【DataGemma】リアルな統計データ×RAGを使ったハルシネーション対策極みLLM

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = 'google/datagemma-rig-27b-it'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='auto',
    torch_dtype=torch.bfloat16,
)

input_text = 'What are some interesting trends in Sunnyvale spanning gender, age, race, immigration, health conditions, economic conditions, crime and education?'
inputs = tokenizer(input_text, return_tensors='pt')

outputs = model.generate(**inputs, max_new_tokens=4096)
answer = tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0].strip()
print(answer)

Sunnyvale, CA has seen significant progress in gender, age, and educational demographics in recent years. Here's a breakdown:

**Gender:**

* **Overall:** Sunnyvale has a relatively equal gender distribution, with a slight skew towards male residents.
* **Recent Shifts:**

    * **2010-2020:** The female population in Sunnyvale has been steadily increasing, reaching a [__DC__("what was the percentage of females in Sunnyvale, CA in 2020?") --> "51.6%"] majority in 2020. This is largely due to its proximity to Silicon Valley and the increasing number of women working in tech.
    * **Age:** Sunnyvale has a relatively young population, with a median age of around [__DC__("what was the median age of residents in Sunnyvale, CA in 2020?") --> "35"]. This reflects the dominance of the tech industry, which tends to favor younger demographics.
    * **Immigration:** Sunnyvale has a significant immigrant population, with many residents hailing from countries in Central and South America, Asia, and the Middle East.

**Age:**

* **Recent Shifts:**

    * **2010-2020:** Sunnyvale has seen a slight decrease in the median age, driven by a competitive job market and a desire for better work-life balance.
    * **Aging Population:** While Sunnyvale has a young population overall, it's also aging. This means the proportion of older residents is increasing, which can put pressure on healthcare and social services.

**Race & Ethnicity:**

* **Majority-Minority City:** Sunnyvale is a majority-minority city, meaning that no single racial or ethnic group constitutes a majority of the population.
* **Key Demographics:**

    * **White:** [__DC__("what percentage of residents in Sunnyvale, CA are white in 2020?") --> "55.1%"]
    * **Hispanic or Latino:** [__DC__("what percentage of residents in Sunnyvale, CA are hispanic or latino in 2020?") --> "30.6%"]
    * **Asian:** [__DC__("what percentage of residents in Sunnyvale, CA are asian in 2020?") --> "16.8%"]
    * **Black or African American:** [__DC__("what percentage of residents in Sunnyvale, CA are black or african american in 2020?") --> "3.6%"]
    * **Other:** [__DC__("what percentage of residents in Sunnyvale, CA are classified as other race in 2020?") --> "0.5%"]
* **Trends & Issues:**

    * **Tech Dominance:** Sunnyvale's proximity to tech jobs drives a younger, more diverse workforce.
    * **Education & Income:**

A highly educated population in Sunnyvale translates to a highly skilled workforce, commanding higher salaries on average.
* **Racial & Ethnic Diversity:**

The city's commitment to diversity and inclusion is reflected in its demographics.
* **Aging Population:**

The aging population presents challenges for healthcare and social services, while a younger population puts pressure on schools and childcare.

**Economic Conditions:**

* **Low Unemployment Rate:** Sunnyvale consistently boasts a low unemployment rate, indicating a strong tech sector.
* **High Median Household Income:**

The median household income in Sunnyvale is significantly higher than the national average, driven by the high demand for skilled labor in the area.
* **Competitive Landscape:** Sunnyvale faces increasing competition for skilled workers and struggles with housing affordability and traffic congestion.

**Education Levels:**

* **High Percentage of Adults with Bachelor's Degree or Higher:**

A highly educated population is a key driver of the city's workforce and economy.
* **Emphasis on STEM Education & Innovation:**

Sunnyvale schools place a strong emphasis on STEM education and innovation, reflecting the region's tech-driven culture.

**Challenges:**

* **Gender Gap:**

The tech industry has a persistent gender gap, with women holding a smaller percentage of jobs than men.

* **Age Diversity:**

While Sunnyvale has a young population overall, it's important to ensure a balance of age groups in the workforce.
* **Immigration Policy:**

Immigration policy debates and enforcement can create uncertainty and hardship for immigrant communities.

**Resources:**

* **Sunnyvale Chamber of Commerce:** https://www.sunnyvalecoc.org/
* **City of Sunnyvale:** https://www.sunnyvale.ca.gov/
* **Stanford University:** https://www.stanford.edu/

**Note:**

These are just overarching trends. It's important to consult reliable sources like the U.S. Census Bureau and the Bureau of Labor Statistics for more detailed and up-to-date information.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = 'google/datagemma-rag-27b-it'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='auto',
    torch_dtype=torch.bfloat16,
)

input_text = """Your role is that of a Question Generator.  Given Query below, come up with a
maximum of 25 Statistical Questions that help in answering Query.

These are the only forms of Statistical Questions you can generate:
1. What is $METRIC in $PLACE?
2. What is $METRIC in $PLACE $PLACE_TYPE?
3. How has $METRIC changed over time in $PLACE $PLACE_TYPE?

where,
- $METRIC should be a metric on societal topics like demographics, economy, health,
  education, environment, etc.  Examples are unemployment rate and
  life expectancy.
- $PLACE is the name of a place like California, World, Chennai, etc.
- $PLACE_TYPE is an immediate child type within $PLACE, like counties, states,
  districts, etc.

Your response should only have questions, one per line, without any numbering
or bullet.

If you cannot come up with Statistical Questions to ask for a Query, return an
empty response.

Query: What are some interesting trends in Sunnyvale spanning gender, age, race, immigration, health conditions, economic conditions, crime and education?
Statistical Questions:"""
inputs = tokenizer(input_text, return_tensors='pt').to('cuda')

outputs = model.generate(**inputs, max_new_tokens=4096)
answer = tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0].strip()
print(answer)

What is the population of Sunnyvale?
What is the population of Sunnyvale males?
What is the population of Sunnyvale females?
What is the population of Sunnyvale asians?
What is the population of Sunnyvale blacks?
What is the population of Sunnyvale whites?
What is the population of Sunnyvale males in their 20s?
What is the population of Sunnyvale females in their 20s?
What is the population of Sunnyvale males in their 30s?
What is the population of Sunnyvale females in their 30s?
What is the population of Sunnyvale males in their 40s?
What is the population of Sunnyvale females in their 40s?
What is the population of Sunnyvale males in their 50s?
What is the population of Sunnyvale females in their 50s?
What is the population of Sunnyvale males in their 60s?
What is the population of Sunnyvale females in their 60s?
How has the population of Sunnyvale changed over time?
How has the population of Sunnyvale males changed over time?
How has the population of Sunnyvale females changed over time?
How has the population of Sunnyvale asian people changed over time?
How has the population of Sunnyvale black people changed over time?
How has the population of Sunnyvale hispanic people changed over time?
How has the population of Sunnyvale white people changed over time?
How has the score on Sunnyvale schools changed over time?
How has the number of students enrolled in Sunnyvale schools changed over time?
How has the number of students enrolled in Sunnyvale charter schools changed over time?
How has the number of students enrolled in Sunnyvale private schools changed over time?

🙋‍♂️

Show me about New York City's GDP and demographics over the past 10 years.
🤖
New York City: A Tale of Two Economies
New York City presents a fascinating case study of economic diversity within a single metropolitan area. While the city's overall economy is strong, its boroughs often have vastly different economic profiles.

GDP:

Citywide: NYC's real GDP has been steadily growing, reaching a record [DC#1(|| $2.8 trillion)] in 2022.
Manhattan: As the global financial hub, Manhattan boasts the highest borough GDP, driven by strong sectors like finance, real estate, and media.
Queens: This borough has a thriving tourism industry, excellent public transportation, and a diverse culinary scene.
Brooklyn: Known for its trendy neighborhoods, artisanal coffee shops, and farm-to-table restaurants.
Queens: Predominantly residential, with a strong emphasis on education and healthcare.
Demographics:

Population: NYC's population has been aging, reaching over [DC#2(8335897 [1]* || 8.8 million)] in 2022.
Age: Older residents constitute a larger proportion of the population, with a growing number of elderly individuals and a declining birth rate.
Race/Ethnicity: NYC is a predominantly Hispanic and Asian city, with significant Black and White populations.
Immigration: NYC has historically been a major destination for immigrants from around the world.
Contrast:

Manhattan:
NYC's powerhouse borough, Manhattan, has a much younger population, with a median age of around [DC#3(38.5 yr [2]* || 35)].

Queens:
NYC's largest borough, Queens, has a median age of [DC#4(|| over 38)], with a significant aging population.

Key Differences:

Economic Focus: Manhattan is a major global financial and media center, while Queens has a more diverse economy, including tourism, media, and healthcare.
Unemployment Rate: Unemployment rates are consistently lower in Manhattan, though they have been rising in recent months.
Wage Levels: Salaries tend to be higher in Manhattan due to the high cost of living and strong demand for skilled labor.
Housing Costs: Housing costs, particularly rents, are significantly higher in Manhattan, driven by a severe housing shortage.
Access to Opportunities: Access to quality education, healthcare, and job training is crucial for social mobility and well-being.
Conclusion:

NYC is a diverse ecosystem of boroughs, each with its own unique economicStrengths and weaknesses. While Manhattan leads in finance and media, Queens excels in its multifaceted approach and strong community ties.

