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Background The agricultural sector remains the main source of livelihoods for rural communities in Ethiopia, but faces the challenge of changing climate. This study investigated how smallholder farmers perceive climate change, what adaptation strategies they practice, and factors that influence their adaptation decisions. Both primary and secondary data were used for the study, and a multinomial logit model was employed to identify the factors that shape smallholder farmers’ adaptation strategies. Results The results show that 90% of farmers have already perceived climate variability, and 85% made attempts to adapt using practices like crop diversification, planting date adjustment, soil and water conservation and management, increasing the intensity of input use, integrating crop with livestock, and tree planting. The econometric model indicated that education, family size, gender, age, livestock ownership, farming experience, frequency of contact with extension agents, farm size, access to market, access to climate information and income were the key factors determining farmers’ choice of adaptation practice. Conclusion In the Central Rift Valley of Ethiopia, climate change is a pressing problem, which is beyond the capacity of smallholders to respond to autonomously. Farmers’ capacity to choose effective adaptation options is influenced by household demography, as well as positively by farm size, income, access to markets, access to climate information and extension, and livestock production. This implies the need to support the indigenous adaptation strategies of the smallholder farmers with a wide range of institutional, policy, and technology support; some of it targeted on smaller, poorer or female-headed households. Moreover, creating opportunities for non-farm income sources is important as this helps farmers to engage in those activities that are less sensitive to climate change. Furthermore, providing climate change information, extension services, and creating access to markets are crucial.

We investigate the relevance of dynamic variables that reflect the purchase history of a household as independent variables in multicategory choice models. To this end, we estimate both homogeneous and finite mixture variants of the multivariate logit model. We consider two types of dynamic variables. Variables of the first type, which previous publications on multicategory choice models have ignored, are exponentially smoothed category purchases, which we simply call category loyalties. Variables of the second type are log-transformed times since the last purchase of any category. Our results clearly show that adding dynamic variables improves statistical model performance with category loyalties being more important than log-transformed times. The majority of coefficients of marketing variables (features, displays, and price reductions), pairwise category interactions, and cross-category relations differ between models either including or excluding dynamic variables. We also measure the effect of marketing variables on purchase probabilities of the same category (own effects) and on purchase probabilities of other categories (cross effects). This exercise demonstrates that the model without dynamic variables tends to overestimate own effects of marketing variables in many product categories. This positive omitted variable bias provides another explanation for the well-known problem of “overpromotion” in retailing.

This paper explores spatial substitution patterns using a choice experiment to estimate the non-market benefits of environmental quality improvements at different sites presented as labelled alternatives. We develop a novel modelling approach to estimate possible disproportional substitution patterns among these alternatives by including cross-effects in site-specific utility functions, combining mixed and universal logit models. The latter model allows for more flexibility in substitution patterns than random parameters and error-components in mixed logit models. The model is relevant to any discrete choice study that compares multiple sites that vary in their comparability and that may be perceived as (imperfect) substitutes. Applying the model in an empirical case study shows that accounting for cross-effects results in a better model fit. We discuss the validity of welfare estimates based on the inclusion of cross-effects. The results demonstrate the importance of accounting for substitution effects in spatial choice models with the aim to inform policy and decision-making.

