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Asymptotic Limit of Consumption and Threshold Level
of Income: A Macroeconometric Analysis of Asian
Countries
R. C. Sharma*
and
Shubhangi Jore**
European countries, which are becoming wealthier and are affluent
enough so that the countries consume food not only for nourishment
reasons but also for enjoyment, preferences, ethics, culture, safety,
prestige, impulse and other factors, whereas, Asian countries are still
mainly dependent on income for their food consumptions. The patterns of
consumption if observed and analysed with the movement of income can
throw some light on the relationship between them. It is likely that with an
increase in income consumption of food increases. But this movement in
the same direction could not continue for a long run and it is expected
that it will either slow down or will stop with a sufficiently large value of
income. In terms of econometrics this level is said to be asymptotic level
of consumption. Major 37 countries of Asian region are included and
statistics of aggregate food consumption in kilo calories per capita per
day are derived from FAO. Per capita GDP for Asian countries has been
collected from http://unstats.un.org. Reciprocal transformation model is
applied to estimate the parameters of interest. The validity of the model is
tested through Durbin Watson d statistic and Ramsey’s RESET test is
used to detect specification error (Gujarati, 2007). The model is applied in
panel data taking three different cases resulting the second case to be
the best fit when slope coefficient was kept constant and the Intercept
varied across countries. Individually each country had different and
significant asymptotic limit of consumption except Turkey, which showed
insignificant value of the same. The material presented in terms of
estimated asymptotic limit of consumption and threshold level of income
will be used to help formulate policies for agricultural production in Asia
and may help the policy makers and traders for determining priorities of
food consumption and marketing.
Field of Research: Economics
*Dr. R. C. Sharma, Professor and Head, School of Future of Studies and Planning and Dean
Management Studies, Devi Ahilya University, Khandwa Road, Indore (M.P.)– 452 001, INDIA.
e-mail: [email protected], [email protected]
Mobile: +91 9685046667, Fax: 91-731-2467378
**Mrs. Shubhangi Jore, Assistant Professor, Prestige Institute of Management and Research,
Indore and Research Scholar, School of Future Studies and Planning, Devi Ahilya University,
Indore (M.P.) – 452 001, INDIA.
e-mailid: [email protected], [email protected], mobile:+91 9425066461
1
1.1 Introduction
European countries, which are becoming wealthier and are affluent enough so
that the countries consume food not only for nourishment reasons but also for
enjoyment, preferences, ethics, culture, safety, prestige, impulse and other
factors, whereas, Asian countries are still mainly dependent on income level for
their food consumptions. The patterns of consumption if observed and analysed
with the movement of income can throw some light on the relationship between
them. It is likely that with an increase in income consumption of food increases.
But this movement in the same direction could not continue for a long run, it is
expected that it will either slow down or will stop with a sufficiently large value of
income. In terms of econometrics this level is said to be asymptotic level of
consumption. The consumption is said to reach a plateau when despite increase
in income the consumption remains unchanged. Talking about the other aspect,
income level, it is likely that any food item cannot be consumed when level of
income is negligibly small. In other words, there has to be a cut off level of
income above which the food is consumed. Specifically studying the cut off level
of income for food is of much need to understand the threshold level of income
for total food and for different food categories. It is of further interest to
understand such a level of income which will indicate the threshold level at which
the food is consumed particularly in Asian countries.
Table 1.1: Global and Regional Per Capita Food Consumption
(kcal per capita per day)
Region
1997
1964 - 1974 – 1984 –
1966
1976
1986
1999
2358
2435
2655
2803
2054
2152
2450
2681
2290
2591
2953
3006
2058
2079
2057
2195
2015
2030
World
2940
3050
Developing countries
2850
2980
Near East and North Africa
3090
3170
Sub-Saharan Africa*
2360
2540
Latin America and the
2393
2546
2689
2824
2980
3140
Caribbean
East Asia
1957
2105
2559
2921
3060
3190
South Asia
2017
1986
2205
2403
2700
2900
Industrialized countries
2947
3065
3206
3380
3440
3500
Transition countries
3222
3385
3379
2906
3060
3180
* Excludes South Africa
Source: Diet, Nutrition and the Prevention of Chronic Diseases…, section 3.2,
Food and Agriculture Organisation (FAO) Corporate Document Repository.
It is well documented by various research papers the way in which food
consumption patterns are changing in Asia as economic growth and
development proceeds. Income growth, urbanisation and the modernisation of
marketing infrastructures are forcing consumption patterns to switch from a
prominence on traditional foods (such as some cereals and root crops) to non2
traditional cereals (eg wheat-based foods) and value-added processed and highprotein foods such as those derived from animal products (Huang and Bouis
2001, Huang and David 1993, Rae 1997 and 1998). This typically involves a
switch in the domestic utilisation of grains from human consumption to feeding of
livestock. Much recent debate has centered on the impacts of such consumption
changes on world food markets, especially those for grains.
To measure and evaluate the evolution of the global and regional food situation
of Asian countries, food consumption is expressed in kilocalories (kcal) which is a
key variable used to evaluate the same. The variable in a more appropriate form
is termed as national average apparent food consumption. Analysis of FAOSTAT
data shows that dietary energy measured in kcals has been steadily increasing
on a worldwide basis; availability of calories per capita from the mid-1960s to the
late 1990s increased globally by approximately 450 kcal per capita per day and
by over 600 kcal per capita per day in developing countries (Table 1.1). This
change has not, however, been equal across regions. The per capita supply of
energy has risen dramatically in East Asia (by almost 1000 kcal per capita per
day, mainly in China). In short, it would appear that the world has made
significant progress in raising food consumption per person. The increase in the
world average consumption would have been higher but for the declines in the
transition economies that occurred in the 1990s. It is generally agreed, however,
that those declines are likely to revert in the near future.
