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Global Information Technology Report 2015

  • Report Highlights
  • Networked Readiness Index
  • Networked Readiness Dataset (xls)
  • Blogs and Opinions
  • Infographics and Shareables
  • Press Releases
  • [ — Divider — ]
  • Preface
  • Acknowledgments
  • Foreword – John Chambers (Cisco Systems)
  • Foreword – Cesare Mainardi (Strategy&, formerly Booz & Company)
  • Executive Summary
  • Part 1: Leveraging ICTs for Shared Prosperity
    • 1.1 The Networked Readiness Index 2015: Taking the Pulse of the ICT revolution
      • Networked Readiness Framework
      • Structure and Methodology
      • Analysis and Key Findings
      • Country Highlights
      • Conclusions
      • References
      • Appendix: Structure and computation of the Networked Readiness Index 2015
    • 1.2 – ICTs, Income Inequality, and Ensuring Inclusive Growth
    • 1.3 – Understanding Digital Content and Services Ecosystems: The Role of Content and Services in Boosting Internet Adoption
    • 1.4 – ICTs for Inclusive Growth: E-Entrepreneurship on the Open Internet
    • 1.5 – Creating the Next Wave of Economic Growth with Inclusive Internet
    • 1.6 – Developing the Network for Growth and Equality of Opportunity
    • 1.7 – CTs in Schools: Why Focusing Policy and Resources on Educators, not Children, Will Improve Educational Outcomes
    • 1.8 – Big Data Analytics for Inclusive Growth: How Technology Can Help Elevate the Human Condition
    • 1.9 – Connected Healthcare: Extending the Benefits of Growth
    • 1.10 – Designing Technology for Inclusive Growth
    • 1.11 – Digital Inclusion and Economic Development: A Regional Analysis from Brazil
  • Technical Notes and Sources
  • About the Authors
  • Partner Institutes
  • Strategic Partner Acknowledgments
  • [ — Divider — ]
  • Downloads
  • Selected Research
  • Contact Us
Global Information Technology Report 2015 Home
  • Report Home
  • Report Highlights
  • Networked Readiness Index
  • Networked Readiness Dataset (xls)
  • Blogs and Opinions
  • Infographics and Shareables
  • Press Releases
  • [ — Divider — ]
  • Preface
  • Acknowledgments
  • Foreword – John Chambers (Cisco Systems)
  • Foreword – Cesare Mainardi (Strategy&, formerly Booz & Company)
  • Executive Summary
  • Part 1: Leveraging ICTs for Shared Prosperity
    • 1.1 The Networked Readiness Index 2015: Taking the Pulse of the ICT revolution
      • Networked Readiness Framework
      • Structure and Methodology
      • Analysis and Key Findings
      • Country Highlights
      • Conclusions
      • References
      • Appendix: Structure and computation of the Networked Readiness Index 2015
    • 1.2 – ICTs, Income Inequality, and Ensuring Inclusive Growth
    • 1.3 – Understanding Digital Content and Services Ecosystems: The Role of Content and Services in Boosting Internet Adoption
    • 1.4 – ICTs for Inclusive Growth: E-Entrepreneurship on the Open Internet
    • 1.5 – Creating the Next Wave of Economic Growth with Inclusive Internet
    • 1.6 – Developing the Network for Growth and Equality of Opportunity
    • 1.7 – CTs in Schools: Why Focusing Policy and Resources on Educators, not Children, Will Improve Educational Outcomes
    • 1.8 – Big Data Analytics for Inclusive Growth: How Technology Can Help Elevate the Human Condition
    • 1.9 – Connected Healthcare: Extending the Benefits of Growth
    • 1.10 – Designing Technology for Inclusive Growth
    • 1.11 – Digital Inclusion and Economic Development: A Regional Analysis from Brazil
  • Technical Notes and Sources
  • About the Authors
  • Partner Institutes
  • Strategic Partner Acknowledgments
  • [ — Divider — ]
  • Downloads
  • Selected Research
  • Contact Us

Analysis and Key Findings

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This section begins with an overview of the NRI 2015 results, including a brief analysis of regional patterns and trends. It then presents some of the key findings and messages from this year’s edition and concludes with a short assessment of the performance of selected countries.

Results overview
Tables 1–5 report the results (ranks and values) for the overall NRI 2015, its four subindexes, and their respective pillars. Table 1 also contains the country classifications used throughout the analysis. Scores are reported with a precision of one decimal, but rankings were derived from the exact figures.

Table 1: The Networked Readiness Index 2015

NETWORKED READINESS INDEX
RankCountry/EconomyValue2014 Rank (out of 148)Income level*Group†
1Singapore6.02HIADV
2Finland6.01HI-OECDADV
3Sweden5.83HI-OECDADV
4Netherlands5.84HI-OECDADV
5Norway5.85HI-OECDADV
6Switzerland5.76HI-OECDADV
7United States5.67HI-OECDADV
8United Kingdom5.69HI-OECDADV
9Luxembourg5.611HI-OECDADV
10Japan5.616HI-OECDADV
11Canada5.517HI-OECDADV
12Korea, Rep.5.510HI-OECDADV
13Germany5.512HI-OECDADV
14Hong Kong SAR5.58HIADV
15Denmark5.513HI-OECDADV
16Australia5.518HI-OECDADV
17New Zealand5.520HI-OECDADV
18Taiwan, China5.514HIADV
19Iceland5.419HI-OECDADV
20Austria5.422HI-OECDADV
21Israel5.415HI-OECDADV
22Estonia5.321HI-OECDADV
23United Arab Emirates5.324HIMENAP
24Belgium5.327HI-OECDADV
25Ireland5.226HI-OECDADV
26France5.225HI-OECDADV
27Qatar5.123HIMENAP
28Portugal4.933HI-OECDADV
29Malta4.928HIADV
30Bahrain4.929HIMENAP
31Lithuania4.931HIEDE
32Malaysia4.930UMEDA
33Latvia4.739HIADV
34Spain4.734HI-OECDADV
35Saudi Arabia4.732HIMENAP
36Cyprus4.737HIADV
37Slovenia4.636HI-OECDADV
38Chile4.635HI-OECDLATAM
39Barbados4.655HILATAM
40Kazakhstan4.538UMCIS
41Russian Federation4.550HICIS
42Oman4.540HIMENAP
43Czech Republic4.542HI-OECDADV
44Puerto Rico4.541HI—
45Mauritius4.548UMSSA
46Uruguay4.556HILATAM
47Macedonia, FYR4.457UMEDE
48Turkey4.451UMEDE
49Costa Rica4.453UMLATAM
50Poland4.454HI-OECDEDE
51Panama4.443UMLATAM
52Jordan4.344UMMENAP
53Hungary4.347UMEDE
54Croatia4.346HIEDE
55Italy4.358HI-OECDADV
56Montenegro4.352UMEDE
57Azerbaijan4.349UMCIS
58Armenia4.265LMCIS
59Slovak Republic4.259HI-OECDADV
60Georgia4.260LMCIS
61Mongolia4.261LMEDA
62China4.262UMEDA
63Romania4.275UMEDE
64Colombia4.163UMLATAM
65Sri Lanka4.176LMEDA
66Greece4.174HI-OECDADV
67Thailand4.067UMEDA
68Moldova4.077LMCIS
69Mexico4.079UMLATAM
70Trinidad and Tobago4.071HILATAM
71Ukraine4.081LMCIS
72Kuwait4.072HIMENAP
73Bulgaria4.073UMEDE
74Seychelles4.066UMSSA
75South Africa4.070UMSSA
76Philippines4.078LMEDA
77Serbia4.080UMEDE
78Morocco3.999LMMENAP
79Indonesia3.964LMEDA
80El Salvador3.998LMLATAM
81Tunisia3.987UMMENAP
82Jamaica3.986UMLATAM
83Rwanda3.985LISSA
84Brazil3.969UMLATAM
85Vietnam3.984LMEDA
86Kenya3.892LISSA
87Cape Verde3.889LMSSA
88Bhutan3.794LMEDA
89India3.783LMEDA
90Peru3.790UMLATAM
91Argentina3.7100UMLATAM
92Albania3.795UMEDE
93Guyana3.788LMLATAM
94Egypt3.691LMMENAP
95Dominican Republic3.693UMLATAM
96Iran, Islamic Rep.3.6104UMMENAP
97Lao PDR3.6109LMEDA
98Kyrgyz Republic3.5118LMCIS
99Lebanon3.597UMMENAP
100Honduras3.5116LMLATAM
101Ghana3.596LMSSA
102Namibia3.5105UMSSA
103Venezuela3.4106UMLATAM
104Botswana3.4103UMSSA
105Paraguay3.4102LMLATAM
106Senegal3.3114LMSSA
107Guatemala3.3101LMLATAM
108Gambia, The3.3107LISSA
109Bangladesh3.3119LIEDA
110Cambodia3.3108LIEDA
111Bolivia3.3120LMLATAM
112Pakistan3.3111LMMENAP
113Suriname3.2113UMLATAM
114Zambia3.2110LMSSA
115Côte d’Ivoire3.2122LMSSA
116Uganda3.2115LISSA
117Tajikistan3.2—LICIS
118Nepal3.2123LIEDA
119Nigeria3.2112LMSSA
120Algeria3.1129UMMENAP
121Zimbabwe3.1117LISSA
122Gabon3.0128UMSSA
123Tanzania3.0125LISSA
124Lesotho3.0133LMSSA
125Swaziland3.0126LMSSA
126Cameroon3.0131LMSSA
127Mali3.0127LISSA
128Nicaragua2.9124LMLATAM
129Mozambique2.9137LISSA
130Ethiopia2.9130LISSA
131Libya2.9138UMMENAP
132Burkina Faso2.8136LISSA
133Malawi2.8132LISSA
134Timor-Leste2.8141LMEDA
135Madagascar2.7139LISSA
136Yemen2.7140LMMENAP
137Haiti2.5143LILATAM
138Mauritania2.5142LMMENAP
139Myanmar2.5146LIEDA
140Angola2.5144UMSSA
141Burundi2.4147LISSA
142Guinea2.4145LISSA
143Chad2.3148LISSA

