This document discusses standardizing statistical tables and measures' labels. It describes a Px-Pro macro and architecture for producing machine-readable statistical tables using standardized metadata. The macro extracts table metadata, classifications, and data to generate tables in px-file format. Over 50% of Statistics Finland's tables are now produced this way, improving completeness, validation, automation and standardization. Future work includes further standardizing measures' labels and handling tables with multiple content dimensions.

1. Toward standardized statistical tables:
metadata-driven px-file production
8/27/2019
Rok Platinovsek, Daniel Davis, Emilia Saarinen
Statistics Finland

2. 8/27/2019 Rok Platinovsek, Statistics Finland 2

3. Machine-readability
8/27/2019 Rok Platinovsek, Statistics Finland 3
The table Index of turnover in
industry, 1995-2018
reports values on
the turnover index and
the trend of the turnover index
by year and industry (NACE).
{
"cube": {
"label": "Index of turnover in industry, 1995-2018",
"dimension": {
"year": {
"label": "Year",
"ID": "urn:stat.fi:representedvariable:a0001",
"role": "time"
},
"industry": {
"label": "Industry (NACE)",
"ID": "urn:stat.fi:representedvariable:a0002",
"role": "grouping"
},
"information": {
"role": "content",
"measures": {
"turnind": {
"label": "Turnover index",
"ID": "urn:stat.fi:representedvariable:a0003"
},
"turnindtrnd": {
"label": "Turnover index, trend",
"ID": "urn:stat.fi:representedvariable:a0004"
}
}
}
}
}
}

4. Machine-readability
8/27/2019 Rok Platinovsek, Statistics Finland 4
{
"cube": {
"label": "Index of turnover in industry, 1995-2018",
"dimension": {
"year": {
"label": "Year",
"ID": "urn:stat.fi:representedvariable:a0001",
"role": "time"
},
"industry": {
"label": "Industry (NACE)",
"ID": "urn:stat.fi:representedvariable:a0002",
"role": "grouping"
},
"information": {
"role": "content",
"measures": {
"turnind": {
"label": "Turnover index",
"ID": "urn:stat.fi:representedvariable:a0003"
},
"turnindtrnd": {
"label": "Turnover index, trend",
"ID": "urn:stat.fi:representedvariable:a0004"
}
}
}
}
}
}
SAS macro Px-file
SAS code

5. PX-Pro’s architecture
8/27/2019 Rok Platinovsek, Statistics Finland 5
PxPro-macro
Table metadata
repository
Classification
repository
Px-fileSAS-dataset
GUI
Classification
editor
GUI
Table metadata
editor
PX-database

6. 8/27/2019 Rok Platinovsek, Statistics Finland 6

7. 8/27/2019 Rok Platinovsek, Statistics Finland 7
%PxPro(
tableId=111h,
inData=data111h,
prelimPeriods=2,
lastUpdated=2019-06-20,
outPath=c:tables
)

8. The impact so far
• Improvements
• Completeness
• Validation
• Automation
• Standardization
• Penetration rate > 50% in StatFin
• To be used on Stats Finland’s new web-site
8/27/2019 Rok Platinovsek, Statistics Finland 8

9. Future development
Standardizing measures’ labels
8/27/2019 Rok Platinovsek, Statistics Finland 9

10. GSIM
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Variable
Represented variable
Instance variable
Unit type
Universe
Population
measures
is a specification of
is a specification of
measures
measures
takes meaning from
takes meaning from Value Domain

11. GSIM example
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Year Industry (NACE) Turnover index Turnover index, trend Domestic turnover index Domestic turnover index, trend
2016 B Mining and quarrying 111,5 110,3 107,5 106,8
2016 C Manufacturing 100,9 101,2 101,6 101,4
2017 B Mining and quarrying 130,2 129,5 127,3 127,2
2017 C Manufacturing 108,7 108,8 107,8 107,7
2018 B Mining and quarrying 140,2 140,5 144,7 145,2
2018 C Manufacturing 115,3 115,2 111,3 111,3
Domestic turnoverTurnoverTime Industry
Instance variableRepresented variable
Variables

12. Elements comprising measures’ labels
1. Content
2. Aggregation method
3. Statistical methods
a) Seasonal adjustment methods
b) Indexation
c) Change percentages
4. Measurement unit
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1. New orders in manufacturing
2. (sum)
3.
a) (none)
b) index (2015=100)
c) annual change
4. (index point)
Annual change of the new orders in manufacturing index
New orders in manufacturing, index (2015=100), annual change

13. Future development
• Standardizing measures’ labels
• Tables with multiple content dimensions
• Establishing comparability of measures
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14. Thank you!
8/27/2019 Rok Platinovsek, Statistics Finland