Showing papers in "Asian Economic Policy Review in 2022"

Measuring Digital Financial Inclusion in Emerging Market and Developing Economies: A New Index

Abstract: Adoption of technology in the financial services industry (i.e. fintech) has been accelerating in recent years. To systematically and comprehensively assess the extent and progress over time in financial inclusion enabled by technology, we develop a novel digital financial inclusion index. This index is based on payments data covering 52 developing countries for 2014 and 2017, taking into account both access and usage dimentions of digital financial services (DFSs). This index is then combined with the traditional measures of financial inclusion in the literature and aggregated into an overall index of financial inlusion. There are two key findings: first, the adoption of fintech has been a key driver of financial inclusion. Second, there is wide variation across countries and regions, with the greatest progress recorded in Africa and Asia and the Pacific regions. This index should offer a useful analytical tool for researchers and policy makers.

Fintech and Financial Inclusion in Southeast Asia and India

Abstract: Financial inclusion, that is, access of excluded households and small firms to financial products and services, is seen as a way to promote more inclusive growth by providing the previously unbanked with access to means for savings, investment, consumption smoothing, and insurance. Financial technology (Fintech), that is, using software, applications, and digital platforms to deliver financial services to consumers and businesses through digital devices such as smartphones, has become recognized as a promising tool to promote financial inclusion. The present paper investigates the developments of financial inclusion and Fintech in the Association of Southeast Asian Nations (ASEAN) member countries and India to identify the ways that Fintech is contributing and can potentially contribute to increased financial inclusion. It also examines potential risks arising from use of Fintech, highlights differences in the strategies and implementation of financial inclusion and Fintech between India and ASEAN, and draws lessons and policy recommendations from these findings.

Developments and Implications of Central Bank Digital Currency: The Case of China e‐CNY

Abstract: China has been both active and cautious in developing a central bank digital currency (CBDC). China CBDC has been in research and development since 2014. The process speeded up in 2019. It is currently at the stage of expanding real field experiments. Residents in 11 areas can open e-wallets linked to nine major banks. It is centralized digital cash designed to gradually replace traditional paper cash and coins (M0). It will be supported by the traditional double-layer banking system. It is not blockchain based at issuance but is technology neutral in distribution. Internet and technology companies may join commercial banks in distributing the China CBDC. In the short run, the China CBDC will help improve domestic financial monitoring and policy implementation. In the long run, it may play a role in the RMB's internationalization or even the international monetary system's evolution.

Chinese Fintech Innovation and Regulation

Abstract: Nowhere has financial technology or “fintech” become larger or as transformative as in China. Fintech, largely developed by technology companies, rapidly turned China from a largely cash-based, backward financial system to a fintech leader globally. At the same time, China's government created an environment conducive to innovation. China's experience with fintech is a rich repository of lessons for other countries aiming to modernize their financial service industries and “leapfrog” up to advanced economies in some respects. It shows how clever reforms can overcome the lack of competition for financial incumbents, but also the mechanisms necessary to ensure regulation and supervision are sufficient for the risks of financial innovation. Finally, policy debates and solutions in China around the power of big tech, including in finance, have relevance to a world increasingly forced to grapple with these issues.

How Inequality Affects Trust in Institutions: Evidence from Indonesia

Abstract: Trust is an important ingredient to improve economic performance and people's welfare by alleviating market failures caused by imperfect information, costly enforcement, or coordination failures. Using the World Values Survey 2018, we estimate the impact of village and district levels inequality on trust in institutions in Indonesia. We find that higher village level inequality has a negative effect only on trust in strangers, while higher district level inequality reduces trust in television, the press, the central government, the courts, and the police. The implication points to the importance of keeping inequality at the aggregate level in check to maintain people's trust in social, political and state institutions.

Income and Wealth Inequality in Asia and the Pacific: Trends, Causes, and Policy Remedies

Abstract: The Asia–Pacific region's rapid growth and poverty reduction in recent decades have been accompanied by rising income and wealth inequality. Technological progress, globalization, deregulation and market-oriented reform, and financialization have generated many new opportunities, but rewarded capital more than labor, benefited skilled workers more than the unskilled, widened spatial inequality, and produced a growing number of the superrich. For some countries, population aging has also contributed to rising inequality. The present paper provides an update on recent trends of income and wealth inequality in the Asia–Pacific region, examines causes behind rising inequality, and discusses policy actions needed to tackle inequality. It also assesses how the COVID-19 has likely worsened inequality in the region. © 2022 Japan Center for Economic Research.

Why Fintech Is Not Changing Japanese Banking

Abstract: Several years have passed since fintech first attracted attention in Japan. Although various new fintech services have emerged due to deregulation and policies to promote fintech's development and adoption, the overall composition and functioning of Japan's financial industry has not changed significantly despite fintech advances in other economies. The industry in Japan continues to be dominated by face-to-face interactions and has experienced little digitization. In Japan, sophisticated financial services were available before the widespread use of the internet, and most people have resisted conducting financial transactions using their smartphones. In addition, there has been almost no progress in digitizing accounting work in corporations. However, this stagnation in digitization on the demand side of financial services is changing for several reasons. Digitization in the government sector and the introduction of a new invoice system in 2023 will provide an opportunity for change. This time, Japan's financial services should undergo a significant digital transformation.

Siamese Twin Troubles: Structural and Regulatory Transformations in Unequal Thailand

Abstract: This paper takes an institutional approach to inequality in Thailand by exploring the country's structural and regulatory transformations. It discusses how Thailand's transition from agriculture to industry and services has been impeded by both the demand and supply sides of government subsidies since the 1950s. The relative failure of structural transformation has slowed down economic catch-up and widened the well-being gap between those inside and outside the agricultural sector. Furthermore, while regulatory transformation has mitigated state-led malaise in certain Asian economies, post-1997 reform in Thailand has incentivized unconventional political actors, such as academics, medical doctors and civil society leaders, to make collective efforts in toppling elected governments in exchange for gaining selection into oversight agencies. The case of Thailand indicates how regulatory reform may create perverse incentives that adversely affect democratization, decentralization, competition, and taxation. Dealing with inequality therefore requires a big push toward progressive structural and regulatory transformations altogether.

What the Welfare State Left Behind—Securing the Capability to Move for the Vulnerable

Abstract: The purpose of the present paper is to measure the disadvantage of the vulnerable in contemporary Japan, focusing on their capabilities in moving both outside and inside the home. Our research interest is to find a new informational base other than consumption expenditure, which provides a strong clue about how to assess the eligibility for social support. We examine theoretical methods to apply the capability approach empirically, extending existing multidimensional poverty measurements. We find that people with disabilities and nursing care users are significantly restricted in their capabilities. The elderly in general, whom we have used as a reference group, are also in a precarious situation.

Social Justice and Affirmative Action in Malaysia: The New Economic Policy after 50 Years

Abstract: Malaysia's New Economic Policy (NEP), promulgated in 1971, established a two-pronged national social justice agenda of poverty reduction, and social restructuring or pro-Bumiputera affirmative action. This distinction of these policy objectives must be appreciated, but various misconceptions, especially regarding affirmative action, have resulted in polarization and stalemate after 50 years of the NEP. Social justice and affirmative action must be conceptualized and evaluated with clarity and rigor, with policy objectives, mechanisms and outcomes aligned. Malaysia needs to systematically formulate a new social justice paradigm, building on the NEP and anchored on the principles of equality and fairness. In the affirmative action sphere, this framework must focus on developing capability and competitiveness, and balance identity, need and merit in the allocation of opportunity.

