Monday, May 6, 2019

Journal Reporting Times, EJMR vs. Self-Reported Stats from Journals.

Methodology: I took the self-reported data I collected here (which come from from ejmr here), and compared to the official journal stats collected by Juan Carlos Su├írez here.

Overall, the data line up fairly well.

Here is the correlation in journal first-response times, conditional on being sent out for review.  The R-squared is a respectable .52, although AEJ: Micro is an outlier on EJMR, where it actually does better than what the official statistics suggest (with N=16 though...). Author-reported weight times are about two weeks longer on average, but on ejmr, you round to the nearest month vs. day, so some difference isn't surprising.





Here is the correlation between desk rejections, author vs. journal reported. The regression coefficient is close to 1, and the R-squared is .63. The intercept is -.072, as average reported desk-rejections are lower on ejmr.



Here's the Data:

Journal Desk Reject Rate (EJMR) Desk Reject (Official) First Response Time, Conditional on Being Sent Out to Referees (EJMR) First Response Time, Conditional on Being Sent Out to Referees (Official)
QJE 61% 66% 1.5 1.5
JPE 50% 49% 8.0 4.0
REStud 34% 49% 4.5 3.4
ECTA 23% 32% 3.6 3.4
AEJ: Macro 21% 38% 2.9 3.4
AEJ: Applied 37% 45% 2.5 2.7
Journal of Finance 33% 32% 3.0 2.2
AER 47% 46% 3.7 3.1
JEEA 58% 49% 2.5 3.2
AEJ: Policy 36% 51% 2.8 3.1
EJ 45% 55% 3.7 3.6
AEJ: Micro 25% 38% 3.4 4.5

Monday, February 18, 2019

Ranking Economic Journals by Speed, Updated

I decided to update my previous ranking on economics journals ranked by speed.

In the table below, # is the journal's rank by citations (may be 2 years old, I didn't update this), and then there is data on the acceptance rate, desk rejection rate, average time to acceptance, median time to acceptance, and the 25th and 75th percentiles (all in terms of months). Only journals with at least 5 observations were included. Data come from here.

There are some similarities with the previous ranking by speed. The QJE is still the fastest, and the JME still brings up the rear -- despite the fact I've doubled the sample size.

One note is that I just copy-pasted and ran my previous code, so if you notice any errors below, please let me know.

My big hesitation with doing this is that I think it may not purely be a good thing to incentive journals to respond quickly. They tend to do this by desk-rejecting more papers, and I worry that desk-rejections are often done on the basis of institutional affiliation, or perhaps after a less-than-fully-thorough examination of the paper. I may do an updated version which conditions on the paper going out to journals. Nevertheless, if you are contemplating which journal to submit a paper to, this may be helpful.

