Micro-Estimates of Wealth for all Low- and Middle-Income Countries
| Autor: | Guanghua Chi, Han Fang, Sourav Chatterjee, Joshua E. Blumenstock |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
poverty maps
FOS: Computer and information sciences I.2 Computer Science - Machine Learning General Economics (econ.GN) J.4 poverty cs.LG Social Sciences K.4 Sustainability Science Economic Sciences Machine Learning (cs.LG) FOS: Economics and business Computer Science - Computers and Society q-fin.EC Computers and Society (cs.CY) low- and middle-income countries cs.CY Economics - General Economics Multidisciplinary sustainable development econ.GN machine learning Physical Sciences |
| Zdroj: | Proceedings of the National Academy of Sciences of the United States of America Proceedings of the National Academy of Sciences of USA, vol 119, iss 3 |
| Popis: | Significance Many critical policy decisions rely on data about the geographic distribution of wealth and poverty, yet only half of all countries have access to adequate data on poverty. This paper creates a complete and publicly available set of microestimates of the distribution of relative poverty and wealth across all 135 low- and middle-income countries. We provide extensive evidence of the accuracy and validity of the estimates and also provide confidence intervals for each microestimate to facilitate responsible downstream use. These methods and maps provide a set of tools to study economic development and growth, guide interventions, monitor and evaluate policies, and track the elimination of poverty worldwide. Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development. |
| Databáze: | OpenAIRE |
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