Information Friction Research Articles

A critical review of information asymmetry in the business cycle: How digital ledger technology can transform and sustain the business cycle

Distributed ledger technology (DLT) is a disruptive technology with the potential to reengineer the entire trading cycle by alleviating inefficiencies such as time lags, multiple record keeping, human errors, and transparency common with the traditional trade cycle. This study evaluates the potential benefits of DLT in mitigating information asymmetry in trading relationships and how a DLT model can be deployed to revamp the trading process. We find that information friction results from differences in stakeholder preferences by identifying and categorizing information friction into 4 groups through a review of key studies in leading management journals. This finding aligns with conclusions reached in scientific research that the benefits of DLT prevail in markets with imperfect information. In addition, we illustrate the potential benefits of DLT in mitigating inefficiencies in trading relationships resulting from information asymmetry. The article concludes with a word of caution for potential users to take gradual steps of adoption to keep pace with changing technology so as not to become laggards.

Optimal Covid-19 Quarantine and Testing Policies

Abstract We study quantitatively the optimality of quarantine and testing policies; and whether they are complements or substitutes. We extend the epidemiological susceptible-exposed-infectious-recovered model to incorporate an information friction. Our main finding is that testing is a cost-efficient substitute for lockdowns, rendering them almost unnecessary. By identifying carriers, testing contains the spread of the virus without reducing output, although the implementation requires widespread massive testing. As a byproduct, we show that two distinct optimal lockdown policy types arise: suppression, intended to eliminate the virus, and mitigation, concerned about flattening the curve. The choice between them is determined by a ‘hope-for-the-cure’ effect, arising due to either an expected vaccine or the belief that the virus can be eliminated. Conditional on the policy type, the intensity and duration of the intervention is invariant to both the trade-off between lives and output and the aversion to GDP variations: the optimal intervention path depends mostly on the virus dynamics.

Identifying information friction in teacher professional development: insights from teacher-reported need and satisfaction

ABSTRACT Professional Development (PD) is regarded a critical channel through which teacher instructional effectiveness improves. However, research shows that teacher discontent with PD is widespread. Bridging literature in organisational decision and labour economics, this study theorises that variation in how schools collect, process and respond to information on teacher needs can influence the degree of information friction in PD provision. Applying a two-step empirical analysis to the Teaching and Learning International Survey (TALIS) 2013 and 2018 United States sample (n = 4,336), findings reveal that teachers demonstrate high levels of concentrated PD need, yet schools do not meet these reported needs. Regression analysis results show that those schools demonstrating higher degrees of plurality in leadership and school autonomy are more effective in addressing teacher needs, while the effect of principal-staff interaction is more complex. Findings contribute new evidence on information friction in teacher PD provisionand identify effective management means in addressing information problems in school settings.

REVISITING INFLATION-GROWTH NEXUS: AN ENDOGENOUS GROWTH MODEL WITH FINANCIAL FRICTIONS

This paper presents a new explanation of why the growth effect of inflation reported in the literature is an inverted-U relationship with considerations of lower bound on bank lending rates. We integrate a non-zero lower bound on the lending rate in the discussion and show the effect of inflation on optimal bank loan contracts. This paper believes that informational friction is a source of the negative relationship between inflation and growth when the loan rate constraint is non-binding. Besides, it finds out that once this loan rate constraint is binding, increasing inflation can reduce the real cost of financing capital investment and then contribute to growth. This paper also proves that the non-zero lower limit can only be reached at the low rate of inflation. It implies developing countries that are more likely to have low capital conversion efficiency and high transaction cost would hold a higher inflation-growth nexus than developed countries.

Extreme price co-movement of commodity futures and industrial production growth: An empirical evaluation

This paper studies how the extreme price co-movement of commodity futures indicates industrial production (IP) growth. In this regard, we model synchronized movements and large price changes into one measure by characterizing upside and downside price extremes. We find that the derived price extremes are positively associated with IP growth over the next quarter. We further conclude that such impact is not symmetric, as the impact led by downside extremes is robust whereas that of upside extremes is not. Our results reinforce the informational friction theory as well as those financial studies that emphasize downside risk.

Distance effects and home bias in patient choice on the Internet: Evidence from an online healthcare platform in China

Telemedicine and telehealth hold promise for reducing access barriers caused by travel distance. However, little is known about how the Internet affects patients' online provider choices and thus the spatial distribution of healthcare utilization. This study investigates the effect of distance on flows of online medical consultations using a unique dataset from one of the leading third-party online healthcare platforms in China: Haodf.com. We show that the geographic distance between doctors and patients is negatively associated with online service utilization, though this effect is almost 40% ~ 50% weaker for online medical services than it is for offline medical services. We also find a strong “home bias” in which patients and doctors tend to locate in the same prefecture and in the same province. Further analyses suggest that prior in-person visits before online consultations, the potential need for a follow-up physical visit after an online consultation, and information friction may contribute to the distance effect. These findings have policy implications for improving healthcare access in China and other nations.

