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Economic Review
Federal Reserve Bank of Dallas
November 1986
1
Distributional Implications
of Reducing Interstate
Energy Price Differences
Ronald H. Schmidt and Jeffery W. Gunther
Energy price deregulation has reduced regional disparities in
residential energy expenditures. Simulation results from a
state-level model of the United States suggest that additional energy price deregulation, such as the deregulation
of bulk electric power, would further reduce differences in
average per capita expenditures across states. Consumers in
the Northeast would have the largest decreases in expenditures, while consumers in the Northwest could see some
increases.
17
Understanding the Texas
Unemployment Rate
William C. Gruben and Keith R. Phillips
The strongest overall influence on fluctuations in the Texas
unemployment rate is the U.S. business cycle. The state's
unemployment rate is also significantly affected by the
Mexican business cycle and by cyclical fluctuations peculiar
to Texas. Two additional factors, however, mean that the
state's unemployment rate can rise even when overall economic activity is expanding. First, permanent shifts in the
relative demands for labor among economic sectors can
lead to temporary increases in the unemployment rate during business cycle upswings. Second, when shifts in the
state's industrial structure lead to rising employment
volatility, the result on average is a higher unemployment
rate.
31
Announcement of New Index
This publication was digitized and made available by the Federal Reserve Bank of Dallas' Historical Library (FedHistory@dal.frb.org)
Distributional Implications
of Reducing Interstate
Energy Price Differences
Ronald H. Schmidt
Jeffery W. Gunther
Senior Economist
Economic Analyst
Federal Reserve Bank of Dallas
Federal Reserve Bank of Dallas
Since the 1973-74 oil embargo, the distributional effect of
energy price shocks has been uneven across regions of the
United States. BecaUse of differences in the mix and quantity of fuels demanded by residential consumers in different
states, the ability to adjust behavior to changing energy
market conditions has been unequal. Furthermore, regional
disparities have been exacerbated by the existence of federal and state regulations that have tended to keep fuel
costs from equalizing across regions.
With the deregulation of oil in the late 1970s and the deregulation of most natural gas accomplished by 1985, some
of the regulatory barriers to fuel price equalization have
been removed. These events, in addition to increasing interest in deregulation of bulk electric power transmission,
suggest that large disparities in regional fuel prices may be
reduced in the foreseeable future-at least to the extent that
regulated prices have prevented competition. States that
presently face high fuel prices due to restricted market entry
are likely to have falling energy prices under deregulation
as a result of increased competition. On the other hand,
states that have had low prices may have higher prices as
energy producers in those areas widen their product
markets.
The purpose of this study is to simulate the effects that
narrowing natural gas and electricity regional price differentials would have on the distribution of residential energy
expenditures across states. 1 Using a model of state residential energy consumption for electricity, natural gas, and
petroleum, scenarios are developed to examine the response of residential energy consumption to a reduction in
regional price differences attributable to deregulation. 2
In general, the results indicate that deregulation of natural
gas will benefit eastern and northwestern consumers at the
expense of consumers in the middle and western parts of
the country. If bulk electric power is also deregulated, the
gains to the East Coast rise, but the benefits of natural gas
deregulation for the Northwest are greatly outweighed by
rising electricity prices.
Economic Review - November 1986
Expenditure and price trends
The extent of past regional expenditure variations can be
seen in Chart 1. Real per capita expenditures for 1970
Chart 1
Real Per Capita Residential Energy Expenditures
1985 DOLLARS
600~------------------------------------------------,
500
400
.- ..---.- ..;~~~~~~-;~~~~:
-------_........,,"
........ - '-. _.........
...............
300
.........................................
______ -------
,,"'SOUTH
~~,~~~--.-.-.-.-.~.~.
___ -------_.-._VVEST
..,.,...--------_._.
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
SOURCES OF PRIMARY DATA:
u.s.
Department of Commerce.
U.S. Department of Energy.
through 1982 are shown for four major regions of the continental United States. 3 Not surprisingly, the Northeast
consistently had the highest expenditures, while the West
and South had the lowest. All the regions had growing real
energy expenditures over the period.
The Northeast also faced the largest rise in energy costs
following the 1973-74 oil embargo and the 1979-80 oil price
increase. As seen in Chart 1, per capita energy expenditures
changed relatively little in the other regions, but costs
jumped rapidly in the Northeast.
The greater responsiveness of expenditures in the Northeast to changes in oil prices is largely a result of differences
between this region and others in the type of fuel consumed. The Northeast has relied less heavily on electricity
than have the other regions, as exhibited in Chart 2, and has
relied more heavily on oil and natural gas. This greater reliance on oil and gas has made that region more susceptible
to changes in oil and gas prices. Although the Northeast has
been reducing its reliance on natural gas and oil by switching to electricity, it still has the smallest share of electricity
among the regions. Furthermore, most of the electricity in
the Northeast is generated by oil and nuclear fuels, linking
electricity costs more closely to oil prices. The North Central region, in contrast, has relied heavily on electricity gen2
erated by burning coal, which is less closely tied to oil price
movements.
These differences in fuel expenditures and fuel shares are
the result of several factors. Geographical factors have been
important in the fuel selection process. Access to coal in
the North Central region, oil in the South, and abundant
hydroelectric power in the West encouraged use of those
resources in the respective regions. The Northeast made
use of oil because of the relatively low cost of transporting
that fuel.
DemographiCS also have played a role. Newer structures,
most of which are in the South and West, have relied more
on electricity and natural gas (instead of heating oil) for
heating and cooling. Furthermore, expansion of airconditioning powered by electricity has occurred more
rapidly in the warmer regions of the country.
The role of regulation
An additional factor important in determining differences in
fuel expenditures and fuel shares across regions has been
energy price regulation. This is particularly true in the case
of natural gas, the regulation of which led to distortions in
the price and availability of the fuel. Before the Natural Gas
Policy Act of 1978 (NGPA), only gas sold through interstate
pipelines was assigned a regulated price. The NGPA did not
Federal Reserve Bank of Dallas
Chart 2
Share of Residential Energy Consumption
Provided by Electricity
PERCENT
50 ~----------------------------------------------~_~--~-.
.,...".-;,.".,.,
40
,,___________-,-,'"
."...,..""..".""."",
"
" "
././././
----_ .
-.~.-.-.-----.-.-.~
20
_._.-.-'
/
" "
" ,"
30
-'
.".------ /
SOUTH
/
'- .-'-
..",.
•.,-"'WEST
............................~.~.~~.~ ..~.~.~.!.~~......................
..........................:::::::-
NORTHEAST
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
SOURCE OF PRIMARY DATA: U.S. Department of Energy.
immediately deregulate prices but, instead, retained an
elaborate schedule of wellhead prices that were based on
the age of the field, depth of well, and whether the natural
gas was committed to interstate or intrastate sales before
1978. Consequently, prices in different states depended on
the relative proportions of low-cost and high-cost gas that
gas distributors in the states had under contract.
The effects of regulation in this regard have also been important for the electricity market. Regulations by public
utility commissions and the Federal Energy Regulatory
Commission determine rates of return for utilities and govern the sale of power outside the immediate region of utilities. Public utility commissions also limit construction of
new power generation facilities.
Since 1978, energy prices increasingly have been deregulated. Oil price controls were phased out between 1978 and
1981. In 1985, most natural gas was released from price
controls, and attempts to release the remaining categories
of controlled gas from regulation have continued. Recently,
interest has emerged in deregulating portions of the electric
utility industry.4 Rulings favoring the sale of cogenerated
power and developing capacity in long-distance transmission have raised the possibility that electricity will be
Economic Review - November 1986
sold with market-determined prices, at least in regional
markets and perhaps in a national power grid.
Deregulation of energy prices is likely to have distributional consequences on residential energy expenditures
across states. Insofar as current price differences reflect
regulatory inefficiencies that have kept prices (adjusted for
transportation costs) from equalizing across regions, deregulation should affect the regional distribution of energy
prices and expenditures by reducing regional energy price
differentials.
Modeling residential energy consumption
To explore the effect of changes in energy prices on residential consumers in different states, it is first necessary to
model the relationship between energy prices and residential energy consumption. In this section, equations relating
consumption of electricity, natural gas, and petroleum to
energy prices and other variables are derived. Later sections
describe the estimation of these equations and use the estimates to model the effect of energy price deregulation.
A large body of empirical and theoretical literature exists
to explain the determinants of residential energy consumption. Because of the difficulty and expense in changing
energy-using capital equipment at the residential level,
3
consumption usually is best modeled as a lagged adjustment process. A typical consumer is expected to react
slowly to changes in energy prices, causing long-run
elasticities to be considerably larger than short-run
elasticities.
Specification of the consumption equations for residential
consumers is derived from previous research. s The consumption equations for electricity, natural gas, and petroleum are expressed as follows:
(1.1)
ELECjt
= a10 + a11ELECjt_1 + a12PRELECjt
+ a13PRGASjt + a14 HEA ~t + a1S COOLjt
+ BEjt'
NGASjt = a20 + a21NGASjt_1 + a22PRGASjt
+ a23PRELECjt + a24 HEA Tjt + BCjt,
PETRjt = a30 + a31PETRjt-1 + a32PRPETRjt
+ a33PRGASjt + a34 HEA Tjt + Bpjt,
(1.2)
(1.3)
where
ELEC
= per capita electricity consumption in
state j
NGAS
= per capita natural gas consumption in
PETR
= per capita petroleum consumption in
state j
state j
price of electricity in state j
price of natural gas in state j
price of petroleum in state j
reported heating degree-days for state j
(in thousands)
COOL = reported cooling degree-days for state j
(in thousands)
t = time index for successive years.
PRELEC =
PRGAS =
PRPETR =
HEA T =
All fuels are measured in millions of British thermal units
(Btu), and average fuel prices are expressed in 1967 dollars
per million Btu after being deflated by the nonfuel consumer
price index. Annual energy consumption and price data for
each of the lower 48 states are taken from the U.S. Department of Energy (DOE) State Energy Price and Expenditure
Data System, covering the years 1970-82. All variables other
than heating and cooling degree-days are used in logarithmic form.
The model represented by equations 1.1 through 1.3 differs in certain key respects from models in many other
studies of residential energy consumption. First, given the
high degree of multicollinearity between fuel prices and that
each fuel often faces only one primary competitor, the
consumption equation for each fuel includes only the ownprice variable and the price of the fuel's major substitute.
During the 1970-82 period, natural gas gained considerable
4
market share from both electricity and petroleum. Consequently, the price of natural gas is used as the substitute
price in the electricity and petroleum consumption equations, while the price of electricity is used as the substitute
price for natural gas consumption.
Second, heating and cooling data are not equally important in explaining consumption of different fuels. Both
heating and cooling degree-days were found to be statistically important in explaining electricity demand, but cooling data were not statistically important for natural gas or
petroleum demand. The apparent insignificance of cooling
data for natural gas and petroleum reflects, in large part, the
dominant role of electricity in air-conditioning. Consequently, the final equations for the two fuels do not include
cooling degree-days.
Third, per capita income is often used in residential energy
demand studies but is not included here. The simulations
presented in this article employ the assumption that residential energy consumption is a necessity and is not significantly affected by differences in per capita income across
states (which tend to be small) or changes in average income over the time span of the sample. Exclusion of income from the model may be important, but when a variety
of techniques were used, per capita income failed to provide significant explanatory power to the model. 6 While the
lack of significance may appear surprising, it is not without
precedent in energy demand studies. 7
The price and quantity data taken from the DOE data set
have the advantage of including periods of both increasing
and decreaSing real energy costs. Furthermore, the data
exhibit substantial price and consumption variability across
individual states and over time, making the estimation of
price coefficients in the model more precise.
