Matthew Olckers Economist by day; graffiti nerd by night.

My favorite papers at the ASSA Meeting 2019

I have started a tradition of selecting my favorite papers from the massive ASSA Annual Meeting. (Does three years make a tradition?) Here are my picks from 2017 and from 2018. Given my research interests, I like most of the papers in the networks sessions (here, here, here and here). Outside of networks, I found the following papers interesting.

“Who Is More Generous with the Most Needy? Experimental Evidence from Bogota’s Stratification” by Mariana Blanco and Patricio Dalton presented at the session titled Social Preferences with Not WEIRD (Western Educated Industrialized Rich Democratic) People.

Over decades, scholars have tried to understand the relationship between generosity and wealth. However, household wealth is not easy to observe without error. It is either self-reported or artificially created in experimental settings. Moreover, comparing generosity of the rich with the poor presents a challenge, since the rich have more monetary resources than the poor to act generously. To address these concerns, we take advantage of a unique feature of the city of Bogota. Bogota is divided by law into six socio-economic strata which are close proxies of household wealth and income. We recruit subjects from different strata and run a series of double-blinded dictator games where the recipient is the NGO Techo-Colombia, which builds transitional housing for homeless families. We twist the design of our experiments to identify the stratum of each subject anonymously and blindly and match their donations with their stratum. In a first experiment we provide a fix endowment to all participants and find that, compared to the poor, the rich donate more. However, in a second experiment, we show that this is not because the rich are intrinsically more generous, but because the experimental endowment has lower real value for them. When we use a strategy-method with endowments equivalent to the average daily expenditure of the strata, we find that both rich and poor are equally generous. The rich and the poor are equally likely to donate and those who donate, give a similar proportion of their strata-equivalent endowment. Moreover, using a matching design, we find that the motivation to donate is also similar across strata. Both, rich and poor generosity is explained more by a feeling of warm-glow rather than by pure altruism.

“Thy Neighbor’s Misfortune: Peer Effect on Consumption” by Sumit Agarwal, Wenlan Qian and Xin Zou presented at the session titled Expectations in Household Finance.

Using a large, representative sample of credit and debit card transactions in Singapore, we study the consumption response of individuals whose same-building neighbors experienced personal bankruptcy. The unique bankruptcy rules in Singapore suggest liquidity shocks drive personal bankruptcy and the bankrupts experience severe consumption decrease afterwards. Peers’ monthly card consumption decreases by 3.4 percent over the one-year post-bankruptcy period. We find no occupation concentration in the bankruptcy-hit buildings, no consumption decrease among individuals in immediately adjacent buildings, or for consumers with diminished post-event social ties with the bankrupt individual. Our findings imply a significant social multiplier effect of 0.8-1.2 times the original consumption shock. The response is more pronounced for consumers with greater interaction and is equally strong in the conspicuous and non-conspicuous goods.

“Crowdsourcing and Optimal Market Design” by Bobak Pakzad-Hurson presented at the session titled Auctions & Mechanism Design.

Solutions to many allocation problems crucially rely on the assumption that agents fully know their preferences over objects to be allocated. I present a general crowdsourcing approach for solving mechanism design problems in which important characteristics of objects are imperfectly observed by agents. The designer first solicits reports of object characteristics by agents and assigns each object a characteristic using a quasi-maximum likelihood method. Second, the designer runs an off-the-shelf “full-information” mechanism using the assessed characteristics. To ensure truth-telling incentives, agents are punished when their reports do not match up with the “wisdom of the crowd.” Assuming mild conditions on the relative growth rates of agents and objects, I show this approach yields the same allocation as in the full-information case with probability exponentially converging to one in the number of agents, with aggregate worst-case waste (punishment) converging exponentially to zero. Neither the aggregation nor punishment schemes rely on details of the market. Therefore, my approach is the first to generate near-optimal outcomes with high probability in a variety of settings, including two-sided matching markets, with interdependent preferences. I give necessary and sufficient conditions for recovering desirable properties when signal acquisition is endogenous and costly for agents.

“Does Elite Capture Matter? Local Elites and Targeted Welfare Programs in Indonesia” by Vivi Alatas, Abhijit Banerjee, Rema Hanna, Benjamin Olken, Ririn Purnamasari and Matthew Wai-Poi presented at the session titled Intentional and Unintentional Effects of Safety Net Programs.

