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Key words: Agricultural policy, Agricultural Resource Management Survey (ARMS), cluster
analysis, farm household, typology
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A New U.S. Farm Household Typology: Implications for Agricultural Policy
Brian C. Briggeman, Allan W. Gray, Mitchell J. Morehart, Timothy G. Baker, and Christine A.
Wilson
Changes in U.S. agriculture have yielded a diversity of farm types. These changes have extended
beyond the farm business and into the farm household. The objective of this research is to
motivate, develop, and discuss the policy implications of a new typology of U.S. farm households
that is based on household economic theory. Using the 2003 Agricultural Resource Management
Survey and statistical analysis, the U.S. Farm Household Typology identifies six mutually
exclusive groups of U.S. farm households. This typology is then compared to the current
Economic Research Service Farm Typology and used to investigate the distribution of government
payments.
@ Brian C. Briggeman is an Assistant Professor at Oklahoma State University.
@ Allan W. Gray is an Associate Professor at Purdue University.
@ Mitchell J. Morehart is a Senior Agricultural Economist at the Economic Research Service.
@ Timothy G. Baker is a Professor at Purdue University.
@ Christine A. Wilson is an Associate Professor at Purdue University.
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The debate concerning U.S. farm programs and farm policy encompasses a number of
issues that are broader than whether the specifics of the 2002 legislation should be continued or
changed. These issues include U.S. competitiveness in the international marketplace; the potential
conflict between U.S. domestic and international trade policy; the impact of farm program
payments on land and other resource values; the distribution of farm program benefits; and
whether price and income support programs are an effective and efficient mechanism for
enhancing the economic well-being of farm families.
Analyses to date have three fundamental deficiencies. First, they ignore the fact that a vast
majority of farms receive substantial income from non-farm sources and that the decision nexus
for most farms with respect to resource allocation decisions (time, labor, management, capital,
etc.) is at the household level rather than farm level. Second, financial impacts of farm programs
are broader than farm income. They include wealth and debt service/creditworthiness as well as
household impacts that may be more or less significant than the impacts on farm income. These
additional household and farm business financial effects are often ignored in policy analyses.
Finally, the differential impacts of farm programs and the distributional consequences for different
farm households are frequently underemphasized or overlooked by analysts, but not by politicians.
For decades, agricultural policy analysts accepted the conventional wisdom that labor and
assets in the agricultural sector earn low returns (for example, see Herdt and Cochrane; Mason).
This is hard to reconcile with data showing high per-farm household wealth compared with nonfarm households. In 2003, average farm household net worth was $663,491 and non-farm
household net worth was $359,400. The issue of high per-farm wealth is often rationalized by
accounting for unrealized capital gains on farmland (Melichar). This leads to conventional
wisdom suggesting that farmers are “cash poor and asset rich.” These views may be incorrect or
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misleading when the sector is viewed from the household perspective. In her AAEA presidential
address, Offutt’s central issue was a household perspective for future policy analysis . Offutt
discusses the importance of future policy analysis taking a more micro-level household approach.
That is, farm policy must view the farm household as an economic decision-making unit.
Segmenting farm households can provide valuable information to policy analysts relative
to the impact of targeted policies. Typically, farm households are segmented on size, e.g. farm
sales. This segmentation is one-dimensional and does not capture the increasing heterogeneity of
farm households in terms of sources of income, wealth, borrowing, saving and consumption
behavior, and financial/business structure. Offutt argues that farm programs that do not recognize
these stated differences in farm households are likely to have dramatically different impacts and
consequences for the varying farm households and farm families. Analysts must account for this
distributional dimension to understand both the intended and unintended economic and political
consequences of farm policy. Therefore, a typology that captures these differential effects would
benefit farm policy.
The objective of this paper is to motivate, develop, and discuss the policy implications of a
U.S. farm household typology based on household economic theory. The farm household’s
resource allocation decisions are the foundation for this new classification system. A dynamic
farm household utility model motivates the decisions of the operator and spouse to allocate farm
household labor and investment (both on and off the farm), consumption, and credit. These
household decisions are used to develop a new typology of U.S. farm households via cluster
analysis. The 2003 Agricultural Resource Management Survey (ARMS), administered jointly by
the Economic Research Service (ERS) and National Agricultural Statistical Service (NASS),
contains the necessary farm household information to develop this typology.
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Results from the cluster analysis identified an intuitive set of six mutually exclusive
groups. These groups represent a new way to capture the heterogeneity of today’s farm household
in the U.S. Farm Household Typology. Policy implications of this typology suggest that economic
safety net program recipients1 have a larger amount of value of production and farm net worth
than non-recipients. Also, households who received a payment have significantly lower off- farm
income but their total household income (farm and off-farm income) is equivalent to farm
households who did not receive a payment. This result suggests that economic safety net program
payments reduce off farm labor by the operator and/or spouse, which is similar to the findings of
Ahearn, El-Osta, and Dewbre. Using a bivariate probit model of off farm labor participation,
Ahearn, El-Osta, and Dewbre found that government payments reduced the probability that the
operator and spouse would work off the farm. By accounting for both the operator and spouse
resource allocation decisions, the U.S. Farm Household Typology provides a set of new and
unique target groups that can be used in the deliberations and analysis of farm policy programs.
