Description
You need to think of a topic about fintech and then follow this topic to complete all parts
Provided some literature and data I collected previous
The methods and results section can be summarized using the data I collected
paper will follow the framework below:
Abstract: up to 250 words
Introduction: background information, purpose of your study, and claims for significance
Literature Review: summary of relevant literature and your hypotheses
Method: data, analytic framework, and statistical analysis
Results: analysis output and interpretation
Discussion: findings in relation to previous literature and business/policy implications
Conclusion: brief summary, study limitations, and direction for the future study
References: in APA style
Submit the draft in a Word file.
Unformatted Attachment Preview
Article
Performance
Naeher, D. and
Narayanan, R. (2023)
‘Attracting private
capital for
development: Are
poorer countries less
efficient?’,
Private capital economic
International
efficiency
Economics and
Economic Policy,
20(1), pp. 1–26.
Available at:
https://doi.org/10.10
07/s10368-022-005492.
Input and Output
-factors
Input factors: Inverse of real
GDP per capita (constant 2010
US$); 2007–2017, GDP growth
(annual %); 2007–2017, Ease of
doing business score, scale from
0 (worst) to 100 (best)
regulatory performance;
2010–2018, Sum of exports and
imports of goods and services (%
of GDP); 2007–2017, Index of
economic freedom, average score
for trade, investment, and
financial freedom, scale from 0
(lowest) to 100 (highest) degree
of freedom; 2007–2018, Inverse
of the inflation rate (annual %);
2007–2017, Logistics Performance
Index, overall score; 2007, 2010,
2012, 2014, 2016, 2018, Fixed
broadband subscriptions per 100
people; 2007–2017, Liquid
liabilities (% of GDP);
2007–2016, Inverse of 5-bank
asset concentration; 2007–2016,
Total natural resources rents (%
of GDP); 2007–2016, Secondary
school enrollment (% net);
2007–2017
– Output factors: Foreign direct
investment, net inflows (% of
– Input factors: percentage of
people with tertiary education
for in all 238 EU NUTS 2
regionsfor the year 2020
Marto, M., Lourenço
Marques, J. and
– Output factors: GDP per capita
Madaleno, M. (2022)
in for all 238 EU NUTS 2 regions
‘An Evaluation of
for the year 2020
the Efficiency of
Tertiary Education in
This study additionally employed
the Explanation of
a two-stage least squares method
the Performance of
The economic promotion efficiency to specifically investigate the
GDP per Capita
of tertiary education
impact of variable spatial lags
Applying Data
on the results, and identified
Envelopment Analysis
that certain countries in Central
(DEA)’,
and Northern Europe, as well as
Sustainability,
some regions in Ireland, exhibit
14(23), p. 15524.
the greatest effectiveness in
Available at:
promoting higher education
https://doi.org/10.33
economics, whereas the
90/su142315524.
performance is poorest in
Southern European and Eastern
European regions of the European
Union
Carracedo, P. and
Puertas, R. (2022)
‘Country Efficiency
– Input factors: Research and
Study Based on
development expenditure in 75
Science & Technology
countries worldwide during the
Indicators: DEA
period 2010–2018.
Approach’,
Research and development
International Journal
– Output factors: number of
technical efficiency
of Innovation and
patents, trademark applications
Technology
and scientific and technical
Management, 19(01),
journal papers in 75 countries
p. 2140005. Available
worldwide during the period
at:
2010–2018
https://doi.org/10.11
42/S0219877021400058.
Data availability
1. Capital Indicator: the level
of capital in the region is
reflected by the indicator of
social fixed investment in the
region.
My own inputs
2. Labor Force Indicator: the
regional urban employment
1. Capital Indicatort: Available
population is used to measure the 2. Labor Force Indicator:
level of human resources in the Available
region.
3. Technology indicator:
Available
3. Technology indicator: the
number of R&D personnel in
industrial enterprises above
large scale is used to measure
the level of technology
development in the region.
1.Economic Development Indicator:
using total GDP to measure the
level of economic development of
the region
My own outputs
1. Economic Development
Indicator: Available
2.People’s Welfare Indicator: the
2. People’s Welfare Indicator:
level of consumption of the
Available
population is used to reflect the
welfare of the people brought
about by the economic development
of the region.
