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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 3 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. 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