3e:null 3f:["$","title",null,{"children":"🙋‍♀️ Gender Bias in Singapore... does it exist? | Matt Osborn | Observable"}] 45:Tea2,beeswarm2 = { const radius = 1; const padding = 0.5; const height = 400; const margin = ({ left: 120, right: 120, top: 100, bottom: 0 }) const xAxis = g => g .attr('transform', `translate(0,${height +30 - margin.top})`) .call(d3.axisBottom(x)) const x = d3.scaleLinear() .domain([0,65]) .nice() .range([margin.left, width - margin.right]) const y = d3.scaleBand() .domain(arr) .range([height + margin.bottom -50, margin.top-50]) // const size = d3.scaleSqrt() // .domain([0.5,d3.max([...joinedData.map(d=>d["percentage-women"])])]) // .range([2, 30]) // const color = d3.scaleOrdinal() // .domain(arr) // .range(d3.schemeCategory10) const svg = d3.create('svg') .attr('viewBox', [0, -30, width, height]); const tooltip = d3 .select("body") .append("div") .attr('id', 'scatterplot-tooltip') .style('position', 'absolute') .style('z-index', '1') .style('visibility', 'hidden') .style('padding', '10px') .style('background', 'rgba(0,0,0,0.6)') .style('border-radius', '4px') .style('color', '#fff'); svg.append('g').call(xAxis); svg.selectAll('.line-consumption') .data(x.ticks()) .join('line') .attr('class', 'line-consumption') .attr('x1', d => x(d)) .attr('x2', d => x(d)) .attr('y1', 10) .attr('y2', height - margin.top +35) .attr('stroke-width', .6) .attr('stroke', 'lightgray'); svg.selectAll('.label-continents') .data(arr) .join('text') .attr('class', 'label-continents') .attr('x', 0) .attr('y', d => y(d)) .style('font-family', 'Helvetica') .style('font-size', '12px') .attr('text-align', 'right') .attr('alignment-baseline', 'right') .text(d => d); const simulation = d3.forceSimulation(ipuQuota) .force('x', d3.forceX((d) => x(d["percentage-women"])).strength(0.3)) .force('y', d3.forceY((d) => y(d["electoral-law-quota"])).strength(0.3)) // .force('collide', d3.forceCollide(d => size(d.gdpPerCap) + padding).strength(1)) .stop(); for(let i = 0; i < 300; i++) { simulation.tick(); } simulation.stop(); svg.selectAll('circle') .data(ipuQuota) .join('circle') .attr('cx', d => d.x) .attr('cy', d => d.y) .attr('r', 5) // .attr('fill', d => color(d.region)) .attr('fill-opacity', 0.4) .on("mouseover", function(e,d) { const tooltipWidth = tooltip.node().offsetWidth; const tooltipHeight = tooltip.node().offsetHeight; tooltip .style("left", e.pageX - tooltipWidth/2 +'px') .style("top", e.pageY-tooltipHeight - 10+'px') .style('visibility', 'visible') .html(`Country: ${d.country}
Percentage of women: ${d["percentage-women"]}
Total women: ${d["total-women"]}
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Caveat on missing data

\nFor both Singapore and Mali, both countries do not have official datasets on candidates and information on candidates who ran in their latest election. This lack of data itself represents the challenges that we confront when we want to study gender bias. It is an additional barrier to studying such an issue.\n\nFor this analysis and project, the team built our own dataset for Singapore's latest elections in 2020. There are some information which could not be found online, and this was listed as 'unknown'.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":540,"value":"

The Data

\n1. [IPU data for international countries](https://data.ipu.org/women-ranking?month=3&year=2023) & IPU [historical data](https://data.ipu.org/historical-women)\n2. [Gender quotas dataset](https://www.idea.int/data-tools/data/gender-quotas/database)\n3. Singapore 🇸🇬 [2020 elections candidates dataset](https://docs.google.com/spreadsheets/d/1JDY4aQ11uXGZtzR7HabSSoT5WuFMyk-ri80tQAs4G3k/edit#gid=1577871075) we built from scratch","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":555,"value":"await FileAttachment(\"generalElectionCandidates20201.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":569,"value":"

