Knowledge that Transforms

This study tests the agency cost hypothesis in the context of geographic earnings disclosures. The agency cost hypothesis predicts that managers, when not monitored by shareholders, make self‐maximizing decisions that may not necessarily be in the best interest of shareholders. These decisions include aggressively growing the firm, which reduces profitability and destroys firm value. Geographic earnings disclosures provide an interesting context to examine this issue. Beginning with Statement of Financial Accounting Standards No. 131 (SFAS 131), most U.S. multinational firms are no longer required to disclose earnings by geographic area (e.g., net income in Mexico or net income in East Asia). Such nondisclosure potentially reduces the ability of shareholders to monitor managers' decisions related to foreign operations. Using a sample of U.S. multinationals with substantial foreign operations, we find that nondisclosing firms, relative to firms that continue to disclose geographic earnings, experience greater expansion of foreign sales, produce lower foreign profit margins, and have lower firm value in the post–SFAS 131 period. Our conclusions are strengthened by the fact that these differences do not exist in the pre–SFAS 131 period and do not relate to domestic operations. We find differences in the predicted direction only for foreign operations and only after adoption of SFAS 131. Our results are robust to the inclusion of an extensive set of control variables related to alternative corporate governance mechanisms, operating performance, and the firm's information environment. Overall, the results are consistent with the agency cost hypothesis and the important role of financial disclosures in monitoring managers.

This paper examines the economic consequences of mandatory International Financial Reporting Standards (IFRS) reporting around the world. We analyze the effects on market liquidity, cost of capital, and Tobin's q in 26 countries using a large sample of firms that are mandated to adopt IFRS. We find that, on average, market liquidity increases around the time of the introduction of IFRS. We also document a decrease in firms' cost of capital and an increase in equity valuations, but only if we account for the possibility that the effects occur prior to the official adoption date. Partitioning our sample, we find that the capital‐market benefits occur only in countries where firms have incentives to be transparent and where legal enforcement is strong, underscoring the central importance of firms' reporting incentives and countries' enforcement regimes for the quality of financial reporting. Comparing mandatory and voluntary adopters, we find that the capital market effects are most pronounced for firms that voluntarily switch to IFRS, both in the year when they switch and again later, when IFRS become mandatory. While the former result is likely due to self‐selection, the latter result cautions us to attribute the capital‐market effects for mandatory adopters solely or even primarily to the IFRS mandate. Many adopting countries make concurrent efforts to improve enforcement and governance regimes, which likely play into our findings. Consistent with this interpretation, the estimated liquidity improvements are smaller in magnitude when we analyze them on a monthly basis, which is more likely to isolate IFRS reporting effects.

This paper examines the market efficiency consequences of accounting disclosure in the context of stock markets as a Keynesian beauty contest, an influential metaphor originally proposed by Keynes [1936] and recently formalized by Allen, Morris, and Shin [2006]. In such markets, public information plays an additional commonality role, biasing stock prices away from the consensus fundamental value toward public information. Despite this bias, I demonstrate that provisions of public information always drive stock prices closer to the fundamental value. Hence, as a main source of public information, accounting disclosure enhances market efficiency, and transparency should not be compromised on grounds of the Keynesian‐beauty‐contest effect.

We study a definition of subjective beliefs applicable to preferences that allow for the perception of ambiguity, and provide a characterization of such beliefs in terms of market behavior. Using this definition, we derive necessary and sufficient conditions for the efficiency of ex ante trade and show that these conditions follow from the fundamental welfare theorems. When aggregate uncertainty is absent, our results show that full insurance is efficient if and only if agents share some common subjective beliefs. Our results hold for a general class of convex preferences, which contains many functional forms used in applications involving ambiguity and ambiguity aversion. We show how our results can be articulated in the language of these functional forms, confirming results existing in the literature, generating new results, and providing a useful tool for applications.

