1 The ESdata Package

ESdata is an R package that allows access to statistical information from Spain structured as ordered data (tidy data). If you have previously installed the devtools package, you can install the package from Github by doing:

devtools::install_github("jmsallan/ESdata")

Once the package is installed, we can access the data by doing:

library(ESdata)

The package allows access to a set of data structured in data frames, with the only dependency on having a version of R equal or higher than 3.5.0. A good alternative to explore this data is to use the tidyverse packages:

library(tidyverse)

In its current version, the package has information related to:

Employment: Active Population Survey, Encuesta de Población Activa (EPA).
Inflation: Consumer Price Index, Índice de precios al consumo (IPC).
Growth: Gross Domestic Product (GDP) at market prices.

In this document we will show the information available on the Gross Domestic Product (GDP).

2 The Gross Domestic Product

The gross domestic product (GDP) is the monetary value of the final goods and services produced by an economy in a period of time. GDP measures the increase in wealth or wealth value of an economy in an aggregate manner. Its measurement allows to evaluate the growth of an economy, how an economy obtains and spends its income, and how it distributes it among its members from a macroeconomic point of view.

In contrast to other aggregates that measure the output of an economy, GDP is:

Interior: Goods produced in a given territory are computed, regardless of whether the people who produced them belong to the territory or not.
Gross: evaluates the gross value of capital goods produced, without subtracting the value of capital goods amortizations during the period.

GDP is the preferred measure of national income evaluation, since it includes the goods available to the inhabitants of the territory (interior), without having to wait to take into account the depreciation of capital goods (gross).

Usually GDP is measured at market prices. This means that the price of the goods includes price increases due to taxes on goods (e.g. Value Added Tax) and decreases due to subsidies.

GDP, like the rest of national income aggregates, can be calculated in three ways:

Through the aggregate demand, also called the spending approach. GDP is obtained through the demand for goods and services from the different economic agents: consumers, companies, the State and the foreign sector.
Through the added offer. In this approach, GDP is the sum of gross added values. The value added of a production stage is equal to the value of its production, minus the value of the inputs used by that stage.
Through income or remuneration of factors. The value added at each stage of production is distributed among the different agents in the supply of productive factors: land, labor and capital.

The three approaches give the same value of GDP, and their analysis makes it possible to obtain additional information on the generation and distribution of income in a country.

The National Institute of Statistics (INE) provides quarterly GDP values at market prices, as well as aggregates for their calculation from the three approaches. The GDP data incorporated into ESdata have been obtained from the tables at https://www.ine.es/dynt3/inebase/es/index.htm?padre=5614&capsel=5615. The standardized methodological report can be found at https://www.ine.es/dynt3/metadatos/es/RespuestaDatos.html?oe=30024.

2.1 Approach Based on Aggregate Demand (Spending)

The following table shows the aggregates reported by the INE for the calculation of GDP through aggregate demand. This approach allows us to assess how it evolves:

the consumption of families,
investment by companies, and public spending on capital goods,
the net impact of the foreign sector, calculated as the difference between exports and imports of goods and services.

Elementos de la demanda agregada
agr_label	agregado	nivel
pib	Producto interior bruto a precios de mercado	1
dn	Demanda nacional	1
xm	Demanda externa	1
C	Gasto en consumo final	2
C_h_isflsh	Gasto en consumo final de los hogares y las ISFLSH	2
C_h	Gasto en consumo final de los hogares	2
C_h_fi	Gasto en consumo final interior de los hogares	2
C_isflsh	Gasto en consumo final de las ISFLSH	2
C_AAPP	Gasto en consumo final de las AAPP	2
C_fie	Consumo final individual efectivo	2
FBC	Formación bruta de capital	2
X_bs	Exportaciones de bienes y servicios	2
M_bs	Importaciones de bienes y servicios	2
C_fi_bd	Gasto en consumo final interior de los hogares. Bienes duraderos	3
C_fi_obs	Gasto en consumo final interior de los hogares. Otros bienes y servicios	3
C_fi_AAPP	Gasto en consumo final individual de las AAPP	3
C_fc_AAPP	Gasto en consumo final colectivo de las AAPP	3
FBCF	Formación bruta de capital fijo (FBCF)	3
VE_OV	Variación de existencias y adquisiciones menos cesiones de objetos valiosos	3
VE	Variación de existencias	3
OV	Adquisiciones menos cesiones de objetos valiosos	3
X_b	Exportaciones de bienes	3
X_s	Exportaciones de servicios	3
M_b	Importaciones de bienes	3
M_s	Importaciones de servicios	3
FBCF_afm	FBCF. Activos fijos materiales	4
FBCF_afi	FBCF. Activos fijos inmateriales. Productos de la propiedad intelectual	4
X_rm	Exportaciones de servicios. Gasto de los hogares no residentes en el territorio económico	4
M_rm	Importaciones de servicios. Gasto de los hogares residentes en el resto del mundo	4
FBCF_afm_con	FBCF. Activos fijos materiales. Construcción	5
FBCF_afm_maq	FBCF. Activos fijos materiales. Maquinaria, bienes de equipo y sistemas de armamento	5
FBCF_afm_rbc	FBCF. Activos fijos materiales.Recursos biológicos cultivados	5
FBCF_afm_con_viv	FBCF. Activos fijos materiales. Construcción. Viviendas	6
FBCF_afm_con_edif	FBCF. Activos fijos materiales. Construcción. Otros edificios y construcciones	6
FBCF_afm_maq_transp	FBCF. Activos fijos materiales. Maquinaria, bienes de equipo y sistemas de armamento. Material de transporte	6
FBCF_afm_maq_arm	FBCF. Activos fijos materiales. Maquinaria, bienes de equipo y sistemas de armamento. Otros	6

