Joshua E Blumenstock
Datos Biográficos
| ID | 946879 |
|---|---|
| NOMBRE | Joshua E Blumenstock |
| NOMBRES | Joshua E |
| APELLIDO | Blumenstock |
| FIRMA | BLUMENSTOCK J E |
| AFILIACIONES | University of California, Berkeley |
| ORCID | 0000-0002-1813-7414 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 10 |
| TOTAL DE CITAS | 24 |
| TOTAL COMO AUTOR | 10 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2015 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2025 |
| ÍNDICE H | 3 |
Probing the limits of mobile phone metadata for poverty prediction and impact evaluation
A series of recent papers demonstrate that mobile phone metadata can, together with machine learning, estimate the wealth of individual subscribers and accurately target cash transfer programs. In the context of an emergency cash transfer program in Haiti, we combine surveys and mobile phone call detail records (CDR) to test whether such methods can be used to estimate the program’s impact on household expenditures . We find that CDR-based predic…
Estimating impact with surveys versus digital traces
We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo, endline survey data indicate positive treatment effects on food security, mental health, and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict base…
Yellow Pages
We study the impact of a low-cost intervention to reduce information frictions in rural markets by randomly assigning small and medium enterprises in Tanzania to be listed in a telephone directory. The listed firms expand their communication networks, increase sales and make greater use of mobile money, with positive spillovers to firms in the same village. Estimated effects are larger and more statistically precise for firms that are more produc…
Welfare Effects of Digital Credit
Digital loans, which provide short-term, high-interest credit via mobile phones, have exploded in popularity across low- and middle-income countries. This paper reports the results of a randomized evaluation of a digital loan product in Nigeria. Being randomly approved for a loan (among those who otherwise would have been denied) substantially increases subjective well-being after 3 months, but being randomly approved for a larger loan does not h…
Violence and Financial Decisions
We provide evidence that violence reduces the adoption and use of mobile money in three separate empirical settings in Afghanistan. First, analyzing nationwide mobile money transaction logs, we find that users exposed to violence reduce use of mobile money. Second, using panel survey data from a field experiment, we show that subjects expecting violence are significantly less likely to respond to random inducements to use mobile money. Finally, a…
Program targeting with machine learning and mobile phone data
Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor hou…
Mobile phone data reveal the effects of violence on internal displacement in Afghanistan
Nearly 50 million people globally have been internally displaced due to conflict, persecution and human rights violations. However, the study of internally displaced persons-and the design of policies to assist them-is complicated by the fact that these people are often underrepresented in surveys and official statistics. We develop an approach to measure the impact of violence on internal displacement using anonymized high-frequency mobile phone…
Mapping poverty using mobile phone and satellite data
Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study de…
Airtime transfers and mobile communications
Predicting poverty and wealth from mobile phone metadata
Predicting unmeasurable wealth In developing countries, collecting data on basic economic quantities, such as wealth and income, is costly, time-consuming, and unreliable. Taking advantage of the ubiquity of mobile phones in Rwanda, Blumenstock et al. mapped mobile phone metadata inputs to individual phone subscriber wealth. They applied the model to predict wealth throughout Rwanda and show that the predictions matched well with those from detai…
Airtime transfers and mobile communications
Mobile phone data reveal the effects of violence on internal displacement in Afghanistan
Nearly 50 million people globally have been internally displaced due to conflict, persecution and human rights violations. However, the study of internally displaced persons-and the design of policies to assist them-is complicated by the fact that these people are often underrepresented in surveys and official statistics. We develop an approach to measure the impact of violence on internal displacement using anonymized high-frequency mobile phone…
Program targeting with machine learning and mobile phone data
Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor hou…
Estimating impact with surveys versus digital traces
We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo, endline survey data indicate positive treatment effects on food security, mental health, and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict base…
Yellow Pages
We study the impact of a low-cost intervention to reduce information frictions in rural markets by randomly assigning small and medium enterprises in Tanzania to be listed in a telephone directory. The listed firms expand their communication networks, increase sales and make greater use of mobile money, with positive spillovers to firms in the same village. Estimated effects are larger and more statistically precise for firms that are more produc…
Predicting poverty and wealth from mobile phone metadata
Predicting unmeasurable wealth In developing countries, collecting data on basic economic quantities, such as wealth and income, is costly, time-consuming, and unreliable. Taking advantage of the ubiquity of mobile phones in Rwanda, Blumenstock et al. mapped mobile phone metadata inputs to individual phone subscriber wealth. They applied the model to predict wealth throughout Rwanda and show that the predictions matched well with those from detai…
Airtime transfers and mobile communications
Mapping poverty using mobile phone and satellite data
Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study de…
Program targeting with machine learning and mobile phone data
Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor hou…
Mobile phone data reveal the effects of violence on internal displacement in Afghanistan
Nearly 50 million people globally have been internally displaced due to conflict, persecution and human rights violations. However, the study of internally displaced persons-and the design of policies to assist them-is complicated by the fact that these people are often underrepresented in surveys and official statistics. We develop an approach to measure the impact of violence on internal displacement using anonymized high-frequency mobile phone…
Violence and Financial Decisions
We provide evidence that violence reduces the adoption and use of mobile money in three separate empirical settings in Afghanistan. First, analyzing nationwide mobile money transaction logs, we find that users exposed to violence reduce use of mobile money. Second, using panel survey data from a field experiment, we show that subjects expecting violence are significantly less likely to respond to random inducements to use mobile money. Finally, a…
Probing the limits of mobile phone metadata for poverty prediction and impact evaluation
A series of recent papers demonstrate that mobile phone metadata can, together with machine learning, estimate the wealth of individual subscribers and accurately target cash transfer programs. In the context of an emergency cash transfer program in Haiti, we combine surveys and mobile phone call detail records (CDR) to test whether such methods can be used to estimate the program’s impact on household expenditures . We find that CDR-based predic…
Estimating impact with surveys versus digital traces
We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo, endline survey data indicate positive treatment effects on food security, mental health, and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict base…
Yellow Pages
We study the impact of a low-cost intervention to reduce information frictions in rural markets by randomly assigning small and medium enterprises in Tanzania to be listed in a telephone directory. The listed firms expand their communication networks, increase sales and make greater use of mobile money, with positive spillovers to firms in the same village. Estimated effects are larger and more statistically precise for firms that are more produc…
Welfare Effects of Digital Credit
Digital loans, which provide short-term, high-interest credit via mobile phones, have exploded in popularity across low- and middle-income countries. This paper reports the results of a randomized evaluation of a digital loan product in Nigeria. Being randomly approved for a loan (among those who otherwise would have been denied) substantially increases subjective well-being after 3 months, but being randomly approved for a larger loan does not h…
Economics (9 obras) · Computer Science (7 obras) · Mobile phone (6 obras) · Telecommunications (6 obras) · Economic growth (5 obras) · Phone (5 obras) · Poverty (5 obras) · Business (4 obras) · Human Mobility and Location-Based Analysis (4 obras) · Mathematics (4 obras)