This document is a shallow investigation, as described here. As we noted in the civil conflict investigation we shared earlier this year, we have not shared many shallow investigations in the last few years but are moving towards sharing more of our early stage work.
This is a shallow on telecommunications infrastructure in low- and middle-income countries (LMICs). I spent about three weeks on it, during which I read major papers in the field and spoke to about ten experts. This document has been read and discussed by Open Philanthropy’s Global Health and Wellbeing cause prioritization team.
We’re continuing to look at the robustness of the social scientific evidence on telecommunications. Many of the papers are quite new and remain unpublished, which limits our ability to review the data behind them in detail. However, we think that the initial evidence is quite positive, and we think that someone looking to launch an impactful organization might want to examine this space more closely. We think there may be both nonprofit and for-profit ideas worth pursuing (the latter could still have large social returns, and provide benefits from an earn-to-give perspective). We see Wave as an interesting model for the latter.
We welcome comments either posted here, emailed to me at firstname.lastname@example.org, or shared via this Google Form.
Major sources of uncertainty
It seems worth flagging two major uncertainties that this shallow does not resolve:
- I am quite unsure what the expansion path for telecommunications technologies looks like. Technologies often spread much faster than expected; it is possible that philanthropic investment is less needed/marginally useful here than I think it is.
- The literature on telecommunications access and income is still quite limited; there are really only two randomized controlled trials (RCTs), both of which have relatively small sample sizes. In addition, there’s only one RCT on cellphone coverage and the other studies in this literature use difference-in-differences with two-way fixed effects, which may yield misleading results. We’d like to have more evidence and are currently considering funding more research in this area.
For the purposes of this document, “telecoms” includes internet access, fixed-line telecommunications infrastructure, and cellular phone access.
We did some initial work on infrastructure in general. Investment in telecoms looked unusually promising in catalyzing income growth. Two RCTs find that access to a form of telecommunications increased incomes by 20% in the first year. Therefore, we decided to look into the topic further.
This shallow seeks to answer the following questions:
- What is the current status of information and communication technology (ICT) infrastructure? Where is ICT infrastructure worst?
- What are the best tactics to improve ICT infrastructure?
- Should Open Phil fund any of those tactics?
Who is already working on it?
Mostly, the market is solving access to at least some telephony and internet - in 20 years, mobile phone usage has increased approximately 85 percentage points. In sub-Saharan Africa, where mobile phone usage has lagged much of the rest of the world, access to 3G networks increased 55 percentage points in the six years through 2020. This increase has happened without particular effort from philanthropists, and with significant barriers to usage. However, neither access nor usage of telecoms are universal, growth is beginning to slow, and there are still large gaps to fill. This is not uncommon for new technologies; initial adoption is generally much faster than rollout to the last 10-15% of users.
ICT is relatively light on philanthropic investment, likely because it is an active area of commercial investment. I’m somewhat uncertain on total funding, as several entities either don’t report their spending or don’t clearly separate it from other spending, but I am fairly certain that it is not a central focus area for any major foundations.
There are four major areas where there is at least some ICT funding, none of which I have managed to come up with a good total investment figure for.
- The Gates Foundation seems to be the only large foundation that is very active in this area. Much of their funding is targeted specifically towards women and/or financial services; they are less interested in ICT as a category than ICT as a catalyst for other things. I’ve had difficulty figuring out what their total ICT spend is, because ICT-centric projects are not necessarily categorized as being such. They funded the original Phillip Roessler RCTs on cellphone provision (targeting women in particular).
- There are a handful of ICT-specific foundations - e.g. the Internet Society Foundation (~$50M a year), the Web Foundation (~$4M a year), the Association for Progressive Communications (~$4M a year), ISIF Asia (~$2M a year). This might be 100M per year if you add up all the small foundations (though that’s potentially high/optimistic).
- Commercial entities also do some amount of philanthropic work - e.g. Google.org’s technology and innovation arm, the Ericsson for Good initiative, Meta’s Connectivity, and Data for Good.
While their overall footprint is relatively large — Google.org gives about $100M per year, and Meta is obviously a large company — their ICT-specific spending appears to be a relatively small percentage of that. If I were to guess, I’d say the companies spend perhaps 50M together on more or less philanthropic ICT work — but this is a highly uncertain estimate.
