**Some Observations and Suggestions for Improving**

**Agricultural and Rural Statistics in Developing Countries**

Isidoro P. David[^1]

**1. Introduction**

In approaching this task of speaking about international standards and
methodology for agricultural and rural statistics, the observations and
suggestions I have chosen to include are based heavily on experience in
the Asian and Pacific countries. Hopefully, some of the observations and
suggestions are relevant also to the developing countries in the other
regions. We hope also that some of views could in some small measure
influence the thinking among some of the donors that are actively
promoting the improvement of agricultural and rural statistics in the
developing countries.

**2. On Some Definitions and Their Implications on Data Analysis and
Availability.**

We dwell on two sets of terms in this note. The first is rural versus
agricultural. Going through the literature, one gets the impression that
these two words are often used interchangeably. Perhaps the statistical
community has not invested enough time to analyze the difference between
the two, besides the obvious that rural implies a condition, state or a
geographical area (with shifting boundaries), while agricultural relates
to activities, like growing crops and raising livestock. In some
developing countries, agricultural statistics is meant generically or
operationally to include fisheries and aquaculture statistics, partly
because the same agency is responsible for the latter also.

It had been pointed out that China's 1996 agriculture census was really
a rural census, because it included more subjects and areas that
transcended the traditional coverage of an agriculture census;
specifically, towns and townships that were classified as rural but had
little agricultural activities were covered fully also.

The rural-urban divide is based on variables (e.g. population density,
presence of theater, central market and other urban amenities) that are
updated in the census of population and housing (CPH). The variables are
seldom changed but their values do, giving rise to rapid urbanization
and a shrinking rural area in developing countries. From a survey design
standpoint, we are reminded that it is not advisable to stratify using
an unstable variable (rural-urban), because this leads to loss in
precision and complications during analysis such as resorting to
post-stratification. Rural and urban areas could instead be treated as
classes or domains that cut across strata, for which estimation
procedures are routine. However, the loss of comparability in the
rural-urban dichotomy from one CPH to the next implies loss in
continuity of the time series statistics, so that their interpretation
could be far from routine. An example is shown in Tables 1a-b, below.
From Table 1b, it is seen that the poverty incidence from 1988 to 1991
increased in both the urban and rural areas of the Philippines -- at the
same time that the incidence for the country decreased. This is not
possible arithmetically, *ceteris paribus.* What made it possible was
that the urban domain - where the poverty incidence was comparatively
much lower - expanded in both population size and area coverage. (Table
1a).

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

+--------------------------------------+---------------+---------------+
|                                      | 1980          | 1990          |
+--------------------------------------+---------------+---------------+
| Population (million)                 | 48.1          | 60.7          |
+--------------------------------------+---------------+---------------+
| Of which: Urban (million)            | 18.0          | 29.4          |
+--------------------------------------+---------------+---------------+
| > Urban (%)                          | 37.5          | 48.5          |
+--------------------------------------+---------------+---------------+
|                                      |               |               |
+--------------------------------------+---------------+---------------+
| Villages (thousand)                  | 40.2          | 41.3          |
+--------------------------------------+---------------+---------------+
| Of which: Urban (thousand)           | 7.7           | 10.2          |
+--------------------------------------+---------------+---------------+
| > Urban (%)                          | 19.2          | 24.8          |
+--------------------------------------+---------------+---------------+

**Table 1b. Poverty Incidences, 1985 -- 1997, Philippines**.

+------------------+---------+---------+---------+---------+---------+
|                  | 1985    | 1988    | 1991    | 1994    | 1997    |
+------------------+---------+---------+---------+---------+---------+
| Poverty          | 49      | 50      | 45      | 41      | 37      |
| Incidence (%)    |         |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| > Urban (%)      | 38      | 34      | 36      | 28      | 22      |
+------------------+---------+---------+---------+---------+---------+
| > Rural (%)      | 56      | 52      | 55      | 53      | 51      |
+------------------+---------+---------+---------+---------+---------+

Source: David and Maligalig (2001)

Confounding rural with agricultural could mask the impact of the short
term (less than one year) duration of many agricultural activities. Take
poverty. The reference period for household income and expenditure
surveys is one year, with the data collected in weekly, monthly,
quarterly or six-month intervals to capture seasonality in the
variables. In the end, however, the data are averaged for the year, with
the resulting annual poverty statistics broken down into rural-urban and
other domains. These fail to capture the transitory or short-term
poverty experienced by households in rainfed and upland areas (who often
belong to minority ethnic groups) who have much reduced incomes during
the dry than in the wet season; and of fisher folk who earn less during
bad weather and live in coastal areas that are at risk to floods and
typhoons. Even off-farm income is affected, as both public and private
infrastructure works that are important sources of temporary employment
show a downturn during the rainy season. These are critical gaps in the
poverty information systems that are currently in place in the
developing countries.

