**Information Needs for Policy Evaluation: The Example of Income
Objectives of Agricultural Policies in OECD Countries**

Catherine Moreddu

Organisation for Economic Co-operation and Development (OECD),
Directorate for Food, Agriculture and Fisheries, Paris, France

**Abstract**

Although securing adequate and stable income levels for farm households
still figures prominently among agricultural policy objectives in many
OECD countries, there is usually no precise definition of income targets
and information to evaluate achievements is often inadequate.

There is, nevertheless, a need to evaluate domestic policy, both in
terms of public accountability and economic efficiency. It is a
challenge for statistical systems to adjust to new policies that create
new needs, all the more when policy makers do not define objectives
clearly and in measurable terms.

In the short term, OECD analysts have chosen to use existing
information, even if imperfect to evaluate policies with regard to their
income objectives. They are, in parallel, trying to raise awareness on
information needs, and to suggest solutions for improvement in data
collection and analysis. This paper summarises the main findings of a
comprehensive OECD study on income issues, while focusing on information
questions. It then suggests how obstacles to improvements in data
collection could be overcome, based on discussions that took place at a
joint OECD/PACIOLI Workshop on Information needs for analysing farm
household income issues, and at various IWG.AGRI Seminars to identify
future needs in the area of agricultural statistics.

**1. Introduction**

As part as its mandate to assess current support policies, not only in
terms of their effectiveness and efficiency to achieve their objectives,
but also in terms of operational criteria such as targeting and equity
(OECD, 1998), the OECD Directorate for Food, Agriculture and Fisheries
has, since the mid-90s, undertook to gather evidence in order to assess
whether the belief that farm households need to be supported on income
grounds still holds and whether current policies are efficient in
pursuing stated income objectives.

Information published in Member countries has therefore been gathered
and analysed in a study of farm household income issues and policy
responses (OECD, 2003). This OECD income study is used to illustrate how
existing data can be used to assess policies with regard to their
(income) objectives, and to shed light on data needed to implement more
efficient policies. Income objectives, stated or implied, were first
identified. The income situation of farm households was then reviewed
and compared to that of other households. The effectiveness and
efficiency of agricultural, fiscal and social policies with regard to
income objectives was assessed and alternative approaches suggested.

In terms of information needs, the OECD income study led to the
conclusion that in many countries, income support policies have been
designed and implemented in the absence of adequate information on the
income situation of farm households. It also outlined the importance of
collecting comprehensive and flexible information on the economic
situation of farm households in order to assess the problems and needs
of the sector and to implement appropriate measures.

This issue of data improvement was considered as meriting further
attention. The 8^th^ IWG.AGRI Seminar "Perspectives for Agriculture and
Rural Indicators and Sustainability" held on 21-22 November 2002 at the
OECD in Paris examined the statistical implications stemming from the
need to develop a "wider view" of agriculture, encompassing the
economic, social and environmental dimension.[^1] In subsequent efforts
to attract attention to the need for better information in this area,
the OECD Secretariat participated in discussions of information needs to
better design and evaluate income policies in various fora, including
the recent joint OECD/PACIOLI Workshop on Information needs for
analysing farm household income issues held in Paris on 29-30 April
2004[^2], recent meetings of the PACIOLI network and the
IWG International Task Force on Statistics on Rural Development and
Agriculture Household Income, which will report in June 2005 on its
results.

Discussion at the Paris Workshop helped clarify data needs and
deficiencies. Data needs for policy design and evaluation are discussed
in Section 2. The main findings of the OECD income study are summarised
in Section 3. Finally, suggestions for lifting obstacles to improvements
in data collection that were made at the OECD/PACIOLI Workshop are
summarised in Section 4. This session of the MEXSAI conference is also
expected to contribute to a better understanding of data needs and uses
in this area.

**2. Information Needs to Evaluate Policies**

***2.1. What Are Income Objectives of Agricultural Policies?***

Although the scope of objectives attributed to agricultural policies has
broadened, income objectives are still appear to be prominent in OECD
countries. They are not often clearly stated but are often expressed in
terms of achieving income parity with other sectors, tackling low income
problems, and reducing the variability of income within agriculture. Do
farm households achieve, on average, income levels that are on a par
with the rest of the economy? Is the incidence of low income higher in
agriculture than in other sectors? How large are income inequalities
within the sector? Is income variability higher among farm households
than other households? These are the most frequently asked questions.
They help analysts and statisticians interpreting policy objectives that
are often not specified precisely in policy declarations.

