Colonialism, Race and Education Inequality

In the words of Engerman and Sokolof, colonialism is back – not in practice, thankfully – but in the minds of scholars as an important ‘deep determinant’ of contemporary development patterns. This post focuses on just one of the many long-term effects of colonialism: its transformation of the construct of race and the effect that has had on education inequality and institutional development.

A Brief History of Race in Africa

Colonialism did not ‘invent’ ethnicity. Many ethnic groups – such as the Ashanti in Ghana and the Baganda in Uganda – have roots that long pre-date European occupation. Back then, ethnicity was an extremely fluid construct: socio-cultural boundaries were marked by fuzziness and flexibility, and many Africans existed within a reality of multiple, overlapping and alternative collective identities. This was largely driven by the scourges of war, famine and disease, which would routinely destroy and dislocate entire communities.

By delineating Africans based on birth and defining their relationship with colonial state in outwardly ethnic terms, colonialism made the concept of race both inflexible and politicised. This was done for several reasons. First, inventing the notion of ‘tribes’ proved to be a costless and efficient way of locating, demarcating and counting the population of a colony for purposes of surveillance and control. Tribal divisions also made it easier for administrators to monitor their local ‘Big Men’: indigenous rulers who served as intermediaries between the colonial state and the wider populace. These relationships were defined by patrimonial logic: colonial elites would keep local Big Men happy through the dispensing of resources who would in turn pacify ethnic constituents by similar means.

The notion of tribalism was also supported by genuine ‘scientific’ beliefs at the time that Africans were naturally tribal and that some tribes were inherently superior to others. For instance, in Rwanda, Belgian occupiers viewed the Tutsi as ‘less African’ than the Hutu and privileged their status accordingly.

Figure 1: The ‘Scientific’ Demarcation of Race in Rwanda

Source: The New Times

The net results of this was a power structure that categorised and prioritised Africans in avowedly ethnic terms. Education was a popular vehicle to deploy this favouritism: for example, in Burundi and Rwanda, Western-style education was reserved almost exclusively for the Tutsi; in Uganda the Baganda were educated at the expense of the Bonyoro, while in Nigeria Christian missionaries set up schools in the Igbo South whilst leaving the Hausa North virtually untouched (though this was for religious as well as ethnic reasons). Largely by design, colonial administrators fomented a new indigenous ‘super-class’ which outwardly reflected the racial biases manifest in colonial rule.

The Stickiness of Colonial Institutions

At independence, inaugural elites – themselves largely beneficiaries of these power structures – found it expedient to maintain rather than dismantle them. By emphasising ethnic divides, they could consolidate support within their ethnic ‘in-group’ while disempowering the rest. Ethnicity proved such a useful ‘mobilising’ tool because it could coexist with other types of consciousness, papering over traditional cleavages like age, religion and class.

As before, education proved an effective means to exhibit ethnic favouritism. Not only were investments in education ‘visible’ to the populace, they also safeguarded the dominance of a group between generations: by deciding who to educate today, elites could effectively choose who would rule tomorrow.

In many countries, the endurance of this logic led to a remarkable stickiness of racial hierarchies. Educational attainment amongst the Brahmin in India, the Igbo/Yoruba in Nigeria and the Baganda in Uganda continues to outstrip those of their ethnic neighbours. In each case, contemporary dominance can largely be traced to the whims of colonial favouritism.

Even in countries where ethnic groups exhibit greater mobility, education policy remains shaped by ethnic considerations. As Franck and Rainer (2009) show, the education enrolment of a particular ethnic group increases when they have 'their man’ in power.

This discussion points to an important consequence of colonialism: by creating communities who were greatly advantaged in terms of human capital and legal rights, colonial administrators left behind societies that were grossly unequal at the outset. This incipient inequality greatly inhibited the development of inclusive political institutions, as the narrow group of elites who inherited power were heavily incentivised to maintain institutions that worked to their advantage (Engerman and Sokolof, 2005).

