Does measurement invariance hold for the official Mexican multidimensional poverty measure? A state-level analysis 2012

One of the main goals in poverty measurement is making comparisons of prevalence and severity across geographical units. This is attained by merely disaggregating the index in question. The underlying assumption is that comparisons across units are tenable, inasmuch as the same indicators are utilised for constructing the index. Nonetheless, in practice, this assumption is very rarely tested. From the statistical perspective, measurement invariance (MI) must hold for comparisons to be valid, and violations thereof indicate that a given poverty index measures different things across different countries, states, counties, etc. Consequently, differentials in severity and prevalence cannot be attributed exclusively to the underlying construct (i.e. poverty) but to factors not considered in the measure. This article tests whether MI holds for two indexes: the Mexican official multidimensional measure (MPM) and an adjusted multidimensional measure (MPM-A) that uses less severe thresholds. The analysis is conducted using a novel method called the ‘alignment method’. It uses these two measures and the method as an illustration of why it is vital to introduce MI tests into poverty measurement. The results suggest that partial strong MI holds for the official measure and MI is violated when the thresholds are adjusted. Partial strong MI guarantees making valid comparisons across the 32 states. Should the official measure requires to be updated with other thresholds, it would be necessary to adjust the threshold or drop the indicator for water deprivation.     

Combining quality and quantity. A psychometric evaluation of the self-anchoring scale

In this study a psychometric comparison is made between a category rating scale with fixed anchors and a self-anchoring category rating scale where respondents themselves define the end anchors by referring to their personal experiences with the construct being measured. A student-satisfaction questionnaire was administered to 301 undergraduate students on two test moments separated by a 1-month period. Both types of category rating scales were crossed with both test moments according to a 2 ×  2 mixed factorial design. Our results show that respondents perceive the construct being measured in the same way with both rating scales. A comparison concerning different indicators of reliability, i.e. test–retest reliability, internal consistency and the error variances associated with each item, also failed to find a difference between both rating scales. In a last analysis it is demonstrated that using a self-anchoring rating scale or a rating scale with fixed anchors has a small but significant effect on the ratings of the respondents. In conclusion, a self anchoring scale may be the scale of choice if additional qualitative information from the anchors is warranted, but when quantitative comparisons between groups are required, rating scales with fixed anchors are preferable thanks to their ease of use.     

An empirical welfare approach to regional income distributions: Alternative specifications and estimates of income inequality measures

The paper attempts to develop a welfare theoretical approach to the analysis and comparison of regional income differences. Three types of alternative methods are discussed, viz. (a) an a priori method of inserting parameter values in a prespecified welfare function, (b) an ex post method of deriving parameter values of a welfare function on the basis of income statistics and (c) a canonical correlation analysis on the basis of a set of underlying explanatory variables. All these three methods are used to define an income inequality measure appropriate for interregional comparisons. The methods employed are illustrated by means of empirical applications to regional income statistics in the Netherlands.     

Potential improvements approach in composite indicators construction: The Multi-directional Benefit of the Doubt model

The aim of the present paper is to propose an innovative method to construct composite indicators, starting from an attractive frontier approach called Multi-directional Efficiency Analysis (MEA), and making it suitable to aggregate simple indicators. The main advantage of this method is the separation of the benchmark selection issue from that of efficiency measurement. The novel method, called Multi-directional Benefit of the Doubt, combines the MEA with the Benefit of the Doubt approach adding some valuable properties to the obtained composite indicator. The proposed method is tested on simulated data for illustrative purposes, and then, an empirical application on OECD ”Better life index” is provided. Results suggest that the method could be a good tool for local policy makers alongside the OECD BLI to investigate and improve some non-performing well-being dimensions.     

