welfare effect of cereal export bans in the united … · 2015-08-14 · bans on cereals in...

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WELFARE EFFECT OF CEREAL EXPORT BANS IN THE UNITED

REPUBLIC OF TANZANIA

Mesia Ilomo

University of Dar es Salaam Business School, United Republic of Tanzania

[email protected]

Abstract

The aim of this paper is to examine the consumer welfare effect of cereal export bans in mainland

Tanzania. The focus of the research is on maize which is the most widely consumed cereal in the country

which also accounts for a substantial share of cereal exports. Maize was the main target of cereal export

bans over the past two decades. As existing research on the issue has provided mixed results, this paper

complements it by looking in greater detail at the impact of the bans on different regions of the country.

Regions are categorized into East African Community (EAC) border regions and peripheral regions

depending on their level of integration into the EAC which is the main export market for Tanzanian

maize.

Drawing on data from the 2007 Household Budget Survey of the Tanzania Bureau of Statistics and price

data from the Ministry of Industry and Trade and the Food and Agriculture Organization of the United

Nations (FAO), the paper runs a series of regressions. The first one aims to determine whether the

transmission from international to domestic prices is affected by the level of integration into the EAC

market. The regression shows that the level of price transmission is high in regions bordering the EAC

and limited in regions far from the EAC border. The subsequent regression at the regional level seeks to

establish the effect of the export ban on domestic prices. The findings indicate that all regions are affected

by the export ban, but to different degrees and in different directions. The export ban was associated with

an increase in prices in most EAC border regions and a decrease in prices in most peripheral regions.

Finally, a non-parametric regression estimates the welfare effect of the export ban on maize in Tanzania.

At the national level, the ban led to a household welfare loss of approximately 1.5 per cent, with female-

headed households suffering more than male-headed households. The welfare loss was slightly higher for

the poorest households. Analysis at the regional level indicates significant variations of the welfare effect

across regions: consumers in most of the EAC border regions lose from the export ban, while those in

most peripheral regions experience a welfare gain. These findings partly support the conclusion by

Dabalen and Paul (2014) that consumers suffered a welfare loss following the introduction of the export

ban in Tanzania. Additionally, they show the heterogeneity of the effect of the export ban in Tanzania,

which can be partly explained by the fragmentation of domestic markets. Regional-level analysis

indicates no significant difference in the welfare effect attributed to the export ban between female-

headed and male-headed households.

Findings in this paper contribute to the debate about the appropriateness of non-tariff measures in trade

policy. In particular, they support a conclusion that export bans are not always beneficial for consumers,

which goes against the expectations that policymakers have when they introduce such measures. The

paper also provides additional insights, particularly regarding the heterogeneous effects of policy

measures, which might encourage targeted interventions that are location-specific.

The author would like to thank Cristian Ugarte, Julia Seiermann, and Vlasta Macku from the UNCTAD Virtual

Institute for their comments on this paper, and David Einhorn for editing the text. The paper was prepared with

financial support from the One UN Fund for Tanzania and the government of Finland.

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1 Introduction

The United Republic of Tanzania is a member of two regional blocs, the East African

Community (EAC) and the Southern African Development Cooperation (SADC), both of which

aim to promote trade among their member states by, among other measures, eliminating all

forms of barriers, including non-tariff barriers.1 Tanzania has regularly imposed export bans on

food, including bans on exports to EAC partner states, in order to ensure the availability of food

and stable prices of cereals in the country (Kagira, 2011; Diao et al., 2013). This policy has

aimed to protect consumers against increases in prices, but has been contested by producers and

some policymakers who believe that a better solution would be to increase production, taking

advantage of Tanzania’s favourable climatic conditions.

The literature shows mixed findings with regard to the effects of export bans on grain consumers

in Africa. Some authors (Diao et al., 2013; Kagira, 2011) find that the effect is positive, even

though marginal, while others such as Woldie and Siddig (2009) find no evidence that export

bans benefit consumers of cereals. Dabalen and Paul (2014) find that consumers suffered a

welfare loss following the introduction of the export ban in Tanzania.

This paper therefore aims to contribute to this debate while taking into account the geographical

characteristics of the domestic market in Tanzania. Statistics indicate that Tanzania mainly

exports cereals to East African countries which account for three-quarters of the country’s total

cereal exports. Most of the exports are transported via roads. Tanzania is a large country with a

fragmented domestic market, which makes it difficult to transport produce from production areas

to markets. For example, the distance by road from Rukwa (a region in the southern highlands

that is a key producer of maize) to Arusha (a region in the north near the border with the EAC) is

1,348 kilometers. Given that cereals are mainly transported by road, the location of the market is

likely to affect price transmission and hence the welfare effect of an export ban on consumers.

Tanzania has a tropical climate with some regional variations. Temperatures in the highlands

range between 10oC and 20

oC during the cold and hot seasons, respectively. In the rest of the

country, temperatures rarely fall below 20oC. Tanzania is considered an agricultural economy,

with 80 per cent of the population in rural areas engaged in agriculture. The agricultural sector is

dominated by smallholder peasants, each of whom cultivates an average of 0.9 to 3 hectares,

mainly rain-fed and cultivated using hand hoes (Makame, 2013). The sector provides

employment for most Tanzanian women, accounting in 2006 for 80 per cent of the country’s

female employment.2 Due to climate change, rainfall-dependent agriculture is increasingly

confronted with low productivity and variations in production levels. Tanzania has more arable

land (approximately 46 per cent of its territory) than other EAC member states, and strategic use

of this resource could take advantage of the market potential available in the SADC, EAC and

1 The member countries of the EAC are Burundi, Kenya, Rwanda, United Republic of Tanzania, and Uganda. The

member countries of the SADC are Angola, Botswana, Democratic Republic of Congo, Lesotho, Madagascar,

Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, United Republic of Tanzania,

Zambia, and Zimbabwe. 2 According to World Bank estimates, the share of agriculture in female employment (80 per cent) was higher than

the share of the agriculture in total employment (77 per cent) in 2006 (World Bank, 2015b).

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the Tripartite Framework countries.3 Regional integration could thus be seen as an opportunity

for farmers to gain better access to regional markets.

Between February 2002 and June 2014, Tanzania had four rounds of export bans – from July

2003 to January 2006, from August to December 2006, from January 2008 to October 2010, and

from May to October 2011. Export bans on staple crops,4 a measure imposed by the Department

of National Food Security, were a controversial policy measure used in the Tanzania up to 2012

when they were abolished following the commitment made by Tanzania’s President Hon. Jakaya

Mrisho Kikwete in bilateral consultation with President Hon. M. Kibaki of Kenya at the G8

Summit in the United States (Makame, 2013). The president’s decision was crucial to honouring

Tanzania’s international commitments, such as the commitment to promote trade among EAC

member states and the commitment to trade liberalization, including the pledge to the World

Trade Organization (WTO) not to impose any new trade barriers.

Export bans on food were introduced in Tanzania when production fell below the level of

domestic demand. These measures were introduced on the assumption that unrestricted exports

of food lead to a limited supply of food in the domestic market, thus causing an increase in

domestic food prices. Consequently, it was assumed that export bans on cereals would increase

the availability (supply) of cereals in the domestic market and eventually lead to lower and more

affordable prices for consumers (Woldie and Siddig, 2009). Another reason for export bans,

particularly in Tanzania, was to address the unethical behaviour of some intermediate traders

who buy cereals from producers – and particularly from financially constrained households – at

lower prices before the harvest, depriving producers of the opportunity to get higher prices in

export markets (Kagira, 2011). Export bans discouraged traders from buying cereals from

farmers because they were denied access to foreign markets where they could attract a higher

price. An alternative was to export informally, but that involved additional costs such as bribes

(Makame, 2013). In the absence of private traders, producers sold their produce to the National

Food Reserve Agency (NFRA), often at a price lower than the price in international markets. The

NFRA, a government agency under the Ministry of Agriculture, Food Security and Cooperatives,

has the mandate to maintain an optimal level of food reserves in the country in order to address

local food shortages and respond to emergency food requirements. The expectation of the

government was that nothing on the export ban list would be exported, but the evidence has

shown otherwise (MAFAP, 2013), including evidence showing that the export ban was

associated with increased informal trade (Makame, 2013). When there was no export ban in

3 The Tripartite Framework was established in 2005 to strengthen and deepen economic integration in southern and

eastern Africa through a merger of three regional economic communities – the EAC, SADC, and the Common

Market for Eastern and Southern Africa (COMESA). These regional communities consist of 26 member countries

and account for at least 58 per cent and 57 per cent of African gross domestic product (GDP) and population,

respectively (COMESA, EAC and SADC, 2011). The Tripartite Framework is one of the attempts to address

overlapping membership through harmonization efforts across the three regional economic communities. 4 The latest export ban was enacted on 16 May 2011, at a time when the demand for food was extremely high in

neighbouring countries. The ban was applicable to all staple crops but with a primary focus on maize. Unfortunately,

the contribution of maize to inflation is modest or marginal, thus limiting the benefits of the ban to consumers.

