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We report on an experiment examining why default options impact behavior. Working with one of the largest private firms in Afghanistan, we randomly assigned each of 949 employees to different variants of a new default savings account. Employees assigned a default contribution rate of 5% are 40 percentage points more likely to contribute than employees assigned to a default contribution rate of zero; to achieve this effect through financial incentives alone would require a 50% match from the employer. Our design permits us to rule out several common explanations for default effects, including employer endorsement, employee inattention, and a lack of awareness about how to switch. Instead, we find evidence that the default effect is driven largely by a combination of present-biased preferences and the cognitive cost of calculating alternate savings scenarios. Default assignment also causes employees to develop savings habits that outlive our experiment: they are more likely to believe that savings is important, less likely to report being too financially constrained to save, and more likely to make an active decision to save at the end of our trial.

Zonder onderwerpscode: economie

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Do non-traditional digital trace data and traditional survey data yield similar estimates of the impact of a cash transfer program? In a randomized controlled trial of Togo's COVID-19 Novissi program, endline survey data indicate positive treatment effects on beneficiary food security, mental health, and self-perceived economic status. However, impact estimates based on mobile phone data - processed with machine learning to predict beneficiary welfare - do not yield similar results, even though related data and methods do accurately predict wealth and consumption in prior cross-sectional analysis in Togo. This limitation likely arises from the underlying difficulty of using mobile phone data to predict short-term changes in wellbeing within a rural population with fairly homogeneous baseline levels of poverty. We discuss the implications of these results for using new digital data sources in impact evaluation.

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One-fifth of the world's population lives in countries affected by fragility, violence and conflict, impeding long-term economic growth. However, little is known about how firms respond to local changes in security, partly because of the difficulty of measuring firm activity in these settings. This paper presents a novel methodology for observing private sector activity using mobile phone metadata. Using Afghanistan as the empirical setting, the analysis combines mobile phone data from over 2,300 firms with data from several other sources to develop and validate measures of firm location, size, and economic activity. Combining these new measures of firm activity with geocoded data on violent events, the paper investigates how the private sector in Afghanistan responds to insecurity. The findings indicate that firms reduce presence in districts following major increases in violence, that these effects persist for up to six months, and that larger firms are more responsive to violence. The paper concludes with a discussion of potential mechanisms, firms' strategic adaptations, and implications for policymakers.

Armed Conflict --- Conflict --- Conflict and Development --- Economic Growth --- Economic Theory & Research --- Industrial Economics --- Industrial Organization --- Insecurity --- Marketing --- Mobile Phones --- Private Sector Development --- Private Sector Development Law --- Private Sector Economics --- Social Development --- Technology Industry --- Technology Innovation --- Violence

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Policymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility -- collected by Google, Facebook, and other providers -- can be used to evaluate the effectiveness of non-pharmaceutical interventions and forecast the spread of COVID-19. This approach relies on simple and transparent statistical models, and involves minimal assumptions about disease dynamics. We demonstrate the effectiveness of this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world.