Comment on “Income and Wealth Inequality in Asia and the Pacific: Trends, Causes, and Policy Remedies”

Abstract: I agree with Zhuang (2022) in his summary assessments of the negative effects of income inequality. Zhuang (2022) includes a section on the inequality in China, so I would like to focus my comments on this section, and particularly on the working-age households in China. Most of the inequality measures cited in Zhang's paper and in other studies are from OECD or World Bank calculations. China's measures come from the National Bureau of Statistics (NBS). Unfortunately, it is difficult to decompose NBS' measures since the micro-level data used by the NBS are not publicly available. Fortunately, there are several large-scale academic household surveys that are available – one of them is the China Household Income Project, led by Zhuang's coauthor on other occasions, Shi Li from Zhejiang University. My analysis here is partially based on another survey, the China Household Finance Survey, that I am the lead investigator for. Zhuang (2022) discusses income inequality decade-wise since the 1980s. I would like to list the biennial changes of income inequality since 2010. Table 1 lists the P90/P50 ratios between 2010–2018. According to Table 1, inequality decreased slightly, from 3.65 to 3.28, between 2010 and 2018. However, working-age households did not see much change in these ratios, while families with retired people (at least one person in the family is older than 60) see a significant drop in inequality, from 5.09 to 3.21. To understand the reason for this, we compare government transfers to the elderly in the form of pension payments, and government transfers to working-age households. The former includes payments to retirees covered by both the Basic Employee Pension Insurance and the Resident Pension Insurance. The latter includes payments from all social assistance programs, such as unemployment benefits and Dibao payments. According to the official statistics, the percentage of pension payments to gross domestic product (GDP) more than doubled from 2.1% in 2010 to 5.4%, in 2019. Meanwhile, the percentage of social assistance to GDP decreased from 0.97% to 0.80%. The increase of social pension payments to GDP is a combination of rising benefits and rising numbers of retirees. The per capita pension payment was RMB 8144 in 2012, and RMB 18,958 in 2020 with an annual growth rate of 11.1%. Meanwhile, nominal per capita GDP only grew 7.74% annually. This is because of the differences in social spending, and the reduction in income inequality among elderly in China while income inequality among working-age families is stagnant. Figure 1 indicates China's social spending relative to other countries. The left panel plots total pension payments as a percentage of GDP, relative to its GDP per capita. This panel shows that China's pension payments are on par with the projected world average indicated by a regression line. However, the right panel of Figure 1 plots the social spending on working-age households relative to its GDP per capita, and we observe that China's figure is far below the projected world average. Figure 1 shows that China is aggressively transferring money to the elderly, but not to working-age households. I believe this is largely due to a (mis)understanding of the consequences of the social welfare system. Official documents show that Chinese government is deeply suspicious of “welfarism.” President Xi recently instructed the government in “preventing ‘welfarism’ trap” as one of the key elements in reaching the “common prosperity” (Xi, August 21, 2021, “Qiushi”). Similar statements have repeatedly appeared in many government documents. However, a large literature has convincingly shown that the “welfare dependent” essentially does not exist if the amount of transfers is not too large. In Banerjee et al. (2017), social assistance in developing countries did not generate disincentives to work since the amount of transfers is only about 10% of consumption. The first Covid-related government program in the US, The CARES Act, raised the unemployment benefit to be 47% higher than the labor income for a median worker. Petroksy-Nadeau and Valletta (2021) find that even such benefits did not generate incentives for people to quit their jobs. This is because the Covid-related unemployment benefits lasted only a few months. Therefore, the present value of the benefit is not large enough to generate disincentives to work. Yet the literature has documented that transfers to the working-age poor could be effective in stimulating consumption (Gan et al., 2018). Social transfers are also beneficial for households' human capital accumulation (García & Saavedra, 2017). Both effects are critical for China's economy now and in the future. In conclusion, the key to reducing income inequality in China is to substantially increase transfers to the working-age poor. However, doing so requires demystifying the widely held belief of “welfarism” among government officials.

Comment on "Measuring Digital Financial Inclusion in Emerging Market and Developing Economies: A New Index"

Abstract: A recent Asian Development Bank (ADB) report reveals that the global digital sector expansion would allow Asia and the Pacific to generate an economic dividend of more than $1.7 trillion per year, and create 65 million new jobs annually over the next 5 years (Asian Development Bank (ADB), 2021). In the finance sector, accelerated digitalization can potentially close the persistent financial inclusion gap between the rich and the poor, especially in developing countries. To empirically examine digital-based financial inclusion, Khera et al. (2022) develop a novel, comprehensive index of digital financial inclusion for 52 emerging and developing economies for 2014 and 2017. The index is constructed by combining the widely used cross-country data on financial inclusion and related aspects. Essentially, Khera et al. determine striking “digital leapfrogging” patterns in financial services. Countries in Asia and the Pacific as well as Africa have increasingly accelerated digital financial inclusion compared to other regions (Kera et al.’s Figure 2). In the wake of rapid digitalization, if the index is extended beyond 2020, it will offer valuable information to determine the impact of COVID-19 and identify suitable “build-better” policies for a desirable new normal. Although Khera et al. and its database are significant, certain issues remain. First, Khera et al. can potentially strengthen the interpretation and analysis of each chart. For example, Khera et al. may discuss potential determinants of differentiated trajectories of the nexus between traditional financial and digital financial inclusion, as demonstrated in their Figure 4. While I agree with Khera et al. that these diverse patterns could be driven by substitution between traditional and digital financial services, they could have verified whether the upper-left group in their Figure 4 corresponds with an initially low level of traditional financial system. Second, according to the list of variables employed, the new index seems to miss the popular use of digital financial transactions in the real world. For example, the poor in developing economies are already using mobile phone apps to receive or send money bilaterally, pay for online and offline purchases and transactions, for loan repayments and savings, and mobile phone recharge load. Third, although the digital divide could drive financial exclusion of the poor, these heterogeneities of digital financial access or use are not considered in the index. The lack of financial and digital literacy could widen the initial digital gap between the rich and the poor. In addition to within-country gaps, digital divide can emerge across countries at an aggregate level, and it will be useful to show, for example, the distribution of the index in Khera et al.’s Figure 1 by subregions in Asia. Fourth, the index may need to directly incorporate information regarding access to necessary infrastructure (Khera et al., 2021). For instance, given that a large proportion of workers and the self-employed receive salaries and payments in cash, to transform cash into digital money, access to digital outlets such as financial kiosks at convenience stores, is indispensable for digital financial transactions.11 A study by Asian Development Bank on collateral-free fintech loans to tricycle drivers in the Philippines examines how fintech, combined with digital infrastructure of online loan repayments at digital kiosks, contributes to financial inclusion of the poor (Asian Development Bank (ADB), 2020). To refine the index, there are cross-country data on digital platform penetration and network readiness (Asian Development Bank (ADB), 2021). Finally, the lack of appropriate laws, rules, and regulations for data privacy and cyber security could lead to mistrust, undermining effective digital financial inclusion. According to the Global Risks Report of the World Economic Forum (2020), 76.1% of respondents identified cyber security as the top five risks in 2020. It is imperative to incorporate these aspects in relation to the quality of governance. These issues should impel further refinement of the index in the future.

