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Why more educated individuals are not always healthier

October 19, 2017

BGSE Voice

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.

Download the full paper:

The causal relationship between education and health-related measures and behaviours: Evidence from England

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Advice for new master’s students from Marc de la Barrera ’17

October 6, 2017

At the welcome event for new students on September 26, alum Marc de la Barrera ’17 shared some advice from his recent experience as a student in the Barcelona GSE Economics Program.

alumni speech

Marc de la Barrera (Economics ’17, GPEFM)

Here is the text of his speech (see if you can spot all the references to a certain television series…)

Dear BGSE students, staff, professors and friends, 

I am very happy to be here giving this speech, remembering myself just one year ago sitting in your place. By that time I was an engineer starting an Economics Master, both amused but nervous for digging in a new field. “You know nothing, Marc Barrera”, I keept saying to myself. One year later, at least I can say I know something.

In the Economics Master, I learnt to play with macroeconomic models, how to gather valuable information from data, and to understand how we take decisions. Also that asking the right question is almost as important as finding the answer. I remember me having troubles understanding the “risk free rate” concept. How is it possible that you get a return on your money for sure? Then someone told me that America always pays its debts. Well, they assume they do, I don’t know if now they are so confident with its new administration. Those in data science will learn that information is power, while these of you taking political economy classes will argue that power is power. For competition ones… well, competition is lack of power. And no matter which master you are enrolled in, you are going to meet, John Maynard Keynes, 1st Baron Keynes, Companion of the Order of the Bath, Fellow of the British Academy and father of modern economics.

I hope you are enjoying your time here, nice weather, meeting new people every day, no pressure… But summer will not last forever. Soon you will realize that winter is coming, and with them, exams. And remember that when exams come and problem sets appear, the lone student dies but the pack survives. Everyone has its studying style, but I deeply encourage you form teams and work altogether. You are here, hence you are all very intelligent, I have no doubt about that, but there is a problem… Your professors more. You will need to merge several minds to solve one problem. You have different backgrounds, someone will be very strong in formal math, others might excel at economic intuition, and others will know coding. These three aspects, and many others, are needed to succed all the masters at BGSE.

But it is not only what I learnt that made last year special, it was the experiences I lived and more importantly, the people I meet. I want to make use of this privilaged attention I have, to encourage BGSE to do more activities outside the academic environment, at the same time that I congratulate them for the ones they are currently performing. Butifarrada, football tournamen, sky trip, fideuà… Go to as many events as you can, if not all. Defying all economic laws, this events provide one thing that economists belief do not exist: “free lunch” (just ignore the tuition fees).

Then the people. You will get in touch with many people from many nationalities, such opportunity must be taken. But is not only the cultural exchange what matters. Feelings, frienship will arise. Some cuples will form with probability one. Networking to get opportunities, information or new jobs is fine, but spending time with people you like and appreciate, is better.

>And finally the faculty. Their level is extraordinary, make the most of them. Not only during the class, they are here to help and guide you. I might have abused of their kindness last year, but every professor and staff member I asked to see, whether for a technical doubt regarding the notes, to more fundamental and vital questions like “should I do a PhD”, received me and helped me as much as they could. Luckily you don’t have to send a raven, although we have more pidgeons here, an e-mail should work. The objective of the faculty is to make the most of you, so let them help.

Whether you stay in Bellaterra at UAB or in the Citadel Campus at UPF, it is time to go beyond the wall. After the master the research frontier will be near, and some of you, like me, will opt to go further, to the unexplored. Those who opt for a professional career, maybe we will make it to the World Economic Forum in Davos.

Congratulations for being admitted to your program. This year will be a great year: you will learn economics, meet people, and discover cultures. I hope that the first weeks have been pleasant, and get ready to work hard, because as bodybuilders say, “no pain, no gain”.

Original post and more from Marc de la Barrera on his personal blog. Connect with Marc on Twitter and LinkedIn


Videos from welcome events for the Class of 2018:

A Study on the Measurement of the Compensating Wage Differentials in European Countries

September 29, 2017

Yusuf Aguş (Economics student ’18) shares a summary of his bachelor’s thesis on the measurement of the compensating wage differentials in European countries.


According to the economic theory, the differences of working conditions are compensated
by wage differentials at the equilibrium in a perfect competition setting. In other words, if a worker
is working in a job with undesirable characteristics, he or she needs to have a higher wage then his
or her counterparts.

