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Mobile phone coverage and infant mortality in sub-Saharan Africa

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Infant mortality is still high in Sub-Saharan Africa. Mobile phone technology has the potential to reduce mortality by facilitating the exchange of information. Matthias Flückiger’s research suggests infant mortality risk drops substantially as mobile phone coverage expands and fertility rates decline with the rollout of mobile coverage.

Image courtesy of interviewee. May 28, 2024

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The study investigates the impact of mobile phone coverage on infant mortality rates in Sub-Saharan Africa, an area characterized by high infant mortality and limited access to health services. It aims to explore whether increased availability of mobile phones can lead to better health outcomes by improving information accessibility for parents regarding health treatments and vaccines.

The research focuses on data collected between 1999 and 2016, during a period of significant mobile coverage rollout—from less than 10% to nearly 90% access. The study employs a longitudinal approach, comparing birth outcomes in specific locations over time as mobile phone coverage changes, allowing for a nuanced analysis of causation rather than mere correlation.

The authors draw on multiple strands of literature, noting evidence that mobile technology can improve incomes and productivity, as well as facilitate access to health services. They employ methods that allow them to control for various confounding factors, enabling them to isolate the effect of mobile coverage on infant mortality.

Their findings indicate that improved mobile access is associated with a reduction in infant mortality rates by approximately 0.9 percentage points, which is significant given the average mortality rate of around 7%. This video video effect is particularly pronounced in rural areas where access to healthcare is limited.

The researchers find no substantial link between mobile access and economic improvements or increased clinic visits, but suggest that mobile phones enhance health awareness. They propose that better-informed individuals may engage in more effective child care practices, contributing to lower mortality.

The study concludes that mobile phones represent a cost-effective intervention to reduce infant mortality. It recommends further exploration of how mobile technology can be leveraged for health education and access, particularly as newer mobile technologies like 3G become more prevalent. The authors highlight the need for better data to fully understand the mechanisms through which mobile coverage impacts health outcomes.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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Basically what we're trying to do in this study is trying

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to understand whether or not the rollout

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of mobile phone coverage, so the availability of,

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mobile phones has an influence on infant mortality.

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And basically the context of the study is

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that we're looking at Sub-Saharan Africa,

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so relatively poor area that has relatively speaking,

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poor access to health services.

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And this is also reflected in high infant mortality rates.

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And if you read policy reports, there's sort a high hope

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that with mobile phones becoming available

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that could improve health outcomes specifically

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through the information channels.

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So you can think of either this being sort

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of a push factor from local governments trying

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to make people aware of different treatments, different

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vaccine programs that are available

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or reminding individuals of that they should be taken up.

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Um, health appointments or you can also think of this

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as a sort of a demand pull factor where individuals,

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so parents specifically that have a child

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that has fallen ill, can draw on help

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by either texting, calling someone.

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So this could be either calling a relative that might,

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know something about treatments or just clinic specifically.

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So there's at least theoretically there is quite a bit of

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potential to improve health outcomes,

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via mobile phone technologies.

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The data that we exploit is that we are very specifically,

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we're looking at the timeframe from 19 9 9 to 2016.

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And this coincides with a massive rollout

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of mobile phone coverage within subs Africa.

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So basically I think at the first year it's less than 10%

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of the individuals actually have

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access to any type of coverage.

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And then by the end it's almost 90%.

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So what we can do is we can basically compare individuals

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or birth outcomes of individuals that live in

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specific area over time

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and link that to changes in, mobile phone coverage

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because there's a lot of variation.

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So you have almost all the locations we observe at least in

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one point in time when they don't have mobile phone coverage

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and then later on when they have mobile phone coverage.

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So in that sense it's a different indifferent type of,

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of setup that we exploit comparing same location

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with itself over time.

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Um, and then compare,

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comparing birth outcomes depending on whether

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or not this location has, mobile phone coverage or not.

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The most relevant for our paper,

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or for our project,

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are two sort of branches of literature.

