Economic

Research

Service

Economic 

Information 

Bulletin

Number 135

February 2015

United States Department of Agriculture

Measuring Access to Food 

in Tanzania: A Food Basket 

Approach

Nancy Cochrane and Anna D’Souza
Economic Research Service 

www.ers.usda.gov

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Recommended citation format for this publication:

Cochrane, Nancy, and Anna D’Souza. Measuring Access to Food in Tanzania: A Food 

Basket Approach, EIB-135, U.S. Department of Agriculture, Economic Research Service, 

February 2015.

Cover images: Nancy Cochrane, USDA, Economic Research Service.

Use of commercial and trade names does not imply approval or constitute endorsement by USDA. 
United States Department of Agriculture

Economic 

Research 

Service

Economic 

Information 

Bulletin

Number 135

February 2015

Abstract

Household access to food over time in Tanzania is measured by comparing the cost of representative food baskets to household income. Consumption patterns, estimated using household data 

from the 2010/11 National Panel Survey conducted by Tanzania’s National Bureau of Statistics 

(NBS), show considerable diversity across the country. Maize (corn) dominates the diets in 

the surplus-maize-producing regions. Households in the maize-deficit regions in the north 

favor other sources of starch such as cassava and banana. The food baskets include 15 food 

groups that make up approximately 67 to 88 percent of average calorie intake. From 2008/09 

to 2010/11, food basket costs rose rapidly in nominal terms but were stable in real terms. 

Combining food basket cost data and income data suggests that households in the bottom two 

income quintiles have significant difficulties with access to food. 

Keywords: household access to food, Tanzania, maize, cassava, National Panel Survey, food 

consumption, food security, dietary diversity.

Acknowledgments

This research was partially supported by the U.S. Agency for International Development 

(USAID), which funded field work in Tanzania under the Feed the Future Program and 

partially funded Washington-based research under USAID’s Borlaug Program. The authors 

thank the USAID mission in Dar es Salaam and the USAID-funded SERA (Swahili for 

“policy”) Project for its logistical support to the U.S. Department of Agriculture (USDA) 

during Economic Research Service (ERS) visits to Tanzania. The authors thank officials at 

the Tanzanian Ministry of Agriculture, Food and Cooperatives for data and insights and the 

World Bank Living Standards Measurement Surveys team for sharing their statistical program 

code. The authors thank Andrea Carlson, USDA, ERS; Todd Benson, International Food 

Policy Research Institute; Clara Cohen, USAID Bureau of Food Security; Kate Snipes, USDA, 

Foreign Agricultural Service; and William Masters, Tufts University, for their reviews. The 

authors thank Maria Williams for editing and Kevin Hunt and Curtia Taylor for design.

Nancy Cochrane and Anna D’Souza

Measuring Access to Food 

in Tanzania: A Food Basket 

Approach
About the Authors

Nancy Cochrane is an economist with USDA, ERS, Market and Trade Economics Division 

(MTED). Anna D’Souza is a development economist and an associate professor at Baruch 

College, City University of New York (CUNY); when this study began, she was an economist 

with ERS.
Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Food Basket Approach Adds Depth to Other Measures of Food Security . . . . . . . . . . . . . . . . . . 5

Methodology and Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Dietary Patterns Vary Across Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Constructing the Food Baskets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Measuring Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Caveats to the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
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Nancy Cochrane and Anna D’Souza

Measuring Access to Food 

in Tanzania: A Food Basket 

Approach

What Is the Issue?

Tanzania, a country rich in agricultural resources and a net exporter of its primary staple, 

maize, is subject to chronic food insecurity and suffers from periodic droughts that reduce 

harvests. Policies to address food insecurity have been hindered by lack of accurate estimates 

of production and existing food needs. This study uses data from the 2010/11 Tanzanian 

National Panel Survey (TZNPS) to construct a set of representative food baskets for mainland Tanzania, Dar es Salaam, and two geographical zones. Using current retail food prices, 

ERS calculated the monthly cost of the food baskets over a 3-year period (January 2010 to 

February 2013). Tanzania is one of 19 focus countries under the U.S. Government’s Feed 

the Future initiative and is a recipient of U.S. foreign assistance. Comparing monthly foodbasket costs with per capita income provides a more precise identification of the segments of 

the Tanzanian population who most need food aid than is possible using existing methods.

What Did the Study Find?

Using data from TZNPS, the authors estimated calorie and expenditure shares for 15 foods 

and food groups for 7 geographic zones of mainland Tanzania plus the business capital Dar 

es Salaam. The authors used the calorie shares to construct representative food baskets for 

Dar es Salaam plus two of the zones: the surplus-maize-producing Southern Highlands and 

the maize-deficit Lake Zone in the northwest surrounding Lake Victoria. The two zones were 

chosen to illustrate the wide divergence in diets across the regions of Tanzania. In particular:

• Diets vary considerably among Tanzania’s geographical regions. Maize dominates the 

diets in the surplus-maize-producing regions of the Southern Highlands. In contrast, 

while maize is still an important component of household diets in the maize-deficit 

regions in the north, it makes up a smaller share of calories. Households in these regions 

favor other sources of starch such as cassava and banana.

February 2015
www.ers.usda.gov

• The food basket cost is lowest in the Southern Highlands because of the ready availability of lowpriced maize. The cost is highest in Dar es Salaam, the business capital and largest city, and the cost is 

also high in the Lake Zone. 

• From January 2010 to February 2013, food basket costs rose rapidly in nominal terms but rose more 

moderately in real terms.

• Households in the bottom two income quintiles face potential problems with access to food. The cost 

of a minimal food basket is close to 100 percent of the average income of the bottom quintile; the 

figure is 80-90 percent for the second quintile. Households in the Lake Zone potentially face greater 

difficulties than those in the Southern Highlands because of higher food costs and lower average 

income. 

• Fewer households in Dar es Salaam face difficulties with food access. Average income in Dar es 

Salaam is nearly twice that of the Southern Highlands and Lake Zone, but the food basket cost, while 

high, is not twice the cost of that in these two zones. Further TZNPS data suggest that a greater share 

of households in Dar es Salaam belong to the country’s upper income quintiles. 

How Was the Study Conducted? 

The TZNPS on households provides estimates of the calorie shares of specific food items in household 

consumption. Using those data, ERS created monthly representative food baskets for each of 7 geographic 

zones that provided a daily per capita average of 2,137 calories (the average daily per capita intake 

reported in the Tanzanian Food Balance Sheet produced by the United Nations Food and Agriculture 

Organization). The study presents food baskets for mainland Tanzania, Dar es Salaam, and two geographical zones—one in the southern grain-growing area, the other in a grain-deficit zone bordering on Lake 

Victoria. The baskets included 15 food groups, which accounted for 67 to 88 percent of daily calorie 

intake. The food groups included maize (corn), rice, beans, cooking bananas, millet and sorghum, potatoes, 

sweet potatoes, wheat and other grains, cassava, poultry, beef and goat, fish, cooking oil, ripe bananas, and 

mangoes and other fruit. ERS used retail prices reported by Tanzania’s National Bureau of Statistics to 

calculate the monthly cost of the food baskets. Comparing the monthly food basket cost with an estimate 

of monthly per capita income by quintile provided a measure of household access to food.
1

Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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Introduction

Tanzania is located in East Africa, just below the equator, and covers an area of 947,340 square 

miles. It has a population of approximately 48 million people, with about 70 percent living in rural 

areas. Tanzania is among the world’s poorest countries; according to the World Bank, per capita 

gross domestic product (GDP) in purchasing power parity (PPP) was $1,700 in 2012 (World Bank, 

2012), and malnutrition is high.1

Agriculture accounts for 27 percent of gross domestic product (GDP) and employs 80 percent of the 

labor force. Major food crops include maize, sorghum, millet (bulrush and finger), rice, wheat, beans 

and other pulses, cassava, potatoes, sweet potatoes, and cooking bananas (unripe bananas consumed 

as a source of starch). A wide variety of vegetables and tropical fruits are grown as well. Major cash 

crops—key sources of export earnings—include coffee, tea, sisal, cashew nuts, cloves, and other 

spices. Tanzania is a surplus producer of maize (in most years), net importer of rice, and largely selfsufficient producer of other food crops. Most of the rural population comprises small-scale farmers, 

cultivating less than one hectare of land. 