Residential consumption parameter estimates
Before the parameters in equations 1.1 through 1.3 were
estimated, the equations were first tested for residual
autocorrelation on a state-by-state basis. Given the lagged
dependent variable, the presence of autocorrelation would
render the estimates biased and inconsistent. Only 3 cases
out of 144 revealed significant first-order positive autocorrelation at the 5-percent significance level. Therefore, the
absence of autocorrelation in the errors was assumed in the
pooled regressions.
Parameter estimates are obtained from a feasible generalized least squares regression procedure that pools the
state-level data for the 12 time periods. A new procedure,
described in the Appendix, was used for effiCiently extracting information about the structure of the variancecovariance matrix. The procedure estimates a block
Federal Reserve Bank of Dallas
Table 1
REGIONAL CLASSIFICATION OF STATES
West
North Central
South
Northeast
Block 1
Block 4
Block 7
Block 10
Arizona
California
Nevada
New Mexico
Minnesota
North Dakota
South Dakota
Wisconsin
Block 2
Block 5
Idaho
Montana
Oregon
Washington
Block 3
Colorado
Kansas
Utah
Wyoming
Illinois
Iowa
Missouri
Nebraska
Block 6
Indiana
Kentucky
Michigan
Ohio
diagonal error covariance structure that assumes that disturbances in states in the same regional block are related
while errors in different regional blocks are not correlated.
As discussed in the Appendix, traditional methods for the
pooled estimation fail to use the available information
efficiently.
The regional breakdown used in forming the disturbance
covariance matrix is presented in Table 1. Covariances between disturbances in states within the same block were
estimated, while errors in different blocks were assumed to
be uncorrelated. The covariance estimates were then used
to generate the final generalized least squares parameter
estimates for equations 1.1 to 1.3.
All parameters, shown in Table 2, have the expected signs
and are statistically significant. Also, the implied elasticities
are within the range of estimates commonly reported by
other researchers.
As shown by the coefficients on the lagged dependent
variables, consumers take a long time to adjust to changes
in energy prices. Reaction to a permanent increase in the
exogenous variable would be only 50 percent complete after 6.6 years in the case of electricity and after 40.2 years
and 10.7 years for natural gas and petroleum, respectively.8
Consequently, changes in consumption are expected to
Economic Review - November 1986
Arkansas
Louisiana
Oklahoma
Texas
Block 8
Alabama
Florida
Georgia
Mississippi
Block 9
North Carolina
South Carolina
Tennessee
Virginia
Maine
Massachusetts
New Hampshire
Vermont
Block 11
Connecticut
New Jersey
New York
Pennsylvania
Block 12
Delaware
Maryland
Rhode Island
West Virginia
occur slowly, especially in the case of natural gas consumption.
As shown in the lower panel of Table 2, the long-run price
elasticities of consumption are generally high, reflecting the
adjustment of households' capital stock to changes in energy costs. The short-run elasticities, on the other hand, in9
dicate little response of consumption to price changes.
The weather variables indicate sensitivity of consumption
to temperature fluctuations. Electricity consumption rose
in response to increases in both heating and cooling
degree-days, while natural gas and petroleum consumption
rose with increases in heating degree-days.
Although the results of the estimation are generally consistent with those of other studies, it is important to note
that data limitations require such estimates to be treated
with caution. Some preliminary evidence suggests that the
parameters may not be stable over the whole time period.
Behavioral changes in response to shortages generated by
price controls and the large price movements in the 1970s
may have increased the price elasticities of residential consumers. These effects, although not large, suggest that gains
and losses to consumers resulting from deregulation may
accrue more rapidly than indicated in the simulation experiments reported below. 1D
5
Table 2
ESTIMATED RESIDENTIAL ENERGY
CONSUMPTION PARAMETERS
Dependent variables
Electricity
Natural gas
Petroleum
Intercept.
-.0833
(-4.09)
-.0490
(-1.25)
-.7037
(-9.88)
Lagged dependent variable
.9006
(118.14)
.9829
(240.94)
.9372
(118.72)
Electricity price
-.1099
(-12.69)
.0358
(3.59)
.0164
(3.65)
-.0355
(-3.63)
Variable
Natural gas price.
Petroleum price.
.0639
(4.23)
-.2601
(-12.45)
Heating.
.1327
(10.72)
Cooling
.4926
(13.13)
.0710
(3.69)
.1573
(4.60)
.98
.98
.97
Own price.
-1.11
-2.08
-4.14
Cross price
.16
2.09
1.02
R2
Implied long-run elasticity
NOTE: Figures in parentheses are asymptotic t ratios.
Estimating price differentials
The consumption equations presented above can be used
to simulate changes in energy consumption patterns, across
states and among fuels, that result from changes in energy
prices. Although it is assumed that the consumption
equation parameters are identical for residential consumers
in different states, regional consumption patterns and expenditures differ in the model because fuel prices and climate vary across states and because demands for energy
sources involve long periods of adjustment. In this section,
a set of equations is developed to model fuel price differentials in different states.
The forecasting model for price differentials is shown in
Table 3. Percentage deviations for each state from average
6
fuel prices across states are assumed to be determined by
two factors. First, a constant is estimated for each state
through the use of dummy variables. This constant represents a structural difference in fuel prices estimated from
data for 1970 through 1982. Second, it is assumed that the
process of adjusting price differentials through changes in
the supply of or demand for different fuels takes time.
Consequently, price differentials are assumed to have a
lagged adjustment formulation.
The equation for petroleum price differentials includes
two additional terms in order to capture the structural
changes after 1978 that resulted from petroleum price deregulation. The first term, <>1' is included to reflect any uniform percentage change across states in the magnitudes or
Federal Reserve Bank of Dallas
Table 3
STRUCTURAL DIFFERENCES IN FUEL PRICES AMONG STATES
Equation
Estimated relationships
+ .7392*Pe,i,t_1'
{3j + .6422*Pn,i,t-1'
'Yj + .0098*Pp,i,t_1 -
2.1
Pe,i,t = {Xj
R2 = .97.
2.2 ...
Pn,j,t =
R2 = .94.
2.3 ...
Pp,i,t =
.86*Ot*'Yj
+
R2 = .71.
.32*O/Pp,i,t-1'
Variable definitions
= percentage deviation
= percentage deviation
= percentage deviation
r.{=
the state.
Pe
Pn
P
from mean electricity price.
from mean natural gas price.
from mean petroleum price.
t = the year.
°
= 0 before 1979 and 1 thereafter.
State
Ci;
(3;
1';
State
West
South
Block 1
Block 7
Arizona
California.
Nevada.
New Mexico ..
Block 2
Idaho
Montana ..
Oregon.
Washington
Block 3
Colorado
Kansas
Utah.
Wyoming
Ci;
(3;
1';
3.47
2.27
-4.82
3.37
-0.74
-8.63
-.62
-9.31
23.18
23.80
10.45
9.05
Arkansas.
Louisiana
Oklahoma
Texas
Block 8
-1.10
-4.34
-3.69
-.90
-15.03
-7.25
-15.14
-4.41
7.06
31.96
1.30
6.37
-17.15
-12.58
-15.31
-25.07
4.49
-10.84
8.29
6.33
3.03
3.76
-12.11
-13.18
Alabama.
Florida
Georgia
Mississippi.
Block 9
-2.97
2.00
-2.63
-1.96
-1.11
10.03
-4.39
-6.01
21.90
34.86
19.30
18.15
-1.37
-.70
-.89
-9.44
-8.77
-15.17
-12.97
-13.20
1.05
-3.80
8.98
9.54
North Carolina ..
South Carolina.
Tennessee
Virginia.
-1.60
-1.90
-9.14
1.18
2.17
3.36
-9.75
4.51
-13.55
-3.75
6.43
-16.24
North Central
Northeast
Block 4
Block 10
Minnesota
North Dakota.
South Dakota.
Wisconsin.
Block 5
-1.22
-3.95
-3.24
-.14
-4.06
-5.97
-7.52
-.44
-9.77
-3.68
-3.25
-14.12
Maine ...
Massachusetts.
New Hampshire.
Vermont.
Block 11
2.28
9.06
7.50
1.64
22.76
15.45
8.48
11.74
-12.40
-15.65
-12.22
-10.59
Illinois
Iowa.
Missouri
Nebraska
Block 6
3.86
.81
-1.13
-5.61
-4.89
-7.10
-5.06
-12.36
-11.48
-1.01
-.47
.05
Connecticut.
New Jersey.
New York ..
Pennsylvania ..
Block 12
8.46
12.16
12.23
4.80
16.89
10.22
8.50
-.29
-14.02
-15.65
-17.41
-18.26
Indiana.
Kentucky
Michigan
Ohio .........
-3.01
-6.36
1.56
1.49
-7.02
-11.09
-5.34
-5.45
-10.65
12.41
-19.99
-16.00
Delaware.
Maryland.
Rhode Island ..
West Virginia ..
9.20
3.87
9.79
-2.64
7.06
4.75
14.61
-5.71
-13.99
-15.33
-15.40
-4.90
NOTE: Percentage deviation of price from mean is approximated by 100 times the differ.ence in the logarithms of actual price and average price.
Economic Review - November 1986
7
Table 4
PROJECTION OF MEAN ENERGY PRICES
Equation
3.1 .
Estimated relationships
+
In(PELEC t ) = -1.29
(-3.51)
.4450 In(PELEC t _ 1 )
(3.02)
+
.0838 In(PCRUDE t ),
(4.21)
R2 = .92; Durbin's h statistic = .58.
3.2. .
+
In(PNATG t ) = - .16
(-.31)
R2
3.3 .
=
.8889 In(PNA TG t (6.15)
1
)
.96; Durbin's h statistic
In(PPETR t ) = - 2.28
(-16.55)
+ .5330
(11.26)
+
.0967 In(PCRUD9
(1.80)
= -
.83.
In(PCRUDE t ),
R2 = .93; Durbin-Watson statistic
1.63.
Variable definitions
PCRUDE = real price of crude
PELEC = real mean price of
real mean price of
PNATG
PPETR = real mean price of
oil at time t.
electricity for residential consumers.
natural gas for residential consumers.
petroleum for residential consumers.
NOTE: Figures in parentheses are asymptotic t ratios except those in equation 3.3, which
are t ratios adiusted for degrees of freedom.
When testing for the alternative of positive first-order autoregressive errors, the
critical value'at the 5-percent level of significance in the Durbin h test is 1.645.
The upper bound and lower bound at the 5-percent level of significance in the
Durbin-Watson test are .971 and 1.331, respectively.
directions of regional petroleum price differentials. The
second additional term, (j2, is included to capture any shift
in the lagged adjustment parameter. The petroleum price
deviation equation can be written as
where Pi,t is the petroleum price deviation for state i in period t, '}Ii is the constant term for state i, D t is a binary variable having a value of 1 after 1978 and 0 otherwise, and Bi,t
represents the unobserved equation error. Notice that the
equation is nonlinear because of the multiplicative interaction between (jl and the constant terms for each state.
The three equations in Table 3 were estimated as a system
of seemingly unrelated regressions, using an iterative nonlinear procedure. 11 Several results in the table are noteworthy. First, electricity prices tend to be lower than average in the West and South and higher than average in the
Northeast. These results reflect the availability of low-cost
8
hydroelectric power in the West and the high cost of
producing electricity in the Northeast with nuclear power
and petroleum.
