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# How effective are cash transfer programs in mitigating income instability ? evidence from the AUH in Argentina

Sebastien Carrere

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Sebastien Carrere. How effective are cash transfer programs in mitigating income instability ? evidence from the AUH in Argentina. 2024. hal-04525248v2

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# How effective are cash transfer programs in mitigating income instability? Evidence from the AUH in Argentina

Sebastien Carrere

► To cite this version:

Sebastien Carrere. How effective are cash transfer programs in mitigating income instability? Evidence from the AUH in Argentina. 2025. hal-04525248v5

HAL Id: hal-04525248

https://hal.science/hal-04525248v5

Preprint submitted on 27 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
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émanant des établissements d'enseignement et de
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# How effective are cash transfer programs in mitigating income instability ? evidence from the AUH in Argentina

Sébastien Carrère<fnref n="1" />

Université de Bordeaux, CNRS, INRAE, BSE, UMR 6060

Version: April 2024

**Abstract:** Income instability is one crucial determinant of household vulnerability to falling back into poverty in developed and developing countries. This paper examines the effectiveness of a nationally implemented cash transfer program as a buffer against income instability among vulnerable households in Argentina. Using nationally representative household surveys from 2004 to 2015, it compares the income stability of eligible and non-eligible households to the Universal Child Allowance (AUH) program by measuring the coefficient of variation of income and transitions into poverty over one and a half years. The findings reveal that the AUH significantly reduces the time spent in poverty by 16% compared to a scenario without the program. Additionally, the program demonstrates a capacity to smooth income fluctuations among eligible households, with an average reduction of 10%. This effect is more pronounced when households experience a drop in income during the observed period. However, the program's impact diminishes in households with lower resilience to economic shocks, such as single mothers or those with dependent grandparents.

**Keywords:** Income instability, poverty, difference-in-difference, social protection, public policy

**JEL:** H53, I38, J31, J6

<footnote n="1">¹ The author is affiliated with the Bordeaux School of Economics (France). Contact: [sebastien.carrere@ubordeaux.fr](mailto:sebastien.carrere@ubordeaux.fr)</footnote>
# How effective are cash transfer programs in mitigating income instability? Evidence from the AUH in Argentina

Sébastien Carrère†*

GAEL (University of Grenoble Alpes, CNRS, INRAE, Grenoble INP), Grenoble, France

Version: September 2025

**Abstract:** Income instability is a crucial determinant of household poverty, especially in developing countries where precarious employment is common and social protection tools are limited. This paper assesses the effectiveness of Argentina's Universal Child Allowance (AUH) cash transfer program in buffering income instability among economically vulnerable households in Argentina. Using nationally representative household surveys from 2004 to 2015, we apply a Matching Difference-in-Difference approach to compare income stability between AUH eligible and non-eligible households. Results show that the AUH effectively reduces income instability among eligible households, lowering both the proportion of periods spent in poverty by 17% and income fluctuations by 5% to 17% relative to a situation without the program introduction. This stabilizing effect is stronger among households experiencing income losses during the period, indicating the program's role in smoothing negative income shocks. While the overall impact is higher for households with young children, its capacity to prevent poverty transitions is weaker among households that are less resilient to shocks, such as single mothers. Finally, analysis of the underlying mechanisms suggests that the non-labor income channel mainly explains income volatility reduction, with the AUH substituting informal risk-coping strategies. The labor income and supply channel appear limited.

**Keywords:** Income instability, poverty, social protection, public policy, Argentina

**JEL:** H53, I38, J31, J6

<footnote>† The author is affiliated with the Grenoble Applied Economics Lab (France). Contact: [sebastien.carrere@univ-grenoble-alpes.fr](mailto:sebastien.carrere@univ-grenoble-alpes.fr) / [sebascarrere@gmail.com](mailto:sebascarrere@gmail.com)</footnote>

<footnote>* The author would like to thank the participants of the AFSE 2024 conference held in Bordeaux (France) for their comments and advice, as well as those present at the WAPLAC 2024 conference in San José (Costa Rica).</footnote>
# 1. Introduction

While the 2030 Agenda for Sustainable Development from the United Nations makes access to social protection a priority worldwide (Target 1.3), the coverage is still limited notably in the Global South (UN, 2015; UNICEF, 2019a). As of 2020, less than one person in two was covered by social protection schemes, leaving more than 4 billion people unprotected (ILO, 2021). Many people have been lifted out of poverty in recent decades but remain largely vulnerable, far from Western middle-class standards (Ravallion, 2010). In particular, individuals with informal or insecure jobs are often excluded from existing contributory social protection systems and anti-poverty programs when their income slightly exceeds eligibility limits (Busso et al., 2021). Lack of access to social protection mechanisms and insurance market leave them largely exposed to idiosyncratic shocks such as job loss or illness, which can keep them or push them into poverty, reducing prospects for economic mobility (De Janvry et al., 2008).

A burgeoning literature has started addressing how income instability profoundly impacts household well-being, both economically and cognitively (Morrissey et al., 2020). Several studies find associations between income instability and various adversities such as material deprivation, deteriorating health, psychological distress, and diminished parenting quality (Gennetian et al., 2015; Hill et al., 2017; Shaefer et al., 2018). Income instability also has detrimental consequences on household's spending patterns and human capital investment, with potentially large negative impact on children's development (Hill et al., 2013). While anti-poverty policies have as their objectives to ease and foster economic mobility for vulnerable households, very few studies have tried to measure precisely the impact of cash transfer (CT) programs on recipients' income stability so far, which is crucial for effective policymaking (Wolf et al., 2014).
# 1. Introduction

While the 2030 Agenda for Sustainable Development from the United Nations prioritizes universal access to social protection (Target 1.3), coverage remains limited, particularly in the Global South (UN, 2015; UNICEF, 2019a). In 2023, only half the global population was covered by social protection schemes, leaving over 3.8 billion people unprotected (ILO, 2024). Despite significant reductions in poverty in recent decades, a large share of the population remains vulnerable to poverty, struggling to achieve stable livelihoods or middle-class standards (Ravallion, 2010). Since the late 1990s, cash transfer (CT) programs have emerged as cost-effective tools to reduce poverty and inequality worldwide (Fiszbein et al., 2009; Millán et al., 2019). In particular, conditional CTs have proliferated in low- and middle-income countries, supporting vulnerable populations and promoting human capital investment through schooling and health conditionalities. However, means-tested programs often provide limited coverage, excluding many households experiencing temporary poverty or at risk due to their focus on the structurally poor (Busso et al., 2021). This is typically the case of informal workers lacking access to social protection schemes.

Beyond relatively low incomes, a critical dimension of vulnerability stems from economic instability. Hill et al. (2017) define economic instability as repeated changes in employment, income, or financial well-being over time, particularly changes that are not intentional, predictable, or part of upward mobility<fnref n="1" />. Economic instability can stem from various dimensions, such as employment (job transitions, hours worked), family structure changes, or volatile income sources.<fnref n="1" /> This paper focuses on income instability, which can arise from internal or external causes for households.

<footnote n="1">¹ We use income 'instability', 'volatility' or 'fluctuations' as synonyms due to a lack of consensus in the literature.</footnote>
This paper fills this gap by investigating the impact of a CT program on income stability and poverty transitions within economically vulnerable households engaged in informal activities<fnref n="2" />. Specifically, this paper focuses on Argentina’s largest CT program implemented nationally in late 2009, the Universal Child Allowance (AUH), one of the most generous non-contributory programs in Latin America (LA). As in many developing countries, informal work is widespread in the LA region, particularly among workers in the two lowest income quintiles (Busso et al., 2021). Argentina provides a highly relevant setting for examining how a massive and nationally coordinated CT program affects household income stability. The AUH program aims at extending social protection to children in poor and economically vulnerable households excluded from the contributory social protection system, such as informal workers, domestic workers, or unemployable individuals. The data used come from several waves of nationally representative household surveys from the *Encuesta Permanente de Hogares* (EPH) covering a broad period from 2004 to 2015. The survey’s rotating panel structure allows to track each household’s income throughout one and a half years through four observations. Household income stability is measured by looking at household poverty transitions, i.e. the time spent in poverty across the observation period, and by computing the coefficient of variation (CV) of income<fnref n="3" />.

