Illicit Supply and Demand: Child Sex Exploitationin South East Asia
Demand for sex
description
Transcript of Demand for sex
Demand for sex
*n n n ; n 1,2, , , N(1) y x
Dependent variable
nj
1 if client n belongs to category j; j 1,2,3,4(2) y
0 otherwise
Ordered structure
*nn1 1
*nn2 1 2*nn3 2 3*nn4 3
y 1 if y
y 1 if y(3)
y 1 if y
y 1 if y
Choice probability
*n n n nnj j 1 j j 1 j(4) P(y 1) P( y ) P( x x )
Probability distribution, Logistic
u1(5) F(u)
1 e
Ordered Logit
n nnj j j 1(6) P(y 1) F( x ) F( x )
Ordered logit
4
nj n4 3 nj 1
[P(y 1)] 1so that P(y 1) 1 F( x )
Likelihood
njyN 4
n nj j-1n=1 j=1
(7) L( , ) = F( - x ) -F( - x )
Marginal effects
n nnj j 1 j
n n n
P(y 1) F( x ) F( x );for j 1,2,3,4
x x x
Marginal effects
n11 n 1 n
n
n21 n 1 n 2 n 2 n
n
n32 n 2 n 3 n 3 n
n
n43 n 3 n
n
P(y 1)F( x )[1 F( x )]
x
P(y 1){F( x )[1 F( x )] F( x )[1 F( x )]}
x
P(y 1){F( x )[1 F( x )] F( x )[1 F( x )]}
x
P(y 1){F( x )[1 F( x )]}
x
Utility function, use of condoms
jnnj nj j 0,1; n 1, 2, , , N(10) U x ;
Probability of using condoms
K K
1k nk k nkk 0 k 0
n1 n0 K K K
0k nk 1k nk k nkk 0 k 0 k 0
n0k 1k 0k
exp( x ) exp( x )(11) P(U U )
exp( x ) exp( x ) 1 exp( x )
where
, and x 1.
Log likelihood
n1 n1N K K
y 1 yn nn1 n1k k
k 0 k 0n 1L( ) [ ( x )] [1 ( x )]
What money buys: clients of street sex workers in the US
• Maria Laura Di Tommaso, Marina della Guista, Isilda Shima and Steinar Strøm
Table A1. Dependent variable for the ordered logit
Frequency of sex with sex worker during last year . No of Obs 582Frequency per cent
=1 never 25.4
=2 once 27.0
=3 more than 1 but less than once per month 35.0
=4 1 to 3 times per month 12.5
VariablesOrdered Logit Logit: Probability of
being a “regular” client Logit: Probability of using condom
Education =1 college or more; =0 otherwise
0.160(0.194)
0.067(0.243)
0.067(0.474)
Work status =1 Full time; =0 otherwise 0.655**(0.281)
0.656*(0.347)
0.476(0.564)
Race =1 if non white; =0 white 0.491***(0.186)
0.201(0.226)
1.121**(0.576)
Job =1executives/business managers;=0 otherwise
-0.125(0.170)
-0.151(0.209)
-0.023(0.415)
Marriage =1 married; =0 otherwise -0.312*(0.173)
-0.118(0.213)
0.090(0.412)
Control dislike 0.276***(0.096)
0.220*(0.118)
-0.062(0.234)
Age 0.017*(0.009)
0.030***(0.011)
-0.031(0.020)
Factor1 'againstg ender violence' 0.181*(0.108)
0.274**(0.136)
0.464*(0.259)
Factor2 'against prostitution'
-0.159*(0.094)
-0.199*(0.112)
-0.400*(0.222)
Factor3 'sex workers not different and dislike their job'
0.198**(0.101)
0.200*(0.124)
-0.102(0.242)
Factor4 'like relationships' -0.536***(0.112)
-0.641***(0.137)
-0.351(0.266)
Factor5 'variety dislike' -0.968***(0.121)
-1.031***(0.151)
0.692***(0.281)
Factor6 'relationship troubles ' -0.026(0.109)
0.006(0.137)
0.482*(0.293)
Threshold 1 0.788(0.550)
Threshold 2 2.233***(0.559)
Threshold 3 4.452***(0.580)
Constant -2.501***(0.692)
3.643***(1.339)
# of observationsMcfaddens rho
5820.14
5820.18
5700.71
Table 7: Marginal effects in the ordered logit
VariablesNever with sex workers
Once with sex workers
More than 1 time but less then once per month
1 to 3 times per month
Education =1 college or more;=0 otherwise
-0.0269(0.033)
-0.012(0.014)
0.027(0.033)
0.012(0.014)
Work status =1 Full time; =0 otherwise
-0.123**(0.059)
-0.033***(0.008)
0.113**(0.048)
0.0429***(0.015)
Race =1 if non white;=0 white -0.077***(0.028)
-0.044**(0.018)
0.079***(0.029)
0.0425**(0.017)
Job =1executives/business managers=0 otherwise
0.02(0.028)
0.01(0.014)
-0.02(0.028)
-0.010(0.013)
Marriage =1 married; 0 otherwise 0.051*(0.0287)
0.026*(0.015)
-0.052*(0.029)
-0.025*(0.014)
Control Dislike -0.045***(0.016)
-0.023***(0.008)
0.046***(0.017)
0.022***(0.008)
Age -0.002**(0.002)
-0.001*(0.0008)
0.002*(0.0015)
0.001*(0.0007)
Factor1 'Against gender violence' -0.029*(0.018)
-0.015*(0.0094)
0.030*(0.018)
0.014*(0.0088)
Factor2 'Against prostitution' 0.026*(0.015)
0.013*(0.0083)
-0.026*(0.015)
-0.012*(0.0077)
Factor3 'Sex workers not different and dislike their job'
-0.032**(0.016)
-0.016*(0.009 )
0.033**(0.0172)
0.016*(0.0083)
Factor4 'Like Relationships' 0.088***(0.0186)
0.045***(0.011)
-0.09***(0.020)
-0.043***(0.009)
Factor5 'Variety dislike' 0.159***(0.02)
0.085***(0.015)
-0.162***(0.024)
-0.078***(0.012)
Factor6 'Relationship troubles' 0.004(0.017)
0.002(0.009)
-0.004(0.018)
-0.002(0.008)
Other examples
• Tax evasion and detection probabilities
• What is the chance for being detected when evading taxes?
4
*n n n
nj
*nn1 1
*nn2 1 2*nn3 2 3
*nn4 3
*nn5 4
; n 1,2, , , N
q
(1) q x
1 if individual n 'sanswer belongs to category j; j 1,2,3,4,5(2) q
0 otherwise
1 if q
q 1 if q
(3) q 1 if q
q 1 if q
q 1 if q
n nnj j j 1
5
nn5 4njj 1
(4) P(q 1) F( x ) F( x )
(5) [P(q 1)] 1so that P(q 1) 1 F( x )
The questions
What is the chance of being detected:
1. Will certainly be detected
2. Will almost certainly be detected
3. Will perhaps be detected
4. Will almost certainly not be detected
5. Will certainly not be detected