Second, the structure of prices reflects the competitiveness of suppliers in different regions. Areas with more than
average shares of consumption of a particular fuel are observed to have lower than average prices for that fuel. For
example, petroleum prices were lower than average for residential consumers in the Northeast. Because most energy
transportation involves decreasing average unit costs at low
volumes, natural monopolies tend to emerge in areas that
use little of the particular fuel. As consumption rises, incentives for entry by other suppliers increase. Resulting
competitive pressure reduces average prices for the fuel. 12
Third, the estimated values of (jl and (j2 in the petroleum
price differential equation are of special interest. The estimates imply that petroleum price deregulation resulted in a
79-percent decrease in the long-run absolute value of the
petroleum price differential in each state. 13 This result is
Federal Reserve Bank of Dallas
consistent with the views expressed earlier concerning the
dampening effect of deregulation on regional energy price
differences.
The equations in Table 3 yield projections of price differentials for each state that deviate from a mean price path.
Given a forecast of mean prices for each period, the
equations in Table 3 can be used to calculate the percentage deviation of each fuel price from that mean to arrive at
a state-specific fuel price.
Simulation design
The equations in Table 3 provide a convenient method for
testing the effects of deregulation. By reducing the magnitude of the constant terms and the lagged adjustment parameter in a given price equation, price differentials across
states can be proportionately lowered. By reducing the
intercept for each state by a constant percentage, prices
could continue to differ across states-reflecting transportation costs-but the differences would be squeezed. Simultaneously reducing the value of the coefficient on the
lagged dependent variable would allow more rapid adjustments in price differentials.
Furthermore, it is assumed that the estimated effects of
petroleum price deregulation, as indicated by 15 1 and 15 2 in
the petroleum price differential equation, can be applied to
the cases of natural gas and electricity price deregulation.
Therefore, in the simulations of the effects of natural gas and
electricity deregulation presented later, the constant terms
in the equations for natural gas and electricity price differentials are reduced by 86 percent, reflecting the estimate of
b1, and coefficients on the lagged dependent variables are
set at 0.33, reflecting the value of the lagged adjustment
parameter in the petroleum price equation after petroleum
price deregulation.
Several important qualifications deserve mention at this
point. First, the simulation strategy assumes that the effect
of deregulation on state price differentials is similar in the
case of all fuels. Although important differences are likely
to exist in these effects, such differences are ignored to keep
the model tractable and because obvious alternative specifications are lacking. Second, it is conceivable that the oil
price shock of 1979 resulted in a decline in petroleum price
differentials between states. Because b1 and 15 2 are assumed
to measure only regulatory effects and cannot separate out
the effect of the price shock on conservation, the simulation
results could be overstating the effects of deregulation.
Forecasts of mean energy prices are accomplished with
the use of the equations presented in Table 4, which were
estimated by ordinary least squares. It is assumed that
crude oil prices are exogenously determined. It is also asEconomic Review - November 1986
sumed that all other fuel prices are ultimately driven by
crude oil price movements. As observed in the recent oil
price decline, petroleum product prices fell rapidly, and
downward pressure was observed on natural gas and electricity prices. Because of the direct physical relationship
between crude oil and petroleum products, the full effect
of crude oil prices on petroleum product prices is assumed
to occur in a single year. Electricity and natural gas prices
also change with oil prices, but the process takes more
time-particularly in the case of natural gas, where the large
coefficient on the lagged dependent variable reflects the
long-term nature of natural gas contracts.
Three scenarios are considered in examining the potential
effect of deregulation. First, a base case is developed.
Crude oil prices are assumed to follow their historical values
through 1985 and then to take on a value of $15 per barrel
in 1986. That price is assumed to remain constant, in real
terms, through the year 2000. 14 The equations in Table 4
translate this exogenously determined crude oil price path
into paths for the mean price of each of the residential fuels.
These paths, combined with forecasts provided by the
equations in Table 3 of the deviations from mean in the
price for each of the three fuels, produce state-specific
projections for residential fuel prices through the year 2000.
Finally, these price projections are translated into
projections for consumption of each of the three fuels in all
48 states by using equations 1.1 through 1.3 and the coefficients in Table 2. The heating and cooling variables are set
at their mean values in the simulation for all periods.
In the second simulation, the effects of deregulation of
natural gas prices are examined. 15 The simulation procedure
is identical to the procedure used in the base case, except
the constants listed in Table 3 are reduced by 86 percent in
the natural gas price deviation equation and the coefficient
on the lagged dependent variable in this equation is set at
0.33.
In the third simulation, bulk electricity deregulation is
proxied by reducing the constants for each state in the
electricity price deviation equation by 86 percent and setting the coefficient on the lagged dependent variable at
0.33. Because natural gas prices were deregulated in 1985,
the previously mentioned modifications to the natural gas
price deviation equation associated with simulating natural
gas deregulation are again empioyed.
Consequently, comparison of scenarios 1 and 2 allows
evaluation of the expected effect of past deregulation of
natural gas prices. Comparison of scenarios 2 and 3 factors
in the additional distributional effects that might occur if
electricity is also deregulated. 16
Table 5
EFFECT OF ENERGY PRICE DEREGULATION
ON RESIDENTIAL ENERGY EXPENDITURES
(Percentage changes in total expenditures)
State
Energy prices deregulated
Energy prices deregulated
Electricity,
natural gas
Electricity,
natural gas
Natural
gas
State
West
South
Block 1
Block 7
Natural
gas
Arizona.
California.
Nevada ...
New Mexico ..
Block 2
-4.57
4.04
6.95
3.04
0.30
7.13
.39
7.30
Arkansas.
Louisiana.
Oklahoma
Texas
Block 8
10.77
9.93
15.27
3.24
9.20
3.61
9.86
2.17
Idaho
Montana.
Oregon ..
Washington
Block 3
24.44
26.18
17.79
36.48
-2.20
8.62
-4.26
-3.17
Alabama.
Florida
Georgia
Mississippi
Block 9
4.66
-5.54
6.19
5.64
.55
-2.52
2.47
2.83
Colorado
Kansas
Utah ..........
Wyoming
9.23
13.25
12.90
24.08
7.56
12.42
11.80
11.31
North Carolina ...
South Carolina ..
Tennessee
Virginia.
1.28
1.50
19.06
-3.77
-.92
-1.29
4.45
-2.32
Northeast
North Central
Block 4
Block 10
Minnesota ..
North Dakota ..
South Dakota ...
Wisconsin ..
Block 5
4.78
9.06
8.98
.45
3.34
4.00
4.93
.32
Maine.
Massachusetts ..
New Hampshire.
Vermont .
Block 11
-12.12
-19.39
-11.98
-7.13
-9.98
-11.58
-4.56
-5.47
Illinois ........
Iowa .....
Missouri ....
Nebraska ...
Block 6
.29
4.78
5.36
17.90
4.79
5.72
3.80
10.07
Connecticut .
New Jersey.
New York ..
Pennsylvania ...
Block 12
-18.43
-19.16
-18.19
-4.89
-10.60
-7.90
-6.78
.25
Indiana ..
Kentucky ...
Michigan ...
Ohio ..........
9.61
16.27
3.75
3.09
5.79
7.20
5.34
4.91
Delaware ...
Maryland ..
Rhode Island
West Virginia .....
-14.07
-7.43
-19.47
7.80
-4.24
-3.15
-10.84
4.20
NOTE: Expenditures are defined as the discounted real value of per capita consumer spending on
residential electricity, natural gas, and petroleum from 1986 through the year 2000.
10
Federal Reserve Bank of Dallas
Effects of Electricity Deregulation
CHANGES IN ENERGY COSTS AS A RESULT OF ElECTRICITY DEREGULATION
~ INCREASE OF MORE THAN 6 PERCENT
Iii DECREASE OF MORE THAN 6 PERCENT
o
CHANGE OF LESS THAN 6 PERCENT
Simulation results
Results from the simulation experiments are fJresented in
Table 5. For each state, the present discounted value of real
energy expenditures for 1986 through 2000 is calculated
under the three scenarios, using a real discount rate of 4
percent. This discounted value of expenditures will be referred to simply as expenditures. Differences in expenditures between the two deregulation scenarios and the base
case are then reported in the table for each state.
As shown in Table 5, deregulation of gas prices alone results in higher expenditures for 31 states and lower expenditures for the remaining 17 states modeled. The major
gainers are in the Northeast, where natural gas prices fall
under deregulation in most states. In contrast, the largest
losers are in the North Central areas and the energyproducing areas of the West (California, New Mexico, and
most of Block 3) and South (Block 7 in particular), where
natural gas prices rise and cause total expenditures to rise.
Deregulating electricity as well as natural gas results in 14
states having lower costs and the remaining 34 facing higher
Economic Review - November 1986
costs, as shown in Table 5. Gains to the Northeast from
natural gas deregulation are strengthened by electricity deregulation, while gains in the Northwest (mostly Block 2
states) are dramatically reversed.
To understand the changes from the scenario in which
only natural gas is deregulated, it is helpful to look at the
incremental effects of electricity deregulation. Winners and
losers from deregulating electricity and gas, instead of only
gas, are shown in the accompanying map. Apparently,
major gainers are in the Northeast, while the largest losers
are in the Northwest. In the Northeast, states rely on highcost nuclear or petroleum-fired generators, and electricity
deregulation results in lower electricity costs. In the Northwest, electricity deregulation has a large positive impact on
electricity prices. This area, especially Washington, has had
low electricity prices as a consequence of plentiful low-cost
hydroelectric power.
In general, the results confirm the view that residential
consumers in energy-producing areas stand to lose the most
from deregulation. Conversely, consumers in energyimporting areas can be expected to enjoy lower costs.
11
Such conclusions, however, depend on important assumptions used in the model. In particular, the assumption
that mean price paths are not affected by the regulatory
environment may bias the results. Modeling the influence
of regulations on the supply of energy is subject to considerable uncertainty. Nevertheless, it is usually assumed that
deregulation will lead to greater supply at lower prices than
would be forthcoming under a regulated environment. In
support of this view, a recent study concludes that the average price of electricity is 33 percent lower with competition than with a monopoly structure. 17 If mean prices are
indeed reduced by deregulation, losses reported in the simulations would be overstated and gains would be understated. The pattern of relative losers and gainers, however,
would be largely unaffected.
In light of these considerations, additional simulations
were conducted to lower the mean price for a deregulated
fuel by 33 percent in each period while also altering regional
price differentials in the manner described above. After incorporation of the assumption that deregulation reduces
mean prices as well as state differentials around those mean
prices, deregulation of natural gas prices alone results in
lower expenditures for all 48 states, while deregulation of
gas and electricity prices results in lower expenditures for
every state except Washington. For Washington, expenditures rise 8.17 percent, in contrast to 36.48 percent when
only regional price effects are allowed for in the simulations.
Although every state except Washington has a decrease in
expenditures in this new round of simulations, the decrease
is larger for states identified as posting decreases in expenditures in Table 5.
Conclusions
Several important conclusions emerge from the simulation
results. First, deregulation is likely to lead to measurable
distributional effects on expenditures by residential consumers. Consumers in the Northeast have the most to gain
by such legislation, while those in the West have the most
to lose, especially if electricity prices are deregulated.
Second, and in contrast, producers of electricity in the
West have the most to gain by deregulating bulk power
transmissions. Because of their cost advantages, sellers of
bulk electric power in the Northwest could increase profits
by selling in a national market, while those in the Northeast
would see lower profits.
From a public policy perspective, a deregulated environment is likely to exhibit far less dramatic distributional consequences in response to future energy price shocks. Unlike
the situation in the 1970s, when lack of price movement
caused shortages in some fuels that imposed severe hard12
ships on certain regions, prices can be expected to be much
more responsive to changes in world oil prices. Also, increased competition among fuels will probably provide a
mechanism to limit price movements in anyone fuel. In
general, the impact of an energy price shock can be expected to have a more even effect on residential consumers
throughout the nation than was the case with regulated
prices.
