George Bangham (Economics of Public Policy ’17) is an economic researcher at the Resolution Foundation, a London-based think-tank that carries out research and policy analysis to improve the living standards of people in the UK on low and middle incomes.
George Bangham (Economics of Public Policy ’17) is an economic researcher at the Resolution Foundation, a London-based think-tank that carries out research and policy analysis to improve the living standards of people in the UK on low and middle incomes. In recent years the Foundation has been influential in advocating for a living wage and for policymakers to consider the intergenerational impact of public policy. George’s own work focuses on labour markets and social security policy, with his recent publications covering issues from working hours to tax reform.
One of his recent papers, “The new wealth of our nation: the case for a citizen’s inheritance,” has received international attention in the media and was featured in an article in La Vanguardia newspaper this May.
The Intergenerational Commission has identified two major trends affecting young adults today, beside the weak performance of their incomes and earnings, which barely featured in political debate for much of the 20thcentury. The first is that risk is being transferred from firms and government to families and individuals, in their jobs, their pensions and the houses they live in. The second is that assets are growing in importance as a determinant of people’s living standards, and asset ownership is becoming concentrated within older generations – on average only those born before 1960 have benefited from Britain’s wealth boom to the extent that they have been able to improve on the asset accumulation of their predecessors. Both trends risk weakening the social contract between the generations that the state has a duty to uphold, as well as undermining the notion that individuals have a fair opportunity to acquire wealth by their own efforts during their working lives.
This paper, the 22nd report for the Intergenerational Commission, makes the case for the UK to adopt a citizen’s inheritance – a universal sum of money made available to every young person when they reach the age of 25 to address some of the key risks they face – as a central component of a policy programme to renew the intergenerational contract that underpins society.
Policy recommendations from the report:
From 2030, citizen’s inheritances of £10,000 should be available from the age of 25 to all British nationals or people born in Britain as restricted-use cash grants, at a cost of £7 billion per year.
To reflect the experiences of those who entered the labour market during and since the financial crisis, and to minimise cliff edges between recipients and non-recipients, the introduction of citizen’s inheritances should be phased in, starting with 34 and 35 year olds receiving £1,000 in 2020. Each subsequent year, citizen’s inheritance amounts should then rise and be paid to younger groups, until the policy reaches a steady-state in 2030 when it is paid to 25 year olds only from then on.
The citizen’s inheritance should have four permitted uses: funding education and training or paying off tuition fee debt; deposits for rental or home purchase; investment in pensions; and start-up costs for new businesses that are also being supported through recognised entrepreneurship schemes.
The citizen’s inheritance should be funded principally by the new lifetime receipts tax, with additional revenues from terminating existing matched savings schemes – the Help to Buy and Lifetime ISAs.
Caleb Hia (Economics ’18) wrote the following article on health economics from his research for his undergraduate dissertation at the University of Edinburgh.
Caleb Hia ’18 wrote the following article on health economics from his research for his undergraduate dissertation at the University of Edinburgh.
From 2006 to 2007, almost half of the UK’s National Health Service’s (NHS) costs were attributed to behavioural risk factors: diet-related sickness, sedentary lifestyles, smoking, alcohol and obesity cost more than £15 billion (Scarborough et al., 2011). This mammoth sum, deemed an economic burden on public resources, attracted the government’s attention. In the recent Budget, the Chancellor introduced a tax on the sugar content of soft drinks from 2018 to tackle childhood obesity aimed at compelling individuals to consider external costs associated with its consumption which they do not bear such as the publicly-funded health costs of treating diet-related diseases. The effectiveness of this or any further government intervention in an attempt to correct this “externality” will influence the way the NHS allocates its limited resources in healthcare provision.
Beyond this political issue runs an underlying discussion of the social determinants of health which have long been studied (Wilkinson and Marmot, 2003; Adams et al., 2003). In particular, the effects of education on health has been of interest since the inception of Grossman’s (1972) health model. Grossman’s model suggests health can be maintained by health investments, depending on goods and activity consumption, which affect health although health depreciates as individuals age. As better health gives an individual more time to work and enjoy consumption, more educated individuals are expected to demand more health and invest more in their health. This implies more educated individuals are also more efficient health producers.