 

Note: Income level classification follows the World Bank classification by income (situation as of July 2014). Group classification follows the International Monetary Fund’s classification (situation as of October 2014).

* Income groups: HI = high-income economies that are not members of the OECD; HI-OECD = high-income OECD members; UM = upper-middle-income economies; LM = lower-middle-income economies; LI = low-income economies.

† Groups: ADV = Advanced economies; CIS = Commonwealth of Independent States; EDA = Emerging and developing Asia; LATAM = Latin America and the Caribbean; MENAP = Middle East, North Africa, and Pakistan; SSA = Sub-Saharan Africa.

Table 2: Environment subindex and pillars

ENVIRONMENT SUBINDEXPolitical & regulatory environmentBusiness & innovation environment
RankCountry/EconomyValueRankValueRankValue
1Singapore5.925.916.0
2New Zealand5.715.965.4
3Finland5.645.8115.4
4United Kingdom5.555.795.4
5Hong Kong SAR5.5125.435.6
6Norway5.565.675.4
7Netherlands5.575.585.4
8Canada5.4115.445.5
9Switzerland5.495.5105.4
10Luxembourg5.435.8275.0
11United Arab Emirates5.4205.125.7
12Ireland5.3145.3135.3
13Sweden5.3105.4195.2
14United States5.3215.055.5
15Qatar5.3175.3165.3
16Denmark5.2165.3185.2
17Australia5.2155.3235.1
18Japan5.285.5354.9
19Germany5.1135.4314.9
20Malaysia5.1235.0215.1
21Belgium5.1225.0245.1
22Iceland5.0274.9175.2
23Estonia5.0264.9255.0
24Austria5.0185.2434.7
25Israel5.0284.6155.3
26France4.8255.0454.7
27Chile4.8354.3145.3
28Taiwan, China4.8384.3125.3
29Saudi Arabia4.8324.5265.0
30Portugal4.8334.4205.2
31South Africa4.8245.0554.5
32Rwanda4.7195.2714.3
33Mauritius4.7314.5384.8
34Korea, Rep.4.6424.1225.1
35Puerto Rico4.6294.6494.6
36Malta4.6304.5514.6
37Barbados4.5374.3404.8
38Jordan4.5394.2364.9
39Cyprus4.5414.1304.9
40Bahrain4.5454.1295.0
41Latvia4.5484.1285.0
42Lithuania4.5494.1334.9
43Oman4.5364.3464.7
44Turkey4.4543.9374.9
45Zambia4.4643.8324.9
46Macedonia, FYR4.4593.9394.8
47Czech Republic4.3444.1584.5
48Hungary4.3464.1574.5
49Uruguay4.3514.0564.5
50Spain4.3603.9474.7
51Slovenia4.2813.6344.9
52Panama4.2733.6414.8
53Poland4.2653.8544.6
54Indonesia4.2623.8594.5
55Kazakhstan4.2613.9614.5
56Montenegro4.1903.5424.8
57Jamaica4.1583.9654.4
58Croatia4.1873.5444.7
59Namibia4.1344.41033.8
60Thailand4.1893.5484.7
61Ghana4.1504.0884.1
62Georgia4.0763.6624.4
63Russian Federation4.0793.6634.4
64Romania4.0723.7664.3
65Cape Verde4.0553.9904.0
66Costa Rica4.0633.8784.1
67Slovak Republic4.0783.6644.4
68Mongolia4.0943.4604.5
69Kuwait3.9743.6704.3
70Guyana3.9683.7734.2
71Botswana3.9474.11063.8
72Kenya3.9663.8894.1
73Lao PDR3.9533.9963.9
74Azerbaijan3.9693.7794.1
75Bhutan3.9434.11143.7
76Bulgaria3.91083.2504.6
77China3.9524.01043.8
78Armenia3.91073.2534.6
79Seychelles3.9563.91013.8
80Morocco3.9713.7834.1
81Mexico3.9703.7874.1
82Gambia, The3.8404.21263.5
83El Salvador3.8853.5754.2
84Philippines3.8753.6854.1
85Lesotho3.8673.7933.9
86Sri Lanka3.8773.6924.0
87Senegal3.8923.5824.1
88Greece3.81063.2684.3
89Trinidad and Tobago3.8993.4764.2
90Italy3.81023.3724.2
91Dominican Republic3.71013.4804.1
92Tajikistan3.7573.91233.5
93Iran, Islamic Rep.3.71003.4864.1
94Albania3.71133.1694.3
95Côte d’Ivoire3.7843.5993.9
96Peru3.71173.0674.3
97Colombia3.7983.4943.9
98Vietnam3.6933.51053.8
99Guatemala3.61183.0744.2
100Serbia3.61103.1844.1
101India3.6823.61153.7
102Kyrgyz Republic3.61143.1814.1
103Tunisia3.6963.41083.8
104Ukraine3.61223.0774.2
105Mali3.6913.51163.7
106Uganda3.6863.51173.6
107Lebanon3.51392.5524.6
108Malawi3.5803.61283.4
109Honduras3.51093.21023.8
110Swaziland3.5883.51253.5
111Brazil3.5953.41213.6
112Moldova3.51243.0914.0
113Ethiopia3.51053.21103.8
114Tanzania3.5833.61303.4
115Cameroon3.51123.11073.8
116Nepal3.41203.01003.9
117Pakistan3.41213.0973.9
118Burkina Faso3.41033.31223.5
119Mozambique3.41043.31203.6
120Nigeria3.41163.11113.8
121Madagascar3.41262.9953.9
122Cambodia3.41193.01133.7
123Egypt3.31153.11243.5
124Gabon3.31113.11293.4
125Bolivia3.3973.41353.2
126Paraguay3.31332.6983.9
127Timor-Leste3.21292.71093.8
128Argentina3.21282.81183.6
129Nicaragua3.21233.01313.4
130Bangladesh3.21352.61123.7
131Zimbabwe3.11252.91323.3
132Suriname3.11302.71273.5
133Libya3.01422.41193.6
134Algeria3.01272.91363.1
135Yemen2.91402.51333.2
136Burundi2.91362.51343.2
137Haiti2.91342.61373.1
138Mauritania2.81312.71393.0
139Myanmar2.71322.71412.8
140Guinea2.71372.51402.9
141Venezuela2.61432.21383.0
142Chad2.51382.51432.5
143Angola2.51412.41422.6

