measuring living standards across space in the developing world · measuring living standards...

Measuring Living Standards Across Space

in the Developing World

Doug GOLLINa

Martina KIRCHBERGERb

David LAGAKOSc

a University of Oxford

b Columbia University

c UC San Diego and NBER

Preliminary and not for distribution – comments welcome!

March 31, 2015

Abstract

This paper shows that basic living standards in Africa vary continuously and increase almost

always monotonically across population density. While there are compelling reasons why indi-

viduals might not move from the most rural to the most densely populated locations, our results

suggest that individuals may be able to improve their welfare by moving within their country to

a marginally more densely populated area. Two reasons this might not happen are the presence

of compensating differentials for rural residents or limited absorptive capacity of cities. We do

not find evidence for either of these explanations and conclude that theories ought to address this

striking empirical regularity.

Email: [email protected], [email protected], [email protected]. All potential errors are our own.

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1. Introduction

By almost any measure, average living standards differ widely across countries. Economists tend to

use real per capita GDP as a crude but effective index of living standards, and by the best available

comparative measures, it appears that real per capita GDP in the richest countries exceeds that in

the poorest countries by a factor of perhaps 30 or more, as has been widely documented (???). The

typical Sub-Saharan African country, for example, has a level of GDP per capita that is roughly five

percent as large as that of the United States. Economists have long recognized that there are also large

differences in income within the typical developing country (?).

What has been less well understood, however, is the extent of income disparities across sectors and

regions within developing countries. In recent years, a number of papers have focused attention on

disparities across sectors – particularly between agriculture and non-agriculture – and between urban

and rural areas within developing countries. For instance, ? compare average labor productivity in

agriculture and non-agriculture, and they find what appear to be substantial differences that remain

after adjusting for sectoral gaps in hours worked and human capital. In a somewhat similar vein, ?

finds substantial differences in realized living standards between urban and rural areas of develop-

ing countries. Average productivity differences could also emerge as a consequence of production

technologies, and they could also reflect selection pressures; e.g., high-skill individuals selecting into

non-agriculture. A similar suite of explanations might apply to ruban-rural comparisons.

Measurement issues represent a particular concern. Sectors and even locations are not easily de-

fined in the data. Individuals and households divide time and effort between agricultural and non-

agricultural activities, and neither outputs nor inputs are well observed at the sectoral level – particu-

larly in developing countries where large fractions of economic activity take place outside the formal

sector. This makes it difficult to be confident of cross-sector comparisons of gross output per unit labor

or value added per unit labor. Slightly different problems arise with comparisons of rural and urban

living standards. Here, categories such as ”urban” and ”rural” may reflect administrative classifica-

tions or population thresholds that make cross-country comparisons tricky. Given these measurement

issues, it might seem premature to focus on cross-sector or cross-region misallocation.

This paper offers new evidence on disparities in living standards within countries – and thus indirectly

on the potential extent of within-country misallocation. Our paper begins by documenting differences

in real living standards at different locations within developing countries. We measure a set of dis-

tinct and well-defined real measures of living standards, very much in the spirit of ? but at a much

richer level of geographic disaggregation. To do this, we draw on recent cross sections from the De-

mographic and Health Surveys (DHS) for a large set of developing countries. For each country we

locate each cluster of survey households using geographical coordinates. We combine these measures

with the WorldPop population density data, which are available at a fine spatial resolution (?). We

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end up with estimates of basic living standards measures and population density in a large number of

geographic regions, each spanning approximately 10 kilometers in diameter.

We show that these distinct measures of living standards vary within countries in consistent ways.

In particular, we find evidence of a strong relationship with population density. According to almost

all of our measures, living standards rise steadily and almost monotonically with population density1.

This is consistent with previous findings about disparities between urban and rural areas2.

This finding adds to puzzles over the allocative efficiency that we observe in the data. Because our

measures are real and readily observable, we argue that we avoid many of the measurement problems

associated with previous studies. Moreover, the consistency of the patterns with respect to population

density suggests that we are not simply observing selection pressures that operate in a simple way

across sectors or locations. For instance, the selection would have to operate within rural areas as

well as between rural and urban locations. We do not claim, however, that this paper can specifically

address the question of misallocation. Nothing in our data will allow us to conclude that misallocation

does or does not exist.

