Historical dataset showing Hong Kong poverty rate by year from N/A to N/A.

Previous investigations of geographic concentration of urban poverty indicate the contribution of a variety of factors, such as economic restructuring and class-based segregation, racial segregation, demographic structure, and public policy. However, the models used by most past research do not consider the possibility that poverty concentration may take different forms in different locations across a city, and most studies have been conducted in Western settings. We investigated the spatial patterning of neighborhood poverty and its correlates in Hong Kong, which is amongst cities with the highest GDP in the region, using the city-wide ordinary least square (OLS) regression model and the local-specific geographically weighted regression (GWR) model. We found substantial geographic variations in small-area poverty rates and identified several poverty clusters in the territory. Factors found to contribute to urban poverty in Western cities, such as socioeconomic factors, ethnicity, and public housing, were also mostly associated with local poverty rates in Hong Kong. Our results also suggest some heterogeneity in the associations of poverty with specific correlates (e.g. access to hospitals) that would be masked in the city-wide OLS model. Policy aimed to alleviate poverty should consider both city-wide and local-specific factors.

Comparison of OLS and GWR for neighborhood poverty rate, Hong Kong, 2011.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

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 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Proportion of LSBs where potential correlates were significantly associated with neighborhood poverty rates in seven poverty clusters, Hong Kong, 2011.

Financial hardship is a common challenge among patients with kidney failure and may have negative health consequences. Therefore, financial status is regarded as an important determinant of health, and its impact needs to be investigated. This cross-sectional study aimed to identify the differences in patient-reported and clinical outcomes among kidney failure patients with different financial status. A total of 354 patients with kidney failure were recruited from March to June 2017 at two hospitals in Hong Kong. The Dialysis Symptoms Index and Kidney Disease Quality of Life-36 were used to evaluate patient-reported outcomes. Clinical outcomes were retrieved from medical records and assessed using the Karnofsky Performance Scale (functional status) and Charlson Comorbidity Index (comorbidity level). Patients were stratified using two dichotomised variables, employment status and income level, and their outcomes were compared using independent sample t-tests and Mann-Whitney U-tests. In this sample, the employment rate was 17.8% and the poverty rate was 61.2%. Compared with other patients, increased distress of specific symptoms and higher healthcare utilization, in terms of more emergency room visits and longer hospital stays, were found in patients with poorer financial status. Low-income patients reported a decreased mental quality of life. Financially underprivileged patients experienced health inequity in terms of impaired outcomes. Attention needs to be paid to these patients by providing financial assessments and interventions. Additional research is warranted to confirm these findings and understand the experience of financial hardship and health equity.

Background characteristics and comparison between different employment groups.

By the end of 2020, with all rural residents living in poverty under the current standard lifted out of absolute poverty, marking a new phase in China’s anti-poverty efforts with new pursuit of the consolidation and expansion of poverty alleviation achievements in the effective connection with rural revitalization. The development and improvement of funding mechanisms for rural preschool education are crucially important to further promoting rural development. Employing an input-oriented three-stage DEA model and the Malmquist index, this study conducts a static and dynamic analysis of resource allocation performance in rural preschool education across 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan). The findings reveal that random factors and environmental variables lead to an underestimation of rural preschool education investment performance. Secondly, economically developed regions are not necessarily equipped with higher performance in rural preschool education investment as regional differences stem from the combined effects of various economic, agglomeration, demographic, and scale factors across different areas of the country. Finally, based on these empirical results, this paper proposes policy recommendations to enhance resource allocation performance in China’s rural preschool education.

By the end of 2020, with all rural residents living in poverty under the current standard lifted out of absolute poverty, marking a new phase in China’s anti-poverty efforts with new pursuit of the consolidation and expansion of poverty alleviation achievements in the effective connection with rural revitalization. The development and improvement of funding mechanisms for rural preschool education are crucially important to further promoting rural development. Employing an input-oriented three-stage DEA model and the Malmquist index, this study conducts a static and dynamic analysis of resource allocation performance in rural preschool education across 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan). The findings reveal that random factors and environmental variables lead to an underestimation of rural preschool education investment performance. Secondly, economically developed regions are not necessarily equipped with higher performance in rural preschool education investment as regional differences stem from the combined effects of various economic, agglomeration, demographic, and scale factors across different areas of the country. Finally, based on these empirical results, this paper proposes policy recommendations to enhance resource allocation performance in China’s rural preschool education.

For most of the 20th century, Ireland stood out as one of the poorest countries in Western Europe, not experience the same post-war boom in prosperity that was felt by virtually all other countries in the region. At the onset of the 1973-1975 Recession, Ireland's GDP per capita was less than 60 percent of GDP per capita in the European Union and less than a quarter of GDP per capita in the U.S. Catching up in the 1980s By the 1980s, a wave of foreign investment saw Ireland's export sector grow exponentially, and between 1975 and 1990, Ireland had the second-fastest growth of exports in the world (behind Japan). Additionally, as Ireland joined the European Communities in 1973, it became more integrated into the European economy; before 1973, around three-quarters of Ireland's exports went to the United Kingdom, but this fell to one-third by the 1990s. Ireland's period of industrialization was relatively short in comparison to its neighbors, as it transitioned from an agriculture-based economy to a producer of high-tech products and services. Ireland's low tax rate and other incentives also attracted many American tech companies in the 1980s, such as Apple, Intel, and Microsoft, who were keen on establishing a presence in the European Union. The Celtic Tiger Named after the Four Asian Tigers (Hong Kong, Singapore, South Korea, and Taiwan), which experienced rapid economic growth in the 1970s and 1980s, the period of prosperity between the 1990s and 2000s in Ireland has been dubbed the "Celtic Tiger." Over this time, Ireland's GDP per capita grew to exceed the average in the EU by 10 percent in 2000, and it would eventually surpass that of the U.S. in 2003. Ireland was severely impacted by the financial crisis of 2008 due to the instability of its property sector and extensive lending by banks, and it was the first European economy to go into recession. By the late 2010s, most sectors of the economy had returned to pre-recession levels, and today, Ireland's GDP per capita remains among the top in the world, second in the EU only to Luxembourg.

Malmquist index and its decomposition of preschool education resource allocation in China.

rural Preschool Education Resources Allocation Efficiency in China.