Joint Research Centre statistical audit of the 2018 Global Attractiveness Index.
Date: 2018-11-20
Institutions: Joint Research Centre
Subjects: Joint Research Centre, business policy, competitiveness, economic development, innovation, job creation, know-how, research report, social development, statistical method

Centre 
Research 
Joint 
Statistical  Audit  of  the  2018 
Global Attractiveness Index  

Saisana, Michaela 

Caperna, Giulio 

Alvarez-Diaz, Marcos 

Tacao Moura, Carlos 

Norlen, Hedvig 

Montalto, Valentina 

2018 

EUR 29373 EN 

 
  
 
 
 
 
 
 
 
 
 
 
 
This  publication  is  a  Technical  report by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science 
and  knowledge  service.  It  aims  to  provide  evidence-based  scientific  support  to  the  European  policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the  European  Commission  nor  any  person  acting  on  behalf  of  the  Commission  is  responsible  for  the  use  that 
might be made of this publication. 

Contact information  
Competence Centre on Composite Indicators and Scoreboards  
jrc-coin@ec.europa.eu 

JRC Science Hub 
https://ec.europa.eu/jrc/coin  

JRC113250  

EUR 29373 EN 

PDF 

ISBN 978-92-79-94186-3 

ISSN 1831-9424 

doi:10.2760/331154 

Luxembourg: Publications Office of the European Union, 2018 

© European Union, 2018 

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All content © European Union, 2018 

How to cite this report: Saisana, M., Caperna, G., Alvarez Diaz, M., Tacao Moura, C., Norlen, H. and Montalto, 
V., Joint Research Centre Statistical Audit of the 2018 Global Attractiveness Index , EUR 29373 EN, Publications 
Office  of  the  European  Union,  Luxembourg,  2018,  ISBN  978-92-79-94186-3  (online),  doi:10.2760/331154 
(online), JRC113250. 

All images © European Union 2018 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents  

Executive Summary ............................................................................................... 4 

1 

Introduction .................................................................................................. 5 

Step 1: Relevance ......................................................................................... 6 

Step 2: Data checks ....................................................................................... 6 

Step 3: Statistical Coherence .......................................................................... 7 

Step 4: Qualitative Review .............................................................................11 

2 

Impact of modelling assumptions in the GAI ....................................................13 

Added value of GAI – From four pillars to one single number of national 

3 
attractiveness ......................................................................................................19 

4 

Conclusions..................................................................................................20 

References and related reading ..............................................................................22 

List of figures .......................................................................................................23 

List of tables ........................................................................................................24 

 
 
 
 
Executive Summary 

Attractiveness is a prerequisite and a symptom for competitiveness and it is valued both 

because  it  implies  a  nation’s  ability  to  attract  talent,  capital  and  assets  (know-how, 

technologies, and other), and because more in general it stimulates the whole process of 

economic and social development.   The  European  House  - Ambrosetti has developed an 

international  monitoring  framework  –  the  Global  Attractiveness  Index  (GAI)  –  that 

measures  a  country’s  attractiveness  as  determining  element  of  its  ability  to  be 

competitive and to grow. The  GAI builds on four attributes of attractiveness:  Openness, 

Innovation, Efficiency, and Endowment. These pillars are used to organise and aggregate 

21 Key  Performance  Indicators (KPIs) into a single summary measure for 144  countries 

that  altogether  cover  approximately  93%  of  the  world’s  population  and  99%  of  Gross 

Domestic Product (in US$) worldwide. This framework inevitably entails both conceptual 

and  practical  challenges1.  The  statistical  audit  discussed  in  this  note  was  conducted  by 

the  European  Commission’s  Joint  Research  Centre,  and  it  aims  at  maximising  the 

reliability  and  transparency  of  the  Global  Attractiveness  Index2.  It  should  enable  policy 

analysts and researchers alike to draw more relevant, meaningful and useful conclusions 

on  good  practices  and  challenges  that  countries  face  in  today’s  competitive  game  to 

business and job creation. 

1  The  Positioning  Index  of  the  Global  Attractiveness  Index  (GAI)  measures  the  current  level  of  a  country’s 
attractiveness  in  relation  to  other  countries.  Two  more  indices  complement  the  conceptual  framework  of  a 
country’s attractiveness. A Dynamism Index that measures the short-to-medium term change in the preceding 
three-year period of the attractiveness level of the 21 KPIs. A Sustainability Index that attempts to show how 
the position achieved in the Positioning Index can be maintained over time. This latter builds on two pillars that 
are  antithetic  and  complementary:  resilience  and  vulnerability.  The  Dynamism  index  and  Sustainability  Index 
results  are  only  communicated  in  qualitative  terms  (traffic  light  approach).  The  present  JRC  audit  focuses  on 
the  GAI  index  (the  Positioning  Index).  Upon  request  of  the  European  House  Ambrosetti,  the  Dynamism  and 
Sustainability indices will be discussed in next year’s audit.  
2 The JRC statistical audit is based on the recommendations of the OECD & JRC (2008) Handbook on Composite 
Indicators,  and  on  more  recent  research  from  the  JRC.  In  principle,  JRC  audits  of  composite  indicators  and 
scoreboards  are  conducted  upon  request  of  their  developers,  see  https://ec.europa.eu/jrc/en/coin  and 
https://composite-indicators.jrc.ec.europa.eu/ 

4 

 
 
                                           
1  Introduction  

Earlier versions of the Global Attractiveness Index were assessed by the JRC in May-June 

2016,  in  May-June  2017  and  June-July  2018.  Fine-tuning  suggestions  made  by  the  JRC 

were taken into account by the European House – Ambrosetti in the final computation of 

the  rankings,  with  a  view  to  setting  the  foundation  for  a  balanced  indicator  framework. 

The entire process followed four steps (Figure 1). 

Figure 1. Conceptual and statistical coherence in the GAI 2018 Framework  

Step 1. Relevance 

Compatibility with existing literature on national attractiveness and competitiveness 

Scaling factors per indicator to represent a fair picture of national differences (e.g., population, GDP, 
world total) 

Step 2. Data checks 
Check for data recency (82% of available data refer to 2016-2017) 

Availability requirements per country: coverage > 66% at the index level (GAI) 

Check for eventual reporting errors in the data (interquartile range) 

Outlier treatment (skewness and kurtosis) 

Step 3. Statistical coherence 

Assessment of grouping key performance indicators (KPIs) to pillars  

Assessment of the importance of KPIs at higher aggregate levels 

Assessment of arithmetic average to aggregate information across KPIs and across pillars 

Step 4. Qualitative review 

Internal qualitative review (Advisory Panel for the European House - Ambrosetti) 

External qualitative review (JRC, International experts) 

Source: European Commission, Joint Research Centre, 2018. 