Lábaj, M., Luptáčik, M. and Nežinský, E. (2014) ‘Data envelopment analysis for measuring economic gro
Lei, M. et al. (2013) ‘DEA analysis of FDI attractiveness for sustainable development: Evidence from
Marto, M., Lourenço Marques, J. and Madaleno, M. (2022) ‘An Evaluation of the Efficiency of Tertiary
Lábaj, M., Luptáčik, M. and Nežinský, E. (2014) ‘Data envelopment analysis for measuring economic gro
Lei, M. et al. (2013) ‘DEA analysis of FDI attractiveness for sustainable development: Evidence from
Marto, M., Lourenço Marques, J. and Madaleno, M. (2022) ‘An Evaluation of the Efficiency of Tertiary
measuring economic growth in terms of welfare beyond GDP’, Empirica, 41(3), pp. 407–424. Available at: https:/
opment: Evidence from Chinese provinces’, Decision Support Systems, 56, pp. 406–418. Available at: https://doi
fficiency of Tertiary Education in the Explanation of the Performance of GDP per Capita Applying Data Envelopmen
measuring economic growth in terms of welfare beyond GDP’, Empirica, 41(3), pp. 407–424. Available at: https:/
opment: Evidence from Chinese provinces’, Decision Support Systems, 56, pp. 406–418. Available at: https://doi
fficiency of Tertiary Education in the Explanation of the Performance of GDP per Capita Applying Data Envelopmen
plying Data Envelopment Analysis (DEA)’, Sustainability, 14(23), p. 15524. Available at: https://doi.org/10.339
plying Data Envelopment Analysis (DEA)’, Sustainability, 14(23), p. 15524. Available at: https://doi.org/10.339
https://doi.org/10.3390/su142315524.
https://doi.org/10.3390/su142315524.
Study Title
Performance Metrics
Assessed
Pham, P. T., & Quddus, A. (2021)
Impact of innovation activities on firm
efficiency
Yeh, L. T., Chang, D. S., & Li, H. M.
(2022)
Network DEA model for banking
efficiency
Syaputra, R., & Abidin, Z. (2022)
Arctic Council’s Arctic and observer
states
Performance analysis of regional
development banks in Indonesia
Efficiency of Arctic and observer states
Inputs
R&D expenditure, number of patents, commercialization performance
Operational costs, innovation investment, loan volumes, profitability
metrics
Capital investment, labor, loan disbursement rates, financial returns
Labor (number of personnel involved in Arctic-related programs),
capital (financial investment in Arctic initiatives), technology
(investment in Arctic exploration and preservation), R&D (spending
on Arctic research and development projects), environmental
protection expenditure (funds allocated to mitigate environmental
impact in the Arctic)
Outputs
Innovation efficiency
Governance, innovation, and operations efficiency
Operational efficiency
Environmental impact (reduction in CO2 emissions from Arctic operations), economic
benefits (revenue from Arctic activities such as shipping routes and natural
resources), research output (number of publications and patents related to the
Arctic), socio-economic development (improvements in living standards for
indigenous and local Arctic communities), sustainability achievements (progress in
achieving sustainable development goals in the Arctic region)
References
Pham, P. T., & Quddus, A. (2021). The impact of innovation activities on firm
efficiency: Data envelopment analysis. Journal of Asian Finance, Economics
and Business.
Yeh, L. T., Chang, D. S., & Li, H. M. (2022). Developing a network data
envelopment analysis model to measure the efficiency of banking with the
governance, innovation, and operations. Managerial and Decision
Economics, 43(7), 2863-2874.
Syaputra, R., & Abidin, Z. (2022). Performance Analysis Of Regional
Development Efficiency Banks In Indonesia Using Data Envelopment
Analysis (DEA) Approach. American International Journal of Business
Management, 5(01), 134-140.
Article
Performance
Ntwiga, D, B. (2020). Technical
Efficiency in the Kenyan Banking
Sector: Influence of Fintech and
Banks Collaboration. SocioEconomic Planning Sciences, 68.
Technical efficiency of banks
Yang, L. & Wang, S. (2022). Do
fintech applications promote
regional innovation efficiency?
Empirical evidence from China.
Technology in Society, 68.
Innovation efficiency
Yao,Y., Hu, D., Yang, C. & Tan,
Y. (2021). The impact and
mechanism of fintech on green
total factor productivity. Green
Finance, 3(2), 198–221. DOI:
10.3934/GF.2021011
Green development effect
My own inputs
1. Financial Investment: The amount of
financial resources allocated to Fintech
research and development projects.
