The Singapore Economic Review

The author shares his theory on market creation and evolution from his perspective as an academic and a practitioner who has been involved in financial innovation for five decades. Rather than being spontaneously created, markets appear to follow a clear set of steps (the “seven-stage” process) from inception to maturity. It is critical that the benefits from transacting are greater than the costs of establishing the market — otherwise the evolution does not occur. Historical examples of the process outlined by the author can be found in commodity, equity, fixed income and environmental markets. The author provides observations and lessons that his evolutionary framework shares with financial inventive activity in new and existing products in our rapidly evolving digital world. Recent digital innovations such as the blockchain have evolved in a framework that did not require a legislative or regulatory framework (or what the author calls a “permissionless” environment). A properly designed regulatory environment can be permissionless regarding inventive activity (withness the Commodity Futures Trading Commission in the United States). A permissionless environment has historical precedent in other “analog” innovations such as wheat trading in the 19th century or financial futures in the mid-1970s. Following the seven-stage process, those innovations arose from structural changes or latent economic demands. Their standards and infrastructure were only much later understood and adopted by the regulators. Given that, the author suggests that since changes now occur at hyper speed, the implications for the future shape of derivatives markets are likely to be profound.

We aim to compare econometric analyses with machine learning approaches in the context of Singapore private property market using transaction data covering the period of 1995–2018. A hedonic model is employed to quantify the premiums of important attributes and amenities, with a focus on the premium of distance to nearest Mass Rapid Transit (MRT) stations. In the meantime, an investigation using machine learning algorithms under three categories — LASSO, random forest and artificial neural networks is conducted in the same context with deeper insights on importance of determinants of property prices. The results suggest that the MRT distance premium is significant and moving 100m closer from the mean distance point to the nearest MRT station would increase the overall transacted price by about 15,000 Singapore dollars (SGD). Machine learning approaches generally achieve higher prediction accuracy and heterogeneous property age premium is suggested by LASSO. Using random forest algorithm, we find that property prices are mostly affected by key macroeconomic factors, such as the time of sale, as well as the size and floor level of property. Finally, an appraisal on different approaches is provided for researchers to utilize additional data sources and data-driven approaches to exploit potential causal effects in economic studies.

Previous studies mostly ignore possible nonlinear behaviors that may be caused by asymmetry, persistence, or structural breaks. This study aims to fill this gap by applying the Nonlinear Autoregressive Distributed Lag technique of [(). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications, pp. 281–314. New York: Springer] to examine the dynamic effect of oil prices on other energy prices based on nonlinear empirical frameworks. We identify how oil prices and four other energy commodity prices behave using daily data from January 07, 1991 to February 25, 2020. The long-run relationships suggest both oil price increases and decreases are significantly and positively related to the prices of all other energy commodities. There seems to be asymmetry in the linkages between oil prices and all the prices of diesel fuel, gasoline, heating oil and natural gas in the long run. Except for natural gas, the effects of oil price increases are significantly larger than those of oil price decreases. However, in the short run, the results are somewhat different. An asymmetric impact of oil price changes is found for gasoline and the effects of oil price decreases are larger than those of oil price increases. In our model, the real effective exchange rate of the US dollar is explicitly incorporated to capture the linkages between US dollar fluctuations and energy price movements. We find that the relative weakness of the US dollar strengthens the prices of energy commodities in the global market.

In this paper, we characterize the position of Singapore in global value chains and identify Singapore’s key upstream and downstream trade partners. We trace how the position of Singapore in global value chains has changed in the past two decades: whether it has moved upstream or downstream, how involved it is in global value chains, how its trend compares with the other major Asian exporters (China, Japan, Korea, Taiwan and Hong Kong), and which key sectors of Singapore play a major role in these global trade networks.

The countercyclical pattern of saving in Thailand in the 1990s and 2000s raised questions about household saving behaviors in the country. Using constructed pseudo-panel data sets from the Thai Household Socioeconomic Surveys (SESs) from 1992 to 2011, this paper estimates the intensity of the precautionary saving motive, measured by the coefficient of relative prudence of households in Thailand. By using a dynamic pseudo-panel approach to address concerns with regard to individual heterogeneity causing bias in estimation, the estimated relative prudence of Thai households is around 2, which shows a low precautionary saving motive among these households compared to other countries. Estimates based on disaggregation by demographic characteristics show that as a result of the government-assistance policy, older cohorts and those who live in the rural areas show lower prudence.

Given the risks of shadow banks and their risk contagion with the housing market, this study investigates the necessity, policy transmission mechanism and impact of incorporating shadow banks into a macroprudential regulation framework. It constructs a dynamic stochastic general equilibrium (DSGE) model incorporating housing market volatility and heterogeneous financial intermediation, where housing market volatility transmits to the financial intermediation sector through the collateral channel. Meanwhile, regulatory asymmetry expands the shadow banking scale and leverage indicators driven by the housing market, increasing the shadow banking risk. Moreover, considering shadow banking credit size, the macroprudential regulation framework can mitigate the volatility of economic variables and maintain macro-financial and economic stability under the conditions of tightening monetary policy and strengthening regulation. However, policy effects are limited in curbing shadow banking risks from housing market fluctuations. Thus, it is necessary to crop the source of shadow banking risks from the transmission mechanism, formulate more targeted and specialized regulatory policies to complement housing market control measures, maximize cross-market effects among different policies, and incorporate shadow banking into the regulatory system.

