Percentages - History

Percentages - History



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Overall Immigration- Percentages

Unemployment tracks the business cycle. Recessions cause high unemployment. Businesses lay off workers and jobless workers have less to spend as a result. Lower consumer spending reduces business revenue, which forces companies to cut more payroll. This downward cycle is devastating.

The highest rate of U.S. unemployment was 24.9% in 1933, during the Great Depression. Unemployment remained above 14% from 1931 to 1940. It remained in the single digits until September 1982 when it reached 10.1%. During the Great Recession, unemployment reached 10% in October 2009.

The government steps in when unemployment exceeds 6%. The Federal Reserve uses expansionary monetary policy to lower interest rates. ​Congress uses fiscal policy to create jobs and provide extended unemployment benefits.

The unemployment rate falls during the expansion phase of the business cycle. The lowest unemployment rate was 1.2% in 1944.

It may seem counterintuitive to think unemployment can get too low, but it can.

The Federal Reserve says that the natural rate of unemployment falls between 3.5% and 4.5%.   If the rate falls any lower than that, the economy could experience too much inflation, and companies could struggle to find good workers that allow them to expand operations.

The unemployment rate is a lagging indicator.   When an economy begins to improve after a recession, for example, the unemployment rate may continue to worsen for some time. Many companies hesitate to hire workers until they regain confidence in the recovery, and it may take several quarters of economic improvement before they feel confident that the recovery is real.

If you’re looking for work after a recession, you’ll find the going is still tough. It might take several months before the unemployment rate falls.


Highest voting percentages in HOF history

There is perhaps no greater honor for a big leaguer than being inducted into the National Baseball Hall of Fame -- especially in the first year on the ballot.

Even more remarkable than being a first-ballot Hall of Famer is a player receiving every single possible vote in his first year on the ballot, something that had never been done until 2019, when Mariano Rivera earned a unanimous induction. Many thought Rivera's former teammate Derek Jeter would follow the same path this year, but Rivera remains the only unanimous selection in the Hall after one voter left Jeter off the ballot.

With that in mind, here are the 10 highest voting percentages of all-time, starting with Rivera's unanimous selection:

1. Mariano Rivera, 2019
Vote total: 100% (425/425)

Rivera was the model of consistency throughout his dominant career in the Yankees' bullpen. The right-hander had a sub-3.00 ERA in 17 of his 19 big league seasons -- and a sub-2.00 ERA in 11 of those campaigns -- on his way to racking up an MLB-record 652 career saves. Rivera was a 13-time All-Star and even earned a share of MVP votes nine times, but he took his game to another level in the postseason. The shutdown closer had a 0.70 ERA over 141 postseason innings, helping lead the Yanks to five World Series titles.

2. Derek Jeter, 2020
Vote total: 99.7% (396/397)

Jeter came one vote shy of joining his fellow Yankees icon as a unanimous selection. The legendary shortstop racked up plenty of hardware throughout his 20-year career, spent entirely in the Bronx. A 14-time All-Star, Jeter earned 1996 American League Rookie of the Year honors and went on to win five Gold Glove Awards and five Silver Sluggers. More important, Jeter helped the Yankees win five World Series titles, and he was named MVP of the 2000 World Series in which the Yankees defeated the crosstown rival Mets. Jeter finished his career with 3,465 career hits (sixth all-time) and another 200 postseason hits (most in MLB history).

3. Ken Griffey Jr., 2016
Vote total: 99.3% (437/440)

Griffey came within three votes of being the first unanimous Hall of Famer following his incredible career. The smooth-swinging all-around talent was a 13-time All-Star, 10-time Gold Glove winner and seven-time Silver Slugger over his illustrious 22-year career. Griffey had seven 40-homer seasons and a pair of 50-homer campaigns, including 56 in his lone MVP season in 1997.

4. Tom Seaver, 1992
Vote total: 98.8% (425/430)

Seaver had no shortage of accolades during his 20-year career before coming within five votes of being a unanimous Hall of Famer in his first year on the ballot. The right-hander won the 1967 NL Rookie of the Year Award and took home the first of his three Cy Young Awards just two years later while helping lead the Mets to the ཱྀ World Series title. Seaver took home three ERA titles and was a five-time strikeout champ. He remains the Mets' franchise leader in nearly every pitching category, including ERA (2.57), wins (198), strikeouts (2,541), complete games (171) and shutouts (44).


Blood Types Around the World

Blood types vary depending on the geographical region: Scandinavians have a high probability of carrying the A blood type, while those indigenous to central Asia are more likely to carry the B blood type. The O blood type is the most common blood type around the world.

