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brassbe1982

Ibrahim Niankara

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Regional level cumulative frequency count of household respondents within WAEMU
Regional level cumulative frequency count of household respondents within WAEMU. This combines household responses from the first and second editions of the survey.
Women respondent's frequency count_Burkina Faso_DHS-2021
The frequency count of women respondent across the 13 Administrative regions of Burkina Faso
Mobile phone access Gender Gap per women respondents' density count
Spatial Bivariate Chart linking the Mobile Phone Access Gender Gap to the women density count in each of the 13 administrative regions in Burkina Faso
Formal Financial Inclusion Gender Gap_Burkina Faso_2021
The Formal Financial Inclusion Gender Gap across the 13 administrative regions in Burkina Faso
The 12 months Gender Usage gap of Mobile Financial Services_Burkina Faso_2021
The 12 months Gender Usage gap of Mobile Financial Services across the 13 administrative regions in Burkina Faso
Gender gap in SmartPhone Access_Burkina Faso_2021
the Gender gap in Smartphone Access across the 13 administrative regions in Burkina Faso
Mobile Access Gender Gap_Burkina Faso_2021
The Gender gap in Mobile Phone Access across the 13 administrative regions in Burkina Faso
Figure (A6): Country level percentage (Weighted proportion) of salary transfer recipients in the global open economy between 2014 and 2021.
Figure (A5): Country level percentage (Weighted proportion) of government transfer recipients in the global open economy between 2014 and 2021.
Figure (A3):Country level percentage (Weighted proportion) of formal saving in the global open economy between 2014 and 2021 fiscal years
Figure (A4): Country level percentage (Weighted proportion) of formal borrowing in the global open economy between 2014 and 2021.
Figure (A2): Country level weighted proportion (percentage) of formal account ownership in the global open economy between 2014 and 2021.
Figure (A1): Global map of respondents’ absolute frequency count by country in the study sample (2014, 2017, and 2021).
It pools together 146688 responses from 2014; 154923 responses from 2017; and 127854 responses from 2021; totaling 429465 responses across 123 countries, and spanning 2014 to 2021.
Data source of IPOdataUAE
Location, Count and Percent Frequency of respondents for the research project "Stated Preferences Data for non-institutional retail investors’ sentiments analysis in the United Arab Emirates’ primary capital market"
figure 13: Round 5 plot of the Provincial weighted Variations of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation policy preferences
figure 12: Round 3 plot of the Provincial weighted Variations of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation policy preferences
figure 11: Baseline (Round 1) plot of the Provincial weighted Variations of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation policy preferences
figure 10: Round 5 plot of the Provincial weighted Averages of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation Policy Preferences
figure 9: Round 3 plot of the Provincial weighted Averages of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation Policy Preferences
figure 8: Baseline (Round 1) plot of the Provincial weighted Averages of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation Policy Preferences
figure 1b: Distribution of Responding Households across the 45 Provinces of Burkina Faso
figure 7: Round 5 plot of the regional weighted Variations of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation Policy Preferences
figure 4: Round 5 plot of the regional weighted Averages of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation Policy Preferences
figure 6: Round 3 plot of the regional weighted Variations of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation policy preferences
figure 3: Round 3 plot of the regional weighted Averages of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation policy preferences
figure 5: Baseline (Round 1) plot of the regional weighted Variations of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation policy preferences
figure 2: Baseline (Round 1) plot of the regional weighted Averages of Household Index of “Self-Directed” and “Government-Directed” COVID-19 risk mitigation policy preferences
Figure1a: Distribution of Responding Households across the 13 Administrative regions of Burkina Faso
RE_Eq1
figure (A8): Spatial distribution of Country level Average Annual Child Health Indicator (CHI-2017 release)
This map represents "figure (A8): Spatial distribution of Country level Average Annual Child Health Indicator (CHI-2017 release)" in the research manuscript entitled "Predicting Youth Planetary Health Interests using Random Utility Theory (RUT)"
figure (A7): Spatial distribution of country level Average Annual Natural Resource Protection Indicator (NRPI-2017 release)
This map represents "figure (A7): Spatial distribution of country level Average Annual Natural Resource Protection Indicator (NRPI-2017 release)" in the research manuscript entitled "Predicting Youth Planetary Health Interests using Random Utility Theory (RUT)"
Figure (A6): Spatial distribution of country level weighted average of youth frequency of Web-browsing on Broad Science
This map represents "figure (A6): Spatial distribution of country level weighted average of youth frequency of Web-browsing on Broad Science" in the research manuscript entitled "Predicting Youth Planetary Health Interests using Random Utility Theory (RUT)"
figure (A5): Spatial distribution of country level weighted average of youth frequency of News Blogs visits
