Title
Amending Title 21 of The Philadelphia Code, entitled “Miscellaneous,” by adding a new Chapter 21-3100, entitled “Voting Machine Languages Requirement;” all under certain terms and conditions.
Body
THE COUNCIL OF THE CITY OF PHILADELPHIA HEREBY ORDAINS:
SECTION 1. Legislative Findings:
(1) On February 20, 2019, the City of Philadelphia Board of Elections approved Election Systems and Software, LLC’s “Express Vote XL” voting machines for use in Philadelphia elections pursuant to the Pennsylvania Election Code.
(2) Subsequently, the City of Philadelphia Procurement Department entered into Contract 196400 with Election Systems and Software, LLC, to purchase new voting machines for use in the City of Philadelphia elections.
(3) That procurement was to satisfy Commonwealth of Pennsylvania Governor Thomas Westerman Wolf’s mandate that Pennsylvania counties select new voting systems that provide a voter verifiable paper record and meet 21st-Century standards of security, auditability, and accessibility, no later than December 31, 2019, and preferably by the 2019 General Election.
(4) While the Express Vote XL voting machines were selected to meet Governor Wolf’s requirements, those voting machines also have the happy capacity to be programable in sixteen languages.
(5) Over 21% of speakers in Philadelphia (308,605) speak a language other than English at home, while nearly 10% of speakers in Philadelphia (138,697) speak English less than “very well.” There are at least 71 languages spoken in Philadelphia:
Table 131. Detailed Languages Spoken at Home and Ability to Speak English for the Population
5 Years and Over for Philadelphia County, PA: 2009-2013
Release Date: October 2015
|
|
Number of speakers1 |
Percentage of Number of Speakers |
Speak English less than "Very Well"1 |
Percentage of Number of Speakers |
|
.Population 5 years and over |
1,432,316 |
100.00% |
138,697 |
9.68% |
|
Speak only English at home |
1,123,711 |
78.45% |
(X) |
(X) |
|
Speak a language other than English at home |
308,605 |
21.55% |
138,697 |
9.68% |
|
Spanish |
141,765 |
9.90% |
56,055 |
3.91% |
|
French |
8,145 |
0.57% |
2,765 |
0.19% |
|
Patois |
485 |
0.03% |
30 |
0.00% |
|
.French Creole |
7,886 |
0.55% |
3,499 |
0.24% |
|
. Italian |
6,675 |
0.47% |
2,310 |
0.16% |
|
Portuguese |
5,035 |
0.35% |
2,715 |
0.19% |
|
German |
2,945 |
0.21% |
500 |
0.03% |
|
. Yiddish |
844 |
0.06% |
108 |
0.01% |
|
Pennsylvania Dutch |
300 |
0.02% |
(D) |
(D) |
|
Dutch |
175 |
0.01% |
(B) |
-- |
|
Afrikaans |
45 |
0.00% |
(D) |
(D) |
|
Swedish |
180 |
0.01% |
80 |
0.01% |
|
Danish |
200 |
0.01% |
(D) |
(D) |
|
Norwegian |
40 |
0.00% |
(D) |
(D) |
|
Icelandic |
35 |
0.00% |
(B) |
-- |
|
. Greek |
1,848 |
0.13% |
442 |
0.03% |
|
. Russian |
11,703 |
0.82% |
7,559 |
0.53% |
|
. Polish |
4,561 |
0.32% |
2,308 |
0.16% |
|
Serbocroatian |
225 |
0.02% |
120 |
0.01% |
|
Croatian |
235 |
0.02% |
70 |
0.00% |
|
Serbian |
220 |
0.02% |
75 |
0.01% |
|
Bielorussian |
(D) |
(D) |
(D) |
(D) |
|
Ukrainian |
3,940 |
0.28% |
1,925 |
0.13% |
|
Czech |
105 |
0.01% |
80 |
0.01% |
|
Slovak |
85 |
0.01% |
(D) |
(D) |
|
Bulgarian |
505 |
0.04% |
250 |
0.02% |
|
Macedonian |
(D) |
(D) |
(D) |
(D) |
|
.Armenian |
305 |
0.02% |
18 |
0.00% |
|
.Persian |
598 |
0.04% |
128 |
0.01% |
|
.Hindi |
2,897 |
0.20% |
588 |
0.04% |
|
.Gujarati |
2,721 |
0.19% |
1,045 |
0.07% |
|
.Urdu |
1,806 |
0.13% |
841 |
0.06% |
|
India n.e.c.3 |
265 |
0.02% |
(D) |
(D) |
|
Bengali |
1,590 |
0.11% |
625 |
0.04% |
|
Panjabi |
480 |
0.03% |
345 |
0.02% |
|
Marathi |
315 |
0.02% |
80 |
0.01% |
|
Assamese |
15 |
0.00% |
(B) |
-- |
|
Nepali |
660 |
0.05% |
550 |
0.04% |
|
Sindhi |
205 |
0.01% |
130 |
0.01% |
|
Pakistan n.e.c.3 |
