**Issues In Integrating Labour\'s Dual Roles of Input to Production and
Social Resource**

> **Berkeley Hill, Federico Perali and Cristina Salvioni**

Abstract

> The conceptual foundations of agricultural labour statistics in the EU
> are examined. In view of the present and anticipated uses within a
> reformed CAP, understanding the behaviour of the households which
> supply this labour is critical. Existing statistics are inadequate at
> answering questions concerning the diversified activities that are
> central to the CAP and the coverage of own-production that is
> significant in the new Member States.

**1 Introduction**

Labour force statistics form an established part of the array of
indicators used within agricultural and rural development policy. In the
European Union (EU) they are based on two sources of data, firstly
general employment statistics in which people are allocated to economic
sectors (such as agriculture) according to the one in which they mainly
work and, secondly, surveys of agricultural holdings that cover all the
labour found there, irrespective of whether agriculture is its main
occupation[^1]. These statistics have traditional roles in providing
information on the size of the agricultural industry in terms of the
numbers of people that are engaged in it and their basic demographic and
socio-economic characteristics. Identifying the people who are engaged
in agriculture is a major step in the process of assessing whether the
fundamental policy aim (in the European Union) of ensuring a fair
standard of living of the agricultural community is being achieved. In a
separate way they also are used to monitor the extent of labour input to
the production process and the characteristics of the units of
production. In the EU labour input estimates are used in combination
with economic accounts for the activity of agricultural production to
show how the residual rewards from agriculture per unit of labour input
are developing over time, a prominent indicator within the Common
Agricultural Policy (CAP) and one highly sensitive to the reliability of
labour input statistics. For this purpose a distinction has to be made
between salaried and non-salaried labour, with the input being measured
in Annual Work Units (Eurostat 2000). A related use is to calculations
on how productivity is changing (both labour productivity and total
factor productivity). The two roles (social orientation and factor-input
orientation), while overlapping, require rather different conceptual
bases and much confusion in the past has resulted from a failure to
distinguish between them.

Important too is the period that the user of statistics is interested
in. In the short term there will be an interest in statistics that show
how the existing labour force is adjusting to the changing economic and
technical environment, including to signals from policymakers. However,
in the longer term adjustments come from the more fundamental decisions
by labour to exit from agriculture. One inherent problem with statistics
that concentrate on labour currently in the agricultural sector is that
they omit coverage of people who have left. This gap is of particular
relevance to users interested in the process of structural change and
the effectiveness of rural development policy in producing an
agriculture that is more economically sustainable. A feature of
developed societies is that the rural labour force is often not
predominantly engaged in agriculture and that the wide range of economic
opportunities found there often means that the population no longer
migrates to urban areas. The shift in the interest of academics and
policymakers toward the identification of the socio-economic situation
of rural areas, rather than solely the agricultural sector, poses a
challenge in terms of collection of labour statistics as well as of
other variables that are needed to monitor and analyse the whole rural
labour market, both agricultural and non agricultural, and the
socio-economic condition of the rural population.

However, labour is not a disembodied resource but is supplied by
individuals who have choice and who live in households that are also
social units. As will be demonstrated below, satisfactory explanation of
behaviour and diversity in agriculture should take into account that
production activities for the market take place within same unit and
concurrently with domestic activities for the home. At the household
level, the various decisions are non separable. As a consequence, rural
statistics should recognize the household as the unit of interest and be
concerned with the basis on which its members allocate their time
between its various activities (market prices, opportunity costs, shadow
prices etc.). A complication is that the households from which
agricultural labour comes are diverse, and while the independent
entrepreneurial farm family may be predominant, consideration should be
given to households of hired workers, co-operative member, owner of
shares in agricultural companies etc.

Taking Italy as a case study, lessons can be learned as how to gather
the information needed to model rural households' behaviour in order to
assess the socio-economic impact of the adjustment to changing economic
and technical environment. Taking Wales as another case study,
experience from evaluating its rural development programme points to how
labour statistics can be improved to better reflect the needs of users
in tracing the responses of households to policy instruments.

