Skills for the 21st Century in LCR May, 2011 BANK TEAM: CRISTIAN AEDO, IAN WALKER, PABLO ACOSTA, ANA MARIA OVIEDO, JAVIER LUQUE CONSULTANTS: TIM GINDLING,

Skills for the 21st Century in LCR

May, 2011

B A N K T E A M : C R I S T I A N A E D O, I A N WA L K E R , PA B LO A C O S TA , A N A M A R I A OV I E D O, J AV I E R LU Q U E

C O N S U LTA N T S : T I M G I N D L I N G , G U I L L E R M O C R U C E S , L E O N A R D O G A S P I R I N I , G R E G V E R A M E N D I , N A N C Y

G U E R R A , K E N D O D G E , J O H N M I D D L E T O N

Outline

Context / motivation: LCR’s declining education earnings premia: is the education system to blame? Does it matter? What should we do about it?

Findings: Falling earnings premia Supply , demand and institutional factors underlying

the trend Should we worry?

Evidence on learning achievement and quality Evidence on the skill content of work and skill shortages.

Policy take-aways Reasons to be cheerful; Reasons for concern.

Future work

2

1. What has happened on education earnings premia?

3

Education earnings premia have declined…….

4

Evolution of education earnings premiums in LCR Coefficients on years of education variable from the Mincer-style monthly earnings regressions, full-time private

sector employees, basic OLS regressions

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

1990 1992 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Nicaragua El Salvador Peru Chile

…continuously for secondary; unevenly for tertiary (kink around 2003) ….

5

6

- Estimated earnings premiums by country, around 2008 Full-time private sector paid employees (asalariados), dependent variable is real monthly earnings.

i. OLS (not including parent's education as an explanatory variable)

Education Level Chile (2006)

Brazil (2008)

Costa Rica (2008)

Nicaragua (2005)

El Salvador (2008)

Colombia (2008)

Mexico (2008)

Peru (2008)

Uruguay (2008)

Years of Education 0.12 0.10 0.09 0.10 0.08 0.12 0.12 0.11 0.10 Vs. primary inc.

Primary comp. 0.11 0.26 0.12 0.29 0.11 0.21 0.19 0.15 0.20 Vs. primary comp.

Secondary tech. comp. 0.39 na 0.43 0.42 na na 0.69 na 0.40 Secondary ac. comp. 0.32 0.25 0.32 0.37 0.38 0.42 0.62 0.26 0.45 Vs. Secondary ac. Comp.

Non-univ. tertiary 0.44 na 0.25 0.31 0.42 0.43 0.30 0.37 0.53 University comp. 1.12 0.92 0.71 0.77 0.99 1.21 0.79 0.94 1.07

ii. OLS (including parent's education as an explanatory variable, data only for children living with parents)

Education Level Chile (2006)

Brazil (2008)

Costa Rica (2008)

Nicaragua (2005)

El Salvador (2008)

Colombia (2008)

Mexico (2008)

Peru (2008)

Uruguay (2008)

Years of Education 0.10 0.09 0.08 0.10 0.08 0.11 0.12 0.10 0.07 Vs. primary inc.

Primary comp. 0.07 0.21 0.07 0.30 0.14 0.16 0.21 0.16 0.18 Vs. primary comp.

Secondary tech. comp. 0.26 na 0.37 0.50 na na 0.63 na 0.26 Secondary ac. comp. 0.22 0.22 0.26 0.40 0.33 0.54 0.55 0.29 0.28 Vs. Secondary ac. comp.

Non-univ. tertiary 0.31 na 0.36 0.31 0.51 0.34 0.52 0.27 0.32 University comp. 0.93 0.71 0.66 0.72 0.78 0.97 0.77 0.78 0.73

Note: Regressions also control for potential experience, gender and region.