- Commercial non-philanthropic spending is potentially very large, but the overlap with our interests (work that makes cell phones/internet access available to new users) is very unclear.
However, there’s clearly a lot of commercial capital overall. Orange Telecom is currently building (pg. 41) 2000 towers in the DRC to (ostensibly) cover 10M additional people in rural areas. Likewise, Google just made first landing of the Equiano cable, their third private international cable, which connects Portugal to West and South Africa (with 20x the capacity of previous cables to the region), which probably cost around $400M. Safaricom is spending $500M expanding to Ethiopia, and Africell is spending $200M on an Angola expansion. Capital spending per year on telecoms in sub-Saharan Africa is ~$10B.
I would guess that total philanthropic spending on telecoms access is probably less than $150M a year, but I am (again) highly uncertain. Adding in relevant commercial spending could vastly increase that number.
What could a new philanthropist do?
Since most interests in the space are commercial entities, they are by definition interested in commercially viable products, and somewhat less interested in the social returns to ICT infrastructure. This means that rural and/or very poor communities are less likely to receive ICT infrastructure.
We could target grants towards these communities - where there might be a large percentage increase in income, but from an extremely low base. These communities might not be commercially viable for telecoms, but investing in them could be above our cost-effectiveness bar. There is pretty good evidence that there are large economic gains to be had in going from no connectivity to some connectivity. (This is discussed further in the importance section.)
The completely unserved populations are now quite small - perhaps 3%-9% of the world’s population. These ~250-750 million people are (somewhat by definition) difficult to reach, but we could invest in some of the companies working on reaching them. One example is Africa Mobile Networks, which is building “network-as-a-service” for mobile operators (pg. 40).
It may be more promising to not only focus only on strictly increasing coverage, but also on decreasing the cost to use mobile devices in areas that already have (at least some) coverage. Most of the ways to drive down cost (competition, deregulation, additional infrastructure) will also likely do something to expand coverage or improve the quality of coverage where it exists now.
- Funding white-label tower companies
- Legal support for civil society interests in telecoms regulation
- Advocating for competition in telecom markets or other regulatory fixes
- Providing low-cost capital for subsea cables
- Providing low-cost capital for land-based fiber networks
- Improving availability of amortized consumer financing arrangements
The limited existing literature suggests that improving communications technology is a surprisingly good way to improve incomes. This appears to be true both when expanding coverage and expanding access, and for both adding cell coverage and internet coverage. The results are remarkably consistent and large.
When expanding coverage, the economics literature has found:
- In the Philippines, an RCT showed that gaining access to a community network increased incomes 17%, with those who could actually place calls from home increasing their incomes 28% in fourteen months. This would be a 7000x return in our units given their per-tower costs.
- In Senegal and Tanzania, a quasi-experimental study found that access to 3G increased incomes 14% and 7% in a year. This would be equivalent to a 3500x-7000x return if building a tower costs roughly the same as it does in the US.
- A similar quasi-experimental study in Nigeria found that access to mobile broadband increased incomes 5.8% in a year, 7.8% in two years, and 9.2% in three years. Based on these numbers, a tower would meet our cost-effectiveness bar if it covered ~2200 people.
- In sub-Saharan Africa, a quasi-experimental study exploiting the rollout of subsea cables showed that gaining access to improved internet raised employment by 7% and GDP/capita by 2%.
When expanding access:
- An RCT in Tanzania showed providing people with phones boosted incomes 7%, and providing people with smartphones boosted incomes 20% in a year. Note that this was true even though 88% of households already had access to a feature phone before the RCT.
Another way of looking at impacts is to look at studies of products enabled by mobile expansions, such as mobile money. Suri and Jack find that MPESA lifted 2% of Kenyan households out of extreme poverty – I think a naive back-of-the-envelope calculation (BOTEC) based on that analysis would imply that MPESA increased average consumption by 3.4%. Having this large of an effect from mobile money alone is another credible signal of large total impacts.
I think these results are positive and consistent enough to suggest that telecoms access gives at least a 5% income boost, and more likely a >10% income boost. That is an extremely “big if true” result.
For instance, a very rough estimate suggests if this is true, perhaps 9% of Africa’s growth in the last two decades has been from increased cell coverage, and an additional 2%-8% came from growth in internet usage. Thus, expanded access to telecommunications alone could explain ~⅙ of the growth Africa has experienced from 2000 to 2020.