**2 Pecularities of farm-households' labour supply**

In most countries, at all levels of economic development, labour in
agriculture is dominated by independent (self-employed) activity (that
is, entrepreneurs and their families providing non-hired labour)[^2].
Hired labour (those engaging in dependent activity in return for a wage)
is in a small minority. This is in sharp contrast with the situation in
most other industries and society in general, where waged or salaried
labour is the largest component, though self-employment (of all sorts)
may be more heavily represented in rural areas than in urban ones. This
poses some particular problems for labour statistics and related income
indicators in agriculture, especially those that are developed for the
population in general and thus have waged labour particularly in mind.

Perhaps the prime problem is the difficulty of measuring labour input of
self-employed people. In contrast with waged labour, where payment is
made in exchange for a specified number of hours worked, there are no
such prescriptions for entrepreneurial labour. An individual who may
consider himself to be full-time in agriculture may work less or more
hours than employees[^3]. Seasonal factors may be important, so no
single week in the year may be typical. While the physical labour that a
farmer and his family contributes to farming operations may be
quantified readily (for example, number of hours driving a tractor) this
does not necessarily reflect the productivity of that labour and thus
its quality as an input. Assuming the physical labour input of a
self-employed farmer of 65 years is equivalent to that of a 25 year old
hired man is unlikely to be valid in many circumstances. When turning to
the entrepreneurial function that differentiates a farmer, problems are
even greater. Time is no reliable guide to managerial input, and many
example can be found (especially among larger farms) where the operator
provides no physical input into operations. Where several self-employed
activities are carried on concurrently (such as combining farming with
some other business) there will probably not be any conscious allocation
of management time between them, particularly where they all take place
on the farm). Being self-employed in several activities permits much
flexibility to allocate resources, including managerial effort, as needs
vary. Entrepreneurial tasks can be closely intermixed and perhaps
carried on simultaneously, and for some (such as considering borrow and
capital allocation) these are only meaningful if they cover all the
range of activities at once. Of course, farmers who take off-farm jobs
as employees have prescribed hours in that activity, but surveys
commonly find that a minority of farm operators claim to be employed in
full-time off-farm jobs yet still manage to operate farm businesses that
can be sizable undertakings.

For self-employed farmers, time spent on leisure may be difficult to
distinguish between that used for work because of their close functional
association (the classic example being attendance at local markets that
serves commercial and social purposes). The production of food for own
consumption which, for someone in another sector might be regarded as a
hobby is not necessarily seen in that light by farmers and their
families. In short, any attempt to measure the labour input of
self-employed in simple quantity terms is fraught with difficulties.
Probably only the very broadest indications can be justified.

But there are other issues of relevance to labour statistics in
agriculture:

-   For convenience, labour statistics have often assumed that there is
    only one farmer per farm. This is increasingly divorced from
    reality, and particularly on larger businesses the entrepreneurial
    role may be shared among several people (partners) who are often
    members of the same family (spouses, siblings, parents and children
    etc.). While all may be equal in decision-taking, one may be the
    senior, but this may vary over time and responsibilities may be
    split up. Where multiple entrepreneurship occurs, this can take the
    form of several members of the same household or be spread across
    several households (in the dwellings sense).

-   A great deal of confusion has been caused in the past (at least in
    the EU) by a failure to distinguish between non-hired labour and
    family labour, a situation that Eurostat has taken steps to resolve
    by developing a target methodology (Eurostat 2000). Some family
    members may receive a wage for working on their farm and possess a
    contract of employment. Or they may be given some cash payment less
    than a market-level wage, with the (often unstated) expectation of
    financial rewards when they eventually inherit or take over the
    business. These fall between the categories of hired and non-hired.
    It is usually assumed that all non-hired labour is family, which
    seems reasonable, though there will be exceptions.