***2.2. What Indicators Are Needed to Evaluate Achievements?***

Because clear criteria about the targeted households and the measure of
income of interest are often missing, analysts have had to interpret
stated objectives and propose indicators to gauge progress.

Farm income provides only a very partial view of the income situation of
a farm household. Farm households derive a significant share of their
income from sources other than farming. In order to reflect the income
situation of farm households all sources of income should be taken into
account. For a full assessment of the economic situation of farm
families, farm and household assets should also be considered in
combination with income in addition to total or disposable household
income, in particular in the case of farm families who own part or all
of the factors of production farmed. Not only does wealth give rise to
income but it provides security and financial leverage. It therefore
affects the ability to consume and, in the case of farms, the viability
of the activity. Additional information on household expenditures and
other social and personal factors such as health or leisure are also
necessary to assess the well-being of farm households.

Such information should be made available on the same basis across the
economy to allow comparison between farm and other groups. To be
relevant and to increase the number of potential uses and users,
information should also be collected in a flexible and disaggregated
way. Structural, but also behavioural and human capital information on
farm households is needed to identify the cause of individual income
problems and design appropriate remedies.

In order to understand and monitor the variability of farm receipts at
the individual level, panel data need to be collected. Three-year
averages (or five years excluding extremes), which smooth farm income
fluctuations and provide better estimates of the income situation, can
then be calculated.

***2.3. What Information Was Used in the OECD Income Study?***

Given data and resource constraints, farm household income was the
indicator chosen to reflect the income situation of farm households. It
includes farm income, defined as net income from farming activities, and
income from non‑farm activities, investments, social transfers and other
sources (Diagram 1).

Measurement issues are discussed briefly in Box 1 and at greater length
in Hill (2003).

**Diagram 1. Components of Farm Household Income**

![](media/image1.wmf){width="5.798611111111111in" height="3.425in"}

*Source:* OECD, 2003.

Various sources of data were used. For some European countries,
aggregate data from a EUROSTAT project that collects aggregate data on
the total income of agricultural households for European Union (EU)
member countries (EUROSTAT, 2002) was used to compare the income of farm
households with that of other households and calculate the share of farm
income in total income. However, this project often adopts a narrow
definition of farm households (main occupation farms of a minimum size
for example). Consequently, whenever possible, national statistics that
define farm households more broadly are used, in order to give a wider
picture of the sector.

To look at the distribution of income or the incidence of low income
among farm households compared to other households, at the change in
income over time, and the impact of agricultural, social and taxation
policies, microeconomic data were used. They either come from specific
surveys (farm, household expenditure, or income surveys), or from tax
and social transfers files. Economy‑wide surveys allow comparison
between farm households and other households. In many cases, however,
the sample of farm households proves to be too small to allow a detailed
and representative distributional analysis. The LIS (Luxembourg Income
Study), which contains micro data from national household surveys,
allows such a comparison for at least some countries and has been used
in the analysis of the incidence of low income in different categories
of households reported in OECD (2001) and summarised in this paper.
Specific farm surveys provide useful structural information on farm
households, allowing the income situation to be related to structural
characteristics, but they do not permit direct comparison with other
households (unless linked with an economy‑wide survey). The OECD
structural database, which has been used to analyse the impact of
support on the distribution of income, contains such data.

Box 1. Measurement Issues

***Is the appropriate information widely available?*** There are
problems of availability, quality and access to relevant data in OECD
countries. Do the data collected allow progress towards income
objectives to be systematically and accurately measured? For many
countries the answer is no. In some cases the data are seriously out of
date. Additional difficulties are created by the fact that in many
countries the definitions adopted for households, income, etc., are too
narrow to allow the real income status of farm households to be
evaluated. The number of farm households in economy‑wide income surveys
is often too small to be representative, which makes it difficult to
compare the situation of farm households with that of other households.
Finally, farm household income can be underestimated. Income in‑kind is
often not taken into account and there can be problems linked to
confidentiality and asymmetric information with reporting income in
surveys. Farm self‑employment income, in particular, might not be fully
captured.

