- 1 -

. *=============================================================================*

. *= [BK21PLUS] Data Management Using Stata =*

. *= 2014.10.17. Oh Ukchan =*

. *=============================================================================*

[목차]

(1) age 1

(2) gender 2

(3) education 2

(4) household characteristics 3

(5) hourly wage rate 5

(6) rent-income ratio 7

(7) suicidal ideation 7

(8) poverty 8

(9) panel dataset 만들기(foreach + append) 10

(10) 결혼상태와 우울의 관계(lag & difference) 12

(11) 데이터의 구조변환과 결합(stack & reshape, merge) 14

(12) 빈곤의 대물림(아동기 경제상태와 현재 빈곤의 관계) 15

(13) 배우자 정보 연결하기(educational assortive mating) 18

(14) 자녀-부모 정보 연결하기(진로성숙도 - 부의 성역할 인식) 20

.

. * current working directory

. cdC:\Users\오욱찬\Desktop\141017-BK방법론워크숍(Data Management Using Stata)

.

. * data : Koweps_hpwc08_2013_beta2 [koweps08p]

. use koweps08, clear

.

. * describe (Describe data in memory)

. describe, short

Contains data from koweps08.dta obs: 17,984 vars: 766 size: 126,391,552 Sorted by:

. *-----------------------------------------------------------------------------*

. * (1) age

. *-----------------------------------------------------------------------------*

.

. * age

. gen age=2012-h08_g4

.

. * summarize (Summary statistics)

. sum age

Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- age | 17984 45.24277 23.96988 0 105

. sum age, detail

age------------------------------------------------------------- Percentiles Smallest 1% 1 0 5% 6 010% 11 0 Obs 1798425% 25 0 Sum of Wgt. 17984

50% 46 Mean 45.24277 Largest Std. Dev. 23.9698875% 67 9890% 76 98 Variance 574.555395% 80 101 Skewness -.148452399% 87 105 Kurtosis 1.880771

.

. * age_cat (age_category)

. recode age (0/19=0) (20/29=1) (30/39=2) (40/49=3) (50/59=4) (60/69=5) ///> (70/200=6), gen(age_cat)(17944 differences between age and age_cat)

. label variable age_cat "" // variable label 제거

.

. * about recode rule

. help recode

.

- 2 -

. * tabulate (One-way table of frequencies)

. tab age_cat

age_cat | Freq. Percent Cum.------------+----------------------------------- 0 | 3,679 20.46 20.46 1 | 1,442 8.02 28.48 2 | 2,164 12.03 40.51 3 | 2,426 13.49 54.00 4 | 2,196 12.21 66.21 5 | 2,294 12.76 78.96 6 | 3,783 21.04 100.00------------+----------------------------------- Total | 17,984 100.00

. *-----------------------------------------------------------------------------*

. * (2) gender

. *-----------------------------------------------------------------------------*

.

. * h08_g3 : 1=male, 2=female

.

. * gender1 (0=female, 1=male)

. recode h08_g3 (1=1) (2=0), gen(gender1)(9765 differences between h08_g3 and gender1)

. la var gender1 "" // variable label 제거

.

. * gender2 (0=female, 1=male)

. gen gender2=(h08_g3==1) // 괄호 안의 조건을 만족하면 1, 그렇지 않으면 0

.

. * about operator

. help operator

.

. * tab1 (One-way table for each variable)

. tab1 gender1 gender2

-> tabulation of gender1

gender1 | Freq. Percent Cum.------------+----------------------------------- 0 | 9,765 54.30 54.30 1 | 8,219 45.70 100.00------------+----------------------------------- Total | 17,984 100.00

-> tabulation of gender2

gender2 | Freq. Percent Cum.------------+----------------------------------- 0 | 9,765 54.30 54.30 1 | 8,219 45.70 100.00------------+----------------------------------- Total | 17,984 100.00

.

.

. *-----------------------------------------------------------------------------*

. * (3) education

. *-----------------------------------------------------------------------------*

.

. * h08_g6 : 1=미취학, 2=무학, 3=초등학교, 4=중학교, 5=고등학교, 6=전문대학, 7=대학교, 8=석사, 9=박사

. * h08_g7 : 0=비해당, 1=재학, 2=휴학, 3=중퇴, 4=수료, 5=졸업

.

. * tabulate (Two-way table of frequencies)

. tab h08_g6 h08_g7, missing

교육수준1( | 교육수준2(h08_g7) h08_g6) | 0 1 2 3 4 5 | Total-----------+------------------------------------------------------------------+---------- 1 | 1,006 0 0 0 0 0 | 1,006 2 | 1,464 0 0 0 0 0 | 1,464 3 | 0 1,166 0 698 0 2,368 | 4,232 4 | 0 656 0 253 0 1,451 | 2,360 5 | 0 624 2 214 0 3,810 | 4,650 6 | 0 129 62 95 1 1,091 | 1,378 7 | 0 418 99 197 2 1,851 | 2,567 8 | 0 36 5 7 14 222 | 284 9 | 0 6 2 0 5 30 | 43 -----------+------------------------------------------------------------------+---------- Total | 2,470 3,035 170 1,464 22 10,823 | 17,984

.

. * educ (categorical, 입학 기준이 아닌 졸업 기준의 최종학력)

. gen educ=h08_g6

. replace educ=educ-1 if h08_g6~=1 & h08_g6~=2 & h08_g7~=5(4691 real changes made)

.

. * educ4 (categorical, 1=중졸이하, 2=고졸, 3=전문대졸, 4=대졸이상)

. recode educ (1/4=1) (5=2) (6=3) (7/9=4), gen(educ4)(16978 differences between educ and educ4)

. la var educ4 "" // variable label 제거

- 3 -

. tab educ4, gen(edu) // 더미변수 만들기(edu1, edu2, edu3, edu4)

educ4 | Freq. Percent Cum.------------+----------------------------------- 1 | 9,902 55.06 55.06 2 | 4,097 22.78 77.84 3 | 1,807 10.05 87.89 4 | 2,178 12.11 100.00------------+----------------------------------- Total | 17,984 100.00

. tab1 edu1 edu2 edu3 edu4

-> tabulation of edu1

educ4== | 1.0000 | Freq. Percent Cum.------------+----------------------------------- 0 | 8,082 44.94 44.94 1 | 9,902 55.06 100.00------------+----------------------------------- Total | 17,984 100.00-> tabulation of edu2

educ4== | 2.0000 | Freq. Percent Cum.------------+----------------------------------- 0 | 13,887 77.22 77.22 1 | 4,097 22.78 100.00------------+----------------------------------- Total | 17,984 100.00

-> tabulation of edu3

educ4== | 3.0000 | Freq. Percent Cum.------------+----------------------------------- 0 | 16,177 89.95 89.95 1 | 1,807 10.05 100.00------------+----------------------------------- Total | 17,984 100.00

-> tabulation of edu4

educ4== | 4.0000 | Freq. Percent Cum.------------+----------------------------------- 0 | 15,806 87.89 87.89 1 | 2,178 12.11 100.00------------+----------------------------------- Total | 17,984 100.00

.

. * educ_year (continuous, 비졸업(재학, 휴학, 중퇴, 수료)은 교육과정의 1/2을 이수한 것으로 가정)

. recode h08_g6 (1 2=0) (3=6) (4=9) (5=12) (6=14) (7=16) (8=18) (9=22), ///> gen(educ_year) // 졸업 상태일 경우의 교육연수(17984 differences between h08_g6 and educ_year)

. label variable educ_year ""

. replace educ_year=educ_year-3 if h08_g6==3 & h08_g7~=5 // 초등학교 비졸업(1864 real changes made)

. replace educ_year=educ_year-1.5 if h08_g6==4 & h08_g7~=5 // 중등학교 비졸업(909 real changes made)

. replace educ_year=educ_year-1.5 if h08_g6==5 & h08_g7~=5 // 고등학교 비졸업(840 real changes made)

. replace educ_year=educ_year-1 if h08_g6==6 & h08_g7~=5 // 전문대 비졸업(287 real changes made)

. replace educ_year=educ_year-2 if h08_g6==7 & h08_g7~=5 // 대학교 비졸업(716 real changes made)

. replace educ_year=educ_year-1 if h08_g6==8 & h08_g7~=5 // 석사 비졸업(62 real changes made)

. replace educ_year=educ_year-2 if h08_g6==9 & h08_g7~=5 // 박사 비졸업(13 real changes made)

.

. * summarize

. sum educ_year

Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- educ_year | 17984 8.831823 5.247909 0 22

.

.

. *-----------------------------------------------------------------------------*

. * (4) household characteristics

. *-----------------------------------------------------------------------------*

.