Although Tanzania is rich in agricultural resources and a net exporter of maize, the country suffers 

from periodic droughts that reduce harvests and is subject to chronic food insecurity. Much of the 

population is engaged in small-scale agriculture, which generates both income and a substantial 

portion of their food. USDA’s Economic Research Service (ERS) estimates that 5 million people, 

out of a total population of 48 million, were food insecure in 2014, and this number is projected 

to grow to 14 million by 2024 (Rosen et al., 2014). The World Food Programme (WFP) estimates 

that as much as 40 percent of the population is affected by malnutrition (WFP, 2010). According to 

UNICEF, the United Nations Children’s Fund, approximately 42 percent of Tanzanian children are 

stunted (United Nations, 2013). 

The principal tool currently used by Tanzania to address food insecurity is the distribution of food 

aid to vulnerable households; recently, the Government of Tanzania (GOT) introduced a system of 

cash transfers on a pilot basis. A key challenge faced by the Governments of Tanzania and other 

1 Purchasing power parity is a concept in which a given amount of U.S. dollars will purchase the same bundle of goods 

in all economies. GDP in purchasing power parity takes into account differences in what a dollar can buy in different 

countries by adjusting exchange rates in the calculations. 

Nancy Cochrane and Anna D’Souza

Measuring Access to Food 

in Tanzania: A Food Basket 

Approach
2

Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

Economic Research Service/USDA

countries coping with food insecurity is to identify the households most in need of assistance. GOT 

relies on estimates of food availability to identify regions potentially in need of assistance. For each 

administrative region, Tanzania’s Ministry of Agriculture forecasts production of 12 basic food crops 

(maize, rice, wheat, sorghum, finger millet, bulrush millet, beans, other pulses, cassava, cooking 

bananas, sweet potatoes, and “Irish” (white) potatoes). Production of the 12 crops is converted to a 

“grain equivalent,” and the Ministry calculates whether this is sufficient to meet the consumption 

needs of the population.2

The current method focusing on availability tends to target regions experiencing production shortfalls. However, availability is just one of four pillars of food security, as defined by the United 

Nations Food and Agriculture Organization (FAO): the other three are access, utilization, and 

stability (UN FAO, 2006). Availability refers to the physical existence of food, which relates to 

production, stocks, and trade. Access refers to a household’s ability to obtain food, which depends 

on income, prices, and distance to local food markets. Utilization refers to an individual’s ability to 

process nutrients and energy from food, which depends on many factors, including dietary diversity 

and nutrient absorption, intra-household allocation of food, and hygienic preparation. The “stability” 

pillar refers to the stability of the other three pillars over time (UN FAO, 2006). 

In this report, we measure household access to food over time, using representative food baskets to 

compare food costs to household income. We use data from the 2010/11 Tanzanian National Panel 

Survey (TZNPS): a nationally representative household survey carried out by Tanzania's National 

Bureau of Statistics (NBS)) to obtain consumption patterns—specifically, calorie shares of different 

foods consumed by households—for various groups of Tanzanian households.3 We then create food 

baskets based on the calorie shares that achieve the per capita daily calorie intake estimated for 

Tanzania by FAO. 

We use time series price data to calculate the monthly cost of these food baskets. The monthly food 

baskets consist of a set of foods that are typically consumed by households in the zone and make up 

67 to 88 percent of total calories consumed by the average household. The ratio of the monthly per 

capita food basket cost and monthly per capita income provides a practical measure of food access. 

Any decline in the cost of food or increase in income is expected to improve the food security of a 

household. Monitoring food costs relative to consumer purchasing power can provide timely feedback on the effectiveness of food security policies and the investment required to address problems 

of food security. 

We present the food baskets for mainland Tanzania (which excludes Zanzibar), for the business 

capital, Dar es Salaam, and for two geographical zones—the Southern Highlands and the Lake Zone 

(bordering on Lake Victoria).4 Mainland Tanzania is divided into 21 administrative regions, which 

we grouped into 7 geographical administrative zones.5 Using data from the TZNPS, we estimated 

calorie and expenditure shares for 15 foods and groups for each of the 7 zones. We then used the 

calorie shares to construct food baskets for Dar es Salaam and two zones: the Southern Highlands, 

2 What GOT calls “grain equivalent” is the dry matter equivalent of the crops being monitored. This is generally similar to a maize equivalent. GOT prefers to use the term “grain equivalent” to downplay the emphasis on maize.

3 The World Bank’s Living Standards Measurement Study-Integrated Surveys on Agriculture team provided assistance 

to NBS.

4 Tanzania is a union of Tanganyika (referred to here as mainland Tanzania) and the island of Zanzibar. We have confined our analysis to mainland Tanzania.

5The seven zones are Eastern, Northern, Lake, Central, Western, Southern, and Southern Highlands.
3

Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

Economic Research Service/USDA

which includes the regions of Mbeya, Iringa, and Rukwa, and the northwest Lake Zone, which 

includes the regions of Mara, Mwanza, and Kagera (all of which border on Lake Victoria). 

Considerable geographical variation exists in agricultural production. The major maize-growing 

region is the Southern Highlands (see figs. 1 and 2). This area is typically a surplus-producing 

region, with maize from this region shipped to other regions, as well as neighboring countries. The 

regions in the Lake Zone and other regions in the north (e.g., Arusha and Kilimanjaro) grow less 

maize and more bananas and cassava, as well as some millet and sorghum (see figs. 3 and 4). The 

regions of Mbeya and Iringa (in the Southern Highlands zone) and Morogoro (in the Eastern zone) 

are the biggest rice producers, although Tanzania is a net importer of rice.

Source: USDA, Economic Research Service.

Figure 1

Political map of Tanzania

Zanzibar

Pemba

Kenya

Uganda

Rwanda

Burundi

Zambia

Dar es Salaam

Democratic

Republic of 

the Congo

Malawi

Mozambique

Lake

Victoria

Lake Tanganyika

Lake Nyasa

Zones

Southern Highlands

Lake Zone

Kigoma

Rukwa

Tabora

Singida

Mbeya Iringa

Ruvuma

Lindi

Morogoro

Coast

Dodoma

Tanga

Manyara

Manyara

Arusha

Mara

Shinyanga

Mwanza

Kagera

Kilimanjaro

Mtwara
4

Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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TZNPS results show wide geographical diversity in dietary preferences that parallel agricultural 

production patterns. The two geographical zones presented in this study were chosen to illustrate 

the geographical diversity of the country, with respect to dietary preferences, as well as the structure of agricultural production.6 The Southern Highlands is a surplus-maize-producing region; the 

Lake Zone, in the northwest along Lake Victoria, produces less maize and more roots and tubers. 

Survey results show significant differences in the typical diets of these two regions: households in 

the Southern Highlands derive 50 percent of their calories from maize, while those in the Lake Zone 

derive just 32 percent of their calories from maize and larger shares from cassava and banana (19 

6 Followup visits were made to these two zones, and interviews with local officials and nutrition experts generally 

confirmed the dietary patterns revealed by the survey results.