The second set of terms that may have yet to be defined or
differentiated adequately are undernourishment or undernutrition on the
one hand, and malnourishment or hunger on the other hand. The Millennium
Development Goals (MDGs) include eradicating hunger, but the road map
indicator chosen to assess progress on the goal seem to be associated
more with undernourishment. This is discussed in more detail later in
this note.

**3. The Future of Agricultural Censuses**

There have been indications that support for censuses of agriculture
(CAs) in some developing countries have been diminishing since the last
two to three rounds. This could not be ascribed solely to the declining
support for the statistical systems and services in these countries; for
one, support for the censuses of population and housing (CPHs) has
remained strong during the same period. Several factors could be at work
here.

-   Users outside the statistical system are generally not clear about
    the role of CAs in the national statistical database vis-à-vis the
    other sources of agricultural information; i.e. to update structural
    benchmark data for small geographic areas. They expect to find small
    area estimates of production and other temporal characteristics that
    are normally obtained through sample surveys. The result is
    disaffection with the CA; on some occasions questions have been
    raised about the wisdom of spending massive money and effort on an
    undertaking that users find do not serve their current needs. The
    situation is exacerbated when the sample surveys also had failed to
    provide reasonably reliable estimates for relatively larger domains
    like provinces, which is the case in many countries.

-   The final CA reports had been released very late and, in the words
    of one major user (from the Philippines), these were like cadavers
    that went straight to bookshelves for burial. The hour of reckoning
    comes four to five years hence when the NSO requests a huge budget
    for yet essentially the same census.

-   When CAs and inter-census sample surveys collected the same
    characteristics (e.g. areas planted to specific crops, counts of
    productive trees like coconuts and mangoes, and of livestock and
    poultry) the results often showed large differences that were
    indicative of divergences in concepts and methods used, in
    measurement errors, and in sampling errors influencing the two
    sources. In many cases the differences were so large that CA
    estimates of [levels]{.underline} were not adopted as the official
    estimates during the census years. Thus, the inter-census surveys
    are conducted also during census years in order to have continuity
    in the official series. The use of CA estimates of levels tended to
    be confined to cross section analysis and to providing small area
    estimates for formulating projects and programs. Other uses tended
    to be based on ratios and proportions instead of levels (e.g. land
    use distributions) and as sources of frames for sample surveys. The
    latter, however, is done mainly by the official statisticians (that
    need no further convincing of the need for CAs), but not by other
    stakeholders, including those who hold the purse strings for
    statistical activities.

Some countries in Asia have managed without CAs. Cambodia has not done a
CA for half a century. After its census in the early 1970s, India has
relied on its Patwaris (village registrars) to update area and land use
data, continuing to conduct every five years a livestock census only. In
Sri Lanka, there was a 20 year pause between the 1991 and 2001 CAs.
Indonesia's CAs are in reality large scale three stage agricultural
sample surveys. Similarly, the Philippines last two CAs were actually
large scale two stage sample surveys. Previously, sampling in CAs had
been confined to the ultimate sampling units; e.g. sampling in the
stratum of small farm households.

Given the funding, operational, timeliness and accuracy problems with
past CAs, the future direction would likely be towards the use of more
in-depth sampling.[^2] This is not necessarily bad. Inter-census surveys
designed as second-phase samples from a first-phase sample census forge
a closer link between census and surveys, thereby narrowing the gap in
the methodologies and statistics from the two sources (David, 1998). For
this to happen, and happen successfully, the national statistical system
will need competent technical assistance from survey statisticians, who
tend to be in short supply, particularly in developing countries that
have decentralized statistical systems in which the agriculture
statistics agency is in the agriculture ministry.

Protracted civil wars, ethnic or religious conflicts, and terrorism
prevent the successful conduct of CAs in parts of a growing number of
countries -- Sri Lanka, Nepal, Philippines, Indonesia, Myanmar, Solomon
Islands in the Asia-Pacific. Other regions have their lists. To the
extent that sample surveys are more doable than censuses in these
situations, then the former could likewise take the place of censuses in
these countries.

**4. Some Outstanding Measurement, Survey Design and Estimation
Problems**

Agricultural statisticians are finding it increasingly difficult to
answer questions that sound simple to users and lay persons, such as:
How many farmers are there? How much income do they make? Among the
reasons for the difficulty is that households that derive their income
solely from farming are becoming rare. Hence, the definition of a farmer
or farm household is no longer that clear, or the old definition based
on combination of minimum area and number of livestock is no longer
useful for policy use. At the same time, the household's income from
agricultural activities should not be estimated and presented separately
from its off-farm incomes. Instead of point estimates, the questions can
be more appropriately answered through estimates of distributions, e.g.
of the number of farm households along a farm income scale, or of total
income through a two-way table with farm income and off-farm income as
coordinates.

