# Women in Parliament Worldwide

This week’s RWeekly digest included an awesome dataset from the World Bank that documents the percentage of women in lower house parliament across the globe from 1997 to 2018. The dataset is nearly complete and also includes summary statistics for numerous geographic aggregations.

All of my code to process and explore the data is available on my GitHub. There’s lots of interesting things to look at in this data, so I will leave most of the boring stuff out of this post.

To get a sense of the World Bank data, here are the top 10 and bottom 10 countries in 2018 in terms of percentage of parliament made up of women.

wip %>%
filter(year == 2018) %>%
top_n(n = 10, wt = prop_women_seats) %>%
arrange(desc(prop_women_seats)) %>%
knitr::kable(caption = "Top 10 Countries with the largest proportion of women in their parliament in 2018")
Table 1: Top 10 Countries with the largest proportion of women in their parliament in 2018
country_name country_code year prop_women_seats
Rwanda RWA 2018 61.3
Cuba CUB 2018 53.2
Bolivia BOL 2018 53.1
Mexico MEX 2018 48.2
Namibia NAM 2018 46.2
Sweden SWE 2018 46.1
Nicaragua NIC 2018 45.7
Costa Rica CRI 2018 45.6
South Africa ZAF 2018 42.3
wip %>%
filter(year == 2018) %>%
top_n(n = 10, wt = -prop_women_seats) %>%
arrange(prop_women_seats) %>%
knitr::kable(caption = "Bottom 10 Countries with the smallest proportion of women in their parliament in 2018")
Table 2: Bottom 10 Countries with the smallest proportion of women in their parliament in 2018
country_name country_code year prop_women_seats
Micronesia, Fed. Sts. FSM 2018 0.0
Papua New Guinea PNG 2018 0.0
Vanuatu VUT 2018 0.0
Yemen, Rep. YEM 2018 0.0
Oman OMN 2018 1.2
Haiti HTI 2018 2.5
Kuwait KWT 2018 3.1
Solomon Islands SLB 2018 4.0
Lebanon LBN 2018 4.7
Thailand THA 2018 5.3

Only 3 countries had over half women in their parliament in 2018! The top 10 are still all over 40%, which seems encouraging! Less encouragingly, four nations still had 0 women in the lower house of parliament in 2018.

# Case study: Rwanda

Though it’s hard to see from the map, at the very beginning we noticed that Rwanda had the highest female representation in its parliament in 2018, at over 60%! Looking into the politics there a bit more, I found that they introduced a quota system in 2003 that required 30% of the parliament to be made up of women. Looking at the data, there is a clear increase at the time this policy began.

wip %>%
drop_na(prop_women_seats) %>%
filter(country_name == "Rwanda") %>%
ggplot(aes(year, prop_women_seats)) +
geom_hline(yintercept = 50, size = 2, color = "grey80") +
geom_vline(xintercept = 2003, size = 1, color = "#b2182b") +
geom_line() +
geom_point() +
annotate(
geom = "text",
x = 2003.25,
y = 40,
label = "30% quota\nintroduced\nin 2003",
hjust = 0,
color = "grey40"
) +
scale_x_continuous(
breaks = seq(1998, 2018, 2),
minor_breaks = NULL
) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1, scale = 1)) +
theme_minimal() +
labs(
title = "Rwanda reached almost 50/50 representation after the introduction of a quota",
subtitle = "Representation continued to increase even 10 years later",
x = NULL,
y = "Percentage of parliament seats held by women"
)

# Quota Systems

The case study of Rwanda made me think more about quotas: how effective are they? where have they been implemented? Conveniently, the Institute for Democracy and Electoral Assistance (IDEA) has a public dataset of the types of quotas implemented all over the world! The only downside is that the database doesn’t include the year the quota was introduced. This is something that could be collated manually but we can work without for now.

