Volume of internationalization (VOI)

Country search scores focus on search volumes for objects in single countries. To compute an object’s volume of internationalization, we focus on global search volumes instead of country search volumes. Using the same approach as outlined above, globaltrends first downloads global search volumes for each control keyword and firm. Next, the package runs the control-object mapping and computes global search scores as the ratio of global search volumes for each object and global control search volumes.

Like country search scores, we can interpret global search scores as the proportion of global search volumes for an object of interest compared to global search volumes for control keywords. This allows researchers to track changes in global interest in objects and highlights phases of fast or receding internationalization. Since the computation of country and global search scores follows the same procedures, the two measures have the same properties. This provides for a direct comparison between country search scores and volume of internationalization in terms of global search scores.

Degree of internationalization (DOI)

As the main measure for the degree of internationalization, the globaltrends package uses the global dispersion of country search scores. The more uniform the distribution of search scores across countries, the higher an object’s degree of internationalization. When the distribution of country search scores is highly skewed, with high search scores in the object’s country of origin and low search scores in other countries, the object has a low degree of internationalization. To compute an object’s degree of internationalization, the packages by default use the inverted Gini coefficient.

Set up and start the database

Research projects that use Google Trends generate a substantial amount of data. To optimally handle this data, the globaltrends package uses an SQLite database to store and handle all data. This ensures efficiency and portability on the one hand and seamless integration with functions implemented in the DBI and dplyr packages on the other hand.

Users create the underlying database through the initialize_db command. The command creates a folder named db within the current working directory and creates an SQLite database file named globaltrends_db.sqlite within this folder. The command also creates all necessary tables within the database. For more information on database tables, please refer to their built-in documentation, e.g., ?globaltrends::data_score. The database initialization is necessary only for the first usage of the globaltrends package.

# initialize_db ----------------------------------------------------------------
setwd("your/globaltrends/folder")
initialize_db()
#> Database has been created.
#> Table 'batch_keywords' has been created.
#> ...
#> Table 'data_global' has been created.
#> Successfully disconnected.

After initialization or when resuming work on an existing database, it is sufficient to call start_db from the respective working directory. This command connects to the globaltrends_db.sqlite database in the folder db and creates connections to all tables in the database.

# start_db ---------------------------------------------------------------------
setwd("your/globaltrends/folder")
start_db()
#> Successfully connected to database.
#> Successfully exported all objects to .GlobalEnv.
print(ls())
#>  [1] "batch_keywords"   "batch_time"       "countries"        "data_control"
#>  [5] "data_doi"         "data_global"      "data_locations"   "data_mapping"
#>  [9] "data_object"      "data_score"       "dir_current"      "dir_wd"
#> [13] "globaltrends_db"  "keyword_synonyms" "keywords_control" "keywords_object"
#> [17] "time_control"     "time_object"      "us_states"

After work with the globaltrends package is complete, the user disconnects from the database with the command disconnect_db.

# disconnect_db ----------------------------------------------------------------
disconnect_db()
#> Successfully disconnected.

Download data from Google Trends

The next step in the globaltrends workflow is data download from Google Trends. The globaltrends package includes four types of download functions that we explain in detail below. Each of these functions uses either the official Google Trends research API (recommended) or the gtrendsR::gtrends function to access Google Trends. Google Trends allows inputs of up to five keywords for a given location and period (see Appendix A for a discussion of data manipulation applied by Google). Therefore, the globaltrends package works with “keyword batches” that combine up to five keywords. The respective batch numbers are an input to all functions – either as list or as single integer objects. In the package, we distinguish two types of batches: control batches that include keywords indicating baseline search activity and object batches that include keywords relating to the objects of interest (e.g., firms, persons, products…). By default, globaltrends only includes two sets of locations. The countries set, which covers all countries that generated at least 0.1% of world GDP in 2018, and the us_states set, covering all US states and Washington, DC, see below for further details.

# initialize python ------------------------------------------------------------
initialize_python(
  api_key = Sys.getenv("GOOGLE_API_KEY"), # Google Trends API key
  conda_env = Sys.getenv("CONDA_ENV"), # Location of conda environment OR
  python_env = Sys.getenv("PYTHON_ENV") # Location of Python environment
)

# add_control_keyword ----------------------------------------------------------
new_control <- add_control_keyword(
  keyword = c("gmail", "maps", "translate", "wikipedia", "youtube"),
  time = "2010-01-01 2020-12-31"
)
#> Successfully created new control batch 1 (gmail ... youtube, 2010-01-01 2020-12-31).

