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Assessing farm owner-operator wealth involves understanding that farmers are making production decisions based on total household wealth, not just on farm production profitabilty We want to explain wealth patterns across regions, farm sizes, and commodity specialization to derive insights into the future financial prospects for American agriculture as a whole, or at least for some agricultural industries. We also want to test the relationships between farm size and productivity, and productivity and profitability. There are three general objectives of the paper. The first objective is to determine whether or not there is convergence of rates of return on farm assets across states over time. The second objective is to derive a system of equations that explains interlinkages between the various components of a farm household's wealth at some point in time. The third objective is to use those equations to empirically assess income and wealth patterns across regions, farm sizes, and commodity specializations.
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Profit and Productivity Patterns from the Farm-Gate to the Global Market-
place: Implications for American Agricultural Competitiveness (Steven C.
Blank, University of California at Davis, presiding)
Recent decades (especially since 1973) have
been an era of decreasing production prof-
its that threaten the survival of many mid-
and small-sized American farms (Blank 2003).
Normally, the survival of a firm depends on
its profitability, both in absolute and relative
terms. To remain viable, a firm must offer
returns that are both sufficient to cover the
owner’s financial obligations and competitive
with returns from alternative investments. If
a firm is profitable, the wealth of its owners
can increase over time. An unprofitable firm,
on the other hand, reduces owners’ wealth.
Yet, American agriculture is full of firms
that routinely earn low or negative returns
on equity from production operations (Blank
2002), thus complicating the evaluation of
the industry’s economic health and prospects.
This suggests that macro-level forecasts of
American agriculture’s future structure and
performance require a micro-level under-
standing of the relationship between farm
profits and owner wealth. This paper addresses
that relationship.
Steven C. Blank is extension economist, Agricultural and Re-
source Economics Department, University of California, Davis,
and member, Giannini Research Foundation. Kenneth W. Er-
ickson is economist, Farm Sector Performance and Well-Being
Branch, U.S. Department of Agriculture, Economic Research Ser-
vice. Charles B. Moss is professor, Food and Resource Economics
Department, University of Florida. Richard Nehring is economist,
U.S. Department of Agriculture, Economic Research Service.
Theauthors thank the reviewers at the USDA-ERS for their
input, and Charles Hallahan of the USDA-ERS for his invaluable
assistance in estimating the ARMS pseudo-panel equations. The
views expressed here are not necessarily those of the Economic
Research Service, U.S. Department of Agriculture.
This article was presented in a principal paper session at the
AAEA annual meeting (Denver, Colorado, August 2004). The ar-
ticles in these sessions are not subjected to the journal’s standard
refereeing process.
Assessing financial stress within American
agriculture involves identifying which groups
are more or less profitable. It also involves
assessing farmers’ well-being in the context
of income, wealth, and consumption at the
household level (Mishra et al.). Previous stud-
ies (e.g., Dodson) raise expectations of prof-
itability differences due to resources available
(and quality) across locations, economies of
scale across farm sizes, and supply/demand
differences across commodity markets caused
by comparative advantage (i.e., competitive-
ness) issues. However, economic theory says
that returns converge over time as resources
flow into more profitable industries and out of
less profitable industries, causing factor price
changes (O’Rourke and Williamson, Caselli
and Coleman). Both traditional growth and
trade theories say factor markets will ad-
just to equalize commodity returns over time
(Andres, Bosca, and Domenech; Ben-David;
Gutierrez; Schott).
Assessing farm owner–operator wealth in-
volves understanding that farmers are making
production decisions based on total household
wealth, not just on farm production profitabil-
ity (Carriker et al., Schmitt). We want to ex-
plain wealth patterns across regions, farm sizes,
and commodity specialization to derive in-
sights into the future financial prospects for
American agriculture as a whole, or at least
for some agricultural industries. We also want
to test the relationships between farm size and
productivity, and productivity and profitability.
There are three general objectives of the pa-
per. The first objective is to determine whether
or not there is convergence of rates of return
on farm assets across states over time. The sec-
ond objective is to derive a system of equations
Amer. J. Agr. Econ. 86 (Number 5, 2004): 1299–1307
Copyright 2004 American Agricultural Economics Association
1300 Number 5, 2004 Amer. J. Agr. Econ.
that explains interlinkages between the vari-
ous components of a farm household’s wealth
at some point in time. The third objective is
to use those equations to empirically assess in-
come and wealth patterns across regions, farm
sizes, and commodity specializations.
Theoretical Relationship between Income
and Wealth
The three components of income (i.e., eco-
nomic gains) contributing to wealth are profits
from farm output, off-farm income, and capital
gains on assets. Total “wealth” (W)isusually
expressed as equity at time t:Wt=Wt1+
Wt.“Wealth changes” during a time period
ending at tequals “farm income” (FInc) plus
“off-farm income” (OFInc) plus some function
of “capital gains” (K) minus “consumption”
(C), or
In this regard, capital gains (even unrealized
gains) immediately improve a farmer’s abil-
ity to borrow, and thus they aid in financing
a larger operation.
There are at least four components of wealth
changes. Those components, on the right-hand
side of (1), are themselves functions of other
Each of these four equations is explained be-
low, beginning with farm income.
For this analysis of American farms, farm
income comes from three sources. Only total
revenues from farmers’ and ranchers’ sales of
production output (R) are considered part of
farm income; government transfers and other
nonfarm income sources are excluded to test
the true sustainability of farm production as an
income source. Yet, to many farm households,
government payments may be significant. This
will be captured in an error term in equations
(1) and (2). In equation (3), government pay-
ments could come from various sources, such
as unemployment benefits. Therefore, the in-
dustry’sfarm income from all agriculturalcom-
modities (i=1, 2, ...,n)attime tis expressed
in (2), where
Qit =Yit Ait
PCt=ijpcjt xijt
OKt=ihokht ziht
and Pi,pcj,okh>0; Yi,Ai,xj,zh0. The num-
ber of commodities produced by the industry
is n.The average unit price of commodity iat
time tis Pit.The quantity of commodity ipro-
duced during the period ending at time tis Qit.