Effects of Risk Attitudes and Information Friction on Willingness to Pay for Precautionary Building Standards

Effects of Risk Attitudes and Information Friction on Willingness to Pay for Precautionary Building Standards

Optimal fund menus

AbstractWe study the optimal design of a menu of funds by a manager who is required to use linear pricing and does not observe the beliefs of investors regarding one of the risky assets. The optimal menu involves bundling of assets and can be constructed from the solution to a calculus of variations problem that optimizes over the indirect utility that each type receives. We provide a complete characterization of the optimal menu and show that the need to maintain incentive compatibility leads the manager to offer funds that are inefficiently tilted towards the asset that is not subject to the information friction.

The Value of Reputation in Trade: Evidence from Alibaba

Abstract We examine the role of an online reputation mechanism in international trade by exploring T-shirt exports on Alibaba. Exploiting rich transaction data and features of search and rating algorithms, we show that exporters displaying a superior reputation perform significantly better than peers with nearly identical true ratings and observables, and the value of reputation rises with the level of information friction and the specificity of information. We develop a dynamic reputation model with heterogeneous cross-country information friction to quantify the effect of the reputation mechanism and find a 20% increase in aggregate exports fueled by a market reallocation towards superstars.

Factors Affecting Technological Diffusion Through Social Networks: A Review of the Empirical Evidence

Abstract Network theory-based interventions could be particularly effective for promoting technology adoption when information friction serves as the major obstacle to technology diffusion. To inform policy makers interested in such interventions, this paper systematically reviews empirical evidence on determinants of how social networks shape technology diffusion. It identifies three sets of factors that individually and jointly affect technological diffusion on social networks: Population characteristics, including those describe overall network structures and key economic agents’ network positions and technology sophistication; technology parameters; and information propagation mechanisms. Accurate social network assessment—crucial for the formulation of network interventions—relies on making careful selection out of the many measures of network characteristics and layers of socioeconomic interactions to examine, and on accurately defining the scope and size of network data to collect. Evidence indicates effective network interventions should aim to introduce new technologies first to economic agents with high centrality or clustering, sufficient resemblance to average population, and whom are incentivized to communicate with others.

The long-term effects of female board representation on accounting quality: Causal evidence

This paper investigates how women’s representation on corporate boards affects financial reporting quality. Drawing on a discussion of existing research, I argue that increasing women’s representation on boards reduces undue CEO influence over the board, and thereby, accounting quality will improve. Using a difference-in-difference (DiD) approach, this paper documents how increasing female representation increases accounting quality. This result holds only in the long run as females gain more experience in the boardroom. In addition, I show that the effect of female representation on accounting quality is attenuated for larger firms and larger boards. This is consistent with the notion that the effect of female representation is likely to be stronger when the information friction is higher. These findings challenge the conventions that underscore the importance of board diversity for today’s firms.

Adapting to Natural Disasters through Better Information: Evidence from the Home Seller Disclosure Requirement

Flood damage is determined by a combination of flood intensity and the population size exposed to the risk, but the US flood policy has primarily focused on managing the former, with little success. This paper studies whether easing information friction in the housing market regarding flood risk could reduce the population in high-risk areas and thus flood damage. By exploiting staggered adoption of the Home Seller Disclosure Requirement and spatial discontinuity in flood risk information, I first show this policy lowers the average price of properties and the population in high-risk areas. Further, using a hydrological measure of flood intensity, I find that the policy reduces expected per capita damage from small to moderate-sized floods by at least 5%. The findings suggest that a policy that facilitates information flow can be a powerful adaptation tool for natural disasters.

Does the investment horizon of institutional investors matter for stock liquidity?

This paper examines the role of the investment horizon of institutional investors on stock liquidity of firms. We show that an increase in long-term institutional ownership is negatively associated with firm liquidity, while an increase in short-term ownership is positively related to a firm's stock liquidity. We identify the ownership-liquidity relationship by examining two major channels: the trading activity channel and the informational friction channel. Long-term investors reduce stock liquidity through low frequency trading and access to value-enhancing and private information, which induces adverse selection bias. In contrast, short-term investors improve liquidity through trading activity and competition with other investors, which lowers transaction costs. Our findings further suggest that the effects of an increase in long-term (short-term) institutional investors on liquidity weaken (strengthen) when a firm has more publicly available information. Finally, we show that the positive impact of an increase in long-term ownership on valuation is more pronounced for firms with higher liquidity and the valuation effect is persistent.

China Markets and Global Stock Return Predictability

This paper explores the China equity market's impact on the global equity markets through the lens of asset pricing. We study the predictive properties of the lagged China returns for global stock returns and find that the lagged China returns can significantly predict other markets' stock returns, but not vice versa. We augmented three selected global equity pricing models (Chen, Roll, and Ross's model (CRR), Ang and Bekaert's model (AB), and Rapach, Strauss, and Zhou's model (RSZ)) with the lagged China returns respectively, and find that these popular global stock return predictors cannot explain the predictive power of the lagged China returns. Moreover, we find that only the lagged China returns can lead the international returns at the one-month horizon but not the lagged US returns. Further evidence reveals it is due to that the information diffusion of the China market occurs at the longer-horizons (e.g. one-month) while the information diffusion of the US market occurs at the shorter-horizons (e.g. one-week). Lastly, we documented that the lagged China returns during the low investor-attention period have stronger predictability compared to the predictive performance during the high attention period, which is in line with the information friction theory that when the attention-constrained investors make investment decisions they fail to allocate attention to certain economic state variables, which causes the slow information diffusion across markets and leads to the cross-market return predictability. Overall, our results indicate that the lagged China returns should be regarded as a global state variable that helps us predict future stock returns.

