Bankruptcy Prediction for Chinese Firms： Comparing Data Mining Tools With Logit Analysis

China＇s capital market is different from that of the US in economic, political, and socio-cultural ways. China＇s dynamic and fast growing economy for the past decade entails some structural changes and weaknesses and as a consequence, there are some business failures. We propose bankruptcy prediction models using Chinese firm data via several data mining tools and traditional logit analysis. We used Chinese firm data one year prior to bankruptcy and our results suggest that the financial variables developed by Altman （1968） and Ohlson （1980） perform reasonably well in determining business failures of Chinese firms, but the overall prediction rate is low compared with those of the US or other countries＇ studies. The reasons for this low prediction rate may be structural weaknesses resulting from China＇s fast growth and immature capital market.     

Mixed Logit Model Performance and Distributional Assumptions: Preferences and GM foods

Using mixed logit models to analyse choice data is common but requires ex ante specification of the functional forms of preference distributions. We make the case for greater use of bounded functional forms and propose the use of the Marginal Likelihood, calculated using Bayesian techniques, as a single measure of model performance across non nested mixed logit specifications. Using this measure leads to very different rankings of model specifications compared to alternative rule of thumb measures. The approach is illustrated using data from a choice experiment regarding GM food types which provides insights regarding the recent WTO dispute between the EU and the US, Canada and Argentina and whether labelling and trade regimes should be based on the production process or product composition. This paper draws work commissioned by UK Department of Environment Food and Rural Affairs (DEFRA). The views presented in this paper are those of the authors alone and should not be regarded as those of DEFRA or of individuals within DEFRA.     

Models and optimal designs for conjoint choice experiments including a no-choice option

In a classical conjoint choice experiment, respondents choose one profile from each choice set that has to be evaluated. However, in real life, the respondent does not always make a choice: often he/she does not prefer any of the options offered. Therefore, including a no-choice option in a choice set makes a conjoint choice experiment more realistic. In the literature, three different models are used to analyze the results of a conjoint choice experiment with a no-choice option: the no-choice multinomial logit model, the extended no-choice multinomial logit model, and the nested no-choice multinomial logit model. We develop optimal designs for the two most appealing of these models using the D-optimality criterion and the modified Fedorov algorithm and compare these optimal designs with a reference design, which is constructed while ignoring the no-choice option, in terms of estimation and prediction accuracy. We conclude that taking into account the no-choice option when designing a no-choice experiment only has a marginal effect on the estimation and prediction accuracy as long as the model used for estimation matches the data-generating model.     

The Value of Changes in Deer Season Length: An Application of the Nested Multinomial Logit Model

Increasing deer populations can be controlled through manipulatingharvest limits or season length. While such actions often result in benefitsto hunters, both motorists and the agricultural sector also benefit as alower deer population leads to fewer incidences of harmful human-deerencounters. Traditional recreation demand models are often employed toexamine the welfare implications of changes in daily hunting bag limits.Studies measuring the effects of changes in season length, however, arenoticeably absent from the literature. This study uses a nested randomutility model to examine hunter choice over site and season selection toderive the values of changes in season length.     

流动人员就业稳定性与专用性人力资本投资研究--基于不完全契约理论的一个解释

文章基于不完全契约理论,在“敲竹杠”模型中引入员工就业不稳定性,分析了员工就业稳定性与专业用性人力资本投资的关系,认为员工稳定性的就业预期对于企业进行专用性人力资本投资是一种激励;而企业在培训后提高薪酬福利待遇对于员工专用性人力资本投资也是一种激励。文章还通过东莞调研数据运用有序logit模型对这些结论进行验证。同时,提出了提高流动人口就业稳定性和专用性人力资本投资的建议,主要包括：改进户籍制度、提供公共服务等政府制度供给。     

