To be discussed last)

9. Future of Census of Agriculture (CA) Declining support for CA because • Users are not clear about the role of CA vis-à-vis surveys • CA reports are released very late • Large deviations between CA and sample surveys, thus limiting the use of CA mainly to rates, proportions and distributions (e.g. land use) instead of levels. Civil strife, ethnic/religious conflicts, terrorism prevent CA in parts of a growing list of countries – Sri Lanka, Nepal, Philippines, Indonesia, Myanmar, Solomon Islands in Asia-Pacific. Other regions have their lists.

10. Future of CAs, cont’d • Sampling in previous CAs are confined to small farm households stratum. • We are bound to see more extensive sampling: 3-stage in Indonesia, 2-stage in Philippines. • Sampling frame problem is solved through multi-phase sampling (David 1998), or through CPH. Some countries have done away with censuses (India), or called a sample survey a census (Indonesia). China, Vietnam are exceptions.

11. Some Design, Measurement and Estimation Problems • Need to strengthen design and analysis capability towards estimating distributions. • Estimation of distributions better served by integrated surveys, e.g. agricultural labor force with nationwide LFS, agricultural/rural income with HIES. • Integration through master sample; e.g. Philippines • Include minimum agric info in CPH to enable injecting at design stage of master sample. • Integration is more difficult with decentralized statistical systems.

12. Why is a large subset of national agricultural database still produced from subjective methods? • Measurement problems too difficult and costly to solve? • Agristats lower priority in agriministry or in national statistical system? • Donor assistance skewed towards non-agristats? Non- agristats already based mainly on objective methods, which will make integration more difficult.

13. Objective methods, e.g. crop-cutting, losing effectiveness. Experiments needed to try other alternatives. Examples: • Regression with two-phase sampling. • Consumption from HIES to adjust crops and livestock output estimates.

14. Improving Statistics on Food Poor, Undernourished and Hungry • MDG1 – eradicate extreme poverty and hunger • WB $1 a day poverty incidence for extreme poverty Uses national estimates based on household survey 2100kcal/capita/day energy threshold Includes food and non-food components Bottom of low income countries, hence severe • FAO proportion of undernourished persons for hunger 2100 threshold also Uses national food supplies and estimate of how supply is distributed to population Includes food only.

15. Both statistics indicators of the same thing? • Or is being extremely poor same as being hungry? • FAO indicator (5) < WB indicator (1) always? • Both indicators showing consistent trend? • Can FAO methodology be aligned with other indicators, e.g. household survey-based.

20. Table 2. Official Poverty Rates (%), NCR, Philippines

21. Multiple Sources of Household Food Consumption Data • HIES, HFCS from NSO Varying quality & availability of prices, quantities, and value of food items. Less objective method of data capture. FCS from Nutrition and Health Institutes Examples: Vietnam, Philippines More objective method of data capture. Use of subject matter specialists as data collectors.

22. Table 3. Energy Consumption Distributions (% of Population) Using Three Different Divisors for Total Consumption, NCR- Philippines, 2003

24. Advantages of Direct Approach over Food Poverty Line Approach • Does not require prices, income, expenditure, reference population – only quantities of food items consumed by household. • Comparable across time and space • Can readily determine incidence for any choice of threshold • Provides sensitivity analysis to different choices of thresholds • Applicable to other nutrients and vitamins, generalizes to joint nutrient adequacy assessment, e.g. energy-protein.

25. Muchas Gracias!