-   When dealing with hired (non-family) labour, the traditional
    statistical approach has been to concentrate on its input to the
    production process with little attention to the households from
    which this labour comes. Thus, while statistics may be available on
    their wages from agriculture (and the cost of employing them),
    little is known in many countries of their overall household
    earnings and the incidence of poverty among them. In the EU no
    harmonised statistics on household income exist for this sector of
    the agricultural community, despite the possibility that they form
    part of the target of the CAP and that their rewards constitute an
    element in the Net Value Added by agriculture.

-   Workers on large-scale agricultural units (some co-operatives, some
    companies) in transition countries present a particular challenge.
    While some only receive wages, the situation is made complex by
    others whose rewards come partly also in the form profit, to which
    should be added the income in kind derived from household plot
    production, inputs to which may be derived from the large
    agricultural units on which they work. This household production is
    perhaps mainly for own consumption but also probably involves some
    exchange by barter or sale. It is important to encompass it in any
    assessment of the resources used in agriculture and the real income
    accruing to households. In many of the new Member States these
    people are numerous and seen definitely as part of the agricultural
    community and thus now part of the target group for support by the
    CAP.

-   Casual or non-regular labour presents technical problems. For hired
    casual labour, circumstances in which it is sometimes employed (for
    example, large numbers of people working for short periods, and for
    cash) are not conducive to detailed record keeping. There is also
    the problem of unpaid casual labour, such as friends and relations
    assisting with seasonal labour demand peaks, and the black
    employment of seasonal immigrants without official papers. The
    households from which this labour comes are, of course, poorly
    documented.

-   Business structure may hide the real nature of labour coming from
    households. While the large majority of the numbers of farms in OECD
    countries are non-incorporated (that is, are sole proprietorships or
    partnerships) a minority in some countries are arranged as
    corporations (companies). Usually these are incorporated for
    taxation reasons, and ownership and management remains typically
    within a small family group. However, where farmers are directors of
    these companies, strictly they are employees and their rewards come
    as salary, though they may also receive dividends as share owners.
    In practice these people can be treated in statistics as if they
    were self-employed without seriously distorting the overall
    representation of the way the labour force functions.

While analysis of labour can be made according to its employment status
(self-employed, hired) and the economic unit that uses the labour
(household-firm, company, co-operative), this is unlikely to be
sufficient for users of labour statistics. The socio-economic
characteristics of the people involved (age, gender, human capital,
degree of pluriactivity etc.) and the types of household from which they
come (size and composition, income level, asset structure, dominant
value sets and so on) and other more policy-related classifications
(members of co-operatives, subsistence producer, hobby farmer etc.) need
to be available and are likely to become of increasing importance as
policy shifts towards encouraging households to develop the resources
currently at their disposal in ways that lead to a greater integration
with the rest of the economy. Of course, though the illustration above
has concentrated on labour and agriculture, a parallel set could have
been given for other sectors in rural areas.

**3 Internal equilibrium of the household**

Understanding the way that the household reaches decisions on the use of
its available labour is an important element of the concepts that
underpin labour statistics. It is generally believed that it is
unrealistic to assume that agricultural labour markets are competitive
either in developed or less developed countries. As noted above, in the
basic economic unit of agriculture (the farm-household) the production
and consumption decision variables are non-separable. In such
circumstances market goods and leisure are not priced at market values.
The evaluation of labour, therefore, is shadow and is revealed by the
value of the marginal farm product. Non-separability is present by
definition in subsistence farming which can be considered as a closed
micro-economy, as first described in Chayanov (1926) and Sen (1966)
often isolated from both output and factor markets The shadow wage
depends on the characteristics of the workers and their hedonic value
(Brown 1983, Barten1964, Benjamin 1992). Accounting for the
heterogeneity between male, female and child work allow estimating a
different shadow wages for the male and female component of the family
or the children employed in farming activities.

When off-farm work and hired labour is zero, i.e. when such decisions
are at a corner and family and hired labour are not perfect substitutes
(Deolalikar and Vijveberg 1987, Jacoby 1992), then implicit shadow
prices must be adopted because the model is non separable. The labour
market may be missing, or binding hours constraints (both for adults and
children) and lack of contractual flexibility in the off-farm labour
market may lead to a failure of the market clearing possibilities. Low
subjective expectations about the probability of finding a job off-farm
(especially among the elderly, low-skilled workers, children or women)
may generate expected off-farm wages that are lower than a return to
labour employed with certainty on their own farm. This observation is
especially appropriate in less developed countries where off-farm
opportunities are often lacking.

