An Atlas of Socio-Economic Statistics 1997–2007 Niger Yemen Maidugurin'djamena El Obeid Aden Djibouti Chad Djibouti Nigeria Sudan Adis Abeba Ethiopia

Home , Maputo Province, Namaacha, Nampula

Mozambique Then and Now An Atlas of Socio-Economic Statistics Public Disclosure Authorized 1997–2007 Public Disclosure Authorized Public Disclosure Authorized

THE WORLD BANK INSTITUTO NACIONAL DE ESTATÍSTICA Public Disclosure Authorized ii Mozambique Then and Now An Atlas of Socio-Economic Statistics 1997–2007 Niger Yemen MaiduguriN'Djamena El Obeid Aden Djibouti Chad Djibouti Nigeria Sudan Adis Abeba Ethiopia

Central African Republic Cameroon Bangui MalaboYaounde Equatorial Guinea Somalia

Equatorial Guinea Muqdisho Kisangani Uganda Mbandaka Kampala Kenya Gabon Congo Nairobi Port Gentil Rwanda Bujumbura RDC Burundi Mombasa Pointe Noire Kinshasa Brazzaville Kigoma Matadi Kananga Tanzania, United Republic of Dar es Salaam Kahemba

Luanda

Mtwara

Lumumbashi BenguelaHuambo Angola Malawi Lilongwe C.Ilha Moçambique ZambiaLusaka

Livingstone Harare Antananarivo Zimbabwe Beira Madagascar Bulawayo

Namibia Botswana Toliara Windhoek Gaborone

Pretoria Maputo Mbabne Johannesburg Swaziland

Kimberley Maseru South Africa Durban Lesotho

East London Cape Town Port Elizabeth 0 250 500 1,000

Kilometers

iv Mozambique Then and Now contents vi I Preface 39 access to services Access to electricity vii Acknowledgement Access to running water 1 Introduction Access to phones and internet Distance to major urban areas 3 The people of mozambique Population 45 education Demographic distribution by age and gender Trend in primary gross enrollment rates Main languages Primary enrollment by gender Religions Primary enrollment across Africa Distance to primary schools Trend in secondary gross enrollment rates 15 wealth Secondary enrollment by gender Poverty Secondary enrollment across Africa Inequality Distance to secondary schools Asset ownership Literacy rates Share of population with complete primary 21 healthy lives Share of population with complete secondary Infant mortality rate Infant mortality in Africa 57 land and agriculture Underweight, prevalence and concentration Topography Stunting, prevalence and concentration Land suitability Maternal mortality rate Rainfall by year Malnutrition Rainfall by month Distance to health facilities Temperature by month Fertility Ownership of animals Access to improved water Trend in accessing water from rivers and lakes Diarrhea and malaria 68 definition of indicators 70 image and map index

72 REFERENCES Preface

The National Statistical Institute of Mozambique is responsible to collect, analyze, to be appropriately targeted. It is our wish that this Atlas will provide an important publish, and disseminate statistical information on a wide range of topics. The mis- source of information for all those who want to have the mission to promote the eco- sion of the National Statistical Institute is to provide and promote accurate, appropri- nomic and social development in Mozambique. ate, high-quality, and timely statistical information for use in both the public and the The maps are elaborated principally from the results of the 2007 Population Census private sectors for policy formulation, decision making, research, and general public and the 2008/09 Household Budget Survey (Inquerito ao Orcamento Familiar, IOF awareness for the advancement of the socio-economic status of Mozambicans. 2009). A central focus of the work of the INE is the data collection on living conditions of Funding for this publication was provided by the Trust Fund for Environmentally and the population of Mozambique. This Atlas was developed as part of efforts to increase Socially Sustainable Development (TF ESSD) at the World Bank and is gratefully ac- the understanding of the living conditions of Mozambicans and to serve as a basis for knowledged. We also gratefully acknowledge the excellent collaboration and support the preparation of the country’s development strategies. provided in the joint preparation of this Atlas. Taken in their entirety, the maps in this Atlas provide profound insights into the characteristics and living conditions of the population of Mozambique and how they João Dias Loureiro vary across the country, thereby enabling poverty reduction programs and policies President of INE

vi acknowledgements

This atlas has been prepared jointly by the National Statistics Office of Mozambique The atlas was produced by a team led by Mr Antonio Nucifora (World Bank, Lead (Instituto Nacional de Estatistica, INE) and the World Bank. Economist), and including Mr Thomas Pave Sohnesen (World Bank Senior Econo- mist, and main author of the Atlas), Mr Vasco Molini (World Bank, Senior Poverty INE provided the 1997 and 2007 Population Census data which is the basis for this Economist), Mr Antonio Adriano (INE Deputy Director for Census and Surveys), Mr Atlas, as well as contributed detailed input and comments that ensured the statistical Cassiano Chipembe (INE Director of Statistics on Demography and Living Condi- accuracy and improved its overall quality. tions ), Mr Paulo Covele (GIS specialist), and Ms Andrea Nieves (Graphic designer). Special thanks goes to INE’s President, Dr João Dias Loureiro, for providing over- Funding for the production was provided by the Trust Fund for Environmentally and all guidance and inspiration, and to the World Bank Country Director for Mozam- Socially Sustainable Development (TF ESSD) at the World Bank. bique, Mr Laurence Clarke, for encouraging and supporting this project. Invaluable comments and guidance was also received by INE Vice-President, Mr Manuel Gas- par, and the World Bank Sector Manager for Poverty Reduction and Economic Man- agement in southern Africa, Mr John Panzer.

vii viii Mozambique Then and Now: An Atlas of Socio-Economic Statistics

It is well known that Mozambique is characterized by wide variations in socio-economic indicators across provinces. According to available statistics, such differences have decreased in recent years. For instance, we know that there has been great progress in both primary and secondary enrollment rates the last decade; however, which areas have made the greatest progress, and which areas are still lacking behind? Infant mortality rates fell the last decade, but where did they decline the most, and where are they still high? This atlas provides maps and illustrations that give insights into these aspects and many more. It does so by showing a range of social and economic statistics at the level of Administrative Posts. For most of the indicators it includes maps which show the situation in 1997 and in 2007. In addition some of the maps specifically illustrate the change over the decade. Overall the Atlas provides a fascinating snapshot of recent socio-economic changes in Mozambique. Antonio Nucifora and Thomas Pave Sohnesen 2 the people of mozambique Population language religion

Mozambique’s growing population speaks a wide range of tongues and belongs to a variety of religious denominations. population

The population of Mozambique is still growing, reaching 20.5 million in 2007.

The northern province of Nampu- 1.2 – Total population (in thousands), 2007 1.3 – Rural population per km2, 2007 la, the central parts of the country (Zambezia, Sofala, Manica and Tete provinces) and the south Eastern coastline (of Inhambane, Gaza and Maputo provinces) have the largest populations (fig 1.1 and 1.2) and the highest rural population densi- ties (fig 1.3). The population density refers to the number people living in a given Administrative Post divided by the size of that Adminis- trative Post measured in squared kilometers.

1.1 – Total population by province (in thousands), 1997 and 2007

1997 2007 0 1,000 2,000 3,000 4,000

Niassa Cabo Delgado Nampula Less than 25 Less than 10 Zambezia 25–50 10–20 Tete 51–75 21–30 Manica 76–100 31–40 Sofala Inhambane Greater than 100 Greater than 40

Gaza 0 75 150 300 0 75 150 300 Maputo province Kilometers Kilometers Maputo City

4 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE

1.4 – Change in population (percentage) between 1997 and 2007 1.5 – Change in population (in thousands) between 1997 and 2007 Population growth from 1997 to 2007 as a percentage was faster in the south and in coastal areas (fig 1.4). However in terms of absolute numbers the increase was greatest in the most populous areas of the country (fig 1.5). In general, population growth was must faster in rural areas (fig 1.6).

1.6 – Urban and rural population (in thousands), 1997 and 2007

8UEDQ 8UEDQ 5XUDO 5XUDO Less than –20 –20–0 Less than –50

1–20 –50–10

21–40 –9–0

41–60 1–10

61–80 11–20

81–100 21–30

Greater than 100 Greater than 30

0 75 150 300 0 75 150 300

Kilometers Kilometers

5 population (cont.)

Mozambique has a very young population, particularly in the Center-north of the country. Most areas of the south are characterized by a larger share of women in the population.

The population of Mozambique 1.8 – Share of population aged 15 or below, 2007 1.9 – Share of population aged 50 or above, 2007 is still expanding (fig 1.7) and the population under 15 years of age is greater than 50 percent in most of the center and the north of the country (fig 1.8). Only in the south and the extreme north there are districts with more than 15 percent of the population aged 50 or above (fig 1.9).

1.7 – Population pyramid by gender, 1997 and 2007

)HPDOHSRSXODWLRQ 0DOHSRSXODWLRQ )HPDOHSRSXODWLRQ 0DOHSRSXODWLRQ

ï ï ï ï Less than 30 Less than 5 ï 30–40 5–10

ï 41–45 11–15 ï 46–50 16–20 ï Greater than 50 Greater than 20 ï ï 0 75 150 300 0 75 150 300 3RSXODWLRQ Kilometers Kilometers

6 Page 7: on this page fig 1.10, 1.11, 1.12;

Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE

1.10 – Number of men per 100 women, 1997 1.11 – Number of men per 100 women, 2007 The ratio of men to women across Mozambique is not even across the country, with the provinces of In- hambane, Gaza and also the southern areas of the provinces of Man- ica and Sofala having more women than men (fig 1.11). The trend is not recent as a similar pattern is found in 1997 (fig 1.10). The pattern ap- pears to be age related, as only after age 17 there are more women than men (and this is true in all provinces mentioned above) (fig 1.12).

1.12 – Ratio of men to women by age, 2007

Inhambane Gaza Other provinces 120

100

Less than 70 Less than 70 80 71–80 70–80

81–90 81–90 60 91–100 91–100

Greater than 100 Greater than 100

0 75 150 300 0 75 150 300 0−5 6−10 11−15 16−20 21−25 26−30 31−35 36−40 41−45 46−50 51−55 56−60 61−65 Age group Kilometers Kilometers

7 Regular text: Note: invert order and start with large map of share who can speak Portuguese Pages 8 and 9: Figures 1.13a to 1.13i; fig 1.14, fog 1.15 language

A variety of languages are spoken across Mozambique. Portuguese is the only one that is spoken in most parts of the country, and almost by everyone in the main urban centers. However, it is the first language for only a relatively small share of Mozambicans.

The ability to speak Portuguese var- 1.13 – Share of population (aged 10 and above) that can speak Portu- ies from less than 20 percent of the guese, 2007 population in some rural areas to over 60 percent in the main urban centers. The ability to speak Por- tuguese also depends critically on age and gender. Most males under 55 speak Portuguese (more than 60 percent), while for females this critically depends on age. For instance, 63 percent of women at age 11 to 15 speak Portuguese compared to 36 percent at age 31 to 35, and 17 percent at age 51 to 55 (fig 1.15).

1.15 – Share of population that can speak Portuguese by age group and gender, 2007

0DOH)HPDOH

Less than 20 20–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers ï ï ï ï ï ï ï ï ï ï ï $JHJURXS

8 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE

1.14a 1.14b Nine languages are spoken by at least 4 percent of the Mozambi- can population, and the geographic prevalence of each of these nine languages is shown in the maps. Each maps show the share of population by whom that language is spoken as their main language (figures 1.14a to 1.14i). The maps show that the common languages are generally geographically concentrated with the far majority in each area speak- ing the same language. It should be noted that the Census 2007 regis- 1.14c tered many variations of the same root language, and to create the maps some of these variations have been combined in the following way: Xitsonga includes the following variations: Xibila, Xidzonga, Xin’walungu, Xihlengue, and Xit- langanu. Sena includes the following variations: Bangwe, Gombe,

Less than 1

1–30

31–60 61–90 1.14a–i – Language spoken (in percentage), 2007 Greater than 90 1.14a – Cindau and variations 0 75 150 300 1.14b – Cinyanja and variations

Kilometers 1.14c – Echuwabo and variations

9 language (CONT.)

