Demand and Price Analysis, Food Consumption/Nutrition/Food Safety,

Bacon, J. Richard

Bonnell, D.

Perez, A.

Toensmeyer, Ulrich C.

English

Research Papers in Economics

Analysis of Trends and Forecasts
In Coffee Prices and Consumer Consumption
In the Northeast and United States
by
D, Bonnell
University of Delaware
Department of Food and Resource Economics
Newarh DE
U. C. Toensmeyer
University of Delaware
Department of Food and Resource Economics
Newark, DE
J. R. Bacon
University of Delaware
Department of Food and Resource Economics
Newariq DE
A. Perez
Universi@ of Delaware
Department of Food and Resource Economics
Newark DE
Introduction
In the five years ending in 1985/86, US.
net imports of coffee averaged 32 percent of total
imports of the International Coffee Organization
(ICO) net importer member nations. The ICO
consumer members account for approximately 90
percent of totai world imports. Although the per-
centage has declined from the approximately 50
percent of world imports held in the 1950s, the
United States is atiil the largest total and net
importer of coffee in the world. Aa such, the
direction of the trend in U.S. coffee consumption
can have a strong bearing on the world trend.
Imports of green coffee into the United
States are dominated by Brazilian (with
unwashed mild arabicas) and Colombian coffee
(colombian mild arabicas), as would be anti-
cipated given the dominance of these two export-
Journal of Food Distribution Research
ing countries along with the rate of absorption
required in the United States. Moreover,
Indonesia is the largest supplier of green robustes
used primarily in the manufacture of soluble or
instant coffee.
Per capita coffee consumption in the
United States peaked in 1962 at 3.12 cups per
person per da~ declining almost steadily, it
reached 1.76 cups per person per day in 1987.
On a per capita basis, the 44 percent decline in
coffee consumption was brought about by the
growth in U.S. population during that period.
Furthermore, loss in consumption was most
heavily felt in the 20-29 age group where coffee
is in strong competition with soft drinks and
increasingly, with fruit juices. Nevertheless,
recent yeara indicate a leveling off in the down-
ward pace of per capita coffee consumption
aithough neither an end in the downward trend
Febmary 90/psge 139nor signs of a rejuvenation in consumption may
be suggested.
The objectives of the study were to deter-
mine past and future trends in coffee prices and
consumption for the Northeast and the United
States and to describe the role of the Interna-
tional Coffee Agreement in affecting supply and
demand and price of coffee to the U.S. consumer.
Methodology
Time series models for per capita coffee
consumption in the United States and average
monthly prices for ground roast coffee in the
Northeast and the United States were developed
using a nine-year series from 1980 to 1988. The
Univariate Box-Jenkins Autoregressive Integrated
Moving-Average (ARIMA) procedure in the Fore-
cast Pro software was applied to the series. An
ARIMA process is an algebraic statement exhibit-
ing how a time series variable can be statistically
related to its past values. Whenever the auto-
covariance function of a variable becomes a com-
bination of intertemporal decay and truncation
such that a zero autocovariance is obtained after
p time periods, the function may be expressed as
an autoregressive moving-average model of
degrees p and q (ARMA [p,q]) and may be speci-
fied as:
Y(t) = a(i)Y(t-i) + b(j) + e(t) EQ(l.0)
where y represents the endogenous variable, a
and b are parameter estimates, t is a time sub-
script p and q denote the number of lags and e
is a white noise error term. The statistical con-
cept of correlation is used to estimate the rela-
tionship between observations within the series.
The ARIMA procedure is a highly recom-
mended forecasting technique. In fac~ when
compared to individual econometric forecasts and
expert opinion methods, the ARIMA performed
substantially better in terms of price forecasting
and tracking price movements (Brandt &
Bessler). Moreover, ARIMA models are most
applicable to short-term forecasting, placing more
emphasis on more recent than distant past obser-
vations.
The use of a stationary time series is a
condition in ARIMA analysis. The stationarity
assumption simplifies the theory underlying
ARIMA models and helps ensure that useful
estimates of the parameters are generated from
a moderate number of observations (Pankratz).
The series is stationary when its mean, variance
and autocorrelation are essentially constant
through time.
Many non-stationary series encountered in
economic applications can be transformed into a
stationa~ series through the differencing proced-
ure. This procedure deals with the calculation of
successive changes in the values of a data series
to eliminate any linear trend. The transformed
series using an appropriate differencing operation
most often has a mean of zero.
The estimated Autocorrelation Function
(ACF) and the estimated Partial Autocorrelation
Coefficient (PACF) were guides in choosing the
appropriate ARIMA models. The estimated ACF