Digital Finance in Asia: Editors' Overview

Abstract: Asian Economic Policy ReviewVolume 17, Issue 2 p. 163-182 Editor's comment Digital Finance in Asia: Editors' Overview Yiping Huang, Yiping Huang Peking UniversitySearch for more papers by this authorTakatoshi Ito, Takatoshi Ito Columbia UniversitySearch for more papers by this authorKazumasa Iwata, Kazumasa Iwata Japan Center for Economic ResearchSearch for more papers by this authorColin McKenzie, Corresponding Author Colin McKenzie mckenzie@z8.keio.jp Keio University Correspondence: Colin McKenzie, Faculty of Economics, Keio University, 2-15-45 Mita, Tokyo 108-8345, Japan. Email: mckenzie@z8.keio.jpSearch for more papers by this authorShujiro Urata, Shujiro Urata Waseda University and the Japan Center for Economic ResearchSearch for more papers by this author Yiping Huang, Yiping Huang Peking UniversitySearch for more papers by this authorTakatoshi Ito, Takatoshi Ito Columbia UniversitySearch for more papers by this authorKazumasa Iwata, Kazumasa Iwata Japan Center for Economic ResearchSearch for more papers by this authorColin McKenzie, Corresponding Author Colin McKenzie mckenzie@z8.keio.jp Keio University Correspondence: Colin McKenzie, Faculty of Economics, Keio University, 2-15-45 Mita, Tokyo 108-8345, Japan. Email: mckenzie@z8.keio.jpSearch for more papers by this authorShujiro Urata, Shujiro Urata Waseda University and the Japan Center for Economic ResearchSearch for more papers by this author First published: 03 July 2022 https://doi.org/10.1111/aepr.12397 Yiping Huang was a Guest Editor for this issue. Read the full textAboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume17, Issue2July 2022Pages 163-182 RelatedInformation

Comment on “Why Fintech Is Not Changing Japanese Banking”

Abstract: Although the term “fintech” has now filtered down into everyday language, fintech services have not yet been fully integrated into everyday life, at least in Japan. Iwashita (2022) overviews the current disappointing state of fintech in Japan, assesses which conditions keep fintech services from taking off, and discusses potential “game changers” that would pave the way for fintech services to gain wider acceptance in the coming decade. Iwashita (2022) points out two major hindrances to which he attributes the low acceptance of fintech services in Japan; one is related to consumers' attitudes to digital devices and the other is related to corporations' readiness for digital technologies. The first hindrance is the hesitation of older people to use digital devices such as personal computers and smartphones. Unlike the younger people who have been using such devices from their childhood, the older people still favor face-to-face communication. Given that the older retired people tend to accumulate more savings than younger working-age people, Japanese banks have an incentive to accommodate those older customers with traditional face-to-face services. The second is the unpreparedness or unwillingness of Japanese companies to adopt new digital technologies. Iwashita's (2022) Table 3 compares Japan with three other developed countries (USA, UK, and Germany) in terms of business information and communication technology (ICT) tool usage, which shows that Japanese companies use ICT tools far less than the others in all respects. This does not necessarily mean that Japanese companies did nothing about information technology (IT) investment. Iwashita states that they did reasonably invest in their IT systems for financial accounting, billing, payments, and other accounting tasks, but their systems are mostly for internal use and have little interoperability with the internet. In addition, national and municipal governments face the same problem of legacy IT systems. As a result, Japanese banks need to maintain their traditional channels of financial transactions for the continuation of business relations with private companies as well as government agencies. Iwashita (2022), however, claims that the inertia can be broken and fintech services will become more widely accepted in Japan for the following five reasons. First, the global expansion of digital finance. Fintech services are rapidly expanding in emerging economies, and this trend will put pressure on Japanese financial service users. Second, the digitization of government services. Japan's Digital Agency was established on September 1, 2021. It is designed to oversee the digitization of government services at both national and municipal levels, which may ease the Japanese people's fears of the internet, in general, and breaches of privacy, in particular. Third, the shifts in strategic goals of financial institutions. Negative interest rates and a shrinking population are forcing Japanese financial institutions to change their traditional “size matters” strategy. Fourth, the generational shift. The older people who abhor digital devices will be superseded by the next generation who actively use the internet. Fifth, the introduction of mandatory invoices. All businesses will be required to issue invoices for paying the consumption tax in 2023, which will promote electric information exchange among business partners. In relation to these reasons, I have several comments and questions on whether and how they will affect the acceptance of fintech services in Japan. First, in my opinion, Japan's national identification number, My Number, is the key to the success of the digitization of government services. The Japanese government tries to tag it on health insurance cards, driver's licenses, bank accounts among other items. How widely will the My Number be accepted by the Japanese people? Will it be as ubiquitous as Aadhaar in India? What kind of initiatives must be implemented to achieve this goal? Second, Japanese regional banks are struggling for their survival, but it seems that they cannot afford to invest in new IT systems due to a lack of resources. To strengthen the operational foundations of the regional banks, the Japanese government encourages consolidations among them by exempting them from anti-monopoly regulations. Will this policy work? Finally, if all transactions among companies are made online, accounting tasks such as billing, payments, and taxation could be fully automated through artificial intelligence, robotic process automation, and other digital technologies in theory. If such a future is coming to Japan, will traditional financial institutions become obsolete and be superseded by digital finance? Can they adapt themselves to those new technologies fast enough?

Comments on “Chinese Fintech Innovation and Regulation”