Final Words

A final word on colonialism. Imagine a spectrum on which theories of colonialism can be placed. At each end are two ridiculously polemical positions: on the left sits the view that colonialism has made no difference to contemporary African development; on the right sits the view that colonialism explains all idiosyncrasies of modern African states.

Clearly, any position too close to the left of this spectrum is untenable – this post has shown just one (of many) long-term consequences of colonialism on economic and institutional outcomes in Africa. However, we should also be wary of arguments that sit too close to the right. Deferring to colonialism as the ultimate cause of everything does a poor job in explaining contemporary differences between Kenya and Tanzania: two countries with ostensibly similar colonial experiences and initial conditions, yet with vastly divergent postcolonial economic, political and social histories. It also robs Africans of agency. Colonialism did not set in motion an unerring chain of events irresponsive to the will of incumbent elites – such a view uniquely misconstrues African leaders as passive objects of manipulation rather than active shapers of history.

Why am I saying this? It is not to exonerate colonialism; rather, it is to undermine the fatalistic idea that colonialism has doomed Africa to underdevelopment. This is not just alarmingly defeatist – it is also patently untrue, as demonstrated by development successes in Botswana, Mauritius and more recently in Ethiopia and Rwanda. Making sense of these necessitates a balanced perspective: we must remain sensitive to the long shadows cast by history while at the same time avoiding overly-deterministic modes of thinking which under-value prospects of future development.

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References

• Engerman and Sokolof (2005), ‘Colonialism, Inequality and Long-Run Paths of Development’
• Franck and Rainer (2009), ‘Does the Leader’s Ethnicity Matter? Ethnic Favouritism, Education and Health in Sub-Saharan Africa’

Making Schools Safe

Open any op-ed, journal article or Econ Twitter thread about education delivery in developing countries and there is a very good chance it will centre on one of two issues: access (how to get kids in school?) or quality (how to make sure kids are learning in school?). Of course, there is good reason for this – if kids aren’t in school (or if they aren’t being taught if they are) then it is unlikely they will be learning key competencies such as reading, writing and numeracy – with devastating consequences for economic growth and individual flourishing. However, in making these issues cardinal, we run the risk of blind-sighting ourselves to issues that are at once simpler yet more fundamental.

Accidents Waiting to Happen

Consider the issue of school safety. If the government mandates a policy of compulsory schooling – which compels children to meet at the same place each day, five days a week – then we had better make damn sure that these places are safe for children to congregate. Safety is multi-faceted. Perhaps the most obvious component is the physical safety afforded by school infrastructure – are schoolchildren at risk of falling debris, open sewage or fire?

It is easy to take these questions for granted – I certainly did before I stepped foot in a school in Zanzibar. But consider these photos I took last week when visiting a pre-primary school – a school designed for children of four and five years of age. These children – barely out of nappies – play largely unsupervised in areas riddled with decaying ceilings, open gutters and precipitous drops unguarded by railing. The fact that no learning is taking place in this school seems secondary to the fact that there is an accident seemingly waiting to happen every single day.

Figure 1: A Pre-Primary School in Zanzibar

Of course, accidents do happen, and the results can be devastating. In 2004, a primary school in the Indian state of Tamil Nadu caught fire due to an illegal thatched roof. 91 primary schoolchildren died that day – a tragedy of a scale that's difficult to comprehend. It is hard to think of a commensurate failure in education delivery.

The Threat Posed by Teachers

Unsafe buildings are not the only threat posed to children: a more frequent and intentional threat is posed by aggressive, lecherous and untrained members of staff. A 2011 study by UNICEF in Zanzibar revealed that over 70% of students reported having experienced some form of physical or humiliating punishment at the hands of educators – a fact which has devastating impacts on children’s incentives to attend school. Despite large decreases in recent years, approximately 69 countries still allow corporal punishment in schools, including parts of the United States, some Australian states and large swathes of Africa and Asia (Gershoff, 2017).

