ชั้น 2 ธนาคารแห่งประเทศไทย
Inflation at Risk in Thailand
Using monthly Thai data from 2003-2020, we examine the determinants of the future distribution of inflation. We evaluate how different risk factors predict 1-year- ahead future distributions of CPI inflation and its components. Risk factors come from 5 different groups of variables: inflation expectations, domestic economic activity, global economic activity, financial conditions, and component-specific factors. We obtain points on the future distributions of inflation through quantile regressions and fitting those points with skewed-t distributions. Our focus is on the outlook in the tails of the distribution, which recent literature referred to as `inflation-at-risk.’ We find, as expected, that the whole inflation distribution has shifted lower, and thus the probability of negative inflation has increased markedly in recent years. There is a structural break around 2015 that affects both the distributions of inflation and their determinants. This structural break makes it challenging to make out-of-sample forecasts, thus, we focus on in-sample evaluation and explanations. For risk factors, we observe that the tightening of financial conditions and the decreasing world production are prominent sources of downside risks to inflation. Inflation expectations also play a smaller role in the lower quantiles, signaling its lower effectiveness in anchoring actual inflation during disinflationary periods. Finally, high global and domestic economic activity can be effective in decreasing downside risks in the lower tail, providing policy makers a way to counter these risks by stimulating the economy.
Long Run Risk Model and Equity Premium Puzzle in Thailand
This paper shows that the long-run risk model of Bansal and Yaron (2004) can potentially solve the equity premium and risk-free rate puzzles in Thailand. In particular, the calibrated values of the risk aversion and the elasticity of intertemporal substitution are empirically plausible. Risk decomposition results indicate that long-run risk is the most important risk component relevant to asset prices; that is, asset prices in Thai financial markets are most sensitive to small changes in news regarding long-term expected growth rates. Volatility risk also has an impact on asset prices but its impact is just about a quarter of the impact of the long-run risk.
Understanding the Dynamic of Digital Economy in the Context of Digital Literacy of Thai Households
Digital economy has led to new business opportunities and growth potential especially for developing countries such as Thailand. However, one crucial factor that could create challenges is the readiness of households in adapting to the digital environment. This research proposes that digital literacy of households is the key indicator that helps policy makers to understand the digital divide situation. Digital literacy should be measured by 4 sub-dimensions, namely, 1) the access to digital technologies 2) the level of digital skills 3) the level of digital knowledge and 4) the digital information awareness. After using the principal component analysis (PCA) to develop the scoring system of digital literacy and using the cluster analysis to classify the sample into 3 levels of digital literacy, it is found that households in the illiterate group are mostly unemployed or work in the labor-intensive sector. When looking at how they use financial services, they appear to significantly use fewer banking services and have lower preference on the personalization of services than the digital fluency group. This evidence suggests that populations in the digital illiterate group may have already suffered from the digital divide which could intensify the problem of wealth inequality in the digital era. Consequently, policies that guarantee all households to have certain levels of digital literacy are needed.
Tax Incentives to Appear Small: Evidence from Thai Firms and Corporate Groups
This paper studies the effects of SME tax incentives on firm behaviors. We use firm-level panel data of all registered firms in Thailand to analyze the effects of a large reduction in corporate income tax rates for SMEs in 2011. First, we find that firms responded strongly to the SME tax incentive as indicated by a sharp bunching of firms just below the threshold after the incentive was introduced. The responses were concentrated among firms with positive EBIT, implying a financial motive for firms to remain small. Second, the bunching was prominent for stand-alone firms, where we observe slower revenue growth for those below the threshold. Third, we do not observe bunching for corporate-group firms, but we find evidence of tax-motivated profit shifting among them instead, especially among firms in small groups with weak corporate governance. Our analysis suggests that transfer pricing was likely a primary channel. Finally, despite the unintended consequences, we find that the incentive significantly raised the probability of firm’s survival and encouraged new firm registration, as the policy intended.
Understanding a Less Developed Labor Market through the Lens of Social Security Data
While understanding labor market dynamics is crucial for designing the country’s social protection programs, prohibitive longitudinal surveys are rarely available in less developed countries. We illustrate that employment history from Social Security records can provide several important insights by using data from a middle-income country, Thailand. First, in contrary to the traditional view, we find that the formal and informal sectors are quite connected. Our analysis of millions of individual histories by a machine learning technique shows that more than half of registered workers left the formal sector either seasonally or permanently long before their retirement age. This finding raises a question of whether the social protection schemes being separately designed for formal and informal workers are effective. Second, the semi-formal workers also had a much flatter wage-age profile compared to those always staying in the formal sector. This observation calls for effective redistributive tools to prevent earnings inequality to translate into disparities in old-age and transmit to the next generation. Lastly, on the employer size, we find that almost half of formally registered firms had fewer than five employees, the benchmark often used to define informal firms. This result suggests that the distributions of firm sizes differ across countries and the employer size alone is unlikely sufficient to define informal workers.