calculates a correlation coefficient for each set of
ordered pairs (Yt, Y~+J and was generated using
the standard formula
n-k
~; (y~ - ‘y) (y~+k -1?-)
r~ =
: (%-32
t= 1 EQ (2.0)
where rk was a notation for the estimated ACF,
Y~for the value at period L Y for mean value, n
for the number of time periods.
The estimated PACF is basically similar to
the estimated ACF although it also considers the
effect of intervening observations. It can be cal-
culated as follows
?% = rl
k-1
‘k -.: $k.11 j%-j
@kk = 1-1 9 (k = 2, 3,...)
k-1
1- x ~~.l, jmj
j=l EQ (3.0)
where Ow is the estimated PACF and @Lj= Ok.l,
j - @kk @k-j> k - j(k = 3, %..., j = L %4 - U.
When the theoretical ACF of a stationary
series decays and damps out toward zero at the
same time that its theoretical PACF rapidly drops
off to zero, after p time periods, the lag length of
the last PACF spike equals the AK order. Alter-
natively, the theoretical ACFS of moving-average
processes, processes that include past random
shocks, cut off to zero after a certain number of
spikes while their theoretical PACFS decay
toward zero. The lag length of the last ACF
spike equals the MA order (q) of the process.
February 90/page 140 Journal of Food Distribution ResearchThe optimal number q AR(p) and M-A(q) lags
were baaed on the Schwarz Criterion (BIC)
whose function is to balance complexity and
goodness of fit of the model. The fit of the
models was also evaluated using the coefficient
of determination (Rz), a measure of the projection
of the variation in the forecasted variable that
was captured by the forecast.
Empirical Results
Based on the Schwarz Criterion, an
ARIMA (1,0,1) was selected to best describe
coffee prices in the Northeast and the United
States. The monthly average coffee prices are
therefore affected by coffee prices one month back
and the corresponding random shocks in the
model with the same lag period. The ARIMA
procedure was unfit in describing the behavior of
per capita coffee consumption in the United
States. The consumption forecasts were unreli-
able because the model generated the same
monthly consumption value for the forecast
period 1988 to 1990. No alternative procedure
was applied in estimating a per capita consump-
tion model for coffee.
The estimated coefficients for the Northeast
and the U.S. coffee prices are presented in
Table 1.
The numbers inside the parentheses and
brackets are the T-ratios and standard errors
corresponding ta the coefficient estimates, respec-
tively. Baaing judgment on the R2 values
obtained in the model estimation process
(Northeast 89%; and United States, 94%), the
price models had a high degree of accuracy in
predicting actual coffee prices over the period
1980 to 1988. The high R2 values indicated that
a high proportion of the variation in the forecasts
were explained by the variable being forecasted.
Table 2 presents the comparison between the
actual and predicted Northeast and U.S. coffee
prices for 1988.
The abiliiy of the two price models to
closely replicate the actual coffee prices implies
that the models can be used in generating out of
sample forecasts. The forecast values for coffee
prices in 1989 and 1990 with the corresponding
95 percent confidence limit are presented in
Tables 3 and 4, respectively.
Implications
The trends in coffee prices and per capita
coffee consumption have significant implications
on the overall market prospects for coffee. Coffee
is an important commodity in international trade
because it is second in rank to oil as an earner of
foreign exchange in developing countries. Inter-
national trade in coffee is dominated by the Inter-
national Coffee Organization (ICO) whose mem-
bers, consisting of both producing and nonproduc-
ing countries, comply with a coffee quota system
under the International Coffee Agreement (ICA).
However, only until recently (July 1989), the ICA
fell apart since the United States, a major
importing member nation of the ICO, disagreed
on the two-tier pricing method of the agreement.
Per capita coffee consumption in the
United States gradually decreased from 2.02 cups
per person per day in 1980 to 1.76 cups per per-
son per day in 1987. U.S. ground roast annual
average coffee prices were $3.14 per pound in
1980 and stabilized at approximately $2.50 per
pound until 1985; then they increased to $3.40
per pound in 1986 and decreased to $2.79 in
1987. Increasing preferences for other beverages
such as fruit juices and soft drinks have been
observed over the years as consumers become
more aware of the negative health effects of
ca.tTeine. The presence of more substitutes
implies that coffee is a more price sensitive com-
modity.
Based on the assumption that the ICA is
still in effe~ the downward trend in the 1989
and 1990 forecasts for coffee prices can be gener-
ally explained by changes in tastes and prefer-
ences as more and more consumers become more
health conscious. In addition, the long time
existence of the ICA indicates the possibility of
promoting overproduction in the long run. In a
perfectly competitive marke~ production invest-
ment is regulated by price. In a market regu-
lated by export quotas that are adjusted up or
down by prescribed prices, however, the price