This paper employs the local Bayesian likelihood methodology to estimate a medium-scale dynamic stochastic general equilibrium (DSGE) model on different frequencies and uses frequency-domain tools to evaluate the time-varying parameter model and the fixed-parameter model. These techniques yield fresh insights into theoretical and empirical implications conveyed by alternative models beyond what conventional time-domain approaches can offer. We show that parameter estimates are sensitive to frequencies, and goodness-of-fit varies substantially with the frequency bands. Overall, the estimated time-varying parameter model captures the properties of the U.S. data better in the business cycle frequency band, and beyond this band, the fixed-parameter model performs better. Additionally, our study also reveals the importance of structural shocks in improving the fit between models and data. Finally, we utilize the spectral representation of generalized forecast error variance decomposition to investigate the frequency dynamics of volatility connectedness. We find shocks to economic activity have an impact on variables at different frequencies with different strengths, and markets become more connected during crisis periods. Furthermore, this study provides insights into a question policymakers are much concerned with: which shocks are major sources of economic volatilities and which sectors serve as major recipients of shocks?

Using data from 133 Chinese commercial banks from 2003 to 2015, we examine the links among Internet finance, net interest margin and noninterest activities. Four main findings emerge. First, the net interest margin exerts significantly negative impacts on the noninterest activities through incentive distortion, cross-subsidization and resource restriction effects. The incentive distortion effect plays a stronger role, while the cross-subsidization and resource restriction effects play weaker roles. Second, Internet finance significantly promotes the development of noninterest activities, meaning that the technology spillover effect of Internet finance is stronger than the customer loss effect in China. Third, Internet finance strengthens the negative effects of the net interest margin on fee and commission activities but weakens the negative effects of the net interest margin on other noninterest activities. Finally, we find that the effects of the net interest margin on noninterest activities are stronger in state-owned banks than in non-state-owned banks, while the effects of Internet finance on noninterest activities are weaker in state-owned banks.

By developing an industrial structure change model, this paper investigates the role of the information technology in explaining the process of industrial structure optimization and upgrading in China from 2001 to 2019. Then, we put forward and discuss the substitution effect and the pervasiveness effect of information technology, and deduce the theoretical propositions from the model. We use the Malmquist index method to estimate the rate of information technology progress, and then choose the genetic algorithm to calibrate the parameters of the model. The research conclusions innovatively explain how the information technology can promote the industrial structure optimization and upgrading in China based on the substitution effect and the pervasiveness effect of information technology in detail. The results point out that when the contribution of information technology to total factor productivity of the tertiary industry is higher than that of the secondary industry, the pervasiveness effect of information technology can promote the upgrading of the industrial structure. Under the comprehensive effect of the pervasiveness effect and substitution effect of information technology, the industrial structure tends to be more optimized. The further heterogeneity analysis shows that the driving effect of information technology on the optimization and upgrading of industrial structure in regions with “servitization” characteristics is more significant. It highlights policy suggestions designed to adopt innovation-driven and technology development strategy, regional industrial structure transformation and economy development strategy.

We use firm-level data from ASEAN5 to examine the significance of tax-motivated profit shifting by multinational enterprises and to analyze how anti-avoidance measures mitigate the profit shifting. We show that (1) tax-motivated profit shifting is statistically and economically significant, especially for manufacturing firms, (2) auditing and transfer-pricing scrutiny is more effective in reducing profit shifting than documentation requirement alone and (3) tax-motivated profit shifting is prominent for large firms, while anti-tax avoidance measures result in the absence of profit shifting detected from small manufacturing firms. The findings have important implications for developing countries with weak governance but dependent on MNEs.

In this paper, we evaluate the preventive behaviors for middle age people in Taiwan through survey wave comparisons for the period 1993–2007. We first investigate the determinants of preventive behavior and how their effects change over time by using the seemingly unrelated bivariate probit model. Evidence shows that income and education levels do have a significantly positive relation with preventive behavior models, while reported poor health, inpatient and outpatient services, and regions are essential determinants of preventive behaviors. Then, we investigate the impact of Taiwan’s National Health Insurance (NHI) on preventive behaviors based on the difference-in-difference (DID) analysis. The results show a lack of evidence for the moral hazard effect on preventive care and regular exercise panels over the period 1993–2007.

The world produces countless tonnes of food to feed everyone, yet the numbers of people who suffer from hunger remain high, especially in developing countries. This issue may highlight the importance of institutions as a foundation for the issue of food security. Hence, this study investigates the role of institutions on food supply in a panel of 56 developing countries. The dynamic generalized methods of moments results indicate that institutional factor plays a vital role in improving the availability of food and access to nutritious food for all people, thereby ameliorating the food supply problem. Therefore, the overall result suggests that policy-makers should improve the level of institutional quality so that it can form the fundamental ground toward alleviating hunger and improving the food supply.

The objective of this paper is to obtain the growth optimizing public debt to GDP ratio ($d$*), based on estimated output elasticity ($\alpha$) of public sector capital under the golden rule of budgetary deficit. After conducting unit root tests and cointegration analysis, value of $\alpha$ which is estimated under OLS, CLS and FMOLS, hovers around 0.281–0.29. Hence, the computed value of $d$* stands around 65–67% of GDP; modestly lower than the current value (73% in 2016). Since large revenue deficit has been persisting, effective value of $d$* would be even lower. Fiscal tightening, especially reducing revenue deficit and offloading the persistently loss making PSUs would be important for macroeconomic stability and accelerating economic growth.