2010 Indonesia Population Census Evaluation

Presented in the “Workshop on Census Evaluation” Hanoi, Viet Nam 2-6 December 2013

BPS-Statistics Indonesia

Background of 2010 Indonesia Population Census (IPC)  Coverage:  33 provinces  497 districts/municipality  6.580 sub-districts  76.581 villages  Around 724.052 census blocks.

 Around 1.100.528 local administrative unit  Estimated 63.645.055 households  Estimated 233.596.970 people   The 2010 Indonesia Population Census took place within the period of May 1 st through May 31 st 2010.

The census date was on 15 May 2010

Background of 2010 Indonesia Population Census (IPC) Field Workers:  Involve 800.000 field workers  Data collection was conducted by enumeration teams  Total teams: 200.000 teams  One team consists of 3 enumerators and 1 supervisor  Workload for field work is 3 – 9 census blocks per team  Every 10 teams, supervised by 1 field coordinator  Note: Beside team, there were also Task Force Unit who had responsibility to enumerate non permanent residence and specific areas (elite and corps diplomatic)

IPC Result: Population by age and sex, 2010

Age Group (1) 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+ Total Male (2) 11 662 369 11 974 094 11 662 417 10 614 306 9 887 713 10 631 311 9 949 357 9 337 517 8 322 712 7 032 740 5 865 997 4 400 316 2 927 191 2 225 133 1 531 459 1 606 281 119 630 913 Female (3) 11 016 333 11 279 386 11 008 664 10 266 428 10 003 920 10 679 132 9 881 328 9 167 614 8 202 140 7 008 242 5 695 324 4 048 254 3 131 570 2 468 898 1 924 872 2 228 308 118 010 413 Both Sexes (4) 22 678 702 23 253 480 22 671 081 20 880 734 19 891 633 21 310 443 19 830 685 18 505 131 16 524 852 14 040 982 11 561 321 8 448 570 6 058 761 4 694 031 3 456 331 3 834 589 237 641 326 4

Demographic Parameters

Laki-laki 12 10 8 6 Jutaan 4 2 0

2010

30-34 25-29 20-24 15-19 10-14 5-9 0-4 75+ 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 Perempuan 0 2 4 6 Jutaan 8 10 12

Demographic Parameters from 2010 PC

TFR IMR Expectation of life Dependency Ratio Percentage pop live in urban : : : : : 2.4

26 70.7

51.3% 49.8%

2010 PC Data evaluation

In general, quality of 2010 PC depends on:  Complicated variables  Unclear manuals  Unproper training  Quality of enumerators  Quality of enumeration prosedure  Quality of monitoring  Data cleaning process in the field  Data processing 

Data evaluation

Purpose of census data evaluation To obtain ”clean raw data” and “tabulation consistencies” -> to produce “realible indicators” -> can be used for “planning purposes” -> for “advanced analysis”

Census data evaluation



During enumeration



Post Enumeration Survey ((PES)



Before preliminary result



During/after processing individual questionnaire



Before final result

During enumeration

 2010 PC field enumeration -> using team, this has impact on data quality  Enumeration using team gives chance to make editing in the field and has resulted in better supervision.

 Any problems relating to enumeration can be solved immediately in the field    Enumeration can be finished on-schedule Self confidence of enumerator is much better Enumeration using team is intended to obtain clean data in the field  Data cleaning is done within one team, and among team coordinators under one field coordinator

During enumeration

      As a part of quality assurance in producing qualified statistics -> BPS conducted “monitoring the quality of the census” and “Post Enumeration Survey” Monitoring quality of the census were conducted in the beginning period of field work (May, week I-II) Monitoring quality is to ensure the quality of PC data -> focusing on the operational procedure in all enumeration process, to minimize the coverage error and content error Monitoring quality is intended to correct the miss procedure during the enumeration 1,676 students, lecturers of Institute of Statistics, and BPS employees involved in this activity Monitoring result is reported through SMS-Gateway and can be observed and download by BPS executives in the website http://kualitassp2010.bps.go.id