# Journal Name Accept % Desk Reject % Avg. Time Median Time 25th Percent. (Months) 75th Percent. (Months) N =
1 Quarterly Journal of Economics 1% 61% 0.59 0 0 1 82
80 Journal of Economic Geography 0% 80% 0.67 0 0 1 5
12 Journal of the European Economic Association 3% 58% 1.16 0 0 2 31
81 Kyklos 8% 33% 1.33 1 0.5 2 12
94 Economics and Human Biology 43% 43% 1.50 1 0 3 7
74 Management Science 18% 45% 1.55 2 0 2 22
15 Review of Financial Studies 11% 37% 1.59 2 0 2 27
78 Demography 0% 80% 1.60 1 0 2 5
14 Journal of Human Resources 15% 54% 1.60 1 0 3 46
31 European Economic Review 28% 44% 1.72 1 0 3 50
6 American Economic Journal: Applied Economics 8% 37% 1.76 2 0 3 49
13 American Economic Journal: Economic Policy 8% 36% 1.83 2 0 3 36
100 China Economic Review 67% 33% 1.83 2 0.5 2 9
7 Journal of Finance 0% 33% 2.05 2 0 3 21
19 Journal of Financial Economics 24% 18% 2.06 2 1 3 17
27 Journal of Financial Intermediation 14% 29% 2.14 3 1 3 7
9 American Economic Review 7% 47% 2.16 1 0 4 91
46 Econometrics Journal 0% 50% 2.17 0.5 0 5 6
55 Review of Income and Wealth 17% 67% 2.17 0.5 0 4 6
38 Journal of Financial and Quantitative Analysis 29% 6% 2.18 2 1 3 17
37 Journal of Population Economics 22% 44% 2.22 3 0 4 9
76 Explorations in Economic History 56% 11% 2.22 2 2 3 9
32 Theoretical Economics 8% 0% 2.23 2 2 2 13
90 Economics Letters 45% 25% 2.24 2 1 2 114
42 Review of Finance 0% 8% 2.25 2.5 1.5 3 12
16 Economic Journal 16% 45% 2.26 2 0 4 56
34 Journal of Health Economics 13% 55% 2.32 2 1 4 31
5 American Economic Journal: Macroeconomics 13% 21% 2.33 2 1.5 3 24
62 Regional Science and Urban Economics 52% 14% 2.35 2 2 3 21
65 International Journal of Industrial Organization 7% 20% 2.47 2 1 4 15
91 Public Choice 14% 48% 2.48 1 0 3 29
35 Labour Economics 9% 27% 2.55 2.5 2 3 22
41 Journal of Law and Economics 0% 43% 2.57 3 1 4 14
61 Journal of Comparative Economics 27% 33% 2.63 2 0.5 4.5 15
71 European Journal of Political Economy 38% 23% 2.69 2 2 3 13
58 B.E. Journals in Economic Analysis & Policy 33% 7% 2.71 3 1 4 15
26 Journal of Urban Economics 22% 17% 2.72 3 1 4 18
30 American Economic Journal: Microeconomics 19% 25% 2.74 3 1 4 16
4 Econometrica 6% 23% 2.77 3 1 4 35
24 Journal of Applied Econometrics 9% 35% 2.78 2 0 5 23
57 Industrial and Labor Relations Review 27% 36% 2.82 2 1 5 11
64 The Journal of Law, Economics, and Organization 18% 27% 2.82 3 2 4 11
85 Energy Journal 38% 0% 2.88 3 2 3.5 8
36 World Bank Economic Review 8% 31% 2.92 2 2 4 13
11 Journal of Labor Economics 0% 32% 3.00 3 0 6 19
21 Journal of Public Economics 12% 27% 3.10 3 1 4 60
83 American Journal of Health Economics 43% 0% 3.14 3 2 4 7
3 Review of Economic Studies 4% 34% 3.16 3 0 5 73
23 Journal of Development Economics 17% 41% 3.16 3 1 4 54
63 Oxford Economic Papers 17% 42% 3.23 3 1 5 12
67 Journal of Banking and Finance 18% 18% 3.25 3 2 4 28
87 Southern Economic Journal 14% 14% 3.29 4 2 4 7
99 American Journal of Agricultural Economics 23% 0% 3.31 3 3 4 13
82 Journal of Macroeconomics 31% 25% 3.31 3 0.5 5.5 16
69 Economics and Politics 40% 0% 3.33 3 1 6 5
89 Journal of Economic History 17% 0% 3.33 3 2 4 6
93 Review of Economics of the Household 20% 10% 3.46 3 1 3 10
17 Journal of Economic Growth 0% 0% 3.50 2.5 2 3 10
92 Small Business Economics 25% 25% 3.50 3 1.5 5 8
10 Review of Economics and Statistics 2% 48% 3.51 2 0 5 65
56 Journal of Economic Behavior and Organization 30% 27% 3.67 4 0 6 33
25 Journal of Business and Economic Statistics 20% 10% 3.70 2.5 2 5 10
49 Canadian Journal of Economics 28% 6% 3.72 4 3 5 18
53 Economics of Education Review 50% 7% 3.79 4 2 5 14
51 Journal of Industrial Economics 13% 33% 3.81 4 1.5 5.5 15
88 World Development 39% 37% 3.92 3 1 6 49
33 Journal of Economic Theory (Elsevier) 21% 18% 3.94 4 3 5 33
22 RAND Journal of Economics 6% 21% 3.97 4 3 5 34
68 Macroeconomics Dynamics (Cambridge) 57% 21% 4.07 4 1 6 14
28 Experimental Economics 38% 8% 4.08 3 2 6 13
40 Journal of Environmental Economics and Management 24% 8% 4.15 3.5 2 5 25
45 Econometric Theory 0% 40% 4.20 4 0 5 5
84 Health Economics 8% 50% 4.21 3 3 6 12
44 Journal of Economic Dynamics and Control 50% 14% 4.22 3 1 4 22
54 Journal of Economics and Management Strategy 54% 8% 4.23 3 3 6 13
66 International Tax and Public Finance 14% 14% 4.29 3 2 7 7
60 B.E. Journal of Macroeconomics 63% 0% 4.33 4 2 6 8
75 Economic Theory 23% 27% 4.35 4 1 6 22
95 Energy Economics 55% 0% 4.42 4 3.5 6 11
98 Economics of Transition 71% 14% 4.50 4.5 2.5 6 7
47 Oxford Bulletin of Economics and Statistics 29% 36% 4.64 4 2 6 14
72 Environmental and Resource Economics 27% 47% 4.69 4 2.5 6.5 15
18 Journal of International Economics 18% 6% 4.71 3 3 6 17
43 Journal of International Money and Finance 40% 5% 4.75 3 2 7 20
97 Journal of Corporate Finance 14% 14% 4.75 3.5 3 6.5 7
73 Review of International Economics 30% 10% 4.91 3 3 6 10
52 Games and Economic Behavior 18% 18% 4.94 4 3 6 50
59 Economic Development and Cultural Change 27% 20% 5.00 4.5 3 6.5 15
48 Economic Inquiry 25% 25% 5.18 3 2 7 36
39 Journal of Economic Surveys 0% 40% 5.20 5 0 7 5
2 Journal of Political Economy 0% 50% 5.34 3 2 8 32
96 Empirical Economics 44% 13% 5.56 5.5 3.5 6 16
50 Economica 11% 53% 5.57 3 1 6 19
20 Journal of Money, Credit, and Banking 13% 13% 5.72 4.5 2.5 9 30
77 Journal of Public Economic Theory 27% 18% 5.86 6 3 9 11
79 The World Economy 40% 0% 6.25 5 3.5 8.5 5
70 Econometric Reviews 33% 0% 6.86 7 4 10 6
29 Journal of Econometrics 10% 10% 6.90 6.5 5 10 10
86 Journal of Empirical Finance 38% 0% 8.22 7 6 13 8
8 Journal of Monetary Economics 21% 11% 9.12 6 4 11 19

Tuesday, October 30, 2018

Are Negative Interest Rates Expansionary? A Review of Eggertsson et al. (2017)

My answer is yes they are.

I recently assigned my students to write a referee report of Eggertsson et al., "Are Negative Nominal Rates Expansionary?" I chose the paper because I'm a huge fan of Eggertsson's work, the paper is well done, it has a nice synthesis of data and theory, and the topic is of central policy importance. The authors find that negative nominal interest rates may not be expansionary, and also, that under certain conditions, quite surprisingly, they may be contractionary.

I like the paper and see a clear contribution. However, I wish the authors would have framed the paper slightly differently. My reading is that they show that negative reserve rates below the cost of hoarding cash can be potentially contractionary under some conditions, and need other policies (negative lending rates for banks/Gesell taxes) to make the policy more expansionary. This is still an important and useful point -- I learned something from them. Yet, since not all of the key modeling assumptions are true, and since "negative interest rates" can include 1. negative rates above the cost of hoarding cash, 2. negative borrowing rates for banks, or 3. Gesell taxes on banks (may seem far-fetched, but something in this spirit has, in fact, happened), I think the paper's actual result is in fact narrower than "negative rates aren't expansionary and may be contractionary", and does not apply to the negative interest rates that we have seen.

