2023初 - 在某S听Econ job market paper talk笔记1

Week 1:

以下notes只是听的过程中的随意摘抄，仅供和我的朋友们分享讨论。后续更新一下我听的感受（主要是从未来preparation+presentation的角度），不过不知道什么时候来填坑，至少是和老板讨论后吧。

Thur, Jan 12: Indira Puri - MIT

Fields: Microeconomic Theory, Financial Economics, Behavioral Economic

Job Market Paper: Simplicity and Risk

Pay attention to the amount of work for this job market talk.

theory (w/ experimental evidence support;

Risk premia increase as complexity increases

(experiment with choices with the same certainty equivalence but different complexity)

Section 1: theory introduction (with support of lab experiment)

Empirical lab evidence + theory

Cognitive ability vs complexity aversion

Separability of complexity and risk

(Discussion: (1) mechanism about the complexity? Some evidence about cognitive ability; but not the focus of today)

Compare with previous literature: can 9 canonical theories explain it?

Eg: sparsity, salient, cognitive-probability weighting (Fit a CPT model to the data, calculate residuals), etc

Build complexity reversion

Re-interpreting classical results with the complexity lens

Section 2: Financial Application

1. Binary Options in markets

0/1 bet on underlying (such as indices, whether to buy or sell something)

Banned in some countries

Test for valuing simplicity:

• Graph: underlying asset price vs payoff; compare the dominating bull spread payoff and binary option payoff, whether dominating bull price always higher than the dominated binary price? The dominated binary is often priced higher than dominating bull spread.
• Data:

1. Binary options: binary options trades from Nadex
2. Bull spreads: option prices from CME

• Standard explanations don't fully capture the results

1. Trading fees; noise traders; collateral; trade size/market depth (restrict the analysis to cases whether the size of purchase on Nadex is at least minimum CME size, similar results)
2. (Q&A: they have the necessary financial literacy but still choose binary options)
3. Short-term appeal, liquidity premia (CME is the more liquid exchange, so pi paying a premium for liquidity, the price of the bull spread should be higher); knowledge or accessibility differences, etc

• Survey: data collection

1. On a binary options trading discussion forum
2. 10 $/ppl, 139 ppl
3. The direct survey confirms the presence of simplicity preferences
4. Other supplemental exercises move in the same direction: fractional contracts, strike distance tests, traded prices relative to the theoretical predictions

Conclusion of the second section:

• Simplicity-seeking in choice
• People value simplicity over and above predictions by canonical theories
• Moving forward, the agenda with possible future papers

Introduction of her ongoing and related research agenda

Fri, Jan 13 in SIEPR: John Conlon - Harvard

Fields: Experimental Economics, Behavior Economics, Labor Economics

Job Market Paper:What Jobs Come to Mind? Stereotypes about Fields of Study

Research questions:

Q 1: Do students know how their major will affect their career?

Q 2: Why are students' beliefs mistaken?

• Related to stereotype --> from the oversimplified image of the major

Agenda:

A: Evidence on stereotyping majors

• Higher education research institute: freshman survey, incoming first-years around the country

1. 1976-2015 wave, 9 million but qualitative
2. Results are also consistent with other explanations: a. qualitative data: what does: probable career" mean? Confidence (aware some jobs are rare, but will nonetheless take them); selection - extreme view about their major.

(a lot of discussions during the talk is about the interpretation of stereotypes; specific to the data)

• Smaller scale survey: Ohio state university self-beliefs, quantitative beliefs
  1. Design to isolate stereotyping: e.g.: ranking 10 groups of majors
  2. Graphs ( the large-scale survey vs belief of top majors vs all majors listed)

B: Implications of stereotyping

• How might stereotypes affect choice?

(Q&A: American students frequently switch majors? A: still affect their commitment and allocation of time)

• Which jobs are risky? 20 most common majors in the ACS:
  1. Graph: percent in stereotypical job vs avg salary of alternative jobs
  2. Correlation/suggestive evidence on long-term outcomes (provide evidence of the importance of this research): e.g.: students in more risky majors are more likely to regret their major choices, have higher unemployment rate and outstanding student debt, and say costs of degrees larger than financial benefits, etc.
  3. Field experiment (attempting) to correct these beliefs

(one comment: it is really hard to do related causal experiment/similar inference in the US as it is hard to keep the students' majors unchanged)

- Scalable intervention; (some details of the experiment)

- What did the information intervention look like? (some information about the major outcome of the participant and the real statistic outcome)

- Effects: on class choices for spring and fall 2022. (Graph: compare risky top major vs non-risky top major vs all students)

C: Mechanism: a memory-based model of belief formation

• Basic mechanics: hypotheses: what share of M majors have career c?
• Assess likelihood:

1. Think of someone she knows
2. Asks about this person's outcome

• Beliefs depend on who comes to mind and recall matters--> memory is associative and imperfect: sometimes the wrong thing comes to mind, interfering with recalling the right things

(My comment: how about the network effect? Examples of who you know are more likely to be your outcome as you have similar social resources and status)

H -> hypothesis

P(recall e when assessing H) ~ similarity between e and H

--> associative memory destroys beliefs

--> students should overestimate rare careers

• Then check with a role model

D: Conclusion part