***Are data comparable across countries?*** In general***, they are
not***. First, the definition of farm households varies both with
respect to who constitutes a household (which family members) and with
respect to what constitutes a farm household (what level of sales,
amount of land farmed, share of income from farming or other indicator
qualifies a household as a farm household). There are enormous
differences among countries with respect to these variables. Second,
there are differences in the indicators of income that are reported,
although with detailed information on farm accounts a common definition
of farm income can be adopted. The coverage of income sources often
differs. In particular, there are still many countries in which off‑farm
sources of income of farm households are not reported. For these
reasons, comparisons across countries have not been attempted in this
report. For each country where data are available, income components are
compared between farm and non‑farm sectors and across various groups in
the agricultural sector.

**3. Main Findings from the OECD Income Study**

***3.1. What is the Income Situation of Farm Households?***

**Farm households achieve income parity at the aggregate level in most
OECD countries.**

In most OECD countries for which data are available, the average income
of farm households is close to the economy‑wide average (Figure 1).

**Figure 1. Total Income of Farm Households as a Ratio of That of
All/Other Households^1\
^(most recent year available)**

![](media/image2.wmf){width="4.334027777777778in"
height="3.2131944444444445in"}

> Data are not comparable across countries.
>
> 1\. All households except for Japan, where it is workers\' households
> and Korea, where it is urban households.
>
> *Source:* OECD, 2004b.

**Farm households derive a significant part of their total income from
non-agricultural sources.**

This is the case even when a very restrictive definition[^3] of farm
households is adopted (Figure 2). Where a broad definition of farm
households is adopted, farm income is not even the main source,
reflecting the diversity of farm households, which include pluriactive,
retirement or hobby farm households.

**Figure 2. Percentage Share of Farm Income in Total Income of Farm
Households\
(average of the three most recent years available)**

![](media/image3.wmf){width="3.38125in" height="4.529861111111111in"}

> Data are not comparable across countries.
>
> 1\. Income from independent activities.
>
> *Source:* OECD, 2004a, 2004b.

Regardless of definition, wages and salaries were the main source of
off‑farm income in three‑quarters of the countries examined. Often, the
farm operator is employed outside the farm but increasingly his/her
spouse may also have off‑farm employment. Cases where social transfers
are higher than salaries and wages are found in countries which restrict
the definition of a farm household to the operator, whose main
occupation is farming, and the spouse. Finally, property income is the
primary source of off‑farm income in the United Kingdom only, but comes
next in importance in close to a third of the countries reviewed. The
results do not generally depend on the year chosen.

**There are large income disparities within the agricultural sector.**

Many factors such as region, the structural characteristics of the farm
and the household, and the economic environment, in particular the
opportunities for off‑farm earnings, affect the total income of farm
households. Differences in average income by farm size and farm type in
selected OECD countries, based on structural farm account data, are
presented here[^4]. In most countries reviewed, the average net
operating income (NOI)[^5] of farms in the top quartile[^6] is two to
three times bigger than that of the average of all farms (Figure 3).

**Figure 3. Average Gross Receipts, Net Operating Farm Income and Total
Income of the Top Quartile (25% Largest Farms) as a Ratio of the Average
of All Farms**

![](media/image4.wmf){width="4.2347222222222225in" height="2.55in"}

> Data are not comparable across countries.
>
> NOI: Net operating income. See Diagram 1 for a definition of income
> indicators.
>
> *Source:* OECD structural database (OECD, 2003).

Owing to differences in farm size, in productivity and in levels of
support between commodities, there are also income disparities between
farm types although they are not as large as between farms classified by
gross sales (OECD, 2003). Similarly, there are income differences by
region, which stem from regional variations in the economic size of
farms, type of farming and rate of support for each commodity, and how
widely regions are defined. These issues were briefly examined in
OECD (1999). In all cases, when non‑agricultural incomes are taken into
account, differences in income by farm size, type and region are
reduced.

**The incidence of low income is often higher among farm households than
in the rest of society.**

In many countries, available evidence suggests that in the mid‑1990s the
incidence of low income was higher among farm households than among
non‑farm households. The low‑income rate (defined in note 1 of Figure 4)
was higher for farm households than non‑farm households in 9 out of
14 countries, slightly lower in three (Canada, the Czech Republic and
Finland), but significantly lower in two (Norway and the United States)
(Figure 4). The low‑income gap was bigger for farm than for non‑farm
households in all the examined countries (note 2 of Figure 4). When the
analysis is repeated using a narrow definition of the farm household,
inequality is greater (OECD, 2001). In other words, farm households
which rely more on farm activities are more frequently included in the
low‑income category. This confirms the importance of off‑farm
activities.