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One is that the economic economics literature

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and there's quite a lot of evidence, empirical evidence,

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sound empirical evidence that, documents

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that mobile phone technology can improve income.

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So for example, you can think of, I dunno, farmers

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or fishing, fishes deciding on

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where they sell their product

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and they can choose the market that has,

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is offering them the highest prices.

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Or there's also evidence that,

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it can increase productivity of firms simply

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by just allowing

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Them to create better networks

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and improve their supply chains could also be very directly

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right if you now have mobile phone technology,

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you need people who sell sim cards

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who build up the infrastructure.

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So, so there's quite a bit of evidence that shows

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that this can create employment opportunities,

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also improve income.

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And at the same time what it will also be sort of important

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for our study is that typically you think

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of mobile phones offering access to insurance markets.

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So this can be either banking or directly health insurance

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and then you can get some of the smoothing of,

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of income shocks, which can then later on translate into,

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differences in in infant mortality.

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So that's of the economics litre.

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Um, and then there is a growing

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sort of branch of literature in health sciences

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that look at the effect much more specifically of

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mobile technology assisted, projects on health outcomes

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and particularly also, some evidence on,

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infant mortality.

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So the, not to drawback,

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but just most of these studies are case studies.

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They look at one specific area, one specific point in time,

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and they look at one specific intervention.

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So you can think of this as a local NGO

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or government deciding to send out personalized,

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health related messages

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or you can think of a local government,

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equipping the healthcare workers

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with mobile phones when they, make house calls.

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They can, if they encounter any problems

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or things that are uncertain, they can call, the center.

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So these kinds of things are the very,

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very specific in the nature.

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And basically what I think where we sit in is

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we look at this from a, a contributing

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to the economic, which we're looking at another

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important outcome.

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So looking at,

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infant mortality rather than let's say income.

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And then we're looking at this, at a large scale.

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So for whole subcontinent basically,

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and we think we get somewhere close to

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causal estimates because there are studies that

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very truly just link regional infant mortality rates to

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mobile phone penetration rates.

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And there you obviously have,

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quite a big risk of conflating things.

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So particularly I think most natural thing natural worry is

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that you're comparing rich

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and poor countries where in rich areas you have a lot

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of people using mobile phones,

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but probably also

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for mortality will be lower compared to poor areas.

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Okay. Just to remind, what we want to do is we want

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to analyze, whether or not we want to test whether

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or not access to mobile phone technologies reduce

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or increase or whatever happens to, to,

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infant mortality.

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And then we need two pieces of information,

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pre crucial pieces of information.

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We need to know what happens to infant mortality rate over

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time in a given location.

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And then also we need to know whether

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or not this location has mobile phone coverage or not.

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And what we can do is we can draw on

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actually quite brilliant service here.

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These are, they're called demographic health service.

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So they have been going for, I think for decades now

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and they're funded by USAD

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and other international organizations.

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And what is really good about them is

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that they're nationally and most

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of the time even sub nationally representative.

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And the primary target of these

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serb are females aged, I think 15 to 49.

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And the crucial bit that is also elicited

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during these interviews is the birth history of women.

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So what that means is that for each woman interviewed,

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we know when she has given

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or if she has given birth to child and whether

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or not this child has survived, beyond the age of one.

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So we know, and the other important point is that we know

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where this individual was interviewed.

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So we basically could, can tie down births

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to locations and years.

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So for each location, let's say it's a village,

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we have a multitude of observations across multiple years

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and we know whether or not this, infant had died after

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or before the, the, the first year after birth.

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And then to that locational information, we can,

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add information whether

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or not this location actually had mobile phone coverage

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during a specific year.

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And this data comes from, I think a lot

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of organizations from, of, of mobile network operators.

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And this is, spatially highly disaggregated.

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So we can really sort of pinpoint

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or answer the question,

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did this location have mobile

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phone coverage in a given year?

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Yes or no, basically.

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And what this allows us to do is basically account for a lot

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of confounding factors that I was talking about before.

