#MakeOverMonday: Visualizing child marriages

[rasagy]

Hi everyone,

As discussed in the class, let’s take a stab at this week’s MakeOverMonday exercise.

• Existing visualization and details
• Excel Dataset
• My slides on Data Storytelling

You can start by sharing the audience you chose, and the insight you got from the data analysis as a comment on this thread.

[nishitanirmal]

Visualizing UNICEF Child-Marriage and Early Childbirth data

Comments on the Existing Visualization

1. Map color scale - The gradient split down the middle seems counterintuitive because the grey saturation increases as the percentage decreases. Also splitting the scale at halfway point implies that 0-38% range of child marriage rates are more normal grey as compared to the 38-76% range which is an alarming red. However, the point of the narrative is to discourage child marriage altogether so normalizing 38% is counterintuitive.

2. Bar Chart - Presenting boys' child marriage rates against girls' child marriage rates in descending order in the bar chart seems to be in poor taste as it creates a conversation around 'competing' gender roles among the victims. While the premise of the article is 'gender equality', the descending order reduces the meaning of the data to mere 'ranking' and belittles the struggle of men who were victims of child marriage Also, yellow text on grey is scarcely visible.

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Tableau visualization for the same dataset

Datasets

UNICEF - Child Marriage UNICEF - Early Childbearing

Doubts while cleaning the Dataset:

1. Is it alright to ignore the inconsistency in 'Reference year' throughout the dataset?

Audience chosen: Policymakers Intent: Inspire/advocate/persuade - To highlight the fact that child marriage is an indicator of discrimination against women, and that higher rates of child marriage are a characteristic of underdeveloped countries.

Child marriage is a violation of human rights regardless of sex and gender of the victims. The point of this piece is to highlight especially how it affects girls. In these visualizations I have compared child-marriage in girls to childbirth in girls and HDI rankings of the respective countries.

Insights from the data (BUS)

1. Concentrated in Africa Africa seems to have the highest rates of child marriage among girls under the age of 18, while Bangladesh is a bit of an outlier in Asia.

(Below) Saturation --> Marriage in girls under 18 y/o

2. Higher rates among under-18 girls The chart below shows that the percentage of child marriage among girls is undeniably higher than that of boys across all given countries. This can only mean that a significant number of under-18 girls are marrying adult men, especially in Niger, Chad, and the Central African Republic.

There is also a slight correlation between the percentages of girls and boys in most countries.

Red --> Marriage in girls under 18 y/o Blue --> Marriage in boys under 18 y/o

3. Age Groups Unsurprisingly, countries with the highest % of marriages of girls under 18 also have the highest % of marriages of girls under 15 - Useful (?) insight

(Below) X-axis is Girls married under 18 y/o Y-axis is Girls married under 15 y/o

4. Childbirth There is a correlation between the number of girls who give birth under 18 years of age and marriage in girls under 18. Big (?) insight

"Adolescence is a vulnerable phase in human development as it represents a transition from childhood to physical and psychological maturity. During this period, adolescents learn and develop knowledge and skills to deal with critical aspects of their health and development while their bodies mature. Adolescent girls, especially younger girls, are particularly vulnerable because they face the risks of premature pregnancy and childbirth. Maternal conditions are the top cause of mortality among girls aged 15-19 globally." - UNICEF, article on Early Childbearing

(Below) X-axis is Childbirth in girls under 18 y/o Y-axis is Marriage in girls under 18 y/o

5. Childbirth in girls in relation to child marriage in boys The chart below shows that the childbirth in girls under 18 remains high even when the marriage rates among boys is relatively lower. This means that a significant number of girls under 18 are either having children out of wedlock or with adult men. (Below) Red --> Childbirth in girls under 18 y/o Blue --> Marriage in boys under 18 y/o

6. HDI Ranking Human Development Index ranking is one of the indicators of the level of development of a country. This chart compares the countries' HDI ranks with the percentage of marriage and childbirth in girls under 18, respectively.

The chart shows that as the HDI rank drops, the percentage of childbirth and marriage in girls under 18 both increase uniformly. This indicates that Higher child marriage and childbirth in girls under 18 is a characteristic of underdeveloped countries. Big (?) insight

(Below) X axis --> Marriage in girls under 18 y/o Y axis --> Country HDI rank Saturation --> Childbirth in girls under 18 y/o

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Feedback:

1. Charts 3,5 and 6 distract from the narrative and are not required in this data story.
2. Charts 2 and 5 are using discreet data points rather than continuous. The chart type should reflect the same.
3. It would be better to further explore the link between child marriage in girls under 18 and childbirth in girls under 18 across African countries.

