# Thoughts on the GOP Tax Bill

A couple weeks ago, my father-in-law asked for my opinion on the GOP tax bill that had just been rushed through the Senate early in the morning of Saturday, December 2nd. As the reconciled version of the bill seems likely to be passed in both houses today, I thought I’d share the response I sent to my father-in-law.

I should make clear that I am not a macroeconomist, nor am I a public finance expert. Corrections and suggestions are extremely welcome on Twitter! I also wrote this over a week ago, before the reconciliation of the Senate and House versions was completed, so some details may have changed.

My father-in-law sent me this Wall Street Journal opinion piece signed by several high-profile economists, so I started by responding to that article:

The article itself lays out a plausible set of effects of the proposed tax cuts. There is plenty of economic theory to support their claims. However, in economics we like to consider both what the theory says is possible and what the data says has happened in similar situations. In this case, it seems like the authors of the WSJ piece have made some very optimistic assumptions relative to what the data has shown in the past. (For details, see this article by two Harvard economists, Lawrence Summers and Jason Furman.) The last time we made a large cut in the corporate tax rate, business investment actually went down. This time around, it sounds like CEOs plan to spend the tax breaks on dividends for their shareholders, not new innovations. So, there is historical evidence that the tax cuts won’t actually work as intended.

You also asked for my impression of the authors as academics. They are all certainly incredibly smart. I personally was taught by Taylor, who is often mentioned as a potential Nobel Prize winner. However, these guys come from a pretty narrow part of the political spectrum of economists. In fact, most of the Stanford names are associated with the Hoover Institution, which is a conservative think tank on campus. You might get a more representative sample from the University of Chicago’s Economic Experts Panel, which surveys professors from top schools in a variety of subfields of economics. The panel was asked about the tax reform debate: nearly all of them thought the proposed bill would increase the national debt but fail to increase GDP.

It is also worth pointing out that the WSJ authors have made similar predictions in the past. Douglas Holtz-Eakin and Michael Boskin predicted runaway inflation after the last financial crisis; Lawrence Lindsey predicted large deficits during the Obama administration that did not materialize; Glen Hubbard predicted massive job growth after the Bush-era tax cuts. Of course, all of us make predictions that don’t pan out, and it’s a bit unfair for me to cherry-pick the failures. But it does go to show that these folks have been wrong before, and could be wrong again.

I worry that the increase in the deficit would limit the government’s ability to respond to another financial crisis or recession. During busts, the federal budget should run a deficit as the government bails out banks and shores up social support programs. Ideally, it would run a surplus during boom years (which we are probably in right now) to pay back the debt incurred during the downturn. I worry that by adding substantially to the deficit, this tax plan would make it impossible for the government to spend more when it is needed the most. (For more details, see this opinion piece from Forbes.) It also means even more of the budget will have to be allocated to paying back debt.

I also worry that the increase in the deficit could be used as justification for cuts to the social safety next (social security, Medicare, Medicaid, CHIP, etc). In fact, Paul Ryan and others have already suggested that cuts to these programs are necessary to tackle the deficit, which they just made larger by passing the tax cuts. For this reason, it’s really hard not to see the GOP’s agenda as a massive transfer of wealth and resources from poor people to rich people and corporations. As I mentioned above, it’s not clear that these changes are going to spur more growth, but they will make life harder for lots of unlucky Americans.

Finally, the process through which the tax cuts were passed is a bit scary. The bill was rushed through before anyone had a chance to literally even read it, much less do a proper economic analysis. As a result, there were several clear mistakes in the bill, including one which actually raised taxes on a lot of corporations. And now that folks have had a chance to actually analyze the pill, the bi-partisan Joint Commission on Taxation found that it may actually lower GDP. Hopefully these mistakes can be fixed when the bill is reconciled with the Senate version, but the process so far does not exactly inspire confidence.

# States spend on average nearly 3 times as much per prisoner as they do per K-12 student

Inspired by this tweet, I went looking for a graph of state spending on students and on prisoners. I found a couple graphics which I felt could have been done better, so I made my own version.