70 |
0.00% |
70 |
0.00% |
|
Sinhalese |
85 |
0.01% |
40 |
0.00% |
|
Jamaican Creole |
680 |
0.05% |
(D) |
(D) |
|
Krio |
(D) |
(D) |
(B) |
-- |
|
Catalonian |
55 |
0.00% |
(D) |
(D) |
|
Romanian |
820 |
0.06% |
350 |
0.02% |
|
Irish Gaelic |
90 |
0.01% |
(D) |
(D) |
|
Albanian |
4,460 |
0.31% |
2,440 |
0.17% |
|
Lithuanian |
130 |
0.01% |
45 |
0.00% |
|
Latvian |
(D) |
(D) |
(D) |
(D) |
|
Pashto |
215 |
0.02% |
135 |
0.01% |
|
Chinese |
18,725 |
1.31% |
12,695 |
0.89% |
|
Cantonese |
4,005 |
0.28% |
2,740 |
0.19% |
|
Mandarin |
3,215 |
0.22% |
2,165 |
0.15% |
|
Fuchow |
125 |
0.01% |
125 |
0.01% |
|
Formosan |
280 |
0.02% |
125 |
0.01% |
|
. Japanese |
896 |
0.06% |
328 |
0.02% |
|
. Korean |
4,605 |
0.32% |
2,385 |
0.17% |
|
. Mon-Khmer, Cambodian |
7,918 |
0.55% |
4,559 |
0.32% |
|
. Hmong |
163 |
0.01% |
42 |
0.00% |
|
.Thai |
581 |
0.04% |
350 |
0.02% |
|
.Laotian |
1,057 |
0.07% |
625 |
0.04% |
|
. Vietnamese |
15,461 |
1.08% |
10,673 |
0.75% |
|
Uighur |
220 |
0.02% |
165 |
0.01% |
|
Turkish |
480 |
0.03% |
145 |
0.01% |
|
Turkmen |
(D) |
(D) |
(B) |
-- |
|
Mongolian |
35 |
0.00% |
(D) |
(D) |
|
Telugu |
340 |
0.02% |
25 |
0.00% |
|
Kannada |
195 |
0.01% |
(D) |
(D) |
|
Malayalam |
6,050 |
0.42% |
3,005 |
0.21% |
|
Tamil |
365 |
0.03% |
80 |
0.01% |
|
Tibetan |
125 |
0.01% |
125 |
0.01% |
|
Burmese |
235 |
0.02% |
180 |
0.01% |
|
. Tagalog |
3,861 |
0.27% |
1,589 |
0.11% |
|
Indonesian |
1,345 |
0.09% |
845 |
0.06% |
|
Javanese |
(D) |
(D) |
(D) |
(D) |
|
Malay |
245 |
0.02% |
175 |
0.01% |
|
Minangkabau |
(D) |
(D) |
(D) |
(D) |
|
Bisayan |
25 |
0.00% |
(B) |
-- |
|
Sebuano |
(D) |
(D) |
(B) |
-- |
|
Ilocano |
40 |
0.00% |
(B) |
-- |
|
Pampangan |
(D) |
(D) |
(B) |
-- |
|
Chamorro |
(D) |
(D) |
(D) |
(D) |
|
Marshallese |
(D) |
(D) |
(D) |
(D) |
|
Palau |
(D) |
(D) |
(D) |
(D) |
|
Melanesian |
(D) |
(D) |
(B) |
-- |
|
Maori |
25 |
0.00% |
(B) |
-- |
|
Algonquian |
(D) |
(D) |
(B) |
-- |
|
Ojibwa |
(D) |
(D) |
(B) |
-- |
|
Kuchin |
(D) |
(D) |
(B) |
-- |
|
Dakota |
45 |
0.00% |
(B) |
-- |
|
Muskogee |
(D) |
(D) |
(B) |
-- |
|
Cherokee |
(D) |
(D) |
(D) |
(D) |
|
. Hungarian |
259 |
0.02% |
36 |
0.00% |
|
. Arabic |
8,770 |
0.61% |
3,092 |
0.22% |
|
. Hebrew |
2,332 |
0.16% |
358 |
0.02% |
|
Amharic |
1,830 |
0.13% |
595 |
0.04% |
|
Berber |
265 |
0.02% |
170 |
0.01% |
|
Chadic |
90 |
0.01% |
(B) |
-- |
|
Cushite |
280 |
0.02% |
120 |
0.01% |
|
Swahili |
305 |
0.02% |
155 |
0.01% |
|
Bantu |
135 |
0.01% |
35 |
0.00% |
|
Mande |
1,550 |
0.11% |
570 |
0.04% |
|
Fulani |
1,010 |
0.07% |
680 |
0.05% |
|
Gur |
170 |
0.01% |
130 |
0.01% |
|
Kru, Ibo, Yoruba |
3,370 |
0.24% |
625 |
0.04% |
|
Efik |
(D) |
(D) |
(B) |
-- |
|
African |
295 |
0.02% |
50 |
0.00% |
|
Finnish |
(D) |
(D) |
(B) |
-- |
|
Caucasian |
135 |
0.01% |
120 |
0.01% |
|
Mayan languages |
(D) |
(D) |
(B) |
-- |
|
Oto - Manguen |
(D) |
(D) |
(D) |
(D) |
|
Uncodable |
85 |
0.01% |
40 |
0.00% |
|
Notes: 1 Detailed-language estimates are rounded to the nearest multiple of five. Aggregate estimates (bold-face entries) are unrounded and appear in table B16001 (http://factfinder.census.gov/bkmk/table/1.0/en/ACS/13_5YR/B16001/0500000US42101). Detailed-language estimates may not sum to aggregate estimates because of rounding. |
|
2 Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data at http://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2013.pdf). The effect of nonsampling error is not represented in these tables. |
|