There are three types of “quotas” in the dataset, explicit quotas about the number or fraction of seats to be held by women, a reserved seats system where certain seats must be held by women, and voluntary systems where some parties elect to hold themselves to a quota but none exists in law. We can visualize how these systems are implemented around the world with a map.

type_order <-
quotas %>%
count(quota_type, sort = TRUE) %>%
pull(quota_type)

world %>%
left_join(
quotas %>%
mutate(
quota_type = factor(
quota_type,
levels = type_order,
ordered = TRUE
)
),
by = c("iso_a3" = "country_code")
) %>%
ggplot() +
geom_sf(aes(fill = quota_type), color = "white", size = 0.1) +
scale_fill_brewer(
type = "qual",
palette = "Dark2",
na.value = "grey80"
) +
theme_minimal() +
theme(legend.position = "top") +
coord_sf(datum = NA) +
labs(x = NULL, y = NULL, fill = "Quota Type")

We can see that South & Central America have almost completely adopted quotas. As we noticed above, this region has had particularly notable improvement in female representation since 1997.

Now to make any causal claims about quotas and representation trends would need much more careful analysis, so I should emphasize I am not trying to do that. There are a couple of simple questions we could answer with this data, though.

1. Do the countries with the biggest increase in representation from 1997 to 2018 also have quota systems in place?
differences <-
wip_quotas %>%
filter(year %in% c(1997, 2018)) %>%
spread(key = year, value = prop_women_seats) %>%
mutate(
difference = 2018 - 1997,
quota_type = if_else(is.na(quota_type), "None", quota_type)
) %>%
drop_na(difference)

differences %>%
top_n(n = 10, wt = difference) %>%
arrange(desc(difference)) %>%
select(country_name, quota_type, difference) %>%
knitr::kable(caption = "The countries with the biggest increase in percentage of parliament seats held by women from 1997 to 2018")
Table 3: The countries with the biggest increase in percentage of parliament seats held by women from 1997 to 2018
country_name quota_type difference
Rwanda Reserved seats 44.2
Ethiopia Voluntary 36.8
North Macedonia Quota 35.0
Nicaragua Quota 34.9
Mexico Quota 34.0
Cuba None 30.4
Senegal Quota 30.1
Costa Rica Quota 29.8
Nepal Reserved seats 29.3

In fact only two of the top 10 countries by increase 1997-2018 did not have some form of legally binding quota system in place.

1. Did countries with quota systems in place see larger year-to-year jumps than those without?
largest_jumps <-
wip_quotas %>%
group_by(country_code) %>%
arrange(year) %>%
mutate(
jump = lag(prop_women_seats) - prop_women_seats,
quota_type = if_else(is.na(quota_type), "None", quota_type)
) %>%
drop_na(jump) %>%
summarize(
largest_jump = max(jump),
quota_type = first(quota_type)
) %>%
filter(
quota_type != "Funding incentives" # only one country
)
## summarise() ungrouping output (override with .groups argument)
median_largest_jump <-
largest_jumps %>%
summarize(med = median(largest_jump)) %>%
pull(med)

largest_jumps %>%
ggplot(aes(quota_type, largest_jump)) +
geom_hline(yintercept = median_largest_jump, size = 2, color = "white") +
geom_violin(draw_quantiles = 0.5) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1, scale = 1)) +
labs(
x = "Quota type",
y = "Largest one-year jump in female representation from 1997 to 2018",
title = "More countries with quota systems saw large year-to-year jumps\nin female  representation",
subtitle = "Though some very large jumps occurred in countries without quotas"
)

To make this plot, I calculated the largest jump in percentage of seats held by women from one year to the next for each country. The distribution of these jumps by quota system is then plotted as a violin plot. The white reference line shows the median largest jump size across all countries. Each line within a violin plot shows the median largest jump size for countries with that type of quota system in place.

Two things are clear:

• More than 50% of countries with some kind of quota system experienced a larger jump than the median largest jump in countries with no quota system at all.
• Some countries with no quota system experienced extremely large jumps from one year to the next. Clearly quotas are not necessary to achieve this.
• Side note: since I was curious, I looked up that enormous outlier. It’s Andorra and they had an election in 2011 where 15 women were elected making them the first country in Europe to have a majority female parliament.

Like I said before, I am being careful not to make any causal claims here. Clearly, representation can be increased with or without quotas. Exploring these datasets has been lots of fun, and there are definitely some very positive examples that give hope for a more equal future!