Search results

This study investigates the effect of business cycles on school enrollment in Turkey. During recessions, school enrollment might increase as opportunity cost of schooling declines, yet it might also decrease because of reduced income households have for education. Which effect dominates depends on the context. We empirically explore this in a context displaying canonical features of developing countries.

Using the Turkish Household Labor Force Survey data for a period covering the Great Recession, we estimate the effect of unemployment rate separately for enrollments in general and vocational high schools and in undergraduate programs. To understand the cyclicality, we use a probit model with the regional and time variations in unemployment rates. We also build a simple theoretical model of work-schooling choice to interpret the findings.

We find that the likelihood of enrolling in general high schools and undergraduate programs declines with higher adult unemployment rates, but the likelihood of enrollment in vocational high schools increases. Confronting these empirical findings with the theoretical model suggests that the major factor in enrollment cyclicality in Turkey is how parental resources allocated to education change during recessions by schooling type.

Our finding of pro-cyclical enrollment in academically oriented programs is in contrast with counter-cyclicality documented for similar programs in developed countries, which highlights the importance of income related factors in developing-country contexts. Our heterogeneous findings for general and vocational high schools are also novel.

In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

The case was developed from two 2-h interviews with the Chief Operating Officer of A-Basin, Alan Henceroth; there is no CEO of A-Basin. The second interview was recorded on a Zoom call to provide accuracy of quotations and information. A variety of secondary sources were used in terms of better understanding the current state of the ski industry, as well as its history.

Arapahoe Basin (A-Basin) is a historic, moderately sized, ski area with proximity to metropolitan Denver, Colorado. For over 20 years A-Basin partnered with Vail, allowing skiers to use the Vail Epic Pass, for which A-Basin received some revenue from Vail for each skier visit. The Epic Pass allowed pass holders unlimited days of skiing at A-Basin. More and more skiers were buying the Epic Pass, thus increasing the customer traffic to A-Basin. However, the skier experience was compromised due inadequate parking, long lift lines and crowded restaurants. The renewal of the contract with Vail was coming due, and A-Basin had to consider whether to renew the contract with Vail. The case is framed primarily as a strategic marketing case. The authors use Porter’s five forces model to assess the external environment of A-Basin, and the authors use the resource-based view and the VRIO tool to assess A-Basin’s internal strengths. Both frameworks provide useful analysis in terms of deciding whether to continue A-Basin’s arrangement with Vail or end the contract and pursue a different strategy. In 2019, after consultation with the Canadian parent company Dream, A-Basin made the decision to disassociate itself from the Epic Pass and Vail to restore a quality ski experience for A-Basin’s customers. No other partner had ever left its relationship with Vail. An epilogue details some of A-Basin’s actions, as well as the outcomes for the ski area. Generally A-Basin’s decision produced positive results and solidified its competitive position among competitors. Other ski areas have since adopted a similar strategy as A-Basin. A-Basin’s success is reflected in a pending offer from Alterra, Inc., to purchase the ski area.

The A-Basin case can be used in both undergraduate and graduate strategic (or marketing) management courses. It is probably best considered during the middle of an academic term, as the case requires students to apply many of the theoretical concepts of strategy. One of the best books to enable students to use Porter’s five forces is Understanding Michael Porter by Joan Magretta (Boston: Harvard Business Review Press, 2012). Magretta was a colleague of Porter for many years and was an Editor of the Harvard Business Review. For a discussion of the VRIN/VRIO concept, see Chapter 4 of Essentials of Strategic Management by Gamble, Peteraf and Thompson (New York: McGraw-Hill Education, 2019).

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.