について調べたNBER論文をエミ・ナカムラ、ジョン・スタインソンらがLeland Farmer, Emi Nakamura & Jón Steinssonが上げているungated版)。論文のタイトルは「Learning About the Long Run」で、著者はLeland Farmer(バージニア大)、Emi Nakamura(UCバークレー)、Jón Steinsson(同)。

Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and forecast revisions predict forecast errors. Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question. We propose that these anomalies arise because professional forecasters don’t know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature. We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976- 2019. Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia.


以前バングラデシュでのマスク着用に関するRCTについて、Ben Rechtによる批判的な検証を取り上げたことがあった。その後、研究者がデータを公開したとのことで、Rechtが公開自体は賞賛しつつも改めてそのデータを批判的に検証している(H/T タイラー・コーエン)。以下はその概要。

  • 公開されたデータは:
    • 対照群(nC):300村の163,861人、陽性者数(iC)=1,106人
    • 処置群(nT):300村の178,322人、陽性者数(iT)=1,086人
  • この結果には、以下のような問題点がある:
    • 34万人以上を8週間検証して差はわずか20人。
    • マスクでは盲検は不可能なので、当然ながら盲検ではない。
    • 処置群では、マスク促進以外に、社会的距離などの他の措置に関する教育も行われた。
    • 研究者による検査に同意して研究対象となった人の比率は、処置群が95%、対照群が92%。この差だけで観測された差を拭い去ってしまう。
    • 血清反応による陽性は、検証以前に感染していた可能性があるため、コロナの指標としては粗い。
  • 生物統計学では、実際の症例数ではなく、相対リスク減少の指標である有効性を見ることが多いが、それは効果を誇張する。
    • 同指標はRR=(iT/nT)/(iC/nC)として計算されるが、マスク研究ではRR=0.9で、感染のリスクの改善率は1.1xに過ぎない。ちなみにmRNAワクチンのRRは0.05で、改善率は20xとなる。
    • ワクチンの学界では有効性EFF=1−RRという指標も使われる。RR=0.9ならばEFF=10%である。0%から20%の有効性は無きに等しいとされ、20%の有効性のワクチンが承認されることはない。また、有効性は非線形性という点でも難点がある。有効性の10%と20%の差は非常に小さいが、85%と95%ではリスク減少にして7倍と20倍という大きな差がある。
  • 研究ではサージカルマスクと布マスクの違いも見ているが、そこでは有効性の馬鹿馬鹿しさがさらに明らかになる。サージカルマスクのデータは以下のようになっている。
    • 対照群(nC):190村の103,247人、陽性者数(iC)=774人
    • 処置群(nT):190村の113,082人、陽性者数(iT)=756人
      • 差は18人で、有効性は11%と低い。
  • 一方、布マスクのデータは以下のようになっている。
    • 対照群(nC):96村の53,691人、陽性者数(iC)=332人
    • 処置群(nT):96村の57,415人、陽性者数(iT)=330人
      • 10万人超の研究で差は僅か2人なので、差の数字に意味はないが、有効性を計算すると7%になる。
  • この場合、7%も11%も「効果なし」として扱われるべきで、その差に大した意味はない。また、このような結果についてはp値のような統計量を計算することにも意味はない。


Philippe Lemoineというコーネルの博士課程にいる研究者が、自らが所属するThe Center for the Study of Partisanship and Ideology(CSPI)という組織のブログに「Have we been thinking about the pandemic wrong? The effect of population structure on transmission」と題した長文のエントリを上げ、タイラー・コーエンが「Why does R vary so much in pandemics?」というコメントを添えて リンクしている。Lemoineはツイートでその内容を解説しているので、以下にその一部を引用してみる。

