An investigation into factors affecting the occupational choices of nominated farm heirs in Ireland

The resilience of family farming is an important feature of the structure of the farming industry in many countries, due largely to the 'smooth' succession of farms from one generation to the next. The stability of this structure is now threatened by the widening gap between the income expected from farming when compared with non-farming occupations in an economy like Ireland, operating at almost full employment. Nominated farm heirs are increasingly unlikely to choose full-time farming as their preferred occupation. To identify the factors that affect this occupational choice, a multinomial logit model is developed and applied to Irish data to examine the farm, economic and personal characteristics that influence a nominated heir's decision to enter farming as opposed to some non-farming occupation. The results show a significant negative relationship between higher education and the choice of full-time farming as an occupation. The interdependence between education and occupational choices is further explored using a bivariate probit model. The main findings are: the occupational choice and the decision to continue with higher education are made jointly; the nominated heirs on more profitable farms are less likely to pursue tertiary education and therefore more likely to enter full-time farming. The model developed is sufficiently general for studying the phenomenon of succession on farms.

Identifying important endemic areas using ecoregions: birds and mammals in the Indo-Pacific

Concentrations of large numbers of endemic species have been singled out in prioritization exercises as significant areas for global biodiversity conservation. This paper describes bird and mammal endemicity in Indo-Pacific ecoregions. An ecoregion is a relatively large unit of land or water that contains a distinct assemblage of natural communities. We prioritize 133 ecoregions according to their levels of endemicity, and explain how variables such as biome type, whether the ecoregion is on an island or continental mass, montane or non-montane, correlate with the proportion of the total species assemblage that are endemic. Following an exploratory principal components analysis we classify all ecoregions according to the relationship between numbers of endemics and overall species richness. Endemicity is negatively correlated with species richness. We show that plotting the logit transformation of the endemicity of birds and mammals against log of species richness is a more effective and useful way of identifying important ecoregions than simply ordering ecoregions by the proportion of endemic species, or any other single measure. The plot, divided into 16 regions corresponding to the quartiles of the two variables, was used to identify ecoregions of high conservation value. These are the ecoregions with the highest endemicity and lowest species richness. Further analysis shows that island and montane ecoregions, regardless of their biome type, are by far the most important for endemic species.

A practical approach to a multi-level analysis with a sparse binary outcome within a large surgical trial

Objectives: To assess the potential source of variation that surgeon may add to patient outcome in a clinical trial of surgical procedures. Methods: Two large (n = 1380) parallel multicentre randomized surgical trials were undertaken to compare laparoscopically assisted hysterectomy with conventional methods of abdominal and vaginal hysterectomy; involving 43 surgeons. The primary end point of the trial was the occurrence of at least one major complication. Patients were nested within surgeons giving the data set a hierarchical structure. A total of 10% of patients had at least one major complication, that is, a sparse binary outcome variable. A linear mixed logistic regression model (with logit link function) was used to model the probability of a major complication, with surgeon fitted as a random effect. Models were fitted using the method of maximum likelihood in SAS((R)). Results: There were many convergence problems. These were resolved using a variety of approaches including; treating all effects as fixed for the initial model building; modelling the variance of a parameter on a logarithmic scale and centring of continuous covariates. The initial model building process indicated no significant 'type of operation' across surgeon interaction effect in either trial, the 'type of operation' term was highly significant in the abdominal trial, and the 'surgeon' term was not significant in either trial. Conclusions: The analysis did not find a surgeon effect but it is difficult to conclude that there was not a difference between surgeons. The statistical test may have lacked sufficient power, the variance estimates were small with large standard errors, indicating that the precision of the variance estimates may be questionable.

Attitudinal and demographic determinants of diet quality and implications for policy targeting

Background:  Poor diet quality is a major public health concern that has prompted governments to introduce a range of measures to promote healthy eating. For these measures to be effective, they should target segments of the population with messages relevant to their needs, aspirations and circumstances. The present study investigates the extent to which attitudes and constraints influence healthy eating, as well as how these vary by demographic characteristics of the UK population. It further considers how such information may be used in segmented diet and health policy messages. Methods:  A survey of 250 UK adults elicited information on conformity to dietary guidelines, attitudes towards healthy eating, constraints to healthy eating and demographic characteristics. Ordered logit regressions were estimated to determine the importance of attitudes and constraints in determining how closely respondents follow healthy eating guidelines. Further regressions explored the demographic characteristics associated with the attitudinal and constraint variables. Results:  People who attach high importance to their own health and appearance eat more healthily than those who do not. Risk-averse people and those able to resist temptation also eat more healthily. Shortage of time is considered an important barrier to healthy eating, although the cost of a healthy diet is not. These variables are associated with a number of demographic characteristics of the population; for example, young adults are more motivated to eat healthily by concerns over their appearance than their health. Conclusions:  The approach employed in the present study could be used to inform future healthy eating campaigns. For example, messages to encourage the young to eat more healthily could focus on the impact of diets on their appearance rather than health.

Designer buildings: an evaluation of the price impacts of signature architects

This study investigates whether commercial offices designed by signature architects in the United States achieve rental premiums compared to commercial offices designed by nonsignature architects. Focusing on buildings designed by winners of the Prizker Prize and the Gold Medal awarded by the American Institute of Architects, we create a sample of commercial office buildings designed by signature architects drawing on CoStar's national database. We use a combination of hedonic regression model and a logit model to estimate the various rent determinants. While the first stage measures the typical rental price differential above the typical building in a particular sub-market over a specific timeframe, the second stage identifies a potential price differential over a set of buildings closely matched on important characteristics (such as age, size, location etc.). We find that in both stages offices design by signature architects exhibit a premium. However these results are preliminary. The premium could be indeed an effect of the name of the architect, but others factors such as micro-market conditions might be the cause. Further tests are needed to confirm the validity of our results.

