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Observations and Suggestions Observations and Suggestions for Improving Agricultural and for Improving Agricultural and Rural Statistics in Developing Rural Statistics in Developing

CountriesCountries

Isidoro P. DavidIsidoro P. David

ICAS III, Cancun, MexicoICAS III, Cancun, Mexico2-4 November 20042-4 November 2004

Observations and Suggestions Observations and Suggestions for Improving Agricultural and for Improving Agricultural and Rural Statistics in Developing Rural Statistics in Developing

CountriesCountries

Isidoro P. DavidIsidoro P. David

ICAS III, Cancun, MexicoICAS III, Cancun, Mexico2-4 November 20042-4 November 2004

ContentsContents

• IntroductionIntroduction• Some definitions: Implications on Some definitions: Implications on

Data Analysis & Availability.Data Analysis & Availability.• The Future of Agricultural CensusesThe Future of Agricultural Censuses• Outstanding Design, Measurement & Outstanding Design, Measurement &

Estimation ProblemsEstimation Problems• Improving Statistics on the Food Improving Statistics on the Food

Poor, Undernourished, & HungryPoor, Undernourished, & Hungry

On DefinitionsOn DefinitionsRural & AgriculturalRural & Agricultural• Used interchangeably?Used interchangeably?• Rural relates to condition, state or Rural relates to condition, state or

geographic area (with shifting boundaries)geographic area (with shifting boundaries)• Agricultural relates to activities, e.g. raising Agricultural relates to activities, e.g. raising

cropscrops

China’s 1996 agri census was really rural China’s 1996 agri census was really rural censuscensus

Table 1a. Population and Number of Villages in 1980 and 1990 CPH, Table 1a. Population and Number of Villages in 1980 and 1990 CPH, PhilippinesPhilippines..

19801980 19901990

Population (million)Population (million) 48.148.1 60.760.7

Of which: Urban (million)Of which: Urban (million) 18.018.0 29.429.4

Urban (%)Urban (%) 37.537.5 48.548.5

Villages (thousand)Villages (thousand) 40.240.2 41.341.3

Of which: Urban (thousand)Of which: Urban (thousand) 7.77.7 10.210.2

Urban (%)Urban (%) 19.219.2 24.824.8

Table 1b. Poverty Incidences, 1985 – 1997 Table 1b. Poverty Incidences, 1985 – 1997 PhilippinesPhilippines

1985198519881988 19911991 19941994 19971997

Poverty Incidence Poverty Incidence (%)(%)

4949 5050 4545 4141 3737

Urban (%)Urban (%) 3838 3434 3636 2828 2222

Rural (%)Rural (%) 5656 5252 5555 5353 5151Source: David and Maligalig (2001)

In Philippines, Rural – Urban In Philippines, Rural – Urban dropped as strata in the new dropped as strata in the new master sample for household master sample for household

surveys that was implemented surveys that was implemented beginning 2003.beginning 2003.

Short-term (less than one year) poverty is averaged Short-term (less than one year) poverty is averaged

out in poverty statistics presented as annual and out in poverty statistics presented as annual and

broken down into rural, urban, regions. Thus, broken down into rural, urban, regions. Thus,

dearth of statistics on transient poverty dearth of statistics on transient poverty

experienced by agricultural households in rainfed experienced by agricultural households in rainfed

and upland areas, fishermen during typhoon and upland areas, fishermen during typhoon

season, also poverty brought about by reduced season, also poverty brought about by reduced

off-farm employment in physical infrastructure off-farm employment in physical infrastructure

projects that slow down during the wet season.projects that slow down during the wet season.

Statistics on food deprivation and Statistics on food deprivation and under-nutrition.under-nutrition.

Is undernourished the same as Is undernourished the same as hungry?hungry?

(To be discussed last)(To be discussed last)

Future of Census of Future of Census of Agriculture (CA)Agriculture (CA)

Declining support for CA becauseDeclining support for CA because

• Users are not clear about the role of CA vis-à-vis surveysUsers are not clear about the role of CA vis-à-vis surveys

• CA reports are released very lateCA reports are released very late

• Large deviations between CA and sample surveys, thus Large deviations between CA and sample surveys, thus limiting the use of CA mainly to rates, proportions and limiting the use of CA mainly to rates, proportions and distributions (e.g. land use) instead of levels.distributions (e.g. land use) instead of levels.