Sena Care, Phondzo, and Tonga. 1.14d 1.14e Emakhuwa includes the following variations: Echirima, Emarevoni, Emetto, Enhara, Esaaka, and Esan- gagi. Echuwabo includes the following variations: Ekarungu and Merenja. Cinyanja includes Cice- wa, Cingoni, and Cinsenga. Xitswa includes the following variations: Xidzivi, Xilhengwe, Xikhambani, and Ximhandla. Cindau includes the following variations: Cidanda, 1.14f Cimachanga, and Ciqwaka.

Less than 1 1.14a–i – Language spoken (in percentage), 2007 1–30 1.14d – Elomwe 31–60 1.14e – Emakhuwa and variations 61–90 1.14f – Portuguese Greater than 90 1.14g – Sena and variations 0 75 150 300

1.14h – Xitsonga and variations Kilometers 1.14i – Xitswa and variations

10 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE

1.14g 1.14h Portuguese seems to be an excep- tion to this general pattern of geographically concentrated languages. Portuguese is spoken as the main language across the country by a significant share of the population, even though it is almost no- where the main language for a majority of the population (fig 1.14f ). Figure 1.14f shows the share of the population which speaks Portu- 1.14i guese as their main languages, while Figure 1.13 showed the share of the population that is able to speak Portuguese.

Less than 1

1–30

31–60

61–90

Greater than 90

0 75 150 300

Kilometers

11 religion

Many denominations are found in Mozambique with distinct geographical patterns to popularity.

Mozambique has a rare rich mix 1.17a of religious beliefs with no religion dominating the country. While all main religions can be found in ev- ery part of the country (fig 1.16), there are marked geographical patterns which characterize the pre- dominance of religious beliefs (fig 1.17a to 1.17f).

1.17b

Less than 20

20–40

41–60 1.17a–f – Share of population belonging to a demonination, 2007 61–80 1.17a – Muslim Greater than 80 1.17b – Catholic 0 75 150 300

1.17c – Ciao Zion Kilometers 1.17d – Evangelical and Pentecostal 1.17e – Anglican 1.17f – Not a member of any religion

12 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE

1.17c 1.17e

1.17d 1.17f

Less than 20

20–40

41–60

61–80

Greater than 80 0 75 150 300

Kilometers

13 14 wealth

Poverty and inequality asset ownership

Indicators of wealth and wellbeing as ownership of assets and consumption per capita show a complex pattern wealth across Mozambique. Page 14: on this page fig 2.1, 2.2 and 2.3 poverty and inequality

Poverty and inequality indicators show an unexpected pattern.

Wealth can be measured in many and non-food consumption per 2.1 – Poverty headcount by province, 2008/2009 ways. In this section we assess capita was calculated, and is re- households’ living standard by ferred to as the poverty line. The measuring their level of con- poverty headcount by province sumption per capita (which is was then calculated as the num- the accepted international in- ber of people who consume less dicator for measuring mone- than the poverty line, over the tary poverty) and also by look- total population in that province ing at the households ownership (fig 2.1). According to the Third of physical assets. The informa- National Poverty Assessment the tion on consumption is taken northern provinces of Niassa, from the Household Budget Sur- Tete and Cabo Delgado were the vey 2008/09 (Inquerito do Orca- least poor provinces in Mozam- mento Familiar, IOF 2009), while bique in 2008/09, while Zambe- the data on ownership of assets zia and Maputo province were comes from the 2007 Population the poorest (fig 2.1).This pattern Census. The Third National Pov- of poverty goes against the gen- erty Assessment (GOM, 2010) erally held view in Mozambique measured the level of poverty of that southern provinces are rel- Mozambican households using atively richer, and may reflect a data from the IOF 2008/09, by problem with the survey data or measuring the household’s total with the methodology used to Less than 35 consumption per capita. The level calculate the poverty line. Addi- 35–45 of expenditures needed to allow tional analysis is ongoing to veri- 46–55 households a basic level of food fy these preliminary results.” 56–65 Greater than 65

0 75 150 300

Kilometers

16 Page 15: on this page fig 2.4 and 2.5

Mozambique Then and Now Section 2 - WEALTH

2.2 – Squared poverty gab by province, 2008/2009 2.3 – Inequality by province, 2008/2009 The level of consumption inequality in each province (how much consumption per capita varies between households within each province) was measured by calculating the Gini coefficient (GOM, 2010). A higher value of the Gini coefficient indicates that inequality is greater. Looking at inequality by province reveals a mixed pattern, with provinces in the north, Center and south that have high levels of inequality, but also provinces with relatively low inequality both in the north and Center (fig 2.3).

Less than 5 Less than 35

5–8 35–37

8–11 37–39

11–14 39–41

Greater than 14 Greater than 41

0 75 150 300 0 75 150 300

Kilometers Kilometers

17 asset ownership

Ownership of expensive assets is for the few and most of them live in urban areas and in the south, while ownership of less expensive assets is common throughout the country.

2.4 – Share of households owning a radio, 2007 2.5 – Share of households owning a bicycle, 2007 The most affordable and frequently owned assets are radios and bicycles. Ownership of radios is common in urban areas, but also widespread in rural areas (fig 2.4). It is worth to highlight that there is substantial variation across provinces and even bordering administrative posts; in many instances there is a difference of more than 20 percentage points in ownership of radios between neighboring administrative posts (fig 2.4). Ownership of bicycles is also spread across the country, but much lower in the south and in Ma- puto city, where ownership of cars is more common and the use of public transport is widespread (fig 2.5).

Less than 30 Less than 10

30–40 10–20

41–50 21–40

51–60 41–60

Greater than 60 Greater than 60

0 75 150 300 0 75 150 300

Kilometers Kilometers

18 asset ownership (CONT.) Mozambique Then and Now Section 2 - WEALTH

Ownership of expensive assets such 2.9a 2.7b as cars, motorcycles, TVs and com- puters is limited to very few households (fig 2.7). Cars, TVs and com- puters are almost exclusively found in urban areas and in the southern part of the country, while motorcycles can be found in a few areas of the country (fig 2.7a–d).

The percentage of households owning any of these assets is much greater in urban areas compared to rural areas, except for bicycles which are more common in rural areas (fig 2.6). 2.7c 2.7d 2.7a–d – Share of households owning specified assets, 2007 2.6 – Asset ownership by urban/ 2.9a – Car rural, 1997/2007 2.9b – Computer 2.9c – Television 2.9d – Motorcycle

Less than 1

1–2

2–5

5–20

Greater than 20

0 75 150 300

Kilometers

19 20 healthy lives infant mortality rate maternal mortality rate malnutrition distance to health facilities fertility accessing water from rivers and lakes lack of access to toilet facilities diarrhea and malaria

A healthy life depends on many aspects and there has been great progress in most of them from 1997 to 2007. infant mortality rate

Infant mortality remains very high, but decreased significantly across Mozambique during the decade from 1997 to 2007, particularly in locations which had very high levels in 1997.

The Infant Mortality Rate (IMR) is 3.1 – IMR per 1000 live births, 1997 3.2 – IMR per 1000 live births, 2007 defined as the number of newborns who die under one year of age divided by the number of live births during the year, multiplied by 1000. Data for Mozambique is from the 2007 Census while the international comparison to other countries in Africa is based on data from the World health Organization online database (available at: www.who. int). There was a substantial reduction in infant mortality from 1997 to 2007 (fig 3.1 and 3.2).

The largest reductions over the decade took place in Administrative Posts that had the highest rates in 1997 (fig 3.4). As a result there is now less variation in infant mortality rate across Administrative Posts in 2007, as is shown by the narrower (more concentrated) distribution of Less than 50 the infant mortality rate by Admin- 50–100 istrative Posts (fig 3.5). Despite prog- 101–150 ress, however, Mozambique’s infant 151–200 mortality rate is not among the low- Greater than 200 est for African countries (fig 3.3). 0 75 150 300 Kilometers

22 Page 2: on this page fig 3.1, 3.5 and 3.6. Note: delete fig 3.4

Mozambique Then and Now Section 3 - HEALTHY LIVES

3.3 – IMR per 1000 live births across Africa, 2007 3.4 – IMR in 1997 and change between 1997 and 2007

100

−100 Change in infant mortality from 1997 to 2007 −200 0 50 100 150 200 250 Infant mortality in 1997

3.5 – Distribution of IMR, 1997 and 2007

Infant mortality 1997 Infant mortality 2007 .03 Less than 30

30–60 61–90 .02 91–120 Greater than 120

No data .01 0 475 950 1,900

Kilometers

0 0 50 100 150 200 250 Infant mortality rate

23 maternal mortality rate

Maternal mortality rates vary substantially between districts.

The Maternal Mortality Rate 3.6– MMR by district, 2007 3.7 – MMR across Africa, 2007 (MMR) is defined as the death of a woman between 15 and 50 of age while pregnant, while giving birth or within 60 days of termination of pregnancy, over 100.000 live births. The data for Mozambique is from the 2007 Population Census while the international comparison to other countries in Africa is from the World Health Organization. Ma- ternal mortality rates are higher in the north, compared to the center and south of the country (fig 3.6). Maternal mortality rates also appear to vary greatly across districts. In part this could be due to few births and deaths related to births within each district making data susceptible to small changes. Less than 500 Less than 500 500–1000

500–1000 1001–1500

1001–1500 1501–2000

1501–2000 Greater than 2000

Greater than 2000 No data

0 75 150 300 0 475 950 1,900

Kilometers Kilometers

24 malnutrition Mozambique Then and Now Section 3 - HEALTHY LIVES

The share of children underweight and stunted is gradually declining, but remains extremely high.

3.8 – Trend in malnutrition between 3.9 – Share of underweight children across Africa, 2007 Child anthropometric measures 1997 and 2008/2009 are widely used to analyze the prevalence of malnutrition among children. Anthropometric measures are measures of height-for- age (stunting), weight-for-height (wasting), and weight-for-age (underweight). The different measures capture different elements of malnutrition, with stunting reflecting sustained past episode or episodes of undernutrition, wasting reflecting weight loss associated with a recent period of starvation or disease, and underweight reflecting a current condition resulting from inadequate food intake, past episodes of undernutrition or poor health conditions.

Malnutrition indicators for Mozam- Less than 6 bican children have been calculat- 5–16 ed using data from the 1996/97and 17–26 2008/09 Household Budget Surveys 27–36 (IOF 1997 and IAF 2009). Measures Greater than 36 of stunting and underweight show a No data declining trend from 1997 to 2009 0 475 950 1,900 (fig 3.8). Compared to other African Kilometers countries Mozambique has an average share of children being underweight (fig 3.9).

25 malnutrition (CONT.)

