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Economic Review
Federal Reserve Bank of Dallas
July 1984
1
Deposit Insurance,
Moral Hazard,
and Credit Unions
Robert T. Clair
The examination of financial ratios of federal credit
unions before and after the provision of federal
deposit insurance in 1971 shows that their exposure to
credit risks grew significantly faster after the provision
of insurance. Following a short transition, both the
quality of loans and the ability of credit unions to
absorb loan losses declined. This evidence supports
the recent literature contending that the pricing of
federal deposit insurance unintentionally encourages
risk taking by insured institutions .
13
The Effects of Oil Prices
and Exchange Rates
on World Oil Consumption
Stephen P. A. Brown and Keith R. Phil/ips
From 1980 to 1983, oil consumption in most industrial
countries declined, even though the real dollar price
of oil fell and world economic activity increased .
Lagged adjustment to the sharp oil price increase in
1979 and appreciation of the dollar more than offset
the effects of economic growth and the oil price
decline. With adjustment to the 1979 oil price increase
nearly complete, continuing economic growth and
any decline in the value of the dollar can be expected
to result in increased oil consumption.
23
Farmers and Economic Shocks:
Ranking Texas Agricultural
Production Regions
Hilary H. Smith
Analysis of income patterns for Texas agricultural
households shows that farmers in West Texas are
the least diversified and, consequently, the most
susceptible to economic shocks. Black/ands and
East Texas farm households generally have smaller
incomes, but their income flows are spread more
evenly over different sources, making these
households better able to withstand unexpected
declines in income from agriculture. West Texas
farmers and ranchers are also exposed to more risk
because of the region ' s low and highly variable
rainfall.
This publication was digitized and made available by the Federal Reserve Bank of Dallas' Historical Library (FedHistory@dal.frb.org)
Deposit Insurance,
Moral Hazard,
and Credit Unions
By Robert T. Clair*
Much attention has recently been given to the
hypothesis that improperly priced federal deposit
insurance has induced greater risk taking by
depository institutions, especially in a deregulated
environment. There have been arguments against
bank deregulation on this basis, and the Federal
Deposit Insurance Corporation (FDIC) recognized
the hypothesis in its 1983 proposal to restructure
federal deposit insurance. In a recent article in this
Review, Eugenie Short and Gerald O'Driscoll proposed regulatory changes to encourage the development of private deposit insurance, because market
forces would cause private insurers to price insurance properly and not subsidize risk taking.'
While a strong theoretical argument can be made
supporting this hypothesis of greater risk taking,
there has been relatively little empirical underpinning. The purpose of this article is to provide em-
* Robert T. Clair is an economist at the Federal
Reserve Bank of Dallas. The views expressed are
those of the author and do not necessarily
reflect the positions of the Federal Reserve
Bank of Dallas or the Federal Reserve System.
Economic ReviewlJuly 1984
pirical evidence. The analysis is conducted using
data from federal credit unions during the postwar
period.
Utilizing data from the credit union industry offers a unique advantage over using data from other
depository institutions in studying the problem of
moral hazard. Federal deposit insurance was made
available to credit unions beginning in 1971.2 It is
feasible, therefore, to examine the impact of deposit
1. See John H. Kareken, "Deregulating Commercial Banks: The
Watchword Should Be Caution," Federal Reserve Bank of
Minneapolis Quarterly Review, Spring-Summer 1981,2; Federal
Deposit Insurance Corporation, Deposit Insurance in a Changing Environment: A Study of the Current System of Deposit
Insurance Pursuant to Section 712 of the Garn-St Germain
Depository Institution Act of 1982, Submitted to the United
States Congress by the Federal Deposit Insurance Corporation
(Washington, D.c.: FDIC, April 1983), 1-7; and Eugenie D. Short
and Gerald P. O'Driscoll, Jr., "Deregulation and Deposit Insurance," Economic Review, Federal Reserve Bank of Dallas,
September 1983,11-22.
2. Legally, a credit union does not accept deposits but issues
shares in the credit union to its members. In fact, credit union
shares are so similar to deposits that all references in this
paper will be to deposits and deposit insurance.
insurance on credit unions by using postwar time
series. Banks and savings and loan associations
(S&Ls) received federal deposit insurance in 1934.
For these institutions, it would be difficult if not impossible to separate the effects of deposit insurance
from the effects of the Great Depression and World
War II.
The statistical analysis conducted here suggests
that federal deposit insurance has resulted in
federal credit unions being more exposed to credit
risks. The empirical evidence also indicates that
credit union liquidity, which declined over the entire
period, was not affected by deposit insurance. On
balance, these results support the basic hypothesis
that federal deposit insurance induced greater risk
taking by insured depository institutions.
The development of moral hazard
The hypothesis that federal deposit insurance has induced greater risk taking by financial institutions is
based on the premise that managers of financial institutions exploit the subsidy provided by deposit insurance by paying less heed to risk and investing in
higher-yield, higher-risk assets. The cost of deposits
is less than it otherwise would be becau~e the
deposits are. insured,. EV,en if the institution becomes
riskier, the cost of deposits will not rise commensurate with the risk undertaken. T.he reason is that
depositors perceive ti)eir accounts to be insured and
will not demand compensation fpr their deposits b~
ing at greater riskJ.Fuft~ermore, the premium pa,id
for deposit insurance does not increase as the institution becomes riskier. Hence, management:can
increase the net interest margin by using lower-cost
insured deposits to fund higher-yield, higher-risk
assets. The result is higher net interest income, but
higher loan .losses are also likely to occur. Managers
expect an increase in interest income to outweigh
the expected increase in loan losses.' This argument
is one of moral hazard: insuring the. deposits of the
3. If managers inappropriately price the lo~ns and do not charge
a sufficient risk premium, it is possible 'for loan loss~s to exceed any gain in interest income. F<;lr a discussion of bow
regulatory safety-net mechanis'ms, such as deposit insurance,
might result in the inappropriate pricing of loans, s'ee Gerald P.
O'Driscoll, Jr., and Eugenie Dudding Short, "Safety-Net·
Mechanisms: The Case of Intemational Lending,'" Federal
Reserve Bank of Dallas Research Paper no. 8404 (Dallas, May
1984)
2
institution causes the institution to act in a riskier
manner.
The moral hazard problem will develop if the
deposit insurance subsidizes risk taking. In the
case of credit unions, this subsidization is easily
established. The insurance premium is the same
percentage of insured deposits for all credit unions
regardless of risk. The premium is currently onetwelfth of 1 percent of the total amount of deposits
in insured accounts, the same as for banks and
S&Ls. This disregard of risk in pricing insurance
takes place despite the fact that at least some risk
characteristics can be measured.' Thus, there is a
problem of moral hazard from improperly pricing insurance, as opposed to an adverse selection problem from not being able to measure risk.
Given that the moral hazard exists, several other
factors will determine how great an increase in risk
exposure is likely to occur. One factor is the extent
of insurance coverage. Another is the availability of
high-yield, high-risk assets that credit unions can acquire. Still another factor is the intensity with which
credit union management seeks to maximize profits.
In the case of federal credit unions, insurance
coverage is extensive. Deposit insurance for federal
credit unions covers nearly 100 percent of total
deposits, and deposits are 90 percent of total
liabilities plus equity. The vast majority of credit
union assets are funded by insured deposits. s
4. Or].e characteristic that could be used to price insurance
premiums for risk is age of the credit union. The probability of
a newly chartered credit union failing is approximately 40 percent This probability declines over time; after 16 years of
operation, the probability of failure approaches zero. Another
credit union characteristic that provides information on the
probability of failure is the type of membership. For example,
a credit union of hotel employees is over three times more
likely to fa)1 than a credit union of telephone workers. The
prob~bility of failure has been calculated for each major type
of membership criterion by the National Credit Union Administration, but this information has not been used to price
insurance premiums. See Donald J. Melvin, Raymond N. Davis,
and Gerald C. Fischer, Credit Unions and the Credit Union Industry: A Study of the Powers, Organization, Regulation and
Competition (New York: New York Institute of Finance, 1977),
55.
5. Coverage has changed over the years as the volume of large
deposits grew and limits to coverage were raised. Over the
1971-81 'period; coverage never fell below 97 percent of total
deposits. See Annual Report of the National Credit Union
Administration, various issues.
Federal Reserve Bank of Dallas
A second factor- abil ity of credit unions to acqu ire high-risk, high-yield assets - is restricted in the
case of federal credit unions by their charters.
Credit unions can only make loans to members, to
other credit unions, or through loan participations
with other credit unions. The result is a loan portfolio composed primarily of secured consumer loans
for the purchase of consumer durables, such as
automobiles. The investment portfolio is even more
restricted, as funds can only be placed in what appear to be low-risk assets. Credit unions can invest
in government obligations, federally sponsored
agency obligations, or obligations guaranteed by the
government. Credit unions can also place funds in
commercial banks or insured thrift institutions. 6
Because of asset restrictions, the riskiest assets
for credit unions are I~kely to be loans to members.
These assets are definitely less liquid than assets in
the investment portfol io, and they are more I ikely to
have a greater risk of default. Hence, it can be
hypothesized that the effect of moral hazard would
be to encourage credit unions to increase their lending to members and reduce their investment portfolios. While this action may not seem to cause a
large increase in risk, it demonstrates the effect of
moral hazard within the confines of the present
regu latory framework.
The willingness of credit union management to
accept greater risk in order to obtain higher returns
is important in determining the increase in exposure
to risk as a result of moral hazard. Credit unions
are, by charter, nonprofit cooperat~ve organizations.
If credit union management is extremely averse to
accepting greater risk or if management has little
incentive to increase risk, there would be little
motivation to trade risk for return in spite of the incentive of the moral hazard. If this were the case, .
the provision of federal deposit insurance should
have little or no effect on credit union portfolios.
If, however, incentives do exist for credit union
management to increase profits, revenues, or credit
availability, then the moral hazard could result in a
sizable increase in exposure to risk. These incentives
may take the form of salary increases or increases
in perquisites. The results of this investigation support the latter description of credit union
management. 7
6. Over most of the period under study, 1948 to 1982, such investments would generally be cons'idered risk-free assets. Any
nongovernment obligation is, however, risky to some extent.
Some credit unions did sustain capital losses as a result of the
failure of Penn Square Bank of Oklahoma City. The perception
of riskiness of some of these investments may have changed in
recent years. For a detailed description of federal credit union
asset powers, see Section 107 (1i U.S.C.-1757) of the Federal
Credit Union Act as amended to May 1,1977, in Melvin, Da'vis,
and Fischer, Credit Unions and the Credit Union Industry, 237-43.
7. While there exist a number of theoretical models of credit
union behavior, almost nothing in the way of empirical tests of
th~se models has been published. Barry Keating did produce
empirical results suggesting that credit union managers maximize their own utility subject to a minimum benefit constraint
for members. This finding would be consistent with the statement that credit union management may be rewarded indirectly through perquisites for increased returns. See Barry P.
Keating, "Prescriptions for Efficiency in No,\profit Firms,'"
Applied Economics 11 (September 1979): 321-32.
Economic ReviewlJuly 1984'
Federal deposit insurance
Charters for federal credit unions were first granted
in 1934. From that time until 1970, there was no
federal deposit insurance for credit unions. In 1970,
legislation providing federal deposit insurance was
enacted, which created the National Credit Union
Share Insurance Fund.