Source: USDA, Economic Research Service using data from the Tanzanian Ministry of Agriculture, Food Security, and 

Cooperatives.

Figure 2

Mbeya in the Southern Highlands is the largest maize producer

Zanzibar

Pemba

Kenya

Uganda

Rwanda

Burundi

Zambia

Dar es Salaam

Maize production (tons)

Democratic

Republic of 

the Congo

Malawi

Mozambique

Lake

Victoria

Lake Tanganyika

Lake Nyasa

No data

<100,000

100,000 to 250,000

250,001 to 400,000

>400,000

Kigoma

Rukwa

Tabora

Singida

Mbeya Iringa

Ruvuma

Lindi

Morogoro

Coast

Dodoma

Tanga

Manyara

Manyara

Arusha

Mara

Shinyanga

Mwanza

Kagera

Kilimanjaro

Mtwara
5

Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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and 5 percent, respectively). As might be expected given their relative wealth, households in Dar es 

Salaam consume a much more diverse diet, deriving just 23 percent of their calories from maize. 

Food Basket Approach Adds Depth to Other Measures of Food Security 

Two alternative approaches are commonly used to measure food security. One, which is used by 

ERS in its Global Food Security Assessments, is a national-level approach based on a food balance, 

calculated according to the methodology used by FAO. Availability of staple foods is calculated 

as the sum of production plus imports minus exports, waste and losses, and change in stocks. 

Availability of each food is converted to a grain equivalent and then summed to give a single 

measure of food availability in the country. This measure is useful for presenting a picture of overall 

Source: USDA, Economic Research Service using data from Tanzania’s Ministry of Agriculture, Food Security, and 

Cooperatives.

Figure 3

The Lake Zone grows more cassava and less maize than the Southern Highlands

Zanzibar

Pemba

Kenya

Uganda

Rwanda

Burundi

Zambia

Dar es Salaam

Democratic

Republic of 

the Congo

Malawi

Mozambique

Lake

Victoria

Lake Tanganyika

Lake Nyasa

Cassava production (tons)

No data

<10,000

10,000 to 100,000

100,001 to 200,000

>200,000

Kigoma

Rukwa

Tabora

Singida

Mbeya Iringa

Ruvuma

Lindi

Morogoro

Coast

Dodoma

Tanga

Manyara

Manyara

Arusha

Mara

Shinyanga

Mwanza Kagera

Kilimanjaro

Mtwara
6

Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

Economic Research Service/USDA

food availability and making comparisons across countries. But it only measures availability; it does 

not provide a measure of access or utilization.

Using the second common approach to measuring food security, WFP calculates a proxy for access 

using data from its Comprehensive Food Security and Vulnerability Assessments, which it carries 

out every 3 to 5 years in a number of developing countries, including Tanzania (WFP, 2010). WFP 

carries out a survey based on 7-day recall; the questionnaire asks for frequency of consumption of 

different food groups rather than quantities consumed. Based on survey results, WFP calculates 

Food Consumption Scores (FCS), “which combine: i) dietary diversity (the number of individual 

foods consumed over the past week is collected); ii) food frequency (the number of days in the past 

week that a specific food item has been consumed is collected); and iii) the nutritional importance of 

the food groups (which are weighted to reflect this)” (WFP, 2010). Based on research, WFP argues 

Source: USDA, Economic Research Service using data from Tanzania’s Ministry of Agriculture, Food Security, and 

Cooperatives.

Figure 4

The Lake Zone includes the largest banana-growing region

Zanzibar

Pemba

Kenya

Uganda

Rwanda

Burundi

Zambia

nj

Dar es Salaam

Democratic

Republic of 

the Congo

Malawi

Mozambique

Lake

Victoria

Lake Tanganyika

Lake Nyasa

Bananas production (tons)

No data

<10,000

10,000 to 100,000

100,001 to 200,000

>200,000

Kigoma

Rukwa

Tabora

Singida

Mbeya Iringa

Ruvuma

Lindi

Morogoro

Coast

Dodoma

Tanga

Manyara

Manyara

Arusha

Mara

Shinyanga

Mwanza Kagera

Kilimanjaro

Mtwara
7

Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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that FCS serves as a good proxy for access. However, FCS does not provide any information on 

quantities of food consumed, nor does it link access to market prices.

Both of these approaches provide a good picture of a country’s food security situation at the national 

level. The food basket approach used in this study can enhance this understanding by providing 

details on subnational differences in diets and linking to markets and prices. This level of detail 

is especially useful for countries seeking to introduce cash transfers as an alternative to food aid. 

This approach can help identify regions where overall food supplies may be adequate but where 

a number of households still experience problems with access. It also provides better information 

on the demand for key commodities such as maize that is used to make national decisions about 

commodity imports and exports. 

The method also allows policymakers to track access across time and, along with other information, 

can provide an early warning of an impending food emergency. The method relies on readily available secondary data sources (food price and GDP income data from NBS), not requiring expensive 

data collection. 

This approach also has its limitations. Consumption levels are held constant across time, so it is 

not possible to account for substitution in response to price or income changes. Because of data 

limitations, it is not possible to account for seasonal variations in monthly income or consumption. 

Available price data are also limited, and some of the price data used to calculate the food basket 

costs may be less than reliable. Additionally, because the food baskets are based on estimated household calorie shares, they represent actual consumption but do not necessarily provide the full range 

of nutrients required for a healthy diet. See the “Caveats to the Analysis” chapter for a more detailed 

discussion of these limitations. 
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Methodology and Data Sources 

The food basket methodology draws on multiple data sources. Here, we briefly list the sources 

and their role in the methodology. We provide more detail later on, presenting summary statistics, 

constructing the food baskets, and measuring household access to food in Tanzania. 

We used food consumption data from Wave 2 of the TZNPS household survey to obtain estimates 

of shares of calories derived from various food groups. To convert calorie shares to quantities 

of specific food items, we started with data on calories per kilogram of food provided by NBS 

(Tanzania's NBS and Ministry of Finance, 2012). The nutritional data were combined with edibleportion conversion factors from USDA to get estimates of per capita consumption (USDA, 2011). 

Representative food baskets (in grams) were then constructed using the estimated calorie shares 

and the estimated per capita calorie consumption (2,137) from the FAO Food Balance Sheet for 

Tanzania. We used the FAO estimate to be consistent with other food security assessments and 

analyses in developing countries (FAO, 2012).7 To calculate the costs of the food baskets we used 

market survey data provided by NBS; using current price data will allow continuous monitoring of 

changes in the cost of these food baskets. To measure access to food, GDP per capita data from the 

Tanzanian National Accounts (provided by NBS) were used. These data too are readily available 

and frequently updated and thus can be used to continuously monitor changes in food access. 

TZNPS was administered between October 2010 and December 2011 and covered 3,924 households 

(3,168 households are from the first round; the remainder are split-off households).8,9 The survey 

was stratified geographically to reflect the country’s overall composition (i.e., using appropriate 

population weights, the estimates are nationally representative). The survey also provides statistically reliable estimates for Tanzania’s eight administrative zones (North, Central, Eastern, South, 

Southern Highlands, West, Lake, and Zanzibar), as well as for Dar es Salaam, other mainland urban 

areas, and mainland rural areas. The final sample for our analysis of mainland Tanzania consists of 

3,313 households. Table 1 displays the number of sample households by region and zone. The fieldwork was spread out over the 14 months to take into account the seasonal variation in consumption 

patterns, which are important in a country like Tanzania where agriculture is a primary source of 

income for most households.10 For details on sample design, see Sandefur (2009). 