The production and consumption sides of the household economy illustrate
the general equilibrium structure of the farm-household. The exogenous
characteristics of the household and the enterprise affect both sides of
the micro economy. Within the theory of the household enterprise this is
an interesting feature since it permits testing the separability
hypothesis between consumption and production decisions (Singh, Squire,
and Strauss 1986, Benjamin 1992, Udry 1996). Under separability, the
general equilibrium program of the household is recursive. Production
decisions are not affected by the household's endowments, preferences,
characteristics or decision processes. On the other hand, consumption
decisions are affected by production choices since profits are part of
the budget constraint.

The separation between production and consumption decisions is ensured
by the household rational behaviour in presence of complete markets.
Recent empirical work (Benjamin 1992, Udry 1996, Pavoni and Perali 2000)
shows that production decisions do depend on farmers' preferences and
endowments. The jointness in decision making is evident even in the
absence of market failures when the same input, such as time, is shared
across the household and home production processes, and in presence of
home consumption of the household marketable product. Imperfections in
the labour, credit and land markets are commonly observed (Benjamin
1992, Udry 1996, Bhalotra and Heady 2001). Under these conditions, farm
production and household consumption decisions are non-separable and
leisure/labour demand on the household is not independent from the
on-farm demand for family labour. As a consequence, shadow wages, rather
than market wages, determine adults and children's labour/leisure
choices. The case of a Chayanovian farm-household closed economy, where
the household members are not employed off-farm and no agricultural
labourers are hired-in, is non recursive by construction (Lambert and
Magnac 1994).

In the estimation of agriculture household income, the calculation of
the implicit wage for household labour is a fundamental issue.
Traditionally, "unpaid" family labour does not appear as an explicit
cost of production but receives its rewards in the residual hybrid of
entrepreneurial income (which goes under a variety of labels).
Nevertheless, the implicit wage of family labour can be estimated using
three different approaches.

*Accounting approach*: the value of family income can be obtained as a
residual subtracting from net entrepreneurial income the remuneration of
all other factors of production. The remuneration of land can be
evaluated either at its rental value or it can be imputed adopting an
interest rate. The cost of using owned operating capital, rather than
borrowing it, can be evaluated by applying the prevailing rate in the
credit market. Individual labour income is then obtained by dividing the
residual by the household labour units. Note that this criterion
compensates both the physical and intellectual labour. Further, the
supplied hour of labour is evaluated uniformly across working family
members.

*Objective market wage under competitive conditions*: this approach
evaluates a hour of household labour at the prevailing market wage
supposing that the labour market is at a competitive equilibrium and the
farmer is indifferent between working in the farm and in the off-farm
market. In this situation, the subjective (from the farmer) and
objective (from the market) evaluation of the opportunity cost of
working inside or outside the household coincide. This "opportunity
cost" approach may differentiate the contribution of the different
working household members when accounting for the individual
characteristics such as age, sex, education, and location of the farm as
a proxy for off-farm market conditions within an econometric estimation
(Huffman 1996). The derived wage corresponds to the potential
compensation that a farmer endowed a specific level of skills could have
potentially obtained if had found an off-farm employment.

*Shadow wage*: when labour markets are not competitive, as it is often
the case, the family "unpaid" labour can be evaluated as the value of
the marginal productivity of labour corresponding to the subjective
evaluation of the disutility associated with an extra hour of work. This
approach requires the estimation of a production or a cost function from
which the marginal productivity can be evaluated. It is important to
realize that the application of this approach implies assuming that a)
the farm-household economy is non-separable, and b) adult (and child)
family labour are quasi-fixed factors in the short run.

