Effect of income on nutrient intake case of households in the southeast region of vietnam 2000

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE Vietnamese-Dutch Project for M.A Program in Development Economics EFFECT OF INCOME ON NUTRIENT INTAKE THE CASE OF HOUSEHOLDS IN THE SOUTHEAST REGION OF VIETNAM- 2000 A thesis presented by Nguyen Phuong Chi In Partial Fulfillment of the Requirements for Obtaining the Degree of MASTER OF ARTS IN ECONOMICS OF DEVELOPMENT Supervisors M.Sc Nguyen Hun Dung Dr Le Bach Mai THU q) V1L~l"~ D J_ ~.?.-.-·~~~ ~~""'"~'~•·-''"r~"="~ CERTIFICATION " I certify that the substance of this dissertation has not already been submitted for any degree and is not being currently submitted for any other degree I certify that to the best of my knowledge any help received in preparing this dissertation, and all sources used, have been acknowledged in this dissertation." ACKNOWLEDGEMENTS First of all, I would like to express my acknowledgement to Dutch Lady Vietnam Food and Beverage Company for their sponsorship to my trip to the National Institution of Nutrition in Hanoi to finish this thesis I also convey my gratitude to the University of Economics - Ho Chi Minh City, the Institute of Social Studies The Hague and the National Institution ofNutrition for creating such good chance for me to the research I am deeply indebted to my supervisors, Mr Kees Voogd, the Innovation Manager and Mr Nguyen Huu Thiet, the Human Resource Manager of Dutch Lady Vietnam Food and Beverage Company; Mr Nguyen Huu Dung, the coordinator of the MDE program; Dr Nguyen Cong Khan, the director of the National Institution of Nutrition, Dr Le Bach Mai, the manager of the Public Nutrition Department and Dr Vu Quynh Hoa, staff of the National Institution of Nutrition for their valuable instructions, comments and suggestions during the process of writing the report Finally, I wish to express my thanks to my parents, relatives and friends for their spiritual encouragement and material support Without those sources, the thesis would have been made impossible I alone bear full responsibility for any errors remaining in the report Nguyen Phuong Chi Ho Chi Minh City, August 2003 ABSTRACT It is a fact that 75 percent undernourished children lives in India, Bangladesh, Pakistan, Indonesia, Nigeria and Vietnam (Foster and Leathers, 1999) Still, developed countries like European and the United States give a lot of attention to this problem They increase incomes of the poor and food production per person to avoid malnutrition (Wolfe and Behrman, 1983) Unfortunately, Engel's law stated that as incomes grow, people would reduce the food spending proportion in their budget and increase the budget share for non-food commodities Besides, people often consume foods to suit their tastes and habits rather than to get more calories in their choice The thesis examines the effect of income on nutrient intake of households in the Southeast region of Vietnam in 2000 All the data is taken from The National Food and Nutrition Survey of the National Institution of Nutrition (NIN) in 2000 Regression result analyzes show that there is a positive effect of expenditure on calorie intake but the effect is rather low People have shifted their spending to animal foods such as meat, eggs, milk and milk products Gender does not have any correlation with calorie intake TABLE OF CONTENTS CERTIFICATION ACKNOWLEDGEMENTS ~ ! ABSTRACT TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES ACRONYMS AND ABBREVIATIONS CHAPTER 1: INTRODUCTION 1.1 PROBLEM STATEMENT 1.2 OBJECTIVES OF THE THESIS 11 1.3 RESEARCH QUESTIONS 11 1.4 RESEARCH HYPOTHESIS 12 1.5 RESEARCH METHODOLOGY 12 1.6 THE ORGANIZATION OF THE THESIS 13 CHAPTER 2: LITERATURE REVIEW AND EMPIRICAL STUDIES 15 2.1 CONCEPTS OF NUTRIENT INTAKE AND THE HOUSEHOLD 15 2.1.1 NUTRIENT INTAKE 15 2.1.2 HOUSEHOLD 16 2.1.3 HOUSEHOLD INCOME 16 2.1.4 l-IOUSEHOLD HEADSHIP 17 2.2 17 LITERATURE REVIEW THE THEORY OF CONSUMER CHOICE 18 2.2.1 2.1.1 The Traditional Model of Consumer Behaviour 18 2.2.1.1.1 Assumptions