<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Census on Kailas Venkitasubramanian</title>
    <link>/tags/census/</link>
    <description>Recent content in Census on Kailas Venkitasubramanian</description>
    <generator>Hugo</generator>
    <language>en</language>
    <lastBuildDate>Sun, 12 May 2024 00:00:00 +0000</lastBuildDate>
    <atom:link href="/tags/census/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Using tidycensus to Analyze ACS PUMS Data</title>
      <link>/blog/posts/2024-05-12-analyzing-census-pums-data-with-tidycensus/</link>
      <pubDate>Sun, 12 May 2024 00:00:00 +0000</pubDate>
      <guid>/blog/posts/2024-05-12-analyzing-census-pums-data-with-tidycensus/</guid>
      <description>&lt;p&gt;If you&amp;rsquo;ve spent any time working with Census data, you know the drill: pull a pre-aggregated table, get median household income by county, move on. It works, and for a lot of questions, it&amp;rsquo;s exactly what you need. But sometimes the published tables just don&amp;rsquo;t cut it. What if you want to look at wage distributions for workers with specific educational credentials? Or model individual-level outcomes rather than tract-level averages? That&amp;rsquo;s where PUMS comes in — and once you start using it, it&amp;rsquo;s hard to go back.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
