├── Ec240a_Fall2023 ├── Data │ ├── NLSY97.zip │ └── NLSY97 │ │ ├── extract.log │ │ ├── NLSY97.NLSY97 │ │ └── NLSY97-value-labels.do ├── Syllabus_Ec240a_Fall2023.pdf ├── Problem_Sets │ ├── Ec240a_Midterm_2023.pdf │ ├── Ec240a_Problem_Set_1_Fall2023.pdf │ ├── Ec240a_Problem_Set_2_Fall2023.pdf │ ├── Ec240a_Problem_Set_3_Fall2023.pdf │ ├── Ec240a_Problem_Set_4_Fall2023.pdf │ ├── Ec240a_Problem_Set_5_Fall2023.pdf │ ├── Ec240a_SecondMidtermReviewSheet_Fall2023_p1.pdf │ └── Ec240a_SecondMidtermReviewSheet_Fall2023_p2.pdf └── Notebooks │ └── Notebook_NLSY97_1.ipynb ├── readme.md └── .gitignore /Ec240a_Fall2023/Data/NLSY97.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bryangraham/Ec240a/HEAD/Ec240a_Fall2023/Data/NLSY97.zip -------------------------------------------------------------------------------- /Ec240a_Fall2023/Syllabus_Ec240a_Fall2023.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bryangraham/Ec240a/HEAD/Ec240a_Fall2023/Syllabus_Ec240a_Fall2023.pdf -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | This repository contains instructional materials for the first semester Ph.D. class in econometrics at the University of California - Berkeley. -------------------------------------------------------------------------------- /Ec240a_Fall2023/Problem_Sets/Ec240a_Midterm_2023.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bryangraham/Ec240a/HEAD/Ec240a_Fall2023/Problem_Sets/Ec240a_Midterm_2023.pdf -------------------------------------------------------------------------------- /Ec240a_Fall2023/Problem_Sets/Ec240a_Problem_Set_1_Fall2023.pdf: 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-------------------------------------------------------------------------------- 1 | # Compiled python modules. 2 | *.pyc 3 | 4 | # Materials to Exclude (for now) 5 | /Ec240a_Fall2015/ 6 | 7 | # Ignore Jupyter/IPython Notebook Checkpoints 8 | **/.ipynb_checkpoints/ 9 | 10 | # Ignore graphics files created by Notebooks/Programs in Repo 11 | *.png 12 | 13 | # Exclude any files in "Not For Repo" subdirectories 14 | **/NotForRepo/ 15 | 16 | # OS Files 17 | .DS_Store 18 | .DS_Store? 19 | ._* 20 | 21 | # Source Lyx/Tex/BibTex files 22 | *.tex 23 | *.lyx 24 | *lyx-format*.lyx 25 | *.*# 26 | *.bib 27 | *.*~ 28 | 29 | # Source spreadsheet files 30 | *.ods 31 | *.xls 32 | *.xlsx -------------------------------------------------------------------------------- /Ec240a_Fall2023/Data/NLSY97/NLSY97.NLSY97: -------------------------------------------------------------------------------- 1 | R0000100 2 | R0489900 3 | R0490000 4 | R0490100 5 | R0490200 6 | R0490300 7 | R0536300 8 | R0536401 9 | R0536402 10 | R1200100 11 | R1200200 12 | R1200300 13 | R1201300 14 | R1204500 15 | R1204600 16 | R1204700 17 | R1235800 18 | R1302400 19 | R1302500 20 | R1302600 21 | R1302700 22 | R1482600 23 | R2340900 24 | R2341000 25 | R2341100 26 | R2341200 27 | R2341300 28 | R2558800 29 | R2563201 30 | R2563300 31 | R3649900 32 | R3650000 33 | R3650100 34 | R3650200 35 | R3650300 36 | R3880300 37 | R3884801 38 | R3884900 39 | R5098600 40 | R5098700 41 | R5098800 42 | R5098900 43 | R5099000 44 | R5464001 45 | R5464100 46 | R5473600 47 | R5473700 48 | R6827200 49 | R6827300 50 | R6827400 51 | R6827500 52 | R6827600 53 | R7222400 54 | R7227701 55 | R7227800 56 | R7237300 57 | R7237400 58 | R7295600 59 | R9829600 60 | S1055500 61 | S1055600 62 | S1055700 63 | S1055800 64 | S1055900 65 | S1535500 66 | S1541601 67 | S1541700 68 | S1552300 69 | S1552600 70 | S1552700 71 | S2005400 72 | S2011401 73 | S2011500 74 | S2022100 75 | S3134400 76 | S3134500 77 | S3134600 78 | S3134700 79 | S3805700 80 | S3812301 81 | 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| U0956900 150 | U0957000 151 | U1838600 152 | U1845400 153 | U2857000 154 | U2857100 155 | U2857200 156 | U2857300 157 | U3438000 158 | U3443900 159 | U4282100 160 | U4282200 161 | U4282300 162 | U4282400 163 | Z9033700 164 | Z9033800 165 | Z9033900 166 | Z9034000 167 | Z9034100 168 | Z9034200 169 | Z9058000 170 | Z9058100 171 | Z9058200 172 | Z9058300 173 | Z9058400 174 | Z9058500 175 | Z9058600 176 | Z9058700 177 | Z9058800 178 | Z9058900 179 | Z9058901 180 | Z9058902 181 | Z9058903 182 | Z9058904 183 | Z9058905 184 | Z9058906 185 | Z9058907 186 | Z9058908 187 | Z9058909 188 | Z9058910 189 | Z9058911 190 | Z9059000 191 | Z9059100 192 | Z9059200 193 | Z9059300 194 | Z9059400 195 | Z9059500 196 | Z9059600 197 | Z9059700 198 | Z9059800 199 | Z9059900 200 | Z9060000 201 | Z9060100 202 | Z9060200 203 | Z9060300 204 | Z9060400 205 | Z9060500 206 | Z9060600 207 | Z9060700 208 | Z9060800 209 | Z9060900 210 | Z9083800 211 | -------------------------------------------------------------------------------- /Ec240a_Fall2023/Data/NLSY97/NLSY97-value-labels.do: -------------------------------------------------------------------------------- 1 | 2 | label define vlR0000100 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 3 | label values R0000100 vlR0000100 4 | 5 | label define vlR0489900 1 "YES" 0 "NO" 6 | label values R0489900 vlR0489900 7 | 8 | label define vlR0490000 1 "YES" 0 "NO" 9 | label values R0490000 vlR0490000 10 | 11 | label define vlR0490100 1 "YES" 0 "NO" 12 | label values R0490100 vlR0490100 13 | 14 | label define vlR0490200 0 "0" 1 "1 TO 499" 500 "500 TO 999" 1000 "1000 TO 1499" 1500 "1500 TO 1999" 2000 "2000 TO 2499" 2500 "2500 TO 2999" 3000 "3000 TO 3499" 3500 "3500 TO 3999" 4000 "4000 TO 4499" 4500 "4500 TO 4999" 5000 "5000 TO 6999" 7000 "7000 TO 9999" 10000 "10000 TO 19999" 20000 "20000 TO 49999" 50000 "50000 TO 999999: 50000+" 15 | label values R0490200 vlR0490200 16 | 17 | label define vlR0490300 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 18 | label values R0490300 vlR0490300 19 | 20 | label define vlR0536300 1 "Male" 2 "Female" 0 "No Information" 21 | label values R0536300 vlR0536300 22 | 23 | label define vlR0536401 1 "1: January" 2 "2: February" 3 "3: March" 4 "4: April" 5 "5: May" 6 "6: June" 7 "7: July" 8 "8: August" 9 "9: September" 10 "10: October" 11 "11: November" 12 "12: December" 24 | label values R0536401 vlR0536401 25 | 26 | label define vlR1200100 0 "0" 1 "1 TO 4" 5 "5 TO 8" 9 "9 TO 14" 15 "15 TO 19" 20 "20 TO 24" 25 "25 TO 29" 30 "30 TO 34" 35 "35 TO 39" 40 "40 TO 44" 45 "45 TO 49" 50 "50 TO 99999999: 50+" 27 | label values R1200100 vlR1200100 28 | 29 | label define vlR1200200 0 "0" 1 "1 TO 4" 5 "5 TO 8" 9 "9 TO 14" 15 "15 TO 19" 20 "20 TO 24" 25 "25 TO 29" 30 "30 TO 34" 35 "35 TO 39" 40 "40 TO 44" 45 "45 TO 49" 50 "50 TO 99999999: 50+" 30 | label values R1200200 vlR1200200 31 | 32 | label define vlR1200300 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 33 | label values R1200300 vlR1200300 34 | 35 | label define vlR1201300 1 "Citizen, born in the U.S." 2 "Unknown, not born in U.S." 3 "Unknown, can't determine birthplace" 36 | label values R1201300 vlR1201300 37 | 38 | label define vlR1204500 -999999 "-999999 TO -3000: < -2999" -2999 "-2999 TO -2000" -1999 "-1999 TO -1000" -999 "-999 TO -1" 0 "0" 1 "1 TO 1000" 1001 "1001 TO 2000" 2001 "2001 TO 3000" 3001 "3001 TO 5000" 5001 "5001 TO 10000" 10001 "10001 TO 20000" 20001 "20001 TO 30000" 30001 "30001 TO 40000" 40001 "40001 TO 50000" 50001 "50001 TO 65000" 65001 "65001 TO 80000" 80001 "80001 TO 100000" 100001 "100001 TO 150000" 150001 "150001 TO 200000" 200001 "200001 TO 999999: 200001+" 39 | label values R1204500 vlR1204500 40 | 41 | label define vlR1204600 1 "Parent" 2 "Youth" 42 | label values R1204600 vlR1204600 43 | 44 | label define vlR1204700 -999999 "-999999 TO -3000: < -2999" -2999 "-2999 TO -2000" -1999 "-1999 TO -1000" -999 "-999 TO -6" 0 "0" 1 "1 TO 1000" 1001 "1001 TO 2000" 2001 "2001 TO 3000" 3001 "3001 TO 5000" 5001 "5001 TO 10000" 10001 "10001 TO 20000" 20001 "20001 TO 30000" 30001 "30001 TO 40000" 40001 "40001 TO 50000" 50001 "50001 TO 65000" 65001 "65001 TO 80000" 80001 "80001 TO 100000" 100001 "100001 TO 150000" 150001 "150001 TO 200000" 200001 "200001 TO 500000" 500001 "500001 TO 1000000" 1000001 "1000001 TO 1500000" 1500001 "1500001 TO 2000000" 2000001 "2000001 TO 999999999: 2000001+" 45 | label values R1204700 vlR1204700 46 | 47 | label define vlR1235800 1 "Cross-sectional" 0 "Oversample" 48 | label values R1235800 vlR1235800 49 | 50 | label define vlR1302400 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 51 | label values R1302400 vlR1302400 52 | 53 | label define vlR1302500 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 54 | label values R1302500 vlR1302500 55 | 56 | label define vlR1302600 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 57 | label values R1302600 vlR1302600 58 | 59 | label define vlR1302700 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 60 | label values R1302700 vlR1302700 61 | 62 | label define vlR1482600 1 "Black" 2 "Hispanic" 3 "Mixed Race (Non-Hispanic)" 4 "Non-Black / Non-Hispanic" 63 | label values R1482600 vlR1482600 64 | 65 | label define vlR2340900 1 "YES" 0 "NO" 66 | label values R2340900 vlR2340900 67 | 68 | label define vlR2341000 1 "YES" 0 "NO" 69 | label values R2341000 vlR2341000 70 | 71 | label define vlR2341100 1 "YES" 0 "NO" 72 | label values R2341100 vlR2341100 73 | 74 | label define vlR2341200 0 "0" 1 "1 TO 499" 500 "500 TO 999" 1000 "1000 TO 1499" 1500 "1500 TO 1999" 2000 "2000 TO 2499" 2500 "2500 TO 2999" 3000 "3000 TO 3499" 3500 "3500 TO 3999" 4000 "4000 TO 4499" 4500 "4500 TO 4999" 5000 "5000 TO 6999" 7000 "7000 TO 99999999: 7000+" 75 | label values R2341200 vlR2341200 76 | 77 | label define vlR2341300 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 78 | label values R2341300 vlR2341300 79 | 80 | label define vlR2558800 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 81 | label values R2558800 vlR2558800 82 | 83 | label define vlR2563201 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 95 "UNGRADED" 84 | label values R2563201 vlR2563201 85 | 86 | label define vlR2563300 -999999 "-999999 TO -3000: < -2999" -2999 "-2999 TO -2000" -1999 "-1999 TO -1000" -999 "-999 TO -1" 0 "0" 1 "1 TO 1000" 1001 "1001 TO 2000" 2001 "2001 TO 3000" 3001 "3001 TO 5000" 5001 "5001 TO 10000" 10001 "10001 TO 20000" 20001 "20001 TO 30000" 30001 "30001 TO 40000" 40001 "40001 TO 50000" 50001 "50001 TO 65000" 65001 "65001 TO 80000" 80001 "80001 TO 100000" 100001 "100001 TO 150000" 150001 "150001 TO 200000" 200001 "200001 TO 999999: 200001+" 87 | label values R2563300 vlR2563300 88 | 89 | label define vlR3649900 1 "YES" 0 "NO" 90 | label values R3649900 vlR3649900 91 | 92 | label define vlR3650000 1 "YES" 0 "NO" 93 | label values R3650000 vlR3650000 94 | 95 | label define vlR3650100 1 "YES" 0 "NO" 96 | label values R3650100 vlR3650100 97 | 98 | label define vlR3650200 0 "0" 1 "1 TO 499" 500 "500 TO 999" 1000 "1000 TO 1499" 1500 "1500 TO 1999" 2000 "2000 TO 2499" 2500 "2500 TO 2999" 3000 "3000 TO 3499" 3500 "3500 TO 3999" 4000 "4000 TO 4499" 4500 "4500 TO 4999" 5000 "5000 TO 6999" 7000 "7000 TO 99999999: 7000+" 99 | label values R3650200 vlR3650200 100 | 101 | label define vlR3650300 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 102 | label values R3650300 vlR3650300 103 | 104 | label define vlR3880300 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 105 | label values R3880300 vlR3880300 106 | 107 | label define vlR3884801 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 95 "UNGRADED" 108 | label values R3884801 vlR3884801 109 | 110 | label define vlR3884900 -999999 "-999999 TO -3000: < -2999" -2999 "-2999 TO -2000" -1999 "-1999 TO -1000" -999 "-999 TO -1" 0 "0" 1 "1 TO 1000" 1001 "1001 TO 2000" 2001 "2001 TO 3000" 3001 "3001 TO 5000" 5001 "5001 TO 10000" 10001 "10001 TO 20000" 20001 "20001 TO 30000" 30001 "30001 TO 40000" 40001 "40001 TO 50000" 50001 "50001 TO 65000" 65001 "65001 TO 80000" 80001 "80001 TO 100000" 100001 "100001 TO 150000" 150001 "150001 TO 200000" 200001 "200001 TO 999999: 200001+" 111 | label values R3884900 vlR3884900 112 | 113 | label define vlR5098600 1 "Yes" 0 "No" 114 | label values R5098600 vlR5098600 115 | 116 | label define vlR5098700 1 "Yes" 0 "No" 117 | label values R5098700 vlR5098700 118 | 119 | label define vlR5098800 1 "Yes" 0 "No" 120 | label values R5098800 vlR5098800 121 | 122 | label define vlR5098900 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 123 | label values R5098900 vlR5098900 124 | 125 | label define vlR5099000 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 126 | label values R5099000 vlR5099000 127 | 128 | label define vlR5464001 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 95 "UNGRADED" 129 | label values R5464001 vlR5464001 130 | 131 | label define vlR5464100 -999999 "-999999 TO -3000: < -2999" -2999 "-2999 TO -2000" -1999 "-1999 TO -1000" -999 "-999 TO -1" 0 "0" 1 "1 TO 1000" 1001 "1001 TO 2000" 2001 "2001 TO 3000" 3001 "3001 TO 5000" 5001 "5001 TO 10000" 10001 "10001 TO 20000" 20001 "20001 TO 30000" 30001 "30001 TO 40000" 40001 "40001 TO 50000" 50001 "50001 TO 65000" 65001 "65001 TO 80000" 80001 "80001 TO 100000" 100001 "100001 TO 150000" 150001 "150001 TO 200000" 200001 "200001 TO 999999: 200001+" 132 | label values R5464100 vlR5464100 133 | 134 | label define vlR5473600 