Download - Public Disclosure Authorized Mozambique Then and …...Mozambique Then and Now Population growth from 1997 to 2007 as a percentage was faster in the south and in coastal areas (fig

Mozambique Then and NowAn Atlas of Socio-Economic Statistics

1997–2007

THE WORLD BANK INSTITUTO NACIONAL DE ESTATÍSTICA

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Mozambique Then and NowAn Atlas of Socio-Economic Statistics

1997–2007

iv

RDC

Sudan

Angola

Ethiopia

South Africa

Zambia

Namibia

Chad

Kenya

Somalia

Botswana

Nigeria

Congo

Cameroon

Madagascar

Gabon

Zimbabwe

Tanzania, United Republic of

Uganda

Central African Republic

Malawi

Niger Yemen

Lesotho

Burundi

Rwanda

Djibouti

Swaziland

Equatorial Guinea

Equatorial Guinea

Aden

Beira

Malabo

Lusaka

Luanda

Harare

Mbabne

Kigoma

Mtwara

Huambo

Matadi

MaseruDurban

Maputo

Bangui

Nairobi

Mombasa

Kampala

Toliara

Kahemba

Kananga

Yaounde

Windhoek

Lilongwe

Djibouti

Mbandaka

El Obeid

Bulawayo

Kinshasa

Pretoria

Gaborone

Benguela

Muqdisho

Cape Town

Kimberley

Maiduguri

Bujumbura

N'Djamena

Kisangani

Lumumbashi

Adis Abeba

Port Gentil

Livingstone

East London

Brazzaville

Antananarivo

Pointe Noire

Johannesburg

Dar es Salaam

Port Elizabeth

C.Ilha Moçambique

0 250 500 1,000

Kilometers

contentsvi I Preface

vii Acknowledgement

1 Introduction

3 The people of mozambique Population Demographic distribution by age and gender Main languages Religions

15 wealth Poverty Inequality Asset ownership

21 healthy lives Infant mortality rate Infant mortality in Africa Underweight, prevalence and concentration Stunting, prevalence and concentration Maternal mortality rate Malnutrition Distance to health facilities Fertility Access to improved water Trend in accessing water from rivers and lakes Diarrhea and malaria

39 access to services Access to electricity Access to running water Access to phones and internet Distance to major urban areas

45 education Trend in primary gross enrollment rates Primary enrollment by gender Primary enrollment across Africa Distance to primary schools Trend in secondary gross enrollment rates Secondary enrollment by gender Secondary enrollment across Africa Distance to secondary schools Literacy rates Share of population with complete primary Share of population with complete secondary

57 land and agriculture Topography Land suitability Rainfall by year Rainfall by month Temperature by month Ownership of animals

68 definition of indicators

70 image and map index

72 REFERENCES

Mozambique Then and Now

vi

Preface

The National Statistical Institute of Mozambique is responsible to collect, analyze, publish, and disseminate statistical information on a wide range of topics. The mis-sion of the National Statistical Institute is to provide and promote accurate, appropri-ate, high-quality, and timely statistical information for use in both the public and the private sectors for policy formulation, decision making, research, and general public awareness for the advancement of the socio-economic status of Mozambicans.

A central focus of the work of the INE is the data collection on living conditions of the population of Mozambique. This Atlas was developed as part of efforts to increase the understanding of the living conditions of Mozambicans and to serve as a basis for the preparation of the country’s development strategies.

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 vary across the country, thereby enabling poverty reduction programs and policies

to be appropriately targeted. It is our wish that this Atlas will provide an important source of information for all those who want to have the mission to promote the eco-nomic and social development in Mozambique.

The maps are elaborated principally from the results of the 2007 Population Census and the 2008/09 Household Budget Survey (Inquerito ao Orcamento Familiar, IOF 2009).

Funding for this publication was provided by the Trust Fund for Environmentally and Socially Sustainable Development (TF ESSD) at the World Bank and is gratefully ac-knowledged. We also gratefully acknowledge the excellent collaboration and support provided in the joint preparation of this Atlas.

João Dias LoureiroPresident of INE

vii

acknowledgements

This atlas has been prepared jointly by the National Statistics Office of Mozambique (Instituto Nacional de Estatistica, INE) and the World Bank.

INE provided the 1997 and 2007 Population Census data which is the basis for this Atlas, as well as contributed detailed input and comments that ensured the statistical accuracy and improved its overall quality.

Special thanks goes to INE’s President, Dr João Dias Loureiro, for providing over-all guidance and inspiration, and to the World Bank Country Director for Mozam-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.

The atlas was produced by a team led by Mr Antonio Nucifora (World Bank, Lead 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 Economist), Mr Antonio Adriano (INE Deputy Director for Census and Surveys), Mr Cassiano Chipembe (INE Director of Statistics on Demography and Living Condi-tions ), Mr Paulo Covele (GIS specialist), and Ms Andrea Nieves (Graphic designer). Funding for the production was provided by the Trust Fund for Environmentally and Socially Sustainable Development (TF ESSD) at the World Bank.

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

the people of mozambiquePopulationlanguagereligion

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

4

population

The northern province of Nampu-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 larg-est populations (fig 1.1 and 1.2) and the highest rural population densi-ties (fig 1.3). The population densi-ty refers to the number people liv-ing in a given Administrative Post divided by the size of that Adminis-trative Post measured in squared ki-lometers.

0 75 150 300

Kilometers

Less than 25

25–50

51–75

76–100

Greater than 100

0 1,000 2,000 3,000 4,000

Maputo City

Maputo province

Gaza

Inhambane

Sofala

Manica

Tete

Zambezia

Nampula

Cabo Delgado

Niassa

1997 2007

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

1.2 – Total population (in thousands), 2007 1.3 – Rural population per km2, 2007

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

Less than 10

10–20

21–30

31–40

Greater than 40

0 75 150 300

Kilometers

5

Section 1 - THE PEOPLE OF MOZAMBIQUEMozambique Then and Now

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, popula-tion growth was must faster in rural areas (fig 1.6).

21–40

41–60

61–80

81–100

Greater than 100

Less than –20

–20–0

1–20

21–30

Greater than 30

Less than –50

–50–10

–9–0

1–10

11–20

1.4 – Change in population (percentage) between 1997 and 2007 1.5 – Change in population (in thousands) between 1997 and 2007

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

0 75 150 300

Kilometers

0 75 150 300

Kilometers

6

population (cont.)

The population of Mozambique 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 dis-tricts with more than 15 percent of the population aged 50 or above (fig 1.9).

Less than 30

30–40

41–45

46–50

Greater than 50

Less than 5

5–10

11–15

16–20

Greater than 20

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.

1.8 – Share of population aged 15 or below, 2007 1.9 – Share of population aged 50 or above, 2007

1.7 – Population pyramid by gender, 1997 and 2007

0 75 150 300

Kilometers

0 75 150 300

Kilometers

7


The ratio of men to women across Mozambique is not even across the country, with the provinces of In-hambane, Gaza and also the south-ern 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).

60

80

100

120

0−5

6−10

11−1

5

16−2

0

21−2

5

26−3

0

31−3

5

36−4

0

41−4

5

46−5

0

51−5

5

56−6

0

61−6

5

Age group

Inhambane Gaza Other provinces

1.10 – Number of men per 100 women, 1997 1.11 – Number of men per 100 women, 2007

1.12 – Ratio of men to women by age, 2007

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

Less than 70

70–80

81–90

91–100

Greater than 100

0 75 150 300

Kilometers

Less than 70

71–80

81–90

91–100

Greater than 100

0 75 150 300

Kilometers

8

languageA 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.

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

1.13 – Share of population (aged 10 and above) that can speak Portu-guese, 2007

Less than 20

20–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers

The ability to speak Portuguese var-ies from less than 20 percent of the 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 crit-ically 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 per-cent at age 51 to 55 (fig 1.15).

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

9


1.14a–i – Language spoken (in percentage), 20071.14a – Cindau and variations1.14b – Cinyanja and variations1.14c – Echuwabo and variations

1.14a

1.14c

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 lan-guages 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-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 follow-ing variations: Xibila, Xidzonga, Xin’walungu, Xihlengue, and Xit-langanu. Sena includes the follow-ing variations: Bangwe, Gombe,

Less than 1

1–30

31–60

61–90

Greater than 90

0 75 150 300

Kilometers

10

language (CONT.)

1.14e

1.14a–i – Language spoken (in percentage), 20071.14d – Elomwe 1.14e – Emakhuwa and variations1.14f – Portuguese1.14g – Sena and variations1.14h – Xitsonga and variations1.14i – Xitswa and variations

Less than 1

1–30

31–60

61–90

Greater than 90

0 75 150 300

Kilometers

1.14d

1.14f

Sena Care, Phondzo, and Tonga. Emakhuwa includes the following variations: Echirima, Emarevoni, Emetto, Enhara, Esaaka, and Esan-gagi. Echuwabo includes the fol-lowing 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, Cimachanga, and Ciqwaka.

11


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

1.14h1.14g

1.14i

Less than 1

1–30

31–60

61–90

Greater than 90

0 75 150 300

Kilometers

12

Mozambique has a rare rich mix 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 pat-terns which characterize the pre-dominance of religious beliefs (fig 1.17a to 1.17f).

