Overview

Dataset statistics

Number of variables10
Number of observations103
Missing cells113
Missing cells (%)11.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory81.2 B

Variable types

Categorical8
Numeric2

Alerts

ชื่อดัชนีหลัก has constant value "The Global Competitiveness Report" Constant
ตัวย่อดัชนีหลัก has constant value "GCR" Constant
ชื่อเดิมดัชนีหลัก has constant value "The Global Competitiveness Report" Constant
ปี has constant value "2019" Constant
หมายเหตุ has constant value "ค่า Score ตาม Original file คือ N/Appl." Constant
รายละเอียดดัชนีย่อย2 has a high cardinality: 103 distinct values High cardinality
Score is highly correlated with RankHigh correlation
Rank is highly correlated with ScoreHigh correlation
Score is highly correlated with RankHigh correlation
Rank is highly correlated with ScoreHigh correlation
ชื่อดัชนีย่อย_lv1 is highly correlated with ชื่อดัชนีย่อย_lv2 and 1 other fieldsHigh correlation
ชื่อดัชนีย่อย_lv2 is highly correlated with ชื่อดัชนีย่อย_lv1 and 1 other fieldsHigh correlation
Score is highly correlated with ชื่อดัชนีย่อย_lv1 and 2 other fieldsHigh correlation
Rank is highly correlated with ScoreHigh correlation
ชื่อดัชนีย่อย_lv2 has 10 (9.7%) missing values Missing
Score has 4 (3.9%) missing values Missing
หมายเหตุ has 99 (96.1%) missing values Missing
รายละเอียดดัชนีย่อย2 is uniformly distributed Uniform
รายละเอียดดัชนีย่อย2 has unique values Unique

Reproduction

Analysis started2026-09-09 04:13:05.009782
Analysis finished2026-09-09 04:13:06.750711
Duration1.74 second
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ชื่อดัชนีหลัก
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
The Global Competitiveness Report
103 

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThe Global Competitiveness Report
2nd rowThe Global Competitiveness Report
3rd rowThe Global Competitiveness Report
4th rowThe Global Competitiveness Report
5th rowThe Global Competitiveness Report

Common Values

ValueCountFrequency (%)
The Global Competitiveness Report103
100.0%

Length

2026-09-09T11:13:06.813821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:13:06.899171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
report103
25.0%
competitiveness103
25.0%
global103
25.0%
the103
25.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
GCR
103 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGCR
2nd rowGCR
3rd rowGCR
4th rowGCR
5th rowGCR

Common Values

ValueCountFrequency (%)
GCR103
100.0%

Length

2026-09-09T11:13:06.983833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:13:07.068863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
gcr103
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
The Global Competitiveness Report
103 

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThe Global Competitiveness Report
2nd rowThe Global Competitiveness Report
3rd rowThe Global Competitiveness Report
4th rowThe Global Competitiveness Report
5th rowThe Global Competitiveness Report

Common Values

ValueCountFrequency (%)
The Global Competitiveness Report103
100.0%

Length

2026-09-09T11:13:07.150784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:13:07.236886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
report103
25.0%
competitiveness103
25.0%
global103
25.0%
the103
25.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct12
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size952.0 B
Institutions
26 
Infrastructure
12 
Labour market
12 
Innovation capability
10 
Financial system
9 
Other values (7)
34 

Length

Max length23
Median length13
Mean length13.70873786
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowInstitutions
2nd rowInstitutions
3rd rowInstitutions
4th rowInstitutions
5th rowInstitutions

Common Values

ValueCountFrequency (%)
Institutions26
25.2%
Infrastructure12
11.7%
Labour market12
11.7%
Innovation capability10
 
9.7%
Financial system9
 
8.7%
Skills9
 
8.7%
Business dynamism8
 
7.8%
Product market7
 
6.8%
ICT adoption5
 
4.9%
Macroeconomic stability2
 
1.9%
Other values (2)3
 
2.9%

Length

2026-09-09T11:13:07.332608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
institutions26
16.5%
market21
13.3%
infrastructure12
 
7.6%
labour12
 
7.6%
innovation10
 
6.3%
capability10
 
6.3%
system9
 
5.7%
financial9
 
5.7%
skills9
 
5.7%
business8
 
5.1%
Other values (8)32
20.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ชื่อดัชนีย่อย_lv2
Categorical