Regularized logistic models for probabilistic forecasting and diagnostics

Logistic models are studied as a tool to convert dynamical forecast information (deterministic and ensemble) into probability forecasts. A logistic model is obtained by setting the logarithmic odds ratio equal to a linear combination of the inputs. As with any statistical model, logistic models will suffer from overfitting if the number of inputs is comparable to the number of forecast instances. Computational approaches to avoid overfitting by regularization are discussed, and efficient techniques for model assessment and selection are presented. A logit version of the lasso (originally a linear regression technique), is discussed. In lasso models, less important inputs are identified and the corresponding coefficient is set to zero, providing an efficient and automatic model reduction procedure. For the same reason, lasso models are particularly appealing for diagnostic purposes.

Stated attribute non-attendance in successive choice experiments

Attribute non-attendance in choice experiments affects WTP estimates and therefore the validity of the method. A recent strand of literature uses attenuated estimates of marginal utilities of ignored attributes. Following this approach, we propose a generalisation of the mixed logit model whereby the distribution of marginal utility coefficients of a stated non-attender has a potentially lower mean and lower variance than those of a stated attender. Model comparison shows that our shrinkage approach fits the data better and produces more reliable WTP estimates. We further find that while reliability of stated attribute non-attendance increases in successive choice experiments, it does not increase when respondents report having ignored the same attribute twice.

Forecast encompassing tests and probability forecasts

We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarithmic scoring rules. We propose test statistics for the null of forecast encompassing, present the limiting distributions of the test statistics, and investigate the impact of estimating the forecasting models' parameters on these distributions. The small-sample performance is investigated, in terms of small numbers of forecasts and model estimation sample sizes. We show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables, and for evaluating survey-based probability forecasts.

A stated preference valuation of the non-market benefits of pollination services in the UK

Using a choice experiment survey this study examines the UK public's willingness to pay to conserve insect pollinators in relation to the levels of two pollination service benefits: maintaining local produce supplies and the aesthetic benefits of diverse wildflower assemblages. Willingness to pay was estimated using a Bayesian mixed logit with two contrasting controls for attribute non-attendance, exclusion and shrinkage. The results suggest that the UK public have an extremely strong preference to avoid a status quo scenario where pollinator populations and pollination services decline. Total willingness to pay was high and did not significantly vary between the two pollination service outputs, producing a conservative total of £379M over a sample of the tax-paying population of the UK, equivalent to £13.4 per UK taxpayer. Using a basic production function approach, the marginal value of pollination services to these attributes is also extrapolated. The study discusses the implications of these findings and directions for related future research into the non-market value of pollination and other ecosystem services.

Do consumers want public or private bodies to monitor animal friendly production and marketing schemes? And does trust matter?

Producing according to enhanced farm animal welfare (FAW) standards increases costs along the livestock value chain, especially for monitoring certified animal friendly products. In the choice between public or private bodies for carrying out and monitoring certification, consumer preferences and trust play a role. We explore this issue by applying logit analysis involving socio-economic and psychometric variables to survey data from Italy. Results identify marked consumer preferences for public bodies and trust in stakeholders a key determinant.

National boundaries and the location of multinational firms in Europe

Using data on 5,102 subsidiaries established in the period 1991–1999, we examine the location choice of multinational firms of different nationalities in 47 regions of five EU countries. In particular we estimate a nested logit model and find that European multinationals consider regions across different countries as relatively closer substitutes than regions within national borders. This is consistent with the hypothesis that European regions compete to attract foreign direct investments relatively more across than within countries. However, in line with previous studies, we also find that national boundaries still play some role in choices made by non-European multinationals.

Location choices of multinational firms in Europe: the role of EU cohesion policy

Using data on 5509 foreign subsidiaries established in 50 regions of 8 EU countries over the period 1991–1999, we estimate a mixed logit model of the location choice of multinational firms in Europe. In particular, we focus on the role of EU Cohesion Policy in attracting foreign investors from both within and outside Europe. We find that, after controlling for the role of agglomeration economies as well as a number of other regional and country characteristics and allowing for a very flexible correlation pattern among choices, Structural and Cohesion funds allocated by the EU to laggard regions have indeed contributed to attracting multinationals. These policies as well as other determinants play a different role in the case of European investors as opposed to non-European ones.

Power series generalized nonlinear models

We introduce in this paper a new class of discrete generalized nonlinear models to extend the binomial, Poisson and negative binomial models to cope with count data. This class of models includes some important models such as log-nonlinear models, logit, probit and negative binomial nonlinear models, generalized Poisson and generalized negative binomial regression models, among other models, which enables the fitting of a wide range of models to count data. We derive an iterative process for fitting these models by maximum likelihood and discuss inference on the parameters. The usefulness of the new class of models is illustrated with an application to a real data set. (C) 2008 Elsevier B.V. All rights reserved.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.

Framework for Skew-Probit Links in Binary Regression

We review several asymmetrical links for binary regression models and present a unified approach for two skew-probit links proposed in the literature. Moreover, under skew-probit link, conditions for the existence of the ML estimators and the posterior distribution under improper priors are established. The framework proposed here considers two sets of latent variables which are helpful to implement the Bayesian MCMC approach. A simulation study to criteria for models comparison is conducted and two applications are made. Using different Bayesian criteria we show that, for these data sets, the skew-probit links are better than alternative links proposed in the literature.