Malnutrition indicators (that is 3.10 – Share of stunted children, 2007 3.11 – Number of stunted children per km2, 2007 stunting, wasting and underweight) are usually not recorded during a census, and indeed the information was not collected as part of the 2007 Population Census (that went to all households in Mozambique). The data available from the House- hold Budget Surveys only refers to a relatively small sample of Mozam- bican households. In order to estimate how many children are stunted or malnourished in each Adminis- trative Post we used a methodology known as ‘Small Area Estimates’. This methodology combines the information on malnutrition available in the 2009 IOF survey with the household and child information available in both the IOF survey and the 2007 Population Cen- sus to estimate how many children Less than 30 Less than 1 are malnourished in each Adminis- 30–40 2–5 trative Post (A full description of the 41–50 6–10 methodology and results is available 51–60 11–15 in Sohnesen (2011). These estimates Greater than 60 Greater than 15 have been used to produce the maps 0 75 150 300 0 75 150 300 of stunting and underweight indica- Kilometers Kilometers tors by administrative post (Fig 3.10 to 3.13)

26 Mozambique Then and Now Section 3 - HEALTHY LIVES

3.12 – Share of underweight children, 2007 3.13 – Number of underweight children per km2, 2007 Much larger shares of children are stunted and/or underweight in the northern and central provinces of Mozambique than in the south. In terms of absolute numbers of children stunted per km2 (which could be relevant if policy makers want to geographically target areas for inter- vention) the highest density of malnourished children are in the most populous areas in the north, Cen- ter and south, including along the densely populated southwestern coastline, and in the urban centers (figs 3.9, 3.10). A similar pattern is found for underweight children (figs 3.12, 3.13).

Less than 10

11–15 Less than 1

16–20 2–5

21–25 6–10

26–30 11–15

Greater than 30 Greater than 15

0 75 150 300 0 75 150 300

Kilometers Kilometers

27 Distance to health facilities

Most people reside relatively close to a health facility

As part of the 2007 Population 3.14 – Location of health facilities across Mozambique, 2007 3.15 – Distance to nearest health facility, 2007 Census the National Statistical Office (INE) collected spatial data on the geographic location of all health facilities (fig 3.14). This has made it possible to calculate and map the average distance of villages from health facilities across the country, where distance to health facilities is measured by the distance as the bird flies between the center of the village to the nearest health facility of any type (health post, health center, or hospital). Most of the country is fairly close to a health facility, but there are also areas where households are lo- cated more than 35 kilometers from the nearest health facility (fig 3.15).

Less than 5

6–15

16–25

26–35 + Health 2007 Greater than 35

0 75 150 300 0 75 150 300

Kilometers Kilometers

28 Access to safe water Mozambique Then and Now Section 3 - HEALTHY LIVES

Despite lack of overall progress in access to clean and safe water, some of the worst areas did see improvements over the past decade.

3.15 – Distance to nearest health facility, 2007 An important aspect of being able to 3.18 – Shared of households with improved water source live a healthy life is access to clean and safe water. The following sources of water are considered improved water sources; household connec- tions, public stand pipes, bore- holes, protected dug wells, protected springs, and rainwater collection. Map 3.x shows the share of households that use an improved water source. The map shows that there are large variations between share of households with improved water source, from less than five per-

cent to some areas with more than Less than 5

85 percent using improved water 5–15

sources. It’s noteworthy that unlike 16–25

many other social aspects illustrated 26–35

in this Atlas, the variation is largely 36–45

within districts more than between 46–55

north-south or East-West. 56–65

66–75

76–85

Greater than 85

0 75 150 300

Kilometers

29 Access to safe water (CONT.)

Figure 3.20 shows location of water 3.20 – Rivers, lakes and water wells across Mozambique, 2007 3.21 - Share of households using water from rivers or lakes, 1997 wells, rivers and lakes across Mo- zambique. Rivers and lakes are not considered a clean and safe source of water. In 1997 and in 2007 around 17 percent of households fetched their drinking water from rivers and lakes. There is a big difference in the percentage of households using rivers and lakes between urban and rural areas (fig 3.19). Adminis- trative Posts with the highest share of households using rivers and lakes as source of drinking water in 2007 are mostly in the Western part of the country (3.21, 3.23).

3.19 – Share of households using rivers as a source of water by urban/ national/rural, 2007

Less than 20

21–40

Water 41–60

Rivers 61–80

Lake Greater than 80

0 75 150 300 0 75 150 300

Kilometers Kilometers

30 Access to safe water (CONT.) Mozambique Then and Now Section 3 - HEALTHY LIVES

Although on average no progress 3.23 – Share of households using water from rivers or lakes, 2007 3.24 – Change of river and lake usage between 1997 and 2007 was recorded in reducing the percentage of households which fetched their drinking water from rivers and lakes, some areas particularly in the West of the country recorded substantial progress (fig 3.24). In fact, the Administrative Posts with the highest share of households using rivers and lakes as a water source in 1997 have seen the most progress (fig 3.22).

3.22 – Share of households using rivers and lakes as water sources in 1997 and change between 1997 and 2007 Less than 20 Less than –15 .5 20–40 –15-–5

41–60 –5–5

61–80 5–15 0 Greater than 80 Greater than 15

0 75 150 300 0 75 150 300

Kilometers Kilometers −.5 Change from 1997 to 2007

−1 0 .2 .4 .6 .8 1 Share that fetch water from rivers and lakes in 1997 31 lack of access to toilet facilities

There was wide progress in access to toilet facilities across the country during the past decade, especially in the north.

The share of households that do not 3.26 – Share of households that do not have access to a toilet, 1997 3.27 – Share of households that do not have access to a toilet, 2007 have access to a toilet facility has fallen by 10 percentage points from 63 percent in 1997 to 53 percent in 2007 (figs 3.25, 3.26, 3.27). Never- theless there is a large gap between urban and rural areas, with more than 65 percent of the households in rural areas still having no access to a toilet in 2007.

3.25 – Share of households without access to a toilet by urban/national/ rural, 2007

Less than 20

20–40 41–60

61–80

Greater than 80 0 75 150 300 8UEDQ 1DWLRQDO 5XUDO Kilometers

32 lack of access to toilet facilities (CONT.) Mozambique Then and Now Section 3 - HEALTHY LIVES

3.28 – Change of households without access to a toilet between 1997 and 2007 In general more progress was observed in the north than the rest of the country (fig 3.28), but unlike other aspects like general fertility and infant mortality we don’t find a relationship between the progress made and the level in 1997 (fig 3.29).

3.29 – Share of households without access to toilet in 1997 and change between 1997 and 2007

Less than –20

–20––10

–10–0 ï 0–10

Greater than 50 &KDQJHLQDFFHVVWRWRLOHWWR

0 75 150 300 ï Kilometers 6KDUHZLWKQRDFFHVVWRWRLOHWLQKRPHLQ

33 diarrhea and malaria

More people are being treated for diarrhea and malaria today than in the past.

The number of treated cases of 3.30 – Number of diarrhea cases per 1000 people, 1997 3.31 – Number of diarrhea cases per 1000 people, 2007 malaria and diarrhea are counted at health facilities throughout the country as reported by the Epide- miology Department of the Min- istry of Health (Gabinete de Epide- miologia, Ministério da Saúde).”

Figures 3.30 to 3.33 show the number of reported cases of diarrhea and malaria as a proportion of the population in health facilities in 1997–1998 and in 2007. They show a substantial increase in number of treated cases in most parts of the country. It is important to under- line, however, that the observed increase in number of cases treated in health facilities does not necessarily indicate that more people are suffering from these diseases— and in fact it is more likely to reflect an increase in the number of peo- Less than 10 Less than 10 ple seeking treatment as access to 10–20 10–20 health facilities has improved over 21–30 21–30 31–40 31–40

Greater than 40 Greater than 40

0 75 150 300 0 75 150 300

Kilometers Kilometers

34 diarrhea and malaria (CONT.) Mozambique Then and Now Section 3 - HEALTHY LIVES

3.32 – Number of malaria cases per 1000 people, 1998 3.33 – Number of malaria cases per 1000 people, 2007 time. The Report on the Millenni- um Development Goals by Min- istry of Planning and Develop- ment (GOM, 2010) indicate a sharp downward trend in on share of children under five suffering from malaria over time (fig 3.33)” with “The report on the Millennium Devel- opment Goals by the Ministry of Planning and Development (GOM, 2010) indicates a sharp downward trend in on share of children under five suffering from malaria.”

Less than 50 Less than 50

50–100 50–100

101–150 101–150

151–200 151–200

201–250 201–250

Greater than 250 Greater than 250

0 75 150 300 0 75 150 300

Kilometers Kilometers

35 fertility

Fertility rates are more homogenous across the country today than they were a decade ago.

3.16 – General fertility rate, 1997 3.17 – General fertility rate, 2007 The general fertility rate is defined as the annual number of live births per 1000 women of childbearing age (between 15 and 49 of age). The national average general fertility rate decreased very little over the decade from 174 in 1997 to 168 in 2007. This small reduction hides a process of equalization where areas with high fertility rates in 1997 had decreasing fertility rates, while areas with very low fertility rates increased (figs 3.16, 3.17). Fertili- ty rates are higher on average in the center and in the north compared to the south, but it’s noteworthy that the fertility rate across the country is more homogeneous in 2007 than it was in 1997 (figs 3.16, 3.17).

Less than 100

100–150

151–200

201–300

Greater than 300

0 75 150 300

Kilometers

36 Mozambique Then and Now Section 3 - HEALTHY LIVES

37 38 access to services electricity, running water, phones and internet distance to major urban areas

Access to services such as electricity, fixed phone lines, and running water are mostly an urban privilege, while the use of cell phones has spread to the rural south. electricity, running water, phones and internet

Access to basic services such as running water, landlines, internet, and electricity for lighting is the privilege of a few households, mainly in urban areas.

Access to services as running wa- 4.2a 4.2b ter, phone landlines, internet, and electricity for lighting is generally a rare luxury. For both electricity and running water 32 percent of the urban households use these services, while only 1 percent of households in rural areas do. In 2007 cell phones were owned by approximately 45 4.2a–d – Share of households using a service, 2007 percent of urban household and less 4.2a – Using electricity as the main source of light than 10 percent of rural households. 4.2b – Smainly using running water (available Electricity and running water were either within the house or outside the house) used by about one third of the urban 4.2c – Owning a fixed phone line households, and almost none in ru- 4.2d – Having used internet in last 12 months ral areas. (fig 4.1).

4.2c 4.2d 4.1 – Share of households using services in urban and rural areas, 2007

Cell phone Electricity Running water Internet Fixed phoneline 50% Less than 2

40% 2–4

5–6 30% 7–8

9–10 20% 11–12

10% Greater than 12

0 37.5 75 150 0% Urban National Rural Kilometers

40 Page 37: 4.3 and 4.4

Mozambique Then and Now Section 4 - ACCESS TO SERVICES

4.3 – Share of households owning at least one cell phone 4.4 – Coverage of mcel masts across Mozambique, 2007 The ownership of cell phones is far more common in urban areas (48 percent of households have at least one cell phone), but it is also spread- ing to rural areas, notably in the south (fig 4.3). Figure 4.3 also reveals that ownership of cell phones is not explained by access to cellular services, as there are many areas that have mcel towers (and therefore net- work coverage) in the north (fig 4.4), but which have very low density of cell phones. (Note: We were not provided the location of cell phone towers by Vodacom, which is the other main cell phone provider in Mo- zambique).

Less than 2

2–4

5–6

7–8

9–10

11–12

Greater than 12

0 75 150 300 0 75 150 300

Kilometers Kilometers

41 distance to major urban areas

Vast areas of the country remain isolated from major markets

PLEASE PROVIDE TITLE FOR FIGURE 4.5!

Access to major urban centers in 4.5 – ??????????? 4.6 – Travel distance to urban centers (in minutes) part depends on road infrastructure and is often key for access to main markets with greater product avail- ability and favorable prices.