The empirical strategy leverages Garganta and Gasparini’s (2015) methodology to estimate the intention-to-treat effect by comparing potentially eligible and non-eligible households based on socioeconomic characteristics. A difference-in-difference (DD) strategy is applied to mitigate selection bias produced by the non-random allocation of the program among the population. To the best of my knowledge, this paper is the first to use a quasi-experimental method to assess

<footnote n="2">² There is no consensus in the literature on how to refer to income variability. Thus, terms such as “income instability”, “income variations”, “income volatility” or “income fluctuations” will be used synonymously.</footnote>

<footnote n="3">³ To consider that income fluctuations impact household welfare differently depending of the direction on income change, the CV is also analyzed separately according to household income’s positive or negative evolution. Further details in section 3.</footnote>
Due to risk aversion, individuals usually prefer stable and predictable income streams over volatile ones. Economic theory, particularly the permanent income hypothesis (Friedman, 1957), posits that households smooth consumption over time by saving or borrowing to absorb temporary income fluctuations. The resulting prediction is that any temporary deviation in income should not impact individual consumption levels (Attanasio and Weber, 2010). Yet, in imperfect credit and insurance markets, low-income households have limited means of smoothing income shocks and resort to risk-coping strategies. Therefore, the absence of formal social protection increases the risk of falling into poverty or remaining trapped in it, reducing prospects for economic mobility (De Janvry et al., 2010; Birdsall et al., 2014).

Experiences of high-income volatility directly impact household welfare by disrupting consumption, hindering investment in human capital, and exacerbating long-term poverty (Morduch, 1995; Dercon, 2002). A growing literature highlights how economic instability profoundly affects households both economically and cognitively (Morrissey et al., 2020), with material deprivation, deteriorating health, psychological distress, and diminished parenting quality (Gennetian et al., 2015; Shaefer et al., 2018; Monahan, 2020). Income instability can thus lead to a wide-ranging negative impact on children's development (Hill et al., 2013). While many studies have examined the effects of CT programs on poverty and human capital outcomes (Fiszbein et al., 2009; Millán et al., 2019) or household resilience to shocks (Haushofer and Shapiro, 2016; Premand and Stoeffler, 2020), very few studies have measured their impact on recipients' income stability so far, which is crucial for policymaking (Wolf et al., 2014; Hill and al., 2017).

In addition to boosting income, regular and predictable cash transfers are expected to help low-income households relax liquidity constraints, enabling better risk management and smoother consumption (Shaefer et al., 2018). Thus, this quasi-permanent source of income is expected to affect overall income stability by offsetting fluctuations in labor income or other volatile
the effect of a safety net program on income stability outcomes. We also conduct heterogeneity
analyzes for differences in family structures and differences in the income shocks to which
potential beneficiaries are exposed.

Overall, the results confirm the AUH's protective role in preventing households from income
swings that lead to poverty and its stabilizing effect on household income flows. The direct
income effect of the program reduces potential beneficiaries' time spent in poverty over the
observation period by 16% compared to what would have happened without the AUH
implementation. This constitutes a substantial positive effect aligned with the program's
objective of alleviating child poverty. Furthermore, the program stabilizes recipients' income
flows, notably for those having experienced a negative income change in prior income, with a
15% reduction in the CV compared to the counterfactual. For those with a positive change in
income, the program also lowers income variations by around 7%. These findings underscore
the program's effectiveness in mitigating and smoothing income streams, particularly in the
face of losses from insecure revenue sources. Robustness tests and heterogeneity analysis
further confirm and qualify these results. The program's capacity to mitigate income instability
holds across different family structures, although the impact is heterogeneous. The AUH's
effect on poverty reduction is higher in households with poorer initial economic conditions or
facing significant expenses, such as larger families or households with a young child. The
impact is considerably reduced in households headed by a woman. Given that most female
heads of household are single in the sample (88%), households' adaptation or resilience to
shocks is likely to be more challenging than for households with both parents. The value of the
transfer is not affected by the parents' family situation but by the number of children. Finally,
the presence of grandparents within the household also diminishes the program's effectiveness,
likely due to their predominantly inactive status, which increases households' economic burden.
sources (Hardy, 2017). Yet, empirical evidence of the effects of safety net programs on income volatility remains mixed and also depends on their design. The available literature – mainly limited to the US or Western Europe – tends to show a protective effect of cash transfer programs on child poverty (Bitler et al., 2017) and a stabilizing effect on income flows (Hardy, 2017; Avram et al., 2022). However, other studies also point out that mean-tested programs exacerbate income instability for beneficiaries (Bania and Leete, 2009; Morduch and Siwicki, 2017), particularly depending on their design, frequency, or eligibility conditions (Wolf et al., 2014). Bazzi et al. (2015) also fail to measure a significant expenditure change for beneficiaries of an unconditional CT program in Indonesia, but stress the importance of the timing and regularity of transfers to smooth consumption.

In this paper, we evaluate the impact of a national CT program on household income instability. We study Argentina’s Universal Child Allowance (AUH) which is targeted at vulnerable and informal workers with children. The AUH is one of Latin America’s (LA) largest non-contributory programs, where 40% of the regional population remains uncovered by social protection with widespread informal work (Busso et al., 2021; ILO, 2024). Using nationally representative household surveys from 2004 to 2015, we track income fluctuations and poverty transitions of potentially eligible and non-eligible households over one and a half years. We adopt a Matched Difference-in-Difference (MDD) strategy to measure the causal impact of the program on income stability among eligible households.

Our findings confirm the AUH’s protective role in preventing households from income swings that lead to poverty and its stabilizing effect on household income flows. We estimate that AUH eligibility reduces poverty transitions by 17% and effectively decreases income volatility from 5% to 17%, with a stronger impact when households experience a loss of income over the period. This constitutes a substantial positive effect aligned with the program’s objective of alleviating child poverty and underscores its effectiveness in mitigating and smoothing income
These results contribute to the literature dealing with safety net programs and income stability.

A large literature has extensively examined the impact of CT programs on various economic and human capital outcomes (Fiszbein et al., 2009; Papadopoulos and Leyer, 2016; Millán et al., 2019; Abramo et al., 2020) as well as their role in enhancing households’ resilience to shocks and ability to manage risks (Haushofer and Shapiro, 2016; Ralston et al., 2017; Premand and Stoeffler, 2020; Macours et al., 2022). By contrast, very few studies have assessed the effects of these programs on the income stability of the growing number of economically vulnerable households relying on informal activities and sources of revenues. While the receipt of regular financial aid is expected to affect households’ welfare by protecting living standards, smoothing consumption, mitigating material hardship, and limiting income loss from other sources (Holzmann and Jorgensen, 2001; Dercon, 2002; De Janvry et al., 2008; Shaefer et al., 2018), safety net policies might exacerbate income instability if households frequently enter and exit programs based on their design and conditionalities (Wolf et al., 2014; Morrissey et al., 2020). The findings of this present paper corroborate recent studies on the stabilizing role of social safety net programs in the US (Hardy, 2017; Bitler et al., 2017) and confirm prior findings of Micha and Trombetta (2020), also for the AUH in Argentina, using a different estimation method. Applying a microsimulation strategy in the post-AUH period (2010-2014), they quantified the contribution of each income source to the total income fluctuations among eligible households and came to a similar conclusion.

Secondly, this article contributes to the limited literature on income stability in developing countries. Mainly due to the scarcity of longitudinal studies in these countries, existing studies dealing with income instability have historically focused on the United States or Western European countries (Dynan, 2012; Hardy, 2017; Avram et al., 2022). In a recent illustrative study, Beccaria et al. (2021) underscore a high level of short-term income mobility in seven countries of the LA region during the 2000s (with mobility defined as income flux or
streams. We discuss further heterogeneous effects by family structure and examine the
underlying mechanisms behind income instability decline.

We contribute to several strands of literature. First, we offer novel causal evidence on safety nets and income dynamics. Our findings confirm prior microsimulation work on income mobility also for the AUH case (Micha and Trombetta, 2020), and align with other studies – although not directly comparable – showing the stabilizing role of safety net programs on beneficiaries’ income in the US (Hardy, 2017) and of the tax-benefit system in the UK (Avram et al., 2022). Secondly, we add to the scarce literature on income stability in developing countries, where high informal work is widespread with significant income insecurity (Beccaria et al., 2021). Due to limited longitudinal data, most existing work has focused on the U.S. or Western European countries (Dynan et al., 2012; Hardy, 2017; Avram et al., 2022). Finally, our study highlights the importance of income-stabilizing mechanisms not only for chronically poor households but also for vulnerable households facing temporary shocks. Regular transfers can be critical role in buffering shocks by stabilizing volatile income sources and avoiding harmful coping strategies. This is especially relevant in Argentina’s volatile macroeconomic environment but also more broadly in developing countries.

