**Policy Analysis with Integrated Rural/Farm Household Data for the
U.S.**

Mitchell Morehart[^1]

Jeffrey Hopkins

James Johnson

Economic Research Service, U.S. Department of Agriculture

Farming and the economic setting for agriculture have changed.
Households have organized their farm businesses to include multiple
families, partners, alliances, ventures, contracts and other
arrangements. The significance of this transformation is that resource
allocation decisions occur at the household level. Farm households
manage a portfolio of business, personal finance, and family activities.
As a result, we can no longer assume that a household's farm business is
its primary economic or financial concern. Households will respond
differently to agricultural, resource, trade, tax, development or other
policies depending upon how incentives and constraints affect business
performance and household well-being.

**1. Introduction**

Farm programs have far-reaching impacts that touch farm households and
rural communities as well as agricultural commodity markets. When first
devised, U.S. agricultural policy had a strong commodity orientation,
which persists today. Structural change in agriculture has disengaged
goals and outcomes of farm policy from the reality of today's farm
sector. Farms are fewer and larger with wide differences in production
costs, marketing approaches, and overall management capabilities. More
importantly, the well-being of farm families is no longer solely (or
even primarily) dependent on commodity market outcomes, but is a
consequence of farm performance and off-farm employment and business
ownership opportunities in rural communities.

Farm household decisions, like decisions made by other self-employed
households, reflect both consumption and production.[^2] As a result,
the farm household structure is a complex system of inter-relationships
between and among a variety of internal and external factors. It is a
resource-allocating unit, where decisions are made on how scarce
household resources are to be distributed among various activities to
attain household goals. Often, decisions on resource allocation affect
the welfare of the entire household as well as the welfare of individual
members of the household. Since the importance of the farm as an
activity included in the household portfolio will vary among households,
demographic, social, and economic characteristics of households, as well
as personal goals held by household members, influence the type of farm
business adjustments made in response to changes in farm policy.

An integrated data collection system is necessary to develop consistent
performance measures that link rural economies, farm households,
business establishments, and commodity enterprises. Important and useful
insights are gained from information about several facets of the farm
sector, including its contribution to the national economy, the
organization and performance of farms, and the income and well-being of
farm households. Each facet can be used to examine how changes in
economic or policy events may affect households and the farms they
operate. Unless households, household members, and their farms are
linked statistically through a survey design that implements a
household-farm paradigm, the nature of the relationship between the
household, household members, the farm, and farm community has to be
asserted or inferred from use of disparate data sets when conducting
descriptive or more analytical research activities. Perhaps even more
troubling, when data are not integrated to measure these relationships,
they are often overlooked. The Economic Research Service (ERS) is
working to overcome these traditional household-farm data limitations
through the development of a comprehensive micro-level data system that
integrates enterprise, farm, and household information in one data
collection activity.

The next section describes how content is determined for the U.S.
information system driven by the Agricultural Resource Management Survey
(ARMS). The ARMS is USDA\'s primary source of annual information on the
financial condition, production practices, resource use, and economic
well-being of America\'s farm households. This is followed by a
description of the various means that information from the ARMS is
disseminated. We then provide several examples of how policy analysis is
supported by the integrated rural/farm household data system.

2.  **A Comprehensive Information System**

The ARMS is critical to the research and analysis mission of the
Economic Research Service, and is a key input to estimates across the
Department of Agriculture and in other agencies. It provides the
information base for sector estimates of value added, income for farms
by type of commodity specialization, costs of producing major crop and
livestock commodities, indices of prices paid by farmers for production
inputs, and a report on the status of family farms. The ARMS also
supports the Department's estimates of household income and wealth, and
contributes to a variety of applied farm production, management,
technology adoption, resource use, and household well-being research
applications. While the ARMS became a stand-alone survey beginning with
the 1996 calendar year survey, it retained and built upon features of
survey activities that date to the 1970's \[Johnson and Morehart,
2004\].

1.  *ARMS Content*

A combination of factors guide the type and scope of questions asked in
the ARMS (figure1). The ARMS supports key uses of enterprise, farm, and
household data that correspond with mandated activities required by the
U.S. Congress, government wide responsibilities delegated to USDA,
assessment of USDA policies and programs, and enabling research to
inform public and private decision makers on agricultural and farm
issues. Congressionally mandated studies and indicators reporting
provide specific direction. For example, costs and return estimates must
"include all typical variable costs, interest, a return on fixed costs
and return for management along with the various production practices
used by farmers in a weighted national average estimate."