Segmentation of U.S. Farm Households Based on Household Economic Theory
To meet the objective of this study, an important marketing concept, customer
segmentation, is utilized. Segmenting a market into subgroups that are homogenous is beneficial
because they are likely to respond in a similar manner. Cluster analysis is a method used to
segment aggregate data into groups that are homogenous within group but are heterogeneous
across groups. Hill, Brennan and Wolman, Shucksmith and Herrmann, and Kim used cluster
analysis to identify a set of homogenous groups to better inform their respective policy questions.
Segmenting U.S. farm households into policy-actionable groups that are based on household
economic theory is beneficial for policy analysts. This segmentation allows policymakers to
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deliberate on the policy impacts for a group of U.S. farm households that are similar in their
decision to allocate their resources rather than groups based on farm size or farm sales.
For example, Blank contends that the U.S. would be better served if agricultural payments
were received by “real” farms as opposed to the “real-estate investment” farms. Blank states that
the difference between these two farm types is the allocation of household labor. “Real” farmers
work on the farm to support the household while “real-estate investment” farms primarily work off
the farm. The U.S. Farm Household Typology presented in this article, being based on household
economic theory, directly addresses Blank’s concerns. If a typology of U.S. farm households is to
be used in farm policy analysis, then it must identify policy-actionable groups that are intuitive
and identifiable. Our U.S. Farm Household Typology meets both of these criteria since it is based
on household economic theory.
Huffman presents a detailed and thorough discussion on the development and estimation of
agricultural household models. He develops a theoretical model that accommodates North
American agriculture or farm households that purchase inputs in the market, sell most or all of
their agricultural outputs, and allocate some or most of their labor off the farm. Other authors
have utilized dynamic models to meet their stated objectives, including Shively and Pagiola;
Fafchamps; Meghir and Weber; Phimister; Deaton. For purposes of this study, a dynamic
household utility model is more realistic than a static household utility model because it captures
the ability of the farm household to use saving/investment and borrowing to smooth consumption
over time. Briggeman developed a dynamic farm household utility model that motivates the
decisions a modern U.S. farm household faces.
Since this typology must sort different farm households in a way that maximizes the
degree of association between households while capturing the heterogeneity of different household
behaviors, the necessary variables must allow for the organization of farm households into
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meaningful structures or mutually exclusive groups. Farm household decisions from the dynamic
farm household utility model serve as the necessary variables. The decisions are household
consumption, on- and off-farm labor by the operator and spouse, investment in farm and non-farm
assets, and borrowing. The 2003 ARMS data set provides the micro-level data of U.S. farm
households necessary for developing the typology2. Table 1 contains the decision variables and
their associated mean and coefficient of variation (CV).
Household Decision Variables
Household consumption is the first decision. From household theory, we know that a
household chooses consumption where the marginal utility of consumption equals the marginal
utility of available funds. Thus, as available funds increase so will the utility of the farm
household. In the 2003 ARMS data set, total minimum farm household expenditure is the variable
that best fits the farm household’s consumption choice. Here, total minimum household
expenditure is the amount of pre-tax income needed for the household’s living expenses (e.g. food,
household supplies, rent/mortgage, utilities, non-farm transportation, appliances/furnishings,
medical expenses, insurance, and all other family living expenses). Average household
consumption within the 2003 ARMS data set is $36,314, with a CV equal to 1.84 percent.
Allocation of labor by the operator and spouse to farm and/or off farm work is the next set
of farm household decisions. From household theory, a farm operator or spouse will work on the
farm as long as their marginal value of farm labor exceeds the off-farm wage and visa versa. In
other words, the operator and spouse will allocate labor to the highest wage rate on and off the
farm. The labor decision will separate households according to wages faced. In addition,
nonpecuniary returns (e.g. working conditions and fringe benefits).may impact the labor decision.
Thus, the labor decision captures the different behaviors of these households relative to the labor
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decision of the operator and spouse. Operator and spouse hours spent working on and off the farm
are reported for each quarter in the ARMS data set. The operator works an average of 1,386 and
1,052 hours per year on and off the farm, respectively. On average, the spouse works more hours
per year off the farm (797) than on the farm (329).
Farm household wealth or asset accumulation affects the production of agricultural goods
as well as the returns from non-farm investments. Mishra and Morehart state that farm households
need to be cognizant of their farm and non-farm investments because of their impact on
profitability management, retirement, taxation, solvency, and liquidity. In addition, wealth
influences the borrowing ability of a farm household through the increase in collateral availability.
Therefore, farm assets, non-farm assets, and borrowing are the final household decisions in
determining the typology. Since the ARMS data set is a cross section, the value of farm and nonfarm assets is used as a proxy for the choice to invest on and/or off the farm. Average value of
farm assets within the 2003 ARMS data set is $557,942 with a CV of 8.48 percent and non-farm
assets average value is approximately half of farm assets at $250,367 with a CV of 5.18 percent.
The ability of farm households to borrow captures the marginal benefit of borrowing or the
borrowing behavior of farm households. A farm household can use borrowing to increase their
available funds, thus creating the ability to smooth consumption over time and to increase their
farm or non-farm investments, i.e. wealth. The farm household debt-to-asset ratio is the proxy
used for borrowed funds by the farm household. The average farm household leverage position is
14 percent.
