# Integrating Rural Household Surveys

Frederic A. Vogel

The World Bank

## 1. The Current Situation

The report of the United Nations Secretary-General (02/04/2003) stated
that "three quarters of the world's poor live in rural areas of
developing countries and depend on agriculture and related activities
for their livelihood. The Millennium Development Goals (MDGs) of halving
the proportion of people living on less than a dollar a day and the
proportion of those who suffer from hunger by 2015 cannot be achieved
unless rural poverty is urgently reduced."

One of the underlying difficulties of assessing rural poverty is that
most of the available data are only available at the national level. The
same problem occurs with assessments of indicators of the other MDGs
including Universal Primary Schooling, Gender Equality, Reducing Child
Mortality, Improving Health, Ensuring Environmental Sustainability, and
Promoting Economic Development. The primary indicators are mostly at the
national level, while the greater problem is in rural areas. The degree
of poverty is masked when only shown at the national level because the
distribution of poverty is not even between rural and urban and can
exist for different reasons.

Many developing countries lack the resources to maintain the statistical
capacity needed to provide sustainable data on their core economic
activities. As a result, resources are even more limited to monitor the
progress towards meeting the millennium development goals.

Most of the funding for rural development at the national level has been
obtained through support for agriculture. A similar statement can be
made about the national level resources for statistics for indicators of
rural development. Agriculture is viewed as a major economic activity in
rural areas in many low income countries, therefore, they mainly support
their major agricultural statistics programs. The quality of rural
statistics has been weaker and more fragmented because there is often no
single department having that responsibility nor are adequate resources
provided. In many cases, there is no national statistical framework for
rural statistics.

Policy for rural development has often grown out of policy for
agriculture. Agricultural policy primarily impacts farmers and their
households; a main objective is to support their incomes. Raising
agricultural productivity is important for reducing poverty and
promoting food security. However, while agriculture is the major
activity and source of income of people living in rural areas of
developing countries, non-farm activities play an increasingly important
role in expanding rural employment and income. The connection between
these issues needs to be included in the monitoring activities to
provide a better understanding of what policies can lead to a reduction
in poverty.

The dilemma is that the primary policies affecting rural areas and the
supporting statistics have been centered on agriculture. There have been
few attempts to provide a statistical system for rural areas. Often this
need can be met by disaggregating national data into urban and rural
sectors.

The purpose of this paper is to provide a framework to integrate
agricultural and rural statistics which is centered on the household as
the unit of measure. This integration needs to take into account the
complex nature of data needs required about households to understand how
the forces affecting poverty are for example, related to education,
health and the environment. The paper also calls for international and
national statistical organizations to join their forces to integrate
data requirements into a sustainable survey and data system. This data
system should integrate the national statistical system's needs to
provide data for core economic and social indicators with those needed
to monitor progress towards meeting the MDGs. In this process, the
statistical capabilities of the countries can be improved and maintained
on a sustainable basis.

## 2. The Need for Agriculture and Rural Statistics integrated with MDG indicators.

Official statistics are used to make plans or decisions and to monitor
their impact over time. These statistics have a myriad of users
including all levels of government, international organizations such as
the IMF, World Bank, regional development banks, private industry, and
individuals . An efficient data system must recognize the different uses
and users of its output whether it be a Consumer Price Index or
measurements of progress towards meeting the MDGs. A brief overview of
the three basic data needs follows.

**Facilitate Sound Policy.** Policy decisions, whether by international
organizations or by national and local governments, are made for a wide
range of reasons and purposes. Examples include decisions about using
resources to improve the infrastructure via better roads, more schools,
improved health care facilities, or incentives to increase investment.
Therefore, priorities must be determined, goals set, and funds or other
economic support such as regulations, tariffs, tax breaks, etc.
furnished.

A very diverse set of indicators is needed to monitor rural development.
The cornerstone should be indicators that measure the results of
developmental efforts to reduce poverty and hunger and understand
factors affecting progress towards meeting the MDGs. Comparable measures
of per capita income, expenditures on health, education, infrastructure,
and the socio demographics of rural households are needed to assess
progress across countries. These however, cannot be viewed
independently. For example, land is one of the world's most important
natural resources. The rising demand for food and fiber has been met by
more intensive use of the land with increased use of irrigation and
fertilizers, expansion of cultivation to marginal land and continual
deforestation. The need to provide food security which is leading to
more intensive use of water and agricultural chemicals are having
environmental consequences. Policy makers, when dealing with reducing
poverty are facing decisions about actions to ensure their agriculture
is competitive in the world markets, yet is sustainable and in harmony
with the environment.