Implementation of the insurance program began
in 1971 and spanned several years. Federal credit
unions were required to obtain insurance and, to
qualify for the insurance, had to meet a standard
for financial condition that was higher than had
been enforced previously. Any federal credit union
that did not qualify for deposit insurance because
of poor financial condition was issued a temporary
insurance certificate. These certificates provided
deposit insurance to the substandard credit unions
for two years. At the end of the two-year period,
such institutions were required either to have improved their financial condition to the point of
being insurable, to have merged with other credit
unions, to have switched to state charters, or to
face liquidation. By the end of 1973, all federal
credit unions were insured, and most transitory effects from the implementation of the insurance program had worked their way through the system.
The method by which the deposit insurance program was implemented suggests that a time series
study of the effects of deposit insurance on federal
credit unions in the postwar period should be di-
3
Chart 1
Total Cancellations of Federal Credit Union Charters
CANCE llA TlONS
1,000 r----------------=~-------__,
-
900
PREINSURANCE
PERIOD
800
TRANSITION
PERIOD
INSURANCE
PERIOD
700
600
500
400
300
200
100
0
'48
SOURCE OF PRIMARY DATA: National Credit Union Administration
vided into three distinct periods. The first period,
spanning 1948 to 1970, will be referred to as the
preinsurance period. The second, a transition period,
includes 1971, 1972, and 1973. Finally, the insurance
period includes 1974 to the latest year for which
suitable data are available. The problem of moral
hazard is hypothesized to have developed during
this third period.
It is important to include the transition period as
a separate period in analyzing the effects of deposit
insurance on federal credit unions. The enforcement
of the higher standard of financial condition had a
significant effect on the financial ratios used here to
assess risk in the credit union industry. Empirical
analysis must control for the changes during the
transition period so that the results will not be
biased.
Two sources of bias might result from not controlling for transition effects. The first is the extremely large number of cancellations of federal
credit union charters during the 1971-73 period. In
1972, cancellations reached a record high of 672
(Chart 1). It is reasonable that the credit unions in
the poorest financial condition were the first to
have their charters canceled. As these credit unions
were eliminated, the financial condition of the in4
dustry as a whole improved.
The second source of bias stems from the sudden
enforcement of stricter financial standards for credit
unions whose charters were not canceled. For example, credit unions wrote off a large amount of
delinquent loans during the transition period, so the
ratio of delinquent loans to total loans improved
substantially. At the same time, writing off the
delinquent loans required reductions in capital.
These transitory effects should span only a year or
two, but it is vitally important to control for them.
Financial ratios as risk measures
The moral hazard hypothesis implies simply that
credit unions took on greater risk following the provision of deposit insurance. To test this hypothesis,
several financial ratios of federal credit unions are
examined to determine if any significant changes
occurred in their trends over time that imply greater
risk taking during the insurance period. The financial ratios used are designed to measure exposure to
two of the basic risks of financial intermediaries: exposure to liquidity risk and exposure to credit risk."
Descriptions of the financial ratios are provided
next, including rationales for their choice, followed
by descriptions of the data and the test procedure.
Federal Reserve Bank of Dallas
Chart 2
Capital Ratio for Federal Credit Unions
RATIO
.20
r---------------
.18
.16
TRANSITION
PERIOD
PREINSURANCE
PERIOD
INSURANCE
PERIOD
.14
.12
.10
.08
.06
.04
.02
'61
'65
'69
73
77
SOURCE OF PRIMARY DATA: National Credit Union Administration.
Finally, regression results are presented and
interpreted.
The first financial ratio examined is a capital
ratio, which measures a credit union's ability to absorb loan losses. The ratio used here is the sum of
undivided earnings plus regular and other reserves
divided by loans. This ratio is the proportion of
loans that could default and be absorbed by capital
surplus. A decrease in this ratio would indicate a
riskier credit union, other things equal.
I n Chart 2 the capital ratio is plotted for 1948 to
1979. The capital ratio was about the same in 1970
as in 1949. During the transition period there was a
slight decline. It was in the insurance period that
the sharp decline occurred, suggesting a greater exposure to credit risks.
The adequacy of a credit union's capital is
directly related to the quality of its loan portfolio.
The second financial ratio, the delinquency rate, is
8. Most depository institutions are also subject to interest rate
risk. It is likely that credit unions, like other depository institutions, borrow short and lend long. Unexpected increases in interest rates can cause a decline in earnings. Unfortunately,
there are no data for measuring interest rate risk exposure.
Economic Review/July 1984
a measure of the quality of the loan portfolio. The
delinquency rate is calculated as the amount of
loans that are delinquent in payments two months
or more divided by total loans. Other things equal,
an increase in the delinquency rate indicates a
decline in the quality of the loan portfolio and
greater exposure to credit risks.
The delinquency rate, plotted for 1949 to 1982 in
Chart 3, trended downward from 1949 to the middle
1960s, indicating an improving loan quality. From
the middle 1960s to the transition period, the rate
was fairly constant. During the transition period the
delinquency rate declined sharply, likely a result of
the enforcement of higher financial standards. As
credit unions wrote off questionable loans, the
delinquency rate improved at the expense of the
capital ratio. Following the transition period, the
delinquency rate was flat initially and then began
an upward trend. The rising delinquency rate beginning in 1977 suggests an increase in exposure to
credit risks.
The third financial ratio measures exposure to
liquidity risk. A credit union is illiquid if it cannot
meet its short-term obligations resulting from either
depositors withdrawing funds or borrowers exercising existing lines of credit. The ratio chosen to
5
Chart 3
Delinquency Rate for Federal Credit Unions
RATE
.10
.09
PREINSURANCE
PERIOD
.08
TRANSITION
PERIOD
INSURANCE
PERIOD
.07
.06
.05
.04
.03
.02
.01
.00
'50
SOURCE OF PRIMARY DATA: National Credit Union Administration.
Chart 4
Loan-to-Deposit Ratio for Federal Credit Unions
RATIO
1.0r-----------------------------~----------------_,
.9
.8
.7
.6
.5
TRANSITION
PERIOD
PREINSURANCE
PERIOD
'56
'60
'64
'68
72
76
INSURANCE
PERIOD
'80
SOURCE OF PRIMARY DATA: National Credit Union Administration.
6
Federal Reserve Bank of Dallas
measure illiquidity is the proportion of loans to
deposits. Loans are relatively illiquid assets compared with a credit union's investments. Thus, as
loans increase relative to deposits, the credit union
has fewer liquid assets available to meet sudden
needs for liquidity, such as deposit withdrawals. As
the loan-to-deposit ratio rises, the credit union is
more exposed to liquidity risks.
The loan-deposit ratio, plotted in Chart 4, maintained a steady upward trend during the preinsurance period. During the transition period, this
ratio declined, consistent with the enforcement of
higher financial standards during the period. Subsequently, the loan-deposit ratio appears to follow
the same upward trend evident for 1960 through
1970. It seems that the higher financial standards
enforced during the transition period had only a
minor effect on this ratio.
A test of the moral hazard hypothesis
The hypothesis that the provision of federal deposit
insurance resulted in greater risk taking was tested
using the financial ratios measuring risk as dependent variables in regressions. These regressions
determined the time trend before insurance, the
change in trend during the transition period, and the
time trend during the insurance period. The hypothesis would predict a decline in riskiness during the
transition period and an increase in risk taking during the insurance period.
The model used here assumes that financial ratios
trend toward optimal values that are functions of
several variables. The optimal value should be sensitive to changes in the expected rates of return on
various assets and should also change in response to
technological changes. These technological changes
might include improvements in cash management
techniques, the availability of new financial instruments, and changes in the regulatory environment. The test of the moral hazard hypothesis is to
show evidence that the optimal values of the financial ratios changed, indicating greater exposure to
risk in response to the provision of deposit
insurance.
The optimal value is affected by both cyclical
fluctuations and secular trends. In the model,
balance sheet variables are used to capture the
effects of cyclical fluctuations and unanticipated
shocks. The secular effect is modeled with a time
trend variable, and the changes in the secular trend
Economic Review/July 1984
resulting from the provision of deposit insurance are
modeled using a binary variable that interacts with
the time variable. The binary variable separates the
data into two subperiods. If the coefficient of the
binary variable interacted with the time variable is
significantly different from zero, the implication is
that the trend of the optimal ratio of the dependent
variable has changed between the two periods.
Suppose credit union management behavior can
be modeled as seeking an optimal value for the
financial ratio, r*, which is a linear function of time,
t:
(1 )
t=1,2, ... ,n.
The management behaves as if it seeks to increase
the financial ratio by b1 in every period and to adjust for any difference between the actual and optimal ratio in the previous period. The financial
ratio that actually occurs in period t will also be affected by any exogenous shocks or cyclical fluctuations that might occur in period t. The financial
ratio in period t, rt , is a function of the ratio in the
previous period, rt - 1 ; the trend of the optimal ratio,
b1; the difference between rt-1 and r t - 1; and any
exogenous shocks, e t :
(2)
r t = rt - 1
+ b1 +
A(rt-1 -
r t - 1)
+
et ,
0<A<1.
If the financial ratio in the previous period was
equal to its optimal value and there are no exogenous shocks in the present period, then
(3)
rt = r t - 1
+
b1 = rt-1
+
b 1 = rt
The first two terms of equation 2 are expected to
pick up the secular trend of the optimal value for
the financial ratio. In this analysis, a simplification
is made whereby the definition of equation rt is
substituted into equation 2, replacing the first two
terms:
The term (rt-1 - r t - 1) measures the impact of past
exogenous shocks on the present ratio. The term e t
measures present exogenous shocks. Neither term
can be measured directly. Assume that variable Yt is
related to e t as a direct proportion with a random
error term. Then,
(5)
7
Furthermore, assume that credit union management
reacts quickly to the exogenous shocks. The quick
reaction implies that A is very close to 1. In this
structure, exogenous shocks before period t-1 have
a negligible effect on 't, as most of the adjustment
for a shock will be accomplished in the next period.
Then equation 4 can be rewritten as
(6)
't =
+
a1
b1 t
+
Ad Yt-1
Et
+
d Yt
= A JAt -1
+
+
Et ,
JAt·
(10)
The provision of deposit insurance changes the
secular trend of
and, consequently, changes a 1
and b 1 . If period x is the period during which
deposit insurance was provided, then
,*
,*
(7)
=
a 1 + b1 t,
a2 + b 2 t
t = 1, ... , x-1.
t = x, ... , n.
,*
To include this formulation of
in the above equation, first define a binary variable, 01' that equals 1
in period x or later and equals zero in all earlier
periods. Equation 6 can be restated as
(8) 't
= a1 +
+
01(a 2
Ad Yt-1
-
a 1)
+
d Yt
+
+
b1 t
+
01(b 2
-
b 1)t
Et ·
This equation can be placed in a regression format,
as follows:
(9) r t = a
+
+
{31 01
+
+
Et ,
{3s Yt
{32 t
+
(33(D1 .
t)
+
{34 Yt-1
where a = a1
{31 = (a 2
{32 = b 1
a1)
{33 = (b 2
b1 )
is estimated using the first four terms in equation 9.
