Russia Bank Lending Conditions: Big Corporations: Loan Diversity data was reported at 1.000 % Point in Mar 2019. This records an increase from the previous number of -1.000 % Point for Dec 2018. Russia Bank Lending Conditions: Big Corporations: Loan Diversity data is updated quarterly, averaging -0.424 % Point from Jun 2009 (Median) to Mar 2019, with 40 observations. The data reached an all-time high of 22.321 % Point in Dec 2014 and a record low of -11.458 % Point in Mar 2010. Russia Bank Lending Conditions: Big Corporations: Loan Diversity data remains active status in CEIC and is reported by The Central Bank of the Russian Federation. The data is categorized under Russia Premium Database’s Monetary and Banking Statistics – Table RU.KAC016: Bank Lending Tightness: Loans to Big Corporations.

In 2024, the trade surplus of goods in Russia amounted to about ****** billion U.S. dollars, having sharply decreased from the previous year. The indicator is calculated as exports minus imports of goods. A positive value means a trade surplus; a negative trade balance means a trade deficit. Russia's politics and the effect on the economy Russia has maintained a positive trade balance over the last 10 years, but in 2009, Russian exports slumped significantly due to the economic crisis. Since then, Russia has recovered and the country reports a greater surplus now than it did prior to the crisis. However, Russia’s economy has been weakened recently because of reductions in global oil and gas prices, upon which the Russian economy is largely dependent, and because of international tensions as a result of Russia's invasion of Ukraine. In the past couple of years, Russia has often reacted with hostility to any developments seen as threatening, and as Russia continues to provoke international conflict, this will affect its economy and likely hurt existing trade relations with both import and export partners. As a result, GDP growth was negative in 2015. This has also contributed to significant reductions in GDP per capita, which will directly affect Russian citizens, and more so as Russia’s inflation is peaking, and the unemployment rate continues to rise. In 2015, the inflation rate was close to ** percent. Economic diversification beyond oil and gas in addition to maintaining trade relations would help Russia’s economy.

Russia Bank Lending Conditions: Households: Loan Diversity data was reported at -3.114 % Point in Mar 2019. This records a decrease from the previous number of -1.442 % Point for Dec 2018. Russia Bank Lending Conditions: Households: Loan Diversity data is updated quarterly, averaging -5.002 % Point from Jun 2009 (Median) to Mar 2019, with 40 observations. The data reached an all-time high of 21.608 % Point in Dec 2014 and a record low of -21.951 % Point in Jun 2010. Russia Bank Lending Conditions: Households: Loan Diversity data remains active status in CEIC and is reported by The Central Bank of the Russian Federation. The data is categorized under Russia Premium Database’s Monetary and Banking Statistics – Table RU.KAC018: Bank Lending Tightness: Loans to Households.

About this dataset

Context

The dataset presents the median household income across different racial categories in Russia town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Russia town population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 96.09% of the total residents in Russia town. Notably, the median household income for White households is $84,649. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $84,649.

https://i.neilsberg.com/ch/russia-ny-median-household-income-by-race.jpeg" alt="Russia town median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Russia town.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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 Russia town median household income by race. You can refer the same here

: Data type: Image

Russia Bank Lending Conditions: Households: Mortgage: Loan Diversity data was reported at -2.381 % Point in Mar 2019. This records a decrease from the previous number of 0.000 % Point for Dec 2018. Russia Bank Lending Conditions: Households: Mortgage: Loan Diversity data is updated quarterly, averaging -3.471 % Point from Dec 2011 (Median) to Mar 2019, with 30 observations. The data reached an all-time high of 17.778 % Point in Dec 2014 and a record low of -10.465 % Point in Dec 2016. Russia Bank Lending Conditions: Households: Mortgage: Loan Diversity data remains active status in CEIC and is reported by The Central Bank of the Russian Federation. The data is categorized under Global Database’s Russian Federation – Table RU.KAC018: Bank Lending Tightness: Loans to Households.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Russia town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Russia town, the median household income for the households where the householder is White increased by $32,051(56.20%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $57,028 in 2013 and $89,079 in 2023.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2013 and 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