1.17a–f – Share of population belonging to a demonination, 20071.17a – Muslim1.17b – Catholic1.17c – Ciao Zion1.17d – Evangelical and Pentecostal1.17e – Anglican1.17f – Not a member of any religion

religion

1.17a

1.17b

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

Less than 20

20–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers

13


Less than 20

20–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers

1.17c 1.17e

1.17d 1.17f

wealthPoverty and inequality asset ownership

Indicators of wealth and wellbeing as ownership of assets and consumption per capita show a complex pattern wealth across Mozambique.

16

poverty and inequality

Poverty and inequality indicators show an unexpected pattern.

Less than 35

35–45

46–55

56–65

Greater than 65

2.1 – Poverty headcount by province, 2008/2009

Page 14: on this page fig 2.1, 2.2 and 2.3

0 75 150 300

Kilometers

Wealth can be measured in many ways. In this section we assess households’ living standard by measuring their level of con-sumption per capita (which is the accepted international in-dicator for measuring mone-tary poverty) and also by look-ing at the households ownership of physical assets. The informa-tion on consumption is taken from the Household Budget Sur-vey 2008/09 (Inquerito do Orca-mento Familiar, IOF 2009), while the data on ownership of assets comes from the 2007 Population Census. The Third National Pov-erty Assessment (GOM, 2010) measured the level of poverty of Mozambican households using data from the IOF 2008/09, by measuring the household’s total consumption per capita. The level of expenditures needed to allow households a basic level of food

and non-food consumption per capita was calculated, and is re-ferred to as the poverty line. The poverty headcount by province was then calculated as the num-ber of people who consume less than the poverty line, over the total population in that province (fig 2.1). According to the Third National Poverty Assessment the northern provinces of Niassa, Tete and Cabo Delgado were the least poor provinces in Mozam-bique in 2008/09, while Zambe-zia and Maputo province were the poorest (fig 2.1).This pattern of poverty goes against the gen-erally held view in Mozambique that southern provinces are rel-atively richer, and may reflect a problem with the survey data or with the methodology used to calculate the poverty line. Addi-tional analysis is ongoing to veri-fy these preliminary results.”

17

Section 2 - WEALTHMozambique Then and Now

Page 15: on this page fig 2.4 and 2.5

Less than 5

5–8

8–11

11–14

Greater than 14

2.2 – Squared poverty gab by province, 2008/2009

0 75 150 300

Kilometers

The level of consumption inequal-ity in each province (how much consumption per capita varies be-tween households within each prov-ince) 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 prov-inces in the north, Center and south that have high levels of inequali-ty, but also provinces with relatively low inequality both in the north and Center (fig 2.3).

Less than 35

35–37

37–39

39–41

Greater than 41

2.3 – Inequality by province, 2008/2009

0 75 150 300

Kilometers

18

Less than 10

10–20

21–40

41–60

Greater than 60

Less than 30

30–40

41–50

51–60

Greater than 60

2.4 – Share of households owning a radio, 2007 2.5 – Share of households owning a bicycle, 2007

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.

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 pub-lic transport is widespread (fig 2.5).

0 75 150 300

Kilometers

0 75 150 300

Kilometers

19

Section 2 - WEALTHMozambique Then and Nowasset ownership (CONT.)

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

The percentage of households own-ing any of these assets is much great-er in urban areas compared to ru-ral areas, except for bicycles which are more common in rural areas (fig 2.6).

Less than 1

1–2

2–5

5–20

Greater than 20

2.7d2.7c

2.7b2.9a

2.6 – Asset ownership by urban/rural, 1997/2007

2.7a–d – Share of households owning specified assets, 20072.9a – Car2.9b – Computer2.9c – Television2.9d – Motorcycle

0 75 150 300

Kilometers

healthy livesinfant mortality ratematernal mortality ratemalnutritiondistance to health facilitiesfertilityaccessing water from rivers and lakeslack of access to toilet facilitiesdiarrhea and malaria

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

22

Less than 50

50–100

101–150

151–200

Greater than 200

3.1 – IMR per 1000 live births, 1997 3.2 – IMR per 1000 live births, 2007

infant mortality rate

The Infant Mortality Rate (IMR) is defined as the number of newborns who die under one year of age di-vided by the number of live births during the year, multiplied by 1000. Data for Mozambique is from the 2007 Census while the internation-al 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 reduc-tion in infant mortality from 1997 to 2007 (fig 3.1 and 3.2).

The largest reductions over the de-cade 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 mortal-ity rate across Administrative Posts in 2007, as is shown by the narrower (more concentrated) distribution of the infant mortality rate by Admin-istrative Posts (fig 3.5). Despite prog-ress, however, Mozambique’s infant mortality rate is not among the low-est for African countries (fig 3.3).

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.

0 75 150 300

Kilometers

23

Section 3 - HEALTHY LIVESMozambique Then and Now

−200

−100

0

100

Cha

nge

in in

fant

mor

talit

y fro

m 1

997

to 2

007

0 50 100 150 200 250Infant mortality in 1997

3.4 – IMR in 1997 and change be-tween 1997 and 2007

3.3 – IMR per 1000 live births across Africa, 2007

0 475 950 1,900

Kilometers

0

.01

.02

.03

0 50 100 150 200 250Infant mortality rate

Infant mortality 1997 Infant mortality 2007

3.5 – Distribution of IMR, 1997 and 2007

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

30–60

61–90

91–120

Greater than 120

No data

Less than 30

24

maternal mortality rate

The Maternal Mortality Rate (MMR) is defined as the death of a woman between 15 and 50 of age while pregnant, while giv-ing 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 in-ternational comparison to oth-er countries in Africa is from the World Health Organization. Ma-ternal mortality rates are high-er in the north, compared to the center and south of the coun-try (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 dis-trict making data susceptible to small changes.

Less than 500

500–1000

1001–1500

1501–2000

Greater than 2000

500–1000

1001–1500

1501–2000

Greater than 2000

No data

Less than 500

3.6– MMR by district, 2007 3.7 – MMR across Africa, 2007

Maternal mortality rates vary substantially between districts.

0 75 150 300

Kilometers

0 475 950 1,900

Kilometers

25


0 475 950 1,900

Kilometers

malnutrition

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

3.8 – Trend in malnutrition between 1997 and 2008/2009

3.9 – Share of underweight children across Africa, 2007 Child anthropometric measures are widely used to analyze the prevalence of malnutrition among children. Anthropometric mea-sures are measures of height-for-age (stunting), weight-for-height (wasting), and weight-for-age (underweight). The different mea-sures capture different elements of malnutrition, with stunting re-flecting sustained past episode or episodes of undernutrition, wast-ing reflecting weight loss associat-ed with a recent period of starva-tion or disease, and underweight reflecting a current condition re-sulting from inadequate food in-take, past episodes of undernutri-tion or poor health conditions.

Malnutrition indicators for Mozam-bican children have been calculat-ed using data from the 1996/97and 2008/09 Household Budget Surveys (IOF 1997 and IAF 2009). Measures of stunting and underweight show a declining trend from 1997 to 2009 (fig 3.8). Compared to other African countries Mozambique has an aver-age share of children being under-weight (fig 3.9).

5–16

17–26

27–36

Greater than 36

No data

Less than 6

26

Less than 1

2–5

6–10

11–15

Greater than 15

3.10 – Share of stunted children, 2007 3.11 – Number of stunted children per km2, 2007Malnutrition indicators (that is stunting, wasting and underweight) are usually not recorded during a census, and indeed the informa-tion 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 esti-mate how many children are stunted or malnourished in each Adminis-trative Post we used a methodolo-gy known as ‘Small Area Estimates’. This methodology combines the in-formation on malnutrition avail-able in the 2009 IOF survey with the household and child informa-tion available in both the IOF sur-vey and the 2007 Population Cen-sus to estimate how many children are malnourished in each Adminis-trative Post (A full description of the methodology and results is available in Sohnesen (2011). These estimates have been used to produce the maps of stunting and underweight indica-tors by administrative post (Fig 3.10 to 3.13)

malnutrition (CONT.)

Less than 30

30–40

41–50

51–60

Greater than 60

0 75 150 300

Kilometers

0 75 150 300

Kilometers

27


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 chil-dren stunted per km2 (which could be relevant if policy makers want to geographically target areas for inter-vention) the highest density of mal-nourished 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).

3.13 – Number of underweight children per km2, 20073.12 – Share of underweight children, 2007

Less than 10

11–15

16–20

21–25

26–30

Greater than 30

Less than 1

2–5

6–10

11–15

Greater than 15

0 75 150 300

Kilometers

0 75 150 300

Kilometers

28

Distance to health facilities

As part of the 2007 Population 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 cal-culate and map the average dis-tance of villages from health fa-cilities 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

Greater than 35

Most people reside relatively close to a health facility

3.14 – Location of health facilities across Mozambique, 2007 3.15 – Distance to nearest health facility, 2007

0 75 150 300

Kilometers

0 75 150 300

Kilometers

+ Health 2007

29


3.15 – Distance to nearest health facility, 2007 3.18 – Shared of households with improved water sourceAn important aspect of being able to live a healthy life is access to clean and safe water. The following sourc-es of water are considered improved water sources; household connec-tions, public stand pipes, bore-holes, protected dug wells, protect-ed springs, and rainwater collection. Map 3.x shows the share of house-holds that use an improved water source. The map shows that there are large variations between share of households with improved wa-ter source, from less than five per-cent to some areas with more than 85 percent using improved water sources. It’s noteworthy that unlike many other social aspects illustrated in this Atlas, the variation is largely within districts more than between north-south or East-West.

