The Current State of ESG ETFs

Author

Bentje Boer

Executive Summary

  • ESG ETFs have seen an increase in AUM over the last 8 years with a steady demand even when other ETFs recorded net outflows in 2022. By end of 2021, AUM of ESG ETFs amounted to $425 bn, roughly a tenth of total equity ETFs’ AUM.
  • Europe represents the largest ESG ETF market by AUM but the number of BlackRock’s North American ESG ETFs has risen steadily over the last 6 years. The single largest ESG ETF is BlackRock’s North America focused ESGU making North America an important ESG ETF market.
  • Analysis of the ESGU shows a close similarity in composition with the comparable S&P 500 ETF IVV. ESGU overweighs the IT sector which likely contributed to a better performance during the pandemic in 2021.
Code 
library(tidyverse)
library(tidyquant)
library(scales)
library(ggrepel)

Introduction

For the last 8 years, investment in ESG related products has been increasing rapidly leading Bloomberg to project that ESG assets will be a third of global AUM or more than 50 trillion USD by 2025 (Bloomberg 2021). ESG ETFs have played a major role in this development, growing at a CGAR of 75% from $4.7 billion in AUM in 2014 to $425 billion at the end of 2021 (Abboud 2022). This amounts to roughly a tenth of the total equity ETF AUM (BlackRock 2023). Demand for ESG ETFs proved resilient even in the difficult investment year of 2022. While broader market funds recorded net outflows of $139 billion in the first half of 2022, ESG ETFs saw a net inflow of $120 billion (Qontigo 2022). 40% of investors planned to increase asset allocation to ESG ETFs by at least 5% (J.P. Morgan 2022). Most AUM of ESG ETFs ($263 billion) are located in Europe, but the American market accounts for $152 AUM (Abboud 2022) and the two largest ESG ETFs focus on the USA and are man-aged by BlackRock (ESGU and SUAS (Barrak 2022)). BlackRock alone manages 33 ESG ETFs, consistently launching new ESG ETFs every year since 2016 to serve the increasing demand (see Figure 1). Analysts are clear that ESG ETFs have established themselves as a persistently growing asset class and the North American market deserves a closer look.

Code 
# assign the url to `github_raw_csv_url`
github_raw_csv_url <- "https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/blackrock_etf_screener_2022-08-30.csv"

# read in the data, and assign it to the object `blackrock_etf_data`
blackrock_etf_data <- read_csv(github_raw_csv_url)

blackrock_etf_data <- blackrock_etf_data |> 
  mutate(across(contains("date"), lubridate::mdy)) |>
  mutate(net_assets_usd_bn = net_assets_usd_mn/10^3) |> 
  select(-net_assets_as_of)|>
  rename(msci_esg_rating = sustainability_characteristics_msci_esg_fund_ratings_msci_esg_fund_rating_aaa_ccc)  |> 
  rename(co2_intensity = msci_weighted_average_carbon_intensity_tons_co2e_m_sales)

color_esg <- c("ESG Fund" = "darkgreen", "Regular Fund" = "darkgrey")

Yearly <- blackrock_etf_data %>% 
  filter(is_esg == "ESG Fund") %>% 
  count(year_launched)

Yearly_R <- blackrock_etf_data %>% 
  filter(is_esg == "Regular Fund") %>%  
  count(year_launched)

Yearly$sum <- cumsum(Yearly[, 2])

Yearly_R$sum <- cumsum(Yearly_R[, 2])

Yearly <- merge(Yearly, Yearly_R, by = "year_launched") %>% 
  filter(year_launched >= 2016)

plot_1 <- ggplot(Yearly, aes(x = year_launched, y = n.x), stat = "identity")+
  geom_point(color = "darkgreen", size = 2) +
  geom_segment(aes(x = year_launched, xend = year_launched, y = 0, yend = n.x), color = "darkgreen", size = 2) +
  geom_point(aes(x = year_launched, y = n.y), stat = "identity", color = "darkgrey", size = 2) +
  geom_segment(aes(x = year_launched, xend = year_launched, y = 0, yend = n.y), color = "darkgrey", size = 2, alpha = .5) +
  geom_line(aes(x = year_launched, y = sum.x$n), stat = "identity", color = "darkgreen", size = 1) +
  labs(
  x = str_wrap("Number of existing ESG ETFs (line) and Launches of ESG (green) and Regular (grey) ETFs each Year"), 
   y = "Number of Funds", 
  title = "Figure 1: The Rise of ESG Funds", 
   subtitle = "Since 2016 the number of ESG Funds has risen steadily.", 
caption = "Source: Blackrock ESG Screener, preprocessed by Teal Emery | Latest Data: 2023 | Calculations by Author", 
color = " ESG Funds"
  )+
  theme_bw()

plot_1

BlackRock’s ESG ETFs with a North American or global focus hold substantial investments and make these markets the two most relevant focus areas for ESG ETFs (Figure 2). The amount of net assets invested in North American BlackRock’s ESG ETFs is comparable to total net assets invested in all regular ETFs by Blackrock with a focus on the Asia Pacific region and more than double the volume of assets invested in all European Regular ETFs. Global ESG ETFs record only slightly fewer net assets than European Regular ETFs. Importantly, BlackRock does not offer ESG ETFs focused on markets other than the North American and Global focus areas. This decision by the largest ESG ETF manager adds to the exceptional relevance and regional concentration on these two specific mar-kets in the ESG ETF landscape.