0 "0: 0%" 1 "1 TO 10: 1%-10%" 11 "11 TO 20: 11%-20%" 21 "21 TO 30: 21%-30%" 31 "31 TO 40: 31%-40%" 41 "41 TO 50: 41%-50%" 51 "51 TO 60: 51%-60%" 61 "61 TO 70: 61%-70%" 71 "71 TO 80: 71%-80%" 81 "81 TO 90: 81%-90%" 91 "91 TO 100: 91%-100%" 135 | label values R5473600 vlR5473600 136 | 137 | label define vlR6827200 1 "YES" 0 "NO" 138 | label values R6827200 vlR6827200 139 | 140 | label define vlR6827300 1 "YES" 0 "NO" 141 | label values R6827300 vlR6827300 142 | 143 | label define vlR6827400 1 "YES" 0 "NO" 144 | label values R6827400 vlR6827400 145 | 146 | label define vlR6827500 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 147 | label values R6827500 vlR6827500 148 | 149 | label define vlR6827600 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 150 | label values R6827600 vlR6827600 151 | 152 | label define vlR7222400 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 153 | label values R7222400 vlR7222400 154 | 155 | label define vlR7227701 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 95 "UNGRADED" 156 | label values R7227701 vlR7227701 157 | 158 | label define vlR7227800 -999999 "-999999 TO -3000: < -2999" -2999 "-2999 TO -2000" -1999 "-1999 TO -1000" -999 "-999 TO -1" 0 "0" 1 "1 TO 1000" 1001 "1001 TO 2000" 2001 "2001 TO 3000" 3001 "3001 TO 5000" 5001 "5001 TO 10000" 10001 "10001 TO 20000" 20001 "20001 TO 30000" 30001 "30001 TO 40000" 40001 "40001 TO 50000" 50001 "50001 TO 65000" 65001 "65001 TO 80000" 80001 "80001 TO 100000" 100001 "100001 TO 150000" 150001 "150001 TO 200000" 200001 "200001 TO 999999: 200001+" 159 | label values R7227800 vlR7227800 160 | 161 | label define vlR7237300 0 "0: 0%" 1 "1 TO 10: 1%-10%" 11 "11 TO 20: 11%-20%" 21 "21 TO 30: 21%-30%" 31 "31 TO 40: 31%-40%" 41 "41 TO 50: 41%-50%" 51 "51 TO 60: 51%-60%" 61 "61 TO 70: 61%-70%" 71 "71 TO 80: 71%-80%" 81 "81 TO 90: 81%-90%" 91 "91 TO 100: 91%-100%" 162 | label values R7237300 vlR7237300 163 | 164 | label define vlR7295600 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 165 | label values R7295600 vlR7295600 166 | 167 | label define vlR9829600 0 "0" 1 "1 TO 999: .001-.999" 1000 "1000 TO 19999: 1.000-19.999" 20000 "20000 TO 39999: 20.000-39.999" 40000 "40000 TO 59999: 40.000-59.999" 60000 "60000 TO 79999: 60.000-79.999" 80000 "80000 TO 100000: 80.000-100.000" 168 | label values R9829600 vlR9829600 169 | 170 | label define vlS1055500 1 "YES" 0 "NO" 171 | label values S1055500 vlS1055500 172 | 173 | label define vlS1055600 1 "YES" 0 "NO" 174 | label values S1055600 vlS1055600 175 | 176 | label define vlS1055700 1 "YES" 0 "NO" 177 | label values S1055700 vlS1055700 178 | 179 | label define vlS1055800 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 180 | label values S1055800 vlS1055800 181 | 182 | label define vlS1055900 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 183 | label values S1055900 vlS1055900 184 | 185 | label define vlS1535500 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 186 | label values S1535500 vlS1535500 187 | 188 | label define vlS1541601 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 95 "UNGRADED" 189 | label values S1541601 vlS1541601 190 | 191 | label define vlS1541700 -999999 "-999999 TO -3000: < -2999" -2999 "-2999 TO -2000" -1999 "-1999 TO -1000" -999 "-999 TO -1" 0 "0" 1 "1 TO 1000" 1001 "1001 TO 2000" 2001 "2001 TO 3000" 3001 "3001 TO 5000" 5001 "5001 TO 10000" 10001 "10001 TO 20000" 20001 "20001 TO 30000" 30001 "30001 TO 40000" 40001 "40001 TO 50000" 50001 "50001 TO 65000" 65001 "65001 TO 80000" 80001 "80001 TO 100000" 100001 "100001 TO 150000" 150001 "150001 TO 200000" 200001 "200001 TO 999999: 200001+" 192 | label values S1541700 vlS1541700 193 | 194 | label define vlS1552300 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 195 | label values S1552300 vlS1552300 196 | 197 | label define vlS1552600 0 "0: 0%" 1 "1 TO 10: 1%-10%" 11 "11 TO 20: 11%-20%" 21 "21 TO 30: 21%-30%" 31 "31 TO 40: 31%-40%" 41 "41 TO 50: 41%-50%" 51 "51 TO 60: 51%-60%" 61 "61 TO 70: 61%-70%" 71 "71 TO 80: 71%-80%" 81 "81 TO 90: 81%-90%" 91 "91 TO 100: 91%-100%" 198 | label values S1552600 vlS1552600 199 | 200 | label define vlS2005400 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 201 | label values S2005400 vlS2005400 202 | 203 | label define vlS2011401 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 95 "UNGRADED" 204 | label values S2011401 vlS2011401 205 | 206 | label define vlS2011500 -999999 "-999999 TO -3000: < -2999" -2999 "-2999 TO -2000" -1999 "-1999 TO -1000" -999 "-999 TO -1" 0 "0" 1 "1 TO 1000" 1001 "1001 TO 2000" 2001 "2001 TO 3000" 3001 "3001 TO 5000" 5001 "5001 TO 10000" 10001 "10001 TO 20000" 20001 "20001 TO 30000" 30001 "30001 TO 40000" 40001 "40001 TO 50000" 50001 "50001 TO 65000" 65001 "65001 TO 80000" 80001 "80001 TO 100000" 100001 "100001 TO 150000" 150001 "150001 TO 200000" 200001 "200001 TO 999999: 200001+" 207 | label values S2011500 vlS2011500 208 | 209 | label define vlS2022100 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 210 | label values S2022100 vlS2022100 211 | 212 | label define vlS3134400 1 "YES" 0 "NO" 213 | label values S3134400 vlS3134400 214 | 215 | label define vlS3134500 1 "YES" 0 "NO" 216 | label values S3134500 vlS3134500 217 | 218 | label define vlS3134600 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 219 | label values S3134600 vlS3134600 220 | 221 | label define vlS3134700 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 222 | label values S3134700 vlS3134700 223 | 224 | label define vlS3805700 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 225 | label values S3805700 vlS3805700 226 | 227 | label define vlS3812301 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 95 "UNGRADED" 228 | label values S3812301 vlS3812301 229 | 230 | label define vlS3823000 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 231 | label values S3823000 vlS3823000 232 | 233 | label define vlS4799400 1 "YES" 0 "NO" 234 | label values S4799400 vlS4799400 235 | 236 | label define vlS4799500 1 "YES" 0 "NO" 237 | label values S4799500 vlS4799500 238 | 239 | label define vlS4799600 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 240 | label values S4799600 vlS4799600 241 | 242 | label define vlS4799700 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 243 | label values S4799700 vlS4799700 244 | 245 | label define vlS5405600 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 246 | label values S5405600 vlS5405600 247 | 248 | label define vlS5412700 0 "None" 1 "1st Grade" 2 "2nd Grade" 3 "3rd Grade" 4 "4th Grade" 5 "5th Grade" 6 "6th Grade" 7 "7th Grade" 8 "8th Grade" 9 "9th Grade" 10 "10th Grade" 11 "11th Grade" 12 "12th Grade" 95 "Ungraded" 249 | label values S5412700 vlS5412700 250 | 251 | label define vlS6500800 1 "YES" 0 "NO" 252 | label values S6500800 vlS6500800 253 | 254 | label define vlS6500900 1 "YES" 0 "NO" 255 | label values S6500900 vlS6500900 256 | 257 | label define vlS6501000 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 258 | label values S6501000 vlS6501000 259 | 260 | label define vlS6501100 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 261 | label values S6501100 vlS6501100 262 | 263 | label define vlS7506100 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 264 | label values S7506100 vlS7506100 265 | 266 | label define vlS7509600 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 267 | label values S7509600 vlS7509600 268 | 269 | label define vlS7513600 0 "None" 1 "1st Grade" 2 "2nd Grade" 3 "3rd Grade" 4 "4th Grade" 5 "5th Grade" 6 "6th Grade" 7 "7th Grade" 8 "8th Grade" 9 "9th Grade" 10 "10th Grade" 11 "11th Grade" 12 "12th Grade" 95 "Ungraded" 270 | label values S7513600 vlS7513600 271 | 272 | label define vlS8496300 1 "YES" 0 "NO" 273 | label values S8496300 vlS8496300 274 | 275 | label define vlS8496400 1 "YES" 0 "NO" 276 | label values S8496400 vlS8496400 277 | 278 | label define vlS8496500 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 279 | label values S8496500 vlS8496500 280 | 281 | label define vlS8496600 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 282 | label values S8496600 vlS8496600 283 | 284 | label define vlT0009400 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 285 | label values T0009400 vlT0009400 286 | 287 | label define vlT0012900 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 288 | label values T0012900 vlT0012900 289 | 290 | label define vlT0014000 0 "None" 1 "1st Grade" 2 "2nd Grade" 3 "3rd Grade" 4 "4th Grade" 5 "5th Grade" 6 "6th Grade" 7 "7th Grade" 8 "8th Grade" 9 "9th Grade" 10 "10th Grade" 11 "11th Grade" 12 "12th Grade" 95 "Ungraded" 291 | label values T0014000 vlT0014000 292 | 293 | label define vlT0889600 1 "YES" 0 "NO" 294 | label values T0889600 vlT0889600 295 | 296 | label define vlT0889700 1 "YES" 0 "NO" 297 | label values T0889700 vlT0889700 298 | 299 | label define vlT0889800 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 300 | label values T0889800 vlT0889800 301 | 302 | label define vlT0889900 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 303 | label values T0889900 vlT0889900 304 | 305 | label define vlT2012100 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 306 | label values T2012100 vlT2012100 307 | 308 | label define vlT2015800 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 309 | label values T2015800 vlT2015800 310 | 311 | label define vlT2016100 0 "None" 1 "1st Grade" 2 "2nd Grade" 3 "3rd Grade" 4 "4th Grade" 5 "5th Grade" 6 "6th Grade" 7 "7th Grade" 8 "8th Grade" 9 "9th Grade" 10 "10th Grade" 11 "11th Grade" 12 "12th Grade" 95 "Ungraded" 312 | label values T2016100 vlT2016100 313 | 314 | label define vlT3002800 1 "YES" 0 "NO" 315 | label values T3002800 vlT3002800 316 | 317 | label define vlT3002900 1 "YES" 0 "NO" 318 | label values T3002900 vlT3002900 319 | 320 | label define vlT3003000 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 321 | label values T3003000 vlT3003000 322 | 323 | label define vlT3003100 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 $250,000" 7 "G. More than $250,000" 324 | label values T3003100 vlT3003100 325 | 326 | label define vlT3602100 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 327 | label values T3602100 vlT3602100 328 | 329 | label define vlT3606100 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 330 | label values T3606100 vlT3606100 331 | 332 | label define vlT3606400 0 "None" 1 "1st Grade" 2 "2nd Grade" 3 "3rd Grade" 4 "4th Grade" 5 "5th Grade" 6 "6th Grade" 7 "7th Grade" 8 "8th Grade" 9 "9th Grade" 10 "10th Grade" 11 "11th Grade" 12 "12th Grade" 95 "Ungraded" 333 | label values T3606400 vlT3606400 334 | 335 | label define vlT4405800 1 "YES" 0 "NO" 336 | label values T4405800 vlT4405800 337 | 338 | label define vlT4405900 1 "YES" 0 "NO" 339 | label values T4405900 vlT4405900 340 | 341 | label define vlT4406000 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 342 | label values T4406000 vlT4406000 343 | 344 | label define vlT4406100 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 - $250,000" 7 "G. More than $250,000" 345 | label values T4406100 vlT4406100 346 | 347 | label define vlT5202300 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 348 | label values T5202300 vlT5202300 349 | 350 | label define vlT5206500 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 351 | label values T5206500 vlT5206500 352 | 353 | label define vlT5206700 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 354 | label values T5206700 vlT5206700 355 | 356 | label define vlT5206800 0 "None" 1 "1st Grade" 2 "2nd Grade" 3 "3rd Grade" 4 "4th Grade" 5 "5th Grade" 6 "6th Grade" 7 "7th Grade" 8 "8th Grade" 9 "9th Grade" 10 "10th Grade" 11 "11th Grade" 12 "12th Grade" 95 "Ungraded" 357 | label values T5206800 vlT5206800 358 | 359 | label define vlT6055300 1 "YES" 0 "NO" 360 | label values T6055300 vlT6055300 361 | 362 | label define vlT6055400 1 "YES" 0 "NO" 363 | label values T6055400 vlT6055400 364 | 365 | label define vlT6055500 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 366 | label values T6055500 vlT6055500 367 | 368 | label define vlT6055600 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 - $250,000" 7 "G. More than $250,000" 369 | label values T6055600 vlT6055600 370 | 371 | label define vlT6652100 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 372 | label values T6652100 vlT6652100 373 | 374 | label define vlT6656300 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 375 | label values T6656300 vlT6656300 376 | 377 | label define vlT6656500 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 378 | label values T6656500 vlT6656500 379 | 380 | label define vlT6656600 0 "None" 1 "1st Grade" 2 "2nd Grade" 3 "3rd Grade" 4 "4th Grade" 5 "5th Grade" 6 "6th Grade" 7 "7th Grade" 8 "8th Grade" 9 "9th Grade" 10 "10th Grade" 11 "11th Grade" 12 "12th Grade" 95 "Ungraded" 381 | label values T6656600 vlT6656600 382 | 383 | label define vlT7545400 1 "YES" 0 "NO" 384 | label values T7545400 vlT7545400 385 | 386 | label define vlT7545500 1 "YES" 0 "NO" 387 | label values T7545500 vlT7545500 388 | 389 | label define