Introduction: Why Supply-side Health?

Author

Ian McCarthy | Emory University

Health Improvements

Major improvements in life expectancy (and many other measures of health) across the world

  • Poverty reduction
  • Technology development and innovation
  • Technology diffusion and adoption
  • Access to better services, including health care
R Code 
gapminder %>%
  group_by(year) %>%
  summarize(lifeExp = median(lifeExp),
            gdpMed = median(gdpPercap)) %>%
  ggplot(aes(year,lifeExp)) + geom_line(alpha = 1/3) + theme_bw() +
    labs(x = "Year",
         y = "Life Expectancy (years)",
         title = "Median life expectancy across the world")

Health and Wealth

R Code 
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + 
  geom_point(size = 1) + theme_bw() + scale_x_continuous(label = comma) +
  labs(x = "GDP Per Capita ($US)",
       y = "Life Expectancy (years)",
       title = "Life expectancy and GDP")

R Code 
ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(alpha = 0.5, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  labs(title = 'Year: {frame_time}', x = 'Log GDP Per Capita ($US)', y = 'Life Expectancy (years)') +
  transition_time(year) +
  ease_aes('linear') +
  theme_bw()
Warning: No shared levels found between `names(values)` of the manual scale and the
data's colour values.

But the U.S. is unique

R Code 
mycolors <- c("US" = "red", "other" = "grey50")
gapminder %>% filter(country %in% c("Canada", "France", "Germany", "Italy", "Japan", "United Kingdom", "United States")) %>%
  mutate(highlight = ifelse(country=="United States", "US", "other")) %>%
  ggplot(aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(alpha = 0.5, show.legend = FALSE, aes(color=highlight)) +
  scale_color_manual("U.S.", values = mycolors) +
  scale_size(range = c(2, 12)) +
  scale_x_comma(limits=c(0,55000)) + 
  labs(title = 'Year: {frame_time}', x = 'GDP Per Capita ($US)', y = 'Life Expectancy (years)') +
  transition_time(year) +
  ease_aes('linear') +
  theme_bw()

Health Care Spending and Outcomes

 

 

What does that mean?

  • Are we just woefully inefficient?
  • Yes, but the complete answer is more complicated
    • U.S. very good in some areas (breast cancer treatment, interventional cardiology)
    • Let’s look at some more graphs from the Commonwealth Fund

Why Study Economics of Health Care?

  1. Health and health care are constantly changing

  2. Health expenditures in 2020: $4.1 trillion, 19.7% of GDP, $12,530 per person (from NHE Fact Sheet)

  3. U.S. health care is uniquely inefficient in many ways

Lots of interesting economic issues in health care, not all unique to the US.

  1. Extremely heterogeneous products
  2. Asymmetric information between patients and physicians
  3. Unobservable quality (experience good)
  4. Unpredictable need (inability to shop in many cases)
  5. Distortion of incentives due to insurance
  6. Adverse selection (asymmetric information between patients and insurers)
  • These factors exist in other markets and in other countries, but…
  • Health care is unique in the combination of these issues
  • U.S. is unique in the extent of these issues in health care (policy problems)

Class Overview