Argentina: Macroeconomic Crisis and Household Vulnerability
Gabriela Inchauste2003, Social Science Research Network
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The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMP policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. Using urban household surveys, we constructed a panel dataset to study the effects of the Argentine macroeconomic crisis of 1999-2002 with the aim of (I) identifying the most vulnerable households, (2) investigating whether employment in the public sector and government spending served to decrease vulnerability, and (3) understanding the mechanisms used by households to smooth the effects of the crisis. Households whose heads were male, less educated, and employed in the construction sector were more vulnerable to the crisis, experiencing larger-than-average declines in income and higher dispersion. Households whose heads were employed in the public sector were more protected from the crisis, although higher public spending did not serve to decrease their vulnerability. A significant source of vulnerability was linked to changes in employment status, and we studied the determinants of the probability of being unemployed and of becoming unemployed. Last, we found that households were unable to perfectly smooth income shocks. Given these results, there is room for broadening social safety nets, particularly in the form of public works programs.
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WP/03/89
IMF Working Paper
Argentina: Macroeconomic Crisis and
Household Vulnerability
Ana Corbacho, Mercedes Garcia-Escribano, and
Gabriela Inchauste
INTERNATIONAL MONETARY FUND
WP/03/89
© 2003 International Monetary Fund
IMF Working Paper
Fiscal Affairs Department
Argentina: Macroeconomic Crisis and Household Vulnerability
Prepared by Ana Corbacho, Mercedes Garcia-Escribano, and Gabriela Inchauste 1
Authorized for distribution by Sanjeev Gupta
April 2003
Abstract
The views expressed in this Working Paper are those of the author(s) and do not necessarily
represent those of the IMF or IMP policy. Working Papers describe research in progress by the
author(s) and are published to elicit comments and to further debate.
Using urban household surveys, we constructed a panel dataset to study the effects of the
Argentine macroeconomic crisis of 1999-2002 with the aim of (I) identifying the most
vulnerable households, (2) investigating whether employment in the public sector and
government spending served to decrease vulnerability, and (3) understanding the
mechanisms used by households to smooth the effects of the crisis. Households whose heads
were male, less educated, and employed in the construction sector were more vulnerable to
the crisis, experiencing larger-than-average declines in income and higher dispersion.
Households whose heads were employed in the public sector were more protected from the
crisis, although higher public spending did not serve to decrease their vulnerability. A
significant source of vulnerability was linked to changes in employment status, and we
studied the determinants of the probability of being unemployed and of becoming
unemployed. Last, we found that households were unable to perfectly smooth income shocks.
Given these results, there is room for broadening social safety nets, particularly in the form
of public works programs.
JEL Classification Numbers:H53, 131
Keywords: Argentina; macroeconomic crisis; household welfare
Authors' E-Mail Address:[email protected]; [email protected];
[email protected]
I The authors would like to thank Sanjeev Gupta, Robert Gillingham, Emmanuel Jimenez, and participants at a
conference at the Fiscal Affairs Department for useful comments and suggestions on earlier versions of this paper.
- 2-
Contents
Page
I.
Introduction ........................................................................................................................... 4
II.
Historical Background .......................................................................................................... 5
III. Description ofthc Data and Summary Statistics .................................................................. 7
A. The Argentine Permanent Household Survey .............................................................. 7
B. Descriptive Statistics of the Panel Sample ................................................................... 8
IV. Who Bore the Burden of the Crisis? ................................................................................... 12
A. Evidence on Heterogeneous Household Vulnerability to the Argentine Crisis ......... 12
B. Most Vulnerable Socioeconomic Groups .................................................................. IS
C. The Role of Government Expenditurcs ...................................................................... 19
D. Transmission Channels of Macro Shocks to Individuals: Employment Status ......... 20
V.
Household Smoothing Mechanisms ................................................................................... 24
VI. Summary and Conclusions ................................................................................................. 32
References ................................................................................................................................... 35
Tables
1. Descriptive Statistics of the Panel Sample
(a) Characteristics of the Head of Household ......................................................................... 9
(b) Household Characteristics ............................................................................................... 10
2. Transition Rates to Unemployment ...................................................................................... 12
3. Average Individual Income, by Deciles ................................................................................ 13
4. Evolution of the Gini Coefficient for Household and Individual Income ............................ 13
5. By Regions, Evolution of Poverty ........................................................................................ 14
6. Movements Across Income Quintiles ................................................................................... 15
7. Selected Regression Rules: Vulnerable Socioeconomic Groups .......................................... 16
8. Selected Regression Results: The Etlects of Government Spending ................................... 21
9. Selected Logit Regression Results ........................................................................................ 23
10. Smoothing Estimation Results .............................................................................................. 28
II. Smoothing Estimation Results .............................................................................................. 29
12. Smoothing Estimation Rcsults--Changc in thc Logarithms Specification: Average
Household vs. Extreme Poor Households ............................................................................. 30
Figures
1. Annual Inflation ........................................................................................................................ 5
2. Real GDP Growth ..................................................................................................................... 5
3. General Government Deficit ..................................................................................................... 6
4. Duration of Employment ........................................................................................................ 10
-3-
5. Unemployment and Underemployment Rates ....................................................................... 11
6. Evolution of Poverty in Greater Buenos Aires ...................................................................... 14
7. Functional Classification of Provincial Government Spending ............................................. 19
8. Economic Classification of Provincial Spending .................................................................. 19
Text Box
I. Social Safety Nets ................................................................................................................. 31
- 4-
I. INTRODUCTION
Macroeconomic shocks can have significant effects on the welfare of individual
households. Shocks at the macro level can be transmitted to households through numerous
channels, including changes in domestic prices, the real exchange rate, and employment.
Macro shocks leading to higher domestic inflation can have a relatively large impact on the
poor, given that they often lack real assets to hedge against inflation and their wages are
defined in nominal terms, translating into declines in the real purchasing power of their
income, A real exchange rate depreciation may have a favorable impact on households
employed in the tradable sector, but can hurt those employed in the production of
nontradables through, for instance, fewer investment opportunities and lower demand for
labor. This may be particularly relevant for urban households, The labor market can playa
crucial role in transmitting macroeconomic shocks especially to the most vulnerable, as they
are likely to derive a larger share of their income from employment 2 Rigidities in the labor
market can amplify the effects of macroeconomic shocks. If real wages are inflexible
downwards, macro shocks that negatively affect labor demand are absorbed through higher
unemployment, underemployment, and informal sector employment, and can lead to
increased employment volatility.
This paper addresses the effects of the macroeconomic crisis on household welfare in
Argentina during 1999-2002. We focus primatily on the effects of the macroeconomic
shock on the labor market, leading to income and employment fluctuations at the household
level. Using survey panel data for 28 urban centers, we identify those characteristics that
contributed the most to household and individual vulnerability. We also investigate whether
employment in the public sector and government spending served as mitigating factors on the
shocks transmitted to the household. This research should be especially useful in the design
of appropriate and targeted social safety nets to alleviate the effects of the crisis on the
poorest groups. In our discussion, we concentrate on four specific questions: (I) Who bore
the cost of adjustment during the crisis and was the main channel of adjustment through
wages or employment? (2) Were households with members employed in the public sector
less vulnerable to the crisis? (3) Were households living in provinces with higher public
spending more protected from adverse shocks? (4) What were the coping mechanisms used
by households to diversify and hedge against the crisis? The remainder of the paper is
organized as follows: Section II presents a brief historical background, and Section III
describes the data and summary statistics. Section IV discusses the empirical strategy and
estimation results, identifying those household characteristics more correlated with income
and employment shocks and studying the role of the public sector. Section V analyzes
mechanisms used by households to smooth the effects of the crisis, and offers some insight
into the need for better design and targeting of social safety nets. Section VI concludes.
~ See Agenor (2002) on the effects of macroeconomic policies on poverty, focusing on transmission channels
through the labor market.
-5-
II. HISTORCA~
BACKGROUND
In 1991, Argentina introdnced the Convertibility Plan, after nearly three decades of
chronic inflation. 3 After two large
Figure L Argentina: Annual Infbtion
hyperinflations in 1989 and 1990, a !
(in perc.-:ntJ
30
cnrrency board was adopted,
.......... 1991 = 172%
pegging the peso to the dollar at a
one-to-one rate. The currency
20
board was successful in controlling
inflation: for the twelve months
15
after the adoption of the
10·
Convertibility Plan, annual
inflation fell to 25 percent, and by
5
the end of 1993, inflation was close
o
to 10 percent. After 1993, inflation
1991 1992 [993 1994 1995 1')% 1997 1998 1999 2(0) 2001 2002
in Argentina was less than
1/
World Economic Outlook (WED).
5 percent, and there were even
II WEO Projection.
deflationary periods (Figure I).
.,
Sourc~:
The adoption of the currency board marked the beginning of a prosperous period in
terms of economic growth and low inflation.' In addition to the new currency system, there
were several structural reforms,
figure}. Argentina: Rcal GDP Growth
(in percent)
including privatization of public
13
enterprises, deregulation, and the
1l
opening of the economy.
Improvements in the fiscal area
9
included simplification and
7
streamlining of the tax system,
5
improvements in tax administf'dtion,
3
and greater control on expenditures.
1
Real GDP growth averaged 8
1994
"~,y
·1 T", 1992 1'I9~
percent per year between 1991 and
·3
1994 (Figure 2). Capital inflows to
·5
the economy were large, leading to
B; timate 2002 = -15'10 '--..
·7
large domestic credit growth and a
Source: World Economic OuLiook (,,"'EO) and INDEC.
11 'i\'EO Projecrion_
consumption and investment boom.
~
Inflation has been a chronic problem in Argentina since the postwar period. Between 1960 and 1991, there
were seven major stabilization programs that generally included a fixed exchange rate system and fiscal and
monetary measures. See Choueiri and Kaminsky (1997) and Alvarez and Zeldes (2001) for a detailed
chronology.
3
Despite favorable macroeconomic indicators, Argentina's economy continued to face high structural volatility.
See, for instance, Caballero (2000).
4
-6 -
After nearly a decade of good macroeconomic performance, Argentina's economy
plnnged into recession by end-1998. Argentina did not suffer during the Asian crisis in
1997, as real GOP grew by over 8 percent. However, economic growth started to decelerate
during the second half of 1998. There were several contributing factors. On the external
front, these included (I) a deterioration in terms of trade; (2) a continuous appreciation of the
real exchange rate, particularly after Brazil's devaluation in 1999; (3) a reversal of capital
flows to emerging markets following Russia's default on its debt in 1998; and (4) the
generalized "flight-to-quality" in all assets after the decline in NASDAQ in 2000 5
In addition to an unfavorable
external environment, there
were several internal factors
that amplified the effects of
external shocks. Fiscal deficits
had been increasing since the
adoption of the Convertibility
Plan (Figure 3). Although the
currency board regime put an
end to bank financing, it
provided no safeguard against
excessive borrowing. In the
event, fiscal performance was
too weak throughout the 1990s
Figure 3. Argentina: General Govemncnt Deficit
(In billions of pesos)
1',---------------------.
16
14
12
10
8
6
4
~ ~
~
~
L-.- L-.- L-.- L-.- L-.L-._2L-______________________________
N~O
I9'lI
•
1993
1994
1995
1996
1997
199&
1999
2000
2001 2002 V
~
Source: World R:onomicOutlook (\\lEO).
to prevent a growing reliance on
!/WEO ProJectlon.