More shockingly, sexual abuse perpetrated by teachers remains ubiquitous, particularly in countries towards the bottom of the income distribution. In Liberia, 1 in 4 primary school age children admitted to having had sex with a teacher, and 1 in 3 admitted to having sex with a member of staff (Steiner et al., 2018). These grim statistics are reflected in Kerala, India, with 21% of schoolchildren reporting having been sexually abused in a school environment over the past year, with surprisingly far more boys reporting abuse (29.5%) than girls (6.2%) (Kumar et al., 2017).

Issues like these strike at our emotions in a way unlike issues related to enrolment or pedagogy. Despite this, they comprise a much smaller part of the overall conversation. If you wanted to find out what works in raising attendance or test scores in schools, there are literally hundreds of studies that have tested all manner of interventions in all manner of contexts. However, if you wanted to assess the prevalence of sexual violence or unsafe infrastructure in schools – and what can be done to improve it – you would struggle to find anything like the body of evidence behind access and quality. As Justin Sandefur notes, there is a strange paradox at play here: what we choose to focus our research on seems disproportionate to what we care about as parents, siblings and people. I'm not a parent - but if I were I imagine I'd care more about whether my child was vulnerable to sexual assault than I would about whether my child learnt anything today in school. 

In a scintillating open letter to a new Minister for Education, Paul Skidmore recommends immediately conducting an independent, internationally-recognised literacy and numeracy assessment to see whether children are learning anything of value in school. This is undoubtedly a good idea – but does not dig deep enough. As well as asking whether children are learning anything in schools, a more fundamental question would be: are schools safe places for children? Such a question would necessitate a school and teacher audit to go alongside any measure of basic competency.

Likewise, academic researchers should recalibrate their focus and make a more concerted effort to identify ways in which unsafe practices in schools can be reduced or eliminated. Recent work by the Behavioural Insights Team which assesses different strategies for changing teachers’ attitudes towards corporal punishment in the Nyarugusu Refugee Camp in Tanzania makes for a promising start (Behavioural Insights Team, 2017).

The benefits of this would be manifold. Crucially, making schools safer will have positive knock-on effects on the two topics currently in the limelight: access and quality. It doesn't take a PhD in Economics to figure out why in environments where they can be hurt and abused, children may be less engaged in class and less willing to show up altogether. More fundamentally though, eliminating unsafe practices can ensure that child's rights are better protected - a benefit which is difficult to overstate.

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References

• Behavioural Insights Team (2017), ‘Changing Teachers’ Attitudes Towards Corporal Punishment’
• Gershoff (2017), ‘School Corporal Punishment in Global Perspective: Prevalence, Outcomes, and Efforts at Intervention’
• Kumar et al. (2017), ‘Prevalence of Child Abuse in School Environment in Kerala, India: An ICAST-CI Based Survey’
• Steiner et al. (2018), ‘Sexual Violence of Liberian School Age Students: An Investigation of Perpetration, Gender, and Forms of Abuse’

Uncovering Causality II: Instrumental Variables

While randomised controlled trials may be the hottest tool in the econometricians toolbox, the most contentious, capricious but potentially brilliant tool is the instrumental variable (IV). A bad instrument will never see the light of day; but a good instrument can elevate the statistician from humble programmer to creative genius, catapulting the researcher to academic stardom.

Instrumental Variables

As is often the case, instrumental variables are best explained by means of example. Suppose I hope to estimate the causal effect of class sizes on student test scores. If I collect data on both, I may get a scatterplot that looks something like this:

What is the problem with extrapolating a causal relationship from this result? As before, there may be ‘confounding’ variables that influence both X and Y, obscuring the true relationship between the two. For example, low class sizes may be correlated with school wealth (e.g. richer schools can afford lower student-classroom ratios) which also affects test scores through other ways (e.g. better textbooks and non-classroom facilities).

One way to avoid this problem is to identify a variable/instrument (Z) which i) affects class size (X) and ii) is not correlated with anything else that affects test scores (Y). In econometrics jargon, i) is known as the relevance requirement and ii) is known as the exogeneity requirement. If both criteria are met, we can effectively ‘isolate’ the random variation in X and identify a true causal effect.