. * 가구원 수

. bysort h08_merkey: egen fnum1=count(h08_pid)

. bysort h08_merkey: gen fnum2=_N // _N: total number within each by-group

. list h08_pid if fnum1~=h0801_1 | fnum2~=h0801_1 // 가구원수 변수가 제대로 생성되었는지 확인

- 4 -

. tab1 fnum1 fnum2 h0801_1 // 가구원수 변수가 제대로 생성되었는지 확인

-> tabulation of fnum1

fnum1 | Freq. Percent Cum.------------+----------------------------------- 1 | 2,000 11.12 11.12 2 | 4,638 25.79 36.91 3 | 3,732 20.75 57.66 4 | 5,100 28.36 86.02 5 | 1,810 10.06 96.09 6 | 516 2.87 98.95 7 | 154 0.86 99.81 8 | 16 0.09 99.90 9 | 18 0.10 100.00------------+----------------------------------- Total | 17,984 100.00

-> tabulation of fnum2

fnum2 | Freq. Percent Cum.------------+----------------------------------- 1 | 2,000 11.12 11.12 2 | 4,638 25.79 36.91 3 | 3,732 20.75 57.66 4 | 5,100 28.36 86.02 5 | 1,810 10.06 96.09 6 | 516 2.87 98.95 7 | 154 0.86 99.81 8 | 16 0.09 99.90 9 | 18 0.10 100.00------------+----------------------------------- Total | 17,984 100.00

-> tabulation of h0801_1

가구원수(h0 | 801_1) | Freq. Percent Cum.------------+----------------------------------- 1 | 2,000 11.12 11.12 2 | 4,638 25.79 36.91 3 | 3,732 20.75 57.66 4 | 5,100 28.36 86.02 5 | 1,810 10.06 96.09 6 | 516 2.87 98.95 7 | 154 0.86 99.81 8 | 16 0.09 99.90 9 | 18 0.10 100.00------------+----------------------------------- Total | 17,984 100.00

.

. * about egen functions

. help egen

.

. * about prefix

. help prefix

.

. * 65세 이상 노인가구원 수

. gen old=age>=65 // 65세 이상 노인 여부(개인별)

. bysort h08_merkey: egen oldfnum=total(old)

. tab oldfnum

oldfnum | Freq. Percent Cum.------------+----------------------------------- 0 | 10,618 59.04 59.04 1 | 4,318 24.01 83.05 2 | 2,997 16.66 99.72 3 | 51 0.28 100.00------------+----------------------------------- Total | 17,984 100.00

.

. * 여성가구주 가구 여부

. tab h08_g3 if h08_g2==10 // 가구주의 성별 빈도 확인

성별(h08_g3 | ) | Freq. Percent Cum.------------+----------------------------------- 1 | 5,113 69.93 69.93 2 | 2,199 30.07 100.00------------+----------------------------------- Total | 7,312 100.00

. gen fhh_i=(h08_g2==10 & h08_g3==2) // 여성가구주 여부(개인별)

. bysort h08_merkey: egen fhh=total(fhh_i)

. tab fhh

fhh | Freq. Percent Cum.------------+----------------------------------- 0 | 14,731 81.91 81.91 1 | 3,253 18.09 100.00------------+----------------------------------- Total | 17,984 100.00

- 5 -

. *-----------------------------------------------------------------------------*

. * (5) hourly wage rate

. *-----------------------------------------------------------------------------*

.

. * h08_pers_income1 : 상용근로자 소득

. * h08_pers_income2 : 임시, 일용근로자 소득

. * p0802_6 : 근로월수

. * p0802_7 : 일한 달의 평균 근로일수

. * p0802_8 : 규칙적으로 일한 경우 주당 평균 근로시간

. * p0802_9 : 불규칙적으로 일한 경우 하루 평균 근로시간

. * p0802_8aq1 : 규칙적으로 일한 경우 일한 달의 월평균 임금 (8차년도 신설)

. * p0802_8aq2 : 불규칙적으로 일한 경우 일한 날의 시간당 임금 (8차년도 신설)

.

. * recode (non-response=missing)

. recode h08_pers_income1 h08_pers_income2 (999999=.), prefix(re_)(0 differences between h08_pers_income1 and re_h08_pers_income1)(0 differences between h08_pers_income2 and re_h08_pers_income2)

. recode p0802_6 p0802_7 p0802_9 (99=.), pre(re_)(1 differences between p0802_6 and re_p0802_6)(1 differences between p0802_7 and re_p0802_7)(0 differences between p0802_9 and re_p0802_9)

. recode p0802_8 (999=.), pre(re_)(1 differences between p0802_8 and re_p0802_8)

.

. * hwage

. egen ywage=rowtotal(re_h08_pers_income1 re_h08_pers_income2), m // yearly wage(11136 missing values generated)

. gen mwage=ywage/re_p0802_6 // monthly wage(12189 missing values generated)

. gen mtime_re=re_p0802_8*366/7/12 // monthly working time (regular)(12149 missing values generated)

. gen mtime_ir=re_p0802_9*re_p0802_7 // monthly working time (irregular)(15594 missing values generated)

. egen mtime=rowtotal(mtime_re mtime_ir), m // monthly working time (all)(9759 missing values generated)

. gen hwage=mwage/mtime // hourly wage(12189 missing values generated)

. gen lnwage=ln(hwage) // natural log of hourly wage(12190 missing values generated)

.

. * summarize (missing case가 얼마나 많나? 극단치가 있나?)

. sum ywage mwage mtime hwage lnwage if inrange(h08_eco4, 1, 4)

Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- ywage | 5436 2507.279 2097.21 0 28684 mwage | 5386 221.8402 185.2268 0 3246 mtime | 5400 185.5566 65.21729 0 457.5 hwage | 5386 1.238428 1.116787 0 21.9276 lnwage | 5385 -.039556 .7060141 -3.827646 3.087746

.

. * 신설변수(p0802_8aq1, p0802_8aq2) 활용 검토

. sum p0802_8aq1 p0802_8aq2

Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- p0802_8aq1 | 5836 1999.503 3763.471 0 9999 p0802_8aq2 | 2390 7284.133 4447.506 0 9999

. sum p0802_8aq1 if p0802_8aq1==9999

Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- p0802_8aq1 | 1056 9999 0 9999 9999

. sum p0802_8aq2 if p0802_8aq2==9999

Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- p0802_8aq2 | 1741 9999 0 9999 9999

.

. * tabstat & tab, sum()

. tabstat hwage if inrange(h08_eco4, 1, 4), by(gender1) s(n mean sd sem)

Summary for variables: hwage by categories of: gender1

gender1 | N mean sd se(mean)---------+---------------------------------------- 0 | 2460 .9339833 .8365322 .0168661 1 | 2926 1.494387 1.250745 .0231223---------+---------------------------------------- Total | 5386 1.238428 1.116787 .0152173--------------------------------------------------

- 6 -

. tab gender1 if inrange(h08_eco4, 1, 4), sum(hwage)

| Summary of hwage gender1 | Mean Std. Dev. Freq.------------+------------------------------------ 0 | .93398334 .83653217 2460 1 | 1.494387 1.2507453 2926------------+------------------------------------ Total | 1.2384284 1.1167871 5386

.

. * outlier(top 1%) deletion

. xtile p100=hwage if inrange(h08_eco4, 1, 4), n(100)

. tab p100

100 | quantiles | of hwage | Freq. Percent Cum.------------+----------------------------------- 1 | 54 1.00 1.00 2 | 54 1.00 2.01 3 | 54 1.00 3.01 4 | 54 1.00 4.01 5 | 56 1.04 5.05 6 | 54 1.00 6.05 7 | 67 1.24 7.30 8 | 43 0.80 8.10 9 | 49 0.91 9.00 10 | 55 1.02 10.03 11 | 53 0.98 11.01 12 | 75 1.39 12.40 13 | 33 0.61 13.02 14 | 54 1.00 14.02 15 | 53 0.98 15.00 16 | 54 1.00 16.00 17 | 64 1.19 17.19 18 | 45 0.84 18.03 19 | 53 0.98 19.01 20 | 54 1.00 20.01 21 | 54 1.00 21.02 22 | 53 0.98 22.00 23 | 65 1.21 23.21 24 | 43 0.80 24.01 25 | 54 1.00 25.01 26 | 57 1.06 26.07 27 | 51 0.95 27.01 28 | 60 1.11 28.13 29 | 48 0.89 29.02 30 | 55 1.02 30.04 31 | 55 1.02 31.06 32 | 51 0.95 32.01 33 | 54 1.00 33.01 34 | 54 1.00 34.01 35 | 54 1.00 35.02