Post Enumeration Survey (PES)

 PES was conducted in 1200 census blocks in 33 provinces, from 5-18 July 2010  Purpose to evaluate instrument, procedure, and result of census related to coverage and content  PES using best enumerators in PC, but working in different CBs of PC  In general, PES result indicates that there is undercount of coverage for population and household  PES also indicates that in general, content quality is still accurate  There are variations in coverage rate and content quality in provinces

Population Net Coverage Error Rate (%)

Indonesia: 3.6 %; range: 0.36 % – 9.77 %

Household Net Coverage Error Rate (%)

Indonesia: 2.5 %; range: -1.20 % – 7.97 %

National Gross Difference Rate %) and Aggregate Index of Inconsistency %) by Variables

Before preliminary result

 Evaluation was also conducted before preliminary result of total population released  Preliminary results of total population -> based on recapitulation from listing questionnaires  Preliminary results were announced on 16 August 2010, during national speech by President on Indonesia’s independence day  Preliminary results include:  Total population by administrative areas: national, province, district  Total population by gender  Population growth rate 2000-2010

Before preliminary result

  BPS prepared worksheet in Excel as template to facilitate BPS Province/District to do the evaluation The evaluation include:  Population distribution 2000 and 2010  Population growth rate: 1990-2000 and 2000-2010  Sex ratio: 2000 and 2010  Total household 2000 and 2010  Household growth rate: 1990-2000 and 2000-2010  Household size Note:  Comparison of population in one region, was based on the recent condition of the administrative region. Proliferation of administrat ve region in Indonesia happens rapidly  Evaluation was done from the smallest administrative area to the highest administrative area  Total population also evaluated by comparing with other sources: ministrative records, population projection

Tabel IA. No Kode 2010

Table Population by Reg/Mun

Kab./Kota

(1) (2) 1 3401000000 KULON PROGO 2 3402000000 BANTUL 3 3403000000 GUNUNG KIDUL 4 3404000000 SLEMAN 5 3471000000 YOGYAKARTA

Provinsi 1990 2000 2010 (P.211) L

(3)

P

(4)

L+P

(5)

L

(6)

P

(7)

L+P

(8)

L

(9) (10)

P L+P

(11) 182.344 189.965 372.309 182.672 188.272 370.944 190.838 198.381 389.219

342.772 354.133 696.905 388.526 392.487 781.013 455.380 455.380 910.760

316.724 334.280 651.004 326.874 343.559 670.433 348.984 348.984 697.968

388.144 392.190 780.334 454.683 446.694 901.377 545.506 545.506 1.091.012

202.002 210.057 412.059 194.106 202.605 396.711 199.684 199.684 399.368

1.431.986 1.480.625 2.912.611 1.546.861 1.573.617 3.120.478 1.740.392 1.747.935 3.488.327

Table population growth rate for evaluation

Tabel IA. No Kode 2010 Kab./Kota

(1) 1 2 3 4 5 3401000000 (2) KULON PROGO 3402000000 3403000000 3404000000 3471000000 BANTUL GUNUNG KIDUL SLEMAN YOGYAKARTA

Provinsi LPP 1990-2000 LPP 2000-2010 v.211

(9) -0,04 1,15 0,29 1,45 -0,38

0,69

(10) 0,48 1,55 0,40 1,93 0,07

1,12

Table sex ratio for evaluation

Tabel IA. No Kode 2010 Kab./Kota

(1) (2) 1 3401000000 KULON PROGO 2 3402000000 BANTUL 3 3403000000 GUNUNG KIDUL 4 3404000000 SLEMAN 5 3471000000 YOGYAKARTA Provinsi

1990

(3) 96,0 96,8 94,7 99,0 96,2 96,7

2000

(4) 97,0 99,0 95,1 101,8 95,8 98,3

2010 v.211

(5) 96,2 100,0 100,0 100,0 100,0 99,6

Table population distribution for evaluation

Tabel IA. No Kode 2010 Kab./Kota

(1) (2) 1 3401000000 KULON PROGO 2 3402000000 BANTUL 3 3403000000 GUNUNG KIDUL 4 3404000000 SLEMAN 5 3471000000 YOGYAKARTA Provinsi