HIGH CORRELATION
MISSING

Distinct25
Distinct (%)26.9%
Missing10
Missing (%)9.7%
Memory size952.0 B
Transport infrastructure
8 
Flexibility
8 
Future orientation of government
7 
Depth
 
5
Skills of current workforce
 
5
Other values (20)
60 

Length

Max length33
Median length22
Mean length20
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)4.3%

Sample

1st rowSecurity
2nd rowSecurity
3rd rowSecurity
4th rowSecurity
5th rowSocial capital

Common Values

ValueCountFrequency (%)
Transport infrastructure8
 
7.8%
Flexibility8
 
7.8%
Future orientation of government 7
 
6.8%
Depth5
 
4.9%
Skills of current workforce5
 
4.9%
Interaction and diversity4
 
3.9%
Security4
 
3.9%
Utility infrastructure4
 
3.9%
Entrepreneurial culture4
 
3.9%
Meritocracy and incentivization4
 
3.9%
Other values (15)40
38.8%
(Missing)10
 
9.7%

Length

2026-09-09T11:13:07.493447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and16
 
7.8%
of14
 
6.8%
infrastructure12
 
5.8%
future10
 
4.9%
workforce9
 
4.4%
transport8
 
3.9%
flexibility8
 
3.9%
orientation7
 
3.4%
government7
 
3.4%
skills7
 
3.4%
Other values (31)108
52.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

รายละเอียดดัชนีย่อย2
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
Trademark applications
 
1
Willingness to delegate authority
 
1
Fibre internet subscriptions
 
1
Quality of land administration
 
1
Energy efficiency regulation
 
1
Other values (98)
98 

Length

Max length57
Median length27
Mean length27.10679612
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)100.0%

Sample

1st rowOrganized crime
2nd rowHomicide rate
3rd rowTerrorism incidence
4th rowReliability of police services
5th rowSocial capital

Common Values

ValueCountFrequency (%)
Trademark applications1
 
1.0%
Willingness to delegate authority1
 
1.0%
Fibre internet subscriptions1
 
1.0%
Quality of land administration1
 
1.0%
Energy efficiency regulation1
 
1.0%
Extent of market dominance1
 
1.0%
Flexibility of wage determination1
 
1.0%
Road connectivity1
 
1.0%
Renewable energy regulation1
 
1.0%
Efficiency of seaport services1
 
1.0%
Other values (93)93
90.3%

Length

2026-09-09T11:13:07.667662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of33
 
9.3%
efficiency7
 
2.0%
to7
 
2.0%
in7
 
2.0%
and6
 
1.7%
services6
 
1.7%
labour4
 
1.1%
subscriptions4
 
1.1%
quality4
 
1.1%
regulation4
 
1.1%
Other values (223)273
76.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ปี
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
2019
103 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019103
100.0%

Length

2026-09-09T11:13:07.818037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:13:07.904322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2019103
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct85
Distinct (%)85.9%
Missing4
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean61.37575758
Minimum9.6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2026-09-09T11:13:08.010657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9.6
5-th percentile26.45
Q149.4
median56.6
Q374.5
95-th percentile100
Maximum100
Range90.4
Interquartile range (IQR)25.1

Descriptive statistics

Standard deviation21.43517561
Coefficient of variation (CV)0.3492449862
Kurtosis-0.1423478716
Mean61.37575758
Median Absolute Deviation (MAD)10.5
Skewness0.159052712
Sum6076.2
Variance459.4667532
MonotonicityNot monotonic
2026-09-09T11:13:08.186407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1008
 
7.8%
52.52
 
1.9%
55.42
 
1.9%
54.32
 
1.9%
51.42
 
1.9%
802
 
1.9%
49.72
 
1.9%
43.82
 
1.9%
64.91
 
1.0%
261
 
1.0%
Other values (75)75
72.8%
(Missing)4
 
3.9%
ValueCountFrequency (%)
9.61
1.0%
11.91
1.0%
12.51
1.0%
21.81
1.0%
261
1.0%
26.51
1.0%
30.31
1.0%
33.31
1.0%
361
1.0%
371
1.0%
ValueCountFrequency (%)
1008
7.8%
98.91
 
1.0%
98.51
 
1.0%
98.11
 
1.0%
961
 
1.0%
94.81
 
1.0%
90.81
 
1.0%
88.91
 
1.0%
86.61
 
1.0%
85.71
 
1.0%

Rank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.14563107
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.0 B
2026-09-09T11:13:08.362041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.2
Q134.5
median53
Q373.5
95-th percentile105.9
Maximum134
Range133
Interquartile range (IQR)39

