Long-term Success of Adaptation Strategies: Evidence from Finnish Companies

This study analyses the success of adaptation strategies applied by Finnish companies during the recession in 1989–93. Five different strategies emerged from data gleaned from a questionnaire answered by 750 companies. In the medium term, investment in new product development and marketing and in the acquisition of new customers was the most successful strategy while a strategy heavily based on negotiating finance contracts and restructuring was the most unsuccessful. To evaluate the long-term effects of adopted strategies, financial statements from 644 companies in 1994 and 1997 were analysed. The results supported the medium-term conclusions. In general, they suggested that in the long-term, active adaptation strategies are most likely to lead to sustained success whereas passive (financial) strategies are associated with a high risk of failure.     

Quantitative Methods for Environmental Justice Assessment of Transportation

Application of Executive Order 12898 to risk assessment of highway or rail transport of hazardous materials has proven difficult; in general, the location and conditions affecting the propagation of a plume of hazardous material released in a potential accident are unknown. Therefore, analyses have only been possible in a geographically broad or approximate manner. The advent of geographic information systems and development of software enhancements at Sandia National Laboratories have made kilometer-by-kilometer analysis of populations tallied by U.S. Census blocks along entire routes practicable. Tabulations of total or racially/ethnically distinct populations close to a route, its alternatives, or the broader surrounding area, can then be compared and differences evaluated statistically. This article presents methods of comparing populations and their racial/ethnic compositions using simple tabulations, histograms, and chi-square tests for statistical significance of differences found. Two examples of these methods are presented: comparison of two routes and comparison of a route with its surroundings.     

Smoothed estimates for nonlinear time series models with irregular data

Time series data observed at unequal time intervals (irregular data) occur quite often and this usually poses problems in its analysis. A recursive form of the exponentially smoothed estimated is here proposed for a nonlinear model with irregularly observed data and its asymptotic properties are discussed An alternative smoother to that of Wright (1985) is also derived. Numerical comparison is made between the resulting estimates and other smoothed estimates.     

Marginally oversaturated designs

A special class of supersaturated design, called marginally over saturated design (MOSD), in which the number of variables under investigation (k) is only slightly larger than the number of experimental runs (n), is presented. Several optimality criteria for supersaturated designs are discussed. It is shown that the resolution rank criterion is most appropriate for screening situations. The construction method builds on two major theorems which provide an efficient way to evaluate resolution rank. Examples are given for the cases n=8, 12, 16, and 20. Potential extensions for future work are discussed.     

Tightened single-level continuous sampling plan

In this paper, a new tightening concept has been incorporated into the single-level continuous sampling plan CSP-1, such that quality degradation will warrant sampling inspection to cease beyond a certain number of sampled items, until new evidence of good quality is established. The expressions of the performance measures for this new plan, such as the operating characteristic, average outgoing quality and average fraction inspected, are derived using a Markov chain model. The advantage of the tightened CSP-1 plan is that it is possible to lower the average outgoing quality limit.     

Robust models in probability sampling

In the estimation of a population mean or total from a random sample, certain methods based on linear models are known to be automatically design consistent, regardless of how well the underlying model describes the population. A sufficient condition is identified for this type of robustness to model failure; the condition, which we call 'internal bias calibration', relates to the combination of a model and the method used to fit it. Included among the internally bias-calibrated models, in addition to the aforementioned linear models, are certain canonical link generalized linear models and nonparametric regressions constructed from them by a particular style of local likelihood fitting. Other models can often be made robust by using a suboptimal fitting method. Thus the class of model-based, but design consistent, analyses is enlarged to include more realistic models for certain types of survey variable such as binary indicators and counts. Particular applications discussed are the estimation of the size of a population subdomain, as arises in tax auditing for example, and the estimation of a bootstrap tail probability.     

State Welfare Rules,TANF Exits,and Geographic Context: Does Place Matter?*

Abstract This research compares the likelihood of exiting TANF with and without employment and the effects of important state TANF rules on welfare exits in more disadvantaged (large Rustbelt cities and poor southern nonmetro) and less disadvantaged (other metro and other nonmetro) areas during the 1996–2003 post‐welfare reform period. Hierarchical competing risk models using individual‐level data from the 1996–99 and 2001–03 Panels of the Survey of Income and Program Participation merged with state‐level data from various sources show that female TANF participants in poor southern nonmetro areas are the least likely to exit TANF with work, and participants in large Rustbelt cities are less likely to exit TANF with work than those in other metro areas. Non‐work TANF exits are more likely to occur in other nonmetro areas than in other metro areas. Importantly, the effects of state welfare rules on TANF exits differ across places of residence. For example, stringent time limit policies promote work exits in large Rustbelt cities but promote non‐work exits in poor southern nonmetro areas. More lenient earned income disregards are significantly related to remaining on TANF in poor southern nonmetro areas but promote work exits in all other places. Findings from this paper imply that states should not take a “one‐size‐fits‐all approach” to reduce welfare caseloads.     

The foundations of econometrics-are there any

The conclusions of alogically consistent economic theory which strictly adheres to Aristotle's axioms of logic are factually true if its sufficient conditions are all factually true. Alternatively, if a conclusion of such a theory is false, then at- least one of its assumptions is false. Unfortunately, the factual truth of sufficient conditions cannot be established because the problem of induction i s impossible t o solve. It is algo true that the falsity of a conclusion cannot be established in the presence of uncertainty. While the philosophy of instrumentalism applied to sufficient and logically consistent explanations may provide useful solutions to immediate practical problems, the principles of simplicity, parsimony and profligacy--all of them requiring conditional deductive arguments--are useless as criteria for model choice.     

Hierarchical Generalized Linear Models and Frailty Models with Bayesian Nonparametric Mixing

This paper proposes Bayesian nonparametric mixing for some well-known and popular models. The distribution of the observations is assumed to contain an unknown mixed effects term which includes a fixed effects term, a function of the observed covariates, and an additive or multiplicative random effects term. Typically these random effects are assumed to be independent of the observed covariates and independent and identically distributed from a distribution from some known parametric family. This assumption may be suspect if either there is interaction between observed covariates and unobserved covariates or the fixed effects predictor of observed covariates is misspecified. Another cause for concern might be simply that the covariates affect more than just the location of the mixed effects distribution. As a consequence the distribution of the random effects could be highly irregular in modality and skewness leaving parametric families unable to model the distribution adequately. This paper therefore proposes a Bayesian nonparametric prior for the random effects to capture possible deviances in modality and skewness and to explore the observed covariates' effect on the distribution of the mixed effects.