{"id":8909,"date":"2023-10-12T15:12:42","date_gmt":"2023-10-12T15:12:42","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/?post_type=chapter&#038;p=8909"},"modified":"2024-10-18T20:58:37","modified_gmt":"2024-10-18T20:58:37","slug":"apportionment-learn-it-4","status":"web-only","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/chapter\/apportionment-learn-it-4\/","title":{"raw":"Apportionment: Learn It 4","rendered":"Apportionment: Learn It 4"},"content":{"raw":"<h2>Webster\u2019s Method<\/h2>\r\n<p>Daniel Webster (1782-1852) proposed a method similar to Jefferson\u2019s in 1832. It was adopted by Congress in 1842, but replaced by Hamilton\u2019s method in 1852. It was then adopted again in 1901. The difference is that Webster rounds the quotas to the nearest whole number rather than dropping the decimal parts. If that doesn\u2019t produce the desired results at the beginning, he says, like Jefferson, to adjust the divisor until it does. (In Jefferson\u2019s case, at least the first adjustment will always be to make the divisor smaller. That is not always the case with Webster\u2019s method.)<\/p>\r\n<section class=\"textbox keyTakeaway\">\r\n<div>\r\n<h3>Webster\u2019s method<\/h3>\r\n<ol style=\"list-style-type: decimal;\">\r\n\t<li>Determine how many people each representative should represent. Do this by dividing the total population of all the states by the total number of representatives. This answer is called the <strong>standard divisor<\/strong>.<\/li>\r\n\t<li>Divide each state\u2019s population by the divisor to determine how many representatives it should have. Record this answer to several decimal places. This answer is called the <strong>quota<\/strong>.<\/li>\r\n\t<li>Round all the quotas to the nearest whole number (but don\u2019t forget what the decimals were). Add up the remaining whole numbers.<\/li>\r\n\t<li>If the total from Step 3 was less than the total number of representatives, reduce the divisor and recalculate the quota and allocation. If the total from step 3 was larger than the total number of representatives, increase the divisor and recalculate the quota and allocation. Continue doing this until the total in Step 3 is equal to the total number of representatives. The divisor we end up using is called the <strong>modified divisor<\/strong> or <strong>adjusted divisor<\/strong>.<\/li>\r\n<\/ol>\r\n<\/div>\r\n<\/section>\r\n<section class=\"textbox example\">\r\n<p>We\u2019ll return to Delaware and apply Webster\u2019s method. As a reminder, the state of Delaware has three counties: Kent, New Castle, and Sussex. The Delaware state House of Representatives has [latex]41[\/latex] members.<\/p>\r\n<p>The populations of the counties are as follows (from the 2010 Census):<\/p>\r\n\r\n<p style=\"text-align: center;\">[latex]\\begin{array}{lr} \\text { County } &amp; \\text { Population } \\\\ \\hline \\text { Kent } &amp; 162,310 \\\\ \\text { New Castle } &amp; 538,479 \\\\ \\text { Sussex } &amp; 197,145 \\\\ \\textbf{ Total } &amp; \\bf{ 897,934 }\\end{array}[\/latex]<\/p> [reveal-answer q=\"4331\"]Show Solution[\/reveal-answer]<br \/>\r\n[hidden-answer a=\"4331\"]\r\n\r\n<p>We begin, as we did with Hamilton\u2019s method, by finding the quotas with the original divisor, [latex]21,900.82927[\/latex].<\/p>\r\n\r\n<p style=\"text-align: center;\">[latex]\\begin{array}{lrrc} \\text { County } &amp; \\text { Population } &amp; \\text{ Quota } \\\\ \\hline \\text { Kent } &amp; 162,310 &amp; 7.4111 \\\\ \\text { Newport } &amp; 538,479 &amp; 24.5872 \\\\ \\text { Providence } &amp; 197,145 &amp; 9.0017 \\\\ \\textbf{ Total } &amp; \\bf{ 897,934 } &amp; \\end{array} [\/latex]<\/p>\r\n\r\n<p>Rounding the quotas up we get:<\/p>\r\n\r\n<p style=\"text-align: center;\">[latex]\\begin{array}{lrrc} \\text { County } &amp; \\text { Population } &amp; \\text{ Quota } &amp; \\text{ Initial } \\\\ \\hline \\text { Kent } &amp; 166,310 &amp; 7.4111 &amp; 7 \\\\ \\text { New Castle } &amp; 538,479 &amp; 24.5872 &amp; 25 \\\\ \\text { Sussex } &amp; 197,145 &amp; 9.0017 &amp; 9 \\\\ \\textbf{ Total } &amp; \\bf{ 897,934 } &amp; &amp; \\bf{ 41 }\\end{array} [\/latex]<\/p>\r\n\r\n<p>This gives the required total, so we\u2019re done.