From where does the contractionary result come from? It comes from assuming banks profits can affect intermediation costs. Since negative interest rates are a tax on banks, they hurt profitability. But, how much did banks actually pay in taxes due to negative interest rates? Color me skeptical it hurt profitability enough to have materially damaged intermediation. We are talking a second or third-order type of effect. And, to the extent negative interest did increase loans, the impact on profitability would be uncertain. Lastly, the ECB had a tiered rate system, in which required reserves were still paid a positive interest rate, but only the marginal reserves over some threshold were taxed. 

One concern is that the ECB also had a program (dubbed TLRTO -- can't central banks print some money and hire better PR people?) where banks could borrow money at negative interest rates on the condition they lend it out. In that case, hundreds of billions of Euros in loans were taken out, likely many as a cause of the policy, and these "negative interest rates" would also add several billion Euros directly to bank profits over the last few years. The model in the paper does not consider this possibility. 

Two additional key assumptions of the model are that banks are taking out 100% financing from deposits (as many of my students noted) and all bank profits are paid to households. To their credit, the authors fully acknowledge that the first of these assumptions is important and not exactly true. In Sweden, for example, the deposit share is less than 50%. To the extent that financing comes from other sources, banks may actually care about the spread between the reserve rate and the loan rate, particularly if there is a cost of holding cash (say, 1-1.5%). In practice, after the financial crisis, banks like Bank of America cut their dividend payments to almost nothing. In this environment, banks will respond to negative interest rates by making more loans and lowering the interest rate on loans, and may even pay more dividends, boosting aggregate demand. Nothing in the paper contradicts this logic. 

My intuition before reading this paper is that a negative interest rate will be an effective tax on excess reserves, and thus it would make a bank want to lend out money instead of having more reserves. This appears not to happen in the model because banks would hoard physical cash instead of reserves (I think the authors could have been more explicit on what banks do with their mountain of excess reserves when negative rates happen -- the calibration exercise starts with few excess reserves).  There is an implicit assumption that because deposit rates didn't drop (much) below zero, that the cost of holding cash must also be close to zero, and also, in particular, lower than the .4-.7% negative interest rates that we've seen. My gut feeling is that the level is likely to be closer to 1-1.5%, since banks did not, in fact, hoard physical cash. One reason they did not, however, is that central banks aren't stupid -- it seems at least some central banks take into account changes in cash holdings of banks when setting the limits for negative-interest free reserves (see box 2 of this BIS document on negative rates). In addition, there is a paper arguing banks responded to negative interest rates by raising fees instead, effectively lowering deposit rates below zero -- a topic the authors do address (hat tip to a student).

Perhaps banks did not lower their deposit rates below zero, not because there is no cost to holding cash, but because they didn't know how depositors would react, or believed the policy was temporary, and were afraid of a hysteretic effect on depositors, or due to ongoing concerns with their quantities of bad long-term loans. It could also be the case that holding small amounts of cash is relatively costless, but once you have football fields of cash, suddenly you need to hire top-notch security, and buy insurance, so the cost could increase non-linearly. The authors, once again, should be commended for being explicit that this assumption would alter the conclusions of the paper. Yet, even I suspect there is a limit to the cost of hoarding cash. 

I could go on here with theory. This is a one period model. A negative headline rate probably sends a stronger signal about future low short-term rates (commitment to irresponsibility) than keeping a rate at zero, and thus could influence longer maturities (as evidence finds). They could also underscore a central bank's commitment to do "whatever it takes" to achieve inflation. It's also a closed economy -- negative interest rates could trigger capital outflows and a depreciated exchange rate, particularly if it had an impact on the yield curve.

It's also possible to introduce other factors which would imply negative nominal rates are expansionary. See this nice paper by Davide Porcellacchia (who we tried unsuccessfully to hire last year at NES), which argues that negative rates might still lead consumers to save less overall. 

Thus, theoretically, the result is ambiguous. (Isn't that always the case?) Thus, it comes down to empirics. 

I also had some minor quibbles with the empirics in the paper. At one point, they write, speaking of Denmark, that "the negative policy rate has not been transmitted to deposit rates." It actually looks to my eye like there were slight declines in the deposit rates each time the Danish central bank crossed into negative territory. The household deposit rate was cut roughly in half after the second cut in 2014. If you squint, it also looks like the corporate deposit rate dipped below zero at several points. 

Admittedly, in their evidence for Switzerland and Japan, going negative did appear to have, at best, a very minor impact on deposit rates (see below). However, for both Germany, and for the Euro Area as a whole, it looks to me like going negative might have had close to a 1-for-1 impact on deposit rates. Certainly, in at least several cases negative policy rates did translate into lower deposit rates. 

Even more important than deposit rates are lending rates (Figure below). Again, for lending rates, it does look like in at least a few cases, a lower deposit rate did translate into lower lending rates. In the Japanese case, this happened despite the fact that deposit rates did not fall. 





It would also have been nice to look at surprise announcements of ZLB episodes, and look at how the announcement of negative rates affect a variety of interest rates in the economy, including the rates that banks borrow from each other at, longer-term yields, and also of exchange rates and the stock market (admittedly, from what I've seen, this evidence looks mixed). The BIS found that negative interest rates passed through fully into money markets. Switzerland and Denmark did, after all, institute negative rates in part to stabilize the exchange rates. 

I think more work needs to be done on this topic, but from the theory and the data, I don't see much that suggests that negative interest rates can't or didn't stimulate the economy. I am persuaded that negative reserve rates are best complemented with other policies, and I credit this paper with making the point. I believe central banks should do more to boost lending during liquidity trap periods, such as through a full program of quantitative targeting of loan levels, regulation, fines, negative interest rates, or subsidies for loans made. China in fact was said to do quantitative targeting during the Great Recession, and France and Germany did things like this during the Bretton Woods period. (See this nice paper by Eric Monnet.)