However, as I argue in the post, I think it's very difficult to deny that the effective reproduction number can undergo large fluctuations even in the absence of significant behavioral changes, which is hard to understand.
Of course, there are other factors that influence transmission (such as meteorological variables), but I argue in the post that they are not sufficient to explain the large fluctuations of the effective reproduction number we observe in the absence of behavioral changes.
Since SARS-CoV-2 is a respiratory virus that is transmitted by contact, transmission should ultimately depend on people's behavior, this is very puzzling. So how can we explain those fluctuations of the effective reproduction number without denying this basic fact?
What I propose in the post is that we can square this circule by taking into account population structure and how it can affect transmission even in the absence of behavioral changes.
Indeed, standard epidemiological models, of the sort that are used to make projections and study the impact of non-pharmaceutical interventions, assume that the population is homogeneous mixing or something close to it.
What this means is that models assume that someone who is infectious has the same probability of infecting everybody in the population or, since models used in applied work often divide the population into age groups, the same probability of infecting everyone in their age group.
Of course, this is totally unrealistic, since in practice if I'm infectious the probability that I'll infect most people in the population or even in my age group is effectively zero, because I don't even have any interaction with them and therefore couldn't possibly infect them.
In practice, the virus doesn't spread in a homogeneous population, but on a network based on people's patterns of interaction with each other. The topology of that network determines what paths the virus can take to spread on the population and not all paths are equally likely.
Now, suppose that this network can be divided into subnetworks that are internally well-connected, but only loosely connected to each other.
In network science, a network that has this property is said to have "community structure", which many real networks are observed to have. For instance, here is a network based on friendship relationships among a few thousand people on Facebook, which has this kind of structure.
If the population has that kind of structure, when one of the subnetworks is seeded, the virus starts spreading in that subnetworks until herd immunity is reached locally, at which point incidence goes down unless the virus manages to reach another subnetwork from there.
Thus, instead of simulating the spread of the virus on a network of individuals, I simulate the spread on a network of homogeneous mixing populations that has community structure. Here is a graph that shows the network generated by the model for one of my simulations.
At the level of each subpopulation in the network, the model is a standard epidemiological model that assume homogeneous mixing, but people who are infected in one subpopulation can "travel" to another along the edges of the network and infect people over there.
(I put "travel" in scare quotes because people in different subpopulations may nevertheless be neighbors. What matters is who they interact with, not physical proximity, though obviously they are related. I discuss this point in more detail in the post.)
As you can see, the network is divided into subnetworks that are internally well-connected, but loosely connected to each other. Moreover, each edge is associated with a probability of "travel" along that edge, which is much greater for edges that stay within the same subnetwork.
For this simulation, I assumed a probability of "travel" of 5% along the edges that stay within the same subnetwork, but only 1 in 10,000 for edges that lead to a subpopulation in another subnetwork. There are more than 10,000 subpopulations, for a total population of ~5 million.
Here is a chart that shows the result of the simulation when I let the virus spread on that network. As you can see, the effective reproduction number undergoes wild fluctuations and the population experiences several waves at the aggregate level.
However, at the level of each subpopulation, the basic reproduction number was assumed to remain constant! Thus, this shows that, when the population has that kind of structure, the effective reproduction number can undergo large fluctuations even without any behavioral changes.
In order to make the process more intuitive, I created this animation showing how the virus spreads across subpopulations, which are represented by rectangles whose area is proportional to their size inside larger rectangles that represent the subnetworks to which they belong.
Unsurprisingly, if we increase the connectivity between subnetworks enough, the model behaves in a way that is more similar to what happens in a homogeneous mixing population.
For instance, if I use the same method to randomly generate a network but multiply the average number of edges between subnetworks by 10 and the probability of "travel" associated to those edges by 100, I obtain this epidemic.
Simulations on networks with community structure can produce all sort of epidemics, not just epidemics with large, sharply defined waves as above, but also epidemics that exhibit long plateaus with ups and downs. Just as we see in real data.
Thus, by relaxing the assumption of homogeneous population mixing and simulating the spread of the virus on a network with community structure, we can get the sort of behavior that we observe in the real world even with a constant basic reproduction number in each subpopulation.
しかしそれぞれの副人口レベルでは、基本再生産数は一定に留まると仮定しているのである! ということで、このことが示しているのは、人口にこうした構造がある場合、行動が何も変化しなくても実効再生産数は大きく振れることがある、ということである。


感染の不均一性が現実のコロナ禍の理解において重要、という話は昨年から取り沙汰されてきた話であるが(cf. ここ、およびそのリンク先)、Lemoineはエントリ本文の追記で、今回の話とその話の違いを以下のように解説している。