of the model 18 2.2.1.1.2 The Budget Constraint and The Budget Line 19 2.2.1.1.3 Indifference Curves 19 2.2.1.1.4 Income effect on Consumption 20 2.2.1.2 Criticisms on The Theory ofConsumer Choice 22 2.2.1.2.1 Advantages of The Theory of Consumer Choice 22 2.2.1.2.2 Disadvantages ofthe Theory of Consumer Choice 23 2.2.2 MODELS OF INTRA-HOUSEHOLD ALLOCATION 24 2.2.2.1 The Unitary Model ·.24 2.2.2.2 The Collective Model 25 2.2.3 EQUIVALENT LAWS OF ECONOMICS 26 2.2.3.1 Engel's Law 27 2.2.3.2 Bennett's Law 27 2 3 ·Slutsky's Law : 28 2.2.4 MEASUREMENT ERRORS OF NUTRIENT INTAKE 29 2.3 EMPIRICAL STUDIES 30 2.3.1 NUTRIENT INTAKE AND INCOME 30 3.1.1 Studies with low calorie income elasticities 2.3.1.1.1 Study ofBehrman and Wolfe (1983) 30 2.3.1.1.2 The study ofBehrman and Deolalikar (1987) 31 2.3.1.1.3 The study ofTimmer and Alderman (1979) 32 3.1 Studies with high calorie income elasticities 3 2.3.1.2.1 The study of Pitt (1983) 33 2.3.1.2.2 The study ofPinstrup-Andersen and Caicedo (1978) 34 2.3.2 NUTRIENT INTAKE AND HOUSEHOLD CHARACTERISTICS 35 2.3.2.1 Nutrient intake and household size and demographic structure 36 2 Calorie intake and household head 3 Calorie intake and female education 38 2.4 38 CONCLUSION CHAPTER 3: SOCIAL CONSUMPTION AND AND ECONOMICAL NUTRIENT INTAKE IN SITUATION, THE FOOD SOUTHEAST REGION OF VIETNAM: GENERAL PICTURE 40 3.1 SOCIAL AND ECONOMICAL SITUATION IN THE SOUTHEAST REGION OF VIETNAM 40 3.2 FOOD AND NUTRITION SITUATION IN THE SOUTHEAST REGION OF VIETNAM 41 3.3 SOME NUTRITIONAL PROGRAMS MALNUTRITION PROBLEMS IN VIETNAM 3.4 CONCLUSIONS IMPLEMENTED TO SOLVE 43 45 CHAPTER 4: ANALYTICAL FRAMEWORK AND RESEARCH METHOD ••.••••••.•.•••••.••••••••••••.••••••••••••••••.•.•.••••••••.•.••.••••••.•••••.•••••••••••••••••••••.•••••••••••••••••••••••••••• 46 46 4.1 ANALYTICAL FRAMEWORK 4.1.1 TRANSFORMING CONCEPTS INTO VARIABLES .46 4.1.1.1 The nutrient intake 46 4.1.1.2 Household income 47 4.1.1.3 Household characteristics 48 4.1.2 MODEL SPECIFICATION · 51 4.2 ~ESEARCH METHOD 52 4.2.1 DATA REQUIREMENT 52 4.2.2 DATA SOURCE : .'53 4.2.3 SAMPLE SELECTION , 53 4.2.3.1 The surveysites 53 4.2.3.2 Sample size and sampling , 53 4.2.4 METHOD OF ANALYZING DATA 54 CHAPTER 5: EFFECTS OF INCOME ON NUTRIENT INTAKE OF HOUSEHOLDS IN THE SOUTHEAST REGION OF VIETNAM IN 2000 55 5.1 FOOD CONSUMPTION PATTERN, NUTRIENT INTAKE AND HOUSEHOLD CHARACTERISTICS 55 5.1.1 FOOD CONSUMPTION PATTERN , 55 5.1.2 NUTRIENT INTAKE 58 1.3 HOUSEHOLD EXPENDITURE 60 5.1.4 HOUSEHOLD CHARACTERISTICS 61 5.1.4.1 Household size 61 5.1.4.2 Household head 62 5.1 Education 63 5.2 REGRESSION RESULTS 64 5.2.1 DESCRIPTION OF VARIABLES 64 5.2.2 RELATIONSHIP BETWEEN CALORIE INTAKE AND TOTAL EXPENDITURE, CALORIE INTAKE AND ENERGY ORIGINATED FROM ANIMAL PRODUCTS 67 2.1 Calories per capita and total expenditure of household 67 · 2 Calories per capita and calories originated from animal foods 68 5.2.3 REGRESSION RESULTS 70 CHAPTER 6: CONCLUSIONS AND POLICY RECOMMENDATIONS 77 APPENDIX 81 APPENDIX2 83 BI~LIOGRAPHY ~87 LIST OF TABLES Table 2.1 : Income and expenditure elasticities of calorie demand Table 3.1: Mean share (%) of food groups in total calories consumed by a household by regions ····:······························································: 43 Table 4.1: List of variables and expected signs in relation with calorie intake 51 Table 4.2: Four forms of commonly used Engel functions .52 Table 4.3: Number of households by city/ provinces in the Southeast region 55 Table 5.1: Major food group- average intake (grams/household/day) 57 Table 5.2: Food consumption pattern by household expenditure quintiles in the Southeast region (grams/household/day) 59 Table 5.3: Energy composition in the Southeast region and the whole country 60 Table 5.4: Highest diploma obtained by gender in the Southeast region in 2000 64 Table 5.5: Percentage of female per household who gets the highest diploma by expenditure quintile in the Southeast regionin 2000 65 Table 5.6: Summary statistics 66 Table 7: Distribution of quantitative variables 667 Table 5.8: Calorie