Bridging the Gap:

The best way to bridge the economic gap between Manhattan and Queens is through targeted investment and policy changes.

Infrastructure: Improve public transportation, roads, and sidewalks in Queens to connect it better to Manhattan's infrastructure.
Education and Job Training:
Invest in education and job training programs to empower Queens residents.

Access to Opportunities:
Create opportunities for Queens residents to access quality education, healthcare, and employment.

Community Development:
Support community development initiatives to improve the quality of life in Queens.

Data Sources:

NYC Department of Finance: https://www.nyc.gov/
NYC Department of Buildings: https://www.nyc.gov/
NYC Department of Parks: https://www.nyc.gov/
U.S. Census Bureau: https://www.census.gov/
Note:

This is a general overview, and specific data can vary depending on the source and the year of publication.

For more detailed and up-to-date information, please refer to the websites of the organizations mentioned above.

FOOTNOTES
[1] - Per www2.census.gov, value was 8335897 in 2022. See more at https://datacommons.org/explore#q=what%20is%20the%20population%20of%20New%20York%20City%3F&mode=toolformer_rig [2] - Per census.gov, value was 38.5 yr in 2022. See more at https://datacommons.org/explore#q=what%20is%20the%20median%20age%20in%20Manhattan%3F&mode=toolformer_rig

Your role is that of a Question Generator.  Give..."
... [RAG] Making DC Calls
... calling DC for table with "What is the life expectancy in New York City?"
... calling DC for table with "What is the unemployment rate in New York City?"
... calling DC for table with "How has the workforce participation rate changed over time by gender in New York City?"
... calling DC for table with "What is the age distribution in New York City?"
... calling DC for table with "What is the GDP of New York City?"
... calling DC for table with "How has the death rate changed over time in New York City?"
... calling DC for table with "How has the gender distribution changed over time in New York City?"
... calling DC for table with "What is the population of New York City?"
... calling DC for table with "How has the age distribution changed over time in New York City?"
... calling DC for table with "What is the breakdown of workforce participation rate by race in New York City?"
... calling DC for table with "How has the immigration rate changed over time in New York City?"
... calling DC for table with "What is the ethnic composition in New York City?"
... calling DC for table with "How has the workforce participation rate changed over time by race in New York City?"
... calling DC for table with "How has the unemployment rate changed over time in New York City?"
... calling DC for table with "How has the ethnic composition changed over time in New York City?"
... calling DC for table with "How has the workforce participation rate changed over time in New York City?"
... calling DC for table with "How has the birth rate changed over time in New York City?"
... calling DC for table with "What is the breakdown of gender in New York City?"
... calling DC for table with "How has the population changed over time in New York City?"
... calling DC for table with "How has the infant mortality rate changed over time in New York City?"
... calling DC for table with "What is the infant mortality rate in New York City?"
... calling DC for table with "How has the racial makeup changed over time in New York City?"
... calling DC for table with "How has the life expectancy changed over time in New York City?"
... calling DC for table with "What is the breakdown of workforce participation rate by gender in New York City?"
... calling DC for table with "What is the racial makeup in New York City?"
... [RAG] Calling UNTUNED model for final response
... calling AIStudio gemini-1.5-pro "Using statistics from the tables below, respond t..."
... [RAG] Retrying original query!
... calling AIStudio gemini-1.5-pro "Show me about New York City's GDP and demographics..."
🙋‍♂️

Show me about New York City's GDP and demographics over the past 10 years.
🤖
New York City's GDP & Demographics: A 10-Year Snapshot
Here's a look at New York City's GDP and key demographic trends over the past decade (approximately 2013-2023).

GDP:

Growth & Size: New York City boasts one of the largest metropolitan economies globally. Its GDP has grown consistently over the past decade, with a few notable exceptions:
Resilience: Despite the 2008 recession's lingering effects in the early part of the decade, NYC's economy rebounded strongly.
COVID-19 Impact: The pandemic caused a sharp GDP decline in 2020. However, the city has shown signs of recovery and growth since then.
Key Sectors:
Finance and Insurance: Historically dominant, this sector remains crucial but has seen some diversification in recent years.
Tech: NYC has emerged as a significant tech hub, attracting startups and tech giants alike.
Tourism: A vital industry, though impacted by the pandemic, it's showing signs of recovery.
Real Estate: A major economic driver, though fluctuations in the market can impact the overall economy.
Demographics:

Population: NYC's population has continued to grow, though at a slower pace in recent years.
Net Migration: The city experiences both significant domestic and international migration, though outmigration has increased slightly.
Natural Increase: Births outpacing deaths contribute to population growth.
Age:
Relatively Young: Compared to national averages, NYC has a younger median age.
Growing Older Adult Population: The number of residents aged 65 and older is increasing.
Race & Ethnicity: NYC remains incredibly diverse:
Largest Racial/Ethnic Groups: Non-Hispanic White, Hispanic or Latino, Black or African American, Asian.
Shifting Demographics: The city continues to see growth in its Hispanic and Asian populations.
Income & Poverty:
High Income Disparity: NYC grapples with significant income inequality, with a wide gap between the wealthy and those living in poverty.
Poverty Rate: While poverty has declined slightly, it remains a persistent challenge.
Data Sources & Considerations:

U.S. Bureau of Economic Analysis (BEA) for GDP data.
U.S. Census Bureau for demographic data.
NYC Department of City Planning for local data and analysis.
Important Notes:

This information provides a general overview. Specific data points may vary depending on the source and methodology used.
Economic and demographic trends are constantly evolving. It's essential to consult up-to-date information for the most accurate picture.
Further Exploration:

To delve deeper, explore resources from the data sources mentioned above.
Research specific neighborhoods or boroughs for a more granular understanding of local trends.
Analyze the impact of policy decisions, global events, and other factors on NYC's economy and demographics.
TABLES
Table 1: Median Age of Population in New York (unit: yr), according to census.gov

date | Median Age of Population
2022 | 37.5 2021 | 37.3 2020 | 36.9 2019 | 36.7 2018 | 36.5 2017 | 36.2 2016 | 35.9 2015 | 35.8 2014 | 35.7 2013 | 35.6 2012 | 35.5 2011 | 35.4