Background Livelihood diversification plays a decisive role for the reduction of poverty, food insecurity and to improve the welfare of rural communities. However, inadequate research attention has been given to explore the determinants of livelihood diversification strategies in resettlement areas of Ethiopia. This study attempts to investigate determinants of livelihood diversification strategies among the resettler households in Chewaka district of Ethiopia. Methods The study utilized both primary and secondary data which are qualitative and quantitative in their nature. Through multistage sampling procedure, a total of 384 households were selected from seven sample kebeles of Chewaka district. Data were collected using interview schedule, focus group discussions and field observations. The collected data were analyzed quantitatively and qualitatively. Descriptive and inferential statistics along with multinomial logit model have been employed to analyze the data. Results The results showed that agriculture (43.2%), agriculture plus non-farm (25.5%), agriculture plus off-farm (19.3%) and a combination of agriculture plus non-farm plus off-farm (12%) activities are the most pertinent livelihood strategies in the study area. It was found that agriculture has a leading contribution to the total households’ income (72.5%) followed by non-farm (20%) and off-farm activities (7.5%). Multinomial logit model result revealed that land holding size, educational status, livestock holding, sex, age, market distance, credit access, annual income, access to training and household sizes were the major determinants of livelihood diversification strategies. Moreover, poor infrastructural development, lack of working capital, absence of technical support, inadequate skill training and lack of awareness are constraints to livelihood diversification in the area. Conclusions The study concludes that agricultural sector alone cannot be relied upon as the core activity for rural households and as a means of reducing poverty, achieving food security and improving livelihoods in the study area. Thus, a comprehensive development plan that enhances successful livelihood diversification is found to be imperative and most urgent. Policies and actions directed towards improving livelihood of the resettlers’ communities should focus on expanding rural infrastructures, enhancing awareness creation activities and cooperation of stakeholders to bring sustainable livelihood outcome in the area.

We study firms that sell multiple substitutable products and customers whose purchase behavior follows a nested logit model, of which the multinomial logit model is a special case. Customers make purchasing decisions sequentially under the nested logit model: they first select a nest of products and subsequently purchase one within the selected nest. We consider the multiproduct pricing problem under the general nested logit model with product-differentiated price sensitivities and arbitrary nest coefficients. We show that the adjusted markup , defined as price minus cost minus the reciprocal of price sensitivity, is constant for all the products within a nest at optimality. This reduces the problem's dimension to a single variable per nest. We also show that the adjusted nest-level markup is nest invariant for all the nests, which further reduces the problem to maximizing a single-variable unimodal function under mild conditions. We also use this result to simplify the oligopolistic multiproduct price competition and characterize the Nash equilibrium. We also consider more general attraction functions that include the linear utility and the multiplicative competitive interaction models as special cases, and we show that similar techniques can be used to significantly simplify the corresponding pricing problems.

We study assortment optimization problems where customer choices are governed by the nested logit model and there are constraints on the set of products offered in each nest. Under the nested logit model, the products are organized in nests. Each product in each nest has a fixed revenue associated with it. The goal is to find a feasible set of products, i.e., a feasible assortment, to maximize the expected revenue per customer. We consider cardinality and space constraints on the offered assortment, which limit the number of products and the total space consumption of the products offered in each nest, respectively. We show that the optimal assortment under cardinality constraints can be obtained efficiently by solving a linear program. The assortment optimization problem under space constraints is NP-hard. We show how to obtain an assortment with a performance guarantee of 2 under space constraints. This assortment also provides a performance guarantee of 1/(1- ) when the space requirement of each product is at most a fraction of the space availability in each nest. Building on our results for constrained assortment optimization, we show that we can efficiently solve joint assortment optimization and pricing problems under the nested logit model, where we choose the assortment of products to offer to customers, as well as the prices of the offered products. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1931 . This paper was accepted by Dimitris Bertsimas, optimization.

Background Consumption of sugar-sweetened beverages (SSBs) or sugary drinks may reduce or even eliminate the household income allocation for other essential commodities. Reducing expenditure for consumption of other household commodities is known as the crowding-out effect of SSB. We aimed to determine the crowding-out effect of SSB expenditure on other household commodities. In addition, we also identified the factors influencing the household's decision to purchase of SSBs. Methods We used the logistic regression (logit and multinomial logit models) and the Seemingly Unrelated Regression (SUR) models. In order to find the probability of a given change in the socio-demographic variables, we also estimated the average marginal effects from the logistic regression. In addition, we regressed the SUR model by gender differences. We used Household Income and Expenditure Survey (HIES) 2016 data to estimate our chosen econometric models. HIES is nationally representative data on the household level across the country and is conducted using a multistage random sampling method by covering 46,075 households. Results The findings from the logit model describe that the greater proportion of male members, larger household size, household heads with higher education, profession, having a refrigerator, members living outside of the house, and households with higher income positively affect the decision of purchasing SSB. However, the determinants vary with the various types of SSB. The unadjusted crowding out effect shows that expenditure on SSB or sugar-added drinks crowds out the household expenditure on food, clothing, housing, and energy items. On the other hand, the adjusted crowding out effect crowds out the spending on housing, education, transportation, and social and state responsibilities. Conclusion Although the household expenditure on beverages and sugar-added drinks is still moderate (around 2% of monthly household expenditure), the increased spending on beverages and sugar-added drinks is a concern due to the displacement of household expenditure for basic commodities such as food, clothing, housing, education, and energy. Therefore, evidence-based policies to regulate the sale and consumption of SSB are required for a healthy nation.

