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DisseminationDissemination
Michael J. LevinMichael J. LevinHarvard Center for Harvard Center for
Population and Development StudiesPopulation and Development [email protected]@yahoo.com
22
Dissemination topicsDissemination topics
I.I. FlowFlow
II.II. TypesTypes
III.III. MediaMedia
33
I.Flow of DisseminationI.Flow of Dissemination
• First releases – within one month of First releases – within one month of the censusthe census
• First level geography tablesFirst level geography tables• Detailed tables – geographyDetailed tables – geography• Detailed tables – cross-tabulationsDetailed tables – cross-tabulations• First analytical reportFirst analytical report• Subsequent analytical reportsSubsequent analytical reports• PUMSPUMS
44
II. Types of DisseminationII. Types of Dissemination
1.1. Frequencies (alone and by geography)Frequencies (alone and by geography)
2.2. First-level cross-tabulationsFirst-level cross-tabulations
3.3. Detailed cross-tabulationsDetailed cross-tabulations
4.4. Graphs *Graphs *
5.5. Maps *Maps *
6.6. Indicators *Indicators *
7.7. Results of direct and indirect techniques *Results of direct and indirect techniques *
8.8. Other evaluationOther evaluation
55
II-4. Population PyramidII-4. Population PyramidSouthern Sudan: 20081. Population by Age and Sex
Population
0
100000
200000
300000
400000
500000
600000
7000000
100000
200000
300000
400000
500000
600000
700000
800000
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
75-79
80-84
85-89
90-94
95+
Male Female
66
Southern Sudan: 1983 and 2008Population by Age and Sex
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
1983.00 2008.00
Female
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
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
75-79
80-84
85-89
90-94
95+Male
II-4. Population Pyramid – two II-4. Population Pyramid – two censusescensuses
77
II-4. Single Year of AgeII-4. Single Year of AgeSouthern Sudan: 2008
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
0 10 20 30 40 50 60 70 80 90
Male Female
3. Population by Sex
88
II-5. MapsII-5. Maps
Legend6,334 to 18,46618,467 to 26,49926,500 to 29,358
Test Variable - Sudan
99
II-6. Indicators: Age II-6. Indicators: Age ReportingReporting
B. Measures of Digit Preference----------- ---- ------------- ------------- ---------------Method and terminal digit Male Female Both sexes----------- ---- ------------- ------------- ---------------WHIPPLE METHOD (23-62)
Index 1.75 1.77 1.76
MYERS METHOD
Index * 32.1 32.9 32.5
0 9.4 10.3 9.81 -4.3 -4.4 -4.32 -0.1 -0.2 -0.23 -3.2 -3.5 -3.44 -2.6 -2.5 -2.55 4.1 3.3 3.76 -1.4 -1.8 -1.67 -2.0 -2.1 -2.18 2.6 2.9 2.89 -2.4 -1.9 -2.1
Southern Sudan: 2008
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
0 1 2 3 4 5 6 7 8 9
Male Female
4. Myers Preference by Digit
1010
II-7. Own Children TFR II-7. Own Children TFR estimates using Kenya estimates using Kenya Censuses: 1955-99Censuses: 1955-99
Figure 1. Own children estimates of TFR for Kenya based on the 1969, 1979, 1989, and 1999 Censuses
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
TFR
Census 1979 Census 1989 Census 1969 Census 1999
1111
II-7. TFRs by Wealth Quintiles, II-7. TFRs by Wealth Quintiles, Kenya: 1975 to 1999Kenya: 1975 to 1999
1212
II-7. TFR for Kenya, Tanzania II-7. TFR for Kenya, Tanzania and Uganda: 1985 to 2002and Uganda: 1985 to 2002
Figure… Own children estimates of TFR for Kenya, Uganda and Tanzania
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
1985 1987 1989 1991 1993 1995 1997 1999 2001
Date
TF
R
KE 1999 TZ 2002 UG 2002
1313
II-7. TFR for Poorest, Kenya, II-7. TFR for Poorest, Kenya, Tanzania and Uganda: 1985 to Tanzania and Uganda: 1985 to 20022002
Estimates of TFR for Kenya (1999), Uganda (2002), and Tanzania (2002) for Quintiles
0.00
2.00
4.00
6.00
8.00
10.00
12.00
1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
Date
TF
R
K99 Q12 TZ02 Q12 UG02 Q12
1414
II-7. ASFRs for 5 year periods, II-7. ASFRs for 5 year periods, Kenya: 1985-89 to 1995-99Kenya: 1985-89 to 1995-99
Own children estimates of trends in fertility patterns for Kenyan women based on 1999 Census
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
Age group
Fert
ility
rate
1995-99 0.077 0.149 0.210 0.193 0.133 0.071 0.029
1990-94 0.084 0.238 0.264 0.215 0.153 0.085 0.035