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2007, 2.4 per cent of maize produced in Tanzania was exported; that share was 2.2 per cent when

an export ban was in place from 2004 to 2005.5

Despite lifting the export ban in 2012, the government still controls the export of maize through

compulsory export clearance. In addition to the seven export documents listed in the Doing

Business Report (World Bank, 2015a), exporters of cereals in Tanzania need to obtain export

clearance from the Ministry of Agriculture, Food Security and Cooperatives.

Against the backdrop described above, this paper aims to analyse the welfare effect of export

bans on cereals in Tanzania on consumers, focusing on consumption at the household level6.

Some studies, such as Diao et al. (2013), examined the welfare effect of the export ban in

Tanzania, but this paper also captures the heterogeneity of the country’s different regions, which

are not uniformly integrated into global markets. Cereals are mainly sold to East African

countries; the EAC accounts for 74 percent of Tanzania exports.7 Given that cereals (and

particularly maize, which is a major produced and traded cereal) are mainly exported to East

African countries, we distinguish between regions that are more integrated into the EAC market

(EAC border regions) and those that are less integrated into this market (peripheral regions). A

peripheral region refers to a region that is at least 500 kilometers from the nearest EAC-Tanzania

border region.8 Dabalen and Paul (2014) argue that north and north-west are the only parts of

Tanzania that export maize to the EAC, and consequently, the only regions that can be affected

by an export ban. Our view, however, is that all regions in Tanzania can be affected by an export

ban, and that it is the degree of impact that differs across regions.9 Border regions are expected to

be more sensitive to policy change, as they are more integrated into the regional market than

their counterparts. We conduct our analysis using data from the 2007 Household Budget Survey

(HBS) (United Republic of Tanzania, 2011), and we focus on maize, which was the main target

of the export ban. Maize is among the most important staple foods in Tanzania; according to

2007 statistics from the Food Agriculture Organization, Tanzania was the second largest country

in Africa in terms of maize harvested.

2 Production and trade in cereals

In 2013, Tanzania exported cereals with a total value of US$40.7 million and imported cereals

with a total value of US$400.1 million, resulting in a trade balance deficit of US$359.4 million.

Production levels increased from approximately 1 million metric tons in 1961 to 8 million metric

tons in 2013 (Figure 1). Production per capita accordingly increased from 0.10 metric tons in

1961 to 0.16 metric tons in 2013. The increase in production is attributed to advancement in

5 The share of exports in total production is computed using data from Barreiro-Hurlé (2012). In any case, the export

of maize has never exceeded 7 per cent of the quantity produced in the period from 2000 to 2007. The export share

of produced maize was 0.7 per cent in 2006, when export bans were in place. 6 Due to the lack of access to data on cereal prices and household survey data for regions in Zanzibar, our analysis is

limited to mainland Tanzania. 7 Author’s calculations using data from the International Trade Centre Trade Map, available at

http://www.trademap.org/Index.aspx (accessed in January 2015). 8 EAC border regions include Arusha, Kilimanjaro, Tanga, Kagera, Mwanza, and Mara; peripheral regions include

Lindi, Mtwara, Ruvuma, Iringa, Mbeya, and Rukwa. 9 The level and pace of transmission from international prices to domestic prices varies across regions in Tanzania

(Table 4).

http://www.trademap.org/Index.aspx

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technology and adherence to recommendable production approaches. Cereal yield in Tanzania is

still low and keeps on fluctuating. Cereals that are widely produced include rice, maize, wheat,

sorghum, and millet. Top produced cereals include maize and paddy. The country’s top maize-

producing regions include Mbeya, Shinyanga, Iringa, Rukwa, Ruvuma, and Morogoro. Maize,

which occupies 45 per cent of cultivated area in Tanzania, accounted for approximately 47 per

cent of the country’s cereal exports and 8.4 per cent of cereals imports in 2012.10

Maize provides

approximately 60 per cent of caloric intake and over 50 per cent of protein content for

Tanzanians, and at least 80 per cent of Tanzanians consider maize as their main food item

(Saidia et al., 2010). In 2009/2010, annual maize consumption was estimated at 3 million tons,

compared with production of approximately 4.7 million tons.11

Top producing regions for paddy

include Arusha, Morogoro, Shinyanga, Rukwa, Mbeya, Mwanza, and Kilimanjaro.

Figure 1: Cereal production and yield and land used for cereal production, 1961–2013

Source: World Bank, World Development Indicators 2014.

Table 1 indicates that Tanzania’s main export destination for cereals is East and Central African

countries. Approximately two-thirds of exports in 2013 went to Kenya, Rwanda, and the

Democratic Republic of the Congo (DRC). Tanzania is an important source of cereals for the

EAC, despite being a net importer of cereals from overseas. Half of cereal imports in 2013 came

from Australia, Canada, and the Russian Federation. This implies that Tanzania depends on a

limited number of trading partners both in terms of export markets and sources of imports.

Imports of cereals to Tanzania from any part of the world except the EAC are subject to a 31.7

per cent tariff, and Tanzanian cereal exports also face tariffs (such as a 20.8 per cent in India, for

example).

10

This is established using trade data from the International Trade Centre (accessed in January 2015). 11

Annual production data were obtained from the website of the Ministry of Agriculture, Food Security and

Cooperatives (accessed in January 2015).

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Table 1: Top five export destinations and sources of imports for Tanzanian cereals Maize Rice Wheat Cereals (per cent share in

exports/imports)

Export destinations Kenya

Rwanda

Democratic

Republic of

the Congo

Burundi

Uganda

Kenya

Democratic

Republic of

the Congo

Rwanda

Congo

Uganda

Burundi

Rwanda

Mali

Congo

Malawi

Kenya (29)

Rwanda (21.5)

Democratic Republic of the

Congo (15.4)

India (9.4)

Burundi (9.2)

Sources of imports Zambia

Uganda

South Africa

Italy

Malawi

Pakistan

Viet Nam

India

China

Egypt

Australia

Canada

Russian

Federation

Ukraine

United States

Australia (28.6)

Canada (16.8)

Russian Federation (11.8)

Ukraine (8.2)

Zambia (6.9)

Source: International Trade Centre Trade Map, available at http://www.trademap.org/Index.aspx (accessed in

January 2015).

Food prices are particularly critical for the well-being of poor households, which dedicate a

higher share of their budget to food. Figure 2 shows the evolution of domestic and international

maize prices. Both prices generally increased between 2002 and 2014. The increase in the

international price can be attributed to the increase in the price of oil, population growth,

speculation on financial markets, and changing weather patterns (Woldie and Siddig, 2009).

Given the nature of Tanzania’s domestic market, we are interested in estimating the transmission

from international to domestic prices while considering the location of the market. Estimation of

the price transmission will allow us to derive some welfare implications of the export bans

applied periodically in Tanzania from February 2002 to June 2014.

Figure 2: Evolution of maize prices from February 2002 to June 2014 (in Tsh. per 100 kg)

Source: Ministry of Industry and Trade; Food and Agriculture Organization of the United Nations, the Food Price

Monitoring and Analysis Tool available at http://www.fao.org/giews/pricetool (accessed in January 2015).