While there are many compelling reasons why migration from the most remote to the most dense

areas does not take place, our results suggest that improvements in basic living standards may be

available to individuals in our sample by moving to a marginally more densely populated location. In

contrast with dichotomous characterizations of the developing world, in which there are (for instance)

only rural and urban locations, we find that many households may be able to achieve distinctly higher

living standards by moving to locations with slightly higher levels of population density; e.g., from a

small town to a slighly larger town. The open question then becomes why we do not observe more

migration of this kind. Compensating differentials might make households in rural areas equally

well off, or cities might have limited absorptive capacity. We do not find evidence for observables

which are consistently worse in more densely populated places, and outcomes appear to be better

consistently for recent migrants currently residing in cities.

Our paper is most closely related to the innovative work of ??, who constructs summary measures

of living standards using the same DHS questions about real outcomes (e.g. electricity ownership)

that we do. Our work differs from his in three main ways. First, we incorporate far richer geographic

detail, allowing us to draw inferences not just at the urban-rural level, but across the entire spectrum

of density. Second, our work avoids the problems of urban-rural classifications that arise in the DHS

data. Most DHS studies follow national classifications of locations. These differ across countries,

making rural-urban comparisons more difficult. Related to this, in light of the rapid growth of many

1As we will discuss later in the paper, there is some evidence that certain variables turn down slightly at the highest

levels of population density, consistent with the presence of congestion effects.2It is also consistent with previous findings about differences between agriculture and non-agriculture, if agriculture is

assumed to take place in locations with lower population density; but this requires an additional assumption.

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African cities, administrative boundaries often have not been adjusted to reflect the actual sizes of

cities.

Our paper is also related to the work of ?, which constructs a comprehensive data set for 30 African

countries combining climate data, night lights data, census data, and multiple rounds of DHS data.

They find that climate affects urbanization, and use the DHS employment data to show that higher

levels of moisture draw women out of home employment into farm employment, and men from off-

farm employment into farm employment. To control for time-invariant unobservables at the level of

the cluster, they create superclusters by matching clusters to their geographically closest neighbor.

Our approach differs from theirs in that we focus on the household level data collected in the DHS for

one cross-section, and link these with population density data in the neighborhood of the cluster.

Our paper also builds on a recent paper by ?, that measures living standards at a small level of

geographic detail using satellite light data from outer space. One advantage of our living-standards

measures relative to theirs is that we have comparable basic living standards across countries, while

the mapping from nightlights into living standards is unlikely to be the same everywhere. Our measure

does not, however, cover every geographic region on the planet, but a sample of them.

This paper is structured as follows: section ?? outlines our data and how we link basic living standards

with population density estimates. Section ?? analyses our living standards data. Section ?? explores

explanations for our findings; section ?? concludes.

2. Measuring Living Standards by Population Density

2.1. Living Standards Measures

To measure household welfare across space, we use data from Demographic and Health Surveys.

These are high-quality nationally representative surveys designed to cover large numbers of house-

holds (typically more than 5,000) in developing countries. The surveys are designed to use consistent

methodology and definitions across countries. Their focus is on variables related to population, health,

and nutrition3.

From the set of available DHS surveys, we select all that satisfy two criteria: (i) the survey was

conducted no earlier than 2005, and (ii) GPS coordinates of the survey clusters were collected and are

available. For Sub-Saharan Africa this leaves us with a sample of 293,517 households and 233,019

births across 25 countries as listed in table ??. From these data, we use the following variables:

durables owned by households (television, car and mobile/landline telephone), housing conditions

(electricity, tap water, constructed floor, and flush toilet), as well as infant survival rates (probability

3The surveys are described in more detail at: http://www.dhsprogram.com.

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http://www.dhsprogram.com

that an infant reaches 12 months)4.

There are several ways to calculate infant mortality rates relying on vital registration statistics, demo-

graphic surveillance systems and houshold surveys. We use the complete fertility histories reported

in the DHS and follow the method suggested by (?). First, we calculate the hypothetical age for each

child by subtracting the date of birth from the date of the interview. We then replace this with the

age at death for children who died, and generate a dummy variable that is equal to one if the child

had died by the time of the survey. The infant mortality rate is then calculated using the command

ltable in Stata, with intervals of 6 months5. The variable is expressed as 1 - (mortality rate) to be

comparable to the US historical data.