With the development of the economy in terms of income and GDP, food
consumption patterns also changes. It affects not only the nutritional status of the
people but also the consumption pattern of the country. It is of much importance
to estimate the level of income at which consumption of commodity under study
starts being consumed. Along with cut off level of income it is of much need to
find the maximum level of consumption of food within Asian countries. Estimation
of asymptotic limit of consumption for different food categories will facilitate to
understand the potential consumption in Asia. Present study is an attempt to
estimate the parameters of interest based on the existing literature using
appropriate econometric model. Considering the need to study the asymptotic
level of food and threshold level of income for Asian countries the objective of
this study is to estimate asymptotic limit of food consumption and to estimate the
threshold level of the income at macro level in Asian countries.
1.2 Literature Review
In the past there have been evidences in the evolution of food consumption in
European countries analyzed by several authors (Besch, 1993; Blandford, 1984;
Frank and Wheelock, 1988; Gracia and Albisu, 2001; Meulenberg and Viane,
1993; Ritson and Hutchins, 1991; Wheelock and Frank, 1989), outlining some of
the important findings as follows: trend of decreased proportion of expenditure
allocated to food, food consumption reached the maximum level in total food
consumption, there was an increase in proportion of taking food away from home
and there was a shift in the food consumption structure. These trends indicated
that economic growth was common to all the European countries. The reason for
consumption reaching the maximum level in the total food category was that
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people wanted to eat better as their daily intake requirements diminished and in
wealthy countries generally quantity surpassed by quality concerns. The
proportion of shift in food consumption structure was not homogenous for all the
European countries, it varied depending on their cultural and historical
evolutions.
Brunso et al. (1996) studied food consumers, from French, German, British and
Danish, and classified them into five segments. With respect to European
countries it was concluded that there had been a saturation point in the markets
of European Union with quantities consumed had reached a peak. New products
and services were searched persistently by agri-food system which would add
value. Gracia and Albisu (2001) had basically showed the similarities and
dissimilarities that existed in different European countries on the basis of
economic factors, lifestyle, and socio-demographic characteristics, just to name a
few. The important factor that influences food consumption are household real
income, product price, and the prices of substitute products including nonfood
products (Deaton and Muellbauer, 1980 and Gracia et al., 1998) and two more,
added by Elsner and Hartmann (1997), as preferences and socio-demographic
factors. The models that are based only on these three factors are quite powerful
and impressive in predicting (Connor, 1994).
Saxon (1975) has studied the saturation level of food particularly in Japan.
During the period considered there were evidences of people approaching or
reached a level of saturation in food consumption taken as a whole measured in
calories. It is generally acknowledged that income and price are by no way the
exclusive determinants of food consumption, although they are thought to be
normally the easiest to measure. Huang (1985) examined the year-to-year
variations in per capita consumption for the food category of beef, pork, chicken,
turkey, eggs, milk and wheat flour for the period 1954-1983. The model used was
able to explain about 97% of the annual variation in demand and was competent
enough to match the turning points like peaks and troughs in consumption.
As per the study of Gil et al. (1995) the countries with low income level has
relatively high food consumption and as income grows, it increases at a lower
rate, up to a threshold which is difficult to surpass because of physical limitations,
although it generally becomes more diversified. Keeping in mind the instances of
total as well as animal products might reach a maximum as income grows. The
regression based on reciprocal functional form was used to derive statistical
estimates. Reason for using reciprocal model was that all European countries,
except Germany, showed an upward trend in the share of animal calories
consumed over the period 1970 to 1980 however, it stabilized or even declined in
the decade from 1980 to 1990.
To describe the relationship between income and average daily calorie intake,
Petrovici et al. (2005) used the same reciprocal functional form. Asymptotic level
was estimated separately for per capita daily calorie consumption, calorie
consumption derived from animal products, protein consumption, and protein
consumption derived from animal products, for the year 2000. The gross
domestic product (GDP) in purchasing power parity terms was used as
independent variable for all the four equations. Other than reciprocal functional
form, Petrovici et at. (2005) has used a log-inverse function, which also allows to
4
estimate asymptotic limit of consumption. To compare the goodness-of-fit
measure of the log-inverse model with the reciprocal model, the approach
recommended by Wooldridge (2000) was followed. To specify the model,
Ramsey-Reset test was used and the model was well specified with no omitted
variables (Ramsey, 1969).
Senugal and Senugal (2006) explored the relationship between income and total
calories and animal calories based on the income calories elasticity using
reciprocal function.The model was tested for its validity and specification error
before estimating the parameters of the variables using Durbin-Watson d statistic
and Ramsey’s RESET. The income elasticities decreased in all countries for both
total and animal products consumption over the period considered due to specific
property of the reciprocal functional form. To estimate the maximum potential
level of consumption and threshold level of income, present study is an attempt
to estimate the parameters of interest particularly in Asian countries.
1.3 Data and Methodology
Major countries of Asian region included in the study are listed as: Armenia,
Azerbaijan, Bangladesh, Brunei Darussalam, China, Cyprus, Georgia, India,
Indonesia, Iran, Israel, Japan, Jordan, Kazakhstan, Korea Dem, People's Rep
Korea, Kyrgyzstan, Laos, Lebanon, Malaysia, Maldives, Mongolia, Myanmar,
Nepal, Pakistan, Philippines, Saudi Arabia, Sri Lanka, Syrian Arab Republic,
Thailand, Timor-Leste, Turkey, Turkmenistan, United Arab Emirates, Uzbekistan,
Vietnam, Yemen. Here China refers to China Mainland, Hong Kong SAR, Macao
SAR and Taiwan Province. Statistics of aggregate food consumption, its product
composition in kilo calories per capita per day are derived from the Food and
Agriculture Organization of the United Nations (FAO) http://faostat.fao.org for the
years from 1990 to 2005.
