The changing food expenditure patterns and trends in Zambia: implications for agricultural policies

Original Paper
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Abstract

Zambia, like many other African countries is undergoing rapid urbanization and rising per capita income, accompanied by rising population. This study sought to understand the changing food expenditure patterns in Zambia and the implications of this transformation on food policy, food market development, and rural development. The main source of data for the study was the Living Conditions Monitoring Survey (LCMS) data collected in 1996, 1998, 2010, and 2015 by the Central Statistical Office (CSO) of Zambia. Trends in expenditure shares were done for each of the food categories over time and by rural and urban areas. The study found that there have been major declines in the shares of food expenditure on maize among rural and urban households between 1996 and 2015. However, wheat shares in urban households’ diets increased while rural households experienced a drop in coarse grains and tubers. Wealthier households spent larger shares of their food expenditure on wheat, rice and potatoes. Further, wealthier households increased their share of expenditure on animal protein, while poorer households doubled their expenditure on vegetables. Thus, transformation of food expenditure patterns is evident mostly among the high income households, mainly in urban areas. Overall the changing pattern of food expenditure is consistent with rising incomes and rapid urbanization. However, the disparities between the different income groups and between rural and urban areas are indicative of a rise in income inequality both in urban and rural parts of Zambia.

Keywords

Expenditure patterns Consumption patterns Agriculture Policy Income Zambia 

1 Introduction

Zambia, like many other African countries is going through a period of population growth, rapid urbanization and rising per capita income (Tschirley et al. 2013; Chauvin et al. 2012; Hassen et al. 2016). While these changes have taken place, it is not clear whether food expenditure patterns have changed among households in urban and rural Zambia. This has a number of implications for food policies, food markets, rural development, and the nutrition and health status of the population.

Failure to understand the changing consumption patterns in Zambia has led to a disjunction between food policies and changes in food preferences that may be taking place. For example, the Zambian government has been pushing for increased maize production through policies such as the Farmer Input Support Programme (FISP) and through the Food Reserve Agency (FRA) disregarding the role of other key staples such as rice, cassava, and wheat in households’ diets. Another facet of the policy disconnect is that farming households remain maize-centric, and fail to respond to changes in demand patterns. Thus, it is difficult to attain diversification in food production and consumption without recognizing changes in consumption patterns in urban and rural Zambia. As some stakeholders have noted “…whilst the agro-ecological zones are favorable to growing other food crops such as cassava, Irish and sweet potatoes, including rice, maize has continued to have a dominant effect on the agricultural system, consumption patterns and on the national economy” (Sichilimo 2016).

Changing food expenditure patterns have important implications for rural development through opportunities created for the rural economy. Rapid urbanization and changes in consumption patterns can increase demand for agricultural produce from rural areas thus creating urban-rural linkages in the form of expanded markets for various crops and livestock products, development of input markets, and increased utilization of rural produce through agro-processing as urban populations demand for processed foods increases (Tschirley et al. 2013). The nexus between the changing expenditure patterns and nutrition status of the population is an important one. In the context of a changing global nutrition landscape, influenced by economic and income growth, urbanization, demographic change and globalization, over-consumption by the rich and under-consumption by the poor has given rise to the double burden malnutrition problem (WHO 2018; Hansen 2018).

Lack of evidence on dietary transformation, therefore, can result in missed opportunities for rural areas to change their food production systems in response to changing expenditure patterns. Against this backdrop, this study seeks to understand the changing expenditure patterns in Zambia and the implications of this transformation on food policy, food market development, rural development and the nutrition status of the population. The study does not only fill the knowledge gap in the literature but provides important information for policy makers in developing countries to enhance the development of food systems for better consumption and nutritional outcomes.

2 Data and methods

2.1 Data and data sources

The main source of data for the study was the Living Conditions Monitoring Survey (LCMS) data collected in 1996, 1998, 2010, and 2015 by the Central Statistical Office (CSO) of Zambia. The LCMS datasets are nationally representative cross-sectional household surveys with varied sample sizes collected over time, and contain among other variables, expenditures on food items. The other variables contained in the LCMS include household expenditure on non-food items; demographics such as age, gender, location (rural/urban); economic variables such as sources of income, incomes and assets among others. The LCMS serves as the official source for national poverty statistics.

For the purpose of conducting sampling for national surveys, Zambia is demarcated into provinces and districts and further subdivided into constituencies, in turn divided into wards. The wards are further divided into Census Supervisory Areas (CSAs), which are subsequently subdivided into Enumeration Areas (EAs). The EAs constitute the Primary Sampling Units (PSUs) for the surveys.

A two-stage stratified cluster sample design was applied for the LCMS. In stage one, EA’s were selected from the probability proportional to estimated size within the respective strata. The measure of size used was population figures taken from the frames developed from the Census of Population and Housing. During the survey, listing of all the households in the selected EAs was done before a sample of households to be interviewed was drawn. In the case of rural EAs, households were listed and stratified according to the scale of their agricultural activity. Therefore, there were four explicit strata created at the second sampling stage in each rural EA: the Small Scale Agricultural Stratum, the Medium Scale Agricultural Stratum, the Large Scale Agricultural Stratum and the Non-Agricultural Stratum. Large scale households were selected on a 100% basis. Urban EAs were explicitly stratified into Low Cost, Medium Cost and High Cost areas based on CSO’s and local authorities’ classification of residential areas. In each rural EA, a minimum of 15 households was selected in the absence of large scale agricultural households, while 25 households in each urban EA were selected.

The sample size in each of the LCMS datasets used in the study was as follows: 1996 (11,961 households); 1998 (16,443 households); 2010 (19,313 households); and 2015 (12,251 households). The proportions of households that were rural and urban were as follows: 1996 (44.7% rural, 55.3% urban); 1998 (50.8% rural, 49.2% urban); 2010 (43.4% rural, 56.6% urban) and 2015 (53.4% rural, 46.6% urban). Although the datasets sets do not constitute a panel dataset they provide a basis for generating comparable statistics over time. This is because they used the same sampling frame derived from the Census of Population and Housing, used similar sampling methods and collected the same variables in each survey. The surveys were designed to provide reliable estimates at the district, provincial, rural/urban and national levels (CSO 1996, 1998, 2010 and 2016).