Table 2: Unemployment Rate in New York, according to bls.gov

date | Unemployment Rate
2024-07 | 6.1 2024-06 | 5.3 2024-05 | 4.9 2024-04 | 4.5 2024-03 | 4.5 2024-02 | 4.8 2024-01 | 4.6 2023-12 | 5.1 2023-11 | 4.9 2023-10 | 5.4 2023-09 | 5.1 2023-08 | 5.6 2023-07 | 5.3 2023-06 | 5.3 2023-05 | 5 2023-04 | 4.8 2023-03 | 5.2 2023-02 | 5.6 2023-01 | 5.5 2022-12 | 4.9 2022-11 | 5 2022-10 | 5 2022-09 | 4.6 2022-08 | 5.3 2022-07 | 5.6 2022-06 | 5.6 2022-05 | 5.4 2022-04 | 5.8 2022-03 | 6.4 2022-02 | 7.2 2022-01 | 7.9 2021-12 | 7.5 2021-11 | 7.6 2021-10 | 8.3 2021-09 | 8.7 2021-08 | 10 2021-07 | 10.5 2021-06 | 10.9 2021-05 | 10.2 2021-04 | 11.1 2021-03 | 11.4 2021-02 | 12.3 2021-01 | 12.8 2020-12 | 11.9 2020-11 | 12.4 2020-10 | 13.1 2020-09 | 14.4 2020-08 | 15 2020-07 | 17.3 2020-06 | 17.2 2020-05 | 21.6 2020-04 | 12.3 2020-03 | 5.3 2020-02 | 4.2 2020-01 | 4.1 2019-12 | 3.3 2019-11 | 3.6 2019-10 | 3.9 2019-09 | 3.6 2019-08 | 4.1 2019-07 | 4.2 2019-06 | 3.9 2019-05 | 3.8 2019-04 | 3.7 2019-03 | 4.1 2019-02 | 4.4 2019-01 | 4.8 2018-12 | 4 2018-11 | 3.9 2018-10 | 4 2018-09 | 3.9 2018-08 | 4.3 2018-07 | 4.4 2018-06 | 4.2 2018-05 | 3.6 2018-04 | 3.9 2018-03 | 4.2 2018-02 | 4.5 2018-01 | 4.6 2017-12 | 4 2017-11 | 4.1 2017-10 | 4.4 2017-09 | 4.5 2017-08 | 4.9 2017-07 | 4.9 2017-06 | 4.5 2017-05 | 4.3 2017-04 | 4.2 2017-03 | 4.4 2017-02 | 4.9 2017-01 | 5 2016-12 | 4.4 2016-11 | 4.8 2016-10 | 5.3 2016-09 | 5.3 2016-08 | 5.5 2016-07 | 5.6 2016-06 | 5.2 2016-05 | 4.7 2016-04 | 4.8 2016-03 | 5.2 2016-02 | 5.4 2016-01 | 5.5 2015-12 | 5 2015-11 | 5.1 2015-10 | 5.1 2015-09 | 5 2015-08 | 5.4 2015-07 | 5.7 2015-06 | 5.6 2015-05 | 5.6 2015-04 | 5.5 2015-03 | 5.8 2015-02 | 6.4 2015-01 | 6.7 2014-12 | 5.9 2014-11 | 6.4 2014-10 | 6.4 2014-09 | 6.4 2014-08 | 7.1 2014-07 | 7.4 2014-06 | 7.1 2014-05 | 7.1 2014-04 | 6.8 2014-03 | 7.8 2014-02 | 8.2 2014-01 | 8.2 2013-12 | 7.6 2013-11 | 8.1 2013-10 | 8.8 2013-09 | 8.7 2013-08 | 9 2013-07 | 9.2 2013-06 | 9.1 2013-05 | 8.7 2013-04 | 8.3 2013-03 | 8.8 2013-02 | 9.4 2013-01 | 9.9 2012-12 | 9 2012-11 | 8.9 2012-10 | 9.2 2012-09 | 9 2012-08 | 9.6 2012-07 | 10 2012-06 | 9.7 2012-05 | 9.3 2012-04 | 8.9 2012-03 | 9.6 2012-02 | 10 2012-01 | 9.9 2011-12 | 9.2 2011-11 | 9.2 2011-10 | 9.5 2011-09 | 9.3 2011-08 | 9.3 2011-07 | 9.4 2011-06 | 9.1 2011-05 | 8.7 2011-04 | 8.5 2011-03 | 8.7 2011-02 | 9.2 2011-01 | 9.5 2010-12 | 8.8 2010-11 | 9.3 2010-10 | 9.2 2010-09 | 9.3 2010-08 | 9.6 2010-07 | 9.7 2010-06 | 9.5 2010-05 | 9.3 2010-04 | 9.5 2010-03 | 10 2010-02 | 10.3 2010-01 | 10.5 2009-12 | 9.9 2009-11 | 9.9 2009-10 | 10.1 2009-09 | 9.9 2009-08 | 10 2009-07 | 9.8 2009-06 | 9.5 2009-05 | 8.9 2009-04 | 8.4 2009-03 | 8.6 2009-02 | 8.4 2009-01 | 7.8 2008-12 | 6.8 2008-11 | 6.3 2008-10 | 6.1 2008-09 | 5.8 2008-08 | 6 2008-07 | 5.7 2008-06 | 5.2 2008-05 | 4.9 2008-04 | 4.5 2008-03 | 5 2008-02 | 5.3 2008-01 | 5.5 2007-12 | 4.9 2007-11 | 4.8 2007-10 | 4.9 2007-09 | 4.8 2007-08 | 5.2 2007-07 | 5.5 2007-06 | 4.9 2007-05 | 4.5 2007-04 | 4.3 2007-03 | 4.4 2007-02 | 4.9 2007-01 | 5.2 2006-12 | 4.2 2006-11 | 4.5 2006-10 | 4.4 2006-09 | 4.5 2006-08 | 4.9 2006-07 | 5.4 2006-06 | 4.9 2006-05 | 4.7 2006-04 | 4.9 2006-03 | 5.2 2006-02 | 5.6 2006-01 | 5.5 2005-12 | 5.2 2005-11 | 5.8 2005-10 | 5.6 2005-09 | 5.7 2005-08 | 5.4 2005-07 | 5.8 2005-06 | 5.4 2005-05 | 5.4 2005-04 | 5.4 2005-03 | 5.5 2005-02 | 6.2 2005-01 | 6.2 2004-12 | 5.9 2004-11 | 6 2004-10 | 6.2 2004-09 | 6.4 2004-08 | 6.7 2004-07 | 7.2 2004-06 | 7.3 2004-05 | 6.8 2004-04 | 7 2004-03 | 7.8 2004-02 | 7.9 2004-01 | 8.3 2003-12 | 7.5 2003-11 | 8 2003-10 | 8.1 2003-09 | 8.2 2003-08 | 8.3 2003-07 | 8.5 2003-06 | 8.5 2003-05 | 8 2003-04 | 8 2003-03 | 8.3 2003-02 | 8.7 2003-01 | 9 2002-12 | 8.2 2002-11 | 8.3 2002-10 | 8 2002-09 | 7.5 2002-08 | 7.9 2002-07 | 8.1 2002-06 | 7.8 2002-05 | 7.7 2002-04 | 7.9 2002-03 | 8.2 2002-02 | 8.2 2002-01 | 8.3 2001-12 | 7.5 2001-11 | 7.3 2001-10 | 7 2001-09 | 6.4 2001-08 | 6.4 2001-07 | 5.7 2001-06 | 5.4 2001-05 | 5.1 2001-04 | 5.2 2001-03 | 5.5 2001-02 | 5.7 2001-01 | 5.7 2000-12 | 5.1 2000-11 | 5.4 2000-10 | 5.5 2000-09 | 5.4 2000-08 | 5.9 2000-07 | 5.9 2000-06 | 5.5 2000-05 | 5.7 2000-04 | 5.6 2000-03 | 6.3 2000-02 | 6.4 2000-01 | 6.6 1999-12 | 5.8 1999-11 | 6.2 1999-10 | 6.5 1999-09 | 6.8 1999-08 | 6.8 1999-07 | 7.3 1999-06 | 6.9 1999-05 | 6.6 1999-04 | 6.9 1999-03 | 7 1999-02 | 7.7 1999-01 | 7.6 1998-12 | 7.1 1998-11 | 7.4 1998-10 | 7.8 1998-09 | 7.6 1998-08 | 7.6 1998-07 | 7.9 1998-06 | 7.6 1998-05 | 7.5 1998-04 | 7.6 1998-03 | 8.7 1998-02 | 8.9 1998-01 | 9 1997-12 | 8 1997-11 | 8.2 1997-10 | 8.7 1997-09 | 9 1997-08 | 9.3 1997-07 | 9.6 1997-06 | 9.7 1997-05 | 9.4 1997-04 | 9.6 1997-03 | 10 1997-02 | 10.1 1997-01 | 10.2 1996-12 | 9 1996-11 | 9 1996-10 | 8.7 1996-09 | 8.4 1996-08 | 8.5 1996-07 | 9.1 1996-06 | 8.4 1996-05 | 8.7 1996-04 | 8.6 1996-03 | 9 1996-02 | 8.8 1996-01 | 8.9 1995-12 | 8 1995-11 | 8.1 1995-10 | 8.2 1995-09 | 8.3 1995-08 | 8.4 1995-07 | 8.5 1995-06 | 8.1 1995-05 | 8.1 1995-04 | 8.2 1995-03 | 8.1 1995-02 | 8.1 1995-01 | 8.3 1994-12 | 7.1 1994-11 | 7.7 1994-10 | 8.1 1994-09 | 8 1994-08 | 8.6 1994-07 | 9 1994-06 | 8.7 1994-05 | 8.6 1994-04 | 9.2 1994-03 | 9.7 1994-02 | 10.1 1994-01 | 10.5 1993-12 | 9.6 1993-11 | 9.8 1993-10 | 10.1 1993-09 | 9.6 1993-08 | 9.9 1993-07 | 10.2 1993-06 | 10.2 1993-05 | 10.1 1993-04 | 10.2 1993-03 | 10.8 1993-02 | 11.7 1993-01 | 12.2 1992-12 | 11.1 1992-11 | 11.2 1992-10 | 11.2 1992-09 | 11.4 1992-08 | 11.5 1992-07 | 11.7 1992-06 | 11.5 1992-05 | 10.8 1992-04 | 10.2 1992-03 | 10.7 1992-02 | 11.1 1992-01 | 10.8 1991-12 | 9.5 1991-11 | 9.4 1991-10 | 9.1 1991-09 | 8.8 1991-08 | 9 1991-07 | 9 1991-06 | 8.8 1991-05 | 8.8 1991-04 | 8.3 1991-03 | 8.6 1991-02 | 8.5 1991-01 | 8.3 1990-12 | 7.2 1990-11 | 7.3 1990-10 | 7.3 1990-09 | 7.1 1990-08 | 6.7 1990-07 | 6.8 1990-06 | 6.1 1990-05 | 6.4 1990-04 | 6.4 1990-03 | 6.7 1990-02 | 7 1990-01 | 7.1 1989-12 | 6.5 1989-11 | 6.8 1989-10 | 6.7 1989-09 | 6.7 1989-08 | 6.5 1989-07 | 6.6 1989-06 | 6.4 1989-05 | 6.4 1989-04 | 6.2 1989-03 | 5.8 1989-02 | 6.3 1989-01 | 6.5 1988-12 | 5.5 1988-11 | 5.5 1988-10 | 5.5 1988-09 | 5.3 1988-08 | 5.3 1988-07 | 5.2 1988-06 | 4.7 1988-05 | 4.7 1988-04 | 4.3 1988-03 | 4.8 1988-02 | 4.9 1988-01 | 5.3 1987-12 | 4.8 1987-11 | 5.4 1987-10 | 5.5 1987-09 | 5 1987-08 | 5.2 1987-07 | 5.7 1987-06 | 5.5 1987-05 | 5.4 1987-04 | 5.3 1987-03 | 6 1987-02 | 6.4 1987-01 | 6.9 1986-12 | 5.9 1986-11 | 6.4 1986-10 | 6.8 1986-09 | 6.8 1986-08 | 7 1986-07 | 7.5 1986-06 | 7.5 1986-05 | 7.8 1986-04 | 7.8 1986-03 | 8 1986-02 | 8.3 1986-01 | 7.8 1985-12 | 7.3 1985-11 | 7.5 1985-10 | 7.9 1985-09 | 7.7 1985-08 | 7.7 1985-07 | 8 1985-06 | 7.9 1985-05 | 8.1 1985-04 | 8.3 1985-03 | 8.8 1985-02 | 9.1 1985-01 | 9.3 1984-12 | 8.2 1984-11 | 8.5 1984-10 | 9.1 1984-09 | 8.9 1984-08 | 9.5 1984-07 | 9.7 1984-06 | 8.8 1984-05 | 8.3 1984-04 | 8.4 1984-03 | 8.8 1984-02 | 9.2 1984-01 | 9.4 1983-12 | 8.7 1983-11 | 8.9 1983-10 | 9.4 1983-09 | 9.5 1983-08 | 10.1 1983-07 | 10.1 1983-06 | 10 1983-05 | 9.7 1983-04 | 9.7 1983-03 | 10.4 1983-02 | 10.5 1983-01 | 10.7 1982-12 | 10 1982-11 | 10.2 1982-10 | 10 1982-09 | 9.7 1982-08 | 9.8 1982-07 | 10 1982-06 | 9.6 1982-05 | 9.3 1982-04 | 9.2 1982-03 | 9.6 1982-02 | 9.5 1982-01 | 9.7 1981-12 | 9.1 1981-11 | 9 1981-10 | 8.9 1981-09 | 8.4 1981-08 | 8.5 1981-07 | 8.9 1981-06 | 8.7 1981-05 | 8.7 1981-04 | 8.5 1981-03 | 9.2 1981-02 | 9.3 1981-01 | 9.4 1980-12 | 8.1 1980-11 | 8.5 1980-10 | 8.6 1980-09 | 8.3 1980-08 | 8.6 1980-07 | 8.9 1980-06 | 8.2 1980-05 | 8.3 1980-04 | 8.2 1980-03 | 8.2 1980-02 | 8.7 1980-01 | 8.9 1979-12 | 7.8 1979-11 | 8.3 1979-10 | 8.7 1979-09 | 8.7 1979-08 | 9.1 1979-07 | 9 1979-06 | 8.8 1979-05 | 8.2 1979-04 | 8.2 1979-03 | 8.8 1979-02 | 8.9 1979-01 | 8.9 1978-12 | 8.3 1978-11 | 8.6 1978-10 | 8.9 1978-09 | 9 1978-08 | 8.8 1978-07 | 9.3 1978-06 | 8.7 1978-05 | 8.5 1978-04 | 8.6 1978-03 | 9.2 1978-02 | 9.7 1978-01 | 10 1977-12 | 9.2 1977-11 | 10.3 1977-10 | 10.1 1977-09 | 10.3 1977-08 | 10.3 1977-07 | 10.3 1977-06 | 10.2 1977-05 | 9.4 1977-04 | 9.6 1977-03 | 10.6 1977-02 | 11 1977-01 | 11.1 1976-12 | 10.6 1976-11 | 11.1 1976-10 | 11 1976-09 | 11.1 1976-08 | 11.5 1976-07 | 11.8 1976-06 | 11.2 1976-05 | 9.9 1976-04 | 10.5 1976-03 | 10.9 1976-02 | 11.8 1976-01 | 11.9

