Economic Modelling最新文献

Non-fungible tokens (NFTs) rose to prominence as a wide-scale implementation of blockchain technology to support the emergence of crypto-asset markets. These nascent digital markets raise questions about the behaviours of investors in the digital economy and their appetite for risk. Using 28,919 auction listings, 4937 sales, and 30,197 Telegram messages, we conduct a field study on the bidding and selling behaviours of NFT investors in a Dutch auction system. We reveal risk-seeking behaviours in our sample of Dutch auction sales. We document that time pressures and value propositions significantly influence NFT investors: fast clock speeds and greater price separations induce underbidding behaviours and are associated with low value retention for sellers. These results are confirmed using a matched-pairs analysis. Our study raises further questions on the risk preferences of investors in emergent digital marketplaces. We propose value maximisation strategies for marketplace developers and participants, while drawing attention to the presence of potentially exploitable biases and heuristics amongst participants, courtesy of bidding incentivisation schemes significantly altering how investors value NFTs.

Reverse innovation, originating in developing countries, is critical to global value chains (GVCs) of multinational corporations (MNCs). However, the impact of knowledge transfer from reverse innovation (KTRI) on MNC returns in the GVCs receives scant attention. Using survey data from 121 subsidiaries in China and applying signaling theory, we examine how knowledge transfer through reverse innovation creates value for the MNCs while inducing perceived adaptation risk, which in turn affects parent company's benefits. We find that KTRI has a positive effect on the perceived value and perceived adaptation risk of reverse innovation. Perceived value increases the benefits to the parent company, while perceived adaptation risk has no significant effect on these benefits. Our study also finds parent company involvement and subsidiary heterogeneity moderate knowledge transfer mechanisms.

The significance of economic complexity in fostering extensive entrepreneurship is increasingly recognized. However, previous research has largely overlooked how economic complexity impacts intensive entrepreneurship, particularly the role of knowledge-intensive business services (KIBS) in this process. This study addresses this gap by analyzing over 12,000 observations from all 47 regions of Japan between 2006 and 2017. Our findings reveal that KIBS complexity significantly promotes new venture growth through three key mechanisms: improved financial access, enhanced professional services, and increased innovation opportunities. Furthermore, we find that the positive effects of KIBS complexity are especially strong in industries with high complexity and in urbanized regions. This study makes several contributions: it refines the analysis of economic complexity to the sub-national level, introduces a micro-level perspective to the literature on economic complexity, and connects KIBS with the intensive margin of entrepreneurship through the complexity lens.

The rapid growth of sustainable investing has led to the global expansion of environmental, social, and governance (ESG) investment products. Existing literature on sustainable investing focuses primarily on corporate social responsibility theory and risk assessment, with relatively little research on ESG investment value and portfolio strategies. Using data from six worldwide ESG exchange-traded funds (ETFs) between 2020 and 2023, we examine return connectedness and portfolio performance by employing a time-varying parameter vector autoregressive (TVP-VAR) and portfolio approaches. The findings reveal that European ETF plays a dominant role in the worldwide ESG system due to the market size and market maturity. Specifically, the European ETF can substantially reduce portfolio volatility. Moreover, the results show that minimum variance and risk-parity portfolios outperform the other portfolio strategies during periods of geopolitical turmoil. These results provide valuable insights for improving the resilience of ESG markets and enhancing sustainable investment strategies.

We exploit provincial variation in minimum wages allowed by the 2004 Chinese regulation to study the impact of minimum wages on the health of workers. We use data from Wave 1 of the World Health Organization's Study on Global Aging and Adult Health (SAGE) conducted in 2007–2010. We consider measures of self-reported health in ten domains and a measure of general health obtained from a factor analysis as dependent variables when estimating ordered probit models and linear models, respectively. We find that real minimum wages have a small, but not negligible, negative effect on most of the health outcomes and general health. The result is robust to several controls, including reporting heterogeneity. The negative effect on health is driven by the deterioration of working conditions, due to the increase in the number of days worked per week for public and private employees.

We propose a novel way to track inflation dynamics by identifying supply and demand shocks at a highly disaggregated level using electronic payments data. We estimate SVAR models and group historical decompositions at the product level into categories of the CPI. Our approach differs from others by explicitly estimating the shocks and retrieving their time-series dynamics. This information is valuable for monetary policy design, as it allows us to assess: (i) the type of shock driving any inflation category, (ii) whether shocks are generalized or driven by large shocks to specific items, and (iii) how the shocks evolve over time. An application to Chile suggests three distinct phases of inflation dynamics since COVID-19. In 2020, negative supply and demand shocks nearly offset each other. In 2021, demand shocks were boosted by massive liquidity injections. In 2022, global supply shocks introduced additional pressures on top of already elevated inflation.

This paper studies the diversity of household consumption spending, i.e. how widely households distribute their spending across different types of goods. Using detailed UK expenditure data (1990–2015), we show that the diversity of household spending rises in income up to a certain level and then declines as richer households increasingly concentrate their spending on specific expenditure categories. As these categories differ across households, spending diversity on the aggregate level can keep rising in income while spending diversity on the household level falls. We build a model with heterogeneous nonhomothetic preferences that can explain the observed patterns, highlighting the role of aggregation. Our model shows that ignoring preference heterogeneity and assuming representative households leads to a (potentially very large) underestimation of the value of product variety. Estimating the welfare effects of trade or innovation policies therefore requires to appropriately account for the empirically observed heterogeneity and nonhomotheticity of preferences.

Strategic emerging industries (SEIs) have become the pillar industries in China. However, prior studies argue that the Chinese stock market fails to adequately support the development of SEIs by primarily considering its financing function while ignoring its market feedback role. Based on data from 2007 to 2020, our study fills this gap by examining whether managers of Chinese SEI firms glean information from stock prices to guide their investment decisions. We find a significantly positive relationship between stock price informativeness (proxied by the volume-synchronized probability of informed trading) and investment-to-price sensitivity in the SEI firms only, indicating that managers of the SEI firms exhibit prominent learning behaviors. We further reveal that high growth prospects and high sensitivity of corporate fundamentals to macroeconomic factors contribute to the learning behaviors in China's SEIs. Our study sheds light on the role of the Chinese stock market in supporting the SEIs' evolution.

We consider all-pay auctions with private and affiliated binary values. The organizer commits ex ante to fully disclosing or concealing bidders’ valuations. We examine the effects of full disclosure on bidders’ welfare and the organizer’s revenue (i.e., expected aggregate bid). We find that full disclosure, as opposed to full concealment, always yields a higher welfare for bidders. On the other hand, full concealment yields a higher revenue if the affiliation in bidders’ valuations is low or if there are only two bidders. Conversely, if the affiliation is high and there are sufficiently many bidders, full disclosure tends to increase revenue.