the place premium lant pritchett (paper with michael clemens, cgd and claudio montenegro, world...
Post on 15-Jan-2016
225 views
TRANSCRIPT
The Place Premium
Lant Pritchett(paper with Michael Clemens, CGD and Claudio
Montenegro, World Bank)LEP Lunch/Development Seminar
Sept 29, 2008
Outline of the presentation
• Empirical estimates of wages differences for observationally equivalent workers on opposite sides of the US Border
• Addressing the issue of migrant self-selection– Simulation with residuals– Data from Latin America– One true experiment– Comparison with macro growth accounting– Experiences with spatially distinct but open borders
• Comparisons of (adjusted) wage gaps with other “similar” numbers (wage discrimination, etc.)
Drilling down through wage surfaces
ln(w)
X (e.g. education, age)
Bolivian (born, educated) workers in Bolivia
USS (born, educated) workersIn USA
Bolivian (born, educated) workersIn USA
)(w
)(w
Bol
USA0
X
XR
New collection of data sets
• 2,015,411 formal-sector wage-earners in 43 countries
• 42 different countries wage surveys—of wage earners– Wages (converted to monthly, PPP)– Country of birth– Amount and country of schooling– Age/experience– Gender– Rural/urban
Comparison of our wage survey results with labor value added per worker
SLE
ETH
TCD
NGA
UGA
YEM
BGD
NPL
KHMVNM
HTIGHA
CMRPAK
BOL
INDIDN
HND
ECU
LKA
JAMNIC
PHL
GUYMARPRY
PERTHA
EGY
GTM
COL
JOR
VEN
PANBLZ
BRA
DOMTURURYCRIZAF
MEXCHL
ARG
USA
62.
51
252
505
001
000
200
04
000
Avg
. P
PP
wag
e, lo
g sc
ale
62.5 125 250 500 1000 2000 4000PPP Labor income, 0.65 share, log scale
45 deg. line Cubic fit w/o HND
The “formal sector” is a bigIssue for the poor African Countries In the sample
We just drop Honduras
Combine with PUMS US Census
• Wages of individuals, with country of birth and age at arrival plus– Schooling– Age– Sex– Urban/rural residence
Results of the wage surface drilling: foreign born, foreign educated (late arrivers), high school or less educated, 35
year old, males, in urban areas in USA vs home
Ratio of US to country wages
Comparing foreign born, foreign educated in US to in home
R0
Predicted annualized wages (2000 PPP)
In US In Home Absolute gap
Mean 7.3 5.1 $20,764 $5,352 $15,411
Median 6.2 4.1 $19,972 $4,675 $15,438
Selected Countries of Interest
Nigeria (2nd highest) 13.5 14.9 $18,394 $1,238 $17,155
Haiti 23.5 10.3 $17,428 $1,690 $15,738
India 10.9 6.3 $23,024 $3,684 $19,340
Philippines 6.2 3.8 $18,436 $4,820 $13,615
Brazil 5.0 3.8 $23,725 $6,302 $17,423
Mexico 3.8 2.5 $17,650 $6,971 $10,679
Dom Rep. (lowest) 3.3 2.0 $17,897 $8,984 $8,912
0
5
10
15
20
25
30Ye
me
n
Nig
eri
a
Egyp
t
Hai
ti
Cam
bo
dia
Sie
rra
Leo
ne
Gh
ana
Ind
on
esi
a
Pak
ista
n
Ve
ne
zuel
a
Cam
ero
on
Vie
tnam
Ind
ia
Jord
an
Ecu
ado
r
Bo
livia
Sri L
anka
Ne
pal
Ban
glad
esh
Uga
nd
a
Eth
iop
ia
Gu
yan
a
Ph
ilip
pin
es
Pe
ru
Bra
zil
Jam
aica
Ch
ile
Nic
arag
ua
Pan
ama
Uru
guay
Gu
ate
mal
a
Co
lom
bia
Par
agu
ay
Sou
th A
fric
a
Turk
ey
Arg
en
tin
a
Me
xico
Be
lize
Thai
lan
d
Co
sta
Ric
a
Mo
rocc
o
Do
min
ican
Re
p.
Ro
Estimates of R0 (predicted wages of observationally workers across the US border) for 42 countries with
95% confidence intervals
38/42 can reject bigger than 1.5
32/42 cannot reject bigger than 4
All kinds of comparability issues: but the biggest is PPP
• Gross versus net• Inclusion of benefits (in-
kind, entitlements) or not• Valuation of workplace
amenities (e.g. safety regulation)
• But we suspect the biggest is imputation of the location of consumption (in US versus home)
Estimates of R0 at various fractions at PPP versus official exchange rates
100%(base)
80% 40% 0%
Mean 5.1 5.8 8.3 16.4
Median 4.1 4.9 7.2 13.9
• Remittances about 20 percentFor Mexicans• Remittances/savings about 60For Philippines overseas workers• Think “optimal” savings of temporaryworker
How much of the observed wage differentials of observationally equivalent workers represent
border restrictions vs. selection or home preference?