Adjustments are also required to reach this new
secular trend. In order to separate these adjustments from other adjustments for exogenous shocks
or cyclical fluctuations, an additional binary
variable, 02' is introduced into the equation. Transition effects are captured by (02 . t). The interactive
variable (02 . t) was chosen to determine the
change in slope."
't = a
+
+
{31 01
{34 Yt-1
+ {32 t + (3i 0 1 . t)
+ {3s Yt + (36(02 . t) +
Et ·
Equation 10 is the format of the estimated regressions. The binary variable 01 equals 1 for 1974 and
later years, separating the insurance period from
earlier periods. The binary variable 02 is defined as
equal to 1 for the years 1971-73 and zero for all
other years. 02 is used to isolate the changes that
occurred during the transition period. The variable
Yt' which captures exogenous shocks and cyclical
movements in 't, is different depending on the
dependent variable. For the capital ratio and the
loan-deposit ratio, Yt is defined as the percentage
change in total assets over the past year divided by
100. For the delinquency rate, Yt is defined as the
percentage change in total loans over the past year
divided by 100.
The data used in the liquidity ratio and capital
ratio regressions are for 1948 to 1979. Post-1948
data were used to avoid any effects from World
War II. The data after 1979 are not comparable with
those for earlier periods because of a binding usury
ceiling in late 1979 and 1980 and the imposition of
credit controls in early 1980. '0
{34 = Ad
{3s = d
Et
= normally distributed random error.
Equation 9 requires one more change in specification. The first four terms on the right-hand side of
equation 8 model the secular trend of the optimal
value of the financial ratio, including its shift in
period x. The remaining terms capture the effects of
exogenous shocks and cyclical fluctuations in this
period and past periods. Under this structure the introduction of deposit insurance will cause a change
in the secular trend. The change in the secular trend
8
9. Because an adjustment is modeled as a process and not a
sudden shift, no binary variable for a change in the intercept
term has been included.
10. The data are collected and published by the National Credit
Union Administration. All the ratios are calculated from the
aggregated outstanding balances reported for all federal
credit unions in the United States, Guam, Puerto Rico, and
the Virgin Islands. Assets and liabilities are reported as
outstanding balances on December 31.
The two economic policies, usury ceilings and credit controls, resulted in a severe decline in loan growth. The decline
in loan growth caused a sharp drop in the liquidity ratio and
an increase in the capital ratio. These ratios did not recover
Federal Reserve Bank of Dallas
The del inquency rate regression uses data from
1949 to 1982 - all the available postwar data. The
years 1980-82 are included for the delinquency rate
because this ratio should not be overly affected by
the usury ceiling or the credit controls.
The postwar period was a relatively stable one for
credit unions. Asset powers were not altered in any
significant way during this period. Credit unions
weathered the business cycles in relatively good
form. Credit unions have a propensity to be organized among workers with stable employment patterns-for example, government employees and
public utility employees-as opposed to workers in
less secure occupations. In addition, credit unions
were probably not affected as much as savings and
loan associations or banks by the problems resulting
from rising interest rates.
Interest rate ceilings on deposits have typically
been higher at credit unions than at banks or other
depository institutions, making credit unions less exposed to disintermediation. When their interest rate
ceilings did become binding, credit unions were innovative in creating unregulated instruments, and
the National Credit Union Administration was quick
to respond by raising the ceiling. Also, credit unions
maintain only a small share of assets in mortgages,
resulting in a relatively shorter average asset maturity than in the case of S&Ls. Consequently, credit
unions did not face the severe earnings pressure
S&Ls did during periods of rising interest rates.
The moral hazard hypothesis implies that the
coefficient {33 is significant, indicating greater risktaking behavior. Increased exposure to credit risk
to their previous levels in 1981 and 1982. It is most likely that
the recession from July 1981 to November 1982 stifled loan
growth. The lack of growth in lenJing prevented the two
ratios from returning to their previous levels.
If the data from 1980 through 1982 are included in the
regressions, the results indicate that there were no significant
changes in the trend of the capital ratio and the loan-deposit
ratio. I n the case of the capital ratio, nothing could be further
from the truth. The capital ratio was fairly constant in the
years before deposit insurance. Following the provision of
insurance, this ratio dropped sharply from 1970 to 1979,
reaching a record postwar low. From 1980 through 1982, it
increased dramatically because of the usury ceiling and the
credit controls. To describe this 12-year period as essentially
flat would be misleading. Rather than attempt to explicitly
model the effects of the usury ceiling and the credit controls,
the last few observations were eliminated.
Economic ReviewlJuly 1984
would be evidenced by {33 being positive in the
delinquency rate regression and negative in the
capital ratio regression. Greater exposure to liquidity risk would be denoted by a positive {33 in the
loan-deposit ratio regression.
The enforcement of higher financial standards
during the transition period should have two effects.
First, the {36 coefficient should show improving
financial condition. This would mean a positive {36 in
the capital ratio regression and a negative {36 in the
regressions for the delinquency rate and the
loan-deposit ratio. Second, credit unions are expected to begin the insurance period in a better
financial condition than existed at the end of the
preinsurance period. Consequently, the {31 coefficient is expected to be positive in the capital ratio
regression and negative in the delinquency rate and
loan-deposit ratio regressions.
The sign of the coefficient {3s can be predicted in
some instances. In the capital ratio regression, {3s is
expected to be negative. In this regression, Yt is
asset growth. If an exogenous shock occurs that
results in an increase in assets, it is likely that the
capital ratio will decline. As earnings from the
additional assets accrue, undivided earnings and
reserves can be increased. The time lag between the
growth in assets and the growth in capital causes
the capital ratio to decline. As a result, {3s is expected to be negative.
I n the del inquency rate regression the coefficient
{3s is also expected to be negative. I n this regression,
Yt is loan growth. An exogenous shock resulting in
higher loan growth would cause the denominator of
the delinquency rate to increase. Furthermore,
economic conditions that encourage loan growth,
high employment, and strong income growth are
also conditions that indicate consumers are less
likely to let their payments lapse. Hence, it would
be likely that the numerator in the delinquency rate
would decrease. Positive loan growth would be
associated with a decline in the delinquency rate.
It is not possible to hypothesize a priori the sign
of {3s in the loan-deposit ratio regression. The
variable Yt in this regression is asset growth. It is impossible to determine whether growth in total assets
would be correlated with growth in loans or
deposits or both.
Regression results
The regressions were estimated using data for all
9
Table 1
REGRESSION RESULTS FOR FINANCIAL RATIOS
Coefficients
a
fJ 4
Independent variables
Regression
Constant
Capital ratio
Yt-1
.1349
(13.22)***
.1847
(3.86)***
.00016
(.69)
-.00524
(-4.81)* * *
-.02926
(-1.24)
-.05838
(-2.80)***
-.00012
(-1.25)
.00551
(1.41 )*
-.02919
(-710)***
.00001
(.18)
-2
R = .9642; DW = 1.82; n = 32; rho = .2345.
(1.36)
Delinquency rate
0963
(16.72)***
-.1061
(-4.88)***
-.00176
(-10.18)***
.00259
(5.14)* * *
-2
R = .9757; DW = 1.56; n = 34; rho = .7328.
(6.28)
Loan-deposit ratio.
.4158
(5.68)* * *
.0326
(.09)
.01341
(817)***
-.00393
(-.50)
.04836
(.26)
.04048
(.24)
-.00236
(-3.48)***
-2
R = .8931; DW = 1.79; n = 32.
Independent variables
01
1 for 1974 or later years; zero otherwise.
a trend variable that takes the values 1,2,.
Yt
(total assets t - total assets t _ 1 ) -;- total assets t _ 1 for the capital ratio regression and the
loan-deposit ratio regression.
(total loans t
02
.,50; 1948 corresponds to (t = 16).
-
total loans t _ 1 ) -;- total loans t _ 1 for the delinquency rate regression.
1 for 1971, 1972, and 1973; zero otherwise.
Dependent variables
Capital ratio
Delinquency rate
Loan-deposit ratio
(undivided earnings
+
reserves) -;- total loans.
loans with payments delinquent for two months or more -. total loans.
total loans -;- total savings.
NOTE: Figures in parentheses are t statistics; * indicates significance of the coefficient at the 10 level,
.01 level, using a one-tailed test when the sign of the coefficient can be hypothesized
R2 is the coefficient of determination adjusted for degrees of freedom.
OW is the Durbin-Watson autocorrelation test statistic
n is the number in sample
Rho is the first-order autocorrelation coefficient
**
at the .05 level, and
***
at the
Capit,al ratio and delinquency rate regressions were corrected for autocorrelation.
10
Federal Reserve Bank of Dallas
Table 2
PREDICTED AND ACTUAL SIGNS OF REGRESSION COEFFICIENTS
AdJuotments to
exogenous shocks
Jl>v1oral ha7drd
Transition period effects
hypothem
fl)
Regrc'l'llon
Predicted
+
Capital ratio
Delinquency rate
Loan-deposit ratio
Predicted
+
+
Actudl
+*
PredICted
Actual
+
n.s.
+*
n.s.
flo
fl6
fl1
I\ctual
Predicted
Actual
n.a.
n.s.
n.s.
n.s.
* Slgnltlcdnt with a equal to 01
n s - Not ~Ignltlcanltv dltterent tram /era
na
-~ot
dpplicable
federal credit unions, and the results are reported in
Table 1 The model fits the data quite well, as most
of the variation in the financial ratios is explained.
The regressions for the capital ratio and the delinquency rate indicate increased exposure to credit
risk following the provision of deposit insurance.
The loan-deposit ratio was unaffected by the provision of deposit insurance; however, it did maintain a
steady upward trend, indicating increasing illiquidity, over the entire 1948-79 period
The fact that credit union liquidity was unaffected does suggest that examination of the basic
hypothesis within the context of a richer theoretical
model of a depository institution might prove productive It may be that deposit insurance lowered
the cost of bankruptcy to credit union members but
did not lower the cost that results from a credit
union being illiquid. Credit unions would then be
more willing to accept greater risk of bankruptcy
for a greater return but not the·gr.eater risk of being
illiquid
The implications of the regression results are summarized in Table 2. The first two columns of the
table provide the predicted and actual signs for the
(33 coefficient based on the moral hazard hypothesis. The coefficient was of the appropriate sign and
significant for the capital ratio and the delinquency
rate. These results imply that federal credit unions
are more exposed to credit risks as a result of the
moral hazard. The (33 coefficient was insignificant in
the loan-deposit ratio regression. This result implies
that deposit insurance had no measurable effect on
Economic Review/July 1984
federal credit union exposure to liquidity risks.
The coefficients (31 and (36 both model the effect
of the transition period. Three of the six coefficients
were significant and appropriately signed. This supports the hypothesis that the transition period was
marked by improving financial conditions as federal
regulators enforced higher standards. The coefficient (35' modeling the effect of cyclical fluctuations
and exogenous shocks on the financial ratios, had
the predicted sign and was significant in both cases
where the sign could be hypothesized.
Conclusion
Regression analysis offers support of the hypothesis
that the provision of federal deposit insurance
resulted in credit unions taking on greater risk. In
particular, there is clear evidence that credit unions
face increasing credit risks in the insurance period.