**Figure 4. Low‑Income Rate and Low Income Gap: Farm Versus Non‑Farm
Households**

  --------------------------------------------------- ---------------------------------------------------
  **Low‑income rate^1^**                              **Low‑income gap^2^**

  ![](media/image5.wmf){width="3.207638888888889in"   ![](media/image6.wmf){width="3.053472222222222in"
  height="1.917361111111111in"}                       height="2.058333333333333in"}
  --------------------------------------------------- ---------------------------------------------------

1\. The low‑income rate is the share of individual farm households with
incomes falling below the low‑income line (50% of median income of all
households).

2\. The low‑income gap is the difference between the average income of
the low‑income farm households and the low‑income line (the average
income gap).

*Source:* OECD, 2001 (LIS data).

***3.2. What is the Impact of Fiscal, Social and Agricultural Support
Policies on Farm Household Income?***

**Support raises farm household income...**

The Producer Support Estimate (PSE) expressed as a percentage of gross
receipts explains, in static terms, the share of gross receipts that
comes from government support. For example, in the OECD area, one third
of gross receipts resulted from support in 2001‑03 (OECD, 2004c). We
cannot deduce from the PSE, however, that farm household incomes would
fall by an equivalent percentage if all government support was removed
given that in the absence of support, adjustments would occur.

**...but its efficiency in transferring income to farm households is
low,**

A large share of the transfers generated by agricultural policy and
included in the PSE does not necessarily translate into net income gains
for farm households. There are two sources of transfer losses that limit
the income transfer efficiency of policy measures. The first is economic
costs, which result from distortions in the use of resources and its
incidence on production and trade patterns. The second source of loss is
distributive leakages, whereby some of the benefits of support accrue to
groups other than the intended beneficiaries. This latter category
includes the costs of administering farm programmes (which are not
accounted for in this analysis), the extra purchases that farmers are
required to make from input suppliers, the share captured by downstream
industries, additional payments to landlords and income transfers to (or
from) other countries.

According to OECD estimates of income transfer efficiency, no support
policy linked to agricultural activity succeeds in delivering more than
half the monetary transfers from consumers and taxpayers as additional
income to farm households. In the case of market price support and
deficiency payments, the share is one fourth or less, and for input
subsidies it is less than one‑fifth (Figure 5).

In the case of market price support and deficiency payments, the
stimulus to output, and hence to input demand, means that much of the
increase in receipts is transmitted back to input suppliers or
capitalized into land values. Not surprisingly, input suppliers reap
most of the benefits of input subsidies. In the case of area payments,
nearly all the benefits are absorbed in increased land values.

**Figure 5. Where Does the Money Go? The Income Transfer Efficiency of
Agricultural Support**

![](media/image7.wmf){width="5.497222222222222in"
height="4.709722222222222in"}

*Source:* OECD, 2003.

**...and support is unequally distributed: most goes to larger
farmers.**

The static comparison between the distribution of support and that of
gross receipts indicates the nature of the impact support has on the
distribution of income[^7]. Distributions by farm size, farm type and
region have been examined for selected countries. Using the same
methodology as in OECD (1999), the distribution of gross receipts,
support and income by farm size is compared in Figure 6, which shows the
share of the 25% largest farms in gross receipts, support and income.

**Figure 6. Share of the 25% Largest Farms in:**

![](media/image8.wmf){width="4.347222222222222in"
height="3.048611111111111in"}

> Data are not comparable across countries.
>
> *Source:* OECD structural database (OECD, 2004b).

The distribution of support by farm size is similar to the distribution
of gross receipts. This is because a large share of support in the OECD
area is linked to the level of production[^8] or the level of input,[^9]
and also because in many cases, support accounts for a significant share
of gross receipts. The largest farms, and often the most prosperous
ones, are therefore the main beneficiaries. Figure 6 shows that the top
25% of farms receive between 40 and 90% of support. In this sense,
support is inequitable. On average, direct payments are more equally
distributed than market price support and gross receipts but the
difference is generally small. We can conclude overall that, in most
countries, support has a rather small redistributional effect by farm
size because it is only slightly less unequally distributed than gross
receipts.