Note: dp_klm is the price in the Kilimanjaro region, dp_rkw is the price in the Rukwa region, and intp_kg is the

international price. Tsh. = Tanzanian shilling.

http://www.trademap.org/Index.aspx

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3 Literature survey

Export restrictions imposed on products that are consumed in raw form such as maize, rice, and

beans are defensive measures pursued by a country to protect consumers. They take different

forms, including export bans, export taxes, export quotas, and export-restricting measures of

State-run trading enterprises (Kagira, 2011). The export ban on maize in Tanzania was

implemented with the assumption that it would increase the quantity of maize available in the

domestic market and thus lower consumer prices. Using descriptive statistics, Kagira (2011)

shows that the export ban had no effect on the price of maize in the domestic market. Six months

after Tanzania put in place its export ban in 2011, the price of 100 kg of maize was virtually

unchanged, with a decline of only 0.05 per cent over the period of four months - from Tsh.

42,265 in May 2011 to Tsh. 42,246 in August 2011. The export ban was not sufficient to

generate a price reduction because of infrastructural challenges in the Tanzanian market which is

highly fragmented and thus causes prices to differ across regions of the country.

Woldie and Sidding (2009) use the standard Global Trade Analysis Project (GTAP) model and

the GTAP Africa database to simulate the impact of an export ban on all grain products in

Ethiopia on food price stabilization and household welfare. Their results show that the price is

likely to decline, but that the policy has a negative effect on overall welfare, with a loss of

approximately US$148 million. The reason is because the export ban is likely to lead to a

reduction in household income and consumption expenditure, and consequently to a decline in

GDP. The export ban is therefore found not to be the best policy option to stabilize food prices.

Using non-parametric techniques, Calvo (2014) establishes the welfare impact of export

restrictions on wheat in Argentina and finds that poor households would be more affected than

rich households by the removal of export restrictions and subsidies. She concludes that the

welfare gains of export restrictions in Argentina were limited to poor households and that the

small gains generated additional costs for the government in the form of subsidies and lost tax

revenue. Her results raise doubts as to whether export restrictions were the appropriate

instruments to achieve welfare goals in Argentina. In light of this research, it is therefore

important to establish the welfare effect of the export ban in Tanzania, as relationships between

trade and poverty tend to be country-specific.

Using a dynamic general equilibrium model, Diao et al. (2013) find that the maize export ban

marginally affected the domestic price in Tanzania. It negatively affected the rural poor,

particularly in maize-producing regions, due to reduced return on labour and land. An export ban

generally works against long-term development goals, as the profitability of maize production

and consequently the incentives to invest in it decline over time. Diao et al. (2013) simulate this

export ban using data from the 2007 Household Budget Survey. The same data are also used in

this paper but we also consider the location of the domestic market in reference to East Africa to

which a large proportion of cereals is exported. Dabalen and Paul (2014) evaluate the price effect

of the export ban on maize in 13 markets of Tanzania and Kenya. The six markets in Tanzania

are Arusha, Dodoma, Singida, Mwanza, Shinyanga, and Tanga.12

The authors consider both the

wholesale price (a proxy of the producer price) and the retail price (a proxy of the consumer

12

These are the markets for which retail price data were available.

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price) for the period from January 2006 to February 2014. They find a drop in the wholesale

price and a rise in the retail price in Tanzania, indicating a potential welfare loss to both

consumers and producers. Their analysis is based on the assumption that the maize exported to

Kenya comes from the northern provinces of Tanzania. However, we assume here that the maize

exported to East Africa may also come from other regions in Tanzania, including regions in the

southern part of the country. The wholesale price is also not a suitable proxy for the producer

price because it incorporates costs beyond farm gate prices, such as transportation costs.

4 Methodology and results

4.1 Data

This paper mainly uses data from the 2007 Household Budget Survey (HBS) collected by the

National Bureau of Statistics and covering the period from 1 January to 31 December 2007

(United Republic of Tanzania, 2011). In total, the analysis includes 10,445 households

distributed as follows: 3,457 households in Dar es Salaam, 3,718 households in other urban

areas, and 3,270 households in rural areas. Dar es Salaam accounts for approximately 33 per cent

of the sample in the 2007 House Budget Survey (HBS) that covered 21 regions in mainland

Tanzania. We will consistently use analytical weights in our analysis when applying HBS 2007

data.13

HBS 2007 provides disaggregated data on consumption expenditure at the household level that

include bread and cereals. However, the data do not go into product-specific details that could

provide particular information on cereals of interest here – wheat (bread), maize, and rice. Wheat

is rarely produced and consumed in Tanzania (Maro and Barreiro-Hurle, 2012) and was not the

primary target of export bans. Production and price data on wheat in Tanzania are not

consistently available to support the intended analysis. Data on other cereals are also

problematic. Regional price data for maize are consistently available for 18 of 21 regions in

Tanzania, and maize accounted for 47 per cent of cereal exports in 2012. Maize was also the

primary target of export bans in Tanzania. This paper will therefore focus on maize which

accounts for a large share of cereals. Domestic and international prices of maize are also

available. We are not able to disaggregate data on household income, which limits our analysis

to the consumption side. Since the export ban aimed to benefit consumers, this paper focuses on

the consumption effects of the export ban on maize on household welfare, with particular

attention to the gender of the household head and the integration of domestic markets into

international markets.

4.2 Income distribution

According to Deaton (1989) and supported more recently in a study by Calvo (2014), a well-

performing proxy of household welfare is the logarithm of per capita consumption. The use of

consumption as a proxy for household welfare is preferred for two reasons: self-reported

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Analytical weights were computed as an inverse of each household’s selection probability while taking into

account the selection of the primary sampling unit and stratification within each primary sampling unit.

Adjustments were made to the sample to ensure that the sum of individuals was equal to 2007 project populations

(United Republic of Tanzania, 2011).

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expenditure is more accurate than self-reported income, and households tend to smooth

consumption over time. Consumption per capita is computed based on total household

consumption and adult equivalent size of households. Both variables are provided in the HBS

2007 dataset. Consumption expenditure is provided on a monthly basis (a span of 28 days) in

Tanzanian shillings.

In our analysis, we categorize regions into three groups; regions that border the EAC; periphery

regions (at least 500 km to the nearest EAC border); and regions that are in between the two

groups. Given the nature of Tanzania’s domestic market, we expect the degree and speed of the

transmission of international prices to domestic prices to vary across markets. Descriptive results

are reported for the two groups of interest: EAC border regions and peripheral regions (Table 2).

We also grouped households into female-headed and male-headed households. Female-headed

households in Tanzania have limited access to productive resources (such as land) and face

difficulties in producing food throughout the year (Karatu, Märker, and Mwakolobo, 2011). They

were expected to spend more on food than male-headed households. An attempt was also made

to group regions based on the ranking in Tanzanian maize production – that is, into high-maize-

producing and low-maize-producing regions, with an assumption that demand for maize will be

high in regions where less maize is produced.

Table 2 provides a summary of household per capita expenditure and the share of total

consumption allocated to food and to bread and cereals in 2007 when the survey was conducted.

It includes both the share of purchased cereals and the share of total cereals in total household

expenditure. Households allocate at least half of their total expenditure to food, and at least 20

per cent of total expenditure to total cereals and 11 per cent to purchased cereals. Urban

households are richer than rural households and allocate a smaller share of expenditure to food

and cereals.14

Dar es Salaam household per capita expenditure is higher than the national average

and shows the smallest share of expenditure dedicated to total cereals and food. However, the

Dar es Salaam household expenditure share of purchased cereals is the highest. EAC border

regions are slightly richer and have a higher share of purchased cereals and a lower share of food

in total expenditure than peripheral regions. Female-headed households have higher per capita

expenditure than male-headed households – there is a slight difference between the two groups in

terms of the expenditure share of cereals, both purchased and total. Regions that produce less

maize have higher per capita expenditure and spend more on purchasing cereals than regions that

are the top maize producers.

We also decided to group households into five income groups (quintiles) ranging from the

poorest to the richest based on total consumption per capita (Table A1 in the Annex). The results

indicate that total consumption per capita of the richest households (5th quintile) is 7.7 times

total consumption per capita of the poorest households (1st quintile) at the national level. The

ratio between consumption per capita of the richest households (5th quintile) and the

consumption per capita of the poorest households (1st quintile) is above the national average in

Dar es Salaam (8.6 times), urban households (7.9 times), female-headed households (8.1 times),

and low-maize-producing regions (8.3 times). On the other hand, the ratio between the

14

We tested whether the four cities other than Dar es Salaam ((Mbeya, Arusha, Tanga, and Mwanza) had any unique

feature in income distribution and in cereal and food expenditure. The result, which is not presented here, does not

show any significant difference among urban households, as indicated in Table 2.