The rest of the paper is structured as follows. Section 2 describes the AUH program. Section 3 presents the database and the measurement of income stability. Section 4 outlines the empirical strategy. Section 5 reports the main results, briefly summarizes robustness tests and discusses heterogeneous effects and underlying mechanisms. Section 6 concludes.

## 2. The AUH program

As in most LA countries, Argentina's social protection system historically followed the Bismarckian model, limiting coverage to formal workers (Bertranou, 2010). In 2009, following years of economic growth, the government restructured its social protection system by
instability). Despite a general improvement in the economic and social situation of countries
over the period, a large proportion of households (40%) experienced a loss of income during
the period, highlighting a high degree of income insecurity, especially in countries with a large
informal sector and lacking adequate social protection systems.

Thirdly, this article provides new evidence supporting the effectiveness of extending social protection to excluded or marginalized populations. Regular transfers ensure a “floor” income that helps vulnerable households to cope with the shocks that harm their disposable income and limits the risk of a loss in living standard, often involving an increase in out-of-pocket expenses, asset sales or indebtedness. These findings carry particular relevance for Argentina, a nation frequently exposed to macroeconomic fluctuations (debt crisis, financial market confidence issues, high inflation), but more generally for all developing countries characterized by widespread informal labor, inadequate investments in social protection and healthcare services, and where political turnover significantly impact access to program benefit (Abramo et al., 2020).

The rest of the paper is structured as follows. Section 2 briefly details social programs in
Argentina and describes the data used. Section 3 presents the methodology adopted to measure
income stability, and the identification and estimation strategies. Section 4 shows the results,
the robustness tests performed, and the program's heterogeneous effects. Section 5 concludes.

**2. Context and data**

2.1. *Context and social programs in Argentina*

While poverty and extreme poverty have fallen sharply in LA in the 2000s, 40% of the
population is still not covered by a social protection mechanism in 2021⁴. Predominantly

<footnote n="4">⁴ Data from ILOSTAT (International Labour Organization) for 2020 or 2021, depending on the latest available period. More details on https://ilostat.ilo.org/data/.</footnote>
introducing the AUH, a non-contributory family allowance program that extended coverage to
informal workers (Pautassi et al., 2013). The AUH was not the first non-contributory program
initiative in Argentina. The main previous program was the *Plan Jefes y Jefas de Hogares
Desocupados* (PJJHD), which emerged in response to the 2001 economic crisis to economically
support unemployed parents (Galasso and Ravallion, 2004). From 2005 onwards, the PJJHD
gradually lost importance, and some of its beneficiaries were absorbed by another conditional
CT program, *Plan Familias*, with more limited coverage (Fenwick, 2013). At the time of its
implementation, the AUH program replaced other existing social programs and was
institutionalized within the existing contributory family allowance system, administered by the
*Administración Nacional de la Seguridad Social* (ANSES) (UNICEF, 2017).

The AUH aims to reduce the number of children living in households at high risk of poverty by
extending social protection coverage to families with under-18 children whose parents are
unregistered in the contributory system. To be eligible for the program, parents must either
work in the informal sector, be in domestic service, or be unemployed without a pension
(incompatibility with other social transfers). AUH eligibility is automatically determined by
government records and verified by the ANSES (Chudnosky and Peeters, 2022).<fnref n="2" /> Though not
only limited to the poorest, the program targets relatively low-income workers who report
earning less than the minimum wage. The initial monthly transfer was 180 pesos per child
(around 48 US$), or 720 pesos if disabled. The transfer is mainly paid to the mother,
representing 90% of cases. The amount is adjusted regularly for inflation and ranks among the
most generous cash transfers in the region (Stampini and Tornarolli, 2012). For a typical eligible
household with two children, the cash transfer accounts for roughly 30% of its monthly income.

<footnote n="2">² The program has undergone several extensions since 2009, such as the inclusion of pregnant women from their 12th week of pregnancy under the *Asignación por Embarazo* in 2011, the adjustment of transfers by region in 2015, and the inclusion of *monotributistas* (specific independent workers) in 2016.</footnote>
adopted in the region since the late 1990s, CT programs have become increasingly popular as policy instruments, mainly through conditional cash transfer (CCT) programs, focusing on specific populations and necessitating compliance with various health and education conditions to foster human capital accumulation<fnref n="5" />. However, the target of these programs still leaves many households uncovered, particularly economically vulnerable households at high risk of poverty whose members work in the informal sector (Busso et al., 2021). In Argentina, the country's main social programs were set up in response to the 2001 economic crisis, which had a devastating impact on the country, dramatically increasing unemployment and poverty rates<fnref n="6" /> (Galasso and Ravallion, 2004). The first large-scale emergency program was implemented in 2002 with the *Plan Jefes y Jefas de Hogares Desocupados* (PJJHD) to economically support households with children affected by the economic crisis. This program consisted of a fixed CT of 150 pesos (around $50 US) per household where the head was unemployed. In 2005, because of improved labor market conditions, the *Plan Familias* program progressively absorbed a substantial part of the PJJHD beneficiaries, restricting access to unemployable individuals with low education levels with two or more children (Ceballos and Lautier, 2013).

After several years of sustained economic growth during the mid-2000s, the Argentine government significantly reorganized its social protection system (Pautassi et al., 2013). In late 2009, the government extended the family allowance system to the informal sector with the non-contributory AUH program, replacing all other safety nets. Unlike previous social programs which depended on a specific ministry, the AUH was added as the second pillar of the existing family allowance system administrated by the *Administración Nacional de la Seguridad Social*, or ANSES (Bertranou, 2010; UNICEF et al., 2017). Its high degree of

<footnote n="5">⁵ Multiple studies underscore the proliferation of social safety net programs in the LA region since the late 1990s, highlighting the bifurcation of the social protection system into formal contributory structures and non-contributory assistance programs (Lavinas, 2015; Barrientos, 2019).</footnote>

<footnote n="6">⁶ The feeling of pauperization, described as the “new poor”, is well documented in Argentina and the LA region (De Riz, 2009; Kessler and Di Virgilio, 2010).</footnote>
The AUH requires compliance with regular health checks and immunization for children under 5 and school attendance up to age 18. 80% of the benefit is paid monthly, while the remaining 20% is disbursed annually upon compliance, which is required for renewal (UNICEF, 2017). By 2019, the AUH covered approximately 4 million children, representing over 30% of Argentina's child population (UNICEF, 2019b). The take-up rate of the program in the country is relatively high, estimated at 82%. However, around 1 million children remained uncovered mainly due to a lack of the necessary documents (Pautassi et al., 2013; UNICEF, 2019b; Chudnosky and Peeters, 2022).

# 3. Data and income stability measurement

## 3.1. Data structure

We draw on nationally representative microdata from the *Encuesta Permanente de Hogares* (EPH) survey conducted by the *Instituto Nacional de Estadística y Censos* (INDEC) between 2004 and 2015. The EPH is a widely used quarterly household survey that covers 32 large urban agglomerations, representing approximately 62% of Argentina's population. The EPH collects detailed information on labor and income outcomes, and other household and individual characteristics such as education or housing conditions. Each wave contains around 19,000 households. One limitation of the EPH is that it only covers populations in urban areas, excluding those in rural areas who might benefit from the program. However, this drawback should be tempered by considering the country's high urbanization rate. More than 90% of Argentines living in urban areas since the early 2000s.

The EPH survey has a rotating panel structure, meaning that part of the sample is renewed during each new wave. Each household can appear in the survey up to four times over an 18-months period. Specifically, a household is interviewed in two consecutive quarters, then leaves the survey for two quarters, and finally reappears for two additional consecutive quarters. We
institutionalization enables it to operate over the long term, independent of the pressures of the political powers in charge (Zucco, 2013; Arcidiácono, 2016).

