Efficiency in fundraising and distributions to cause-related social profit enterprises

Managerial efficiency is as important in social profit enterprises (SPEs) as it is for more traditional financial-profit organizations. In this regard, both donors and SPE executives use efficiency information in making decisions. Here, we suggest a linked, two-stage Data Envelopment Analysis (DEA) methodology for assessing efficiency in both charitable fundraising and cause delivery, while empirically investigating results for international aid organizations. The model allows efficiency assessment for both the fundraising and utilization of generated funds when directed for cause-related purposes. This, in particular, allows for measurement of the organization’s managerial efficiency relative to both multiple phased goals and peer organizations. Additionally, the approach provides benchmarks for identifying sources of improved performance in fundraising and program/cause service delivery. It can also project the results of changes in inputs on the amount of resources available for the charitable organization’s cause.The proposed model(s) allow the examiner to assess performance while, at the same time, identifying those instances wherein the simple ratio measures commonly used in non-profit assessment are (1) deficient, and/or (2) misleading because of the use of ‘incorrect’ variables, or the ‘hiding’ of inefficiency if/when tax form categories are filed by an SPE. Importantly, the suggested two-stage DEA methodology can be useful for any organization with multiple-linked goals.     

The economic meaning of Data Envelopment Analysis: A ‘behavioral’ perspective

We reconsider the motivation of Data Envelopment Analysis (DEA), the non-parametric technique that is widely employed for analyzing productive efficiency in academia, the private sector and the public sector. We first argue that the conventional engineering motivation of DEA can be problematic since it often builds on unverifiable production axioms. We then provide a dual viewpoint and highlight the ‘behavioral’ interpretation of DEA models. We start from a specification of the production objectives while imposing minimal structure on the production possibilities, and construct tools to meaningfully quantify deviations of observed producer behavior from optimizing behavior. This brings to light the economic meaning of DEA, provides guidelines for selecting the appropriate model in practical research settings, and prepares the ground for instituting new DEA models. We also provide an empirical application that demonstrates the practical relevance of our arguments. We hope that our insights will contribute to the further dissemination of DEA, and stimulate public sector applications of DEA that build on its behavioral interpretation.     

The zero-information-limit condition and spurious inference in weakly identified models

In many econometric models the asymptotic variance of a parameter estimate depends on the value of another structural parameter in such a way that the data contain little information about the former when the latter is close to a critical value. This paper introduces the zero-information-limit-condition (ZILC) to identify such models where ‘weak identification’ leads to spurious inference. We find that standard errors tend to be underestimated in these cases, but the size of the asymptotic t-test may either be too great (the intuitive case emphasized in the ‘weak instrument’ literature) or too small as in two cases illustrated here.     

The origins,development and future directions of data envelopment analysis approach in transportation systems

Over the last two decades, application of Data envelopment analysis (DEA) in transportation problems have gained considerable research attention. This paper presents a literature review and classification of the applications of DEA in transportation systems (TSs). First by classifying 40 papers from 2007 to 2018, the origins of DEA in transportation problems have been reviewed. Then the development and an overall view of DEA applications in TSs have been presented. We have classified the applications of DEA into six different contexts. In each context, published papers have deeply been analyzed. Content of analysis includes “Number of published papers during the time”, “target journals”, “countries”, “keyword frequency”, “most cited papers”, “map of most co-cited publications”. More important, we reported the “inputs and outputs variables” used in each paper. Further “a review of the selected papers” and “gaps/future research directions” have been given within each cluster. The results show that DEA is one of the most useful approach in evaluating TSs for policy makers. On the other hand, DEA can help the decision makers in transportation especially regarding environmental factors, sustainable development and eco-design. Finally, we proposed subjects for future researches including guidance for new studies in the field of DEA applications in TSs.     

Making unilateral trade liberalisation beneficial to the poor

In this paper, we investigate the impacts of unilateral import liberalisation by a representative South Asian developing economy, Nepal, and demonstrate those conditions required to make the impacts ‘pro-poor.’ Applying the Computable General Equilibrium model to Social Accounting Matrix data, we conclude that import liberalisation is growth-enhancing but that, unfortunately, the rich benefit more than do the poor. We envisage a restructured but plausible model economy that requires a transformational period of ten years, and simulate unilateral trade liberalisation but, in the context of a dynamic model. We conclude that improvement in efficiency parameters, reorganisation of investment patterns, along with reallocation of factors of production by both household group and activity type are required to make growth accrued by import liberalisation ‘pro-poor’ in developing economies such as that of Nepal.     