Yiis the average yield per acre of commodity
i.Aiis the total acreage devoted to commod-
ity i.PCtis the total production costs of all
commodities at time t. Unit costs of jvariable
inputs is pcj. Quantities of jvariable inputs to
be applied in the production of commodity i
is xij.OKtis the total ownership costs of all
commodities at time t. Unit costs of hcapital
inputs (land, improvements, equipment, etc.)
is okh. Quantities of hcapital inputs used in
the production of commodity iis zih.
Equation (3) states that off-farm income
for a period ending at time tconsists of the
sum of off-farm salary or wages earned (Sal)
and nonfarm investment or “unearned” in-
come (Inv). Off-farm employment is the pri-
mary source of nonfarm income for a major-
ity of farm and ranch households, representing
over 90% of average farm household income
in recent years (Mishra et al.). Nonfarm invest-
ment includes income sources such as interest
income on savings, Social Security and other
retirement benefits, and capital gains and div-
idends on nonfarm assets such as stocks and
Equation (4) specifies how farmers’ change
in wealth is influenced by capital gains. To be-
gin, capital gains are simply the change in value
of a farmer’s capital from one period to the
next (i.e., KtKt1). Capital gains are only
realized if the asset is sold. However, some
portion of unrealized capital gains can be used
to improve a farmer’s operation. Lenders will
usually loan a farmer up to some specific por-
tion of the market value of the assets, referred
to as the “loan-to-value” ratio (LTV). In (4),
is an estimate of how much of unrealized capi-
tal gains are immediately converted into cash,
Blank et al. Agricultural Profits and Farm 1301
and is assumed to be a function of the LTV. In
all cases, 1 >LTV 0.
The capital variable (K)in(4) can be ex-
pressed as the sum of the market values for all
assets (real estate, nonreal estate, and nonfarm
assets) held by a farm at time t,
where LV is the “value of land” and improve-
ments (buildings, irrigation systems, etc.), MV
is the “value of nonreal estate assets” (e.g.,
machinery and other equipment), and FV is
the “value of nonfarm financial assets” (stocks,
bonds, etc.). Farmland has historically repre-
sented about 75% of assets held by farm house-
holds. Also, farmland values vary much more
than do the other agricultural assets because
they are a function of numerous variables
(Drozd and Johnson). A simple model for the
expected price of farmland can be specified
=E(Rft CKft +TFPft +Dft ).
An empirical version of (4b) is
ft =1Rft 2CKft +3TFPft
+4ft +vf+εt
where LVft is the value of farmland and build-
ings in state or farm fat time t,Rft is the cash
flow (revenue, as specified in (2a)) from agri-
cultural production in state or farm fat time t,
CKft is the average “cost of capital” at time t,
TFPft is a technology variable (state-level “to-
tal factor productivity”), we also use a farm-
level estimate of “productivity” (PROD), the
“population density” (people per acre) in
county or state fat time tis Dft,is a coef-
ficient to be estimated, and vfand εtare errors
for, respectively, state or farm for for time tif
a random effects model is used.
Equation (5) specifies farm household con-
sumptionduring a period endingat time tas the
sum of the basic cost of living (CLt), such as the
cost of providing a minimum level of food and
shelter to members of the household, and the
extra expenditures made by household mem-
bers to raise the quality of life to the desired
level (QLt).
Industry sales and profit totals are simply
the sum of results from decisions made by the
individual firms that constitute the industry.
In American agriculture, individuals are as-
sumed to make production decisions based on
the goal of maximizing expected profits. This
study follows Klepper in recognizing that re-
sults are influenced by both the innovation ex-
pertise and capital available within an indus-
try. Thus expected profit, for firm fat time t,is
specified as
E(ft)=E[Rft PCft OKft
+(mf)g(crft )G(crft)]
where R,PC, and OK are defined as above,
but are for firm fonly. E(·)isthe expected
value of (·). The innovation expertise of firm
fis denoted mfand influences the firm’s suc-
cess at improving productivity. The probabil-
ity of firm fimproving its productivity in pe-
riod tis (mf)g(crft), where crft is defined as the
firm’s cumulative investment in human capi-
tal and productive resources through time t
and is some function of profits earned in all
prior periods. The function g(crft ) reflects the
opportunities for improving productivity. The
potential increase in profits earned by an in-
novation that improves productivity is G.This
can result from either reduced input costs per
unit (PC/Q and/or OK/Q)orincreased rev-
enue from a higher yield (Y). Gis defined
to equal (Rft PCft OKft )(R
ft PC
ft), where the asterisk indicates a value
that would have existed for firm fin period t
without the innovation. The change in cumula-
tive investment during period t[(crft)] equals
crft crft1, and it is constrained to be 0.
A firm’s expected sales revenues are
+E[(mf)g(crft )G+(crft)].
Current revenues are expected to equal the
previous year’s revenues plus expected im-
provements from a productivity component
[(mf)g(crft)G] and an investment component
We use state-level data from the USDA Eco-
nomic Research Service (ERS) for 1960–2002
to test for convergence of rates of return
on farm assets and over time. Next, we use
farm-level data from the USDA’s Agricul-
tural Resource Management Survey (ARMS)
tohelp explain the inter-linkagesbetween farm
household wealth, returns, and productivity
(USDA). We construct a unique pseudo-panel
data set from pooled ARMS data for 1996–
2002 over three regions: the Lake States, the
1302 Number 5, 2004 Amer. J. Agr. Econ.
Corn Belt, and the Southeast. These regions
were selected to represent production sectors
dominated by dairy and wheat, corn, and spe-
cialty crops, respectively. Then, we estimate
the equations using a two-way fixed effects
approach (Baltagi, Chapter 3), examining fac-
tors affecting profitability and the change in
wealth across regions.
Model of Convergence
In this study, we determine whether the rate
of return on agricultural assets is converging
across regions. A typical formulation of con-
vergence (Sala-i-Martin) can be expressed as
ln yit
yt=0+1ln yi,t1
+2zit +εit
where ln (·) denotes the natural logarithm, yit is
the level of income per capita in region or state
iin time t,ytis the index income per capita at
time t,zit is a vector of other economic vari-
ables (such as initial capital) in region or state
iat time t,εit is an error term, and 0,1, and 2
are estimated coefficients. In this formulation,
if 00,1<1, and 2=0, the income in re-
gion iconverges over time toward the income
of the index. Further, this convergence is un-
conditional, or does not depend on other vari-
ables (such as initial capital). The convergence
is conditional if 00,1<0, and 2= 0.