2.2 Approach Vased on the Added Offer (Added Value)

The following table shows the aggregates reported by the INE for the calculation of GDP through aggregate supply. This approach allows us to assess how various economic activities contribute to total output.

Elementos de la oferta agregada
agr_label	agregado	nivel
pib	Producto interior bruto a precios de mercado	1
vab_A	VABpb Agricultura, ganadería, silvicultura y pesca (A, CNAE 2009)	1
vab_BE	VABpb Industria (B-E, CNAE 2009)	1
vab_F	VABpb Construcción (F, CNAE 2009)	1
vab_GT	VABpb Servicios (G-T, CNAE 2009)	1
imp_sub	Impuestos menos subvenciones sobre los productos	1
vab_C	VABpb Industria. Industria manufacturera (C, CNAE 2009)	2
vab_GI	VABpb Servicios. Comercio, transporte y hostelería (G-I, CNAE 2009)	2
vab_J	VABpb Servicios. Información y comunicaciones (J, CNAE 2009)	2
vab_K	VABpb Servicios. Actividades financieras y de seguros (K, CNAE 2009)	2
vab_L	VABpb Servicios. Actividades inmobiliarias (L, CNAE 2009)	2
vab_MN	VABpb Servicios. Actividades profesionales, científicas y técnicas y otras (M-N, CNAE 2009)	2
vab_OQ	VABpb Servicios. Administración pública, educación y sanidad (O-Q, CNAE 2009)	2
vab_RT	VABpb Servicios. Actividades artísticas, recreativas y otros servicios (R-T, CNAE 2009)	2
imp	Impuestos sobre los productos	2
sub	Subvenciones sobre los productos	2

2.3 Income-Based Approach

The following table shows the aggregates reported by the INE for the calculation of GDP through income. This approach makes it possible to assess how income is distributed between labor (salaries) and capital (profits and rents).

