While this is a signed posting, I do that only to take responsibility for the content, rather than to claim originality. These notes draw on enumerable sources from media, emails, conversations, as well as my personal observations and knowledge drawing on the more mainstream literature. The post is provided as a contribution to public debate, not as any attempt at a definitive statement.
The lack of systematic testing for the Covid-19 virus makes nonsense of the counts of incidence, especially in the developing world. Death counts are probably more reliable, but still flawed. Nonetheless, from what we know so far, the developing world is on roughly the same trajectory as for the developed world, just with a lag. Death rates might be somewhat lower, given younger populations. However, less well-nourished and less healthy young populations could well turn out to be just as vulnerable.
On balance, it is a reasonable expectation that this will be a huge health and economic shock to the developing world, and especially to poor people.
Reliable public data and communication is crucial at this time
Yet in many developing countries, the government is pretending that it is in control, or that the threat is minimal. This delusion does not help. A different approach is needed.
Sharper trade-offs and harder constraints can emerge in poor places, with bearing on policy responses
Isolation at home slows the spread of the virus, and reduces the strain on health systems. It also comes with a large cost, especially to poor people.
Is the case for isolation weaker in poor countries? No. But the case for lockdown is weaker. Lockdown can pose new threats in some poor places.
Administrative and fiscal constraints loom large in poor countries.
A lot of what we routinely recommend for social protection in poor countries may need to be re-thought in this pandemic
Some of the classic policy prescriptions for dealing with other shocks don’t make as much sense during a pandemic, and this is especially so in poor places.
As in rich countries, a rapid, bold and forward-looking response is needed
A large, albeit time-bound, scaling up is needed of existing programs that are considered to work adequately. Anything less than an immediate fiscal transfer of something like 2% of GDP would probably be judged inadequate.
There is an important role for the private sector
The immediate health risk naturally take center stage, but getting people back to work is a high priority, and certainly no less for poor workers.
In the 1850s, Ernst Engel famously studied household budgets for 200 working class Belgian families, and found that the share devoted to food tends to decline with total household spending—a property that came to be known as Engel’s Law. Since then, “Engel curves” for budget shares have been widely used and much studied across the world, with near universal confirmation for Engel’s Law. Engel curves have found a wide range of applications, including in the assessment of policies related to agriculture, taxation, trade, industrial organization, housing, and in the measurement of poverty and inequality.
While there have been methodological and computational advances in the specification and estimation of Engel curves over the last 100 years or so, a common and persistent feature has been the reliance on household aggregates that Engel pioneered, along with a degree of imposed homogeneity in the Engel curves, allowing only limited variability in the parameters across and within households.
In a new paper, “Unpacking Household Engel Curves,” Philippe De Vreyer, Sylvie Lambert and myself have studied some neglected but potentially confounding sources of heterogeneity in standard household Engel curves. Three sources are postulated.
First, there can be latent household effects on individual demand behavior. Members of a given household are not autonomous individuals who happen to be living together, but rather they come together selectively, and then interact and influence each other’s behavior through the process of consuming (and often working) together. While we may reject the unitary model, it can be expected that there are aspects of the household, and shared local environment, that can have a powerful influence on individual choices. This can happen via individual preferences, which are to some extent formed within a household. Or it can stem from household- or location-specific aspects of the constraints on exercising personal preferences.
Second, there are differences in individual demand parameters within households. Engel’s Law may cease to hold at the household level when income gains are assigned to people with different consumption patterns and different preferences over how the extra money should be spent.
Third, there is heterogeneity in the extent of inequality within households. The existence of intra-household inequality is known to be a source of bias in the measurement of poverty and inequality. It is less well known that intra-household inequality can also bias estimates of empirical consumer demand functions, as invariably estimated from household aggregate data. Yet for many goods, and (hence) expenditures, there is a typically an unobserved individual assignment within the household, that may be a source of intra-household inequality, reflecting different reservation utilities outside the household. Furthermore, intra-household inequality can interact with individual parameter heterogeneity in influencing household demands, whereby greater intra-household inequality magnifies the effect of differences in preferences.
In our new paper, Philippe, Sylvie and I use an unusual survey for Senegal (which Philippe and Sylvie developed, in collaboration with others) that gives us a window on consumption distribution within the household. The families are typically multigenerational. Polygamous unions are common, with 25% of married men and 39% of married women engaged in such unions, which mostly comprise a husband and two wives.
Using these data, and with suitable modelling, we can unpack the traditional household Engel curve. In essence, what we do is estimate individual Engel curves (strictly, they are for sub-household units called “cells”) and then aggregate these up to the household level. We then compare the results we get this way with the traditional household Engel curve, pretending that we do not have the sub-household cell data.
We find that the traditional household Engel curve hides quite a lot about distribution within the household and preference heterogeneity, and these hidden factors are quite confounding about the true Engel curve. For example, intra-household inequality (not observed in standard data sets) surfaces in the error term of the traditional Engel curve. (To be more concrete, for those familiar with the literature on Engel curves, a form of the Theil index of intra-household inequality is found in the error term of the traditional Working-Leser household Engel curve.)
Two key lessons emerge. First, the (often-assumed) two-stage structure in bargaining-collective models of the household carries a testable implication with our data, namely that household spending should only matter to individual choices via the intra-household allocation of total spending. This exclusion restriction is generally consistent with our results. The exception is education spending, for which cell-specific budget shares are independently, and significantly, affected by the household’s overall standard of living. We suspect that this may be a “social effect” on cell Engel curves whereby the father exercises influence over the spouse(s) to spend more on his children’s schooling (including making conditional monetary transfers to), though some role may also be played by competition among the wives.
Second, our data reveal large biases in the standard household-level Engel curves. The sources of bias do not all go in the same direction; in particular, the bias associated with intra-household inequality tends to offset that due to latent heterogeneity in preferences. However, large net biases are indicated. For example, for the food share Engel curve, the coefficient on log total household spending is -0.11 using only household data but -0.28 when one estimates the Engel curve from the sub-household data and aggregates up to the household data. This is enough to reduce the income elasticity of demand for food (evaluated at mean food share) by one third, from 0.82 to 0.55.
In these data, we find that the bulk of the bias in standard household-level Engel curves is accountable to the influence of household fixed effects on sub-household Engel curves. The fact that the channel of bias via intra-household inequality partially offsets that due to the latent household effects in standard Engel curves implies that only adding controls to reflect intra-household inequality will tend to increase the bias in household-level Engel curves.
Given that we find that the bulk of the bias in standard household Engel curves is due to household effects on sub-household Engel curves, it may be expected that the most promising means of removing (or at least attenuating) the bias is to use longitudinal data, assuming that the confounding household effect in individual consumption behavior is time invariant. That conclusion is to be investigated further in future work.
