Chapter 2 Introduction

The first edition of the IGI consisted of three pillars, namely economy, living conditions and equality (Barnat et al., n.d.) and was composed of 21 indicators, including one environmental indicator (carbon dioxide - CO$_2$ emissions under pillar 2). This new, expanded IGI includes more equality metrics addressing gender inequality more broadly, and includes a new separate pillar dedicated to environmental issues. These were highlighted as potential development areas of the original index, and can now be addressed also due to progress with data availability for countries.

The current edition of the IGI is comprised of four pillars and 27 indicators. Indicators were selected as for the SDG Pulse 2022, however updated for new available data series such as when constant prices were rebased (GDP, poverty etc.).

Overview:

Number of countries: 130 economies
Number of pillars: 4 pillars
Number of indicators: 27 indicators
Years coverage:
- Data collected, imputed and forecasted for the period 2000-2024
- Index compiled only for one year - 2022

Pillars:

Pillar 1 – Economic growth
Pillar 2 - Living conditions
Pillar 3 – Equality
Pillar 4 - Environment

Table with pillars and indicators

Note: Names of indicators are taken as from their original sources

Pillar 1	Economic growth
Indicator 1.1	GDP per capita, PPP (constant 2017 international $)
Indicator 1.2	Adjusted net national income per capita (constant 2015 US $)
Indicator 1.3	Labour productivity - GDP per person employed (constant 2017 PPP USD)
Indicator 1.4	Employment to population ratio, 15+, total (%) (modeled ILO estimate)
Indicator 1.5	Electricity consumption/population (kWh per capita)
Indicator 1.6	Exports of goods and services (% of GDP)

Pillar 2	Living conditions
Indicator 2.1	Logistics performance index: Overall (1=low to 5=high)
Indicator 2.2	Fixed Internet broadband subscriptions per 100 people, units
Indicator 2.3	Under-five mortality rate (deaths per 1.000 live births)
Indicator 2.4	People using safely managed drinking water services (% of population)
Indicator 2.5	School enrollment, secondary (% gross)
Indicator 2.6	Coverage of essential health services
Indicator 2.7	Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider

Pillar 3	Equality
Indicator 3.1	Income concentration ratio (Gini index), units
Indicator 3.2	Poverty headcount ratio at 5.50 USD a day (2011 PPP) (% of population)
Indicator 3.3	School enrollment, secondary (gross), gender parity index (GPI)
Indicator 3.4	Ratio of female to male employment rate (modeled ILO estimate)
Indicator 3.5	Ratio of youth to adult employment rate (modeled ILO estimate)
Indicator 3.6	Gender parity in the number of seats held by women and men in national parliaments
Indicator 3.7	Ratio of female to male labour force participation rate (%) (ILO modeled estimate)
Indicator 3.8	Ratio of female age of first marriage to male age of first marriage
Indicator 3.9	Ratio of the share of wage and salaried workers in women’s employment to men’s employment
Indicator 3.10	Share of women’s service employment to total employment, raised to the power of the inverse of the Palma ratio

Pillar 4	Environment
Indicator 4.1	CO$_2$ emissions (kg per PPP USD of GDP)
Indicator 4.2	Energy intensity level of primary energy (MJ/$2017 PPP GDP)
Indicator 4.3	Effeciency of water use (water productivity)
Indicator 4.4	Terrestrial protected areas (% of total land area)

2.1 Data description

All data information is stored as an R object which includes original downloaded data, interpolated, extrapolated and imputed values, final index with its components, and all relevant metadata.

IGI <- readRDS("IGI.rds")
class(IGI)

## [1] "IGI"

The IGI object returned as an output is a list containing the following elements:

IGI: the scores for each component or category and the IGI values for all countries and all years, in this case only 2022 is available
date_created: a time stamp
intermediate_data: a list containing the data at the various steps of the data pipeline
- read: original data downloaded
- interpolated: interpolated data
- forecast: nowcasts and forecasts
- Imputed: imputed data
- final: imputed and transformed data
paths: a list containing the paths set by the user
controls: a list of various controls set by the user
indicators_metadata: a tibble of the input file indicators_metadata.csv
countries_metadata: a tibble of the input file countries_metadata.csv
countries_names: a vector of the names of the countries that are considered
indicators_initial: a vector of names of the initial indicators
indicators_initial_main: a vector of names of the main initial indicators
indicators_initial_auxiliary: a vector of names of the auxiliary initial indicators
indicators_initial_categories: a named list with names corresponding to the various categories of the initial indicators and whose elements are vectors of the names of the indicators for these categories.
indicators_final: a vector of names of the final indicators (those used in the PCA)
indicators_categories: a named list with names corresponding to the various categories of the final indicators and whose elements are vectors of the names of the indicators for these categories.
NA_diagnostics: a list of 2 summary tibbles containing the proportion of missing data (one where the result is summarized by country, and the other where the result is summarized by years)
Miss: a tibble with boolean values for the indicators (TRUE: data is missing, FALSE: data is not missing)
loadings: a list of selected principal components including loadings for each category
groups: the scores for each component or category and the IGI values for groups (this would probably change and the groups will be calculated directly in the system)