Civil strife, ethnic/religious conflicts, terrorism prevent CA Civil strife, ethnic/religious conflicts, terrorism prevent CA in parts of a growing list of countries – Sri Lanka, Nepal, in parts of a growing list of countries – Sri Lanka, Nepal, Philippines, Indonesia, Myanmar, Solomon Islands in Philippines, Indonesia, Myanmar, Solomon Islands in Asia-Pacific. Other regions have their lists.Asia-Pacific. Other regions have their lists.

Future of CAs, cont’dFuture of CAs, cont’d

• Sampling in previous CAs are confined to small Sampling in previous CAs are confined to small farm households stratum.farm households stratum.

• We are bound to see more extensive sampling: 3-We are bound to see more extensive sampling: 3-stage in Indonesia, 2-stage in Philippines. stage in Indonesia, 2-stage in Philippines.

• Sampling frame problem is solved through multi-Sampling frame problem is solved through multi-phase sampling (David 1998), or through CPH.phase sampling (David 1998), or through CPH.

Some countries have done away with censuses Some countries have done away with censuses (India), or called a sample survey a census (India), or called a sample survey a census (Indonesia). (Indonesia).

China, Vietnam are exceptions.China, Vietnam are exceptions.

Some Design, Measurement and Some Design, Measurement and Estimation ProblemsEstimation Problems

• Need to strengthen design and analysis capability Need to strengthen design and analysis capability towards estimating distributions.towards estimating distributions.

• Estimation of distributions better served by integrated Estimation of distributions better served by integrated surveys, e.g. agricultural labor force with nationwide surveys, e.g. agricultural labor force with nationwide LFS, agricultural/rural income with HIES.LFS, agricultural/rural income with HIES.

• Integration through master sample; e.g. PhilippinesIntegration through master sample; e.g. Philippines

• Include minimum agric info in CPH to enable injecting Include minimum agric info in CPH to enable injecting at design stage of master sample.at design stage of master sample.

• Integration is more difficult with decentralized Integration is more difficult with decentralized statistical systems.statistical systems.

Why is a large subset of national Why is a large subset of national agricultural database still produced agricultural database still produced

from subjective methodsfrom subjective methods??• Measurement problems too difficult and costly to Measurement problems too difficult and costly to

solve?solve?• Agristats lower priority in agriministry or in Agristats lower priority in agriministry or in

national statistical system?national statistical system?• Donor assistance skewed towards non-agristats?Donor assistance skewed towards non-agristats?

Non- agristats already based mainly on Non- agristats already based mainly on objective methods, which will make objective methods, which will make integration more difficult. integration more difficult.

Objective methods, e.g. crop-cutting, Objective methods, e.g. crop-cutting, losing effectiveness. Experiments losing effectiveness. Experiments needed to try other alternatives.needed to try other alternatives.

Examples:Examples:• Regression with two-phase sampling.Regression with two-phase sampling.• Consumption from HIES to adjust crops and Consumption from HIES to adjust crops and

livestock output estimates.livestock output estimates.

Improving Statistics on Food Poor, Improving Statistics on Food Poor, Undernourished and HungryUndernourished and Hungry

• MDG1 – eradicate MDG1 – eradicate extreme povertyextreme poverty and and hungerhunger• WB $1 a day poverty incidence for extreme povertyWB $1 a day poverty incidence for extreme poverty

Uses national estimates based on household Uses national estimates based on household surveysurvey2100kcal/capita/day energy threshold2100kcal/capita/day energy thresholdIncludes food and non-food componentsIncludes food and non-food componentsBottom of low income countries, hence severeBottom of low income countries, hence severe

• FAO proportion of undernourished persons for hungerFAO proportion of undernourished persons for hunger 2100 threshold also 2100 threshold also Uses national food supplies and estimate of Uses national food supplies and estimate of

how how supply is distributed to populationsupply is distributed to populationIncludes food only.Includes food only.

• Both statistics indicators of the same thing? Both statistics indicators of the same thing? • Or is being extremely poor same as being Or is being extremely poor same as being

hungry?hungry?• FAO indicator (5) < WB indicator (1) FAO indicator (5) < WB indicator (1)

always?always?• Both indicators showing consistent trend?Both indicators showing consistent trend?• Can FAO methodology be aligned with other Can FAO methodology be aligned with other

indicators, e.g. household survey-based.indicators, e.g. household survey-based.

