Multivariate Small Area Estimation of Multidimensional Latent Economic Well-being Indicators

Factor analysis models are used in data dimensionality reduction problems where the variability among observed variables can be described through a smaller number of unobserved latent variables. This approach is often used to estimate the multidimensionality of well-being. We employ factor analysis models and use multivariate empirical best linear unbiased predictor (EBLUP) under a unit-level small area estimation approach to predict a vector of means of factor scores representing well-being for small areas. We compare this approach with the standard approach whereby we use small area estimation (univariate and multivariate) to estimate a dashboard of EBLUPs of the means of the original variables and then averaged. Our simulation study shows that the use of factor scores provides estimates with lower variability than weighted and simple averages of standardised multivariate EBLUPs and univariate EBLUPs. Moreover, we find that when the correlation in the observed data is taken into account before small area estimates are computed, multivariate modelling does not provide large improvements in the precision of the estimates over the univariate modelling. We close with an application using the European Union Statistics on Income and Living Conditions data.     

What are the determinants of financial well-being? A Bayesian LASSO approach

The financial well-being (FWB) of individuals is a topic that is becoming increasingly important across a multitude of disciplines. In this study, we use the 2016 National Financial Well-Being Survey administered by the Consumer Financial Protection Bureau to assess the determinants of an individual's FWB. We identify 144 potential covariates that could explain variation in the FWB score of individuals. The statistical methodology of choice is the Bayesian LASSO, which is a covariate selection algorithm that also allows for the importance ranking of covariates. Out of the 144 potential covariates, we find that 26 have 95% credible intervals that do not contain zero. Broadly speaking, the results show that objective measures of financial competency and psychological and sociological factors contribute the bulk of the explanatory power that help explain an individual's FWB score.     

Modelling stock price behaviour by financial ratios

The main purpose of this study is to find out which economic dimensions of the firm are reflected in stock price behaviour in the Finnish stock market. Based on the previous theoretical articles, four economic dimensions are chosen: profitability, financial leverage, operating leverage and corporate growth. Twelve (12) financial ratios are then selected to represent these four dimensions. All the Finnish firms common series listed for the whole 1974–1986 period are included in the empirical analysis.All of the four expected dimensions above are found in the empirical classification pattern of ratios. On the cross-sectional level, profitability and financial leverate are reported as determinants of stock price behaviour. Corporation growth is merely connected to the risk of the common stock. Somewhat weaker results concerning the association between stock price behaviour and operating leverage factor may be due to difficulties measuring operating leverage on an empirical level.When studying the intra-year explanatory power of financial ratios, it is reported that the explanatory power of financial ratios tends to increase when the reporting day approaches, and starts to decrease after that releasing day of financial statement numbers. Empirical evidence strongly indicates that financial ratios represent pricing relationships in a substantive manner.The financial support by the Academy of Finland as well as the helpful comments and suggestions of an anonymous referee are gratefully acknowledged.     

Which formula for national happiness?

The World Happiness Report is published by the United Nations Sustainable Development Solutions Network and contains an international ranking of national average happiness, as measured by surveys of personal life evaluations. It also contains an analysis which tries to explain the happiness figures from more than 150 countries using data on six key variables. That analysis assumes the factors combine in an additive manner and therefore operate independently of each other. By contrast, we explore a multiplicative model, which allows for interactivity or synergy between factors, as well as the possibility of diminishing marginal benefit at higher levels of achievement. We find that this model provides a better fit to the data and is therefore superior in its explanatory power. The implication for policy-makers is that they should focus on improving those factors which are the lowest for their nation as this will provide greater relative benefits to subjective well-being. At an individual level this means focusing on improving conditions for those who are experiencing the lowest levels of well-being.     

A MODEL OF GROWTH AUGMENTED WITH INSTITUTIONS

This paper shows that the inclusion of institutional indicators in a traditional model of growth substantially improves its explanatory capacity. The results have implications for economic policy, because not all the dimensions of institutional quality influence growth to the same extent. A large sample of 165 countries and estimation methods with instrumental variables are used to solve endogeneity problems.     

