The Architecture of Global Technological Diffusion

The five arguments build on each other. If innovation and diffusion are different processes, then a technology's invention date says little about when it reaches a given population. If diffusion determines who benefits, then the distribution of technological benefits is a policy question rather than a by-product of invention. If infrastructure, institutions and finance set the speed, those are the levers. If diffusion speeds convergence, it is a development strategy. And if scaling proven technologies reduces many exposures at once, it is also a polycrisis strategy.

Each is testable, and this dashboard tests each with data rather than assertion. Two caveats run through all of them. First, the technologies that have diffused furthest are also those most tied to income, so separating the effect of the technology from that of the wealth that accompanies it is hard, and every statistical link here is an association. Second, diffusion is not the only constraint: some problems (an effective malaria vaccine, long-duration storage, cheap clean steel) still await invention or cost reduction, and the proposition is about balance, not exclusion.

The nine technologies are chosen because each is mature, proven and measured consistently across countries, and each reduces a distinct form of exposure: energy, health, information, finance. Together they span the infrastructure-dependent (electricity, water, sanitation), the service-delivery (vaccination), and the device-based and network-based (phones, the internet, accounts), which is what makes them useful for studying how architecture shapes speed.

Comin and Hobijn's data, extended by Our World in Data, track the share of United States households or producers using each technology for up to a century. Of 45 series, 18 start below 10% and pass 50%, so the timing of the whole middle of the S-curve can be measured. The span from 10% to 50% ranges from about two years for radial tyres and electronic ignition to 46 years for the flush toilet, 38 for the electric range, 29 for the dishwasher and 27 for the clothes dryer.

The ranking is not random. The slowest are the technologies that need something else to be built first: sewers and water mains for the flush toilet, wiring for electric ranges and dryers, plumbing for dishwashers, a distribution network for central heating and cable. The fastest are components and products that slot into an existing system: radial tyres, disc brakes, microwave ovens, tablets. Classifying the 18 into infrastructure-embedded (8) and stand-alone (10) gives medians of 26.5 and 5.0 years, a five-fold difference. The classification is a judgement, and some technologies could be put either way, but the pattern is large.

There is also a general speed-up. Technologies that reached 10% later reached 50% faster: each later year of start is associated with 0.29 fewer years of run, and the correlation is −0.68. Social media and tablets went from 10% to 50% in about five years. But the fast technologies are the stand-alone ones, which also happen to be the recent ones. For a poor country today, the technologies that matter most (power, water, sanitation, immunisation) belong to the slow, infrastructure-embedded class.

Mobile phones are the nearest thing to a universal technology. Low-income countries now have 66 subscriptions per 100 people, upper-middle-income countries 100, and the median low-income country went from 10 to 50 per 100 between about 2007 and 2012. The ITU and World Bank data show the lag between the median rich country crossing 10 per 100 (1997) and the median poor one (2007) at ten years, and the median run from 10 to 50 per 100 takes 4.1 years in high-income countries and 6.9 in low-income countries, a far smaller difference than for the internet.

The internet has not followed. The median high-income country passed 10% use in 1999 and 50% in 2006; the median upper-middle-income country in 2006 and 2015; the median lower-middle-income country in 2012 and 2019. Low-income countries passed 10% in a median of 2016, 17 years after the rich, and none of the 22 that have passed 10% has yet reached 50%. Among countries that have completed the climb, the median run from 10% to 50% takes 6.7 years in high-income, 9.6 in upper-middle- and 9.3 in lower-middle-income countries. Low-income countries use the internet at about a fifth of the rate of rich countries (20% against 93%), a gap the ITU puts at 23% against 94%, and about 24 years of history.

Where the technology needs infrastructure the gap is wider still. Electricity reaches 49% of people in low-income countries; high-income countries had passed that level before 1990, so the lag is at least 34 years. Clean cooking (16% against 100%), safely managed water (20% against 95%) and sanitation (19% against 90%) show the same pattern. Vaccination is the one infrastructure-light service that has come furthest (71% against 94%) and financial accounts, at 43% against 93%, show how a service on top of mobile networks can move faster than the physical networks themselves.

