Five Connecticuts Report 2013 Preliminary Edition Methodology September 1, 2013 Edition
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1 PRELIMINARY HUMAN DEVELOPMENT INDEX FOR CONNECTICUT 1 September 2013 Kate Johnson INTRODUCTION This report provides a description of the methods used to create the Human Development Index for the 169 towns in Connecticut. The methods and calculations used were consistent with those used and described in the Measure of America reports by Kristen Lewis and Sarah Burd-Sharps to create the human development index for the United States 1. These reports were in turn based on human development indices that had been created by the United Nations to assess development at global levels 2. The Measure of America reports use three major indices (Education, Income, and Health) to assign a 0-10 score to each state. Each index is calculated by comparing an actual number to a minimum and maximum goalpost set by Burd-Sharps and Lewis at specific numbers based on ranges they observed in their data (see below). In order for the Connecticut HDI to be compared with the Measure of America reports, the same goalposts were used in calculating the indices for each town despite the fact that the range of the data for Connecticut was quite different. Dimension Index = All of the data used to construct the indices was taken from the American Community Survey, except for the raw life expectancy data, which was provided by the Connecticut Department of Public Health and which dates to This data was then compiled into an abridged life table for each town in Connecticut using the age groups described in the Measure of America reports. See below for more detailed information on the calculation of the life expectancies for each town. All of the calculations were performed in ArcGIS by using the geodatabase available from the Connecticut State Data Center which contains town boundaries as well as all of the necessary ACS tables, except the table used in calculating Educational Attainment (S1501), which was downloaded from American FactFinder and joined to the shapefile. GOALPOSTS Goalposts used in calculating the indices for Connecticut were the same that were used in calculating those for the Measure of America reports, as well as those for Louisiana (2009), Mississippi (2009), California (2011) and Marin County (2012). The goalposts used for the American HD index represent the range of values that the authors observed in the data. So that the results for Connecticut towns would be comparable to the American indices, we used the same goalposts. The goalposts for the median earnings differ in all 3 reports because they ve been adjusted for inflation. For this report (Connecticut) we used the CPI-U-RS inflation calculator 3 to adjust the goalposts from the 2012 Marin County report to 2011 earnings because the Marin report Bureau of Labor Statistics CPI Inflation Calculator,
2 used the ACS data which was adjusted to 2010 earnings. 4 The same method of adjusting inflation was used by Bud-Sharps and Lewis in their more recent reports as well. Indicators Minimum Maximum Life Expectancy at birth (years) Educational attainment score Combined gross enrollment ratio (%) Median personal earnings (adj for inflation 2011 $) 14,977 63,366 **Note: 2011 Poverty threshold for an individual is $11,484 (Taken from US Census website: TABLES/FIELDS All of the tables used in calculating the index for Connecticut were the same tables used in the Measure of America reports, as mentioned in the various methodologies. The tables and fields from the ACS data that were used to calculate their respective data category are as follows: Data description Table Fields Percent high school grad or higher S1501 HC01_EST_VD17 Percent bachelor s deg or higher S1501 HC01_EST_VD16 Percent graduate degree S1501 HC01_EST_VD14 Educational Attainment Index Educational Attainment Score # of enrolled students, ages 3-35 B14003 Multiple, see Educational Enrollment Index below Total population, ages 3-24 B14003 Multiple, see Educational Enrollment Index below Gross enrollment ratio Gross enrollment ratio capped Educational Enrollment Index Education Index Median earnings estimate (2011) B20017 B _EST Numerator for Income Index Denominator for Income Index Median Earnings Index Life Expectancy Data created at CTSDC Health Index Human Development Index INDEX CALCULATIONS The calculations for the index were performed in ArcGIS. Below is a list and accompanying description of each of the fields that were created and calculated to obtain the final HDI. Field Name Description Formula (all calculations performed in ArcGIS) HiSch Percent high school grad or higher Percentage / 100 Bach Percent bachelor s deg or higher Percentage / Burd-Sharps and Lewis, A Portrait of Marin: Marin County Human Development Report p.67.