An integral part of rural development is the reallocation of household
labor from agriculture to other industries. As a result, it is not
enough to measure agricultural income which is often the unit of
measure, because as economies develop, the increase in household income
often comes from non farm sources. The health of the farm and non-farm
economies in rural areas is tightly linked.

The level of education of the rural labor force is an important
indicator in any assessment of rural employment conditions. Unemployment
rates differ significantly according to levels of education achieved.
Again, a common measure of the effect of policies regarding education is
the resulting changes in household income as education levels increase.

##  Enhance Investment. Economic growth is essential for rural development and to help lift people out of poverty, and is largely driven by investment whether from public or private resources. Investment in public goods such as roads, irrigation systems, etc are done to improve the competitiveness and thus the income of farmers and rural households. For example, decisions to locate additional schools with incentives for families to spare their children's labor on their farm need to be based on knowledge about current numbers of households, numbers of children of both pre-school and school age, and the levels of income by the distribution of the education of the adults in the households. For large countries, these decisions need to be based on small area statistics to determine where to focus the developmental efforts.

Private investment involves everything from the construction of
processing facilities to forming new enterprises for a multitude of
reasons all the way to the individual farmer deciding to invest in a new
technology or equipment. Decisions about where to invest, how much and
when are best made with the availability of data that first describe the
current situation starting with the availability of resources and
infrastructure to support the effort. Suppose a food processing firm
wishes to build a plant to process soybeans into oil, meal, and related
products. Data requirements include the availability and sustainability
of soybean production, a labor force, infrastructure such as roads and
railways, and finally an assessment of the demand for its products.
These data requirements include a combination of very specific small
area statistics to what could be national and global measures of demand.
The data system needs to ensure that investment decisions are made to
ensure progress continues towards meeting the MDGs of ensuring
environmental sustainability and fostering partnerships for development.

**Promote Efficient Markets**. A factor often overlooked by
organizations, both national and international, in monitoring progress
towards meeting the MDGs is that agriculture is an important part of
many country's trade balances. Agricultural production and the
subsequent prices, more than in any other sector, are very volatile as
they suffer from the vagaries of weather on top of market and policy
driven effects. The shifting of major producing countries away from
planned systems to market driven policies has created a market economy
for agricultural products. Efforts to improve the production
capabilities of a nations' farms needs to be accompanied by an
information system that supports effective marketing of the increased
production. Statistical organizations need to ensure initiatives to
monitor progress towards meeting the MDGs are integrated with a data
system that supports the functioning of the marketing system.

The production in developing countries is an important part of the world
supply situation. For example, 60 percent of the world's rice production
comes from China, India, and Indonesia, and over a fourth of the wheat
production comes from India and China. The soybean market is driven by
production in Brazil and Argentina.

A key element that ensures that markets function efficiently for both
buyers and sellers is basic information on what is produced, where it is
produced and the total supplies including quantities in storage. Market
information needs to be timely. Even more important is market
information that provides projections of future supplies. The time to be
marketing a bumper crop is before harvest, not after storage facilities
are overflowing and the product rotting. On the flip side, if there is a
shortage, procurements need to be made before the poor face a famine. .

**3. Data Requirements**

Both policy and investment decisions have long run implications. In
other words, once a policy decision is made or investment committed and
expended, it is not easily changed or discontinued. Another important
point often overlooked is that the various policy and investment
decisions are not independent. A policy decision or commitment to invest
can have an effect on the consequences of other decisions previously
made.

Considerable effort has gone into identifying indicators of economic
development and measurement of factors to monitor the progress towards
meeting the MDGs. Another fact that can be overlooked when preparing
data to support policy and investment decisions is to consider what is
needed to forecast the consequences of those actions before the
decisions are actually made.

While the need for data to support all possible policy and investment
decisions can be overwhelming, the core data that supports all decisions
is that related to the rural household as the unit of measure. This is
based on the assumption that the primary impetus behind rural
development is to improve the well-being of rural households and reduce
the number living below the poverty line. These core data are basically
represented by the household income and expenditures by source whether
on or off farm, education levels, employment status, and health
situation, to name a few.