The AUH aims to reduce the number of children living in households at high risk of poverty<fnref n="7" /> by extending social protection coverage to households with under-18 children whose parents are unregistered in the contributory system. Parents must work in the informal sector or be unemployed or inactive without pensions (the AUH is incompatible with other social transfers). Even if the AUH is not exclusively reserved for the poor, the program targets relatively low-income workers, officially earning less than the minimum wage. Although both parents must be eligible, nearly all the program transfers go directly to mothers (more than 90%). The initial transfer value per child was 180 monthly pesos (around 48 US$ per child) and 720 pesos for one disabled member. For a typical eligible household with two children, the cash transfer accounts for roughly 30% of its monthly income. The transfer value is regularly adjusted for inflation and is one of the most generous programs in the LA region (Stampini and Tornarolli, 2012). Like traditional CCT programs, the AUH requires compliance with regular health checks and immunization for children under age four and school attendance for children aged 5 through 18 (Garganta et al., 2017). Most of the transfer is paid monthly (80%), with the remainder paid at the end of the year when the required conditions are met. It is important to note that even though these conditions are standard for this type of program, compliance with them is a prerequisite for renewal the following year, which can be restrictive if certificates are not issued

<footnote n="7">⁷ Since its implementation, the government has made a few extensions to include more children not covered yet. First, in 2011, the AUH widened for pregnant women from their 12th week of pregnancy until birth with the Asignación por Embarazo. In 2015, the transfer was adjusted according to the household's residence region to account for geographical disparities and living standards. It also provided supplementary transfers to finance school fees in the same year. In 2016, the program also extended the coverage for children from *monotributistas* parents (specific independent workers).</footnote>
exploit this survey design to compute several measures of household income stability and
poverty transitions over a period of one and a half years. For the analysis, we consider
households entering the survey in the same year as belonging to the same cohort, and then stack
these cohorts across years from 2004-2005 to 2014-2015.<fnref n="3" /> The resulting dataset is therefore
best described as a set of repeated cross sections. We provide an illustration of the data structure
in Figure SA1 in Supplementary Materials. To ensure comparability, we restrict the main
sample to households interviewed for a full year (i.e., three or four waves), which represent
56% of the total sample.<fnref n="4" /> As a robustness check, we also include households observed for only
two waves (82% of the total sample), which yield very similar conclusion (Supplementary
Materials Table SD5).

All income values are deflated to 2018 Argentine pesos and converted to 2011 purchasing
power parity (PPP) dollars.<fnref n="5" /> Given the consensus in the literature on the poor quality of
INDEC's official data on consumer price index (CPI) over the 2007-2015 period, two different
sources are used. From 2004 to 2007, the official CPI data is used. However, an alternative CPI
source is preferred for the following years based on the Billion Prices Project, using numerous
online prices available on the web that correct for bias coming from official sources (Cavallo
and Rigobon, 2016).

## 3.2. *Income stability measurement*

We use three main indicators to measure household income instability. The first one is the
Poverty Trends (PT) indicator. The PT measures the proportion of periods spent in poverty
during a household's observation period, i.e., the number of periods in poverty out of the total

<footnote n="3">³ The EPH data includes only the first semester of the 2014-2015 panel, and the 3rd quarter of 2007 was not published by INDEC.</footnote>

<footnote n="4">⁴ To address potential non-random attrition, we perform an inverse-probability weighting adjustment based on a probit model predicting panel retention using household characteristics (Paz, 2013). Since the main results remain robust to this correction (Supplementary Materials Table SD3), we keep the original survey weights for simplicity.</footnote>

<footnote n="5">⁵ Argentina’s 2011 PPP factor conversion in 2018 is 14.23 (World Bank, last access in November 2023).</footnote>
on time. In 2019, the AUH covered around 4 million children, representing more than 30% of the child population in the country<fnref n="8" /> (UNICEF, 2019b).

## 2.2. Data

This study uses nationally representative microdata from the EPH survey collected by the Instituto Nacional de Estadística y Censos (INDEC) from 2004 to 2015. The EPH is a widely used national household survey carried out quarterly for around 18,000 households per wave and covers 31 large urban areas accounting for around 62% of the Argentine population. However, the survey does not cover rural areas. It addresses work and income-related dimensions and provides various socioeconomic information on households (education, housing equipment, geographical information). The survey is carried out quarterly and has a rotating panel structure, with part of the sample replaced in the next wave (see Figure A1 in Appendix). The data structure allows the construction of several short panels from 2004 to 2015⁹.

In each panel, households are interviewed a maximum of four times over one and a half years. A household with a complete follow-up is interviewed in two consecutive quarters of a year *t* when it enters the survey, exits the survey for the following two quarters, and is interviewed again in the same two quarters the following year in *t+1*. In this case, a household has two pairs of observations between years *t* and *t+1*. However, for some households, the follow-up is not fully complete. Only households interviewed three or four times are kept in the sample for

<footnote n="8">⁸ Yet, around 16% of children in 2016 were still not covered by any social protection scheme because of administrative barriers, such as the lack of identity documents or birth certificates, or parents having migrated for less than three years (Pautassi et al., 2013; UNICEF, 2019b). Similarly, delays on the supply side (administration, health services) in receiving certificates of compliance with conditionalities can sometimes compromise program renewal for the following year, particularly for rural or geographically remote populations.</footnote>

<footnote n="9">⁹ The EPH data includes only the first semester of the 2014-2015 panel.</footnote>
number of periods. It shows the extent to which income swings push a household below the poverty line over the interviews through one and a half years. The poverty line is set at $5.5 per capita a day (2011 PPP), corresponding to moderate poverty, and is commonly used for the LA region (Jolliffe and Prydz, 2016; Gasparini et al., 2023).

The second indicator is the Coefficient of Variation (CV) of per capita income, which captures the overall dispersion of a household's income, i.e., the extent to which its income fluctuates over its observation period. The CV facilitates comparisons across heterogeneous groups and has been used in the literature to assess household income variability in both the US (Gennetian et al., 2015; Morduch and Siwicki, 2017; Monahan, 2020) and LA contexts (Beccaria and Groisman, 2008; Micha and Trombetta, 2020; Beccaria et al., 2021). For household i, the CV is computed as the ratio of the standard deviation of income over time to its mean, as written in Equation (1):

$$CV_i = \frac{\sqrt{\frac{1}{T} \sum_{t=1}^{T} (y_i^t - \bar{\mu}_i)^2}}{\bar{\mu}_i} \quad (1)$$

Where $y_i^t$ is the per capita income of household i observed at period t, and $\bar{\mu}_i$ the average income of household i over its entire observation period. However, as the CV is relative to the mean, an average increase in household income could mechanically lower the indicator without reducing income volatility. Therefore, we use an alternative measure of income variability, the Standard Deviation of Arc Percentage Change (SD-APC), which captures the magnitude of income fluctuations between consecutive periods $y_i^t$ and $y_i^{t+1}$, as in Equation (2). The SD-APC reduces the influence of large income swings caused by outliers, is symmetrical, and is bounded between -200% and +200% (Dyna et al., 2012; Hill, 2021).

$$SD-APC_{i}^{t+1;t} = \sqrt{\text{Variance} \left[ \left( \frac{y_{i}^{t+1} - y_{i}^{t}}{\frac{y_{i}^{t+1} + y_{i}^{t}}{2}} \right) \times 100 \right]} \quad (2)$$
analysis to measure income stability more accurately and to cover a broader period. The
percentage of households meeting this condition accounts for around 56% of the total sample<fnref n="10" />.

Another point concerns income imputation. In some cases, a household member may not declare all sources of income at the time of the survey. Around 20% of households are in this situation. Although this could lead to a measurement bias in the variation of income at the household level, it should be noted that this phenomenon is more prevalent among the wealthiest households, i.e. those in the bottom top deciles of the per capita income distribution (around 14% for deciles D1 and D2, against 25% for D9 and D10). Since the AUH does not target these households, these households are kept in the analysis. Further tests check the results' robustness to the sample size's extension or restriction in Section 4.

Finally, all income values are deflated to 2018 Argentine pesos and converted to 2011 purchasing power parity (PPP) dollars<fnref n="11" />. Given the consensus in the literature on the poor quality of INDEC's official data on consumer price index (CPI) over the 2007-2015 period, two different sources are used. From 2004 to 2007, the official CPI data is used, but an alternative source is preferred for the following years from the Billion Prices Project<fnref n="12" /> (Cavallo et al., 2016; Cavallo and Rigobon, 2016).