1990

(3) 12,8 23,9 22,4 26,8 14,1 100,0

% Distribusi 2000

(4) 11,9 25,0 21,5 28,9 12,7 100,0

2010 v.211 2010 v.212

(5) 11,2 26,1 20,0 31,3 11,4 100,0 (6)

During/after processing individual questionnaire  There were evaluations based on individual questionnaires during/after processing individual questionnaires  Internal Evaluation (BPS), based on 80 primary tables for publication, this includes:  Evaluation on consistencies within tables  Evaluation on content  Evaluation based on indicators trend  Evaluation on consistency and imputation related to data processing , and evaluation on some demographic parameters by expert Michael J. Levin, from Harvard Center for Population and Development Studies.

Internal Evaluation (BPS), based on 80 primary tables for publication  Consistencies evaluation:  Within tables  Out of range data in table  Wrong recognizion by machine   Editing and rule validation programe are not strictly Tracing to raw data  Checking trend indicators (national and province), such as:  Percentage population by age group          Percentage of urban population Percentage of population by religion Population by gender Percentage of single women Percentage of population by schooling status Mean age at marriage Mean children ever born per woman Mean children surviving per woman Percentage of migrant population  Percentage of labour force  Percentage of working people  Content checking: disability data, deaths by age group, labour force -> also compared by other sources

Evaluation assisted by International Expert

 Evaluation of consistency and imputation  Consistency was done to confirm that the data is consistent with the master file and range, for field which has certain range  Imputation was done for inconsistent data and empty field that should be filled. Imputation used cold-deck and hot-deck  Rule validation program was upgraded gradually to produce consistent data

Evaluation assisted by International Expert

 Evaluation on some demographic parameters  Population pyramids  UN Age Sex Indices  Single-Year ages: Whipple’s index, Myer’s, producing various graphs for interpreting data  Running Own-children methods -> to see ASFRs and TFRs and compares with last censuses  Children ever born and children surviving -> to see the change in parity over time and the percentage of children surviving by age group

Evaluation on age of males using Index Myers

Evaluation on age of females using Index Myers

Evaluation on age using UN Index

Evaluation before final result

 Evaluation on maternal death data  Starting with the consultation to international expert from Harvard University: Prof.Kenneth Hill -> to finalize questions on maternal mortality in PC questionnaires  Workshop on calculation of maternal mortality ratio from census data  Evaluation of maternal mortality data from 2010 PC by senior experts/demographers -> maternal mortality ratio (pregnancy-related death ratio).

 Comparing MMR with PMDF method, MMR with other sources

Further evaluation on maternal death data

  Detail evaluation by tracing the raw data and verifying/visiting the household again to ascertain death as maternal death.

Household is revisited and verified, if: 1.

2.

3.

4.

there was an ever married woman who died during pregnancy, delivery or childbirth within two months after delivery there was a woman age 15-49 died there was a male household member with the marital status widower there was household member age 0 year and there was no married women living in the house    Revisiting the households for cases no.1 and 2 Sampling the households for cases no.3 and 4 Using snowballing method (by asking to head of community), whether there is a maternal death in their neighbouring unit.

Maternal mortality ratio (pregnancy-related death ratio)

 Total cases of maternal deaths from Popultion Census: 13.956

 Total cases of maternal deaths after revisiting the household: 8.437

 Maternal Mortality Ratio: 259 deaths per 100.000

births.

Challenges in evaluation

 Methods of evaluation on census data should be planned and organized since the beginning of census preparation; and there should be a standard format of census evaluation (from UN) particularly on assessing demographic parameters  Evaluation on census data should be done in all BPS regional offices, started from lowest level administrative area (bottom up areas). Staffs in regional offices know exactly their area’s condition  The role of international and internal experts are important in the collaboration of census evaluation

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