document_title: Aer Lingus destinations

question_text: where does aer lingus fly to in italy

question_tokens: where does aer lingus fly to in italy

text: Aer Lingus destinations

text: Aer Lingus serve the following destinations as of November 2016:[1]

text: The airline currently operates scheduled and limited charter flights to/from a total of 92 airports, across 24 countries. Some destinations are served seasonally as indicated and some routes are operated under the Aer Lingus Regional brand by Stobart Air.[1]

text: Aer Lingus also serves over an additional 100 destinations across the World through codeshare and interline agreements with Air Canada, British Airways, Etihad Airways, Flybe, JetBlue and United Airlines.[1][2]

text: List[edit]

text:
[Hub]
Hub

[Base]
Operating base

[F]
Future destination

[S]
Seasonal

text:
City
Country
IATA
ICAO
Airport
Notes
Refs

Aberdeen
United Kingdom
ABZ
EGPD
Aberdeen Airport
Aer Lingus Regional

Alicante
Spain
ALC
LEAL
Alicante–Elche Airport

Amsterdam
Netherlands
AMS
EHAM
Amsterdam Airport Schiphol

Athens
Greece
ATH
LGAV
Athens International Airport

Barcelona
Spain
BCN
LEBL
Barcelona–El Prat Airport

Belfast
United Kingdom
BHD
EGAC
George Best Belfast City Airport [Base]

Berlin
Germany
TXL
EDDT
Berlin Tegel Airport

Bilbao
Spain
BIO
LEBB
Bilbao Airport

Birmingham
United Kingdom
BHX
EGBB
Birmingham Airport

Bologna
Italy
BLQ
LIPE
Bologna Guglielmo Marconi Airport

Bordeaux
France
BOD
LFBD
Bordeaux–Mérignac Airport

Boston
United States
BOS
KBOS
Logan International Airport

Bristol
United Kingdom
BRS
EGGD
Bristol Airport
Aer Lingus Regional

Brussels
Belgium
BRU
EBBR
Brussels Airport

Budapest
Hungary
BUD
LHBP
Budapest Ferenc Liszt International Airport

Burgas
Bulgaria
BOJ
LBBG
Burgas Airport

Catania
Italy
CTA
LICC
Catania–Fontanarossa Airport

Chicago
United States
ORD
KORD
Chicago O'Hare International Airport

Corfu
Greece
CFU
LGKR
Corfu International Airport

Cork
Ireland
ORK
EICK
Cork Airport [Base]

Donegal
Ireland
CFN
EIDL
Donegal Airport
Aer Lingus Regional

Dublin
Ireland
DUB
EIDW
Dublin Airport [Hub]

Dubrovnik
Croatia
DBV
LDDU
Dubrovnik Airport

Düsseldorf
Germany
DUS
EDDL
Düsseldorf Airport

Edinburgh
United Kingdom
EDI
EGPH
Edinburgh Airport
Aer Lingus Regional

Faro
Portugal
FAO
LPFR
Faro Airport

Frankfurt
Germany
FRA
EDDF
Frankfurt Airport

Fuerteventura
Spain
FUE
GCFV
Fuerteventura Airport

Geneva
Switzerland
GVA
LSGG
Geneva Airport

Glasgow
United Kingdom
GLA
EGPF
Glasgow Airport
Aer Lingus Regional

Gran Canaria
Spain
LPA
GCLP
Gran Canaria Airport

Hamburg
Germany
HAM
EDDH
Hamburg Airport

Hartford
United States
BDL
KBDL
Bradley International Airport

Isle of Man
Isle of Man
IOM
EGNS
Isle of Man Airport
Aer Lingus Regional

İzmir
Turkey
ADB
LTBJ
Adnan Menderes Airport

Jersey
Jersey
JER
EGJJ
Jersey Airport
Aer Lingus Regional

Kerry
Ireland
KIR
EIKY
Kerry Airport
Aer Lingus Regional

Knock
Ireland
NOC
EIKN
Ireland West Airport Knock

Lanzarote
Spain
ACE
GCRR
Lanzarote Airport

Leeds
United Kingdom
LBA
EGNM
Leeds Bradford Airport
Aer Lingus Regional

Lisbon
Portugal
LIS
LPPT
Lisbon Portela Airport

London
United Kingdom
LGW
EGKK
Gatwick Airport

London
United Kingdom
LHR
EGLL
Heathrow Airport

Los Angeles
United States
LAX
KLAX
Los Angeles International Airport

Lyon
France
LYS
LFLL
Lyon–Saint-Exupéry Airport

Madrid
Spain
MAD
LEMD
Adolfo Suárez Madrid–Barajas Airport

Málaga
Spain
AGP
LEMG
Málaga Airport

Manchester
United Kingdom
MAN
EGCC
Manchester Airport

Marseille
France
MRS
LFML
Marseille Provence Airport

Miami
United States
MIA
KMIA
Miami International Airport

[3]

Milan
Italy
LIN
LIML
Linate Airport

Milan
Italy
MXP
LIMC
Milan–Malpensa Airport

Montpellier
France
MPL
LFMT
Montpellier–Méditerranée Airport

Munich
Germany
MUC
EDDM
Munich Airport

Murcia
Spain
MJV
LELC
Murcia–San Javier Airport

Nantes
France
NTE
LFRS
Nantes Atlantique Airport

Naples
Italy
NAP
LIRN
Naples International Airport

New York City
United States
JFK
KJFK
John F. Kennedy International Airport

Newark
United States
EWR
KEWR
Newark Liberty International Airport

Newcastle upon Tyne
United Kingdom
NCL
EGNT
Newcastle Airport
Aer Lingus Regional

Newquay
United Kingdom
NQY
EGHQ
Cornwall Airport Newquay
Aer Lingus Regional

Nice
France
NCE
LFMN
Nice Côte d'Azur Airport

Orlando
United States
MCO
KMCO
Orlando International Airport

Palma, Majorca
Spain
PMI
LEPA
Palma de Mallorca Airport

Paris
France
CDG
LFPG
Charles de Gaulle Airport

Perpignan
France
PGF
LFMP
Perpignan–Rivesaltes Airport

Philadelphia
begins 26 March 2018
United States
PHL
KPHL
Philadelphia International Airport

[4]

Pisa
Italy
PSA
LIRP
Pisa International Airport

Prague
Czech Republic
PRG
LKPR
Václav Havel Airport Prague

Pula
Croatia
PUY
LDPL
Pula Airport

Rennes
France
RNS
LFRN
Rennes–Saint-Jacques Airport
Aer Lingus Regional

Reykjavík
Iceland
KEF
BIKF
Keflavík International Airport

[5]

Rome
Italy
FCO
LIRF
Leonardo da Vinci–Fiumicino Airport

Rovaniemi
Finland
RVN
EFRO
Rovaniemi Airport

[5]

Salzburg
Austria
SZG
LOWS
Salzburg Airport

[5]

San Francisco
United States
SFO
KSFO
San Francisco International Airport

Santiago de Compostela
Spain
SCQ
LEST
Santiago de Compostela Airport

Shannon
Ireland
SNN
EINN
Shannon Airport [Base]

Southampton
United Kingdom
SOU
EGHI
Southampton Airport
Aer Lingus Regional

Split
Croatia
SPU
LDSP
Split Airport

[3]

Stuttgart
Germany
STR
EDDS
Stuttgart Airport

Tenerife
Spain
TFS
GCTS
Tenerife–South Airport

Toronto
Canada
YYZ
CYYZ
Toronto Pearson International Airport

Toulouse
France
TLS
LFBO
Toulouse–Blagnac Airport

Venice
Italy
VCE
LIPZ
Venice Marco Polo Airport

Verona
Italy
VRN
LIPX
Verona Villafranca Airport

Vienna
Austria
VIE
LOWW
Vienna International Airport

Warsaw
Poland
WAW
EPWA
Warsaw Chopin Airport

Washington, D.C.
United States
IAD
KIAD
Washington Dulles International Airport

Zürich
Switzerland
ZRH
LSZH
Zürich Airport


document_title: Median

question_text: is the 50th percentile the mean or median

question_tokens: is the 50th percentile the mean or median

root

text: Median

text: Finite set of numbers[edit]

text: The median of a finite list of numbers can be found by arranging all the numbers from smallest to greatest.

tag: p

text: If there is an odd number of numbers, the middle one is picked. For example, consider the list of numbers

text: This list contains seven numbers. The median is the fourth of them, which is 6.

text: If there is an even number of observations, then there is no single middle value; the median is then usually defined to be the mean of the two middle values.[1][2] For example, in the data set

text: the median is the mean of the middle two numbers: this is (4 + 5) ÷ 2, which is 4.5 or 4 1/2. (In more technical terms, this interprets the median as the fully trimmed mid-range).

text: The formula used to find the middle number of a data set of n numerically ordered numbers is (n + 1) ÷ 2. This either gives the middle number (for an odd number of values) or the halfway point between the two middle values. For example, with 14 values, the formula will give 7.5, and the median will be taken by averaging the seventh and eighth values. So median can be represented by the following formula:

text:
Comparison of common averages of values { 1, 2, 2, 3, 4, 7, 9 }

Type
Description
Example
Result

Arithmetic mean
Sum of values of a data set divided by number of values:

x
¯

=

1
n

∑

i
=
1

n

x

i

{\displaystyle \scriptstyle {\bar {x}}={\frac {1}{n}}\sum _{i=1}^{n}x_{i}}

(1+2+2+3+4+7+9) / 7
4

Median
Middle value separating the greater and lesser halves of a data set
1, 2, 2, 3, 4, 7, 9
3

Mode
Most frequent value in a data set
1, 2, 2, 3, 4, 7, 9
2

tag: table

text: One can find the median using the Stem-and-Leaf Plot.