2. Human Capital: The expertise and skills
of individuals involved in Fintech
research, including researchers, engineers,
and financial analysts.
3. Regulatory Framework: The level of
regulatory support and infrastructure in
place to facilitate Fintech innovation and
implementation.
4. Technology Infrastructure: The
availability and sophistication of
technological infrastructure, including
internet connectivity, data centers, and
cybersecurity measures.
My own outputs
1. Fintech Innovation Index: A measure of
the quantity and quality of Fintech
innovations produced by Arctic and observer
states, including new technologies,
platforms, and financial products.
2. Financial Inclusion Index: The extent
to which Fintech initiatives have
contributed to improving access to
financial services for marginalized or
underserved populations in Arctic and
observer states.
3. Environmental Impact Index: An
assessment of the environmental
consequences of Fintech activities,
including energy consumption, carbon
emissions, and electronic waste generation.
This would include both desirable outcomes,
such as reduced paper consumption, and
undesirable outcomes, such as increased
electronic waste.
4. Economic Growth Index: The overall
economic impact of Fintech initiatives on
Arctic and observer states, including GDP
growth, job creation, and investment
attraction.
Input and Output factors
– Input factors: Financial statement data (e.g., possibly
operational costs, capital investments, etc.) from 2009-2018.
– Output factors: Efficiency scores derived from the DEA model,
which include technical efficiency (TE), pure technical
efficiency (PTE), and scale efficiency (SE). Additionally,
specific financial variables such as loan intensity, return on
assets, and cost of intermediation are mentioned as influencing
factors in the panel regression model.
– Input factors: The study likely considers factors related to
R&D investments, such as expenditures on research and
development, human capital investment, technological
infrastructure, etc.
– Output factors: Output factors might include measures of
innovation outcomes, such as the number of patents, successful
product launches, market penetration of new products, etc.
Additionally, the study may consider measures related to the
commercialization of innovations, such as profitability and
– Input factors: The study likely considers factors related to
financial technology (fintech) development, such as investments
in fintech infrastructure, technological innovation in
financial services, regulatory policies, etc. Additionally,
the financial support policy index (FSP) may serve as an input
factor.
– Output factors: Output factors would include measures related
to green development, specifically green total factor
productivity (GTFP), which reflects the efficiency of resource
utilization in generating environmentally friendly outputs.
The study may also consider intermediate factors such as
Data availability
1. Financial Investment: Available
2. Human Capital: Available
3. Regulatory Framework: Available
4. Technology Infrastructure: Available
1. Fintech Innovation Index: Available
2. Financial Inclusion Index: Available
3. Environmental Impact Index: Available
4. Economic Growth Index: Available
Instruction:
1. make a separate sheet for each input or output factor
2. put data source on each sheet
3. use the wide format: data by each DMU (by column) and time (by row)
4. locate data at the same place (e.g., DMU 1’s 2009 data in cell D6 across all input and output factors)
5. See the example: Population and GDP
Name
Institution Affiliation
Date
tput factors)
All Years
Input
Year
DMU
2019 Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2018 Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2017 Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Charges for the use of IP
R&D (% of GDP)
(BoP, current US$)
1.755730033
13588255249