The impact of stakeholder heterogeneity on risk perceptions in technological innovation

Managing risk has been widely acknowledged as a crucial managerial task in the development of new technology. More recently, the acceptance of new technologies has increasingly been influenced by secondary stakeholders, some of which are difficult to identify, or whose concerns are not easily reconciled. This paper develops a conceptual framework based on the management of technology and research & development literature, stakeholder theory, risk and social judgment to describe how traditional approaches based on reducing uncertainties through estimating probabilities may not work for social uncertainties; different heuristics are needed to understand and resolve such heterogeneous stakeholder perspectives. We contribute to the discourse by describing how risk perceptions among stakeholders vary, and how this may change over time. The framework suggests that the perception of primary stakeholder towards a specific innovation is ‘Standard’ when information is well known, but becomes riskier when information is unclear. For secondary stakeholders, when there is a low degree of imperfect information, the stakeholder relationship is an ‘Irritant’ but becomes increasingly ‘Dangerous’ when information becomes ambiguous. We conclude with implications for management and future research.     

Forecasting Austrian national elections: The Grand Coalition model

Forecasting the outcomes of national elections has become established practice in several democracies. In the present paper, we develop an economic voting model for forecasting the future success of the Austrian ‘grand coalition’, i.e., the joint electoral success of the two mainstream parties SPOE and OEVP, at the 2013 Austrian Parliamentary Elections. Our main argument is that the success of both parties is strongly tied to the accomplishments of the Austrian system of corporatism, that is, the Social Partnership (Sozialpartnerschaft  ), in providing economic prosperity. Using data from Austrian national elections between 1953 and 2008 (n=18$n=18$), we rely on the following predictors in our forecasting model: (1) unemployment rates, (2) previous incumbency of the two parties, and (3) dealignment over time. We conclude that, in general, the two mainstream parties benefit considerably from low unemployment rates, and are weakened whenever they have previously formed a coalition government. Further, we show that they have gradually been losing a good share of their voter basis over recent decades.     

An alternative to the weighted score card approach to measuring performance: An analysis of English and Welsh probation boards

The UK government published a weighted score card approach in 2003 to analyse the performance of Probation Boards in England and Wales. However, there has not been a thorough analysis of whether non-parametric methods could provide more advanced options for analysing performance than the standard Weighted Score Card approach – a variant of the Balanced Score Card. Our results show there is considerable divergence in the ranks of Probation Boards from that of the WSC when we include the input variable ‘resource expenditure’ within a DEA model, calling into question recent policy initiatives to increase efficiency in the national probation service.     

The measurement of pay discrimination between job assignments

The traditional Becker/Arrow model of taste discrimination in pay depicts majority and minority labour as perfectly substitutable, implying that all workers perform precisely the same job assignment and have the same qualifications. The model is thus only appropriate for determining whether ceteris paribus pay differences between white workers and non-white workers, for example, performing job assignment A are attributable to prejudice (‘within-assignment discrimination’). The model is inappropriate for determining whether ceteris paribus pay differences between white workers in assignment A and non-white workers in assignment B reflect prejudice (‘cross-assignment discrimination’). We extend the traditional model to allow for cross-assignment discrimination and we propose an empirical methodology for its estimation. In so doing we address two broad questions: (1) Do predictions about cross-assignment discrimination vary with the form of the production function?; and (2) How can one estimate such discrimination when there is no common measure of productivity? We address the first question by deriving a measure of cross-assignment discrimination for four different production functions—Generalized Leontief, Quadratic, CES, and Cobb-Douglas. The Generalized Leontief provides the most general results, although closed form solutions are not possible. Closed form solutions are obtainable from the other three functions, but only under restrictive assumptions. There are two main findings. First, most predictions are generally robust across functional forms. Second, cross-assignment discrimination depends upon productivity and labour supply differences between the two worker groups, labour market structure, and the interaction between relative group productivity and prejudice. We address the second question by outlining, for future exploration, a two-stage regression methodology in which a standardised (i.e. common) measure of productivity is estimated separately for each occupation. This measure is then incorporated as a right-hand-side explanatory variable in a second-stage, all-occupation regression designed to estimate cross-assignment discrimination. We discuss the proposed methodology with reference to a valuable and interesting test case: The market for professional sports players.     

Using the past to map out the future of occupational health and safety research: where do we go from here?