Estimated travel time to nearest city of at least 50.000 people has been calculated based on available roads and assumptions about average travel speed by type of road, also tak- ing into account landscape and elevation conditions (see Dorosh and Schmidt, 2008, for full details). The model calculates the quickest travel time from each village to the clos- est market within Mozambique. The Primary map is based on the 2007 Census Secondary map (fig 4.6). Tertiary Vicinal Less than 200 Unclassified 201–400 Ohter 401–600 Rail Greater than 600 Capitals

0 75 150 300 0 75 150 300

Kilometers Kilometers

42 Mozambique Then and Now Section 4 - ACCESS TO SERVICES

4.6 – Travel distance to urban centers (in minutes)

43 44 education Primary gross enrollment rates Secondary gross enrollment rates Distance to primary and secondary schools Education of the labor force

There was substantial progress in primary and secondary enrollment over the decade from 1997 to 2007, both for boys and girls, and in urban and rural areas. primary gross enrollment rates

Gross primary enrollment increased substantially during the past decade

The Primary Gross Enrollment 5.1 – PGE rates, 2007 5.2 – Change in PGE between 1997 and 2007 (PGE) rate provides a measure of the share of primary school-age children which are actually enrolled in school. It is calculated as the number of students attending primary school (grades 1 to 7, and including technical elementary ETE), divided by number of children between 6 and 12 years of age. The Prima- ry Gross Enrollment rate increased substantially the last decade, from an average of 66 percent in 1997 to 99 percent in 2007. This reflects substantial increases in primary enrollment both in urban and rural areas and for boys and girls. Geographi- cally there was progress in primary enrollment rates in almost all Ad- ministrative Posts, particularly in the central part of the country (figs 5.1, 5.2). In fact, a simple analysis shows that the administrative posts Less than 25 Less than 15 with the lowest enrollment rates in 25–50 15–30 1997 saw the largest improvements 51–75 31–45 between 1997 and 2007 (fig 5.5). It is 76–100 46–60 useful to keep in mind that the rel- Greater than 100 Greater than 60 atively small progress made in Ad- 0 75 150 300 0 75 150 300 ministrative Posts in the south and Kilometers Kilometers in Maputo City and other urban centers does not necessarily reflect

46 Page 43: fig 5.6, 5.7

Mozambique Then and Now Section 5 - EDUCATION

5.4 – Male PGE rate, 2007 5.5 – Female PGE rate, 2007 lack of progress and could also indicate that enrollment rates are ap- proaching universal enrollment in those areas.

It is also noteworthy that many Ad- ministrative Posts still have low primary enrollment rates. Around 20 percent of Administrative Posts have primary gross enrollment rates below 75. Many of these areas with low enrollment rates are concentrated in northern provinces as Niassa, Cabo Delgado, Nampula, and Tete. Further, female enrollment rates remain lower than that for boys, particularly in rural areas (figs 5.3, 5.4).

5.5 – PGE rates in 1997 and change between 1997 and 2007

Less than 25 Less than 25

25–50 25–50

51–75 51–75 76–100 76–100

Greater than 100 Greater than 100

0 75 150 300 0 75 150 300 Kilometers Kilometers &KDQJHLQHQUROOPHQWUDWHIURPWR ï (QUROOPHQWUDWHLQ

47 New page: this is new material not included in the first version of the atlas. New map is called “distance primary” Regular text: primary gross enrollment rates (CONT.)

Access to primary schools is good in most of the country

Distance to primary schools is mea- 5.6 – Distance to nearest primary school, 2007 sured as the bird flies, e.i. the geographical distance between any location and the nearest primary school, irrespective of the existence of a road or other means of trans- portation. A better measure would be the actual travel distance given available paths and roads, however, the map still gives a good indication of how well the country is covered by primary schools. The map shows that in most parts of the country, there is a primary school within six kilometers. There are however, also some areas with much longer distance to the nearest primary school.

Less than 2

3–4

5–6

7–8

Greater than 8

0 75 150 300

Kilometers

48 Mozambique Then and Now Section 5 - EDUCATION

5.7 – PGE across Africa, 2007 With primary enrollment levels ap- proaching universal enrollment Mo- zambique does well compared to many other African countries (fig 2.5).

Less than 70

70–90

91–110

111–130

Greater than 130

No data

0 475 950 1,900

Kilometers

49 secondary gross enrollment rates

Gross secondary enrollment increased around four fold during the last decade with progress concentrated in urban areas.

The Secondary Gross Enrollment 5.8 – SGE rates, 2007 5.9 – Change in SGE between 1997 and 2007 (SGE) rate provides a measure of the share of secondary school-age children which are actually enrolled in secondary school. It is defined as number of students attending secondary school (grade 8 to 12, including technical schools ETB and ETM), divided by the number of children between 13 and 16 years of age. The gross secondary enrollment rate increased four fold from an average of 10 percent in 1997 to 42 percent in 2007. Progress across administrative posts was relatively equal with almost all administrative posts making progress (figs 5.8, 5.9). However the increase in enrollment was concentrated in urban areas and mostly took place in areas that already had higher enrollment Less than 10 Less than 5 in 1997 (fig 5.12). 10–20 21–25 5–15

26–45 16–25

46–65 26–35

66–85 36–45

86–100 46–55

Greater than 100 Greater than 55

0 75 150 300 0 75 150 300

Kilometers Kilometers

50 Page 45: fig 5.13, 5.14

Mozambique Then and Now Section 5 - EDUCATION

5.10 – Male SGE rate, 2007 5.11 – Female SGE rate, 2007 A gender gap prevails in secondary education. In 2007, approximately 47 percent of boys were enrolled compared to 37 percent of girls. Un- like some other social aspects there is not a clear North-South difference in secondary enrollment rates for both genders. There is however disparities between administrative posts, and within all provinces there are wide difference across Adminis- trative Posts (figs 5.10, 5.11).

5.12 – SGE rates in 1997 and change between 1997 and 2007

Less than 10 Less than 10

10–20 10–20 21–25 21–25

26–45 26–45

46–65 46–65 66–85 66–85

86–100 86–100 Greater than 100 Greater than 100

0 75 150 300 0 75 150 300 &KDQJHLQHQUROOPHQWUDWHIURPWR Kilometers Kilometers (QUROOPHQWUDWHLQ

51 secondary gross enrollment rates (CONT.)

Access to secondary schools remains limited

Distance to secondary schools is 5.13 – Distance to nearest secondary school, 2007 measured as the bird flies, e.i. the geographical distance between any location and the nearest secondary school, irrespective of the existence of a road or other means of trans- portation. A better measure would be the actual travel distance given available paths and roads, however, the map still gives a good indication of how well the country is covered by secondary schools. The map shows that in most parts of the country there is no secondary school within 40 kilometers.

Less than 10

10–20

21–30

31–40

Greater than 40

0 75 150 300

Kilometers

52 Page 47: fig 5.16

Mozambique Then and Now Section 5 - EDUCATION

5.14 – SGE rates across Africa, 2007 Secondary enrollment rates are on average lower in Mozambique than in most other African countries (fig 5.14).

Less than 10

10–30

31–50

51–70

Greater than 70

No data

0 475 950 1,900

Kilometers

53 Education of labor force

Educated labor is scarce

An educated labor force is a key 5.19 – Adult male literacy rate, 2007 5.20 – Adult female literacy rate, 2007 element in development and growth. Unfortunately, in most rural areas only about half the men can read and write (i.e. they are literate), and only very few women are literate (fig 5.19, 5.20).

Less than 25 Less than 25

25–40 25–40

41–60 41–60

61–80 61–80

Greater than 80 Greater than 80

0 75 150 300 0 75 150 300

Kilometers Kilometers

54 Mozambique Then and Now Section 5 - EDUCATION

5.21 – Share of labor force with completed primary education or 5.22 – Share of labor force with completed secondary education or The population of labor force age above, 2007 above, 2007 (15 to 65) with some formal education (primary education or above) is mostly concentrated in urban areas. There is hardly anyone of labor force age (with secondary (or above) outside urban areas (fig 5.21, 5.22, 5.23).

5.23 – Share of labor (aged 15 to 65) with complete primary or above, and secondary and above, by gender and urban/rural, 2007

3ULPDU\ 6HFRQGDU\

Less than 25 Less than 25

25–40 25–40 41–60 41–60

61–80 61–80

Greater than 80 Greater than 80 0 75 150 300 0 75 150 300

Kilometers Kilometers 8UEDQ 5XUDO 8UEDQ 5XUDO 0DOH)HPDOH

55 56 land and agriculture

land weather animals

Land and agriculture is of great importance to most in Mozambique. This section shows how the land is distributed across the country and how rain and temperature varied in 2007 and over the last decade. It also shows where ownership of animals is more common and how it has changed over time. Regular text: Page 56: fig 7.1, and 7.2 land and weather

Mozambique’s topography varies mostly from East to West while rain patterns often vary mostly from north to south

Elevation generally increas- 6.1 – Topography of Mozambique 6.2 – Land suitability es as you move from East to West in the Northern parts of the country (fig 6.1). Land capabilities and its suitability for use vary mostly from North to South as land suit- ed for intensive agricultural use is concentrated in the North (fig 6.2).

Land suitable for intensive agricultural use Water bodies –112 – 100 Suitable for all uses 101 – 200 Suitable for all uses, but requires some conservation 201 – 300 Suitable for all uses, but requires intensive conservation 301 – 400 Land not suitable for agricultural use, but suitable for 401 – 500 pasture, reforestation or natural vegetation. 501 – 600 Suitable for several uses

601 – 1,000 Suitable for pasture, reforestation or wild life 1,010 – 2,420 Suitable for reforestation or wild life, inadequate for grass

0 75 150 300 0 75 150 300

Kilometers Kilometers

58 Page 57: figure 7.3a-l rain by year

Mozambique Then and Now Section 6 - LAND & AGRICULTURE

The north-south pattern 6.3a – 1997 6.3b – 1998 6.3c – 1999 6.3d – 2000 6.3e – 2001 6.3f – 2002 seen in agricultural suitability is also seen in rainfall with the north receiv- ing much more rain than the south. Figures 6.3a–l show how rain varies across years and location. In particular the floods in 2000 and the droughts around 2002 and 2005 are easily observable

The rainfall and temperatures were produced using daily NASA weather satellite data, over the period 6.3g – 2003 6.3h - 2004 6.3i – 2005 6.3j – 2006 6.3k – 2007 6.3l – 2008 from 1997 to 2008..

6.3a–l – Rainfall by year, 1997–2008

400mL

500mL

600mL

700mL

800mL

900mL

1000mL

1300mL

1600mL

1900mL 0 75 150 300

Kilometers

59 Page 58: figure 7.4a-l.x rain by months land and weather (CONT.)

The 12 maps in fig- 6.4a – January 6.4b – February 6.4c – March 6.4d – April 6.4e – May 6.4f – June ures 6.4a–l dramatical- ly illustrate the seasonal rainfall in Mozambique as it was in 2007. The rain season started in November in the south and Center, covering the whole country by De- cember, and declining from the south towards the north in January, February and March.