text: There is no widely accepted standard notation for the median, but some authors represent the median of a variable x either as x͂ or as μ1/2[1] sometimes also M.[3][4] In any of these cases, the use of these or other symbols for the median needs to be explicitly defined when they are introduced.

text: The median is used primarily for skewed distributions, which it summarizes differently from the arithmetic mean. Consider the multiset { 1, 2, 2, 2, 3, 14 }. The median is 2 in this case, (as is the mode), and it might be seen as a better indication of central tendency (less susceptible to the exceptionally large value in data) than the arithmetic mean of 4.

text: The median is a popular summary statistic used in descriptive statistics, since it is simple to understand and easy to calculate, while also giving a measure that is more robust in the presence of outlier values than is the mean. The widely cited empirical relationship between the relative locations of the mean and the median for skewed distributions is, however, not generally true.[5] There are, however, various relationships for the absolute difference between them; see below.

text: With an even number of observations (as shown above) no value need be exactly at the value of the median. Nonetheless, the value of the median is uniquely determined with the usual definition. A related concept, in which the outcome is forced to correspond to a member of the sample, is the medoid.

text: In a population, at most half have values strictly less than the median and at most half have values strictly greater than it. If each group contains less than half the population, then some of the population is exactly equal to the median. For example, if a < b < c, then the median of the list {a, b, c} is b, and, if a < b < c < d, then the median of the list {a, b, c, d} is the mean of b and c; i.e., it is (b + c)/2. Indeed, as it is based on the middle data in a group, it is not necessary to even know the value of extreme results in order to calculate a median. For example, in a psychology test investigating the time needed to solve a problem, if a small number of people failed to solve the problem at all in the given time a median can still be calculated.[6]

text: The median can be used as a measure of location when a distribution is skewed, when end-values are not known, or when one requires reduced importance to be attached to outliers, e.g., because they may be measurement errors.

text: A median is only defined on ordered one-dimensional data, and is independent of any distance metric. A geometric median, on the other hand, is defined in any number of dimensions.

text: The median is one of a number of ways of summarising the typical values associated with members of a statistical population; thus, it is a possible location parameter. The median is the 2nd quartile, 5th decile, and 50th percentile. Since the median is the same as the second quartile, its calculation is illustrated in the article on quartiles. A median can be worked out for ranked but not numerical classes (e.g. working out a median grade when students are graded from A to F), although the result might be halfway between grades if there is an even number of cases.

text: When the median is used as a location parameter in descriptive statistics, there are several choices for a measure of variability: the range, the interquartile range, the mean absolute deviation, and the median absolute deviation.

text: For practical purposes, different measures of location and dispersion are often compared on the basis of how well the corresponding population values can be estimated from a sample of data. The median, estimated using the sample median, has good properties in this regard. While it is not usually optimal if a given population distribution is assumed, its properties are always reasonably good. For example, a comparison of the efficiency of candidate estimators shows that the sample mean is more statistically efficient than the sample median when data are uncontaminated by data from heavy-tailed distributions or from mixtures of distributions, but less efficient otherwise, and that the efficiency of the sample median is higher than that for a wide range of distributions. More specifically, the median has a 64% efficiency compared to the minimum-variance mean (for large normal samples), which is to say the variance of the median will be ~50% greater than the variance of the mean—see asymptotic efficiency and references therein.

text: Probability distributions[edit]

text: For any probability distribution on the real line R with cumulative distribution function F, regardless of whether it is any kind of continuous probability distribution, in particular an absolutely continuous distribution (which has a probability density function), or a discrete probability distribution, a median is by definition any real number m that satisfies the inequalities

text: or, equivalently, the inequalities

text: in which a Lebesgue–Stieltjes integral is used. For an absolutely continuous probability distribution with probability density function ƒ, the median satisfies

text: Any probability distribution on R has at least one median, but in specific cases there may be more than one median. Specifically, if a probability distribution is zero on an interval [a, b], and the cumulative distribution function at a is 1/2, any value between a and b will also be a median.

text: Medians of particular distributions[edit]

text: The medians of certain types of distributions can be easily calculated from their parameters; furthermore, they exist even for some distributions lacking a well-defined mean, such as the Cauchy distribution:

text:
The median of a symmetric unimodal distribution coincides with the mode.
The median of a symmetric distribution which possesses a mean μ also takes the value μ.

The median of a normal distribution with mean μ and variance σ2 is μ. In fact, for a normal distribution, mean = median = mode.
The median of a uniform distribution in the interval [a, b] is (a + b) / 2, which is also the mean.

The median of a Cauchy distribution with location parameter x0 and scale parameter y is x0, the location parameter.
The median of a power law distribution x−a, with exponent a > 1 is 21/(a − 1)xmin, where xmin is the minimum value for which the power law holds[8]
The median of an exponential distribution with rate parameter λ is the natural logarithm of 2 divided by the rate parameter: λ−1ln 2.
The median of a Weibull distribution with shape parameter k and scale parameter λ is λ(ln 2)1/k.

tag: ul

text: Populations[edit]

text: Optimality property[edit]

text: The mean absolute error of a real variable c with respect to the random variable X is

text: Provided that the probability distribution of X is such that the above expectation exists, then m is a median of X if and only if m is a minimizer of the mean absolute error with respect to X.[9] In particular, m is a sample median if and only if m minimizes the arithmetic mean of the absolute deviations.

text: More generally, a median is defined as a minimum of

text: as discussed below in the section on multivariate medians (specifically, the spatial median).

text: This optimization-based definition of the median is useful in statistical data-analysis, for example, in k-medians clustering.

text: Unimodal distributions[edit]

text:
It can be shown for a unimodal distribution that the median

X
~

{\displaystyle {\tilde {X}}}

 and the mean

X
¯

{\displaystyle {\bar {X}}}

 lie within (3/5)1/2 ≈ 0.7746 standard deviations of each other.[10] In symbols,

text: where |·| is the absolute value.

text: A similar relation holds between the median and the mode: they lie within 31/2 ≈ 1.732 standard deviations of each other:

text: Inequality relating means and medians[edit]

text: If the distribution has finite variance, then the distance between the median and the mean is bounded by one standard deviation.

text: This bound was proved by Mallows,[11] who used Jensen's inequality twice, as follows. We have

text: The first and third inequalities come from Jensen's inequality applied to the absolute-value function and the square function, which are each convex. The second inequality comes from the fact that a median minimizes the absolute deviation function

text: This proof also follows directly from Cantelli's inequality.[12] The result can be generalized to obtain a multivariate version of the inequality,[13] as follows:

text:
where m is a spatial median, that is, a minimizer of the function

a
↦
E

(
‖
X
−
a
‖
)
.

{\displaystyle a\mapsto \operatorname {E} (\|X-a\|).\,}

 The spatial median is unique when the data-set's dimension is two or more.[14][15] An alternative proof uses the one-sided Chebyshev inequality; it appears in an inequality on location and scale parameters.

text: Jensen's inequality for medians[edit]

text: Jensen's inequality states that for any random variable x with a finite expectation E(x) and for any convex function f

text: It has been shown[16] that if x is a real variable with a unique median m and f is a C function then

text: A C function is a real valued function, defined on the set of real numbers R, with the property that for any real t

text: is a closed interval, a singleton or an empty set.

text: Medians for samples[edit]

text: The sample median[edit]

text: Efficient computation of the sample median[edit]

text: Even though comparison-sorting n items requires Ω(n log n) operations, selection algorithms can compute the k'th-smallest of n items with only Θ(n) operations. This includes the median, which is the n/2'th order statistic (or for an even number of samples, the arithmetic mean of the two middle order statistics).

text: Selection algorithms still have the downside of requiring Ω(n) memory, that is, they need to have the full sample (or a linear-sized portion of it) in memory. Because this, as well as the linear time requirement, can be prohibitive, several estimation procedures for the median have been developed. A simple one is the median of three rule, which estimates the median as the median of a three-element subsample; this is commonly used as a subroutine in the quicksort sorting algorithm, which uses an estimate of its input's median. A more robust estimator is Tukey's ninther, which is the median of three rule applied with limited recursion:[17] if A is the sample laid out as an array, and

text: then

text: The remedian is an estimator for the median that requires linear time but sub-linear memory, operating in a single pass over the sample.[18]

text: Easy explanation of the sample median[edit]

text: In individual series (if number of observation is very low) first one must arrange all the observations in order. Then count(n) is the total number of observation in given data.

text: If n is odd then Median (M) = value of ((n + 1)/2)th item term.

text: If n is even then Median (M) = value of [(n/2)th item term + (n/2 + 1)th item term]/2

text: As an example, we will calculate the sample median for the following set of observations: 1, 5, 2, 8, 7.

text: Start by sorting the values: 1, 2, 5, 7, 8.

text: In this case, the median is 5 since it is the middle observation in the ordered list.

text: The median is the ((n + 1)/2)th item, where n is the number of values. For example, for the list {1, 2, 5, 7, 8}, we have n = 5, so the median is the ((5 + 1)/2)th item.

text: As an example, we will calculate the sample median for the following set of observations: 1, 6, 2, 8, 7, 2.

text: Start by sorting the values: 1, 2, 2, 6, 7, 8.

text: In this case, the arithmetic mean of the two middlemost terms is (2 + 6)/2 = 4. Therefore, the median is 4 since it is the arithmetic mean of the middle observations in the ordered list.

text: Sampling distribution[edit]

text:
The distributions of both the sample mean and the sample median were determined by Laplace.[19] The distribution of the sample median from a population with a density function

f
(
x
)

{\displaystyle f(x)}

 is asymptotically normal with mean

m

{\displaystyle m}

 and variance[20]

text:
where

m

{\displaystyle m}

 is the median of

f
(
x
)