Linking job insecurity to well-being and organizational attitudes in Belgian workers: the role of security expectations and fairness

The aim of this study was to investigate how perceptions of job insecurity and fairness associate with individual well-being (job satisfaction and general health) and organizational attitudes (organizational commitment and turnover intention), under the condition that the psychological contract as perceived by the employee includes a promise on job security, or not. More specifically, we suggest that (H1) job insecurity is negatively related to individual well-being and organizational commitment and positively related to turnover intentions when job security is expected as part of the psychological contract, (H2) that job insecurity is negatively related to fairness perceptions when job security is expected as part of the psychological contract, (H3) that fairness associates positively with individual well-being and organizational commitment and negatively with turnover intentions and (H4) that the association between job insecurity, individual well-being and organizational outcomes is mediated by fairness under the condition that job security is expected as part of the psychological contract. Thus, we propose a model of mediated moderation. Results based on a sample of Belgian employees (N = 559) supported our hypotheses for organizational outcomes. For individual well-being, the mediation framework was not conditional upon a perceived promise of job security as part of the psychological contract. We conclude that employees' perceptions of job insecurity and fairness are important factors in employees' well-being and their attitudes towards the organization. More importantly, these perceptions can in part be shaped by organizational agents such as managers and supervisors. Additionally, the active utilization of the psychological contract in management strategies seems to have favourable results for the fostering of pro-organizational attitudes among employees.     

Measuring well-being by a multidimensional spatial model in OECD Better Life Index framework

One of the most influential measures of multidimensional well-being, the Better Life Index, launched by OECD in 2011, contains a detailed overview of the social, economic, and environmental performances of different countries. Since its launch, a relevant number of empirical studies have been proposed on these data, but the role played by the distance between societal priorities and country-level performance in Better Life Index as well as in multidimensional well-being remains underexplored. We propose to address this issue by means of a multidimensional spatial model. We position the countries in the Euclidean K-dimensional space in which each dimension is a specific aspect of well-being, and we consider each individual's opinion on the same dimensions to calculate the personal optimal point. The distance between the optimal point of well-being and the actual observed point at individual level is the individuals' loss in well-being. We show that the societal loss at country-level is negatively related to the overall well-being and the main indices of quality of democracy. Based on the above evidence, we would argue that a multidimensional spatial framework represents a promising tool for the analysis of the whole class of multidimensional measures of well-being in which a group of individuals expresses the weights individually assigned to a set of dimensions within a pre-established range.     

The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns

In this article, we construct mixed-frequency individual stock sentiment using MIDAS model. We first investigate the influence power of mixed-frequency individual stock sentiment on excess returns. The results indicate that the higher the frequency of individual stock sentiment is, the better it explains the variation of excess returns, that mixed-frequency individual stock sentiment, especially mixed high-frequency sentiment, exerts greater influence on excess returns than the same frequency one and that the mixed-frequency sentiment has a stronger explanatory power to the variation of excess returns than size factor, book-to-market factor, profitability factor and investment factor do. Then, we study the predictive content of mixed-frequency individual stock sentiment. The results show that the higher the frequency of individual stock sentiment is, the better the forecast performs. Moreover, by comparing the corresponding statistics in influence and predictive power models, we find that the influence power of mixed-frequency individual stock sentiment is more significant than its predictive power.     