LINK

[tdeepikatiwari]

Make Over Monday UNICEF Child-Marriage data

Target audience: General public. Show impact of child marriages to overall growth/ wellbeing/ success of the country and if there is varying impact based on which gender is disadvantaged more

I found forming insights directly from excel hard, so I started the project with quickly plotting the data on different chart types to get a sense of what was happening across genders. I wanted to see if there was any correlation between child marriage in boys and child marriage in girls. So I sorted data in descending order of %of boys under 18 and picked first 20 countries.

Insights -

• There seemed to be no direct correlation between boys and girls percentage. However, in every case % of girls always dominated the boys.

I then plotted top 20 countries with child marriage under 18 for both males and females.

Top 20 countries with Child marriages for men under 18 spread throughout the world

MMR

Top 20 countries with Child marriages for girls uner 18 concentrated in africa.

MMR

Next Steps

• Plot top 20 countries doing best with reference to child marriage.
• Compare these countries against infant mortality/ maternal death

Link to final article LINK

[Noopurkumarikashyap]

MakeOverMonday Child-Marriage data Visualization

Audience: Organizations Dedicated To Establish Gender Equality

Problem: Almost all the countries contribute to overall female marriage below 18( figure 1). Figure 1

IS CHILD MARRIAGE ACTUALLY ROOTED IN GENDER INEQUALITY?

Initial observations on the data: After plotting different visualization on the available data, I decided to derive a new field from the given data, namely ' difference between women' and men' marriage below 18 across different countries'( as shown in the figure2).
Figure 2

The following visualizations are done in Tableau. Figure 3(countries with a difference of less than 10%)

Figure 4(countries in order of difference between female and male marriage below 18)

Surprising Insight: Tonga has more Male marriage below 18 as compared to female marriages + the difference is low Useful Insight: countries with lower difference have a lower female marriage( based on this can we say that child marriage is rooted in gender inequality towards female ?)

One message that the audience should take away: Child Marriage can be reduced by Gender Equality. Format of my story: Interactive Storytelling (the audience can interact and the data presented will have a narrative ) Format of the story( rough sketch in figure 5 and 6 below): Figure 5 Figure 6

General feedback received( on Figures 5 and 6): The story is more for the general public and less for the Audience considered above.

Final visualization( I decided to stick to my initial audience i.e. Organizations Dedicated To Establish Gender Equality. The updated final data visualisation is given in the link below. ) LINK

[raaghavishan]

Visualizing child marriages

Audience- Common people Purpose- The purpose of this visualization is to find which countries follow their legal marriage laws strictly and in which countries the marriage laws have mostly been ignored.

The given data set was cleaned with no blanks in both female and male columns. Then I separated the male and female data. I visualized the data separately using Tableau to get an idea of what are all the countries present and their percentage of child marriages. Analysing the dataset

• The dataset ranges between 2013 - 2019 but still, some of it was outside the range
• In most countries, the data of females and the data of males were in different years.
• Child marriages under 15 are the subset of child marriages under 18
• In the data it is said that it was collected from women age 20-24, so is the percentage is based on their marriage year which was before 4-5 yrs, or is the percentage is based on the year of data collected?

This sheet shows the child marriage of female, it is clear from the map that countries in Africa have more no. of child marriages than the countries in Asia and America This sheet shows the child marriages of male, it is clear from this that the concentration is African countries is not high as compared to Female child marriages

Analyzing the data based on the child marriages at 15 and marriages between 16-18 It can be directly seen that most of the child marriages happen between 16-18 age for girls. This ratio is can be high because in some countries the lowest age to get married in 15 or 16

After all these initial analyses,

Legal and Illegal Child marriages around the world

Link to the visualization Globally, the average legal age of marriage for boys is 17 and 16 for girls but many countries permit them, particularly girls, to marry much younger. Many countries have laws specifying which age is right for marriage for both girls and boys. Some countries follow it strictly while some don't. The purpose of this visualization is to find which countries follow their legal marriage laws strictly and in which countries the marriage laws has mostly been ignored.

