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CMGPD-LN Methodological Lecture Day 4

CMGPD-LN Methodological Lecture Day 4. Households. Outline. Existing household variables Identifiers Characteristics Dynamics Household relationship Creation of new variables Use of bysort / egen. Identifiers. HOUSEHOLD_ID

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CMGPD-LN Methodological Lecture Day 4

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  1. CMGPD-LNMethodological LectureDay 4 Households

  2. Outline • Existing household variables • Identifiers • Characteristics • Dynamics • Household relationship • Creation of new variables • Use of bysort/egen

  3. Identifiers • HOUSEHOLD_ID • Identifies records associated with a household in the current register • HOUSEHOLD_SEQ • The order of the current household (linghu) within the current household group (yihu) • UNIQUE_HH_ID • Identifies records associated with the same household across different registers • New value assigned at time of household division • Each of the resulting households gets a new, different

  4. Characteristics • HH_SIZE • Number of living members of the household • Set to missing before 1789 • HH_DIVIDE_NEXT • Number of households in the next register that the members of the current household are associated with. • 1 if no division • 0 if extinction • 2 or more if division • Set to missing before 1789

  5. histogram HH_SIZE if PRESENT & HH_SIZE > 0, width(2) scheme(s1mono) fraction ytitle("Proportion of individuals") xtitle("Number of members")

  6. This isn’t particularly appealing • A log scale on the x axis would help • In STATA, histogram forces fixed width bins, even when the x scale is set to log • We can collapse the data and plot using twoway bar or scatter table HH_SIZE, replace twoway bar table1 HH_SIZE if HH_SIZE > 0, xscale(log) scheme(s1mono) xlabel(0 1 2 5 10 20 50 100 150)

  7. What if we would like to convert to fractions? • Compute total number of households by summing table1, then divide each value of table 1 by the total • sum(table1) returns the sum of table 1 up to the current observation • total[_N] returns the value of total in the last observation drop if HH_SIZE <= 0 generate total = sum(table1) generate hh_fraction = table1/total[_N] twoway bar hh_fraction HH_SIZE if HH_SIZE > 0, xscale(log) scheme(s1mono) xlabel(0 1 2 5 10 20 50 100 150) ytitle("Proportion of households")

  8. Households as units of analysis • The previous figures all treated individuals as the units of an analysis • Every household was represented as many times as it had members • A household with 100 members would contribute 100 observations • In effect, the figures represent household size as experienced by individuals • Sometimes we would like to treat households as units of analysis • So that each household only contributes one observation per register

  9. Households as units of analysis • One easy way is to create a flag variable that is set to 1 only for the first observation in each household • Then select based on that flag variable for tabulations etc. • This leaves the original individual level data intact bysort HOUSEHOLD_ID: generate hh_first_record = _n == 1 histogram HH_SIZE if hh_first_record & HH_SIZE > 0, width(2) scheme(s1mono) fraction ytitle("Proportion of households") xtitle("Number of members")

  10. Another approach to plotting trends • We can plot average household size by year of birth without ‘destroying’ the data with TABLE, REPLACE or COLLAPSE bysort YEAR: egenmean_hh_size = mean(HH_SIZE) if HH_SIZE > 0 bysort YEAR: egenfirst_in_year= _n == 1 twoway scatter mean_hh_size YEAR if first_in_year & YEAR >= 1775, scheme(s1mono) ytitle("Mean household size of individuals") xlabel(1775(25)1900)

  11. Mean household size of individuals by age keep if AGE_IN_SUI > 0 & SEX == 2 & YEAR >= 1789 & HH_SIZE > 0 bysort AGE_IN_SUI: egenmean_hh_size = mean(HH_SIZE) bysort AGE_IN_SUI: generate first_in_age = _n == 1 twoway scatter mean_hh_size AGE_IN_SUI if first_in_age & AGE_IN_SUI <= 80, scheme(s1mono) ytitle("Mean household size of individuals") xlabel(1(5)85) xtitle("Age in sui") lowessmean_hh_size AGE_IN_SUI if first_in_age & AGE_IN_SUI <= 80, scheme(s1mono) ytitle("Mean household size of individuals") xlabel(1(5)85) xtitle("Age in sui") msize(small)

  12. Household divisionIndividuals by next register . tab HH_DIVIDE_NEXT if PRESENT & NEXT_3 & HH_DIVIDE_NEXT >= 0 Number of | household in | the next | available | register | Freq. Percent Cum. ---------------+----------------------------------- 1 | 789,250 94.98 94.98 2 | 33,000 3.97 98.95 3 | 5,815 0.70 99.65 4 | 1,812 0.22 99.87 5 | 383 0.05 99.91 6 | 314 0.04 99.95 7 | 196 0.02 99.98 8 | 34 0.00 99.98 9 | 82 0.01 99.99 10 | 86 0.01 100.00 ---------------+----------------------------------- Total | 830,972 100.00

  13. Household divisionHouseholds by next register . bysort HOUSEHOLD_ID: generate first_in_hh = _n == 1 . tab HH_DIVIDE_NEXT if PRESENT & NEXT_3 & HH_DIVIDE_NEXT >= 0 & first_in_hh Number of | household in | the next | available | register | Freq. Percent Cum. ---------------+----------------------------------- 1 | 117,317 97.80 97.80 2 | 2,287 1.91 99.71 3 | 272 0.23 99.94 4 | 57 0.05 99.98 5 | 8 0.01 99.99 6 | 7 0.01 100.00 7 | 2 0.00 100.00 9 | 1 0.00 100.00 10 | 1 0.00 100.00 ---------------+----------------------------------- Total | 119,952 100.00

