Journal of the Japanese and International Economies最新文献

Recent advances in AI represent improvements in prediction. We examine how decision-making and risk management strategies change when prediction improves. The adoption of AI may cause substitution away from risk management activities used when rules are applied (rules require always taking the same action), instead allowing for decision-making (choosing actions based on the predicted state). We provide a formal model evaluating the impact of AI and how risk management, stakes, and interrelated tasks affect AI adoption. The broad conclusion is that AI adoption can be stymied by existing processes designed to address uncertainty. In particular, many processes are designed to enable coordinated decision-making among different organizational actors. AI can make coordination even more challenging. However, when the cost of changing such processes falls, then the returns from AI adoption increase.

Employing a novel dataset on Tokyo condominium resales with details regarding their equipment and renovation status, this study provides a background for the rapid depreciation of Japanese condominiums, particularly due to obsolete housing equipment and weak renovation. Condominiums over the age of 20 years tend to lack contemporary equipment and exhibit severe economic obsolescence. This is measured as a greater rate of price decline caused by the lack of contemporary equipment for old condominiums than new condominiums. While intensive renovation can slow down depreciation by adding contemporary equipment to old condominiums, only around half of such condominiums undergo intensive renovation. These trends are consistent across the Tokyo metropolitan area. The results imply the potential to shift to a society where properties can attain longer economic lives through appropriate renovations and contemporary housing equipment is provided through frequent new constructions, as often seen in Asian countries.

This study considers the retrogression event of convenience for bank users as a natural experiment and analyzes the effect of this event on cash demand. Using bank account transaction data, we find that branch/ATM consolidations reduce the amount of cash withdrawals by past users considerably. Particularly, older or less wealthy users were severely affected by the discontinuation of branches. However, we find that branch/ATM consolidations reduce the total expenditure and inflows that include non-cash transactions almost by the same amount, which implies that the retrogression in convenience possibly caused users to shift to other banks for their daily payments. We also document facts about cash withdrawals from ATMs using bank account transaction data.

We examine the impact of COVID-19-related school closures on student learning and screen time. We find that between January 2020 (pre-COVID-19) and May 2020, as the length of a COVID-19-related school closure increased, there was a decrease in learning time and an increase in screen time. These adverse effects tend to be more pronounced for students in low-income households, low academic achievers, and elementary school students living in single-parent households. Moreover, these adverse effects might have persisted until January 2021 for elementary school students in single-parent households. On average, while live online classes might mitigate the effects of decreased learning time for junior high school students, that effect is not found for low academic achievers.

This study examines the effects of the Bank of Japan's (BOJ) large-scale equity purchases on the Nikkei 225 during the coronavirus (COVID-19) pandemic. Although the BOJ started equity purchases in 2010, the purchased amount reached unprecedented levels when the pandemic broke out. The large-scale purchases provide a natural experiment to examine how effective the central bank's equity purchases were in the crisis. Unlike previous studies, we use intra-day data and investigate the purchase effects allowing for endogeneity. We first derive the BOJ's intra-day reaction function by estimating probit models. From this reaction function, we then calculate the BOJ's unexpected and expected purchases and examine their effects on the Nikkei 225 returns in the Tokyo Stock Exchange's afternoon session. We find that the BOJ's unexpected large-scale purchases had large positive instantaneous impacts on intra-day returns during the pandemic. However, the large positive impacts arose because most of the purchases came as big surprises to the markets. We argue that the policy would be effective only if the BOJ continues to surprise the market. We also argue that the BOJ's purchases increased the volatility of the Nikkei 225.

This study examines the effects of public investment on the stock market between the zero lower bound (ZLB) and the non-ZLB periods using Japanese sectoral panel data. The empirical results first show that while public investment shocks have stimulating effects on stock returns during the ZLB period, this is not the case outside of the ZLB. Furthermore, the impulse responses for the manufacturing industry are estimated to be positive and significant regardless of model specification. Our results suggest that the government is recommended to increase public investment under the ZLB to prop up the stock market but cut back once the economy is no longer in a liquidity trap.

After the late 2000s, there were substantial fluctuations in unemployment during the Global Financial Crisis (GFC), Abenomics, and COVID-19 crisis, and their impact was uneven by gender and industry. This study examines the property of worker flows in Japan by focusing on gender and industry perspectives using monthly data from the Labour Force Survey. During the COVID-19 crisis, the transition probability from employment to inactivity rose sharply compared to the period of the GFC, mainly driven by women in the service sectors. The contribution of inflows to the dynamics of unemployment was on the rise for both men and women until around 2018, and the decreased unemployment rate during Abenomics can be accounted for by the decline in separation rates.

The use of changes in short-term interest rates (STIRs) within 30 min before and after monetary policy announcements, or so-called high-frequency identification (HFI), has been attracting attention as a method of extracting monetary policy surprises. In this paper, we use the Japanese data during the 2000s and 2010s, which includes periods when interest rates hovered around the ELB, to construct an indicator of monetary policy surprises using HFI and document its properties. We find that the STIR futures variations within 30 min around monetary policy announcements are more closely correlated with key financial variables than those outside that window. We also find that the impulse responses of macroeconomic variables to the identified shocks are overall in line with what conventional theory predicts.

The high-profile failures of a number of crypto firms have re-ignited the debate on the appropriate policy response to address the risks in crypto. The “shadow financial” functions enabled by crypto markets share many of the vulnerabilities of traditional finance and risks are often exacerbated by specific features of crypto. Authorities may consider different, and not mutually exclusive, lines of action to tackle the risks in crypto. These include (i) bans, which could tackle specific aspects of the crypto ecosystem, (ii) containment so that the real economy is insulated from crypto risks, and (iii) the regulation of the crypto sector. The paper highlights the pros and cons of the different approaches and proposes a framework to choose when bans, containment and regulation are most appropriate. It also describes the approach taken in Japan, which has been a pioneer in tacking such risks. With each approach, central banks and public authorities can also work to make traditional financial more attractive, thereby allowing responsible innovation to thrive.

We examine how a firm's political connection measured by the membership of its CEO in the People's Congress (PC) or Chinese People's Political Consultative Conference (CPPCC) influences its likelihood of receiving the innovation subsidies given by the state. We find that politically connected firms are more likely to receive innovation subsidies. The political connection measured in this way is found much more important than state ownership in explaining the allocation of innovation subsidies. We also investigate if the firms that receive innovation subsidies are more innovative, productive, or profitable. Our results show that the firms that receive innovation subsidies file and receive more patents, but that their patents are not necessarily of high quality. They do not have higher productivity or profitability, either. The results collectively suggest politically induced inefficiency in the allocation of innovation subsidies in China.