A possible causal link between education and health exists possibly because higher productivity from more education directly translates to a higher level of health production through allocative efficiency (Kenkel, 1991; Rosenzweig, 1995) and productive efficiency (Grossman, 1972). For example, low literacy is associated with a poor understanding of hospitals’ discharge instructions (Spandorfer et al., 1995) while higher educated individuals are more likely to follow medical treatments (Goldman and Smith, 2002). Relatedly, higher educated people spend more time on health-related activities because they are better at allocating inputs (Grossman, 1972). Additionally, higher educated individuals use their higher earnings to purchase healthier lifestyles (Glied and Lleras-Muney, 2003) which entail more expensive medical treatments, healthier food consumption and living in healthier areas.
I use a natural experiment in England, the increase in compulsory schooling laws from fifteen to sixteen years old following the Raising of School Leaving Age Order in 1972, and an instrumental variable (IV) regression model to examine the relationship between education and health in greater detail. My sample incorporates additional years of data from Health Survey England between 1991 and 1993 which were not analysed before. I measure various health-related measures and behaviours including Body Mass Index (BMI) which has not been considered before. I run Ordinary Least Squares (OLS) and two-stage least squares (2SLS) regressions in a sample containing all individuals and a discontinuity sample comprising individuals born only in January and February using February-born individuals as my instrument. I show education has no causal effect on various health-related measures and behaviours.
A possible explanation for this lies in time inconsistent preferences supported by behavioural economics. Quasi-hyperbolic discounting (Phelps and Pollak, 1968; Laibson, 1997) induces dynamically inconsistent preferences contrary to geometric discounting. The following payoff matrices models a hypothetical situation where an individual fails to quit smoking due to quasi-hyperbolic discounting:
Under geometric discounting where ∝ ≈ 1 and β ≈ 0.8,
he makes time consistent choices regardless of when benefits to those choices are delayed. Since he gets more utility from quitting in both periods, he quits immediately.
However, under Quasi-hyperbolic discounting where ∝ ≈ 1 and β ≈ 0.8,
he changes his choices based on his distance in the future. Unlike geometric discounting, he gets more utility from quitting only in future and not at present and hence do not quit.
The empirical evidence from Gruber and Köszegi’s (2001) addictive behaviour model which incorporates time-inconsistent preferences to the standard “rational addiction” model (Becker et al., 1994) suggests smokers exhibit forward-looking behaviour with time inconsistent preferences concerning smoking. Thus, individuals start smoking often as adolescents when they are most present biased (Hammond, 2005) and do not anticipate the difficulty of quitting.
Therefore, lifestyle habits may not be correlated with education. In the case of smoking, individuals who quit smoking successfully may have used commitment devices (Ashraf et al., 2006; Kaur et al., 2010; Beshears et al., 2011) like quitting with friends to constrain their own future choices by deciding ahead of time to make future deviations costly. Increasing the education budget may be a sound way to promote public health but understanding behaviours and exploring policies to incentivise individuals to adopt healthy habits may be more effective in the long-run.
By Cox Bogaards, Marceline Noumoe Feze, Swasti Gupta, Mia Kim Veloso
Almost a year since the UK voted to leave the EU, uncertainty still remains elevated with the UK’s Economic Policy Index at historical highs. With Theresa May’s snap General Election in just under two weeks, the Labour party has narrowed the gap from Conservative lead to five percentage points, which combined with weak GDP data of only 0.2 per cent growth in Q1 2017 released yesterday, has driven the pound sterling to a three-week low against the dollar. Given potentially large repurcussions of market sentiment and financial market volatility on the economy as a whole, this series of events has further emphasised the the need for policymakers to implement effective forecasting models.
In this analysis, we contribute to ongoing research by assessing whether the uncertainty in the aftermath of the UK’s vote to leave the EU could have been predicted. Using the volatility of the Pound-Euro exchange rate as a measure of risk and uncertainty, we test the performance of one-step ahead forecast models including ARCH, GARCH and rolling variance in explaining the uncertainty that ensued in the aftermath of the Brexit vote.