vlT7545600 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 390 | label values T7545600 vlT7545600 391 | 392 | label define vlT7545700 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 - $250,000" 7 "G. More than $250,000" 393 | label values T7545700 vlT7545700 394 | 395 | label define vlT8123700 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 396 | label values T8123700 vlT8123700 397 | 398 | label define vlT8128700 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 399 | label values T8128700 vlT8128700 400 | 401 | label define vlT8128900 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 402 | label values T8128900 vlT8128900 403 | 404 | label define vlT8129000 0 "None" 1 "1st Grade" 2 "2nd Grade" 3 "3rd Grade" 4 "4th Grade" 5 "5th Grade" 6 "6th Grade" 7 "7th Grade" 8 "8th Grade" 9 "9th Grade" 10 "10th Grade" 11 "11th Grade" 12 "12th Grade" 95 "Ungraded" 405 | label values T8129000 vlT8129000 406 | 407 | label define vlT8976500 1 "YES" 0 "NO" 408 | label values T8976500 vlT8976500 409 | 410 | label define vlT8976600 1 "YES" 0 "NO" 411 | label values T8976600 vlT8976600 412 | 413 | label define vlT8976700 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 414 | label values T8976700 vlT8976700 415 | 416 | label define vlT8976800 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 - $250,000" 7 "G. More than $250,000" 417 | label values T8976800 vlT8976800 418 | 419 | label define vlU0001900 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 420 | label values U0001900 vlU0001900 421 | 422 | label define vlU0008600 1 "Citizen, born in the U.S." 2 "Citizen, naturalized" 3 "Applicant for Naturalization" 4 "Permanent Resident" 5 "Applicant for Residence" 6 "Other" 423 | label values U0008600 vlU0008600 424 | 425 | label define vlU0008800 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 426 | label values U0008800 vlU0008800 427 | 428 | label define vlU0956700 1 "YES" 0 "NO" 429 | label values U0956700 vlU0956700 430 | 431 | label define vlU0956800 1 "YES" 0 "NO" 432 | label values U0956800 vlU0956800 433 | 434 | label define vlU0956900 0 "0" 1 "1 TO 999" 1000 "1000 TO 1999" 2000 "2000 TO 2999" 3000 "3000 TO 3999" 4000 "4000 TO 4999" 5000 "5000 TO 5999" 6000 "6000 TO 6999" 7000 "7000 TO 7999" 8000 "8000 TO 8999" 9000 "9000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 49999" 50000 "50000 TO 99999999: 50000+" 435 | label values U0956900 vlU0956900 436 | 437 | label define vlU0957000 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 - $250,000" 7 "G. More than $250,000" 438 | label values U0957000 vlU0957000 439 | 440 | label define vlU1838600 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 441 | label values U1838600 vlU1838600 442 | 443 | label define vlU1845400 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 444 | label values U1845400 vlU1845400 445 | 446 | label define vlU2857000 1 "YES" 0 "NO" 447 | label values U2857000 vlU2857000 448 | 449 | label define vlU2857100 1 "YES" 0 "NO" 450 | label values U2857100 vlU2857100 451 | 452 | label define vlU2857200 0 "0" 1 "1 TO 4999" 5000 "5000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 29999" 30000 "30000 TO 39999" 40000 "40000 TO 49999" 50000 "50000 TO 59999" 60000 "60000 TO 69999" 70000 "70000 TO 79999" 80000 "80000 TO 89999" 90000 "90000 TO 99999" 100000 "100000 TO 149999" 150000 "150000 TO 99999999: 150000+" 453 | label values U2857200 vlU2857200 454 | 455 | label define vlU2857300 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 - $250,000" 7 "G. More than $250,000" 456 | label values U2857300 vlU2857300 457 | 458 | label define vlU3438000 1 "Northeast (CT, ME, MA, NH, NJ, NY, PA, RI, VT)" 2 "North Central (IL, IN, IA, KS, MI, MN, MO, NE, OH, ND, SD, WI)" 3 "South (AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN , TX, VA, WV)" 4 "West (AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY)" 459 | label values U3438000 vlU3438000 460 | 461 | label define vlU3443900 0 "NONE" 1 "1ST GRADE" 2 "2ND GRADE" 3 "3RD GRADE" 4 "4TH GRADE" 5 "5TH GRADE" 6 "6TH GRADE" 7 "7TH GRADE" 8 "8TH GRADE" 9 "9TH GRADE" 10 "10TH GRADE" 11 "11TH GRADE" 12 "12TH GRADE" 13 "1ST YEAR COLLEGE" 14 "2ND YEAR COLLEGE" 15 "3RD YEAR COLLEGE" 16 "4TH YEAR COLLEGE" 17 "5TH YEAR COLLEGE" 18 "6TH YEAR COLLEGE" 19 "7TH YEAR COLLEGE" 20 "8TH YEAR COLLEGE OR MORE" 95 "UNGRADED" 462 | label values U3443900 vlU3443900 463 | 464 | label define vlU4282100 1 "YES" 0 "NO" 465 | label values U4282100 vlU4282100 466 | 467 | label define vlU4282200 1 "YES" 0 "NO" 468 | label values U4282200 vlU4282200 469 | 470 | label define vlU4282300 0 "0" 1 "1 TO 4999" 5000 "5000 TO 9999" 10000 "10000 TO 14999" 15000 "15000 TO 19999" 20000 "20000 TO 24999" 25000 "25000 TO 29999" 30000 "30000 TO 39999" 40000 "40000 TO 49999" 50000 "50000 TO 59999" 60000 "60000 TO 69999" 70000 "70000 TO 79999" 80000 "80000 TO 89999" 90000 "90000 TO 99999" 100000 "100000 TO 149999" 150000 "150000 TO 99999999: 150000+" 471 | label values U4282300 vlU4282300 472 | 473 | label define vlU4282400 1 "A. $1 - $5,000" 2 "B. $5,001 - $10,000" 3 "C. $10,001 - $25,000" 4 "D. $25,001 - $50,000" 5 "E. $50,001 - $100,000" 6 "F. $100,001 - $250,000" 7 "G. More than $250,000" 474 | label values U4282400 vlU4282400 475 | 476 | label define vlZ9033700 1 "200 - 300" 2 "301 - 400" 3 "401 - 500" 4 "501 - 600" 5 "601 - 700" 6 "701 - 800" 0 "Have not yet received the scores" 477 | label values Z9033700 vlZ9033700 478 | 479 | label define vlZ9033800 1 "Round 1" 2 "Round 2" 3 "Round 3" 4 "Round 4" 5 "Round 5" 6 "Round 6" 7 "Round 7" 8 "Round 8" 9 "Round 9" 10 "Round 10" 11 "Round 11" 12 "Round 12" 13 "Round 13" 14 "Round 14" 15 "Round 15" 16 "Round 16" 17 "Round 17" 18 "Round 18" 19 "Round 19" 480 | label values Z9033800 vlZ9033800 481 | 482 | label define vlZ9033900 1 "200 - 300" 2 "301 - 400" 3 "401 - 500" 4 "501 - 600" 5 "601 - 700" 6 "701 - 800" 0 "Have not yet received the scores" 483 | label values Z9033900 vlZ9033900 484 | 485 | label define vlZ9034000 1 "Round 1" 2 "Round 2" 3 "Round 3" 4 "Round 4" 5 "Round 5" 6 "Round 6" 7 "Round 7" 8 "Round 8" 9 "Round 9" 10 "Round 10" 11 "Round 11" 12 "Round 12" 13 "Round 13" 14 "Round 14" 15 "Round 15" 16 "Round 16" 17 "Round 17" 18 "Round 18" 19 "Round 19" 486 | label values Z9034000 vlZ9034000 487 | 488 | label define vlZ9034100 1 "0 - 6" 2 "7 - 12" 3 "13 - 18" 4 "19 - 24" 5 "25 - 30" 6 "31 - 36" 0 "Have not yet received the scores" 489 | label values Z9034100 vlZ9034100 490 | 491 | label define vlZ9034200 1 "Round 1" 2 "Round 2" 3 "Round 3" 4 "Round 4" 5 "Round 5" 6 "Round 6" 7 "Round 7" 8 "Round 8" 9 "Round 9" 10 "Round 10" 11 "Round 11" 12 "Round 12" 13 "Round 13" 14 "Round 14" 15 "Round 15" 16 "Round 16" 17 "Round 17" 18 "Round 18" 19 "Round 19" 492 | label values Z9034200 vlZ9034200 493 | 494 | label define vlZ9058000 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 495 | label values Z9058000 vlZ9058000 496 | 497 | label define vlZ9058100 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 498 | label values Z9058100 vlZ9058100 499 | 500 | label define vlZ9058200 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 501 | label values Z9058200 vlZ9058200 502 | 503 | label define vlZ9058300 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 504 | label values Z9058300 vlZ9058300 505 | 506 | label define vlZ9058400 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 507 | label values Z9058400 vlZ9058400 508 | 509 | label define vlZ9058500 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 510 | label values Z9058500 vlZ9058500 511 | 512 | label define vlZ9058600 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 513 | label values Z9058600 vlZ9058600 514 | 515 | label define vlZ9058700 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 516 | label values Z9058700 vlZ9058700 517 | 518 | label define vlZ9058800 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 519 | label values Z9058800 vlZ9058800 520 | 521 | label define vlZ9058900 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 522 | label values Z9058900 vlZ9058900 523 | 524 | label define vlZ9058901 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 525 | label values Z9058901 vlZ9058901 526 | 527 | label define vlZ9058902 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 528 | label values Z9058902 vlZ9058902 529 | 530 | label define vlZ9058903 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 531 | label values Z9058903 vlZ9058903 532 | 533 | label define vlZ9058904 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 534 | label values Z9058904 vlZ9058904 535 | 536 | label define vlZ9058905 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 537 | label values Z9058905 vlZ9058905 538 | 539 | label define vlZ9058906 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 540 | label values Z9058906 vlZ9058906 541 | 542 | label define vlZ9058907 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 543 | label values Z9058907 vlZ9058907 544 | 545 | label define vlZ9058908 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 546 | label values Z9058908 vlZ9058908 547 | 548 | label define vlZ9058909 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 549 | label values Z9058909 vlZ9058909 550 | 551 | label define vlZ9058910 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 552 | label values Z9058910 vlZ9058910 553 | 554 | label define vlZ9058911 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 555 | label values Z9058911 vlZ9058911 556 | 557 | label define vlZ9059000 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 558 | label values Z9059000 vlZ9059000 559 | 560 | label define vlZ9059100 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 561 | label values Z9059100 vlZ9059100 562 | 563 | label define vlZ9059200 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 564 | label values Z9059200 vlZ9059200 565 | 566 | label define vlZ9059300 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 567 | label values Z9059300 vlZ9059300 568 | 569 | label define vlZ9059400 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 570 | label values Z9059400 vlZ9059400 571 | 572 | label define vlZ9059500 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 573 | label values Z9059500 vlZ9059500 574 | 575 | label define vlZ9059600 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 576 | label values Z9059600 vlZ9059600 577 | 578 | label define vlZ9059700 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 579 | label values Z9059700 vlZ9059700 580 | 581 | label define vlZ9059800 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 582 | label values Z9059800 vlZ9059800 583 | 584 | label define vlZ9059900 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 585 | label values Z9059900 vlZ9059900 586 | 587 | label define vlZ9060000 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 588 | label values Z9060000 vlZ9060000 589 | 590 | label define vlZ9060100 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 591 | label values Z9060100 vlZ9060100 592 | 593 | label define vlZ9060200 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 594 | label values Z9060200 vlZ9060200 595 | 596 | label define vlZ9060300 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 597 | label values Z9060300 vlZ9060300 598 | 599 | label define vlZ9060400 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 600 | label values Z9060400 vlZ9060400 601 | 602 | label define vlZ9060500 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 603 | label values Z9060500 vlZ9060500 604 | 605 | label define vlZ9060600 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 606 | label values Z9060600 vlZ9060600 607 | 608 | label define vlZ9060700 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 609 | label values Z9060700 vlZ9060700 610 | 611 | label define vlZ9060800 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 612 | label values Z9060800 vlZ9060800 613 | 614 | label define vlZ9060900 0 "0: weeks" 1 "1 TO 5: weeks" 6 "6 TO 10: weeks" 11 "11 TO 15: weeks" 16 "16 TO 20: weeks" 21 "21 TO 25: weeks" 26 "26 TO 30: weeks" 31 "31 TO 35: weeks" 36 "36 TO 40: weeks" 41 "41 TO 45: weeks" 46 "46 TO 50: weeks" 51 "51 TO 999: weeks" 615 | label values Z9060900 vlZ9060900 616 | 617 | label define vlZ9083800 0 "None" 1 "1st grade" 2 "2nd grade" 3 "3rd grade" 4 "4th grade" 5 "5th grade" 6 "6th grade" 7 "7th grade" 8 "8th grade" 9 "9th grade" 10 "10th grade" 11 "11th grade" 12 "12th grade" 13 "1st year college" 14 "2nd year college" 15 "3rd year college" 16 "4th year college" 17 "5th year college" 18 "6th year college" 19 "7th year college" 20 "8th year college or more" 95 "Ungraded" 618 | label values Z9083800 vlZ9083800 619 | /* Crosswalk for Reference number & Question name 620 | * Uncomment and edit this RENAME statement to rename variables for ease of use. 621 | * This command does not guarantee uniqueness 622 | */ 623 | /* *start* */ 624 | /* 625 | rename R0000100 PUBID_1997 626 | rename R0489900 YINC_1400_1997 // YINC-1400 627 | rename R0490000 YINC_1500_1997 // YINC-1500 628 | rename R0490100 