Moving Towards Register-based Censuses in Korea:

A Long Way to go

Kun LeeUniversity of Seoul

March 3, 2008Spatial Lab @ UCSB

Contents

• Prologue• Starting Point

– The Model• Korean Census

– Possibles and Probables– But-not-Possible

• Epilogue: At the Crossroad

Prologue: Background• Pressure from Government Officials, Congressman, Academics

– Population Register• Implemented in 1962• Almost every person has his/her own Personal Identification Number• Widely used in administration and Internet identification

– Began to advocate in 2002• Rapidly spread and accomplish

• Growing Concerns in Censuses– Quality, Response, Cost, E-Gov, Gov 2.0, and etc

• Resistance from the other sides– Confidentiality and Big Brother Issue

• From Control to Privacy

Those Who Control Information, Control the World (& Society)

“He who controls the spice, controls the universe!” - Frank Herbert. 1999(1965). Dune. New York: Ace

Prologue: Technical Issues• Survey vs. Administrative Records

– de facto vs. de jure• Which one is more accurate?• Can they be used to compensate each other?• Can they converge to ONE, that is de jure=de facto?

– Administrative Modifications, if NOT Reformation, Necessary

• Issues– How to Replace Surveys with Administrative Records based

Data Construction?– How to Utilize Administrative Records in Surveys?– How to Pursue Two Goals Together?– Finally, are all these possible?

• Given Situation: Using Administrative Records is the Must– Expand the Use of Administrative Records in Surveys

• To Reduce Cost, Response Burden• To Increase the Use of Data by Timely Production of Data

Starting Point• The Model: Case of Northern Europe

PersonID

Building & Land ID

BusinessID

Household Employee

Statistics Denmark

• Elements in the Model– Units in Administrative Records

• Actors– Persons (individuals)– Corporations (Juridical persons) and proprietorship

• Spatial boundaries– Administration within a jurisdiction

– To identify units, necessary to have ID number systems

• ID systems for persons and corporations• ID system for locations of persons and

corporations, i.e. addresses for dwelling and business units

• Constructing Information by Combining Records via ID’s – Some examples– Using Person and Building ID’s

• Identifying household unit• Identifying household members• Constructing household information

– Using Person, Business, and Building ID’s• Constructing household income• Identifying household’s economic activity

– Using Business and Building ID’s• Identifying establishment• Constructing establishment information

Korean Census

• Possibles and Probables– Population Register

• PID for everyone with Korean nationality• Identify household members• Example 1: Matching Items between Register and

Census Questionnaire (Kim and Kang, 2005)– Building Register

• Address as an ID• Providing Information about Housing Census• Example 2: Matching Items between Register and

Census Questionnaire (Kim and Kang, 2005)

Example 1: Population Register

Example 2: Building Register

But-Not-Possible 1

• Population Register– Unidentifiable individuals

• Moving without reporting• Registering differently from the fact

– Unidentifiable households• Different Household Concepts• Several Households in one address

• Because of Unidentifiable Individuals– Differences in population counts

• Total Population– Census (2005. 11. 1): 47,041,434– Register (2005. 12. 31): 48,782,274

» Difference: 1,740,840» Census counts is 96.4% of Register counts

(Census/Register) at 7 metropolitan areas and 9 provinces

• Histogram of (Register-Census)/Register by dongs– Of 3,584 dongs, at 1,239 (34.7%) dongs > 10%– Mean Difference Ratio=6.4%, S.D=8.7%

• Difference Ratio = ((Register-Census)/Register)*100

Census Population is larger than Register population by 30% Register Population is larger than Census population by 30%

adminstrative area Census Register Diff. (%) adminstrative area Census Register Diff. (%)

