diff --git a/analysis/data/raw/data_population_households_ingram.csv b/analysis/data/raw/data_population_households_ingram.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e632cb8f9c682417e0e1a331238a1c27eaf92970
--- /dev/null
+++ b/analysis/data/raw/data_population_households_ingram.csv
@@ -0,0 +1,150 @@
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diff --git a/analysis/figures/unnamed-chunk-1-1.png b/analysis/figures/unnamed-chunk-1-1.png
new file mode 100644
index 0000000000000000000000000000000000000000..d4254e709ed22d7cae30149a18958178e7a70058
Binary files /dev/null and b/analysis/figures/unnamed-chunk-1-1.png differ
diff --git a/analysis/paper/si.Rmd b/analysis/paper/si.Rmd
index 9eeaf7a0c36316e5099a33b1d41395e4b66784af..48932c57d8c40201f2a920566cd5161b63f12ae9 100644
--- a/analysis/paper/si.Rmd
+++ b/analysis/paper/si.Rmd
@@ -599,6 +599,36 @@ flextable(eemrio_bp) %>%
Here we aggregated the data of 28 European countries with 5 income groups each into 10 European expenditure groups, to decompose the total European household energy and carbon footprint by European expenditure decile, ranking each national income group according to their mean consumption expenditure in PPS euros. We call these European expenditure deciles, although only countries with EUROSTAT HBS data from 2005 to 2015 are included, which excludes Italy and Luxembourg, but includes the UK, Norway and Turkey.
+## Unit of analysis
+
+```{r }
+
+### ae vs. per capita
+
+data_population_households = read_csv(here("analysis","data","raw","data_population_households_ingram.csv")) %>%
+ rename(total_population = pop_estimate_per_quintile)
+
+data_population_households %>%
+ group_by(eu_q_rank) %>%
+ select(-total_households) %>%
+ summarise(total_adult_eq = sum(total_adult_eq),
+ total_population = sum(total_population)) %>%
+ pivot_longer(cols = -eu_q_rank, names_to = "indicator", values_to="value") %>%
+ ggplot(aes(x=factor(eu_q_rank), y=value)) +
+ geom_col() +
+ facet_wrap(~indicator, scales="free_y") +
+ theme_ipsum()
+
+
+#eu_ntile_name = pdat_country_summary_by_eu_ntile %>%
+# ungroup() %>%
+# select(eu_q_rank, eu_ntile_name) %>%
+# unique() %>%
+# arrange(eu_q_rank)
+
+```
+
+
## Alternative method
Our methodology used for the main paper, and explained in the sections above, keeps the production sector shares of EE-MRIO household final demand expenditure (and subsequently the footprint) the same as they are found in the original EE-MRIO household final demand expenditure when not decomposed by income quantile. The alternative method is to keep the consumption category shares of total HBS expenditure the same as they are found in the HBS. This means taking the total sum of household final demand expenditure from the EE-MRIO and decomposing it first based on the share of each income quantile's consumption expenditure of the total consumption expenditure as found in the HBS, before decomposing into sectors as well using the HBS 'parts per mille' per sector. This leads to a different total footprint than the original EE-MRIO footprint (when not decomposed by income quantile), because a different amount of final demand expenditure in each sector is now multiplied by the same total intensities per sector that are originally calculated in the EE-MRIO.
diff --git a/analysis/paper/si.docx b/analysis/paper/si.docx
index a2708bad995505f15f7a94bdb58a2b240bd43fe4..7b6a3dd785fa0e401958269df9b3f2c4349b8fb1 100644
Binary files a/analysis/paper/si.docx and b/analysis/paper/si.docx differ