6.4a–l – Rainfall by Month, 2007 6.4g – July 6.4h – August 6.4i – September 6.4j – October 6.4k – November 6.4l – December 5mL

10mL

20mL

40mL

60mL

80mL

100mL

200mL

400mL

700mL

0 75 150 300

Kilometers

60 Page 59: temperature by month

Mozambique Then and Now Section 6 - LAND & AGRICULTURE

6.5a – January 6.5b – February 6.5c – March 6.5d – April 6.5e – May 6.5f – June Figures 6.5a–l show variation in mean day temperatures in Celsius across the same year. Compared to rain, temperatures do not vary as drastically over the year or across the country. The higher elevated plains in the West do see some temperature swings around the winter months of June and July that are up to 10 degrees Celsius colder than the summer months

6.5g – July 6.5h – August 6.5i – September 6.5j – October 6.5k – November 6.5l – December 6.5a–l – Average temperature by month, 2007

<18°C

19°C

20°C

21°C

22°C

23°C

24°C

25°C

26°C

27°C

28°C 0 75 150 300

Kilometers

61 ownership of animals

Ownership of animals varies with location, but is generally very little, except for poultry

There are geographical patterns in 6.6 – Share of households owning sheep 6.7 – Share of households owning cattle ownership of animals. Sheep owners are mostly found in the south (fig 6.6), cattle owners are mostly found in the south and the West (fig 6.7), ownership of swine is most common in southern part of Inhambane and the central part of the country (fig 6.8), while ownership of poultry is common throughout the country, except in the Maputo province (fig 6.9).

Less than 1 Less than 1

1–5 1–5

6–10 6–10

11–15 11–15

16–20 16–20

21–25 21–25

26–30 26–30

Greater than 30 Greater than 30

0 75 150 300 0 75 150 300

Kilometers Kilometers

62 Mozambique Then and Now Section 6 - LAND & AGRICULTURE

6.8 – Share of households owning swine 6.9 – Share of households owning poultry

Less than 1 Less than 1

1–5 1–5

6–10 6–10

11–15 11–15

16–20 16–20

21–25 21–25

26–30 26–30

Greater than 30 Greater than 30

0 75 150 300 0 75 150 300

Kilometers Kilometers

63 ownership of animals (CONT.)

6.13 – Percentage point change in share Ownership of most animals is the 6.11 – Share of households owning owning cattle between 1997 and 2007 priviledge of few households; in sheep between 1997 and 2007 2007 only 4 percent of households owned cattle, 2 percent sheep, and 10 percent swine (figs 6.10, 6.11, 6.12). Poultry on the other hand is relatively common with more than 50 percent of households owning some poultry. Compared to 1997, some households have shifted away from sheep (–10 percentage points from 1997 to 2007), while there has been little change in the percentage of household owning swine (–2 percentage points) and cattle (+1 percentage point) (figs 6.13, 6.14, 6.15).

6.10 – Share of households owning 6.12 – Share of households owning cattle between 1997 and 2007 swine between 1997 and 2007

Less than –6

–6––2

–2–2

2–6

6–10

Greater than 10

0 75 150 300

Kilometers

64 Mozambique Then and Now Section 6 - LAND & AGRICULTURE

6.14 – Percentage point change in share 6.15 – Percentage point change in share owning sheep between 1997 and 2007 owning swine between 1997 and 2007

Less than –6

Less than –25 –6––2

–25––15 –2–2

–15––5 2–6

–5–5 6–10

Greater than 5 Greater than 10

0 75 150 300 0 75 150 300

Kilometers Kilometers

65 Cod Adm. Posts District Province Cod Adm. Posts District Province 1 CIDADE DE LICHINGA CIDADE DE LICHINGA NIASSA 73 IMBUO MUEDA CABO DELGADO 2 CUAMBA CUAMBA NIASSA 74 NEGOMANO MUEDA CABO DELGADO 3 ETARARA CUAMBA NIASSA 75 N’GAPA MUEDA CABO DELGADO 4 LURIO CUAMBA NIASSA 76 MUIDUMBE MUIDUMBE CABO DELGADO Location of 5 METANGULA LAGO NIASSA 77 CHITUNDA MUIDUMBE CABO DELGADO 6 COBUE LAGO NIASSA 78 MITEDA MUIDUMBE CABO DELGADO 7 MANIAMBA LAGO NIASSA 79 NAMUNO NAMUNO CABO DELGADO 8 CHIMBONILA LICHINGA NIASSA 80 HUCULA NAMUNO CABO DELGADO Administration Posts 9 LIONE LICHINGA NIASSA 81 MACHOCA NAMUNO CABO DELGADO 10 MEPONDA LICHINGA NIASSA 82 MELOCO NAMUNO CABO DELGADO 11 MALANGA MAJUNE NIASSA 83 N’CUMPE NAMUNO CABO DELGADO 12 MUAQUIA MAJUNE NIASSA 84 PAPAI NAMUNO CABO DELGADO 13 NAIRRUBI MAJUNE NIASSA 85 NANGADE NANGADE CABO DELGADO