There may be a need to reorient activities and strengthen capacity in
the agricultural statistics bureaus towards estimating distribution
functions from survey data. The integration of rural household surveys,
as proposed by Fred Vogel in a paper in this conference, is almost a
necessary condition to make estimations of many useful distributions
possible. We would go even further by suggesting that all major
household surveys of the national statistical system be integrated. For
instance, rural or agricultural household income and expenditure are
more economically and rationally analyzed from a household income and
expenditure survey; agricultural labor force statistics can be derived
from or improved by labor force surveys; etc.

The integration of household surveys can be pursued through a master
sample. For example, the Philippines developed and began implementing in
2003 a master sample for all household surveys of the National
Statistics Office (NSO), including Family Income and Expenditure Survey
(FIES), Labor Force Survey (LFS), and Health and Demographic Survey. As
mentioned in the next section, another agency used one-fourth of the
master sample for its National Nutrition Survey (NNS); this opens up the
possibility of using the returns from the 41,000 sample households of
FIES as concomitant information to improve the analysis of the NNS.
There should be no serious technical difficulties in revising the
sampling rates of the quarterly LFS, e.g. increase/decrease the rates in
the rural/urban areas, and do supplemental data collection in the rural
areas order to come up with agricultural labor force statistics --
without having to do independent agricultural labor force surveys.

Since the master sample uses the most recent census of population and
housing (CPH) as sampling frame, it would be preferable if future CPH
questionnaires include a minimum set of agricultural information that
can be used in developing master samples that are more agriculture
sector-friendly.

After the major crops such as rice, maize and wheat and inventory of
livestock during the survey date, the remaining statistics on other
crops and production of livestock and livestock products are derived
using subjective methods. This sentence is true in the majority of Asian
developing countries, and possibly in the other regions. It is important
to know the reasons for this lack of advance in design and measurement
methodologies, especially since the household-based statistics in the
non-agriculture sector are based by and large on objectively designed
sample surveys. Have the measurement problems proved insurmountable and
costly given the resources made available to the agricultural statistics
agency? Is it because the agricultural statistics subsystem is accorded
lower priority in the agriculture ministry (in the case of decentralized
statistical systems), or compared to the other subsystems like
population, demography and industry? Has donor assistance been skewed in
favor of non-agriculture statistics? Placing the agriculture statistics
at par with the non-agricultural statistics is an important step towards
integration of survey systems. Many crops, livestock and fishery
production statistics can be improved by pulling in relevant information
from household consumption surveys, for example. While this can be done
at aggregate levels, higher efficiencies are possible if the linking is
done at household or small area levels.

Some objective methods of data capture are losing their effectiveness.
Missing observations from crop-cutting have been rising because farmers
who own or rent harvesting machines, or who hire labor for harvesting,
have increasingly been unwilling to skip the designated crop-cut areas.
Experiments are needed to test alternative methods; for example,
two-phase sampling where the crop-cuts are limited to a much smaller
sub-sample, regress these on yields obtained by interview, and use the
results to adjust the interview responses from the first-phase sample.

**5. Improving Statistics on the Food Poor, Undernourished and Hungry**

*5.1 Poverty and hunger indicators for the Millennium Development Goals*

Poverty is predominantly a rural phenomenon. In cases where rural
poverty incidence has been disaggregated further, subsistence farming
and fishing households and those living in agriculturally marginal areas
like arid and high lands have been found to harbor most of the poor.

The first of the millennium development goals - eradicate extreme
poverty and hunger -- aims to halve, between 1990 and 2015, both the
proportion of people whose income is less than \$1 a day and the
proportion who suffer from hunger. Of the five indicators chosen to
monitor progress toward the goal, the first three - (1) proportion of
people living below the \$1 a day poverty line, (2) poverty gap ratio,
and (3) share of poorest 20% in national income or consumption -- are
for extreme poverty, while the remaining two -- (4) proportion of
underweight children, and (5) proportion of undernourished persons --
are for hunger (see UN MDG website). All are short-term input or
intermediate indicators since they are compiled/updated from annual
data, except indicator (4) which is a manifestation of long-term chronic
undernourishment of children. All are based on data from household
surveys (and in the case of (4) on observations from individual children
in the sample households), except (5) in which the method of compilation
"takes into account the amount of food available per person nationally
and the extent of inequality in access to food" (FAO -- SOFI 2003).