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consumption per capita of the richest households and the consumption per capita of the poorest

households is below the national average for male-headed households (7.5 times), EAC border

regions (7.3 times), peripheral regions (7.2 times), and high-maize-producing regions (7 times).

The households in the lowest quintile in Tanzania allocate 76 per cent and 10 per cent of

household expenditure to food and to cereals and bread, respectively. The highest quintile

allocates 58.5 per cent and 18.9 per cent of household expenditure to food, and to cereals and

bread, respectively. Detailed results in Table A1 consistently show that rich households spend

relatively less on food and cereals. This confirms the Engel postulate that the share of food in

expenditure decreases with increasing income (Calvo, 2014).

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Table 2: Summary statistics for households by regions and household groups, 2007

Measure National Urban Rural Dar es

Salaam

region

EAC

border

regions

Peripheral

regions

Male-

headed

Female-

headed

High-

maize-

producing

regions

Low-

maize-

producing

regions

Expenditure per capita

(Tsh.)

30,162 38,098 24,479 53,679 29,026 27,986 29,484 32,248 29,007 37,094

Share of food in total

expenditure (per cent)

70.4 65.6 73.5 58.0 67.6 71.4 69.9 71.8 72.0 66.5

Share of purchased

cereals in total

expenditure (per cent)

13.4 17.9 11.2 20.1 12.3 11.6 13.5 13.2 11.1 18.8

Share of total cereals in

total expenditure (per

cent)

27.5 23.0 29.7 20.5 21.7 28.7 27.3 27.8 27.2 26.7

Share of cereals in total

food expenditure (per

cent)

38.0 34.1 39.6 34.8 31.4 39.5 38.2 37.6 37.0 39.3

Average number of

adult equivalents per

household

3.8 3.6 4.0 3.1 4.0 3.6 4.0 3.2 4.0 3.3

Number of observations 10,445 3,718 3,270 3,457 2,605 2,082 7,724 2,721 2,961 4,355

Source: Author’s calculations, based on data from the 2007 Household Budget Survey.

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We explore the income distribution of different groups using the kernel density plot of the log of

per capita consumption expenditure. Panel (a) in Figure 3 confirms that Dar es Salaam

households are better off compared with other areas. Dar es Salaam households are also better

off than the national average, as indicated by the shift of the distribution curve to the right. On

average, rural households are concentrated in lower quintiles of the income distribution. Panel

(b) in Figure 3 shows the income distribution based on the gender of the household head. With

the exception of middle-income households, it shows that female-headed households are slightly

better off compared with male-headed households. This is consistent with summary statistics

reported in Table 2. Panel (a) in Figure 4 presents a comparison of income distribution of regions

that border EAC member states and regions that are far from the EAC border. As indicated,

households in EAC border regions are slightly better off than households in peripheral regions,

but there is no major difference in income distribution between the two geographical areas. Panel

(b) in Figure 4 presents the income distribution for areas with different population density.

Regions with high population density are slightly better off than regions with low population

density.

Figure 3: Density estimates of per capita expenditure by area and by gender of the

household head

0.2

.4.6

.8

Dens

ity

9 10 11 12Log per capita expenditure (natural log)

National Dar es Salaam

Urban Rural

(a) All

0.2

.4.6

Dens

ity

9 10 11 12

Log per capita expenditure (natural log)

All Male Female

(b) Gender of the household head


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Figure 4: Density estimates of per capita expenditure by distance to border with EAC

countries and by population density

0.2

.4.6

Den

sity

9 10 11 12


All EAC border regions Peripheral regions

(a) Border to EAC

0.2

.4.6

.8

Den

sity

9 10 11 12


All High density Low density

(b) Population density


For the purpose of this paper, the analysis will be limited to two categories of households:

households in regions grouped according to their level of integration into the EAC, and

households grouped by the gender of the household head. The analysis of regions with different

levels of production of maize and population density is left for future studies. The share of

cereals in subsequent analysis in this paper refers to the share of purchased cereals in total

expenditure.

4.3 Income level and cereal consumption

To assess the relationship between income levels and cereal consumption, we run non-parametric

regressions of the share of purchased cereals in per capita household expenditure. Our analysis is

14

limited to household consumption, as data on production of cereals are not available in the 2007

HBS. Non-parametric regressions are useful in this case because they do not require specific

assumptions on the data distribution or an econometric specification of the functional form of the

relationship between variables (Deaton, 1997; Calvo, 2014). The dependent variable in our case

is the expenditure share of cereals and the regressor is the log per capita expenditure.

Figures 5 and 6, as well as Figure A1 in the Annex, yield mixed results. While for consumers in

Dar es Salaam, urban areas, and EAC border regions the results are consistent with the

assumption that poor households spend a higher share on cereals than rich households, poor

households in rural and peripheral regions actually spend less on purchasing cereals than rich

households. The pattern of cereal consumption at the national level is not so obvious: the poorest

and richest households spend less on purchasing cereals than do middle-income households. The

difference in consumption between female-headed and male-headed households is generally

insignificant, except in rural and peripheral regions where female-headed households

consistently spend less on purchasing cereals than male-headed households (see panel (b) in

Figure 6 and panel (a) in Figure A1 in the Annex). The Dar es Salam share of purchased cereals

is above the national average and all other groupings. The share of purchased cereals for the

poorest households is approximately 30 per cent in Dar es Salaam, while it is around 8 per cent

at the national level and in rural areas, 7 per cent in peripheral regions, 12 per cent in EAC

border regions, and 17 per cent in urban areas.

Figure 5: Share of cereals in total household expenditure – national and Dar es Salaam,

2007

68

1012

1416

9 10 11 12


All Male head Female head

Sha

re o

f cer

eals

(pe

r ce

nt)

(a) National

15

510

1520

2530

9 10 11 12 13



Sha

re o

f cer

eals

(pe

r ce

nt)

(b) Dar es Salaam


Figure 6: Share of cereals in total household expenditure – EAC border regions and

peripheral regions, 2007

46

810

1214

8 9 10 11 12



Sha

re o

f cer

eals

(pe

r ce

nt)

(a) EAC border regions

16

510

15

8 9 10 11 12



Sha

re o

f cer

eals

(pe

r ce

nt)

(b) Peripheral regions


5 Price transmission and construction of counterfactual scenarios

Theoretically, changes in international food prices should be reflected in domestic prices of

cereals. However, the extent of this transmission depends on transport and marketing costs,

policy measures, local currency valuation, market structures, and the degree of processing of

final consumer goods (FAO et al., 2011). Measures such as import duties, export taxes, non-

tariff barriers, and domestic policies that influence supply or demand of food affect the

transmission of international prices to domestic prices. Empirical studies indicate that the degree

and speed of price transmission vary across countries and markets within these countries (FAO et

al., 2011). The price will be instantly and more fully transmitted in markets that are better

integrated into global markets. Thus, in the absence of non-tariff measures such as an export ban

on cereals, the movement of the domestic price of cereals in a well-integrated market should

follow the pattern of international prices of cereals (Calvo, 2014). At present, cereal exports in

Tanzania are free of duties and taxes.15

As previously indicated, most of Tanzanian exports go to EAC countries. The degree of

transmission of international prices to domestic prices is likely to vary across domestic markets,

given the fact that those markets are fragmented. The degree of price transmission is expected to

be stronger in the EAC border regions than peripheral regions (Dabalen and Paul, 2014). We use

equation (1) to assess such potential differences while controlling for yearly and monthly fixed

effects that capture yearly macroeconomic shocks and monthly seasonality of prices,

respectively.

ldpmyi = λ0 + λ1*lintpmy + λ2*EAC_Borderi*lintpmy+ λ3*EAC_Borderi+δy + δm+εmyi, (1)

15

Information available on the Tanzania Revenue Authority website at http://www.tra.go.tz/index.php/export-

procedures.

http://www.tra.go.tz/index.php/export-procedures

http://www.tra.go.tz/index.php/export-procedures

17

where ldpmyi stands for the natural logarithm of the domestic price for month m in year y in

region i and lintpmy is the natural logarithm of the international price in month m and year y.