In a new NBER Working Paper, “A Market for Work Permits,” Michael Lokshin and I have explored further the case for introducing a market in work permits (building on a shorter World Bank paper we wrote a few months ago). The policy idea we study is that working-age citizens in high-wage economies should be given the option of renting out their work permit—viewed as an asset of citizenship, though an asset that is not currently marketable. On the other side of the market, foreigners can buy (taxable, time-bound) work permits. The price of a work permit would balance supply and demand.
We argue that creating such a market would help capture the economic gains from freer migration, while keeping the host-country government in control of the migration flows, and aggregate labor supply. A minimum income can be assured for workers in host countries, financed by tapping into the currently unexploited gains from international migration. Thus, this market would offer a new instrument for social protection, as well as an efficient, growth-promoting, means of managing immigration, which would now be seen as an asset to workers in host countries, rather than a threat.
In the new paper, Lokshin and I elaborate further on the idea and provide illustrative calculations for the US and Mexico. We simulate the economic returns to sampled Mexican workers from migrating, and compare this to the likely costs, including the equilibrium price of the work permit, also allowing this to be taxed by the host country.
The results suggest that the missing market for work permits is large, with 18-36 million participants (depending on the chosen tax rate on work permits and other parameters for the costs of migration). For example, with a 10% host-country tax on the work permits and a 10-20% “remittance tax” on the US wage earnings of the Mexican migrants, the equilibrium price of the WPs would be about $20,000 per year, and around 30 million workers would participate. The US tax revenue would be around $300 billion, and the gain in earnings would represent about 6% of US GDP. The poverty rate in the US would fall by two percentage points, reflecting the pro-poor feature of the market’s implicit targeting mechanism.
Our simulations for the US and Mexico are only intended to be broadly indicative of likely orders of magnitude. More research is needed, including on the costs of migration and the spillover effects on the labor markets for workers who do not directly participate. Lokshin and I argue that further exploration of these and other issues discussed in our new paper is warranted, given the huge potential benefits of a market for work permits.
A recent paper with Michael Lokshin, “Market for Work Permits,” (with many updates to the earlier draft) proposed the creation of a market for work permits. This would allow citizens to rent out their right-to-work (RTW) for a period of their choice. On the other side of the market, foreigners can purchase time-bound work permits (WPs). This would help tap into the (potentially huge) unexploited gains from restrictions on international migration. Yet host countries would retain control over the flow of migrants and total employment. There are many gains to the host country, as discussed in the paper.
There is one aspect of the policy proposal that is worth considering. Under certain conditions, this policy will create a new binding floor to labor earnings in the host country—a new lower bound, above the current floor. The only estimate of the level of the income floor in America (averaged over reported incomes of the poor, with higher weight on poorer people) puts the floor at about $5 per person per day (Jolliffe et al., 2019). Allowing for (say) one dependent, this implies an income of $10 a day. It would be reasonable to assume that this is lower than the equilibrium price of a WP in our proposal. Indeed, $10 a day is lower than the minimum wage rate in the US for an eight hour day.
Workers in the host country will sell their RTW if they earn less than the going price in this new market (and some earning more than it will also do so if they experience a disutility of work). Similarly, foreign workers will only take up migration under this scheme if they earn something more than the going price of WPs (sufficiently higher to cover costs of moving and any tax levied). This holds for all contracted time periods of the WPs. Thus, creating a market in WPs along the lines Lokshin and I suggest can be thought of as a new way of providing a guaranteed minimum income for each time period. And it is self-financing.
To better understand this argument, we can posit a first-best distribution in the host country that maximizes some weighted aggregate of utilities, with the weights reflecting the government’s social preferences. The first-best distribution of income is bounded below by some value, lets call it ymin. However, in the absence of this policy, the first-best is not implementable given other constraints (notably on information and administrative capabilities). Thus, the observed distribution has incomes below ymin due to uninsured shocks or longer-term disadvantage. With the policy in place, the host government can now solve for the tax rate on WPs required to assure ymin (as explained in the new version of the paper). Thus, the market for WPs now makes it feasible to implement the host country’s socially optimal minimum income.
There is another control available to the host country, namely its power over eligibility to purchase WPs, or sell the RTW. For example, the US might (initially at least) choose to make the market only available to citizens of (say) Mexico. Restricting migrant eligibility, or expanding eligibility to sell the RTW among citizens of the host country, will reduce the equilibrium price.
The big difference between these two policy instruments—the tax on WPs and eligibility conditions—is that the tax instrument can raise revenue, albeit at the expense of both citizens selling their RTW and foreigners buying WPs. It is reasonable to assume that the (positive) partial equilibrium effect of a higher tax rate on revenue dominates the (negative) effect stemming from the deterrent effect of a higher tax on migration. Then the host government faces a trade-off between the level of the income floor and the extra revenue generated by a higher tax on WPs.
Under certain conditions (explained in the paper) one can solve for the host government’s optimal tax on the new WPs, obtained by balancing its desire for revenue against its desire to implement its first-best level of the floor to living standards.
Jolliffe, Dean, Juan Margitic, and Martin Ravallion, 2019, “Food Stamps and America’s Poorest,” NBER WP 26025.
A Universal Basic Income (UBI) gives everyone the same transfer amount. Of course, the net benefit may not be uniform after the extra taxes, or spending cuts, used to finance the UBI. However, the question here is whether it is feasible to do better than a UBI—to assure that more goes to poor people who clearly need it more. There are many ways in practice of doing that—or at least trying to do so. The solutions proposed, or found in practice, vary greatly in their efficacy. Information and incentive constraints are known to loom large. (Incentive effects may well be less of a concern than information.)
In a new paper, “The Missing Market for Work Permits,” Michael Lokshin and I have argued that creating a two-sided market in work permits would provide both pro-poor social protection in high-wage economies and new options for migration from low-wage economies. (A revised version is found here, with a fuller treatment of the literature.)
The idea is to create a market that helps capture the gains from international economic migration, while keeping the host government in control of domestic employment. An anonymous market exchange would allow workers to rent out their right-to-work (RTW). There is clearly much they could then do, including financing education or training, homecare of loved ones, or taking a long vacation. Simultaneously, someone else pays for a work permit (WP) and is then free to take up any job offer in that country.
A competitive market mechanism can be implemented (such as through a computerized double auction) to determine the market prices of these new WPs, conditional on the stipulated length of time and start date. Once that period ends, the seller gets back her RTW. The marketable WP is fully disembodied from the person selling it, and also independent of who is buying it. The market is anonymous.
Transaction costs would probably be low—almost certainly lower than for immigrant sponsorship schemes. The WPs could be taxed to finance the scheme’s costs, and (if desired) support other objectives. Development agencies and financial institutions could help applicants from developing countries, including in financing the costs of the WPs.
