Higher minimum wages and poverty revisited

One of the key purposes of a minimum wage is to decrease poverty. However, there is no certainty that poverty would be reduced by a higher minimum wage. In the simplest sense, we can think about several effects of higher minimum wages on poverty. Income goes up for those earning the minimum wage, and therefore poverty may decrease. However, if there is any disemployment (workers losing their jobs) resulting from the minimum wage, poverty may increase. Poverty may also increase if there is significant pass-through of higher minimum wages into prices (as noted in this post), increasing the cost of living for all households. So, whether higher minimum wages increase or decrease poverty, or don't affect poverty at all, is essentially an empirical question (and one that I have written about before).

Until relatively recently though, there was some general consensus among economists that the minimum wage is ineffective at reducing poverty. Aside from the offsetting effects I noted above, the minimum wage isn't really well targeted at the poor (consider, for example, the number of teens from relatively high-income families who work in jobs paying the minimum wage).

So, the lack of an effect of minimum wages on poverty was generally agreed on by economists. Until this 2019 article by Arindrajit Dube, published in the American Economic Journal: Applied Economics (open access), which claimed to show that there was a large effect of higher minimum wages on poverty in the US between 1983 and 2012. That Dube article got a lot of press at the time, and encouraged support for a much higher federal minimum wage, as encapsulated in the US Raise the Wage Act of 2021 (which died in Committee).

Dube's article also shook the consensus among economists, and thus it also attracted attention from other researchers. And unsurprisingly, some have looked closely at the analyses. This 2023 NBER Working Paper by Richard Burkhauser (Cornell University), Drew McNichols (Amazon), and Joseph Sabia (San Diego State University) is one such effort. As they explain:

This study revisits the relationship between minimum wage increases and poverty. We highlight four key results. First, we replicate and reassess the findings of Dube (2019), based on poverty data from the March 1984 to March 2013 CPS (corresponding to calendar years 1983-2012). After precisely replicating his estimates, we show that his results are driven by two specification choices: (1) the inclusion of macroeconomic controls (the state unemployment rate and per capita state Gross Domestic Product) that may also capture a mechanism through which the minimum wage affects poverty: its employment and hours effects, and (2) restricting treatment states’ counterfactuals to states within the same census division (“close controls”), even when geographically proximate states are rejected by a data-driven synthetic control approach to generate counterfactuals. When we (1) use the state house price index and the unemployment and average wage rate among more highly educated individuals to control for state macroeconomic conditions that are less likely to capture pathways through which minimum wages affect poverty in a difference-in-differences framework, or (2) allow states outside a treatment state’s census division to serve as potential donors in a synthetic control framework, we find no evidence of poverty-reducing effects of the minimum wage over the 1983-2012 period... The 95 percent confidence intervals around our preferred estimates rule out poverty elasticities with respect to the minimum wage of less than -0.138, which include central estimates reported by Dube (2019).

In other words, Burkhauser et al. show that Dube's results are not robust to several modelling choices that Dube made. When the models are run with different control variables, or with a different selection of control states, there are no negative effects of higher minimum wages on poverty. Also:

...when we explore the most recent decade of CPS data, which captures the years following the Great Recession (2010-2019), the contemporaneous and longer-run poverty findings reported by Dube (2019) are largely absent, including in models that use Dube’s preferred macroeconomic controls or controls for spatial heterogeneity. Specifically, we find no evidence that post-Great Recession minimum wage increases had a statistically significant or economically important effect on poverty.

So, Burkhauser et al. show that Dube's results are sensitive to the choice of the time period that the dataset covers. And then:

...when we combine the two data windows discussed above and amass our “full panel” from 1983-2019, we find little support for the hypothesis that minimum wage increases reduce poverty over this 37-year period. Estimated elasticities below -0.131 for non-elderly individuals (and below -0.129 for all persons) lie outside of our 95% confidence interval, which would rule out the central long-run estimate reported by Dube (2019). Our preferred estimate shows that a 10 percent increase in the minimum wage is associated with a (statistically insignificant) 0.17 percent increase in the probability of poverty among all persons.