3.170490026
42273000000
1.885059953
15161436539
0.993529975
72719073.99
2.184350014
40693815668
3.387579918
6949656309
1.631459951
63213681.88
3.167789936
16959534532
1.250249982
6838705335
2.7996099
1000220941
2.191790104
12396256258
1.225270033
94262327695
4.627029896
9909400000
2.896850109
1528895648
2.244630098
34370466478
3.218240023
26774027971
1.737200022
12724585776
3.010099888
42736000000
1.809579968
17249096447
0.936619997
61738440.48
2.138799906
45150660652
3.321059942
4891649272
1.409989953
66881559.96
3.110110044
16367799982
1.241510034
6644636046
2.75748992
1066485645
2.196660042
14350603897
1.167199969
85482296094
4.516329765
9812300000
2.966029882
1584730653
2.140579939
35782953953
3.219199896
21993730962
1.687019944
11833542181
2.904320002
44406000000
1.899049997
15810634176
0.896260023
67754950.86
2.178570032
42021672733
3.362790108
5119181581
1.276849985
62014803.52
3.047100067
14364977912
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2016 Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2015 Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2014 Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
1.209769964
2.727869987
2.198879957
1.251870036
4.292059898
2.931240082
2.116029978
3.166359901
1.729030013
2.853499889
2.07291007
0.842410028
2.150810003
3.247359991
1.243059993
2.94039011
1.189859986
2.724420071
2.222379923
1.175070047
3.987040043
3.092829943
2.100330114
3.106659889
1.693240047
2.786999941
2.174449921
1.043409944
2.14605999
3.219029903
1.46776998
2.933789968
1.221789956
2.871959925
2.227020025
1.182119966
3.978199959
3.054970026
2.057009935
3.24071002
1.714169979
2.717859983
2.082279921
1.030110002
2.173300028
3.101840019
1.430230021
5065245409
1022698642
15849154072
75297536577
9701600000
1488297357
28746479819
21381387584
11567469044
41974000000
15666647584
66926847.32
37624231190
3332624578
51669819.03
11295106926
4987966185
876927845
14758389447
76560417794
9429400000
1625778239
23979580208
20246369822
10732654496
35178000000
19400287309
48349772.01
72163826761
4264682982
43591116.46
10117945574
4520612371
832316827.1
15660393096
70703012434
10055900000
1530214735
22022366055
17033762146
11712157364
37562000000
20877550215
47451213.57
47319178096
3900845338
61551392.44
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2013 Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2.877840042
1.241590023
3.147510052
2.275919914
1.52098
4.077859879
2.914089918
2.022429943
3.367880106
1.70539999
2.702150106
1.921040058
0.948719978
2.15605998
3.260420084
1.724120021
2.835989952
1.27481997
3.271369934
2.237030029
1.567579985
3.951240063
2.970479965
1.997859955
3.27895999
10729008199
4474717118
1178587922
12764952188
57267745573
10546000000
1695330425
22613800782
20941598563
11801326421
35294000000
22992008311
51742886.95
37444374674
2850908443
49014653.79
8696432170
4514279640
1917947814
11054534501
42172564892
9836800000
1735972102
21033078371
17831143180
Output
Foreign direct
Researchers in R&D
investment (BoP,
Portfolio Investment
Commercial bank branches
(per million people)
current US$)
(BoP, current US$)
(per 100,000 adults)
4894.20752
26965431145.15
-1565030418.94
19.73
4308.874023 -201057000000.00
-244920000000.00
30.46
7275.631348 -39191638311.78
108603888844.77
7.82
3364.467285
-1272922205.62
-2655256488.71
10.08
5640.245605
-836227817.91
37191877518.64
9.24
7697.835938
6379736740.78
12012913841.36
16.42
3769.836914
-1190231245.84
237065479.64
8.93
5428.215332
98812746279.98
81950239568.62
10.97
3067.661133
8921032437.20
-56072941242.53
49.6
7246.271973
-8568455133.47
-28373260159.58
4.49
4870.456055
30864497546.20
-69492047430.73
34.17
4836.074219 -97444443421.70
32398324973.00
19.89
8322.587891
25604700000.00
42377000000.00
15.08
7727.239258
10135346257.05
4024653536.71
20
1485.817505 -50259822364.76
-57947645385.64
8.8
5409.535645 218323780735.71
86642953831.49
33.82
4758.102539
20156533320.49