The shadow wage can be derived as the marginal effect of a change in
fixed factors on total costs, which, in the long run, corresponds to
total revenues or directly from the estimated production function
(Jacoby 1993 and Skoufias 1994). The effective shadow wage of adult and
child labour is given by the sum of the effective shadow wage with the
total contribution to the marginal productivity of labour provided by
the characteristics of the worker.

The knowledge of shadow wages is fundamental in order to explain
individual labour choices. Farmers decide to work on the farm by
comparing the shadow wage with the market objective wage when the
subjective perception of the probability to find a job, either in
agriculture or in other sectors, and the objective probability to be
hired is equal to 1. If subjective and objective probabilities diverge,
then the proper wage comparison is between shadow wages and expected
market wages which thus incorporate information about the probability to
find a job conditional on the level of education, age, experience and,
more generally, skills of the farmers.

Shadow wages from agricultural activities can be estimated on an
individual basis if data are collected about who does what in the farm.
Still, the derivation of individual incomes incorporating also an
assessment of the change in the household's net worth during the
accounting period require that non labour income is assigned to each
household member given the knowledge of the rule governing the
allocation of resources within the household. Note that the shadow wage
approach is often the only one available when evaluating child labour.

**4 Time Use and Valuation of "Unpaid" Domestic Labour**

Conventional accounting systems for households that include both the
rewards from economic production in agriculture and other industries,
wages actually received, interest and other property returns and
miscellaneous items permit the derivation of total income and (after
non-optional deductions such as tax) disposable farm household income
(Hill; Eurostat; Organisation for Economic Co-operation and Development
(OECD); Smeeding; Smeeding and Weinberg). However, the family portfolio
of labour choices also includes employment in domestic activities. This
form of self-employment is valued at the "unpaid" equilibrium shadow
wage, and, if a competitive environment is assumed, corresponds to the
opportunity cost of time. The incorporation of this implicit source of
income in the computation of household incomes gives the *extended*
income (Lazear and Michael; Jenkins and O'Leary; INSTRAW). The sum of
extended income and the value of leisure time forms the Beckerian notion
of *full* income (Becker).

Under both behavioral and policy points of view, it is relevant to take
into formal consideration that decisions made by the household unit are
conditional on the information sets related to both the production and
consumption side of the household economy. What is often neglected is
that the production side of the household economy consists of both
farming and domestic activities. Considering both extended and full
incomes explicitly recognizes the contribution of home activities to the
formation of household resources. Farm and "home produced" incomes are
traditionally pooled within the family. In order to derive individual
incomes, we assign these sources of income to each worker in proportion
to the amount of contributed labour. We can then estimate the relative
contribution of the husband and wife to the paid and unpaid sources of
household income and to undertake a gender-specific analysis of the
income distributions.

The implementation of the Beckerian notion of full income requires
evaluating the time endowment, which is employed in both paid and unpaid
working activities and leisure, and measuring non-labour incomes derived
from returns on nonfarm assets and/or pensions. The accomplishment of
this task requires the derivation of total farm household and extended
incomes along with the evaluation of leisure time. Both farming and home
production are family enterprises, the difference being that farm output
is marketable, while domestic output, often composed by public
components, is sold within the household at an implicit price.

Each member (*i*) of a farm household of size *N* can allocate its time
endowment among the following activities ![](media/image1.wmf)*,* where
*f~i~* is hours of time devoted to farm labour activities; *o~i~* is
time devoted to off-farm labour either in agriculture or in other
sectors (paid at the market wage) and commuting time; *h~i~* is time
devoted to unpaid home production activities; *l~i~* is time devoted to
pure leisure, such as recreational activities; *I~i~* is time devoted to
rest and personal care. The amount of disposable time is
![](media/image2.wmf).

Traditionally, economists define "unpaid work" as the time spent at home
doing housework, producing goods and services for the family. Similarly,
for farm households, farm labour supplied by household members is not
directly paid to farm operators. Farmers remunerate themselves at an
implicit wage. The time contributed by farm operators is often referred
to as unpaid farm labour (Huffman). Both farm labour (*f~i~*) and
domestic work (*h~i~*) are defined as unpaid work. The availability of
individual time-use data permits separating the time devoted to domestic
work from the time allocated to pure leisure.

