Showing papers on "Poverty published in 2022"

The impact of a poverty reduction intervention on infant brain activity

Abstract: Significance This study demonstrates the causal impact of a poverty reduction intervention on early childhood brain activity. Data from the Baby’s First Years study, a randomized control trial, show that a predictable, monthly unconditional cash transfer given to low-income families may have a causal impact on infant brain activity. In the context of greater economic resources, children’s experiences changed, and their brain activity adapted to those experiences. The resultant brain activity patterns have been shown to be associated with the development of subsequent cognitive skills. Early childhood poverty is a risk factor for lower school achievement, reduced earnings, and poorer health, and has been associated with differences in brain structure and function. Whether poverty causes differences in neurodevelopment, or is merely associated with factors that cause such differences, remains unclear. Here, we report estimates of the causal impact of a poverty reduction intervention on brain activity in the first year of life. We draw data from a subsample of the Baby’s First Years study, which recruited 1,000 diverse low-income mother–infant dyads. Shortly after giving birth, mothers were randomized to receive either a large or nominal monthly unconditional cash gift. Infant brain activity was assessed at approximately 1 y of age in the child’s home, using resting electroencephalography (EEG; n = 435). We hypothesized that infants in the high-cash gift group would have greater EEG power in the mid- to high-frequency bands and reduced power in a low-frequency band compared with infants in the low-cash gift group. Indeed, infants in the high-cash gift group showed more power in high-frequency bands. Effect sizes were similar in magnitude to many scalable education interventions, although the significance of estimates varied with the analytic specification. In sum, using a rigorous randomized design, we provide evidence that giving monthly unconditional cash transfers to mothers experiencing poverty in the first year of their children’s lives may change infant brain activity. Such changes reflect neuroplasticity and environmental adaptation and display a pattern that has been associated with the development of subsequent cognitive skills.

Impacts of poverty alleviation on national and global carbon emissions

Abstract: Wealth and income are disproportionately distributed among the global population. This has direct consequences on consumption patterns and consumption-based carbon footprints, resulting in carbon inequality. Due to persistent inequality, millions of people still live in poverty today. On the basis of global expenditure data, we compute country- and expenditure-specific per capita carbon footprints with unprecedented details. We show that they can reach several hundred tons of CO2 per year, while the majority of people living below poverty lines have yearly carbon footprints of less than 1 tCO2. Reaching targets under United Nations Sustainable Development Goal 1, lifting more than one billion people out of poverty, leads to only small relative increases in global carbon emissions of 1.6–2.1% or less. Nevertheless, carbon emissions in low- and lower-middle-income countries in sub-Saharan Africa can more than double as an effect of poverty alleviation. To ensure global progress on poverty alleviation without overshooting climate targets, high-emitting countries need to reduce their emissions substantially. Carbon inequality mirrors extreme wealth and income inequalities globally, with a high level of consumption-based carbon emissions in rich nations. This study shows that lifting people out of poverty does not impact much emissions globally, though in poorer countries emissions could more than double.

Adults Meeting Fruit and Vegetable Intake Recommendations — United States, 2019

Abstract: The 2020-2025 Dietary Guidelines for Americans* advise incorporating more fruits and vegetables into U.S. residents' diets as part of healthy dietary patterns. Adults should consume 1.5-2 cup-equivalents of fruits and 2-3 cup-equivalents of vegetables daily.† A healthy diet supports healthy immune function (1) and helps to prevent obesity, type 2 diabetes, cardiovascular diseases, and some cancers (2); having some of these conditions can predispose persons to more severe illness and death from COVID-19 (3). CDC used the most recent 2019 Behavioral Risk Factor Surveillance system (BRFSS) data to estimate the percentage of states' adult population who met intake recommendations overall and by sociodemographic characteristics for 49 states and the District of Columbia (DC). Overall, 12.3% of adults met fruit recommendations, ranging from 8.4% in West Virginia to 16.1% in Connecticut, and 10.0% met vegetable recommendations, ranging from 5.6% in Kentucky to 16.0% in Vermont. The prevalence of meeting fruit intake recommendations was highest among Hispanic adults (16.4%) and lowest among males (10.1%); meeting vegetable intake recommendations was highest among adults aged ≥51 years (12.5%) and lowest among those living below or close to the poverty level (income to poverty ratio [IPR] <1.25) (6.8%). Additional policies§ and programs that will increase access to fruits and vegetables in places where U.S. residents live, learn, work, and play, might increase consumption and improve health.

