Land degradation is a major problem undermining land productivity in the highlands of Ethiopia. This paper analyses the decisions made by individual household to adopt and intensify land conservation investment. The paper used a Box-Cox Double Hurdle specification and offers the advantage of exploiting panel data collected in a household survey of 6,408 plots in Ethiopia. The results suggest that adoption and intensification decisions appear to be explained by different processes, justifying the use of Box-Cox double hurdle approach over more restrictive models. Poverty-related factors seem to have mixed effect on both adoption and intensification decisions. While farmer's adoption decision is affected by expectation of the certainty of cultivating the land for the next five years (risk for long term), intensification of land conservation investment is determined by whether or not the plot is owner-operated (risk for immediate period) and plot-home distance. A lesson for policymakers is that major changes in land conservation investments will require attention to many factors because no single factor can be used as a major policy leverage instrument. Some of these factors (such as land tenure security, plot size, and total farm holdings) can be directly influenced by government policies and programs.

Purpose This paper aims to examine house price drivers in Dubai, addressing nonlinearity and heterogeneity. Design/methodology/approach The study applies a combination of linear and nonlinear, as well as quantile regression, specifications to address these concerns and better explain the real-world phenomenon. Findings The study shows the double-log quantile regression approach is an overarching description of house price drivers, confirming that not only the price of housing and its determinants are non-linearly related but also that their relationship is heterogeneous across house price quantiles. The findings reveal the prevalence of sub-market differentials in house price sensitivity to house attributes such as size (in square meters), location and type of house, as well as government laws. The study also identifies the peaks and deflation, as well as the rebounding nature of the house price bubble in Dubai. Research limitations/implications The data used are limited, in that information on only a few house attributes was available. Future research should include data on other house attributes such as house quality, zip codes and composition. Practical implications The findings of this study are expected to suggest results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution. Social implications This study allows policy makers, developers and buyers of higher-priced houses to behave differently from buyers of lower-priced or medium-priced houses. Originality/value Methodologically, it demonstrates alternative linear and nonlinear, as well as quantile regression, specifications to address two increasing concerns in the house price literature: nonlinearity and heterogeneity. Unlike most other studies, this study used a rich data (140,039 day-to-day transactions of 10 years’ pooled data). The Dubai housing market presents an interesting case. UAE (Dubai, in particular) is named as the second-hottest marketplace for global residential property investors, ahead of Singapore, the UK and Hong Kong (Savills plc, 2015).

Service quality, being an integral part of urban growth, has been a subject of interest to city transport planners. The provision of better quality public transport service is a challenge in the United Arab Emirates, as this country strives to meet the transport requirements fundamental to sustain the investment and economic as well as urban population growth exhibited over the last few years. United Arab Emirates Government intervention strategies include increasing share of public transportation and decreasing transport by private vehicles through bettering bus service delivery. Such shift of demand for public service requires not only understanding of people's willingness to use and pay for improved public transport services but also market incentives to pull-down personal vehicle ownership demand into public transport services. This study examines the willingness of United Arab Emirates residents to use and pay for improved public transport services. A contingent valuation survey is conducted on 852 representative sample of cross section of United Arab Emirates residents. Probit and Ordered logit models are used to analyze the survey data. The study has generated results with significant ramifications for policy. It suggests that there is the knowledge that residents, in general, are willing to use and pay higher fees for using public bus provided its service quality is improved. This has to provide reassurance to the Roads and Transport Authority who may be interested about the potential demand for improved public bus service. In order for the United Arab Emirates Government to increase the share of public transportation and decrease transport by private vehicles, effort will have to continue to improve the public bus service quality. In particular, marketing of such improved service will have to consider the residents' socioeconomic and demographic as well as some travel attributes.

