Research Collection

Doctoral Thesis

The impact of socio-economic characteristics on demand for food and nutrition in Tanzania evidence from a household survey

Author(s): Aubert, Dominique

Publication Date: 2002

Permanent Link: https://doi.org/10.3929/ethz-a-004378923

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The Impact of Socio-Economic Characteristics on Demand for Food and Nutrition in Tanzania:

Evidence from a Household Survey

A Dissertation submitted to the

SWISS FEDERAL INSTITUTE OF TECHNOLOGY

For the degree of

DOCTOR OF TECHNICAL SCIENCES

Presented by

Dominique Aubert

Dipl. Ing.-Agr. ETH

Born on 20 June 1969

Citizen of le Chenit, VD

Switzerland

Accepted on the recommendation of

Prof. Dr. A. Abdulai, examiner

Prof. Dr. P. Rieder, co-examiner

Zurich 2002 I

Acknowledgements

This thesis was written while working as a scientific investigator at the Department of

Agricultural Economics (IAW) of the Federal Institute of Technology in Zurich (ETHZ).

not have been There are many people I have to thank without whom this work would possible.

First of all I want to express my gratitude to Prof. Dr. A. Abdulai for his immense support and for giving me the opportunity to learn a lot from him. I also want to thank Prof.

Dr. P. Rieder for accepting to be my co-examiner and for giving good comments.

This work was possible thank to a project financed by the Swiss Agency for

Development and Cooperation (SDC) through the Swiss Center for International Agriculture

(ZIL). For this I express my profound gratitude.

An important part of this study was to carry out an extended data collection in Dar es

Salaam and Mbeya regions of Tanzania. This would have been difficult without the help of many.

Special thanks go to the Department of Agricultural Economics and Agribusiness of the Sokoine University of Agriculture in Morogoro, Tanzania. I am particularly thankful to

Prof. Dr. N. Mdoe and Prof. Dr. M. Mlambiti for their precious support in preparing and while data collection.

I also must admit that the study would have been difficult without the support of the

Southern Highlands Dairy Development Project in Iringa and Mbeya. My special thanks go to

Mrs. L. Maarse and to Dr. S. Mpate for helping me in my research.

Thanks to the Commission of Science and Technology in Dar es Salaam for permitting the survey.

I also want to thank my enumerators for collecting good data and each household participating in the study for taking time to accurately respond to the questions.

I finally want to thank all my friend and relatives in Zurich, Bern, Lausanne, Dar es

Salaam, Morogoro, Mbeya, Iringa, and wherever in the world for being my friends. Seite Leer / Blank leaf Ill

Abstract

Most of the African countries south of Sahara belong to low income or middle income countries with large parts of the population living with less than 1 US$ per day. Many of the

is a poor living in these countries suffer from food insecurity. If food security political goal, poverty will have to be eliminated. Every policy aiming at alleviating poverty can be considered a food security policy, and must be encouraged.

Sectoral long-run efficiency policies supporting agriculture are most likely to be effective in Africa for poverty alleviation because agriculture is labor intensive and because

in most of the poor live in rural areas, and rely on agriculture or on employment agriculture.

The livestock agricultural sub-sector contributes about 18% of the agricultural GDP in Africa

role in income and milk makes up 20% to 25% of this. Thus milk production plays a major generation and provision of employment in rural areas of Sub-Saharan Africa countries. This study therefore examines the determinants of the supply of marketed surplus of milk and the demand for food and nutrition in Tanzania. In the first part, the survey data on the production and household consumption of milk and milk products is used to analyze the factors that determine the supply of marketed surplus of milk and milk products in Mbeya and Iringa regions. The second part employs data of the household expenditure survey to investigate the demand for food and nutrition in Mbeya and Dar es Salaam regions.

The analysis on determinants of marketed surplus of milk (MSM) was carried out using a farm household model. The results reveal a positive relationship between price of milk and milk products and MSM, albeit statistically insignificant. Higher prices are likely to increase farmers' profits and incomes, thus increasing their own consumption of milk and milk products, as well as other food products. The results also show that the use of other farm inputs tends to increase MSM. A tabular analysis of the sources of fresh and fermented milk

milk of the survey households indicates that most customers in Dar es Salaam buy milk and products from traders, while only few do so in Mbeya region, where direct sales are more current.

As countries go through structural transformation in their economies and urbanization, changing tastes and lifestyles can have significant impact on the demand for food and other commodities. The food demand analysis estimates the demand for different food commodities, separately for low and high-income households. Both economic and socio- demographic effects on food demand were examined using a two stage budgeting procedure. IV

From these coefficients, expenditure and price elasticities of the food commodity categories were then computed for the two expenditure groups.

The results indicate that poor households tend to allocate large parts of their budget to food providing cheap calories and protein. Furthermore, low-income households are much

the assertion that low- more price responsive than high-income households which supports income households are compelled to adjust their consumption patterns to relatively inexpensive commodities. The results also suggest that demand for food will increase with rising expenditure, especially for meat and milk products.

It is commonly assumed that nutritional intake increases with rising income. However, when household incomes increase, they also tend to purchase more expensive goods, and thus nutrient availability could stagnate with increasing expenditure. Therefore, high food expenditure elasticities do not necessarily imply that human nutrient intake increase with rising income; even for low-income households. Nonparametric and parametric procedures were used to relate nutrient availability to total household expenditure and to compute nutrient-expenditure elasticities.

The representation of Engel curves computed with the nonparametric procedure revealed that demand for nutrients continues to rise with increasing expenditure and that demand for nutrition is in linear relation to expenditure. The parametric result confirmed this assumption.

Therefore, the main conclusion that can be drawn from these studies, is that income policies are likely to be very effective in improving household nutrient availability, especially

which their of the poor, since higher incomes allows them to buy more foods clearly improves nutrient availability. Price interventions are likely to be less effective in improving nutritional

which can result status of the poor. Increasing incomes will raise demand for food products, in higher food prices. Price increase of cheap and calorie rich foods would have a negative

and would influence on the poor, since they allocate large parts of their budget to these goods thus suffer a reduction of their budget available, and that will impair their nutrient availability.

Assuring low prices for basic foods can be achieved by increasing production of these goods by improving farmer productivity, as well as by facilitating trade within the country. V

Kurzfassung

Die meisten afrikanischen Länder südlich der Sahara gehören in die Gruppe der

Länder mit mittleren bis tiefen Einkommen. Grosse Teile der dortigen Bevölkerung müssen

Damit mit bis zu einem Dollar am Tag auskommen und leiden unter Ernährungsunsicherheit. das politische Ziel der Ernährungssicherheit erreicht werden kann, muss die Armut eliminiert werden. Alle Massnahmen zur Armutsbekämpfung benötigen deshalb politische

Unterstützung, da sie auch die Ernährungssicherheit verbessern.

Sektorielle, politische Massnahmen, die die Landwirtschaft langfristig unterstützen

Armen sind ein gutes Mittel, um die Armut im südlichen Afrika zu bekämpfen. Die meisten leben auf dem Land und somit von der Landwirtschaft. Auch die Tierhaltung spielt dabei eine wichtige Rolle: Dieser Produktionszweig steuert rund 18% des landwirtschaftlichen

Einkommens in Afrika bei, davon stammen 20-25% vom Milchsektor. Diese Arbeit untersucht deshalb die Faktoren, die das Angebot an vermarkteten Milchüberschüssen beeinflussen und die Nachfrage nach Nahrungsmitteln und nach Nährstoffen in Tansania. In einem ersten Teil werden Daten einer Umfrage in den Regionen von Mbeya und Iringa zur

Produktion und Verbrauch von Milch von landwirtschaftlichen Haushalten benutzt, um die

Milch Faktoren zu analysieren, die das Angebot von vermarkteter, überschüssigen beeinflussen. Im zweiten Teil werden Angaben über Haushaltausgaben benutzt, um die

Nachfrage der Haushalte nach Nahrungsmitteln und Nährstoffen zu analysieren.

Die Bestimmungsfaktoren vermarkteter Milchüberschüsse wurden mit einem landwirtschaftlichen Haushaltsmodell untersucht. Die Resultate zeigen, dass der Preis für

Milch die vermarktete Menge an Milch positiv beeinflusst, jedoch nicht auf statistisch signifikantem Niveau. Höhere Preise verbessern die Rentabilität der Milchproduktion und somit das Einkommen der Betriebe. Dadurch steigt auch der Verbrauch der Produzenten von

Milch und Milchprodukten sowie anderer Lebensmitteln, was die verkaufte Menge an Milch reduziert. Die Resultate zeigen auch, dass die gesteigerte Anwendung von

Produktionsmitteln, zu einem grösseren Angebot an Milch führt. Weiter wurde die Herkunft der konsumierten Milch der Haushalte, die an der Datensammlung von 1998-1999 teilnahmen, untersucht. Herbei zeigt sich, dass die meisten Haushalte in Dar es Salaam ihre

Milch bei einem Händler beziehen, während die Haushalte in Mbeya ihre Milch lieber direkt beim Produzenten kaufen.

In Ländern mit strukturellem Wandel der Wirtschaft und mit zunehmender

Urbanisierung, können sich Geschmack und Lebensweise verändern, was die Nachfrage nach VI

Lebensmitteln und anderen Gütern wesentlich beeinflussen kann. Die Analyse der Nachfrage nach Lebensmitteln untersucht deshalb Einflüsse von Preis, Einkommen und anderen sozio- demografischen Variablen auf die Nachfrage für Haushalte mit tiefen und solche mit hohen

Einkommen. Dafür wird eine zweistufige Budgetzuteilung angenommen. Mit den geschätzten

Koeffizienten werden Einkommens- und Preiselastizitäten beider Einkommensgruppen berechnet.

Die Resultate der Nachfrageanalyse zeigen, dass arme Haushalte einen grossen Anteil ihres Budget für Lebensmittel ausgeben, die reich an Kalorien und Eiweissen sind. Dazu reagieren diese Haushalte stärker auf Preisveränderungen als Haushalte mit hohen Ausgaben.

Das bedeutet, dass Haushalte mit tiefen Einkommen gezwungen sind, die billigste Quelle für

Kalorien und Eiweissen zu benutzen.

Im Allgemeinen wird angenommen, dass die Aufnahme von Nährstoffen mit zunehmendem Einkommen steigt. Allerdings werden Haushalte mit höheren Einkommen zunehmend auch teurere Lebensmittel kaufen, die weniger Kalorien beinhalten als andere

Güter, und somit könnten die verfügbaren Nährstoffe auch mit steigenden Einkommen stagnieren. Steigendes Einkommen garantiert somit keine Verbesserung der Ernährungslage der Armen, sogar wenn die Einkommenselastizitäten für Lebensmittel hoch sind. Der Einfluss sämtlicher Ausgaben, als Ausdruck des Haushaltseinkommens, auf die Verfügbarkeit verschiedener Nährstoffe wurde ebenfalls untersucht. Dabei wurden nicht-parametrische und parametrische Verfahren angewendet.

Mit den Resultaten der nicht-parametrischen Berechnungen konnten Engelskurven für die untersuchten Nährstoffe grafisch dargestellt werden. Es stellte sich heraus, dass die

Verfügbarkeit der Nährstoffe mit steigendem Einkommen zunimmt und das auch bei hohem

Einkommensniveau. Deshalb konnte angenommen werden, dass die Verfügbarkeit der

Nährstoffe mit steigendem Einkommen linear zunimmt, was bei den parametrischen

Untersuchungen bestätigt wurde.

Die wichtigste Schlussfolgerung, die aufgrund der Ergebnisse der Analysen gezogen werden kann, ist, dass politische Massnahmen, die zur einer breit verteilten

Einkommensverbesserung der Haushalte führt, das effizienteste Mittel zur

Armutsbekämpfung und der Ernährungssicherung ist. Politischer Einfluss auf den Preis wird weniger nützlich sein. Allerdings sind tiefe Preise für die Ernährungssicherheit trotzdem wichtig, denn arme Haushalte geben grosse Mittel für billige Lebensmittel aus. Ein steigender

Preis dieser Lebensmittel würde das verfügbare Einkommen der armen beeinträchtigen, und VII

somit zu einer allgemeinen Verschlechterung der Ernährungslage führen. Indem grössere

Mengen an Grundnahrungsmittel hergestellt werden, können tiefe Preise sichergestellt werden. Dieses kann mit verbesserter Produktivität der Landwirte erreicht werden, oder indem der Handel mit Agrargütern vereinfacht wird. Seite Leer / Blank leaf Content

Acknowledgements I

Abstract Ill

Kurzfassung V

Content IX

List of Tables XIII

List of Figures XV Acronyms XVII

1. Introduction 1

2. Tanzania 5

2.1. Arusha Declaration and Ujamaa Socialism 8

2.2. Structural Adjustment and Trade Liberalization 12

2.3. Agriculture in Tanzania 14

3. The Structure of Market, and the Supply of Milk and Milk Products 19

3.1. Overview of Milk Production, Marketing and Consumption in Sub-Saharan Africa 19

3.2. The Dairy Sector in Tanzania 22

3.3. The Structure of the Market of Milk and Milk Products in the Dar es Salaam and Mbeya Regions 23

3.4. Determinants of Marketed Surplus of Milk 29 3.4.1. Methodology 29 3.4.2. Empirical Specification 32 3.4.3. Data for MSM Analysis 34 3.4.4. Results and Conclusions 36

4. Household Expenditure Survey 41

4.1. Area of Data Collection 41 4.1.1. Dares Salaam 41 4.1.2. Mbeya 42

4.2. Selection of Household Sample 43

4.3. The Enumerators 44

4.4. The Questionnaire 44

4.5. Data Collection 46

4.6. Description of Household Characteristics 47

4.7. Segmentation of the Sample Households 49

4.8. Description of Household Expenditure 50 X

5. Analysis of Demand for Food 55

5.1. Introduction 55

5.2. Estimating Demand Functions 56

5.3. Conditional Demand Modeling Techniques and Two-Stage Budgeting Approach 56

5.4. Computing Elasticities Using the Two Stage Budgeting Approach 61

5.5. Uncompensated and Compensated Price Effects on Demand for Goods 63

5.6. Model specification 64 5.6.1. Linear Expenditure System 64 5.6.2. Linear Approximate Almost Ideal Demand System 65

5.7. Estimations of the Food Demand System 68 5.7.1. First Stage 68 5.7.2. Second Stage 70

5.8. Computed Price and Expenditure Elasticities 73

5.9. Discussion and Conclusions 80

6. Nutrition Analysis 85

6.1. Introduction 85

6.2. Description of Nutrient Availability 86

6.3. Explaining Household's Nutrient Availability 89

6.4. Nonparametric Estimations 91 6.4.1. Procedure 92 6.4.2. Results 93

6.5. Parametric Estimations 99

6.6. Econometric Considerations of Parametric Estimations 101 6.6.1. Nonlinearity 101 6.6.2. Simultaneity bias 102 6.6.3. Measurement Error 103 6.6.4. Two Stage Least Squares (2SLS) Method 106

6.7. Results of Parametric Estimations for Calories and Protein 107

6.8. Results of Parametric Estimations for Other Nutrients 112

6.9. Conclusions from Parametric Estimations 113

6.10. Computing Nutrient-Expenditure Elasticities 115 6.10.1. Nutrient-Expenditure Elasticities from Food Demand Analysis 115 6.10.2. Elasticities From Parametric Linear Estimations 116

7. Conclusions and Implications 119

7.1. The Supply of Milk and Milk Products 119

7.2. Analysis of Demand for Food 120

7.3. Nutrition Analysis 123

References 127

Appendix 135 XI

Curriculum Vitae 153 Seite Leer / Biank leaf XIII

List of Tables

Table 1: NMC Purchases of Maize Grain from Surplus Regions 10

Table 2: Distribution of Area Harvested 16

Table 3: Households' Source of Fresh and Fermented Milk 27

Table 4: Descriptive Representation of the Farm Data Used for the MSM Analysis 36 (550TSh = 1US$)

Table 5: Marketed Surplus Function for Milk and Milk Products for Iringa and Mbeya Regions 37

Table 6: Socio-economic Profile of Sample Households 48

Table 7: Average and Relative Household Total Expenditure per Capita and Year by Location and Area, and by Income Groups(average in TSh) 52

Table 8: Average and Relative Household Food Expenditure per Capita and Year by Location and Area, and by Income Groups (average in TSh) 52

Table 9: Parameters of LES Estimation of the First Stage of TSB 68

Table 10: Computed Marshallian Price and Expenditure Elasticities of the First Stage of the Two Stage Budgeting Procedure 69

Table 11: Estimated Coefficients for the LA/AIDS Model for Different Food Items with 71 Pooled Data in Dar es Salaam and Mbeya Regions, Tanzania

Table 12: Estimated Coefficients for the LA/AIDS Model for Different Food Items with

Data of Low-Income Households in Dar es Salaam and Mbeya Regions, Tanzania 72

Table 13: Estimated Coefficients for the LA/AIDS Model for Different Food Items with Data of High-Income Households in Dar es Salaam and Mbeya Regions, Tanzania 73

Table 14: Marshallian Price and Expenditure Elasticities for Food Groups with Pooled Data 74

Table 15: Marshallian Price and Expenditure Elasticities for Food Groups of Low- Income Households 75

Table 16: Marshallian Price and Expenditure Elasticities for Food Groups of High- Income Households 77

Table 17: Hicksian Price Elasticities of all Households for Food Groups 78

Table 18: Hicksian Price Elasticities of Low-Income Households for Different Food Groups 79

Table 19: Hicksian Price Elasticities of High-Income Households for Different Food Groups 79

Table 20: Unconditional Marshallian Price and Expenditure Elasticities of all Households 80

Table 21: Calorie and Protein Consumption, and Prices per Calorie and per Protein, 88 Mbeya and Dar es Salaam Regions 1998/1999

Table 22: Nomenclature of Variables in Nutrient Availability Analysis 108

Table 23: Second Stage of 2SLS Procedure of Log-linear Model for Calories and Proteins 109 xrv

Table 24: Second Stage of 2SLS Procedure of Quadratic Model for Calories and Proteins Ill

Table 25: Nutrient Elasticities Computed from Food Demand Elasticities 116

Table 26: Comparing Nutrient-Expenditure-Elasticities 117

Table 27: First Stage of 2SLS Procedure 147

Table 28 Parametric Estimations of Calorie and Protein Availability 148

Table 29: Parametric Estimations of Fat and Cholesterol Availability 148

Table 30: Parametric Estimations of Fiber and Vitamin A Availability 149

Table 31: Parametric Estimations of Vitamin E and Vitamin C Availability 149

Table 32: Parametric Estimations of Vitamin B6 and Vitamin B12 Availability 150

Table 33: Parametric Estimations of Thiamin and Riboflavin Availability 150

Table 34: Parametric Estimations of Niacin and Folate Availability 151

Table 35: Parametric Estimations of Calcium and Magnesium Availability 151

Table 36: Parametric Estimations of Iron and Zinc Availability 152 XV

List of Figures

Figure 1: Map of Tanzania 5

Figure 2: Landuse in Mainland Tanzania (94.3million ha) 6

Figure 3: Distribution of the Population by Sex and Age, Mainland Tanzania 7

Figure 4: Monopoly with Price Control and Second Economy 11

Figure 5: Average GDP and agriculture GDP real growth rates compared with annual growth of population 14

Figure 6: Contribution of Economic Sectors to GDP in 1997 15

Figure 7: Distribution of Cereal Area Harvested in 1997 (3.3 million ha) 16

Figure 8: Milk Production and Imports, and Consumption per Capita in Sub-Sahara Africa 20

Figure 9: Milk Marketing Channels from Dairy Cattle Production in Tanzania 25

Figure 10: Quantities of Fresh and Fermented Milk Consumed by Source 28

Figure 11: Distribution of the Sample Households by Size 49

Figure 12: Ranked Plot of the Logarithm of Total Household Expenditure per Capita and Year (lUS$=670TSh) 50

Figure 13: Utility Tree 60

Figure 14: Income and Substitution Effects (geometric representation of the Slutsky equation) 63

Figure 15: Distribution of Total Expenditure Resulting from LES Estimation 69

Figure 16: Scatter Diagram of the Logarithms of per Capita Calories and Expenditure 90

Figure 17: Scatter Diagram of the Logarithms of per Capita Protein and Expenditure 91

Figure 18: Nonparametric Representation of Calorie 94

Figure 19: Nonparametric Representation of Protein 94

Figure 20: Nonparametric Representation of Fats 94

Figure 21: Nonparametric Representation of Cholesterol 94

Figure 22: Nonparametric Representation of Fiber 95

Figure 23: Nonparametric Representation of Vitamin A 95

Figure 24: Nonparametric Representation of Vitamin E 95

Figure 25: Nonparametric Representation of Vitamin C 95

Figure 26: Nonparametric Representation of Vitamin B6 96

Figure 27: Nonparametric Representation of Vitamin B12 96

Figure 28: Nonparametric Representation of Thiamin 96

Figure 29: Nonparametric Representation of Riboflavin 96

Figure 30: Nonparametric Representation of Niacin 97

Figure 31: Nonparametric Representation of Folate 97 XVI

Figure 32: Nonparametric Representation of Calcium 97

Figure 33: Nonparametric Representation of Magnesium 97

Figure 34: Nonparametric Representation of Iron 98

Figure 35: Nonparametric Representation of Zinc 98

Figure 36: The Relation of Wage and Efficiency 102 XVII

Acronyms

2SLS two stage least squares FAO Food and Agriculture Organization of the United Nations

GDP gross demographic product IMF International Monetary Fond IV Instrumental variable LA/AIDS linear approximation of the almost ideal demand system LES linear expenditure system MoAC Ministry of Agriculture and Co-operatives MSM marketed surplus of milk

OLS ordinary least squares SDC Swiss Agency for Development and Cooperation SHDDP Southern Highlands Dairy Development Project SSA Sub-Saharan Africa SUA Sokoine University of Agriculture

TDL Tanzania Dairies Limited

TPCE total per capita expenditure TSB two stage budgeting system TSh Tanzanian shilling USDA United States Department of Agriculture WHO World Health Organization 1

1. Introduction

Most of the African countries south of Sahara belong to low or middle income countries with large parts of the population living with less than 1 US$ per day, which is a broadly accepted limit for hard core poverty. Many of the poor in these countries also suffer from food insecurity. Food security is defined as the access by all people at all times to

and enough food for an active, healthy life. Its essential elements are the availability of food the households' ability to acquire it (World Bank, 1986). A life free of poverty and thus with food security is an internationally recognized Human Right since the International Covenant on Economic, Social and Cultural Rights states that "the States Parties to the present

and his Covenant recognize the right of everyone to an adequate standard of living for himself family, including adequate food, clothing and housing, and to the continuous improvement of living conditions" (Article 11, Paragraph 1).

FAO (1996) estimated that 841 million persons suffered from malnutrition world wide in developing countries in 1990-92. These are 20% of the total population in these countries.

In 1969-71 the undernourished were 918 million or 35% of the population. As can be seen, the number of malnourished worldwide diminished in this period, which is a considerable

Africa success of poverty alleviation policies. Unfortunately the situation in Sub-Saharan

(SSA) worsened. In 1969-71; 103 million persons suffered from undernutrition, which represented 38% of the population in SSA. In 1990-92, they were 215 million or 43% of the population. At the 1996 World Food Summit, representatives from 185 countries and the

European Community vowed to achieve universal food security. They pledged to cut the number of hungry people by half until 2015.

For a household to achieve food security it has to be entitled with either land for subsistence farming or with income to buy food (Geier, 1995). Those who are not able to achieve food security belong to the poor, since they lack of resources to produce food or to generate income. If food security is to be attained, poverty will have to be eliminated, thus every policy aiming at alleviating poverty must also be considered a food security policy.

This goal can be attained if incomes of the poor increase, and for this a broad based growth of

with the economy is a prerequisite. Therefore policies supporting economic growth emphasize on income generation of the poor have to be given highest priority.

Since beginning of the 1980s many developing countries started economic reforms, implementing stabilization and adjustment programs. These programs became necessary because the former policies, often with strong implication of the state in the economy were 2

unsustainable, as these countries faced high macro-economic instability, economic decline and a rising number of poor. Stabilization policies are designed to reduce macro-imbalances in the economy, and are usually supported by the IMF. Adjustment policies restructure national economies aiming at increasing long-run efficiency. The World Bank often supports these programs. Stabilization and adjustment policies generally consist of demand restraint policies, which are deficit reduction (fiscal policy) and monetary policy, switching policies including exchange rate policies and wage policy, and long-run efficiency policies, which consist of trade policies, sectoral policies including industry, energy, and agriculture, financial sector reform, rationalization of government administration, and public enterprise reforms.

Sometimes the programs also include social policy reforms and other policies (Steward,

1995).

Sectoral long-run efficiency policies supporting agriculture are most likely to be effective in Africa for poverty alleviation because agriculture is labor intensive and because

in most of the poor live in rural areas and thus rely on agriculture or on employment agriculture for their living. Furthermore agriculture plays a major role in the economy of most of the SSA countries. The sector accounts for about 42% of the GDP in low income countries and for 27% in middle income countries of SSA. Cash crops account for at least 60% of export earnings in more than half the countries and 65 to 80% of the labor force is primarily employed in agriculture. Agricultural growth also contributes to overall economic growth, as the sector contributes to increased foreign export earnings; increases the supply of food for domestic consumption and raw materials for domestic industries; can and should make a net contribution to the capital required for investment and expansion of secondary industry; releases labor for manufacturing, and enlarges the size of the market for industrial output

(Abdulai and Delgado, 1995; Abdulai and Hazell, 1995).

The livestock sub-sector of agriculture contributes about 18% of the agricultural GDP in Africa and milk makes up 20% to 25% of this (Walshe et al., 1991) and thus plays a major role in income generation and provision of employment in rural areas of SSA countries.

Commercial dairying is also a regular source of cash income for the farmers in comparison to most crops, which generate unsteady income over the year. All of this allow to consider the dairy sector as a mean to contribute to poverty alleviation in SSA, although it is constrained by difficulties in production and marketing.

Improved dairy cattle were introduced in the Southern Highlands of Tanzania with the goal to increase protein consumption of rural population. Today production and marketing of 3

milk must be seen as a means to increase income of rural farmers, and thus to reduce poverty

farmers' in this area. An understanding of how economic and non-economic factors influence supply of marketed surplus of milk (MSM) may help to improve dairy related extension, and thus increase productivity and income of small-scale dairy farmers. Therefore, an attempt is made to examine the market supply function for milk and milk products in Iringa and Mbeya regions of Tanzania using cross-sectional data of small scale milk producers. The supply function of MSM used in the analysis is derived from an agricultural household model.

The characteristics of fresh whole milk and of its production have a large influence on requirements of milk marketing systems of small scale dairy farmers and of other milk marketing actors. Knowing the actual marketing channels will help dairy producers in rural

at the areas to adapt their offer to requirements of customers. Therefore, a close look is taken source of fresh and fermented milk consumed by the households during the survey period.

and As countries go through structural transformation in their economies urbanization, changing tastes and lifestyles can have significant impact on the demand for food and other commodities. The contribution of a food demand analysis in this study is to estimate the demand for different commodities separately, for low and high-income households in Dar es

Salaam and Mbeya regions of Tanzania. Besides prices and income effects, the influence of factors such as location and household size on demand for these commodities is also examined. To be able to measure these effects micro-data on household level were collected in Dar es Salaam and Mbeya regions of Tanzania. 500 households participated in the survey that was carried out from June 1998 until April 1999.

The collected data brought together information on household expenditure permitting an insight and analysis of household budget allocation. In a first stage the allocation of the total budget to food, to other non-durables, and durables was examined using a linear expenditure system. In a second stage only food expenditures were explored, segmenting household food expenditures into several categories. The influence of economic and socio- demographic variables on demand for food was estimated using a linear approximation of the almost ideal demand system. Expenditure and price elasticities were then computed using the estimated coefficients of the two stages.

An analysis on how income and other socio-economic variables impact on nutrient availability of households was also carried out. This kind of analysis is particularly important for developing countries where large parts of the population strive to meet their nutritional requirement. Information on how economic and non-economic household characteristics 4

influence households nutrient availability are important for policy makers to optimize policy measures aiming at improving nutrition of the population.

It is commonly assumed that nutritional intake increases with rising income

(Ravallion, 1990; and others). However, households also tend to purchase more expensive goods when their income increases, and thus nutrient availability could stagnate with increasing expenditure. Therefore, high food expenditure elasticities do not necessarily imply that human nutrients intake increase with rising income; even for low-income households.

nutrient This is the reason for carrying out an analysis directly relating household availability with total expenditure.

the data The data necessary for this analysis were obtained by multiplying brought

nutrient together from the survey with nutrient content tables. The total availability per capita

the on household level was put in relation with households' per capita total expenditure. First,

in Then data were explored using a nonparametric procedure and were presented figures.

and of parametric estimations were carried out to investigate effects of rising expenditures

is other socio-demographic variables using a two stage least squares procedure. This

and total because of necessary, due to simultaneity of available nutrients expenditures,

income. measurement errors and the possible effect of an adequate nutrition on Finally, nutrients expenditure elasticities were computed from coefficients of the food demand analysis and compared to the results of the direct parametric estimates.

of Tanzania is The study is organized as follows. In chapter 2 a short presentation provided. Chapter 3 investigates the sources of fresh and fermented milk of the survey households and the analysis of economic and non-economic influences on marketed surplus of milk and milk products of small scale dairy farms in Iringa and Mbeya regions of Tanzania is presented. Chapter 4 describes the data collection carried out in Mbeya and Dar es Salaam regions of Tanzania in 1998-1999, and presents the households that participated in the survey.

The food demand analysis is presented in Chapter 5, and chapter 6 focuses on the relation of nutrient availability and household total expenditure. Finally, chapter 7 presents the major conclusions, and implications of this study. 5

2. Tanzania

Tanzania is the biggest of the three East Africa countries (i.e. Kenya, Uganda and

Tanzania), and stretches from equator down to 12 degrees south Latitude and from 29 to 41 degrees east Longitude. On the north Tanzania borders to Kenya and Uganda, on the south to

Mozambique and to Malawi and to the west to Rwanda, Burundi, Zambia and Congo-

Kinshasa. On the west Tanzania has a coastline of 700 km to the Indian Ocean. Mainland

Tanzania covers 942,800 square kilometers, and the islands of Zanzibar make up another

2000 square kilometers. The land area of mainland Tanzania is 881,300 square km; an additional 61,500 square km are under inland lakes.

Figure 1: Map of Tanzania

The climatic conditions vary from arid in central regions to afro-alpine climate on

Mount Kilimanjaro. The rainy season is bimodal in the north and on the coast, and unimodal 6

in the rest of the country. Annually rainfalls vary from 500mm in the Central Plains up to

and more than 1500mm in some parts of the Southern Highlands (Pratt Gwynne, 1977).

