Factor Price Equalization in the UK?

This paper develops a general test of factor price equalization that is robust to unobserved regional productivity differences, unobserved region-industry factor quality differences and variation in production technology across industries. We test relative factor price equalization across regions of the UK. Although the UK is small and densely-populated, we find evidence of statistically significant and economically important departures from relative factor price equalization. Our estimates suggest three distinct relative factor price areas with a clear spatial structure. We explore explanations for these findings, including multiple cones of diversification, region-industry technology differences, agglomeration and increasing returns to scale.

Relative Wage Variation and Industry Location

Relative wages vary considerably across regions of the United Kingdom, with skill-abundantregions exhibiting lower skill premia than skill-scarce regions. This paper shows that thelocation of economic activity is correlated with the variation in relative wages. U.K. regionswith low skill premia produce different sets of manufacturing industries than regions withhigh skill premia. Relative wages are also linked to subsequent economic development: overtime, increases in the employment share of skill- intensive industries are greater in regionswith lower initial skill premia. Both results suggest firms adjust production across and withinregions in response to relative wage differences.Deindustrialization, Relative Factor Prices, Diversification Cones

Relative Wage Variation and Industry Location

Relative wages vary considerably across regions of the United Kingdom, with skill-abundant regions exhibiting lower skill premia than skill-scarce regions. This paper shows that the location of economic activity is correlated with the variation in relative wages. U.K. regions with low skill premia produce different sets of manufacturing industries than regions with high skill premia. Relative wages are also linked to subsequent economic development: over time, increases in the employment share of skill-intensive industries are greater in regions with lower initial skill premia. Both results suggest firms adjust production across and within regions in response to relative wage differences.

All is not equal.

Theory suggests that market forces should bring the relative pay of skilled workers into line in different regions within a country. Andrew Bernard, Stephen Redding, Peter Schott and Helen Simpson show that this is not the case for the UK and argue that regional industrial policy needs to take this into account.

Maternal iron status in early pregnancy and birth outcomes : insights from the Baby's Vascular health and Iron in Pregnancy study

Date of Acceptance: 16/03/2015 Acknowledgements N. A. A. was funded by a Wellcome Trust Research Training Fellowship (WT87789). H. J. M. and H. E. H. are supported by the Scottish Government’s Rural and Environment Science and Analytical Services. N. A. B. S. is supported by Cerebra. The authors’ contributions are as follows: N. A. A. was responsible for organising the study conduct, data collection and database management, performed the statistical analysis, interpreted the results and drafted the paper. N. A. A., N. A. B. S., J. E. C., H. J. M. and D. C. G. contributed to the study concept and design, and interpretation of results. H. J. M. and H. E. H. analysed the laboratory samples. J. E. C. and D. C. G. provided advice on statistical strategy and analysis. All authors have fully participated in the reporting stage and have critically reviewed and approved the final draft of the paper. The authors declare no conflict of interestPeer reviewedPublisher PD

Randomized sham-controlled trial of repetitive transcranial magnetic stimulation in treatment-resistant obsessive–compulsive disorder

In open trials, 1-Hz repetitive transcranial magnetic stimulation (rTMS) to the supplementary motor area (SMA) improved symptoms and normalized cortical hyper-excitability of patients with obsessive–compulsive disorder (OCD). Here we present the results of a randomized sham-controlled double-blind study. Medication-resistant OCD patients (n=21) were assigned 4 wk either active or sham rTMS to the SMA bilaterally. rTMS parameters consisted of 1200 pulses/d, at 1 Hz and 100% of motor threshold (MT). Eighteen patients completed the study. Response to treatment was defined as a ≽25% decrease on the Yale–Brown Obsessive Compulsive Scale (YBOCS). Non-responders to sham and responders to active or sham rTMS were offered four additional weeks of open active rTMS. After 4 wk, the response rate in the completer sample was 67% (6/9) with active and 22% (2/9) with sham rTMS. At 4 wk, patients receiving active rTMS showed on average a 25% reduction in the YBOCS compared to a 12% reduction in those receiving sham. In those who received 8-wk active rTMS, OCD symptoms improved from 28.2±5.8 to 14.5±3.6. In patients randomized to active rTMS, MT measures on the right hemisphere increased significantly over time. At the end of 4-wk rTMS the abnormal hemispheric laterality found in the group randomized to active rTMS normalized. The results of the first randomized sham-controlled trial of SMA stimulation in the treatment of resistant OCD support further investigation into the potential therapeutic applications of rTMS in this disabling condition

