SJTU CMGPD 2012 Methodological Lecture Day 4
description
Transcript of SJTU CMGPD 2012 Methodological Lecture Day 4
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SJTU CMGPD 2012Methodological Lecture
Day 4
Household and Relationship Variables
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Outline
• Existing household variables– Identifiers– Characteristics– Dynamics– Household relationship
• Creation of new variables– Use of bysort/egen
• Household relationship variables
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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
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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
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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
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• 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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0 1 2 5 10 20 50 100 150Household size
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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
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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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510
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1775 1800 1825 1850 1875 1900Year
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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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bandwidth = .8
Lowess smoother
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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
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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
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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
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. 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
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. 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------------------------------------------------------------------------------
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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
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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
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RELATIONSHIP
• String describes relationship of individual to the head of the household– Before 1789, describes relationship to head of
yihu• This is the basis of our kinship linkage
– Automated linkage of children to their parents– Automated linkage of wives to their husband’s– All based on processing of strings describing
relationship
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RELATIONSHIPCore
• e is household head• w is a household head’s wife• m is household head’s mother• f is household head’s father (usually dead)• 1yb, 2yb, 2ob etc. are head’s brothers
– Older brothers of the head are unusual• 1yz, 2yz, 2oz etc. are head’s unmarried sisters• 1s, 2s, etc. are head’s sons• 1d, 2d, etc. are the head’s unmarried daughters
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RELATIONSHIPCombining codes
• More distant relationships are built up from these core relationships by combining them
• Examples– ff is grandfather of head– fm is grandmother of head– f2yb is an uncle: father’s second younger brother
• f2ybw is his wife– f2yb1s is a cousin: father’s 2nd younger brother’s 1st son– 3yb2s is a nephew: 3rd younger brother’s 2nd son– 3s2s is a grandson: 3rd son’s 2nd son
• 3s2sw is his wife
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RELATIONSHIPLinking wives to husbands
• Strip the w off of a married woman’s relationship and search the household for the remaining string. – f2yb1sw -> search for f2yb1s
• Exceptions– For w, search for e– For f, search for m– For fm, search for ff– Etc.
• Basically prepare a target string, and then make use of merge on HOUSEHOLD_ID and the target
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RELATIONSHIPLinking children to fathers
• In most cases, strip off the last relationship code and look for the remainder.– 1s1s -> look for 1s– ff2yb3s2s -> look for ff2yb3s
• Exceptions– e look for f– 2yb look for f– f2yb look for ff
• To link married women to their fathers-in-law, strip off w first, then convert to father’s relationship
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RELATIONSHIPIndicators of specify basic relationships to head
generate head = RELATIONSHIP == “e”
generate head_wife = RELATIONSHIP == “w”
generate mother = RELATIONSHIP == “m”
generate father = RELATIONSHIP == “f”
. tab head SEX if PRESENT & SEX >= 1, row col
+-------------------+| Key ||-------------------|| frequency || row percentage || column percentage |+-------------------+
| Sex head | Female Male | Total-----------+----------------------+---------- 0 | 539,935 671,972 | 1,211,907 | 44.55 55.45 | 100.00 | 98.69 78.90 | 86.64 -----------+----------------------+---------- 1 | 7,148 179,658 | 186,806 | 3.83 96.17 | 100.00 | 1.31 21.10 | 13.36 -----------+----------------------+---------- Total | 547,083 851,630 | 1,398,713 | 39.11 60.89 | 100.00 | 100.00 100.00 | 100.00
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RELATIONSHIPProcessing for distant relationships
• Strip out numbers, seniority modifiers y and b, etc.
• In a .do file, this will create a new variable with a stripped relationship
generate new_RELATIONSHIP = RELATIONSHIPlocal for_removal "1 2 3 4 5 6 7 8 9 o y w"foreach x of local for_removal {
replace new_RELATIONSHIP = subinstr(new_RELATIONSHIP,"`x'","",.)