Earlier studies failed to find significant results for the effect of most of the working
conditions on wages, which could be possibly caused by several different biases, and focused on the
effect of the risk of fatal or non-fatal accident. These biases can be summarized as the effect of
unobserved characteristics, survey errors, heterogeneity of individual preferences on job
characteristics and endogeneity of job riskiness. In this direction, the effect of risk perception on
wages has been tried to be estimated by using 2010 and 2015 waves of the data set of European
Working Conditions Survey (EWCS) which includes a wide set of data from 25 countries. As it is a
very wide set of data, it allows us to control for a lot of heterogeneities across the individuals. A
three-staged estimation strategy has been used in order to show the cross-national differences
clearly. Firstly, the estimation is done for all the countries. Secondly, the models are estimated only
for Turkey. In the third and the last stage the estimation is done separately for two different
country groups, which are constructed according to their GDP levels. For the sake of simplicity,
countries with higher GDPs are addressed as the developed countries and the rest as less developed
countries. The lists of country groups can be found in the following table:

 

The estimation gave insignificant results for most of the cases. However, the most salient
result has appeared in the estimation for the less developed countries. A negative and significant
effect of risk perception on wages has been received for the group of less developed countries,
which can be the sign of a segmented labor market across European countries in terms of
compensation of working conditions.

For the case of Turkey, It can be observed that Turkish workers receive a positive wage premium for being informed about risk, but they do not think that their wage is compensated for risk. Pooled results are quite confusing as well. Risk perception did not bring a significant wage premium, but workers who think that their wages are compensated for risk have higher wages than their counterparts. According to these somewhat controversial results, we might say that European workers are not perceiving the risk correctly.

The full article can be read here.

 

 

Inflation Expectations and Forward Guidance

September 27, 2017

Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2017. The project is a required component of every master program.


Authors:

Marc de la Barrera, Juraj Falath, Dorian Henricot and Jean-Alexandre Vaglio

Master’s Program:

Economics

Paper Abstract:

Our paper empirically investigates the impact of forward guidance announcements on inflation expectations in the Eurozone. The ECB first resorted to forward guidance on July 4, 2013 thereby expanding its array of unconventional policy instruments in the vicinity of the zero-lower bound. We use an ARCH model and identify forward guidance shocks as changes in the 2-year nominal ECB yield on specific announcement days to measure changes in daily inflation swaps of different maturities. In the process, we also separately identify the effect of quantitative easing and interest rate change announcement shocks. We find that forward guidance was successful in reviving inflation expectations in the medium to long term. Analyzing the transmission channels of forward guidance, we find evidence that both a reanchoring channel and a portfolio effect might have been at play.

chart

Sources: authors’ calculations and Thomson Reuters Datastream

Conclusions:

Forward guidance shocks have a strong impact on inflation expectations with a one point decrease in 2-year nominal ECB yields pushing inflation expectations 37bps upwards five years ahead with high significance. Normalizing, a negative shock of one standard deviation in ECB yields had a 11bps positive impact. In Campbell’s terminology (Campbell et al. (2012)), market participants’ interpretation was Odyssean. Thereby, we broadly match the results found by Hubert & Labondance (2016) for the Eurozone. Since the impact persists at all horizons, albeit with decreasing amplitude, we suggest that a reanchoring channel à la Andrade et al. (2015) explains the bulk of the transmission. ECB forward guidance announcements have thus been effective in reducing the growing gap between agents’ beliefs in future monetary policy and ECB’s targets. Our results are also consistent with a portfolio effect à la Hanson & Stein (2015). We also document that QE announcements were more effective in amplitude than forward guidance announcements, probably through a reduction in the term premium.

In contrast, studies run by Nakamura & Steinsson (2013) or Campbell et al. (2012) suggested a larger Delphic channel was at play in the US. More precisely however, they found that their results were lower than those predicted by a New Keynesian model with sticky prices. Thus, a natural extension of this paper would be to explore how our results would compare to the predictions of a New Keynesian model. Another approach would be to build a counter-factual for inflation expectations in the absence of forward guidance. In any case, given that the ECB implemented forward guidance at a time of heightened uncertainty and while long-term inflation expectations were dropping, there are reasons to believe it could have been more efficient in the Eurozone than in the US.

On the theoretical side, it is important to understand the transmission mechanisms of forward guidance within a structural model. This would allow to understand the potential gap to empirical outcomes. A number of authors have already striven to embed forward guidance within New Keynesian models and it is still an active area of research. The objective is then to derive an optimal policy function for further times of monetary policy management under the ZLB constraint.