Abstract

The purpose of this study is to take stock of the extant research on occupational health and safety (OHS) with the aim of identifying gaps and mapping out a future research agenda for human resource management (HRM) scholars. A comprehensive review of OHS research from 1956 to 2019 was first conducted. A total of 564 articles from 17 leading journals were then identified and categorized into five distinct, yet inter-related, themes: (1) antecedents and work-related factors influencing OHS; (2) industrial policy and regulations surrounding OHS; (3) OHS management practices; (4) approaches to, and models of, managing OHS and (5) outcomes of OHS management. The review also discusses OHS research methodologies and design foci. Overall, we found that OHS research is poorly integrated into the field of HRM, and we identify a plethora of opportunities for HR researchers to add value to this field of research. A future agenda is formulated, encompassing new OHS theory-building, novel directions for empirical research, and innovations in research design and methodology.     

Local disaggregation of negative demand and excess demand functions

The literature on the characterization of aggregate excess and market demand has generated three types of results: global, local, or ‘at a point’. In this note, we study the relationship between the last two approaches. We prove that within the class of functions satisfying standard conditions and whose Jacobian matrix is negative semi-definite, only n/2+1$n/2+1$ agents are needed for the ‘at’ decomposition. We ask whether, within the same class, the ‘around’ decomposition also requires only n/2+1$n/2+1$ agents.     

Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer–supplier relationships

Inter-organizational communication has been documented as a critical factor in promoting strategic collaboration among firms. In this paper, we seek to extend the stream of research in supply chain management by systematically investigating the antecedents and performance outcomes of inter-organizational communication. Specifically, inter-organizational communication is proposed as a relational competency that may yield strategic advantages for supply chain partners. Using structural equation modeling, we empirically test a number of hypothesized relationships based on a sample of over 200 United States firms. Our results provide strong support for the notion of inter-organizational communication as a relational competency that enhances buyers’ and suppliers’ performance. Implications for future research and practice are offered.     

Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda

We review and integrate the extant knowledge on group-based forecasting, paying particular attention to the papers included in this special issue of the International Journal of Forecasting. We focus on the relative merits of different methods of aggregating individual forecasts, the advantages of heterogeneity in group memberships, the impact of others’ opinions on group members, and the importance of perceptions of trust. We conclude that a change of opinion following group-based deliberation is most likely to be appropriate where the group membership is heterogeneous, the minority opinion is protected from pressure to conform, information exchange between group members has been facilitated, and the recipient of the advice is able — by reasoning processes — to evaluate the reasoning justifying the proffered advice. Proffered advice is least likely to be accepted where the advisor is not trusted — an evaluation which is based on the advisor having different perceived values to the recipient and being thought to be self-interested. In contrast, the outcome of a group-based deliberation is most likely to be accepted when there is perceived to be procedural fairness and the participants in the process are perceived to be trustworthy. Finally, we broaden our discussion of group-based forecasting to include a consideration of other group-based methodologies which are aimed at enhancing judgment and decision making. In particular, we discuss the relevance of research on small-group decision making, the nature and quality of the advice, group-based scenario planning, and public engagement processes. From this analysis, we conclude that, for medium- to long-term judgemental forecasting, a variety of non-outcome criteria need to be considered in the evaluation of alternative group-based methods.     

Firm heterogeneity, productivity and spatially bounded knowledge externalities

In this paper, we argue that conceptually disentangling the ‘context versus composition’ aspects of regional growth is a multilevel issue. By applying multilevel models (also called random-effects models), we show (1) the importance of considering firm-specific characteristics simultaneously with region-specific characteristics, as we find that a large part of what is traditionally assigned to the impact of the region should be assigned to firm-specific characteristics and (2) that existing single-level methodologies can be problematic, as they are vulnerable to the charge of estimating significance levels that are too liberally assigned and promote exaggerations. This is illustrated empirically by showing that single-level approaches would lead to the conclusion that innovation spillovers are highly significant in a setting of Dutch urban growth differentials, while multilevel analyses shows less liberally assigned significance levels. We conclude that multilevel-effect models better fit research questions that combine firm and spatial characteristics simultaneously, especially because they allow firm-specific characteristics to be differently linked to their regional contexts.     

Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA

Since the original Data Envelopment Analysis (DEA) study by Charnes et al. [Measuring the efficiency of decision-making units. European Journal of Operational Research 1978;2(6):429–44], there has been rapid and continuous growth in the field. As a result, a considerable amount of published research has appeared, with a significant portion focused on DEA applications of efficiency and productivity in both public and private sector activities. While several bibliographic collections have been reported, a comprehensive listing and analysis of DEA research covering its first 30 years of history is not available.This paper thus presents an extensive, if not nearly complete, listing of DEA research covering theoretical developments as well as “real-world” applications from inception to the year 2007.A listing of the most utilized/relevant journals, a keyword analysis, and selected statistics are presented.     

