Graph and download economic data for Gross Domestic Product: All Industry Total in New York (NYNGSP) from 1997 to 2024 about NY, GSP, industry, GDP, and USA.

New York City GDP - Historical chart and current data through 2023.

This layer illustrates the contribution of the ocean economy to each coastal county’s GDP (dollars) in 2013. Data for 33 coastal counties from Maine to New York show similar patterns as ocean economy employment. Results are based on research from the Center for the Blue Economy and the National Oceanic and Atmospheric Administration’s 2013 Economics: National Ocean Watch (ENOW) database. ENOW provides time-series data on the coastal and ocean economy from 2005 to 2013 derived from national accounts of the Bureau of Labor Statistics and the Bureau of Economic Analysis. ENOW’s four economic indicators are the number of business establishments, number of people employed, wages paid to employees, and contribution to gross domestic product. For more information, users are encouraged to consult the Northeast Ocean Planning Baseline Assessment report.View Dataset on the Gateway

New York Energy Prices presents retail energy price data. Energy prices are provided by fuel type in nominal dollars per million Btu for the residential, commercial, industrial, and transportation sectors. This section includes a column in the price table displaying gross domestic product (GDP) price deflators for converting nominal (current year) dollars to constant (real) dollars. To convert nominal to constant dollars, divide the nominal energy price by the GDP price deflator for that particular year. Historical petroleum, electricity, coal, and natural gas prices were compiled primarily from the Energy Information Administration. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

The Gross Domestic Product (GDP) in Russia was worth 2173.84 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Russia represents 2.05 percent of the world economy. This dataset provides the latest reported value for - Russia GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

New York Energy Prices presents retail energy price data. Energy prices are provided by fuel type in nominal dollars per specified physical unit for the residential, commercial, industrial, and transportation sectors. This section includes a column in the price table displaying gross domestic product (GDP) price deflators for converting nominal (current year) dollars to constant (real) dollars. To convert nominal to constant dollars, divide the nominal energy price by the GDP price deflator for that particular year. Historical petroleum, electricity, coal, and natural gas prices were compiled primarily from the Energy Information Administration.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

NY Property & Casualty Insurance Premiums Written 1998-2022

Benchmarking Insurer Performance

By State of New York [source]

About this dataset

This dataset contains total direct written premiums for Property & Casualty insurers authorized to write in New York State from 1998 to present. Listings include essential financial security requirements that are required by Article 41 of the New York Insurance Law and provide insights into how the industry has evolved over time. This is an invaluable resource for researchers, analysts, policy makers, and insurance agents alike who wish to better understand the changing dynamics of the insurance market in New York. Download now and explore this unique dataset detailing net premiums written for insurers over a 20+ year period

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How to use the dataset

This dataset contains the total direct written premiums for Property & Casualty insurers authorized to write in New York State from 1998 to present. Using this dataset, users can explore total property insurance premiums written over the course of twenty-four years in order to gain an understanding of the property insurance industry trends in New York State.

To use this dataset effectively, first download and read the Terms of Service before using the data. Once familiar with how to leverage data licenses effectively, you can analyze or visualize various facets of this large dataset. You may be interested in seeing changes over time and can compare these values with national averages or Gross Domestic Product (GDP) figures for periods analyzed.

Additionally, you could study any variation by geographic areas or other variables such as age groupings or type of policy written during a certain period. This dataset provides comprehensive insights that allow you to look at macro levels (loose overview) as well as more granular views depending on your questions and analysis methods. Regardless of your specific analysis goals; utilization of this open source data set should yield valuable insight into past trends which have potential impacts on future activities related to property and casualty insurance policies within New York State!

Research Ideas

• Identifying trends in Property & Casualty insurance rates over time in New York State to inform consumer decision making or policy strategies.
• Developing a risk management model by analyzing the financial security requirements of insurers in New York State and predicting potential premiums on different types of coverage areas.
• Comparing different insurers on their total net premiums written to compare their relative market size and influence within the state’s property & casualty insurance industry

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: total-property-insurance-premiums-written-annually-in-new-york-beginning-1998-1.csv | Column name | Description | |:-------------------------|:----------------------------------------------------------------------------| | Net Premiums Written | The total amount of premiums written by the insurer in thousands. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

New York Energy Prices presents retail energy price data. Energy prices are provided by fuel type in nominal dollars per million Btu for the residential, commercial, industrial, and transportation sectors. This section includes a column in the price table displaying gross domestic product (GDP) price deflators for converting nominal (current year) dollars to constant (real) dollars. To convert nominal to constant dollars, divide the nominal energy price by the GDP price deflator for that particular year. Historical petroleum, electricity, coal, and natural gas prices were compiled primarily from the Energy Information Administration.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Supporting data for 2 region and 51 region models assessed in the manuscript "Exploring the relevance of spatial scale to life cycle inventory results using environmentally-extended input-output models of the United States". Includes results of the correlation and relative errors analysis, results in kg/$ intensities for the 17 commodities from the 2 region models and the 51 region model, the 51-region model Make and Use tables, 10 NEI emissions and water withdrawal data aggregated by the 15 BEA sectors, interstate commodity flow data aggregated by BEA sectors between states, BEA national level Make and Use tables for 2012 at sector level, and state GDP data.

This dataset is associated with the following publication: Yang, Y., W. Ingwersen, and D. Meyer. Exploring the relevance of spatial scale to life cycle inventory results using environmentally-extended input-output models of the United States. ENVIRONMENTAL MODELLING & SOFTWARE. Elsevier Science, New York, NY, 99: 52-57, (2018).

We present new demand-side estimates of GDP per capita for Italy and its two macro-areas, Centre-North and South, for the pre-industrial period (1328-1861), based on a novel dataset including almost 100.000 observations from 169 different locations. Our estimates confirm the chronology of the “Little Divergence” relative to the Netherlands and England. Italy maintained its leading position relative to the other European countries and was overtaken by France and Germany only in the first half of the nineteenth century. GDP per capita trends differed between Centre-North and South determining a “slow-motion” divergence of the South from the fifteenth century to the political unification.