So, using the broader dataset from 1983 to 2019, the effect of higher minimum wages on poverty is small and statistically insignificant. Finally, Burkhauser et al. reiterate earlier findings in the literature, by showing that:

...less than 10 percent of those whose hourly wage rate would be directly impacted by a $15 minimum wage live in poor families. Approximately two-thirds live in families with incomes over two times the poverty line and nearly half live in families with incomes over three times the poverty line.

Burkhauser et al. conclude that:

In summary, our findings provide little compelling evidence that raising the minimum wage will be an effective or target efficient policy tool for alleviating poverty.

Burkhauser et al.'s paper is a comprehensive and systematic take-down of Dube's earlier work. And, it reestablishes the earlier consensus - higher minimum wages do not reduce poverty (at least, in the US - the paper I discussed in this earlier post showed some short run, but not long run, effects on poverty in Brazil). As with all research, it pays not to overcorrect greatly on the basis of a single new research paper. Policy makers would do well to remember that.

[HT: Marginal Revolution, back in 2023]

Read more:

• A bunch of research findings on minimum wages in New Zealand
• The disemployment effects of Canadian minimum wages
• Jeffrey Clemens on the disemployment effects of the minimum wage
• The impact of an online minimum wage
• Seattle's minimum wage, revisited
• The latest evidence supports negative employment effects of the minimum wage
• Latest research suggests the minimum wage DOES reduce employment
• More empirical support for the disemployment effects of the minimum wage
• The minimum wage and job vacancies
• The minimum wage and hiring standards
• The employment effects of the minimum wage, from American Community Survey data
• The minimum wage and homelessness
• New York restaurants find a new way to respond to the minimum wage
• The minimum wage and workplace safety
• How employers respond to minimum wage increases
• Minimum wage increases and employer noncompliance

An extraordinary (but weak) claim about China's poverty reduction performance

Over the last forty years, global poverty has reduced dramatically. To see how dramatically, run this animation from Our World in Data (source here):

That animation is based on a poverty line of US$30 per day, which is about the average poverty line in a developed country. A poverty line is a level of income that separates the poor (who have income below the poverty line) from the non-poor (who have income above the poverty line). The poverty rate is then the proportion of the population who are poor. For example, the World Bank uses a poverty line that is currently US$2.15 per day (which is US$1 per day in 1996, adjusted for inflation) to determine rates of extreme poverty. By that measure, over the last 40 years, 800 million people have been lifted out of poverty, and China contributed close to three-quarters of that number.

The level of the extreme poverty line has been the subject of significant debate over the years (with many articles and several books devoted to the topic). Nevertheless, that global poverty has declined, and that China has contributed significantly to this success, is generally accepted. So, I was very surprised to read this article in The Conversation by Dylan Sullivan (Macquarie University), Jason Hickel (Autonomous University of Barcelona), and Michail Moatsos (Maastricht University), which presents a counter-view. They argue that:

In contrast to the World Bank, we find that from 1981 to 1990 – at the end of the socialist period – China’s rate of extreme poverty was one of the lowest in the developing world. It averaged only 5.6%, compared to 51% in India, 36.5% in Indonesia and 29.5% in Brazil.

We find extreme poverty increased dramatically during the market reforms of the 1990s. It reached a peak of 68% as price deregulation pushed up the cost of basic food and housing, cutting the buying power of low-income people.

Extreme poverty then slid during the 2000s, but has yet to fall to the levels calculated by the World Bank.

This is an extraordinary claim, so I read the underlying research article, which was just published in the journal New Political Economy (open access). The key difference between Sullivan et al.'s results and those of the World Bank lie in how the poverty line is calculated. As they explain:

In recent years, scholars have developed an alternative approach to measuring extreme poverty, which compares incomes against the cost of basic needs in different contexts (Moatsos 2016, Allen 2017, 2020). In 2021, the OECD published estimates of the share of the population below this ‘basic needs poverty line’ (BNPL), for all countries with available household survey based data from 1981 to 2008.

It is the use of this Basic Needs Poverty Line (BNPL) that explains the extraordinary results, and that is because when it comes to basic needs:

Socialist policies of public provisioning and price controls may keep the cost of meeting basic needs quite low compared to capitalist contexts characterised by high levels of commodification and privatisation. This means that any given level of broad-gauge PPP income would have a greater welfare purchasing power – in terms of basic needs – under socialism than under capitalism; people would have better access to the key goods, such as food and housing, that are necessary for escaping extreme poverty.