Elementos de ingresos
agr_label	agregado	nivel
pib	Producto interior bruto a precios de mercado	1
rem	Remuneración de los asalariados	1
rem_A	Remuneración de los asalariados. Agricultura, ganadería, silvicultura y pesca (A, CNAE 2009)	1
rem_BE	Remuneración de los asalariados. Industria (B-E, CNAE 2009)	1
rem_C	Remuneración de los asalariados. Industria manufacturera (C, CNAE 2009)	1
rem_F	Remuneración de los asalariados. Construcción (F, CNAE 2009)	1
rem_GT	Remuneración de los asalariados. Servicios (G-T, CNAE 2009)	1
rem_GI	Remuneración de los asalariados. Comercio, transporte y hostelería (G-I, CNAE 2009)	1
rem_J	Remuneración de los asalariados. Información y comunicaciones (J, CNAE 2009)	1
rem_K	Remuneración de los asalariados. Actividades financieras y de seguros (K, CNAE 2009)	1
rem_L	Remuneración de los asalariados. Actividades inmobiliarias (L, CNAE 2009)	1
rem_MN	Remuneración de los asalariados. Actividades profesionales, científicas y técnicas y otras (M-N, CNAE 2009)	1
rem_OQ	Remuneración de los asalariados. Administración pública, educación y sanidad (O-Q, CNAE 2009)	1
rem_RT	Remuneración de los asalariados. Actividades artísticas, recreativas y otros servicios (R-T, CNAE 2009)	1
ben	Excedente de explotación bruto y Renta mixta bruta	1
imp_sub	Impuestos menos subvenciones sobre la producción y las importaciones	1
sal	Sueldos y salarios	2
css	Cotizaciones sociales a cargo de los empleadores	2
sal_A	Sueldos y salarios. Agricultura, ganadería, silvicultura y pesca (A, CNAE 2009)	2
css_A	Cotizaciones sociales a cargo de los empleadores. Agricultura, ganadería, silvicultura y pesca (A, CNAE 2009)	2
sal_BE	Sueldos y salarios. Industria (B-E, CNAE 2009)	2
css_BE	Cotizaciones sociales a cargo de los empleadores. Industria (B-E, CNAE 2009)	2
sal_C	Sueldos y salarios. Industria manufacturera (C, CNAE 2009)	2
css_C	Cotizaciones sociales a cargo de los empleadores. Industria manufacturera (C, CNAE 2009)	2
sal_F	Sueldos y salarios. Construcción (F, CNAE 2009)	2
css_F	Cotizaciones sociales a cargo de los empleadores. Construcción (F, CNAE 2009)	2
sal_GT	Sueldos y salarios. Servicios (G-T, CNAE 2009)	2
css_GT	Cotizaciones sociales a cargo de los empleadores. Servicios (G-T, CNAE 2009)	2
sal_GI	Sueldos y salarios. Comercio, transporte y hostelería (G-I, CNAE 2009)	2
css_GI	Cotizaciones sociales a cargo de los empleadores. Comercio, transporte y hostelería (G-I, CNAE 2009)	2
sal_J	Sueldos y salarios. Información y comunicaciones (J, CNAE 2009)	2
css_J	Cotizaciones sociales a cargo de los empleadores. Información y comunicaciones (J, CNAE 2009)	2
sal_K	Sueldos y salarios. Actividades financieras y de seguros (K, CNAE 2009)	2
css_K	Cotizaciones sociales a cargo de los empleadores. Actividades financieras y de seguros (K, CNAE 2009)	2
sal_L	Sueldos y salarios. Actividades inmobiliarias (L, CNAE 2009)	2
css_L	Cotizaciones sociales a cargo de los empleadores. Actividades inmobiliarias (L, CNAE 2009)	2
sal_MN	Sueldos y salarios. Actividades profesionales, científicas y técnicas y otras (M-N, CNAE 2009)	2
css_MN	Cotizaciones sociales a cargo de los empleadores. Actividades profesionales, científicas y técnicas y otras (M-N, CNAE 2009)	2
sal_OQ	Sueldos y salarios. Administración pública, educación y sanidad (O-Q, CNAE 2009)	2
css_OQ	Cotizaciones sociales a cargo de los empleadores. Administración pública, educación y sanidad (O-Q, CNAE 2009)	2
sal_RT	Sueldos y salarios. Actividades artísticas, recreativas y otros servicios (R-T, CNAE 2009)	2
css_RT	Cotizaciones sociales a cargo de los empleadores. Actividades artísticas, recreativas y otros servicios (R-T, CNAE 2009)	2
imp	Impuestos sobre la producción y las importaciones	2
sub	Subvenciones sobre la producción y las importaciones	2

3 GDP in ESdata

The information on GDP in ESdata is presented in separate tables for each of the GDP approaches. All three approaches have a table for the aggregates that I have presented in the previous section and another for the data:

Datos de PIB en `ESdata`
enfoque	agregados	datos
demanda	`pib_demanda_agregados`	`pib_pm_demanda`
oferta	`pib_oferta_agregados`	`pib_pm_oferta`
rentas	`pib_rentas_agregados`	`pib_pm_rentas`

The variables of each of the data tables are:

Variables en cada una de las tablas
datos	periodo	agregado	ajuste	tipo	dato	valor
`pib_pm_demanda`	✓	✓	✓	✓	✓	✓
`pib_pm_oferta`	✓	✓	✓	✓	✓	✓
`pib_pm_rentas`	✓	✓	✓		✓	✓

El contenido de las variables es:

periodo: el último día del trimestre del dato correspondiente a la fila, en formato de fecha Date.
el agregado correspondiente a cada una de las filas.
el ajuste de cada fila: ajustado para datos ajustados a estacionalidad y calendario, o no_ajustado.
el tipo de dato: o valor o `indice’.
el dato, que puede ser base, variación trimestral var_trim, variación anual var_anual, aportación trimestral ap_trim o aportación anual ap_anual.
el valor, en índice o en millones de euros. Para el enfoque de rentas no están disponibles datos en índice.