I
.-~
private capital flows, resulting in
the accumulation of external debt throughout the period.' By the end of 2000, both domestic
and international investors feared that Argentina would default on its debt. The spread on the
yield of dollar-denominated bonds over similar U.S. Treasury bills, commonly referred to as
"country risk," reached a record high, and the country lost access to external capital markets.
Rigidities in the labor market contributed to increased unemployment and lower job
security.7 On the political front, the De la Rua Administration faced serious constraints to
implement fiscal adjustment measures. Additionally, the deterioration of social conditions
and a freeze on bank deposits in 200 I contributed to widespread discontent and unrest in the
population. After violent protests in end-December 2001, De la Rua resigned. A transitory
government is in place until new presidential elections are held in 2003.
5 See
IMF (2001).
6 See IMF (2002).
7 See Llach and Llach (1998) for an analysis of the labor market in Argentina during the Convertibility Plan
period.
-7-
Argentina abandoned the fixed exchange rate in January 2002 in the midst of severe
economic and political turmoil. By January 2003, the peso had depreciated by over
300 percent. Social indicators had been on a negative path since 1992, and deteriorated
considerably after October 200 I. Poverty increased from 38.3 percent to 52.2 percent
between October 2001 and May 2002;8 unemployment reached an alamling 21.4 percent in
May 2002; 9 and income inequality, as measured by the Gini coefficient, 10 had risen from
0.53 to 0.57 by May 2002.
III. DESCRIPTION OF TIlE DATA AND SUMMARY STATISTICS
In this section, we describe the data used to analyze the effects of the crisis on household
welfare. We also define the variables used in the study, and present summary statistics for the
estimation sample.
A. The Argentine Permanent Household Survey
The empirical analysis is based on the Argentine Permanent Household Survey
(En cuesta Permanente de Hogares, EPH) for the period 1999-2002. The National
Statistical and Census Institute (INDEC) conducts the survey biannually in May and October
in 28 urban centers. The EPR has a rolling unbalanced panel stmcture: once a household is
chosen, it remains in the sample for four periods, that is, for a total of two years. The survey
contains information at the household level, such as dwelling attributes and family
composition, as well as at the individual level with demographic, labor market, and income
data for each household member. Therefore, the behavior of individuals and the households
in which they reside can be followed over time.
Some limitations of the data have implications for our study. First, the survey covers only
urban areas. Our results should not be applied to mral households since these households
might experience different income shocks and have access to different smoothing strategies.
Second, consumption data are not available. Therefore, the ability of households to smooth
consumption in the face of income shocks by savingldissaving and borrowing/lending
mechanisms cannot be determined. Finally, while some information on transfers, such as
interhousehold transfers, provision of food, unemployment insurance, severance payments,
scholarships, and pensions is collected in the survey, other types of government interventions
(including taxes) are not recorded. For this reason, our empirical strategy uses the EPR panel
data to identify the socioeconomic groups vulnerable to the current economic shock; and
macro-level data on public expenditures at the provincial level to evaluate if public programs
8
Source: National Statistical and Census Institute (INDEC).
9
Source: INDEC.
10 Based on the distribution of individual income. Authors' calculations are based on the Permanent Household
Survey_ See Section III for further details.
-8-
are being targeted to help these groups, or on the contrary, if such programs increase their
vulnerability.
This paper follows the definition of "vulnerability" in Glewwe and Hall (1998):
vnlnerable groups are those that experience larger than average declines in
socioeconomic status. Hence. vulnerability is a dynamic concept tbat focuses on changes in
socioeconomic status l l We use changes in pretax household income and its dispersion as our
measure of the effect of the economic shock on household welfare.
B. Descriptive Statistics of the Panel Sample
Changes in household income and composition refer to a six-montb period. The sample
includes a total of 55.325 observations for 35.614 households. Households misreporting
information for the head of household on any of the variables relevant for the empirical
analysis are excluded 12. 13 The distribution of the observations across the six subsamples and
the descriptive statistics are shown in Table I. Table I a includes characteristics of the head of
household, and Table 1b shows characteristics of the household.
Table Ib indicates that living conditions, measured in terms of per capita household
income, have deteriorated since May 1999, especially during the six months after
October 2001. The average change in per capita household income is negative except for the
subsample May 2000-0ctober 2000. Note that the biggest decline in per capita household
income occurred between October 2001 and May 2002.
11 Tn contrast, poverty is a static concept since it concerns one's current socioeconomic status (Glewwe and
Hall, 1998).
12 The head of household is defmed as the household member between ages 15-64 with the highest reported
income at the beginning of the six~month
period. If no household member reports personal income, we
designate as head of household the member that declares himself or herself as the decision maker.
13 We excluded from the sample households with missing information on the education of the head of
household, or with no infonnation on any of the following variables for the head of household if employed:
total hours worked and hourly earnings at the main occupation, years in that occupation, expected job duration
(pennanem, temporal, unknown), type of employer (public or private), or economic sector (primary,
construction, manufacturing, services, and trade), if employed in the private sector; and type of worker
(entrepreneur, self-employed, or wage worker), if employed in the private sector. Further, to minimize reporting
errors, households with discrepancies on information on the gender of the head of household, with age declining
over time, or with recorded hourly earnings-if working both periods-ten times larger or smaller than the
amount recorded at the initial of the six-month period were excluded from the sample.
-9-
Table I a. Descriptive Statistics of the Panel Sample
Characteristics of the Head of Household 1/21
May 99Oct-99
Number of households
Schooling (years)
Age (years)
Male (percent)
If active, change in total personal income
Oct 99May-DO
May 00Oct-OO
Oct 00May-O!
May 01-
OctOI-
Oet-Ol
May-02
11.066
9.279
9.701
9,155
9,458
6.666
10.08
10.15
10.17
10.17
10.27
10.22
(4.28)
(4.30)
(4.30)
(4.30)
(4.30)
(4.31 )
40.20
40.47
(] 1.65)
40.45
(11.74)
40.77
40.68
41.00
(11.741
(11.67)
(11.69)
(11.74)
69.16
6K43
67.21
67.36
66.70
66.52
-27.26
-37.75
-27.35
-40.87
-50.60
-159.86
(519.78)
(47<'1.06)
(436.64)
(428.4l'l)
(496.73)
(362.12)
Employed to unemployed
4.54
5.96
4.89
6.07
6.31
8.84
Unemployed to employed
2.69
3.01
3.31
3.45
3.25
3.41
90.88
S8.95
89.07
SH.24
87.07
83.48
1.90
2.08
2.73
2.23
3.37
4.27
If employed, type of employer (percent) 31
Public
26.15
26.21
27.01
27.48
27.62
27.16
Private
73.85
73.79
72.99
72.S2
72.38
72.84
If active. change in labor market status (percent)
Employed to employed
Unemployed to unemployed
If in private sector, type of work (percent) 31
Owner 1 employer
5.18
5.81
5.88
5.48
5.53
4.97
Self-employed
26.16
26.46
26.11
27.03
26.70
28.62
Worker
68.66
67.73
68.01
67.50
67.77
66.41
If in the private sector, economic sector (percent) 3/
Primary
Construction
2.48
2.66
2.32
1.96
2.71
3.00
16.06
15.29
14.44
14.41
13.90
13.57
Manufacturing
17.48
16.65
16.30
16.20
15.87
16.81
Services
44.47
45.26
46.08
47.37
47.32
47.27
Trade
19.51
20.15
20.86
20.06
20.21
19.36
If employed, years in that occupation 3/
8.46
(8.62)
8.48
(8.55)
8.45
(8.62)
8.76
(8.78)
8.76
(8.62)
(8.79)
If employed, expected job duration (percent)
Permanent
8.93
31
87.06
88.25
87.55
86.89
86.25
85.94
TemporaJ
5.36
5.57
5.49
5.78
6.00
6.27
Unknown
7.59
6.18
6.95
7.33
7.75
7.79
Source: Authors' estimations. Panel data constructed using EPH from May 1999 to May 2002.
II Mean. Standard deviation in parenthesis.
21 Change refers to the change over the six-month interval period.
31 Descriptive statistics at the beginning of the six-month interval period.
- 10 -
Table 1b. Descriptive Statistics of the Panel Sample (cont.)
Household Characteristics 11 21
Oct 01-
May 99-
Oct 99-
May 00-
Oct 00-
May 01-
Oct-99
May-OO
Oct-GO
May-O!
Ocr-Ol
13.92
19.54
14.84
14.89
15.50
14.47
18.59
Northwest
21.91
21.44
22.15
22.51
21.35
May~02
Region (percent)
Greater Buenos Aires
Northeast
13.26
12.42
12.1X
1234
12.52
11.61
Cuyu
14.26
7.21
8.14
8.13
8.41
10.19
Pampeana
26.83
29.55
29.14
28.84
27.78
24.65
Patagonia
12.20
14.07
14.20
13.04
14.32
13.62
Change per capira household income
Number of household members 3/
Propol1ion children 3/
Presence of elderly (percent) 31
Proportion working members 31
Proportion unemployed members 3/
-0.82
-3.59
3.65
-4.99
-11.32
-64.61
(242.08)
(234.40)
(230.38)
([85.33)
<:232.10)
066.64)
4.02
4.0l
3.99
3.98
3.97
3.97
(1.99)
(1.<)8)
(l.97)
(1.99)
(2.01)
(2.06)
0.24
0.24
0.24
0.24
0.23
0.23
(0.24)
(0,24)
(0.24)
m.24 )
10.24)
(0.24)
22.00
21.92
22.36
22.26
22.12
22.28
0.36
0.36
0.35
0.36
0.36
0.35
(0.24)
(0.23)
<0.23)
(0.24)
<0.24)
(0.24)
0.05
0.05
0.06
0.06
0.07
0.07
<0.12)
(0.13)
(0.14)
(0.13)
(0.14)
(0. [4)
Source: Authors' estimations. Panel data constructed using EPH from May 1999 to May 2002.
11 Mean. Standard deviation in parenthesis.
21 Change refers to the change over the six-month interval period.
31 Descriptive statistics at the beginning of the six-month interval period.
The decline in per capita
household income was
associated mainly with a drop
in the total income of the head
of household, and more
precisely from the decline in the
household head's eamings-the
main source of household
income (Table tal. The main
reasons for the decline in the
head of household's
earnings were an increase in
unemployment, and a striking
decline in the earnings of those
employed. Rates for transition
Figure 4. Argentina: Duration ofEmploYIrent
60~-,
55
50
45
40
35
30
25
20
15
"~=:
than 2 months
th"-n 2 months. but less Ihao 6 monIhs
_x_more than 6 mooIh:;. hut leSll than a ye::rr
........ more thall a year
_l~,s
~more
Source: [NOt£:, -Sample includes all adults.
- II -
into unemployment rose remarkably; the number of household heads becoming unemployed
rose from 4.75 percent between May 1999 and October 1999 to 9.57 percent between
October 2001 and May 2002 (Table 2). At the same time, unemployment duration remained
relatively constant (Figure 4). As a result, unemployment rates increased (Figure 5). The
second source of the decline in the household head's earnings arose from a reduction in the
number of hours worked,14 and from a slight decline in hourly earnings.