2.2 Methods

Studies that have examined food expenditure patterns often disaggregate consumption (usually measured by expenditure shares in the total food basket) into categories in order to conduct more detailed analysis of changes in consumption patterns. The standard procedure for the aggregation of food items consists of grouping together items that are close substitutes in consumption (Tschirley et al. 2013; Cirera and Masset 2010; Chauvin et al. 2012;Hassen et al. 2016). Other studies have gone further to examine calories consumed per capita and the associated expenditures expressed in real terms (Hassen et al. 2016). However, data on expenditure shares are more readily available compared to actual caloric intake at household level. Further, a good number of studies on consumption explore the disparities between rural areas and urban areas by disaggregating food expenditure or expenditure shares in these areas. Greater insight into the effects of income on expenditure was gained by further disaggregating food expenditure data into expenditure quintiles. Comparing the differences in expenditure patterns between well off and poorer households is often indicative of how transformation of food systems will shape food economies in the country (Hassen et al. 2016).

Following Tschirley et al. (2013) the analysis of expenditure patterns of households was conducted in order to determine changes in consumption patterns using the LCMS datasets of 1996, 1998, 2010, and 2015. The expenditure share of each food item out of total food expenditure (expressed as a percentage) was used as an indicator of consumption. Food items were categorized into the following sub-groups: food groups, commodity groups, and level of processing.
  1. i)

    Main commodity item: This included the main commodities making up the food items (for example maize is the main commodity in maize meal and wheat is the main commodity in bread). A total of 24 commodity items were identified, as shown in Table 1. Note that in this grouping of foods, some of the foods such as pulses, tubers, fruits and vegetables have still remained as food groups for analytical convenience.

     
  2. ii)

    Food groups: each food item was appropriately placed in one of the groups: a) starchy staples such as maize and cassava; b) pulses such as groundnuts; c) fruits and vegetables such as oranges and cabbage; d) animal foods such as meat, milk, eggs, and fish; e) beverages such as tea, coffee, and munkoyo1; and f) other food not classified in any of the main categories.

     
  3. iii)

    Level of processing and perishability: food items were grouped according to the amount of processing and perishability (the extent to which the item can be stored). Food items were firstly identified as non-perishable (i.e., food items with a long shelf life such a maize grains or wheat), and perishable (i.e., food items with short shelf life such as fruits, vegetables, fresh meat, and fish).

     
Table 1

Main commodity food items

1.

Rice

2.

Maize

3.

Wheat

4.

Other cereals (e.g., millet and sorghum)

5.

Cassava

6.

Potatoes

7.

Other tubers (e.g., sweet potatoes)

8.

Pulses

9.

Oil crops

10.

Vegetable oils and animal fats

11.

Vegetables

12.

Fruits

13.

Beef

14.

Other meat

15.

Fish

16.

Dairy products

17.

Poultry

18.

Eggs

19.

Sugar and sweets

20.

Spices

21.

Non-alcoholic beverages

22.

Alcoholic beverages

23.

Food consumed away from home

24.

Other food

Authors

The food items were then identified by the processing level (i.e., unprocessed (e.g., whole maize grains, cassava, sweet potatoes, rice), processed, which was further broken down into low processing, which is processing that involves little value addition, such as maize meal and high processing, which involves greater value addition, such as breakfast cereals, beverages, and sugar (see Table 2). The food was then grouped into: non-perishable unprocessed; non-perishable low processed; non-perishable high processed; perishable unprocessed; perishable low processed; and perishable high processed.
Table 2

Level of perishability and processing

1.

Non-Perishable – Unprocessed

2.

Non-Perishable - Low Processed

3.

Non-Perishable - High Processed

4.

Perishable – Unprocessed

5.

Perishable - Low Processed

6.

Perishable - High Processed

Authors

Comparisons of expenditure shares were done for each of the food categories discussed above across the different years. Comparison of expenditure shares was also done between rural and urban households. To examine the variation in expenditure patterns across the different income groups, households were further grouped into quartiles. Total household expenditure quartiles was used as a proxy for income groups similar to Hichaambwa et al. (2009) where expenditure terciles were used in a study on urban consumption and expenditure in Zambia.

To assess the responsiveness of expenditure to changes in income, income elasticity of food expenditure were computed. This statistic measures the percentage change in food expenditure in response to a one-percentage increase in income (Wilde 1989). The Engel curve represents the relationship between household expenditure e i on item and household income, y i . In this function, price is assumed to be independent of y i and the relationship between e i and y i reflects changes in the quantity purchased in response to a change in y i while holding prices fixed (Gale and Huang 2007).

The following relationship between expenditure on food items was estimated using Ordinary Least Squares (OLS):
$$ {e}_i={\eta}_i{y}_i+{\varepsilon}_i $$
(1)
Where: e i is the expenditure on food commodity item i; y i is the income for the household measured by the total expenditure on all food and non-food items as a proxy; and η i is the income elasticity of expenditure on food item i. Using the LCMS datasets of 1996, 1998, 2010, and 2015, Eq. (1) was estimated for 25 food items and seven food groups for each year. From Eq. (1), income elasticity of food expenditure was estimated as follows:
$$ {\eta}_i=\frac{\partial \ln {e}_i}{\partial \ln {y}_i} $$
(2)
Where η i is as defined already, lne i is the natural logarithm of expenditure on food item i and lny i is the natural logarithm of income for the household using total expenditure on all food and non-food items as the proxy for income.

3 Drivers of expenditure patterns

This section discusses the drivers of expenditure patterns drawing from literature and trends in each of the identified drivers. This informs our subsequent analysis of the patterns of food expenditure over time.

3.1 The main determinants of changing food expenditure patterns

In the literature, the main drivers of the changing consumption patterns are growth in income, population, and urbanization (Tschirley et al. 2013; Wilde 1989; Hichaambwa et al. 2009; Hassen et al. 2016; Cirera and Masset 2010). In theory, income influences food expenditure patterns, giving rise to the establishment of two fundamental laws in economics. Lorenz Engel, a German economist, proposed the economic law that the share of income spent on food falls as income rises. Another economist, M.K. Bennett proposed what is termed Bennett’s Law, which stipulates that the proportion of starchy staples in the diet also decreases with increasing income. Both propositions have been widely tested and in most cases confirmed. As income increases, consumers choose to spend it on foods that are more desirable. Cereals become less important and the share of high-value crops, such as fruits and vegetables, dairy and animal products, and fish, in the food expenditure basket increases (Wilde 1989; Hassen et al. 2016). Also as incomes change, household expenditure patterns vary, depending on the elasticity associated with a particular food commodity. Intuitively, this income elasticity of demand reflects whether a commodity is a necessity or a luxury, as well as a reflection of households’ consumption tastes and preferences (Cirera and Masset 2010).