Total calorie intake is used as data input. The unit of measurement of dietary
energy is in terms of kilocalories (kcal). The reason for using caloric measure of
food consumption was that it facilitates aggregation of different foods and the
derivation of shares by types of foodstuff. Also caloric equivalents are assumed
to be constant over time and to be common across countries simplifying crosscountry comparisons of changes in consumption. The conversion from product
weights to caloric equivalents is one of the limitations of using caloric measure.
Statistical Year Book – Asia and Pacific 2009 and per capita GDP for Asian
countries has been collected from http://unstats.un.org.
To estimate asymptotic limit the most suitable econometric tool is reciprocal
transformation model. Physiological considerations suggests that per capita
consumption of food will increase only upto a limit as income grows and there will
exist a maximum potential level of consumption (Senugal and Senugal, 2006).
Hence for such case the reciprocal functional form is more appropriate. For the
validity of the reciprocal model the durbin Watson d statistic and Ramsey’s
RESET (regression specification error test) test is used to detect specification
error (Gujarati, 2007).
The reciprocal model is given by
5
 1
Yi  1   2 
 Xi
where

  u i

… (1.1)
Yi = Food Consumption in kcal for ith country
X i = GDP at factor cost in US $ for ith country
The model (1.1) has built in itself an asymptote or limit value that the dependent
variable will take when the value of X variable increases indefinitely. As X
 1 

 Xi 
 2 
increases indefinitely, the term
approaches zero and Y approaches the
limiting or asymptotic value β1. The threshold level of income is estimated by
taking negative ratio of β2 to β1.
1.4 Findings and Discussion
It is likely that in long run with an increase income there is an increase in food
consumption. Hence economically income is the determinant of long-run
changes in per capita food consumption. From Figure 1.1 it is apparent that there
is an indication of positive relationship between average per capita GDP in
dollars and average total calories in Asia. The relationship is moderately
correlated, as Karl Pearson’s coefficient of correlation is showing a positive 0.35
value. However, food consumption does not increase with the same proportion
as that of real per capita income and hence attains a maximum potential level
after a period of time and there also exists a threshold level of income below
which the food is not consumed.
Figure 1.1 Scatter Plot of Average GDP at Factor Cost in Dollars vs Average
Total Calories in Asia
Average Food Consumption
2700
2680
2660
2640
2620
2600
2580
2560
0
1000
2000
3000
4000
Average GDP
6
5000
6000
7000
The table 1.2 indicates that average apparent consumption of total calories for 37
Asian countries is 2687 kcal in 1990 and decreased to reach 2684 kcal in 2005.
The total calories are consumed in Asia with a wide variety ranging from a
minimum of 1590 kcal in Yemen to a maximum 3695 kcal in Israel in 2005.
Trends in food consumption also vary at country level. Of considered countries
almost half of the countries such as Azerbaijan, Bangladesh, China, Cyprus,
Georgia, Indonesia, Iran, Israel, Jordan, Korea Republic, Lao People Democratic,
Lebanon, Malaysia, Maldives, Myanmar, Pakistan, Philippines, Thailand,
Turkmenistan and Vietnam are showing a steady upward trend of total calories
consumption and rest others like Armenia, Brunei Darussalam, India, Japan,
Kazakhstan, Korea Dem., Kyrgyzstan, Mongolia, Nepal, Saudi Arab Emirates, Sri
Lanka, Syrian Arab, Timor-Leste, Turkey, United Arab Emirates, Uzbekistan and
Yemen are showing a steady downfall in the consumption of total calories during
the considered period.
Considering food consumption in kcal, Yi, as dependent variables; and the gross
domestic product (GDP) at factor cost price in $, Xi, as independent variable. Due
to some of the important features of the reciprocal model such as when X (GDP
 1
at factor cost price in US $) increases indefinitely, the term 2 X approaches
zero and Y (food consumption in kcal) approaches the limiting or asymptotic
value β1. Hence the reciprocal model has built in it the asymptote or limit value
that the dependent variable takes when the value of the X variable increases
indefinitely (Gujarati, 2007). To estimate the parameters in Asia, the next section
analyzes panel data.
There are 37 cross-sectional units in terms of countries of Asia and 16 time
periods. The data of food consumption in kcal is pooled by taking a random
sample of countries at each time period. In this case each cross-sectional unit
has the same number of time series observations and hence called balanced
panel. There are various reasons to use independently pooled cross-sectional
data. It increases the sample size, in the present study it is 592.
By pooling random samples drawn from the same population, but at different
points in time, the resultant estimators are more precise and the test statistics is
more powerful. Pooling is helpful if the relationship between the dependent
variable (food consumption) and the independent variable (GDP at factor cost in
$) remains constant over time. The pooled cross-sectional data raises minor
statistical complications. There is no issue of serial correlation because of
independent sample across time. Estimation of pooled data can be done by
ordinary least square method (OLS). Estimation of panel data regression model
taking the fixed effects approach is done using the equation (1.1) which can be
rewritten as equation (1.2) given below, by combining, all 592 observations:
1
Yit  1   2 .
 u it
X it
… (1.2)
 
where
i = 1, 2, …., 37 (ith cross-sectional unit)
t = 1990, 2000, …, 2005 (tth time period)
Yit = Food consumption for ith country (cross-sectional unit) at tth time (year)
Xit = GDP at factor price in $ for ith country (crossectional unit) at tth time (year)
7
Table 1.2: Evolution of Food Consumption and Per Capita GDP in Asian Countries (daily calories and dollars)
1990
Country
Armenia
Azerbaijan
Bangladesh
Brunei Dar.
