Application 1: Health and Earnings

Application 1: Health and Earnings Methods of Economic Investigation Lecture 3

Why are we doing this? • Want to apply what we’ve talked about this week to real-life situation • Better able to understand academic papers • Even if you go to industry (finance, consulting, etc.) or government/policy—academic work is often used • Being able to see why something works and doesn’t is critical

What are we doing today? • Think about the causal relationship between health and earnings • Review: • How to define a research question • How to develop a way to distinguish correlation from causation • Thinking about problems with measurement and data • Applying econometrics to real-life

A little background: Facts • Strong relationship between income and health (health gradient) • Lots of correlates to income and health • Education • Race/Ethnicity • Need to know relationship for determining actual policies

How can we show a relationship? • In a cross-section: Do richer people (or countries) have better average health? • In a time-series: As people (or countries) get richer, does the average level of health increase? • In a panel: Do people, after getting more money, become healthier? • In a repeated cross-section: Do cohorts (groups of people in the same year) who appear to have more money, have better health?

In a time series… Source: Source: National Center for Health Statistics, National Vital Statistics Reports, vol. 54, no. 19, June 28, 2006; Source: U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplements.

In a cross-section Source: Lynch, et al (1998) AJPH, p 1078

In a Panel… Source: Smith, JEP 1999, pp 147

What is the research question? • What is it we want to know? • If we gave some people money—would that make them healthier? • Accumulation effects over time? • Does it matter who (which income group) we give the money to? • Does it matter when we give them the money? • If we improve the income of some group of people, with that improve their health outcomes? • Long-term or short-term? • Holding other stuff fixed? Do we care how that extra income affects other things that later affect health?

What is the fundamental identification problem? INCOME of INDIVIDUAL i HEALTH OF INDIVIDUAL i Too sick for school? No Access to Medical Care? Parents didn’t know about med/docs/etc?

Where is the bias? –I • Reverse causation • Health might affect inputs (like education) which then affect income • Health might make it hard to work • In that example—good health is positively correlated with income. What does that imply for the bias?

Poor health can cost money

Where’s the bias? –II • Third Factors • Education might make people better able to earn and better at taking up health protective behaviors • Ability might make people more educated, have higher income and healthier? • Underlying genetics might make people more able, higher educated, higher income, etc.

What is health anyway? • Want to see if income causes better health but how to we quantify better health? • This about what it is we’re after? • Quality of life? • Extreme outcomes (death, dismemberment)? • Things that are costly for society (infectious diseases, eg.)

What is the outcome?-I • Maybe there’s something in existing data… • Mortality • Extreme outcome: Small changes might be hard to see • Maybe not what we care about (if everyone lives but some are very sick…) • Illness/disease • Hard to get info on • Diagnosis bias • LOTS of third factor causation here… • What about doctor/hospital visits?

What is the outcome?-II • Maybe we could just ask people… • How good is your health (1 being excellent 4 being poor) • This is called “Self-reported health status” • Commonly used measure • Survey Response • Can we compare answers across people? • Bias especially bad if response type varies by SES/Income characteristics

What experiment would we design? • Thought experiment: If we took a random sample of the population and divided them in half, arbitrarily gave half an extra income and measured their health, would they be different than the others? • Can we do this (maybe?!!) • Does something like this happen in real life? • What will change and can we measure it?

What should we estimate? • If we could do the experiment we just described: we would want to test: E(Health | Treatment) > E(Health | Control) • Very simple econometric specification: Regression is indexed by: i: the individual c:the group (e.g. either treatment or control) (This works because our sample is randomly drawn and assigned, next class…)

How do we interpret estimates? • Recall that our OLS estimate is: • Our estimate is very simple: • We can put a dollar value on this since we designed the change!

Research Design • If we can give people extra income, then we can measure their health afterwards and see what happens • If we can’t do this experiment, can we think of sometimes when people completely randomly get money? • Example: The Lottery (Paper by Mikael Lindhal, Journal of Human Resources, 2005) • Identifying assumption: People who win the lottery look like people who play the lottery but don’t win

Data problems • The survey didn’t ask if you played the lottery—so can we compare lottery players to non-players? • Can we do anything else? • New Identifying assumption: Playing the lottery is NOT correlated with characteristics that are correlated with health • Can we prove this? Compare lottery players and non-players

Comparing Players to Non-Players

What does he estimate? • Want to see what the effect of a lottery prize is on health Health Measure in 1981 SES Characteristics: Age, gender family background, etc. in 1968 Lottery Winnings between 1969-1981

Results • Evidence of reduced health (other results index these measures and get significant results) • Evidence of reduced mortality

Interpreting results • If we know the change in income for lottery—we can estimate an effect size • Results imply: 10 percent increase in income increased general health by 0.04 standard deviations • How to put this into context? • What do other interventions find? • Is this a replicatable policy?

Internal Validity • Do we believe the identifying assumption? • Maybe not • People who win the lottery might play a lot so they have more disposable income or may be more risk-loving and that affects other characteristics too • If he only looked at players and compared big winners to little winners—Effects are EVEN bigger (40 percent) what does that tell you?

External Validity • If we believe the identifying assumption, can we generalize this? • Who plays the lottery? We might only be identifying this for a certain point on the distribution • Do we think the effect size would be the same for very rich people? • It happened in Sweden—with compressed income distribution and good health system/safety net • Is this comparable to the US? • Is this comparable to a developing country?

Next Steps • If we had done our thought experiment, we might have had some of these problems • Who participates in our experiment? • Is the change in health the same for all people on the income distribution? • What is the mechanism by which this works? (if it’s access to health care—better make sure that’s in place in our experiment too…) • Next week: Experimental Evaluations…

Application 1: Health and Earnings