Urbanization is another important driver of expenditure patterns. “Urbanization refers to a rising share of urban population in total population; a country is urbanizing if year-over-year a larger share of its population is living in urban rather than rural areas” (Tschirley et al. 2013). As cities grow in size and number, national food expenditure patterns increasingly reflect the dietary behavior of urban consumers (Wilde 1989). The combination of per capita income growth and urbanization rate can result in dramatic changes in consumption patterns. Rising incomes and urbanization results in households eating more fresh perishable and more processed foods. This, together with the growth in numbers of people, delivers explosive growth in some types of foods (meat, dairy, some fresh produce items, wheat and wheat products, and many new highly processed items) and slow growth or even decline in others (maize and other coarse grains, roots, and tubers), and vast increases over time in the total amount of food that the system has to produce, process, and distribute (Tschirley et al. 2013).

3.2 Trends in the main drivers of food expenditure in Zambia

In order to contextualize expenditure patterns in Zambia, a brief review of the main drivers, namely population, urbanization, and income is undertaken in this section.

High population growth rate affects consumption and expenditure patterns in that there is growth in aggregate demand for food with increasing population growth. Figure 1 shows the population growth for Zambia between 1950 and 2015. The population of Zambia has increased rapidly from two million people in 1950 to 15.5 million people in 2015.
Fig. 1

Zambia’s population growth. Source: United Nations 2014, Department of Economic and Social Affairs, Population Division

Urbanization growth rate affects consumption and expenditure patterns in that as urban areas grow in proportion, national food expenditure patterns increasingly reflect the dietary behavior of urban consumers (Wilde 1989). Figure 2 shows the growth rate of the population in urban and rural areas.
Fig. 2

Zambia Population Growth Rate in Urban and Rural Areas. Source: United Nations 2014, Department of Economic and Social Affairs, Population Division

Although there has been variation in the patterns of urban and rural growth rates over the years, compared to its neighbors, Zambia has been highly urbanized for several decades and there has been further sustained growth in the urban population relative to rural areas since 2000. Zambia’s urban population in 2015 stood at 40.9% compared to the Sub-Saharan Africa average at 37.9%. The high level of urbanization has led to an expansion of supermarkets in most urban areas. Over the last 10 years, supermarkets have influenced the way urban consumers procure food items and the types of foods consumed, by offering a wider range of grocery retail products at relatively cheaper prices, given economies of scale and global sourcing strategies (Das Nair and Chisoro 2015). The market share of supermarket purchases, however, still remains relatively low as compared to the traditional and informal market outlets (Tschirley et al. 2013).

High per capita income growth implies that householders’ purchasing power increases and however, they demand more nutritious and diversified diets. Zambia’s GDP per capita has grown from around $800 per annum in 1990 to around $1000 per annum by 2014 (World Bank 2014). While GDP per capita shows that income levels have increased in Zambia over the years, this measure hides the inequalities that may exist in the distribution of income. Thus, it is necessary to look at per capita income based on survey data, which also shows per capita income in rural and urban areas. In addition to this, income distribution measures using the Gini Coefficient and poverty rates are imperative.

Drawing from survey data, Fig. 3 shows per capita income, Gini Coefficient and poverty rates for the period 1996 to 2015 in rural and urban areas. Inflation adjusted per capita income (2010 constant prices) increased from K273 to K308 between 1996 and 2015 (13% increase). Urban households experienced growth in real per capita income which rose from K461 in 1996 to K553 in 2015 (20% increase). By contrast, rural households experienced a drop in real per capita income from K171 to K129 over the period (25% decrease). Over the entire sample Zambia experienced growth in per capita income: however, this income growth was more concentrated in urban areas as there was a reduction in real per capita income in rural areas.
Fig. 3

Per capita income, gini coefficient, and poverty rates: 1996 to 2015. Source: CSO 1996, 2005, 2012, and 2016

Between 1996 and 2015, the Gini Coefficient2 over the entire sample increased from 0.61 in 1996 to 0.69, which means that income inequality increased over the period. Income inequality worsened in both rural and urban areas with higher inequality experienced in urban than in rural areas.

Overall, national poverty declined from 78% in 1996 to 54% in 2015. Urban poverty decreased tremendously from 60% in 1996 to 23% in 2015, while rural poverty declined moderately from 89% in 1996 to 77% in 2015. The trends in poverty underline the finding that per capita income growth has been concentrated in urban areas. However, the higher inequality especially in urban areas indicates a widening gap in the standards of living by households in the different income groups.

4 Study findings

The study findings are presented by first showing the household expenditure patterns of the main food groups based on the individual food items, followed by the level of processing and perishability. Then the expenditure pattern on the main food items are presented to give more insight into the patterns observed in the food groups. Finally, the income elasticities of food expenditure are also discussed.

4.1 Household food expenditure patterns on the main food groups

In order to observe consumption patterns on the main food groups, the food commodity items were categorized into six main food groups, namely starchy staples; pulses; vegetables and fruits; meat, milk, eggs and fish; beverages; and others. Figure 4 shows the proportion of total expenditure on food that is spent on the various categories of food. Starchy staples, which constituted 41% of households’ food expenditure in 1996, showed a decline in 2015 to 28%. The expenditure share of vegetables and fruits on the other hand exhibited a marked increase from 14% in 1996 to 26% in 2015. The share of meat, milk, eggs, and fish in households’ total food expenditure increased moderately from 25% in 1996 to 29%. There were minimal changes in the expenditure shares of pulses while the share of beverages declined over the period.
Fig. 4

Household food expenditure share on food groups. Source: Authors’ computation from CSO 1996, 1998, 2010, and 2016

In general, the above patterns of expenditure are in agreement with Bennett’s law: as the expenditure shares on starch in Zambia have fallen, the shares on other more nutritious foods such as meat and vegetables have increased. Nevertheless, there are salient differences in the patterns of expenditure by households in rural and urban areas. Note, for example, that per capita income dropped among rural households, meaning that their increased share of vegetable expenditure is an indication of declining per capita income.

Figure 5 shows expenditures shares on different food groups disaggregated by rural and urban households. Rural households experienced a greater drop in the share of their food expenditure going to starchy staples between 1996 and 2015 (from 45% to 29%) than urban households which experienced a moderate drop (from 33% to 28%). Meanwhile, the share of vegetables and fruits in rural households’ food expenditure rose substantially from 13% to 29% over the same period. In urban areas, the increase in vegetables and fruits was more modest.
Fig. 5

Rural and urban household’s expenditure shares on different food groups. Source: Authors’ computation from CSO 1996, 1998, 2010, and 2016

Urban households experienced a larger increase in expenditure share on meat, milk, eggs and fish than rural households. While average per capita income declined among rural households between 1996 and 2015, there was a corresponding decline in the starchy staples’ share in rural households’ food expenditure as well as significant increase in vegetables and fruits and a moderate increase in animal food. The increase in vegetable expenditure over animal food for the rural households may be due to the decline in per capita income among these households.