… but remain high, even when controlling for parents’ education

2. Understanding the trends: supply and demand factors

7

Educational expansion is transforming emerging cohort in LCR (25-35 non students)

8

23 20 16

15 1514

2721

18

2125

28

14 19 23

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1997 2003 2008

Colombia

14 16 17 16 16 15 16 15 15 15 14 13

36 37 37 35 36 35 34 34 33 30 32 31

21 20 19 21 20 19 20 21 21 22 21 21

15 14 13 14 13 14 14 13 13 14 15 15

13 12 13 14 15 16 16 17 17 19 18 20

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Costa Rica

Tertiary

Secondary comp.

Secondary inc.

Primary comp.

Primary inc.

38 37 34 32 33

11 11 10 9 9

23 23 24 25 24

16 18 20 21 22

11 12 12 13 12

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1998 2001 2003 2006 2008

El Salvador

28 25 22 18 16 15 14 14 14 14

24 2320

20 20 19 17 16 18 16

2323

27 31 33 33 39 37 37 37

13 15 17 16 16 17 14 15 15 15

13 13 14 13 13 15 16 17 17 18

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1992 1994 1996 1998 2000 2002 2004 2005 2006 2008

Mexico

Tertiary

Secondary comp.

Secondary inc.

Primary comp.

Primary inc.

60 57 57 56 54 53 50 48 46 44 41 38 36

11 12 11 12 12 11 11 11 11 10 10 11 11

5 5 5 5 5 5 5 5 5 5 6 5 6

17 18 19 19 21 23 25 26 28 30 32 33 34

8 8 8 8 8 9 9 10 10 11 12 13 14

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1995 1996 1997 1998 1999 2001 2002 2003 2004 2005 2006 2007 2008

Brazil

23 21 20 16 15 12 10

12 12 1110 11

10 9

20 21 2017 16

1514

27 30 3334 36

37 40

18 15 16 22 22 26 27

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1990 1992 1994 1998 2000 2003 2006

ChileTertiary

Secondary comp.

Secondary incomp.

Primary comp.

Primary inc.

Mean years of education

9

But other regions are doing better

10

0%

20%

40%

60%

80%

100%19

50

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Latin America and the Caribbean

No educ Primary Secondary Tertiary

0%

20%

40%

60%

80%

100%

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Eastern Europe and Central Asia


0%

20%

40%

60%

80%

100%

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Asian Tigers


0%

20%

40%

60%

80%

100%

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Advanced economies


… and we are still miles behind

11

67

89

10

11

Ave

rag

e ye

ars

of s

cho

olin

g fo

r 1

5+ p

opul

atio

n

1990 2010Year

Latin America and the Caribbean Advanced economies

East Asian Tigers Eastern Europe and Central Asia

Educational attainment 1990-2010

Demand shifters seem to be the main drivers of falling wage premia (Katz-Murphy aproach)

12

Changes in the Wage Premium and the Supply and Demand for Skilled (Tertiary Educated) vs. Unskilled (Other Educational Groups) Workers

Source: Own calculations based on microdata from household surveys. Details on sample years, survey and

methodology are available in Acosta, Cruces, Gasparini (2011).

