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Hidden costs of the recession

26 Apr

In a previous post I considered whether the current Great Recession had been good for your health. Evidence suggests that temporary reductions in income may improve your health for a number of reasons. In part, when I lose my job I may have expectations of finding work again in the short term, my skills may not depreciate in the short term, and I may be able to smooth my consumption with access to credit or savings and do more time-consuming, health-promoting things. But, the longer my spell of unemployment, the less access to health promoting goods I have and the greater the effects of socioeconomic deprivation. A number of studies have remarked on the link between income inequality and poor health (e.g. see here and here).

In the last post, I looked at a cross section of data from the 2011 census. I presented some correlations between the proportion of individuals who were unemployed and the proportion reporting bad health. I, and I am certainly not alone, may argue that myriad other factors could cause this observed relationship. I can’t prove or disprove any hypothesis in the space that this blog permits but I will add the following figure in support of the relationship. Here, I took data from both the 2001 and 2011 censuses for all lower super output areas (LSOAs; geographical areas of approximately 1,500 people) and looked at the relationship between the difference in the proportion unemployed and the difference in the proportion reporting bad health between 2001 and 2011:

change in prop bad health vs change unemployed

Given the long lag between 2001 and 2011, the arguments from the previous post, that this represents changes to structural unemployment rather than short term cyclical unemployment, may still stand. But, for whatever reason, there is a correlation between unemployment and self-reported bad health.

I should mention that the questions about health differed between the two censuses from three options in 2001: ‘good health’, ‘fair health’, or ‘bad health’, compared to five options in 2011: ‘very good health’, ‘good health’, ‘fair health’, ‘bad health’, and ‘very bad health’. I have compared here the percentage reporting the 2001 option ‘bad health’ to the combined ‘bad health’ and ‘very bad health’ option. You may think this is an affront to good data analysis, so to allay your fears I have provided versions of the following two figures that use only 2011 data. You will see that they tell the same story.

The increase to poor health as a result of increased socioeconomic deprivation is costly for a number of reasons. Considering healthcare, direct costs such as hospital admissions for physical and mental health problems may increase, along with the accompanying costs of providing pharmaceuticals and other treatments. One cost that is not well reported in the media is that of unpaid care. One study in the UK estimated the costs of services provided by unpaid carers to be as much as £87 billion per year. Now, those in poor health require care. The following figure shows the relationship between the change in the proportion of people reporting bad health and the change in the proportion of people providing more than 20 hours a week of unpaid care between 2001 and 2011 in each LSOA:

bad health vs unpaid care

bad health vs unpaid care 2011

2011 data only

I am not surprised by this relationship, and I doubt you are either. Then, it should also come as no surprise, given the previous two figures, that when I plot the relationship between the difference in the proportion unemployed and the difference in the proportion providing more than 20 hours unpaid care per week that there is also a strong relationship:

unemployed vs unpaid care

2011 data only

2011 data only

The relationship between health and economic conditions is complicated to say the least. What these data may indicate is that the cost due to increased unemployment may be far more than just reduced growth and output. Unpaid carers often have to leave employment to provide their services. Cutting back on health and social care funding in real terms will only shift the growing burden to individuals in poor areas, where health is worse, rather than to the state.

I would like to point out as a final note, and perhaps one of optimism, that the percentage of people reporting bad health has on average declined between 2001 and 2011. Although this may just be a case of hedonic adaptation…

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Posted by on April 26, 2013 in Health and the Economy

 

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Is this recession good for your health?

25 Apr

There have been a good number of articles to document the phenomenon of a counter-cyclical relationship between unemployment and health. As unemployment rises, deaths from a number of causes have been found to decline. These include accidents, infant mortality, heart disease, and liver disease (Ruhm, 2000; Dehejia and Lleras-Muney, 2004). That such a relationship is observed may at first seem counterintuitive; the reduction in income must surely damage our health. But, there are a number of reasons why we may see this relationship:

  1. Opportunity cost of time: In economic upturns leisure time decreases and health improving behaviours such as exercise decrease. Thus, in an economic downturn, since our time is less precious we have more time to engage in time-intensive and health-promoting activities. We could even visit the doctor more.
  2. Health as an input to production: The production of goods and services requires healthy people. But this production may be hazardous or stress-inducing. Furthermore, some of the most hazardous sectors, such as construction, are the most affected by economic downturns.
  3. External sources of death: Less time spent commuting means less time on the road and so fewer vehicular accidents. We may also see less drink driving, which is more common in economic upturns.
  4. Income effect: Our consumption of alcohol and tobacco as well as other goods that damage our health may decline.