Table 3: Readiness subindex and pillars

READINESS SUBINDEXInfrastructureAffordabilitySkills
RankCountry/EconomyValueRankValueRankValueRankValue
1Finland6.757.096.616.5
2Taiwan, China6.417.0136.5235.8
3Iceland6.467.0256.3135.9
4Sweden6.437.0186.4285.7
5Norway6.417.0276.2125.9
6Austria6.3126.656.7275.7
7Australia6.367.0286.2175.9
8Singapore6.3196.2306.126.5
9Germany6.2136.6415.9106.1
10Switzerland6.2106.8655.436.4
11Canada6.267.0605.596.1
12United States6.147.0535.6335.6
13Denmark6.0206.2336.1195.8
14Belgium6.0216.1565.646.3
15Japan6.0176.3435.8155.9
16Korea, Rep.6.0116.6455.8395.5
17Hong Kong SAR6.0285.8206.4225.8
18Netherlands6.0146.4725.366.2
19Luxembourg5.9186.3505.7185.8
20Cyprus5.9305.6346.1116.0
21United Kingdom5.9156.3515.7315.6
22Estonia5.8236.1625.5165.9
23Slovenia5.8255.9585.6245.8
24New Zealand5.896.91014.276.2
25Malta5.7166.3765.1295.7
26France5.7246.0735.2145.9
27Russian Federation5.6395.0156.5525.3
28Ukraine5.6464.7106.6365.6
29Ireland5.6265.9874.786.1
30Poland5.6365.1266.2435.4
31Lithuania5.6504.6226.3255.7
32Italy5.5375.0366.0375.6
33Portugal5.5414.9356.0345.6
34Spain5.5335.3405.9565.3
35Kazakhstan5.5494.6116.6495.4
36Czech Republic5.5226.1805.0535.3
37Israel5.4315.6685.3485.4
38Latvia5.4434.8475.8325.6
39Croatia5.4474.7425.9405.5
40Bahrain5.3355.2665.4415.5
41Turkey5.3534.686.6804.8
42Mongolia5.3754.066.7555.3
43Mauritius5.3773.936.7505.4
44Armenia5.3574.4316.1545.3
45Georgia5.3594.376.6784.9
46Macedonia, FYR5.3584.4296.1645.2
47Romania5.2524.6595.5385.5
48Serbia5.2424.8615.5665.1
49Montenegro5.2454.7755.2355.6
50Panama5.2634.3196.4824.8
51Costa Rica5.2913.3166.4265.7
52Trinidad and Tobago5.1674.3525.7465.4
53Moldova5.1694.2376.0715.0
54United Arab Emirates5.1275.81143.6215.8
55Barbados5.0385.01004.3205.8
56Qatar5.0295.71263.156.3
57Puerto Rico5.0803.8146.5874.7
58Mexico5.0813.746.7924.5
59Colombia4.9684.2555.6774.9
60Greece4.9405.0964.4585.3
61Seychelles4.9444.7934.5425.4
62Oman4.9614.3675.4754.9
63Malaysia4.9704.2795.1575.3
64Azerbaijan4.9604.3775.1685.1
65Slovak Republic4.8714.1695.3695.1
66Kuwait4.8484.6854.8705.0
67Uruguay4.8514.6745.2844.7
68Hungary4.8654.3864.8475.4
69Tunisia4.8863.4326.1764.9
70Sri Lanka4.81102.7386.0305.6
71Bulgaria4.8345.21103.8605.3
72Venezuela4.7933.2126.5904.5
73Thailand4.7664.3844.9735.0
74Chile4.7544.5914.5725.0
75Saudi Arabia4.7325.41223.2455.4
76China4.7923.2575.6595.3
77Jamaica4.6783.9715.3834.7
78Bhutan4.6724.1445.81063.9
79Argentina4.6624.3n/an/a794.9
80El Salvador4.6744.0635.4974.3
81Jordan4.6963.0705.3445.4
82Kyrgyz Republic4.61003.0396.0864.7
83India4.61152.617.01024.1
84Vietnam4.51272.126.8884.6
85Philippines4.5734.11034.2615.3
86Iran, Islamic Rep.4.5973.0465.8854.7
87Morocco4.5873.4246.31103.8
88Albania4.4843.5924.5655.2
89Paraguay4.4644.3815.01053.9
90Egypt4.3993.0176.41183.6
91Brazil4.3564.5894.61083.9
92Cape Verde4.31042.9835.0744.9
93Peru4.3903.3785.1964.3
94Libya4.2763.9984.3934.4
95Suriname4.2554.51193.4814.8
96Indonesia4.2983.0994.3635.2
97Algeria4.2833.7944.5944.4
98Lebanon4.1823.71173.4515.3
99Guyana4.11032.91024.2625.2
100Bangladesh4.01092.8216.31253.0
101Lao PDR4.01072.8645.41123.7
102South Africa4.0853.51074.1954.4
103Cambodia3.91082.8485.71203.3
104Nepal3.91331.9236.31173.6
105Honduras3.91132.6825.01014.1
106Dominican Republic3.9883.3974.41044.0
107Kenya3.8943.11064.11004.1
108Uganda3.81122.7545.61263.0
109Pakistan3.61192.5495.71332.6
110Bolivia3.61022.91203.3914.5
111Ghana3.51242.31054.11034.0
112Gabon3.31182.61083.91163.6
113Nicaragua3.3793.81342.41143.7
114Namibia3.31013.01233.21133.7
115Rwanda3.31062.81113.71213.2
116Botswana3.31142.61312.6894.6
117Guatemala3.2953.01243.11193.5
118Côte d’Ivoire3.2893.31273.01233.2
119Zimbabwe3.21282.1n/an/a994.2
120Yemen3.11292.0884.71342.5
121Lesotho3.11302.01213.31073.9
122Swaziland3.01162.61362.2984.2
123Nigeria3.01212.31044.11352.5
124Tajikistan3.01361.61372.1675.1
125Tanzania3.01172.61123.71322.6
126Timor-Leste2.81052.91292.81302.8
127Gambia, The2.81252.21283.01223.2
128Myanmar2.81312.0n/an/a1153.6
129Senegal2.71202.51302.61282.9
130Mozambique2.61371.3904.61402.1
131Angola2.61222.31183.41382.2
132Burundi2.61232.31332.41243.1
133Ethiopia2.61351.71133.61372.3
134Guinea2.51341.81153.61412.1
135Haiti2.51421.01163.51273.0
136Cameroon2.41411.21322.41113.7
137Zambia2.41322.01381.61093.8
138Chad2.41431.0954.41431.8
139Mauritania2.31391.21093.81422.0
140Malawi2.31112.71391.51312.6
141Burkina Faso2.21401.21253.11392.2
142Madagascar2.11262.21401.31292.8
143Mali1.91381.21352.31362.4