3 Grad Percent graduate degree Percentage / 100 EdAttInd Educational Attainment Index (AttScore 0.5)/( ) AttScore Educational Attainment Score [HiSch] + [Bach] + [Grad] Enr3to35p # of enrolled students, ages 3-35 B _EST+ B _EST+ B _EST+ B _EST EnrPop3to24 Total population, ages 3-24 See Educational Enrollment Index below GER Gross enrollment ratio [Enr3to35p] / [EnrPop3to24]*100 GER_capped Gross enrollment ratio capped All values > 100 changed to 100 EdEnrInd Educational Enrollment Index ([GER_capped])-70)/(100-70) EDINDEX Education Index (2/3*[EdAttInd])+(1/3*[EdEnrInd]) MedEar_Est Median earnings estimate (2011) None see below for table info Num_Log Numerator for Income Index math.log10(!medear_est!)-math.log10(14977) **see note Den_Log Denominator for Income Index math.log10(63366)-math.log10(14977) **see note INCINDEX Median Earnings Index [Num_Log]/[Den_Log]*10 LifeExp Life Expectancy No formula see below for info HINDEX Health Index ([LifeExp]-66)/(90-66)*10 HDI Human Development Index ([EDINDEX]+[INCINDEX]+[HINDEX])/3 ** Because VB Script in ArcGIS field calculator only performs natural log, this calculation was done using Python in order to use the log10 function. EDUCATIONAL ATTAINMENT INDEX Table S1501 In order to maintain consistency with the Measure of America reports, table S1501 was used because it provided the percentages of individuals with a bachelor s degree or higher, or a high school degree or higher. The raw data that was used to create S1501 can be found in B Fields & Calculations: Percent high school or higher [HiSch]: HC01_EST_VD16 Percent bachelors or higher [Bach]: HC01_EST_VD17 Graduate degree [Grad]: HC01_EST_VD14 AttScore (Attainment Score) = [HiSch] + [Bach] + [Grad] EdAttInd (Attainment Index) = ([AttScore] 0.5)/( )
4 EDUCATIONAL ENROLLMENT INDEX Table B14003 The data in this table are organized not only by sex, but by age groups and whether those groups are enrolled in public or private school, or unenrolled. Enr3to35p (Total enrolled for ages 3 to 35): Male Public + Male Private + Female Public + Female Private [B14003.B _EST]+ [B14003.B _EST] + [B14003.B _EST]+ [B14003.B _EST] EnrPop3to24 (Total population ages 3 to 24): Male Public/Private/Unenrolled each group Female Public/Private/Unenrolled each group 3-24 [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST]+ [B14003.B _EST] GER = [Enr3to35p] / [EnrPop3to24]*100 GER_capped = All values of GER that are above 100, change to 100. EdEnrInd = ([GER_capped] 70) / (100-70) EDINDEX = (2/3*[EdAttInd]) + (1/3*[EdEnrInd])
5 MEDIAN EARNINGS MedEar_Est = B20017 In order to calculate this field in ArcGIS, Python was used because VBScript does not provide a log10 option only natural log. Num_Log = math.log10(!medear_est!)-math.log10(14977) Den_Log = math.log10(63366)-math.log10(14977) INCINDEX =!Num_Log!/!Den_Log!*10 HEALTH INDEX Life expectancy data was calculated using abridged life tables. The abridged life tables were created using the number of deaths per age group and the total population for that age group (see pg. 285 in Demographic methods and concepts by Donald T. Rowland, 2003 Oxford University Press, Oxford) The age ranges were grouped based on the methods in the original Measure of America report from (Under 1, 1-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85+.) The value interpreted as life expectancy was the value of e x for the Under 1 age group. The raw data was obtained from the Connecticut Department of Public Health. HINDEX = ([LifeExp] 66) / (90-66) HUMAN DEVELOPMENT INDEX The total index is calculated as the average of the three other main indices: HDI = ([EDINDEX] + [INCINDEX] + [HINDEX]) / 3 Notes The HDI does not appear to take into account areas with low population numbers. So, for example, the tract in Connecticut that contains the airport has a very poor in the Education Index, because there are no people there between the ages of 3 and 24 (it receives a low negative value). Other areas with similar low values for that population group also appear to get lower Education Index values, thus bringing down their overall HDI. For the airport tract, all of the values for each index and corresponding fields were set to 0 so as not to negatively influence the range of HDI values. Negative values and values over 10 are a product of the 3 different indices being higher or lower than the national goalposts that were set for each specific index. Tracts that contain group quarters (universities, prisons, nursing homes) can influence the data at the town level. For instance, the median earnings for the UConn tract are very low compared with the rest of the tracts in Mansfield. As a result, the median earnings at the town level are the lowest
6 in the entire state, and thus Mansfield receives a low HDI score even though the other three tracts in the town have much higher median earnings. This is likely the case for other towns in Connecticut that have universities, prisons, or nursing homes. The existence of nursing homes can also influence life expectancy due to a proportionally higher number of people in older age groups.
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