Counting people rather than percentages shows the scale of what has been invented but not delivered. In the latest World Bank data, 671 million people have no electricity, 2.0 billion have no clean cooking, at least 2.0 billion lack safely managed drinking water and 2.8 billion safely managed sanitation (these two figures cover only the 73% and 87% of the world's people living in countries with data), and 2.3 billion do not use the internet. The backlog is concentrated: low-income countries hold 58% of those without electricity but 32% of those without clean cooking. Lower-middle-income countries, home to India, Nigeria, Pakistan and Bangladesh, hold the largest share of every other backlog: 53% of those without clean cooking, 49% of those without safely managed water, 43% of those without safely managed sanitation and 50% of those offline.

The backlog is strongly related to income, which is why it is called a development problem. The diffusion index, the mean coverage of seven core technologies, is 96 in the median high-income country, 82 in upper-middle-income, 66 in lower-middle-income and 40 in low-income countries. Income per person alone explains 83% of the cross-country variation in the logit of the index. But the scatter is wide at every income level, and the next section examines what lies behind the spread.

The backlog is also a measure of unrealised benefit. Each of these technologies has a record of reducing exposure: electricity and clean cooking to household air pollution, water and sanitation to diarrhoeal disease, vaccination to infectious disease, phones and the internet to isolation from information, markets and early warnings. Section 7 shows how closely the index and each technology track child survival at the same income.

For each of eight technologies and for the index, the dashboard fits a cross-country model of the logit of coverage on seven standardised variables in six groups. For the index, the seven together explain 86% of the variation, income alone 83%: wealth is the dominant correlate, and any attempt to pull apart its parts has to reckon with how closely income, schooling, power use, institutions and credit move together. The Shapley decomposition splits the shared explanatory power evenly between correlated variables. Of the index's 86 points of R², income carries 21, power use 20, place (mostly rurality) 14, institutions 13, human capital 12 and finance 5.

The technologies differ in revealing ways. Internet use (R² 0.85) is gated by power and income. Electricity (0.76), clean cooking (0.82) and water (0.80) depend on income and human capital, and for water and sanitation institutions carry almost as much weight as income. Financial accounts (0.71) are the most institution-dependent: government effectiveness and regulatory quality carry 24 of the 71 points of R², and the sign-constrained coefficient on institutions is four times that on income. Mobile phones (0.37), which have nearly saturated, retain little structure. DTP3 vaccination (0.26) escapes income almost entirely, and its largest residuals are Rwanda (98% coverage), Nepal and Costa Rica above prediction and Sudan, Lebanon and Azerbaijan below: delivery systems, not wealth, decide it.

The residuals suggest where the architecture can be beaten. After accounting for capacity, the countries furthest above the prediction include Kuwait, Iran, South Korea, Brunei and Denmark, where coverage is near-universal, and several lower-income countries: Kyrgyzstan, Tajikistan, Sudan and Rwanda. Below it are mostly upper-middle-income countries whose safely managed water and sanitation lag their income (Serbia 24% on sanitation, Mexico 43% on water, Montenegro, North Macedonia, the Dominican Republic), plus Norway and Ireland, where reported safely managed sanitation (78% and 81%) is low relative to income. Measurement quirks contribute, so these should be read as questions rather than verdicts.

Emerging economies can raise productivity and living standards by adopting technologies already developed elsewhere, which is the logic of convergence. The data show both halves of the argument. In coverage, low-income countries have gone from almost nothing in 1995 to 66 mobile subscriptions per 100 and 20% internet use, so the ratio of their coverage to that of rich countries has risen sharply for every technology that spread. Yet for the infrastructure technologies the ratio is still low, at 49% for electricity and 16% for clean cooking, and the proportion of rich-country coverage that low-income countries have reached in internet use (22%) is far below mobile (66%).