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So specifically what allow, what it allows us to do is

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to only compare within one given village.

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So we can ask the question, did a child

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or was the risk of a child

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being born in a different location was a difference

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depending on whether that location had access

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to mobile phone coverage.

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So technically speaking what we're doing very simple terms,

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we are using OLS or linear probability model

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and the depend variable is just an indicator

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that the child died before the age of one, yes or no.

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And then our main explanatory factor is gonna be did this

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location where that child was born,

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did the location have access

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to mobile phone coverage in that year?

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Yes or no? And then we can control

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for location fixed effects.

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So that means that we're just comparing birth outcomes

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within the same location over time.

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And we can actually go a bit beyond that

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because for most of the women in that sample,

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we observe multiple births.

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So we can actually compare the likelihood of

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children dying just amongst siblings.

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So, and there we can really sort get rid of,

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of potential worries

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that you might be comparing different households,

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rich and poor households.

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So it's really just exploiting the fact that

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one sibling was born during a period

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where the village didn't have mobile phone access

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and the other one afterwards.

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So that's sort of, what we can do.

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Yeah, I mean the, the sort of the headline finding is

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that there's quite a strong negative effect,

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which is a good thing in this case.

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So what we see specifically is that,

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getting access to mobile phone

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signals reduces infant mortality rates by around,

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I think it was 0.9 percentage points, which might sound,

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not particularly impressive.

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But if we compare it to the average mortality rate, which is

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around 7%, then 0.9 percentage points

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makes up about 10% of that effect.

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So that's quite substantial.

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And I think the, what we also see is

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that which intuitive will make sense is that

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pretty much this whole effect is driven by rural areas

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where you would expect naturally I guess is

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that this positive effect of, of mobile phones is great

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because in urban areas you have access to,

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health clinics for example,

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or you, it's much easier to get into contact with

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with individuals and get some advice off of them.

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I think the second important thing for us that sort

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of points to towards our ability to

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identify causal effects of getting somewhere near to that is

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that we don't see any relationship to sort

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of village level GDP changes

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because one natural worry for us was the might the rollout

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of, of mobile phone coverage might just go along with normal

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economic development and then we will be conflating things.

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And what we also see is that there is no correlation with,

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with other, infrastructure projects.

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So for example, it's unrelated to, likelihood

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that you get, health clinics or electricity.

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So that, that's quite reassuring.

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So that's sort from a canal's point of view that's sort

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of the major result.

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And then obviously from a sort of policy implication point

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of view, it is super important to know where,

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where this effect is coming from.

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So, and there, just caveat out

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before I start my whole spiel here is that,

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the data is not particularly good.

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So, so everything I wanna say now is basically just

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suggested evidence, but

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going into this project we had sort of two types

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of mechanisms in mind.

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The one is sort of a straightforward economic one.

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So I was mentioning before, so you, you could imagine that

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mobile phone coverage creates new income opportunities

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because people are now wealthier or better off.

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That implies for example, that nutrition improves

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for individuals and that reduces income mortality

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or you can get access to risk moving instruments like,

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like insurance or banking.

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And for both of these aspects we don't find any evidence.

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So that was a bit, surprising for us

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and again, might be, due to data

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or might just be that in our sample this

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is simply not the case.

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And then we,

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looked at some more health specific, mechanisms.

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And one natural expectation that I had was

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that maybe people just use healthcare services more often

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can call for health or they're reminded

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that they should be taken up appointments.

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But again here we don't see any evidence.

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So for example, mobile phone coverage

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or getting access to that coverage isn't related to

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higher probability of people, going to clinics

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or seeing a doctor before or after birth.

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So there doesn't seem to be any, effect,

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at least we do not observe it.

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Where we see some evidence is that getting access

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to mobile phone coverage seems to be related to,

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an improved of health awareness in the sense that

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people are now more informed about health related issues

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like STIs and how to treat them, how

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to intimate, what the risks are.