东北地区粮食型农户家庭农场经营风险认知的影响因素分析

［目的］东北地区作为维护国家粮食安全的“压舱石”,准确而系统地掌握东北地区粮食型农户家庭农场经营风险认知水平,为政府部门精准实施粮食型农户家庭农场配套扶持政策提供参考。［方法］文章利用黑龙江、吉林、辽宁3省301个粮食型农户家庭农场调研数据,运用交叉列表和多元有序logit模型分析家庭农场经营风险认知水平及其影响因素。［结果］（1）6312%的粮食型家庭农场有一定风险认知能力,土地经营面积在333~1333hm2（50~200亩）与1333~3333hm2（200~500亩）的家庭农场风险认知处于中等水平, 3333hm2（500亩）以上家庭农场经营风险认知低; （2）玉米种植型家庭农场风险认知水平高于水稻种植型家庭农场; （3）年龄、是否村干部、劳动力数量、土地经营规模、是否有自然灾害、农产品市场价格波动、是否“三品一标”认证、金融支持、农推人员技术指导和新型经营主体间合作稳定对东北地区粮食型农户家庭农场经营风险认知有影响。［结论］应培养家庭农场主个人特质,鼓励支农惠农政策向规模适度的家庭农场倾斜,从流程入手提高家庭农场风险防范能力。     

我国天然橡胶种植户生产行为的影响因素分析——基于橡胶价格持续低迷的背景

[目的]通过分析在天然橡胶价格持续低迷背景下种植户的生产行为,从农户及家庭特征、生产特征、市场因素和政策因素4个方面选取16项指标,研究影响天然橡胶种植户生产行为的因素。[方法]选取2015年在天然橡胶主植区云南、海南的农户调研数据,运用多元Logit模型分析天然橡胶种植户生产行为改变的影响因素。[结果]种植户生产行为选择降低和提高割胶频率是种植户权衡各类因素追求利益最大化而产生的不同行为模式,其中橡胶收入占比、种植年限、投入肥料变化、雇工割胶、生产成本、后期价格态度、补贴满意度、收益满意度8项因素对种植户选择降低割胶频率具有显著影响;受教育子女数量、种植规模、雇工割胶、是否享受补贴4项因素对种植户选择提高割胶频率具有显著影响。[结论]天然橡胶种植户的生产行为受多种因素共同作用,并且天然橡胶价格持续低迷破坏了天然橡胶产业的可持续发展。     

粮食主产区农户节水灌溉采用意愿分析

黑龙江垦区已形成科学农业节水体系,但广大市县节水灌溉率非常低,大多采用传统漫灌方式.该文依据黑龙江省364个市县农户的调查数据,运用logit模型对农户采用节水灌溉技术意愿的影响因素进行分析,结果表明:种植业收入所占比重、政府扶持、耕地面积、年龄等因素具有正相关作用.最后提出构建节水灌溉技术推广的投融资体制、加大政府补贴力度、构建农户农业用水激励机制、扩大农户的耕地规模等政策建议.     

An analysis on perceived crowding level reported by domestic visitors of South Korean National Parks: a multilevel ordered logit approach

Perceived Crowding Level (PCL) is an indicator of the social carrying capacity of recreational sites such as National Parks. Using a sample of national-level visitor survey data across 21 National Parks in South Korea, this paper aims to apply a multilevel ordered logit model as a method in testing for statistical relationships between the PCL and the covariates, accounting for site- and individual-specific heterogeneity. The results hint that levels of perceived crowding in South Korean National Parks are related to factors such as the behaviour of other users and the quality of park facilities such as comfort in accommodation. The measured level of heterogeneity can provide useful information about the level (nation-wide or park-specific) of intervention policies and management strategies.     

Public preferences for post 2020 agri-environmental policy in the Czech Republic: A choice experiment approach

In November 2017 European Union commission presented a communication report summarizing the reform proposal of the post 2020 Common Agriculture Policy (CAP). The reform aims to address the environmental degradation associated with agricultural production as well as change in the structure of CAP payments. To this end, the Ministry of Agriculture in Czech Republic is preparing to set its priorities towards CAP’s reform. In this study we applied a choice experiment to investigate the public preferences for a set of environmental goods and services delivered by agri-environment-climatic voluntary measures (AECMs). A mixed logit model is employed to elicit preferences and explore their heterogeneity. We find that respondents oppose strongly funding removal. Among environmental attributes, water and food quality are the ones with the highest implicit marginal willingness-to-pay values. Preferences for no funding option are heterogeneous with socio-demographic and attitudinal variables explaining some sources of this heterogeneity. A continuation of national funding for the AECMs is expected to lead to a better state of environment with an anticipated positive welfare change of 669–932 mil EUR as opposed to funding removal. The change reflects the estimated welfare change resulting from moving from a low to a medium or to a high preservation state of agri-environmental attributes. We also project the budget change for AECMs considering the level of national funding and given the transfer share between Pillar I and II. Based on our results, we suggest that national funding can be informed by the welfare change scenarios and transfer shares are projected accordingly.