σsu=2 σsu=31990s 2000s 1990s 2000s 1990s 2000s 1990s 2000s

Argentina 3.5 -2.4 4.6 2.4 11.5 -2.3 15.0 -4.7Bolivia 7.9 -4.6 -0.2 5.1 15.6 -4.1 23.5 -8.7Brazil -0.4 -3.2 1.6 4.4 0.8 -1.9 0.4 -5.1Chile 0.5 -1.9 3.1 1.1 4.1 -2.7 4.6 -4.7Colombia 2.5 -2.0 6.4 6.0 11.5 2.1 14.0 0.1Costa Rica 0.4 -0.2 4.0 3.4 4.9 3.0 5.3 2.8Ecuador -3.2 3.4 -3.0 -6.3El Salvador 1.7 -0.1 5.5 -0.3 8.9 -0.4 10.6 -0.5Honduras 0.0 -1.9 2.6 2.3 2.6 -1.4 2.6 -3.3Mexico 1.8 -2.8 3.6 2.2 7.2 -3.5 9.0 -6.3Nicaragua 3.5 -6.9 4.6 6.6 11.6 -7.2 15.0 -14.1Panama 0.3 -2.3 2.3 2.4 2.9 -2.2 3.1 -4.4Paraguay 0.8 -5.6 5.3 6.1 6.9 -5.2 7.6 -10.8Peru 0.6 -2.8 0.2 3.8 1.3 -1.8 1.9 -4.6Uruguay 2.2 -1.3 2.9 -0.8 7.3 -3.5 9.6 -4.8Venezuela 1.1 -4.8 3.9 4.2 6.2 -5.4 7.3 -10.3Mean 1.8 -2.9 3.4 3.3 6.9 -2.5 8.6 -5.4

Relative demand Relative demandRelative supplyWage premium

in Cono Sur…

13

Argentina Brazil

Paraguay Uruguay

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.6019

86

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Wag

e Pre

miu

m In

dex

Dem

and

and

Supp

ly In

dexe

s

Relative supply Relative demand Wage premium

-0.20-0.15-0.10-0.050.000.050.100.150.200.250.30

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

Wag

e Pre

miu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Wag

e Pre

miu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


0.000.050.100.150.200.250.300.350.400.45

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Wag

e Pre

miu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


The Andes…

14

Bolivia Chile

Peru Venezuela

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

-0.20-0.100.000.100.200.300.400.500.600.700.80

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Wag

e Pr

emiu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


-0.15

-0.10

-0.05

0.00

0.05

0.10

0.000.050.100.150.200.250.300.350.400.450.50

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Wag

e Pr

emiu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Wag

e Pr

emiu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Wag

e Pr

emiu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


…and Central America

15

Costa Rica Honduras

Mexico Panama

-0.05

0.00

0.05

0.10

0.15

0.20

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Wag

e Pr

emiu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

Wag

e Pr

emiu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Wag

e Pr

emiu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Wag

e Pr

emiu

m In

dex

Dem

and

and

Supp

ly In

dexe

s


In both decades changes in skills demand reflect mainly “within” sector effects

16

Source: Own calculations based on household surveys. Details on sample years, survey and methodology are available in Acosta, Cruces, Gasparini (2011).

Minimum wages likely played a role in compressing secondary skill premia

17

Evolution of Real Minimum Wages, 1995-2007

Source: Oficina Internacional de Trabajo (ILO), Santiago office, "Evolucion de los salarios en America Latina," mimeo, Santiago, October 2008

0

50

100

150

200

250

300

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Argentina

Brazil

Chile

Colombia

Costa Rica

El Salvador

Mexico

Peru

Uruguay

…when the MW (blue line) trends up, secondary premium (red line) trends down

18

Comparing the real minimum wage index with secondary and university earnings premia

0.00

0.50

1.00

1.50

2.00Brazil

University Secondary Real MW

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1990 1992 1994 1996 1998 2000 2003 2006

Chile


0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1997 2003 2008

Colombia


0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1998 2000 2002 2004 2006 2008

Costa Rica


3. Should we worry?

19

Secondary expansion has not eroded learning attainment

20

- PISA results Math Read 2000 2009 2000 2009 Argentina 387 389

412 399

Brazil 334 386

391 412 Chile 383 421

409 450

Mexico 390 418

422 425 Peru 293 365

323 370

2003 2009

2003 2009

Uruguay 422 427

435 426

2006 2009

2006 2009

Colombia 370 381 385 413 Source: OECD

Better grade-age correspondence is an important trend …..

21

Average Grade Attainment in PISA 2000 2009 Argentina 9.6 9.4 Brazil 8.5 9.1 Chile 9.5 9.7 Mexico 9.4 9.5 Peru 9.3 9.8

2003 2009

Uruguay 9.5 9.4

2006 2009

Colombia 9.6 9.7 Source: Authors´ elaboration based on OECD.