The content of the variables is:

periodo: the last day of the quarter of the data corresponding to the row, in date format Date.
agregado: the aggregate corresponding to each of the rows.
ajuste: the adjustment of each row: ajustado means adjusted for seasonality, and no_ajustado not adjusted.
tipo: the data type: either valor (actual value) or indice (indexed value).
dato: the data, which can be base value, quarterly variation var_trim, annual variation var_anual, quarterly contribution ap_trim or annual contribution ap_anual.
valor: the actual value, in index or in millions of euros. For the income approach, index data are not available.

4 Examples of Use

A large number of visualizations and tables can be obtained with the data incorporated into the package. In each case it is necessary to do the previous filtering that allows us to obtain the required data. In this section we will present some examples of visualizations of GDP data.

4.1 Adjusted and Unadjusted GDP

In this graph we see the difference between unadjusted and adjusted GDP. We can see that to evaluate the evolution of GDP it is convenient to see the adjusted.

pib_pm_demanda %>% 
  filter(agregado == "pib" & tipo == "indice" & dato == "base") %>%
  ggplot(aes(periodo, valor, group=ajuste)) + 
  geom_line(aes(linetype=ajuste)) +
  scale_linetype_manual(values = c("solid", "dotted"), labels = c("ajustado", "no ajustado")) +
  labs(title="Índice de PIB ajustado y no ajustado", x="", y="índice PIB") + 
  theme_bw() + 
  theme(legend.position = "bottom")

The graphic also shows how to use ggplot to differentiate series by line type.

4.2 The Impact of COVID-19 on GDP

In this chart, we show the impact of COVID-19 on economic growth using annual and quarterly variations.

library(lubridate)
pib_pm_demanda %>% 
  filter(agregado == "pib" & ajuste == "ajustado" & tipo == "valor" & dato %in% c("var_anual", "var_trim")) %>%
  filter(year(periodo) > 2017) %>%
  mutate(valor = valor/100) %>%
  ggplot(aes(periodo, valor, col=dato)) +
  geom_point() +
  geom_line() +
  labs(title = "Impacto del COVID-19 en el IPC", x="", y="variación") +
  scale_color_brewer(name = "variación", labels = c("anual", "trimestral"), palette = "PuOr") +
  scale_y_continuous(labels = scales::percent) +
  theme_dark() +
  theme(legend.position = "bottom")

The graph shows the following resources:

Using the year function of lubridate to select data from 2017 onward.
Overlapping points and lines on the same graph.
The y-axis in percent with scale_y_continuous.
Selection of colors with a brewer palette.
The use of theme_dark for screen presentations.

4.3 Distribution ofIncome

With pib_pm_rentas we can evaluate how the income distribution evolves. To do this we must take into account that GDP is equal to remuneration of labor rem plus benefits ben. Since we are evaluating GDP at market prices pib, we must also add taxes and subsidies imp_sub.

\[ \text{pib} = \text{rem} + \text{ben} + \text{imp_sub} \]

In the income aggregates, we see that remunerations include salaries sal and company contributions to Social Security css.

\[ \text{rem} = \text{sal} + \text{css} \]

We will use the first equation to find the weight of net wages, benefits, and indirect taxes in GDP at market prices:

pib_pm_rentas %>% filter(agregado %in% c("pib", "rem", "ben", "imp_sub") & dato == "base" & ajuste == "ajustado") %>%
  pivot_wider(names_from = "agregado", values_from = "valor") %>%
  mutate(pr_rem = rem/pib, pr_ben = ben/pib, pr_imp = imp_sub/pib) %>%
  select(periodo, pr_rem, pr_ben, pr_imp) %>%
  pivot_longer(cols = -periodo, names_to = "dato", values_to = "valor")

## # A tibble: 339 × 3
##    periodo    dato    valor
##    <date>     <chr>   <dbl>
##  1 2023-01-31 pr_rem 0.458 
##  2 2023-01-31 pr_ben 0.438 
##  3 2023-01-31 pr_imp 0.104 
##  4 2022-12-31 pr_rem 0.463 
##  5 2022-12-31 pr_ben 0.459 
##  6 2022-12-31 pr_imp 0.0785
##  7 2022-09-30 pr_rem 0.471 
##  8 2022-09-30 pr_ben 0.426 
##  9 2022-09-30 pr_imp 0.103 
## 10 2022-06-30 pr_rem 0.468 
## # ℹ 329 more rows