Table 2. $1 a day poverty and undernutrition estimates in eight most populous developing countries

 

Country$1 a Day Undernutrition Populatio

n in 20001990 1995 2000   1990 1995 2000

China 1275

Incidence (%) 33 17.4 16.6 17 12 11

Persons (Mns) 381 212 212 193 145 135

India 1017

Incidence 42.1 42.2 34.7 25 21 21

Persons 356 393 353 215 195 214

Indonesia 212

Incidence 17.4 13.9 7.2 9 6 6

Persons 32 27 15 17 11 13

Brazil 172

Incidence 14 10.5 8.2 12 10 9

Persons 21 15 14 19 17 16

Table 2. $1 a day poverty and undernutrition estimates in eight most populous developing countries (con’t)

 

Country$1 a Day Undernutrition Populatio

n in 20001990 1995 2000   1990 1995 2000

Pakistan 143

Incidence 47.8 33.9 13.4 26 19 19

Persons 53 42 19 29 24 27

Bangladesh 138

Incidence 35.9 28.6 36 35 38 32

Persons 39 35 49 39 48 44

Nigeria 115

Incidence 59.2 70.2* …* 13 8 8

Persons 51 70* 80* 11 8 9

Mexico 99

Incidence 15.8 8.4 9.8 5 5 5

Persons 13 8 10 4 5 5

Totals                

Incidence (weighted) 34.7 27.1 23.7 19.4 15.4 14.6

Persons (Mns) 945 802 752   524 453 463  

0

5

10

15

20

25

30

35

40

1990 1995 2000

$1 a Day

Undernutrition

0

100

200

300

400

500

600

700

800

900

1000

1990 1995 2000

$1 a Day

Undernutrition

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

Type / YearType / Year 19971997 20002000

Total PovertyTotal Poverty

FamiliesFamilies 4.84.8 5.75.7

PersonsPersons 6.5 6.5 7.67.6

Food PovertyFood Poverty

FamiliesFamilies 0.60.6 0.70.7

PersonsPersons 0.80.8 1.01.0

Multiple Sources of Household Multiple Sources of Household Food Consumption DataFood Consumption Data

• HIES, HFCS from NSOHIES, HFCS from NSO

Varying quality & availability of prices, Varying quality & availability of prices, quantities, and value of food items.quantities, and value of food items.

Less objective method of data capture.Less objective method of data capture.

FCS from Nutrition and Health InstitutesFCS from Nutrition and Health Institutes

Examples: Vietnam, PhilippinesExamples: Vietnam, PhilippinesMore objective method of data capture.More objective method of data capture.Use of subject matter specialists as data Use of subject matter specialists as data

collectors.collectors.

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

Divisor/Cut-Off (kcal)Divisor/Cut-Off (kcal) <1500<1500 <1800<1800 <2000<2000 <2100<2100

Family Size, NFamily Size, N 48.048.0 74.074.0 83.083.0 88.088.0

Consumption Units, TCUConsumption Units, TCU 29.029.0 53.053.0 69.069.0 74.074.0

Adjusted for Scale Economies, Adjusted for Scale Economies, N*N*

7.97.9 16.016.0 22.522.5 26.326.3

E s ti mated E mpi r i c al C DF

0

0. 2

0. 4

0. 6

0. 8

1

1. 2

0 1500 1800 2000 2100 >2100

E n er gyLevel C ut - Off

per capita per consumption unit per adult equivalent

Advantages of Direct Approach Advantages of Direct Approach over Food Poverty Line Approachover Food Poverty Line Approach

• Does not require prices, income, expenditure, Does not require prices, income, expenditure, reference population – only quantities of food reference population – only quantities of food items consumed by household.items consumed by household.

• Comparable across time and spaceComparable across time and space• Can readily determine incidence for any Can readily determine incidence for any

choice of thresholdchoice of threshold• Provides sensitivity analysis to different Provides sensitivity analysis to different

choices of thresholdschoices of thresholds• Applicable to other nutrients and vitamins, Applicable to other nutrients and vitamins,

generalizes to joint nutrient adequacy generalizes to joint nutrient adequacy assessment, e.g. energy-protein.assessment, e.g. energy-protein.

Muchas Gracias!Muchas Gracias!Muchas Gracias!Muchas Gracias!