Given that the rates of return on agricul-
tural assets are stationary (results not reported
here), we reformulate convergence in (8) into
ln(yit )ln(yt)
+2zit +εit
dit =0+1di,t1+2zit +εit
where dit is the logarithmic difference between
returns in state iand the index state at time t.
Since the rate of return data for agricultural as-
sets in (9) are stationary, convergence can be
estimated directly. Unfortunately, the formula-
tion in (9) cannot be directly applied to agricul-
tural returns because negative rates of return
are sometimes observed in the data. Thus, we
redefine (9) so that dit =rtrit, where rtis
the maximum rate of return to agricultural
assets in each of ERS’s 10 regions. Finally,
we estimate 0and 1in (9) using maximum
Estimation of Income and Wealth Patterns
Ideally, one would like to take repeated cross-
section surveys of U.S. farm households over
time. However, it is impossible to track the
same farm household over time. Instead, we
construct a pseudo-panel data set. For empiri-
cal studies using such panel data, the temporal
pattern of a given farm’s production behavior
must be established. In the absence of genuine
panel data, repeated cross-sections of data
across farm typologies may be used to con-
struct pseudo-panel data (Deaton, Verbeek
and Nijman). A pseudo panel is created by
grouping individual observations into homo-
geneous cohorts, distinguished according to
time-invariant characteristics such as fixed as-
sets, geographic location, or land quality. The
empirical analysis is then based on the cohort
means rather than the individual farm-level
We assigned the farm-level data to cohorts,
based on the ERS farm typology (TYP) groups
(Hoppe and MacDonald). A cohort group is
formed for each state in the sample. There are
thirteen cohorts per state and fourteen states,
resulting in a total of 182 cross-sectional en-
tities per year. We refer to these entities as
The problem when using time series and
cross-sectional data is to specify a model that
will adequately allow for differences in behav-
ior over cross-sectional units as well as for
differences in behavior over time for a given
cross-sectional unit. Fixed effects regression is
a method of controlling for omitted variables
in panel data when the omitted variables vary
across“firms” but do not changeover time.The
fixed effects regression model has ndifferent
dummy intercepts, one for each firm.
To test if some omitted variables are con-
stant over time but vary across regions, while
other variables are constant across states but
vary over time, we include both location and
time effects. This is done by including both
n1 state binary variables and T1 time
binary variables in the regression, plus an in-
tercept. The combined time and firm fixed re-
gression model is
yit =0+i+Xit +vi+εt
where Xit is a vector of other variables to be
estimated (from (4c)). This model has an over-
all constant term as well as a “group” effect
for each group and a “time” effect for each
time period. The combined time and state fixed
effects regression model eliminates omitted
Blank et al. Agricultural Profits and Farm 1303
variables bias arising from unobserved vari-
ables that are constant over time and/or con-
stant across states.
We estimated reduced forms of equa-
tions (1), (2), (4c), and (6) for the three
regions using unbalanced panels. The equa-
tions were estimated by ordinary least squares
(OLS) since these equations constitute a re-
cursive system (Baltagi; Greene, p. 659). If
we take the i’s to be identical across loca-
tions, OLS provides consistent and asymptoti-
cally efficient estimations of and (Greene,
pp. 560–62).
Table 1. Estimated Autoregression Coefficients for Difference in Rate of Return on Assets,
State 10State 10
Connecticut 0.427 0.039 Kentucky 0.904 0.066
(0.140) (0.008) (0.062) (0.029)
Maine 0.165 0.054 Tennessee 0.946 0.093
(0.154) (0.007) (0.044) (0.040)
Maryland 0.548 0.038 Virginia 0.931 0.087
(0.131) (0.007) (0.050) (0.032)
Massachusetts 0.663 0.050 West Virginia 0.413 0.159
(0.114) (0.013) (0.141) (0.027)
New Hampshire 0.299 0.106 Alabama 0.338 0.018
(0.150) (0.023) (0.147) (0.003)
New Jersey 0.557 0.049 Georgia 0.485 0.002
(0.128) (0.009) (0.137) (0.004)
New York 0.722 0.051 South Carolina 0.629 0.030
(0.105) (0.014) (0.131) (0.008)
Pennsylvania 0.684 0.072 Louisiana 0.210 0.015
(0.111) (0.013) (0.153) (0.003)
Rhode Island 0.626 0.038 Mississippi 0.327 0.015
(0.119) (0.014) (0.147) (0.003)
Vermont 0.676 0.044 Oklahoma 0.466 0.006
(0.112) (0.014) (0.139) (0.002)
Michigan 0.191 0.030 Arizona 0.880 0.009
(0.153) (0.004) (0.075) (0.017)
Wisconsin 0.449 0.014 Colorado 0.880 0.014
(0.138) (0.005) (0.085) (0.015)
Illinois 0.365 0.014 Montana 0.824 0.022
(0.145) (0.003) (0.096) (0.014)
Indiana 0.297 0.023 Nevada 0.755 0.039
(0.149) (0.004) (0.102) (0.009)
Missouri 0.646 0.042 New Mexico 0.743 0.018
(0.118) (0.006) (0.101) (0.009)
Ohio 0.358 0.045 Utah 0.853 0.041
(0.147) (0.004) (0.082) (0.012)
Kansas 0.576 0.012 Wyoming 0.877 0.035
(0.127) (0.004) (0.078) (0.018)
North Dakota 0.516 0.011 Oregon 0.705 0.048
(0.133) (0.007) (0.108) (0.005)
South Dakota 0.750 0.003 Washington 0.557 0.011
(0.103) (0.007) (0.128) (0.004)
Note: Numbers in parenthesis denote standard deviations and all numbers are rounded to the third decimal.