36 | 53 0.98 36.00 37 | 54 1.00 37.00 38 | 56 1.04 38.04 39 | 52 0.97 39.01 40 | 54 1.00 40.01 41 | 55 1.02 41.03 42 | 53 0.98 42.02 43 | 53 0.98 43.00 44 | 55 1.02 44.02 45 | 53 0.98 45.01 46 | 55 1.02 46.03 47 | 53 0.98 47.01 48 | 54 1.00 48.01 49 | 55 1.02 49.03 50 | 52 0.97 50.00 51 | 56 1.04 51.04 52 | 52 0.97 52.01 53 | 54 1.00 53.01 54 | 54 1.00 54.01 55 | 54 1.00 55.01 56 | 55 1.02 56.03 57 | 62 1.15 57.19 58 | 44 0.82 58.00 59 | 62 1.15 59.15 60 | 46 0.85 60.01 61 | 54 1.00 61.01 62 | 55 1.02 62.03 63 | 53 0.98 63.02 64 | 58 1.08 64.09 65 | 49 0.91 65.00 66 | 54 1.00 66.00 67 | 54 1.00 67.01 68 | 54 1.00 68.01 69 | 54 1.00 69.01 70 | 54 1.00 70.01 71 | 55 1.02 71.04 72 | 52 0.97 72.00 73 | 54 1.00 73.00 74 | 54 1.00 74.01 75 | 54 1.00 75.01 76 | 54 1.00 76.01 77 | 54 1.00 77.01 78 | 54 1.00 78.02 79 | 53 0.98 79.00 80 | 54 1.00 80.00 81 | 54 1.00 81.01 82 | 54 1.00 82.01 83 | 54 1.00 83.01 84 | 54 1.00 84.01 85 | 54 1.00 85.02 86 | 53 0.98 86.00 87 | 54 1.00 87.00 88 | 54 1.00 88.01 89 | 54 1.00 89.01 90 | 54 1.00 90.01 91 | 54 1.00 91.01

- 7 -

92 | 54 1.00 92.02 93 | 53 0.98 93.00 94 | 54 1.00 94.00 95 | 54 1.00 95.01 96 | 54 1.00 96.01 97 | 54 1.00 97.01 98 | 54 1.00 98.01 99 | 54 1.00 99.02 100 | 53 0.98 100.00------------+----------------------------------- Total | 5,386 100.00

. scatter hwage age if inrange(h08_eco4, 1, 4)

. scatter hwage age if inrange(h08_eco4, 1, 4) & p100~=100

.

.

. *-----------------------------------------------------------------------------*

. * (6) rent-income ratio

. *-----------------------------------------------------------------------------*

.

. * h0809_aq7 : 주거관련 부채의 이자(연간)

. * h0807_3aq3 : 월평균 주거비-월세

. * h0807_3aq4 : 월평균 주거비-주거관리비

. * h0807_3aq5 : 월평균 광열수도비

.

. * recode (non-response=missing)

. recode h0809_aq7 (9999999=.), pre(re_)(0 differences between h0809_aq7 and re_h0809_aq7)

. recode h0807_3aq3 h0807_3aq4 h0807_3aq5 (9999=.), pre(re_)(0 differences between h0807_3aq3 and re_h0807_3aq3)(0 differences between h0807_3aq4 and re_h0807_3aq4)(0 differences between h0807_3aq5 and re_h0807_3aq5)

.

. * rir (rent-income ratio, 30% 초과 여부)

. gen rent=(re_h0809_aq7/12)+re_h0807_3aq3+re_h0807_3aq4+re_h0807_3aq5

. gen rir=(rent/h08_din*12>0.3 | rent/h08_din*12<0) if rent~=. & h08_din~=.

. tab rir

rir | Freq. Percent Cum.------------+----------------------------------- 0 | 16,915 94.06 94.06 1 | 1,069 5.94 100.00------------+----------------------------------- Total | 17,984 100.00

.

. * about missing values

. help missing

.

.

. *-----------------------------------------------------------------------------*

. * (7) suicidal ideation

. *-----------------------------------------------------------------------------*

.

. * p0805_6aq1 : 지금까지 자살하는 것에 대해 생각한 적이 있는지 여부(신규가구원만 응답)

. * p0805_6aq3 : 자살하는 것에 마지막으로 생각했을 때의 만나이(신규가구원만 응답)

. * p0805_7aq1 : 지난 한해 동안 자살하는 것에 대해 생각한 적이 있는지 여부(원가구원만 응답)

.

. * recode (non-response=missing)

. recode p0805_6aq1 p0805_7aq1 (9=.), pre(re_)(55 differences between p0805_6aq1 and re_p0805_6aq1)(598 differences between p0805_7aq1 and re_p0805_7aq1)

. recode p0805_6aq3 (99=.), pre(re_)(0 differences between p0805_6aq3 and re_p0805_6aq3)

.

. * tab1

. tab1 re_p0805_6aq1 re_p0805_7aq1

-> tabulation of re_p0805_6aq1

RECODE of | p0805_6aq1 | (지금까지 |자살에 대한 | 생각을 | 한번이라도 | 한적이 | 있는지 | 여부( | Freq. Percent Cum.------------+----------------------------------- 1 | 18 5.88 5.88 2 | 288 94.12 100.00------------+----------------------------------- Total | 306 100.00

- 8 -

-> tabulation of re_p0805_7aq1

RECODE of | p0805_7aq1 | (지난 한해 | 동안 | 자살하는 | 것에 대해 | 진지하게 |생각한 적이 | 있?| Freq. Percent Cum.------------+----------------------------------- 1 | 565 4.26 4.26 2 | 12,699 95.74 100.00------------+----------------------------------- Total | 13,264 100.00

.

. * suiidea (suicidal ideation)

. recode re_p0805_6aq1 (1=2) if re_p0805_6aq3<(age-1) | re_p0805_6aq3>(age+1), ///> pre(re_) copy // 왜 age+-1세로 범위를 제한하였을까?(9 differences between re_p0805_6aq1 and re_re_p0805_6aq1)

. egen suiidea=rowtotal(re_re_p0805_6aq1 re_p0805_7aq1), m(4414 missing values generated)

. tab suiidea gender1, column nokey

| gender1 suiidea | 0 1 | Total-----------+----------------------+---------- 1 | 375 199 | 574 | 4.86 3.40 | 4.23 -----------+----------------------+---------- 2 | 7,343 5,653 | 12,996 | 95.14 96.60 | 95.77 -----------+----------------------+---------- Total | 7,718 5,852 | 13,570 | 100.00 100.00 | 100.00

.

.

. *-----------------------------------------------------------------------------*

. * (8) poverty

. *-----------------------------------------------------------------------------*

.

. * rpoor (relative poverty, poverty line: 50% of the median equivalized income)

. gen eqinc=h08_din/(h0801_1^0.5) // disposable equivalized income

. quietly: sum eqinc [aw=p08_wgc], d // 중위소득값을 구하기 위한 기술통계문석(quietly!)

. gen rpoor=eqinc<(r(p50)*0.5) // r(p50): 앞에서 구한 중위소득값의 stored results

.

. * about weight

. help weight

.

. * about Stored results & Classification of Stata commands

. help stored_results

.

. qui sum eqinc [aw=p08_wgc]

. return list

scalars: r(N) = 17984 r(sum_w) = 50004441.00424577 r(mean) = 2800.606286450974 r(Var) = 3351661.104254521 r(sd) = 1830.754244636489 r(min) = -7313.60546875 r(max) = 31054.958984375 r(sum) = 140042751826.9576

.

. qui sum eqinc [aw=p08_wgc], d

. return list

scalars: r(N) = 17984 r(sum_w) = 50004441.00424577 r(mean) = 2800.606286450974 r(Var) = 3351661.104254521 r(sd) = 1830.754244636489 r(skewness) = 3.767188215143093 r(kurtosis) = 40.01284745968434 r(sum) = 140042751826.9576 r(min) = -7313.60546875 r(max) = 31054.958984375

- 9 -

r(p1) = 436.7999877929688 r(p5) = 799.7999877929688 r(p10) = 1063.473876953125 r(p25) = 1716.693237304688 r(p50) = 2490.68896484375 r(p75) = 3475 r(p90) = 4650.5 r(p95) = 5742.669921875 r(p99) = 8952.970703125

.

. * apoor (absolute poverty, poverty line: minimum cost of living, 2012)

. gen apline=h0801_1

. recode apline (1=55.3354) (2=94.2197) (3=121.8873) (4=149.5550) ///> (5=177.2227) (6=204.8904) (7=232.5580) (8=260.2257) (9=287.8934)(apline: 17984 changes made)

. gen apoor=(h08_din/12)<apline

.

. * poverty rate

. tab1 rpoor apoor

-> tabulation of rpoor

rpoor | Freq. Percent Cum.------------+----------------------------------- 0 | 13,345 74.20 74.20 1 | 4,639 25.80 100.00------------+----------------------------------- Total | 17,984 100.00

-> tabulation of apoor

apoor | Freq. Percent Cum.------------+----------------------------------- 0 | 16,199 90.07 90.07 1 | 1,785 9.93 100.00------------+----------------------------------- Total | 17,984 100.00

. tab rpoor [aw=p08_wgc]

rpoor | Freq. Percent Cum.------------+----------------------------------- 0 | 15,497.893 86.18 86.18 1 | 2,486.1071 13.82 100.00------------+----------------------------------- Total | 17,984 100.00

. tab apoor [aw=p08_wgc]

apoor | Freq. Percent Cum.------------+----------------------------------- 0 | 17,102.074 95.10 95.10 1 | 881.926391 4.90 100.00------------+----------------------------------- Total | 17,984 100.00

.

