This paper studies how people make decisions over preventive behaviors under ambiguity (i.e., Knightian uncertainty) where they do not even know the probability of a loss. In the context of the current COVID-19 pandemic, scientific uncertainty makes it hard to evaluate not only whether one will be infected, but also probabilities such as the infection rate. We constructed a simple model and demonstrated how its effect was heterogeneous depending on ambiguity-attitudes. Motivated by the model, we further conducted a survey experiment in Japan where we manipulated the information regarding scientific uncertainty on COVID-19. We found that higher ambiguity induced by scientific uncertainty increased the level of social distancing among ambiguity-loving people, but such evidence was nonexistent for ambiguity-averse counterparts.

This study presents a general equilibrium model of a small open developing economy with pollution generated by the tourism industry. The national government issues emission permits and constructs tourism infrastructure for the tourism sector. We examine the effects of a stricter environmental regulation on welfare, production, and income distribution. If the elasticity of substitution in the tourism sector is sufficiently low, a stricter environmental regulation paradoxically expands the tourism sector and narrows domestic wage inequality, even under constant tourism terms of trade. In this model, in addition to the two traditional channels, there is a new channel through which a stricter environmental regulation affects the tourism terms of trade and domestic welfare. The new channel, which arises from the difference between the marginal value product of tourism infrastructure and its price, improves the tourism terms of trade and domestic welfare if (1) the marginal value product of tourism infrastructure is greater than its price, (2) the output of tourism infrastructure is increased by a stricter environmental regulation, and (3) the excess supply of a tourism service decreases with a stricter environmental regulation.

This paper quantitatively analyzes the trade-off between job losses and the spread of COVID-19 in Japan. We derive an empirical specification from the social planner's resource constraint under the susceptible, infected, recovered, and deaths (SIRD) model and estimate how job losses and the case growth rate are related to people's mobility using the Japanese prefecture-level panel data on confirmed cases, involuntary job losses, people's mobility, and teleworkability. Our findings are summarized as follows. First, we find that a decrease in mobility driven by containment policies is associated with an increase in involuntary job separations, but the high teleworkability mitigates the negative effect of decreased mobility on job losses. Second, estimating how the case growth is related to people's mobility and past cases, we find that the case growth rate is positively related to an increase in people's mobility but negatively associated with past confirmed cases. Third, using these estimates, we provide a quantitative analysis of the trade-off between job losses and the number of confirmed cases. Taking Tokyo in July 2020 as a benchmark, we find that the cost of saving 1 job per month is 2.3 more confirmed cases per month in the short run of 1 month. When we consider a trade-off for 3 months from July to September of 2020, protecting 1 job per month requires 6.6 more confirmed cases per month. Therefore, the trade-off becomes worse substantially in the longer run of 3 months, reflecting the exponential case growth when the people's mobility is high.

In this paper, we review the structure of various epidemic models in mathematical epidemiology for the future applications in economics. The heterogeneity of population and the generalization of nonlinear terms play important roles in making more elaborate and realistic models. The basic, effective, control and type reproduction numbers have been used to estimate the intensity of epidemic, to evaluate the effectiveness of interventions and to design appropriate interventions. The advanced epidemic models includes the age structure, seasonality, spatial diffusion, mutation and reinfection, and the theory of reproduction numbers has been generalized to them. In particular, the existence of sustained periodic solutions has attracted much interest because they can explain the recurrent waves of epidemic. Although the theory of epidemic models has been developed in decades and the development has been accelerated through COVID-19, it is still difficult to completely answer the uncertainty problem of epidemic models. We would have to mind that there is no single model that can solve all questions and build a scientific attitude to comprehensively understand the results obtained by various researchers from different backgrounds.

To assess the promotion of life saving behaviors and determine the sustainability of nudge message effects, this paper examines nudges that promote evacuation during heavy rainfall, preventative COVID-19 infection behaviors, and COVID-19 vaccination. The results showed that altruistic gain messages may have more sustained effects than others in promoting both evacuation during heavy rainfall and contact reduction behaviors as a measure against COVID-19 infection. Specifically, social influence nudges that use a gain frame to convey that a person's behavior promotes the behavior of others are effective for both heavy rainfall evacuations and encouraging COVID-19 vaccination.

This paper discusses the importance of the community mechanism that complements the market and power mechanisms in an economic system during an era of crisis, defined as a mechanism for resource allocation by which at least one person proposes voluntary cooperation, and the proposal is not rejected. While this community mechanism can function alongside in win-win situations, it can be further activated with social preferences for altruism and reciprocity and with norms or worldviews that encourage cooperation. Other factors that relate to these include the character strengths that contribute to community and society known as virtues, with the concept of wellbeing related to virtues being known as eudaimonia. Some aspects of the acquisition of virtues can be viewed as changing preferences, and there is empirical evidence suggesting changes in trust relate to changes in preferences. Leadership is an example of the virtue of justice, and servant leadership seems important for the community mechanism, as does perspective taking. For evaluating policies, normative economics based only on consequentialism may not be sufficient, and virtue ethics seems essential when the community mechanism is important.