**3. Methodology**

3.1. *Income groups and income stability measurement*

Two indicators are used to offer a more comprehensive understanding of household income dynamics: the poverty trends indicator and the CV. The first one measures the share of periods

<footnote n="10">¹⁰ Households with no follow-up account for 18% in the sample, and those with only two interviews for around 26%. Robustness checks also include households with only two interviews over time and find similar results.</footnote>

<footnote n="11">¹¹ The 2011 PPP factor conversion for Argentina in 2018 is 14.23 (World Bank, last access in November 2023).</footnote>

<footnote n="12">¹² Their CPI data come from numerous online prices available on the web that correct for bias coming from INDEC sources. See Cavallo and Rigobon (2016) for further details.</footnote>
Because income increases or decreases differ in causes and effects on household well-being, distinguishing income trends from overall variability is important. Therefore, for both the CV and SD-APC indicators, we also analyse changes in average income $\bar{y}_i$ between the first and last year of observation. The $CV^U$ and $SD-APC^U$ correspond to income variability measures for the subsample of households experiencing an upward income mobility between year $t$ and $t+1$ – i.e., when $\bar{y}_i^t < \bar{y}_i^{t+1}$ – while the $CV^D$ and $SD-APC^D$ for the subsample of households with a downward income mobility between $t$ and $t+1$, i.e. when $\bar{y}_i^t \ge \bar{y}_i^{t+1}$.

For the calculation of indicators, households with one member omitting a source of income are kept in the sample (around 26% of households, representing 11% of total individuals). Although this may lead to measurement errors, the non-declaration of income is more prevalent among wealthier households, which are not targeted by the AUH program (5% of non-reporting in the lowest income deciles compared to 20% in the highest). Robustness tests modifying the sample size do not alter our results (Supplementary Materials Tables SD4 and SD5).

# 4. Methodology

## 4.1 Groups identification

Since the AUH beneficiaries are not directly identified in the EPH, we adopt the methodology from Garganta and Gasparini (2015) to estimate the program's intention-to-treat (ITT) effect. Households' eligibility for the program is therefore determined according to official criteria based on their socio-economic characteristics. Households are considered as eligible if they have children under 18 and parents who are unemployed, inactive, informal workers, domestic workers, or retired without health coverage. Eligibility is confirmed if these conditions are met in both the household's first two interviews. Although it is impossible to know whether households receive assistance continuously between survey waves, official ANSES reports
during which a household is in poverty during its observation period, i.e. the time spent in
poverty during its observation period. It shows how much income swings push a household
below the poverty line. In the manner of transition matrices, it identifies households that are
always, transiently or never poor during their observation period, based on four interviews over
one and a half years. Three income groups are built based on the LA context to establish a
relevant poverty line: the poor, the vulnerable, and the middle and upper groups. The
identification of the vulnerable group is based on that of the “strugglers” by Birdsall et al.
(2014), comprising economically vulnerable households earning between $4 and $10 per capita
per day (expressed in 2005 PPP). The lower threshold of $4 is sufficiently above the (older)
international poverty lines of $1.25 and $2 a day for extreme and modest poverty to not include
people who are likely to be poor in the vulnerable group. López-Calva and Ortiz-Juarez (2014)
also find that the $4 threshold provides a more accurate estimate of poverty in LA than using
standard international poverty lines for low-income countries. On the other hand, they also
demonstrate that the upper limit of $10 is associated with a low probability of falling into
poverty (around 10%), which depicts the beginning of the (lower) middle class in LA. Then,
the vulnerable group includes people who are not economically secure enough to be inside the
middle class because of their substantial risk of falling back into poverty (Ferreira et al., 2012).

As the cut-off values discussed above are from the 2005-era PPP, all values used to construct
income groups are converted to 2011 PPP to adjust to global price changes. The adjusted
absolute thresholds in 2011 PPP for the vulnerable group are $5.5 for the lower bound, and
$11.5 for the upper bound. A household with a daily per capita income of less than $5.5 will be
considered poor, and vulnerable if it does not exceed $11.5. Otherwise, it will be considered
middle class and upper groups. Table 1 summarizes the size of the monetary groups in the total
EPH survey and in the sample of households with at least three interviews between 2004 and
have shown that around 90% of AUH recipients receive it continuously over a year (UNICEF, 2017). Transitions over a year are, therefore, somewhat limited.<fnref n="6" />

Informality among employees is proxied by lack of pension contributions, a standard approach in the literature to determine informality in LA (Tornarolli et al., 2014). Self-employed workers, mainly low-skilled and without healthcare in the sample (73% without secondary education and 77% uninsured), are also considered as eligible due to their low social protection coverage in Argentina (Garganta and Gasparini, 2015). Unemployed are also included in this group, given that less than 3% report receiving unemployment benefits. In contrast, the non-eligible group comprises households with minor children but include members with formal employment or social protection, which disqualifies them from the AUH (employers, formal employees, retired with health coverage).

Access to the program also requires that households' reported incomes falls below the national minimum wage. However, measuring income from informal activities (non-declared income, reporting errors, and lack of or poorly developed accounting) remains difficult for the ANSES, and this requirement is rarely met in practice (Garganta and Gasparini, 2015). Because the AUH targets low-income households, we restrict the sample to those in the bottom three deciles of national capita income during their first year of interview. Households in the upper deciles are therefore not considered to avoid including households with a low probability of benefiting from the program and to compare similar economic situations. To validate this approach, we use an alternative nationally representative dataset which directly identify the AUH recipients: the *Encuesta Nacional de Gastos de los Hogares* (ENGHo), a household expenditure survey available from 2012 to March 2013. The ENGHo dataset shows that 76% of AUH recipients

<footnote n="6">⁶ Most households have a stable eligibility status: 8% of them change status between the first two quarters, and 80% of those eligible in the first year remain so the following year. Results are robust to various sample restrictions (Supplementary Materials Table SD5).</footnote>
2015. Group sizes are very similar in both cases. As expected for Argentina, many households belong to the middle and upper groups.

<table><caption>Table 1: Identification of income-groups in the EPH</caption><thead><tr><th>Income groups</th><th>$US per capita/day (PPP)</th><th>Group sizes in total EPH survey (%)</th><th>Group sizes in the sample used (%)</th></tr></thead><tbody><tr><td>Poor</td><td>[$0; $5.5[</td><td>8.7</td><td>7.9</td></tr><tr><td>Vulnerable</td><td>[$5.5; $11.5[</td><td>16.1</td><td>17.0</td></tr><tr><td>Middle and upper groups</td><td>≥$11.5</td><td>75.3</td><td>75.1</td></tr></tbody></table>

Source: Authors’ elaboration based on the EPH microdata (2004-2015).

Note: Households are classified by averaging their per capita income over the first year of interview, or first interview when follow-up is not carried out. The sample of households used for the last column includes households interviewed at least three times in the survey. PPP = purchasing power parity. The PPP (2011) factor conversion is 14.23 adjusted for 2018 prices in Argentina (World Bank). CPI come from official sources (INDEC) and the Billion Prices Project (BPP) for 2007-2015 period (Cavallo and Rigobon, 2016).

In addition to the first indicator, the CV of per capita household income is calculated to quantify how much household income fluctuates over its observation period. This traditional indicator has been used in the literature to examine household income variability in the US (Newman, 2008; Gennetian et al., 2015) but also in LA (Beccaria and Groisman, 2008; Micha and Trombetta, 2020; Beccaria et al., 2021). The CV for a household *i* is computed as the ratio of the standard deviation of income measured over time to the mean income:

$$CV_i = \frac{\sqrt{\frac{1}{T} \sum_{t=1}^{T} (y_{i,t} - \bar{\mu}_i)^2}}{\bar{\mu}_i} \quad (1)$$

Where $y_{i,t}$ is the per capita income of household *i* observed at period *t*, and $\bar{\mu}_i$ the average income of household *i* over its entire observation period. However, the variation in household income measured by the CV does not consider the direction in which income evolves. Since an upward or downward change in income does not have the same impact on a household's welfare, the CV analysis will also be carried out on two sub-samples, taking into account the change in average household income $\bar{y}_i$ between the first and final year of interview. Thus, the CV-up computes the CV for household experiencing an upward income mobility between year *t* and *t+1*, i.e. when $\bar{y}_i^t < \bar{y}_i^{t+1}$. The CV-down, for those with a downward income mobility between *t* and *t+1*, i.e. when $\bar{y}_i^t \ge \bar{y}_i^{t+1}$.
fall within this range, supporting that the income threshold applied in the EPH data adequately captures the target population.<fnref n="7" />