To complete the policy recommendation, one needs to weigh out the benefits of forward guidance against its undesirable side-effects. Poloz (2014) suggested that successful forward guidance could results in increased future volatility when restoring conventional communication. Campbell et al. (2012) highlighted that central bank commitment could have a cost in terms of inflation or credibility. It would then be interesting to assess the negative externalities of forward guidance.

References:

Andrade, P., Breckenfelder, J., De Fiore, F., Karadi, P. & Tristani, O. (2015), ‘The ECB’s asset purchase programme: an early assessment’, ECB Working Paper (1956).

Campbell, J., Evans, C., Fisher, J. & Justiniano, A. (2012), ‘Macroeconomic Effects of Federal Reserve Forward Guidance’, Brookings Papers on Economic Activity 43(1), 1–80.

Hanson, S. & Stein, J. (2015), ‘Monetary policy and long-term real rates’, Journal of Financial Economics 115(3), 429–448.

Hubert, P. & Labondance, F. (2016), ‘The effect of ECB Forward Guidance on Policy Expectations’, Sciences Po publications (30).

Nakamura, E. & Steinsson, J. (2013), ‘High frequency identification of monetary non-neutrality: The information effect’, NBER Working Paper (w19260).

Poloz, S. (2014), ‘Integrating uncertainty and monetary policy-making: A practitioner’s perspective’, Bank of Canada Discussion Paper (2014-6).

International Asset Allocations and Capital Flows: The Benchmark Effect

September 15, 2017

Tomas Williams (Economics ’12, GPEFM ’17) is Assistant Professor of International Finance at George Washington University in Washington, DC. His paper, “International Asset Allocations and Capital Flows: The Benchmark Effect” (with Claudio Raddatz, Central Bank of Chile and Sergio Schmukler, World Bank Research Group) is forthcoming at the Journal of International Economics.


International Asset Allocations and Capital Flows: The Benchmark Effect

As financial intermediaries such as open-end funds with benchmark tracking grow in importance around the world, the issue of which countries belong to relevant international benchmark indexes (such as the MSCI Emerging Markets) has generated significant attention in the financial world (Financial Times, 2015). The reason is that the inclusion/exclusion of countries from widely followed benchmarks has implications for the allocation of capital across countries. As institutional investors become more passive, they follow benchmark indexes more closely. These benchmark indexes change over time, as index providers reclassify countries, implying that investment funds have to re-allocate their portfolio among the countries they target. The capital flows generated by these portfolio re-allocations are important since worldwide open-end funds that follow a few well-known stock and bond market indexes manage around 37 trillion U.S. dollars in assets (ICI, 2016). These changes in benchmark indexes can produce unexpected effects in international capital flows, linked to how financial markets work, not necessarily to economic fundamentals.

One clear example of these counterintuitive reallocations happened when MCSI announced in 2009 that it would upgrade Israel from emerging to developed market status, moving it from the MSCI Emerging Markets (EM) Index to the World Index. When the upgrade became effective in May 2010, Israel faced equity capital outflows of around 2 billion dollars despite its better status (Figure 1 below, click image to enlarge). The reason is that Israel became a smaller fish in a bigger pond. Israel’s weight in the MSCI EM Index decreased from 3.17 to 0, while it increased from 0 to 0.37 in the MSCI World Index. Israeli stocks in the MSCI index fell almost 4 percent in the week of the announcement and significantly underperformed the stocks not included in the index. The week prior to the effective date (when index funds rebalanced their portfolio) there was a 4.2 percent drop in the MSCI Israel Index, versus a 1.5 fall in the Israeli stocks outside the index.

Figure 1. Direct Benchmark Effect: Aggregate Flows

This figure shows aggregate data on flows in Israel around the time of large benchmark weight changes. Figure 1 shows data for portfolio equity liability flows and portfolio debt liability flows for Israel quarterly between 2007 and 2011. Figure 2 shows the cumulative flows from frontier markets passive funds around the upgrade of Qatar and United Arab Emirates to the MSCI Emerging Markets.

The effects of index reclassifications go beyond the countries and asset classes being specifically targeted. Spillovers could occur to other countries that share a certain benchmark with countries affected by reclassifications. A clear example of this is the upgrade in June 2013 of Qatar and United Arab Emirates (UAE) from the MSCI Frontier Markets (FM) Index to the MSCI EM Index. Together, these two countries were around 40 percent of the MSCI FM Index before the reclassification. When this reclassification took place, funds tracking closely the MSCI FM Index had to sell securities from these two countries and use the money to invest in the rest of the countries in the MSCI FM Index. This resulted in significant capital inflows and stock market price increases in countries such as Nigeria, Kuwait, and Pakistan (Figure 2, click image to enlarge).