Of the five indicators, (1) and (5) are the most used for monitoring
extreme poverty and hunger respectively at regional and global, and
sometimes at national, levels. We discuss three topics in this section.
First is the possibility that, on the one hand, the two indicators
conceptually might be trying to measure the same phenomenon; on the
other hand, since the methodologies for the two indicators use data from
different sources, the results could in some actual cases point in
different directions. Second, to the extent that being undernourished
does not necessarily mean going hungry or malnourished, (5) may be
improved to make it a more appropriate indicator for monitoring the
other half of the first goal -- that of halving the proportion of hungry
persons. Third, we explore the possibility of estimating the proportion
of hungry, malnourished or undernourished persons -- or food deprivation
in general - directly from household level food consumption data.

*5.2 Empirical Comparison of the Indicators*.

Most developing countries use a 2100 kcalories per capita energy
threshold as basis for computing the food poverty line (fpl). This is
inflated into a total poverty line (tpl) by adding an amount for a
bundle of basic non-food goods and services that are considered
essential and hence availed of by the poor. This is done in two ways:
sum up the costs of a prescribed list of items comprising the bundle,
nfpl say, so that tpl = fpl + nfpl; or calculate the food expenditure
(fe) and total expenditure (te) of a small reference population, such as
those whose per capita income or expenditure falls within a narrow band,
say ± 10% around fpl, and compute tpl = fpl/(fe/te). This tpl is the
basic input for the World Bank methodology, hence the resulting \$1 a
day estimate includes both food and essential non-food components.
However, since the original choice of \$1 a day line was guided by
countries near the bottom of the group of low income countries (see
WB-WDR 1990), the World Bank estimates are considered severe or very
conservative.[^3] The primary data sources for food consumed, prices,
income and expenditure are household surveys.

FAO also uses 2100 kcalories threshold for its indicator (5); however,
similarities with the World Bank methodology stop there. Per capita kcal
consumption is derived from estimates of total national food supply
(viz. national food balance sheets) and distribution of the supply to
the population. Since the methodology does not take into account
non-food requirements, the indicator -- conceptually - could or should
yield lower incidence estimates than the World Bank's \$1 a day
indicator. This is not at all clear or certain to happen in practice,
however, since the basic data sources are different and, as mentioned,
the original \$1 threshold was strongly influenced by a subset of low
income countries.

In 2000, two out of three people in the developing world lived in the
eight most populous developing countries (Table 2). Almost one out of
two lived in China or India. Thus the combined trend in extreme poverty
and under-nutrition in these eight countries should pretty much reflect
what transpires in the developing world. The sizes of China and India
likewise exert heavy influence on regional and global trends.

The values of indicators (1) and (5) for the milestone years 1990, 1995
and 2000 are shown in Table 2. The combined estimates for the eight
countries show that indeed (1)\> (5). However, the differences are
perhaps more than what one would expect: In 1990, the proportion of
persons below the \$1 a day poverty line was 34.7%, while the prevalence
of undernourishment was 19.4%. By 2000, the proportions had declined to
23.7% and 14.6% respectively; the decline was faster in (1) than in (5),
which can be seen more clearly in Figure 1a. Thus, the prospects are
higher in halving extreme poverty incidence by 2015 than in halving the
prevalence of hunger. This gets plainer when the proportions are turned
into persons (Figure 1b). There were an estimated 945 million extremely
poor in 1990, 802 million in 1995, and 752 million in 2000. On the other
hand, while the number of undernourished declined from 524 million in
1990 to 453 in 1995, there appeared to be a setback to a higher 463
million in 2000.[^4] Thus, the two indicators show

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

                                                                                       

  Country                   \$1 a                       Undernutrition                 Population
                            Day                                                        in 2000

                            1990   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     

  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     

                                                                                       

  Notes: Some values under                                                             
  1995 and 2000 are for                                                                
  1996 and 2001                                                                        
  respectively. FAO                                                                    
  undernutrition values are                                                            
  3-year                                                                               

  averages, e.g. 2000 are                                                              
  for 1999-2001.                                                                       

  \* Nigeria\'s 1995 values                                                            
  are actually in 1997. For                                                            
  purposes of estimating                                                               
  weighted incidence and                                                               
  total of eight countries,                                                            

  the same 70.2% incidence                                                             
  was applied to the 1995                                                              
  and 2000 populations.                                                                

  ... Not available                                                                    

                                                                                       

  Sources: UN Statistics                                                               
  Division, MDG Web site                                                               

  UN Population Division                                                               
  Website                                                                              

                                                                                       

                                                                                       
  ------------------------- ------ -------- ------- --- ---------------- ------ ------ ------------

![](media/image1.wmf){width="5.572916666666667in" height="3.5in"}

**Fig. 1a. \$1 a Day Poverty and Undernutrition in Eight Biggest Dev.
Countries (%)**

![](media/image2.wmf){width="5.59375in" height="3.375in"}

**Fig. 1b. \$ a Day Poverty and Undernutrition in Eight Biggest Dev.
Countries (Million Persons)**

discordance in two ways: (1) show much higher estimates than (5) and the
two point to opposing trends. It would be paradoxical if the number of
extremely poor declines significantly at the same time that number of
undernourished increases appreciably.