Code 
rise_etf <- blackrock_etf_data %>% 
  filter(region == "Europe" | region == "Asia Pacific" | region == "North America" & is_esg == "ESG Fund" | region == "Global" & is_esg == "ESG Fund") %>% 
  select("is_esg", "region", "net_assets_usd_bn") %>% 
  group_by(region, is_esg) %>%
  summarize(total_assets =          sum(net_assets_usd_bn)) %>% 
  ungroup()

plot_2 <- ggplot(rise_etf, aes(x = total_assets, y = fct_reorder(.f = region, .x = total_assets), fill = is_esg)) + 
  geom_bar(stat = "identity") +
  scale_fill_manual(values = color_esg)+
  labs(title = str_wrap("Figure 2: Comparison of Total Net Assets for ESG Funds and European/Asian Regular Funds"), 
       subtitle = str_wrap("North American ESG ETFs are a hold a sizable amount of net assets."), 
       x = "Total Net Assets (in Bn)", 
       y = "", 
       caption = "Source: Blackrock ESG Screener, preprocessed by Teal Emery | Latest Data: 2023 | Calculations by Author", 
       fill = "Funds' Type"
       ) +
  theme(plot.caption.position = "plot",
        plot.caption = element_text(hjust = 1.5)) +
  scale_x_continuous(labels = scales::label_dollar(), expand = c(0,0)) +
  geom_text(aes(label = scales::dollar(total_assets, accuracy = .1, suffix = " Bn")), 
            hjust = 1.2, size = 3, color = "white") +
  theme_classic()
  
ggsave(
  filename = "plot_2.jpeg",
  path = "/Users/Bentje/Documents/JHU/Spring23/SustFin", 
  plot = plot_2, 
  scale = 1
)

plot_2 +
  theme(plot.caption = element_text(hjust = 0))

The largest ESG ETF worldwide (ESGU) mirrors the comparable regular ETF IVV closely in composition but slightly overweighs Information Technology (Figure 3). BlackRock’s ESG Aware MSCI USA ETF (ESGU) is the largest ESG ETF globally, managing over $20 Bn in AUM (VettaFi 2023). Its ESG strategy comprises a negative screening of the holdings of its benchmark index and a tilting towards favorable companies on ESG rating basis. As a result, it shares its six largest engagements with the comparable but regular Core S&P 500 ETF (IVV) which are big tech firms like Apple, Microsoft, Google, Amazon and Tesla. While both ETFs heavily weigh the IT sector, the ESGU tends to slightly overweigh it in comparison reporting an exposure of 28% to the IT sector vs 25% of the IVV.

Code 
etf_comparison_data_github_url <- "https://raw.githubusercontent.com/t-emery/sais-susfin_data/main/datasets/etf_comparison-2022-10-03.csv"

etf_comparison <- read_csv(etf_comparison_data_github_url)

esg_fund <- etf_comparison |> 
  filter(esg_etf > 0)
#esg_fund

stand_fund <- etf_comparison |> 
  filter(standard_etf > 0)
#stand_fund

options(ggrepel.max.overlaps = 50)

highlight_it <- etf_comparison %>% 
  filter(sector == "Information Technology")

it_names <- c("AAPL", "MSFT", "GOOGL", "AMZN", "TSLA")

etf_comparison <- etf_comparison %>%
  mutate(plotname = as.character(ticker)) %>% 
  mutate(plotname = ifelse(plotname %in% it_names, plotname, ""))

set.seed(23)
ggplot(etf_comparison, aes(x= esg_etf, y = standard_etf))+
  geom_point(alpha = .6, size = 1) +
  geom_point(data = highlight_it, 
             aes(x = esg_etf, y = standard_etf),
             color = "blue", size = 1)+
  geom_text_repel(aes(x = esg_etf, y = standard_etf, label = plotname)) +
  geom_abline(alpha = .5, intercept = 0, slope = 1, color = "darkgrey")+
  labs(
    title = "Figure 3: Correlation of IVV and ESGU Holdings", 
    subtitle = str_wrap("The effect of the ESG strategy on companies' weights in the ESGU is marginal, both firms highly value big tech companies and IT firms (blue)."), 
    x = "Weight of Company in ESGU (in %) ", 
    y = "Weight of Company in IVV (in %)", 
    color = "Information Technology", 
    caption = "Source: Teal Emery RPubs | Latest Data 2023 | Calculations by Author") +
  theme_bw()