3092046327.93
20.13
4261.989258 -345435000000.00
78785000000.00
30.88
6786.695801 -58349843384.14
53044179882.74
8.13
3092.745361
-260129818.34
2078680042.26
11.45
5535.476074
57011679381.86
8466672375.70
10.94
7433.210449
14173567423.59
-10655491353.38
14.91
3765.621826
-1459135847.16
999816138.55
9.68
5239.97168
29011101778.83
176504907851.64
11.17
3003.562744 -20511155392.23
28823038904.11
55.11
6872.983887
13725083434.58
-24611541884.88
4.8
4756.583984
61682963434.95
18859193994.19
34.79
4749.617188
27441629240.53
43994088661.39
20.44
7913.501465
26037800000.00
47420700000.00
15.28
7635.777344
-2041425450.86
52837283466.38
20.85
1319.355957 -92338473352.48
-106873525546.72
8.81
5361.96582 134928938193.78
92911832022.28
33.94
4467.15625
52829985894.71
-75109610881.83
20.75
3958.563965
28590000000.00
-250083000000.00
31.21
6777.637207 -36941108315.08
19903760160.76
8.22
2994.609863
-932827114.81
1772186359.70
13.6
5302.344727
43897890313.69
43681990207.61
11.92
7308.65918
15239796026.02
3858366621.68
16.17
3552.230469
-1085198792.99
3014901302.83
10.06
5087.254395
36124571462.22
223066732686.92
12.95
2863.750244
14575952991.29
6731.791992
-3464257880.13
4624.696777
12702511140.09
5155.370605 -55346188075.96
7450.557617
16156500000.00
7683.441895
5836604485.43
1237.741943 -27790987919.51
5331.271484 154947985695.97
4428.172852
33404095512.75
3830.462646 -174573000000.00
6897.044434 -25861166110.97
2891.363525
-264618974.69
5128.771484
49999336305.92
7107.980469 -14370211554.91
3296.207275
-560729295.01
4862.625
47174192459.00
2727.428711
12384254171.60
6543.96582
15487777451.94
4467.428223
41841316016.55
5184.715332
-9219282218.49
7056.342773
17785200000.00
7873.96875
10187621831.32
1210.741089
41674876169.52
5235.156738 137656207444.10
4582.379395
23927632715.34
3874.786621 -209363000000.00
6977.669434 -24551342898.19
2741.438477
-680317054.69
4911.127441
60422293503.45
6810.790039
4645839623.08
3188.26123
140346638.37
4733.358398
68388393318.93
2636.847656
33299356666.04
6856.334961 -18241204965.20
4386.236328
8404253503.82
5282.949707 -47433558092.54
7018.358887
19583000000.00
7561.016602
5406579386.65
1164.940308 -68098649764.78
5198.097656 133162791214.28
4595.637695
805191688.45
3836.598145 135673000000.00
6632.226074 -16220985754.31
3014.187012
188404473.21
4950.793457
786049364.97
6872.72168
4819176705.87
3285.461182
-645747876.49
34991893869.69
-3154351263.56
25219875130.84
-62993130040.17
57853000000.00
2198675055.05
-29497793098.26
-49213900461.95
-102982524308.76
-193776000000.00
14489054096.83
3707438117.70
28549509631.20
6307394456.19
2716915396.53
218090929462.71
64996063918.37
4310875885.56
-169240236.29
30264537968.93
66970200000.00
-11701036788.40
52270616754.19
268042440742.74
-37884016917.95
-106755000000.00
61651555652.54
-85242496.91
-39371656227.61
-13132270344.38
599721575.29
213012801836.65
11991763762.09
618494105.51
42635898465.63
-99086557299.09
49529800000.00
-17028220834.30
66470061883.31
132433986065.34
-33792951517.00
-114932000000.00
46534154137.56
-1309719655.42
59571398822.21
23471019154.11
645485436.70
58.57
6.46
35.98
20.75
15.44
20.71
8.75
33.95
22.25
32.1
8.68
13.44
12.46
17.54
10.42
13.55
61.81
7.31
37.19
21.61
16.24
24.05
8.76
34.07
22.84
32.7
8.95
14.55
13.99
19.32
11.15
14.06
67.51
8.49
37.6
21.86
16.75
24.69
8.48
34.14
23.14
32.42
9.01
16.24
14.79
21.11
12.39
4304.865234
87919371869.14
2628.943848
12972045515.77
7022.442871 -17108721387.90
4282.457031
47288734074.28
5332.266602
-6747671077.87
6864.206055
18724900000.00
7345.600098
3342265730.45
1104.08313 -144967626758.58
5352.762207 118172189795.21
4678.261719 -12563236012.73
3735.776611 104665000000.00
6631.40625 -19110684887.72
2809.611328
-436800371.87
4920.688477
87174417702.78
6686.092773
26576115126.54
3338.216553
-246699019.41
4343.317383
26093621454.52
2638.027832 -14263389021.58
7223.024902
-2236027725.24
4199.837402 -13973846914.22