As a response to the economic shock,
other household members increased
their labor participation; the mean
change in the proportion of inacti ve
members is negative, but labor market
conditions were not favorable to them
either. Consequently, the proportion of
unemployed members-excluding the
head of household-rose (Table Ib).
Figure 5. Argentina: Unemployment and UnderernploYImnt Rates
",-------------------------------------,
Given our definition for the head of
household, Table la shows that the
. - . - UnclIl'loyment _Underemployment
evolution of labor market conditions in
Source' lNOF.c. Sample includes all adults.
1999-2002 was similar across
education groups. In particular, the number of years of schooling of the head rose; the
number of female-headed households increased; the number of household heads employed in
the public sector rose relative to employment in the private sector except for the period
October 200l-May 2002; the number of household heads employed in the construction and
manufacturing sectors declined, while those in the service sector rose; and household heads'
years in the same occupation rose. The proportion of self-employed workers rose, while the
proportion of wage workers fell; this probably indicates also an expansion of informal-sector
employment, and, therefore, the lack of a social protection scheme for the labor force.
Similar conclusions are drawn from the descriptive statistics on adult characteristics (Table
Ib). The probability of becoming unemployed rose from 5.63 percent for the sample May
1999-0ctober 1999 to 11.40 percent for the sample October 200 I-May 2002 (Table 2). Last,
consistent with the household traits commented on earlier, adult labor force participation
rates increased (the transition from inactive to active individuals was 15.53 percent in May
1999-0ctober 1999 and 19.56 percent in October 200 I-May 2002).
14 Underemployment rates increased throughout the period. Underemployment refers to the percentage of
occupied adults in the active population working less than 35 hours per week for involuntary reasons.
- 12 -
Table 2. Transition Rates to Unemployment
May 99
Oct 99
Oct 99MayDa
May 00Dc -00
Oct 00-
May 01
May 01
Oct 01-
Oct Ot
May 02
Employed heads at / ifcmployed at 1-1
Unemployed heads at I if employed at /-1
95.25
93.72
94.79
93.56
93.24
90.43
4.75
6.28
5.21
6.44
6.76
9.57
Employed adults at I if employed at /-1
UnemElo;red adults at I if emEl0;ted at /-1
94.37
92.3
93.77
92.01
91.62
88.6
5.63
7.7
6.23
7.99
8.38
11.4
Source: Authors' estimations. Panel data constructed using EPH from May 1999 to May 2002.
IV. WHO BORE THE BURDEN OF THE CRISIS?
Tn this section of the paper, we discuss which socioeconomic groups in the population
disproportionately bore the burden of the adjustment during thc macroeconomic crisis. First,
we present evidence on household vulnerability. Second, using panel regression analysis, we
identify which socioeconomic groups were affected the most by the crisis in Argentina.
Then, we focus on the labor market as a transmission channel of macroeconomic shocks to
households and analyze the effects on employment. Last, we examine mechanisms
households relied on to smooth those shocks.
A. Evidence on Heterogeneous Household Vulnerability to the Argentine Crisis
It stands to reason that households are uot affected equally by macroeconomic shocks,
such as economic crises or adjustment programs. However, little research has been done
to identify the socioeconomic groups most vulnerable to macro shocks, and the reasons why
these groups suffer the most. Until recently, the required data did not exist; in contrast to
economic aggregates, micro-level data on household expenditures or income allows a deeper
exploration of the social implications of adverse macroeconomic events. Examples of this
line of research are Glewwe and Hall (1994 and 1998) for Peru, Eble and Koeva (2002) for
Russia, and Frankenberg, Thomas, and Beegle (1999) for Indonesia. In this paper, by
exploiting the pancl structure of microeconomic data for Argentina, we will apply similar
methodologies to quantify the effects of the macroeconomic crisis on household
vulnerability.
Average per capita household income declined throughout the period 1999-2002, but
more so after October 2001. The average household experienced a decline of 3.8 percent in
per capita household income between May 1999 and October 2001. Household income then
collapsed by 23.8 percent between October 2001 and May 2002 (Table 3). Individuals in the
poorest decile of the income distribution experienced the largest declines in personal income.
This result was even more pronounced during the period October 200 l-May 2002 (Table 3),
when per capita household income of the poorest decile collapsed by 41 percent, compared to
a 23 percent decline in the per capita household income of the richest decile, indicating that
the relative vulnerability of poorest groups increased during the peak of the crisis. Individuals
- 13 -
in the wealthiest decile were less affected by tbe economic shock tban tbe poor, but also
experienced significantly larger declines in income after October 2001,
Table 3, Average Per Capita Household Income, by Deciles
Average Individual Income, by Deciles
Per CaEita Household Income
Active Adult Income
Pcn:entaB,e Chan~e
May 99-
Decile
Mav-99
Oct-Ol
1
32.0
20A
Ylav-02
11.9
Oct 01
-36.3
2
71.0
56.9
37.4
-19.9
3
98.7
89.3
60.0
-9.5
-9.9
Change
Oct 01Mar 02
Percnta~
Oct 01Mar 02
ltCJ.S
85.2
L32.6
4
115.0
151.3
-10.3
5
168.6
153.6
-5.5
206.8
195.5
6
195.9
-4.7
7
266.2
253.8
256.6
-5.1
333.9
8
351.8
494.6
489.5
368.2
-1.0
9
-1.8
808.1
10
1054.2
1073.6
-3.8
Total
282.5
271.8
207.2
Source: Authors' estimations. Panel data constructed using EPH
Oct-Ol
Mar-99
Mar-02
\1ay 99 Oct 01
-41.7
-34.3
0.0
0.0
ttl.3
70A
0.0
12.8
-36.7
-fl7.7
-32.8
215.3
173.4
73.9
-19.5
-28.7
246.6
146.7
-15.4
-24.0
291.6
371.3
233.7
-14.8
-21.4
439.0
316.2
399.7
-57.4
-40.5
-26.1
287.8
-22.8
535.2
485.8
348.8
-23.1
687.1
458.8
-9.0
-9.2
-7.7
6+5.1
-0.8
1354.0
343.5
-4.6
-10.7
-24.S
901.8
634.1
894.7
-23.3
-23.8
1920.6
531.6
1832.2
474.9
-28.0
-28.2
-27.6
-27.9
-')6.1
-27.7
from May 1999 to May 2002.
Note: Monthly per capita household income. Values are in I YY9 pesos.
Active adults, induding head of household.
As a result, the distributions of household and individual income were more unequal at
the end of the period under study than in May 1999. Gini coefficients were computed for
each of the six subsamples at tbe beginning and at tbe end of tbe six-month period to evaluate
the evolution of inequality across individuals and across households (Table 4).
Table 4. Evolution of tbe Gini Coefficient for Household and Individual Income
May 99-
Oct 99-
May 00-
Oct 00-
MayOl-
Oct 99
May 00
Oct 00
MayOI
Oct 01
Oct 01Ma~02
Per capita household income
Initial
Final
0.50
0.49
0.49
0.50
0.50
0.51
0.50
0.50
0.52
0.52
0.52
0.53
Active adult individual income
Initial
Final
0.49
0.50
0.49
0.51
0.51
0.52
0.51
0.52
0.52
0.54
0.53
0.57
Source: Authors' estimations. Panel data constructed using EPH from May 1999 to May 2002.
The percentage of population under the poverty line in greater Buenos Aires has
steadily increased since 1994,15 but even more so since the start of the economic crisis
in 1999 and particularly since October 2001 (Figure 6). Also, the proportion of poor
15 Poverty is measured as the percentage of households under the poverty line. Lengthy time series for regions
other than greater Buenos Aires are not available.
- 14-
persons exceeds that of poor households, implying that poor households have
more members, Indeed, change in household size is one of the mechanisms households rely
on to insulate households' welfare from shocks to personal labor income, The percentage of
population under the poverty
Figure 6. Argentina: Evolution ofPoveny in Q-eater Buenos Aires
line significantly increased in
other regions as welL In May
2002, poverty reached levels
,50 I
over 60 percent in the North40
eastern and Northwestern
regions, but was below 40
30 "
percent in Patagonia (Table 5),
,
20
Exploiting the panel structure
10
of our data set, Table 6
presents the movements across
per capita household income
quintiles and individual
-+- % households un~r
poverty line
--<>-- % persolls WIder poverty lin"
income quintiles for each of
_
% hOll.'ieholds under puverty line that are extremely poor
the subsamples. As a result of
Source: Sistema de InfnrIllliCi6n, Monitorco y Evaluaci6n de Programas S()ciales (SIEMPRO)
differences in how hard
households were hit by the macroeconomic shock and in their ability to smooth those shocks,
a large number of households moved into a different quintile, especially during the last
period. The share of active adults that shifted quintiles was even larger, indicating that
significant changes occurred within the household.
Table 5. By Regions, Evolution of Poverty
Households 11
Oct-Ol
Ma~-Ot
Total
euyo
Greater Buenos Aires
Northeast
Northwest
26.20
29.30
23.50
44.00
37.lO
24.70
18.10
28.00
30.30
25.50
45.51
37.90
27.20
18.00
Mar-02
41.4
44.9
37.7
59.4
53.0
4U
30.9
Percentage
change
OctOI-May
02
0.48
0.48
0.48
0.31
0.40
Percentage
Persons 2/
Mar- Ot
Oct-Ot
Mar-02
35.90
38.60
32.70
56.60
47.50
33.80
23.90
38.30
39.60
35.40
57.20
48.30
37.10
23.20
53.00
54.90
49.70
69.80
63.50
52.70
39.10
Pampeana
0.54
0.72
PataB,0nia
Source: TI\IDEC.
11 Households in poverty measured as percentage of household under poveny line.
21 Persons in poverty measured as percentage of persons in the popUlation under poverty line.
change
Oct 01- May
02
0.38
0.39
0.40
0.22
0.31
0.42
0.69
- 15 Table 6. Movements Across Income Quintiles
Household Per CaEita Income Quintiles
Active Adults Income Quintiles
Percentage of
Percentage of
Households
that Moved to
Active Adults
that Moved to
Another
Anomer
Quintile 1/
Quintile
Max-SlY
Oct-99
1
13.6
Quintiles
2
3
4.S
1.1
0.4
0.1
4
5
May-02
1
Quintiies
2
3
4
5
13.3
5.2
1.3
0.4
0.1
2
4.6
3
1.1
4
0.5
11.7
1.2
4.4
1.4
4.1
8.1
3.9
0.2
0.5
3.8
12.3
3.2
Oct-Ol
3
4
2
4.1
9.7
5.0
1.0
D.2
1.7
4.0
10.2
3.5
0.8
0.7
1.3
4.3
10.1
3.4
I
5
0.2
39.6
10.7
4.9
0.3
0.6
3.1
1.4
0.7
0.2
14.7
5
0.2
0.2
1.2
3.1
15.3
41.4
1
8.8
7.2
1.8
0.9
0.2
2
3.8
11.2
4.0
1.6
0.4
M<lX-99
3
1.1
4
0.7
5
0.3
1.9
3.3
11.0
0.3
0.6
2.8
3.8
16.6
Oct-Ol
3
2.0
4
1.1
4.3
1.5
8.2
7.4
0.9
2.6
11.1
5
0.6
0.2
0.5
2.8
15.0
4.1
9.9
4.1
0.8
2
3.1
9.3
4.3
1.2
0.2
4.9
40.7
47.7
Source: Authors' estimations. Panel data constructed using EPH from May 1999 to May 2002.