Descriptive statistics

Standard deviation28.90939455
Coefficient of variation (CV)0.5242372604
Kurtosis-0.09049693425
Mean55.14563107
Median Absolute Deviation (MAD)20
Skewness0.3926402754
Sum5680
Variance835.7530935
MonotonicityNot monotonic
2026-09-09T11:13:08.710873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
483
 
2.9%
373
 
2.9%
563
 
2.9%
753
 
2.9%
793
 
2.9%
553
 
2.9%
663
 
2.9%
333
 
2.9%
263
 
2.9%
362
 
1.9%
Other values (58)74
71.8%
ValueCountFrequency (%)
12
1.9%
21
1.0%
51
1.0%
81
1.0%
91
1.0%
111
1.0%
141
1.0%
181
1.0%
191
1.0%
212
1.9%
ValueCountFrequency (%)
1341
1.0%
1301
1.0%
1161
1.0%
1131
1.0%
1071
1.0%
1061
1.0%
1051
1.0%
1041
1.0%
991
1.0%
961
1.0%

หมายเหตุ
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)25.0%
Missing99
Missing (%)96.1%
Memory size952.0 B
ค่า Score ตาม Original file คือ N/Appl.
4 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowค่า Score ตาม Original file คือ N/Appl.
2nd rowค่า Score ตาม Original file คือ N/Appl.
3rd rowค่า Score ตาม Original file คือ N/Appl.
4th rowค่า Score ตาม Original file คือ N/Appl.

Common Values

ValueCountFrequency (%)
ค่า Score ตาม Original file คือ N/Appl.4
 
3.9%
(Missing)99
96.1%

Length

2026-09-09T11:13:08.891305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:13:08.985801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
n/appl4
14.3%
คือ4
14.3%
file4
14.3%
original4
14.3%
ตาม4
14.3%
score4
14.3%
ค่า4
14.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2026-09-09T11:13:05.660775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:13:05.409695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:13:05.788782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:13:05.527546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2026-09-09T11:13:09.054582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2026-09-09T11:13:09.230188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2026-09-09T11:13:09.404567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2026-09-09T11:13:09.581796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2026-09-09T11:13:06.036855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2026-09-09T11:13:06.323908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-09-09T11:13:06.513676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2026-09-09T11:13:06.636218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ชื่อดัชนีหลักตัวย่อดัชนีหลักชื่อเดิมดัชนีหลักชื่อดัชนีย่อย_lv1ชื่อดัชนีย่อย_lv2รายละเอียดดัชนีย่อย2ปีScoreRankหมายเหตุ
0The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsSecurityOrganized crime201954.395NaN
1The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsSecurityHomicide rate201990.881NaN
2The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsSecurityTerrorism incidence201969.9134NaN
3The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsSecurityReliability of police services201943.7105NaN
4The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsSocial capitalSocial capital201953.249NaN
5The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsChecks and balancesBudget transparency201956.036NaN
6The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsChecks and balancesJudicial independence201949.764NaN
7The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsChecks and balancesEfficiency of legal framework in challenging regulations201938.675NaN
8The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsChecks and balancesFreedom of the press201955.9113NaN
9The Global Competitiveness ReportGCRThe Global Competitiveness ReportInstitutionsPublic-sector performanceBurden of government regulation201945.850NaN

Last rows

ชื่อดัชนีหลักตัวย่อดัชนีหลักชื่อเดิมดัชนีหลักชื่อดัชนีย่อย_lv1ชื่อดัชนีย่อย_lv2รายละเอียดดัชนีย่อย2ปีScoreRankหมายเหตุ
93The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityInteraction and diversityDiversity of workforce201968.027NaN
94The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityInteraction and diversityState of cluster development201951.447NaN
95The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityInteraction and diversityInternational co-inventions20199.661NaN
96The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityInteraction and diversityMulti-stakeholder collaboration201952.140NaN
97The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityResearch and developmentScientific publications201984.039NaN
98The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityResearch and developmentPatent applications201912.566NaN
99The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityResearch and developmentR&D expenditures201926.048NaN
100The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityResearch and developmentResearch institutions prominence201911.943NaN
101The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityCommercializationBuyer sophistication201955.426NaN
102The Global Competitiveness ReportGCRThe Global Competitiveness ReportInnovation capabilityCommercializationTrademark applications201967.870NaN