Fifteen years ago, the OECD started evaluating education systems worldwide by testing the knowledge and competences of 15 year-old students through the Programme for International Student Assessment (PISA). Right from very first PISA exercise in 2000, we noted that the although the results for France were around the OECD average, they revealed a system where children’s socio-economic status had a disproportionate influence on their school grades, and where children from disadvantaged backgrounds did not receive enough support.
The OECD PISA 2015 results are now in. Even if France’s performance hasn’t deteriorated since the last series in 2012, it has not improved much compared to previous rounds either. France’s results for science and mathematics are around the OECD average, while reading comprehension is slightly above average.
Nonetheless, the French system is still markedly two-tier. The number of high-achieving students is stable and higher than the OECD average, but lower levels are not improving, with a proportion of 15 year-olds in difficulty in science higher than the OECD average.
According to PISA 2015, students from the most disadvantaged backgrounds have four times less chance of succeeding than the others. This is not only a human tragedy. It is also a brake on economic development, which can only be solid and sustainable when it is inclusive.
Reconciling educational excellence and success for all is not just the best way to tackle social inequalities at the root, but also to obtain good results.
Results from around the globe illustrate various best practices applied to improve the equity and performance of the education system. Portugal’s TEIP programme for example (Priority Intervention Education Territories) targets investment in geographical regions where the population is socially disadvantaged and where school drop-out rates are higher than the national average. Singapore, first in the PISA science rankings, has a comprehensive teacher evaluation system that includes in particular the contribution to students’ personal and academic development, as well as the quality of parent-teacher relations.
In short, the capacity of a system to help students in difficulty and those from disadvantaged backgrounds to improve raises the general quality of the system and thus its overall performance.
In France however, investments in education do not always reach these groups. I had some personal experience of this malfunctioning when I arrived in France and asked people to recommend primary schools for my own children. The answer was: “Don’t pick a school, pick a neighbourhood”.
How can we ensure that success at school isn’t the result of a postcode lottery? France has already implemented reforms going in the right direction.
As recommended by the OECD, more resources, teachers, scholarships and support have been made available for disadvantaged students. The July 2003 Education Act (Loi d’orientation et de programmation pour la refondation de l’école de la République du 8 juillet 2013) designed to tackle school drop-out and failure from the earliest age marks an important step. The recent implementation of numerous reforms inspired by the Act at primary and junior high levels, could, depending on their practical application, respond to certain ongoing challenges and help to improve students’ learning and outcomes.
Of course it is too early to see any impact of these reforms on PISA 2015 scores. However, they were necessary and should be strengthened and evaluated regularly.
In France, as elsewhere in the past, teachers will play a key role in the reforms and will have to take ownership of the main objectives. Reform of teacher training should therefore be continued and made a priority.
It is important to stress that contrary to a commonly-held belief in France, the PISA 2015 results do not show that reforms designed to reduce social and educational inequalities result in a lowering of the overall level. On the contrary. In countries that carried out such reforms, the number of students failing dropped in the following decade, while the good students got even better. OECD countries that have managed to achieve high performance in science along with equity in terms of educational outcomes include Canada, Denmark, Estonia, Finland, Japan, Korea, Norway, and the UK according to PISA 2015.
We chose science as the focus of PISA 2015 because a good understanding of science and the technologies derived from it is indispensable, especially in our age of digital revolution. This is not only a necessity for those whose career depends directly on science, but for every citizen who wants to take an enlightened position on any number of questions facing society today, from health to sustainable development or climate change. Today, everyone should be able to “think like a scientist”.
More generally, education is fundamental in these troubled times, when populism is on the rise, when France has been shaken by several terrorist attacks, and social inequalities in the world have left by the wayside a number of citizens who no longer have any trust in institutions.
More than ever, we have to invest in our children’s science education, to respond to the “post-fact” era with an open and informed dialogue. More than ever, we have to strengthen our education systems to face up to the challenges that threaten increasingly to divide us.
