Refugees, Education Outcomes and Gambling for Redemption

Over 80% of refugees live in developing countries – a fact you’d be forgiven forgetting amidst the media frenzy surrounding the refugee crisis in Europe. The infographic below demonstrates this, showing East Africa and the Middle East to be global refugee hotspots: the former due to insurgency in DRC, South Sudan and Somalia; the latter due to the collapse of the Syrian state.

Source: UNHCR statistics

Another oft-neglected fact is that most refugees do not live in camps. UNHCR estimate that globally, less than a 1/3 of refugees reside in camps – the rest live almost exclusively in cities and peri-urban areas. Many refugees relocate to the capital: for example, in Lebanon, over 25% of refugees reside in Beirut.

Source: UNHCR Syria Regional Response data

Refugee Access to Education

Education delivered in refugee camps is imperfect. Curriculums are often not aligned with the system in their home country; the language of instruction is often foreign; teaching materials are scarce, and teachers are in dangerously short supply. This final point is particularly vexing: many refugees are qualified teachers but are barred from formal employment, leading to the surreal situation where there are both not enough teachers and teachers without jobs.

Despite these issues, most refugee children in camps have access to education. This is largely due to the logistical convenience that refugee camps provide: by concentrating refugees in one place, host governments and aid agencies can quickly build tents, distribute food, deliver inoculations and set up rudimentary schools.

By contrast, the average urban refugee is less likely to be in school. Two years ago, I volunteered for Xavier Project, an NGO focused on increasing urban refugee access to education in East Africa. We estimated that in Nairobi, 65% of primary school-aged and just 33% of secondary school-aged children were attending school – making refugee children five times more likely to be out of school than their non-refugee peers. There is significant variation amongst country of origin: in Kampala, we estimated that just 26% of school-age Congolese refugee children were attending school.

There are several reasons for this. Most obviously, refugees from Somalia and DRC often cannot speak English – the primary mode of instruction in both countries – creating a Catch 22 whereby they can’t attend school because they can’t speak English, but they can’t learn English because they can’t attend school. Refugees also face a minefield of bureaucratic obstacles: many can’t enrol in school because they lack identity cards; many more can’t enrol as they lack previous academic transcripts, which in many cases were left behind in a rush to flee violence.

It is not just a lack of education that urban refugees must contend with. They must also tolerate slum dwellings, frequent xenophobia and similarly hard-to-access healthcare facilities. In many cases, it is often only through remarkable social networks that refugees manage to survive.

Faced with this situation, a reasonable question to ask is: why do refugees migrate to the city in the first place? I think I can answer this question, but to do so requires a brief detour to one of the most famous models in Development Economics: the Harris-Todaro model of rural-urban migration.

The Harris-Todaro Model

The puzzle that Harris and Todaro sought to address was this: why do so many people migrate from rural to urban areas in the face of high urban unemployment and poverty? Their answer: calculated risk. In agriculture, workers are assumed to be all-but-guaranteed low-income/subsistence work as a farmer. In the city, there is a high probability they will end up unemployed, living in a slum and forced to eke out a living in the informal sector. But there is also a chance they will land a formal job – with all the salary and non-salary perks that this implies. People migrate until the low-risk, low-reward prospect of agriculture equates with the high-risk, high-reward returns offered in the city.

A numerical example may help illustrate this. In the table below, people are guaranteed an income of 50 in the countryside. If they migrate to the city, there is a 50% chance they will be unemployed (earning an income of 20 in the informal sector) and a 50% chance they land a formal job, which pays 80.

Assuming agents are not risk averse, they should be indifferent between remaining in the countryside and migrating to the city. Subsequently, anything that increases their likelihood of landing a formal job – or increases the salary these jobs command – should incentivise greater rural-urban migration.

Gambling for Redemption

While this model was originally devised to explain rural-urban migration, I think it does a good job explaining camp-urban migration decisions too. For a refugee, the low-risk, low-reward outcome is often remaining in the camp: while they are least virtually guaranteed basic housing, healthcare and education, they are nonetheless prohibited from formal employment, or registering a business. The risks of migrating to the city are extensive and have been described above. But the potential rewards are also great. There is a possibility, however small, that they will obtain formal employment. There is also a chance they will get sponsored by an NGO like Xavier Project, which tend to be disproportionately located in cities. Finally, through being near UNHCR headquarters, they may be better able to ‘play the system’ and make their case heard, maximising their chances of being resettled in a developed country. Hence, camp-urban migration can be considered a similar gamble to rural-urban migration – though one with arguably higher risks and rewards.