Abstract: China has shown an impressive development of the fintech industry over the past decade or so. The fintech industry has grown fast in several areas, including mobile payments, online lending, and investment. The progress of the fintech industry has expanded access to financial services to hundreds of millions of people who could not get access to traditional financial institutions. The stunning performance of China's fintech industry has raised interesting questions: what are the key contributing factors to its success?; what is the government's role in supporting fintech development?; and is China's experience applicable to other countries? Chorzempa and Huang (2022) provide a concise and informative analysis of China's fintech development. It also discusses how policies and regulatory measures have evolved since the advent of fintech and evaluates the government's recent crackdown on big fintech companies. It can be a useful reference for readers who want to understand the main characteristics of China's fintech industry and policy. Nevertheless, I have several comments. First, it would be useful if Chorzempa and Huang analyze the main factors of China's fintech development more thoroughly with an empirical framework and data. They argue that China's fintech industry development since 2004 when Alibaba launched the online payment system is attributed to a number of factors including a big market size, a highly repressive financial policy, the rapid development of digital technology, and the relatively friendly approach of the authorities to fintech. To strengthen this main argument, further empirical exercises are recommended. Regarding this, a useful exercise would be to assess, quantitatively with cross-country data, the extent to which each of these key factors has contributed to the adoption and growth of fintech in China much faster than in other countries. Adopting a regression method can be helpful to investigate the role of demand and supply factors in the faster rise of fintech in China. Furthermore, it would be interesting to apply this empirical analysis to China's microdata and examine the determinants of fintech proliferation across regions in China. The Beijing University Digital Finance Inclusion Index of China's Peking University shows that despite a significant expansion of the fintech industry, there are considerable disparities across China's provinces and cities. Second, it is necessary to carefully evaluate how the recent government crackdown on the fintech giants will affect the whole financial industry and the overall economy in the long term. Chorzempa and Huang (2022) points out that “change in the policy stance toward the Ant Group affects not just fintech activities, but also the policy enablers that will affect the whole sector.” While fostering innovation, China's lax regulation of the fintech industry in the past has created many risks and loopholes in the financial system as exemplified by the P2P scandals in 2016. Regulatory frameworks need to be strengthened to create safer and healthier environments for the fintech industry. The key challenge for regulators is balancing regulation and innovation. Recently, the Chinese government is aiming to enhance competition in the fintech market and control over market power of tech giants. This brings concerns over the Chinese government's overregulation of the private sector, undermining confidence among businesses and consumers, entrepreneurship, and innovation. As the authors point out, assessing the long-term impact of tighter government regulations on the competitiveness and productivity of the financial sector and the overall economy remains an important issue. Another key issue is privacy protection. In China and many other countries, tech giants are accused of having too much power to use, share, and create value for their digital data. They are tempted to use vast amounts of personal information for profit. In this regard, the Chinese government's attempt to break up the data monopoly is commendable. However, simply handing over data to the government cannot solve the issue of privacy protection. Moreover, there are concerns that states may abuse digital data and technology. In fact, the aggressive use of surveillance tools and excessive disclosure of personal data during the COVID-19 pandemic have raised privacy concerns in China and other East Asian countries. I agree with Chorzempa and Huang that China's data protection regime is still work in progress. Establishing appropriate checks and balances, including the rule of law and the media, would be necessary to control tech giants and states' undue power and protect civil liberties and privacy. Third, Chorzempa and Huang highlight valuable lessons from China's fintech success, including a flexible regulatory regime in the early stage of financial innovation, high levels of political support, and adequate control over tech giants to ensure competition. China's fintech giants have shown that they can leapfrog to new forms of finance when consumers and businesses are eager to use new financial technologies. Without the active role of the state, the dynamism of the private sector appears to be a key factor in China's remarkable performance in the fintech industry. Although state-led reform and industrialization strategies since the 1980s have acted as the driving force behind China's economic miracle, markets and entrepreneurship may play a more important role in developing the fintech industry and the overall economy in the coming decades.

Comment on “How Inequality Affects Trust in Institutions: Evidence from Indonesia”

Abstract: Suryahadi et al. (2022) address the challenging task of identifying how income inequality affects trust in others, organizations, and institutions through a cross-section analysis of Indonesia. As previous studies have suggested, the extent of trust is considered an important factor for economic development, for example, by reducing transaction costs. This has led many social scientists to analyze the determinants of trust, but few studies have shed light on the impact of inequality on trust (Gustavsson & Jordahl, 2008; Barone & Mocetti, 2016). The importance of the topic seems to be even greater under the current Covid-19 pandemic. It has been noted that low trust in governments may lead to vaccine hesitancy (SAGE Working Group on Vaccine Hesitancy, 2014), which might have lowered the coverage of Covid-19 vaccination in many countries. In order to identify the effects of income inequality on various aspects of trust, Suryahadi et al. construct an impressive dataset comprising various surveys, such as the World Value Survey, the National Socioeconomic Survey (Susenas), Village Census (PODES), and village-level data on estimated poverty and inequality (PovertyMap). Using the informative dataset, Suryahadi et al. reveal statistically significant negative correlations between district-level inequality and trust in political and state institutions. On the other hand, they also find that higher village-level inequality has a negative effect on trust in strangers. The estimation results are intuitively consistent, and Suryahadi et al. provide a useful perspective on the relationship between inequality and trust in Indonesia, though there appear to be some issues that still need to be cleared up. First, their cross-section analysis might induce an endogeneity problem. Suryahadi et al. employ ordinary least squares results with reference to endogeneity test outcomes (Suryahadi et al.'s table A.2), which depend on the assumption that their instrumental variable is valid and strong. As Suryahadi et al. are cautious about the results of their analysis, I am still afraid that the inequality variables correlate with the error term (endogeneity). For example, I am concerned that Suryahadi et al. do not control for ethnic diversity among villages in the empirical specifications, while ethnic heterogeneity displays a strong negative correlation with the extent to which people trust each other (Gustavsson & Jordahl, 2008; Jordahl, 2009). Second, in line with the literature, Suryahadi et al. assume that respondents are well acquainted with the objective level of income inequality in their districts and villages. In other words, this means that the subjective perception of inequality is supposed to coincide with, or at least be proportional to, the objective inequality index, but it seems questionable to make such a simple assumption. Hu (2017) suggests that objective inequality and subjective inequality are not correlated and shows a statistically significant nonlinear relationship between subjective inequality and general trust. Third, linked to the above comment, it appears that there is room for consideration as to which inequality index is appropriate for the analysis. Previous studies have used inequality indices such as the percentile ratio, top income shares, and the standard deviation of logs in addition to the Gini coefficient, and it is suggested that the estimation results may be sensitive to the choice of indicators (Gustavsson & Jordahl, 2008). If such alternative inequality indices are available for Indonesia, it may be worth trying them to check the robustness of the estimation results. Fourth, Suryahadi et al. show that while Indonesia's Gini ratio increased at the national level in the 2000s, trust in government organizations and institutions remained flat or increased slightly over the same period (Suryahadi et al.'s figure 3). This simple relationship between disparity and trust seen in the time series data seems to contrast with the results of their cross-sectional analysis, which shows a negative correlation between inequality and trust. It would be useful if Suryahadi et al. could explain the reason for this. Fifth, in contrast to Zmerli and Castillo (2015) who explore Latin American cases, Suryahadi et al. find a negative correlation between higher inequality and trust in the central government rather than trust in political parties (Suryahadi et al.'s figure 5). This may be brought about by differences in model specifications, but it would be helpful if Suryahadi et al. can elaborate on the possible reason behind the differences. Finally, one of their main challenges for the future will be the search for another appropriate instrumental variable that will allow them to reveal causal relationships between inequality and trust. The information-rich dataset they have constructed will make it possible to identify the causal inference and contribute to the literature.

