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The purpose of this paper is to investigate the role played by the geographic distance between the poor and non‐poor in the local demand for income redistribution and, in particular, to provide an empirical test of the geographically limited altruism model proposed by Pauly, incorporating the possibility of participation costs associated with the provision of transfers.

First, the authors motivate the discussion by allowing for an “iceberg cost” as participation for the poor individuals in Pauly's original model. Next, using data from the 2000 Brazilian Census and a panel based on the National Household Sample Survey (PNAD) from 2001 to 2007, the authors estimate the effect of the proximity between poor and non‐poor on the demand for redistribution.

All of the authors' distance‐related explanatory variables indicate that an increased proximity between poor and non‐poor is associated with better targeting of the programs (demand for redistribution). For instance, a one‐hour increase in the time spent commuting by the poor reduces the targeting by 3.158 percentage points. This result is similar to that of Ashworth et al., but is definitely not due to the program leakages. To empirically disentangle participation costs and spatially restricted altruism effects, an additional test is conducted using unique panel data based on the 2004 and 2006 PNAD, which assess the number of benefits and the average benefit value received by beneficiaries. The estimates suggest that both cost and altruism play important roles in the demand for redistribution and might reduce targeting in Brazil. Lastly, the results indicate that “size matters”; i.e. the budget for redistribution has a positive impact on targeting.

Our results suggest that a totally centralized supply of transfers may be more inefficient than local redistribution in terms of targeting, either due to higher participation costs or because of the eventual greater geographical distance between the national median voter and poor individuals. However, a partial role for the federal government, such as providing funds for redistribution, seems to improve targeting.

In particular, the paper provides an empirical test for the geographically limited altruism model proposed by Pauly, incorporating the possibility of participation costs associated with the provision of transfers. The authors motivate this discussion by adding the possibility of distance‐related “iceberg costs” of delivering benefits to poor individuals and show that these two effects of distance may act to lower the demand for transfers, making it difficult to distinguish between the two effects. These two effects of distance act by lowering the demand for transfers, making it difficult to disentangle the effect of altruism from the effect of cost. The authors' empirical strategy seems to allow to identify each of them and to provide a suggestion on whether it is advantageous to carry out redistribution at the local level.

Forecasters have frequently been concerned with designing seasonal adjustment procedures that satisfy particular theoretical criteria (e.g. orthogonality, idempotency, symmetry, Lovell). In evaluating the merits of a particular technique, Monte Carlo studies are often undertaken and the results are then compared to those derived from the Census Bureau's X—11 routine (Wallis, Stephenson, Grether). However, many practical questions have not been addressed, such as to what extent can seasonal routines affect parameter estimates, forecast values, and policy scenarios? The purpose of this article is to focus upon these questions. Data from a short‐term petroleum demand model is seasonally adjusted six different ways. The seasonally adjusted data is then used to estimate the demand relationships of the model using the same structural equation in each case. The results of these estimations provide illuminating information about how seasonality affects parameter values. For policy purposes, this information can be crucial as various policies can be predicated upon an estimated response to a particular variable (e.g. the price of gasoline). The question answered here is how sensitive are the expected policy results to the type of seasonal routine employed in making the estimations.

The purpose of this paper is to test the linkage between innovation strategies (process innovation and service innovation) and organizational performance in the context of Malaysia hotel industry.

A total of 475 sets of questionnaires administrated through mail to all three star and above rating hotel managers in Malaysia, and only 24 percent of it, which is 114 were usable. Regression was utilized to test the link of innovation strategies and performance.

The results showed that hotels in Malaysia used process innovation and service innovation as their functional-level strategy. Specifically, both process innovation and service innovation strategies positively linked with performance. But, the paired sample t-test result indicated that process innovation has slightly greater effect on performance than service innovation. In addition, this study found that hotel size significantly affected the performance.

This study is one of the limited number of studies which has empirically addressed the effect of innovation strategies on performance in hotel industry. Additional researches are needed to address effect of potential mediators or even moderators in innovation strategies and performance linkage.

This study found that implementation of process innovation and service innovation as their functional strategy could assist to attain better performance in Malaysia hotel industry. Entirely the findings provided new insight to the hotel management in understanding the role of innovation strategies in generating enhanced performance.

Process innovation and service innovation in the service industry have received very limited empirical attention in current innovation literature. This study extended the body of knowledge in innovation literature, particularly in hospitality sector in Malaysia.

The purpose of this paper is to analyze the effect of the relationship between stakeholder demand, resources, knowledge and product uniqueness on green marketing and its implication on sustainability performance.

This study used a quantitative research approach that explains the phenomenon by collecting numerical data analyzed using mathematically based methods. The research location was Batik Lawean Center of Surakarta, which is the centers of Batik industry and heritage. These locations were chosen because Laweyan and Kedung Baruk have a vision as the center of Batik industry and environment-friendly heritage through sustainable development.

Stakeholder demand, resource, knowledge and the uniqueness of the product have a significant effect on the application of green management, and the green management has a significant effect on the sustainability performance. It means that the stakeholder demand, resources, knowledge and product uniqueness have a significant effect on green management, and green management simultaneously shows a significant effect on sustainability performance. The application of green management will also improve sustainability performance.