This simple model can be applied to risk taking in more extreme scenarios. Consider refugees taking rubber dinghies across the Mediterranean to Europe. This is an extremely high-risk strategy; but one that must be compared to the often-dire alternative of staying put. When framed this way, such a decision seems undeniably risky but not irrational. Rather, the decision simply reflects the brutal reality that refugees must confront.

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References

This blog post was largely inspired by my time volunteering for Xavier Project, an NGO based in Kenya and Uganda. They do great work: sponsoring refugee children through school, teaching English to those without and running adult education hubs to teach vocational skills. Please check them out here: http://xavierproject.org/

•           Harris, J. & Todaro, M. (1970), ‘Migration, Unemployment and Development: A Two-Sector Analysis’

Are Humans Getting More Intelligent?

Human beings are getting smarter – at least according to a recent meta-analysis conducted by researchers at Kings College London. They base this claim on the observation, first noticed by James Flynn, that worldwide IQ test performance (the de facto measure of intelligence) has profoundly and consistently increased over the last century. These gains have been virtually universal, with the biggest leaps seen in China and India.

Since IQ tests are always standardised to 100, one way to measure this increase is to have new test subjects take older tests. When Americans today take IQ tests from a century ago, they score an extraordinarily high average IQ of 130. Conversely, Flynn argues, if the American’s of 100 years ago took today’s tests, they would have an average IQ of 70 – the recognised cut-off for people with intellectual disabilities. Progress is most pronounced in the Raven’s Matrices: a non-verbal test designed to measure abstract reasoning (example below). These findings are colloquially known as the ‘Flynn effect’.

Above: US gains in IQ. Source: BBC

Above: Ravens Progressive Matrices, which should be familiar to anyone who sat the 11+.

Test Wiseness

When I first heard of this effect, I was quite sceptical. My initial reaction was that the improvement was most likely due to humans getting better at sitting IQ tests (a phenomenon known as ‘test wiseness’) – not due to their getting more intelligence. For example, in Estonia, when psychologists Olev and Aasa Must laid IQ tests from the 1930s alongside papers from 2006, they found an increase in correct answers – but also incorrect ones. Students had realised they would not be penalised for incorrect answers – they had become better at ‘gaming’ the system.

I no longer think this explanation captures the entire story. Firstly, standardised IQ tests have been an accepted part of American culture for a long time; secondly, the frequency of IQ testing has waned during the last 50 years. In other words, people have ‘gotten used’ to taking IQ tests while simultaneously have had less chance to practice them. Despite this, IQ increases have remained steady over the last 50 years (at about 3 points per decade). Test wiseness alone struggles to explain this fact, which has led theorists to propose several alternative explanations that couch these gains in the vastly different world we inhabit.

Education

For example, one explanation cites the vast increase in the quantity and quality of education as the main reason for the improvement in IQ scores. Globally, more people are in school than ever before. Curriculums have also shifted further towards mathematics and natural sciences, requiring a greater emphasis being placed on classification, logical consistency and abstract reasoning (Blair et al., 2005). This shift away from memorisation towards abstraction has permeated through all levels of schooling. For example, in Ohio in 1910, 14-year olds would have sat state examinations which predominantly tested them for concrete, socially-valuable information, such as the names of all 45 state capitals. In 1990, these same examinations had shifted to almost exclusively abstract questions like “why is the largest city in the state rarely the capital?”. The tenor of education has changed: children are more encouraged to think in the abstract and take the hypothetical seriously – traits we tend to associate with fluid intelligence and higher IQ scores.

Environment

These shifts in education practices have occurred contemporaneously with a radically changing working environment. In 1900, only 3% of Americans worked in professions deemed ‘cognitively demanding’, such as doctors, lawyers or bankers. Today, this figure has risen to 35%. Not only this, but there has been an ‘upgrading’ of these professions: a doctor today must confront a much more complex and information-laden working environment than a doctor a century ago.