1/ Adults, including head of household.
B. Most Vulnerable Socioeconomic Groups
This section analyzes the determinants of changes in income and draws inferences regarding
socioeconomic characteristics and vulnerability. The test consists in regressing vulnerability
measured by the difference in the logarithm of household income
dLHYh ,= log
,r.
Yhouseholdh , )
.'.
on a vector of characteristics of the head of household,
( Yhousehold r I-I
h
and a vector of household characteristics, Wh.,",.},; plus time dummies, (4.}.I; region
dummies, <\; and a constant term, r The sUbscript h indexes households; r indexes
geographical region; and t indexes time. Household welfare is measured as the logarithm of
total household income, Yhousehold. Interaction terms of the characteristics with the year
dummy aO,lObaO}.May02 are included to test for differences in the beta coefficients for the
period October 2001 and May 2002 during the peak of the crisis. We have estimated equation
(I) using a random effects specification for the error term:
Xh.'.r.};
log
Yhousehold h.,., )
[ Yhousehold h,,,,-1
= y+ al_l,r
+ <:Sr + f3
1
-1
'xh,r,i-l
+ aOctoberOl,May2002 . f3
+j32· WlI,r,I-1 +aOcroberOl,May02 .j3-2. Wh,r.l-l +Eh,r,1
,xh.r,t-l
+
(1)
- 16 Table 7: Selected Regression Results: Vulnerable Socioeconomic Groups II
logiYhOimdwld ,lYhOllsehold 1_' )
.0.057
-0.057
-0,056
[)ependent variable:
IX 0""",,,,", Mm'OO
-0021
(4,73)**
-0.024
(3.67)*"
-(J.028
(1.71)
0.(5)
(U2)
-(H160
14.73)"~
a MayOO. OelvimOa
-0050
(3.91~*
-0,051
-Il,ll2
Suburban Buenos Air~s
()
Northeast L ulh~rwse
1, o\herwi~
0
15.161*"
-0.013
(0.54)
-0.041
(4,80)~*
(1.68)
-0,054
Pampe:ma I, otherwise 0
(2"+3)~
-0,478
-0.030
(U4)
-0.05(,
\2.28)<
-0.067
(2.99)"*
0,004
Ikud year>; u[ ~ducation
(3.9n~*
0.002
(L05)
0005
(1.66)
Household size
Ag~
-0.123
(,).73~
of h",llli
(3,13)**
-0.007
12.26)'
0.000
12.23)*
-0.060
(1.7Y)
-0,0:;0
(4.70)**
0.000
(11.21)
-0.041
(2.47)"
-0.021
(0.87)
0.039
(3.21)**
-0.\35
(8.38)**
Head gender (1 if male, 0 if female)
Years in tbat oc<:upuljol]
Expected juh dllration: I if pennanent
Expected job dLlralion: 1 if transitory
Employer of he:\d (1 if public, 0 if private)
Cunstruction Sector I, otherwise 0
Presence of elderly I,
othcrwis~
0
Proportion of children
Proportion of occLlpk<l m"mbers
-0,541
(4.24)**
-0.027
(0.94)
1.449
(3.72)**
0.304
12.37)*
0.246
0.005
(4.501*"
-11.1109
0.000
(Age of hcud)"
(6.12)*~
-(J.ool
(5.6y~
0.000
(0,10)
-0.038
(2.5)~
0.01(,
(0.68)
(6.0)~;
-0_1110
(2.68)**
-O'(J07
(1.84)
0.000
(l 90)
-(J.on
(7.03 )~*
0.000
(0.27)
-0.026
(1.311
-0.023
(0.821
O,03R
(3.17)U
-0.132
(8.20)**
-0.038
(2.97)**
-0,204
(7.83)**
-0,311
(13,07/,'"
Entrepreneur 1, otherwise 0
(2.39'1~
(2.37)~
0.081
(1.52)
0.151
0.354
-0.122
(7,53)~
(2.111)*
-0.062
(2.30)*
0.009
I Yhousehold t_;))
0,120
0.151
(2.66)H
(2,64)*~
(3,69)~*
(.l.9Q)M
1<).611*"
-0._n9
Dh (lug( Ylw~sehod,
0.118
(2.36F
0,081
(1.52 \
0.150
O_J55
(6.!3J~*
(191)
0.328
1,320
(2.90)'"~
0.2n
(2.31 }*~
-0.023
(0.19)
0]05
0.115
(1.68)
0.199
(2,73)'*
D 312
(4.20)M
1.617
(2.751"*
0.310
(2.01 )**
0.268
(1.67)
0.274
(2.7)~
f2.57)~
-0.037
(6.21)**
-0.026
(2.11)*
0.007
((),47)
-0.034
(5.72)**
-0,070
(4,63)**
-0,024
-0.046
(608)**
-0.035
(4.52\*'"
·0.032
11.(,0)
(1.73)
().OOO
D.OOO
0.000
(0,57)
0.06(,
(1.201
-(Ull1
(L7Y)
0.026
(Um
(3.94)~*
-(J.23Y
(3.16)**
0.594
(5,64)*'
-0.200
(3.35)*'
(LYO)
(0.41')
-0.011
(3,93)u
-0.233
(3.08)**
0.600
(5.69)~
·1).279
(3.15J**
0,805
{6.20y"
·0.205
(3,43?'*
0.453
(5,70y~
0.156
(2.19)*
-0.014
(3,77)**
0.444(S.59)~*
-0.224
0.44)**
(J.440
(3.34)**
-0,324
-0.042
(2.5Y)**
-0.228
(6.82Y~
·0.322
(l0.64)**
-(1.063
(2.80)*~
-0.244
{2D2)M
0.562
(3.2)~*
-0.405
(2.74)~*
0.042
(0.]5)
(2,46)~
IntcraCli(l!llt:nns with
((QU"{,,,,OJ,MuyO:
Head years of education
Head gender (1 if male, 0 if female)
Employer of head (I if public, 0 if private)
Ye:m in that occupation
0.003
(1.11)
-0.014
(0.50)
0.084
(2.63)"'''
0.002
(l,45)
Presence of elderly I. othcrwioe 0
O.0(J4
( 1.20)
-0.008
(0.27)
0.082
(2.57)""
0.003
(1.54)
0.058
(1.70)
ConSlrLlct)OO Sector 1, otherwise 0
C()!JsLunt
-0,193
(4.20)**
0.110
-0191
(4,17)**
(l.79)
(6.11)**
0.418
0.009
(2.21)*
-0.044
(1.20)
0.00]
(1.21 )
(J.on
0·76)
-0.024
-0.024
-0.041\
{I 72)
(1.74)
0.161
(1.26)
.0.140
0.154
(1.20)
(2.68)**
0.410
(2,40)*
-0.140
(0,96)
(0.97)
·0.016
(2.18)*
-0.016
(2.171"
-0,37(;
-0.022
(2,26 )~
-0.493
(2.40F
1,292
(2.4)~
1.302
(J.370
(442)**
(6.19)~*
(6.14)~
0.954
(3.02F*
(S.OO)**
1.724
.t]Y90
43990
43990
Oh8erva[ions
3215::1
439YIJ
286H2
28682
28682
28682
21611
Number of households
using EPH from May 1999 to May 2002
Sonrce: Authors' estimations. I'and data cou~tr[ed
Note: T-statistics m parentheses; * significant at 5%; "'''' significant at 1 %.
II Cmnplde regression results are available upon request. Regressions include dunnnies for all regions, a dunnny [or calegory work~,
dUITllllies for primary, manufactm1ng and trade sectors, and interaction terms with all variables and the period October 2001-May 2002.
J'he excluded dummies ure: lime dunnny: May99-Ocwber99; expected job duration: unlrnown; ["gioo: city of Buenos Aires;
type of work in the private sector: scJf-~"Ilpoyed;
mJd ecunumk :.ector: 'Service sector.
2,155
(5.03)**
32158
21611
- 17 -
A household with a certain socioeconomic trait is more (less) vulnerable when the beta
coefficient for that particular trait is negative (positive). Since the time and region dummies
a, and 0, control for economy-wide shocks, or uninsurable shocks, a negative (positive) beta
coefficient indicates that the group with this characteristic experienced a decline in welfare
larger (smaller) than the average, holding all other characteristics constant.
In addition to measuring differential vulnerability among socioeconomic groups, we tested
for variations in vulnerability within groups. It could be the case that on average, households
with a particular trait do better than others during the crisis, but still inequality across
households with that trait might increase. To capture whether there is a relationship between
traits and changes in inequality, we used the speciflcation (I) but replaced the dependent
variable by the dispersion of the difference in the logarithm of household income:
Dis".,.,
= (dLHY".,.I - mean(dLHY".,.,))' , where Dis is deflned for household h in region r, at
time t, and mean(dLHY) is the mcan of the difference in the logarithm of household income.
Table 7 presents the coefficients and t-statistics from these regressions. Only the most
signifleant results are shown. The vector oftraits of the head of household includes years of
education, age, age squared, gender, type of cmployer (private or public, if employed),
working experience measured as the number of years in that occupation, expected job
duration (permanent, temporal, or unknown), economic sector for those working in the
private sector (primary, construction, manufacturing, services, trade, or other); and type of
worker for those working in the private sector (entrepreneur, self-employed, or worker).
Household traits included are household size, presence of elderly people, proportion of
children, and proportion of employed household members. The flrst two columns of each
rcgrcssion prcsent results for the wholc samplc, while thc third and sixth columns correspond
to households whose head was employed in the private sector. 16
Household welfare deteriorated from the beginning of the period, with the biggest
decline in October 200l-May 2002. In addition, inequality across households also
increased remarkably, especially after October 2001. Regional dummies capture
differences in the magnitude of the economic shock among regions. The Northeast and
Pampeana regions experienced larger declines in welfare, while the city of Buenos Aires
experienced the smallest.
16 In order to control for initial poverty levels that could lead to a bias in the estimated coefficients, an initial
wealth variable was added identifying households in extreme poverty in the initial period. Households in
extreme poverty were defined by housing characteristics provided in the EPH survey. We identified the poorest
households as those that were living in a house with no running water, no electricity, no private bathroom, or
made with construction materials inferior to the typical brick and stucco. The resulting estimates were not
substantially different from the results reported below. Households initially classified as extremely poor were
more vulnerable, although this variable becomes less significant when the interaction term with the dummy for
the period of October 200 I-May 2002 is included.
- 18 -
Households with better-educated heads were less vulnerable. In addition to time ,md
regional dummies, head of households' traits help explain changes in income levels. On
average and holding other traits constant. households headed by individuals with more
education were less susceptible to the changing economic conditions. Households whose
head was more educated experienced smaller declines in income (or larger increases) of
about 0.5 percent per extra year of education. Inequality across households with bettereducated heads was also lower. Dispersion of household income was about 3 percent lower
per extra year of education. Inequality across households with better-educated heads declined
even further during the last period under analysis, particularly for those employed in the
pri vate sector.