The UK’s referendum on EU membership is a prime example of an event which perpetuated financial market volatility and wider uncertainty. On 20th February 2016, UK Prime Minister David Cameron announced the official referendum date on whether Britain should remain in the EU, and it was largely seen as one of the biggest political decisions made by the British government in decades.
Assessment by HM Treasury (2016) on the immediate impacts suggested “a vote to leave would cause an immediate and profound economic shock creating instability and uncertainty”, and in a severe shock scenario could see sterling effective exchange rate index depreciate by as much as 15 percent. This was echoed in responses to the Centre for Macroeconomics’ (CFM) survey (25th February 2016), where 93 percent of respondents agreed that the possibility of the UK leaving the EU would lead to increased volatility in financial markets and the broader economy, expressing uncertainty about the post-Brexit world.
Resonating these views, the UK’s vote to leave the EU on 23rd June 2016 indeed led to significant currency impacts including GBP devaluation and greater volatility. On 27th June 2016, the Pound Sterling fell to $1.315, reaching a 31-year low against the dollar since 1985 and below the value of the Pound’s “Black Wednesday” value in 1992 when the UK left the ERM.
In this analysis, we assess whether the uncertainty in the aftermath of the UK’s vote to leave the EU could have been predicted. Using the volatility of Pound-Euro exchange rate as a measure of risk and uncertainty, we test the performance of one-step ahead forecast models including ARCH, GARCH and rolling variance. We conduct an out-of-sample forecast based on models using daily data pre-announcement (from 1st January 2010 until 19th February 2016) and test performance against the actual data from 22nd February 2016 to 28th February 2017.
Descriptive Statistics and Dynamic Properties
As can be seen in Figure 1, the value of the Pound exhibits a general upward trend against the Euro over the majority of our sample. The series peaks at the start of 2016, and begins a sharp downtrend afterwards. There are several noticeable movements in the exchange rate, which can be traced back to key events, and we can also comment on the volatility of exchange rate returns surrounding these events, as a proxy for the level of uncertainty, shown in Figure 2.
Figure 1: GBP/EUR Exchange Rate
Source: Sveriges Riksbank and authors’ calculations
Notably, over our sample, the pound reached its lowest level against the Euro at €1.10 in March 2010, amid pressure from the European Commission on the UK government to cut spending, along with a bearish housing market in England and Wales. The Pound was still recovering from the recent financial crisis in which it was severely affected during which it almost reached parity with the Euro at €1.02 in December 2008 – its lowest recorded value since the Euro’s inception (Kollewe 2008).
However, from the second half of 2011 the Pound began rising against the Euro, as the Eurozone debt crisis began to unfold. After some fears over a new recession due to consistently weak industrial output, by July 2015 the pound hit a seven and a half year high against the Euro at 1.44. Volatility over this period remained relatively low, except in the run up to the UK General elections in early 2015.
However, Britain’s vote to leave the EU on 23rd June 2016 raised investors’ concerns about the economic prospects of the UK. In the next 24 hours, the Pound depreciated by 1.5 per cent on the immediate news of the exit vote and by a further 5.5 per cent over the weekend that followed, causing volatility to spike to new record levels as can be seen in Figure 2.
Figure 2: Volatility of GBP/EUR Exchange Rate
Source: Sveriges Riksbank and authors’ calculations
As seen in Figure 1, the GBP-EUR exchange rate series is trending for majority of the sample, and this may reflect non-stationarity in which case standard asymptotic theory would be violated, resulting in infinitely persistent shocks. We conduct an Augmented Dickey Fuller test on the exchange rate and find evidence of non-stationarity, and proceed by creating daily log returns in order to de-trend the series. Table 1 summarises the first four moments of the log daily returns series, which is stationary.
Table 1: Summary Statistics
Source: Sveriges Riksbank and authors’ calculations
The series has a mean close to zero, suggesting that on average the Pound neither appreciates or depreciates against the Euro on a daily basis. There is a slight negative skew and significant kurtosis – almost five times higher than that of the normal distribution of three – as depicted in the kernel density plot below. This suggests that the distribution of daily returns for the GBP-EUR, like many financial time series, exhibits fat tails, i.e. it exhibits a higher probability of extreme changes than the normal distribution, as would be expected.