5 

 
 
 
 
 
 
Step 1: Relevance 

Almost  200  variables  were  initially  considered  by  the  European  House  –  Ambrosetti  for 

their  relevance  to  the  four  attractiveness  attributes  –  Openness,  Innovation,  Efficiency, 

Endowment  -  on  the  basis  of  a  literature  review  and  expert  consultation  in  2016. 

Openness  captures  a  country’s  efforts  to  promoting  the  circulation  of  economic,  human 

and  business  resources  both  internally  and  externally.  Innovation  synthesizes  how  a 

country’s ecosystem (research network, public institutions, businesses, financial system) 

promote  scientific  and  technological  progress.  Efficiency  monitors  the  ability  of 

organisational  and  function-related  structures  to  guarantee  proper  functioning  (and 

quality)  of  capital  markets,  the  labor  market,  services  and  government.  Finally, 

Endowment captures high-quality assets that are capable of being sources of competitive 

advantage.  

After  screening  for  data  coverage  and  subsequently  testing  for  statistical  coherence, 

twenty-one  key performance  indicators (KPIs) were selected. To  represent a fair picture 

of country differences, two types of denominators for the indicators were used. External 

factors: for those KPIs that express magnitudes related to the attractiveness of a country 

in relation to others, raw data values were divided by the world total (e.g., KPI 7 Exports 

of  high-technology  goods,  compared  with  world  total)3.  Internal  factors:  for  those  KPIs 

that capture aspects of internal attractiveness, raw data values were divided by relevant 

national  factors  (e.g.,  KPI  4  Foreign  university  students,  compared  with  youth 

population).  

Step 2: Data checks 

The  most  recently  released  data  within  the  period  2013–17  were  used  for  each  country 

(total 144 countries): 82% of available data for the GAI refer to 2016 or 2017. Countries 

are  included  in  the  GAI  if  data  availability  is  at  least  66%  (i.e.,  14  out  of  21  KPIs). 

Exceptionally, eleven countries with lower data coverage have been included in the GAI: 

Libya,  Puerto  Rico,  Swaziland,  Syrian  Arab  Republic,  and  Timor-Leste  (with  48%  up  to 

57% data available) and Chad, Gabon, Guinea, Liberia, Venezuela, and Yemen (with 62% 

data  availability,  i.e.  13/21  KPIs  available).  In  practice,  data  availability  in  the  GAI  is 

overall very good: at least 80% data available for 100 (out of 144) countries. That said, 

for  some  countries  data  coverage  is  not  satisfactory  at  the  pillar level.  For  example,  for 

Libya  and  Venezuela  only  one  out  of  the  six  KPI  values  under  the  Endowment  pillar  is 

available. This is in general undesirable because the single KPI value available will dictate 

the  pillar  score  for  those  countries.  The  impact  of  missing  values  on  the  GAI  results  is 

discussed in Section 2.  

3 See Giampietro, 2014 for a discussion on scaling factors for indicators (intensive versus extensive properties). 

6 

 
 
                                           
Potentially problematic indicators that could bias the overall results were identified on the 

basis of two measures related to the shape of the indicators’ distribution: skewness and 

kurtosis.  Values  were  treated  if  the  indicators  had  absolute  skewness  greater  than  3.0 

and kurtosis greater than 3.5.4 These criteria were proposed by the JRC back in 2016 for 

the  specific  dataset  underpinning  the  GAI  model.  These  indicators  were  treated  by 

winsorization (five or less   outliers per indicator) in order to avoid that few very high/low 

values result in polarised indicators and scores, and introduce distortion in the correlation 

coefficients that are subsequently used for the analysis of the statistical coherence in the 

GAI framework.  

Step 3: Statistical Coherence 

The  reliability  of  the  Global  Attractiveness  Index  depends,  inter  alia,  on  the  degree  of 

coherence between the conceptual framework – 21 KPIs grouped into 4 pillars and finally 

into an index – and the statistical structure of the data. The more the statistical structure 

of the data is compatible with the GAI conceptual framework, the higher the reliability of 

the GAI will be. The coherence of the GAI framework was assessed by analysing whether 

the  21  KPIs  explain  a  sufficient  amount  of  variation  in  the  aggregated  scores  (either  in 

the  four  pillars  or  the  overall  index)  by  means  of  correlation,  cross-correlation,  and 

principal component analysis.  

Given  that  this  type  of  analysis  of  the  Global  Attractiveness  Index  is  based  on 

correlations,  the  correspondence  of  the  GAI  to  a  real-world  phenomenon  needs  to  be 

critically  addressed  by  experts  in  the  field  because  ‘correlations  need  not  necessarily 

represent  the  real  influence  of  the  individual  indicators  on  the  phenomenon  being 

measured’.5 The point made here is that the validity of the GAI framework relies on the 

combination  of  both  statistical  and  conceptual  soundness.  In  this  respect,  the  GAI 

framework  has  been  developed  following  an  iterative  process  that  went  back  and  forth 

between the theoretical understandings of national competitiveness and attractiveness on 

the one hand, and data observations on the other.    

Principal  component  analysis  was  used  to  assess  the  extent  to  which  the  conceptual 

framework  underpinning  the  GAI  –  21  indicators  grouped  in  4  pillars  and  finally  into  an 

index  –  is  compatible  with  the  data  statistical  properties.  Results  suggest  that  the 

expectation of a single statistical dimension (i.e., no more than one principal component 

with eigenvalue greater than 1.0) is confirmed for two of the four pillars, namely for the 

Openness and Innovation pillars. Instead there are two statistical dimensions in the other 

two  pillars:  Efficiency  and  Endowment.  The  presence  of  more  than  one  statistical 

4  Groeneveld  and  Meeden  (1984)  set  the  criteria  for  absolute  skewness  above  1  and  kurtosis  above  3.5.  The 
skewness  criterion  was  relaxed  in  the  GAI  case  after  having  conducted  ad-hoc  tests  in  the  2013-2017 
timeseries.   

5 OECD & EC JRC (2008). 

7 

 
                                           
dimension in the Efficiency and Endowment pillars suggests that some of the information 

content of some KPIs does not arrive at the pillar level.  

