The Getaway

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Summary: An ex-con and his devoted wife must flee from danger when a heist doesn't go as planned.

Release Date: February 11, 1994

Genres: Action, Adventure, Crime

Director: Roger Donaldson

Writers: Jim Thompson

, Walter Hill

, Amy Holden Jones

Runtime: 1 h 55 min

MPAA: R

Production: Universal Pictures, Largo Entertainment, JVC Entertainment Networks, Turman-Foster Company, David Foster Productions

Distributor: Universal Pictures, MCA/Universal Home Video, Fox Network, Image Entertainment, Universal Studios Home Video, Warner Bros.

Countries: Japan, United States

Language: English

Domestic: $ 16,094,974

International: $ 13,963,000

Box office:

Domestic:$16.1MInternational:$14MWorldwide:$30.1M

Adaptation/remake:

based on novel, remake

Possible Big Business:

Production:

Universal Pictures

N/A

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22%25%

22% of the Stars & Main Cast are Female.

25% of the Stars & Main Cast are "diverse."

White75%Asian12.5%Latino6.25%Mixed / Other6.25%

N/A

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CONTENT BREAKDOWN

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Race	White	Asian	Latino	Mixed / Other	Black	Total	Visuals	Info
Ethnic	7			1		8		This is the most reliable data source for accurate ethnic breakdowns of famous people. Often the information is crowdsourced by users and they cite all their sources (family trees, obituaries, census archives, etc.) on the bottom of the page. The only issue is converting their ambiguous and inconsistent verdicts into quantifiable variables. Source: https://ethnicelebs.com/
Kairos	3	2	1			6		A third party facial recognition software company we used to maintain objectivity. Upload images to the facial recognition demo page to check the veracity of our data set. Or use their API and do a bulk peer review if you'd like. Source: https://www.kairos.com/demos
Forebears	1		1		1	3
Surname	1					1		A third party free program that uses US Census data to create an algorithm that analyzes surnames and guesses what ethnicity the person is. Obviously there are issues with this data set such as African Americans who have the last names of their ancestors' slave owner. However, when no other data set is available, this can give a rough estimate and expand the data pool for the less important cast members with no IMDb or TMDb image. Please consider downloading their source code via GitHub to verify the accuracy of our data set, and to peer review our conclusions. Source: https://pypi.org/project/ethnicolr/
Total	12	2	2	1	1	18
Cast Percentages	66.67%	11.11%	11.11%	5.56%	5.56%	100%	{"name":"Cast Percentages","series":[{"name":"White","y":66.67,"color":"#2b908f","sliced":true,"selected":true},{"name":"Asian","y":11.11,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Latino","y":11.11,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Black","y":5.56,"color":"#f45b5b","sliced":true,"selected":true}],"prefix":""}
U.S. Population (1990) Percentage	75.6%	2.78%	9%	3.9%	12.1%		{"name":"U.S. Population (1990)","series":[{"name":"White","y":75.6,"color":"#2b908f"},{"name":"Arab","y":0.35,"color":"#DF5353"},{"name":"Asian","y":2.78,"color":"#90ee7e"},{"name":"Black","y":12.1,"color":"#f45b5b"},{"name":"Indian","y":0.33,"color":"#7798BF"},{"name":"Indigenous","y":0.8,"color":"#aaeeee"},{"name":"Latino","y":9,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":3.9,"color":"#55BF3B"},{"name":"Jewish","y":2.22,"color":"#ff0066"}],"prefix":""}	Source: US Census (Note: Racial and ethnic categories overlap so the sum will not equal 100%.)
U.S. Population (1990) Representation	-8.93	+8.33	+2.11	+1.66	-6.54		{"name":"U.S. Population (1990)","series":[["White",75.6],["Asian",2.78],["Latino",9],["Mixed \/ Other",3.9],["Black",12.1]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"},{"name":"Black","y":5.56,"color":"#f45b5b"}]}
U.S. Buying Power (2000) Percentage	81.25%	3.5%	6.26%	0.76%	7.72%		{"name":"U.S. Buying Power (2000)","series":[{"name":"White","y":6413300000000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":276400000000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":609100000000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":40300000000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":494000000000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":60300000000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2000) Representation	-14.58	+7.61	+4.85	+4.8	-2.16		{"name":"U.S. Buying Power (2000)","series":[["White",81.25],["Asian",3.5],["Latino",6.26],["Mixed \/ Other",0.76],["Black",7.72]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"},{"name":"Black","y":5.56,"color":"#f45b5b"}]}
U.S. Average Individual Income (2018) Percentage	80.3%	3.28%	4.3%	2.87%	8.65%		{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":12390076235472,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":505394534547,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1335090043788,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":94074954628,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":663077520000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":442226921916,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	-13.63	+7.83	+6.81	+2.69	-3.09		{"name":"U.S. Average Individual Income (2018)","series":[["White",80.3],["Asian",3.28],["Latino",4.3],["Mixed \/ Other",2.87],["Black",8.65]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"},{"name":"Black","y":5.56,"color":"#f45b5b"}]}
World population (1994) Percentage	17.5%	34.81%	5.68%	1.94%	10.71%		{"name":"World population (1994) ","series":[{"name":"Mixed \/ Other","y":10858008714,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Latino","y":31824086856,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Indigenous","y":7464553788,"color":"#aaeeee","sliced":true,"selected":true},{"name":"White","y":97977676579,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":59956007624,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Asian","y":194901148969,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Arab","y":40885774057,"color":"#DF5353","sliced":true,"selected":true},{"name":"Indian","y":114090347331,"color":"#7798BF","sliced":true,"selected":true},{"name":"Jewish","y":1899620891,"color":"#ff0066","sliced":true,"selected":true}],"prefix":""}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World population (1994) Representation	+49.17	-23.7	+5.43	+3.62	-5.15		{"name":"World population (1994) ","series":[["White",17.5],["Asian",34.81],["Latino",5.68],["Mixed \/ Other",1.94],["Black",10.71]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"},{"name":"Black","y":5.56,"color":"#f45b5b"}]}
World Buying Power Percentage	36.48%	29.46%	9.35%	1.81%	6.38%		{"name":"World Buying Power","series":[{"name":"White","y":3010602822170000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Latino","y":771525888400000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Jewish","y":91700962050000,"color":"#ff0066","sliced":true,"selected":true},{"name":"Black","y":526264226360000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indigenous","y":174285727610000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":148984454560000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Arab","y":526400066100000,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":2431392083750000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Indian","y":572687873900000,"color":"#7798BF","sliced":true,"selected":true}],"prefix":"$"}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World Buying Power Representation	+30.19	-18.35	+1.76	+3.75	-0.82		{"name":"World Buying Power","series":[["White",36.48],["Asian",29.46],["Latino",9.35],["Mixed \/ Other",1.81],["Black",6.38]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Latino","y":11.11,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":5.56,"color":"#55BF3B"},{"name":"Black","y":5.56,"color":"#f45b5b"}]}
Notes		The US Census considers Pakistani, Sri Lankan, Indian, and Bangladeshi as Asians. At ZR we group Pakistanis with the Arab world. And we group Sri Lankan and Bangladeshi with India because 1.) they are all genetically similar as "South Asians and 2.) there are only a handful of actors from those regions in our database. However, you are viewing the "Asian Alone" category from the US Census when comparing it with Domestic representation. Also note that the 2000 and 2010 estimates include MultiRacial Asians not just "Asian Alone" estimates, and in some categories they include Native Hawaiians as "Asian." So there is a margin of error less than 1% and some inconsistencies with the grouping.	Many Latinos check "White" or "Other" on the Census. So this overlaps with "Mixed" race and "White." Also, when there was a discrepancy with the percentage listed on the Wikipedia table and our calculated numbers, the Wiki table's numbers were used. Likewise, when the population total was missing from the Census pdf, the estimate was calculated using the percentage from the Wiki table. NOTE: It turns out that the reason America doesn't have a separate "Latino" race is because the League of Latin American Citizens protested against discrimination towards Mexicans and got themselves to legally be considered "White." Check the research links in our Google Doc for more info.	The "Mixed / Other" categories overlap, and the 2 or more category was not an option until the 2000 Census. So for the combined column, all data will be taken from the "Other" column except for 2000, 2010, & 2020. For those, the average between the "2 or More Races" and "Some Other Race" will be used.