…linked to improved equality of educational opportunity in many countries

22

… and contributing to improved PISA scores, apart from Argentina and Uruguay.

23

Importance of Grade Attainment on PISA Mathematics scores

Country BASE YEAR (BY) YEAR 2009 ADJUSTMENT % EXPLAINED BY

(STR_BY*SCORE_BY) (STR_09*SCORE_09) (STR_09*SCORE_BY)GRADE

STRUCTUREA B C (C-A)/(B-A)

Base year 2000Argentina 387 388 366 -2,100%Brazil 334 386 355 41%Chile 383 421 398 40%Mexico 390 418 392 7%Peru 293 365 312 26%Base year 2003Uruguay 423 427 415 -200%Base year 2006Colombia 370 381 373 27%

STR: Estimated structure

Tertiary quality also appears stable, based on analysis of earnings variance

24

Comparing the coefficient of variation of real monthly earnings to earnings premiums for workers with university education

0.000.200.400.600.801.001.201.401.601.802.00

1995 1996 1997 1998 1999 2001 2002 2003 2004 2005 2006 2007 2008

Brazil

Earnings Premiums for University Complete Education (Basic regressions, full-time paid employees)

Coefficient of Variation (for the earnings of those with a university complete education)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1990 1992 1994 1996 1998 2000 2003 2006

Chile

Earnings Premiums for University Complete Education (Basic regressions, full-time paid employees)Coefficient of Variation (for the earnings of those with a university complete education)

0.00

0.50

1.00

1.50

2.00

2.50

1997 2003 2008

Colombia



0.00

0.20

0.40

0.60

0.80

1.00

1.20

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Costa Rica



0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

El Salvador



0.00

0.50

1.00

1.50

2.00

2.50

1992 1994 1996 1998 2000 2002 2004 2005 2006 2008

Mexico



But there ARE reasons to worry

East Asian trends look very different The quality gap for LAC in PISA scores

remains large LAC’s pattern of occupational expansion is

not at the cutting edge Signs of skill shortage and mismatch

25

Asian tigers: a contrasting pattern of increasing attainment AND premia

26

Based on PISA, LCR’s secondary quality gap remains big, versus OECD

27

Comparison of LAC countries and OECD in PISA*

* LAC line represents the average performance of Argentina, Brazil, Chile, Mexico, and Peru in PISA. Source: Own elaboration based on OECD.

MATH READING

300

350

400

450

500

550

2000 2003 2006 2009

OECD (Non LAC) LAC

300

350

400

450

500

550

2000 2003 2006 2009

OECD (Non LAC) LAC

and LCR under-performs, relative to income level

28

ARGBRA

CHL

COL

MEX

PANPER

TTOURY

200

300

400

500

600

MA

TH S

CO

RE

-2 0 2 4 6LOG GDP PER CAPITA

2009 PISA MATH SCORE AND GDP PER CAPITA

ARG

BRA

CHL

COL

MEX

PANPER

TTOURY

300

350

400

450

500

550

MAT

H S

CO

RE

-2 0 2 4 6LOG GDP PER CAPITA

2009 PISA READING SCORE AND GDP PER CAPITA

LACLAC

LCR skill quotients are inferior to USA, for high end “new economy” skills (Autor- Levy-Murnane approach)

29

Distribution of skills in the labor force1

USA Brazil Costa Rica Mexico Nicaragua

El Salvador

Non routine Cognitive Analytical 3.04 2.81 2.80 2.76 2.66 2.65 Non Routine Cognitive Interpersonal 3.10 2.95 2.95 2.95 2.90 2.86 Routine Cognitive 3.13 3.05 3.03 2.94 2.93 2.99 Routine Manual 2.47 2.63 2.72 2.63 2.76 2.70 Non Routine Manual Physical. 2.31 2.55 2.58 2.60 2.70 2.62 1/ Scale 1 to 5. A score of 5 denotes high importance of skill.