Dynamic measurement of news-driven information friction in China's carbon market: Theory and evidence

This paper dynamically measures news-driven information friction in China's carbon market theoretically and empirically. The biggest difference between this paper and the existing literature is that this paper does not assume that the information shock is a white noise process but that the information shock comes from the news. This study does not use fiscal expenditure budget data and fiscal expenditure final data to estimate information friction but rather extends the standard Fève and Pietrunti (2016) theoretical framework to identify effective information and noise signals from market policy news by machine learning. A latent Dirichlet allocation model is employed to estimate the variance of effective information. China's carbon policy uncertainty as indicated by Chinese-language newspapers is used to estimate the variance of the noise signal. Therefore, we can calculate time-varying high-frequency information friction. The results of this study show that enterprises with different attributes in the same industry face different information friction. In China, state-owned industrial enterprises have the lowest levels of information friction. Joint-stock industrial enterprises have the highest levels of information friction. The higher the degree of policy information friction, the weaker the impact of effective information shocks and the greater the reduction in policy effects. These results have policy implications – the government should pay attention to enterprise attributes to reduce policy uncertainty and the degree of policy information friction, thereby enhancing the effectiveness of policy regulation.

Informational Friction as a Lens for Studying Algorithmic Aspects of Privacy

This paper addresses challenges in conceptualizing privacy posed by algorithmic systems that can infer sensitive information from seemingly innocuous data. This type of privacy is of imminent concern due to the rapid adoption of machine learning and artificial intelligence systems in virtually every industry. In this paper, we suggest informational friction, a concept from Floridi's ethics of information, as a valuable conceptual lens for studying algorithmic aspects of privacy. Informational friction describes the amount of work required for one agent to access or alter the information of another. By focusing on amount of work, rather than the type of information or manner in which it is collected, informational friction can help to explain why automated analyses should raise privacy concerns independently of, and in addition to, those associated with data collection. As a demonstration, this paper analyze law enforcement use of facial recognition, andFacebook's targeted advertising model using informational friction and demonstrate risks inherent to these systems which are not completely identified in another popular framework, Nissenbaum's Contextual Integrity.The paper concludes with a discussion of broader implications, both for privacy research and for privacy regulation.

Coordinated bubbles and crashes

In a market with information friction, investors strategically consider the actions of others and evolutionarily switch between fundamental and technical strategies to maximize their payoffs. The collective actions of all investors exert feedback on asset price, which affects investors’ subsequent actions. We find that investors have the incentive to adopt fundamental strategy to restore market efficiency only if the mispricing is sufficiently large. When investors fail to coordinate on the fundamental strategy, market inefficiency increases, which blows the bubble. As market inefficiency grows, the coordination on fundamental strategy strengthens, which eventually bursts the bubble.

Corporate Bond Illiquidity and Bond Return Synchronicity

This paper provides an innovative theoretical model and empirical evidence for how the illiquidity of corporate bonds, as trading noise, dampens firm-specific information incorporated into bond prices. We find a negative relation between bond illiquidity and synchronicity, and this empirical relation remains after applying robustness checks and endogeneity controls. Consistent with theoretical model implications, the effect of bond illiquidity as information friction is more pronounced for bonds with lower market sensitivity and for firms with higher degrees of information uncertainty and operating in weaker information environments. We also explore general bond return synchronicity determinants, including both bond attributes and firm fundamentals.

Asymmetric Attention and Stock Returns

This paper constructs a new measure of attention allocation by local investors relative to nonlocals using aggregate search volume from Google. We first present a conceptual framework in which local investors optimally choose to focus their attention on local stocks when they receive private news, leading to an asymmetric allocation of attention between local and nonlocal investors. Consistent with the main prediction of this framework, we find that firms attracting abnormally high asymmetric attention from local relative to nonlocal investors earn higher returns. A portfolio that goes long in stocks with high asymmetric attention and short in stocks with low asymmetric attention has an alpha of 32 basis points per month. The results are stronger for stocks with a greater degree of information friction. The new measure of asymmetric attention allows one to infer the arrival of unobservable private information by observing investors’ attention allocation behavior. This paper was accepted by Karl Diether, finance.

A retrospective study on the epidemiological characteristics and establishment of an early warning system of severe COVID-19 patients.

This paper estimates the magnitude of an informational friction limiting credit reallocation to firms during the 2007‐2009 financial crisis. Because lenders rely on private information when deciding which relationship to end, borrowers looking for a new lender are adversely selected. I show how to identify private information separately from information common to all lenders but unobservable to the econometrician by using bank shocks within a discrete choice model of relationships. Quantitatively, these informational frictions seem too small to explain the credit crunch in the U.S. syndicated corporate loan market.