The currently missing market would no longer be missing. This can be seen as a social protection policy as well as an efficient policy for managing immigration, while capturing at least some of the (seemingly huge) economic gains from freer international migration. And freer migration would become a more popular idea—relieving public concerns by helping to internalize the externalities in host countries generated by migrants (or at least perceived to be). If the option of selling your RTW is confined to those in the workforce then aggregate labor supply would stay the same. A broader base of eligibility would allow rising employment. That is a policy choice.
Going back to the question I posed at the outset, our proposal will undoubtedly have a more pro-poor incidence than a UBI; specifically, it will bring both direct (first-order) gains to relatively low-wage workers who take up the option of renting out their RTW—a “self-targeting” mechanism—and indirect gains to others via the likely tightening in the low-wage labor market.
We probably can do better than a UBI, which can be a rather blunt instrument. For example, a UBI has been advocated as a means of addressing job-loss due to automation. But why would one give the transfer to everyone, including those who stay working? Our scheme would directly help those who lose their job due to automation.
Also, unlike a UBI, it is self-financing. This overcomes a widespread concern about UBI proposals that require higher domestic taxes or are only available as an option to existing welfare programs, thus reducing the net gains to poor people from the UBI.
Today we find two main approaches to measuring poverty and monitoring progress in reducing it. The first focuses on “absolute” measures that strive to use poverty lines with constant real value. For example, this is essentially what the official poverty measures for the US strive to do. It is also how the World Bank measures global poverty, aiming to apply a “rigidly unchanged” real line across countries as well as over time.
The second approach uses “relative” measures for which the poverty line varies in real terms, being set at a constant proportion of the current mean or median—an approach that emerged in the 1960s and became popular in Western Europe in the late C20th. There has been much debate on the choice between absolute versus relative measures.
In a new paper, “On Measuring Global Poverty,” I argue that neither approach makes economic sense. A new approach is needed for measuring and monitoring global poverty going forward.
The nub of the problem is that existing poverty measures tend to opt for one of two very different assumptions, neither of which can be seen as acceptable any longer:
When applied globally, the fixed real line cannot capture relative economic deprivation at country level or the need for higher outlays for economic well-being in richer countries—a higher cost-of-living not reflected in the existing Purchasing Power Parity (PPP) rates. However, it is no less obvious that the absolute standard of living, at given relative income, also matters, thereby ruling out measures in which the poverty line is set at a constant proportion of the mean or median.
The new paper proposes a welfarist interpretation of global poverty lines, which is augmented by the idea of normative functionings, the cost of which varies across countries. In this light, current absolute measures are seen to ignore important social effects on welfare, while popular strongly-relative measures ignore absolute levels of living. It is argued that a new hybrid measure is called for, combining absolute and weakly-relative measures consistent with how national lines vary across countries.
Illustrative calculations indicate that we are seeing a falling incidence of poverty globally over the last 30 years. This is mainly due to lower absolute poverty counts in the developing world. While fewer people are poor by the global absolute standard, more are poor by the country-specific relative standard. The incidence of purely relative poverty has been rising in the developing world, as can be seen from this graph. The vast bulk of poverty, both absolute and relative, is now found in the developing world.
It is a sad task to be reviewing the last book by Anthony (Tony) Atkinson, who passed away in January 2017, before the book could be completed. Shortly before his death, he asked two of his past collaborators, Andrea Brandolini and John Micklewright, to bring the book to publication under the title Measuring Poverty around the World .
Throughout his career, Tony Atkinson bridged his considerable technical skill as an economist with a commitment to rigorous thinking about distributional measurement and policies. He combined deep scholarship with social concern. It is a combination that has long made him a role model for all those who seriously study, and care about, poverty and inequality, and social issues more broadly.
Here is my review: Review of Atkinson’s Measuring Poverty Around the World.
Two recent interviews may be of interest. The first is an interview with World Bank economists Kathleen Beegle and Berk Ozler, posted on the World Bank’s Development Impact Blog. This relates to differences between the job of being a researcher at the World Bank and that of an academic, as well as my research on poverty and policy evaluation.
The second is an interview with Ignacio Fariza, a journalist with the El Pais. This was done after my public lecture at El Colegio de Mexico in Mexico City, June 2019, co-organized by Oxfam Mexico. The full article can be found here. The El Pais article is in Spanish. Here is a translation into English that includes (identified in [ ]) a few minor explanations from me: “El Pais Interview 2019“. Ignacio asks some good questions, touching on poverty, inequality, human development and modern capitalism. I hope it is of interest.
Over the last 20 years or so, the Supplemental Nutrition Assistance Program (SNAP)—colloquially referred to by its old label “food stamps”—has become one of the most important elements of America’s social safety net. My new research paper (with Dean Jolliffe and Juan Margitic), “Food stamps and America’s poorest” focuses on an aspect of SNAP that has received little or no attention, namely it efficacy in reaching America’s poorest and so lifting the floor to US living standards. While this is a novel feature in the context of the literature on poverty in America, the paper’s focus on the floor has deep roots in theories of distributive justice whereby a society’s progress is judged in part by its ability to enhance the economic welfare of the least advantaged group.
Assessing the impact of any social program on the floor poses methodological challenges, given that conventional surveys are not designed for this purpose. Even in high quality surveys, the tails of the distribution are not likely to be measured accurately. Recorded incomes in surveys can be very low for some household in a survey response period, but this can be deceptive about living standards. Our method recognizes that there are both measurement errors and smoothable transient income effects in the observed survey data. Some averaging is called for. The idea is to measure the floor as a weighted mean of those deemed to be poor, with higher weight on observationally poorer people.
The new paper implements an operational approach to measuring the floor in the US using cross-sectional national surveys spanning 30 years, and uses these data to study the impact of SNAP. We estimate the level of the floor before and after recorded receipts from SNAP at the household level. This requires some potentially strong assumptions, so we provide various robustness tests, including tests of alternative weighting schemes, possible behavioral responses to SNAP among the poorest and the sensitivity of our results to including non-positive incomes. We also provide confidence intervals for the floor, taking account of survey design.
The paper finds that America’s poorest gained from SNAP. Their mean gain exceeded mean spending on SNAP—implying that the program has historically done better than a universal basic income (UBI) with the same budget (and ignoring any differences in administrative cost, which could well be lower for a UBI). Alarmingly, we find that America’s floor has been on a long-term decline, although SNAP helped prevent further decline in some periods, notably during the 2008-11 financial crisis. However, the efficiency of SNAP in raising the floor has also fallen over time, such that the per capita amount received by the poorest in 2016 is about the same as mean spending per capita.
In summary, the paper provides the first assessment of America’s progress in lifting the floor of the distribution of real income, and whether the country’s largest antipoverty program helped do so. Alarmingly, America’s floor has been sinking over the last 30 years. SNAP has lifted the floor, but its efficiency in doing so has declined over time. Full SNAP coverage of the poorest would lift the floor appreciable, from around $5 a day (per person, or about one third of the official poverty line) to $8.