87 14 MANDIMBA MANDIMBA NIASSA 86 M’TAMBA NANGADE CABO DELGADO United Republic of Tanzania 89 85 88 15 MITANDE MANDIMBA NIASSA 87 PALMA PALMA CABO DELGADO 86 63 16 MARRUPA MARRUPA NIASSA 88 OLUMBI PALMA CABO DELGADO 75 73 64 74 71 65 17 MARANGIRA MARRUPA NIASSA 89 PUNDANHAR PALMA CABO DELGADO 38 76 25 78 77 58 6 22 72 18 NUNGO MARRUPA NIASSA 90 QUIONGA PALMA CABO DELGADO 26 56 37 21 55 57 19 MAUA MAUA NIASSA 91 METUGE PEMBA CABO DELGADO 69 93 53 61 62 54 20 MAIACA MAUA NIASSA 92 MIEZE PEMBA CABO DELGADO Zambia 7 30 94 35 17 95 5 68 40 21 MAVAGO MAVAGO NIASSA 93 QUISSANGA QUISSANGA CABO DELGADO 36 91 11 7042 92 29 16 46 39 22 M’SAWIZE MAVAGO NIASSA 94 BILIBIZA QUISSANGA CABO DELGADO 66 41 60 10 1 67 8 12 44 43 47 59 18 49 48 50 23 INSACA MECANHELAS NIASSA 95 MAHATE QUISSANGA CABO DELGADO 9 79 82 52 Malawi 13 34 45 126 24 CHIUTA MECANHELAS NIASSA 96 URBANO CENTRAL CIDADE DE NAMPULA NAMPULA 32 83 80 51 106 31 19 125 81 25 MECULA MECULA NIASSA 97 MUATALA CIDADE DE NAMPULA NAMPULA 15 20 33 84 108 107 127 27 122 Nampula 227 14 121 160 124 26 MATONDOVELA MECULA NIASSA 98 MUHALA CIDADE DE NAMPULA NAMPULA 28 111 159 129 114 228 158 112 149 157 154 27 METARICA METARICA NIASSA 99 NAMIKOPO CIDADE DE NAMPULA NAMPULA 238 218 2 4 114 120 231 219 24 143156 115 123 150 144 403 232 247 165 148 147 405 28 NACUMUA METARICA NIASSA 100 NAPIPIME CIDADE DE NAMPULA NAMPULA 250 236 226 113 145 400 251 3 167 163 161 116 142 23 180 99 110 29 MUEMBE MUEMBE NIASSA 101 NATIKIRE CIDADE DE NAMPULA NAMPULA 237 248 152 119 166 164 162 402 239 179 173 131 146 249 230 229 151 117 404 30 CHICONONO MUEMBE NIASSA 102 CIDADE DE ANGOCHE ANGOCHE NAMPULA 220 242 181 134 401 197 211 178 153 132 128 222 184 139 139 31 MASSANGULO NGAUMA NIASSA 103 AUBE ANGOCHE NAMPULA 234 233 172 135 133136 129 235 225 241 210 137 104 32 ITEPELA NGAUMA NIASSA 104 NAMAPONDA ANGOCHE NAMPULA 217240 190 182 139 221 196 195 177 105102 187 33 NIPEPE NIPEPE NIASSA 105 BOILA_NAMITORIA ANGOCHE NAMPULA 188 183 141 103 224 198 189 264 245 216 140 200 138 34 MUIPITE NIPEPE NIASSA 106 NAMAPA ERATI NAMPULA 266 205 223 286 284 201 215 193 35 UNANGO SANGA NIASSA 107 ALUA ERATI NAMPULA 265 299 243 206 199 214 298 244 36 LUSSIMBESSE SANGA NIASSA 108 NAMIROA ERATI NAMPULA 263 285 204 194 192 300 207 191 316 208 297246 37 MACALOGE SANGA NIASSA 109 C. ILHA DE MOÇAMBIQUE C. ILHA DE MOÇAMBIQUE NAMPULA 255 212 209 271 269 296 315 203 213 38 MATCHEDJE SANGA NIASSA 110 LUMBO C. ILHA DE MOÇAMBIQUE NAMPULA 295 202 169 254 185 39 CIDADE DE PEMBA CIDADE DE PEMBA CABO DELGADO 111 LALAUA LALAUA NAMPULA 319 253 317 302 186 270 175 176 40 ANCUABE ANCUABE CABO DELGADO 112 METI LALAUA NAMPULA 311 275 310 301 318 174 41 METORO ANCUABE CABO DELGADO 113 MALEMA MALEMA NAMPULA 273 276 261 309 258 320 272 321 42 MESA ANCUABE CABO DELGADO 114 CHIHULO MALEMA NAMPULA 274 252 257 322 262 43 BALAMA BALAMA CABO DELGADO 115 MUTUALI MALEMA NAMPULA Zimbabwe 282 259 260 324307 Beira 283 323 308 44 IMPIRI BALAMA CABO DELGADO 116 MECONTA MECONTA NAMPULA 280 429 306291 428 429 45 KUEKUE BALAMA CABO DELGADO 117 CORRANE MECONTA NAMPULA 281 292 289290 46 MAVALA BALAMA CABO DELGADO 118 NAMIALO MECONTA NAMPULA 304 293 425 279 303 294 423 47 CHIURE CHIURE CABO DELGADO 119 7 DE ABRIL MECONTA NAMPULA 277 305 48 CHIURE VELHO CHIURE CABO DELGADO 120 MECUBURI MECUBURI NAMPULA 278 313314 424 267 422 49 KATAPUA CHIURE CABO DELGADO 121 MILHANA MECUBURI NAMPULA 312 373 421 328 50 MAZEZE CHIURE CABO DELGADO 122 MUITE MECUBURI NAMPULA 268 329 340 51 NAMOGELIA CHIURE CABO DELGADO 123 NAMINA MECUBURI NAMPULA 335 390 389 334 52 OCUA CHIURE CABO DELGADO 124 MEMBA MEMBA NAMPULA 338 349 53 IBO IBO CABO DELGADO 125 CHIPENE MEMBA NAMPULA 366 339 350 54 QUIRIMBA IBO CABO DELGADO 126 LURIO MEMBA NAMPULA 369 367 327 342 55 MACOMIA MACOMIA CABO DELGADO 127 MAZUA MEMBA NAMPULA 56 CHAI MACOMIA CABO DELGADO 128 NAMIGE MOGINCUAL NAMPULA 368 380 326 341 57 MUCOJO MACOMIA CABO DELGADO 129 QUINGA MOGINCUAL NAMPULA 370 345 58 QUITERAJO MACOMIA CABO DELGADO 130 CHUNGA MOGINCUAL NAMPULA 381 344 392 331 379 348 343 59 MECUFI MECUFI CABO DELGADO 131 QUIXAXE MOGINCUAL NAMPULA 363 347 330 325 391 393 378 346 336 60 MURREBUE MECUFI CABO DELGADO 132 LIUPO MOGINCUAL NAMPULA 377 337 361 386 075 150 300 373 333 61 MELUCO MELUCO CABO DELGADO 133 NAMETIL MOGOVOLAS NAMPULA 403 405 376 332 362364 384 351 South Africa 404 372 352 Km 62 MUAGUIDE MELUCO CABO DELGADO 134 CALIPO MOGOVOLAS NAMPULA 365387 406 356 385 Maputo Matola 394 402 355 395388 361 63 MOCIMBOA DA PRAIA MOCIMBOA DA PRAIA CABO DELGADO 135 IULUTI MOGOVOLAS NAMPULA 353 423 412359 396 382 407 408358 64 DIACA MOCIMBOA DA PRAIA CABO DELGADO 136 MUATUA MOGOVOLAS NAMPULA 420410 420 422 414 418 65 MBAU MOCIMBOA DA PRAIA CABO DELGADO 137 NANHUPO RIO MOGOVOLAS NAMPULA 421413 407408 424401399 66 MONTEPUEZ MONTEPUEZ CABO DELGADO 138 MOMA MOMA NAMPULA 432 400 363419 406 410 410 416418 67 MAPUPULO MONTEPUEZ CABO DELGADO 139 CHALAUA MOMA NAMPULA 425 427 415 68 MIRATE MONTEPUEZ CABO DELGADO 140 LARDE MOMA NAMPULA 364 409 Swaziland417 419 69 NAIROTO MONTEPUEZ CABO DELGADO 141 MUCUALI MOMA NAMPULA 70 NAMANHUMBIR MONTEPUEZ CABO DELGADO 142 MONAPO MONAPO NAMPULA 71 MUEDA MUEDA CABO DELGADO 143 ITOCULO MONAPO NAMPULA 72 CHAPA MUEDA CABO DELGADO 144 NETIA MONAPO NAMPULA 66 Cod Adm. Posts District Province Cod Adm. Posts District Province Cod Adm. Posts District Province Cod Adm. Posts District Province Cod Adm. Posts District Province 73 IMBUO MUEDA CABO DELGADO 145 MOSSURIL MOSSURIL NAMPULA 217 CIDADE DE TETE CIDADE DE TETE TETE 289 URBANO Nº3 CIDADE DA BEIRA SOFALA 361 ALTO CHANGANE CHIBUTO GAZA 74 NEGOMANO MUEDA CABO DELGADO 146 LUNGA MOSSURIL NAMPULA 218 ULONGUE ANGONIA TETE 290 URBANO Nº4 CIDADE DA BEIRA SOFALA 362 CHAIMITE CHIBUTO GAZA 75 N’GAPA MUEDA CABO DELGADO 147 MATIBANE MOSSURIL NAMPULA 219 DOMUE ANGONIA TETE 291 URBANO Nº5 CIDADE DA BEIRA SOFALA 363 CHANGANINE CHIBUTO GAZA 76 MUIDUMBE MUIDUMBE CABO DELGADO 148 MUECATE MUECATE NAMPULA 220 SONGO CAHORA BASSA TETE 292 BUZI BUZI SOFALA 364 GODIDE CHIBUTO GAZA 77 CHITUNDA MUIDUMBE CABO DELGADO 149 IMALA MUECATE NAMPULA 221 CHINTHOLO CAHORA BASSA TETE 293 ESTAQUINHA BUZI SOFALA 365 MALEHICE CHIBUTO GAZA 78 MITEDA MUIDUMBE CABO DELGADO 150 MUCULOENE MUECATE NAMPULA 222 CHITIMA CAHORA BASSA TETE 294 NOVA-SOFALA BUZI SOFALA 366 VILA EDUARDO MONDLANE CHICUALACUALA GAZA 79 NAMUNO NAMUNO CABO DELGADO 151 MURRUPULA MURRUPULA NAMPULA 223 LUENHA CHANGARA TETE 295 CAIA CAIA SOFALA 367 MAPAI CHICUALACUALA GAZA 80 HUCULA NAMUNO CABO DELGADO 152 CHINGA MURRUPULA NAMPULA 224 CHIPEMBERE (CHIOCO) CHANGARA TETE 296 MURRAÇA CAIA SOFALA 368 PAFURI CHICUALACUALA GAZA 81 MACHOCA NAMUNO CABO DELGADO 153 NIHESSIUE MURRUPULA NAMPULA 225 KACHEMBE (MARARA) CHANGARA TETE 297 SENA CAIA SOFALA 369 CHIGUBO CHIGUBO GAZA 82 MELOCO NAMUNO CABO DELGADO 154 MAIAIA NACALA-PORTO NAMPULA 226 CHIFUNDE CHIFUNDE TETE 298 CHEMBA CHEMBA SOFALA 370 DINDIZA CHIGUBO GAZA 83 N’CUMPE NAMUNO CABO DELGADO 155 MUANONA NACALA-PORTO NAMPULA 227 MUALADZE CHIFUNDE TETE 299 CHIRAMBA CHEMBA SOFALA 371 CIDADE DE CHOKWE CHOKWE GAZA 84 PAPAI NAMUNO CABO DELGADO 156 NACALA-A-VELHA NACALA-A-VELHA NAMPULA 228 NSADZU CHIFUNDE TETE 300 MULIMA CHEMBA SOFALA 372 LIONDE CHOKWE GAZA 85 NANGADE NANGADE CABO DELGADO 157 COVO NACALA-A-VELHA NAMPULA 229 KAZULA CHIUTA TETE 301 INHAMINGA CHERINGOMA SOFALA 373 MACARRETANE CHOKWE GAZA 86 M’TAMBA NANGADE CABO DELGADO 158 NACAROA NACAROA NAMPULA 230 MANJE CHIUTA TETE 302 INHAMITANGA CHERINGOMA SOFALA 374 XILEMBENE CHOKWE GAZA 87 PALMA PALMA CABO DELGADO 159 INTETE NACAROA NAMPULA 231 FURANCUNGO MACANGA TETE 303 CHIBABAVA CHIBABAVA SOFALA 375 CANIÇADO GUIJA GAZA 88 OLUMBI PALMA CABO DELGADO 160 SAUA-SAUA NACAROA NAMPULA 232 CHIDZOLOMONDO MACANGA TETE 304 GOONDA CHIBABAVA SOFALA 376 CHIVONGOENE GUIJA GAZA 89 PUNDANHAR PALMA CABO DELGADO 161 RAPALE RAPALE-NAMPULA NAMPULA 233 MPHENDE MAGOE TETE 305 MUXUNGUE CHIBABAVA SOFALA 377 MUBANGOENE GUIJA GAZA 90 QUIONGA PALMA CABO DELGADO 162 ANCHILO RAPALE-NAMPULA NAMPULA 234 CHINTHOPO MAGOE TETE 306 CIDADE DE DONDO DONDO SOFALA 378 NALAZI GUIJA GAZA 91 METUGE PEMBA CABO DELGADO 163 MUTIVASSE RAPALE-NAMPULA NAMPULA 235 MUKUMBURA MAGOE TETE 307 MAFAMBISSE DONDO SOFALA 379 MABALANE MABALANE GAZA 92 MIEZE PEMBA CABO DELGADO 164 NAMAITA RAPALE-NAMPULA NAMPULA 236 CHIPUTU MARAVIA TETE 308 SAVANE DONDO SOFALA 380 COMBOMUNE MABALANE GAZA 93 QUISSANGA QUISSANGA CABO DELGADO 165 RIBAUE RIBAUE NAMPULA 237 FINGOE MARAVIA TETE 309 GORONGOSA GORONGOSA SOFALA 381 NTLAVENE MABALANE GAZA 94 BILIBIZA QUISSANGA CABO DELGADO 166 CUNLE RIBAUE NAMPULA 238 MALOWERA MARAVIA TETE 310 NHAMADZE GORONGOSA SOFALA 382 MANDLAKAZE MANDLAKAZE GAZA 95 MAHATE QUISSANGA CABO DELGADO 167 IAPALA RIBAUE NAMPULA 239 CHIPERA MARAVIA TETE 311 VANDUZI GORONGOSA SOFALA 383 XHALALA MANDLAKAZE GAZA 96 URBANO CENTRAL CIDADE DE NAMPULA NAMPULA 168 Urbano Nº1 QUELIMANE ZAMBEZIA 240 MOATIZE MOATIZE TETE 312 MACHANGA MACHANGA SOFALA 384 CHIBONZANE MANDLAKAZE GAZA 97 MUATALA CIDADE DE NAMPULA NAMPULA 169 Urbano Nº2 QUELIMANE ZAMBEZIA 241 KAMBULATSISI MOATIZE TETE 313 DIVINHE MACHANGA SOFALA 385 CHIDENGUELE MANDLAKAZE GAZA 98 MUHALA CIDADE DE NAMPULA NAMPULA 170 Urbano Nº3 QUELIMANE ZAMBEZIA 242 ZOBUE MOATIZE TETE 314 CHILOANE MACHANGA SOFALA 386 MACUACUA MANDLAKAZE GAZA 99 NAMIKOPO CIDADE DE NAMPULA NAMPULA 171 Urbano Nº4 QUELIMANE ZAMBEZIA 243 NHAMAYABUE MUTARARA TETE 315 MARINGUE MARINGUE SOFALA 387 MAZUCANE MANDLAKAZE GAZA 100 NAPIPIME CIDADE DE NAMPULA NAMPULA 172 ALTO MOLOCUE ALTO MOLOCUE ZAMBEZIA 244 CHARRE MUTARARA TETE 316 CANXIXE MARINGUE SOFALA 388 NGUZENE MANDLAKAZE GAZA 101 NATIKIRE CIDADE DE NAMPULA NAMPULA 173 NAUELA ALTO MOLOCUE ZAMBEZIA 245 DOA MUTARARA TETE 317 SUBWE MARINGUE SOFALA 389 MASSANGENA MASSANGENA GAZA 102 CIDADE DE ANGOCHE ANGOCHE NAMPULA 174 CHINDE CHINDE ZAMBEZIA 246 INHANGOMA MUTARARA TETE 318 MARROMEU MARROMEU SOFALA 390 MAVUE MASSANGENA GAZA 103 AUBE ANGOCHE NAMPULA 175 LUABO CHINDE ZAMBEZIA 247 NTENGO-WA-MBALAME TSANGANO TETE 319 CHUPANGA MARROMEU SOFALA 391 MASSINGIR MASSINGIR GAZA 104 NAMAPONDA ANGOCHE NAMPULA 176 MICAUNE CHINDE ZAMBEZIA 248 TSANGANO TSANGANO TETE 320 MALINGAPASSE MARROMEU SOFALA 392 MAVODZE MASSINGIR GAZA 105 BOILA_NAMITORIA ANGOCHE NAMPULA 177 GILE GILE ZAMBEZIA 249 ZUMBU ZUMBU TETE 321 MUANZA MUANZA SOFALA 393 ZULO MASSINGIR GAZA 106 NAMAPA ERATI NAMPULA 178 ALTO LIGONHA GILE ZAMBEZIA 250 MUZE ZUMBU TETE 322 GALINHA MUANZA SOFALA 394 CHICUMBANE XAI-XAI GAZA 107 ALUA ERATI NAMPULA 179 CIDADE DE GURUE GURUE ZAMBEZIA 251 ZAMBUE ZUMBU TETE 323 NHAMATANDA NHAMATANDA SOFALA 395 CHONGOENE XAI-XAI GAZA 108 NAMIROA ERATI NAMPULA 180 LIOMA GURUE ZAMBEZIA 252 CIDADE DE CHIMOIO CIDADE DE CHIMOIO MANICA 324 TICA NHAMATANDA SOFALA 396 ZONGOENE XAI-XAI GAZA 109 C. ILHA DE MOÇAMBIQUE C. ILHA DE MOÇAMBIQUE NAMPULA 181 MEPUAGIUA GURUE ZAMBEZIA 253 CATANDICA BARUE MANICA 325 CIDADE DE INHAMBANE CIDADE DE INHAMBANE INHAMBANE 397 MATOLA CIDADE DA MATOLA MAPUTO 110 LUMBO C. ILHA DE MOÇAMBIQUE NAMPULA 182 ILE ILE ZAMBEZIA 254 CHOA BARUE MANICA 326 FUNHALOURO FUNHALOURO INHAMBANE 398 MACHAVA CIDADE DA MATOLA MAPUTO 111 LALAUA LALAUA NAMPULA 183 MULEVALA (NAMIGONHA) ILE ZAMBEZIA 255 NHAMPASSA BARUE MANICA 327 TOME FUNHALOURO INHAMBANE 399 INFULENE CIDADE DA MATOLA MAPUTO 112 METI LALAUA NAMPULA 184 SOCONE ILE ZAMBEZIA 256 GONDOLA GONDOLA MANICA 328 NOVA MAMBONE GOVURO INHAMBANE 400 BOANE BOANE MAPUTO 113 MALEMA MALEMA NAMPULA 185 INHASSUNGE (MUCUPIA) INHASSUNGE ZAMBEZIA 257 AMATONGAS GONDOLA MANICA 329 SAVE GOVURO INHAMBANE 401 MATOLA RIO BOANE MAPUTO 114 CHIHULO MALEMA NAMPULA 186 GONHANE INHASSUNGE ZAMBEZIA 258 CAFUMPE GONDOLA MANICA 330 HOMOINE HOMOINE INHAMBANE 402 MAGUDE MAGUDE MAPUTO 115 MUTUALI MALEMA NAMPULA 187 LUGELA LUGELA ZAMBEZIA 259 INCHOPE GONDOLA MANICA 331 PEMBE HOMOINE INHAMBANE 403 MAPULANGUENE MAGUDE MAPUTO 116 MECONTA MECONTA NAMPULA 188 TACUANE LUGELA ZAMBEZIA 260 MACATE GONDOLA MANICA 332 INHARRIME INHARRIME INHAMBANE 404 MOTAZE MAGUDE MAPUTO 117 CORRANE MECONTA NAMPULA 189 MUNHAMADE LUGELA ZAMBEZIA 261 MATSINHO GONDOLA MANICA 333 MOCUMBI INHARRIME INHAMBANE 405 MAHELE MAGUDE MAPUTO 118 NAMIALO MECONTA NAMPULA 190 MUABANAMA LUGELA ZAMBEZIA 262 ZEMBE GONDOLA MANICA 334 INHASSORO INHASSORO INHAMBANE 406 PANJANE MAGUDE MAPUTO 119 7 DE ABRIL MECONTA NAMPULA 191 MAGANJA DA COSTA MAGANJA DA COSTA ZAMBEZIA 263 GURO GURO MANICA 335 BAZARUTO INHASSORO INHAMBANE 407 MANHIÇA MANHIÇA MAPUTO 120 MECUBURI MECUBURI NAMPULA 192 BAJONE MAGANJA DA COSTA ZAMBEZIA 264 MANDIE GURO MANICA 336 JANGAMO JANGAMO INHAMBANE 408 CALANGA MANHIÇA MAPUTO 121 MILHANA MECUBURI NAMPULA 193 MOCUBELA MAGANJA DA COSTA ZAMBEZIA 265 MUNGARI GURO MANICA 337 CUMBANA JANGAMO INHAMBANE 409 ILHA JOSINA MACHEL MANHIÇA MAPUTO 122 MUITE MECUBURI NAMPULA 194 NANTE MAGANJA DA COSTA ZAMBEZIA 266 NHAMASSONGE GURO MANICA 338 MABOTE MABOTE INHAMBANE 410 MALUANA MANHIÇA MAPUTO 123 NAMINA MECUBURI NAMPULA 195 MILANGE MILANGE ZAMBEZIA 267 CHITOBE (MACHAZE) MACHAZE MANICA 339 ZIMANE MABOTE INHAMBANE 411 XINAVANE MANHIÇA MAPUTO 124 MEMBA MEMBA NAMPULA 196 MAJAUA MILANGE ZAMBEZIA 268 SAVE MACHAZE MANICA 340 ZINAVE MABOTE INHAMBANE 412 PALMEIRA (3 DE FEVEREIRO) MANHIÇA MAPUTO 125 CHIPENE MEMBA NAMPULA 197 MOLUMBO MILANGE ZAMBEZIA 269 MACOSSA MACOSSA MANICA 341 MASSINGA MASSINGA INHAMBANE 413 MARRACUENE MARRACUENE MAPUTO 126 LURIO MEMBA NAMPULA 198 MONGUE MILANGE ZAMBEZIA 270 NGUAWALA MACOSSA MANICA 342 CHICOMO MASSINGA INHAMBANE 414 MACHUBO MARRACUENE MAPUTO 127 MAZUA MEMBA NAMPULA 199 CIDADE DE MOCUBA MOCUBA ZAMBEZIA 271 NHAMAGUA MACOSSA MANICA 343 CIDADE DE MAXIXE CIDADE DE MAXIXE INHAMBANE 415 BELA VISTA MATUTUINE MAPUTO 128 NAMIGE MOGINCUAL NAMPULA 200 MUGEBA MOCUBA ZAMBEZIA 272 CIDADE DE MANICA MANICA MANICA 344 MORRUMBENE MORRUMBENE INHAMBANE 416 MUGAZINE (CATEMBE) MATUTUINE MAPUTO 129 QUINGA MOGINCUAL NAMPULA 201 NAMANJAVIRA MOCUBA ZAMBEZIA 273 MACHIPANDA MANICA MANICA 345 MUCODUENE MORRUMBENE INHAMBANE 417 CATUANE MATUTUINE MAPUTO 130 CHUNGA MOGINCUAL NAMPULA 202 MOPEIA MOPEIA ZAMBEZIA 274 MESSICA MANICA MANICA 346 PANDA PANDA INHAMBANE 418 NDELANE (MACHAMGULO) MATUTUINE MAPUTO 131 QUIXAXE MOGINCUAL NAMPULA 203 CAMPO MOPEIA ZAMBEZIA 275 MAVONDE MANICA MANICA 347 MAWAYELA PANDA INHAMBANE 419 ZITUNDO MATUTUINE MAPUTO 132 LIUPO MOGINCUAL NAMPULA 204 MORRUMBALA MORRUMBALA ZAMBEZIA 276 VANDUZI MANICA MANICA 348 URRENE PANDA INHAMBANE 420 MOAMBA MOAMBA MAPUTO 133 NAMETIL MOGOVOLAS NAMPULA 205 CHIRE MORRUMBALA ZAMBEZIA 277 ESPUNGABERA MOSSURIZE MANICA 349 VILANKULO VILANKULO INHAMBANE 421 PESSENE MOAMBA MAPUTO 134 CALIPO MOGOVOLAS NAMPULA 206 DERRE MORRUMBALA ZAMBEZIA 278 CHIURAIRUE MOSSURIZE MANICA 350 MAPINHANE VILANKULO INHAMBANE 422 RESSANO GARCIA MOAMBA MAPUTO 135 IULUTI MOGOVOLAS NAMPULA 207 MEGAZA MORRUMBALA ZAMBEZIA 279 DACATA MOSSURIZE MANICA 351 QUISSICO ZAVALA INHAMBANE 423 SABIE MOAMBA MAPUTO 136 MUATUA MOGOVOLAS NAMPULA 208 NAMACURRA NAMACURRA ZAMBEZIA 280 SUSSUNDENGA SUSSUNDENGA MANICA 352 ZANDAMELA ZAVALA INHAMBANE 424 NAMAACHA NAMAACHA MAPUTO 137 NANHUPO RIO MOGOVOLAS NAMPULA 209 MACUZE NAMACURRA ZAMBEZIA 281 DOMBE SUSSUNDENGA MANICA 353 CIDADE DE XAI-XAI CIDADE DE XAI-XAI GAZA 425 CHANGALANE NAMAACHA MAPUTO 138 MOMA MOMA NAMPULA 210 NAMARROI NAMARROI ZAMBEZIA 282 MUOHA SUSSUNDENGA MANICA 354 MACIA BILENE GAZA 426 DISTRITO URBANO Nº1 DISTRITO URBANO Nº1 CIDADE DE MAPUTO 139 CHALAUA MOMA NAMPULA 211 REGONE NAMARROI ZAMBEZIA 283 ROTANDA SUSSUNDENGA MANICA 355 CHISSANO BILENE GAZA 427 DISTRITO URBANO Nº2 DISTRITO URBANO Nº2 CIDADE DE MAPUTO 140 LARDE MOMA NAMPULA 212 NICOADALA NICOADALA ZAMBEZIA 284 NHACOLO TAMBARA MANICA 356 MAZIVILA BILENE GAZA 428 DISTRITO URBANO Nº3 DISTRITO URBANO Nº3 CIDADE DE MAPUTO 141 MUCUALI MOMA NAMPULA 213 MAQUIVAL NICOADALA ZAMBEZIA 285 BUZUA TAMBARA MANICA 357 MESSANO BILENE GAZA 429 DISTRITO URBANO Nº4 DISTRITO URBANO Nº4 CIDADE DE MAPUTO 142 MONAPO MONAPO NAMPULA 214 PEBANE PEBANE ZAMBEZIA 286 NHACAFULA TAMBARA MANICA 358 PRAIA DE BILENE BILENE GAZA 430 DISTRITO URBANO Nº5 DISTRITO URBANO Nº5 CIDADE DE MAPUTO 143 ITOCULO MONAPO NAMPULA 215 MUALAMA (MULELA) PEBANE ZAMBEZIA 287 URBANO Nº1 CIDADE DA BEIRA SOFALA 359 MAKLUANE BILENE GAZA 431 DISTRITO URBANO Nº6 DISTRITO URBANO Nº6 CIDADE DE MAPUTO 144 NETIA MONAPO NAMPULA 216 NABURI PEBANE ZAMBEZIA 288 URBANO Nº2 CIDADE DA BEIRA SOFALA 360 CIDADE DE CHIBUTO CHIBUTO GAZA 432 DISTRITO URBANO Nº7 DISTRITO URBANO Nº7 CIDADE DE MAPUTO 67 definitions of indicators this needs to be updated