# keywords_control and dplyr interaction ---------------------------------------
dplyr::filter(keywords_control, keyword == "gmail")
#> # A tibble: 1 x 2
#>   batch keyword
#>   <int> <chr>
#> 1     1 gmail

# download_control -------------------------------------------------------------
download_control(control = new_control, locations = countries)
#> Successfully downloaded control data | control: 1 | location: US [1/66]
#> ...
#> Successfully downloaded control data | control: 1 | location: DO [66/66]

# download_control_global ------------------------------------------------------
download_control_global(control = new_control)
#> Successfully downloaded control data | control: 1 | location: world [1/1]

# add_object_keyword -----------------------------------------------------------
new_object <- add_object_keyword(
  keyword = list(
    c("coca cola", "facebook", "microsoft"),
    c("alaska air group", "illinois tool works", "jm smucker")
  ),
  time = "2010-01-01 2020-12-31"
)
#> Successfully created new object batch 1 (coca cola ... microsoft, 2010-01-01 2020-12-31).
#> Successfully created new object batch 2 (alaska air group ... jm smucker, 2010-01-01 2020-12-31).

# keywords_object and dplyr interaction ----------------------------------------
dplyr::filter(keywords_object, keyword == "coca cola")
#> # A tibble: 1 x 2
#>   batch keyword
#>   <int> <chr>
#> 1     1 coca cola

# download_object --------------------------------------------------------------
download_object(object = new_object, locations = countries)
#> Successfully downloaded object data | object: 1 | location: US [1/66]
#> ...
#> Successfully downloaded object data | object: 2 | location: DO [66/66]

# download_object_global -------------------------------------------------------
download_object_global(object = new_object)
#> Successfully downloaded object data | object: 1 | location: world [1/1]
#> Successfully downloaded object data | object: 2 | location: world [1/1]

Compute search scores and internationalization

Once the user has completed all control and object downloads, globaltrends computes search scores for each keyword-time-location combination and at a global level (volume of internationalization). Next, the package uses the across-country distribution of these search scores to measure the degree of internationalization of an object keyword.

# compute_score ----------------------------------------------------------------
compute_score(control = new_control[[1]], object = new_object, locations = countries)
#> Successfully computed search score | control: 1 | object: 1 | location: US [1/66]
#> ...
#> Successfully computed search score | control: 1 | object: 2 | location: DO [66/66]

# compute_voi ------------------------------------------------------------------
compute_voi(control = new_control[[1]], object = new_object)
#> Successfully computed search score | control: 1 | object: 1 | location: world [1/1]
#> Successfully computed search score | control: 1 | object: 2 | location: world [1/1]

# compute_doi ------------------------------------------------------------------
compute_doi(control = new_control[[1]], object = new_object, locations = "countries")
#> Successfully computed DOI | control: 1 | object: 1 [1/2]
#> Successfully computed DOI | control: 1 | object: 2 [2/2]

Exports and plots

Functions in globaltrends write all data directly to tables in the database. With the help of functions from the dplyr package and connections exported from start_db, users can access database tables and prepare their own analysis.

# manual exports ---------------------------------------------------------------
library(dplyr)
data_score %>%
  filter(keyword == "coca cola") %>%
  collect()
#> # A tibble: 8,040 x 6
#>    location keyword    date   score     batch_c  batch_o
#>    <chr>    <chr>     <int>     <dbl>   <int>    <int>
#>  1 US       coca cola 14610   0.00362   1        1
#>  ...
#> 10 US       coca cola 14883   0.00347   1        1
#> # ... with 8,030 more rows

To enhance usability, the globaltrends package includes a set of export functions that offer filters and return data as a tibble. The default value for the batch/keyword, for which export_xxx exports data, is NULL. In this case, all values from the database are exported. Alternatively, users can specify filters (e.g., keywords, batches, locations) individually, as a vector or as a list.