Malaysia’s colonial legacy left both high inequality—with a marked ethnic/racial dimension—and high poverty—with an especially high incidence of poverty among the ethnic majority, the Malay people and other (non-Malay) “Bumiputera.” And ethnic inequality was somewhat neglected as a policy issue in the 12 years or so after the county’s Independence in 1957.
Ethnic riots broke out in Malaysia in 1969, prompting a national effort at affirmative action favoring the Bumiputera. The main policy instrument was the New Economic Policy (NEP). Its twin aims of Malaysia’s were ethnic redistribution and poverty reduction. The Bumiputera were to receive favorable treatment in access to education, housing, public-sector hiring, and corporate share ownership. The NEP lasted 20 years. To varying degrees, successive governments have intervened against ethnic inequality. The policy has been controversial, with both ardent supporters and critics (seemingly correlated, though imperfectly, with ethnicity).
Since 1969, Malaysia’s official poverty measures indicate one of the fastest long-term rates of poverty reduction in the world, due to both economic growth and falling inequality. Indeed the official poverty rate has gone from about 50% to virtually zero. Judged by Malaysia’s poverty line, the only country I know that has had as seen a faster rate of poverty reduction over the longer-term is neighboring Thailand. (Yes, they both beat China in terms of the pace of decline in the poverty rate, though not, of course, in terms of the count of numbers of poor.)
As an aside, I am not convinced that Malaysia has virtually eliminated poverty. The current official poverty line is almost certainly too low by prevailing standards of what “poverty” means in a country such as Malaysia. I take up this issue in another blog post, which can be found here.
Malaysia can also claim more success than most countries in managing inequality. The Gini index of household incomes fell from 0.51 in 1970 to 0.40 in 2016. This was due to progress in reducing ethnic inequality. For developing countries as a whole, average inequality has been roughly unchanged over a similar period.
Did ethnic inequality fall since 1969 and was that a key factor in the country’s success in reducing poverty and in managing inequality? Thankfully, we have data from 18 reasonably comparable, nationally representative, household surveys to draw on in addressing that question, though the historical tabulations are rather limited, and the micro data are not publically available.
A natural question to ask of the available data is how much ethnic inequality fell, and how much that contributed to poverty reduction. My new NBER working paper, “Ethnic Inequality and Poverty in Malaysia since 1969″, tries to answer these questions. (My research for the paper was done while visiting the Ungku Aziz Centre in the Faculty of Economics, University of Malaya in January 2019. Many thanks to the Faculty for their hospitality.)
The paper provides new measures of ethnic inequality spanning 50 years. These show a large reduction in relative ethnic inequality. And the decline in the national Gini index is fully accountable to the country’s progress in reducing the Gini index of between-group inequality (see Figure below). Nonetheless, the ethnic differential in growth rates has not been enough to attenuate the large absolute gaps in mean incomes by ethnicity, given the extent of the initial ethnic inequality. Relative ethnic inequality has fallen, but absolute ethnic inequality has risen.
Importantly, I find no sign in the data of a robust effect of the growth process on inequality within ethnic groups. It is not the case, for example, that the higher growth rate for the Bumiputera in the wake of the NEP was shared unevenly among the Bumiputera when one defines “uneven” in terms of proportionate changes. However, that growth did come with rising absolute inequality. Nor do I find any sign that the policy efforts to reduce ethnic inequality came at a cost to the overall rate of growth, though this is an issue that merits further research.
The paper also finds that Malaysia’s success in reducing income inequality over the last 50 years played a non-negligible role in the country’s success at reducing poverty, in combination with economic growth. Using the official poverty measures, about 10% of the overall rate of poverty reduction is accountable to reduced inequality in average incomes between the main ethnic groups. However, overall economic growth has been the more important driver quantitatively. Changes in the ethnic composition of the population tended to be poverty increasing, though this effect turns out to be small.
While the reduction in ethnic inequality has not been as quantitatively important to poverty reduction as overall growth that does not imply that ethnic redistribution is a blunt tool against poverty in this setting. Nor do the facts that both ethnic inequality and poverty have been reduced substantially imply that ethnic inequality no longer matters to poverty. Indeed, the paper finds quite sizable elasticities of national poverty to inequality-reducing ethnic redistribution. And the elasticities have stayed high—indeed, they have increased—through this period of ethnic redistribution and poverty reduction spanning 50 years.
In short: Malaysia’s long-term effort to reduce ethnic/racial disparities in living standards has played an important role in its ability to manage overall relative inequality. That effort has also helped reduce absolute poverty, although on that score overall economic growth has been more important. However, the potential gains to poor Malaysians from progress toward ethnic equality do not appear to have been exhausted. Going forward, even small reductions, or increases, in ethnic inequality can still matter.
Officially there is almost no poverty left in Malaysia. Yet since coming to visit the country this month, I have read and heard a few people question this claim. So I thought I should look at the issue more closely. (I have been based at the University of Malaya, as the visiting Royal Ungku Aziz Professor; many thanks to this great University for their hospitality. This blog post is based on a presentation I gave at the University.)
The data available from Malaysia’s Department of Statistics (DOSM) suggest that the country has made huge progress against poverty. The official poverty rate has fallen from nearly 50% in 1970 to just 0.4% in 2016 (the latest year available).
However, when one probes further there are reasons to question the claim that Malaysia has virtually eliminated poverty. The official poverty counts are based on a line that has had a fixed real value over time. Currently the line is MYR 920 per household per month, or a little over MYR 7 per person per day, at average household size.
How does that compare with other countries? For that purpose we need to use Purchasing Power Parity (PPP) exchange rates from the International Comparison Program (ICP). The last ICP was for 2011, when the official line was MYR 800. At average household size this is equivalent to MYR 6.41 per person per day. When converted using the PPP for consumption from the 2011 ICP that comes out at almost exactly $4 per day. (Note that the PPP rate is well below the exchange rate given that many goods and services are cheaper in Malaysia than in the US. Unlike market exchange rates, PPP rates are based on the actual prices paid by people.)
Malaysia’s official line is well above the World Bank’s international poverty line of $1.90 a day. But it should be well above that line! The $1.90 line is deliberately low, being anchored to poverty lines found in the poorest countries. If one looks instead at countries with a roughly similar average income as Malaysia, one would expect the poverty line to be about $12 a day—three times the current line in Malaysia. That would indicate a national poverty rate of about 20%, not 0.4%! The following graph shows how Malaysia’s poverty line compares to the poverty lines found in other (non-OECD) countries. Malaysia’s current official line is clearly well below what one would expect for a country with its current average standard of living.