In response to the COVID-19 crisis, governments worldwide have been formulating and implementing different strategies to mitigate its social and economic impacts. We study the household consumption responses to Japan's COVID-19 unconditional cash transfer program. Owing to frequent delays in local governments' administrative procedures, the timing of the payment to households varied unexpectedly. Using this natural experiment, we analyze households' consumption responses to cash transfers using high-frequency data from personal finance management software that links detailed information on expenditure, income, and wealth. We construct three consumption measures: one captures the baseline marginal propensity to consume (MPC), and the other two are for the lower and the upper bound of MPC. Additionally, we explore heterogeneity in MPCs by household income, wealth, and population characteristics, as well as consumption categories. Our results show that households exhibit immediate and non-negligible positive responses in household expenditure. There is significant heterogeneity depending on various household characteristics, with liquidity constraint status being the most crucial factor, in line with the standard consumption theory. Additionally, this study provides policymakers with insights regarding targeted cash transfer programs, conditioning on labor income, and liquidity constraints.

The Labor Force Survey, a large-scale government statistics, and the causal forest algorithm are used to estimate the group average treatment effect of the COVID-19 on the employment status for each month from January to June 2020. We find that (1) because of the seasonality in employment status at monthly level, whether we use January 2020 as the base month for comparison, as done in most of the studies or whether we use the same month last year as the base comparison group makes a large difference; (2) whether we include those who are absent from work among the employed or not makes a large difference in the measure of the impact of COVID-19 and its changes; (3) if we use the employment measure which does not include those who are absent from work among the employed, 25-30% among the employed are adversely affected and that 10% of the employed experienced more than 10% decline in employment probability in April, 2020; (4) those who are the most affected by the COVID-19 are those who are unemployed or work part-time in the hotel and restaurant industry and service occupations; (5) in addition, younger and female respondents are more affected than are older and male respondents; and (6) we observe no clear differences in the impacts of COVID-19 with respect to living location, education status, and firm size among the most affected.

In recent years, many developments have been made in matching theory and its applications to market design. This paper surveys some selected topics from this research area and describe our own work. We also describe the newly established University of Tokyo Market Design Center (UTMD), which works as a vehicle for practical implementation.

[This corrects the article DOI: 10.1007/s42973-021-00098-4.].

This paper reviews recent findings on the normative analysis of private and governmental countermeasures against infectious diseases, focusing on COVID-19. Based on a model that relates the economic activity to infectious disease epidemics, policies that maximize social welfare are considered. Lockdowns in many countries are measures that restrict economic activity over a wide area, and the economic damage they cause is extremely large. Existing studies on the net benefit of lockdown implemented in 2020 have reached mixed conclusions as to whether it is warranted or not. Although the estimates of costs and effects are relatively stable, the setting of the value of a statistical life for converting effects into benefits has a wide range and is also likely to overestimate benefits. Therefore, a careful procedure for setting is particularly crucial to obtain a reliable evaluation of countermeasures. Compared to uniform restriction of activities, taking measures to restrict activities by selecting targets may improve efficiency. Attributes that can be used to select targets include those that can be identified at little or no cost, such as age and industry, and those that can only be identified at a cost, such as close contact with infectious individuals and the presence of pathogens. In comparison to lockdown, these measures may reduce human suffering and economic suffering. No trade-off exists between uniform activity restrictions and selective activity restrictions.

We identify monetary policy shocks in Japan during the unconventional monetary policy period using high-frequency data for interest rate futures. Following the empirical strategy of Gürkaynak et al. (Int J Cent Bank 1: 55-93, 2005), we conduct an event-study analysis to estimate the effects of the monetary policy surprises on asset prices around the timing of policy announcements made by the Bank of Japan between 1999 and 2020. We find that a monetary policy shock can be described by two factors that have statistically significant effects on the financial market. A surprise monetary tightening has negative effects on stock returns and positive effects on government bond yields, even in the low-interest environment. We also find that the responses of the longer term yields tend to be larger than those of the shorter term yields. The response is the largest for the 10-year government bond yield, which has, in the last 2 decades, been effectively targeted by the Bank of Japan. This finding contrasts with those of previous studies of the conventional monetary policy period, in which responses are larger for the shorter term yields.

In this paper, I use a simple SIR Macro model to examine Japan's soft lockdown policies in 2021 under the COVID-19 crisis. As real-time research, this paper consists of two parts written during two different research periods. The first part, which was originally reported in February 2021, studies the Japan's second soft lockdown policy (state of emergency declaration) from January to March 2021. After the model is calibrated using 2020 data, the results show that a long enough lockdown can avoid future lockdowns, improving both the infection and the economy. In addition, I propose the ICU targeting policy, which keeps the number of severe patients at a constant level, mimicking the monetary policy's inflation targeting. The model's prediction is evaluated from an ex-post perspective in the second part, written in July 2021. I find that the model broadly captures the realized consequences of the second soft lockdown and the subsequent paths. Furthermore, the simulation is projected to the end of the pandemic under a revised scenario, incorporating the proliferation of COVID-19 variants. Finally, I discuss the effectiveness of the inverse lockdown (economic stimulus) policy in the fall of 2021 under the dynamic infection externality.