On the back of this evidence, I asked myself, has this effect been present in the UK during the current Great Recession? Overall, unemployment has risen over the last five years, and the average weekly wage has declined in real terms (thanks to @peterpannier for the graph):

Click for larger image

A proper analysis of the data would be a full paper, something that someone, somewhere, may be in the process of writing – but, for the purposes of a preliminary investigation, let’s just look at the raw data. The 2011 census asked people how they would rate their health and provided them with five possible responses from ‘very good’ through to ‘very bad’. The census also provides us with the number of economically active but unemployed individuals. All this information is aggregated at the level of lower super output area (LSOA); of which there are around 32,000 in the UK each with a population of around 1,500. The following figure shows a plot of the proportion of unemployed individuals (as a proportion of 16-74 year olds) against the proportion reporting ‘bad’ or ‘very bad’ health:

Click for larger image

Clearly, there is a strong upward trend; areas with more unemployed have more people reporting bad health. Does this contradict our initial hypothesis? One of the crucial points about the aforementioned arguments are that they are arguments to explain the relationship between a change in health and a change in economic circumstances. The papers cited above used a fixed effects analysis; an analysis to examine the effects of changes. Thus, the correlations in the figure above may be picking up structural unemployment: we may be seeing the relationship between health and unemployment for those for whom the recession doesn’t affect health behaviour because they don’t experience a change as they are already unemployed. So let’s look instead at the relationship between short-term unemployment and the proportion reporting ‘bad’ or ‘very bad’ health. I defined short term unemployed here as having last been employed in 2011, i.e. a maximum of three months prior to the census. I looked at this in two ways; firstly, by looking at the number of short term unemployed as a proportion of the total number of people between 16 and 74:

Click for larger image

As you can see, there is now a downward trend, albeit not very steep. One issue is that areas with high short-term unemployment may also have high long-term unemployment making it hard to distinguish their effects. Therefore, my second approach was to look at the proportion of short term unemployed as a proportion of the total unemployed:

Click for larger image

Now there is clearly a strong downward trend. At a superficial level, these data seem to preliminarily support the hypothesis that short-term changes to unemployment may improve health. However, we also see that long-term unemployment is related to negative health. This is certainly not unexpected.

It is well evidenced that longer spells of unemployment lead to a reduced probability of finding work. From the macroeconomic point of view, the longer a downturn in the economy lasts, the greater the structural unemployment. This, as the above data suggest, may therefore lead to a reduction in average population health. Reducing unemployment and the duration of employment spells is certainly important but an ambitious policy goal. A better understanding of how socioeconomic deprivation and poor health are related would identify other methods to combat this negative effect on health.

These data may also shine a different light on Keynes’s well quoted line that ‘In the long run we are all dead’.

 
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Posted by on April 25, 2013 in Determinants of Health and Ill-Health, Health and the Economy, Public Health

 

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A comment on health inequality

26 Mar

A recent article by Benjamin Ho and Sita Nataraj Slavov, which I picked up via Marginal Revolution, argues that health inequality is falling. The argument is that life expectancy for the 1% dying at the bottom end of the age-at-death distribution has increased by more than the life expectancy for the 1% at the top. I’m struggling to think of much academic work being done to look at levels of health inequality in this way. However, I’m not sure what answering such questions could add.

Existing work

Plenty of work has been done on how to measure health inequality. It seems a pretty heinous crime to talk about health equality without mentioning Culyer and Wagstaff. More recently, new models of health inequality have been developed that bare varying levels of equivalence to a standard concentration curve (see here, here, here, here etc). But the authors of the aforementioned article are really interested in pure health inequality, irrelevant of income or socio-economic indicators. Some work has been done here too (see here, here, here etc); indeed, the age-at-death distribution thing was done by Le Grand.