Both the quality of the credit union industry's loan
portfolio and its ability to absorb loan losses have
diminished during the insurance period. Deposit insurance seems to have had no effect on credit
union liquidity; however, credit union liquidity
declined significantly over the entire 1948-79
period.
The conditions that resulted in federal credit
unions becoming more exposed to credit risks also
exist for other depository institutions. The National
Credit Union Share Insurance Fund was modeled on
the Federal Deposit Insurance Corporation and the
Federal Savings and Loan I nsurance Corporation.
Subsidized deposit insurance is provided to com11
mercial banks, savings and loan associations, and
mutual savings banks. The problems resulting from
moral hazard that developed in the credit union
industry are likely to exist for all these insured
institutions.
The problems would be dealt with most directly
by pricing the premiums for deposit insurance to
reflect the risk of individual institutions. This might
be accomplished within the present system of
federal insurance. It could also be accomplished by
encouraging the development of a private deposit
12
insurance industry. A strong argument can be made
that the private industry would have a greater incentive to price risk properly than would the federal insurance programs. In either case, given that deregulation of the financial industry will continue, it is
vitally important to remove the subsidy inherent in
the present insurance system in order to eliminate
moral hazard and to provide a "level playing field"
for insured depository institutions to compete with
uninsured financial institutions.
Federal Reserve Bank of Dallas
The Effects of Oil Prices
and Exchange Rates
on World Oil Consumption
By Stephen P. A. Brown and Keith R. Phillips*
From 1980 to 1983, oil consumption in most industrial countries declined, even though the real
dollar price of oil fell and world economic activity
increased. A common explanation for this decline is
that consumers continued to adjust to the sharp oil
price increase occurring in 1979. A more complete
analysis reveals that exchange rate movements have
also reduced oil consumption. Because world oil
prices are denominated in U.S. dollars, movements
in exchange rates can alter the price of oil faced by
countries other than the United States. In fact, increases in the value of the dollar raised the effective price of oil for some major industrial countries
to levels that were higher in 1983 than in 1980.
* Stephen P. A. Brown is an economist and Keith R.
Phil/ips is a research associate at the Federal
Reserve Bank of Dallas. The authors wish to
thank the participants in the 1984 Federal
Reserve System Conference on Energy Economics
for helpful comments. The views expressed are
those of the authors and do not necessarily
reflect the positions of the Federal Reserve
Bank of Dallas or the Federal Reserve System.
Economic Review/luly 1984
For the seven largest industrial economies
(Canada, France, Italy, Japan, the United Kingdom,
the United States, and West Germany), simple
econometric models and simulation analysis were
used to estimate the effects that three factorslagged adjustment to oil prices, changes in the
dollar price of oil, and appreciation of the
dollar-have had on world oil consumption. In
1983, these seven economies consumed 27 million
barrels of oil per day, accounting for about threefourths of free-world oil consumption and about
half of total world oil consumption.
Results of the simulations indicate that a lagged
adjustment to the oil price shock occurring in 1979
was the largest single factor reducing world oil consumption from 1980 to 1983 but that the adjustment
was largely completed by the end of 1983. The
results further indicate that the stimulative effects
of a decline in the real dollar price of oil from 1980
to 1983 were more than offset by appreciation of
the dollar against other major currencies.
Modeling oil demand
A simple model of oil demand was developed for
each of the seven economies. Each model was constructed with two equations. The first equation in13
dicates the quantity of oil that would be demanded
in the long run for a given gross national product
and price of oil. The second indicates the rate at
which adjustment to the long run occurs. A reduced
f~rm was estimated to obtain both short-run and
long-run price elasticities of oil demand and to
obtain the rate of adjustment.
The consumption of oil products is commonly
thought to be a fu nction of the general level of
economic activity, the real prices of oil products,
and other variables. If other variables are ignored as
being of secondary importance and if the elasticity
of aggregate oil demand with respect to gross national product (GNP) is assumed to be 1,' oil demand can be expressed as a long-run consumptionto-GNP ratio that is a function of price:
- -) = A
(-crt
(1 )
GNP
J,t
GNPj,t
Aj
PRICEj,t
Ylj
I
PRICE
Substituting (2) into (1), reducing and rearranging
terms, and then taking the natural logarithm yield
the reduced-form equation that was used for
estimation:
In(~)
(3)
'1'
I,t'
aj
+
+
Yj In(
(3j In(PRICEj,t)
Cj,t-1 ) ,
CNPj,t_1
in which
long-run value of oil consumption in
country j during period t (the consumption that would prevail with GNPj,t if
there was complete adjustment to the
price of oil, PRICEj,t)
real gross national product in country j
during period t, as measured in country
j's own currency
a scaling constant for country j's crude
oil demand
real price of crude oil faced by country
j during period t, denominated in the
country's own currency (a proxy for internal oil product prices)
= long-run price elasticity of crude oil
demand for country j.
Reflecting the difference between short-run and
long-run adjustment, oil consumption is modeled
as achieving only a partial adjustment to changes
For estimating convenience it is assumed that the aggregate
elasticity of oil demand with respect to GNP is 1 Given the
short period over which demand is estimated, this assumption
is probably not critical. There is some evidence, however, that
the GNP elasticity of oil demand for the United States varies
over the business cycle. See Stephen P. A. Brown, "A U.S.
Economic Recovery Could Solidify World Oil Prices," Energy
Highlights, Federal Reserve Bank of Dallas, April 1983.
14
(2)
GNPj,t
in which
Crt
in price during a given quarter. This approach
creates a gap between the consumption-GNP ratio
that prevails in a given quarter and the long-run
consumption-GNP ratio. The gap was modeled as
closing at a constant rate of dj percent in each
time period:
aj -
dj In(A j )
{3j - dj Ylj , the short-run price elasticity of oil
demand
Yj
-
(1 -
d j ).
The estimated parameters from equations 1 and 2
are obtained as follows:
(1 -
Yj), the estimated rate of adjustment
~/(1 I
"
Yfj
yA)
I
~jl(1 - Yj), the estimated long-run price
elasticity of oil demand.
The model used for estimation has two advantages. It allows oil demand to show a partial (or
lagged) adjustment to changes in the oil price, and
it requires only a limited number of observations.'
Real oil prices
Because sufficient data on internal product prices
2. The partial-adjustment model chosen for estimation imposes a
Koyck lag structure on the crude oil prices, in which the coefficient of PRICE/. t _ k for any period k preceding t is
This
functional form implies that there is a geometrically declining
rate at which past prices influence the current consumptionto-GNP ratio. See G. S Maddala, Econometrics (New York:
McGraw-Hili Book Company, 1977),142-43.
{3,Y/.
Federal Reserve Bank of Dallas
Real Prices of Mideastern Light Crude Oil
(1980 AVERAGE = 100)
150r---------------------------------------------------------------------
,-
; .... -- ....\\ \
..... --/./~ '- ..... _ J
\\
~\~
'/
.......
------ ...-.... ,--,
/',,
//
/
// /
./
//~/
\ \
/-\
,;;
/
/
Ijl
I
/
///
IX/
h'
t---::;;_~.......
_J
/
OFFICIAL,
-
\'"
\\\ . . , . . ./-.. . . . . .
... ~
.;----- ./"
~;--
OFFICIAL J9BO •
-----
FRANCE
WEST GERMANY
UNITED KINGDOM
ITALY
~
-- -
........... _
-......,
Xj,1'180. ~ • 100
-~,t
1981
~
D},1980
JAPAN
UNITED STATES
CANADA
1983
1982
SOURCES OF PRIMARY DATA: ~edelal Rp\ervp Bank of Ne\\ York
International Monetarv fund
Pptroleum & E-nergv Intelligence VY{'ckl y , Inc
were not readily available, an exchange-rateadjusted crude price was used as a proxy for internal product prices in the estimation of oil demand.
The price of crude oil faced by a country is only
one component of the country's internal product
prices. Other important components include taxes,
refining costs, and transportation costs. Ignoring
these other components of product prices implies
that there is a constant relationship between the
exchange-rate-adjusted price of crude oil and
internal product prices.
Adjusted for inflation, the official U.S. dollar
price of Mideastern light crude oil fell 17.5 percent
from 1980 to 1983, as shown in the accompanying
chart. During the same period the real price of officially priced Mideastern light crude oil, measured
in each country's respective currency, decreased
21.2 percent for Canada and 3.3 percent for Japan.
It increased 10.7 percent for Italy, 20.5 percent for
the United Kingdom, 23.3 percent for West Germany, and 29.6 percent for France. Differences in
movements in the price of oil across these seven
Economic Review/July 1984
countries reflect changes in real exchange rates.
The real price of crude oil faced by a country, as
denominated in the country's currency, is a product
of the country's exchange rate, inflation in that
country, and the dollar price of oil:
(4)
PRICE t = X/. t '
/,
,
(00/,°) OFFICIAL
J,t
t,
in which
Xi,!
nominal exchange rate of country j's
currency for U.S. dollars
country j's implicit price deflator in
the base period
country j's implicit price deflator for
period t
OFFICIAL t
official nominal U.S. dollar price of
Mideastern I ight crude oil in period t. 3
3. Spot prices for a raw material might be expected to be more
indicative of the state of the market for its products. The spot
15
Alternatively, the price of crude oil faced by a
country can be expressed as a product of the real
U.S. dollar price of crude oil and the real exchange
rate between the country's currency and the U.S.
dollar:
(5)
PRICEj,t
in which
Ej,t
the real exchange rate, or
Xu . (OJ,ol OJ,tl(D$/ 0$,0)
the real dollar price of oil, or
(0$,01 O$,t)OFFICIAL t ·
Estimating oil demand
Using the price series described in the preceding
section, equation 3 was estimated for each of the
seven countries with quarterly data from the first
quarter of 1975 through the fourth quarter of 1983 4
Oil consumption data proved to be the factor
limiting the time period for which the model was
estimated. For dates before 1975, data that were
consistent with the post-1974 series cou Id not be
found.
crude oil market is very thin, however, and there is little
statistical relationship between product prices and spot prices.
There is a strong relationship between product prices and the
official price of Mideastern light crude oil. See SPA. Brown
and Keith R. Phillips, "Exchange Rates and the Estimation of
World Oil Demand" (Work in progress, Federal Reserve Bank
of Dallas).
4. Monthly oil consumption data for all seven countries were
obtained from the US. Department of Energy. The data were
transformed to quarterly values of average barrels per day and
then seasonally adjusted with the Xll procedure contained in
the Statistical Analysis System (SAS)
The quarterly GNP series were obtained or estimated with
data from the International Monetary Fund, the Federal
Reserve Bank of New York, and the economic staff of the
British Embassy in the United States. For all seven countries
except the United Kingdom, seasonally adjusted data were
provided. The Xll procedure contained in SAS was used in
seasonally adjusting the GNP series for the United Kingdom.
Exchange rate series were obtained from the International
Monetary Fund. Implicit price deflators were obtained or
estimated with data from the International Monetary Fund and
the Federal Reserve Bank of New York. Oil price data are from
the Petroleum Intelligence Weekly series for Mideastern light
crude.
16
Using Durbin's H statistic, autocorrelation was
judged to be present in original estimates for the
models representing Canada, France, Italy, and West
Germany. The autocorrelation was corrected in
those cases.