**Figure 1**

![](media/image1.wmf){width="6.178472222222222in"
height="2.298611111111111in"}

Standardization of components and definitions for financial statements
also helps to determine the necessary items to collect. For example, the
Farm Financial Standards Council develops and publishes standardized
*Financial Guidelines for Agricultural Producers* that incorporates
generally accepted accounting practices (GAAP) for income statement,
balance sheet, and financial ratios to the extent feasible for farming
businesses. Commodity costs and returns are estimated according to the
guidelines established by a task force created by the American
Agricultural Economics Association. Guidelines for sector accounting are
part of the government-wide standards for national, regional, industry,
and international economic accounts (Seskin and Parker). The ARMS is
unique in that the collection of information to support financial
statements covers all four major levels of interest (commodity
enterprises, farm businesses, farm operator households, and the farm
sector) within one sample design and set of survey instruments.

In addition to the factors that drive the demand for information, the
ARMS content also reflects the circumstances of those who supply the
information. To capture the diversity that exists among farms and farm
households, several different attributes are measured. These include
operator and spouse labor allocation decisions, educational attainment,
age, and other demographic characteristics. The ARMS also captures the
wide variety of farm operational and organizational structures that
constitute the complexity of modern farming. Specifically, the
information system must distinguish among the many stakeholders that
participate in agricultural production. They principally include
lenders, property owners, and firms with contractual arrangements. In
2002 for example, of the more than 2 million farms, 633,000-rented land
for cash, another 209,000 rented land for a share of production, 910,000
had outstanding debt at the end of the year, and 50,000 grew commodities
under contract arrangements. Conceptually correct economic assessment of
farms and farm households not only requires that survey content reflect
this complexity, but the array of stakeholders must also be taken into
account in the survey design.

2.  *ARMS Design and Coverage*

The ARMS is a multiple phase survey. In the fall, producers of major
commodities such as feed grains, food grains, or cotton are surveyed
about production practices and land use for a selected field on their
operation. In order to link commodity production activities and
conservation practices with the farm business and operator household,
farmers that successfully complete the fall interview about production
practices and input usage are interviewed again in the spring. Spring
data collection focuses on the structural and economic characteristics
of the farm business and farm operator households.

In ARMS, the household of the senior or dominant operator is enumerated.
Other contributing households and operators are identified. Survey
instruments used with the farms are developed to provide sufficient data
to develop for each farm an indication of value contributed to national
output, net income after accounting for capital use and inventory
adjustments, and for the disposition of income among parties that have
an ownership interest in production. For the farm\'s controlling
household, data are obtained to establish human capital, financial and
other asset allocations to farm and non-farm activities.

Within each phase of the survey, there are also multiple questionnaire
versions. The commonality of questions across versions provides one
facet of integration. In the fall data collection, the target commodity
distinguishes questionnaires. In addition to the commodity follow-up
interviews, the spring data collection has two distinct survey
instruments. The larger of these two versions (both in terms of sample
size and content) is personally enumerated and designed to collect
important policy related information, in addition to basic farm and
operator household financial and demographic information. A smaller core
version, which collects essential farm and household financial
information, ensures a sufficient sample to generate statistically
reliable information for major agricultural states. The core version is
enumerated using three stages of collection beginning with a mail
questionnaire form, followed by phone and, if necessary, personal
interview.

Entities sharing in the risk of production encompass not only individual
proprietors, but also a myriad of individual investors and legal
entities. These stakeholders contribute resources with the expectation
of receiving financial rewards commensurate with their share in the
risks of production. The ARMS captures the financial participation of
these various stakeholders in a manner consistent with the value added
accounting framework. In some cases, payments to stakeholders occur
prior to production (land, labor, and capital) and are relatively
straightforward to determine. The questionnaire also captures more
complex, production-related relationships such as contracts so that
expense and income can be correctly attributed to the appropriate
stakeholders.