This map represents "figure (A5): Spatial distribution of country level weighted average of youth frequency of News Blogs visits" in the research manuscript entitled "Predicting Youth Planetary Health Interests using Random Utility Theory (RUT)"
figure (A4): Spatial distribution of country level weighted average of youth frequency of Ecological Website visits
This map represents "figure (A4): Spatial distribution of country level weighted average of youth frequency of Ecological Website visits" in the research manuscript entitled "Predicting Youth Planetary Health Interests using Random Utility Theory (RUT)"
figure (A3): Spatial distribution of country level weighted average youth interest in Preventive Science
This map represents "figure (A3): Spatial distribution of country level weighted average youth interest in Preventive Science" in the research manuscript entitled "Predicting Youth Planetary Health Interests using Random Utility Theory (RUT)"
figure (A2): Spatial distribution of country level weighted average youth interest in the Biosphere
This map represents "figure (A2): Spatial distribution of country level weighted average youth interest in the Biosphere" in the research manuscript entitled "Predicting Youth Planetary Health Interests using Random Utility Theory (RUT)"
figure (A1): Spatial distribution of youth respondents count across countries
This map represents "figure (A1): Spatial distribution of youth respondents count across countries" in the research manuscript entitled "Predicting Youth Planetary Health Interests using Random Utility Theory (RUT)"
Figure (B10): Country level percentage (Weighted proportion) of digital purchases between 2014 and 2017 globally
This map represents Figure 10 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (B9): Country level percentage (Weighted proportion) of credit cards ownership between 2014 and 2017 globally
This map represents Figure 9 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (B8): Country level percentage (Weighted proportion) of debit cards ownership between 2014 and 2017 globally
This map represents Figure 8 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (B7): Country level percentage (Weighted proportion) of access to emergency funds between 2014 and 2017 globally
This map represents Figure 7 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (B6): Country level percentage (Weighted proportion) of salary transfer recipients between 2014 and 2017 globally
This map represents Figure 6 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (B5): Country level percentage (Weighted proportion) of government transfer recipients between 2014 and 2017 globally
This map represents Figure 5 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (B10): Country level percentage (Weighted proportion) of home loan between 2014 and 2017 globally
This map represents Figure 10 in Appendix B of the research manuscript entitled "The impact of public sector and private sector financial transfers on the global promotion of financial inclusion: A Spatio-temporal Random Utility Modeling Approach".
Figure (A4): Country level weighted proportion of borrowers for entrepreneurial (farming or business) purposes between 2014 and 2017, globally.
This map represents Figure 9 in Appendix B of the research manuscript entitled "The impact of public sector and private sector financial transfers on the global promotion of financial inclusion: A Spatio-temporal Random Utility Modeling Approach".
Figure (A3): Country level weighted proportion of savers for entrepreneurial (farming or business) purposes between 2014 and 2017, globally.
This map represents Figure 8 in Appendix B of the research manuscript entitled "The impact of public sector and private sector financial transfers on the global promotion of financial inclusion: A Spatio-temporal Random Utility Modeling Approach".
Figure (B4): Country level percentage (Weighted proportion) of formal borrowing at a financial institution between 2014 and 2017 globally
This map represents Figure 4 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (B3): Country level percentage (Weighted proportion) of formal saving at a financial institution between 2014 and 2017 globally
This map represents Figure 3 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (A2): Country level weighted proportion (percentage) of formal account owners between 2014 and 2017 globally.
This map represents Figure 2 in Appendix B of the research manuscript entitled "The Impact of Digital Innovations in Public and Private Sectors Financial Management on Formal Financial Inclusion in the Global Economy: A Random Utility Analysis".
Figure (B4): Country level percentage (Weighted proportion) of any borrowing between 2014 and 2017 globally
This map represents Figure 4 in Appendix B of the research manuscript entitled "The impact of public sector and private sector financial transfers on the global promotion of financial inclusion: A Spatio-temporal Random Utility Modeling Approach".
Figure (B3): Country level percentage (Weighted proportion) of any Savings between 2014 and 2017 globally
This map represents Figure 3 in Appendix B of the research manuscript entitled "The impact of public sector and private sector financial transfers on the global promotion of financial inclusion: A Spatio-temporal Random Utility Modeling Approach".
Figure (B2): Country level percentage (Weighted proportion) of any account ownership between 2014 and 2017 globally
This map represents Figure 2 in Appendix B of the research manuscript entitled "The impact of public sector and private sector financial transfers on the global promotion of financial inclusion: A Spatio-temporal Random Utility Modeling Approach".