Overall, I'm a big believer of much more aggressive monetary policy at the zero lower bound than what we've seen. While I think a healthy degree of skepticism about new policy tools during liquidity trap periods is prudent (probably, mistakes were made with the rollout and PR around QE, even if it does seem to have been stimulative), and while I also think papers like this one move the debate forward, I've also long been skeptical of the skepticism of the idea that monetary policy can't be effective at the ZLB. I worry that this paper may be misinterpreted to suggest that banks shouldn't try to use negative interest rates at the ZLB, and should opt to do nothing instead. This despite the correct conclusion being that central banks should use negative reserve rates in conjunction with other policies. Some like this happened with people who read Paul Krugman only occasionally, but not in the round. They walked away believing that monetary policy had "shot its wad" -- in the words of a former president. Had they read every word Krugman wrote, they would have believed instead that central banks needed to credibly commit to higher future levels of inflation to stimulate the economy, but only that traditional monetary policy was ineffective. I observed this when I worked in the Obama CEA, and some economists there too believed that nothing more could be done on monetary policy, leading them to not want to recommend to the President that he make his vacant FOMC picks. There's a case to be made that this was the single biggest policy mistake Obama made while in office. And that mistake, to a large extent, explains how we got to where we are. 

Note: I should mention that many of the points above were motivated, directly or indirectly, by the insightful referee reports of my students! 



Tuesday, February 6, 2018

On the Uses (and Abuses) of Economath: The Malthusian Models

Many American undergraduates in Economics interested in doing a Ph.D. are surprised to learn that the first year of an Econ Ph.D. feels much more like entering a Ph.D. in solving mathematical models by hand than it does with learning economics. Typically, there is very little reading or writing involved, but loads and loads of fast algebra is required. Why is it like this?

The first reason is that mathematical models are useful! Take the Malthusian Model. All you need is four simple assumptions: (1) that the birth rate is increasing in income, (2) that the death rate is decreasing in income, (3) that income per person is negatively related to population, and (4) the rate of technological growth is slow relative to population growth, and you can explain a lot of world history, and it leads you to the surprising conclusion that income in a Malthusian economy is determined solely by birth and death rate schedules, and is uncorrelated with technology. Using this model, you can explain, for example, why incomes before 1800 were roughly stagnant for centuries despite improving technology (technological advance just resulted in more people; see the graph of income proxied by skeletal heights below). It also predicts why the Neo-Europes -- the US/Australasia/Southern Cone countries are rich -- they were depopulated by disease, and then Europeans moved in with lots of land per person. It is a very simple, and yet powerful, model. And it makes (correct) predictions that many historians (e.g., Kenneth Pomeranz), scientists (e.g., Jared Diamond), and John Bates Clark-caliber economists (see below) get wrong.



A second beneficial reason is signalling. This reason is not to be discounted given the paramount importance of signalling in all walks of life (still not sufficiently appreciated by all labor economists). Smart people do math. Even smarter people do even more complicated-looking math. I gratuitously put a version of the Melitz model in my job market paper, and when I interviewed, someone remarked that I was "really teched up!" Simple models are not something that serious grown-ups partake in. Other social science disciplines have their own versions of peacock feathers. In philosophy, people write in increasingly obtuse terms, using obscure language and jargon, going through enormous effort to use words requiring as many people as possible to consult dictionaries. Unfortunately, the Malthusian model above, while effective in terms of predictive power, is far too simple to play a beneficial signalling role, and as a result would likely have trouble getting published if introduced today. 

A third reason to use math is that it is easy to use math to trick people. Often, if you make your assumptions in plain English, they will sound ridiculous. But if you couch them in terms of equations, integrals, and matrices, they will appear more sophisticated, and the unrealism of the assumptions may not be obvious, even to people with Ph.D.'s from places like Harvard and Stanford, or to editors at top theory journals such as Econometrica. A particularly informative example is the Malthusian model proposed by Acemoglu, Johnson, and Robinson in the 2001 version of their "Reversal of Fortune" paper (model starts on the bottom of page 9). Note that Daron Acemoglu is widely regarded as one of the most brilliant economic theorists of his generation, is a tenured professor of Economics at MIT, was recently the editor of Econometrica (the top theory journal in all of economics), and was also awarded a John Bates Clark medal (the 2nd most prestigious medal in the profession) in large part for his work on this paper (and a closely related paper). Also keep in mind this paper was eventually published in the QJE, the top journal in the field. Very few living economists have a better CV than Daron Acemoglu. Thus, if we want to learn about how economath is used, we'll do best to start by learning from the master himself.

What's interesting about the Acemoglu et al. Malthusian model is that they take the same basic assumptions, assign a particular functional form to how population growth is influenced by income, and arrive at the conclusion that population density (which is proportional to technology) will be proportional to income! They use the model:

p(t+1) = rho*p(t) + lambda*(y-ybar) + epsilon(t),

where p(t+1) is population density at time t+1, p(t) is population at time t, rho is a parameter (perhaps just less than 1), lambda is a parameter, y is income, ybar is the level of Malthusian subsistence income, and epsilon is an error term. If you impose a steady state (p* and y*) and solve for p*, you get:

p* = 1/(1-rho)*lambda(y*-ybar)

I.e., you get that population density is increasing in income, and thus that income per person should have been increasing throughout history. Thus, these guys from MIT were able to use mathematics and overturn one of the central predictions of the Malthusian model. It is no wonder, then, that Acemoglu was then awarded a Clark medal for this work.

Except. This version doesn't necessarily fit the skeletal evidence above, although that evidence may be incomplete and imperfect (selection issues?). What exactly was the source of the difference in the classical Malthusian model and the "MIT" malthusian model? The crucial assumption, unstated in words but there in greek letters for anyone to see, was that income affects the level of population, but not the growth rate in population. Stated differently, this assumption means that a handful of individuals could and would out-reproduce the whole of China and India combined if they had the same level of income. (With rho less than one, say, .98, the first term will imply a contraction of millions of people in China/India. With income over subsistence, we then need to parameterize lambda to be large enough so that overall population can grow in China. But once we do this, we'll have the implication that even a very small population would have much larger absolute growth than China given the same income.) Obviously, this is quite a ridiculous assumption when stated in plain language. A population can grow by, at most, a few percent per year. 100 people can't have 3 million offspring. What this model does successfully is reveal how cloaking an unrealistic assumption in terms of mathematics can make said assumption very hard to detect, even by tenured economics professors at places like MIT. Math in this case is used as little more than a literary device designed to fool the feebleminded. Fortunately, someone caught the flaw, and this model didn't make the published version in the QJE. Unfortunately, the published version still included the view that population density is a reasonable proxy for income in a Malthusian economy, which of course it is not. And the insight that Malthusian forces led to high incomes in the Neo-Europes was also lost. 

