Research in Economics最新文献

Digital transformation entails new sources of economic information in the form of rich texts, which can provide a deeper understanding of banking sector developments. With textual data available and accessible in digital format, this paper develops three distinct indices based on a large corpus of economic news articles to forecast banking crises. The methodological approaches feature the identification of key topics within a large volume of texts. A Banking Crisis Lexicon Index and Sentiment Index are developed through analysing a vast number of economic articles to detect the evolution of banking sector discourse. Findings from Granger causality highlight leading indicator status of the Banking Crisis Lexicon Index, signalling a change in the banking crisis series four years in advance, accentuated by innovations from a VAR analysis using Cholesky decomposition, and substantiated by receiver operating characteristics with under the curve estimates suggesting robust predictive performance strength above 70%, on a global scale, for developed economies and crisis countries. Serving as benchmark, the Sentiment Index constitutes a concurrent indicator, which moves in tandem with the crisis series, thereby providing more granular information on banking developments. A combined Banking Crisis Lexicon and Sentiment Index exhibits solid forecasting performance, which is comparable to the Banking Crisis Lexicon Index, yet with shorter lead time. In a robustness test, German-based indices outperform those based on English reporting in a predominantly German speaking region, highlighting the value of textual analysis in the vernacular. In reading between the lines, this paper contributes to the literature on quantitative analyses of textual data in constructing text-based banking crisis indicators to support a preemptive policy response.

This research uses a dynamic modelling approach to design and simulate an equilibrium model of the interaction between CBDC issuance, deposits and bank reserves. With many central banks announcing their intention to issue central bank digital currencies (CBDCs), it has become imperative to rigorously analyse the potential impact of these issues on the banking system. This study aims to examine the implications of the introduction of CBDCs within a robust analytical framework, in order to inform policymakers and financial sector players about the possible consequences of this major monetary innovation. The study results show that overconfidence of economic agents towards CBDCs can drastically reduce bank reserves, thereby limiting the lending capacity of banks and creating liquidity problems. Similarly, increasing reserve requirements in fiat currency for each unit of CBDC issued can constrain bank reserves and restrict loans and deposits. Additionally, a rise in interest rates on CBDC-related loans can discourage borrowers, thereby reducing loan demand and affecting banking activity. An increase in interest rates leads to a decrease in the quantity of CBDC in circulation, an increase in bank reserves and deposits in CBDC, and a decrease in bank loans. Monitoring the level of confidence of economic agents towards CBDCs is crucial to avoid excessive speculation.

Motivated by empirical evidence, I study the properties of a monopolistic competition model with private ownership. Toward that goal, I consider a monopolistic competition model with additive preferences, homogeneous workers and homogeneous firms. I then introduce in such a standard framework a single additional ingredient: private ownership. Private ownership means that each firm is owned and managed by a single household called the entrepreneur, and this entrepreneur receives profits as sole revenues. In turn, private ownership changes the nature of entry. Free entry in the industry is no longer satisfied. Rather, the number of firms is now determined through occupational choice. Armed with this new framework, I provide a full characterization of a market equilibrium, and compare it to the standard model with collective ownership and free entry. Notably, I find new results concerning optimality, the transmission of aggregate shocks and the ability of the new model to replicate well-established empirical facts.

This essay explores the mechanics of financial shocks within a globalized framework, employing a two-country macroeconomic model to distinguish between core and periphery economies. Our research indicates that intangible assets, serving as stable collateral, respond more robustly to economic shocks than traditional physical forms. Moreover, the study highlights the complex transmission mechanisms of productivity and leverage shocks, revealing that while the financial sector effectively assimilates the positive economic outcomes resulting from a productivity shock, leverage shocks, despite depleting capital, paradoxically stimulate overall economic output.

This paper analyzes the nonlinear effect of trade openness on economic growth over a sample of developed and developing countries during the period 1980–2020. Using a Dynamic Panel Threshold Model advanced by Seo and Shin (2016), and Seo et al., (2019), the results prove the existence of a human capital's threshold level above it openness to trade has a positive effect on economic growth. More precisely, trade can enhance economic performance if the human capital is high, otherwise, the effect is negative.

We model a stochastic, demographic-based framework to examine how an empirically proven, so-called first mover advantage affects the long-term sporting success of states by considering, among other things, demographic developments and the accumulation of sport-related human capital. In order to clarify and formalize this advantage in a sports economics framework, we draw on the concept of the stochastic dominance. Since our model is dynamic, it allows us to show how the probability of success of a nation evolves and under which conditions the stochastic dominance reduces.

Climate finance is an increasingly sought-after instrument for reducing greenhouse gas emissions by financing adaptation and mitigation measures. There is a global commitment to achieve the Sustainable Development Goals (SDGs), particularly with regard to tackling climate change. The mobilization and use of climate finance could influence environmental quality. This paper focuses on analyzing the impact of climate finance on environmental quality in 111 countries worldwide over the period 2000-2019. This study uses the generalized method of moments (GMM) in panel data. The main results indicate a positive effect of climate finance on environmental quality, reflecting the theory of financial ecology. More specifically, climate finance targeting climate change mitigation measures has a significant effect on environmental quality. Member countries of the United Nations Framework Convention on Climate Change (UNFCCC) and private sector actors should implement strategies to monetize climate finance and invest heavily in mitigation and adaptation measures to improve environmental quality.

This paper conducts a comprehensive review of the relationship between inflation and unemployment in the U.S. as described by the Phillips curve in a New Keynesian framework and investigates whether this relationship has changed systematically over time. We also aim to identify possible explanations for these changes. Three different hypotheses are discussed to assess whether they are consistent with more recent developments, such as the evolution of inflation expectations, the impact of globalization, and possible nonlinearities in the functional shape of the Phillips curve. We find that the relationship between inflation and unemployment has weakened since the 1980s and especially during the Covid-19 pandemic due to forces of globalization and better anchored inflation expectations resulting from more credible monetary policy. This has led to question of whether the Phillips curve is outdated. We conclude with the implications of these findings for the conduct of monetary policy.

The relationship between cognitive biases and infection prevention behavior remains unexplored in the existing literature. This study uses data from a questionnaire survey conducted in Japan on the first wave of Coronavirus Disease 2019 (COVID-19) from February to May 2020 to empirically investigate the impact of Bayesian probability inference, the influence of cognitive biases of PCR test results on infection prevention behavior, and the discrepancy between infection prevention intentions and behaviors. We used a bivariate ordinal probit model when considering the correlation between behaviors. The results showed that the higher probability responses, implying pessimistic biases, were more likely to indicate that declaring a state of emergency was necessary and effective, and were more health-oriented in ensuring infection prevention behavior even at the expense of the economy. However, the study found that although they wanted to reduce the frequency of their outings and the number of people they met, they did not reduce them in terms of actual behavior change. It also found that those with pessimistic biases had a higher WTP for the vaccine.

Latin American and Caribbean countries have had a singular path of growth throughout their history. This paper aimed to analyze the economic growth in Latin America and the Caribbean (LAC) countries, setting the focus on the politics and trade outcomes. Working with a dynamic panel of biannual growth from 1981 to 2018 for nineteen countries, we apply the System Generalized Method of Moments (SYS-GMM), allowing to some covariates to be endogenous to the growth process. We do not observe income convergence in LAC countries, as well as any effect of being a democracy on growth. We found a positive and significant effect of trade measures on growth. We perform a series of alternative specifications and our main results do not change.