Permanent effects of economic crises on household welfare: Evidence and projections from

Argentina’s downturns

Guillermo CrucesPablo GluzmannCEDLAS – Universidad Nacional de La Plata, CONICETLuis Felipe López-CalvaUNDP-RBLAC

NIP Network MeetingNew Orleans, Tulane University, April 9 2010

One slide presentation Objective: estimate the impact of the economic

crisis on household welfare. Extension: document permanent effects. Motivation:

Transient or permanent? Not easy to find (beyond poverty & employment) Ambiguous impacts for some outcomes

Use data from Argentina’s up and downs to estimate crisis effects, and extrapolate.

Significant effects on maternal and infant outcomes, more ambiguous for education.

What is not included: flawless id strategy, fancy IV estimates.

© Hugo Ñopo, 2006

Motivation and objective

Motivation Part of a regional UNDP-RBLAC project

on the impact of the recent financial crisis on household welfare in LAC.

Case study for Argentina. Objective: estimate the impact of the

economic crisis on household welfare. Feasible short term estimates:

No ideal data (in fact no data in ARG) No flawless identification strategy

Objective No contemporary data for Argentina… access to

surveys, quality, prices, and GDP. Exploit Argentina’s up and downs to estimate

past crisis effects and extrapolate. Some relatively obvious (& easy) impacts:

Poverty Unemployment

Extension (and focus): permanent effects. Motivation:

Some seem obvious, but not easy to document (e.g.: Ernesto’s paper later today)

Ambiguous impacts for some outcomes

200,000

220,000

240,000

260,000

280,000

300,000

320,000

340,000

360,000

380,000

400,000

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

7%

GDP % of change in GDP

GDP and changes in GDP

Discussion: Potential effects of crises

Ambiguous effects of crises: Ferreira and Schady’s 2009 framework

Aggregate income shocks can have ambiguous impacts on investments in child education and health.

Not necessarily pro-cyclical: income and substitution effects.

In F&S: income effect dominates for poorer countries, with less developed credit markets, and less protected public spending.

Ambiguous effects in middle-income countries.

Previous evidence Crises result in higher school enrollment:

Brazil (Duryea and Arends-Kuenning 2003) Mexico (McKenzie 2003) Argentina (Lopez-Boo 2008) Peru (Schady 2004)

Negative shocks to crop prices increase enrollment: Nicaragua (Maluccio 2005) Brazil (Kruger 2007)

Crises increase infant mortality: Mexico (Cutler et al. 2002) Peru (Paxson and Schady 2005)

+ This project: Aguero & Valdivia, …

Source: Ferreira and Schady, Aggregate Economic Shocks, Child Schooling and Child Health, WBRO and PEGNET Presentation, 2009.

Protected?

Social expenditure as percentage of GDP

3.0

3.5

4.0

4.5

5.0

5.5

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

6.0

6.5

7.0

7.5

8.0

8.5

Education Health Employment, assistance, housing & infraestructure Social Security

Estimation strategies

Estimation strategy Objective: estimate the impact of the current

crisis on socioeconomic outcomes. Methodology: projections based on past

experience.1. Methodology 1: growth elasticities of relevant

outcomes over 15 years (provincial panels – id through regional variation).

2. Methodology 2: estimates of the impact of the 2001 crisis (difference in differences - ad hoc/upper bound).

Outcomes: schooling, child and overall poverty, maternal and infant mortality, low weight at birth.

GDP elasticities (regional data)

Compute growth elasticities of the outcomes.

Limitation at the national level: Short time span – little variation.

Solution: Exploit time and regional variability from a panel of per capita GDP and outcomes at the province (25) level to identify the relevant elasticities.

Yjt denotes the outcome of interest for province j in time t.

Xjt are a series of covariates (e.g. gender composition and mean age) for each province

Fj captures regional fixed effects. is the semi-elasticity of the selected outcome with

respect to per capita GDP. Regression with fixed effects at the province level,

weighted by province population, robust standard errors.

jtjjttjtjtjtj FXGDPpcGDPpcYY ''loglog 11

Fixed Effect Model

Poverty

children 1.25 USD

Poverty children 2.5

USD

Poverty children 4

USD

Poverty 1.25 USD

Poverty 2.5 USD

Poverty 4 USD

Maternal mortality

Children mortality

-0.4192 -0.6610 -0.5859 -0.2389 -0.4675 -0.5398 -3.3174 -4.0781(7.28)*** (7.96)*** (6.82)*** (6.59)*** (7.11)*** (6.61)*** (1.96)* (1.79)*-0.0001 -0.0065 -0.0155 -0.0019 -0.0071 -0.0115 -0.0891 -0.0340(0.01) (0.77) (1.78)* (0.49) (1.23) (1.65) (0.38) (0.14)0.2877 -0.1766 -0.4027 0.0581 -0.1035 -0.2031 -5.4329 -39.2225(0.39) (0.19) (0.40) (0.13) (0.15) (0.24) (0.25) (1.71)*