Pure health inequality

Health and income are very different in a number of ways, and it seems a misnomer to compare income inequality with health inequality. The most important difference, probably, is how society views the two. Society has some aversion to income inequality and also aversion to health inequality. However, we don’t just prefer a more equal distribution of health; we want equal full health (i.e. health maximisation). Assuming diminishing marginal returns to health care (in terms of health), we will tend to prioritise those in worse health and tend towards equality. I would argue that health can only increase indefinitely in terms of longevity. We may live longer and longer but I think ‘full health’ is a very real ceiling while we’re alive. It simply isn’t possible for a super-rich elite to develop in terms of health. What would these people be like? Bionic presumably, but that’s a different debate. Even if health could be amassed indefinitely it wouldn’t be, as health has no value in exchange.

For me (given society’s aversion to inequality, technological progress and a maximum level of health at any point in time), movement towards equal health seems inevitable. You don’t need to agree with the Grossman model to accept that health represents a kind of ‘stock’. It therefore bares more resemblance to wealth than to income. Health requires some effort to maintain, but not to the same degree as income. Ho and Slavov’s article also introduces the idea of a lottery; luck plays an important role here. Society reacts differently to an income shock (say, unemployment) than it does to a health shock (say, being hit by a car). As with income there might be fair and unfair inequalities, but either way society is going to attach more weight to reimbursing an individual’s loss of health than an individual’s loss of income (unless, maybe, the latter is a result of the former). The same applies to those dealt a nasty hand at birth. In countries where health care is dependent on ability to pay there will certainly be more of a link between health and income; and thus between health inequality and income inequality. In countries like the UK, income inequality seems less likely to affect health inequality.

Health is becoming more equal; I won’t disagree with that. But, for the reasons outlined above, this seems somewhat inevitable. I suppose that doesn’t mean we shouldn’t celebrate it, but it does raise into question the value of doing so when there are real discrepancies between different demographics’ health that need addressing.

Cynics may spot the benefit of such an approach for those at the top of the income distribution…

 
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Posted by on March 26, 2013 in Demand for Health and Health Care, Efficiency and Equity

 

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#HEJC for 01/04/2013 (new time!)

25 Mar

This month’s meeting will take place Monday 1st April, at 5pm London time. That’ll be 6pm in Cape Town and 7pm in Riga. Join the Facebook event here. We’ll also hold an antipodal meeting on Tuesday 2nd April, at 5am London time. That’ll be 2pm in Brisbane and 9pm on Monday in Seattle. Join the Facebook event here. For more information about the Health Economics Twitter Journal Club and how to take part, click here.

The paper for discussion this month is a working paper published by the Research Institute of Industrial Economics in Sweden. The authors are Sara Fogelberg and Jonas Karlsson. The title of the paper is:

“Competition and antibiotics prescription”

Following the meeting, a transcript of the discussion can be downloaded here.

Links to the article

Direct: http://www.ifn.se/wfiles/wp/wp949.pdf

RePEc: http://ideas.repec.org/p/hhs/iuiwop/0949.html

Other: tbc

Summary of the paper

Antibiotics resistance is an increasingly apparent problem in medicine, with the prevalence of multi-resistant bacteria on the rise. Over-prescription of antibiotics has short- and long-term implications for public health. Furthermore, there is much debate about the role of competition in healthcare provision. This paper investigates the effect of increased competition between healthcare providers on the prescription of antibiotics. The authors hypothesise that, as a result of increased competition, doctors may be inclined to prescribe more antibiotics in order to meet patients’ demand. The study makes use of a natural experiment where competition-inducing reform was implemented in different counties in Sweden at different points in time during 2007 to 2010. The dataset contains monthly data on all prescribed antibiotics in Sweden, including those defined as narrow spectrum and broad spectrum antibiotics. The authors implement a difference in differences model. The results indicate that increased competition had a positive and significant effect on antibiotics prescription.

Discussion points

  • What is the significance of Swedish reimbursement processes?
  • What does this study tell us about patients’ and doctors’ preferences for antibiotics?
  • What are the implications for the UK and other countries?
  • How can this study inform the debate about competition in healthcare?

Missed the meeting? Add your thoughts on the paper in the comments below.

 
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Posted by on March 25, 2013 in #HEJC

 

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Some thoughts on evidence-based policy

22 Mar

I’m currently reading Les Mis (I have been for about 2 years – it’s half a million words long). A few months ago, Hugo described economists to me as “geologists of politics” (géologues de la politique). A pretty smart observation for 1862. It reminded me of a slightly more recent quip by Oswald Falk; telling his friend John Maynard Keynes that all he had really done was codify “the moral feeling of an age”.