Female Child Marriage:

This visualization shows which countries have most disobeyed their laws and which countries have least disobeyed their laws

Male Child Marriage

This visualization shows the legal age of marriage for boys in each country and the percentage of boys getting married before 18. By filtering the legal age from the dropdown, one can get which countries have most violated the law and which countries have least violated the law

[divoojilly]

Neighbouring countries that have a stark difference

Audience Chosen: Students of Gender Studies/Laypeople Insight: There are some countries that are right next to each other, but have a surprisingly different way to look at Child Marriages for Women under 18. Some numbers are starker than others, some are fairly similar.

Bangladesh and Myanmar

Haiti and the Dominican Republic

Turkmenistan and Afghanistan

Rwanda and the Democratic Republic of Congo

Mozambique and South Africa

Algeria and Niger

Suriname and Peru

When I looked at the data, some countries were far more heterogeneous in their practices. For eg., most of Africa is quite different from each other, with the numbers being in extremes. South America, on the other hand, is homogenous and consistent. The gap between the lowest and highest numbers isn't that high.

This pool of countries could be expanded as and when more data comes in. The current dataset is sparse; it does not include many developed countries such as Japan, USA and the entirety of Europe. Due to this, we already are assuming that the lesser developed parts of the world are facing this problem.

I started to compare neighbouring countries that have a difference of more than 20% between them.

Could there be a reason for so? What is holding some of these countries back? This is the question I want to ask. Although there is no reasoning for it, it prevents generalization.

The draft for the final presentation is here: https://jillydivoo.wixsite.com/mysite-2

[jon-swn]

Child marriages in the highest countries and India

Going through the data, it's clear that the percentage of females greatly exceeds the percentage of male children who are getting married. I found it interesting however, that in some countries there is an equally high number of both genders. For example the numbers are quite high in No.3 Central African Republic but it's neighboring country No.4 Chad has lesser males. The similar case in Nepal, Madagascar, but size of the population is a factor here.

I plotted the data and sorted it according to females under 18 and the thickness of the lines represents the number of males under 18 who are married. Here India is lost somewhere in between too because of the population percentage The visualization did not work out.

I was looking out if there is any particular reason for a high or a low difference between the sexes, Or if it makes sense at all to look at it. I'll focus on the countries with the highest rates for both sexes and compare them with their neighboring countries

I was also looking at India in the dataset and saw that it seems to be hidden among the other countries, I was thinking maybe displaying the numbers with respect to country population rather than displaying the numbers as a percentage would have a bigger impact as done by this article

• The references for the data sources also vary a lot, some are very recent while others are 13 years old. I am considering dropping this from the visualization.

I'll be taking one of the two approaches

1. Showing real numbers instead of percentages that will tell the story of how our country compares with countries like Niger and the Central African Republic
2. Look at countries with higher rates for both male and female child marriages and try to see if there is any reason for that.

I tried to look at Gender Ratios of countries and looking for any correlation between the difference of male and female gender ratios.

Version 1.0

Audience: Layman I went ahead with the first idea, looking at absolute numbers of India and compared them to the top 5 countries that were available in the dataset. The title of my story is "High numbers are closer to Home", I know it's a working title. I wanted to tell the a story that, yes although there are countries where the rates of child marriage are high. India is not far from eradicating it completely. My aim is to encourage readers of the visualization to realize the magnitude of the problem and maybe provide steps as to how to make a difference. Link to Version 1.0

After Feedback

There was some confusion on how I calculated the data. So I mistakenly called it % of marriages but instead it was % of male+female who were married below 18. So I corrected that in the visualization.

Here is the Final Version after Feedback: FINAL VERSION

References

• Child Marriage in Central African Republic
• Girls not Brides, India

[arinjitdas]

Child Marriage and Illiteracy for Females Above 15

Initial Exploration - Rates of Child Marriage in the Indian subcontinent and Southeast Asian Countries

Initial Chosen Audience: Indian News Readers Initial Intent: Show how India compares with its neighboring countries when it comes to Child Marriage.

For the interactive data visualization assignment, I initially wanted to explore how prevalent child marriages are in India and neighbouring Asian countries. Analyzing the data set, I noticed that countries in Africa had incredibly high rates of child marriage and may be skewing the dataset towards the higher end. In comparison, Asian countries seem to be doing better though the situation is bad enough.