{\displaystyle f(x)}

 and

n

{\displaystyle n}

 is the sample size.

text:
These results have also been extended.[21] It is now known for the

p

{\displaystyle p}

-th quantile that the distribution of the sample

p

{\displaystyle p}

-th quantile is asymptotically normal around the

p

{\displaystyle p}

-th quantile with variance equal to

text:
where

f
(

x

p

)

{\displaystyle f(x_{p})}

 is the value of the distribution density at the

p

{\displaystyle p}

-th quantile.

text:
In the case of a discrete variable, the sampling distribution of the median for small-samples can be investigated as follows. We take the sample size to be an odd number

N
=
2
n
+
1

{\displaystyle N=2n+1}

. If a given value

v

{\displaystyle v}

 is to be the median of the sample then two conditions must be satisfied. The first is that at most

n

{\displaystyle n}

 observations can have a value of

v
−
1

{\displaystyle v-1}

 or less. The second is that at most

n

{\displaystyle n}

 observations can have a value of

v
+
1

{\displaystyle v+1}

 or more. Let

i

{\displaystyle i}

 be the number of observations which have a value of

v
−
1

{\displaystyle v-1}

 or less and let

k

{\displaystyle k}

 be the number of observations which have a value of

v
+
1

{\displaystyle v+1}

 or more. Then

i

{\displaystyle i}

 and

k

{\displaystyle k}

 both have a minimum value of 0 and a maximum of

n

{\displaystyle n}

. If an observation has a value below

v

{\displaystyle v}

, it is not relevant how far below

v

{\displaystyle v}

 it is and conversely, if an observation has a value above

v

{\displaystyle v}

, it is not relevant how far above

v

{\displaystyle v}

 it is. We can therefore represent the observations as following a trinomial distribution with probabilities

F
(
v
−
1
)

{\displaystyle F(v-1)}

,

f
(
v
)

{\displaystyle f(v)}

 and

1
−
F
(
v
)

{\displaystyle 1-F(v)}

. The probability that the median

m

{\displaystyle m}

 will have a value

v

{\displaystyle v}

 is then given by

text:
Summing this over all values of

v

{\displaystyle v}

 defines a proper distribution and gives a unit sum. In practice, the function

f
(
v
)

{\displaystyle f(v)}

 will often not be known but it can be estimated from an observed frequency distribution. An example is given in the following table where the actual distribution is not known but a sample of 3,800 observations allows a sufficiently accurate assessment of

f
(
v
)

{\displaystyle f(v)}

.

text:
v
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5

f(v)
0.000
0.008
0.010
0.013
0.083
0.108
0.328
0.220
0.202
0.023
0.005

F(v)
0.000
0.008
0.018
0.031
0.114
0.222
0.550
0.770
0.972
0.995
1.000

text:
Using these data it is possible to investigate the effect of sample size on the standard errors of the mean and median. The observed mean is 3.16, the observed raw median is 3 and the observed interpolated median is 3.174. The following table gives some comparison statistics. The standard error of the median is given both from the above expression for

p
r
(
m
=
v
)

{\displaystyle pr(m=v)}

 and from the asymptotic approximation given earlier.

text:
Sample size
Statistic

3
9
15
21

Expected value of median
3.198
3.191
3.174
3.161

Standard error of median (above formula)
0.482
0.305
0.257
0.239

Standard error of median (asymptotic approximation)
0.879
0.508
0.393
0.332

Standard error of mean
0.421
0.243
0.188
0.159

text: The expected value of the median falls slightly as sample size increases while, as would be expected, the standard errors of both the median and the mean are proportionate to the inverse square root of the sample size. The asymptotic approximation errs on the side of caution by overestimating the standard error.

text:
In the case of a continuous variable, the following argument can be used. If a given value

v

{\displaystyle v}

 is to be the median, then one observation must take the value

v

{\displaystyle v}

. The elemental probability of this is

f
(
v
)

d
v

{\displaystyle f(v)\,dv}

. Then, of the remaining

2
n

{\displaystyle 2n}

 observations, exactly

n

{\displaystyle n}

 of them must be above

v

{\displaystyle v}

 and the remaining

n

{\displaystyle n}

 below. The probability of this is the

n

{\displaystyle n}

th term of a binomial distribution with parameters

F
(
v
)

{\displaystyle F(v)}

 and

2
n

{\displaystyle 2n}

. Finally we multiply by

2
n
+
1

{\displaystyle 2n+1}

 since any of the observations in the sample can be the median observation. Hence the elemental probability of the median at the point

v

{\displaystyle v}

 is given by

text:
Now we introduce the beta function. For integer arguments

α

{\displaystyle \alpha }

 and

β

{\displaystyle \beta }

, this can be expressed as

B

(
α
,
β
)
=
(
α
−
1
)
!
(
β
−
1
)
!

/

(
α
+
β
−
1
)
!

{\displaystyle \mathrm {B} (\alpha ,\beta )=(\alpha -1)!(\beta -1)!/(\alpha +\beta -1)!}

. Also, we note that

f
(
v
)
=
d
F
(
v
)

/

d
v

{\displaystyle f(v)=dF(v)/dv}

. Using these relationships and setting both

α

{\displaystyle \alpha }

 and

β

{\displaystyle \beta }

 equal to

(
n
+
1
)

{\displaystyle (n+1)}

 allows the last expression to be written as

text:
Hence the density function of the median is a symmetric beta distribution over the unit interval which supports

F
(
v
)

{\displaystyle F(v)}

. Its mean, as we would expect, is 0.5 and its variance is

1

/

(
4
(
N
+
2
)
)

{\displaystyle 1/(4(N+2))}

. The corresponding variance of the sample median is

text:
However this finding can only be used if the density function

f
(
v
)

{\displaystyle f(v)}

 is known or can be assumed. As this will not always be the case, the median variance has to be estimated sometimes from the sample data.

text:
The value of

(
2
f
(
x
)

)

−
2

{\displaystyle (2f(x))^{-2}}

—the asymptotic value of

n

−

1
2

(
ν
−
m
)

{\displaystyle n^{-{\frac {1}{2}}}(\nu -m)}

 where

ν

{\displaystyle \nu }

 is the population median—has been studied by several authors. The standard "delete one" jackknife method produces inconsistent results.[22] An alternative—the "delete k" method—where

k

{\displaystyle k}

 grows with the sample size has been shown to be asymptotically consistent.[23] This method may be computationally expensive for large data sets. A bootstrap estimate is known to be consistent,[24] but converges very slowly (order of

n

−

1
4

{\displaystyle n^{-{\frac {1}{4}}}}

).[25] Other methods have been proposed but their behavior may differ between large and small samples.[26]

text:
The efficiency of the sample median, measured as the ratio of the variance of the mean to the variance of the median, depends on the sample size and on the underlying population distribution. For a sample of size

N
=
2
n
+
1

{\displaystyle N=2n+1}

 from the normal distribution, the efficiency for large N is

text:
The efficiency tends to

2
π

{\displaystyle {\frac {2}{\pi }}}

 as

N

{\displaystyle N}

 tends to infinity.

text: Other estimators[edit]

text: For univariate distributions that are symmetric about one median, the Hodges–Lehmann estimator is a robust and highly efficient estimator of the population median.[27]

text: If data are represented by a statistical model specifying a particular family of probability distributions, then estimates of the median can be obtained by fitting that family of probability distributions to the data and calculating the theoretical median of the fitted distribution.[citation needed] Pareto interpolation is an application of this when the population is assumed to have a Pareto distribution.

text: Coefficient of dispersion[edit]

text: The coefficient of dispersion (CD) is defined as the ratio of the average absolute deviation from the median to the median of the data.[28] It is a statistical measure used by the states of Iowa, New York and South Dakota in estimating dues taxes.[29][30][31] In symbols

text: where n is the sample size, m is the sample median and x is a variate. The sum is taken over the whole sample.

text: Confidence intervals for a two-sample test in which the sample sizes are large have been derived by Bonett and Seier[28] This test assumes that both samples have the same median but differ in the dispersion around it. The confidence interval (CI) is bounded inferiorly by

text: where tj is the mean absolute deviation of the jth sample, var() is the variance and zα is the value from the normal distribution for the chosen value of α: for α = 0.05, zα = 1.96. The following formulae are used in the derivation of these confidence intervals

text: where r is the Pearson correlation coefficient between the squared deviation scores

text: a and b here are constants equal to 1 and 2, x is a variate and s is the standard deviation of the sample.

text: Multivariate median[edit]

text: Previously, this article discussed the univariate median, when the sample or population had one-dimension. When the dimension is two or higher, there are multiple concepts that extend the definition of the univariate median; each such multivariate median agrees with the univariate median when the dimension is exactly one.[27][32][33][34]

text: Medoid[edit]

text:
Let be a set of points in a space with a distance function

d

{\displaystyle d}

. Medoid is defined as

text:
x

medoid

=

argmin

y
∈
{

x

1

,

x

2

,
⋯
,

x

n

}

∑

i
=
1

n

d
(
y
,

x

i

)
.

