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We provide evidence from field experiments with three different banks that reminder messages increase commitment attainment for clients who recently opened commitment savings accounts. Messages that mention both savings goals and financial incentives are particularly effective, whereas other content variations such as gain versus loss framing do not have significantly different effects. Nor do we find evidence that receiving additional late reminders has an additive effect. These empirical results do not map neatly into existing models, so we provide a simple model where limited attention to exceptional expenses can generate undersaving that is in turn mitigated by reminders. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2296 . This paper was accepted by Teck-Hua Ho, behavioral economics .

This article defines and analyzes a ‘‘sparse max’’ operator, which is a less than fully attentive and rational version of the traditional max operator. The agent builds (as economists do) a simplified model of the world which is sparse, considering only the variables of first-order importance. His stylized model and his resulting choices both derive from constrained optimization. Still, the sparse max remains tractable to compute. Moreover, the induced outcomes reflect basic psychological forces governing limited attention. The sparse max yields a behavioral version of basic chapters of the microeconomics textbook: consumer demand and competitive equilibrium. I obtain a behavioral version of Marshallian and Hicksian demand, Arrow-Debreu competitive equilibrium, the Slutsky matrix, the Edgeworth box, Roy’s identity, and so on. The Slutsky matrix is no longer symmetric: nonsalient prices are associated with anomalously small demand elasticities. Because the consumer exhibits nominal illusion, in the Edgeworth box, the offer curve is a two-dimensional surface rather than a one-dimensional curve. As a result, different aggregate price levels correspond to materially distinct competitive equilibria, in a similar spirit to a Phillips curve. The Arrow-Debreu welfare theorems typically do not hold. This framework provides a way to assess which parts of basic microeconomics are robust, and which are not, to the assumption of perfect maximization.

Many of the insights of economics seem to be qualitative, with many fewer reliable quantitative laws. However a series of power laws in economics do count as true and nontrivial quantitative laws—and they are not only established empirically, but also understood theoretically. I will start by providing several illustrations of empirical power laws having to do with patterns involving cities, firms, and the stock market. I summarize some of the theoretical explanations that have been proposed. I suggest that power laws help us explain many economic phenomena, including aggregate economic fluctuations. I hope to clarify why power laws are so special, and to demonstrate their utility. In conclusion, I list some power-law-related economic enigmas that demand further exploration. A formal definition may be useful.

We build a game-theoretic model to examine how better demand forecasting resulting from algorithms, machine learning, and artificial intelligence affects the sustainability of collusion in an industry. We find that, although better forecasting allows colluding firms to better tailor prices to demand conditions, it also increases each firm’s temptation to deviate to a lower price in time periods of high predicted demand. Overall, our research suggests that, despite concerns expressed by policy makers, better forecasting and algorithms can lead to lower prices and higher consumer surplus. This paper was accepted by Joshua Gans, business strategy.

In poverty measurement, differential weighting aims to take into account the unequal importance of the diverse dimensions and aspects of poverty and to add valuable information that improves the classification of the poor and the not-poor. This practice, however, is in contention with both classical test theory and modern measurement theories, which state that high reliability is a necessary condition for consistent population classification, while differential weighting is not so. The literature needs a clear numerical illustration of the relationship between high/low reliability and good/poor population classification to dissolve this tension and assist applied researchers in the assessment of multidimensional poverty indexes, using different reliability statistics. This paper uses a Monte Carlo study based on factor mixture models to draw up a series of uni-and multidimensional poverty measures with different reliabilities and predefined groups. The article shows that low reliability results in a high proportion of the poor group erroneously classified as part of the not poor group. Therefore, reliability inspections should be a systematic practice in poverty measurement. The article provides guidelines for interpreting the effects of unreliability upon adequate population classification and suggest that the classification error of current unreliable multidimensional indexes is above 10%.

In recent years, electrification has reemerged as a key priority in low-income countries, with a particular focus on electrifying households. Yet the microeconomic literature examining the impacts of electrifying households on economic development has produced a set of conflicting results. Does household electrification lead to measurable gains in living standards or not? Focusing on grid electrification, we discuss how the divergent conclusions across the literature can be explained by differences in methods, interventions, potential for spillovers, and populations. We then use experimental data from Lee, Miguel, and Wolfram (2019)—a field experiment that connected randomly selected households to the grid in rural Kenya—to show that impacts can vary even across individuals in neighboring villages. Specifically, we show that households that were willing to pay more for a grid electrification may gain more from electrification compared to households that would only connect for free. We conclude that access to household electrification alone is not enough to drive meaningful gains in development outcomes. Instead, future initiatives may work better if paired with complementary inputs that allow people to do more with power.

We investigate the hypothesis that macroeconomic fluctuations are primitively the results of many microeconomic shocks. We define fundamental volatility as the volatility that would arise from an economy made entirely of idiosyncratic sectoral or firm-level shocks. Fundamental volatility accounts for the swings in macroeconomic volatility in the major world economies in the past half-century. It accounts for the \"great moderation\" and its undoing. The initial great moderation is due to a decreasing share of manufacturing between 1975 and 1985. The recent rise of macroeconomic volatility is chiefly due to the growth of the financial sector.

We use daily transaction data in 214 cities to study the impact of COVID-19 on consumption after China’s outbreak in late January 2020. Based on difference-in-difference estimation, daily offline consumption—via bank card and mobile Quick Response code transactions—fell by 32 percent, or 18.57 million renminbi (RMB) per city, during the 12-week period. The effect is prevalent across cities and is more pronounced in the dining-and-entertainment and travel categories. We infer that China’s offline consumption decreased by over 1.22 trillion RMB, or 1.2 percent of China’s 2019 GDP, in the 3-month postoutbreak period.

The measurement of well-being of people is very difficult because it is characterized by a multiplicity of aspects or dimensions. Principal Components Analysis (PCA) is probably the most popular multivariate statistical technique for reducing data with many dimensions and, often, well-being indicators are reduced to a single index of well-being by using PCA. However, PCA is implicitly based on a reflective measurement model that is not suitable for all types of indicators. In this paper, we discuss the use and misuse of PCA for measuring well-being, and we show some applications to real data.

The use of real decision-making incentives remains under debate after decades of economic experiments. In time preferences experiments involving future payments, real incentives are particularly problematic due to between-options differences in transaction costs, among other issues. What if hypothetical payments provide accurate data which, moreover, avoid transaction cost problems? In this paper, we test whether the use of hypothetical or one-out-of-ten-participants probabilistic—versus real—payments affects the elicitation of short-term and long-term discounting in a standard multiple price list task. We analyze data from a lab experiment in Spain and well-powered field and online experiments in Nigeria and the UK, respectively ( N  = 2,038). Our results indicate that the preferences elicited using the three payment methods are mostly the same : we can reject that either hypothetical or one-out-of-ten payments change any of the four preference measures considered by more than 0.18 SD with respect to real payments.