Measuring operational and quality-adjusted efficiency of Chilean water companies

The path to a sustainable management of the urban water cycle requires the assessment of both operational and quality-adjusted efficiency in a unified manner. This can be done by the use of non-radial Data Envelopment Analysis models. This study used Range Adjusted Measure models to evaluate the operational, quality-adjusted, and operational & quality-adjusted efficiency (O&QAE) scores of the Chilean water industry including water leakage and unplanned interruptions as undesirable outputs. It was found that on average water utilities presented large O&QAE scores over time. The mean O&QAE score was 0.964 which means that water utilities could further reduce costs and undesirable outputs by 3.6% on average, while trying to expand the scale of operation. This finding suggests that excellent quality-adjusted efficiency at an efficient expenditure could be feasible. It was also evidenced that customer density, mixed water resources, and ownership influenced the O&QAE of Chilean water companies.

Irrigation Water Use Efficiency in Olive Trees in Kairouan, Tunisia

This study evaluated the technical efficiency and irrigation water use efficiency of olive farms in Tunisia, using Data Envelopment Analysis. In order to calibrate and validate the findings, data related to area, water use, water quality, cultivar, input, and yield were collected based on interviews from 45 irrigated olive farms in Kairouan Governorate. The results show that average input-oriented water use efficiency under the CRS and VRS specifications is 17.2% and 36.3%, respectively, indicating that the sampled olive farms could reduce the use of water by an average of 82.8% and 63.7% by improving the performance of irrigation systems. Also, it was found that there are large differences in irrigation water use efficiency between the CRS and VRS specifications. Consequently, this indicates that a number of olive farms can enhance overall efficiency by improving the scale of operation. In practical terms, this study provides significant insights for the olive growers in this study regarding the importance of removing scale inefficiency. Specifically, they need to consider the effects of water and soil quality on irrigated fields to improve the efficiency of irrigation water use.

Assessing the Role of Social and Solidarity Initiatives in exploring Blue Economy: Evidences from Indian Sundarbans

Blue economy approach can mitigate the impacts of climate change on marine and coastal ecosystems. Sundarbans landscape is posed by the climate change of the region as reflected in the continued global warming, rising sea level, erosion of island area and increased frequency of cyclones. Thematic mapping of the Sundarbans region reveals a high ecological security in this region, whereas it is characterised by insecure economic efficiency and social equity. Through participatory observation methods, qualitative information of the operation of selected Social and Solidarity Economic (SSE) initiatives is screened in this study in the light of three sustainability pillars of the blue economy: ecological conservation measures, economic sustainability and social development services. Empirical evidence reveals a variation in the performance of the SSE actors, which is conditioned upon their scale of operation, level of financial assistance and range of activities.

Determinants of scale of farm operation in the eastern region of Ghana

Background Ghana’s smallholder share area under cultivation is witnessing a gradual decline, relative to the share of farmland under medium scale that is growing rapidly. Little attention has, however, been given to examining the drivers that influence scale of operation. Method Using survey data from 231 farmers, this study employed the binary probit regression to assess factors that influence scale of farm operation among cassava and maize farmers in Ghana’s Eastern Region. Results The findings showed that factors that were significant and positively related to farm size were age, secondary education, land acquisition for maize farmers, and tertiary education for cassava farmers. On the other hand, factors that were significant and negatively related to farm size were gender, marital status, access to extension services for cassava farmers, and household size, membership of farmer-based organization and access to credit for maize farmers. Conclusion The study recommends the provision of mechanization support for medium-scale farmers coupled with the improvement of extension service delivery to medium-scale farmers. With messages focused on the adoption of improved technologies and mechanization of farm operations.

Awareness and Constraints of Aquaculture Biosecurity Among Fish Farmers in Ekiti State, Nigeria

The study investigated the level of awareness and constraints the farmers were facing in adopting effective biosecurity measures in Ekiti State. Primary data were collected using multi-stage sampling technique to randomly select 150 fish growers, out of which 144 questionnaires were recovered. Analysis was carried out using descriptive statistics, four-point Likert scale and multiple regression. The result showed that 76.4% of the fish farmers with mean age of 40.48±10.28years were married, 61.1% owned the land on which they farm their fish, 73.6% operated as small scale fish farmers with farm size of <1 hectare. About 91.0% knew about fish diseases, the main source of pollution on the farm was flooding while some (61.1%) experienced outbreak of fish diseases at juvenile stage. High cost of facilities, lack of financial assistance, poor knowledge on biosecurity, expensive laboratory charges and inadequate monitoring by extension agents was perceived as the major constraints. The farmers need financial help to get adequate facilities and to enlarge their scale of operation. Adequate extension services to enlighten them on the effective biosecurity measures should be adopted.