The discordance is more marked at the country levels. For example,
Mexico's proportion of extremely poor persons declined by five
percentage points from 1990 to 2000, which when converted to population
implied 3 million fewer extremely poor. On the other hand, the
proportion of undernourished remained flat at five per cent; this
implied more undernourished persons on account of a positive population
growth. The discordance is most acute in Nigeria, where in 1990 the
estimates for indicators (1) and (5) were 59% and 13% respectively. By
1997 the estimate for (1) pointed to a sharp increase in the proportion
of extremely poor people, to 70%. On the other hand indicator (5) showed
an improvement from 13 % to 8 % during the same period. These examples
emphasize that these indicators are intended mainly for regional and
global monitoring and should be used for national assessments -- if at
all- with great caution.

*5.3 Undernourished versus Malnourished or Hungry?*

National food poverty lines and FAO's indicator (5) are based on
thresholds that are guided by WHO energy requirement or recommended
daily allowance (RDA) defined as 'the amount needed to maintain health,
growth, and an "appropriate" level of physical activity' (WHO, 1985, p.
34). Thus, while persons who consume less than the RDA may be
undernourished, not all of them go hungry -- or stay hungry for a long
period. Indeed, FAO refers to its indicator as the proportion of the
population below the minimum level of dietary energy consumption, or
proportion of undernourished persons, or prevalence of undernourishment
(FAO, Naiken, op.cit.). It may not, therefore, be an apt indicator for
the second target under the first MDG \-- halving the proportion of
persons who suffer from hunger.[^5]

A hunger prevalence indicator can be derived by lowering the energy
threshold. Some countries have been doing that. For instance, Bangladesh
computes two poverty lines: an absolute poverty line based on a 2,122
kcal threshold and a "hard core poverty line" based on 1,805 kcalories
(Ahmed, 2000). A 1971 FAO/WHO Expert Committee on Energy and Protein
Requirements accepted a 15% coefficient of variation of energy
requirement between individuals in a population or group with similar
demographics; (WHO, 1985, p.6). Assuming a normal or bell-shaped
distribution of energy requirement in the group, this CV and RDA = 2100
kcal implies RDA - SD = 1785 (which nicely rounds off to 1800 kcal).
This could be put forward as justification for Bangladesh's hard core
poverty threshold, for instance. Furthermore, WHO (op.cit) and earlier
writers (e.g. Sukhatme, 1978) suggest a lower threshold of RDA -- 2SD. A
15% CV implies 2SD = 30%, or a lower threshold of 1470 kcal (which
rounds off nicely to 1500).

Thus, with a 2100 kcal food poverty threshold that most countries use, a
hunger threshold rounded to 1500 may be recommended. In fact, it may be
a good idea to have three thresholds, by adding an in-between 1800 kcal.

*5.4 Direct Estimation of Food Poverty and Hunger Prevalence*

No international agency has resources and reach like the World Bank's to
launch an expensive global undertaking such as the International
Comparison Program, the results of which are required for updating
indicator (1). Hence, the use by FAO of indicator (5) which is in
kilocalories and not in different national currencies, and therefore has
the potential for direct universal comparability, is commendable. What
is needed is to revise the methodology to accept household food
consumption surveys as inputs instead of national food supply
estimates.[^6] Such a move will align the FAO indicator with the others.

Computationally, there are several ways of implementing the new
threshold -- or any other threshold for that matter. One that is simple
is to compare the per capita energy consumption of each household
(adjusted into adult equivalent or economies of scale) against 1500. An
alternative that is more appealing analytically is to compute the
lowered threshold for each household, by reducing the individual age by
sex RDAs by the same proportion (e.g. 30%), and compare this against the
estimated actual per capita consumption of the same household. More
generally, estimating the per capita energy consumption cumulative
distribution function (CDF) opens up the flexibility of determining the
proportion below any choice of threshold(s). An example is provided
sub-section 5.6.

*5.5 Multiple Sources of Food Consumption and Nutrition Data*

In many developing countries there are specialized agencies that conduct
time series surveys on nutrition and food consumption. These are,
typically, health and nutrition institutes in either health or science
ministries. Their surveys generally employ comparatively more objective
methods of data capture than those used by NSOs, e.g. food weighing,
clinical observations, and anthropometric and biochemical measurements.
They also use specialists such as nutritionists, chemists, and health
personnel like nurses data collectors, as opposed to NSO statisticians
and enumerators hired (sometimes temporarily) for specific surveys.
However, these specialized agencies are often outside their country's
national statistical system; hence, their surveys' results get into
research reports but seldom in official statistical series.