A more detailed analysis of the correlation structure within and across the four GAI pillars 

confirms  the  expectation  that  the  indicators  are  generally  more  correlated  to  their  own 

pillar  than  to  any  other  (see  Table  1).  This  result  suggests  that  the  allocation  of  the  21 

KPIs  to  a  specific  attribute  of  a  country’s  attractiveness  is  consistent  both  from 

conceptual  and  statistical  perspectives.  Furthermore,  all  associations  between  indicators 

and  the  respective  pillar  are  statistically  significant,  and  most  correlation  coefficients 

within a GAI pillar are close to or greater than 0.70, which suggests that at least half of 

the variance in the GAI pillar scores can be explained by an underlying indicator.  

Finally,  the  four  GAI  pillars  also  share  a  single  statistical  dimension.  The  GAI  captures 

75%  of  the  total  variance  in  the  four  pillars,  and  the  four  correlation  coefficients 

(between the index and each pillar) are high, close to 0.8 or greater. This result supports 

the aggregation of four GAI pillars into one number and suggests that all four pillars of a 

country’s attractiveness can explain more than half of the variation of the GAI scores, as 

envisaged  by  the  developing  team.  The  reliability  of  the  Global  Attractiveness  Index, 

measured  by  the  Cronbach-alpha  value,  is  very  good  at  0.89—well  above  the  0.7 

threshold for a reliable aggregate of the four pillars.6  

Concluding, the statistical coherence tests corroborate the two-level structure in the GAI 

framework,  and  confirm  the  desired  unidimensionality  of  two  out  of  the  four  pillars 

(Openness  and  Innovation),  and  the  overall  index.  Furthermore,  all  21  indicators  are 

found to be influential at least at the first aggregation level (pillars) and for 17 out of the 

21  indicators,  this  influence  arrives  up  to  the  overall  index.  This  is  a  highly  desirable 

outcome  as  it  suggests  that  the  information  content  in  the  majority  of  the  underlying 

indicators is maintained at all levels of aggregation in the GAI framework.  

At the same time, the analysis has also helped to evidence several issues that are worth 

of  further  reflection  either  because  they  indicate  avenues  for  refining  the  index  or  for 

further policy analysis.   

First, there are four indicators that do not have statistically significant correlation to the 

overall  index:  Employed  in  high-technology  sectors  (KPI6)  within  Innovation,  Total 

productivity  of  factors  (KPI13)  and  Total  tax  rate  (KPI16)  within  Efficiency,  and  Gross 

fixed investment (KPI18) within Endowment. Although conceptually enriching the overall 

GAI  framework,  these  KPIs  are  found  not  to  co-vary  with  the  overall  index.  This  means 

that  countries  may  have  achieve  high  GAI  scores  irrespective  of  high  or  low  values  in 

KPIs 6, 13, 16, 18, and the same holds for low GAI scores.  The JRC recommendation to 

6 See Nunnally (1978). 

8 

 
                                           
the GAI development team is to carefully monitor how these four indicators behave in the 

coming releases of the index and eventually to fine-tune the framework by considering a 

different formulation or different data source for these indicators. This refinement can be 

inspired  by  the  positive  impact  to  the  coherence  of  the  GAI  of  the  new  data  source  on 

migrants (next point).  

Second,  the  new  data  source  selected  by  the  developing  team  for  capturing  the  Net 

number  of  migrants  (KPI5  within  Openness,  which  is  now  based  on  United  Nations 

Population  Division  data)  has  contributed  to  increasing  the  statistical  coherence  in  this 

year’s  GAI  (correlation  0.65  with  the  Openness  pillar  compared  to  0.35  in  last  year’s 

version). 

Third, although the PISA Test score (KPI21) belongs to the Endowment pillar, it presents 

a  much  stronger  correlation  to  the  other  three  pillars  and  in  particular  to  the  Efficiency 

pillar  (Pearson  correlation  0.74).  The  link  between  PISA  test  scores  and  a  country’s 

Efficiency calls for further reflection.  

Forth,  unlike  what  one  may  expect,  a  country’s  unemployment  level  is  not  strongly 

related  to  the  overall  index.  This  outcome  merits  further  analysis  as  it  suggests  that 

countries can achieve high levels of attractiveness despite high unemployment levels (as 

it  is  the  case  for  Spain,  which  ranks  25th  in  the  GAI  despite  one  of  the  highest 

unemployment level worldwide). 

Fifth,  although  Gross  National  Product  belongs  to  the  Endowment  pillar,  it  is  found  to 

have much stronger statistical association to the Openness pillar. Similarly, the Logistics 

Performance  Index  is  found  to  be  equally  related  to  all  four  pillars  of  a  country’s 

attractiveness;  this  transversal  impact  of  the  Logistics  Performance  Index  across  all 

pillars may be worth of further analysis.  

Finally, while most of the 21 KPIs are influential at the index level, two of them  - Export 

+  Import  (KPI2)  and  the  Logistics  Performance  Index  (KPI  12)  are  the  best  single 

predictors for a country’s attractiveness level.  

9 

 
Table 1. Statistical coherence: correlations between GAI components 

Notes: Numbers represent the Pearson correlations coefficients between the GAI components (pillars or index) 
and the underlying indicators (for 144 countries). Values greater than 0.7 are desirable as they imply that the 
pillar captures at least 50% (≈ 0.7×0.7) of the variation in the underlying KPIs. Instead, values lower than 0.23 
are  not  presented  as  they  are  not  statistically  significant  (p-values  >0.01).  Grey  boxes  show  the  conceptual 
grouping of the indicators. KPIs for which lower values are desirable are marked with (-1).            

Source: European Commission Joint Research Centre, 2018. 