2.1.1 Collected data

### load original data
dat_original <- IGI$intermediate_data$read
str(dat_original)

## tibble [4,850 × 37] (S3: tbl_df/tbl/data.frame)
##  $ ISO3                : chr [1:4850] "ABW" "ABW" "ABW" "ABW" ...
##  $ Year                : num [1:4850] 2000 2001 2002 2003 2004 ...
##  $ NY.GDP.PCAP.PP.KD   : num [1:4850] 39934 40875 40008 40051 42586 ...
##  $ NY.ADJ.NNTY.PC.KD   : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SL.GDP.PCAP.EM.KD   : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SL.EMP.TOTL.SP.ZS   : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ ELEPOP              : num [1:4850] 7871 7459 7582 7600 7829 ...
##  $ NE.EXP.GNFS.ZS      : num [1:4850] NA NA NA NA NA ...
##  $ LP.LPI.OVRL.XQ      : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ IT_NET_BBND         : num [1:4850] NA NA NA 1.51 7.44 ...
##  $ SH_DYN_MORT         : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SH_H2O_SAFE         : num [1:4850] 76.6 77.1 77.5 77.9 78.4 ...
##  $ SE.SEC.ENRR         : num [1:4850] 93.8 95.3 100.3 99.4 97.2 ...
##  $ SH_ACS_UNHC         : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ FB_BNK_ACCSS        : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SI.POV.GINI         : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SI.POV.LMIC         : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SE.ENR.SECO.FM.ZS   : num [1:4850] 1.03 1.05 1.05 1.05 1 ...
##  $ SL.EMP.TOTL.SP.FE.ZS: num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SL.EMP.TOTL.SP.MA.ZS: num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ AGE_YTHADULT_Y15-24 : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ AGE_YTHADULT_YGE25  : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SG_GEN_PARL         : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SL.TLF.CACT.FM.ZS   : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ MAR_AGE_FEM         : num [1:4850] 26.8 NA NA NA NA NA NA NA NA NA ...
##  $ MAR_AGE_MAL         : num [1:4850] 29.9 NA NA NA NA NA NA NA NA NA ...
##  $ SL.EMP.WORK.FE.ZS   : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SL.EMP.WORK.MA.ZS   : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ PALMA               : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SL.SRV.EMPL.FE.ZS   : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ SL.TLF.TOTL.FE.ZS   : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ EN_ATM_CO2GDP       : num [1:4850] 0.0755 0.0732 0.0808 0.0921 0.0876 ...
##  $ EG_EGY_PRIM         : num [1:4850] 15.4 14.9 15.2 15.5 14.6 ...
##  $ ER_H2O_WUEYST       : num [1:4850] NA NA NA NA NA NA NA NA NA NA ...
##  $ ER_PTD_TERR         : num [1:4850] 37.1 37.1 37.1 37.1 37.1 ...
##  $ POP                 : num [1:4850] 88.8 90.3 91.4 92.3 93.2 ...
##  $ GDP_USD2015         : num [1:4850] 2.93e+09 2.88e+09 2.88e+09 2.90e+09 3.12e+09 ...

2.1.2 Imputed data

### load imputed data
dat_imputed <- IGI$intermediate_data$imputed

### load transformed data
dat_final <- IGI$intermediate_data$final

### change labels of variables to IGI codes
new_label <- IGI$indicators_metadata %>%
  select("Indicator_code", Series_code1="final indicator") %>%
  distinct()
dat_final_label <- dat_final %>%
  pivot_longer(-c(ISO3, Year), names_to = "Series_code", values_to = "Value") %>%
  inner_join(new_label, by=c("Series_code"="Series_code1")) %>%
  select(-Series_code) %>%
  rename(Series_code=Indicator_code) %>%
  pivot_wider(names_from = Series_code, values_from = Value) %>%
  # remove POP and GDP
  select(-c("IGI_0-1","IGI_0-2"))

2.1.3 Country selection for IGI computation

Data from various sources are collected and merged into one data set. The total number of economies for which data are collected is 194. However, data availability varies by economy.

Although values are imputed to obtain a full panel data set, for some countries they are pure estimates not based on any official data. For this reason, only countries with at least one data point available for each series are considered for the final IGI compilation.

One exception is made for Singapore which was included in previous editions if the index. No poverty data are available for Singapore, estimates are considered to be good enough to include the country for IGI.

Further research is needed on how to fill data gaps and increase the index coverage, especially for those series without any available data points.

References

Barnat, Nour, Steve MacFeely, Fernando Cantu, Anu Peltola, Anastasia Khazhgerieva, Andrey Panteleev, and Nikolay Ryabtsev. n.d. “Compiling an Inclusive Growth Index: Methodological Challenges, Considerations and Conclusions.” Statistical Journal of the IAOS, no. Preprint: 1–21.

Pillar 4	Environment
Indicator 4.1	CO\(_2\) emissions (kg per PPP USD of GDP)
Indicator 4.2	Energy intensity level of primary energy (MJ/$2017 PPP GDP)
Indicator 4.3	Effeciency of water use (water productivity)
Indicator 4.4	Terrestrial protected areas (% of total land area)