. * top & bottom coding

. qui sum h08_din [aw=p08_wgc], d // top & bottom coding의 기준은 균등화 하기 전의 원래 가처분소득

. gen din_tb=h08_din

. replace din_tb=10*r(p50) if din_tb>10*r(p50) // top: 가처분소득 중위값의 10배 초과는 10배 값으로

(8 real changes made)

. replace din_tb=0 if din_tb<0 // bottom: 가처분소득 0 미만은 0으로

(44 real changes made)

.

. gen eqinc_tb=din_tb/(h0801_1^0.5)

. qui sum eqinc_tb [aw=p08_wgc], d

. gen rpoor_tb=eqinc_tb<(r(p50)*0.5)

.

. tab rpoor [aw=p08_wgc]

rpoor | Freq. Percent Cum.------------+----------------------------------- 0 | 15,497.893 86.18 86.18 1 | 2,486.1071 13.82 100.00------------+----------------------------------- Total | 17,984 100.00

. tab rpoor_tb [aw=p08_wgc]

rpoor_tb | Freq. Percent Cum.------------+----------------------------------- 0 | 15,497.893 86.18 86.18 1 | 2,486.1071 13.82 100.00------------+----------------------------------- Total | 17,984 100.00. .

- 10 -

. *-----------------------------------------------------------------------------*

. * (9) panel dataset 만들기(foreach + append)

. *-----------------------------------------------------------------------------*

.

. * make panel dataset

. foreach i in 06 07 08 {

2. use koweps`i', clear //(a) 데이터 열기

3. gen wave=`i' //(b) 조사차수 변수 만들기 4. rename h`i'_pid pid //(b) pid 변수명 통일하기 5. label var pid "" 6. gen age=2004+`i'-h`i'_g4 //(b) 연령(모든 차수에 적용되도록!) 7. gen gender=(h`i'_g3==1) //(b) 성별(0=여성, 1=남성) 8. recode h`i'_g10 (5=1) (1=2) (2/4=3) (else=.), gen(marital) 9. label var marital "" //(b) 결혼상태(1=미혼, 2=유배우,

3=사별/이혼/별거) 10. recode p`i'05_9 p`i'05_11 p`i'05_12 p`i'05_13 p`i'05_14 p`i'05_16 ///> p`i'05_17 p`i'05_18 p`i'05_19 (1=0) (2=1) (3=2) (4=3) (9=.), pre(re_) 11. recode p`i'05_10 p`i'05_15 (4=0) (3=1) (2=2) (1=3) (9=.), pre(re_) 12. gen dep=(re_p`i'05_9+re_p`i'05_10+re_p`i'05_11+re_p`i'05_12+ ///> re_p`i'05_13+re_p`i'05_14+re_p`i'05_15+re_p`i'05_16+re_p`i'05_17+ ///> re_p`i'05_18+re_p`i'05_19)*20/11 //(b) 우울변수(CESD-11, 0~60) 13. gen dep_d=(dep>16) if dep~=. //(b) 우울증 변수(dep 16점 초과) 14. keep if age>=20 & p`i'_cp==1 //(b) 20세 이상이고 가구원용 조사표에

응답을 완료한 사람만 남기기

15. if `i'~=06 { 16. append using koweps678 //(c) 앞서 저장되었던 자료와 long-type 결합 17. }

18. keep wave pid age gender marital dep dep_d //(d) 필요한 변수만 남기기 19. save koweps678, replace //(d) 기존 데이터에 덮어 저장하기

20. }

(14163 differences between h06_g10 and marital)(11368 differences between p0605_9 and re_p0605_9)(11368 differences between p0605_11 and re_p0605_11)(11368 differences between p0605_12 and re_p0605_12)(11368 differences between p0605_13 and re_p0605_13)(11368 differences between p0605_14 and re_p0605_14)(11368 differences between p0605_16 and re_p0605_16)(11368 differences between p0605_17 and re_p0605_17)(11368 differences between p0605_18 and re_p0605_18)(11368 differences between p0605_19 and re_p0605_19)(10591 differences between p0605_10 and re_p0605_10)(10279 differences between p0605_15 and re_p0605_15)(3783 missing values generated)(3783 missing values generated)(3547 observations deleted)

(note: file koweps678.dta not found)file koweps678.dta saved(17913 differences between h07_g10 and marital)(14676 differences between p0705_9 and re_p0705_9)(14676 differences between p0705_11 and re_p0705_11)(14676 differences between p0705_12 and re_p0705_12)(14676 differences between p0705_13 and re_p0705_13)(14676 differences between p0705_14 and re_p0705_14)(14676 differences between p0705_16 and re_p0705_16)(14676 differences between p0705_17 and re_p0705_17)(14676 differences between p0705_18 and re_p0705_18)(14676 differences between p0705_19 and re_p0705_19)(13752 differences between p0705_10 and re_p0705_10)(13502 differences between p0705_15 and re_p0705_15)(4678 missing values generated)(4678 missing values generated)(4193 observations deleted)file koweps678.dta saved(17279 differences between h08_g10 and marital)(14237 differences between p0805_9 and re_p0805_9)(14237 differences between p0805_11 and re_p0805_11)(14237 differences between p0805_12 and re_p0805_12)(14237 differences between p0805_13 and re_p0805_13)(14237 differences between p0805_14 and re_p0805_14)(14237 differences between p0805_16 and re_p0805_16)(14237 differences between p0805_17 and re_p0805_17)(14237 differences between p0805_18 and re_p0805_18)(14237 differences between p0805_19 and re_p0805_19)(13295 differences between p0805_10 and re_p0805_10)(13116 differences between p0805_15 and re_p0805_15)(4402 missing values generated)(4402 missing values generated)(3970 observations deleted)file koweps678.dta saved

.

. * (6a)->(6b)->(6d) -> (7a)->(7b)->(7c)->(7d) -> (8a)->(8b)->(8c)->(8d)

.

. * xtset

. xtset pid wave panel variable: pid (unbalanced) time variable: wave, 6 to 8, but with gaps delta: 1 unit

.

- 11 -

. * xtdescribe

. tab wave

wave | Freq. Percent Cum.------------+----------------------------------- 6 | 11,149 28.16 28.16 7 | 14,429 36.44 64.60 8 | 14,014 35.40 100.00------------+----------------------------------- Total | 39,592 100.00

. xtdescribe

pid: 101, 201, ..., 980003 n = 15354 wave: 6, 7, ..., 8 T = 3 Delta(wave) = 1 unit Span(wave) = 3 periods (pid*wave uniquely identifies each observation)

Distribution of T_i: min 5% 25% 50% 75% 95% max 1 1 2 3 3 3 3

Freq. Percent Cum. | Pattern ---------------------------+--------- 10109 65.84 65.84 | 111 3477 22.65 88.49 | .11 524 3.41 91.90 | 11. 497 3.24 95.13 | 1.. 409 2.66 97.80 | ..1 319 2.08 99.88 | .1. 19 0.12 100.00 | 1.1 ---------------------------+--------- 15354 100.00 | XXX

.

. * listwise deletion & make balanced panel data

. drop if marital==. | dep==. // 결측값이 있는 케이스 삭제(listwise deletion)(1709 observations deleted)

. bysort pid: gen tnum=_N // 개인별 관찰빈도 변수 만들기

. tab tnum

tnum | Freq. Percent Cum.------------+----------------------------------- 1 | 1,483 3.91 3.91 2 | 8,026 21.19 25.10 3 | 28,374 74.90 100.00------------+----------------------------------- Total | 37,883 100.00

. keep if tnum==3 // 관찰빈도가 3인 케이스만 남기기(balanced panel data)(9509 observations deleted). . * xtsum. tab wave, sum(dep)

| Summary of dep wave | Mean Std. Dev. Freq.------------+------------------------------------ 6 | 7.2923356 8.6380096 9458 7 | 6.3917031 8.0761632 9458 8 | 6.7725255 8.7095687 9458------------+------------------------------------ Total | 6.8188547 8.4870452 28374

. xtsum dep

Variable | Mean Std. Dev. Min Max | Observations-----------------+--------------------------------------------+----------------dep overall | 6.818855 8.487045 0 60 | N = 28374 between | 6.817233 0 47.27273 | n = 9458 within | 5.055545 -19.24175 39.54613 | T = 3

.

. * xttab

. xttab marital

Overall Between Within marital | Freq. Percent Freq. Percent Percent----------+----------------------------------------------------- 1 | 3303 11.64 1167 12.34 94.34 2 | 19206 67.69 6558 69.34 97.62 3 | 5865 20.67 2067 21.85 94.58----------+----------------------------------------------------- Total | 28374 100.00 9792 103.53 96.59 (n = 9458)

.

- 12 -

. * xttrans

. xttrans marital, freq

| marital marital | 1 2 3 | Total-----------+---------------------------------+---------- 1 | 2,136 109 6 | 2,251 | 94.89 4.84 0.27 | 100.00 -----------+---------------------------------+---------- 2 | 0 12,647 178 | 12,825 | 0.00 98.61 1.39 | 100.00 -----------+---------------------------------+---------- 3 | 7 36 3,797 | 3,840 | 0.18 0.94 98.88 | 100.00 -----------+---------------------------------+---------- Total | 2,143 12,792 3,981 | 18,916 | 11.33 67.63 21.05 | 100.00

. xttrans dep_d, freq

| dep_d dep_d | 0 1 | Total-----------+----------------------+---------- 0 | 14,787 1,466 | 16,253 | 90.98 9.02 | 100.00 -----------+----------------------+---------- 1 | 1,487 1,176 | 2,663 | 55.84 44.16 | 100.00 -----------+----------------------+---------- Total | 16,274 2,642 | 18,916 | 86.03 13.97 | 100.00

.