This high variability of climatic conditions allows Tanzania to have many different farming and production systems. First there is the coffee-banana and horticulture system found in the densely populated highlands areas within the regions of Kagera, Arusha,

Kilimanjaro, and Tanga in the north, and Mbeya, Iringa, and Ruvuma in the south. The most

This is common farming system among smallholders is the one of maize and legumes. system

in the Western found in zones with medium to good agricultural potential, and predominates

Plateaus and the Southern Highlands. Pastoralism is prevalent in the arid and semi-arid regions in Central Tanzania and cover large areas, as 63% of the land in Tanzania is pasture.

Central Other systems are the sorghum, millet, livestock system in the north of the Plateau; wetland paddy and sugarcane in alluvial river valley and the cassava, cashewnut, coconut system in the Coast region, Eastern Lindi and Mtwara (World Bank, 1994b). Even though

of Mainland Tanzania has high potential for many farming activities, only 3% of the surface

Tanzania is cultivated (Figure 2).

Figure 2: Landuse in Mainland Tanzania (94.3million ha)

(Source: World Bank, 1994b)

In 1997 total population of Tanzania was estimated at 30.0 million people, of whom

29.1 millions lived in Mainland Tanzania and nearly 900,000 in Zanzibar. The population is

the estimated to increase at an average rate of 2.8% per annum. The 1988 census revealed 7

much urban population to represent about 18% of the total. But the urban population grows

is estimated at more than 3 faster than the rural one; currently the population in Dar es Salaam millions, compared to the 1.36 millions in 1988. This makes an average growth rate of 8.2%

of the Tanzanian lives now in Dar es per annum, and thus approximately 10% population

19 Salaam. As Figure 3 shows, more than half of the Tanzanian population is under years old,

in the next which means that the population will continue to grow rapidly years.

Figure 3: Distribution of the Population by Sex and Age, Mainland Tanzania (in thousands)

60-64

0 500 1000 1500 2000

(Source: Household Budget Survey 1991/92)

After independence in 1961, social services such as schooling and health services were

of the improved. This resulted in an increase of the enrolment of pupils, an increase literacy

increased from rate, and progresses of other social indicators. The estimated adult literacy

65.8 and 34.1 in 1980 to 79.4 and 56.8% in 1995 for men and women, respectively. The gross enrolment ratio1 at primary education level increased from 33.5% in 1970 to 90.4% in 1981, but then decreased to 66.4% in 1996 due to the introduction of schooling fees. Swahili is

the Therefore pushed as the national language and is the teaching language all over country. most of the Tanzanians are able to communicate with each other, although there are more than

to their The gross enrolment ratio is the total of pupils enrolled in education, regardless age, school of expressed as a percentage of the population corresponding to the official age primary education (Source: UNESCO). 8

declined from one hundred and thirty different local languages in Tanzania. Infant mortality

while life at birth 108 per thousand in 1980 to 85 in 1998 (World Bank, 2001), expectancy

because of the remained stable at 51 years in the last ten years, but it is most likely to decline

AIDS epidemic.

At the end of the 19th century Tanganyika (today called Mainland Tanzania) became a

German colony. As Germany lost the first world war, Tanganyika became a British protectorate, and gained independence in 1961. The primary focus of the young government was on "Africanisation" of the colonial administration. In 1963 a revolution against the sultanate in Zanzibar took place. Tanganyika intervened and brought peace onto the islands.

Tanganyika and the islands of Zanzibar formed the United Republic of Tanzania in 1964. In

1967 after the Arusha Declaration, the Tanzanian government implemented policies to

in north achieve an African socialism and self reliance. The occupation of Kagera region the west of Tanzania by troops of Idi Amin in 1978, led to the war with Uganda until 1979. In the mid-1980s, the government had to give up its socialistic policies and started economy

on the Tanzanian recovery policies. The period of Ujamaa socialism had a large impact society, and is thus described in the next section in more details.

2.1. Arusha Declaration and Ujamaa2 Socialism

The Arusha Declaration in 1967 and its implementation had a large impact on the

Tanzanian population and economy, and can still be felt today. The goal of the policy was to

all the needs of the build up an African socialism aiming at self-reliance, thus to meet

Tanzanian population. Another goal was also the Africanisation of trade and other parts of the

the second world economy, which had been under control of persons of Asian origins since war (Hewitt, 1999). All the major financial and commercial institutions as well as some private agricultural estates were nationalized, and the national and international trade were put under control of parastatals with exclusive market rights. Exchange rates were also controlled accompanied by stiff regulation on imports and capital flows. The agricultural policies were based on the "villagization" that saw rural population resettled in so called Ujamaa villages.

Trade of agricultural goods as well as provision of agricultural inputs became sole responsibility of parastatal authorities. These public authorities also replaced farmers' cooperatives. The government however made significant strides toward improving social services, particularly access to education and safe water (Mans, 1994; Biermann and Moshi,

1997; Sijm, 1997). 9

The policy of rural development revolved around two poles - large-scale agriculture and ranching under parastatals, and small-scale agriculture under villagization. Parastatals took over nationalized assets including land that belonged to peasants under customary rights

(Shivji, 1998). Under villagization, the rural population was resettled in Ujamaa villages and the farmer's land collectivized to communal land to be able to increase agricultural productivity through modernization and economies of scale. Farmers were required to work

Later on the communal land, which was very unpopular and led to very low production. they

sell their were allowed to produce crops on their own plots, but were still confined to produce to primary public society in the village. By 1977, 13 million peasants and pastoralists had moved into Ujamaa villages.

The government also intervened in marketing, distribution, and pricing in the agricultural sector. In 1963 the National Agricultural Products Board (NAPB) received

from monopoly powers in the pricing and marketing of agricultural crops to prevent peasants being exploited by Asian traders. After the Arusha Declaration, the eight major milling companies were nationalized and were amalgamated to form the National Milling

The Corporation (NMC). In 1973 NAPB was abolished and replaced by regional authorities. regional organizations, however, also showed to be highly ineffective. Thus NMC received

and beans in monopoly power over maize, rice, wheat, cassava, millet, sorghum, marketing

that no 1975. Private trade was illegal and punishable, and road-blocks were set up to ensure unauthorized inter-regional transactions of food grains took place. In 1970 the government introduced pan-territorial producer prices, which replaced pan-territorial in-store prices, which benefited farmers in remote areas. At the same time Government sought to protect earners of low salaries in urban areas by keeping food prices low through subsidies.

The pan-territorial price policy penalized regions with little transport costs and subsidized regions with high transport costs. Farmers in remote regions such as Iringa and

Mbeya increased their production, while those nearby important urban centers or near the

Kenyan border reduced their sales to NMC and sold large quantities of cereals to the illegal

and parallel market, where their produce fetched up to double of official prices. Maliyamkono

Bagachwa (1990) estimated that two third of the marketed surplus of maize and three quarters

as can be seen of paddy were traded in the parallel markets between 1971-72 and 1986-87

market from Table 1. When harvest was bad, the proportion of maize sold outside the official rose well above 90% and fell under 50% in years of bumper harvest, when prices on parallel markets declined. Urban households that had no access to subsidized food were compelled to

2 Ujamaa is a Swahili word and means brotherhood 10

to meet their purchase food on parallel markets and spent more on food due to higher prices needs. Households that benefited of the subsidies usually were employed by the government or by a parastatal organization (Maliyamkono and Bagachwa, 1990).

Table 1: NMC Purchases of Maize Grain from Surplus Regions

1976-7 1980-1 1986-7

Percentage Percentage Percentage Tons Tons Tons of total of total of total

Surplus regions

Iringa 14,700 11.5 21,754 26.3 38,006 21.3

Mbeya 5,500 4.3 5,251 6.5 15,987 9.0

Rukwa 11,800 9.2 17,717 21.5 29,338 16.4

Ruvuma 10,000 7.8 14,082 17.0 29,116 16.3

Former surplus» regions

Kilimanjaro 6,100 4.8 134 0.16 769 0.43

Morogoro 9,200 7.2 733 0.88 776 0.43

Tanga 20,800 16.3 89 0.12 625 0.35

Tabora 3,500 2.7 2,381 2.9 1,428 0.8

Source: Maliyamkono and Bagachwa, 1990

The situation was similar for export crops. Public authorities were the sole authorized exporters of agricultural products and sole supplier of necessary inputs. Producer prices were not determined by the world market price but by a recommendation of the Marketing

of Development Bureau based on a cost-plus calculation of the production. Internal marketing

and was the six major export crops (tea, coffee, cotton, cashew nuts, tobacco, pyrethrum) confined to cooperative societies, while export marketing was handled largely through parastatal marketing boards (Mans, 1994). Agricultural exports were also discriminated by control of exchange rate that led to an overvalued Tanzanian shilling. A parallel market for foreign currencies emerged with a premium over official exchange rate that peaked at 800% in 1986 (Kaufmann and O'Connell, 1999).

Trade and industrial production of consumer goods were also put under state control.

In 1986, industrial parastatals accounted for around 47% of production value-added and 48% of employment in the manufacturing sector. Prices were also controlled to protect consumers from prices set by parastatal monopolies, which would be exorbitant. After the Arusha

Declaration (1967) the State Trading Company (STC) started to control the nationalized 11

internal import-export business. STC was also assumed to control internal trade, when wholesale was nationalized in 1971. In 1973 STC was decentralized and reorganized into six parastatal importing companies and 20 regional trading companies (RTCs) under the

that management system of the newly created Board of Internal Trade (BIT). Figure 4 shows

which a a monopoly with a fixed price PI for its output, produces the quantity A, represents parastatal monopoly in Tanzania during Ujamaa socialism. Demand at the same price is assumed be B, thus excess and uncovered demand results if imports are prohibited. Secondary trade in the gray-market for the good produced by the monopoly will lead to higher price P3.

on the If illegal imports of similar goods occur, represented by S, price P2 will be fetched

at the official PI are gray market. Those, who have access to the locally produced good price most likely to resell these goods in the second market and will capture a large rent.

Figure 4: Monopoly with Price Control and Second Economy

p

P3

P2

P1

0 A C B Q

D: Demand

MC: Marginal costs of monopoly

PI: Official price

S: Supply second economy (illegal imports)

P2: Second market price with illegal imports

P3: Second market price without illegal imports

The results of these policies were dramatic. Due to low prices of export goods at farm gate, production declined and thus revenues form exports. This meant a study tightening of import restrictions, the results of which were declining import volumes. This and state control

of both on domestic production resulted in shortages of intermediate imports and imported 12

of official and domestically produced consumer good. This shortages led to limited increases prices and to increased reliance on direct rationing of goods. Shortage of consumer good were particularly severe in rural areas, where the led to a reduction in peasant labor supply, leading to further declines in export production in a vicious circle of output decline and declining availability of goods (Bevan et al., 1990).

Because of external shocks in the 1970s, the war with Uganda 1978-79, and because of these ineffective economic structures, which led to large macroeconomic instability, the

Tanzanian government had to stop its socialist policies in 1985 and started to reform the

the IMF and the World Tanzanian economy with structural adjustment programs supported by

Bank. It is most likely that the system collapsed because of the inefficiencies of the system and not because of external shocks. For example, Tanzania also benefited from high coffee prices on the world market from 1976 to 1978 (Bevan et al., 1990).

2.2. Structural Adjustment and Trade Liberalization

The Tanzanian structural adjustment policies were based on macroeconomic and sectoral reforms. Macroeconomic reforms included fiscal policy and management, monetary policy, financial sector policies, reforms on the exchange rate management, and external trade liberalization. The sectoral reforms contained price and market deregulation, labor and wage policy reforms, public enterprise reforms, and social sector policies .

In fiscal policy and management reforms, the government adopted measures to increase revenue and reduce the growth of both current and development expenditures.

Difficulties in reforming the taxing system emerged because of weak administrative capacities. Furthermore, the tax base declined due to a change in the structure from easy-to-

dominated tax public sector-dominated economy to a hard-to-tax private and informal sector

reduced economy. In the 1990s many smaller taxes were eliminated and customs duty rates and rationalized. In 1996 the Tanzanian Revenue Authority (TRA) was established, which

VAT was an important step on the road to improve tax administration, and in 1998 the replaced the much more cumbersome sales tax (IMF, 1999). On the expenditure side explicit parastatal subsidies were reduced and a hiring freeze was imposed on new recruitment in the civil service, with exemption of certain specified categories such as teachers and medical personnel.

-5 A detailed overview on the implemented reforms is described by Mans (1994). 13

with the External trade was gradually liberalized. In 1984, liberalization started

in the introduction of an "own-funds" import scheme, because of a severe shortage of goods country. This scheme allowed individuals to import a specified range of consumer, intermediate, and capital goods into the country using unofficial sources of foreign exchange.

In 1987 the open general license was introduced, which allowed importers to access to foreign

and exchange on a first-come first-served basis, and permitted imports of few agriculture

that transport sector goods. Later many other goods were allowed to be imported through

from This means, which suddenly exposed local producers to a stiff competition imports. competition was enhanced as the Tanzanian shilling continued to be overvalued, even though the official exchange rate was lowered. In 1991-93 all administrative allocations of foreign exchange were eliminated and the system of import licensing was abolished (IMF, 1999).

In comparison to imports, the liberalization of exports has been promoted less vigorously, especially for the six major agricultural export crops4, where internal trade was controlled by cooperative societies and export controlled by a parastatal marketing board. It is only in the early 1990 that trade of these commodities was liberalized, but exports remain difficult due to bureaucratic red tape and costs involved (Mans, 1994). By 1993/94, the system of export licensing was abolished, including for exports of traditional crops.

Mandatory registration of export companies was eliminated, and surrender requirements were no longer imposed (IMF, 1999).

Trade liberalization for goods of the agricultural sector was faster for foodcrops than

and for export crops. In the early 1980s the Tanzanian government allowed cooperatives individuals to market foodgrains, and removed all restrictions on their transport. In 1987 regional cooperative unions and primary societies were allowed to sell foodgrains directly to private traders, although market outlets for farmers were still confined to the primary societies. One season later the trade with foodgrains was fully liberalized. Official price for foodgrains first became the minimum price to be paid to farmers. Later it became an indicative price and then was completely removed leaving market forces to determine the price (Maliyamkono and Bagachwa, 1990).

The situation of the poor as well as of the better off improved after structural

line adjustment began. In rural areas, the proportion of Tanzanians living under the poverty decreased from 65% in 1983 to 50.5% in 1991, and those living under the hard core poverty line decreased from 54% to 41.8%. This also means that the absolute number of people living

4 Tea, coffee, cotton, cashew nuts, tobacco, and pyrethrum 14

in poverty declined from 10.8 to 9.7 millions, and those living under the hard core poverty limit from 8.9 to 8 millions (Ferreira, 1996). The urban poor probably also benefited from trade liberalization as most of them did not have access to subsidized food and as they had to

food rely on the more expensive food provided in parallel markets. After liberalization prices

and in parallel markets declined and even undercut the official prices (Maliyamkono

Bagachwa, 1990). Those who lost from the structural adjustment are the former employees of the government and of parastatals who were released.

in How policy regimes impacted on the growth of the economy in Tanzania is shown

Figure 5. Real growths are compared to the annual percentage growth of the population.

Growth in agriculture lagged behind GDP during the initial period of increased government

economic intervention in the economy. In the early 1980s, the bottom of the general decline, agriculture had actually started to recover, even while GDP growth was negative. Agriculture

from growth has led the economy through the periods of reform and economic recuperation the early 1980s responding rapidly to the earliest reforms in the marketing of agricultural produce.

Figure 5: Average GDP and agriculture GDP real growth rates compared with annual growth of population

1993-97

EJGDP DAgriculture GDP

(Sources: World Bank, 1994b; World Bank, 1995; Planning Commission, 1998)

2.3. Agriculture in Tanzania

Agriculture must be considered as the backbone of the Tanzanian economy, supporting employment, food production and exports. It contributes almost 50% to the 15

Tanzanian GDP (Figure 6), and accounts for about 75% of the country's foreign exchange earnings. Furthermore, about 80% of the population depends on agriculture for their

livestock livelihood. In 1997 crops contributed 69.7% of the agricultural GDP, 16.2%, forestry and hunting 4.6%, and fishing 9.5%.

Figure 6: Contribution of Economic Sectors to GDP in 1997

Public Services

Source: The Planning Commission, 1998 Table 2 shows that production of cereals increased most rapidly since independence,

in 1997 and that it gained most importance in crop production. Only 9% of the area harvested

Still traditional was for export crops, which is a clear decline since independence. export

of Tanzania in 1997 crops accounted for about 50% of the export earnings (The Planning

Commission, 1998). 16

Table 2: Distribution of Area Harvested

1969-71 1983-84 1996-98

million ha percent million ha percent million ha percent

Cereals 1728'603 36.5% 2'444267 41.4% 3182729 47.6%

15.3% 891274 13.3% Root crops 753'500 15.9% 904713

Pulses 419140 8.9% 838700 14.2% 779TXX) 11.7%

Tree nuts 198TXX) 4.2% 71167 1.2% 120TO0 1.8%

595300 8.9% Oil crops 364'567 7.7% 515267 8.7%

Vegetables 160707 3.4% 181127 3.1% 165237 2.5%

Fruits 209 Ü67 4.4% 263'500 4.5% 269'600 4.0%

Coffee 107X)00 2.3% 109333 1.8% 115TO0 1.7%

Cotton 425'400 9.0% 452263 7.7% 409'667 6.1%

Sisal 239'565 5.1% 63333 1.1% 53 TOO 0.8%

Other cash 124'622 2.6% 66778 1.1% 100217 1.5%

Total 4730170 100.0% 5909948 100.0% 6'681X)23 100.0%

(Source: FAOSTAT Database, three years averages)

Among cereals, Maize is the preferred, and is grown where climate and soils permit.

Sorghum and millet are cultivated in less favorable areas. Rice has become increasingly important, because of the good returns it fetches in the urban markets.

Figure 7: Distribution of Cereal Area Harvested in 1997 (3.3 million ha)

Wheat Paddy 2.2% 15.0% Millet 6.2%.

Sorghu Maize 14.9% 61.7%

Source: The Planning Commission, 1998

Livestock keeping contributed about one third of the agricultural GDP in Tanzania. Of this beef accounts for about 40%, milk for 30% and poultry and small stock for the remaining 17

30%. Out of the 15.6 million heads of cattle in 1995, 246,000 are improved dairy cows, which

which are 1.6% of all. However, they contributed 25% of the milk produced in Tanzania, amounted to 680 million liters in 1995 (MoAC, 1998b). Pastoralists own 40% of the cattle in

Tanzania. However pastoralists represent only 20% of the cattle keepers, while the other livestock owners are settled, and usually also produce crop in a mixed farming system (World

Bank, 1995). Beside meat and milk, livestock provide animal traction, manure used on crops and as fuel, and plays an important social role in many communities. Wealth is linked to livestock ownership, as it is a relatively secure form of saving and investment. Seite Leer / Blank leaf 19

3. The Structure of Market, and the Supply of Milk and Milk Products

This chapter examines the structure and sources of milk and milk products in the Dar

milk es Salaam and Mbeya regions, as well as the supply of marketed surplus of (MSM) by farms in the Mbeya and Iringa regions. First, the role of milk production in Africa is described, and second production, marketing, and consumption of milk and milk products in

Tanzania is presented. The structure of the market of milk and milk products in the Dar es

Salaam and Mbeya regions is then described using tables and figures. Second, an analysis of the marketed surplus of milk is done with a supply function.

3.1. Overview of Milk Production, Marketing and Consumption in Sub-Saharan Africa

The livestock sub-sector of agriculture contributes about 18% of the agricultural GDP in Africa and milk makes up 20% to 25% of this (Walshe et al., 1991) and thus plays a major role in income generation and provision of employment in rural areas of SSA countries. From

1970 to 1997 milk production in Africa grew by an average of 2.6% per year from 7.934 to

15.732 metric tons (Figure 8) while the number of cattle increased by 1.4% per year in the

The eastern same period. Milk production in Sub-Saharan Africa is unevenly distributed. region includes only 30% of the human population but accounts for three-quarters of all reported milk production, while only 15% of the milk is produced in West Africa, where 40% of the human population lives. 20

Figure 8: Milk Production and Imports, and Consumption per Capita in Sub-Sahara Africa

18000 40

g 16000 ti 35

14000 30

« 12000

25 c

10000 I Imports 20 £ 3 Production E -Consumtion per capita •£ 8000

c É 6000 o Ü

10

I- 0 ^ ^ ^ ^

(Source: FAOSTAT Databank)

Five main milk production systems are known in Sub-Saharan Africa: pastoralism, agro-pastoralism, mixed-farming, intensive dairy farming, and peri-urban milk production.

Pastoralists own large herds of camels, sheep, goats, and cattle, and are continuously on the move looking for fresh grazing, and are mainly found in arid areas. Milk is the major source of nutrition for pastoralists. Agro-pastoralists are sedentary farmers who cultivate food crops, and feed their livestock on communal grazing land. Mixed system farmers produce food or cash crops and keep livestock, which are used for draught, utilization of crop residues, improving soil fertility and providing additional income in form of milk or meat. Intensive dairy farmers hold dairy cattle and use part or all their land for fodder production, since milk is a major farm income. Large parastatal and commercial, as well as small scale dairy farms belong to this production system. Peri-urban milk production developed around cities and towns that have a high demand for milk. The main feeds are agro-industrial by-products and cultivated fodder crops or crop residues. Milk is often traded directly to the consumers in the city and is the major source of income for the farmers (Walshe et al., 1991).

From 1995 to 1997 SSA countries imported on average 2 million metric tons of milk products in milk equivalents, valued more than seven hundred million US dollars, which is about 12% of the total milk consumed in Africa. Therefore, in most SSA countries dairy 21

in production must be considered as an import substitute for imported milk products, usually form of milk powder. Considering that a large share of the milk produced is consumed by the dairy farmer's household, and that only a small share of the milk produced is commercialized in local markets, there is a large potential of the local milk production for further substitution of milk imports.

Since the early 1980s, consumption of milk declined from more than 35 liters to under

milk 30 liters of fresh milk equivalents per capita and per year in Africa (Figure 8). Imports of products in Africa also declined especially due to changes of export policies in surplus

to the counties such as the European Union. This decline in consumption is probably due

and also general decline of per capita income and thus purchasing power in this period because of the reduced proportion of the population living as pastoralists, who's major source

other of protein and energy is milk. In SSA, most of the milk is consumed fresh or fermented; milk products take only an insignificant part of the consumption (Walshe et al., 1991).

However, the demand for milk in Sub Saharan Africa could increase again if incomes raise. Delgado et al. (1999) projected the milk demand to boost in the next years in developing countries. They estimated that milk demand will grow by an average annual rate of 3.3 in the developing world and by a rate of 3.8 in SSA from 1993 to 2020. The driving forces of this development are changes of the incomes and population growth. By 2020 per capita total milk consumption in SSA would be 31 million liter of milk equivalents per year, but per capita consumption would remain almost constant at 30 liters per annum.

The dairy sector in Africa faces many constraints, but also holds opportunities. First of all there are technical constraints, which include genetic structure of the cattle, feed and nutrition, health and disease problems, management, water shortage, and appropriate technologies. Then there are also institutional constraints such as land tenure, marketing facilities, and support services at all levels. The principal opportunity for dairy development in Africa is the growing urban population, which is likely to produce an increasing demand for marketed milk. There are plenty of indigenous cattle and livestock keeper traditionally milk their animals, where cattle keeping is not constrained by trypanosomiasis. Therefore, late starters in marketing milk will catch up rapidly, since they are already used to produce milk for home consumption. Smallholder manual systems are appropriate under African conditions because they are much less capital intensive and risky than large mechanized systems (Walshe et al., 1991). Commercial dairying is also labor intensive and represents a regular source of cash income for the farmers in comparison to most crops, which generate unsteady income 22

milk offers to over the year. Considering the opportunities the marketing of milk and products rural population, the dairy sector is likely to contribute to poverty alleviation in SSA, although the constraints and difficulties in production and marketing.

3.2. The Dairy Sector in Tanzania

All five dairy production systems known in Africa can also be found in Tanzania.

First, pastoralists who own large herds of indigenous cattle in arid areas, and who produce and consume milk for subsistence. Milk is the largest source of energy and protein for them.

However their potential for commercial dairying is considered to be low, since they are continuously on the move, and since milk output of zebu cattle is low and faces a high seasonal variability, due to feeding problems when fodder becomes scarce in the dry season.

Second, the agro-pastoralists who are former pastoralists who settled down and started to

the cultivate food crops, but who kept their cattle. The third category is mixed-farming system, where farmers owns at least one improved dairy cow, but for whom milk is only a secondary output from farming. Intensive dairy farming is the fourth known system, where milk is the main output. Both, large scale intensive dairy farms with hundreds of heads of cattle and small scale intensive dairy farms with few dairy cattle exist in Tanzania. Best agro- climatic conditions for mixed-farming and for intensive dairy farming are met in the Northern and in the Southern Highlands, and to a lesser extend in the Coastal area. Finally, peri-urban and urban milk production can be found in regional urban centers and in Dar-es-Salaam. The development potential is considered to be good for all four production systems especially for small scale farms, which have lower investment cost and which are considered to be more efficient than large scale farms. (Walshe et al, 1991; MoAC, 1998b).

After independence the Tanzanian government started to support the development of milk production. Efforts were directed towards increasing stock numbers and performance per head. The dairy industry was also encouraged as a means of improving the nutritional status of people in both rural and urban areas, and reducing the country's need to import milk products. In the early 1970s Tanzania embarked on several dairy development programs based on introducing more exotic cattle, farm machinery, better feeding and other management practices on large-scale farms. Large scale farms owned by parastatal bodies such as the Dairy Farming Company (DAFCO) and other government institutions were build

Tanzania a up. Due to poor performance of these large scale units, the government of started new strategy in 1979 that also encouraged small scale farmers to produce milk for the urban markets (Ngigwana, 1992). 23

The role of organizing, collecting and processing milk in Tanzania was given to the

Tanzania Dairies Limited (TDL), a parastatal organization. Milk processing plants were settled in several urban centers, some of them with facilities to recombine milk from dried skim milk and butter oil, often provided by multinational donor agencies or by governments of developed countries as food aid. Inflow of fresh whole milk was provided by the large- scale dairy farms, and later on also from small-scale farmers who delivered their produce to collection centers (Ngigwana, 1992). TDL faced many problems. Shortage of foreign exchange and local funds to purchase essential spares for the processing and cooling machines, chemicals, cultures and packaging materials limited the milk processing capacity.

Milk collection was also hampered by poor roads, especially in the rainy season and by inadequate milk collection vehicles and lack of spare parts (Ashimogo and Kurwijila, 1992).

Furthermore, farmers became reluctant in supplying milk to the TDL collection centers due to delays in payment (Kunze et. al, 1997). From 1979 to 1988 local supply of fresh whole milk declined from 11.3 to 5.5 million liters (Ashimogo and Kurwijila, 1992). In the 90s changes in international trade policies that saw a reduction of export subsidies in developed countries and an increase of the price for skimmed milk powder made recombination of milk economically less interesting (Mdoe and Wiggins, 1996). By the middle of the 90s, TDL had stopped to exist and the milk processing plants were sold to private entrepreneurs. Milk processing and trade is now undertaken either directly by the farmers or by private milk traders or processors.

3.3. The Structure of the Market of Milk and Milk Products in the Dar es Salaam and Mbeya Regions

This section describes the source of fresh and fermented milk consumed by the households of the 1998-1999 survey in the Dar es Salaam and Mbeya regions of Tanzania.

After a short review of the actual knowledge on the markets of milk and milk products in these two regions, the source of supply used by customers of milk and milk products participating with the survey is described using tabular presentations.

The characteristics of fresh whole milk and of its production have a large influence on the requirements of the milk marketing activity of the farmer and of the other marketing actors. Fresh whole milk is highly perishable and has a high bulkiness, which increases the costs of its transfer to the markets. The milk output of a cow is daily, and it varies with the availability of fodder and water and so with the seasons. The dairy farmer therefore has to market his production every day and thus needs an outlet that is reliable and not too far away 24

to keep the transaction costs low (Jaffee and Morton, 1995). One way to increase shelf-live of milk is to ferment it. Fermented milk can be produced with simple technology appropriate to the Tanzanian situation and is already widely consumed (Kurwijila, 1992). Therefore,

The fermented milk and fresh milk are the milk products that are included in this study. major

because of the area of interest for developing outlets for dairy fanners are urban markets, higher population density than in rural areas, which reduces distribution costs. Urban areas

is to rise in these are also growing fast and thus demand for milk and milk products likely areas, even if household incomes do not change.

Several studies have already been undertaken in Tanzania on the marketing systems for milk and milk products (SHDDP 1997a and 1997b; Mdoe and Wiggins, 1996i Kurwijila, and Henriksen, 1995, Mdoe et al, 2000), which were summarized in the rapid appraisal of the dairy sub-sector of Tanzania (MoAC, 1998b). In Tanzania, there are at least three possibilities for dairy farmers to market their output. First, there is the local market, where the dairy farmer sells his production to his neighbors. Second, small and large regional urban centers are usually the nearest locations in Tanzania for dairy farmers to sell their output. The largest urban centers in regions with good conditions for intensive milk production are Moshi and

Arusha in the Northern Highlands, and Mbeya, Iringa and Morogoro in the Southern

Highlands. The distance of the farm gate to these urban markets increase the requirements of the marketing activity. The dairy producer will have to carry his production himself to the

will urban center if he can not sell it to a small trader (middlemen) or to a co-operative, which transport and sell the milk in the urban area. Third, Dar es Salaam with 3 million inhabitants is the largest market for dairy products in Tanzania. However the distance of 300 to 600km

of the from the main milk sheds make a well organized marketing channel necessary. Most milk consumed in Dar es Salaam comes from the Coastal and from the peri-urban area or is produced directly there. Dairy products brought into Dar es Salaam from other regions will have or already do compete with this local production. 25

Figure 9: Milk Marketing Channels from Dairy Cattle Production in Tanzania

TOTAL FARM PRODUCTION - 188 million liters

67% 33% v MARKETED MILK 126 NON-MARKETED MILK 22% 4% 1% 24% 9%

Vendors and Family Calf Kiosks 42 Consumption 44 Consumption 18 <1% i r v

Co-operatives6

209 5 r 'r 40% Private Processors 4 (75) 1% <1% ! r

Agent/Retailer2.5 1% 1% 3% ^ r i r ^ r v CONSUMERS

Source: Rapid Appraisal(MoAC, 1998b) 26

The Rapid Appraisal (MoAC, 1998b) carried out in 1997, estimated the total fresh milk production from dairy cows in Tanzania at 188 million liters per year. One third was estimated to be consumed by the farming households. The bulk of the milk that is marketed

is annually in Tanzania (126 million liters of milk5) passes through informal channels as shown in Figure 9. The informal marketing channels are the direct sales of the farmers to the

and sold middlemen consumers living in rural or urban areas (60%), the milk bought by

(22%) and the milk collected by dairy co-operatives (4%). The dairy products passing through

4 liters these channels are fresh whole milk and fermented milk, both unpacked. Only million

whole (3%) are processed in private units, which produce packed fresh milk, packed

such as and fermented milk, very little UHT milk, and few other transformed products, yogurt butter. These more advanced transformation plants are a part of the formal milk marketing channel in Tanzania (MoAC, 1998b).