A similar macro BOTEC suggests that increasing usage of mobile broadband in sub-Saharan Africa to South Asian levels (28% usage → 34% usage – which is only a modest change compared to high-income country levels of usage) would be worth $171 - $342 billion to Open Philanthropy, using our framework for valuing income increases.
Mobile communications is not the most neglected of topics when viewed in terms of total spending, as global telecoms spending is somewhere around 1.6 trillion dollars a year. But this is obviously not spent evenly around the globe; US telecom providers spend about $226/person/year on capex, while African ones spend about $13/person/year.
I found it more helpful to separately consider the number of people who have no coverage (the coverage gap) and the number of people who do not use mobile devices (the usage gap).
The coverage gap is closing fairly rapidly over time. GSMA claims that 93% of the world’s population currently has access to 3G or better mobile data; the International Telecommunications Union claims it is 94%, with an additional 3% having access to 2G data. This would leave about 600 million people without access to coverage. This is down from 750 million just two years before; if coverage continued to increase at that rate, it would take just eight years to reach global coverage.
However, this data is likely to be optimistic; it is based on self-reported data by telecoms, who are likely to have incentives to overstate how good their networks are. As I noted above, I am uncertain about this data, and it is possible that the number of people with no cell phone coverage is substantially greater than 600 million.
I do think it’s likely that >90% of the world has access to cell service, but I’m not willing to swear to any exact percentage. I’d estimate the true coverage gap is somewhere between 3% (the number claimed by GSMA) and 9% (3x what GSMA claims). This would mean ~240M-710M people lack coverage globally.
If defined like the coverage gap, the usage gap would be the percentage of people who have access to any kind of cell phone coverage, but don’t use it. But for reasons I frankly don’t understand, this is almost never what people actually mean by the usage gap. Indeed, finding that number is fairly difficult; since SIM swapping and sharing phones is common in low and middle income countries, good estimates of the percentage of the population with access to a cell phone are thin on the ground.
My guess is that most but not all residents of L&MICs have access to a feature phone, as surveys in several sub-Saharan Africa countries suggest that about 80% of people have access to a phone and the number of subscriptions is over 100 per 100 people. This is likely less true the poorer the country you consider; the number of subscriptions / 100 people in LICs is nearly 50% lower than it is in MICs.
Instead, the “usage gap” generally actually means the percentage of people who have theoretical access to mobile internet but do not use it. This is now about 6x the percentage that don’t have any access, or about 43% of the global population. Usage is lower among the groups you would expect - rural residents (37% less likely to use mobile broadband), lower income people, women (20% less likely to use mobile broadband), speakers of minority languages, etc. Like the coverage gap, the size of usage gap is decreasing over time - but the experts we spoke to were less than convinced that usage will ever be universal.
The most common reason for people not to use telecom services is the cost of both the device and the coverage. Per the GSMA, an internet-enabled device costs an average of 44% of monthly income in a low or middle income country. Philanthropic investment is unlikely to change device costs, but we might be able to reduce tariffs (often 7-10% of the purchase price) through policy work.
Even if you have a phone, though, the cost of using data can be prohibitive in developing countries. In sub-Saharan Africa, for instance, 2 GB of data can cost 10-20% of monthly income, and it is fairly obvious why internet penetration in Nigeria is 2x that of Togo (see image below).
Prices are dropping over time — GSMA reports that the price of a marginal GB of data has dropped 40% in four years – but I think we could speed this process along.
It’s not clear that telecoms companies have the incentives to lower their prices as much as possible – many are monopolies or duopolies – but we could advocate for changes. As physical infrastructure improves, regulatory costs decrease, or competition increases, the marginal GB becomes cheaper, and the marginal consumer becomes more likely to be able to afford to use mobile internet.
Possible “Grant Ideas”
All of these are somewhat more nebulous than I would like; I am not sure what organization you would literally write a check to in order to make these things happen. In many cases, the ideal organization would be a company doing this work to which we could potentially provide low-cost capital, which has different costs than a typical grant.
I split these into two sections — possible grants that would address the coverage gap, and possible grants that would address the usage gap.
White-Label Tower Companies
In the US, many cell phone towers are owned by companies that are not the telecoms that run services through them. This is not true elsewhere, where towers are largely built, owned and maintained by the telecoms. This is less than efficient – if a new carrier wants to expand into an area, they have to build a new set of towers, since they cannot use their competitors’ existing towers.