So, as China opened their economy up in the 1980s, privatising housing and other markets, the cost for a household to satisfy their basic needs increased, meaning that more households would be defined as extremely poor based on the BNPL measure. So, they end up with this comparison between China and several other 'middle income' countries (Figure 4 from the article):

Sullivan et al. then go on to compare those countries' performance across a range of measures that should be correlated with living standards, including literacy, school enrolment, life expectancy, health access (physicians and hospital beds per 1000 people), and calorie availability. They compare China with those other countries in 1981 and in 1990, a period where extreme poverty was reducing based on the World Bank poverty line, but where poverty based on the BNPL was relatively flat (as shown above). With these comparisons, they show that:

In sum, the empirical data on social indicators raises significant questions about the validity of the World Bank’s estimates of the poverty rate in China during the 1980s. In 25 out of 29 comparisons, China achieved the 1st or 2nd best score of the countries reviewed here, as we would expect from China’s relative performance on the BNPL.

There is a problem with the comparisons that they make though. They look at how China ranked in 1981 and 1990 compared with these other countries, and use China's declining ranking compared to these other countries as evidence in support of the BNPL data. However, they don't make the very obvious comparison of China in 1990 with China in 1981 on those same measures. If extreme poverty was decreasing, as the World Bank data suggests, then we would expect an improvement in these other measures of living standards. And indeed, that is what we see. The literacy rate in China increased from 66% to 78%, life expectancy increased from 67 to 69, and calorie availability increased from 103% to 119% of basic needs. None of that is inconsistent with a reduction in extreme poverty. Of course, that is also consistent with the BNPL data, which shows an (albeit slight) decrease in extreme poverty between 1981 and 1990 as well. An even better comparison would have been to look at the 1990s, where poverty based on the World Bank data and poverty based on the BNPL dramatically diverged. However, Sullivan et al. don't make that comparison (presumably because it doesn't support their argument).

The setting of a global extreme poverty line is quite fraught, and there will always be disagreements about how it should be determined, and at what level it should be set. However, as the American astronomer Carl Sagan noted, extraordinary claims require extraordinary evidence. The evidence that Sullivan et al. have brought to support their extraordinary claim is not extraordinary. Before we overturn the consensus that China dramatically reduced extreme poverty during the past 40 years, we need something more.

The minimum wage and poverty and inequality in Brazil

One of the purposes of a minimum wage is to reduce poverty, by ensuring that the lowest paid workers are paid more than they would be without the minimum wage (at the cost of potentially reducing employment among low-wage workers, as I most recently noted here). The minimum wage might also reduce inequality, if it raises the income of low-income groups, while not affecting higher-income groups, and provided any disemployment effects are not too great. It is an open question how effective the minimum wage is at reducing poverty and inequality.

Most of the empirical evidence on the minimum wage comes from developed countries (and a lot of it from the US, where the federal minimum wage is very low and less likely to be binding than in other countries). So, I was interested to see this recent article by Orlando Sotomayor (University of Puerto Rico), published in the journal World Development (sorry, I don't see an ungated version), because it looks at poverty and inequality, and examines the case of Brazil. Sotomayor uses data drawn from the Monthly Employment Surveys conducted by the Brazilian Institute of Geography and Statistics, and a difference-in-differences analysis.

The choice of a difference-in-differences analysis is interesting. Although the minimum wage is specified at the national level in Brazil, differences in the income distribution will mean that the minimum wage is more binding in some states than in other states. For example, Sotomayor notes that earnings in Rio de Janeiro are lower than in São Paulo, so comparing changes in poverty (or inequality) in Rio de Janeiro with those in São Paulo after an increase in the minimum wage will provide some idea of its impact on poverty (or inequality). Sotomayor also makes use of a synthetic control method as well (which I've written about before, for example here).

Sotomayor finds that:

...within three months of a minimum wage hike, poverty and income inequality decline, on average, by 2.8% and 2.4%, respectively. Effects fade over time, particularly with respect to bottom-sensitive distribution measures, a process that is consistent with resulting job loses that fall more heavily among poorer households.

