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Date

Attendees

 

 

Agenda

 

1)  Use Case reminder.

2)  Where  we are on our road map. 

3)  Open Action Items

4 ) JIRA Issues Review

 https://jira.edmcouncil.org/browse/IND/?selectedTab=com.atlassian.jira.jira-projects-plugin:issues-panel

5)  Todays content discussion.

 

            UML

            Spreadsheet

            Protégé

6)  For next week.

Proceedings

Slides from today's call – 

 

Notes from the call:

The focus this week has been on CPI – Consumer Price Index.  In that context, but with respect to the more general definition of Economic Indicator, we discussed datatype definitions in a bit more depth.  With respect to replacing QuantityValue with StatisticalValue and laying out datatypes for statistics, there are some broad classes of statistical data to consider:  

Statistical Data

     Categorical Data – which is something akin to qualitative data from an ontological perspective

          Nominal Data – for definitions of concepts such as Gender, which might be enumerated/valid values that are qualitative, including Preference Scales

          Ordinal Data – in cases where order matters

     Quantitative Data

          Interval Data – for datatypes such as integer, where you can do some calculations such as differences, but not necessarily other more complex algorithms

          Ratio Data – that can be used in complex calculations (e.g., real numbers, fixed decimal numbers, etc.)

Statisticians focused on the mathematical side of statistics might or might not agree with this classification, but we might find something in a statistics text for experimental design – Dan will look around for a reference.  We could build this up using ISO 11404 as the starting point potentially.  (Not for the August 15th submission date, though).

Datatypes we ultimately need to represent with respect to averages include weighted averages and moving averages, for example, for chained CPI.

For CPI, we need to add the definition of MSA to what we now have (metropolitan statistical area).  The OMB defines a Metropolitan Statistical Area as one or more adjacent counties or county equivalents that have at least one urban core area of at least 50,000 population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties.  See https://en.wikipedia.org/wiki/List_of_Metropolitan_Statistical_Areas.  Note that the definition for MSA includes a CPI for small MSA and for a collection of small MSAs.


From our working session on this, earlier in the week, for CPI at BLS: 

Three CPI series. The Bureau of Labor Statistics (BLS; the Bureau) publishes CPI data every month. The three main CPI series are

                        CPI for All Urban Consumers (CPI-U)

                        CPI for Urban Wage Earners and Clerical Workers (CPI-W)

                        Chained CPI for All Urban Consumers (C-CPI-U)

CPI populations. A consumer price index measures the price-change experience of a particular group called its tar­get population. The CPI uses two target populations for its main series:

                        All Urban Consumers (the “CPI-U” population)

                        Urban Wage Earners and Clerical Workers (the “CPI-W” population)

Both the CPI-U and the C-CPI-U target the CPI-U popula­tion. The CPI-U population, which covers about 88 percent of the U.S. population, covers households in all areas of the United States except people living in rural nonmetropoli­tan areas, in farm households, on military installations, in religious communities, and in institutions such as prisons and mental hospitals.

The CPI-W population, the target of the CPI-W, is a sub­set of the CPI-U population. The CPI-W population consists of all CPI-U population households for whom 50 percent or more of household income comes from wages and clerical workers’ earnings. The CPI-W’s share of the total U.S. popu­lation has diminished over the years; the CPI-W population is now about 28 percent of the total U.S. population. The CPI-W population excludes households of professional and salaried workers, part-time workers, the self-employed, and the un­employed, along with households with no one in the labor force, such as those of retirees.


Survey references: 

Consumer Expenditure - http://www.bls.gov/cex/ 

Telephone Point of Purchase Survey - http://www.bls.gov/respondents/cpi/tpops/


Census Bureau definition of Household / HousingUnit (which we will need when we get into the detail of some of the indicators):

http://www.census.gov/glossary/#term_Household

A household includes all the people who occupy a housing unit (such as a house or apartment) as their usual place of residence.

A household includes the related family members and all the unrelated people, if any, such as lodgers, foster children, wards, or employees who share the housing unit. A person living alone in a housing unit, or a group of unrelated people sharing a housing unit such as partners or roomers, is also counted as a household. The count of households excludes group quarters. There are two major categories of households, "family" and "nonfamily."

Household is a standard item in Census Bureau population tables

 

http://www.census.gov/glossary/#term_Housingunit 

A house, an apartment, a mobile home or trailer, a group of rooms, or a single room occupied as separate living quarters, or if vacant, intended for occupancy as separate living quarters. Separate living quarters are those in which the occupants live separately from any other individuals in the building and which have direct access from outside the building or through a common hall. For vacant units, the criteria of separateness and direct access are applied to the intended occupants whenever possible.


Definition of Market Basket for CPI from BLS:  Market basket (Consumer Price Index)

The market basket is a package of goods and services that consumers purchase for day-to-day living. The weight of each item is based on the amount of expenditure reported by a sample of households.


We also discussed the issue of using ISO definitions in our standards, which Dan agreed should not be an issue in the way that it has become in our current discussions at OMG, but he also can understand that ISO might see OMG as a competitor and might have lawyers combing through OMG specs for violations of copyright.  He thought the process we agreed to in Orlando is a reasonable approach.  SC32 went to great lengths to make sure that several of their specifications were made publicly available, and referenceable with attribution, and JTC1 is likely a good place to start because they understand this. 

Decisions

Once we have the defintions for CPI incorporated that are cited above, we'll work on PPI next, then if time permits look at how to model the comparison indicators (e.g. current month vs. prior 2 months and prior year, as is done for the employment survey for household data).

Action items

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