1. Residential energy consumption covers fuels used in the home for space
heating, cooking, and power. It does not include consumption outside
the home, such as gasoline for transportation.
2.
The incidence of changes in energy costs considered in this article is
limited to effects on direct expenditures of residential consumers.
Other distributional consequences, such as effects on income and
wealth, are outside the scope of this study. For a recent examination
of the effect of changes in natural gas price deregulation on the regional
distribution of wealth, see Joseph P. Kalt and Robert A. leone, "Regional
Effects of Energy Price Decontrol: The Roles of Interregional Trade,
Stockholding, and Microeconomic Incidence,' Rand Journal of Economics 17 (Summer 1986): 201-13.
3.
Nominal expenditures were adjusted for inflation by using the nonfuel
consumer price index.
4.
See Paull. Joskow and Richard Schmalensee, Markets for Power: An
Analysis of Electric Utility Deregulation (Cambridge: MIT Press, 1983).
5.
See Roger H. Dunstan and Ronald H. Schmidt, "Structural Changes in
Residential Energy Demand" (Federal Reserve Bank of Dallas, April 1986,
Photocopy).
6.
See Dunstan and Schmidt, "Structural Changes in Residential Energy
Demand."
7.
See james G. Beierlein, james W. Dunn, and james C. McConnon, jr.,
"The Demand for Electricity and Natural Gas in the Northeastern United
States,' Review of Economics and Statistics 63 (August 1981): 403-8.
8. These figures are calculated as T = In(.5)/ln(p), where
coefficient on the lagged dependent variable.
pis the estimated
9. The short-run elasticities are equal to (j, and the long-run elasticities are
equal to ()/(1 - P), where () is the coefficient on the respective price
variables and fJ is the coefficient on the lagged dependent variable.
10. For a discussion of possible changes in the structural parameters, see
Dunstan and Schmidt, "Structural Changes in Residential Energy
Demand:
11. For a discussion of the iterative generalized least squares procedure
used, see SAS Institute Inc., SAS/ETS User's Guide, Version 5 Edition
(Cary, N.C: SAS Institute Inc., 1984), 505-50.
12. The lower than average prices for the fuel in areas that are heavier users
indicate two forces at work. First, as a causal factor, lower costs encourage the consumption of that fuel. Second, because some degree
of monopoly power exists in the residential energy market, more intensive use of one particular fuel encourages more competition in the areas
to bid away the monopoly rent.
Federal Reserve Bank of Dallas
13. The long-run price deviation for a particular state is calculated as
81(1 - P), where 8 is the estimated constant term for the state and P is
the estimated lagged adjustment parameter. The estimated interactive
terms show that after deregulation, each of the constant terms in the
petroleum price deviation equation was reduced by 86 percent while
the value of the lagged adjustment parameter increased from 0.01 to
0.33. The combined effect of these changes on the long-run price
deviations for each state is then easily calculated.
modeled by the parameter estimates of the various equations used in
the simulations, are not addressed.
14. Because of the design of the experiments, the results are invariant to the
path specified for crude oil prices. Allowing higher oil prices, for example, increases the present value of total expenditures significantly for all
states, but the percentage differences in expenditures between scenarios are not affected.
16. The simulation methodology does not allow for asymmetries in the effects of deregulation on energy prices in low-cost and high-cost areas.
For example, it could be argued that although high-cost areas would
have reductions in prices as barriers to entry are removed and low-cost
producers expand their product markets under deregulation, low-cost
areas would not have increases in prices, or at least not of the same
magnitude. Upward pressure on prices in low-cost areas, stemming
from suppliers widening their product markets into high-cost areas,
could be offset by increases in supply. Insofar as such considerations
are pertinent, the simulated effects of deregulation on expenditures in
energy-producing areas should be viewed as upper bounds.
15. The simulation strategy captures only the effects of narrowing price
differentials across states. Other possible consequences of deregulation, induding other changes in the consumer and market behavior
17. See Walter J. Primeaux, Jr., "Estimate of the Price Effect of Competition:
The Case of Electricity," Resources and Energy 7 (December 1985):
325-40.
Economic Review - November 1986
13
Appendix
Pooled Estimation with a Block
Covariance Structure
Estimation of parameters for residential energy consumption equations typically assumes that the disturbances can
be specified in the structure of an error components model.
An error components model assumes that the variance of
the disturbance is the same for all states, that the correlation between disturbances for a given state across time
is unchanged no matter how far apart in time the disturbances are, and that the contemporaneous covariance of
disturbances across states is the same for all states. 1
Formally, the error components model assumes the following relationships among disturbances:
(A1)
E(eit) = 0,
E(eitejS) =
for i i= j and t i= 5,
°
=
(i
=
u1 for i = j and t i=
=
for i = j and t =
u! for i i= j and
t
5,
5,
= 5,
where i and j are states and t and 5 are time periods.
The main computational advantage of the error components model derives from the strict set of assumptions
imposed on the disturbance structure. Only three parameters need to be estimated to complete the variancecovariance matrix. This is a particular advantage for the
current application, given the limited number of time series
observations available. The weakness of the error components model for the present study, however, is that the
model forces the disturbance covariances among all states
to be the same and, so, ignores information about regional
similarities and differences.
An alternative approach is to allow disturbance
covariances and variances to differ for each state, using the
estimation method described by Arnold Zellner and
Richard Parks. 2
Under this methodology, separate
covariances are estimated for each pair of cross sections.
However, not all disturbance variances and covariances
can be estimated for a pooled regression where the number of cross sections is larger than the number of time seIn such cases, the estimated full
ries observations.
disturbance variance-covariance matrix is singular. 3
This estimation problem presents itself in the study here
because only 12 time series observations are used for each
of the 48 states. The usual procedure in such cases is to
estimate separate disturbance variances for each state .and
to assume that there is no correlation of the disturbances
across states. This "heteroskedasticity" model fails, however, to capture many of the important correlations among
states.
14
An alternative method, used in the study here, improves
on the heteroskedasticity model by grouping the states in
regional blocks with the following disturbance structure:
(A2)
E(eivt) = 0,
E( eivtejwS) =
for t i= 5,
°
= u~ for i = j,
= Uij for i i= j and v = w,
=
°for v i= w,
where v and ware the regions to which states i and j belong. The disturbance structure in (A2) allows variances to
differ across states and assumes that disturbances in states
within the same region are correlated but errors in states
in different regions are not correlated.
Inclusion of nonzero correlations between states in the
same region results in parameter estimates that are significantly more efficient (that is, they have smaller variances)
than those in the heteroskedasticity model, as long as the
disturbances in these states are actually contemporaneously correlated. Furthermore, this technique incorporates considerably more information in the variancecovariance matrix than does the error components model,
which has only three estimated parameters. Insofar as the
correlations of disturbances across states depend on identifiable factors, such as the geographical proximity of
states (that is, the correlation between disturbances in
Washington and Oregon is probably greater than that between disturbances in Washington and Georgia), the block
covariance structure in (A2) is less restrictive than the
structure in (A1).
In this study, the states were divided into 12 regional
blocks, each containing 4 states. Theoretically, estimation
would have been possible if the states were divided into 4
regional blocks, each containing 12 states. This strategy
would have allowed estimation of a greater number of
covariance terms than the one employing 12 regional
blocks. However, estimation of disturbance covariance
terms for states in which the disturbances are actually not
correlated results in a decrease in efficiency for estimates
of the effects of the explanatory variables on energy consumption. 4 Therefore, only covariance terms that are, on
the basis of prior economic information, likely to be significantly different from zero should be estimated.
Energy consumption not explained by the model is likely
to be correlated only among states with similar energy industry structures. States exhibiting these similarities tend
to be small in number and closely situated geographically,
such as the electricity-producing states of the Northwest.
Federal Reserve Bank of Dallas
Pretests of the significance levels of the estimated disturbance correlations across states confirmed these priors,
revealing that the disturbances were, in most cases, highly
correlated across states only within small geographical regions. Therefore, the states were grouped according to regional similarities, and only four states were included in
each region. The regional breakdown is presented in Table
1 in the preceding text.
2.
See Arnold Zellner, "An Efficient Method of Estimating Seemingly
Unrelated Regressions and Tests for Aggregation Bias," Journal
of the American Statistical Association 57 (june 1962): 348-68; and
Richard W. Parks, "Efficient Estimation of a System of Regression
Equations When Disturbances Are Both Serially and Contemporaneously Correlated," Journal of the American Statistical
Association 62 (June 1967): 500-509.
3.
See Henri Theil, Principles of Econometrics (New York:
john
Wiley & Sons, 1971), 310.
4.
1.
For a discussion of the error components model, see Thomas B.
Fomby, R. Carter Hill, and Stanley R. johnson, Advanced
Econometric Methods (New York: Springer-Verlag, 1984), 334-36.
Economic Review - November 1986
See Fomby, Hill, and johnson, Advanced Econometric Methods,
164-66, for a discussion of issues related to this consideration.
15
Understanding the Texas
Unemployment Rate
William C. Gruben
Keith R. Phillips
Senior Economist
Federal Reserve Bank of Dallas
Economic Analyst
Federal Reserve Bank of Dallas
Even though the Texas economy expanded rapidly during
the 1970s and early 1980s, the state's average unemployment rate rose. At the same time, however, the u.s. unemployment rate rose faster than the state's, causing the ratio
of the Texas unemployment rate to the nation's to fall. Despite this decline, both Texas' and the nation's series generally moved up and down at about the same time.
These various facets of the state's unemployment rate
raise questions about the nature of Texas' growth during this
period. Why did the average unemployment rate in Texas
incre\lse? Was it because Texas' economic growth was accompanied by growing instability in demand for labor? Or
was increasing U.S. joblessness simply pushing workers displaced in other parts of the nation into the Texas job market
rapidly enough that the state could not absorb its new immigrants into the workforce? Further, was unemployment
in Texas linked to economic events in Mexico and to resultant increases in immigration?
In attempting to answer these questions, this paper assesses the effects of aggregate business cycle fluctuations in
the United States and Mexico upon the match between
Texas jobs and Texas workers available. Also shown is that
even when the overall u.s. economy is growing, permanent
shifts in the relative demands for labor by each economic
sector in the nation can increase the unemployment rate in
Texas. In addition, the paper examines the response of
Texas' aggregate output to world economic events, together
with the impact on the state's unemployment rate. Attention is given to the impact of changes in the Texas industrial
structure upon the volatility of employment in the state.
The analysis here links increasing employment volatility to
a rising average unemployment rate in Texas.
These influences are captured in a single-equation model
that is able to explain a large portion of the total variation
in the Texas unemployment rate. Using the estimation derived from the model for the sample period (1970.l-1981.1V),
the Texas unemployment rate was simulated for later periods.
The model demonstrates that aggregate business cycle
fluctuations in Mexico and in the United States have significant impacts on fluctuations in the Texas unemployment
rate. Fluctuations in aggregate output in Texas likewise
strongly affect the state's unemployment. Over the sample
period, a measure of fluctuations in U.s. aggregate output
was shown to explain more variation in the Texas unemployment rate than did any other explanatory variable considered.
Economic Review - November 1986
17
Permanent U.S. realignments of relative demand for labor
among industries were found to strongly affect the rate of
unemployment in Texas. Even when national economic
growth is occurring, if a U.S industry goes into a long-term
decline, some of its laid-off workers are likely to swell the
ranks of the unemployed in Texas.
Although the shift in Texas' industrial structure from a
low-employment volatility to a higher one also affected the
state's average unemployment rate, this transformation had
the smallest impact on unemployment variation of any factor considered during the sample period.
