This statistic depicts the percentage of prekindergarten children, ages 3 to 5 years, who were read to frequently by a family member in the U.S. in 2012, distinguished by race and Hispanic origin. In 2012, the percentage of non-Hispanic White children who were read to 3 or more times per week by a family member stood at 91 percent.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Reading town by race. It includes the distribution of the Non-Hispanic population of Reading town across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Reading town across relevant racial categories.

Key observations

Of the Non-Hispanic population in Reading town, the largest racial group is White alone with a population of 21,861 (90.05% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Reading town
• Population: The population of the racial category (for Non-Hispanic) in the Reading town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Reading town total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Reading town Population by Race & Ethnicity. You can refer the same here

In 2018, more than three fourths (77 percent) of people who identified as white surveyed in Brazil were deemed functionally literate – that is, minimally able to read and interpret memos, pieces of news, instructions, narratives, graphs, tables, ads, and other types of text. Among brown and black-skinned respondents, the functional literacy rate stood at 70 and 65 percent respectively. Less than half of Brazilians aged between 50 and 64 years were considered functionally literate.

In the United States in 2023, Asian Americans spent an average of 17.4 minutes reading per day. White readers spent the most time with books each day, whereas Hispanic Americans read for just six minutes on average.

About this dataset

Context

The dataset tabulates the population of Reading town by race. It includes the population of Reading town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Reading town across relevant racial categories.

Key observations

The percent distribution of Reading town population by race (across all racial categories recognized by the U.S. Census Bureau): 89.24% are white, 0.29% are Asian and 10.47% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Reading town
• Population: The population of the racial category (excluding ethnicity) in the Reading town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Reading town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Reading town Population by Race & Ethnicity. You can refer the same here

There is a gender gap in the global literacy rate. Although literacy rates have generally increased worldwide for both men and women, men are on average more literate than women. As of 2023, about 90.6 percent of men and a little less than 84.1 percent of women in the world were literate. Adult literacy rate is defined as the percentage of people aged 15 years and above who can both read and write with understanding a short simple statement about their everyday life. Youth literacy rate Not only does the literacy gender gap concern adults, it also exists among the world’s younger generations aged 15 to 24. Despite an overall increase in literacy, young men are still more literate than young women. In fact, the global youth literacy rate as gender parity index was 0.98 as of 2023, indicating that young women are not yet as literate as young men. Gender pay gap Gender gaps occur in many different spheres of global society. One such issue concerns salary gender gaps in the professional life. Regarding the controlled gender pay gap, which measures the median salary for men and women with the same job and qualifications, women still earned less than men as of 2024. The difference was even bigger when measuring the median salary for all men and women. However, not everyone worries about gender pay gaps. According to a survey from 2021, 54 percent of the female respondents deemed the gender pay gap a real problem, compared to 45 percent of the male respondents.

Out of all ethnic groups, pupils from the White Irish and Chinese ethnic groups were most likely to meet the expected standard in reading in 2018/19.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Reading by race. It includes the distribution of the Non-Hispanic population of Reading across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Reading across relevant racial categories.

Key observations

Of the Non-Hispanic population in Reading, the largest racial group is White alone with a population of 1,210 (98.37% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Reading
• Population: The population of the racial category (for Non-Hispanic) in the Reading is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Reading total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Reading Population by Race & Ethnicity. You can refer the same here

From 2020 to 2022, reading test scores declined for nine year olds of every race except for those who were Asian in the United States. Within this time period, the most significant decrease was for nine year olds who were two or more races, as their scores dropped from 224 in 2020 to 216 in 2022.

The statistic shows the degree of adult literacy in China from 1982 to 2020. In 2020, the literacy rate, which is defined as people aged 15 and above who can read and write, had reached about 97.15 percent in China.

Global literacy rates

By 2020, around 86.8 percent of the world population aged 15 years and above had been able to read and write. While in developed regions this figure ranged a lot higher, only around 67 percent of the population in Sub-Saharan Africa was literate. Countries with the lowest literacy rates are also the most underdeveloped worldwide. According to UNESCO, literacy is a human right, especially in a fast-changing and technology-driven world. In China, the literacy rate has developed from 79 percent in 1982 to 97 percent in 2020, indicating that almost one million people per year had become literate over three decades. In India, the situation was entirely different. The second most populous country in the world displayed a literacy rate of merely 76 percent in 2022.

Literacy in China

The dramatic increase in literacy in China has a lot to do with the efficacy of numerous political, economic and educational policies. In 1982, compulsory education was written into the Chinese constitution, postulating a nine-year compulsory education funded by the government. As is shown by the graph above, there was a large gender gap in literacy rate in China as of 1982. Though this gap still existed in 2020, it was narrowed down to three percent, starting from 28 percent in 1982. Since 1990, the national education policy was directed at females, especially from poor and/or minority families. Over the past years, China has achieved gender parity in primary schooling.

However, regional literacy disparities in China should not to be overlooked. Regions with a strong economic background tend to display illiteracy rates below national average. In contrast, economically underdeveloped regions have a much larger share of people who cannot read nor write. Tibet for instance, a region where 92 percent of the population belong to an ethnic minority, showed the highest illiterate rate nationwide, with around 34 percent in 2022.

The ReAding Comprehension dataset from Examinations (RACE) dataset is a machine reading comprehension dataset consisting of 27,933 passages and 97,867 questions from English exams, targeting Chinese students aged 12-18. RACE consists of two subsets, RACE-M and RACE-H, from middle school and high school exams, respectively. RACE-M has 28,293 questions and RACE-H has 69,574. Each question is associated with 4 candidate answers, one of which is correct. The data generation process of RACE differs from most machine reading comprehension datasets - instead of generating questions and answers by heuristics or crowd-sourcing, questions in RACE are specifically designed for testing human reading skills, and are created by domain experts.

In 2018, the adult literacy rate in Morocco was around 83 percent among men and 65 percent among women. In the years under review, literacy levels in the country gradually increased and the gender gap narrowed. In comparison, only 18 percent of the women in Morocco were literate in 1982, compared to 44 percent of the male population.

Literacy or numeracy - Average scores and distribution of proficiency levels, by Aboriginal status (off-reserve), immigrant status, minority language status and sex, population aged 16-65, selected provinces and territories 2012.

This statistic presents the share of people in the United States who have read at least one e-book in the past 12 months as of January 2018, broken down by ethnicity. During a survey, 19 percent of Hispanic respondents stated they had read at least one e-book in the previous 12 months.

Some other race Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Reading, Pennsylvania by age, education, race, gender, work experience and more.

A survey held in 2021 found that 74 percent of non-Hispanic Black respondents had read at least one book in the previous 12 months, up from 65 percent who said the same during the 2019 study. Non-Hispanic White adults were the most likely group to have read a print or digital book in the last year.

In 2020, 65.9 percent of people aged 15 years and above in Sub-Saharan Africa were able to read and write a simple statement. The adult literacy rate varied, however, according to gender. While 72.5 percent of males were literate, the share among females was measured at 59.4 percent. Overall, the literate population has been slowly growing in the region, with the prevalence of a wide gender gap.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Berks County, PA (B03002021E042011) from 2009 to 2023 about Berks County, PA; Reading; PA; latino; hispanic; estimate; persons; 5-year; population; and USA.

Two or more races Health Insurance Coverage Statistics for 2022. This is part of a larger dataset covering consumer health insurance coverage rates in Reading, Massachusetts by age, education, race, gender, work experience and more.