Human Development Index (HDI) – Global Dataset (1990–2022) This dataset provides comprehensive data on the Human Development Index (HDI), a summary measure of average achievement in key dimensions of human development:

Life Expectancy at Birth – representing health and longevity

Average Education Level – combining mean years of schooling and expected years of schooling The HDI is used by the United Nations Development Programme (UNDP) and researchers worldwide to compare levels of development across countries.

source:https://hdr.undp.org/data-center

Material wealth is a key factor shaping human development and well-being. Every year, hundreds of studies in social science and policy fields assess material wealth in low- and middle-income countries assuming that there is a single dimension by which households can move from poverty to prosperity. However, a one-dimensional model may miss important kinds of prosperity, particularly in countries where traditional subsistence-based livelihoods coexist with modern cash economies. Using multiple correspondence analysis to analyze representative household data from six countries—Nepal, Bangladesh, Ethiopia, Kenya, Tanzania and Guatemala—across three world regions, we identify a number of independent dimension of wealth, each with a clear link to locally relevant pathways to success in cash and agricultural economies. In all cases, the first dimension identified by this approach replicates standard one-dimensional estimates and captures success in cash economies. The novel dimensions we identify reflect success in different agricultural sectors and are independently associated with key benchmarks of food security and human growth, such as adult body mass index and child height. The multidimensional models of wealth we describe here provide new opportunities for examining the causes and consequences of wealth inequality that go beyond success in cash economies, for tracing the emergence of hybrid pathways to prosperity, and for assessing how these different pathways to economic success carry different health risks and social opportunities.

Url of original source: http://hdr.undp.org/en/data. The mission of the Human Development Report Office (HDRO) is to advance human development. The goal is to contribute towards the expansion of opportunities, choice and freedom. The office works towards this goal by promoting innovative new ideas, advocating practical policy changes, and constructively challenging policies and approaches that constrain human development. The office works with others to achieve change through writing and research, data analysis and presentation, support to national and regional analysis and outreach and advocacy work.

Data presents ten countries selected from twenty-eight case studies covered in a literature review, as subjects in analysing the impact of tourism to human development using panel estimation approach. It shows the government structure/system, as referenced from CIA World Factbook (2021) and World Bank (2021) and travel and tourism's total contribution to GDP in 2019, as referenced from World Travel and Tourism Council (2021).

Context

The Human Development Index is a statistic composite index of life expectancy, education, and per capita income indicators, which are used to rank countries into four tiers of human development.

Content

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The health dimension is assessed by life expectancy at birth, the education dimension is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. The standard of living dimension is measured by gross national income per capita. The HDI uses the logarithm of income, to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean. Refer to Technical notes for more details.

The HDI simplifies and captures only part of what human development entails. It does not reflect on inequalities, poverty, human security, empowerment, etc. The HDRO offers the other composite indices as broader proxy on some of the key issues of human development, inequality, gender disparity and poverty.

A fuller picture of a country's level of human development requires analysis of other indicators and information presented in the statistical annex of the report.

The Human Development Index (HDI) was developed by the United Nations Development Programme (UNDP) in the Human Development Report 1990 to measure the level of economic and social development of the United Nations member countries. The HDI is a composite indicator based on three basic variables: life expectancy, educational attainment and quality of life, and is calculated according to a certain methodology. "The One Belt One Road (OBOR) human development resilience dataset is a comprehensive indicator of human development resilience in each country. "The human development resilience dataset for countries along the Belt and Road is a comprehensive diagnosis based on sensitivity and adaptability analysis using year-by-year data of the Human Development Index for countries along the Belt and Road from 2000 to 2020. The Human Development Resilience Indicator (HDRI) data was prepared based on sensitivity and adaptation analysis. Please refer to the documentation for the methodology of preparing the dataset. "The Human Development Resilience Dataset for countries along the Belt and Road is an important reference for analysing and comparing the current state of human development resilience in each country.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2006/2007. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2012 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2006/2007:
In order to develop an effective poverty reduction policies and programs, Iraqi policy makers need to know how large the poverty problem is, what kind of people are poor, and what are the causes and consequences of poverty. Until recently, they had neither the data nor an official poverty line. (The last national income and expenditure survey was in 1988.)