Comment on “Measuring Digital Financial Inclusion in Emerging Market and Developing Economies: A New Index”

Abstract: Khera et al. (2022) provides a novel measurement of digital financial inclusion using a three-stage principal component approach (PCA) for 52 emerging market and developing economies. Based on this new index, they have found that the adoption of digital financial services has been a key driver of financial inclusion, and countries/regions in Africa and Asia and regions have achieved greater progress. They also warn against a digital divide and call for policies to close the gap. The novelty of this new index rests on three characteristics: it is focused; it is comprehensive, and it utilizes the PCA approach. This index focuses on the payment aspects of financial inclusion, and considers the “access” and “usage” aspects of both digital and traditional aspects of financial inclusion. The three-stage PCA approach first extracts the supply-side and demand-side aspects of financial inclusion for both traditional and digital financial services, then extracts the principal components of the access and usage indices for the traditional and digital financial inclusion, respectively, and finally builds up a comprehensive index encompassing all these subcomponents. The constructed index provides a good chance to measure the level of the adoption of digital financial services in a specific country, and hence provides an instrument for evaluating the policy implications of financial inclusion, especially digital financial inclusion. For example, the subindex provides a chance to evaluate the severeness of the digital divide and the risk of financial exclusion. The indices show wide variations in digital financial inclusion across countries, whether it is mainly driven by a reluctance in constructing more digital financial infrastructure due to financial constraints, or a distrust of digital technology will need further investigation. Overall, this index has great potential for deepening our understanding of the relationships between comprehensive financial inclusion, digital financial inclusion as well as traditional financial inclusion. In this aspect, Khera et al. (2022) may wish to provide more discussions so that the importance of subindices can be better appreciated. For example, Khera et al.’s Figures 1 and 2 indicate that African countries excel in digital financial inclusion, so it would be insightful to explain which of the access and the usage components are relevant in promoting the development in digital financial inclusion. Another example is Khera et al.’s Figure 3 that contains the interesting finding, namely, for countries with low traditional financial inclusion, the variance of traditional financial inclusion is larger than the variance of countries with high-traditional financial inclusion. This implies that efforts in pursuing digital financial inclusion vary more in countries with low levels of traditional financial inclusion. Some additional empirical evidences may also help to convince readers about the validity of this index. For example, Khera et al.'s (2022) Figure 3 ranks Mongolia as the most advanced country in terms of both comprehensive as well as digital financial inclusion, and their Figure 4 shows that Ghana ranks No. 1 in improvements of digital financial inclusion. Presentations of some statistics about access and usage, and the development in traditional financial inclusion between 2014 and 2017 of these two countries would be helpful. Some robustness checks may also help readers and users to appreciate the importance of this index. For example, one way to construct the index is to apply the PCA approach to all the variables in one stage instead of in three stages. Such a strategy can avoid the prediction errors caused by treating the first-stage and second-stage indices directly as raw data, and can also provide readers with a broader view about the financial inclusion status quo.

Comment on “Siamese Twin Failures: Structural and Regulatory Transformations in Unequal Thailand”

Abstract: Kanchoochat (2022) takes an institutional approach to explaining the persistence of inequality, poverty, and low growth rates in Thailand over recent decades. He focuses on two institutional transformations: a “structural transformation,” meaning a move away from agriculture, and a “regulatory transformation,” meaning efficiency-enhancing reforms in public administration, decentralization, anti-monopoly policies, and taxation. Kanchoochat argues that the high-growth countries of East Asia, especially Taiwan and South Korea, achieved these two transformations, resulting in higher growth and declining inequality, while Thailand has failed. This is a succinct, elegant, and original approach to an important issue. On the structural transformation, Kanchoochat has excellent charts showing the extent of Thailand's failure compared to other Asian countries to move people out of agriculture and to improve productivity. He attributes this failure to two causes: government subsidies of inefficient agriculture, especially since the 1990s, and the continuing role of the farm as a form of social security in the absence of state provision. He suggests that ending subsidies and constructing a comprehensive social security system would overcome the problem. While I would welcome these reforms, I doubt they would achieve a “transformation,” because I think other factors are important in sustaining this inefficient agricultural sector. Most important of all, access to and use of land, the single most important input into agricultural production, is still lumbered with many restrictions. Around 60% of land is still ultimately controlled by government. Large areas are not available for economic use, and others have restrictions on their use (Cripps, 2020). Without a far-reaching reform of the tenure system, the potential of agriculture will not be realized. Another restraining factor is the very low rate of public investment in agriculture over the long term. On the regulatory transformation, Kanchoochat shows how moves toward democratization, decentralization, and progressive polices on tax and competition foundered on the intransigence of the “traditional elite,” meaning the military, and segments of the bureaucracy, professions, and politically connected entrepreneurs. This alliance created a new institutional framework featuring appointed bodies and the judicial system which blocked or reversed reforms. Kanchoochat argues that change requires a larger role for electoral institutions and a “new social contract” under which citizens will agree to pay more tax and entrepreneurs will be happy with less monopoly. But it is not clear what social forces might drive such changes. Kanchoochat, following North, Aoki, and others, argues that institutions are created by human will to form a stable structure for the conduct of everyday life. According to this definition, institutions are susceptible to change but there is a tendency for “a self-sustaining system of shared beliefs” to hinder reform of institutions that fail to perform. My major concern is that explanations which see institutions as the critical players beg the question of what social forces shape these institutions and also what social forces might alter the situation. In short, what can disturb the dominant role of the “traditional elite” which seems quite happy to live with an inefficient agricultural sector, an inefficient state, and high inequality?

Comments on “Fintech and Financial Inclusion in S.E. Asia and India”

Abstract: Morgan (2022) writes an interesting and valuable survey on fintech and financial inclusion in Association of Southeast Asian Nations (ASEAN) and India. This paper is very timely and topical. It provides a good and informative description of the development of financial technology and digital payments in India and ASEAN. This paper also shows how fintech can help promote financial inclusion. Morgan argues that fintech adoption is spreading faster in higher income countries, and, within countries, it is spreading faster among higher income and more highly educated groups. In addition, Morgan points out the potential of digital payments for expanding financial inclusion. The information that Morgan provides is very useful for readers who want to know about the development of fintech in India and ASEAN. Morgan examines differences in the strategies and implementation of financial inclusion and fintech between India and ASEAN and draws lessons and policy recommendations from these findings. This paper also provides a comparative perspective on regulatory developments in several ASEAN countries and India. I agree very much with the author's analyses and findings, however, to enrich and sharpen Morgan's analysis, I suggest several things: First, Morgan does discuss general concepts of how fintech can promote financial inclusion, but it would be more interesting for example if he could provide examples or discuss how fintech in India or ASEAN can reduce transaction costs in financial transactions. Second, Morgan points out that despite the rapid growth of alternative finance in recent years, the overall rate of penetration is still small. It will be useful if he could elaborate on the prospects of alternative finance in the future. Third, one of the objectives of Morgan's paper is to discuss the relationship between financial inclusion and financial stability as well as the various risks that arise from the development of this fintech. However, I found the argument is very general. I think Morgan should be more articulate and more explicit about this so that readers can see this argument clearly. Fourth, on regulatory issue, we understand that digital innovation happens so fast, the production cycle becomes so short. Goods or services made today will become obsolete in a short period of time. So how does the government regulate it? Regulations will tend to lose ground quickly when the new technologies emerge. How can the government make regulations for one industry or one product, if the rules soon become obsolete due to new innovations. Government regulations or laws, I believe, will need to be more general and flexible in the future. The issue is that if the regulations are not explicit, how can they adequately govern them? The conundrum is that while innovation cannot be stifled, it must be protected. What is the best way to draw this line? This is a big issue that might become a potential problem in the future. In my opinion, regulators from various countries must change their mind set from agreements on rules to agreements on principles. It would be useful if Morgan can touch this issue when elaborating on the regulatory issues. Fifth, in the discussion on regulation, Morgan does convey descriptively about the development of regulations in several ASEAN countries and India, but it would be very interesting if he also discusses the potential problems that arise related to this regulation. Sixth, in the conclusion section, Morgan points out that fintech tends to widen the gap in income and wealth. This is an important issue. It would be useful if Morgan can elaborate more on this issue. Despite all these comments and questions, in sum, this paper is worth reading and offers an important contribution for survey on fintech development in India and ASEAN. Furthermore, various lessons can be drawn from this paper, particularly a comparative study with other countries with similar problems.