Methodology
The statistical model selected to define the U.S. Farm Household Typology is cluster
analysis. Milligan argues that irrelevant variables can blur the true data structure and that only
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those variables that help distinguish clusters in a meaningful way should be included in the
analysis. Therefore, the household decisions discussed above serve as the variables used within
the cluster analysis to determine the U.S. Farm Household Typology.
An issue with cluster analysis is determining the number of cluster groups. In their survey
of hierarchical and non-hierarchical clustering algorithms, Punj and Stewart find that empirical
studies that identify a logical set of non-random groups to serve as starting values select a nonhierarchical clustering algorithm because they outperform a hierarchical clustering algorithm. The
current ERS Farm Typology segments U.S. farm households into seven groups that are based on
farm sales and operator’s occupation (Hoppe, Perry, and Banker). Thus, the ERS Farm Typology
represents a logical set of non-random groups that can serve as starting values to inform the
proposed typology.
Although the ERS Farm Typology extends the typical one-dimensional segmentation, i.e.
farm sales, it is still largely determined by farm sales and is missing a vital component of today’s
farm household – the spouse’s occupation. A spouse’s labor decision impacts household income
in terms of stabilizing and/or increasing household income, e.g. through off-farm employment. In
addition, studies have found that government payments significantly influence the spouse’s labor
decision (Mishra and Goodwin; Ahearn, El-Osta, and Dewbre). Given that the proposed typology
in this paper intends to better inform farm policy, we directly account for the spouse’s labor
decision.
Since the seven groups in the ERS Farm Typology represent a logical set of non-random
groups and will serve as the starting seed values, a non-hierarchical clustering algorithm is
selected. We selected the weighted k-means clustering algorithm because Euclidean distances are
minimized or the similarity between observations in a cluster is maximized. Since the Euclidean
distances are the measure of similarity, the scale of the variables must be controlled for in the
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clustering algorithm. Also, the household decision variables chosen will be skewed within a given
distribution (e.g. farm assets). This skewed distribution should be retained because policy analysis
must consider this distribution. To accomplish this, each variable is segmented or binned into 10
groups based on the weighted distribution of the data. Each household below the 5th percentile is
in group 1 and each household above the 95th percentile is in group 10 for their respective
explanatory variable. Remaining groups are evenly divided between the 5th and 95th percentile.
Finally, to control for those households who do not have a spouse, a separate bin group is created
for on- and off-farm spouse labor which equals zero. Approximately 20 percent of the
representative farm households do not have a spouse.
Combining the household decisions and the ERS Farm Typology, allows the weighted kmeans algorithm to identify a typology that is based on household theory, i.e. operator and spouse
labor, investment, borrowing, and consumption decisions, as well as incorporates farm sales
information through the ERS Farm Typology starting seed values. Table 2 shows the mean and
CV for each binned variable for the entire 2003 ARMS data set as well as the ERS Farm
Typology.
Limited Resource Farms are small farms with sales less than $100,000 in 2003 and low
operator household income in 2002 and 2003. Household income is low if it is less than the
poverty level in both 2002 and 2003 or if it is less than half the county median income for both
years. Therefore, it is not surprising that Limited Resource Farms have an average consumption
(2.71) and value of farm assets (2.96) less than the average for all farm operator households of
5.05 and 3.56, respectively.
Retirement Farms have the lowest average borrowed funds or household debt-to-asset ratio
(2.69). This is typical for the latter stages of the life-cycle. Residential farms have the highest
average binned consumption (5.55) and the highest average binned off-farm labor hours by the
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operator (7.61) and spouse (5.32). Note that the ERS Farm Typology does not consider the
spouse’s labor allocation decision, but the residential farm group has the highest spouse off farm
average binned labor hours among all groups. Given the importance of spouse hours both to farm
and off-farm employment, this is a vital farm household decision that must be directly captured
within a typology of farm households.
The operators in the next four groups report farming as their main occupation3. As such,
average binned operator farm hours and the value of farm assets for each of these groups exceeds
the average values for all farm households. Also, the average operator’s off-farm labor is less than
the aggregate data’s average. Another trend in these four groups is that as the gross farm sales
increases, the average operator farm labor and value of farm assets increases as well.
The ideal clustering solution is observable, actionable and intuitive. To determine if it is
intuitive, descriptive statistics of the clustering solution are analyzed relative to the household
decisions. This will define and identify the farm type each cluster group represents. Cluster group
means of the household decisions are tested for differences by the pairwise t-test statistic.
Significant differences between the means of these clustering variables are expected since the
clustering algorithm maximizes the differences between clusters and the similarities within each
cluster. Additional household variables that relate to the theoretical model will be tested to further
demonstrate that the cluster groups are indeed different. If statistical significance between two
groups is not found, then this is justification for combining the two groups.
Finally, the clustering solution must be validated. Two approaches are implemented to
ensure the validity of the final solution. First, the stability or robustness of the cluster solution is
tested. Stability of the cluster solution is a concern because the structure of the solution can shift
even if the same data is reordered (Levine, Jones and Williams). Similar to Levine, Jones and
Williams, a stability index is calculated. To create this statistic, the cluster solution developed on
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the entire sample is treated as the base and then the sample is randomly segmented into groups of
10 percent, 20 percent, 30 percent…90 percent of the total sample (creating 9 subsamples).