The data on the household's purchase location of milk and milk products were collected in October to December 1998 in the second round of the food demand survey (see

each chapter 4). A tabular analysis was employed to explore the number of households using

of fresh source to purchase milk and milk products and figures are used to represent quantities and fermented milk that passed through the different marketing channels. Since the importance of the channels strongly varies, dependent on the location of the households, the

The flow of fresh data are presented separately for region and for urban and rural households.

milk consumed and fermented milk was computed by summing up the total quantity of by households dependent on the source they indicated.

classified into 3 For reasons of simplification the sources of these two products were

cattle categories. First, own production is the source of milk if the consuming households own and produced milk themselves. The second channel "from farm" is milk that households purchased directly from the producer, either at the farm gate or home delivered by farmers.

Other sources of fresh and fermented milk, such as street vendors, milk kiosks, corner shops, and supermarkets are included into the last category "from trader".

The results of the study are presented in Table 3 and in Figure 10

5 From improved dairy cows. Marketed milk from traditional herds are not included in that figure as well as in the percentages in the brackets. 27

Table 3: Households' Source of Fresh and Fermented Milk

Number of responses Frequency of responses

From _ Own „ Own „ From c

. From farm , Area Milk From farm , , ^ ^ production trader production trader

Fresh 5 27 123 3.2% 17.4% 79.4% Dar urban Fermented 9 14 69 9.8% 15.2% 75.0%

Fresh 0 0 63 0.0% 0.0% 100.0% Darrura I Fermented 0 0 11 0.0% 0.0% 100.0%

Fresh 18 45 8 25.4% 63.4% 11.3% Mbeya urban Fermented 10 11 11 31.3% 34.4% 34.4%

Fresh 21 29 1 41.2% 56.9% 2.0% Mbeya rural Fermented 17 44 0 27.9% 72.1% 0.0%

Fresh 44 101 195 12.9% 29.7% 57.4% All Fermented 36 69 91 18.4% 35.2% 46.4%

- Most of the customers in Dar es Salaam - as well in urban as in rural areas purchase fresh whole milk and fermented milk from traders, usually from a milk kiosk or from a corner-shop. The proportion of fresh milk and fermented milk that was purchased from traders attained 71% and 79% respectively.

milk Not one household in rural areas of Dar es Salaam purchased fresh and fermented

the was from a dairy producer since no dairy producers were found in the area where study carried out. Thus all milk consumed in this area came from middlemen.

Most of the households in Mbeya urban and rural obtained fresh whole milk directly

in estimated from the farm of a dairy producer or produced the milk themselves. A study 1996 that approximately 68.7% of milk traded in the city of Mbeya is produced locally (SHDDP,

1997a). In comparison this study indicates that only 8 out of 53 households (15%) do not purchase fresh whole milk from a local producer. Out of the total fresh milk consumed in

Mbeya urban, only 6.6% is purchased from middlemen. The largest part is milk consumed

In from the own production (41.1%) or purchased directly from the producer (52.3%). contrary to fresh whole milk, half of the purchasers of fermented milk in the city of Mbeya obtained it from a merchant and 38.5% of the fermented milk consumed came from that channel. However farmers consumed only little part of their production as fermented milk but large quantities (60%) of fermented milk were purchased from the producer. 28

Figure 10: Quantities of Fresh and Fermented Milk Consumed by Source

Fresh milk Fermented milk

Dar es Salaam Urban Dar es Salaam Urban

Own production From farm D From trader 0 Own production^ From farmO From traiden

Dar es Salaam Rural Dar es Salaam Rural

Own From farmo From traiden ^3 Own production H From farm D From trader | productiorB

Mbeya Urban Mbeya Urban

| Own production H From farm DFromtraider] Own production From farm G From trader

Rural Mbeya Rural Mbeya

3 Own production From farm DFromtraider | Own production From farm DFrom trader

Mbeya rural is the only location where more households consumed fermented milk

from a than fresh milk. Only a marginal part of the milk consumed in this area is purchased 29

trader. Fresh and fermented milk is thus consumed either by the producers' household or is directly sold from farmer to neighboring households. However, milk producers do almost not

of consume fermented milk, but most of their intake of milk is fresh. The bulk (94%) fermented milk is purchased by household directly from farmers.

in In Dar es Salaam most customers buy milk from traders, while only few do so

know Mbeya region. Consumers seem to prefer fresh and fermented milk from producers they

thus or produce it themselves. In the city of Dar es Salaam milk production is rather difficult, offer of locally produced milk is limited, and consumers are compelled to satisfy their demand for milk and milk products with purchased products from middlemen. In Mbeya

in this area and does urban, many people hold cows and thus a lot of fresh milk is available not have to be transported over long distances.

The analysis also suggests that customers in Mbeya urban are more likely to consume fermented milk from traders, and fresh milk from farm gate. Similarly, households who produce milk in rural Mbeya will consume fresh milk and the other households will prefer to buy fermented than fresh milk, even when purchased at farm gate. There is no good reason why household should prefer one milk product to the other due to the place of purchase.

However fresh milk spoils much faster than fermented milk; this gives an advantage to fermented milk when milk is marketed (Jaffee and Morton, 1995). In view of this, fermented milk is most likely to be the best adapted milk product for trade and transport from rural to urban areas.

An analysis of the factors that impact farmer supply of milk and milk products are presented in the next section.

3.4. Determinants of Marketed Surplus of Milk

milk In this section an attempt is made to examine the market supplies function for and milk products in Iringa and Mbeya regions of Tanzania. The influence of economic and non-economic variables on farmers' marketed surplus of milk (MSM) is analyzed.

Understanding how these factors impact on farmers' MSM may help to improve dairy related extension, and thus increase productivity and income of small-scale dairy farmers.

3.4.1. Methodology

The supply function of MSM results from a joined production and consumption decision of the farm household. Therefore, the analytical framework employed is the 30 agricultural household model developed in Gronau (1977), and Singh, Squire, and Strauss

(1986).

The household is assumed to maximize a utility function of the form:

U = u{Xa,Xm,Xl) (3.1) where Xa represents an agricultural staple, Xm is a market purchased food and Xi is leisure.

Equation (3.1) is maximized subject to a number of constraints. The first is a cash income constraint:

PmXm=pa{Q-Xa)-w(L-F)

and of the where pm and pa are the prices of the market-purchased commodity staple, respectively, Q is the household's production of the staple (so W - Xa is the marketed

that L - surplus), w is the market wage, L is total labor input, and F is family labor input (so

F, is positive, if labor is hired, and negative, if off-farm labor is supplied).

The household also faces a time constraint. That is, it cannot allocate more time to leisure, on-farm production, or off-farm employment than the total time available to the household.

X,+F = T where T is the total stock of household time.

The household faces a production constraint or production technology that depicts the selection between inputs and output:

Q = Q{L,A) where A is the household's fixed quantity of land.

It is assumed in this presentation that family labor and hired labor are perfect substitutes and can be added directly. For the moment, other variable inputs are omitted. It is also assumed that the three prices in the model - pa, pm and w - are not affected by the actions of the household. That is, the household is assumed to be a price-taker in the three markets.

Now, substituting the production constraint into the cash income constraint for Q and substituting the time constraint into the cash income constraint for F yield an single constraint of the form

PmXm+paXa+wXt=wT + fc (3.2) 31

= total where n paQ(L,À)-wL and is a measure of farm profits. The left-hand side shows household "expenditure" on three items - the market-purchased commodity, the household's

of "purchase" of its own output, and the household's "purchase of its own time" in the form leisure. The right-hand side is the full income in which the value of the stock of time (wT) owned by the household is explicitly recorded.

In these equations, the household can choose the levels of consumption for the three commodities and the total labor input into agricultural production. The first-order conditions for maximizing each of the choice variables therefore need to be explored.

The first-order condition for the labor input is:

PadQ/dL = w (3.3)

That is, the household will equate the marginal revenue product of labor to the market

it can be solved for wage. Given that equation (3.3) contains only one endogenous variable, L,

L as a function of prices (pa and w), the technological parameters of the production function,

in and the fixed area of land. This result actually parallels the commonly found condition agricultural household models that production decisions can be made independently of consumption and labor-supply (or leisure) decisions.

Suppose the solution for L is

Ü=L'(w,pa,Ä), (3.4) then this solution can be substituted into the right-hand side of equation (3.2) to obtain the value of full income when farm profits have been maximized through an appropriate choice of labor input. Equation (3.2) could therefore be rewritten as

Pmxm + püxl,+wxl=r where y* is the value of full income associated with profit-maximizing behavior. Maximizing utility, equation (3.1), subject to this new version of the constraint yields the following first- order conditions:

dU/dXm=Apm dU/dXa=tpa (3.5)

dU/dX, = Aw and

pmXm + paXu+wX,=Y' 32

which are the standard conditions from consumer-demand theory.

The solution to equation (3.5) yields standard demand curves of the form

x, = x,\Pm>Pa>w>Y*) i = m,a,l (3.6)

That is, demand depends on prices and income. For agricultural households, however, income is determined by the household's production activities. Hence, changes in factors influencing production will change Y and hence consumption behavior.

the which For the purpose of this analysis, it is necessary to introduce "profit effect", influences the supply of the agricultural staple, when prices change. Suppose the price of the agricultural staple increases, what is the effect on consumption of the staple? From equation

(3.6), the following relationship can be derived

^ = ^+<^H. (3.7) dpa dpa ay* dpa

The first term in the right-hand side is the standard result of consumer-demand theory,

effects. A in and for a normal good, it is negative. The second term captures the profit change the price of the staple increases farm profits and hence full income. From equation (3.7) the following equation can be obtained:

^-dPa=^-dpa=QdPa. (3.8)

The profit effects equals therefore output times the change in price and is, unambiguously positive. This positive effect of an increase in profits will definitely dampen and may outweigh the negative price effect of standard consumer-demand theory.

3.4.2. Empirical Specification

The above theoretical framework can be used to specify an empirical model to examine marketed surplus of the agricultural staple. Assuming a quantity Q of the agricultural

the marketed staple is produced and a quantity C is consumed by the farm household, then surplus will be given as Q - C. A marketed surplus function can be specified as

S,=S{p,Z) (3.9)

where St is the marketed surplus of farm i and Z represents a vector of exogenous

Cobb- shifters, such as farm inputs and demographic variables. Specifically, if we assume a 33

Douglas function, the following log-linear model can be used to describe the MSM supply function:

+ In C + lnMSM =a0+al InPm + a2 In Ls + a3 InLn + a4 InLh a5

a6 In A + a7 ln If + as ln Ih + u

where Pm is the price of milk; Ls, Ln, and L/, are skilled, non-skilled, and hired labor respectively; C is the size of fodder plot; and A is the total surface of the farm; If and /„ are

to be purchased food inputs and health expenses respectively; the a are parameters estimated, while u represents the error term.

The price of milk is important for profitability of milk production in Tanzania (Mdoe

milk and and Wiggins, 1997). High prices are likely to make farmers produce and sell more milk products. With increasing profitability household incomes also increases and thus household demand for milk rises. This effect will damper farms MSM, as discussed above.

Thus the effect of milk price is likely to be positive on MSM, although its magnitude could be small.

and Labor is expected to have a positive influence on MSM since labor is an input directly contributes to the production of milk. Especially semi-skilled and hired labor are likely to have positive influence on MSM. Semi-skilled labor is likely to be more productive than non-skilled labor provided by the households, and it is thus expected to have a larger

is also positive effect on MSM than non-skilled labor. Hired labor, although non-skilled, very

labor. Hired labor likely to have a more favorable impact on MSM than non-skilled household

to household labor. A farmer will is a production cost that has to be paid in cash in contrary thus allocate hired labor to milk production only if the household can not provide labor itself, and if the farm household has enough cash income to pay the salary. This cash income is most likely to be generated by sales of milk and milk products. Therefore farms hiring labor are

that can meet labor more likely to provide more MSM to the market than households

on MSM is requirements with own household forces. The effect of non-skilled labor

effect on MSM. uncertain. On one hand it is a production input and ought to have a positive

On the other hand, the supply of non-skilled labor in rural area is high and is usually only

absorbed by the farming sector at harvest period. The rest of the time, rural non-skilled labor

often remains idle if there are no labor opportunities off farm, and thus the opportunity costs

of non-skilled household labor is almost zero. Therefore, if a farm household allocate own

non-skilled labor to milk production, this could be not to increase output but to find an

occupation in free time and thus to be leisure. In this case, non-skilled household labor will 34

since have no positive effect on milk output and thus non on MSM either. In contrary, large

labor force than households are more likely to be affected by over-availability of household small households, and since large households are likely to consume more milk themselves, allocation of non-skilled household labor to milk production could even lead to a negative effect on MSM.

The fodder plot is a plot on which the farmers grow grass to be fed to improved dairy cattle. The size of that plot is expected to have a positive influence on MSM, because fodder

collected from fallow land. The grown on a plot is likely to be of better quality than fodder

and larger the fodder plot, the more quality grass is available, that sustains milk production thus permits the farmers to sell more MSM.

The effect of total area of farm on MSM is ambiguous, since dairying is not directly

MSM because linked to available surface like crops are. Large farms could have a higher

farms could allocate production is more likely to be higher than consumption. However, large

and could more own resources to agricultural ventures that depend on land available, neglect

is other economic activities, that are less land-dependant such as dairying. The situation different for small farms that could find in dairying a mean to increase total agricultural

Thus output by increasing the number of dairy cattle, while keeping land surface constant.

and thus farm size small farms can produce and sell at least as much milk as large farms, could have no or even negative effect on MSM.

Purchased feed inputs and health expenses are expected to both have a positive

since influence on MSM. Especially purchased feed inputs should have significant effect, their application leads to improved animal nutrition, also of micronutrient, which will allow the cattle to improve the effectiveness of the utilization of proteins and energy in the fodder.

health This will directly lead to an increase of the output of milk and thus of MSM. Animal expenditure should also have a positive effect on MSM, since healthy animals produce more

disease. milk than ailing ones. However, animal health expenditures often come up in case of

than those That means that farmers owning ailing cattle are likely to bear more health costs with healthy animals. In that case, animal health expenditure would be a proxy for sick cattle, which produce less milk, and would thus have negative influence on MSM.

3.4.3. Datafor MSM Analysis

The data used in this analysis came from a farm-level survey that was conducted by the Southern Highlands Dairy Development Project (SHDDP) in the Iringa and Mbeya regions of Tanzania in 1994 and 1995. The survey involved a random sample of 109 dairy 35

farms in the two regions. Information were gathered through questionnaires. Table 4 presents the summary statistics of the data.

The marketed surplus of milk is the quantity of fresh and fermented milk that the

1995. The mean farms sold within the survey period that is from October 1994 to September

minimum quantity of MSM was 1,420 liters. Some farmers sold milk just occasionally, as the of MSM by one farm was 6 liters and others supplied large quantities of MSM up to 12,180

of liters on a regular basis. This shows how large the range of market participation dairy farmers in marketing their surplus of milk is. This reveals that the supply of milk in these two regions could be increased by allowing more farmers to participate in the milk market and by helping dairy farmers to increase their supply of MSM.

with a of The average price farmers obtained per liter of milk was 134.10 TSh, range

80 TSh to more than 230 TSh per liter. These are very large differences that are most likely to

in be due to remoteness of some areas where data collection took place. These differences prices thus reflect transaction costs from rural areas to outlet markets. Thus improving infrastructure to remote areas will reduce these transaction costs and price differences will

who decline. Farmers in remote areas are likely to benefit from such changes, while farmers already have access to reliable market outlets for milk and milk products will face a more stringent competition.

Semi-skilled labor include time spend by household members on milking, marketing, and veterinary care. Non-skilled labor consists of time spent by household members on operations such as cattle grazing, forage gathering, or cleaning the cowshed. Female and child

and for labor was converted into man-day equivalents using a factor of 0.7 and 0.5 for female

443 child labor, respectively. The average of semi-skilled labor allocated to dairying was man-days, ranging from 1.1 to 1,383 days, and non-skilled labor accounted to 296 man-days

These data on average, with a minimum of 0.85 and a maximum of 766 man days. indicate, that dairying provides more semi-skilled labor than non-skilled labor. Costs for hired labor

that range from 0 to 133,000 TSh, and reaches 13,845 TSh on average. This indicates dairying not only provides job opportunities for members of dairy farms but also for persons who are not member of dairy farm households.

A fodder plots is farmland, where farmers grow green fodder to feed dairy cattle. Their

and the average size was 0.44 hectares, with some farms having no fodder plot, largest

to 8 reaching 2 hectares. The average farm size was 2.35 hectares, with a range of 0.4 hectares hectares. Thus the data covered a large range of farm sizes in these two regions. However 36

unit. The of even the largest farm of the sample is a small scale production presence very small farms shows that dairying is possible, although little land is available.

The costs for purchased fodder inputs range from 0 to 374,850 TSh with an average

was lowest at of 21,087 TSh, and the mean cost for animal health inputs was 33,906 TSh, and

0 and highest at 1,036,824 TSh. These figures indicate that there are large variations in farmers application of purchased inputs for the production of milk in the two regions.

Table 4: Descriptive Representation of the Farm Data Used for the MSM Analysis (550TSh = 1US$) Standard Mean Minimum Maximum deviation

Marketed surplus of milk (It.) 1,420.33 1,592.50 6 12,180.00

80 232.32 Price per liter (TSh) 134.07 28.65

Semi-skilled labor (man days) 48.21 48.76 0 278.01

Non-skilled labor (man days) 295.99 151.25 0.85 766.14

Costs of hired labor (TSh) 13,849.61 22,317.28 0 133,000

Size of fodder plot (ha) 0.52 0.443 0 2

Farm size (ha) 2.35 1.339 0.4 8

Purchased fodder inputs (TSh) 21,087.54 41,042.75 0 374,850

Animal health inputs (TSh) 33,906.78 116,975 0 1,036,824.25

3.4.4. Results and Conclusions

Table 5 presents the results of the estimations of equation (3.10). Several estimations were carried out to see how the results alter if some of the variables are missing.

The results show that the price of milk has a positive, although statistically

on the marketed insignificant, impact on marketed surplus of milk. This positive effect

make surplus of milk indicates that, even where the profit effect is strong enough to

to offset consumption response positive, the total output response is always large enough increased household consumption. Since the price of milk probably also reflects information

other on the location of the farm, the insignificant effect is probably due to possible marketing

not be of production restrictions that are linked with the location of the farm, and that could estimated with the data set; for example it could be difficult to raise the number of cattle in urban areas, although price are likely to be high in these areas. 37

Table 5: Marketed Surplus Function for Milk and Milk Products for Iringa and Mbeya Regions

Constant Semi-skilled Non-skilled Size of Purchased Animal Price Hired labor Farm size AdjustedR term labor labor fodder plot fodder inputs health inputs

4.7360362 0.223907 0.5991683 -0.6069643 0.022559 0.193107 0.301171 0.1195383 0.075331 Estimation 1 0.3347 (2.31452) (0.48105) (0.11934) (0.19207) (0.02162) (0.47681) (0.30963) (0.03937) (0.03797)

Constant Semi-skilled Non-skilled Size of Purchased Animal Price Hired labor Farm size AdjustedR2 term labor labor fodder plot fodder inputs health inputs

4.9693891 0.215651 0.5832283 -0.5723253 0.026012 0.444446 0.1057913 0.0772652 Estimation 2 0.3350 (2.30144) (0.48084) (0.11818) (0.18869) (0.02132) (0.40062) (0.03673) (0.03791)

Constant Semi-skilled Non-skilled Size of Purchased Animal Price Hired labor Farm size AdjustedR2 term labor labor fodder plot fodder inputs health inputs

5.435512 0.179824 0.618113 -0.6468543 0.027232 0.1211323 0.0811392 Estimation 3 0 0.3335 (2.26532) (0.4803) (0.11405) (0.17653) (0.02132) (0.03407) (0.03779)

Constant Semi-skilled Non-skilled Size of Purchased Animal Price Hired labor Farm size AdjustedR term labor labor fodder plot fodder inputs health inputs

5.4492672 0.14409 0.6466663 -0.6115023 0.116153 0.0876932 Estimation 4 0 0.3294 (2.27223) (0.48095) (0.11218) (0.17488) (0.03395) (0.03755)

Constant Semi-skilled Non-skilled Size of Purchased Animal Price Hired labor Farm size AdjustedR2 term labor labor fodder plot fodder inputs health inputs

6.0940863 0.652644 -0.6074143 0.1167693 0.0889262 Estimation 5 0 0.3353 (0.72517) (0.1099) (0.17358) (0.03374) (0.03716) Note: = Figuresin parenthesesindicate standard error of regressioncoefficients

different from at 1 5 cent and 10 cent confidence level. , , , significantly zero per cent, per per 38

Semi-skilled household labor has a positive and statistically significant impact on

MSM. The positive influence of semi-skilled household labor was expected due to its higher productivity than non-skilled labor. Therefore, increasing the allocation of semi-skilled labor

since to dairying is likely to be a mean to increase farms milk production and sales. However, semi-skilled labor in dairying comprise activities like animal health care and milking, there are technical limitations to an increase of allocation of semi-skilled household labor to milk

cattle would production. For many farms, only a raise of the number of improved dairy permit an increase of semi-skilled labor allocation.

Non-skilled household labor has a negative and significant impact on MSM. Although the allocation of non-skilled labor to dairying must be considered as an input, its effect on

MSM is negative. This confirms the hypotheses that non-skilled household labor is allocated to milk production in periods of low labor requirements on the rest of the farm. Since large

will allocate households are likely to have more idle labor force than small households, they more non-skilled labor to dairying than small farming households. Furthermore, large

MSM. This households are likely to consume more milk than small ones, which reduces

but would mean that large households allocate more non-skilled labor to milk production supply less MSM than small households. This explains the negative influence of non-skilled labor on MSM.

Employment of hired labor has a positive influence on MSM as expected, but its magnitude is not significantly different from zero. Hired labor is usually non-skilled. As seen above non-skilled household labor does not exert positive influence on MSM. The reason for low significance level is due to the fact that labor is hired if there is no household labor available. This is usually the case at harvest period when a lot of work has to be done on the farms. In this period, if the household is not large enough, it is possible that part of the total workload is borne by hired labor, and that a part of it is allocated to milk production. Even if the household does not have enough cash income from sales of MSM to pay salaries, the allocation of hired labor to milk production in harvest period is possible. The household can pay its employee with cash incomes from sales of harvested crops.

Farm size, which is the surface of the arable land of the farm, exerts a positive but not significant impact on MSM. While larger farms may be associated with high MSM, the result does not support the hypothesis that larger farms provide more MSM than smaller farms.

The size of fodder plot has a positive although not significant influence on MSM. The

fallow quality of fodder grown on a specific plot should be better than fodder from land, 39

which should increase milk output and thus MSM due to improved animal nutrition; but no significant increase of MSM could be measured. The actual return of the allocation of arable land to fodder production must therefore be considered as insufficient.

Purchased fodder and animal health inputs both have a positive outcome on dairy

maize cotton farmer's supply of MSM as could be expected. Purchased fodder such as bran, and sunflower cake, and salts increase the productivity of dairying that results in a larger supply of MSM. Applications of animal health inputs increase the quantity of MSM, as

this can mean that healthy dairy cattle are more productive than ailing ones. On one hand,

also mean that some preventive health measures are justified. On the other hand, this could

a low farmers with sick animals can not afford veterinary services and thus remain on production level compared to those who can heal their animals with veterinary support.

Prices of milk and milk products are important for the small scale dairy farms. Higher prices increase their profits and because of this also their demand for milk and milk products.

low influence of However, higher prices are not likely to boost production in Tanzania due to the price on MSM. Good prices are thus important for farmers to generate enough income,

is intended. price policies, however, are not likely to be sufficient if increased market supply

to be An effective marketing chain that leads to a reduction of transaction costs is likely

milk and milk effective to prevent prices to drop or will even allow farm gate prices for products to increase, and thus to benefit the farmers as well as consumers.

Semi-skilled tasks in dairying, which are milking and animal health tasks boost production of milk and thus of MSM. However, the technology of dairying offers only a limited quantity of semi-skilled tasks. If the market allows small scale dairy farmers to sell

least fulfil all more MSM they should be taught and encouraged to at technically possible semi-skilled tasks.

The results show that increasing allocation of non-skilled household labor leads to a decline of the farms supply of MSM. This is most likely to be linked to the size of the farm

of non-skilled household as described above. Thus advising farmers to reduce the allocation labor to dairying is not likely to increase the farm's supply of MSM. Family members whose labor input in dairying fetch such little return are likely to look for work off farm. If they can

where off-farm not find work in the area they live, they are likely to migrate to urban centers

for non- work opportunities are larger. Creating off-farm job opportunities in rural areas

It is skilled labor is likely to be a contribution to prevent migration from rural to urban areas. 40

harvest when important that these new off-farm activities can be interrupted in period,

demand for the agricultural sector for labor is high.

The results suggest that hired labor has a positive although not significant effect on

members and can MSM. Thus dairying can be considered to provide labor to non-household

for from be a benefice for the poorest in rural areas, who are seeking job opportunities away

is available. This home. However, dairy farmers are likely to hire labor if no household labor

is usually the case at harvest, the period where most of the labor opportunities are anyway

available in rural areas.

Like hired labor, the size of fodder plot does have a positive influence on MSM.

However, it is not significant. That means that land allocated to fodder production brings only

little additional return to the farm's dairy unit. Therefore, the allocation of land to other crops

This is is likely to be more profitable to the farm than its use for fodder production. especially

low return from fodder the case if enough fodder is available on fallow land. The plot is,

however, most likely due to mismanagement and to lack of know-how on production of good

the fodder. Therefore fodder should only be grown on arable land if the farmer has knowledge

leave it. on how to produce high quality fodder, else he should better

Improved animal health facilities and increased animal feed can help farmers to

be considered as a increase animal productivity, as well as increase MSM. Animal feed can

normal input. Facilitating farmers' access to input markets is likely to be the best way to

increase milk production and supply of MSM through increased use of feed inputs. The same

health. It could be conclusions can be drawn looking at the coefficient of expenses for animal

sick worthy to support veterinary services, since the results suggest that some farmers with

animals can not afford this service and thus remain on a low production level. 41

4. Household Expenditure Survey

This chapter describes how the data necessary for the food demand analysis were

as well collected and gives a first insight into the socio-economic structure of the households

The area where as into households' budget allocation to non food and to various food groups. the data collection took place, the questionnaire used in the survey, the procedure used for household selection, and the accomplishment of the survey are presented first. Then the collected data are presented in details. Households' socio-economic data by location and area are presented in Table 6. Households' total expenditure per capita and year for food and non¬ food items are drawn in Table 7 and the allocation of food expenditure in various food groups is presented in more details in Table 8. The book on data collection in developing countries by Casely and Lury (1981) was of good help to prepare and conduct the survey.

4.1. Area of Data Collection

A multistage random sampling procedure was used to select households for the survey. In the first stage of the sampling procedure, Mbeya and Dar es Salaam regions were chosen as the locations where the survey should be carried out. Dar es Salaam was chosen because it is the largest city of the country, and the political and economic center of Tanzania.

Dar es Salaam is also the largest market for food in Tanzania and for milk and milk products in particular. Mbeya was chosen to represent the periphery of Tanzania. The fact that

Intercooperation (IC) conducts a milk development project funded in large parts by SDC in that region is a major reason why Mbeya was chosen and not another part of the country. Both of the two regions are now presented in more details.

4.1.1. Dar es Salaam

Dar es Salaam is the largest city in Tanzania and its name means harbor of peace. It is located on the African East Coast in the south of the islands of Zanzibar. The region covers an area of km and is surrounded the Coast Dar es Salaam is 1,393 , by region. Administratively, split into three districts: Kinondoni in the north, Hala in the center, and Temeke in the south.

Dar es Salaam was established in 1862 by the Sultan of Zanzibar. The German East

Africa Company established a station there in 1887, due to the good characteristics of the bay of Dar es Salaam for large commercial ships. In 1891, Dar es Salaam became the capital of

German East Africa. With the construction of the railway to Mwanza and Kigoma, Dar es

Salaam became also the economic center of the country. In 1974 Nyerere declared Dodoma in 42

the center of the country, to become the new capital of the United Republic of Tanzania and

and to decided to move the whole administration to that place. However, due to lack of funds organizational problems, only the parliament has moved its seat to Dodoma, yet. Thus, even though Dar es Salaam is no longer the official capital of Tanzania, it remains the most important town of Tanzania, economically as well as politically.

Like many other large towns in Africa, Dar es Salaam is growing rapidly at an average annual rate of 4.6%. Officially, the population was estimated at 2,23 millions in 1999 (The

Planning Commission). However, local newspaper reported a population of more than three

of millions in Dar es Salaam in the same period. This would mean that approximately 10% the Tanzanian population lives in Dar es Salaam. The region contributes more than 20% of the national GDP, which underlines that it is the economic center of Tanzania. These figures

as the indicate that the GDP per capita in Dar es Salaam is likely to be at least double as high national average.

4.1.2. Mbeya

Mbeya region lays in the south-west of Tanzania and borders to Zambia and Malawi.

With Rukwa, Iringa, and Ruvuma regions, Mbeya belongs to the Southern Highlands of

Tanzania that is considered to be the corn chamber. The area of the Mbeya region covers

63,617 km2 of which 57,000 km2 is arable. The rest of the area is game and forest reserves, and water bodies. Large parts of the land of Mbeya remain unused, as only 28% of the arable land lies under agriculture. This figure drops to 0.3% and 2.2% in Heje and Chunya districts, respectively, which are located in the north, and attains 100% in Kyela district in the south of the region. These variations can easily be explained by looking at the large differences in the agro-climatic conditions between the semi-arid north and the humid south of the region. The city of Mbeya is located on the highway leading to Zambia and lies 1,800 meters above sea level.

From 1978 to 1988, the population in Mbeya region grew by an average annual rate of

3.17% and attained 1.47 millions in 1988. Thus the population in the region of Mbeya in 1999 can be estimated to be larger than 2.2 millions if growth of population is assumed to be unchanged. The population of the city of Mbeya grew in the same period by an annual rate of

6.94%, which is much faster compared to the whole region. In 1999, the population of urban

Mbeya must thus be estimated at approximately 320,000 inhabitants compared to 153,000 in

1988 (own calculation with data of the 1978 and 1988 census, The Planning Commission,

1997). 43

About 80% of the population in Mbeya lives in rural areas, and most of them depend

other are on agriculture for their livelihood. The main crop is maize, but many crops cultivated such as paddy, pulses, roots and tubers, and fruits and vegetables. Cash crops are

cattle an also common, especially coffee and tea. Livestock keeping, particularly plays important role in the agricultural sector of Mbeya region. In 1995 the population of cattle was

Other estimated at almost one million, of which only 4,500 were improved dairy cattle. commonly hold animals are goats, sheep, and pigs. Other important economic activities are forestry, fishing, large and small scale mining and some small to medium scale industrial production (The Planning Commission, 1997). Many people also take advantage of the long international borders of Mbeya region to improve their income by smuggling goods from and to Zambia and Malawi.

