Great Recession & Slow Recovery

The Good, The Bad, and the Ugly

The Good:

• Student explored data on both FRED and World Bank Data

• Student picked relevant variables, explained it correctly

• Cogent international comparisons of the great recession
  
  

The Bad:

• Great interactive data tools can make for poor presentation visual aids

• Time consuming to reproduce - Return to website, select features and time periods, download image, etc.
  
  

The Ugly:

• Inconsistent style across FRED and World Bank Data graphs

• Very difficult to see much of the text

Introducing the ECODATA R Package

• I created an R package, ecodata, to facilitate visualizing data in undergraduate macroeconomics courses

• Designed for students and novices in R, statistics, and data analysis

• Wrapper for the fredr, wbstats, and ggplot2 packages

• Makes it easy to:

  • Download data from Federal Reserve Economic Data (FRED) and World Bank Data

  • Produce professional-quality data visualizations

  • View descriptions of the data

  • Cite sources

• Do everything with 3-4 lines of code

• Not a substitute for fredr, wbstats, or ggplot2, nor for exploring data and using interactive tools in FRED and World Bank Data

Let’s Try It Out!

Fast Example: Two Lines of Code

mydata <- get_ecodata("https://fred.stlouisfed.org/series/MORTGAGE30US")
ggplot_ecodata_ts(mydata, title = "Mortgage Interest Rate - Fixed Rate 30-Year", plot.recessions = TRUE)

Student Example: Fetch the Data

# Identify what variables I want
# Includes both FRED and World Bank Data sources
variable_sources <- c(
  "https://fred.stlouisfed.org/series/UNRATE",
  "https://fred.stlouisfed.org/series/AURUKM",
  "https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG?locations=US",
  "https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG?locations=GB"
)

# Optionally, provide my own names for the variables
variable_names <- c(
  "Unemployment Rate: United States",
  "Unemployment Rate: United Kingdom",
  "Inflation Rate: United States",
  "Inflation Rate: United Kingdom"
)

# Fetch the data 
mydata <- get_ecodata(variable_sources, variable_names)

# Get only Great Recession + Recovery
mydata <- mydata |>
  filter(Date >= "2007-01-01" & Date <= "2016-12-31")

Student Example: Plot the Data

ggplot_ecodata_facet(mydata, 
                     ncol = 2, 
                     title = "Unemployment and Inflation in U.S. and U.K.",
                     plot.recession = TRUE)

Student Example: Data Description

ecodata_description_table(mydata)

Data for All U.S. States

unemployed <- get_ecodata_allstates_fred("https://fred.stlouisfed.org/series/MOUR")
ggplot_ecodata_bar(unemployed,
                   plot_at = "2020-04-01",
                   highest = 10, 
                   highlight = "Illinois",
                   title = "States with Highest Unemployment During COVID")

Documentation

• All the documentation: https://murraylax.org/ecodata/

• Getting Started Guide

  • Walks through many of the most common uses of the package.

  • Download data for multiple states or countries with ease

  • Plots with multiple variables, bar plots

  • Data transformations, computing new quantities

• Full reference manual

• Instructional Vignettes

  • Focus is learning macroeconomics, using data

  • Worked-out examples using the package to explore macroeconomic principles

Why Scripting Languages?

• Used in other courses: R is used in econometrics, statistics, data science, etc.

• Used in industry, even among those who are not data scientists

• Even more important / relevant with AI

  • AI assistance makes coding more accessible

  • Verification and reproducibility is key with AI-generated content

Why Avoid Scripting Languages?

ECODATA Package

• Gentle learning curve!

• Easy to do easy things

• Use simple reproducible code to analyze macroeconomics principles

Why ECODATA for Principles of Macroeconomics?

• Data visualization is a key skill for everyone

• Early introduction makes subsequent classes easier

• Data is used throughout the economics discipline, it should be used throughout economics curriculum

• Repeated practice throughout undergraduate degree builds mastery

Instructional Vignettes

• Supply and Demand for Housing

  • Data on construction prices, new house prices, and housing starts.

  • Does a supply and demand model prediction explain the data?

• Exchange Rates and Interest Rates

  • Data on exchange rates and interest rates interest rates in U.K. and U.S.

  • Does a supply and demand model for currencies explain the data?

• Unemployment and Recessions

  • State and national data on unemployment and during the 2008-2009 recession.

• And I will be making a lot more!

What Are Your Ideas?

• Do you have or envision a short low-stakes assignment where students visualize macroeconomic data?

• Answer these questions about one such assignment:

  1. What is the macroeconomics concept or theory that students apply?

  2. What variables do the students need?

  3. What will the students discover?

• Share your ideas! Email: jmurray@uwlax.edu

• What me to create instructional vignette around your idea? Let me know!

Thank you!