While the DHS aim to make survey instruments and samples comparable across countries, the ex-

act sampling differs according to the particular survey. The target population of most DHS surveys

are women aged 15-49 and children under the age of five living in residential households with the

most common sampling following a two-stage cluster sampling procedure (?). If a recent census is

available, the sampling frame of the census is used to define primary sampling units which are usually

enumeration areas. Alternative sample frames include lists of electoral zones, estimated structures per

pixel derived from high-resolution satellite imagery or lists of administrative units. Clusters will then

be stratified depending on the number of domains that are desired for the particular survey, where a

typical stratification is first at the geographical level and then at rural/urban clusters. In the first stage,

from each of the strata a random sample of enumeration areas is selected inversely proportional to

size. Unless a reliable listing of households exists, households will be listed for each of the selected

primary sampling units. In the second stage, households are selected with equal probability.

2.2. Population Density Measures

To measure population density, we use data from WorldPop6, which provides population density

estimates at a resolution of 0.000833333 decimal degrees which corresponds to about 100m at the

equator. These estimates are derived either from land cover based methods or random forest regression

tree-based mapping7. Since our focus is on comparing distributions within countries, the fact that the

method used to estimate population density differs across countries does not affect our results8. The

unit of observation in the dataset is the estimated number of persons per grid square. We use the

4See section ?? in the appendix for the exact variable names.5Alternatively, we have also computed the mortality rate by dropping children younger than 12 months so that the base

are births that occurred 1-5 years before the survey. Across all these cohorts we then compute the percentage of children

who died before reaching 12 months. The pairwise correlation between these two infant mortality measures is 0.899 with

a p-value of 0.6http://www.worldpop.org.uk.7For details on the methodology see ?? and ?. Notes on the improved random forest regression tree-based mapping can

be found herehttp://www.worldpop.org.uk/resources/docs/WorldPop-Random-Forest-Mapping.pdf.8We also use data from the global map which is available at a 1km resolution and the results are very consistent.

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http://www.worldpop.org.uk

http://www.worldpop.org.uk/resources/docs/WorldPop-Random-Forest-Mapping.pdf

adjusted population estimates which match the rates reported in the UN population prospects (?).

2.3. Spatially linking Living Standards and Density

Ideally, we would take the location of DHS households or clusters and locate them precisely with

geo-coordinates. But for understandable reasons, the data cannot be located so precisely. To preserve

anonymity, the DHS follows a practice of reporting approximate GPS coordinates for sampled house-

holds. This protects the identities of the specific households and communities, while also making it

possible to identify the regional and locational characteristics of the clusters.

To be precise, the DHS clusters are re-assigned a specific GPS location that falls within a specified

distance of its actual location. Urban DHS clusters are randomly displaced by 0-2km and rural clusters

are randomly displaced by 0-5km, with 1 percent of clusters randomly selected to be displaced by

10km (?)9. We take into account the random offset of DHS cluster locations when linking DHS GPS

data with continuous raster data, by taking 5 km buffers around both urban and rural DHS clusters

as suggested by ?10. Figure ?? shows the circle of 5km radius around a selected cluster in Dar Es

Salaam11 12.

One concern with using our population density estimates is that nightlights data are used as inputs

to produce the population density data. It could be that the positive relationship between basic living

standards and population density that we observe in the data is driven by an underlying variable,

wealth, which affects night lights intensity and thus estimated population density. To check whether

this is the case we use data from Tanzania for which we have the enumeration area shapefile for the

2002 census. We compute the population density for each of the 1,842 enumeration areas, measured in

people per square kilometer. This is a measure of the maximum level of population dispersion in each

enumeration area, free of any auxiliary input data. We then extract the average population density

around clusters within a 5km radius as before. The correlation between the Worldpop population

density estimate and the population density from the census data is 0.93 with a p-value of 0.000.

Thus, we have reasons to believe that the population density measures are reasonably accurate. Al-

though we are dependent on the procedures and algorithms used to generate these data, we are satisfied

that the procedures are independent of the kinds of data used to generate our living standards mea-

sures. As a result, we feel confident in mapping and reporting living standards in DHS clusters in

9The displacement is done by selecting a random displacement angle between 1-360 degrees as well as a random

distance.10An alternative measure would be the distance to the closest city. The log of euclidian distance to the nearest CBD

and the log of density have a correlation of 0.79. The patterns are very similar when using different buffer sizes or using

the distance to the nearest CBD of a city instead on the density measure.11Given that buffers are overlapping in urban areas, and the zonal statistics tool does not perform well when extracting

statistics for overlapping polygons, we use the new Zonal Statistics 2 tool developed for ArcGIS 10.212We multiply the number of persons per grid square by 1000 and for expositional simplicity drop 194 observations for

which the log density is negative (36 households in Mali and 158 households in Namibia).

