Obesity of politicians and corruption in Ukraine in 2000–2020– Reply

We collected 469 frontal face images of Ukrainian cabinet ministers who were in office in 2000–2020. For each image, the minister's body mass index was estimated using a computer vision algorithm. The time series of median estimated body mass index of cabinet ministers co-moves over time with two existing measures of corruption – World Bank worldwide governance indicator Control of Corruption, and the discrepancy between the quantity of luxury wristwatches exported from Switzerland to Ukraine (reported by Swiss statistics) and the corresponding quantity imported by Ukraine from Switzerland (reported by Ukrainian statistics).     

Cointegration and control: Assessing the impact of events using time series data

Control groups can provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. We argue that fitting a multivariate time series model offers potential gains over a direct comparison between the target and a weighted average of controls. More importantly, it highlights the assumptions underlying methods such as difference in differences and synthetic control, suggesting ways to test these assumptions. Gains from simple and transparent time series models are analysed using examples from the literature, including the California smoking law of 1989 and German reunification. We argue that selecting controls using a time series strategy is preferable to existing data‐driven regression methods.     

All work intensity is not created equal: Effort motives,job satisfaction and quit intentions at a grocery chain

Prior research has shown that the well-being of employees engaged in intensive work can vary with the discretion their jobs afford regarding how and when to carry out the work. This article explores a different avenue. It argues that well-being also varies with employees’ individual motives for working intensively. The article introduces self-determination theory to the domain of work intensity and focuses on two hypotheses. The first is whether intensive work driven by explicit or implicit incentives is more positively associated with an employee's job satisfaction than intensive work driven by job demands. The second is whether intensive work driven by intrinsic motives is more positively associated with job satisfaction than that driven by explicit or implicit incentives. In both these cases, the article also examines whether equivalent effects exist on (reduced) quit intentions. Original data from a major Greek grocery chain provide corroborative evidence that is robust to a rich set of covariates, including increasingly demanding adjustments for job discretion. The findings contribute to a more complete understanding of why differences in well-being exist among employees performing intensive work, with implications for workers and employers.     

Hayek on the essential dispersion of market knowledge

The Review of Austrian Economics - A key insight in Hayek’s thought is the importance of so-called “local knowledge” in economic activity. In The Fatal Conceit, he states that...     

互联网使用与个体生育意愿——基于信息成本和家庭代际视角的分析

探究生育意愿的影响因素,助力人口转变升级成为当前研究的重要方向.文章置身中国具体情境,基于中国社会状况综合调查数据(CGSS2017),从信息成本视角和家庭代际视角两个维度考察互联网使用对个体生育意愿的影响.研究结果表明,使用互联网以及互联网使用频率越高的个体,其生育意愿越低.进一步研究发现,已育史对于互联网使用频率影响个体再育意愿存在负向调节效应,对于已育个体而言,上网频率越高对再育意愿的抑制作用越加显著;代际"数字鸿沟"对于互联网使用频率影响个体生育意愿也存在显著的负向调节效应.当子代和父辈存在较大的代际"数字鸿沟"时,对于互联网使用频繁的个体,父辈沿袭下来的传统育儿经验和子代通过网络获取的育儿知识相冲突可能会引致更多的家庭矛盾,进而抑制个体生育意愿.反之,当代际"数字鸿沟"较小时,互联网使用频率对个体生育意愿的抑制效应亦有所减弱.文章的发现对于提升个体生育意愿以及生育率的研究具有一定启示意义.     

Asymmetric effects of housing wealth on college enrolment

We investigate the impact of housing wealth, credit availability and financial distress on college enrolment decisions. We find that housing wealth is negatively related to enrolment in public schools and positively related to enrolment in private schools. This evidence suggests that, on average, students substituted away from private schools towards public institutions during the recent financial crisis.     

Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.