Access to safe water

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

0 75 150 300

Kilometers

Less than 5

46–55

5–15

56–65

16–25

66–75

26–35

76–85

36–45

Greater than 85

30

Figure 3.20 shows location of water 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 us-ing 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

Access to safe water (CONT.)

Lake

Water

Rivers

3.20 – Rivers, lakes and water wells across Mozambique, 2007 3.21 - Share of households using water from rivers or lakes, 1997

Less than 20

21–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers

0 75 150 300

Kilometers

31

Section 3 - HEALTHY LIVESMozambique Then and NowAccess to safe water (CONT.)

3.23 – Share of households using water from rivers or lakes, 2007

Less than 20

20–40

41–60

61–80

Greater than 80

Although on average no progress was recorded in reducing the per-centage of households which fetched their drinking water from rivers and lakes, some areas particularly in the West of the country recorded sub-stantial 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).

3.24 – Change of river and lake usage between 1997 and 2007

Less than –15

–15-–5

–5–5

5–15

Greater than 15

−1

−.5

0

.5

Cha

nge

from

199

7 to

200

7

0 .2 .4 .6 .8 1Share that fetch water from rivers and lakes in 1997

3.22 – Share of households using rivers and lakes as water sources in 1997 and change between 1997 and 2007

0 75 150 300

Kilometers

0 75 150 300

Kilometers

32

0 75 150 300

Kilometers

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, 2007The share of households that do not 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.

Less than 20

20–40

41–60

61–80

Greater than 80

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

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

lack of access to toilet facilities

33


–20––10

–10–0

0–10

Greater than 50

Less than –20

In general more progress was ob-served 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).

3.28 – Change of households without access to a toilet between 1997 and 2007

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

lack of access to toilet facilities (CONT.)

0 75 150 300

Kilometers

34

3.30 – Number of diarrhea cases per 1000 people, 1997 3.31 – Number of diarrhea cases per 1000 people, 2007

diarrhea and malaria

The number of treated cases of 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 num-ber 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 in-crease in number of cases treated in health facilities does not nec-essarily 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-ple seeking treatment as access to health facilities has improved over

Less than 10

10–20

21–30

31–40

Greater than 40

Less than 10

10–20

21–30

31–40

Greater than 40

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

0 75 150 300

Kilometers

0 75 150 300

Kilometers

35


3.32 – Number of malaria cases per 1000 people, 1998 3.33 – Number of malaria cases per 1000 people, 2007

50–100

101–150

151–200

201–250

Greater than 250

Less than 50

50–100

101–150

151–200

201–250

Greater than 250

Less than 50

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 chil-dren under five suffering from ma-laria 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.”

diarrhea and malaria (CONT.)

0 75 150 300

Kilometers

0 75 150 300

Kilometers

36

fertility

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

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 fer-tility rate decreased very little over the decade from 174 in 1997 to 168 in 2007. This small reduction hides a process of equalization where ar-eas 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).

3.16 – General fertility rate, 1997 3.17 – General fertility rate, 2007

Less than 100

100–150

151–200

201–300

Greater than 300

0 75 150 300

Kilometers

37


access to services

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 internetdistance to major urban areas

40

4.2a–d – Share of households using a service, 20074.2a – Using electricity as the main source of light4.2b – Smainly using running water (available either within the house or outside the house)4.2c – Owning a fixed phone line 4.2d – Having used internet in last 12 months

4.2d4.2c

4.2b4.2a

electricity, running water, phones and internet

Access to services as running wa-ter, phone landlines, internet, and electricity for lighting is generally a rare luxury. For both electricity and running water 32 percent of the ur-ban households use these services, while only 1 percent of households in rural areas do. In 2007 cell phones were owned by approximately 45 percent of urban household and less than 10 percent of rural households. Electricity and running water were used by about one third of the urban households, and almost none in ru-ral areas. (fig 4.1).

Greater than 12

11–12

2–4

5–6

7–8

9–10

Less than 2

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.

0%

10%

20%

30%

40%

50%

Urban National Rural

Cell phone Electricity Running waterInternet Fixed phoneline

4.1 – Share of households usingservices in urban and rural areas,2007

0 37.5 75 150

Kilometers

41

Section 4 - ACCESS TO SERVICESMozambique Then and Now

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 re-veals 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 pro-vided the location of cell phone tow-ers by Vodacom, which is the other main cell phone provider in Mo-zambique).

Greater than 12

11–12

2–4

5–6

7–8

9–10

Less than 2

Page 37: 4.3 and 4.4

0 75 150 300

Kilometers

0 75 150 300

Kilometers

42

distance to major urban areas

Access to major urban centers in 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 trav-el speed by type of road, also tak-ing into account landscape and el-evation conditions (see Dorosh and Schmidt, 2008, for full details). The model calculates the quickest trav-el time from each village to the clos-est market within Mozambique. The map is based on the 2007 Census map (fig 4.6).

Vast areas of the country remain isolated from major markets

Primary

Secondary

Tertiary

Vicinal

Unclassified

Ohter

Rail

Capitals

4.5 – ??????????? 4.6 – Travel distance to urban centers (in minutes)

Less than 200

201–400

401–600

Greater than 600

0 75 150 300

Kilometers

0 75 150 300

Kilometers

PLEASE PROVIDE TITLE FOR FIGURE 4.5!

43

Section 4 - ACCESS TO SERVICESMozambique Then and Now

4.6 – Travel distance to urban centers (in minutes)

educationPrimary gross enrollment ratesSecondary gross enrollment ratesDistance to primary and secondary schoolsEducation 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.

46

primary gross enrollment rates

The Primary Gross Enrollment (PGE) rate provides a measure of the share of primary school-age chil-dren which are actually enrolled in school. It is calculated as the num-ber of students attending primary school (grades 1 to 7, and including technical elementary ETE), divid-ed 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 sub-stantial increases in primary enroll-ment 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 with the lowest enrollment rates in 1997 saw the largest improvements between 1997 and 2007 (fig 5.5). It is useful to keep in mind that the rel-atively small progress made in Ad-ministrative Posts in the south and in Maputo City and other urban centers does not necessarily reflect

5.1 – PGE rates, 2007

Gross primary enrollment increased substantially during the past decade

5.2 – Change in PGE between 1997 and 2007

Greater than 60

46–60

Less than 15

15–30

31–45

0 75 150 300

Kilometers

Less than 25

25–50

51–75

76–100

Greater than 100

0 75 150 300

Kilometers

47

Section 5 - EDUCATIONMozambique Then and Now

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

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

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

Page 43: fig 5.6, 5.7Page 43: fig 5.6, 5.7

Less than 25

25–50

51–75

76–100

Greater than 100

Less than 25

25–50

51–75

76–100

Greater than 100

0 75 150 300

Kilometers

0 75 150 300

Kilometers

48

primary gross enrollment rates (CONT.)

Distance to primary schools is mea-sured as the bird flies, e.i. the geo-graphical distance between any lo-cation 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 dis-tance to the nearest primary school.

New page: this is new material not included in the first version of the atlas. New map is called “distance primary”

Regular text:

Access to primary schools is good in most of the country

5.6 – Distance to nearest primary school, 2007

Less than 2

3–4

5–6

7–8

Greater than 8

0 75 150 300

Kilometers

49


With primary enrollment levels ap-proaching universal enrollment Mo-zambique does well compared to many other African countries (fig 2.5).

5.7 – PGE across Africa, 2007

Less than 70

70–90

91–110

111–130

Greater than 130

No data

0 475 950 1,900

Kilometers

50

secondary gross enrollment rates

The Secondary Gross Enrollment (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 sec-ondary school (grade 8 to 12, in-cluding technical schools ETB and ETM), divided by the number of children between 13 and 16 years of age. The gross secondary enroll-ment rate increased four fold from an average of 10 percent in 1997 to 42 percent in 2007. Progress across administrative posts was relative-ly equal with almost all administra-tive posts making progress (figs 5.8, 5.9). However the increase in enroll-ment was concentrated in urban ar-eas and mostly took place in areas that already had higher enrollment in 1997 (fig 5.12).

5.8 – SGE rates, 2007

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

5.9 – Change in SGE between 1997 and 2007

Greater than 100

86–100

21–25

26–45

46–65

66–85

10–20

Less than 10

0 75 150 300

Kilometers

Greater than 55

46–55

5–15

16–25

26–35

36–45

Less than 5

0 75 150 300

Kilometers

51


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

5.10 – Male SGE rate, 2007 5.11 – Female SGE rate, 2007 A gender gap prevails in secondary education. In 2007, approximate-ly 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 differ-ence 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).