Further analysis of the variation in expenditure patterns is presented in the annexes (Figs. 10 and 11). The more urbanized provinces, that is, Lusaka and the Copperbelt are distinct from the other provinces, showing low shares of starchy staples consistently over the period of our analysis. Southern and Central provinces show a catching-up effect with a declining trend in terms of the share of expenditure on staples. The poorer provinces of Western, Luapula and Eastern Provinces have had larger shares of households’ expenditure on starchy staples and higher shares going to vegetables by 2015.

The Southern, Eastern and Western Provinces account for the highest household expenditure shares on maize. Southern and Eastern have traditionally been regarded as the maize-belt of Zambia with high dependence on the crop. The Copperbelt and Lusaka, the most urbanized, show low maize expenditure shares. North Western, Luapula and Northern Provinces depend more on cassava, hence the higher shares of cassava expenditure in these provinces and lower shares of maize. The share of animal food increases most in the urbanized provinces of the Copperbelt and Lusaka but also in Southern Province, which is a major livestock producing province. Vegetable expenditure shares increased across all provinces by 2015 but more so in the poorer provinces.

To further observe the effects of income on the expenditure patterns of the food groups, households were categorized into income groups and their expenditure shares determined over the study period as summarized in Fig. 6. The lowest income quartile households experienced a significant drop in the share of their food expenditure on starchy staples from 49% to 28% between 1996 and 2015. At the same time, these households’ share of the food budget spent on vegetables more than doubled from 14% to 32%, while meat, milk, fish, and eggs increased less dramatically from 21% to 25%.
Fig. 6

Proportion of food budgets spent on each food group by income quartiles. Source: Authors’ computation from CSO 1996, 1998, 2010, and 2016

The highest income quartiles experienced a less dramatic transformation over the period 1996 to 2015. There was a drop in the expenditure shares on starchy staples, a moderate increase in the shares on vegetables and fruits and a considerably larger increase in expenditure on meat, milk, eggs and fish.

4.2 Household food expenditure shares by level of processing and perishability

Figure 7 shows the rural and urban households’ expenditure shares by level of processing and perishability. There was a consistent drop in both urban and rural areas in the expenditure shares of unprocessed non-perishable foods from 41% to 13% between 1996 and 2015 in rural areas and 16% to 5% in urban areas. During the same period, there was a remarkable increase in the expenditure shares on perishable, low processed food in both rural and urban areas. However, while there was an increase in the expenditure shares of perishable unprocessed foods in rural areas, these foods experienced a decline in urban areas.
Fig. 7

Rural and Urban Food Expenditure Shares by Level of Processing/Perishability. Source: Authors’ computation from CSO 1996, 1998, 2010, and 2016

Figure 8 shows the shares of total food expenditure on processed versus unprocessed food. Both rural and urban households experienced a significant rise in the share of their expenditure on processed food between 1996 and 2015. Larger expenditure shares were observed in urban than rural areas.
Fig. 8

Expenditure Shares of Processed and Unprocessed Food. Source: Authors’ computation from CSO 1996, 1998, 2010, and 2016

4.3 Household expenditure patterns on the main commodity groups

To obtain further insight into what is driving the changes in expenditure in the different food groups, the percentage share of the total food expenditure on the main constituent food items was determined. Table 3 shows the proportion of household food budgets spent on each of the commodity groups in Zambia for the periods 1996, 1998, 2010, and 2015. The share of household food budgets spent on maize has significantly decreased from 23% in 1996 to 14% in 2015. On the other hand, the share of food budgets spent on wheat increased from 4% to 6% while the share of rice and potatoes remained constant. Budget shares of other cereals such as millet and sorghum, cassava, and other tubers decreased significantly over the same period, explaining the reduction in the starchy staples food group. Households’ expenditure shares of pulses such as groundnuts reduced slightly from 6% to 5%. The budget share of food spent on vegetables increased from 11% in 1996 to 20% in 2015. The beef expenditure share of total food budgets dropped from 6% to 3% over the same period, while the share of expenditure on other meat types increased. Fish expenditure has remained high over the period and increased slightly from 11% to 12% and poultry budget shares increased from 5% to 7%. The share of food budgets spent on dairy products declined slightly and at the same time, the share of eggs increased slightly. These increases in the budget share of poultry and eggs explains the increases in the meat, milk, fish, and eggs food group.
Table 3

Proportion of food budgets spent on each commodity group

  

1996

1998

2010

2015

(%)

(%)

(%)

(%)

1.

Rice

1.8

1.78

1.96

1.98

2.

Maize

23.36

23.23

22.27

13.93

3.

Wheat

3.84

4.83

5.27

6.48

4.

Other Cereals (e.g., Millet and Sorghum)

3.44

1.79

0.82

0.37

5.

Cassava

5.09

7.95

5.75

2.81

6.

Potatoes

0.53

0.55

0.7

0.89

7.

Other Tubers (e.g., Sweet potatoes)

2.67

0.25

1.06

1.96

8.

Pulses

5.89

5.56

5.47

5.21

9.

Oil Crops

0

0

0.13

0.07

10.

Vegetable Oils and Animal Fats

2.6

2.79

4.49

5.13

11.

Vegetables

11.09

11.55

15.88

19.66

12.

Fruits

0.68

1.66

2.86

1.45

13.

Beef

5.98

3.27

2.45

2.98

14.

Other Meat

0

4.48

2.4

2.1

15.

Fish

11.47

9.68

9.26

12.47

16.

Dairy Products

1.93

2.12

1.58

1.36

17.

Poultry

4.63

4.57

5.6

7.47

18.

Eggs

1.44

1.45

1.39

2.18

19.

Sugar and Sweets

4.6

4.86

4.23

4.49

20.

Spices

1.26

1.78

1.47

1.62

21.

Non-Alcoholic Beverages

2.36

0.64

2.71

3.39

22.

Alcoholic Beverages

5.32

3.93

1.95

1.79

23.

Food consumed away from home

0

0

0.27

0.15

24.

