Joshua Evan Blumenstock

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 detailed boots-on-the-ground surveys of the population. Science, this issue p. 1073 Metadata from individuals’ phones can be used to predict aggregate-level characteristics such as access to electricity. Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist. We show that an individual’s past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.

Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda

We combine data from a field survey with transaction log data from a mobile phone operator to provide new insight into daily patterns of mobile phone use in Rwanda. The analysis is divided into three parts. First, we present a statistical comparison of the general Rwandan population to the population of mobile phone owners in Rwanda. We find that phone owners are considerably wealthier, better educated, and more predominantly male than the general population. Second, we analyze patterns of phone use and access, based on self-reported survey data. We note statistically significant differences by gender; for instance, women are more likely to use shared phones than men. Third, we perform a quantitative analysis of calling patterns and social network structure using mobile operator billing logs. By these measures, the differences between men and women are more modest, but we observe vast differences in utilization between the relatively rich and the relatively poor. Taken together, the evidence in this paper suggests that phones are disproportionately owned and used by the privileged strata of Rwandan society.

Global Analysis of Gene Expression: Methods, Interpretation, and Pitfalls

Over the past 15 years, global analysis of mRNA expression has emerged as a powerful strategy for biological discovery. Using the power of parallel processing, robotics, and computer-based informatics, a number of high-throughput methods have been devised. These include DNA microarrays, serial analysis of gene expression, quantitative RT-PCR, differential-display RT-PCR, and massively parallel signature sequencing. Each of these methods has inherent advantages and disadvantages, often related to expense, technical difficulty, specificity, and reliability. Further, the ability to generate large data sets of gene expression has led to new challenges in bioinformatics. Nonetheless, this technological revolution is transforming disease classification, gene discovery, and our understanding of regulatory gene networks.

Inferring patterns of internal migration from mobile phone call records: evidence from Rwanda

Understanding the causes and effects of internal migration is critical to the effective design and implementation of policies that promote human development. However, a major impediment to deepening this understanding is the lack of reliable data on the movement of individuals within a country. Government censuses and household surveys, from which most migration statistics are derived, are difficult to coordinate and costly to implement, and typically do not capture the patterns of temporary and circular migration that are prevalent in developing economies. In this paper, we describe how new information and communications technologies (ICTs), and mobile phones in particular, can provide a new source of data on internal migration. As these technologies quickly proliferate throughout the developing world, billions of individuals are now carrying devices from which it is possible to reconstruct detailed trajectories through time and space. Using Rwanda as a case study, we demonstrate how such data can be used in practice. We develop and formalize the concept of inferred mobility, and compute this and other metrics on a large data set containing the phone records of 1.5 million Rwandans over four years. Our empirical results corroborate the findings of a recent government survey that notes relatively low levels of permanent migration in Rwanda. However, our analysis reveals more subtle patterns that were not detected in the government survey. Namely, we observe high levels of temporary and circular migration, and note significant heterogeneity in mobility within the Rwandan population. Our goals in this research are thus twofold. First, we intend to provide a new quantitative perspective on certain patterns of internal migration in Rwanda that are unobservable using standard survey techniques. Second, we seek to contribute to the broader literature by illustrating how new forms of ICT can be used to better understand the behavior of individuals in developing countries.

Promises and pitfalls of mobile money in Afghanistan: evidence from a randomized control trial

Despite substantial interest in the potential for mobile money to positively impact the lives of the poor, little empirical evidence exists to substantiate these claims. In this paper, we present the results of a field experiment in Afghanistan that was designed to increase adoption of mobile money, and determine if such adoption led to measurable changes in the lives of the adopters. The specific intervention we evaluate is a mobile salary payment program, in which a random subset of individuals of a large firm were transitioned into receiving their regular salaries in mobile money rather than in cash. We separately analyze the impact of this transition on both the employer and the individual employees. For the employer, there were immediate and significant cost savings; in a dangerous physical environment, they were able to effectively shift the costs of managing their salary supply chain to the mobile phone operator. For individual employees, however, the results were more ambiguous. Individuals who were transitioned onto mobile salary payments were more likely to use mobile money, and there is evidence that these accounts were used to accumulate small balances that may be indicative of savings. However, we find little consistent evidence that mobile money had an immediate or significant impact on several key indicators of individual wealth or well-being. Taken together, these results suggest that while mobile salary payments may increase the efficiency and transparency of traditional systems, in the short run the benefits may be realized by those making the payments, rather than by those receiving them.

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 demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.

Social and spatial ethnic segregation: a framework for analyzing segregation with large-scale spatial network data

While ethnic segregation plays an important role in determining the development trajectories of many countries, empirical measures of the dynamics of segregation remain rudimentary. In this paper, we develop a new computational framework to model and measure fine-grained patterns of segregation from novel sources of large-scale digital data. This framework improves upon prior work by providing a method for decomposing segregation into two types that previous work has been unable to separate: social segregation, as observed in interactions between people, and spatial segregation, as determined by the co-presence of individuals in physical locations. Our primary contribution is thus to develop a set of computational and quantitative methods that can be used to study segregation using generic spatial network data. A secondary contribution is to discuss in detail the strengths, weaknesses, and implications of this approach for studying segregation in developing countries, where ethnic divisions are common but data on segregation is often plagued by issues of bias and error. Finally, to demonstrate how this framework can be used in practice, and to illustrate the differences between social and spatial segregation, we run a series of diagnostic tests using data from a single city in a large developing country in South Asia. The case study we develop is based on anonymized data from a mobile phone network, but the framework can generalize easily to a broad class of spatial network data from sources such as Twitter, social media, and networked sensors.

Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda

A large literature describes how local risk sharing networks can help individuals smooth consumption in the face of idiosyncratic economic shocks. However, when an entire community faces a large covariate shock, and when the transaction costs of transfers are high, these risk sharing networks are likely to be less effective. In this paper, we document how a new technology – mobile phones – reduces transaction costs and enables Rwandans to share risk quickly over long distances. We examine a comprehensive database of person-to-person transfers of mobile airtime and find that individuals send this rudimentary form of “mobile money” to friends and family affected by natural disasters. Using the Lake Kivu earthquake of 2008 to identify the effect of a large covariate shock on interpersonal transfers, we estimate that a current-day earthquake would result in the transfer of between

Differences in phone use between men and women: quantitative evidence from Rwanda

22,000 and

Using mobile phone data to measure the ties between nations

30,000 to individuals living near the epicenter. We further show that the pattern of transfers is most consistent with a model of reciprocal risk sharing, where transfers are determined by past reciprocity and geographical proximity, rather than one of pure charity or altruism, in which transfers would be expected to be increasing in the wealth of the sender and decreasing in the wealth of the recipient.