Page 45: fig 5.13, 5.14

Greater than 100

86–100

21–25

26–45

46–65

66–85

10–20

Less than 10

0 75 150 300

Kilometers

Greater than 100

86–100

21–25

26–45

46–65

66–85

10–20

Less than 10

0 75 150 300

Kilometers

52

secondary gross enrollment rates (CONT.)

Distance to secondary schools is 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 giv-en available paths and roads, how-ever, the map still gives a good in-dication 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.

Access to secondary schools remains limited

5.13 – Distance to nearest secondary school, 2007

Less than 10

10–20

21–30

31–40

Greater than 40

0 75 150 300

Kilometers

53


Less than 10

10–30

31–50

51–70

Greater than 70

No data

Secondary enrollment rates are on average lower in Mozambique than in most other African countries (fig 5.14).

5.14 – SGE rates across Africa, 2007

Page 47: fig 5.16

0 475 950 1,900

Kilometers

54

An educated labor force is a key 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).

Education of labor force

Educated labor is scarce

5.19 – Adult male literacy rate, 2007 5.20 – Adult female literacy rate, 2007

Less than 25

25–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers

Less than 25

25–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers

55


The population of labor force age (15 to 65) with some formal educa-tion (primary education or above) is mostly concentrated in urban areas. There is hardly anyone of labor force age (with secondary (or above) out-side 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

5.21 – Share of labor force with completed primary education or above, 2007

5.22 – Share of labor force with completed secondary education or above, 2007

Less than 25

25–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers

Less than 25

25–40

41–60

61–80

Greater than 80

0 75 150 300

Kilometers

land and agricultureland weatheranimals

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.

58

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

6.1 – Topography of Mozambique 6.2 – Land suitability

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

land and weather

Regular text: Page 56: fig 7.1, and 7.2

0 75 150 300

Kilometers

601 – 1,000

501 – 600

101 – 200

201 – 300

301 – 400

401 – 500

–112 – 100

1,010 – 2,420

0 75 150 300

Kilometers

Suitable for reforestation or wild life, inadequate for grass

Suitable for pasture, reforestation or wild life

Suitable for all uses

Suitable for all uses, but requires some conservation

Suitable for all uses, but requires intensive conservation

Suitable for several uses

Water bodies

Land suitable for intensive agricultural use

Land not suitable for agricultural use, but suitable for pasture, reforestation or natural vegetation.

59

Section 6 - LAND & AGRICULTUREMozambique Then and Now

6.3a – 1997 6.3c – 1999 6.3d – 2000 6.3e – 2001 6.3f – 20026.3b – 1998

6.3g – 2003 6.3i – 2005 6.3j – 2006 6.3k – 2007 6.3l – 20086.3h - 2004

The north-south pattern seen in agricultural suit-ability is also seen in rain-fall 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 tempera-tures were produced using daily NASA weather sat-ellite data, over the period from 1997 to 2008..

6.3a–l – Rainfall by year, 1997–2008

1900mL

1600mL

800mL

900mL

1000mL

1300mL

700mL

600mL

500mL

400mL

Page 57: figure 7.3a-l rain by year

0 75 150 300

Kilometers

60

6.4a – January 6.4c – March 6.4d – April 6.4e – May 6.4f – June6.4b – February

6.4g – July 6.4i – September 6.4j – October 6.4k – November 6.4l – December6.4h – August

6.4a–l – Rainfall by Month, 2007

700mL

400mL

60mL

80mL

100mL

200mL

40mL

20mL

10mL

5mL

land and weather (CONT.)

Page 58: figure 7.4a-l.x rain by months

The 12 maps in fig-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.

0 75 150 300

Kilometers

61


6.5a – January 6.5c – March 6.5d – April 6.5e – May 6.5f – June6.5b – February

6.5g – July 6.5i – September 6.5j – October 6.5k – November 6.5l – December6.5h – August

Figures 6.5a–l show varia-tion in mean day tempera-tures 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

6.5a–l – Average temperature by month, 2007

28°C

27°C

23°C

24°C

25°C

26°C

22°C

21°C

20°C

19°C

<18°C

Page 59: temperature by month

0 75 150 300

Kilometers

62

ownership of animals

There are geographical patterns in ownership of animals. Sheep own-ers 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 com-mon in southern part of Inhambane and the central part of the country (fig 6.8), while ownership of poultry is common throughout the coun-try, except in the Maputo province (fig 6.9).

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

6.6 – Share of households owning sheep 6.7 – Share of households owning cattle

Greater than 30

26–30

6–10

11–15

16–20

21–25

1–5

Less than 1

Greater than 30

26–30

6–10

11–15

16–20

21–25

1–5

Less than 1

0 75 150 300

Kilometers

0 75 150 300

Kilometers

63


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

Greater than 30

26–30

6–10

11–15

16–20

21–25

1–5

Less than 1

Greater than 30

26–30

6–10

11–15

16–20

21–25

1–5

Less than 1

0 75 150 300

Kilometers

0 75 150 300

Kilometers

64

6.10 – Share of households owning cattle between 1997 and 2007

6.11 – Share of households owning sheep between 1997 and 2007

6.12 – Share of households owning swine between 1997 and 2007

6.13 – Percentage point change in share owning cattle between 1997 and 2007

ownership of animals (CONT.)

Ownership of most animals is the priviledge of few households; in 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 per-centage points) and cattle (+1 per-centage point) (figs 6.13, 6.14, 6.15).

