We focus on Italy -- one of the first country struck by #COVIDー19 -- where the lockdown design offers a source of exogenous variation in the intensity of the lockdown at a granular level 2/n
In the second (economic) lockdown (March 22) the Italian government defined a detailed list of essential economic activities. All other activities were either suspended or allowed to operate only remotely 3/n
This list provides a source of exogenous variation in the share of active workers at the municipal level that we recovered by matching the list with data on the number of workers at the municipal-NACE 3 digits level 4/n
We apply a diff-in-diff methodology by comparing excess deaths in municipalities experiencing a reduction in the share of active population above vs below the provincial median and before vs after the lockdown addressing potential violations of the parallel trend assumption 5/n
We use excess deaths at the municipal-day level as proxy of Mortality by Covid-19. This overcomes, at least partially, issues related to differences in classifications of deaths due to Covid-19, testing, and hospital capacity (Buonanno et al., 2020; Galeotti and Surico, 2020) 6/n
The key results show that the intensity of the economic lockdown is associated to a statistically significant reduction in mortality by Covid-19 and, in particular, for age groups between 40-64 and older, with large and more significant effects on the elderly. 7/n
Back of the envelope calculations indicate that 4,793 deaths were avoided, in the 26 days between April 5 to April 30, in the 3,518 municipalities which experienced a more intense lockdown. 8/n
We do not find effects of the lockdown in the south of Italy. This is not surprising because the effects of the epidemic were modest in the south. But this finding is useful as a “placebo exercise” and tends to exclude other confounding mechanisms. 9/n
By exploiting mobility data at the municipal-daily level (EnelX) we also observe that municipalities with a larger contraction in the share of active population experienced a reduction in daily aggregate mobility of around 53 kilometers per 1,000 residents 10/n
Why are our results important? They provide a simple methodology to assess the effect of the lockdown in other countries and help to understand the overall cost effectiveness of the Italian lockdown. 11/n
Some additional details on our analysis: we also control for municipality-specific dynamics (7-lags of daily excess deaths) and daily-shocks at the provincial level. 12/n
Our analysis is based on quite granular data because Italian municipalities are rather small having a median population of around 2,500 residents and a median size of around 21 kilometers. 13/n
The results are robust to a battery of checks and hold also on various sub-samples of municipalities with more similar or almost identical pre-trends, as for example municipalities with less than 5,000 residents 14/n
In the paper, we discuss possible alternative channels that may explain our results such as a reversion-to-the mean or the effect of the first Italian lockdown on mobility. The available evidence does not seem to support these alternative stories. 15/n
Caveat: our diff-in-diff exercise is clearly not based on experimental evidence and cannot pin down the mechanism through which the lockdown reduced excess mortality. 16/n
As such, we do not claim same effects would necessarily hold in different settings (e.g., when + masks available). Therefore, our results cannot provide direct policy implications. Yet, we hope our results might be informative on effects of the Italian lockdown measures n/n
I am very excited to share my new working paper titled “#Crypto Risk Premia” (with Daniele Massacci, @RubinMirco and Dario Ruzzi). A short 🧵 follows. Please, share it if you like it. Comments are very welcome [1/n]
Before “crypto winter” hit markets at the beginning of 2022, cryptocurrency was getting “boring” as some of the craziness of the earlier times was fading out and institutional investors had started to pour in, allocating a part of their large portfolios to crypto. [2/n]
To inform investment and risk management decisions, and guide portfolio allocation to crypto assets, it is fundamental to i) identify the set of risk factors driving crypto returns and ii) correctly quantify the prices associated with these sources of risk. [3/n]
The paper shows that sudden and large price moves in bitcoin prices (jumps) explain a large portion in the variation in bitcoin returns [2/n]
Study tail-risk in crypto markets is important for at least two reasons 1/ is tail-risk priced similarly to that in equity markets? 2/ to characterize the SDF of the marginal investor and price alternative cryptocurrencies and tokens and do risk-management [3/n]
The narrative of the rollercoaster day for cryptocurrency markets centers around the fears of stricter regulation in China (which might want to push its future CBDC). I shamlessy take the opportunity to advertise some of my prior work [1/n] #EconTwitter ft.com/content/c4c29b…
In Borri and Shakhnov (FRL 2019) we look at a similar big shock when China de facto ordered the closing of cryptocurrency exchanges. [2/n]
The shock had a huge effect on the global share of trading volume that took place on Chinese cryptocurrency exchanges: in a matter of months it went from 90% to less than 1% (caveat: part of it could have been wash trading) [3/n]
Our paper is motivated by recent work by @HannoLustig et al. (AER 2019) who found that currency carry trade strategies with T-bonds are different from those with T-bills because local currency term premia offset currency premia 2/n
Results in Lustig et al. (AER 2019) are for advanced economies with no/low default risk and imply that the volatility of the permanent component of investors’ SDF must be equalized across countries 3/n
I am very happy to share that my paper "Optimal Taxation with Home Ownership and Wealth Inequality" with Pietro Reichlin has been now accepted for publication at the @RevEconDyn [1/n] #EconTwitter
In the paper we consider optimal taxation in a model with wealth-poor and wealth-rich households, where wealth derives from business capital and home ownership, and investigate the consequences of a rising wealth inequality at steady state on these tax rates [2/n]
We find that the optimal tax structure includes some taxation of labor, zero taxation of financial and business capital, and critically a housing wealth tax on the wealth-rich households and a housing subsidy on the wealth-poor households [3/n]
My paper with K. Shakhnov on Regulation spillovers across cryptocurrency markets is now available on FRL at this link authors.elsevier.com/c/1bjLs5VD4Kcw… (with 50 days free access) #EconTwitter [thread 1/n]
In this paper we look at the unprecedented drop in trading volume on the Chinese cryprocurrency market after a significant regulatory change that de facto banned bitcoin in early 2017 in China [thread 2/n]
We find large spillovers of this regulatory shock on other cryptocurrency markets: 1) we observe a large increase in trading volume for bitcoin vs. Korean won, Japanese yen and U.S. dollars; ... [3/n]