Given that this paper then formed part of the basis of Acemoglu's Clark medal, I think we can safely conclude that people are very susceptible to bullshit when written in equations. More evidence will come later in the comments section, as, conditioned on getting hits, I suspect several people will be taken in by the AJR model, and will defend it vigorously. 

This episodes shows some truth to Bryan Caplan's view that "The main intellectual benefit of studying economath ... is that it allows you to detect the abuse of economath." 

Given the importance of signaling in all walks of life, and given the power of math, not just to illuminate and to signal, but also to trick, confuse, and bewilder, it thus makes perfect sense that roughly 99% of the core training in an economics Ph.D. is in fact in math rather than economics.


Update: Sure enough, as I predicted above, we have a defender of the AJR model in the comments. He argues the AJR model shows why math clarifies, even while his posts unwittingly convey the opposite.

Above, I took issue with the steady state relationship in the model and the fallacious assumption which yields it. The commenter points out correctly, that, outside of the steady state, the AJR model actually implies that there are two conflicting forces. But, so what? My argument was about the steady state. If one fixes the wrong assumption, steady-state income in the Malthusian model will be equal to subsistence income, and thus the main argument for correlation between population density and income outside of the steady state will also be shut down.

Second the commenter unfairly smears Acemoglu & Co., writing that the real problem is not with their model, but that they didn't interpret their model correctly: "goes ahead in the empirical work to largely, in contrast to what their model says, take population density as a proxy for income!".

Thus I'd like to defend Acemoglu against this unfair smear. In preindustrial societies, there were vast differences in population densities between hunter-gatherer groups, and agricultural societies, even though there were not vast income differences between the two. In fact, quite surprisingly, hunter-gatherer societies often look to have been richer despite working less (read Greg Clark), and despite far more primitive technology. Thus it is quite reasonable to assume, as AJR did, that it is likely that differences in technology would swamp the differences in other population shocks (A more important than epsilon). The Black Death might have doubled or tripled incomes, but settled agrarian societies might have population densities 1000 times as large as primitive hunter-gatherer tribes. This isn't an airtight argument, but, given their model, I believe AJR's empirical extension is reasonable, particularly given that they provide a caveat. The problem is that their model is not reasonable.

The commenter goes on to argue that I've gotten AJR's conclusion backward: "You claim that the point they are making is "population density will be a decent proxy for income in a Malthusian model." The point they are making is explicitly the exact opposite: that "caution is required in interpreting population density as a proxy for income per capita." 

Huh? The first two lines of the abstract of the AJR paper read: "Among countries colonized by European powers during the past 500 years, those that were relatively rich in 1500 are now relatively poor. We document this reversal using data on urbanization patterns and population density, which, we argue, proxy for economic prosperity."

Seems clear here they are arguing for using it as a proxy.



Wednesday, January 24, 2018

Reminder: Most Published Research is Probably Wrong!

At least in some way. Don't get me wrong, there is a lot of great research out there. However, it has occured to me that many people are much too trusting of published research, particularly when written by people from fancy universities with fancy letters behind their names and when published in prestigious journals. I saw this recently during a very lively session on the Decline in US Manufacturing Growth and Productivity at the AEA meetings in Philadelphia several weeks ago. Several people asked David Autor why his results on the impact of China on US innovation was different from what other prominent researchers had found. (One of the answers, of course, is that there is little reason to believe the competing research, but I digress...) Similarly, one of my complaints of the otherwise excellent Trade Talks podcast with Chad Bown is that published results, particularly by prominent researchers, are generally taken at face value, with not enough discussion, in my view, about potential caveats and shortcomings of the methodologies employed.

The reality is that science is difficult, and that Cowen's First Law (there is something wrong with everything!) applies to economic research.

Here's a moving video from Neil Degrasse Tyson which I mostly love. My only issue was his description of science:
One of the great things about science, is that it's an entire exercise in finding what is true.
You have a hypothesis, you test it. I get a result. A rival of mine double checks it, because they think I might be wrong. They perform an even better experiment than I did, and they find out, “Hey, this experiment matches! Oh my gosh. We’re on to something here!” And out of this rises a new, emergent truth.
This is a description of everything I wish science was! Perhaps it is an accurate description of hard sciences (I'm skeptical), but this is not how the social sciences operate. In practice, when a top researcher has a major finding, other top researchers, with rare exceptions, do not check it. Occasionally, grad students or less prominent researchers will overturn the result, but they will find that journals simply aren't the least bit interested in publishing papers which reverse seminal papers. Thinking like an economist, this creates some rather perverse incentives. If you are a well-connected researcher in a prominent department, you are well-incentivized to publish as much as possible. This means creating research which appears sophisticated, and it also means not pissing off the people who will judge your research. On the contrary, implies that there are benefits from having a lot of close friends (what deGrasse calls your "rivals") in the profession. You don't accomplish this by pointing out that another researcher's results disappear when you control for latitude. As a result, many top researchers are in fact incentivized to crank out many low-quality papers but with seemingly blockbuster results.

Part of the way this system survives is because there is a culture frowning on writing "comment papers", and the other reason is that there is, fortunate for the existence of this system, a willing population of "sheep", the "true believers", available to consume and believe this research.

In any case, on my ride back to Moscow from Philadelphia, I fired up Stata, and took a second look at some of the research which found that the China shock led to a huge increase in productivity and patenting in Europe, published in a leading journal. The thesis sounded quite dubious to me from the beginning. It turned out that including sectoral fixed effects -- a very basic control -- killed the results. If I were to write this up, the journal that published it would never, in a million years, accept it. Secondly, although the original authors seem to me like fine people, traditionally, economists behave in a way which is mafia-level shady (see the comments) when their research comes under attack. Partly, they have to do this, since the masses believe that most top research is correct, it is seen as a huge black mark on someone's reputation to have a paper overturned. If there was widespread knowledge that science is difficult and most papers have flaws, this might not be so necessary. Then, perhaps, we could get a bit closer to Neil Degrasse Tyson's idealized view of science.









