The impact of testosterone on wages

Men earn more than women, this is still rue despite much effort over the past decades. The difference in wages, of course, needs to be measured ceteribus paribus, which means we need to take into account any observable characteristic so that we really compare men and women with the same characteristics doing the same job. This could mean that there is some unobservable characteristic that still matters. Men have more testosterone, and maybe this brings an increase in productivity they are rewarded for. But we typically do not measure that in labor force surveys.

Anne Gielen, Jessica Holmes and Caitlin Myers find a way around this difficulty by looking at twins. Indeed, females with a male twin have been exposed to more testosterone than all-female twin pairs. And males with a female twin got less. Using administrative data from the Netherlands with 80,000 twins, they find that this proxy for testosterone levels has a positive impact on hourly wages for males, but not for females. This new "observable" allows thus to better explain wage dispersion among males, but cannot explain the still remaining male-female wage gap.

What kind of jobs are academic scholars looking for?

Any university ranking that is published these days features a majority of US-based schools at the top. It is clear that across every field the United States is able to attract the best talent, at least when looking at top schools. Why is that so? What attracts scholars to the US?

Jürgen Janger and Klaus Nowotny have created an interesting data set by surveying 10,000 academics across the world and letting them choose between various hypothetical jobs. Those jobs varied along a series of characteristics, which allows to understand what academics value most. The results indicate that the standard US tenure track system is pretty much close to optimal. What matters most is pay, which should have a performance component, valuable peers, internal grants and a good mix of teaching and research. Location does not matter much, presumably because academicians are so focused on their work. Those early in their careers value financial and intellectual autonomy, as well as having some prospect for internal promotion based on performance. The more senior ones do not like being bound to a particular research stream and prefer being in a departmental setup rather than a chair-like system. Given all this, it is no surprise that the US manages to attract the best talent. But one can wonder whether the responses also reflect the realization that the United States has attracted the best researchers, so its system must be better independently from personal preferences.

The Economics of Christian Reformation

The social fabric of a country has a lot of inertia. It takes generations for norms to change, in large part because people rarely change during their lifetime, and if there is any change it is towards conservatism, that is, preserving the status quo. Yet sometimes change spreads quickly, like a revolution. In some sense we see this with the Arab Spring. All it needed was a small spark, and that spark may seem irrelevant at first. Another dramatic social change was the Christian Reformation that started with a simple priest in a completely irrelevant town of Saxony. Martin Luther was a spark that somehow set on fire an existing social norm, Catholicism, and set in motion a revolution that would keep Europe busy for centuries. How could this happen?

Philipp Robinson Rössner points out that central Germany suffered at the time from economic depression and deflation, at least partly as a consequence from a decline in silver supplies. This context deeply influenced Martin Luther's thinking, which found a receptive audience throughout the region. One thing that I take away from this is that the Reformation possibly happened because currency was tied to silver. Had the region had a modern central bank with fiat money, the money supply could have adapted to economic circumstances and the Reformation may have never happened. Europe would have suffered from much fewer wars, and the world's history (and economy) would have been quite different.

How to increase savings: add a lottery!

Households with low incomes save little. In one way, this should not surprise us, as their propensity to consume is high because the marginal utility of consumption is high. If they temporarily have low income, this is not a problem at all. However, if you have persistent low income, then you absolutely need to accumulate some savings to supplement any retirement pension income. This does not seem to be happening, and a frightening share of the population is hitting retirement age with little in the bank. Even worse, the use of lotteries and other gambling operations seems to be rather popular for lower incomes. While one can rationalize playing an actuarially unfair lottery under some circumstances (see here), it is generally considered to be a poor use of scarce income. Now, could one use the temptation of lotteries to get low income households to save more?