In this study, the minimum wage is associated with small improvements in poverty and inequality through raising incomes of the poor, but these improvements are reduced or eliminated by the disemployment effects in the longer run. This adds to the literature from more developed countries such as the US and suggests that, in the longer run, there may be more effective ways to reduce poverty and inequality than relying on the minimum wage. Redistribution through a more expansive social security system, or a wage subsidy for low-wage employers, are two options that deserve more consideration, as they would not be associated with the same disemployment effects as the minimum wage.

What is a developing country, anyway?

I've always thought it problematic that we define "developing countries" (or "less-developed countries" or "low income countries" if you prefer) purely on the basis of income (or GDP per capita). There are many aspects of development that are not captured by income (although they may be correlated with income), such as health, education, good institutions, individual freedoms, and so on. The focus on income is one of the main reasons why we conflate under-development with poverty. While they may be related, they are not the same thing.

The Human Development Index (HDI) goes some way towards improving the categorisation of countries, but isn't really used when the big multilateral agencies consider categorising the countries that are most in need of assistance. Moreover, by its very nature as an index, it is uni-dimensional. What we need is a more multi-dimensional way of categorising the level of development of countries.

So, I was quite excited to recently read this 2014 CGD Working Paper by Andy Sumner (King's College London) and Sergio Tezanoz Vazquez (University of Cantabria). It builds on some earlier work of theirs that was published in the Journal of Development Studies in 2013 (sorry I don't see an ungated version online), and looks at a taxonomy of developing countries constructed in an explicitly multidimensional way.

The authors use cluster analysis to categorise countries, using variables across four main dimensions of development:

1. Development as structural transformation - GDP in non-agricultural sectors (as a % of GDP), exports of primary commodities (as a % of GDP), GDP per worker (in constant 2005 PPP dollars, as a measure of productivity), number of scientific articles (per million people), and external finance (overseas development assistance, foreign direct investment, foreign portfolio investment, and remittances, as a share of GDP);
2. Development as human development - poverty headcount (using a $2 per day poverty line), Gini coefficient (a measure of inequality), and malnutrition prevalence (low weight-for-age among those aged under five years);
3. Development as democratic participation and improved governance - World Governance Indicators index, and POLITY 2 index; and
4. Development as environmental sustainability - CO2 emissions (in metric tons per capita).

Cluster analysis is a really useful way of identifying observations (in this case, countries) that are similar across many dimensions. The important thing is that, unlike the HDI, the multi-dimensionality of the data is preserved. That means that you don't have a dichotomy (poor/non-poor countries), nor do you necessarily have a single development trajectory (from low income to high income). The authors note:

hierarchical cluster analysis allows one to build a taxonomy of countries with heterogeneous levels of development in order to divide them into a number of groups so that: i) each country belongs to one – and only one – group; ii) all countries are classified; iii) countries of the same group are, to some extent, internally ‘homogeneous’; and iv) countries of different groups are noticeably dissimilar. The advantage of this procedure is that it allows one to discern the ‘association structure’ between countries, which – in our analysis – facilitates the identification of the key development characteristics of each cluster.

Moreover, one of the great things about this working paper is that they look at two points in time (1995-2000, and 2005-2010), which allows them to:

...this analysis allows us to... analyse the dynamics of the development process of a single country in comparative terms (that is, in terms of the average development indicators of the "peer" countries belonging to the same cluster.

In both time periods, the authors identify five clusters of developing countries. The three variables in order with the greatest discriminating power (the variables that make countries in each country most different from each other) are poverty, quality of democracy, and productivity in the 1995-2000 data, and poverty, productivity, and quality of democracy in the 2005-2010 data. The consistency is reassuring.

The five clusters in 1995-2000 (in order from lowest average Gross National Income per capita to highest) were:

1. Very poor countries with largely 'traditional' economies - 31 countries, including Democratic Republic of Congo, Rwanda, Pakistan, and Swaziland;
2. Poor countries with democratic regimes but poor governance - 18 countries, including Ethiopia, India, Indonesia, and the Philippines;
3. Countries with democratic regimes but high levels of inequality and dependency on external flows - 18 countries, including Moldova, Honduras, Colombia, and the Dominican Republic;
4. "Emerging economies" that were primary product exporting with low inequality but high environmental pollution and severely constrained political freedoms - 11 countries, including Azerbaijan, China, Egypt, and Gabon; and
5. Highly polluting and unequal emerging economies - 21 countries, including Ukraine, Thailand, Mexico, and Argentina.