In response to this situation, the Iraqi Ministry of Planning and Development Cooperation established the Household Survey and Policies for Poverty Reduction Project in 2006, with financial and technical support of the World Bank. The project has been led by the Iraqi Poverty Reduction Strategy High Committee, a group which includes representatives from Parliament, the prime minister's office, the Kurdistan Regional Government, and the ministries of Planning and Development Cooperation, Finance, Trade, Labor and Social Affairs, Education, Health, Women's Affairs, and Baghdad University.

The Project has consisted of three components: - Collection of data which can provide a measurable indicator of welfare, i.e. The Iraq Household Socio Economic Survey (IHSES).

• Establishment of an official poverty line (i.e. a cut off point below which people are considered poor) and analysis of poverty (how large the poverty problem is, what kind of people are poor and what are the causes and consequences of poverty).

• Development of a Poverty Reduction Strategy, based on a solid understanding of poverty in Iraq.

The survey has four main objectives. These are:

• To provide data that will help in the measurement and analysis of poverty. • To provide data required to establish a new consumer price index (CPI) since the current outdated CPI is based on 1993 data and no longer applies to the country's vastly changed circumstances. • To provide data that meet the requirements and needs of national accounts. • To provide other indicators, such as consumption expenditure, sources of income, human development, and time use.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2012 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Total sample size and stratification:

The total effective sample size of the Iraq Household Socio Economic Survey (IHSES) 2007 is 17,822 households. The survey was nominally designed to visit 18,144 households - 324 in each of 56 major strata. The strata are the rural, urban and metropolitan sections of each of Iraq's 18 governorates, with the exception of Baghdad, which has three metropolitan strata. The Iraq Household Socio Economic Survey (IHSES) 2007 and the MICS 2006 survey intended to visit the same nominal sample. Variable q0040 indicates whether this was indeed the case.

----> Sample frame:

The 1997 population census frame was applied to the 15 governorates that participated in the census (the three governorates in Kurdistan Region of Iraq were excluded). For Sulaimaniya, the population frame prepared for the compulsory education project was adopted. For Erbil and Duhouk, the enumeration frame implemented in the 2004 Iraq Living Conditions Survey was updated and used. The population covered by Iraq Household Socio Economic Survey (IHSES) included all households residing in Iraq from November 1, 2006, to October 30, 2007, meaning that every household residing within Iraq's geographical boundaries during that period potentially could be selected for the sample.

----> Primary sampling units and the listing and mapping exercise:

The 1997 population census frame provided a database for all households. The smallest enumeration unit was the village in rural areas and the majal (census enumeration area), which is a collection of 15-25 urban households. The majals were merged to form Primary Sampling Units (PSUs), containing 70-100 households each. In Kurdistan, PSUs were created based on the maps and frames updated by the statistics offices. Villages in rural areas, especially those with few inhabitants, were merged to form PSUs. Selecting a truly representative sample required that changes between 1997 and the pilot survey be accounted for. The names and addresses of the households in each sample point (that is, the selected PSU) were updated; and a map was drawn that defined the unit's borders, buildings, houses, and the streets and alleys passing through. All buildings were renumbered. A list of heads of household in each sample point was prepared from forms that were filled out and used as a frame for selecting the sample households.

----> Sampling strategy and sampling stages:

The sample was selected in two stages, with groups of majals (Census Enumeration Areas) as Primary Sampling Units (PSUs) and households as Secondary Sampling Units. In the first stage, 54 PSUs were selected with probability proportional to size (pps) within each stratum, using the number of households recorded by the 1997 Census as a measure of size. In the second stage, six households were selected by systematic equal probability sampling (seps) within each PSU. To these effects, a cartographic updating and household listing operation was conducted in 2006 in all 3,024 PSUs, without resorting to the segmentation of any large PSUs. The total sample is thus nominally composed of 6 households in each of 3,024 PSUs.

