Elasticities across all potential variable pairs and for different time frames, non-interpolated — ppts_easia_weuro_world_elas

Compute elasticities for different numerator and denominator combinations, using non-interpolated data.

Usage

ppts_easia_weuro_world_elas_raw

Format

A data frame:

location_code: Short string code for country and regions
location_level: Country, or regional or sub-country
variable: The variable associated with value, also the variable, whose percentage change is computed, in the denominator
year_bins_type: The type of year binning, 1 year, 10 years, 20 years, etc.
year_bins: Year bins given year binning, labels of different years, decades, bi-decades, etc.
pchg: Percentage change from start to end of bin associated with variable (for the denominator)
value: Level values for variables
variable_numerator: The variable, whose percentage change is computed, in the numerator
pchg_numerator: pchg but for the variable_numerator variable
elasticity: pchg_numerator over pchg to compute elasticities over different windows for all possible variable variable_numerator and variable combinations.

Source

World bank, national statistics websites of W. Europe and E. Asia countries

Examples

data(ppts_easia_weuro_world_elas_raw)
summary(ppts_easia_weuro_world_elas_raw)
#>  location_code          location_level      variable          year_bins_type 
#>  AUT    :   915   country      :96940   gdp     :29475   1920t2020i05:60015  
#>  CHN    :   780   multicountry :24760   school  : 3430   1920t2020i10:31805  
#>  JPN    :   780   multiprovince:  750   student :31845   1920t2020i20:17180  
#>  CHE    :   690   province     : 6375   teacher :30290   1925t2015i15:19825  
#>  KOR    :   625                         youthpop:33785   1940t2020i01:    0  
#>  BGD    :   530                                                              
#>  (Other):124505                                                              
#>   year_bins              pchg           value           variable_numerator
#>  Length:128825      Min.   :-0.71   Min.   :2.200e+01   Length:128825     
#>  Class :character   1st Qu.:-0.01   1st Qu.:7.013e+03   Class :character  
#>  Mode  :character   Median : 0.10   Median :1.436e+05   Mode  :character  
#>                     Mean   : 0.17   Mean   :2.937e+07                     
#>                     3rd Qu.: 0.25   3rd Qu.:2.715e+06                     
#>                     Max.   : 9.76   Max.   :1.977e+09                     
#>                     NA's   :47830   NA's   :19255                         
#>  pchg_numerator    elasticity   
#>  Min.   :-0.71   Min.   :-Inf   
#>  1st Qu.:-0.01   1st Qu.:0.08   
#>  Median : 0.10   Median :1.00   
#>  Mean   : 0.17   Mean   : NaN   
#>  3rd Qu.: 0.25   3rd Qu.:1.00   
#>  Max.   : 9.76   Max.   : Inf   
#>  NA's   :67378   NA's   :79417  
print(ppts_easia_weuro_world_elas_raw)
#> # A tibble: 128,825 × 10
#>    location_code location_level variable year_bins_type year_bins  pchg value
#>    <fct>         <fct>          <fct>    <fct>          <chr>     <dbl> <dbl>
#>  1 ABW           country        gdp      1920t2020i05   1981-1985    NA    NA
#>  2 ABW           country        gdp      1920t2020i05   1981-1985    NA    NA
#>  3 ABW           country        gdp      1920t2020i05   1981-1985    NA    NA
#>  4 ABW           country        gdp      1920t2020i05   1981-1985    NA    NA
#>  5 ABW           country        gdp      1920t2020i05   1981-1985    NA    NA
#>  6 ABW           country        gdp      1920t2020i05   1986-1990    NA 27884
#>  7 ABW           country        gdp      1920t2020i05   1986-1990    NA 27884
#>  8 ABW           country        gdp      1920t2020i05   1986-1990    NA 27884
#>  9 ABW           country        gdp      1920t2020i05   1986-1990    NA 27884
#> 10 ABW           country        gdp      1920t2020i05   1986-1990    NA 27884
#> # ℹ 128,815 more rows
#> # ℹ 3 more variables: variable_numerator <chr>, pchg_numerator <dbl>,
#> #   elasticity <dbl>