Improvement and Transfer of Practice-directed Knowledge

In this paper, we propose a method of working that aims to explicitly organize action research and consultancy activities. Principles of action research are discussed and comparisons are made with practice-directed research in general, particularly Argyris idea of an action science. The main feature of our proposal is a distinction between implementation (the how to or organization of an intervention) and justification (the why or plausibility of the intended effect occuring). Some simple rules are proposed to register and use these two aspects (which we label modules) to improve and disseminate practice-directed knowledge. The method is illustrated by an intervention in socio-technical re-designing.     

Epistemology of data envelopment analysis and comparison with other fields of OR/MS for relevance to applications

Periodically, professionals in a given field must reflect and assess where the field has been, where it is heading, and what if anything, should be done to change that field's course. This article discusses statistical trends within the data envelopment analysis (DEA) literature. The number of articles published per year in refereed journals over the entire lifespan of the field, authorship and publishing outlets-of-choice statistics are used to indicate DEA's vitality, relevance, diffusion to other disciplines/professions and its worldwide acceptance. Lastly, based on published meta reviews, comparisons are made with other OR/MS sub-disciplines.     

Interaction effects in cross-national studies

One may analyse interaction effects either by adding a multiplicative term to an ordinary regression equation or by group comparisons. The effects of human capital formation and government revenues on economic growth in LDCs serve as an example. The proposition under discussion claims that high government revenues (as a share of GDP) cancel the otherwise positive effects of human capital formation on economic growth. This proposition cannot be supported in regression analyses with an interaction term, although it receives rather strong support from comparisons of linear-additive regressions of growth of rates on human capital formation separately done for high and low revenue countries. This result leads to the question which technique and thereby which finding should be adopted. Since regression analyses with interaction terms investigate, whether an interaction of a restricted, symmetrical type applies, group comparisons should often be preferred.     

A lagrange multiplier interpretation of disturbance estimators with an application to testing for nonlinearity

This paper describes a Lagrange multiplier interpretation of LUF disturbance estimators and an associated means of constructing residuals and procedures for testing for certain kinds of misspecification. Together with a recent technique for partially ordering multivariate data, the methods are used to devise an F test for nonlinearity in some or all of the explanatory variables in multiple regression. Unlike several alternatives, the proposed test does not require all misspecified variables to be positively correlated. Power computations, and comparisons with other procedures, yield encouraging results.     

Sequential optimization three-way decision model with information gain for credit default risk evaluation

To categorize credit applications into defaulters or non-defaulters, most credit evaluation models have employed binary classification methods based on default probabilities. However, while some loan applications can be directly accepted or rejected, there are others on which immediate accurate credit status decisions cannot be made using existing information. To resolve these issues, this study developed an optimized sequential three-way decision model. First, an information gain objective function was built for the three-way decision, after which a genetic algorithm (GA) was applied to determine the optimal decision thresholds. Then, appropriate accept or reject decisions for some applicants were made using basic credit information, with the remaining applicants, whose credit status was difficult to determine, being divided into a boundary region (BND). Supplementary information was then added to reevaluate the credit applicants in the BND, and a sequential optimization process was employed to ensure more accurate predictions. Therefore, the model’s predictive abilities were improved and the information acquisition costs controlled. The empirical results demonstrated that the proposed model was able to outperform other benchmarking credit models based on performance indicators.     

Defining and measuring the development of a country over time: a proposal of a new index

This paper introduces the concept of harmonic growth as an extended acceptation of the notion of development, and discusses its measurement via the Harmonic Growth Index (HGI). The growth is seen as harmonic when the behaviour of a benchmark time series, which here is a measure of wealth, such as per capita GDP, is followed by a similar pattern in socio-economic series. Unlike most widely used indicators in the literature, which take into account the measurement of development over a single time, HGI measures the degree to which a social indicator’s time series pattern matches with the GDP’s. The index is a function, ranging in [0, 1], of the coefficients of the uniform B-splines fitted to each time series, according to the functional data framework. A case study on Mediterranean welfare countries (Greece, Italy, Portugal, and Spain), in the period 1996–2007, shows critical differences in the selected indicators which can be ascribed to their dissimilar specific development models. HGI can be also considered as a general index to measure the similarity between time patterns, or as an alternative to correlation for (non-necessarily linear) time series.     

