Rows: 393 Columns: 22
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (14): ticker, name, incept_date, net_assets_as_of, asset_class, sub_asse...
dbl (8): gross_expense_ratio_percent, net_expense_ratio_percent, net_assets...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
blackrock_etf_data <- blackrock_etf_data |>mutate(incept_date = lubridate::mdy(incept_date))|>mutate(incept_year = lubridate::year(incept_date))#creating a subset that only includes funds set up in or after 2016 when climate awareness presumably reached a new level through the Paris agreementblackrock_post_2016 <- blackrock_etf_data |>subset(incept_year >="2016")#subsetting esg and non esg fundsblackrock_etf_esg <- blackrock_etf_data |>filter(is_esg =="ESG Fund")
Graph 1
I noticed large differences in the net volume of the funds and wanted to see if ESG-funds were generally more often smaller than non-ESG funds or if there was any other pattern. I created a variable that would cluster funds according to their volume (high: > 1000 mn USD; medium: 100 - 999 mn USD; low: 0-99 mn USD) and created the following barplot. Most non ESG-funds (more than 150) have very high net assets, many are in the medium-range (around 125), and comparably few in the low range (around 75). ESG-funds on the other hand seem to be quite equally distributed across these classes, maybe because they are younger and haven’t had as much time to grow?
#Graph 1 #creating a new variable that clusters funds according to their volumeblackrock_etf_data$volume <-if_else(blackrock_etf_data$net_assets_usd_mn >1000,"high", ifelse(blackrock_etf_data$net_assets_usd_mn >=100& blackrock_etf_data$net_assets_usd_mn <1000, "medium", "low"))#barplotggplot(blackrock_etf_data, aes(x =fct_infreq(volume), fill = is_esg)) +geom_bar()
So, I looked at the same graph focusing only on the funds launched in or after 2016 (still capturing most ESG funds, but excluding many old regular funds). The pattern reversed for non-ESG funds and ESG funds now contribute almost half of the high-volume funds. This is a very superficial approach towards asking whether ESG funds can over time accumulate as much capital as regular ones but from these preliminary two graphs there doesn’t seem to be an inhibiting factor.
###Graph 2 With this plot I wanted to see whether a higher cost/expense ratio is somehow related to a higher sustainability rating (after all you could spend more time with improving your sustainability rating if you were paid more). I differentiated ESG and non-ESG funds by color and their volume by shape. Both plots show clouds and no real relationship. Generally, both ESG and regular funds concentrate in the high MSCI-Score, lower expense ratio field. ESG funds are more concentrated (do not reach MSCI ratings lower than 0.5 and net expense ratios higher than 0.75%), but they are also fewer than the regular ones and might thus look more concentrated (I did not calculate statistical variance measures).
#Graph 2: Scatterplot Sustainability Rating and Expense Ratio#removing rows without ESG Quality Scoreblackrock_etf_data_w_score <-drop_na(blackrock_etf_data, msci_esg_quality_score_0_10)#plotting net expense ratioggplot(blackrock_etf_data_w_score, aes(x = msci_esg_quality_score_0_10, y = net_expense_ratio_percent, color = is_esg, shape = volume)) +geom_point()
#plotting gross expense ratioggplot(blackrock_etf_data_w_score, aes(x = msci_esg_quality_score_0_10, y = gross_expense_ratio_percent, color = is_esg, shape = volume)) +geom_point()
###Graph 3 Thirdly, I looked at the relationship between carbon intensity and MSCI ratings, hoping to find that high carbon intensity would be correlated with lower MSCI ratings as it would make sense to weigh this measure heavily in a rating system. From the first graph, I couldn’t identify this pattern, I only saw again that ESG funds seemed to be more concentrated in the higher rating/lower carbon intensity field than regular funds. To have a better look at ESG-funds only, I excluded regular funds and also tried to add a curve in the graph to help identify any pattern. Similarly low carbon intensity was displayed along the range of MSCI ratings, maybe the funds were too similar in this regard and thus the differences in ratings only came from other factors (SG rather than E for example).
#Graph 3: Scatterplot of Carbon Intensity and MSCI ratingsblackrock_etf_data_w_scores <-drop_na(blackrock_etf_data_w_score, msci_weighted_average_carbon_intensity_tons_co2e_m_sales)ggplot(blackrock_etf_data_w_scores, aes(x = msci_esg_quality_score_0_10, y = msci_weighted_average_carbon_intensity_tons_co2e_m_sales, color = is_esg)) +geom_point()
# closer look at only ESG Fundsblackrock_etf_data_w_scores |>filter(is_esg =="ESG Fund") |>ggplot(aes(x = msci_esg_quality_score_0_10, y = msci_weighted_average_carbon_intensity_tons_co2e_m_sales, color = is_esg)) +geom_point() +geom_smooth()
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
###Graph 4 Lastly, I wanted to use a different (maybe better) txpe of plot to show the concentration of ESG vs regular funds and their carbon intensity. These graphs (the boxplot more clearly) build on statistical measures such as mean, median and SE and thus allow a more confident claim that ESG funds generally have a lower carbon intensity and are less spread in terms of carbon intensity. At least in these terms, they are on average “better for the environment”.
