Module 2: Demand for Cigarettes and Instrumental Variables

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# Module 2: Demand for Cigarettes and Instrumental Variables
## Part 1: CDC Data on Smoking and Cigarettes
### Ian McCarthy | Emory University
### Econ 470 & HLTH 470

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# History of Smoking

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# History of Smoking
- Widespread smoking began in late 1800s
- Lung cancer becoming more common after 1930s
- First evidence of link in 1950s
- Surgeon general's report in 1964
- Very important in causal inference! ([Section 5.1.1](https://mixtape.scunning.com/matching-and-subclassification.html#some-background) of Causal Inference Mixtape)

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# Why it matters
1. Extreme public health concerns
  - Lung cancer prevalence
  - Fetal and baby health
  
2. Economic questions
  - Is it an information problem?
  - Externalities (second-hand smoke)
  - Moral hazard due to insurance
  
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# In our case
We want to focus on estimating demand for cigarettes. By this, I mean estimating price elasticity of demand.

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<br>

We'll show that standard OLS isn't going to do this very well.

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# Cigarette Data

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# The Data

- Data from [CDC Tax Burden on Tobacco](https://data.cdc.gov/Policy/The-Tax-Burden-on-Tobacco-1970-2018/7nwe-3aj9/data)

- Visit GitHub repository for other info: [Tobacco GitHub repository](https://github.com/imccart/CDC-Tobacco)

- Supplement with CPI data, also in GitHub repo.

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# Summary stats
We're interested in cigarette prices and sales, so let's focus our summaries on those two variables

```r
stargazer(as.data.frame(cig.data %>% select(sales_per_capita, price_cpi, cost_per_pack)), type="html")
```

<table style="text-align:center"><tr><td colspan="8" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Statistic</td><td>N</td><td>Mean</td><td>St. Dev.</td><td>Min</td><td>Pctl(25)</td><td>Pctl(75)</td><td>Max</td></tr>
<tr><td colspan="8" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">sales_per_capita</td><td>2,499</td><td>95.150</td><td>41.133</td><td>12.500</td><td>63.050</td><td>122.400</td><td>296.200</td></tr>
<tr><td style="text-align:left">price_cpi</td><td>2,499</td><td>3.396</td><td>1.641</td><td>1.307</td><td>2.088</td><td>4.520</td><td>9.651</td></tr>
<tr><td style="text-align:left">cost_per_pack</td><td>2,499</td><td>2.678</td><td>2.238</td><td>0.287</td><td>0.780</td><td>4.237</td><td>10.376</td></tr>
<tr><td colspan="8" style="border-bottom: 1px solid black"></td></tr></table>

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# Cigarette Sales

```r
cig.data %>% 
  ggplot(aes(x=Year,y=sales_per_capita)) + 
  stat_summary(fun.y="mean",geom="line") +
  labs(
    x="Year",
    y="Packs per Capita",
    title="Cigarette Sales"
  ) + theme_bw() +
  scale_x_continuous(breaks=seq(1970, 2020, 5))
```
.plot-callout[
<img src="02-smoking1_files/figure-html/cig-sales-callout-1.png" style="display: block; margin: auto;" />
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# Cigarette Sales

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# Cigarette Prices

```r
cig.data %>% 
  ggplot(aes(x=Year,y=price_cpi)) + 
  stat_summary(fun.y="mean",geom="line") +
  labs(
    x="Year",
    y="Price per Pack ($)",
    title="Cigarette Prices in 2010 Real Dollars"
  ) + theme_bw() +
  scale_x_continuous(breaks=seq(1970, 2020, 5))
```
.plot-callout[
<img src="02-smoking1_files/figure-html/cig-price-callout-1.png" style="display: block; margin: auto;" />
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# Cigarette Prices