China
Cyprus
Georgia
India
Indonesia
Iran
Israel
Japan
Jordan
Kazakhstan
Korea Dem
Korea Rep.
Kyrgyzstan
Lao Peop
Lebanon
Malaysia
Maldives
Mongolia
Myanmar
Nepal
Pakistan
Philippines
1995
2000
2005
Total
Total
Total
Total
GDP
GDP
GDP
GDP
Calories
Calories
Calories
Calories
(US $)
(US $)
(US $)
(US $)
(kcal)
(kcal)
(kcal)
(kcal)
2676
609
2065
399
2083
620
2380
1624
2166
903
1940
395
2313
647
2744
1586
2122
269
2051
300
2260
326
2310
376
3147 13391
3044 16950
2965 17997
2611
25497
2825
358
2831
635
2861
956
3019
1785
3049
9970
3027 14090
2995 13399
3295
22399
1272
1563
2150
541
1946
648
1797
1433
2428
380
2493
387
2451
447
2417
713
2737
685
2949
1120
2862
780
2893
1265
2805
1595
2949
1760
3036
1557
3082
2766
2760 12611
2912 17601
3485 19871
3695
19612
2727 24432
2861 41823
2710 36742
2679
35593
2641
1235
2272
1564
2238
1763
2741
2275
5105
1794
3667
1291
3152
1223
3200
3756
2602
735
2257
222
1999
462
2291
550
2959
6153
2975 11490
2848 10937
2970
16533
5212
253
2480
325
2888
277
3053
473
2667
212
2337
379
2676
332
3064
507
2795
945
2812
3141
3039
4421
3009
5375
2640
2525
2671
4479
2548
4030
3013
5378
2146
997
2501
1610
2598
2287
2791
2542
2041
656
1864
595
2033
441
1955
894
2434
129
2960
180
2803
159
3305
249
2371
214
2102
227
2198
254
2341
334
2358
506
2455
638
2459
544
2446
807
2427
724
2368
1081
2474
996
2497
1167
8
1990
1995
2000
2005
Total
Total
Total
Total
GDP
GDP
GDP
GDP
Calories
Calories
Calories
Calories
(US $)
(US $)
(US $)
(US $)
(kcal)
(kcal)
(kcal)
(kcal)
Saudi Arab
3275
7174
2777
7795
2688
9057
2631
13365
Sri Lanka
2310
479
2069
739
2302
893
2200
1269
Syrian Arab
2979
877
2622
927
2695
1190
2906
1487
Thailand
2514
1572
2858
2921
2639
2023
2657
2800
Timor-Leste
2619
197
2356
371
2641
386
2136
328
Turkey
3781
2628
3637
2699
3391
2924
3212
4969
Turkmenistan
1243
837
2388
522
2684
923
3112
1198
United Arab
3636 18093
3361 17605
2783 21718
2446
32547
Uzbekistan
2132
717
2333
587
2198
557
2074
517
Viet Nam
2209
98
2556
283
2443
394
2762
621
Yemen
1620
281
1752
335
1873
530
1590
797
9
Estimation of equation (1.2) depends on the assumptions made about the intercept, the
slope coefficient and the error term, uit. There are several possibilities:
Case 1: Assume that the intercept (β1) and the slope coefficient (β2) are constant
across time and space and the error term captures differences over time and
individuals. This approach, being the simplest and naïve, disregards the space and time
dimensions of the pooled data and estimation is done with the usual OLS regression.
Total 16 observations of calories derived from total food consumption for each
considered Asian country is stacked one on top of the other, giving in all 592
observations for each of the dependent and independent variables (Yit and Xit ) in the
model. The OLS results are as enumerated in Table 1.3.
Table 1.3: OLS Estimation of Food Consumption in Panel Data Assuming the
Intercept (β1) and Slope Coefficient (β2) Constant across Time and Space
Y  2783.471 
110962
X
SE (25.294)
(11896.978)
t
(-9.327)
(110.046)
R2 = 0.128
Durbin –Watson = 0.314
n = 592 d.f. = 591
RESET test F = 51.04
Examining the results of the pooled regression, all the coefficients are individually
significant; slope coefficient has the expected negative sign. Hence the null hypothesis
of slope coefficient being zero is rejected at 5 percent level of significance. The
estimated asymptotic limit of consumption for pooled data is 2783.471 kcal as depicted
from table 1.3. But R2 is very low and Durbin-Watson is also very low. The low values of
R2 and Durbin-Watson statistics could be due to specification error. The result of
RESET test (F-value) is greater than the F-table value at the 5 percent significance
indicating that the model given by eq. (1.2) is miss-specified. The same conclusion is
reached on the basis of the Durbin-Watson d value.
The estimated model assumes that the intercept values of considered Asian countries
are the same. It also assumes that the slope coefficient of independent variable X is
identical for all 37 countries. Noticeably, these are highly restricted assumption in the
sense that conditions for these considered Asian countries are not same. Hence despite
its simplicity, the pooled regression (1.2) may distort the true picture of the relationship
between Y and the X’s across the countries. There is a need to take into account the
specific nature of the different region of Asia, which is explained further. The next case
considers variations across individual region (central, eastern, western, southern and
south-eastern).
