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Policymakers and investors often conceptualize trend growth as simply a medium/long term average growth rate. In practice, these averages are usually taken over arbitrary periods of time, thereby ignoring the large empirical growth literature which shows that doing so is inappropriate, especially in developing countries where growth is highly unstable. This paper builds on this literature to propose an algorithm, called "iterative Fit and Filter" (iFF), that extracts the trend as a sequence of medium/long term growth averages. iFF separates important countryspecific historical episodes and trend growth durations -number of years between two consecutive trend growth shifts, vary substantially across countries and over time. We relate the conditional probabilities of up and down-shifts in trend growth next year to the country's current growth environment, level of development, demographics, institutions, economic management and external shocks, and show how both iFF and the predictive model could be employed in practice. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means without the prior permission in writing of the publisher nor be issued to the public or circulated in any form other than that in which it is published.Requests for permission to reproduce any article or part of the Working Paper should be sent to the editor at the above address. © Inna Grinis submitted 2017 TREND GROWTH DURATIONS & SHIFTSINNA GRINIS* Abstract. Policymakers and investors often conceptualize trend growth as simply a medium/long term average growth rate. In practice, these averages are usually taken over arbitrary periods of time, thereby ignoring the large empirical growth literature which shows that doing so is inappropriate, especially in developing countries where growth is highly unstable. This paper builds on this literature to propose an algorithm, called "iterative Fit and Filter" (iFF), that extracts the trend as a sequence of medium/long term growth averages. iFF separates important countryspecific historical episodes and trend growth durations -number of years between two consecutive trend growth shifts, vary substantially across countries and over time. We relate the conditional probabilities of up and down-shifts in trend growth next year to the country's current growth environment, level of development, demographics, institutions, economic management and external shocks, and show how both iFF and the predictive model could be employed in practice.

Do employers in "non-STEM" occupations (e.g. Graphic Designers, Economists) seek to hire STEM (Science, Technology, Engineering, and Mathematics) graduates with a higher probability than non-STEM ones for knowledge and skills that they have acquired through their STEM education (e.g. "Microsoft C#", "Systems Engineering") and not simply for their problem solving and analytical abilities? This is an important question in the UK where less than half of STEM graduates work in STEM occupations and where this apparent leakage from the "STEM pipeline" is often considered as a wastage of resources. To address it, this paper goes beyond the discrete divide of occupations into STEM vs. non-STEM and measures STEM requirements at the level of jobs by examining the universe of UK online vacancy postings between 2012 and 2016. We design and evaluate machine learning algorithms that classify thousands of keywords collected from job adverts and millions of vacancies into STEM and non-STEM. 35% of all STEM jobs belong to non-STEM occupations and 15% of all postings in non-STEM occupations are STEM. Moreover, STEM jobs are associated with higher wages within both STEM and non-STEM occupations, even after controlling for detailed occupations, education, experience requirements, employers, etc. Although our results indicate that the STEM pipeline breakdown may be less problematic than typically thought, we also find that many of the STEM requirements of "non-STEM" jobs could be acquired with STEM training that is less advanced than a full time STEM education. Hence, a more efficient way of satisfying the STEM demand in non-STEM occupations could be to teach more STEM in non-STEM disciplines. We develop a simple abstract framework to show how this education policy could help reduce STEM shortages in both STEM and non-STEM occupations. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means without the prior permission in writing of the publisher nor be issued to the public or circulated in any form other than that in which it is published.Requests for permission to reproduce any article or part of the Working Paper should be sent to the editor at the above address. © Inna Grinis submitted 2017 THE STEM REQUIREMENTS OF "NON-STEM" JOBS: EVIDENCE FROM UK ONLINE VACANCY POSTINGS AND IMPLICATIONS FOR SKILLS & KNOWLEDGE SHORTAGESINNA GRINIS* Abstract. Do employers in "non-STEM" occupations (e.g. Graphic Designers, Economists) seek to hire STEM (Science, Technology, Engineering, and Mathematics) graduates with a higher probability than non-STEM ones for knowledge and skills that they have acquired through their STEM education (e.g. "Microsoft C#", "Systems Engineering") and not simply for their problem solving and analytical abilities? This is an important question in the UK where less than half of STEM graduates work in STEM occupations and where this apparent leakage from the "STEM pipeline" is often considered as a wastage of resources. To addre...

How does the change in the creditworthiness of a financial institution or sovereign impact its creditors' solvency? I address this question in the context of the recent European sovereign debt crisis. Considering the network of Eurozone member states, interlinked through investment cross-holdings, I model default as a multi-stage disease with each credit-rating corresponding to a new infection phase, then derive systemic importance and vulnerability indicators in the presence of financial contagion, triggered by the change in the creditworthiness of a network member. I further extend the model to analyse not only negative, but also positive credit risk spillovers.Keywords: financial networks, systemic risk, contagion, multi-stage disease. JEL classifications: F34, G01, G15This paper is published as part of the Systemic Risk Centre's Discussion Paper Series. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means without the prior permission in writing of the publisher nor be issued to the public or circulated in any form other than that in which it is published.Requests for permission to reproduce any article or part of the Working Paper should be sent to the editor at the above address. © Inna Grinis submitted 2015Credit Risk Spillovers, Systemic Importance and Vulnerability in Financial Networks* Inna Grinis 1 AbstractHow does the change in the creditworthiness of a financial institution or sovereign impact its creditors solvency? I address this question in the context of the recent European sovereign debt crisis. Considering the network of Eurozone member states, interlinked through investment cross-holdings, I model default as a multi-stage disease with each credit-rating corresponding to a new infection phase, then derive systemic importance and vulnerability indicators in the presence of financial contagion, triggered by the change in the creditworthiness of a network member. I further extend the model to analyse not only negative, but also positive credit risk spillovers.JEL classification: F34, G01, G15.

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