The world around us has also changed. Society asks us to solve a wider range of cognitive problems and process a much larger amount of information than before. To deal with this, we have developed mental models to categorise, analyse and make sense of an increasingly chaotic world. These models are often faulty: but our sheer frequency of using them stretches our cognitive faculties to a degree incomparable with a century ago.

One of the most striking ways the world has changed is in it becoming more visual. From pictures on the wall to movies to television to video games, each successive generation has been exposed to far richer optical displays than the one before. Beyond merely looking at pictures, we also analyse them. Picture puzzles, mazes, exploded views and complex montages appear everywhere—on cereal boxes, on McDonald's wrappers, in the instructions for assembling toys and in books intended to help children pass the time. This may have helped us develop a skill deemed an important component of intelligence: visual reasoning. Indeed, the fact that the greatest IQ gains can be seen in Ravens Progressive Matrices (which predominantly test for visual reasoning) may stand testament to the importance of this trend.

Humans 2.0

What to make of all this? It is important not to get carried away. A human brain today is indistinguishable from a human brain a century ago[1]: 100 years is far too brief a period for evolutionary processes to have taken place - nature has not upgraded us to a smarter breed of human. Instead, if we are to take the Flynn effect seriously, it makes more sense to view it as a case of nurture over nature. Consider two identical twins, one of whom becomes a professional athlete while the other becomes an investment banker. At autopsy these twins would look physically very different, despite their similar physiology at birth. In the same way, a human born today will have to endure far more rigorous cognitive training than one born a century ago. While they may have had similar brains at birth, comparing their brains during middle age will likely reveal significant discrepancies in a range of cognitive functions – discrepancies which reflect the different demands placed on them by the different worlds they inhabit.

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References

For anyone interested in this topic, I would recommend James Flynn’s Ted Talk on why our IQ levels are higher than our grandparents. Blair et al. (2005) provide a compelling case for curriculum change being the primary driver of the increase in test scores. The BBC News article also provides a nice overview.

•          BBC News, ‘Are humans getting cleverer?’ https://www.bbc.com/news/magazine-31556802
•          Blair et al. (2005), ‘Rising mean IQ: Cognitive demand of mathematics education for young children, population exposure to formal schooling and the neurobiology of the prefrontal cortex’
•         Flynn, J. ‘Why our IQ levels are higher than our grandparents’ https://www.youtube.com/watch?v=9vpqilhW9uI&t=36s
•          Kings College London, ‘A Cross-Temporal Meta-Analysis of Raven’s Progressive Matrices: Age groups and developing versus developed countries’

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[1] One challenge to this view stems from the observation that human beings are taller today than a century ago, having grown at a rate of 1cm per decade. It is not inconceivable that this increase in height has led to a corresponding increase in brain size. Some scientists, such as Richard Lynn, argue that this could account for the entirety of the Flynn effect. I omit a full discussion of this explanation, as the evidence for the link between height and brain size and between brain size and intelligence is inconclusive.

Why Does Education Reform Happen Where and When It Does?

Education reform fundamentally changes the quality, composition and demands of a workforce. It does so in a way that does not benefit all sections of society equally. This post attempts to outline a simple model to help us think about the winners and losers of education reform, in order to explain why reform happens when and where it does. Doing so will reveal the curious relationship between land reform and education reform, which offers a clue as to why the timing and extent of reform differs so greatly between countries.

A Model of Education Reform

In economic models, we often make crude abstractions which simplify reality. We do this to make our analysis workable and to remove irrelevant details which risk obscuring the main message we wish to convey. These abstractions are often criticised as being ‘unrealistic’. However, the relevant criterion is not whether such abstractions are realistic but whether they are useful. Maps provide a good analogy to this: a map which detailed every single crack, pothole and tree along a road would be realistic but ultimately useless. By contrast, an ‘unrealistic’ map which only detailed the most salient features of a road (such as the name, type and direction the road is headed) would be far more useful.