Monday, December 11, 2017

Janet Yellen's Tenure, in Retrospect (Has the Economy Really Recovered?)

On Twitter, Paul Romer lauds the job that Janet Yellen has done, writing that "In an extraordinarily difficult political context, J. Yellen did an extraordinarily important public policy job extraordinarily well."

However, I've long been a skeptic of the job that both Ben Bernanke and Janet Yellen have done. (Seems I'm the only one who remembers that 2010 discount rate hike, with GDP 20% below trend, she voted for, which helped spawn the Tea Party...) The economy never had a full recovery, as growth is still slow and inflation is still below target. I once explained this to a colleague, and she told me that "Sorry, but I don't think you are smarter than Ben Bernanke. He knows more about monetary policy than you." When I was at the CEA, virtually everyone else there thought I was the stupid one, and that Ben-"When Growth is Not Enough"-Bernanke's policy was roughly optimal.

Of course, this is long before Ben Bernanke himself amended his views, to say that central banks should do price-level targeting when exiting a liquidity trap. I.e., Ben Bernanke (2017) thinks the Fed should have aimed for higher inflation in the 2009 to 2014 period, whereas Ben Bernanke (2009-2014) seemed to be content with below-target inflation, much less inflation over and above the inflation target. That Bernanke (and Yellen) also believed that when growth and inflation were below forecast, a central bank should not provide more stimulus, but instead lower the forecasts.

However, Bernanke's reappraisal isn't just a repudiation of the views of the 2009 to 2014 Ben Bernanke, but also a repudiation of the views of Janet Yellen over this period. Of course, Janet has only been the Chair since 2014. Since that time, she has seen fit to end QE and raise interest rates repeatedly. So, let's do some Monday-morning quarterbacking on how well this has gone.
























In terms of the Core PCE deflator, inflation has been below the Fed's own target under Yellen's entire term, and is currently nowhere near the target. In addition, there's a good case to be made that the Fed's inflation target is, itself, too low. And then there is Bernanke's argument, that, coming out of a liquidity trap period, central banks should aim for temporarily high inflation. Yellen's record here is not good.

How about with GDP growth and employment? If all is well there, slightly lower inflation would not be a real problem. However, here is Real GDP relative to the long-run trend.















Note that the US is well below it's long-run growth trend, and getting further away from it. The near-consensus among economists, interestingly enough, is that most everything that can be invented has already been invented, and there just isn't that much "stuff" left. Yes, really. I think that is nonsense (the iphone was invented in 2007!). In fact the cause is the China (+RER) shock, and then poor regulation during the housing bubble, and poor monetary policy managing the liquidity trap. This is all fairly obvious by now. Strange it isn't already the consensus. But slow inflation along with slow growth suggests that the problem isn't some structural supply problem, but due to a shortfall of demand.

However, to be fair to Paul Romer, unemployment is way down. This is a good sign, and an indicator that the labor market has improved.











However, it is not the only labor market indicator, and thus, by itself, does not provide a full picture of the economy. The employment rate for prime-aged workers is another legitimate measure to look at, as the unemployment rate might look good if many people have simply left the labor force. And, the prime-age employment rate below shows that, while the US has made steady progress, it is not quite back to the level it was at in 2007. In addition, the 2007 peak, which came after 7 years of relatively slow GDP growth despite a housing bubble, and was also the decade of the collapse in manufacturing employment, was significantly lower than the 2000 peak. (The overall employment-to-population ratio still looks terrible.)























However, even this measure is flawed in several respects. One problem is that, given heavy baby boomer retirement, more jobs have opened up for younger workers than would otherwise be the case. While I don't think an adjustment for this would change the picture that much, we might actually deduct a quarter to a half of a percent for this.

A second factor is that the several decades since the 1970s had saw increasing numbers of women enter the labor force. Optimists may say that this trend was simply complete by 2000. But, even since then, we have seen female employment continue to increase on a relative basis. Thus, I would say that our baseline shouldn't be the 2000 peak, but that we should have expected emp-to-pop to have increased more than this. How much more? Perhaps another .25 or .5%. A good paper could probably tease this out. Again, I don't think this necessitates a large adjustment. But, these are starting to add up, and means we might still be 3.5-4% below where we should be.

The graph of the female prime-aged employment rate shows that female employment has essentially recovered back to its level in 2007. This means that it is still gaining ground relative to male employment, even since 2000. And, it is still about 7% lower than the overall employment rate.





A third factor is that just because people are working, it doesn't mean they are doing work they are happy with, or have seen the wage growth they would like. Here is the part-time employment rate, which is still elevated, and presents a rather pessimistic figure. But, if more people are working part-time, this is an indication that other people who are working full-time are not employed in their ideal jobs, but would rather have better jobs with higher salaries. And, of course, given the slower GDP growth, incomes have not grown as fast as they used to.

























Obviously, incomes are also not increasing as fast as they used to.

























Sure, inflation is also low, but GDP growth is slow. That both are slow is an indication that the economy is demand constrained. Why is it demand constrained? Well, the end of QE and four rate hikes are certainly part of the story. Those rate hikes caused the dollar to appreciate, inflation to subside, and more manufacturing jobs to be lost. And this is Janet Yellen's doing. This wasn't a one-time mistake either. She repeatedly failed to hit her inflation target, with slow GDP, and never re-thought the course the Fed was on.

The defence of Yellen (and also of Bernanke) is that she might have liked to have been more accommodative over much of this period, but also had to deal with more conservative elements on the Board (see here).

