Global Homicide Rates: A Gender Perspective

The violence in the regions is essential to indicate the peace and security reached by the countries. Fortunately, the global homicide rate has been decreasing while it is slowly. But as for men, the situation does not look so bright. The global homicide rate per 100.000 people is about four times higher for men when compared to women.

First, we will examine this situation using a radar chart for the year 2020.

library(tidyverse)
library(WDI)

#Intentional homicides, female (per 100,000 female)
df_vi_fe <-
  WDI(indicator = "VC.IHR.PSRC.FE.P5",
      extra = TRUE) %>%
  as_tibble() %>%
  mutate(gender = "female") %>%
  rename(rate = VC.IHR.PSRC.FE.P5)

#Intentional homicides, male (per 100,000 male)
df_vi_ma <-
  WDI(indicator = "VC.IHR.PSRC.MA.P5",
      extra = TRUE) %>%
  as_tibble() %>%
  mutate(gender = "male") %>%
  rename(rate = VC.IHR.PSRC.MA.P5)

#Combining all the datasets
df_merged <-
  df_vi_fe %>%
  rbind(df_vi_ma) %>%
  #removing labels attribute for fitting process
  crosstable::remove_labels() %>%
  drop_na()

#The data frame of the international homicide rate by gender, 2020
df_2020 <-
  df_merged %>%
  filter(year == 2020,
         region != "Aggregates") %>%
  select(region, gender, income, rate)

#Radar/spider chart
library(fmsb)

#Preparing the radar data frame for fmsb package
df_radar <-
  df_2020 %>%
  group_by(region, gender) %>%
  summarise(mean = mean(rate)) %>%
  pivot_wider(names_from = "region",
              values_from = "mean") %>%
  column_to_rownames("gender")


#Adding the max and min of each variable to use the fmsb package
df_radar <- rbind(rep(32,7),
                  rep(0,7),
                  df_radar)

#Plotting the average homicide rates(per 100.000 people)
#by gender in the Regions, 2020
radarchart(df_radar,
           pcol = c("orange","steelblue"))

#Setting font family
par(family = "Bricolage Grotesque")

#Plot title
title("Average Homicide Rates by Gender in the Regions, 2020",
      sub = "(per 100.000 people)",
      font = 2)

#Legend
legend(x= 0.7,
       y= 1.2,
       legend = c("Female", "Male"),
       bty = "n",
       pch=20 ,
       col=c("orange","steelblue"),
       text.col = "black",
       cex=0.9,
       pt.cex=1.6)

As you can see from the above chart, Latin America & the Caribbean had the highest average number (per 100.000 people) of male homicides in 2020 by far; this could be related to organized crime, which is common in the area.

Now, we will model the homicide rates of the regions, with and without gender, using bootstrap confidence intervals to understand the motives behind it.

#Bootstrap intervals
library(rsample)

set.seed(12345)
without_gender <-
  reg_intervals(rate ~ region + income,
                data = df_2020,
                times = 500)

set.seed(12345)
with_gender <-
  reg_intervals(rate ~ region + income + gender,
                data = df_2020,
                times = 500)

#Bootstrap confidence intervals plot

#Legend colors for the title
legend_cols <- RColorBrewer::brewer.pal(3, "Dark2")

bind_rows(
  without_gender %>% mutate(gender = "without"),
  with_gender %>% mutate(gender = "with")
) %>%
  mutate(term = str_remove_all(term, "gender|income|region")) %>%
  mutate(term = str_to_title(term)) %>%
  ggplot(aes(.estimate,
             term %>% reorder(.estimate),
             color = gender)) +
  geom_vline(xintercept = 0,
             linewidth = 1.5,
             lty = 2,
             color = "gray50") +
  geom_errorbar(size = 1.4,
                alpha = 0.7,
                aes(xmin = .lower,
                    xmax = .upper)) +
  geom_point(size = 3) +
  scale_x_continuous() +
  scale_color_brewer(palette = "Dark2") +
  labs(x = "Higher indicates more important",
       y = "",
       title = glue::glue("Bootstrap Intervals <span style='color:{legend_cols[1]}'>with</span> or <span style='color:{legend_cols[2]}'>without</span> Gender")) +
  theme_minimal(base_family = "Bricolage Grotesque",
                base_size = 15) +
  theme(legend.position="none",
        panel.grid.minor = element_blank(),
        panel.grid.major.y = element_blank(),
        plot.background = element_rect(fill = "#eaf7fa"),
        axis.title.x = element_text(size = 12),
        plot.title = ggtext::element_markdown(hjust = 0.5, face = "bold"))

Passing the vertical dashed line (zero point) in the related intervals indicates significantly not the importance of the related variables, which confirms the spider chart above for the Male and Latin America & Caribbean variables.

Continue reading: Homicide Rates from Gender Perspective: Analysis using Radar Chart and Bootstrap Intervals

Long-term implications and potential future developments of global homicide rates

The data analysis presented in the text paints an important picture of the global trends in homicide rates, revealing that men are four times more likely to be victims of intentional homicide compared to women. Moreover, Latin America & the Caribbean emerged as the region demonstrating the highest average count of male homicides per 100,000 people in 2020. This may point towards persistent issues related to organized crime and security in the region.

Implications

The disproportionate impact of violence on men globally prompts reflection on societal structures and gender dynamics that lead to these disparities. This trend could be connected to traditional roles attributed to men, exacerbating their exposure to risky situations or violent confrontation. The marked discrepancy also highlights the potential need for strategies to examine and address gendered violence more effectively.

The high incidence of homicide specific to Latin America and the Caribbean underscores the importance of addressing security challenges in these regions. This could involve adopting robust and comprehensive strategies to mitigate the prevalence of organized crime and violence.

Future Developments

Long-term, it would be beneficial to perform continued data analysis annually to monitor and understand evolving trends. The inclusion of additional variables in the data analysis, such as qualitative data on societal attitudes, could also provide a more robust understanding of the factors driving these trends.

Actionable Advice

Tackling violence and reducing global homicide rates is multifaceted and requires concerted effort on several fronts:

1. Targeted Interventions: Implementing initiatives that specifically address the high incidence of male victims of intentional homicide and violence in Latin America and the Caribbean is important. Crime prevention strategies, such as community policing and education, may be effective.
2. Policy Review: Policymakers should consider reviewing existing strategies to tackle violence against men and assess their effectiveness. This can ensure current approaches are fit for purpose and respond to the data-driven landscape.
3. Research and Data: Continual collection and analysis of global homicide data will provide important insights to inform policy and intervention efforts. Expanding data collection to include additional variables, can deepen understanding and inform strategic policy development.

By using a data-driven approach to understand the issue of homicide from a gender perspective, targeted and effective solutions can be formed to protect those most at risk.

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