The impact of support on income disparities by farm type depends on how
wide differences in support level are in the country and on how narrowly
farm types have been defined. In the European Union, for example,
support has widened disparities between dairy and intensive livestock
farms on the one hand and field crop and cattle farms on the other.
There are, nonetheless, few examples where support narrows disparities
between farm types but the effect is relatively small. Overall, support
increases income disparities between farm types (Figure 14 in OECD,
2003).

There are also regional differences in the distribution of support.
While support linked to output automatically goes to larger farms,
direct payments can be targeted to less favoured areas. Although this is
done to some extent in Switzerland and the EU, inequality nonetheless
persists despite these efforts (OECD, 1999).

***3.3. Are Policy Measures Effectively Achieving the Objectives Set for
them in Terms of the Level, Variability and Distribution of Farm
Household Incomes?***

**Overall, support policies, whatever their objectives, do raise farm
income levels to some extent and reduce their variability, but this
would seem to be achieved at significantly greater cost to consumers and
taxpayers than necessary.**

The evidence presented here suggests that there are significant problems
in delivering income support to farm households through the types of
sector specific measures and policies that have been pursued to date.
The main problems relate to **targeting** -- the great bulk of the
measures used are broad and untargeted in nature ‑ to **equity
--** because the measures are still predominantly based on production or
factors of production they fail to change the income distribution in any
significant way and most of the support that reaches the sector goes to
larger farm households, who do not usually need it ‑ and to **leakages
--** much of the support is transferred to unintended beneficiaries.

In addition, as much of the support in OECD countries is linked to
production or input use there have been **significant international
spillover effects**. Production enhancing support raises domestic farm
income but contributes to lower world prices, which in turn depress farm
income in other parts of the world. Policies that reduce income risk
faced by farmers also affect production decisions, often to the same
extent as price support. In addition, by reducing adjustment in the
domestic market, they transfer domestic instability to the world market
and therefore switch the burden of adjustment to other countries (OECD,
2003).

***3.4. Which Policy Instruments Would Transfer Income to Farm
Households More Effectively and More Equitably?***

To design and implement efficient policies, **income objectives have to
be clearly defined** in the national policy process. In particular, some
income criteria need to be developed to define and identify the targeted
households. All sources of income should be taken into account in
identifying the households to be targeted, as well as household wealth.
For example, criteria could be set concerning the level of income or the
variability of individual farm household income that would trigger
intervention, if indeed the prevailing policy concerns involve those
criteria.

There are several possible policy responses to low‑income problems among
farm households. Government should first consider ways to **develop
market solutions**. It is important to understand the cause of low
income in order to find the most effective remedy. If governments are
unwilling to see less efficient farmers leave the sector because they
provide economic and social benefits that are not, and cannot be,
rewarded by the market, the optimal policy would be to give farmers the
appropriate incentive to provide these benefits, using for example
decoupled and targeted payments

Similarly, regarding income risk management, government **should
encourage the development of contingency arrangements** such as
insurance and futures markets, for example through the collection and
transmission of information to reduce problems created by information
asymmetry; or training programmes in the use of futures markets to
reduce income risk. Agricultural safety nets could then be envisaged to
address any remaining risk management failure.

**From an income transfer efficiency point of view, support that is
decoupled from agricultural activity and targeted specifically to income
would be much better as a way to transfer income to farm households.**
Such direct income payments minimise economic distortions and
distributive leakages because their effects on production decisions are
minimal, and they can be targeted and delivered to those households that
are deemed to warrant assistance.

More generally, government could **invest in general services** for the
sector, such as expenditures on infrastructure, training, research and
development, that improve the functioning of agricultural markets and
allow farmers to increase their competitiveness. Low income may be
experienced by farm households that are resource‑poor and located in
areas where there is also a problem of lack of viable economic
alternatives. The solution in this type of situation is not necessarily
a sector‑specific income support scheme. Investment in infrastructure to
make rural areas more attractive to investors and transitional
assistance to more viable economic activities may be of greater benefit.

**Sequencing is important.** As policies to address market failures in
the agricultural sector will have an impact on the income of farmers,
there is a logical case for applying measures that first correct market
failures and then address any outstanding concerns about incomes, using
the types of measures indicated above. Finally, **general tax and social
security systems** are in place in most, if not all, OECD countries.
These structures **are well placed to identify remaining low‑income
problems** among agricultural households **and ensure equal treatment**
vis‑à‑vis other classes of households.