The generalized multinomial logit (GMNL) model accommodates scale heterogeneity to the random parameters logit (RPL) model. It has been often used to study people’s preferences and predict people’s decisions in many areas, such as health economics, marketing, agricultural studies, transportation research and public policy. However, there are few works studying the efficiency of this model estimator and the corresponding estimation and prediction risks. In this paper, we use a frequentist model averaging (FMA) estimator to reduce the estimation and prediction risks of the GMNL model estimator. We show that the asymptotic squared error risk of the FMA estimator dominates that of the GMNL model estimator, and it is consistent with the results of our Monte Carlo experiments. The accuracy of the predicted choices is also higher based on the FMA estimates compared to the results based on the GMNL estimates. In the empirical analyses, using the FMA estimator improves the percentage of correct predicted choices by 10% compared to the results with GMNL estimates. This paper provides a more efficient alternative to the GMNL model to capture people’s preferences and predict people’s choices.

The 2007–2008 French National Travel Survey (FNTS) included questions about the trip experience for a random subsample of the respondents’ daily travel, offering a rare opportunity to examine a national profile of attitudes toward travel. This study analyzes the self-reported (mental and/or physical) fatigue associated with the selected trip, and its (un)pleasantness. Only 8 % of trips were tiring, and fewer than 4 % were unpleasant, indicating that travel is by no means universally distasteful. We present a bivariate probit model of the mental and physical fatigue associated with the trip, and binary logit models of whether the trip was pleasant (yes/no) or unpleasant (yes/no). For the most part, socioeconomic variables and indicators of trip length, distance, purpose, and mode have logical relationships to fatigue and pleasantness. However, 11 variables out of 31 common to both sets of models have impacts on fatigue that are opposite to those on un/pleasantness, pointing to conditions under which a trip can be fatiguing but pleasant, or conversely. Accordingly, a key contribution of the research is to demonstrate the value of jointly considering both constructs in order to more comprehensively capture the overall attitudes toward the travelling activity. It is also of interest that activities conducted during the trip appear in both sets of models. In particular, the results suggest that although listening to the radio/music decreases the tendency to rate the trip as mentally fatiguing, it tends to be seen as ameliorating the disutility of a tedious trip more than increasing the pleasantness of the trip. Among the policy-relevant findings, we note the especially negative attitudes towards multimodal trips and trips mainly involving driving cars.

Museums represent a fundamental asset for the Italian cultural and social background, and the use of digital technologies can be considered as a keystone for their attractiveness. Thus, assessing the specific determinants which stimulate to invest in new digital solutions and to provide a competitive museum offer is of crucial interest. For this reason, a performing multilevel approach for modeling the probability of including digital innovations in museums will be discussed and different modeling options will be compared. In particular, the implementation of a multilevel binary logit model will be useful to detect the factors of adopting at least basic digital tools. Then, the development of an innovative and flexible multilevel multinomial ordered model will be suitable to further investigate on the probability for the museums to move towards medium/low or high levels of digitalization, on the basis of an increasing sorting criterion. This will be realized by considering the variation of such probability both at regional and provincial levels for some key specific museums features, as well as by including some regional/provincial contextual factors.