# export_control ---------------------------------------------------------------
export_control(control = 1)
#> # A tibble: 39,600 x 5
#>    location keyword date        hits control
#>    <chr>    <chr>   <date>     <dbl>   <int>
#>  1 US       gmail   2010-01-01    22       1
#>  ...
#> 10 US       gmail   2010-10-01    27       1
#> # ... with 39,590 more rows

# export_score -----------------------------------------------------------------
export_score(object = 1, control = 1)
#> # A tibble: 23,760 x 6
#>    location keyword   date       score    control  object
#>    <chr>    <chr>     <date>     <dbl>    <int>    <int>
#>  1 US       coca cola 2010-01-01 0.00362  1        1
#>  ...
#> 10 US       coca cola 2010-10-01 0.00347  1        1
#> # ... with 23,750 more rows

# export_doi and purrr interaction ---------------------------------------------
purrr::map_dfr(c("coca cola", "microsoft"), export_doi, control = 1)
#> # A tibble: 240 x 8
#>    keyword   date       gini   hhi entropy control object locations
#>    <chr>     <date>     <dbl> <dbl>   <dbl>   <int>  <int> <chr>
#>  1 coca cola 2010-01-01 0.397 0.874  -0.938       1     1 countries
#>  ...
#> 10 coca cola 2010-10-01 0.574 0.968  -0.303       1     1 countries
#> # ... with 230 more rows

The export functions from globaltrends also allow direct interaction with dplyr or other packages for further analysis.

# export and dplyr interaction -------------------------------------------------
library(dplyr)
export_doi(object = 1, control = 1) %>%
  filter(lubridate::year(date) == 2019) %>%
  summarise(gini = mean(gini), .by = keyword)
#> # A tibble: 3 x 2
#>   keyword    gini
#>   <chr>     <dbl>
#> 1 coca cola 0.615
#> 2 facebook  0.707
#> 3 microsoft 0.682

Additional options

The globaltrends package offers several options that allow adjustments for default computations. Users can use other measures besides the inverted Gini coefficient, or change the set of locations.

Alternative dispersion measures

The globaltrends package computes degree of internationalization based on the across-location distribution of search scores. By default, the package uses an inverted Gini coefficient. In addition, the package provides the inverted Herfindahl index and Entropy as robustness checks. In general, outcomes for all three dispersion measures are similar.

# change locations -------------------------------------------------------------
download_control(control = 1, locations = us_states)
download_object(object = 1:2, locations = us_states)
compute_score(control = 1, object = 2, locations = us_states)
compute_doi(control = 1, object = 1:2, locations = "us_states")

add_locations(c("AT", "CH", "DE"), type = "dach")
#> Successfully created new location set dach (AT, CH, DE).
data <- export_score(keyword = "coca cola", locations = dach)
dplyr::count(data, location)
#> # A tibble: 3 x 2
#>   location     n
#>   <chr>    <int>
#> 1 AT         127
#> 2 CH         127
#> 3 DE         127

Releveling of search volumes

Google Trends does not provide raw search queries for downloads. Instead, Google Trends expresses the number of search queries as search volumes relative to the total number of search queries and then normalizes this data. To use Google Trends data, we first have to bring all search volumes to the same level.
For object keyword \(ko\), included in object batch \(bo\), Google Trends observes \(SQ_{ko,bo,l,t}\) search queries for location \(l\) at time \(t\). The number of raw search queries is transformed to search volumes \(SV_{ko,bo,l,t}\) by division through the total number of search queries for the given location-time pair \(l,t\):

\[\begin{equation} SV_{ko,bo,l,t}=\frac{SQ_{ko,bo,l,t}}{\sum SQ_{l,t}}. \tag{1} \end{equation}\]

Next, Google Trends divides search volumes \(SV_{ko,bo,l,t}\) by the maximum search value within object batch \(bo\) at location \(l\) to normalize search volumes to \(\tilde{SV}_{ko,bo,l,t}\):

\[\begin{equation} \tilde{SV}_{ko,bo,l,t}=\frac{SV_{ko,bo,l,t}}{max(SV_{bo,l})*100}. \tag{2} \end{equation}\]

Since this normalization step is contingent on the maximum search volume within object batch \(bo\), normalized search volumes \(\tilde{SV}\) depend on the other keywords included in the object batch, the choice of location, and time span \(T\) (\(t \in T\)) for which data is obtained. To prepare normalized search volumes \(\tilde{SV}\) for further usage, the globaltrends packages follows Castelnuovo and Tran (2017, pp. A1-A2) to relevel \(\tilde{SV}\) through mapping to a benchmark. To this end, we map all \(\tilde{SV}\) values in object batch \(bo\) to the same level as \(\tilde{SV}\) values in control batch \(bc\). The function download_object automatically adds a control keyword \(kc\) to all object batches \(bo\). In functions compute_score and compute_voi, \(\tilde{SV}_{kc,bc,l,t}\) of control keyword \(kc\) in control batch \(bc\) is divided by \(\tilde{SV}_{ko,bc,l,t}\) in object batch \(bo\). By multiplying the result of this division with normalized search volumes \(\tilde{SV}_{ko,bo,l,t}\), we get releveled search volumes \(\tilde{SV}_{ko,bc,l,t}\) for object keyword \(ko\), at location \(l\), at time \(t\):