To get a sense of how competent the people around Yellen at the Fed have been, read this stream of jaw-dropping quotes, stolen from a commenter here on Scott Sumner's excellent blog:

The 2008 “Dream Team”
SEPTEMBER 16, 2008 FOMC TRANSCRIPT
SELECTED QUOTES EXCERPTED FROM ROUNDTABLE DISCUSSION
MR DUDLEY
Either the financial system is going to implode in a major way, which will lead to a significant further easing, or it is not.
MR LOCKHART
But I should follow the philosophy of Charlie Brown, who I think said, “Never do today what you can put off until tomorrow.” [Laughter]
MR ROSENGREN
Deleveraging is likely to occur with a vengeance as firms seek to survive this period of significant upheaval… I support alternative A to reduce the fed funds rate 25 basis points. Thank you.
Mr HOENIG.
I also encourage us to look beyond the immediate crisis, which I recognize is serious. But as pointed out here, we also have an inflation issue. Our core inflation is still above where it should be.
MS YELLEN. I agree with the Greenbook’s assessment that the strength we saw in the upwardly revised real GDP growth in the second quarter will not hold up. Despite the tax rebates, real personal consumption expenditures declined in both June
and July, and retail sales were down in August. My contacts report that cutbacks in spending are widespread, especially for discretionary items. For example, East Bay plastic surgeons and dentists note that patients are deferring elective procedures. [Laughter]
MR BULLARD
Meanwhile, an inflation problem is brewing. The headline CPI inflation rate, the one consumers actually face, is about 6¼ percent year-to-date…My policy preference is to maintain the federal funds rate target at the current level and to wait for some time to assess the impact of the Lehman bankruptcy filing, if any, on the national economy.
MR PLOSSER
As I said, it is my view that the current stance of policy is inconsistent with price stability in the intermediate term and so rates ultimately will have to rise.
MR STERN
Given the lags in policy, it doesn’t seem that there is a heck of a lot we can do about current circumstances, and we have already tried to address the financial turmoil. So I would favor alternative B as a policy matter. As far as language is concerned with regard to B, I would be inclined to give more prominence to financial issues. I think you could do that maybe by reversing the first two sentences in paragraph 2. You would have to change the transitions, of
course.
MR. EVANS
But I think we should be seen as making well-calculated moves with the funds rate, and the current uncertainty is so large that I don’t feel as though we have enough information to make such calculations today.
MS PIANALTO
Given the events of the weekend, I still think it is appropriate for us to keep our policy rate unchanged. I would like more time to assess how the recent events are going to affect the real economy. I have a small preference for the assessment-of-risk language under alternative A.
MR LACKER
In fact, it’s heartening that compensation growth is coming in a little below expected in response to the energy price shock this year. This has allowed us to accomplish the inevitable decline in real wages without setting off an inflationary acceleration in wage rates.
MR. HOENIG
I think what we did with Lehman was the right thing because we did have a market beginning to play the Treasury and us, and that has some pretty negative consequences as well, which we are now coming to grips with.
MR. ROSENGREN
I think it’s too soon to know whether what we did with Lehman is right. Given that the Treasury didn’t want to put money in, what happened was that we had no choice…I hope we get through this week. But I think it’s far from clear, and we were taking a bet, and I hope in the future we don’t have to be in situations where we’re taking bets.
Mr. FISHER. All of that reminds me—forgive me for quoting Bob Dylan—but money doesn’t talk; it swears. When you swear, you get emotional. If you blaspheme, you lose control. I think the main thing we must do in this policy decision today is not to lose control, to show a steady hand. I would recommend, Mr. Chairman, that we embrace unanimously—and I think it’s important for us to be unanimous at this moment—alternative B
MR WARSH.
Those would be my suggestions to try to strike that balance—that we are keenly focused on what’s going on, but until we have a better view of its implications, we are not going to act.


The optimistic view of Yellen is that, while overall policy was quite inappropriately tight for essentially the entire period since 2008, Yellen may have been struggling all the time against these clowns in private, leading policy on a less-bad course. It seems this was partly true, but this case remains to be made, however, as I see no evidence that she wasn't in favor of the rate hikes as Fed Chair.  She also could have talked Obama into making timely appointments in 2009 and 2010, to try to get policy back on track. Instead, she voted for a rate hike in 2010. I viewed this as unforgivable at the time, and it even looks worse in retrospect.

Of course, she also got unlucky. Had it not been for Comey, her mistakes as Fed Chair would likely not have led to Donald Trump.


Note: follow me on twitter @TradeandMoney





Wednesday, November 29, 2017

An Economist's Take on Bitcoin and Cryptocurrencies: It's a Giant Scam and the Mother of All Bubbles

Bitcoin has climbed over $10,000 (when I first drafted this post last week, it was at just $8,200). The cryptocurrency market now appears like a full stock market of fake stocks, with a market cap of $245 billion (update a week later: $345 billion), more than 1% of the capitalization of the US stock market. My take on bitcoin is the standard boring economists' take: bitcoin and other cryptocurrencies are the mother of all irrational bubbles. The South Seas Bubble, Tulip Mania, the Nifty Fifty, and the dot.com bubble were all similar. And, if you'd listened to me (and us economists), you're continuing to live in relative poverty as your friends get rich, with money and wealth coming out of nowhere and millionaires minted overnight.
Pictured here with Nobel Prize Winner Robert Shiller,
fortunate to experience a balmy -7 degrees in Moscow. He
also believes that bitcoin is in a huge bubble.

Despite my view that this is a standard bubble, I tried to buy bitcoin last summer (back at the bargain price of $4,000...), in part because I wanted to see how easy it was to use bitcoin to send money back to the US from Russia. After all, the logic behind bitcoin is that it is a super easy, cheap and fast way to send money. Exactly what I needed. The difficulty I went through in trying to purchase bitcoin only confirmed my worst fears of why I think it is a scam/ponzi scheme. Part of the problems I faced were no doubt specific to me, as a US national living in Russia. Many bitcoin exchanges are country specific, and didn't like my Russian IP address. Others did, but required a lot of information, including a picture of my with an ID, and also a picture of me with a bank statement with my home address (in the US) written on it. I ended up never getting approved, and never got a straight answer from some of these exchanges on why not. Probably, they are just minting money so fast why should they invest in customer support.

But all the information required, even if I had been approved immediately, kind of shoots down some of the logic. If I'm a drug-dealer looking to accept payment in bitcoin, I'm still going to have to provide a lot of information to the exchanges. And, while my troubles may have been unique, bitcoin isn't that easy to use. Your grandma isn't going to be buying groceries or trading bitcoin anytime soon. Indicative of the inconvenience of buying bitcoin, there is a closed-end investment fund which traded on the stock-market that owns only bitcoin, and was recently trading at twice the par value of bitcoin (see Figure below). That is, people who wanted to buy bitcoin in their brokerage accounts were too lazy to cash out their accounts and buy bitcoin directly, so they paid double the price to avoid the hassle.

