Oxford Bulletin of Economics and Statistics最新文献

This paper combines administrative data from the Italian social security administration and proprietary data from a major Italian commercial bank to analyse the impact of job protection legislation on mortgage conditions. An exogenous change in the degree of job protection against individual dismissals of workers with open-ended contracts is identified by exploiting the labour market reform of 2015, the ‘Jobs Act’, which weakened the employment protection of new hires at medium-sized and large private firms. We find that the lessening of job security led to lower mortgage amounts and a fall in leveraging capacity, as measured by the loan-to-value ratio. The impact of job insecurity is mitigated by the presence of co-mortgagors; it is aggravated for young and low-income mortgagors.

We introduce a Bayesian mixed frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii) enhancing the economic interpretation of the main movements in the labour market. We find satisfactory results in terms of nowcasting and forecasting, especially for employment growth. Furthermore, we look into the shocks that drove the labour market and macroeconomic dynamics from 2002 to 2022, with an insight also on the COVID-19 recession. While demand shocks were the main drivers during the Global Financial Crisis, technology and wage bargaining factors, reflecting the degree of lockdown-related restrictions and job retention schemes, have been important drivers of key labour market variables during the pandemic.

We examine whether the decline of routine occupations contributed to rising wage inequality between young and prime-age non-college educated women in the UK over 2001-2019. We estimate age, period, and cohort effects for the likelihood of employment in different occupations and the wages earned therein. For recent generations, cohort effects indicate a higher likelihood of employment in low-paying manual jobs relative to high-paying abstract ones. Cohort effects also underpin falling wages for post-1980 cohorts across all occupations. We find that the latter channel, rather than job polarization, has been the main driver of rising inter-age inequality among non-college females.

For linear panel data models with fixed effects, cluster-robust covariance estimation does not use variability over time. The extant heteroskedasticity-robust methods available under strict exogeneity do not generalize to dynamic models. We propose novel robust covariance estimators under a strong version of serial uncorrelatedness, where serial uncorrelatedness is required to identify dynamic panel models. Asymptotics are established, and simulations verify theoretical findings. The estimator can apply to the popular dynamic IV-GMM estimators and be a sharper alternative for cluster-robust covariance estimators in panel data models with limited cross-sectional information.

For VAR models with common explosive root, the OLS estimator of the autoregressive coefficient matrix is inconsistent (refer to Nielsen, 2009 and Phillips and Magdalinos, 2013). Although Phillips & Magdalinos (2013) proposed using the future observations as the instrumental variable for removing the endogeneity from VAR models, type I error occurs when testing for a common explosive root from the distinct explosive roots before the implementation of IV estimation. Such error creates bias and variance in the estimate and further causes incorrect inference in the structural analysis such as forecast error decomposition (FEVD). Hence, we propose using of seemingly unrelated regression (SUR) estimation for VAR models with explosive roots. Our SUR estimator is consistent in the case of both distinct explosive roots and common explosive root. We also consider models with drift in the system for generalization. Simulations show that the SUR estimate performs better than OLS and IV estimate in the case of both a common explosive root and distinct explosive roots case. In structural FEVD analysis, simulations show that SUR yields a different result from OLS and IV. We demonstrate the use of SUR in FEVD for agricultural commodity markets between 3 July 2010, and 29 January 2011.

This study examines US productivity growth through the lens of R&D-based growth models. A general R&D-based model, nesting different model varieties, is developed. These varieties are tested using a novel cointegrating relationship and US data for the period 1953–2018. The results provide evidence against the widely used fully endogenous variety and support for other varieties including the semi-endogenous variety. Further, the results are systematically consistent with the presence of fishing-out effects in knowledge production, implying that productivity-enhancing innovations become increasingly harder to achieve as technologies become more advanced. Forecasts suggest that the US productivity growth slowdown continues over the coming decades.

In cross-section gravity models the two-way cluster-robust standard errors of the Poisson pseudo maximum likelihood (PPML) estimates tend to be considerably downward biased. However, two-way clustering can be avoided if intra-cluster correlation is induced by country-specific trade shocks with uniform pass through (equi-correlation) and the gravity model includes exporter and importer country fixed effects. In this case the pseudo-within-transformation of the PPML estimator projects out the corresponding components of the disturbances. In Monte Carlo simulations the Pustejovsky and Tipton (2018) bias correction for independent disturbances (i.e. ignoring clustering) reveals just a small downward bias of the estimated standard errors and confidence intervals with nearly correct coverage rates. Under deviations from equi-correlation the bias is somewhat larger, but still comparable to the bias of the cluster-robust standard errors with Pustejovsky and Tipton (2018) bias correction.

We study the effects of early life exposure to above average levels of rainfall on adult mental health. While we find no effect from prenatal exposure, postnatal positive rainfall shocks decrease average Center for Epidemiological Studies Depression (CESD) mental health scores by 13% and increase the likelihood of depression by 6%, a more than 26% increase relative to the mean. These effects are limited to females. We rule out prenatal stress and income shocks as pathways. Early life exposure to infectious diseases appears to play a limited role but further research is required.

Using high-quality nationwide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals are on income support in the subsequent 4 years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R-squared), compared to the latter. This gain can be achieved at no extra cost to practitioners since the algorithms use administrative data currently available to caseworkers. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients, which can potentially enable governments and institutions to offer timely support to these at-risk individuals.

We analyse why conventional monetary policy tightening in the euro area leads to a deterioration of output in Central-, East and Southeastern Europe (CESEE). Our findings show that negative spillovers mainly arise through a decline in CESEE imports and exports, induced by a decrease in euro area demand. Negative spillovers are amplified through knock-on effects through third-countries and cannot be cushioned by favourable exchange rate movements. We also find evidence for a broad-based retrenchment of cross-border bank flows to the region. For the CESEE policymaker, our results indicate a limited power of exchange rate policies to buffer foreign, adverse monetary policy shocks.