Other food

0

0

0.01

0.02

Source: Authors’ computation from CSO 1996, 1998, 2010, and 2016

In order to determine whether the changes in the percentage share of budgets spent on each commodity is due to changes in consumption patterns or changes in prices, Table 4 shows the real (inflation adjusted) prices between 1996 and 2015. The prices of cereals, especially maize, products show a significant decline. Among animal proteins, beef prices remained constant while the rest of the animal foods declined, except for kapenta (sardines), which increased. Milk and cooking oil prices declined; however, egg prices recorded a slight increase. All the fruits and vegetables showed a decline, except sweet potato leaves, which show a marginal price increase. Overall, there is variation in the way prices changed between 1996 and 2015 for different commodities; however, the general picture is that prices of most food items dropped in real terms over this period. This is also supported by the drop in the national annual inflation rate from 43% in 1996 to 10% in 2015 (CSO 2017).
Table 4

Real prices of selected food items

Food Items

Unit

Real (2015) Food Price per unit in ZMK

1996

1998

2010

2015

Breakfast mealie meal

25 kg

163.41

169.41

86.11

72.94

Roller mealie meal

25 kg

134.79

148.42

61.59

51.58

Maize grain

20 l tin (17 kg)

53.03

68.76

30.78

30.20

Hammer milling charge

20 l tin

6.24

17.59

4.25

4.60

Rice

kg

17.30

15.52

9.71

Wheat flour

3 kg

18.26

16.72

25.11

19.14

Cassava meal

1 kg

8.35

7.87

5.75

5.07

Irish potatoes

1 kg

11.15

10.34

5.92

5.75

Sweet potatoes

1 kg

5.74

6.00

2.15

2.65

Millet

5 l tin

23.57

8.16

13.76

15.82

Sorghum

each

35.73

8.06

16.20

17.39

Dried beans

1 kg

19.14

20.89

12.13

13.57

Beef (Mixed Cut)

1 kg

28.89

27.75

28.32

Pork Chops

1 kg

42.81

46.10

30.71

Chicken (frozen)

1 kg

54.77

50.46

19.46

Fish (frozen)

1 kg

39.43

30.30

23.19

Dried Kapenta (sardines)

1 kg

100.16

100.35

110.25

Fresh Milk

500 mls

6.38

6.19

4.39

Eggs

1 unit (10 eggs)

28.36

22.90

29.44

Cooking oil

2.5–3 l

99.94

95.54

40.85

Oranges

1 kg

10.35

13.34

6.87

7.90

Tomatoes

1 kg

10.43

8.42

5.05

5.74

Onion

1 kg

17.68

12.67

8.79

9.43

Rape (Kale)

1 kg

6.24

17.59

4.25

4.00

Sweet potato leaves

1 kg

6.87

6.26

3.82

7.00

Sugar

2 kg

30.27

22.31

19.04

Tea bags

each

16.81

8.66

CSO 2017

Some food prices for 1998 and 2010 were not collected

While real price changes can partly help explain changes in household expenditure, the question of whether real prices or income changes account for changing expenditure patterns cannot be adequately answered in this study. One assumption, though, is that changes in prices also have an income effect in the long term because as items become cheaper over time, households’ disposable income increases.

Other than price, production trends over the period of analysis for selected commodities or crops is worth noting. Figure 9 shows that maize, wheat and Irish potatoes experienced the largest per capita production growth, while cassava and sweet potatoes declined in terms of per capita production. Per capita production of course grains i.e. millet and sorghum steadily declined over the period. In general, production trends have been responding to changing consumption patterns, although in some cases policies such as government support for maize have had an influence on production. In the case of Irish potatoes, there has been significant growth in per capita production although coming from a low production base. Nevertheless, the production is still very low making Zambia import dependent. Rice, whose expenditure share is growing, especially in urban and wealthier households, experienced slow growth, meaning that the country remains largely dependent on imports.
Fig. 9

Per capita production of selected staples: 1996, 1998, 2010 and 2015. Source: Ministry of Agriculture Crop Forecast Surveys, 1996; 1998, 2010 and 2015; United Nations, Department of Economic and Social Affairs, Population Division 2014. Note: Per capita production was computed as annual production (kg) /annual population (persons)

The expenditure share of each of the commodity items was disaggregated by rural and urban areas (Table 5). The expenditure shares of maize have been consistently higher for rural than urban areas over the period. In 1996, the share of maize in rural areas was 26% while in urban areas it stood at 18%. By 2015, the share of maize in rural budgets declined to 16%, while it also declined to 12% in urban areas. The share of rice has remained constant in both urban and rural areas and that of wheat increased in both rural and urban areas, i.e., 2% and 8% in rural and urban areas, respectively, in 1996 and 4% and 10% in rural and urban areas, respectively, in 2015. The expendiure share of cassava has been higher among rural households but there has been a significant drop. Urban households’ expenduture share of cassava which was already lower has further dropped. The budget share of potatoes has remained low in urban and rural areas but the share of vegetables increased in both urban and rural areas between 1996 and 2015, with rural areas recording a higher share than in urban areas by 2015. The share of beef dropped in both urban and rural areas, with urban areas accounting for a higher share of beef expenditure. Rural areas, however, accounted for a higher share of other meat types than urban areas. These include small livestock such as goats, pigs, and sheep among others. The share of fish has been higher in rural than in urban areas while the share of poultry was significantly higher in urban areas by 2015. The expenditure shares of dairy products and eggs have been higher in urban than in rural areas and yet the increase over the period has not been that high.
Table 5

Proportion of food budgets spent on each commodity groups by rural and urban

  

1996 (%)

1998 (%)

2010 (%)

2015 (%)

Rural

Urban

Rural

Urban

Rural

Urban

Rural

Urban

1.

Rice

1.30

2.76

1.30

2.62

1.29

3.14

1.26

2.92

2.

Maize

26.01

18.41

28.90

13.25

25.78

16.03

15.61

11.75

3.

Wheat

1.60

8.03

1.49

10.71

3.23

8.89

3.83

9.89

4.

Other Cereals

5.09

0.35

2.69

0.19

1.22

0.11

0.61

0.04

5.

Cassava

7.49

0.59

11.79

1.18

8.63

0.63

4.70

0.37

6.

Potatoes

0.30

0.97

0.16

1.23

0.36

1.29

0.58

1.30

7.

Vegetables

10.32

12.53

9.49

15.17

17.33

13.28

23.07

15.24

8.

Fruits

0.53

0.95

1.82

1.37

2.68

3.17

1.03

2.00

9.

Beef

4.95

7.92

1.60

6.21

1.52

4.12

1.88

4.40

10.

Other Meat

0.00

0.00

6.07

1.68

2.54

2.15

2.63

1.41

11.

Fish

11.51

11.39

9.28

10.40

8.47

10.66

12.70

12.17

12.

Dairy Products

1.37

2.97

1.39

3.41

1.12

2.41

1.00

1.84

13.