4.2. Selection of Household Sample

A multistage random sampling procedure was applied to select households to participate in the study. After the selection of Dar es Salaam and Mbeya regions for

in rural areas in both conducting the survey, wards were selected in urban areas and villages

and regions. The wards were chosen to make sure that the different areas of Dar es Salaam

since Mbeya urban are represented and that various income groups participate in the survey,

in low to medium wards are either in high density areas with low-income households or density areas where households with better income live. In the last stage of the sampling procedure, ten households were randomly selected in each ward in urban areas, and twenty households in each village in rural areas.

Twenty wards were selected in the city of Dar es Salaam: these are Manzese,

Mbuharati, Buguruni-Malapa, Mgomeni, Mtoni-Temeke, Bala, Tabata-Kimanga, Tabata,

Ubungo, Mikocheni, Kariakoo, Kurasini, Mwenge, Sinza, Upanga, Oysterbay, Kinondoni,

Malakua, Chang'ombe, and Tazara. The five villages that were chosen to represent the rural surrounding of Dar es Salaam are Kiluya Madukoni, Kibaha Maili moja, Kisarawe, Tangi

Kigamboni, and Pugu Kajiungeni. With this selection of wards and villages, and the application of the procedure described above for the selection of the households, the sample for Dar es Salaam region comprised 200 urban and 100 rural households.

Ten wards were selected in urban Mbeya and five villages in rural Mbeya. The ten wards are Isanga, Nonde, Itigi, Demi, Ghana, Uyole, Mabatini, Forest, Rwanda, and Sinde;

The and the five villages are Shibolya, Igoma, Shamwengo, Usohamungano, and Ulenje. household sample in Mbeya region thus contained 100 households as well in urban as in rural 44

areas, which makes a total of 200 households selected in Mbeya region. Total number of households that participated in the study ad up to five hundred. Since the survey included three rounds in which each selected household participated, a total of 1,500 interviews were carried out.

4.3. The Enumerators

Local enumerators had to be hired due to the size of the sample of households, and to the fact that many Tanzanians do not speak English but only Swahili. Three enumerators were

in the hired to carry out the interviews in Dar es Salaam and two enumerators participated study in Mbeya region.

The three enumerators for the interviews in Dar es Salaam were hired with the support of Dr. Mdoe of the Department for Agricultural Economics and Agribusiness (DAEA) of the

Sokoine University of Agriculture (SUA) in Morogoro. The enumerators were freshly graduated students in agricultural economics, who had not yet found a job. The enumerators were introduced to the study and as a part of the training; each of them tested the questionnaire in three households in the town of Morogoro.

The enumerators who conducted the interviews in Mbeya region were selected with the highly appreciated aid of Dr Mpate, Regional Livestock Development Officer, and then

Director of the Southern Highlands Dairy Development Project (SHDDP) in Mbeya. The enumerators in Mbeya were also introduced to the research and trained before data collection started. The first five questionnaires of each of the two enumerators were checked directly after they conducted the interviews. This was done to ascertain the enumerators understood the questionnaire properly, and the few mistakes that occurred could be corrected immediately.

4.4. The Questionnaire

The questionnaire was build to gain insight into households' total yearly expenditures for food and non-food items, to be able to apply a total expenditure demand system. The questionnaire contained four schedules. The first schedule aimed at identification of the households and at collecting general information regarding the households' characteristics.

The goal of the second schedule was to receive information on the households' expenditures on food and some non-food items on a monthly consumption recall bases. The third schedule gathered information on consumption habits for milk and milk products beside quantities consumed and prices paid. Finally, the fourth schedule intended to collect household yearly 45

in the expenditure on non-food items. All four schedules of the questionnaire are displayed appendix.

information Schedule 1 was used to identify the households and to gather general

the regarding households' socio-demographics. The two pages of schedule 1 were printed on

three rounds. Thus front and back of one sheet, which topped the questionnaires of the survey

find the the enumerators could rely on the information of the first interview on the top sheet to

in households in the second and third round of the survey. They also could check for changes the households' structure as some members left or new one arrived from one to the other round of data collection.

The goal of the second schedule was to gather information on the household monthly total expenditure on food and expenditure for some non-food items on a monthly consumption recall bases. For each food items on the list, respondents had to indicate the

list for the quantity consumed and the price paid per unit. Thus schedule 2 was a check

for various food items. To ease data collection, food items were grouped into categories; instance into cereals, tubers and roots, vegetables, pulses, meat, fish and eggs, edible oils, fruits, milk and milk products, and other food including meals taken away from home. Data

items such as alcohol and were also brought together on monthly expenditure for non-food cigarettes, firewood, charcoal and other sources of energy for household use. Some other monthly expenditures for non-food items were grouped into categories such as health

rounds and was for expenses or transport costs. Schedule 2 was part of the three reprinted each of them so that the data from the previous round was not available to the enumerators.

The third schedule gathered information on household's purchase and consumption habits for milk and milk products beside quantities and prices paid, which were already collected in schedule 2. It especially focused on purchasing place of milk and milk products

and income. and on factors that confine consumption of milk and milk products besides price

Schedule 3 was part of the questionnaire in the second round of the survey.

The fourth schedule intended to collect household yearly expenditure on non-food items. Non-food expenditure were grouped into 13 categories, such as clothing, schooling fees, and housing. The respondent was asked to estimate in lump sums the expenditure of the household for the various categories in the last twelfth months. This last schedule was incorporated in the questionnaire of the third and last round of the survey; thus the yearly household expenditure collected in schedule 4 covered the period of the two previous rounds 46

of data collection, which allowed a consistent assessment of households' total yearly expenditure, necessary for a full budget analysis.

in The entire questionnaire was tested in 9 households in the town of Morogoro May

allowed for 1998 as a part of the training of the enumerators of Dar es Salaam. This procedure

2 could be a consistent improvement of the questionnaire, especially as the flow of schedule adapted to the food consumption habits of the population, and some missing food items were included. The final version of the four schedules of the questionnaire can be found in the appendixes.

4.5. Data Collection

This section describes the three rounds of the data collection, including economic and climatic background of the period and problems faced while conducting the interviews.

The first found of the data collection took place from June to August 1998. This period is shortly after the rainy season, and also the cold season in Mbeya region, as temperature

and may drop below zero in exposed areas. Farmers harvest and sell their produce purchase non-food durables and non-durables. The enumerators did not face major problems while conducting the interviews. Even though some households were somehow reluctant in answering the questions, especially in urban areas, each of the selected households finally

schedule which had to gave information on their expenditures. The questionnaire included 1, be made out in that round and contained questions on the socio-economic background of the household and of the household members, and schedule 2, which is the check list on monthly expenditures for food and some non-food items.

The second series of interviews began in October 1998 and was completed by middle

rains start of December of the same year. This period is the end of the dry season as the short in November. The enumerators again did not face major problems, especially since the respondents already knew them. The questionnaire included the head-sheet (schedule 1) of the first round, schedule 2, the check-list of food and non-food items, and schedule 3 containing some additional questions on household's purchasing habits of milk and milk products. The list of members of household, a part of schedule 1, was also checked to catch changes of the composition of the households.

The third and last series of interviews started in February 1999 and was completed in

April 1999. This period was in the rainy season and as crops can not be harvested yet, it is also commonly called the hungry season. The major difficulty in that round was the hampered 47

walk access to a remote village of Mbeya region due to heavy rains. The enumerators had to

four to reach the village and I am recognizant for their perseverance. In Dar es Salaam region, households cold not be found because they had moved away. Therefore, 197 households in

Dar es Salaam urban and 99 households in Dar es Salaam rural took part in the study as well

of as hundred households in each urban and rural areas of Mbeya region, which makes a total

496 households participating in the whole of the study. Like the two first rounds of the

members and survey, the questionnaire included schedule 1 with the list of household

4 schedule 2, the checklist on monthly expenditures for food and non-food items. Schedule finalized that questionnaire to completely measure the total expenditure of the households.

All in all data collection took place rather smoothly, which would had been probably different without the unconditional support of Dr. Mdoe in Morogoro and Dr. Mpate in

Mbeya, and without the good and reliable work of the enumerators. The collected data are presented in the following parts of this chapter and analyses that were carried out with these data are presented and discussed in chapters 5 and 6.

4.6. Description of Household Characteristics

Households' socio-demographic information, especially family size and household composition as well as household location and background can be important non-economic variables to explain variations in food demand and in human nutrient availability. These information are presented in Table 6. The average size of the sample households is 5.62

families in person. Households in Mbeya region are larger than in Dar es Salaam and living urban areas tend to be larger than in rural areas. Figure 11 shows the exact distribution of the sample households by their size. Most households embrace four or five members, and fifteen

is 52%. persons lived in the largest ones. The proportion of females of the sample population

However this proportion attains only 48% and 49% in urban and rural area of Mbeya region, respectively, and reaches 54% and 56% in urban and rural areas of Dar es Salaam, respectively. About 16% of the household members are less of equal to 5 years of age; this group is larger in Dar es Salaam than in Mbeya region as well as in rural than in urban areas.

Approximately 17% of the population is more than 5 years and less or equal to 11 years of

These age, and 15% of the population more than 11 years and less or equal to 17 years of age. two groups tend to be larger in Mbeya region than in Dar es Salaam. In general the population tends to be older in urban than in rural areas as well as in Dar es Salaam compared to Mbeya region. The fact is that 48% of all household members in the sample are less than 18 years of age. 48

Table 6: Socio-economic Profile of Sample Households Dar es Salaam Dar es Salaam Mbeya urban Mbeya rural All households urban rural

Number of households 197 99 100 100 496

5.44 4.75 6.81 5.7 5.62 Average size of the households (2.33) (2.17) (2.72) (2.21) (2.44)

Proportionof household members that are female 54% 56% 48% 49% 52%

of household members that are less or to Proportion equal 15% 20% 14% 19% 16% 5 years of age of household members that are more than 5 Proportion 15% 18% 17% 20% 17% years and less or equalto 11 years of age of household members that are more than 11 Proportion 12% 12% 19% 17% 15% years and less or equalto 17 years of age of household members that are more than 17 Proportion 58% 50% 50% 44% 52% years of age

Number of households headed by a women 26 23 14 8 71

Number of Muslim households 108 58 15 0 181

Number of bigamous households 0 17

40.87 38.95 50 41.49 42.39 Average age of households' heads in years (11.01) (12.36) (12.62) (10.87) (12.16) 7.34 5.12 5.05 4.78 5.92 Average education of households' heads in years (4.22) (2.72) (2.12) (1.82) (3.39) 33.13 30.03 38.66 32.51 33.48 Average age of the heads' spouse in years (10.69) (8.87) (10.31) (9.13) (10.25) 6.05 4.61 4.48 3.8 4.99 Average education of the heads' spouse in years (3.46) (2.15) (2.55) (2.15) (2.99) Note: Figuresin the brackets are standard deviations. 49

Figure 11: Distribution of the Sample Households by Size

Hon c

o

o

o

Households o

—

o

n n 3 n ö ....

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Members

Other specifications of the households indicate that 71 of the households are headed

while this by a woman. More than half of the households in Dar es Salaam region are Muslim,

rural. is the case for only 15% of the households in Mbeya urban and none in Mbeya Only

7 and 9 one household in Dar es Salaam region is bigamous (a witch doctor), compared to households in Mbeya urban and rural, respectively. The average age of the household heads is

in Dar es and 42.4 years. In Mbeya region the age of the heads is higher than Salaam, higher in urban than in rural areas. The heads average number of years of education is 5.9. It is

heads' highest in Dar es Salaam urban and lowest in Mbeya rural. The average age of the

The spouses is 33.5 years and tends to be higher in urban areas and in Dar es Salaam region.

educated in Dar es spouses enjoyed 5.0 years of education on average and tend to be better

Salaam and in urban areas compared to those in Mbeya region and in rural areas.

4.7. Segmentation of the Sample Households

To accomplish the goal of estimating the effects of expenditure level on household food consumption, the data were partitioned into a low-income group and a high-income

from Ferreira who group of households. The basis for this segmentation was taken (1996), proposed a soft-core poverty border for Tanzania in 1995. This level was corrected with the national consumer price index and gave 188,888 Tanzanian shillings of total expenditure per

chosen to capita and year as the soft core poverty level for 1998-1999. This limit was segment the survey households.

Partitioning the data in this manner resulted in the creation of a group of 313

of households above the soft core poverty limit with average annual expenditure per capita

465,728 TSh and a low-income group of 183 households with average annual expenditure per 50

capita of 116,206 TSh. Thus, about 38% of the households participating in the survey lived

that about 23.9% of below the soft core poverty level. A closer look at the two groups shows the household in urban and 58.8% in rural areas live below that border. 10.8% of the

in households participating in the survey in Dar es Salaam and 78.0% of them Mbeya region

differences in food are in the low-income group. This partition also allows to compare demand behavior of the tow group by estimating parameters for two groups separately as presented in the food demand analysis of chapter 5.

4.8. Description of Household Expenditure

Figure 12 is a plot of the households ranked by the total household expenditure per

and are capita and year. In this figure average total household expenditure per capita year

is the limit represented in a logarithmic scale. The limit at 188,888 Tanzanian shillings separating low from high expenditure households. In a large part of the sample, income

than the increases constantly. At the low end, some households have much less expenditure others, and at the upper end households' expenditure per capita expand rapidly.

Figure 12: Ranked Plot of the Logarithm of Total Household Expenditure per Capita and Year (lUS$=670TSh)

IO'000'OOO

rooo'ooo

TSh 188'888

lOO'OOO

lO'OOO

500 400 300 200 100 1

Rank

and Table 7 presents average and relative total household expenditure per capita year

of the by location and area, and by income groups. The average total household expenditure

The households sample households are 339,455 Tanzanian shillings per capita and year. participating in the study spent 175,104 TSh or 51.6% of their budget for food and 164,352

TSh on non-food items, which is 48.4% of their total budget. A comparison with other budget

62 to 87 surveys indicate higher food budget shares in Africa ranging around percent (Teklu, 51

1996). The lower food budget share in this study could be due to the fact that housing were

for. included in the household expenditure, although many households did not have expenses

that An examination of the households' average total expenditure by location shows

and that it is more than total expenditure per capita is highest in the city of Dar es Salaam

total double as high as in rural areas of the same region. In Mbeya town average expenditure

much less than in urban areas of Dar per capita and year reach almost 200,000 TSh, which is

with TSh at mean. es Salaam. Total expenditure per capita is lowest in rural Mbeya 135,000

Food expenditures represent 45 % of total expenditure in urban areas of Dar es Salaam. The relative total expenditure share for food is 63% in Dar es Salaam rural 63% in Mbeya urban, and 62% in Mbeya rural. The low food expenditure share in Dar es Salaam urban is due to high non-food household expenditures specific to Dar es Salaam, such as high housing rents and high transport costs due to large distances from work to home. Due to the higher GDP per

much more than capita in Dar es Salaam it is not surprising, that households there in spend from other areas.

A comparison of high-income and low-income households shows that total per capita expenditure of high expenditure households is four times larger than the average of low expenditure households. While high-income households allocate almost half of their budget to food, low-income households will spend 60% of their available budget on food. This confirms the hypotheses that households reduce the total budget share for food as income increases.

shares in Household average food expenditure and the relative food group expenditure total food expenditure are displayed in Table 8. All households spent an average of 61,206

and contribute TSh on cereals and pulses, which is 35.0% of food budget. Thus cereals pulses

Tanzania. The the largest part of food expenses in Dar es Salaam and Mbeya regions of second largest food budget share is allocated to meat, fish, and eggs with 19.1% of the food

and budget, which are 33,405 TSh. The food budget shares allocated to fruits and vegetables, to other food is a little bit more than 16% for both food groups. Finally sample households spent 6.7% and 6.6% of their food budget to milk and milk products and to edible oils,

urban respectively. As seen above total food expenditure per capita and year is highest in areas of Dar es Salaam, which is to a large extend due to higher food prices. 52

Table 7: Average and Relative Household Total Expenditure per Capita and Year by Location and Area, and by Income Groups(average in TSh)

es es . - . . . . Dar Salaam Dar Salaam „ , „ , TT. T „, ... Mbeya urban rural High income Low income All households , , J Mbeya ° urban rural

Average Relative Average Relative Average Relative Average Relative Average Relative Average Relative Average Relative Food 63.1% 62.6% 61.8% 49.9% 60.0% 51.6% ... 249,562 44.9% 165,670 121,668 83,610 232,480 69,693 175,104 expenditure Non-food 306,185 55.1% 96,713 36.8% 72,653 37.4% 51,741 38.2% 233,248 50.1% 46,512 40.0% 164,352 48.4% expenditure TotalJ. 555,747 194,321 135,352 465,728 116,206 100.0% 100.0% ' 100.0% 262,383 100.0% ' 100.0% ' 100.0% ' 100.0% ' 339,455 expenditure Source: Survey Data

Table 8: Average and Relative Household Food Expenditure per Capita and Year by Location and Area, and by Income Groups (average in TSh)

Dar es Salaam Dar es Salaam , ,„ . TT- . • T • l A„ Mbeya Mbeya rural High income *nuAll households u , , J urban income Low urban rural jo Average Relative Average Relative Average Relative Average Relative Average Relative Average Relative Average Relative Cereals and ?1 gg5 2g 5% 72754 A23% 53,317 43.1% 37,550 45.6% 78,116 33.6% 32,284 46.3% 61,206 35.0% pulses Meat, fish and 66g 2QJ% 2g lg 3% 3J n 2% 54? 193% 45 l9J% m 1? $% Q5 19J% eggs Fruits and 3g 154% 29 n 6% B 1Q5% Q2 Ul% ^ J5 {% 5 u5% 2g 162% vegetables Milk and milk 3Q 6Q% 6 42% ^ g g% ? gQ% 15 ?5 6 g% 46fâ 6?% x ?3g 6J% products Edible oils 15,667 6.3% 11,589 6.9% 10,292 8.8% 4,744 5.3% 15,618 6.7% 4,384 6.3% 11,473 6.6%

Other food 54,795 22.1% 16,108 10.7% 13,631 11.4% 7,586 8.7% 41,889 18.0% 6,747 9.7% 28,924 16.5%

Total 249,562 100.0% 165,670 100.0% 121,668 100.0% 83,611 100.0% 232,480 100.0% 69,693 100.0% 175,104 100.0%

Source: Survey Data 53

wheat and The food group of cereals and pulses contains all cereals, i.e. maize, rice, wheat products, and other cereals. Furthermore it contains roots and tubers, which are sweet

all sorts of potatoes, Irish potatoes, cassava, yams and also cooking bananas, as well as pulses,

the beans and peas. With 28.5% of the food expenses, cereals and pulses represent largest

in other area of the food expenditure group in urban Dar es Salaam. This share is much larger study. Households in rural Dar allocate 42.3%, in urban Mbeya 43.1% and in rural Mbeya

45.6% of their food budget to cereals and pulses, which is a larger share than in urban Dar. In

it is real terms, 71,885 TSh expenses for cereals and pulses in urban Dar is almost as large as in rural Dar with 72,754 TSh, while it is only 53,317 and 37,550 in urban and rural Mbeya, respectively. This is the consequence of the smaller total household budget in Mbeya region compared to the budget of households in the city of Dar es Salaam.

The food budget share for meat, fish and eggs is second largest in rural Dar and in urban and rural Mbeya and third largest in urban Dar. However, households in urban Dar es

than Salaam allocate a share of 20.7% of their food budget to this food group, which is more households in the three other areas do. The budget share for meat, fish and eggs in rural Dar, urban Mbeya and rural Mbeya reach 18.3%, 17.2%, and 19.3%, respectively.

Relative food expenditure to fruits and vegetables is larger in Dar es Salaam than in

Mbeya region. Food budget shares for this food group range from 17.6% in rural areas of Dar es Salaam to 10.5% in urban Mbeya.

The food budget share for milk and milk products, in contrary to fruits and vegetables, is larger in Mbeya region than in Dar, and is also larger urban areas compared to rural areas. It attains 8.8% in urban Mbeya, 8.0% in rural Mbeya, 6.9% in the city of Dar es Salaam and only 4.6% in the rural surroundings of Dar.

The largest food expenditure share for edible oils was found in urban areas of Mbeya at 8.8% and the lowest one in Mbeya rural at 5.3%; households in urban areas of Dar es

Salaam allocated 6.3% of their food budget to edible oils, and household in rural areas of Dar es Salaam 6.9%.

with 22.1% The largest food group in urban Dar es Salaam is the group of other foods of the food budget. The group of other foods include sugar, salt and spices, tea and coffee, soft drinks, snacks, and meals taken away from home. Beyond locations, there are large differences in allocating expenditure to other food, since other food is third largest in urban

Mbeya with 11.4% and only fourth in rural Dar and rural Mbeya with 10.7% and 8.7% of the food budget, respectively. Meals taken away from home is probably the reason for this 54

from do not difference, since many habitants of Dar es Salaam, who often work away home,

this become affordable with go home for lunch, but also since foods which are within group increasing incomes.

- the The data show the large discrepancy of per capita income between Dar es Salaam economic center of Tanzania - and the periphery areas of Tanzania represented here by

is Mbeya region. Therefore, the real expenditure per capita and year for each food group

real largest in urban areas of Dar es Salaam, and households in rural Mbeya have the smallest

also show that relative food expenditure per capita and year for every food group. The data

of food in expenditure are still very high in Tanzania compared to the share expenditures developed countries.

A comparison of low and high expenditure households reveal large differences in absolute and relative food budget allocation. On the average, high expenditure households

TSh to food. spent 232,480 TSh for food, while poor households could only allocate 69,693

Of this, the low-income households used 32,284 TSh for cereals and pulses, which is almost half of their food budget. Households that are better off can allocate more than double as

of their food Since many TSh to this food group, which however is only one third budget.

is not cereals and pulses take such a large part of the food budget of poor households, it surprising that they allocate fewer resources to every other food group than their richer counterparts, in particular for the group of other foods, which contains many luxuries. 55

5. Analysis of Demand for Food

5.1. Introduction

and As countries go through structural transformation in their economies urbanization,

other changing tastes and lifestyles can have significant impact on the demand for food and commodities (Huang and Bouis, 1996). In particular, the rising consumption of meat and milk in developing countries presents a potential for improving nutrition and direct income growth for those who need them most.

For example the volume of meat consumed in developing countries increased almost

is three times as much as it did in developed countries, and the annual growth in consumption expected to be four times greater than in developed countries (Delgado et al., 1999). While such developments could significantly improve the well-being of many rural poor, much

in to ensure that could go wrong if long-run policies and investments are not put place poor households have access to sufficient food, particularly animal products.

Some studies (e.g. Delgado and Sil, 1994; Mdoe and Wiggins, 1996) have empirically considered the consumption patterns of households and the factors that influence the demand for food in Sub-Saharan Africa. Fewer still have employed theoretically consistent frameworks to estimate the demand for food at the household level. An overview on food demand analyses carried out in SSA is given by Teklu (1996).

As argued by Pinstrup-Andersen and Caicedo (1978), the utilization of average estimates of price and income elasticities for the population as a whole for the projection of individual commodity demands is not likely to be very successful if significant changes occur in income distribution. Especially where changes have occurred in income distribution, commodity demand projections should be based on individual income strata rather than in average estimates of price and income elasticities (Goungetas et al., 1993).

The contribution of this study is to estimate the demand for different commodities separately, for low and high-income households in Dar es Salaam and Mbeya regions of

Tanzania. Besides prices and income effects, the influence of factors such as location and household size on demand for these commodities is also examined.

First, the modeling technique used in this analysis is presented. Second, the systems of demand equations used to estimate coefficients are portrayed. Last, the coefficients estimated and the elasticities computed are displayed and discussed. 56

5.2. Estimating Demand Functions

As indicated by Deaton and Muellbauer (1980b) the early history of empirical demand analysis is marked, not by an attention to theory, but by the extensive use of single equation methodology centered around the measurement of elasticities. Thus analysts employed the

has the single demand equation such as equation (3.6) given in chapter 3. This approach advantage of flexibility and is clearly the best way of modeling the demand for an individual commodity in isolation.

However in moving from simple demand equations to complete systems of equations, the theory becomes much more directly relevant and many restrictions of demand systems not considered in single equations become important. The advantage of the complete systems of equations is that theoretical properties of demand can be imposed as restrictions in the demand model. These properties are adding up, homogeneity and symmetry. The fourth property of non-negativity, however, can not be imposed. Furthermore, when using single equation estimations, it is not particularly crucial to make the distinction between income on one hand and total expenditure, on the other.

5.3. Conditional Demand Modeling Techniques and Two-Stage Budgeting Approach

When modeling household food demand, two different techniques can be applied, the conditional and the unconditional modeling techniques. Browning and Meghir (1991) described the theoretical underpinnings for empirical investigation, using the conditional demand equation approach of Pollak (1969).

All consumed goods are divided into two exclusive classes. Firstly, there are the

"goods of interest"; the quantity and price vectors of these goods are denoted by q and p respectively. The second set of goods is "conditioning goods". These goods may affect preferences over the goods of interest but are not themselves of primary interest. The quantity and price vectors of these goods are denoted by h and r respectively. Finally, the vectors of some "demographic variables" that may also affect preferences over the goods of interest are denoted by a.

The goods of interest are assumed to be a group of nondurable commodities and the set of conditioning goods to be other nondurables, durables, and public goods. The

as well as demographic variables a may include the age and composition of the household variables like education, social class, and race. 57

then the If preferences over all goods are represented by the utility function U(q, h, a), conditional cost function is defined as

c(p,h,a,u,) = min[pq\u(q,h,a) = u) (5.1) i

Under conventional assumptions the conditional cost function has the following

fixed properties: (i) it is concave, linear homogeneous, and nondecreasing in p for (h, a, u);

is for (ii) it is convex in h for fixed (p, a, u); (iii) it is monotone in h (that is, it decreasing goods which increase utility (for example, durables)). Browning (1983) gives a full account of the conditional cost function and its relation to the (unconditional) cost function that is

which defined on (p, r, a, u). It is generally a matter of convenience preference representation

for is used; for food demand purposes the conditional cost function is the most appropriate reasons that will be made explicit in the course of this sections.

Given a conditional cost function, a conditional demand system can easily be derived.

Firstly, the gradient of the conditional cost function with respect to p gives conditional

= = x is the total compensated demands, i.e. q V c(p, h, a, u). If the identity c( •, u) x (where

then we expenditure on the goods of interest q) is inverted to derive u in terms of (p, h, a, x),

demand can substitute this into the compensated demands to give the uncompensated system

0, =/,(/>.M,*) (t = l,2..ji) (5.2)

The conditional demand system described above is sometimes preferred than the unconditional system represented by:

q.,=fl\P>r>a>x) 0' = l,2...n) (5.3) where x* is the total expenditure on (q, h).

A result that relates the structure of conditional cost functions to the structure of the

does direct utility function is now presented. Generally structure on the direct utility functions

As shown this is not not have any obvious implications for structure on dual functions. below, the case for the conditional cost function; it is this fact that makes this representation useful in the context of testing for separability. The goods of interest are weakly separable from the conditioning goods if the direct utility function can be written in the form F(U(q, a), h, a).

if conditional cost Therefore the set of goods q is weakly separable from h if and only the function takes the form c(p, a, g(h, a, «)). 58

Thus, under weak separability, conditioning goods have only income effects. This result has the corollary that under weak separability the conditional demand system for the goods of interest has the form:

q, = /, (p, a, x) (i = 1,2.. jn) (5.4)

This result is due to Pollak (1971). Hence a simple test of weak separability consists of

that we have testing whether the demands q, depend on the quantities of goods h, given conditioned on the prices of the goods of interest p, the total expenditure on these goods, x, and on a.

One thing to note about the demand system in (5.2) is that this system is unchanged if

• is we start with the cost function c(p,h,a,¥(h,a,u)) rather than c(p, h, a, u) where W{ ) any

for arbitrary function that is increasing in u. Thus we can choose an arbitrary normalization the utility function which depends on h and a. This means that we cannot in general infer

the anything about preferences over h and a from observing demands alone. Indeed, just about only thing for which can be checked is separability of q from h. In particular, it is not possible to test the integrability conditions neither on the conditioning variables nor to test whether even the simplest properties (like monotonicity) hold for them.

A number of reasons have been advanced to explain why conditional demand systems

if some are preferable to unconditional systems. The first was noted by Pollak (1969); goods is given in predetermined quantities, (that is, it is rationed), then it is appropriate to put the level of the good on the right-hand side. As an example, Deaton (1981) models housing in this way.

A second advantage of the conditional demand approach is that testing for weak

set of variables separability is very easy. All we have to do is to test whether a particular should be excluded from the right-hand side of a regression. This is in marked contrast to the case for unconditional demand systems which do not typically have simple parametric restrictions that are equivalent to weak separability, except in the case of quasihomothetic preferences (Gorman and Myles, 1980). The conditional demand approach allows to test for weak separability without specifying the structure of preferences for the goods that are separable under the null. Moreover, we can use flexible preference representations for the goods of interest.

The third advantage of the conditional approach is that a general conditional demand system is valid whether or not demanded goods of interest are equal to zero. Thus corner 59

solutions in the conditioning goods do not lead to switching in the demand system. This gets around many of the problems raised by Lee and Pitt (1987).

the A fourth advantage is that we do not need to model the determination of conditioning goods explicitly. Indeed, the conditional demand approach does not require an explicit structural model for the conditioning good at all. Moreover, the conditional demand system will be correctly specified whether or not a and h are chosen optimally. Additionally,

This is we do not need to model explicitly the budget constraint for the conditioning goods. particularly significant for durables since it involves an unobservable rental or user cost.

Conditional demand functions are an economical way of relaxing separability and still maintaining the focus on the goods of interest.

The advantages of conditional modeling are often compelling, particularly in the case

that where the goods focussed on may be nonseparable from say durables, leisure, or goods

all behavioral are not consumed by many households. There is, however, one disadvantage;

consumed. and policy implications are conditional on the quantities of the conditioning goods

conditional To see this, suppose that we are interested in the own price elasticity for good /,

This is defined on total expenditure in the group (that is, the Marshallian own price elasticity). as (taking the case of a single conditioning good for simplicity)

dlnqt/dlnPl =dlnf/dp, =(3ln / ldh\dhldp,) (5.5)

The conditional demand system will yield estimates of the parameters of the first two

be estimated derivatives on the right-hand side but not of the third. These latter must separately unless the conditioning variable is believed to be genuinely predetermined in which case the final derivative is zero. Similar care must be exercised in interpreting income effects.

How demand elasticities are computed in this study, is denoted in the next section.

is Given that the focus is on food in the present study, a conditional demand system employed in the analysis. The basic assumption is that food is weakly separable from the

in the other goods purchased by the households in the survey. Thus the approach adopted following estimations is to represent the consumer expenditure allocation problem in two stages. The procedure is displayed in Figure 13 as a utility tree. In the first stage, the consumer determines the allocation of his total expenditure to food, clothing, fuel, housing and other non-food items. In the second stage, households allocate their expenditure among

fish and fruits and different food groups, which are cereals, roots and pulses, meat, eggs, vegetables, milk and milk products, edible oils and other food. 60

Figure 13: UtilityTree

Total expenditure

CD O) CO -I—> co Food Other non¬ expenditure food

CD O) CO c/> Cereals and Meat, Fish, Fruits and Milk and Edible Oils Other Food "O c Pulses and Eggs vegetables Milk Expenditure o o Products CD CO 61

5.4. Computing Elasticities Using the Two Stage Budgeting Approach

As stated above, precaution must be exerted when computing elasticities in a conditional demand system. The elasticities computed in the second stage of the consumption analysis are within group elasticities and are thus conditional elasticities. Unconditional elasticities depend on the results of the first and of the second stage of the analysis. How these elasticities are computed is shown in this section.