Part #1: Analysing female representation across regions and internationally

\n\nQuestions to explore:\n- How did Singapore and Mali fare, regionally and internationally?\n- Does historical data tell us anything about gender bias?\n- How do quotas impact the proportion of female representation?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":575,"value":"TO DO: \nSmall multiples (% of female candidates in Singapore) compared to regionally (Asia)\nSmall multiples (% of female candidates in Mali) compared to regionally (Africa)","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":579,"value":"\nExplorational tool: Select one country, then select the country you want to compare it to?","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":620,"value":"viewof country = Inputs.select([... new Set(ipuHistoricalFiltered.map(d => d.country))], {label: \"Country\", multiple: false})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":736,"value":"chart = LineChart(ipuHistoricalFiltered.filter(d => d[\"region\"] == \"ASIA\"), {\n x: d => +d.year,\n y: d => d[\"percentage-women\"],\n z: d => d.country,\n fixedZ: \"Singapore\",\n dropDownZ: country,\n yLabel: \"Proportion of female candidates (%)\",\n width,\n height: 500,\n color: (z) => z === 'Singapore' ? \"red\" : \"steelblue\",\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":925,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":630,"value":"ipuHistoricalFiltered = aq.from(ipuHistorical).filter(aq.escape( d => [\"Lower\", \"Single\"].includes(d[\"chamber-type\"]) ) ).derive({year: aq.escape(d => new Date(d.year,0,1))}).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":720,"value":"

So do quotas work?