According to the National Center for Biotechnology Information (a molecular biology resource funded by the government), the breakdown of blood type by region is:

Blood Type A: Central and Eastern Europe

The A blood group is common in central Europe. Nearly half the population in Denmark, Norway, Austria, and the Ukraine have this blood type. This blood type is also found in high levels among small, unrelated groups of people. In Montana, 80% of the Blackfoot tribe has the A blood group.

Blood Type B: Asia

The B blood type is rare in Europe (about 10% of the population), but fairly common in Asia. Nearly 25% of the Chinese population demonstrates this blood type. This blood type is also fairly common in India and other Central Asian countries.

Blood Type AB: Asia

The AB blood type is the rarest of all. It is found in up to 10% of the population in Japan, Korea, and China, but is extremely rare in other regions.

Blood Type O: The Americas

The O blood type is the most common around the globe, and is carried by nearly 100% of those living in South America. It is the most common blood type among Australian Aborigines, Celts, those living in Western Europe, and in the United States.

The majority of people in any geographical region are Rh positive. Caucasians are the most likely to be Rh negative, with approximately 17% of blood donors demonstrating a lack of this protein. Native Americans are the next highest proportion of the population to test as Rh negative: approximately 10% of donors from this population lack this protein.


The Surveillance, Epidemiology, and End Results (SEER) Program

NCI’s Surveillance, Epidemiology, and End Results (SEER) Program collects and publishes cancer incidence and survival data from population-based cancer registries that cover approximately 35% of the US population. The SEER program website has more detailed cancer statistics, including population statistics for common types of cancer, customizable graphs and tables, and interactive tools.

The Annual Report to the Nation on the Status of Cancer provides an annual update of cancer incidence, mortality, and trends in the United States. This report is jointly authored by experts from NCI, the Centers for Disease Control and Prevention, American Cancer Society, and the North American Association of Central Cancer Registries.


U.S. Inflation Rate History and Forecast

The best way to compare inflation rates is to use the end-of-year CPI. This creates an image of a specific point in time.

The table below compares the inflation rate (December end-of-year) with the fed funds rate, the phase of the business cycle, and the significant events influencing inflation. A more detailed forecast is in the U.S. Economic Outlook.