For example, Vietnam's official poverty statistics are produced by the
General Statistics Office (GSO) from its Multipurpose Household Survey
up to the 1980s and later from the Vietnamese Living Standard Surveys
(VLSS). The National Institute of Nutrition of the Ministry of Health
conducts General Nutrition Surveys, the last one in 2000 which had 7,658
sample households. The data capture method was described as a "24-hour
recall combined with controlled weighing ...standardized by food
weighing as a checking method" (General Nutrition Survey 2000 Report).
Adjustments were made for food taken outside the home and for guests..
The quantities so obtained were converted into energy (and other
nutrients) units using food composition tables developed by the
Institute. Instead of estimating poverty lines in (Vietnamese Dong) as
GSO does, the Institute derived empirical CDFs of per capita consumption
of nutrients, including kilocalories of energy. The Philippines provides
another example which is presented separately in the next subsection for
illustrative purposes and to point out problems and potential areas of
improvement of food poverty statistics.

*5.6 An Illustrative Example*

The main source of basic data for poverty measurement in the Philippines
is the Family Income and Expenditure Survey (FIES) that the National
Statistics Office conducts every three years. Two face-to-face
interviews are done, the first in July for data pertaining to the first
half of the year, and the second in January for the July-December
period. For food, quantities, prices and values are elicited using as
recall or reference period either the actual or usual consumption for a
week or a month depending on the item. Data from the two visits are
combined to derive annualized estimates. The last FIES was conducted in
2003, with a sample of about 41,000 households. As of this writing,
preliminary results (of income and expenditure distributions) have been
published, but poverty statistics have yet to be released. Clean family
level data files (containing food consumption details) also have yet to
be made available.

The official poverty rates for Metropolitan Manila or National Capital
Region (NCR) for 1997 and 2000 are shown in Table 3. (Why NCR only will
become clear shortly). The Philippines methodology in compiling these
statistics is unique in Asia in that the food poverty line (fpl) is the
cost of the ingredients that go into a prescribed one-day menu that
provides 2000 kcal and constructed based on the consumption behavior of
poor families. This is inflated into a total poverty line (tpl) using
Engel's coefficient for families whose per capita expenditures fall
within ± 10 percentage points of fpl. To estimate the poverty incidences
(of families), these poverty lines are then compared with household per
capita income distribution. The poverty incidences (of persons) is
obtained from the sizes of the poor families and inflated to reflect the
entire population. Because poor families tend to be bigger, it is seen
from Table 3 that family poverty incidence rates are appreciably lower
than the population rates. The much higher rates for total poverty
compared to food poverty is another noticeable feature of the entries in
Table 3. Granted that the costs of other non-food basic needs,
particularly housing and transport, are much higher in NCR than in other
regions and rural areas especially; nevertheless, it would be important
to ask or ascertain whether non-food basic needs should really cause a
3- to 5-fold increase in total poverty from food poverty incidences.
(Or, is food poverty incidence underestimated? This question is
suggested by alternative data and ways of estimating food poverty
incidence, as discussed below.)

**Table 3. Official Poverty Rates (%), National Capital Region,
Philippines**

+---------------------------------+-----------------+------------------+
| ### Type / Year                 | **1997**        | **2000**         |
+---------------------------------+-----------------+------------------+
| *Total Poverty*                 |                 |                  |
+---------------------------------+-----------------+------------------+
| Families                        | 4.8             | 5.7              |
+---------------------------------+-----------------+------------------+
| Persons                         | 6.5             | 7.6              |
+---------------------------------+-----------------+------------------+
| *Food Poverty*                  |                 |                  |
+---------------------------------+-----------------+------------------+
| Families                        | 0.6             | 0.7              |
+---------------------------------+-----------------+------------------+
| Persons                         | 0.8             | 1.0              |
+---------------------------------+-----------------+------------------+

Source: National Statistical Coordination Board (NSCB); NSO for basic
data.

Note: NSCB also calls food poverty as subsistence poverty or core
poverty, and is the estimate of the proportion of families or persons
whose per capita income \< cost of 2000 kilocalorie menu prescribed for
the National Capital Region.

The Food and Nutrition Research Institute (FNRI) in the Department of
Science and Technology conducts a National Nutrition Survey (NNS) every
five years. The 2003 NNS is a one-fourth sub-sample of the 2003 FIES
sample.[^7] NNS has four modules: Food Consumption Survey (FCS),
Anthropometric Survey, Clinical Survey, and Biochemical Survey. The data
capture method used in FCS has been one-day actual weighing of all food
items cooked by the sample family in the course of the day, with the
sample families spread over the seven days of the week. Left over
portions, amounts fed to pets, etc. were weighed also. Family members
who ate outside were asked to recall their meals particulars, at the
same time that consumption of guests who ate with the family during the
day were netted out as well.