----> Sample Points Trios, teams and survey waves:

The PSUs selected in each governorate (270 in Baghdad and 162 in each of the other governorates) were sorted into groups of three neighboring PSUs called trios -- 90 trios in Baghdad and 54 per governorate elsewhere. The three PSUs in each trio do not necessarily belong to the same stratum. The 12 months of the data collection period were divided into 18 periods of 20 or 21 days called survey waves. Fieldworkers were organized into teams of three interviewers, each team being responsible for interviewing one trio during a survey wave. The survey used 56 teams in total - 5 in Baghdad and 3 per governorate elsewhere. The 18 trios assigned to each team were allocated into survey waves at random. The 'time use' module was administered to two of the six households selected in each PSU: nominally the second and fifth households selected by the seps procedure in the PSU.

----> Time-use sample:

The Iraq Household Socio Economic Survey (IHSES) questionnaire on time use covered all household members aged 10 years and older. A subsample of one-third of the households was selected (the second and fifth of the six households in each sample point). The second and fourth visits were designated for completion of the time-use sheet, which covered all activities performed by every member of the household.

A more detailed description of the allocation of sample across governorates is provided in the tabulation report document available among external resources in both English and Arabic.

Sampling deviation

----> Exceptional Measures

The design did not consider the replacement of any of the randomly selected units (PSUs or households.) However, sometimes a team could not visit a cluster during the allocated wave because of unsafe security conditions. When this happened, that cluster was then swapped with another cluster from a randomly selected future wave that was considered more secure. If none were considered secure, a sample point was randomly selected from among those that had been visited already. The team then visited a new cluster within that sample point. (That is, the team visited six households that had not been previously interviewed.) The original cluster as well as the new cluster were both selected by systematic equal probability sampling.

This explains why the survey datasets only contain data from 2,876 of the 3,024 originally selected PSUs, whereas 55 of the PSUs contain more that the six households nominally dictated by the design.

The wave number in the survey datasets is always the nominal wave number, corresponding to the random allocation considered by the design. The effective interview dates can be found in questions 35 to 39 of the survey questionnaires.

Remarkably few of the original clusters could not be visited during the fieldwork. Nationally, less than 2 percent of the original clusters (55 of 3,024) had to be replaced. Of the original clusters, 20 of 54 (37 percent) could not be visited in the stratum of “Kirkuk/other urban” and

This dataset contain data of different countries. This dataset is about best country to line in 2024. This dataset is best for Exploratory Data Analysis. If you like this dataset then please upvote it.

Here we present the development of a novel minimally invasive sample preparation technique for application in the mass spectrometric analysis of bioarchaeological materials. The extraction protocol was developed by applying commercially available, dermatology grade skin sampling strips to modern skin surfaces as a surrogate, and then applied to cranial and bone fragments belonging to a 26th Dynasty Egyptian mummified individual from the coffin of a woman named Mer-Neith-it-es. Extracted proteins were subjected to electrophoretic separation and proteolytic digestion, resulting in peptides that were separated, fragmented and identified using nanoflow liquid chromatography - high resolution tandem mass spectrometry. We have identified a number of ancient brain and intracellular proteins on the surfaces of the various cranial and bone fragments, without causing any significant damage to these valuable remains.

ABSTRACT The present paper proposes an approach to human management that privileges conversational spaces in the organization through verbalization, which results in symbolization, socialization and responsibilization of individuals in the organization groups. Our special concern is responsibilization, emphasizing that participation through conversation generates commitment, inclusion and a sense of belonging in individuals. In the cases studied, we have privileged the analytical method, understood as the method of discourse analysis, which underlies the attitude of the professional who grounds his work on listening, analyzing and intervening. The research findings allow the set out of a path to corporate responsibility.