Comment on “Big Data in Asian Central Banks”

Abstract: My first comment is on why central banks have started using big data in the first place. Even before the Covid-19 pandemic, private financial institutions and investors extensively used big data, “alternative data” in their terminology, in predicting the future of stock prices. Central banks, including the Bank of Japan (BOJ), were concerned that with the rapid growth in the use of big data by the private sector, they would fall behind if it looked only at traditional data, and this was the driving force behind central banks' use of big data. In 2020, as Covid-19 spread, the use of big data by the Fed, the BOJ, and other central banks accelerated even further. Here is what happened in Japan at that time. The Japanese government declared a state of emergency on April 7, 2020. In the Monthly Economic Report released on April 23, the Japanese government stated that “[t]he Japanese economy is getting worse rapidly in an extremely severe situation, due to the Novel Coronavirus” (sic). What is important to note here is how this assessment was made. Normally, assessments in monthly reports are based on official government statistics. In this case, it is clear that the impact of Covid-19 on consumer spending, especially service spending, was pronounced, so the assessment in April last year should have been based on the “Family Income and Expenditure Survey” (FIES). However, this was not the case. The reason is simple: as of April 23, the latest information was for February 2020, which hardly reflected the impact of Covid-19. Instead of the FIES data, the government used alternative (big) data. Specifically, in the April Monthly Report, expenditure figures calculated from credit card data (e.g., spending on eating out and spending at supermarkets) were included to show that an unprecedented sharp decline was occurring. Credit card spending figures had never been used in a monthly report before. The crisis thus suddenly provided an opportunity for the use of alternative data. How will the use of alternative data by governments and central banks develop in the future? Once Covid-19 is under control, the need to rely on alternative data may diminish. Statistics, be they traditional or nontraditional, are a tool for responding to crises: In normal times, no one needs statistics and, in fact, no one pays much attention to them. However, once a crisis occurs, statistics are indispensable to obtain a real picture of the crisis. Since traditional data tend to be available too late to play a role in a crisis, we have no choice but to supplement traditional data with alternative data. Alternative data represent one of the means to deal with crises, and societies should make sure such data are available as part of their preparations for the next crisis. Given the prospect that the use of alternative data by central banks will expand in the future, who is best placed to produce such data? This brings me to my second comment. There are two possibilities. The first is that central banks do everything themselves: they contact data holders, process the data, and make policies based on it. The second possibility is that central banks are involved only as users of alternative data compiled by the private sector. Cornelli et al. (2022) seem to prefer the first possibility. Specifically, they state that it is desirable to reduce the dependence on private data service providers as much as possible because of high costs and the “significant legal and operational risks” of relying on them. However, many of the examples of alternative data use in Asian central banks cited by Cornelli et al. (2022) rely on data service providers such as Google. Even in the Japanese example given earlier, the government relied on alternative data provided by private companies. If it is not practical for central banks to prepare an environment for alternative data on their own, they should seriously consider the second possibility: relying on the private sector. It is the private sector that holds the big data, not central banks or governments. It is also the private sector that is skilled in processing and analyzing big data. Based on this reality, central banks should start discussing how best to work with the private sector. In doing so, as pointed out by Cornelli et al. (2022), it is necessary to pay sufficient attention to the protection of personal information and data ethics. In addition to these general matters, there are issues specific to central banks. The first of these is ensuring that data are provided on a sustainable basis. It is important to avoid a situation where data become unavailable due to changes in the business policies of private companies. The second is the need to deal appropriately with fraud by private data service providers. Central banks and governments will need to play a role in monitoring for and preventing such fraud. Further, countries will need to develop and coordinate rules for the handling of alternative data.

Comment on “Chinese Fintech Innovation and Regulation”

Abstract: While the digitalization of finance is a global phenomenon, the trend is most remarkable in China, which is now home to two of the largest tech companies—big techs—in the world (Alibaba and Tencent). Against this backdrop, Chinese regulators have been confronted with the Herculean challenge to find a balance that embraces the promise of fintech while reducing potential harm to the financial system. Chorzempa and Huang (2022) offer a review of the development of fintech in China and related regulatory actions. They highlight a number of yet unresolved policy issues, draw lessons from China's experience and offer some recommendations for Chinese authorities. Their paper concludes by assessing the regulatory response to fintech in China as being largely successful. In China, to a large part, discussing fintech means discussing big techs. China is the largest market for both fintech credit and big tech credit in the world (FSB, 2020); and two big tech firms jointly account for 94% of its mobile payments market (Carstens et al., 2021). In 2018, big techs processed payments equivalent to 38% of gross domestic product (GDP) and they now have market capitalizations and credit ratings comparable to those of large banks (FSB, 2020). Big techs have unique business models that differ from those of fintech players. Most importantly, big techs are exploiting activities with strong network effects, under which every additional user creates value for all others (see chapter 3 in BIS, 2019). Chui (2021) argues that these network effects can help explain the rise of Alibaba and Tencent, and that they ventured into financial services as a means to survive against competition, which is why he sees them as “accidental financiers” rather than “aggressive invaders”, at least during the initial stage. Big techs' activities in financial services come with several policy challenges. Some are traditional policy concerns: financial risks, consumer protection and operational resilience; others are new challenges surrounding the concentration of market power and data governance (Carstens et al., 2021). These challenges are increasingly attracting the attention of policymakers. China is one of the countries where policy initiatives have emerged to cope with the risks presented by big techs, specifically in the areas of competition, data, conduct of business, operational resilience, and financial stability (Crisanto et al., 2021a). Against this backdrop, Chorzempa and Huang review how fintech developed in China and what factors contributed to its growth. Their discussion usefully refers to the underlying economics of financial services in the context of digital innovation (Feyen et al., 2021); and contrasts the Chinese experience with that of other countries. Building on this, Chorzempa and Huang provide an overview of fintech policy developments based on a conceptual framework, the “Fintech Tree” (Ehrentraud et al., 2020). Policy areas reviewed are payments, peer-to-peer lending, cryptocurrencies, financial holding companies, competition policy, data protection and the “rectification” of Ant Group. As one of their key recommendations, in line with Restoy (2021) and Crisanto et al. (2021b), Chorzempa and Huang advocate for more entity-based regulation to address the unique risks posed by big tech firms' unique characteristics and reduce the scope for regulatory arbitrage. This recommendation should receive much support. The main rationale for an entity-based approach is that the risks generated by different entities performing a similar activity are not necessarily the same. Fintech/big tech players offering financial services must already satisfy regulatory requirements which are aligned, in principle, with those imposed on other market participants. But these activity-based rules are probably not enough. To the extent that risks emerge not only from the provision of a particular service but also from the combination of activities (as is the case for big techs), entity-based rules are called for.