In 2005-2010, four of the clusters maintain a similar definition, but Cluster 2 becomes "Countries with high poverty and malnutrition rates that are primary product exporting and have limited political freedoms".

To a large extent, the most interesting aspects of the paper are the dynamics, i.e. which countries move from one cluster to another over the period, and which countries remain in the same cluster. There is a lot of movement between clusters - too much to effectively summarise here. Some notable (to me!) movements though include Vietnam moving from Cluster 1 to the new Cluster 2, India (and Nigeria, Ethiopia, and Papua New Guinea) moving from Cluster 2 to Cluster 1, Indonesia (and Sri Lanka) moving from Cluster 2 to Cluster 3, Thailand (and Ukraine) moving from Cluster 5 to Cluster 3, and Iran moving from Cluster 5 to Cluster 4. All countries in Clusters 3 and 4 in the first period were still in the same clusters in the second period, which is also interesting.

Obviously, some improvements can be made in terms of which variables should be included in developing the clusters (the authors make this point themselves). However, I can see a lot of mileage in further exploring not only the results in this paper, but the approach to categorising developing countries and their development paths more generally.

How my views on inequality have evolved over the last year

I've been teaching about poverty and inequality for a number of years, but that teaching (at both undergraduate and graduate levels) focuses on the measurement of poverty and inequality, particularly using income as a metric (although my graduate class did cover multidimensional poverty approaches and classes at both levels were introduced to Sen's capabilities approach). The focus on measurement has meant that differences in the problems associated with poverty and inequality (as separate issues) are somewhat muddied. This isn't helped of course by one of the main tools used to reduce poverty - income redistribution through progressive taxation, transfers, etc. - also reduces inequality. Yesterday I did a guest lecture for a second-year social policy class, which gave me an opportunity to reflect on how I've changed my views on inequality over the last year or so, which I thought I would share here.

In the last year, my thinking about inequality has come almost full-circle. Initially I was of the opinion that greater inequality created problems of unequal access to basic needs such as healthcare, education, etc. and lower social cohesion (more crime, etc.), lower economic growth, and lower happiness (or subjective wellbeing, in economic parlance). These views were based on a range of research that I have read over the years.

Then I read the Max Rashbrooke-edited book "Inequality: A New Zealand Crisis". This book changed my views greatly, but not in the way that the contributing authors would have intended. Almost every chapter of the book described the 'problems of inequality' as pertaining to low-income households. As I read the book I realised they were often conflating 'problems of inequality' with 'problems of poverty'. Max Rashbrooke was clear in the introduction on the reason for this:

If incomes do matter, the question remains: why put so much stress on a relative issue, the difference in incomes between various groups? Why not focus simply on the fact that some people are too poor to get by, in a basic material sense?

The short answer is that poverty is not just a problem for the poor; it concerns and involves everyone, including the very well off. In these pages, we describe the levels of poverty now found in New Zealand as a crisis for the society as a whole.

Which would be great, if that's what the authors did. I didn't see anywhere in the book where they clearly made the case for why poverty is a problem for the well-off (other than through general concern for the wellbeing of others). Which brings me back to the difference between 'problems of poverty' and 'problems of inequality'. To understand the difference, consider the following thought experiment:

Let's say you rounded up all of the richest people in an area, took 10% of all of their wealth, and burned it. What would happen to poverty and inequality?

If we took this action, it would reduce the wealth of the rich, but it does nothing for the wealth of the poor. Clearly burning part of the rich people's wealth must reduce inequality as the wealth difference between rich and poor has narrowed. However, it wouldn't affect poverty at all, as it doesn't give the poor any more resources. So, if you're wondering whether a problem is primarily related to poverty or primary related to inequality, you need to ask: Would the problem be reduced by burning 10% of the wealth of all of the richest people?

If the answer is yes, then the problem probably stems primarily from inequality. Otherwise it is more likely to be primarily a problem of poverty. For example, say you are concerned about the large number of parents unable to feed their children a nutritious breakfast before school. It seems unlikely that burning wealth would reduce the problem, so it is more likely a poverty problem not an inequality problem.

The difference between problems of poverty and problems of inequality may appear to be semantic, but the policy prescriptions for reducing inequality and for reducing poverty need not be the same. For instance, an unconditional basic income is argued as a means of reducing absolute poverty, but would do little or nothing to reduce inequality as it simply shifts up the whole income distribution.

