We design a laboratory experiment to study bargaining behaviour when negotiations involve multiple issues. Parties must discover both trading prices and agreement scopes, giving rise to unexplored information structures and bargaining strategies. We find that bargainers often trade the efficient set of issues despite lacking information about individual aspects. However, beneficial agreements critically hinge on integrated negotiations that allow deals on bundles of issues. Moreover, access to more information boosts agreement rates in small-surplus negotiations but can also backfire as it triggers increased risk-taking and conflicting fairness preferences in large-surplus negotiations. Finally, successful negotiations display a specific bargaining convention that emerges endogenously. It involves alternating offers that meet the other side’s most recent demand halfway.

We examine how executive compensation can be designed to facilitate product market collusion. We look at the 2013 decision to close several regional offices of the U.S. Department of Justice, which lowered antitrust enforcement for firms located near these closed offices. We argue this made collusion more appealing to shareholders, and find that these firms increased the sensitivity of executive pay to local rivals' performance, consistent with rewarding the managers for colluding with them. The affected CEOs were also granted longer vesting periods, which provides long-term incentives that could foster collusive arrangements.

This paper studies the real effects of foreign real estate capital inflows. Using transaction-level data, we document (i) a “China shock” in the U.S. housing market characterized by surging foreign Chinese housing purchases after 2008, and (ii) “home bias” in these purchases, as they concentrate in neighborhoods historically populated by ethnic Chinese. Exploiting their temporal and spatial variation, we find that these capital inflows raise local employment, with the effect transmitted through a housing net worth channel. However, they displace local lower-income residents. Our results show that real estate capital inflows can both stimulate the real economy and induce gentrification.

We present a rational expectations model of credit-driven crises, providing a new perspective to explain why credit booms can lead to severe financial crises and aftermath slow economic recoveries. In our model economy, banks can operate in two types of business. They are sequentially aware of the deterioration of fundamentals of the speculative business and decide whether to continue credit extension in that business or liquidate capital and move into the traditional business. However, because individual banks face uncertainty about how many of their peers have been aware, they rationally choose to extend credit in the speculative business for a longer time than is socially optimal, leading to an over-delayed crisis and consequently more banks being caught by the crisis. This in turn renders the financial crisis more severe and the subsequent economic recovery slower. Extending to a standard textbook macroeconomic growth setting, our model also generates rich dynamics of economic booms, slowdowns, crashes, a

We present a tractable model that accommodates asset-market sentiment in a standard Dynamic Stochastic General Equilibrium (DSGE) setting, allowing us to quantitatively evaluate sentiment-driven macroeconomic fluctuations. In our model, changes in households' perceived uncertainty about housing prices lead to self-fulfilling fluctuations in housing prices, which then impact investment and output through entrepreneurs' collateral constraints. Household sentiment shocks hence are transmitted and propagated to the macroeconomy, generating boom–bust cycles. Uncertainty, housing prices, and the real economy are linked. Quantitatively, the sentiment shock in the form of risk–panic is a crucial driver of business cycle fluctuations despite the presence of various competing shocks.

Over the period 1992–2018, we investigate the economic impact of scandal-tainted congresspersons on politically connected firms. Following the first media report of a scandal, firms connected to the scandal-tainted congressperson experience a relative loss in market value. The loss manifests through both a reputational spillover and a reduced effectiveness of the congressperson in granting political favors to firms. Our findings indicate an undocumented cost of corporate political connections—the loss that occurs when a connected politician is caught up in a scandal.

We examine the role of peer (e.g., Lipper manager indices) versus pure (e.g., S&P 500) benchmarks in fund manager compensation. We model their impact on manager incentives and then test those predictions using novel data. We find that 71% of managers are compensated based on peer benchmarks. Consistent with the model, peer-benchmarked fund managers exhibit higher effort generating higher gross performance and collect higher fee income. Analyzing advisors’ choice between benchmark types, we show that peer-benchmarking advisors cater to more sophisticated and performance-sensitive investors, and are more likely to sell through direct channels, consistent with investor heterogeneity and market segmentation.

Using a novel dataset of US companies’ regular board meeting schedules, we find evidence of informed trading by outside directors prior to board meetings. During the days prior to board meetings, when outside directors possess private information, they make more profitable and larger purchases than they do during other periods. We find no such patterns among inside directors who possess private information regardless of the timing of board meetings. We further find that the profitability of outside directors’ purchases made prior to board meetings is associated with subsequent news disclosures and is realized shortly after the purchases, consistent with the opportunistic use of private board meeting information. Our findings suggest that providing private information to outside directors in preparation for board meetings—a process deemed necessary for effective board monitoring—can facilitate opportunistic insider trading.

The coronavirus disease 2019 (COVID-19) pandemic has led to an increase in cases of racial discrimination against Asians, especially Chinese people. Despite an emerging stream of studies investigating various aspects of the COVID-19 pandemic, research on the behavioral consequences of racial discrimination during the pandemic remains scarce. In this work, we examined how racial discrimination stemming from the COVID-19 pandemic and subsequent anti-discrimination were manifested on online platforms. By combining difference-in-differences analyses on two large-scale panel data sets from Yelp.com and SafeGraph and two controlled experiments, we explored the impact of COVID-19 on Chinese restaurants relative to non-Chinese restaurants at different phases of the COVID-19 pandemic. We found that the COVID-19 pandemic led to an immediate increase in racial discrimination, which was reflected in a significant drop in the customer patronage frequency of Chinese restaurants as compared with that of non-Chinese restaura

We use unique data on daily labor-market outcomes for Indian casual workers to study labor reallocation between agricultural and non-agricultural activities within rural areas. Controlling for both individual time-invariant attributes and time-varying shocks, we find that workers who switch sectors across years or even within a week can obtain 23% higher wages by taking non-agricultural jobs. We then estimate a discrete choice model of daily labor allocation that decomposes preferences for jobs into two types of disamenities: (i) those associated with job characteristics and (ii) those associated with location. We find that the first type of disamenity is 23% of wages for men and 38% for women, and the second type is 36% of wages for men and 31% for women.