Deborah M. Gordon, Department of Biology, Stanford University
Systems without central control are ubiquitous in nature. The activities of brains, such as thinking, remembering and speaking, are the outcome of countless electrical interactions among cells. Nothing in the brain tells the rest of it to think or remember. I study ants because I am interested in how collective outcomes arise from interactions among individuals, and how collective behaviour is tuned to changing environments.
There are more than 14,000 species of ants, which all live in colonies consisting of one or more reproductive females, and many sterile workers, which are the ants that you see walking around. Although the reproductive females are called “queens”, they have no power or political authority. One ant never directs the behaviour of another or tells it what to do. Ant colonies manage to collect food, build and maintain nests, rear the young, and deal with neighbouring colonies – all without a plan.
The collective behaviour of colonies is produced by a dynamical network of simple interactions among ants. In most ant species, the ants can barely see. They operate mostly by smell. As an ant moves around it briefly contacts other ants with its antennae, or it may contact a short-lived patch of a volatile chemical recently left behind by another ant. Ants smell with their antennae, and when one ant touches another with its antennae, it assesses whether the other ant is a nestmate, and sometimes what task the other ant has been performing. The ant uses its recent experience of chemical interactions to decide what to do next. In the aggregate, these simple interactions create a constantly shifting network that regulates the behaviour of the colony.
The process that generates simple interactions from colony behavior is what computer scientists call a distributed algorithm. No single unit, such as an ant or a router in a data network, knows what all the others are doing and tells them what to do. Instead, interactions between each unit and its local connections add up to the desired outcome.
The distributed processes that regulate the collective behaviour of ants are tuned to environmental conditions. For example, harvester ants in the desert face high operating costs, and their behaviour is regulated by feedback that limits activity unless it is necessary. A colony must spend water to get water. The ants get water by metabolizing the fats in the seeds they eat. A forager out in the desert sun loses water while out searching for food. Colonies manage this tradeoff by a simple form of feedback. An outgoing forager does not leave the nest until it meets enough returning foragers with seeds. This makes sense because each forager searches until it finds food. Thus the more food is available, the more quickly they find it and return to the nest, stimulating more foragers to go out to search. When food is not available, foraging activity decreases. A long-term study of a population of colonies shows that the colonies that conserve water in dry conditions by staying inside are more successful in producing offspring colonies.
By contrast, another species called “turtle ants”, living in the trees of a tropical forest in Mexico, regulate their behaviour very differently. The turtle ants create a highway system of trails that links different nests and food sources. Operating costs are low because it is humid in the tropical forest, but competition from other species is high. These ants interact using trail pheromones, laying down a chemical trail everywhere they go. An ant tends to follow another and this simple interaction keeps the stream of ants going, except when it is deterred by encounters with other species. In conditions of low operating costs, interactions create feedback that makes ongoing activity the default state, and uses negative feedback to inhibit activity. Thus this is the opposite of the system for desert ants that require positive feedback to initiate activity.
What can we learn from ants about human society? Ants have been used throughout history as examples of obedience and industry. In Greek mythology, Zeus changes the ants of Thessaly into men, creating an army of soldiers, who would become famous as the Myrmidons ready to die for Achilles (from myrmex – μύρμηξ – ant). In the Bible (Proverbs 4:4), we are told to “Look to the ant” who harvests grain in the summer to save for the winter. But ants are not acting out of obedience, and they are not especially industrious; in fact, many ants just hang around in the nest doing nothing.
Ants and humans are very different. Power and identity are crucial to human social behaviour, and absent in ants. Ants do not have relations with other ants as individuals. As an ant assesses its recent interactions with others, it does not matter whether it met ant number 522 or ant number 677. Even more fundamental, an ant does not act in response to any assessment of what needs to be done.