5181.936035 -21251151864.34
6453.847168
15551200000.00
7102.160645
6327998758.53
1081.9646 -217957551783.69
5169.265137 145036160640.49
167003343430.67
-7982916147.77
5242410422.90
-26765450439.10
-38061113346.20
30608900000.00
10568098227.10
-82429447386.75
-40342220314.65
-34483049123.13
-30689000000.00
64035962598.90
1888693892.87
5344474297.89
-48767101424.78
721511621.22
209538930526.72
-86445930881.29
-4433931669.80
-79782483832.98
47693796508.43
9344500000.00
-11916788706.90
-52891117744.13
-274651950434.83
14.56
69.68
9.42
38.06
21.8
17.2
27.79
8.01
33.9
23.32
33.64
9.11
18.08
17.35
21.54
13.95
14.91
73.61
14.1
38.63
22.79
18.02
30.01
7.8
33.91
YR2019
Input
DMU
Canada
Top
Fintech United States
Countries Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
Charges for the use of IP
R&D (% of GDP) (BoP, current US$)
1.755730033
13588255249
3.170490026
42273000000
1.885059953
15161436539
0.993529975
72719073.99
2.184350014
40693815668
3.387579918
6949656309
1.631459951
63213681.88
3.167789936
16959534532
1.250249982
6838705335
2.7996099
1000220941
2.191790104
12396256258
1.225270033
94262327695
4.627029896
9909400000
2.896850109
1528895648
2.244630098
34370466478
3.218240023
26774027971
Output
Researchers in R&D
Foreign direct investment
Portfolio Investment
(per million people)
(BoP, current US$)
(BoP, current US$)
4894.20752
26965431145.15
-1565030418.94
4308.874023
-201057000000.00
-244920000000.00
7275.631348
-39191638311.78
108603888844.77
3364.467285
-1272922205.62
-2655256488.71
5640.245605
-836227817.91
37191877518.64
7697.835938
6379736740.78
12012913841.36
3769.836914
-1190231245.84
237065479.64
5428.215332
98812746279.98
81950239568.62
3067.661133
8921032437.20
-56072941242.53
7246.271973
-8568455133.47
-28373260159.58
4870.456055
30864497546.20
-69492047430.73
4836.074219
-97444443421.70
32398324973.00
8322.587891
25604700000.00
42377000000.00
7727.239258
10135346257.05
4024653536.71
1485.817505
-50259822364.76
-57947645385.64
5409.535645
218323780735.71
86642953831.49
Commercial bank branches
(per 100,000 adults)
19.73
30.46
7.82
10.08
9.24
16.42
8.93
10.97
49.6
4.49
34.17
19.89
15.08
20
8.8
33.82
Research and development expenditure (% of GDP)
Source:
World Development Indicators, World Bank
https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD
DMU
Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2013
1.70539999
2.702150106
1.921040058
0.948719978
2.15605998
3.260420084
1.724120021
2.835989952
1.27481997
3.271369934
2.237030029
1.567579985
3.951240063
2.970479965
1.997859955
3.27895999
2014
1.714169979
2.717859983
2.082279921
1.030110002
2.173300028
3.101840019
1.430230021
2.877840042
1.241590023
3.147510052
2.275919914
1.52098
4.077859879
2.914089918
2.022429943
3.367880106
s, World Bank
dicator/NY.GDP.MKTP.PP.KD
2015
1.693240047
2.786999941
2.174449921
1.043409944
2.14605999
3.219029903
1.46776998
2.933789968
1.221789956
2.871959925
2.227020025
1.182119966
3.978199959
3.054970026
2.057009935
3.24071002
2016
1.729030013
2.853499889
2.07291007
0.842410028
2.150810003
3.247359991
1.243059993
2.94039011
1.189859986
2.724420071
2.222379923
1.175070047
3.987040043
3.092829943
2.100330114
3.106659889
2017
1.687019944
2.904320002
1.899049997
0.896260023
2.178570032
3.362790108
1.276849985
3.047100067
1.209769964
2.727869987
2.198879957
1.251870036
4.292059898
2.931240082
2.116029978
3.166359901
2018
2019
1.737200022 1.75573003
3.010099888 3.17049003
1.809579968 1.88505995
0.936619997 0.99352998
2.138799906 2.18435001
3.321059942 3.38757992
1.409989953 1.63145995
3.110110044 3.16778994
1.241510034 1.25024998
2.75748992 2.7996099
2.196660042 2.1917901
1.167199969 1.22527003
4.516329765 4.6270299
2.966029882 2.89685011
2.140579939 2.2446301
3.219199896 3.21824002
Charges for the use of intellectual property, payments (BoP, current US$)
Source:
World Development Indicators, World Bank
https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD
DMU
Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2013
11801326421
35294000000
22992008311
51742886.95
37444374674
2850908443
49014653.79
8696432170
4514279640
1917947814
11054534501
42172564892
9836800000
1735972102
21033078371
17831143180
2014
11712157364
37562000000
20877550215
47451213.57
47319178096
3900845338
61551392.44
10729008199
4474717118
1178587922
12764952188
57267745573
10546000000
1695330425
22613800782
20941598563
2015
10732654496
35178000000
19400287309
48349772.01
72163826761
4264682982
43591116.46
10117945574
4520612371
832316827.1
15660393096
70703012434
10055900000
1530214735
22022366055
17033762146
.MKTP.PP.KD
2016
11567469044
41974000000
15666647584
66926847.32
37624231190
3332624578
51669819.03
11295106926
4987966185
876927845
14758389447
76560417794
9429400000
1625778239
23979580208
20246369822
2017
11833542181
44406000000
15810634176
67754950.86
42021672733
5119181581
62014803.52
14364977912
5065245409
1022698642
15849154072
75297536577
9701600000
1488297357
28746479819
21381387584
2018
2019
12724585776 13588255249
42736000000 42273000000
17249096447 15161436539
61738440.48 72719073.99
45150660652 40693815668
4891649272 6949656309
66881559.96 63213681.88
16367799982 16959534532
6644636046 6838705335
1066485645 1000220941
14350603897 12396256258
85482296094 94262327695
9812300000 9909400000
1584730653 1528895648
35782953953 34370466478
21993730962 26774027971
Researchers in R&D (per million people)
Source:
World Development Indicators, World Bank
https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD
DMU
Canada
Top
Fintech United States
Countries Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2013
4678.261719
3735.776611
6631.40625
2809.611328
4920.688477
6686.092773
3338.216553
4343.317383
2638.027832
7223.024902
4199.837402
5181.936035
6453.847168
7102.160645
1081.9646
5169.265137
2014
4595.637695
3836.598145
6632.226074
3014.187012
4950.793457
6872.72168
3285.461182
4304.865234
2628.943848
7022.442871
4282.457031
5332.266602
6864.206055
7345.600098
1104.08313
5352.762207
2015
4582.379395
3874.786621
6977.669434
2741.438477
4911.127441
6810.790039
3188.26123
4733.358398
2636.847656
6856.334961
4386.236328
5282.949707
7018.358887
7561.016602
1164.940308
5198.097656
.MKTP.PP.KD
2016
4428.172852
3830.462646
6897.044434
2891.363525
5128.771484
7107.980469
3296.207275
4862.625
2727.428711
6543.96582
4467.428223
5184.715332
7056.342773
7873.96875
1210.741089
5235.156738
2017
4467.15625
3958.563965
6777.637207
2994.609863
5302.344727
7308.65918
3552.230469
5087.254395
2863.750244
6731.791992
4624.696777
5155.370605
7450.557617
7683.441895
1237.741943
5331.271484
2018
4758.102539
4261.989258
6786.695801
3092.745361
5535.476074
7433.210449
3765.621826
5239.97168
3003.562744
6872.983887
4756.583984
4749.617188
7913.501465
7635.777344
1319.355957
5361.96582
2019
4894.20752
4308.874023
7275.631348
3364.467285
5640.245605
7697.835938
3769.836914
5428.215332
3067.661133
7246.271973
4870.456055
4836.074219
8322.587891
7727.239258
1485.817505
5409.535645
Foreign direct investment, net (BoP, current US$)
Source:
World Development Indicators, World Bank
https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD
DMU
Canada
Top
Fintech United States
Countries Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2013
-12563236012.73
104665000000.00
-19110684887.72
-436800371.87
87174417702.78
26576115126.54
-246699019.41
26093621454.52
-14263389021.58
-2236027725.24
-13973846914.22
-21251151864.34
15551200000.00
6327998758.53
-217957551783.69
145036160640.49
2014
805191688.45
135673000000.00
-16220985754.31
188404473.21
786049364.97
4819176705.87
-645747876.49
87919371869.14
12972045515.77
-17108721387.90
47288734074.28
-6747671077.87
18724900000.00
3342265730.45
-144967626758.58
118172189795.21
or/NY.GDP.MKTP.PP.KD
2015
23927632715.34
-209363000000.00
-24551342898.19
-680317054.69
60422293503.45
4645839623.08