  14. Household divisionExample of a simple analysis generate byte DIVISION = HH_DIVIDE_NEXT > 1 generate l_HH_SIZE = ln(HH_SIZE)/ln(1.1) logit DIVISION HH_SIZE YEAR if HH_SIZE > 0 & NEXT_3 & HH_DIVIDE_NEXT >= 0 & first_in_hh logit DIVISION l_HH_SIZE YEAR if NEXT_3 & HH_DIVIDE_NEXT >= 0 & first_in_hh

  15. . logit DIVISION HH_SIZE YEAR if HH_SIZE > 0 & NEXT_3 & HH_DIVIDE_NEXT >= 0 & first_in_hh Iteration 0: log likelihood = -15419.716 Iteration 1: log likelihood = -14310.848 Iteration 2: log likelihood = -14127.244 Iteration 3: log likelihood = -14126.276 Iteration 4: log likelihood = -14126.276 Logistic regression Number of obs = 132688 LR chi2(2) = 2586.88 Prob > chi2 = 0.0000 Log likelihood = -14126.276 Pseudo R2 = 0.0839 ------------------------------------------------------------------------------ DIVISION | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- HH_SIZE | .0882472 .0016549 53.32 0.000 .0850036 .0914908 YEAR | -.0122989 .0005941 -20.70 0.000 -.0134633 -.0111345 _cons | 18.23519 1.087218 16.77 0.000 16.10428 20.3661

  16. . logit DIVISION l_HH_SIZE YEAR if NEXT_3 & HH_DIVIDE_NEXT >= 0 & first_in_hh Iteration 0: log likelihood = -15419.716 Iteration 1: log likelihood = -13953.268 Iteration 2: log likelihood = -13468.077 Iteration 3: log likelihood = -13463.036 Iteration 4: log likelihood = -13463.032 Iteration 5: log likelihood = -13463.032 Logistic regression Number of obs = 132688 LR chi2(2) = 3913.37 Prob > chi2 = 0.0000 Log likelihood = -13463.032 Pseudo R2 = 0.1269 ------------------------------------------------------------------------------ DIVISION | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- l_HH_SIZE | .1341566 .0023316 57.54 0.000 .1295867 .1387265 YEAR | -.0130866 .0005775 -22.66 0.000 -.0142185 -.0119547 _cons | 17.75924 1.048066 16.94 0.000 15.70507 19.81342 ------------------------------------------------------------------------------

  17. Creating household variables • bysort and egen are your friends • Use household_idto group observations of the same household in the same register • Let’s start with a count of the number of live individuals in the household bysort HOUSEHOLD_ID: egennew_hh_size = total(PRESENT) . corr HH_SIZE new_hh_size if YEAR >= 1789 (obs=1410354) | HH_SIZE new_hh~e -------------+------------------ HH_SIZE | 1.0000 new_hh_size | 1.0000 1.0000

  18. Creating measures of age and sex composition of the household bysort HOUSEHOLD_ID: egen males_1_15 = total(PRESENT & SEX == 2 & AGE_IN_SUI >= 1 & AGE_IN_SUI <= 15) bysort HOUSEHOLD_ID: egen males_16_55 = total(PRESENT & SEX == 2 & AGE_IN_SUI >= 16 & AGE_IN_SUI <= 55) bysort HOUSEHOLD_ID: egen males_56_up = total(PRESENT & SEX == 2 & AGE_IN_SUI >= 56) bysort HOUSEHOLD_ID: egen females_1_15 = total(PRESENT & SEX == 1 & AGE_IN_SUI >= 1 & AGE_IN_SUI <= 15) bysort HOUSEHOLD_ID: egen females_16_55 = total(PRESENT & SEX == 1 & AGE_IN_SUI >= 16 & AGE_IN_SUI <= 55) bysort HOUSEHOLD_ID: egen females_56_up = total(PRESENT & SEX == 1 & AGE_IN_SUI >= 56) generate hh_dependency_ratio = (males_1_15+males56_up+females_1_15+females56_up)/HH_SIZE bysort AGE_IN_SUI: generate first_in_age = _n == 1 bysort AGE_IN_SUI: egenmean_hh_dependency_ratio = mean(hh_dependency_ratio) twoway line mean_hh_dependency_ratio AGE_IN_SUI if first_in_age & AGE_IN_SUI >= 16 & AGE_IN_SUI <= 55, scheme(s1mono) ylabel(0(0.1)0.5) xlabel(16(5)55) ytitle("Household dependency ratio (Prop. < 15 or >= 56 sui)") xtitle("Age in sui")

  19. Numbers of individuals who co-reside with someone who holds a position . bysort HOUSEHOLD_ID: egenposition_in_hh = total(PRESENT & HAS_POSITION > 0) . tab position_in_hh if PRESENT & YEAR >= 1789 position_in | _hh | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,177,575 90.23 90.23 1 | 87,517 6.71 96.94 2 | 24,204 1.85 98.79 3 | 8,019 0.61 99.41 4 | 4,893 0.37 99.78 5 | 1,712 0.13 99.91 6 | 651 0.05 99.96 7 | 241 0.02 99.98 8 | 136 0.01 99.99 9 | 101 0.01 100.00 ------------+----------------------------------- Total | 1,305,049 100.00 . replace position_in_hh = position_in_hh > 0 (49183 real changes made) . tab position_in_hh if PRESENT & YEAR >= 1789 position_in | _hh | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,177,575 90.23 90.23 1 | 127,474 9.77 100.00 ------------+----------------------------------- Total | 1,305,049 100.00

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