The 2003 NNS data are currently being processed; only the questionnaires
from the 931 sample families in the National Capital Region (NCR) or
Metropolitan Manila have been cleaned and the energy values derived from
the NCR-FCS questionnaires. (This is the reason why Table 3 entries are
confined to NCR also). Empirical cumulative distribution functions (CDF)
of per capita energy consumption were calculated from this sub-sample
(Table 4). Owing to the smallish sub-sample and lack of time to compute
sampling errors, the results should be regarded as preliminary and
mainly for illustration of the method.

There are three choices of denominator for the estimated total family
energy consumption, to arrive at family per capita energy consumption.
One is the family size, N. Since there is no adjustment at all (for
family size, composition, elasticity of need in general), the results
lead predictably to very high food poverty incidence rates (first row,
Table 4). Forty-eight percent of the population consumed less than 1500
kcal per day. The food poverty incidence at the official 2000 kcal
threshold is an unrealistically high 83%.

FNRI uses what it calls a total consumption unit (TCU) in place of N.
TCU adjusts N downwards taking into account the family's meal pattern
and number of meals taken by each member; however, it does not adjust
for family size and composition. The results (second row, Table 4),
while lower, are still much too high compared to the official estimates
(Table 3). Estimates of similar magnitudes have been observed before.
These point to the inadequacy of N and TCU for per capita nutrient
consumption estimation, as they lead to inordinately high estimates of
food deprivation incidence.

A third conceptually and analytically more appealing denominator for
total family consumption is N adjusted for elasticities of (food) need.
Bantilan et. al. (1992) estimated such elasticity , є , to be 0.7 using
a double log function of food expenditure, income, and family size N
using data from 1985 FIES.[^8] Pending more recent data on food
expenditure and income (e.g. from 2003 FIES), N\* = N^0.7^ is tried on
the 2003 NCR- FCS data. The results (third row, Table 4) look more
reasonable, though still higher than the official estimates. They show a
(tentative) 7.9% hunger prevalence and a 22.5% food poverty or dietary
energy inadequacy incidence.[^9]

**Table 4**. **Energy Consumption Distributions (% of Population)
Using**

**Three Different Divisors for Total Consumption, NCR- Philippines,
2003**

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

  Family Size, N                 48.0         74.0         83.0         88.0

  Consumption Units, TCU         29.0         53.0         69.0         74.0

  Adjusted for Scale Economies,  7.9          16.0         22.5         26.3
  N\*                                                                   
  ------------------------------ ------------ ------------ ------------ ------------

Source: David, et. al. (2004)

*5.7. Comparison with Food Poverty Line Method*

Estimating a food poverty line (fpl) requires constructing a food bundle
or basket that provides the prescribed per capita energy threshold,
converting these into energy units, choosing a reference poor
population, and collecting the unit prices of the food items in the
bundle; then fpl = Σ pq , where p is price, q is quantity, and the
summation is over the items in the bundle. To estimate food poverty
incidence, fpl is compared against either the household per capita
income or expenditure distribution that is estimated from an income and
expenditure survey.

On the other hand, all that the direct food poverty estimation method
described in sub-section 5.6 needs are household food consumption data
in quantities for each food item. These are converted into energy units
and into per capita energy consumption using an appropriate divisor. The
per capita energy consumption distribution is estimated (e.g. an
empirical CDF), from which the proportion of the population below any
chosen energy threshold is readily determined. Prices, choosing a
reference population, collecting income or expenditure data do not enter
the picture. Thus, work on improving basic data quality can be
concentrated on accurately measuring food consumed. To the extent that a
person is a person and a calorie is a calorie (disregarding quality
difference between sources of calories for the moment), then the
estimates are, or can be easily made comparable across space and time.
An empirical CDF allows an assessment of the sensitivity of the food
poverty estimates to different choices of energy threshold. For
instance, a shift by the Philippines of its current 2000 kcal threshold
to 2100 that is used by most countries, would mean a 3.8 percentage
points increase in the food poverty incidence estimate (last row, Table
4). Last but not least, the method is uniformly applicable to protein,
vitamin A and any other nutrient of choice. It can be generalized to
more than one nutrient, e.g. by estimating the joint per capita
consumption of energy and protein.

**6. References**

Ahmed, F., 2000. Poverty Incidence in Bangladesh 1999: Regional and
National Estimates. Presented at the Regional Seminar on Poverty
Monitoring Survey, 21 May 2000, Dhaka

Bantilan, C. S., J. M Pattugalan and M. de Castro (1992). Adult
Equivalent Income and Expenditure Distributions: Measures of Poverty and
Inequality in the Philippines, 1985 and 1988. Presented at the
Consultative Workshop on the Methodologies for an Integrated Analysis of
Poverty and Income Distribution and Their Uses for Policy Formulation,
21-24 July 1992, Quezon City, Philippines

David, I. P. (1998). Sampling Strategy for Agricultural Censuses and
Surveys in Developing Countries. In *Proceedings of the First
International Conference on Agricultural Statistics*, 18-20 March 1998,
Washington DC.