Table 4: Usage subindex and pillars

USAGE SUBINDEXIndividual usageBusiness usageGovernment usage
RankCountry/EconomyValueRankValueRankValueRankValue
1Sweden5.926.735.9205.1
2Singapore5.9116.2145.316.2
3Finland5.956.645.9175.2
4Japan5.9136.226.075.4
5Netherlands5.976.565.8135.3
6Korea, Rep.5.996.4125.435.7
7Luxembourg5.866.5115.4115.4
8Norway5.736.7105.5245.1
9Denmark5.716.885.7404.6
10United States5.7186.075.7145.3
11Switzerland5.6106.416.1484.4
12United Kingdom5.646.6165.1165.2
13United Arab Emirates5.6205.9274.526.2
14Germany5.5176.055.8314.8
15Israel5.5285.695.7155.2
16New Zealand5.4225.9195.0105.4
17Qatar5.4196.0254.655.5
18Austria5.3215.9135.4324.7
19Hong Kong SAR5.3126.2185.1364.7
20Australia5.3156.1244.7235.1
21Iceland5.386.5214.9424.5
22Taiwan, China5.3265.7175.1215.1
23Estonia5.3166.0284.465.5
24France5.3245.8204.9185.1
25Bahrain5.2146.2493.945.7
26Canada5.2295.6234.8225.1
27Belgium5.1255.8155.1434.5
28Ireland5.1275.7224.8334.7
29Saudi Arabia4.9365.3424.085.4
30Malaysia4.9574.6264.695.4
31Malta4.8235.8374.0384.7
32Lithuania4.7375.3314.3354.7
33Spain4.7315.4453.9374.7
34Portugal4.7464.9334.2264.9
35Oman4.6415.1733.5195.1
36Latvia4.6305.6414.0514.3
37Chile4.5524.7473.9294.8
38Uruguay4.4455.0893.4274.8
39Russian Federation4.4435.1663.6474.4
40Kazakhstan4.4514.7673.6284.8
41Azerbaijan4.3594.5583.7344.7
42Slovenia4.3345.3364.1843.6
43Barbados4.3405.2304.31013.5
44Costa Rica4.3564.6394.0544.3
45Czech Republic4.3325.3324.21133.3
46Italy4.2335.3603.7763.7
47Puerto Rico4.2634.4294.4683.9
48Slovak Republic4.2355.3553.8883.6
49Hungary4.2425.1643.7693.9
50Cyprus4.2504.7513.9664.0
51Jordan4.1694.0503.9444.5
52Macedonia, FYR4.1494.8853.5594.1
53Mauritius4.1664.1573.8464.4
54Poland4.1445.1713.6863.6
55Montenegro4.1604.5833.5524.3
56Croatia4.1395.2923.4833.6
57China4.1803.6463.9394.7
58Kuwait4.1385.2933.4913.6
59Colombia4.0773.8813.5304.8
60Brazil4.0624.4523.8713.9
61Panama4.0723.9404.0574.2
62Turkey4.0674.0533.8554.2
63Greece3.9484.8963.4823.6
64Morocco3.9703.91053.3414.6
65Armenia3.9743.81003.3454.5
66Romania3.9614.5763.5853.6
67South Africa3.9684.0344.21053.4
68Trinidad and Tobago3.8584.5863.5963.5
69Sri Lanka3.81062.6483.9255.0
70Seychelles3.8654.2683.6793.7
71Moldova3.8644.21143.2654.0
72Georgia3.8763.81043.3504.3
73Bulgaria3.8474.9913.41183.1
74Philippines3.8893.2384.0614.1
75Thailand3.7753.8543.8803.7
76Argentina3.7544.61013.31153.3
77Indonesia3.7973.0354.1634.1
78Mongolia3.7883.3693.6534.3
79Mexico3.7873.3723.6564.2
80Serbia3.7554.61263.01113.3
81Tunisia3.6813.51063.3584.2
82Vietnam3.6863.3873.5604.1
83Kenya3.61102.5433.9494.4
84El Salvador3.6963.0593.7644.0
85Rwanda3.61321.8703.6125.4
86Lebanon3.6534.61083.21302.8
87Albania3.5793.61033.3783.7
88Jamaica3.5843.4633.7943.5
89Cape Verde3.5823.4973.4773.7
90Egypt3.5733.91253.11023.5
91Peru3.4943.0903.4703.9
92Botswana3.4853.31023.3813.7
93Dominican Republic3.4903.1773.5933.6
94Ukraine3.4783.7783.51242.9
95Namibia3.4953.0613.7973.5
96Ghana3.4913.1843.5923.6
97Venezuela3.3713.91283.01173.1
98Senegal3.31112.5623.7733.8
99Honduras3.31032.7563.81063.4
100Gambia, The3.31152.3743.5674.0
101Guatemala3.3992.9443.91232.9
102Guyana3.21072.6823.5893.6
103India3.21212.0883.5624.1
104Nigeria3.21142.4793.5953.5
105Bhutan3.11082.61203.1743.8
106Bolivia3.11012.71233.1983.5
107Zambia3.11222.0653.7873.6
108Iran, Islamic Rep.3.11002.91293.01093.4
109Paraguay3.1933.11113.21252.9
110Suriname3.0833.41223.11332.7
111Zimbabwe3.01042.61123.21123.3
112Mali3.01132.41173.1993.5
113Lao PDR3.01281.9753.5903.6
114Cambodia3.01052.6993.41203.1
115Kyrgyz Republic3.0982.91133.21262.9
116Cameroon3.01301.9803.51033.5
117Côte d’Ivoire2.91192.1953.41143.3
118Pakistan2.91232.0943.41103.3
119Gabon2.91092.51183.11193.1
120Bangladesh2.91291.91243.1753.7
121Tajikistan2.91162.31073.31163.1
122Uganda2.71351.71103.21073.4
123Swaziland2.71182.21093.21272.9
124Tanzania2.71371.61213.11003.5
125Burkina Faso2.71331.81312.91043.5
126Ethiopia2.71401.51352.8723.8
127Mozambique2.71361.61163.11083.4
128Nicaragua2.71122.51193.11372.5
129Algeria2.71022.71372.71342.7
130Madagascar2.71381.6983.41223.1
131Nepal2.61202.11273.01292.8
132Malawi2.61411.51153.21213.1
133Mauritania2.61172.21322.91382.5
134Lesotho2.51242.01303.01352.7
135Yemen2.51272.01332.91322.7
136Libya2.5923.11412.51431.8
137Timor-Leste2.41252.01382.61312.7
138Angola2.41262.01432.41282.8
139Haiti2.41311.91342.81402.5
140Guinea2.31341.71362.81412.5
141Myanmar2.21391.61392.61392.5
142Chad2.11421.31422.51362.6
143Burundi2.11431.31402.51422.4