As shown by Table 1, five of the seven models
performed well. The Italian model was judged inappropriate because it exhibited a poor fit and the
hypothesis of a zero coefficient on price could not
be rejected with statistical confidence. Despite its
good fit, the Canadian model was judged inappropriate because the hypothesis of a unitary coefficient on the lagged dependent variable could not be
rejected with statistical confidence. s
The poor results obtained with the models representing oil demand for Canada and Italy are indicative of the problems inherent in using crude oil
prices as a proxy for product prices. Government
regulation and changes in oil product taxation over
the period of estimation altered the relationship between crude oil prices and the oil product prices
faced by consumers in these countries. The five
other countries modeled also regulated their internal energy markets and changed their taxes to some
extent during the period. Nevertheless, their internal
product prices, unlike those in Canada and Italy,
showed a strong relationship to exchange-rateadjusted real crude oil prices'"
For the five acceptable models, short-run (onequarter) price elasticities were estimated to range
from - 0.047 to - 0.125, indicating a relatively low
response of consumption to changes in price. The
estimated rate of adjustment toward the long run
varied from the Japanese low of 8.7 percent of the
remaining gap per quarter to the French high of 37.2
percent of the remaining gap per quarter. With the
estimated adjustment rates, 90 percent of the longrun adjustment to a price change would require 4
quarters for France, 25 quarters for Japan, 9
quarters for the United Kingdom, 24 quarters for the
United States, and 12 quarters for West Germany.
Long-run price elasticities were estimated to extend
from -0.380 to -0.634, indicating some-but not a
5. A coefficient on the lagged dependent variable equal to or
greater than 1 implies that the model is unstable.
6. See Brown and Phillips, "Exchange Rates and the Estimation of
World Oil Demand u
Federal Reserve Bank of Dallas
Table 1
ESTIMATED PARAMETERS OF CRUDE OIL DEMAND
Short-run
price
elasticity
Country
Canada
(t)
(t, Ho: Yj
(t)
(t, Ho: Yj
(t)
(t, Ho: Yj
(t)
(t)
(t)
-.022
(-1.612)
.999
(21.683)
{-.012)
-.157
(-3.370)
(t)
= 1)
.628
R2
"R2
-40.688
.9294
.9250
-.421
.8786
.8710
Durbin's
H
(5.102)
(-3.018)
-.026
{-1.343)
.413
(2.095)
(-2.972)
-.044
.1837
.1327
*
-.055
(-2.265)
.913
(17.111)
(-1.639)
-.634
.9746
.9730
-1.75
-.125
(-2.467)
.783
(8.056)
{-1.892J
-.575
.9254
.9255
-.19
-.047
(-2.360)
.910
(17.275)
(-1.708)
-.527
.9737
.9721
-1.14
-.1{)3
(-3.668)
.728
(8.797)
(-3.279)
-.380
.9477
.9444
*
= 1)
West Germany
(t, Ho: Yj
'Ii
= 1)
United States
(t, Ho: Yj
A
Yj
= 1)
United Kingdom.
(t, Ho: Yj
A
= 1)
Japan ..........
(t, Ho: Yj
A
= 1)
Italy
Long-run
price
elasticity
(Jj
= 1)
France
Coefficient
on lagged
dependent
variable
* Corrected for autocorrelation using a computational routine that adjusts the variance-covariance matrix of the
estimates for the presence of a lagged dependent variable. See Phoebus J. Dhrymes, Distributed Lags: Problems
of Estimation and Formulation, 2d ed. (Amsterdam, New York, and Oxford: North·Holland Publishing Company,
1981), 199 (Theorem 7.1). Reported R2 is based on the Durbin equation.
NOTE: "R 2 is the coefficient of determination adjusted for degrees of freedom. There are 32 degrees of freedom for
each regression.
(t) is the standard t statistic for the null hypothesis that the true coefficient is zero.
(t, Ho: Yj = 1) is the t statistic for the null hypothesis that the true value of Yj is equal to 1.
Economic ReviewlJuly 1984
17
Table 2
ESTIMATED EFFECT ON 1983 Oil CONSUMPTION
OF CONTINUED ADJUSTMENT TO THE 1980
EXCHANGE-RATE-ADJUSTED Oil PRICE
France
Japan
United
Kingdom
West
Germany
United
States
Thousands of barrels per day
Simulated 1983 oil consumption
Assuming exchange-rate-adjusted price
held constant at 1980 level'
1,860
4,306
1,435
14,998
2,230
-105
-912
-44
-2,579
-181
Assuming consumption-GNP ratio
set at 1980 level'
Change in oil consumption attributable
to continued adjustment to 1980 price
1. Entry equals:
GNP
j,1983
~
)Yj12
Cj'1980
--'-GNP
j,1980
.n
11
)yk
(
e"j • PRICE ,1980 (3j
j
j
k=O
2. Entry equals: GNPj,1983(Cj,1980/GNPj.1980)'
Table 3
ESTIMATED EFFECT ON 1983 Oil CONSUMPTION
OF CHANGES IN THE EXCHANGE-RATE-ADJUSTED Oil PRICE
OCCURRING BETWEEN 1980 AND 1983
France
Japan
United
Kingdom
United
States
West
Germany
Thousands of barrels per day
Simulated 1983 oil consumption
Using fitted value from regression' .
Assuming exchange-rate-adjusted price
held constant at 1980 level
Change in oil consumption attributable
to changes in adjusted oil price.
1
18
1,640
4,193
1,307
15,270
2,033
1,860
4,306
1,435
14,998
2,230
-220
-113
-128
272
-197
The fitted value of consumption is equal to the simulated value of consumption obtained under the assumption that the
adjusted price of oil took its actual values from 1980 to 1983
Federal Reserve Bank of Dallas
I
l
great- response to price changes. The estimated
price elasticities of oil demand are higher and the
estimated adjustment rates are faster than those
that would be obtained if exchange rates were not
taken into account.
Effects of conservation,
oil prices, and exchange rates
For five of the seven economies, simulations were
used to break out the effects of conservation,
changes in crude oil prices, and changes in exchange rates on 1983 oil consumption.? Estimates
derived from these simulations indicate that between 1980 and 1983 the oil consumption of the
five countries fell a total of 4.2 million barrels per
day as a result of conservation and changes in
exchange-rate-adjusted crude oil prices. Conservation accounted for 3.8 million barrels, while the net
effect of changes in the real dollar price of oil and
in exchange rates accounted for 0.4 million barrels.
Conservation, which can be regarded as a lagged,
price-induced reduction in oil consumption, had a
considerable influence on 1983 oil consumption. As
shown by Table 2, lagged adjustment to the real
dollar price of oil and to the exchange rates prevailing in 1980 reduced the oil consumption of the five
cou ntries an average of 13.3 percent in 1983.
Estimates for individual countries ranged from a
3.0-percent decrease in the United Kingdom to a
17.5-percent decrease in Japan. The 1983 reduction
attributed to lagged adjustment might be regarded
as the response to the oil price shock of 1979
because the 1980 real dollar price of oil largely
reflects the shock.
Changes in the exchange-rate-adjusted price of oil
between 1980 and 1983 had a smaller effect on 1983
oil consumption than did conservation. As shown by
Table 3, movements in the exchange-rate-adjusted
price of oil reduced the oil consumption of the five
countries an average of 1.6 percent in 1983.
7. Simulations were not performed for oil consumption in Canada
and Italy because the estimated models for those countries
had been rejected.
8. I nterestingly, the path that exchange-rate-adjusted prices took
for Japan between 1980 and 1983 (rising through the fourth
quarter of 1982 and then falling sharply in 1983) resulted in a
2.6-percent decrease in consumption even though the adjusted
crude price was lower in 1983 than in 1980.
Economic Review/July 1984
Estimates for individual countries ranged from a
1.8-percent increase in the United States to an
11.8-percent decrease in France· Because changes
in exchange-rate-adjusted prices reflect movements
in both real exchange rates and the real dollar price
of oil, this reduction in consumption reflects the net
effects of an appreciation of the dollar and a
decline in the real dollar price of oil.
Given that the net effects of price and exchange
rate movements have been to reduce oil consumption and that the real dollar price of oil fell 17.5
percent between 1980 and 1983, it is clear that exchange rate movements have had a strong effect in
reducing world oil consumption. In fact, as shown
by Table 4, appreciation of the dollar reduced total
oil consumption an average of 9.8 percent (1 million
barrels per day) for the four foreign countries examined. Estimates for individual countries ranged
from a 4.6-percent decrease in Japanese oil consumption to a 16.3-percent decrease in French oil
consumption. Because oil is priced in U.S. dollars,
movements in exchange rates had no estimated effect on U.S. oil consumption.
The state of the oil market
Although lags in adjustment to 1980 prices had a
dramatic effect in reducing world oil consumption,
the decline in oil consumption is not necessarily expected to continue. As shown by Table 5, 1983 oil
consumption in four of the five countries examined
was within 2 percent of the level that would eventually prevail if oil prices and GNP were to remain
at their 1983 levels indefinitely. Japanese oil consumption was within 5 percent.
Despite unfavorable movements of their exchange
rates against the dollar, France, the United
Kingdom, and West Germany reached levels of oil
consumption in 1983 that were close to the levels
consistent with 1983 GNP, exchange rates, and oil
prices over the long run as a result of their rapid adjustment to price changes. On the other hand, a
decline in the real price of oil, denominated in their
respective currencies, helped Japan and the United
States near oil consumption levels consistent with
1983 GNP, exchange rates, and oil prices over the
long run despite relatively slow rates of adjustment.
World oil market outlook
The decline in oil consumption from 1980 to 1983
can be attributed largely to a lagged adjustment to
19
Table 4
ESTIMATED EFfECT ON 1983 Oil CONSUMPTION
OF CHANGES IN EXCHANGE RATES
OCCURRING BETWEEN 1980 AND 1983
France
United
Kingdom
Japan
United
States
West
Germany
Thousands of barrels per day
Simulated 1983 oil consumption
Using fitted value from regression
Assuming exchange rate held
constant at 1980 level'
Change in oil consumption attributable
to changes in exchange rates.
1.
1,640
4,193
1,307
15,270
2,033
1,959
4,396
1,502
15,270
2,315
-319
-203
-195
0
-282
Entry equals:
n
11 ( Ct .
(J
C,'1980 )1/2
,
e I • Ej ,1980 I 'PRICE $,k (Jj
GNP
--,-'~j,1983 ( GNP.
)
/,1980
k""O
~.
I
k
.
Table 5
ESTIMATED DIFFERENCE BETWEEN 1983 Oil CONSUMPTION
AND CONSUMPTION CONSISTENT WITH 1983 GNP,
OIL PRICES, AND EXCHANGE RATES OVER. THE lONG RUN
france
Japan
United
Kingdom
United
States
West
Germany
Thousands of barrels per day
••
4
Oil consumption consistent with
1983 GNP, oil prices, and
exchange rates over the long run'
1,669
3,995
1,287
15,394
2,056
Average 1983 fitted oil consumption ..
1,640
4,193
1,307
15,270
2,033
29
-198
-20
124
23
Gap remaining'
1. Entry is the level of consumption that would eventually prevail if real gross national product, the real dollar oil
price, and the real exchanjJe rate were to remain at 1983 levels thereafter.
2. The gap is indicative of the direction in which consumption would change if GNP. oil prices, and exchange rates
were to remain at their 1983 levels.