The Tribune Express cites (without a source) that “[in Pakistan] a dismal reality is that 40% of the 34,000 towers are just 300 metres apart, which results in wastage of $1 billion worth of capital due to the existence of parallel towers at close distances.” In many places, this can make coverage expansion unviable. The population density might be enough to support one company and one infrastructure buildout, but certainly not three or four.
This might also allow telecom companies to lower prices. If each company must complete a full build-out, they must also pay for said full build-out. If companies are sharing towers, the cost per company is lower, and the amount that the company must recoup from their customers is also lower. Thus, this intervention might be able to address both the coverage gap (by making expansion into new areas more economical) and the usage gap (by lowering the cost of service in all areas).
There is at least one group working on this in Pakistan. It appears to be an incumbent telecom company, so it’s not clear if they need funding. African Mobile Networks and BRCK are working on related issues in Africa (though BRCK is building a wifi rather than mobile solution).
More Subsea Cables
Most internet traffic is processed via subsea cable. Some countries in sub-Saharan Africa have enough undersea cables to have reasonable bandwidth, but some countries (particularly on the West African coast) are still underserved. The more cables serve a country, the cheaper the internet there is, and the more people are likely to use the internet. Google seems to agree with these claims; they believe their infrastructure investments in Asia (including subsea cables) have had a naive return of 215x in raw dollar terms.
While we likely would not want to support the entire subsea cable (as they cost about $400M even when built at scale for ~$30k per km), adding a branch to a new country is remarkably inexpensive ($10-30M). One could add, say, a connection to Senegal. Senegal is currently served by four subsea cables, but each is relatively small and the country has low total bandwidth available. A very rough BOTEC puts a landing in Senegal in the range of 700x ROI in our terms assuming a three year speedup (using the higher end $30M cost per connection and assuming no private co-financing or return on capital).
More/Cheaper Land-Based Fiber Optic Cables
Two experts said that this was the most promising way to increase access.
When building a terrestrial fiber network in Africa, capital costs are very high; >10% interest is common when putting together a buildout. Such high costs limit the amount of fiber built. When companies are able to raise the capital to build fiber, they often pass these high costs on to consumers, limiting the user base.
OP could conceptually underwrite capital investments for <10% interest. If the cost to reach each additional consumer is lower, telecoms would be able to reach more consumers, as well as charge lower rates (to the extent this brings in more customers and allows for higher profits). We could conceivably spend huge amounts of money on this; existing telecoms capex in sub-Saharan Africa is about $11B a year.
It is cost-prohibitive in some countries to put down more land-based fiber. In some municipalities in Nigeria, there are policies that require companies to pay extremely high fees to put down cable. These towns are often just skipped during buildout.
If we are interested in subsidizing capital for land-based fiber, I think we would have to fund advocacy for reducing these fees as well; it’s not that helpful to lower interest rates if it is still extremely costly to build cable.
Almost all telecoms markets have only a few entrants, and some remain monopolies. As long as there is no competition in the market, there are few incentives for telecom companies to improve or increase their coverage.
One of the most obvious examples of this is EthioTelecom. EthioTelecom is the only telecommunications provider in Ethiopia, and while it claims to cover 88% of the country with at least 2G service (and 66% with 3G), it is unusably bad. South Africa’s average internet speed is about 10,000x faster than Ethiopia’s. Unsurprisingly, Ethiopia has low internet penetration and essentially no digital economy.
By comparison, when Cambodia introduced competition into the mobile market, the cost per gigabyte of data dropped from $4.56 in 2013 to $0.13 in 2019 (pg. 166 of the World Development Report) and mobile usage spiked to 6.9 gigabytes per capita per month.
However, introducing competition without improving infrastructure has its own challenges. When pricing pressure is substantial, the infrastructure can’t keep up. This has been most notable in India, but also has happened elsewhere in southeast Asia. Carriers are not making enough money to upgrade their infrastructure, and so service is somewhat worse.
Spectrum licensing fees can be 20-30% of buildout costs. When you buy spectrum in most countries, you have exclusive use of that part of the spectrum.