***3.5. Better Information is Needed to Design More Efficient and
Targeted Policies***

In most countries, systems exist to monitor the income situation of farm
households but they are incomplete, out of date and not consistent
between micro and macro data, between different types of farm surveys,
between farm and general surveys, and across countries. Income concepts
and typologies based on commodity production are outdated and no longer
relevant given the increasing diversity in income sources and the trend
towards the decoupling of policy measures from production.

It is important, in order to assess the problems and needs of the sector
and to implement targeted measures, that comprehensive information on
the economic situation of farm households be available, as described
above. Issues related to farm household income data availability and
quality are not just, or even primarily, of interest to analysts and
statisticians. The principal beneficiaries of improved information would
be policy makers and the public they serve. Until the coverage,
timeliness and consistency of national microeconomic data is improved,
policy measures, ostensibly aimed at improving the incomes of farm
households, will be implemented without adequate knowledge of the
nature, incidence or even existence of the problem that they are
attempting to solve.

**4. How to Obtain Better Information?**

There are obstacles to obtaining required information. They are
administrative, political and, to some extent, technical. Administrative
obstacles may occur when policy-making ministries -- the potential data
users \-- fail to communicate their needs to statistical agencies
\-- the data collectors. The costs in designing and setting up new or
revised surveys, to take account of new needs, can be an obstacle. Costs
also affect the frequency and timeliness of surveys. There can also be
legal/confidentiality difficulties, preventing for example the merging
of data from different sources (e.g. farm and tax filer records). These
kinds of difficulties often prevent the transmission of information to
analysts outside ministries. Finally, to ensure the co-operation of
participants to surveys, who are usually volunteers, there are limits to
the burden that can be placed on them.

Political obstacles are not negligible. Participants may not understand
or agree that it is legitimate to seek information on total income,
*i.e.* on income sources that are not part of the farm business, in
particular a spouse's income, investment income and wealth. This affects
the rate and quality of responses. Vested interests more generally limit
the political will to understand the full income situation of farm
households, as it could threaten the legitimacy of income support. As a
result, the status quo is often vigorously defended.

To a lesser extent, there are a number of technical obstacles. Coverage
of farm households in general surveys is often not representative
because they are so few. This can limit the value of general surveys for
farm household income analysis and for combining them with farm account
information. Wealth, in particular some types of assets such as
livestock, forests or vineyards, is difficult to evaluate although there
are International Financial Reporting Standards (IFRS), which include
International Accounting Standards on agriculture. Another difficulty in
keeping track of a panel of farm households is that farms and households
are not stable over time.

There are, however, examples of successful countries, where
policy-relevant information is collected and used. The need for policy
evaluation -- it can be made compulsory---and demands for better
accountability with respect to public funds are playing an important
role. Increasingly, improvements in data collection are requested by
Audit Offices. This has happened in Canada and more recently in the
European Union.

In terms of costs, budget constraints and the resulting need to better
target policies should work as incentives for improving data collection,
rather than obstacles. Costs will be lower if data collection is
adequate at the central level: individual researchers or local
government would not need to collect the same data several times, hence
saving for government and surveyed farmers. Data should also be
available at a disaggregated level in order to increase the number of
potential uses and users (including from the private sector).

Changes in policy should prompt changes in data collection systems. The
cost of evaluating a policy should be attached to its funding. Specific
data may be generated by the implementation of a policy and the
collection cost should be part of programme funds.

There are many ways to reduce the cost of data collection and
transmission, for example the use of existing administrative or
non-agricultural data sets; the use of telephone interviews or Internet
for filling questionnaires or accessing data.

Communication on income issues should be simple and effective so that
the need to monitor the income situation and evaluate policies is
understood by all. It should focus on key players, who can influence
political, policy or funding decisions. Efforts should be particularly
concentrated on improving communication between statisticians, policy
makers, and the industry.

International estimation and definition standards, when they exist, can
contribute to solving technical, estimation problems and ensuring a
certain degree of international consistency. Information technology
facilitates access to information, through web sites ensuring
confidentiality (FADN, Luxembourg Income Study). It also helps to reduce
the time lags in making data available to the public and in answering
requests.

