# Markets and Measurement II

Materials for class on Wednesday, January 10, 2018

## Contents

## Slides

Download the slides from today’s lecture.

## Adjusting for inflation

### Indicators

### Adjusting for inflation

\[ \text{Real GDP} = \frac{\text{Nominal GDP}}{\text{Price Index / 100}} \]

- Worksheet we created in class
- Or, use the BLS’s CPI inflation calculator

## Measuring inequality with Gini coefficients

Calculating Gini coefficients with R is trivial with the `ineq`

package—create a vector of incomes and feed it into `ineq()`

. Here’s what you do for a fictional country with the following incomes:

Person | Income |
---|---|

1 | $10,000 |

2 | $20,000 |

3 | $50,000 |

4 | $100,000 |

5 | $200,000 |

`## [1] 0.4842105`

There are other types of inequality measures too. The default is Gini, but if you look at the documentation of `ineq()`

with `?ineq`

, you can see the others.

You can plot the Lorenz curve easily too:

Alternatively you can extract the Lorenz curve that’s returned from `Lc()`

and use ggplot2 to create a nicer plot:

```
library(tidyverse)
# This file is on GitHub at https://github.com/andrewheiss/econw18.classes.andrewheiss.com/blob/master/lib/graphics.R
source(file.path(here::here(), "lib", "graphics.R")) # Load theme and colors
lorenz <- Lc(incomes)
plot_data <- data_frame(prop_population = lorenz$p,
prop_income = lorenz$L)
plot_labels <- tribble(
~x, ~y, ~label,
0.6, 0.4, "A",
0.8, 0.2, "B"
)
ggplot(plot_data, aes(x = prop_population, y = prop_income)) +
geom_line() +
geom_segment(x = 0, xend = 1, y = 0, yend = 1) +
geom_ribbon(aes(ymax = prop_population, ymin = prop_income), fill = nord_red) +
geom_ribbon(aes(ymax = prop_income, ymin = 0), fill = "grey80") +
geom_point(size = 2) +
geom_text(x = 0.2, y = 0.8, label = "Gini == frac(A, A + B)", parse = TRUE,
size = 6, family = "Roboto Condensed Light") +
geom_text(data = plot_labels, aes(x = x, y = y, label = label),
color = "white", size = 9, family = "Roboto Condensed Bold") +
labs(x = "Cumulative % of population", y = "Cumulative % of income") +
scale_x_continuous(labels = scales::percent, breaks = seq(0, 1, 0.2)) +
scale_y_continuous(labels = scales::percent, breaks = seq(0, 1, 0.2)) +
theme_econ(13) +
theme(panel.grid = element_blank())
```