Purpose: With an alarmingly high percentage of the human population living in countries impacted by violent conflict and social, environmental and economic fragility exacerbated by major global challenges, entrepreneurship is considered key to sustainable development and human prosperity. Entrepreneurs operating in fragile contexts contribute to peace building, poverty reduction and the advancement of institutional structures. However, little is known about who those entrepreneurs are and what makes them thrive amidst and despite fragility. This thesis aims to understand the human capital assets of fragile-country entrepreneurs that enable their success at various stages of the entrepreneurial process as well as the status quo of human capital investment efforts pertaining to entrepreneurship and their contribution to sustainable outcomes in fragile contexts. Literature Analysis: Entrepreneurship research has been criticized for lacking focus on diversity and the shortage of scholarly works combining theoretical rigor with social relevance. Research on entrepreneurship outside of stable economies is quite limited with existing literature lacking empirical and theoretical robustness. As a result, much remains unknown about the characteristics and success enablers of fragile-country entrepreneurs. For instance, studies on the drivers behind individuals’ decision to found new companies largely classify fragile-country entrepreneurship motivation into a necessity/opportunity dichotomy, thereby ignoring the complex interplay of personal and environmental factors that feed into this decision. As for studies during early business start-up and growth stages, they primarily view entrepreneurial success in terms of economic gain rather than human capital assets and outcomes. Accordingly, little is known about the entrepreneurial and managerial behaviors of fragile-country entrepreneurs or personality predictors of their success. At later stages along the entrepreneurial path, the constructs of entrepreneurial orientation and global mindset, although frequently assessed in studies of entrepreneurial internationalization, have been little analyzed with respect to the globalization of fragile-county startups. In response to those gaps in entrepreneurship literature, the first three papers in this thesis specifically address human capital assets pertaining to the motivation, personality, behavior, orientation and mindset needed for entrepreneurial success in the pre-startup, initiation and early growth and internationalization stages respectively. This is complimented by an analysis of entrepreneurship education and training’s contribution to sustainable development in fragile contexts, in recognition of education’s role as prime investment in human capital. Research Design: Human capital assets, specifically drivers behind entrepreneurship motivation in the pre-startup stage (paper 1), personality characteristics and entrepreneurial and managerial behaviors in the initiation and early growth stage (paper 2) and entrepreneurship orientation and global mindset in the startup internationalization stage (paper 3), are analyzed quantitatively through the use of questionnaires. Quantitative approaches have been chosen to complement existing qualitative findings and are preferred due to their use of standardized, validated scales and large sample sizes that allow for the generalization and replication of findings. The first paper concerns Syrian entrepreneurs and employs an exploratory factor analysis to identify key motivational factors behind their decision to pursue entrepreneurship, then proceeds to comparatively assess differences in entrepreneurship motivation between Syrian entrepreneurs in Damascus and Berlin using MANCOVA and non-parametric methods. The second paper employs linear regression to analyze the personality-behavior relationship in a sample of sub Saharan African entrepreneurs across 22 countries while moderating for country fragility, while a student t-test was used to confirm behavioral differences between highly successful and less successful entrepreneurs. The third paper compares various facets of entrepreneurial orientation and global mindset between Pakistani and German entrepreneurs using MANOVA and non-parametric methods. Finally, a systematic literature review guided the analysis of entrepreneurship education and training programs’ contribution to sustainable development, zooming in on the role they play and challenges they face in fragile contexts. Results: The first study identified self-realization and the perceptions of institutional support, cultural influence and the economic milieu as key aspects of Syrians’ entrepreneurship motivation with no notable differences between local entrepreneurs in Damascus and newcomers in Berlin. As for the second study, positive relationships have been confirmed between managerial and entrepreneurial behaviors and entrepreneurial success. Additionally, agreeableness and conscientiousness have been identified as the strongest personality predictors of entrepreneurial and managerial behaviors among sub Saharan African entrepreneurs, partially moderated by country fragility. The third study reveals that Pakistani entrepreneurs have lower risk-taking tendencies, international cognition and international knowledge compared with German ones yet higher levels of international behavior. The final study uncovers shortcomings of current entrepreneurship education and training initiatives in their contribution to positive social and environmental outcomes. Implications: This thesis extends multiple theories to fragile contexts (e.g. the eclectic theory of entrepreneurship, the trait activation theory and the mindset theory) while combining primary, micro-level data with country-level comparisons. The first study reveals motivations beyond necessity and opportunity, combining individual and environmental perspectives. The second study challenges existing literature by uncovering agreeableness, rather that extraversion, as key predictor of desired behaviors alongside conscientiousness, providing evidence for the conditional expression of personality depending on context. The third study contributes to the growing literature stream of comparative entrepreneurial internationalization and affirms differences in internationalization tendencies between entrepreneurs in stable and fragile economies. Combined with the identification of several gaps in the entrepreneurship education and training literature with respect to fragile context development, those findings set the stage for future explanatory analyses. On a practical level, this research provides tools to assess the success of investments in entrepreneurship from a human capital perspective while supporting the customization of educational and financial support programs based on context-relevant knowledge. For instance, it may be wise to financially invest in entrepreneurs with higher levels of intrinsic motivation, agreeableness, conscientiousness as well as entrepreneurial and managerial behaviors while supporting entrepreneurs who lack those assets to develop them through education and training. Practitioners are also encouraged to invest in socially underrepresented groups, outside of university settings and using experiential and technology-powered educational approaches.