To determine whether there is any dependence in our series, we assess the autocorrelation in the returns. Carrying out a Ljung-Box test using 22 lags, as this corresponds to a month of daily data, we cannot reject the null of no autocorrelation in the returns series, which is confirmed by an inspection of the autocorrelograms. While we find no evidence of dependence in the returns series, we find strong autocorrelations in the absolute and squared returns.
The non-significant ACF and PACF of returns, but significant ACFs of absolute and squared returns indicate that the series exhibits ARCH effects. This suggests that the variance of returns is changing over time, and there may be volatility clustering. To test this, we conduct an ARCH-LM test using four lag returns and find that the F-statistic is significant at the 0.05 level.
For the in-sample analysis we proceed using the Box-Jenkins methodology. Given the evidence of ARCH effects and volatility clustering using an ARCH-LM test but lack of any leverage effects in line with economic theory, we proceed to estimate models which can capture this: ARCH (1), ARCH (2), and the GARCH (1,1). Estimation of ARCH (1) suggests low persistence as captured by α1 and relatively fast mean reversion. The ARCH(2) model generates greater persistence measured by sum of α1 and α2 and but still not as large as the GARCH(1,1) model, sum of α1 and β as shown in table 2.
Table 2: Parameter Estimates
We proceed to forecast using the ARCH(1) as it has the lowest AIC and BIC in-sample, and GARCH (1,1) which has the most normally distributed residuals, no dependence in absolute levels, and the largest log-likelihood. We compare performance against a baseline 5 day rolling variance model.
Figure 3 plots the out of sample forecasts of the three models (from 22nd February 2016 to 28th February 2017). The ARCH model is able to capture the spike in volatility surrounding the referendum, however the shock does not persist. In contrast, the effect of this shock in the GARCH model fades more slowly suggesting that uncertainty persists for a longer time. However neither of the models fully capture the magnitude of the spike in volatility. This is in line with Dukich et al’s (2010) and Miletic’s (2014) findings that GARCH models are not able to adequately capture the sudden shifts in volatility associated with shocks.
Figure 3: Volatility forecasts and Squared Returns (5-day Rolling window)
We use two losses traditionally used in the volatility forecasting literature namely the quasi-likelihood (QL) loss and the mean-squared error (MSE) loss. QL depends only on the multiplicative forecast error, whereas the MSE depends only on the additive forecast error. Among the two losses, QL is often more recommended as MSE has a bias that is proportional to the square of the true variance, while the bias of QL is independent of the volatility level. As shown in table 3, GARCH(1,1) has the lowest QL, while the ARCH (1) and rolling variance perform better on the MSE measure.
Table 3: QL & MSE
Table 4: Diebold- Mariano Test (w/5-day Rolling window)
Employing the Diebold-Mariano (DM) Test, we find that there is no significance in the DM statistics of both the QL and MSE. Neither the GARCH nor ARCH are found to perform significantly better than the 5-day Rolling Variance.
In this analysis, we tested various models to forecast the volatility of the Pound exchange rate against the Euro in light of the Brexit referendum. In line with Miletić (2014), we find that despite accounting for volatility clustering through ARCH effects, our models do not fully capture volatility during periods of extremely high uncertainty.
We find that the shock to the exchange rate resulted in a large but temporary swing in volatility but this did not persist as long as predicted by the GARCH model. In contrast, the ARCH model has a very low persistence, and while it captures the temporary spike in volatility well, it quickly reverts to the unconditional mean. To the extent that we can consider exchange rate volatility as a measure of risk and uncertainty, we may have expected the outcome of Brexit to have a long term effect on uncertainty. However, we observe that the exchange rate volatility after Brexit does not seem significantly higher than before. This may suggest that either uncertainty does not persist (unlikely) or that the Pound-Euro exchange rate volatility does not capture fully the uncertainty surrounding the future of the UK outside the EU.
Abdalla S.Z.S (2012), “Modelling Exchange Rate Volatility using GARCH Models: Empirical Evidence from Arab Countries”, International Journal of Economics and Finance, 4(3), 216-229
Allen K.and Monaghan A. “Brexit Fallout – the Economic Impact in Six Key Charts.” www.theguardian.com. Guardian News and Media Limited, 8 Jul. 2016. Web. Accessed: March 11, 2017
Brownlees C., Engle R., and Kelly B. (2011), “A Practical Guide to Volatility Forecasting Through Calm and Storm”, The Journal of Risk, 14(2), 3-22.
