u/SonnytheFlame

Here's the link to this week's paper, The Origins of State Capacity: Property Rights, Taxation, and Politics (Timothy Besley and Torsten Persson, American Economic Review)

Another theory paper this week, this time on political economy. State capacity is what the name implies, the capacity of a government to perform political or economic functions. It's an interesting topic and one explored both by economists and political scientists, sometimes even using the same tools. The interesting part of this paper is theory, though there is some limited estimation at the end.

The main capacities the paper is interested in are legal and fiscal, or the ability to enforce contracts and taxes respectively. Using a two period model, the government invests in state capacity in the first period while the citizens/investors invest in assets across the two periods. When war breaks out or other crisis events occur, the incentives for investing in state capacity rise, which is designed to emulate the real-world implementation of income taxes during the US civil war and Napoleonic wars in Europe. The most interesting thing to me was that legal and fiscal capacity are complementary, and nations which tend to have one grow in the other. This matches the empirical fact that most (but not all) highly developed nations are high tax nations.

The paper is straightforward and part of a large literature on state capacity. The tools in it are very easy, and I chose it in part because it can be read and commented upon by anyone who's taken undergraduate macroeconomics. It was written in 2009, which was a long time ago in academic terms, but on the whole I find it didn't tell me much I didn't already know from reading the Acemoglu macro-development literature. I didn't find the estimation at the end particularly compelling, and the consumer side of the economy seems simplified to the point of not being interesting, and is ripe for extension in light of the growth of heterogeneous agent macro. Honestly, if this was submitted today I don't think it would be published in AER, but that's more a sign of how much political economy has frown in the past 15-20 years.

Feel free to DM me if you have a suggestion for next week's paper of the week!

Fellow Economists,

Hope you've all been enjoying the paper of the week and the (hopefully) reduced spam on this sub. The admissions posts seem to be better, we have fewer of them and they seem to be more informative. I added some resources to the sub's sidebar but only realized earlier today that most of it is only viewable on old reddit, and since the vast majority of our users are on mobile or new reddit it's not very useful. As such, I'm going to start work an r/academiceconomics wiki page to act as a centralized source of knowledge for economists, and wanted to hear what you guys would like to see.

We're going to include a comprehensive guide on admissions for masters and PhD programs to hopefully act as a repository similar to how testmagic was back in the day. I'd also like to work on a document on the academic job market and flyouts, if any faculty are interested in that please DM me. There's a couple of miscellaneous papers I'm going to add about research workflow, document management, and coding, but I'm curious to hear what you guys think would be helpful. Feel free to suggest resources you find useful as well, I'll take a look at them and see about adding them.

I'll pin this thread for the next week or so then start writing it up, and aim to have the wiki up by the end of the month.

Here's the link to this week's paper, Risks For the Long Run: A Potential Resolution of Asset Pricing Puzzles (Ravi Bansal & Amir Yaron, Journal of Finance)

Our first finance paper looks at what's likely the biggest outstanding question in the financial literature, the equity premium puzzle. For those who haven't studied finance, the equity premium puzzle refers to the outsized difference in returns between the market and government bonds relative to reasonable risk aversion coefficients. There's a very, very large literature attempting to measure and explain the source of this equity premium.

The crux of the model is a persistent term added to the growth of cosumption and dividends and stochastic violatility (contrasted with iid growth usually assumed). Under normal preferences, a positive shock to growth rates would result in bond rates rising, but Bansal and Yaron use Epstein-Zin preferences to decouple IES and CRRA, where if IES if is greater than 1 investors actually feel wealthier and invest more, driving up the price/consumption ratio. Similarly, if there's a negative shock to consumption, this massively affects the asset's return and hurts the investor more. The persistence of growth creates a 'long run risk', and this risk creates the asset premium. This covers the first 10 pages or so, and the rest of the paper attempts to justify the persistent growth term ahead of the expected criticism. The main empirical result is that their callibration predicts an IES of 1.5 and a risk aversion of about 10. For those who aren't in finance, risk aversion is empircally thought to be somewhere between 3 and 10, and independent of Bansal and Yaron's model, would require something like 40 to be consistent with current asset returns and bond rates. Long-run risk clearly has a lot of explanatory power.

So how does their explanation hold up to criticism? Beeler and Campbell (2012) have a good paper which finds that consumption growth isn't as consistent as the model would imply, and Campbell specifically has disagreed with it publically on a couple occassions. Bansal has a couple other paper's defending it from these criticisms, but by and large it hasn't fully solved the puzzle. This isn't to say it isn't a valuable good model-this is an outstanding theoretical contribution, and was cited in the 2013 Nobel Prize article.

I'm a fan of Bansal's work. I'm somewhat of an amateur when it comes to asset pricing (the empirical literature is seemingly endless), but this paper takes a shot at one of the biggest questions in economics and presents a viable explanation in relatively simple mathematical terms. I replicated this paper when I took asset pricing and aside from some excessive algebra in Case II, I found it pretty straightforward, and would strongly reccomend anyone interested in pursuing finance at least give the derivations a try.

Feel free to DM me if you have a suggestion for next week's paper of the week!

Here's the link to this week's paper, Structural vs. atheoretic approaches to econometrics (Michael P Keane, Journal of Econometrics)

This week's paper is a bit different, and is more of a meta commentary on the state of econometrics c. 2010. In short, Keane argues that the rise of reduced form (here called atheoretical) work isn't as auspicious as thought by Angrist and his ilk of causal inference researchers. Rather than relying on fewer assumptions, reduced form estimation just makes the need for certain assumptions less obvious.