I am not the only one to ask whether the current official line is defensible. For example, in one comment in the Star, in 2018, a Malaysian politician (the Deputy Minister of International Trade and Investment), Dr Ong Kian Ming, wrote that “As wages climbed and we became a middle-income nation, we didn’t increase the standard for what is considered decent living above the poverty line.” Dr Ong has a good point in my view. $4 a day may well have been a sensible line for Malaysia in 1970. But that is no longer clear; real income per household in Malaysia has increased over five fold since 1970! In terms of the World Bank’s income classifications for countries, Malaysia has gone from “low-income” to “upper middle income.”
To see how much all this might matter, I have calculated illustrative alternative lines that rise with average income over time in a seemingly sensible way. The slope as mean income rises is 1:3; so for each MYR 10 increase in the mean, my line rises by MYR 3.33. But I also gave the line a lower bound; I set this at the official “hard-core” poverty line, which I reckon to be about $2.50.
This gives what I call “weakly relative lines.” The “weakly” refers to the fact that the poverty line is not directly proportional to the mean (so that is has an elasticity less than unity). Making the line directly proportional to the mean (“strongly relative”) has the very odd property that if all incomes (including that of the poor) rise by the same percentage then poverty does not change. (They also have the strange property that the poverty rate can fall in a recession. For example, if one uses strongly relative lines for Malaysia I have found that the poverty rate fell during the Global Financial Crisis of 2008-10. That is clearly wrong.) Weakly relative lines are much more sensible.
The following graph compares my new series of poverty measures for Malaysia with those for $4 a day. We see a very similar pattern over time, and a marked longer-term decline in poverty incidence. But there is still a lot of poverty left. Progress, yes, but there is no room for complacency about poverty in Malaysia!
By the way, when I said above, “from the data available” I was hinting at another fact about Malaysia. Unlike many countries today, Malaysia does not have full open access to its household survey data on incomes and expenditures—the data used to measure poverty and inequality. I have had to work from (less than ideal) published tabulations, helped by the World Bank’s PovcalNet data site. I understand that researchers can apply to DOSM for selected variables for sub-samples of the survey data. But the full household-level data are not available. And only the full data can reveal the many dimensions of living standards and their correlates–the joint distribution of the household data.
Access to public data also needs to improve if Malaysia is to maintain high standards of research on poverty, inequality and socio-economic policy going forward.
Academics often try to educate the public at large, by explaining important stuff in an accessible way. This can be socially valuable. But that is not so clear if they oversimplify, or even get it wrong, in their expository zeal. Then bad ideas can get perpetuated, and have excessive influence.
For example, all of the following “expository” claims one hears about randomized control trials (RCTs) are deceptive, if not wrong:
My paper, “Should the randomistas (continue to) rule” gives other examples, and tries to explain, in reasonably transparent terms, why all this matters to sound development policy making.
And here is my review of the Banerjee and Duflo book, “Poor Economics.”
Please tell me if you think I have oversimplified anything!
The evidence we are seeing of falling relative inequality between countries has left some observers optimistic about the prospects of falling absolute inequality. (Relative inequality is about the ratios of incomes—typically normalized by the mean—while absolute inequality is about the absolute gaps, such as the $ gap in consumption between “rich” and “poor”.) Based on evidence from economic research, some observers anticipate the prospect of a far more equal world ahead. In this vein, Zanny Minton Beddoes wrote a few years ago that: “The gap between the world’s rich and poor will be far narrower in 2050.”
Is that right? We are not talking here about countries (as in the recent paper by Johnson and Papageorgiou). We are talking about people. The answer then depends on what absolute gaps we focus on within the distribution of global incomes and whether relative inequality is declining. If we are talking about the world’s richest 1% (say) and the poorest 1% then it is plain from what we know that we will not see a declining absolute gap in the foreseeable future if recent trends continue, since we are not even seeing falling relative inequality between the two groups of people.
However, there is a range of “middle” incomes for which recent trends do suggest declining absolute inequality over the next few decades. Compare the world’s middle incomes—the 50-60th percentiles, say (just above the global median)—with the income of the 80-90th percentiles, i.e., the group that Branko Milanovic identifies as the rich world’s “middle class.” Branko’s famous “elephant graph” shows that the former group has seen its incomes growing strongly at 3.6% per annum over 1988-2008, while the latter group has seen little growth (0.23% per annum). The ratio of mean incomes in 2008 was 5.7 (based on Lakner and Milanovic, Table 3). Then it can be readily verified that absolute inequality between the two will decline, though it will take 53 years for the two income levels to converge if recent trends continue.
One might question whether a near zero growth rate of the rich world’s middle class is sustainable. Suppose instead that this income group sees a 1% per annum growth rate, with all else unchanged. Then absolute divergence between the rich world’s middle class and the world’s middle will rise for the next 20 years, and only then start to fall, vanishing after about 70 years.
Of course, such calculations should be taken with a grain of salt. Here they only serve to illustrate that absolute inequality is very likely to persist for some time even with falling relative inequality. Indeed, with current trends, the gap will rise between the world’s richest and poorest, and may well also do so between the rich world’s middle classes and the new middle class of the developing world.
Bourguignon, Francois, 2016, The Globalization of Inequality, Princeton: Princeton University Press.
Lakner, Christoph, and Branko Milanovic, 2016, “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession,” World Bank Economic Review 30(2): 203-232.
Milanovic, Branko, 2016, Global Inequality: A New Approach for the Age of Globalization. Cambridge, Mass: Harvard University Press.
Johnson, Paul, and Chris Papageorgiou, 2018, “What Remains of Cross-County Convergence?,” Journal of Economic Literature, forthcoming.
Ravallion, Martin, 2010, “The Developing World’s Bulging (but Vulnerable) Middle Class,” World Development 38(4): 445-454.
______________,, 2018, “Inequality and Globalization: A Review Essay,” Journal of Economic Literature 56(2): 1-23.
The prevailing approach to measuring global inequality pools all household incomes—as measured in sample surveys—across the world. One then measures inequality in this global distribution the same way one measures inequality within one country. A good example of this approach is found in Lakner and Milanovic (2016).
These measures of global inequality attribute no economic advantage to living in a richer country beyond what is already reflected in the household incomes measured from household surveys. This restriction is hard to defend on either theoretical or empirical grounds.
One way that national income may matter stems from the longstanding idea of relative deprivation. This postulates negative economic gains to co-residents. Then we can rationalize a nationalistic view that “global inequality” is just the average national inequality across countries. This emerges as the limiting case in which it is relative income within the country of residence that matters.
However, that is hardly plausible. One can point to reasonable arguments for positive external effects of living in a richer country at given own income. Examples of how this can happen include the likely positive correlation between national income and factors conducive to a higher long-run personal income, better public services, and greater security. None of these gains are likely to be properly reflected in current incomes as measured in surveys.
The implication is clear: the (large) differences in average incomes found between rich and poor countries create an extra (horizontal) inequality between their residents, not reflected in their current incomes, as observed from surveys. This is a source of downward bias in prevailing measures of global inequality. Yet the likelihood that living in a richer country delivers gains to economic welfare that are not reflected in survey-based incomes has been entirely ignored by past measures.