The household questionnaire includes detailed questions on food consumption inside and outside 

the household, as well as key demographic information. The food consumption section asks female 

7 The 2009 data are the most recent available for Tanzania. The per capita calorie estimate is derived from the total 

food available for human consumption divided by the total population. To calculate the total food available for human 

consumption, FAO begins with the total food available in the country (production, imports, and aid) and then accounts 

for food exported, fed to livestock, used for seed, and lost during storage and transportation.

8 Split-off households occur when one or more individuals move from one household to another (either forming a new 

household or joining an existing household). 

9 The survey’s first wave was administered between October 2008 and October 2009 and covered 3,265 households 

in all 26 regions of Tanzania (21 in Mainland and 5 in Zanzibar). The first wave of the panel used a multistage cluster 

design and was constructed using the National Master Sample Frame (list of all populated enumeration areas in the 

country) that was developed for the 2002 Population and Housing Census. The survey team was able to track 97 percent 

of first-round households (i.e., at least one household member from Round 1 lived in the Round 2-tracked household). 

Attrition rates were similar across strata. Approximately 18 percent of Round 1 households split before Round 2, largely 

because of marriage and migration. For more details, see Tanzania's NBS and Ministry of Finance (2012).

10 We average the data over the sample period using survey weights to obtain representative estimates. 
9

Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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respondents (who are the primary caregivers and are typically in charge of the preparation and 

distribution of food) about food consumed within the household by any member over the past 

week.11 In particular, the survey asks for the amount of food consumed (in kilograms or liters) for 

59 food items from purchases, own-production, or gifts and other sources. Expenditure data, in 

Tanzanian shillings (TSH), are collected for purchased items (1 U.S. dollar = 1,666 Tanzanian shillings (2014)). A separate section asks respondents about individual expenditure on meals and food 

eaten away from home over the previous week. In this analysis, we focus exclusively on in-home 

food to calculate calorie availability by food group, since food away from home cannot be disaggregated by food group.

11 The survey does not solicit information on the intra-household allocation of food, and therefore, we cannot examine 

differences in food consumption within the household.

Table 1

Tanzanian National Panel Survey sample sizes by zone and region 

Zone, sample size Region, sample size

Eastern, 846 Dar es Salaam, 626

Morogoro, 135

Pwani, 85

Northern, 443 Arusha, 122

Kilimanjaro, 114

Manyara, 83

Tanga, 124

Lake, 396 Kagera, 157

Mara, 64

Mwanza, 175

Central, 195 Dodoma, 109

Singida, 66

Western, 468 Kigoma, 180

Shinyanga, 194

Tabara, 144

Southern, 575 Lindi, 180

Mtwara, 237

Ruvuma, 158

Southern Highlands, 410 Iringa, 137

Mbeya, 173

Rukwa, 100

Source: Government of Tanzania, 2010-11 Tanzanian National Panel Survey.
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Dietary Patterns Vary Across Zones

To examine the diversity of dietary patterns across Tanzania, we estimated shares of calories 

devoted to various food groups. From the 59 food items in the TZNPS, we chose 15 foods groups 

because of their important role in the Tanzanian diet and the availability of retail prices; the foods 

make up over 80 percent of average daily calories, except in Dar es Salaam, where they make up just 

67 percent of calorie intake. 

Figures 5 through 8 display calorie shares by food group for mainland Tanzania, Dar es Salaam, 

the Southern Highlands, and the Lake Zone, respectively. The Tanzanian diet relies heavily on 

starchy staples. Maize is a major staple food throughout mainland Tanzania, providing over 40 

percent of household calories. However, there is considerable geographical variation. Maize 

accounts for 51 percent of total calories in the Southern Highlands, and even in the Lake Zone, 

where it accounts for 32 percent of calories, it makes up a larger share of calories than any other 

food category. For households in the Lake Zone, cassava is the other key staple, providing about 

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.

Figure 5

Mainland Tanzania—41 percent of calories derive from maize

41.3%

6.2% 10.9%

2.3%

2.9% 0.4%

0.2% 1.5%

8.5%

0.3%

1.1%

1.0%

5.1%

0.3%

0.6%

17.5%

 Maize

 Rice

 Beans

 Cooking bananas

 Millet/sorghum

 Potatoes

 Sweet potatoes

 Wheat/other 

 grains

 Cassava

 Poultry

 Beef/goat

 Fish

 Cooking oil

 Ripe bananas

 Mangoes/other fruit

 Other
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19 percent of total calories; cassava consumption is much lower in the Southern Highlands (4 

percent).12 Rice, beans, and cooking bananas are important to the diets in both zones, although 

banana consumption is higher in the Lake Zone. Irish potato consumption is low throughout the 

country. Consumption of sweet potatoes is somewhat higher, especially in the Lake Zone. In most 

areas, beans are the main source of protein. 

Consumption of animal products is low throughout the country, but is lowest in the Southern 

Highlands. Fish is an important source of protein in the Lake Zone, followed by beef and goat and 

then poultry. Fish is also eaten in the Southern Highlands but makes up a smaller share of total 

calories. Dairy products were not included in the food basket because their calorie shares were negligible. Vegetables (apart from the starchy vegetables) were also not included—the total calorie share 

for all vegetables was just 1 percent, even in Dar es Salaam.

12 Cassava is consumed both in its fresh form and in the form of flour. In the Lake Zone, it is consumed mainly as 

flour; in other regions it is more commonly consumed fresh during the cassava season.

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.

Figure 6

Dar es Salaam—households consume a more diverse diet than in the two zones

23.4%

21.2%

5.5%

1.0%

0.8%

0.6%

0.7% 0.3%

1.1%

0.3%

1.5%

0.9%

8.5%

0.4%

0.8%

32.7%

 Maize

 Rice

 Beans

 Cooking bananas

 Millet/sorghum

 Potatoes

 Sweet potatoes

 Wheat/other 

 grains

 Cassava

 Poultry

 Beef/goat

 Fish

 Cooking oil

 Ripe bananas

 Mangoes/other fruit

 Other
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In Dar es Salaam, maize makes up 23 percent of calories; rice is the other major staple, providing 

about 21 percent of calories. Diets in the city are generally more diverse, perhaps because of higher 

average incomes and/or greater access to markets. (Foods from the 15 tracked food groups make up 

only 67 percent of total calories consumed, compared with 83-88 percent for other areas.) 

The survey data also suggest that a lot of heterogeneity in consumption exists within zones, partly 

because of intra-zone variations in climatic and growing conditions and partly because of transportation costs. For example, the three regions that make up the Lake Zone (Mara, Mwanza, and Kagera) 

display quite different dietary patterns. Because Kagera is a major banana-growing region, households there derive a large portion of their calories (20 percent) from bananas. In contrast, households 

in Mara rely more heavily on cassava and millet and derive a negligible portion of their calories 

from bananas (0.4 percent). Because the sample sizes in these regions were small (64 in Mara and 

157 in Kagera), these calorie shares are suggestive rather than statistically conclusive. 

Maize dominates the diets in all three regions of the Southern Highlands (Mbeya, Iringa, Rukwa), 

though households in Mbeya have a slightly smaller calorie share for maize and a larger share for 

rice than those in the other two regions. In interviews conducted by ERS staff during field visits in 

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.