Another reason I wanted to focus on Asian countries was that socio-cultural dynamics in India may have more in common with those of other Asian nations than those of African nations. It would have been interesting to see whether there would be any correlation between child marriage rates and another measure such as the GINI coefficient or dominant religion. Perhaps one could also compare continental data in such a way.

I tried creating a scatter plot the rates of child marriage by the age of 18 against the GINI coefficients of the Asian countries in the dataset, but it seems there was no clear trend or correlation at least among Asian countries. What I probably was thinking of was the HDI of the countries which measure Human Development instead of GINI that measures income inequality.

Problems with Approach

1. No clear correlation between child marriage and something like GINI Coefficient.
2. HDI (Human Development Index) would have been a more interesting dimension than GINI (which measure income inequality)
3. Too narrow a scope with no clear insight into child marriage - simply representing data set in a map

Other Ideas

1) Create classes of nations (Very High, High, Moderate, Low) and see how many countries from each continent appear in them instead of one continuous scale 2) Create a cartogram of nations by their rates of child marriage 3) Visualize which countries show the highest jump in child marriages between ages 16-18

I explored the third idea, since the data set provided two key pieces of information - percentage of girls married before 15 and percentage of those married before 18, with the former being a subset of the latter. So I computed the difference and attempted to visualize the data.

Final Approach - The Relationship Between Child Marriage and Literacy Rates of Women 15 and Above

Revised Audience: UNDP Policy Makers Revised Intent: Highlight which countries have the highest rates of child marriage between ages 15-18 and the implications for female literacy

The idea for the final interactive data visualization was inspired by the following visualization, in which one can notice that rates of child marriage in some countries such as South Sudan skyrocket between the ages of 15-18. This would signal towards girls being married off soon after they hit puberty and during hugely important teenage years. This also has major implications for education since those are the years when a person would typically be finishing high school.

The original data set only included rates of child marriage for girls and boys, but I felt it would be insightful to explore another dimension as well. For the final interactive data visualization, I derived the child marriage rates in the 15-18 age range, legal marriage ages across countries and rates of literacy (and thereby, rates of illiteracy) of females above the age of 15.

Interactive Data Visualization on Tableau Desktop

Interactive Version Accessible Here

1. Map Visualization of Child Marriage: The original map These can be filtered by continent to view the regions separately. Annotations. Legal Marriage Age. There was a gradient going from grey to white to red; this was problematic since the saturation of grey regions did not directly suggest that rates were low but instead that rates were higher than areas in white (which lay around the centre of the spectrum and had higher rates than grey areas but lower than red ones). Also, information about countries in the middle of gradient/spectrum was lost against a light background.

In the made over version, the gradient goes from white to red and is set against a dark background. Hovering over the countries shows the rates of child marriages and legal marriage age in the country. I have also added annotations of countries that are doing the worst in their regions and other outliers. Lastly, users can filter by region now and focus on one particular area in the map.

Original Map Visualization

Redesigned Map Visualization

2. Continent-Wise Rates and Averages: One of the problems with the original data viz was that it couldn't be sorted by continent. The current dashboard consists of a worksheet where you can see child marriage rates between 15 and 18 for countries arranged by continent, the mean child marriage rate for that particular continent and see which countries are doing better or worse than the mean.

3. Correlation Between Child Marriage and Female Illiteracy (15 and Above): The final element of the interactive dashboard illustrates the correlation between Child Marriage (15 to 18) and Female Illiteracy for 15 and Above (p value < 0.0001) to make the point that interventions in protecting females from child marriages and focussing on policies that focus on their education may have immense positive impact, especially in African countries (as can be seen by the cluster of black circles).

[Abhi98krishna]

Child marriage data visualisation

---------wip--------

Audience: Policy makers of developing countries Intent: Policy makers of countries rely heavily on numbers and statistics to draw up plans for the future years. One way a developing country can address a particular issue is by following certain models that developed countries have followed. So I'm attempting to create an interactive visualization that might help these policymakers to compare the child marriage statistics of their country with that of another developed country, majorly acting as a helping stick to pick a country that they might want to study about.

I imagine to be selecting a few countries like the BRICS countries, and a few developed countries, and trying to tell a story through comparative visualizations. To go about doing this, I first thought of just exploring with data of marriages of girls and boys before the age of 18, before I start narrowing down my countries, just to see if I can notice any patterns...