Table 5: Impact subindex and pillars

IMPACT SUBINDEXEconomic impactsSocial impacts
RankCountry/EconomyValueRankValueRankValue
1Singapore6.045.816.2
2Netherlands5.955.836.1
3Finland5.816.1125.6
4Sweden5.726.0165.5
5Korea, Rep.5.6105.246.0
6United States5.675.6115.6
7Israel5.565.7195.4
8Switzerland5.535.9345.0
9United Kingdom5.5135.165.8
10Norway5.4115.275.7
11Japan5.4125.1135.6
12Luxembourg5.385.3205.4
13Canada5.3145.195.6
14Estonia5.3254.656.0
15Taiwan, China5.3174.985.7
16Hong Kong SAR5.2165.0185.4
17Germany5.295.3315.1
18United Arab Emirates5.2274.326.1
19Australia5.1244.6145.6
20New Zealand5.0264.5155.5
21Denmark5.0184.9305.1
22Iceland5.0214.7245.3
23France5.0224.7255.3
24Ireland5.0155.0384.9
25Belgium4.9204.8295.1
26Austria4.9234.7265.2
27Qatar4.8324.0105.6
28Portugal4.7304.0225.4
29Lithuania4.7284.2275.2
30Malaysia4.6314.0285.2
31Malta4.5334.0335.0
32Latvia4.5353.9325.1
33Bahrain4.5483.5175.5
34Spain4.5344.0364.9
35Chile4.4443.5235.3
36Uruguay4.4563.4215.4
37Barbados4.3194.9863.7
38Saudi Arabia4.3413.7374.9
39Slovenia4.3294.0534.5
40Puerto Rico4.2373.8514.5
41Costa Rica4.1473.5414.8
42Russian Federation4.1393.7484.6
43Jordan4.1423.6444.6
44Kazakhstan4.1523.5424.8
45Oman4.1623.3354.9
46Panama4.1453.5464.6
47China4.0713.2404.9
48Azerbaijan4.0493.5494.5
49Hungary4.0383.8634.3
50Cyprus4.0433.6594.4
51Kenya4.0593.4524.5
52Colombia3.9693.2434.7
53Czech Republic3.9363.9744.0
54Armenia3.9503.5584.4
55Macedonia, FYR3.9533.4554.4
56Rwanda3.9983.0394.9
57Montenegro3.9463.5614.3
58Slovak Republic3.9573.4574.4
59Turkey3.9633.3504.5
60Sri Lanka3.9753.1474.6
61Mauritius3.8653.3564.4
62Philippines3.8553.4674.2
63Croatia3.8403.7803.9
64Georgia3.8973.0454.6
65Mongolia3.8833.1544.4
66Italy3.7513.5754.0
67Moldova3.7793.1604.3
68Greece3.7743.1654.3
69Poland3.7543.4784.0
70Thailand3.6863.1664.2
71Vietnam3.61012.9624.3
72Mexico3.6723.2764.0
73India3.6923.0684.2
74Indonesia3.6783.1724.1
75Brazil3.6763.1734.0
76El Salvador3.6943.0694.2
77Bulgaria3.6613.3843.8
78Senegal3.6663.3813.8
79Peru3.5963.0704.1
80Romania3.5853.1774.0
81Tunisia3.51032.9714.1
82Ukraine3.5673.3893.7
83Morocco3.41202.6644.3
84Egypt3.4603.31003.5
85Seychelles3.4903.0853.8
86Honduras3.4643.3993.5
87Mali3.4683.2983.5
88Dominican Republic3.4703.2963.6
89Serbia3.4803.1903.7
90Cape Verde3.4773.1943.6
91Gambia, The3.4893.0883.7
92South Africa3.4583.41103.3
93Trinidad and Tobago3.4843.1923.6
94Argentina3.3913.0913.7
95Bhutan3.31112.7794.0
96Lao PDR3.3883.0953.6
97Guyana3.31072.8833.8
98Guatemala3.2733.21093.3
99Tajikistan3.2933.01033.5
100Bolivia3.21082.8933.6
101Jamaica3.2823.11063.4
102Kuwait3.21192.7873.7
103Albania3.21252.5823.8
104Nigeria3.1813.11163.2
105Pakistan3.11022.91083.4
106Bangladesh3.11062.81053.4
107Côte d’Ivoire3.1993.01143.3
108Venezuela3.11162.7973.5
109Namibia3.11052.81073.4
110Cameroon3.1873.01183.1
111Botswana3.11132.71013.5
112Zambia3.11092.71043.4
113Ghana3.01212.61023.5
114Kyrgyz Republic3.01142.71123.3
115Paraguay3.0953.01243.0
116Iran, Islamic Rep.3.01102.71153.2
117Lebanon2.91042.91252.9
118Cambodia2.91122.71233.1
119Mozambique2.91172.71203.1
120Zimbabwe2.91282.51133.3
121Burkina Faso2.91002.91312.8
122Tanzania2.91322.41113.3
123Uganda2.81222.51223.1
124Madagascar2.81292.51213.1
125Malawi2.81152.71272.8
126Swaziland2.71232.51262.9
127Nepal2.71372.31193.1
128Ethiopia2.71392.21173.2
129Suriname2.61182.71332.6
130Gabon2.61302.51292.8
131Nicaragua2.61262.51322.7
132Timor-Leste2.61312.41302.8
133Lesotho2.51382.21282.8
134Algeria2.51272.51362.6
135Haiti2.41352.31342.6
136Angola2.41342.31352.6
137Mauritania2.41242.51392.3
138Yemen2.41332.31372.5
139Myanmar2.41362.31382.4
140Chad2.11402.11402.2
141Burundi2.11412.11422.2
142Guinea2.11422.01412.2
143Libya1.81431.81431.7

Not unexpectedly, advanced economies are better than developing ones at leveraging ICTs. High-income economies dominate the NRI, taking the first 31 places in the overall NRI rankings (see Table 1). The performance of countries in the NRI largely mirrors their position on the development ladder: a higher level of income is typically associated with a higher NRI score (see Figure 2). Forty-four of the 50 high-income economies covered in the NRI rank in the top 50, which otherwise features six upper-middle-income countries, the highest-ranked being Malaysia at 32nd place. At the bottom of the rankings, 26 of the 30 worst-performing countries are low-income or lower-middle-income countries. The only exceptions are Algeria (120th), Gabon (122nd), Libya (131st), and Angola (140th). These oil-rich countries belong to the upper-middle-income category, and they all face major challenges across all components of the Index.

FIGURE2

The composition of the top 10 would seem to suggest that “smaller” nations are at an advantage when it comes to networked readiness: seven of the 10 best performers have a population of less than 20 million. Yet, when considering the full sample of economies, Figure 3 reveals that this relationship does not hold and that size is not a key determinant of networked readiness.

FIGURE3

Singapore tops the rankings this year, and even though Finland drops to 2nd place, seven of the top 10 economies this year are European. That is one more than in 2014, thanks to Luxembourg (9th), which—along with Japan—enters the top 10 at the expense of the Republic of Korea (12th, down two spots) and Hong Kong SAR (14th, down six). As a result, Singapore is now the only representative of the Asian Tigers in the top 10. Taiwan (China) (18th, down four) also loses significant ground.8 Meanwhile, Japan (10th, up six) continues its progression and enters the top 10. Besides Singapore and Japan, the United States (stable at 7th) is the only other non-European country in the top 10.

In Europe, Northern and Western Europe are home to some of the best connected and most innovation-driven economies in the world. In particular, the Nordics—Finland (2nd), Sweden (3rd), Norway (5th), Denmark (15th), and Iceland (19th)—continue to perform well in the NRI. Indeed, these five countries have featured in the top 20 of every edition since 2012.