Figure 7

Southern Highlands—50 percent of calories derive from maize

1.8%

0.8%

0.9%

0.9%

0.1%

0.9%

0.4%

0.5%

50.6%

9.2%

7.1%

4.1%

5.1%

16.6%

0.8%

 Maize

 Rice

 Beans

 Cooking 

 bananas

 Millet/sorghum

 Potatoes

 Sweet 

 potatoes

 Wheat/other 

 grains

 Cassava

 Poultry

 Beef/goat

 Fish

 Cooking oil

 Ripe bananas

 Mangoes/other fruit

 Other
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March 2013, respondents pointed out some notable differences in dietary preferences among districts 

(subcomponents of regions). Rice is favored in the district of Kyela, near the Malawi border, while 

bananas are favored in the Rungwe district. Respondents also pointed out that many households 

that had not traditionally consumed much maize are switching to a more maize-based diet, in part 

because public food assistance is in the form of maize. In addition, they said that in all three regions 

of the Southern Highlands, maize has become more of a cash crop in recent years and is displacing 

more traditional crops such as millet and sorghum.

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.

Figure 8

Lake Zone diets contain higher shares of bananas and cassava than do diets 

of Dar es Salaam and the Southern Highlands

0.5%

0.3%

1.1%

0.4%

1.2%

31.8%

8.5%

6.7%

19.4%

12.3%

Cassava

3.9%

1.8%

3.4%

3.6% 5.1%

 Maize

 Rice

 Beans

 Cooking bananas

 Millet/sorghum

 Potatoes

 Sweet 

 potatoes

 Wheat/other 

 grains

 Cassava

 Poultry

 Beef/goat

 Fish

 Cooking oil

 Ripe bananas

 Mangoes/other fruit

 Other
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Constructing the Food Baskets

To construct the food baskets, calorie shares from the TZNPS data were estimated, after accounting 

for the inedible portion of the foods (husks, peels, seeds, pits, etc.), using edible-portion conversion 

factors from USDA (USDA, 2011).13 Using these calorie shares, we imposed a target of a daily per 

capita intake of 2,137 calories, which is the average daily per capita calorie intake calculated by 

FAO in its 2009 Food Balance Sheet for Tanzania (FAO, 2012).14 The daily quantities (grams) of 

food were derived using estimates of calories per kilogram provided by NBS (Tanzania's NBS and 

Ministry of Finance, 2012). The typical food baskets for mainland Tanzania, Dar es Salaam, the 

Southern Highlands, and the Lake Zones are shown in tables 2 through 5, respectively. 

13 The edible portion of a Tanzanian product could differ somewhat from the USDA estimates because of differences 

in variety or techniques used to remove the inedible portion. However, the USDA database is the only comprehensive 

source of these conversion factors. Most of these factors were derived from the basic biological properties of the food 

item (thickness of the peel, weight of the seeds, etc.), so we assumed the differences will be minimal. 

14 The 2009 data are the most recently available for Tanzania. The per capita calorie estimate was derived from the 

total food available for human consumption divided by the total population. The FAO calculations began with the total 

food available in the country (production, imports, and aid) and then accounted for food exported, fed to livestock, used 

for seed, and lost during storage and transportation, in order to get the total food available for human consumption.

Table 2

Food basket calculations for mainland Tanzania

(1) (2) (3) (4) (5) (6)

Food group/item

Calorie 

shares

Calories 

per kg

Calories per day assuming 

a diet of 2,137 calories

Grams/

day

Kilograms/

month

Maize 0.413 3,680 882 240 7.19

Rice 0.109 3,640 233 64 1.92

Beans 0.062 3,330 132 40 1.19

Cooking bananas 0.023 1,350 49 36 1.09

Millet/sorghum 0.029 3,450 62 18 0.54

Potatoes 0.004 790 8 10 0.31

Sweet potatoes 0.015 1,050 31 30 0.89

Wheat/other grains 0.002 3,400 4 1 0.04

Cassava 0.085 1,492 182 122 3.66

Poultry 0.003 1,390 6 4 0.12

Beef/goat 0.011 1,550 23 15 0.45

Fish 0.010 820 22 26 0.79

Cooking oil 0.051 8,840 109 12 0.37

Ripe bananas 0.003 920 7 7 0.22

Mangoes/other fruit 0.006 850 13 15 0.46

Total calorie share 0.825

Notes: Column 4 displays the calories for each food based on daily total calories of 2,137; column 4 equals column 2 

multiplied by 2,137 calories. Column 5 equals column 4 divided by column 3 (which yields kilograms per day) multiplied by 

1,000. Column 6 equals column 5 multiplied by 30 (days in month) and divided by 1,000 (to convert grams to kilograms). 

kg = kilogram.

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.
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Note that these food baskets are representative: they reflect the typical diet patterns for each zone, 

but do not necessarily comprise a nutritionally optimal diet. Preliminary analysis suggests that the 

Southern Highlands diets are sufficient in protein, according to average USDA daily requirements, 

because of the dominance of maize, but are deficient in many vitamins and minerals. The Lake 

Zone diet is deficient in protein, as well as most micronutrients. More nutritious food baskets would 

include more beans, animal products, groundnuts, and leafy green vegetables. (Groundnuts and leafy 

green vegetables are grown in Tanzania, but not frequently consumed.)15

To calculate the total cost of these food baskets, we used retail market prices reported by the NBS 

Department of Labor and Price Statistics. For Dar es Salaam, prices were used from retail sources 

throughout the city; for the Southern Highlands Zone, prices were used from the main market in 

Mbeya (the retail center of the maize growing region); and for the Lake Zone, average prices were 

15 These preliminary calculations were based on recommended daily requirements taken from the Food and Nutrition Board, Institute of Medicine, National Academy of Sciences (2004). Nutrient content of selected foods was taken 

from the Tanzania Food Composition Tables (2008), compiled by Muhimbili University of Health and Allied Sciences 

(MUHAS), Dar es Salaam; the Tanzania Food and Nutrition Centre (TFNC), Dar es Salaam; and the Harvard School of 

Public Health (HSPH), Boston, MA, November 2008.

Table 3

Food basket calculations for Dar es Salaam

(1) (2) (3) (4) (5) (6)

Food group/item

Calorie 

shares

Calories 

per kg

Calories per day assuming 

a diet of 2,137 calories

Grams/

day

Kilograms/

month

Maize 0.234 3,680 500 136 4.08

Rice 0.212 3,640 454 125 3.74

Beans 0.055 3,330 117 35 1.05

Cooking bananas 0.010 1,350 21 15 0.46

Millet/sorghum 0.008 3,450 18 5 0.15

Potatoes 0.006 790 14 18 0.53

Sweet potatoes 0.003 1,050 7 7 0.21

Wheat/other grains 0.007 3,400 15 4 0.13

Cassava 0.011 1,492 25 16 0.49

Poultry 0.003 1,390 7 5 0.14

Beef/goat 0.015 1,550 33 21 0.64

Fish 0.009 820 19 24 0.71

Cooking oil 0.085 8,840 182 21 0.62

Ripe bananas 0.004 920 9 10 0.30

Mangoes/other fruit 0.008 850 18 21 0.63

Total calorie share 0.673

Notes: Column 4 displays the calories for each food based on daily total calories of 2,137; column 4 equals column 2 

multiplied by 2,137 calories. Column 5 equals column 4 divided by column 3 (which yields kilograms per day) multiplied by 

1,000. Column 6 equals column 5 multiplied by 30 (days in month) and divided by 1,000 (to convert grams to kilograms). 

kg = kilogram.

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.
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used from the three markets in the regional capitals—Mwanza, Bukoba, and Musoma—weighted by 

the population of each region given the wide variations in prices within the zone.16

TZNPS results indicate that, on average, mainland households derive 25 percent of their food 

consumption from their own production. This share is 32 percent among rural households. To derive 

the total food basket cost, we valued this portion of food consumption at local market prices. 