groups by mean total expenditure per household 68 Table 5.9: Daily calories per capita originated from animal foods by calorie groups and expenditure quintiles 70 Table A2.1: Multicollinearity test 84 Table A2.2: Heterokedasticity test 85 Table A2.3: Functional form test 85 Table A2.4: Autocorrelation test 86 LIST OF FIGURES Figure 2.1: Matching Consumer Desires with Available Consumer Choices 21 Figure 2.2: Income-Consumption curve and its form 22 Figure 3.1 : Percentage of children under five suffering from mild underweitght and stunting (malnutrition level 1) 44 ·Figure 5.1: Average consumption level of other foods in the Southeast region (grams/household/day) 58 Figure 5.2: Total expenditure and food expenditure by quintiles in the Southeast region (1 000 dongs/person/month) 62 Figure 5.3: Histogram of quantitative variables with normal curve 667 Figure 5.4: The relationship between daily calories per capita and total expenditure per household 69 Figure 5.5: The relationship between calories per capita and calories from animal foods 70 Figure A2.1: Histogram of residual from the regression on linear function with unstandardized residual 87 Figure A2.2: Histogram of residual from the regression on linear function with standardized residual 87 ACRONYMS AND ABBREVIATIONS ADB Asian Development Bank BMI Body Mass Index BRVT BaRia- Vung Tau em Centimeters FAO Food and Agriculture Organization GDP Gross Domestic Product GSO General Statistical Office HCMC Ho Chi Minh City kcal Kilocalories NGO Non Governmental Organization NIN National Institution ofNutrition OLS Ordinary Least Squares PEM Protein Energy Malnutrition SPSS Statistical Package for the Social Sciences UN United Nations us United States UNICEF United Nations International Children's Fund VER Vietnam Economic Review VSED Vietnam's Socio-Economic Development WB World Bank capita than those in rural area provided that households in urban area earn higher income and thus spend more on everything, including foods However, the findings from the study give an opposite result It may be because price level in urban area is more expensive than that in rural area and households in rural area may selfsupply foods as well Therefore, when energy intake of people is still low in general, policy makers and nutritional experts should encourage them to pay more attention to their food diets towards more nutritious and high-calorie foods wherever they live, either in rural or urban area The Southeast region has the rapid speed of urbanization and thus needs to pay much attention to calorie intake of households in urban areas, especially the poorest ones ' Emphasizing on households with large size Apart from income, household size is an important determinant of calorie consumption According to Ayalew (2000), large household size benefits from sharing household public goods and enjoying economies of scale due to saving resources on food consumption as well as the consumption of other items At the same time, household size may reduce calorie intake per capita, especially with limited resources In the Southeast region of Vietnam, the latter effect seems to dominate Therefore, households with large size should be focused on keeping from energy insufficiency Encouraging female education and providing nutritional knowledge for females In this study, the variables representing all levels of female education not have statistically significant effects on calorie intake, except for the level of finished primary school This finding is interesting since it suggests that females are not necessarily well educated but still can help the household to improve quality of food consumed and nutrient intake of members in the household Besides, the finding that further female education does not have a significant influence on calorie intake indicates that efforts to promote broad basic education as a way to fight malnutrition may be misplaced It might be more useful to focus on ' providing more specialized nutritional information, such as about the composition of