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relation to the estimated population density. For example, we can consider the case of electricity in

Tanzania as in figure ??, where we compare the relationship between electricity and population den-

sity when population density is measured directly from the census (figure on the left) as well as from

the WorldPop data (figure on the right). The figure reveals nearly identical patterns when comparing

population density data from the 2002 census with the WorldPop population density data, suggesting

that our procedure is accurately capturing the reality – at least for this one country x variable set of

observations.

3. Analysis of Living Standards Data

In this section, we analyze the living standards data in relation to population density. It goes without

saying that for many of the countries in our sample, living standards are very low compared to today’s

rich countries. But we focus in particular on the patterns and spatial distribution of living standards.

The consistency with which we see the same patterns across a large set of countries suggests that

there are underlying forces behind the spatial distribution of well-being. In particular, we will need

explanations for why large numbers of people live in areas of relatively low population density where

material living standards are low.

Note that we cannot observe and therefore do not account for differences in intangibles associated

with quality of life. Differences in such intangibles may very well compensate people who live in

areas of lower population density.

The measures that we do use have the advantage of being real and directly observable; i.e., they are

not based on prices or values, nor do they require imputation. As argued by ?, such measures have the

advantage that they admit direct comparison across time and space. There may of course be substantial

quality differences that are hidden by the variables that we consider. Thus, a ”constructed floor”

implies only that the floor is made of a finished material rather than dirt or a simple covering such a

tarpaulin or carpet. But within the category of ”constructed floors,” households could in principle have

anything from rough-hewn boards or bamboo slats to poured cement or even marble tile. Similarly

having access to tap water could mean a single cold-water tap, or it could mean a plethora of chrome-

plated faucets. The data do not allow us to distinguish variation within the categories reported in the

DHS data.

We begin by looking at basic descriptive statistics on how living standards vary across locations with

respect to population density, according to the DHS data.

3.1. Basic Descriptive Statistics of Population Density across Space

Our set of observations relates to the distribution of population by density. The DHS sample is

chosen to be representative at the second administrative level, as well as rural/urban within the second

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administrative level; unless the sampling frame is specifically selected to match the population along

the lines of population density, it is possible that the distribution of the DHS sample according to

population density might not match that of the entire population. In practice, the studies that we have

examined show very little effort to oversample or undersample with respect to population density.

For Tanzania we can compare the population density distribution of the DHS clusters with those of

the overall population from the census data where we weight the population density of enumeration

areas by the population. As is evident from figure ??, the DHS appears to do capture a representative

sample of the population with respect to density.

As one might expect, population density distributions vary considerably across the countries in our

sample. Some are heavily urbanized, while others are more rural as illustrated in figure ??. Quite

a few countries display bimodal distributions of population density. For instance, Uganda and Zim-

babwe have pronounced bimodal distributions, with both showing spikes in population in what are

presumably dense urban areas, to go along with substantial numbers of people living in rural areas.

Looking within rural areas, some countries show relatively narrow distributions of population den-

sity; for instance, Rwanda and Burundi both have very narrow supports for the density distribution.

Others (e.g., Cameroon, Ethiopia, Gabon, and Ivory Coast, for example) have very flat distributions

suggesting that even in rural areas, population density varies substantially.

The next point worth noting is that the population density measure provides a different – and we

will argue, more useful – way of thinking about the spatial distribution of the population than does

the typical urban-rural dichotomy, which is largely based on administrative classifications. Figure

?? shows population density distributions for those DHS households that are classified as urban and

rural based on the administrative designation of each cluster. This figure shows that in many countries

there are people living in ”urban” locations with quite low population density, while there are at

least a few households classified as rural that occupy quite dense locations13. The data show that

some countries have quite distinct distributions of urban and rural population densities, as one might

expect. This is true, for instance, in Burkina Faso and the Congo (DRC), as well as in Liberia, Mali,

Zambia and Zimbabwe. Other countries have urban and rural population density distributions that

overlap considerably; e.g., Guinea, Kenya, and Malawi. The key point to make is that the frequently

used urban-rural characterization offers a different view from the density-based measures that we

use here. We do not argue that the urban-rural classification is flawed; for some purposes, we may

care precisely about the administrative designation, which may dictate access to public services and

resources. But for other purposes where location in space matters – e.g., agglomeration externalities,

market thickness, and transaction costs – the density-based measures are likely to be more informative.