Part #2: Guided in-depth analysis into Singapore's 🇸🇬 political gender bias

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":71,"value":"We'll be analysing the proportion of female candidates running for Singapore Parliament, the nations decision making body. For context: All genders in the database are either male (M) or female (F). Singapore has no candidate who's openly non-binary. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":74,"value":"**Female candidates are a minority, forming about a fifth of a total of 192 candidates.**
\nThe disparity is wider when we apply an intersectionality lens and filter for minority race representation i.e. non-Chinese","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":693,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"viewof race = Inputs.select([... new Set(gecandidates_race.map(d => d.race))], {label: \"Race\", multiple: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":122,"value":"overallProp.legend(\"color\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"value":"overallProp = Plot.plot({\n x: {\n percent: true\n },\n marks: [\n Plot.barX(gecandidates_race.filter(d => d.race === race), Plot.stackX(Plot.groupZ({x: \"proportion\", title: \"count\"}, {fill: \"gender\"}))),\n Plot.text(gecandidates_race.filter(d => d.race === race), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"gender\", text: \"gender\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel2\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"Moreover, the **spread of female candidates is uneven**. Some political parties remain male-only.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":128,"value":"addTooltips(Plot.plot({\n y: {\n grid: true\n },\n marks: [\n Plot.barY(generalElectionCandidates20201, Plot.groupX({y: \"count\"}, {x: \"party-2020\", title: \"gender\", fill: \"gender\",sort:{x:\"y\", reverse:true}})),\n Plot.ruleY([0])\n ], color: { scheme: \"pastel2\"}\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":379,"value":"Low female proportion also plays out among elected members of parliament who are also **political office holders**.\nIn fact, out of 27 female elected candidates, only 3 of them are cabinet members. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":340,"value":"addTooltips(Plot.plot({ marginLeft: 200,\n y: { domain: ['Prime Minister','Deputy Prime Minister','Senior Minister','Minister', 'Senior Minister of State', 'Minister of State','Senior Parliamentary Secretary'] },\n marks: [\n Plot.barX(position, {x: \"count\", y: \"highest_pos_if_pol_appointee\", title: \"count\", fill: \"gender\", }),\n Plot.ruleX([0]),\n ], color: { scheme: \"pastel2\"}, \n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":199,"value":"**Most candidates are married and many use this martial status to show themselves as a \"family person\" in their campaigns.** However, a higher proportion of female candidates are single, relative to male candidates (even if we were to assume that those with \"unknown\" status are also legally single). This tells us that as a female, being married is something that costs them more than males.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":227,"value":"addTooltips(Plot.plot({\n x: {\n percent: true\n }, label: \"Proportion for female candidates (%)\",\n marks: [\n Plot.barX(generalElectionCandidates20201.filter(d => d.gender === \"F\"), Plot.stackX(Plot.groupZ({x: \"proportion\", title: \"count\"}, {fill: \"relationship-status\"}))),\n Plot.text(generalElectionCandidates20201.filter(d => d.gender === \"F\"), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"relationship-status\", text: \"relationship-status\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel1\"}\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"Plot.plot({\n x: {\n percent: true\n }, label: \"Proportion for male candidates (%)\",\n marks: [\n Plot.barX(generalElectionCandidates20201.filter(d => d.gender === \"M\"), Plot.stackX(Plot.groupZ({x: \"proportion\", title: \"count\"}, {fill: \"relationship-status\"}))),\n Plot.text(generalElectionCandidates20201.filter(d => d.gender === \"M\"), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"relationship-status\", text: \"relationship-status\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel1\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":185,"value":"relationship = aq.from(generalElectionCandidates20201).select(\"gender\", \"relationship-status\").groupby(\"relationship-status\", \"gender\").count().orderby(\"relationship-status\",\"gender\").view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":652,"value":"Compared to male candidates, more female candidates do not have children. This could also imply that there is gender bias as the weight/responsibility of having children for female is more than male.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":644,"value":"Plot.plot({\n x: {\n percent: true\n }, label: \"Proportion of male candidates with children (%)\",\n marks: [\n Plot.barX(generalElectionCandidates20201.filter(d => d.gender === \"M\"), Plot.stackX(Plot.groupZ({x: \"proportion\", title: \"count\"}, {fill: \"children\"}))),\n Plot.text(generalElectionCandidates20201.filter(d => d.gender === \"M\"), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"children\", text: \"children\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel1\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":646,"value":"Plot.plot({\n x: {\n percent: true\n }, label: \"Proportion of female candidates with children (%)\",\n marks: [\n Plot.barX(generalElectionCandidates20201.filter(d => d.gender === \"F\"), Plot.stackX(Plot.groupZ({x: \"proportion\", title: \"count\"}, {fill: \"children\"}))),\n Plot.text(generalElectionCandidates20201.filter(d => d.gender === \"F\"), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"children\", text: \"children\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel1\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":330,"value":"**A greater proportion of female candidates have backgrounds in charity/social service relative to male candidates.** But the top professional background is business for both male and female candidates. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":254,"value":"Plot.plot({\n marginLeft: 200,\n facet: {data: background, y: \"gender\", marginRight: 80},\n marks: [\n Plot.barX(background, {x: domain: d3.sort(background, d => -d.count).map(d => d.gender),\n y: \"background\", fill: \"gender\"}),\n Plot.ruleX([0]),\n ], color: { scheme: \"pastel2\"}, \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"**Ministry portfolio of members of parliament who are also political office holders**: No female representation in Defence, but a relatively higher female representation (40% vs 33%) in Home Affairs (policing and internal security), National Development (national land use planning and development), and Sustainability and the Environment (environment, and water and food supplies)\n\nJosephine Teo is the Minister-in-charge of Smart Nation and Cybersecurity, the only area that's owned by a female MP. This is related to her duties as Minister of Communications and Information.\n\nNote: Economic Policies, National Security, and Social Policies do not refer to ministries, but refer to areas overseen by the three Senior Ministers.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":397,"value":"Plot.plot({ marginLeft: 200,\n marks: [\n Plot.barX(portfolio, {x: \"count\", y: \"portfolio\", fill: \"gender\", title:\"gender\", sort:{y:\"x\", reverse:true}}),\n Plot.ruleX([0]),\n ], color: { scheme: \"pastel2\"}, \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":323,"value":"## Appendix (Imports)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":95,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":468,"value":"import {Plot} from \"@mkfreeman/plot-tooltip\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":564,"value":"generalElectionCandidates20201 = FileAttachment(\"generalElectionCandidates20201.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":701,"value":"gecandidates_race = aq.from(generalElectionCandidates20201).derive({race: d => d.race === \"Indo-Aryan\" ? \"Other\" : d.race === \"Indian-Chinese\" ? \"Indian\" : d.race}).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":609,"value":"ipuHistorical = FileAttachment(\"ipu-historical-data@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":611,"value":"ipuHistoricalbyCountry = aq.from(ipuHistorical).filter(d => d[\"chamber-type\"] == \"Lower\").groupby(\"country\").objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":747,"value":"ipuQuota = FileAttachment(\"ipu-quota-lowerhouse@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":252,"value":"background = aq.from(generalElectionCandidates20201).select(\"gender\", \"background\").groupby(\"background\",\"gender\").count().groupby(\"background\").pivot(\"gender\",\"count\").impute({ \"F\": () => 0 }).fold(['F', 'M'], { as: ['gender', 'count'] }).objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":336,"value":"position = aq.from(generalElectionCandidates20201).filter(d => d[\"Political-appointee\"] == \"Y\").select(\"gender\", \"highest_pos_if_pol_appointee\").groupby(\"highest_pos_if_pol_appointee\",\"gender\").count().groupby(\"highest_pos_if_pol_appointee\").pivot(\"gender\",\"count\").impute({ \"F\": () => 0 }).fold(['F', 'M'], { as: ['gender', 'count'] }).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":387,"value":"portfolio = aq.from(generalElectionCandidates20201).filter(d => d[\"Political-appointee\"] == \"Y\").select(\"gender\", \"portfolio\").derive({ portfolio: d => op.split(d.portfolio, '_')}).unroll(\"portfolio\").derive({ portfolio: d => op.trim(d.portfolio)}).groupby(\"portfolio\",\"gender\").count().groupby(\"portfolio\").pivot(\"gender\",\"count\").impute({ \"F\": () => 0, \"M\": () => 0}).fold(['F', 'M'], { as: ['gender', 'count'] }).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":734,"value":"import {LineChart} from \"78265e2cdf49d773\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W46","stdlib":"1"}} 44:"$43:body"

Caveat on missing data

The Data

**Part #1: Analysing female representation across regions and internationally**

So do quotas work?

Part #2: Guided in-depth analysis into Singapore's 🇸🇬 political gender bias

Part #1: Analysing female representation across regions and internationally