<\/p>\r\n\r\n[\/hidden-answer]<\/section>\r\n<section class=\"textbox tryIt\">\r\n<p>[ohm2_question hide_question_numbers=1]13232[\/ohm2_question]<\/p>\r\n<\/section>\r\n<p>Like Jefferson\u2019s method, Webster\u2019s method carries a bias in favor of states with large populations, but rounding the quotas to the nearest whole number greatly reduces this bias. Also like Jefferson\u2019s method, Webster\u2019s method does not always follow the quota rule, but it follows the quota rule much more often than Jefferson\u2019s method does. (In fact, if Webster\u2019s method had been applied to every apportionment of Congress in all of American history, it would have followed the quota rule every single time.)<\/p>\r\n<p>In 1980, two mathematicians, Peyton Young and Mike Balinski, proved what we now call the <strong>Balinski-Young Impossibility Theorem<\/strong>.<\/p>\r\n<section class=\"textbox keyTakeaway\">\r\n<div>\r\n<h3>Balinski-Young Impossibility Theorem<\/h3>\r\n\r\nThe <strong>Balinski-Young Impossibility Theorem<\/strong> shows that any apportionment method which always follows the quota rule will be subject to the possibility of paradoxes like the Alabama, New States, or Population paradoxes. In other words, we can choose a method that avoids those paradoxes, but only if we are willing to give up the guarantee of following the quota rule.<\/div>\r\n<\/section>","rendered":"<h2>Webster\u2019s Method<\/h2>\n<p>Daniel Webster (1782-1852) proposed a method similar to Jefferson\u2019s in 1832. It was adopted by Congress in 1842, but replaced by Hamilton\u2019s method in 1852. It was then adopted again in 1901. The difference is that Webster rounds the quotas to the nearest whole number rather than dropping the decimal parts. If that doesn\u2019t produce the desired results at the beginning, he says, like Jefferson, to adjust the divisor until it does. (In Jefferson\u2019s case, at least the first adjustment will always be to make the divisor smaller. That is not always the case with Webster\u2019s method.)<\/p>\n<section class=\"textbox keyTakeaway\">\n<div>\n<h3>Webster\u2019s method<\/h3>\n<ol style=\"list-style-type: decimal;\">\n<li>Determine how many people each representative should represent. Do this by dividing the total population of all the states by the total number of representatives. This answer is called the <strong>standard divisor<\/strong>.<\/li>\n<li>Divide each state\u2019s population by the divisor to determine how many representatives it should have. Record this answer to several decimal places. This answer is called the <strong>quota<\/strong>.<\/li>\n<li>Round all the quotas to the nearest whole number (but don\u2019t forget what the decimals were). Add up the remaining whole numbers.<\/li>\n<li>If the total from Step 3 was less than the total number of representatives, reduce the divisor and recalculate the quota and allocation. If the total from step 3 was larger than the total number of representatives, increase the divisor and recalculate the quota and allocation. Continue doing this until the total in Step 3 is equal to the total number of representatives. The divisor we end up using is called the <strong>modified divisor<\/strong> or <strong>adjusted divisor<\/strong>.<\/li>\n<\/ol>\n<\/div>\n<\/section>\n<section class=\"textbox example\">\n<p>We\u2019ll return to Delaware and apply Webster\u2019s method. As a reminder, the state of Delaware has three counties: Kent, New Castle, and Sussex. The Delaware state House of Representatives has [latex]41[\/latex] members.<\/p>\n<p>The populations of the counties are as follows (from the 2010 Census):<\/p>\n<p style=\"text-align: center;\">[latex]\\begin{array}{lr} \\text { County } & \\text { Population } \\\\ \\hline \\text { Kent } & 162,310 \\\\ \\text { New Castle } & 538,479 \\\\ \\text { Sussex } & 197,145 \\\\ \\textbf{ Total } & \\bf{ 897,934 }\\end{array}[\/latex]<\/p>\n<div class=\"qa-wrapper\" style=\"display: block\"><button class=\"show-answer show-answer-button collapsed\" data-target=\"q4331\">Show Solution<\/button><\/p>\n<div id=\"q4331\" class=\"hidden-answer\" style=\"display: none\">\n<p>We begin, as we did with Hamilton\u2019s method, by finding the quotas with the original divisor, [latex]21,900.82927[\/latex].