1 Seoul Songpa Jamsil2 15 0 * 1 Seoul Sungbuk Wolgok3 233 702 66.8

2 Seoul Songpa Jamsil1 152 26 584.6 2 JunBuk Gunsan Okdo 2,289 4,395 47.9

3 Chungbuk Chungju Dalchun 8,699 5,124 69.8 3 GyungNam Hapcheon Deokgok 909 1,713 46.9

4 Gangwon Wonju Heungup 10,703 6,402 67.2 4 Seoul Jung-gu Euljiro 1,052 1,780 40.9

5 Inchoen Seo-gu Suknam1 23,031 13,913 65.5 5 Inchon Jung-gu Youngyoo 2,176 3,559 38.9

6 GyungBuk Gumi Gongdan2 7,255 4,491 61.5 6 Daejun Dong-gu Jungang 2,031 3,297 38.4

7 GyungBuk Gyungsan Bukbu 18,811 11,646 61.5 7 Kangwon Samchuk Seongnae 7,997 12,769 37.4

8 GyungGi Ansung Daeduk 14,175 8,922 58.9 8 Sungnam Bundang Pankyo 572 902 36.6

9 GyungBuk Gumi Jinmi 22,954 15,504 48.1 9 JunBuk Imsil Oonam 1,366 2,141 36.2

10 Yongin Giheung Seonong 14,204 9,625 47.6 10 GyungNam Jinju Daepyong 853 1,325 35.6

11 Gangwon Samchuk Jungra 12,102 8,537 41.8 11 Inchon Seo-gu Seoknam2 13,211 20,410 35.3

12 Busan Youngdo Dongsam2 6,812 4,972 37 12 JunNam Yoesu Myodo 971 1,468 33.9

13 JunBuk Iksan Sun 24,478 18,079 35.4 13 Seoul Yongsan Hangangro 2,723 4,073 33.1

14 Gangwon Gangreung Naegok 14,827 10,955 35.3 14 Ansan Danwon Wonkok2 5,833 8,531 31.6

15 Gangwon Choonchun Dongsan 2,270 1,701 33.5 15 Jeju Bukjeju Oodo 1,241 1,796 30.9

16 Gangwon Choonchun Hyoka 19,332 14,522 33.1 16 Inchon Ongjin Youngheung 2,778 3,986 30.3

17 GyungGi Ansung Samjuk 4,671 3,522 32.6

18 Gangwon Gangreung Kyungpo 7,275 5,545 31.2

19 Daegu Buk-gu Daehyun1 11,091 8,522 30.1

20 ChungNam Asan Tangjung 17,034 13,093 30.1

Districts of ( |Register – Census| / Register) > 0.3

• Seoul only

• Because of Unidentifiable Households– Difference in household counts

Register Census Diff.(%)

Whole Nation 17,857,511 15,887,128 11.0Seoul 3,871,024 3,309,890 14.5Busan 1,270,612 1,186,378 6.6Daegu 865,766 814,585 5.9

Inchoen 933,686 823,023 11.9Gwangju 481,935 460,090 4.5Daejun 505,650 478,865 5.3Ulsan 365,197 339,095 7.1

Gyunggi 3,910,886 3,329,177 14.9Gangwon 574,019 520,628 9.3Chun Buk 547,213 505,203 7.7Chun Nam 756,073 659,871 12.7

Jun Buk 683,683 619,958 9.3Jun Nam 751,732 666,319 11.4

Gyung Buk 1,006,080 938,840 6.7

Gyung Nam 1,129,320 1,056,007 6.5

Jeju 204,635 179,199 12.4

• Because of unidentifiable Households– Worst Cases of Differences in household counts

Top 10 Districts of the Highest Difference Ratio* Top 10 Districts of the Lowest Difference Ratio

Metro Polis/Province

City /County Dong Register Census DR* Metro Polis

/ProvinceCity /

County Dong Register Census DR

Seoul Songpa Jamsil1 11 0 100.0 Chung Buk Chungju Dalchun 2,155 4,127 -91.5

Seoul Sungbuk Wallgok2 346 78 77.5 GyungBuk Gyungsan Bukbu 5,739 10,343 -80.2

Chung Buk Chungju Gangseo-gu 2,399 899 62.5 Gyunggi Ansung Daeduk 4,104 6,542 -59.4

Gyunggi Paju Wallreung 4,576 1,848 59.6 Gangwon Samchuk Jungra 2,965 4,491 -51.5

Chun Nam Asan Tangjung 8,304 3,697 55.5 Gangwon Gangreung Gyungpo 2,229 3,237 -45.2