13We must exercise some caution here; because of the displacement of DHS clusters in the data, it is conceivable that

some of these apparent discrepancies are linked to the displacement procedure, which might move change the measured

density in our data. But quantitatively speaking, this does not appear to be a strong enough effect to account for much of

the overlap in population density between urban and rural locations.

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Another advantage of the density measures is that they allow us to differentiate rural locations. Par-

ticularly in countries where large fractions of the population remain rural, it is useful to consider the

density heterogeneity within rural areas. Small towns and densely populated rural areas – which are

usually in proximity to markets, as noted above – may have quite different characteristics than remote

rural areas14. We see in the data that the rural population densities vary enormously in many of the

DHS countries. In this paper, we will argue that the variation in densities is important and has im-

plications for the types and magnitude of frictions that could perhaps prevent people from moving to

areas with higher average living standards. We will return to this issue at the close of the paper.

3.2. Basic Descriptive Statistics of Living Standards across Space

Next we turn to descriptions of the living standards distributions across space. In this section, we

will make three claims with respect to the data. First, we argue that there is enormous variation

within countries in the level of each indicator. Different DHS clusters display strikingly different

levels of these indicators. Second, there is a strong spatial pattern with respect to living standards.

Specifically, in almost all cases, living standards seem to improve with population density – although

at very high levels of density, some indicators indicate a slight decline – though never to the level of

the most remote areas. Third, the spatial pattern of variation is typically quite continuous and nearly

monotonic with respect to population density – at least until the highest levels of density. Admittedly,

to some extent the smoothness reflects the fact that we use local polynomial approximations to smooth

the data. But the raw data are nearly as smooth.

As an example, we consider a single indicator – electrification – in a set of four countries. Figure

?? shows a local polynomial smooth of whether a household has electricity and the log of density

including a 95% confidence interval for Tanzania, Nigeria, Ethiopia and Senegal. Several facts are

worth noticing. First, there is a large dispersion in electricity – one of our real indicators of living

standards – from the least populated areas to the most populated areas, with a support from zero

to one. Second, across the whole range of densities, basic living standards are increasing almost

monotonically and continuously. Third, this relationship is not driven by wealthy households in cities.

If this were the case we would expect large variances in cities and confidence intervals are in fact not

getting larger in the most densely populated areas. To see that the data are not driven simply by

inequality within clusters, we look at the distribution of electricity in relation to population density

for the subset of households for which the household head has varying levels of education. For this

population, as well as for the total population, electricity increases with population density deciles

(see figure ??). Although the gradient is slightly flatter for less educated households than for the

population as a whole, the positive relationship between outcomes and population density holds for

14For instance, ? find that rural locations that are ex ante identical will take on different characteristics ex post based

only on transport costs.

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all three categories of households15.

Leaving aside the particular case of electricity in Tanzania, we turn to the broader data. The same

indicator – household electricity – can be reproduced for each of the countries in our sample. These

data are shown in Figure ??. The individual country graphs are plotted on the same scale. These

graphs show substantial variation across countries in the levels of electrification attained. Some coun-

tries, such as Ivory Coast, have quite high levels of electrification. Others, such as Liberia, have

strikingly low levels. Almost all countries show substantial increases in electrification as population

density rises. A number of countries show slight dips in electrification levels at the extreme high end

of the density distribution; e.g., Lesotho, Namibia, Tanzania, Zimbabwe. In a few countries (Ghana,

Swaziland, Uganda, Zambia, and Zimbabwe), there appear to be slightly higher levels of electrifica-

tion at the least dense areas. This may be a real observation – but it is worth recalling that very few

individuals live in these areas of extremely low population density, making the estimates somewhat

imprecise and subject to outliers.

From electricity, we can move on to other indicators. We can construct similar figures for the entire

set of indicators that we consider. These are shown in the appendix in sections ?? to ??.

