5  Price FrameWork (pfw) data frame

The price framework data frame (pfw) is the most important source of technical metadata of the PIP project, which makes pertinent the have of a separate chapter for it. The general explanation of the structure of the pfw can be found in Section @ref(pfw-join). This chapter focuses more on the use of each of the variables. Yet, it is worth repeating a few things already mentioned in Section @ref(pfw-join).

# pfw <- pipload::pip_load_aux("pfw")

5.1 Variables

The table provides a short description of each variable of the Price FrameWork data frame. We provide additional information for variable.

5.2 Additional explanation

ID vars

Variables of identification are wb_region_code, pcn_region_code, country_code, ctryname, year, surveyid_year, survey_acronym.

The main difference between wb_region_code and pcn_region_code is that the former only include geographical regions internally used by the world bank, whereas the latter has an additional category, “OHI,” for Other High Income countries.

# janitor::tabyl(pfw, wb_region_code, pcn_region_code)

For most of the data points, there should be no difference in between year and surveyid_year. In some extreme cases (not available at the moment), year could be different due to the reporting criteria from the NSO. For our purpose, we use rep_year as the reporting year in our system for integer ones, and ref_year for decimal reporting purpose

altname

Alternative survey name for some surveys.

# head(pfw[altname != "", c("altname", "survey_acronym")])

survey_coverage

This variable represent the househol survey coverage. This is different from the disaggregation data level.

# janitor::tabyl(pfw, survey_coverage)

welfare_type

This variable contains the welfare type of the main welfare aggregate variable in a survey in case it has more than one. The welfare type of alternative welfare aggregates is found in variable oth_welfare1_type.

# janitor::tabyl(pfw, welfare_type)

use_imputed

Whether the welfare aggregate has been imputed. There are just few countries with this kind of data.

use_microdata

Whether the welfare aggregate vector used in PIP is directly extracted from microdata without any sort of aggregation. Mos of the countries have this kind of data. Below you can see those that do not.

# pfw[use_microdata != 1, unique(country_code)]

use_bin

Whether the welfare aggregate was aggregated to 400 bins from microdata before being incorporated to the PIP repository. This is the case of houshold surveys only available in the Luxembourg Data Center (LIS). Countries with bin data is considered micro data for technical purposes.

# pfw[use_bin == 1, unique(country_code)]

use_groupdata

Whether welfare aggregate comes from grouped data. Information about this type of data is available in [@dattComputationalToolsPoverty1998; @krauseCorrigendumEllipticalLorenz2013; @villasenorEllipticalLorenzCurves1989]. The following countries have this kind of data.

# pfw[use_groupdata == 1, unique(country_code)]