Less than –6

–6––2

–2–2

2–6

6–10

Greater than 10

0 75 150 300

Kilometers

65


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

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

Less than –25

–25––15

–15––5

–5–5

Greater than 5

Less than –6

–6––2

–2–2

2–6

6–10

Greater than 10

0 75 150 300

Kilometers

0 75 150 300

Kilometers

69

25

26

816

6

17

19

326

369

267

11

3822

368

61

75

37

367

2

370

21

30

33

322

390

339

269

4

301

342

231

5

238

215

221

380

177

268

338

18

249

237

327

340

206

15

68

334

13

74

12

366

197

23

230

3

350

202

27

178

224

240

305

7

251 236

172

45

312

203

120

199

245

235

216

227

195

193

292

28

205

250 22624

265

281

318

40

204

222

72

234

35

392

201

229

309

253

85

180

111

20

239

346

212

347

242

183

225

403

167

9

415

173

361

228

29

49

63

138

323

279

423

31

113

117

294

14

248

329

270

223

317

315

91

165

190

378

4142

304

3282

278

219

200

176

182

341

379

310

115

295

319

255

285

46

107

148

233

389

79

232

241

146

276

66

62

88

311

71

391

114

211

57

76

56

34

191

64

43

131179166

260

81

266

135132

208

210

424

247

87

188

105

149

65

264

48

106

55

345

151

402

349

313

381

302

271

283

316

328

112

189

333

321

300

127

52

207

86

324

108122

214

293

420

198

139

124

84

363

192

162

243

194

95

261

348

181

121

280

286

143

417

142

299

36

10

377

94

89

44

80

218

298

67

175

141

263

332

160

134

83 51

386

351

50

144

330

284

174

373

303

259308

275

70

150

58

404 352

187

158

5947

384

60

393

372

282

133

297

331

184

405

419

145

128

156

257

254

164

274

163

73

77

125

123

425

246

258

1

129

307

104

395

140

376

137

126

161

385

78

147

159

93

296

336

406

196

209

244

337

213

136

400

220

408

273

152

153

412

394

320

119

364

396

344

421

306

359

362

277

355365

185

102

116

413410

388

416

291

407

103

356

262

157

358

217

343

387

186

154

414

401

325

92

422

314

252

418

39

399

353

110

272

99

289

335

169

290

53

432

54Zambia

Zimbabwe

Malawi

South Africa

United Republic of Tanzania

Swaziland

129

400

139

405

404401

403

139

402

114

425

428

423

373

429

424422421

429

420

382

418

409

363

408

419

407

410

361

427406 410

364

Nampula

Beira

Maputo Matola

0 150 30075Km

66

Cod Adm. Posts District Province1 CIDADE DE LICHINGA CIDADE DE LICHINGA NIASSA2 CUAMBA CUAMBA NIASSA3 ETARARA CUAMBA NIASSA4 LURIO CUAMBA NIASSA5 METANGULA LAGO NIASSA6 COBUE LAGO NIASSA7 MANIAMBA LAGO NIASSA8 CHIMBONILA LICHINGA NIASSA9 LIONE LICHINGA NIASSA10 MEPONDA LICHINGA NIASSA11 MALANGA MAJUNE NIASSA12 MUAQUIA MAJUNE NIASSA13 NAIRRUBI MAJUNE NIASSA14 MANDIMBA MANDIMBA NIASSA15 MITANDE MANDIMBA NIASSA16 MARRUPA MARRUPA NIASSA17 MARANGIRA MARRUPA NIASSA18 NUNGO MARRUPA NIASSA19 MAUA MAUA NIASSA20 MAIACA MAUA NIASSA21 MAVAGO MAVAGO NIASSA22 M’SAWIZE MAVAGO NIASSA23 INSACA MECANHELAS NIASSA24 CHIUTA MECANHELAS NIASSA25 MECULA MECULA NIASSA26 MATONDOVELA MECULA NIASSA27 METARICA METARICA NIASSA28 NACUMUA METARICA NIASSA29 MUEMBE MUEMBE NIASSA30 CHICONONO MUEMBE NIASSA31 MASSANGULO NGAUMA NIASSA32 ITEPELA NGAUMA NIASSA33 NIPEPE NIPEPE NIASSA34 MUIPITE NIPEPE NIASSA35 UNANGO SANGA NIASSA36 LUSSIMBESSE SANGA NIASSA37 MACALOGE SANGA NIASSA38 MATCHEDJE SANGA NIASSA39 CIDADE DE PEMBA CIDADE DE PEMBA CABO DELGADO40 ANCUABE ANCUABE CABO DELGADO41 METORO ANCUABE CABO DELGADO42 MESA ANCUABE CABO DELGADO43 BALAMA BALAMA CABO DELGADO44 IMPIRI BALAMA CABO DELGADO45 KUEKUE BALAMA CABO DELGADO46 MAVALA BALAMA CABO DELGADO47 CHIURE CHIURE CABO DELGADO48 CHIURE VELHO CHIURE CABO DELGADO49 KATAPUA CHIURE CABO DELGADO50 MAZEZE CHIURE CABO DELGADO51 NAMOGELIA CHIURE CABO DELGADO52 OCUA CHIURE CABO DELGADO53 IBO IBO CABO DELGADO54 QUIRIMBA IBO CABO DELGADO55 MACOMIA MACOMIA CABO DELGADO56 CHAI MACOMIA CABO DELGADO57 MUCOJO MACOMIA CABO DELGADO58 QUITERAJO MACOMIA CABO DELGADO59 MECUFI MECUFI CABO DELGADO60 MURREBUE MECUFI CABO DELGADO61 MELUCO MELUCO CABO DELGADO62 MUAGUIDE MELUCO CABO DELGADO63 MOCIMBOA DA PRAIA MOCIMBOA DA PRAIA CABO DELGADO64 DIACA MOCIMBOA DA PRAIA CABO DELGADO65 MBAU MOCIMBOA DA PRAIA CABO DELGADO66 MONTEPUEZ MONTEPUEZ CABO DELGADO67 MAPUPULO MONTEPUEZ CABO DELGADO68 MIRATE MONTEPUEZ CABO DELGADO69 NAIROTO MONTEPUEZ CABO DELGADO70 NAMANHUMBIR MONTEPUEZ CABO DELGADO71 MUEDA MUEDA CABO DELGADO72 CHAPA MUEDA CABO DELGADO

Cod Adm. Posts District Province73 IMBUO MUEDA CABO DELGADO74 NEGOMANO MUEDA CABO DELGADO75 N’GAPA MUEDA CABO DELGADO76 MUIDUMBE MUIDUMBE CABO DELGADO77 CHITUNDA MUIDUMBE CABO DELGADO78 MITEDA MUIDUMBE CABO DELGADO79 NAMUNO NAMUNO CABO DELGADO80 HUCULA NAMUNO CABO DELGADO81 MACHOCA NAMUNO CABO DELGADO82 MELOCO NAMUNO CABO DELGADO83 N’CUMPE NAMUNO CABO DELGADO84 PAPAI NAMUNO CABO DELGADO85 NANGADE NANGADE CABO DELGADO86 M’TAMBA NANGADE CABO DELGADO87 PALMA PALMA CABO DELGADO88 OLUMBI PALMA CABO DELGADO89 PUNDANHAR PALMA CABO DELGADO90 QUIONGA PALMA CABO DELGADO91 METUGE PEMBA CABO DELGADO92 MIEZE PEMBA CABO DELGADO93 QUISSANGA QUISSANGA CABO DELGADO94 BILIBIZA QUISSANGA CABO DELGADO95 MAHATE QUISSANGA CABO DELGADO96 URBANO CENTRAL CIDADE DE NAMPULA NAMPULA97 MUATALA CIDADE DE NAMPULA NAMPULA98 MUHALA CIDADE DE NAMPULA NAMPULA99 NAMIKOPO CIDADE DE NAMPULA NAMPULA100 NAPIPIME CIDADE DE NAMPULA NAMPULA101 NATIKIRE CIDADE DE NAMPULA NAMPULA102 CIDADE DE ANGOCHE ANGOCHE NAMPULA103 AUBE ANGOCHE NAMPULA104 NAMAPONDA ANGOCHE NAMPULA105 BOILA_NAMITORIA ANGOCHE NAMPULA106 NAMAPA ERATI NAMPULA107 ALUA ERATI NAMPULA108 NAMIROA ERATI NAMPULA109 C. ILHA DE MOÇAMBIQUE C. ILHA DE MOÇAMBIQUE NAMPULA110 LUMBO C. ILHA DE MOÇAMBIQUE NAMPULA111 LALAUA LALAUA NAMPULA112 METI LALAUA NAMPULA113 MALEMA MALEMA NAMPULA114 CHIHULO MALEMA NAMPULA115 MUTUALI MALEMA NAMPULA116 MECONTA MECONTA NAMPULA117 CORRANE MECONTA NAMPULA118 NAMIALO MECONTA NAMPULA119 7 DE ABRIL MECONTA NAMPULA120 MECUBURI MECUBURI NAMPULA121 MILHANA MECUBURI NAMPULA122 MUITE MECUBURI NAMPULA123 NAMINA MECUBURI NAMPULA124 MEMBA MEMBA NAMPULA125 CHIPENE MEMBA NAMPULA126 LURIO MEMBA NAMPULA127 MAZUA MEMBA NAMPULA128 NAMIGE MOGINCUAL NAMPULA129 QUINGA MOGINCUAL NAMPULA130 CHUNGA MOGINCUAL NAMPULA131 QUIXAXE MOGINCUAL NAMPULA132 LIUPO MOGINCUAL NAMPULA133 NAMETIL MOGOVOLAS NAMPULA134 CALIPO MOGOVOLAS NAMPULA135 IULUTI MOGOVOLAS NAMPULA136 MUATUA MOGOVOLAS NAMPULA137 NANHUPO RIO MOGOVOLAS NAMPULA138 MOMA MOMA NAMPULA139 CHALAUA MOMA NAMPULA140 LARDE MOMA NAMPULA141 MUCUALI MOMA NAMPULA142 MONAPO MONAPO NAMPULA143 ITOCULO MONAPO NAMPULA144 NETIA MONAPO NAMPULA

Location of Administration Posts

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Cod Adm. Posts District Province73 IMBUO MUEDA CABO DELGADO74 NEGOMANO MUEDA CABO DELGADO75 N’GAPA MUEDA CABO DELGADO76 MUIDUMBE MUIDUMBE CABO DELGADO77 CHITUNDA MUIDUMBE CABO DELGADO78 MITEDA MUIDUMBE CABO DELGADO79 NAMUNO NAMUNO CABO DELGADO80 HUCULA NAMUNO CABO DELGADO81 MACHOCA NAMUNO CABO DELGADO82 MELOCO NAMUNO CABO DELGADO83 N’CUMPE NAMUNO CABO DELGADO84 PAPAI NAMUNO CABO DELGADO85 NANGADE NANGADE CABO DELGADO86 M’TAMBA NANGADE CABO DELGADO87 PALMA PALMA CABO DELGADO88 OLUMBI PALMA CABO DELGADO89 PUNDANHAR PALMA CABO DELGADO90 QUIONGA PALMA CABO DELGADO91 METUGE PEMBA CABO DELGADO92 MIEZE PEMBA CABO DELGADO93 QUISSANGA QUISSANGA CABO DELGADO94 BILIBIZA QUISSANGA CABO DELGADO95 MAHATE QUISSANGA CABO DELGADO96 URBANO CENTRAL CIDADE DE NAMPULA NAMPULA97 MUATALA CIDADE DE NAMPULA NAMPULA98 MUHALA CIDADE DE NAMPULA NAMPULA99 NAMIKOPO CIDADE DE NAMPULA NAMPULA100 NAPIPIME CIDADE DE NAMPULA NAMPULA101 NATIKIRE CIDADE DE NAMPULA NAMPULA102 CIDADE DE ANGOCHE ANGOCHE NAMPULA103 AUBE ANGOCHE NAMPULA104 NAMAPONDA ANGOCHE NAMPULA105 BOILA_NAMITORIA ANGOCHE NAMPULA106 NAMAPA ERATI NAMPULA107 ALUA ERATI NAMPULA108 NAMIROA ERATI NAMPULA109 C. ILHA DE MOÇAMBIQUE C. ILHA DE MOÇAMBIQUE NAMPULA110 LUMBO C. ILHA DE MOÇAMBIQUE NAMPULA111 LALAUA LALAUA NAMPULA112 METI LALAUA NAMPULA113 MALEMA MALEMA NAMPULA114 CHIHULO MALEMA NAMPULA115 MUTUALI MALEMA NAMPULA116 MECONTA MECONTA NAMPULA117 CORRANE MECONTA NAMPULA118 NAMIALO MECONTA NAMPULA119 7 DE ABRIL MECONTA NAMPULA120 MECUBURI MECUBURI NAMPULA121 MILHANA MECUBURI NAMPULA122 MUITE MECUBURI NAMPULA123 NAMINA MECUBURI NAMPULA124 MEMBA MEMBA NAMPULA125 CHIPENE MEMBA NAMPULA126 LURIO MEMBA NAMPULA127 MAZUA MEMBA NAMPULA128 NAMIGE MOGINCUAL NAMPULA129 QUINGA MOGINCUAL NAMPULA130 CHUNGA MOGINCUAL NAMPULA131 QUIXAXE MOGINCUAL NAMPULA132 LIUPO MOGINCUAL NAMPULA133 NAMETIL MOGOVOLAS NAMPULA134 CALIPO MOGOVOLAS NAMPULA135 IULUTI MOGOVOLAS NAMPULA136 MUATUA MOGOVOLAS NAMPULA137 NANHUPO RIO MOGOVOLAS NAMPULA138 MOMA MOMA NAMPULA139 CHALAUA MOMA NAMPULA140 LARDE MOMA NAMPULA141 MUCUALI MOMA NAMPULA142 MONAPO MONAPO NAMPULA143 ITOCULO MONAPO NAMPULA144 NETIA MONAPO NAMPULA

