Purpose
The purpose of this paper is to investigate the determinants of the gender gap in the gross value of rice yield in Vietnam.
Design/methodology/approach
A panel data set of 12 provinces of Vietnam from 2010 to 2014 was used, collected from the Vietnam access to resources household survey. To measure the gender gap in the gross value of rice yield, two-stage least squares and Blinder – Oaxaca decomposition methods were used.
Findings
The gross value of rice productivity of male-headed households was 10.3 percent higher than that of female-headed households. The gender gap in rice productivity is caused by the endowment and structural effects; the endowment effect explained 53 percent of the gender gap in rice productivity and the structural effect 42 percent.
Practical implications
In order to reduce the gender gap and improve the gross value of rice yield, the following policies are suggested: female education and access to institutional services (extension and credit) should be improved and future research is needed to determine the reasons for gender discrimination in the agricultural production system.
Originality/value
The findings suggest that the difference in the gross value of rice yield between male- and female-headed households were mainly caused by endowments and returns from those endowments.
A central problem in marketing is the clear understanding of consumer's choice or preferences. Designing questionnaires and then analyzing the answers of probable customers can achieve this. The traditional approach in the marketing analysis has been the designing of nonadaptive questionnaires, questionnaires that are predetermined and not at all influenced by respondent's answers. The aim of this paper is to design a questionnaire that is influenced by respondent's answer through implementation of soft computing and approximate reasoning methodologies. The learning of particular pattern on respondent's fuzzy responses has also been envisaged in the post-survey (Postconjoint) and further better clustering of choices and segregation is accomplished. The module of learning and finer clustering from respondent's choice pattern could be a major pre-requisite for construction of adaptive questionnaires. Further extensions of the soft computing methods for product recommender system have also been mentioned for the design of adaptive questionnaire.
A time series is a sequence of observations that a variable takes with respect to times. It has a wide range of applications in decision making and forecasting in economics, agriculture, medicine, industry, energy sector and other scientific fields. Time series modeling and forecasting contain some of the classical issues that are widely addressed in the literature based on traditional statistical models with low interpretability. Fuzzy time series has become a powerful tool that can counter the problem of prediction of historical data in linguistic terms. This study proposes a new framework for modeling the fuzzy time series approach in the environment of intuitionistic fuzzy set theory to play viable role in ensuring robustness to the uncertainty involved in data series. In order to get the optimized length of intervals, the principles of fuzzy c-means (FCM) clustering and information granules are integrated. To fuzzify the historical data, intuitionistic fuzzy triangular function is practiced to acquire the intuitionistic fuzzy sets. Furthermore, the distance measures between the elements of the intuitionistic fuzzy set of the fuzzified historical data and the centers of the corresponding clusters are computed for all fuzzy sets. Finally, a robust fuzzy time series model is designed by extracting fuzzy logical relationships and employing weighted association reasoning as an exhaustive defuzzification approach. The parameters of accuracy measures such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used to identify the strength of the proposed modeling and forecasting. Findings demonstrate that the proposed forecasting method is robust in determining the highly accurate forecasts.
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