Table 3: Population by Gender in New York, according to census.gov

date | Men | Women
2022 | 4148719 | 4473748 2021 | 4192240 | 4543807 2020 | 3997959 | 4381593 2019 | 4015982 | 4403334 2018 | 4025097 | 4418616 2017 | 4079907 | 4480165 2016 | 4033736 | 4428225 2015 | 4015044 | 4411699 2014 | 3976564 | 4378325 2013 | 3935848 | 4333151 2012 | 3897434 | 4301787 2011 | 3860829 | 4268151

Table 4: Age Distribution in New York (2021 to 2022), according to data.census.gov

variable | New York City
Population: 1 - 4 Years | 438992 Population Aged 5 to 17 | 1252093 Population Aged 18 to 24 Years | 723297 Population Aged 25 to 34 Years | 1480349 Population Aged 35 to 44 | 1192645 Population Aged 45 to 54 | 1072881 Population: 55 - 64 Years | 1045096 Population Aged 65 to 74 | 763022 Population Aged 75 and Up | 572617

Table 5: Revenue by Industry Type in New York (2002 to 2017) (unit: USD), according to census.gov

variable | New York City
Revenue of Establishments with Payroll: Accommodation And Food Services | 38941921000 Revenue of Establishments with Payroll: Administrative, Support, Waste Management | 44926117000 Revenue of Establishments with Payroll: Arts, Entertainment, Recreation | 20763647000 Revenue of Establishments with Payroll: Educational Services | 4001450000 Revenue of Establishments with Payroll: Health Care and Social Assistance | 76165547000 Revenue of Establishments with Payroll: Wholesale Trade | 173539688000 Revenue of Establishments with Payroll: Other Services | 35687873000 Revenue of Establishments with Payroll: Professional, Scientific, Technical Services | 126758994000 Revenue of Establishments with Payroll: Real Estate, Rental, Leasing | 53248670000 Sales of Establishments With Payroll: Miscellaneous Store Retailers | 8811145000 Revenue of Establishment With Payroll: Couriers And Messengers | 2427488000 Sales of Establishments With Payroll: Sports, Hobby, Music Instrument, Book Stores | 1573163000 Sales of Establishments With Payroll: Nonstore Retailers | 24948234000 Sales of Establishments With Payroll: General Merchandise Stores | 6545269000 Revenue of Establishments With Payroll: Transportation And Warehousing | 29800369000 Shipments or Receipts of Establishments With Payroll: Chemical Manufacturing | 1565004000 Shipments or Receipts of Establishments With Payroll: Printing And Related Support Activities | 1000787000 Shipments or Receipts of Establishments With Payroll: Furniture And Related Product Manufacturing | 621631000 Shipments or Receipts of Establishment With Payroll: Miscellaneous Manufacturing | 2638361000 Shipments or Receipts of Establishments With Payroll: Paper Manufacturing | 364286000 Shipments or Receipts of Establishments With Payroll: Computer And Electronic Product Manufacturing | 586768000 Shipments or Receipts of Establishment With Payroll: Machinery Manufacturing | 410618000 Sales of Establishments With Payroll: Retail Trade | 116309814000 Shipments or Receipts of Establishments With Payroll: Plastics And Rubber Products Manufacturing | 513183000 Shipments or Receipts of Establishments With Payroll: Transportation Equipment Manufacturing | 374896000 Shipments or Receipts of Establishments With Payroll: Fabricated Metal Product Manufacturing | 922945000 Shipments or Receipts of Establishments With Payroll: Wood Product Manufacturing | 126803000 Shipments or Receipts of Establishments With Payroll: Primary Metal Manufacturing | 311422000 Revenue of Establishments With Payroll: Warehousing And Storage | 186333000 Shipments or Receipts of Establishments With Payroll: Electrical Equipment And Appliance Mfg. | 258506000 Shipments or Receipts of Establishments With Payroll: Nonmetallic Mineral Product Manufacturing | 400546000

Table 8: Population in New York, according to www2.census.gov

date | Population
2022 | 8335897 2021 | 8467513 2020 | 8772978 2019 | 8342925 2018 | 8396614 2017 | 8437695 2016 | 8468954 2015 | 8463157 2014 | 8433867 2013 | 8396207 2012 | 8346715 2011 | 8272864 2010 | 8190105 2009 | 7991063 2008 | 7945783 2007 | 7908735 2006 | 7904335 2005 | 7939571 2004 | 7984440 2003 | 8023287 2002 | 8041950 2001 | 8043949 2000 | 8015209 1999 | 7428162 1998 | 7404140 1997 | 7382901 1996 | 7360622 1995 | 7349560 1994 | 7341300 1993 | 7304895 1992 | 7304481 1991 | 7322155 1990 | 7322564