140346638.37
68388393318.93
33299356666.04
-18241204965.20
8404253503.82
-47433558092.54
19583000000.00
5406579386.65
-68098649764.78
133162791214.28
2016
33404095512.75
-174573000000.00
-25861166110.97
-264618974.69
49999336305.92
-14370211554.91
-560729295.01
47174192459.00
12384254171.60
15487777451.94
41841316016.55
-9219282218.49
17785200000.00
10187621831.32
41674876169.52
137656207444.10
2017
52829985894.71
28590000000.00
-36941108315.08
-932827114.81
43897890313.69
15239796026.02
-1085198792.99
36124571462.22
14575952991.29
-3464257880.13
12702511140.09
-55346188075.96
16156500000.00
5836604485.43
-27790987919.51
154947985695.97
2018
20156533320.49
-345435000000.00
-58349843384.14
-260129818.34
57011679381.86
14173567423.59
-1459135847.16
29011101778.83
-20511155392.23
13725083434.58
61682963434.95
27441629240.53
26037800000.00
-2041425450.86
-92338473352.48
134928938193.78
2019
26965431145.15
-201057000000.00
-39191638311.78
-1272922205.62
-836227817.91
6379736740.78
-1190231245.84
98812746279.98
8921032437.20
-8568455133.47
30864497546.20
-97444443421.70
25604700000.00
10135346257.05
-50259822364.76
218323780735.71
Portfolio Investment, net (BoP, current US$)
Source:
World Development Indicators, World Bank
https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD
DMU
Canada
Top
Fintech United States
Countries Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2013
-34483049123.13
-30689000000.00
64035962598.90
1888693892.87
5344474297.89
-48767101424.78
721511621.22
209538930526.72
-86445930881.29
-4433931669.80
-79782483832.98
47693796508.43
9344500000.00
-11916788706.90
-52891117744.13
-274651950434.83
2014
-33792951517.00
-114932000000.00
46534154137.56
-1309719655.42
59571398822.21
23471019154.11
645485436.70
167003343430.67
-7982916147.77
5242410422.90
-26765450439.10
-38061113346.20
30608900000.00
10568098227.10
-82429447386.75
-40342220314.65
or/NY.GDP.MKTP.PP.KD
2015
-37884016917.95
-106755000000.00
61651555652.54
-85242496.91
-39371656227.61
-13132270344.38
599721575.29
213012801836.65
11991763762.09
618494105.51
42635898465.63
-99086557299.09
49529800000.00
-17028220834.30
66470061883.31
132433986065.34
2016
-102982524308.76
-193776000000.00
14489054096.83
3707438117.70
28549509631.20
6307394456.19
2716915396.53
218090929462.71
64996063918.37
4310875885.56
-169240236.29
30264537968.93
66970200000.00
-11701036788.40
52270616754.19
268042440742.74
2017
-75109610881.83
-250083000000.00
19903760160.76
1772186359.70
43681990207.61
3858366621.68
3014901302.83
223066732686.92
34991893869.69
-3154351263.56
25219875130.84
-62993130040.17
57853000000.00
2198675055.05
-29497793098.26
-49213900461.95
2018
3092046327.93
78785000000.00
53044179882.74
2078680042.26
8466672375.70
-10655491353.38
999816138.55
176504907851.64
28823038904.11
-24611541884.88
18859193994.19
43994088661.39
47420700000.00
52837283466.38
-106873525546.72
92911832022.28
2019
-1565030418.94
-244920000000.00
108603888844.77
-2655256488.71
37191877518.64
12012913841.36
237065479.64
81950239568.62
-56072941242.53
-28373260159.58
-69492047430.73
32398324973.00
42377000000.00
4024653536.71
-57947645385.64
86642953831.49
Commercial bank branches (per 100,000 adults)
Source:
World Development Indicators, World Bank
https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD
DMU
Top Fintech Canada
Countries United States
Singapore
Lithuania
Netherlands
Sweden
Estonia
Germany
Spain
Finland
France
Ireland
Korea, Rep.
Denmark
China
Japan
2013
23.32
33.64
9.11
18.08
17.35
21.54
13.95
14.91
73.61
14.1
38.63
22.79
18.02
30.01
7.8
33.91
2014
23.14
32.42
9.01
16.24
14.79
21.11
12.39
14.56
69.68
9.42
38.06
21.8
17.2
27.79
8.01
33.9
or/NY.GDP.MKTP.PP.KD
2015
22.84
32.7
8.95
14.55
13.99
19.32
11.15
14.06
67.51
8.49
37.6
21.86
16.75
24.69
8.48
34.14
2016
22.25
32.1
8.68
13.44
12.46
17.54
10.42
13.55
61.81
7.31
37.19
21.61
16.24
24.05
8.76
34.07
2017
20.75
31.21
8.22
13.6
11.92
16.17