1985-89 0.171 0.351 0.347 0.290 0.202 0.114 0.044
15-19 20-24 25-29 30-34 35-39 40-44 45-49
1515
III. Media of disseminationIII. Media of dissemination
1.1. PaperPaper
2.2. CD/DVD/Flash drivesCD/DVD/Flash drives
3.3. Internet – already prepared tablesInternet – already prepared tables
4.4. Internet – user developed tables on Internet – user developed tables on demanddemand
5.5. Public Use Microdata Sets (PUMS)Public Use Microdata Sets (PUMS)
1616
III-1. PaperIII-1. Paper
• Traditional method – still importantTraditional method – still important• But movement is away from paper to But movement is away from paper to
electronic mediaelectronic media• Summary tablesSummary tables• Basic tables – geography for variablesBasic tables – geography for variables• Basic tables – crosstabs for two or more Basic tables – crosstabs for two or more
variablesvariables• Graphs, maps, other analytical toolsGraphs, maps, other analytical tools• Detailed tables at low levels of geographyDetailed tables at low levels of geography• Detailed tables for small groupsDetailed tables for small groups
1717
III-2. Compact disk/DVD/flash III-2. Compact disk/DVD/flash drivesdrives• For large tables For large tables
• For low levels of geographyFor low levels of geography
• For selected variables – religion and For selected variables – religion and ethnicityethnicity
• For small groups – sensitivity issuesFor small groups – sensitivity issues
• Direct dissemination -- watch Direct dissemination -- watch confidentialityconfidentiality
• Electronic media in office for use on Electronic media in office for use on requestrequest
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III-3. Internet disseminationIII-3. Internet dissemination
• Tables downloaded directly from the Tables downloaded directly from the webweb
• Other forms of presentation stored – Other forms of presentation stored – pyramids, graphs, maps, etcpyramids, graphs, maps, etc
• Note: importance of media fitting on Note: importance of media fitting on an A4 pagean A4 page
1919
III-4. Internet III-4. Internet crosstabulationscrosstabulations• Model is US Census Bureau’s American Model is US Census Bureau’s American
FactfinderFactfinder• Computer makes takes described by the Computer makes takes described by the
user – (1) geographic hierarchy, (2) user – (1) geographic hierarchy, (2) columns, and (3) rowscolumns, and (3) rows
• Limited to those items availableLimited to those items available• Confidentiality is maintainedConfidentiality is maintained• User cannot access micro-dataUser cannot access micro-data• Next step: open use of micro-data onlineNext step: open use of micro-data online
2020
III-5. Public Use Micro-data III-5. Public Use Micro-data Samples (PUMS)Samples (PUMS)
• Provides users opportunity to develop own Provides users opportunity to develop own tablestables
• Provides users opportunity for statistical Provides users opportunity for statistical analysis analysis
• Provides users opportunity to test hypothesesProvides users opportunity to test hypotheses• Does not provide detail for small groups and Does not provide detail for small groups and
small areassmall areas• Consider size of one or more PUMS – 1 %, 5 Consider size of one or more PUMS – 1 %, 5
%, 10 % or more%, 10 % or more• Consider confidentialityConsider confidentiality
2121
III-5. Disclosure Controls Example: III-5. Disclosure Controls Example: Saint Lucia, 1991 CensusSaint Lucia, 1991 Census1.1. Restrict access to samples: 10% Restrict access to samples: 10% (13,405 persons)(13,405 persons)
2.2. Limit geographical detail (n<2,000): suppress Limit geographical detail (n<2,000): suppress region, district, town, settlement, enumeration region, district, town, settlement, enumeration district, school identification; retain urban-ruraldistrict, school identification; retain urban-rural
3.3. Recode sparse categories (n<25)Recode sparse categories (n<25) “other”. “other”.» Type of dwelling: suppress townhouse, barracksType of dwelling: suppress townhouse, barracks» Land occupation: suppress sharecropLand occupation: suppress sharecrop» And othersAnd others» Ethnic origin: suppress Chinese, Portuguese, Syrian-Ethnic origin: suppress Chinese, Portuguese, Syrian-