EAC_Borderi is a dummy variable that takes the value 1 in cases where the region borders the

EAC and 0 otherwise.16

We include the interaction term between the natural logarithm of the

international price and the dummy for the EAC border. We expect the coefficient of interaction

term to be positive, implying that price transmission is expected to be higher in border regions

than peripheral regions. The model includes δy, yearly fixed effects to capture any year-specific

factors that could affect price transmission, and monthly fixed effect (δm) to capture any seasonal

effect across the whole period. It also includes a regional fixed effect to capture any regional

specific characteristics. εmyi stands for the statistical error term. We conduct a panel data analysis

for 12 regions (six regions bordering the EAC and six regions that are at least 500 kilometers

from the nearest EAC border and Dar es Salaam).17

As suggested by results in Table 3 (columns 3 and 4), the coefficient of the interaction term

between the logarithm of the international price and the dummy for the EAC border region is

positive, though insignificant. These results provide little evidence regarding the assumption that

price transmission is higher in regions bordering EAC countries.18

Alternatively, we used actual

road distance (dist, in km) to the nearest border, which also supports the conclusion that the

transmission from international to domestic prices is higher in regions that are closer to the EAC

border, even though the relationship is not statistically significant (columns 5 and 6 in Table 3).

Table 3: Pass-through from international to domestic prices, 2002–2014

(1) (2) (3) (4) (5) (6) (7) (8)

Ldp ldp Ldp Ldp Ldp ldp Ldp ldp

Lintp 0.93***

(0.02)

0.13***

(0.02)

0.93***

(0.02)

0.13***

(0.03)

0.95***

(0.02)

0.15***

(0.03)

1.22***

(0.03)

0.13***

(0.03)

lintp*EAC_border 0.008

(0.03)

0.008

(0.03)

lintp*dist -0.0001

(0.0001)

-0.0001

(0.0001)

lintp*ban -0.44***

(0.05)

-0.009

(0.03)

Ban 4.70***

(0.48)

0.14

(0.32)

16

“EAC border regions” refers to geographical regions in Tanzania that physically border any member country of

the EAC. “Peripheral regions” refers to regions that are at least 500 km away from the nearest EAC border region.

The definition of these regions is based on the fact that most cereals are transported to Kenya on lorries and trucks

(Karugia et al., 2009). 17

Dar es Salaam is the exception in the second category as it is 331 kilometers from the nearest EAC border region. 18

The dummy variable (EAC_border) and the variable dist are omitted for multicollinearity reasons. We had to

conduct the Wald test to confirm whether the coefficient of lintp and the EAC-border dummy or distance (dist), or

the dummy for the export ban, are jointly and statistically different from zero. The test produced a significance level

that is close to zero in all specifications (with EAC-border dummy, distance, and dummy for the export ban). We

thus strongly accept that the estimated coefficients are significantly different from zero.

18

Regional fixed

effects

Yes Yes Yes Yes Yes Yes Yes Yes

Yearly fixed

effects

No Yes No Yes No Yes No Yes

Monthly fixed

effects


Constant 0.83***

(0.16)

9.48***

(0.25)

0.83***

(0.16)

9.48***

(0.26)

0.83***

(0.15)

9.48***

(0.27)

-2.29***

(0.35)

9.35***

(0.32)

R-squared 0.54 0.81 0.55 0.82 0.52 0.79 0.60 0.81

Observations 2,682 2,682 2,682 2,682 2,682 2,682 2,682 2,682

Source: Author’s estimations, based on data from the Ministry of Industry and Trade and the Food and Agriculture

Organization of the United Nations.

Note: Robust standard errors in parentheses. Dependent variable: logarithm of domestic prices of maize (for a given

region/domestic market); *** p < 0.01.

We had to establish transmission from international to domestic prices of maize in the presence

of the export ban. Theoretically, for well-integrated markets, changes in international prices are

expected to match changes in domestic prices in the absence of the export ban. To test this

relationship, we run the ordinary least square (OLS) regression of the logarithm of domestic

prices on the logarithm of international prices. The model specification is provided in equation

(2), where ldpimy is the logarithm of the domestic price of maize in region i expressed in

Tanzanian shillings in year y and month m. lintpmy is the logarithm of the international price of

maize expressed in Tanzanian shillings in year y and month m.19

It includes the dummy variable

for the export ban (ban), which takes a value of 1 if an export ban was observed during the

period and 0 otherwise. An interaction term between the logarithm of the international price and

the dummy for the export ban is included in the model. We expect the coefficient of the

interaction term to be negative, meaning that in the presence of an export ban, a rise in the

international price is associated with a decrease in the domestic price. The coefficient on the

export ban dummy is also expected to be negative, implying that the export ban is correlated

with lower domestic prices and thus beneficial to consumers. The model also includes δy, which

are yearly fixed effects designed to capture any year-specific factors that could affect price

transmission, and monthly fixed effects (δm), which are designed to capture any seasonal effects

affecting a particular market across the year. εmy stands for the statistical error term. This

specification is run individually for each region, given previous findings pointing out that price

transmission is not uniform in Tanzania.

ldpimy = λ0 + λ1*lintpmy + λ2lintp*banmy+ λ3*banmy+ δy + δm+εmy. (2)20

Table 4 presents a summary of the average ratio of the domestic price to the international price

for selected regions/markets in Tanzania. There are 73 months without an export ban and 76

19

International prices are converted to national currency using the official exchange rate obtained from the Bank of

Tanzania. 20

All prices were tested for time series challenges before being used for intended estimation. Data were subjected to

a unit root test where all prices were found not stationary but long-term trended. Tests were also done for potential

spurious regression. All regressions were found not spurious, except for Mbeya, which is, however, insignificant,

and Rukwa where the regression becomes non-spurious with the increase in lags.

19

months with an export ban between February 2002 and June 2014. The data present an

inconsistent picture, as in most cases consumers pay more in periods with export bans than what

they pay in periods with no export bans. Such inconsistency is common in EAC border regions.

The ratio of domestic prices to international prices varies across regions. It appears to be higher

in EAC border regions than in peripheral regions, implying that consumers in border regions pay

more than their counterparts for the same amount of maize. Frequently, consumers in EAC

border regions pay more than the average international price.

Table 4: Ratio of domestic to international prices of maize, 2002–2014

EAC border regions Peripheral regions

Duration Ban Dar es

Salaam

Kilimanjaro Mwanza Tanga Iringa Mbeya Rukwa Lindi

February 2002 -

June 2003

No 95 77 76 80 83 81 77 88

July 2003 -

January 2006

Yes 127 139 139 128 94 89 87 135

February - July

2006

No 130 137 147 133 119 100 84 113

August -

December 2006

Yes 79 71 108 59 79 68 62 76

January -

December 2007

No 65 62 84 62 48 54 53 80

January 2008 -

October 2010

Yes 148 145 150 144 124 117 101 156

November 2010 -

April 2011

No 119 106 134 125 95 105 87 129

May - October

2011

Yes 109 114 134 94 76 87 67 83

November 2011 -

June 2014

No 142 135 154 127 105 115 98 134

Source: Author’s calculations, based on data from the Ministry of Industry and Trade and the Food and Agriculture


Subject to domestic price data availability, our analysis is limited to 18 of 21 regions; price data

for three regions (Kigoma, Manyara and Pwani were missing for some years/months. Detailed

results for equation (2) are presented in Tables A2–A5 in the Annex. The value of R-squared is

higher in cases where fixed effects are considered, which means that year-specific factors and

seasonality are relevant control variables in estimating the transmission from international to

domestic prices of maize in Tanzania. Table 5 provides a summary of results, particularly the

coefficient on the export ban dummy and the coefficient on the interaction between the export

ban dummy and the international price. Subsequent discussion dwells on specification (2) which

includes yearly and monthly fixed effects; specification (1) does not include yearly and monthly

fixed effects.