## 4.2. Impact identification

Since the AUH program was not randomly assigned, the analysis may suffer from selection bias, raising endogeneity concerns that hinder causal inference. We adopt a quasi-experimental DD approach to isolate the program's effect on income stability. This compares outcomes for eligible and non-eligible groups before and after the AUH's introduction, controlling for time-invariant characteristics. To clearly delineate the periods before and after the introduction of the AUH in November 2009, the transitional period of 2009-2010 is not considered, leaving five periods before the program (from 2004-2005 to 2008-2009), and five after (2010-2011 to 2014-2015). Equation (3) presents the standard DD specification:

$$Y_i = \beta_1 D_t + \beta_2 T_i + \beta_3 (T_i * D_t) + \eta_r + \eta_t + \theta X'_i + u_i \quad (3)$$

With $Y_i$ is a measure of income instability for household $i$, $D_t$ a post-treatment dummy equal to one for years 2010-2015 and zero for 2004-2009, $T_i$ a treatment indicator equal to one if household $i$ was eligible for the AUH in its first year of observation, $X'_i$ a vector of household-level baseline controls, $\eta_r$ and $\eta_t$ region and year fixed effects, and $u_i$ the error term. As expressed in Equation (4), the DD estimator compares the average changes in outcomes $Y$ between eligible (T=1) and non-eligible (T=0) groups before (D=0) and after (D=1) the AUH introduction, conditional on the set of covariates $X$.

$$\alpha^{DD} = (E[Y_i | T_i = 1, D_t = 1, X'_i] - E[Y_i | T_i = 1, D_t = 0, X'_i]) - (E[Y_i | T_i = 0, D_t = 1, X'_i] - E[Y_i | T_i = 0, D_t = 0, X'_i]) \quad (4)$$

In this framework, the identification of the ITT effect relies on the conditional parallel trends assumption – that is, eligible and non-eligible households would have followed similar trends

<footnote n="7">⁷ Descriptive statistics on AUH beneficiaries from the ENGHo are available in Supplementary Materials Table SA1.</footnote>
## 3.2. Empirical approach: groups identification

The rotating structure of the EPH allows the construction of eleven yearly panels covering 2004-2015. Since the AUH program appeared during the last quarter of 2009, the 2009-2010 panel is excluded from the analysis to delimit a clear cut-off between the pre- and post-implementation of the program<fnref n="13" />. Then, five-yearly panels are entirely located in the pre-intervention period (from 2004-2005 to 2008-2009), and the following five panels after the AUH implementation (from 2010-2011 to 2014-2015). However, the EPH survey does not include questions allowing us to identify AUH beneficiaries directly. The questionnaire asks only: “Did you receive any monetary transfers from the State, church, etc., in the past three months ?” with the corresponding monetary amounts. There is no way to be sure that households are part of the AUH program and that the amount received does not come from other public institutions or alternative sources. Therefore, the methodology Garganta and Gasparini (2015) adopted is followed to estimate the program’s intention-to-treat. Households are assigned to treatment or control groups according to their initial labor and socioeconomic characteristics, which mimic the program’s official eligibility criteria. The treatment group includes households with children under 18 years old whose parents are either working in the informal sector, as domestic employees, inactive, unemployed, or retired without health coverage, in the first year of interview<fnref n="14" />. Only households meeting these criteria in their first two interviews are considered eligible (i.e. the first year of entry into the survey) or non-eligible otherwise<fnref n="15" />. The status of the declared head of household and the spouse are checked for

<footnote n="13">¹³ Similarly, households interviewed during the implementation of the AUH are not considered.</footnote>

<footnote n="14">¹⁴ Workers in the domestic service, even those registered in the formal sector, are specifically targeted by the AUH program (Edo et al., 2017).</footnote>

<footnote n="15">¹⁵ Only 5% of households in the sample change category between the first two quarters of interview. In the next section, some robustness checks introduce additional eligibility constraints to test the sensitivity of the results. It restricts the analysis to households “stable” in their formal/informal categorization over the entire period, instead of the first year. Results remain mostly unchanged.</footnote>
in the absence of the AUH, after controlling for covariates. Under this assumption, the parameter $\beta_3$ in Equation (3) identifies the impact of AUH eligibility on household income instability. However, two concerns may bias this estimate. First, mean comparisons before the AUH implementation show that eligible and non-eligible groups differ significantly in several dimensions (Table 1). For instance, both groups have different income stability and poverty levels, which justify using the DD strategy. Yet, other differences also exist in terms of family structure or education, which could compromise the comparability of the two groups. Second, since the analysis uses repeated cross-sectional data, compositional changes in the treatment and control groups over time may violate the DD identifying assumptions.

<table><caption>Table 1: Characteristics of eligible and non-eligible households before the AUH implementation</caption><thead><tr><th rowspan="2">Variables</th><th colspan="4">Pre-AUH</th></tr><tr><th>Eligible (1)</th><th>Non-eligible (2)</th><th>Difference (2)-(1)</th><th>t</th></tr></thead><tbody><tr><td>PT</td><td>0.52</td><td>0.24</td><td>-27.8</td><td><strong>-45.18</strong></td></tr><tr><td>CV</td><td>0.43</td><td>0.34</td><td>-0.09</td><td><strong>-21.25</strong></td></tr><tr><td>SD-APC in per capita income (%)</td><td>62.07</td><td>48.44</td><td>-13.62</td><td><strong>-17.86</strong></td></tr><tr><td>Average daily income per capita</td><td>6.32</td><td>8.90</td><td>2.58</td><td><strong>34.88</strong></td></tr><tr><td>Household size</td><td>5.24</td><td>5.52</td><td>0.28</td><td><strong>7.67</strong></td></tr><tr><td>Number of minor children</td><td>2.66</td><td>2.74</td><td>0.08</td><td><strong>2.81</strong></td></tr><tr><td>Number of major children</td><td>0.70</td><td>0.72</td><td>0.02</td><td>0.91</td></tr><tr><td>Age of the youngest child</td><td>5.71</td><td>6.00</td><td>0.29</td><td><strong>3.24</strong></td></tr><tr><td>Age of the head</td><td>42.7</td><td>43.22</td><td>0.55</td><td><strong>2.41</strong></td></tr><tr><td>Woman head of household</td><td>0.38</td><td>0.19</td><td>-0.19</td><td><strong>-23.52</strong></td></tr><tr><td>Married head of household</td><td>0.37</td><td>0.59</td><td>0.22</td><td><strong>23.70</strong></td></tr><tr><td>Single head of household</td><td>0.33</td><td>0.15</td><td>-0.19</td><td><strong>-24.37</strong></td></tr><tr><td>Parents' pluri-activity</td><td>0.11</td><td>0.06</td><td>-0.05</td><td><strong>-9.74</strong></td></tr><tr><td>Parents' highest level of education</td><td></td><td></td><td></td><td></td></tr><tr><td>Primary incomplete</td><td>0.13</td><td>0.08</td><td>-0.05</td><td><strong>-9.20</strong></td></tr><tr><td>Primary complete</td><td>0.36</td><td>0.28</td><td>-0.08</td><td><strong>-9.88</strong></td></tr><tr><td>Secondary incomplete</td><td>0.25</td><td>0.23</td><td>-0.02</td><td><strong>-2.12</strong></td></tr><tr><td>Secondary complete</td><td>0.17</td><td>0.24</td><td>0.07</td><td><strong>9.81</strong></td></tr><tr><td>University incomplete</td><td>0.05</td><td>0.08</td><td>0.03</td><td><strong>6.23</strong></td></tr><tr><td>University complete</td><td>0.03</td><td>0.08</td><td>0.05</td><td><strong>10.57</strong></td></tr><tr><td>Non-reporting of income</td><td>0.27</td><td>0.26</td><td>0.01</td><td>-0.90</td></tr><tr><td>Observations</td><td>8,791</td><td>4,288</td><td></td><td></td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2009.

Note: The poverty trends indicator measures the proportion of periods classified as poor for a household over its observation period, i.e. income per capita below $5.5/day (PPP 2011). The CV is the coefficient of variation of per capita income. CV-down and CV-up measure the CV of sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. Values in bold indicate significant differences between the two groups at 95% level.
program eligibility. In cases where one of the child's grandparents is declared head of household, only the status of the child's parents is considered.