Figure 2. Cumulative Flows from Frontier Passive Funds

Figure 2. Cumulative Flows from Frontier Passive Funds

These movements in financial markets have led to speculations and market movements related to potential new reclassifications. One recent and prominent example is that of China. For the past two years, MSCI delayed numerous times the introduction of China A-shares as a part of the MSCI Emerging Markets. Finally, in June 2017, they confirmed the inclusion of only a fraction of these stocks, creating capital inflows into the Chinese stock markets, and increases in stock prices (Financial Times, 2017). Chinese sovereign bonds may see similar capital inflows if J.P. Morgan, Citibank and Barclays decide to add China into their flagship bond indexes (CNBC, 2017).

In a recent study (Raddatz et al., 2017), we systematically document these benchmark effects, showing the various channels through which prominent international equity and bond market indexes affect asset allocations, capital flows, and asset prices across countries. Benchmarks have statistically and economically significant effects on the allocations and capital flows of mutual funds across countries. For example, a 1 percent increase in a country’s benchmark weight results on average in a 0.7 percent increase in the weight of that country for the typical mutual fund that follows that benchmark. These benchmark effects on the mutual fund portfolios are relevant even after controlling for time-varying industry allocations and country-specific or fundamental factors. Exogenous events that modify benchmark indexes affect benchmark weights. Furthermore, asset prices move both during the announcement and effective dates of the benchmark changes in response to the capital movements.

Academics, financial institutions, and policy makers have already started paying attention to the potential effects of benchmarks on capital flows and asset prices, as well as on herding, momentum, and risk taking (BIS, 2014; Arslanalp and Tsuda, 2015; IMF, 2015, Shek et al., 2015; Vayanos and Woolley, 2016). More work in this area would be welcomed as passive investing continues expanding.

References

Arslanalp, S., Tsuda, T., 2015. Emerging Market Portfolio Flows: The Role of Benchmark-Driven Investors. IMF Working Paper 15/263, December.

BIS, 2014. International Banking and Financial Market Developments. BIS Quarterly Review.

CNBC, 2017. Chinese Stocks got their Global Stamp of Approval, and now Bonds may be next.

Financial Times, 2015. Emerging Market Investors Dominated by Indices. August 4.

Financial Times, 2017. China Stocks Set for $500bn Inflows after MSCI Move. June 21.

ICI, 2016. Investment Company Institute: Annual Factbook.

IMF, 2015. Global Financial Stability Report.

MSCI, 2016. Potential Impact on the MSCI Indexes in the Event of the United Kingdom’s Exit from the European Union (“Brexit”). June.

Raddatz, C., Schmukler, S., Williams, T., 2017. International Asset Allocations and Capital Flows: The Benchmark Effect. Working Papers 2017-XX, The George Washington University, Institute for International Economic Policy.

Shek, J., Shim, I., Shin H.S., 2015. Investor Redemptions and Fund Manager Sales of Emerging Market Bonds: How Are They Related? BIS Working Paper 509.

Vayanos, D., Woolley, P., 2016. Curse of the Benchmarks. LSE Discussion Paper 747.

Wall Street Journal, 2014. Colombia Wins Investors’ Favor – And That’s the Problem. August 13.

About Tomas Williams

From his website:

I am an Assistant Professor of International Finance at George Washington University in Washington, D.C. My main fields of research are International Finance, Financial Economics and Empirical Banking. I have a special interest on financial intermediaries and how they affect international capital flows and economic activity. More specifically, I have been working on how the use of well-known benchmark indexes by financial intermediaries affects both financial markets and real economic activity.

More personally, I grew up in Buenos Aires, and studied economics at Universidad del CEMA. Afterwards, I moved to Barcelona and completed the Master’s Degree in Economics and Finance (Economics Program) at Barcelona GSE. Later on, I received my Ph.D. in Economics and Finance from Universitat Pompeu Fabra. I also spent one year as a visiting doctoral student in the Financial Markets Group (FMG) at the London School of Economics and Political Science.

Connect with Tomas on Twitter

Can misguided monetary policy explain the European housing bubble?

September 12, 2017

Patrick Altmeyer (Finance student ’18) who has an interest in monetary policy, shares his work on whether misguided monetary policy can explain the European housing bubble.


Property prices surged throughout Europe in the early 2000s before collapsing during the crisis and causing tremendous welfare losses. This dissertation uses Structural Vector Autoregression (SVAR) to analyse the role of house prices within the monetary transmission mechanism in Europe over the past decades in order to understand whether lax interest rate policy had caused the bubble. Quarterly observations of inflation, output, consumption, real estate prices and mortgage variables for eight European countries were used. Sample periods vary by model specification but generally four decades.

