Oxford Bulletin of Economics and Statistics最新文献

This paper studies the evolution of government spending multipliers in the post-war USA using a time-varying parameter VAR model. We achieve identification by imposing sign and zero restrictions on the systematic component of policy rules and impulse responses. Our results show that the US multipliers in the post-OBRA93 period are smaller than those in the 1970s. The multipliers are found to be more strongly correlated with the estimated coefficients of the debt-stabilizing rule than the debt-to-gross domestic product ratios. The increased magnitude of fiscal adjustments appears to be the major driving force behind the decline in multipliers rather than debt accumulation itself.

Here, we present an unexplored issue regarding temporal aggregation. When a model contains frequency-dependent coefficients, such as a distinct long- and short-term coefficient, temporal aggregation leads to inconsistent least squares estimates. Because the sub-sampled variable's spectrum is equal to its folded original spectrum, the low-frequency variable may exhibit a mixture of distinct linear relations for a given frequency. We propose a new method to disentangle the frequencies superposition based on band spectrum regression, thus avoiding the inconsistency problem. As a result, we can test for the presence of frequency-dependent coefficients. We use stationary and non-stationary linear semi-parametric models to demonstrate our findings. Our Monte Carlo simulations show good finite sample size and power properties. Finally, our empirical study rejects the presence of a single coefficient for all frequencies between quarterly GDP and monthly US indicators.

In recent years, the USA observed a substantial increase in the adoption of robotic technology. The use of industrial robots in the US economy increased rapidly from about 1 robot per 1,000 workers in 2005 to 1.7 robots per 1,000 workers in 2017, a 70% increase. At the same time, there is a concern that the rapid adoption of robots will transform our society in a way that we have never seen before. In this article, we investigate whether individuals are responding to the increasing use of robots in their locality by altering their schooling decision. The results of the analysis suggest that a 10% increase in robot exposure is associated with an approximately 2.5% rise in college enrolment rate. In the long run, we find evidence that more intense exposure to robots during school ages is associated with an increase in the probability of an individual obtaining a college degree.

This study investigates the effects of speed competition in classrooms on young pupils' learning outcomes. To examine how faster peers' speed affects slower pupils' speed and learning, we employ students' daily progress data in a self-learning programme at BRAC primary schools in Bangladesh. The programme's unique setting allows us to address the reflection problem reasonably well. While speed competition could generate negative consequences, our results show overall positive peer effects on problem-solving time and scores. The effects are stronger among peers with similar abilities, without negatively affecting others. Our results show efficiency gains from non-market competition in education and learning.

We examine the extent to which monetary policy should respond to movements in sectoral inflation rates using a Generalized Taylor model that takes specific account of the sectoral make-up of the consumer price index. We calibrate the model for each sector using the UK consumer price microdata. We find that a policy rule allowing for different responses to inflation in different sectors outperforms a rule targeting only aggregate inflation, as does a rule responding only to core inflation. However, we find that the optimal sectoral rule only leads to a small absolute improvement in terms of extra consumption.

We assess the impact of the ECB's unconventional monetary policy, specifically of quantitative easing (QE), on the distribution of household wealth in nine euro area countries. For this purpose, we estimate the effects of a QE shock on housing and risky financial asset prices by means of local projections. We then use these estimates to carry out micro-simulations based on data from the Household Finance and Consumption Survey (HFCS). For the majority of the countries under review, expansionary QE via asset prices leads to net wealth inequality increases when measured using wealth indicators that are sensitive to changes at the tails of the wealth distribution. This finding contrasts with results based on the Gini coefficient which point to an equalizing impact of QE. One-third of the households in our sample holds neither housing nor financial wealth and is thus not directly affected by QE measures through the asset prices channel.

Panel data are rarely available for developing countries. Departing from traditional pseudo-panel methods that require multiple rounds of cross-sectional data to study poverty mobility at the cohort level, we develop a procedure that works with as few as two survey rounds and produces point estimates of transitions along the welfare distribution at the more disaggregated household level. Validation using Monte Carlo simulations and real cross-sectional and actual panel survey data – from several countries, spanning different income levels and geographical regions – perform well under various deviations from model assumptions. The method could also inform investigation of other welfare outcome dynamics.

GDP is measured with error. But data uncertainty is rarely communicated quantitatively in real-time. An exception are the fan charts for historical real GDP growth published by the Bank of England. To assess how well data uncertainty is understood, we first evaluate the accuracy of the historical fan charts. We find that data uncertainties can be accurately quantified, even without judgement, using past revisions data. Secondly, we conduct an online survey to gauge perceptions of GDP data uncertainty across a wider set of experts. Our results call for greater communication of data uncertainties to anchor experts' dispersed expectations.

Given the financial and economic damage that can be caused by the collapse of an asset price bubble, it is of critical importance to rapidly detect the onset of a crash once a bubble has been identified. We develop a real-time monitoring procedure for detecting a crash episode in a time series. We adopt an autoregressive framework, with bubble and crash regimes modelled by explosive and stationary dynamics, respectively. The first stage of our approach is to monitor for a bubble; conditional on which, we monitor for a crash in real time as new data emerges. Our crash detection procedure employs a statistic based on the different signs of the means of the first differences associated with explosive and stationary regimes, and critical values are obtained using a training period of data. We show that the procedure has desirable asymptotic properties in terms of its ability to rapidly detect a crash while never indicating a crash earlier than one occurs. Monte Carlo simulations further demonstrate that our procedure can offer a well-controlled false positive rate during a bubble regime. Application to the US housing market demonstrates the efficacy of our procedure in rapidly detecting the house price crash of 2006.

We analyze the effects of early-life shocks in the Philippines and find that in utero exposure to severe typhoons is associated with adverse outcomes. We exploit variations in typhoon exposure and sharp increases in short-term disaster relief efforts in the 1960s. Before the increase in disaster relief efforts, in utero exposure to severe typhoons was associated with higher mortality (a 9% reduction in cohort size); survivors exhibited similar levels of human capital as the unaffected. After the increase in disaster relief, the mortality effects were mitigated; however, survivors exhibited lower human capital in the long term. We offer suggestive evidence that the observed changes in adverse long-term effects are due to the relief efforts' effectiveness in increasing survival probability.