Investigating the incremental validity of employee engagement in the prediction of employee effectiveness: A meta-analytic path analysis

The current study used meta-analytic estimates and path analysis to examine whether the construct of employee engagement (EE) shows incremental validity in the prediction of employee effectiveness (a broad measure of performance-related behaviors) over other job attitudes such as job satisfaction, job involvement, and organizational commitment. Meta-analytic estimates between EE and various employee effectiveness indicators were computed from 49 published correlations representing a total of 22,090 individuals. We combined these estimates with published meta-analytic estimates between employee effectiveness and job attitudes to produce a meta-matrix representing 1,161 unique correlations. Using this meta-matrix, a series of path model comparisons produced two results: (1) EE bears low to moderate incremental validity over individual job attitudes (R² change of 0.02 to 0.06), and (2) EE bears low incremental validity over a higher-order job attitude construct representing the combination of other job attitudes in the prediction of a higher-order employee effectiveness construct (R² change of 0.01). Given the brevity of popular EE measures, the results suggest EE is better conceptualized as a higher-order measure of job attitudes that is an effective and concise predictor of employee effectiveness.     

Selection of Indicators for the Interaction Term in Structural Equation Models with Interaction

Kenny and Judd (1984, Psychological Bulletin 96: 201–210) suggested using structural equation models to model interaction effects since they allow correction for measurement error. They proposed using all possible products of the indicators of the two interacting variables as indicators for the interaction term. Jöreskog and Yang (1996, Advanced Structural Equation Modeling. Mahwah, NJ: Lawrence Erlbaum, pp. 57–88.) defended that this is not necessary; one product variable is sufficient to estimate the interaction effect. However, they did not specify which indicators should be chosen if there is more than one possibility. We prove that the optimal choice is to select the indicators with the highest reliabilities. But this is only true if certain assumptions hold. We go on to show that one can get very different results depending on the indicators chosen for the interaction term if the indicators are not congeneric which is often the case. These methodological arguments will be illustrated by a study of the purchasing or boycotting of certain products for environmental reasons.     

Computing marginal likelihoods from a single MCMC output

In this article, we propose new Monte Carlo methods for computing a single marginal likelihood or several marginal likelihoods for the purpose of Bayesian model comparisons. The methods are motivated by Bayesian variable selection, in which the marginal likelihoods for all subset variable models are required to compute. The proposed estimates use only a single Markov chain Monte Carlo (MCMC) output from the joint posterior distribution and it does not require the specific structure or the form of the MCMC sampling algorithm that is used to generate the MCMC sample to be known. The theoretical properties of the proposed method are examined in detail. The applicability and usefulness of the proposed method are demonstrated via ordinal data probit regression models. A real dataset involving ordinal outcomes is used to further illustrate the proposed methodology.     

Factor models for large and incomplete data sets with unknown group structure

Most economic applications rely on a large number of time series, which typically have a remarkable clustering structure and they are available over different spans. To handle these databases, we combined the expectation–maximization (EM) algorithm outlined by Stock and Watson (JBES, 2002) and the estimation algorithm for large factor models with an unknown number of group structures and unknown membership described by Ando and Bai (JAE, 2016; JASA, 2017) . Several Monte Carlo experiments demonstrated the good performance of the proposed method at determining the correct number of clusters, providing the appropriate number of group-specific factors, identifying error-free group membership, and obtaining accurate estimates of unobserved missing data. In addition, we found that our proposed method performed substantially better than the standard EM algorithm when the data had a grouped factor structure. Using the Federal Reserve Economic Data FRED-QD, our method detected two distinct groups of macroeconomic indicators comprising the real activity indicators and nominal indicators. Thus, we demonstrated the usefulness of our group-specific factor model for studies of business cycle chronology and for forecasting purposes.     