10 

ATTRIBUTEKey Performance Indicators (KPIs)OpennessInnovationEfficiencyEndowmentINDEXKPI1(Foreign Direct Investment flows into the country IN + the country’s investment abroad OUT), % of world total0.750.660.470.660.74KPI2(Export + Import), % of world total0.800.810.570.770.85KPI3(No. foreign tourists IN + No. national tourists abroad OUT), compared with national population 0.630.410.290.230.45KPI4Foreign university students, compared with youth population 0.680.530.520.370.61KPI5Net number of migrants, compared with population 0.650.380.270.200.43KPI6Employed in high-technology sectors, compared with employed -0.160.500.260.100.20KPI7Exports of high-technology goods, compared with world total0.600.750.480.670.73KPI8ICT Development Index0.760.860.580.580.81KPI9Number of scientific publications, compared with world total0.610.760.500.770.77KPI10Internet users, % of population0.740.850.550.550.79KPI11Unemployment level(-1)0.170.110.600.200.28KPI12Logistics Performance Index0.790.780.770.700.86KPI13Total productivity of factors0.060.100.380.090.15KPI14Rule of Law Index0.730.700.800.570.76KPI15Total tax rate (% commercial profits)-0.150.030.280.130.07KPI16Gross Domestic Product (GDP), compared with world total0.590.710.460.750.73KPI17Gross National Product, (GNP), per capita 0.850.760.580.670.82KPI18Gross Fixed Investment, compared with GDP -0.05-0.03-0.020.280.04KPI19Natural Resource Index0.120.210.100.570.29KPI20College graduates, compared with world total0.030.390.290.780.48KPI21PISA Test Score0.580.510.740.270.59Attributes of AttractivenessOpennessInnovationEfficiencyEndowmentINDEXOpenness1.000.800.580.660.88Innovation0.801.000.620.770.94Efficiency0.580.621.000.590.78Endowment0.660.770.591.000.87OpennessInnovationEfficiencyEndowment 
 
 
 
Step 4: Qualitative Review  

Finally, the GAI results were evaluated by an ad-hoc Advisory Panel and by international 

experts  invited  by  the  European  House  –  Ambrosetti  to  verify  that  they  are,  to  a  great 

extent, consistent with current evidence, existing research and prevailing theory.  

To complement this qualitative evaluation, the GAI results are compared herein vis-à-vis 

other  similar  indices.  The  expectation  is  that  the  GAI  correlates  strongly  to  other 

international  indices  on  competitiveness,  innovation  and  human  capital.  Table  2 

compares  the  GAI  2018  with  both  the  World  Economic  Forum’s  2017–2018  Global 

Competitiveness  Index  and  the  2017  Global  Human  Capital  Index,  with  Cornell 

University,  INSEAD,  and  WIPO’s  2018  Global  Innovation  Index  and  with  INSEAD’s  2018 

Global  Talent  Competitiveness  Index.  The  rank  correlation  between  GAI  2018  with  all 

four international indices is substantially high (correlation ≈ 0.9), which suggests that the 

GAI framework has many elements in common with other international frameworks that 

monitor innovation, competitiveness and human capital at national level worldwide.  

Table  2.  Distribution  of  differences  between  the  GAI  2018  and  other  international 

rankings 

Global Innovation 
Index  
(Cornell, INSEAD, 
WIPO, 2018) 

Global 
Human 
Capital Index  
(WEF, 2017) 

Global 
Competitiveness 
Index  
(WEF, 2017-2018) 

Global Talent 
Competitiveness 
Index  
(INSEAD, 2018) 

More than 30 positions 

20 to 29 positions 

10 to 19 positions 

More than 10 positions (*) 

 5 to 9 positions 

Less than 5 positions 

0 positions 

4% 

12% 

32% 

48% 

22% 

21% 

9% 

13% 

16% 

32% 

61% 

17% 

19% 

2% 

8% 

11% 

35% 

54% 

26% 

20% 

1% 

10% 

11% 

34% 

55% 

18% 

24% 

3% 

Total 

100% 

100% 

100% 

100% 

Pearson correlation coefficient 
with the GAI 

Spearman rank correlation 
coefficient with the GAI 

Common countries with the 
GAI 

0.89 

0.90 

114 

0.78 

0.82 

124 

0.89 

0.91 

132 

0.86 

0.88 

120 

Notes:  The  comparison  between  the  GAI  and  the  other  indices  was  based  on  the  common  set  of  countries. 
(*)This row is the sum of the prior three rows. 

Source: European Commission Joint Research Centre, 2018. 

11 

 
 
  
 
At the same time, looking at the shifts in rankings, one finds that 48% up to 61% out of 

the  countries  included  in  the  GAI  2018  that  feature  in  the  other  four  indices  differ  in 

ranking  by  more  than  10  positions  when  comparing  the  GAI  2018  with  the  recent 

releases  of  the  Global  Competitiveness  Index,  the  Global  Human  Capital  Index,  the 

Global  Competitiveness  Index  and  the  Global  Talent  Competitiveness  Index.  This  result 

suggests  that  the  GAI  2018  receives  validity  when  compared  to  other  relevant 

international  indices,  and  that  the  GAI  offers  additional  insights  into  nations’  human 

capital and competitiveness that go beyond the findings of other international indices.   

Notwithstanding  these  statistical  tests  and  the  positive  outcomes  on  the  statistical 

coherence  together  with  the  suggestions  for  refinement  made  above,  the  GAI  model,  in 

its third edition now, has been and should remain open for future improvements as better 

data, more comprehensive surveys and assessments, and new relevant research studies 

on national attractiveness and competitiveness become available. 

12 

 
 
2  Impact of modelling assumptions in the GAI  

Assessing the effect of varying modelling assumptions in the GAI inside plausible ranges 

is an important part of the statistical audit. The rationale for the choices made by the GAI 

development  team  is  manifold.  For  instance,  literature  review  and  expert  opinion  on 

national  attractiveness  and  competitiveness,  coupled  with  statistical  analysis,  is  behind 

the selection of the 21 individual indicators and their grouping in four pillars and into an 

overall index; common practice and easy of interpretation suggests the use of a min-max 

normalization approach in the [0–100] range for the indicators; statistical analysis guides 

the  choice  on  the  treatment  of  outliers;  and  simplicity  seems  to  advocate  for  not 

estimating missing data, assigning equal weights at all levels and adopting an arithmetic 

average formula.  

Despite  the  well-substantiated  rationale  for  the  choices  made  during  the  GAI 

development, there is an unavoidable subjectivity (or uncertainty), which is accounted for 

in  the  robustness  assessment  carried  out  by  the  JRC.  More  precisely,  the  uncertainly 

analysis is conducted herein in order to allow for the  joint analysis of the impact of the 

modelling choices on the GAI results, resulting in error estimates and confidence intervals 

calculated for the 144 countries included in the GAI.  

As  suggested  in  the  relevant  literature  on  composite  indicators7,  the  robustness 

assessment of the GAI  model was based on Monte Carlo simulation and multi-modelling 

approaches,  applied  to  ‘error-free’  data  where  eventual  errors  and  typos  have  already 

been  corrected  in  a  preliminary  stage.  In  particular,  the  three  key  modelling  issues 

considered  in  the  assessment  of  the  GAI  were  the  treatment  of  missing  data,  the 

aggregation formula at the pillar level and finally the pillar weights.  