The effect of the export ban on domestic prices is directly captured by the coefficient on the

export ban dummy (λ3) and indirectly captured by the coefficient on the interaction between the

export ban dummy and the international price (λ2). The signs of these coefficients are

consistently opposite, implying that the two effects counteract each other. As expected, results

vary significantly across regions, and both coefficients are statistically insignificant in most

regions. The two coefficients are only statistically significant at the 5 per cent level in Rukwa,

20

Singida, and Mbeya, and the coefficient on the interaction term is statistically significant at 10

per cent in Mtwara. To obtain the net effect on domestic prices, we multiply the coefficient on

the interaction term with the average change in international prices during the months with an

export ban (1.53 per cent) and add it to the coefficient on the export ban dummy. The aggregate

coefficient (λ2*1.53+λ3) shown in Table 5 is computed from specification (2) is positive in

Kilimanjaro, Iringa, Lindi, Mwanza, Singida, Tanga, Arusha, Kagera, Dodoma, Morogoro,

Tabora, and Shinyanga, and negative in Dar es Salaam, Rukwa, Mbeya, Mara, Ruvuma, and

Mtwara. A positive aggregate coefficient indicates that the export ban was correlated with an

increase in the domestic price, which suggests a welfare loss to consumers, while a negative

aggregate coefficient implies that the export ban was correlated with a decrease in the domestic

price, thus suggesting a welfare gain to consumers. Our results do not strongly show that the

export ban was beneficial for consumers in Tanzania. The export ban had heterogeneous effects

on Tanzanian regions: consumers in six regions benefited from it, while consumers in 12 regions

suffered. Consumers in all EAC border regions except Mara suffered from the export ban, while

consumers in all peripheral regions except Iringa and Lindi benefited from it.

Table 5: Summary results: Coefficient on the export ban dummy (λ3) and coefficient on the

interaction between the export ban dummy and the logarithm of the international price

change (λ2)

Specification (1) (2) Aggregate coefficient

Region/Coefficient λ2 λ3 λ2 λ3 (λ2 *1.53)+ λ3

Dar es Salaam -0.35***

(0.10)

3.74***

(0.99)

0.02

(0.12)

-0.20

(1.18)

-0.1694

Kilimanjaro -0.64***

(0.12)

6.69***

(1.24)

-0.07

(0.09)

0.81

(0.94)

0.7029

Rukwa -0.19*

(1.00)

2.02**

(1.00)

0.21**

(0.11)

-2.13**

(1.05)

-1.8087

Iringa -0.09

(0.11)

1.07

(1.10)

-0.05

(0.09)

0.53

(0.90)

0.4535

Lindi -0.57***

(0.12)

5.92***

(1.28)

-0.05

(0.12)

0.53

(1.19)

0.4535

Mwanza -0.64***

(0.12)

6.71***

(1.23)

-0.07

(0.08)

0.85

(0.77)

0.7429

Singida -0.64***

(0.12)

6.77***

(1.24)

-0.26**

(0.11)

2.85**

(1.16)

2.4522

Tanga -0.49***

(0.14)

5.15***

(1.41)

-0.06

(0.13)

0.63

(1.29)

0.5382

Mbeya -0.18**

(0.09)

1.91**

(0.90)

0.20**

(0.08)

-2.04**

(0.78)

-1.734

Arusha -0.54***

(0.11)

5.68***

(1.11)

-0.08

(0.10)

0.88

(1.00)

0.7576

Kagera -0.48***

(0.13)

5.08***

(1.39)

-0.02

(0.11)

0.21

(1.06)

0.1794

Mara -0.52***

(0.11)

5.45***

(1.17)

0.007

(0.07)

-0.05

(0.68)

-0.03929

Dodoma -0.53***

(0.12)

5.64***

(1.20)

-0.099

(0.11)

1.09

(1.14)

0.93853

Ruvuma -0.05 0.63 0.18 -1.80 -1.5246

21

Specification (1) (2) Aggregate coefficient

Region/Coefficient λ2 λ3 λ2 λ3 (λ2 *1.53)+ λ3

(0.11) (1.13) (0.12) (1.24)

Morogoro -0.43***

(1.11)

4.64***

(1.16)

-0.097

(0.12)

1.00

(1.23)

0.85159

Mtwara -0.40**

(0.16)

4.22**

(1.61)

0.23*

(0.14)

-2.32

(1.41)

-1.9681

Tabora -0.57***

(0.11)

5.95***

(1.14)

-0.08

(0.10)

0.92

(1.03)

0.7976

Shinyanga -0.69***

(0.12)

7.26***

(1.27)

-0.07

(0.10)

0.82

(1.00)

0.7129

All regions (Tanzania) -0.44***

(0.05)

4.70***

(0.48)

-0.009

(0.03)

0.14

(0.32)

0.12623




region/domestic market); *** p < 0.01, ** p < 0.05, * p < 0.1.

6 Welfare impact of export ban on maize

Finally, we present the welfare effect of Tanzania’s export ban on maize on households. As

discussed in the previous section, counterfactual scenarios are established for each

region/market, given the fact that price transmission is affected by the export ban to different

degrees across regions/markets. To estimate the welfare effect of exports, we compute the

welfare effect using equation (3). The welfare effect is estimated using 2007 HBS data.

Household Welfare Effects = %ΔPriceMaize * Cereal_Share, (3)

where %ΔPriceMaize = – (λ3+λ2*% increase in international price). The percentage increase in

the international price is computed as the average monthly increase in the international price

during the period with the export ban, that is, 1.53 per cent. λ3 is the coefficient on the dummy

of the export ban, and λ2 is the coefficient on the interaction of the dummy of the export ban and

the international price. Both λ3 and λ2 are estimated for each region individually. This refers to

the coefficients of the equations including all fixed effects; the improvement of R-squared when

fixed effects are considered implies that macroeconomic shocks and seasonality are relevant in

estimating the transmission from international to domestic prices in Tanzania. A negative value

of %ΔPriceMaize means that the export ban led to a smaller increase in the domestic price, hence

benefitting consumers. Cereal_Share is computed as a share of purchased cereals in total

household consumption expenditure based on 2007 HBS data.

Household welfare effects are presented in Figure 7 and Figures A2–A4 in the Annex. As

indicated in Figure 7, export ban measures in Tanzania led to a loss in consumer welfare, with a

national average welfare loss of 1.5 per cent. The welfare loss was slightly higher for poor

households, while high-income male-headed households gained from the export ban. The male-

headed household welfare loss (averaging 1 per cent) was lower than the national average and

the average for female-headed households (3 per cent). Otherwise, the welfare effect of the

export ban varied significantly across regions (Figures A2–A4 in the Annex).

22

Detailed analysis of the welfare effect of the export ban at the regional level presents mixed

results (Figures A2–A4 in the Annex). Consumers in all EAC border regions except Mara

experienced a welfare loss as a result of the export ban, with an average loss of 6.5 per cent

(panels (a) and (e) in Figure A3 and panels (a), (c), and (d) in Figure A4 in the Annex).

Consumers in Mara had a marginal gain of approximately 0.5 per cent as a result of the export

ban. Consumers in peripheral regions except Iringa and Lindi gained as a result of the export

ban, with an average welfare gain of 13.3 per cent (panels (b) and (d) in Figure A2, panel (b) in

Figure A3, and panel (b) in Figure A4 in the Annex). Most peripheral regions are among the top-

maize-producing regions in Tanzania, while EAC border regions are not. Thus consumers in

peripheral regions are expected to benefit from the export ban due to the increase in the supply of

maize in the absence of export business. Again, although our analysis is limited to the short-term

effect of the export ban, it shows that consumers in EAC border regions suffer from the export

ban because maize traders lack incentives to transport maize from producing regions to EAC

border regions. Traders also face challenges in transporting maize from producing regions that

are far from the EAC border. Some producing regions are not well connected to market centres,

thus magnifying the complexity of the distribution of maize in Tanzania. These results may

partly suggest that maize exported to the EAC does not originate from border regions, as

maintained by Dabalen and Paul (2014), but rather comes from other regions in Tanzania. In

particular, the export ban led to household welfare losses for consumers in Dodoma, Morogoro,

Tabora, Shinyanga, Kilimanjaro, Iringa, Lindi, Mwanza, Singida, Tanga, Arusha, and Kagera,

and to household welfare gains in Dar es Salaam, Mtwara, Ruvuma, Mbeya, Mara, and Rukwa.21

The greatest loss was experienced by household consumers in Singida (approximately 31.6 per

cent), while the smallest loss was observed in Kagera (approximately 1.6 per cent). The welfare

gain to consumers in Mtwara (32.7 per cent), which is approximately 920 kilometers from the

nearest EAC border, was the highest, while the welfare gain for consumers in Mara (0.5 per

cent), an EAC border region, was the lowest.