.

. *-----------------------------------------------------------------------------*

. * (10) 결혼상태와 우울의 관계(lag & difference)

. *-----------------------------------------------------------------------------*

.

. * marital status transition

. gen mar_trans=.(28374 missing values generated)

. replace mar_trans=1 if (L.marital==1 | L.marital==3) & marital==2 // 1=결혼(145 real changes made)

. replace mar_trans=2 if (L.marital==1 | L.marital==2) & marital==3 // 2=이별(184 real changes made)

. replace mar_trans=3 if L.marital==1 & marital==1 // 3=미혼유지(2136 real changes made)

. replace mar_trans=4 if L.marital==2 & marital==2 // 4=유배우유지(12647 real changes made)

. replace mar_trans=5 if L.marital==3 & (marital==3 | marital==1) // 5=이별유지(3804 real changes made)

.

. * | marital

. * L.marital | 1 | 2 | 3 |

. *-----------+-------+-------+-------|

. * 1 | 3 | 1 | 2 |

. *-----------+-------+-------+-------|

. * 2 | X | 4 | 2 |

. *-----------+-------+-------+-------|

. * 3 | X(5) | 1 | 5 |

. *------------------------------------

.

. * dep difference(x_t - x_t-1)

. gen dep_diff=D.dep(9458 missing values generated)

.

. * about Time-series varlists

. help tsvarlist

.

. * overall relation

. tab marital, sum(dep)

| Summary of dep marital | Mean Std. Dev. Freq.------------+------------------------------------ 1 | 5.2954615 7.5024104 3303 2 | 5.5754358 7.4031534 19206 3 | 11.748586 10.327135 5865------------+------------------------------------ Total | 6.8188547 8.4870452 28374

. tab mar_trans, sum(dep_diff)

| Summary of dep_diff mar_trans | Mean Std. Dev. Freq.------------+------------------------------------ 1 | -1.3291536 8.3766236 145 2 | -2.4703557 13.320451 184 3 | -.07490636 8.3045347 2136 4 | -.14865185 7.7212671 12647 5 | -.58598605 10.514083 3804------------+------------------------------------ Total | -.25990504 8.4976384 18916

- 13 -

.

. * by wave

. tab mar_trans if wave==7, sum(dep_diff)

| Summary of dep_diff mar_trans | Mean Std. Dev. Freq.------------+------------------------------------ 1 | -1.197861 8.9229195 85 2 | -5.2964427 13.01837 92 3 | -.4491193 8.068992 1089 4 | -.78981405 7.638734 6326 5 | -1.3095586 10.456276 1866------------+------------------------------------ Total | -.90063247 8.4064957 9458

. tab mar_trans if wave==8, sum(dep_diff)

| Summary of dep_diff mar_trans | Mean Std. Dev. Freq.------------+------------------------------------ 1 | -1.5151516 7.6053864 60 2 | .35573126 13.084023 92 3 | .31431798 8.5290866 1047 4 | .49301751 7.7506663 6321 5 | .11070456 10.525216 1938------------+------------------------------------ Total | .38082239 8.5403182 9458

.

. * by gender

. tab mar_trans if gender==0, sum(dep_diff)

| Summary of dep_diff mar_trans | Mean Std. Dev. Freq.------------+------------------------------------ 1 | -.29850751 7.4064633 67 2 | -3.8445524 13.455001 131 3 | -.01463326 8.207754 994 4 | -.12284636 8.1589934 6527 5 | -.56733844 10.504523 3099------------+------------------------------------ Total | -.28639137 8.980999 10818

. tab mar_trans if gender==1, sum(dep_diff)

| Summary of dep_diff mar_trans | Mean Std. Dev. Freq.------------+------------------------------------ 1 | -2.2144522 9.0821561 78 2 | .92624359 12.462902 53 3 | -.12736825 8.3911026 1142 4 | -.1761735 7.2258343 6120 5 | -.66795617 10.563093 705------------+------------------------------------ Total | -.22452233 7.8057691 8098

.

. * by age group

. tab mar_trans if age<50, sum(dep_diff)

| Summary of dep_diff mar_trans | Mean Std. Dev. Freq.------------+------------------------------------ 1 | -1.4876033 8.4956642 121 2 | -.43290032 14.168107 42 3 | -.00362368 8.1778384 2007 4 | -.07811047 6.7266162 5377 5 | -.71881608 8.577937 430------------+------------------------------------ Total | -.117155 7.3106652 7977

. tab mar_trans if age>=50, sum(dep_diff)

| Summary of dep_diff mar_trans | Mean Std. Dev. Freq.------------+------------------------------------ 1 | -.53030317 7.8702954 24 2 | -3.0729834 13.050484 142 3 | -1.1839324 10.045483 129 4 | -.20082531 8.3814056 7270 5 | -.5690575 10.736741 3374------------+------------------------------------ Total | -.36400203 9.2667963 10939

.

.

- 14 -

. *-----------------------------------------------------------------------------*

. * (11) 데이터의 구조변환과 결합(stack & reshape, merge)

. *-----------------------------------------------------------------------------*

.

. * 한국복지패널은 가구용, 가구원용, 부가조사 데이터를 개인단위로 결합하여 제공하기 때문에 아래의 내용은 실제로 거의 필요하지 않다.

. * 하지만 타 자료의 경우 가구데이터와 가구원데이터를 분리하여 제공하는 경우가 종종 있기 때문에 기본적인 방법은 알아둘 필요가 있다.

.

. * hid : h08_merkey

. * pid : h08_pid1 h08_pid2 h08_pid3 h08_pid4 h08_pid5 h08_pid6 h08_pid7 h08_pid8 h08_pid9

. * 성별 : h0801_4 h0801_16 h0801_28 h0801_40 h0801_52 h0801_64 h0801_76 h0801_88 h0801_100

. * 출생연도 : h0801_5 h0801_17 h0801_29 h0801_41 h0801_53 h0801_65 h0801_77 h0801_89 h0801_101

. * 기초보장수급형태 : h0801_5aq1(가구공통 변수)

. * 주택유형 : h0806_1(가구공통 변수)

.

. * 가구데이터의 개인 단위 구조변환(stack을 활용한 wide-type -> long-type)

. use koweps08h, clear

. keep h08_merkey h08_pid1 h08_pid2 h08_pid3 h08_pid4 h08_pid5 h08_pid6 ///> h08_pid7 h08_pid8 h08_pid9 h0801_4 h0801_16 h0801_28 h0801_40 h0801_52 ///> h0801_64 h0801_76 h0801_88 h0801_100 h0801_5 h0801_17 h0801_29 h0801_41 ///> h0801_53 h0801_65 h0801_77 h0801_89 h0801_101 h0801_5aq1 h0806_1. // 이 변수들만 있다고 가정.. stack h08_merkey h08_pid1 h0801_4 h0801_5 h0801_5aq1 h0806_1 ///> h08_merkey h08_pid2 h0801_16 h0801_17 h0801_5aq1 h0806_1 ///> h08_merkey h08_pid3 h0801_28 h0801_29 h0801_5aq1 h0806_1 ///> h08_merkey h08_pid4 h0801_40 h0801_41 h0801_5aq1 h0806_1 ///> h08_merkey h08_pid5 h0801_52 h0801_53 h0801_5aq1 h0806_1 ///> h08_merkey h08_pid6 h0801_64 h0801_65 h0801_5aq1 h0806_1 ///> h08_merkey h08_pid7 h0801_76 h0801_77 h0801_5aq1 h0806_1 ///> h08_merkey h08_pid8 h0801_88 h0801_89 h0801_5aq1 h0806_1 ///> h08_merkey h08_pid9 h0801_100 h0801_101 h0801_5aq1 h0806_1, ///> into(h08_merkey h08_pid h08_g3 h08_g4 h0801_5aq1 h0806_1) clear. . drop if h08_pid==. // 가구당 9명이 무조건 생성되기 때문에 실존하지 않는 사람은 삭제(47824 observations deleted)

. sort h08_merkey h08_pid // hid, pid 기준으로 정렬

.