}
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ExamplesRELATIONSHIP new_RELATIONSHIPe ewf fm m1ob b1obw b1ob1s bs3yb b3ybw b3yb1s bs3yb1d bd4yb b4ybw bf2yb fbf2ybw fb
RELATIONSHIP new_RELATIONSHIPf2yb1d fbdf3yb fbf3ybw fbf3yb1s fbsf3yb1sw fbsf3yb1s1s fbssf3yb1s1d fbsdf3yb2s fbsf3yb2sw fbsf3yb2s1d fbsdf4ybw fbf4yb1sw fbsf4yb1s1d fbsdf4yb1d fbdf4yb2d fbd
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generate brother = new_RELATIONSHIP = “b” & SEX == 2
generate brothers_wife = “b” & SEX == 1 & MARITAL_STATUS !=2 & MARITAL_STATUS > 0
generate sister = new_RELATIONSHIP = “z” & SEX == 1
generate male_cousin = new_RELATIONSHIP = “fbs” & SEX == 2
generate nephew = new_RELATIONSHIP = “bs” & SEX == 2
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Proportions of different relationships by age
generate brother = new_RELATIONSHIP == "b"bysort AGE_IN_SUI: egen males = total(SEX == 2 & PRESENT)bysort AGE_IN_SUI: egen brothers = total(SEX == 2 & brother & PRESENT)generate proportion_brothers = brothers/malesby AGE_IN_SUI: generate first_in_age = _n == 1twoway line proportion_brothers AGE_IN_SUI if AGE_IN_SUI >= 1 & AGE_IN_SUI <= 80
& first_in_age, ytitle("Proportion of males who are brother of a head") scheme(s1mono)
bysort AGE_IN_SUI: egen heads = total(SEX == 2 & RELATIONSHIP == "e" & PRESENT)generate proportion_heads = heads/malestwoway line proportion_heads AGE_IN_SUI if AGE_IN_SUI >= 1 & AGE_IN_SUI <= 80 &
first_in_age, ytitle("Proportion of males who are household head") scheme(s1mono)bysort AGE_IN_SUI: egen sons = total(SEX == 2 & new_RELATIONSHIP == "s" & PRESENT)generate proportion_sons = sons/malestwoway line proportion_sons AGE_IN_SUI if AGE_IN_SUI >= 1 & AGE_IN_SUI <= 80 &
first_in_age, ytitle("Proportion of males who are son of a head") scheme(s1mono)
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Relationship at first appearancebysort PERSON_ID (YEAR): generate fa_nephew = new_RELATIONSHIP[1] == "bs" & AGE[1] <= 10 &
SEX == 2 & PRESENTbysort PERSON_ID (YEAR): generate fa_son = new_RELATIONSHIP[1] == "s" & AGE[1] <= 10 & SEX == 2
& PRESENTgenerate fa_nephew_head = fa_nephew & headgenerate fa_son_head = fa_son & headbysort AGE_IN_SUI: egen fa_sons = total(fa_son)bysort AGE_IN_SUI: egen fa_nephews = total(fa_nephew)bysort AGE_IN_SUI: egen fa_sons_head = total(fa_son_head)bysort AGE_IN_SUI: egen fa_nephews_head = total(fa_nephew_head)generate p_fa_sons_head = fa_sons_head/fa_sonsgenerate p_fa_nephews_head = fa_nephews_head/fa_nephewstwoway line p_fa_sons_head p_fa_nephews_head AGE_IN_SUI if AGE_IN_SUI >= 1 & AGE_IN_SUI <=
80 & first_in_age, ytitle("Proportion") scheme(s1mono)twoway line p_fa_sons_head p_fa_nephews_head AGE_IN_SUI if AGE_IN_SUI >= 1 & AGE_IN_SUI <=
80 & first_in_age, ytitle("Proportion now head") scheme(s1mono) legend(order(1 "Appeared as sons of head" 2 "Appeared as nephews of head"))
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0 20 40 60 80Age in Sui
Appeared as sons of head Appeared as nephews of head