The importance of households as the unit of measure has been recognized
by the World Health Organization and World Bank by their World Health
Surveys and Living Standard Measurement Surveys, respectively. The
administrative data collected by the International Labor Organization
and the United Nations Educational Social and Cultural Organization
would be enhanced if they could be connected to households and related
demographics. The data need to be measured at the household level to
enable analysis to obtain predictions of the consequences of policy or
investment decisions.

There is a basic need for household level data that are available on a
repeated basis to not only monitor changes that are occurring over time,
but also to understand what caused the changes. For examples, various
policy and investment decisions can be made regarding health, education,
the environment. How does one know they were effective?

A framework for such a data system follows in section 5.

.

**4. Frequency and Level of Detail**

Many policy and investment decisions take years to bear fruit.
Therefore, periodic surveys to measure the progress towards the goals
may be sufficient. However, these surveys will probably need to produce
data below the national level. Certainly, to measure the progress
towards improving income and reducing poverty in rural areas as well as
making progress towards meeting the other MDGS requires that household
survey data be at that level. It may be necessary to have rural data at
the provincial level for large, diverse countries.

If one accepts the goal to integrate data needs to provide core economic
indicators with those to monitor MDGs in a national statistical system,
then some basic decisions need to be made about how often the data are
required and at what level of detail. The data needed for each indicator
needs to be subjected to the test of time and geographic detail
required. The various indicators need to be categorized by degree of
importance. This three way look at determining the content of a
statistical system needs to be based on the realities of available
resources and statistical capabilities.

Table 1 shows how the frequency and level of geographic detail can be
viewed for data items. Because of cost and other resources, there is a
necessary trade-off between the degree of frequency of providing data
and the level of geographic detail.

This exercise needs to be done before establishing the framework for
integrating the overall data requirements into the statistical system.
Once this process has been completed and the integrated data system
established, it will need to be reviewed on a periodic basis such as on
5-year intervals if the survey framework presented below is developed.

Table 1. Frequency and Level of Geographic Detail

+--------------+---------+----------+----------+--------+------------+
| #  Detail    | **Nat   | **Re     | **Pro    | **Co   | **GIS**    |
|              | ional** | gional** | vince/** | unty** |            |
| ## Frequency |         |          |          |        | **Re       |
|              |         |          | *        |        | ferenced** |
|              |         |          | *State** |        |            |
+--------------+---------+----------+----------+--------+------------+
| *            |         |          |          |        |            |
| *Decennial** |         |          |          |        |            |
+--------------+---------+----------+----------+--------+------------+
| **Qu         |         |          |          |        |            |
| inquennial** |         |          |          |        |            |
+--------------+---------+----------+----------+--------+------------+
| **Bi         |         |          |          |        |            |
| Annual**     |         |          |          |        |            |
+--------------+---------+----------+----------+--------+------------+
| **Annual**   |         |          |          |        |            |
+--------------+---------+----------+----------+--------+------------+
| **Semi       |         |          |          |        |            |
| Annual**     |         |          |          |        |            |
+--------------+---------+----------+----------+--------+------------+
| *            |         |          |          |        |            |
| *Quarterly** |         |          |          |        |            |
+--------------+---------+----------+----------+--------+------------+
| **Monthly**  |         |          |          |        |            |
+--------------+---------+----------+----------+--------+------------+

**5 Frame work for integrating rural household surveys.**

A framework and overall design of an integrated annual rural household
survey program is proposed. This would integrate a country's core
statistical needs with those needed to monitor progress towards meeting
the MDGs. It should be recognized that some of the MDG indicators should
be part of the core items. There are several issues to keep in mind when
considering the proposed survey design. One is that there are core data
items at the household level that need to be monitored every year. It
should also be recognized that there is no need to monitor some items
every year because situations do not change that rapidly. There are two
reasons for proposing an annual survey framework. One is to build a data
base of household data that can be used for analysis purposes for
current and future policy and investment decisions, especially to
forecast the consequences of the proposed actions. A primary goal is to
provide a survey framework that provides the data needs to monitor
progress towards meeting the MDGs, and more importantly, the inter
relationships between them. The other is to foster statistical capacity
building. When countries do periodic household surveys, they receive
training and technical assistance. However, when the survey is completed
the sponsor or donor goes elsewhere leaving the country without the
capacity to continue.

Table 2 provides a brief description of the proposed survey framework.
Note that the proposal is to have a national level rural household
survey to provide a cross section of information over time.