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 of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Russia town.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

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 Russia town median household income by race. You can refer the same here

This dataset comprises 348 files, each representing a unique economic indicator for the BRICS nations—Brazil, Russia, India, China, and South Africa—spanning from 1970 to 2020. The dataset includes a wide array of economic metrics such as government consumption expenditure, GDP growth, adjusted savings, and various other national accounts data. This comprehensive dataset is ideal for economic research, financial analysis, and policy evaluation, offering a robust foundation for exploring economic trends and making data-driven decisions.

Key Features: - Diversity of Indicators: Covers a wide range of economic indicators, including net national income, government expenditure, GDP, and more. - Historical Coverage: Provides data spanning five decades, enabling both historical trend analysis and long-term forecasting. - Country Focus: Specifically tailored to the BRICS nations, offering insights into some of the world’s most influential emerging economies.

Usage

This dataset can be utilized for various purposes, such as: - Economic Analysis: Researchers can use the dataset to study economic trends and performance in BRICS countries. - Machine Learning: Data scientists can train models to predict future economic indicators or identify patterns in the data. - Policy Development: Policymakers can analyze the data to develop informed strategies for economic development.

Example Use Case: Suppose you want to analyze the trend in GDP per capita growth across BRICS nations. You could load the relevant files, clean the data, and use statistical tools or machine learning models to study the trend and make predictions.

System

This dataset is self-contained and can be integrated into broader economic research systems. The data files are in CSV format, making them easy to load and manipulate with standard data analysis tools like Python, R, and Excel.

Integration: While the dataset is standalone, it can be combined with other datasets or models for more complex analyses, such as predicting future economic performance or simulating policy impacts.

Data Provenance

The dataset is sourced from the World Bank’s BRICS Economic Indicators, a trusted and comprehensive source of economic data. The data was compiled, cleaned, and structured to facilitate easy analysis and integration into various analytical workflows.

Source: Kaggle - BRICS World Bank Indicators Dataset Coverage: The dataset includes data from Brazil, Russia, India, China, and South Africa, from 1970 to 2020.

Data Preprocessing: Each file was cleaned to remove inconsistencies, and missing values were handled appropriately to ensure the quality and reliability of the data.

Data Overview

The dataset is organized into 348 CSV files, each focusing on a specific economic indicator. Examples include: - GDP per Capita (Constant 2010 US$): Tracks the GDP per capita adjusted for inflation. - Government Final Consumption Expenditure (% of GDP): Measures government spending as a percentage of GDP. - Adjusted Net Savings: Accounts for environmental depletion and degradation in national savings.

Each file contains the following columns: - SeriesName: Describes the economic indicator. - CountryName: The name of the BRICS country. - Year: The year the data was recorded. - Value: The numerical value of the indicator for that year.

This dataset provides a rich resource for anyone looking to delve into the economic history and performance of BRICS countries, offering the data necessary to explore past trends and project future developments.

This study examines the correlation among Bitcoin (BTC), gold, equity, bonds, and exchange rate volatility in the context of new developments during Russia Ukraine conflict using daily data from January 01, 2018, to May 30, 2023. Three GARCH estimation models are utilized to capture the hedging, diversification, and safe haven properties of Bitcoin in Russian financial market. The results indicate from GJR-GARCH estimation model exhibits that BTC has hedging ability against the bonds and gold that enables investors to diversify the risk among the underline financial assets. In addition, value at risk and conditional value at risk estimations are employed to estimate potential losses in the portfolio during the crisis. The study observes a significant increase in Bitcoin investments during crisis, leading to heightening the volatility and uncertainty where negative news has a stronger impact compared to positive news which underscores the importance of prudent asset allocation for risk mitigation. The study provides notable policy implications within the context of the ongoing crisis between Russia and Ukraine.