It is worth noting that this approach appears to perpetuate the
simplistic view that the use of labour for agricultural entrepreneurial
activities is competitive with its use for non-agricultural ones. As
noted above, while this may be true for physical labour input, its
validity for managerial input is less valid. But a model that
accommodated non-rival circumstances would no doubt be far more complex.

**5 Lessons from Wales and Italy**

*5.1 Case study from Italy*

In response to this change about the importance of understanding the
behaviour of both rural households and enterprises in the context of
each specific local economies, the Institute for Studies on Agricultural
Markets (Ismea) designed and analyzed, in collaboration with the
*Microsimulation-Unit* based at the University of Verona, a survey aimed
to collect data on the socio-economic conditions of Italian agricultural
households and their incomes with the objective to meet the demand for
information requested by the design and implementation of rural
policies.

The objectives of the Ismea survey were to gather data on the farm and
the household that could be used to describe both the structure and the
behavior of the farm, and to understand household behavior and welfare
as a step in evaluating the effect of agricultural and rural policies on
the living conditions of the agricultural population by making use of a
collective household approach[^4]. Accordingly, a multi-topic
questionnaire was designed to collect data on many dimensions of the
farm and the household well-being, including consumption at the
individual level, income, savings, financial wealth, governmental and
intra-household transfers, education and housing.

The design of the Ismea questionnaire was inspired by the questionnaires
in use for the collection of data on farm production (for example that
used by the EU RICA-FADN), those on the consumption of household members
(such as the one used by ISTAT), by the EU time budget and by the
questionnaire used by the Bank of Italy to collect data on household
incomes. The final result is a set of questions very close to those
suggested by the Living Standards Measurement Study of the World Bank to
assess the welfare of rural households.

An important characteristic of the questionnaire is that the attention
is shifted from the traditional farm operation perspective to the farm
household-firm unit one. For examples, information on the social
characteristics such as gender, age, level of education, professional
characteristics, etc. of each family members are collected. In addition,
the questionnaire contains a stylized time sheet[^5] describing how much
time each family component is devoting to activities such as on and
off-farm work, household work, child care and pure leisure time. This
kind of information is useful when the work roles and off-farm labour
participation of different members of the family are analyzed and to
estimate shadow wages when labour markets are not competitive. This is a
crucial undertaking. Market non-agricultural wages, wages for hired
labour and shadow unpaid wages differ significantly across household
types within the Ismea sample (Menon and Perali 2004). In addition, the
data gathered in the time budgets are also essential for estimating the
labour supplied to paid and unpaid working activities and the associated
extended and full household incomes (Castagnini *et al*. 2004).

Another peculiarity is that the Ismea survey was designed to provide the
information needed to assess the economic of policy programs at the farm
level and the social impact at the household level with a special regard
to labour choices and employment conditions of each member of the
household. In order to allow the analyst to evaluate and measure such a
socio-economic impact the Ismea survey contains a module of questions
gathering information on the quality of life and on other
characteristics of farm households.

There is a first group of questions on housing characteristics. The
answers to these questions can be used to infer the standard of living
of the agricultural household. A second group of questions collects
detailed information on household consumption such as the consumption of
food, either bought from the market (recording both quantities and
prices) or grown on the farm, and the consumption of both semi durables
and durable goods (distinguishing between goods for children and for
adults). Measurement of consumption is emphasized in the questionnaires
not only because consumption data can give a more direct picture, with
respect to labour income, of how household individuals actually
live[^6], but also because this kind of information allows the
researcher to estimate the rule governing the intra-household allocation
of material resources and time and to explain labour choices both at the
household and individual level.

