First review of “Foreign States in Domestic Markets”
Professor Lucia Quaglia has just published in Regulation and Governance the first review of my recent book Foreign States in Domestic Markets (joint with Professor Mark Thatcher and published with Oxford University Press).
It makes a number of interesting points worth reading in full here and is very generous in its positive overall assessment of the book:
“This book is a “must” for scholars of International Political Economy and Comparative Political Economy. It is impressive in terms of analytical rigor, breath of empirical coverage and importance of the findings and how they contribute to the existing literature.”
Our WEP article has been ranked by altmetric 4 out of 924 outputs
You can access it on the West European Politics journal or on this website .
Our EPSR article among top 3 most cited piece of last 3 years
You can access on EPSR website or find it in free access on this website!
It has also done well in terms of online attention:
New LSE blog on Partisanship and vaccination rates
In a new LSE blog, we show that Conservative constituencies and individuals are more likely to be vaccinated than Labour supporters.
New book on Foreign States in Domestic Markets is finally out with Oxford University Press!
Political economy debates have focused on the internationalisation of private capital, but foreign states increasingly enter domestic markets as financial investors. How do policy makers in recipient countries react? Do they treat purchases as a threat and impose restrictions or see them as beneficial and welcome them? What are the wider implications for debates about state capacities to govern domestic economies in the face of internationalisation of financial markets?
In response, Foreign States in Domestic Markets have developed the concept of ‘internationalised statism’, where governments welcome the use of foreign state investments to govern their domestic economies. These foreign state investments are applied to the most prominent overseas state investors, Sovereign Wealth Funds (SWFs). Many SWFs are from Asia and the Middle East and their number and size have greatly expanded, reaching $9 trillion by 2020.
This book examines policies towards non-Western SWFs buying company shares in four countries: the US, UK, France, and Germany. Although the US has imposed significant legal restrictions, the others have pursued internationalised statism in ways that are surprising given both popular and political economy classifications. This book argues that the policy patterns found are related to domestic politics, notably the preferences and capacities of the political executive and legislature, rather than solely economic needs or national security risks.
The phenomenon of internationalised statism underlines that overseas state investment provides policy makers in recipient states with new allies and resources. The study of SWFs shows that internationalisation and liberalisation of financial markets offer national policy makers opportunities to govern their domestic economies.
You can order the book at Oxford University Press , Waterstones , or Amazon.
Witness at International Trade Committee
I recently gave evidence as a witness in the inquiry into Inward Foreign Direct Investment organised by the International Trade Committee. Other witnesses included Diego López, Managing Director, Global SWF; Duncan Bonfield, Chief Executive Officer, International Forum of Sovereign Wealth Funds; Nicolai Tangen, Chief Executive Officer, Norges Bank Investment Management; Trond Grande, Deputy Chief Executive Officer, Norges Bank Investment Management; Lord Grimstone, Investment Minister, Department for International Trade / Department for Business, Energy and Industrial Strategy; and Lord Callanan, Minister for Business, Energy and Corporate Responsibility, Department for Business, Energy and Industrial Strategy.
New working paper on Weather and Lockdown Compliance
The effectiveness of containment measures depends on both epidemiological and sociological mechanisms, most notably compliance with national lockdown rules. Yet, there is growing discontent with social distancing rules in many countries, which is expected to intensify further during summer. Using a highly granular dataset on compliance of over 105,000 individuals in the United Kingdom (UK), we find that compliance with lockdown policies tends to be high in the overall population, but that specific segments of society are substantially less compliant. Our findings show that warmer temperatures decrease non-compliance with governmental guidelines of individuals who are male, divorced, part-time employed, and/or parent of more than two children. Thus, as long as heard immunity through vaccination is not achieved and new strains demand containment measures to remain in place, understanding the individual determinants of non-compliance behaviour in different seasons of the year will remain important for policymakers to design effective policies in the future.
Full paper can be accessed at: https://www.researchgate.net/publication/352355506_Weather_and_Lockdown_Compliance
My piece on Pandemic misery index in top 5 most read in LSE EUROPP blog in 2020
To mark the end of 2020, the LSE EUROPP blog compiled a list of top 5 EUROPP articles with the highest readership in 2020 and my recent post introducing a pandemic misery index was ranked 3rd:
1. The implications of Brexit for the UK economy
2. The economic consequences of Covid-19
3. A pandemic ‘misery index’: Ranking countries’ economic and health performance during Covid-19
4. Beating Covid-19: The problem with national lockdowns
Pandemic Misery Index
Ranking countries economic and health performance during Covid19
Tim Vlandas, University of Oxford. This post first appeared on LSE Europe blog
What has been the economic and health performance of different countries since the covid19 crisis began? I propose to rank countries on the basis of how they have fared since the ongoing pandemic began by combining data on two dimensions: a health dimension capturing mortality data; and an economic dimension capturing increases in unemployment.
While the two indicators I select cannot provide an exhaustive picture, they are nevertheless useful in giving us some sense of how countries have fared across two of the dimensions that the covid19 crisis has affected most.
A measure of health costs
The health dimension is based on the so-called p-score. This data is available from the ourworldindata website (data extracted on 14th November 2020) and captures the weekly deviation of current mortality from the 5 years average for that week.
Excess mortality have two advantages over more direct measures of covid19 deaths. First, they do not depend on the testing capacity of the country under consideration, nor are they dependent on definitions of what it means to have died from covid19.
Second, they include the total ‘health cost’ of the pandemic in terms of mortality, i.e. the excess deaths that are the product of both the pandemic and the policy responses to the pandemic.