The originality of this study is on the testing of simultaneous relationships between the factors making up the application of green marketing, namely stakeholder demand, resources, knowledge and product uniqueness, as well as the impact of green marketing implementation on sustainability performance. This study focuses on the application of green management by involving the measurement of environmental performance and financial performance, as has been investigated by Karagiorgos (2010) and Earnhart and Lizal (2006). On the other hand, this study attempts to review the application of green management in the form of environmental performance as studied by Filbeck and Gorman (2004) and Sarah and Peter (2000), which reveal several determinants of environmental performance, as suggested by Mutamimah and Handoko (2011). However, this study focuses on the qualitative determinants that have been found by researchers (Raharjo, 2016) that the low or high level of green management application is determined by the demand of stakeholders, resources, knowledge, and product uniqueness considering the object of research is the Batik industry, which is certainly different from other industries, such as those that have been investigated by Karagiorgos (2010), Earnhart and Lizal (2006), Mutamimah and Handoko (2011), Filbeck and Gorman (2004), and Sarah and Peter (2000). This study also combines the measurement of financial performance and non-financial performance in the form of sustainability performance variables.

The purpose of this paper is to compare the perceived transformational leadership practices of charge and staff nurses. Transformational leadership is effective in promoting change in organisations, with the leader guiding followers towards a common vision.

A quantitative, descriptive, comparative survey design was used. All charge nurses (N = 151) and staff nurses (N = 1,950) in six health entities in Malta were included. A mixed mode survey design was used. Data were collected using the Leadership Practices Inventory and analysed using ANOVA and the Kruskal–Wallis test.

An overall response rate of 15% (n = 315) was achieved. Both staff and charge nurses perceived transformational leadership to be practiced. Charge nurses scored consistently higher than staff nurses. In long-term care environments, charge nurses are more likely to “model the way”, while in acute settings, they were more likely to “enable others to act”.

Transformational leadership appears to be applied by charge nurses in Malta. The response rate achieved was low and may limit the generalisability of the results of the study.

Nurse managers need to adapt their transformational leadership style based on the context in which they work.

Regular feedback from nursing staff should be sought for charge nurses to be aware of the extent to which they are implementing transformational leadership.

Reflects on three years′ experience of Annual Reports on the Public Health. Argues that the material benefits do not warrant an overly substantial amount of dedicated time – a five‐year strategic public health programme, supported by a periodic cross‐sectional analysis, is more appropriate and would exploit much wider sources of data than those generally appearing in Annual Reports, Considers the value of the Decennial Census of Population, as a data source. Suggests that it must be used in conjunction with other sources of “hard” data. Also argues that methods for collecting and incorporating qualitative data must be developed, together with a framework for informed policy analysis, which would facilitate a participative dialogue compatible with the aims of the “new” public health.

Reviews methods of estimating shopping centre turnover in the absence of published census information. Discusses use of market area analysis and reviews the experience of surveying retailers themselves in order to measure turnover. Reports a case study of small towns in mid Wales which found that about 70 per cent of the retailers approached were willing to be surveyed, and of these about 70 per cent were willing to provide information on turnover. Examines the use of surrogate measures such as retail employment and concludes that, while such surveys are worthwhile in present circumstances, reinstatement of a centrally organized compulsory census is essential in the longer term.

The purpose of this paper is to examine credit usage by beginning farmers and ranchers (BFR). BFR credit usage is stratified by location (state) and by socially disadvantaged farmer and rancher (SDFR, also known as historically underserved) status. SDFR groups are defined to include women; individuals with Hispanic, Latino or Spanish Origin; individuals who identify as American Indian or Alaskan Native, Black or African American, Asian, Native Hawaiian or other Pacific Islander. Non-SDFR is defined as individuals who identify as non-Hispanic, White men.

The US Department of Agriculture’s Census of Agriculture, Agricultural Resource Management Survey (ARMS) is linked with Farm Service Agency (FSA) loan program administrative data to estimate shares of BFR operations using FSA credit. Census data provided information on population changes in total farms and BFR operations from 2012 to 2017 which are compared by SDFR status.

Results reveal differences among BFR operations active in agricultural credit markets by SDFR status and state. BFR were more common among SDFR groups as well as in regions where farms tend to be smaller, such as the Northeast, compared to a more highly agricultural upper Midwest. Among BFR, non-SDFR are more likely to utilize credit than SDFR, however, FSA appeared to be crucial in enabling BFR and especially beginning SDFR groups to access loans.

The results are timely and of keen interest to researchers, industry and policymakers and are expected to assist in developing and adjusting policies to effectively promote and improve BFR success in general and for beginning SDFR groups.

In this study, the relative accuracy of four well known methods for forecasting are compared The methods are applied to the time series of earnings per share for a random sample of United States corporations over a lengthy period of time. All the time series exhibit both period‐to‐period movements and seasonal fluctuation. The four models are, (1) Holt‐Winters multiplicative exponential smoothing model, (2) univariate Box‐Jenkins model, (3) linear autoregression of data seasonally adjusted by the Census II–XII method, and (4) linear autoregression of the data seasonally adjusted by the X11‐ARIMA method. The study of financial data of this type is important because (1) these data exhibit time series properties of trend, seasonality, and cycle, (2) earnings per share forecasts are important for purposes of financial planning and investment; and (3) previous studies of this nature were not as exhaustive in terms of the statistical analysis of the results

The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems.

This study employs NHPP to model the failure data. Initially, Nelson's graph method is employed to estimate the mean number of repairs and the MCRF value for the repairable system. Second, the time series decomposition approach is employed to predict the mean number of repairs and MCRF values.

The proposed method can analyze and predict the reliability for repairable systems. It can analyze the combined effect of trend‐cycle components and the seasonal component of the failure data.

This study only adopts simulated data to verify the proposed method. Future research may use other real products' failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems.

Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.

The time series decomposition approach was used to model and analyze software aging and software failure in 2007. However, the time series decomposition approach was rarely used for modeling and analyzing the failure data for repairable systems. This study proposes the time series decomposition approach to analyze and predict the failure data of the repairable systems and the proposed method is better than the ARIMA model and neural networks in predictive accuracy.