G
20
~
Federal Reserve Bank of Dallas
the oil price shock occurring in 1979. For the countries examined, appreciation of the dollar also had a
strong effect in reducing oil consumption, more
than offsetting a downturn in the real dollar price of
oil. Because appreciation of the dollar preceded
downward movement in the price of oil, the
estimates provide some support for the notion that
appreciation of the dollar against other major currencies has reduced oil demand and added to the
downward pressure on the real dollar price of oil.
Economic Review/July 1984
It appears that world oil consumption is poised to
increase. For the economies examined, adjustment
to the sharp oil price increase of 1979 appears
nearly complete. Continuing or emerging economic
growth of these economies can be expected to
stimulate oil consumption further. Any decline in
the foreign exchange value of the dollar will reduce
crude prices faced by foreign economies and further
stimulate world oil consumption.
21
Farmers and Economic Shocks:
Ranking Texas Agricultural
Production Regions
By Hilary H. Smith*
Agriculture in Texas, as in the rest of the country, is
prone to various kinds of economic turmoil: unfavorable weather, surges of pestilence, prices
below the cost of production, and political
maneuvers either here or abroad. In Texas during
1982 and 1983, there were massive hailstorms, a
damaging drought, and inopportune freezing
temperatures. Are farmers in some parts of Texas
better able to deal with such economic disruptions
than farmers elsewhere in the state?
To answer this question, Texas is divided into production regions, and various gauges of farmers'
shock-bearing capacity are examined. The results
show the western part of Texas, where about half
the farm households are located and where about
half the agricultural cash receipts are generated, is
the most vulnerable. This area is most likely to suffer weather-related agricultural shocks and is the
least capable of coping with the agricultural income
* Hilary H. Smith is an economist at the Federal
Reserve Bank of Dallas. The views expressed are
those of the author and do not necessarily reflect
the positions of the Federal Reserve Bank of Dallas
or the Federal Reserve System.
Economic Review/July 1984
disturbances.
Shocks and the Texas agricultural economy
Agricultural shocks affect farmers, farm workers,
agribusinesses, and the general economy. This paper
examines only the relative capacity among Texas
farm households to handle agricultural income
disruptions but acknowledges that other sectors of
the agricultural and general state economy must absorb the disturbances as well. Agriculture is important to the state economy, generating around $10
billion of cash receipts and perhaps four times that
much related economic activity. Moreover, farmers
and ranchers in Texas are a powerful interest group,
so their ability to handle agricultural income disruptions is of political significance.
The relative capacity of farm households to withstand economic shocks is analyzed here by comparing farmers in different regions. Texas is divided into
10 agricultural production regions, based on crop
reporting districts as fitted to 1980 Census county
groups. Map 1 shows these regions, their 1982
agricultural cash receipts, and the number of farm
households in 1980. This article examines possible
ways for farm households to ride out agricultural income shocks. These ways include diversified income
23
Map 1
Agricultural Cash Receipts and Farm Households
in Texas Agricultural Production Regions
•
•
CASH RECEIPTS, 1982 (MILLIONS OF DOLLARS)
FARM HOUSEHOLDS WITH NONNEGATIVE INCOMES
IN ALL CATEGORIES, 1980
WESTTEXAS---------t~~_+~~~~
•
•
$3,084.3
16,540
EDWARDS ----~--+--I-~**'"'F4~~
PLATEAU
•
•
CENTRAL TEXAS
• $410.0
• 5,860
$483.2
4,700
SOUTHTEXAS------------~~+_~~
•
•
UPPER COAST
• $1,075.7
• 7,020
$443.8
2,940
LOWER RIO GRANDE
VAllEY
• $567.7
• 2,340
COASTAL BEND
• $402.6
• 2,280
SOURCES OF PRIMARY DATA: Texas Department of Agriculture and U.S. Department of Agriculture.
U.S. Bureau of the Census
sources, savings and investments, temporary or
alternative employment, and, to some extent,
government programs.
Farmers are not able to take advantage of all the
means available to cushion income disruptions.
Most agricultural production is by farmers working
full-time. These farmers are considered to be small
businessmen. It is assumed that the full-time farmer
is sufficiently engaged on the farm or ranch to
preclude taking a job elsewhere. The part-time
farmer or rancher is more diversified and, consequently, better able to withstand an agricultural income disturbance. To the extent that a region is
composed of part-time farmers, it is more insulated
24
from agricultural shocks. But for full-time farmers,
temporary wage employment is not an alternative.
Agricultural emergency programs of the Federal
Government do help farmers ride out economic
disruptions, but most of the aid is available as loans
to farmers who cannot qualify for commercial
credit. Many farmers do not seek emergency government loans, as shown by Texas farmers' slight interest in Farmers Home Administration loans during
last year's drought. In addition, agricultural programs are relatively uniform across the state, so
regional differences because of government programs are small. In the event of an agricultural income disturbance, farmers frequently cannot rely on
Federal ReseA'e Bank of Dallas
the option of outside employment nor on government agricultural emergency programs.
proportional agricultural income shocks, although
some wealth comparisons are made.
Absolute versus proportional shocks
The data
The capacity to withstand a disruption in agricultural income depends on a farm household's income
diversification, its level of income, and, in some
cases, diversification of agricultural enterprises. The
last category is assumed to be of minimal importance because the disturbances are assumed to be
agriculture-wide. Drought, for example, affects most
farm and ranch enterprises. As long as the income
flows are not perfectly correlated, income diversification can hel p. 1
Shocks to agricultural income are generally of
two types: proportional and absolute. If cotton
farmers in West Texas suffer a 10-percent yield loss
because of drought, that is a proportional loss. An
example might be two farmers who suffer a
10-percent yield loss in their cotton crops, one with
200 acres and the other with 2,000 acres. Each has
suffered the same proportional loss of cotton income. An example of absolute loss would be spotty
hail or insect damage. Considering the same pair of
farmers, suppose that the first suffered a $10,000
"hail-out" while the second had $10,000 of weevil
damage. I n this case, the amount of loss is the
same, but the loss as a percentage of gross income
from farming is very different.
For a discussion of proportional versus absolute
losses, see the accompanying box. In the event of a
proportional loss, the farm household with more
diversified income sources is better off than a
household with income largely from farming. For
absolute losses, the wealthier the household, the
better.
Most agricultural income losses are probably a
mixture of both, with the proportional type predominating. This article primarily examines the
capacity of farmers in different regions to withstand
Household income data for analyzing the Texas
regional differences in capacity to withstand
agricultural income shocks were provided by the
Public-Use Microdata Sample of the 1980 Census of
Population and Housing 2 The income of all
members of the household was summed by the
seven income categories provided by the Census
data. The income categories are wages and salaries;
nonfarm self-employment; farm self-employment;
interest, dividends, royalties, and rental payments
(hereafter referred to as interest and dividends);
social security; public assistance; and all other income. These income sources were also calculated
as percentages of total household income.
The original subset sample totaled 4,132
households. Two sets of deletions were made. One
set was households without any income in any
category, of which there were 42. These households
could not be affected by income losses. Another
619 households were deleted because they had
negative income in at least one category. This second set of deletions was necessary to rule out "tax
farmers" - individuals showing losses from farming
year after year- and to exclude individuals having a
single bad year. Every year, some genuine farmers
lose money, but the composition of this group
constantly changes. 3
1. The risk to a household's total income can be reduced if the
income flows do not have a correlation coefficient of 1. Differences in the income categories make it clear that such
perfect correlation does not occur here. Social security
payments, interest and dividends, nonfarm wages and salaries,
and farm self-employment income should not show high
positive correlation; for example, a large drop-off in farm income will not be associated with a large fall in social security
income.
Economic Review/July 1984
2. Of the three available Census samples, the 5-percent A Sample
was used. On the basis of the county groups identified in that
sample, Texas was divided into production regions. The
5-percent sample was subset by selecting only households that
contained at least one member with farmer occupation code
473, "farmers, except horticultural" The income reported in
the 1980 Census was earned in 1979, one of the best years
overall for farm income in Texas. Because all regions did not
report record cash receipts that year, using 1979 created some
bias. For example, West Texas had a greater share of Texas
agricultural receipts in 1979. Thus, income comparisons will be
more dramatic than if other years were used. Overall though,
every region had a good year, so the use of 1979 income data
should not distort the results substantially.
3. There were methodological reasons for exclusion. Including
negative income categories naturally reduces total household
income, so calculating category income as a percentage of
total household income can result in shares over 100 percent
or shares that are negative. To make the sample more typical,
households with negative income categories were dropped.
25
The model
The purpose of this article is to determine whether
regionai differences exist in the capacity of
households to withstand agricultural disruptions, not
to investigate the causes of those differences. To
quantify the differences, regression analysis could
be used or a very simple linear one-factor analysis
of variance (ANOYA) model. Regression analysis
would employ the dependent variable regressed on
a constant and a series of dummy variables
representing the regions. Analysis of variance is, in a
sense, a subset of regression analysis but is formulated specifically to deal with the types of comparisons undertaken here, making the ease of use
considerable.
Results equivalent to those from using regression
were obtained from a simple ANOYA model:
household income, or mean household income, then
the regions are equally capable of withstanding a
disturbance in their agricultural income. The null
hypothesis was
(4)
against the alternative that the regional means are
not all equal. Recalling equation 2, this is equivalent to testing whether the individual regional
effects (Tj) are significantly different from region to
region.
Further comparisons were made between individual regional means using t tests. s The mean of
every region was compared with the mean of each
of the other regions. Such null hypotheses were
(5)
Ho: /Aj = /Ak
for all j i:-k,
j,k = 1,10,
(1 )
against the alternative sets of hypotheses that the
where y is the observation. The different observation classes used were income category, income
category as a percentage of household income, and
household income. In this model the observation is
composed of three parts: the Texas mean, or grand
mean, for the variable is /A, the "treatment effect"
stemming from regional differences from the grand
mean is T, and the random component is E. Because
the treatment effects are modeled as deviations
from the grand mean, they sum to zero. The subscript i is the observation, while the subscript j is
the region.
Regional means (/Aj) are the sum of the grand
mean and the treatment effects:
(2)
Substituting equation 2 into equation 1 gives the
estimating equation:
(3)
Yariation within a region is caused by all the
unexamined factors that make up the random error
term. Yariation between regions is the result of any
differences in regional means and random variation.
The first hypothesis tested, using an F statistic,
was whether there are any differences between
regional means. 4 If there are no differences in mean
income levels by category, category shares of
26
4. An F statistic can be constructed from a ratio of the mean
square of the variation between regions (which includes the
variation from random sources and variation from different
treatment effects) to the mean square of variation within all
the regions (which results from only the random error term):
10
:L:
nj(l'-j -
1'-)2
j=l
10
nj
:L: :L:
j=l
(Yjj -
1'-/
j=l
where the summation j equals 1 to 10 is over regions and the
summation i equals 1 to nj is over observations. The number 0
observations in the jth region is njThe value of the F statistic depends on the variation from
differences in regional means or treatment effects. If the
regional means are the same, the F statistic will be close to 1,
and the null hypothesis cannot be rejected. Conversely, if the
treatment effects are significantly different, the between-region
variation will be larger than the purely random within-region
variation, and the F statistic will be significantly larger than 1.