Context

The Gender Development Index (GDI) is a composite measure designed to assess gender disparities and inequalities in a society by considering factors related to human development. It is an extension of the Human Development Index (HDI) and focuses on three key dimensions: health, education, and income. In the GDI, these dimensions are assessed separately for males and females, allowing for a comparison of gender-based development gaps. Health indicators typically include life expectancy at birth for both genders. Education indicators encompass literacy rates and enrollment in primary, secondary, and tertiary education for both males and females. The income component typically examines income levels and workforce participation for both genders.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021, and Gender Development Index spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Gender Development Index from 1990 to 2021 - Gender Development Index from 1990 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/NI4UY57.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

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Determination of ivermectin in plasma and whole blood using LC-MS/MS

This is a metadata only record. The datasets used in this thesis are open and available via https://databank.worldbank.org/source/world-development-indicators We use panel dataset for 115 countries for the time span 1990-2016. The countries are categorized into four groups as per gross national income (GNI) measured using World Bank Atlas (2018) method [the 9 of low ($1005 or less), 32 of lower-middle ($1006-$3955), 35 of upper-middle ($3956-$12,235), and 39 of high ($12,236 or more) income panels]. The data on different variable of interests are collected from World Development Indicators (CD-ROM, 2018). We use real estimation adjusting inflation. The collected datasets of dependent variables are carbon dioxide (CO2) measured in metric tons per capita, methane (CH4) in Kt. of CO2 equivalent, and the particulate matter (PM2.5) in microgram per cubic meter. The independent variables of the collected datasets are gross domestic product (GDP) per capita (constant 2010 US$), energy consumption (EC) in kg of oil equivalent per capita, trade openness (TO) measured as the share of total trade volume in GDP, urbanization (UR) in terms of the share of urban population in total population and TR is the total transport services in percentage of total commercial service of exports and imports, financial development (FD) measured in domestic credit to private sector, foreign direct investment (FDI) is measured by the net inflows of FDI as a percentage of GDP, the human development index (HDI) measured by the UNDP as a proxy for human capital formation. Moreover, we measure the agricultural sector by its output share of GDP (constant 2010 US$) and the manufacturing sector by its output share of GDP (constant 2010 US$).