Although the sample period was characterized by high
rates of economic growth in Texas, out-of-sample predictions show that much of the state's unemployment variation during both the economic downturn of 1982 and the
subsequent economic weakness of 1983 is captured by this
model.
Texas employment and unemployment
in the 19705 and 19805
During the 1970s and early 1980s, the economy of Texas
grew faster than that of the nation, with the Texas gross
state product growing slightly more than twice as fast as
U.S. gross national product for the 1970-80 period. 1 For the
same period, nonagricultural employment in Texas grew
one and two-thirds as fast as that for the United States. As
Texas' employment rose during this period, the state's population also expanded more rapidly than the national population in a ratio of 27.1 percent to 11.4 percent.
The takeoff in Texas growth was part of a transformation
of the industrial structure of the United States which occurred because of realignments in the prices of various
goods and services. In Texas, the most obvious realignment
was the rapid rise in energy prices relative to others. Texas
was well equipped to benefit from this realignment, not only
because of its energy reserves but also because of its human
capital.
Even in 1970, Texas had a relatively high proportion of
total employment in oil and gas extraction, oilfield equipment, and other energy-related industries. In comparison
to Texas' 5.1 percent of total U.S. nonagricultural employment, the state showed a 36.0-percent share of U.S. oil and
gas extraction employment and a 65.7-percent share of U.S.
oilfield equipment employment. In addition, the state still
had a potential for further development of its energy resources, as well as a well-developed set of extraction and
extraction-related industries capable of serving world markets.
The state experienced not only a rapid overall growth in
employment but also a shift in the shares of employment
18
by industry. For the decade 1970-80, the share of total
nonagricultural employment in Texas due to oil and gas extraction rose from 2.7 percent to 4.0 percent, and that for
oilfield equipment increased from 0.8 percent to 1.1 percent,
while the share of nonagricultural employment in food
products manufacturing fell from 2.3 percent to 1.7 percent.
Significant shifts in employment shares also occurred in
other states and in nonenergy sectors. The share of durable
goods employment in the United States fell, while the shares
of services and of mining employment rose. Overall, the
decade of the 1970s was particularly susceptible to extended sectoral shifts of employment for both Texas and the
United States.
While Texas employment and population grew rapidly
during this period, the average unemployment rate for Texas
rose. As Chart 1 shows, for both Texas and the nation, the
average unemployment rate for the period 1976-80 was
clearly above the average for 1970-75. Nevertheless, the
U.S. average unemployment rate rose more rapidly than
that of Texas. With the rapid rise of industries nationallywith disproportionately large shares of employment in
Texas-the unemployment rate for the state fell relative to
that of the nation (see Chart 2). The Texas unemployment
rate relative to the national rate fluctuated a good deal
during the 1970s and 1980s, but this ratio generally declined
during the 1970s.
Despite the perception of some that the state of Texas
was "recession-proof" during this period, unemployment
rates moved more or less in tandem with the nation. Chart
1 demonstrates that while Texas' unemployment rate remained below the nation's until 1984, the movement in the
Texas rate generally reflected national economic patterns,
including unemployment shifts up or down, though with a
lag.
Why unemployment rates change
Changes in the natural rate. The causes of unemployment rate changes are complex. Some unemployment is
always present-even in periods of economic
growth-because of fluctuations in demand for individual
products and in the cost of inputs to production. Economists have defined this rate of unavoidable unemployment
as the "natural rate" of unemployment. 2
The natural rate may be defined also in terms of its relationship to wage changes as one at which there is neither
upward nor downward pressure on the rate of change in
wages. Because the rate of increase in wages moves closely
with the overall rate of inflation, it is not unusual to consider
the natural rate of unemployment as having neither upward
nor downward pressure on the rate of inflation. Many
Federal Reserve Bank of Dallas
Chart 1
Texas and
u.s.
Unemployment Rates
PERCENT
10
UNITED STATES
8
6
4
'70 '71 '72 '73 '74 '75 '76 '77 '78 '79 '80 '81 '82 '83 '84 '85 '86
SOURCE OF PRIMARY DATA: U.S. Department of Labor.
Chart 2
Ratio of Texas to U.S. Unemployment Rates
RATIO
1.40
r-------------------------,
1.20
1.00
0.80
'70 '71 '72 '73 '74 '75 '76 '77 '78 '79 '80 '81 '82 '83 '84 '85 '86
SOURCE OF PRIMARY DATA: U.S. Department of Labor.
Economic Review - November 1986
19
economists traditionally have viewed fluctuations in the
overall unemployment rate as deviations from a natural rate
that varies little over time. 3 That is, when sales slump overall, workers are laid off and the jobless rate rises; and when
sales rise, some of the jobless are hired, and the unemployment rate falls. These movements in the unemployment
rate are seen as the normal and expected results of
upswings and downswings in the business cycle. But if the
natural rate has not changed, a particular ratio of actual to
potential GNP will be associated with some particular rate
of unemployment. For example, the unemployment rate
that occurs when actual GNP equals potential GNP will be
about the same, no matter when this event occurs.
Some economists recently have begun to offer a different
explanation for unemployment rate fluctuations, although
the concept is also linked to the idea of a natural rate. They
claim that a portion of what traditionally have been considered cyclical movements in the unemployment rate are
actually fluctuations in the natural rate. 4
In one version of this explanation, the natural rate rises
when permanent shifts in relative labor demands across
economic sectors induce workers from one industry to seek
jobs in another. Transitory, rather than permanent, shifts in
relative demands for labor across sectors are normal characteristics of the business cycle, but the stress on the permanent component of such shifts is important. For
example, an aggregate cyclical downswing usually has a
more profound impact on capital goods industry employment than on services employment. The consequence of a
cyclically generated decline in the capital goods share of
overall employment is, technically speaking, a sectoral shift.
But this is simply a result of the particular way in which an
overall economic downturn generally hits a certain
industry-capital goods. In this case, though unemployment may rise because of job losses in capital goods, this
increase does not Signal a change in the natural rate of unemployment. It only marks the the rise of overall unemployment to a point above the natural rate. (We refer to
sectoral shifts that are normal to the business cycle as
"transitory" because when the economic downswing ends
and the upswing gets under way, the industry in question
will regain its former share of workers.)
When a sectoral shift is permanent, however, workers
who have lost their jobs because of weakness in a given industry will not be rehired in that industry. A permanent
shift thus persists in the face of aggregate growth Or declines.
A permanent sectoral shift in demand for labor affects the
unemployment rate because the job search process is
time-consuming and costly. If an industry undergoes a
20
permanent downturn, its laid-off workers require time to
find and take new jobs. When workers have skills that are
not easily transferable from one firm or industry to others,
they may be slow to take employment in other sectors of
the economy. The most quickly available new job may not
use the skills which the worker developed in his old firm and
thus may pay a lower wage rate than the employee formerly
received.
A permanent sectoral shift in demand for labor causes a
temporary increase in the natural rate of unemployment.
This increase in the overall rate of unemployment is independent of the traditional, aggregate demand-generated effects of the business cycle. It is possible, then, to have
economic growth and rising unemployment at the same
time. Overall economic growth does not preclude a permanent downshift in the demand for one industry's products or the sudden rise of another's. Because they disrupt
the traditional relationship between a given rate of economic growth and a given rate of unemployment, permanent sectoral shifts are seen as temporarily altering the
natural rate of unemployment rather than the cyclical
rate. s
Thus far, only the temporary effects of a permanent sectoral shift have been discussed. A permanent sectoral shift,
however, can have both permanent and temporary effects.
When a permanent sectoral shift induces workers to seek
new jobs, its unemployment effect is over when all searchers find work. Thus, this effect is temporary. The more
permanent effect of a sectoral shift is a changed industrial
structure. This change in industrial structure may also have
a significant effect upon a state's patterns of unemployment
fluctuations. Specifically, such a change can affect the unemployment effects of the business cycle. Paradoxically, the
permanent effect of a sectoral shift can be a change in the
cyclical patterns of unemployment itself.
Changes in employment variability. As is well recognized,
differences in industrial structures-either over time or
across states-can correspond to differences in average unemployment rates. Because the demands for capital goods
tend to fluctuate more than those for services, a capital
goods worker may be more likely to work overtime than a
service worker during an upswing in the business cycle. For
the same reason, during a downswing, a capital goods
worker might be more likely to be unemployed than would
a service worker. Furthermore, because of the relatively
great heights of upswings and the depths of downswings in
capital goods industries, the average period of time unemployed over the business cycle is likely to be greater for
workers in these industries than for those in service industries. A capital goods firm may compensate workers at a
Federal Reserve Bank of Dallas
higher rate than does a service firm, given comparable skill
levels, in order to adjust for their relatively high risk of being
unemployed. Even so, the introduction of capital goods industries into a region~or expansion within it~may, other
things being equal, result in a higher average rate of unemployment. In general, the states dominated by industries
with high employment variation are also likely to have
higher average unemployment rates than those whose industries have low employment variation.
When there are several industries in a state, the timing of
their individual employment variations is also linked to the
state's average unemployment rate. If all industries in a
state layoff workers at the same time, it is particularly difficult for workers from one industry to find jobs in another
within the state. The average unemployment rate in such
states is likely to be higher than in those where some industries are tending to layoff workers at times when other
local industries are adding to their workforces. It is common
to refer to states with industries whose downswings tend to
occur simultaneously as having high employment
covariance.
States with both high employment variance and high
employment covariance are said to have high employment
portfolio variance. Differences in the degree of employment
portfolio variance not only can occur across states at a
point in time but also may differ within a state over time.
When industries with a high variance or covariance grow
more rapidly than industries with low variance or
covariance, the portfolio variance of the state will rise. As
the portfolio variance of a state rises, so does its average
unemployment rate.
Because these explanations of unemployment rate fluctuations are general, they must be tailored to the
particularities of the Texas experience. In the following section, these explanations are related to the fluctuations of the
Texas unemployment rate during recent years.
Explaining the Texas unemployment rate
The Texas economy is a small, open economy affected by
national and world events. As an open economy within the
United States, the state is naturally affected by fluctuations
in income and output that occur in the larger, national
economy. Because of its geographic location and industrial
structure, the Texas economy is also influenced by economic growth and contraction in Mexico. Nevertheless,
Texas' industrial mix, legal structure, and portfolio of natural
and human resources make it sufficiently different from either the rest of the United States or Mexico that the state's
economic fluctuations are not identical with those of either.
The Texas unemployment rate thus is influenced by fluctuEconomic Review - November 1986
ations in the aggregate supply of, and demand for, goods
and services in the United States and in Mexico as well as
by those that occur within the state itself.
U.S. aggregate business cycle fluctuations
and Texas unemployment
U.S. aggregate business cycle fluctuations have a strong effect on Texas' unemployment. When upturns in the U.S.
business cycle occur, this expansion generally pushes up
demand for Texas' products, thus fueling employment increases. But if the labor force does not expand in the state
faster than the increase in employment, the unemployment
rate falls.
Even if growth in the United States occurs but does not
result in increased demand for Texas' particular mix of products, unemployment can fall in Texas because U.S. expansion also increases demands for labor in areas of the country
outside Texas. In this case, workers outside Texas become
less prone to leave other portions of the United States to
seek work in Texas. Conversely, Texas workers become
more prone to leave the state for jobs elsewhere. Even if the
demand for labor does not change in Texas because of U.S.
economic growth, the supply of labor will. In sum, when
U.S. aggregate output growth occurs, both supply and demand effects are likely to lower the unemployment rate in
Texas.
Similar effects of Mexican
aggregate output fluctuations
Similar arguments apply to Mexico's impact on Texas unemployment. Mexico shares a border of more than a thousand miles with Texas. Mexican consumers come to Texas
to buy a wide array of Texas consumer goods and services.