Missing  data.  The  GAI  developers,  for  transparency  and  replicability  and  following 

common  practice  on  composite  indicator  development,  opted  not  to  estimate  missing 

data.  Technically,  the  ‘no  imputation’  choice  is  equivalent  to  replacing  an  indicator’s 

missing  value  for  a  given  country  with  the  respective  pillar  score.  Hence,  the  available 

data (indicators) in the incomplete pillar may dominate, sometimes biasing the ranks up 

or down. Furthermore, the ‘no imputation’ choice might encourage countries not to report 

low  data  values.  To  test  the  impact  of  the  ‘no  imputation’  choice,  the  JRC  estimated 

missing values in the GAI dataset using the Expectation Maximization (EM) algorithm that 

was applied in the entire set of 21 indicators.8   

7 Saisana et al., 2005; Saisana et al., 2011 ; Vértesy 2016; Vértesy and Deiss, 2016 

8  The  Expectation-Maximization  (EM)  algorithm  (Little  and  Rubin,  2002;  Schneider,  2001)  is  an  iterative 
procedure that finds the maximum likelihood estimates of the parameter vector by repeating two steps. Step 1: 
The expectation E-step: Given a set of parameter estimates, such as a mean vector and covariance matrix for a 
multivariate  normal  distribution,  the  E-step  calculates  the  conditional  expectation  of  the  complete-data  log 
likelihood  given  the  observed  data  and  the  parameter  estimates.  Step  2:  The  maximization  M-step:  Given  a 

13 

 
                                           
Aggregation.  Regarding  the  aggregation  formula,  decision-theory  practitioners  challenge 

the  use  of  simple  arithmetic  averages  because  of  their  fully  compensatory  nature,  in 

which a comparative high advantage on a few indicators can compensate a comparative 

disadvantage on many indicators.9 To assess the impact of this compensability issue, the 

strong perfect substitutability assumption inherent in the arithmetic average was relaxed 

in this analysis; instead the geometric average across the four GAI pillars was considered 

as an alternative. Nevertheless, the arithmetic average has been maintained at the KPIs 

level,  where  full  compensability  may  be  justifiable.  The  geometric  average  is  a  partially 

compensatory  approach  that  rewards  countries  with  balanced  profiles  and  motivates 

countries to improve in the GAI pillars in which they perform poorly, and not just in  any 

GAI pillar.10 

Weights.  While  the  term  multi-modelling  refers  to  testing  alternative  assumptions—that 

is,  an  alternative  aggregation  method,  and  missing  data  estimation  method—the  Monte 

Carlo  simulation  explored  the  issue  of  weighting  and  comprised  1,000  runs,  each 

corresponding  to  a  different  set  of  weights  for  the  four  pillars,  randomly  sampled  from 

uniform  continuous  distributions  centred  in  the  reference  values  (equal  weighting;  pillar 

weights are 25%). The  choice of the range for  the weights’ variation was driven by two 

opposite needs: to ensure a wide enough interval to have meaningful robustness checks, 

and  to  respect  the  rationale  of  GAI  that  places  equal  importance  on  all  four  pillars  – 

Openness, Innovation, Efficiency, Endowment. Given these considerations, limit values of 

uncertainty intervals for the pillar weights are 15% to 35% for the four pillars (see Table 

3). In all simulations, sampled weights are then rescaled so that they always sum to 1.  

Four  models  were  tested  based  on  the  combination  of  no  imputation  versus  EM 

imputation at the indicator level, arithmetic versus geometric average at the pillar level. 

Combined  with  1,000  simulations  per  model  (random  weights  versus  fixed  weights),  a 

total of 4,000 simulations for the Global Attractiveness Index were run.   

complete-data  log  likelihood,  the  M-step  finds  the  parameter  estimates  to  maximize  the  complete-data  log 
likelihood from the E-step. The two steps are iterated until the iterations converge. 

9  Munda, 2008. 

10  In  the  geometric  average,  pillars  are  multiplied  as  opposed  to  summed  in  the  arithmetic  average.  Pillar 
weights  appear  as  exponents  in  the  multiplication.  A  constant  of  0.001  was  added  to  the  pillar  scores  to 
avoid zero values that would have led to zero geometric averages. 

14 

 
                                                                                                                                    
Table 3. Uncertainty parameters in the GAI: missing values, weights, aggregation 

Source: European Commission Joint Research Centre, 2018. 

The main results of the robustness analysis are shown in Figure 2 with median ranks and 

the 90% confidence intervals computed across the 4,000 Monte Carlo simulations for the 

Global  Attractiveness  Index.  Countries  are  ordered  from  high  to  low  performance 

according to their reference GAI rank (black line), the dot being the median rank over the 

simulations.  

All published GAI 2018 ranks lay within the simulated 90% confidence intervals, and for 

the vast majority of the countries these ranks can be considered as representative of the 

plurality  of  scenarios  simulated  herein.  Taking  the  median  rank  as  the  yardstick  for  an 

economy’s  expected  rank  in  the  realm  of  the  GAI’s  unavoidable  methodological 

uncertainties,  70%  of  the  economies  are  found  to  shift  fewer  than  three  positions  with 

respect to the median rank in the GAI.    

Furthermore,  for  most  economies  the  simulated  rank  intervals  are  narrow  enough  for 

meaningful inferences to be drawn: there are fewer than 10 positions for 67  of the 144 

economies.  Nevertheless,  several  country  ranks  vary  significantly  with  changes  in  the 

four  pillar  weights,  the  aggregation  formula  across  the  four  pillars  or  the  estimation  of 

missing  data  (where  applicable):  confidence  interval  widths  are  30  or  greater  for  the 

following  14  countries  that  are  placed  between  the  45th  (Qatar)  and  the  142nd 

(Swaziland) position: Qatar, Oman, Jordan, Venezuela, Gabon, Montenegro, Cabo Verde, 

Armenia,  Tanzania,  Tajikistan,  Seychelles,  Namibia,  Libya,  and  Swaziland.  For  these 

countries the GAI ranks should not be taken at face value. 

For  full  transparency  and  information,  Table  4  reports  the  GAI  2018  country  ranks 

together  with  the  simulated  90%  confidence  intervals  in  order  to  better  appreciate  the 

robustness  of  the  results  to  the  estimation  of  missing  data,  the  choice  of  the  four  pillar 

weights and of the aggregation formula at pillar level. 