Poultry

4.85

4.22

4.01

5.5

4.72

7.18

6.16

9.16

14.

Eggs

1.13

2.04

0.91

2.39

0.83

2.39

1.54

3.02

Authors’ computation from CSO 1996, 1998, 2010, and 2016

Table 6 shows the proportion of food budgets spent on each commodity group by income group. For the bottom 25% income group, maize constituted the highest share of household expenditure in 1996. For the top 25% income group, maize consistently accounted for a smaller share of the food budget i.e., 15% in 1996 and 9% in 2015. By 2015, wheat accounted for a larger share of the food budget for the top 25% income households than maize. The expenditure on rice and potatoes has been consistently higher among the high-income households throughout the period and these higher income households have increased the share of their expenditure on these commodities. This finding is similar to that of Hichaambwa et al. (2009) who found that wheat (and not maize) had the biggest expenditure share among staples in Lusaka, followed by maize and rice. Cassava, other tubers and other cereals such as millet and sorghum have been predominantly consumed by low income households throughout the period and the shares among the higher income households has declined even further.
Table 6

Proportion of food budgets spent on each commodity group by income (expenditure) groups

 

1996

1998

2010

2015

Qa1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Rice

0.97

1.59

2.42

2.97

1.26

1.56

2.16

2.38

0.62

1.56

2.97

4.13

0.75

1.34

2.67

3.63

Maize

30.52

22.96

19.85

14.63

33.15

25.88

19.38

9.86

28.97

23.65

18.47

10.83

16.60

16.29

12.82

8.50

Wheat

0.80

2.80

5.62

9.04

1.39

4.52

7.33

7.34

1.94

4.47

7.90

10.16

2.89

5.56

8.72

9.62

Cassava

8.32

5.61

2.86

0.86

14.93

7.92

4.21

2.18

9.89

6.12

2.40

0.91

4.94

3.47

1.70

0.46

Potatoes

0.24

0.38

0.70

1.14

0.12

0.38

0.74

1.17

0.23

0.49

0.97

1.69

0.39

0.59

0.97

1.89

Other Tubers

2.54

3.13

2.78

2.09

0.15

0.20

0.27

0.45

1.09

1.23

1.02

0.76

2.19

2.56

1.86

0.98

Vegetables

12.28

10.80

10.68

9.75

10.03

11.57

12.52

12.60

19.88

16.63

12.98

10.00

26.42

21.34

17.09

11.50

Fruits

0.38

0.56

0.75

1.34

1.56

1.79

1.56

1.76

2.20

2.87

3.25

3.71

0.56

0.99

1.72

2.95

Beef

4.11

5.58

6.53

9.51

1.59

2.56

3.97

5.80

1.02

1.96

3.09

5.49

1.56

2.03

3.31

5.77

Other Meat

0.00

0.00

0.00

0.00

1.64

3.37

4.48

10.04

1.94

2.61

2.68

2.63

2.28

1.89

2.04

2.21

Fish

10.78

12.62

12.19

10.10

11.12

10.09

9.34

7.47

8.08

9.16

10.31

10.49

13.94

12.05

12.01

11.64

Dairy Products

1.07

1.39

2.31

3.94

1.05

1.53

2.59

3.90

0.68

1.15

2.15

3.51

0.62

0.85

1.52

2.88

Poultry

3.72

4.56

4.82

6.25

3.02

4.72

5.06

6.05

3.54

5.61

6.59

8.63

5.40

7.12

8.55

9.35

Eggs

0.88

1.27

1.76

2.38

0.69

1.48

1.91

1.96

0.59

1.17

2.03

2.59

1.31

2.15

2.59

2.89

Authors’ computation from CSO 1996, 1998, 2010, and 2016

aQ Quartile

By 2015, vegetables accounted for the largest share of food expenditure for the bottom 25% income group followed by maize. Vegetables increasingly constituted higher budget shares among the lower income households compared to the higher income households such that by 2015 the bottom 25% income group spent 26% of their food budgets on vegetables compared to 11% for high-income households.

Beef share of household’s food budgets is significantly higher among the top 25% households over the period. In 2015, the share of other meats was similar in both urban and rural areas. The share of fish increased over the period especially among the bottom 25%, accounting for 14% of their total food expenditure compared to 12% for the top 25% by 2015. Poultry shares have been consistently higher for the high-income households and the share of poultry increased significantly by 2015. Dairy products and eggs have also been higher among the higher income households over the period.

The major points discovered from changes in expenditure patterns over time are that: 1) The share of starchy staples in households total food expenditure have declined over the years while that of other more nutritious foods have risen in line with growing incomes and urbanization 2) Households’ expenditure shares of maize have fallen over the same period while the share of other staples such as wheat and Irish potatoes have risen 3) The changes in expenditure patterns have been more pronounced among the wealthier households mainly in urban areas where incomes have grown much faster and 4) The share of rural households’ expenditure on vegetables had risen significantly, which presents important opportunities for addressing nutritional problems associated with rural households in Zambia.

4.4 Income elasticities of food expenditure

Income elasticities of expenditure shows the responsiveness of expenditure on each commodity item to an increase in income. Table 7 shows the income elasticities of expenditure for food groups over the period 1996 to 2015. The income elasticity of starchy staples declined over the period indicating that as income rises (as seen by the high per capita income), household demand less starchy foods. However, the demand for more high value food such as meat, milk, eggs, and fish increased. There has also been an appreciable increase in the income elasticity of beverages. The income elasticity of fruits and vegetables declined marginally, while the elasticity of meat, milk, fish, and eggs showed an increase.
Table 7

Income elasticities of expenditure for food groups

 

Food Group

Year

1996

1998

2010

2015

1.

Starchy staples

0.72

0.54

0.40

0.39

2.

Pulses

0.29

0.40

0.51

0.38

3.

Fruits and vegetables

0.46

0.67

0.43

0.42

4.

Meat, milk, eggs and fish

0.53

1.52

0.66

0.67

5.

Beverages

0.64

0.81

0.91

1.30

6.

*Prepared Foods

1.17

0.89

7.

Others

0.33

0.53

0.45

0.49

Authors

*Data on prepared foods was not collected in 1996 and 1998

Table 8 summarizes the income elasticities of expenditure for the individual food items for the periods 1996, 1998, 2010, and 2015. The elasticity for maize is quite low and has decreased between 2010 and 2015 reflecting lower expenditure on maize as incomes grow. In comparison, rice, wheat and potatoes have had higher income elasticities of expenditure meaning that growth in income results in increased household expenditures on these items. Income elasticities of food expenditure on other cereals such as sorghum and millets as well as cassava are negative, implying a reduction in expenditures as household incomes rise. The elasticity of other tubers such as sweet potatoes is low and has declined over the period but they have been positive.
Table 8

Income elasticities of expenditure for commodity items

  

Year

1996

1998

2010

2015

1.