I found state-by-state data on correctional department spending per prison inmate from vera.org (see figure 4). I found roughly corresponding data on per-pupil K-12 education department spending from the U.S. Census (see table 8).

It is important to note that the prison spending data is from 2010 but the education spending data is from 2013. Comparing across years like this is somewhat dangerous, because the economic and political climates in each state might be very different in those time periods. However, this is the best I could find. Also, there is complete data for only 40 states.

Here is the resulting graph:

Here’s how to interpret the graphs: Each data point is one state. The x-axis is the state’s corrections department spending per prisoner, while the y-axis is the state’s education departments spending per student. If you hover over the state’s data point, you can see the exact numbers for per-prisoner and per-pupil spending, as well as the ratio.

The grey dashed lines indicate where the ratio of per-prisoner to per-student spending would be 1:1, 2:1, 3:1, and 4:1. Note that every state spends more per prisoner than they spend per K-12 student. The highest ratio actually belongs to California, at 5.14:1. The average ratio across the 40 states with complete data is 2.9:1.

The code and data for this graph can be found here.

# New Working Paper: Observability Increases the Demand for Commitment Devices

I recently completed a new paper with my friend and classmate Christine Exley. The paper addresses an issue that has gotten much attention in the experimental economics literature: commitment devices.

To understand what I mean by a commitment device, let’s take a step back and talk about time preferences more generally. Suppose you are planning your activities for the coming day or week. It would be strange to limit your options of activities without some form of compensation. Yet that is exactly what we see people do in a variety of settings: They voluntarily open savings accounts with restricted access to their own cash. They choose wage contracts that are strictly dominated. They choose to set early deadlines for classroom assignments.

One possible explanation has been very popular in recent literature: people suffer from present-bias, which essentially means they lack perfect self control. That is, they have trouble following through on activities in future that they know are good for them in the long run. A widely-used model of this problem imagines that you are playing a game with your future selves: you all agree what the best outcome is, but each version of yourself would prefer to push off the hard work to tomorrow’s version. So, it then makes sense for today’s version to force tomorrow’s version to take the action. Thus people put money in lockbox account to force their future self to save. They choose a goal-oriented wage contract or early deadlines to prevent tomorrow’s self from shirking. These are all examples of commitment devices.

The plethora of lab, field, and theoretical work on this model leaves little doubt that present bias is a real phenomena, or that it is driving commitment demand in many settings. In our paper, Christine and I note an interesting phenomenon in a field experiment: increasing observability of the commitment choice leads to greater demand for that device. If demand for commitment devices were driven by present bias alone, the observability of the level of commitment should have no effect on the choice itself.

In our experiment, students signed up for workshops at a campus center through a website that we set up. After indicating which workshop they wanted to go to, our website offered them the following prospect: If they attended the workshop they had selected, they would receive a gift card for \$15.00. If they did not attend, they would receive an amount of their choosing between \$0 and \$15.00. If they wanted to commit themselves to going (at least partially), they could choose to receive less than the full amount. And many students did, putting about \$5.00 on the line on average. This was our private treatment.

For a second treatment group, we then made a very simple change: The amount they choose to forgo would be made public to the people running the workshop. This small change increased the average commitment choice to almost \\$9.00 on average. Our hypothesis is that at least some of the students in our experiment chose to commit themselves in order to signal something about themselves: they were interested in the center’s mission, or that they knew that failing to follow through with their actions was a bad thing.

The takeaway message from this field experiment is simple: We may be looking for present bias when there are other mechanisms at play. Returning to the examples I cited earlier: People may choose lockbox accounts to show others that they value saving. They may choose restrictive deadlines or goals to signal to their boss or teacher than their are dedicated and conscientious workers. When running experiments such as these, we need to be cautious about inferring that these actions are driven necessarily by time preference issues. If we are not careful, our estimates about the prevalence of present bias or the parameters of time preferences may be biased.