Figure (A1): Global map of respondents’ absolute frequency count by country in the pooled sample (2014 and 2017).
This map represents Figure 1 in Appendix B of the research manuscript entitled "The impact of public sector and private sector financial transfers on the global promotion of financial inclusion: A Spatio-temporal Random Utility Modeling Approach".
figure 5EU: Regional mapping of country level averages of total SSRs (left panel) and ADRs (right panel) on bookings by EU leisure consumers at a resort and city hotel in Portugal
Figure 5EU in the manuscript titled: " Testing the complementarity hypothesis between Average Daily Rate (ADR) and special services requests (SSR) during hotel stay: Evidence from EU leisure consumers at a resort and a city hotel in Portugal"
figure 4EU: Regional mapping of country level average of the ADRs on EU consumers’ bookings at a resort and city hotel in Portugal
Figure 4EU in the manuscript titled: " Testing the complementarity hypothesis between Average Daily Rate (ADR) and special services requests (SSR) during hotel stay: Evidence from EU leisure consumers at a resort and a city hotel in Portugal"
figure 3EU: Regional mapping of country level average total requests of special services by European Union leisure consumers at a resort and city hotel in Portugal
Figure 3EU in the manuscript titled: " Testing the complementarity hypothesis between Average Daily Rate (ADR) and special services requests (SSR) during hotel stay: Evidence from EU leisure consumers at a resort and a city hotel in Portugal"
figure 1EU: Regional mapping of booking counts by country of origin in the European Union
Figure 1EU in the manuscript titled: "Testing the complementarity hypothesis between Average Daily Rate (ADR) and special services requests (SSR) during hotel stay: Evidence from EU leisure consumers at a resort and a city hotel in Portugal"
figure 2EU: Spatial distribution of the Joint probabilities (in %) of ADR and SSR from the dependent copula model (left panel), and the Joint probabilities (in %) from independence model (right panel)
Figure 2EU in the manuscript titled: " Testing the complementarity hypothesis between Average Daily Rate (ADR) and special services requests (SSR) during hotel stay: Evidence from EU leisure consumers at a resort and a city hotel in Portugal"
figure 5: spatial distribution of the joint probabilities (in %) that the dependent variables ICTRES and BELONG are both less than their respective standardized mean values in the study sample.
Figure 5 in “Cross national comparative analysis of youth access to ICT resources and subjective well-being in the Middle East: A spatial bivariate copula regression modelling”
figure 3: Geographical maps of country level weighted average of standardized ICT resource availability (left panel) and youth subjective well-being (right panel)
Figure 3 in “Cross national comparative analysis of youth access to ICT resources and subjective well-being in the Middle East: A spatial bivariate copula regression modelling”
figure 2: Geographical map of respondents count by country
Figure 2 in “Cross national comparative analysis of youth access to ICT resources and subjective well-being in the Middle East: A spatial bivariate copula regression modelling”
figure 8: Spatial distribution of pooled (2015 and 2018) country level weighted average of "youth's index of economic and socio-cultural status"
Figure 9 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
figure 8: Spatial distribution of pooled (2015 and 2018) country level weighted average of "youth's family wealth"
Figure 8 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
figure 7: Spatial distribution of pooled (2015 and 2018) country level weighted average of "youth's sense of belonging in school"
Figure 7 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
figure 6: Spatial distribution of pooled (2015 and 2018) country level weighted average "number of phones with internet access at youth's home (desktop, laptop and notebook)"
Figure 6 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
figure 4: Spatial distribution of pooled (2015 and 2018) country level weighted average "number of computers at youth's home (desktop, laptop and notebook)"
Figure 5 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
figure 4: Spatial distribution of pooled (2015 and 2018) country level weighted average of "youth access to ICT resources at home"
Figure 4 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
figure 3: Global Mapping of youth respondents count by country over for the year 2018
Figure 3 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
figure 2: Global Mapping of youth respondents count by country over for the year 2015
Figure 2 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
figure 1: Global Mapping of respondents count by country over the two years 2015 and 2018
Figure 1 in "Pooled cross-sectional panel of the 2015-2018 PISA student questionnaire data files for the evaluation of youth related strategies implemented under UN 2030 Agenda for sustainable development"
Spatial distribution of Welfare across the 45 provinces of Burkina Faso
Welfare is measured by households' Inflation Adjusted (real) per-capita consumption expenditure for the year 2014.
Per Capita Welfare Map BF
Cartographie Nationale de la distribution Provinciale du Bien Etre Economique, utilisant les données de l'enqette Multi-sectorielle Continue (EMC), collectée par l'Institue Nationale de la Statistique et de la démographie (INSD), Burkina Faso.