Empirical Results
The empirical results of the convergence
model in (9) are presented in table 1. Based on
the data for 1960–2002, the state with the high-
estrate of return withineachregion was chosen
as the index state (yin equation (9)). Follow-
ing this criterion, we use Delaware: Northeast,
Minnesota: Lake States, Iowa: Corn Belt,
Nebraska: Northern Plains, North Carolina:
Appalachia, Florida: Southeast, Arkansas:
1304 Number 5, 2004 Amer. J. Agr. Econ.
Table 2. Regression Results for Farm Income and Farmland Value Equations by Region: Lake
States, Corn Belt, and Southeast (1996–2002)
Lake States Corn Belt Southeast
Variable Estimate t-Value Estimate t-Value Estimate t-Value
Farm income equation
CashFlow 0.6300 10.410.1445 5.360.0935 2.48∗∗
TotalCashExpenses 0.4124 5.820.1232 5.070.0918 2.22∗∗
Depreciation 0.3212 0.32 1.0920 4.831.1513 8.98
Fixed effects
Firm NS NS
Farmland value equation
CashFlow 0.0961 7.880.0367 4.730.1558 3.35∗∗
CostCapital 0.2414 2.03∗∗ 0.1301 0.84 0.0730 0.36
Productivity 17.9303 1.42 19.4439 3.12∗∗ 8.2662 1.45
PopDensity 3.1103 0.22 4.8009 1.24 0.2555 0.11
Fixed effects
Firm ∗∗
and ∗∗ denote statistical significance at the 0.01 and 0.05 confidence levels. NS denotes “not significant.”
Delta, Texas: Southern Plains, Idaho: Moun-
tain region, and California: Pacific States. In
general, convergence occurs if 1is less than 1,
implying that the difference between the rate
of return for a particular state and the regional
index declines over time. The results indicate
that all the rates of return to agricultural as-
sets converge over time in all regions except
Appalachia. Within the Appalachian region,
we fail to reject 1=1atthe 0.05 confidence
level for Kentucky, Tennessee, and Virginia.
Thus, at least conditional convergence for the
rate of return on agricultural assets is sup-
ported in all regions except Appalachia.
To test unconditional convergence, we next
examine convergence between each of the in-
dex states. North Carolina, the state with the
highest average returns over the period, is used
to normalize the index states for each region.
Again, the estimated autoregression coeffi-
cient for each region is less than 1 at any con-
ventional level of statistical significance. Thus,
we conclude that the rates of return on agri-
cultural assets are converging across regions.
Farm Income, Land Values, Wealth,
and Profits Equations
The regression results were generally “best”
for the Lake States and Corn Belt since coef-
ficients’ signs were generally consistent with
economic theory, and levels of significance
were high, especially for the farm income
Farm income (equation (2)). The results in
the top section of table 2 show some differ-
ences across regions. CashFlow and TotalCash-
Expenses were significant in all three regions,
but with varied coefficients. Depreciation was
not significant in the Lake States, possibly in-
dicating farm structures with relatively greater
fixed assets. Firm fixed effects were significant
in the Corn Belt, indicating that other firm-
related variables also possibly affecting farm
income (such as commodities produced) are
Farmland value (equation (4c)). CashFlow
was significant in all three regions (bottom sec-
tion of table 2). This is consistent with the ex-
pectation that land value is determined pri-
marily by its ability to generate agricultural
revenues. The Productivity variable was only
significant in the Corn Belt. This may be due to
the more heterogeneous nature of operations
in the Southeast and Lake States.
Change in wealth (equation (1)). Wealth
consists of both farm and nonfarm capital, al-
though most farm household wealth is held
in the form of farmland. Both components
were highly significant in the combined three-
region area when examining changes in farm
wealth across farm sizes (top of table 3). In-
come generally was not significant, thus wealth
comes from capital, not income, for all farm
Blank et al. Agricultural Profits and Farm 1305
Table 3. Regression Results for Change in Wealth and Profits Equations by Farm Size: Lake
States, Corn Belt, and Southeast Regions Combined (1996–2002)
Farm Size 1 Farm Size 2 Farm Size 3
Variable Estimate t-Value Estimate t-Value Estimate t-Value
Change in wealth equation
FarmInc 0.1662 0.48 0.3110 1.63 0.0005 0.00
NonFarmInc 0.1638 1.47 0.4293 1.881.6816 0.88
ChngFarmCap 0.9908 118.62∗∗∗ 0.9374 69.83∗∗∗ 0.2568 21.68∗∗∗
ChngNFarmCap 0.8597 30.58∗∗∗ 0.9439 18.94∗∗∗ 0.6524 2.22∗∗
Consumption 0.2698 0.93 1.0688 1.96∗∗ 2.3527 0.92
Fixed effects
Year ∗∗∗ ∗∗∗ NS
Profits equation
CashFlow 0.0129 0.51 0.0771 3.48∗∗ 0.0054 5.82∗∗∗
TotalExpenses 0.0663 2.32∗∗ 0.0237 3.25∗∗ 0.0038 3.69∗∗
Depreciation 0.0600 1.15 0.1678 9.35∗∗∗ 0.0485 5.11∗∗∗
Productivity 1.7527 2.23∗∗ 0.7212 2.09∗∗ 0.0456 0.24
HumanCapitalEd 1.5789 1.37 6.8049 3.49∗∗ 0.0022 0.05
Fixed effects
Firm ∗∗∗ ∗∗
Year ∗∗
Note: Farm Size 1 corresponds to limited resource, retirement, and residential farms. Farm Size 2 corresponds to farm/lower sales and farm/higher sales. Farm
Size 3 are large family farms and very large farms.
,∗∗,and ∗∗∗ denote statistical significance at the 0.10, 0.05, and 0.01 confidence levels. NS denotes “not significant.”
As shown in table 4, both farm and nonfarm
capital were significant in all regions, but had
differential impacts on wealth. For example, a
$1,000 change in farm capital in the Lake States
would raise wealth by $453, compared to $103
in the Corn Belt and $278 in the Southeast.