This allows incumbent carriers to essentially block new entrants (by licensing the part of the spectrum they would use) — but does not require the incumbent to use it. A licensing regime that does not allow you to license spectrum unless you plan to use it could be helpful, and allow more entrants into the market.
This might be more effective than pushing for competition more generally.
Handset costs are a significant barrier for many people in low and middle income countries (LMICs). The phone supply chain is relatively efficient, so we are unlikely to be able to reduce the cost of the actual device, but we could reduce the effective cost to consumers by reducing tariffs.
Tariffs of 7-10% are common on imported electronics in LMICs. This is pretty substantial, and makes electronics more expensive to purchase in poor countries than in rich ones. We could advocate for tariffs on these devices to be reduced, so that more people could afford handsets.
Legal Support for Consumer Access to Telecoms
One expert said that if he was given $5M, he would “endow a few universities with a chair of affordable telecommunications regulation.”
He argued that regulators everywhere hear mostly from lobbyists. In poor countries (particularly in sub-Saharan Africa, where he mostly works), they hear only from telecommunications companies, and “civil society doesn’t get much of a voice in that space”.
He believes this could be combated by drawing budding lawyers into the affordable access telecoms space. He also argued that this supports local expertise; infrastructure can be profoundly localized, and people with a deep knowledge of local conditions will be better-positioned to push for effective regulatory changes.
I’m a bit skeptical; I’m not entirely sure what lawyers could do (though perhaps they could push through the regulatory fixes described above). But it was certainly an interesting and unique suggestion.
Consumer Financing Structures
In some places, it is still relatively difficult to finance handsets over time. Instead, prospective users must pay for the entire cost of the phone upfront - which is not doable for many poor people. Like tariff reduction, allowing for financing over time might be a way to get more phones into hands.
Google is working with Safaricom on this, and I’m not sure what additional leverage or value we’d be able to provide. I don’t think this idea is as promising as providing capital for build outs.
In 2014, Steve Song gave a talk noting that 20% of Africans had access to 3G. By 2020, GSMA said that 75% of Africans have access to 3G.
It’s possible that this number is much larger — see my notes on the global coverage gap.
Indeed, I am sometimes a little sloppy in distinguishing between the two.
They say that 2% of Kenyan HHs exit extreme poverty based on MPESA overall. The coefficient on “leave extreme poverty” w.r.t. “agent density” is -0.007, so 0.02/0.007 ~ 2.9x multiplier on that coefficient. The coefficient on “log(per capita consumption)” is 0.012, so 0.012*2.9 ~ 0.034.
Depending on whether you expect a 5% income increase or a 10% income increase
Note that for the above I am considering mobile internet only. In the near future, satellites in low orbit will make it possible to access broadband in almost all parts of the world in the near future. However, satellite internet is quite pricey. Starlink terminals are currently $500 loss-leaders for the company, plus a monthly cost of $99. While this makes coverage possible throughout the world, it does not mean that this is actually useful to the majority of the world (let alone the ~7% of the world that does not have access to mobile broadband yet).
Rural coverage can be 40X the cost of urban coverage.
Cell phone towers should be 1-2 km apart.
They find a return of $430B for an investment of $2B.
This is without using a log scale for consumption. Adjusting for how Open Phil generally thinks about income increases (we value a $1 income increase more highly the lower the initial income is), this would be closer to a return of 1380x.
This effort could be compared to our work on land use reform, which often involves funding advocates for reduced housing regulation.
Systematic scoping review that might support further investigation on impact of mobile networks in low- and middle-income countries:
In case helpful, we recently published a Gates-funded systematic scoping review synthesizing 315 articles evidencing use and/or impacts of digital farming services in low- and middle-income countries. (We interpreted digital farming services as any agriculture-related information service , market linkage service, farming tool or financial service with a digital user interface). Potentially relevant findings include:
- Importance of mobile networks for digitizing farming services (for good and bad): Use of digital farming services was influenced by mobile network availability (according to 51 empirical studies) and mobile network affordability (according to 19 empirical studies)
- Impact evidence of digital farming services: We found 173 empirical studies reporting digital farming services outcomes (e.g. increased social inclusion, reduced income) with variable levels of rigor (which we coarsely categorized). Only a handful of studies directly analysed how mobile network access influenced these outcomes (e.g. Jensen 2007)
- Leverage of mobile networks: numerous reviewed studies found farmers creating informal digital farming services using mobile networks (e.g. pastoralists in Tanzania using their mobile phones to reduce human-wildlife conflict - Lewis et al., 2016, and farmers in Cambodia using mobile phones to get better rice prices - Shimamoto et al., 2015)
Linked here is a database of reviewed studies (filtered by country, reported outcome type etc) and linked here is the journal paper documenting the review itself. I personally read almost all of the 315 articles and would be very happy to informally help you (or anyone else reading this) navigate the resources or support in whatever other way (spent ages on this work and would love to help make it useful for OpenPhil or any other impact-oriented people) - samcoggins55 at gmail dot com
Thanks for your great work on this investigation in any case
I wish I had useful comments Lauren but all I can say is that this was a really interesting read on a topic I haven't thought much about.
