Consumers rely on the price changes of goods in their grocery bundles when forming expectations about aggregate inflation. We use micro data that uniquely match individual expectations, detailed ...information about consumption bundles, and item-level prices. The weights consumers assign to price changes depend on the frequency of purchase, rather than expenditure share, and positive price changes loom larger than negative price changes. Prices of goods offered in the same store but not purchased do not affect inflation expectations, nor do other dimensions. Our results provide empirical guidance for models of expectations formation with heterogeneous consumers.
We evaluate an intervention targeting early life nutrition and well-being for households in extreme poverty in Northern Nigeria. The intervention leads to large and sustained improvements in ...children’s anthropometric and health outcomes, including an 8 percent reduction in stunting 4 years, post-intervention. These impacts are partly driven by information- related channels. However, the certain and substantial flow of cash transfers is also key. They induce positive labor supply responses among women, and enables them to undertake productive investments in livestock. These provide protein rich diets for children, and generate higher household earnings streams long after the cash transfers expire.
In 2018, the editors of the Journal of Economic Perspectives invited faculty to send us examples of JEP articles that they had found useful for teaching. We received 250 responses. On the JEP ...website, we have created a landing page (https://www.aeaweb.org/journals/jep/classroom) that organizes the recommended articles into 33 categories. If you click on any of the categories at that link, you will see a list of JEP papers that were recommended by faculty members for classroom use for that category, presented in reverse date order. Each paper is listed with a hyperlink to its article page on the JEP website. In this article, I offer some thoughts about how this exercise was carried out, along with its strengths and weaknesses. Although we make no pretense of presenting a complete syllabus for any specific course, we offer the milder hope that these recommendations from peers might suggest some additional readings for your students.
•Naturalistic data was used to model the cyclist-vehicle interaction.•The model predicts the yielding decision at an unsignalized intersection.•The model uses both the kinematics and the cyclists’ ...behavioral cues for prediction.•Head movement and pedaling were significant predictors.
When a cyclist’s path intersects with that of a motorized vehicle at an unsignalized intersection, serious conflicts may happen. In recent years, the number of cyclist fatalities in this conflict scenario has held steady, while the number in many other traffic scenarios has been decreasing. There is, therefore, a need to further study this conflict scenario in order to make it safer. With the advent of automated vehicles, threat assessment algorithms able to predict cyclists’ (other road users’) behavior will be increasingly important to ensure safety.
To date, the handful of studies that have modeled the vehicle-cyclist interaction at unsignalized intersections have used kinematics (speed and location) alone without using cyclists’ behavioral cues, such as pedaling or gesturing. As a result, we do not know whether non-verbal communication (e.g., from behavioral cues) could improve model predictions.
In this paper, we propose a quantitative model based on naturalistic data, which uses additional non-verbal information to predict cyclists’ crossing intentions at unsignalized intersections. Interaction events were extracted from a trajectory dataset and enriched by adding cyclists’ behavioral cues obtained from sensors. Both kinematics and cyclists’ behavioral cues (e.g., pedaling and head movement), were found to be statistically significant for predicting the cyclist’s yielding behavior. This research shows that adding information about the cyclists’ behavioral cues to the threat assessment algorithms of active safety systems and automated vehicles will improve safety.
Labor Rationing Breza, Emily; Kaur, Supreet; Shamdasani, Yogita
The American economic review,
10/2021, Volume:
111, Issue:
10
Journal Article
Peer reviewed
Open access
This paper measures excess labor supply in equilibrium. We induce hiring shocks—which employ 24 percent of the labor force in external month-long jobs—in Indian local labor markets. In peak months, ...wages increase instantaneously and local aggregate employment declines. In lean months, consistent with severe labor rationing, wages and aggregate employment are unchanged, with positive employment spillovers on remaining workers, indicating that over a quarter of labor supply is rationed. At least 24 percent of lean self-employment among casual workers occurs because they cannot find jobs. Consequently, traditional survey approaches mismeasure labor market slack. Rationing has broad implications for labor market analysis.
Employers use various proxies to predict the future labour productivity levels of the job applicants. Success in school, especially in high‐level coursework, is among the most widely used proxies to ...screen entry‐level candidates. We estimate the causal effect of graduating with honours (i.e. with a grade point average of 3.00 and above out of 4.00) on the starting wages of economics majors in Türkiye. Using comprehensive micro data on all economics majors between 2014 and 2018, matched with administrative records about their first jobs, we implement a regression discontinuity analysis to investigate whether there is any statistically significant jump in the starting wages at the honours‐degree cutoff. We find that graduating with honours increases the wages of males, while there is no impact on females. We further document that the impact on males is almost entirely driven by the graduates of non‐elite universities. In particular, graduating with an honours degree increases the entry wages of males from non‐elite universities by about 4%, on average. We provide an explanation for these patterns using the theory of statistical discrimination. We discuss the potential reasons behind the heterogeneous signal value of graduating with honours between males vs. females and elite versus non‐elite university graduates.
In Germany, employers used to pay union members and non‐members in a plant the same union wage in order to prevent workers from joining unions. Using recent administrative data, we investigate which ...workers in firms covered by collective bargaining agreements still individually benefit from these union agreements, which workers are not covered anymore and what this means for their wages. We show that about 9 per cent of workers in plants with collective agreements do not enjoy individual coverage (and thus the union wage) anymore. Econometric analyses with unconditional quantile regressions and firm‐fixed‐effects estimations demonstrate that not being individually covered by a collective agreement has serious wage implications for most workers. Low‐wage non‐union workers and those at low hierarchy levels particularly suffer since employers abstain from extending union wages to them in order to pay lower wages. This jeopardizes unions' goal of protecting all disadvantaged workers.
Learning from Law Enforcement Dušek, Libor; Traxler, Christian
Journal of the European Economic Association,
04/2022, Volume:
20, Issue:
2
Journal Article
Peer reviewed
Open access
Abstract
This paper studies how punishment affects future compliance behavior and isolates deterrence effects mediated by learning. Using administrative data from speed cameras that capture the full ...driving histories of more than a million cars over several years, we evaluate responses to punishment at the extensive (receiving a speeding ticket) and intensive margins (tickets with higher fines). Two complementary empirical strategies—a regression discontinuity design and an event study—coherently document strong responses to receiving a ticket: The speeding rate drops by a third and re-offense rates fall by 70%. Higher fines produce a small but imprecisely estimated additional effect. All responses occur immediately and are persistent over time, with no backsliding toward speeding even two years after receiving a ticket. Our evidence rejects unlearning and temporary salience effects. Instead, it supports a learning model in which agents update their priors on the expected punishment in a coarse manner.
Using high‐quality nationwide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian ...social security system between 2014 and 2018. We show that machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals are on income support in the subsequent 4 years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R‐squared), compared to the latter. This gain can be achieved at no extra cost to practitioners since the algorithms use administrative data currently available to caseworkers. Consequently, our machine learning algorithms can improve the detection of long‐term income support recipients, which can potentially enable governments and institutions to offer timely support to these at‐risk individuals.
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