For parents with employee status, the distinction between formal and informal is approximated by asking whether pension contributions are deducted from wages. Lack of contribution to the pension system through deductions from wages is the most commonly used proxy in the literature to determine informality in LA (Tornarolli et al., 2014). Self-employed are also considered eligible since social protection is poorly developed for them in Argentina (Gasparini et al., 2009). In this income bracket, most are unskilled self-employed (73% with less than a secondary degree) and have no healthcare coverage (78%). Unemployed are also included in this group given that only 3% report receiving unemployment benefits. Finally, retired people are considered eligible if they have no healthcare coverage. On the other hand, the control group is made up of households with a similar family structure with minor children but with different labor characteristics since they are registered or are paying contributions to the social system (employers, formal employees, retired with health coverage), which is not compatible with the AUH eligibility criterion.

A final condition for access to the program is that household members should earn less than the national minimum wage. However, measuring income from informal activities (non-declared income, reporting error, no or poorly developed accounting) remains difficult for the ANSES and this requirement is rarely met in practice (Garganta and Gasparini, 2015). Given that the AUH program targets economically vulnerable households, only those classified as vulnerable or poor during the first year of interview are kept, i.e. with an average per capita income of less than $11.5 per day. Households belonging to the middle and upper-income groups are excluded due to their low probability of being targeted by the program and to limit the inclusion of households that are too wealthy. To ensure that potential AUH beneficiaries are correctly identified and similarly distributed across the income groups, an alternative database allowing
To address these concerns and construct a more convincing counterfactual, we implement the MDD method, combining the standard DD approach with a kernel-based matching (Heckman et al., 1998; Blundell and Dias, 2009). The MDD controls for potential differences in group composition while retaining most observations (Garrido et al., 2014; Fernández and Villar, 2017). Matching is performed separately across three groups: the treatment group before the intervention, and the control group before and after the intervention. Using Blundell and Dias (2009) notations, the MDD estimator is written as in Equation (5):

$$ \alpha^{\text{MDD}} = (E[Y_i | T_i = 1, D_t = 1, X_i] - \tilde{w}_{i0}^T \times E[Y_i | T_i = 1, D_t = 0, X_i]) \\ - (\tilde{w}_{i1}^C \times E[Y_i | T_i = 1, D_t = 0, X_i] - \tilde{w}_{i0}^C \times E[Y_i | T_i = 0, D_t = 0, X_i]) \quad (5) $$

With $\tilde{w}_{i0}^C$, $\tilde{w}_{i1}^C$, and $\tilde{w}_{i0}^T$ the kernel weights for the control group in the baseline and follow-up periods, and for the treatment group in the baseline period, respectively. Covariates included in the matching are the number of minor children, having a young child (5 or under), being a large family (6 or more members), if the head is under 30 or over 60, the head is a woman, married parents, parents' highest level of education and quarter dummies. To maintain the representativeness of the data at the national level, we adjust the survey expansion weights by multiplying them with the kernel weights (Ridgeway et al., 2015; Lenis et al., 2019). More details on matching quality can be found in Supplementary Materials (Section E). Overall, matching reduces standardised differences between groups by 80%.<fnref n="8" />

We complement the previous MDD analysis with an event study specification to examine the dynamic ITT effects and test the assumption of parallel trends. Equation (6) includes a series of event-time dummies $T_{i\tau}$, where $\Upsilon_\tau$ estimates pre-treatment differences (leads) and $\delta_\tau$ the post-treatment effects (lags). Year -1 is omitted and therefore taken as the reference point.

<footnote n="8">⁸ The -diff- Stata package is used to implement the kernel-based matching estimator (Villa, 2016), and -psmatch2 - for the matching quality graphs (Leuven and Sianesi, 2003).</footnote>
for direct identification of AUH beneficiaries is used. The *Encuesta Nacional de Gastos de los Hogares* (ENGHo) is a household expenditure survey only available for the year 2012 until March 2013 that is also nationally representative<fnref n="16" />. Table 2 shows the distribution of AUH beneficiaries across income groups for 2012 using the ENGHo and EPH database. The direct identification is based on the ENGHo survey (Column 1), while the non-direct identification is made via the EPH survey (Column 2). For 2012, the proportion of beneficiaries in each income groups are very similar using both methods. Using the direct method, around 60% of the AUH beneficiaries are classified in the poor and vulnerable income groups, and 67% for the non-direct method. The majority of beneficiaries belong to the poor and vulnerable groups. As 40% of beneficiaries' households are classified in the middle and upper-income groups, several methods verifying the sensitivity of the results to various sample sizes are presented in Section 4.

<table><caption>Table 2: Identification of the AUH beneficiaries (2012/2013)</caption><thead><tr><th>Income groups (per capita/day)</th><th>(1)<br>Direct identification (ENGHo)</th><th>(2)<br>Non-direct identification (EPH)</th></tr></thead><tbody><tr><td>Poor (&lt; $5.5)</td><td>18.6%</td><td>14.3%</td></tr><tr><td>Vulnerable ($5.5 ; $11.5)</td><td>40.6%</td><td>43.1%</td></tr><tr><td>Middle and upper groups (&gt; $11.5)</td><td>40.5%</td><td>42.6%</td></tr><tr><td>Observations</td><td>2806</td><td>2261</td></tr></tbody></table>

*Source:* Authors’ elaboration based on the EPH (2012) and ENGHo (2012/2013).

*Note:* Households are classified according to their average per capita income in the first year of interview. Incomes were adjusted to 2018 pesos and converted to US PPP (2011). The conversion factor used comes from the World Bank (=14.23). In the ENGHo survey, the following question is asked: “Did you receive any income in cash or in-kind in the last 6 months from Universal Child Allowance (AUH) ?”; In the EPH survey, potential beneficiaries are households with an under-18 child and whose parents are either working in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview.

### 3.3. Empirical approach: impact identification and assumptions

Since the AUH program was not randomly assigned to the population across the country, the analysis may suffer from a selection bias, bringing endogeneity concerns. The treatment and control groups have many observable and unobservable differences (consumption behavior, saving strategies, and budget allocation like health expenditures) that could prevent us from

<footnote n="16">¹⁶ Descriptive statistics of the AUH beneficiaries from the ENGHo are available in Appendix (Table A1).</footnote>
$$Y_i = \eta_r + \eta_t + \sum_{\tau=-q}^{-2} \gamma_{\tau} T_{i\tau} + \sum_{\tau=0}^{m} \delta_{\tau} T_{i\tau} + \theta X'_i + u_i \quad (6)$$

$X'_i$ includes the matching covariates and supplementary controls such as dummies for single parent households and whether any household member failed to report a source of income. Standard errors are clustered at the large urban area level – equivalent to the city or metropolitan level – to account for potential intra-cluster correlation in errors over time within the same area (Bertrand et al., 2004).<fnref n="9" />

As presented later, we also assess the robustness of our findings through a series of complementary exercises (Supplementary Materials, Section D), including the Doubly Robust estimators proposed by Sant'Anna and Zhao (2020). These specific estimators remain consistent if either the outcome regression or the treatment model is correctly specified, and enable valid inference with uniform confidence intervals accounting for multiple testing issues (Baker et al., 2025). As the program was implemented nationwide (not staggered) and the results closely match our MDD estimates, we only report the estimates in Supplementary Materials (Section D).

### 4.3. MDD assumptions

We now discuss some essential assumptions of the MDD strategy. As stated previously, one of the main assumptions is that trends in the outcome variables should have evolved in the same way in the absence of the program. While this cannot be proven, checking the evolution of income stability indicators before the AUH implementation could help us gain confidence in its validity. Figure (1) shows the unconditional means of the indicators for eligible and non-eligible households for the total sample (1.a), and by sub-sample following the fall (1.b) or rise (1.c) in income over the observation period. Before introducing the AUH in late 2009, both groups followed fairly similar trends across all figures. As shown earlier, eligible households

<footnote n="9">⁹ Alternative levels of clustering (city-year or city-quarter level) were tested and do not affect the results.</footnote>
identifying the program's causal effect on household income stability. Table (3) below presents some household characteristics of the treatment (eligible) and control (non-eligible) groups before the implementation of the AUH.