Cod Adm. Posts District Province145 MOSSURIL MOSSURIL NAMPULA146 LUNGA MOSSURIL NAMPULA147 MATIBANE MOSSURIL NAMPULA148 MUECATE MUECATE NAMPULA149 IMALA MUECATE NAMPULA150 MUCULOENE MUECATE NAMPULA151 MURRUPULA MURRUPULA NAMPULA152 CHINGA MURRUPULA NAMPULA153 NIHESSIUE MURRUPULA NAMPULA154 MAIAIA NACALA-PORTO NAMPULA155 MUANONA NACALA-PORTO NAMPULA156 NACALA-A-VELHA NACALA-A-VELHA NAMPULA157 COVO NACALA-A-VELHA NAMPULA158 NACAROA NACAROA NAMPULA159 INTETE NACAROA NAMPULA160 SAUA-SAUA NACAROA NAMPULA161 RAPALE RAPALE-NAMPULA NAMPULA162 ANCHILO RAPALE-NAMPULA NAMPULA163 MUTIVASSE RAPALE-NAMPULA NAMPULA164 NAMAITA RAPALE-NAMPULA NAMPULA165 RIBAUE RIBAUE NAMPULA166 CUNLE RIBAUE NAMPULA167 IAPALA RIBAUE NAMPULA168 Urbano Nº1 QUELIMANE ZAMBEZIA169 Urbano Nº2 QUELIMANE ZAMBEZIA170 Urbano Nº3 QUELIMANE ZAMBEZIA171 Urbano Nº4 QUELIMANE ZAMBEZIA172 ALTO MOLOCUE ALTO MOLOCUE ZAMBEZIA173 NAUELA ALTO MOLOCUE ZAMBEZIA174 CHINDE CHINDE ZAMBEZIA175 LUABO CHINDE ZAMBEZIA176 MICAUNE CHINDE ZAMBEZIA177 GILE GILE ZAMBEZIA178 ALTO LIGONHA GILE ZAMBEZIA179 CIDADE DE GURUE GURUE ZAMBEZIA180 LIOMA GURUE ZAMBEZIA181 MEPUAGIUA GURUE ZAMBEZIA182 ILE ILE ZAMBEZIA183 MULEVALA (NAMIGONHA) ILE ZAMBEZIA184 SOCONE ILE ZAMBEZIA185 INHASSUNGE (MUCUPIA) INHASSUNGE ZAMBEZIA186 GONHANE INHASSUNGE ZAMBEZIA187 LUGELA LUGELA ZAMBEZIA188 TACUANE LUGELA ZAMBEZIA189 MUNHAMADE LUGELA ZAMBEZIA190 MUABANAMA LUGELA ZAMBEZIA191 MAGANJA DA COSTA MAGANJA DA COSTA ZAMBEZIA192 BAJONE MAGANJA DA COSTA ZAMBEZIA193 MOCUBELA MAGANJA DA COSTA ZAMBEZIA194 NANTE MAGANJA DA COSTA ZAMBEZIA195 MILANGE MILANGE ZAMBEZIA196 MAJAUA MILANGE ZAMBEZIA197 MOLUMBO MILANGE ZAMBEZIA198 MONGUE MILANGE ZAMBEZIA199 CIDADE DE MOCUBA MOCUBA ZAMBEZIA200 MUGEBA MOCUBA ZAMBEZIA201 NAMANJAVIRA MOCUBA ZAMBEZIA202 MOPEIA MOPEIA ZAMBEZIA203 CAMPO MOPEIA ZAMBEZIA204 MORRUMBALA MORRUMBALA ZAMBEZIA205 CHIRE MORRUMBALA ZAMBEZIA206 DERRE MORRUMBALA ZAMBEZIA207 MEGAZA MORRUMBALA ZAMBEZIA208 NAMACURRA NAMACURRA ZAMBEZIA209 MACUZE NAMACURRA ZAMBEZIA210 NAMARROI NAMARROI ZAMBEZIA211 REGONE NAMARROI ZAMBEZIA212 NICOADALA NICOADALA ZAMBEZIA213 MAQUIVAL NICOADALA ZAMBEZIA214 PEBANE PEBANE ZAMBEZIA215 MUALAMA (MULELA) PEBANE ZAMBEZIA216 NABURI PEBANE ZAMBEZIA

Cod Adm. Posts District Province217 CIDADE DE TETE CIDADE DE TETE TETE218 ULONGUE ANGONIA TETE219 DOMUE ANGONIA TETE220 SONGO CAHORA BASSA TETE221 CHINTHOLO CAHORA BASSA TETE222 CHITIMA CAHORA BASSA TETE223 LUENHA CHANGARA TETE224 CHIPEMBERE (CHIOCO) CHANGARA TETE225 KACHEMBE (MARARA) CHANGARA TETE226 CHIFUNDE CHIFUNDE TETE227 MUALADZE CHIFUNDE TETE228 NSADZU CHIFUNDE TETE229 KAZULA CHIUTA TETE230 MANJE CHIUTA TETE231 FURANCUNGO MACANGA TETE232 CHIDZOLOMONDO MACANGA TETE233 MPHENDE MAGOE TETE234 CHINTHOPO MAGOE TETE235 MUKUMBURA MAGOE TETE236 CHIPUTU MARAVIA TETE237 FINGOE MARAVIA TETE238 MALOWERA MARAVIA TETE239 CHIPERA MARAVIA TETE240 MOATIZE MOATIZE TETE241 KAMBULATSISI MOATIZE TETE242 ZOBUE MOATIZE TETE243 NHAMAYABUE MUTARARA TETE244 CHARRE MUTARARA TETE245 DOA MUTARARA TETE246 INHANGOMA MUTARARA TETE247 NTENGO-WA-MBALAME TSANGANO TETE248 TSANGANO TSANGANO TETE249 ZUMBU ZUMBU TETE250 MUZE ZUMBU TETE251 ZAMBUE ZUMBU TETE252 CIDADE DE CHIMOIO CIDADE DE CHIMOIO MANICA253 CATANDICA BARUE MANICA254 CHOA BARUE MANICA255 NHAMPASSA BARUE MANICA256 GONDOLA GONDOLA MANICA257 AMATONGAS GONDOLA MANICA258 CAFUMPE GONDOLA MANICA259 INCHOPE GONDOLA MANICA260 MACATE GONDOLA MANICA261 MATSINHO GONDOLA MANICA262 ZEMBE GONDOLA MANICA263 GURO GURO MANICA264 MANDIE GURO MANICA265 MUNGARI GURO MANICA266 NHAMASSONGE GURO MANICA267 CHITOBE (MACHAZE) MACHAZE MANICA268 SAVE MACHAZE MANICA269 MACOSSA MACOSSA MANICA270 NGUAWALA MACOSSA MANICA271 NHAMAGUA MACOSSA MANICA272 CIDADE DE MANICA MANICA MANICA273 MACHIPANDA MANICA MANICA274 MESSICA MANICA MANICA275 MAVONDE MANICA MANICA276 VANDUZI MANICA MANICA277 ESPUNGABERA MOSSURIZE MANICA278 CHIURAIRUE MOSSURIZE MANICA279 DACATA MOSSURIZE MANICA280 SUSSUNDENGA SUSSUNDENGA MANICA281 DOMBE SUSSUNDENGA MANICA282 MUOHA SUSSUNDENGA MANICA283 ROTANDA SUSSUNDENGA MANICA284 NHACOLO TAMBARA MANICA285 BUZUA TAMBARA MANICA286 NHACAFULA TAMBARA MANICA287 URBANO Nº1 CIDADE DA BEIRA SOFALA288 URBANO Nº2 CIDADE DA BEIRA SOFALA