{\displaystyle {\displaystyle x_{\text{medoid}}={\text{argmin}}_{y\in \{x_{1},x_{2},\cdots ,x_{n}\}}\sum _{i=1}^{n}d(y,x_{i}).}}

text: The medoid is often used in clustering using the k-medoid algorithm.

text: Marginal median[edit]

text: The marginal median is defined for vectors defined with respect to a fixed set of coordinates. A marginal median is defined to be the vector whose components are univariate medians. The marginal median is easy to compute, and its properties were studied by Puri and Sen.[27][35]

text: Spatial median[edit]

text: For N vectors in a normed vector space, a spatial median minimizes the average distance

text: where xn and a are vectors. The spatial median is unique when the data-set's dimension is two or more and the norm is the Euclidean norm (or another strictly convex norm).[14][15][27] The spatial median is also called the L1 median, even when the norm is Euclidean. Other names are used especially for finite sets of points: geometric median, Fermat point (in mechanics), or Weber or Fermat-Weber point (in geographical location theory).[36] In the special case where the norm is an L1-norm, then the spatial median and the marginal median are the same.

text: More generally, a spatial median is defined as a minimizer of

text: this general definition is convenient for defining a spatial median of a population in a finite-dimensional normed space, for example, for distributions without a finite mean.[14][27] Spatial medians are defined for random vectors with values in a Banach space.[14]

text: The spatial median is a robust and highly efficient estimator of a central tendency of a population.[27][37][38][39]

text: Other multivariate medians[edit]

text: An alternative generalization of the spatial median in higher dimensions that does not relate to a particular metric is the centerpoint.

text: Other median-related concepts[edit]

text: Interpolated median[edit]

text:
When dealing with a discrete variable, it is sometimes useful to regard the observed values as being midpoints of underlying continuous intervals. An example of this is a Likert scale, on which opinions or preferences are expressed on a scale with a set number of possible responses. If the scale consists of the positive integers, an observation of 3 might be regarded as representing the interval from 2.50 to 3.50. It is possible to estimate the median of the underlying variable. If, say, 22% of the observations are of value 2 or below and 55.0% are of 3 or below (so 33% have the value 3), then the median

m

{\displaystyle m}

 is 3 since the median is the smallest value of

x

{\displaystyle x}

 for which

F
(
x
)

{\displaystyle F(x)}

 is greater than a half. But the interpolated median is somewhere between 2.50 and 3.50. First we add half of the interval width

w

{\displaystyle w}

 to the median to get the upper bound of the median interval. Then we subtract that proportion of the interval width which equals the proportion of the 33% which lies above the 50% mark. In other words, we split up the interval width pro rata to the numbers of observations. In this case, the 33% is split into 28% below the median and 5% above it so we subtract 5/33 of the interval width from the upper bound of 3.50 to give an interpolated median of 3.35. More formally, if the values

f
(
x
)

{\displaystyle f(x)}

 are known, the interpolated median can be calculated from

text:
Alternatively, if in an observed sample there are

k

{\displaystyle k}

 scores above the median category,

j

{\displaystyle j}

 scores in it and

i

{\displaystyle i}

 scores below it then the interpolated median is given by

text: Pseudo-median[edit]

text: For univariate distributions that are symmetric about one median, the Hodges–Lehmann estimator is a robust and highly efficient estimator of the population median; for non-symmetric distributions, the Hodges–Lehmann estimator is a robust and highly efficient estimator of the population pseudo-median, which is the median of a symmetrized distribution and which is close to the population median.[citation needed] The Hodges–Lehmann estimator has been generalized to multivariate distributions.[37]

text: Variants of regression[edit]

text: The Theil–Sen estimator is a method for robust linear regression based on finding medians of slopes.[40]

text: Median filter[edit]

text: In the context of image processing of monochrome raster images there is a type of noise, known as the salt and pepper noise, when each pixel independently becomes black (with some small probability) or white (with some small probability), and is unchanged otherwise (with the probability close to 1). An image constructed of median values of neighborhoods (like 3×3 square) can effectively reduce noise in this case.[citation needed]

text: Cluster analysis[edit]

text: In cluster analysis, the k-medians clustering algorithm provides a way of defining clusters, in which the criterion of maximising the distance between cluster-means that is used in k-means clustering, is replaced by maximising the distance between cluster-medians.

text: Median–median line[edit]

text:
This is a method of robust regression. The idea dates back to Wald in 1940 who suggested dividing a set of bivariate data into two halves depending on the value of the independent parameter

x

{\displaystyle x}

: a left half with values less than the median and a right half with values greater than the median.[41] He suggested taking the means of the dependent

y

{\displaystyle y}

 and independent

x

{\displaystyle x}

 variables of the left and the right halves and estimating the slope of the line joining these two points. The line could then be adjusted to fit the majority of the points in the data set.

text: Nair and Shrivastava in 1942 suggested a similar idea but instead advocated dividing the sample into three equal parts before calculating the means of the subsamples.[42] Brown and Mood in 1951 proposed the idea of using the medians of two subsamples rather the means.[43] Tukey combined these ideas and recommended dividing the sample into three equal size subsamples and estimating the line based on the medians of the subsamples.[44]

text: Median-unbiased estimators[edit]

text: Any mean-unbiased estimator minimizes the risk (expected loss) with respect to the squared-error loss function, as observed by Gauss. A median-unbiased estimator minimizes the risk with respect to the absolute-deviation loss function, as observed by Laplace. Other loss functions are used in statistical theory, particularly in robust statistics.

text: The theory of median-unbiased estimators was revived by George W. Brown in 1947:[45]

text: An estimate of a one-dimensional parameter θ will be said to be median-unbiased if, for fixed θ, the median of the distribution of the estimate is at the value θ; i.e., the estimate underestimates just as often as it overestimates. This requirement seems for most purposes to accomplish as much as the mean-unbiased requirement and has the additional property that it is invariant under one-to-one transformation.

text: Further properties of median-unbiased estimators have been reported.[46][47][48][49] Median-unbiased estimators are invariant under one-to-one transformations.

text: There are methods of construction median-unbiased estimators that are optimal (in a sense analogous to minimum-variance property considered for mean-unbiased estimators). Such constructions exist for probability distributions having monotone likelihood-functions.[50][51] One such procedure is an analogue of the Rao–Blackwell procedure for mean-unbiased estimators: The procedure holds for a smaller class of probability distributions than does the Rao—Blackwell procedure but for a larger class of loss functions.[52]

text: History[edit]

text: The idea of the median appeared in the 13th century in the Talmud [53][54] (further[citation needed] for possible older mentions)

text: The idea of the median also appeared later in Edward Wright's book on navigation (Certaine Errors in Navigation) in 1599 in a section concerning the determination of location with a compass. Wright felt that this value was the most likely to be the correct value in a series of observations.

text: In 1757, Roger Joseph Boscovich developed a regression method based on the L1 norm and therefore implicitly on the median.[55]

text:
In 1774, Laplace suggested the median be used as the standard estimator of the value of a posterior pdf. The specific criterion was to minimize the expected magnitude of the error;

|

α
−

α

∗

|

{\displaystyle |\alpha -\alpha ^{*}|}

 where

α

∗

{\displaystyle \alpha ^{*}}

 is the estimate and

α

{\displaystyle \alpha }

 is the true value. Laplaces's criterion was generally rejected for 150 years in favor of the least squares method of Gauss and Legendre which minimizes

(
α
−

α

∗

)

2

{\displaystyle (\alpha -\alpha ^{*})^{2}}

 to obtain the mean.[56] The distribution of both the sample mean and the sample median were determined by Laplace in the early 1800s.[19][57]

text: Antoine Augustin Cournot in 1843 was the first[citation needed] to use the term median (valeur médiane) for the value that divides a probability distribution into two equal halves. Gustav Theodor Fechner used the median (Centralwerth) in sociological and psychological phenomena.[58] It had earlier been used only in astronomy and related fields. Gustav Fechner popularized the median into the formal analysis of data, although it had been used previously by Laplace.[58]

text: Francis Galton used the English term median in 1881,[59] having earlier used the terms middle-most value in 1869, and the medium in 1880.[60][61]


document_title: List of the verified oldest people

question_text: who is the oldest living person on record

text: List of the verified oldest people

text: This is a list of the 100 verified oldest people, arranged in descending order of each individual's age in years and days. A year typically refers to a calendar year, the time between two dates of the same name. However, years can be of different lengths due to the presence or absence of a leap day within the year, or to the conversion of dates from one calendar to another.

text: The oldest person ever whose age has been verified is Jeanne Calment (1875–1997) of France, who died at the age of 122 years, 164 days.

text: There are five living people on this list, all of whom are women and the oldest of whom is Kane Tanaka of Japan, aged 115 years, 223 days.

text: Since all the people on this list have lived past the age of 110, all of them have been, or still are, supercentenarians.

text: 100 verified oldest people[edit]

text:
Deceased
  Living

†^  denotes age at death, or, if living, age as of  today, 13 August 2018

text:
Rank

Name

Sex

Birth date

Death date

Age [†]

Place of death or residence

1

Jeanne Calment[1]

F

21 February 1875

4 August 1997

122 years, 164 days

France

2

Sarah Knauss[2]

F

24 September 1880

30 December 1999

119 years, 97 days

United States

3

Nabi Tajima[3]

F

4 August 1900

21 April 2018

117 years, 260 days

Japan

4

Lucy Hannah[4]

F

16 July 1875

21 March 1993

117 years, 248 days

United States

5

Marie-Louise Meilleur[5]

F

29 August 1880

16 April 1998

117 years, 230 days

Canada

6

Violet Brown[3]

F

10 March 1900

15 September 2017

117 years, 189 days

Jamaica

7

Emma Morano[3][6]

F

29 November 1899

15 April 2017

117 years, 137 days

Italy

8

Chiyo Miyako[3][7][8]

F

2 May 1901

22 July 2018

117 years, 81 days

Japan

9

Misao Okawa[3]

F

5 March 1898

1 April 2015

117 years, 27 days

Japan

10

María Capovilla[9]

F

14 September 1889

27 August 2006

116 years, 347 days

Ecuador

11

Susannah Mushatt Jones[3]

F

6 July 1899

12 May 2016

116 years, 311 days

United States

12

Gertrude Weaver[3]

F

4 July 1898

6 April 2015

116 years, 276 days

United States

13

Tane Ikai[10]