Elasticities are defined as the relative change in consumption of a commodity for an infinitesimal change in expenditure or price. Following Edgerton (1997) the total (or unconditional) expenditure elasticity E, for the rth commodity within the Fth commodity group can be defined as

(5.1) 3 In*

where x is total expenditure and f, is the uncompensated or Marshallian demand for good i from form (5.2), subject to total expenditure x. In a similar manner the within-group

(or conditional) expenditure elasticity EFl can be denoted as

*fl=|^k (5-2) amxF where xp is expenditure for the group of goods F and/^ is the uncompensated or Marshallian demand for good /, subject to group expenditure xf. The uncompensated or Marshallian group expenditure elasticity Ef for the Fth commodity group is defined as

B,=^ (5.3) amx where gp is the uncompensated or Marshallian demand for the group of goods F. The quantities consumed of a group of commodities can only be defined in terms of quantity indices. In the two stage budgeting approach indices are also used to measure the prices of groups of commodities and, as expenditure equals price multiplied with quantity, an obvious quantity index is given by dividing the group expenditure by the group price indices.

The uncompensated or Marshallian total price elasticity etJ for the ith commodity (in the Fth group) and the/th commodity (in the 5th group) can be defined as

«.-^ (5.4) dlnpSj 62

within the The within-group price elasticity e^y between the ith and y'th commodities

Fth commodity group is denoted as

(5-5) ^-^— d\npFj

Fth and 5th and the uncompensated or Marshallian group price elasticity eFS for the commodity groups as

*/*=^f SfS (5-6> dlni» s where Ps is the price indices of commodity group 5. Finally we can define the equivalent

or Hicksian elasticities ê and as compensated price , eF eFS

ev = eXJ + wfi (5.7)

êF,j=eFil+wFjEFt (5.8)

and

eFS = eFs + wsef (5-9)

with w being budget shares at different levels, which are defined as wi=(pFlqFl)/x,

the indices of wfi ={pFiclFi)lXF' and wF ={PFQF)/x, with QF being quantity commodity group F.

Assuming that the price indices Pf is approximately independent of the level of

total expenditure x, which is implied by the two stage budgeting approach (Edgerton, 1997), expenditure elasticities can be written as

E^En-Ef, (5.10)

the total uncompensated or Marshallian price elasticities as

e,j = SFSeFlJ + EF,WSjeFS (5-11)

and the total compensated or Hicksian price elasticities as

etJ = SFSeFlJ + EFlwS]eFS. (5.12)

ops is the Kronecker's delta, equal to one when F = 5 and zero otherwise. Since the objective of this study is to analyze food consumption, forms (5.11) and (5.12) can be written 63

= as - and The Marshallian and Hicksian et] eFlJ +EFlwFjeFF el} eFlJ +EFlwFjeFF , respectively.

This is or uncompensated and compensated price effects can also be illustrated graphically. done below.

5.5. Uncompensated and Compensated Price Effects on Demand for Goods

Figure 14 represents geometrically in a two-goods model (qi and qi) the effects of a

constraint DA moves price increase of good qi. Due to the change in price the original budget to DB, equilibrium from A to B, and purchases of good qi from OZ to OX. This is the total price effect and is also termed the Marshallian own-price effect. This price effect contains two components, the substitution and the income effect. The substitution effect reflects the change of consumption due to the change of relative prices of qi and q2, with the household remaining at constant utility level. In this case, the hypothetical equilibrium is C and quantity

effect of good qi is reduced from OZ to OY. This change is called the compensated own-price and is represented by the Hicksian own-price elasticity. The price change also has an income effect, and is represented by the move from C to B at constant relative prices. The income

from Y to X. effect of the price increase of good qi leads to a reduction of its consumption

Figure 14: Income and Substitution Effects (geometric representation of the Slutsky equation)

D

M

N

L

0 X Y Z 64

The total An increase of the price of good qj also affects demand for good q2. price

to N. effect is the move from A to B and leads the increase of consumption of good q2 from L

in a This Marshallian cross-price effect can, similarly to the own-price effect, be separated

A to substitution and an income component. The substitution effect is the move from hypothetical equilibrium C and leads to an increased demand for good q2 from OL to OM. This effect is also called compensated cross-price or Hicksian cross-price effect. In this case the substitution effect is positive, since an increase of the price of good qi lead to a higher

reduces then demand for good q2. Thus these goods are substitutes. The income component

than the cross- demand for good q2 from M to N. In Figure 14 the income effect is smaller price effect. It is however possible that this income effect overcompensates the substitution effect. This means that Marshallian cross-price effects can be negative although both goods

of are substitutes. A Hicksian cross-price effect that is negative means that price increase good qi leads to a reduction in demand for good q2, and thus both goods are complementary.

This effect, however, can not be shown in the figure above.

The addition of substitution and income effects to the total price effect is also called the Slutsky equation and is used in equation (5.11) below.

5.6. Model specification

Since we are interested in obtaining both conditional and unconditional elasticities, both stages of the two stage budgeting approach are estimated separately. We employ a Linear

Expenditure System (LES) in the analysis of the first stage and a Linear Approximate Almost

Ideal Demand System (LA/AIDS) model in the analysis of the second. The specifications of these two models are presented in the following sections. The demand elasticities of the first stage and the conditional elasticities of the second stage are then used to compute unconditional elasticities.

5.6.1. Linear Expenditure System

The Linear Expenditure System (LES) is derived from a direct utility function that takes the form

"(?)=£«>(*-r,) (5-1) 1=1

where qi > yi. This utility is maximized such that 65

^PA=x (5.2) 1=1

= written as If we let Zi qt- yu the utility maximization problem can be k

max 2ailnzi (5-3) "i=i

ft A- such that £p,z, =x-^p,yl (5.4) i=i 1=1

The demand function for q, then has the form of

^^ 9, =7,+a, • (5.5)

Multiplying (5.5) by pt yields the linear expenditure system (LES)

= ~ pa p,r,+a, \x S*=i PJ< ) • (5-6)

= can as the minimum of with 2_! _,«, 1 • The term y; be interpreted required quantities good qt. Thus pvqx are committed expenditure bought first, leaving a residual, "supernumerary

x - which is allocated between the in the fixed at. expenditure" ^ /?,<£ , goods proportions

Following Fan et al. (1995), uncompensated or Marshallian own- and cross-price elasticities are

e!,=(l-tf>,/,/(A<7,)-l (5-7)

and

e^-aXpjMpa)- (5-8)

The expenditure elasticities are

E^as/faq,). (5.9)

5.6.2. Linear Approximate Almost Ideal Demand System

The Almost Ideal Demand System (AIDS), developed by Deaton and Muellbauer

(1980a), is a popular framework for estimating price and income elasticities when expenditure or budget data are available (e.g. Gao et al., 1996; Abdulai et al, 1999; Abdulai and Jain,

1999). The AIDS model satisfies the axioms of choice exactly, and does not impose additive 66 preferences, and, under certain conditions, allows consistent aggregation of individual demands to market demands. The expenditure share equations for each commodity or

the form: commodity group are derived by differentiating an expenditure function of

\ne(u,p) = a0 + ^aj\npJ+^^rl\np[\npJ+ßß0Y[p^ (5.1) j j ' j

where e(u,p) is the expenditure function for given utility u, and price vector p.

Applying Shephard's lemma to the expenditure function yields a demand system in terms of utility and prices. Since utility is unobservable, the resulting demand system cannot be estimated. Using the inverse of the expenditure function to express utility in terms of incomes and prices, however, results in the following budget share equations:

w, = «i + X rv In Pj + A Mx'P) (5-2) J

is total P is where wt is budget share of good i; p} price of good j; x expenditure; price index defined by:

logP = a0 + X«7 In Pj + £]T £yv ln Pl ln P] (5.3) j i j

The theoretical properties of adding-up, homogeneity in prices and income, and symmetry of the cross effects of demand functions, imply the following parametric restrictions on (5.2):

Adding up 5>,=1; 5>y=0; £/?, =0; (5.4.1) 1=1 1=1 1=1

Homogeneity ^/y =0; (5.4.2)

Symmetry yi} = yß (5.4.3)

The fourth restriction involves concavity of the expenditure function. This restriction has, however, no obvious parametric representation.

Following Heien and Pompelli (1988), demographic effects are incorporated in the model by allowing the intercept a, in (5.2) to be a function of demographic variables, or:

«i=Ao+I>,/A (5-5) t=i

where dk is the Ath demographic variable of which there are 5. The new model, including the demographic variables and an error term (v,) is then defined as: 67

j w, =P,o + 1LP*dk+1Lr,jtopJ+ßMxtp)+vt (5-6) k=\

The adding-up requirement, under the specification with the demographic variables now requires that:

jrAo=land]TAi=0 {k = l,..,S). (5.7)

i i

Using the price index as defined in equation (5.3), the system of equation (5.2) becomes non-linear and requires the estimation of a large number of parameters. As in most empirical studies applying the AIDS, the overall (5.2) price index P employed in these estimations is approximated by the Stone price index defined as:

In P = ]>>/>, (5.8)

The AIDS model using Stone's price index is a Linear Approximate Almost Ideal

Demand System (LA/AIDS)

Following Chalfant (1987), the uncompensated or Marshallian price elasticity of commodity i with respect to commodity/s price in the LA/AIDS model conditional on food expenditures can be computed as:

y ßw <=^-^-£y (5.9) w, w>

where <^7 is the Kronecker delta and is equal to 1 when i = j, otherwise S,j = 0.

Expenditure elasticities are obtained from

A 1 + (5.10) w.

Using the Slutsky equation, the compensated, or Hicksian price elasticities, e*, can be computed as:

<=<Ç+WjE, (5-11)

Both (5.9) and (5.11) take into account the Stone-index approximation for the total expenditure deflator and exhibit variation between households. It is obvious from equations

(5.9), (5.10) and (5.11) that the demographic variables through their influence on the budget share (w,), will affect the magnitude, but not the sign of the elasticities. The classification of 68

the goods as to luxuries or necessities is not affected by the presence of demographic variables. Whether demand is elastic or not is, however, affected by the presence of the demographic variables. (Heien and Pompelli, 1988)

5.7. Estimations of the Food Demand System

5.7.1. First Stage

In the first stage of the food demand analysis the linear expenditure system presented in section 5.6.1 was applied on total expenditure. The results presented in Table 9 were obtained from the estimation of form (5.6) using food, clothing, fuel, housing, and other expenditures as groups of goods.

Table 9: Parameters of LES Estimation of the First Stage of TSB

Y t-value a t-value

Food 312.0 26.60 0.324 41.14

Clothing 7.35 5.18 0.068 16.96

Fuel 138.5 7.42 0.168 36.85

Housing 1.76 49.21 0.091 17.79

Other 6.72 1.53 0.350 44.08

in form of The parameter y in Table 9 can be seen as the required quantity quantity

indices will indices for the five commodity groups. Multiplying y with the commodity price give the committed expenditure for that commodity. This is the starting value of the expenditure distribution represented in Figure 15. The estimated parameters a are fixed proportions at which "supernumerary expenditure" are allocated to the groups of goods. The

with largest share of supernumerary expenditure is allocated to other non-food expenditures

35.0% and to food with 32.4%. A share of 16.8% of the supernumerary expenditure is

How this allocated for fuel and energy expenses, 9.1% to housing, and 6.8% to clothing.

shown in allocation of supernumerary expenditure influences the household budgets is Figure

15. 69

Figure 15: Distribution of Total Expenditure Resulting from LES Estimation

450000

400000

350000

300000

250000

200000

150000

100000 -i

50000

Table 10 displays the elasticities computed from the coefficients obtained with the

LES estimation presented in Table 9. The forms (5.7) and (5.8) presented in section 5.6.1

for the were used to compute own and cross-price elasticities and form (5.9) was used expenditure elasticities. The framed values are uncompensated or Marshallian own-price elasticities and the others are uncompensated or Marshallian cross-prices elasticities. All values are at the households' sample means.

Table 10: Computed Marshallian Price and Expenditure Elasticities of the First Stage of the Two Stage Budgeting Procedure

food clothes fuel housing other expenditure

Food -0.52393 -0.00004 -0.00008 -0.00017 -0.00004 0.65165

Clothes -0.00043 -0.62055 -0.00005 -0.00011 -0.00003 1.22806

Fuel -0.00412 -0.00027 -0.73471 -0.00111 -0.00025 1.21847

Housing -0.00001 0.00000 0.00000 -0.32085 0.00000 0.72260

Other -0.00064 -0.00004 -0.00008 -0.00017 -0.92846 1.85581

Special interest is given to expenditure elasticities. The food group total expenditure

shown in elasticity is necessary to obtain the food subgroups total expenditure elasticities as 70

is a as form (5.10). The food group total expenditure elasticity is 0.65 and thus food necessity could be expected from the presentation of the data in chapter 3. Housing is also a necessity

of 1.23 with an expenditure elasticity of 0.72. Clothes and fuel with expenditure elasticities

The and 1.22, respectively, are luxuries, as well as other expenditures with 1.86.

non-food uncompensated price elasticities range from -0.32 for housing to -0.92 for other goods. Cross-price effects are almost zero for all groups.

5.7.2. Second Stage

A Linear Approximate Almost Ideal Demand System presented above was used in the second stage of the analysis. Ordinary least squares can not be used to estimate the demand functions of the AIDS model since their error terms interact. A shock affecting demand for

Unrelated one good may spill over and affect demand for other goods. Thus Seemingly

Regression Estimation (SURE) by Zellner (1962) was applied to estimate the model. Iterating

SURE yields the maximum likelihood estimates of the parameters (Kennedy, 1998).

The results of the estimations of the equations of the LA/AIDS presented above are shown in Table 11 to 14. Table 11 presents the estimated parameters of the pooled data described in chapter 4. The parameters displayed in Table 12 and 14 are the estimates of the segmented data, which are low-income and high-income households, respectively.

The results of the second stage of the food demand analysis with the pooled data are presented in Table 11. The parameters estimated for expenditure and prices are used to compute the elasticities that are displayed below. The results reveal, that households in Dar es

and Salaam spend more on meat, fish and eggs, and fruits and vegetables, and less on cereals pulses, and on milk and milk products than their counterparts in Mbeya region. Urban households allocate less of their budget to cereals and pulses, and to fruits and vegetables, while their expenditures on meat, fish and eggs, milk and milk products, and edible oils are larger than households in rural areas. Large households have significantly larger expenditure shares for milk and milk products, while their budget shares of meat, fish and eggs, fruits and vegetables, and edible oils tend to be smaller than those of small households. 71

Table 11: Estimated Coefficients for the LA/AIDS Model for Different Food Items with

Pooled Data in Dar es Salaam and Mbeya Regions, Tanzania Items Mean Constant Cereals Meat, fish Fruits and Milk and budget term and pulses and eggs vegetables milk share products Cereals and 0.39790 1.03485 -0.025191 pulses Meat, fish and 0.18251 0.13515 -0.012721 0.031703 eggs Fruits and 0.15902 0.29013 -0.025203 -0.00458 0.027273 vegetables Milk and milk 0.06363 -0.09518 0.019863 -0.00030 -0.009061 -0.00133 products

Edible oils 0.06431 0.08309 0.00221 -0.00483 -0.00264 -0.00568

Other food 0.13263 -0.44804 0.041053 -0.009271 0.014223 -0.00348

Items Edible oils Other food Food Dar Urban Family Expenditure size Cereals and -0.104423 -0.031343 -0.039453 0.00151 pulses Meat, fish and 0.00733 0.014151 0.011583 -0.019803 eggs Fruits and -0.019563 0.054713 -0.013943 -0.017363 vegetables Milk and milk 0.026233 -0.031833 0.008921 0.015043 products

Edible oils 0.00587 -0.00333 0.00101 0.015453 -0.007152

Other food 0.00508 -0.04761 0.09375 -0.00670 0.01745 0.02776

Note: different from zero at 1 5 cent and 10 cent confidence level. , , , significantly per cent, per per 72

Table 12: Estimated Coefficients for the LA/AIDS Model for Different Food Items with

Data of Low-Income Households in Dar es Salaam and Mbeya Regions, Tanzania Items Mean Constant Cereals Meat, fish Fruits and Milk and budget term and pulses and eggs vegetables milk share products Cereals and 0.49011 0.59949 -0.03186 pulses Meat, fish and 0.16339 0.14816 0.01152 0.00135 eggs Fruits and 0.13897 0.37278 -0.017051 -0.01271 0.02922 vegetables Milk and milk 0.06327 -0.17628 0.03274 -0.00384 -0.01339 -0.032562 products

Edible oils 0.06049 -0.03064 -0.00566 0.00255 -0.00027 0.00034

Other food 0.08378 0.08650 0.01032 0.00113 0.014201 0.016712

Items Edible oils Other food Expenditure Dar Urban Family size

Cereals and -0.03242 0.02386 0.02048 0.01791 pulses Meat, fish and 0.01041 -0.02315 -0.024872 -0.025202 eggs Fruits and -0.030142 0.048233 -0.034633 -0.018692 vegetables Milk and milk 0.031641 -0.05 1113 -0.01610 0.036813 products

Edible oils -0.00221 0.01392 0.015861 0.032433 -0.00311

Other food 0.00524 -0.04761 0.00658 -0.01369 0.02269 -0.00772

3 2 1 level. Note: significantly different from zero at 1 per cent, 5 per cent and 10 per cent confidence

The results for low-income households presented in Table 12 show that low-income

and less on households in Dar es Salaam spend more on fruits and vegetables, and edible oils milk and milk products than the households in Mbeya. Households in urban areas consumed

their more edible oils, and less meat, fish and eggs, and fruits and vegetables than

and on counterparts in rural areas. Large families tend to allocate more on meat, fish and eggs, milk and milk products, and less on fruits and vegetables than small households. 73

Table 13: Estimated Coefficients for the LA/AIDS Model for Different Food Items with Data of High-Income Households in Dar es Salaam and Mbeya Regions, Tanzania Items Mean Constant Cereals Meat, fish Fruits and Milk and budget term and pulses and eggs vegetables milk share products Cereals and 0.34065 1.13211 0.00445 pulses Meat, fish and 0.19439 0.13021 -0.01394 0.046513 eggs Fruits and 0.17147 0.26095 -0.037383 -0.01216 0.033843 vegetables Milk and milk 0.06385 0.02120 0.016641 -0.00474 -0.00669 0.00748 products

Edible oils 0.06668 0.16103 -0.00627 -0.009611 -0.00459 -0.00569

Other food 0.16296 -0.70550 0.036503 -0.00605 0.026983 -0.00699

Items Edible oils Other food Expenditure Dar Urban Family size

Cereals and -0.115033 -0.021791 -0.040363 -0.023803 pulses Meat, fish and 0.00348 0.021501 0.015381 -0.01182 eggs Fruits and -0.014181 0.047803 -0.00254 -0.014332 vegetables Milk and milk 0.01099 -0.037203 0.011412 0.00621 products

Edible oils 0.028993 -0.016653 -0.00810 0.008352 -0.011433

Other food -0.00283 -0.04761 0.13139 -0.00220 0.00775 0.05518

Note:3,2, ', significantly different from zero at 1 per cent, 5 per cent and 10 per cent confidence level.

The parameters of the empirical analysis of high-income households are presented in

Table 13. In Dar es Salaam, high-income households consumed more meat, fish and eggs, and fruits and vegetables, and less cereals and pulses, and milk and milk products than those in

Mbeya. Compared to households in rural areas, those located in urban areas spent more on

and meat, fish and eggs, on milk and milk products, and on edible oil, and less on cereals pulses. Large high-income households tend to spend less on cereals and pulses, on fruits and vegetables, and on edible oils than smaller ones.

5.8. Computed Price and Expenditure Elasticities

This section displays computed price and expenditure elasticities with the parameters presented in the section above. Table 14 shows Marshallian price and expenditure elasticities from pooled data, Table 15 and 16 the ones of low and high-income households, respectively.

These elasticities are conditional on total expenditure elasticities presented in section 5.7.1. 74

Form (5.9) was used to compute own-price and cross-price elasticities and form (5.10) to compute food expenditure elasticities. The marginal expenditure shares indicate how households allocate one additional shilling of expenditure. The marginal expenditure shares

The are computed by multiplying food budget shares with food expenditure elasticities.

Hicksian price elasticities are displayed in Table 17 to 19, and were computed using the

Slutsky equation (form (5.11)). The Hicksian price elasticities show the substitution effects of price changes described in Figure 14.

Table 14: Marshallian Price and Expenditure Elasticities for Food Groups with Pooled Data Milk Cereals Meat, Fruits Food Marginal and Edible Other Items and fish and and expend. expend. milk oils food share pulses eggs veget. elast. products Cereals and -0.9589 0.0159 -0.0216 0.0666 0.0224 0.1380 0.7376 0.2935 pulses Meat, fish -0.0857 -0.8336 -0.0315 -0.0042 -0.0291 -0.0561 1.0402 0.1898 and eggs Fruits and -0.1096 -0.0064 -0.8089 0.0492 -0.0087 0.1057 0.8770 0.1395 vegetables Milk and 0.1480 -0.0799 -0.2080 -1.0472 -0.1158 -0.1093 1.4122 0.0899 milk products

Edible oils 0.0549 -0.0657 -0.0329 -0.0851 -0.9054 0.0858 0.9483 0.0610

Other food 0.0283 -0.1989 -0.0052 -0.0712 -0.0072 -1.4527 1.7068 0.2264

Table 14 displays the Marshallian price and expenditure elasticities, computed from the coefficients presented in Table 11, as well as the marginal expenditure shares for the various food groups. These results were obtained with the pooled data of the 1998-1999

in survey in Mbeya and Dar es Salaam regions of Tanzania. Own-price elasticities, depicted frames, range from -1.45 for the group of other food to -0.81 for fruits and vegetables. Only the own-price elasticity of milk and milk products attains at -1.04 an absolute value of more than 1. The other own-price elasticities are still high but households do not reduce their demand at the same proportions as the price variations. Uncompensated or Marshallian cross- price elasticities do not reveal the relation of the food groups6 to each other, due to the high income effects. Hicksian cross-price elasticities are more relevant and are presented below.

Conditional food expenditure elasticities range from 0.74 for cereals and pulses to 1.71

and for the group of other food. Cereals and pulses at 0.74, fruits and vegetables at 0.87, edible oils at 0.95 have food expenditure elasticities below one. These goods are thus 75 necessities. The expenditure elasticity of the food categories of milk and milk products; meat,

three fish, and eggs; and other food are above 1. Thus, within food expenditures, these groups of food goods are luxuries.

The marginal expenditure share in Table 14 reveal that on average, Tanzanian households will allocate 29.4% of an additional shilling of food expenditure to cereals and

is the pulses, even though expenditure elasticity for this food group is low. The reason for this large budget share for cereals and pulses, which is however likely to decline with increasing incomes. A share of 22.6% of additional food expenditures will be spent to the food group of other food. Since food expenditure elasticity of the group of other food is high, budget shares for this group is expected to increase with augmenting expenditures. The marginal budget share of milk and milk products is low at 9.0%.

Table 15: Marshallian Price and Expenditure Elasticities for Food Groups of Low- Income Households Milk Cereals Meat, Fruits Food Marginal and Edible Other Items and fish and and expend, expend. milk oils food share pulses eggs veget. elast. products Cereals and -1.0326 0.0343 -0.0256 0.0710 -0.0076 0.0266 0.9338 0.4577 pulses Meat, fish 0.0393 -1.0021 0.0867 -0.0275 0.0117 0.0016 1.0637 0.1738 and eggs Fruits and -0.0164 -0.0561 -0.7596 0.0826 0.0112 0.1204 0.7831 0.1088 vegetables Milk and 0.2724 -0.1424 -0.2811 1.5463 0.0248 0.2222 1.5001 0.0949 milk products

Edible oils -0.2065 0.0045 -0.0364 -0.0089 -1.0504 0.0674 1.2302 0.0744

Other food 0.0847 0.0006 0.1586 0.1944 0.0578 -1.5748 1.0786 0.0904

Income, price, and cross-price elasticities computed from the parameters estimated with the data of low-income and high-income households are presented in Table 15 and Table

16, respectively. How figures change from one to the other group of household is of particular interest. This will indicate how households will change their behavior to allocate their food budget if their income increases.

When comparing own-price elasticities, it becomes clear that low-income households are more price responsive than high-income households. Only price responsiveness for the fruit and vegetable category is lower for low-income than for high-income households. The

Substitute or complementary goods 76

own-price elasticity for fruit and vegetable of the poorer households is also the only one that

of is smaller than one in absolute value. Own-price elasticities of all other food categories low-expenditure households are above 1 in absolute value. The most price elastic food categories are milk and milk products at -1.55, and other food at -1.59. High-income households are far less price responsive than their poorer counterparts. Just their own-price elasticity for other food is above one in absolute value. The absolute value of the own-price elasticities of all the other categories are below one, and range from -0.90 for milk and milk products to -0.56 for edible oils.

A look at the food expenditure elasticities also reveals changes in food budget allocations with increasing incomes. For example, the food expenditure elasticity for cereal and pulses of the low-income households is 0.93, while it is 0.66 for high-income households.

This indicates that households of the low expenditure group raise their expenses for cereal and pulses at almost the same rate than their total food outlays, while high expenditure households will only increase their expenses for this food category by two thirds of a food budget increase. Both income groups have conditional expenditure elasticities for meat, fish and eggs that are slightly higher than one. The food budget elasticity for fruits and vegetables reveals that this group is a necessity for both income groups. It is higher for high-income households than for low-income households. Milk and milk products are luxury goods within food expenditure, as well for low-income as for high-income households with values of 1.50 and

1.17, respectively. The largest difference in income elasticities between low and high-income households can be observed for edible oils. To high-income households edible oils are necessary goods with an elasticity of 0.75, while edible oils are luxury goods to low-income households with an elasticity of 1.23. The group of other food is luxury for both income categories. High-income households increase their expenditure on this category of food much faster than low-income households when food budgets increase. 77

Table 16: Marshallian Price and Expenditure Elasticities for Food Groups of High- Income Households Milk Cereals Meat, Fruits Food Marginal and Edible Other Items and fish and and expend, expend. milk oils food elast. share pulses eggs veget. products Cereals and -0.8719 0.0247 -0.0518 0.0704 0.0041 0.1622 0.6623 0.2256 pulses Meat, fish -0.0778 -0.7642 0.0656 -0.0256 -0.0506 -0.0341 1.0179 0.1979 and eggs Fruits and -0.1898 -0.0548 -0.7885 0.0337 -0.0212 0.1708 0.9173 0.1573 vegetables Milk and 0.2020 -0.1078 -0.1343 -0.8938 0.1006 -0.1375 1.1721 0.0748 milk products

Edible oils -0.0090 -0.0956 -0.0260 -0.0694 -0.5486 0.0017 0.7503 0.0500

Other food -0.0507 -0.1939 0.0273 -0.0944 -0.0711 -1.4235 1.8063 0.2944

The comparison of the marginal expenditure shares reveals expected differences. Poor household tend to spend almost half of food budget increases on cereal and pulses, while the richer household will allocate only 22.6% of additional food expenses to that food group.

other Low-income households are likely to allocate only 9% of a food budget increase to food, while high-income households will allocate almost 30% to this food group. The marginal expenditure shares of the remaining food categories do not differ much between the two income groups.

Table 17 presents the Hicksian or compensated own and cross-price elasticities for the food categories with pooled data in Dar es Salaam and Mbeya regions. Negative cross-price elasticities mean that the goods are complementary; substitutes have positive cross-price elasticities.

The substitution effects of price changes on demand for food categories range from -

0.64 for meat, fish, and eggs, to -1.23 for other food. The category of other food is the only one with a compensated own-price elasticity that is higher than one in absolute value; every other is below one. Each food category is a substitute to the others, only the cross-price elasticities of milk and milk products and edible oils are negative, and thus these two goods, can not be considered to be substitutes. 78

Table 17: Hicksian Price Elasticities of all Households for Food Groups Milk and Cereals Meat, fish Fruits and Items milk Edible oils Other food and pulses and eggs vegetables products Cereals and -0.6654 0.1505 0.0957 0.1135 0.0698 0.2358 pulses Meat, fish and 0.3282 -0.6438 0.1339 0.0620 0.0378 0.0819 eggs Fruits and 0.2394 0.1537 -0.6695 0.0066 0.0477 0.2221 vegetables Milk and milk 0.7100 0.1778 0.0166 -0.9573 -0.0250 0.0780 products

Edible oils 0.4322 0.1074 0.1179 -0.0247 -0.8444 0.2116

Other food 0.7074 0.1127 0.2662 0.0374 0.1026 -1.2263

The Hicksian price elasticities of the low-income households are presented in Table 18 and those of the households with higher incomes in Table 19. The comparison of the two tables reveals that also compensated own-price elasticities tend to be higher for the low

households expenditure group. The compensated own price effects of low expenditure range from -0.57 for cereals and pulses to -1.48 for other food. Beneath other food, milk and milk products is the sole food category that also reaches an absolute value above one with a compensated own-price elasticity of-1.45. The richer households react with elasticities of-

0.50 for edible oils to -1.13 for other food to price changes. Only the category of other food

The reaches a compensated own-price elasticity of more than one in absolute value. compensated own-price elasticity of cereals and pulses is the only one that is higher for low expenditure households than for the high expenditure group, with -0.57 compared to -0.65, respectively. 79

Table 18: Hicksian Price Elasticities of Low-Income Households for Different Food Groups Milk and Cereals Meat, fish Fruits and Items milk Edible oils Other food and pulses and eggs vegetables products Cereals and -0.5749 0.1869 0.1042 0.1301 0.0489 0.1048 pulses Meat, fish and 0.5606 -0.8283 0.0611 0.0398 0.0761 0.0907 eggs Fruits and 0.3674 0.0719 -0.6508 -0.0331 0.0586 0.1860 vegetables Milk and milk 1.0076 0.1027 -0.0727 -1.4514 0.0659 0.3478 products

Edible oils 0.3965 0.2055 0.1346 0.0690 -0.9760 0.1705

Other food 0.6133 0.1769 0.3085 0.2627 0.1231 -1.4844

The data also show that low expenditure households do not substitute vegetables and fruits with milk and milk products, and high expenditure households do not substitute milk and milk products with edible oils. Changes of prices of cereals and pulses has an important

low effect on all the other food categories. This effect is even more accentuated for the expenditure households. This is due to the large food budget share that households allocate to

the cereals and pulses, especially the poorer ones. These effects were not visible from

Marshallian cross-price elasticities due to the large income effect of price changes of cereals and pulses on households.

