External Debt in Mexico increased to 623302.60 USD Million in the first quarter of 2025 from 592728.30 USD Million in the fourth quarter of 2024. This dataset provides - Mexico External Debt - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is about countries per year in Mexico. It has 64 rows. It features 4 columns: country, central government debt, and expense.

Mexico recorded a Government Debt to GDP of 49.70 percent of the country's Gross Domestic Product in 2024. This dataset provides - Mexico Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Historical chart and dataset showing Mexico external debt by year from 1970 to 2023.

External Debt to GDP in Mexico decreased to 6 percent of GDP in 2023 from 7.50 percent of GDP in 2022. This dataset includes a chart with historical data for Mexico External Debt To GDP.

This dataset is about countries per year in Mexico. It has 64 rows. It features 4 columns: country, currency, and population.

Mexico recorded a Current Account deficit of 7613 USD Million in the first quarter of 2025. This dataset provides - Mexico Current Account - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is about countries in Mexico. It has 1 row. It features 5 columns: currency, capital city, continent, and tax revenue.

The Gross Domestic Product per capita in Mexico was last recorded at 10313.49 US dollars in 2024. The GDP per Capita in Mexico is equivalent to 82 percent of the world's average. This dataset provides - Mexico GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mexico recorded a Government Budget deficit equal to 5.70 percent of the country's Gross Domestic Product in 2024. This dataset provides - Mexico Government Budget - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Mexico, NY, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 24,923, while the mean income for the highest quintile (20% of households with the highest income) is 190,157. This indicates that the top earners earn 8 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 291,021, which is 153.04% higher compared to the highest quintile, and 1167.68% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Mexico median household income. You can refer the same here

The Global Forest Observations Initiative (GFOI) is an initiative of the inter-governmental Group on Earth Observations (GEO) that aims to: foster the sustained availability of observations for national forest monitoring systems; support governments that are establishing national systems by providing a platform for coordinating observations, providing assistance and guidance on utilizing observations, developing accepted methods and protocols, and promoting ongoing research and development; and work with national governments that report into international forest assessments (such as the global Forest Resources Assessment (FRA) of the Food and Agriculture Organization, FAO) and the national greenhouse gas inventories reported to the UN Framework Convention on Climate Change (UNFCCC) using methods of the Intergovernmental Panel on Climate Change (IPCC).

In 2010, the Deepwater Horizon oil spill occurred in the Gulf of Mexico and the Natural Resources Damage Assessment (NRDA) was initiated to determine the extent of damage to the resources and habitat of the area impacted by the spill. The Southeast Fisheries Science Center Mississippi Laboratories has collected standardized data in the Gulf of Mexico since the 1980s through various fisheries...

Original provider: GVI Mexico

Dataset credits: Lluvia Soto

Abstract: GVI working in partnership with Amigos de Sian Kaan (ASK), have been monitoring Mexico’s Caribbean reefs since 2003. Data are collected at Pez Maya (20.00222 degrees N, 87.28624 degrees W) and Punta Gruesa (19.00865 degrees N, 87.58977 degrees W) to support the Mesoamerican Barrier Reef System project and provide information to the National Parks Commission (CONANP). These data are now being used by GVI and its partners to promote the management of the reserve’s resources in a more sustainable manner.

Purpose: The objective of this monitoring program is the creation of a database that will help to assess the health of the Mexican-Caribbean reefs.

We dive regularly and all incidences of target species are recorded and the global positioning system (GPS) coordinates are known from our monitoring program.

Supplemental information: [2020-09-30] The following invalid species names were corrected according to the Integrated Taxonomic Information System (ITIS). Turtles: Testudines (173749) => Testudines (948936)

[UPDATE] 2017 Jan - Mar data were added on 2017-04-10. [UPDATE] 2016 Jul - Dec data were added on 2016-12-14. [UPDATE] Coordinates for some sites were corrected on 2016-09-14 affecting more than 500 records. [UPDATE] 2016 Jan - Jun data were added on 2016-07-15. [UPDATE] 2015 Jan - Jun data were added on 2015-06-30. [UPDATE] 2014 Jul - Dec data were added on 2015-01-12. [UPDATE] 2014 Jan - Jun data were added on 2014-07-09. [UPDATE] 2012 and 2013 data were added on 2014-02-07.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Mexico is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

About this dataset

Context

The dataset tabulates the Mexico Beach population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Mexico Beach across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Mexico Beach was 1,122, a 6.65% increase year-by-year from 2022. Previously, in 2022, Mexico Beach population was 1,052, an increase of 7.90% compared to a population of 975 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Mexico Beach increased by 112. In this period, the peak population was 1,296 in the year 2006. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Mexico Beach is shown in this column.
• Year on Year Change: This column displays the change in Mexico Beach population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Mexico Beach Population by Year. You can refer the same here

This dataset is about countries per year in Mexico. It has 1 row and is filtered where the date is 2023. It features 4 columns: country, currency, and net migration.

Government Spending in Mexico decreased to 2762162.03 MXN Million in the first quarter of 2025 from 2852959.47 MXN Million in the fourth quarter of 2024. This dataset provides - Mexico Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gulf Coast Vulnerability Assessment utilized expert opinion that was gathered through the Standardized Index of Vulnerability and Value (SIVVA) tool, which is an Excel-based vulnerability and prioritization tool that enables assessors to provide input in a relatively short time and allows for relatively seamless compilation of results. The vulnerability of each ecosystem and associated species was conducted by subregion, excluding those subregions where the species did not occur in significant numbers. Assessors were asked to evaluate species based on the habitats they use in a particular subregion. Because vulnerability can vary with life-stage for many species, assessors were asked to consider the most vulnerable life-stage of the species for each criterion scored.

Mexico has one of the largest overweight and obesity epidemics in the world and as a response, several actions aiming to reduce the obesity epidemic have been already set in place. Some of these actions include a specific action program for schools looking to turn the scholar environments into supportive environments for the infants to make healthier food choices. The influence of the environment (the so-called “choice architecture”) on people’s perceptions and decisions is studied by economists with the aim of supporting individuals’ to make healthier decisions, using tools known as “nudges”. However, "nudges" are not commonly integrated into anti-obesity strategies. We designed an intervention trying to find out whether such a small, liberty-preserving intervention could increase the effectiveness of a water-promotion campaign, when compared to the common approach of an educative talk. The intervention was developed in three schools in Mexico City and the State of Mexico. The body mass index, standardized by Z-scores, was used as the indicator of campaign success. Although – mainly due to problems within the sample and a yet too-short follow-up – our results do not show considerable differences between the approaches, they provide insights suggesting that including “nudges” into a health promoting campaign may indeed have a positive impact.