However, we may be able to learn from ants about the behaviour of very large dynamical networks by focussing on the pattern or structure of interactions rather than the content. While we care about what our emails say, the ants care only about how often they get them. It is clear that many human social processes operate without central control. For instance, we see all around us the effects of climate change driven by many different social processes that are based on the use of fossil fuel. No central authority decided to pump carbon into the atmosphere, but the CO2 levels are the result of human activity. Another obvious example is the internet, a huge dynamical network of local interactions in the form of email messages and visits to websites. The role of social media in the recent US election reflects how the gap between different networks can produce completely disparate views of what is happening and why.
The most useful insights may come from considering how the dynamics of distributed algorithms evolve in relation to changing conditions. The correspondences between the regulation of collective behaviour and the changing conditions in which it operates might provide insight, and even inspire thinking about policy, in human social systems. For ants or neurons, the network has no content. Studying natural systems can show us how the rhythm of local interactions creates patterns in the behaviour and development of large groups, and how such feedback evolves in response to a changing world.
Ants at Work: How an Insect Society is Organized Deborah M. Gordon
Adrian Blundell-Wignall, Special Advisor to the OECD Secretary-General on Financial and Enterprise Affairs
Global finance is the perfect example of a complex system, consisting as it does of a highly interconnected system of sub-systems featuring tipping points, emergence, asymmetries, unintended consequences, a “parts-within-parts” structure (to quote Herbert Simon), and all the other defining characteristics of complexity. It is shaped by numerous internal and external trends and shocks that it also influences and generates in turn. And as the system (in most parts) also reacts to predictions about it, it can be called a “level two” chaotic system (as described, e.g. by Yuval Harari)
Numerous developments combined to contribute to the 2008 crisis and several of them led to structures and institutions that might pose problems again. Two important trends that would play a huge role in the crisis were the opening up of OECD economies to international trade and investment after 1945, and rapid advances in digital technology and networks. These trends brought a greater complexity of financial products and structures needed to navigate this new world, going well beyond the needs to meet the increased demand for cross-border banking to include new products that would facilitate hedging of exchange rate and credit default risks; financial engineering to match maturities required by savers and investors, and to take advantage of different tax and regulatory regimes; mergers and acquisitions not only of businesses, but of stock exchanges and related markets with global capabilities; and new platforms and technological developments to handle the trading of volatile new products.
The freeing up of financial markets followed the opening of goods markets, and in some respects was the necessary counterpart of it. However, the process went very far, and by the end of the 1990s policies encouraged the “financial supermarket” model, and by 2004 bank capital rules became materially more favourable to bank leverage as did rule changes for investment banks. The banking system became the epicentre of the global financial crisis, because of the under-pricing of risk, essentially due to poor micro-prudential regulation, excessive leverage, and too-big-to-fail business models. The rise of the institutional investor, the expansion of leverage and derivatives, the general deepening of financial markets and technological advances led to innovations not only in products but also in how securities are traded, for example high-frequency trading. The increasing separation of owners from the governance of companies also added a new layer of complexity compounding some of these issues (passive funds, ETFs, lending agents custody, re-hypothecation, advisors and consultants are all in the mix).
The trends towards openness in OECD economies were not mirrored in emerging market economies (EMEs) generally, and in Asia in particular. Capital controls remained strong in some EMEs despite a strengthening and better regulated domestic financial system. Furthermore, capital control measures have often supported a managed exchange rate regime in relation to the US dollar. When countries intervene to fix their currencies versus the dollar, they acquire US dollars and typically recycle these into holdings of US Treasuries, very liquid and low-risk securities . There are two important effects of the increasingly large size of “dollar bloc” EME’s: first, they compress Treasury yields as the stock of their holdings grows, second, their foreign exchange intervention means that the US economy faces a misalignment of its exchange rates vis-à-vis these trading partners.
Low interest rates, together with the more compressed yields on Treasury securities, have encouraged investors to search for higher-risk and higher-yield products. In “risk-on” periods this contributes to increased inflows into EME high-yield credit which, in turn, contributes to more foreign exchange intervention and increased capital control measures. The potential danger is that in “risk-off” periods, the attempt to sell these illiquid assets will result in huge pressures on EME funding and a great deal of volatility in financial markets.