Mexico imports oilfield equipment and other Texas-made
capital goods, as well as Texas agricultural products. Thus,
not only Mexican demand for goods and services but flows
of Mexican labor influence the Texas unemployment rate.
The length and porosity of Texas' border with Mexico also
mean that the arrival and departure of Mexican workers,
both legal and illegal, are daily events of considerable magnitude. When economic growth occurs in Mexico, the expansion generally means an increase in demand for Texas
products and a consequent increase in the demand for labor in Texas. Again, the state's unemployment rate will fall
unless the labor supply increases more rapidly than the upturn in Texas employment.
Even if upturns in Mexican income and output do not
translate into an increase in demand for Texas' output, they
do imply an expansion in the demand for labor in Mexico.
Then, if the supply of Mexican workers to Texas diminishes,
21
other things being equal, the Texas labor force also declines.
The Texas unemployment rate thus could still fall. In the
event of a downturn in the Mexican economy-and reduced
employment opportunities-more Mexican workers would
come to Texas. But if the Texas labor demand were insufficient to absorb the increase, the Texas unemployment rate
would rise.
Aggregate output fluctuations in Texas
An additional aggregate effect is specific to Texas. Texas'
endowment of natural resources, its institutional structure,
the characteristics of its labor force, and the extent and nature of its entrepreneurship all provide comparative advantages for some types of products but not for others. In part,
because the portfolio of industries in which Texas has a
comparative advantage is different from that of the United
States or Mexico, the state's individual components of
overall aggregate output are different. Consequently, the
Texas business cycle does not coincide exactly with those
either of the United States or Mexico. For example, Texas
can be in the growth portion of its cycle while the United
States is in a slump. Conversely, while the u.s. aggregate
output is growing, the particular attributes that characterize
the Texas economy can put it in the recessionary portion
of its cycle.
To account fully, therefore, for the influence of business
cycles on the Texas unemployment rate, it is necessary to
model both the Texas business cycle and the aggregate U.S.
and Mexican business cycles. The usefulness of considering
the state's business cycle as separate from the nation's has
become particularly evident in 1986 when the aggregate
growth for the United States has been accompanied by aggregate declines in Texas output.
Permanent sectoral shifts
affecting Texas unemployment
If the sectoral shift theorists are correct, the influence of U.S.
economic events upon the Texas unemployment rate is not
limited to national aggregate fluctuations. Even in periods
of economic growth, national permanent sectoral shifts can
result in a rising U.S. unemployment rate. When changes in
costs, tastes, or technologies force long-lived rearrangements of the distribution of demand for labor among industries, these changes are likely to affect such distributions
throughout the country. In the wake of a permanent sectoral shift, displaced workers search for employment in industries where they have not worked before. In their
search, some of these workers are likely to move across
state boundaries, and in Texas they cannot always imme22
diately find jobs. With a national permanent sectoral shift,
then, the unemployment rate in Texas could rise.
Employment portfolio
variance increases in Texas
Finally, growth in portfolio variance is linked to the longterm increases in the average Texas unemployment rate.
During the 1970s and early 1980s, changes in the industrial
structure of the Texas economy resulted in steadily rising
employment portfolio variance. While Texas employment
grew overall, some volatile industries such as oilfield equipment and construction grew especially rapidly. In addition,
this growth was particularly strong among industries whose
employment patterns were highly covariant with one another. That is, during the 1970s and early 1980s-as the
Texas economy became less diversified--employment became increasingly unstable.
A model of Texas unemployment
A quarterly linear regression model was used to test the
significance of the relationship between the Texas unemployment rate and three general types of influences: (1) aggregate output fluctuations, including those of Texas, the
United States, and Mexico; (2) national permanent sectoral
shifts in labor demand; and (3) Texas portfolio variance.
The sample period 1970.l-1981.IV begins with 1970 because of changes that occurred in that year in the procedure for estimating the Texas unemployment rate.
Specifically, 1970 marks the beginning of the use of the
Census Bureau's Current Population Survey to derive estimates of the Texas unemployment rate. Formerly, estimates
were derived from unemployment insurance statistics. The
period ends with 1981 because the calculation procedures
for the permanent sectoral shift variable require 16 quarters
of observations past the regression sample. This means that
to estimate the permanent sectoral shift variable through
1981.1V, it was necessary to have employment data through
1985.IV.
In order to estimate the effects of aggregate output fluctuations in the United States, Mexico, and Texas upon the
Texas unemployment rate, measures of aggregate "gaps"
were used as variables in the regression equation. The aggregate gaps for the United States, Mexico, and Texas may
each be considered as the percentage difference between
the potential level of output at full employment and the
actual level of aggregate output. Other things being equal,
as actual output falls farther below potential output for the
United States, Mexico, or Texas, the state's unemployment
rate is expected to rise. Likewise, if actual output rises toward or above the estimates of potential output, the Texas
Federal Reserve Bank of Dallas
unemployment rate is expected to fall. The relation between each gap variable and the Texas unemployment rate
thus is expected to be negative. Discussions of the procedures for calculating such gaps appear in subsequent paragraphs.
GNP gap data provided by the Federal Reserve Bank of st.
Louis were used to capture the influence of u.s. business
cycle fluctuations upon Texas unemployment. This variable
is measured as potential real GNP in the United States minus
actual real GNP expressed as a percentage of real GNP.6
Because gap variables are not readily available for Texas or
Mexico, it was necessary to create Texas and Mexican gap
variables.?
Although economic theory allows linking fluctuations in
these gap variables to changes in the Texas unemployment,
it provides little about the exact timing of these relationships. In the beginning of an economic downturn, firms
commonly are reluctant to fire their employees. Though
they may cut employee hours, firms often prefer to keep
their workers during a downturn because of the firmspecific skills they have developed. Hiring new workers after
an upturn in the business cycle also can mean that additional training costs will be incurred. Also, when employees
are laid off in one state, they may seek other jobs there before fanning out to other parts of the country. As a result
of these factors, lags can be expected between downturns
in U.S., Texas, or Mexican output and in increases in the
Texas unemployment rate. On the other hand, it is difficult
to know a priori which particular lag relationship that the
fluctuations in each of these variables would have to the
Texas unemployment rate.
Two separate selection criteria were used in order to
specify optimal lag configurations for the aggregate gaps of
the United States, Mexico, and Texas in the regression.
These measures included the Akaike Information Criterion
and the MSEp Criterion. 8 Both approaches to selection of
lags resulted in the same lag configuration, which included
contemporaneous values for the U.S., Mexican, and Texas
gaps. In the equation selected by both criteria, the lagged
variables included lags of one and three quarters for the
Texas gap, lags of one and four quarters for the U.s. gap, and
a four-quarter lag for the Mexican gap.9
The model accounts for permanent U.s. sectoral shifts
through an estimation procedure that is described mathematically in Appendix A. In brief, this variable is a measure
of the permanent component of the difference between a
series of past employment distributions by sector and a series by sector that occurs for an extended period after the
observation point in question. If the observation point is
1981.1V, for example, this variable measures the permanent
Economic Review - November 1986
component of the difference between a weighted average
of sectoral employment distributions occurring as far back
as 1977.1V, with a similar average of sectoral distributions of
employment occurring as far ahead as 1985.1V. A large
change in these distributions over time is interpreted as
meaning that a significant number of workers have been
induced to seek work in industries different from the ones
in which they have most recently been employed. Permanent sectoral employment shifts are expected to be positively related to changes in the Texas unemployment rate. 10
Also considered was the role of employment portfolio
variance in explaining the Texas unemployment rate. The
measure used to construct the variable (see Appendix B) is
a matrix of the u.s. employment portfolio variance adjusted
to consider the employment share-weights particular to the
state of Texas in each quarter of the observation period. As
noted in previous sections of this discussion, increases in
the estimates of portfolio variance are expected to be related positively to increases in the Texas unemployment
rate.
Although the gap variables in the equation were lagged,
neither the permanent sectoral shift nor the portfolio variance variables were lagged because fluctuations in these
variables-even in their contemporaneous form-reflect
long-term changes in economic structure. It should also be
noted that in other studies in which conceptually similar or
comparable variables have been used, lags in such variables
were either not significant or not used.
Estimation results
The results of the regression analysis are reported in Table
1. Results for the gap variables are reported in terms of the
summed coefficient values. The equation was able to explain 85.99 percent of the variation in the Texas unemployment rate over the sample period.
The sum of the contemporaneous and lagged values of
u.s. GNP gap coefficients was negative and significant at the
0.0003 level. Those for Texas and Mexico were also of the
expected negative sign and significant at the 0.0027 and
0.0062 levels, respectively. Significance levels in the cases
of U.s., Texas, and Mexican gaps were estimated using
F-tests of joint significance.
The coefficient of the permanent sectoral shift variable is
positive, as was hypotheSized. As estimated on the basis of
a t-test, the coefficient value was significant at the 0.0386
level. Permanent U.S. sectoral shifts apparently have acted
upon the Texas unemployment rate in the expected way.
That is, as permanent sectoral shifts occur nationally, displaced workers sometimes come to Texas to find work and
are not always immediately successful in their job searches.
23
Table 1
REGRESSION RESULTS FOR A QUARTERLY MODEL
OF UNEMPLOYMENT IN TEXAS'
Parameter
estimate
Variable
Intercept ........... .
Mexican GOP gap
Texas portfol io variance'
Texas GSP gap
U.S. sectoral employment
shift'
U.S. GNP gap
-2.749671
-3.466021
1.391644
-1.829559
0.908656
-15.648165
t- or Fstatistic
-1.961
5.85
4.599
5.67
Significance
level
(t)
(F)
.0575
.0062
.0001
.0027
2.145 (t)
8.02 (F)
.0386
.0003
(t)
(F)
R2
.8599.
R2 adjusted
.8221.
OW
1.70.
F = 22.72.
RMSE
.2689.
First-order autocorrelation =
.142.
1. The dependent variable is the quarterly average Texas unemployment rate.
2. Observations were multiplied times 10' for convenience of reporting coefficients and
standard errors.
3. Observations were multiplied times 10' for convenience of reporting coefficients and
standard errors.
SOURCES OF PRIMARY DATA: Baylor University Forecasting Services,
Professor M. Ray Perryman.
International Monetary Fund.
U.S. Bureau of Labor Statistics.
U.S. Department of Commerce.
Table 2
TOTAL DIRECT AND INDIRECT CONTRIBUTION OF
EACH EXPLANATORY VARIABLE TO TOTAL VARIATION
IN THE ESTIMATED TEXAS UNEMPLOYMENT RATE
Variable
Mexican GOP gap.
U.S. GNP gap.
Texas GSP gap.
Texas portfolio variance.
U.S. sectoral employment shift .
Total
Percent
20.65
37.87
7.53
6.53
27.42
100.00
SOURCES OF PRIMARY DATA: Baylor University Forecasting Services,
Professor M. Ray Perryman.
International Monetary Fund.
U.S. Bureau of Labor Statistics.
U.S. Department of Commerce.
24
Federal Reserve Bank {)f Dallas
The coefficient of the Texas portfolio variance expression
was positive and significant at the 0.0001 level. Because the
value of this variable rose steadily during the sample period,
it appears to have played a role in explaining the secular
increase in the Texas unemployment rate during the sample
period. The restructuring of the Texas economy during this
period apparently injected a degree of employment instability that had not formerly been seen in the state.
While the results of the regression equation show that all
of the variables had statistically significant explanatory
power, this finding offers little information as to the relative
importance of each variable in explaining the Texas unemployment rate. Because of the complicated nature of some
of the variables, particularly those depicting permanent U.S.
sectoral employment shifts and Texas portfolio variance, the
interpretation of even the coefficient elasticities is not
straightforward.
