WHICH FISCAL MULTIPLIERS ARE REGIME‐DEPENDENT? A META‐REGRESSION ANALYSIS

We analyse whether estimated multiplier effects are systematically higher if the economy suffers a downturn. For that purpose, we conduct a meta‐regression analysis on a unique data set of 98 empirical studies with more than 1800 observations on multiplier effects and control for regime‐dependence of the multiplier. We find that spending multipliers are much higher (by about 0.7–0.9 units) during a downturn. Tax multipliers are not sensitive to the economic regime, and generally lower than spending multipliers. Finally, for all spending categories other than government consumption, the multiplier significantly exceeds one during downturns.     

衡水市农户土地资源转出意愿的影响因素与行政干预

[目的]通过了解河北省衡水市农户土地转出的意愿状况,分析影响农户土地资源转出意愿的关键因素,并制定正确的战略决策,为构建农地适度规模化经营奠定理论基础,促进衡水市农民的现代化发展。[方法]文章结合调研情况,对样本区农户土地使用权流转行为进行了统计描述和分析研究,并运用Logistic模型对农户土地使用权转出意愿的影响因素进行了分析,找到了影响衡水市土地流转的关键性因素。[结果]研究表明,户主年龄、从事职业和文化程度、农户家庭非农收入以及农户家庭兼业人数等与农户土地转出意愿呈显著正相关关系,显著性数值分别为0. 018、0. 000、0. 010、0. 000、0. 028,而农户家庭农用机械数量则与农户土地转出意愿呈显著负相关关系,显著性数值为0. 033。此外农户社会保障程度和农户对政府土地流转的相关政策了解程度等因素也影响着农户土地流转的意愿。[结论]影响农户土地转出意愿的显著性因素包括家庭非农收入、户主年龄和职业;另外,户主的年龄、职业、文化程度、非农收入、家庭兼业人数和农用机械数量均是影响农户土地转出意愿的重要内容。     

广东省农村基础设施建设与农业经济增长的关系探究

作为提高农业生产效率、改善农民生活质量及促进农村经济增长的重要物质基础,农村基础设施建设对扩大农村地区内需增长点,解决农村剩余劳动力就业问题以及全面推进城乡统筹发展具有至关重要的意义。广东省作为我国南部地区经济发达省份,具有丰富的农业生产自然资源,但其农村基础设施发展程度相对来说却比较低,其成为农村经济发展的"瓶颈",不利于农村经济结构优化升级。为了保障农村基础设施的供给与农村经济发展水平保持协调一致,文章以广东地区农村为例,采用多元非线性回归分析的方法,在论证农村基础设施建设对农村经济增长的作用机制的基础上,从农村基础设施建设与农业生产、非农生产和农民人均纯收入3方面的内在关系进行计量分析,结果表明,首先农村基础设施对影响农业经济增长的3方面因素都发挥了显著的规模经济效益;其次农村基础设施建设中的教育变量对农业经济增长的作用最为显著;再次,农业机械设施在农业生产中的作用相对于其他影响因素而言不太显著;最后,道路、通讯设施对非农生产和农民人均收入收入的贡献率相对其他变量数值较低。并提出健全农村基础设施建设投融资机制、加大农村基础设施的资金投入力度、提高农村基础设施建设和利用水平、全面推进农村基础设施建设进程等优化策略。     

Global value chains,large-scale farming,and poverty: Long-term effects in Senegal

This paper presents panel data evidence on the impact of expansion of global value chains and large-scale export-oriented farms in developing countries over almost a decade. We estimate the income effects of wage employment on large-scale farms in the horticultural export sector in Senegal, using data from two survey rounds covering a seven-years period of rapid expansion of the sector. We estimate average income effects as well as heterogeneous income effects, using fixed effects and quantile fixed effects regressions. We find that poverty and inequality reduced much faster in the research area than elsewhere in Senegal. Employment in the horticultural export sector is associated with higher household income and the income effect is strongest for the poorest households. Expansion of the horticultural export sector in Senegal has been particularly pro-poor through creating employment that is accessible and creates substantial income gains for the poorest half of the rural population. These pro-poor employment effects contrast with insights in the literature on increased inequality from rural wage employment.     

GDP nowcasting: application and constraints in a small open developing economy

Despite data limitations, an attempt is made to find out if a GDP nowcasting model can provide reliable forecasts for a small open economy. Two competing Bayesian vector autoregressive models are tested rigorously to obtain the optimal model by minimizing in-sample forecasting errors. The main finding of this study is that GDP nowcasting can produce reliable results for a small open economy despite the unavailability of sufficient data sets and the lack of high frequency indicators.     

A Meta‐Analysis on the Elasticity of Demand for Pesticides

There is an increasing policy interest in pesticide taxation schemes as a measure to reduce harmful effects of pesticide use. The effectiveness of such tax depends, however, on the price elasticity of demand for pesticides. Moreover, information on these demand elasticities and their determinants is of crucial relevance for policy‐making and normative modeling approaches. In this article, we present a meta‐analysis based on studies that have estimated pesticide demand elasticities in Europe and North America. Our meta‐analysis reveals that the own‐price elasticities of demand for pesticides are, with a median of ?0.28, significantly smaller than zero, but also significantly larger than ?1, i.e. to be inelastic. We find that the demand for pesticides for special crops is less elastic than that for arable and grassland. In addition, the demand for herbicides is more elastic than for other pesticides. Studies that consider only short‐term horizons and little flexibility for farmers to adjust to price changes generate significantly less elastic pesticide demands. The results also indicate that more recent studies identify lower pesticide price elasticities of demand. Furthermore, we find that peer‐reviewed studies tend to find more inelastic results compared to grey literature.     

空间分析在住宅价值评估中应用研究综述

住宅是一种异质性商品，它的构成使用价值的各个特征之间有明显的差异，因此特征价格定价模型广泛用于住宅的价值评估。但是由于住宅数据中常常表现出空间问题，使得传统的特征价格模型不再适用，空间效应在不动产领域的存在引起了学者们的广泛关注。对空间分析在住宅价值评估中的应用研究进行了回顾和阐述，首先分析空间效应的来源和影响，讨论了在传统模型中纳入空间效应的重要性，接着阐述如何对这些空间效应进行有效建模和检验，并分析了进一步研究的方向。     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.