This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

United States US: Income Share Held by Highest 20% data was reported at 46.900 % in 2016. This records an increase from the previous number of 46.400 % for 2013. United States US: Income Share Held by Highest 20% data is updated yearly, averaging 46.000 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 46.900 % in 2016 and a record low of 41.200 % in 1979. United States US: Income Share Held by Highest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

This statistic shows the median household income in the United States from 1970 to 2020, by income tier. In 2020, the median household income for the middle class stood at 90,131 U.S. dollars, which was approximately a 50 percent increase from 1970. However, the median income of upper income households in the U.S. increased by almost 70 percent compared to 1970.

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

In the Post-industrial Era there has been an apparent weakening of the relationship between class and voting in the U.S., with lower class voters becoming less likely to support the Democratic Party. We argue that this reflects that lower class status predicts liberal economic attitudes, but conservative views on cultural and racial issues, while the parties are consistently liberal or conservative, creating conflicts for many voters. How do voters settle such internal conflicts? We argue that the salience voters attach to these different types of issues determines how policy attitudes, and indirectly class, shapes voting. Using ANES and GSS data since the 1970s, we find that class consistently predicts economic and cultural/minority policy attitudes, and that lower class voters who place more salience on economic issues, and upper class voters for whom cultural issues are more salient, are more likely to support the Democratic Party in presidential elections.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the Data DictionaryWhat is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

The present research investigated the effects of social class on interpersonal trust. In a series of experiments, we showed how the contextualist socio-cognitive tendencies of the lower class and the solipsistic tendencies of the upper class were reflected in their trusting attitudes and behaviors. In Study 1 (N = 491), upper class individuals expressed the same levels of trust towards all partners, while lower class individuals adjusted their trust choices to the affect-rich information about their interaction partner and trusted warm partners more than cold partners. The results of Study 2 (N = 210) showed that when threatened, lower class individuals had generally less trusting attitudes, while upper class members were equally trusting as in a neutral situation. Study 3 (N = 200) revealed that upper class individuals explained a betrayal of their trust with dispositional factors to a higher degree than lower class individuals. We discuss how these differences contribute to perpetuating the disadvantage of the lower class.

Between April 2008 and March 2024, households from the Pakistani and Bangladeshi ethnic groups were the most likely to live in low income out of all ethnic groups, before and after housing costs.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the Data DictionaryWhat is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

In 2021, the birth rate in the United States was highest in families that had under 10,000 U.S. dollars in income per year, at 62.75 births per 1,000 women. As the income scale increases, the birth rate decreases, with families making 200,000 U.S. dollars or more per year having the second-lowest birth rate, at 47.57 births per 1,000 women. Income and the birth rate Income and high birth rates are strongly linked, not just in the United States, but around the world. Women in lower income brackets tend to have higher birth rates across the board. There are many factors at play in birth rates, such as the education level of the mother, ethnicity of the mother, and even where someone lives. The fertility rate in the United States The fertility rate in the United States has declined in recent years, and it seems that more and more women are waiting longer to begin having children. Studies have shown that the average age of the mother at the birth of their first child in the United States was 27.4 years old, although this figure varies for different ethnic origins.

Graph and download economic data for Real Median Family Income in the United States (MEFAINUSA672N) from 1953 to 2024 about family, median, income, real, and USA.

If politicians in the United States were paid better, would more middle- and working-class people become politicians? Reformers often argue that the low salaries paid in state and local governments make holding office economically infeasible for lower-income citizens and contribute to the enduring numerical under-representation of the working class in our political institutions. Of course, raising politicians’ salaries could also make political office more attractive to affluent professionals, increasing competition for office and ultimately discouraging lower-income citizens from running and winning. In this paper, we test these hypotheses using data on the salaries and economic backgrounds of state legislators. Contrary to the notion that paying politicians more promotes economic diversity, we find that the descriptive representation of the working class is the same or worse in states that pay legislators higher salaries. These findings have important implications for research on descriptive representation, political compensation, and political inequality.

Vietnam Grade School: Class: Lower Secondary data was reported at 153.600 Unit th in 2017. This records an increase from the previous number of 151.700 Unit th for 2016. Vietnam Grade School: Class: Lower Secondary data is updated yearly, averaging 150.000 Unit th from Sep 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 170.900 Unit th in 2004 and a record low of 73.300 Unit th in 1991. Vietnam Grade School: Class: Lower Secondary data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G050: Education Statistics.