3 N.E.C. stands for not elsewhere classified. These are languages where respondents indicated they spoke either Indian or Pakistan. For Indian, it cannot be determined if the respondent spoke a native American language or spoke a language from India. For Pakistan, respondents wrote in Pakistan but it cannot be determined which one of the languages spoken in Pakistan is actually being spoken. To distinguish these languages, n.e.c. is used to indicated they are not classified in any other language code. |
|
4 This category includes literal write-ins of Chinese as well as Hakka, Kan, Hsiang, Cantonese, Mandarin, Fuchow, Formosan, and Wu. |
|
(D) Data withheld to avoid disclosure. |
|
(B) Either no sample observations or too few sample observations were available to compute an estimate. |
|
(X) Question does not apply. |
|
-- Either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. |
|
Source: U.S. Census Bureau, 2009-2013 American Community Survey |
(6) For the 2019 General Election, the Philadelphia voting machines are being programmed for use with only two languages: English and Spanish.
(7) Currently, lack of facility in English or Spanish is a barrier to effective participation in voting and, therefore, is antithetical to Democracy in Philadelphia.
(8) On June 20, 2019, Philadelphia City Council passed Resolution 190584 urging the Office of the Philadelphia City Commissioners to maximize language access services available through the City’s new voting machines.
(9) As a City known for being immigrant-friendly, it behooves the City of Philadelphia to make the voting process as accessible as possible to immigrants living amongst us to allow them to participate in and enrich our Democracy.
(10) Technology has made it possible to enable voters whose command of English is limited to vote in a language in which they are fluent.
(11) In the future, unlimited access to languages other than English will make it possible to greatly broaden participation in elections, and this opportunity should be maximized to enrich our Democracy in Philadelphia.
SECTION 2. Title 21 of The Philadelphia Code is hereby amended to read as
follows:
TITLE 21. MISCELLANEOUS.
* * *
CHAPTER 21-3100. VOTING MACHINE LANGUAGES REQUIREMENT
§ 21-3101. Definitions, Usages, and Data.
(1) For purposes of this Chapter, the definitions and usages of words shall be the same as the definitions and usages of such words in the most current Pennsylvania Election Code.
(2) For purposes of this Chapter, the data used in mathematical calculations shall be sourced from the most current United States Census Bureau American Community Survey data set for “Detailed Languages Spoken at Home and Ability to Speak English for the Population 5 Years and Over for Philadelphia County, Pennsylvania.”
§ 21-3102. Voting Machine Languages Requirement.
(1) In all Philadelphia elections, voting machines shall be programmed to utilize the maximum capacity for languages that such machines afford, with the selection of languages determined by numerical calculation of percentages starting with the highest language usage for specific language speakers who speak English less than “Very Well” divided by the total number of speakers in the population 5 years and over for Philadelphia County using the data as reported by the most current United States Census Bureau American Community Survey, and extending until the maximum capacity for languages such machines afford is fully utilized.
(2) No voting machine shall be used in an election that has not been programmed to maximize capacity for languages that such machines afford in accordance with this Section.
(3) Nothing in this Section shall be construed as inconsistent with the most current Pennsylvania Election Code.
SECTION 3. This Ordinance shall take effect immediately.
___________________________________
Explanation:
[Brackets] indicate matter deleted.
Italics indicate matter added.
End