-0.1255 0.3044 0.6986 0.0382 0.2849 0.4731 5.3942 19.1320(0.24) (0.49) (1.05) (0.13) (0.64) (0.84) (0.35) (1.37)

Observations 235 235 235 235 235 235 244 244R-squared 0.389 0.498 0.405 0.384 0.488 0.454 0.027 0.070

Dependent Variable: Change for each socioeconomic indicator

% change in GDPpc

Mean age

Male rate

Constant

Strongly significant effects on poverty (expected)Significant effects on health outcomes

and no significant effects on educational indicators

No significant negative effects on education (an ambiguous impact in years of education)

School attendance 6 to 12 years

old

School attendance

13 to 17 years old

School gap 6 to 12 years

old

School gap 13 to 17 years old

Years of education 6 to 12 years

old

Years of education 13 to 17 years

old

Years of education

Not working nor attending school 13 to 17 years old

0.0180 -0.0530 -1.5171 -0.7750 0.6840 0.4745 1.6455 -0.0152(0.72) (0.49) (1.83)* (1.16) (1.07) (0.57) (2.37)** (0.16)

-0.0344 0.0552 0.1931 0.2473 -0.3157 -0.1849 -0.8465 -0.0461(0.91) (0.80) (0.73) (0.88) (0.51) (0.67) (2.70)*** (0.68)

-0.0011 -0.0065 0.0287 0.0100 0.0449 0.0355 0.0009 0.0038(0.87) (1.49) (0.95) (0.28) (1.37) (0.64) (0.03) (1.01)0.0455 0.6714 5.2069 -0.4839 -2.9136 1.4048 -4.7483 -0.8212(0.40) (1.07) (1.68)* (0.14) (0.96) (0.31) (1.50) (1.39)0.0127 -0.1026 -3.3094 -0.0579 -0.0716 -1.8028 2.2137 0.2694(0.17) (0.31) (1.74)* (0.02) (0.03) (0.50) (1.02) (0.88)

Observations 235 235 235 235 235 235 235 235R-squared 0.047 0.095 0.101 0.038 0.062 0.040 0.100 0.086

Dependent Variable: Change for each socioeconomic indicator

Constant

% change in GDPpc (Positives)

% change in GDPpc (Negatives)

Mean age

Male rate

Also: asymmetrical relationship for growth and recession periods

Asymmetrical relationship for growth and crisis periods

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

-15%

-10%

-5%

0%

5%

10%

15%

Change in PP of poverty 2.5 USD (inverted) GDPpc Growth

Larger poverty & child mortality effects during crises, no effect on maternal mortality

Poverty children 1.25

USD

Poverty children 2.5

USD

Poverty children 4

USD

Poverty 1.25 USD

Poverty 2.5 USD

Poverty 4 USD

Maternal mortality

Children mortality

-0.3993 -0.5389 -0.4470 -0.2417 -0.3499 -0.3980 -7.5769 3.9059(2.83)*** (2.86)*** (2.15)** (2.94)*** (2.64)*** (2.22)** (2.19)** (0.92)-0.4358 -0.7624 -0.7013 -0.2365 -0.5651 -0.6576 0.2378 -10.7419(6.69)*** (6.37)*** (6.38)*** (4.39)*** (4.75)*** (4.79)*** (0.07) (4.77)***-0.0003 -0.0080 -0.0173 -0.0019 -0.0087 -0.0133 -0.0339 -0.1374(0.05) (0.86) (1.76)* (0.44) (1.34) (1.69)* (0.14) (0.60)0.2754 -0.2523 -0.4888 0.0598 -0.1764 -0.2910 -2.7856 -44.1845(0.35) (0.25) (0.45) (0.13) (0.24) (0.32) (0.13) (1.93)*

-0.1126 0.3836 0.7888 0.0363 0.3612 0.5651 2.6402 24.2940(0.20) (0.56) (1.08) (0.11) (0.75) (0.92) (0.17) (1.75)*

Observations 235 235 235 235 235 235 244 244R-squared 0.390 0.503 0.410 0.384 0.496 0.461 0.033 0.098

Constant

% change in GDPpc (Possitives)

% change in GDPpc (Negatives)

