Calculate distributional impacts (EU)

Step by step tutorials

Before running any of the examples below, make sure you have processed the Eurostat HBS microdata with hbs_eu(). See Preparing the data for details.

hbs <- hbs_eu(year = 2015, country = "all", path = "raw_data")

Example 1. How to enter price shocks in calc_di_eu

1. Download the example file into your working directory by running the following function in the R terminal:

ex_shocks_eu()

2. Go to your working directory and open the csv “Example_shocks_eu.csv”.

3. Define the scenarios: each column named after a country code and a scenario suffix (e.g. AT_s1, BE_s1, …, AT_s2, BE_s2, …) represents a scenario for each country. To run calc_di_eu for a single scenario, delete all columns for Scenario 2. To add another scenario, copy the Scenario 2 columns to the right and change the suffix to _s3. You can rename scenarios by changing the suffix (e.g. _shock1), but keep the country code prefix intact.

4. Enter the price shocks: each row corresponds to a COICOP code. Enter the price change to be applied to each COICOP category for each country and each scenario. A value greater than 1 indicates a price increase (e.g. 1.1 for a 10% increase) and less than 1 indicates a price decrease (e.g. 0.9 for a 10% decrease). If there is no shock in a category, keep 1.

5. Save the edited csv file.

6. Upload the edited file to R:

shocks <- read.csv("Example_shocks_eu.csv",
                   header = TRUE,
                   sep = ",",
                   dec = ".")

7. Run calc_di_eu with the processed HBS data and the price shocks:

results <- calc_di_eu(data = hbs,           # Output from hbs_eu()
                      shocks = shocks)       # Edited shocks file

By default, calc_di_eu calculates distributional impacts for all available socioeconomic variables and saves the results and figures in an outputs/ folder within your working directory.

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Example 2. How to select variables for distributional impacts

Individual variables (var)

1. To see the variables available for distributional impact analysis, run:

available_var_eu()

2. Select a variable (e.g. "decile") and pass it to the var argument of calc_di_eu:

results <- calc_di_eu(data = hbs,
                      shocks = shocks,
                      var = "decile")     # Single variable

3. To calculate impacts for several variables, pass a vector:

vars <- c("decile", "zone", "gender")

results <- calc_di_eu(data = hbs,
                      shocks = shocks,
                      var = vars)

For more information on available variables, see Available Variables.

Intersectional variables (var_intersec)

1. To see the combinations of variables available for intersectional distributional impacts, run:

available_var_intersec_eu()

2. Download the intersectional variables file into your working directory:

ex_var_intersec_eu()

3. Open “Var_Intersec_eu.csv” and delete the rows for combinations you do not want to analyse. Save the edited file.

4. Upload the file to R:

example_vars <- read.csv("Var_Intersec_eu.csv",
                         header = TRUE,
                         sep = ",",
                         dec = ".")

5. Pass the file to the var_intersec argument of calc_di_eu:

results <- calc_di_eu(data = hbs,
                      shocks = shocks,
                      var_impact = NULL,           # Skip individual variables
                      var_intersec = example_vars) # Intersectional combinations

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Example 3. Country-level vs. EU-level results

By default, calc_di_eu calculates distributional impacts both at the EU level (across all households jointly) and at the country level (separately for each member state). To disable country-level results and obtain only EU-level results, set by_country = FALSE:

results <- calc_di_eu(data = hbs,
                      shocks = shocks,
                      by_country = FALSE)

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Example 4. Update the microdata to a different year

If your price shocks come from a macroeconomic model calibrated to a year different from the HBS wave, you can update the HBS expenditure data before running the simulation using the update_hbs argument:

results <- calc_di_eu(data = hbs,           # HBS 2015 microdata
                      update_hbs = 2018,    # Update expenditure to 2018 prices
                      shocks = shocks)

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Outputs

calc_di_eu returns a list with two elements:

• di: a data frame with the basic distributional impacts per selected variable and scenario.
• dii (if var_intersec is specified): a data frame with the intersectional distributional impacts.

By default, results are saved to the outputs/ folder and figures are generated automatically. To suppress saving, set save = FALSE and fig = FALSE.