Year Inflation Rate YOY Fed Funds Rate* Business Cycle (GDP Growth) Events Affecting Inflation
1929 0.6% NA August peak Market crash
1930 -6.4% NA Contraction (-8.5%) Smoot-Hawley
1931 -9.3% NA Contraction (-6.4%) Dust Bowl
1932 -10.3% NA Contraction (-12.9%) Hoover tax hikes
1933 0.8% NA Contraction ended in March (-1.2%) FDR's New Deal
1934 1.5% NA Expansion (10.8%) U.S. debt rose
1935 3.0% NA Expansion (8.9%) Social Security
1936 1.4% NA Expansion (12.9%) FDR tax hikes
1937 2.9% NA Expansion peaked in May (5.1%) Depression resumes
1938 -2.8% NA Contraction ended in June (-3.3%) Depression ended
1939 0.0% NA Expansion (8.0% Dust Bowl ended
1940 0.7% NA Expansion (8.8%) Defense increased
1941 9.9% NA Expansion (17.7%) Pearl Harbor
1942 9.0% NA Expansion (18.9%) Defense spending
1943 3.0% NA Expansion (17.0%) Defense spending
1944 2.3% NA Expansion (8.0%) Bretton Woods
1945 2.2% NA Feb. peak, Oct. trough (-1.0%) Truman ended WWII
1946 18.1% NA Expansion (-11.6%) Budget cuts
1947 8.8% NA Expansion (-1.1%) Cold War spending
1948 3.0% NA Nov. peak (4.1%)
1949 -2.1% NA Oct trough (-0.6%) Fair Deal, NATO
1950 5.9% NA Expansion (8.7%) Korean War
1951 6.0% NA Expansion (8.0%)
1952 0.8% NA Expansion (4.1%)
1953 0.7% NA July peak (4.7%) Eisenhower ended Korean War
1954 -0.7% 1.25% May trough (-0.6%) Dow returned to 1929 high
1955 0.4% 2.50% Expansion (7.1%)
1956 3.0% 3.00% Expansion (2.1%)
1957 2.9% 3.00% Aug. peak (2.1%) Recession
1958 1.8% 2.50% April trough (-0.7%) Recession ended
1959 1.7% 4.00% Expansion (6.9%) Fed raised rates
1960 1.4% 2.00% April peak (2.6%) Recession
1961 0.7% 2.25% Feb. trough (2.6%) JFK's deficit spending ended recession
1962 1.3% 3.00% Expansion (6.1%)
1963 1.6% 3.5% Expansion (4.4%)
1964 1.0% 3.75% Expansion (5.8%) LBJ Medicare, Medicaid
1965 1.9% 4.25% Expansion (6.5%)
1966 3.5% 5.50% Expansion (6.6%) Vietnam War
1967 3.0% 4.50% Expansion (2.7%)
1968 4.7% 6.00% Expansion (4.9%) Moon landing
1969 6.2% 9.00% Dec. peak (3.1%) Nixon took office
1970 5.6% 5.00% Nov. trough (0.2%) Recession
1971 3.3% 5.00% Expansion (3.3%) Wage-price controls
1972 3.4% 5.75% Expansion (5.3%) Stagflation
1973 8.7% 9.00% Nov. peak (5.6%) End of gold standard
1974 12.3% 8.00% Contraction (-0.5%) Watergate
1975 6.9% 4.75% March trough (-0.2%) Stop-gap monetary policy confused businesses and kept prices high
1976 4.9% 4.75% Expansion (5.4%)
1977 6.7% 6.50% Expansion (4.6%)
1978 9.0% 10.00% Expansion (5.5%)
1979 13.3% 12.00% Expansion (3.2%)
1980 12.5% 18.00% Jan. peak (-0.3%) Recession
1981 8.9% 12.00% July trough (2.5%) Reagan tax cut
1982 3.8% 8.50% November (-1.8%) Recession ended
1983 3.8% 9.25% Expansion (4.6%) Military spending
1984 3.9% 8.25% Expansion (7.2%)
1985 3.8% 7.75% Expansion (4.2%)
1986 1.1% 6.00% Expansion (3.5%) Tax cut
1987 4.4% 6.75% Expansion (3.5%) Black Monday crash
1988 4.4% 9.75% Expansion (4.2%) Fed raised rates
1989 4.6% 8.25% Expansion (3.7%) S&L Crisis
1990 6.1% 7.00% July peak (1.9%) Recession
1991 3.1% 4.00% Mar trough (-0.1%) Fed lowered rates
1992 2.9% 3.00% Expansion (3.5%) NAFTA drafted
1993 2.7% 3.00% Expansion (2.8%) Balanced Budget Act
1994 2.7% 5.50% Expansion (4.0%)
1995 2.5% 5.50% Expansion (2.7%)
1996 3.3% 5.25% Expansion (3.8%) Welfare reform
1997 1.7% 5.50% Expansion (4.4%) Fed raised rates
1998 1.6% 4.75% Expansion (4.5%) LTCM crisis
1999 2.7% 5.50% Expansion (4.8%) Glass-Steagall repealed
2000 3.4% 6.50% Expansion (4.1%) Tech bubble burst
2001 1.6% 1.75% March peak, Nov. trough (1.0%) Bush tax cut, 9/11 attacks
2002 2.4% 1.25% Expansion (1.7%) War on Terror
2003 1.9% 1.00% Expansion (2.9%) JGTRRA
2004 3.3% 2.25% Expansion (3.8%)
2005 3.4% 4.25% Expansion (3.5%) Katrina, Bankruptcy Act
2006 2.5% 5.25% Expansion (2.9%) Bernanke became Fed Chair
2007 4.1% 4.25% Dec peak (1.9%) Bank crisis
2008 0.1% 0.25% Contraction (-0.1%) Financial crisis
2009 2.7% 0.25% June trough (-2.5%) ARRA
2010 1.5% 0.25% Expansion (2.6%) ACA, Dodd-Frank Act
2011 3.0% 0.25% Expansion (1.6%) Debt ceiling crisis
2012 1.7% 0.25% Expansion (2.2%)
2013 1.5% 0.25% Expansion (1.8%) Government shutdown. Sequestration
2014 0.8% 0.25% Expansion (2.5%) QE ends
2015 0.7% 0.50% Expansion (3.1%) Deflation in oil and gas prices
2016 2.1% 0.75% Expansion (1.7%)
2017 2.1% 1.50% Expansion (2.3%) Core inflation rate 1.7%
2018 1.9% 2.50% Expansion (3.0%) Core rate 2.2%
2019 2.3% 1.75% Expansion (2.2%) Core rate 2.3%
2020 1.2% 0.25% Contraction (-2.4%) Forecast: Core rate 1.4%
Impact of COVID
2021 1.8% 0.25% Expansion (4.2%) Forecast: Core rate is 1.8%
2022 1.9% 0.25% Expansion
(3.2%)
Forecast: Core rate is 1.9%
2023 2.0% 0.25% Expansion (2.4%) Forecast: Core rate is 2.0%
*Top of the range for the targeted fed funds rate.