Case 2: Slope coefficient constant but the intercept varies across individuals (region of
Asia): The fixed effects or least squares dummy variables (LSDV) regression model. To
take into account the individuality of each region of Asia or cross-sectional unit, there is
10
an assumption of varying intercept for each region with still constant slope coefficient.
The model used is given in equation (1.2) with following notations
1
Yit  1i   2 .
 u it
X it
… (1.3)
i = 1, 2, 3 and 4 (ith cross-sectional unit or ith region)
t = 1990, 2000, …, 2005 (tth time period)
Yit = Food consumption for ith region (cross-sectional unit) at tth time (year)
Xit = GDP at factor price in $ for ith region (cross-sectional unit) at tth time (year)
The differences may be due to special features of each region of Asia. These features
in particular may be geographic conditions, income, economic environment, production,
exports, imports, availability etc. In the literature, model (1.3) is known as the fixed
effects (regression) model (FEM). The term “fixed effects” is due to the fact that,
although the intercept may differ across regions, each region intercept does not vary
over time; that is, it is time invariant. The FEM given in (1.3) assumes that the slope
coefficients of the regressors do not vary across regions or over time. To actually allow
for the (fixed effect) intercept to vary between regions, the dummy variable technique
particularly, the differential intercept dummies is used. Hence the model given in (1.3)
can be rewritten as
Yi ,t  1   2 D2i   3 D3i   4 D4i   5 D5i 
where
D2i = 1
0
D3i = 1
0
D4i = 1
0
D5i = 1
0
2
X it
 u it
… (1.4)
if
region = East Asia
otherwise
if
region = South Asia
otherwise
if
region = South East Asia
otherwise
if
region = West Asia
otherwise
Table 1.4: OLS Estimation of Food Consumption in Panel Data Assuming Slope
Coefficient Constant but the Intercept Varies across Individuals (Region of Asia)
Y  3029.675  371.866 D2i  352.184 D3i  95.135D4i  246.116 D5i 
124025.59
X it
SE (56.195)
(66.147)
(65.798)
(63.240)
(62.781)
(12309.578)
t
(-5.622)
(-5.352)
(-1.504)
(-3.92)
(10.076)
(53.914)
R2 = 0.204
Durbin –Watson = 0.357 n = 592
d.f. = 586
RESET test F = 13.18
Here there are five regions but we have used only four dummies to avoid falling into the
dummy - variable trap (i.e. situation of perfect co-linearity). Hence there is no dummy for
Central Asia region. Here α1 represents the intercept of Central Asia, α2, α3, α4, α5 the
11
differential intercept coefficient tell how much the intercepts of East, South, South East,
West Asia region differ from the intercept of Central Asia. In short Central Asia becomes
the comparison region. Model (1.4) is also known as LSDV model or covariance mod el
where X is covariate. The results of estimation of parameters of model (1.4) are as
given in table 1.4.
All the estimated coefficients except for D4i (South East Asia) are individually highly
significant, as the p value of the estimate t coefficients are extremely small. The
intercept values of the five regions are statistically different.
Coefficient for Central Asia α1 = 3029.675
Coefficient for East Asia α1 + α2 = 3029.675 – 371.866 = 2657.809
Coefficient for South Asia α1 + α3 = 3029.675 – 352.184 = 2677.491
Coefficient for South East Asia α1 + α4 = 3029.675 – 95.135 = 2934.54
Coefficient for West Asia α1 + α2 = 3029.675 – 246.116 = 2783.559
These differences in the intercepts may be due to unique features of each region, such
as climate, living style, eating habits, product range availability, production etc. Here still
R2 and D-W is low and RESET test is highly significant which implies the model is still
miss-specified. Hence taking dummy for each region of Asia is not fulfilling the need of
estimation of parameters. The variation within the region provokes us to consider
dummy corresponding to each country. Now to allow for the (fixed effect) intercept to
vary between countries, the dummy variables corresponding to each country is
introduced and the model can be written in the equation (1.5).
Yit  1   2 C 2i   3C3i  ...   37C37i 
where
C2i = 1
0
C3i = 1
0
C4i = 1
0
C37i = 1
0
2
X it
 uit
… (1.5)
if
country = Kyrgystan
otherwise
if
country = Turkmenistan
otherwise
if
country = Uzbekistan
otherwise
if
country = Yeman
otherwise
α1 = intercept of Kazakhstan
α2 = differential intercept coefficient of how much the intercept of Kyrgystan country
differ from the intercept of Kazakhstan
α3 = differential intercept coefficient of how much the intercept of Turkmenistan country
differ from the intercept of Kazakhstan
α37 = differential intercept coefficient of how much the intercept of Yemen country differ
from the intercept of Kazakhstan
Here there are 37 countries but we have used 36 dummies to avoid falling into the trap
of dummy-variable (i.e. the situation of perfect co-linearity). Here there is no dummy for
the first country Kazakhstan. This country becomes the comparison country (otherwise
any country can be chosen as comparison country). Model (1.5) is also known as LSDV
model or covariance model. The estimated parameters are as enumerated in table
(1.5).