Rice

0.48

0.71

1.26

0.59

2.

Maize

0.82

0.19

0.22

0.21

3.

Wheat

1.40

0.48

0.65

0.59

4.

Other cereals

−0.19

0.05

−0.19

−0.08

5.

Cassava

−0.13

−0.11

−0.24

−0.13

6.

Potatoes

0.68

0.93

1.52

0.91

7.

Other tubers

0.20

0.26

0.29

0.14

8.

Pulses

0.29

0.25

0.52

0.38

9.

Oil crops

0.32

0.50

10.

Vegetable Oils and Animal Fats

0.32

0.93

0.41

0.42

11.

Staple vegetables

0.47

0.34

0.49

0.37

12.

Other vegetables

0.28

0.23

13.

Fruits

0.92

0.53

0.72

1.96

14.

Beef

0.78

0.70

2.12

1.03

15.

Other Meat

−0.23

0.61

0.76

16.

Fish

0.32

0.44

0.46

0.46

17.

Dairy Products

0.60

0.76

1.13

1.71

18.

Poultry

0.58

0.71

0.64

0.55

19.

Eggs

0.59

0.58

0.55

0.58

20.

Sugar and Sweets

0.36

0.60

0.41

0.44

21.

Spices

0.13

0.26

0.61

0.46

22.

Non-Alcoholic Beverages

1.13

2.56

0.84

1.33

23.

Alcoholic Beverages

0.49

0.22

1.04

1.43

24.

Food consumed away from home

1.17

3.78

25

Other Foods

1.67

−0.85

Authors’ computation from CSO 1996, 1998, 2010, and 2016

Pulses, oil crops, vegetable oils and animal fats (edible oils) all have positive elasticities. There was a marginal increase in the expenditure elasticities for pulses over the period. Similarly, the expenditure elasticity for oil crops shows a slight increase as does the elasticity of edible oils.

Staple vegetables such as tomato, onion, and green leafy vegetables have positive elasticity; however, this has declined over the years. In comparison, other vegetables including traditional vegetables have a lower elasticity of expenditure. Fruits on the other hand have a much higher elasticity of expenditure, which was greater than one by 2015.

The expenditure elasticity of beef was less than one in 1996 and 1998 but rose above one in 2010 and 2015. Similar to beef, the expenditure elasticity of dairy products increased substantially by 2015.

The rising beef and dairy expenditure elasticities shows that these items have associated with income households over the years. There is also a rise in the elasticity for other meat types; however, these elasticities are lower than beef. The fish expenditure elasticities have been more consistent, increasing only marginally since 1996. Fish expenditure has been less responsive to income changes over time than other sources of animal protein, meaning that even lower income households can afford it. Kapenta (sardines) is prominent in poorer households’ diets, while wealthier households consume bream fish. Zambia has a deficit in fish, with fish imports coming from as far afield as China. This is surprising, given the vast potential that Zambia has in local fish production and that the country holds 35–45% of Southern Africa’s fresh water. The expenditure elasticity of poultry has been low over the years and shows a marginal decline by 2015. Eggs also show a similar trend with poultry products. This also explains why poultry (particularly chicken) is now more widely consumed than before. There is a booming broiler poultry industry in Zambia’s urban areas while village chickens are kept by almost all households in rural areas. Food consumed away from home shows the highest expenditure elasticity among all commodity items. Alcoholic and non-alcoholic expenditures also exhibit high expenditure elasticities. Expenditure on these items is associated with high incomes. Table 9 provides a comparison of expenditure elasticities for commodity items for rural and urban areas.
Table 9

Expenditure elasticities for commodity groups by rural and urban households

  

1996 (%)

1998 (%)

2010 (%)

2015 (%)

Rural

Urban

Rural

Urban

Rural

Urban

Rural

Urban

1.

Rice

0.45

0.44

0.39

0.68

1.16

1.20

1.41

0.43

2.

Maize

0.18

0.89

0.22

0.17

0.33

0.26

0.49

0.17

3.

Wheat

1.37

1.50

0.33

0.39

1.36

0.55

1.16

0.45

4.

Other Cereals

0.06

0.09

0.11

0.21

0.04

0.11

0.13

0.17

5.

Cassava

0.05

−0.04

0.02

−0.02

0.09

−0.18

0.08

0.02

6.

Potatoes

0.56

0.60

1.21

0.69

1.07

1.12

2.39

0.55

7.

Other Tubers

0.26

0.18

0.24

0.23

0.78

0.25

0.30

0.15

8.

Pulses

0.30

0.28

0.17

0.25

0.44

0.64

0.61

0.39

11.

Vegetables

0.30

0.48

0.28

0.33

0.71

0.45

0.43

0.37

12.

Fruits

0.76

0.87

0.27

0.84

0.66

0.65

1.86

1.40

13.

Beef

0.55

0.78

0.48

0.75

1.29

1.22

1.93

0.65

14.

Other Meat

0.32

2.35

0.71

0.74

1.08

1.04

15.

Fish

0.52

0.25

0.25

0.56

0.51

0.46

0.54

0.45

16.

Dairy Products

0.68

0.53

0.89

0.70

1.56

0.96

1.15

1.18

17.

Poultry

0.40

0.67

0.45

0.87

0.59

0.63

0.72

0.50

18.

Eggs

0.40

0.58

0.47

0.59

1.01

0.47

0.96

0.49

19.

Sugar and sweets

0.43

0.31

1.16

0.42

0.55

0.40

0.81

0.37

20.

Spices

0.14

0.14

0.31

0.25

0.84

0.70

0.39

0.57

21.

Non-Alcoholic Beverages

6.26

0.74

1.16

1.16

1.19

0.76

4.08

0.95

22.

Alcoholic Beverages

0.42

0.50

0.71

3.44

0.58

1.03

7.43

1.06

23.

Food consumed away from home

   

1.08

0.23

13.55

2.91

24.

Other food

   

1.29

0.93

0.76

0.52

Authors’ computation from CSO 1996, 1998, 2010, and 2016

The main limitation of the study is that while efforts were made by the CSO -the main government department charged with collecting the datasets used in the study- there may have been variation in sampling methods used in the different years. Further, there was variation in the proportions of households sampled in rural and urban areas from year to year. Another limitation is that the partial effects of population, income and urbanization on the observed expenditure patterns was not conducted as it was beyond the scope of the study. Further research is required to determine the partial effects of the important drivers of expenditure and consumption patterns.