Cod Adm. Posts District Province289 URBANO Nº3 CIDADE DA BEIRA SOFALA290 URBANO Nº4 CIDADE DA BEIRA SOFALA291 URBANO Nº5 CIDADE DA BEIRA SOFALA292 BUZI BUZI SOFALA293 ESTAQUINHA BUZI SOFALA294 NOVA-SOFALA BUZI SOFALA295 CAIA CAIA SOFALA296 MURRAÇA CAIA SOFALA297 SENA CAIA SOFALA298 CHEMBA CHEMBA SOFALA299 CHIRAMBA CHEMBA SOFALA300 MULIMA CHEMBA SOFALA301 INHAMINGA CHERINGOMA SOFALA302 INHAMITANGA CHERINGOMA SOFALA303 CHIBABAVA CHIBABAVA SOFALA304 GOONDA CHIBABAVA SOFALA305 MUXUNGUE CHIBABAVA SOFALA306 CIDADE DE DONDO DONDO SOFALA307 MAFAMBISSE DONDO SOFALA308 SAVANE DONDO SOFALA309 GORONGOSA GORONGOSA SOFALA310 NHAMADZE GORONGOSA SOFALA311 VANDUZI GORONGOSA SOFALA312 MACHANGA MACHANGA SOFALA313 DIVINHE MACHANGA SOFALA314 CHILOANE MACHANGA SOFALA315 MARINGUE MARINGUE SOFALA316 CANXIXE MARINGUE SOFALA317 SUBWE MARINGUE SOFALA318 MARROMEU MARROMEU SOFALA319 CHUPANGA MARROMEU SOFALA320 MALINGAPASSE MARROMEU SOFALA321 MUANZA MUANZA SOFALA322 GALINHA MUANZA SOFALA323 NHAMATANDA NHAMATANDA SOFALA324 TICA NHAMATANDA SOFALA325 CIDADE DE INHAMBANE CIDADE DE INHAMBANE INHAMBANE326 FUNHALOURO FUNHALOURO INHAMBANE327 TOME FUNHALOURO INHAMBANE328 NOVA MAMBONE GOVURO INHAMBANE329 SAVE GOVURO INHAMBANE330 HOMOINE HOMOINE INHAMBANE331 PEMBE HOMOINE INHAMBANE332 INHARRIME INHARRIME INHAMBANE333 MOCUMBI INHARRIME INHAMBANE334 INHASSORO INHASSORO INHAMBANE335 BAZARUTO INHASSORO INHAMBANE336 JANGAMO JANGAMO INHAMBANE337 CUMBANA JANGAMO INHAMBANE338 MABOTE MABOTE INHAMBANE339 ZIMANE MABOTE INHAMBANE340 ZINAVE MABOTE INHAMBANE341 MASSINGA MASSINGA INHAMBANE342 CHICOMO MASSINGA INHAMBANE343 CIDADE DE MAXIXE CIDADE DE MAXIXE INHAMBANE344 MORRUMBENE MORRUMBENE INHAMBANE345 MUCODUENE MORRUMBENE INHAMBANE346 PANDA PANDA INHAMBANE347 MAWAYELA PANDA INHAMBANE348 URRENE PANDA INHAMBANE349 VILANKULO VILANKULO INHAMBANE350 MAPINHANE VILANKULO INHAMBANE351 QUISSICO ZAVALA INHAMBANE352 ZANDAMELA ZAVALA INHAMBANE353 CIDADE DE XAI-XAI CIDADE DE XAI-XAI GAZA354 MACIA BILENE GAZA355 CHISSANO BILENE GAZA356 MAZIVILA BILENE GAZA357 MESSANO BILENE GAZA358 PRAIA DE BILENE BILENE GAZA359 MAKLUANE BILENE GAZA360 CIDADE DE CHIBUTO CHIBUTO GAZA

Cod Adm. Posts District Province361 ALTO CHANGANE CHIBUTO GAZA362 CHAIMITE CHIBUTO GAZA363 CHANGANINE CHIBUTO GAZA364 GODIDE CHIBUTO GAZA365 MALEHICE CHIBUTO GAZA366 VILA EDUARDO MONDLANE CHICUALACUALA GAZA367 MAPAI CHICUALACUALA GAZA368 PAFURI CHICUALACUALA GAZA369 CHIGUBO CHIGUBO GAZA370 DINDIZA CHIGUBO GAZA371 CIDADE DE CHOKWE CHOKWE GAZA372 LIONDE CHOKWE GAZA373 MACARRETANE CHOKWE GAZA374 XILEMBENE CHOKWE GAZA375 CANIÇADO GUIJA GAZA376 CHIVONGOENE GUIJA GAZA377 MUBANGOENE GUIJA GAZA378 NALAZI GUIJA GAZA379 MABALANE MABALANE GAZA380 COMBOMUNE MABALANE GAZA381 NTLAVENE MABALANE GAZA382 MANDLAKAZE MANDLAKAZE GAZA383 XHALALA MANDLAKAZE GAZA384 CHIBONZANE MANDLAKAZE GAZA385 CHIDENGUELE MANDLAKAZE GAZA386 MACUACUA MANDLAKAZE GAZA387 MAZUCANE MANDLAKAZE GAZA388 NGUZENE MANDLAKAZE GAZA389 MASSANGENA MASSANGENA GAZA390 MAVUE MASSANGENA GAZA391 MASSINGIR MASSINGIR GAZA392 MAVODZE MASSINGIR GAZA393 ZULO MASSINGIR GAZA394 CHICUMBANE XAI-XAI GAZA395 CHONGOENE XAI-XAI GAZA396 ZONGOENE XAI-XAI GAZA397 MATOLA CIDADE DA MATOLA MAPUTO398 MACHAVA CIDADE DA MATOLA MAPUTO399 INFULENE CIDADE DA MATOLA MAPUTO400 BOANE BOANE MAPUTO401 MATOLA RIO BOANE MAPUTO402 MAGUDE MAGUDE MAPUTO403 MAPULANGUENE MAGUDE MAPUTO404 MOTAZE MAGUDE MAPUTO405 MAHELE MAGUDE MAPUTO406 PANJANE MAGUDE MAPUTO407 MANHIÇA MANHIÇA MAPUTO408 CALANGA MANHIÇA MAPUTO409 ILHA JOSINA MACHEL MANHIÇA MAPUTO410 MALUANA MANHIÇA MAPUTO411 XINAVANE MANHIÇA MAPUTO412 PALMEIRA (3 DE FEVEREIRO) MANHIÇA MAPUTO413 MARRACUENE MARRACUENE MAPUTO414 MACHUBO MARRACUENE MAPUTO415 BELA VISTA MATUTUINE MAPUTO416 MUGAZINE (CATEMBE) MATUTUINE MAPUTO417 CATUANE MATUTUINE MAPUTO418 NDELANE (MACHAMGULO) MATUTUINE MAPUTO419 ZITUNDO MATUTUINE MAPUTO420 MOAMBA MOAMBA MAPUTO421 PESSENE MOAMBA MAPUTO422 RESSANO GARCIA MOAMBA MAPUTO423 SABIE MOAMBA MAPUTO424 NAMAACHA NAMAACHA MAPUTO425 CHANGALANE NAMAACHA MAPUTO426 DISTRITO URBANO Nº1 DISTRITO URBANO Nº1 CIDADE DE MAPUTO427 DISTRITO URBANO Nº2 DISTRITO URBANO Nº2 CIDADE DE MAPUTO428 DISTRITO URBANO Nº3 DISTRITO URBANO Nº3 CIDADE DE MAPUTO429 DISTRITO URBANO Nº4 DISTRITO URBANO Nº4 CIDADE DE MAPUTO430 DISTRITO URBANO Nº5 DISTRITO URBANO Nº5 CIDADE DE MAPUTO431 DISTRITO URBANO Nº6 DISTRITO URBANO Nº6 CIDADE DE MAPUTO432 DISTRITO URBANO Nº7 DISTRITO URBANO Nº7 CIDADE DE MAPUTO

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Section 1 – Healthy Lives

Infant Mortality Rate is defined as number of newborns dying under a year of age divided by the number of live births during the year times 1000.

Maternal Mortality Rate 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. 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.

General fertility rate is defined as the annual number of live births per 1000 women of childbearing age (between 15 and 49 of age).

Stunting. A child is considered stunted if it has a Height-for-Age z-score that is 2 standard deviations below the reference population.

Malnutrition. Child anthropometric measures are widely used to analyze the prevalence malnutrition among children under five. Anthropometric measures are measures of height for 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 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 of undernutrition, wasting reflecting weight loss associated with a recent period of starvation or disease, and underweight ref lecting a current condition resulting from inadequate food intake, past episodes of undernutrition or poor health conditions.

Small Area Estimates of Malnutrition: Most of the other social indicators shown in this Atlas were recorded in the 2007 census that went to all households in Mozambique. Malnutrition

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 in each administrative post a method known as Small Area Estimates was used. This method 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 how many children are malnourished in each Administrative post. A full description of the method and results can be found in Sohnesen et all.

Underweight. A child is considered underweight if it has a Weight-for-Age z-score that is 2 standard deviations below the reference population.

Accessing water from rivers and lakes is the share of households that uses rivers or lakes as their source of drinking water.

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)

Lack of access to toilet facilities is the share of households that do not have access to a toilet.

Section 2 – Access to Education

Gross Primary Enrollment Rate is defined as number of students attending primary school (grade 1 to 7), including technical elementary (ETE), divided by number of children between 6 and 12 years of age.