A very interesting read, thanks for preparing. Is there any research into whether those who do not have access to telecommunications technology in these LMICs actually demand or desire access to this technology?
There is a risk here that we may be imposing our biases on potential beneficiaries that I believe would be helpful to investigate further. I could plausibly see a world where there is a significant demand for the secondary benefits that arise from telecommunication technology access.
However, similarly, I could imagine plausible situations where the 3-9% of individuals that you referenced may have strong preferences for their status quo existing lifestyle and technology situation.
When we spoke to experts in the field, this was not a major concern for them. Indeed, a couple mentioned that often convincing people to use a development intervention is an uphill battle - but people needed no convincing to use cell phones.
This seems to be borne out by usage statistics; even though devices are expensive (44% of monthly income is a lot), usage is growing a lot. GSMA has smartphone usage doubling in sub-Saharan Africa doubling from 2014-2019 (pg. 17). World Bank research suggests the major barrier entry to using a mobile device is not lack of interest, but affordability.
Thanks for sharing your thoughts on this very interesting topic!
Did you consider the impact that companies like or in particular Elon Musk's Starlink is going to have on the situation? Starlink seems to be focusing their efforts exactly at countries like those studied here, countries with high entry-costs for cable-bound telecom means which they don't have since they already largely invested in their now existing satellite fleet and a significant remaining gap in the population without access to telecoms.
Without knowing much about it, it seems to make much sense to run a business model that is based on either very low prices for those that weren't able to afford telecoms before Starlink and/or heavy advantage over the existing (weak) competition in terms of quality. Looking at Starlink's current activities, they could change the situation in many LMICs drastically within a few years.
That being said, I have also heard that Starlink seems to be quite expensive to deploy atm (but as before, I'm no expert on the topic).
Starlink is AFAIK a much discussed topic at least in Mozambique, Nigeria and Zimbabwe atm.
It looks like my footnote on Starlink didn't make it over the forum version; will fix that! In the interim, these are my thoughts: "in the near future, satellites in low orbit will make it possible to access broadband in almost all parts of the world in the near future. However, satellite internet is quite pricey. Starlink terminals are currently $500 loss-leaders for the company, plus a monthly cost of $99. While this makes coverage possible throughout the world, it does not mean that this is actually useful to the majority of the world (let alone the ~7% of the world that does not have access to mobile broadband yet)."
Most experts we talked to were skeptical on Starlink for the average person in a LMIC, just because of the cost.
I think the relevance of Starlink here is not for serving individuals in LMICs, but for providing backhaul for towers in places too remote to easily backhaul via fiber or line of sight microwave links.
Very interesting read!
I'd just like to add that all other humanitarian work would become easier if there were better network coverage and access to smartphones. In my work with AMF, we put a strong emphasis on electronic collection of mosquito net campaign data, and we often work around problems related to network coverage / device availability / ...
To give a concrete example, this year's mosquito net campaign in Guinea has faced delays because it was difficult to obtain the several thousand tablets needed for electronic data collection, and because the system did not work well in areas with no network coverage.
Oh, that's really interesting. I've passed this along to the team.
Fascinating work. Thanks for putting this together. I have done some research on the impact of ICT related to democratization efforts in Latin American and SSA. The main deterrent I have faced in the way of research has been the reticence of telecom companies to share proprietary data with researchers, presumably due to the competitiveness of the sector in these regions. This makes it extremely difficult to conduct low- and medium-cost studies. In my opinion, improving access to this data would be a great start in enabling more effective assessments of the potential of similar interventions across a range of outcomes.