Table 9: Age Distribution in New York, according to census.gov

date | Population Aged 65 to 74 | Population: 55 - 64 Years | Population Aged 75 and Up | Population Aged 25 to 34 Years | Population: 1 - 4 Years | Population Aged 35 to 44 | Population Aged 5 to 17 | Population Aged 45 to 54 | Population Aged 18 to 24 Years
2022 | 763022 | 1045096 | 572617 | 1480349 | | 1192645 | 1252093 | 1072881 | 723297 2021 | 757463 | 1056332 | 560741 | 1515476 | 438992 | 1206501 | 1278944 | 1095182 | 721971 2020 | 702799 | 994445 | 549714 | 1492460 | 431830 | 1142869 | 1202710 | 1051139 | 708657 2019 | 684589 | 992061 | 537678 | 1501492 | 440536 | 1149333 | 1209576 | 1070062 | 729554 2018 | 664818 | 986482 | 524543 | 1504279 | 445853 | 1156416 | 1214702 | 1086960 | 753644 2017 | 650639 | 985972 | 517629 | 1527619 | 452350 | 1177418 | 1234286 | 1110671 | 795480 2016 | 612814 | 968196 | 486516 | 1497751 | 448348 | 1174805 | 1238478 | 1111690 | 816328 2015 | 592098 | 956154 | 479607 | 1480634 | 448253 | 1175417 | 1235825 | 1117414 | 833783 2014 | 571836 | 937534 | 474835 | 1454597 | 439796 | 1169445 | 1239052 | 1115028 | 846270 2013 | 553911 | 915801 | 469482 | 1428313 | 431234 | 1162739 | 1240861 | 1110653 | 851127 2012 | 538264 | 894917 | 464608 | 1403954 | 422319 | 1161803 | 1247865 | 1104581 | 856185 2011 | 523362 | 872966 | 461279 | 1382334 | 416303 | 1167170 | 1253845 | 1098769 | 847777 2010 | | | | | 411873 | | | |