YINC_1600_1997 // YINC-1600 629 | rename R0490200 YINC_1700_1997 // YINC-1700 630 | rename R0490300 YINC_1800_1997 // YINC-1800 631 | rename R0536300 KEY!SEX_1997 632 | rename R0536401 KEY!BDATE_M_1997 633 | rename R0536402 KEY!BDATE_Y_1997 634 | rename R1200100 CV_BIO_MOM_AGE_CHILD1_1997 635 | rename R1200200 CV_BIO_MOM_AGE_YOUTH_1997 636 | rename R1200300 CV_CENSUS_REGION_1997 637 | rename R1201300 CV_CITIZENSHIP_1997 638 | rename R1204500 CV_INCOME_GROSS_YR_1997 639 | rename R1204600 CV_HH_INCOME_SOURCE_1997 640 | rename R1204700 CV_HH_NET_WORTH_P_1997 641 | rename R1235800 CV_SAMPLE_TYPE_1997 642 | rename R1302400 CV_HGC_BIO_DAD_1997 643 | rename R1302500 CV_HGC_BIO_MOM_1997 644 | rename R1302600 CV_HGC_RES_DAD_1997 645 | rename R1302700 CV_HGC_RES_MOM_1997 646 | rename R1482600 KEY!RACE_ETHNICITY_1997 647 | rename R2340900 YINC_1400_1998 // YINC-1400 648 | rename R2341000 YINC_1500_1998 // YINC-1500 649 | rename R2341100 YINC_1600_1998 // YINC-1600 650 | rename R2341200 YINC_1700_1998 // YINC-1700 651 | rename R2341300 YINC_1800_1998 // YINC-1800 652 | rename R2558800 CV_CENSUS_REGION_1998 653 | rename R2563201 CV_HGC_9899_1998 654 | rename R2563300 CV_INCOME_GROSS_YR_1998 655 | rename R3649900 YINC_1400_1999 // YINC-1400 656 | rename R3650000 YINC_1500_1999 // YINC-1500 657 | rename R3650100 YINC_1600_1999 // YINC-1600 658 | rename R3650200 YINC_1700_1999 // YINC-1700 659 | rename R3650300 YINC_1800_1999 // YINC-1800 660 | rename R3880300 CV_CENSUS_REGION_1999 661 | rename R3884801 CV_HGC_9900_1999 662 | rename R3884900 CV_INCOME_GROSS_YR_1999 663 | rename R5098600 YINC_1400_2000 // YINC-1400 664 | rename R5098700 YINC_1500_2000 // YINC-1500 665 | rename R5098800 YINC_1600_2000 // YINC-1600 666 | rename R5098900 YINC_1700_2000 // YINC-1700 667 | rename R5099000 YINC_1800_2000 // YINC-1800 668 | rename R5464001 CV_HGC_0001_2000 669 | rename R5464100 CV_INCOME_GROSS_YR_2000 670 | rename R5473600 CV_PIAT_PERCENTILE_SCORE_2000 671 | rename R5473700 CV_PIAT_STANDARD_SCORE_2000 672 | rename R6827200 YINC_1400_2001 // YINC-1400 673 | rename R6827300 YINC_1500_2001 // YINC-1500 674 | rename R6827400 YINC_1600_2001 // YINC-1600 675 | rename R6827500 YINC_1700_2001 // YINC-1700 676 | rename R6827600 YINC_1800_2001 // YINC-1800 677 | rename R7222400 CV_CENSUS_REGION_2001 678 | rename R7227701 CV_HGC_0102_2001 679 | rename R7227800 CV_INCOME_GROSS_YR_2001 680 | rename R7237300 CV_PIAT_PERCENTILE_SCORE_2001 681 | rename R7237400 CV_PIAT_STANDARD_SCORE_2001 682 | rename R7295600 CV_CITIZEN_CURRENT_2001 683 | rename R9829600 ASVAB_MATH_VERBAL_SCORE_PCT_1999 684 | rename S1055500 YINC_1400_2002 // YINC-1400 685 | rename S1055600 YINC_1500_2002 // YINC-1500 686 | rename S1055700 YINC_1600_2002 // YINC-1600 687 | rename S1055800 YINC_1700_2002 // YINC-1700 688 | rename S1055900 YINC_1800_2002 // YINC-1800 689 | rename S1535500 CV_CENSUS_REGION_2002 690 | rename S1541601 CV_HGC_0203_2002 691 | rename S1541700 CV_INCOME_GROSS_YR_2002 692 | rename S1552300 CV_CITIZEN_CURRENT_2002 693 | rename S1552600 CV_PIAT_PERCENTILE_SCORE_2002 694 | rename S1552700 CV_PIAT_STANDARD_SCORE_2002 695 | rename S2005400 CV_CENSUS_REGION_2003 696 | rename S2011401 CV_HGC_0304_2003 697 | rename S2011500 CV_INCOME_GROSS_YR_2003 698 | rename S2022100 CV_CITIZEN_CURRENT_2003 699 | rename S3134400 YINC_1400_2003 // YINC-1400 700 | rename S3134500 YINC_1600_2003 // YINC-1600 701 | rename S3134600 YINC_1700_2003 // YINC-1700 702 | rename S3134700 YINC_1800_2003 // YINC-1800 703 | rename S3805700 CV_CENSUS_REGION_2004 704 | rename S3812301 CV_HGC_0405_2004 705 | rename S3823000 CV_CITIZEN_CURRENT_2004 706 | rename S4799400 YINC_1400_2004 // YINC-1400 707 | rename S4799500 YINC_1600_2004 // YINC-1600 708 | rename S4799600 YINC_1700_2004 // YINC-1700 709 | rename S4799700 YINC_1800_2004 // YINC-1800 710 | rename S5405600 CV_CENSUS_REGION_2005 711 | rename S5412700 CV_HGC_0506_2005 712 | rename S6500800 YINC_1400_2005 // YINC-1400 713 | rename S6500900 YINC_1600_2005 // YINC-1600 714 | rename S6501000 YINC_1700_2005 // YINC-1700 715 | rename S6501100 YINC_1800_2005 // YINC-1800 716 | rename S7506100 CV_CENSUS_REGION_2006 717 | rename S7509600 CV_CITIZEN_CURRENT_2006 718 | rename S7513600 CV_HGC_0607_2006 719 | rename S8496300 YINC_1400_2006 // YINC-1400 720 | rename S8496400 YINC_1600_2006 // YINC-1600 721 | rename S8496500 YINC_1700_2006 // YINC-1700 722 | rename S8496600 YINC_1800_2006 // YINC-1800 723 | rename T0009400 CV_CENSUS_REGION_2007 724 | rename T0012900 CV_CITIZEN_CURRENT_2007 725 | rename T0014000 CV_HGC_0708_2007 726 | rename T0889600 YINC_1400_2007 // YINC-1400 727 | rename T0889700 YINC_1600_2007 // YINC-1600 728 | rename T0889800 YINC_1700_2007 // YINC-1700 729 | rename T0889900 YINC_1800_2007 // YINC-1800 730 | rename T2012100 CV_CENSUS_REGION_2008 731 | rename T2015800 CV_CITIZEN_CURRENT_2008 732 | rename T2016100 CV_HGC_0809_2008 733 | rename T3002800 YINC_1400_2008 // YINC-1400 734 | rename T3002900 YINC_1600_2008 // YINC-1600 735 | rename T3003000 YINC_1700_2008 // YINC-1700 736 | rename T3003100 YINC_1800_2008 // YINC-1800 737 | rename T3602100 CV_CENSUS_REGION_2009 738 | rename T3606100 CV_CITIZEN_CURRENT_2009 739 | rename T3606400 CV_HGC_0910_2009 740 | rename T4405800 YINC_1400_2009 // YINC-1400 741 | rename T4405900 YINC_1600_2009 // YINC-1600 742 | rename T4406000 YINC_1700_2009 // YINC-1700 743 | rename T4406100 YINC_1800_2009 // YINC-1800 744 | rename T5202300 CV_CENSUS_REGION_2010 745 | rename T5206500 CV_CITIZEN_CURRENT_2010 746 | rename T5206700 CV_HGC_EVER_EDT_2010 747 | rename T5206800 CV_HGC_1011_2010 748 | rename T6055300 YINC_1400_2010 // YINC-1400 749 | rename T6055400 YINC_1600_2010 // YINC-1600 750 | rename T6055500 YINC_1700_2010 // YINC-1700 751 | rename T6055600 YINC_1800_2010 // YINC-1800 752 | rename T6652100 CV_CENSUS_REGION_2011 753 | rename T6656300 CV_CITIZEN_CURRENT_2011 754 | rename T6656500 CV_HGC_EVER_EDT_2011 755 | rename T6656600 CV_HGC_1112_2011 756 | rename T7545400 YINC_1400_2011 // YINC-1400 757 | rename T7545500 YINC_1600_2011 // YINC-1600 758 | rename T7545600 YINC_1700_2011 // YINC-1700 759 | rename T7545700 YINC_1800_2011 // YINC-1800 760 | rename T8123700 CV_CENSUS_REGION_2013 761 | rename T8128700 CV_CITIZEN_CURRENT_2013 762 | rename T8128900 CV_HGC_EVER_EDT_2013 763 | rename T8129000 CV_HGC_1314_2013 764 | rename T8976500 YINC_1400_2013 // YINC-1400 765 | rename T8976600 YINC_1600_2013 // YINC-1600 766 | rename T8976700 YINC_1700_2013 // YINC-1700 767 | rename T8976800 YINC_1800_2013 // YINC-1800 768 | rename U0001900 CV_CENSUS_REGION_2015 769 | rename U0008600 CV_CITIZEN_CURRENT_2015 770 | rename U0008800 CV_HGC_EVER_EDT_2015 771 | rename U0956700 YINC_1400_2015 // YINC-1400 772 | rename U0956800 YINC_1600_2015 // YINC-1600 773 | rename U0956900 YINC_1700_2015 // YINC-1700 774 | rename U0957000 YINC_1800_2015 // YINC-1800 775 | rename U1838600 CV_CENSUS_REGION_2017 776 | rename U1845400 CV_HGC_EVER_EDT_2017 777 | rename U2857000 YINC_1400_2017 // YINC-1400 778 | rename U2857100 YINC_1600_2017 // YINC-1600 779 | rename U2857200 YINC_1700_2017 // YINC-1700 780 | rename U2857300 YINC_1800_2017 // YINC-1800 781 | rename U3438000 CV_CENSUS_REGION_2019 782 | rename U3443900 CV_HGC_EVER_EDT_2019 783 | rename U4282100 YINC_1400_2019 // YINC-1400 784 | rename U4282200 YINC_1600_2019 // YINC-1600 785 | rename U4282300 YINC_1700_2019 // YINC-1700 786 | rename U4282400 YINC_1800_2019 // YINC-1800 787 | rename Z9033700 CVC_SAT_MATH_SCORE_2007_XRND 788 | rename Z9033800 CVC_SAT_MATH_RND_2007_XRND 789 | rename Z9033900 CVC_SAT_VERBAL_SCORE_2007_XRND 790 | rename Z9034000 CVC_SAT_VERBAL_RND_2007_XRND 791 | rename Z9034100 CVC_ACT_SCORE_2007_XRND 792 | rename Z9034200 CVC_ACT_RND_2007_XRND 793 | rename Z9058000 CVC_WKSWK_YR_ET_00_XRND // CVC_WKSWK_YR_ET.00 794 | rename Z9058100 CVC_WKSWK_YR_ET_01_XRND // CVC_WKSWK_YR_ET.01 795 | rename Z9058200 CVC_WKSWK_YR_ET_02_XRND // CVC_WKSWK_YR_ET.02 796 | rename Z9058300 CVC_WKSWK_YR_ET_03_XRND // CVC_WKSWK_YR_ET.03 797 | rename Z9058400 CVC_WKSWK_YR_ET_04_XRND // CVC_WKSWK_YR_ET.04 798 | rename Z9058500 CVC_WKSWK_YR_ET_05_XRND // CVC_WKSWK_YR_ET.05 799 | rename Z9058600 CVC_WKSWK_YR_ET_06_XRND // CVC_WKSWK_YR_ET.06 800 | rename Z9058700 CVC_WKSWK_YR_ET_07_XRND // CVC_WKSWK_YR_ET.07 801 | rename Z9058800 CVC_WKSWK_YR_ET_08_XRND // CVC_WKSWK_YR_ET.08 802 | rename Z9058900 CVC_WKSWK_YR_ET_09_XRND // CVC_WKSWK_YR_ET.09 803 | rename Z9058901 CVC_WKSWK_YR_ET_10_XRND // CVC_WKSWK_YR_ET.10 804 | rename Z9058902 CVC_WKSWK_YR_ET_11_XRND // CVC_WKSWK_YR_ET.11 805 | rename Z9058903 CVC_WKSWK_YR_ET_12_XRND // CVC_WKSWK_YR_ET.12 806 | rename Z9058904 CVC_WKSWK_YR_ET_13_XRND // CVC_WKSWK_YR_ET.13 807 | rename Z9058905 CVC_WKSWK_YR_ET_14_XRND // CVC_WKSWK_YR_ET.14 808 | rename Z9058906 CVC_WKSWK_YR_ET_15_XRND // CVC_WKSWK_YR_ET.15 809 | rename Z9058907 CVC_WKSWK_YR_ET_16_XRND // CVC_WKSWK_YR_ET.16 810 | rename Z9058908 CVC_WKSWK_YR_ET_17_XRND // CVC_WKSWK_YR_ET.17 811 | rename Z9058909 CVC_WKSWK_YR_ET_18_XRND // CVC_WKSWK_YR_ET.18 812 | rename Z9058910 CVC_WKSWK_YR_ET_19_XRND // CVC_WKSWK_YR_ET.19 813 | rename Z9058911 CVC_WKSWK_YR_ET_20_XRND // CVC_WKSWK_YR_ET.20 814 | rename Z9059000 CVC_WKSWK_YR_ET_80_XRND // CVC_WKSWK_YR_ET.80 815 | rename Z9059100 CVC_WKSWK_YR_ET_81_XRND // CVC_WKSWK_YR_ET.81 816 | rename Z9059200 CVC_WKSWK_YR_ET_82_XRND // CVC_WKSWK_YR_ET.82 817 | rename Z9059300 CVC_WKSWK_YR_ET_83_XRND // CVC_WKSWK_YR_ET.83 818 | rename Z9059400 CVC_WKSWK_YR_ET_84_XRND // CVC_WKSWK_YR_ET.84 819 | rename Z9059500 CVC_WKSWK_YR_ET_85_XRND // CVC_WKSWK_YR_ET.85 820 | rename Z9059600 CVC_WKSWK_YR_ET_86_XRND // CVC_WKSWK_YR_ET.86 821 | rename Z9059700 CVC_WKSWK_YR_ET_87_XRND // CVC_WKSWK_YR_ET.87 822 | rename Z9059800 CVC_WKSWK_YR_ET_88_XRND // CVC_WKSWK_YR_ET.88 823 | rename Z9059900 CVC_WKSWK_YR_ET_89_XRND // CVC_WKSWK_YR_ET.89 824 | rename Z9060000 CVC_WKSWK_YR_ET_90_XRND // CVC_WKSWK_YR_ET.90 825 | rename Z9060100 CVC_WKSWK_YR_ET_91_XRND // CVC_WKSWK_YR_ET.91 826 | rename Z9060200 CVC_WKSWK_YR_ET_92_XRND // CVC_WKSWK_YR_ET.92 827 | rename Z9060300 CVC_WKSWK_YR_ET_93_XRND // CVC_WKSWK_YR_ET.93 828 | rename Z9060400 CVC_WKSWK_YR_ET_94_XRND // CVC_WKSWK_YR_ET.94 829 | rename Z9060500 CVC_WKSWK_YR_ET_95_XRND // CVC_WKSWK_YR_ET.95 830 | rename Z9060600 CVC_WKSWK_YR_ET_96_XRND // CVC_WKSWK_YR_ET.96 831 | rename Z9060700 CVC_WKSWK_YR_ET_97_XRND // CVC_WKSWK_YR_ET.97 832 | rename Z9060800 CVC_WKSWK_YR_ET_98_XRND // CVC_WKSWK_YR_ET.98 833 | rename Z9060900 CVC_WKSWK_YR_ET_99_XRND // CVC_WKSWK_YR_ET.99 834 | rename Z9083800 CVC_HGC_EVER_XRND 835 | 836 | */ 837 | /* *end* */ 838 | /* To convert variable names to lower case use the TOLOWER command 839 | * (type findit tolower and follow the links to install). 840 | * TOLOWER VARLIST will change listed variables to lower case; 841 | * TOLOWER without a specified variable list will convert all variables in the dataset to lower case 842 | */ 843 | /* tolower */ 844 | -------------------------------------------------------------------------------- /Ec240a_Fall2023/Notebooks/Notebook_NLSY97_1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "dc136a70", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "# Direct Python to plot all figures inline (i.e., not in a separate window)\n", 11 | "%matplotlib inline\n", 12 | "\n", 13 | "# Load libraries\n", 14 | "import numpy as np\n", 15 | "import pandas as pd\n", 16 | "import seaborn as sns \n", 17 | "import matplotlib.pyplot as plt\n", 18 | "import statsmodels.api as sm" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 2, 24 | "id": "8de44ac4", 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "# Directory where NLSY79_TeachingExtract.csv file is located\n", 29 | "data = '/Users/bgraham/Dropbox/Teaching/Berkeley_Courses/Ec240a/Ec240a_Fall2023/Data/NLSY97/'" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 3, 35 | "id": "218eacae", 36 | "metadata": { 37 | "scrolled": true 38 | }, 39 | "outputs": [], 40 | "source": [ 41 | "# Read in NLSY79 Extract as a pandas dataframe\n", 42 | "nlsy97 = pd.read_csv(data+'NLSY97.csv') # Reading .csv file as DataFrame\n", 43 | "nlsy97.set_index('R0000100', inplace=True)\n", 44 | "nlsy97.index.name = 'pubid'\n", 45 | "\n", 46 | "# Read in CPI data as a pandas dataframe\n", 47 | "# https://www.bls.gov/cpi/research-series/r-cpi-u-rs-home.htm on 27 Oct 2023\n", 48 | "cpi = pd.read_excel(data+'r-cpi-u-rs-allitems.xlsx', sheet_name='Table 1', skiprows=5) # Reading .xlsx file as DataFrame\n", 49 | "cpi.set_index('YEAR', inplace=True)" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 4, 55 | "id": "c93508a2", 56 | "metadata": {}, 57 | "outputs": [ 58 | { 59 | "data": { 60 | "text/html": [ 61 | "
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R0489900R0490000R0490100R0490200R0490300R0536300R0536401R0536402R1200100R1200200...Z9060100Z9060200Z9060300Z9060400Z9060500Z9060600Z9060700Z9060800Z9060900Z9083800
pubid
10-4-4-4-42919811926...00000026525216
20-4-4-4-41719821919...00000000014
30-4-4-4-42919832626...00000000016
\n", 201 | "

3 rows × 209 columns

\n", 202 | "