Vital Signs: Changes in Firearm Homicide and Suicide Rates — United States, 2019–2020

Abstract: Introduction The majority of homicides (79%) and suicides (53%) in the United States involved a firearm in 2020. High firearm homicide and suicide rates and corresponding inequities by race and ethnicity and poverty level represent important public health concerns. This study examined changes in firearm homicide and firearm suicide rates coinciding with the emergence of the COVID-19 pandemic in 2020. Methods National vital statistics and population data were integrated with urbanization and poverty measures at the county level. Population-based firearm homicide and suicide rates were examined by age, sex, race and ethnicity, geographic area, level of urbanization, and level of poverty. Results From 2019 to 2020, the overall firearm homicide rate increased 34.6%, from 4.6 to 6.1 per 100,000 persons. The largest increases occurred among non-Hispanic Black or African American males aged 10–44 years and non-Hispanic American Indian or Alaska Native (AI/AN) males aged 25–44 years. Rates of firearm homicide were lowest and increased least at the lowest poverty level and were higher and showed larger increases at higher poverty levels. The overall firearm suicide rate remained relatively unchanged from 2019 to 2020 (7.9 to 8.1); however, in some populations, including AI/AN males aged 10–44 years, rates did increase. Conclusions and Implications for Public Health Practice During the COVID-19 pandemic, the firearm homicide rate in the United States reached its highest level since 1994, with substantial increases among several population subgroups. These increases have widened disparities in rates by race and ethnicity and poverty level. Several increases in firearm suicide rates were also observed. Implementation of comprehensive strategies employing proven approaches that address underlying economic, physical, and social conditions contributing to the risks for violence and suicide is urgently needed to reduce these rates and disparities.

Mining policy conflict:

Abstract: This study aims to determine the MINING POLICY CONFLICT: Recruitment of Local Workers in Morosi Industrial Estate, Konawe Regency, Southeast Sulawesi. The focus of the study is to find the design of mining policies and the impact of mining as well as the adoption model for the application of mining policies in the recruitment of local workers in the Morosi Industrial Estate. This type of research is qualitative with an analytical descriptive design, used to collect data by researchers and present descriptive data with in-depth analysis. This research was conducted in the administrative area of the Morosi Industrial Estate which is within the territory of the Konawe Regency Government of Southeast Sulawesi Province. The results of the study explain that the Konawe Regency Government is able to take advantage of opportunities to increase regional economic growth even during the Covid-19 pandemic by opening up investment opportunities and massive employment through collaboration with PT. VDNI and PT. OSS. The dynamics of mining conflicts in the recruitment of local workers can be minimized so that the Konawe Regency Government and the Konawe people feel that it is a reduction in unemployment and poverty. Furthermore, currently, the adaptation that can be done by the Konawe Regency Government is to increase the competence of the local community of prospective workers according to the needs of mining companies

Nexus Between Energy Poverty and Technological Innovations: A Pathway for Addressing Energy Sustainability

Abstract: Pakistan has experienced energy poverty, as most of the people live in rural areas. Poor people are stereotyped as collecting the firewood and using the unclean energy sources to meet their residential energy needs. As a result, respondents in the provinces with the highest rates of energy poverty set a high priority on this research. Structured interviews were used to conduct the research in rural parts of Punjab and Sindh provinces. Due to the apparent country’s large population and rapid industrialization, conventional energy sources cannot meet the country’s present energy needs. Results revealed that energy poverty in rural areas had exposed the residents to security problems such as health dangers, fire accidents, time poverty, financial poverty, illiteracy, and other issues at various levels of severity. As a result, alternative energy sources must be explored. This research aims to determine the best renewable energy choice for Pakistan’s rural areas. In terms of pricing, life duration, operation, and maintenance costs, the results show that solar energy is the best renewable energy source for Pakistan. The key barriers that continue to promote energy poverty have been identified. Finally, the study suggests policy recommendation for public and private sectors to overcome energy related barriers to alleviate energy poverty in rural areas by utilizing maximum solar energy.