Levine and Domany rigorously prove that resampling is a viable method to validate a cluster
solution. Therefore, the original cluster solution or base result, which is based on the aggregate
sample, is compared to each random sample cluster solution. If the results are equivalent, then the
individual farm household is labeled as being identified correctly. It is unreasonable to assume
that 100 percent will be identified correctly but the results should fit reasonably well or at least
identify with a 75 percent success rate (Levine, Jones and Williams).
The second validation method is to analyze the U.S. Farm Household Typology relative to
the ERS Farm Typology. Pairwise t-tests of the means of each group within the U.S. Farm
Household Typology and the ERS Farm Typology are calculated relative to the household
decisions. The objective is to demonstrate that the two typologies differ significantly relative to
the farm household resource allocation decisions.
Results
The cluster algorithm uses variables that are binned to control for scale issues within the
data; however, analyzing continuous variables is more meaningful to determine what each cluster
group represents. Therefore, continuous variables used in the clustering algorithm and additional
variables related to the dynamic household utility model are discussed. Standard ANOVA tests
show that the U.S. Farm Household Typology is statistically different relative to the household
decisions used to determine the typology. Table 3 shows the means and pairwise t-tests of these
continuous variables for the U.S. Farm Household Typology4.
Single Income Ruralpolitan
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The first cluster group, Single Income Ruralpolitan (SIR), comprises 22.3 percent of the
overall weighted sample. Labor allocation, non-farm assets, household farm income, and earned
off-farm income are the distinguishing variables for this group. Earned off-farm income consists
of operator and spouse wages/salaries, and any income earned from operating a non-farm
business. The operator is the primary worker and is employed full-time off the farm (operator offfarm hours equal 2,016). This labor allocation aids in explaining why earned off-farm income is
high ($64,768) and statistically different relative to all other groups. These results support the
characterization of single income. Ruralpolitan refers to the relatively large amount of non-farm
assets compared to farm assets for this group. Also, labor primarily being allocated off the farm
and off-farm income being the main source of funds for the household, further supports the
Ruralpolitan designation.
Double Income Ruralpolitan
The Double Income Ruralpolitan (DIR) represents 23.5 percent of the weighted sample.
Looking at table 3, DIR is similar to SIR. The means and statistical significance on the value of
farm assets, non-farm assets, operator farm hours, and spouse farm hours support the ruralpolitan
designation. What makes DIR unique is that the mean earned off-farm income ($82,376) and the
mean of spouse off-farm hours (1,974) are high and statistically different relative to SIR and all
other groups. Also, the Double Income title is appropriate for this cluster group because all
observations reported having a spouse who worked off the farm. Another differentiating factor for
this group is that household expenditures ($49,131) are the highest and statistically different
relative to all groups. Finally, these results aid in explaining why household size (3.2) is the
second highest among all groups.
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Active Seniors
Active Seniors (AS) represent 24.4 percent of the sample. All mean levels of the household
decision variables are below all other groups except for operator farm hours relative to the two
ruralpolitan groups. This group is titled AS because the operator primarily works on the farm,
they have the lowest mean household debt-to-asset ratio, household consumption is the lowest,
they have unearned off-farm income (i.e. social security, pensions, interests, dividends, etc.) and
the operator is the oldest relative to all other groups. Also, statistically significant differences on
all of these variables relative to all other groups further support their designation as AS5.
Farm Operator with Spouse Working Off Farm
It is argued within the literature that off-farm work is a risk-mitigating strategy for the
variability in farm income. The next group, Farm Operator with Spouse Working Off Farm
(FOSO), is supported by that body of literature. Here, the farm operator has the third highest
mean farm labor hours and the spouse has the highest mean off-farm labor hours, both of which
are statistically different than the other groups. Assuming the operator and spouse allocate their
labor to the highest wage explains why farm and earned off-farm income are both positive and
statistically different for FOSO relative to the other groups. Another reason why the operator
takes a more active role on the farm is because the value of farm assets ($626,909) is the third
highest average among all groups. Finally, this group comprises 12.3 percent of the sample and
all observations reported having a spouse who worked off the farm.
Traditional Farms
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Knutson, Penn and Flinchbaugh contend that the operator and his/her family provide the
majority of the management and work on a traditional farm. However, today’s farm household
may be working on and off the farm. The important distinction for this group is that the operator
and spouse are spending a significant amount of time on the farm. We titled this group
Traditional Farms (TRAD) since the operator and spouse have the highest mean on-farm hours.
TRAD derives nearly 50 percent of their total earned income from farming, which is second
highest among all groups. Also, this group has the second highest mean value of farm assets,
which is statistically significant, showing a further commitment to the farm. This group represents
9 percent of the U.S. farm household population and all observations reported having a spouse
who worked on the farm.
Commercial Farms
Commercial Farms (COM) comprise 8.5 percent of the sample. This group has the
operator primarily working on the farm and the spouse not taking as active of a role on the farm
compared with TRAD. “Commercial” refers to the high mean value of farm assets relative to
labor allocation by the operator and spouse. In addition to the highest and statistically significant
value of farm assets, COM has the highest value of non-farm assets. COM has the largest amount
of wealth and a relatively low amount of debt given that the household debt-to-asset ratio is the
second lowest among all groups. Finally, COM has the highest and statistically significant mean
value of household farm income ($47,584).