To assess the economic significance of each of the independent variables, the average percentage change in the
model's predicted value due to each independent variable
was estimated (see Table 2).11 This estimation procedure
captures the total contribution provided by a given variable,
both indirectly through its influence on other variables in
the equation and directly through its influence on the Texas
unemployment rate. Essentially, this procedure involves
calculating each variable's share of within-sample unemployment rate variation estimated by the total model in
each quarter and averaging each share over the sample period. The dominant variable was the U.S. GNP gap, in
which-as estimated by the model-variations over the
sample period accounted for 37.87 percent of the Texas
unemployment rate fluctuations. Second in dominance was
the permanent sectoral employment shift variable, which
accounted for 27.42 percent of the fluctuations in the estimated unemployment rate. The total effect of the Mexican
gross domestic gap variable accounted for 20.65 percent of
the unemployment rate fluctuations, while variations in the
Texas gross state product gap accounted for only 7.53 percent of estimated unemployment rate fluctuations.
This latter estimation requires some care in interpretation.
A number of variables that affect the Texas unemployment
rate also may be expected to influence the Texas gross state
product gap. These variables include the U.S. and Mexican
GNP gaps. The impacts of these two variables on the Texas
unemployment rate may be both direct and indirectthrough their impacts on the Texas gross state product gap.
The measure of the proportion of total estimated unemployment captures both the direct and indirect effects of
these variables. As a result, the Texas gross state product
gap may be said to account for 7.53 percent of unemployEconomic Review - November 1986
ment rate fluctuations-net of the portion of its overall variation that is linked to the u.s. GNP gap and the Mexican
GOP gap.
The smallest impact of fluctuations in any variable on
variations in the Texas unemployment rate, as estimated by
the model, was that of Texas employment portfolio
variance-6.53 percent. It should be noted that the
elasticity of the portfolio variance variable was the largest
of any of the variables in the equation. Nevertheless, fluctuations in the value of this variable over the sample period
were so small that it accounted for only a relatively small
portion of total estimated variation.
Out-of-sample estimations of the model
During the sample period for which this model was estimated, Texas grew rapidly. Indeed, one reason that the
state's unemployment rate rose during this period was attributable to labor supply effects rather than demand effects. In the wake of the 1973 and 1979 oil price shocks, for
example, unemployment in Texas grew-not because employment was falling but because the labor supply was increasing faster than total employment.
In the second quarter of 1982, shortly after the end of the
sample period, Texas fell into a recession that had already
commenced in the United States. The decline in Texas was
a response to growing weakness in the u.s. economy, together with a drop in oil prices that induced a reduction in
oil and gas drilling activity and a major downturn in Mexico.
Oil prices had peaked in 1980.lV, reaching an average onthe-spot market price of $38.63 per barrel. By 1981.1V, spot
prices had fallen 12.8 percent to an average price of $33.68
per barrel. By 1982.1V, prices were averaging $31.75. Partially in response to the repercussions of this decline, the
Texas gross state product fell 4.2 percent between 1982.1
and 1982.1V, while the Mexican gross domestic product
dropped 6.2 percent. The unemployment rate in Texas rose
from an average of 5.4 percent in 1981.1V to 8.4 percent in
1983.1.
In order to test the predictive power of the model, outof-sample predictions of the Texas unemployment rate were
performed for each quarter of the period 1982.1-1983.1V.12
Recognizing the difference between Texas' economic experience within the sample period and what occurred in the
out-of-sample period, we considered that the prediction
would prove a rigorous test of the model's validity.
Chart 3 depicts both in-sample and out-of-sample predictions of the Texas unemployment rate, together with the
actual values of the unemployment rate for the period
1970.l-1983.IV. The predicted values generally move in a
direction that is consistent with actual values. Nevertheless,
25
Chart 3
Texas Unemployment Rate vs. Estimated Texas
Unemployment Rate for the Period
1970.l-1983.1V
PERCENT
9
8
TEXAS UNEMPLOYMENT RATE
.'.
7
i\
6
5
4
3
'70
'72
'74
'76
'78
'80
'82
'84
SOURCE OF PRIMARY DATA: U.S. Department of Labor.
the predicted values fall far short of reaching the peaks
posted by the actual unemployment values. The most extreme error occurred in 1983.1, when the predicted value
was 6.9 percent and the actual value was 8.4 percent. By
1983.1V, however, the predicted and actual values had converged and differed by only 0.2 percentage points. Some
of the error in the predicted values can be attributed to the
out-of-sample permanent sectoral shift variable being
measured with error, for reasons described in footnote 11.
For the entire out-of-sample prediction period, however, the
correlation between the real and predicted fluctuations in
Texas unemployment rates was 87 percent.
Conclusions
The above findings help to answer in some detail the
questions posed at the beginning of this paper. Was Texas'
economic growth accompanied by growing instability in
demand for labor? Or was increasing U.S. joblessness simply
pushing workers displaced in other parts of the nation into
the Texas job market at such a high rate that the state could
not absorb its new immigrants into the workforce?
26
The results of the regression equation suggest affirmative
answers to both questions, because the coefficients for
variables that accommodate these two concepts were significant. These two explanations are not mutually exclusive,
however. The increasing value of Texas employment portfolio variance over time attests to the rising instability of labor demand in the Texas economy. Consistent with the
displaced-worker argument is the evidence of a strong influence of fluctuations in the U.S. GNP gap on the Texas
unemployment rate, together with Texas' rapid employment
growth often being accompanied by an even more rapid
expansion in the labor force.
Were fluctuations in unemployment in Texas significantly
linked to events in Mexico? The regression results clearly
suggest that they were. When the rate of change in
Mexican gross domestic product fell below its trend rate of
growth, the Texas unemployment rate was shown to increase. A particularly striking finding is not simply that
fluctuations in Mexico help to explain fluctuations in the
Texas unemployment rate but that such a large portion of
total Texas unemployment rate fluctuations can be explained by a Mexican variable.
Federal Reserve Bank of Dallas
Furthermore, this model is able to provide some answers
for other questions that could be raised in response to remarks in preceding sections of this paper. For example,
permanent national sectoral shifts appear to have supply
effects that can raise the Texas unemployment rate even
when overall U.S. growth is positive. The U.S. permanent
sectoral employment shift variable was shown to have significant explanatory power in the model, despite the inclusion of the U.S. GNP gap.
Finally, it can be asked what this model means for the future of unemployment in Texas. The model suggests that
aggregate economic events in the United States have an
extremely strong impact on Texas but that much of this impact occurs with a substantial lag. When oil price declines
shock Texas, they negatively impact the state quickly and
powerfully. These effects ultimately may be moderated by
increases in U.S. demand and output. These moderating
responses, however, occur with a considerable lag after the
price decline. They are the result of the lagged growth reaction of the u.s. economy to such a decline and of the
lagged impact of that national growth on the Texas unemployment rate. Pulling from the other direction, however,
are the unemployment effects of a continued weakness in
the Mexican economy. While Mexico's influence on Texas
employment is not as strong as that of the United States, it
still exerts an important influence on the state's labor markets. As long as economic weakness occurs in Mexico, this
problem will temper the positive effects for Texas of U.S.
growth.
1.
Estimates of the Texas gross state product are regularly published by,
and are available from, Professor M. Ray Perryman, Baylor University
Forecasting Services (Waco, Texas). The authors are grateful for the use
of these data.
2.
For a discussion of the fundamentals of the traditional natural rate theory, see Robert J. Gordon, Macroeconomics (Boston: Little, Brown and
Co., 1978), 212-15.
3. This somewhat different emphasis in interpreting the natural rate or
nonaccelerating inflation rate of unemployment stems from attempts
to examine the stability of the Phillips Curve relationship. See, for example, Milton Friedman, "The Role of Monetary Policy," The American
Economic Review 58 (March 1968): 1-17; and Edmund S. Phelps, "Introduction: The New Microeconomics in Employment and Inflation Theory," in Edmund S. Phelps, ed., Microeconomic Foundations of
Employment and Inflation Theory (New York: W. W. Norton and Co.,
Inc., 1970), 1-23. It should be noted that even in the earliest casting of
this approach to the analysis of the natural rate, clear statements appear indicating that the natural rate is not immutable. For example,
fluctuations in real minimum wages and in the strength of labor unions
are cited in Friedman (see above) as causes of fluctuations in the natural
rate. Researchers who focus on the unemployment rate/price relationship commonly estimate. the natural rate for different periods and find
Economic Review - November 1986
some variation over time. (See A. Steven Englander and Cornelis A. Los,
"The Stability of the Phillips Curve and Its Implications for the 1980s,"
Research Paper, Federal Reserve Bank of New York, January 1983.
Englander and Los also note four survey articles dealing with this topic,
each of which cites a plethora of other research papers on the subject.
Also see three publications by Robert J. Gordon: "Inflation, Flexible Exchange Rates, and the Natural Rate of Unemployment," in Martin Neil
Baily, ed., Workers, Jobs, and Inflation [Washington, D.C.: The Brookings
Institution, 1982], 89-152; "Unemployment and Potential Output in the
1980s," in William C. Brainard and George C. Perry, eds., Brookings Papers on Economic Activity, vol. 2 [Washington, D.C.: The Brookings Institution, 1984], 537-64; and "Understanding Inflation in the 1980s," in
William C. Brainard and George C. Perry, eds., Brookings Papers on Economic Activity, vol. 1 [Washington, D.c.: The Brookings Institution,
1985], 263-99.) In many cases, however, these studies find the natural
rate to be fairly stable over time. Gordon (1985, see above) finds the
natural rate to have ranged between 5.8 percent and 6.0 percent over
the period 1971-84, and in an earlier paper (1982, see above) he finds no
significant change in the natural rate between the 1950s and the late
1970s except for movements associated with long-term demographic
trends. Arthur M. Okun, in The Political Economy of Prosperity
(Washington, D.C.: The Brookings Institution, 1970), 136, cites two approaches to analyzing the relationship between aggregate economic
activity and the unemployment rate. One approach involves comparing changes in the ratio of potential to actual aggregate economic output with changes in the unemployment rate. The other addresses the
relation between the level of the ratio of the potential to actual aggregate economic output and the level of the unemployment rate. This
second approach-which uses levels but assumes "the trend of output
growth at constant unemployment rates"-is the focus of my discussion
in the text which follows.
4. The theoretical foundations of the aspects of the "variable" natural rate
approach that are addressed in this paper lie in a series of arguments in
which changes in the unemployment rate may occur without any aggregate fluctuations. In these models, increases in the stochastic variability of labor demands, either between industrial sectors or in the
aggregate, result in increased equilibrium unemployment and job mobility. (See, for example, Phelps, ed., Microeconomic Foundations of
Employment and Inflation Theory.) In competitive models where search
is costly and occurs among spatially distinct "islands," a perceived
change in the distribution of labor demand among markets increases
the amount of search unemployment resulting from the option price
character of the reservation wage. In another model, where search
costs are constant, increased variance in the distribution of sectoral
demands increases the return to search. As a result of this increase, the
equilibrium amount of search unemployment rises. (See Robert E.
Lucas, Jr., and Edward C. Prescott, "Equilibrium Search and Unemployment," Journal of Economic Theory 7 [February 1974J: 188-209.)
5.