The baskets of foods in tables 2-5 represent 67 to 88 percent of the total calories typically consumed 

by one person in a month. To give policymakers an accurate picture of the food security situation, 

we estimated the cost of a total food basket by including the cost of other foods that households 

consume, including dairy products, vegetables, nuts and spices, sugar and sweets, and beverages 

(alcoholic and nonalcoholic). These food groups include small quantities of a wide array of products 

that are often difficult to measure and to value. 

To estimate the value of these other foods, estimated shares of food expenditure were used. In 

particular, we calculated the share of total food expenditures devoted to the 15 food groups, as well 

16 Ideally, the prices would have been weighted by the volume of goods traded on each market, but those data were not 

available. 

Table 4

Food basket calculations for Southern Highlands Zone

(1) (2) (3) (4) (5) (6)

Food group/item

Calorie 

shares

Calories 

per kg

Calories per day assuming 

a diet of 2,137 calories

Grams/

day

Kilograms/

month

Maize 0.506 3,680 1,082 294 8.82

Rice 0.092 3,640 197 54 1.62

Beans 0.071 3,330 152 46 1.37

Cooking bananas 0.018 1,350 38 28 0.85

Millet/sorghum 0.008 3,450 17 5 0.15

Potatoes 0.009 790 19 24 0.71

Sweet potatoes 0.009 1,050 19 18 0.55

Wheat/other grains 0.001 3,400 2 1 0.02

Cassava 0.041 1,492 87 58 1.75

Poultry 0.001 1,390 3 2 0.07

Beef/goat 0.009 1,550 19 12 0.37

Fish 0.008 820 18 22 0.65

Cooking oil 0.051 8,840 108 12 0.37

Ripe bananas 0.004 920 10 10 0.31

Mangoes/other fruit 0.005 850 11 13 0.38

Total calorie share 0.834

Notes: Column 4 displays the calories for each food based on daily total calories of 2,137; column 4 equals column 2 

multiplied by 2,137 calories. Column 5 equals column 4 divided by column 3 (which yields kilograms per day) multiplied by 

1,000. Column 6 equals column 5 multiplied by 30 (days in month) and divided by 1,000 (to convert grams to kilograms). 

kg = kilogram.

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.
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as to the “other foods” category. For in-home consumption of purchased food, we used the value of 

expenditure reported by households. For in-home consumption of own-production and gifts, expenditure was estimated using the quantities reported by households and median unit-value prices from 

purchases of the same food items.17 For away-from-home consumption, we aggregated the value of 

expenditure reported for individuals to the household level.

The expenditure shares, shown in table 6, denote the share of total food expenditures for each of the 

foods in the baskets. Expenditures on the basket of 15 food groups made up 57 percent of total food 

expenditures in Dar es Salaam, 69 percent in the Southern Highlands, and 80 percent in the Lake 

Zone. We used these expenditure shares to estimate the value of other foods and add that value to 

the value of the 15 food groups in the baskets. These estimates were included in the total food basket 

costs shown in figures 6 and 9 and in table 7. (Note that the expenditure shares of foods such as 

maize, rice, beans and cassava are typically lower than the calorie shares, since these are relatively 

17 Unit-value prices for purchased foods were calculated by dividing expenditures by quantities for each food item. 

Assigned unit-value prices were taken from nearest geographical area given a minimum of three unit price observations. 

The minimum of three unit price observations helped to ensure that the price represented the area and to guard against 

potential outliers. Ideally, shadow prices would be used to calculate the value of food produced at home. Because the 

survey instrument did not provide enough information to create shadow prices, we used market prices, as is common in 

the literature.

Table 5

 Food basket calculations for Lake Zone

(1) (2) (3) (4) (5) (6)

Food group/item

Calorie 

shares

Calories 

per kg

Calories per day assuming 

a diet of 2,137 calories

Grams/

day

Kilograms/

month

Maize 0.318 3,680 679 184 5.53

Rice 0.085 3,640 182 50 1.50

Beans 0.067 3,330 143 43 1.29

Cooking bananas 0.051 1,350 110 82 2.45

Millet/sorghum 0.036 3,450 76 22 0.67

Potatoes 0.005 790 10 12 0.37

Sweet potatoes 0.034 1,050 73 69 2.08

Wheat/other grains 0.000 3,400 1 0 0.01

Cassava 0.194 1,490 415 279 8.36

Poultry 0.003 1,390 7 5 0.15

Beef/goat 0.011 1,550 23 15 0.44

Fish 0.018 820 39 47 1.42

Cooking oil 0.039 8,840 83 9 0.28

Ripe bananas 0.004 920 8 8 0.25

Mangoes/other fruit 0.012 850 25 30 0.89

Total calorie share 0.877

Notes: Column 4 displays the calories for each food based on daily total calories of 2,137; column 4 equals column 2 

multiplied by 2,137 calories. Column 5 equals column 4 divided by column 3 (which yields kilograms per day) multiplied by 

1,000. Column 6 equals column 5 multiplied by 30 (days in month) and divided by 1,000 (to convert grams to kilograms). 

kg = kilogram.

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.
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cheap foods, in terms of calories per TSH. Conversely, expenditure shares of more expensive foods, 

like beef and poultry, are higher than their calorie shares because they are more expensive, in terms 

of calories per TSH.)

Table 7 displays food basket costs by zone for December 2012—a month chosen simply as an illustration. The cost was lowest in the Southern Highlands, driven by relatively low maize prices and 

a relatively high-calorie share devoted to maize, as well as low consumption of high-priced items 

such as meat and fish. The food basket in Dar es Salaam was the most expensive, driven by generally higher food prices and greater consumption of higher priced foods such as rice, meat, and fish. 

The food basket for the Lake Zone was also relatively costly. Maize prices were high, and the calorie 

share of maize, while lower than in the Southern Highlands, was still fairly high. Also, consumption of poultry, beef, and fish (relatively expensive items) was higher in the Lake Zone. The primary 

driver of the high cost, however, was the large calorie share devoted to cassava, which was relatively 

expensive. The “Caveats to the Analysis” chapter raises the possibility that the cassava prices were 

measured with error and thus that the cost of the Lake Zone food basket may have been exaggerated. 

Figure 9 displays the nominal monthly cost of each region’s food basket from January 2010 to 

February 2013. The sharp rise in the nominal costs of the food baskets that began in mid-2011 can 

be attributed largely to a surge in maize and rice prices in Tanzania (shown in figures 10 and 11). 

From January 2010 to February 2013, food inflation was higher than non-food inflation. Price 

increases were the most pronounced for maize and rice, which make up substantial shares of the 

Table 6

Three Tanzanian regions’ calorie and expenditure shares

Food item

Dar es Salaam Southern Highlands Lake Zone

Calorie Expenditure Calorie Expenditure Calorie Expenditure

Shares (percent)

Maize 23.4 9.9 50.6 28.0 31.8 17.5 

Rice 21.2 13.5 9.2 6.8 8.5 6.2 

Beans 5.5 4.8 7.1 7.5 6.7 6.7 

Cooking bananas 1.0 1.3 1.8 2.6 5.1 5.5 

Millet/sorghum 0.8 0.6 0.8 0.6 3.6 2.1 

Potatoes 0.6 1.4 0.9 2.1 0.5 0.8 

Sweet potatoes 0.3 0.5 0.9 1.2 3.4 4.2 

Wheat/other grains 0.7 0.5 0.1 1.2 0.0 4.2 

Cassava 1.1 0.3 4.1 0.1 19.4 0.0 

Poultry 0.3 1.1 0.1 1.9 0.3 9.6 

Beef/goat 1.5 2.6 0.9 1.5 1.1 3.4 

Fish 0.9 9.3 0.8 5.3 1.8 6.2 

Cooking oil 8.5 5.7 5.1 4.4 3.9 9.2 

Ripe bananas 0.4 4.9 0.4 4.7 0.4 3.0 

Mangoes/other fruit 0.8 1.2 0.5 1.2 1.2 0.9 

Total 66.9 57.4 82.5 69.3 84.3 79.5 

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.
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food baskets in both zones. We calculated changes in the real cost of food by deflating the cost of the 

food basket using the consumer price index (CPI) from NBS. The rise in the real costs of the food 

baskets was more moderate than the rise in the nominal costs (fig. 12).18

18 According to NBS, food and nonalcoholic beverages make up 47.8 percent of the basket of goods used to compute 

the consumer price index.