I explore this issue in a new paper, “Global inequality when national income matters,” which proposes a new way of measuring global inequality that allows national income to matter at given “own-income” as measured in surveys (Ravallion, 2018).
I find that this issue is highly salient to the quantitative measures obtained for global inequality. If one defines economic welfare in terms of relative income alone then one sees far less inequality in the world than if one puts a sizable value on the external benefits of living in a richer country. Using what can be considered the ideal inequality measure for this purpose, namely the Mean Log Deviation (MLD), the paper finds that relative deprivation theory implies that global interpersonal inequality is far lower than prevailing measures suggest since it is then entirely within countries.
This changes dramatically when one allows a positive value of national income (at given own-income), such as when living in a richer country brings benefits in terms of access to non-market goods and services, and better opportunities for private support in times of need. From what we know based on past global studies using micro data, the national income effect could well be 50% or more of the own-income effect on subjective wellbeing. Then global inequality is far higher than prevailing measures suggest, and far higher than found in even the most unequal country.
Indeed, the differences in levels of inequality due to even rather modest differences in how one values national mean income tend to swamp the differences seen over time in standard measures, or the differences we see between countries, and are also large relative to the impact of even a substantial underestimation of the incomes of the rich.
For example, suppose that incomes of all the richest 1% in the world are actually double the numbers in Lakner and Milanovic (2016) for 2008. This would add about 0.1 to the global MLD. I find that this is about the same as adding a modest 10% of log national-mean income to log own income to allow for the gains from living in an economically-better off country.
So the level of global inequality could well be very much higher than current measures suggest, once one allows for the likely external gains from living in a richer country. Nonetheless, I still find that the stylized fact that global inequality has been falling since around 1990 is robust to all except a seemingly high negative weight on national income, such as due to relative deprivation. The finding of falling between-country inequality is robust whatever value one attaches to national income in assessing individual economic welfare.
There is almost certainly more inequality in the world than we think, but it is likely to be falling.
Lakner, Christoph, and Branko Milanovic, 2016, “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession,” World Bank Economic Review 30(2): 203-232.
Ravallion, Martin, 2018, “Global Inequality when Unequal Countries Create Unequal People,” European Economic Review, forthcoming.
With today’s release of an updated version of the World Bank’s PovcalNet data site, it is a good time to take stock of progress toward Sustainable Development Goal 1.1 (SDG1.1). SDG1.1 aims to “eradicate extreme poverty” by 2030, as defined by the World Bank’s international poverty line of $1.90 a day line. ($1.90 is an update to 2011 prices of the $1.25 a day line in 2005 prices, which is explained in this paper, “Dollar-a-Day Revisited”.
What does the new data suggest about progress toward SDG1.1?
The following Figure plots the series from PovcalNet for the overall poverty rate for the developing world over the period 1981-2015. This looks very encouraging. Of course, there will be less progress at higher lines than the (deliberately frugal) $1.90 line. But the rate of decline we are seeing for extreme poverty is clearly good news. The poverty rate is falling by a little over 1% point per year, on average, over the period as a whole. (The regression coefficient on time is -0.012, with a standard error of 0.0004, though this only reflects the time series variation.)
A simple linear extrapolation of this series indicates that extreme poverty will be eliminated by 2025. (A crude calculation of the standard error, again based solely on the time series, gives a value of 0.9 years, implying a 95% confidence interval is 2023-2027.)
However, if we dig a bit deeper there is another perspective on the data that is far less encouraging. Let us ask instead how long it will take for the “floor” of the distribution of consumption in the developing world to reach $1.90 a day. This is another way of defining “eradicating extreme poverty,” namely that the poorest person has $1.90 a day or more. The floor is not easily estimated, but I have outlined an approach in another paper, “Are the World’s Poorest Being Left Behind?.” The following Figure gives my estimates of the floor over the same period. This is a weighted mean of the consumption levels of the poor, with highest weight on the poorest, and linearly declining weight as consumption rises, until $1.90 is reached.
This perspective is not nearly as encouraging. The floor in 2015 is almost exactly $1.00 a day, up from $0.87 a day in 1981. There is a (statistically significant) positive slope over time to how the floor has evolved, but the slope is very small. At this rate of progress, extreme poverty will not be eliminated until 2278 (with a 95% confidence interval of 2169 to 2387). From this perspective we are way off target.
The reason is clear: the developing world is not making enough progress in reaching the poorest—well below the $1.90 line. Numbers of poor (by this frugal standard) are falling, which is undeniably good news. But the progress is not being shared enough by the developing world’s poorest. They are not exactly being “left behind” but pretty close to it.
The prospect of a much slower progress in lifting the last few % out of poverty should not be too surprising. In 2012, the President of the World Bank, Jim Yong Kim, asked me to propose a goal for poverty reduction (at a time when I worked for the World Bank, as Director of its research department). An elaboration of the note I wrote for him was subsequently published as, “How Long will it Take to Lift One Billion People out of Poverty?” That paper outlined both pessimistic and optimistic paths to lifting one billion people out of extreme poverty, and the optimistic path evolved into SDG1.1. But an important difference crept in along the way. My “optimistic path” would still leave a few % of the world’s population in poverty by 2025-30. My calculations suggested that the last few % would be a much harder job. Even my optimistic path for economic growth and income inequality would not “eradicate extreme poverty.”
The good news from the latest PovcalNet is that we are making progress in reducing numbers of poor. But a closer look at the new data suggests that “business as usual” will not achieve SDG1.1, which might well take another 200 years!
Greater effort at reaching the poorest will almost certainly be needed.
One sometimes hears claims that rising top incomes are helping to lift up the poorest. This is a version of what is often called “trickle down.” The idea has been much debated.
Is there any evidence that this could be happening in America, where the marked rise in top incomes is (understandably) getting a lot of attention, and raising concerns about the country’s future. These concerns remain even if there are trickle-down benefits to the poorest. But let us focus here on whether there is any sign that the trickle down idea might be right.
Some terminology first. “Top incomes” refers here to the incomes of the richest 1% say. The “floor” refers to the lower bound of the distribution of income—a level of income below which very few people are likely to be found for any reasonable length of time. (There are many methodological issues in measuring these two extremes of the income distribution; while I will mention some, this post is not the place to go into those issues in much detail.)
To help address the question of whether there is any sign that trickle down has been at work, this post draws on my new research paper written with Dean Jolliffe and Juan Margitic, “Social Protection and Economic Development: Are the Poorest Being Lifted-Up or Left-Behind” (coming out on June 4 as NBER WP 24665). The main data source is the microdata from the annual Current Population Surveys done by the US Census Bureau for the Bureau of Labor Statistics, spanning 1988-2016.