The first part of the questionnaire is complemented by a module
containing questions on the intra-household decision making process for
both farm and household decisions, on the household goods (household
header growths in farm, time spent in family, farm inheritance and farm
legacy), on intra-household transfers, on subjective measures about the
risk associated to future investments in agriculture and intentions
about the future development of the farm. This is a set of information,
usually not available in the traditional agricultural statistics, that
proved to be very useful, for example, in order to tackle problems such
as modelling the intergenerational transfer of household farms, or the
on- and off-farm labour decisions within the farm household.

The set of data on the household welfare is completed by a group of
questions on household income and wealth comparable to the survey on
household income and wealth conducted by the Bank of Italy. A consistent
reconstruction of non-labour income is fundamental in understanding
labour choices (El-Osta, Mishra and Morehart 2004).

In conclusion, the Ismea survey gathers a very large subset of the
information on the household suggested by the Living Standards
Measurement Study of the World Bank to measure labour statistics in an
integrated context which allows a researcher also to provide robust
explanations of labour choices both at the household and individual
level. The information collected recognizes the agricultural household
as the statistical unit of interest. In order to measure labour
statistics and living standards of both the agricultural and the rural
household, it seems necessary to extend the focus of the data collection
to non farming enterprises and services by coupling the household
questionnaire with a module run at the level of the rural community.

*5.2 Case study from Wales*

The Rural Development Programmes (RDPs) for the period 2000-06 that have
been implemented in EU Member States carry the obligation of a mid-term
evaluation by the end of 2003. To assist with the overall assessment of
RDPs the European Commission put forward some required questions and
prescribed indicators that were to be used to answer them[^7].
Experience in carrying out the mid-term evaluation for the Welsh RDP
(Hill and AgraCEAS 2003) and the baseline study for the parallel
evaluation in England (Hill *et al.* 2002) has led to a number of
lessons for labour statistics These exercises involved making full use
of all existing official statistics concerning labour in agriculture
and, more generally, in areas deemed to be rural. A few special analyses
of data were provided by the statistical authorities. Where the RDP
evaluation encountered gaps in official statistics, these had to be
filled by special surveys.

First is the restriction imposed by a sector approach in labour
statistics. Rural development involves support to the diversification of
activities on holdings, yet the available statistics do not give much
information on the use of the household's labour resources for other
uses. Though the EU's Farm Structure Survey permits an analysis of
labour according o the presence of an Other Gainful Activity (OGA), and
whether this is the person's main activity or a secondary one, annual
snapshots give little idea of the way in which changes are made on
individual holdings, and the relatively static picture they present
possibly hides a far more dynamic situation in which some changes cancel
out (as is found with structural change generally in agriculture). What
is needed is a panel approach with longitudinal analysis. Also, the unit
of observation should be the household rather than the individual
(though data at this level is important), as many diversification
responses are associated with decisions at this level (such as a spouse
developing an OGA).

Second, and related to the above, information on people (and households)
that leave farming, some of whom may still live in the farm dwelling, is
important, as is the reasons behind these readjustments in their use of
resources (levels of monetary reward, other gains and losses etc.). Many
farming changes (environmental, enterprise adjustment, investment, land
acquisition etc.) are associated with transfers of land, associated with
exit, entry and inter-generational transfers of assets. Though some
structural change is made by management within its career, a great deal
comes about by the disparity between the rate of entry and exit.
Evidence from the USA (Gale 2003) suggests that, while the rate of exits
is not greatly affected by the changing economic conditions in
agriculture, and neither is the (lower) rate of entrants aged below 35
years (many of whom are likely to be farmers' sons), older new entrants
are more sensitive. Presumably, the decisions of those already in
established careers is much more a choice between alternatives in which
the potential opportunity cost of transferring more of their time to
agriculture is a major factor. Such decisions are more likely to be made
on a household rather than an individual basis. In such matters it would
be highly interesting to be able to trace the development of the
farm/non-farm mix, and what happens to those who leave farming
completely, though labour statistics are rarely set up in a way that
makes this possible.