3.3. Understanding Differences in Living Standards across Space

Moeover, we can construct the comparisons between households that have heads with less than com-

plete primary education and those with heads that have completed primary education. Some of these

are shown in figures ?? to ??.

4. Why are not more people moving across space?

The descriptive statistics of outcomes across population densities in section ?? suggest that basic

living standards gradually increase across population density. While it was not possible for people in

the 1900s to travel across time towards better living standards, our results beg the question why not

more people move to more densely populated areas and enjoy higher basic living standards. Even

small movements to more densely populated locations would seem to be desirable. The aim of this

section is not to give an answer to this question; rather, we provide some suggestive evidence on how

plausible two explanations seem to be: compensating differentials and limited absorptive capacities

of cities.

15Section ?? in the appendix shows these graphs also for whether the household has a phone, tap water, a constructed

floor, flush toilet, and a bank account.

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4.1. Compensating differentials

One reason individuals might not be willing to move to cities are compensating differentials in rural

areas. We have explored a range of potential variabels, including child mortality and the number

of rooms per person above 5 years as a measure for living space. Child mortality remains roughly

constant across the density distribution and the number of rooms per person above 5 years is relatively

constant, although there is a negative density gradient for some countries here. Overall, at least in

terms of observables, it is difficult find to dimensions of living standards that worsen in cities.

4.2. Limited absorptive capacity of cities

A second conjecture why individuals might prefer to remain in rural areas is that their living standards

drop sharply for some time when they initially arrive in a city due to limited absorptive capacity of

cities. We do not have exact migration histories of the individuals in the dataset. The fact that we

do not know where they migrated from precludes us from using, for example, climate variation as an

instrument for their migration decision. Our results are therefore correlations and we do not make

claims about causation. For surveys in which migration questions were asked, we use the individual

level data and define a rural-urban migrant as an individual who currently lives in an urban area, has

done so for no more than 10 years, and lived in the countryside before. We then link the individual

level data with the household data to get outcomes and compare rural-urban migrants to individuals

who currently reside in rural areas, as well as the whole sample. For some countries there are few

recent rural-urban migrants so sometimes we are not able to estimate the relationship with population

density precisely for this sub-group. As we are interested in outcomes for recent urban migrants,

this differs from ? who defines an rural-urban migrant as an individual whose childhood residence is

”countryside” and whose current residence is ”town”, ”city” or ”capital”. The results in figure ?? do

not suggest that those individuals who are recent migrants in a city have suffered from severe drops

in living standards compared to non-migrants when it comes to access to electricity. Section ?? in the

appendix shows the same graphs for further outcomes. Overall, the figures suggest that rural-urban

migrants often but not always face slightly lower living conditions than urban non-migrants. However,

they have substantially higher living conditions than rural people, even allowing for variance.

5. Conclusion

[TO COME]

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Figure 1: DHS clusters in Dar Es Salaam

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Figure 2: Population data from 2002 Census and Worldpop in Tanzania0

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Figure 3: Distribution of population and DHS clusters in Tanzania0

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1.5

0.5

11.

5

0 2 4 6 8 10

0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10

Albania Bolivia BurkinaFaso Burundi Cambodia Cameroon

DRC DominicanRepublic Egypt Ethiopia Gabon Ghana

Guinea Guyana Haiti Honduras IvoryCoast Jordan

Kenya KyrgyzRepublic Lesotho Liberia Madagascar Malawi

Mali Moldova Mozambique Namibia Nigeria Peru

Philippines Rwanda Senegal SierraLeone Swaziland Tajikistan

Tanzania TimorLeste Uganda Zambia Zimbabwe

kden

sity

Log of population densityGraphs by country

16

Figure 5: Urban/Rural classifications

0.5

11.

50

.51

1.5

0.5

11.

50

.51

1.5

0.5

11.

50

.51

1.5

0.5

11.

5

0 2 4 6 8 10

0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10








Urban Rural

kden

sity

logd

ens_

gpw

v4

Log of population density

Graphs by country

17

Figure 6: Living Standards Across Space - Electricity

LBRMWI

GIN

LSO ZMBTZABFA

SWZ

SLEBDIRWAMLI

ZWE

UGAKENMDGDRC

NAM

GHASENNGA

CMR

COT

GAB

ETH

0.2

.4.6

.81

Bot

tom

Dec

ile

0 .2 .4 .6 .8 1Top Decile

electricity

18

Figure 7: Electricity by education−

.50

.51

0 2 4 6 8 10Log of population density

Complete Secondary or MoreComplete Primary and Incomplete SecondaryNo or incomplete primary

electricity

0.2

.4.6

.81



electricity

0.5

11.

