Data
The following examples use mainly fictitious data to present the
functions. However, if you want to test the functionality in more
detail, the package comes with a small data set of real Paraguayan
credit cooperatives (creditcoops). There are 22 paired observations for
real Paraguayan credit cooperatives (with assets > 11 Mio. USD) for
2016 and 2018 with their respective total loans granted. For a better
visualization there is an additional column with the transformed total
loans. For further information on the data please see the
data help file.
data("creditcoops")
head(creditcoops)
#> # A tibble: 6 x 5
#> coop_id year total_loans paired total_loans_log
#> <dbl> <fct> <dbl> <int> <dbl>
#> 1 1 2016 173892358 1 19.0
#> 2 1 2018 199048199 1 19.1
#> 3 2 2016 323892456 2 19.6
#> 4 2 2018 461609439 2 20.0
#> 5 3 2016 179981404 3 19.0
#> 6 3 2018 227232008 3 19.2
Group wrapper overview
At the moment, there are the following groups of functions
available:
- mstruct is a wrapper for market structure measures
- inequ is a wrapper for inequality and diversity
measures
- comp is a wrapper with different concentration
measures
- concstats is a function which calculates a set of
pre-selected measures in a one step procedure to get a quick overview of
a given market structure
The functions will be presented in more details in the following
short step-by-step guide.
Examples
We will use a vector which represents market participants with their
respective market shares (in decimal form):
mstruct
The wrapper includes the following individual functions:
firm, nrs_eq, top,
top3, top5, and all
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04, 0, 0)
test_share_top5 <- top5(test_share) # top 5 market share
test_share_top5
#> [1] 96
You should have noticed that the market shares are in decimal form.
There are eight market participants, however, two have no market shares,
by default concstats treats 0 as NA. The result is a top 5
market share of 96 %. You can also access each function through their
respective argument in the group wrapper:
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04, 0, 0)
test_share_top <- mstruct(test_share, type = "top") # top market share
test_share_top
#> [1] 40
Or, just calculate all measures of the group wrapper.
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04, 0, 0)
test_share_mstruct <- mstruct(test_share, type = "all")
test_share_mstruct
#> Measure Value
#> 1 Firms 6.0
#> 2 Nrs_equivalent 3.3
#> 3 Top (%) 40.0
#> 4 Top3 (%) 85.0
#> 5 Top5 (%) 96.0
The result is a table with the market structure measures.
inequ
The inequality and diversity group includes the functions
entropy, gini, berry,
palma, grs, and all.
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04)
test_share_entropy <- entropy(test_share)
test_share_entropy
#> [1] 2.036449
# and as a standardized value
test_share_entropy2 <- entropy(test_share, unbiased = TRUE)
test_share_entropy2
#> [1] 0.787806
comp
The group wrapper for competition measures includes the functions
hhi, hhi_d, hhi_min,
dom, sten, and all
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04, 0, 0)
test_share_hhi <- hhi(test_share)
test_share_hhi
#> [1] 0.3002
# a standardized value
test_share_hhi2 <- hhi(test_share, unbiased = TRUE)
test_share_hhi2
#> [1] 0.16024
# the min average of the hhi
test_share_hhi3 <- comp(test_share, unbiased = TRUE, type = "hhi_min")
test_share_hhi3
#> [1] 0.1666667
concstats
A single function which calculates a set of eight pre-selected
measures in a one step procedure for a first overview of a given market
structure.
test_share <- c(0.2, 0.3, 0.5)
test_share_conc <- concstats(test_share)
test_share_conc
#> Measure Value
#> 1 Firms 3.00
#> 2 Nrs_equivalent 2.63
#> 3 Top (%) 50.00
#> 4 Top3 (%) 100.00
#> 5 Top5 (%) NA
#> 6 HHI 0.38
#> 7 Entropy(RE) 0.94
#> 8 Palma ratio 2.50
Visualization
The scope of the package is to calculate market structure and
concentration measures to get a quick and more informed overview of a
given market situation. However, it is good practice to visualize your
data in an exploratory step or in reporting your results. The package
concstats works fine with other EDA or data visualization
packages e.g. overviewR,
dataexplorer,
kableExtra
or ggplot2
to name a few.
Some examples how you can accomplish this. Let us assume one would
like to use the group measure for e.g. market structure, and keep the
resulting table. We can refine the table using kableExtra
which works nice with knitr.
This time, we will use our creditcoops data set again,
which comes with the package.
data("creditcoops")
head(creditcoops)
#> # A tibble: 6 x 5
#> coop_id year total_loans paired total_loans_log
#> <dbl> <fct> <dbl> <int> <dbl>
#> 1 1 2016 173892358 1 19.0
#> 2 1 2018 199048199 1 19.1
#> 3 2 2016 323892456 2 19.6
#> 4 2 2018 461609439 2 20.0
#> 5 3 2016 179981404 3 19.0
#> 6 3 2018 227232008 3 19.2
You will need the following two packages. Make sure you have these
packages installed.
library(dplyr)
library(kableExtra)
Now, we will filter out data for the year 2016.
coops_2016 <- creditcoops %>% dplyr::filter(year == 2016)
head(coops_2016)
coops_2016 <- coops_2016[["total_loans"]] # atomic vector of total loans
coops_2016 <- coops_2016/sum(coops_2016) # convert the vector in decimal form
# of market shares
# We then use the new object `coops_2016` to calculate the market structure measures
# as a group in a one-step-procedure:
coops_2016_mstruct <- mstruct(coops_2016, type = "all")
coops_2016_mstruct_tab <- coops_2016_mstruct %>%
kableExtra::kbl(caption = "Market structure 2016", digits = 2,
booktabs = T, align = "r") %>%
kableExtra::kable_classic(full_width = F, html_font = "Arial")
coops_2016_mstruct_tab
The result is a nice reusable table.
Now, let’s go a step further. We will compare the two samples for
2016 and 2018. For this purpose, we will select from our
creditcoops data set the relevant columns (coop_id, year,
paired, and total_loans_log) and make a new data frame.
Make sure you have installed the ggplot2 package. Load
the package.
library(ggplot2) # Create Elegant Data Visualizations Using the Grammar of Graphics
Having a look a the output, we see a box plot with paired values of
the cooperatives and the evolution of their respective total loans over
time for the two sample years 2016 and 2018.