Kadir Atalay, Fayzan Bakhtiar, Stephen Cheung and Robert Slonim show one can combine saving accounts effectively with lottery jackpots. In an experiment conducted online in the US, they had participant allocate funds under various savings schemes. While this is not quite like the real world, it still reveals some interesting findings. Introducing a jackpot lottery does indeed increase savings, and significantly so (the authors find a 12 percentage point boost). These savings come both from delayed consumption and reduced lottery participation outside of the savings account. And all these effects are stronger among those with lower incomes. Can we believe those results? After all, the jackpots in savings lotteries are supposed to be "life changing", something that such an experiment cannot simulate. But it is encouraging to still see a strong impact.

Seigniorage loss and the fall of the Roman Empire

Early in the 20th century, the United States took over from the United Kingdom the role of the preeminent economic and political power. Since the last turn of the century, some people are seeing hints that the United States may be losing that role (but it is not clear who would take it), often seeing parallels with the fall of the Roman Empire. We do not know, however, why the Roman Empire fell. There are several explanations, and several may be necessary for the fall. But there is no smoking gun that the United States should particularly look out for.

John Hartwig has an interesting suggestion: the Roman Empire fell because it lost the steady revenue from seigniorage. Of course, it is difficult to get detailed data from this period, thus Hartwig proceeds by formulating a four-sector (C, I, G, gold mining) model where the government gets a substantial fraction from minting gold and silver and issuing at a value substantially above cost. About 165AD, gold deposits were exhausted and Rome had to tax significantly more to sustain itself, thus switching from one type of tax to the other. Forced to mint low quality coins, inflation sets in but this does not bring sufficient revenue. In addition, the center of the Empire being used to import goods from the periphery suffers from a lack of productive capacity once there is no new gold to pay for imports. All in all, the Roman Empire lost the benefits of holding the keys to the world currency. This is something the US has definitely been benefiting from, through seigniorage and through low interest rates, and one motivation for the creation of the Euro has been to capture this rent. But it is so far hard to find evidence that the dollar is losing its status. The US thus seems safe on that front.

The Lucas Critique, DSGE models and the Phillips Curve

The Lucas Critique in 1976 has been a major motivation behind the building of RBC models, the folow-up DSGE models as well as the structural estimation of these models. The idea was that estimating reduced-form elasticities was not immune to policy variations, and those elasticities were being estimated to determine the impact of policy in the first place. The resulting bias reduced the trust in the Philipps curve. The structural models, however, had and still have at their core supposedly invariant parameters that describe some fundamentals of the economy. But it turns out that some of those are not invariant over time. I recently discussed the case of the labor income share (here). And misspecification can be problematic in the estimation of such structural models, with possibly important consequences.

Samuel Hurtado tries to sort that out by including parameter shifts in the estimation of a standard DSGE model, but misspecifying it in such a way that it ignores this shift. Using data form the 1970s, he then shows that the policy responses from his model look surprisingly close to those of a reduced-form Philipps curve. In other words, it seems the DSGE model without parameter shift is just as misspecified as the old Philipps curve. What this means is that one has to either include ad hoc parameter shifts or that one needs to go even deeper in the fundamentals to understand why and how these parameters shifts are occurring. The latter gives an even stronger meaning to the Lucas Critique.

The fuss about big data

"Big data" is the latest buzzword describing the next technological revolution wherein enormous amounts of data can be collected about our daily lives and can be used to improve our choices and better understand what is going on in all sorts of dimensions. That includes very detailed information about transactions, locations, and even online behavior. Who has not noticed that ads suddenly turn to what one has searched for a few days ago, if not getting emails about that. Whether big data will keep its promise will depend in part on what will happen with privacy protection. Europe has already taken steps, for example imposing that web cookies need to be accepted by users. In the US, people have been so far very tolerant with companies (but not the government) spying on them, but the tide could turn. But what are really the promises of big data?

Liran Einav and Jonathan Levin focus on economic policy and research. Quite obviously, we complain when data is not available when we want to measure something. Will big data make that possible? While I do not think the (mostly) random collection of big data will allow us to get exactly what we need, the authors thinks that with new statistical techniques and computer algorithms being developed specifically for big data, there should be something useful for economists. They hope to achieve better statistical power from massively larger and finer data. The opening of larger administrative data sets also has a lot of potential, especially, I would add, if the researcher is allowed to link them to each other. Denmark has shown how great data allows for better research and policy, and also makes researchers flock to you. But again, this is all dependent on how privacy laws will evolve.