Section 1 – Healthy Lives indicators as stunting and underweight are usually not recorded during a census and they were not part of the 2007 census. In order to estimate how many children are stunted or malnourished Infant Mortality Rate is defined as number of newborns dying under a year of age divided by the in each administrative post a method known as Small Area Estimates was used. This method number of live births during the year times 1000. combines the information available on malnutrition in the 2008/09 IAF survey with the household and child information available in both the AIF survey and the 2007 census to estimate Maternal Mortality Rate is defined as the death of a woman between 15 and 50 of age while how many children are malnourished in each Administrative post. A full description of the pregnant, while giving birth or within 60 days of termination of pregnancy, over 100.000 live method and results can be found in Sohnesen et all. births. Note that the international standard definition usually only include the first 42 days after birth and not the 2 months used in the Mozambique census. Underweight. A child is considered underweight if it has a Weight-for-Age z-score that is 2 standard deviations below the reference population. General fertility rate is defined as the annual number of live births per 1000 women of childbearing age (between 15 and 49 of age). Accessing water from rivers and lakes is the share of households that uses rivers or lakes as their source of drinking water. Stunting. A child is considered stunted if it has a Height-for-Age z-score that is 2 standard deviations below the reference population. Distance to health facilities is measured as the distance as the bird flies between the center of the village to the nearest health facility of any type (health post, health center, or hospital) Malnutrition. Child anthropometric measures are widely used to analyze the prevalence malnutrition among children under five. Anthropometric measures are measures of height for Lack of access to toilet facilities is the share of households that do not have access to a toilet. age (stunting), weight for height (wasting), and weight for age (underweight). These measures are compared to what you would expect to find in a healthy population, and a child is considered moderate malnourished if it has a value that is lower than two standard deviations from Section 2 – Access to Education the mean of the healthy reference population. The different measures can be said to capture different elements of malnutrition, with stunting reflecting sustained past episode or episodes Gross Primary Enrollment Rate is defined as number of students attending primary school of undernutrition, wasting reflecting weight loss associated with a recent period of starvation or (grade 1 to 7), including technical elementary (ETE), divided by number of children between 6 disease, and underweight ref lecting a current condition resulting from inadequate food intake, and 12 years of age. past episodes of undernutrition or poor health conditions. Gross Secondary Enrollment Rate is defined as number of students attending secondary school Small Area Estimates of Malnutrition: Most of the other social indicators shown in this Atlas (grade 8 to 12), including technical schools (ETB and ETM) and primary school teachers, divided were recorded in the 2007 census that went to all households in Mozambique. Malnutrition by number of children between 13 and 16 years of age.