Comment on “Why Fintech Is Not Changing Japanese Banking”

Abstract: Iwashita (2022) argues that Japan's FinTech is underdeveloped as banks and their customers, especially the elderly and conservative corporate customers, cling to the traditional financial service formats. He further argues that the supremacy of banking services before the Internet era may have hindered changes on the supply side, that is, the service level was sufficiently convenient therefore the relevant parties did not want to expose themselves to the risks of cyberattacks and the costs of system renewals. Iwashita also mentions that the aging population may have worked against digitization due to the low digital literacy of the elderly. Iwashita (2022) also touches on the possibility of near-future developments, to be prompted by the global advancements in digitalization, the supporting attitude of the Japanese government to promote technological progresses, and the changing attitudes of customers. The change of tax filing obligations expected in 2023 is also mentioned as a factor which may promote the electronification of receipts and invoices, leading to more digital payments. I agree with most of the points presented in Iwashita (2022). While Japan's FinTech industry became active in around 2015,11 In 2015, the FinTech Association of Japan was founded and the Financial Service Agency activated a FinTech support desk to provide information for the industry. which almost coincided with the timing of the start of FinTech activities in other parts of the world, the progress of less-cash and more-efficient financial services has been slower in Japan in comparison to her international peers since then. As Iwashita indicates, the Covid crisis has accelerated the change of attitudes of financial institutions, customers and the government in Japan. To further deepen the analysis and enhance the value of the recommendations presented by Iwashita (2022), I would like to suggest following points. First, to expand the list of possible actions by banks and the government to promote FinTech in Japan, it may be worthwhile to identify changes of the payment systems in some European countries achieving less-cash status while, like Japan, their societies are aging and their banking systems are well developed. Sweden, where cash usage has decreased in an extraordinary manner (Riksbank (Sweden), 2017; Bach et al., 2018), suggests that the central bank's actions to promote an effective cash handling system by imposing the costs on to the private sector, and technological developments to make digital methods cheaper and more convenient may have contributed to the extraordinary rapid decline of cash usage. Another analysis (Riksbank (Sweden), 2020) offers the hypothesis that the combination of a couple of events, such as the introduction of mandatory receipt issuance for cash and the popularization of a bank-consortium-led digital payment application, made cash less attractive to digital methods. By learning from these examples, Japan may be able to build concrete action plans to promote FinTech. Second, the discussion of the possible risks of digitalized financial services, and solutions to such risks, may be appreciated as the negative consequences need to be considered in the promotion of FinTech. For example, the Bank of Japan has mentioned some concerns over a private sector-driven cashless society, such as the imperfect substitution to legal tender cash payments in light with the level of security or the geographic distribution, and concluded that the issuance of Central Bank Digital Currency might serve as the solution (Bank of Japan, 2020). It may also be useful to list the desirable features of FinTech services to reduce potential problems, such as security, resilience, universal access, the instant payment capability (settlement finality), and interoperability.22 These five points are listed as the desirable features of CBDC in Bank of Japan (2020) and considered to be applicable to private sector digital payments in the discussant's opinion. I would like to conclude my comment by echoing Iwashita's remark to put the importance in the “meeting users'” needs and providing greater profitability. The final goal is not the promotion of FinTech itself, but the improvement of the productivity from the users' viewpoints. I believe the root-cause analysis of the current status presented by Iwashita (2022) will greatly contribute to the near future changes of Japanese banking industry.

Comment on “Fintech and Financial Inclusion in Southeast Asia and India”

Abstract: Morgan (2022) provides a comprehensive analysis of the Fintech scene and its influence on financial inclusion in Southeast Asia and India. He describes how Fintech can contribute to promoting financial inclusion which poses a pressing challenge in this region. Among the various categories of Fintech, Morgan focuses on digital payments and alternative finance, taking account of their importance in enhancing financial inclusion. He explains each category in detail and how they are developing in the region, as well as how they are regulated/promoted. Morgan successfully deals with the daunting task of bringing together and evaluating the various characteristics of the relevant countries that differ significantly from one another. Morgan points out that digital payments such as mobile money and digital remittances are growing rapidly, including among the unbanked and under-banked. But as for alternative finance, for instance crowdfunding and P2P (peer to peer) lending, even though they are also developing very fast, Morgan argues that the overall penetration rate is still substantially lower than conventional lending. In addition, Morgan discusses that even though Fintech can enhance financial inclusion, at the same time it can bring various risks to consumers. One of them is the potential increase in income and wealth inequality, given that Fintech adoption can be seen more in higher income countries, and within one country there is an adoption gap depending on income and educational backgrounds. Morgan stresses that policy measures are necessary so that the benefits of Fintech are distributed more broadly and in a more equal manner. There has been a huge amount of hype surrounding the Fintech landscape in both Southeast Asia and India in recent years. The spread of the internet and smartphones on the one hand and the underdevelopment of financial services on the other in this region have brought various business opportunities for Fintech services. Numerous Fintech startups have been founded and have attracted investments from all over the world. In Southeast Asia, venture capital investment into startups dedicated to the payments and financial services sector amounted to US$1.3 billion in 2020, the second largest after the multi-vertical sector in which many startups included here also provide Fintech services (Cento Ventures, 2021). In the meantime, Fintech investment in India in the same year was US$1.2 billion, close to Southeast Asia's figures and made up one of the top three investment categories (Sheth et al., 2021). The surge in Fintech startups raised alarm among the traditional financial institutions, and they too started adopting Fintech in order not to be left behind. A prominent example is DBS: a Singapore-based bank that has been proactively embracing Fintech to become “digital to the core” (DBS, 2017). Its efforts have paid off and today DBS is recognized as the world's frontrunner in digital banking. Other banks, from state-owned banks in Vietnam and public sector banks in India to private banks in Thailand, are also taking on Fintech, even if they may not have gone as far as DBS. Considering all these developments, it would only be natural to assume that Fintech has penetrated the economies of Southeast Asia and India and has brought about positive effects toward financial inclusion. Therefore, Morgan's observation is somewhat sobering: The penetration of Fintech has so far been uneven, and it is also breeding new disparities in the region. In this regard, Morgan contributes to eliminating the illusion that we unintentionally tend to fall into, that technology can solve all sorts of problems including social issues. This leads to the following two questions. First, how can we evaluate the current “unequal” distribution of merits provided by Fintech in Southeast Asia and India? Morgan discusses Fintech's limited effects, but this may be because it is fairly new and developing, and it can be just a matter of time before we are able to see it utilized more extensively. Or Fintech startups, although the number has increased dramatically, may still be too small to make notable macroeconomic differences. Also, there are possibilities that traditional financial institutions are embracing Fintech but at a limited scope, for example, using Fintech mainly to serve their existing customers and not to expand their customer base to segments they have not previously reached out for. Secondly, how can we make sure Fintech can bring positive results to the region with minimum side effects? Morgan mentions the need for policy intervention and makes several suggestions. Detailed studies are necessary to find out what kind of policy measures would be effective without at the same time preventing innovative Fintech solutions from coming out. Overall, Morgan makes a meaningful contribution to our understanding of what is currently going on in Fintech and financial inclusion in Southeast Asia and India, and provides an important basis for further research on this topic.

