CMGPD-LN Methodological Lecture Day 4

CMGPD-LNMethodological Lecture

Day 4

Households

Outline

• Existing household variables– Identifiers– Characteristics– Dynamics– Household relationship

• Creation of new variables– Use of bysort/egen

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

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

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

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• 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, replacetwoway bar table1 HH_SIZE if HH_SIZE > 0,

xscale(log) scheme(s1mono) xlabel(0 1 2 5 10 20 50 100 150)

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0 1 2 5 10 20 50 100 150Household size

• 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 <= 0generate 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")

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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

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")

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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: egen mean_hh_size = mean(HH_SIZE) if HH_SIZE > 0

bysort YEAR: egen first_in_year = _n == 1twoway scatter mean_hh_size YEAR if first_in_year & YEAR >= 1775, scheme(s1mono) ytitle("Mean household size of individuals") xlabel(1775(25)1900)

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1775 1800 1825 1850 1875 1900Year

Mean household size of individuals by age

keep if AGE_IN_SUI > 0 & SEX == 2 & YEAR >= 1789 & HH_SIZE > 0

bysort AGE_IN_SUI: egen mean_hh_size = mean(HH_SIZE)

bysort AGE_IN_SUI: generate first_in_age = _n == 1twoway 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")

lowess 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") msize(small)

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1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86Age in sui

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1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86Age in sui

bandwidth = .8

Lowess smoother

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

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

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

. 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 = 0.0000Log 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

. 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 = 0.0000Log 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------------------------------------------------------------------------------

Creating household variables• bysort and egen are your friends• Use household_id to 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: egen new_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

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 == 1bysort AGE_IN_SUI: egen mean_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")

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Numbers of individuals who co-reside with someone who holds a position

. bysort HOUSEHOLD_ID: egen position_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