Embedded in the value-added measurement of farm sector income is the
return to farm operator households from farming activities (figure 2).
Stakeholders' shares in net value added vary depending on region of the
country, type of farm or ranch, and size of operation. Many factors
influence these differences, including the mix of commodities on the
operation, the types of inputs used, the prevalence of business
arrangements such as contracting and share leasing, the size of
operation and number of operators on the farm, the mix of operator
versus hired labor services, and the use of debt. The ARMS is uniquely
equipped to correctly attribute income and expenses to the different
stakeholders and measure the diversity of outcomes for farm businesses
and operator households. The result is a consistently derived annual
measurement of value added to the farm sector, farm business
performance, operator household well-being, and the profitability of
commodity production.

Figure 2

![](media/image2.wmf){width="5.929861111111111in"
height="2.797222222222222in"}

Source: 2002 Agricultural Resource Management Survey (ARMS), USDA.

**3.** **How the Information System Supports Policy Analysis**

Important insight into how ARMS unit-level data contribute to policy
analysis can be gained by comparing unit-level data, such as the ARMS,
to aggregate net cash farm income, a statistic collected and published
by USDA since 1910 and frequently used as a policy benchmark \[Morehart
et al., 2001\]. Net cash farm income tends to track well with both
global and domestic events, but problems can arise when changes in the
statistic are interpreted as changes in farm household well-being.
Because aggregate data can only reflect the total or the average,
difficulties also arise when impacts from domestic or international
events are unevenly distributed, as they most often are. Increasingly,
the distribution across regions of the country, farm types, and
commodity specialization is used to reveal both where impacts are
greatest as well as where policy intervention has been most successful.

Some are willing to forego distributional content for the parsimony and
simplicity that a single aggregate number provides. Even these find net
farm income to be problematic as a policy benchmark because it is
overly-restrictive as a measure of well-being. Net cash farm income
excludes off-farm income, a significant determinant of well-being for
over 60 percent of farm operators who report that their primary
occupation is something other than farming. The measure also omits debt
obligations, the capacity of farm businesses to service their debts, and
is not indicative of farm business failures. As a flow measure of
returns, it doesn't reflect the ability of families to increase the
level of well-being by accumulating wealth. Morehart *et al*. point out
that a more comprehensive measure than net farm income is needed to
track the financial health, needs, and well-being of farm businesses and
households. Desirable characteristics of an aggregate measure include an
ability to subsume both farm and off-farm components of income and
wealth for the households involved in farming, as well as the capacity
to distinguish between farm business and farm household financial
well-being.

*3.1 Research on Policy Issues -- 3 examples*

Policy analysis, including how farmers cope with risk, adjust to policy
shocks, and make labor and technology adoption decisions, is greatly
assisted with data on the economic activities of individual farm
households. The utility of the research is considerably enhanced by the
ability to carry out disaggregated analysis. Disaggregation ranges from
a group-wise estimate of financial well-being based on farm and/or
household characteristics (e.g. the ERS farm typology) as well as
behavioral hypothesis testing with unit-level data.

*3.1.1 Savings, Income Volatility, and Basic Needs*

We begin with the case of what agricultural policy might do to address
income volatility of farm households. Simple comparisons such as Figure
3 reveal that farm household income is more volatile than household
incomes in the economy as a whole. Maintaining emergency savings to draw
on during income shortfalls is one way that households can address
volatility in their entire earning portfolio, and the U.S. has
considered programs that seek to increase the level of farm household
savings.

One analysis, by Mishra and Morehart, shows that individual farm
households vary greatly in their use of savings (including liquid
accounts, financial assets including stocks and bonds, retirement
accounts, and residential or other nonfarm real estate). Mishra and
Morehart's analysis suggests that those farms that are best positioned
to weather a volatile income stream are the ones that would be most
likely to participate, substantially cutting into the overall net
benefits of a subsidized savings program, and also suggestive of
important interactions with other risk management tools such as crop
insurance.