. * 가구데이터의 개인 단위 구조변환(reshape을 활용한 wide-type -> long-type)

. use koweps08h, clear

. keep h08_merkey h08_pid1 h08_pid2 h08_pid3 h08_pid4 h08_pid5 h08_pid6 ///> h08_pid7 h08_pid8 h08_pid9 h0801_4 h0801_16 h0801_28 h0801_40 h0801_52 ///> h0801_64 h0801_76 h0801_88 h0801_100 h0801_5 h0801_17 h0801_29 h0801_41 ///

> h0801_53 h0801_65 h0801_77 h0801_89 h0801_101 h0801_5aq1 h0806_1. // 이 변수들만 있다고 가정.. ren (h0801_4 h0801_16 h0801_28 h0801_40 h0801_52 h0801_64 h0801_76 h0801_88 ///> h0801_100 h0801_5 h0801_17 h0801_29 h0801_41 h0801_53 h0801_65 h0801_77 ///> h0801_89 h0801_101) (gender1 gender2 gender3 gender4 gender5 gender6 ///> gender7 gender8 gender9 byear1 byear2 byear3 byear4 byear5 byear6 byear7 ///> byear8 byear9) // reshape을 하려면 변수명에 규칙이 필요함(공통문자+숫자열)

. reshape long h08_pid gender byear, i(h08_merkey) j(member)(note: j = 1 2 3 4 5 6 7 8 9)

Data wide -> long-----------------------------------------------------------------------------Number of obs. 7312 -> 65808Number of variables 30 -> 7j variable (9 values) -> memberxij variables: h08_pid1 h08_pid2 ... h08_pid9 -> h08_pid gender1 gender2 ... gender9 -> gender byear1 byear2 ... byear9 -> byear-----------------------------------------------------------------------------

.

. drop if h08_pid==. // 가구당 9명이 무조건 생성되기 때문에 실존하지 않는 사람은 삭제(47824 observations deleted)

. sort h08_merkey h08_pid // hid, pid 기준으로 정렬

.

. * reshape 데이터에 가구원용데이터의 변수 결합(1:1 merge)

. merge 1:1 h08_pid using koweps08p, keepusing(p08_cp p0803_12)

Result # of obs. ----------------------------------------- not matched 3,454 from master 3,454 (_merge==1) from using 0 (_merge==2)

matched 14,530 (_merge==3) -----------------------------------------

. tab _merge

_merge | Freq. Percent Cum.------------------------+----------------------------------- master only (1) | 3,454 19.21 19.21 matched (3) | 14,530 80.79 100.00------------------------+----------------------------------- Total | 17,984 100.00

- 15 -

. tab p08_cp, m

최종조사결?| ?|확인(p08_cp | ) | Freq. Percent Cum.------------+----------------------------------- 1 | 14,237 79.16 79.16 2 | 293 1.63 80.79 . | 3,454 19.21 100.00------------+----------------------------------- Total | 17,984 100.00

.

. * 가구원용데이터에 가구용데이터의 변수 결합(m:1 merge)

. use koweps08p, clear

. merge m:1 h08_merkey using koweps08h, keepusing(h08_din h0801_110)

Result # of obs. ----------------------------------------- not matched 1 from master 0 (_merge==1) from using 1 (_merge==2)

matched 14,530 (_merge==3) -----------------------------------------

. tab _merge

_merge | Freq. Percent Cum.------------------------+----------------------------------- using only (2) | 1 0.01 0.01 matched (3) | 14,530 99.99 100.00------------------------+----------------------------------- Total | 14,531 100.00

.

.

. *-----------------------------------------------------------------------------*

. * (12) 빈곤의 대물림(아동기 경제상태와 현재 빈곤의 관계) : 1:1 merge의 응용

. *-----------------------------------------------------------------------------*

.

. * np0806_2 : 아동기의 경제적 생활상태(신규가구원에게만 조사하는 문항)

. * 1=매우가난, 2=가난, 3=보통, 4=부유, 5=매우부유

.

. * 1~8차 데이터 merge

.

. foreach i in 01 02 03 04 05 06 07 08 {

2. use koweps`i', clear

3. if `i'~=08 { 4. ren h`i'_pid h08_pid 5. ren *p`i'06_2 np0806_2 6. save pov`i' 7. }

8. if `i'==08 { 9. merge 1:1 h08_pid using pov07, nogen keepusing(np0806_2) keep(1 3 4

5) update replace 10. merge 1:1 h08_pid using pov06, nogen keepusing(np0806_2) keep(1 3 4

5) update replace 11. merge 1:1 h08_pid using pov05, nogen keepusing(np0806_2) keep(1 3 4

5) update replace 12. merge 1:1 h08_pid using pov04, nogen keepusing(np0806_2) keep(1 3 4

5) update replace 13. merge 1:1 h08_pid using pov03, nogen keepusing(np0806_2) keep(1 3 4

5) update replace 14. merge 1:1 h08_pid using pov02, nogen keepusing(np0806_2) keep(1 3 4

5) update replace 15. merge 1:1 h08_pid using pov01, nogen keepusing(np0806_2) keep(1 3 4

5) update replace 16. }

17. }

file pov01.dta savedfile pov02.dta savedfile pov03.dta savedfile pov04.dta savedfile pov05.dta savedfile pov06.dta savedfile pov07.dta saved

- 16 -

Result # of obs. ----------------------------------------- not matched 432 from master 432 from using 0

matched 17,552 not updated 14,189 missing updated 3,363 nonmissing conflict 0 -----------------------------------------

Result # of obs. ----------------------------------------- not matched 4,663 from master 4,663 from using 0

matched 13,321 not updated 13,098 missing updated 223 nonmissing conflict 0 -----------------------------------------

Result # of obs. ----------------------------------------- not matched 4,836 from master 4,836 from using 0

matched 13,148 not updated 12,864 missing updated 284 nonmissing conflict 0 -----------------------------------------

Result # of obs. ----------------------------------------- not matched 5,113 from master 5,113 from using 0

matched 12,871 not updated 12,574 missing updated 297 nonmissing conflict 0 -----------------------------------------

Result # of obs. ----------------------------------------- not matched 5,442 from master 5,442 from using 0

matched 12,542 not updated 12,243 missing updated 299 nonmissing conflict 0 -----------------------------------------

Result # of obs. ----------------------------------------- not matched 5,707 from master 5,707 from using 0

matched 12,277 not updated 12,068 missing updated 209 nonmissing conflict 0 -----------------------------------------

Result # of obs. ----------------------------------------- not matched 5,890 from master 5,890 from using 0

matched 12,094 not updated 2,754 missing updated 9,340 nonmissing conflict 0 -----------------------------------------

.

- 17 -

. * 아동기 경제상태 변수

. tab np0806_2 p08_cp, m

아동기에 | 경제적 |생활상태(n | 최종조사결과 확인(p08_cp) p0806_2) | 1 2 . | Total-----------+---------------------------------+---------- 1 | 1,432 30 0 | 1,462 2 | 4,691 77 1 | 4,769 3 | 6,393 51 4 | 6,448 4 | 1,513 24 0 | 1,537 5 | 129 5 0 | 134 9 | 26 0 0 | 26 . | 53 106 3,449 | 3,608 -----------+---------------------------------+---------- Total | 14,237 293 3,454 | 17,984

. recode np0806_2 (9=.), gen(childpov)(26 differences between np0806_2 and childpov)

. la var childpov ""

.

. * 현재 빈곤 변수

. gen eqinc=h08_din/(h0801_1^0.5)

. qui sum eqinc [aw=p08_wgc], d

. gen adultpov=eqinc<(r(p50)*0.5)

.

. * 연령 변수

. gen age=2012-h08_g4

.

. * 연령대별 아동기 경제상태와 현재 빈곤의 관계

. tab adultpov childpov if age>=20 & age<40, col nokey

| childpov adultpov | 1 2 3 4 5 | Total-----------+-------------------------------------------------------+---------- 0 | 76 705 2,210 265 13 | 3,269 | 76.00 87.47 94.40 95.67 86.67 | 92.37 -----------+-------------------------------------------------------+---------- 1 | 24 101 131 12 2 | 270 | 24.00 12.53 5.60 4.33 13.33 | 7.63 -----------+-------------------------------------------------------+---------- Total | 100 806 2,341 277 15 | 3,539 | 100.00 100.00 100.00 100.00 100.00 | 100.00

. tab adultpov childpov if age>=40 & age<60, col nokey

| childpov adultpov | 1 2 3 4 5 | Total-----------+-------------------------------------------------------+---------- 0 | 236 1,307 1,938 391 19 | 3,891 | 73.98 82.88 87.89 84.27 79.17 | 84.79 -----------+-------------------------------------------------------+---------- 1 | 83 270 267 73 5 | 698 | 26.02 17.12 12.11 15.73 20.83 | 15.21 -----------+-------------------------------------------------------+---------- Total | 319 1,577 2,205 464 24 | 4,589 | 100.00 100.00 100.00 100.00 100.00 | 100.00

. tab adultpov childpov if age>=60, col nokey

| childpov adultpov | 1 2 3 4 5 | Total-----------+-------------------------------------------------------+---------- 0 | 373 1,134 903 397 54 | 2,861 | 36.25 48.77 51.10 50.90 56.84 | 47.72 -----------+-------------------------------------------------------+---------- 1 | 656 1,191 864 383 41 | 3,135 | 63.75 51.23 48.90 49.10 43.16 | 52.28 -----------+-------------------------------------------------------+---------- Total | 1,029 2,325 1,767 780 95 | 5,996 | 100.00 100.00 100.00 100.00 100.00 | 100.00

.

.