Third, despite shortcomings, labour statistics for agriculture are far
better that those for other sectors in rural areas (at least in Wales
and England). The overwhelming majority of self-employed people in rural
area are in sectors other than agriculture (some 90% in rural England
are non-agricultural)(Kempson and White 2001), leaving farmers as a
small but relatively intensively studied sub-sector. Yet
non-agricultural businesses are seen as drivers in the process of rural
development. The lack of adequate statistics about them, their
adjustment decisions and incomes was identified as a major gap. Of
course, developing statistics for these entrepreneurial households
encounters many of the conceptual issues outlined above for
self-employed labour in agriculture.

**Conclusions**

The most important conclusion is that the supply of labour reflects the
nature of the households that supply it, so that the conceptual basis of
labour statistics must acknowledge this relationship. This important
feature calls for a redesign of agricultural surveys which should
recognize the household, both farming and non farming, as the
statistical unit of interest. This change would permit a more precise
measurement of the investment of family time in paid off-family work and
unpaid on-family activities and of the linkages of the household supply
of labour with the local non-agricultural business environment.

Second, the nature of self-employment, the form which most labour takes
in agriculture, makes its quantification in terms of input to the
productive process problematic. Attempts to apply measures developed in
the context of hired labour, for which the notion of a wage or salary
forms part of a contract that requires the supply of a certain number of
hours/days of input in return, to self-employment is likely to give
artificial and misleading results. It is therefore important for the
statistician to provide sufficient information to the economist with the
aim of estimating reliable shadow wages when competitive labour markets
are not in function. With this send in mind, it would be important to
know who does what in the farming, domestic and other productive
activities. The phenomenon of pluriactivity further complicates the
issue, as does the emergence of a separate management function that may
be divorced from labour input among the larger farms.

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[^1]: The latest available edition of the annual *Agricultural Situation
    in the European Union* gives from the general employment statistics,
    for both agriculture and industry, numbers employed and separate
    breakdowns into paid and self-employed, full-time and part-time, and
    age band. Statistics based on surveys of agricultural holdings give
    total numbers working on the holdings, and their volume of labour in
    Annual Work Units. Labour is broken down into family and non-family
    (subdivided into regular and non-regular), by gender, whether there
    is other gainful employment (subdivided into main or secondary) and
    according to the percentage of working time spent on the holding, in
    three broad bands.

[^2]: For EU15in 2002 of the people for whom agriculture was the main
    activity, numbers of self-employed were approximately double that of
    employees (65m in contrast with 35m). In terms of labour input to
    agricultural holdings, family labour was responsible for 73% in
    2000.

[^3]: The European Commission's Farm Accountancy Data Network
    (FADN/RICA) does not allow farmers who claim to work hours greater
    than a normal working week to count as more than one Annual Work
    Unit, though fewer hours may lead to recording as a fraction of a
    AWU. A similarly approach is adopted in Eurostat's measure of labour
    input in terms of Annual Work Units. However there may be some
    attempt to permit a full-time worker to count for more than 1 AWU in
    some productivity studies.(see Eurostat 2000, para 3.22).

[^4]: That is, models that explicitly take into account the existence of
    differences in resource allocation decisions across the individuals
    of a same household.

[^5]: Comparable to that used by ISTAT in the "Multiscopo survey" and in
    the Communitarian survey on time budget conducted by Eurisko.

[^6]: Income data indicate the living standard that the recipient could
    prudently afford.

[^7]: These include, for schemes involving aid to investment on
    holdings, the output per hour of labour on assisted holdings, share
    of working time spent on alternative activities on the holding,,
    number of full-time equivalent jobs maintained or created thanks to
    the assistance for alternative activities, the training needs of the
    labour force and the nature of trainees and non-pecuniary factors
    affecting job quality, Taking the cross-cutting questions (covering
    the combined impact of all RDP activities), there are questions on
    the age distribution and gender of labour on assisted holdings, and
    evidence of positive influences on the reduction in rural
    depopulation, employment directly and indirectly created for holders
    and non-family labour and whether this is full-time jobs and other
    than in basic agriculture and forestry. For jobs created or
    maintained elsewhere in the rural areas by the RDP, questions cover
    age, gender, pluriactivity and part-time farming.