\[\begin{equation} \tilde{SV}_{ko,bc,l,t}=\tilde{SV}_{ko,bo,t,l}*\frac{\tilde{SV}_{kc,bc,l,t}}{\tilde{SV}_{kc,bo,l,t}}. \tag{3} \end{equation}\]

After the releveling, search volumes from all object batches use control batch \(bc\) as basis for normalization.

Computing search scores

The outcome of the releveling is not a de-normalization but that search volumes are releveled to control batch \(bc\). This means that \(\tilde{SV}_{ko,bc,l,t}\) may still be distorted by \(max(SV_{bo,l})\). To overcome such distortion, functions compute_score and compute_voi divide \(\tilde{SV}_{ko,bc,l,t}\) by search volumes for a set of control keywords \(KC\). Since gmail, maps, translate, wikipedia, and youtube allow an approximation of “standard” search volumes on Google, we propose them as control keywords for global trend analysis. These keywords approximate the baseline search traffic on Google. For specific research settings, we suggest adapting control keywords to the respective setting and testing them on the Google Trends portal beforehand. To compute search score \(SC_{ko,l,t}\), we divide search volumes for object keywords by the sum of search volumes for control keywords \(kc \in KC\):

\[\begin{equation} SC_{ko,l,t}=\frac{\tilde{SV}_{ko,bc,l,t}}{\sum_{kc \in KC} \tilde{SV}_{kc,bc,l,t}}. \tag{4} \end{equation}\]

Using equation (3) for normalization from above, we can rewrite the equation (4) for \(SC\) as follows:

\[\begin{equation} SC_{ko,l,t}=\frac{\tilde{SV}_{ko,bo,t,l}*\frac{\tilde{SV}_{kc,bc,l,t}}{\tilde{SV}_{kc,bo,l,t}}}{\sum_{kc \in KC} \tilde{SV}_{kc,bc,l,t}} \tag{5} \end{equation}\]

\[\begin{equation} SC_{ko,l,t}=\frac{\frac{SV_{ko,bo,t,l}}{max(SV_{bo,l})*100}*\frac{\frac{SV_{kc,bc,l,t}}{max(SV_{bc,l})*100}}{\frac{SV_{kc,bo,l,t}}{ max(SV_{bo,l})*100}}}{\sum_{kc \in KC} \frac{SV_{kc,bc,l,t}}{ max(SV_{bc,l})*100}} \tag{6} \end{equation}\]

\[\begin{equation} SC_{ko,l,t}=\frac{SV_{ko,bo,t,l}*\frac{SV_{kc,bc,l,t}}{SV_{kc,bo,l,t}}}{\sum_{kc \in KC} SV_{kc,bc,l,t}} \tag{7} \end{equation}\]

\[\begin{equation} SC_{ko,l,t}=\frac{SV_{ko,bc,l,t}}{\sum_{kc \in KC} SV_{kc,bc,l,t}}. \tag{8} \end{equation}\]

Using the equation (1) for \(SV\) from above, we can reformulate \(SC\) as:

\[\begin{equation} SC_{ko,l,t}=\frac{\frac{SQ_{ko,l,t}}{\sum SQ_{l,t}}} {\sum_{kc \in KC} \frac{SQ_{kc,l,t}}{\sum SQ_{l,t}}}. \tag{9} \end{equation}\]

\[\begin{equation} SC_{ko,l,t}=\frac{SQ_{ko,l,t}}{\sum_{kc \in KC} SQ_{kc,l,t}}. \tag{10} \end{equation}\]

Based on these transformations, we can interpret search score \(SC\) as the ratio of search queries \(SQ_{ko,l,t}\) for object keyword \(ko\) divided by the sum of search queries \(SQ_{kc,l,t}\) for control keywords \(ko \in KC\) at location \(l\) for time \(t\). Since \(SC\) is independent from any keyword batch \(bo\) or \(bc\), search scores therefore allow comparison across objects of interest, time, and countries.