F

18 January 1879

12 July 1995

116 years, 175 days

Japan

14

Elizabeth Bolden[11]

F

15 August 1890

11 December 2006

116 years, 118 days

United States

15

Besse Cooper[3]

F

26 August 1896

4 December 2012

116 years, 100 days

United States

16

Jiroemon Kimura[3]

M

19 April 1897

12 June 2013

116 years, 54 days

Japan

17

Ana María Vela Rubio[3]

F

29 October 1901

15 December 2017

116 years, 47 days

Spain

18

Giuseppina  Projetto[3]

F

30 May 1902

6 July 2018

116 years, 37 days

Italy

19

Jeralean Talley[3]

F

23 May 1899

17 June 2015

116 years, 25 days

United States

20

Maggie Barnes[12]

F

6 March 1882

19 January 1998

115 years, 319 days

United States

21

Dina Manfredini[3]

F

4 April 1897

17 December 2012

115 years, 257 days

United States[a]

22

Christian Mortensen[13]

M

16 August 1882

25 April 1998

115 years, 252 days

United States[b]

23

Charlotte Hughes[14]

F

1 August 1877

17 March 1993

115 years, 228 days

United Kingdom

24

Kane Tanaka[3]

F

2 January 1903

Living[3]

7004422270000000000♠115 years, 223 days

Japan

25

Edna Parker[15]

F

20 April 1893

26 November 2008

115 years, 220 days

United States

26

Mary Ann Rhodes[16]

F

12 August 1882

3 March 1998

115 years, 203 days

Canada

27

Anonymous of Tokyo[3]

F

15 March 1900

27 September 2015

115 years, 196 days

Japan

28

Margaret Skeete[17]

F

27 October 1878

7 May 1994

115 years, 192 days

United States

29

Bernice Madigan[18]

F

24 July 1899

3 January 2015

115 years, 163 days

United States

30

Gertrude Baines[19]

F

6 April 1894

11 September 2009

115 years, 158 days

United States

31

Emiliano Mercado del Toro[13]

M

21 August 1891

24 January 2007

115 years, 156 days

Puerto Rico

32

Bettie Wilson[20]

F

13 September 1890

13 February 2006

115 years, 153 days

United States

33

Maria Giuseppa Robucci[3]

F

20 March 1903

Living[3]

7004421500000000000♠115 years, 146 days

Italy

34

Julie Winnefred Bertrand[21]

F

16 September 1891

18 January 2007

115 years, 124 days

Canada

35

Maria de Jesus[22]

F

10 September 1893

2 January 2009

115 years, 114 days

Portugal

36

Marie Josephine Gaudette[3][23]

F

25 March 1902

13 July 2017

115 years, 110 days

Italy[c]

37

Susie Gibson[24]

F

31 October 1890

16 February 2006

115 years, 108 days

United States

38

Shimoe Akiyama[3]

F

19 May 1903

Living[3]

7004420900000000000♠115 years, 86 days

Japan

39

Augusta Holtz[25]

F

3 August 1871

21 October 1986

115 years, 79 days

United States[d]

40

Hendrikje van Andel-Schipper[26]

F

29 June 1890

30 August 2005

115 years, 62 days

Netherlands

41

Maude Farris-Luse[27]

F

21 January 1887

18 March 2002

115 years, 56 days

United States

42

Marie Brémont[28]

F

25 April 1886

6 June 2001

115 years, 42 days

France

43

Koto Okubo[29]

F

24 December 1897

12 January 2013

115 years, 19 days

Japan

44

Antonia Gerena Rivera[3]

F

19 May 1900

2 June 2015

115 years, 14 days

United States[e]

45

Chiyono Hasegawa[30]

F

20 November 1896

2 December 2011

115 years, 12 days

Japan

46

Annie Jennings[31]

F

12 November 1884

20 November 1999

115 years, 8 days

United Kingdom

47

Eva Morris[32]

F

8 November 1885

2 November 2000

114 years, 360 days

United Kingdom

48

Kama Chinen[33]

F

10 May 1895

2 May 2010

114 years, 357 days

Japan

49

Maria Gomes Valentim[34]

F

9 July 1896

21 June 2011

114 years, 347 days

Brazil

50

Mary Bidwell[35]

F

19 May 1881

25 April 1996

114 years, 342 days

United States

51

Mary Josephine Ray[36]

F

17 May 1895

7 March 2010

114 years, 294 days

United States[f]

52

Goldie Steinberg[3][37][38]

F

30 October 1900

16 August 2015

114 years, 290 days

United States[g]

53

Kiyoko Ishiguro[3]

F

4 March 1901

5 December 2015

114 years, 276 days

Japan

54

Maria do Couto Maia-Lopes[13]

F

24 October 1890

25 July 2005

114 years, 274 days

Portugal

Eudoxie Baboul[3]

F

1 October 1901

1 July 2016

114 years, 274 days

France (French Guiana)

56

Ramona Trinidad Iglesias-Jordan[39]

F

31 August 1889

29 May 2004

114 years, 272 days

Puerto Rico

57

Yukie Hino[3]

F

17 April 1902

13 January 2017

114 years, 271 days

Japan

58

Delphine Gibson[3]

F

17 August 1903

9 May 2018

114 years, 265 days

United States

59

Eugénie Blanchard[40]

F

16 February 1896

4 November 2010

114 years, 261 days

France (Saint Barthélemy)

60

Venere Pizzinato-Papo[41]

F

23 November 1896

2 August 2011

114 years, 252 days

Italy[h]

61

Neva Morris[42]

F

3 August 1895

6 April 2010

114 years, 246 days

United States

62

Hide Ohira[13]

F

15 September 1880

9 May 1995

114 years, 236 days

Japan

63

Blanche Cobb[43][44]

F

8 September 1900

1 May 2015

114 years, 235 days

United States

64

Ethel Lang[3]

F

27 May 1900

15 January 2015

114 years, 233 days

United Kingdom

65

Mathew Beard[13]

M

9 July 1870

16 February 1985

114 years, 222 days

United States

66

Yone Minagawa[45]

F

4 January 1893

13 August 2007

114 years, 221 days

Japan

67

María Antonia Castro[46]

F

10 June 1881

16 January 1996

114 years, 220 days

Spain

68

Carrie Lazenby[13]

F

9 February 1882

14 September 1996

114 years, 218 days

United States

Ura Koyama[47]

F

30 August 1890

5 April 2005

114 years, 218 days

Japan

70

Myrtle Dorsey[48]

F

22 November 1885

25 June 2000

114 years, 216 days

United States

71

Iso Nakamura[3]

F

23 April 1903

23 November 2017

114 years, 214 days

Japan

72

Anna Eliza Williams[49]

F

2 June 1873

27 December 1987

114 years, 208 days

United Kingdom

73

Walter Breuning[13]

M

21 September 1896

14 April 2011

114 years, 205 days

United States

74

Eunice Sanborn[50]

F

20 July 1896

31 January 2011

114 years, 195 days

United States

75

Grace Clawson[51]

F

15 November 1887

28 May 2002

114 years, 194 days

United States[i]

76

Mitsue Toyoda[3]

F

15 February 1902

25 August 2016

114 years, 192 days

Japan

77

Tase Matsunaga[52]

F

11 May 1884

18 November 1998

114 years, 191 days

Japan

78

Yukichi Chuganji[13]

M

23 March 1889

28 September 2003

114 years, 189 days

Japan

79

Kame Nakamura[53]

F

8 March 1898

12 September 2012

114 years, 188 days

Japan

80

Lydie Vellard[13]

F

18 March 1875

17 September 1989

114 years, 183 days

France

Wilhelmina Kott[54]

F

7 March 1880

6 September 1994

114 years, 183 days

United States

Adelina Domingues[55]

F

19 February 1888

21 August 2002

114 years, 183 days

United States[j]

Lucile Randon[3]

F

11 February 1904

Living[3]

7004418220000000000♠114 years, 183 days

France

84

Mitoyo Kawate[56]

F

15 May 1889

13 November 2003

114 years, 182 days

Japan

85

Charlotte Benkner[57]

F

16 November 1889

14 May 2004

114 years, 180 days

United States[k]

Camille Loiseau[58]

F

13 February 1892

12 August 2006

114 years, 180 days

France

87

Anne Primout[59]

F

5 October 1890

26 March 2005

114 years, 172 days

France[l]

88

Ettie Mae Greene[60]

F

8 September 1877

26 February 1992

114 years, 171 days

United States

89

Dominga Velasco[3]

F

12 May 1901

11 October 2015

114 years, 152 days

United States[m]

90

Toshie Yorimitsu[3]

F

30 September 1901

28 February 2016

114 years, 151 days

Japan

91

Irene Frank[13]

F

1 October 1881

28 February 1996

114 years, 150 days

United States

92

Christina Cock[61]

F

25 December 1887

22 May 2002

114 years, 148 days

Australia[n]

93

Olivia Patricia Thomas[62]

F

29 June 1895

16 November 2009

114 years, 140 days

United States

94

Shin Matsushita[3]

F

30 March 1904

Living[3]

7004417740000000000♠114 years, 136 days

Japan

95

Tae Ito[3]

F

11 July 1903

13 November 2017

114 years, 125 days

Japan

96

Anna Henderson[63]

F

5 March 1900

1 July 2014

114 years, 118 days

United States

97

Emma Verona Johnston[64]

F

6 August 1890

1 December 2004

114 years, 117 days

United States

Mamie Rearden[65]

F

7 September 1898

2 January 2013

114 years, 117 days

United States

99

Bettie Chatmon[13]

F

30 April 1884

16 August 1998

114 years, 108 days

United States

100

Odie Matthews[13]