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Um, unfortunately what we don't have

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is really precise information or questions about, infant

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or child related health aspects.

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But so of we can partially at least infer that

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information is one of the chance for which we,

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to which these main effects work so

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that individuals are better informed about how to, to care

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for children in either just preventative terms or,

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or if they have fallen or what to do.

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We see the strong effect

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and the implications if we were to take these,

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at face values.

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I guess one implication would be that

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mobile phones is a relatively cheap way of reducing,

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infant mortality rates.

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And also the evidence suggests that

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because information seems to be at least one

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of the drive driving mechanisms that

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governments could potentially achieve a big reduction if

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they were to, use this.

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So for example, creating information campaigns

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or setting up, centers

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where individuals could call if they needed help.

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Um, as I said, I think that's

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also the mechanism part is also one area where,

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from my point of view we need more work just understanding

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better exactly where this is,

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where this effect is, is coming from.

14:36.405 --> 14:38.385
Um, so that might be possible with,

14:38.415 --> 14:39.745
with better data in the future.

14:40.365 --> 14:42.305
And the other important thing that I haven't mentioned yet

14:42.405 --> 14:44.665
so far is that basically the results

14:45.365 --> 14:48.505
or our results, they document the effects of

14:49.125 --> 14:50.825
two G networks. So you

14:50.825 --> 14:53.485
Can really think of this as calling or texting

14:54.355 --> 14:58.525
because at up until the end of our sample period, that was

14:59.745 --> 15:03.285
by far the most popular or most available technology and

15:03.525 --> 15:05.725
although I think only South Africa had 3G at the end

15:05.725 --> 15:07.485
of a period, but now obviously this has become much

15:07.485 --> 15:08.765
more widely available.

15:08.865 --> 15:11.245
So basically you can use your phones to access the internet

15:11.245 --> 15:12.965
and this could potentially,

15:13.195 --> 15:15.205
have an add-on effect I would expect.

15:15.265 --> 15:16.605
So, so understanding and,

15:16.605 --> 15:19.205
and looking at this would be something that,

15:19.205 --> 15:20.765
would be really interesting for the future.

Mobile Phones and Infant Mortality in Africa

00:00:00 - Basically what we're trying to do in this study is trying to understand whether or not the rollout of mobile phone coverage, so the availability of, mobile phones has an influence on infant mortality. And basically the context of the study is that we're looking at Sub-Saharan Africa, so relatively poor area that has relatively speaking, poor access to health services. And this is also reflected in high infant mortality rates. And if you read policy reports, there's sort a high hope that with mobile phones becoming available that could improve health outcomes specifically
00:00:36 - So you can think of either this being sort of a push factor from local governments trying to make people aware of different treatments, different vaccine programs that are available or reminding individuals of that they should be taken up. health appointments or you can also think of this as a sort of a demand pull factor where individuals, so parents specifically that have a child that has fallen ill, can draw on help by either texting, calling someone.
00:01:08 - know something about treatments or just clinic specifically. So there's at least theoretically there is quite a bit of potential to improve health outcomes, via mobile phone technologies. The data that we exploit is that we are very specifically, we're looking at the timeframe from 19 9 9 to 2016. And this coincides with a massive rollout of mobile phone coverage within subs Africa. So basically I think at the first year it's less than 10%
00:01:39 - access to any type of coverage. And then by the end it's almost 90%. So what we can do is we can basically compare individuals or birth outcomes of individuals that live in specific area over time and link that to changes in, mobile phone coverage because there's a lot of variation. So you have almost all the locations we observe at least in one point in time when they don't have mobile phone coverage and then later on when they have mobile phone coverage. So in that sense it's a different indifferent type of,
00:02:12 - with itself over time. and then compare, comparing birth outcomes depending on whether or not this location has, mobile phone coverage or not. The most relevant for our paper, or for our project, are two sort of branches of literature. One is that the economic economics literature and there's quite a lot of evidence, empirical evidence, sound empirical evidence that, documents that mobile phone technology can improve income.
00:02:42 - or fishing, fishes deciding on where they sell their product and they can choose the market that has, is offering them the highest prices. Or there's also evidence that, it can increase productivity of firms simply by just allowing Them to create better networks and improve their supply chains could also be very directly right if you now have mobile phone technology, you need people who sell sim cards who build up the infrastructure. So, so there's quite a bit of evidence that shows