Table 19: Hicksian Price Elasticities of High-Income Households for Different Food Groups Milk and Cereals Meat, fish Fruits and Items milk Edible oils Other food and pulses and eggs vegetables products Cereals and -0.6463 0.1535 0.0617 0.1127 0.0483 0.2701 pulses Meat, fish and 0.2689 -0.5663 0.1089 0.0394 0.0172 0.1318 eggs Fruits and 0.1227 0.1235 -0.6312 0.0248 0.0399 0.3203 vegetables Milk and milk 0.6013 0.1201 0.0667 -0.8190 -0.0225 0.0535 products

Edible oils 0.2466 0.0502 0.1027 -0.0215 -0.4986 0.1206

Other food 0.5646 0.1572 0.3370 0.0210 0.0493 -1.1292 80

Table 20 displays the unconditional food category expenditure elasticities computed from the food expenditure elasticities for all households presented in Table 14 multiplied with

with the the food group total expenditure elasticity of Table 10 that were computed coefficients estimated in the first stage of the two stage budget analysis. The conditional food expenditure elasticities are multiplied with the food total expenditure elasticity to obtain the unconditional food category expenditure elasticities as shown in form (5.10).

Table 20: Unconditional Marshallian Price and Expenditure Elasticities of all Households

fish Fruits and Edible Other , Meat, .„ Expend. Items oils food Elast. and , , eggs veget. A pulses products Cereals and -0.9495 0.0202 -0.0178 0.0681 0.0239 0.1411 0.4835 pulses Meat, fish -0.0724 -0.8275 -0.0262 -0.0021 -0.0269 -0.0517 0.6818 and eggs Fruits and -0.0984 -0.0012 -0.8045 -0.0474 -0.0069 0.1095 0.5749 vegetables Milk and 0.1661 -0.0717 -0.2008 1.0443 -0.1129 -0.1033 0.9257 milk products

Edible oils 0.0670 -0.0601 -0.0280 -0.0831 -0.9034 0.0899 0.6216

Other food 0.0501 -0.1889 0.0035 -0.0677 -0.0036 -1.4454 1.1189

These results show that only the category of other food can be considered to be luxuries for the population in Dar es Salaam and Mbeya regions. The total expenditure elasticity of milk and milk products is 0.92 and is thus not far from unity. The other unconditional expenditure elasticities range from 0.68 for meat, fish, and eggs, to 0.48 for cereals and pulses. This means that the average household will raise its expenditure for cereals and pulses by 0.5% if its total budget increases by 1%.

5.9. Discussion and Conclusions

The results of the study show that price and income are significant determinants for the demand of food commodities.

The results of the first stage of the demand analysis suggest that the total expenditure elasticity for food is 0.65. This means that food is a necessity, which is an outcome that was expected. This elasticity means that households raise their food budget by 6.5% when their total expenditure increase by 10%, and that they do increase food consumption with rising income. The unconditional expenditure elasticities for the food categories indicate that only 81

and other food is a luxury and all the others are necessities. Especially the cereals pulses

its total is but group has a low response to additional income since expenditure elasticity 0.48, it is still positive, and thus a normal good.

increases of Important questions are to which food groups and to which extent, household food budget are allocated, and if there are differences from low expenditure households to households which are better off. Results in the second stage of the analysis indicate, considering marginal expenditure shares, that households tend to spend 30% on cereals and pulses, 22% on other food and less than 20% on each of the other food categories.

tend to However, there is a clear difference between the two income groups. Poor households spend almost 50% of an additional Tanzanian shilling for cereals and pulses, and high-income households only 23%. On the other hand, richer households will allocate 30% of a food budget increase to other food, while the poorer will only spend 9% of new food expenses on

half of that category. Poor households are most likely to have good reasons to spend additional food expenditures to cereal and pulses. This food category is very effective in providing calories and also protein. These results therefore suggest that poorer households are

that the situation not able to meet their basic energy requirements. The results also show drastically improves with rising food budget, and thus with better incomes. High-income households tend to allocate food budget increases to more valuable food items, e.g. other food which indicates that they do not suffer from undernutrition.

The conditional food expenditure elasticity for cereals and pulses of the low-income households is 0.94, while it is 0.67 for high-income households. This indicates that the

is much than that response of low-income households to changes in total expenditure higher of high-income households. This is probably due to the fact that high-income household

that low-income already consume relatively large quantities of cereals and pulses, and households still strive to attain a normal nutritional status with increased consumption of cereals and pulses as income grows.

The income elasticity for fruits and vegetables is higher for high-income households than for low-income households. This is probably due to some fruits and vegetables being

items in this are absolute necessary goods, such as tomatoes and onions, while other category

and can luxuries and are only purchased by households after they attained a certain wealth, afford to diversify food expenditures to less nutritious foods.

The income elasticities of meat, fish and eggs, and of milk and milk products are high in both, low and high-income groups. This suggests that rapid economic growth, particularly 82

if it is accompanied by reduction in poverty could lead to escalating demand for meat, fish

would be reduced. This and eggs, and milk and milk products. As a consequence malnutrition results confirms the assumption of Delgado et al. (1999), who expect the demand for animal products to boost in the next years.

The largest difference in income elasticities between low and high-income households

households edible oils are can be observed for the group of edible oils. For high-income

for low-income necessary goods with an elasticity of 0.75, while they are luxury goods

in households with a food expenditure elasticity of 1.23. Oil is an important ingredient

Tanzanian cooking. Poor households probably can not afford as much oil as they wish. With increasing incomes, they will increase their consumption of oil, up to a level that satisfies them. Once this level is reached there is little sense in consuming even more edible oils, and since oil in Tanzania is homogenous they do not switch consumption to more expensive oils, which would lead to a higher expenditure elasticity of high-income households.

Items in the group of other food are luxuries for both income categories. High-income households increase their expenditure on this category of food much faster than low-income households, due to the typical luxury items included in this category, which are not purchased by low-income households at all.

The results also show that there are differences between the two income categories when prices change. Low-income households are much more price responsive than high- income households which supports the assertion that for a given level of expenditure and prices, low-income households are compelled to adjust their consumption patterns to

low- relatively inexpensive commodities, away from expensive ones. This suggests that income households face human nutrient deficiency.

Price changes are especially important when they affect the cereals and pulses group.

The Hicksian own-price elasticity for this food category is smaller in the low expenditure

when of cereals and household group, than in the richer one. This indicates that even prices

due to the pulses increase, poor households continue to consume this food group. However, high budget share of cereals and pulses, price changes of this food category has an important income effect. This becomes obvious when Marshallian and Hicksian cross-price effects of

effect of cereals and pulses on the other food categories are compared. The substitution price changes of cereals and pulses on other food categories is important for each of them (Hicksian cross-price elasticities). The income effect of such a price change however overcompensates 83

the substitution effect, and therefore, Marshallian cross-price elasticities of cereals and pulses are negative.

Non-price variables are also important in explaining variations in food consumption patterns. A household moving from rural areas to a city will, per se, tend to shift its food budget allocation from cereals and pulses, and fruits and vegetables to meat, fish and eggs, milk and milk products, and edible oils. The reduction of consumption of cereals and pulses with urbanization, however, can only be observed for high-income households.

The households' size exerts some influence on food budget allocation of the households. Large households have significantly larger expenditure shares for milk and milk products, while their budget shares of meat, fish and eggs, fruits and vegetables, and edible oils tend to be smaller than those of small households. As indicated, the results suggest that small households consume less milk than large households, especially small low-income households. This could mean that the smallest purchasable quantity of milk available on the market is not small enough. Small households may not be able to consume all the purchased fresh milk before it spoils. It could be worthy for milk sellers to try to sell fresh whole milk in

that smaller quantities, and so permitting small households to consume milk without leftovers will spoil. This could also lead to an increased human nutrient intake of small low-income households.

The food analysis presented here allow for some important conclusions. The results suggest that low expenditure households face nutrient deficiency, since they tend to allocate a large part of their food budget to cereals and pulses, which is the food category most likely to be the cheapest source of calorie and protein. Their high response to price changes, compared to their richer counterparts, also indicates that they switch consumption away from expensive to cheaper food.

Policies aiming at a broad growth of income especially of low-income households are likely to be the most effective approach to increase their nutritional status. Poor households tend to allocate large amounts of increasing food expenditure to cereals and pulses, which indicates that they look for increased calorie availability. With larger incomes, households could tend to diversify their food expenditure, to tastier but not necessarily more nutritious goods.

Increasing efficiency of production and marketing of food, especially of cereals and

almost half pulses will benefit poor households. As indicated, poor households tend to spend

will of their food budget on these goods. Therefore, price changes of cereals and pulses 84

will have to reduce particularly affect poor households, and, if prices increase, they consumption of other goods due to the income effect. For the same reason, declining prices of cereals and pulses due to increased productivity will have a positive income effect on the

of cereals and poor. Increased efficiency of production and marketing of food, especially pulses can lead to a reduction of prices of food which will especially benefit the poor.

from Considering that many poor households live in rural areas and make a living producing cereals and pulses, policies should make sure that especially small producers can improve their efficiency to prevent their incomes to be affected if prices decline, and to increase their incomes if prices remain constant. Improving infrastructure in rural areas, and assuring

urban competition among food traders is likely to reduce transaction costs from farm gate to markets, which could lead to price reduction in urban areas or to price increase at farm gate.

With increasing income, demand for food is going to augment; especially demand for

which could lead to meat, fish and eggs, and for milk and milk products is going to expand increases of food prices which could have a negative effect on nutrition of households. This could be prevented by supporting the supply of animal products which will increase the quantity of animal products supplied, and thus will make sure prices to increase too much.

This policy will also make sure that local producer fully benefit from these changes in demand and that this demand will not be satisfied by competing imports. Thus animal production can continue to be an important mean to reduce poverty in rural areas of Tanzania. 85

6. Nutrition Analysis

6.1. Introduction

An analysis on how income and other socio-economic variables impact on nutrient availability of households is presented in this chapter. This kind of analysis is particularly important for developing countries where large parts of the population attempt to meet their nutritional requirements. Tanzania belongs to the poorest countries with a GDP of 240$ per capita in 1999 (World Bank, 2001). In 1991-93, 17-24% of the children were either stunted or wasting7, and 18% of newborns weighed less than 2500 grams at birth (WHO Basic Health

Indicators). These are indicators for prevailing undernutrition. Thus food security and the role of public interventions in the production, consumption, distribution and foreign trade of food are important policy issues in Tanzania. Knowing how economic and non-economic household characteristics impact on nutrient availability is important in providing information to optimize policy measures to improve nutrition of the population.

It is commonly assumed that nutritional intake improves with increasing income

(Behrman and Wolfe, 1984; Ravallion, 1990; Submarian and Deaton, 1996; Srinivasan,

2000). As could also be shown in the food demand analysis (chapter 5), expenses for food increase with increasing total expenditure. However, households also tend to purchase more expensive goods as their income increases. Therefore, positive food expenditure elasticities do not necessarily imply that availability of human nutrients increases with rising income; even for low-income households. This part of the research shows if policies aiming at increasing household income, especially of the lowest, are likely to be effective for improving nutritional status of the population.

Many analyses investigating the link of income and nutritional intake have been undertaken in developing countries due to the importance of this kind of analysis. The data used for these analyses were usually obtained from cross-sectional household data on food expenditure or on recalled or weighted food consumption over a short period, usually 24 hours or 7 days. The studies estimated nutrient-income elasticities, usually the income elasticity for calories. Bouis and Haddad (1992) provide a list of calorie-expenditure elasticities from various studies ranging from 1.18 to 0.01 at mean. Most of these analyses were undertaken in Asia and South America. There are also some estimates from Africa; e.g. 86

Bouis et al. (1992) estimated calorie-expenditure elasticities in Kenya ranging from 0.19 to

0.40. Bouis and Haddad (1992) showed how measurement method and statistical procedure

To the best of our may impact on the computation of nutrient-income elasticities. knowledge, there is no existing nutrient-expenditure elasticity for Tanzania.

This chapter therefore examines the response of households demand for nutrients to changes of total expenditure and other household demographics. First, household availability of calories and protein is presented using tabular representations. Then, a nonparametric procedure is used to graphically represent the influence of per capita expenditure on nutrient availability. In another part, parametric estimations of the influence of household expenditure and other economic and socio-demographic variables on nutrient availability are carried out.

Finally, nutrient-expenditure elasticities obtained from the parametric estimations are compared to nutrient-expenditure elasticities computed from food demand elasticities.

6.2. Description of Nutrient Availability

To obtain total nutrient availability per capita at the household level, the data collected with the survey and described in chapter 4 had to be transformed. Reported monthly quantities of food items consumed were multiplied with nutrient content tables of the USDA

Nutrient Database for Standard Reference (Release 13)8 and then divided by the number of household members. Since 496 households participated in the entire study, and since three data sets are available for each household, the number of observations reaches 1,488. The data obtained that way are the availability of calories and proteins to households' members rather than their exact intake. Thus, if income elasticities for "food wastage" is large, the results of our study could be compromised (Bouis and Haddad, 1992). The monthly total expenditure per capita were computed by adding up monthly expenditure for food and non-food items, plus one twelfth of the large yearly non-food expenditure.

Table 21 shows from which food groups, households get their calories and protein, and how much each calorie and protein costs if purchased through each of the various food

above. groups. The same food groups were used as in the food demand analysis in the chapter

half of Since edible oils and sugar do not contain protein, they are not reported in the lower

Table 21. Columns 1-3 show the distribution of calories and protein over the various food

in groups. They are calculated from calorie and protein shares of each of the 496 households

7 Conventional definition of a stunted (wasting) child is one having a height (weight) below two standard deviations of the median value for a reference population of children of the same age (height). http://www.nal.usda.gov/fnic/foodcomp/index.html 87

and bottom deciles the survey, averaged over the whole sample in column 1 and over the top

The last row for of per capita household total expenditure in columns 2 and 3, respectively.

and in the calories shows that per capita daily calories are 2,270 on average and 1,414 3,040 two extreme deciles, respectively. The daily per capita availability of proteins is 66.8 grams on average and 40.0 and 98.6 grams for the bottom and the top ten percent households, respectively.

Cereals and pulses are the largest source of energy for Tanzanian households, with

needs of 69.2% on average. This food group is particularly important in fulfilling the energy

83.2% of total poor households, since the calorie share of cereals and pulses amounts to calorie availability of the bottom 10% of the households in the study. With rising income the importance of this group as a source of energy declines, but even the high-income group covers more than 50% of their energy requirement with this food group. Cereals and pulses are also the most important source of protein for the Tanzanian households. While the poorest obtain three fourths of their protein from that group, the richest get less than half, and on average two thirds of protein availability is provided by this food group.

Due to its composition, the food group meat, fish, and eggs is more important for providing protein than calories. On average of all households it is more than one quarter of protein, while it provides only 9% of calories. It is an important source of protein for better- off households, since their protein share from this group is almost as large as from cereals, roots and pulses. The share of protein provided to poor households by this food group reaches

10.9%. The calorie share of meat, fish, and eggs reaches 16.4% for high-income households and only 3.6% for the poorest. 88

Table 21: Calorie and Protein Consumption, and Prices per Calorie and per Protein, Mbeya and Dar es Salaam Regions 1998/1999 Price per Calorie Calorie Share (TSh per 1000 Calories) Mean All Bottom 10% Top 10% Mean All Bottom 10% Top 10% (1) (2) (3) (4) (5) (6) Cereals and 69.2% 83.2% 53.4% 98.94 59.70 142.32 Pulses Meat, Fish, and 9.0% 3.6% 16.4% 397.16 353.04 413.96 Eggs Fruits and 5.1% 5.1% 4.9% 644.79 240.81 960.45 Vegetables Milk and Milk 2.2% 1.1% 3.6% 665.60 433.59 744.28 Products

Edible Oils 8.4% 3.7% 13.6% 145.00 136.64 159.96

Sugar 6.2% 3.2% 8.1% 109.32 110.00 116.86

Total Food 2,270 1,414 3,040 164.09 89.96 246.74 (Calories) Price of Protein Protein Share (TSh per gram of Protein)

Mean All Bottom 10% Top 10% Mean All Bottom 10% Top 10%

Cereals and 62.8% 78.5% 45.2% 3.80 2.26 5.26 Pulses Meat, Fish, and 25.2% 10.9% 42.3% 4.65 4.11 4.89 Eggs Fruits and 8.2% 8.6% 6.6% 13.34 4.94 22.21 Vegetables Milk and Milk 3.8% 2.0% 5.8% 12.79 8.00 14.31 Products Total Food 66.8 40.0 98.6 6.34 3.16 10.56 (Protein) of Note: Mean refers to mean over the whole sample, bottom 10% to mean over households in the bottom decile household per capita household expenditure, and top 10% to mean over households in the top decile of per capita expenditure. Shares of calories and of expenditures are calculated on an individual household basis and are averaged over all appropriate households. Calorie prices are averages over consuming households.

The other food groups are relatively small in providing calories and protein to households. While the calorie and protein share of fruits and vegetables does not increase with rising expenditure, the share of protein and calories of milk and milk products and the share of calories from edible oils and sugar increase rapidly with rising income. Poor households get 1.1% of calories from milk and milk products, 3.7% from edible oils and 3.2% from sugar, while high-income households get 3.6%, 13.6%, and 8.1% from these groups, respectively.

Columns 4-6 of Table 21 show how many Tanzanian shillings of expenditure on each

On food group were required to generate 1,000 calories and one gram of protein, respectively.

90 average households spent 164 shillings per 1,000 calories with the poorest decile paying shillings and the richest paying 247 shillings. Similarly, the price households paid per gram of 89

protein was 6.34 shillings on average and 3.16 and 10.56 shillings per gram of protein paid by households with the lowest and highest expenditures, respectively. Cereals and pulses provide cheap calories and protein to all households, but especially to the poorest. The group of meat,

of fish, and eggs is a rather expensive source of calorie but a relative cheap source protein.

Fruits and vegetables, and milk and milk products are expensive sources of calorie as well as of protein. On the other hand, edible oils and sugar provide rather cheap calories.

The figures in columns 4-6 show that rich households spend more for the same amount

for this. of calories and protein than poor households. There are two plausible explanations

First, it is possible that households do not only increase their availability of calories and protein with rising income, but also tend to buy more expensive goods, which are of higher quality. For example, the increase of the price paid for calories from cereals and pulses is most likely due to the shift of consumption to more refined and processed cereal products as income increase. Second, the difference in the price between low and high-income households could also be explained by the location of the households, since most of the households with

in urban areas of Dar es very low expenditure are found in rural Mbeya, and the richest live

Salaam. This becomes clear considering fresh food, which are fruits and vegetables, and milk and milk products. Due to the high perishability of these goods, marketing costs tend to increase (Jaffee and Morton, 1995). Indeed, prices of local produces in Mbeya rural are very low compared to the price these goods fetch in urban Dar es Salaam.

The small differences of the price for 1000 calories of edible oils and sugar from low to high-income households are due to the homogeneity in the quality of these goods, which leads to relatively constant prices for all households. It is interesting to note that calories from

of all sugar are more expensive for households of the lowest decile than the average households. An explanation for this could be that, sugar is not produced locally and has to be transported to the villages increasing its price for rural households, where most of the poorest of the households are located.

6.3. Explaining Household's Nutrient Availability

Increasing total income is likely to be the most important factor for explaining improved nutrient availability, as discussed above. Assuring that households are always able to meet their nutrient requirements is the main goal of any policy aiming at achieving food security. This is most likely to be possible if households are able to generate sufficient income to purchase the essential foods to meet their needs, and is regarded as conventional wisdom

(Subramanian and Deaton, 1996). 90

calories and the Figure 16 is a scatter diagram displaying the logarithm of per capita logarithm of household total expenditure per capita. Figure 17 shows the same for protein.

of The shape of these figures anticipate a good many of the results to come, since availability calories and proteins visibly increase with rising income. Since the scale of the axes of Figure

16 and Figure 17 are logarithmic, log-linear models are most likely to adequately represent the relationship between income and nutrient availability. Similar results are obtained in the nonparametric estimations of the relation of nutrient availability with household total expenditure displayed in Figure 18 and Figure 19 below.

Figure 16: Scatter Diagram of the Logarithms of per Capita Calories and Expenditure

13

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12

E 11 5

8. • ..«•JLZXS&tMmHtVfl

' WVflttfï >•} • -VJ> *! 7%

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Ä*% 10 * X * «

9 10 11 13

ln(expenditure (in TSh) per capita per month) 91

Figure 17: Scatter Diagram of the Logarithms of per Capita Protein and Expenditure

, 5 T

* , .

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65 .t.*

55 4

9 10 11 12 13

ln(expenditure (In TSh) per capita per month)

Parametric and nonparametric approaches are employed to examine the relationship of

method per capita nutrient availability and total per capita expenditure. First, nonparametric are used to estimate nutrients Engel curves that show the relationship between per capita nutrient availability and household total per capita expenditure. An observation of the Engel

and curves reveals how total household expenditure influences per capita nutrient availability if the shape of the curve changes with increasing expenditure. Nonparametric regression

is provides a powerful set of tools that can be extremely useful for data analysis when there little a priori knowledge of the shape of the function to be estimated, and when the shape may

functional form comes vary over the distribution to the covariate. However, the escape from

The is that at the expense of limiting the number of covariates that can be handled. problem

that nutrient availability may be affected by other factors than expenditure. To handle problem, parametric estimations of the Engel curves that allow the consideration of other

the factors are also carried out. Parametric procedures are applied to regress the logarithm of quantities of available nutrients with respect to the logarithm of total per capita expenditure and other variables.

6.4. Nonparametric Estimations

One of the main concerns, when estimating classical linear regressions, is the possibility of non-linearity. It is quite plausible that poor people, whose income is insufficient 92

is much to buy sufficient food, should have an elasticity of calories to total expenditure that higher than that for those who have enough to eat. Thus, the slope of the Engel curve of the nutrients could be steeper for low-income than for high-income households. With the nonparametric estimations a possible non-linearity can be visualized.

6.4.1. Procedure

of Nonparametric procedure was used to represent in logarithms the influence per

allows for non-linear capita expenditure on per capita nutrient availability, which relationships. The expected quantity of nutrients conditional on per capita household

of nutrients expenditure is estimated, using a local regression function. The expected quantity can be written as

m(x) = E(y\x), (6.1)

and jc is the of where y is the logarithm of per capita nutrient availability, logarithm per capita total household expenditure.

This function is estimated using the Loess smoothing procedure, which is a local

linear regression technique that works as follows: At any given expenditure level x a

this is a local regression of available nutrients on per capita expenditure is carried out. Since

all chosen levels of regression, only a restricted quantity of observations nearby x is used. For

< a < and n is the total numbers of expenditure x, a*n neighbors are selected, with 0 1,

The used observations. The optimal a is determined by generalized cross validation.

Tricube observations in each estimation i are also weighted in function of their distance to x.

with distance weights are chosen to be largest for sample points close to x and to diminish

= - the levels of from x. Let us assume that A( (jc) |jc, x\ is the distance from x to expenditure jc, the used observations, and &(q){x) is the distance from x to the nearest neighbor not considered in estimation i. If T(u) is assumed as the tricube weight function

far\u\

then the to the observation for the fit at x is neighborhood weight given (jc, , y, )

AM. = w.\(x) T (6.3) 93

and decrease as For each jc,- such that A,(jc)< A^(x), the weights are positive A((x) increases. For A;(jc)> A(ç)(jc), the weights are zero. A detailed description of the Loess smoothing procedure can be found in Cleveland (1993). To choose the expenditure levels jc, an evenly spaced grid of 128 points in the distribution of log per capita expenditure was applied, and local Loess regressions for each was calculated. The estimation of m(x) is the predicted value of the local regression at x, and is used to plot the nutrient Engel curves displayed below.

6.4.2. Results

Figure 18 and 20 show the local regression estimate of the scatter plots in Figure 16 and 18, respectively, and Figure 20 to 36 show the same for the other nutrients analyzed. As is to be expected, all curves show that nutrients available augment with increasing expenditure.

In general, the slope of the curves is steeper at lower levels of total per capita expenditure

(TPCE). For some curves the slope increases with higher levels of TPCE compared to the middle ranges of TPCE. Since all Engel curve continue to increase with growing expenditure, one must assume that they are linear.

Figure 18 shows the influence of total expenditure per capita on calories available.

with With very low expenditure, calories available are also very low, but increase rapidly raising expenditure. After reaching 2000 calories per capita and per day (log of per capita calories = 11) the quantity of available calories continues to increase, however on a lower rate. Similarly, proteins available increase rapidly until approximately 54 grams per capita and per day is attained. After that, availability of protein continues to increase, however, at a lower rate than before. The shape of the curve of vitamin B6, thiamin, niacin, folate, magnesium, and iron are almost similar to the shape of calories and proteins.

In comparison to this, availability of fats and cholesterol (Figure 20 and 22) increase

of almost linearly up to high expenditure levels, and this at very large rates. A similar shape the curve can be observed for vitamin E, vitamin B12, riboflavin, and zinc, however at much lower rates.

Consumption of fibers in Figure 22 increase at low levels of expenditure, but then remains almost constant. The relation of TPCE to availability of Vitamin A, Vitamin C, and calcium are quite similar to this curve. 94

Figure 18 : Nonparametric Representationof Calorie Figure 19: Nonparametric Representationof Protein

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Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin grams

Figure 20: Nonparametric Representationof Fats Figure 21: Nonparametric Representationof Cholesterol

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Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin grams Note: quantityin milligrams 95

Figure 22 : Nonparametric Representationof Fiber Figure 23: Nonparametric Representationof Vitamin A < a 14-

> cd +-» Ü 'cf 12- u. O

8 10 12 8 10 12

Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin grams Note: quantityin micrograms

Figure 24: Nonparametric Representationof Vitamin E Figure 25: Nonparametric Representationof Vitamin C

rj 10- c c

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8 10 12 8 10 12 Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin milligrams Note: quantityin milligrams 96

Figure 26: Nonparametric Representationof Vitamin B6 Figure 27: Nonparametric Representationof Vitamin B12

SOm c C I "> cd > cd -4-» 'H* 4- cd O 'cf u o

Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin milligrams Note: quantityin micrograms

Figure 28: Nonparametric Representationof Thiamin Figure 29: Nonparametric Representationof Riboflavin

Si 5- c > S cd 5=1 cd o IS 4- £> cd c -4-» cd 4-» 'cf 'a. o cd 3- o Ö 11 Oh &tu o <4-l (50 o o bû O —i— -1

8 10 12 8 10 12

Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin milligrams Note: quantityin milligrams 97

Figure 30 : Nonparametric Representationof Niacin Figure 31: Nonparametric Representationof Folate

iii

td

S a 10- cd 'I o o u. t-

8 10 12 8 10 12

Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin milligrams Note: quantityin micrograms

Figure 32: Nonparametric Representationof Calcium Figure 33: Nonparametric Representationof Magnesium

a S u j3 10- c 10- "cd o SP cd s -4-* cd 'if o 'I 9- 8- o ul- «4M a. o bO o O bO O

8 10 12 8 10 12

Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin milligrams Note: quantityin milligrams 98

Figure 34 : Nonparametric Representationof Iron Figure 35: Nonparametric Representationof Zinc

o a 'n cd

'cf Ü l-

5-

bOO

8 10 12 8 10 12

Log of per capitaexpenditure Log of per capitaexpenditure

Note: quantityin milligrams Note: quantityin milligrams 99

6.5. Parametric Estimations

The nonparametric estimations in the previous section fail to consider the effects of factors that may influence nutrient demand, that are not related to household expenditure.

Given that the nutrient requirements of children and adults, as well as males and females can differ, it is important to use a framework that considers these effects.

The practice of estimating a log-linear model relating per capita nutrient availability «,

food item and a set with per capita total monthly expenditure jc, the price of a representative p, of demographic variables dk is applied in this analysis.

As in Bouis and Haddad (1992), the specification is given as

In «, = a,o + a,iln x + «*ln P + Xa<*J* + ". (6-4) k

nutrient-income- where the «'s are parameters and u, is an OLS error term. In this case, the elasticities are equal to cc,i and remain constant at any expenditure level of the sample.

The price of maize was included in the analyses. The price of maize was chosen because it is the main food staple of the Tanzanian population. A decreasing price of maize is expected to have a positive impact on household ability to meet its nutritional needs and thus the sign of this parameter is anticipated to be negative.

The other variables used in the model are household socio-economic variables and other characteristics. DAR is a dummy variable and takes the value of one if the household is located in Dar es Salaam and zero if it is not. Similarly URBAN is a dummy variable for household background; one if urban, zero if rural. These variables indicate how nutrient

rural to urban availability change per se, when households change their location or move from areas.

SIZE is the number of household members in the three rounds of data collection. If the parameter is negative, persons living in large households face a smaller nutrient availability than those living in small households. This could also indicate that especially children living in small households are likely to be better off than children in large households, since it can be expected that the adults within the household will meet their nutrient requirements inconsiderate of their children's needs. On the other hand, a negative sign can also be expected due to the fact that the data collected do not report total intake of nutrients of household members, but the available quantities. Thus food wastage is included in the reported quantities. If one assumes that food wastage is more or less equal in every 100

household, independently to its size, it can be expected that household nutrient availability per capita declines with the increasing size of households, as food wastage per capita declines.

DEMU5 is the proportion of household members with an age of 5 years or less.

DEM611 is the proportion of household members with an age of between 6 and 11 years, and

DEM1217 is the proportion of household members with an age of between 12 and 17 years.

These three variables are expected to have a negative effect on nutrient availability, since nutrient requirements are lower for children than for adults.

WOMEN is a dummy variable, and is equal to one if the household head is a woman, and zero if the household head is a man. A positive sign of the coefficient will indicate that household members living in a household headed by woman are better provided with nutrients than those living in households headed by men. A negative sign will show the opposite.

will indicate AGE is the age in years of the household head. The sign of this parameter how the nutrient availability of households could change over time in Tanzania due to a change of generation. This is valid if one assumes that food habits changes more from one generation to the other than households change their food habits over time. This variable may therefore indicate possible long run changes in the Tanzanian diet.

EDUCATION is the education in years of the household head. The sign of this variable will indicate how diet changes with better education of the household head. A negative sign will indicate that nutrient availability declines the better a household head is educated. This could also mean that well educated people pursue work that is less physical than less educated people, and thus their nutrient requirement is per se lower. Negative sign

less could also mean that the well educated tend to spend more on non-food items than the educated. A positive sign would indicate that household members with a well educated head

and have an improved nutrient availability, and that educating household heads have a direct positive influence on nutritional status of the household members.

FIRST ROUND and SECOND ROUND are dummy variables for the rounds of interviews. The first one took place from June to August 1998 at the beginning of the dry season, the second one from October to December 1998 during the dry season and finally the third and last one from February to April 1999, which is the rainy season and the reference round in these estimations. 101

6.6. Econometric Considerations of Parametric Estimations

When estimating equation (6.4), three main concerns arise. First, potential nonlinearity

is that of is an issue. The second issue is that of simultaneity bias. The third concern potential measurement error. These possible errors are discussed below.