# David, I. P and D. S. Maligalig (2001). Issues in Estimating the Poverty Line. Presented at the Paper presented at the WBI-Philippine Institute for Development Studies Regional Workshop in Strengthening Poverty Data Collection and Analysis, 30 April -- 3 May 2001, Manila.

David, I. P., D. S. Maligalig, W. L. Molano, Ma. R. A. Pedro and Z. VJ.
Albacea. Estimating Food Poverty and Hunger Prevalence Directly from
Food Consumption Data. Paper presented at the UN Statistics Division
Regional Workshop on Poverty Statistics, 7-8 October 2004, Manila.

Food and Agriculture Organization. *State of Food Insecurity (SOFI)
2003*. Rome

Ministry of Health -- National Institute of Nutrition. *General
Nutritional Survey 2000 Report.* Medical Publishing House, 2003. Hanoi,
Vietnam.

Naiken, L. (2002). FAO Methodology for Estimating the Prevalence of
Undernourishment. In *Proceedings of International Symposium on
International Measurement and Assessment of Food Deprivation and
Undernutrition, FAO, Rome, 26-28 June 2002, pp. 7-42.*

Sukhatme, P. V. (1978). Assessment of Adequacy of Diets at Different
Income Levels. *Economic and Political Weekly*, Vol. XII, No. 31-32.

Vogel, F. (2004). Integrating Rural Household Surveys. Paper in the
Third International Conference on Agricultural Statistics, 2-4 November
2004, Cancun, Mexico.

World Bank. *World Development Report 1990*. Oxford University Press

World Health Organization. (1985). *Energy and Protein Requirements.*
Report of a Joint FAO/WHO/UNU Expert Consultation. WHO Technical Report
Series 724, Geneva.

[^1]: Consultant, 16 Whitefield, White Plains, Quezon City, Philippines,
    email: <ip_david@yahoo.com> This note is presented at the Third
    International Conference on Agriculture Statistics, 2-4 November
    2004, Cancun, Mexico

[^2]: China, Vietnam and other countries that continue to have one party
    governments will be exceptions. In these countries, the old
    administrative reporting systems that are still wholly or partly in
    place are harnessed to do complete enumeration censuses.

[^3]: The World Bank also compiles estimates based on a more generous
    \$2 a day poverty line.

[^4]: While the MDGs which sprung from the UN Millennium Summit talk
    about halving the *proportion* of people who suffer from hunger, the
    1996 World Food Summit's goal is to reduce by half the *number* of
    undernourished people, also by the year 2015 (see. e.g. FAO-FIVIMS
    website). The fact that indicator (5) from FAO is used to monitor
    both goals raises the possibility that undernourishment and hunger
    are used interchangeably or synonymously, as well as the
    likelihood - small as it might be -- that success is achieved in
    terms of proportions but not in numbers of people lifted out of food
    deprivation.

[^5]: It may be difficult to get a consensus on a conceptual definition
    of the state of being hungry. This has not stopped survey
    practitioners from using pragmatic operational definitions; e.g.
    going hungry means missing a meal during the reference period
    because there was no food available.

[^6]: It appears that FAO is seriously studying this possibility, as
    mentioned in FAO-SOFI (State of Food Insecurity) and FIVIMS (Food
    Insecurity and Vulnerability Information and Mapping System)
    websites.

[^7]: The National Statistics Office introduced a new master sample for
    its household surveys starting in 2003. The sampling design employed
    replicated sampling with four replicates. All NSO household surveys,
    e.g. Labor Force Survey, National Demographic and Health Survey,
    share the same sampling design, with variations only in sampling
    rates and sample sizes. Other publicly-funded household surveys are
    encouraged to use the same master sample.

[^8]: The double-log function relating family food expenditure (F),
    income (Y) and size (N) is log F = α + β log Y + τ log N. F/Y
    connotes levels of welfare (It is Engel's ratio when Y is replaced
    by expenditure). For F/Y constant, the family elasticity of income
    is є = ( ∂log Y) / ( ∂log N) = τ / (1 -- β).

[^9]: Coincidentally, a private polling company did a survey in the
    third quarter of 2004. Based on the company's definition that anyone
    who missed a meal for lack of food during the reference period is
    considered hungry, hunger prevalence in the NCR was estimated at
    15.6% (a figure that was quoted in all the major Philippine
    dailies). This is very close to the 16.0 incidence in Table 4 under
    N\* and 1800kcal threshold.