First, note that the sample is divided into replicates with 5 being used
each year.

Also note that by the 5^th^ year, each replicate will have been in the
sample for 5 consecutive years.

Every sample household in every replicate will receive the same core
questionnaire that will remain essentially the same over time. The core
questionnaire will obtain information needed on an annual basis, have
considerable year to year volatility, are important to monitor food
security issues, and provide an overall overview of progress to meeting
the MDG's. The design will provide longitudinal analysis of the core
data so that short run evaluations of the effects of policy or
investment decisions can be apprised.

The key to the design is that each year each household is also surveyed
by a detailed questionnaire that rotates by subject matter each year.
The variables to be measured on a rotating basis are those for which
change would be difficult to monitor on an annual basis. During the
period a household is in the sample, it will be queried by each of the
detailed questionnaires at least once with one fifth of the households
surveyed twice for one of the detailed questionnaires.

At the end of the 5^th^ year a household is in the sample, it will have
been surveyed annually for the core items, once for each detailed
questionnaire, and twice for one of the detailed version to provide a
matching sample comparison over time.

The content of the detailed questionnaires to be used each year will be
targeted to include indicators to measure progress the Millennium
Development Goals. An overview of the content of the detailed
questionnaires follows:

-   A. (MDG 1) Indicators of progress toward eradicating poverty and
    hunger. This would also information about employment and wage rates
    included in the October Inquiry. This would provide the primary data
    to enable computing Purchasing Power Parities for the poor in
    conjunction with the International Comparison Program.

-   B. (MDGs 2 and 3) Indicators of progress toward achieving universal
    primary education and gender equality for women.

-   C. (MDGs 4, 5, and 6) Indicators of progress made to reduce child
    mortality, improve maternal health and combat diseases.

-   D. (MDGs 7 and 8) Indicators at the household level to measure the
    consequences of improving the environment and improving the
    competitiveness of markets by removing distortions to trade.

Table 2. Description of distribution of sample replicates by year and
detailed questionnaire to be used.

<table>
<colgroup>
<col style="width: 13%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
</colgroup>
<tbody>
<tr class="odd">
<td><h2 id="replicate">Replicate</h2>
<h3 id="year">Year</h3></td>
<td><p><strong>Rep</strong></p>
<p><strong>1</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>2</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>3</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>4</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>5</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>6</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>7</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>8</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>9</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>10</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>11</strong></p></td>
<td><p><strong>Rep</strong></p>
<p><strong>12</strong></p></td>
</tr>
<tr class="even">
<td><strong>1</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>2</strong></td>
<td></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>3</strong></td>
<td></td>
<td></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>4</strong></td>
<td></td>
<td></td>
<td></td>
<td><strong>D</strong></td>
<td><strong>D</strong></td>
<td><strong>D</strong></td>
<td><strong>D</strong></td>
<td><strong>D</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>5</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>6</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>7</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>8</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><strong>D</strong></td>
<td><strong>D</strong></td>
<td><strong>D</strong></td>
<td><strong>D</strong></td>
<td><strong>D</strong></td>
</tr>
<tr class="even">
<td><strong>9</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
<td><strong>A</strong></td>
</tr>
<tr class="odd">
<td><strong>10</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
<td><strong>B</strong></td>
</tr>
<tr class="even">
<td><strong>11</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><strong>C</strong></td>
<td><strong>C</strong></td>
</tr>
<tr class="odd">
<td><strong>12</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><strong>D</strong></td>
</tr>
</tbody>
</table>

## 

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## Summary

The primary themes in this paper are the need to disaggregate national
data to better describe and understand the rural sector, to base the
statistics on the household as the unit of measure, and to call for an
integration of international and national resources to provide a
sustainable statistical system to monitor progress towards meeting the
MDGs using an integrated survey approach.

International and National statistical offices need to view data systems
from a global point of view down to the local level and recognize there
are data uses beyond their own needs when designing data systems.

The recognition of the many needs for statistical information by a
myriad of data users will foster support to sustain the survey programs
to meet those needs.

The integration of the household data will provide information that will
improve the understanding of the relationships between the different
items being measured. This will widen the audience of data users and
analysts that will further foster support for the data system.

The question is whether the donor organizations, international
institutes and national governments have the will to work together to
meet the challenge to harmonize their data requirements, combine
resources and strive for a sustainable international statistical system