This particular method didn't bring about any tangible insights, and I thought visualizing them on a map might give something more useful, plotting the females and males separately

Only after going through this visualization I realized that there were very few/almost no developed country having child marriages. It is also possible that the data set is lacking in that respect. So I might now need to re-think about the intent of my visualization. If I get values about the change in child marriage percentages over time, I could give a comparative narration of data of countries.

Another option is to shorten the data set into the most well performing, and worst performing countries with respect of female child marriages, and then try to find a correlation (eg: economic index, female mortality) that might help the lower performing countries follow a particular better performing country. The top and bottom 20 countries are below:

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So on analyzing this, I noticed a few things:

• There isn't any data for Russia or China, which are two major developing countries, nor is there any data for USA, which is a developed nation. So comparison over country types might be difficult due to lack of data.
• Africa has a high number of countries with data, and it also has a wide range of child marriage percentages across nations in the continent. So I thought it might be interesting to study just the African continent alone, and try to uncover some co-relation which can help form a model for understanding and lowering the child marriage numbers in these countries.

Re-worked motivation

Audience: Policy makers of African nations Intent: Policy makers of countries rely heavily on numbers and statistics to draw up plans for the future years. One way a developing country can address a particular issue is by understanding co-relations and causation that helps find solutions to the child marriage problem. So I'm attempting to create an interactive visualization that might help these policymakers to see trends with other metrics that might be a co-relation to child marriages.

I went through the World Bank data for Africa to see if I could find any data set that might bring out some interesting co-relation with my existing child marriage data set. I was especially interested to see if Education and Literacy levels affected Child Marriage, so after going through different data sets related to education and literacy, I decided to pick the Gender Parity data set.

An inherent problem with the data set was that different countries had different years on the record, so I wanted to somehow make the data set more generic, and tell a story of a particular trend. Hence I broke the Gender Parity data into 3 categories: 1970-85, 1986-2000, 2000-2018. The first two categories will be used to tell the story of the current visualization of Child marriage, and the last category, on comparison with the previous two, would help people imagine what the future of Child marriage rates will be, and policy makers can then take action on the trend if that acts as a causation to Child Marriages. Below is the final visualization:

Does Gender Parity affect female child marriage numbers in African countries, if yes, can we map a trend and use the data to start solving the issue?

Insights:

• Gender Parity seems to have a direct correlation to female child marriage percentages in Africa, so policy makers should looks at improving female literacy to be able to reduce female child marriages in the future.
• Central Africa needs to take immediate measures to lower female child marriage numbers

[AkhilGuthula]

Child-Marriage data Visualization

Audience: Policy makers

Initial Intent: Throughout the last decade, BRICS (which has almost 45% of the world population) has developed sectorial cooperations in different areas, such as science and technology, trade promotion, energy, health, education, innovation and fight against transnational crime. The intent of this data visualization is to compare the compare the trend of child marriages in BRICS with other developing Nations.

• Cleaning and analyzing the dataset: After cleaning the dataset, I realized that the data of China and Russia which plays a major role in BRICS is not available.

So I just augmented the data on the map to see where the countries stand globally in this specific child marriage context

From the visualizations, its interesting to see how the percentage of child marriages in Central Africa is so high and how it is being diluted when moved towards the boundaries (for both men and women). Since data for most of the African countries is available when compared with other countries in the world, it would be interesting to visualize and compare the percentage of child marriages across African countries, this could also be compared with other BRICS nations (India and Brazil)

Does child marriage has any significance relevance with the literacy rate of both male and female in the country?

There might be several factors which influences the child marriage in any country. It could be insightful if we could show the relevance between one of these factors and the child marriage.

[richavagrawal]

Child Marriage Data Visualization

Audience - Laymen Intent - To raise awareness of Gender Inequality. To show the dataset of child marriage of girls under 18 and boys under 18 as a manifestation of Gender Inequality in different cultures across the world.

Some Issues with the visualization - The scale uses a gradient which uses a dark color on both sides

To do this, I first I decided to plot the percentage of girls married under 18 and boys married under 18 on the same scale using an appropriate color gradient to get an visual idea of the inequality.

This visually showed a clear difference between the percentage of girls married under 18 and boys married under 18. Also, brings up that young girls are married to older men. To strengthen the narrative I also plotted girls married under 15 on the same scale.

Link to visualization.

This visually showed that more girls were married under 15 globally than boys married under 18, barring a very few countries.