Who owns cellphones and smartphones

A substantial majority of Americans are cellphone owners across a wide range of demographic groups. By contrast, smartphone ownership exhibits greater variation based on age, household income and educational attainment.

% of U.S. adults who say they own a …

Cellphone Smartphone Cellphone, but not smartphone
Total 97% 85% 11%
Men 97% 85% 11%
Women 98% 85% 12%
Ages 18-29 100% 96% 4%
30-49 100% 95% 5%
50-64 97% 83% 12%
65+ 92% 61% 29%
White 97% 85% 11%
Black 99% 83% 15%
Hispanic 100% 85% 14%
High school or less 96% 75% 19%
Some college 98% 89% 9%
College graduate 98% 93% 5%
Less than $30,000 97% 76% 19%
$30,000-$49,999 97% 83% 14%
$50,000-$74,999 97% 85% 12%
$75,000+ 100% 96% 3%
Urban 98% 89% 9%
Suburban 97% 84% 12%
Rural 94% 80% 14%

Note: Respondents who did not give an answer are not shown. White and Black adults include those who report being only one race and are not Hispanic. Hispanics are of any race.
Source: Survey of U.S. adults conducted Jan. 25-Feb. 8, 2021.


Underage Drinking in the United States

Prevalence of Underage Alcohol Use

Prevalence of Drinking:According to the 2019 NSDUH, 39.7 percent of 12- to 20-year-olds reported that they have had at least 1 drink in their lives. 25 About 7.0 million people ages 12 to 20 24 (18.5 percent of this age group 25 ) reported drinking alcohol in the past month (17.2 percent of males and 19.9 percent of females 25 ).

Prevalence of Binge Drinking: According to the 2019 NSDUH, approximately 4.2 million people ages 12 to 20 24 reported binge drinking in the past month. This represents 11.1 percent of people in this age group (10.4 percent of males ages 12 to 20 and 11.8 percent of females ages 12 to 20 25 ).

Prevalence of Heavy Alcohol Use:According to the 2019 NSDUH, approximately 825,000 people ages 12 to 20 24 reported heavy alcohol use in the past month. This represents 2.2 percent of this age group 25 (2.1 percent of males ages 12 to 20 and 2.3 percent of females ages 12 to 20 25 ).

Trend in Underage Alcohol Use

NSDUH findings have demonstrated a decline in underage drinking. From 2002 to 2019, the prevalence of past-30-day alcohol use decreased 41.1 percent for 16- to 17-year-olds, 54.7 percent for 14- to 15-year-olds, and 61.9 percent for 12- to 13-year-olds. 26

Consequences of Underage Alcohol Use

Research indicates that alcohol use during the teenage years can interfere with normal adolescent brain development and increase the risk of developing AUD. In addition, underage drinking contributes to a range of acute consequences, such as injuries, sexual assaults, alcohol overdoses, and deaths—including those from motor vehicle crashes. 27

Alcohol is a factor in the deaths of thousands of people younger than age 21 in the United States each year. This includes:

1,092 from motor vehicle crashes 28

208 from alcohol overdose, falls, burns, and drowning 29


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Win Expectancy, Run Expectancy, and Leverage Index calculations provided by Tom Tango of InsideTheBook.com, and co-author of The Book: Playing the Percentages in Baseball.

Total Zone Rating and initial framework for Wins above Replacement calculations provided by Sean Smith.

Full-year historical Major League statistics provided by Pete Palmer and Gary Gillette of Hidden Game Sports.

Some defensive statistics Copyright © Baseball Info Solutions, 2010-2021.

Some high school data is courtesy David McWater.

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Dick Stuart

Career Fielding Percentage: .982

League Average 1B: .990

Career dWAR: -6.1

Career Rtot: -59

No list regarding defense in baseball, especially bad defense, would be complete without the name of Dick Stuart, aka Dr. Strangeglove.

Stuart did rack up 228 home runs during his career, leading the American League in RBI for the 1963 season, but the man who would also be called “Stonefingers” and “The Man with the Iron Glove" made his mark as the poorest defensive fielding first baseman in major league history.

Doug Mead is a Featured Columnist with Bleacher Report. His work has been featured on the Seattle Post-Intelligencer, SF Gate, CBS Sports, the Los Angeles Times and the Houston Chronicle. Follow Doug on Twitter, @Sports_A_Holic.


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