" 203 | ], 204 | "text/plain": [ 205 | " R0489900 R0490000 R0490100 R0490200 R0490300 R0536300 R0536401 \\\n", 206 | "pubid \n", 207 | "1 0 -4 -4 -4 -4 2 9 \n", 208 | "2 0 -4 -4 -4 -4 1 7 \n", 209 | "3 0 -4 -4 -4 -4 2 9 \n", 210 | "\n", 211 | " R0536402 R1200100 R1200200 ... Z9060100 Z9060200 Z9060300 \\\n", 212 | "pubid ... \n", 213 | "1 1981 19 26 ... 0 0 0 \n", 214 | "2 1982 19 19 ... 0 0 0 \n", 215 | "3 1983 26 26 ... 0 0 0 \n", 216 | "\n", 217 | " Z9060400 Z9060500 Z9060600 Z9060700 Z9060800 Z9060900 Z9083800 \n", 218 | "pubid \n", 219 | "1 0 0 0 26 52 52 16 \n", 220 | "2 0 0 0 0 0 0 14 \n", 221 | "3 0 0 0 0 0 0 16 \n", 222 | "\n", 223 | "[3 rows x 209 columns]" 224 | ] 225 | }, 226 | "execution_count": 4, 227 | "metadata": {}, 228 | "output_type": "execute_result" 229 | } 230 | ], 231 | "source": [ 232 | "nlsy97[0:3]" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 5, 238 | "id": "66fb2cdb", 239 | "metadata": {}, 240 | "outputs": [ 241 | { 242 | "data": { 243 | "text/html": [ 244 | "
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JANFEBMARAPRMAYJUNEJULYAUGSEPOCTNOVDECAVGUnnamed: 14
YEAR
1977NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN100.0NaNNaN
1978100.5101.1101.8102.7103.6104.5105.0105.5106.1106.7107.3107.8104.4NaN
1979108.7109.7110.7111.8113.0114.1115.1116.0117.1117.9118.5119.5114.3NaN
\n", 349 | "
" 350 | ], 351 | "text/plain": [ 352 | " JAN FEB MAR APR MAY JUNE JULY AUG SEP OCT \\\n", 353 | "YEAR \n", 354 | "1977 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 355 | "1978 100.5 101.1 101.8 102.7 103.6 104.5 105.0 105.5 106.1 106.7 \n", 356 | "1979 108.7 109.7 110.7 111.8 113.0 114.1 115.1 116.0 117.1 117.9 \n", 357 | "\n", 358 | " NOV DEC AVG Unnamed: 14 \n", 359 | "YEAR \n", 360 | "1977 NaN 100.0 NaN NaN \n", 361 | "1978 107.3 107.8 104.4 NaN \n", 362 | "1979 118.5 119.5 114.3 NaN " 363 | ] 364 | }, 365 | "execution_count": 5, 366 | "metadata": {}, 367 | "output_type": "execute_result" 368 | } 369 | ], 370 | "source": [ 371 | "cpi[0:3]" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": 6, 377 | "id": "cdd54d81", 378 | "metadata": {}, 379 | "outputs": [], 380 | "source": [ 381 | "nlsy97['female'] = 1*(nlsy97['R0536300']==2)\n", 382 | "\n", 383 | "nlsy97.rename(columns={'R0536401': \"birth_month\", \"R0536402\": \"birth_year\"})\n", 384 | "\n", 385 | "nlsy97['black'] = 1*(nlsy97['R1482600']==1)\n", 386 | "nlsy97['hispanic'] = 1*(nlsy97['R1482600']==2)\n", 387 | "\n", 388 | "nlsy97['asvab'] = np.nan\n", 389 | "nlsy97.loc[nlsy97['R9829600']>=0,'asvab'] = nlsy97['R9829600'][nlsy97['R9829600']>=0]/1000" 390 | ] 391 | }, 392 | { 393 | "cell_type": "code", 394 | "execution_count": 7, 395 | "id": "43d6281e", 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "data": { 400 | "text/html": [ 401 | "
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hgc_99hgc_00hgc_01hgc_02hgc_03hgc_04hgc_05hgc_06hgc_07hgc_08...hgc_10hgc_11hgc_13hgc_ever10hgc_ever11hgc_ever13hgc_ever15hgc_ever17hgc_ever19hgc_ever
count8128.0000007972.0000007773.0000007815.0000007675.0000007425.0000007267.0000007480.0000007345.0000007419.000000...7408.0000007342.0000007069.0000007411.0000007345.0000007060.0000007028.0000006663.0000006871.0000008884.000000
mean10.09190510.68966411.12183211.35994911.43035811.45279511.44695211.44010711.43730411.439547...11.43372011.43326111.44334413.41883713.48563613.64589213.74174713.90304713.95735713.676835
std1.4296321.3034331.1999971.1622011.1667981.1761431.1808821.1821171.1813601.180046...1.1831961.1748631.1655882.9307792.9548333.0407993.0946433.1309693.1403953.163758
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%9.00000010.00000011.00000011.00000011.00000012.00000012.00000012.00000012.00000012.000000...12.00000012.00000012.00000012.00000012.00000012.00000012.00000012.00000012.00000012.000000
50%10.00000011.00000012.00000012.00000012.00000012.00000012.00000012.00000012.00000012.000000...12.00000012.00000012.00000013.00000013.00000013.00000014.00000014.00000014.00000013.000000
75%11.00000012.00000012.00000012.00000012.00000012.00000012.00000012.00000012.00000012.000000...12.00000012.00000012.00000016.00000016.00000016.00000016.00000016.00000016.00000016.000000
max12.00000012.00000012.00000012.00000012.00000012.00000012.00000012.00000012.00000012.000000...12.00000012.00000012.00000020.00000020.00000020.00000020.00000020.00000020.00000020.000000
\n", 637 | "

8 rows × 21 columns

\n", 638 | "
" 639 | ], 640 | "text/plain": [ 641 | " hgc_99 hgc_00 hgc_01 hgc_02 hgc_03 \\\n", 642 | "count 8128.000000 7972.000000 7773.000000 7815.000000 7675.000000 \n", 643 | "mean 10.091905 10.689664 11.121832 11.359949 11.430358 \n", 644 | "std 1.429632 1.303433 1.199997 1.162201 1.166798 \n", 645 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 646 | "25% 9.000000 10.000000 11.000000 11.000000 11.000000 \n", 647 | "50% 10.000000 11.000000 12.000000 12.000000 12.000000 \n", 648 | "75% 11.000000 12.000000 12.000000 12.000000 12.000000 \n", 649 | "max 12.000000 12.000000 12.000000 12.000000 12.000000 \n", 650 | "\n", 651 | " hgc_04 hgc_05 hgc_06 hgc_07 hgc_08 ... \\\n", 652 | "count 7425.000000 7267.000000 7480.000000 7345.000000 7419.000000 ... \n", 653 | "mean 11.452795 11.446952 11.440107 11.437304 11.439547 ... \n", 654 | "std 1.176143 1.180882 1.182117 1.181360 1.180046 ... \n", 655 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 ... \n", 656 | "25% 12.000000 12.000000 12.000000 12.000000 12.000000 ... \n", 657 | "50% 12.000000 12.000000 12.000000 12.000000 12.000000 ... \n", 658 | "75% 12.000000 12.000000 12.000000 12.000000 12.000000 ... \n", 659 | "max 12.000000 12.000000 12.000000 12.000000 12.000000 ... \n", 660 | "\n", 661 | " hgc_10 hgc_11 hgc_13 hgc_ever10 hgc_ever11 \\\n", 662 | "count 7408.000000 7342.000000 7069.000000 7411.000000 7345.000000 \n", 663 | "mean 11.433720 11.433261 11.443344 13.418837 13.485636 \n", 664 | "std 1.183196 1.174863 1.165588 2.930779 2.954833 \n", 665 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 666 | "25% 12.000000 12.000000 12.000000 12.000000 12.000000 \n", 667 | "50% 12.000000 12.000000 12.000000 13.000000 13.000000 \n", 668 | "75% 12.000000 12.000000 12.000000 16.000000 16.000000 \n", 669 | "max 12.000000 12.000000 12.000000 20.000000 20.000000 \n", 670 | "\n", 671 | " hgc_ever13 hgc_ever15 hgc_ever17 hgc_ever19 hgc_ever \n", 672 | "count 7060.000000 7028.000000 6663.000000 6871.000000 8884.000000 \n", 673 | "mean 13.645892 13.741747 13.903047 13.957357 13.676835 \n", 674 | "std 3.040799 3.094643 3.130969 3.140395 3.163758 \n", 675 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 676 | "25% 12.000000 12.000000 12.000000 12.000000 12.000000 \n", 677 | "50% 13.000000 14.000000 14.000000 14.000000 13.000000 \n", 678 | "75% 16.000000 16.000000 16.000000 16.000000 16.000000 \n", 679 | "max 20.000000 20.000000 20.000000 20.000000 20.000000 \n", 680 | "\n", 681 | "[8 rows x 21 columns]" 682 | ] 683 | }, 684 | "execution_count": 7, 685 | "metadata": {}, 686 | "output_type": "execute_result" 687 | } 688 | ], 689 | "source": [ 690 | "nlsy97['hgc_99'] = np.nan\n", 691 | "nlsy97.loc[nlsy97['R3884801']>=0,'hgc_99'] = nlsy97['R3884801'][nlsy97['R3884801']>=0]\n", 692 | "nlsy97['hgc_99'].replace(95, 0, inplace=True)\n", 693 | "\n", 694 | "nlsy97['hgc_00'] = np.nan\n", 695 | "nlsy97.loc[nlsy97['R5464001']>=0,'hgc_00'] = nlsy97['R5464001'][nlsy97['R5464001']>=0]\n", 696 | "nlsy97['hgc_00'].replace(95, 0, inplace=True)\n", 697 | "\n", 698 | "nlsy97['hgc_01'] = np.nan\n", 699 | "nlsy97.loc[nlsy97['R7227701']>=0,'hgc_01'] = nlsy97['R7227701'][nlsy97['R7227701']>=0]\n", 700 | "nlsy97['hgc_01'].replace(95, 0, inplace=True)\n", 701 | "\n", 702 | "nlsy97['hgc_02'] = np.nan\n", 703 | "nlsy97.loc[nlsy97['S1541601']>=0,'hgc_02'] = nlsy97['S1541601'][nlsy97['S1541601']>=0]\n", 704 | "nlsy97['hgc_02'].replace(95, 0, inplace=True)\n", 705 | "\n", 706 | "nlsy97['hgc_03'] = np.nan\n", 707 | "nlsy97.loc[nlsy97['S2011401']>=0,'hgc_03'] = nlsy97['S2011401'][nlsy97['S2011401']>=0]\n", 708 | "nlsy97['hgc_03'].replace(95, 0, inplace=True)\n", 709 | "\n", 710 | "nlsy97['hgc_04'] = np.nan\n", 711 | "nlsy97.loc[nlsy97['S3812301']>=0,'hgc_04'] = nlsy97['S3812301'][nlsy97['S3812301']>=0]\n", 712 | "nlsy97['hgc_04'].replace(95, 0, inplace=True)\n", 713 | "\n", 714 | "nlsy97['hgc_05'] = np.nan\n", 715 | "nlsy97.loc[nlsy97['S5412700']>=0,'hgc_05'] = nlsy97['S5412700'][nlsy97['S5412700']>=0]\n", 716 | "nlsy97['hgc_05'].replace(95, 0, inplace=True)\n", 717 | "\n", 718 | "nlsy97['hgc_06'] = np.nan\n", 719 | "nlsy97.loc[nlsy97['S7513600']>=0,'hgc_06'] = nlsy97['S7513600'][nlsy97['S7513600']>=0]\n", 720 | "nlsy97['hgc_06'].replace(95, 0, inplace=True)\n", 721 | "\n", 722 | "nlsy97['hgc_07'] = np.nan\n", 723 | "nlsy97.loc[nlsy97['T0014000']>=0,'hgc_07'] = nlsy97['T0014000'][nlsy97['T0014000']>=0]\n", 724 | "nlsy97['hgc_07'].replace(95, 0, inplace=True)\n", 725 | "\n", 726 | "nlsy97['hgc_08'] = np.nan\n", 727 | "nlsy97.loc[nlsy97['T2016100']>=0,'hgc_08'] = nlsy97['T2016100'][nlsy97['T2016100']>=0]\n", 728 | "nlsy97['hgc_08'].replace(95, 0, inplace=True)\n", 729 | "\n", 730 | "nlsy97['hgc_09'] = np.nan\n", 731 | "nlsy97.loc[nlsy97['T3606400']>=0,'hgc_09'] = nlsy97['T3606400'][nlsy97['T3606400']>=0]\n", 732 | "nlsy97['hgc_09'].replace(95, 0, inplace=True)\n", 733 | "\n", 734 | "nlsy97['hgc_10'] = np.nan\n", 735 | "nlsy97.loc[nlsy97['T5206800']>=0,'hgc_10'] = nlsy97['T5206800'][nlsy97['T5206800']>=0]\n", 736 | "nlsy97['hgc_10'].replace(95, 0, inplace=True)\n", 737 | "\n", 738 | "nlsy97['hgc_11'] = np.nan\n", 739 | "nlsy97.loc[nlsy97['T6656600']>=0,'hgc_11'] = nlsy97['T6656600'][nlsy97['T6656600']>=0]\n", 740 | "nlsy97['hgc_11'].replace(95, 0, inplace=True)\n", 741 | "\n", 742 | "nlsy97['hgc_13'] = np.nan\n", 743 | "nlsy97.loc[nlsy97['T8129000']>=0,'hgc_13'] = nlsy97['T8129000'][nlsy97['T8129000']>=0]\n", 744 | "nlsy97['hgc_13'].replace(95, 0, inplace=True)\n", 745 | "\n", 746 | "nlsy97['hgc_ever10'] = np.nan\n", 747 | "nlsy97.loc[nlsy97['T5206700']>=0,'hgc_ever10'] = nlsy97['T5206700'][nlsy97['T5206700']>=0]\n", 748 | "nlsy97['hgc_ever10'].replace(95, 0, inplace=True)\n", 749 | "\n", 750 | "nlsy97['hgc_ever11'] = np.nan\n", 751 | "nlsy97.loc[nlsy97['T6656500']>=0,'hgc_ever11'] = nlsy97['T6656500'][nlsy97['T6656500']>=0]\n", 752 | "nlsy97['hgc_ever11'].replace(95, 0, inplace=True)\n", 753 | "\n", 754 | "nlsy97['hgc_ever13'] = np.nan\n", 755 | "nlsy97.loc[nlsy97['T8128900']>=0,'hgc_ever13'] = nlsy97['T8128900'][nlsy97['T8128900']>=0]\n", 756 | "nlsy97['hgc_ever13'].replace(95, 0, inplace=True)\n", 757 | "\n", 758 | "nlsy97['hgc_ever15'] = np.nan\n", 759 | "nlsy97.loc[nlsy97['U0008800']>=0,'hgc_ever15'] = nlsy97['U0008800'][nlsy97['U0008800']>=0]\n", 760 | "nlsy97['hgc_ever15'].replace(95, 0, inplace=True)\n", 761 | "\n", 762 | "nlsy97['hgc_ever17'] = np.nan\n", 763 | "nlsy97.loc[nlsy97['U1845400']>=0,'hgc_ever17'] = nlsy97['U1845400'][nlsy97['U1845400']>=0]\n", 764 | "nlsy97['hgc_ever17'].replace(95, 0, inplace=True)\n", 765 | "\n", 766 | "nlsy97['hgc_ever19'] = np.nan\n", 767 | "nlsy97.loc[nlsy97['U3443900']>=0,'hgc_ever19'] = nlsy97['U3443900'][nlsy97['U3443900']>=0]\n", 768 | "nlsy97['hgc_ever19'].replace(95, 0, inplace=True)\n", 769 | "\n", 770 | "nlsy97['hgc_ever'] = np.nan\n", 771 | "nlsy97.loc[nlsy97['Z9083800']>=0,'hgc_ever'] = nlsy97['Z9083800'][nlsy97['Z9083800']>=0]\n", 772 | "nlsy97['hgc_ever'].replace(95, 0, inplace=True)\n", 773 | "\n", 774 | "\n", 775 | "nlsy97[['hgc_99','hgc_00','hgc_01','hgc_02','hgc_03','hgc_04','hgc_05','hgc_06','hgc_07','hgc_08', \\\n", 776 | " 'hgc_09','hgc_10','hgc_11','hgc_13','hgc_ever10','hgc_ever11','hgc_ever13','hgc_ever15','hgc_ever17','hgc_ever19','hgc_ever']].describe()" 777 | ] 778 | }, 779 | { 780 | "cell_type": "code", 781 | "execution_count": 8, 782 | "id": "d18987a4", 783 | "metadata": {}, 784 | "outputs": [], 785 | "source": [ 786 | "# Construct annual earnings measure 1996\n", 787 | "# NOTE: No top-coding during this calendar year\n", 788 | "nlsy97['earn96'] = np.nan\n", 789 | "nlsy97.loc[nlsy97['R0490200']>=0,'earn96'] = nlsy97['R0490200'][nlsy97['R0490200']>=0] # Reports earnings\n", 790 | "nlsy97.loc[pd.isna(nlsy97['earn96']) & (nlsy97['R0489900']==0),'earn96'] = 0 # Reports no earnings (#1)\n", 791 | "nlsy97.loc[nlsy97['R0489900']==-1 & (nlsy97['R0490100']==0),'earn96'] = 0 # Reports no earnings (#2)\n", 792 | "nlsy97.loc[nlsy97['R0489900']==-2 & (nlsy97['R0490000']==0),'earn96'] = 0 # Reports no earnings (#3)\n", 793 | "nlsy97.loc[nlsy97['R0490200'].isin([-1,-2]) & (nlsy97['R0490300']==1),'earn96'] = 2500 # $1 to $5000 range\n", 794 | "nlsy97.loc[nlsy97['R0490200'].isin([-1,-2]) & (nlsy97['R0490300']==2),'earn96'] = 7500 # $5001 to $10000 range\n", 795 | "nlsy97.loc[nlsy97['R0490200'].isin([-1,-2]) & (nlsy97['R0490300']==3),'earn96'] = 17500 # $10001 to $25000 range\n", 796 | "nlsy97.loc[nlsy97['R0490200'].isin([-1,-2]) & (nlsy97['R0490300']==4),'earn96'] = 37500 # $25001 to $50000 range\n", 797 | "nlsy97.loc[nlsy97['R0490200'].isin([-1,-2]) & (nlsy97['R0490300']==5),'earn96'] = 75000 # $50001 to $100000 range\n", 798 | "nlsy97.loc[nlsy97['R0490200'].isin([-1,-2]) & (nlsy97['R0490300']==6),'earn96'] = 