5 Conclusions

The study analyzed trends in expenditure patterns over time using nationally representative surveys for the periods 1996, 1998, 2010 and 2015 in order to understand the changing food expenditure patterns in Zambia and the implications of this transformation on food policy, food market development, and rural development.

Zambia experienced per capita income growth and rapid urbanization between 1996 and 2015. However, this period was also characterized by rising income inequality. The main findings of the study are as follows: firstly the share of starchy staples in households total food expenditure has declined over the years while that of other more nutritious foods has risen in line with growing incomes and urbanization. Secondly households’ expenditure shares of maize have fallen over the same period while the share of other staples such as wheat and Irish potatoes has risen. Thirdly the changes in expenditure patterns have been more pronounced among the wealthier households, mainly in urban areas where incomes have grown much faster. Fourthly, the share of rural households’ expenditure on vegetables has risen significantly, which presents important opportunities to address nutritional problems associated with rural households. The share of households’ food expenditure on maize has declined substantially over the years, while other staples such as wheat are becoming important in peoples diets. The findings in this paper are in agreement with the main literature from previous studies such as that of Tschirley et al. (2013) who found that the starchy staples share of expenditure of countries in Sub Sahara Africa ranged from 30% to 55% for the countries studied. They also found that there were variations in the patterns of expenditure by income groups and by rural and urban areas, which is similar to our findings.

From the findings it is noted that the excessive policy focus on maize is misplaced as it fails to recognize the transformation that has taken place over the years. Instead, policies should shift in line with what households are consuming. This entails creating an enabling environment for the production of more nutritious foods, creating marketing linkages to facilitate rural urban linkages and nutrition messaging for improving nutritional outcomes to address the double burden nutrition challenge.

While these changing expenditure patterns could be a result of changing preferences as per capita income grows and in some cases prices, maize centric policies seem to have also played a role. Thus, Zambia’s agricultural policies have failed to recognize that consumption patterns have changed over time. The beginnings of dietary transformation in Zambia are evident from the reduction in households’ expenditure shares on staple foods and the increase in the share of other foods. More work will be needed to see how changes in expenditure translate into dietary changes within households, and on to affect the nutrition status of the population. However, there are major variations between urban and rural households as well as across the different income groups, an indication of growing income inequality as well as the concentration of income growth among urban households.

The increasing share of vegetable expenditure among poorer households is a promising development from a nutrition point of view. This could be an important point for policy leverage as it already points towards diversification of the poor’s diet away from staples to vegetables. This trend could be further harnessed to generate positive nutritional outcomes, particularly if the poor could increase their consumption of animal foods .

Fish remains the major animal-source food among poor households whose expenditure share has remained quite high in both rural and urban areas as well as across income groups. By contrast, the shares of beef and dairy products in households’ food budgets have fallen even among the wealthier households, while the shares of poultry and eggs have experienced growth in rural and urban areas as well across all income groups. There has also been an increase in the expenditure shares of perishable and processed food in both rural and urban areas, which presents opportunities in agro-processing horticultural and fresh produce sub-sectors.

In line with the findings above, income elasticity of expenditure results indicate that households’ expenditure on maize does not increase in response to an increase in income. Households actually reduce expenditure on cassava, sorghum, and millet in response to income growth. However, households’ expenditures are more responsive towards wheat, potatoes, rice, beef, dairy products, fruits, poultry and eggs, which are some of the most nutritious and aspirational foods. Expenditure on vegetables and pulses, however, are only moderately responsive to growth in per capita income.

6 Policy implications

  • While maize is Zambia’s main staple food, the focus on maize undermines positive nutritional outcomes as households tend to produce more of it, at the expense of more nutritious foods. Market and trade policies that try to keep maize prices low through price controls and keeping large stocks of maize as buffer stock (strategic grain reserves) may have prevented real prices from increasing over time, thus making maize both accessible and affordable. However, the downside of government subsidies and price controls, which have had a disproportionate focus on maize, affect production and consumption decisions of farmers in a way that leads to poor nutrition outcomes. Government needs to reduce its excessive focus on maize and encourage diversification in food production systems in line with changing demand patterns. This should encourage the production of various crops such as vegetables, fruits, rice, potatoes and wheat, and small livestock as well as poultry. In particular policy should focus on stimulating production for vegetables which exhibit high consumption among rural households and creating market linkages with urban centres where demand is also growing rapidly.

  • As the study findings suggest, there has been an increase in vegetable expenditure among poor households. There should be increased policy focus on the production and marketing of diversity of vegetable crops to promote nutrition enhancement of the poor.

  • Fish remain the most important source of nutrients from animal foods, especially among poor households. However, Zambia is still a net importer of fish. There is need to strengthen the local production capacity of fish to reduce the deficit through aquaculture development. Likewise, rice and Irish potatoes whose demand is growing with income are largely imported. There is scope for substituting such imports with domestic production especially as Zambia has the right growing conditions and available water resources for these crops.

  • Rising demand for animal proteins such as poultry implies rapid growth in feed demand and maize is among the major inputs in feed. The competing uses of maize as feed or for human consumption may become a major issue in the near future, given the fast growth of poultry demand; although at the moment Zambia is a net surplus producer and exporter of maize grain and feed.

Footnotes

  1. 1.

    This a traditional beverage made from maize

  2. 2.

    The Gini Coefficient helps our understanding of the equality of income distribution in the population. This measures household income distribution using an index of inequality, which ranges from 0 to 1. A coefficient of 0 represents total equality in income distribution, while a coefficient of 1 represents total inequality (CSO 2012).

Notes

Funding information

We wish to acknowledge the financial and substantive support of the Swedish International Development Agency (SIDA) and the United States Agency for International Development (USAID) in Lusaka.

Compliance with ethical standards

Statement on conflict of interest

We as authors hereby declare that there is no conflict of interest arising from the publication of this article in Food Security.

Details of ethical approval

The analysis done for this article used datasets collected by the Government of the Republic of Zambia through the Central Statistical Office (CSO), which is the department charged with national statistics. The CSO complied with ethical standards during the collection of data.

Statement of informed consent

As part of standard practice by the CSO, respondents were given full information on the survey prior to the commencement of the questionnaire based interviews. Further, respondents were also given an option to discontinue the interview.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature and International Society for Plant Pathology 2018

Authors and Affiliations

  1. 1.Indaba Agricultural Policy Research InstituteLusakaZambia

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