<\/p>\n<p style=\"text-align: center;\">[latex]\\begin{array}{lrrc} \\text { County } & \\text { Population } & \\text{ Quota } \\\\ \\hline \\text { Kent } & 162,310 & 7.4111 \\\\ \\text { Newport } & 538,479 & 24.5872 \\\\ \\text { Providence } & 197,145 & 9.0017 \\\\ \\textbf{ Total } & \\bf{ 897,934 } & \\end{array}[\/latex]<\/p>\n<p>Rounding the quotas up we get:<\/p>\n<p style=\"text-align: center;\">[latex]\\begin{array}{lrrc} \\text { County } & \\text { Population } & \\text{ Quota } & \\text{ Initial } \\\\ \\hline \\text { Kent } & 166,310 & 7.4111 & 7 \\\\ \\text { New Castle } & 538,479 & 24.5872 & 25 \\\\ \\text { Sussex } & 197,145 & 9.0017 & 9 \\\\ \\textbf{ Total } & \\bf{ 897,934 } & & \\bf{ 41 }\\end{array}[\/latex]<\/p>\n<p>This gives the required total, so we\u2019re done.<\/p>\n<\/div>\n<\/div>\n<\/section>\n<section class=\"textbox tryIt\">\n<iframe loading=\"lazy\" id=\"ohm13232\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=13232&theme=lumen&iframe_resize_id=ohm13232&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><br \/>\n<\/section>\n<p>Like Jefferson\u2019s method, Webster\u2019s method carries a bias in favor of states with large populations, but rounding the quotas to the nearest whole number greatly reduces this bias. Also like Jefferson\u2019s method, Webster\u2019s method does not always follow the quota rule, but it follows the quota rule much more often than Jefferson\u2019s method does. (In fact, if Webster\u2019s method had been applied to every apportionment of Congress in all of American history, it would have followed the quota rule every single time.)<\/p>\n<p>In 1980, two mathematicians, Peyton Young and Mike Balinski, proved what we now call the <strong>Balinski-Young Impossibility Theorem<\/strong>.<\/p>\n<section class=\"textbox keyTakeaway\">\n<div>\n<h3>Balinski-Young Impossibility Theorem<\/h3>\n<p>The <strong>Balinski-Young Impossibility Theorem<\/strong> shows that any apportionment method which always follows the quota rule will be subject to the possibility of paradoxes like the Alabama, New States, or Population paradoxes. In other words, we can choose a method that avoids those paradoxes, but only if we are willing to give up the guarantee of following the quota rule.<\/div>\n<\/section>\n","protected":false},"author":15,"menu_order":16,"template":"","meta":{"_candela_citation":"[{\"type\":\"cc-attribution\",\"description\":\"Math in Society (Lippman)\",\"author\":\"Mike Kenyon & David Lippman\",\"organization\":\"LibreTexts Mathematics\",\"url\":\"https:\/\/math.libretexts.org\/Bookshelves\/Applied_Mathematics\/Math_in_Society_(Lippman)\/04%3A_Apportionment\/4.04%3A_Websters_Method\",\"project\":\"4.4: Webster\\'s Method\",\"license\":\"cc-by-sa\",\"license_terms\":\"Access for free at https:\/\/math.libretexts.org\/Bookshelves\/Applied_Mathematics\/Math_in_Society_(Lippman)\"}]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":90,"module-header":"learn_it","content_attributions":null,"internal_book_links":[],"video_content":null,"cc_video_embed_content":{"cc_scripts":"","media_targets":[]},"try_it_collection":null,"_links":{"self":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/8909"}],"collection":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/users\/15"}],"version-history":[{"count":6,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/8909\/revisions"}],"predecessor-version":[{"id":12804,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/8909\/revisions\/12804"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/parts\/90"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/8909\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/media?parent=8909"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=8909"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/contributor?post=8909"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/license?post=8909"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}