Stability of the U.S. Farm Household Typology
The previously discussed stability index is the robustness check of the U.S. Farm
Household Typology. According to figure 1, the hypothesis that the clustering solution correctly
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identifies at least 75 percent within each random sample holds. The smallest random sample (10
percent) is the closest to the 75 percent stability index benchmark but it is still above it at 80.2
percent. Therefore, the presented clustering solution is stable and robust given that the data was
randomly segmented into smaller subgroups6.
U.S. Farm Household Typology versus the ERS Farm Typology
Pairwise t-test statistics show statistical evidence of the difference between the ERS Farm
Typology and the U.S. Farm Household Typology. Table 4 presents means of the ERS Farm
Typology and pairwise t-test statistics relative to the U.S. Farm Household Typology for the
household decision and additional variables.
Table 4 shows that at least one group in either of the two typologies is statistically different
relative to the household decision and additional household variables. This result occurs because
both typologies, as discussed earlier, are based on different factors. The ERS Farm Typology is
determined more on farm attributes. For example, do you consider your major occupation
farming? Are you retired? What are your gross farm sales? Even though the ERS Farm
Typology serves as the starting seed values for the U.S. Farm Household Typology, the weighted
k-means clustering algorithm is able to identify unique groups based on those theoretically derived
household decision variables. Therefore, the U.S. Farm Household Typology is statistically
different relative to the ERS Farm Typology.
Table 4 confirms our earlier contention that the spouse of a farm household plays a
significant role in allocating resources or labor. Nearly all group means relative to spouse farm
and off labor are statistically different from each other. This result is apparent in the spouse offfarm labor allocation. SIR and DIR are similar to the Residential group relative to the reported
means. The biggest difference is in the spouse’s labor decision. This also aids in explaining why
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the earned off farm income for SIR is significantly lower than the Residential earned off-farm
income (i.e. the spouse is not working off the farm for SIR).
Policy Implications
The 2007 U.S. Department of Agriculture (USDA) budget proposal focuses on six
strategic goals that center on enhancing U.S. agriculture. Of special interest for this research is the
strategic goal to enhance the competitiveness and sustainability of rural and farm economies.
Economic safety net programs provide income stability for producers and are a mechanism to
meet this strategic goal. Table 5 shows the policy implications of assessing the U.S. Farm
Household Typology relative to those who received and did not receive economic safety net
program payments (referred to as payments for the remainder of the paper).
If a payment was received, then a higher and statistically significant mean level of farm
income and total value of production is found for each typology group except COM. For the
average household in the SIR and DIR groups who received a payment, the mean farm income is
positive and statistically significant. This positive farm income is partially attributed to these farm
households having an increase of approximately $45,000 total value of production for each group.
Households that receive payments have a lower average off-farm income. This result
supports Mishra’s and Goodwin’s.findings that off-farm income is an income- stabilizing strategy
for farm households. Payments in table 5 appear to also stabilize income because the total
household income, on average, for each group remains constant even though off-farm income
decreases. It is interesting to note that the FOSO and TRAD groups have significantly lower offfarm household income. Perhaps payments are “buying” the spouse out of the off-farm labor
market in the case of FOSO. Alternatively, these payments might allow operators to work more
on the farm in the case of TRAD. The results of Ahearn, El-Osta, and Dewbre support these
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assertions. They found that an increase in government payments decreased the likelihood of the
spouse and operator working off the farm.
The average net worth from farm assets (i.e. farm net worth) for those groups who received
a payment is higher than non-recipients except for the COM group. Payment groups also have a
higher total household net worth even though there is a reduction in the average net worth from
non-farm assets (i.e. non-farm net worth). This result may be capturing the capitalization of
payments into land values since these payments are received by land owners who qualify for the
payments.
Those who received payments show significant differences from those who did not receive
payments; however, what about the concentration of all government payments relative to this new
typology? Figure 2 shows the concentration of different government payments does vary
substantially across the U.S. Farm Household Typology. Commodity program payments are more
heavily directed toward the three household types who allocate more resources to the farm (FOSO,
TRAD, and COM). But, conservation payments such as the Conservation Reserve Program
(CRP) tend to be more heavily concentrated toward farm households that are not dependent on
farming for household income. Is this an intended consequence of U.S. farm policy?
These examples demonstrate some of the policy questions that can be addressed with the
U.S. Farm Household Typology. The U.S. Farm Household Typology and the ERS Farm
Typology do exhibit some similarities. For example, COM and Very Large Farms or the largest
gross farm sales groups in the U.S. Farm Household Typology and the ERS Farm Typology,
respectfully, receive the highest average amount of payments relative to other groups in their
respective typology. Thus, primary payment recipients can be identified in each typology. A
deviation between the two typologies is noted since the U.S. Farm Household Typology accounts
for the resource allocation decisions of the operator and spouse as opposed to the ERS Farm
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Typology that does not consider the spouse’s labor decision. This is an advantage of the U.S.