I decided to go with three separate visualizations as I felt these were not additive (or subtractive) parameters. A number of confounds would arise if one simply subtracted the percentage of boys married under 18 from the percentage of girls married under 18. However, the limitation is that I am currently using three separate visualizations. I would like to improve it to make the narrative more focused and concise. Another possibility is to also map the gender equality index for these countries to show a direct correlation.

Final Visualization

I decided to pick on the single topic of girls under 15 married to men above 18. To create this visualization from the available data, I had to make 2 assumptions:

1. The number of men and women in a country are similar - Hence, the percentages would be subtract able.

2. I did not consider polygamy or homosexual marriage, considered marriage between a single male and female.

3. This visualization would show the percentage of girls under 15 married to men over 18 atleast.

Link to Final Visualisation

This visualization shows that the marriage of girls under 15 (children) to men over 18 (adults) is a significant number over the world (countries who's data is available for this visualization, which highlights such child marriage being an implication of gender inequality.

[rajsreekanth]

Child marriage data visualization Audience: Policymakers, NGOs I don't see how this data will make awareness among the public or someone who is part of this community who follow the practice of child marriage. These are just numbers and don't convey the problems associated with the practice. The government body or an organization can act based on this data.

Intent: I approached the dataset with the intention of plotting the change in the numbers over the years. However, the year was missing in some places which made me drop that idea. Then I decided to try plotting this data in different forms to get some insight.

Comparing the number of boys and girls getting married by 18 was also something I had difficulty in understanding because it gave a feeling of competing. Whatever gender it is, child-marriage is wrong and it needs to be stopped, is what I want to tell. Instead of saying 'the number of girls getting married by 18 is more than that of boys' I want to say 'there are these many child-marriages happening around the world and in that this is the number of girls and the rest are boys'. It should also be easier for the audience to see the difference between these numbers, even though it is not explicitly mentioned. So I tried to plot the total number of child-marriages in each country as follows:

Then I decided to put it alongside the world map and make it interactive. I was not trying to tell a particular story but to plot the whole data into a more understandable and interactive format.

Then I realized it was a mistake to add percentages of boys and girls to get the total number. So I changed the format. In the dataset provided, it shows 20% of girls around the world get married before they turn 18. I decided to use this as the title of my story.

Child Marriage V01

[rishi4git]

Visualizing child marriages across the world.

Audience: Policy Makers

Map the childhood lost due to child marriages and explore the relations of various factors like Literacy, Human Development Index, average Schooling age, Maternal mortality rate on the Child marriages.

Looking For Insights

1. Most of the cases of child marriages were from African Countries.
2. Bangladesh was the Significant Outlier

Childhood Lost (only for reference)

Observing other factors

1. Countries with a minimum year of schooling.

   

2. Maternal Mortality rate

3. Literacy Rate

[rounaksengupta]

Child Marriages Across the World

Audience : WHO Policy Maker

Dr. Charles Lautner is the director of the Department of Maternal, Newborn, Child and Adolescent Health (MCA), World Health Organization. He has a task at hand which involves dispersing funds to the various WHO and UN centers for an awareness program but he doesnt know which countries or regions require immediate attention.

He also needs to know the possible reasons for child marriages, whether social, Religious or sheer lack of development/awareness, so that he can make the program more appropriate for the audience.

I first looked at countries across the world that actively practice child marriage.

Then I tried to look at which of these countries had a larger section of females under 15 getting married

In comparison I tried to see how many of these countries also practices child marriage among male children

And I compared the numbers with the number of girls under 18 getting married , to see whether there were any countries which accepted child marriage irrespective of gender, pointing to a socio-cultural norm/practice ( maybe? )

I divided the world into conventional regions and tried to look at the numbers regionally, instead of nationally

child marriage drilldown 2.pdf

[sugandha-123]

The scope of the chart could be towards talking about the countries where child marriages are highly prevalent, and how gender percentages also occur differently within the same. I could create the chart catering to news / awareness requirements.

I first wanted to visualise the numbers on the map.

For women that marry before the age of 18:

For men that marry before the age of 18:

On comparing the two, I realised that the number of females that marry before 18 years is much more than the number of males that marry before that age. I calculated the total number of marriages before the age 18, and tried visualising the three categories on a simple plot.

After sorting the information.