Impact of Mobile Phones on Infant Mortality

00:03:14 - also improve income. And at the same time what it will also be sort of important for our study is that typically you think of mobile phones offering access to insurance markets. So this can be either banking or directly health insurance and then you can get some of the smoothing of, of income shocks, which can then later on translate into, differences in in infant mortality. So that's of the economics litre. and then there is a growing
00:03:49 - that look at the effect much more specifically of mobile technology assisted, projects on health outcomes and particularly also, some evidence on, infant mortality. So the, not to drawback, but just most of these studies are case studies. They look at one specific area, one specific point in time, and they look at one specific intervention. So you can think of this as a local NGO or government deciding to send out personalized,
00:04:21 - or you can think of a local government, equipping the healthcare workers with mobile phones when they, make house calls. They can, if they encounter any problems or things that are uncertain, they can call, the center. So these kinds of things are the very, very specific in the nature. And basically what I think where we sit in is we look at this from a, a contributing to the economic, which we're looking at another important outcome.
00:04:52 - infant mortality rather than let's say income. And then we're looking at this, at a large scale. So for whole subcontinent basically, and we think we get somewhere close to causal estimates because there are studies that very truly just link regional infant mortality rates to mobile phone penetration rates. And there you obviously have, quite a big risk of conflating things.
00:05:25 - that you're comparing rich and poor countries where in rich areas you have a lot of people using mobile phones, but probably also for mortality will be lower compared to poor areas. Okay. Just to remind, what we want to do is we want to analyze, whether or not we want to test whether or not access to mobile phone technologies reduce or increase or whatever happens to, to, infant mortality. And then we need two pieces of information,
00:05:54 - We need to know what happens to infant mortality rate over time in a given location. And then also we need to know whether or not this location has mobile phone coverage or not. And what we can do is we can draw on actually quite brilliant service here. These are, they're called demographic health service. So they have been going for, I think for decades now and they're funded by USAD and other international organizations. And what is really good about them is that they're nationally and most of the time even sub nationally representative.

Analyzing Birth Outcomes and Mobile Coverage

00:06:28 - serb are females aged, I think 15 to 49. And the crucial bit that is also elicited during these interviews is the birth history of women. So what that means is that for each woman interviewed, we know when she has given or if she has given birth to child and whether or not this child has survived, beyond the age of one. So we know, and the other important point is that we know
00:06:57 - So we basically could, can tie down births to locations and years. So for each location, let's say it's a village, we have a multitude of observations across multiple years and we know whether or not this, infant had died after or before the, the, the first year after birth. And then to that locational information, we can, add information whether or not this location actually had mobile phone coverage
00:07:27 - And this data comes from, I think a lot of organizations from, of, of mobile network operators. And this is, spatially highly disaggregated. So we can really sort of pinpoint or answer the question, did this location have mobile phone coverage in a given year? Yes or no, basically. And what this allows us to do is basically account for a lot of confounding factors that I was talking about before. So specifically what allow, what it allows us to do is
00:07:59 - So we can ask the question, did a child or was the risk of a child being born in a different location was a difference depending on whether that location had access to mobile phone coverage. So technically speaking what we're doing very simple terms, we are using OLS or linear probability model and the depend variable is just an indicator that the child died before the age of one, yes or no. And then our main explanatory factor is gonna be did this
00:08:30 - did the location have access to mobile phone coverage in that year? Yes or no? And then we can control for location fixed effects. So that means that we're just comparing birth outcomes within the same location over time. And we can actually go a bit beyond that because for most of the women in that sample, we observe multiple births. So we can actually compare the likelihood of children dying just amongst siblings.
00:09:03 - of potential worries that you might be comparing different households, rich and poor households. So it's really just exploiting the fact that one sibling was born during a period where the village didn't have mobile phone access and the other one afterwards. So that's sort of, what we can do. Yeah, I mean the, the sort of the headline finding is that there's quite a strong negative effect, which is a good thing in this case. So what we see specifically is that,