Table 7

Monthly food-basket cost, December 2012

Product Dar es Salaam Southern Highlands Lake Zone

Tanzanian shillings

Maize 3,606 5,865 4,399

Rice 7,308 3,241 2,690

Beans 1,753 2,193 1,895

Cooking bananas 421 353 944

Millet/sorghum 197 226 1,254

Potatoes 451 313 342

Sweet potatoes 294 329 912

Wheat/other grains 157 28 12

Cassava 287 947 5,471

Poultry 862 653 1,032

Beef/goat 3,457 1,851 2,055

Fish 5,113 3,894 7,133

Cooking oil 1,664 1,296 1,130

Ripe bananas 338 228 290

Mangoes/other fruit 421 335 634

Other 17,887 9,145 8,917

Total 44,217 30,896 39,108

Source: USDA, Economic Research Service using data from Government of Tanzania, 2010-11 National Panel Survey.
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Note: 1 U.S. dollar = 1,666 Tanzanian shillings.

Source: USDA, Economic Research Service using data from Government of Tanzania, National Bureau of Statistics, 

Department of Labor and Price Statistics, and the 2010-11 Tanzanian National Panel Survey.

Figure 9

Nominal per month, per household food basket costs rose sharply from 2011 to 2013

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Jan-10

Apr-10

Jul-10

Oct-10

Jan-11

Apr-11

Jul-11

Oct-11

Jan-12

Apr-12

Jul-12

Oct-12

Jan-13

Tanzanian shillings

Dar es Salaam Lake Zone Southern Highlands

Note: 1 U.S. dollar = 1,666 Tanzanian shillings. Prices for the Southern Highlands were collected from the main open-air 

market in the city of Mbeya. Prices for the Lake Zone are an average of prices collected from three main markets: 

Bukoba, Mwanza, and Musoma. kg = kilogram.

Source: Government of Tanzania, National Bureau of Statistics, Department of Labor and Price Statistics.

Figure 10

Maize prices, paralleling nominal food basket costs, rose sharply from 2011 to 2013

Tanzanian shillings per kg

Dar es Salaam Lake Zone Southern Highlands

0

200

400

600

800

1,000

1,200

Jan-10

Mar-10

May-10

Jul-10

Sep-10

Nov-10

Jan-11

Mar-11

May-11

Jul-11

Sep-11

Nov-11

Jan-12

Mar-12

May-12

Jul-12

Sep-12

Nov-12

Jan-13
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Note: 1 U.S. dollar = 1,666 Tanzanian shillings. Prices for the Southern Highlands were collected from the main open-air 

market in the city of Mbeya. Prices for the Lake Zone are an average of prices collected from three main markets: 

Bukoba, Mwanza, and Musoma. kg = kilogram.

Source: Government of Tanzania, National Bureau of Statistics, Department of Labor and Price Statistics.

Figure 11

Rice prices, mirroring nominal food basket costs and maize prices, rose sharply 

Tanzanian shillings per kg

Dar es Salaam Lake Zone Southern Highlands

0

500

1,000

1,500

2,000

2,500

Jan-10

Mar-10

May-10

Jul-10

Sep-10

Nov-10

Jan-11

Mar-11

May-11

Jul-11

Sep-11

Nov-11

Jan-12

Mar-12

May-12

Jul-12

Sep-12

Nov-12

Jan-13

Note: 1 U.S. dollar = 1,666 Tanzanian shillings.

Source: USDA, Economic Research Service, using data from Government of Tanzania, National Bureau of Statistics, 

Department of Labor and Price Statistics, and the 2010-11 Tanzanian National Panel Survey.

Figure 12

Real per month, per household food basket costs rose moderately

Tanzanian shillings, Sept. 2010=100

Dar es Salaam Lake Zone Southern Highlands

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

Jan-10

Mar-10

May-10

Jul-10

Sep-10

Nov-10

Jan-11

Mar-11

May-11

Jul-11

Sep-11

Nov-11

Jan-12

Mar-12

May-12

Jul-12

Sep-12

Nov-12

Jan-13
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Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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Measuring Access

Food is just one of the expenditures for Tanzanian households; households also spend money on 

housing, clothing, school fees, and other essential goods and services. As in many developing countries, poor households in Tanzania spend the majority (on average, 75 percent) of their income on 

food. 

To measure access to food, we calculated the ratio of the monthly food basket cost to monthly per 

capita income. If the ratio was greater than 0.50 (that is, if the cost of the food basket is over 50 

percent of total household income), we defined the household as potentially food insecure. Decreases 

or increases in the ratio indicated improvements or declines, respectively, in access to food. 

Because data on Tanzanian GDP by region were only available annually, we divided by 12 to obtain 

monthly per capita income for each region. While the assumption of constant monthly income is 

a strong one (for a country in which many households engage in the agricultural sector), we were 

limited by data availability.19

Access appears to be a potential problem only in the Lake Zone. In December 2012, for example, 

the average nominal cost of the Lake Zone food basket was 39,108 TSH. Average monthly per capita 

nominal GDP was 71,906 TSH, giving a ratio of 54 percent. The ratio for the Southern Highlands is 

31 percent. The ratio for Dar es Salaam, where average monthly per capita GDP was 144,570 TSH, 

was also 31 percent (table 8).

However, the aggregate numbers mask significant variation across households within these zones. 

Income inequality in Tanzania is pronounced, and those in the lower income quintiles have more 

difficulty accessing food and buffering price increases than those in the upper quintiles.

19 Per capita GDP from the National Accounts is an imperfect proxy for household income. An alternative approach 

would be to use total consumption derived from TZNPS as a proxy for income. The principal motivation for developing 

the food-basket measure, however, was to be able to monitor changes in access to food. GDP data are updated regularly 

and are available with only a short lag, which allows users to follow monthly changes in access to food; whereas data 

from TZNPS are only available every few years.

Table 8

Food-basket cost as share of income by quintile, December 2012

Income group Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Share of GDP held by quintile 6.8 11.1 15.6 21.7 44.8

Total GDP held by quintile (million TSH) 3,040,801 4,963,661 6,975,955 9,703,733 20,033,513

Per capita GDP (TSH) 348,513 568,896 799,530 1,112,167 2,296,086

Average monthly per capita GDP (TSH) 29,043 47,408 66,627 92,681 191,341

Monthly food-basket cost as percent of income, December 2012

Dar es Salaam 152 93 66 48 23

Southern Highlands 106 65 46 33 16

Lake Zone 135 82 59 42 20

Note: GDP = gross domestic product.

TSH = Tanzanian shillings (1 U.S. dollar = 1,666 TSH).

Source: USDA, Economic Research Service using data from the World Bank and the Tanzanian National Bureau of 

Statistics.
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Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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Combining the GDP data with income quintile share data from the World Bank, we created the 

national average per capita GDP by quintile (see table 8). Based on these estimates, households 

in the bottom quintiles appeared vulnerable to food insecurity, with the food basket costs greatly 

exceeding average incomes. However, these aggregate estimates could be misleading given the 

considerable variation in income across areas—for example, average per capita income in Dar es 

Salaam was twice that in the Lake Zone and close to twice the average income in the Southern 

Highlands. Therefore, it is likely that more households in the country’s top quintiles lived in Dar es 

Salaam and fewer of them lived in other areas; similarly, households in the bottom quintiles (with 

difficulty accessing food) were more likely to live outside of Dar es Salaam. 