Gross Secondary Enrollment Rate is defined as number of students attending secondary school (grade 8 to 12), including technical schools (ETB and ETM) and primary school teachers, divided by number of children between 13 and 16 years of age.

definitions of indicators this needs to be updated

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Section 3 – Access to Services

Estimated travel time to nearest city with over 50.000 people. This is an estimate of travel time based on location and available roads among other aspects, please see Dorosh and Schmidt for full details.

Share of households with cell phones. A household is defined as being a call phone if at least one person within the household has a cell phone.

Share of household using electricity. A household is defined as using electricity if this is its main source of light.

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 4 – Labor Force and Employment

Share active in labor market is defined as any person over 15 that during the last week of June 2007 said that they 1) are working, 2) wasn’t working, but has a job, Worked with a family 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.

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.

Section 5 – Land and Agriculture

The rainfall and temperature maps in section 5 were produced using daily NASA weather satellite data, over the period from 1997 to 2007.

Section 6 – The People of Mozambique

Population density is defined as number people living in an administrative post divided by the size measured km2 of the Administrative post.

Share that speak Portuguese is defined for ages 10 and up.

Section 7 – Wealth

Poverty headcount is defined as the number of peopled considered poor over total population.

Squared poverty gap measures the depth of poverty. A high value indicates that many people are very poor. For more details see MEPD 2010.

Inequality is measured by the Gini coefficient.

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image and map index

Section 1 - Healthy Lives1.1 - IMR by administrative post per 1000 live births - 19971.2 - IMR by administrative post per 1000 live births - 20071.3 - Distribution of IMR in administrative posts - 1997 and 20071.4 - Box plot of IMR in administrative posts - 1997 and 20071.5 - IMR in 1997 and change between 1997 and 2007, by administrative post1.6 - IMR per 1000 live births across Africa- 20071.7 - Malnutrition by region and urban/rural - 2008/091.8 - Malnutrition by province - 2008/091.9 - Trend in malnutrition - 1997–2008/091.10 - Share of underweight children across Africa - 20071.11 - Share of stunted children by administrative post, Share of stunted children per km2 by administrative

post - 20071.12 - Share of underweight children by administrative posts, Share of underweight children per km2 by

administrative post - 20071.13 - MMR across Africa - 20071.14 - MMR in urban and rural areas - 20071.15 - MMR by province - 20071.16 - MMR by district - 20071.17 - General fertility rate [defined as the annual number of live births per 1000 women of childbearing age

(15 to 49)] - 19971.18 - General fertility rate [defined as the annual number of live births per 1000 women of childbearing age

(15 to 49)] - 20071.19 - General fertility rates in 1997 and change between 1997 and 2007, by administrative post1.20 - General fertility rate by province - 1997 and 20071.21 - Distribution of general fertility rates in administrative post - 1997 and 20071.22 - Absolute change of fertility rate by administrative post - 1997–20071.23 - Rivers, lakes and water wells across Mozambique - 20071.24 - Share of households using water from rivers or lakes by administrative post - 20071.25 - Absolute change of river and lake usage by administrative post - 1997–20071.26 - Share of households using rivers and lakes as water source in 1997 and change between 1997 and 2007,

by administrative post1.27 - Location of health facilities across Mozambique - 20071.28 - Distance to nearest health facility, as birds fly - 20071.29 - Absolute change of households without access to a toilet by administrative post between 1997–20071.30 - Share of households that do not have access to a toilet by administrative post - 1997

1.31 - Share of households that do not have access to a toilet by administrative post - 20071.32 - Change in share of households with no access to toilets 1997–2007

Section 2 - Access to Education2.1 - Male gross primary enrollment by administrative post - 19972.2 - Male gross primary enrollment by administrative post - 20072.3 - Female gross primary enrollment by administrative post - 19972.4 - Female gross primary enrollment by administrative post - 20072.5 - Gross primary enrollment across Africa - 20072.6 - Male gross secondary enrollment by administrative post - 19972.7 - Male gross secondary enrollment by administrative post - 20072.8 - Female gross secondary enrollment by administrative post - 19972.9 - Female gross secondary enrollment by administrative post - 20072.10 - Gross secondary enrollment across Africa - 20072.11 - Absolute change of male gross primary enrollment by administrative post - 1997–20072.12 - Absolute change of female gross primary enrollment by administrative post - 1997–20072.13 - Absolute change of male gross secondary enrollment by administrative post - 1997–20072.14 - Absolute change of female gross secondary enrollment by administrative post - 1997–20072.15 - Primary gross enrollment rate in urban and rural areas, 1997 and 20072.16 - Secondary gross enrollment rate in urban and rural areas, 1997 and 20072.17 - Increase in total number of students in primary and secondary from 1997 to 2007, by gender2.18 - Primary gross enrollment rate for boys and girls, 1997 and 20072.19 - Secondary gross enrollment rate for boys and girls, 1997 and 20072.20 - Increase in total number of students in primary and secondary from 1997 to 2007, by urban/rural2.21 - Gross enrollment rates by administrative post - 1997 and 20072.22 - Primary gross enrollment rates in 1997 and change between 1997 and 2007, by administrative post2.23 - Secondary gross enrollment rates in 1997 and change between 1997 and 2007, by administrative post

Section 3 - Access to Services3.1 - Share of households using electricity as main source of light by administrative post - 20073.2 - Share of households having running water by administrative post - 20073.3 - Share of households with at least one cell phone by administrative post - 20073.4 - Share of households that own a fixed phoneline by administrative post - 20073.5 - Share of households that have used internet in the last year at least once by administrative post - 20073.6 - Share of household using service in urban and rural areas - 20073.7 - Travel distance to urban centers by administrative post – 2007

needs update when all changes are implemented

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Section 4 - Labor Force and Employment4.1 - Adult female literacy rate by administrative post - 20074.2 - Adult male literacy rate by administrative post - 20074.3 - Share of labor force with primary education or above by administrative post - 20074.4 - Share of labor force with secondary education or above by administrative post - 20074.5 - Share of labor (aged 15 to 65) with complete primary or above, and secondary and above, by gender and

urban/rural - 20074.6 - Share of adults active in labor market in 1997 and change between 1997 and 2007, by administrative post4.7 - Box plot share of adults active in labor market by administrative post - 1997 and 20074.8 - Labor force participation by gender and urban/rural - 20074.9 - Labor force participation by gender and education level - 20074.10 - Female labor force participation rate by administrative post - 20074.11 - Male labor force participation rate by administrative post - 20074.12 - Labor force participation rate by administrative post - 19974.13 - Labor force participation rate by administrative post - 20074.14 - Change in labor force participation rate by administrative post - 1997–20074.15 - Type of employer by urban/rural - 20074.16 - Type of employer by gender - 20074.17 - Type of employer by province - 2007

Section 5 - Land and Agriculture5.1 - Share of households owning cows - 1997 and 20075.2 - Share of households owning sheep - 1997 and 20075.3 - Share of households owning swine - 1997 and 20075.4 - Absolute change of households owning swine by administrative post - 1997–20075.5 - Absolute change of households owning sheep by administrative post - 1997–20075.6 - Absolute change of households owning cows by administrative post - 1997–20075.7 - Share of households that own sheep by administrative post - 20075.8 - Share of households that own cattle by administrative post - 20075.9 - Share of households that own swine by administrative post - 20075.10 - Topography of Mozambique5.11 - Temperature per month in Mozambique - 20075.12 - Rainfall per year in Mozambique - 1997–20085.13 - Share of cashew farmers by administrative post - 20075.14 - Share of fish farmers by administrative post - 2007

Section 6 - The People of Mozambique6.1 - Change in population (percentage) - 1997–20076.2 - Change in population by administrative post - 1997–20076.3 - Ratio of men to women by age and province - 20076.4 - Population by province - 1997 and 20076.5 - Population by gender - 1997 and 20076.6 - Share of population aged 15 or below by administrative post - 20076.7 - Share of population aged 50 or above by administrative post - 20076.8 - Total population by administrative post - 20076.9 - Rural population per km2 by administrative post - 20076.10 - Number of men per 100 women by administrative post - 19976.11 - Number of men per 100 women by administrative post - 20076.12 - Main languages spoken in northern provinces - 20076.13 - Main languages spoken in central provinces - 20076.14 - Main languages spoken in southern provinces - 20076.15 - Share of population that know Portuguese by age group and gender - 20076.16 - Share of population (aged 10 and above) that speak Portuguese by administrative post - 20076.17 - Share of population belonging to denomination - 20076.18 - Religion by province - 2007

Section 7 - Wealth7.1 - Share of households that own a car by administrative post - 20077.2 - Share of households that own a TV by administrative post - 20077.3 - Share of households that own a computer by administrative post - 20077.4 - Asset ownership by urban/rural7.5 - Share of households that own a radio by administrative post - 20077.6 - Share of households that own a bicycle by administrative post - 20077.7 - Share of households that own a motorcycle by administrative post - 20077.8 - Poverty headcount by province - 2008/097.9 - Poverty headcount by urban/rural - 2008/097.10 - Squared poverty gab by province - 2008/097.11 - Inequality by province - 2008/09

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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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Download - Public Disclosure Authorized Mozambique Then and …...Mozambique Then and Now Population growth from 1997 to 2007 as a percentage was faster in the south and in coastal areas (fig

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