Antecedents and organizational consequences of family supportive supervisor behavior: A multilevel conceptual framework for research

Family supportive supervision has emerged as an important prerequisite for effective work-family integration and employees' well-being. Scholars are addressing the need to develop family supportive managers and have introduced a new construct and measure, ‘family supportive supervisor behavior’. So far, little attention has been focused on the underlying behavioral process and managerial characteristics that triggers family supportive supervisor behavior. In response, a multilevel conceptual framework is developed that identifies individual-level and contextual-level factors that would predict managers' overall tendency to engage in family supportive supervisor behavior. The consequences of family supportive supervisor behavior on organizational outcomes such as the subordinate and the team level and its practical implications are outlined. In presenting a multilevel conceptual framework for family supportive supervisor behavior, a research agenda is proposed that can guide future researchers in the field of family supportive supervision.     

Modelling security market events in continuous time: Intensity based,multivariate point process models

A continuous time econometric modelling framework for multivariate financial market event (or ‘transactions’) data is developed in which the model is specified via the vector conditional intensity. Generalised Hawkes models are introduced that incorporate inhibitory events and dependence between trading days. Novel omnibus specification tests based on a multivariate random time change theorem are proposed. A bivariate point process model of the timing of trades and mid-quote changes is then presented for a New York Stock Exchange stock and related to the market microstructure literature. The two-way interaction of trades and quote changes in continuous time is found to be important empirically.     

Using DEA to estimate potential savings at GP units at medical specialty level

The aim of this paper is to identify benchmark cost-efficient General Practitioner (GP) units at delivering health care in the Geriatric and General Medicine (GMG) specialty and estimate potential cost savings. The use of a single medical specialty makes it possible to reflect more accurately the medical condition of the List population of the Practice so as to contextualize its expenditure on care for patients. We use Data Envelopment Analysis (DEA) to estimate the potential for cost savings at GP units and to decompose these savings into those attributable to the reduction of resource use, to altering the mix of resources used and to those attributable to securing better resource ‘prices’. The results reveal a considerable potential for savings of varying composition across GP units.     

Treatment effects in sample selection models and their nonparametric estimation

In a sample-selection model with the ‘selection’ variable Q$Q$ and the ‘outcome’ variable Y^∗$Y^{\ast }$, Y^∗$Y^{\ast }$ is observed only when Q=1$Q=1$. For a treatment D$D$ affecting both Q$Q$ and Y^∗$Y^{\ast }$, three effects are of interest: ‘participation  ’ (i.e., the selection) effect of D$D$ on Q$Q$, ‘visible performance  ’ (i.e., the observed outcome) effect of D$D$ on Y≡QY^∗$Y\equiv Q{Y}^{\ast }$, and ‘invisible performance  ’ (i.e., the latent outcome) effect of D$D$ on Y^∗$Y^{\ast }$. This paper shows the conditions under which the three effects are identified, respectively, by the three corresponding mean differences of Q$Q$, Y$Y$, and Y|Q=1$Y|Q=1$ (i.e., Y^∗|Q=1$Y^{\ast }|Q=1$) across the control (D=0$D=0$) and treatment (D=1$D=1$) groups. Our nonparametric estimators for those effects adopt a two-sample framework and have several advantages over the usual matching methods. First, there is no need to select the number of matched observations. Second, the asymptotic distribution is easily obtained. Third, over-sampling the control/treatment group is allowed. Fourth, there is a built-in mechanism that takes into account the ‘non-overlapping support problem’, which the usual matching deals with by choosing a ‘caliper’. Fifth, a sensitivity analysis to gauge the presence of unobserved confounders is available. A simulation study is conducted to compare the proposed methods with matching methods, and a real data illustration is provided.     

Forecasting with equilibrium-correction models during structural breaks

When location shifts occur, cointegration-based equilibrium-correction models (EqCMs) face forecasting problems. We consider alleviating such forecast failure by updating, intercept corrections, differencing, and estimating the future progress of an ‘internal’ break. Updating leads to a loss of cointegration when an EqCM suffers an equilibrium-mean shift, but helps when collinearities are changed by an ‘external’ break with the EqCM staying constant. Both mechanistic corrections help compared to retaining a pre-break estimated model, but an estimated model of the break process could outperform. We apply the approaches to EqCMs for UK M1, compared with updating a learning function as the break evolves.