With respect to gender of the head of household, the average decline in income of maleheaded households was larger, ceteris paribus, than that of female-headed households.
Male-headed households experienced a 6 percent larger decline in income (or smaller
increase) than female-headed households. Results also suggest that dispersion was higher for
those households headed by males employed in the private sector, especially after October
2001 (last column, Table 7).
Households whose head was employed in the public sector were less vulnerable to the
economic shock. Results suggest that households headed by public sector employees
experienced nearly a 4 percent smaller decline in income than households headed by private
sector employees, and this difference increased to about 12 percent between October 2001
and May 2002. Also, dispersion in living standards was lower when compared to dispersion
of households whose head was employed in the private sector. Within the private sector,
households whose head was employed in construction were more vulnerable to the economic
conditions as they experienced a 13 percent larger decline in their income than those whose
head was employed in the services sector. In addition, inequality in household welfare within
the construction sector widened, especially after October 2001. When looking at the
regressions for households whose head was employed in the private sector ouly (Column 3),
we find that, on average, households whose head was an entrepreneur experienced larger
declines in income than households whose head was a wage worker or self-employed.
Results relative to the occupation of the head of household are robust to variables controlling
for working experience and expected job duration.
With respect to household characteristics, the coefficient for household size indicates
that total household income declined less for smaller households. Results show that larger
households experienced larger declines in income of 0.9 percent per extra household
member. This result might capture the fact that poor households, which are characterized by
larger families, are on average more vulnerable. Dispersion in income growth across was
smaller for large households, suggesting smaller variation in how well these households are
able to cope with shocks.
Households with a larger share of employed members experienced a higher decline in
incomes, but lower dispersion. Given that the macroeconomic shock mostly affected labor
- 19-
income, households deri ving most of their income from the labor market were affected more
than households who potentially derived their income from other sources,
Similarly, households with elderly members experienced larger declines in their income,
while dispersion within these families fell, potentially as a result of social security
transfers,
Last, households with a higher proportion of children experienced a larger decline in
income than the average, In addition, dispersion in income rose for these households. Both
results agree with the intuition that households with more children are more vulnerable.
C. The Role of Government Expenditures
Provincial government
spending, roughly 40 percent
of consolidated government
spending, remained fairly
constant during 1998-2001,
with some marginal increase
in 2001. As shown in Figure 7,
the largest share of spending is
allocated to the social sector,
which includes mainly health,
education, social insurance,
social assistance, and urban
development. With regard to
the economic classification of
expenditures, the bulk of
spending is allocated to wages
(Figure 8).
Figure 7. Argentina: Functional Classitication of Provincial
Glvemnnlt SpemLing
40,000
35,0(1(1
•-
30,QOO
51 25,000
0
c
1
"
20,000
15,000
10,000
5,000
1998
19<,19
• Public Administnttion
I'll Economic services
Source: Ministry of Economy,
l1li Security
2000
2001
o Social services
• Public debt
Naci"nal de Coordinaci6n Fiscal con la, Provincia,
Dir~"Cc(jn
Figure 8. Argentina: &onomic Oassificatioll of Provincial Spending
Results suggest that higher
government spending did not
protect household welfare,
and, in fact, may have
contributed to its decline. The
lack of detailed data on access
to public transfers and social
programs at the household level
limits our analysis to studying
the impact of aggregate
provincial public spending on
vulnerability. The log of
provincial government
spending per capita incurred in
40,000
35,000
,
""
)(J,(JOO
•
15,000
-"
25,000
20,000
0
§
10,000
5,000
1999
1998
l!lI wods and services
l!lI Capital transfers
Sourc~:
[J
Financial investment
2000
2001
• Capital expenditures
IlII Current transfers
Ministry of Economy, Direccion Nacional de C'nordinaciou risen! con loS Proviucias
~
20
~
the previous year was included in the regressions above. As shown in Table 8, households in
provinces with higher spending experienced a larger decline in their income levels of about
5 percent per additional;ercent of spending per capita. Additionally, dispersion across these
households was larger. l
The results from disaggregating spending by economic classification suggest that
households in provinces that had spent more on wages and salaries fared worse, while
households in provinces that had spent more on capital expenditures were relatively
better off. This is particularly interesting, implying that although households whose head
was employed in the public sector suffered less than the average, as discussed in the previous
section, higher spending on wages and salaries made the average households in those
provinces worse off. Moreover, it could indicate that provinces investing in capital
expendimres could have spurred growth leading to improvements in household welfare and a
decline in dispersion. Finally, results from disaggregating by functional classification show
that social spending was not significant in its impact on household income changes,
potentially pointing to its lack of effectiveness and poor targeting. ls
D. Transmission Channels of Macro Shocks to Individuals: Employment Status
After having examined the determinants of changes in income at the household level, we
study the determinants of changes on income at the individual level. We focus on labor
income changes, and in particular, on the determinants of unemployment, since this is the
main shock to personal income.
17 To control for potential endogeneity, we estimated an IV model using lags of spending as instruments. The
IV results were virtually identical when using log of total spending per capita. When using functional
classification, the effects of debt service appear equally significant to the non-IV model, but the effects are
somewhat larger. When using economic classification, results are not statistically significant, but coefficients go
in the same direction with somewhat smaller sizes. In addition, the sensitivity of the results to the inclusion of
provincial dummies was tested. Regressions including provincial dummies reduce the significance of the
coefficients. However, the magnitudes of the estimated coefticients are larger with the same signs. Results of
these regressioT1,) are available upon request.
18 Bonari and Gasparini (2002) find that consolidated social spending in Argentina was generally progressive
1997-98, but do not comment on its effectiveness or targeting. This includes spending on social security, which
is regressive, and spending in the social sectors, which is progressive. Within the social sectors, health, and
social assistance are highly pro-poor, while education is slightly progressive. Spending on water, housing, and
other services is regressive.
- 21 -
Table 8: Selected Regression Results: The Effects of Government Spending 1/
Dependent variable:
Log of per capita total public spending
Log of per capita spending on:
(Functional Classification)
Public Administration
Log (Yhuusehold ,IYhousehold ,.j )
-0.047
(3.16)**
Dis Dog (Yhousehold, IYhousehold I_I )1
0.17
(2.21)*
-0.013
-0.258
-1.71
0.184
-0.77
0.453
(2.27)*
-0.057
-1.01
0.14
(3.97)**
10.44)
Security services
Social services
Economic services
Debt service
(Economic classification)
Wages
Goods
Services
Rent
Current transfers to the private sector
Current transfers to the public sector
Current transfers to the external sector
Capital expenditures
Capital transfers
Financial investments
-0.037
(0.77)
-0.031
(0.75)
0.009
(0.73)
-0.024
(3.42)"
-0.178
(4.50)"
-0.001
(0.07)
0.001
(0.34)
-0.008
(1.06)
-0.002
(0.14)
0.041
(2.18)*
0.002
(2.10)'
0.047
(3.79)"
0.000
(0.57)
-0.001
(0.32)
43990
43990
43990
43990
Observations
Number of households
28682
28682
28682
28682
SOllTce: Authors' estimations. Panel data conslructed using EPH from May 1999 to May 2002.
Note: T-statistics in parentheses; * significant at 5%; ** significant at 1%.
1/ Complete regression results are available upon request. Based on the second specification of Table 7.
0.876
(4.45)"
-0.138
( 1.82)
-0.007
(0.89)
0.041
( 1.05)
-0.032
(0.40)
-0.238
(2.51)'
-0.005
(0.91)
-0.146
(2.44)*
0.000
(0.04)
0.027
(2.47)*
43990
28682
43990
28682
- 22-
First, we investigate the relationship between individual characteristics and the employment
status of each adult participating in the labor market, We model the probability of being
unemployed as:
Unemployed, "
..
I
=
if
(2)
{ 0 otherwise
where Zi.Lt are individual characteristics and UMav2002 is a time dummy. We assume a random
effects logit specification for the cumulative distribution function of u. The subscript i
indexes individuals; r indexes geographical region; and t indexes time.
Second, we focus on the susceptibility to changing economic conditions. To capture this
dynamic concept, we model the transition into unemployment, that is, the probability of
becoming unemployed conditioned on being employed at the initial period, as a function of
initial individual traits:
Unemployedi,r,I/Emp[oyedul_l =
,
I if y+a.I-\,f +6r +(3. "'1,r.(-1 +aOcrober2001,May2002 '(3-' "'I,r.I-1 +u I,r,{ >0 (3)
{ 0 otherwIse
7.
7
The results show that the incidence of both unemployment spells and unemployment
rates rose during the recent economic crisis in Argentina (Table 9), The probability of
being unemployed was 5,5 percent higher in May 2002 than in May 1999, holding all else
constant Regions have been affected differently, Compared to the city of Buenos Aires,
unemployment in suburban Buenos Aires is higher, while the Northeastern region, Cuyo, and
Patagonica are characterized by lower rates, However, the incidence of unemployment
increased in greater Buenos Aires, and in the Northeast, Northwest, and Pampeana regions,
Turning to the characteristics of the individual, we fmd unemployment rates were
higher for individuals with low levels of education. An extra year of education lowered the
probability of unemployment by about 05 percent Moreover, workers with less education
were more likely to lose their jobs as the economic environment worsened, potentially
because their low human capital made them less valuable to their firms, An extra year of
education lowered the probability of becoming unemployed by approximately 0,3 percent A
similar argument can be made for younger workers and those with less working experience,
who appeared to be more likely to be unemployed and to lose their jobs (note the negative
relationship between age and unemployment rates, between age and transition into
unemployment, and between years in an occupation and transition into unemployment),
Another finding is that while unemployment rates were higher among female workers
by about 2 percent, the gender gap had vanished by the end of the period (see the
coefficient for the interaction of gender and the May 2002 time dummy), This follows from
the higher vulnerability to unemployment-measured as incidence of unemployment or the
- 23 -
Table 9. Selected Logit Regression Results 11
Event'
Tm!lSIliotl into
l:Ieing unemploye,d
LlI~mpJ!)yn,e(
Privale
Time dummies
0,000
(0.81)
0,000
ao.,""",OlJ,M",(J()
(0.11)
om",
0,014
(6_97)~
a~fuwO,nJ
0.012
(2,')01~"
(2.{il)·~
0,021
a."",vl-"ITV'''",
0.038
a,,,,",,,,{JI,M,,..a;;
(13,9TI~*
(16,UI)~*
O.06R
(19 47)~
0.055
(5.07)**
-O,OOS
I2S,36)H
Years of education
-0.014
(42 20l~*
a.ouo
(Age)'
(34,70'0'*
-0.017
112.39)**
0.012
if m31e. 0 if female)
Suburt-an Buellos i\lre, I. mhetwi.,e 0
0.028
«).98)**
0.014
(L23)
(1~.7)U
(lL97)~*
(lO.97)~*
Cuyu 1. otherwise 0
0007
(1,05)
-0.004
0.000
(4.-14)**
0.007
(1,70'1
-OJ)()7
{l,6:'.)
0.014
11.95)
-(1.008
11.24)
0.000
(0.33',
-0_00:1
11.46)
IJ.()OO
a.oo:!
-O.oo:!
(O.ol )
10,25)
-0,013
(3,04)**
-oms
ILon
-0.002
11,23)
-0.002
(081)
-().OO4
(2.46)*
-11.001
(2.83)H
0.007
0.01:'.