An example of the early empirical development of the notion of sectoral
shifts is in David M. Lilien, "Sectoral Shifts and Cyclical Unemployment,"
Journal of Political Economy 90 (August 1982): 777-93. Lilien's work,
however, did not include attempts to separate the effects of permanent
sectoral shifts from the transitory component that later work suggested
was simply an effect of the business cycle. An attempt to separate
permanent from transitory components of sectoral shifts appears in
George R. Neumann and Robert H. Topel, "Employment Risk, Sectoral
Shifts and Unemployment," Research Paper, Economics Research Center, NORC, supported by the u.s. Department of Labor, Office of the
Assistant Secretary for Policy, January 1984; rev., October 1984.
27
Neumann and Topel's paper, however, is not alone in criticizing lilien's
approach as capturing the detailed impacts of the business cycle. For
a paper that begins with a similar criticism but develops an argument in
opposition to that of Neumann and Topel, see Katherine G. Abraham
and Lawrence F. Katz, "Cyclical Unemployment: Sectoral Shifts or Aggregate Disturbances?" Working Paper No. 1410, NBER Working Paper
Series (Cambridge, Mass.: National Bureau of Economic Research, July
1984); this paper has been published by the authors under the same title
in the Journal of Political Economy 94 (june 1986): 507-22. Abraham and
Katz build upon their critique of lilien by attempting to show that aggregate fluctuations are the major explainers of unemployment rate
fluctuations. Abraham and Katz, however, do not distinguish between
the roles of permanent and transitory sectoral shifts. Although the
controversy surrounding the role of aggregate fluctuations versus sectoral shifts is not the focus of the present paper, the controversy is important. To the extent that sectoral shifts-rather than aggregate
fluctuations-determine variations in unemployment, the power of national aggregate fiscal policy is diminished as a tool for reducing
joblessness.
6. The application of the U.S. GNP gap to explain unemployment rates is
based on a version of Okun's Law, which essentially suggests that a decline in aggregate demand will show up as an increase in GNP gap. According to Okun's approach, the drop in aggregate demand is seen as
reducing the demand for labor and thus increasing unemployment (see,
for example, Okun, The Political Economy of Prosperity). The actual
procedure for estimation of GNP gap as used in the present paper,
however, is found in Robert H. Rasche and John A Tatom, "Energy Resources and Potential GNP," Federal Reserve Bank of St. Louis, Review
59 (june 1977): 10-24.
7. To derive a Texas gap variable, the natural log of real gross state product
was regressed on a quadratic time trend, and the predicted values were
used as a proxy for potential gross state product for Texas. This variable
minus estimates of the natural log of the actual real GSP served as the
gap variable for Texas. The real GSP estimates were provided by Professor Perryman of Baylor University Forecasting Services. For deriving
a Mexican gap variable, real annual gross domestic product data from
the Mexican government were used. Because these data were not
quarterly, they were expressed quarterly by means of the Chow-lin
Procedure. (See Gregory C. Chow and An-Ioh lin, "Best linear Unbiased
Interpolation, Distribution, and Extrapolation of Time Series by Related
Series," The Review of Economics and Statistics 53 [November 1971l:
372-75.) Changes in annual Mexican industrial production indexes were
related to changes in Mexican gross domestic product. Quarterly
Mexican industrial production indexes, readily available from the Banco
de Mexico, were used to estimate quarterly real Mexican gross domestic
product. The natural log of Mexican GOP was then regressed on a
quadratic time trend, and the predicted value was used as a proxy for
potential Mexican GOP. Subtracting estimates of actual quarterly
Mexican GOP from this proxy for potential GOP produced a measure of
Mexican GOP gap.
8.
28
The Akaike Information Criterion was used (as described in George G.
Judge, William E. Griffiths, Carter Hill, and Tsoung-Chao Lee, The Theory
and Practice of Econometrics [New York: John Wiley and Sons, 1980l).
A full explanation of the MSEp Criterion, as used in the present paper,
appears in John Neter and William Wasserman, Applied Linear Statistical
Models: Regressions, Analysis of Variance, and Experimental Designs
(Homewood, III.: Richard D. Irwin, Inc., 1974). According to these selection processes, regressions were performed for all combinations of
lags from zero to five for each of the three gaps. It should be noted that
the set of all combinations of lags includes combinations in which distant lags are included but nearer lags are excluded. Thus, a fourthquarter lag may appear even when first, second, and third-quarter lags
are not included. In addition, these procedures do not constrain all lag
lengths for all lagged variables to be identical.
9. A method of lag-length selection in which intermediate lags are retained
was also applied. Under this procedure, the lags on the variables were
simultaneously increased until the minimum mean square error was
achieved, given the constraint that no intervening lags were allowed to
be deleted. Under this method, four lags were chosen as optimal for
each of the three gap variables. The resulting mean square error was
slightly higher than in the model in which intermediate lags were not
included, and the out-of-sample estimations from the four-lags model
were slightly less accurate than in the model for which results appear in
the table.
Because the reported model leaves out some intermediate lags, its dynamic multipliers are not smooth. However, as the explanatory contribution made through the inclusion of intermediate lags is essentially
insignificant, the alternative model changes this result very little. Also,
little change in the dynamic multipliers was realized through the application of Almon lag structures, an approach that was also tried. The lag
configurations of the reported model (and even of the unreported
models) are consistent with the notion not only that gap variables have
both labor demand and labor supply impacts but also that the bulk of
each of these two classes of impacts occurs at different times and, in
terms of timing, that they may be relatively discrete events.
10. It is reasonable to hypothesize that permanent sectoral shifts in Mexico
would also affect the unemployment rate in Texas. Anecdotal evidence
reveals that such permanent shifts occurred in Mexico during the 1970s.
Mexican labor data, however, that over the observation period were
consistent, reliable, and applicable to the estimation of a Mexican permanent sectoral employment shift variable were unavailable. Construction of a Mexican permanent shift variable was attempted by using
labor data extrapolated by the Mexican government from information
gathered during past census years. The resulting variable did not, however, provide significant explanatory power.
11. This procedure estimates the contribution that fluctuations in each
variable-in both contemporaneous and lagged form-have made collectively to fluctuations in the Texas unemployment rate over the sample period. This estimation was accomplished by taking the absolute
value of the change in the Texas unemployment rate predicted by a
given variable, dividing it by the sum of these predicted changes for
each quarter in the sample, and then averaging each variable's contribution to predicted variation for the entire sample period.
12. All explanatory variables but one were available in their standard forms
for the out-of-sample period. In order to construct out-of-sample
predictions, it was necessary to estimate values for the permanent sectoral employment shift. A measure of the permanent sectoral shift was
not available in its standard form out of sample because 16 quarters of
employment data observations past the end of the sample period were
required to estimate this variable. Thus, to estimate the standard permanent sectoral shift variable for 1983.1V, employment data through
1987.1V would be required. In order to estimate permanent sectoral
shifts through 1983.1V, a separate variable was created that required
only eight observations after the sample period. To estimate the
standard permanent shift variable out of sample, past values of perma-
Federal Reserve Bank of Dallas
nent shift were regressed on values of the eight-quarter permanent shift
approximation. Once the relationship between these two variables had
been estimated, it was used to simulate the standard permanent shift
variable over the prediction period.
Appendix A
Portfolio Variance
The employment portfolio variance may be defined as the
summation of variances and covariances of employment
within and across industries, weighted by measures of
long-run employment shares. Since it represents such a
summation, the portfolio variance may be disaggregated
into the sum of the individual employment variances
multiplied by the squares of the share weights of the individual employment sectors and an appropriately weighted
sum of the employment covariances. This latter pair of
summations can be expressed mathematically, as follows,
where (JJ represents the employment variance of industry
j; (Jij represents the covariance of employment between
industry i and industry j; and Si and Sj represent the respective long-term employment share-weights of industry
i and industry j:
Every industry thus contributes to the regional portfolio
variance, not only through the first term on the right-hand
side of the equation but also through the weighted sum of
all the covariances with the other industries in the portfolio.
Employment was disaggregated by 27 standard industrial
classifications (SICs) for the United States. However,
single-digit SICs were used in all industries except mining
and manufacturing. In manufacturing, two-digit SICs were
applied, except in nonelectrical machinery. Here, the importance of oilfield equipment manufacturing to Texas was
taken into consideration. Oilfield equipment, a three-digit
SIC, was disaggregated from the rest of nonelectrical machinery. Likewise, in the mining classification, oil and gas
extraction was separated out. Based on these data, a relative variance-covariance matrix was estimated for the
sample period 1970-81. More specifically, a variance-
Economic Review - November 1986
covariance matrix was estimated from the residuals of employment around an estimate based on a quadratic time
trend standardized with respect to the mean of each series.
Thus, each element of the matrix includes a relative
covariance of the following form:
Here, fit and fjt represent the observed levels of employment il), indu~tries i and j, respectively, during quarter
t; while fit and fjt represent the expected levels of employment in industries i and j for month t given by an estimate
based on a quadratic time trend for each industry; and E;
and Ej represent the arithmetic means of the individual industry time series.
This matrix can be condensed to a variable describing the
employment variance for a given region by applying
region-specific weights to the portfolio variance formula as
noted in the first equation and substituting (iij from the
second equation into the first equation in place of (Jij" In
sum, (Jp offers a measure of employment variance for a
geographic region under analysis based on the industrial
structure (as reflected in the weights) of the region but using a national matrix (for the components of (iij)'
As weights, estimates were used of the relative proportions of quarterly employment (again based on quadratic time trends) in each of the industries represented
within SICs described for the state of Texas. Thus, the
weights Si and Sj are taken to be expected proportions of
employment in industries i and j in Texas. In the report on
the regression results, (Jp is referred to as Texas Portfolio
Variance, and fluctuations in this variable are expected to
be related positively to fluctuations in the unemployment
rate.
29
Appendix B
Permanent Sectoral Employment Shifts
Neumann and Topel 1 develop a permanent sectoral shift
variable using Euclidian lengths. They begin by generating
a variable, be;, that represents the difference between
moving averages of future and past vectors of employment
shares at each time t. This variable, which is taken as a
measure of the direction of permanent change in the sectoral distribution of employment, is generated as follows.
In any quarter, et = (e1t . .. ent ) be the vector of employment shares across n industry groups. Then the direction
of permanent change in this distribution is the vector
J
J
Lle; = Iptt+j - IPje t-1,
j=1
j=1
J
where
2:.Pj =
1. In practice, J = 16 quarters, with smoothly
'-1
decliniri-g weights Pj = (.9) j /(7.33).
While the preceding measure defines the direction of
permanent change, the actual difference between the current employment distribution and the conformable past
distribution is defined as the vector
J
Llet = et - Ipj et-i"
j=1
Because this vector has both permanent and transitory
components, a consideration of the permanent component of this actual change requires disaggregation of the
permanent and transitory components. A permanent
component of such a reallocation in each period is defined
as the least squares projection of the actual difference be-
30
tween the current employment distribution and the past
distribution onto the vector Lle;. That is, the permanent
component of a change in distribution is the portion of the
actual change that can be explained by changes in the difference between moving averages of future and past vectors of employment shares at each t. Where Lle t denotes
the vector representing the actual difference between the
current employment distribution and the conformable past
distribution, then the vector of permanent changes in employment shares can be expressed as
Finally, the size of the permanent shock to the distribution of employment was measured by using the
Euclidean length of Llei:
This left-hand-side variable is referred to as SHIFT, and
changes here are expected to be positively related to
changes in unemployment rates. In this model, industries
are disaggregated by the same 27-industry configuration
as that used in the portfolio variance estimate. The result
is a disaggregation that includes all nonagricultural wage
and salary employment in the United States.
1.
George R. Neumann and Robert H. Topel, "Employment Risk,
Sectoral Shifts and Unemployment/' Research Paper, Economic
Research Center, NORC, supported by the U.S. Department of
Labor, Office of the Assistant Secretary for Policy, January 1984;
revised, October 1984.
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