This work critically examines the emergence of a post-industrial economy in China as it continues to transform into a 21st century global leader. On August 15th, 2010, the Financial Times published an article stating that recently released figures from the International Monetary Fund show that China had surpassed Japan as the second-largest economy in the world and predicted that China will maintain its lead going forward . This is an astonishing feat for an emerging economy, as Japan had previously held the second-place position for over four decades. In recent years, China has outperformed other large emerging economies such as Brazil, Russia and India. As a result, it is important to examine China more closely and understand what is occurring within the country as it continues to grow and develop as a global leader. In the contemporary global environment, lasting economic advantage comes from attracting and retaining a talented and creative workforce. As China begins to transition from an industrial economy to a post-industrial economy, several factors including a more educated workforce, the development of domestic intellectual property and openness to a more diverse range of ideas and people are becoming more important. Against this backdrop, this report explores the emergence of a creative, service-driven, post-industrial economy in China by employing two methods of analysis developed by Richard Florida (2002). The first part of the analysis examines the changing occupational structure of China’s workforce. To execute this part of the analysis, we divide China’s workforce into the four occupational categories defined by Florida (2002): creative class, service class, working class and fishing, farming and forestry class. The second part of the analysis employs what are known as the “3Ts of economic development” to rank China’s regions according to their strengths in supporting a creative economy. The 3Ts of regional economic development include technology (high-tech employment and innovation), talent (education and skills), and tolerance (diversity and openness). The report explores China’s provincial-level regions and three of its four Municipalities, with a special interest in the dynamics and geography of the creative economy.

Bank Lending Conditions: Big Corporations: Loan Diversity在2019-03达1.000 % 点，相较于2018-12的-1.000 % 点有所增长。Bank Lending Conditions: Big Corporations: Loan Diversity数据按季度更新，2009-06至2019-03期间平均值为-0.424 % 点，共40份观测结果。该数据的历史最高值出现于2014-12，达22.321 % 点，而历史最低值则出现于2010-03，为-11.458 % 点。CEIC提供的Bank Lending Conditions: Big Corporations: Loan Diversity数据处于定期更新的状态，数据来源于The Central Bank of the Russian Federation，数据归类于Russia Premium Database的Monetary and Banking Statistics – Table RU.KAC016: Bank Lending Tightness: Loans to Big Corporations。

Bank Lending Conditions: Households: Loan Diversity在2019-03达-3.114 % 点，相较于2018-12的-1.442 % 点有所下降。Bank Lending Conditions: Households: Loan Diversity数据按季度更新，2009-06至2019-03期间平均值为-5.002 % 点，共40份观测结果。该数据的历史最高值出现于2014-12，达21.608 % 点，而历史最低值则出现于2010-06，为-21.951 % 点。CEIC提供的Bank Lending Conditions: Households: Loan Diversity数据处于定期更新的状态，数据来源于The Central Bank of the Russian Federation，数据归类于Russia Premium Database的Monetary and Banking Statistics – Table RU.KAC018: Bank Lending Tightness: Loans to Households。

Bank Lending Conditions: Small & Medium Business: Loan Diversity在2019-03达-1.020 % 点，相较于2018-12的-1.000 % 点有所下降。Bank Lending Conditions: Small & Medium Business: Loan Diversity数据按季度更新，2009-06至2019-03期间平均值为-2.859 % 点，共40份观测结果。该数据的历史最高值出现于2014-12，达24.528 % 点，而历史最低值则出现于2010-03，为-16.667 % 点。CEIC提供的Bank Lending Conditions: Small & Medium Business: Loan Diversity数据处于定期更新的状态，数据来源于The Central Bank of the Russian Federation，数据归类于Russia Premium Database的Monetary and Banking Statistics – Table RU.KAC017: Bank Lending Tightness: Loans to Small & Medium Business。