15 

ReferenceAlternativeNo estimation of missing dataExpectation Maximization (EM)Arithmetic averageGeometric average Reference value for the weightDistribution assigned for robustness analysis0.25U[0.15,0.35]  0.25U[0.15,0.35]  0.25U[0.15,0.35]  0.25U[0.15,0.35]  EndowmentII. Uncertainty in the aggregation formula at pillar levelIII. Uncertainty intervals for the weights of the four GAI pillarsOpennessInnovationEfficiencyI. Uncertainty in the treatment of missing values 
Figure 2. Robustness analysis (GAI rank vs. median rank, 90% confidence intervals) 

Notes:  Median  ranks  and  intervals  are  calculated  over  4,000  simulated  scenarios  based  on  imputing  (or  not) 
missing values, random weights plus/minus 25% around the reference weights for the four pillars on Openness, 
Innovation,  Efficiency,  Endowment,  and  aggregation  formula  at  pillar  level  (as  shown  in  Table  3).  The 
Spearman rank correlation between the median rank of the simulations and the GAI 2018 rank is 0.991.  

Source: European Commission Joint Research Centre, 2018. 

16 

 
Table 4. GAI 2018: Index ranks and simulated 90% intervals 

Notes: Rank intervals are calculated over 4,000 simulated scenarios based on imputing (or not) missing values, 
random  weights  plus/minus  25%  around  the  reference  weights  for  the  four  pillars  on  Openness,  Innovation, 
Efficiency, Endowment, and aggregation formula at pillar level. Countries with reduced data coverage (less than 
14/21 indicators) are highlighted in grey.  

Source: European Commission Joint Research Centre, 2018. 

Next, the impact of treating missing values in the GAI is analysed in more detail. Table 5 

lists 27 countries that are strongly affected (moving 20 positions or more in a given GAI 

pillar) when missing values are estimated via the EM algorithm as opposed to not being 

estimated  at  all  (reference  scenario),  together  with  the  data  availability  per  pillar.  Most 

17 

United States1[1, 1]Romania51[48, 55]Jamaica101[94, 102]Germany2[2, 3]Puerto Rico52[50, 63]Armenia102[85, 115]Japan3[2, 3]Greece53[43, 66]Tanzania103[97, 128]United Kingdom4[4, 4]Suriname54[48, 66]Botswana104[97, 108]Singapore5[5, 8]Panama55[52, 64]Kenya105[105, 117]China6[5, 7]Lithuania56[52, 63]Bosnia and Herzegovina106[98, 125]France7[5, 8]Indonesia57[53, 75]Senegal107[103, 114]Canada8[7, 8]Croatia58[48, 62]Benin108[103, 129]Australia9[9, 11]Uruguay59[52, 62]Cambodia109[105, 110]Korea, Rep.10[9, 12]Cyprus60[45, 65]Rwanda110[104, 127]Netherlands11[9, 11]Colombia61[57, 75]Cameroon111[109, 130]Hong Kong12[10, 13]Ukraine62[54, 70]Guatemala112[109, 116]Switzerland13[12, 13]Vietnam63[59, 70]Lao PDR113[103, 119]Austria14[14, 17]Philippines64[60, 72]Zambia114[106, 131]Belgium15[15, 18]Kazakhstan65[55, 65]Tajikistan115[104, 134]Italy16[14, 19]Costa Rica66[56, 68]Nepal116[105, 117]Sweden17[15, 22]Bhutan67[66, 79]Mongolia117[111, 125]Ireland18[14, 20]Oman68[43, 100]Pakistan118[113, 119]Luxembourg19[16, 23]Algeria69[68, 86]Mauritania119[116, 126]Iceland20[17, 22]Lebanon70[53, 72]El Salvador120[108, 121]Norway21[18, 26]Bulgaria71[65, 72]Mali121[116, 137]Spain22[18, 23]Peru72[69, 80]Myanmar122[109, 125]Denmark23[20, 23]Jordan73[54, 87]Uganda123[119, 125]New Zealand24[24, 26]Azerbaijan74[74, 84]Honduras124[114, 126]United Arab Emirates25[22, 27]Moldova75[72, 96]Seychelles125[62, 132]Czech Republic26[25, 35]Venezuela76[72, 120]Timor-Leste126[122, 135]India27[24, 30]Georgia77[65, 78]Guinea127[122, 139]Finland28[27, 30]Trinidad and Tobago78[74, 84]Chad128[126, 144]Russian Federation29[26, 32]Serbia79[77, 85]Gambia129[113, 131]Estonia30[29, 36]Dominican Republic80[76, 84]Siria130[125, 140]Poland31[30, 35]Gabon81[66, 100]Burundi131[128, 142]Bahrain32[23, 40]South Africa82[73, 85]Malawi132[128, 139]Malaysia33[31, 34]Guyana83[77, 93]Liberia133[124, 137]Brazil34[30, 40]Morocco84[81, 89]Nigeria134[120, 135]Israel35[30, 37]Albania85[70, 88]Zimbabwe135[128, 139]Slovenia36[35, 40]Egypt86[81, 87]Madagascar136[133, 141]Slovak Republic37[37, 46]Ecuador87[86, 91]Namibia137[104, 138]Hungary38[38, 42]Bolivia88[85, 103]Mozambique138[130, 143]Saudi Arabia39[31, 39]Montenegro89[63, 94]Libya139[108, 144]Kuwait40[34, 43]Cote d'Ivoire90[86, 97]Sierra Leone140[133, 140]Mexico41[38, 43]Mauritius91[75, 94]Haiti141[134, 142]Portugal42[41, 46]Sri Lanka92[89, 108]Swaziland142[101, 142]Malta43[37, 44]Tunisia93[91, 97]Yemen143[142, 144]Iran44[42, 58]Paraguay94[90, 95]Lesotho144[132, 144]Qatar45[27, 78]Bangladesh95[91, 109]Latvia46[46, 52]Cabo Verde96[92, 124]Chile47[46, 65]Nicaragua97[95, 110]Argentina48[46, 58]Ghana98[93, 105]Turkey49[44, 58]Kyrgyz Republic99[84, 101]Thailand50[44, 51]Macedonia100[87, 110] 
 
countries’  ranks  are  sensitive  to  the  missing  data  estimation  in  one  of  the  pillars, 

primarily  the  Efficiency  or  the  Endowment  pillar.  Only  four  countries  are  sensitive  to 

missing  values  in  two  pillars:  Chad,  Guinea,  Lesotho  and  Venezuela.  The  JRC 

recommendation is to consider pillar ranks (and scores) for these  countries with a grain 

of salt when drawing inferences on their performance.   