(1.8n
-0.017
(Ui3)
-0,014
12.70)~*
I1S.90)H
0.009
Expecredjoh duration: 1 if trdnoilory
0.002
(3.15)*~
-0,002
-O.OOl
(12.31 )H 1l0.48)M
-0.0.'12
-0.078
duration: 1 if permunent
0.000
\.l21)~*
0.015
(3,56)~
Years in thac oC~"Ipaln
-0.001
0.000
.941~*
i5,M)~'
(6.94)~
F~peCldJob
0.1141
0.020
(Ll21
(4.07)·~
(3.0)*~
0,007
(1.90)
-0.022
1. otherw;"" 0
Palg()nic~
O.orB
12.1 1)~
0.003
-D.006
0.005
(6.38)~
0.033
(2AO'o~
0.000
(10.51 )*~
(0.001
-0.0:'.0
0.000
(0.41)
(9.27)~*
·0'()03
<12.19)**
-0.005
-O.OIS
(5,59)**
OJ)OO
Northwes( 1. othCrI'ThC 0
O.otl
(7.6)~*
(2,93)~"
Northeas{ I, mherwi,,, 0
0007
(4,3m*~
0.023
(S.J)~*
(9.76)'~
0.Q35
j
(6.~:)
1l(J"""'NO."'."oJ
(6.1I)H
Publi~
0.027
O.3SY*
0,008
(7.31)'~
O.Oll
(5.2S)"
0.019
(8.3:1)<*
Gender i
0.020
n8,Q)"~
0.021
(3.201~
1·.3)*~
a,]3)*"
-0.012
(6.09)>>
0.000
(0.36)
-0.033
(4.75)**
-0.030
Entrepreneur 1. otherwise 0
Worker 1, mllem;,e 0
(10.23)~
Employer (I if public. 0 if privall-)
-0.042
(16.68)M
Primary 1, olhc['.1.1Se 0
0.003
(0.56)
0.037
Construction 1. otherlvise 0
02.6)~?
Manufacmring I, otb"""ise 0
0,004
(1.49)
T mole 1. oth~_rwi,e
-0.001
(0.61)
0
Gender (1 if male. U if [emal~)
0.021
15.34)**
Conmuction Sector 1, othe""j'e 0
Observatioll.l
Number of Household,
0.007
(1.53)
11.016
(2.16)*
(0.40)
0.001
49483
323:3
10973
0.026
Cl.b 7)~
14<)590
57608
14<)590
~760
66948
42214
17--!ti5
Source' Autllors' ""Iima,;,,",_ P~nel
data comlmcted millg EPH from May 1'!'!9 to MdY 2002.
NOle: Marginal effect, are reponed. Marginal effect, on dummy variable-, coresp~d
to lhe discrete change of [he dummy from 0 10 1. Tsnui:;lics in paremhescs: "sigmficant at 5%; .~ ,tgniEcant at 1%.
1/ Complete regression resulls am available upon reque'l. Regr"'hlo~
ru,ull, induur o[her interoction telms and a Cr>t),tant.
Ibc excluded dummie, are=
time dummy: initial period; expeaccijob dul-ation: unknown; region: oity ofBucno. AiITS:
t)'Fe 01' work in the private sectOlC self-employed: and economic secto" service sector.
Coefficient' for meinlt'r""tion oflhetime dummy for the period with no Cll""oc;'hrnrd w,th tlx: I,,!lowing dtpmJett! variables wtrenotslgnific311C
year, "f roucation. y= in mot oc~"r.t!On,
3lld dummy va..iables: permanent job duration. tnmsitory job dUJation, worker, crtlrepTeIl<:UJ. public
employer. primary sector, manufacOJring 'OClor. ond Irode sl;Clor
- 24-
probability of becoming unemployed-among males, especially for the last period under
analysis. This result supports the notion of male-headed households' being more vulnerable
to the crisis, as described in the previous section.
Stability of jobs differed across types of jobs. Consistent with the previous results on
income changes. public employces were less likely to lose their jobs, and within the private
scctor, adjustments in employment levels affected the construction sector the most.
particularly during the period October 2001 to May 2002. Expected job duration also helps
predict the probability of becoming unemployed: on average, pemlanent jobs are more stable
than a series of temporary jobs during a macroeconomic crisis.
Tn the last two columns of Table 9 we present results on the probability of becoming
unemployed for the private sector and the public sector separately. Within the private sector,
we found that self-employed workers experienced higher transition rates into unemployment
(about 3 percent more) than entrepreneurs and wage workers did. Moreover, since selfemployed individuals gencrally lack social protection schemes like severance payments or
unemployment insurance, they faced serious difficulties in buffering unemployment shocks.
The results for public sector employees are similar to the private sector employees, except
that the effects are smaller in scale. For instance, while more-educated public sector workers
were less likely to loose their jobs compared to less-educated public sector workers, the
contribution of education was not as large as for private sector workers.
v.
HOUSEHOLD SMOOTHING MECHA'iTSMS
This section investigates the extent to which the labor income shock experienced by the head
of household affected total household income. The following spccification is used:
1og Yhousehold".,."
( Yhousehold1v.r_1
J= Y+ ur-l,r + < + fJu . Iog(Yhead;,:.:,:,
J+
Yhead~o1lH'r
U,.
,l,r,l-i
(4)
where a.-I.! and S. are. respectively, time and regional fixed effects controlling for systemic
fluctuations in the economy (that is, uninsurable shocks); and Yheai ahu , denotes earnings of
the head of household. 19 An interaction term of dlog(Yhead labw ) with the time dummy
aOo OI..Mm()2 is included to lest for ditferences in the responsiveness of household welfare to
19 To avoid losing observations with zero labor income for the household head-which indeed are very
informative about labor income shocks-we added one peso to both tbe labor income of the household head and
to total household income.
- 25 -
labor income shocks experienced by honsehold heads after October 2001. Equation (4) is
estimated using a random effects model.
The estimate for ;P, presented in Colunrn 1 of Table 10, suggests that 32.2 percent of
the head of household's labor income shock was not smoothed for each percentage
change in his or her labor income. The unsmoothed part of the shock to the household
head's earnings rose by 3.4 percent during the period between October 2001 and May 2002.
This implies that the other sources of household income-such as other income sources of
the head of household, or the labor income of the other household members-were less
effecti ve in protecting household welfare from changes in the income of household heads
after October 200 I.
Next, we focus on the evolution of the other income sources of the head of household
during the period analyzed. The panel includes infonnation on the following income
sources: (1) pensions and other retirement, (2) rental and interest income, (3) profits and
dividends, (4) unemployment insurance, (5) severance payments, (6) scholarships, (7) food
subsidies, (8) private transfers by nonhousehold members, and (9) other. The question is
whether these alternative income sources buffered the shock to the head of household's
earnings, or if, on the contrary, they were pOSitively correlated with the labor income shock
experienced by the head of household. We estimated the following regression using a random
effects specification:
10
Yhead,("
''
g ( Yh d j
ea
J
~
y+ a
lI,r,r-1
Yhead ;"::'
+ v~ + P" ·Iog( Yh
., +
d labor
ea IV,t-i
c
t-l,t
J
r
+a
Oc/oberOI,May2002
J
(5)
'"',,' +c j
. - U·l o [ Yhead h,r,r
jJ
g Yh dlabor
h,r,1
ea h,r,l-1
wherej ~ I, ... , 9 denotes each of the other income sources of the head mentioned above.
Results are shown in Columns 3 through II of Table 10. We also estimated specification (5)
for the summation of all the other income sources of the household head (Column 2,
Table 10).
While estimates show no difference across regions in the evolution of each of the
alternative income sources of the head of household, there are differences across
periods. In particular, rental and interest income, unemployment insnrance, and
private transfers declined sharply during May 1999-0ctoher 2001. Regarding the beta
coefficients, pj, a negative estimate indicates a negative correlation between the labor
income shock and the other income sources of the head of household. In other words,
negative (positive) beta reflects smoothing (dis-smoothing) of the earning shock. The
coefficients corresponding to Columns 2 (hrough II are negative, indicating that during the
time period analyzed, each of the other income sources of the head of household contributed
to protect household welfare from the labor income shock. Note that for a percentage fall in
labor income of the head of household, his or her nonlabor income rose by 25.2 percent.
- 26 -
Food subsidies (Column 9), transfers from nonhousehold members (Column 10) and the
category "other" (Colnmn 11) were the most responsive in insulating the head of household's
labor income shock.
Interactions of changes in the head of household's labor income with the October 2001May 2002 time dummy sbow that unemployment insurance, scholarships, and
nonhousebold transfers were more negatively correlated with the labor income shock
experienced by the head of household during the peak of the crisis; the opposite holds for
severance payments. But on the whole, as Column 2 of Table 10 shows, the head of
household's nonlabor income contributed to further buffer the shock to the head after
October 2001.
We also investigated the role of alternative household income sources. Specifically, we
examined the correlation between the earnings shock experienced by the head of
household and the income of other members of the household.(Ymembers). The
estimation results corresponding to specification (6) are presented in Table II.
log (
Ymembersh""
j
Ymembers h,r,t-l
J= y+ a
+a
t -l,I
~ + fJu . log (
+ VI'
OrroberOt,May2002
Yhead:'~;
In-bar
Yhead h,r.l-i
J
J+
(6)
lcobo,
. - G, -10 ( Yhead h,r,1
+E j
j3
g Yh d'abor
h.r,1
ea h,r,t-l
where the superscriptj denotes either labor income (Column I in Table II) or other sources
of income (Column 2 in Table II). Equation (6) is estimated using a random effects model.
As before, "other" sources of income consists of the summation of pensions, other
retirement, rental and interest income, profits, dividends, unemployment insurance, severance
payments, scholarships, food subsidies, and private transfers by nonhousehold members.
Although the labor income of other members did not serve to smooth the shock to labor
income of the head of household, the nonlabor income of other members did serve to
buffer the shock after October 2001. The positive correlation between changes in the head
of household's labor income and changes in the labor income of other members (Column 1 in
Table II) reflects that work prospects are being affected by similar factors. The interaction
term of the shock to the household head's earnings and the time dummy, txocwb"Ol.May02,
indicates that the nonlabor income of other members was more effective in insulating the
household income from the shock to the head of household's earnings during the peak of the
crisis (Column 2 in Table II).
Extremely poor households found it harder to buffer a shock to the labor income of the
household head. Additional insight regarding the economic environment and household
accessibility to the mechanisms to smooth income shocks is provided by testing for
differences in the responsiveness of household income to the head of household's labor
income shock across groups in the population. In particular, attention was given to the
- 27 -
poorest households. We used a dummy for households categorized as the poorest and
included it in regression (5) as a constant and also interacting with the head of household's
labor income shock. Results are presented in Column I of Table 12. Column I shows that for
extremely poor households, the unsmoothed fraction of the shock to the household head's
labor is about 0.15 larger than for the rest of households. We also tested for differences in the
performance of the other income sources of the head of household (Columns 2 through II in
Table 12). We did not find a significant gap in the responsiveness of the summation of all the
other income sources of the head of household (Column 2). However, while several income
sources (pensions and other retirement; rental and interest income; dividends; unemployment
insurance; and severance payments) protected the average household better, scholarships and
the category "other" were more efficient in buffering the head of household's labor income
shock among those households in extreme poverty.
