Farm size and productivity growth in the United States Corn Belt

In recent decades, agricultural production in the U.S. has continued to shift to large-scale operations, raising concerns about the economic viability of small and midsized farms. To understand whether economies of size provided an incentive for the consolidation of production, the study estimates the total factor productivity (TFP) of five size classes of grain-producing farms in the U.S. Heartland (Corn Belt) region. Using quinquennial Agricultural Census data from 1982 to 2012 the study also compares TFP growth rates across farm sizes to gain insight into whether observed productivity differences are likely to persist. The finding of a strong positive relationship between farm size and TFP suggests that consolidation of production has contributed to recent aggregate productivity growth in the crop sector. The study estimates the extent to which sectoral productivity growth can be attributed to structural change versus other factors including technological change. The study also explores some tradeoffs associated with policies that raise the productivity of small versus large farms.     

Discouraging Rainforest Transformation: An Ex-ante Policy Impact Analysis

Despite extensive efforts made by national and international certification agencies, Indonesian smallholder farmers’ participation in palm oil certification schemes adoption remains low. A fundamental obstacle is the smallholder practice of rainforest transformation into oil palm plantation which is forbidden by the agencies. In this context, we investigate three policies that could lead to a reduction in rainforest deforestation by smallholders: price premium on certified palm oil; the provision of environmental information; contributor recognition. In order to evaluate the influence of the policies ex-ante, we conduct a social dilemma experiment involving rubber and oil palm smallholders in Jambi Province, Sumatra, Indonesia. The findings indicate that the price premium and provision of context-specific environmental information could reduce rainforest transformation. However, a statistically significant effect of contributor recognition was not found.     

Drivers of research impact: evidence from the top three finance journals

We study the characteristics of all published papers in the top three finance journals (JF, JFE and RFS), and how these paper characteristics affect the number of citations in Google Scholar and the Web of Science database. First, we find the characteristics in the universalist perspective remain constant while the characteristics in the constructivist and presentation perspectives increase over time. Second, some characteristics are significantly different between the high-impact and the low-impact papers. Third, paper quality, research method, journal placement and paper age are the most important drivers. Last, different drivers play different roles in different journals.     

组织场域视角下水利水电工程BIM技术扩散的影响因素 ——基于TOE框架的分析

在梳理国内外BIM采纳与扩散相关研究的基础上，从新制度主义理论出发，提出了水利水电工程组织场域并分析了其特征；进而运用技术-组织-环境(TOE)框架和匹配理论，分别讨论了水利水电工程组织场域BIM扩散的主要影响因素，形成一个整体的理论分析框架。分析表明，水利水电工程组织场域BIM扩散的影响因素主要包括:相对优势、兼容性和复杂性等技术因素，组织沟通、资源就绪度和高层领导支持等组织因素，以及制度环境、市场环境等环境因素。     

Understanding precedents for frontline employee turnover in luxury hotels: Emotional intelligence as a unifying factor

Current turnover research fails to serve the needs of an industry that is long plagued by employee turnover. Existing literature focuses more on evaluating bundles of human resource practices and fail to provide precise and clear guidance for practitioners. This study proposes that emotional intelligence (EI) unifies sufficient individual factors and organizational factors that affect employee turnover and serves as a single significant precedent for turnover. Data were collected from frontline employees at eight luxury hotels. The direct, indirect, and total impacts of employee EI on employee turnover were tested by structural equation modeling and bootstrap tests. The results suggest that EI has significant indirect impacts through the mediation of perceived organizational support, pay satisfaction and job burnout, and significant total impacts on turnover. Implication suggestions include integrating EI into the recruiting process for new employees and providing training opportunities for current employees to improve their EI.     

Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

This paper aims to comprehensively uncover bank risk factors from qualitative textual risk disclosures reported in financial statements, which contain a huge amount of information on bank risks. We propose a new semi‐supervised text mining approach named naive collision algorithm to analyse the textual risk disclosures, which can more accurately identify bank risk factors compared with the typical unsupervised text mining approach. We identified 21 bank risk factors in total, which is far more than identified in previous studies. We further analyse the importance of each bank risk factor and how the importance of each risk factor changes over time.     

Is DNA fingerprinting the gold standard for estimation of adoption and impacts of improved lentil varieties?

In the early 1980s, disease susceptibility in short-season lentil landraces began to limit productivity in areas where relay cropping took place in Bangladesh. Since then, several improved high-yielding lentil varieties, which are resistant to rust and blight and suitable in the relay cropping system, have been released jointly by national and international research centers. This study used three methods, namely a panel of experts, a survey of 1000 households where the respondents named the variety they used, and DNA fingerprinting of seed samples collected from all lentil plots cultivated by survey households to estimate adoption. Double hurdle and instrumental variables regression methods were applied to the household survey and DNA fingerprinting data to identify determinants of adoption and measure their impacts. Of particular interest was whether estimates of adoption, determinants of adoption and impacts varied by method of variety identification. Results showed that the expert panel overestimated the adoption of more recent varieties while about 89% of the farmer-reported varieties were accurate, as verified by DNA fingerprinting. DNA fingerprinting appears to have little advantage for estimating the level of adoption in this case, where few varieties of lentils are found, local variety names do not exist, and most seed is obtained through a formal system. However, even under these conditions, determinants of adoption vary by identification method, and use of farmer-reported information on the variety can lead to erroneous conclusions about determinants of adoption. Because recent breeding efforts have focused on taste and cooking considerations, yield impacts were not significantly different from zero.     

Green human resource management: Development of a valid measurement scale

The objective of the study was to develop a valid measurement scale for green human resource management (HRM). Even though the common practices of green HRM have been presented in much of the literature, the previous studies focused only on a small number of functions in integrating environmental management with HRM. Additionally, the measurement of green HRM practices still calls for empirical validation. The two‐stage methodology of structural equation modeling in AMOS was employed for data analysis. Exploratory factor analysis revealed seven dimensions of the construct measured by 28 items. Confirmatory factor analysis confirmed the factor structure. The measuring instruments revealed convergent and discriminant validity. Several model fit indices indicated the model fitness. The study provided supplementary evidence on the underlying structure of the construct that can be valuable to researchers and practitioners in this area.     

The impact of real exchange rate shocks on manufacturing workers: An autopsy from the MORG

We study the impact of large real exchange rate shocks on workers in sectors initially more exposed to international trade using the Current Population Survey’s (CPS) Merged Outgoing Rotation Group (MORG) from 1979 to 2010 combined with new annual measures of imported inputs, a proxy for offshoring. We find that in periods when US relative prices are high, and imports surge relative to exports, workers in sectors with greater initial exposure to international trade were more likely to be unemployed or exit the labor force a year later, but did not experience significant declines in wages conditional on being employed. Contrary to the usual narrative, we find negative wage effects for higher-wage, but not lower-wage workers, particularly for those who are less-educated.     

How many factors are important in U.K. stock returns?

I use the sequential approach of Harvey and Liu ([2018]. Lucky factors (Working Paper). Duke University) to build linear factor models in U.K. stock returns among a set of 13 candidate factors using individual stocks and three groups of test portfolios between July 1983 and December 2017. My study finds that the Market factor is the dominant factor in reducing mispricing in individual stocks and test portfolios regardless of the pricing error metric used. The Market factor has a bigger impact when using a value weighting pricing error metric. Whether a second factor is used or not depends upon which metric is used for mispricing and the time period examined. My study finds support for a two-factor model for the whole sample period of the Market factor and the Conservative Minus Aggressive (CMA) factor of Fama and French ([2015]. “A five-factor asset pricing model.” Journal of Financial Economics 116: 1–22) when giving greater weight to the mispricing of larger companies.