signal to reduce production is diluted (Bonnell
1988). The structure regulations set by the ICA
encourages individual nations to invest in produc-
tion because noninvestment would cause a reduc-
tion in their export market share.
In light of the recent termination of the
ICA, coffee prices have been unstable and gener-
ally falling in the short run because producers
can sell at quantities higher than the set quotaa,
i.e., selling at prices below the set agreement,
Efforts are, however, being made by the United
States and Colombia to reinstate international
quotas on coffee exports to support world coffee
prices.
Journal of Food Distribution Research February 90/page 141Table 1
Parameter Estimates, T-Ratios, and Standard Errors for the
Northeast and U.S. Coffee Price Models, 1980-1988.
Estimated Coefficient
Parameter ------------------------------------------------------------------------------------------------------------------
Northeast United States
A[l] 0.87132 0.951439
(9.359) (10.728)
[0.093931] [0.0886901
B[l] -0.338800 -0.480524
(-3.436) (-5.484)
[0.098606] [0.0876201
CONSTANT 0.331853 0.133836
R’ 0.892 0.940
BIC 0.114282 0.085909
Source: Calculations
Table 2
Actual and Predicted Average Monthly Prices Per Pound of Ground Roast Coffee
In the Northeast and United States, January - December 1988.
Actual Predicted
Month -----------------------------------------------------------------------------------------------------------------------
NE us NE us
1 2.715 2.635 2.639 2.566
2 2,730 2.665 2.744 2.674
3 2.710 2.634 2.727 2.665
4
5
6
7
8
9
10
11
12
2.783
2.863
2.872
2.909
2.958
2.927
2.975
2.952
3.063
Source: Calculations
2.699
2.729
2.735
2.784
2.834
2.840
2.855
2.892
2.925
2.709
2.804
2.869
2.858
2.907
2.950
2.897
2.974
2.920
2.625
2.737
2.726
2.740
2.804
2.845
2.834
2.860
2.901
February 90/page 142 Journal of Food Distribution ResearchTable 3
Predicted Average Monthly Coffee Prices in the Northeast and the United Stites,
January - December 1989
Forecast Lower (95%) Upper (95%)
Month --------------------------------------------------------------------------u-S-------------------NE------------------
NE us NE us
1 3.073 2.920 2.845 2.757 3.302 3.100
2 3.034 2.920 2.673 2.620 3.394 3.220
3 2.999 2.912 2.563 2.532 3.434 3.293
4 2.968 2.904 2.482 2.464 3.454 3.346
5 2.941 2.897 2.420 2.408 3.463 3.387
6 2.918 2.891 2.370 2.361 3.465 3.420
7 2.897 2.884 2.331 2.321 3.463 3.447
8 2.879 2.878 2.298 2.286 3.459 3.470
9 2.862 2.872 2.271 2.255 3.454 3.489
10 2.848 2.866 2.249 2.228 3.442 3.518
11 2.836 2.861 2.230 2.203 3.442 3.524
12 2.825 2.856 2.214 2.182 3.436 3,530
Source: Calculations
Table 4
Predicted Average Monthly Coffee Prices in the Northeast and the United States,
January - December 1990
Forecast Lower (95%) Upper (95%)
Month
NE US NE us NE us
1
2
3
2.815
2.807
2.800
2.851
2.846
2,842
2.201
2.189
2.180
2.172
2.164
2.158
2.162
2.144
2.128
3.430
3.425
3.419
3.540
3.549
3.556
2.113
2.100
2.088
3.414
3.410
3.406
3.562
3.568
3.572
4
5
6
2.793
2.787
2.782
2.838
2.834
2.830
2.826
2.823
2.820
2.153
2.149
2,145
2.077
2.067
2,057
3.402
3.399
3.396
3.576
3.579
3.582
7
8
9
10
11
12
2.778
2.774
2.770
3.584
3.586
3.588
2.768
2.765
2.762
2.817
2.814
2.811
2.141
2.139
2.136
2.050
2.041
2.034
3.394
3.391
3.389
Source: Calculations
Journal of Food Distribution Research February 90/page 143Summary and Conclusions
In general, the coffee price models for the
Northeast and the United States had a high
degree of accuracy in predicting the actual prices
in the period 1980 to 1988. There was a general
downward trend in the out-of-sample foreeast
values generated by the two models for the
period 1989 to 1990. Changes in coffee per
capita consumption were explained by a decrease
in coffee demand due to changes in consumer
tastes and preferences rather than to price. The
trend in the foreeast prices, on the other hand, is
attributed to the export quotas set by the Inter-
national Coffee Agreement. The possibilities of
reinstating the export quotas may be one solution
to increase coffee prices in the short run.
References
Bonnel, Deirdre. Selected Aspects of Interna-
tional Green Coffee Marketing.
Unpublished Master’s thesis, Department
of Food and Resource Economics. The
University of Delaware. August 1988.
Brandt, J.A. and D.A. Bessler. “Composite Fore-
casting An Application with U.S. Hog
Prices.” American Journal of Agricultural
Economics 63:135-40, 1981.
Goodrich, Robert L. and E. A. Stillwagen. Fore-
cast Pro Operations Manual.
Massachusetts: Business Forecast Systems,
Inc., 1988.
Pankratz, Alan. Forecasting with Univariate Box-
Jenkins Models: Concepts and Cases.
New York John Wiley and Sons, 1983.
February 90/page 144 Journal of Food Distribution Research

Composite Forecasting An Application with U.S. Hog Prices.”

February 90/page 144

Pankratz, Alan. Forecasting with Univariate BoxJenkins Models: Concepts and Cases.

ANALYSIS OF TRENDS AND FORECASTS IN COFFEE PRICES AND CONSUMER CONSUMPTION IN THE NORTHEAST AND UNITED STATES

ANALYSIS OF TRENDS AND FORECASTS IN COFFEE PRICES AND CONSUMER CONSUMPTION IN THE NORTHEAST AND UNITED STATES

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