The link to economic convergence is suggestive. Across 158 countries, a ten-point larger gain in the average of mobile subscriptions and internet use between 2005 and 2022 is associated with 0.19 percentage points of additional annual growth in income per person (standard error 0.08), conditional on starting income. That is modest but not trivial: a country gaining 40 points more than another over 17 years would have grown about 0.8 points a year faster, or some 14% more in total. The regression explains 12% of the variation in growth.

Causality is not established, and probably runs both ways: richer, faster-growing economies can afford to adopt, and adoption can raise productivity. Comin and Mestieri's study of technology diffusion over two centuries found that adoption lags between poor and rich countries have converged, while the intensity of use of adopted technologies in poor countries relative to rich ones has diverged, and that the divergence in intensity of use accounts for the divergence in income in the twentieth century: technology has arrived almost everywhere but income has not converged. That is the point of the architecture: adoption requires local adaptation and complementary investment.

The diffusion index is closely related to child survival. Across 167 countries, at the same income per person, a country with ten more points of diffusion index has 19% lower under-5 mortality (standard error about 5 percentage points). Individual technologies tell a consistent story: ten more points of safely managed water coverage go with 15% lower under-5 mortality, of internet use 10%, of DTP3 vaccination 8%, of electricity 7%, of sanitation 6% and of clean cooking 5%. Mobile phones show no relation at all (+0.2%), which is expected of a technology that has nearly saturated and that reduces child mortality, if at all, indirectly.

These are associations at a point in time, subject to reverse causation and omitted variables, and the coverage variables are correlated with each other and with other determinants of child health. They cannot say what would happen if a given country raised its coverage. What they do show is that the technologies H Heuristics has studied separately, from clean electricity and cooking to safe water and digital connectivity, form a single pattern: the gap in child survival between countries of equal income lines up with the gap in diffusion.

That makes diffusion a mechanism of polycrisis mitigation in the sense used by earlier H Heuristics reports. Clean electricity and cooking cut air pollution and heat exposure; water and sanitation cut infectious disease; vaccination underwrites pandemic preparedness; connectivity and accounts underpin early warning and cash transfers in a crisis, although the digital-resilience study found that governance dominates. Scaling proven technologies reduces several sources of exposure simultaneously, and often at lower cost per unit of exposure than developing new ones.

For each technology, the simulator applies the fitted model's coefficients to the change you specify, standardised by the cross-country standard deviation, and adds the result to the country's own current coverage on the logit scale. That keeps the country's distance from the model, its over- or under-performance, constant, and moves only the architecture. The index change uses the model for the index itself, and the implied change in child mortality applies the −19% per ten points gradient from section 7.

Read it as a map of gradients, not a forecast. Cross-country models cannot say what a particular country would experience, because their coefficients combine causal effects with correlations among the variables, and because large changes may fall outside the range the data cover. Variables that move together in the data, such as income and power use, are not independent in practice either: doubling income with no change in power use implies an unusual economy. The coefficient matrix in section 5 shows which variables matter most for which technology; a coefficient of zero means the variable adds nothing once the others are held constant, not that it is irrelevant.

Two experiments are instructive. For Nigeria, raising institutions by one point (about 1.2 standard deviations) implies financial-account ownership rising from 63% to about 82%, while doubling income implies 66%. For Ethiopia, 20 more points of secondary enrolment implies electricity access rising from 57% to about 73%, more than a doubling of income (67%). And vaccination barely moves under any variable, since it is so weakly tied to the architecture; for it, the lessons are in the countries that beat it.

Build the shared infrastructure first. Technologies embedded in networks take five times as long to spread, and they are the ones that matter most for survival. Grids, water and sanitation systems, roads and digital backbones are public goods that every later technology depends on. Where the gap is widest, distributed options (solar home systems and mini-grids, shared water points, mobile networks) can deliver service before the trunk network arrives, as mobile phones skipped fixed lines.

Invest in delivery institutions. Vaccination, financial accounts and water have less to do with income than with delivery systems and rules. Rwanda reaches 98% DTP3 coverage with a low-income economy; Kenya, Mongolia and Iran have account ownership above 90%; Kyrgyzstan, Tajikistan and Uzbekistan lead their income peers on the diffusion index. Strong delivery systems, effective regulators and credible rules for providers are not luxuries of rich countries.