Based on the response to this post, many people seem to think what I’m saying is the same thing as what people who argued back in 2020 that heterogeneity in social activity might lower the herd immunity threshold, but while this is related to what I’m talking about here it’s actually different so I thought it might be useful to briefly explain why. I’m actually familiar with the debate that took place about that last year, since I even wrote a post about it at the time. In both cases, the point is that heterogeneity affects the dynamic of the epidemic, but it’s not the same kind of heterogeneity. What people were arguing last year is that, if people’s level of social activity varies a lot, herd immunity will be reached sooner because the people who spread the virus the most are also the most likely to be infected early in the pandemic.41 This intuitive argument is supported by models showing that, when you introduce that kind of heterogeneity, herd immunity does in fact occur sooner. If we model the spread of the virus on a network, this debate was mostly about the degree distribution, i. e. the distribution of the number of edges connected to each individual in the network. The point was that, when this distribution is more dispersed than standard epidemiological models implicitly assume, the herd immunity threshold will be lower than predicted by those models.
However, the kind of epidemic behavior I discuss in this post only arises when the network has community structure, which is about a lot more than the variance of the degree distribution.42 In particular, the network must exhibit a specific kind of clustering, but this doesn’t just depend on its degree distribution. In fact, it’s conceivable that at the level of the parts of the network that I idealized as homogeneous mixing population in my simulations, the herd immunity threshold is lower than predicted by standard epidemiological model due to heterogeneity in social activity, even though at the aggregate level it’s higher due to community structure, as I explained above. So while most people have interpreted the fact that many places with a high prevalence of immunity have recently experienced large outbreaks as proof that people who argued that heterogeneity in social activity could lower the herd immunity threshold were wrong, this is not actually the case if the network on which the virus is spreading has the kind of structure assumed in this post. Of course, like the rest of this post, this is very speculative, but it goes to show that the spread of infectious diseases is a lot more complicated than people generally assume.

*1:原注:Actually, some people also talked about other kinds of heterogeneity, such as heterogeneity in susceptibility. If you are modeling the spread of a virus on a network, whose edges have a weight indicating the probability of transmission along that edge, this presumably depends on a combination of the degree distribution and the distribution of the weights. But this is also different from the kind of heterogeneity I’m discussing in this post.

*2:原注:In general, the topology of a network can’t be reduced to the properties of its degree distribution, because it depends on facts about how the network was generated that go beyond the degree distribution that was used.


というNBER論文(原題は「A Primer on Trade and Inequality」)をダニ・ロドリックが上げている(H/T Mostly Economicsungated版)。以下はその要旨。

In the public imagination globalization’s adverse effects have loomed large, contributing significantly to the backlash against the political mainstream and the rise of far-right populism. The literature on trade and inequality is in fact exceptionally rich, with important theoretical insights as well as extensive empirical findings that sheds light on this recent experience. Some of the key results of this literature, discussed here, are as follows: Redistribution is the flip side of the gains from trade, and it becomes larger relative to net gains from trade in the advanced stages of globalization. Compensation is difficult for both economic and political reasons. International trade often differs from other market exchanges, raising fairness concerns in ways that domestic markets do not. The economic benefits of deep integration are generally ambiguous. Dynamic or growth gains from trade are uncertain.


というBIS論文が上がっている(H/T Mostly Economics)。原題は「Financial crises and political radicalization: How failing banks paved Hitler's path to power」で、著者は Sebastian Doerr(BIS)、Stefan Gissler(FRB)、Jose-Luis Peydro(インペリアル・カレッジ・ロンドン)、Hans-Joachim Voth(チューリッヒ大)。

Do financial crises fan the flames of fanaticism? Many have argued that the financial crisis of 2007–09 not only wrought havoc on employment and output but that its problematic aftermath of failing financial institutions, public bailouts and austerity may also have paved the way for populists around the world. We examine the canonical case of a radical movement's rise to power: Hitler's Nazi party, which took office in the wake of the severe 1931 banking crisis in Germany – a turning point in modern history.
Several cross-country studies have concluded that a link exists between financial crises and right-wing populist movements. What is still missing are studies demonstrating that a financial shock can lead to a broad-based radicalisation of the electorate, with major political consequences. It has also remained unclear how economic and financial shocks interact with cultural identity in the turn toward radicalisation.
Using newly collected data on the exposure of individual cities to the failure of Danatbank – the bank at the heart of Germany's 1931 financial crisis – we show that a financial shock led to a generalised radicalisation of the electorate. This directly helped the Nazi party to gain power. Importantly, we demonstrate that the financial shock interacted with pre-existing cultural attitudes: the surge in support for the Nazis in response to the shock was greatest in places with a previous history of antisemitism. Voters were radicalised both at the ballot box and in their actions. Once the Nazis were in power, both pogroms and deportations were more likely to occur in places worse affected by the banking crisis.
Do financial crises radicalize voters? We study Germany's 1931 banking crisis, collecting new data on bank branches and firm-bank connections. Exploiting cross- sectional variation in pre-crisis exposure to the bank at the center of the crisis, we show that Nazi votes surged in locations more affected by its failure. Radicalization in response to the shock was exacerbated in cities with a history of anti- Semitism. After the Nazis seized power, both pogroms and deportations were more frequent in places affected by the banking crisis. Our results suggest an important synergy between financial distress and cultural predispositions, with far-reaching consequences.