Mean age

Male rate

Dependent Variable: Change for each socioeconomic indicator

Beyond simple elasticities…

70%

75%

80%

85%

90%

95%

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 20065,000

5,500

6,000

6,500

7,000

7,500

8,000

8,500

9,000

School attendance 13 to 17 years old Real GDPpc

0

1

2

3

4

5

6

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 20065,000

5,500

6,000

6,500

7,000

7,500

8,000

8,500

9,000

Maternal mortality Real GDPpc

0

5

10

15

20

25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 20065,000

5,500

6,000

6,500

7,000

7,500

8,000

8,500

9,000

Children mortality Real GDPpc

0.975

0.98

0.985

0.99

0.995

1

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

5,000

5,500

6,000

6,500

7,000

7,500

8,000

8,500

9,000

School attendance 6 to 12 years old Real GDPpc

School attendance 6 to 12

School attendance 13 to 17 Child Mortality

Maternal Mortality

Upper bounds of crisis effects: evidence from a “worst-case” scenario

The evolution of outcomes over the 1999-2001 recession and the large 2001-2002 crisis reveals a specific effect.

Not the long(ish) term elasticity: a “crisis” elasticity.

Computing a “worst case scenario” impact: Panel of outcomes at the province (25) – but calibrate the timing of the “exposure period” to obtain an upper bound.

Yjt denotes the outcome of interest for province j in time t.

T is dummy variable that is equal to 1 during the crisis period (1999-2002, or equivalently 2000-2003),

D identifies the last year in each period (crisis and reference).

is an idiosyncratic shock uncorrelated with the regressors.

captures the difference in outcomes attributable to the crisis (interaction term in DiD estimation).

jtjjttj FXDTDTY ''*

jt

Results...

Dependent Variable

School attendance 6 to

12 years old

School attendance 6 to 12 years old in the first income

quintil

School attendance 13 to 17 years old

School attendance 13 to 17 years old

in the first income quintil

Not working nor attending

school 13 to 17 years old

Not working nor attending

school 13 to 17 years old in the

first income quintil

Maternal Mortality

Children Mortality

T = 0 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998T = 1 2000-2003 2000-2003 2001-2004 2001-2004 2001-2004 2001-2004 2000-2003 2000-2003

0.0041 0.0026 0.1134 0.1573 -0.0825 -0.1309 -0.9466 -5.7840(1.82)* (0.58) (4.72)*** (4.04)*** (4.43)*** (3.64)*** (2.62)** (9.48)***0.0035 0.0047 0.0633 0.0857 -0.0519 -0.0900 -0.4304 -2.8863(1.65) (1.01) (2.96)*** (2.54)** (3.23)*** (2.98)*** (1.07) (6.58)***

-0.0092 -0.0146 -0.0882 -0.1132 0.0707 0.1090 1.1508 2.8380(2.48)** (1.77)* (3.24)*** (2.54)** (3.44)*** (2.63)** (1.91)* (3.94)***0.0026 0.0037 0.0044 -0.0009 0.0047 0.0111 0.2052 0.1703(1.80)* (1.12) (0.55) (0.06) (0.88) (0.71) (0.68) (0.79)0.0427 -0.2773 0.8782 1.6829 -0.2919 -0.0158 -17.4014 -47.6226(0.25) (0.67) (0.93) (1.00) (0.40) (0.01) (0.48) (1.42)0.8858 0.9990 0.2392 -0.0833 0.1403 -0.0894 5.8367 39.0193

(8.04)*** (3.85)*** (0.42) (0.08) (0.32) (0.08) (0.27) (2.09)**

Observations84 84 84 84 84 84 84 84

R-squared 0.549 0.351 0.828 0.712 0.806 0.636 0.811 0.925

Age

Constant

T

D

T*D

Male

Effect on poverty according to the elasticity approach

Dependent Variable

Poverty Children 1.25

USD

Poverty Children 2.5

USD

Poverty Children 4 USD

Poverty 1.25 USD

Poverty 2.5 USD

Poverty 4 USD

T = 0 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998 1995-1998T = 1 1999-2002 1999-2002 1999-2002 1999-2002 1999-2002 1999-2002

0.0016 0.0202 0.0296 0.0028 0.0153 0.0204(0.20) (2.29)** (2.74)*** (0.62) (1.94)* (1.87)*0.0038 0.0189 0.0266 0.0014 0.0094 0.0116(0.53) (2.27)** (3.48)*** (0.32) (1.25) (1.20)0.1448 0.2189 0.2084 0.0861 0.1623 0.1984

(9.52)*** (12.89)*** (12.41)*** (8.75)*** (9.51)*** (8.86)***-0.0028 -0.0140 -0.0283 -0.0004 -0.0044 -0.0170(0.50) (2.23)** (4.82)*** (0.12) (0.79) (2.77)***

