Tag Archives: Poverty

Living on Less Than One Dollar per Day

Sam Watson recently drew my attention to this fascinating article by my heroes – Adhijit Banerjee and Esther Duflo.[1] How do people in the “bottom billion” spend an income of around $1 per day? The authors turn to household surveys covering 13 countries in Asia, Africa and Central America (one assembled by the World Bank, and the others by the RAND Corporation). Even though it is hard to get a full stomach on $1 per day and many are hungry, not all money is spent on food – the proportion varies from a half to three-quarters of income spent on food. Nor are the cheapest foods always selected – taste crowds out Calories, even if that leaves you hungry. The second largest source of expenditure is festivals, such as weddings and funerals. Radios are a priority and show elastic demand on income. There is an inverse relationship across countries between spending on radios and on festivals. Asset ownership is very low – even in rural areas bicycle ownership is low – at a third of households or less. Education attracts a very low proportion of expenditure; 2-3% of the household budget in Pakistan, for example. People often feel hungry, many are anaemic, and energy levels are low. Illness rates are high and anxiety common when compared to high income countries. I guess many are in a poverty trap and need a little help to get them out of it, but the results resonate with the Gospel of Matthew, ‘Man shall not live by bread alone’.

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Banerjee AV & Duflo E. The Economic Lives of the Poor. J Econ Perspect. 2007; 21(1): 141-67.

Relative Wealth and Health

It has been known since the time of Condorcet, over 200 years ago, that poverty is bad for you – an income effect.[1] Studies have also shown an association between relative poverty and life expectancy – a relative income effect.[2] It has become common to interpret these associations as evidence that relative poverty causes poor health, net of absolute wealth. Previous studies are trumped by the largest association study ever conducted, based on nearly 1.4 trillion person years of observation and 1.4 billion de-identified tax records across the United States of America.[3] Beat that! So, what did it find?

  1. The association between wealth and longevity (the income effect) is confirmed. The difference in male life expectancy at age 40 differs by a colossal 15 years between the top and bottom 1% on the income scale. Interestingly, there is no threshold above which the association fades; rather the reverse.
  2. Inequality has increased in recent years because life expectancy has increased faster among people on high incomes than among those on low incomes.
  3. Differences in mortality adjusted for race and ethnicity and net of income varies by geographic area. In other words, the differences in life expectancy between rich and poor itself differs by geographic area.
  4. Differences in income effect by area are mostly explained by differences in health behaviour (rather than, for example, access to healthcare).

The finding that different areas have very different survival rates net of income, allows the effect of numerous other variables on the income effects (absolute and relative) to be explored. Some places have large gradients in wealth, others smaller. The existence of a relative income effect is confirmed by a negative correlation between inequality (measured by the Gini co-efficient) and longevity. But this is an artefact of the concave nature of the relationship between income and life expectancy. In fact, among the lowest quartile by income there is no correlation between wealth disparity and health, whereas, ironically, it is strongest among the upper quartile. So the idea that it is the poor who exhibit the strongest relative income effect is completely wrong. In fact, poor people have healthier behaviours and live longer when they live in rich cities alongside a highly-educated, high income populations than in poorer cities with ‘better’ Gini co-efficients. Perhaps this is because rich cities can raise more in tax at a given tax rate. Slum formation is more rapid in cities with a high Gini co-efficient. This is sometimes interpreted as a high Gini causing poverty, rather than the more plausible interpretation that rational internal migrants gravitate to richer cities. These data are important because they call into doubt the simplistic idea that all we have to do to right the world’s wrongs is to tax rich people more heavily.

— Richard Lilford, CLAHRC WM Director

References:

  1. Benzeval M, Bond L, Campbell M, et al. How Does Money Influence Health? York: Joseph Rowntree Foundation. 2014.
  2. Wilkinson RG, Pickett KE. Income inequality and population health: a review and explanation of the evidence. Soc Sci Med. 2006; 62(7): 1768-84.
  3. Chetty R, Stepner M, Abraham S, et al. The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA. 2016; 315(6):1750-66.

Poverty and Cognitive Function

It would appear that people who are chronically poor have lower cognitive functioning than the well-off.[1] Of course, which way round causality is working is not clear from this finding alone. However, even temporary poverty appears to affect cognitive reasoning, even if nutrition, time to complete the cognitive test, and work-effects do not vary. This latter study was based on Indian farmers tested at various points as their wealth changed across the seasons.[2] The authors postulate that the stress associated with poverty consumes mental resources that are not available for other concerns. These results provide a further argument for focussing resources on the poorest of the poor. For instance, people in registered slums in India have many amenities that are denied those in unregistered slums.[2] It would be better to spread meagre resources even more thinly in order to provide at least some help those who are poorly equipped to help themselves.+

— Richard Lilford, CLAHRC WM Director

Reference:

  1. Mani A, Mullainathan S, Shafir E, Zhao J. Poverty Impedes Cognitive Function. Science. 2013; 341: 976-80.
  2. Subbaraman R, O’Brien J, Shitole, T, et al. Off the map: the health and social implications of being a non-notified slum in India 2012. Environ Urban. 2012; 24(2): 643-63.

Are Slums Part of the Transition from Rural Poor to Urban Middle Class?

Successive waves of immigrants have arrived in East London over the 400-plus years since the Huguenots arrived following the revocation of the Edict of Nantes in 1685. Like their successors, they arrived impoverished, found work and moved on to a better life elsewhere. East London stayed the same, but the inhabitants progressed.

Nearly one billion people live in informal settlements or slums worldwide. Are these people in transition from rags to riches, or are they stuck in a poverty trap? Sadly, the evidence suggests that the second scenario is closer to reality. Poorly nourished, inadequately educated, chronically unwell and exploited by “slumlords”, they tend to sink, rather than rise, according to a recent review.[1] Work carried out by my collaborations at the Africa Population and Health Research Center (APHRC) in Nairobi confirms that net emigration from slums is a slow and uncertain business. Sojourn times are prolonged, often encompassing more than one generation.[2]

Various solutions have been proposed for this problem over the last 50 years – more or less sequentially:

  1. Slum clearance – hardly gets to the underlying cause.
  2. Benign neglect to halt immigration from the country – they still come.
  3. Investment in slums – may be the best option, but benefits often prove ephemeral, as in Jakarta.
  4. Land titling – often makes matters worse because “slumlords”, now with title deeds in their files, evict residents or raise rents.

The only thing that really works is a rising per capita GDP, as in China and India, but “trickle down” can take a long time.

Like many social phenomenon, the solutions are not straightforward. We would be grateful for further contributions to this debate. However, early education (and I mean really early) and improved sanitation/healthcare may be the best bet for now. Small effects are likely to be cost-effective, I would suggest.

— Richard Lilford, CLAHRC WM Director

References

  1. Marx B, Stoker T, Suri T. The Economics of Slums in the Developing World. J Econ Perspect. 2013; 27(4):187-210.
  2. African Population & Health Research Center – APHRC. KENYA – NUHDSS – Residency Table for all DSS Residents. 2014. [Online].