This paper highlights the contributions made by lab-in-the-field experiments, which are also known as artefactual, framed and extra-lab experiments. We present a curated sample of lab-in-the-field experiments and discuss how they can be conducted on their own or combined with conventional laboratory experiments, natural experiments, randomised control trials and surveys to provide unique insights into the behaviour of a diverse population. Using our recent research on gender and leadership, we demonstrate how lab-in-the-field experiments have offered new perspectives about gender differences in decision-making. Finally, we outline the ethical and implementational challenges researchers may face while conducting these experiments and share some of the strategies we employed to address them.

This study aims to empirically investigate the role of E-commerce (EC) on the trade impacts of COVID-19. To this end, we estimate gravity equations for bilateral trade among 34 reporting countries and their 145 partner countries during January-August in 2019 and 2020. Our major findings can be summarized as follows. A larger number of confirmed cases or deaths in both importing and exporting countries significantly decrease international trade. However, we found that EC development in importing countries contributes to mitigating this negative effect of COVID-19 on trade while that in exporting countries does not. These results are robust for our use of multiple measures of EC development.

Utilizing a unique firm-level survey in Japan that contains five-bin forecasts for sales, we document three findings. First, firm-level subjective uncertainty is highly and positively related to volatility of past firm growth. Second, there are substantial variations in subjective uncertainty across firms, with a long right tail with extremely high subjective uncertainty. In addition, firms that have exposure to international businesses either through international trade or foreign direct investment have both higher average expected sales and subjective uncertainty. Finally, the sudden escalation of the COVID-19 pandemic in January-February 2020 led to a substantial increase in firms' subjective uncertainty. Our triple-difference estimation results show that this effect is especially large for firms that have direct exposure to China through international trade and foreign direct investment.

Face-to-face communication increases human trust, which is crucial for making important decisions with others. Due to technological breakthroughs and the COVID-19 pandemic, human interactions now predominantly occur online, leading to two situations: other peoples' faces cannot be seen, but yours can, and vice versa. However, the relationships among watching, being watched, and face-to-face interaction are unclear in existing papers. This paper separately measures the effects of both watching and being watched on human interactions using a trust game. I derive the optimal behaviors of senders and receivers in the trust game and empirically validate it through a controlled experiment. The results show that more than half of the participants perform the optimal behavior. They also indicate that both watching and being watched enhance human trust and reciprocity, while the synergy effect of face-to-face is not observed. Additionally, women reciprocate more when they are watched, and trust increases when participants are paired with the opposite gender and can watch their partner. This paper theoretically concludes that the former comes from women's social pressure that they should be reciprocators, and the latter from participants' beliefs that the opposite gender reciprocates more than the same gender does. These results propose a framework based on watching and being watched affecting human behaviors and emphasize the importance of face-to-face communication in online human interactions.

Changes in people's behavior during the COVID-19 pandemic can be regarded as the result of two types of effects: the "intervention effect" (changes resulting from government orders for people to change their behavior) and the "information effect" (voluntary changes in people's behavior based on information about the pandemic). Using age-specific mobile location data, we examine how the intervention and information effects differ across age groups. Our main findings are as follows. First, the age profile of the intervention effect shows that the degree to which people refrained from going out was smaller for older age groups, who are at a higher risk of serious illness and death, than for younger age groups. Second, the age profile of the information effect shows that the degree to which people stayed at home tended to increase with age for weekends and holidays. Thus, while Acemoglu et al. (2020) proposed targeted lockdowns requiring stricter lockdown policies for the oldest group in order to protect those at a high risk of serious illness and death, our findings suggest that Japan's government intervention had a very different effect in that it primarily reduced outings by the young, and what led to the quarantining of older groups at higher risk instead was people's voluntary response to information about the pandemic. Third, the information effect has been on a downward trend since the summer of 2020. It is relatively more pronounced among the young, so that the age profile of the information effect remains upward sloping.

We build a tractable SIR-macro-model with time-varying parameters and use it to explore various policy questions such as when to lift the state of emergency (SOE). An earlier departure from the SOE results in smaller output loss and more deaths in the short run. However, if the SOE is lifted too early, the number of new cases will surge and another SOE may need to be issued in the future, possibly resulting in both larger output loss and more deaths. That is, the tradeoff between output and infection that exists in the short run does not necessarily exist in the long run. Our model-based analysis-updated weekly since January 2021, frequently reported by media, and presented to policymakers on many occasions-has played a unique role in the policy response to the COVID-19 crisis in Japan.