Analysis at the regional level provides an inconsistent comparison of the welfare effect between

female-headed and male-headed households. There was no apparent difference between the

welfare effects of the export ban on female-headed versus male-headed households in most

regions. This can be explained by the small difference between the two groups in the share of

purchased cereals in total expenditure (Table 2). The difference is significant for consumers in

Ruvuma, Tabora, Arusha, and Mara where female-headed households are worse off compared

with male-headed households. These are the regions where women are marginalized in terms of

ownership of economic resources. The pattern of the welfare loss or gain from imposition of the

export ban by the gender of the household head in other regions was generally mixed.

Our results provide complementary evidence to the findings of Dabalen and Paul (2014) because

they confirm that export bans have a heterogeneous impact across regions/markets in Tanzania.

The findings are consistent with our expectations that the price of maize would react differently

21

The average welfare effect of the export ban in different regions in Tanzania is as follows; Mtwara (32.7 per cent),

Ruvuma (14.7 per cent), Mbeya (18.9 per cent), Rukwa (18.6 per cent), Mara (0.5 per cent), Dodoma (-11.4 per

cent), Arusha (-9.7 per cent), Kilimanjaro (-7.7 per cent), Tanga (-8.2 per cent), Morogoro (-13.9 per cent), Lindi (-

6.11 per cent), Iringa (-3.7 per cent), Singida (-31.6 per cent), Tabora (-10 per cent), Shinyanga (-8.1 per cent),

Kagera (-1.6 per cent), Mwanza (-10 per cent), and Dar es Salaam (3.4 per cent). As shown, the average welfare

effect ranges from negative to positive, where a negative number implies a welfare loss in a given region.

23

across regions in Tanzania, and this is not limited to EAC border regions as presented in Dabalen

and Paul (2014). Most of the regions bordering the EAC lose from the export ban, while regions

that are far away from the border gain from the ban.

As indicated in the introduction to this paper, export ban measures covered all cereals, but the

emphasis was on maize. A future study might include comparative analysis of the welfare effect

of export bans covering multiple products for regions with sufficient price information. Such a

study would be important to establish whether the welfare effect of an export ban is region-

specific, product-specific, or both.

Figure 7: Potential welfare effect of export bans on households based on the gender of the

household head

-4-3

-2-1

0

9 10 11 12 13Log per capita expenditure (natural log)


We

lfare

ch

an

ge

Source: Author’s estimations, based on data from the 2007 Household Budget Survey.

7 Conclusions

The United Republic of Tanzania has periodically imposed export bans on cereals as a way to

stabilize consumer prices in the domestic market. This paper aimed to quantify the welfare effect

of export bans on consumers in Tanzania using data from the 2007 Household Budget Survey.

We described the income distribution in the country and showed the relationship between the

level of livelihood of households and the share of cereals in their total expenditure. Poor

households have a higher expenditure share for cereals and food than rich households,22

and

22

Rural households, which are poorer, have higher relative expenditure on cereals and food compared with urban

households.

24

female-headed households are better off compared with male-headed households as measured by

per capita consumption expenditure. EAC border regions allocate a higher share of expenditure

to purchasing cereals than households in peripheral regions.

The paper concludes that the degree of transmission from international to domestic prices varies

across regions in Tanzania and that price transmission is higher in regions/markets that border

the EAC. Export bans also affect regions/markets in Tanzania differently. Consumers in regions

that border the EAC lose from export bans, while consumers in peripheral regions gain from the

bans. Mara is an exception among the EAC border regions where consumers gain from the

export ban, though marginally. Iringa and Lindi are exceptional peripheral regions in that the

export ban is associated with a loss in those regions. At the national level, the export ban is

associated with an average welfare loss of 1.5 per cent, with female-headed households

registering greater welfare losses than male-headed households. At the regional level, there is no

clear difference in how male-headed and female-headed households were affected by the export

ban. Results are generally mixed and vary across regions in Tanzania.

The findings in this paper support a conclusion that export bans are not always beneficial for

consumers, which goes against the expectations that policymakers have when they introduce

such measures. Some consumers gain while others lose from the same policy, but in any case the

benefits from export bans are not statistically significant. This is seen from the price transmission

estimations, where results are statistically significant for only a few regions. Results also

complement the findings of Dabalen and Paul (2014) by showing that not only the north and

north-west regions of Tanzania are affected by export bans.

25

Annex

Table A1: Summary statistics by quintile of income distribution, 2007 (in per cent unless

otherwise indicated)

Measure

Quintile

1st 2nd 3rd 4th 5th

National Expenditure per capita (Tsh.) 9,340 15,678 22,209 32,149 71,471

Share of food in total expenditure 76.0 74.9 73.5 68.9 58.5

Share of purchased cereals in total

expenditure 10.0 12.3 14.6 15.8 14.4

Share of total cereals in total expenditure 32.2 31.5 29.2 25.5 18.9

Share of cereals in total food expenditure 41.4 41.3 39.1 36.6 31.6

Dar es Salaam Expenditure per capita (Tsh.) 9,407 15,930 22,667 33,072 81,163



expenditure 28.3 25.8 25.7 21.8 16.2



Urban Expenditure per capita (Tsh.) 9,271 15,950 22,431 32,550 73,737


Share of purchased cereals in total expenditure

17.0 18.9 20.3 19.4 15.1



Rural Expenditure per capita (Tsh.) 9,347 15,624 22,109 31,782 64,344



8.7 10.6 12.1 13.1 13.0



EAC border regions Expenditure per capita (Tsh.) 9,392 15,593 22,233 31,850 68,997



expenditure 10.0 11.9 14.3 13.9 11.6



Peripheral regions Expenditure per capita (Tsh.) 9,304 15,784 22,058 32,211 67,263



expenditure 8.7 11.1 11.5 13.3 14.0



26

Measure

Quintile

1st 2nd 3rd 4th 5th

Female-headed households Expenditure per capita (Tsh.) 9,256 15,583 22,227 32,124 75,054



expenditure 9.9 12.2 13.7 16.5 14.1



Male-headed households Expenditure per capita (Tsh.) 9,368 15,710 22,205 32,156 70,050



expenditure 10.0 12.3 14.9 15.6 14.6



High-maize-producing regions Expenditure per capita (Tsh.) 9,568 15,717 22,062 31,825 66,756



7.8 9.4 11.3 13.0 13.2



Low-maize-producing regions Expenditure per capita (Tsh.) 9,380 15,680 22,446 32,945 77,548



expenditure 14.0 19.1 21.9 21.7 17.1




27

Table A2: Pass-through from international to domestic prices (Dar es Salaam, Kilimanjaro,

Rukwa, and Iringa), 2002–2014

Region/Market Dar es Salaam Kilimanjaro Rukwa Iringa

(1) (2) (1) (2) (1) (2) (1) (2)

ldp_dar ldp_dar ldp_klm ldp_klm ldp_rkw ldp_rkw ldp_ir ldp_ir

Lintp 1.17***

(0.09)

0.12

(0.15)

1.29***

(1.00)

0.09

(0.14)

1.06***

(0.08)

0.06

(0.18)

1.01***

(0.08)

0.27*

(0.16)

lintp*ban -0.35***

(0.10)

0.02

(0.12)

-0.64***

(0.12)

-0.07

(0.09)

-0.19*

(1.00)

0.21**

(0.11)

-0.09

(0.11)

-0.05

(0.09)

Ban 3.74***

(0.99)

-0.20

(1.18)

6.69***

(1.24)

0.81

(0.94)

2.02**

(1.00)

-2.13**

(1.05)

1.07

(1.10)

0.53

(0.90)

Constant -1.67*

(0.87)

8.66***

(1.43)

-3.01***

(1.04)

8.89***

(1.31)

-0.87

(0.85)

8.95***

(1.70)