12
Table 1.5: OLS Estimation of Food Consumption in Panel Data Assuming Slope
Coefficient Constant but the Intercept Varies across Individuals (Countries)
Y  3621.084  541.58C 2i  1020.76C3i  ...  569.726C36i  1725.35C37i 
SE (57.71)
(87.22)
(81.11)
…
t
(-6.21)
(-12.59)
… (-7.04)
(62.75)
R2 = 0.778
(80.09)
Durbin –Watson = 0.899 n = 592
d.f. = 591
54084.3
X it
(85.45)
(15338.82)
(-20.19)
(-3.53)
RESET test F = 0.0090
The value of RESET test F is 0.009 which is lower than the table F-value, indicating that
the model (6.4) do not contain any specification error. Value of R2 = 0.778 has
increased significantly as there is inclusion of large number of variables in the model. All
the estimated coefficients are individually highly significant as depicted from table the p
values of the estimated t coefficients are extremely small except for the 35th country
which is corresponding to Turkey. The values of αi are depicting the asymptotic limit of
consumption for the said period for ith country. Hence individually country has different
asymptotic limit which is significant also. Graphically the asymptotic limit of the
considered Asian countries is displayed in figure 1.2. Further to estimate the effect of
time on the model and to execute the same, the next case describes the situation in
detail.
Case 3: The Time Effect. To study the time effect in the sense that the reciprocal
function shifts over time because of several economical factors, or external effect such
as wars or other environmental effect. The section deals with the estimation of
parameters considering time effect. Such time effect can be easily accounted for if time
dummies are introduced, one for each year. Since the data is available for 16 years, 15
dummies can be introduced to avoid falling in the trap of dummy variable. The model
along with time effect can be given by equation (1.6).
Yit  0  1d1991  2 d1992  ...  16 d 2005 
where
d1991 = 1
0
d1992 = 1
0
.
d2005 = 1
0
2
X it
 uit
… (1.6)
if
year = 1991
otherwise
if
year = 1992
otherwise
if
year = 2005
otherwise
Here there are 16 countries but we have used 15 dummies to avoid falling into the trap
of dummy-variable (i.e. the situation of perfect co-linearity). Here there is no dummy for
the first year 1990. This year becomes the comparison year (otherwise any year can be
13
chosen as comparison year). Model (1.6) is also known as LSDV model or covariance
model. The estimated parameters are as enumerated in table (1.7).
Table 1.6: Regression Model (1.5) Estimation
Model
Unstandardized
Coefficients
Std.
Error
(Constant)
3621.084
57.71
C2
-541.58
87.22
C3
-1020.76
81.11
C4
-1266.23
82.49
C5
-687.977
81.36
C6
-540.846
80.89
C7
-1636.5
81.17
C8
-887.976
80.97
C9
-1250.08
83.81
C10
-687.268
80.85
C11
-1541.47
82.50
C12
-1258.71
89.96
C13
-1026.86
84.85
C14
-634.087
80.44
C15
-1073.65
80.44
C16
-1178.83
97.50
C17
-1096.38
82.14
C18
-1320.35
81.11
C19
-650.508
80.91
C20
-703.33
80.81
C21
-845.015
89.03
C22
-909.124
80.64
C23
-431.597
114.66
C24
-1145.3
80.69
C25
-921.66
80.48
C26
-1002.87
90.26
C27
-923.264
95.37
C28
-1402.55
82.40
C29
-1288.05
81.73
C30
-385.953
80.92
C31
-1153.5
80.43
C32
-680.854
80.55
C33
-858.502
80.82
C34
-795.193
80.55
C35
-101.088
80.55
C36
-569.726
80.93
C37
-1725.35
85.45
RECGDP
-54084.3 15338.82
Dependent Variable: FOODCONS
B
Country
Kyrgyzstan
Turkmenistan
Uzbekistan
China
Cyprus
Goergia
Japan
Korea DPR
Korea Repbl
Mongolia
Bangladesh
India
Iran
Maldives
Nepal
Pakistan
Sri-Lanka
Brunei
Indonesia
Laos
Malaysia
Myanmar
Philippines
Thailand
Timor-Leste
Vietnam
Armenia
Azerbaijan
Israel
Jordan
Lebanon
Saudi Arabia
Syrian A Rep
Turkey
UAE
Yemen
Stand.
Coefficients
t
Sig.
Beta
-0.188
-0.354
-0.440
-0.239
-0.188
-0.568
-0.308
-0.434
-0.239
-0.535
-0.437
-0.356
-0.220
-0.373
-0.409
-0.381
-0.458
-0.226
-0.244
-0.293
-0.316
-0.150
-0.398
-0.320
-0.348
-0.320
-0.487
-0.447
-0.134
-0.400
-0.236
-0.298
-0.276
-0.035
-0.198
-0.599
-0.175
62.75
-6.21
-12.59
-15.35
-8.46
-6.69
-20.16
-10.97
-14.91
-8.50
-18.69
-13.99
-12.10
-7.88
-13.35
-12.09
-13.35
-16.28
-8.04
-8.70
-9.49
-11.27
-3.76
-14.19
-11.45
-11.11
-9.68
-17.02
-15.76
-4.77
-14.34
-8.45
-10.62
-9.87
-1.25
-7.04
-20.19
-3.53
Source: Authors estimation of parameters using equation 6.4
14
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.21
0.00
0.00
0.00
Asymptotic
Limit of
consumption
αi
Threshhold
Level of
Income
3621.08
3079.50
2600.33
2354.85
2933.11
3080.24
1984.58
2733.11
2371.01
2933.82
2079.61
2362.37
2594.22
2987.00
2547.43
2442.26
2524.70
2300.74
2970.58
2917.75
2776.07
2711.96
3189.49
2475.78
2699.42
2618.21
2697.82
2218.53
2333.03
3235.13
2467.58
2940.23
2762.58
2825.89
3520.00
3051.36
1895.74
0.067
0.057
0.048
0.044
0.054
0.057
0.037
0.051
0.044
0.054
0.038
0.044
0.048
0.055
0.047
0.045
0.047
0.043
0.055
0.054
0.051
0.050
0.059
0.046
0.050
0.048
0.050
0.041
0.043
0.060
0.046
0.054
0.051
0.052
0.065
0.056
0.035
Figure 1.2: Asymptotic Limit of Consumption of countries of Asia
Value of R2 = 0.135 has decreased significantly with a low Durbin Watson value 0.318
and RESET test F value is 50.06 is indicating that the model specified by eq (1.6) is
miss-specified. All the dummy variables are insignificant. Hence none of the time
dummies are showing any effect on the food consumption. Hence it could be concluded
that instead of time series data there is a need to study cross-sectional data to estimate
the parameters. Further the section 4.1.3 focuses on the estimation of maximum
  2 

1 
potential level of consumption (β1) and threshold level of income 
individually
for each cross-sectional unit.