This paper investigates the impact of elite capture on the allocation of targeted government welfare programs in Indonesia, using both a high-stakes field experiment that varied the extent of elite influence and non-experimental data on a variety of existing government transfer programs. Conditional on their consumption level, there is little evidence that village elites and their relatives are more likely to receive aid programs than non-elites. However, this overall result masks stark differences between different types of elites: those holding formal leadership positions are more likely to receive benefits, while informal leaders are less likely to receive them. We show that capture by formal elites occurs when program benefits are actually distributed to households and not during the processes of determining who should be on the beneficiary lists. However, while elite capture exists, the welfare losses it creates appear small: since formal elites and their relatives are only 9 percent richer than non-elites, are at most about 8 percentage points more likely to receive benefits than non-elites and represent at most 15 percent of the population, eliminating elite capture entirely would improve the welfare gains from these programs by less than one percent.

“What Do Consumers Consider Before They Choose? Identification from Asymmetric Demand Responses” by Abi Adams and Jason Abaluck presented at the session titled Identification and Inference in Limited Attention Models.

Consideration set models relax the assumption that consumers are aware of all available options. Thus far, identification arguments for these models have relied either on auxiliary data on what options were considered or on instruments excluded from consideration or utility. In a discrete choice framework subsuming logit, probit and random coefficients models, we prove that utility and consideration set probabilities can be separately identified without these data intensive methods. In full-consideration models, choice probabilities satisfy a symmetry property analogous to Slutsky symmetry in continuous choice models. This symmetry breaks down in consideration set models when changes in characteristics perturb consideration and we show that consideration probabilities are constructively identified from the resulting asymmetries. In a lab experiment, we recover preferences and consideration probabilities using only data on which items were ultimately chosen and we apply the model to study hotel choices on Expedia.com and insurance choices in Medicare Part D.

“Causal Methods for Panel Data” by Susan Athey and Guido Imbens presented at the session titled Applied Machine Learning.

In estimation of treatment effects in panel data settings, researchers have often used fixed effect models where variation between units and over time is captured completely by additive components. Such specifications may be restrictive if in fact there is heterogeneity in the treatment effects between units and over time. In this paper we explore machine learning methods to assess such heterogeneity and to develop richer, data-driven specifications. A key challenge is that simply treating the unit and time indicators as features leads to models with many features where sparsity may not be plausible. Instead we develop methods that build flexible models for the heterogeneity between units and over time that can be viewed as generalizing fixed effect models.

“The Effect of Superstar Firms on College Major Choice” by Darwin Choi, Dong Lou and Abhiroop Mukherjee presented at the session titled Behavioral Corporate Finance.

We study the effect of superstar firms on college students’ major choice. The occurrence of superstar performers in an industry is followed by a significant rise in the number of college students choosing to major in related fields, after controlling for lagged industry returns and wages. The tendency to follow superstars, however, results in a temporary over-supply of human capital, as evidenced by the lower real wage earned by entry-level employees when students enter the job market. Further evidence from the National Survey of College Graduates shows that this adverse impact on career outcomes can last for decades.

“Time Discounting, Savings Behavior and Wealth Inequality” by Claus Kreiner, Helga Duda-Fehr, Ernst Fehr, David Dreyer Lassen, Søren Leth-Petersen, Gregers Nytoft Rasmussen and Thomas Epper presented at the session titled Wealth Inequality & Wealth Taxation.

The distribution of wealth in society is very unequal and has important economic and political consequences. According to standard life-cycle savings theory, differences in time discounting behavior across individuals can play an important role for their position in the wealth distribution. Empirical testing of this hypothesis has been difficult because of serious data limitations. We overcome these limitations by linking an experimental measure of time discounting for a large sample of middle-aged individuals to Danish high-quality administrative data with information about their real-life wealth over the life-cycle as well as a large number of background characteristics. The results show that individuals with relatively low time discounting are persistently positioned higher in the wealth distribution. The relationship is of the same magnitude as the association between years of education and the position in the wealth distribution and it robustly persists after controlling for a large number of theoretically motivated confounders such as education, risk aversion, school grades, income, credit constraints, initial wealth and parental wealth. These findings support the view that individual differences in time discounting affect individuals’ positions in the wealth distribution through the savings channel.