A measure of economic costs
However, as is well recognised and widely discussed, the pandemic and our policy responses entail significant economic costs. Many economic indicators could be relevant to capture the economic costs. For my purpose, I focus on monthly unemployment rate data, which is extracted from the OECD website.
Partly, this choice is based on data availability constraints, since the alternative to use instead GDP growth data would be hindered by more limited, less recent and less frequent data at the time of writing.
But partly this choice finds inspiration in the so-called misery index which was created following the stagflation in the 1970s. At the time, governments were attempting – and more or less able – to jointly minimise inflation and unemployment.
Adverse unemployment performance can be captured in two distinct ways. The first is simply to look at the average monthly unemployment rate. However, this does not account for the fact that when the pandemic hit, countries started from different relative position. Since the p-score is calculated as a percentage increase from a previous average, I calculate an ‘unemployment score’ as the percentage increase in unemployment from one month to the next.
Country coverage and time period
The following countries are included in my analysis: Austria, Belgium, Canada, Chile, Czech Republic, Denmark, England & Wales, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Israel, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, South Korea, Spain, Sweden, and the United States.
Because ourworldindata.org does not report excess deaths by age groups for the UK as a whole but instead for England&Wales, this is the data I use throughout. For most countries. I have data from January to September 2020 (inclusive). More recent data was not available for all countries at the time of writing so what follows does not capture what has happened since the ‘second wave’.
Excess mortality by age group
I start by showing the weekly evolution of excess mortality by four age groups (Figure 1): 15-64; 65-74; 75-84 and 85+.
The figure reveals the (by now) familiar worse performers, most notably Belgium and England. Note further that English experience is not atypical in the UK given the similar picture for Scotland and to a lesser extent Northern Ireland.
Other countries that did not fare well include Chile, Spain, Netherlands, Italy; although we can observe differences in the timing of the peak. In all cases, excess mortality is strongly a function of age, as has been well documented elsewhere.
Figure 1: Weekly excess mortality by age group
Economic and health costs over time
If we abstract from the cross-national variation, we can see February was actually below the excess mortality average of the last five years for that time of the year and in March the average for our countries only experienced a mild increase. By contrast, in April most countries experienced very significant rises in their excess mortality (Figure 2).
Figure 2: Monthly excess mortality and unemployment score
Bringing in the labour market deterioration into the picture, the increase in unemployment associated with the lockdown measures many countries introduced is also apparent. From May onwards and well into September, both unemployment and mortality stopped to increase in any substantial way across this sample of countries.
The correlation between the unemployment score and different measures of excess mortality is statistically significant and positive but modest (between 0.13 and 0.15), which suggests that countries’ performance in one dimension does not relate strongly for its performance on the other dimension.
This contrasts with claims about the automatic inevitable adverse effects of addressing pandemic for the economy, but also about the presumed positive effects of addressing the pandemic for the economy.
Finally, the heterogeneity is apparent when it comes to excess mortality by age group (figure 3): while some countries experienced the highest increases in mortality for the very old (85+), in others the figures were worst for the 65 to 84, and we can observe both below and above average excess mortality rates for the 16-64 age group.
Figure 3: Excess mortality by country and age group over whole period (from Febuary 2020 onwards)
Pandemic Misery Index (PMI)
Policy makers are therefore faced with a joint minimisation problem whereby they are trying to minimise both health and economic costs. It is in this respect worth keeping in mind that both are – at least in the medium to long term – intrinsically linked to each one another.
On the one hand, mass health issues end up undermining economic productivity and growth. On the other hand, economic decline, insecurity and deprivation generate health problems while also limiting our ability to fund health interventions.
If we combine our economic and health performance indicators into a single pandemic misery index (PMI), we can see that the peak in April hides significant cross-national variation as captured by the standard deviation, and that as the mean of the PMI falls, so does its standard deviation (Figure 4).
In Figure 5, we plot the cross-national variation in the PMI. In the top worst performers we find three liberal market economies (the UK and the US) and two southern European countries (Spain, Italy and Portugal). Although not in top five worst performers, two small open economies in continental Europe – Belgium and Netherlands – also fared poorly.
Figure 4: Pandemic misery index over time
Note: the standard deviation statistic with weights returns the bias-corrected standard deviation, which is based on the factor sqrt(N_i/(N_i-1)), where N_i is the number of observations.
Figure 5: Pandemic misery index across countries
To assess what’s driving poor performance we can disaggregate the PMI along its two dimensions (Figure 6): the PMI in Spain, Italy and the UK is driven by mortality rates with relatively mild increases in unemployment, compared to the US and to Canada, where the increases in unemployment was much more acute.
Among good performers, we find several central and eastern European countries, including Latvia, Hungary, and Slovakia; and also Nordic countries such as Norway, Denmark and Iceland.
Of course, different countries started with different labour market positions, so plotting average levels (instead of changes) in unemployment rates, reveals a slightly different picture (Figure 7). Greece and Spain now look worsts in terms of unemployment rate over the period, followed by Chile, Canada, US and Italy. Czech Republic, Poland, Netherlands, England and Wales, among others kept a low level of unemployment rate.
Finally, excess mortality among the very old (85+) reveals especially dire numbers for parts of Southern Europe (Figure 8), but contrast Spain and Italy, and to a lesser extent Portugal on one hand, and Greece on the other hand. The US, England and Wales, and Canada score high but are not in the top 6 worst performers, while Iceland, Norway, Hungary, Denmark and Slovakia do especially well.
Figure 6: Disaggregating the pandemic misery index across countries
Figure 7: Disaggregated PMI using levels in average monthly unemployment
Figure 8: Disaggregated PMI using mortality rates for over 85 rather than all ages
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