Suggesting possibility that LCR skill use evolution reflects supply constraints….

30

Dynamic trends in skill distribution in Brazil, Costa Rica and Nicaragua compared with the USA

NRCA: Non Routine Cognitive Analytical, NRCI: Non Routine Cognitive Interpersonal, RC: Routine Manual, NRMP: Non Routine Manual Physical

303540455055606570

1980 2008

USA

NRCA NRCI RC RM NRMP

303540455055606570

1981 2009

BRAZIL


303540455055606570

2001 2009

COSTA RICA


303540455055606570

2001 2009

NICARAGUA


Home country education quality correlates to immigrant graduate skill uses in US market

31

- Correlation of education quality in migrants’ country of origin and skill content of work done in the USA

Mexico

Costa Rica

El Salvador

Guatemala

Nicaragua

Panama Cuba

Dominican Republic

Argentina

Brazil

Chile

Colombia

Ecuador

Paraguay

Peru

Uruguay

2.8

2.9

33

.13

.23

.3A

nal

ytic

al s

kills

- s

cale

1 to

5

400 450 500 550 600 650SERCE SCORE

Includes only individuals with tertiary edu.

CONTENT OF LABOR FORCE BY COUNTRY OF ORIGINCOGNITIVTE SKILLS AND ANALYTICAL SKILL

Mexico

Costa Rica

El Salvador

Guatemala

Nicaragua

Panama

Cuba

Dominican Republic

Argentina

Brazil

Chile

Colombia

Ecuador

Paraguay

Peru

Uruguay

33

.05

3.1

3.1

53

.23

.25

Inte

rper

sona

l ski

lls -

sca

le 1

to 5

400 450 500 550 600 650SERCE SCORE

Includes only individuals with tertiary edu.

CONTENT OF LABOR FORCE BY COUNTRY OF ORIGINCOGNITIVTE SKILLS AND INTERPERSONAL SKILL

Enterprise survey evidence on time to fill skilled vacancies in LCR reinforces this message…

32

Average time to fill vacancy by regions of the world

… as does the evidence on time taken by innovative firms: a constrained sector?

33

Average weeks to fill vacancy by firm’s innovation status

3. Conclusions

34

Reasons to be cheerful LCR’s large education expansion hasn’t eroded

quality Downturns in skill premia likely reflect easing of

(relative) shortages (winning Tinbergen’s race) rather than erosion of quality (Katz-Murphy analysis)

Improved outcomes for children from poor backgrounds in some countries (EOI)

Reduced skill premia help reduce Ginis and poverty Institutional factors such as minimum wages have

helped, in most places Skill premia still high enough to stimulate demand

for secondary and tertiary education

35

Reasons to worry Big quality gap may be constraining path of

development East Asia still has rising premia in spite of higher

and faster growth of attainment: not the same thing?

LCR lags OECD by the equivalent of 2 years schooling @ age 15 (PISA).

Quality improving only slightly and due to better grade-age matches, not intra grade efficiency.

Shortage of New Economy skills may constrain growth at cutting edge: a second class continent?

36

Future work Improving our understanding of skill – labor

market linkages Getting data with direct observations of skills,

beyond the “Big Five”. High end (New economy skills) Low end (social literacy skills)…. New skills surveys in FY 12 in Bolivia, Colombia, El

Salvador (MDTF, BNPP) Improving regulatory information in TVET

Benchmarking methodology designed Piloting underway in Argentina Rollout next year

37

Thank you

38

Skills for the 21st Century in LCR May, 2011 BANK TEAM: CRISTIAN AEDO, IAN WALKER, PABLO ACOSTA, ANA MARIA OVIEDO, JAVIER LUQUE CONSULTANTS: TIM GINDLING,

Documents

lac slide

world slide

university education

parents education slide

john middleton slide

secondary skill premia

earnings premia supply

lcr skill quotients