5



electricity

0.2

.4.6

.81



electricity

19

Figure 8: Electricity0

.51

0.5

10

.51

0.5

10

.51

0.5

10

.51

0 2 4 6 8 10 12 0 2 4 6 8 10 12

0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12



Guinea Guyana Haiti IvoryCoast Jordan Kenya

KyrgyzRepublic Lesotho Liberia Madagascar Malawi Mali

Moldova Mozambique Namibia Nigeria Peru Philippines

Rwanda Senegal SierraLeone Swaziland Tajikistan Tanzania

TimorLeste Uganda Zambia Zimbabwe

Electricity


20

Figure 9: Phone−

.50

.51

−.5

0.5

1−

.50

.51

−.5

0.5

1−

.50

.51

0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

BurkinaFaso Burundi Cameroon DRC Ethiopia

Gabon Ghana Guinea IvoryCoast Kenya

Lesotho Liberia Madagascar Malawi Mali

Namibia Nigeria Rwanda Senegal SierraLeone

Swaziland Tanzania Uganda Zambia Zimbabwe

95% CI lpoly smooth: (mean) phone

lpoly smoothing grid

Graphs by country

21

Figure 10: Tap water0

.51

0.5

10

.51

0.5

10

.51

0.5

10

.51

0 2 4 6 8 10 12 0 2 4 6 8 10 12

0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

Albania Bolivia BurkinaFaso Burundi Cameroon DRC

DominicanRepublic Egypt Ethiopia Gabon Ghana Guinea

Guyana Haiti Honduras IvoryCoast Jordan Kenya

KyrgyzRepublic Lesotho Liberia Madagascar Malawi Mali

Moldova Mozambique Namibia Nigeria Peru Philippines

Rwanda Senegal SierraLeone Swaziland Tajikistan Tanzania

TimorLeste Uganda Zambia Zimbabwe

Tap Water


Graphs by country

22

Figure 11: Constructed Floor0

.51

1.5

0.5

11.

50

.51

1.5

0.5

11.

50

.51

1.5

0.5

11.

50

.51

1.5

0 2 4 6 8 10 12

0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12








Constructed Floor


Graphs by country

23

Figure 12: Bank account0

.51

0.5

10

.51

0.5

10

.51

0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0 2 4 6 8 10 12

BurkinaFaso Burundi Cameroon Ethiopia Gabon

Ghana Guinea IvoryCoast Lesotho Liberia

Madagascar Malawi Namibia Nigeria Rwanda

Senegal SierraLeone Swaziland Tanzania Zambia

Zimbabwe

95% CI lpoly smooth: =1 if hh member owns bank account


Graphs by country

24

Figure 13: Electricity by migration.2

.4.6

.81

elec

tric

ity


Rural−Urban Migrant All

electricity

0.2

.4.6

.81

elec

tric

ity



electricity

0.2

.4.6

.8el

ectr

icity



electricity

0.2

.4.6

elec

tric

ity

2 4 6 8Log of population density


electricity

0.2

.4.6

.81

elec

tric

ity



electricity

25

Appendix

A. Additional Tables and Figures

Table A.1: Sample of countries

Country Year Households WorldPop Mapping Approach

Burkina Faso 2010 14,424 Land cover based

Burundi 2010 4,866 Land cover based

Cameroon 2011 14,214 Land cover based

Congo, Dem. Rep. 2007 8,886 Land cover based

Cote d’Ivoire 2011-2012 9,686 Land cover based

Ethiopia 2011 16,702 Land cover based

Gabon 2012 9,755 Land cover based

Ghana 2008 11,778 Land cover based

Guinea 2005 6,282 Land cover based

Kenya 2008-09 9,057 Random Forest

Lesotho 2009 9,391 Land cover based

Liberia 2007 4,162 Land cover based

Madagascar 2008-09 17,857 Land cover based

Malawi 2010 24,825 Random Forest

Mali 2006 12,998 Land cover based

Namibia 2006-07 9,200 Land cover based

Nigeria 2008 34,070 Land cover based

Rwanda 2010 12,540 Random Forest

Senegal 2010-11 7,902 Land cover based

Sierra Leone 2008 7,284 Land cover based

Swaziland 2006-07 4,843 Land cover based

Tanzania 2010 9,623 Random Forest

Uganda 2011 9,033 Random Forest

Zambia 2007 7,164 Land cover based

Zimbabwe 2010-11 9,756 Land cover based

26

B. Variables used from DHS data

The main variables we use are:

• Durables: television (hv208), car (hv221), mobile or landline (hv221 and hv243a)16.