The Unheard Story of Organizational Motivations Towards User Privacy

There could be numerous reasons that drive organizations to provide privacy protections to end users in the applications they develop and maintain. Organizational motivations towards privacy affects the quality of privacy received by end users. Understanding these motivations and the approaches taken by organizations towards privacy protection would assist the policymakers and regulators to define effective frameworks encouraging organizational privacy practices. This study focuses on understanding the motivations behind organizational decisions and the approaches they take to embed privacy into the software applications. The authors analyzed 40 organizations different in size, scope, scale of operation, nature of data used, and revenue. they identified four groups of organizations characterized by the approach taken to provide privacy protection to their users. The taxonomy contributes to the organizational perspective of privacy. The knowledge presented here would help addressing the challenges in the domain of user privacy in software applications and services.

Government Strategies to Minimize the COVID-19 Fallout on MSMEs in India

Currently, the whole country is going across a stressful era. Pandemic has swept the whole planet into its grasp and has smudged the lives of citizens as well as the global economies as a result. COVID-19 has been one of the 2020 mega tragedies. The correlation is clear since the primary issue does seem to be unemployment and financial losses, which causes a huge decrease in consumer spending for all industrialized nations. Consequently, workers have been eliminated, and consumers have a drastic lack of their wages, creating a large fall in prices. Findings reflect that economies all around the world are suffering from COVID-19, which has made the entire world panic and the pandemic virus has taken over almost 195 countries in its grip. It is quite evident that the enterprises in the MSME sector are the most vulnerable ones in the era of the COVID-19 pandemic because of their size, the scale of operation, limited financial managerial resources, and more importantly, they do not have the capacity to deal with something so unexpected.

Nonparametric estimation of a primary care production function in urban Brazil

Background The Brazilian public health system is one of the largest health systems in the world, with a mandate to deliver medical care to more than 200 million Brazilians. The objective of this study is to estimate a production function for primary care in urban Brazil. Our goal is to use flexible estimates to identify heterogeneous returns and complementarities between medical capital and labor. Methods We use a large dataset from 2012 to 2016 (with more than 400 million consultations, 270 thousand physicians, and 11 thousand clinics) to nonparametrically estimate a primary care production function and calculate the elasticity of doctors’ visits (output) to two inputs: capital stock (number of clinics) and labor (number of physicians). We benchmark our nonparametric estimates against estimates of a Cobb-Douglas (CD) production function. The CD model was chosen as a baseline because it is arguably the most popular parametric production function model. By comparing our nonparametric results with those from the CD model, our paper shed some light on the limitations of the parametric approach, and on the novelty of nonparametric insights. Results The nonparametric results show significantly heterogeneity of returns to both capital and labor, depending on the scale of operation. We find that diseconomies of scale, diminishing returns to scale, and increasing returns to scale are possible, depending on the input range. Conclusions The nonparametric model identifies complementarities between capital and labor, which is essential in designing efficient policy interventions. For example, we find that the response of primary care consultations to labor is steeper when capital level is high. This means that, if the goal is to allocate labor to maximize increases in consultations, adding physicians in cities with a high number of clinics is preferred to allocating physicians to low medical infrastructure municipalities. The results highlight how the CD model hides useful policy information by not accounting for the heterogeneity in the data.

INDIAN INVESTMENT IN CHINESE PROVINCES: SPATIAL DISTRIBUTION, LOCATIONAL DETERMINANTS, CHALLENGES AND IMPLICATIONS

The paper empirically examines Indian investment in China and its spatial distribution across 17 Chinese provinces during 2005–2015. Indian investments in China have spread across heterogeneous provinces. The location of ventures has been shaped by several provincial characteristics and locational advantages that include infrastructural facilities, lower wages, markets and openness; economic geography forces that include agglomeration of foreign and private firms and policies and engagements of the local governments. As such multiple determinants of location choice can be observed, which provide insights into the locational determinants of firms from one emerging country in another. Chinese inland provinces trying to attract Indian investment need to focus on developing locational advantages at par with the coastal regions and on economic geography. However, the scale of operation of Indian companies in China remains smaller due to several China-specific challenges. Industrial bodies and investment promotion agencies may aid in building firm-capacity to deal with China-specific challenges, with adequate emphasis on the spatial characteristics of provinces, to overcome business challenges. The increase in Indian investment in China shall be beneficial provided the firms and their activities generate complementarity in the bilateral economic relationship between the two countries and in the product and factor markets.

Analyzing Spatial Variance of Airbnb Pricing Determinants Using Multiscale GWR Approach

A sharing economy accommodation service like Airbnb, which provides trust between strangers to connect them for profiting from underutilized assets, was born and has thrived thanks to the innovations in the platform technology. Due to the unique structure of Airbnb, the pricing strategies of hosts are very different from the conventional hospitality industry. However, existing Airbnb pricing studies have limitations considering the varying scale of operation among hosts, spatial variances in pricing strategies, and crucial geographic information for estimating the influence of the pricing variables, as well as ignoring inter-city variances. In this research, we explored the spatially heterogeneous relationship between price and pricing variables using an innovative spatial approach, Multiscale Geographically Weighted Regression (MGWR). Analysis results for Airbnb listing in Log Angeles and New York in the US showed the effectiveness of MGWR regarding estimating the influence of pricing variables spatially. By revealing spatially heterogeneous and dependent relationships, this research fills gaps in Airbnb pricing research and deepens the understanding of the pricing strategies of the hosts.