The group performance of Western European countries is also strong. The Netherlands (4th), Switzerland (6th), the United Kingdom (8th), and Luxembourg (9th) all appear in the top 10. Ireland (25th) has been stable since 2012, and France (26th)—which has lost three places since 2012—closes the group in the subregion. In Southern Europe, Portugal (28th, up five), Italy (55th, up three), and Greece (66th, up eight) improve significantly from last year on the back of major improvements in government usage, whereas Malta (29th, down one), Spain (34th), and Cyprus (36th, up one) remain quite stable. These largely positive trends contribute to narrowing Southern Europe’s gap with the rest of the region, which had been widening since 2012.

Farther east, thanks to the strong performance of Estonia (22nd) and the steady rise of Latvia (33rd, up six), which is catching up with Lithuania (31st), the Baltic countries are slowly but surely bridging the gap with the Nordics—a remarkable achievement for the three former Soviet Republics. While Estonia has always been in the vanguard, Lithuania and Latvia are breaking away from what was once a fairly homogenous group of Eastern European countries that have joined the European Union (EU) since 2004: Slovenia (37th, down one), the Czech Republic (43rd, down one), Hungary (53rd, down six), Croatia (54th, down eight), and the Slovak Republic (59th, no change) are either stable or losing ground. Meanwhile, Poland has jumped four places to enter the top 50, and Romania—once the worst performer in the European Union—has leapfrogged 12 positions to reach 63rd place, ahead of Bulgaria (73rd, no change).

The divide within the Middle East, North Africa, and Pakistan (MENAP) is the largest among all regions. The United Arab Emirates (UAE; 23rd, up one) and Qatar (27th, down four) continue to lead, ahead of Bahrain (30th), Saudi Arabia (35th), and Oman (42nd), which are all members of the Gulf Cooperation Council (GCC). All owe their success to a very strong commitment to ICT development by their respective governments. Kuwait’s performance (72nd) stands at odds with that of its GCC peers. In the rest of the region, only Jordan (52nd) features in the top half of the rankings. Morocco follows at a middling 78th, but it is the country that has improved the most (21 places) over the past year. Mauritania (138th) remains the region’s worst-performing country, 115 places behind the UAE.

Emerging and developing Asia also presents contrasting pictures. Over 100 places separate the region’s best- and worst-performing economies. Second, with only Malaysia (32nd) featured in the top 60, two-thirds of the countries from the region appear in the bottom half of the rankings; Mongolia (61st), Sri Lanka (65th), and Thailand (67th) all lag some 30 places behind. China is stable in 62nd position, while India continues its decline, dropping a further six to 89th place, both contributing to the disappointing group performance of the BRICS.

Chile (38th, up one) leads in Latin America and the Caribbean, almost 100 places ahead of Haiti (137th), the region’s worst performer. Overall, though, trends in the region are encouraging: 14 of the 23 countries in the region have increased their score since last year; 19 of them have done so since 2012. In particular, Costa Rica (49th, up nine since 2012), Panama (51st, up six), El Salvador (80th, up 23), Peru (90th, up 16), and Bolivia (111th, up 16) have posted some of the largest score gains since 2012.

The performance of sub-Saharan Africa is particularly poor: 30 of the 31 countries included in the sample appear in the bottom half of the NRI rankings. The only exception is Mauritius, at 45th. The country has progressed three places since last year and eight since 2012. Among the large economies of the region, Nigeria drops seven places to 119th. South Africa drops five to 75th—it is now third in the region behind Mauritius and Seychelles (74th). In contrast, Kenya (86th, up six) has been slowly improving since 2012.

When considering the results of the different pillars of the NRI, the relationship with income is very strong for eight of the ten pillars, the two exceptions being the Affordability and the Government usage pillars. Advanced economies outperform the rest of the world in every pillar (see Figure 4) except Affordability. Sub-Saharan Africa is the worst-performing region in all pillars except for the Political and regulatory environment, in which Latin America and the Caribbean obtains the lowest average score.

The divide among the best- and worst-performing countries runs the deepest in terms of Infrastructure, Affordability, and Individual usage. Not unexpectedly, the results in the Infrastructure and Individual usage pillars are similar, because a well-developed infrastructure is a pre-condition to ICT adoption. In addition, although ICTs are becoming increasingly affordable in many developing countries, most sub-Saharan Africa countries lag behind. The difficulty faced by this region in mastering the infrastructure-affordability-usage nexus is particularly worrisome.

Finally, looking at the trends since 2012 reveals that all regions have improved their overall performance to some extent (see Figure 5).9With an average NRI score up 0.5 points since 2012, the Commonwealth of Independent States (CIS) has seen the most progress as a whole. Five of the 10 countries that have improved their NRI score the most are from the CIS, including Armenia and Georgia (see Table 6). Emerging and developing Europe improves by 0.2 points, as does Latin America and the Caribbean. The other regions posts small gains of about 0.1 points, with the exception of sub-Saharan Africa, whose performance has remained stable since 2012.

TABLE6

FIGURE5

Key findings
Among the many insights that emerge from the NRI results, five key findings stand out because of their important policy implications: (1) the persistence of digital divides, (2) the need for an Internet revolution, (3) the policymakers’ low-hanging fruit to boost ICT use, (4) ICTs’ contributions to shared prosperity, and (5) a call for better data.

The limits of the mobile revolution and the persistence of digital divides
In the span of just two decades, the number of mobile telephone subscriptions exploded from essentially zero to 6.9 billion.10 The mobile revolution originated in the rich world, and by the year 2000, high-income OECD countries already boasted 50 subscriptions per 100 population. In low-income countries, however, the rate was still less than 1 subscription per 100 population (see Figure 6).11 Thanks to fast-paced growth, the developing world started to bridge this “mobile divide.” Whereas high-income countries still boasted 18 times more subscriptions per 100 population than low-income countries in 2005, this ratio had dropped to 2 times by 2013.

FIGURE6

Arguably, the mobile revolution’s influence has been greatest in the developing world, where it has helped address the critical lack of telecommunication infrastructure and improve access and productivity in sectors such as agriculture, health, education, and finance. While this is truly remarkable, one must acknowledge the limits of this mobile revolution.

First, even though there are almost as many mobile telephone subscriptions as people on the planet, this does not imply that everyone owns or is using a mobile phone. The number of mobile subscriptions far exceeds the number of mobile phone users.12 Based on the GSM Association’s estimates that unique mobile subscribers account for about half of mobile cellular subscriptions, ITU reckons that mobile telephony penetration has reached approximately 48 percent globally and 30 percent in least-developed countries.

Second, even in countries where penetration rates exceed 50 percent, vast disparities exist between urban and rural areas. Indeed, parts of the developing world are not yet covered by a mobile network signal. ITU calculates that, at the end of 2012, around 450 million people worldwide still lived out of reach of a mobile signal.13

Third, only so much can be done through 2G mobile telephony, which can carry only voice and text messages. The most compelling and promising solutions for development require more sophisticated technologies: first and foremost is fast and reliable access to the Internet, be it mobile, wireless, or wired access. But the Internet is neither as ubiquitous nor is it spreading as fast as many believe. Beyond mobile telephony, the digital divide still runs deep.

FIGURE7

Figure 7 reveals the stubbornly high correlation between income and performance in the ICT usage pillar. In this category, the score gap between high-income economies and the rest of the world is large and has actually been widening since 2012: lower-middle-income and low-income countries are now farther behind than they were in 2012 (see Figure 9). Figure 8 shows that the relationship between income and ICT impacts is almost as strong, providing an illustration of the new digital divide as we termed it in 2013.14

FIGURE8

FIGURE9

The need for an Internet revolution
The United Nations’ Open Working Group (OWG) on Sustainable Development Goals recommends that the international community “… strive to provide universal and affordable access to internet in least-developed economies by 2020.”15 In light of the current levels and growth trends described below, this milestone appears highly optimistic and will most likely be missed.