diets, nutritious foods and how to have a balanced food pattern 79 This study has some limitations and thus further research will be needed First, one important determinant of food consumption and also of calorie demand is price of food Because the !actor price is not collected during the survey, it is difficult to interpret causes of low calorie intake in a particular region Furthermore, foods consumed are grouped aggregately and thus not show the substitution among one category of food but with big different quantities of calorie supplies Besides, it would be useful ·to focus especially on households who have the current energy intake per capita lower than 1,800 kcal to reduce the proportion of these households Finally, it would be more precise if further studies can find determinants of calorie intake of each individual in a household with different calorie demand based on his or her age, sex, level of activity and health status 80 APPENDIX REGRESSTION RESULTS WITH DOUBLE LOGARITHM FUNCTION AND SEMILOGARITHMIC FUNCTION Regression results from the double logarithm function Dependent variable is Ln Calorie per capita 1,031 observations used for estimation Regressor Coefficient Standard Error Coefficient T -Ratio[Prob] (Standardized) Constant 7.182 077 -3.855E-03 015 -.008 -0.263[.793] 6.408E-02 007 300 9.666(.000] -7.250E-02 019 -.119 -3.917(.000] -.142 016 -.313 -8.847[.000] Ln Total expenditure 4.197E-02 010 149 4.168[.000] Not finished primary l.l06E-02 023 018 484[.628] Finished primary 3.443E-02 019 069 824[ 068] Finished secondary school 5.261E-03 018 010 290[.772] Head sex (female =1) Ln of Calorie from animal products Ln Household size equivalent Area (urban = 1) 93.326 [.000] R-Squared 0.176 F-statistic F( 8, 1023) Adjusted R-Squared 0.169 S.E of Regression 27.269(.000] Among eight independent variables (excluding constant), four variables are significant at the five percent level and have the right signs: ln calorie from animal products, In household size equivalent, area and In total expenditure Finished primary school is significant at ten percent level Other variables (head sex, not finished primm'y, and finished secondary) arc insignificant at ten percent level 81 20728 Regression results from the semilogarithmic function Dependent variable is Calorie per capita 1,031 observations used for estimation Regressor Coefficient Standard Error Coefficient T -Ratio[Prob] (Standardized) Constant 1115.173 175.062 -2.361 33.376 -.002 -.071 [.944] 135.349 15.219 280 8.893[.000] Ln Household size equivalent -I 61.605 42.I09 -.I I -3.838[.000] Area (urban =1) -322.507 36.550 -.314 -8.824[.000] Ln Total expenditure 97.055 22.905 153 4.237[.000] Not finished primary 38.619 51.948 028 743[.457] Finished primary 82.312 42.945 073 1.917[ 056] Finished secondary 16.352 41.262 028 743[.447] Head sex (female =I) Ln of Calorie from 6.370 [.000] animal products R-Squared 0.166 F-statistic F( 8, 1023) Adjusted R-Squared 0.160 S.E ofRegression 25.460[.000] Among eight independent variables (excluding constant), four variables are significant at five percent level and have the right signs: In calorie from animal products, In Household size equivalent, area and In total expenditure Variable finished primary is significant at ten percent level Other variables (head sex, not finished primruy and finished secondruy) are insignificant 82 471.5480 APPENDIX2 REGRESSION DIAGNOSTIC TESTS AND HISTOGRAM OF RESIDUAL Multicollinearity test As mentioned by Gujarati (1995), when some independent variables are highly correlated, the regression model faces a multicollinearity problem The consequence is that variance and covariance will be larger The confident intervals therefore will be larger, and R squared is spuriously high despite an insignificant t ratio In testing multicollinearity, Gujarati ( 1995) suggests the rule of thumb that a variable is to be highly collinearity if its variance inflation factor (VIF) exceeds 10 Table A2.1: Multicollinearity test Variable Linear function VIF 1/VIF Double logarithm Semilogarithmic function