**Figure 3**

![](media/image3.wmf){width="5.184722222222222in"
height="2.7618055555555556in"}

Average farm household income declined by 3.7 percent in 2000 and
increased by 3.5 percent in 2001. However, despite the swings in average
income, ARMS data reveal that in each year about 18 percent of farm
households had lower incomes than the previous year. Because households
may make different choices in the face of financial stress such as an
income shortfall, especially when income drops below the basic need
level, an accurate measure of an income shortfall is necessary. Indeed,
about one third of those households with an income shortfall in 2001
earned income below their stated "basic needs" level (the remaining two
thirds reported earnings above basic household needs). Although not all
farm households found themselves under financial stress due to farm
business performance, Hopkins et al., showed that farm-associated income
shocks and shocks associated with sickness or divorce were more likely
to prevent a household from meeting basic needs than an income shortfall
due to a reduction in off-farm wages or off-farm business losses.
Accordingly, diversification in earnings, to include off-farm earnings
by the operator and spouse as well as a diversification in agricultural
production, were characteristic of those households that had income
shocks but still managed to meet basic needs. In contrast, aggressive
cost reduction in the farm business, and higher levels of debt relative
to assets were important predictors of failure to meet basic needs and
could be considered ultimately inefficient short-term coping strategies.
Intuitively, the evidence suggests that households with greater capacity
to cope with income shortfalls tend to have diversified income streams,
while those more reliant on debt and cost-cutting are less likely to be
able to meet their basic needs. While off-farm income shocks also occur
for farm households, these are less likely to prevent farm households
from meeting their basic needs.

ARMS data on non-farm assets, the size and the source of negative shocks
to farm household income, and a self-assessment of basic needs were used
in a policy-relevant study of the decisions that farm households make in
the face of volatility and risk. Without unit-level ARMS data, both the
assessment of the risks that farm households undertake, and the
consequences for well-being, would have been considerably more open to
speculation.

*3.1.2. Farm Household Impacts from Policy Reform*

The preceding analyses treat income shocks but not policy-induced
shocks. In fact, little work has been done on how households respond
when subsidies are removed or when global trade rules are liberalized.
The principal tool that policymakers have used to address the negative
impacts on farmers from trade liberalization is through Trade Adjustment
Assistance programs (established under the Trade Act of 1974). However,
more localized effects within a group of commodity producers attract
considerable attention from producers, consumers, and policymakers. If
they can, producers and households that are affected by trade or subsidy
reform will shift resources away from production, employment, or
investment in sectors where returns are falling, and into the sectors
where returns are rising. Opportunities for alternative investments or
alternative ways to allocate labor are region-specific. A study by
Burfisher, et al. shows that local gains will ultimately be determined
by variability in exposure to policy reform as well as variability in
the capacity to respond to policy reform. To reflect variability in the
impact as well as the means to respond to policy shocks, general
equilibrium gains from trade liberalization were simulated based on
existing commodity specialization, farm production efficiency, and the
ability to find employment off the farm. Using ARMS data,
household-specific exposure and adjustment response showed that impacts
were highly variable, and that most of the gains from commodity price
changes were captured by either the largest, most efficient farm
households or those households that were able to compensate for
commodity price declines by entering off-farm labor markets. Burfisher
*et al*. (figure 4) show that the net benefits after considering
response heterogeneity were even more unevenly distributed than exposure
to price changes. ARMS data on the heterogeneity of response to policy
reform -- some find it economical to switch farm enterprises, some seek
alternative labor opportunities, and some have little opportunity at all
to adjust -- provides information that policymakers need weigh the
benefits of trade liberalization as well as improve domestic adjustment
capacity.

**Figure 4**

![](media/image4.png){width="4.282638888888889in"
height="3.036111111111111in"}

*3.1.3. On-farm Technology Adoption*

The final area of policy analysis with ARMS data that we consider is the
adoption of new technologies by farm households. Economic assessments of
technology adoption using the traditional tools of agricultural
economists pioneered by Grilliches have been demonstrated to be
inadequate and incapable of explaining differing adoption rates of
agricultural innovations introduced in recent decades. The standard
measure of farm profitability, net returns to management, gives an
incomplete picture of economic returns because it excludes the value of
management time. Management time on modern operations is a scarce
resource, and is often used in off-farm employment. Recent work (Smith;
Fernandez-Cornejo and McBride; Fernandez-Cornejo and Hendricks) is
suggestive of the emergence of a new classification of technological
innovations as either management-saving or management-using.
Particularly for smaller farms that spend a great deal of time off of
the farm in wage-earning activities, technologies that do not appear to
change the net returns in farming can in fact increase overall household
well-being as management time is released. It appears that farm
households are increasingly making technology adoption decisions based
on how total household income is affected rather than simply residual
farm income. Without ARMS data on the range of farm household
activities, less would be known about what can constrain on-farm
adoption of newer production and conservation technologies. Moreover,
ARMS data are a key component in the evaluation of incentive-based and
voluntary programs put in place to encourage adoption of conservation
practices in meeting domestic environmental protection goals.