Anyway, reading the Rashbrooke book left me feeling a little bit unsettled and wondering a bit whether there were any problems associated with inequality that weren't inherently poverty issues, given the authors focused on poverty issues rather than inequality issues. So I followed it up by reading "The Spirit Level", by Richard Wilkinson and Kate Pickett. This book has been roundly criticised by economists and others (especially see Christopher Snowdon here), but I didn't think it was all that bad. I had two main takeaways from the book. One was negative, and I'll blog about that separately later. The other was summed up nicely by the following passage from the book:

We concluded that, rather than telling us about some previously unknown influence on health (or social problems), the scale of income differences in a society was telling us about the social hierarchy across which gradients in so many social outcomes occur. Because gradients in health and social problems reflect social status differences in culture and behaviour, it looks as if material inequality is probably central to those differences. (Wilkinson and Pickett, p.28).

In other words, the observed relationship between inequality and social problems reflects (in part) differences in social status between groups. Since inequality perpetuates differences in social status between groups (particularly when social mobility is low), this makes inequality self-reinforcing. This self-reinforcement of inequality relates to an argument that social distance makes us more accepting of inequality (as Wilkinson argues in his earlier book, "The Impact of Inequality", and incidentally is also raised in the chapter by Paul Barber in his chapter in the Rashbrooke book), and recent research that higher levels of inequality make us more accepting of inequality. So, because inequality is self-reinforcing this leads to increasing segregation by social class, reducing social cohesion. So, I felt a little better as I had re-discovered at least one problem that related to inequality but not necessarily to poverty.

Finally, at the start of this year I read the Robert Frank book "Falling Behind: How Rising Inequality Harms the Middle Class". I'm a big fan of Robert Frank and he brought a different perspective on inequality to the other two books. In this book, he highlights that the actions of high income individuals change what is deemed socially acceptable in terms of housing and other 'positional goods' (positional goods are goods that signal the owner's relative social standing - similar to conspicuous consumption that I have discussed before here). Frank makes the case that there is competition by middle class families for houses in desirable areas, close to good schools, where public safety is higher, and closer to work and amenities, etc. This competition drives the price of those positional goods higher, leaving less income for middle class families to spend on non-positional goods. As Frank describes:

Increased spending at the top of the income distribution has imposed not only psychological costs on families in the middle, but also more tangible costs. In particular, it has raised the cost of achieving goals that most middle-class families regard as basic.

The process starts when sharply higher incomes prompt top earners to build larger mansions. To the extent that middle-income families even notice these mansions, there is no evidence that they are offended by them. On the contrary, many seem to derive pleasure from seeing images of them in magazines and on television.

But for those just below the top, the new mansions alter the frame of reference that defines what kind of house is considered necessary or desirable. Perhaps it is now the custom to host one’s daughter’s wedding reception in the home, or to host larger dinner parties. And when the near rich, in turn, build larger houses, others just below them find their own 10,000-square-foot houses no longer adequate, and so on all the way down the income ladder.

People who live in less expensive houses not only feel relatively deprived (a psychological cost), but also miss out on good schools, safe neighbourhoods, and face more expensive and time-consuming commutes to work (also costs). So rational people trying to avoid these costs can't easily opt out of the battle for positional goods. All of which links inequality to relative deprivation, and to lower happiness.

So, in the end that short journey through three books has brought me back more-or-less to where I started the year. At least, in the sense that inequality is a problem because of lower social cohesion and lower happiness (and lower economic growth, see this OECD report (PDF) from late last year).

Of course, I reserve the right to change my position again, and I have a few more books on inequality that I'd like to read. But more on those later.

Try this: Tradeoffs for the poor

The beautiful future-Mrs-Cameron shared this link to an online game called Spent with me, from one of her students. In the game, you have to try and survive for one month as an unemployed American with no savings.

It's a tough game, but underlines the sorts of tough choices and trade-offs that real people have to make every day. It's very Americentric (as you would expect), but if you have a spare few minutes, I suggest you try it out for yourself.

How did I go? Apparently, I could survive as a poor American since I got through all 30 days unscathed, but with what would have been a pretty wretched standard of living.