Egypt's puzzling income inequality

The Arab Spring is the result of growing inequalities and iniquities and has been a wake-up call for the leaders of other countries where a few privileged dominate the masses. That could be a short summary of the commentary coming out the mainstream media about what happened in Tunisia, Libya, Syria and in particular Egypt. And it is all wrong.

Indeed, Vladimir Hlasny and Paolo Verme point out that there is really nothing special about the income distribution in Egypt, and if anything it has become more egalitarian during this millennium. They can turn the data whichever way, same result. However, it appears from the World Value Survey that the tolerance for inequality has sharply declined. And this change must be coming from other factors that the income distribution.

About exploding offers

The academic job market is characterized by much uncertainty about the job candidates, at least in Economics where students who have yet to publish anything (in most cases) and have not even completed their studies are hired. The fact that they are supposed to be at the research frontier and that very few people, if any, can evaluate their potential makes it no surprise that recruiting committees stick to signals: who the dissertation adviser is, where the degree is from, and always glowing recommendation letters. When a recruiter has managed to identify a particularly good candidate, it does not want to let others benefit from this discovery. To avoid the job candidate from continuing to shop around, the typical strategy is to make an exploding offer: The offer letter is valid for, say, a week, and thereafter becomes void. This is quite frustrating for a candidate who may still be waiting for a preferred department to make its move, but this is well proven strategy for recruiting departments.

Mark Armstrong and Jidong Zhou show that this does not necessarily have to be so. Other options are to let candidates make a down-payment to keep a job offer alive or offer a bonus if they sign quickly (I am reinterpreting the papers results for my example). Yet, I do not think I have ever seen this happen, even a signing bonus. The model, which is actually about a seller who may offer a buy-now discount, ask for a deposit or make an exploding offer, highlights that the uncertainty about the outside options of the buyer (or the job candidate) is crucial. The search wants to deter the buyer from looking elsewhere. How much the uncertainty affects the buyer determines which strategy is best. In the case of the academic market, I guess this means that job candidates are very risk averse, thus the exploding offer strategy is optimal for the recruiters.

Empirics under uncertain beliefs are difficult

The typically way we model uncertainty is by assuming economic agents know the stochastic process they are facing, and we call this uncertainty. That is wrong. This should be called risk as probabilities are known. Uncertainty is when those probabilities are unknown. That does not mean the agent is not rational, it is simply that the information set is smaller than what we typically assume.

Nabil Al-Najjar and Jonathan Weinstein point out that the uncertain agent trying to smooth consumption may look like he is excessively precautionary to someone thinking he has known probabilities. They frame it within a Bayesian framework, where beliefs including subjective probabilities are updated with incoming information. This makes it very difficult to do any empirical work, including measuring time preference or risk aversion.

I am surprised, though, the Al-Najjar and Weinstein misunderstand rational expectations. They claim an uncertain agent does not have rational expectations if beliefs over probabilities do not coincide with observed frequencies. This does not need to be if the econometrician has information the agent did not have at the time of the decision. If the agent uses all the available information, then it is still rational expectations. There may be so little that he cannot determine probabilities precisely, unlike perfect foresight on probabilities, like Al-Najjar and Weinstein seem to imply. In any case, this is more about semantics than results.

Is early childhood really that important for adult life?

How important are the preschool years for adult outcomes? Empirical evidence from rich countries mostly shows that treatments during preschool years persist to adult years. James Heckman, for example, has pushed very hard this result. How robust is it once you look at more extreme cases?