For example, in the context of class sizes and test scores, a candidate instrument could be the occurrence of earthquakes. In places like California, schools sometimes shut down due to infrastructure damage caused by periodic earthquakes. While the schools are being repaired, affected students are temporarily transferred to undamaged nearby schools, causing a sudden increase in class sizes.

We could also argue that earthquakes do not affect test scores (Y) in any other way apart from via their effect on class sizes (X). They do not change the school syllabus, they do not change the timing of the exams and they do not change the underlying ability of students. Assuming this argument is correct, we are left with a nice natural experiment: we have a random, isolated increase in class sizes (X) that allows us to identify its independent contribution to test scores (Y).

The Exogeneity Requirement: An Achilles Heel

Unfortunately, plausible instruments are difficult to identify. The relevance requirement is fairly easy to meet: it is not difficult to find variables that are correlated with the treatment (e.g. class sizes). However, the exogeneity requirement represents a perennial stumbling block. In my previous example, the exogeneity requirement is almost certainly not met. We can theorise many potential effects of earthquakes on test scores besides its impact on class sizes: for instance, earthquakes can have localised income effects as well as psychological effects on affected students, both of which effect test results. Alas, academic stardom will have to wait.

To make matters worse, the standards for an acceptable IV have generally become a lot more stringent. This is partly due to the emergence of new causal identification techniques that have raised the bar when it comes to credible identification strategies. The graph below gives a nice illustration of these new econometric techniques: some of which I may discuss in future posts.

Source: The Economist

As a result, some of the most iconic IVs appear unable to bear the weight of contemporary scrutiny. For example, perhaps the most famous IV in all of Economics is the use of settler (colonial) mortality rates to isolate the causal effect of institutions on growth (Acemoglu and Robinson, 2001). The authors theorised that in places where settler mortality rates were high (e.g. Africa, South America), settlers were more likely set up ‘extractive’ institutions in which a small group of individuals exploited the rest of the population. Conversely, in places where settler mortality rates were low (e.g. North America), settlers were more likely to establish trade relationships and ‘inclusive’ institutions, which included many people in the process of governing.

The authors go on to argue that these initial conditions greatly influenced the quality of economic and political arrangements today, due to the ‘sticky’ nature of institutions. Hence, the relevance requirement is met: Z (settler mortality rates) affect X (modern institutions). Then comes the leap of faith: the authors claim that historic settler mortality rates (Z) have not affected modern day growth (Y) except via their effect on the institution’s they brought about (X).

Needless to say, the validity of this instrument has received a hammering in recent years. For instance, settler mortality rates (Z) seem correlated with historic disease environment which is itself correlated with modern day growth (Y). These criticisms appear unanswerable: I remember a senior faculty member at Oxford once telling me that the IV would probably not get published today. Still, the above example serves as a testament to human creativity and to the rapid advancement of scientific standards.

All Hope is Not Lost

With this being said, instrumental variables are not completely defunct. One of the best IVs I have seen concerns estimating the effect of incarceration length (X) on subsequent employment outcomes (Y) (Kling, 2006). The immediate difficulty in estimating this effect is that people who get longer prison sentences are likely to be different from those who get shorter sentences, in ways that matter for future labour earnings.

To get around this, we can use the fact that judges are randomly assigned to cases, and that some judges appear systematically harsher at sentencing than others. This appears to meet the conditions of a valid instrument: the particular judge (Z) a defendant gets certainly effects his/her likely incarceration length (X), but is unlikely to be correlated with any other factor which effects his/her future earnings (Y).

This, I think, represents a plausible identification strategy with many avenues for future research (see this blog post). Though standards have risen, instrumental variables are certainly not obsolete. But remember: IV regression is only as good as the instrument itself!