Grayscale Bitcoin Investment Trust



In addition, the fees associated with buying bitcoins in Russia using rubles, sending them to myself in the US, and then converting them back into dollars are at least an order of magnitude larger than just buying dollars using my currency broker, and then sending money to myself directly. The total cost of my normal fees for doing this set of transactions run about $25 for a $10,000 transaction using the banking system and my currency broker. By contrast, I'm told the bitcoin broker in Russia charges 3%, and one in the US (Coinbase) charges 2% per transaction (maybe this is now 1.49% for Coinbase users in the US, although it looks as though they charge 4% to fund an account using Visa/Mastercard), plus whatever the bitcoin miners charge (perhaps .2%?). Even the miner's fees are calculated in a super non-transparent way. It's probably that way for a reason. 

Theoretically, some other problems with bitcoin is that there is free entry. Anyone can create an infinite amount of cryptocurrency out of thin air. The marginal cost is zero. The saving grace is that there are network effects -- a currency becomes more valuable the more people that use it, and so it will be tough for other cryptocurrencies to displace bitcoin. However, that can't explain why there are thousands of cryptocurrencies with huge market caps. Only 1-2 of these will ultimately be the victor, and bitcoin is likely to be one of them.

Another issue with bitcoin/cryptocurrencies long-term is that if they ever did replace actual currencies in everyday transactions, governments could really lose out. The Federal Reserve would lose control over monetary policy, for example, and to the extent cryptocurrencies enable drug smugglers and hackers and others to evade the authorities and paying taxes, this should be something which governments will have a real interest in illegalizing. Thus, there is no endgame where bitcoin replaces the US dollar, the Chinese Yuan, or the Euro as the primary currency of a major economy. It is simply too volatile, and there will be nothing to stabilize its value.

The real economic argument for bitcoin is not that it actually provides cheap transaction fees, but rather that it is a really good scam/meme. It's techy, it's complicated, and few people understand it. Those who spent the time to learn how it really works then become part of the cult and evangelize over it. It could be compared to the spread of a religion: If many people very fervently buy into it, it could be a bubble that lasts a long time. This is the optimistic case for bitcoin. There are a group of Japanese in Brazil who went to their graves believing that Japan didn't lose WWII, and it was just US propaganda that suggested otherwise. The bitcoin true believers/dead-enders may hold bitcoin until the day they die, giving it a positive value for a long time to come.

Or, it could be more like the spread of a disease (I'm stealing this from Robert Shiller). To grow, the disease needs a lot of new people to infect. Once about 20-30% of the people are infected, it's growth will be at a maximum. But, over time, there are fewer and fewer new people to infect, as most people have had the disease, and the rate of new infections crashes. Bitcoin may not be so different -- the early adopters buy in, sending the price up. The higher price means more news, and is a positive feedback loop as the mainstreamers start to buy. Doubt creeps into the minds of naysayers, who might have believed it to be a scam initially, but now see the price soaring, against their predictions.

Usually the moment to sell is after almost everyone who is a quick adopter has already adopted, the median person has too, and the moment at which people who are typically late adopters start to invest. At that point, the economy will run out of suckers, and the price will start to stagnate and fall. Legend has it that Joe Kennedy sold his stocks in 1929 after a shoeshine boy started giving him stock tips. An older family member of mine was day-trading tech stocks in the 1990s, and then bought a condo in Florida in 2006. This person is my bellwether.

Given this may be a reason to buy in the near term, before the late adopters get wind (and, damnit!, why didn't I realize early on that this was a good scam!), be warned that just as the positive feedback loop works well on the way up, and it can work in reverse on the way down. A few bad days, and panic selling can ensue. Once it crashes, a generation of people could be so turned off by crypto they'll never touch it again.

What crypto does is settle the debate over whether fundamentals drive stock prices and exchange rates. I gave a talk at LSE a few weeks ago on my research on exchange rates and manufacturing, and someone stated their belief that exchange rates are driven by fundamentals (monetary policy) and so it was monetary policy which drove my results and not exchange rates, per se. However, as we see with bitcoin, which isn't driven by any kind of fundamental economic value, as it pays no dividends and has high transaction fees, bubbles can happen and markets aren't that efficient. (OK, even if you believe in bitcoin, how much do you believe in Sexcoin, Dogecoin, or "Byteball bites", the latter of which has a market cap of a cool $187 million...) There is never going to be a day when everyday people use "byteball bites" to buy groceries.

It also shows another reason why governments might want to tax windfall profits or large capital gains at a higher rate. Those profiting from cryptocurrency are incredibly lucky. Their "investments" don't leave any reason to deserve favorable income treatment relative to wage income. Stock market earnings are similar. Luck is involved just as much as skill.

Lastly, though, let me state my agreement with others that government-sponsored electronic currencies are probably a thing of the near future. If an electronic currency allows every transaction (or most transactions) to be traced by the government, it can cut down on illegal activity, narcotics, and tax evasion. A government could really very easily broaden the tax base, and raise more revenue while cutting taxes on law abiding citizens. This will probably help developing countries (like Russia) where tax evasion is rampant the most. I guess this will happen soon. Greece should do this and leave the Euro system (but not the EU!). Obviously, a digital currency also solves the problem of the zero lower bound on interest rates, reason enough to do it. Were I the Autocrat of All the Russians, I'd have implemented this already.

In any case, I don't want to give anyone investment advice. I have no clue what will happen to the price of bitcoin, although that should be a warning. I hope none of my friends miss out on the huge boom as bitcoin goes from $10,000 to $100,000 just because they read this. Just be for-warned that what goes up must come down. If you do ride the wave up, think about taking something off the table and try to remain diversified. (That goes for the US stock market too, which also now looks quite overvalued...) Once your parents start to buy bitcoin, that's probably a good time to cash out.