F

28 December 1878

14 April 1993

114 years, 107 days

United States

text:
a^  Manfredini was born in Italy.
b^  Mortensen was born in Denmark.
c^  Gaudette was born in the United States.
d^  Holtz was born in the German Empire; her birthplace is now in Poland.
e^  Gerena was born in Puerto Rico.
f^  Ray was born in Canada.
g^  Steinberg was born in Kishinev, then part of the Russian Empire. It is now the capital of Moldova.
h^  Pizzinato-Papo was born in Ala, which was then part of Austria-Hungary; it is now located in Italy.
i^  Clawson was born in the United Kingdom.
j^  Domingues was born in Cape Verde, at that time a colony of Portugal.
k^  Benkner was born in Germany.
l^  Primout was born in French Algeria; it is now Algeria.
m^  Velasco was born in Mexico.
n^  Cock was born in the former British colony Victoria, which is now part of Australia.


document_title: Cape Fear (1991 film)

question_text: where does the movie cape fear take place

text: Cape Fear (1991 film)

text: Plot[edit]

text: Sam Bowden is a lawyer in the town of New Essex, North Carolina. Max Cady is a former client whom Bowden defended 14 years earlier when he was working as a public defender in Atlanta. Cady was tried for the violent rape and battery of a young woman. Bowden, appalled by Cady's crime, buried evidence that might have lightened Cady's sentence or even secured his acquittal. After his release from prison, Cady tracks down Bowden. He studied law in prison and assumed his own defense, unsuccessfully appealing his conviction several times. Cady hints he has learned about Bowden burying the report.

text: Several incidents involving Cady impact the Bowden family, which consists of Bowden's wife Leigh and their teenage daughter Danielle. The family dog is poisoned. Cady lurks near their property. Bowden attempts to have Cady arrested, but police lieutenant Elgart says there is no evidence of a crime. At a bar, Cady meets Bowden's colleague Lori Davis. At her house, Cady cuffs her hands, breaks her arm, and rapes her. Lori refuses to press charges, ashamed of what happened. Bowden hires private investigator Claude Kersek to follow Cady.

text: Cady approaches Danielle at her school by impersonating her new drama teacher and kisses her. Bowden warns him to leave his family and him alone or suffer the consequences. Cady secretly tapes the conversation with a hidden recorder. Kersek persuades Bowden to hire three men to beat Cady, but as Bowden watches from a hiding place, Cady turns the tide on his attackers and viciously beats them. Cady uses the recording of Bowden's threat and an exaggerated display of his own injuries to file for a restraining order against Bowden. Cady's new lawyer, Lee Heller, files a complaint with the North Carolina State Bar, vowing to have Bowden disbarred.

text: Kersek reasons that Cady may try to enter the Bowden house during Bowden's appearance at a bar hearing out of town. They fake Bowden's departure and hide in the house, hoping that Cady will break in so that he can be shot in self-defense. Cady kills Kersek and a housekeeper before escaping. Bowden, Leigh, and Danielle discover the bodies. Horrified, they flee to their houseboat, which is docked upstate along Cape Fear. Cady follows them by tying himself to the chassis of their car. He attacks the family on the boat, beating and tying up Bowden, and prepares to rape Leigh and Danielle while making Bowden watch. Danielle sprays Cady with lighter fluid while he lights a cigar, engulfing him in flames and causing him to jump off the boat. However, Cady clings to a rope and pulls himself back on board.

text: As the boat is rocked by a violent thunderstorm, a badly burned Cady confronts Bowden. The storm knocks him off his feet, allowing Bowden to gain the upper hand once the women make it to shore. Bowden uses Cady's handcuffs to shackle Cady to the boat. When the boat hits a rock and is destroyed, the fight continues on shore. A raging tide carries Cady away, and he drowns. Bowden washes the blood from his hands before rejoining Leigh and Danielle.

text: Cast[edit]

text:
Robert De Niro as Max Cady
Nick Nolte as Sam Bowden
Jessica Lange as Leigh Bowden
Juliette Lewis as Danielle Bowden
Joe Don Baker as Claude Kersek
Robert Mitchum as Lt. Elgart
Gregory Peck as Lee Heller
Illeana Douglas as Lori Davis
Fred Dalton Thompson as Tom Broadbent
Martin Balsam as Judge

text: Production[edit]

text: The film was adapted by Wesley Strick from the original screenplay by James R. Webb, which was an adaptation from the novel The Executioners by John D. MacDonald.

text: It was originally developed by Steven Spielberg, who eventually decided it was too violent and traded it to Scorsese to get back Schindler's List, which Scorsese had decided not to make. Spielberg stayed on as a producer, through his Amblin Entertainment, but chose not to be credited personally on the finished film.[2]

text: Despite having worked with Nolte in New York Stories (1989), Scorsese did not have him in mind to portray Sam Bowden and wanted Harrison Ford to play the part instead.  Ford, however, agreed to do the film only if he was going to portray Max Cady.  Nolte, who was interested in portraying Bowden, managed to convince Scorsese to cast him for the part.  In addition, Drew Barrymore and Reese Witherspoon both auditioned for the part of Danielle Bowden and Spielberg reportedly wanted Bill Murray to portray Cady.[3]

text: Nick Nolte is taller than Robert De Niro, but for the movie, Nolte lost weight and De Niro developed muscles until De Niro appeared to be the stronger man. De Niro reportedly took his body fat down to 4%.[4] De Niro also paid a doctor $20,000 to grind down his teeth for the role to give the character a more menacing look.[5]

text: The work of Alfred Hitchcock was also influential on the style of Cape Fear. As with the 1962 film version, where director J. Lee Thompson specifically acknowledged Hitchcock's influence, strove to use Hitchcock's style, and had Bernard Hermann write the score, Scorsese made his version in the Hitchcock manner, especially through the use of unusual camera angles, lighting, and editing techniques. Additionally, Scorsese's version has opening credits designed by regular Hitchcock collaborator Saul Bass and the link to Hitchcock is cemented by the reuse of the original score by Herrmann, albeit reworked by Elmer Bernstein.[6]

text: Reception[edit]

text: Box office[edit]

text: The film was a box office success, making $182,291,969 worldwide[7] on a $35 million budget.

text: Critical response[edit]

text: The film received generally positive reviews by critics, with praise garnered towards its performances, direction, cinematography and editing. It received a 75% "Certified Fresh" rating on the film aggregator website Rotten Tomatoes based on 47 reviews, with an average score of 7.1 out of 10. The consensus reads, "Smart and stylish, Cape Fear is a gleefully mainstream shocker from Martin Scorsese, with a terrifying Robert De Niro performance."[8] On Metacritic, the film holds a score of 73 out of 100 based on 9 reviews, indicating "generally favorable reviews."[9] Audiences polled by CinemaScore gave the film an average grade of "B+" on an A+ to F scale.[10]

text: Roger Ebert gave the film three stars, commenting: "Cape Fear is impressive moviemaking, showing Scorsese as a master of a traditional Hollywood genre who is able to mold it to his own themes and obsessions. But as I look at this $35 million movie with big stars, special effects and production values, I wonder whether it represents a good omen from the finest director now at work."[11]

text: Awards and honors[edit]

text:
Award

Category

Subject

Result

Academy Awards

Best Actor

Robert De Niro

Nominated

Best Supporting Actress

Juliette Lewis

Nominated

Golden Globe Awards

Best Actor – Motion Picture Drama

Robert De Niro

Nominated

Best Supporting Actress – Motion Picture

Juliette Lewis

Nominated

BAFTA Awards

Best Cinematography

Freddie Francis

Nominated

Best Editing

Thelma Schoonmaker

Nominated

Berlin International Film Festival

Golden Berlin Bear[12]

Martin Scorsese

Nominated

Broadcast Music, Inc.

BMI Film Music Award

Elmer Bernstein

Won

CFCA Awards

Best Director

Martin Scorsese

Nominated

Best Supporting Actress

Juliette Lewis

Nominated

Most Promising Actress

Won

David di Donatello Award

Best Foreign Actor

Robert De Niro

Nominated

Jupiter Award

Best International Actor

Won

Kansas City Film Critics Circle Award

Best Supporting Actress

Juliette Lewis

Won

MTV Movie Awards

Best Kiss

Nominated

Robert De Niro

Nominated

Best Male Performance

Nominated

Best Villain

Nominated

National Society of Film Critics Award

Best Supporting Actress

Juliette Lewis

2nd place

New York Film Critics Circle Awards

Best Supporting Actress

2nd place

Best Cinematographer

Freddie Francis

2nd place

text: In popular culture[edit]

text: The film was parodied in the 1993 Simpsons episode "Cape Feare", with Sideshow Bob in the role of Cady. They also pay homage to another Robert Mitchum film The Night of the Hunter in which Sideshow Bob's knuckles (scaled down for a cartoon character with one fewer finger on each hand) say "Luv" (Love) and "Hāt" (Hate, with the diacritical mark providing the long vowel).

text: In 1995, professional wrestler Waylon Mercy made his WWE (then WWF) debut, with his gimmick based on De Niro's portrayal of Max Cady. However, he only lasted a few months with the company due to a buildup of previous injuries, and the character was abandoned. In 2013, WWE debuted Bray Wyatt, another character partly based on De Niro's portrayal.[13]

text: In the Seinfeld episode "The Red Dot", as Elaine's boyfriend rampages through her office to get revenge on Jerry for making him lose his job, Elaine makes a reference to the film.

text: Also in the “Seinfeld” episode “The Bookstore”, in a dream after Jerry rats on Uncle Leo for shoplifting in a bookstore, Jerry has a nightmare where Uncle Leo is doing chin ups, working out in prison to get his revenge on Jerry once he finally received his release. A close up, sharing the same scary score of the movie, Uncle Leo is seen with “Jerry” & “Hello” tattooed on each hand. It’s a clear reference to Robert DeNiro’s character in Cape Fear.