Leveraging on energy security to alleviate poverty in asian economies

Abstract: This study examines the role of energy security in poverty reduction in the 12 poorest Asian economies from 2000 to 2019. We postulated an energy security index using principal component analysis. We adopted the system generalized method of a moment technique to manage endogeneity and dynamism in the model. For robustness, we applied a panel-corrected standard error (PCSE). We found a negative relationship between energy security and poverty reduction, suggesting that energy security helps reduce poverty. We conclude that energy security promotes sustainable poverty alleviation and recommends feed-in tariffs, net metering, tax credits, and energy resource diversification away from fossil fuels.

Flood exposure and poverty in 188 countries

Abstract: Abstract Flooding is among the most prevalent natural hazards, with particularly disastrous impacts in low-income countries. This study presents global estimates of the number of people exposed to high flood risks in interaction with poverty. It finds that 1.81 billion people (23% of world population) are directly exposed to 1-in-100-year floods. Of these, 1.24 billion are located in South and East Asia, where China (395 million) and India (390 million) account for over one-third of global exposure. Low- and middle-income countries are home to 89% of the world’s flood-exposed people. Of the 170 million facing high flood risk and extreme poverty (living on under $1.90 per day), 44% are in Sub-Saharan Africa. Over 780 million of those living on under $5.50 per day face high flood risk. Using state-of-the-art poverty and flood data, our findings highlight the scale and priority regions for flood mitigation measures to support resilient development.

Impact of COVID-19 on food insecurity using multiple waves of high frequency household surveys

Abstract: In response to the rapid spread of COVID-19, governments across the globe have implemented local lockdowns that have led to increased unemployment and have disrupted local and international transport routes and supply chains. Whilst such efforts aim to slow or stop the spread of the SARS-CoV-2 virus, they have also resulted in increased food insecurity, whether due to reduced incomes or increased food prices. This is the first paper to track food insecurity and its determinants during the pandemic using multi-country and multi-wave evidence. Using data from 11 countries and up to 6 waves of High-Frequency Phone Survey data (household-level surveys) on COVID-19 and its impacts, we use a fixed-effects linear probability model to investigate the socioeconomic determinants of food insecurity during the pandemic for each country using household-level data over multiple waves. We control for socioeconomic characteristics including gender and education of the household head; income and poverty status of the households during the pandemic; safety nets in the form of cash and food assistance; coping strategies adopted by households; and price effects of major food items. Our findings suggest that cash safety nets appear to have been more effective than food in terms of reducing food insecurity during the pandemic; and that those particularly hard hit are female headed-households (highest in Malawi: 0.541, 95% CI 0.516, 0.569; lowest in Cambodia: 0.023, 95% CI 0.022, 0.024), the less educated (highest in Djibouti: - 0.232, 95% CI - 0.221, - 0.244; lowest in Nigeria: 0.006, 95% CI - 0.005, - 0.007), and poorer households (highest in Mali: 0.382, 95% CI 0.364, 0.402; lowest in Chad: 0.135, 95% CI 0.129, 0.142). In line with the existing literature, our results show that, even controlling for income loss and poverty status, those households who had to borrow rather than rely on savings had a higher probability of suffering from food insecurity. Distinct differences in the efficacy of safety nets across the 11 countries, and the differential impact of the pandemic on different groups within societies, suggest in-depth country-specific studies are needed to understand why some countries have coped better than others. Our paper highlights the importance of improving household resilience to future systemic crises, and using evidence-based best practice in the design of relevant policy instruments.

Unveiling hidden energy poverty using the energy equity gap

Abstract: Income-based energy poverty metrics ignore people's behavior patterns, particularly reducing energy consumption to limit financial stress. We investigate energy-limiting behavior in low-income households using a residential electricity consumption dataset. We first determine the outdoor temperature at which households start using cooling systems, the inflection temperature. Our relative energy poverty metric, the energy equity gap, is defined as the difference in the inflection temperatures between low and high-income groups. In our study region, we estimate the energy equity gap to be between 4.7-7.5 °F (2.6-4.2 °C). Within a sample of 4577 households, we found 86 energy-poor and 214 energy-insecure households. In contrast, the income-based energy poverty metric, energy burden (10% threshold), identified 141 households as energy-insecure. Only three households overlap between our energy equity gap and the income-based measure. Thus, the energy equity gap reveals a hidden but complementary aspect of energy poverty and insecurity.

Machine learning and phone data can improve targeting of humanitarian aid

Abstract: The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards1. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people2. Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data3,4. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes-including exclusion errors, total social welfare and measures of fairness-under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4-21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date.