Traditional discrete-choice models assume buyers are aware of all products for sale. In markets where products change rapidly, the full information assumption is untenable. I present a discrete-choice model of limited consumer information, where advertising influences the set of products from which consumers choose to purchase. I apply the model to the U.S. personal computer market where top firms spend over $2 billion annually on advertising. I find estimated markups of 19% over production costs, where top firms advertise more than average and earn higher than average markups. High markups are explained to a large extent by informational asymmetries across consumers, where full information models predict markups of one-fourth the magnitude. I find that estimated product demand curves are biased toward being too elastic under traditional models. I show how to use data on media exposure to improve estimated price elasticities in the absence of micro ad data.

In the past few decades multistore retailers, especially those with 100 or more stores, have experienced substantial growth. At the same time, there is widely reported public outcry over the impact of these chain stores on other retailers and local communities. This paper develops an empirical model to assess the impact of chain stores on other discount retailers and to quantify the size of the scale economies within a chain. The model has two key features. First, it allows for flexible competition patterns among all players. Second, for chains, it incorporates the scale economies that arise from operating multiple stores in nearby regions. In doing so, the model relaxes the commonly used assumption that entry in different markets is independent. The lattice theory is exploited to solve this complicated entry game among chains and other discount retailers in a large number of markets. It is found that the negative impact of Kmart's presence on Wal-Mart's profit was much stronger in 1988 than in 1997, while the opposite is true for the effect of Wal-Mart's presence on Kmart's profit. Having a chain store in a market makes roughly 50% of the discount stores unprofitable. Wal-Mart's expansion from the late 1980s to the late 1990s explains about 40–50% of the net change in the number of small discount stores and 30–40% for all other discount stores. Scale economies were important for Wal-Mart, but less so for Kmart, and the magnitude did not grow proportionately with the chains' sizes.

We design experiments to jointly elicit risk and time preferences for the adult Danish population. Since subjects are generally risk averse, we find that joint elicitation provides estimates of discount rates that are significantly lower than those found in previous studies and more in line with what would be considered as a priori reasonable rates. The statistical specification relies on a theoretical framework that involves a latent trade-off between long-run optimization and short-run temptation. Estimation of this specification is undertaken using structural, maximum likelihood methods. Our main results based on exponential discounting are robust to alternative specifications such as hyperbolic discounting. These results have direct implications for attempts to elicit time preferences, as well as debates over the appropriate domain of the utility function when characterizing risk aversion and time consistency.

We prove existence of equilibrium in a continuous-time securities market in which the securities are potentially dynamically complete: the number of securities is at least one more than the number of independent sources of uncertainty. We prove that dynamic completeness of the candidate equilibrium price process follows from mild exogenous assumptions on the economic primitives of the model. Our result is universal, rather than generic: dynamic completeness of the candidate equilibrium price process and existence of equilibrium follow from the way information is revealed in a Brownian filtration, and from a mild exogenous nondegeneracy condition on the terminal security dividends. The nondegeneracy condition, which requires that finding one point at which a determinant of a Jacobian matrix of dividends is nonzero, is very easy to check. We find that the equilibrium prices, consumptions, and trading strategies are well-behaved functions of the stochastic process describing the evolution of information. We prove that equilibria of discrete approximations converge to equilibria of the continuous-time economy.

This paper derives asymptotic power envelopes for tests of the unit root hypothesis in a zero-mean AR(1) model. The power envelopes are derived using the limits of experiments approach and are semiparametric in the sense that the underlying error distribution is treated as an unknown infinite-dimensional nuisance parameter. Adaptation is shown to be possible when the error distribution is known to be symmetric and to be impossible when the error distribution is unrestricted. In the latter case, two conceptually distinct approaches to nuisance parameter elimination are employed in the derivation of the semiparametric power bounds. One of these bounds, derived under an invariance restriction, is shown by example to be sharp, while the other, derived under a similarity restriction, is conjectured not to be globally attainable.

We develop a framework to assess how successfully standard time series models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric series. The properties of these weighted averages are then compared to the asymptotic implications of a number of common time series models. We apply the framework to twenty U.S. macroeconomic and financial time series using frequencies lower than the business cycle.