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 1st through May 31st 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
Male
Female
Both Sexes
(1)
(2)
(3)
(4)
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
11
11
11
10
9
10
9
9
8
7
5
4
2
2
1
1
662
974
662
614
887
631
949
337
322
032
865
400
927
225
531
606
369
094
417
306
713
311
357
517
712
740
997
316
191
133
459
281
119 630 913
11
11
11
10
10
10
9
9
8
7
5
4
3
2
1
2
016
279
008
266
003
679
881
167
202
008
695
048
131
468
924
228
333
386
664
428
920
132
328
614
140
242
324
254
570
898
872
308
118 010 413
22
23
22
20
19
21
19
18
16
14
11
8
6
4
3
3
678
253
671
880
891
310
830
505
524
040
561
448
058
694
456
834
702
480
081
734
633
443
685
131
852
982
321
570
761
031
331
589
237 641 326
4
Demographic Parameters
2010
75+
Demographic Parameters from 2010 PC
70-74
65-69
Laki-laki
Perempuan
60-64
55-59
TFR
:
IMR
:
Expectation of life
:
Dependency Ratio
:
Percentage pop live in urban :
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
12
10
8
6
Jutaan
4
2
0
0
2
4
6
Jutaan
8
10
12
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 missprocedure 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
Table Population by Reg/Mun
Tabel IA.
No Kode 2010
(1)
1
2
3
4
5
Kab./Kota
(2)
3401000000 KULON PROGO
3402000000 BANTUL
3403000000 GUNUNG KIDUL
3404000000 SLEMAN
3471000000 YOGYAKARTA
Provinsi
L
1990
P
L+P
(3)
(4)
(5)
L
2000
P
L+P
2010 (P.211)
L
P L+P
(6)
(7)
(8)
(9)
(10)
(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)
(2)
LPP 1990-2000 LPP 2000-2010 v.211
(9)
(10)
1
3401000000
KULON PROGO
-0,04
0,48
2
3
4
5
3402000000
3403000000
3404000000
3471000000
BANTUL
GUNUNG KIDUL
SLEMAN
YOGYAKARTA
1,15
0,29
1,45
-0,38
1,55
0,40
1,93
0,07
0,69
1,12
Provinsi
Table sex ratio for evaluation
Tabel IA.
No Kode 2010
(1)
1
2
3
4
5
Kab./Kota
1990
2000
2010 v.211
(2)
(3)
(4)
(5)
96,0
96,8
94,7
99,0
96,2
96,7
97,0
99,0
95,1
101,8
95,8
98,3
96,2
100,0
100,0
100,0
100,0
99,6
3401000000 KULON PROGO
3402000000 BANTUL
3403000000 GUNUNG KIDUL
3404000000 SLEMAN
3471000000 YOGYAKARTA
Provinsi
Table population distribution for evaluation
Tabel IA.
% Distribusi
No Kode 2010
(1)
1
2
3
4
5
Kab./Kota
(2)
3401000000 KULON PROGO
3402000000 BANTUL
3403000000 GUNUNG KIDUL
3404000000 SLEMAN
3471000000 YOGYAKARTA
Provinsi
1990
2000
(3)
(4)
(5)
11,9
25,0
21,5
28,9
12,7
100,0
11,2
26,1
20,0
31,3
11,4
100,0
12,8
23,9
22,4
26,8
14,1
100,0
2010 v.211 2010 v.212
(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
THANK YOU