Investigating the psychometric properties and assessment capabilities of the short version of the Health and Safety Executive’s Management Standards Indicator Tool

Abstract

Policies on psychosocial hazards at work are gaining importance. Subsequently, a number of indicators have been proposed to assess the phenomenon such as the Management Standards Indicator Tool (MSIT). This study focuses on the short-version of the MSIT and aims to evaluate the convergent and discriminant validities of the measures, explore whether these measures can be represented by a higher-order factor of work-related stress and to contribute towards better understanding of its concurrent validity. Data were obtained from 452 full-time managerial-grade employees working in a multinational manufacturing plant in Malta. The study shows that the original 25-item MSIT measures have convergent and discriminant validity issues. Following the removal of five items, the revised indicator demonstrated good construct validity and was well-represented by a higher order factor. Additionally, the second-order factor of the modified MSIT mediated the relationship between psychosocial safety climate and psychological well-being and slightly interacted with engagement to explain improved well-being in the mediational pathway. This study suggests that the modified MSIT is effective in assessing psychosocial factors in stress-management interventions. The MSIT has good prospects to be further adopted in the process of developing better policy and standards for the improvement of psychological conditions at work.     

员工幸福感研究述评

员工幸福感是积极组织行为学的研究热点,它是一种可测量、可开发的积极心理状态,对工作绩效、工作满意度和组织承诺水平有正向影响。其研究有员工主观幸福感和员工心理幸福感两条取向,前者关注工作满意度,后者侧重工作动机和积极心理功能。员工幸福感的影响因素包括人格、积极心理资源和工作特征。未来的研究方向是整合两种研究取向去建构新的理论模型、开发新的测量工具。     

基于混沌模型的港口竞争力问题研究

目前,随着港口企业的不断增多及国家政策的鼓励,港口企业间的竞争逐渐加剧,因此港口竞争力问题成为港口企业关注的焦点问题。同时,港口竞争力系统又是一个复杂的、对初始条件具有敏感依赖性的系统。本文首先采用因子分析法对数据进行降维处理,得到对港口竞争力影响最大的因子,而后结合混沌模型通过对数据进行分析进而对港口竞争力问题进行研究,希望为港口企业在日常的管理决策过程中提供帮助。     

Gender analysis of entrepreneurial intentions as a function of economic development across three groups of countries

The aim of this paper is to analyze the gender differences in entrepreneurial intentions by studying a range of socioeconomic and psychosocial factors. Furthermore, we aim at analyzing these differences in three groups of countries depending on the stage of economic development of each country according to the Global Competitiveness Report. Using a logistic regression analysis and data from the 2008 APS (Adult Population Survey) database which is part of the Project GEM (Global Entrepreneurship Monitor), results show the existence of gender differences in entrepreneurial intentions. The model proposed in the study has decreasing explanatory power as the degree of economic development increases, and is also more conclusive for men than women. An important methodological innovation we implemented in the current study is the use of the variable gender as a dependent variable rather than a variable of a socio demographic nature.     

Factor‐Based Identification‐Robust Interference in IV Regressions

Robust methods for instrumental variable inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade‐offs resulting from weak or many instruments. We show that information reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor‐based modifications to three popular weak‐instrument‐robust statistics, and illustrate their validity asymptotically and in finite samples. Results are derived using asymptotic settings that are commonly used in both the factor and weak‐instrument literature. For the Anderson–Rubin statistic, we also provide analytical finite‐sample results that do not require any underlying factor structure. An illustrative Monte Carlo study reveals the following. Factor‐based tests control size regardless of instruments and factor quality. All factor‐based tests are systematically more powerful than standard counterparts. With informative instruments and in contrast to standard tests: (i) power of factor‐based tests is not affected by k even when large; and (ii) weak factor structure does not cost power. An empirical study on a New Keynesian macroeconomic model suggests that our factor‐based methods can bridge a number of gaps between structural and statistical modeling. Copyright © 2015 John Wiley & Sons, Ltd.     

On the comprehensive method of testing non-nested regression models

The paper derives the specific form of the exponentially combined likelihood function of two competing multivariate non-linear regression models and shows that the application of the comprehensive approach to testing non-nested regression models will, in general, be indeterminate. It establishes that in the univariate case there exists a large number of tests of non-nested regression models which are consistent in addition to having the same asymptotic distribution under the null hypothesis. The paper then derives a set of conditions under which all these consistent tests are asymptotically equivalent not only under the null hypothesis but also under local alternatives. As an application of this latter result the paper establishes the asymptotic equivalence of the tests recently proposed by Davidson and MacKinnon, and Fisher and McAleer under local alternatives, and shows that within the class of tests considered in the paper these proposed tests possess maximum local power. The latter test has this property only when the number of explanatory variables of the ‘true’ model is not more than that of the ‘false’ model.     