Farm Household Typology relative to the ERS Farm Typology in policy discussions. For
example, FOSO and TRAD farms who receive payments have a lower amount of off-farm income
but have a higher total household income relative to FOSO and TRAD farms who do not receive
payments. Therefore, the U.S. Farm Household Typology can be used in deliberations when
discussing how farm policy will affect the entire farm household’s resource allocation decisions
relative to a set of target groups based on household economic theory.
Conclusions
The primary objective of this research was to develop a U.S. Farm Household Typology
that uses household economic theory to determine how farm households should be segmented.
The household decisions used to define the U.S. Farm Household Typology were based on a
dynamic household utility model. From these factors, six mutually exclusive groups were
identified using the 2003 ARMS data. These groups comprise the U.S. Farm Household
Typology.
The U.S. Farm Household Typology provides multiple avenues for future research beyond
the scope of the present research. One particular extension of this typology is to incorporate it into
a U.S. computable general equilibrium (CGE) model. From the results in this paper, one
hypothesis is that each group in the U.S. Farm Household Typology will allocate their resources,
in particular operator and spouse labor, differently if current payments are changed. Another
extension is to assess the financial risks and opportunities each group within the U.S. Farm
Household Typology pose to creditors. Since the SIR and DIR have a more diversified asset and
income portfolio compared to the other groups, they pose lower financial risk to creditors. Also,
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SIR and DIR represent nearly 50 percent of today’s U.S. farm households or they are a significant
portion of customers for creditors.
Finally, this research contributes a new typology of U.S. farm households to the literature.
This typology identifies intuitive groups of U.S. farm households which are based on household
economic theory. Policymakers can assess the impacts of government payments relative to how
U.S. farm households allocate their resources and not by their farm sales. Combining this new
typology with the current ERS Farm Typology can provide a new richness in analyzing the
implications of agricultural policy changes for various agricultural constituents.
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Acknowledgements
The authors wish to thank Daniel Pick, Hisham El-Osta, and two anonymous reviewers for their
insightful comments on earlier drafts of this paper. Partial funding for this research was provided
by a Cooperative Agreement between USDA-ERS and Purdue University.
The views expressed here are those of the authors, and may not be attributed to the Economic
Research Service or the U.S. Department of Agriculture.
22
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26
Table 1. Household decisions summary statistics
Household Decisions
ARMS Variable
Consumption
Minimum Household Expenditures
Operator Farm Labor
Operator Farm Hours
Spouse Farm Labor
Spouse Farm Hours
Operator Off Farm Labor Operator Off Farm Hours
Spouse Off Farm Labor
Spouse Off Farm Hours
Farm Investment
Value of Farm Assets
Non-Farm Investment
Value of Non-Farm Assets
Borrowed Funds
Household Debt-to-Asset Ratio
Mean
$36,314
1,386
329
1052
797
$557,942
$250,367
14%
C.V.
1.84%
1.22%
3.56%
3.00%
3.10%
8.48%
5.18%
7.14%
Note: 2003 ARMS data is used with 17,728 samples and 2,084,715 weighted U.S. family farm households.
C.V. is the coefficient of variation.
27
Table 2. 2003 ARMS Data and ERS Farm Typology means and coefficients of variation of the binned household decisions
ERS Farm Typology
Household Decisions
Consumption
Operator Farm Hours
Spouse Farm Hours
Operator Off Farm Hours
Spouse Off Farm Hours
Farm Assets
Non-Farm Assets
Household Debt-to-Asset
Ratio
Note:
2003
ARMS
Data
Limited
Resource
Farm
Retirement Residential
4.37
(1.4%)
5.97
(1.0%)
2.96
(1.9%)
2.97
(2.3%)
3.74
(2.3%)
5.07
(1.1%)
3.33
(1.8%)
3.97
(1.6%)
2.56
(3.0%)
4.38
(6.8%)
1.40
(9.6%)
2.05
(13.2%)
1.05
(15.1%)
3.09
(3.1%)
2.26
(4.3%)
2.69
(5.1%)
3.77
(3.6%)
3.30
(2.2%)
1.51
(5.9%)
1.54
(6.9%)
2.09
(7.0%)
3.23
(3.3%)
3.37
(4.6%)
2.52
(2.4%)
5.01
(1.8%)
3.26
(1.2%)
2.20
(4.1%)
7.61
(0.9%)
5.13
(2.9%)
3.04
(1.4%)
3.66
(2.9%)
4.03
(2.0%)
Farming
Occupation/
Low Sales
Farming
Occupation/
High Sales
Large
Very
Large
3.94
(1.9%)
5.23
(2.2%)
2.79
(3.8%)
2.63
(4.5%)
3.86
(3.2%)
3.84
(1.8%)
2.96
(2.6%)
3.51
(2.6%)
3.98
(1.5%)
7.59
(0.9%)
3.65
(3.6%)
1.74
(5.0%)
3.93
(5.1%)
5.51
(1.6%)
2.88
(2.4%)
3.84
(2.7%)
4.57
(1.7%)
7.42
(1.3%)
3.49
(4.4%)
1.96
(4.1%)
4.08
(3.9%)
6.28
(1.9%)
3.31
(3.2%)
4.27
(2.2%)
4.86
(1.3%)
7.48
(1.0%)
3.56
(2.5%)
1.86
(4.5%)
3.30
(4.1%)
7.19
(0.9%)
3.86
(2.7%)
4.79
(1.5%)
2003 ARMS data is used with 17,728 samples and 2,084,715 weighted U.S. family farm households.
Numbers in the parentheses are coefficients of variation.