text: In the Rick and Morty episode "Ricksy Business", Lucy (the deranged maid who tries to rape Jerry) is shown clinging to the underside chassis of their car as they drive home, maniacally shouting "Ha, I'm doing like in Cape Fear!"


document_title: Welfare

question_text: when did the welfare system began in the us

text: Welfare

text: Terminology[edit]

text: Terminology in this area is somewhat different in the United States than in the rest of the English-speaking world. The general term for an action program in support of the well being of poor people in the United States is welfare program, and the general term for all such programs is simply welfare. In American society, the term welfare arguably has negative connotations. In the United States, the term Social Security refers to the US social insurance program for all retired and disabled people. Elsewhere the term social security is used in a much broader sense, referring to the economic security that society offers when people are faced with certain risks.

text: Recent UK official use of the term welfare includes not only minimal help for people in need but also some services traditionally called benefits and social security in most English-speaking countries except the United States. The term is even used to include government help in finding employment.[1]

text: Non-native English speakers are often confused by the use of welfare to refer to different things in different countries and in the same country, for example well being, but native speakers can usually understand from the context which meaning is intended.

text: History[edit]

text: In the Roman Empire, the first emperor Augustus provided the Cura Annonae or grain dole for citizens who could not afford to buy food every month. Social welfare was enlarged by the Emperor Trajan.[2] Trajan's program brought acclaim from many, including Pliny the Younger.[3] The Song dynasty government (c.1000AD in China) supported multiple programs which could be classified as social welfare, including the establishment of retirement homes, public clinics, and paupers' graveyards. According to economist Robert Henry Nelson, "The medieval Roman Catholic Church operated a far-reaching and comprehensive welfare system for the poor..."[4][5]

text: Early welfare programs in Europe included the English Poor Law of 1601, which gave parishes the responsibility for providing welfare payments to the poor.[6] This system was substantially modified by the 19th-century Poor Law Amendment Act, which introduced the system of workhouses.

text: Public assistance programs were not called welfare until the early 20th century when the term was quickly adopted to avoid the negative connotations that had become associated with older terms such as charity.[7]

text: It was predominantly in the late 19th and early 20th centuries that an organized system of state welfare provision was introduced in many countries. Otto von Bismarck, Chancellor of Germany, introduced one of the first welfare systems for the working classes. In Great Britain the Liberal government of Henry Campbell-Bannerman and David Lloyd George introduced the National Insurance system in 1911,[8] a system later expanded by Clement Attlee. The United States inherited England's poor house laws and has had a form of welfare since before it won its independence[citation needed]. During the Great Depression, when emergency relief measures were introduced under President Franklin D. Roosevelt, Roosevelt's New Deal focused predominantly on a program of providing work and stimulating the economy through public spending on projects, rather than on cash payment.

text: Modern welfare states include Germany, France, the Netherlands,[9] as well as the Nordic countries, such as Iceland, Sweden, Norway, Denmark, and Finland[10] which employ a system known as the Nordic model. Esping-Andersen classified the most developed welfare state systems into three categories; Social Democratic, Conservative, and Liberal.[11]

text: In the Islamic world, Zakat (charity), one of the Five Pillars of Islam, has been collected by the government since the time of the Rashidun caliph Umar in the 7th century. The taxes were used to provide income for the needy, including the poor, elderly, orphans, widows, and the disabled. According to the Islamic jurist Al-Ghazali (Algazel, 1058–111), the government was also expected to store up food supplies in every region in case a disaster or famine occurred.[12][13] (See Bayt al-mal for further information.)

text: Forms[edit]

text: Welfare can take a variety of forms, such as monetary payments, subsidies and vouchers, or housing assistance. Welfare systems differ from country to country, but welfare is commonly provided to individuals who are unemployed, those with illness or disability, the elderly, those with dependent children, and veterans. A person's eligibility for welfare may also be constrained by means testing or other conditions.

text: Provision and funding[edit]

text: Welfare is provided by governments or their agencies, by private organizations, or a combination of both. Funding for welfare usually comes from general government revenue, but when dealing with charities or NGOs, donations may be used. Some countries run conditional cash transfer welfare programs where payment is conditional on behavior of the recipients.[14][15][16][17]

text: Welfare systems[edit]

text: Australia[edit]

text: Prior to 1900 in Australia, charitable assistance from benevolent societies, sometimes with financial contributions from the authorities, was the primary means of relief for people not able to support themselves.[18] The 1890s economic depression and the rise of the trade unions and the Labor parties during this period led to a movement for welfare reform.[19]

text: In 1900, the states of New South Wales and Victoria enacted legislation introducing non-contributory pensions for those aged 65 and over. Queensland legislated a similar system in 1907 before the Australian labor Commonwealth government led by Andrew Fisher introduced a national aged pension under the Invalid and Old-Aged Pensions Act 1908. A national invalid disability pension was started in 1910, and a national maternity allowance was introduced in 1912.[18][20]

text: During the Second World War, Australia under a labor government created a welfare state by enacting national schemes for: child endowment in 1941 (superseding the 1927 New South Wales scheme); a widows’ pension in 1942 (superseding the New South Wales 1926 scheme); a wife’s allowance in 1943; additional allowances for the children of pensioners in 1943; and unemployment, sickness, and special benefits in 1945 (superseding the Queensland 1923 scheme).[18][20]

text: Canada[edit]

text: Canada has a welfare state in the European tradition; however, it is not referred to as "welfare", but rather as "social programs". In Canada, "welfare" usually refers specifically to direct payments to poor individuals (as in the American usage) and not to healthcare and education spending (as in the European usage).[21]

text: The Canadian social safety net covers a broad spectrum of programs, and because Canada is a federation, many are run by the provinces. Canada has a wide range of government transfer payments to individuals, which totaled $145 billion in 2006.[22] Only social programs that direct funds to individuals are included in that cost; programs such as medicare and public education are additional costs.

text: Generally speaking, before the Great Depression, most social services were provided by religious charities and other private groups. Changing government policy between the 1930s and 1960s saw the emergence of a welfare state, similar to many Western European countries. Most programs from that era are still in use, although many were scaled back during the 1990s as government priorities shifted towards reducing debt and deficits.

text: Denmark[edit]

text: Danish welfare is handled by the state through a series of policies (and the like) that seeks to provide welfare services to citizens, hence the term welfare state. This refers not only to social benefits, but also tax-funded education, public child care, medical care, etc. A number of these services are not provided by the state directly, but administered by municipalities, regions or private providers through outsourcing. This sometimes gives a source of tension between the state and municipalities, as there is not always consistency between the promises of welfare provided by the state (i.e. parliament) and local perception of what it would cost to fulfill these promises.

text: France[edit]

text: Solidarity is a strong value of the French Social Protection system. The first article of the French Code of Social Security describes the principle of solidarity. Solidarity is commonly comprehended in relations of similar work, shared responsibility and common risks. Existing solidarities in France caused the expansion of health and social security.[23]

text: Germany[edit]

text: The welfare state has a long tradition in Germany dating back to the industrial revolution. Due to the pressure of the workers' movement in the late 19th century, Reichskanzler Otto von Bismarck introduced the first rudimentary state social insurance scheme. Under Adolf Hitler, the National Socialist Program stated "We demand an expansion on a large scale of old age welfare".[24] Today, the social protection of all its citizens is considered a central pillar of German national policy. 27.6 percent of Germany's GDP is channeled into an all-embracing system of health, pension, accident, longterm care and unemployment insurance, compared to 16.2 percent in the US. In addition, there are tax-financed services such as child benefits (Kindergeld, beginning at €192 per month for the first and second child, €198 for the third and €223 for each child thereafter, until they attain 25 years or receive their first professional qualification),[25] and basic provisions for those unable to work or anyone with an income below the poverty line.[26]

text: Since 2005, reception of full unemployment pay (60–67% of the previous net salary) has been restricted to 12 months in general and 18 months for those over 55. This is now followed by (usually much lower) Arbeitslosengeld II (ALG II) or Sozialhilfe, which is independent of previous employment (Hartz IV concept).

text: Under ALG II, a single person receives €391 per month plus the cost of 'adequate' housing and health insurance. ALG II can also be paid partially to supplement a low work income.

text: Italy[edit]

text: The Italian welfare state's foundations were laid along the lines of the corporatist-conservative model, or of its Mediterranean variant.[citation needed] Later, in the 1960s and 1970s, increases in public spending and a major focus on universality brought it on the same path as social-democratic systems. In 1978, a universalistic welfare model was introduced in Italy, offering a number of universal and free services such as a National Health Fund.[27]

text: Japan[edit]

text: Social welfare, assistance for the ill or otherwise disabled and for the old, has long been provided in Japan by both the government and private companies. Beginning in the 1920s, the government enacted a series of welfare programs, based mainly on European models, to provide medical care and financial support. During the postwar period, a comprehensive system of social security was gradually established.[28][29]

text: Latin America[edit]

text: History[edit]

text: The 1980s marked a change in the structure of Latin American social protection programs. Social protection embraces three major areas: social insurance, financed by workers and employers; social assistance to the population’s poorest, financed by the state; and labor market regulations to protect worker rights.[30] Although diverse, recent Latin American social policy has tended to concentrate on social assistance.

text: The 1980s had a significant effect on social protection policies. Prior to the 1980s, most Latin American countries focused on social insurance policies involving formal sector workers, assuming that the informal sector would disappear with economic development. The economic crisis of the 1980s and the liberalization of the labor market led to a growing informal sector and a rapid increase in poverty and inequality. Latin American countries did not have the institutions and funds to properly handle such a crisis, both due to the structure of the social security system, and to the previously implemented structural adjustment policies (SAPs) that had decreased the size of the state.

