permanent effects of economic crises on household welfare: evidence and projections from...
TRANSCRIPT
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.