action hero adulterous wife adultery ambush american in mexico anti hero anti heroine arm in a sling arrest bare chested male based on novel beating betrayal black panties blonde blood splatter border crossing breaking a lock breast sucking breasts bulletproof vest bus buttocks car chase car crash career criminal caressing carjacking cat chase con artist condom corpse corrupt businessman cowboy hat cuckold cunnilingus diner distrust dog race dog track dysfunctional marriage el paso texas elevator elevator crash escape escape from police custody ex convict exploding car exploding truck explosion face slap female frontal nudity female pubic hair female rear nudity fight flashlight food fight foot chase foreplay framed for murder frisking someone fugitive fugitive sex garbage dump garbage truck gun in mouth gun store heist held at gunpoint hitman hostage hotel hotel manager hotel room husband slaps wife husband wife relationship husband wife team imprisonment infidelity kiss kissing while having sex kitten knocked unconscious light airplane loading a gun love lust male rear nudity man and woman in shower man removes a woman's panties man slaps a woman man wears sunglasses man wears underwear manhole manhunt masked robber mexican prison money motel motorcycle mouse murder neo noir nipples old man on the run one last job panties pulled down parking garage pistol planning police police car police van power outage prison prison visit prisoner pump action shotgun punched in the face racetrack reconciliation reference to john dillinger release from prison remake revolver safe screaming woman security guard seduction semiautomatic pistol sex in bed sex scene sexual favor sexual torture shaving shooting cans shooting practice shootout shootout in a hotel shot in the arm shot in the chest shot in the leg shot to death shotgun showdown shower sleazy man submachine gun suicide suicide by hanging suitcase full of money taxi taxi driver texas thief thrown from a car tied to a chair time bomb torture train station train station locker train toilet truck tv news u.s. mexico border uncle murders nephew undressing veterinarian violence walkie talkie watching sex white bra and panties wife slaps husband woman on top woman punches a woman woman slaps a man woman wears a bra and panties woman wears a towel woman wears sunglasses wounded man

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last updated: 2024-10-26Update data

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last updated: 2024-09-27Update data

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rating: 7.2/10

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