Abstract Purpose: This study aims to analyze the impact on human development of rates of innovative entrepreneurship and necessity entrepreneurship. Design/methodology/approach: Our empirical study is based on samples from countries with information about rates of entrepreneurship, human development, and social progress. The data are analyzed by means of pooled least squares and panel data techniques. Findings: Innovative entrepreneurship improves the quality of life in the dimensions measured by the Social Progress Index and Modified Human Development Index. Necessity entrepreneurship does not favor an increase of human development, at least in the dimensions measured by the two indexes, since this is a subsistence entrepreneurship type. Originality/value: This study presents new evidence that contributes to the knowledge on how entrepreneurship improves quality of life.

Context

The Gender Inequality Index (GII) is a comprehensive measure devised to evaluate gender disparities and inequities within a society by taking into account various critical dimensions. This index provides insights into the differences and imbalances experienced by individuals based on their gender. The GII is an extension of the Human Development Index (HDI) and concentrates on three principal dimensions: reproductive health, empowerment, and economic activity. Reproductive health is a significant dimension of the GII, encompassing indicators such as maternal mortality rates and adolescent birth rates. These indicators reflect the disparities in health outcomes experienced by women, especially in terms of maternal health and reproductive rights.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021, GII Rank (2021) representing the Gender Inequality Index Rank for 2021 and Gender Inequality Index spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• GII Rank (2021) - Gender Inequality Index Rank for 2021
• Gender Inequality Index from 1990 to 2021 - Gender Inequality Index from 1990 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/E64Y2Be.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by pikisuperstar on Freepik

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Context

In the intricate tapestry of gender disparities, the Labour Force Participation Rate (LFPR) serves as a crucial thread that weaves through the fabric of economic activity. Examining LFPR through the lens of the Gender Inequality Index (GII) sheds light on the distinctive experiences of men and women in the workforce, unraveling disparities and inequities that persist in our societies.

Male Labour Force Participation Rate: For men, the LFPR becomes a gauge of economic engagement and contribution to societal progress. Traditionally, societal expectations have often encouraged a high male LFPR, positioning men as primary breadwinners. The index, when analyzed within the context of GII, reveals not only the quantity but also the quality of male participation in the workforce. High LFPR for men might suggest economic activity, but it doesn't necessarily capture the nuances of workplace gender dynamics, occupational segregation, or disparities in income.

Female Labour Force Participation Rate: Conversely, the LFPR for women emerges as a pivotal indicator of empowerment and gender equality. A rising female LFPR signals a departure from traditional norms, reflecting increased opportunities, access to education, and a broader recognition of women's roles in society. However, the GII prompts a deeper examination, delving into the quality of female participation. Disparities may persist in terms of wage gaps, representation in leadership roles, and challenges related to work-life balance.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021 and Labour force participation rate for male and female (% ages 15 and older) spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Labour force participation rate, male and female (% ages 15 and older) from 1990 to 2021 - Labour force participation rate for male and female from 1990 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/r2urCHa.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by iconicbestiary on Freepik

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Context

Income inequality is a global issue reflecting the uneven distribution of wealth within and between countries. Developed nations exhibit varying income levels due to economic policies and labor dynamics, resulting in Gini coefficients of around 0.3 to 0.4. Conversely, developing nations often experience higher income disparities due to limited access to education, healthcare, and jobs, leading to Gini coefficients exceeding 0.4, exacerbating poverty cycles and social tensions. This inequality hampers economic growth, social cohesion, and upward mobility. Addressing it requires comprehensive policies, including progressive taxation and equitable resource distribution, to promote a more just and inclusive society.