Then we will make a graph with geom_area to see how the variables evolve. The use of mutate is to change the order of the factor levels of the variable value:

pib_pm_rentas %>% 
  filter(agregado %in% c("pib", "rem", "ben", "imp_sub") & dato == "base" & ajuste == "ajustado") %>%
  pivot_wider(names_from = "agregado", values_from = "valor") %>%
  mutate(pr_rem = rem/pib, pr_ben = ben/pib, pr_imp = imp_sub/pib) %>%
  select(periodo, pr_rem, pr_ben, pr_imp) %>%
  pivot_longer(cols = -periodo, names_to = "dato", values_to = "valor") %>%
  mutate(dato = factor(dato, levels = c("pr_ben", "pr_rem", "pr_imp"))) %>%
  ggplot(aes(periodo, valor, fill=dato)) +
  geom_area() +
  scale_fill_brewer(name = "rentas", labels = c("capital", "trabajo", "impuestos - subvenciones")) +
  labs(title = "Reparto del PIB entre capital y trabajo", x="", y="") +
  scale_y_continuous(labels = scales::percent) +
  theme_bw() +
  theme(legend.position = "bottom")

In this case, we have selected the colors using the default palette of scale_fill_brewer.

4.4 Changes in the Weight of Construction in the Economy

The data pib_pm_oferta make it possible to evaluate the weight in the economy of the different sectors of activity. In this graph we see how the weight of the construction sector has evolved. We put the data in context by reporting on the 2008 crocumentisis.

pib_pm_oferta %>% 
  filter(agregado %in% c("pib", "vab_F") & ajuste == "ajustado" & tipo == "valor" & dato == "base") %>%
  pivot_wider(names_from = agregado, values_from = valor) %>% 
  mutate(prop = vab_F/pib) %>%
  ggplot(aes(periodo, prop)) +
  geom_line(size=1.2) + 
  labs(title="Peso de la construcción en la economía", y="construcción vs PIB", x="") +
  geom_vline(xintercept = as.Date("2008-12-31"), linetype="dashed", size=0.5,col ="#FF0000") +
  annotate("text", x=as.Date("2010-06-30"), y=0.12, label="crisis del 2008", hjust="left") +
  annotate("curve", x=as.Date("2010-06-15"), y =0.12, xend=as.Date("2008-12-31"), yend=0.11, curvature=0.2, arrow = arrow(length = unit(2, "mm"))) +
  scale_y_continuous(labels = scales::percent) +
  theme_bw() +
  theme(legend.position = "bottom")

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Here we have again used the pivot_wider and pivot_longer functions of tidyr to calculate construction to GDP ratios.

4.5 Annual Evolution of Construction and GDP

Line graphs are used to evaluate the evolution of a quantity over time. The graph that we have presented shows the drop in the weight of construction after the 2008 crisis. To illustrate the absolute values of a magnitude, it is better to use a bar graph. Here we present the weight of construction in the economy, adding the GDP of the quarters of each year.

pib_pm_oferta %>% 
  filter(agregado %in% c("pib", "vab_F") & ajuste == "ajustado" & tipo == "valor" & dato == "base") %>%
  pivot_wider(names_from = agregado, values_from = valor) %>% 
  mutate(year=year(periodo)) %>%
  group_by(year) %>%
  summarise(pib=sum(pib), constr=sum(vab_F)) %>% 
  mutate(prop = constr/pib) %>%
  ggplot(aes(year, prop)) +
  geom_bar(stat = "Identity", fill="#DC143C") +
  labs(title="Peso de la construcción en la economía", y="construcción vs PIB", x="") + 
  scale_y_continuous(labels = scales::percent) +
  theme_bw() +
  theme(legend.position = "bottom")

We can also compare the weight of construction in GDP as a whole with bar graphs:

library(lubridate)  
pib_pm_oferta %>% 
  filter(agregado %in% c("pib", "vab_F") & ajuste == "ajustado" & tipo == "valor" & dato == "base") %>%
  pivot_wider(names_from = agregado, values_from = valor) %>% 
  mutate(year=year(periodo)) %>%
  group_by(year) %>%
  summarise(pib=sum(pib), constr=sum(vab_F)) %>%
  pivot_longer(-year, values_to = "valor", names_to = "dato") %>%
  ggplot(aes(year, valor, fill=dato)) +
  geom_bar(stat = "Identity", position = "dodge") + 
  labs(title="Evolución del PIB y la construcción", y="millones euros", x="año") +
  scale_fill_manual(name="Agregado", labels=c("VAB construcción", "Total PIB"), values = c("#B22222", "#228B22")) +
  theme_bw() +
  theme(legend.position = "bottom")

5 Improvement Proposals

If you have any proposals or have detected any errors, you can make a pull request at: https://github.com/jmsallan/ESdata

ESdata Package: the Gross Domestic Product

Jose M Sallan

04/05/2023