- 18 -

. *-----------------------------------------------------------------------------*

. * (13) 배우자 정보 연결하기(educational assortive mating) : 1:1 merge의 응용

. *-----------------------------------------------------------------------------*

.

. * 복지패널에서 배우자의 정보를 별도의 변수로 제공하지 않지만, 가구id와 가구주와의 관계코드로 배우자를 식별할 수 있음

. * 가구id와 '배우자가 있다면 가질 수 있는 가구주와의 관계코드' 변수를 key변수로 사용하여 1:1 merge

.

. * using dataset(배우자 데이터세트)

. use koweps08, clear

. keep if h08_g10==1 // 유배우자만 남기기(8802 observations deleted)

. drop if h08_g2==997 | h08_g2==998 // 997, 998은 가구 내에 두 명 이상이 있을 수 있음

(0 observations deleted)

.

. rename h08_g2 g2_s // 배우자의 가구주와의 관계코드

. isid h08_merkey g2_s // 두 변수를 결합할 때 unique identifier가 되는지 확인

.

. gen gender_s=(h08_g3==1) // 배우자의 성별(0=여성, 1=남성)

. ren h08_g6 educ_s // 배우자의 교육수준

. label var educ_s ""

. gen age_s=2012-h08_g4 // 배우자의 연령

.

. keep h08_merkey g2_s gender_s educ_s age_s // 필요한 변수만 남기기

. save spouse // 다른 이름으로 파일 저장file spouse.dta saved

.

. * master dataset(본인 데이터세트)

. use koweps08, clear

. keep if h08_g10==1 // 유배우자만 남기기(8802 observations deleted)

.

. gen g2_s=h08_g2

. recode g2_s (1=2) (2=1) (3=4) (4=3) (5=6) (6=5) (7=8) (8=7) (10=20) (11=21) ///> (12=22) (13=23) (14=24) (15=25) (16=26) (17=27) (18=28) (19=29) (20=10) ///> (21=11) (22=12) (23=13) (24=14) (25=15) (26=16) (27=17) (28=18) (29=19) ///> (31=51) (32=52) (33=53) (34=54) (35=55) (36=56) (37=57) (38=58) (39=59) ///> (41=61) (42=62) (43=63) (44=64) (45=65) (46=66) (47=67) (48=68) (49=69) ///> (51=31) (52=32) (53=33) (54=34) (55=35) (56=36) (57=37) (58=38) (59=39) ///> (61=41) (62=42) (63=43) (64=44) (65=45) (66=46) (67=47) (68=48) (69=49) ///> (111=211) (112=212) (113=213) (114=214) (115=215) (116=216) (117=217) ///> (118=218) (119=219) (121=221) (122=222) (123=223) (124=224) (125=225) ///> (126=226) (127=227) (128=228) (129=229) (131=231) (132=232) (133=233) ///> (134=234) (135=235) (136=236) (137=237) (138=238) (139=239) (141=241) ///> (142=242) (143=243) (144=244) (145=245) (146=246) (147=247) (148=248) ///> (149=249) (151=251) (152=252) (153=253) (154=254) (155=255) (156=256) ///> (157=257) (158=258) (159=259) (161=261) (162=262) (163=263) (164=264) ///> (165=265) (166=266) (167=267) (168=268) (169=269) (171=271) (172=272) ///> (173=273) (174=274) (175=275) (176=276) (177=277) (178=278) (179=279) ///> (181=281) (182=282) (183=283) (184=284) (185=285) (186=286) (187=287) ///> (188=288) (189=289) (191=291) (192=292) (193=293) (194=294) (195=295) ///> (196=296) (197=297) (198=298) (199=299) (211=111) (212=112) (213=113) ///> (214=114) (215=115) (216=116) (217=117) (218=118) (219=119) (221=121) ///> (222=122) (223=123) (224=124) (225=125) (226=126) (227=127) (228=128) ///> (229=129) (231=131) (232=132) (233=133) (234=134) (235=135) (236=136) ///> (237=137) (238=138) (239=139) (241=141) (242=142) (243=143) (244=144) ///> (245=145) (246=146) (247=147) (248=148) (249=149) (251=151) (252=152) ///> (253=153) (254=154) (255=155) (256=156) (257=157) (258=158) (259=159) ///> (261=161) (262=162) (263=163) (264=164) (265=165) (266=166) (267=167) ///> (268=168) (269=169) (271=171) (272=172) (273=173) (274=174) (275=175) ///> (276=176) (277=177) (278=178) (279=179) (281=181) (282=182) (283=183) ///> (284=184) (285=185) (286=186) (287=187) (288=188) (289=189) (291=191) ///> (292=192) (293=193) (294=194) (295=195) (296=196) (297=197) (298=198) ///> (299=199) (else=.) // 배우자가 있다면 가질 수 있는 가구주와의 관계코드(g2_s: 9182 changes made)

. isid h08_merkey g2_s // 두 변수를 결합할 때 unique identifier가 되는지 확인

.

. gen gender=(h08_g3==1) // 본인의 성별(0=여성, 1=남성)

. ren h08_g6 educ // 본인의 교육수준

. label var educ ""

. gen age=2012-h08_g4 // 본인의 연령

.

. keep h08_merkey g2_s gender educ age // 필요한 변수만 남기기

.

- 19 -

. * 1:1 merge

. merge 1:1 h08_merkey g2_s using spouse, nogen keepusing(gender_s educ_s ///> age_s) keep(1 3)(note: variable g2_s was float, now double to accommodate using data's values)

Result # of obs. ----------------------------------------- not matched 102 from master 102 from using 0

matched 9,080 -----------------------------------------

.

. * 배우자 정보가 제대로 들어왔는지 확인

. tab gender gender_s, m

| gender_s gender | 0 1 . | Total-----------+---------------------------------+---------- 0 | 0 4,540 58 | 4,598 1 | 4,540 0 44 | 4,584 -----------+---------------------------------+---------- Total | 4,540 4,540 102 | 9,182

.

. * educational assortive mating?

. tab educ educ_s if gender==0

| educ_s educ | 2 3 4 5 6 7 8 9 | Total-----------+----------------------------------------------------------------------------------------+---------- 2 | 131 139 36 17 0 3 0 0 | 326 3 | 55 581 260 183 6 25 3 0 | 1,113 4 | 5 64 234 275 11 39 5 0 | 633 5 | 3 22 75 893 132 272 33 0 | 1,430 6 | 1 3 6 96 129 144 16 2 | 397 7 | 0 1 4 68 42 373 68 12 | 568 8 | 0 0 1 2 4 27 25 4 | 63 9 | 0 0 0 1 0 3 4 2 | 10 -----------+----------------------------------------------------------------------------------------+---------- Total | 195 810 616 1,535 324 886 154 20 | 4,540

.

. gen amating=(educ==educ_s) if educ_s~=. // 동질혼(102 missing values generated)

. replace amating=2 if educ<educ_s & educ_s~=. // 상향혼(2172 real changes made)

. replace amating=3 if educ>educ_s & educ_s~=. // 하향혼(2172 real changes made)

.

. tab amating gender, col nokey

| gender amating | 0 1 | Total-----------+----------------------+---------- 1 | 2,368 2,368 | 4,736 | 52.16 52.16 | 52.16 -----------+----------------------+---------- 2 | 1,685 487 | 2,172 | 37.11 10.73 | 23.92 -----------+----------------------+---------- 3 | 487 1,685 | 2,172 | 10.73 37.11 | 23.92 -----------+----------------------+---------- Total | 4,540 4,540 | 9,080 | 100.00 100.00 | 100.00

. tab amating gender if age<40, col nokey

| gender amating | 0 1 | Total-----------+----------------------+---------- 1 | 545 391 | 936 | 55.11 55.46 | 55.25 -----------+----------------------+---------- 2 | 267 126 | 393 | 27.00 17.87 | 23.20 -----------+----------------------+---------- 3 | 177 188 | 365 | 17.90 26.67 | 21.55 -----------+----------------------+---------- Total | 989 705 | 1,694 | 100.00 100.00 | 100.00

.

.

- 20 -

. *-----------------------------------------------------------------------------*

. * (14) 자녀-부모 정보 연결하기(진로성숙도 - 부의 성역할 인식) : m:1 merge의 응용

. *-----------------------------------------------------------------------------*

.

. * 복지패널에서 부모의 정보를 별도의 변수로 제공하지 않지만, 가구id, 가구주와의 관계코드, 성별로 부/모를 식별할 수 있음

. * 가구id, '부모가 있다면 가질 수 있는 가구주와의 관계코드' 변수, 성별을 key변수로 사용하여 m:1 merge

.

. * using dataset(부 데이터세트)

. use koweps07, clear

. drop if h07_g2==997 | h07_g2==998 // 997, 998은 가구 내에 두 명 이상이 있을 수 있음

(37 observations deleted)

.

. gen g2_f1=h07_g2 // 부의 가구주와의 관계코드1

. gen g2_f2=h07_g2 // 부의 가구주와의 관계코드2

. gen gender_f=(h07_g3==1) // 부의 성별(0=여성, 1=남성)

. isid h07_merkey g2_f1 gender_f // 세 변수를 결합할 때 unique identifier가 되는지 확인

. isid h07_merkey g2_f2 gender_f

.