Comment on “Income and Wealth Inequality in Asia and the Pacific: Trends, Causes, and Policy Remedies”

Abstract: Zhuang (2023) covers a large amount of ground when it comes to issues, countries, and literature. The latter is indicated by the fact that the list of references includes more than one hundred titles. My first comment is that Zhuang's paper actually does not do what the title originally indicates. On one side, the spatial coverage is broader than suggested by the title. It covers Australia and New Zealand, that usually are not considered parts of Asia. More importantly, the coverage is narrower as it does not treat income and wealth inequality in Asia as a unit. True the paper deals with the development of inequality in Asian countries and its possible causes. However, it does not attempt to address how inequality at the household level in Asia as an entity has developed. For some years a literature studying how household income inequality from a global perspective has evolved. In a recent contribution, Milanovic (2022) reports that the Gini coefficient at the household level for income in Asia as a whole decreased from 59% in 2008 to 55% in 2013, a rather large decrease over a short period. It can be claimed that the evolution of income inequality in Asia during the most recent years does not at most, have to do with how inequality within countries has evolved. Instead the main factor is how the average incomes in various countries have changed. For example average income in China and in India has increased more rapidly than in Asia's high-income countries like Japan. As a consequence the middle classes in China and India have grown. On this see, for example, Sicular et al. (2022) who define the “global middle class” as being neither poor nor rich if the people are living in the developed world. In 2018 China's global middle class constituted not less than 25% of China's population and the middle class in India was estimated to 6% of its population. The absolute size of the Chinese middle class was in 2018 nearly double the size of the global middle class in the USA and similar in size to that in Europe. My second comment relates to if we should care about rising inequality. Zhuang touches on this issue in his concluding section and refers to the literature on the inequality of opportunity (IOp). A point of departure taken in this literature is that public policy better not try to counteract inequality that is due to effort, but it should focus on inequality due conditions individuals cannot affect (circumstances). Most of the empirical literature aiming to quantify IOp concerns high-income countries but by now there are some papers on China. For example, Yang et al. (2021) show that between 2002 and 2013 and especially between 2013 and 2018 IOp in China declined. In 2002 the large contributors to IOp were region and hukou type at birth. However, in 2018 the contributions of those circumstances had decreased, but that of parents' education had increased. This study also finds that IOp is higher for older than younger birth cohorts. My third comment relates to the data situation. An important issue is how comparable the data is across countries and time. For several decades there have been ambitious efforts to harmonize survey data on income for different countries in the Luxembourg Income Survey (LIS). Starting with high-income countries, now more and more countries have been included in the databank. Today LIS has data from the following Asian countries: China, India, Israel, Japan, Laos, Palestine, South Korea, Taiwan, and Vietnam. Thus, the LIS data covers slightly more than 70% of Asia's total population. One would expect that progress beyond the present paper can be made by using LIS data more intensively. My last comment relates to how research represents inequality and its changes. Today there is a large awareness that in many cases it is not advisable to summarize the degree of inequality through the numerical value of only one index. Perhaps more progress can be made in the future by considering more than one dimension for a particular household, see, for example, the study on the USA by Fischer et al. (2022).

Comment on “How Inequality Affects Trust in Institutions: Evidence from Indonesia”

Abstract: Suryahadi et al. (2022) is an ambitious and innovative paper. Highlighting the importance of trust as a key ingredient in the process of economic and political development, the authors examine the impact of inequality on trust, and by extension institutions, at the village and district levels in Indonesia. The paper has several distinctive features. First, the authors carefully disaggregate the concept of institutions, into economic, social, and political (“the state”) dimensions, plausibly conjecturing that each of these may have different behavioral relationships to the key variables of interest. Second, the analysis is also geographically disaggregated. This adds richness to the study given that Indonesia is the world's largest archipelagic state. Third, they introduce an intermediate variable in the analysis, education, finding that more highly educated individuals' trust in the political and state institutions is more sensitive to higher inequality. The authors' main conclusions are reassuring. In general, the levels of trust are high (Suryahadi et al.'s figures 2 and 3), in some cases arguably higher than might have been expected, and mostly rising. Not surprisingly, trust is particularly high for social ties. Evidently, the village respondents are least trusting only toward “strangers.” Financial institutions enjoy high trust; one might surmise that the absence of any major bank crashes in Indonesia this century might contribute to this finding. Trust in governments and the civil service is very high, approaching 80% in 2018. This is perhaps a little unexpected given the endless “coffee shop” discussions of corruption. Nevertheless, in discussing their figure 5 the authors add an important qualifier, that “lower trust does not pertain to political institutions like elections or the parliament, but more on state apparatus.” The exceptions to the conclusion of high trust include that across religious communities, not a major surprise in view of the country's occasional religious tensions, and some decline in the press and media, which is a global phenomenon in this era of proliferating “fake news.” The finding that trust is comparatively high is also of interest given Indonesia's inequality outcomes. Historically expenditure inequality was moderately low, but it has risen significantly for much of this century. Not surprisingly, therefore, the authors conclude that keeping inequality “in check” is important for healthy institutional development. I have several comments on this fine paper, which might be explored in future work on the subject. First, it would be interesting to set out some analytical “priors,” of what one might hypothesize to be the likely relationships. For example, Indonesia has had episodes of quite serious conflict over the past 50 years, including ongoing unrest in the two Papua provinces (which presumably were not in the survey). But they have generally been contained, and in addition the level of reported criminality is quite low. In passing, both these variables, conflict and criminality, could be introduced as intermediate variables in the analysis. Moreover, presumably the fact that Indonesia has successfully conducted five national elections in the democratic era, that is, from 1999 onward, with mostly credible processes and outcomes, and similarly so (on most occasions) for sub-national elections, also provides prima facie support for the high recorded trust. And even if people are unhappy with the leadership at various tiers of government they know they can vote them out at the next election (as they do quite often). It might even be inferred that the high trust levels were a factor in Indonesia navigating the Covid crisis without major stress on its political and social fabrics. In this context, the authors document that the Covid pandemic increased inequality in Indonesia, especially for workers in the informal sector. But overall the effects were relatively mild and, importantly, about two-thirds of the negative effects of Covid were mitigated by the government's social protection programs. Connecting to the paper's central topic, they might want to emphasize that this significant achievement has presumably been a trust-enhancing outcome, as has the almost continuous reduction in head count poverty since the 1970s, apart from during the Asian financial crisis. A second set of questions relate to the World Value Survey (WVS) data, which are employed to generate the paper's most original empirical contribution. The Indonesian WVS data were obtained from interviews with 3200 adults, who were geographically dispersed and also identified by education, gender, age, and location (urban/rural). For those (like this reviewer) who have not used the WVS data, some brief commentary on its origins and construction would be helpful. For example, how were the data collected (phone, internet, face-to-face interviews), who were the interviewers, and how well do these potentially value-laden concepts travel across international boundaries, particularly from developed to developing countries? Third, given Indonesia's great subnational diversity, it would be interesting to explore whether there are significant spatial variations in the relationships. For example, one might hypothesize that the relationship between trust and inequality would differ between densely-settled, industrialized Java districts and those in lightly-settled, resource-rich, remote regions, where personal connections are assumed to be more important. But the sample size is presumably not large enough to undertake such analysis, and in any case the authors do employ island dummies.