To conclude, it should be emphasized that households, even those that are not extremely
poor, are unable to perfectly smooth shocks. Therefore, social intervention via direct
transfers can be useful mechanisms to assist households during macroeconomic crises. Given
the high unemployment rate and the fact that vulnerable groups are likely to participate in the
informal sector, transfers should not necessarily be tied to the labor market. Mechanisms
used by households to cope with income shocks should be strengthened. There are already
several such programs in place in Argentina (see Box I). However, for the interventions to be
targeted properly, programs should be evaluated on a routine basis. Those that prove to be
ineffective in increasing household welfare, for instance via increased earnings, reductions in
poverty, improved access to the labor market, or better education and health of children,
should be discontinued and funds be reallocated to programs that do work. Finally, an
optimal policy cannot ignore that some of the smoothing channels households engage in,
such as changes in the labor force participation of household members, including children,
are costly, or suboptimal.
Table 10: Smoothing Estimation Results II
Responsiveness of the Other Income Sources of the Head to Fluctuations in Labor Income of the Head
dlogYIU'ad
1awr
aOc,_J,Ma,'02
a O"I,>h~
. dlo<~Y1zeab"'
MoyVii
a MayOO. o.'ob,riJu
a(k''"-O().M",,-IJJ
aM",oJ.O"ol,o,',;)J
dl0'iYhousehold
dloe ¥heado,.«
dlosYhNJdo'her J
dlucYhea{I<.,her 2
dloS YhoadOlh,'r _ j
0.322
( 122.46)**
-0.252
(59.39)**
-0.035
(16.87)H
-0.015
(7.90)**
-O,O())
0,0.14
-0.030
(3.33)**
·0.023
(l JJ9)
-(L009
(O.44J
-0.033
(1.57)
-0,023
OJ)()<)
O,O(J4
(0,83)
-0.019
(1,97)*
-OJ)12
(5,12)**
-0,022
(Lti8)
0.1)119
(0.69)
0.000
(0,03)
-OJ)36
(1.74)
-0,008
(0.71i)
0.002
(0.22)
-0,003
(0.29)
-o.mJ7
(2.1\ 1)*'
(1.24)
-0.026
(2,7nn
-0.014
(1.50)
-0,039
(3.66)**
-0.1)14
(().87)
55325
35614
(4.36)**
n,OO]
(O,6t;)
-0,004
(U8)
-{],1I02
(0.69)
,
Ibe excluded dummies arc: lime dummy:
Q;.\Iayj9,<k'ob"",,:
region dUlmny: the city ofBuenus Aires.
d!op'IJeado,"e> 6
dloKfh,'a,)",hcr 7
(J/')"Y/teadorha 8
-0,024
(21 12)H-
-0.001
(l_oor;
(4.39)M
-0,072
(26.67)**
(32.26)**
-(J,022
(7,07)**
-0,01 J
(1,84)
-0,016
(1.62)**
0,007
-0,005
(2.86)**
-0,003
(1.11)
,0.003
-O,OD2
(11.39)
0,002
-0.021
-(LOO4
(3,04)"Q
(0,7'))
0,013
(1.25)
(L05)
(1,40)
-0,004
(1.27)
0.002
(0,61)
-0.005
(1.36)
-().OO5
((),79)
55324
3561.l
-0.013
(2.5')**
0.003
(0.31 )
-0.003
(0.30)
-0,007
(0.50)
55325
35614
-O,OlO
(0.78)
0,000
(0,(10)
-0,006
(0.46)
-0.009
(0.66)
-(),O74
(4-'>7)*'1
.0.001
-o.mj)
(0.31)
(0.44)
-0.008
-0.002
(0.61)
0,001
(fU2)
-0.004
(UO)
(0.li8)
-0.179
-0 128
-0.012
(l2,32)*,f
(1.08)
(5.46)**
0.037
-0.098
-0.019
({),711)
(1.76)
(1.16)
(2.87)**
Ohocrvation"
55325
55125
55325
55325
Number ofhousebolds
-'5614
35614
35614
35614
Source: AlIthnrs' estimations.Pancl data eDn,trucled using r':PH rrom May 1999 to May 2002.
Notes: GLS random dfects estimates. 'i'-slatis(ics in pm-en(heses, * significant aL 5%; ** significant at 1%,
Coefficients for (he regionalltummies are not reported since Ihey were not statistically significantly diffen:nt from each oiller.
labor income.
C,)juJiln (I) measures the fraction of head's shock left ullsmoothed for a percentage ehangc in b"d'~
Columns (2-11) measure the responsiveness of the other income OOllrces oI the head to a perccntage cbang(: in heau's Illbur iucome'
Other: summation of all the other income SOUTces of tile bead (column 2, Table !OJ.
Other_I' ponsions and otbor rctirement
Othec2: rental or interest income
OthcT_:"I: profits or dividends.
Other 4' unemployment insUrallCt:
Other_5: severance payments
Othec6 scholarships
Otllec7: food suh~ide.
Otllccll: privatc U'allsrers hy non hous~bld
melubt%
Olber _9: other
11 Complete reg~sion
resultll are available upon request
aOcto/>,"~JM;
lIlo:;l'l",ud,,'her ,
-IUl37
(30.12)**
dlvSYh,,,,dOlh,',
(1.33)
0.023
(3,31) ~-l<
0,001
ro,13)
55325
35614
~
(2.59)~
0.014
(2.51)*
0,005
(0,87)
0,009
(1.65)
0.002
(0.]1 )
0.1101
(().O9)
O()()9
(().%)
55325
35614
(2.3)~-
((J,19)
11.012
-0016
(0,74)
55325
35614
dlogYhead,"""'-
~
(LOri?
-D.O()]
«(U2)
0.1)16
(1.59)
0.001
(0.12)
OJ)O')
(0,82)
-0.031
(L8~)
55325
1';614
tv
00
- 29 Table II: Smoothing Estimation Results II
Responsiveness of the Income of the Other Adults to Fluctuations in Labor Income of the Head
dlog Yhead labOr
a Goober-Of ,MaV()2 ,dIOl?Yhead !~bor
a OcroberGO,Ma\'OI
1
2
dlog Ymembers lahar
dlogYmemiJers 0I1l",
0.017
(2.77)'*
0.016
(0.99)
-O.OSI
(2.62)*'
0.050
( 1.64)
0.002
W.4I)
-0.026
(2.451 )*
-0.032
(1.51)
-0.032
([.54)
-0.054
(2.598)"
0.006
(0.30)
-0.054
(2.325)*
0.109
(3.188)"*
55325
35614
-0.010
(0.31)
-0.097
IX ~fav(JI
,Or/a",,,)1
(3.15)*'
-0.245
IX OcioberOlNay02
(7.08)*'
0.167
y
(3.31)'*
Observations
55325
35614
Number of households
Source: Authors' estimations. Panel data constructed using EPH from .May 1999 to May 2002.
Notes: GLS random effects estimates. T-statistics in parentheses. '" significant at 5%; ** signitlcant at 1%.
11 Complete regression results are available upon request.
The excluded dummies are: time dummy: CtMay99.0clOber99; region dummy: the city of Buenos Aires.
Regressions control for regional effects.
Table 12: Smoothing Estimation Results-Change in Logarithms Specification: Average
Household vs Extreme Poor Households 1/
Responsiveness of the Other Income Sources of the Head to Fluctuations in Labor Income of the Head
dlo/!tl",,,,,,,,,/,j
J!oB),]"",d",I... ,
OA',3
(82A7)"
·0.264
(29.63)"
d1ngYI<pa~'"
(dwmny a if~-'IP"
poor, I ul"e,'s~
J d108iJea~l,
P""', 1 olhe,wise J
a,,,",p,,,,,-",',.,,,
"'~,wo1
,,,.,,,,,!
(] mil
(O_K_lJ
(1.17)
-(1.05J
1l.1l1'!
-1J.un
-(l,om
IUI31
(1.16)
_0,\122
-0,023
(L691
il.ll)
10.75)
(I(J()X
-().OO9
tUKl2
((),tic})
(0-44)
(0.22)
0.000
-0.033
_OJXI3
(L~XI
-0,024
(0.29)
(W16
(US)
() 113
(O,63)
-0.016
-0,194
(13.73»>
0.083
(4.93)~·
(3.01·~
55_US
35614
(U~)K
(1.431
-11.116
p.54)··
Observations
l',-umber oJItoIl,eltohh
Seo lJbJe 10.
0.00;
rl~
-0.012
(2.71)*'
(LI>I!)"
ao"",,,,,, ",","
.11o"jJ"od",j,,, ;
0.020
(4.110)"
(0,02)
-0.\134
""")<iI,V ••
_0
dl"d!"·,,,!"",", 2
(4.11)"
0,1107
(4.58)·~
aO"'I~
~
(0.76)
_0.155
(25~4)'
l,h"mJiY U if ~_,IJ"me
d!O,lJ~"NI.
55325
35(014
-o,om
_n,005
(.~
,0",,,, ,",·"h.-,
11.(131
(12.08·~
II (''''''1'1"", "'g' ""iOIl ,","II, aIe aHuiahlu Hpon request.
rho "cludmn~,
"'~:
lirn~1u"'(Y
a,IL"'i_~w:
cegi{," dummy: the c~y
01 BU~1loS
Aires.
dl"st)""d,,'h"'n'
-O.ll!]
(7.45)"
-O.(J{~
-0.012
(4.(.11"
(2301'
,!IeA)',-,',,],,;',',6
-O,OOU
(4.J~)"
O.Oll
(O.SIl)
(1),06)
(),2(I}
(LOn
.(j,(J(J.t
-lIJ112
(I.ot~
(I.()()_I
(0.4~)
55325
35014
'i~32
_,5614
(I".K:!)"
(1.71)
-0,019
10 .39)
_[UIK4
(1.U8)
(I,IlW
(Hli)"
0.1)03
Cl.X9)H
(I.UO-:
JI,st)",,,""""" .,
(I.{I(J5
-0.()(16
(1.97)"
-0.012
(1.24)
-0.026
12.71)"
-0,014
(1.47)
-0.041
·11.1,12
13 15)"
o,(X)u
(9)"
7
JI"*Y,~i'
(UIl>t
0.000
(0,01)
"
dJosH,~"r'
oms
(7,8bJ'~
O_Oj8
!l.O(J<)
(1.3.J)
(1.5~)
-lUX)}
-I1'()ll
(UI4)
0'(11.1
(l,B)
(0.21)
((J.B!)
( Ubi
_OJ)1l3
(1.(112
{I,n!XI
-11003
(11.31)
(L1~)
(I.Q())
-(J,lIlh
(2 ..H)'
O,O(J5
(O.M)
d_631"
(0_S7)
(1.05)
(1.40)
(IIJ)!"))