. recode p0704_3aq1 p0704_3aq2 p0704_3aq3 p0704_3aq4 (9=.), pre(re_)(0 differences between p0704_3aq1 and re_p0704_3aq1)(0 differences between p0704_3aq2 and re_p0704_3aq2)(0 differences between p0704_3aq3 and re_p0704_3aq3)(0 differences between p0704_3aq4 and re_p0704_3aq4)

. gen grole=re_p0704_3aq1+re_p0704_3aq2+re_p0704_3aq3+re_p0704_3aq4(4651 missing values generated). // 부의 성역할 인식(4~20, 높을수록 보수적)

. gen age_f=2011-h07_g4 // 부의 연령

.

. keep h07_merkey g2_f1 g2_f2 gender_f grole age_f // 필요한 변수만 남기기

. save father // 다른 이름으로 파일 저장file father.dta saved

.

. * master dataset(자녀 데이터세트)

. use koweps07, clear

. keep if c07_cp==1 // 아동부가조사에 응답완료한 아동만 남기기(18110 observations deleted)

.

. gen g2_f1=h07_g2

. recode g2_f1 (1=5) (3=7) (10=1) (11/19=10) (20=3) (31/39=1) (41/49=3) ///> (111/119=11) (121/129=12) (131/139=13) (141/149=14) (151/159=15) ///> (161/169=16) (171/179=17) (181/189=18) (191/199=19) (else=.)(g2_f1: 512 changes made). // 부가 있다면 가질 수 있는 가구주와의 관계코드1

. gen g2_f2=h07_g2

. recode g2_f2 (1=6) (3=8) (10=2) (11/19=20) (20=4) (31/39=2) (41/49=4) ///> (111/119=21) (121/129=22) (131/139=23) (141/149=24) (151/159=25) ///> (161/169=26) (171/179=27) (181/189=28) (191/199=29) (else=.)(g2_f2: 512 changes made). // 부가 있다면 가질 수 있는 가구주와의 관계코드2.. gen gender_f=1 // 부가 있다면 가질 수 있는 성별(무조건 1)

.

. recode c0707_4aq3 c0707_4aq7 c0707_4aq8 c0707_4aq10 c0707_4aq13 c0707_4aq15 ///> c0707_4aq16 c0707_4aq17 c0707_4aq19 c0707_4aq23 (1=0) (2=1) (3=2) (4=3) ///> (9=.), pre(re_)(512 differences between c0707_4aq3 and re_c0707_4aq3)(512 differences between c0707_4aq7 and re_c0707_4aq7)(512 differences between c0707_4aq8 and re_c0707_4aq8)(512 differences between c0707_4aq10 and re_c0707_4aq10)(512 differences between c0707_4aq13 and re_c0707_4aq13)(512 differences between c0707_4aq15 and re_c0707_4aq15)(512 differences between c0707_4aq16 and re_c0707_4aq16)(512 differences between c0707_4aq17 and re_c0707_4aq17)(512 differences between c0707_4aq19 and re_c0707_4aq19)(511 differences between c0707_4aq23 and re_c0707_4aq23)

. recode c0707_4aq4 c0707_4aq5 c0707_4aq6 c0707_4aq9 c0707_4aq11 c0707_4aq12 ///> c0707_4aq14 c0707_4aq18 c0707_4aq20 c0707_4aq21 c0707_4aq22 (4=0) (3=1) ///> (2=2) (1=3) (9=.), pre(re_)(273 differences between c0707_4aq4 and re_c0707_4aq4)(288 differences between c0707_4aq5 and re_c0707_4aq5)(282 differences between c0707_4aq6 and re_c0707_4aq6)(212 differences between c0707_4aq9 and re_c0707_4aq9)(276 differences between c0707_4aq11 and re_c0707_4aq11)(289 differences between c0707_4aq12 and re_c0707_4aq12)(266 differences between c0707_4aq14 and re_c0707_4aq14)(302 differences between c0707_4aq18 and re_c0707_4aq18)(275 differences between c0707_4aq20 and re_c0707_4aq20)(275 differences between c0707_4aq21 and re_c0707_4aq21)(313 differences between c0707_4aq22 and re_c0707_4aq22)

- 21 -

. gen maturity=re_c0707_4aq3+re_c0707_4aq4+re_c0707_4aq5+re_c0707_4aq6+ ///> re_c0707_4aq7+re_c0707_4aq8+re_c0707_4aq9+re_c0707_4aq10+re_c0707_4aq11+ ///> re_c0707_4aq12+re_c0707_4aq13+re_c0707_4aq14+re_c0707_4aq15+ ///> re_c0707_4aq16+re_c0707_4aq17+re_c0707_4aq18+re_c0707_4aq19+ ///> re_c0707_4aq20+re_c0707_4aq21+re_c0707_4aq22+re_c0707_4aq23(1 missing value generated). // 자녀의 진로성숙도(0~63)

. gen gender=(h07_g3==1) // 자녀의 성별(0=여성, 1=남성)

. gen age=2011-h07_g4 // 자녀의 연령

.

. keep h07_merkey g2_f1 g2_f2 gender_f maturity gender age // 필요한 변수만 남기기

.

. * m:1 merge

. merge m:1 h07_merkey g2_f1 gender_f using father, nogen keepusing(grole ///> ge_f) keep(1 3) // 첫번째 가구주와의 관계코드로 m:1 merge

Result # of obs. ----------------------------------------- not matched 107 from master 107 from using 0

matched 405 -----------------------------------------

. ren (grole age_f) (grole1 age_f1) // 동일한 변수를 다시 merge해야 하기 때문에 변수명 변경

. merge m:1 h07_merkey g2_f2 gender_f using father, nogen keepusing(grole ///> age_f) keep(1 3) // 두번째 가구주와의 관계코드로 m:1 merge

Result # of obs. ----------------------------------------- not matched 509 from master 509 from using 0

matched 3 -----------------------------------------

. ren (grole age_f) (grole2 age_f2) // 앞에서 merge한 변수와 구분하기 위해 변수명 변경

.

. * 최종 부의 변수 생성

. egen grole=rowtotal(grole1 grole2), m // 둘 중에 하나만 값이 있거나, 둘 다 없을 수도 있음

(127 missing values generated)

. egen age_f=rowtotal(age_f1 age_f2), m // 둘 중에 하나만 값이 있거나, 둘 다 없을 수도 있음

(104 missing values generated)

.

. * 데이터 확인

. sum age maturity age_f grole

Variable | Obs Mean Std. Dev. Min Max-------------+-------------------------------------------------------- age | 512 15.85742 .8898116 14 18 maturity | 511 43.43053 7.400223 22 62 age_f | 408 47.32843 4.588199 34 65 grole | 385 13.96623 2.006864 8 19

.

. * 부의 성역할 인식과 자녀의 진로성숙도 사이의 관계

. tw (sc maturity grole) (lfit maturity grole), by(gender)

. bysort gender: regress maturity grole i.age

----------------------------------------------------------------------------------> gender = 0

Source | SS df MS Number of obs = 190-------------+------------------------------ F( 4, 185) = 2.77 Model | 632.51478 4 158.128695 Prob > F = 0.0286 Residual | 10555.4642 185 57.0565631 R-squared = 0.0565-------------+------------------------------ Adj R-squared = 0.0361 Total | 11187.9789 189 59.1956558 Root MSE = 7.5536

------------------------------------------------------------------------------ maturity | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- grole | -.5526148 .2905979 -1.90 0.059 -1.125927 .020697 | age | 15 | -.9942933 2.697573 -0.37 0.713 -6.316255 4.327668 16 | -.1345084 2.685691 -0.05 0.960 -5.433027 5.16401 17 | 2.785573 2.71994 1.02 0.307 -2.580516 8.151661 | _cons | 50.89743 4.757055 10.70 0.000 41.51238 60.28248------------------------------------------------------------------------------

- 22 -

----------------------------------------------------------------------------------> gender = 1

Source | SS df MS Number of obs = 194-------------+------------------------------ F( 5, 188) = 1.22 Model | 307.093696 5 61.4187392 Prob > F = 0.3004 Residual | 9450.24651 188 50.2672687 R-squared = 0.0315-------------+------------------------------ Adj R-squared = 0.0057 Total | 9757.34021 193 50.5561669 Root MSE = 7.0899

------------------------------------------------------------------------------ maturity | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- grole | -.1371066 .2435136 -0.56 0.574 -.6174768 .3432636 | age | 15 | 2.153722 2.241127 0.96 0.338 -2.267266 6.574709 16 | .9257842 2.244635 0.41 0.680 -3.502123 5.353691 17 | 2.093571 2.274224 0.92 0.358 -2.392705 6.579848 18 | -12.87432 7.38282 -1.74 0.083 -27.43813 1.689495 | _cons | 43.79381 3.786732 11.57 0.000 36.32387 51.26376------------------------------------------------------------------------------. . * about Factor variables. help fvvarlist. .