The power of positivity? The influence of positive psychological capital language on crowdfunding performance

We extend the entrepreneurship literature to include positive psychological capital — an individual or organization's level of psychological resources consisting of hope, optimism, resilience, and confidence — as a salient signal in crowdfunding. We draw from the costless signaling literature to argue that positive psychological capital language usage enhances crowdfunding performance. We examine 1726 crowdfunding campaigns from Kickstarter, finding that entrepreneurs conveying positive psychological capital experience superior fundraising performance. Human capital moderates this relationship while social capital does not, suggesting that costly signals may, at times, enhance the influence of costless signals. Post hoc analyses suggest findings generalize across crowdfunding types, but not to IPOs.     

Inequality and entrepreneurial thresholds

We explore the relationship between inequality and entrepreneurial activity. Drawing on cross-sectional data from a largescale survey of the economic conditions of individuals across India, we develop a number of dimensions of inequality to explore empirically how inequality interacts with entrepreneurship, operationalized as self-employment or as employing other people. We find compelling evidence that there are thresholds to becoming self-employed, and even more so to assembling the combinations of resources and personal attributes required to become an employer. Greater inequality leaves more people unable to make the transition to self-employment, leaving casual laboring as the occupation of necessity. At the same time, inequality increases the number of employers in a society, by concentrating resources - particularly land and finance - enough for significant numbers of people to be able to cross this higher threshold. Lastly, greater differentiation into social or religious groups curtails the ability to cross either entrepreneurial threshold, presumably by limiting the extent and benefits of social networks of value for entrepreneurship.     

Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

资源依赖、制度安排与区域创新能力 —基于空间杜宾模型

区域创新能力提升是我国加快建设创新型国家、实现高质量发展的现实需求。 本文通过构建涵盖资源依赖、制度安排与区域创新能力的理论模型分析其内在关系并提出假 说，运用2000-2016年全国30个省（区、市）的面板数据与空间杜宾模型，检验三者之间的影 响及空间溢出效应。研究结果表明：（1）资源依赖程度越高，越不利于本地区创新能力提升；（2）有效率的制度安排对区域创新能力有正向促进作用；（3）从空间溢出效应来看，本地区 资源依赖程度越高，越抑制周边地区区域创新能力提升，有效的制度安排能助推周边地区创新能力提高。     

珠三角工业设计与工业互动比较研究：双重需求拉动与设计驱动增长的区域验证

动态比较珠三角工业发展阶段和设计水平不同的典型区域内，工业用户和最终消费者用户双重需求拉动设计创新以及设计驱动工业增长作用机制的异同，提出克服互动系统短板并因地制宜实施设计驱动转型升级的对策建议。基于SVAR建模的脉冲响应和方差分解分析发现：珠三角工业确实可以通过工业设计创新驱动实现内生性可持续增长，而设计水平的地区差异对设计驱动绩效有调节作用；工业发展质量和结构欠佳，无法有效拉动区域工业设计创新，是珠三角多数地区工业设计有效需求不足的根本原因。区域工业设计与工业互动系统的短板是缺乏内行和挑剔的最终消费者有效需求。最后基于模型稳健性检验，提出因地制宜科学实施设计驱动发展战略的对策建议。     

Increasing linkages among European regions. The role of sectoral composition

This paper analyses changes in economic regional interlinkages in Europe over time and investigates the factors that could explain the dynamics of these changes. Our four main findings are the following: (i) we detect a significant surge in regional synchronisation after the Great Recession; (ii) we identify the regions most interrelated with the rest of Europe, namely, Ile de France, Inner London and Lombardia; (iii) we find that sectoral composition explains regional synchronisation in Europe, mainly after the Great Recession and (iv) we document that sectoral composition has important implications for aggregate economic fluctuations, in particular, that similarities in services-related sectors across regions explain a nonlinear relationship between sectoral composition and regional business cycle synchronisation. We also propose a new method to measure time-varying synchronisation in small samples that combines regime-switching models and dynamic model averaging.     

广东省县域农业生产效率分析——基于2015-2019年揭阳市5县域DEA模型为例

农业生产效率提升是实现农业现代化的重要手段。本文以《揭阳市统计年鉴(2015-2019)》的农业相关数据,从县域的视角研究揭阳市农业生产效率,运用DEA模型对揭阳市5个县域的3个投入指标和1个产出指标进行实证分析。结果显示,2015-2019年揭阳市农业生产效率均属于DEA无效,总体效率提升并不明显,且各县域呈现发展不平衡的状态,仅惠来县位于生产前沿面上,普宁市的农业生产效率最低,规模报酬属于递减态势。因此,恰逢“十四五”谋划之时,对揭阳市各县域农业生产效率做出分析研究,为现阶段提升揭阳市农业生产效率提出有参考价值的建议,为广东省县域农业生产效率分析提供范本。     

Unbiased estimation of expected return using CAPM

Estimation of expected return is required for many financial decisions. For example, an estimate for cost of capital is required for capital budgeting and cost of equity estimates are needed for performance evaluation based on measures such as EVA. Estimates for expected return are often based on the Capital Asset Pricing Model (CAPM), which states that expected excess return (expected return minus the risk-free rate) is equal to the asset's sensitivity to the world market portfolio (β) times the risk premium on the “world market portfolio” (the market risk premium). Since the world market portfolio, by definition, contains all assets in the world, it is not observable. As a result, an estimate for expected return is commonly obtained by taking an estimate for β based on some index (as a proxy for the world market portfolio) and an estimate for the market risk premium based on a potentially different index and multiplying them together. In this paper, it is shown that this results in a biased estimate for expected return. This is undesirable since biased estimates lead to misallocation of funds and biased performance measures. It is also shown in this paper that the straightforward procedure suggested by Fama and MacBeth [J. Financ. Econ. 1 (1974) 43] results in an unbiased estimate for expected return. Further from the analysis done, it follows that, for an unbiased estimate, it does not matter what proxy is used, as long as it is used correctly an unbiased estimate for expected return results.     

A Bayesian analysis of a variance decomposition for stock returns

We apply Bayesian methods to study a common vector autoregression (VAR)-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy, which involves expressing beliefs about the components of the variance decomposition. Previous Bayesian work elicited priors from the difficult-to-interpret parameters of the VAR. With a commonly used data set, we find that the posterior standard deviations for the variance decomposition based on these previously used priors, including “non-informative” limiting cases, are much larger than classical standard errors based on asymptotic approximations. Therefore, the non-informative researcher remains relatively uninformed about the variance decomposition after observing the data. We show the large posterior standard deviations arise because the “non-informative” prior is implicitly very informative in a highly undesirable way. However, reasonably informative priors using our elicitation method allow for much more precise inference about components of the variance decomposition.