175000 # $100001 to $250000 range\n", 799 | "\n", 800 | "# Keep track of respondents reporting only an earnings range\n", 801 | "nlsy97['earn96_range'] = np.nan\n", 802 | "nlsy97.loc[~pd.isna(nlsy97['earn96']),'earn96_range'] = 0\n", 803 | "nlsy97.loc[nlsy97['R0490300']>=1,'earn96_range'] = 1\n", 804 | "\n", 805 | "# Deflate earnings measures\n", 806 | "nlsy97['earn96'] = nlsy97['earn96']*(cpi.loc[2022]['AVG']/cpi.loc[1996]['AVG'])\n", 807 | "\n", 808 | "# Construct annual earnings measure 1997\n", 809 | "# NOTE: Top-coded at $14,048 (lowest value of top 2%)\n", 810 | "nlsy97['earn97'] = np.nan\n", 811 | "nlsy97.loc[nlsy97['R2341200']>=0,'earn97'] = nlsy97['R2341200'][nlsy97['R2341200']>=0] # Reports earnings\n", 812 | "nlsy97.loc[pd.isna(nlsy97['earn97']) & (nlsy97['R2340900']==0),'earn97'] = 0 # Reports no earnings (#1)\n", 813 | "nlsy97.loc[nlsy97['R2340900']==-1 & (nlsy97['R2341100']==0),'earn97'] = 0 # Reports no earnings (#2)\n", 814 | "nlsy97.loc[nlsy97['R2340900']==-2 & (nlsy97['R2341000']==0),'earn97'] = 0 # Reports no earnings (#3)\n", 815 | "nlsy97.loc[nlsy97['R2341200'].isin([-1,-2]) & (nlsy97['R2341300']==1),'earn97'] = 2500 # $1 to $5000 range\n", 816 | "nlsy97.loc[nlsy97['R2341200'].isin([-1,-2]) & (nlsy97['R2341300']==2),'earn97'] = 7500 # $5001 to $10000 range\n", 817 | "nlsy97.loc[nlsy97['R2341200'].isin([-1,-2]) & (nlsy97['R2341300']==3),'earn97'] = 17500 # $10001 to $25000 range\n", 818 | "nlsy97.loc[nlsy97['R2341200'].isin([-1,-2]) & (nlsy97['R2341300']==4),'earn97'] = 37500 # $25001 to $50000 range\n", 819 | "nlsy97.loc[nlsy97['R2341200'].isin([-1,-2]) & (nlsy97['R2341300']==5),'earn97'] = 75000 # $50001 to $100000 range\n", 820 | "nlsy97.loc[nlsy97['R2341200'].isin([-1,-2]) & (nlsy97['R2341300']==6),'earn97'] = 175000 # $100001 to $250000 range\n", 821 | "\n", 822 | "# Keep track of respondents reporting only an earnings range\n", 823 | "nlsy97['earn97_range'] = np.nan\n", 824 | "nlsy97.loc[~pd.isna(nlsy97['earn97']),'earn97_range'] = 0\n", 825 | "nlsy97.loc[nlsy97['R2341300']>=1,'earn97_range'] = 1\n", 826 | "\n", 827 | "# Deflate earnings measures\n", 828 | "nlsy97['earn97'] = nlsy97['earn97']*(cpi.loc[2022]['AVG']/cpi.loc[1997]['AVG'])\n", 829 | "\n", 830 | "# Construct annual earnings measure 1998\n", 831 | "# NOTE: Top-coded at $25,249 (lowest value of top 2%)\n", 832 | "nlsy97['earn98'] = np.nan\n", 833 | "nlsy97.loc[nlsy97['R3650200']>=0,'earn98'] = nlsy97['R3650200'][nlsy97['R3650200']>=0] # Reports earnings\n", 834 | "nlsy97.loc[pd.isna(nlsy97['earn98']) & (nlsy97['R3649900']==0),'earn98'] = 0 # Reports no earnings (#1)\n", 835 | "nlsy97.loc[nlsy97['R3649900']==-1 & (nlsy97['R3650100']==0),'earn98'] = 0 # Reports no earnings (#2)\n", 836 | "nlsy97.loc[nlsy97['R3649900']==-2 & (nlsy97['R3650000']==0),'earn98'] = 0 # Reports no earnings (#3)\n", 837 | "nlsy97.loc[nlsy97['R3650200'].isin([-1,-2]) & (nlsy97['R3650300']==1),'earn98'] = 2500 # $1 to $5000 range\n", 838 | "nlsy97.loc[nlsy97['R3650200'].isin([-1,-2]) & (nlsy97['R3650300']==2),'earn98'] = 7500 # $5001 to $10000 range\n", 839 | "nlsy97.loc[nlsy97['R3650200'].isin([-1,-2]) & (nlsy97['R3650300']==3),'earn98'] = 17500 # $10001 to $25000 range\n", 840 | "nlsy97.loc[nlsy97['R3650200'].isin([-1,-2]) & (nlsy97['R3650300']==4),'earn98'] = 37500 # $25001 to $50000 range\n", 841 | "nlsy97.loc[nlsy97['R3650200'].isin([-1,-2]) & (nlsy97['R3650300']==5),'earn98'] = 75000 # $50001 to $100000 range\n", 842 | "nlsy97.loc[nlsy97['R3650200'].isin([-1,-2]) & (nlsy97['R3650300']==6),'earn98'] = 175000 # $100001 to $250000 range\n", 843 | "\n", 844 | "# Keep track of respondents reporting only an earnings range\n", 845 | "nlsy97['earn98_range'] = np.nan\n", 846 | "nlsy97.loc[~pd.isna(nlsy97['earn98']),'earn98_range'] = 0\n", 847 | "nlsy97.loc[nlsy97['R3650300']>=1,'earn98_range'] = 1\n", 848 | "\n", 849 | "# Deflate earnings measures\n", 850 | "nlsy97['earn98'] = nlsy97['earn98']*(cpi.loc[2022]['AVG']/cpi.loc[1998]['AVG'])\n", 851 | "\n", 852 | "# Construct annual earnings measure 1999\n", 853 | "# NOTE: Top-coded at $23,000 (lowest value of top 2%)\n", 854 | "nlsy97['earn99'] = np.nan\n", 855 | "nlsy97.loc[nlsy97['R5098900']>=0,'earn99'] = nlsy97['R5098900'][nlsy97['R5098900']>=0] # Reports earnings\n", 856 | "nlsy97.loc[pd.isna(nlsy97['earn99']) & (nlsy97['R5098600']==0),'earn99'] = 0 # Reports no earnings (#1)\n", 857 | "nlsy97.loc[nlsy97['R5098600']==-1 & (nlsy97['R5098800']==0),'earn99'] = 0 # Reports no earnings (#2)\n", 858 | "nlsy97.loc[nlsy97['R5098600']==-2 & (nlsy97['R5098700']==0),'earn99'] = 0 # Reports no earnings (#3)\n", 859 | "nlsy97.loc[nlsy97['R5098900'].isin([-1,-2]) & (nlsy97['R5099000']==1),'earn99'] = 2500 # $1 to $5000 range\n", 860 | "nlsy97.loc[nlsy97['R5098900'].isin([-1,-2]) & (nlsy97['R5099000']==2),'earn99'] = 7500 # $5001 to $10000 range\n", 861 | "nlsy97.loc[nlsy97['R5098900'].isin([-1,-2]) & (nlsy97['R5099000']==3),'earn99'] = 17500 # $10001 to $25000 range\n", 862 | "nlsy97.loc[nlsy97['R5098900'].isin([-1,-2]) & (nlsy97['R5099000']==4),'earn99'] = 37500 # $25001 to $50000 range\n", 863 | "nlsy97.loc[nlsy97['R5098900'].isin([-1,-2]) & (nlsy97['R5099000']==5),'earn99'] = 75000 # $50001 to $100000 range\n", 864 | "nlsy97.loc[nlsy97['R5098900'].isin([-1,-2]) & (nlsy97['R5099000']==6),'earn99'] = 175000 # $100001 to $250000 range\n", 865 | "\n", 866 | "# Keep track of respondents reporting only an earnings range\n", 867 | "nlsy97['earn99_range'] = np.nan\n", 868 | "nlsy97.loc[~pd.isna(nlsy97['earn99']),'earn99_range'] = 0\n", 869 | "nlsy97.loc[nlsy97['R5099000']>=1,'earn99_range'] = 1\n", 870 | "\n", 871 | "# Deflate earnings measures\n", 872 | "nlsy97['earn99'] = nlsy97['earn99']*(cpi.loc[2022]['AVG']/cpi.loc[1999]['AVG'])\n", 873 | "\n", 874 | "# Construct annual earnings measure 2000\n", 875 | "# NOTE: Top-coded at $28,000 (lowest value of top 2%)\n", 876 | "nlsy97['earn00'] = np.nan\n", 877 | "nlsy97.loc[nlsy97['R6827500']>=0,'earn00'] = nlsy97['R6827500'][nlsy97['R6827500']>=0] # Reports earnings\n", 878 | "nlsy97.loc[pd.isna(nlsy97['earn00']) & (nlsy97['R6827200']==0),'earn00'] = 0 # Reports no earnings (#1)\n", 879 | "nlsy97.loc[nlsy97['R6827200']==-1 & (nlsy97['R6827400']==0),'earn00'] = 0 # Reports no earnings (#2)\n", 880 | "nlsy97.loc[nlsy97['R6827200']==-2 & (nlsy97['R6827300']==0),'earn00'] = 0 # Reports no earnings (#3)\n", 881 | "nlsy97.loc[nlsy97['R6827500'].isin([-1,-2]) & (nlsy97['R6827600']==1),'earn00'] = 2500 # $1 to $5000 range\n", 882 | "nlsy97.loc[nlsy97['R6827500'].isin([-1,-2]) & (nlsy97['R6827600']==2),'earn00'] = 7500 # $5001 to $10000 range\n", 883 | "nlsy97.loc[nlsy97['R6827500'].isin([-1,-2]) & (nlsy97['R6827600']==3),'earn00'] = 17500 # $10001 to $25000 range\n", 884 | "nlsy97.loc[nlsy97['R6827500'].isin([-1,-2]) & (nlsy97['R6827600']==4),'earn00'] = 37500 # $25001 to $50000 range\n", 885 | "nlsy97.loc[nlsy97['R6827500'].isin([-1,-2]) & (nlsy97['R6827600']==5),'earn00'] = 75000 # $50001 to $100000 range\n", 886 | "nlsy97.loc[nlsy97['R6827500'].isin([-1,-2]) & (nlsy97['R6827600']==6),'earn00'] = 175000 # $100001 to $250000 range\n", 887 | "\n", 888 | "# Keep track of respondents reporting only an earnings range\n", 889 | "nlsy97['earn00_range'] = np.nan\n", 890 | "nlsy97.loc[~pd.isna(nlsy97['earn00']),'earn00_range'] = 0\n", 891 | "nlsy97.loc[nlsy97['R6827600']>=1,'earn00_range'] = 1\n", 892 | "\n", 893 | "# Deflate earnings measures\n", 894 | "nlsy97['earn00'] = nlsy97['earn00']*(cpi.loc[2022]['AVG']/cpi.loc[2000]['AVG'])\n", 895 | "\n", 896 | "# Construct annual earnings measure 2001\n", 897 | "# NOTE: Top-coded at $34,000 (lowest value of top 2%)\n", 898 | "nlsy97['earn01'] = np.nan\n", 899 | "nlsy97.loc[nlsy97['S1055800']>=0,'earn01'] = nlsy97['S1055800'][nlsy97['S1055800']>=0] # Reports earnings\n", 900 | "nlsy97.loc[pd.isna(nlsy97['earn01']) & (nlsy97['S1055500']==0),'earn01'] = 0 # Reports no earnings (#1)\n", 901 | "nlsy97.loc[nlsy97['S1055500']==-1 & (nlsy97['S1055700']==0),'earn01'] = 0 # Reports no earnings (#2)\n", 902 | "nlsy97.loc[nlsy97['S1055500']==-2 & (nlsy97['S1055600']==0),'earn01'] = 0 # Reports no earnings (#3)\n", 903 | "nlsy97.loc[nlsy97['S1055800'].isin([-1,-2]) & (nlsy97['S1055900']==1),'earn01'] = 2500 # $1 to $5000 range\n", 904 | "nlsy97.loc[nlsy97['S1055800'].isin([-1,-2]) & (nlsy97['S1055900']==2),'earn01'] = 7500 # $5001 to $10000 range\n", 905 | "nlsy97.loc[nlsy97['S1055800'].isin([-1,-2]) & (nlsy97['S1055900']==3),'earn01'] = 17500 # $10001 to $25000 range\n", 906 | "nlsy97.loc[nlsy97['S1055800'].isin([-1,-2]) & (nlsy97['S1055900']==4),'earn01'] = 37500 # $25001 to $50000 range\n", 907 | "nlsy97.loc[nlsy97['S1055800'].isin([-1,-2]) & (nlsy97['S1055900']==5),'earn01'] = 75000 # $50001 to $100000 range\n", 908 | "nlsy97.loc[nlsy97['S1055800'].isin([-1,-2]) & (nlsy97['S1055900']==6),'earn01'] = 175000 # $100001 to $250000 range\n", 909 | "\n", 910 | "# Keep track of respondents reporting only an earnings range\n", 911 | "nlsy97['earn01_range'] = np.nan\n", 912 | "nlsy97.loc[~pd.isna(nlsy97['earn01']),'earn01_range'] = 0\n", 913 | "nlsy97.loc[nlsy97['S1055900']>=1,'earn01_range'] = 1\n", 914 | "\n", 915 | "# Deflate earnings measures\n", 916 | "nlsy97['earn01'] = nlsy97['earn01']*(cpi.loc[2022]['AVG']/cpi.loc[2001]['AVG'])\n", 917 | "\n", 918 | "# Construct annual earnings measure 2002\n", 919 | "# NOTE: Top-coded at $35,000 (lowest value of top 2%)\n", 920 | "nlsy97['earn02'] = np.nan\n", 921 | "nlsy97.loc[nlsy97['S3134600']>=0,'earn02'] = nlsy97['S3134600'][nlsy97['S3134600']>=0] # Reports earnings\n", 922 | "nlsy97.loc[pd.isna(nlsy97['earn02']) & (nlsy97['S3134400']==0),'earn02'] = 0 # Reports no earnings (#1)\n", 923 | "nlsy97.loc[nlsy97['S3134400'].isin([-1,-2]) & (nlsy97['S3134500']==0),'earn02'] = 0 # Reports no earnings (#2)\n", 924 | "nlsy97.loc[nlsy97['S3134600'].isin([-1,-2]) & (nlsy97['S3134700']==1),'earn02'] = 2500 # $1 to $5000 range\n", 925 | "nlsy97.loc[nlsy97['S3134600'].isin([-1,-2]) & (nlsy97['S3134700']==2),'earn02'] = 7500 # $5001 to $10000 range\n", 926 | "nlsy97.loc[nlsy97['S3134600'].isin([-1,-2]) & (nlsy97['S3134700']==3),'earn02'] = 17500 # $10001 to $25000 range\n", 927 | "nlsy97.loc[nlsy97['S3134600'].isin([-1,-2]) & (nlsy97['S3134700']==4),'earn02'] = 37500 # $25001 to $50000 range\n", 928 | "nlsy97.loc[nlsy97['S3134600'].isin([-1,-2]) & (nlsy97['S3134700']==5),'earn02'] = 75000 # $50001 to $100000 range\n", 929 | "nlsy97.loc[nlsy97['S3134600'].isin([-1,-2]) & (nlsy97['S3134700']==6),'earn02'] = 175000 # $100001 to $250000 range\n", 930 | "\n", 931 | "# Keep track of respondents reporting only an earnings range\n", 932 | "nlsy97['earn02_range'] = np.nan\n", 933 | "nlsy97.loc[~pd.isna(nlsy97['earn02']),'earn02_range'] = 0\n", 934 | "nlsy97.loc[nlsy97['S3134700']>=1,'earn02_range'] = 1\n", 935 | "\n", 936 | "# Deflate earnings measures\n", 937 | "nlsy97['earn02'] = nlsy97['earn02']*(cpi.loc[2022]['AVG']/cpi.loc[2002]['AVG'])\n", 938 | "\n", 939 | "# Construct annual earnings measure 2003\n", 940 | "# NOTE: Top-coded at $43,000 (lowest value of top 2%)\n", 941 | "nlsy97['earn03'] = np.nan\n", 942 | "nlsy97.loc[nlsy97['S4799600']>=0,'earn03'] = nlsy97['S4799600'][nlsy97['S4799600']>=0] # Reports earnings\n", 943 | "nlsy97.loc[pd.isna(nlsy97['earn03']) & (nlsy97['S4799400']==0),'earn03'] = 0 # Reports no earnings (#1)\n", 944 | "nlsy97.loc[nlsy97['S4799400'].isin([-1,-2]) & (nlsy97['S4799500']==0),'earn02'] = 0 # Reports no earnings (#2)\n", 945 | "nlsy97.loc[nlsy97['S4799600'].isin([-1,-2]) & (nlsy97['S4799700']==1),'earn03'] = 2500 # $1 to $5000 range\n", 946 | "nlsy97.loc[nlsy97['S4799600'].isin([-1,-2]) & (nlsy97['S4799700']==2),'earn03'] = 7500 # $5001 to $10000 range\n", 947 | "nlsy97.loc[nlsy97['S4799600'].isin([-1,-2]) & (nlsy97['S4799700']==3),'earn03'] = 17500 # $10001 to $25000 range\n", 948 | "nlsy97.loc[nlsy97['S4799600'].isin([-1,-2]) & (nlsy97['S4799700']==4),'earn03'] = 37500 # $25001 to $50000 range\n", 949 | "nlsy97.loc[nlsy97['S4799600'].isin([-1,-2]) & (nlsy97['S4799700']==5),'earn03'] = 75000 # $50001 to $100000 range\n", 950 | "nlsy97.loc[nlsy97['S4799600'].isin([-1,-2]) & (nlsy97['S4799700']==6),'earn03'] = 175000 # $100001 to $250000 range\n", 951 | "nlsy97.loc[nlsy97['S4799600'].isin([-1,-2]) & (nlsy97['S4799700']==7),'earn03'] = 250000 # $100001 to $250000 range\n", 952 | "\n", 953 | "# Keep track of respondents reporting only an earnings range\n", 954 | "nlsy97['earn03_range'] = np.nan\n", 955 | "nlsy97.loc[~pd.isna(nlsy97['earn03']),'earn03_range'] = 0\n", 956 | "nlsy97.loc[nlsy97['S4799700']>=1,'earn03_range'] = 1\n", 957 | "\n", 958 | "# Deflate earnings measures\n", 959 | "nlsy97['earn03'] = nlsy97['earn03']*(cpi.loc[2022]['AVG']/cpi.loc[2003]['AVG'])\n", 960 | "\n", 961 | "# Construct annual earnings measure 2004\n", 962 | "# NOTE: Top-coded at $69,342 (lowest value of top 2%)\n", 963 | "nlsy97['earn04'] = np.nan\n", 964 | "nlsy97.loc[nlsy97['S6501000']>=0,'earn04'] = nlsy97['S6501000'][nlsy97['S6501000']>=0] # Reports earnings\n", 965 | "nlsy97.loc[pd.isna(nlsy97['earn04']) & (nlsy97['S6500800']==0),'earn04'] = 0 # Reports no earnings (#1)\n", 966 | "nlsy97.loc[nlsy97['S6500800'].isin([-1,-2]) & (nlsy97['S6500900']==0),'earn04'] = 0 # Reports no earnings (#2)\n", 967 | "nlsy97.loc[nlsy97['S6501000'].isin([-1,-2]) & (nlsy97['S6501100']==1),'earn04'] = 2500 # $1 to $5000 range\n", 968 | "nlsy97.loc[nlsy97['S6501000'].isin([-1,-2]) & (nlsy97['S6501100']==2),'earn04'] = 7500 # $5001 to $10000 range\n", 969 | "nlsy97.loc[nlsy97['S6501000'].isin([-1,-2]) & (nlsy97['S6501100']==3),'earn04'] = 17500 # $10001 to $25000 range\n", 970 | "nlsy97.loc[nlsy97['S6501000'].isin([-1,-2]) & (nlsy97['S6501100']==4),'earn04'] = 37500 # $25001 to $50000 range\n", 971 | "nlsy97.loc[nlsy97['S6501000'].isin([-1,-2]) & (nlsy97['S6501100']==5),'earn04'] = 75000 # $50001 to $100000 range\n", 972 | "nlsy97.loc[nlsy97['S6501000'].isin([-1,-2]) & (nlsy97['S6501100']==6),'earn04'] = 175000 # $100001 to $250000 range\n", 973 | "nlsy97.loc[nlsy97['S6501000'].isin([-1,-2]) & (nlsy97['S6501100']==7),'earn04'] = 250000 # $100001 to $250000 range\n", 974 | "\n", 975 | "# Keep track of respondents reporting only an earnings range\n", 976 | "nlsy97['earn04_range'] = np.nan\n", 977 | "nlsy97.loc[~pd.isna(nlsy97['earn04']),'earn04_range'] = 0\n", 978 | "nlsy97.loc[nlsy97['S6501100']>=1,'earn04_range'] = 1\n", 979 | "\n", 980 | "# Deflate earnings measures\n", 981 | "nlsy97['earn04'] = nlsy97['earn04']*(cpi.loc[2022]['AVG']/cpi.loc[2004]['AVG'])\n", 982 | "\n", 983 | "# Construct annual earnings measure 2005\n", 984 | "# NOTE: Top-coded at $80,471 (lowest value of top 2%)\n", 985 | "nlsy97['earn05'] = np.nan\n", 986 | "nlsy97.loc[nlsy97['S8496500']>=0,'earn05'] = nlsy97['S8496500'][nlsy97['S8496500']>=0] # Reports earnings\n", 987 | "nlsy97.loc[pd.isna(nlsy97['earn05']) & (nlsy97['S8496300']==0),'earn05'] = 0 # Reports no earnings (#1)\n", 988 | "nlsy97.loc[nlsy97['S8496300'].isin([-1,-2]) & (nlsy97['S8496400']==0),'earn05'] = 0 # Reports no earnings (#2)\n", 989 | "nlsy97.loc[nlsy97['S8496500'].isin([-1,-2]) & (nlsy97['S8496600']==1),'earn05'] = 2500 # $1 to $5000 range\n", 990 | "nlsy97.loc[nlsy97['S8496500'].isin([-1,-2]) & (nlsy97['S8496600']==2),'earn05'] = 7500 # $5001 to $10000 range\n", 991 | "nlsy97.loc[nlsy97['S8496500'].isin([-1,-2]) & (nlsy97['S8496600']==3),'earn05'] = 17500 # $10001 to $25000 range\n", 992 | "nlsy97.loc[nlsy97['S8496500'].isin([-1,-2]) & (nlsy97['S8496600']==4),'earn05'] = 37500 # $25001 to $50000 range\n", 993 | "nlsy97.loc[nlsy97['S8496500'].isin([-1,-2]) & (nlsy97['S8496600']==5),'earn05'] = 75000 # $50001 to $100000 range\n", 994 | "nlsy97.loc[nlsy97['S8496500'].isin([-1,-2]) & (nlsy97['S8496600']==6),'earn05'] = 175000 # $100001 to $250000 range\n", 995 | "nlsy97.loc[nlsy97['S8496500'].isin([-1,-2]) & (nlsy97['S8496600']==7),'earn05'] = 250000 # $100001 to $250000 range\n", 996 | "\n", 997 | "# Keep track of respondents reporting only an earnings range\n", 998 | "nlsy97['earn05_range'] = np.nan\n", 999 | "nlsy97.loc[~pd.isna(nlsy97['earn05']),'earn05_range'] = 0\n", 1000 | "nlsy97.loc[nlsy97['S8496600']>=1,'earn05_range'] = 1\n", 1001 | "\n", 1002 | "# Deflate earnings measures\n", 1003 | "nlsy97['earn05'] = nlsy97['earn05']*(cpi.loc[2022]['AVG']/cpi.loc[2005]['AVG'])\n", 1004 | "\n", 1005 | "# Construct annual earnings measure 2006\n", 1006 | "# NOTE: Top-coded at $70,000 (lowest value of top 2%)\n", 1007 | "nlsy97['earn06'] = np.nan\n", 1008 | "nlsy97.loc[nlsy97['T0889800']>=0,'earn06'] = nlsy97['T0889800'][nlsy97['T0889800']>=0] # Reports earnings\n", 1009 | "nlsy97.loc[pd.isna(nlsy97['earn06']) & (nlsy97['T0889600']==0),'earn06'] = 0 # Reports no earnings (#1)\n", 1010 | "nlsy97.loc[nlsy97['T0889600'].isin([-1,-2]) & (nlsy97['T0889700']==0),'earn06'] = 0 # Reports no earnings (#2)\n", 1011 | "nlsy97.loc[nlsy97['T0889800'].isin([-1,-2]) & (nlsy97['T0889900']==1),'earn06'] = 2500 # $1 to $5000 range\n", 1012 | "nlsy97.loc[nlsy97['T0889800'].isin([-1,-2]) & (nlsy97['T0889900']==2),'earn06'] = 7500 # $5001 to $10000 range\n", 1013 | "nlsy97.loc[nlsy97['T0889800'].isin([-1,-2]) & (nlsy97['T0889900']==3),'earn06'] = 17500 # $10001 to $25000 range\n", 1014 | "nlsy97.loc[nlsy97['T0889800'].isin([-1,-2]) & (nlsy97['T0889900']==4),'earn06'] = 37500 # $25001 to $50000 range\n", 1015 | "nlsy97.loc[nlsy97['T0889800'].isin([-1,-2]) & (nlsy97['T0889900']==5),'earn06'] = 75000 # $50001 to $100000 range\n", 1016 | "nlsy97.loc[nlsy97['T0889800'].isin([-1,-2]) & (nlsy97['T0889900']==6),'earn06'] = 175000 # $100001 to $250000 range\n", 1017 | "nlsy97.loc[nlsy97['T0889800'].isin([-1,-2]) & (nlsy97['T0889900']==7),'earn06'] = 250000 # $100001 to $250000 range\n", 1018 | "\n", 1019 | "# Keep track of respondents reporting only an earnings range\n", 1020 | "nlsy97['earn06_range'] = np.nan\n", 1021 | "nlsy97.loc[~pd.isna(nlsy97['earn06']),'earn06_range'] = 0\n", 1022 | "nlsy97.loc[nlsy97['T0889900']>=1,'earn06_range'] = 1\n", 1023 | "\n", 1024 | "# Deflate earnings measures\n", 1025 | "nlsy97['earn06'] = nlsy97['earn06']*(cpi.loc[2022]['AVG']/cpi.loc[2006]['AVG'])\n", 1026 | "\n", 1027 | "# Construct annual earnings measure 2007\n", 1028 | "# NOTE: Top-coded at $74,900 (lowest value of top 2%)\n", 1029 | "nlsy97['earn07'] = np.nan\n", 1030 | "nlsy97.loc[nlsy97['T3003000']>=0,'earn07'] = nlsy97['T3003000'][nlsy97['T3003000']>=0] # Reports earnings\n", 1031 | "nlsy97.loc[pd.isna(nlsy97['earn07']) & (nlsy97['T3002800']==0),'earn07'] = 0 # Reports no earnings (#1)\n", 1032 | "nlsy97.loc[nlsy97['T3002800'].isin([-1,-2]) & (nlsy97['T3002900']==0),'earn07'] = 0 # Reports no earnings (#2)\n", 1033 | "nlsy97.loc[nlsy97['T3003000'].isin([-1,-2]) & (nlsy97['T3003100']==1),'earn07'] = 2500 # $1 to $5000 range\n", 1034 | "nlsy97.loc[nlsy97['T3003000'].isin([-1,-2]) & (nlsy97['T3003100']==2),'earn07'] = 7500 # $5001 to $10000 range\n", 1035 | "nlsy97.loc[nlsy97['T3003000'].isin([-1,-2]) & (nlsy97['T3003100']==3),'earn07'] = 17500 # $10001 to $25000 range\n", 1036 | "nlsy97.loc[nlsy97['T3003000'].isin([-1,-2]) & (nlsy97['T3003100']==4),'earn07'] = 37500 # $25001 to $50000 range\n", 1037 | "nlsy97.loc[nlsy97['T3003000'].isin([-1,-2]) & (nlsy97['T3003100']==5),'earn07'] = 75000 # $50001 to $100000 range\n", 1038 | "nlsy97.loc[nlsy97['T3003000'].isin([-1,-2]) & (nlsy97['T3003100']==6),'earn07'] = 175000 # $100001 to $250000 range\n", 1039 | "nlsy97.loc[nlsy97['T3003000'].isin([-1,-2]) & (nlsy97['T3003100']==7),'earn07'] = 250000 # $100001 to $250000 range\n", 1040 | "\n", 1041 | "# Keep track of respondents reporting only an earnings range\n", 1042 | "nlsy97['earn07_range'] = np.nan\n", 1043 | "nlsy97.loc[~pd.isna(nlsy97['earn07']),'earn07_range'] = 0\n", 1044 | "nlsy97.loc[nlsy97['T3003100']>=1,'earn07_range'] = 1\n", 1045 | "\n", 1046 | "# Deflate earnings measures\n", 1047 | "nlsy97['earn07'] = nlsy97['earn07']*(cpi.loc[2022]['AVG']/cpi.loc[2007]['AVG'])\n", 1048 | "\n", 1049 | "# Construct annual earnings measure 2008\n", 1050 | "# NOTE: Top-coded at $84,000 (lowest value of top 2%)\n", 1051 | "nlsy97['earn08'] = np.nan\n", 1052 | "nlsy97.loc[nlsy97['T4406000']>=0,'earn08'] = nlsy97['T4406000'][nlsy97['T4406000']>=0] # Reports earnings\n", 1053 | "nlsy97.loc[pd.isna(nlsy97['earn08']) & (nlsy97['T4405800']==0),'earn08'] = 0 # Reports no earnings (#1)\n", 1054 | "nlsy97.loc[nlsy97['T4405800'].isin([-1,-2]) & (nlsy97['T4405900']==0),'earn08'] = 0 # Reports no earnings (#2)\n", 1055 | "nlsy97.loc[nlsy97['T4406000'].isin([-1,-2]) & (nlsy97['T4406100']==1),'earn08'] = 2500 # $1 to $5000 range\n", 1056 | "nlsy97.loc[nlsy97['T4406000'].isin([-1,-2]) & (nlsy97['T4406100']==2),'earn08'] = 7500 # $5001 to $10000 range\n", 1057 | "nlsy97.loc[nlsy97['T4406000'].isin([-1,-2]) & (nlsy97['T4406100']==3),'earn08'] = 17500 # $10001 to $25000 range\n", 1058 | "nlsy97.loc[nlsy97['T4406000'].isin([-1,-2]) & (nlsy97['T4406100']==4),'earn08'] = 37500 # $25001 to $50000 range\n", 1059 | "nlsy97.loc[nlsy97['T4406000'].isin([-1,-2]) & (nlsy97['T4406100']==5),'earn08'] = 75000 # $50001 to $100000 range\n", 1060 | "nlsy97.loc[nlsy97['T4406000'].isin([-1,-2]) & (nlsy97['T4406100']==6),'earn08'] = 175000 # $100001 to $250000 range\n", 1061 | "nlsy97.loc[nlsy97['T4406000'].isin([-1,-2]) & (nlsy97['T4406100']==7),'earn08'] = 250000 # $100001 to $250000 range\n", 1062 | "\n", 1063 | "# Keep track of respondents reporting only an earnings range\n", 1064 | "nlsy97['earn08_range'] = np.nan\n", 1065 | "nlsy97.loc[~pd.isna(nlsy97['earn08']),'earn08_range'] = 0\n", 1066 | "nlsy97.loc[nlsy97['T4406100']>=1,'earn08_range'] = 1\n", 1067 | "\n", 1068 | "# Deflate earnings measures\n", 1069 | "nlsy97['earn08'] = nlsy97['earn08']*(cpi.loc[2022]['AVG']/cpi.loc[2008]['AVG'])\n", 1070 | "\n", 1071 | "# Construct annual earnings measure 2009\n", 1072 | "# NOTE: Top-coded at $88,000 (lowest value of top 2%)\n", 1073 | "nlsy97['earn09'] = np.nan\n", 1074 | "nlsy97.loc[nlsy97['T6055500']>=0,'earn09'] = nlsy97['T6055500'][nlsy97['T6055500']>=0] # Reports earnings\n", 1075 | "nlsy97.loc[pd.isna(nlsy97['earn09']) & (nlsy97['T6055300']==0),'earn09'] = 0 # Reports no earnings (#1)\n", 1076 | "nlsy97.loc[nlsy97['T6055300'].isin([-1,-2]) & (nlsy97['T6055400']==0),'earn09'] = 0 # Reports no earnings (#2)\n", 1077 | "nlsy97.loc[nlsy97['T6055500'].isin([-1,-2]) & (nlsy97['T6055600']==1),'earn09'] = 2500 # $1 to $5000 range\n", 1078 | "nlsy97.loc[nlsy97['T6055500'].isin([-1,-2]) & (nlsy97['T6055600']==2),'earn09'] = 7500 # $5001 to $10000 range\n", 1079 | "nlsy97.loc[nlsy97['T6055500'].isin([-1,-2]) & (nlsy97['T6055600']==3),'earn09'] = 17500 # $10001 to $25000 range\n", 1080 | "nlsy97.loc[nlsy97['T6055500'].isin([-1,-2]) & (nlsy97['T6055600']==4),'earn09'] = 37500 # $25001 to $50000 range\n", 1081 | "nlsy97.loc[nlsy97['T6055500'].isin([-1,-2]) & (nlsy97['T6055600']==5),'earn09'] = 75000 # $50001 to $100000 range\n", 1082 | "nlsy97.loc[nlsy97['T6055500'].isin([-1,-2]) & (nlsy97['T6055600']==6),'earn09'] = 175000 # $100001 to $250000 range\n", 1083 | "nlsy97.loc[nlsy97['T6055500'].isin([-1,-2]) & (nlsy97['T6055600']==7),'earn09'] = 250000 # $100001 to $250000 range\n", 1084 | "\n", 1085 | "# Keep track of respondents reporting only an earnings range\n", 1086 | "nlsy97['earn09_range'] = np.nan\n", 1087 | "nlsy97.loc[~pd.isna(nlsy97['earn09']),'earn09_range'] = 0\n", 1088 | "nlsy97.loc[nlsy97['T6055600']>=1,'earn09_range'] = 1\n", 1089 | "\n", 1090 | "# Deflate earnings measures\n", 1091 | "nlsy97['earn09'] = nlsy97['earn09']*(cpi.loc[2022]['AVG']/cpi.loc[2009]['AVG'])\n", 1092 | "\n", 1093 | "\n", 1094 | "# Construct annual earnings measure 2010\n", 1095 | "# NOTE: Top-coded at $94,000 (lowest value of top 2%)\n", 1096 | "nlsy97['earn10'] = np.nan\n", 1097 | "nlsy97.loc[nlsy97['T7545600']>=0,'earn10'] = nlsy97['T7545600'][nlsy97['T7545600']>=0] # Reports earnings\n", 1098 | "nlsy97.loc[pd.isna(nlsy97['earn10']) & (nlsy97['T7545400']==0),'earn10'] = 0 # Reports no earnings (#1)\n", 1099 | "nlsy97.loc[nlsy97['T7545400'].isin([-1,-2]) & (nlsy97['T7545500']==0),'earn10'] = 0 # Reports no earnings (#2)\n", 1100 | "nlsy97.loc[nlsy97['T7545600'].isin([-1,-2]) & (nlsy97['T7545700']==1),'earn10'] = 2500 # $1 to $5000 range\n", 1101 | "nlsy97.loc[nlsy97['T7545600'].isin([-1,-2]) & (nlsy97['T7545700']==2),'earn10'] = 7500 # $5001 to $10000 range\n", 1102 | "nlsy97.loc[nlsy97['T7545600'].isin([-1,-2]) & (nlsy97['T7545700']==3),'earn10'] = 17500 # $10001 to $25000 range\n", 1103 | "nlsy97.loc[nlsy97['T7545600'].isin([-1,-2]) & (nlsy97['T7545700']==4),'earn10'] = 37500 # $25001 to $50000 range\n", 1104 | "nlsy97.loc[nlsy97['T7545600'].isin([-1,-2]) & (nlsy97['T7545700']==5),'earn10'] = 75000 # $50001 to $100000 range\n", 1105 | "nlsy97.loc[nlsy97['T7545600'].isin([-1,-2]) & (nlsy97['T7545700']==6),'earn10'] = 175000 # $100001 to $250000 range\n", 1106 | "nlsy97.loc[nlsy97['T7545600'].isin([-1,-2]) & (nlsy97['T7545700']==7),'earn10'] = 250000 # $100001 to $250000 range\n", 1107 | "\n", 1108 | "# Keep track of respondents reporting only an earnings range\n", 1109 | "nlsy97['earn10_range'] = np.nan\n", 1110 | "nlsy97.loc[~pd.isna(nlsy97['earn10']),'earn10_range'] = 0\n", 1111 | "nlsy97.loc[nlsy97['T7545700']>=1,'earn10_range'] = 1\n", 1112 | "\n", 1113 | "# Deflate earnings measures\n", 1114 | "nlsy97['earn10'] = nlsy97['earn10']*(cpi.loc[2022]['AVG']/cpi.loc[2010]['AVG'])\n", 1115 | "\n", 1116 | "# Construct annual earnings measure 2012\n", 1117 | "# NOTE: Top-coded at $112,000 (lowest value of top 2%)\n", 1118 | "nlsy97['earn12'] = np.nan\n", 1119 | "nlsy97.loc[nlsy97['T8976700']>=0,'earn12'] = nlsy97['T8976700'][nlsy97['T8976700']>=0] # Reports earnings\n", 1120 | "nlsy97.loc[pd.isna(nlsy97['earn12']) & (nlsy97['T8976500']==0),'earn12'] = 0 # Reports no earnings (#1)\n", 1121 | "nlsy97.loc[nlsy97['T8976500'].isin([-1,-2]) & (nlsy97['T8976600']==0),'earn12'] = 0 # Reports no earnings (#2)\n", 1122 | "nlsy97.loc[nlsy97['T8976700'].isin([-1,-2]) & (nlsy97['T8976800']==1),'earn12'] = 2500 # $1 to $5000 range\n", 1123 | "nlsy97.loc[nlsy97['T8976700'].isin([-1,-2]) & (nlsy97['T8976800']==2),'earn12'] = 7500 # $5001 to $10000 range\n", 1124 | "nlsy97.loc[nlsy97['T8976700'].isin([-1,-2]) & (nlsy97['T8976800']==3),'earn12'] = 17500 # $10001 to $25000 range\n", 1125 | "nlsy97.loc[nlsy97['T8976700'].isin([-1,-2]) & (nlsy97['T8976800']==4),'earn12'] = 37500 # $25001 to $50000 range\n", 1126 | "nlsy97.loc[nlsy97['T8976700'].isin([-1,-2]) & (nlsy97['T8976800']==5),'earn12'] = 75000 # $50001 to $100000 range\n", 1127 | "nlsy97.loc[nlsy97['T8976700'].isin([-1,-2]) & (nlsy97['T8976800']==6),'earn12'] = 175000 # $100001 to $250000 range\n", 1128 | "nlsy97.loc[nlsy97['T8976700'].isin([-1,-2]) & (nlsy97['T8976800']==7),'earn12'] = 250000 # $100001 to $250000 range\n", 1129 | "\n", 1130 | "# Keep track of respondents reporting only an earnings range\n", 1131 | "nlsy97['earn12_range'] = np.nan\n", 1132 | "nlsy97.loc[~pd.isna(nlsy97['earn12']),'earn12_range'] = 0\n", 1133 | "nlsy97.loc[nlsy97['T8976800']>=1,'earn12_range'] = 1\n", 1134 | "\n", 1135 | "# Deflate earnings measures\n", 1136 | "nlsy97['earn12'] = nlsy97['earn12']*(cpi.loc[2022]['AVG']/cpi.loc[2012]['AVG'])\n", 1137 | "\n", 1138 | "# Construct annual earnings measure 2014\n", 1139 | "# NOTE: Top-coded at $135,000 (lowest value of top 2%)\n", 1140 | "nlsy97['earn14'] = np.nan\n", 1141 | "nlsy97.loc[nlsy97['U0956900']>=0,'earn14'] = nlsy97['U0956900'][nlsy97['U0956900']>=0] # Reports earnings\n", 1142 | "nlsy97.loc[pd.isna(nlsy97['earn14']) & (nlsy97['U0956700']==0),'earn14'] = 0 # Reports no earnings (#1)\n", 1143 | "nlsy97.loc[nlsy97['U0956700'].isin([-1,-2]) & (nlsy97['U0956800']==0),'earn14'] = 0 # Reports no earnings (#2)\n", 1144 | "nlsy97.loc[nlsy97['U0956900'].isin([-1,-2]) & (nlsy97['U0957000']==1),'earn14'] = 2500 # $1 to $5000 range\n", 1145 | "nlsy97.loc[nlsy97['U0956900'].isin([-1,-2]) & (nlsy97['U0957000']==2),'earn14'] = 7500 # $5001 to $10000 range\n", 1146 | "nlsy97.loc[nlsy97['U0956900'].isin([-1,-2]) & (nlsy97['U0957000']==3),'earn14'] = 17500 # $10001 to $25000 range\n", 1147 | "nlsy97.loc[nlsy97['U0956900'].isin([-1,-2]) & (nlsy97['U0957000']==4),'earn14'] = 37500 # $25001 to $50000 range\n", 1148 | "nlsy97.loc[nlsy97['U0956900'].isin([-1,-2]) & (nlsy97['U0957000']==5),'earn14'] = 75000 # $50001 to $100000 range\n", 1149 | "nlsy97.loc[nlsy97['U0956900'].isin([-1,-2]) & (nlsy97['U0957000']==6),'earn14'] = 175000 # $100001 to $250000 range\n", 1150 | "nlsy97.loc[nlsy97['U0956900'].isin([-1,-2]) & (nlsy97['U0957000']==7),'earn14'] = 250000 # $100001 to $250000 range\n", 1151 | "\n", 1152 | "# Keep track of respondents reporting only an earnings range\n", 1153 | "nlsy97['earn14_range'] = np.nan\n", 1154 | "nlsy97.loc[~pd.isna(nlsy97['earn14']),'earn14_range'] = 0\n", 1155 | "nlsy97.loc[nlsy97['U0957000']>=1,'earn14_range'] = 1\n", 1156 | "\n", 1157 | "# Deflate earnings measures\n", 1158 | "nlsy97['earn14'] = nlsy97['earn14']*(cpi.loc[2022]['AVG']/cpi.loc[2014]['AVG'])\n", 1159 | "\n", 1160 | "# Construct annual earnings measure 2016\n", 1161 | "# NOTE: Top-coded at $149,000 (lowest value of top 2%)\n", 1162 | "nlsy97['earn16'] = np.nan\n", 1163 | "nlsy97.loc[nlsy97['U2857200']>=0,'earn16'] = nlsy97['U2857200'][nlsy97['U2857200']>=0] # Reports earnings\n", 1164 | "nlsy97.loc[pd.isna(nlsy97['earn16']) & (nlsy97['U2857000']==0),'earn16'] = 0 # Reports no earnings (#1)\n", 1165 | "nlsy97.loc[nlsy97['U2857000'].isin([-1,-2]) & (nlsy97['U2857100']==0),'earn16'] = 0 # Reports no earnings (#2)\n", 1166 | "nlsy97.loc[nlsy97['U2857200'].isin([-1,-2]) & (nlsy97['U2857300']==1),'earn16'] = 2500 # $1 to $5000 range\n", 1167 | "nlsy97.loc[nlsy97['U2857200'].isin([-1,-2]) & (nlsy97['U2857300']==2),'earn16'] = 7500 # $5001 to $10000 range\n", 1168 | "nlsy97.loc[nlsy97['U2857200'].isin([-1,-2]) & (nlsy97['U2857300']==3),'earn16'] = 17500 # $10001 to $25000 range\n", 1169 | "nlsy97.loc[nlsy97['U2857200'].isin([-1,-2]) & (nlsy97['U2857300']==4),'earn16'] = 37500 # $25001 to $50000 range\n", 1170 | "nlsy97.loc[nlsy97['U2857200'].isin([-1,-2]) & (nlsy97['U2857300']==5),'earn16'] = 75000 # $50001 to $100000 range\n", 1171 | "nlsy97.loc[nlsy97['U2857200'].isin([-1,-2]) & (nlsy97['U2857300']==6),'earn16'] = 175000 # $100001 to $250000 range\n", 1172 | "nlsy97.loc[nlsy97['U2857200'].isin([-1,-2]) & (nlsy97['U2857300']==7),'earn16'] = 250000 # $100001 to $250000 range\n", 1173 | "\n", 1174 | "# Keep track of respondents reporting only an earnings range\n", 1175 | "nlsy97['earn16_range'] = np.nan\n", 1176 | "nlsy97.loc[~pd.isna(nlsy97['earn16']),'earn16_range'] = 0\n", 1177 | "nlsy97.loc[nlsy97['U2857300']>=1,'earn16_range'] = 1\n", 1178 | "\n", 1179 | "# Deflate earnings measures\n", 1180 | "nlsy97['earn16'] = nlsy97['earn16']*(cpi.loc[2022]['AVG']/cpi.loc[2016]['AVG'])\n", 1181 | "\n", 1182 | "# Construct annual earnings measure 2018\n", 1183 | "# NOTE: Top-coded at $178,000 (lowest value of top 2%)\n", 1184 | "nlsy97['earn18'] = np.nan\n", 1185 | "nlsy97.loc[nlsy97['U4282300']>=0,'earn18'] = nlsy97['U4282300'][nlsy97['U4282300']>=0] # Reports earnings\n", 1186 | "nlsy97.loc[pd.isna(nlsy97['earn18']) & (nlsy97['U4282100']==0),'earn18'] = 0 # Reports no earnings (#1)\n", 1187 | "nlsy97.loc[nlsy97['U4282100'].isin([-1,-2]) & (nlsy97['U4282200']==0),'earn18'] = 0 # Reports no earnings (#2)\n", 1188 | "nlsy97.loc[nlsy97['U4282300'].isin([-1,-2]) & (nlsy97['U4282400']==1),'earn18'] = 2500 # $1 to $5000 range\n", 1189 | "nlsy97.loc[nlsy97['U4282300'].isin([-1,-2]) & (nlsy97['U4282400']==2),'earn18'] = 7500 # $5001 to $10000 range\n", 1190 | "nlsy97.loc[nlsy97['U4282300'].isin([-1,-2]) & (nlsy97['U4282400']==3),'earn18'] = 17500 # $10001 to $25000 range\n", 1191 | "nlsy97.loc[nlsy97['U4282300'].isin([-1,-2]) & (nlsy97['U4282400']==4),'earn18'] = 37500 # $25001 to $50000 range\n", 1192 | "nlsy97.loc[nlsy97['U4282300'].isin([-1,-2]) & (nlsy97['U4282400']==5),'earn18'] = 75000 # $50001 to $100000 range\n", 1193 | "nlsy97.loc[nlsy97['U4282300'].isin([-1,-2]) & (nlsy97['U4282400']==6),'earn18'] = 175000 # $100001 to $250000 range\n", 1194 | "nlsy97.loc[nlsy97['U4282300'].isin([-1,-2]) & (nlsy97['U4282400']==7),'earn18'] = 250000 # $100001 to $250000 range\n", 1195 | "\n", 1196 | "# Keep track of respondents reporting only an earnings range\n", 1197 | "nlsy97['earn18_range'] = np.nan\n", 1198 | "nlsy97.loc[~pd.isna(nlsy97['earn18']),'earn18_range'] = 0\n", 1199 | "nlsy97.loc[nlsy97['U4282400']>=1,'earn18_range'] = 1\n", 1200 | "\n", 1201 | "# Deflate earnings measures\n", 1202 | "nlsy97['earn18'] = nlsy97['earn18']*(cpi.loc[2022]['AVG']/cpi.loc[2018]['AVG'])" 1203 | ] 1204 | }, 1205 | { 1206 | "cell_type": "code", 1207 | "execution_count": 9, 1208 | "id": "25d0c489", 1209 | "metadata": {}, 1210 | "outputs": [ 1211 | { 1212 | "data": { 1213 | "text/html": [ 1214 | "
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earn97earn97_rangeearn98earn98_rangeearn99earn99_rangeearn00earn00_rangeearn01earn01_range...earn10earn10_rangeearn12earn12_rangeearn14earn14_rangeearn16earn16_rangeearn18earn18_range
count8341.0000008341.0000008144.0000008144.0000008002.0000008002.0000007816.0000007816.0000007792.0000007792.000000...7354.0000007354.0000007065.0000007065.0000007003.0000007003.0000006644.0000006645.0000006862.0000006862.000000
mean1542.8514990.0618632944.7120020.0931975228.3770460.1273437958.2541530.11118210741.7055020.186473...35347.6560720.07207038902.6183880.07784942885.3150820.10224248576.8230310.05568154126.8214270.040367
std3519.9892580.2409216472.2823640.2907278943.2419540.33337811577.1319970.31437814025.1142650.389513...35814.1163170.25862139714.4976910.26795244619.4673600.30298850500.6955960.22932260689.1880640.196833
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...4047.8424020.0000007684.7729300.0000009307.5927520.00000012230.7256240.00000014028.7185040.000000
50%0.0000000.00000090.1964880.0000001413.5954140.0000003417.8217820.0000004153.8313440.000000...29684.1776110.00000032019.8872070.00000035989.3586430.00000040361.3945580.00000043255.2153890.000000
75%1462.7118640.0000003607.8595320.0000005300.9828010.00000011962.3762380.00000015950.7123600.000000...51272.6704190.00000056355.0014840.00000061461.1374750.00000067268.9909300.00000073650.7721480.000000
max68564.6186441.00000067647.3662211.00000066262.2850121.000000128168.3168321.000000290768.1940701.000000...236124.1400881.000000320198.8720691.000000310253.0917461.000000305768.1405901.000000383978.8851261.000000
\n", 1450 | "

8 rows × 36 columns

\n", 1451 | "
" 1452 | ], 1453 | "text/plain": [ 1454 | " earn97 earn97_range earn98 earn98_range earn99 \\\n", 1455 | "count 8341.000000 8341.000000 8144.000000 8144.000000 8002.000000 \n", 1456 | "mean 1542.851499 0.061863 2944.712002 0.093197 5228.377046 \n", 1457 | "std 3519.989258 0.240921 6472.282364 0.290727 8943.241954 \n", 1458 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 1459 | "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 1460 | "50% 0.000000 0.000000 90.196488 0.000000 1413.595414 \n", 1461 | "75% 1462.711864 0.000000 3607.859532 0.000000 5300.982801 \n", 1462 | "max 68564.618644 1.000000 67647.366221 1.000000 66262.285012 \n", 1463 | "\n", 1464 | " earn99_range earn00 earn00_range earn01 earn01_range \\\n", 1465 | "count 8002.000000 7816.000000 7816.000000 7792.000000 7792.000000 \n", 1466 | "mean 0.127343 7958.254153 0.111182 10741.705502 0.186473 \n", 1467 | "std 0.333378 11577.131997 0.314378 14025.114265 0.389513 \n", 1468 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 1469 | "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 1470 | "50% 0.000000 3417.821782 0.000000 4153.831344 0.000000 \n", 1471 | "75% 0.000000 11962.376238 0.000000 15950.712360 0.000000 \n", 1472 | "max 1.000000 128168.316832 1.000000 290768.194070 1.000000 \n", 1473 | "\n", 1474 | " ... earn10 earn10_range earn12 earn12_range \\\n", 1475 | "count ... 7354.000000 7354.000000 7065.000000 7065.000000 \n", 1476 | "mean ... 35347.656072 0.072070 38902.618388 0.077849 \n", 1477 | "std ... 35814.116317 0.258621 39714.497691 0.267952 \n", 1478 | "min ... 0.000000 0.000000 0.000000 0.000000 \n", 1479 | "25% ... 4047.842402 0.000000 7684.772930 0.000000 \n", 1480 | "50% ... 29684.177611 0.000000 32019.887207 0.000000 \n", 1481 | "75% ... 51272.670419 0.000000 56355.001484 0.000000 \n", 1482 | "max ... 236124.140088 1.000000 320198.872069 1.000000 \n", 1483 | "\n", 1484 | " earn14 earn14_range earn16 earn16_range \\\n", 1485 | "count 7003.000000 7003.000000 6644.000000 6645.000000 \n", 1486 | "mean 42885.315082 0.102242 48576.823031 0.055681 \n", 1487 | "std 44619.467360 0.302988 50500.695596 0.229322 \n", 1488 | "min 0.000000 0.000000 0.000000 0.000000 \n", 1489 | "25% 9307.592752 0.000000 12230.725624 0.000000 \n", 1490 | "50% 35989.358643 0.000000 40361.394558 0.000000 \n", 1491 | "75% 61461.137475 0.000000 67268.990930 0.000000 \n", 1492 | "max 310253.091746 1.000000 305768.140590 1.000000 \n", 1493 | "\n", 1494 | " earn18 earn18_range \n", 1495 | "count 6862.000000 6862.000000 \n", 1496 | "mean 54126.821427 0.040367 \n", 1497 | "std 60689.188064 0.196833 \n", 1498 | "min 0.000000 0.000000 \n", 1499 | "25% 14028.718504 0.000000 \n", 1500 | "50% 43255.215389 0.000000 \n", 1501 | "75% 73650.772148 0.000000 \n", 1502 | "max 383978.885126 1.000000 \n", 1503 | "\n", 1504 | "[8 rows x 36 columns]" 1505 | ] 1506 | }, 1507 | "execution_count": 9, 1508 | "metadata": {}, 1509 | "output_type": "execute_result" 1510 | } 1511 | ], 1512 | "source": [ 1513 | "nlsy97[['earn97','earn97_range','earn98','earn98_range','earn99','earn99_range', \\\n", 1514 | " 'earn00','earn00_range','earn01','earn01_range','earn02','earn02_range', \\\n", 1515 | " 'earn03','earn03_range','earn04','earn04_range','earn05','earn05_range', \\\n", 1516 | " 'earn06','earn06_range','earn07','earn07_range','earn08','earn08_range',\\\n", 1517 | " 'earn09','earn09_range','earn10','earn10_range','earn12','earn12_range',\\\n", 1518 | " 'earn14','earn14_range','earn16','earn16_range','earn18','earn18_range']].describe()" 1519 | ] 1520 | }, 1521 | { 1522 | "cell_type": "code", 1523 | "execution_count": 10, 1524 | "id": "6ff344e8", 1525 | "metadata": {}, 1526 | "outputs": [], 1527 | "source": [ 1528 | "nlsy97['avg_earn_2014_to_2018'] = nlsy97[['earn14','earn16','earn18']].mean(1)" 1529 | ] 1530 | }, 1531 | { 1532 | "cell_type": "code", 1533 | "execution_count": 11, 1534 | "id": "1399b0d2", 1535 | "metadata": {}, 1536 | "outputs": [], 1537 | "source": [ 1538 | "nlsy97[['avg_earn_2014_to_2018', 'hgc_ever', 'asvab', 'female', 'black', 'hispanic']].dropna(how='any', axis=0).to_csv(data+'nlsy97ss.csv',index=True)" 1539 | ] 1540 | } 1541 | ], 1542 | "metadata": { 1543 | "kernelspec": { 1544 | "display_name": "Python 3", 1545 | "language": "python", 1546 | "name": "python3" 1547 | }, 1548 | "language_info": { 1549 | "codemirror_mode": { 1550 | "name": "ipython", 1551 | "version": 3 1552 | }, 1553 | "file_extension": ".py", 1554 | "mimetype": "text/x-python", 1555 | "name": "python", 1556 | "nbconvert_exporter": "python", 1557 | "pygments_lexer": "ipython3", 1558 | "version": "3.8.8" 1559 | } 1560 | }, 1561 | "nbformat": 4, 1562 | "nbformat_minor": 5 1563 | } 1564 | --------------------------------------------------------------------------------