-0.4138 -1.4391 -1.4397 -0.4562 -0.9845 -1.2957(0.65) (1.66) (1.96)* (0.96) (1.14) (1.30)0.3613 1.3216 1.9521 0.2746 0.7254 1.3952(0.92) (2.69)*** (4.48)*** (1.00) (1.53) (2.70)***

Observations81 81 81 81 81 81

R-squared 0.917 0.968 0.981 0.917 0.952 0.967

T

D

T*D

Male

Age

Constant

Conclusion

Conclusion Regional and time variation in Argentina

provides evidence of permanent effects of crises through maternal and infant health.

Asymmetric effects (hysteresis?). Middle income country: no major negative

effects on education (some ambiguous), but negative effects in upper bound estimates.

Conclusion Current crisis: difficult to state without

credible GDP (and other) data. But probably milder than worst case

estimates. Protect social expenditure during crises,

even in middle income countries.

Thanks!

Fixed Effect Model Projections

Poverty children 1.25

USD

Poverty children 2.5

USD

Poverty children 4

USD

Poverty 1.25 USD

Poverty 2.5 USD

Poverty 4 USD

Maternal mortality

Children mortality

8.1% 19.9% 36.7% 4.6% 11.5% 22.8% 4.8 22.9

-0.4192 -0.6610 -0.5859 -0.2389 -0.4675 -0.5398 -3.3174 -4.07812.2% 10.7% 28.5% 1.3% 5.0% 15.3% 4.3 22.3

123

2.4% 11.0% 28.8% 1.4% 5.2% 15.5% 4.4 22.3

2.6% 11.3% 29.1% 1.5% 5.4% 15.8% 4.4 22.4

4.3% 14.0% 31.4% 2.5% 7.3% 18.0% 4.5 22.5

6.4% 17.3% 34.4% 3.7% 9.6% 20.7% 4.7 22.7

Extrapolated level to Official GDP Growth at 2009q1

Extrapolated level to scenario 1

Extrapoled level to scenario 2

Extrapolated level to scenario 3

Alternative scenarios-10.0%-5.0%-1.0%

Partial elasticityExtrapolated level at 2008q3

Official GDP Growth from 2008q3 to 2009q1

-0.5%

Variable

Level at 2006Official GDP Growth from

2006q4 to 2008q313.9%

...and new projections

Poverty children 1.25

USD

Poverty children 2.5

USD

Poverty children 4

USD

Poverty 1.25 USD

Poverty 2.5 USD

Poverty 4 USD

Maternal mortality

Children mortality

8.1% 19.9% 36.7% 4.6% 11.5% 22.8% 4.8 22.9

+ -0.3993 -0.5389 -0.4470 -0.2417 -0.3499 -0.3980 -7.6 3.9- -0.4358 -0.7624 -0.7013 -0.2365 -0.5651 -0.6576 0.2 -10.7

2.5% 12.4% 30.4% 1.3% 6.6% 17.3% 3.7 23.4

123

2.7% 12.7% 30.8% 1.4% 6.9% 17.6% 3.7 23.5

2.9% 13.1% 31.1% 1.5% 7.2% 17.9% 3.7 23.6

4.7% 16.2% 33.9% 2.4% 9.4% 20.6% 3.7 24.0

6.9% 20.0% 37.4% 3.6% 12.2% 23.8% 3.7 24.5

-1.0%

-0.5%

13.9%

Variable

Level at 2006Official GDP Growth from

2006q4 to 2008q3

Partial elasticity

Extrapolated level at 2008q3Official GDP Growth from

2008q3 to 2009q1

Extrapolated level to scenario 3

Alternative scenarios

Extrapolated level to Official GDP Growth at 2009q1

Extrapoled level to scenario 1

Extrapolated level to scenario 2

-10.0%-5.0%

Previous evidence Education outcomes Health and nutrition outcomes Rich countries Positive impact

United States

Positive impact United States

Middle-income countries Ambiguous impact Examples of positive impact Mexico Brazil Argentina Peru Nicaragua Examples of negative impact Costa Rica

Ambiguous impact Examples of positive impact Colombia Examples of negative impact Peru Mexico Russia

Poor countries Negative impact Indonesia Cote d’Ivoire Malawi South Africa (Nicaragua)

Negative impact Nicaragua India Cote d’Ivoire Zimbabwe Ethiopia Tanzania Cameroon South Africa

Source: Ferreira and Schady, Aggregate Economic Shocks, Child Schooling and Child Health, WBRO and PEGNET Presentation, 2009.