This is a late July 2013 YouGov political tracker survey combining data on attitudes to race and immigration with questions on mobility history as well as voting intention, media consumption and other background variables. Data is also geocoded to ward level and ward-level census variables appended. The quantitative research will be based on ONS longitudinal survey and census data, as well the large-scale Citizenship Surveys and Understanding Society surveys. We will identify individual respondents from the quantitative research and explore their responses through qualitative work, in the form of three focus groups - two in Greater London, one in Birmingham. These will probe connections between respondents' local and national identities, their intentions to move neighbourhood, and their opinions on immigration, interethnic relations, community cohesion and voting behaviour.In the past decade in Britain, the 'white working-class' has been the focus of unprecedented media and policy attention. While class is a longstanding discursive category, the prefix 'white' is an important rider. We live in an era of global migration. Population pressure from the global South, and demand for workers in the developed North, will power what some term a 'third demographic transition' involving significant declines in the white majority populations of the western world (Coleman 2010). In the UK, the upsurge in diversity arguably presents a greater challenge for the working-class part of the white British population than for the middle class. Why? First, because for lower-status members of dominant groups, their ethnic identity tends to be their most prestigious social identity (Yiftachel 1999). Second, minorities tend to be from disadvantaged backgrounds and are therefore more likely to compete for housing and jobs with the white working class. Finally, because the white working-class is less comfortable navigating the contours of the new global knowledge economy than the middle class, it is more attached to existential securities rooted in the local and national context (Skey 2011). How might the white working class respond to increasing diversity? Drawing upon Albert O. Hirschman's classic book Exit, Voice and Loyalty (1970), we posit three possible responses: 'exit', 'voice' and 'accommodation.' The first possibility is white 'exit': geographic segregation, or, in the extreme, 'white flight'. A second avenue is 'voice': spearheading an identity politics based on opposition to immigration and voting for white nationalist parties. A third possibility is accommodation, in which members of the white working-class become more comfortable with elevated levels of ethnic diversity in their neighbourhood and nation. From exploratory research and existing literature, we suggest that a three-stage pattern of voice, exit and accommodation may be a useful way of thinking about white working-class responses to diversity in the UK. In other words, initial diversity meets strong white working-class resistance, expressed in attitudes and voting. This is followed by a degree of white out-migration, and then by a decline in anti-immigration sentiment and far right voting. Yet these broad patterns require finer-grained analysis that takes both individual characteristics and local context into account. This project will test these propositions through quantitative and qualitative research. There are three major dimensions of white working class attitudes and behaviour we seek to explain. Namely, whether members of the white working-class: 1) are more likely than other groups to leave or avoid areas with large or growing minority populations; 2) oppose immigration more strongly if they reside in diverse or ethnically changing wards and local authorities; and 3) support far right parties more if they reside in diverse or ethnically changing wards and local authorities. A central question we seek to answer is whether inter-ethnic contact reduces white working-class antagonism toward minorities (the contact hypothesis), or whether increased diversity leads to white flight, leaving relatively tolerant whites remaining in diverse neighbourhoods. The latter, 'hydraulic' process mimics the contact hypothesis but does not signify increased accommodation. Telephone interview of 1869 individuals (YouGov) in Britain. Further details available in the YouGov Archive Birbeck results pdf which is available in the related resources section of this project record.

Educators are familiar with evidence of the benefit that frequent and immediate feedback provides to our students, regardless of class size. We also often find ourselves in a situation where we do not have enough time planned or TA support available to give enough feedback to enable learners to monitor their progress. Here we describe an approach in which students take a low-stakes quiz about the assigned material at the beginning of each class period. The quiz is then peer graded as part of the class and thus provides another chance for all students to think about the problems in context.

Although we have applied this format in our smaller class setting, it is feasible and scalable for larger courses that want to provide additional or alternative options for formative assessment and opportunities to ask clarifying questions. As a strategy, it may be particularly fruitful for students challenged by gaps in learning skills or in classes in which faculty-written formative assessment feedback options are limited.

