Is doing good good for you? how corporate charitable contributions enhance revenue growth

This study examines the impact of corporate philanthropy growth on sales growth using a large sample of charitable contributions made by U.S. public companies from 1989 through 2000. Applying Granger causality tests, we find that charitable contributions are significantly associated with future revenue, whereas the association between revenue and future contributions is marginally significant at best. We then identify the mechanism underlying our findings. Our results are particularly pronounced for firms that are highly sensitive to consumer perception, where individual consumers are the predominant customers. In addition, we document a positive relationship between contributions and customer satisfaction. Overall, our evidence suggests that corporate philanthropy, under certain circumstances, furthers firms' economic objectives. Copyright © 2009 John Wiley & Sons, Ltd.     

Monotonicity in asset returns: New tests with applications to the term structure,the CAPM,and portfolio sorts

Many theories in finance imply monotonic patterns in expected returns and other financial variables. The liquidity preference hypothesis predicts higher expected returns for bonds with longer times to maturity; the Capital Asset Pricing Model (CAPM) implies higher expected returns for stocks with higher betas; and standard asset pricing models imply that the pricing kernel is declining in market returns. The full set of implications of monotonicity is generally not exploited in empirical work, however. This paper proposes new and simple ways to test for monotonicity in financial variables and compares the proposed tests with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.     

Are microfinance markets monopolistic?

Do microfinance institutions (MFIs) operate in a monopoly, monopolistic competition environment or are their revenues derived under perfect competition markets? We employ the Panzar–Rosse revenue test on a global panel data to assess the competitive environment in which MFIs of five selected countries operate: Ecuador, India, Indonesia, Peru and Philippines, over the period 2005–2009. We estimate the static and the dynamic revenue tests, with analyses of the interest rate and the return on assets. We control for microfinance-specific variables such as capital-assets-ratio, loans-assets and the size of the MFI. The analyses also account for the endogeneity problem by employing the fixed-effects two-stage least squares and the fixed-effects system generalized method of moments. Our results suggest that MFIs in Peru and India operate in a monopolistic environment. We also find weak evidence that the microfinance industry in Ecuador, Indonesia and Philippines may operate under perfect competition.     

压力测试及其在金融机构风险管理中的运用

本文研究和评价了目前被国际大型金融机构积极采用的风险管理工具:压力测试。作者通过将压力测试与VaR作比较等方法详细阐述了压力测试的特征、使用方法和缺陷,并且进一步分析指出压力测试在金融机构风险管理中的作用及其最新发展状况和趋势,最后着重分析了压力测试在我国运用和发展的前景并提出建议。     

Efficient rank tests for semiparametric competing risk models

We consider a semiparametric competing risk model given by k independent survival times. The paper offers an asymptotic treatment of tests for the semiparametric null hypothesis of equality of the underlying risks. It turns out that modified rank tests are asymptotically efficient for certain semiparametric submodels, where the baseline hazard is a nuisance parameter. In addition, the asymptotic relative efficiency of the present tests is derived. A comparison of asymptotic power functions can then be used to classify various tests proposed earlier in the literature. For instance a chi-square type test is efficient for proportional hazards. Data driven tests of likelihood ratio type are proposed for cones of alternatives. We will consider certain stochastically increasing alternatives as a special example. The paper shows how the concept of local asymptotic normality of Le Cam works for hazard oriented models.     

Huntington's disease,critical illness insurance and life insurance

We describe briefly a model of Huntington's disease (HD), a highly penetrant, dominantly inherited, fatal neurological disorder. Although it is a single-gene disorder, mutations are variable in their effects, depending on the number of times that the CAG trinucleotide is repeated in a certain region of the HD gene. The model covers: (a) rates of onset, depending on CAG repeat length as well as age; (b) post-onset rates of mortality; and (c) the distribution of CAG repeat lengths in the population. Using these, we study the critical illness and life insurance markets. We calculate premiums based on genetic test results that disclose the CAG repeat length, or more simply on a family history of HD. These vary widely with age and policy term; some are exceptionally high, but in a large number of cases cover could be offered within normal underwriting limits. We then consider the possible costs of adverse selection, in terms of increased premiums, under various possible moratoria on the use of genetic information, including family history. These are uniformly very small, because of the rarity of HD, but do show that the costs would be much larger in relative terms if family history could not be used in underwriting. We point out some difficulties involved in applying a moratorium that recognises simply a dichotomy between ‘carriers’ and ‘non-carriers’ of any mutation in a gene when these mutations are, in fact, very variable in their effects. These complexities suggest that restrictions on the disclosure, rather than on the use, of genetic information, if it became established as a principle, could deprive insurers of information needed for risk management even if not used in underwriting.     

The Spatial Durbin Model and the Common Factor Tests

Abstract

The spatial Durbin model occupies an interesting position in the field of spatial econometrics. It is the reduced form of a model with cross-sectional dependence in the errors and it may be used as the nesting equation in a more general approach of model selection. Specifically, in this equation we obtain the common factor tests (of which the likelihood ratio is the best known) whose objective is to discriminate between substantive and residual dependence in an apparently misspecified equation. Our paper tries to delve deeper into the role of the spatial Durbin model in the problem of specifying a spatial econometric model. We include a Monte Carlo study related to the performance of the common factor tests presented in the paper in small sample sizes.     

Local asymptotic normality for randomly censored models with applications to rank tests

For randomly right censored models we study the asymptotic behaviour of linear (rank) statistics under local alternatives. The results can be used to evaluate the asymptotic power of the corresponding tests. For instance we treat the question how to choose the best scores in order to derive asymptotically optimal (rank) tests under certain alternatives.     

Reciprocity,Multiplexity, and Exchange: Measures

We introduce measures and statistical tests for multiplexity and exchange that are modeled on similar ideas developed for reciprocity quite early in the history of social network research. As properties of a multi-relation network, multiplexity, and exchange have almost as ancient a history as reciprocity, but have not been as intensively investigated from a methodological perspective. Multiplexity refers to the extent to which two ties, for example, advice and friendship, coincide over population; that is, do respondents name the same people as friends as the persons they nominate as individuals from who they seek advice. Exchange refers to the extent to which a tie of one type directed from person i to person j is returned by a tie of another type from j to i. We conceive of the current paper as the first part of a two-part paper, wherein the second part explores specific theoretical models for multiplexity and exchange.     

Diagnostics cannot have much power against general alternatives

Model diagnostics are shown to have little power unless alternative hypotheses can be narrowly defined. For example, the independence of observations cannot be tested against general forms of dependence. Thus, the basic assumptions in regression models cannot be inferred from the data. Equally, the proportionality assumption in proportional-hazards models is not testable. Specification error is a primary source of uncertainty in forecasting, and this uncertainty will be difficult to resolve without external calibration. Model-based causal inference is even more problematic.