Divergence between the richest and the poorest: Figure 1 plots the per capita pre-tax incomes of various quantiles, including the top 1%—the income level above which one finds 1% of America’s population. Call this q(99). The graph also gives the quantiles for 95% and 90% as well as the mean and median.
And Figure 1 gives our estimate of the floor. This is a weighted mean of the incomes of those officially designated as poor in the US, with highest weight on the poorest, and declining weight as incomes rise. We see a marked absolute divergence—roughly speaking, a rising absolute gap between richest and poorest.
Figure 1: Absolute real incomes per capita
Two methodological points are notable here. First, in measuring the floor for the purpose of this graph, incomes are calculated the same way that the US Census Bureau uses for its official poverty measures. This includes money income before taxes from several sources (such as wages, salary, net-income from self-employment, social security payments, pensions, interest, dividends, alimony, other forms of periodic monetary income) but excludes capital gains and non-cash benefits such as fringe benefits or noncash government social assistance programs. Importantly, its excludes a major income source for poor people, namely the Supplemental Nutrition Assistance Program (SNAP), though more often called “food stamps”. This helps recipients cover their food spending and is explicitly targeted to the poor (living below 130% of the official poverty line). SNAP now covers about 14% of the US population. I will return to SNAP shortly.
Second, there is likely to be a downward bias in survey-based incomes of the rich, and correcting for this is likely to make the top few quantiles in the graph higher, and probably steeper. The estimates combining CPS with income tax and national accounts data in the Worldwide Inequality Database (based at the Paris School of Economics) indicate a higher growth rate for incomes of the top 1% than indicated by only using the CPS.
Figure 2: Log real incomes per capita
Since the floor is so low—we estimate that it is around $6 per person per day—relative to the top incomes it is hard to see what is going on in the graph above. So Figure 2 uses logs instead. Now the divergence between the top and the bottom is easy to see. Not only is there no sign that rising top incomes have lifted the floor, the floor is actually falling over time. The growth rate of the floor over the whole period is -1.3% per annum. By contrast the growth rate of q(99) is 1.6% per annum. For a family of four people, the floor fell from about 9% of q(99) in 1988 to 4% in 2016. We see relative divergence between the richest and the poorest.
Food stamps: There is another potential channel for “trickle down” namely via public spending on antipoverty programs. The new paper with Jolliffe and Margitic looks more closely at the role played by SNAP. We find that SNAP has benefited the poorest. Figure 3 gives our estimates of the floor before and after SNAP. (This is expressed as a proportion of the official poverty line, which was $15.15 per person per day in 2010, for a family of two adults and two children.)
Figure 3: The floor before and after food stamps
We find that SNAP helped raise the floor, and to stabilize it after 2000, though with signs of a decline since 2012. SNAP helped prevent the floor falling even further, especially in the wake of the financial crisis starting in 2008. Without the “SNAP stimulus” in the wake of that crisis we would have seen the floor dropping even more. Less encouragingly, we also find that the efficiency of SNAP in reaching the poorest (the gain in the floor per $ spent on the program) has declined over time. However, it is clear from our calculations that SNAP has helped America’s poorest.
So is this a form of trickle down that has worked? That is not so clear. SNAP spending per recipient has grown with rising top incomes, though whether the latter caused the former is an open question, with arguments both ways. More tellingly, SNAP participation rates have fluctuated over time since around 1990, buffeted by politics and reform efforts, and this has led to similarly fluctuating levels of overall spending per capita of the US population. It seems likely that politics has played a more important role than a trickle down process through social spending.
It has long been known that social outcomes in terms of poverty and human development vary across countries at given mean income, such as measured by GDP per capita. An early demonstration of this point is found in Amartya Sen’s 1981 paper “Public Action and the Quality of Life in Developing Countries” (Oxford Bulletin of Economics and Statistics 43(4): 287-319). Sen pointed to countries such as Sri Lanka that had used social policies to attain the social indicators (such as life expectancy) that are more typical of high-income countries. Costa Rica is another example.
There are hazards in inferring that social policies explain why some countries have better social outcomes at given mean income. This relates to a general class of problems in inferring “social efficiency” from regressions of outcomes on mean income, as explained in my paper “On Measuring Aggregate Social Efficiency” (Economic Development and Cultural Change 53:.273-92). However, let me put those problems to one side here, and assume that good social policies help explain cases such as Sri Lanka and Costa Rica.
Does this imply that rich countries can maintain their better social outcomes (in terms of health, education, absolute poverty) at a lower mean income? That is the claim made by the Degrowth Movement, which has argued that the way to improve environmental outcomes (and, in particular, to reach targets for carbon emissions) is to contract aggregate economic activity. Clearly, this would meet with near universal opposition if it was thought that social outcomes would worsen as a result. That is where the experience of countries such as Sri Lanka and Costa Rica becomes so important in the eyes of the Degrowth Movement, which points to those countries to support their claim that degrowth need not have a social cost.
The problem in this argument is that better social outcomes are not only attributable to better social policies. Higher average incomes have also played a role, both directly (through poor people’s greater command over commodities that matter to those outcomes) and indirectly (by creating the resource availability needed to finance better social policies). (Sudhir Anand and I elaborate this argument in our paper, “Human Development in Poor Countries,” Journal of Economic Perspectives 7(Winter 1993): 133-150.)
There lies the Degrowth Fallacy: The fact that some countries have better social outcomes at a given level of mean income does not imply that richer countries can attain the same social outcomes at lower mean income.
In an example of the Degrowth Fallacy, in a radio interview (posted today) Jason Hickel (introduced as an economist and anthropologist at the London School of Economics) points to Costa Rica to support his claim that rich countries can maintain their social outcomes at lower mean income. Yes, Costa Rica has had good social and environmental policies over many decades, and other countries can learn from that experience. However, the country did this in combination with economic growth. Indeed, mean income has tripled in real terms since 1960, and the average growth rate has been above average for Latin America (World Bank). By combining social policies with policies that directly supported economic growth, Costa Rica was able to attain over time good social outcomes for a country at its level of mean income. Costa Rica is definitely not an example of how good social outcomes are possible without economic growth.
The degrowth advocates have also disputed claims that social outcomes tend to improve with growth. For example, Item 3 of the Degrowth Manifesto says that “Global economic growth has not succeeded in reducing poverty.” I will grant that there are cases in which economic growth has by-passed poor people; there is no reason why growth will inevitably come with lower poverty or better outcomes in terms of human development. However, the bulk of country experiences have indicated that better social outcomes (including poverty reduction) generally come side-by-side with economic growth. In marked contrast to the Degrowth Manifesto, economic growth has succeeded in reducing poverty. Also, lower poverty has helped promote economic growth. (On all this see Chapter 8 of the Economics of Poverty.)
Of course, this does not mean that growth on its own, without good social policies, will improve social outcomes. There are also examples of countries that have squandered much of the social welfare gain from economic growth from this perspective—the gains have been largely captured by those who are already well-off, with modest benefit to the broader society. The US today is an example.