This article is a must-read today, in a time where causal inference plays a central role in graduate training. In my view, the prominence of causality in econometrics has two causes. The first is the overcorrection from the heavily structural work used in macro and IO in the 90s. Economists recognized that relative to other disciplines, they were making a large number of assumptions about the data they were analyzing using, and sought to course-correct by borrowing things like RCTs from medicine. Intuitively, establishing a strong causal link between two variables makes the arguments in economic papers stronger, and further legitimizes research in the field. The second is that reduced form econometrics require objectively less training, and allows for economists with less rigorous technical backgrounds or scholars in non-social science fields to understand the papers. Structural estimation is difficult (I remember working through the BLP model in IO!) and not widely performed by political scientists, sociologists, or data scientists. By shifting the econometric focus from the labour-intensive theory-driven structrual work, more economists were able to capitalize on the increasingly available statistical tools that became prominent in the late 1990s.

I'd be curious to hear from people who do work with reduced form models, both on the paper itself and how the field has changed. I'm a theorist but I only worked with hardcore structural estimation in the second year of my PhD. I imagine this sub will lean towards reduced-form simply by virture of not having PhD econometrics yet, though I strongly encourage any prospective graduate students to read this paper before declaring one methodology superior.

Feel free to DM me if you have a suggestion for next week's paper of the week!

Here's the link to this week's paper, Taxation and Innovation in the 20th century (Ufuk Akcigit, John Grigsby, Tom Nicholas, & Stefanie Stantcheva, QJE).

This paper is a interesting mix of micro, macro, and econometrics, in contract to last week's macro theory. The authors (and presumably an army of RAs) construct databases of innovations (proxied by patents) and state-level taxes, and along with data on individual income taxes, to measure whether taxes at the state level and/or individual level actually deter innovation.

The paper is fairly straightforward-taxes clearly deter innovation, but there are both micro and macro mechanisms through which they do so. The main dependent variable is # of patents registered, so the expectation would be that federal taxes reduce patents overall, and state-level taxes would decrease patents in that state, with innovators potentially moving to other states. Identification is done through a range of fixed-effects, and elasticities are also calculated. The main result comes in Table 4, which finds that corporate and marginal taxes lead to statistically significant decreases in patents and citations.

This is an excellent paper, and an example of how interesting research questions are more important than being an outstanding mathematician or theoretician. On the other hand, this means the value of the paper largely boils down to the value of the data the authors were able to get. The authors assembled two new datasets to write this paper and the main regression result has more than 6,000,000 observations, which takes a herculean effort-and access to data is not randomly distributed across departments. For those of us who are still in grad school, this is something to consider as you begin to specialize and figure out what kind of research you want to do.

Feel free to DM me if you have a suggestion for next week's paper of the week!

Here's the link to our inagural Paper of the Week, Automation and Polarization (Daron Acemoglu & Jonas Loebbing, JPE).

This is a particularly salient paper given the changes happening in the tech sector with the advent of AI. It's a macro-labour paper which discusses why/how certain types of jobs get automated. Specific tasks are either capital or labor intensive, and as capital becomes more productive, workers whose skillset is similar to capital become more likely to be replaced. Since the tasks performed by low-income workers can be done at low cost, increased capital efficiency results in middle income workers being most impacted by automation, as higher wages make it more profitable to lay off higher-skill workers. This automation leads to the titular polarization, as workers are increasingly pooled into low-skill and high-skill work.

Methodologically, this is a pretty standard macro model with some added conditions on equilibrium. There's some calibration work done at the end as well using public wage data and data from one of Acemoglu's other papers. I also found this paper quite readable compared to some of the metrics-heavy applied micro papers-it requires an understanding of workhorse macro models and interior solutions, but a decent masters student should be able to work through it without much issue.

Have a read of it and let us know what you think. Hopefully we have a macroeconomist somewhere in our comments who can give us a deeper analysis (my macro knowledge is limited to my first year PhD courses), but I think framing AI as reducing the amount of labor needed to perform *specific* tasks, and in turn raising unemployment, rather than an exogeneous shock to the value of low-skill workers wholesale is a useful approach.

Feel free to DM me if you have a suggestion for next week's paper of the week!

Fellow Economists,

I've been a user of this sub for the past couple years, and have been dissapointed to see fewer and fewer posts about interesting research papers and more and more of the same admissions questions about the same 4 or 5 masters programmes. This sub was helpful when I was applying to PhDs, and I'd like it to be that helpful again. To help improve the quality of this sub, I've been added to the mod team. I'm pinning this post to solicit feedback from current users, but here are some of the changes I've made/will be coming in the next couple of days.

1. Rules have been added. Pretty straightforward stuff-use the search bar before you post, don't post un-academic economics content, be polite.
2. The sidebar is going to be updated with more recent/relevant links. Open to reccomendations as to what you guys think would be interesting.
3. Weekly paper discussion threads. Starting next week I'll be pinning a paper I think people would enjoy reading each week, with comments open to questions and comments on the paper. The topic will change from week to week, but I'll aim to have it general enough to be of interest/readable for a first year PhD student. Feel free to DM me if you want to make a suggestion.

Thanks for reading,

u/SonnytheFlame