28
Table 3. Household decisions and additional household variables means for the U.S. Farm Household Typology and pairwise ttests
Note:
2003 ARMS data is used with 17,728 samples and 2,084,715 representative U.S. family farm households.
At least 10% statistically significant pairwise t-test statistics are represented by A, B, C, D, E, and F.
A) Single Income Ruralpolitan relative to the household factor and the given cluster group.
B) Double Income Ruralpolitan relative to the household factor and the given cluster group.
C) Active Seniors relative to the household factor and the given cluster group.
D) Farm Operator with Spouse Working Off Farm relative to the household factor and the given cluster group.
E) Traditional Farms relative to the household factor and the given cluster group.
F) Commercial Farms relative to the household factor and the given cluster group.
na stands for pairwise t-test is not applicable.
29
Table 4. Household decisions and additional household variable means for the ERS Farm Typology and pairwise t-tests relative
to the U.S. Farm Household Typology
Household Decisions
Household
Farm Income
Earned Off
Farm Income
Unearned Off
Farm Income
Operator
Age
Operator
College Household
Education
Size
Farm Assets
Non-Farm
Assets
Limited Resource
$20,450ABCDEF 1,398ABCDEF
188ACDE
311ABCD
149BCD
$389,257CDEF
$87,432ABDEF
7.3%ABDE
$-7,238ACDEF
$5,686ABDEF
$8,562CEF
62ABCDE
10%na
2.2ABDEF
Retirement
$31,951ABCDF
918CDEF
138BCDE
168ABCDE
337ABCDEF
$414,151CDEF
$261,100C
6.2%ABDEF
$394BCDEF
$16,724ABCD
$32,823ABCDE
69ABDEF
18%na
2.1ABDEF
Residential
$45,936CEF
887CDEF
265ACEF
2,008BCDEF
1,110ABCDEF
$416,680CDEF
$269,017CEF
19%CF
$-4,476ACDEF
$78,882ACDEF
$11,229CF
50BCDEF
24%na
3.0ABCEF
$33,682ABCDF 1,793ABCDEF
439ABCDEF
487ABCDF
811ABCDEF
$544,978ABCDEF
$171,389BF
16.2%ACF
$2,317BDEF
$31,745ABCDF
$15,891BCEF
57ABCD
15%na
2.8BCE
$34,292ABCDF 2,906ABCDEF
656ABCDEF
205ABCDE
784ABCDEF
$953,104ABCDF
$148,530ABF
16.7%ACF
$29,248ABCDF
$19,904ABCD
$10,471CF
52BCEF
21%na
3.1ACF
$62,775ABCDE
$29,892ABCDF
$9,383CF
51BCDEF
23%na
3.1ACF
$176,938ABCDEF $30,379ABCD
$13,726CF
51BCDEF
30%na
3.0ACF
ERS Farm Typology Consumption
Farming Occupation
Low Sales
Farming Occupation
High Sales
Operator Spouse Farm Operator Off Spouse Off
Farm Hours
Hours
Farm Hours Farm Hours
Additional Household Variables
Household
Debt-to-Asset
Ratio
Large
$41,229BCE
2,863ABCDF
622ABCDEF
280ABCDEF
813ABCDEF
$1,277,462ABCDEF $217,837BCEF
20.7%CEF
Very Large
$46,199ACEF
2,821ABCDF
594ABCDEF
255ABCDEF
618ABCDEF
$2,025,006ABCDE $316,957ACE
26.2%BCDEF
Note:
2003 ARMS data is used with 17,728 samples and 2,084,715 representative U.S. family farm households.
At least 10% statistically significant pairwise t-test statistics are represented by A, B, C, D, E, and F.
A) Single Income Ruralpolitan and the given ERS Farm Typology relative to the pertinent household factor.
B) Double Income Ruralpolitan and the given ERS Farm Typology relative to the pertinent household factor.
C) Active Senior and the given ERS Farm Typology relative to the pertinent household factor.
D) Farm Operator with Spouse Working Off Farm and the given ERS Farm Typology relative to the pertinent household factor.
E) Traditional Farm and the given ERS Farm Typology relative to the pertinent household factor.
F) Commercial Farm and the given ERS Farm Typology relative to the pertinent household factor.
na stands for pairwise t-test is not applicable.