Purpose Considering the importance of human capital in innovation, entrepreneurship and thus economic development, this study attempts to examine formal learning as a mechanism of human capital development in institutions of higher learning. Ironically, students in such institutions are automatically assumed to learn and accumulate pertinent capacity, which would then enable them to compete in the business world or pursue further studies in future. Consequently, lack of this cognizance culminates in little being known about how students learn to accumulate knowledge, skills and requisite competencies. Notwithstanding this, the challenges posed in the twenty-first century require well-rounded students those especially who can address the global transformations witnessed in the business arena. The purpose of this paper is an attempt to fill this gap using data from the University of Dubai (UD) to examine how formal learning takes place in an institution and what determines it. Design/methodology/approach Learning is conceptualized in terms of knowledge, skills and competencies accumulated as proxied by cumulative general point aggregate. All the data used came from the UD. In addition to in-depth descriptive analysis, the study uses limited dependent techniques to identify the most significant determinants of institutional learning. Findings The empirical results generated indicate that demographic characteristics such as age, nationality and gender had a positive effect on learning. Moreover, a student’s initial condition influenced his/her learning positively. Whereas the mode of study under personal preferences did not seem to affect learning, the number of course sections taken had a positive influence on learning. As anticipated, student transfer had a negative influence on learning. The number of credit hours accumulated affected learning positively. Research limitations/implications The main limitation of this study is that results are only applicable within a limited geographical scope, and thus they cannot be generalized for global consumption. Nonetheless, the discussion and results obtained make insights to any future-related studies. Originality/value As pointed out in the previous sections, learning will be conceptualized in the form of knowledge, skills and competency acquisition. In a school setting, knowledge, skills and competencies are better captured by the grade attained in each subject. The general student learning can, therefore, be equally captured by the cumulative grade point aggregate. The authors purport that learning can be visualized, or in other words conceptualized, as a complex process that is determined by five main factors that include demographic characteristics; student initial condition; personal preferences and choices; and time factor curriculum and anticipated future career.

Purpose The purpose of this paper is to analyze significant determinants to assess the probability of insureds’ intent to buy (ITB) insurance and willingness to pay (WTP) quantum of dollars for security benefits. Design/methodology/approach The authors use the Double Hurdle Model (DHM) and Neural Network (NN) architecture to analyze the insureds’ behavior for ITB and WTP. The authors apply these frameworks to all the 503 insureds of a branch of a leading insurer in the United Arab Emirates. Findings The DHM identified age, loans & liabilities, body mass index, travel outside the UAE, salary and country of origin (Middle Eastern and African) as significant determinants to predict WTP for social security benefits. In addition to these determinants, NN architecture identified insurance replacement, holding multiple citizenship, age of parents, mortgages, country of origin: Americas, length of travel, income of previous year and medical conditions of insured as additional important determinants to predict WTP for social security benefits; thus, NN is found to be superior to DHM due to its lowest RMSE and AIC in the holdout sample and also its flexibility and no assumptions unlike econometric models. Research limitations/implications Insureds’ data used from one UAE Branch limit the generalizability of empirical findings. Practical implications The study findings will enable the insurers to appropriately design the insurance products that match the insurers’ behavior of ITB and WTP for social security benefits. Social implications The study findings have the potential for insurance institutions to be more flexible in their insurance practices through public–private partnerships. Originality/value This is the authors’ original research work.

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The paper examines sector specific characteristics to analyse the factors affecting the sustainability of the economies of Dubai and rest of the United Arab Emirates (UAE).The study applies system design to analyse the research questions. Consequently, Zellner's seemingly unrelated regressions (SURE) technique is used to examine the relative contribution of sectors to the economies Dubai, as an individual Emirate, and the rest of UAE as a group of Emirates using time series sectoral level data for 2001-2015.The study shows that there exists positive interdependencies between Dubai and rest of UAE economies. This signifies that the core competencies across various sectors in Dubai and rest of UAE economies need to be promoted further to have overall diversified impact on UAE economy.The positive sizable impact of the finance sector in Dubai and negative sizable impact in the rest of the UAE provide many opportunities for designing diversification programs for sustained economic development of the entire UAE economy. The small sample size, non-availability of detailed sectoral data in four of the seven emirates constrained the scope of the study for generalization to other economies in the middle east.

This research addresses the problem of predicting the user’s responses through multivariate choice (MVC) and neural network (NN) frameworks for predicting quality, quantity and overall User satisfaction of public water supply organization, BWSSB (Bangalore Water Supply and Sewerage Board) in Bangalore - India for policy initiatives. The MVC study identifies statistically significant factors that explain users’ loyalty to express satisfaction and voice to express dissatisfaction. The MVC model correctly predicts 85% of satisfied customers across satisfaction dimensions.  Wald test on 1940 responses confirms that there exits cross equation correlation across quality, quantity and overall Users’ satisfaction dimensions and thus appropriateness of MVC framework over traditional logit for predicting the user responses. NN framework outperforms the econometric model with 94% correct classification of user responses. The study opens up potential research opportunities for applying the advanced analytical frameworks for predicting user responses in various public and private settings for Policy initiatives so that the service providers could improve their service delivery.