Table 4. Regression Results for Change in Wealth and Profits Equations by Region: Lake
States, Corn Belt, and Southeast (1996–2002)
Lake States Corn Belt Southeast
Variable Estimate t-Value Estimate t-Value Estimate t-Value
Change in wealth equation
FarmInc 0.4028 2.48∗∗ 0.3323 1.41 0.9596 2.26∗∗
NonFarmInc 1.5043 1.50 0.4800 0.81 0.2574 0.20
ChngFarmCap 0.4533 17.14∗∗∗ 0.1028 5.27∗∗∗ 0.2776 23.32∗∗∗
ChngNFarmCap 0.6321 3.85∗∗ 1.3783 7.25∗∗∗ 2.2177 9.27∗∗∗
Consumption 2.5824 1.25 0.9489 0.88 0.1850 0.07
Fixed effects
Year NS ∗∗
Profits equation
CashFlow 0.0177 4.67∗∗∗ 0.0232 7.27∗∗∗ 0.0070 1.77
TotalExpenses 0.0031 0.66 0.0161 5.30∗∗∗ 0.0044 0.96
Depreciation 0.0566 3.00∗∗ 0.0910 3.50∗∗ 0.0789 4.97∗∗∗
Productivity 5.1918 3.43∗∗ 1.5778 2.86∗∗ 0.1505 0.63
HumanCapitalEd 16.1208 5.26∗∗∗ 3.9766 1.32 2.0530 2.43
Fixed Effects
Firm ∗∗ ∗∗∗ ∗∗∗
Year NS ∗∗ NS
,∗∗,and ∗∗∗ denote statistical significance at the 0.10, 0.05, and 0.01 confidence levels. NS denotes “not significant.”
Also, a $1,000 change in nonfarm capital would
raise wealth by $632 in the Lake States, by
$1,378 in the Corn Belt, and by $2,218 in the
Southeast. The different impacts across re-
gions may be partly due to differences in the
opportunities and multiplier effects available
1306 Number 5, 2004 Amer. J. Agr. Econ.
off-farm in the regional economies. In all re-
gions, the higher regression coefficients for
“changes in nonfarm capital” imply that there
are economic incentives for shifting resources
out of agriculture and into nonagricultural
Nonfarm income” was significant for mid-
sized farms when farms from all three regions
were combined (table 3). This may be because
off-farm income is more stable over time than
is farm income for small-sized firms, and off-
farm income may be a small part of large farms’
total wealth, thus the lack of statistical signifi-
cance in explaining changes in wealth (Mishra
et al.).
Farm profits (equation (6)). There were di-
verse results across regions for the prof-
its equation (table 4) reflecting different
commodity specializations across regions.
“CashFlow” (gross sales) and “Depreciation”
were significant in all three regions. “Human-
CapitalEd,” which represents the productivity
and investment components of human capital
was significant in the Lake States and South-
east (table 4), and for mid-sized farms (table 3).
The productivity variable was significant in the
Lake States and Corn Belt. Combining the
state-level total factor productivity variable
with the farm-level variable (i.e., gross value
of production divided by total cash expenses)
gives a significant relationship between profits
and productivity for small and mid-sized farms
(table 3). The coefficient for productivity de-
creases as farm size increases.
Implications of the Results
These results generally agree with other stud-
ies of convergence of time-series returns on
farm investments, and with other studies that
have used farm-level data to empirically assess
wealth and income patterns across states, farm
types, and commodity specializations. We sug-
gest three implications of these results.
First, although U.S. farm sector returns are
converging over the 1960–2002 period and
across regions, farm profits still vary widely by
farm type, farm size, location, and by other fac-
tors. Constructing a pseudo panel using pooled
farm-level data and estimating the system of
equations linking wealth, income, profits, and
productivity helps explain the linkages be-
tween the various components. For example,
the finding that both the changes in farm and
nonfarm capital are significant in all three re-
gions suggests that nonfarm capital is a substi-
tute for farm capital. This indicates that farm
households have diversified their portfolios.
Second, changes in farm and nonfarm cap-
ital have differential impacts on farm wealth
by farm location and by farm size. In general,
the fact that changes in nonfarm capital have
larger impacts than do changes in farm cap-
ital across all regions implies that there are
economic incentives to shift resources out of
agriculture. However, this may not happen be-
cause there appears to be incentives for small-
scale farms to increase their capital levels.
Third, we found evidence that farm size af-
fects both farm wealth and profits, and that
the relative impacts of the “farm size” vari-
able vary across these three regions, indicating
differences in profitability across the different
commodities produced in each region. We also
found evidence that a firm’s cumulative in-
vestment in human capital and productive re-
sources is important in the Lake States and the
Southeast, but is not statistically significant in
the Corn Belt. This implies productivity differ-
ences exist across the commodities that domi-
nate production in each region. This is impor-
tant because productivity growth is expected
to be a key to future profitability for each re-
gion’s agricultural sector.
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... er fördert die Spezialisierung eines Betriebs (Dabbert und Braun, 2012). Der wirtschaftliche Erfolg eines Betriebs wird vom Betriebstyp beeinflusst (Mishra et al., 2012;Blank et al., 2004). ...
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Economic Heterogeneity at Farm and Farm-Enterprise Level The Farm Accountancy Data Network’s annual estimates of agricultural revenue and earned income point to major differences between farms. This report contains three analyses investigating the heterogeneity of earned income per family annual labour unit, i.e. the remuneration of a full-time family labour unit of the farm manager’s family. The reference farms from 2010 to 2014 serve as the data source. First, all farms are classified into decile intervals according to their earned income. The ten groups are characterised descriptively inter alia according to farm size and orientation, composition of revenues and costs, and education and age of the farm manager. The second analysis investigates the determinants of earned income for commercial dairy farms by means of two regression approaches: a random-effects model and quantile regressions for deciles. In the third analysis, cost/performance calculations are created for the seven farm enterprises (production branches), also denoted as activities, of wheat, feed grain, oilseed rape, sugar beet, potatoes, dairy cattle and suckler cows. The basis for this are 63 to 941 observations, each stemming from a particular farm type. From the resulting labour utilisation per hour, a top and bottom group are created, and checked for statistically significant differences. Even if important site-specific conditions such as climate and soil conditions of the farm location cannot be taken into account, the three analyses allow us to deduce four characteristics of successful farms, i.e. farms with high earned income. Firstly, the size of both the farm and of the farm enterprises has a positive influence on the remuneration of labour, which can also be seen from the fact that full-time farms perform significantly better than secondary-, and in particular, part-time farms. Secondly, farm orientation towards plant production (field crops) or finishing (pig- and poultry fattening) leads to higher earned incomes than a focus on dairy cattle or suckler cows. Thirdly, the success factor of human capital or (strategic) farm management described in the literature is made tangible by means of the identified differences at the level of farm enterprises. High earned incomes are associated with systematically higher performances and lower costs per hectare or livestock unit. With commercial dairy farms, the influence of farm management on milk yield also becomes clear: for high-earning farms, farm management significantly increases earned income, while it has no significant influence on unsuccessful farms. Lastly, earned income rises with educational level: there is a positive statistical correlation between both the agricultural and non-agricultural education of farm managers and partners on the one hand, and earned income on the other. The three analyses highlight the fact that at least part of the differences in earned income per family annual labour unit are dependent on factors that are amenable to influence. Accordingly, we conclude that there are extensive options in practice for increasing earned income.