6.6.1. Nonlinearity

valued It is generally agreed that as expenditure increase, households switch to higher food, not necessarily with higher nutrient content. Poor people, whose income is insufficient

assure sufficient nutrient to buy enough food, are likely to use increases of their income to availability for household members. Once all household members have enough to eat,

when their incomes households are likely to continue to increase their food expenditure

if increase. However they may remain on a constant level of consumed nutrients, they increase the quality and not the quantity of food consumed. Calorie intake among the poor is likely to respond positively to income, but when income increase the elasticity could decline,

levels. This that possibly to zero, or even become negative at high enough income suggests

have nonlinearities may be key. While some studies (e.g. Sahn, 1988; Ravallion, 1990) reported a concave relationship, other descriptive studies (Poleman, 1981; Lipton, 1983)

were the case the results argue that the calorie-expenditure curve may be elbow-shaped. If this

the of an application of OLS to the linear equation (6.4) would be inaccurate since assumption

would be violated. on linearity of the variables in the classical linear regression models

transformed Therefore, the logarithm of total expenditure per capita in equation (6.4) can be by adding the squared term in the equation resulting in

Inn, =al0+aa\nx + al2(\nxf+al3\np + ^ialkdk+ul (6.5) k

which is a new linear model since it is linear in parameters and since the squared term

used to estimate of expenditure can be considered as another linear variable. OLS can also be equation (6.5). With coefficients estimated that way, point elasticities can be computed for the

can be whole sample and for groups of different income categories. The elasticities e

this to computed with e = d(ln n) I J(ln x) (Simon and Blume, 1994, p. 102). Applying equation (6.5) leads to the form

= e, aa + 2ai2 ln jc . (6.6)

As can be seen the nutrient-expenditure elasticity can decline when expenditure

in 18 to increase if al2 is negative. However, the nonparametric estimations displayed Figure 102

Figure 35 do not suggest that the Engel curves of the nutrients to be nonlinear, and thus equation (6.5) is not likely to represent nutrient expenditure relationship correctly.

the Nevertheless, equation (6.5) was estimated for calories and protein to be able to compare results to those of the estimation of equation (6.4).

6.6.2. Simultaneity bias

of a As Figure 36 shows, the efficiency wage hypothesis argues that the productivity

is based worker increases with increasing wage. The traditional explanation of this hypothesis

and thus on nutrition. Below a certain level of consumption of food, workers lack of nutrients, are not very productive (Bliss and Stern, 1978).

to the curve h. The The efficiency wage w* is given by the tangent from the origin

then at an wage productivity curve h is drawn as starting at a positive wage wo, rising

of increasing rate and later rising at a declining rate. The supposition is that a certain amount consumption is required to enable someone to undertake any work as opposed to merely existing. Once that basic consumption has been provided, increasing food consumption has increasing returns and then later diminishing returns (Mirrlees, 1976).

Figure 36: The Relation of Wage and Efficiency

efficiency

wage

In developing countries where poverty and malnutrition is frequent employers would thus increase the salaries to allow their employees to improve their nutritional status and thus 103

in to be more productive. However, farmers and many other non-farmers are self-employed developing countries. Therefore, their income does not depend on a salary but directly on

their their own productivity. If their nutritional status is so low that it affects productivity, their income and thus their expenditure depend on available nutrients. Therefore, household

for the incomes could depend on nutritional status of households' members. The implication present analysis is that household expenditure is not exogenous in the nutrition functions (6.4)

To this and (6.5). Hence, the variable x is likely to be correlated with the error term «,. prevent

this method the effect the two stage least square (2SLS) method is also applied, since prevents explanatory variable jc from correlating with disturbance term w, (see below).

6.6.3. Measurement Error

Bouis and Haddad (1992) showed that OLS estimates of nutrient Engel curves with cross-sectional data can be biased due to random measurement errors. First we consider the standard case of OLS bias towards zero due to random measurement error. If u represents an

and the variance = error term in measuring x, such that the expected value E(u)=0, Var(u) o2,

the result can be derived from

-bo2 ' (6.7) plimb0LS=b+ 2 2 CTX+(JU + 20XJU

the direction of the bias on If x is the only explanatory variable measured with error,

be b0LS is unambiguously toward zero. Because more than one explanatory variable may

the measured with error, to conclude that b0LS is biased towards zero relies on assumption that total expenditure x is the only variables that is measured with any significant error.

For all estimations presented here, the set of explanatory variables used (in addition to

household total expenditure) are the number of household members, percentage of total

head's members falling into various age categories, survey round dummies, households years

and real of schooling, households head's age, a dummy for households headed by women, maize prices. This group of explanatory variables used in the estimations represents a fairly standard set of Engel curve regressors. These regressors are, by nature, less susceptible to

in the short measurement error than the expenditure variable, and exogenous to the household

should be well run. Hence, the direction of the biases on b0LS due to measurement error fairly defined. 104

Due to commonality of measurement error OLS bias could be upwards. The

and x downward bias on b0LS holds when n is nutrient availability or intake, represents current income. It is the combination of nutrient availability on the left-hand side together with total household expenditure, instead of income, on the right-hand side that results in a departure from this general rule of thumb, because of the commonality in error terms between the dependent and independent variables.

in error In deriving a complete expression for the bias due to this commonality terms,

of measurement error shown below it is necessary to consider five separate potential sources

(ux is decomposed into the first two sources): ufe = the error in measuring household food expenditures (FE); unfe = the error in measuring household non-food expenditures (NFE); fed to upg = the error in proportion of household food expenditures being guests (PG); fed to workers upw = the error in proportion of household food expenditures being (PW);

= food to and uol the error in measuring leakages other than expenditures going guests workers.

The simplification assumption is made that leakages due to meals fed to guests and workers dominate other leakages so that uol may be ignored. It is necessary to distinguish between meals fed to guests and meals fed to workers because meals fed to workers are a production expense which need to be subtracted from total expenditure.

nutrients Letting k equal a multiplicative factor that converts total food expenditures to and denoting an observed variable with an asterisk, we can write

NA'-L'=a + b-x*+u (6.8) where NA are available nutrients, L are leakages, and

- - - + u u = [k\l PG* PW*)-b\iFE -buNFE NA(upg + upw) + b(FE)upw

of the bias For illustrative purposes, using terms that dominate the magnitude empirically, we can write

[k{l-PG-PW)-b]c72' (6.9) p\imb0LS=b+^—2 2

of b in will The measurement error component of the bias due to OLS estimation (6.9) be upwards provided [k(l - PG - PW) - b] > 0. The multiplicative factor k represents the

b the nutrients made available on average for each shilling spent on food, whereas represents marginal change in family nutrient availability for every extra shilling of total expenditure. 105

Both because (PG*+PW*) is likely to be low, but more importantly because households

that purchase more expensive sources of calories at the margin, it is most probable

[k(l-PG-PW)-b]>0 for most of the households.

Instrumental variable (IV) estimation is a potentially useful technique for correcting for contemporaneous correlation between an explanatory variable and the disturbance term. In this estimation the instrumental variable estimator is unbiased if an instrument, z, can be found such that cov(z,ufe) = co\(z,unfe) = cov(z,upg) = cov(z,upw) = cov(z,v) =0, while having a reasonably high correlation with jc*.

Bearing this in mind, an instrument set is expected to be able to neutralize commonality covariances involving the random error components - ufe, unfe, and u - but not the covariance terms involving the error in measuring leakages due to guests and workers - upg and uPW. Because the latter two error terms are so strongly linked to income, they very likely will be linked to any otherwise appropriate instrument for income.

Because it is so difficult (if not impossible) to find instruments correlated with x but uncorrected with upg and upw, the IV estimator may be more biased than the OLS estimator.

To see this, (6.7) is reformulated.

-b<72 -cov(x,upr+upw)PW> Vlunb0LS =b+ \ ; :° (6.10)

The first term in the numerator of (6.10) is associated with the familiar bias towards zero of measurement errors in jc. The second term is associated with the guest and worker meals that have not been purged from the dependent variable. These two terms have opposite signs so that it is impossible, a priori, to determine the sign of the overall bias. What is more

in the pertinent for the purposes of this discussion, however, is the fact that if the second term numerator dominates, the overall positive bias is dampened both by the presence of the first term in the numerator and by the fact that jc is inaccurately measured, which, ceteris paribus, implies a relatively large denominator.

When a 2SLS IV estimator is used: (i) the first term in the numerator of (6.10) disappears (assuming the instrument has the desired standard properties); (ii) the denominator is x* where x the value of that serves as the instrument; replaced by cov(jc* ) , predicted x*, and (iii) the second term in the numerator is replaced by cov(jc\ upg+upw)- Because var(jc*), the denominator of (6.10), will always be greater than cov(jc*, x* ), the denominator for the expression for the bias for the IV estimate will always be smaller. These first two factors, 106

ceteris paribus, will lead to a higher positive bias. It is impossible, however, a priori, to determine the relative magnitudes of cov(jc, upg+uPw) and cov(jc*, upg+upw); depend on the

components of the instrument set which combine linearly to create jc* .

Household-specific effects, which are not included as variables in the regression estimations, but which affect the demand for calories, may be correlated with x and its associated measurement error (ux). It is difficult to come up with specific (immeasurable)

of variables that are intuitively appealing, so that it is difficult to speculate as to the direction this potential bias. The unobserved household-specific effects can be represented as an additional error component which is time-invariant.

The econometric procedure of two stage least squares (2SLS), which is a special case of instrumental variables (IV) method, and that was applied to correct for measurements error and for possible simultaneity due to possibly increased efficiency and income with better nutrition is described in the next section.

6.6.4. Two Stage Least Squares (2SLS) Method

The simultaneity due to the measurement error and the supposition that expenditure is endogenous to total nutrient availability will lead to an overestimation of the parameter cc,i in equations (6.4) and (6.5). Therefore the 2SLS method was applied to estimate equations (6.4) and (6.5). In the first stage total expenditure jc is estimated using all exogenous variables of equations (6.4) and (6.5) plus some other household variables di. Thus the equation estimated in the first stage is

lnx, = ßl0+ßl3lnp + ^ßlzdz+Ul (6.11)

z

where ß are parameters, household's characteristics dz are equal to dk+di which are the household's variables used in equations (6.4) and (6.5) plus the variables required by the

used application of 2SLS, and w, are the usual OLS residuals. Five additional variables were in the first stage of the 2SLS procedure. These variables are: DPGMY is a dummy variable that is one if the household is bigamous or polygamous, and zero if the household is

is a monogamous; EDUSP is the education in years of the household head's spouse; DWOOD

if dummy variable that is one if the household uses wood as a source of energy and zero not;

DELEC is a dummy variable that is one if the household is connected to the electricity network and zero if not; and DOWNHS is a dummy variable that is one if the household owns its accommodation and zero if the household rents the rooms it lives in. 107

From equation (6.11), we obtain an estimation of logarithm of total expenditure per capita dependent on households' socio-economic variables.

lnjc,=/?!0 + #3ln/> + X/U (6.12)

z

households where ln jc is an estimate of the logarithm of expenditure, conditional on characteristics and price of maize.

and The second stage is then the estimation of an equation similar to equations (6.4)

(6.5); however with ln jc replaced by ln jc, the instrumental variable. Thus following equations are estimated

= +u* ln nt al0 + aa ln x + ai3 ln p + ^ alk dk (6.13)

and

= + + u*. ln nt ai0 + aa ln jc + al2 (ln Jc)2 + al3 \np ^cclkdk (6.14) k

of In x The variable ln Jc is an exogenous variable, since ln jc is an estimation using

of of available exogenous variables and linear parameters, and the problem simultaneity nutrients and total expenditure in equations (6.4) and (6.5) is also resolved in equations (6.13) and (6.14).

6.7. Results of Parametric Estimations for Calories and Protein

In this section the results of the parametric estimations are presented. As described in the section above, the 2SLS procedure was used to estimate functional forms (6.4) and (6.5).

The coefficients presented in Table 23 and 24 are the results for calories and protein of the

of second stage of 2SLS for linear and quadratic functional forms, respectively. The results the estimations of the linear functional form (6.4) for the other nutrients are also discussed in this section. All coefficients are presented in tables in the appendix.

An overview of the variables used in the analyses of nutrient availability is provided in

Table 22. The coefficients estimated in the first stage of the 2SLS procedure is drawn in Table

in the 27 in the appendix on page 147. These coefficients were used for assessing expenditure second stage of the analyses (IV/LNTPCE). The coefficients estimated in the second stage

is show the sole effect of expenditure on nutrient availability, since the assessed expenditure

calorie and exogenous due to the two stage procedure. First the results for protein availability are presented, and then the results for all other nutrients interpreted. 108

Table 22: Nomenclature of Variables in Nutrient Availability Analysis NAME Description

Logarithm of per capita expenditure per month LNTPCE (Monthly current expenditure plus large yearly expenditure divided by 12) IV/LNTPCE Instrumental variable / estimation of LNTPCE

DAR Zero-one dummy for households in Dar es Salaam

URB Zero-one dummy for households in urban areas

SIZE Number of household members

DEMU5 Percent of SIZE that are less or equal to 5 years of age

Percent of SIZE that are greater than 5 years and less or equal to 11 DEM611 years of age Percent of SIZE that are greater than 11 years and less or equal to 17 DEM1217 years of age WOMEN Zero-one dummy for households headed by a women

AGE Age of head of household in years

EDUCATION Education of head of household in years

FIRST ROUND Zero-one dummy for first round survey

SECOND ROUND Zero-one dummy for second round survey

LNP MAIZE Logarithm of the price of maze DPGMY* Zero-one dummy for by- and polygamous households

AGESP* Age of spouse of head of household in years

EDUSP* Education of spouse of head of household in years DWOOD* Zero-one dummy for wood used by household

DELEC* Zero-one dummy for presence of electricity for house DOWNHS* Zero-one dummy for house owned by household

* Note: The variables marked with are only used in the first stage of 2SLS as regressors. 109

Table 23: Second Stage of 2SLS Procedure of Log-linear Model for Calories and Proteins

Calories Protein

Parameter Coefficients t Value Coefficients t Value

Intercept 8.305999 16.20 3.833057 5.73

IV/LNTPCE 0.322836 5.55 0.427446 6.81

DAR -0.140670 -2.74 -0.207654 -2.93

URBAN -0.069239 -2.21 -0.100964 -2.94

SIZE -0.026322 -4.42 -0.021975 -3.42

DEMU5 -0.132784 -2.57 -0.100665 -1.84

DEM611 -0.080041 -1.46 -0.012360 -0.20

DEM1217 -0.018262 -0.30 0.00166622 0.03

WOMEN 0.030968 1.17 -0.00858169 -0.29

AGE 0.00083656 0.98 -0.00059430 -0.63

EDUCATION -0.00832635 -1.69 -0.00688354 -1.28

FIRST ROUND 0.124366 4.14 0.192461 6.38

SECOND ROUND 0.032440 1.21 0.132913 4.50

LNP MAIZE -0.031544 -0.89 -0.044122 -0.62

Adjusted R2 0.2271 0.2596

The logarithm of total expenditure per capita (IV/LNTPCE) is as expected important in explaining calorie and protein availability of households, since for both the influence is positive and significant. Household total expenditure as a proxy for household income is thus important in explaining increases of household's availability of calories and protein and its ability to meet the calorie and protein requirements of its members. The results also suggest that availability of protein increase faster than availability of calories if expenditure increases.

Since these estimations are based on a log-linear model, the parameters estimated are also nutrient-expenditure elasticities.

The location of the households also impacts on the calorie and protein availability.

Households located in Dar es Salaam tend to have less calorie and protein available than households in Mbeya, and households in urban areas consume significantly less calories and protein than households in rural eras. 110

Family size (SIZE) is important in explaining per capita calorie and protein availability. For both the influence of family size is negative and significant. Household composition (DEMU5, DEM611, and DEM1217) also exerts influence on the calorie and protein availability. Since these variables indicate the proportions of children and youth in the households, and since children and youth have lower calorie and protein requirements than adults, it is not surprising that the signs are negative. For calories only the presence of children under five years of age is significant, and for protein none.

Calorie and protein availability of households headed by a woman do not significantly differ from those headed by a man. However, the sign is positive for calorie, and negative for protein.

The older the household head, the lower the per capita protein availability is. Calorie availability is positively related to the age of the household head. Both effects, however, are not significant. The number of years of education of the head exert a negative, but not significant influence on both calorie and protein availability.

The seasons also play a role in explaining calorie and protein availability. In the third round of data collection (February to April, which is the rainy season before harvest) both calorie and protein were significantly less available than in the two other rounds since latter is the lean season. Ill

Table 24: Second Stage of 2SLS Procedure of Quadratic Model for Calories and Proteins

Calories Proteins

Parameter Coefficients t Value Coefficients t Value

Intercept -0.664842 -0.09 1.501048 0.18

IV/LNTPCE 2.147223 1.41 0.881658 0.53

IV/LNTPCESQ -0.091759 -1.19 -0.022555 -0.27

DAR -0.161257 -3.02 -0.231858 -3.97

URBAN -0.083163 -2.7 -0.108246 -3.22

SIZE -0.01991 -3.33 -0.018816 -2.88

DEMU5 -0.202694 -2.6 -0.15267 -1.79

DEM611 -0.097452 -1.37 -0.029782 -0.38

DEM1217 -0.10316 -1.4 -0.040906 -0.51

WOMEN 0.029563 1.11 -0.010321 -0.35

AGE 0.0005755 0.64 -0.00083795 -0.85

EDUCATION -0.00315821 -0.46 -0.00526602 -0.7

FIRST ROUND 0.130538 4.34 0.181771 5.53

SECOND ROUND 0.065234 2.54 0.13038 4.64

LNP MAIZE -0.044083 -1.24 -0.03184 -0.82

Adjusted R2 0.2363 0.2735

The price of the major staple food maize exerts a negative influence on the calorie and protein availability. However this influence is not significant.

Table 24 shows for calories and protein the second stage of the 2SLS procedure including the quadratic term in household's revealed expenditure per capita IV/LNTPCESQ, which is the form (6.5). Neither expenditure nor its quadratic term exerts significant influence on availability of calories and protein. Therefore, the hypotheses that the nutrients Engel curve is concave must be rejected, as was forecasted with the representation of the nonparameritc estimations represented in Figure 18 to 35. 112

6.8. Results of Parametric Estimations for Other Nutrients

The coefficients of the estimation of the availability of the other nutrients are

in displayed in tables in the appendix from page 148 to 152. The results are discussed this section.

Like for calories and protein, the logarithm of total expenditure per capita

(IV/LNTPCE) is important in explaining nutrient availability of households. Household total

meet the expenditure as a proxy for household income is thus important for its ability to human nutrient requirements of its members. Total expenditures always exerts a positive influence, which is statistically significant for all nutrients but not for fibers and for folate.

The location of the households also impacts on the nutrient availability. Households located in Dar es Salaam tend to have fewer nutrients available than households in Mbeya.

A is This effect can be observed for almost all nutrients. Solely the availability of vitamin larger in households located in Dar es Salaam. Households with an urban background tend to consume less nutrients than households in rural areas. In urban areas, household consumption

in rural areas. Just of many nutrients is lower than the consumption of nutrients of households the availability of fats tends to be larger for households in urban areas.

Family size (SIZE) is not only important in explaining per capita calorie and protein availability, but also for the availability of the other nutrients. For all nutrients but for cholesterol and vitamin B12 its influence is negative. The parameters estimated for SIZE on fat, vitamin A, vitamin E, calcium, and riboflavin are in contrary to the others not statistically different from zero at the 5% confidence level.

The larger the proportion of children in the household (DEMU5, DEM611, and

DEM 1217) the less nutrients will be available, at least for most of them. The presence of

of children of any age is positively correlated with consumption of cholesterol. Availability fiber is negatively influenced especially by the presence of children under eleven years of age.

Vitamin A, C and E are not notably influenced by the presence of children, while availability of vitamin B6 significantly decreases.

Nutrient availability of households headed by a woman do not significantly differ from those headed by a man. The signs are positive for fat, fiber, vitamin A, vitamin E, vitamin B6, thiamin, folate, magnesium, and iron, and they are negative for cholesterol, vitamin C, vitamin B12, riboflavin, niacin, calcium, and zinc. 113

In most cases, the older the household head is, the lower the per capita nutrient availability is. The nutrients that are positively related to the age of the household head are fibers, vitamin A, thiamin, folate, and magnesium. For every other nutrient, its influence is negative.

The number of years of education of the head of household in general exerts a

for negative influence on household nutrient availability. This influence is significant fat, cholesterol, vitamin A, vitamin C, and calcium. The signs are positive only for fiber and for folate.

The price of maize exerts a negative influence on the availability of all nutrients analyzed. Like for calories and protein, this influence does not show any significance, only for vitamin A.

6.9. Conclusions from Parametric Estimations

The logarithm of total expenditure per capita (IV/LNTPCE) is as expected important in explaining household nutrient availability. Household total expenditure as a proxy for household income is important in explaining increases of households' nutrient availability and their ability to meet the human nutrient requirements of their members. Thus policies aiming at increasing incomes, and thus household expenditure, are likely to be very effective in improving peoples nutritional status, especially if these policies are focussing on the poor,

nutritional who's incomes are to small to purchase sufficient food, and thus to satisfy their needs.

The results also indicate that the nutrient Engel curves are not concave. That means that nutrient availability continues to increase even when the nutritional requirements are met.

Considering the rapid increase of fats and cholesterol with increasing expenditure also at high

heart levels of expenditure, one must assume that Tanzania is likely to face increasing rates of diseases and other obesity born diseases if broad revenues continue to increase.

The negative influence that the price of the major staple food, maize, exerts on the

the nutrient availability appears to be low. This suggests that policies aiming at reducing price of this staple are not likely to significantly improve nutritional status of the Tanzanian population. Furthermore, a policy of administrated low prices could lead to a reduction of the marketed quantities of maize like Tanzania experienced in the 1970s and early 1980s

(Ashimogo, 1995. Maliyamkono and Bagachwa, 1990). 114

The parameters of the dummies for Dar es Salaam as well as the one for the urban location of the households impact on the nutrient availability. Households located in Dar es

Salaam tend to have fewer nutrients available than households in Mbeya, and households with

rural area. This an urban background tend to consume fewer nutrients than households in difference in human nutrient availability with changing location may be explained by the fact that people living in rural areas and in Mbeya region are more likely to be occupied in heavy physical activities, such as farming, than persons living in urban areas of Dar es Salaam, as they are more likely to work in offices. Thus persons living in rural areas or in Mbeya are likely to have higher nutrient requirements especially for calories. This is reflected in a higher per capita nutrient availability in the analysis.

Family size (Size) is important in explaining per capita nutrient availability. The negative influence indicates that members of a large household encounter difficulties in covering their nutrients requirements in comparison to members of small households.

Especially children living in large households are likely to be more frequently affected by undernutrition compared to children growing in smaller households. Therefore, policies aiming at helping women to control the number of births they give is likely to prevent large household sizes and will thus contribute to reduce the number of children suffering from undernutrition.

The negative influence exerted by household composition variables on the nutrient availability can be easily explained by the lower nutrient requirements of children and youth compared to adults.

The education level of the household head has a negative effect on nutrient availability. This effect is, however, not significant. Those with a better education are thus likely to adapt their food intake to their reduced human nutrients requirements if their workload is less physical than the less educated. The negative and significant influence of household head's education on availability of fats and cholesterol could also indicate that education leads to a larger health awareness since these two "nutrients" are known for being risk factors for cardiovascular diseases (Wildman and Medeiros, 2000). The result could also indicate that the better educated tend to allocate higher budget shares to non-food goods, thus reducing food expenditures and nutrient availability as well. 115

6.10. Computing Nutrient-Expenditure Elasticities

The nutrient-expenditure coefficients presented in the previous sections are nutrient- expenditure elasticities, since they are estimated from linear double-logarithmic functions.

Alderman et al. (1997) have argued that elasticities obtained directly from nutrient-

food expenditure functions are usually lower than those obtained from estimated elasticities of demand functions. Since we estimated food demand elasticities earlier on, we intend to compute nutrient-expenditure elasticities from these estimates and compare them to the estimates from the direct approach.

6.10.1. Nutrient-Expenditure Elasticities from Food Demand Analysis

The approach employed by Ramezani et al. (1995) is used to compute the nutrient elasticities from our estimated food demand elasticities (chapter 5). Supposing that the total availability of nutrient /, Nu is "produced" via a production technology of the form

and is the total Nt=^ n q.(p,x), where riß is the amount of nutrient i per unit of foody qj consumption of food j. Differentiating this expression with respect to prices and expenditure, the following forms are obtained

*„=IV» (6-15) i

where 0in and Wn are nutrient-price and nutrient-expenditure elasticities, respectively, and s,„ is the share of nutrient from the respective food group. Equations and (6.16) are used to compute the elasticities. Table 25 presents these results.

As can be seen from Table 25, the nutrient-expenditure elasticities, computed with form (6.15), rank from 0.72 for cholesterol and vitamin B12 to 0.50 for fiber. Due to the large contribution of cereals and pulses to the availability of many nutrients, it is not surprising that the nutrient-price elasticities of cereals and pulses are the largest for many nutrients. The most sensitive is fiber with a price elasticity of -0.77. Demand for calories declines by 0.66% and demand for protein by 0.61% for a group price increase of 1% of cereals and pulses. Nutrient price elasticity of meat, fish, and eggs is particularly high for cholesterol and vitamin B12 at -

0.75 and -0.72, respectively. Especially demand for vitamin A, vitamin C, and calcium are affected by price changes of fruits and vegetables, since their elasticities range from -0.57 to

-0.37. Since milk and milk products do not contribute to a large extend to nutrition, price 116

changes will not affect demand for nutrients heavily. Price changes of edible oils only influence availability of fats with an elasticity of-0.43.

Table 25: Nutrient Elasticities Computed from Food Demand Elasticities

Price Elasticities Expenditure Elasticities Milk and Cereals and Meat, fish Fruits and milk Edible Oils pulses and eggs vegetables products

Calories 0.57 -0.66 -0.07 -0.04 -0.02 -0.08

Protein 0.56 -0.61 -0.20 -0.07 -0.04 -

Fats 0.64 -0.15 -0.24 -0.02 -0.07 -0.43

- Cholesterol 0.72 - -0.75 - -0.12

- Fiber 0.50 -0.77 - -0.16 -

Vitamin A 0.55 -0.38 -0.03 -0.44 -0.02 -0.01

Vitamin E 0.57 -0.40 -0.12 -0.26 -0.03 -0.07

Vitamin C 0.55 -0.27 -0.00 -0.57 -0.01 -0.00

Vitamin B6 0.54 -0.59 -0.11 -0.19 -0.02 -0.00

Vitamin B12 0.72 - -0.72 - -0.14 -0.00

Thiamin 0.53 -0.67 -0.08 -0.14 -0.03 -0.00

Riboflavin 0.60 -0.38 -0.18 -0.23 -0.11 -0.00

Niacin 0.54 -0.59 -0.16 -0.15 -0.01 -0.00

Folate 0.52 -0.65 -0.04 -0.21 -0.01 -0.00

Calcium 0.66 -0.23 -0.04 -0.37 -0.18 -0.00

Magnesium 0.55 -0.60 -0.05 -0.20 -0.03 -0.00

Iron 0.56 -0.57 -0.09 -0.19 -0.00 -0.00

Zinc 0.57 -0.54 -0.21 -0.11 -0.04 -

6.10.2. Elasticities From Parametric Linear Estimations

The parametric estimations of the direct influence of total pre capita expenditure on nutrition availability (form (6.4)) were presented in section 6.7. Since form (6.4) takes a log- linear form, the coefficients estimated for the logarithm of total expenditure per capita are the 117

nutrient expenditure elasticities. All these estimates are summarized in Table 26 next to the elasticities computed from the food demand system and already presented in Table 25 above.

The highest elasticity from the parametric estimation is 2.13 for cholesterol and the lowest is

0.02 for fiber. The directly estimated elasticities clearly differ from those estimated indirectly.

This is due to the aggregation bias linked to the indirect method (Alderman et al, 1997).

Because expenditure shares are used as the dependent variable in estimating the expenditure elasticities for these aggregate food groups, the estimates take no account of possible

household income. the increasing cost per nutrient as food expenditures increase with Using indirect method could lead to exaggerated expectations of dietary response to income.

Table 26: Comparing Nutrient-Expenditure-Elasticities

_ . from food ^

Parametric , , Nutrient demand

. estimations , A. . elasticities

Calories 0.34 0.57

Proteins 0.44 0.56

Fat 0.88 0.64

Cholesterol 2.13 0.72

Fiber 0.02 0.50

Vitamin A 0.38 0.55

Vitamin E 0.68 0.57

Vitamin C 0.55 0.55

Vitamin B6 0.29 0.54

Vitamin B12 1.50 0.72

Thiamin 0.26 0.53

Riboflavin 0.65 0.60

Niacin 0.42 0.54

Folate 0.10 0.52

Calcium 0.65 0.66

Magnesium 0.22 0.55

Iron 0.30 0.56

Zinc 0.47 0.57 118

The comparison of the elasticities obtained from the two methods reveals that the range of values from direct estimations is larger than the range of indirectly computed elasticities. For most of the nutrients the indirect method to compute the nutrient-expenditure elasticity leads to an overestimation. For example the indirectly computed calorie-expenditure elasticity is 0.57 instead of 0.34 and the one of protein is 0.56 compared to 0.44 from the direct method. The most clearly overestimated values from the indirect methods are fibers with 0.50 compared to 0.02, and folate with 0.52 instead of 0.10. But for some other nutrients, application of the indirect method leads to an underestimation. The direct estimation of the impact of increasing expenditures on availability of cholesterol is 2.13, and 0.72 when indirectly assessed. Similarly nutrient-elasticity of vitamin B12 and fats are 1.5 and 0.88 as against 0.72 and 0.64, respectively. 119

7. Conclusions and Implications

In the present study econometric analyses on farmer's supply of marketed surplus of milk, on household demand for food, on household food demand, and on nutrient availability

in are carried out. Conclusions and implications deduced from these analyses are presented this section.

7.1. The Supply of Milk and Milk Products

This part of the study focussed on the determinants of small scale dairy farmer's supply of milk and milk products to the market; the so called marketed surplus of milk

(MSM), in Iringa and Mbeya regions of Tanzania. Since the supply function of MSM results from a joined production and consumption decision of the farm households, an agricultural household model was employed as analytical framework. This resulted into a log-linear

data used were equation that was estimated using an ordinary least square regression. The micro-data on smallholder dairy production that were obtained from the Southern Highlands

Dairy Development Project (SHDDP) in Iringa and Mbeya regions.

The results allow for several conclusions and implications. First of all the price of milk and milk products are important for the small scale dairy farms although it has no significant

of the influence on MSM. Higher prices for milk and milk products increase profits producers, and because of this also their demand for milk and milk products. Due to low

market is response, price policies are not likely to be sufficient if increased supply intended,

this will but could be a tool if small scale dairy farms' income shall be sustained. However,

Policies bring losses of consumer benefits or could lead to a loss of farmers competitiveness.

is in favor of an effective milk marketing chain that leads to a reduction of transaction costs likely to be effective to prevent prices to drop if supply increases or will even allow farm gate prices for milk and milk products to increase, and thus to benefit the farmers.