• Six different methods/data for examining wage selection, all of which suggest our predicted mean wage ratios of observationally equivalent workers over-state wage ratios of equal intrinsic productivity workers by between 1 and 1.4.
The question of selection on unobservable
• Our estimates of compare what those who moved to US make versus what those who are observationally equivalent make in home.
• But those who did move might have made more than the o.e. counter-parts so R0 overstates the gain
• We are not talking about the upper end but the low skill end—people making 10$/hour
• Not obvious that there is positive self-selection on unobservable productivity in the home market—theory is that people would maximize the gain from moving if either:
– productivity is a market match phenomena (e.g. having an uncle with a good business), or
– Individually differential obstacles (e.g. family unification visas)
then one might expect zero or negative selection.
0.0
0.2
0.4
0.6
0.8
Kern
el
densi
ty
0 5 10 15Component plus residual from ln(wage) regression
USA born, USA res, USA educ IND born, IND res, IND educ
IND born, USA res, USA educ IND born, USA res, IND educ
IndiaR0 compares means
Could compare to otherpercentile of the home distribution of unobservables,e.g. 70th
1st approach: Wage ratios under various assumptions about where in the home distribution
of unobservables migrants came
50th 70th 90th 95th
Median across countries 4.5 3.4 2.1 1.6
Ratio to assumption of 50th 1.34 2.20 2.85
Selected Countries of Interest
Nigeria 10.34 6.92 4.24 3.49
Haiti 8.76 4.08 1.34 0.86
India 7.05 5.16 3.28 2.6
Philippines 3.77 2.73 1.76 1.44
Brazil 4.23 3.2 2.03 1.6
Mexico 3.32 2.44 1.57 1.24
Dominican Rep. 2.95 2.26 1.71 1.07
Wage ratios of equally productive workers at various assumptions of source of migrants in
distribution of unobservables
0
1
2
3
4
5
6
7
8
9
10
Egyp
tYe
men
Nig
eria
Sier
ra L
eone
Jord
anVe
nezu
ela
Indo
nesi
aPa
kist
anVi
etna
mIn
dia
Nep
alCa
mer
oon
Cam
bodi
aEc
uado
rBa
ngla
desh
Sri L
anka
Gha
naG
uyan
aBo
livia
Jam
aica
Braz
ilCh
ileTu
rkey
Ethi
opia
Uga
nda
Phili
ppin
esPa
nam
aPe
ruN
icar
agua
Colo
mbi
aPa
ragu
ayU
rugu
ayBe
lize
Arge
ntina
Gua
tem
ala
Mex
ico
Cost
a Ri
caSo
uth
Afri
caM
oroc
coH
aiti
Thai
land
Dom
inic
an R
ep.
Re
30th
50th
70th
90th
95th
2nd Approach: Data from the Latin American Migration Project (LAMP)
• Tracks migrants from seven Latin American countries and does surveys in their origin localities of non-migrants
• Wage histories of migrants including last wage before migrating
• Compare wages of migrants before moving and non-migrants, with distribution of residuals
Distribution of the unobserved component on wages (residuals) in home for migrants and non-
migrants: Mexico0.
00.
20.
40.
60.
8K
ern
el d
ensi
ty
0 2 4 6 8 10ln(wage)
Migrant in home Non-migrant in home
Mean migrant at 53rd
Percentile of non-migrants
0.0
0.2
0.4
0.6
0.8
Kern
el
densi
ty
0 5 10 15Component plus residual from ln(wage) regression
USA born, USA res, USA educ MEX born, MEX res, MEX educ
MEX born, USA res, USA educ MEX born, USA res, MEX educ
Mexico
Actual distributionOf residuals for MexicoSo we can compute 50th
Of movers to 53rd of home
Distribution of the unobserved component on wages (residuals) in home for migrants and non-
migrants: Haiti0.
00.
10.
20.
30.
4K
ern
el d
ensi
ty
2 4 6 8 10 12ln(wage)
Migrant in home Non-migrant in home
Mean migrant at 61st
Percentile of non-migrants
0.0
0.2
0.4
0.6
0.8
Kern
el
densi
ty
-5 0 5 10 15Component plus residual from ln(wage) regression
USA born, USA res, USA educ HTI born, HTI res, HTI educ
HTI born, USA res, USA educ HTI born, USA res, HTI educ
Haiti
Typical migrant percentile in distribution of non-migrants' unobserved component of wages
Mean migrant: 53 50 54 58 51 61 69
Median migrant: 49 50 50 50 50 64 62
Ratio of migrant home wage to non-migrant home wage, conditional on observables = exp(βmigrant)
1.07 1.00 1.10 1.19 1.06 1.46 1.42
US wage (our data)
1471 1553 1561 1606 1491 1452 1714
Non-migrant wage (our data)
581 529 443 775 749 141 452
Ro 2.53 2.94 3.52 2.07 1.99 10.31 3.79
Re 2.37 2.93 3.19 1.74 1.89 7.07 2.67
Ro/Re 1.07 1.00 1.10 1.19 1.06 1.46 1.42
México Guatemala Nicaragua Costa Rica Dominican Rep.