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References

• Acemoglu & Robinson (2001): ‘The Colonial Origins of Comparative Development: An Empirical Investigation’
• Kling (2006): ‘Incarceration Length, Employment, and Earnings’
• McKenzie has a nice recent blog post on the promise of judge leniency IV-designs: https://blogs.worldbank.org/impactevaluations/judge-leniency-iv-designs-now-not-just-crime-studies?cid=SHR_BlogSiteShare_XX_EXT

A Tribute to the Kremer O-Ring Model, and its Application to Education Economics

The Kremer O-Ring Model

My favourite model in all of Economics is the Kremer O-Ring Model. Devised in 1993, the model depicts a firms’ production process as a series of sequential tasks that all inter-depend on each other. In order for the firms’ final product to be of any high value, all tasks need to be executed proficiently. Consequently, a single mistake in the chain can ruin the contribution of all the others.

To illustrate this, imagine the production process at Dominos: a chain of workers all responsible for a different component of the pizza. If the baker messes up the dough this ruins the value of the whole pizza, regardless of how expertly the sauce is cooked or the toppings are arranged.

Until now I have refrained from using a single equation in my blogs; however, I cannot resist introducing one now to illustrate this central idea:

Here, y represents a generic term for ‘output’ (e.g. pizzas, clothes) while q represents the quality of a particular worker i. For simplicity, assume q is a variable distributed between 0 and 1. For example, a q of 0.9 means a worker executes her task perfectly 90% of the time (and fails the other 10%). The large pi symbol is simply a multiplicative operator: it instructs us to multiply together each q from q(1) to q(n).

A numerical example helps bring this equation to life. Imagine two 5-person production teams: Team A has four talented workers (0.9) and one dud (0.1); Team B has five average workers (0.6). Their respective outputs are as follows:

Team A: 0.9*0.9*0.9*0.9*0.1 = 0.066

Team B: 0.6*0.6*0.6*0.6*0.6 = 0.078

Crucially, even though the average worker is more talented in Team A, Team B still produces more. The dud worker ruins the output of the rest; anyone who has played 5-aside football without a proper goalkeeper may appreciate this idea.

The model takes its name from the Challenger Space Shuttle disaster of 1986, where the shuttle exploded 73 seconds after take-off. Catastrophe was caused by the failure of a single O-ring: a small rubber gasket (costing a few cents) that was supposed provide a tight seal between metal parts. This echoes the central message of the model: an enormously complex process can be destroyed by the failure of any one, seemingly trivial part.

Figure 1: The Challenger Space Shuttle at Take-Off

Source: www.snopes.com

Why do I like this model? I like it because from so little we can learn so much. The equation I outlined above – less than 2cm of algebra – generates many testable hypotheses that appear borne out in the real world.

For example, due to the sequential nature of production, the model predicts that people of similar abilities will seek each other out and band together to form firms. This reflects the type of assortative matching we see in the modern labour market, especially amongst highly talented people.

The model also implies that the wages paid to these people will be disproportionately large, due to the non-linear effects implied by the multiplicative production function. To see this, plug in a value of 0.9 for each member of Team A and 0.8 for each member of Team B. This small difference in skill amplifies into large differences in output, which itself drives large differentials in wages. This gives rise to a very skewed income distribution: similar to the one we see today.

The model also explains the positive correlation we observe between firm sizes and wages (Haugen, 2016). The idea is simple: if you are a large firm with many stages of production, a mistake at a single stage becomes extremely costly. To ensure mistakes are minimised, firms must hire highly talented people who command higher wages.

A similar idea underlies another prediction of the model: that more complicated products will be made in richer countries. Again, complicated products imply extremely costly mistakes; hence firms are better off locating in environments with an abundance of high-skilled labour. This is why Germany specialises in automobiles and satellite technology while the Democratic Republic of Congo specialises in agriculture and mineral extraction.