In the same way, ‘unrealistic’ Economic models can nonetheless greatly aid our understanding of the world. For example, in the context of 19^th century Britain, a crude (but useful) abstraction to illustrate the timing of education reform is to categorise people into three separate groups: landowners, capitalists and workers. Landowners own land (e.g. farms), capitalists own capital (e.g. factories) and workers own their labour time, which they can sell to either landowners or capitalists. In this model, both landowners and capitalists make up the political elite, who must decide on a variety of public sector measures including education policy.

Above: A cartoon parodying the dynamic between the three actors in our model. Source: Henry Devon George Society

Crucial to the timing of reform is the idea that landowners and capitalists demand fundamentally different things from their workforce. Crudely, landowners desire a workforce that is predominantly rural and immobile, so they can extract rent from tenants who have little in the way of ‘outside options’ to recourse to. On the other hand, capitalists desire a workforce that is educated, so that there are engineers to fix their machines, architects to build their factories and at the very least literate factory workers who are able to read instructions. Faced with these contrasting incentives, the pace and timing of reform is dictated by the political clout of capitalists relative to landowners at any one moment in time. When landowners are more powerful, education reform is likely to stall, as such a policy would likely require tax increases and threaten the immobility of their workforce.

To tie this back to land ownership, consider that the political salience of the landlord class is more pronounced when land ownership is more concentrated in the hands of an elite group: the landed aristocracy. Thus, land reforms which equalise land ownership will reduce the power of landlords relative to capitalists, increasing the likelihood for education reform to take place. Not only do they make landlords less important as a political class, but they can may also prompt landowners to diversify their portfolio away from land and towards capital, lowering their economic incentive to block education reform.

Historical Evidence

History provides us with many instances of this sequence taking place. For example, in Japan in 1871, an Imperial Decree initiated the abolishment of the feudal system. Decisions on land utilisation and choice crops were transferred from landlords to farmers and prohibitions on the sale of farmland were removed. These reforms had the effect of greatly reducing the power of Japan’s landed aristocracy. Education reform soon followed: in 1872, the Education Code established compulsory and locally funded education. While in 1873 only 28% of school-age children attended schools, this ratio increased to 51% by 1883 and 94% by 1903 (Gubbins, 1973).

This pattern was mirrored in Russia a few decades later. As the Tsar’s grip on power weakened during the early 1900s, the political power of the wealthy landowners gradually declined leading to a series of agrarian reforms initiated by the Stolypin in 1906. Restrictions on the mobility of peasants were abolished, fragmented land-holdings were consolidated, and the formation of individually owned farms was encouraged and supported through the provision of government credit. As a result, the land holdings of the aristocracy declined from about 35-45% in 1860 to 17% in 1917. As their political power correspondingly weakened, the Duma began to initiate a series of education reforms, leading to the education share of the Provincial Council’s budget increasing from 20.4% in 1905 to 31.1% in 1914 (Florinsky, 1961).

Interestingly, this converse relationship between the concentration of land ownership and the extent of education delivery remains visible today. In Costa Rica and Colombia, where coffee is typically grown in small farms, education expenditure and schooling outcomes are significantly higher than in Guatemala and El Salvador, where large plantations still dominate.

From this simple model of landowners, capitalists and workers, we can derive several interesting conclusions. First, anything that reduces the political power of the landowner class should speed up education reform. Land reform is one way to do this, though certainly not the only way. Electoral reform and a gradual diversification away from agriculture should both have similar effects. Second, in countries where the boundary between landowners and capitalists is more fluid, this should increase the likelihood of education reform, as it enables landowners to diversify away from land in response to political change. Finally, the model implies that while land abundance may initially be a positive thing, it may eventually hamper education reform (and ultimately growth) to the extent that it intensifies the political power of landowners. This may help explain what Acemoglu and Robinson refer to as the ‘reversal of fortune’: the observation that many historically rich civilisations (e.g. the Aztecs) have moved towards the bottom of the income distribution since the industrial revolution.

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References

The model outlined in this article was devised by Galor et al. (2009).

•         Florinsky (1961), ‘Encyclopaedia of Russia and the Soviet Union’
•     Galor et al. (2009), ‘Inequality in Land Ownership, the Emergence of Human-Capital Promoting Institutions, and the Great Divergence’
•     Gubbins (1973), ‘The Making of Modern Japan’