The euro affects financial stability too, often in unexpected ways.. European countries trade not only with each other but with the rest of the world. However, the north of Europe is, through global value chains, more vertically integrated into strongly growing Asia due to the demands for high-quality technology, infrastructure, and other investment goods, while the south of Europe is competing with EMEs to a greater degree in lower-level manufacturing trade. Asymmetric real shocks to different euro area regions, such as divergent fiscal policy or changes in EME competitiveness, mean that a one-size-fits-all approach to monetary policy creates economic divergence. Resulting bad loans feed back into financial fragility issues, and interconnectedness adds to the complexity of the problem.
Population ageing adds to these concerns, notably due to the interactions among longer life spans, low yields on the government bonds that underpin pension funds, and lack of saving by the less wealthy who were hardest hit by the crisis and may also suffer from future changes in employment and career structures. To meet yield targets, institutions have taken on more risk in products that are often less transparent and where providers are trying to create “artificial liquidity” that does not exist in the underlying securities and assets.
However big and complex the financial system, though, it is not an end in itself. Its role should be to help fund the economic growth and jobs that will contribute to well-being. But despite all the interconnectedness, paradoxically, as the OECD Business and Finance Outlook 2016 argues, fragmentation is blocking business investment and productivity growth.
In financial markets, information technology and regulatory reforms have paved the way for fragmentation with respect to an increased number of stock trading venues and created so-called “dark trading” pools. Differences in regulatory requirements and disclosure among trading venues raise concerns about stock market transparency and equal treatment of investors. Also, corporations may be affected negatively if speed and complexity is rewarded over long-term investing.
Different legal regimes across countries and in the growing network of international investment treaties also fragment the business environment. National laws in different countries sanction foreign bribery with uneven and often insufficient severity, and many investment treaties have created rules that can fragment companies with respect to their investors and disrupt established rules on corporate governance and corporate finance.
Complexity is in the nature of the financial system, but if we want this system to play its role in funding inclusive, sustainable growth, we need to put these fragmented pieces back together in a more harmonious way.
Few economic indicators make the newspapers’ front pages. One that often does though is house prices. This is because, as witnessed during the financial crisis, movements in house prices can have a direct impact on households’ wealth and economic growth. At the same time, fluctuations in economic activity can also drive trends in house prices. House price indicators are therefore among the indicators that are closely monitored by policymakers.
However, despite their importance, until recently, largely reflecting a variety of conceptual and measurement differences across countries, no harmonised internationally comparable measure of house prices existed. In 2013, a new statistical handbook on house price indices was endorsed by several international organisations, and since then the OECD has been working with countries to develop a new internationally comparable database on house prices.
The new data confirm the positive association between house prices and economic activity but they also reveal significant differences in the strength of the link across countries, especially in the wake of the recent financial crisis.
There is a positive correlation between fluctuations in house prices and in economic activity…
As Figure 1 shows, fluctuations in real house prices (i.e. adjusted for general inflation) and economic activity in the OECD are positively related (with a correlation coefficient of around 0.6 over the period 1971 to 2015). This relationship reflects three drivers, that may differ in intensity and over time: a leading component, as the wealth effects from increases in real estate prices can boost final consumption of home owners, through re-mortgaging for example; a lagging component, as stronger economic growth may boost house prices; and a co-incident component as both house prices and economic activity may be explained by the same underlying factors, such as credit market conditions and population growth.
Note: The real house price index for the OECD area is computed from real house price indices for the 35 OECD countries, weighted using their nominal GDP weights in PPP terms. This real house price index is sourced from the OECD Analytical house price indicators dataset and real GDP from the OECD Quarterly national accounts database.