Table 5. Impact of missing data estimation on countries with most sensitive pillar ranks  

Source: European Commission Joint Research Centre, 2018. 

Concluding,  the  published  GAI  2018  ranks  are  reliable  and  for  the  vast  majority  of 

countries  the  simulated  90%  confidence  intervals  are  narrow  enough  for  meaningful 

inferences  to  be  drawn.  Given  the  sensitivity  of  some  countries’  pillar  ranks  to  the 

estimation of missing values, the JRC recommendation to the index developers is to find 

a  suitable  way  for  approximating  missing  values,  where  possible  by  contacting  national 

statistical offices or finding additional data sources. For the readers and policy analysts of 

the  GAI  2018  report,  the  recommendation  is  to  consider  country  ranks  within  the  90% 

confidence  intervals  in  order  to  better  appreciate  to  what  degree  a  country’s  rank 

depends  on  the  three  modelling  choices  accounted  for,  namely  estimation  of  missing 

data, weights and aggregation formula at the pillar level.  

18 

Country rank sensitive to the treatment of missing dataOpennessInnovationEfficiencyEndowmentOpennessInnovationEfficiencyEndowmentBahrainYES80%80%80%67%BeninYES100%80%60%67%BurundiYES80%60%60%67%ChadYESYES60%60%60%67%El SalvadorYES100%80%80%83%EstoniaYES80%100%100%83%GreeceYES80%100%100%83%GuineaYESYES80%60%60%50%HaitiYES80%60%60%67%Hong KongYES100%80%100%67%IranYES80%60%100%83%JordanYES100%80%100%50%LesothoYESYES80%60%60%67%LiberiaYES60%40%80%67%MaliYES60%80%80%67%MozambiqueYES100%60%80%83%MyanmarYES80%80%100%50%NepalYES80%60%80%67%NigeriaYES80%80%100%50%OmanYES100%80%80%50%QatarYES100%80%80%50%SeychellesYES80%80%40%67%SiriaYES40%60%80%33%TajikistanYES100%40%80%67%Timor-LesteYES80%60%40%50%UkraineYES100%60%100%83%VenezuelaYESYES80%60%100%17%Data availability 
 
 
 
3  Added  value  of  GAI  –  From  four  pillars  to  one  single 

number of national attractiveness 

This  last  section  aims  at  touching  upon  the  added  value  of  the  Global  Attractiveness 

Index  as  a  summary  measure  of  the  four  pillars.  Table  6  shows  that  the  GAI  2018 

ranking and any of the four pillar rankings differ by 10 positions or more for at least 48% 

(up to 65%) of the 144 countries. This finding suggests  that there is  an added  value in 

referring to the GAI results in order to identify aspects of countries’ attractiveness that do 

not  directly  emerge  by  looking  into  the  four  pillars  separately.  At  the  same  time,  this 

outcome  points  to  the  value  of  examining  individual  GAI  pillars  and  indicators  on  their 

own merit in order to see which components are driving a country’s attractiveness. 

Table 6. Distribution of differences between pillars and GAI rankings 

Source: European Commission Joint Research Centre, 2018. 

19 

Shift with respect to the GAIOpennessInnovationEfficiencyEndowmentMore than 30 positions13%5%25%17%20 to 29 positions17%15%13%15%10 to 19 positions33%28%26%26% 5 to 9 positions15%23%17%17%Less than 5 positions20%25%15%20%0 positions2%4%3%4%Total100%100%100%100%More than 10 positions63%48%65%58% 
 
 
 
 
4  Conclusions 

The  European  House  –  Ambrosetti  developed,  in  its  third  edition  now,  the  Global 

Attractiveness  Index  (GAI)  with  a  view  to  measure  national  attractiveness  in  144 

countries around the world. The JRC statistical audit has delved around in the workings of 

the GAI framework to assess the statistical properties of the data, and the methodology 

used in the index construction. Overall the GAI framework is well-constructed, into which 

a  lot  of  thought  has  clearly  been  put  and  extensive  original  research  into  the  multiple 

determinants of a county’s attractiveness has been conducted by the developers. The key 

findings of the statistical assessment conducted herein are the following: 

First,  the  coherence  tests  suggest  that  the  conceptual  grouping  of  the  21  indicators 

into four pillars and an overall index  is corroborated by  statistical analysis,  and that the 

GAI scale is unidimensional and has high statistical reliability (Cronbach alpha 0.89)  well 

above  the  recommended  threshold  (0.7)  for  a  reliable  aggregate.  Seventeen  out  of  the 

21 indicators in the GAI framework are also found to be influential all the way up to the 

index level. Nevertheless, four  indicators ‒  Employed in high-technology sectors (KPI6), 

Total  productivity  of  factors  (KPI13),  Total  tax  rate  (KPI16)  and  Gross  fixed  investment 

(KPI18) – account for a small (almost negligible) amount of variation in the GAI scores. 

Although  these  indicators  are  conceptually  enriching  the  GAI  framework  and  their 

statistical  impact  arrives  up  to  the  pillar  level,  it  is  recommended  to  carefully  monitor 

how  these  four  indicators  behave  in  the  coming  releases  of  the  index  and  eventually  to 

fine-tune the framework in this respect. The new data source used in this GAI edition for 

capturing the Net number of migrants (KPI5) has contributed to increasing the statistical 

coherence in this year’s GAI.  

Second,  the  GAI  dataset  has  very  good  data  coverage  and  82%  of  the  data  refer  to 

2016 or 2017. Uncertainty and sensitivity analysis have shown that it is important to find 

reliable  estimates  for  the  missing  values  in  27  countries  because  of  the  impact  on  the 

country ranks along specific GAI pillars.    

Third, the tests helped to single out 14 countries with GAI ranks that are very sensitive 

to  the  modelling  choices  and  hence  these  ranks  should  be  interpreted  cautiously.  On 

the  other  hand  and  compared  to  the  reference  GAI  rank,  70%  of  the  economies  are 

found  to  shift  fewer  than  three  positions  with  respect  to  the  median  rank  over  4,000 

simulations.  Thereafter,  the  GAI  framework  allows  to  reliably  benchmark  national 

attractiveness in the vast majority of the countries analysed.  