... Improvements in small farm production could assuage problems of food inadequacy in at-risk rural communities. In order for small farms to survive, however, new strategies must be developed to produce high value fruit and vegetable crops that will reward limitedresource farmers and ranchers by both maximizing profits and minimizing costs [10]. ...
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Small farms are particularly important for local food production in the Mississippi Delta, a region identified as having substantial food deserts. In order for small farms to survive, however, management strategies are needed that simultaneously yield high value fruits and vegetables and also enable farmers to remain economically solvent. The research reported here tested the economic and productive feasibility of implementing irrigation in sweet potato (Ipomoea batatas L. Lam) production in Mississippi US. Historical production records and management expenses were used to determine sweet potato production expenses and returns over a ten-year period from 2002 - 2011. Crop water use over this 10-year period was calculated from historical weather records. Yield improvements resulting from implementing irrigation were then used to determine potential increased return on investment. Irrigation costs increased yearly production expenses 3-4%, depending on pumping fuel costs. Costs associated with harvest and post-harvest processing of the greater yields added an additional 8 – 70% to production expenses, depending on yield increase. However, even very modest (10%) improvements in yield are sufficient to economically justify implementing irrigation, as the net return on investment increased by 5% or more. Irrigation is a relatively simple tool that farmers could use to enhance their management practices and maximize profits. However, access to startup capital and knowledge of irrigation management are still critically needed to assist small, limited resource farmers in adopting tools and skills that will improve the output and economic return of their production systems. The results from this research will be used to develop management tools for farmers to improve access to production information and assist in making crop management and business decisions.
... The second reason that farmland values are a useful indicator of agriculture's economic performance is that those values are a major component of farm household wealth (Koenigstein and Lins, 1990; Blank et al., 2004, 2009). As noted in bullet 10 above, land represents a majority of farmers' wealth and that wealth impacts a farmer's ability to borrow to cover short-term operating expenses or to make long-term capital investments. ...
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Purpose – The Agricultural Resource Management Survey (ARMS) conducted annually by the USDA's Economic Research Service collects data on US agriculture, ranging from production practices to the financial condition of farm and ranch enterprises and the farm household. The purpose of this article is to consider what could make ARMS useful from a farmer's point of view. Design/methodology/approach – A Delphi method is used to gather input from a panel of experts. Findings – Results show that increasing the usability of the ARMS to agricultural producers involves expanding the content and relevance of the data collected. Specific types of data needed are identified. Also, recommendations are made concerning how the usefulness and relevance of the data could be increased by refining the sample frame. Finally, it is argued here that after making some adjustments to the ARMS sample frame to create nationally representative data, the ARMS project could serve as a hugely important basis for reporting economic performance levels for American agriculture. Originality/value – This study offers insights from agricultural finance experts on how the ARMS could be improved to expand the quality and usefulness of its output for both professionals and agricultural producers.
... The availability of employment outside of agriculture has meant that a broad range of options are available to farm families in order to supplement farm income, with the result that farmers must now closely examine their farming systems in terms of labour input and working hours (DAFRD, 2006b). Blank et al. (2004) in a study on agricultural profit and household wealth came to the conclusion that non-farm employment can help substitute for farm capital. This highlights the fact that farm households are diversifying their portfolios in search of alternative forms of revenue to supplement household income. ...
The objective of this study was to examine the opportunities and limitations facing Irish dairy producers based on a survey of 800 suppliers from the largest dairy processor in Ireland. The survey sample was randomly selected based on a proportionate representation of suppliers broken down by quota size, supplier region and system of production. The sample was subsequently broken into five quota categories to investigate the effect of scale on survey variables. Findings reveal an average milk yield of 4,808 l per cow and 8,346 l per hectare at an average stocking rate of 1.78 (LU/ha). Stocking rate, milk yield per cow and per hectare increased as quota size increased. Furthermore the proportion of farmers paying for labour increased as quota size increases with results showing that 55% of all respondents had paid labour on their farms. A successor has been identified on 45% of farms and in greater frequency on larger farms. Despite the relatively small scale of enterprises, only 15 percent of dairy farms had off-farm employment. Of those surveyed, 20% have winter housing facilities capable of holding more animals than they currently have, with 5% having capacity for more than 15 additional cows. The results show considerable underutilisation of land with potential for increases in productivity through increased per animal production, increased stocking density and increased specialisation in dairying
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Islam is a religion of monotheism. Monotheism has two dimensions: theoretical and practical dimension. Social justice is one of the dimensions of practical monotheism and justice is achieved through the implementation of divine rules. The implementation of Islam and the fulfillment of divine rules require the organization and the executive apparatus. The executive government, which wants to implement the religion of God, is called "Islamic State". This research will explore the Islamic State's tasks from Islamic documents and texts. The research uses the method of research and the approach of extracting the government's duties from revelation, hadiths, and laws approved in Iran by using the method of research and approach in Iran. By selecting the verses, narratives, and principles of the constitution of the Islamic Republic of Iran, the analysis their content and content and describes, defines and categorizes the Islamic duties of the Islamic State. The results of the research show that Islamic government is obligated to perform revelation in all aspects of human life and society and based on which prayers should be made. Zakat and donations are collected and used. To settle the claims of people. The government has a duty to explain the equipment used for revelation and to organize the economic, social, religious, political, military and cultural issues required by the individual and the Islamic society. Support the poor and the oppressed and implement justice in all its dimensions. In the absence of Kobra, the Islamic State has all the duties of the government of the Prophet (PBUH) except for revelation. In the course of the rule of the supreme leader, the government has all the duties of the Islamic State during the presence of the Imams (AS), in so many cases. Keywords: revelation, justice, government, executive machinery, settlement of claims, propaganda of religion, implementation of Islamic law, constitution, Iran.