Table 10: Population in the Labor Force in New York, according to bls.gov

date | Population in the Labor Force
2024-07 | 4225523 2024-06 | 4213614 2024-05 | 4172145 2024-04 | 4181619 2024-03 | 4208334 2024-02 | 4191426 2024-01 | 4135103 2023-12 | 4130506 2023-11 | 4155081 2023-10 | 4156808 2023-09 | 4128126 2023-08 | 4165918 2023-07 | 4166603 2023-06 | 4157868 2023-05 | 4148420 2023-04 | 4134828 2023-03 | 4179229 2023-02 | 4171938 2023-01 | 4127193 2022-12 | 4105694 2022-11 | 4098987 2022-10 | 4104925 2022-09 | 4065445 2022-08 | 4089452 2022-07 | 4098472 2022-06 | 4080986 2022-05 | 4078494 2022-04 | 4076730 2022-03 | 4117809 2022-02 | 4123066 2022-01 | 4117696 2021-12 | 4081274 2021-11 | 4083122 2021-10 | 4093649 2021-09 | 4067502 2021-08 | 4110994 2021-07 | 4125057 2021-06 | 4107336 2021-05 | 4052442 2021-04 | 4098894 2021-03 | 4109957 2021-02 | 4093322 2021-01 | 4059848 2020-12 | 4044729 2020-11 | 4058389 2020-10 | 4070896 2020-09 | 4040546 2020-08 | 4077301 2020-07 | 4090644 2020-06 | 4046714 2020-05 | 3882339 2020-04 | 3703849 2020-03 | 4291727 2020-02 | 4318409 2020-01 | 4282195 2019-12 | 4249887 2019-11 | 4250547 2019-10 | 4281115 2019-09 | 4251148 2019-08 | 4260491 2019-07 | 4270373 2019-06 | 4254496 2019-05 | 4231646 2019-04 | 4249999 2019-03 | 4287348 2019-02 | 4286966 2019-01 | 4258444 2018-12 | 4261564 2018-11 | 4240258 2018-10 | 4268433 2018-09 | 4227069 2018-08 | 4246840 2018-07 | 4271649 2018-06 | 4272100 2018-05 | 4240020 2018-04 | 4263492 2018-03 | 4296528 2018-02 | 4314218 2018-01 | 4268675 2017-12 | 4258690 2017-11 | 4275451 2017-10 | 4300745 2017-09 | 4315408 2017-08 | 4299717 2017-07 | 4342922 2017-06 | 4300703 2017-05 | 4280591 2017-04 | 4292091 2017-03 | 4329153 2017-02 | 4317178 2017-01 | 4274446 2016-12 | 4083277 2016-11 | 4083100 2016-10 | 4097777 2016-09 | 4089711 2016-08 | 4099613 2016-07 | 4107522 2016-06 | 4072837 2016-05 | 4048212 2016-04 | 4064296 2016-03 | 4114382 2016-02 | 4114599 2016-01 | 4075349 2015-12 | 4074367 2015-11 | 4066094 2015-10 | 4073342 2015-09 | 4040138 2015-08 | 4083965 2015-07 | 4108503 2015-06 | 4087605 2015-05 | 4097712 2015-04 | 4095893 2015-03 | 4113967 2015-02 | 4125207 2015-01 | 4101838 2014-12 | 4082915 2014-11 | 4086151 2014-10 | 4108693 2014-09 | 4066890 2014-08 | 4094263 2014-07 | 4112690 2014-06 | 4102543 2014-05 | 4075161 2014-04 | 4060289 2014-03 | 4113307 2014-02 | 4111823 2014-01 | 4078202 2013-12 | 4050764 2013-11 | 4065804 2013-10 | 4077369 2013-09 | 4064725 2013-08 | 4092475 2013-07 | 4113334 2013-06 | 4077096 2013-05 | 4052254 2013-04 | 4038537 2013-03 | 4037902 2013-02 | 4060225 2013-01 | 4052335 2012-12 | 4046401 2012-11 | 4018436 2012-10 | 4047864 2012-09 | 4018492 2012-08 | 4028346 2012-07 | 4056621 2012-06 | 4025481 2012-05 | 4004064 2012-04 | 3987795 2012-03 | 4020958 2012-02 | 4018696 2012-01 | 3979300 2011-12 | 3987622 2011-11 | 3979070 2011-10 | 3996428 2011-09 | 3976121 2011-08 | 4000563 2011-07 | 3985503 2011-06 | 3954743 2011-05 | 3952800 2011-04 | 3951588 2011-03 | 3973757 2011-02 | 3973426 2011-01 | 3970651 2010-12 | 3950896 2010-11 | 3947260 2010-10 | 3963088 2010-09 | 3962730 2010-08 | 4000557 2010-07 | 4000835 2010-06 | 3982079 2010-05 | 3974782 2010-04 | 4007358 2010-03 | 3999416 2010-02 | 4004226 2010-01 | 3973888 2009-12 | 3956410 2009-11 | 3967110 2009-10 | 3981035 2009-09 | 3948585 2009-08 | 3999983 2009-07 | 4025128 2009-06 | 3972401 2009-05 | 3935372 2009-04 | 3939632 2009-03 | 3959032 2009-02 | 4026041 2009-01 | 3993241 2008-12 | 3987359 2008-11 | 3982757 2008-10 | 3991440 2008-09 | 3958818 2008-08 | 3977418 2008-07 | 3975574 2008-06 | 3939528 2008-05 | 3898530 2008-04 | 3903762 2008-03 | 3907982 2008-02 | 3923817 2008-01 | 3911556 2007-12 | 3890956 2007-11 | 3892200 2007-10 | 3874656 2007-09 | 3859126 2007-08 | 3874120 2007-07 | 3896542 2007-06 | 3842934 2007-05 | 3817662 2007-04 | 3812072 2007-03 | 3845965 2007-02 | 3852194 2007-01 | 3840195 2006-12 | 3829449 2006-11 | 3816596 2006-10 | 3829322 2006-09 | 3797593 2006-08 | 3836217 2006-07 | 3853958 2006-06 | 3823688 2006-05 | 3812857 2006-04 | 3810358 2006-03 | 3835690 2006-02 | 3851443 2006-01 | 3822015 2005-12 | 3816760 2005-11 | 3815103 2005-10 | 3833662 2005-09 | 3812408 2005-08 | 3818044 2005-07 | 3834043 2005-06 | 3785217 2005-05 | 3770616 2005-04 | 3773851 2005-03 | 3779662 2005-02 | 3792854 2005-01 | 3767538 2004-12 | 3777744 2004-11 | 3789654 2004-10 | 3779489 2004-09 | 3758685 2004-08 | 3789991 2004-07 | 3820342 2004-06 | 3756104 2004-05 | 3723803 2004-04 | 3723599 2004-03 | 3760050 2004-02 | 3720632 2004-01 | 3719478 2003-12 | 3690056 2003-11 | 3717716 2003-10 | 3697617 2003-09 | 3680070 2003-08 | 3707436 2003-07 | 3718424 2003-06 | 3705043 2003-05 | 3680147 2003-04 | 3708963 2003-03 | 3713445 2003-02 | 3740174 2003-01 | 3729686 2002-12 | 3721973 2002-11 | 3709337 2002-10 | 3727666 2002-09 | 3710696 2002-08 | 3737915 2002-07 | 3737712 2002-06 | 3721104 2002-05 | 3705068 2002-04 | 3712494 2002-03 | 3730575 2002-02 | 3735713 2002-01 | 3697121 2001-12 | 3685708 2001-11 | 3674531 2001-10 | 3666879 2001-09 | 3624268 2001-08 | 3643512 2001-07 | 3665489 2001-06 | 3604546 2001-05 | 3606826 2001-04 | 3626022 2001-03 | 3673315 2001-02 | 3675505 2001-01 | 3658619 2000-12 | 3664418 2000-11 | 3649337 2000-10 | 3644383 2000-09 | 3628523 2000-08 | 3666868 2000-07 | 3675725 2000-06 | 3662277 2000-05 | 3644320 2000-04 | 3662782 2000-03 | 3694899 2000-02 | 3679052 2000-01 | 3639408 1999-12 | 3616905 1999-11 | 3605532 1999-10 | 3612945 1999-09 | 3596431 1999-08 | 3616880 1999-07 | 3647599 1999-06 | 3588723 1999-05 | 3558387 1999-04 | 3556835 1999-03 | 3578429 1999-02 | 3603157 1999-01 | 3582517 1998-12 | 3572209 1998-11 | 3570352 1998-10 | 3571223 1998-09 | 3547596 1998-08 | 3561436 1998-07 | 3598183 1998-06 | 3546186 1998-05 | 3529850 1998-04 | 3528187 1998-03 | 3586741 1998-02 | 3583879 1998-01 | 3556369 1997-12 | 3541167 1997-11 | 3531397 1997-10 | 3539712 1997-09 | 3528752 1997-08 | 3558298 1997-07 | 3573965 1997-06 | 3516642 1997-05 | 3498110 1997-04 | 3490884 1997-03 | 3527693 1997-02 | 3502006 1997-01 | 3477670 1996-12 | 3452935 1996-11 | 3446649 1996-10 | 3434693 1996-09 | 3398885 1996-08 | 3418570 1996-07 | 3450884 1996-06 | 3374410 1996-05 | 3374680 1996-04 | 3364239 1996-03 | 3387410 1996-02 | 3348538 1996-01 | 3318556 1995-12 | 3311863 1995-11 | 3314292 1995-10 | 3323938 1995-09 | 3308864 1995-08 | 3332486 1995-07 | 3344010 1995-06 | 3273065 1995-05 | 3264690 1995-04 | 3279319 1995-03 | 3287481 1995-02 | 3276151 1995-01 | 3260651 1994-12 | 3262661 1994-11 | 3274121 1994-10 | 3286425 1994-09 | 3247106 1994-08 | 3303602 1994-07 | 3321121 1994-06 | 3280167 1994-05 | 3269058 1994-04 | 3274638 1994-03 | 3313229 1994-02 | 3307880 1994-01 | 3300320 1993-12 | 3280611 1993-11 | 3281777 1993-10 | 3300343 1993-09 | 3263617 1993-08 | 3322199 1993-07 | 3356304 1993-06 | 3309785 1993-05 | 3287344 1993-04 | 3275800 1993-03 | 3325678 1993-02 | 3344850 1993-01 | 3323677 1992-12 | 3333323 1992-11 | 3317206 1992-10 | 3307986 1992-09 | 3324662 1992-08 | 3365734 1992-07 | 3385158 1992-06 | 3339151 1992-05 | 3287661 1992-04 | 3289088 1992-03 | 3309258 1992-02 | 3296617 1992-01 | 3282260 1991-12 | 3270284 1991-11 | 3272729 1991-10 | 3279459 1991-09 | 3268995 1991-08 | 3288707 1991-07 | 3324483 1991-06 | 3289762 1991-05 | 3266723 1991-04 | 3276511 1991-03 | 3299027 1991-02 | 3277984 1991-01 | 3286256 1990-12 | 3289433 1990-11 | 3298105 1990-10 | 3324697 1990-09 | 3315431 1990-08 | 3355689 1990-07 | 3378104 1990-06 | 3369202 1990-05 | 3346156 1990-04 | 3330533 1990-03 | 3359836 1990-02 | 3366993 1990-01 | 3357022 1989-12 | 3346939 1989-11 | 3358150 1989-10 | 3344489 1989-09 | 3319976 1989-08 | 3357679 1989-07 | 3384939 1989-06 | 3343663 1989-05 | 3306282 1989-04 | 3315064 1989-03 | 3305911 1989-02 | 3308911 1989-01 | 3294432 1988-12 | 3271776 1988-11 | 3268349 1988-10 | 3262478 1988-09 | 3223449 1988-08 | 3266082 1988-07 | 3273307 1988-06 | 3232682 1988-05 | 3187203 1988-04 | 3200061 1988-03 | 3228142 1988-02 | 3225213 1988-01 | 3222482 1987-12 | 3229700 1987-11 | 3252704 1987-10 | 3245322 1987-09 | 3210148 1987-08 | 3284724 1987-07 | 3284243 1987-06 | 3229786 1987-05 | 3217799 1987-04 | 3211800 1987-03 | 3249221 1987-02 | 3225550 1987-01 | 3227758 1986-12 | 3221434 1986-11 | 3227572 1986-10 | 3229217 1986-09 | 3209742 1986-08 | 3254817 1986-07 | 3286062 1986-06 | 3245484 1986-05 | 3224994 1986-04 | 3224922 1986-03 | 3265003 1986-02 | 3240989 1986-01 | 3244140 1985-12 | 3242264 1985-11 | 3252193 1985-10 | 3255362 1985-09 | 3234536 1985-08 | 3267152 1985-07 | 3283007 1985-06 | 3217107 1985-05 | 3193592 1985-04 | 3182639 1985-03 | 3142780 1985-02 | 3122809 1985-01 | 3125775 1984-12 | 3102068 1984-11 | 3086952 1984-10 | 3099393 1984-09 | 3081254 1984-08 | 3135504 1984-07 | 3170653 1984-06 | 3092106 1984-05 | 3076031 1984-04 | 3054251 1984-03 | 3087109 1984-02 | 3077957 1984-01 | 3067464 1983-12 | 3070960 1983-11 | 3075917 1983-10 | 3068028 1983-09 | 3086309 1983-08 | 3130098 1983-07 | 3126322 1983-06 | 3083234 1983-05 | 3010156 1983-04 | 3028331 1983-03 | 3060446 1983-02 | 3046925 1983-01 | 3056984 1982-12 | 3054449 1982-11 | 3074542 1982-10 | 3083492 1982-09 | 3070026 1982-08 | 3108534 1982-07 | 3144793 1982-06 | 3092859 1982-05 | 3090069 1982-04 | 3063222 1982-03 | 3102160 1982-02 | 3079154 1982-01 | 3075723 1981-12 | 3072958 1981-11 | 3089340 1981-10 | 3090375 1981-09 | 3056736 1981-08 | 3103943 1981-07 | 3138026 1981-06 | 3091343 1981-05 | 3084054 1981-04 | 3089944 1981-03 | 3101651 1981-02 | 3090688 1981-01 | 3073969 1980-12 | 3051306 1980-11 | 3053721 1980-10 | 3060997 1980-09 | 3032016 1980-08 | 3092739 1980-07 | 3119955 1980-06 | 3049472 1980-05 | 3029423 1980-04 | 3036569 1980-03 | 3058373 1980-02 | 3078581 1980-01 | 3061114 1979-12 | 3064619 1979-11 | 3062418 1979-10 | 3070285 1979-09 | 3061768 1979-08 | 3106370 1979-07 | 3115554 1979-06 | 3056420 1979-05 | 3008871 1979-04 | 3039290 1979-03 | 3076519 1979-02 | 3069182 1979-01 | 3053354 1978-12 | 3059046 1978-11 | 3064808 1978-10 | 3065572 1978-09 | 3051863 1978-08 | 3083327 1978-07 | 3105802 1978-06 | 3065022 1978-05 | 3020706 1978-04 | 3040893 1978-03 | 3048503 1978-02 | 3043953 1978-01 | 3045581 1977-12 | 3051051 1977-11 | 3064434 1977-10 | 3055759 1977-09 | 3029858 1977-08 | 3095203 1977-07 | 3093248 1977-06 | 3069799 1977-05 | 3013828 1977-04 | 3046346 1977-03 | 3072754 1977-02 | 3080802 1977-01 | 3056764 1976-12 | 3075767 1976-11 | 3085568 1976-10 | 3068813 1976-09 | 3056267 1976-08 | 3124702 1976-07 | 3145241 1976-06 | 3084330 1976-05 | 3016803 1976-04 | 3043937 1976-03 | 3062814 1976-02 | 3064176 1976-01 | 3061214

Table 11: Population by Nativity in New York (unit: %), according to census.gov

date | Population: Native | Population: Foreign Born
2022 | 59.7777 | 37.5862 2021 | 59.5033 | 37.4308 2020 | 54.799 | 34.7918 2019 | 57.4053 | 37.1639 2018 | 56.9381 | 37.2446 2017 | 57.3187 | 37.6892 2016 | 56.3981 | 37.1488 2015 | 56.1116 | 37.0804 2014 | 55.9706 | 36.8008 2013 | 55.8445 | 36.4335 2012 | 55.8568 | 36.2282 2011 | 55.9853 | 36.1401