Indeed, the Internet remains nonexistent, scarce, unaffordable, or too slow in vast swaths of the developing world. Figure 10 shows the Internet penetration rate by income group and by year since 1997, when data coverage became sufficiently large.16 At the end of 2013, 81 percent of the population of high-income OECD countries used the Internet. The rate among low-income countries was 10 times less—a mere 7.6 percent, which is lower than the penetration rate among OECD countries was in 1997.

FIGURE10

In 2013, among the 25 low-income countries studied, five had a penetration rate above 10 percent and only one—Kenya—had a rate exceeding 20 percent. Kenya liberalized its telecommunications sector in the late 1990s and created the Kenya Internet Exchange Point in 2002, which led to a dramatic fall in providers’ operating costs and retail prices and an increase in local content.17 As a result, Internet penetration in Kenya increased from 1 percent in 2002 to 39 percent today—five times the low-income group’s average.

As in the case of mobile telephony, the rural-urban gap in terms of Internet penetration is large. According to ITU, it is even widening in parts of the world.18Data are extremely scant, but the few data points that do exist are telling. In Guatemala, for instance, an urban household is 12 times more likely to be connected to the Internet than a rural one. And ITU reckons that this ratio could be much higher in low-income countries.

The difference in the speed of Internet adoption across countries is striking, too (see Table 7). It took only six years on average for high-income OECD countries to attain 20 percent penetration.19 In contrast, only half of lower-middle-income countries have reached this mark and it took those almost twice as much time. Furthermore, while 90 percent of high-income countries have exceeded the 60 percent threshold, only 15 percent of upper-middle-income countries—and not a single lower-middle-income or low-income country—have reached this mark yet.

In low-income countries, Internet penetration has been growing at double-digit rates, but from a very low base and growth has been slowing lately (see Figure 11). If penetration continued to grow at the same rate as it did from 2011 to 2013—an optimistic supposition given the trajectory usually assumed by technology diffusion—it will take at least another 12 years for the Internet to reach 75 percent of the world’s population. This is very far from the objective set out by the OWG to achieve universal penetration by 2020.

FIGURE11

Finally, beyond affordability and infrastructure, the lack of availability of digital content and services represents another significant obstacle to more widespread adoption. Many individuals do not get online simply because there is little content relevant to them. Chapter 1.3 points to solutions for jumpstarting digital content and services ecosystems.

As developing countries leapfrog to 4G technology, thus enabling owners of smartphones to access the Internet, Internet diffusion may accelerate in coming years. Prices of 4G smartphones remain high, but—thanks to innovation and competition—prices are expected to keep falling. Already one-sixth of smartphones sold in 2013 cost less than US$100.20 Leapfrogging and falling prices could usher in the mobile revolution 2.0, a rapid expansion of mobile broadband throughout the world.

At the same time that prices fall, innovative projects could address the lack and cost of infrastructure that hampers the use of smartphones. For instance, Google’s Loon project plans a network of balloons placed in the stratosphere to broadcast a 4G wireless signal in rural and remote areas. This project, still in a pilot phase, is not expected to provide a solution in the short term, but it does indicate the role that breakthrough innovations could play in alleviating the obstacle represented by poor or lacking infrastructure.

Yet it would be ill-advised to assume that the Internet will become ubiquitous soon without further policy action. Policymakers must accelerate liberalization, boost public investment, and work closely with international and domestic businesses to attract private investment and encourage innovation. In this effort, connecting rural areas of developing countries to broadband networks must be a priority. Since those areas lack other infrastructure and access to public services, the benefits brought about by ICTs will have especially momentous impact. Improving the framework conditions and the readiness of the population will also increase the potential of this impact.

Policymakers’ low-hanging fruit to boost ICT use
To achieve this Internet revolution and bridge the digital divides, countries need to build their ICT readiness. This implies long-term, costly investments in infrastructure and in education. But a low-hanging fruit exists in the policymaker toolkit. Governments can accelerate the process through sound regulation and more intense competition. By displaying leadership, they can create an enabling environment and orient private operators toward the best solutions for the system’s long-term cost-effectiveness, quality, and sustainability.

Of course, liberalization bears political costs because it implies breaking the dominant position of well-connected or government-owned firms. However, countries can and must overcome these costs to reap the benefits, which are significant. Liberalization attracts more players and creates competition, which in turn tends to increase the quality of products and services and reduce retail prices. This better system lures more customers and encourages investment, both domestic and foreign, which is used to improve infrastructure and the availability of services. Larger markets also generate economies of scale for operators, thus reducing retail prices further and attracting even more customers. In short, liberalization creates a virtuous circle with lasting and far-reaching effects across the economy.

Figure 12 shows the state of liberalization in 17 categories of ICT services on a scale from 0 (monopoly in all services) to 2 (all services fully liberalized). The blue bars delineate the interquartile range within each region, while the black squares and the blue dots identify the median value and outliers, respectively. Although advanced economies perform better on average than any other group of economies, countries from all regions and at different development stages have liberalized their ICT markets.

FIGURE12

The performance of sub-Saharan Africa is noteworthy: on average, the region performs better in terms of liberalization than Emerging and developing Asia or the MENAP regions. Many sub-Saharan African countries have fully liberalized their ICT markets, including several Least Developed Countries (LDCs) and fragile economies: Burkina Faso, Cape Verde, Kenya, Lesotho, Madagascar, Mauritius, Nigeria, Tanzania, and Uganda. This strategy bodes well for the future, and some countries—such as Kenya and Tanzania—are already reaping the benefits of this liberalization in the form of increased investments and use and the introduction of new business models and services.

A byproduct of market liberalization is the creation of Internet exchange points (IXPs). IXPs are physical infrastructures for the exchange of traffic between Internet service providers (ISPs) and other content providers. As countries develop their digital infrastructure, IXPs are used to route domestic traffic exclusively within the country without needing to exchange data through international carriers. This significantly improves the network performance in terms of latency and stability, and it also decreases costs for domestic ISPs.

IXPs can be established with the direct support of the government (as in Nigeria) or by a group of private ISPs (as in Kenya). In both cases, governments provide an essential element, either by playing an active, leadership role in spurring the adoption of this type of technology, or by creating an enabling, competitive environment and properly regulating the existence and provision of this type of services. Governments also play a strategic role in developing IXPs through the construction of Internet backbone networks to connect IXPs to potential users both domestically and abroad.21

ICTs’ contributions to shared prosperity
If harnessed properly, ICTs can create economic opportunities and foster social and political inclusion, ultimately contributing to shared prosperity. The socioeconomic benefits brought about by ICTs are precisely what the Impact subindex of the NRI aims to measure.

ICTs hold the potential for transforming our economies through multiple channels. They boost productivity and reduce transaction and information costs. They allow new models of collaboration that increase workers’ efficiency and flexibility for better work-life balance.

ICTs foster entrepreneurship and create new business models. The past two decades have witnessed the emergence of startups that have disrupted entire industries or created entirely new ones. Some of these startups have since become corporate giants that are transforming our world. Startup incubators now exist in most major cities and provide affordable training, mentorship, and resources to those who wish to start a business. Associated with 3D printing and other technologies, user-friendly, open-source software and inexpensive hardware are contributing to the spread of digital manufacturing among aspiring entrepreneurs, especially among the youth (see Box 2).