Fourth,  results  show  that  there  is  an  added  value in  referring  to  the  GAI  results  in 

order  to  identify  aspects  of  countries’  attractiveness  that  do  not  directly  emerge  by 

20 

 
looking into the four pillars separately. In fact, the GAI ranking and any of the four pillar 

rankings differ by 10 positions or more for at least 48% up to 65% of the 144 countries.   

Fifth,  the  external  validity  testing  of  the  GAI  confirms  the  high  degree  of  association 

(correlation ≈ 0.9) to the latest releases of four international indices: the World Economic 

Forum’s  Global  Competitiveness  Index  and  Global  Human  Capital  Index,  the  Cornell 

University, INSEAD, and WIPO’s Global Innovation Index, and the INSEAD’s Global Talent 

Competitiveness  Index.  At  the  same  time,  one  finds  that  48%  up  to  61%  out  of  the 

countries included in the GAI 2018 that feature in the other four indices differ in ranking 

by  more  than  10  positions  when  comparing  the  GAI  2018  with  the  recent  releases  of 

these international indices. This latter result suggests that the GAI 2018 offers additional 

insights  into  nations’  human  capital  and  competitiveness  that  go  beyond  the  findings  of 

other international indices.   

All  things  considered,  the  present  JRC  audit  findings  confirm  that  the  Global 

Attractiveness  Index  2018  meets,  at  large,  international  quality  standards  for  statistical 

soundness.  Consequently,  the  GAI  framework  offers  a  sound  starting  point  for  more 

informed discussions on what determines national attractiveness worldwide. Readers and 

policy  analysts  of  the  Global  Attractiveness  Index  should  hence  go  beyond  the  overall 

index scores (and ranks) and duly take into account the 21 individual indicators and four 

pillars  on  their  own  merit.  The  Global  Attractiveness  Index  cannot  possibly  serve  as  the 

ultimate and definitive yardstick for monitoring national attractiveness. Instead, the GAI 

best  represents  an  ongoing  attempt  by  the  European  House  Ambrosetti  to  stimulate 

public  interest  and  help  focus  policy  discussions  on  the  multiple  facets  of  a  country’s 

attractiveness,  continuously  adapting  the  Global  Attractiveness  Index  framework  to 

reflect the improved availability of statistics and the theoretical advances in the field.  

21 

 
 
References and related reading 

Becker, W., Saisana, M., Paruolo, P., Vandecasteele, I. 2017. Weights and importance in 

composite indicators: Closing the gap, Ecological Indicators 80: 12–22.  

Cornell  University,  INSEAD,  and  WIPO.  2018.  The  Global  Innovation  Index  2018: 
Energizing the World with Innovation. Ithaca, Fontainebleau, and Geneva. 

Giampietro,  M.,  2014.  ‘Mono-dimensional  accounting  and  multidimensional  measures  of 
sustainable growth’. Report produced by the European Commission-Joint Research 
Centre, Econometrics and Applied Statistics (DDG.01). EUR 26917 EN. 

 Groeneveld,  R.  A.  and  G.  Meeden.  1984.  ‘Measuring  Skewness  and  Kurtosis’.  The 

Statistician 33: 391–99.  

INSEAD. 2018. The Global Talent Competitiveness Index 2018, Fontainebleau, France. 

Little, R. J. A. and D. B. Rubin. 2002. Statistical Analysis with Missing Data. 2nd edition. 

Hoboken, NJ: John Wiley & Sons, Inc. 

Munda,  G.  2008.  Social  Multi-Criteria  Evaluation  for  a  Sustainable  Economy.  Berlin 

Heidelberg: Springer-Verlag. 

Nunally, J. 1978. Psychometric Theory. New York: McGraw-Hill. 

OECD/EC  JRC  (Organisation  for  Economic  Co-operation  and  Development/European 
Commission, Joint Research Centre). 2008. Handbook on Constructing Composite 
Indicators: Methodology and User Guide. Paris: OECD. 

Paruolo, P., M. Saisana, and A. Saltelli. 2013. ‘Ratings and Rankings: Voodoo or Science?’ 

Journal of the Royal Statistical Society  A 176 (3): 609–34. 

Saisana,  M.,  B.  D’Hombres,  and  A.  Saltelli.  2011.  ‘Rickety  Numbers:  Volatility  of 
University Rankings and Policy Implications’. Research Policy 40: 165–77. 

Saisana,  M.,  A.  Saltelli,  and  S.  Tarantola.  2005.  ‘Uncertainty  and  Sensitivity  Analysis 
Techniques  as  Tools  for  the  Analysis  And  Validation  Of  Composite  Indicators’. 
Journal of the Royal Statistical Society A 168 (2): 307–23. 

Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M., Saisana, and 
S.  Tarantola.  2008.  Global  Sensitivity  Analysis:  The  Primer.  Chichester,  England: 
John Wiley & Sons. 

Schneider, T. 2001. ‘Analysis of incomplete climate data: Estimation of mean values and 
covariance  matrices  and  imputation  of  missing  values.  Journal  of  Climate,  14, 
853–871. 

Vertesy, D., Deiss, R. 2016. The Innovation Output Indicator 2016. Methodology Update. 

EUR 27880. European Commission, Joint Research Centre.  

Vértesy,  D.  (2016,  July).  A  Critical  Assessment  of  Quality  and  Validity  of  Composite 
Indicators  of  Innovation.  Paper  presented  at  the  OECD  Blue  Sky  III  Forum  on 
Science and Innovation Indicators. Ghent, 19-21 Sept 2016. 

World Economic Forum. 2017. The Global Competitiveness Report 2017–2018. Geneva 

World Economic Forum. 2017. The Global Human Capital Report 2017 - Preparing people 

for the future of work. Geneva. 

22 

 
 
 
 
 
List of figures  

Figure 1: Conceptual and statistical coherence in the GAI 2018 Framework ................. 5 

Figure 2: Robustness analysis (GAI rank vs. median rank, 90% confidence intervals) .. 16 

23 

 
 
List of tables 

Table 1. Statistical coherence: correlations between GAI components ........................ 10 

Table 2. Distribution of differences between the GAI 2018 and other international 
rankings .............................................................................................................. 11 

Table 3. Uncertainty parameters in the GAI: missing values, weights, aggregation ...... 15 

Table 4. GAI 2018: Index ranks and simulated 90% intervals ................................... 17 

Table 5. Impact of missing data estimation on countries with most sensitive pillar ranks
 .......................................................................................................................... 18 

Table 6. Distribution of differences between pillars and GAI rankings ......................... 19 

24 

 
 
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