Browsing by Subject "Information Acquisition"

This dissertation consists of two chapters studying monetary and fiscal policy. In the first chapter, I study the welfare benefits and costs of increased central bank transparency in a dynamic model of costly information acquisition where agents can either choose to gather new costly information or remember information from the past for free. Information is costly to acquire due to an agent's limited attention. Agents face an intratemporal decision on how to allocate attention across public and private signals within the period and an intertemporal decision on how to allocate attention over time. The model embeds a coordination externality into the dynamic framework which motivates agents to be overly attentive to public information and creates the possibility of costly transparency. Interestingly, allowing for intratemporal and intertempral tradeoffs for attention amplifies (attenuates) the benefits (costs) of earlier transparency whereas it attenuates (amplifies) the benefits (costs) of delayed transparency.

The second chapter, co-authored with Barbara Rossi and Atsushi Inoue, studies the empirical effects of unexpected changes in government spending and tax policy on heterogeneous agents. We use data from the Consumption Expenditure Survey (CEX) to estimate individual-level impulse responses as well as multipliers for government spending and tax policy shocks. The main empirical finding of this paper is that unexpected fiscal shocks have substantially different effects on consumers depending on their age, income levels, and education. In particular, the wealthiest individuals tend to behave according to the predictions of standard RBC models, whereas the poorest individuals tend to behave according to standard IS-LM (non-Ricardian) models, due to credit constraints. Furthermore, government spending policy shocks tend to decrease consumption inequality, whereas tax policy shocks most negatively affect the lives of the poor, more so than the rich, thus increasing consumption inequality.

This paper analyzes a manager's optimal ex-ante reporting system using a Bayesian persuasion approach (Kamenica and Gentzkow (2011)) in a setting where investors affect cash flows through their decision to finance the firm's investment opportunities, possibly assisted by the costly acquisition of additional information (inspection). I examine how the informativeness and the bias of the optimal system are determined by investors' inspection cost, the degree of incentive alignment between the manager and the investor, and the prior belief that the project is profitable. I find that a mis-aligned manager's system is informative

only when the market prior is pessimistic and is always positively biased; this bias decreases as investors' inspection cost decreases. In contrast, a well-aligned manager's system is fully revealing when investors' inspection cost is high, and is counter-cyclical to the market belief when the inspection cost is low: It is positively (negatively) biased when the market belief is pessimistic (optimistic). Furthermore, I explore the extent to which the results generalize to a case with managerial manipulation and discuss the implications for investment efficiency. Overall, the analysis describes the complex interactions among determinants of firm disclosures and governance, and offers explanations for the mixed empirical results in this area.

This paper establishes the life-cycle dynamics of Corporate Venture Capital (CVC) to explore the information acquisition role of CVC investment in the process of corporate innovation. I exploit an identification strategy that allows me to isolate exogenous shocks to a firm's ability to innovate. Using this strategy, I first find that the CVC life cycle typically begins following a period of deteriorated corporate innovation and increasingly valuable external information, lending support to the hypothesis that firms conduct CVC investment to acquire information and innovation knowledge from startups. Building on this analysis, I show that CVCs acquire information by investing in companies with similar technological focus but have a different knowledge base. Following CVC investment, parent firms internalize the newly acquired knowledge into internal R&D and external acquisition decisions. Human capital renewal, such as hiring inventors who can integrate new innovation knowledge, is integral in this step. The CVC life cycle lasts about four years, terminating as innovation in the parent firm rebounds. These findings shed new light on discussions about firm boundaries, managing innovation, and corporate information choices.

Prior literature finds that algorithmic trading (AT) benefits the financial market by improving liquidity and accelerating the incorporation of existing information into prices. This paper shows that AT also has negative real effects: it reduces the sensitivity of corporate investment to stock prices. Moreover, firms’ future operating performance deteriorates following periods of high AT activity. The evidence is consistent with algorithmic traders crowding out fundamental traders’ information acquisition, leading to less information in prices for managers to learn and hence worse investment efficiency. Supporting evidence is also observed among financial analysts, who also learn less from stock prices when making forecast revisions.