Factor pricing of cryptocurrencies

In this paper, we study the cryptocurrency pricing factors. We review the literatures which state that the cryptocurrency market is weakly efficient. We use the Fama–MacBeth method to investigate the pricing factors. The classical equity-based risk factors including size, momentum, and value to growth from the Fama–French three factor model are studied. We use crypto-unique coin-to-token as a proxy for value-to-growth. For volatility risk factor category, we investigate realized volatility, skewness and jump. We also investigate liquidity factors including bid–ask, volume growth and Roll’s measure. The macro factors are found not to be an explanatory factor. The attention factor works sometimes. The factor model constructed by the significant factors explain most of the excess return of cryptocurrencies.     

Fairness in supply chain contracts: A laboratory study

Various contracts can be designed to coordinate a simple supplier–retailer channel, yet the contracts proposed in prior research and tested in a laboratory setting do not perform as standard theory predicts. The supplier, endowed with all bargaining power, can neither fully coordinate the channel nor extract all of the channel profit. We report on a sequence of laboratory experiments designed to separate possible causes of channel inefficiency. The three causes we consider are inequality aversion, bounded rationality, and incomplete information. It turns out that all three affect human behavior. Inequality aversion has by far the most explanatory power regarding retailers’ behavior. Incomplete information about the retailer's degree of inequality aversion has the most explanatory power in regards to the suppliers’ behavior. Bounded rationality affects both players, but is of secondary importance.     

Simultaneous Parameter Estimation in Exploratory Factor Analysis: An Expository Review

The classical exploratory factor analysis (EFA) finds estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample correlation matrix with respect to some goodness-of-fit criterion. Common factor scores can be obtained as a function of these estimates and the data. Alternatively to the classical EFA, the EFA model can be fitted directly to the data which yields factor loadings and common factor scores simultaneously. Recently, new algorithms were introduced for the simultaneous least squares estimation of all EFA model unknowns. The new methods are based on the numerical procedure for singular value decomposition of matrices and work equally well when the number of variables exceeds the number of observations. This paper provides an account that is intended as an expository review of methods for simultaneous parameter estimation in EFA. The methods are illustrated on Harman's five socio-economic variables data and a high-dimensional data set from genome research.     

What governs firm-level R&D: Internal or external factors?

Two parallel streams of research investigating the determinants of corporate R&D exist: one from economics and the other from management. The economists’ variables tend to reflect the firm’s external environment while the explanatory variables used by management scientists are commonly internal to the firm. This paper combines both approaches to test for the relative importance of each type of factor using firm-level data on large Australian companies from 1990 to 2005. Our evidence suggests that most of a firm’s R&D activity can be explained by time-invariant factors which we believe relate to internal and specific characteristics such as the firm’s managerial dimensions, competitive strategy and how it communicates with employees. Of the remaining time-varying portion, we find that past profits, the rate of growth of the industry and the level of R&D activity over the firm’s industry is pertinent. These results are suggestive since we cannot clearly identify the extent to which the firm’s internal behaviour is conditioned by its external environment.     

Consistent selection of explanatory variables

We consider a linear regression model where some explanatory variables are unknown members of sets of alternative explanatory variables. It will be shown that under weak conditions the minimum residual variance criterion for selecting these explanatory variables has the property that the probability of selecting wrong explanatory variables vanishes if the number of observations increases to infmity. Moreover, the O.L.S. estimator of the resulting "specified" model turns out to be consistent, while in the case that all the parameters are nonzero it can be shown that this O.L.S. estimator has the same limiting distribution as the O.L.S. estimator of the true model.