From Firm Visibility to Wider Economic Change
The subject now shifts from an individual firm's credit file to the economy-wide relationships associated with digital-payment use. The specific question is what can be learned when payment adoption is compared with changes in financial engagement, informal employment, GDP per capita, and productivity across countries. The value of this shift is also its danger: it shows how far the visibility mechanism might reach, but it makes differences in measurement, timing, and causal interpretation impossible to ignore.
A payment trail can help a lender assess one firm. At economy level, the relevant measure is not that firm's receipts but the share of people using digital payments. The result is a much wider indicator. It may reflect households, workers, and businesses at once, and it moves together with internet access, financial accounts, institutional quality, and income. The BIS study is useful for testing whether digital-payment adoption is associated with broader development outcomes, but it cannot simply be read as a larger version of the firm-level credit study.
What the BIS comparison measures
The BIS paper uses the World Bank's Global Findex data to measure the percentage of people over age 15 who report making a digital payment in the previous year. The measure includes payments made through a mobile-money account, debit or credit card, mobile phone account, or the internet, including bill payments, purchases, and some remittances. It is a population measure of reported use, not a count of transactions received by firms and not a direct measure of how much revenue is visible to a lender.
For its main regressions on growth, productivity, and informality, the paper uses the 2014 and 2017 survey rounds and studies the period 2014–19. It excludes the 2021 payment-use observation from these outcomes because the Covid-19 pandemic changed payment behavior and economic conditions at the same time. The estimation relates a two-year change in an outcome to the lagged level of digital-payment adoption, the lagged level of the outcome, controls, country random effects, and time effects. One comparison uses growth from 2014 to 2016 and adoption measured in 2014; the other uses growth from 2017 to 2019 and adoption measured in 2017.
This timing arrangement addresses one obvious problem: an outcome that occurs after the adoption measure is less likely to be a simple reflection of the same year's shock. It does not make adoption random. Countries with higher incomes, stronger institutions, more broadband, or more developed financial systems may both use more digital payments and grow faster for other reasons. The paper therefore describes its contribution as cross-country evidence on relationships and channels, not a final causal estimate.
The outcomes also do not cover the same countries equally. The GDP-per-capita growth regressions contain 196 observations across 101 countries. The TFP regressions contain 162 observations across 83 countries. The informal-labour regressions contain only 50 observations across 29 countries. That difference is not a technical footnote. A result based on 29 countries has a narrower empirical base than one based on 101, even when the coefficient is reported in the same table. It should not be generalized as if every outcome were observed for the full country set.
The study measures growth with real GDP per capita and total factor productivity, or TFP. Informality is measured as the share of informal labour in total employment. These measures capture different parts of economic change. GDP per capita can rise because an economy expands its use of labor and capital, because demand grows, or because resources are used more productively. TFP is intended to capture efficiency after accounting for measured inputs, but it is difficult to estimate.