Low Cost Carrier (LCC) Sales Market Outlook

The global market size for Low Cost Carrier (LCC) sales was valued at approximately USD 200 billion in 2023 and is expected to reach USD 350 billion by 2032, exhibiting a CAGR of 6.5% over the forecast period. This growth can be attributed to the rising demand for affordable air travel and the increasing number of middle-class travelers around the world. The low-cost carrier model continues to gain popularity, driven by its ability to offer competitive pricing and efficient service, making air travel accessible to a broader audience.

One of the primary growth factors for the LCC market is the continuous rise in disposable incomes, particularly in emerging economies. As more individuals enter the middle class, the demand for affordable travel options increases. LCCs have capitalized on this trend by offering lower fares compared to traditional full-service airlines, thereby attracting price-sensitive travelers. Furthermore, the growth of tourism in regions such as Asia Pacific and Latin America has significantly contributed to the expansion of the LCC market. Governments in these regions are investing heavily in aviation infrastructure to support the increasing passenger traffic, further propelling the market growth.

Technological advancements have also played a crucial role in the growth of the LCC market. The adoption of modern, fuel-efficient aircraft has enabled LCCs to reduce operational costs, which in turn has allowed them to offer competitive ticket prices. Additionally, the use of online booking platforms and mobile applications has streamlined the ticket purchasing process, making it more convenient for passengers. This digital transformation has not only increased the reach of LCCs but also enhanced customer experience, driving higher sales and market penetration.

The evolving consumer preferences towards travel have also contributed to the growth of the LCC market. With an increasing number of people prioritizing experiences over material possessions, travel, particularly budget-friendly travel, has become a significant aspect of their lifestyle. LCCs cater to this demographic by providing affordable and no-frills services, making air travel more accessible to a larger segment of the population. Moreover, frequent promotional offers and discounts have further incentivized travelers to choose LCCs over traditional airlines.

Regionally, Asia Pacific is expected to dominate the LCC market over the forecast period, followed by North America and Europe. The rapid urbanization, growing middle-class population, and increasing tourism activities in countries like China, India, and Southeast Asian nations are driving the demand for low-cost carriers in the Asia Pacific region. North America and Europe, although mature markets, continue to see steady growth due to the presence of established LCCs and the continuous introduction of new routes and services. Latin America and the Middle East & Africa are also witnessing significant growth, albeit from a smaller base, driven by economic development and increasing air travel demand.

Service Type Analysis

In the LCC market, service types can be broadly categorized into passenger services, cargo services, and ancillary services. Passenger services dominate the market, as they represent the primary revenue source for low-cost carriers. The demand for passenger services continues to rise due to the increasing number of budget-conscious travelers and the expansion of LCC networks. These carriers focus on maintaining high load factors by offering frequent flights on popular routes and utilizing point-to-point networks, which reduces operational costs and allows for lower ticket prices. Additionally, the adoption of innovative in-flight services and amenities enhances the overall travel experience, attracting more passengers to opt for LCCs.

Cargo services, although a smaller segment compared to passenger services, play a vital role in the revenue generation for low-cost carriers. The rise in e-commerce and the need for efficient logistics solutions have driven the demand for air cargo services. LCCs are increasingly exploring cargo services as a supplementary revenue stream, capitalizing on unused cargo space in passenger flights. Some LCCs have even started dedicated cargo operations to cater to the growing demand. The integration of digital technologies in cargo management and the development of partnerships with logistics companies are further enhancing the capabilities and efficiency of LCCs in this segment.