In conclusion, obtaining political commitment is crucial. Technical
problems can be overcome with sufficient will and resources. Good
co-operation between policy makers, analysts and statisticians is
essential, but also co-operation between countries is useful.
International fora, such as this meeting, networks or the OECD, are
places where national statisticians, policy-makers and analysts can work
together on harmonisation, exchange best practices between countries and
systems, and take account of the diversity of situations. They are also
places where awareness of needs can be raised and where practical
examples of data usefulness can be given.

**References**

EUROSTAT (2002), Incomes of agricultural households sector, Luxembourg.

Hill, B. (2003), "A conceptual framework for estimating agricultural
household incomes in EU countries", in *Agricultural and Rural
Development Policies in the Baltic Countries*, OECD, Paris, 2003. Paper
presented at the Seminar on Agriculture and Rural Incomes, Labour
Mobility and Rural Development Policies in Estonia, Latvia and
Lithuania, 10‑12 June 2003, Tallinn, Estonia.

OECD (1998), *Agricultural policy reform: stocktaking of achievements*,
discussion paper for the meeting of the OECD Committee for Agriculture
at Ministerial Level, 5‑6 March 1998, OECD, Paris.

OECD (1999), Distributional effects of agricultural support in selected
OECD countries, available at www.oecd.org/agr/policy.

OECD (2001), *Low incomes in agriculture in OECD countries*, available
at www.oecd.org/agr/policy.

OECD (2003), *Farm household income: issues and policy responses*, OECD,
Paris.

OECD (2004a), *Farm Household Income: Towards Better Informed Policies*,
OECD policy Brief, Paris.

OECD (2004b), "Farm Household Income Issues: Where Do We Start From?"
published in OECD/PACIOLI (2004).

OECD (2004c), *Agricultural policies in OECD countries: At a glance*,
Paris.

OECD/PACIOLI (2004), *Proceedings of the OECD/PACIOLI Workshop on
Information Needs for Analysing Farm Households Income Issues* held in
Paris on 29-30 April 2004, LEI, The Hague.

[^1]: . The 8^th^ IWG.AGRI PARIS Seminar attracted over 120 experts from
    OECD and non-OECD countries and has been key in leading to the
    preparation of MEXSAI. See in particular the following papers
    presented at the Seminar and available at
    http://www.oecd.org/document/28/0,2340,en_2649_34231_1942236_1_1_1_1,00.html:\
    Offutt, S. (2002), "Policy change and data obsolescence"
    STD/NAES/AGR(2002)1.\
    Lindner, A. (2002), "Statistical challenges of a wider view of
    agriculture" STD/NAES/AGR(2002)2.\
    Galarza, J.-M. (2002), "A national information system for
    sustainable rural development" STD/NAES/AGR(2002)6.\
    Hill, B. (2002), "Measurement issues to be addressed when
    calculating income and integrated accounts" STD/NAES/AGR(2002)14.\
    Vogel, F. (2002), "Future issues for agricultural statistics"
    STD/NAES/AGR(2002)19.

[^2]: . Presentations made at this workshop are available on
    [www.oecd.org/agr/policy](http://www.oecd.org/agr/policy) under
    publications and documents, then events and meetings. An OECD Policy
    Brief (OECD, 2004a) summarises the discussion that took place at the
    workshop and the proceedings will be jointly published by OECD and
    PACIOLI in OECD/PACIOLI (2004).

[^3]: . A more restrictive definition involves the exclusion of smaller
    farms (based on gross sales or area) and part‑time farmers, for whom
    farm income is not the main source of income or for whom
    agricultural activity is not the main activity.

[^4]: . See OECD (1999) for a description of the characteristics of
    national farm account data. Whenever possible, the analysis reported
    here has been updated to use the most recent data available.

[^5]: . See Diagram 1 for a definition of income indicators.

[^6]: . The top quartile contains the 25% largest farms, based on gross
    sales.

[^7]: . It should be noted that support is included in the value of
    gross receipts.

[^8]: . Over 65% of the PSE in the OECD area came from market price
    support measures and payments based on output in 2001‑03 (OECD,
    2004c).

[^9]: . In 2001‑03, payments based on area planted or animal numbers and
    payments based on input use accounted respectively for 15 and 9% of
    the PSE for the OECD area (OECD, 2004c).