Knowledge that Transforms

This paper investigates the effect tax havens and other foreign jurisdictions have on the income tax rates of multinational firms based in the United States. We develop a new regression methodology using financial accounting data to estimate the average worldwide, federal, and foreign tax rates on worldwide, federal, and foreign pretax book income for a large sample of U.S. firms with and without tax haven operations. We find that on average U.S. firms that disclosed material operations in at least one tax haven country have a worldwide tax burden on worldwide income that is approximately 1.5 percentage points lower than firms without operations in at least one tax haven country. Our results also show that U.S. firms face a 4.4% current federal tax rate on foreign income whether or not they have tax haven operations. Finally, we find that U.S. firms with operations in some tax haven countries have higher federal tax rates on foreign income than other firms. This result suggests that in some cases, tax haven operations may increase U.S. tax collections at the expense of foreign country tax collections.

As barriers to international investment fall and technology improves, the cost advantages for a firm's securities to trade publicly in the country in which that firm is located and for that country to have a market for publicly traded securities distinct from the capital markets of other countries will progressively disappear. Securities laws remain an important determinant of whether and where securities are issued, how they are valued, who owns them, and where they trade. I show that there is a demand from entrepreneurs for mechanisms that allow them to commit to credible disclosure because disclosure helps reduce agency costs. Under some circumstances, mandatory disclosure through securities laws can help satisfy that demand, but only provided investors or the state can act on the information disclosed and the laws cannot be weakened ex post too much through lobbying by corporate insiders. With financial globalization, national disclosure laws can have wide‐ranging effects on a country's welfare, on firms and on investor portfolios, including the extent to which share holdings reveal a home bias. In equilibrium, if firms can choose the securities laws they are subject to when they go public, some firms will choose stronger securities laws than those of the country in which they are located and some firms will do the opposite.

We examine how firms design bonus plans of their CFOs. CFOs participate in decision making much like other executives, but they also have significant fiduciary responsibilities for reporting firms’ financial results. Responsibility for financial reporting raises the question of whether it is appropriate to pay CFOs annual bonuses contingent on self‐reported financial performance. In this paper, we provide a framework that characterizes CFO bonuses as a tradeoff between CFOs’ decision‐making responsibilities and their fiduciary duties over financial reporting. This framework yields a number of implications that we examine empirically using a proprietary survey of CFO compensation practices of public and private firms. Our main finding shows that from 2003 to 2007 public entities (relative to private entities) reduced the percentage of CFO bonuses contingent on financial performance. We interpret this result as evidence that firms mitigate misreporting practices in part by deemphasizing CFO incentive compensation.

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for it. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in applied work under different assumptions on the Markov property, stationarity, and type-invariance in the transition process. Three elements emerge as the important determinants of identification: the time-dimension of panel data, the number of values the covariates can take, and the heterogeneity of the response of different types to changes in the covariates. For example, in a simple case where the transition function is type-invariant, a time-dimension of T = 3 is sufficient for identification, provided that the number of values the covariates can take is no smaller than the number of types and that the changes in the covariates induce sufficiently heterogeneous variations in the choice probabilities across types. Identification is achieved even when state dependence is present if a model is stationary first-order Markovian and the panel has a moderate time-dimension (T 6).

I study individuals who use frequentist models to draw uniform inferences from independent and identically distributed data. The main contribution of this paper is to show that distinct models may be consistent with empirical evidence, even in the limit when data increases without bound. Decision makers may then hold different beliefs and interpret their environment differently even though they know each other's model and base their inferences on the same evidence. The behavior modeled here is that of rational individuals confronting an environment in which learning is hard, rather than individuals beset by cognitive limitations or behavioral biases.

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill-posed inverse problems. In functional cointegrating regressions where the regressor is an integrated time series, it is shown here that inverse and ill-posed inverse problems do not arise. Remarkably, nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving simple useable asymptotics in practical work. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series. The methods may be applied to a wide range of empirical models where functional estimation of cointegrating relations is required.

Conventional wisdom suggests that increased life expectancy had a key role in causing a rise in investment in human capital. I incorporate the retirement decision into a version of Ben-Porath's (1967) model and find that a necessary condition for this causal relationship to hold is that increased life expectancy will also increase lifetime labor supply. I then show that this condition does not hold for American men born between 1840 and 1970 and for the American population born between 1890 and 1970. The data suggest similar patterns in Western Europe. I end by discussing the implications of my findings for the debate on the fundamental causes of long-run growth.

I discuss the failure of the canonical search and matching model to match the cyclical volatility in the job finding rate. I show that job creation in the model is influenced by wages in new matches. I summarize microeconometric evidence and find that wages in new matches are volatile and consistent with the model's key predictions. Therefore, explanations of the unemployment volatility puzzle have to preserve the cyclical volatility of wages. I discuss a modification of the model, based on fixed matching costs, that can increase cyclical unemployment volatility and is consistent with wage flexibility in new matches.

We propose bootstrap methods for a general class of nonlinear transformations of realized volatility which includes the raw version of realized volatility and its logarithmic transformation as special cases. We consider the independent and identically distributed (i.i.d.) bootstrap and the wild bootstrap (WB), and prove their first-order asymptotic validity under general assumptions on the log-price process that allow for drift and leverage effects. We derive Edgeworth expansions in a simpler model that rules out these effects. The i.i.d. bootstrap provides a second-order asymptotic refinement when volatility is constant, but not otherwise. The WB yields a second-order asymptotic refinement under stochastic volatility provided we choose the external random variable used to construct the WB data appropriately. None of these methods provides third-order asymptotic refinements. Both methods improve upon the first-order asymptotic theory in finite samples.

We create an analytical structure that reveals the long-run risk-return relationship for nonlinear continuous-time Markov environments. We do so by studying an eigenvalue problem associated with a positive eigenfunction for a conveniently chosen family of valuation operators. The members of this family are indexed by the elapsed time between payoff and valuation dates, and they are necessarily related via a mathematical structure called a semigroup. We represent the semigroup using a positive process with three components: an exponential term constructed from the eigenvalue, a martingale, and a transient eigenfunction term. The eigenvalue encodes the risk adjustment, the martingale alters the probability measure to capture long-run approximation, and the eigenfunction gives the long-run dependence on the Markov state. We discuss sufficient conditions for the existence and uniqueness of the relevant eigenvalue and eigenfunction. By showing how changes in the stochastic growth components of cash flows induce changes in the corresponding eigenvalues and eigenfunctions, we reveal a long-run risk-return trade-off.