Consequently, the performance of ESGU and IVV align closely and only deviate during the pandemic when the ESGU performed slightly better due to its higher IT exposure (Figure 4). The high correlation of company weights in the composition of both ESGU and IVV leads to their very similar performance over time. Only during the pandemic, the ESGU decouples from the IVV, peaking at 237 bps in December 2021 compared to 233 bps for the IVV. In general, their developments never deviate more than 7.3 bps demonstrating their extremely close similarity. The marginal outperformance of the ESGU in 2021 is probably caused by the higher IT exposure which allowed it to profit more from pandemic heights in this industry. Preparation Performance

Code 
esgu_prices <- tq_get("ESGU", get = "stock.prices", from = "2016-12-06") %>% 
  tq_transmute(select = adjusted, 
               mutate_fun = to.period, 
               period = "months")
#esgu_prices

esgu_index <- esgu_prices %>% 
  mutate(index_value_esgu = adjusted/44.76545   *100) %>% 
  select(date, index_value_esgu)

ivv_prices <- tq_get("IVV", get = "stock.prices", from = "2016-12-06") %>% 
   tq_transmute(select = adjusted, 
               mutate_fun = to.period, 
               period = "months")
#ivv_prices

ivv_index <- ivv_prices %>% 
  mutate(index_value_ivv = adjusted/201.7690*100) %>% 
  select(date, index_value_ivv)

indices <- merge(esgu_index, ivv_index) %>% 
  mutate(index_diff = esgu_index - ivv_index)

plot_4 <- ggplot(esgu_index, aes(x = date, y = index_value_esgu)) +
  geom_line(color = "darkgreen", size = .7)+
  geom_line(data = ivv_index, aes(x = date, y = index_value_ivv), stat = "identity", color = "darkgrey", alpha = .8, size = .5)+
  labs(
    title = "Figure 4: Similar Development of ESGU and IVV", 
    subtitle = str_wrap("ESGU (green) develops slightly better than the IVV (grey) during the pandemic probably due to higher IT weight."), 
    x = "", 
    y = "ESGU Index and IVV Index (2016: 100)", 
    color = "ESGU", 
    caption = "Source: Yahoo!  Finance | Latest Data 2023 | Calculations by Author")+
  theme_bw()

ggsave(plot_4, filename = "/Users/Bentje/Documents/JHU/Spring23/SustFin/plot_4.png")

#Shares of Funds in IT
it_in_esgu <- esg_fund %>% 
  filter(sector == "Information Technology") %>% 
  summarise(sum_it_esgu = sum(esg_etf))

it_in_ivv <- esg_fund %>% 
  filter(sector == "Information Technology") %>% 
  summarise(sum_it_ivv = sum(standard_etf))

#it_in_esgu
#it_in_ivv
plot_4

Conclusion

Overall, the North American ESG ETF market is extremely relevant both to ESG and ETF investing more general. The largest ESG ETF worldwide is BlackRock’s ESGU focusing on North America. It closely tracks the S&P 500 but applies negative screening and conscious tilting to its holdings based on ESG ratings. This results in slightly favoring the IT sector more than the comparable IVV which likely contributed to a better performance in 2021. Demand for ESG ETFs has grown persistently over years and proved especially resilient to the shocks of 2022. This investment category is very likely to remain relevant in the future.

References

Abboud, Rony. 2022. “ESG ETF Investing Outlook for 2022.” Accessed Feb 17, 2023. https://www.trackinsight.com/en/etf-news/esg-etf-investing-outlook-2022.

Barrak, Eddie. 2022. “Introducing the Three Largest ESG ETFs.” Accessed Feb 18, 2023. https://www.trackinsight.com/en/etf-news/introducing-three-largest-esg-etfs.

BlackRock. 2023. “Visualizing the Expanse of the ETF Universe.” https://www.blackrock.com/americas-offshore/en/insights/visualizing-the-expanse-etf-universe.

Bloomberg. 2021. “ESG Assets may Hit $53 Trillion by 2025, a Third of Global AUM | Insights.” Bloomberg Professional Services, 2021. https://www.bloomberg.com/professional/blog/esg-assets-may-hit-53-trillion-by-2025-a-third-of-global-aum/.

J.P. Morgan. 2022. “ESG ETF Investments | J.P. Morgan Asset Management.” Accessed Feb 17, 2023. https://am.jpmorgan.com/dk/en/asset-management/per/funds/etfs/esg-etf/.

Qontigo. 2022. “ESG Fund Flows show Resilience Amid 2022 Market Sell-Off.” Accessed Feb 17, 2023. https://qontigo.com/esg-fund-flows-show-resilience-amid-2022-market-sell-off/.

VettaFi. 2023. “iShares ESG Aware MSCI USA ETF.” Accessed Feb 18, 2023. https://etfdb.com/etf/ESGU/.