A Latent Class Application to the Multidimensional Measurement of Poverty

The paper presents the multidimensional measurement as a transparent and easy-to-interpret method to measure poverty, where poverty is measured with a set of direct and indirect poverty indicators side-by-side. Multidimensional measurement is formalised and compared to the traditional, one-dimensional measurement. This formalisation is based on the idea about a set of indicators that are measuring different manifestations of the same latent variable. The Latent Class Model (LCM) is proposed as a method to select a valid and reliable set of poverty indicators for multidimensional measurement. The LCM is used to test if these different poverty indicators really measure the same latent referent – an assumption on which the multidimensional measurement is based. Before this method presented here, constructing and selecting indicators for the multidimensional measurement of poverty has relied practically on theory and substance only. Naturally, the method presented here can be used generally for studying and developing multidimensional measurements.     

Selecting the key research areas in nano-technology field using technology cluster analysis: A case study based on National R&D Programs in South Korea

In the early 21st century, Korean government issued a policy recommendation that Korean public research institutes should select strategic research fields to concentrate their resources, based on a careful review of the various strategic R&D factors. The government has emphasized the “selection and concentration” strategy for the efficient use of R&D resources and as a way to increase the national competitiveness of Korea. This paper suggests a method, a “Technology Cluster Analysis,” for selecting the strategic research areas, mainly targeting large, multi-disciplinary and long-term programs. The technology cluster analysis groups near technologies together based on key indicators. In this study, the method is applied to national R&D programs in the nano-technology field. Fifty-six nano-technologies are analyzed and grouped into three main clusters based on the survey data from 180 experts. Technological distances and correlations among individual technologies are depicted by hierarchical dendrogram. Three main clusters in nano-technology field are found and termed nano-materials, nano-devices, and nano-bio. These three clusters are expected to be the core technology clusters in nano-technology field in South Korea.     

Three useful but often forgotten education system indicators

This paper discusses three indicators of education systems. Although previously mentioned in the literature, these indicators have not been generalised fully nor their behaviour patterns explained. The necessary computing formulae are presented and an example discussed to demonstrate how interpretations of calculated values might be made.     

An international comparison of economic leading indicators of telecommunications traffic

The purpose of this paper is to draw international comparisons of the coherence of indexes of leading economic indicators with selected telecommunications traffic series. The traffic series under consideration are total Australian telephone outgoing and U.S. outgoing telephone to Australia with data consisting of monthly observations spanning the period 1970–1983. The response of the telecommunications traffic to these indexes is analysed using cross-spectral techniques. Additionally, a dynamic regression forecasting model for Australian traffic is estimated using the Australian index as an explanatory variable. In comparison to an ARIMA model for the telecommunications data this model reduces post-sample MSE by 19 percent.     

Consumption in the closed von Neumann model

Conclusion This new method for introducing extra consumption into the closed von Neumann model is not without flaw. In addition to requiring collusion by labor, the mechanism increases the dimension of the commodity space so that comparisons between intensity (or price) vectors before and after the introduction of consumption are difficult to make. It is claimed, however, that the method is free from the objection of Malinvaud. The method allows alternative processes for producing extra consumption and provides a mechanism for selecting among the alternative processes. Further, the new method solves the problem of allocating the extra consumption to the labor force.W. W. Cooper, Leif Johansen, and M. C. Lowell made helpful suggestions on earlier darfts of this article. This version is better for their help but any remaining errors are to be credited to my account.     

Crude oil price forecasting incorporating news text

Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic model and further affecting the forecasting performance. Exploiting advanced and appropriate methods to construct high-quality features from news headlines becomes crucial in crude oil forecasting. This paper introduces two novel indicators of topic and sentiment for the short and sparse text data to tackle this issue. Empirical experiments show that AdaBoost.RT with our proposed text indicators, with a more comprehensive view and characterization of the short and sparse text data, outperforms the other benchmarks. Another significant merit is that our method also yields good forecasting performance when applied to other futures commodities.