Application to Education Economics

Fortunately for the theme of this blog, the model also has a lot to say about education decisions. For example, the model can explain education-poverty ‘traps’ whereby people in poor countries have no incentive to invest in education to improve their skills. Consider two people: Amy who is born in rich country A and Ben who is born in poor country B. Amy and Ben are of comparable skill (0.3) and both have the chance to invest in education to make them more productive (0.9). Each is confronted with the following prospective labour force:

Country A: 0.9*0.9*0.9*0.9

Country B: 0.3*0.3*0.3*0.3

Will both invest in education? Assuming education is costly, Ben may not invest in education as there are no similarly educated workers for him to match with in Country B. This is not the case for Amy, who is able to realise the full benefits of her education by teaming up with other talented workers who amplify her newly-developed skills.

The model can also explain a ubiquitous phenomenon in developing countries: brain drain. Suppose Ben does end up investing in education. Does he have any incentive to remain in country B? He is likely to be far more productive in country A where he can be matched with other high-quality workers and command much higher wages.

We see the same phenomena at a local level: imagine Country A is now UNICEF and Country B is a generic Ministry in a developing country. Faced with this choice, many talented local staff will eschew civil service work in favour of working for donors, sapping the capacity of the local public sector. I am witnessing this type of domestic brain drain first-hand in Zanzibar; it is an issue both donors and NGOs should be more mindful of to avoid outcomes that run counter to their objectives.

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References

Thanks to this post goes to Doug Gollin, my Microeconomics Professor at Oxford who first introduced me to the O-ring model and whose slides this blog draws inspiration from.

•   Haugen (2016), ‘Firm Size Wage Premiums Around the World: Evidence from PIAAC’
•   Kremer (1993), ‘The O-Ring Theory of Economic Development’

The Case for Prioritising Female Education

A famous African proverb tells us that “if you educate a man, you educate an individual, but if you educate a woman you educate a nation”. Should we take this claim seriously? There is a lot of evidence to suggest we should, which together forms a case for prioritising female education.

Workforce Emancipation

First, education emancipates women by increasing their chances of entering the workforce. For example, Erten and Keskin (2018) show that women induced to get more education in Turkey by a 1997 reform that increased compulsory schooling from five to eight years were more likely to work outside the home. Grepin and Bharadwaj study a similar reform in Zimbabwe that relaxed constraints on black Zimbabwean’s access to secondary school to find that each additional year of education led to a 3-percentage point increase in the probability of a woman working outside the home. This is good not just for women but for the economy as a whole, as it diversifies the talent pool of its workforce which, in almost all countries, is predominantly male (OECD).

An increase in female labour force participation can also have a positive feedback loop via the effect it has on the aspirations of younger generations. There are numerous studies that show how ‘mould-breakers’ can move social groups out of self-reinforcing low-aspirations traps. For example, in 1993 India passed a law that reserved leadership positions for women in randomly selected village councils. Policy changes like these are gold dust to social science researchers – the randomisation of the intervention creates a nice ‘natural experiment’ whereby we can viably compare ‘treated’ and ‘non-treated’ villages. Beaman et al. (2012) exploit this to show that in villages assigned a female leader, the gender gap in aspirations was closed by 32% and the gender gap in adolescent educational attainment was completely erased. Educating girls today can create leaders tomorrow; the effects of which can echo through generations.

Health Spillovers

While increased education usually increases female labour force participation, in some countries this is not the case. For example, in Malawi, Baird et al. (2016) show that out-of-school females who were prompted to re-enter school by a cash transfer were not more likely to be working two years after the transfers ended.

Even in contexts such as these, a case remains for prioritising female education. Educated mothers know more about nutrition (UNICEF), health and sanitation (Glewwe, 2009) and are more likely to immunise their children (Keats, 2014), leading to substantial health spillovers in the household. These spillovers are likely to be far greater for women than for men for the simple reason that in developing (and developed) countries, women almost always do the lion-share of domestic duties (UN Women).  

A specific example helps illustrate this point. A recent chart published by Our World in Data shows that 3% of global deaths (~1.7 million) are caused by diarrhoea: more than suicide, homicide, conflict and terrorism combined.