Ancillary services have

This interview is part of the Women, Risk and Aids Project (1989-90) archive which was created as part of the Reanimating Data Project (2018-20).Anonymised transcript of an interview with Tiffany, who had trained as a hairdresser but didn't enjoy it very much - she is now in dental nursing instead. She had been engaged to her Catholic partner of five years, but realised she wasn't happy in the relationship and had been too young to settle down. She has quite a cynical attitude towards marriage, as she has seen a lot of divorce while growing up. Tiffany is now in a relationship with Ryan, which had started out as a casual, 'no-strings attached' affair, but is now becoming quite serious. The formal sex education she received at school, in biology, only covered conception and early child development, and was not taken seriously. She mainly sought information through friends and the youth club. She feels that if sex education had been taken more seriously at school, been offered younger and the risks involved through sex properly explained, that young people would not engage in precocious sexual activity. None of her friends had used condoms or other contraceptions when they were younger. She would like to think that she would use condoms if she were to have casual sexual relationships or one-night stands in the future, but would find it embarrassing and thinks condoms would 'ruin the moment'. Her (male) friends are not too bothered about contracting AIDS, despite having many sexual partners, but Tifffany is cautious as she has learnt about AIDS transmission through her work as a dental nurse. She feels women are typically more concerned than men, as they tend to be responsible for protecting themselves against unwanted pregnancies. There is a wider discourse among her peers that they are unlikely to contract AIDS, that it 'wouldn't happen to them', and they do not position themselves as at risk. She thought public campaigning around AIDS through advertisements was effective, but needs to be more consistent and should be taught properly through sex education in schools. The leaflets she has seen about AIDS do not do enough to convey the risks and volume of the disease, and should be more realistic rather than fear-mongering. She hopes attitudes towards sex will change and people will be more cautious in casual sexual encounters, and especially hopes that men will take more responsibility.

What is the Tucson Equity Tree Index (TETI)?The Tucson Equity Tree Index (TETI) is a tool that describes the distribution of tree canopy as it relates to social vulnerability. It categorizes the dataset into 5 classes that represent the differing prioritization needs for improving tree canopy coverage: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for tree planting prioritization, as they tend to have more existing tree canopy and less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for tree planting prioritization, as they tend to have the least existing tree canopy and the highest proportions of socially vulnerable demographics. How is tree canopy measured?To measure tree canopy, the TETI calculates a Gap Score using the difference between the goal canopy coverage per feature (15%) and the percent of existing tree canopy coverage. The existing tree canopy coverage was calculated from PAG’s 2015 tree canopy dataset, which can be viewed here.How is social vulnerability measured?The TETI incorporates the Tucson Equity Priority Index (TEPI), which examines the proportion of vulnerability per feature using 11 demographic indicators:• % of households below poverty• % unemployed• % with no personal vehicle access• % with no health insurance• % that experience renter cost-burden• % that experience homeowner cost-burden• % that are a vulnerable age (children under 18 and seniors 65 and older)• % of households with 1 or more disabilities• % people of color• % highest educational attainment is high school or less• % with low English proficiencyAn overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?The two variables (tree canopy gap score and vulnerability score) for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined using the mean aggregation method and weighting the tree canopy gap score and the vulnerability score equally. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable and have the largest tree canopy gap scores. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability and tree canopy gap scores compared to the median. While not the highest, these areas are more vulnerable and in need of tree canopy than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability and canopy gap scores. These areas may show a balanced mix of high and low vulnerability and canopy indicators. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable and have more tree canopy than most but may still exhibit certain vulnerable characteristics or tree canopy needs. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable and have the greatest existing tree canopy, making them the most resilient compared to all other features in the dataset. How is the TETI different from the Tucson Tree Equity Score index?The TETI is designed to be the updated version of the Tucson Tree Equity score index in that:The demographic variables of vulnerability use the most current data and reflect how the City of Tucson as a whole is defining social vulnerabilityThis version of TETI uses census block groups instead of Tucson neighborhoods, which results in areas that are more comparable to one another in population size and an overall index that is more comparable to other existing indices (Tucson Equity Priority Index (TEPI), the Climate and Economic Justice Screening Tool (CEJST), the American Forests Tree Equity Score National Explorer, etc.)The classification method for the TETI results in equal ranges for each classification based on relative need for prioritization, whereas the Tucson Tree Equity Index has a more arbitrary classification scheme and does not have equal classification ranges

The March of Dimes Global action report indicated that preterm birth (PTB) rates are increasing in most countries. It is the most important cause of neonatal deaths and the second leading cause of death in children under age 5. Literature reporting the relationship between maternal pre-pregnancy body mass index (BMI) and PTB has previously yielded inconsistent conclusions. Our objective is to fill in the knowledge gap by evaluating the interaction of socio-economic status (SES) and BMI and its relationship to the rate of PTB. This is a case control study using the Natality Data of the National Vital Statistics System from the years 2020–2022. BMI was a significant factor in PTB for lower socioeconomic status (LSES) women. For every increase in BMI, there was a decrease in the probability of PTB (OR = 0.923, 95% CI 0.915–0.931, P