68 Section 3 – Access to Services Section 5 – Land and Agriculture Estimated travel time to nearest city with over 50.000 people. This is an estimate of travel time The rainfall and temperature maps in section 5 were produced using daily NASA weather satellite based on location and available roads among other aspects, please see Dorosh and Schmidt for data, over the period from 1997 to 2007. full details. Share of households with cell phones. A household is defined as being a call phone if at least one Section 6 – The People of Mozambique person within the household has a cell phone. Population density is defined as number people living in an administrative post divided by the Share of household using electricity. A household is defined as using electricity if this is its main size measured km2 of the Administrative post. source of light. Share that speak Portuguese is defined for ages 10 and up. Share of households with running water. A household I defined to have access to running water if this its main source of water either within the house or outside the house.

Section 7 – Wealth Section 4 – Labor Force and Employment Poverty headcount is defined as the number of peopled considered poor over total population. Share active in labor market is defined as any person over 15 that during the last week of Squared poverty gap measures the depth of poverty. A high value indicates that many people are June 2007 said that they 1) are working, 2) wasn’t working, but has a job, Worked with a family very poor. For more details see MEPD 2010. business, 3) worked at home, or was looking for a job. Following the practice of INE young people looking for their first job are not included as active in the labor market. Inequality is measured by the Gini coefficient. Share literate is any person that can read and write. Share of labor aged with primary or above is the share of adults between 15 and 65 that have primary education or above. Share of labor aged with secondary or above is the share of adults between 15 and 65 that have secondary education or above.

69 image and map index needs update when all changes are implemented

Section 1 - Healthy Lives 1.31 - Share of households that do not have access to a toilet by administrative post - 2007 1.1 - IMR by administrative post per 1000 live births - 1997 1.32 - Change in share of households with no access to toilets 1997–2007 1.2 - IMR by administrative post per 1000 live births - 2007 1.3 - Distribution of IMR in administrative posts - 1997 and 2007 Section 2 - Access to Education 1.4 - Box plot of IMR in administrative posts - 1997 and 2007 2.1 - Male gross primary enrollment by administrative post - 1997 1.5 - IMR in 1997 and change between 1997 and 2007, by administrative post 2.2 - Male gross primary enrollment by administrative post - 2007 1.6 - IMR per 1000 live births across Africa- 2007 2.3 - Female gross primary enrollment by administrative post - 1997 1.7 - Malnutrition by region and urban/rural - 2008/09 2.4 - Female gross primary enrollment by administrative post - 2007 1.8 - Malnutrition by province - 2008/09 2.5 - Gross primary enrollment across Africa - 2007 1.9 - Trend in malnutrition - 1997–2008/09 2.6 - Male gross secondary enrollment by administrative post - 1997 1.10 - Share of underweight children across Africa - 2007 2.7 - Male gross secondary enrollment by administrative post - 2007 1.11 - Share of stunted children by administrative post, Share of stunted children per km2 by administrative 2.8 - Female gross secondary enrollment by administrative post - 1997 post - 2007 2.9 - Female gross secondary enrollment by administrative post - 2007 1.12 - Share of underweight children by administrative posts, Share of underweight children per km2 by 2.10 - Gross secondary enrollment across Africa - 2007 administrative post - 2007 2.11 - Absolute change of male gross primary enrollment by administrative post - 1997–2007 1.13 - MMR across Africa - 2007 2.12 - Absolute change of female gross primary enrollment by administrative post - 1997–2007 1.14 - MMR in urban and rural areas - 2007 2.13 - Absolute change of male gross secondary enrollment by administrative post - 1997–2007 1.15 - MMR by province - 2007 2.14 - Absolute change of female gross secondary enrollment by administrative post - 1997–2007 1.16 - MMR by district - 2007 2.15 - Primary gross enrollment rate in urban and rural areas, 1997 and 2007 1.17 - General fertility rate [defined as the annual number of live births per 1000 women of childbearing age 2.16 - Secondary gross enrollment rate in urban and rural areas, 1997 and 2007 (15 to 49)] - 1997 2.17 - Increase in total number of students in primary and secondary from 1997 to 2007, by gender 1.18 - General fertility rate [defined as the annual number of live births per 1000 women of childbearing age 2.18 - Primary gross enrollment rate for boys and girls, 1997 and 2007 (15 to 49)] - 2007 2.19 - Secondary gross enrollment rate for boys and girls, 1997 and 2007 1.19 - General fertility rates in 1997 and change between 1997 and 2007, by administrative post 2.20 - Increase in total number of students in primary and secondary from 1997 to 2007, by urban/rural 1.20 - General fertility rate by province - 1997 and 2007 2.21 - Gross enrollment rates by administrative post - 1997 and 2007 1.21 - Distribution of general fertility rates in administrative post - 1997 and 2007 2.22 - Primary gross enrollment rates in 1997 and change between 1997 and 2007, by administrative post 1.22 - Absolute change of fertility rate by administrative post - 1997–2007 2.23 - Secondary gross enrollment rates in 1997 and change between 1997 and 2007, by administrative post 1.23 - Rivers, lakes and water wells across Mozambique - 2007 1.24 - Share of households using water from rivers or lakes by administrative post - 2007 Section 3 - Access to Services 1.25 - Absolute change of river and lake usage by administrative post - 1997–2007 3.1 - Share of households using electricity as main source of light by administrative post - 2007 1.26 - Share of households using rivers and lakes as water source in 1997 and change between 1997 and 2007, by administrative post 3.2 - Share of households having running water by administrative post - 2007 1.27 - Location of health facilities across Mozambique - 2007 3.3 - Share of households with at least one cell phone by administrative post - 2007 1.28 - Distance to nearest health facility, as birds fly - 2007 3.4 - Share of households that own a fixed phoneline by administrative post - 2007 1.29 - Absolute change of households without access to a toilet by administrative post between 1997–2007 3.5 - Share of households that have used internet in the last year at least once by administrative post - 2007 1.30 - Share of households that do not have access to a toilet by administrative post - 1997 3.6 - Share of household using service in urban and rural areas - 2007 3.7 - Travel distance to urban centers by administrative post – 2007

70 Section 4 - Labor Force and Employment Section 6 - The People of Mozambique 4.1 - Adult female literacy rate by administrative post - 2007 6.1 - Change in population (percentage) - 1997–2007 4.2 - Adult male literacy rate by administrative post - 2007 6.2 - Change in population by administrative post - 1997–2007 4.3 - Share of labor force with primary education or above by administrative post - 2007 6.3 - Ratio of men to women by age and province - 2007 4.4 - Share of labor force with secondary education or above by administrative post - 2007 6.4 - Population by province - 1997 and 2007 4.5 - S hare of labor (aged 15 to 65) with complete primary or above, and secondary and above, by gender and 6.5 - Population by gender - 1997 and 2007 urban/rural - 2007 6.6 - Share of population aged 15 or below by administrative post - 2007 4.6 - S hare of adults active in labor market in 1997 and change between 1997 and 2007, by administrative post 6.7 - Share of population aged 50 or above by administrative post - 2007 4.7 - Box plot share of adults active in labor market by administrative post - 1997 and 2007 6.8 - Total population by administrative post - 2007 4.8 - Labor force participation by gender and urban/rural - 2007 6.9 - Rural population per km2 by administrative post - 2007 4.9 - Labor force participation by gender and education level - 2007 6.10 - Number of men per 100 women by administrative post - 1997 4.10 - Female labor force participation rate by administrative post - 2007 6.11 - Number of men per 100 women by administrative post - 2007 4.11 - Male labor force participation rate by administrative post - 2007 6.12 - Main languages spoken in northern provinces - 2007 4.12 - Labor force participation rate by administrative post - 1997 6.13 - Main languages spoken in central provinces - 2007 4.13 - Labor force participation rate by administrative post - 2007 6.14 - Main languages spoken in southern provinces - 2007 4.14 - Change in labor force participation rate by administrative post - 1997–2007 6.15 - Share of population that know Portuguese by age group and gender - 2007 4.15 - Type of employer by urban/rural - 2007 6.16 - Share of population (aged 10 and above) that speak Portuguese by administrative post - 2007 4.16 - Type of employer by gender - 2007 6.17 - Share of population belonging to denomination - 2007 4.17 - Type of employer by province - 2007 6.18 - Religion by province - 2007

Section 5 - Land and Agriculture Section 7 - Wealth 5.1 - Share of households owning cows - 1997 and 2007 7.1 - Share of households that own a car by administrative post - 2007 5.2 - Share of households owning sheep - 1997 and 2007 7.2 - Share of households that own a TV by administrative post - 2007 5.3 - Share of households owning swine - 1997 and 2007 7.3 - Share of households that own a computer by administrative post - 2007 5.4 - Absolute change of households owning swine by administrative post - 1997–2007 7.4 - Asset ownership by urban/rural 5.5 - Absolute change of households owning sheep by administrative post - 1997–2007 7.5 - Share of households that own a radio by administrative post - 2007 5.6 - Absolute change of households owning cows by administrative post - 1997–2007 7.6 - Share of households that own a bicycle by administrative post - 2007 5.7 - Share of households that own sheep by administrative post - 2007 7.7 - Share of households that own a motorcycle by administrative post - 2007 5.8 - Share of households that own cattle by administrative post - 2007 7.8 - Poverty headcount by province - 2008/09 5.9 - Share of households that own swine by administrative post - 2007 7.9 - Poverty headcount by urban/rural - 2008/09 5.10 - Topography of Mozambique 7.10 - Squared poverty gab by province - 2008/09 5.11 - Temperature per month in Mozambique - 2007 7.11 - Inequality by province - 2008/09 5.12 - Rainfall per year in Mozambique - 1997–2008 5.13 - Share of cashew farmers by administrative post - 2007 5.14 - Share of fish farmers by administrative post - 2007

71 References

Third National Poverty Assessment, Ministry of Planning and Development (GOM, 2010) Report on the Millennium Development Goals, Ministry of Planning and Develop- ment (GOM, 2010) Sohnesen, Thomas Pave (2011), Children’s Health on a Map: Infant Mortality and Malnutrition at Local Levels in Mozambique. Unpublished manuscript. The World Bank, Washington DC Dorosh, Paul, Schmidt, Emily. 2008. Mozambique Corridors: Implications of Invest- ments in Feeder Roads. Unpublished manuscript. The World Bank, Washington DC

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