Figure 1: Annual Number of Deaths by Cause, 2016

Source: Our World in Data

In a separate study, Datta and Mullainathan (2014) show that 35-50% of Indian mothers surveyed believed that the correct treatment for diarrhoea is to withhold fluids, due to a faulty mental model that associates it with ‘leaking’. This is one of the saddest but simultaneously most hopeful findings I’ve come across. How many of these 1.7 million deaths could have been prevented by extremely basic maternal education? The welfare implications of this are difficult to comprehend.

Cool Heads, Warm Hearts: A Foray into Moral Philosophy

Of course, there is also a moral dimension to this debate that is hard to ignore. Another of the most memorable (but harrowing) statistics in Economics is Amartya Sen’s (1990) calculation that there are 100 million ‘missing’ women in the world: a result of sex-selective abortions, female infanticide and inadequate healthcare and nutrition for female children. This is not just an artefact of the present day: in almost every society that has ever existed, women’s claims to equal rights, freedoms, authority and resources have been systematically violated (Harari, 2015). This is particularly the case in contemporary developing countries, which almost exclusively languish towards the bottom of cross-country gender parity indexes (UNESCO).

              

Faced with a world like this, how should we react? Economics offers little guidance on normative questions like these: we need to venture into the field of moral philosophy. In a world where the returns to male and female education were equal across every dimension, women would still command a greater claim to society’s resources due to both present day inequalities and historic injustices bestowed upon them.[1] It is worth reemphasising that this claim holds even in absence of the ‘instrumental’ arguments I have outlined thus far.

Who is responsible for meeting this claim: the state, the firm or the individual? While all three have the capacity to redistribute resources, the ability of states to move resources and coordinate people en masse is unparalleled – which makes their obligation correspondingly much larger. This does not absolve individuals (particularly men) and firms entirely: both certainly have a role to play in challenging unfair gender norms and practices in the workplace and home. However, this does not change the fact that states are the most suitably placed to fulfil women’s moral claim to a greater share of society’s resources. States can fulfil this claim by – amongst other things – prioritising female access to education and other public goods.

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References

• Baird et al. (2016), ‘When the Money Runs Out: Do Cash Transfers Have Sustained Effects on Human Capital Accumulation?’
• Beaman et al. (2012), ‘Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India’
• Datta & Mullainathan (2014), ‘Behavioural Design: A New Approach to Development Policy'
• Erten & Keskin (2018), ‘For Better or for Worse?: Education and the Prevalence of Domestic Violence in Turkey.’
• Glewwe (2009), ‘Why Does Mother’s Schooling Raise Child Health in Developing Countries? Evidence from Morocco’
• Grepin & Bharadwaj (2015), ‘Maternal Education and Child Mortality in Zimbabwe’
• Harari (2015), ‘Sapiens’
• Keats (2014), ‘Women’s Schooling, Fertility and Child Health Outcomes: Evidence from Uganda’s Free Primary Education Programme’
• OECD: https://stats.oecd.org/index.aspx?queryid=54741
• Our World in Data: https://ourworldindata.org/what-does-the-world-die-from
• Sen (1990), ‘More than 100 Million Women are Missing’
• UN Women: http://www.unwomen.org/en/what-we-do/economic-empowerment/facts-and-figures
• UNESCO http://uis.unesco.org/en/glossary-term/gender-parity-index-gpi
• UNICEF: https://www.unicef.org/nutrition/burkinafaso_35979.html

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[1] Theories of distributive justice can be roughly split into two camps: ‘time-slice’ theories (a la John Rawls) say that the justice of a distribution depends on present-day inequalities: all that matters is what people get. ‘Historic’ principles of justice (a la Robert Nozick) instead argue that all that matters is how a particularly distribution came about (i.e. did it violate anyone’s rights?). Crucially, the distribution of resources in most countries is unfavourable to women and came about via the violation of their rights. Therefore, women deserve a greater share of society’s resources, regardless of what camp you are in

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.