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Islam has a government and government. The Islamic state has a structure and institution. Institutions in the Islamic government are diverse and numerous. At the time of the prophet (PBUH) Institutions focused on the mosque. In the era of Imam Ali (AS), the Islamic state had the institutions of Beit Al-Qaza, Jaish, Jebayeh, Charity, and the divisions of spoils and fixtures. During the absence, The Islamic Republic of Iran has many political, economic, and social institutions. This research is trying, by reviewing the institutions and organizations of the Prophet (PBUH), Imam Ali (AS) governments, compare them with the institutions and structures outlined in the constitution of the Islamic Republic of Iran, extract and explain the decision-making bodies in the Islamic government. The method of collecting information is the study of historical texts and authentic Islamic sources and in the form of a library. To extract the structure, content analysis method has been used in authentic Islamic documents. The research, by the legal method, attempts to extract and analyze the institutions contained in the constitutional principles of the Islamic Republic of Iran in contemporary times and describes and explains the criteria of the legitimacy of the structure during the rule of the Velayat-e faqih. The results of the research show that the structure of the Islamic republic is different from the structure of prophethood (PHUB) and Alavi (A), but used in a small amount of them.
Purpose The purpose of this paper is to examine the impact of changes in farm economic conditions and macroeconomic trends on US farm capital expenditures between 1996 and 2013. Design/methodology/approach A synthetic panel is constructed from Agricultural Resource Management Survey (ARMS) data. A dynamic system GMM regression model is estimated for farms as a whole and separately within farm typology categories. The use of farm typologies allows for comparison of the relative magnitudes of these estimates across farms by farm sales level and the operator’s primary occupation. Findings Changes in gross farm income levels, tax depreciation rates, and interest rates have a significant impact on crop farm investment, while changes in output prices, net cash farm income levels, tax depreciation rates, and farm specialization levels have significant impacts on livestock farm capital investment. The relative significance and magnitudes of these impacts differ within farm typologies. Significant differences include a greater responsiveness to change in tax policy variables for residential crop farms, greater responsiveness to changes in output prices and debt to asset ratios for intermediate livestock farms, and larger changes in commercial crop and livestock farm investment given equivalent changes in farm sales or the returns to investment. Research limitations/implications These findings are of interest to agricultural economists when constructing farm investment models and employing pseudo panel methods, to those in the agricultural equipment and manufacturing sector when constructing models to manage inventories and plan for production needs across regions and over time, to those involved in drafting tax policy and evaluating the potential impacts of tax changes on agricultural investment, and for those in the agricultural lending sector when designing and executing agricultural capital lending programs. Originality/value This study uniquely identifies differences in the level of investment and the magnitude of investment responsiveness to changes in farm economic conditions and macroeconomic trends given differences in income levels and primary operator occupation. In addition, this study is one of the few which utilizes ARMS data to study farm capital investment. Utilizing ARMS data provides a rich panel data set, covering producers across many different crop production types and regions. Finally, employing pseudo panel construction methods contributes to efforts to effectively employ cross-sectional data and dynamic models to study farm behavior across time.
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Für die sieben Betriebszweige Weizen, Futtergetreide, Raps, Zuckerrüben, Kartoffeln, Verkehrsmilch und Mutterkühe werden Kosten-/Leistungsrechnungen erstellt, wobei zwischen 63 und 941 Beobachtungen vorliegen, die von Referenzbetrieben der Zentralen Auswertung von Buchhaltungsdaten aus den Jahren 2010 bis 2014 stammen. Bei allen Betriebszweigen zeigt sich, dass die Gemeinkosten für Arbeit, Maschinen und Gebäude den Hauptanteil der Kosten bilden. Basierend auf der aus den Kosten-/Leistungsrechnungen resultierenden Arbeitsverwertung pro Stunde wird eine obere und untere Gruppe gebildet und für alle Leistungs- und Kostenpositionen überprüft, ob sich die beiden Gruppen statistisch signifikant unterscheiden. Es zeigt sich hier ein „doppeltes Optimierungspotential“: Die obere Gruppe hebt sich signifikant von der un-teren Gruppe mit jeweils höheren Leistungen und tieferen Gemeinkosten ab, wobei die Kosteneinsparungen jeweils den grössten Einfluss auf die Wirtschaftlichkeit haben. Unterschiede bei den Direktkosten hingegen sind nur vereinzelt signifikant. Die Resultate zeigen deutlich auf, dass eine Zuteilung der Gemeinkosten auf die Betriebszweige und damit der Wechsel von der Deckungsbeitrags- zur Vollkostenrechnung für die Analyse der Wirtschaftlichkeit und der daraus folgenden Betriebsplanung von grossem Vorteil ist.