function VIF 1NIF VIF 1NIF Head sex (female =1) 1.10 0.9128 1.10 0.9104 1.10 0.9104 (Ln) Calorie from animal 1.15 0.8671 1.20 0.8350 1.20 0.8350 1.09 0.9153 1.14 0.8771 1.14 0.8771 Area (urban = 1) 1.38 0.7239 1.55 0.6451 1.55 0.6451 (Ln) Total expenditure 1.10 0.9058 1.59 0.6288 1.59 0.6288 Not finished primary 1.64 0.6105 1.71 0.5858 1.71 0.5858 Finished primary 1.74 0.5741 1.78 0.5622 1.78 0.5622 Finished secondary 1.51 0.6642 1.50 0.6647 1.50 0.6647 Mean VIF 1.34 products (Ln) Household size equivalent 1.45 1.45 The larger the value of VIF is, the more collinearity is The discussion of high or low value of VIF, according to Gujarati (1995: 339), is just in relative sense The 83 ' above VIF values are far from 10; or the existence and the degree of multicollinearity are expected to be not serious in the regression models Heteroskedasticity test In order to test heteroskedasticity, Cook-Weisberg test is applied by using fitted values of calorie per capita or ln of calorie per capita The null" hypothesis is no heteroskedasticity in the model If probability of the test comparing with chi2 is larger than 0.05, the null hypothesis is accepted From table A2.2, among three functions, only the double logarithm function has heteroskedasticity problem Table A2.2: Heteroskedasticity test Function Linear Chi-squared(!) Prob > chi-squared Conclusion Double logarithm Semilogarithmic 2.3 14.49 0.52 0.1290 0.0001 0.4714 No Heteroskedasticity heteroskedasticity No heteroskedasticity Functional form test In order to test functional form, Ramsey RESET test is applied by using powers of the fitted values of calorie per capita or ln of calorie per capita The null hypothesis is that the model has no omitted variables If probability of the test comparing with F probability is larger than 0.05, the null hypothesis is accepted From table A2.2, among three functions, only the linear function satisfies the test Table A2.3: Functional form test Function F(3,1019) Prob>F Conclusion Linear Double logarithm Semilogarithmic 2.33 8.33 8.78 0.0727 0.0000 0.0000 Correct specification Misspecification 84 Misspecification Autocorrelation test In order to test autocorrelation, Durbin-Watson test is used According to Norusis (1992), the more the Durbin-Watson statistic is closer to 2, the more probability the model has no autocorrelation Table A2.3 shows that all functions have DurbinWatson statistics that are close to 2, meaning that the problem of autocorrelation in the models is not serious Table A2.4: Autocorrelation test Function Linear Double logarithm Semilogarithmic Durbin-Watson statistic 1.744 1.792 1.780 In conclusion, based on numbers of variables that has t-probability significant and the four above tests, all functions have four variables that are significant at the five percent level, one variable that is significant at ten percent level and only the linear function satisfies all tests Therefore, the linear function is selected as the regression model and the results are interpreted based on this function 85 Figure A2.1: Histogram of residual from the regression on linear function with unstandardized residual 140 ~ -, 120 100 80 60 40 Std Dev 20 =443.23 Mean= 0.0 - _ N =1032.00 Unstandardized Residual Figure A2.2: Histogram of residual from the regression on linear function with standardized residual 160 140 120 100 80 60 40 Std Dev = 1.00 20 Mean= 0.00 N ~~~~-~-~~~ ~~ ~ -~ ~ ~~~~~ Standardized Residual 86 =1032.00 BIBLIOGRAPHY 11 Asian Development Bank (2001) Human Capital of The Poor in Vietnam Manila: Asian Development Bank 2/ Ayalew, T (2000) 'Liquidity Constraint and the Demand for Food: Income Elasticity of Calorie in Rural Ethiopia' Department of Economics, KU Leuven Belgium http://www.econ.kuleuven.ac.be/ew/admin/Pulications/DPSOO/DPS0027.pdf accessed on 10 March, 2003 3/ Begg, D., S Fisher and R Dornbusch (1991) Economics Massachusetts: McGraw Hill 4/ Behrman, J.R and A.B Deolalikar (1988) 'Health and Nutrition' in Chenery, H and T.N Srinivasan (ed.) 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