LebaneseLebanese» Religion: suppress 6 categoriesReligion: suppress 6 categories» School, work mode of transport: bicycleSchool, work mode of transport: bicycle» Occupation, industry, training code: reduce from 4 Occupation, industry, training code: reduce from 4
digits to 1/2/3digits to 1/2/3» And others And others
2222
III-5. Technical Disclosure Controls Example: III-5. Technical Disclosure Controls Example: Saint Lucia, 1991Saint Lucia, 19914.4. Top-bottom codeTop-bottom code
» Number of rooms: 10+Number of rooms: 10+» Number of radios: 4+Number of radios: 4+» Age: 81+Age: 81+» Age at first child: <= 14Age at first child: <= 14» And othersAnd others
5.5. Suppress: Suppress: » date of birth, precise place of birth, type of work wanteddate of birth, precise place of birth, type of work wanted
6.6. Migration: timing/place not identified in detail Migration: timing/place not identified in detail » Country last lived: suppress 37 categoriesCountry last lived: suppress 37 categories» Year of immigration: <1948Year of immigration: <1948
7.7. Identify place of residence by major civil division Identify place of residence by major civil division (pop>20k, 60k, 100k, 250k, 1 million—i.e., national (pop>20k, 60k, 100k, 250k, 1 million—i.e., national convention)convention)
» all suppressedall suppressed8.8. Suppress any sensitive variable requested by NSI: Suppress any sensitive variable requested by NSI:
» none (as yet) none (as yet)
2323
III-5. Implementing IPUMS technical III-5. Implementing IPUMS technical protocols using CSPro – Designprotocols using CSPro – Design
if DWELL = 4 or DWELL = 7 let DWELL = 8 endifif LAND = 5 let LAND = 8 endifif OWNER = 2 or OWNER = 5 let OWNER = 7 endifif ROOF = 3:7 let ROOF = 8 endifif WALL = 4:7 let WALL = 8 endifif WATER = 6 let WATER = 7 endifif LIGHT = 1 let LIGHT = 4 endif
. Top-Bottom codingif ROOMS > 10 let ROOMS = 10 endifif BEDR > 7 let BEDR = 7 endifif RADIOS > 4 let RADIOS = 4 endifif TV > 3 let TV = 3 endifif NBEMIG > 2 let NBEMIG = 2 endif
2424
III-5. Design – Some Pop III-5. Design – Some Pop ItemsItems
if RACE = 4:6 let RACE = 9 endifif RELIG = 3,6,7,9,11,13,14 let RELIG = 16 endif. Migration variablesif BTHPL = 3,9,15,27,38,41,100else let BTHPL = 99 endifif CNLVD = 3,9,15,27,38,41,100else let CNLVD = 99 endifif CNTRY = 3,9,15,27,38,41,100else let CNTRY = 99 endifif RESCN = 3,9,15,27,38,41,100else let RESCN = 99 endif
2525
III-5. Demonstration of Concept:III-5. Demonstration of Concept:
Source: Unpublished data, St. Lucia Statistics
22.120.521.321.720.521.1Median
1,0505701,6201,0956291,72480 _ 84 years
8806901,5708577551,61275 _ 79 years
1,4301,0702,5001,2661,0482,31470 _ 74 years
1,6501,4603,1101,7171,3243,04165 _ 69 years
1,6901,4503,1401,6781,4023,08060 _ 64 years
1,9101,3503,2601,7961,4733,26955 _ 59 years
2,2701,8804,1502,0141,7013,71550 _ 54 years
2,2002,3304,5302,3672,3114,67845 _ 49 years
2,9702,8205,7902,9062,7195,62540 _ 44 years
3,9303,3707,3003,6423,4197,06135 _ 39 years
4,7604,3709,1304,7844,2098,99330 _ 34 years
5,9705,45011,4205,9235,57911,50225 _ 29 years
6,6906,36013,0506,5796,37512,95420 _ 24 years
7,5207,46014,9807,4777,29114,76815 _ 19 years
7,4507,61015,0607,9778,06816,04510 _ 14 years
9,0608,87017,9308,6778,57317,2505 _ 9 years
7,6907,94015,6307,9087,76915,6770 _ 4 years
69,12065,050134,17068,66364,645133,308Total
femalemaleTotalfemalemaleTotalAge Group
SampleCensus
Table 1. Comparison of Age and Sex Distributions, Census and Sample: St. Lucia, 1991
Source: Unpublished data, St. Lucia Statistics
22.120.521.321.720.521.1Median
1,0505701,6201,0956291,72480 _ 84 years
8806901,5708577551,61275 _ 79 years
1,4301,0702,5001,2661,0482,31470 _ 74 years
1,6501,4603,1101,7171,3243,04165 _ 69 years
1,6901,4503,1401,6781,4023,08060 _ 64 years
1,9101,3503,2601,7961,4733,26955 _ 59 years
2,2701,8804,1502,0141,7013,71550 _ 54 years
2,2002,3304,5302,3672,3114,67845 _ 49 years
2,9702,8205,7902,9062,7195,62540 _ 44 years
3,9303,3707,3003,6423,4197,06135 _ 39 years
4,7604,3709,1304,7844,2098,99330 _ 34 years
5,9705,45011,4205,9235,57911,50225 _ 29 years
6,6906,36013,0506,5796,37512,95420 _ 24 years
7,5207,46014,9807,4777,29114,76815 _ 19 years
7,4507,61015,0607,9778,06816,04510 _ 14 years
9,0608,87017,9308,6778,57317,2505 _ 9 years
7,6907,94015,6307,9087,76915,6770 _ 4 years
69,12065,050134,17068,66364,645133,308Total
femalemaleTotalfemalemaleTotalAge Group
SampleCensus
Table 1. Comparison of Age and Sex Distributions, Census and Sample: St. Lucia, 1991