Todd Schoellman does this by looking at refugees from Indochina who arrived in the US. He finds no difference in adult wages, education and anything else he can throw at the data between refugees who arrived in the United States at different preschool ages (before 5). One would have expected a huge effect, as they were exposed to very dire environments in their home countries or the refugee camps. It runs also counter to other empirical evidence and standard models. So what is going on here? Schoellman argues that what really matters in early childhood were the parents, much less the environment. That result changes somewhat once the arrival date falls into school age.

The central bank should be sector-agnostic

Large Scale Asset Purchases (LSAPs) of mortgage-backed securities have been a major component of recent monetary policy. It is not without critics as this is a policy that has been targeted towards a specific sector of the economy, the real estate sector. This is principle a big no-no, as a central bank should only care about the overall economy, not specific sectors or firms. In a similar fashion, the ECB has been criticized for buying out specific countries following conditions that were differentiated by country, instead of applying one rule to all, or even not buying country debt at all. But if all sectors benefit equally or if that was the most efficient way to conduct policy, that is all good.

In the case of LSAPs, Meixing Dai, Frédéric Dufourt and Qiao Zhang find it was certainly not the most efficient way to deal with a confidence shocks in the banking sector, and it obviously privileged the real estate industry. One could have done better by buying corporate bonds, but in a uniform manner across sectors. This works better than mortgage-backed securities because they are less leveraged and thus free up more bank capital. This is more true if financial markets are more segmented, that is, if bankers cannot freely reallocate resources between sectors. What the paper does not say is how this would have compared to a conventional policy, buying government bonds.

Taylor rules with assets and credit

One thing we have learned from the last recession is that the financial sector is quite important, that its dysfunction can have important consequences, and this can happen even in the most financially elaborate economy. Some thus call for the health of the financial sector to become a component of every policy maker's dashboard. From a dashboard it is only a small step to include the financial sector into a policy formula such as the Taylor Rule.

Leonardo Gambacorta and Federico Signoretti take that step by deriving from a DSGE model a Taylor Rule that includes asset prices and the amount of credit. So far so good, but why not also include the exchange rate? And more indicators? This is not the purpose of the Taylor Rule. It was devised to be a simple guide to policy, from which you want to deviate when circumstances call you to do so, for example with unconventional policies that cannot be captured with a Taylor Rule, simple or not. The best example is when the Taylor Rule calls for negative nominal interest rates. Would anybody blindly follow this? Of course not, and this is why we should stop thinking in terms of a single equation, especially when one has several policy goals. You needs at least as many instruments as goals. The policy interest rate cannot do everything.

Political connections pay off in the US

It is no secret that political connections help your business. The more more regulated or corrupt your economy, the more likely this is to be true. How much this helps is difficult to quantify. For one, political connections cannot be measured on some sort of scale, and gathering such data would be very difficult as people usually try to hide such connections from the public. And second, how would you measure the impact of of these connections.

Yen-Teik Lee, Bang Dang Nguyen and Quoc-Anh Do find a way by first looking at the university networks among CEOs and candidates to US state governor races and then looking at the stock valuation of firms around the time of close gubernatorial contests. Firms connected to the right candidate gain 1.36% right after the election, and the bump persists. Imagine how large this can become in countries where such patronage is less scrutinized and where regulation is more prevalent.

Entrepreneurship cannot be taught

A lot of classes in business schools teach rather fluffy material, especially MBA classes. It is all about entertaining the students who pay dearly for their education and expect a diploma. The signaling value from the diploma happened with entry into the school and not through the selection process during classes. And quite a few classes are all about making the students believe they are learning important skills that will make them CEOs. Nowhere is that more true than with "entrepreneurship" classes, whose teachers are often adored by students who think they will turn into the next Bill Gates.

Michael Stuetzer, Martin Obschonka, Per Davidsson, and Eva Schmitt-Rodermund Do not empirical research into what it takes to be an entrepreneur, and I presume their sample includes only successful ones. It turns out education has no bearing at all. It is all about having a varied work experience. Thus, working a long time on the same job will not make you a successful entrepreneur once you quit. And taking entrepreneurship classes or getting an MBA will not help you either.