As Hickel also argues, many social outcomes were better in the US in the 1970s, when mean income was roughly half what it is today. But one must seriously doubt that halving today’s average income in the US will restore the social outcomes of 50 years ago.
(This is the original English version of my oped in Le Monde, 12/15/2017.)
Probably more than any time in history, we have a broad political consensus today on the desirability of eliminating the worst forms of poverty in the world. We see this consensus in many places, including the UN’s Sustainable Development Goals. There is no such consensus against inequality.
From a global perspective, we are making progress against poverty. Higher rates of economic growth in the developing world during the current period of globalization (though not just due to globalization) have come with success in reducing the numbers of very poor people. In the world as a whole, the number of people living below the frugal poverty lines found in low-income countries has fallen, from around 1,800 million in 1990 to just under 800 million in 2013 living under the World Bank’s international line of $1.90 a day (2011 prices). China has played a major role in this success. But since 2000 we are seeing progress in reducing the numbers of very poor people in all regions of the developing world. We are also seeing progress in human development, with rising literacy rates and falling infant mortality rates.
Growth in the developing world has also helped attenuate global inequality. This is typically measured by pooling all household incomes in the world (ignoring country of residence and) and measuring relative inequality—a summary statistic of the proportionate differences in incomes—in this anonymous global distribution. By this measure, inequality has been falling over the last 30 years or so.
But when we look more closely at the data, there are some troubling features, pointing to serious policy challenges going forward. Relative inequality within countries is trending upwards on average, and markedly so in many countries, now famously so in the US. And absolute inequality—the absolute income and wealth gaps between rich and poor—is probably rising within all growing economies.
We have also learnt that growth processes in all countries have had both winners and losers. Some people have been pushed into poverty, while others escape it. This creates social tensions despite overall progress. We are also seeing rising numbers of relatively poor people, meaning that they are poor by the standards of what “poverty” means in their own countries, even though they live above $1.90 a day.
Nor are we seeing much progress in raising the level of living of the poorest—the world’s consumption floor. Despite higher growth rates in most developing countries since 2000, the consumption floor is still somewhere near the biological minimum in many countries, at around $1 per day. The developing world has been far more successful in reducing the numbers of people living near the floor than in raising the floor.
Before we can expect to see public effort to address these distributional concerns, there needs to be a political consensus that effort is called for. The lack of consensus for inequality is stalling public action. Granted we will never want to eliminate inequality, as that would surely leave too little incentive for innovation and growth. The consensus we need is about reducing high inequality. To some eyes, such inequality is morally objectionable, but not to other eyes.
Broader public support for redistributive effort may well emerge from the body of research that has indicated that high inequality undermines the scope for sustained economic growth, which makes it harder to maintain progress in reducing poverty. Also, the higher the initial level of inequality, the less poor people tend to share in the gains from economic growth. In short, high-inequality countries tend to see less growth and less of it helps those who need it the most. Inequality is thus a policy concern even if we think poverty reduction is the far more important goal.
Not all inequalities matter equally. There is scope for greater consensus about reducing those aspects of high inequality that matter most to growth and poverty reduction. These are the specific inequalities that constrain economic opportunities for poor and middle-class people. Inadequacies in schooling, health care and social protection are socially and economically disabling—creating a loss of freedom to fulfill people’s aspirations in life. So too do the many imperfections in markets for credit and land—creating inequalities in access to production inputs and technologies. All such inequalities limit employment opportunities, impede physical and social mobility, weaken democracy, and render people vulnerable to subordination to the will of others and exploitation in all domains of life. This represents lost economic opportunities, such that poor people have little hope of sharing in the new sources of wealth and in helping to generate that wealth. We then see an inequitable growth process going forward, perpetuating poverty across generations.
This can be the basis for developing a broad consensus against high inequality, as we now have for poverty.
A new paper by Robert Allen, “Absolute Poverty: When Necessity Displaces Desire” (American Economic Review, December 2017), has proposed and implemented a method of measuring global poverty. Allen advocates this as an alternative to the World Bank’s longstanding method using Purchasing Power Parity (PPP) rates across countries in trying to assure that the international poverty line has constant purchasing power globally.
At the core of Allen’s method is the use of Linear Programming (LP) to set a least-cost diet for attaining stipulated nutrients, following George Stigler (“The Cost of Subsistence,” Journal of Farm Economics, 1945). Allen estimates country-specific least-cost diets anchored to globally-constant nutritional requirements with an allowance for spending on his stipulated bundle of non-food goods. Allen does not present his method as a complement to the Bank’s, but as superior. He claims that his method yields substantially high global poverty counts than the World Bank’s $1.90 a day line.
My comment on Allen’s paper, “An interesting step backwards,” argues that his proposed method is the resurrection of a method famously rejected long ago, including by Stigler, because it produces poverty lines of little social relevance. From the point of view of the history of thought on poverty, it is interesting to see what this old method delivers with new data for the developing world.
On a close inspection of his results and on doing some extra calculations, I find that his claim that the global absolute poverty count is much higher than currently thought, based on the Bank’s $1.90 line, is no longer true when one uses nutritional requirements that would seem to accord more closely with practice in low-income countries. This gives a much lower poverty count than the Bank’s $1.90 line. And the trend over time is virtually identical. I also argue that there is an urban bias in the prices Allen uses for valuation, which raises his counts relative to the national lines in low-income countries that have been used to determine the $1.90 line.
The comment argues that Stigler was right, and the method Allen proposes should still be rejected, based on Allen’s own findings. PPP’s remain essential for global poverty measurement.
Partly in response to concerns about chronic undernutrition, there is an expanding effort at social protection in developing countries and this effort is typically focused on transfers targeted to poor families (as discussed in Chapter 10 of The Economics of Poverty). These policies regularly assume that targeting poor households suffices in reaching poor individuals. Is that right?
A new paper, “Are poor individuals mainly found in poor households?“, with Cait Brown and Dominique van de Walle, studies the effectiveness of these household targeting efforts when one is trying to reach poor individuals, as identified by the nutritional status of women and children. Our answers will surprise many readers.
As expected, there is a “wealth gradient” in that poorer families are more likely to include undernourished women and children. But other factors are at work, including intra-household inequality and shared risks from the health environment. In our comprehensive assessment for 30 countries in sub-Saharan Africa we find that undernourished women and children are spread quite widely across the household wealth and consumption distributions.
Strikingly we find that roughly three-quarters of underweight women and undernourished children are not found in the poorest 20% of households, and around half are not found in the poorest 40%. Countries with higher undernutrition tend to have higher shares of undernourished individuals in non-poor households.
It is clear from our results that to have any hope of reaching undernourished women and children in Africa, policy interventions will either require much more individualized intra-household information or they will need to be nearly-universal in coverage.