Dieses Kapitel untersucht Bestimmungsgrössen für den jährlichen Arbeitsverdienst je Familien-Jahres-Arbeitseinheit (FJAE) von Schweizer Verkehrsmilchbetrieben, basierend auf den Daten der Zentralen Auswertung von Buchhaltungsdaten (ZA) aus den Jahren 2010 bis 2014. Um den finanziellen Erfolg eines solchen Betriebs zu untersuchen, benutzen wir zwei Arten von Regressions-Modell: ein “einfaches” Random-Effects-Modell und eine Quantil-Regression, die es ermöglicht, die weniger erfolgreichen von den erfolgreichen Betrieben abgegrenzt zu analysieren. Diese Methodik wird einerseits auf die Verkehrsmilchbetriebe in allen Regionen, andererseits je separat auf die Betriebe innerhalb der einzelnen Regionen Berg, Hügel und Tal angewandt. Übergreifend lässt sich feststellen, dass folgende Faktoren für jede Region positiv zum finanziellen Erfolg eines Verkehrsmilchbetriebs beitragen: Milchleistung pro GVE, Grösse des Betriebs (in Tieren oder Fläche), biologischer Landbau und der Anteil familienfremder Arbeitskräfte. Der Einsatz von Kraftfutter pro Milchkuh wirkt sich stets negativ aus. In mehreren Regionen signifikant und positiv wirken Laufstallhaltung, silofreie Produktion, paralandwirtschaftliche Aktivitäten und die Tatsache aus, dass es sich nicht um einen Nebenerwerbsbetrieb handelt. Ein negativer Einfluss für mehrere Regionen entsteht durch die Hanglage des Betriebs, die Grösse des Haushalts der Betriebsleiterfamilie und eine tiefe Ausbildung des Betriebsleitenden und seines Partners / seiner Partnerin ausserhalb des land- und hauswirtschaftlichen Sektors. Abschliessend kann man feststellen, dass die Methode der Quantil-Regression einen wesentlichen Beitrag dazu liefern kann, die Heterogenität zwischen den Verkehrsmilch-Betrieben besser zu verstehen.
Policy settings influence how farmers manage pests. To successfully grow and market a crop an individual farmer has to engage in pest management. Their management strategy is subject to the relevant domestic policies. These policies are in turn shaped by international agreements concerning maximum residue levels for pesticides and the sanitary and phytosanitary (SPS) agreements on trade. Policies are designed to solicit a response by using incentives and penalties to achieve a set of social objectives. These policies create signals to which the wider domestic settings and international economies respond. Consequently the ultimate outcome from these signals may be counter to the initial design (or intention) of the policy. This chapter outlines some of the economic underpinnings required for good pest management policy and it explores why farmers respond to the same pest problem differently. The discussion will examine the national drivers behind pest management in Australia and discuss the implications for both on-farm pest management and the wider community. To enable this discussion the economics of integrated pest management is presented to articulate individual responses to a policy setting. Finally we examine the policies required to create successful area- wide management systems in rural Australia. © 2014 Springer Science+Business Media Dordrecht. All rights reserved.
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The increasing globalization of agricultural markets in recent decades appears to be changing the economics of the American production agriculture sector, reducing its economic importance and raising questions about its life cycle. This study contributes to the product life cycle literature by creating tests of hypotheses about the economic life of American production agriculture. A methodology to test the hypotheses is proposed and then applied in an empirical analysis. In general, it appears that a new stage in American agriculture's life began during the 1973-1983 period. Finally, the results and their implications for the American production agriculture sector are discussed.
The concepts of σ-convergence, absolute β-convergence and conditional β-convergence are discussed in this paper. The concepts are applied to a variety of data sets that include a large cross-section of 110 countries, the sub-sample OECD countries, the states within the United States, the prefectures of Japan, and regions within several European countries. Except for the large cross-section of counties, all data sets display strong evidence of σ-convergence and absolute β-convergence. The cross-section of countries exhibits σ-divergence and conditional β-convergence. The speed of conditional convergence, which is very similar across data sets, is close to 2% per year.
Farm family disposable income is generated from farm operations, off-farm sources, and government payments. If these three income components are fungible (a dollar from one source is a perfect substitute for a dollar from another source), then the propensities to consume each should be the same. This study examines the farm family propensity to consume from separate income sources. Results indicate that the propensity to consume off-farm income and government payments is higher than the propensity to consume farm income.
This paper finds strong and robust evidence of convergence of labour productivity in agriculture across all US states and 11 EU countries during 1970-1992. Moreover, off-farm migration has a positive effect on the speed of convergence, especially in the EU. Holding the off-farm migration rate constant, the speed of convergence for EU countries (2.9 per cent per year) is stronger than for US states (1.9 per cent per year). A convergence speed of 2.9 per cent means that an EU country needs about 10 years to eliminate one-quarter of the gap between its initial output per worker and its own steady state, and half a century to eliminate three-quarters of the gap.
Due primarily to transport improvements, commodity prices in Britain and the United States tended to converge between 1870 and 1913. Heckscher and Ohlin, writing in 1919 and 1924, thought that these events should have contributed to factor-price convergence. It turns out that Heckscher and Ohlin were right: a significant share of the Anglo-American real-wage convergence was due to commodity-price convergence. It appears that this late nineteenth-century episode was the dramatic start of world-commodity and factor-market integration that continues today.
This study analyzes an urban-influenced real estate market that is experiencing land use transitions. Evaluating a three-year period of unimproved real estate sales in Saunders County, Nebraska, has identified components that contribute to farmland values. Applying these components illustrates that buyers having special motivations often pay premiums to obtain agricultural land. A model based on farmland productivity determines a crossover point where it becomes economically justifiable to convert farmland into acreage tracts, illustrating productivity levels where concerns over development hold merit. Areas experiencing farmland development will obtain valuable information for land use and planning decisions from applying this research.
We present a joint study of the U.S. structural transformation (the decline of agriculture as the dominating sector) and regional convergence (of southern to northern average wages). We find empirically that most of the regional convergence is attributable to the structural transformation: the nationwide convergence of agricultural wages to nonagricultural wages and the faster rate of transition of the southern labor force from agricultural to nonagricultural jobs. Similar results describe the Midwest's catch-up to the Northeast (but not the relative experience of the West). To explain these observations, we construct a model in which the South (Midwest) has a comparative advantage in producing unskilled laborintensive agricultural goods. Thus it starts with a disproportionate share of the unskilled labor force and lower per capita incomes. Over time, declining education/training costs induce an increasing proportion of the labor force to move out of the (unskilled) agricultural sector and into the (skilled) nonagricultural sector. The decline in the agricultural labor force leads to an increase in relative agricultural wages. Both effects benefit the South (Midwest) disproportionately since it has more agricultural workers. With the addition of a less than unit income elasticity of demand for farm goods and faster technological progress in farming than outside of farming, this model successfully matches the quantitative features of the U.S. structural transformation and regional convergence, as well as several other stylized facts on U.S. economic growth in the last century. The model does not rely on frictions on interregional labor and capital mobility, since in our empirical work we find this channel to be less important than the compositional effects the model emphasizes.