…but the relationship may have weakened over time…
Understanding the dynamic contribution these drivers make over time is clearly of interest, especially as they provide insights on the potential build-up of vulnerabilities stemming from strong household spending growth driven by rising leverage and inflated asset prices.
The latest edition of the OECD’s Economic Outlook provides evidence of a post-crisis weakening of the relationship between house price growth and the propensity to consume, in part reflecting the changes in financial regulations and credit standards introduced after the crisis which have reduced the ability of households to use rising housing values as collateral for additional borrowing to fund current spending.
…and it differs across countries.
A number of factors influence house price movements, including real household incomes, real interest rates, mortgage market regulations and supervision, lending patterns (at fixed rate versus variable rates), tax relief on mortgage debt financing, and transaction costs such as stamp duty. Therefore, differences in institutional arrangements combined with differences in economic activity may explain heterogeneity in housing markets across countries.
The internationally comparable house price indices from the OECD database show that the relationship between house prices and economic activity is indeed stronger in some countries than others. For example, in countries like Finland, Ireland, Japan and the United Kingdom, fluctuations in house prices and economic activity are closely related, with a correlation coefficient of around 0.7 from 1971 to 2015, whereas it is much weaker in countries like France, Italy and Norway, with a correlation coefficient lower than 0.3 over the same period.
Similarly, while house prices dropped in many countries at the time of the crisis, factors that affect house price developments differ markedly across countries. Figure 2 shows how real house price developments have diverged significantly across countries since 2005. Notwithstanding some differences within each group, four broad groups of OECD countries can be distinguished:
- An initial fall in real house prices followed by a subsequent rebound in New Zealand, the United Kingdom and the United States.
- A continuous increase in real house prices pre and post crisis in Australia, Mexico and Sweden.
- A severe and prolonged fall in house prices post the crisis with only recent signs of stabilisation in Greece and Spain.
- Relatively stable house prices since 2005 in Belgium and Korea.
In respect of the above, it should also be noted that there may be significant differences across local housing markets within countries. Unfortunately internationally comparable data at sub-national level are typically not available.
Note: House price indices for individual countries are sourced from the OECD RPPI – Headline indicators dataset and deflated by the Consumer Price Indices (CPIs) for all items. The real house price index for the OECD area is sourced from the OECD Analytical house price indicators dataset.
The measure explained
House price indices, also called Residential Property Prices Indices (RPPIs), are index numbers measuring the rate at which the prices of all residential properties (flats, detached houses, terraced houses, etc.) purchased by households are changing over time. Both new and existing dwellings are covered if available, independently of their final use and their previous owners. Only market prices are considered. They include the price of the land on which residential buildings are located.
Where to find the underlying data
The OECD database on house prices is available on OECD.STAT and includes the three following datasets:
- Residential Property Price Indices – Headline Indicators: This dataset covers OECD members and some non-member countries. For each country, the RPPI that is available at the most aggregate level has been singled out in this dataset, but due to data availability, headline indicators are country specific. For example, the RPPI at the most aggregate level for the United States only covers single-family dwellings and not all types of dwellings as is the case for most other OECD countries.
- Residential Property Price Indices – Complete database: This dataset contains nominal house price indices with various breakdowns for OECD members and some non-member countries. Headline indicators are a subset of this complete dataset.
- Analytical house price indicators: This dataset contains, in addition to nominal RPPIs, information on real house prices, rental prices and the ratios of nominal prices to rents and to disposable household income per capita. It should be noted that for Brazil, Canada, China, Germany, the United States and the Euro area, the datasets “Analytical house price indicators” and “Residential Property Price Indices (RPPIs) – Headline Indicators” do not refer to the same nominal price indices. These differences are further documented in country-specific metadata that are attached to this dataset.
In the future, the OECD database on house prices will include other housing-related indicators in order to provide a more comprehensive picture of real-estate markets.
ILO, IMF, OECD, UNECE, Eurostat, World Bank (eds.), (2013), Handbook on Residential Property Price Indices, Eurostat, Luxembourg