4.  **Implications**

The farm household serves as the focal point for understanding how
policy impacts farm production activities, the ownership of production
assets, the disposition of income, and, even, operational and strategic
management of the business. From an agricultural industry perspective,
the U.S. agricultural income accounting system is not diminished in its
ability to generate national estimates of value-added to the U.S.
economy and to track sector-wide trends over time. The accounting system
has been honed to more accurately account for the participation of
multiple operators/households in farm business establishments along with
other entities. This enables the income accounting system to recognize
multiple owners of assets and to correctly distribute income to
participants. This new system explicitly recognizes that an operator
household may not receive all the income generated by its farm business,
and that farming income is but one source of earnings for the dominant
share of farm households.

ARMS is structured to develop for each farm an measure of value added,
net income, and the disposition of income among stakeholders, including
the controlling household of the senior or dominant operator. For the
farm\'s controlling household, data are obtained to establish human
capital, financial and other asset allocations to farm and non-farm
activities. This integrated data collection system enables the
development of farm business and household accounts for the same unit of
observation. A result has been the development of a series of income
measures each of which contributes to a clearer understanding of the
contribution of farming to the U.S. economy, the performance of farms as
business establishments, and the welfare of households of farm
operators.

5.  **References**

American Agricultural Economics Association. 2000. Commodity Costs and
Returns Estimation Handbook. Available at
[http://waterhome.brc.tamus.edu/care/Aaea/pdf/AAEA%20Handbook.pdf](http://waterhome.brc.tamus.edu/care/Aaea/pdf/AAEA Handbook.pdf)

Burfisher, Mary, Kenneth Hanson, Jeffrey W. Hopkins, and Agapi Somwaru.
"Global Agricultural Policy Reform and U.S. Agricultural Adjustment
Capacity," invited paper for the International Agricultural Trade
Research Consortium (IATRC) workshop on Policy Reform and Agricultural
Adjustment in Industrial Countries, Philadelphia, PA, June 6-7 2004

Fernandez-Cornejo, J. and W.D. McBride. Adoption of Bioengineered Crops.
Agricultural Economic Report No. 810. U.S. Dept. of Agriculture, ERS.
May 2002.

Fernandez-Cornejo, J. and C. Hendricks. "Off-Farm Work and the Economic
Impact of Adopting Herbicide-Tolerant Crops." Selected Paper presented
at the AAEA meetings, Montreal, Canada, July 27-30, 2003.

Hopkins, Jeffrey W., Mitch Morehart, James Johnson, David Culver and
John Caldwell. "What Affects Farmers' Ability to Adjust? Evidence from
the United States and Canada", invited paper for the International
Agricultural Trade Research Consortium (IATRC) workshop on Policy Reform
and Agricultural Adjustment in Industrial Countries, Philadelphia, PA,
June 6-7 2004.

Johnson, James and Mitch Morehart. "Agricultural Resource Management
Survey: A Case Study" IWG.AGRI Task Force on Rural Development
Statistics and Household Income. Verona, Italy, June 30, 2004.

Mishra, Ashok and Mitch Morehart, "Farm Families' Savings: Findings from
the ARMS Survey", *Agricultural Outlook,* April 2002, pp. 27-30.

Morehart, Mitch, James Johnson, C. Edwin Young and Greg Pompelli. "Using
Farm-Sector Income as A Policy Benchmark", *Agricultural Outlook*,
June-July 2001, pp. 14-19.

Seskin, Eugene P and Roger P. Parker. "A Guide to the NIPA's", *Survey
of Current Business*, March 1998, pp. 26-68.

Smith, Katherine R. "Does Off-farm Work Hinder 'Smart Farming'?",
*Agricultural Outlook*, September 2002, pp. 28-30.

[^1]: Presented at the Third International Conference on Agricultural
    Statistics, Cancun Mexico, November 2-4 2004. The views expressed
    are those of the authors and not necessarily those of the U.S.
    Department of Agriculture.

[^2]: Farm operator households exclude households associated with farms
    organized as non-family corporations or cooperatives, as well as
    households where the operator is a hired manager. Household members
    include all persons dependent on the household for financial
    support, whether they reside in the household or not.