Dairying provides semi-skilled and non-skilled labor opportunities but non-skilled labor does not contribute to increased supply ofmarketed surplus of milk. The effects of labor allocated to milk production are contradictory. The allocation of skilled household labor has a

Hired positive influence on MSM but the impact of non-skilled household labor is negative. labor (non-skilled) is positively correlated to MSM, but not significantly. These results are

labor signs for underemployed rural population that is very likely to move to urban areas if no opportunities are created in rural areas. Dairying may be a mean for creating jobs in rural

milk area, but the results of this analysis show that allocation of non-skilled labor to 120

production does not lead to an increase of milk sales. In seasons where little work can be

at least an completed on farm land, household members of small scale dairy farms can find occupation in milk production, although it is probably economically not rewarded.

Fodder production must be qualitatively improved or ceased. The size of fodder plot does not significantly increase farmers' supply of MSM. This result indicates that fodder

from fallow land. Under grown on a specific plot is probably not of better quality than fodder this circumstance, allocation of land to fodder production is not advisable. However, the purchase of food supplements increases MSM. This means that MSM can be increased by improving animal nutrition, and this also indicates that milk production could be improved if management of fodder production is improved, which would result in fodder of better quality.

Therefore fodder production on a plot of arable should only be advised if the farmer is able to produce fodder of a high quality. This could be achieved with continuous education.

Improved farmers' accessibility to the markets is likely to boost MSM. Accessibility to the market is not only important for reducing transaction costs for marketing milk and milk products. It is as well very important for farmers to have access to inputs at lower costs, which can be achieved with good market access. This will likely increase the use of inputs, also in milk production. As the results show, animal nutrients supplements and animal health inputs increase MSM. Therefore good access to these inputs is likely to increase MSM.

Veterinary service should be supported. The results show that animal health expenses increase MSM. Since animal health expenses technically prevent losses of productivity, this positive influence means that there are many farmers who have sick animals but who can not afford to treat them, else they would sell as much milk as those who purchase veterinary services. Therefore veterinary services and provisions of animal health drugs should be supported to allow farmers to keep productivity high, even if they lack liquidity to pay for veterinary services and drugs.

7.2. Analysis of Demand for Food

This analysis estimates the demand for food in Dar es Salaam and Mbeya regions of

Tanzania, with pooled data, and separately for low and high-income households. The approach adopted in this part of the study was to represent the consumer expenditure allocation problem in two stages assuming a utility tree. In the analysis of the first stage, the

Linear Expenditure System (LES) was applied and in the analyses of the second stage a

Linear Approximate Almost Ideal Demand System (LA/AIDS) was used. 121

The results of the study show that price and income are significant determinants of the demand for food commodities. In the first stage, expenditure elasticity for food was found to be 0.65 supporting the assumption that food is a necessity. Multiplying this result with the

shows that of all food food group expenditure elasticities obtained in the second stage

and other as categories only the group of other food can be considered as luxury, every group necessities. Especially the cereals and pulses group has a low response to additional income since its total expenditure elasticity is 0.48.

Computed marginal expenditure shares showed that poor households tend to spend

households almost 50% of a food budget increase to cereals and pulses and high-income only

23%. On the other hand, the rich households will spend 30% of a food budget increase to

These other food, while the poor will only spend 9% of new food expenses to that category.

basic results suggest that the poorer households are not able to meet their energy

of calories and requirements, since the group of cereals and pulses is an important source protein. However, the situation drastically improves with rising food budgets, and thus with better incomes. Indeed, high-income households tend to allocate food budget increases to

that do not more valuable food items, e.g. to the group of other food, which indicates they suffer from undernutrition.

The results also show that the two income categories react differently when prices change. Low-income households are much more price responsive than high-income households which supports the assertion that for a given level of expenditure and prices, low- income households are compelled to adjust their consumption patterns to relatively inexpensive commodities, away from expensive ones. This also suggests that low-income households face human nutrient deficiency.

Price changes are especially important when they affect the cereals and pulses group.

The Hicksian own-price elasticity for this food category is smaller in the low expenditure

when of cereals and group, than in the high expenditure group. This indicates that even prices

but pulses increase, poor households continue to consume goods of this food group, they suffer a loss of purchase power.

Non-economic variables are also important in explaining variations in food consumption patterns. For example, a household moving from rural areas to a city will, per se, tend to shift its food budget allocation from cereals and pulses, and fruits and vegetables to

size also exerts meat, fish and eggs, milk and milk products, and edible oils. The household some influence on food budget allocation of the households. 122

Low-income households are food deficient. The results suggest that low expenditure households face food deficiency, since they tend to allocate a large part of their food budget to cereals and pulses, which is the food category most likely to be the cheapest source of calories and protein. They also highly respond to price changes, compared to their richer counterparts.

This is also an indication that poor households switch consumption away from expensive to cheap food goods, when trying to meet their nutritional requirements.

Policies aiming at a broad growth of income especially of low-income households are likely to be the most effective approach to improve the nutritional status of low-income households. Poor households tend to allocate large amounts of increasing food expenditure to cereals and pulses, which indicates that they look for increased calorie availability. That

income means that poor households are able to increase availability of food if their improve.

However, with even larger incomes, households tend to diversify their food expenditure, to tastier but not necessarily more nutritious goods.

Increasing efficiency of production and marketing offood, especially of cereals and

will pulses will benefit poor households. Price changes of cereals and pulses particularly

and if affect poor households, since they spend a lot of money for these goods, prices increase, they will have to reduce consumption of other goods due to the income effect. For

will have the same reason, declining prices of cereals and pulses due to increased productivity a positive income effect on the poor. Increased efficiency of production and marketing of food, especially of cereals and pulses can lead to a reduction of prices of food which will

and especially benefit the poor. Considering that many poor households live in rural areas make a living from producing cereals and pulses, policies should make sure that especially small producers can improve their efficiency to prevent their incomes to be affected if prices decline, and to increase their incomes if prices remain constant. Improving infrastructure in rural areas, and assuring competition among food traders is likely to reduce transaction costs from farm gate to urban markets, which could lead to price reductions in urban areas or to price increases at farm gates.

Demandforfood is going to increase with rising incomes, especially demand for meat,

could lead to fish and eggs, and for milk and milk products is going to expand which increases of food prices which could have a negative effect on nutrition of households. This could be prevented by supporting the supply of animal products which will increase the quantity of animal products supplied, and thus will make sure prices to increase too much.

This policy will also make sure that local producer fully benefit from these changes in 123

demand and that this demand will not be satisfied by imports. Thus animal production can continue to be an important mean to reduce poverty in rural areas of Tanzania.

7.3. Nutrition Analysis

An analysis on how income and other socio-economic variables impact on nutrient availability of households is presented in chapter 6. This kind of analysis is particularly important for developing countries where large parts of the population do not yet meet their nutritional requirements; and Tanzania belongs to them. Food security and the role of public interventions in the production, consumption, distribution and foreign trade of food are important policy issues in Tanzania. Knowing how economic and household non-economic characteristics impact on nutrient availability are important information to optimize policy measures to improve the nutrition of the population.

The data used in this analysis were the same as in the food demand analysis. To obtain

of food total nutrient availability per capita on household level, reported monthly quantities items consumed were multiplied with nutrient content tables and then divided by the number of household members. Two econometric approaches were employed in this analysis, the parametric and the nonparametric approach. The nonparametric approach was used to observe the nutrient-Engel curves, and to see if the shape of the curve changes with increasing expenditure. Since other variables influence the availability of nutrients, parametric estimations were carried out that included households' expenditure plus other households' characteristics. To prevent simultaneity biases due to measurement error, and to the possible endogeneity of expenditures9 as an explaining variable, two stage least squares procedure was used to estimate a log-linear form.

The representation of the Engel curves computed with the nonparametric procedure revealed that demand for nutrients continues to increase with increasing expenditure. Thus one must assume that they are linear, and thus that nutrient-expenditure-elasticities do not significantly decrease with higher expenditures. Since the presentation of the date in logarithmic scales seemed plausible, log-linear forms were used in the parametric estimations.

The results of the parametric estimations show that the total expenditure per capita, as

household nutrient a proxy for household income, is important in explaining increases of availability, and thus households' ability to meet the human nutrient requirements of their members. This is valid for each nutrient, since expenditure always exerts a positive effect on

9 Efficiency wage theory 124

their availability. The price of the major staple food maize exerts a negative influence on the availability of most nutrients. However, this influence does not show any significance, only for vitamin A.

Non-economic factors can also influence household nutrient availability. The

in households' location impacts on the nutrient availability, and also family size is important

size on all nutrients is explaining per capita nutrient availability. The influence of family negative, but for cholesterol and vitamin B12. Household composition also exerts influence

than on the nutrient availability. Since children and youth have lower nutrient requirements adults, it is not surprising that the signs are in general negative. Nutrient availability in

In households headed by a woman does not significantly differ from those headed by a man.

will most cases, the older the household head is, the lower the per capita nutrient availability

influence on be, and the number of years of education of the head has in general a negative household nutrient availability.

round of The seasons also play a role in explaining nutrient availability. In the third data collection (February to April, which is the rainy season before harvest) each nutrient was significantly less available than in the two other rounds.

The Income policies are likely to be very effective in improving nutrient availability. results of the parametric estimations showed the importance of total expenditure in explaining nutrient availability. Thus policies aiming at raising incomes and thus household expenditure

if this are likely to be very effective in improving peoples nutritional status, especially

nutritional policies focus on those with low incomes, and who are not able to satisfy their needs.

Tanzania is likely to face increasing rates of heart and other obesity born diseases.

The results indicate that the nutrient-Engel curves are not concave, which means that the consumption of nutrients continues to increase, even when the nutritional requirements are met. If the rapid increase of the consumption of fat and cholesterol with increasing expenditure is considered, also at high levels of expenditure, and if broad incomes continue to increase one must assume that Tanzania is likely to face rising rates of heart, and of other obesity born diseases. The results also suggest that education of the household head reduces consumption of fats and cholesterol. Therefore educating people on effects of this kind of malnutrition is likely to be most effective to prevent these diseases, and is likely to become necessary in the future, if incomes continue to increase. 125

that the Price interventions are likely to be less effective. The negative influence price

This of the major staple food maize exerted on the nutrient availability is shown to be low. suggests that policies aiming at reducing the price of this staple is not likely to significantly improve nutritional availability of the Tanzanian population, and is likely to reduce farmers income, which would be contra-productive. However it is important to make sure that food prices do not increase with increasing incomes that raise demand for food products. This can be achieved by increasing production of food by improving farmers' productivity, as well as by facilitating trade of food within the country.

Family planning is likely to reduce rate of undernourished children. The results show

size. The that per capita nutrient availability declines with increasing family negative

in influence indicates that members of a large household encounter more difficulties meeting their nutrients requirements in comparison to members of small households. Thus children

undernutrition living in large households are likely to be more frequently affected by compared to children growing in small households. Therefore, policies aiming at helping

household sizes women to control the number of births they give, is likely to prevent large and will thus contribute to reduce the number of children suffering from undernutrition.

The influence of other household variables on nutrient availability can be explained with diverting nutrient requirements. The negative influence exerted by household

lower nutrient composition variables on nutrient availability can be easily explained by the requirements of children and youth compared to adults. The coefficients of the dummy

the households variable for Dar es Salaam as well as the dummy variable for urban location of have negative signs. This difference in availability of human nutrients may be explained by the fact that people who live in rural areas or in Mbeya region are more likely to be occupied

which in heavy physical activities such as farming. This increases their nutrient requirements, are reflected in these results. Seite Leer / Blank leaf 127

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Appendix Seite Leer / Blank leaf 137

Demand Analysis Tanzania (Collaborative Research Project of the Swiss Centre for International Agriculture (ZIL), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, and the Sokoine University of Agriculture (SUA), Morogoro)

Schedule 1 : General Information Regarding Household Note: strike out whichever is not applicable

Household identification number:. (to be filled in before Interview)

Region: Dar es Salaam & Coast / Mbeya

Background of Household: Urban/Rural

Full name of Head of Household

Full name of respondent

Complete address

Best time and day for interview.

Date Start End Respondent Enumerator

Introduction

1st interview

2nd interview

3rd interview 138

Occupation of the household: Main:

Subsidiary:

Salary income from main occupation/month (facultative): TSh

Operational land holding: acres

Religion :

Number of total households headed by the household's head:

Demographic particulars of the members of the household:

Relation to head of Code Marital Code Education Code No. Sex Age Occupation household 1 Status 2 status 3

Not married 1 None 1 Married 2 Adult 2 Male 1 widower 3 education Years Main Subsidiary Female 2 divorced or 4 Primary 3 separated Secondary 4 Graduate 5

1 Head

2

3

4

5

6

7

8

9

10

11

12 139

Demand Analysis Tanzania (Collaborative Research Project of the Swiss Centre for International Agriculture (ZIL), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, and the Sokoine University of Agriculture (SUA), Morogoro)

Schedule 2: Information on Consumption/Expenditure on Food and Non-Food Items Note: Strike out whichever is not applicable

Household identification number:

First/second/third interview.

Enumerator :

/. Monthly consumption/expenditure onfood items (last month)

Quantity consumed Expenditure Price/Unit Remarks in TSh Item Home Pur-chased Total produced 1 2 3 4 5 6 7

Cereals: A) Nafaka: Maize 1 Mahindi kg Wheat/Bread 2 Ngano kg Rice 3 Mchele kg Millet and sorghum 4 ulezi na mtama kg Other cereals 5

Roots and tubers: B)

Sweet potato 1 Kiazi kitamu bndl kg/bndl

Potato 2 Kiazi Ulaya bndl kg/bndl Cassava fresh 3 Muhogo bndl kg/bndl dry Yams 4 Viazi vikuu bndl kg/bndl

Banana 5 Ndizi pes Other 6 140

Vegetables all C) expenditures: Mboga: Amaranths 1 Mchicha bndl kg/bndl

Tomatos 2 Nyanya bndl kg/bndl Onion 3 Kitunguu bndl kg/bndl Ladies' finger, cauliflower, cabbage, carrot, pumpkin, 4 aubergine, and others Bamia, koliflari, kebichi, karoti, boga, biringani,... Beans/Peas all D) expenditures: Maharagwe/:

1 kg

2 kg

3 kg

Chick, cow, garden, and pigeon pea, bonavist, kidney, soya, sword, and 4 velvet bean Dengu, kunde, njegere, mbaazi, fiwi, maharagwe, magobi, upupu

Meat, fish and eggs: E) Nyama, samaki na mayai: Beef 1 Nyama ya ng'ombe kg Goat 2 Nyama ya mbuzi kg Mutton 3 Nyama ya kondoo kg Pork 4 Nyama ya nguruwe kg Chicken 5 Kuku kg Fish fresh 6 Samaki kg dry

Other meat 7 Nyama nyingine kg Eggs 8 Mayai pes 141

Edible oils and oil seeds, F) Mafuta:

It. 1 kg It. 2 kg Margarine 3 kS Sunflower, groundnuts, cashew nut, cottonseed, sesame, castor been, palm 4 oil, alizeti, karanga, njugu, korosho, pamba, ufuta, nyonyo, chikichi, Fruits: G) Matunda: Banana large 1 Ndizi pes small bndl 2 kg bndl/kg bndl 3 kg bndl/kg Coconut/copra, apple, banana, orange, grapes, pineapple, avocado, watermelon, pawpaw, guava, palm date, jack fruit, Java plum, sugar cane, and 4 others nazi, dafu, mbata; tofaa,, chungwa, zabibu, nanasi, parachichi, tango,, papai, pera, tende, fenesi, zambarau, muwa, na matunda mengine Milk and milk products: H) Maziwa: Fresh whole milk 1 Maziwa mabichi It. Fermented milk 2 Mgando It. Pasteurized milk 3 It.

UHT milk 4 It. Fermented milk packed 5 It. Yogurt 6 It. Cheese 7 Jibini kg Butter / ghee 8 Siagi / samli kg Condensed milk 9 kg Milk powder 10 kg Other milk products 11 142

Sugar: I) Sukari: kg Salt and spices: J) Chumvi na viungo Salt kg Spices S Refreshments: K)

Biscuits, and snaks.... 1

Carbonated drinks (Coca 2 Cola, Fanta, Pepsi, ) botls

Tea and coffee at home 3 Chai na kahawa s Meals of household

L) members taken away from home: Breakfast 1

Lunch 2

Dinner 3

Other 4 143

II. Last Month Expenditures on non-food items

Quantity consumed Expenditure Price/Unit Remarks in TSh Item Home Purchased Total produced l 2 3 4 5 6 7 Alcoholic beverages, 7.£ 7 M) tobacco and other intoxicants: Beer (local and bottled) It. 1 Pombe ya kienyeji na bia bottles

Leaf tobacco, cigarette g 2 Tumbaku na sigara pks Fuel, light and water N)

Firewood 1 bndl kg/bndl Coal/charcoal bag kg/bag 2 bndl kg/bndl

Cooking gas 3

Biogas 4

Kerosene 5 It. Electricity charges 6

Water charges 7

Expenditures in TSh Item Remarks last month

1 6 7

O) Miscellaneous goods and services:

1 Literary activities (newspaper, books, etc.) Last month

2 and Postage telephones Last month

3 Medical expenses Last month

4 Transport and petrol expenses Last month

Social customs and festivals (gifts to relatives, religious 5 offerings), recreation and entertainment Last month Others (washing, hair dressing, cosmetics, domestic 6 servant, pets, hotels and restaurants, etc.) Last month 144

Demand Analysis Tanzania (Collaborative Research Project of the Swiss Centre for International Agriculture (ZIL), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, and the Sokoine University of Agriculture (SUA), Morogoro)

Schedule 3: Knowledge gaps dairy products

1. Spot of purchase of the various dairy products Where do you get from? Answers to be ranked if more than one purchase spot (1-most important, 2-second most important...).

Own Producer Home Street Milk Corner Super¬ Not production gate delivery vendor kiosk shop market consumed

Fresh whole milk

Fermented milk

Pasteurised milk

UHT milk

Fermented milk packed

Butter / Ghee

Cheese

Only if household produces its own milk: Did you sell milk last three months? To whom and to which price/liter?

At Other To To Not sold consumer's Farmer group collection neighbours middlemen gate centre

Fresh whole milk TSh TSh TSh TSh TSh

Fermented milk TSh TSh TSh TSh TSh 2. When you could afford to consume and you do 1 : Not available/No deliveryservice not, what are the reasons for? 2: Purchase spot too far away A: Do not consume anyway 3: possiblynot available at purchasespot B: Can not afford anyway 4: Bad quality C: Only consume sometimes as much as I can afford 5: Low hygienicstandard of purchasespot D: Always consume as much as I can afford 6: Do not like Answers to be ranked if more than one is a reason for less consumption (1-most Other reasons for not buying: important,2=second most important...).

A B C D 1 2 3 4 5 6

Fresh whole milk

Fermented milk

Pasteurised milk

UHT milk

Fermented packed milk

Butter / Ghee

Cheese

Date: (Signatureof Enumerator) 146

Schedule 4: Last year's expenditure on non-food items

Item Expenditures

last year Remarks

1 6 7

1 Clothing

2 Footwear

3 Suitcase, trunks, handbags etc.

4 Schooling fees and tuition, schooling material

5 Large medical expenses

6 Large social customs and festivals

Furniture and decoration (Bed sheet, dressing table, stool, benches, sofa, chair, table, desk, carpets and 7 other floor mating, painting, drawing and other show pieces) Cooking and household appliances (cutlery and crockery, electric fan, lamp, iron, pressure cooker, 8 refrigerator, air cooler, washing machines, radio, television, VCP/VCR, record player, tape recorder, stereo, musical instruments etc.) Personal transport equipment (car, scooter, moped, 9 motor cycle, bicycle)

10 Building material

House and land rent (if house and land owned assess Paid 11 rent that would be paid) Owned

12 Taxes

13 Insurance

Date: Signature:. 147

Appendix 2:

Table 27; First Stage of 2SLS Procedure

Parameter Coefficients t Value

Intercept 8.740053 35.45

DAR 0.54183 11.71

URBAN 0.207088 6.7

SIZE -0.054508 -8.31

DEMU5 -0.615071 -6.87

DEM611 -0.490557 -5.79

DEM1217 -0.33139 -3.55

WOMEN 0.114363 2.78

AGE -0.00082341 -0.68

EDUCATION 0.049508 11.2

FIRST ROUND 0.235364 6.53

SECOND ROUND 0.159822 5.06

LNP MAIZE 0.130935 2.9

DPGMY -0.245038 -3.55

EDUSP 0.016711 3.33

DEWOOD -0.24312 -6.21

DELEC 0.271734 8.86

DOWNHS 0.058338 1.88

Adjusted R2 0.6851 148

Table 28; Parametric Estimations of Calorie and Protein Availability Calorie Protein

Parameter Coefficients t Value Coefficients t Value

Intercept 8.214311 15.78 3.68357 6.46

IV/LNTPCE 0.336889 5.8 0.436673 6.87

DAR -0.145033 -2.82 -0.22787 -4.05

URBAN -0.076765 -2.55 -0.106673 -3.23

SIZE -0.022087 -3.9 -0.019351 -3.12

DEMU5 -0.177251 -2.37 -0.146416 -1.79

DEM611 -0.090623 -1.29 -0.028103 -0.36

DEM1217 -0.08377 -1.17 -0.03614 -0.46

WOMEN 0.028828 1.08 -0.010502 -0.36

AGE 0.0005894 0.66 -0.00083453 -0.85

EDUCATION -0.00908941 -1.89 -0.00672393 -1.28

FIRST ROUND 0.131197 4.38 0.181933 5.55

SECOND ROUND 0.0647 2.53 0.130249 4.65

LNP MAIZE -0.037872 -1.08 -0.030314 -0.79

Adjusted R2 0.2433 0.2768

Table 29: Parametric Estimations of Fat and Cholesterol Availability Fat Cholesterol

Parameter Coefficients t Value Coefficients t Value

Intercept -1.107646 -1.5 -11.877082 -6.35

IV/LNTPCE 0.881072 10.73 2.127559 10.2

DAR -0.243397 -3.35 -1.228446 -6.64

URBAN 0.00749549 0.18 -0.204943 -1.89

SIZE -0.00543654 -0.68 0.077232 3.79

DEMU5 -0.042812 -0.4 0.196505 0.73

DEM611 -0.064543 -0.65 0.395505 1.56

DEM1217 -0.179279 -1.77 0.161573 0.63

WOMEN 0.038098 1.01 -0.157656 -1.65

AGE -0.00203467 -1.61 -0.016168 -5

EDUCATION -0.0208 -3.06 -0.048122 -2.79

FIRST ROUND 0.093761 2.22 0.305076 2.84

SECOND ROUND 0.08477 2.34 0.29785 3.24

LNP MAIZE -0.040105 -0.81 -0.041372 -0.33

Adjusted R2 0.5021 0.3247 149

Table 30: Parametric Estimations of Fiber and Vitamin A Availability

Fiber Vitamin A

Parameter Coefficients t Value Coefficients t Value

Intercept 6.921233 12.26 12.492619 9.90

IV/LNTPCE 0.020042 0.32 0.382967 2.97

DAR -0.089923 -1.61 0.490933 3.89

URBAN -0.111737 -3.42 -0.195898 -2.73

SIZE -0.036359 -5.92 -0.00933453 -0.68

DEMU5 -0.293318 -3.62 -0.148495 -0.81

DEM611 -0.184866 -2.42 -0.190121 -1.10

DEM1217 -0.121715 -1.57 -0.185272 -1.03

WOMEN 0.030105 1.04 0.042033 0.63

AGE 0.00222135 2.29 0.00285013 1.27

EDUCATION 0.00015925 0.03 -0.037549 -3.33

FIRST ROUND 0.220967 6.81

SECOND ROUND 0.154926 5.58

LNP MAIZE -0.00666758 -0.18 -0.691413 -9.96

Adjusted R2 0.1498 0.1297

Table 31: Parametric Estimations of Vitamin E and Vitamin C Availability

Vitamin E Vitamin C

Parameter Coefficients t Value Coefficients t Value

Intercept -1.66819 -2.36 3.142094 3.86

IV/LNTPCE 0.682136 8.66 0.547628 6.03

DAR -0.402331 -5.77 -0.436295 -5.42

URBAN -0.156115 -3.82 -0.42278 -8.96

SIZE -0.011155 -1.45 -0.019404 -2.19

DEMU5 -0.136457 -1.34 -0.037555 -0.32

DEM611 -0.073236 -0.77 -0.0233 -0.21

DEM 1217 -0.159723 -1.64 -0.193969 -1.73

WOMEN 0.010767 0.3 -0.00645131 -0.16

AGE -0.0012955 -1.07 -0.00175749 -1.26

EDUCATION -0.011258 -1.73 -0.019297 -2.57

FIRST ROUND 0.237749 5.85 0.297994 6.37

SECOND ROUND 0.23768 6.84 0.349794 8.74

LNP MAIZE -0.075765 -1.59 0.036084 0.66

Adjusted R2 0.3301 0.2268 150

Table 32: Parametric Estimations of Vitamin B6 and Vitamin B12 Availability

Vitamin B6 Vitamin B12

Parameter Coefficients t Value Coefficients t Value

Intercept 1.580586 2.9 -10.427604 -8.75

IV/LNTPCE 0.285355 4.7 1.501095 11.29

DAR -0.303031 -5.64 -0.773157 -6.56

URBAN -0.1208 -3.83 -0.086035 -1.25

SIZE -0.026052 -4.39 0.024731 1.9

DEMU5 -0.237002 -3.03 0.1285 0.75

DEM611 -0.154674 -2.1 0.218046 1.35

DEM1217 -0.173825 -2.32 -0.037079 -0.23

WOMEN 0.00457513 0.16 -0.0648 -1.06

AGE -0.00065184 -0.7 -0.00451894 -2.2

EDUCATION -0.00490952 -0.98 -0.03881 -3.53

FIRST ROUND 0.253411 8.1 0.181588 2.65

SECOND ROUND 0.164756 6.16 0.166651 2.85

LNP MAIZE 0.01094 0.3 0.00936777 0.12

Adjusted R2 0.2294 0.4009

Table 33: Parametric Estimations of Thiamin and Riboflavin Availability Thiamin Riboflavin

Parameter Coefficients t Value Coefficients t Value

Intercept 1.19859 2.23 -2.466149 -3.43

IV/LNTPCE 0.262454 4.39 0.648317 8.09

DAR -0.189394 -3.57 -0.373328 -5.26

URBAN -0.118747 -3.82 -0.178841 -4.3

SIZE -0.027904 -4.78 -0.00726947 -0.93

DEMU5 -0.174444 -2.26 -0.107496 -1.04

DEM611 -0.082114 -1.13 -0.054969 -0.57

DEM1217 -0.081381 -1.1 -0.051106 -0.52

WOMEN 0.00987806 0.36 -0.020365 -0.55

AGE 0.00047306 0.51 -0.00297047 -2.4

EDUCATION -0.00386033 -0.78 -0.010768 -1.62

FIRST ROUND 0.220817 7.16 0.283474 6.86

SECOND ROUND 0.1694 6.42 0.234747 6.65

LNP MAIZE -0.021243 -0.59 -0.030926 -0.64

Adjusted R2 0.2132 0.3087 151

Table 34: Parametric Estimations of Niacin and Folate Availability Niacin Folate

Parameter Coefficients t Value Coefficients t Value

Intercept 2.131535 3.82 8.566098 14.5

IV/LNTPCE 0.419914 6.75 0.100227 1.52

Dar -0.199591 -3.62 -0.027208 -0.47

Urban -0.158554 -4.9 -0.145906 -4.27

Size -0.022649 -3.72 -0.034213 -5.32

DEMU5 -0.13431 -1.67 -0.274549 -3.24

DEM611 -0.032742 -0.43 -0.132552 -1.66

DEM 1217 -0.110912 -1.44 -0.065965 -0.81

Women -0.00350349 -0.12 0.012071 0.4

Age -0.00052248 -0.54 0.00056582 0.56

Education -0.00992443 -1.93 0.00193717 0.36

First round 0.224125 6.98 0.280834 8.27

Second round 0.133096 4.85 0.230982 7.96

LNP maize -0.034087 -0.91 0.00221771 0.06

Adjusted R2 0.2914 0.1863

Table 35: Parametric Estimations of Calcium and Magnesium Availability Calcium Magnesium

Parameter Coefficients t Value Coefficients t Value

Intercept 3.565321 4.47 7.244558 13.84

IV/LNTPCE 0.650598 7.32 0.221409 3.79

DAR -0.416391 -5.28 -0.155703 -3.01

URBAN -0.352646 -7.63 -0.158371 -5.22

SIZE -0.00178865 -0.21 -0.028311 -4.97

DEMU5 -0.010974 -0.1 -0.221004 -2.94

DEM611 -0.00400227 -0.04 -0.121494 -1.72

DEM1217 0.041521 0.38 -0.094723 -1.32

WOMEN -0.020005 -0.49 0.011289 0.42

AGE -0.00230253 -1.68 0.00036825 0.41

EDUCATION -0.015838 -2.15 -0.0041808 -0.87

FIRST ROUND 0.240237 5.24 0.235241 7.82

SECOND ROUND 0.258738 6.6 0.171097 6.65

LNP MAIZE -0.014939 -0.28 -0.00540445 -0.15

Adjusted R2 0.2129 0.2176 152

Table 36: Parametric Estimations of Iron and Zinc Availability

Iron Zinc

Parameter Coefficients t Value Coefficients t Value

Intercept 3.352028 5.9 1.348962 2.37

IV/LNTPCE 0.296465 4.68 0.467866 7.36

DAR -0.117013 -2.08 -0.160123 -2.84

URBAN -0.163253 -4.96 -0.124228 -3.76

SIZE -0.024224 -3.91 -0.018153 -2.92

DEMU5 -0.205453 -2.52 -0.159095 -1.94

DEM611 -0.068296 -0.89 -0.030803 -0.4

DEM1217 -0.082882 -1.06 -0.063472 -0.81

WOMEN 0.00793998 0.27 -0.016731 -0.57

AGE -0.00018554 -0.19 -0.00065252 -0.67

EDUCATION -0.00444946 -0.85 -0.00819753 -1.56

FIRST ROUND 0.248404 7.6 0.186407 5.69

SECOND ROUND 0.197725 7.08 0.133187 4.75

LNP MAIZE -0.020415 -0.53 -0.042676 -1.11

Adjusted R2 0.2461 0.3285 Curriculum Vitae

Name: Dominique Antoine Surname: Aubert Date of birth: June 20th, 1969 Place of Origin Le Chenit VD Nationality: Swiss Marital status: Single

Occupation and professional experience:

Since 1997 Scientific investigator at the Department of Agricultural Economics, Swiss Federal Institute of Technology (ETH) Zurich

Education

1991 - 1996 Department of Agriculture, Swiss Federal Institute of Technology (ETH) Zurich, Dipl. Engineer of Agriculture, in Agricultural Economics.

1990 - 1991 Centre International De Recherche et Formations Appliquées, Biot France

1984 - 1990 Gymnasium Koniz; Federally Recognized Matura Typus C