Haití Perú
Results from 7 countries
• The medians of the migrant and non-migrants are exactly the same for 5 of the 7—the selection is mostly an upper tail thing
• Using the means to adjust out Ro estimates lowers them by a ratio of between 1 (no adjustment for Guatemala) to 1.46 (Haiti)
• In no country is the typical migrant from as high as the 70th percentile of non-migrants (which, from table above, implies an adjustment of 1.34 using the actual residuals data).
3rd Approach: Comparison with experimental estimates of wage effects
• Movers from Tonga to New Zealand chosen from applicants based on a lottery
• OLS wage ratio: 6.14 (chosen versus all stayers)
• Experimental wage ratio: 4.91 (foreign wages of randomly selected chosen versus home wages of applicants).
• Bias from not correcting for selection: 6.12/4.91=1.25
4th Approach: Comparison to macro growth decomposition (Hall and Jones)
Hall and Jones estimates
Ratio wage based
estimates to macro
accountingR
estimates,
Ratio of USA A and K
to country A and K
Ratio of USA A
to country
A
Median 3.82 3.07 2.44 1.25
Average 5.11 3.69 2.71 1.39
Average without fouroutliers 4.53 3.92 2.90 1.16
5th approach: Use comparisons of average wages of observationally equivalent in home
and foreign (allowing for country specific schooling)
• Doesn’t involve movers at all—so should understate the marginal mover if there is positive selection.
• In fact, these are larger than bilateral estimates• But one has to correct for the quality of
schooling as S in Bolivia is not S in USA• Under various plausible adjustments of S
“evaporation” suggest selection at most increases R0 by factor of 1.2
6th approach: wage ratios in spatially distinct but legally integrated labor
markets: Puerto Rico
1.3 1.4 1.4 1.5 1.6 1.8 1.8
0
1
2
3
4
5
Guam=1.36
When borders were open wage ratios above 2 caused massive mobility, leading to wage
convergence0
12
34
5
1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930
Germany Great BritainI reland I talyNorway Sweden
Shall I compare thee to a summer’s day…thou are much bigger
• Wage discrimination—comparing wage discrimination against disfavored social groups within borders to consequence of local of birth/citizenship/market access based wage differentials
• Border differentials in prices of goods or capital
• Impacts of poverty programs
Our average cross-border wage differential (5.1) is larger by a factor of 3 than racial
discrimination in the US in 1939
1.1 1.1 1.3 1.4 1.4 1.4 1.61.9
0
1
2
3
4
5Using our wage data we can estimate the largest discrimination against females in the world, Pakistan, 3.1
Using historical data one can estimate the gap between marginal product (rental price) and subsistence wage for 19th century North American slaves: around 3.8
Estimates of the remaining price gaps across countries
0
10
20
30
40
50
60
Me
an
pe
rce
nta
ge
ab
so
lute
va
lue
d
iffe
ren
ce
in p
rod
uc
er
pri
ce
s a
cro
ss
g
oo
ds Canada-USA
Germany-USAUK-USAJapan-USA
Source: Bradford and Lawrence, 2004
Combination of small price gaps and large wage gaps implies the estimated gains from even minor relaxations in labor mobility are big relative to the
largest gains in remaining trade liberalization
79.5 86
305
Bil
lio
ns
Doubling net ODA
Net gains to developingcountries fromliberalization in Doharound
Value of welfare gains tocurrent developing countryresidents (including gainsto movers)from 3% ofOECD labor force increase
`
Source: Winters et al 2004
Comparing estimated gains from anti-poverty interventions in poor countries to wage differences
Intervention Country
Present-value lifetime income
increment due to
intervention (US$ at PPP)
Annual wage difference of
observationally equivalent
male low skill worker
Weeks of US work equivalent to lifetime NPV of intervention
Microcredit Bangladesh 700 $14,891 2.4Anti-
sweatshop Indonesia 2,700 $17,478 8.0Additional year
of schooling Bolivia 2,250 $15,455 8.0
Deworming Kenya 71 $16,265 0.2
Conclusion
• Massive gaps in wages between observationally equivalent workers in 42 poor countries—average 5.1, median 4.1--$15,000 per year (PPP)
• The bulk of the evidence suggests that the self-selection might cause these to overstate gains from movement of unskilled workers by a modest amount (scale back by between 1 and 1.4)
• These make the wage differentials across borders:– Bigger than any wage discrimination– Bigger than any price distortion due to borders– Bigger than any poverty impact
by factor multiples (if not orders of magnitude)