15
Table 1.7: Estimation of Parameters of Regression Model (1.6)
Unstandardized
Standardized
tCoefficients
Coefficients
statistic
Year
B
Std. Error
Beta
(Constant)
2907.737
76.271
38.124
1991
D91
-74.248
102.516
-0.038 -0.724
1992
D92
-99.119
102.530
-0.051 -0.967
1993
D93
-126.091
102.547
-0.065 -1.230
1994
D94
-135.310
102.562
-0.070 -1.319
1995
D95
-140.023
102.592
-0.073 -1.365
1996
D96
-157.133
102.705
-0.081 -1.530
1997
D97
-169.060
102.737
-0.088 -1.646
1998
D98
-131.487
102.568
-0.068 -1.282
1999
D99
-138.157
102.645
-0.072 -1.346
2000
D00
-145.556
102.707
-0.075 -1.417
2001
D01
-129.814
102.711
-0.067 -1.264
2002
D02
-126.234
102.790
-0.065 -1.228
2003
D03
-128.697
102.882
-0.067 -1.251
2004
D04
-123.244
103.016
-0.064 -1.196
2005
D05
-112.260
103.179
-0.058 -1.088
RECGDP
-113124.348 12182.379
-0.365 -9.286
Dependent Variable: FOODCONS
Source: Authors estimation of parameters using equation 6.5
Sig.
(p)
0.00
0.47
0.33
0.22
0.19
0.17
0.13
0.10
0.20
0.18
0.16
0.21
0.22
0.21
0.23
0.28
0.00
There are a very few studies which have dealt with topic of estimation of maximum
potential level of consumption of food in Asia. The initial evidence that people are
approaching or has reached a level of saturation in food consumption taken as a whole
was given by Saxon (1975). The average intake in Japan, which was little more than
2000 kilocalories before the war, rose steadily after the war, reaching 2526 in 1973.
However, the rate of increase slowed appreciable after 1966. Thus, it was shown that
the consumption of food per person, as measured by energy value, appeared to have
reached or almost reached a level of saturation in most economically advanced
countries. The author further clarified that even after reaching a saturation level, was not
an indication that there will be no further change in food consumption but it meant that
increased consumption of some foods will be at the expense of decreased consumption
of some other food products which are decreasing its popularity due to one or the other
reason.
There are evidences in earlier times that the composition of food consumption in
western countries had been changing in favour of higher quality foods, with greater
emphasis on animal proteins; the trend was especially strong in Japan as evidenced by
(Saxon, 1975). In reality, such a saturation level for each and every product category is
attainable and once the level is reached the consumption patterns would remain fixed.
Yet growth in the consumption of even the more preferred food cannot continue
16
indefinitely and it is possible to think of an ultimate maximum (or minimum) level for
each product. But it should be kept in mind that these levels are unlikely to be reached
simultaneously. And it is likely that the sum of the conceptual saturation levels for
individual products is likely to exceed the saturation level for food as a whole or for
groups of similar foods.
Gil et al (1995) has studied food consumption pattern of European countries for the
period 1961 to 1990 and has estimated asymptotic limit, threshold level of income and
income elasticities for 1970, 1980 and 1990. The income elasticities decreased in all
countries for both total and animal products consumption over the period considered.
The authors have described two noteworthy features of the elasticities, first being the
higher income elasticities for animal products than those for total food consumption and
second, it was found that higher income elasticities belong to higher maximum potential
level of consumption.
The results of the present study are somewhat in line with that by Senugal and Senugal
(2006). The authors have estimated asymptotic limit, threshold level of income and
income elasticities for European Union countries along with Turkey for the period from
1970 to 2000. The results of the study revealed that in most of the EU countries per
capita food consumption has either reached or is rapidly reaching a plateau. Although
this plateau has a very narrow chance of variation and is different for different countries.
There still exists some potential for some countries for further growth in food
consumption as a result of increase in income. The countries with higher income level
have higher maximum potential level of consumption. In Turkey the income elasticity of
food consumption was relatively higher than that of EU countries. It was also found that
the income elasticity was higher than that of total food consumption.
1.5 Conclusion
The food consumption in Asian countries does not appear to have reached a plateau in
many of the instances. Food consumption may become progressively less influenced by
further increase in income. Aggregate food consumption may increase but the speed
may be slow in future, the principle factor influencing total consumption being growth in
population. Moreover, food consumption patterns could not be expected to be
completely similar among countries. The reason for such differences may be socioeconomic, demographic factors and culture differences among them. The distribution of
income is also unequal between major Asian countries. Reducing these income gaps
will be very difficult because of the dependence of low-income regions on agriculture.
There are large differences in quality of life indicators among different regions of the
continent.
In Asia, per capita income is relatively low as compared to other developed countries of
the world except few exceptions. Increase in food consumption can be seen with an
increase in income, but thereby population is also increasing. Looking to the relationship
between income and food consumption overall there is a potential for total calories to
grow and hence yet not reached a plateau in many of the instances.