Economic theory often follows political theory, no doubt, and policy can follow from either. But there are now calls in the UK for ‘evidence’ to enter the equation; most recently in eduction. In regard to economic and public policy, the argument is presumably that the story should go:

political theory > economic theory > evidence > policy.

A loose parallel in medicine might go:

medical theory > treatment > evidence > policy.

In medicine this is usually feasible, as human biology is relatively predictable. It is reasonably clear how medical questions can be answered; usually by randomised controlled trials and epidemiological studies. But is the step from theory to evidence as simple in public policy?

Evidence-based policy

In public policy, the story rarely goes as described above; evidence can fall anywhere in the schema – usually at the end. Evidence is retrospective, while policy is prospective. Human evolution is relatively slow, and what a drug does to a person now it is likely to do in 12 months’ time. Evidence collected in a trial is therefore largely applicable in the future. The same cannot be said for economies and societies. Evidence becomes heavily dependent on projections of what will happen in the future, and we (economists, humans) are notoriously bad at making predictions.

Evidence-based medicine (future edition)

Medicine is less dependent on projections, so evidence-based medicine is usually a safe bet. However, with the rise of personalised medicine, evidence-based medicine as we know it could be off the table. In personalised medicine, n=1. It won’t be possible to stratify trials by the four quadrillion different human genetic combinations; let alone different socio-economic indicators. Furthermore, some pressing questions are proving to be beyond the scope of evidence and prediction. For example, Richard Smith and Joanna Coast recently highlighted the limitations of evidence in antimicrobial resistance.

I’m all in favour of evidence-based medicine, as I’m not a moron! I’m also in favour of evidence-based policy wherever we can do it. But we need to acknowledge its limitations and avoid hubris whenever we do have ‘evidence’. Health economists live in a very evidence-based world, which is no bad thing, but we mustn’t restrict ourselves to it. We need to consider that, if we can’t find evidence of support for a policy (say, attaching a greater weight to end of life care), it may be that our theory is wrong.

When would the NHS have been created, had we waited for the evidence (or economic theory, for that matter)? How long can we wait for evidence in the case of antimicrobial resistance? In the long run we could all, quite literally, be dead. Sometimes it will be necessary to charge forward with policies that we know are right, but just can’t prove. The economists will add the veneer of theory later.

 
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Posted by on March 22, 2013 in Determinants of Health and Ill-Health, Public Health

 

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A minimum price for alcohol

15 Mar

The current UK government is toying with the idea of introducing a minimum price for alcohol in England and Wales of around 45p per unit. However, just this week it was revealed that some senior cabinet members opposed the policy; putting it in jeopardy.

As with any policy there is a burden of evidence. The impact of such a policy should be established as best as possible. Basic economic arguments and the current evidence about alcohol may or may not lead us to expect that the policy would: i) reduce overall consumption (income effect), ii) increase consumption of other drugs (substitution effect), iii) not affect consumption of alcohol among alcoholics (inelastic demand among addicts), and iv) reduce the welfare of the poorest households (tighter budget constraint).

As was discussed in a previous post, based on arguments presented by David Nutt, the primary policy goal should be a reduction in the harm caused by alcohol; not a reduction in the prevalence of alcohol consumption. Of the above effects, presumably only the first is what the government desires; and, since it is a minimum price increase, only those who purchase the cheapest alcohol would see an income effect. The understanding is that alcoholics are the ones who would thusly be affected. But this leads to point iv); poor households who are not problematic drinkers would see an increase to the price of alcohol, while wealthier households who purchase more expensive alcohol (fine wine is a luxury good, cheap cider an inferior good), wouldn’t be affected. Yet there is certainly evidence (e.g. here and here) to suggest that alcohol consumption among the middle classes is problematic.

A precursory glance at the literature reveals the evidence of the effect of a minimum price of alcohol is fairly limited. It does reveal that, in Canada, it was found that a 10% increase in the minimum price of alcohol led to both a reduction in alcohol consumption and a 31.7% reduction in alcohol-attributable deaths. Epidemiological models set in the UK estimate the same effect.