Table 13: Monoracial Breakdown in New York, according to census.gov

date | Population Asian Alone | Hispanic Population | Population Identifying as Some Other Race Alone | Population American Indian or Alaska Native Alone | Population of Native Hawaiian or Other Pacific Islander Alone | Black Monoracial Population | White Monoracial Population
2022 | 1247994 | 2503005 | 1325866 | 52828 | 5431 | 1992200 | 3230647 2021 | 1244510 | 2521022 | 1301785 | 45784 | 5600 | 2042560 | 3474805 2020 | 1197261 | 2423869 | 1209179 | 36884 | 5283 | 1996121 | 3463286 2019 | 1186608 | 2447862 | 1241614 | 36250 | 4802 | 2046877 | 3597217 2018 | 1177700 | 2457137 | 1277050 | 36075 | 4339 | 2049418 | 3603057 2017 | 1198334 | 2491496 | 1294497 | 34487 | 4358 | 2081507 | 3661800 2016 | 1159883 | 2457945 | 1277265 | 33388 | 3995 | 2064927 | 3644982 2015 | 1139294 | 2437297 | 1269789 | 32790 | 3389 | 2065597 | 3648214 2014 | 1106989 | 2407983 | 1231976 | 32887 | 3572 | 2067708 | 3646761 2013 | 1077554 | 2371116 | 1189355 | 32421 | 4403 | 2057226 | 3659155 2012 | 1053649 | 2343458 | 1169421 | 30743 | 3866 | 2059279 | 3646181 2011 | 1032214 | 2310163 | 1200833 | 30011 | 3909 | 2046365 | 3598277

Table 15: Median Age by Gender in New York (unit: yr), according to census.gov

date | Men's Median Age | Women's Median Age
2022 | 36.2 | 38.9 2021 | 36 | 38.6 2020 | 35.6 | 38.4 2019 | 35.4 | 38.1 2018 | 35.2 | 37.8 2017 | 34.9 | 37.5 2016 | 34.6 | 37.2 2015 | 34.5 | 37.1 2014 | 34.4 | 37 2013 | 34.2 | 37 2012 | 34.1 | 36.9 2011 | 34 | 36.9

Table 17: Population: 16-50 Years, Birth in the Past 12 Months, Female (As Fraction of Count Person 16 to 50 Years Female) in New York, according to data.census.gov

date | Population: 16 - 50 Years, Birth in The Past 12 Months, Female (As Fraction of Count Person 16 To 50 Years Female)
2022 | 46 2021 | 45 2020 | 47 2019 | 47 2018 | 47 2017 | 48 2016 | 48 2015 | 49 2014 | 51 2013 | 51 2012 | 53 2011 | 53 2010 | 53

Table 25: Monoracial Breakdown in New York (2022), according to census.gov

variable | New York City
White Monoracial Population | 3230647 Black Monoracial Population | 1992200 Hispanic Population | 2503005 Population Asian Alone | 1247994 Population American Indian or Alaska Native Alone | 52828 Population of Native Hawaiian or Other Pacific Islander Alone | 5431 Population Identifying as Some Other Race Alone | 1325866

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Is the amount of natto(fermented soybeans) consumed in Japan increasing?
🤖
Yes, the amount of natto consumed in Japan has been steadily increasing.

According to the National Agricultural Research Center (NARC), Japan's natto production has reached:

[DC#1(|| 1,500,000 metric tons)] in 2921 (fiscal year)
Factors contributing to this increase include:

Health benefits: Natto is a good source of protein, fiber, and other nutrients. It's also known for its potential health benefits, such as improving heart health, reducing stroke risk, and supporting gut health.
Taste and texture: While natto has a strong, pungent flavor that may be off-putting to some, it's generally considered a delicacy with a unique taste and texture.
Affordability: Natto is a relatively inexpensive food, making it accessible to a wide range of consumers.
However, it's important to note that:

Consumption varies regionally: Urban areas tend to have higher per capita natto consumption than rural areas.
Demographic differences exist: Older adults and pregnant women are among the most frequent consumers of natto.
Organic farming is increasing: While not as widespread as conventional soybean production, the number of acres devoted to organic soybeans is gradually increasing, driven by growing awareness of environmental sustainability and health benefits.
Overall, while natto consumption is increasing, it's crucial for Japan to continue promoting sustainable agricultural practices to ensure a secure and ethical:

Food supply: Japan relies heavily on imported soybeans, making it vulnerable to weather events and geopolitical instability.
Environmental protection: The production of wasted food, including natto, is a significant contributor to greenhouse gas emissions and food insecurity.
Promoting sustainable consumption and production of natto is essential for mitigating these issues and ensuring the long-term viability of this traditional food.

It's important to consult reliable sources like the National Agricultural Research Center (NARC) and the Japan Times for the most up-to-date information on natto consumption and production in Japan.

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Is the amount of natto(fermented soybeans) consumed in Japan increasing?
🤖
It's difficult to say definitively whether natto consumption in Japan is increasing or decreasing without access to very specific and recent data. Here's why:

Conflicting Trends: There's evidence pointing in both directions:
Potential Increase: Health benefits of fermented foods are becoming more widely known, which could lead to increased interest in natto.
Potential Decrease: Younger generations in Japan may not consume natto as much as older generations due to changing tastes and lifestyles.
Data Availability: racking food consumption trends accurately requires large-scale surveys and data collection, which may not be readily available or updated frequently.
To find a more definitive answer, you could try to research:

Japanese Ministry of Agriculture, Forestry and Fisheries: They may have statistics on soybean production and consumption, which could offer some insights.
Market Research Firms: Companies specializing in the Japanese food industry might have data on natto sales trends.
Academic Research: Look for studies on Japanese dietary habits or the natto market specifically.
Keep in mind: Even with data, it's important to consider the limitations of the information and any potential biases.

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米国の出生率の推移を教えて
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ください。

出生率は、ある社会における1000人あたりに生まれる子供の数を指します。

Recent Trends:

Overall Decline: The US birth rate has been steadily declining for years, reaching a record low in 2020. This trend is attributed to various factors, including economic uncertainty, delayed childbearing, and rising living costs.
Pandemic Impact: The COVID-19 pandemic further accelerated the decline, with a sharp drop in births in 2021.
Longer-Term Trends:

1950s-1960s: The US experienced a "baby boom" after World War II, with birth rates peaking around [DC#1(|| 4 million)] births per year.
1970s-1980s: Birth rates started to decline in the mid-1970s and early 1980s.
1990s-Present: The decline in birth rates has continued, though at a slower pace.
Reasons for Decline:

Economic Concerns: Raising children is expensive, particularly in affluent areas.
Delayed Childbearing: Women are increasingly delaying childbirth, pursuing education and careers first.
Rising Living Costs: The cost of living, especially housing, has soared in recent years.
Regional Variations:

It's important to note that birth rates vary significantly across different regions and demographic groups within the US.

Northeast and West: These regions tend to have lower birth rates than the South and Midwest.
Urban vs. Rural: Birth rates are generally lower in urban areas due to higher living costs and greater access to education and career opportunities for women.
Age and Education:
Birth rates are highest for women in their early 20s, and decline with age and education level.

Consequences of Low Birth Rates:

Shrinking Workforce: Fewer young people entering the workforce lead to labor shortages and slower economic growth.
Increased Pressure on Social Security: With fewer working-age people supporting a larger elderly population, Social Security faces significant strain.
Social and Cultural Changes: An aging population can lead to shifts in consumption patterns, family structures, and social values.
Efforts to Reverse the Decline:

There are several initiatives underway to encourage childbirth in the US, including:

Improved economic benefits: Some companies are implementing paid parental leave and childcare assistance programs to help women balance work and family life.
Targeted interventions: The CDC and other organizations are developing and implementing programs to address specific factors contributing to the birth rate decline.
Cultural shifts:
There are ongoing discussions and initiatives to promote breastfeeding, healthy pregnancies, and affordable childcare, aiming to create a more supportive environment for mothers and children.

Conclusion:

The US birth rate has reached historically low levels, prompting concerns about workforce shortages and the aging population.

Addressing these issues requires a multifaceted approach, including improving economic support for families, promoting breastfeeding, and tackling the underlying social determinants of health that contribute to the decline.