Gyunggi Siheung Gwarim 1,191 558 53.1 Gangwon Wongju Heungup 2,771 4,056 -46.4

Inchoen Chung-gu YoungYoo 1,729 902 47.8 JunBuk Iksan Sindong 6,871 10,030 -46.0

Daejun Dong-gu Joongang 1,850 998 46.1 Inchon Nam-gu Yonghyun4 3,435 4,906 -42.8

Gyunggi Bundang Pangyo 370 197 46.8 Seoul Gwanak Sinrim9 12,053 17,267 -43.3

Jun Buk Gunsan Dokdo 1,578 869 44.9 Gangwon Gangreung Naegok 3,893 5,479 -40.7

* Difference Ratio = ((Register - Census) / Register)*100

• Building Register– Unidentifiable Housing Unit

• Land Based Address System– Many Buildings in One Address, and vice versa– Several households in one address

• Incomparability among Registers– Information Not Usable in Census

• Units in Building Register are not the same as those of Census

• Information in Building Register is neither complete nor updated

But-Not-Possible 2

• Because of Land Based Address– Difficult to match buildings– 3 types of matching

1 Building= 1 Address

1 Building= Many Addresses

Many Building= 1 Addresses

• As a Result, Matching is Difficult

• An Example (Changsin-dong, Seoul)

• Register based matching rate: 82.1%

• Building Map based matching rate: 78.0%

Not Matched

• Because of Incomparability of Registers– Hard to Find the Actual Figures– Example: Numbers of houses in Map, Building Register, and Tax

Register by Counties in Seoul

County Map BuildingRegister

TaxRegister County Map Building

RegisterTax

Register종로구 34,907 30,912 26,679 마포구 38,402 34,742 31,880

중구 24,363 26,282 20,567 양천구 22,723 21,562 20,135

용산구 32,076 28,825 24,965 강서구 30,890 28,515 26,198

성동구 30,573 26,144 22,719 구로구 31,893 25,723 23,304

광진구 29,427 27,635 25,965 금천구 20,206 17,583 15,497

동대문구 45,380 37,672 33,769 영등포구 40,630 34,538 29,555

중랑구 34,447 31,782 30,218 동작구 36,168 32,368 28,662

성북구 55,482 48,024 42,734 관악구 41,579 35,663 34,033

강북구 37,185 32,585 31,184 서초구 25,430 19,873 18,094

도봉구 21,548 16,922 16,328 강남구 28,263 23,792 22,700

노원구 21,062 14,828 13,392 송파구 29,266 25,650 24,471

은평구 50,069 45,563 40,293 강동구 24,900 23,154 21,951

서대문구 37,596 32,553 30,468 총합계 824,465 722,890 655,761

• Because of Incomparable Measurments– Difficult to use together

• Example : % of Matching, Changsin-dong, Seoul

Match < 10% < 20% < 50% 50% <=

1,525 431 150 156 65 57 3

34.8% 36.2% 15.1% 13.2% 0.7%

1,631 325 199 83 28 9 6

61.2% 25.5% 8.6% 2.8% 1.8%

339 1617 1214 238 21 22 122

75.1% 14.7% 1.3% 1.4% 7.5%

1,631 325 64 128 79 45 9

19.7% 39.4% 24.3% 13.8% 2.8%

1,631 325 44 54 43 141 43

13.5% 16.6% 13.2% 43.4% 13.2%

Area of Building

Floor Space

B-to-L Ratio

Floor Area Ratio

NullNot Null (Compared Cases)

Area of Land

In a Word

• Hard to Use Administrative Records Directly for Statistical Purposes– cannot replace survey in census

• What else is Possible?– Admin. Records as References

• In Determining Enumeration Districts– Registered Records of Households, Hotel, and Other

Institutions in the Local Governments– In Combination

• Previous Census Info. and New Building Register– Some Data about Housing Census

» Especially, Apartment Buildings (51%), and other multiple residence buildings

Epilogue: At the Crossroad• Choices and Steps

– What Is the Final Goal?• Fully Replacing or Complementary to Survey

– What Steps to Take?• Depending on the Final Goal• Expanding the Use of Admin. Records is the Must• Needs Researches in Various Aspects

– How to Relates Population Census and Population Register– Whatever the goal is, it will take a long time, very

long time.– All helps the Increasing Use of Statistics in

Administration, anyway.