IGF-I responses in young adults

BACKGROUND/AIMS: Physiological growth hormone (GH) secretion and insulin-like growth factor-I (IGF-I) levels are greater in young compared to older adults. We evaluated IGF-I levels and predictors of IGF-I responses in young adults on GH replacement. DESIGN: From the KIMS database, 310 young adults (age 15-26 years) with severe GH deficiency related to childhood-onset disease and commenced on 'adult GH replacement' were identified. 'IGF-I responses' were estimated from first-year increments in IGF-I standard deviation scores (SDS) and adjusted for GH dose. Body composition was assessed by bioimpedance in 143 patients. RESULTS: IGF-I levels increased markedly from baseline to 1 year of replacement (-3.75 ± 1.94 vs. -1.36 ± 1.86 SDS, p < 0.0001), but remained low compared to normative data despite dose titration. In multivariate models, IGF-I responses were positively associated with age [B (SE) SDS/(mg/m2); 0.52 (0.15), p = 0.0007] and BMI SDS [1.06 (0.25), p < 0.0001] and inversely associated with female gender [-4.45 (0.79), p < 0.0001] and baseline IGF-I SDS [-1.44 (0.20), p < 0.0001]. IGF-I responses were positively associated with first-year increases in lean body mass (r = 0.19, p = 0.003) and haemoglobin A1c (r = 0.15, p = 0.031). CONCLUSIONS: Low IGF-I levels in young adults on treatment may reflect suboptimal GH replacement. Identification of predictors for IGF-I responses could lead to a more appropriate replacement strategy. Association between IGF-I responses and lean body mass suggests that maintaining age-appropriate IGF-I levels is important during therapy.The study was funded by an investigator-initiated research (IIR) grant from Pfizer Inc. Pfizer provided statistical support as well as advice as to logistical aspects of interrogating the KIMS database. D Capalbo and H Simpson have nothing to declare. A Thankamony received salary support from the IIR grant and speaker honoraria from Pfizer Inc. D.B Dunger was a member of the KIGS steering committee and received consultant and speaker honoraria from Pfizer Inc. P.J Jonsson is an employee of Pfizer Inc., Sweden and provided statistical support for the study.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Karger Publishers

Temporal trends and forecasting of COVID-19 hospitalisations and deaths in Scotland using a national real-time patient-level data platform: a statistical modelling study

This study is part of the EAVE II project. EAVE II is funded by the MRC (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director General Health and Social Care. The original EAVE project was funded by the NIHR Health Technology Assessment programme (11/46/23).Background   As the COVID-19 pandemic continues, national-level surveillance platforms with real-time individual person-level data are required to monitor and predict the epidemiological and clinical profile of COVID-19 and inform public health policy. We aimed to create a national dataset of patient-level data in Scotland to identify temporal trends and COVID-19 risk factors, and to develop a novel statistical prediction model to forecast COVID-19-related deaths and hospitalisations during the second wave.  Methods   We established a surveillance platform to monitor COVID-19 temporal trends using person-level primary care data (including age, sex, socioeconomic status, urban or rural residence, care home residence, and clinical risk factors) linked to data on SARS-CoV-2 RT-PCR tests, hospitalisations, and deaths for all individuals resident in Scotland who were registered with a general practice on Feb 23, 2020. A Cox proportional hazards model was used to estimate the association between clinical risk groups and time to hospitalisation and death. A survival prediction model derived from data from March 1 to June 23, 2020, was created to forecast hospital admissions and deaths from October to December, 2020. We fitted a generalised additive spline model to daily SARS-CoV-2 cases over the previous 10 weeks and used this to create a 28-day forecast of the number of daily cases. The age and risk group pattern of cases in the previous 3 weeks was then used to select a stratified sample of individuals from our cohort who had not previously tested positive, with future cases in each group sampled from a multinomial distribution. We then used their patient characteristics (including age, sex, comorbidities, and socioeconomic status) to predict their probability of hospitalisation or death.  Findings   Our cohort included 5 384 819 people, representing 98·6% of the entire estimated population residing in Scotland during 2020. Hospitalisation and death among those testing positive for SARS-CoV-2 between March 1 and June 23, 2020, were associated with several patient characteristics, including male sex (hospitalisation hazard ratio [HR] 1·47, 95% CI 1·38–1·57; death HR 1·62, 1·49–1·76) and various comorbidities, with the highest hospitalisation HR found for transplantation (4·53, 1·87–10·98) and the highest death HR for myoneural disease (2·33, 1·46–3·71). For those testing positive, there were decreasing temporal trends in hospitalisation and death rates. The proportion of positive tests among older age groups (>40 years) and those with at-risk comorbidities increased during October, 2020. On Nov 10, 2020, the projected number of hospitalisations for Dec 8, 2020 (28 days later) was 90 per day (95% prediction interval 55–125) and the projected number of deaths was 21 per day (12–29). Interpretation The estimated incidence of SARS-CoV-2 infection based on positive tests recorded in this unique data resource has provided forecasts of hospitalisation and death rates for the whole of Scotland. These findings were used by the Scottish Government to inform their response to reduce COVID-19-related morbidity and mortality.Publisher PDFPeer reviewe

Cohort profile : early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II) database

Funding: The original EAVE project was funded by the National Institute for Health Research Health Technology Assessment Programme (project number 13/34/14). EAVE II is funded by the Medical Research Council [MR/R008345/1] and supported by the Scottish Government. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK.PostprintPeer reviewe