Impulse response functions for the baseline SVAR suggest that real estate prices did indeed respond positively to dovish monetary policy and thereby amplified conventional effects on consumer spending. However, the interpretation of these preliminary results is complicated by explosive house price dynamics during the early 2000s. The linear vector autoregressions fail to fully capture these non-linear elements of the time series. A statistical test developed by Homm and Breitung (2012) is therefore used to identify bubble periods in the various countries analysed. Explosive house price dynamics are found in all countries but Germany as shown in Figure 1.

Figure 1: House price trends in European countries. Shaded areas indicate bubble periods.

Information about house price bubbles is subsequently used to augment the baseline SVAR in various ways. Consequently, the measured effect of a decrease in interest rates on house prices remains positive, but to a lesser extent. Overall, evidence found here suggests that interest rate policy alone was not responsible for the European housing bubble. Rather, it appears that the boom could be better explained by joint effects of loose monetary policy, financial liberalisation and associated mortgage market innovations. Note, for example, that total securitisation activity measured in terms of the number of euro-denominated asset-backed securities outstanding increased six fold from 2000 until the credit bubble burst in mid 2007. Unsurprisingly, many have drawn a connection between monetary policy and securitisation commonly arguing that the latter amplified the conventional credit effects of the former. Information about mortgage rates and lending activity is used as a proxy for mortgage securitisation and added to the SVAR in the final section of the empirical part. Indeed, these variables are found to have high explanatory power with respect to house price trends in most countries as evident in Figure 2, which plots forecast error variance decompositions for each country under the preferred model specification.

Figure 2: Forecast error variance decompositions.

The paper therefore concludes that stricter interest rates more closely aligned with policy rules could not have entirely avoided the bubbles, hence this approach is not recommended for the future. Putting more focus on asset price stability and thereby departing from the policy rate’s traditional role of smoothing consumption and consumer prices would be too complicated and is therefore not advisable, either. In light of the finding that financial innovations have greatly contributed to bubbles, policy makers should continue current efforts on imposing stricter regulation through macroprudential measures.

The full article can be read here.

Modeling IPP Capital and its Effect on the Labor Share

August 29, 2017

Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2017. The project is a required component of every master program.


Authors:

Alfredo Andonie, Andrea Greppi, Ece Yagman, Lorenzo Pisati and Moritz Degler

Master’s Program:

Economics

Paper Abstract:

We investigate the effects of intellectual property products capital in the evolution of the labor share for five European countries. Using post-revision national accounts data, we construct a benchmark labor share with the contribution of both, traditional and IPP capital, against which we measure a counterfactual LS which isolates the effects of IPP. We report that the labor share in Austria, France, Germany, and Spain has been consistently declining, with high variation across countries. Our results show that part of this decline is explained by the inclusion and growing importance of IPP capital in the economy. A closer look at France reveals that the main channels through which IPP has an impact on the labor share are a higher depreciation rate and investment flow relative to traditional capital.

Conclusions:

Our analysis of IPP capital and its impact on the LS reveals three main findings. First, we observe a decline in the LS of Austria, France, Germany and Spain, part of which is explained by the impact of IPP capital on aggregate income. Second, a cross-country comparison discloses great variation in both the magnitude at which the LS is falling in these countries and the extent up to which IPP capital can account for such a decline. Finally, a deeper analysis for France, in which we study the dynamics and composition of its aggregate capital, allows us to identify the higher depreciation rate and investment flow of IPP capital as the main channels driving the change in the trend of the LS.

We conclude that, to an extent, the behavior of the LS attests to the transition into more IPP capital-intensive economies. Since the inclusion of intangible capital in the revised European national accounts (ESA 2010), the growing importance of IPP relative to traditional capital has altered essential properties of aggregate capital, such as the depreciation rate. In particular, these changes have translated into new dynamics and ways in which factors are allocated in the economy.

In using revised data, our analysis presents novel evidence for LS dynamics in Europe and its relation with the composition of capital in the economy. From a measurement point of view, it highlights the way in which we have begun to think differently of developments and aggregate indicators. From a theoretical point of view, it compels us to reformulate models that can accommodate these new measurements and their implications for the rest of the economy.

As a final remark, we have not attempted to establish a connection between the LS and inequality. However, the relation between the compensation to labor and the concentration of IPP capital poses interesting challenges for future research. Particularly relevant to this study are the potential ways in which a decline in the LS propelled by an increase in IPP capital maps onto the evolution of inequality.