The purpose of this policy does seem to be prevention of alcohol-related disease. But changing the minimum price of alcohol doesn’t address many of the issues surrounding the causes and effects of alcohol addiction; in particular, the effect of socioeconomic status. Higher socioeconomic status individuals are at least as likely to consume risky amounts of alcohol but appear to be less at risk of the adverse consequences. Indeed, one way of abrogating these effects would be to reduce consumption among the lower status individuals, but this would certainly be inequitable. It is widely accepted that there is a relationship between low socioeconomic status and alcohol addiction due to adverse social factors and poor life circumstances with the arrow of causality pointing in both directions. Perhaps addressing socioeconomic problems could be a more effective solution.

 
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Posted by on March 15, 2013 in Determinants of Health and Ill-Health, Public Health

 

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Diagnosing the cost disease

13 Mar

Last year William Baumol published a book entitled The Cost Disease in which he discussed his theory for why healthcare costs continue to rise as a proportion of GDP, while at the same time manufactured goods get cheaper. This idea wasn’t new; he’d first published it in 1967. However, even with 45 years for people to digest his idea, it has generally been overlooked. I shan’t go into much detail about the theory here as it is the subject of a previous post; nevertheless a short outline is probably warranted given its centrality to the rest of this post.

Baumol divides the economy into productive and stagnant sectors; the former includes industries with a high degree of capital input that see increases to labour productivity greater than average as a result of innovation (i.e. computers), while the latter has a less than average labour productivity growth rate since labour is the main input and there is a low degree of substitution between capital and labour (e.g. in healthcare). Wages increase in the productive sector in line with productivity growth. In the stagnant sector wages increase at the same rate to ‘keep up’ and not lose skilled workers, despite the smaller increases to productivity. As a result of productivity growth, overall the economy grows. The costs in the stagnant sector grow relative to the output, whereas costs in the productive sector don’t increase relative to output. Hence, the economy is bigger but the stagnant sector takes up a larger part of it.

The question arises, then, as to how we might test for the presence of the cost disease. The idea is simple and is based on the principle that wages in the economy as a whole rise in line with the productive sector, and that, in the productive sector, wages are tied to productivity. In the productive sector we should not expect to see any deviation in the difference between wages and productivity. Therefore, when we look at differences in wages and productivity in the economy as a whole, any variation in the difference between wages and productivity should be due to the stagnant sector. Shortfalls in productivity in the stagnant sector are not associated with changes to the wage rate. The test for the cost disease is then whether changes to the difference between wages and productivity in the economy as a whole are related to the costs in the stagnant sector, since the theory proposes that the driver of increased stagnant sector costs is the increase in wages relative to productivity.

In a very recent paper, using a panel of 50 US states between 1980 and 2009, Bates and Santerre estimated the effect of the difference between wages and productivity, in the economy as a whole, on unit costs in healthcare. This difference has been called the ‘Baumol variable’. It doesn’t seem to have much direct economic interpretation, but a positive coefficient is interpreted as evidence for the cost disease. They also included controls for the other possible determinants of rising healthcare costs: an aging population, unemployment and income per capita. In a previous paper, Hartwig estimated a similar model.

Both papers find a positive and statistically significant coefficient on the Baumol variable. Bates and Santerre argue that their method and results are an improvement over those of Hartwig, and since they support his findings they further strengthen the evidence for the presence of the cost disease in the US.

As Baumol himself identifies, if we accept the cost disease hypothesis, rising healthcare costs are not that great a problem. While healthcare costs take up a greater share of the pie, the pie is expanding at least as fast. However, Bates and Santerre also find evidence that population aging and unemployment cause increases to healthcare costs. This may mean that the pie does not grow proportionally to the healthcare slice but it does provide some reassurance that the problem is not as severe as depicted by many politicians.

From a policy perspective it may appear as if the solution is a Logan’s Run style scenario where, once people reach a certain age, they are vaporised and ‘renewed’. This would at once solve the aging problem and probably reduce unemployment too. Increases in healthcare costs would then only be a cost disease effect, but I doubt this idea would pass ethics approval. What is certainly true is that a greater understanding of the determinants of healthcare costs are required to better control them and understand the limits of our ability to control them.

 
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Posted by on March 13, 2013 in Health and the Economy

 

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