Content

This dataset comprises historical information encompassing various indicators concerning Inequality in Income on a global scale. The dataset prominently features: ISO3, Country, Continent, Hemisphere, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Income from 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Income from 2010 to 2021 - Inequality in Income from year 2010 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/LIrXWPP.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by Image by pch.vector on Freepik

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ABSTRACT The ultimate goal of the present work lay in creating a vector methodology for measuring QoL. Application of an integrated approach to the results of the classification analysis and SWOT analysis enabled elaborating a vector methodology of a recommendatory type aimed at improving QoL measurement approaches. It was established that this methodology should include four major updates taking into account the challenges of tomorrow. The study results may be of interest to public authorities responsible for taking measures directed at raising the country’s international ranking as well as be used for reducing contradictions on the part of QoL measuring procedures.

To support evidence-based policymaking and research in the transport sector, the United Nations Conference on Trade and Development (UNCTAD) and the World Bank have developed a Global Transport Costs dataset for International Trade.

In order to provide a more visual understanding of global transport costs related to international trade, an interactive map has been created to visualize the transport costs (incl. insurance costs) of individual commodities (Harmonized System, six-digit level) traded between two countries in 2016. The map focuses on sea mode while the chart shows total transport costs by mode of transport. This visualization aims to complement the underlying quantitative data by offering alternative access to the data and providing high-level insights into how transport costs may differ as a function of mode of transport, distance, economies of scale, and other factors.

Being very interested in enhancing the user experience with regard to this visual approach to global transport costs, we look forward to any feedback (statistics@unctad.org) to further optimize the display of global transports data for international trade.

s a dynamic and practical tool to support developing countries in understanding the status of their productive capacity and how this can be improved.

It builds on UNCTAD’s long-standing work on productive capacities, which are essential for generating inclusive and sustained economic growth and achieving sustainable development.

The PCI covers 194 economies for the period 2000-2022. The set of productive capacities and their specific combinations are mapped across 42 indicators. This makes our PCI multidimensional in its analytical abilities.

The PCI can help diagnose the areas where countries may be leading or falling behind, spotlighting where policies are working and where corrective efforts are needed. It suggests a roadmap for future policy actions and interventions under each of its eight components: human capital, natural capital, energy, ICTs, structural change, transport, institutions and the private sector.

The PCI was developed in response to the ECOSOC resolution (E/RES/2017/29), encouraging UNCTAD “to pursue its methodological work to measure progress in and identify obstacles to the development of productive capacities in developing countries”.

The PCI has been peer-reviewed and validated at national and regional levels by leading technical experts across the UN system, as well as by academics and government stakeholders.

Stakeholders in select countries have been trained on how to use the index in their development policymaking processes. UNCTAD stands ready to conduct more training sessions at the request of countries.

What are productive capacities? UNCTAD has long defined productive capacities as “the productive resources, entrepreneurial capabilities and production linkages, which together determine the capacity of a country to produce goods and services and enable it to grow and develop.”

Productive resources are factors of production, including different types of productive resources and capital. They include financial capital and physical capital, the latter comprising both machinery and equipment (typically operating at the firm / farm level) and infrastructure. Entrepreneurial capabilities are the skills, knowledge, and information which enterprises have. They comprise entrepreneurship, entrepreneurial capabilities, and technological capabilities. They include the important skills required for investment, production and establishing linkages at the firm / farm level. Production linkages are flows among productive units (firms / farms) of goods and services, knowledge, technology and information, and productive resources, including human resources). They include exchanges among productive units of different sizes (micro, small and medium-sized enterprises and large ones), and ownership structures (domestic / foreign, public / private), operating in different sectors. What role do productive capacities play? Developing productive capacities, plays a central role in setting in motion the long-term process of structural transformation, which is the backbone of sustainable development. The available evidence shows no nation has developed without fostering productive capacities and structural economic transformation.

Building the economic resilience of developing countries remains a daunting challenge. It depends fundamentally on creating, maintaining and using productive capacities to realize development objectives. This requires a shift from the current fragmented and project-based interventions towards coherent, economy-wide and programme-based approaches to removing binding constraints on development. Actions and interventions at the domestic level need to be supported and complemented by additional robust international support.

The PCI makes a...