Markups of Exporters and Importers: Evidence from Hungary

In this paper, we study the relationship between different proxies of firm‐level markups and trade status, using balance sheet information linked to detailed trade data from Hungary between 1995 and 2003. We find that importing is strongly positively correlated with markup measures, both across and within firms. We argue that this correlation can reflect three channels: self‐selection, higher physical productivity resulting from access to a larger variety of inputs, and quality upgrading based on high‐quality imported intermediate inputs. We present evidence for the relevance of the third channel by showing that importers’ markup premium is higher when inputs arrive from developed countries, and that importing is correlated with higher‐quality (price‐adjusted revenue) exports. We find no robust evidence for exporter premium when controlling for importing. We argue that the non‐existent exporter premium might result from the stronger competition in export markets relative to domestic markets.     

长江经济带中三角地区农业结构的分析与优化对策

[目的]从宏观、中观和微观3个层面深入分析长江经济带中三角地区湖北、湖南和江西3省的农业产业结构及其各产业差异性和相对优势及其竞争力。[方法]在宏观层面运用了传统的基尼系数分析3个省份历史发展上的地区经济水平差异,判断3个地区经济发展是否存在虹吸效应;从中观层面运用区位熵指数分析法分析区域农业产业结构,从农畜林渔等方面判断各区域产业专业化水平程度;从微观层面运用偏离—份额分析法(SSM)分离出3个地区的各部门产业的优势及其竞争力。[结果]区域内的经济差异变化呈不断增大的趋势,基尼系数不断扩大;湖北的种植业、渔业和农林牧渔服务业,湖南的种植业、林业、畜牧业和农林牧渔服务业,江西的林业、渔业都较中三角地区对应的子产业的区位熵指数高;江西木材和竹加工业和烟草业具有较大的产业竞争优势,湖北的茶叶、水产和瓜果疏菜产业优势明显,湖南地区农业产业综合优势主要在于烟草、水果、肉类以及木材和竹类加工业。[结论]通过比较地区农业产业竞争优势,提出发展地区产业特色,合理布局全域农业产业,提高农产品附加收益,稳固对结构优化的资金支持等对策建议。     

中国海外耕地投资的区位特征及其影响因素*——基于2000—2016年土地矩阵网络数据

［目的］分析中国海外耕地投资的区位特征及其影响因素，以促进我国海外耕地投资的合理布局，提高农业投资效率。［方法］以2000—2016年中国137宗海外耕地投资项目及其所涉及的35个东道国作为研究样本，运用Spearman相关分析方法，分别以耕地投资面积和耕地投资项目数量作因变量，研究其与各影响因素的相关关系，并用对应分析法进一步探究各东道国的相对优势。［结果］（1）我国海外耕地投资区域集中在东南亚且由周边国家向外扩展，被投资耕地用途多样，投资规模自2004年开始急剧上升，近年来呈下降趋势。（2）我国海外耕地投资以效率寻求型和市场寻求型为主，倾向于资源丰富、与我国距离较近、制度相似、经济关系良好但发展水平不高的国家。（3）根据各地区的比较优势，亚洲国家适合效率寻求型和市场寻求型的企业投资，欧洲和美洲国家适合资源寻求型和战略资产寻求型的企业投资，非洲国家适合资源寻求型、效率寻求型和市场寻求型的企业投资。［结论］中国农业企业应结合企业投资目标和各地区的比较优势，合理选择投资区位，适时转变投资策略与模式，积极防范海外耕地投资过程中的自然风险、政治环境风险和道德风险等，以提高投资效果。     

Are You a Chocolate Lover? An Investigation of the Repurchase Behavior of Chocolate Consumers

This study aimed to understand the factors affecting repurchase behavior of chocolate brands and, consequently, customer retention and acquisition. The study adopted a qualitative, inductive approach using in-depth interviews with 31 Australian consumers. The factors identified in the extant literature as antecedents of customers’ repurchase intention in the chocolate industry, including brand recognition, sales promotion, product price value, variety, taste, texture, size, packaging, and customer satisfaction, were confirmed. The results also indicated that functional value, product selection value, self-gratification value, socialization value, and transactional value were also considered during the consumer decision-making process. Implications for practitioners are provided.     

A proposed configurable approach for recommendation systems via data mining techniques

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.     

Policy lessons from spatiotemporal enrollment patterns of payment for ecosystem service programs in Argentina

Payment for ecosystem services schemes (PES) are lauded as a market-based solution to curtail deforestation and restore degraded ecosystems. However, PES programs often fail to conserve sites under strong long-term deforestation pressures. Underperformance, in part, is likely due to adverse selection. Spatial adverse selection occurs when landowners are more likely to enroll parcels with low deforestation pressure than parcels with high deforestation pressure. Temporal adverse selection arises when parcels are enrolled for short time periods. In both cases, financial resources are allocated without having a sizeable impact on long-term land use change. Improving program performance to overcome these shortcomings requires understanding attributes of landowners and their parcels across large scales to identify spatial and temporal enrollment patterns that drive adverse selection. In this paper, we examine these patterns in Argentina’s PES program in Chaco forest, a global deforestation hotspot. Our study area covers 252,319 km². Results from multinomial logistic regression models showed that large parcels of enrolled land and parcels owned by absentee landowners exhibit greater evidence of spatiotemporal adverse selection than smaller parcels or parcels owned by local landowners. Furthermore, parcels managed under land use plans for conservation and restoration are more likely to be associated with adverse selection than parcels managed for financial returns such as harvest of non-timber forest products, silviculture, and silvopasture. However, prior to recommending that PES programs focus on land uses with higher potential earnings, a greater understanding is needed of the degree to which these land uses meet ecological and biodiversity goals of PES programs. We suggest that increased spatial targeting of enrollment, along with enrollment of local landowners and further incentives for land uses that support conservation and restoration, could promote long-term conservation of forest lands.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

Existence of solutions to principal–agent problems with adverse selection under minimal assumptions

We prove an existence result for the principal–agent problem with adverse selection under general assumptions on preferences and allocation spaces. Instead of assuming that the allocation space is finite-dimensional or compact, we consider a more general coercivity condition which takes into account the principal’s cost and the agents’ preferences. Our existence proof is simple and flexible enough to adapt to partial participation models as well as to the case of type-dependent budget constraints.     

Reluctant Generalised Additive Modelling

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.     

出口经验能助推对外直接投资吗?--基于中国与“一带一路”沿线国家宏观数据的实证研究

以"一带一路"沿线45国2003~2014年数据为样本,实证检验了出口经验能否提升对外直接投资。研究结果显示,出口经验显著提升中国对"一带一路"沿线国家的直接投资。在基准模型的基础上,引入公共治理、能源禀赋和地理区位与出口经验交互项的实证结果还显示,出口经验对直接投资的促进效应随着东道国治理水平提升而提高,对陆上丝绸之路国家的作用显著低于海上丝绸之路国家,而能源禀赋并未直接或间接通过出口经验对直接投资产生作用。这些研究结论说明,企业在不确定性外部环境下进行对外直接投资时,倾向于通过建立出口学习曲线并依此进行直接投资决策。