Nevertheless, it was considered that the saturation levels calculated in the present
study could be useful, if used in conjunction with other substantiation, in making some
assessment of the likely growth (or contraction) in the Asian countries. Thus it could be
concluded that there are chances of increased consumption but will only be at the cost
17
of decreased consumption of other food category. Estimated asymptotic limit of food
consumption give a very slight indication of saturation, while there is much potential for
growth in the consumption with increase in income in Asian countries.
This study will provide information for government officers who are required to prepare,
for policy purposes, demand projections and production targets for the basic food
categories. This study may be treated as a document for the benefit of universities and
other organizations interested in research into the food consumption pattern. The
material presented in terms of estimated asymptotic limit of consumption and threshold
level of income will be used to help formulate policies for agricultural production in Asia
at macro level. The results may help the policy makers and traders for determining
priorities of food consumption and marketing. Further attention is to be paid for the
extent of marketing practices which may be adopted in the emerging markets, given the
large size of these markets and their positive impact on trade flows following Asia
enlargement.
References
Besch, M. 1993. “Agricultural marketing in Germany”, in Meulenberg, M. (Eds.), Food
and agribusiness marketing, International Business Press, Europe, pp. 5-35.
Blandford, D. 1984, “Changes in food consumption patterns in the OECD area’,
European Review of Agricultural Economics”, vol. 11, pp. 43-65.
Brunso, K., Grunert, F. G. and Brdahl, L. 1996. “An analysis of national and crossnational consumer segments using the food-related lifestyle instrument in
Denmark, France, Germany and The United Kingdom”, MAPP, Working Paper 35,
Aarhus: The Aarhus School of Business.
Connor, J. M. 1994. “Northern America as a precursor of changes in western European
food purchasing patterns’, European Review of Agricultural Economics”, vol. 21,
no. 2, pp. 155-173.
Deaton A. and Muellbauer J. 1980, Economics and Consumer Behavior, Cambridge
University Press, Cambridge.
Elsner, K. and Hartmann, M. 1997. “Convergence in food consumption patterns
between Eastern Europe and Western Europe”, in Loader, R.J., Henson, S.J. and
Traill, W.B. (Ed.), Globalisation and the Food Industry: Policy Implications,
Reading, Centre for Food Economics Research, pp. 85-104.
Frank, J. and Wheelock V. 1988, “International trends in food consumption’, British
Food Journal”, vol. 90, no. 1, pp. 22-29.
Gil, J. M., Gracia, A. and Perez, L. P. 1995. “Food consumption and economic
development in the European Union’, European Review of Agricultural
Economics”, vol. 22, pp. 385-399.
Gracia, A. and Albisu L.M. 2001. “Food consumption in the European Union: Main
determinants and country differences”, paper presented at the 71th EAAE Seminar
on The Food Consumer in the Early 21st Century, 19-20 April, Zaragoza.
Gracia, A., Gil, J. M. and Angulo, A. M. 1998. “Spanish food demand: a dynamic
approach’, Applied Economics”, vol. 30, pp. 1399-1405.
Gujarati D. 2007, Basic Econometrics 4/e, Fourth Edition, TATA McGraw-Hill, Delhi.
18
Huang, K. S. 1985. “US Demand for food: A Complete System of Price and Income
Effects Technical”, Bulletin No. 1714. Washington, DC: Economic Research
Service, US Department of Agriculture.
Huang, J. and David, C. 1993. “Demand for Cereal Grains in Asia: The Effect of
Urbanization’, Agricultural Economics”,vol. 8, pp. 107-124.
Huang, J. and Bouis, H. 2001. “Structural Changes in Demand for Food in Asia:
Empirical Evidence from Tiwan’, Agricultural Economics”, vol. 26, pp. 57-69.
Meulenberg, M. and Viane, J. 1993. “Agricultural marketing in Belgium and The
Netherlands”, in Meulenberg, M. (Ed.), Food and Agribusiness Marketing in
Europe, International Business Press, London, pp. 141-162.
Petrovici, D. A., Ritson, C. and Ness, M. 2005. “Exploring Disparities and Similarities in
European Food Consumption Patterns’, Cahiers D'Economie et Sociologie
Rurales”, vol. 75, pp. 24-49.
Rae, A. N. 1997. “Changing food consumption patterns in East Asia: implications of the
trend towards livestock products’, Agribusiness: an International Journal”, vol. 13,
no. 1, pp. 33-44.
Rae, A. N. 1998. “The effects of expenditure growth and urbanisation on food
consumption in East Asia: a note on animal products’, Agricultural Economics”,
vol. 18, pp. 291-299.
Ramsey, J.B. 1969. “Tests for specification error in classical linear least squares
regression analysis’, Journal of Royal Statistical Society”, Series B 31, pp. 350371.
Ritson, C. and Hutchins R. 1991. “The consumption revolution”, in Slater, J. (Ed.), Fifty
Years of the National Food Survey, HMSO, London, pp. 35-46.
Saxon, E. A. 1975. “A Review of Selected Japanese Data and Research Results”,
Occasional Paper No. 32., Australian Government Publishing Service, Canberra.
Senugal, H. and Senugal, S. 2006. “Food consumption and Economic development in
Turkey and European Union countries’, Applied Economics”, vol. 38, pp. 24212431.
Wheelock, V. and Frank J. 1989. “Food consumption patterns in developed countries”,
in Trail, B. (Ed.), Prospects for the European Food Systems, Elsevier Applied
Science, London.
Wooldridge J.M. 2000, Introductory Econometrics: A Modern Approach, South-Western
College Publishing, Cincinnati.
19