The data itself does not give us information related to the reasons why this might be the case. It does not explain the higher number of women compared to men, nor does it explain the countries with higher number of child marriages. We can assume it is due to the socio economic and cultural reasons, but for the sake of visualising a chart, I would prefer using only the data that is available to us.

I created the above on Flourish. https://public.flourish.studio/visualisation/3899374/

[raaghavlaxman]

Child Marriage & Religion in Sub-Saharan Africa

Purpose : To determine the influence of religion on child marriages.

Traditional African religions have mostly been replaced by Christianity & Islam. However, these religions co-exist with traditional practices like ancestor worship. Child marriage also seems to be one of these deeply rooted traditions, according to this article.

I picked only the Sub-Saharan African countries from the data set and looked at the total percentage of child marriages irrespective of gender.

I then looked around for a reliable dataset on religious affiliations & beliefs in Sub-Saharan Africa. I found a detailed survey on religious attitudes in the region from the Pew Research Center.

I then took this data and tabulated it alongside the child marriage data.

Based on this data I first mapped out the percentage of child marriages in these countries.

Next I mapped out the percentage of people who said religion is important in their lives

I then created a stacked bar chart showing the religious affiliation in the respective countries

I also looked at what percentage of the population in each of these countries favours law guided by religion.

I was looking to find a correlation between child marriage prevalence and religiosity. I was also trying to look at whether Christian dominated countries or Muslim dominated countries have a higher prevalence of child marriages.

Limitation : In the process of making these charts I realised that for this data story to provide an insight I would require data of child marriage prevalence in these countries before 1900, which is probably data that has not been documented. This could also give an insight into how child marriage has changed after the spread of Christianity & Islam.

[dikshasingh13]

Audience chosen: Policymakers Purpose: To visualize and showcase the trends of child marriage to the country’s literacy growth, economic growth.

Problems with current visualization: The color gradient does not seem natural. The colors selected made the map challenging to lead and misinform the reader. The whites are usually used to show lower values. Here, they’ve used it in the middle. Moreover, the extremities on either end make it harder to understand their importance in the first go.

1. Extent Since the dataset is usually quite large, it becomes difficult to draw insights from the statistics available with no visualization. To understand the extent and visualize the data myself, I started plotting the girls who are getting married before 18 on the map. Niger reports the most girl child marriages at 76%.

Similarly, the boys who were married under 18 were also plotted to understand the extent. 28% of boys of the Central African Republic were found to be married before they reach the age of 18.

The degree of child marriage is somewhat dependent on geography, ie African countries seem to be doing worse than the other continents.

2. Comparision Looking at these graphs, it is visible that female child marriage has a larger extent. Also, where the legend goes to 76 in females married under 18, the legend goes to 28 in males married under 18. I decided to put them side to side to understand the context and the difference between the values.

The graphs confirm that the girls under 18 are getting married to more when compared to their counterparts. This could signify that they are getting married off to much older men.

3. School Completion Rate Further, I decided to see any correlation between the child marriage dataset and the percentage of children that complete schools.

In a place like Niger, the school completion rate for girls was 4% and it also reports the worst case of child marriages for girls (at 76%) As predicted, it is evident that there is a significant drop in the number of students dropping out where the cases of child marriages are high in both boys and girls. One surprising insight was that there are many more unmarried boys who have left the school compared to girls. It could mean that the countries that believe in child marriages also do not educate and send their daughters to school in the first place.

4. GDP Lastly, I wanted to see if there is a correlation between the GDP of the countries and their rate of child marriages. There was no significant trend that I could find using this metric. This could probably be due to the fact that GDP doesn’t really capture the economic condition of the country alone. Anyway, it was interesting to see the continent-wise breakdown of GDP and child marriage dataset.

[advaitmb]

Critical mass theory in politics suggests that when a certain community can make a substantial difference in policy after they reach a certain critical mass. For women, it is said to be 30% ie. if women have 30% representation in a country's parliament, they can make substantial policy changes that ensure their rights and interests are taken care of.

The following chart shows what is the relationship between female representation in the government and female child marriages. Click on the image preview to open the interactive visualization

As seen in this chart, there seems to be no relationship

Furthermore, if one tries to compare child marriage rates for men and women, they seem to be correlated:

Click on the image preview to open the interactive visualizations

Click on the image preview to open the interactive visualization

It is also clear that more women are married below the age of 18 than men in almost all of the countries, but as seen before, gender representation in government doesn't seem to have any effect on the same.