5. A t statistic can be calculated to test whether the difference is
significant:
abs(l'-j RMSE(1/nj
1'-;)
+
1/n;)V,
where abs is the absolute value, RMSE is the root mean square
error, and nj and n; are the number of observations for regions
i and I.
Federal Reserve Bank of Dallas
means are different from each other. 6
Finally, use of the F and t tests through the
ANOYA technique required that the random error
terms be normally distributed and the variances in
each region be the same. The samples tested out to
be normally distributed, and the variances were
shown to be similar for most comparisons. 7
Texas farm households
Wealth and diversification are two criteria for
measuring the capacity of Texas farm households to
withstand agricultural shocks. Table 1 shows that
there is considerable dispersion in terms of categorical income distribution, as well as shares of
total household income, and in the variance in aggregate household income. For example, farm selfemployment income ranges from an average high of
$16,732 in West Texas to a low of $7,995 in East
Texas. On average, Texas farm households earn less
than half, 47 percent, of their money from farming.
This proportion varies widely by region, from a high
of 58 percent in West Texas to a low of 38 percent
6. These simple t tests are not as reliable as in regression
analysis. Given that there are 10 regions, to test each region
against all the other regions involves 45 comparisons. In single
t tests, as in a regression, the possibility of error is not of as
much concern. For example, at the 95-percent significance
level, the t test will reject a false null hypothesis 95 times out
of 100. For a single t test, those are good odds that the t test is
indicating significance of the null hypothesis correctly. If
multiple t tests are conducted, as between all pairs of regions,
then the probability that the t test will reject the true null
hypothesis-a Type I error- increases as the number of t tests
conducted goes up. For the 45 comparisons the probability of
making at least one Type I error is close to .90.
7. All the samples were normally distributed according to a
modified version of the Kolmogorov-Smirnov 0 statistic, which
was calculated for each income and percentage category for
each region. See M. A. Stephens, "Use of the KolmogorovSmirnov, Cramer-Von Mises and Related Statistics Without Extensive Tables," Journal of the Royal Statistical Society, set. B,
32, no. 1 (1970): 115-22.
The regional variances for each major income category were
tested against the variance of the entire Texas sample. It was
found that 55 percent of the sample statistics could not be
rejected and over 71 percent were within 110 percent of the
critical value. Most of the statistics that did reject the null
hypothesis were from the mean income categories, rather than
the more critical categories based on the percentage of total
income. Although compliance with the equal variance requirement was not strict, the bias that is introduced should not do
much violence to the aggregated results.
Economic Review/July 1984
in East Texas and the Blacklands. Total household
income varies from a low of $20,013 in Central
Texas to almost 50 percent more in the Coastal
Bend. Overall, if all Texas farm households had the
same income distribution as did the average Texas
farm household in Table 1, the impact of agricultural income stream disruptions would be far less.
The categories accounting for three-quarters or
more of the farm household incomes are farm selfemployment, wages and salaries, interest and
dividends, and social security payments. The
analysis presented here concentrates on these four
categories and ignores the minor categories of nonfarm self-employment, public assistance, and "other
income."
Are the magnitudes of income and categorical
percentages statistically significant for the regions?
If so, how were these regions ranked? Following the
ANOY A test proced ures, both F tests and t tests
were performed for each of the major income
categories. Presentation of the F-test resu Its is
straightforward because an F-statistic value shows
for a particular income category whether regional
means as a group differ from the mean for Texas.
The t test elaborates on the group results shown
by the F test. 8 For the fou r income categories, t tests
were performed between all possible regional pairings within each category: every region was tested
against nine others. The results of these t tests were
then aggregated and linked. For example, if one
region proved to have a significantly greater mean
in one income category than all the other regions, it
was given a score of + 9. Similarly, if one regiop
had a significantly lower mean than five other
regions, it would score a - 5. For a particular income category, therefore, a region might score
+ 2/3/- 4, which means that it had a higher mean
than two other regions, was not statistically different from three regions, and had a lower mean
than four others.
Taking the diversification issue first, it is contended above that a diversified region is better able
to deal with proportional agricultural income shocks
8. The t test, in a strict sense, does not need to be consistent with
the F test That is, even though the F test may reject the
hypothesis that the regional means as a group are different
from the state mean, the t test can still pick out significant differences between individual regional means.
27
Table 1
FARM HOUSEHOLD INCOME IN TEXAS, BY CATEGORY
(Averages, based on 1979 data)
Wages
and
Public
aSSIS-
Nonfarm
Farm
Interest
and
dividends
Social
salaries
security
tance
Other
income
TOTAL
INCOME
West Texas.
Percent of total
$4,153
16.3
$1,347
4.1
$16,732
57.9
$3.734
11.3
$1,004
7.3
$ 49
.6
$ 534
2.5
$27,554
Rolling Plains
Percent of total
4,948
17.0
909
3.5
14,417
52.7
3,362
11.2
1,427
12.0
46
.7
873
3.0
25,982
Blacklands .
Percent of total
4,876
19.9
1,077
4.0
8,838
37.9
3,133
12.5
1,793
17.4
145
2.0
1,183
6.5
21,046
East Texas
Percent of total
5,372
21.6
1,221
4.8
7,995
38.1
2,927
11.5
1,673
16.6
122
2.3
1,096
5.1
20,407
Upper Coast.
Percent of total
5,213
19.8
1,626
4.5
11,885
44.9
4,549
14.5
1,311
13.1
110
.7
534
2.5
25,229
Central Texas.
Percent of total
4,835
17.8
1,101
3.0
8,080
39.8
2,976
14.1
1,807
18.9
120
1.2
1,094
5.2
20,013
Coastal Bend.
Percent of total
4,686
17.7
1,207
2.8
14,844
48.8
6,136
15.7
1,808
12.8
88
.4
331
1.9
29,101
Lower Rio Grande Valley.
Percent of total
4,594
24.3
1,078
3.0
8,688
38.8
5,118
11.1
1,486
13.3
393
5.8
594
3.7
21,951
South Texas.
Percent of total
4,428
19.3
1,364
3.6
9,247
45.9
4,518
16.2
1,039
8.2
88
.9
1,373
5.8
22,056
Edwards Plateau.
Percent of total
3,376
14.8
1,906
4.4
11,760
52.1
2,769
10.7
1,429
14.3
95
1.5
521
2.2
22,257
$4,698
18.4
$1,268
3.9
$11,984
47.2
$3,609
12.3
$1,428
13.0
$101
1.3
$819
3.8
$23,908
Agricultural
production region
TEXAS
Percent of total
SOURCE OF PRIMARY DATA:
28
u.s.
Self-employment
Bureau of the Census.
Federal Reserve Bank of Dallas
Table 2
SIGNIFICANCE TESTS OF SHARES OF FARM HOUSEHOLD
INCOME IN TEXAS REGIONS, BY MAJOR CATEGORIES
Agricultural
production region
West Texas.
Rolling Plains.
Blacklands .
East Texas
Upper Coast.
Central Texas.
Coastal Bend.
Lower Rio Grande Valley ..
South Texas .
Edwards Plateau.
F-test statistic
Wages
and
salaries
Farm
selfemployment
Interest
and
dividends
Social
security
0/5/-4
0/7/- 2
+ 2/7/0
+ 3/6/0
+ 2/7/0
0/8/-1
0/9/0
+4/5/0
0/9/0
0/5/-4
+9/0/0
+6/2/-1
0/3/-6
0/3/-6
+2/4/-3
0/5/-4
+4/4/-1
0/5/-4
+2/5/-2
+5/3/-1
0/5/-4
0/6/- 3
0/9/0
0/7/- 2
+4/5/0
+ 1/8/0
+ 3/6/0
0/9/0
+4/5/0
0/6/- 3
011/-8
+1/5/-3
+4/5/0
+4/5/0
+2/4/-3
+ 7/2/0
+1/7/-1
+1/7/-1
0/4/- 5
+ 2/6/-1
2.70
19.17
2.21
11.92
NOTE: The income share, by category, for each region was tested against each of the income shares of the
other nine regions by individual t tests within the same category. The results of the nine tests are
grouped as to whether the region's categorical income share is statistically larger (the first number
in each entry), no different (the second entry number), or statistically smaller (the last number in
each entry) than the same categorical income shares in other regions.
than are regions with incomes more concentrated in
farming or ranching. Table 2 gives the F-test statistic
for each major income category as a percentage of
total household income.
Several things are of interest in Table 2. First, all
the F statistics are significant at the 10-percent
level. Also, the regions with farm household incomes concentrated in farming are easy to spot. The
t-test scores indicate that West Texas farm households earn a larger portion of income from farming,
in a statistical significance sense, than do households of any other Texas region, In the areas of
diversification out of farming-wages and salaries,
interest and dividends, and social security
payments-scores are markedly lower for West
Texas than for many other regions. Other regions
with heavy farm income concentrations are the
Rolling Plains and the Edwards Plateau, with scores
of + 6 and + 5, respectively, under farm selfemployment income. Both regions are at a disadvantage in most of the diversification categories.
Regions having the lowest percentage of their
household income in farm self-employment are
Economic Review/July 1984
those that obviously earn more income in the nonfarming categories and are more diversified. Table 2
reveals that the Blacklands and East Texas farm
households have noticeably lower mean percentages of income from farming. Both regions have
similar distribution patterns in the other income
categories- no noticeable diversification in interest
and dividends but strength in social security
payments and some strength in wages and salaries,
Central Texas and the Lower Rio Grande Valley also
show signs of diversification, with low percentages
of income from farming and some significant mean
percentages in social security and wages and
salaries. The remaining regions show a mixture of
concentration and diversification.
Overall, Table 2 contains the following information. West Texas, the Rolling Plains, and the
Edwards Plateau are the least diversified regions,
while the Blacklands and East Texas head the list
of the most diversified. This diversification is the
household income attribute that largely cushions
the impact of proportional agricultural income
disruptions.
29
Table 3
SIGNIFICANCE TESTS OF MEANS OF SELECTED
FARM HOUSEHOLD INCOME IN TEXAS REGIONS
Agricultural
production region
West Texas
Roll ing Plains
Blacklands .
East Texas
Upper Coast.
Central Texas
Coastal Bend
Lower Rio Grande Valley
South Texas.
Edwards Plateau
F-test statistic
Major categories
Interest
and
dividends
Wages
and
salaries
Farm
selfemployment
0/8/-1
0/9/0
0/9/0
+2/7/0
0/9/0
0/9/0
0/9/0
0/9/0
0/9/0
0/8/-1
+8/1/0
+7/1/-1
0/4/- 5
0/4/-5
+4/3/- 2
0/4/- 5
+5/4/0
0/5/-4
0/6/-3
+3/4/- 2
1.03
19.98
security
Social
Total
income
0/8/-1
0/8/-1
0/7/- 2
0/6/-3
+3/6/0
0/8/-1
+6/3/0
+2/7/0
0/9/0
0/6/-3
0/1/-8
+1/6/- 2
+5/4/0
+3/6/0
+1/4/-4
+4/5/0
+3/6/0
+1/8/0
0/5/-4
+1/7/-1
+6/3/0
+4/5/0
0/5/-4
0/5/-4
+3/6/0
0/5/-4
+6/3/0
0/7/-2
0/7/-2
0/6/-3
2.17
7.20
7.58
NOH: The mean of income, by category, for each region was tested against each of the means of the other nine regions by
individual t tests for the same category The results of the nine tests are grouped as to whether the region's mean is
statistically larger (the first number in each entry), no different (the second entry number), or statistically smaller (the
last number in each entry) than the means for the other regions
Farm household wealth
To complement the examination of income
categories as a percentage of total household income, farm household wealth was considered. The
Census does not offer data on household wealth
other than the value of the house. However, the
means of income categories were compared across
regions, instead of comparing the categorical
average shares of household income. Many farmers
are land-rich and cash-poor. I n that sense, the
proper wealth measure of capacity to withstand
agricultural income disruptions may be levels of income and returns from assets rather than equity in
land and machinery. Equity-backed loans might be
an alternative, but with agricultural income disturbances often creating cash-flow difficulties for
farmers and ranchers, loans on equity would only
threaten longer-term viability.