The TZNPS survey data appear to support these hypotheses. For example, less than 2 percent of 

households in Dar es Salaam were in the bottom income quintile of Tanzania, while as many as 76 

percent of Dar es Salaam households were in the country’s top income quintile; and in the Lake 

Zone, a larger portion of households were in Tanzania’s bottom quintile and a smaller portion 

were in its top quintile. Therefore, access was likely a bigger problem in the Lake Zone and in the 

Southern Highlands than in Dar es Salaam. Further note that these ratios do not necessarily mean 

that the bottom quintile verged on starvation. The food basket calculations were based on average 

consumption patterns for all households in the areas (not on quintile-specific consumption patterns). 

While households at the bottom likely consumed fewer calories, they also likely consumed a cheaper 

(in calories per TSH), less diverse, and less nutritious diet—consisting of less meat, less fruit, less 

rice, and more maize and other starches. Unfortunately, the survey design does not allow us to calculate food baskets by quintile within zone.
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Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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Caveats to the Analysis

Limitations of the methodology and data must be kept in mind when interpreting the results of this 

study. Limitations include:

Combining data from multiple sources. The current methodology combined data from several 

sources (e.g., household surveys, national accounts, and FAO food balances). We took this approach 

to allow continuous monitoring of changes in food access using the most recent GDP and price data; 

using price and/or income data estimated from household surveys would have provided outdated 

food-access information. (It was necessary to use the household data to estimate calorie shares since 

such data are not available from other sources.) However, this approach has limitations. GDP per 

capita data from national accounts provide only a rough proxy for household income and thus may 

not reflect actual or disposable income for measuring food access of households. Similarly, the price 

data may not reflect accurately the true prices that households paid for their food. Finally, the FAO 

estimates of daily calories per capita do not take into account wastage that may have occurred at 

the household level; they also do not account for varying levels of activity—assuming only a “light 

work” level of activity. Thus, the constructed food baskets may not have reflected accurately the 

combination of food necessary to achieve the calorie threshold for certain Tanzanian households. 

Scope of the analysis. Our analysis was limited to examining access to food—one pillar of food 

security. While food availability and utilization are important, our food basket methodology did not 

take them into account. Nevertheless, our analysis complements current food availability measures. 

With timely price reports, it is possible to track changes in the cost of the food basket. This analysis, combined with reports from the districts on crop conditions, can provide early warning of an 

impending food security problem. 

Survey limitations. Another set of caveats concerns the nature of the survey. The food consumption data came from a 7-day recall method. As in other household surveys, there was the potential of 

measurement error associated with recall and waste. Recall bias stems from respondents’ memory 

lapses about food consumed over long periods of time, causing them to underreport or overreport 

the quantities of food consumed. This may occur systematically across foods or it may occur when 

recalling certain foods; in the latter case, the calorie shares across food groups would suffer from 

measurement error bias. Furthermore, household consumption data do not typically account for food 

waste (food prepared or purchased but not eaten), and thus estimates of food intake may be larger 

than actual values. Again, bias in the calorie share estimates occurs if there is more waste in certain 

food groups than others. 

Food away from home. The food baskets calculated in this report accounted only for food 

consumed inside the home because we could not disaggregate food away from home by food group. 

The share of food expenditures outside the home was quite large, especially in Dar es Salaam, where 

food outside the home accounted for almost a third of all food expenditures. Thus we may not have 

captured the full food access experience of households in Dar es Salaam. 

Income assumptions. Annual GDP was divided by 12 to obtain average monthly income; however, 

in an agriculture-dominated economy, monthly income may vary considerably by season. Therefore, 

our measures of access may have better reflected the true access to food in certain months during 

the years. 
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Retail price data. Serious limitations existed to the available retail price data in Tanzania. Prices 

used were provided by the NBS Department of Labor and Price Statistics, which is responsible for 

calculating the CPI. Market reporters in each region are responsible for sending the prices to NBS. 

District and regional governments determine which offices will be responsible for reporting prices, 

and there is little oversight. The methods used to collect and report prices are not consistent across 

regions, in terms of quality standards or units of measure. For example, maize and rice prices are 

reported on a per kilogram basis, but other food items, such as bananas, potatoes, cassava, and most 

fruit, are reported by the piece, bunch, or pile. NBS converts these measurements into kilogram 

equivalents, but because no standard definition exists for a pile or bunch, such prices likely suffer 

from measurement error.

Market prices reported for cassava are particularly problematic. Cassava is consumed mainly in the 

form of cassava flour, and in interviews conducted by ERS, households indicated that they preferred 

to buy cassava flour in the market because great care must be taken when processing raw cassava 

into flour, given its toxic nature. Since retail prices of cassava flour are not available, we converted 

quantities of flour to their raw cassava equivalent and then used the market price for raw cassava. 

There is little consistency across regions in reporting cassava prices; some regions report prices by 

the kilogram and others by piece (which can vary considerably in size). Cassava makes up a large 

share of the food basket in the Lake Zone and so its food basket costs and, thus, vulnerability to 

food security (driven by poor access) may be exaggerated.
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Measuring Access to Food in Tanzania: A Food Basket Approach, EIB-135

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Conclusion

Considerable variation in diets exists among the geographical regions of Tanzania. Maize dominates 

the diets in the surplus-maize-producing regions of the Southern Highlands. However, in the maizedeficit regions in the north, maize, while still important, makes up a smaller share of food intake; 

households in these regions supplement maize with other starchy staples such as cassava and banana. 

The food basket cost is lowest in the Southern Highlands, because of the ready availability of lowpriced maize. The cost is highest in Dar es Salaam but also quite high in the Lake Zone. Food basket 

costs have risen rapidly in nominal terms but remained stable in real terms. 

The bottom two income quintiles of Tanzania’s population, for which the cost of a minimal monthly 

food basket is over 50 percent of the average monthly income, face potential problems with access. 

Households in the Lake Zone could potentially face greater difficulties than those in the Southern 

Highlands because of higher food costs and lower average income. Fewer households in Dar es 

Salaam face difficulties with access. Average income in Dar es Salaam is nearly twice that of the 

two zones, and the cost of the food basket, while high, is not twice the cost. Further TZNPS data 

suggest that a greater share of households in Dar es Salaam than in the two zones is in the country’s 

upper income quintiles. 

Results of this analysis can help better pinpoint Tanzanian regions that may be more vulnerable 

to food insecurity. In the Lake Zone, the cost of the food basket is higher, because of generally 

higher food prices, and the average income is lower than the respective counterparts in the Southern 

Highlands. 

Furthermore, knowing the monthly costs for a representative food basket across the country can 

help identify vulnerable households and calculate the appropriate resources for poverty alleviation 

programs, such as a recently launched pilot program of conditional cash transfers to vulnerable 

households (conditions include enrollment of children in school, regular health checkups, participation in nutrition workshops, among others), combined with “labor-intensive public works.” 

Finally, while this analysis has described representative food baskets, future work can focus on 

creating affordable food baskets that provide a better balanced diet using foods that are readily available and acceptable given current cultural tastes and preferences. For example, the baskets could 

include a greater variety of leafy green vegetables, which are widely grown in Tanzania, and could 

constitute a good source of vitamin A, and millet could be an alternative to maize, offering more 

minerals such as calcium and phosphorus than maize does.
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