(J,nOl
-O,om
O.OIJ9
-()_IJ04
_onn
_OJK)(,
1iJ)1(,
(0.32)
(0047)
(l.h"i)
O.fX)2
(1-28)
(LGfi)H
(0048)
(1.61)
o,w.:
-(l,{lJ{I
(lOOI
(lAO)
(Il ~'I
((J.n)
(0.(19)
-11.1114
(Ubi
-0,002
-0,0(13
11_001
OO()(J
(2()6)'
((JAil)
(11M)
10,24)
O.OCI]
-0,(l(1_1
(1.(10'1
_(1.(1]7
14,49)"
_0_033
0.012
(I (141
OJUY
(0.07)
55325
356)4
10.33 )
(I.IS)
(1,(15'1
(1.37)
{l.IIK)'
55325
351>14
'i'i_Ht
5V_~
5~V
_~n5
35613
3'i61--l
3501·\
:lSO( \
-0,002
(0,61)
OJNI1
(0.17)
_IIJ)04
(0.71)
55325
},';(,(4
_(UX)~
{I.nU2
IO_)~
"
-0.098
(22h(],"
_{W02
,~gjoJl"1
dummIes are nol reportcd "in~
Ihey ",ere not stali,tic"lly 'ignifwamly dille,"nl 1m", "ad othe!:
jlO<w if li"ing in" hoo", wilh no "'J{"r. no do::lrK,ly, no pri"Jt" bothr<lom,
Based on It", informal ion on hO!1.'ing chameleris/les, we <"lcg"TI/.cd" household "ex\rcm~
NOles: ('oelficienl, h" Ih,'
or with ",,,,'«uthOn material, infer,or to Ihe typical bnckand stucco.
w
-I
W
0
- 31 -
Box 1. Argentina: Social Safety Nets
Argentina's social welfare system is narrow and fragmented. The social safety net is made up of many
small social assistance programs with overlapping national and provincial strategies. They serve various
objectives: promoting rural development, improving housing and infrastructure, developing social and
human capital, ensuring minimum food access, and providing assistance in the case of weather
emergencies. However, there is yet no unified information system to prevent leakage of funds or use of
competing programs by the same beneficiary,l Most programs lack built-in evaluation mechanisms, and
although eligibility criteria exist, it is unclear how funds are allocated when demand for benefit...;; exceeds
availability. In many instances, the distribution of benefits is subject to political influence by government
officials.
In response to the crisis, the government introduced "ide-ranging social emergency programs,
focusing on providing financial assistance to support health, education, nutrition, temporary employment,
income support and community development. Direct social assistance expenditures are estimated to have
doubled as a share of GDP in 2002, and spending priorities were reoriented toward an identified core safety
net program. The recently created National Council for Coordination of Social Policies (Consejo Nacional
de Coordinacion de Politicas Sociales, under the Presidency) is currently developing a system of
information, evaluation and monitoring of national social programs. Despite these efforts, key challenges
continue to be the effective and proper implementation of safety nets, education, and health programs.:2
In particular, the largest cash transfer program, Plan Jefes y Jefas de Hogar Desocupados, targets
unemployed heads of households living with children under the age of 18 or handicapped family
members. It is under the supervision of the Ministry of Labor, funded through the national budget and
administered by municipalities. To receive a monthly cash allowance of $150 transferred through the
banking system, beneficiaries must perfonn activities for the municipality or for qualified NGOs. To be
eligible, heads of households must certify that they are unemployed, that children in the household attend
school and that they are vaccinated according to the national health program. Control of execution and
transparency is the responsibility of municipal councils. The program reached over 1.5 million beneficiaries
during 2002, with a budget of nearly 1.3 billion pesos.) However, the program does not have proper
evaluation mechanisms to measure whether it has had significant effects on poverty, school attendance, or
children's health, and does not have any built-in incentives so that heads of households go back to the labor
market. Finally, the assignment of benefits is not devoid of political influence by local government
officials.
The Programa Familias por la Inclusion Social provides cash transfers to poor families with schoolaged children, under the requirement of continued school attendance and health check-ups. This
program is under the supervision of the Ministry of Social Development and administered by local
governments. Potential households' beneficiaries must present information at local centers to be processed
in a unified system. Participants must attend regular talks and present certified compliance with the
requirements of the program. In this sense, it is similar to Mexico's Progresa. 4 However, unlike Progresa,
qualified applicants are not randomly selected to participate in the program, preventing the application of
the best evaluation mechanisms. Benefits amount to $150 per month and are transferred through the
banking system.
11 For example, there is a food program tor low income fantilies (Programa de Emergencia Alimentaria) under the
supervision of the Ministry of Social Development and administered by provincial governments. which is not integrated
with the food program for children in schools in rural and poor urban areas (Proyecto Comedores), under (he Ministry of
Education, or other food progranls targeted to the old (Programa Probienestar de los Mayores). under the Ministry of Health.
21 See IMF (2002).
3/ Source: SlEMPRO and INDEC.
4/ For details on Progresa, see for instance Corbacho and Schwartz (2002) and Hillman and Jenkner (2002).
- 32 -
Box 1. Argentina: Social Safety Nets (concluded)
The Trabajar program provides short-term work at relatively low wages on socially useful projects
in poor areas. The projects are proposed by local governments and NGOs, and workers can only join if
recruited for an approved project. Projects last a maximum of six months and workers can switch to new
projects after this period. The monthly wage is set low enough to ensure proper self-targeting from the
pool of poor unemployed. Beneficiaries cannot receive unemployment benefits or participate in other
similar programs. Trabajar was started in the mid 19905 and is currently in its third phase.
Trabajar 111 reached about 21,000 beneficiaries in March 2002 with a budget of 5i 14 million. s Funding is
transferred to local governments by the central government, leaving considerable amount of discretion to
local government officials to allocate funds within the province. This has generated a fair amount of
controversy in terms of haw transparent these allocation mechanisms have been. The World Bank has
perfonned studies to evaluate the impact of the second phase of the program. 6 Main conclusions were that
program participants arc morc likely to be poor than nonparticipants, and that it has generated significant
income gains to participants. Studies have also found evidence of horizontal inequality in the allocation of
funding across provinces, with equally poor districts receiving very diffcrent amounts of funding.
5/ Source: SIElvfPRO and f:\IDEC.
6/ See for instance Jalan and Ravallion (1999), Ravallion (l999b), and Ravallion and others (2001).
VI.
SUMMARY AND CONCLUSIONS
It stands to reason that households are not affected equally by macroeconomic shocks, such
as economic crises or adjustment programs. However, little research has been done to
identify which socioeconomic groups are more vulnerable to macro shocks, and the reasons
why these groups suffer the most. In this paper, we have studied the effects of the
macroeconomic crisis in Argentina between 1999 and 2002 with the aim of (I) identifying
households that have been more vulnerable to the crisis, (2) investigating whether
employment in the public sector and government spending served to decrease vulnerability,
and (3) understanding mechanisms used by households to smooth the effects of the crisis.
Both poverty and income inequality increased over the 1999 to 2002 period, particularly
between October 2001 and May 2002. In fact, individuals in the poorest decile of the income
distribution experienced the largest declines in personal income. This result was even more
pronounced during the period October 200 I-May 2002-when per capita household income
of the poorest decile collapsed by 41 percent compared to a 23 percent decline in the per
capita household income of the riches! decile-indicating that the relative vulnerability of
poorest groups increased during the peak of the crisis.
The regression results suggest that household vulnerability was higher for households whose
head was less educated and employed in the private sector versus the public sector,
particularly in construction. In fact, households headed by public sector employees
experienced a 4 percent smaller decline in income when compared to households headed by
private sector employees, and this difference increased to 12 percent between October 2001
and May 2002. Larger households, those with more children, and those with a higher
- 33 -
proportion of working members showed greater declines in income. In terms of the role of
public sector expenditures. we found that households in provinces with higher public
spending appear to be more vulnerable, particularly in provinces with high spending on
wages and low capital expenditure. Interestingly, it seems that households in provinces that
spent more on wages were on average more vulnerable, although public sector employees
were better off. This suggests that provinces that allocated a large share of expenditures to
wages had lower ability to assist households in the event of the crisis, given that it is difficult
to reduce public wages in the short run to increase expenditures that may be better targeted to
the most vulnerable groups.
Turning to the transmission channels of macroeconomic shocks to indi viduals, we focused on
changes in the employment status of individuals. The results show that both the incidence of
unemployment spells and unemployment rates rose during the recent economic crisis.
Unemployment rates were higher for individuals with low levels of education, while job
stability differed across types of jobs. Consistent with the previous results on income
changes, public employees were less likely to lose their jobs, and within the private sector,
adjustments in employment levels affected the construction sector the most, particularly
during the period October 200 I to May 2002. Self-employed workers experienced higher
transition rates into unemployment than entrepreneurs and wage workers did. Moreover,
since self-employed individuals generally lack social protection schemes like severance
payments or unemployment insurance, they faced serious difficulties in buffering
unemployment shocks.
Last, we found evidence that households were unable to smooth shocks perfectly, especially
households living in the poorest conditions. While estimates present no difference across
regions in the evolution of alternative income sources, rental and interest income,
unemployment insurance, and private transfers declined sharply on average as a result of the
economy-wide shock. Finally, although several income sources (pensions and other
retirement; rental and interest income; dividends; unemployment insurance; and severance
payments) protected the average household better, scholarships and other transfers were more
efficient in buffering the head of household's labor income shock among households in
extreme poverty.
These results highlight the conclusion that social intervention can be useful to assist
households in the event of a crisis. In particular, there is room for increasing the outreach of
social safety nets. Public works programs (which can promote employment and capital
investment leading to growth, and, therefore, lower vulnerability) can be especially useful.
Direct transfers to the most vulnerable households could also be considered, although this
should not be tied to the labor market since those more vulnerable are likely to participate in
the informal sector. Finally, there is need to strengthen mechanisms households use to cope
with shocks, such as through greater labor market flexibility allowing easier access to jobs.
Argentina has a number of such programs in place, but there is still significant work to be
done in this area. There is great need for better coordination among different governmental
agencies providing social assistance to prevent program overlap, waste of funds, and abuse
by beneficiaries. For social safety nets to be effective, proper targeting and transparency in
- 34-
allocalion mechanisms-devoid of political interference-must be ensured, so that funds
truly reach those that need them the most. Also, programs must be evaluated routinely using
statistical techniques, and those programs that prove to be useful should be expanded while
others should be discontinued. In addition, it is critical that good incentives be provided for
heads of households to continue the search for employment ,md not rely on social assistance
on a continuons basis.
Our analysis can be extended in several directions. By exploiting the data for households
present in the sample for four consecutive surveys, we could trace the impact of negative
income and employment shocks [or more than one period and identify the characleristics that
could help to rcverse negative trends in welfare. Additionally, we could study the
determinants of poverty and model the transition into poverty. Should more detailed data on
public spending become available, further investigation on the impact of public social
programs would be warranted, to beller disentangle the effects of public spending on
household vulnerability. Also, more analysis on the effects of household composition could
address pOlential endogeneily in the result that larger households may experience larger
income declincs. Finally. the cffects of firm composition could be studied, to gain insight on
whether targeted credit programs, for instance to small firms, could serve as an additional
instrument to assist heads of households employed in these firms to cope with the effects of
macroeconomic crisis.
- 35 -
References
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and Cross-Country Evidence," Policy Research Working Paper 1\'0. 2788
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Economica (Argentina), Vol. 42,
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No. 1~2,
pp. 1~28.
Altimir, Oscar, 2001, "Long-Term Trends of Poverty in Latin American Countries," Estudios
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