Impact of contracts in high yielding varieties seed production on profits and yield: The case of Nepal

Nepal’s population continues to grow, but the agricultural sector’s performance remains almost stagnant. This has led to a decline in the per capita availability of food. Increasing agricultural productivity is the key to agricultural growth, and one strategy for increasing agricultural productivity is to use improved seeds. This study investigates the impact of contract farming (CF) in high yielding varieties (HYV) of paddy seed production on costs, yield, and profits of smallholder farms in Nepal. Using farm-level data and a non-parametric propensity score matching estimator, the study finds a significant positive impact of contract HYV seed farming on revenues, profits, and yield, and a significant negative impact on total costs of production. Additionally, very small farms (⩽0.43 ha) with CF in HYV paddy seeds tend to gain the most when it comes to yield per hectare. Our estimates reveal that the average smallholder household in Nepal engaged in CF with input conditions receives higher profits. However, farmers engaged in CF with output conditions tend to have higher yields but smaller profits. Finally, farmers engaged in CF in HYV paddy seeds with both input and output conditions have the highest yield gains and significantly higher profits.     

Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes

This paper contributes to the nascent literature on nowcasting and forecasting GDP in emerging market economies using big data methods. This is done by analyzing the usefulness of various dimension-reduction, machine learning and shrinkage methods, including sparse principal component analysis (SPCA), the elastic net, the least absolute shrinkage operator, and least angle regression when constructing predictions using latent global macroeconomic and financial factors (diffusion indexes) in a dynamic factor model (DFM). We also utilize a judgmental dimension-reduction method called the Bloomberg Relevance Index (BRI), which is an index that assigns a measure of importance to each variable in a dataset depending on the variable’s usage by market participants. Our empirical analysis shows that, when specified using dimension-reduction methods (particularly BRI and SPCA), DFMs yield superior predictions relative to both benchmark linear econometric models and simple DFMs. Moreover, global financial and macroeconomic (business cycle) diffusion indexes constructed using targeted predictors are found to be important in four of the five emerging market economies that we study (Brazil, Mexico, South Africa, and Turkey). These findings point to the importance of spillover effects across emerging market economies, and underscore the significance of characterizing such linkages parsimoniously when utilizing high-dimensional global datasets.     

All of work? All of life? Reconceptualising work‐life balance for the 21st century

This paper argues that the study of work‐life balance to date has, in the main, adopted a restricted conception of both “work” and “life”, which does not take account of recent developments in life worlds, working arrangements and employment relationships. “Life” has hitherto been viewed as largely comprising caring activities for dependent children, whereas “work” has been premised largely on a traditional model of work, characterised by full‐time, permanent employment with one employer and a conventional understanding of what work involves. This means that extant research and theory only provides a partial view of the work‐life needs and experiences of the workforce. In the paper, we propose extending conceptions of both work and life to incorporate different life worlds and social groups and different working arrangements and employment relationships.     

高职院校环境监测实践教学改革探索

本文分析了高职院校环境监测实践教学中存在的问题,从实践参与教学模式、实践教学方法及考核、评价方式等方面进行了一系列的探索和尝试。     

‘I didn't feel like I was alone anymore’: evaluating self‐organised employee coping practices conducted via Facebook

The long‐term fracturing of the labour movement has led to increased attention to employee coping practices under new management practices and labour processes. However, the literature caters little for the recent rise of employees taking to social networking sites (SNSs), such as Facebook, to find ways to cope with the pressures of contemporary employment. To explore the self‐organised coping qualities of SNSs, interviews were conducted with front line workers, employed by a large anti‐trade union US retailer, who contribute to a self‐organised Facebook group set up as a place for fellow employees to deal with collective employment‐related problems. The main findings suggest employee self‐organised Facebook groups represent an important development and extension to the coping practices available to individual and groups of employees. The main implication of the findings is that Facebook groups appear to strengthen and widen the options for employee resilience in an age of continuing trade union retreat.     

A comparison of programming languages in macroeconomics

We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++14, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and briefly comment on the strengths and weaknesses of each language.     

Repeated moral hazard and recursive Lagrangeans

This paper shows how to solve dynamic agency models by extending recursive Lagrangean techniques à la Marcet and Marimon (2011) to problems with hidden actions. The method has many advantages with respect to the promised utilities approach (Abreu et al., 1990): it is a significant improvement in terms of simplicity, tractability and computational speed. Solutions can be easily computed for hidden actions models with several endogenous state variables and several agents, while the promised utilities approach becomes extremely difficult and computationally intensive even with just one state variable or two agents.     

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.     

Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach

It is commonly observed that high grade loans with better ratings are often associated with low recoveries if they default (i.e. with relatively high loss-given-default (LGD)). To address the mismatch problem, this paper proposes a credit risk approach by minimizing LGD for higher rated loans as a risk-rating matching standard in the sense that the decreasing LGD from creditors’ perspective is associated with higher credit rating for the borrower. This standard forces customers’ credit rating of each grade to be optimally determined in correspondence to its LGD, which means the LGD of high grade loans tends to be low. The approach is then tested using three credit datasets from China, i.e. credit data from 2044 farmers, 2157 small private businesses and 3111 SMEs. The empirical results show that the proposed approach indeed guides the way to solve the mismatch phenomenon between credit ratings and LGDs in the existing credit rating literature. By optimally determining credit ratings, the findings derived from this paper help provide a valuable reference for bankers, and bond investors to manage their credit risk.     

基于供应链的物流企业战略匹配能力研究

文中通过回顾之前对物流能力所作的研究及相关理论背景的基础上，认为物流企业战略匹配能力是企业物流能力的一个重要方面，并做了简单的探讨分析，丰富了对物流企业能力的研究。