30
Table 5. U.S. Farm Household Typology income and wealth means and pairwise t-tests of those who received and did not
receive economic safety net payments
Group
Variable
Economic Safety Net Payments***
Total Value of Production***
Single Income
Farm Household Income***
Ruralpolitan
Earned Off Farm Income*
Farm Net Worth
Non-Farm Net Worth
Representative Number of Households
Economic Safety Net Payments***
Total Value of Production***
Farm Household Income***
Double Income
Earned Off Farm Income
Ruralpolitan
Farm Net Worth**
Non-Farm Net Worth
Representative Number of Households
Economic Safety Net Payments
Received
Did Not
Mean
Mean
$4,911
$0
$69,914
$20,888
$8,163
-$2,658
$56,550
$66,017
$360,705
$310,669
$179,954
$167,164
61,379
403,992
$4,203
$56,505
$4,853
$79,980
$385,322
$208,517
84,675
$0
$11,523
-$5,066
$82,877
$303,712
$216,194
405,219
Group
Variable
Economic Safety Net Payments***
Total Value of Production***
Farm Operator
Farm Household Income***
with Spouse
Earned Off Farm Income*
Working Off
Farm Net Worth***
Farm
Non-Farm Net Worth
Representative Number of Households
Traditional
Farms
Economic Safety Net Payments***
Total Value of Production***
Farm Household Income***
Earned Off Farm Income**
Farm Net Worth***
Non-Farm Net Worth
Representative Number of Households
Economic Safety Net Payments
Received
Did Not
Mean
Mean
$16,614
$0
$243,480
$74,259
$35,813
$6,683
$35,785
$59,310
$721,118
$433,749
$135,559
$249,526
94,473
162,072
$12,691
$254,838
$36,104
$14,593
$938,689
$100,255
72,496
$0
$143,940
$11,876
$32,641
$677,858
$139,629
116,068
Economic Safety Net Payments***
$5,248
$0
Economic Safety Net Payments***
$17,108
$0
Total Value of Production***
$60,736
$15,460
Total Value of Production
$306,020
$357,774
Active Seniors Farm Household Income***
$17,306
$617
Farm Household Income
$50,539
$45,048
Commercial
Earned Off Farm Income
$5,294
$6,791
Earned Off Farm Income**
$14,549
$28,748
Farms
Farm Net Worth***
$346,955
$286,778
Farm Net Worth
$1,499,577
$1,994,493
Non-Farm Net Worth
$104,406
$125,548
Non-Farm Net Worth***
$219,332
$358,560
Representative Number of Households
88,547
419,303
Representative Number of Households
81,516
94,975
Note: 2003 ARMS data is used with 17,728 samples and 2,084,715 representative U.S. family farm households.
Economic safety net payments include direct payments, counter-cyclical payments, loan deficiency payments, marketing loan gains, and for net value of
commodity certificates.
Pairwise t-tests are for a given typology group and across those who did and did not receive a payment.
*, **, and *** represent 10%, 5%, and 1% statistical significance, respectively.
31
100%
96.5%
95.5%
92.6%
90%
88.6%
88.7%
89.5%
89.3%
40%
50%
60%
70%
87.3%
80.2%
80%
Stability Index
70%
60%
50%
40%
30%
20%
10%
0%
10%
20%
30%
Random Sample
Note: Aggregate 2003 ARMS data is 17,728 samples and 2,084,715 representative U.S. family farm households.
Figure 1. Stability Index for the U.S. Farm Household Typology
80%
90%
32
35%
Percent of Government Payments Received
30%
25%
20%
15%
10%
5%
0%
Single Income
Ruralpolitan
Double Income
Ruralpolitan
Conservation Payments
Active Senior
Farm Operator with
Spouse Working
Off Farm
Traditional Farm
Economic Safety Net Payments
Commercial Farm
Other Payments
Note: Aggregate 2003 ARMS data is 17,728 samples and 2,084,715 representative U.S. family farm households.
Figure 2. Distribution of government payments by type across the U.S. Farm Household Typology
33
Endnotes
1
These include direct payments, counter-cyclical payments, loan deficiency payments, marketing
loan gains, and for net value of commodity certificates.
2
Given the complex survey design of ARMS, a survey weight or expansion factor is calculated to
control for the survey design. This survey weight is the inverse probability of the surveyed
household being selected for the survey. The weighted k-means clustering algorithm uses this
survey weight. All standard errors for estimates and significance tests are estimated using the
delete-a-group jackknife variance estimator (Dubman).
3
Delineation between these four groups is based on gross farm sales: less than $150,000 is the
Farming Occupation/Low Sales group; greater than $150,000 but less than $250,000 is the
Farming Occupation/High Sales group; greater than $250,000 but less than $500,000 is the Large
group; greater than $500,000 is the Very Large group.
4
Since the weighted k-means clustering algorithm was used with the ERS Farm Typology
providing the starting seed values, seven cluster groups was the initial solution; however, the
seventh group was combined with the first group. A reason these groups were combined is
because the seventh group was the smallest group identified (5.6 percent). Another reason is
because the main objective of the U.S. Farm Household Typology is to identify policy actionable
groups. Groups seven and one were not statistically different across policy or welfare proxies (i.e.
household income and consumption).
5
Cluster analysis groups individuals together based on multiple factors and in the case of AS, it is
possible that a “young” operator may fall into this category. However, over 80 percent of AS are
over the age of 60 and none are younger than 54. Thus, the AS group captures households who
have adopted a retirement lifestyle.
33
6
Multiple runs of the weighted k-means clustering algorithm were conducted with and without the
starting seed values. Results show that five groups consistently appear – SIR, DIR, AS, TRAD,
and FOSO. Since the presented typology contains these groups, and the presented typology is
more robust using the stability index relative to the other clustering runs, and the other runs yield
varying groups, the six mutually exclusive groups presented are used as the U.S. Farm Household
Typology.