-0.23

(0.86)

6.75***

(1.50)

Yearly fixed

effects


Monthly fixed

effects


R-squared 0.63 0.89 0.63 0.91 0.65 0.88 0.56 0.89

Observations 149 149 149 149 149 149 149 149





Table A3: Pass-through from international to domestic prices (Lindi, Mwanza, Singida, and

Tanga), 2002–2014

Region/Market Lindi Mwanza Singida Tanga

(1) (2) (1) (2) (1) (2) (1) (2)

ldp_lindi ldp_lindi ldp_mwz ldp_mwz ldp_sgd ldp_sgd ldp_tanga ldp_tanga

Lintp 1.24***

(0.11)

0.05

(0.15)

1.46***

(0.11)

0.27***

(0.10)

1.39***

(0.10)

0.40**

(0.16)

1.21***

(0.11)

0.02

(0.19)

lintp*ban -0.57***

(0.12)

-0.05

(0.12)

-0.64***

(0.12)

-0.07

(0.08)

-0.64***

(0.12)

-0.26**

(0.11)

-0.49***

(0.14)

-0.06

(0.13)

Ban 5.92***

(1.28)

0.53

(1.19)

6.71***

(1.23)

0.85

(0.77)

6.77***

(1.24)

2.85**

(1.16)

5.15***

(1.41)

0.63

(1.29)

Constant -2.41**

(1.11)

9.49***

(1.52)

-4.53***

(1.15)

7.27***

(0.96)

-4.11***

(1.09)

5.67***

(1.54)

-2.17*

(1.18)

9.55***

(1.79)

Yearly fixed

effects


Monthly fixed

effects


R-squared 0.59 0.89 0.76 0.95 0.69 0.90 0.56 0.87

Observations 149 149 149 149 149 149 149 149



28



Table A4: Pass-through from international to domestic prices (Mbeya, Arusha, Kagera, and Mara),

2002–2014

Region/Market Mbeya Arusha Kagera Mara

(1) (2) (1) (2) (1) (2) (1) (2)

ldp_mby ldp_mby ldp_ar ldp_ar ldp_bkb ldp_bkb ldp_mr ldp_mr

Lintp 1.17***

(0.08)

0.09

(0.13)

1.24***

(0.09)

0.15

(0.13)

1.25***

(0.12)

0.002

(0.13)

1.30***

(0.10)

0.09

(0.09)

lintp*ban -0.18**

(0.09)

0.20**

(0.08)

-0.54***

(0.11)

-0.08

(0.10)

-0.48***

(0.13)

-0.02

(0.11)

-0.52***

(0.11)

0.007

(0.07)

Ban 1.91**

(0.90)

-2.04**

(0.78)

5.68***

(1.11)

0.88

(1.00)

5.08***

(1.39)

0.21

(1.06)

5.45***

(1.17)

-0.05

(0.68)

Constant -1.82

(0.77)

8.70***

(1.21)

-2.52**

(0.97)

8.17***

(1.27)

-2.44*

(1.28)

9.79***

(1.24)

-3.04

(1.09)

8.98***

(0.86)

Yearly fixed

effects


Monthly fixed

effects


R-squared 0.69 0.93 0.65 0.91 0.67 0.93 0.72 0.95

Observations 149 149 149 149 149 149 149 149





29

Table A5: Pass-through from international to domestic prices (Dodoma, Ruvuma, Morogoro, Mtwara, Tabora, and Shinyanga), 2002–

2014

Region/Market Dodoma Ruvuma Morogoro Mtwara Tabora Shinyanga

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

ldp_dm ldp_dm ldp_rvm ldp_rvm ldp_moro ldp_moro ldp_mtr ldp_mtr ldp_tbr ldp_tbr ldp_shy ldp_shy

Lintp 1.32***

(0.10)

0.23

(0.17)

0.90***

(0.08)

0.02

(0.19)

1.16***

(0.099)

0.05

(0.17)

1.09***

(0.14)

-0.08

(0.19)

1.32***

(0.10)

0.34**

(0.14)

1.41***

(0.12)

0.22

(0.14)

lintp*ban -0.53***

(0.12)

-0.099

(0.11)

-0.05

(0.11)

0.18

(0.12)

-0.43***

(1.11)

-0.097

(0.12)

-0.40**

(0.16)

0.23*

(0.14)

-0.57***

(0.11)

-0.08

(0.10)

-0.69***

(0.12)

-0.07

(0.10)

Ban 5.64***

(1.20)

1.09

(1.14)

0.63

(1.13)

-1.80

(1.24)

4.62***

(1.16)

1.00

(1.23)

4.22**

(1.61)

-2.32

(1.41)

5.95***

(1.14)

0.92

(1.03)

7.26***

(1.27)

0.82

(1.00)

Constant -3.20***

(1.04)

7.43***

(1.61)

0.73

(0.82)

9.22***

(1.88)

-1.65

(1.03)

9.18***

(1.64)

-0.89

(1.45)

10.72

(1.84)

-3.23***

(1.05)

6.39***

(1.37)

-4.15***

(1.20)

7.55

(1.34)

Yearly fixed

effects

No Yes No Yes No Yes No Yes No Yes No Yes

Monthly fixed

effects

No Yes No Yes No Yes No Yes No Yes No Yes

R-squared 0.61 0.91 0.52 0.83 0.59 0.88 0.52 0.85 0.73 0.91 0.72 0.92

Observations 149 149 149 149 149 149 149 149 149 149 149 149

Source: Author’s estimations, based on data from the Ministry of Industry and Trade and the Food and Agriculture Organization of the United Nations.

Note: Robust standard errors in parentheses. Dependent variable: logarithm of domestic prices of maize (for a given region/domestic market); *** p < 0.01, ** p

< 0.05, * p < 0.1.

30

Figure A1: Share of cereals in total household expenditure – rural and urban households, 2007

68

1012

14

8 9 10 11 12



Sha

re o

f cer

eals

(per

cen

t)

(a) Rural

1012

1416

1820

9 10 11 12



Shar

e of

cer

eals

(per

cen

t)

(b) Urban


31

Figure A2: Potential welfare effect on households in different regions of Tanzania – Batch 1 -2

0-1

5-1

0-5



We

lfare

ch

an

ge

(a) Dodoma

05

10

15

20

25



We

lfare

ch

an

ge

(b) Ruvuma

-20

-18

-16

-14

-12

-10



We

lfare

ch

an

ge

(c) Morogoro

020

40

60



We

lfare

ch

an

ge

(d) Mtwara

-20

-15

-10

-50



We

lfare

ch

an

ge

(e) Tabora

-14

-12

-10

-8-6

-4



We

lfare c

han

ge

(f) Shinyanga


32

Figure A3: Potential welfare effects on households in different regions of Tanzania – Batch 2 -1

2-1

0-8

-6-4

9 10 11 12



We

lfare

ch

an

ge

(a) Kilimanjaro

010

20

30

40

8 9 10 11 12


All Male head Female headW

elfare

ch

an

ge

(b) Rukwa

-6-5

-4-3

9 10 11 12



We

lfare c

han

ge

(c) Iringa

-10

-8-6

-4

8 9 10 11 12



We

lfare

ch

an

ge

(d) Lindi

-12

-11

-10

-9-8

-7

8 9 10 11 12



We

lfare

ch

an

ge

(e) Mwanza

-60

-50

-40

-30

-20

8 9 10 11 12



We

lfare

ch

an

ge

(f) Singida


33

Figure A4: Potential welfare effects on households in different regions of Tanzania – Batch 3 -1

1-1

0-9

-8-7

9 9.5 10 10.5 11 11.5



We

lfare

ch

an

ge

(a) Tanga

16

18

20

22

24

9 10 11 12


All Male head Female headW

elfare

ch

an

ge

(b) Mbeya

-12

-10

-8-6

-4

8 9 10 11 12



We

lfare

ch

an

ge

(c) Arusha

-2-1

.5-1

-.5

9 10 11 12



We

lfare

ch

an

ge

(d) Kagera

.2.3

.4.5

.6.7

8 9 10 11 12



We

lfare

ch

an

ge

(e) Mara

12

34

5

9 10 11 12 13



We

lfare c

han

ge

(f) Dar es Salaam


34

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