金融危機は狂信的行為の炎を煽るのだろうか? 2007-09年の金融危機が雇用と生産に大きな損害をもたらしただけでなく、金融機関の破綻、公的救済、および緊縮財政というその問題含みの余波が、世界のポピュリストの台頭に寄与したと論じる者は多い。我々は過激運動の政権奪取に関する典型的なケースを調べた。即ち、現代史の転換点となった、1931年のドイツの深刻な銀行危機の後に政権の座に就いたヒトラーのナチ党である。
金融危機有権者を過激化するのだろうか? 我々は1931年のドイツの銀行危機を調べるため、銀行支店ならびに企業と銀行の関係の新たなデータを収集した。危機の中心にあった銀行との危機前の関係の深さの地域ごとの違いを利用し、ナチへの投票がその銀行の破綻に影響された地域ほど大きく急増したことを我々は示す。ショックによる過激化は、反ユダヤ主義の過去がある都市ほど悪化した。ナチスの権力掌握後は、銀行危機の影響を受けた地域ほどユダヤ人への迫害と追放が頻繁に生じた。我々の結果は、重大な帰結をもたらす、金融危機と従来からの文化的傾向との間の重要なシナジー効果を示唆している。


スキデルスキーのProject Syndicate論説からの引用をもう一丁。ヴェブレン、ケインズ、ハーシュマンの伝記をレビューした論説(H/T タイラー・コーエン)の冒頭でスキデルスキーは、傑出した経済学者を2種類に分類している。

There are two types of extraordinary economist. The first type includes pioneers of the field such as David Ricardo, William Stanley Jevons, and, in our own time, Robert Lucas. They all aimed to economize knowledge in order to explain the largest possible amount of behavior with the smallest possible number of variables.
The second category, which includes Thorstein Veblen, John Maynard Keynes, and Albert O. Hirschman, sought to broaden economic knowledge in order to understand motives and norms of behavior excluded by mainstream analysis but important in real life. The first type of economist is fiercely exclusive; the second has tried, largely in vain, to make economics more inclusive.
The first type of economist rather than the second has come to define the field, owing partly to the successful drive to professionalize the production of knowledge. Economics and other social sciences are heirs of the medieval guilds, each jealously preserving its chosen method of creating intellectual products. It also reflects the increasing difficulty in a secular age of developing moral content for the social sciences in general. We lack an agreed standpoint from outside “the science” by which to judge the value of human activity.
2番目のカテゴリーにはソースティン ・ヴェブレン、ジョン・メイナード・ケインズ、および、アルバート・O・ハーシュマンが含まれ、主流派の分析では除外されているが実生活では重要な行動の動機と規範を理解するために、経済的知識を広げようとした。第一のタイプの経済学者は極めて排他的である。第二のタイプは、概ね失敗に終わったものの、経済学をもっと包摂的なものにしようとした。第二のタイプよりは第一のタイプが分野を規定するようになったが、その理由の一つは知識の生産をうまく職業化したためである。経済学や他の社会科学は中世のギルドを受け継いでおり、知的生産物を生み出す選ばれた手法をそれぞれ油断なく保持している。別の理由は、世俗の時代において社会科学全般に関する道徳的な内容を構築することがますます困難になっていることにある。人間の活動の価値を判断する拠り所となる皆が合意した「科学」の外側の立場を我々は欠いているのである。


というProject Syndicate論説(原題は「What Killed Macroeconomics?」)の結論部でロバート・スキデルスキーが以下のように書いている(H/T Mostly Economics)。

The relationship between theory and practice is thus not as Bernanke saw it. Monetary policy works in theory but not in practice; fiscal policy works in practice but not in theory. Fiscal Keynesianism is still a policy in search of a theory. Acemoglu, Laibson, and List supply a piece of the missing theory when they note that shocks are “hard to predict.” Keynes would have said they are impossible to predict, which is why he rejected the standard view that economies are cyclically stable in the absence of shocks (which is as useless as saying that leaves don’t flutter in the absence of wind).
The supply and demand models that first-year economics students are taught can illuminate the equilibrium path of the hairdressing industry but not of the economy as a whole. Macroeconomics is the child of uncertainty. Unless economists recognize the existence of inescapable uncertainty, there can be no macroeconomic theory, only prudential responses to emergencies.

*1:論説の冒頭でスキデルスキーは、「QEの問題点は実際には効果があるのに理論上は効果が無いことだ(The problem with QE is it works in practice but it doesn't work in theory)」というバーナンキの有名なセリフ(cf. ここ)を引いている。