text: New Welfare programs have integrated the multidimensional, social risk management, and capabilities approaches into poverty alleviation. They focus on income transfers and service provisions while aiming to alleviate both long- and short-term poverty through, among other things, education, health, security, and housing. Unlike previous programs that targeted the working class, new programs have successfully focused on locating and targeting the very poorest.

text: The impacts of social assistance programs vary between countries, and many programs have yet to be fully evaluated. According to Barrientos and Santibanez, the programs have been more successful in increasing investment in human capital than in bringing households above the poverty line. Challenges still exist, including the extreme inequality levels and the mass scale of poverty; locating a financial basis for programs; and deciding on exit strategies or on the long-term establishment of programs.[30]

text: Latin America’s most recent shift in social policies[edit]

text: The economic crisis of the 1980s led to a shift in social policies, as understandings of poverty and social programs evolved (24). New, mostly short-term programs emerged. These include:[31]

text:
Argentina: Jefes y Jefas de Hogar, Asignación Universal por Hijo
Bolivia: Bonosol
Brazil: Bolsa Escola and Bolsa Familia
Chile: Chile Solidario
Colombia: Solidaridad por Colombia
Ecuador: Bono de Desarollo Humano
Honduras: Red Solidaria
Mexico: Prospera (earlier known as Oportunidades)
Panama: Red de Oportunidades
Peru: Juntos

text: Major aspects of current social assistance programs[edit]

text:
Conditional cash transfer (CCT) combined with service provisions. Transfer cash directly to households, most often through the women of the household, if certain conditions are met (e.g. children’s school attendance or doctor visits) (10). Providing free schooling or healthcare is often not sufficient, because there is an opportunity cost for the parents in, for example, sending children to school (lost labor power), or in paying for the transportation costs of getting to a health clinic.
Household. The household has been the focal point of social assistance programs.
Target the poorest. Recent programs have been more successful than past ones in targeting the poorest. Previous programs often targeted the working class.
Multidimensional. Programs have attempted to address many dimensions of poverty at once. Chile Solidario is the best example.

text: New Zealand[edit]

text: New Zealand is often regarded as having one of the first comprehensive welfare systems in the world. During the 1890s a Liberal government adopted many social programmes to help the poor who had suffered from a long economic depression in the 1880s. One of the most far reaching was the passing of tax legislation that made it difficult for wealthy sheep farmers to hold onto their large land holdings. This and the invention of refrigeration led to a farming revolution where many sheep farms were broken up and sold to become smaller dairy farms. This enabled thousands of new farmers to buy land and develop a new and vigorous industry that has become the backbone of New Zealand's economy to this day. This liberal tradition flourished with increased enfranchisement for indigenous Maori in the 1880s and women. Pensions for the elderly, the poor and war casualties followed, with State-run schools, hospitals and subsidized medical and dental care. By 1960 New Zealand was able to afford one of the best-developed and most comprehensive welfare systems in the world, supported by a well-developed and stable economy.

text: Sweden[edit]

text: Social welfare in Sweden is made up of several organizations and systems dealing with welfare. It is mostly funded by taxes, and executed by the public sector on all levels of government as well as private organizations. It can be separated into three parts falling under three different ministries; social welfare, falling under the responsibility of Ministry of Health and Social Affairs; education, under the responsibility of the Ministry of Education and Research and labor market, under the responsibility of Ministry of Employment.[32]

text: Government pension payments are financed through an 18.5% pension tax on all taxed incomes in the country, which comes partly from a tax category called a public pension fee (7% on gross income), and 30% of a tax category called employer fees on salaries (which is 33% on a netted income). Since January 2001 the 18.5% is divided in two parts: 16% goes to current payments, and 2.5% goes into individual retirement accounts, which were introduced in 2001. Money saved and invested in government funds, and IRAs for future pension costs, are roughly 5 times annual government pension expenses (725/150).

text: United Kingdom[edit]

text:
State pension (46.32%)
Housing Benefit (10.55%)
Disability Living Allowance (7.87%)
Pension Credit (5.06%)
Income Support (4.31%)
Rent rebates (3.43%)
Attendance Allowance (3.31%)
Jobseeker's Allowance (3.06%)
Incapacity Benefit (3.06%)
Council Tax Benefit (3%)
Other (10.03%)[33]

text: The United Kingdom has a long history of welfare, notably including the English Poor laws which date back to 1536. After various reforms to the program, which involved workhouses, it was eventually abolished and replaced with a modern system by laws such as National Assistance Act 1948.

text: In more recent times, comparing the Cameron–Clegg coalition's austerity measures with the Opposition's, the respected Financial Times commentator Martin Wolf commented that the "big shift from Labour ... is the cuts in welfare benefits."[34] The government's austerity programme, which involves reduction in government policy, has been linked to a rise in food banks. A study published in the British Medical Journal in 2015 found that each 1 percentage point increase in the rate of Jobseeker's Allowance claimants sanctioned was associated with a 0.09 percentage point rise in food bank use.[35] The austerity programme has faced opposition from disability rights groups for disproportionately affecting disabled people. The "bedroom tax" is an austerity measure that has attracted particular criticism, with activists arguing that two thirds of council houses affected by the policy are occupied with a person with a disability.[36]

text: United States[edit]

text: In the United States, depending on the context, the term “welfare” can be used to refer to means-tested cash benefits, especially the Aid to Families with Dependent Children (AFDC) program and its successor, the Temporary Assistance for Needy Families Block Grant, or it can be used to refer to all means-tested programs that help individuals or families meet basic needs, including, for example, health care through Medicaid, Supplemental Security Income (SSI) benefits and food and nutrition programs (SNAP). It can also include Social Insurance programs such as Unemployment Insurance, Social Security, and Medicare.

text: AFDC (originally called Aid to Dependent Children) was created during the Great Depression to alleviate the burden of poverty for families with children and allow widowed mothers to maintain their households. The New Deal employment program such as the Works Progress Administration primarily served men. Prior to the New Deal, anti-poverty programs were primarily operated by private charities or state or local governments; however, these programs were overwhelmed by the depth of need during the Depression.[37] The United States has no national program of cash assistance for non-disabled poor individuals who are not raising children.

text: Race is brought up constantly in policies as to categorize whether it is a Black or White issue and in welfare's history there's a switch in the way the public is educated through news media. Until early in the year of 1965, the news media was conveying only Whites as living in poverty however that perception had changed to Blacks.[38] Some of the influences in this shift could have been the civil rights movement and urban riots from the mid 60s. Welfare had then shifted from being a White issue to a Black issue and during this time frame the war on poverty had already begun.[38] Subsequently, news media portrayed stereotypes of Blacks as lazy, undeserving and welfare queens. These shifts in media don't necessarily establish the population living in poverty decreasing.[38]

text: In 1996, the Personal Responsibility and Work Opportunity Reconciliation Act changed the structure of Welfare payments and added new criteria to states that received Welfare funding. After reforms, which President Clinton said would "end Welfare as we know it",[40] amounts from the federal government were given out in a flat rate per state based on population.[41] Each state must meet certain criteria to ensure recipients are being encouraged to work themselves out of Welfare. The new program is called Temporary Assistance for Needy Families (TANF).[42][43] It encourages states to require some sort of employment search in exchange for providing funds to individuals, and imposes a five-year lifetime limit on cash assistance.[40][42][44] In FY 2010, 31.8% of TANF families were white, 31.9% were African-American, and 30.0% were Hispanic.[43]

text: According to the U.S. Census Bureau data released September 13, 2011, the nation's poverty rate rose to 15.1% (46.2 million) in 2010,[45] up from 14.3% (approximately 43.6 million) in 2009 and to its highest level since 1993. In 2008, 13.2% (39.8 million) Americans lived in relative poverty.[46]

text: In a 2011 op-ed in Forbes, Peter Ferrara stated that, "The best estimate of the cost of the 185 federal means tested Welfare programs for 2010 for the federal government alone is nearly $700 billion, up a third since 2008, according to the Heritage Foundation. Counting state spending, total Welfare spending for 2010 reached nearly $900 billion, up nearly one-fourth since 2008 (24.3%)".[47] California, with 12% of the U.S. population, has one-third of the nation's welfare recipients.[48]

text: In FY 2011, federal spending on means-tested welfare, plus state contributions to federal programs, reached $927 billion per year. Roughly half of this welfare assistance, or $462 billion went to families with children, most of which are headed by single parents.[49]

text: The United States has also typically relied on charitable giving through non-profit agencies and fundraising instead of direct monetary assistance from the government itself. According to Giving USA, Americans gave $358.38 billion to charity in 2014. This is rewarded by the United States government through tax incentives for individuals and companies that are not typically seen in other countries.

text: Criticism[edit]

text: Income transfers can be either conditional or unconditional. Conditionalities are sometimes criticized as being paternalistic and unnecessary.

text: Current programs have been built as short-term rather than as permanent institutions, and many of them have rather short time spans (around five years). Some programs have time frames that reflect available funding. One example of this is Bolivia’s Bonosol, which is financed by proceeds from the privatization of utilities—an unsustainable funding source. Some see Latin America’s social assistance programs as a way to patch up high levels of poverty and inequalities, partly brought on by the current economic system.

text: Some opponents of welfare argue that it affects work incentives. They also argue that the taxes levied can also affect work incentives. A good example of this would be the reform of the Aid to Families with Dependent Children (AFDC) program. Per AFDC, some amount per recipient is guaranteed. However, for every dollar the recipient earns the monthly stipend is decreased by an equivalent amount. For most persons, this reduces their incentive to work. This program was replaced by Temporary Aid to Needy Families (TANF). Under TANF, people were required to actively seek employment while receiving aid and they could only receive aid for a limited amount of time. However, states can choose the amount of resources they will devote to the program.