Box 2: Fab Labs and digital makers: How information technology is fostering youth entrepreneurship

 

With the advent of digital manufacturing, “fabrication laboratories” are spreading around the world. These centers provide access to hardware, machines, and open-source software, along with affordable training and mentoring. They encourage collaboration among stakeholders and across disciplines, and are increasingly seen as a powerful way to spur entrepreneurship, address the skills gap, and alleviate youth unemployment while revolutionizing production processes.

In June 2013, the French Ministry for the Economy and Finance (Ministère de l’économie, de l’Industrie et du Numérique) called for projects to finance 14 new fabrication laboratories. In doing so, the government recognized the key role of such structures in spreading a culture of innovation and creating bridges between civil society, the private sector, and the education system. The projects were presented by firms, universities, and private associations. All of these projects were based on partnerships among different stakeholders. Moreover, in the context of the Initiative French Tech—a program launched by the French government to support the creation of startups—the presence of a fabrication lab is one of the requirements for a city (or any geographic entity) to be officially recognized as a “Métropole French Tech.”1

In Italy, the North East Foundation (Fondazione Nord Est), a public-private foundation partnered by local business associations and public institutions, is leading an effort to create a fabrication lab in every high school of the northeast of the country. An online crowdfunding platform was launched in January 2015 to finance labs in 10 schools and a professional training centre.2 Within the current context of reforming the school system, in 2015 the Italian government also plans to launch introductory courses on digital manufacturing in some secondary schools, teaching pupils how to code and use digital technologies to make objects on their own or connecting existing ones to the Internet.3

The largest network of labs is one supported by the Fab Foundation, born as an educational outreach component of MIT’s Center for Bits and Atoms. Today, this community is composed of 472 “Fab Labs” in 71 countries (see Figure A).4 It engages schools, academia, entrepreneurs, and research institutions. To be certified as a Fab Lab by the Fab Foundation, a fabrication laboratory must provide a common set of tools and services and share the objectives and the principles of the “Fab Charter.”

Box2-FigA

Box2-TableA

 

Through crowdfunding and equity-crowdfunding platforms, ICTs also provide alternative sources of credit for individuals and entrepreneurs who do not have access to traditional sources of funding, or even for more established businesses that need to finance their operations. Online marketplaces, such as Lending Club, allow borrowers and lenders to connect directly online, while big data makes it possible to compute a credit score for virtually every human being.

ICTs offer significant social benefits, notably by enabling access to basic services, including financial services and education. Perhaps one of the best examples of how the mobile revolution is changing financial services is M-PESA, the mobile-based money transfer system that was launched in Kenya and Tanzania and is now spreading to the rest of the developing world. In the education arena, the proliferation of massive online open courses (MOOCS) allows people around the world to upgrade their skills, train, or re-train more frequently, more flexibly, and more cheaply than through traditional channels.

Technology is also allowing for a more direct interaction between populations and governments. Improved government online presence can significantly increase the efficiency of public administration. The Internet provides new ways for citizens to participate in the policy- and decision-making processes, especially for those whose voice is usually further from the boardrooms. Open-data initiatives and stronger commitments by governments to making information available online improve transparency, governance, and accountability, because citizens and civil society can now monitor more closely the conduct of civil servants.

Most governments have responded—more or less promptly—to demand for e-participation and have enhanced the provision of e-information, the launch of e-consultation initiatives, and the use of e-decision-making. As a result, we observe significant improvement by most countries in the latest edition of the E-participation Index (indicator 10.04) compiled by UNPAN.

Widespread ICT use by businesses, government, and the population at large is a pre-condition for all these benefits and opportunities to materialize, as confirmed by the NRI results. Figure 13 reveals the nearly perfect relationship between the Usage and Impact subindexes—a linear regression of the latter on the former yields a coefficient of determination (R2) of 0.94.

FIGURE13

Better data for better policies
The lack of good data on some of the most basic indicators of socioeconomic performances, let alone ICT-related concepts, is truly alarming, as it can lead to misguided policies and misallocation of resources. In August 2014, UN Secretary-General Ban Ki-moon appointed an Independent Expert Advisory Group (IEAG) on a Data Revolution for Sustainable Development. In its report Mobilising for the Data Revolution, the IEAG referred to data as “the lifeblood of decision-making and the raw material for accountability.”22

To a certain extent, the NRI also suffers from data paucity. Like any benchmarking exercise, it is only as good as its underlying data. The World Economic Forum is fully aware of the limitations of the data and acknowledges the gaps, particularly when it comes to measuring the impacts of ICTs. A handful of data points composing the NRI pre-date 2006, a lag of 10 years, which by ICT standards is appallingly long.

Echoing the UN Secretary-General, the plea for more and better data is reiterated. Governments around the world need to strengthen the capacity of national statistical offices to collect data and preserve their independence, and to support the United Nations’ agencies and other international institutions in their hugely important efforts to collect more reliable, more granular, more timely, more complete, and more harmonized data.

ICTs will both contribute to ushering in the data revolution and benefit from it. ICTs—in all their forms, such as mobile phones, the Internet of things, satellite imagery, and sensors—are revolutionizing the way data are being collected. The new data thus collected will in turn further our understanding of how ICTs are impacting our society.

8
8 It must be noted that in the case of Hong Kong SAR and Taiwan (China), data remain unavailable for four key indicators: PCT patents (indicator 7.03), Government Online Service Index (8.02), ICT PCT patents (9.02), and E-Participation Index (10.04). Other evidence suggests that both economies tend to perform well in the areas of innovation and e-government. Therefore, the data gaps likely penalize these two economies and the overall results should be interpreted with caution.
9
9 Trend analyses are based on a constant sample of the economies that have been covered in every NRI edition since 2012. The 2014 IMF classification was used to compute the averages in every edition.
10
10 ITU 2014.
11
11 The analysis in this paragraph is based on a sample of 188 economies for which data on mobile telephony subscriptions and population existed for every year over the period 1997–2013. The country classification by income is from the World Bank (situation as of July 2014). The breakdown is as follows: 63 high-income countries, 49 upper-middle-income countries, 44 lower-middle-income countries, and 32 low-income countries. Penetration rates are weighted by population. Detailed calculations are available from the authors ([email protected]).
12
12 ITU 2014.
13
13 ITU 2014.
14
14 Bilbao-Osorio et al. 2013.
15
15 United Nations 2014, Goal 9, p. 17.
16
16 The analysis covers 165 countries for which data on Internet penetration and population is available for every year over the period 1997-2013. The country classification by income is from the World Bank (situation as of July 2014). The breakdown is as follows: 62 high-income countries, 41 upper-middle-income countries, 37 lower-middle income countries, and 25 low-income countries. Penetration rates are weighted by population. Detailed calculations are available from the authors.
17
17 Amega-Selorm et al. 2009. An IXP is a physical connection point that helps keep local Internet traffic local. This reduces costs associated with traffic exchange between Internet Service Providers (ISPs).
18
18 ITU 2014.
19
19 This is the median time in years necessary for countries of a given income group to increase Internet penetration and the number of mobile telephone subscriptions per 100 population to the specified threshold. Time is measured from the latest year at the end of which the Internet penetration rate and the number of subscriptions were less than, respectively, 1 percent and two subscriptions.
20
20 The Economist 2014.
21
21 See http://www.ixptoolkit.org/. For more information about IXPs, see also Amega-Selorm et al. 2009.
1
1 http://www.labuonascuola.gov.it.
2
2 http://www.fablabs.io (accessed on February 20th, 2015)
3
3 http://www.lafrenchtech.com.
4
4 http://www.fablabascuola.it.
22
22 IEAG 2014, p. 2.
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