• Housing conditions: electricity (hv206=1), tapped water (hv201¡21), constructed floor (hv213

is not earth, sand, dung), flush toilet (hv205¡20), number of rooms (hv216).

• Any member of hh has a bank account (hv247).

• Child health: mortality rate (v008,b3,b7,b5);

• Migrant: hv103, hv104, hv10517.

16For Liberia there is no data on whether the household has a landline17For the women dataset in Liberia none of the women have countryside as childhood residence.

27

C. Education

3.1. Ethiopia

0.2

.4.6

.81



phone

−.5

0.5

1



electricity

0.2

.4.6

.81



watert

0.2

.4.6

.81



floorc

0.1

.2.3

.4



toiletf

0.2

.4.6

.8



bank

28

3.2. Senegal.6

.81

4 6 8 10 12

Senegal



Graphs by country

0.5

11.

5



electricity

0.5

11.

5



watert

0.5

11.

5



floorc

0.5

11.

5



toiletf

0.5

11.

5

4 6 8 10 12

Senegal



Graphs by country

29

3.3. Nigeria0

.51

4 6 8 10 12

Nigeriaphone



Graphs by country

0.2

.4.6

.81



electricity

0.1

.2.3

.4



watert

0.2

.4.6

.81



floorc

0.2

.4.6

.8



toiletf

0.2

.4.6

.8

4 6 8 10 12

Nigeriabank



Graphs by country

30

3.4. Tanzania0

.2.4

.6.8

1



phone

0.2

.4.6

.81



electricity

0.2

.4.6

.81



watert

0.2

.4.6

.81



floorc

0.2

.4.6

.8



toiletf

0.2

.4.6

.81



bank

31

D. Migration

4.1. Ghana

.2.4

.6.8

1ph

one


Rural−Urban Migrant RuralAll

phone

.2.4

.6.8

1el

ectr

icity



electricity

0.2

.4.6

.81

wat

ert



watert.6

.7.8

.91

floor

c



floorc

0.1

.2.3

.4.5

toile

tf



toiletf

0.2

.4.6

.8ba

nk



bank

32

4.2. DRC0

.2.4

.6.8

0 5 10

DRC


phon

e


Graphs by country

0.2

.4.6

.81

elec

tric

ity



electricity

0.2

.4.6

.81

wat

ert



watert

0.2

.4.6

.81

floor

c



floorc

0.1

.2.3

.4to

iletf



toiletf

33

4.3. Sierra Leone0

.51

4 6 8 10 12

SierraLeone


phon

e


Graphs by country

0.2

.4.6

.8el

ectr

icity



electricity

0.2

.4.6

.81

wat

ert



watert

0.2

.4.6

.81

floor

c



floorc

0.1

.2.3

toile

tf



toiletf

0.2

.4.6

4 6 8 10 12

SierraLeone


bank


Graphs by country

34

4.4. Malawi.2

.4.6

.81

phon

e


Rural−Urban Migrant Rural All

0.2

.4.6

elec

tric

ity



electricity

0.2

.4.6

.81

wat

ert



watert

0.2

.4.6

.81

floor

c



floorc

0.0

5.1

.15

.2to

iletf



toiletf

0.2

.4.6

bank


Rural−Urban Migrant Rural All

35

4.5. Nigeria0

.51

4 6 8 10 12

Nigeria


phon

e


Graphs by country

0.2

.4.6

.81

elec

tric

ity



electricity

0.1

.2.3

.4w

ater

t



watert

.2.4

.6.8

1flo

orc



floorc

0.2

.4.6

.8to

iletf



toiletf

0.2

.4.6

.8

4 6 8 10 12

Nigeria


bank


Graphs by country

36

measuring living standards across space in the developing world · measuring living standards...

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