Impact of Mobile Phones on Infant Mortality

00:09:33 - signals reduces infant mortality rates by around, I think it was 0.9 percentage points, which might sound, not particularly impressive. But if we compare it to the average mortality rate, which is around 7%, then 0.9 percentage points makes up about 10% of that effect. So that's quite substantial. And I think the, what we also see is that which intuitive will make sense is that
00:10:04 - where you would expect naturally I guess is that this positive effect of, of mobile phones is great because in urban areas you have access to, health clinics for example, or you, it's much easier to get into contact with with individuals and get some advice off of them. I think the second important thing for us that sort of points to towards our ability to identify causal effects of getting somewhere near to that is
00:10:37 - of village level GDP changes because one natural worry for us was the might the rollout of, of mobile phone coverage might just go along with normal economic development and then we will be conflating things. And what we also see is that there is no correlation with, with other, infrastructure projects. So for example, it's unrelated to, likelihood that you get, health clinics or electricity.
00:11:07 - So that's sort from a canal's point of view that's sort of the major result. And then obviously from a sort of policy implication point of view, it is super important to know where, where this effect is coming from. So, and there, just caveat out before I start my whole spiel here is that, the data is not particularly good. So, so everything I wanna say now is basically just suggested evidence, but going into this project we had sort of two types
00:11:39 - The one is sort of a straightforward economic one. So I was mentioning before, so you, you could imagine that mobile phone coverage creates new income opportunities because people are now wealthier or better off. That implies for example, that nutrition improves for individuals and that reduces income mortality or you can get access to risk moving instruments like, like insurance or banking. And for both of these aspects we don't find any evidence. So that was a bit, surprising for us
00:12:11 - or might just be that in our sample this is simply not the case. And then we, looked at some more health specific, mechanisms. And one natural expectation that I had was that maybe people just use healthcare services more often can call for health or they're reminded that they should be taken up appointments. But again here we don't see any evidence. So for example, mobile phone coverage

Mobile Phones and Improved Health Awareness

00:12:44 - higher probability of people, going to clinics or seeing a doctor before or after birth. So there doesn't seem to be any, effect, at least we do not observe it. Where we see some evidence is that getting access to mobile phone coverage seems to be related to, an improved of health awareness in the sense that people are now more informed about health related issues
00:13:14 - to intimate, what the risks are. unfortunately what we don't have is really precise information or questions about, infant or child related health aspects. But so of we can partially at least infer that information is one of the chance for which we, to which these main effects work so that individuals are better informed about how to, to care for children in either just preventative terms or, or if they have fallen or what to do.
00:13:46 - and the implications if we were to take these, at face values. I guess one implication would be that mobile phones is a relatively cheap way of reducing, infant mortality rates. And also the evidence suggests that because information seems to be at least one of the drive driving mechanisms that governments could potentially achieve a big reduction if they were to, use this. So for example, creating information campaigns or setting up, centers
00:14:22 - as I said, I think that's also the mechanism part is also one area where, from my point of view we need more work just understanding better exactly where this is, where this effect is, is coming from. so that might be possible with, with better data in the future. And the other important thing that I haven't mentioned yet so far is that basically the results or our results, they document the effects of
00:14:50 - Can really think of this as calling or texting because at up until the end of our sample period, that was by far the most popular or most available technology and although I think only South Africa had 3G at the end of a period, but now obviously this has become much more widely available. So basically you can use your phones to access the internet and this could potentially, have an add-on effect I would expect. So, so understanding and, and looking at this would be something that,
00:15:19 -

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