Centre for Macroeconomics (2016), “Brexit and Financial Market Volatility”. Accessed: March 9, 2017.
Cox, J. (2017) “Pound sterling falls after Labour slashes Tory lead in latest election poll”, independent.co.uk. Web. Accessed May 26, 2017
Diebold F. X. (2013), “Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests”. Dukich J., Kim K.Y., and Lin H.H. (2010), “Modeling Exchange Rates using the GARCH Model”
HM Treasury (2016), “HM Treasury analysis: the immediate economic impact of leaving the EU”, published 23rd May 2016.
Sveriges Riksbank, “Cross Rates” www.riksbank.se. Web. Accessed 16 Feb 2017
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We want to know what the BGSE community is thinking and reading about the Brexit.
We invite all Barcelona GSE students and alumni to share their early reflections on the potential economic consequences of the UK’s recent vote to leave the EU. Did you focus on a related topic in your master project? Are you working at a think tank, central bank, or consulting firm where your projects will be impacted by this decision? Have you seen any articles or links that you found useful for understanding what lies ahead?
Here are a couple of pieces we’ve found to get the discussion going:
The BGSE participates in A Dynamic Economic and Monetary Union (ADEMU), a project of the EU Horizon 2020 Program. Last week, ADEMU researchers held a webinar to discuss the Brexit.
Europe has grown out of its crises when reason and solidarity have prevailed, but it has also been devastated by its crises when fear and nationalism have taken the lead. Brexit, in the aftermath of the euro crisis, brings this dichotomy back to the foreground. Since 2010 there have been important advances in the development of the Economic and Monetary Union (EMU) and flexible forms of participation have allowed other EU countries, reluctant to join the euro, to share the basic principles that define the EU and have a common presence in the interdependent global world.
According to the panelists, Brexit raises 3 crucial questions:
Should the EMU be accelerated to become a centre of gravity within the EU, or slowed down to avoid a centrifugal diaspora? If accelerated, how?
Should an ‘exit’ country be allowed free entry to the single market and other EU public goods without accepting freedom of movement?
Should the EU remain as it is, or increase its capacity to offer common public services (Banking Union, border security, research funding, environment, etc.), or limit its scope of activity to the EU single and integrated market?
– Joaquín Almunia (Former Vice-President of the European Commission, honorary president of the Barcelona GSE)
– Ramon Marimon (European University Institute and UPF – Barcelona GSE; ADEMU)
– Gorgio Monti (European University Institute; ADEMU)
– Morten Ravn (University College London; ADEMU)
Annika Zorn (European University Institute; Florence School of Banking & Finance)
Nobel Laureate and Barcelona GSE Scientific Council member Joseph Stiglitz shares some reflections in the wake of the Brexit decision
What are you thoughts on Brexit?
We want to know what the BGSE community is thinking and reading about the Brexit. Please share your ideas, favorite sources for analysis, or observations from economists you respect in the comments below.
Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2015. The project is a required component of every master program.
Adrian Ifrim and Önundur Páll Ragnarsson
Macroeconomic Policy and Financial Markets
We propose a new identification method in open economy models by restricting both the systematic component of monetary policy and the IRFs to a monetary policy shock, at the same time remaining agnostic with respect to the effects of monetary policy shocks on output and open economy variables. We estimate the model for the U.S/U.K economies and find that a U.S monetary shock has a significant and permanent effect on output. Quantitatively a 0.4% annual increase in the interest rates causes output to contract by 1.2%. This contradicts the findings of Uhlig (2005) and Scholl and Uhlig (2008). We compute the long-run multipliers implied by the monetary policy reaction function and compare our identification with to the ones proposed by Uhlig (2005), Scholl and Uhlig (2008) and Arias et al. (2015). We argue that neither of the above schemes identify correctly the monetary policy shock since the latter overestimates the effects of the shock and the former implies a counterfactual behavior of monetary policy. We also find that the delayed overshooting puzzle is a robust feature of the data no matter what identification is chosen.
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