Table 1 reveals considerable differences in mean
total household income and in the various components of income. If total household income is used
as a proxy for wealth, the Coastal Bend is the
30
wealthiest region while Central Texas is the least
well off. The interest and dividends column shows a
wide variation in income, from a low in the Edwards
Plateau to a high in the Coastal Bend region. Similar
differences exist in other categories.
To examine whether the means of the income
categories are significantly different from one
another on a regional basis, ANOY A resu Its are
presented in Table 3 in a manner identical with
Table 2. Only the regional mean incomes from
wages and salaries do not seem different from the
Texas average, as judged by an insignificant F
statistic at the 90-percent level. All the other income categories have regional means significantly
different from the respective statewide means. In
withstanding an absolute agricultural income disturbance, it is household wealth or a proxy, household
income, that is important. It is also likely that interest and dividends income is proportional to some
measures of wealth, so such income can be considered a proxy as well.
Comparing the means of household income in
Table 3, the West Texas, Coastal Bend, and Rolling
Federal Reserve Bank of Dallas
Proportional and Absolute
Losses to Farm Income
Particular assumptions about farmers' utility functions
are necessary in order to use the proportional-absolute
loss analysis. It is assumed that all farmers and
ranchers have the same utility function. Further, so
that farmers react to proportional losses the same
way, it is assumed that this utility function has
decreasing marginal utility of income and a constant
elasticity of utility with respect to income, with a
value between zero and 1. The simple utility function
was
of both sides; then the derivative of log of util ity with
respect to log of income was taken:
(4)
v
aIn(Y)
.
For expositional purposes, the elasticity of utility can
be expressed as
(5)
I1U!U
a=--,
11 Y/Y
(1 )
where U, Y, and a are utility, income, and the elasticity
of utility, respectively.
Taking the first derivative gives the marginal utility
of income,
au
(2)
= a*y(a -
(6)
ay
a2 u
ay2
'=
a*(a _ 1)*y(a -
2)
and a is assumed to be between zero and 1, marginal
utility is decreasing as income is increasing.
Utility functions with constant elasticity of utility,
coupled with decreasing marginal utility of income,
give farmers the same loss of util ity for a proportional
loss of income. To derive the elasticity of utility (E )
from the above utility function, logarithms were takeYn
Economic ReviewlJuly 1984
where I1U and 11 Yare discrete changes in util ity and income, respectively. Rearranging terms shows that the
change in utility depends on the elasticity of utility,
total utility, and the percentage change in income:
I1U = a*U*(I1YjY)
1)
For marginal utility to be decreasing, its derivative
must be negative. Because
(3)
aln(U)
E=---=a
Assuming that total utility and elasticity of utility
are the same for all farmers, then for a given proportional income change, farmers' utility will change by
the same amount. The implication is that a farmer with
losses of $7,000 and total income of $70,000 suffers
the same reduction in utility as does the farmer with
losses of $70,000 and income of $700,000. Proportional losses hurt the same.
By the same token, absolute losses give the above
farmers different values of (I1YjY) and different reductions in utility. For example, losses of $10,000 result in
a utility loss 10 times greater for the farmer with the
smaller income. For absolute losses, the wealthier the
farm household, the better.
31
Map 2
Income Diversification Within Farm Households
Across Texas Agricultural Production Regions
II FARM HOUSEHOLDS WITH DIVERSIFIED INCOMES
FARM HOUSEHOLDS WITH INCOMES CONCENTRATED IN FARMING
OlUNG PLAINS
CENTRAL TEXAS
EDWARDS
PLATEAU
UPPER COAST
lOWER
RIO
GRANDE
VAllEY
Plains regions do have higher household incomes
than many other regions, and these differences are
statistically significant. But West Texas and the
Rolling Plains have the largest absolute incomes
from farming and, hence, are in a position to lose
the most in the event of a proportional agricultural
shock. The Coastal Bend, with a + 6 score, is the
only region that stands out in the comparison of
mean levels of interest and dividends, suggesting
that this region's household portfolio of incomeproducing assets is measurably larger than for most
other regions.
Table 3 also reveals which regions have significantly smaller mean household incomes. Central
Texas and South Texas have lower mean household
incomes, as do the Blacklands and East Texas.
32
COASTAL BEND
These four also have lower incomes from farming.
The less diversified regions show only modestly
lower means in the nonfarm-originating income
categories.
Interestingly, the most diversified regions (the
Blacklands and East Texas) seem to be the least
able to withstand an absolute income disturbance
because the households in those regions are less
wealthy than in other regions in Texas. The more
concentrated regions (West Texas and the Rolling
Plains) are wealthier than the more diversified
regions and, thus, are better equipped to handle an
absolute agricultural income disruption.
Risk and shock-bearing capacity
From the above analysis, it is clear that West Texas,
Federal Reserve Bank of Dallas
the Rolling Plains, and the Edwards Plateau are the
regions most dependent on farm income, while the
Blacklands and East Texas are the most diversified
regions. I n terms of withstanding proportional
shocks to agricultural income streams, the areas
that are most dependent on farm income are the
least able to cope. For less likely absolute income
shocks, the tables are turned. Farming-dependent
households generally earn more income, implying
that their wealth - and therefore their capacity to
withstand an absolute shock-is larger. The other
regions in the state (Central Texas, the Coastal Bend,
the Valley, the Upper Coast, and South Texas) lie
somewhere between the two groups above in their
ability to withstand the different kinds of shocks.
Map 2 shows the different regions and their capacity to withstand proportional shocks.
Given the relative capacity to withstand
agricultural income disruptions, which regions are
more I ikely to experience a disturbance in their
farm income streams? Weather events, unlike many
price effects or policy actions, are one type of
shock that is often both specific to regions individually and important to all types of agriculture.
Texas is a borderline state in terms of climate.
The eastern part of the state receives agriculturally
bountiful amounts of rainfall-over 40 inches a
year-while the western part is bone-dry much of
the time, with as little as 8 inches of yearly rainfall.
West of a line through Fort Worth, Waco, Austin,
and Corpus Christi lies the portion of Texas that gets
less than 30 inches of rain a year. Drought becomes
a frequent, if not regular, event in these drier parts
of Texas. For example, based on the Palmer Drought
Severity Index, the Edwards Plateau experienced at
least moderate drought in about half of the past 50
years .. Moreover, West Texas, the Rolling Plains,
and the Edwards Plateau are prone to hailstorms,
early frosts, and other weather calamities.
The results so far present one conundrum: farmers
and ranchers in the western half of Texas apparently
choose to work in an area with large weatherrelated risks and yet are less able to deal with proportional agricultural income disturbances. But
farmers may have several choices in dealing with
the risks imposed on West Texas agriculture. The
9. The Palmer data were provided by Meteorologist George
Bomar of the Texas Department of Water Resources.
Economic Review/July 1984
risks can be reduced through risk spreading or
neutralizing the risk or can be compensated with
risk premiums.
Risk spreading occurs, probably implicitly, when a
household pools the income from all its members,
then allocates shares. If farming is the riskiest of all
the income categories, risk spreading reduces the
risks faced by the farmers in the household. Wages
and salaries of household members are generally
the largest source of nonfarm income for such farm
households. But the western half of the state is
sparsely settled, and density of nonfarm economic
activity is much lower than in the rest of the state.
This lower density of economic commerce
precludes extensive off-farm employment of
household members, making the household more
dependent on agriculturally based income.
If risk spreading is not viable, the farmer has
strategies for effectively neutralizing the risk. Knowing that drought, for instance, is a long-run fact of
life has most likely spurred successful farmers and
ranchers to formulate agricultural management
plans that take into account periodic dry-outs. For
example, rain-fed agriculture may use standby irrigation to augment rainfall during abnormally dry
periods.
If the farmers are risk-averse, as assumed
throughout this analysis,'° then economic theory
would suggest that in order to bear increased risk,
farmers must receive a premium over what farmers
receive in less risky regions. Such premiums may be
higher returns to agricultural endeavors, or they
may be nonpecuniary rewards. Examination of cash
receipts and farm household self-employment income shows that the western half of Texas earns
more gross farm income," and part of this return
could be considered a risk premium.
If the risks were not compensated by additional
returns from farming, the farmers and ranchers
might be expected to have an incentive to move
elsewhere. Migration theory predicts that as long as
the discounted benefits from moving outweigh the
10. In the discussion of proportional losses, the box covers the
assumption of declining marginal utility of income. Declining marginal utility of income also implies risk aversion.
11. Net farm income is a preferable measure to cash receipts
but is not available on a regional basis. Both net farm income and cash receipts are proxies for return on investment.
33
discounted costs, there is an incentive to migrate."
The obvious costs to a farmer in moving to a less
risky region are that much farm and ranch capital
may be suitable only for particular operations and a
farmer's skills and knowledge are likely the products
of several generations of special ized experience.
These are not traded I ightly for the uncertainties of
a different way of farming, and the costs may be so
high that there are no short-run methods of financially surviving the sure losses that initially result
from a change in environment.
Recent developments in migration theory also
suggest that migration takes place as a result of
fixed-place amenities or compensating differentials.
Incomes and rents adjust to keep utility constant
over geographic areas. 13 These compensating differentials may include intangible aspects that
reward farmers for staying in higher-risk farming
12. See Larry A. Sjaastad, "The Costs and Returns of Human
Migration," Journal of Political Economy 70 (October 1962,
pt. 2): 80-93.
13. See Philip E. Graves, "Migration with a Composite Amenity:
The Role of Rents," Journal of Regional Science 23
(November 1983): 541-46.
34
areas. Farmers generally consider farming and
ranching to be a way of life, one in which the land,
tradition, and the part of the country they live in
play important roles in their utility functions.
Conclusions
The major finding of this article is that different
regions of Texas are not uniformly able, on average,
to deal with shocks to agricultural income. For example, while farmers in the Lower Rio Grande
Valley did suffer large losses in the Christmas
freeze, the average farm household there receives
only 39 percent of its income from farming and 24
percent from wages and salaries. I n contrast, the
drought in West Texas over the last two years has
hit households that are very concentrated in farming, which accounts for 58 percent of total
household income there.
Farmers in the western portions of the state face
a riskier environment. Because the risks cannot be
spread, farmers are likely to be compensated with
higher monetary returns and nonpecuniary differential compensation factors-largely the intangible
aspects of farm life that are very difficult to quantify but are highly important in a household utility
function.
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