.. calling DC for table with "What is the birth rate in US?"
... calling DC for table with "How has birth rate changed over time in US states?"
... calling DC for table with "What is the birth rate in US states?"
... [RAG] Calling UNTUNED model for final response
... calling AIStudio gemini-1.5-pro "Using statistics from the tables below, respond t..."
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米国の出生率の推移を教えて
🤖
[Table 1] によると、米国の粗出生率は1960年の23.7人から2021年には11人に減少しています。特に1970年代から1990年代にかけて大きく減少し、その後は緩やかな減少傾向にあります。

TABLES
Table 1: Crude Birth Rate in United States (unit: Per 1000 people), according to datacatalog.worldbank.org

date | Crude Birth Rate
2021 | 11 2020 | 10.9 2019 | 11.4 2018 | 11.6 2017 | 11.8 2016 | 12.2 2015 | 12.4 2014 | 12.5 2013 | 12.4 2012 | 12.6 2011 | 12.7 2010 | 13 2009 | 13.5 2008 | 14 2007 | 14.3 2006 | 14.3 2005 | 14 2004 | 14 2003 | 14.1 2002 | 14 2001 | 14.1 2000 | 14.4 1999 | 14.2 1998 | 14.3 1997 | 14.2 1996 | 14.4 1995 | 14.6 1994 | 15 1993 | 15.4 1992 | 15.8 1991 | 16.2 1990 | 16.7 1989 | 16.4 1988 | 16 1987 | 15.7 1986 | 15.6 1985 | 15.8 1984 | 15.6 1983 | 15.6 1982 | 15.9 1981 | 15.8 1980 | 15.9 1979 | 15.6 1978 | 15 1977 | 15.1 1976 | 14.6 1975 | 14.6 1974 | 14.8 1973 | 14.8 1972 | 15.6 1971 | 17.2 1970 | 18.4 1969 | 17.9 1968 | 17.6 1967 | 17.8 1966 | 18.4 1965 | 19.4 1964 | 21.1 1963 | 21.7 1962 | 22.4 1961 | 23.3 1960 | 23.7

Table 2: Population: 16-50 Years, Birth in the Past 12 Months, Female (As Fraction of Count Person 16 to 50 Years Female), according to data.census.gov

date | Alabama | Alaska | Arizona | Arkansas | California | Colorado | Connecticut | Delaware | District of Columbia | Florida | Georgia | Hawaii | Idaho | Illinois | Indiana | Iowa | Kansas | Kentucky | Louisiana | Maine | Maryland | Massachusetts | Michigan | Minnesota | Mississippi | Missouri | Montana | Nebraska | Nevada | New Hampshire | New Jersey | New Mexico | New York | North Carolina | North Dakota | Ohio | Oklahoma | Oregon | Pennsylvania | Rhode Island | South Carolina | South Dakota | Tennessee | Texas | Utah | Vermont | Virginia | Washington | West Virginia | Wisconsin | Wyoming | Puerto Rico
2022 | 52 | 64 | 56 | 55 | 50 | 50 | 44 | 48 | 42 | 49 | 52 | 58 | 58 | 52 | 56 | 58 | 62 | 54 | 57 | 52 | 53 | 45 | 53 | 58 | 56 | 56 | 57 | 63 | 52 | 47 | 52 | 56 | 49 | 52 | 67 | 57 | 59 | 47 | 52 | 47 | 55 | 66 | 53 | 58 | 61 | 43 | 50 | 54 | 50 | 52 | 64 | 28 2021 | 52 | 63 | 56 | 56 | 49 | 50 | 44 | 46 | 41 | 50 | 52 | 60 | 59 | 52 | 54 | 58 | 62 | 53 | 55 | 51 | 53 | 46 | 53 | 59 | 56 | 57 | 55 | 63 | 52 | 48 | 52 | 56 | 48 | 52 | 67 | 56 | 59 | 49 | 52 | 47 | 56 | 64 | 53 | 58 | 63 | 39 | 50 | 55 | 49 | 53 | 63 | 28 2020 | 53 | 63 | 56 | 57 | 50 | 51 | 43 | 46 | 40 | 49 | 53 | 62 | 61 | 53 | 55 | 61 | 60 | 54 | 57 | 51 | 53 | 46 | 54 | 60 | 56 | 58 | 55 | 64 | 52 | 47 | 51 | 57 | 49 | 52 | 68 | 57 | 61 | 49 | 53 | 49 | 56 | 68 | 52 | 58 | 66 | 42 | 51 | 56 | 53 | 54 | 66 | 31 2019 | 54 | 64 | 56 | 58 | 50 | 52 | 43 | 47 | 44 | 49 | 54 | 63 | 64 | 53 | 55 | 61 | 60 | 54 | 57 | 51 | 53 | 46 | 54 | 60 | 53 | 57 | 57 | 65 | 53 | 50 | 50 | 55 | 48 | 52 | 71 | 57 | 61 | 49 | 53 | 48 | 56 | 68 | 52 | 59 | 69 | 45 | 52 | 55 | 53 | 54 | 61 | 32 2018 | 54 | 64 | 56 | 60 | 50 | 54 | 46 | 48 | 45 | 49 | 54 | 63 | 63 | 53 | 56 | 62 | 60 | 56 | 56 | 50 | 52 | 46 | 54 | 60 | 54 | 56 | 55 | 67 | 53 | 51 | 49 | 56 | 48 | 52 | 69 | 56 | 60 | 50 | 53 | 48 | 56 | 68 | 52 | 59 | 71 | 45 | 53 | 55 | 51 | 54 | 62 | 35 2017 | 52 | 62 | 56 | 61 | 51 | 54 | 45 | 50 | 48 | 49 | 55 | 63 | 63 | 52 | 55 | 61 | 61 | 55 | 56 | 50 | 52 | 47 | 54 | 59 | 54 | 56 | 56 | 66 | 53 | 52 | 49 | 57 | 48 | 52 | 69 | 55 | 61 | 51 | 52 | 48 | 55 | 70 | 53 | 60 | 73 | 44 | 53 | 55 | 50 | 53 | 59 | 36 2016 | 53 | 61 | 56 | 59 | 52 | 54 | 45 | 52 | 48 | 49 | 56 | 64 | 62 | 52 | 57 | 60 | 62 | 55 | 55 | 47 | 52 | 46 | 53 | 59 | 55 | 55 | 54 | 65 | 53 | 50 | 50 | 57 | 48 | 52 | 70 | 54 | 60 | 50 | 51 | 47 | 53 | 69 | 54 | 61 | 75 | 43 | 54 | 55 | 48 | 53 | 60 | 38 2015 | 55 | 65 | 56 | 58 | 53 | 56 | 46 | 52 | 46 | 49 | 56 | 65 | 66 | 52 | 57 | 59 | 64 | 55 | 55 | 47 | 53 | 46 | 53 | 60 | 53 | 55 | 56 | 66 | 54 | 48 | 51 | 60 | 49 | 54 | 70 | 54 | 59 | 51 | 51 | 47 | 55 | 67 | 55 | 61 | 75 | 42 | 55 | 55 | 47 | 52 | 60 | 40 2014 | 56 | 66 | 57 | 56 | 54 | 57 | 48 | 53 | 46 | 49 | 57 | 65 | 67 | 53 | 56 | 58 | 64 | 55 | 56 | 48 | 53 | 47 | 54 | 60 | 57 | 57 | 57 | 65 | 57 | 49 | 51 | 60 | 50 | 55 | 66 | 54 | 60 | 53 | 50 | 46 | 55 | 68 | 57 | 62 | 77 | 42 | 55 | 56 | 47 | 53 | 59 | 42 2013 | 57 | 70 | 59 | 57 | 55 | 58 | 48 | 55 | 45 | 51 | 58 | 65 | 70 | 55 | 58 | 59 | 66 | 56 | 58 | 48 | 55 | 48 | 54 | 59 | 59 | 57 | 58 | 66 | 59 | 48 | 52 | 61 | 51 | 56 | 66 | 55 | 61 | 53 | 51 | 47 | 57 | 68 | 60 | 64 | 78 | 42 | 55 | 56 | 47 | 54 | 61 | 43 2012 | 60 | 74 | 61 | 58 | 57 | 59 | 50 | 53 | 42 | 52 | 60 | 66 | 73 | 56 | 58 | 61 | 66 | 57 | 60 | 48 | 56 | 49 | 53 | 61 | 62 | 58 | 60 | 66 | 61 | 47 | 55 | 62 | 51 | 58 | 65 | 56 | 62 | 56 | 52 | 48 | 59 | 66 | 62 | 66 | 80 | 43 | 55 | 59 | 50 | 56 | 64 | 45 2011 | 60 | 76 | 63 | 59 | 58 | 59 | 51 | 49 | 44 | 52 | 59 | 64 | 76 | 57 | 59 | 60 | 66 | 58 | 61 | 49 | 56 | 49 | 54 | 61 | 62 | 57 | 60 | 66 | 62 | 46 | 56 | 64 | 52 | 58 | 62 | 56 | 62 | 56 | 52 | 50 | 59 | 66 | 62 | 66 | 80 | 44 | 54 | 59 | 47 | 56 | 60 | 46 2010 | 58 | 76 | 64 | 60 | 58 | 59 | 51 | 52 | 46 | 53 | 59 | 60 | 75 | 58 | 59 | 62 | 65 | 58 | 61 | 49 | 58 | 49 | 55 | 62 | 64 | 56 | 61 | 68 | 62 | 46 | 56 | 62 | 52 | 58 | 60 | 56 | 61 | 57 | 52 | 49 | 58 | 66 | 63 | 67 | 83 | 45 | 54 | 60 | 49 | 57 | 62 | 46