inflation_adjusting.qmd

---
title: 'Inflation adjusting with Realtalk'
---

**Note:** Users will need to install realtalk and epiextractr for this example. Refer to [EPI packages for R](epi_libraries.qmd) for installation instructions.

## Loading CPI Indices using the [realtalk](https://economic.github.io/realtalk/) library

The following chunk of code loads the R libraries necessary for this exercise. You may need to install them to run this code.

```{r Load libraries, message=FALSE}
#Load necessary libraries
library(tidyverse)
library(realtalk)
library(epiextractr)
library(here)
```

The RealTalk package includes several datasets of common US price indices. You may view those by executing the available_price_indexes() command.

```{r List price indices}

#list available cpi series
realtalk::available_price_indexes
```

EPI uses the CPI-U-RS series to inflation adjust wages, so we'll select that series and assign it to a dataframe.

```{r }
#this creates a dataframe with the annual CPI-U-RS index from 1937-2022
cpi_data <- realtalk::cpi_u_rs_annual

#Set base year to 2022
cpi2022 <- cpi_data$cpi_u_rs[cpi_data$year==2022]
```

## A refresher on inflation adjustment

Before jumping into the full code. Let's refresh on how to calculate inflation using the Consumer Price Index.

Inflation in a given year is calculated by dividing the price of a market basket in a particular year by the price of the same basket in the base year, like so:

$$
\frac{\text{Given year}}{\text{Base year}} * 100
$$

For example, let's calculate how much the CPI-U-RS index has increased from 1978 to 2022.

$$
\frac{\text{CPI}_{2022}}{\text{CPI}_{1978}} = \frac{431.5}{104} \approx 4.149038 \text{ or } 414.9\%
$$

And voila - we see that inflation has caused the basket of goods to increase 414.9% since 1978.

## Applying inflation adjustment to CPS ORG wage data

This section uses [epiextractr](https://economic.github.io/epiextractr) to load Current Population Survey data. Refer to the [Using EPI's CPS Extracts](epi_microdata.qmd) to learn how to use this library.

Below, I load CPS ORG data and define my sample:

```{r Wage data, message=FALSE}

org <- load_org(2012:2022, year, month, orgwgt, wage, age, lfstat) %>% 
    #define sample universe
    filter(age>=16, lfstat %in% c(1,2))

```

Next, calculate median wages in the CPS. and merge the annual CPI index to the dataframe using left_join()

```{r Calculate median wages in the CPS ORG}

#Calculate median wages in the CPS ORG
wage_data <- org %>% 
  #use MetricsWeighted package to calculate a weighted median
  #Note: I am pooling 12 months of CPS data, 
  #so I adjust orgwgt—the survey weight— variable, dividing it by 12.
  summarize(nominal_median_wage = 
               MetricsWeighted::weighted_median(wage, w=orgwgt/12, na.rm=TRUE),
            .by=year) %>% 

#Merge annual CPI data to dataframe by year.
  left_join(cpi_data, by='year') 

wage_data
```

Finally, calculate the inflation rate relative to 2022, and the inflation adjusted median wage using the CPI index as follows:

```{r Clean and prepare data for inflation adjustment}

adjusted_data <- wage_data %>% 
 
  #This mutate command calculates inflation in a given year, relative to 2022
  #and multiplies nominal median wages by the inflation quotient
  mutate(infl_rel_to_2022 = signif(x = ((cpi2022/cpi_u_rs)-1), digits=2),
         real_wage_2022 = nominal_median_wage*(cpi2022/cpi_u_rs)) %>% 
  
  relocate(infl_rel_to_2022, nominal_median_wage, .after=cpi_u_rs)
  
adjusted_data
```