Finance adoption, and adapt it locally. The cost of capital is higher in the countries that most need to adopt, as the financing-access report found for clean energy, and affordability binds at the household level. Concessional finance, guarantees, local-currency lending and results-based subsidies lower the price of the first connection. Adoption also needs local adaptation and complementary investment (skills, maintenance, supply chains), which the cross-country record shows is where adoption most often stalls.

Where the proposition needs qualifying. The data support the claim that distribution is at least as important as invention for the problems studied here, but not that invention is unimportant. Solar modules became 95% cheaper in 15 years, and that is why distributed solar is now the quickest route to the last connections; mobile phones diffused faster than any infrastructure technology because they needed little infrastructure of their own. The relationship between invention and diffusion is therefore two-way: invention that lowers the architecture a technology requires is itself a way of accelerating diffusion.

What would count as progress. Three indicators track the architecture: the length of the run from 10% to 50% for the next technologies (in particular for low-income countries, none of which has yet reached 50% internet use); the distance of each country from the level its capacity predicts; and the backlog in people. The first measures speed, the second delivery, the third the unfinished work.

What this dashboard cannot say. It cannot say that raising any variable would raise a country's diffusion by the model's coefficient, because the coefficients are cross-country correlations. It cannot say how diffusion would change if the world adopted a particular policy. And it covers only nine technologies, all of which are old enough to have standard measures. Technologies still at the start of their S-curves, from AI to long-duration storage, will meet the same architecture.

Technologies: World Bank WDI series IT.CEL.SETS.P2 (mobile subscriptions per 100, capped at 100), IT.NET.USER.ZS, IT.NET.BBND.P2, EG.ELC.ACCS.ZS, EG.CFT.ACCS.ZS, SH.H2O.SMDW.ZS, SH.STA.SMSS.ZS, SH.IMM.IDPT (DTP3) and FX.OWN.TOTL.ZS (Global Findex), latest year since 2016 (2017 for accounts). Countries with at least 0.3 million people (177). Income-group curves are population-weighted for a fixed set of countries per technology with data near the start and in 2022–2025. Crossing years interpolate linearly between annual observations and count only series that start below the threshold. US adoption: Comin and Hobijn technology diffusion data via Our World in Data; of 50 series, those starting at or below 12% and passing 50% (18 of 45) are used. Infrastructure classification: flush toilet, central heating, electric range, dryer, dishwasher, cable TV, shipping container port infrastructure and real-time gross settlement systems.

Models: for each technology the logit of coverage (clipped to 0.5–99.5%) is regressed on seven standardised variables, with R² and the Shapley decomposition from ordinary least squares and the coefficients (used in the matrix, residuals and simulator) from a sign-constrained ridge regression whose penalty is chosen by leave-one-out cross-validation (capacity variables cannot lower coverage, rurality cannot raise it; trade is unconstrained), because income, schooling, institutions and power use are highly collinear. The seven variables are ln GDP per person (PPP), gross secondary enrolment (capped at 120), institutions (mean of WGI government effectiveness and regulatory quality), private credit (capped at 150% of GDP), ln electricity use per person (excluded for electricity and cooking), rural share and trade (capped at 200% of GDP); countries with all variables (97–122). Shapley (LMG) decomposition over the six groups. Residuals are in logit points. Outcome gradients: ln(under-5 mortality) on ln GDP and coverage/10. Growth: average annual log growth of GDP per person 2005–2022 on initial ln GDP and the average gain in mobile and internet coverage/10.

Caveats: all relationships are cross-sectional associations; income, schooling, power and institutions are collinear, so decompositions share explanatory power by construction; coverage measures differ in quality (safely managed water and sanitation especially); mobile subscriptions are not unique users; time-to-threshold excludes countries that have not crossed (biasing later runs toward faster); and the infrastructure classification is a judgement. Backlog totals cover only countries with data, and are shown for context against the ITU's own estimates.