<table><caption>Table 3: Characteristics of eligible and non-eligible households before the AUH implementation</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">Pre-AUH</th><th rowspan="2">t</th></tr><tr><th>Eligible (1)</th><th>Non-eligible (2)</th><th>Difference (2)-(1)</th></tr></thead><tbody><tr><td>Poverty trends (%)</td><td>48.6</td><td>21.1</td><td>-27.5</td><td><strong>-51.46</strong></td></tr><tr><td>CV</td><td>0.43</td><td>0.35</td><td>-0.08</td><td><strong>-20.28</strong></td></tr><tr><td>CV – Down</td><td>0.40</td><td>0.33</td><td>-0.07</td><td><strong>-9.58</strong></td></tr><tr><td>CV – Up</td><td>0.45</td><td>0.36</td><td>-0.09</td><td><strong>-18.62</strong></td></tr><tr><td>Mean income per capita/day (initial year)</td><td>5.52</td><td>7.78</td><td>2.26</td><td><strong>54.40</strong></td></tr><tr><td>Mean income per capita/day (final year)</td><td>7.66</td><td>10.51</td><td>2.85</td><td><strong>28.31</strong></td></tr><tr><td>Household size</td><td>5.15</td><td>5.34</td><td>0.19</td><td><strong>5.73</strong></td></tr><tr><td>Nb. of minor children</td><td>2.57</td><td>2.57</td><td>0.00</td><td>0.19</td></tr><tr><td>Nb. of major children</td><td>0.70</td><td>0.71</td><td>0.01</td><td>0.40</td></tr><tr><td>Age of the youngest child</td><td>5.90</td><td>6.21</td><td>0.31</td><td><strong>3.75</strong></td></tr><tr><td>Age of the head</td><td>42.9</td><td>43.16</td><td>0.23</td><td>1.14</td></tr><tr><td>Woman head</td><td>0.37</td><td>0.19</td><td>-0.19</td><td><strong>-26.25</strong></td></tr><tr><td>Single parent household</td><td>0.34</td><td>0.16</td><td>-0.19</td><td><strong>-27.80</strong></td></tr><tr><td>Parents' pluri-activity</td><td>0.11</td><td>0.07</td><td>-0.05</td><td><strong>-10.25</strong></td></tr><tr><td>Grandparents in the household</td><td>0.25</td><td>0.16</td><td>-0.09</td><td><strong>-12.95</strong></td></tr><tr><td>Parents' highest level of education</td><td></td><td></td><td></td><td></td></tr><tr><td>Primary incomplete</td><td>0.13</td><td>0.07</td><td>-0.06</td><td><strong>-11.78</strong></td></tr><tr><td>Primary complete</td><td>0.36</td><td>0.27</td><td>-0.09</td><td><strong>-12.01</strong></td></tr><tr><td>Secondary incomplete</td><td>0.25</td><td>0.23</td><td>-0.02</td><td><strong>-2.59</strong></td></tr><tr><td>Secondary complete</td><td>0.17</td><td>0.24</td><td>0.07</td><td><strong>10.29</strong></td></tr><tr><td>University incomplete</td><td>0.06</td><td>0.09</td><td>0.03</td><td><strong>7.51</strong></td></tr><tr><td>University complete</td><td>0.03</td><td>0.09</td><td>0.06</td><td><strong>14.61</strong></td></tr><tr><td>Observations</td><td>9795</td><td>5676</td><td></td><td></td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2009.

Note: The poverty trends indicator measures the time spent in poverty by a household during its observation period. i.e. income per capita below $5.5/day (PPP 2011). The CV is the coefficient of variation of household income. CV-down and CV-up measure the CV of sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample comprises households with at least one minor child and classified as poor or vulnerable in the first year of interview, i.e. average income per capita below $11.5/day. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. Values in bold indicate significant differences between the two groups at 95% level.

As expected, both groups differ in income stability and poverty levels. On average, households eligible (1) for the AUH have a 48% probability of being in poverty during their observation period, i.e. around two periods out of four, compared with 21% for non-eligible (2) households. Eligible households also have higher income fluctuations than non-eligible households, confirming that income from individuals working in the informal sector is more volatile<fnref n="17" />. While the family structure is quite similar between groups (number of members in the

<footnote n="17">¹⁷ It should be noted there are no significant differences between the two groups in terms of income imputation by one or more household members before and after the AUH implementation.</footnote>
consistently exhibit higher levels of poverty and income instability relative to those in the formal sector, reflecting their greater exposure to income risks and less stable income sources.<fnref n="10" /> Only a slight divergence can be observed for the 2008-2009 period and may be explained by the existence of other programs during the period, in particular *Plan Familias* (Figure A1 in Appendix).

A second essential assumption is that no contemporary event other than the AUH should explain any differences in outcome trends for the two groups. On this point, the literature is unanimous in stating that the AUH was the only central public policy implemented in Argentina in 2009 and the following years (Bertranou, 2010; Garganta and Gasparini, 2015). Furthermore, possible anticipation of the program implementation is unlikely since the AUH was not expected in the country, and its roll-out was rapid, covering over 3 million children in its first month. It was by far the largest program in the country regarding benefits and participants.<fnref n="11" /> In the years following the AUH introduction, only the PROGESAR program was introduced in 2014, which aimed to provide additional monetary resources to households with children aged 18 to 24 enrolled in university and whose resources are below the national minimum wage. The exclusion of years after 2013 does not change the results (Supplementary Materials Table SD5).

<footnote n="10">¹⁰ Figure SA2 in Supplementary Materials shows a similar trend in household income structure between groups in the pre-AUH period.</footnote>

<footnote n="11">¹¹ See Figure A1 in the Appendix for the evolution of the number of beneficiaries of Argentina's main social programs.</footnote>
household, age of the head, number of children), parents in eligible households are, on average, less educated than their formal counterparts (75% with less than a secondary degree). The proportion of female heads of household and single parents is also much higher (mostly single mothers). Eligible households are also more likely to have dependent parents inside the house, which could indicate a higher financial burden since most of them are inactive. The characteristics of the group of eligible households are very similar to those of the beneficiaries identified in the ENGHO database (Appendix Table A1).

Thus, a quasi-experimental DD strategy is applied to compare the outcomes of heterogeneous groups, controlling their stable characteristics over time. The strategy consists of comparing the dependent variables of the treatment and control groups before and after the AUH implementation<fnref n="18" />. One of the main identification assumptions of the DD strategy is that trends in the outcome variables should have evolved in the same way in the absence of the program. In other words, the evolution of household income stability and poverty should have followed a similar pattern without implementing the program. While this cannot be proven, looking at trends for the different outcome variables before the AUH implementation could help us gain confidence in its validity. Figure (2) shows trends for each income stability outcome with a visual inspection of the unconditional mean for the eligible and non-eligible groups. As can be seen, the eligible and non-eligible groups followed very similar trends for each outcome before the AUH appeared at the end of 2009. Levels of poverty and income fluctuations among eligible households are consistently higher than those of non-eligible ones, which is to be expected since informal workers have higher income risks, less regular income sources, and generally lower incomes than those in the formal sector. Also, it has to be noted that the structure of household income for both groups was stable over the period before the AUH implementation (Figure A3

<footnote n="18">¹⁸ Other articles assess the impact of the AUH on other outcomes using the DD strategy, such as labor formalization (Garganta and Gasparini, 2015), female labor participation (Garganta and Gasparini, 2017), or educational outcomes (Edo et al., 2017; Edo and Marchionni, 2019).</footnote>
Figure 1: Income stability trends among eligible and non-eligible households

1.a) Total sample

PT

<figure><img src="image_2.png" /></figure>

CV

<figure><img src="image_3.png" /></figure>

SD-APC

<figure><img src="image_4.png" /></figure>

1.b) Sub-sample: downward trend in income

CV

<figure><img src="image_5.png" /></figure>

SD-APC

<figure><img src="image_6.png" /></figure>
in Appendix). The gap between the two groups is narrowing after 2009, coinciding with the implementation of the AUH. Although the decline in poverty is visible for both groups up to 2008 (Figure 2.a), poverty keeps declining after 2009 for the eligible group, while the other remains constant at around 15%. Similarly, the gap in income fluctuations between the two groups narrowed after 2009 (Figure 2.b), mainly due to the CV-down indicator which restricts the sample to households experiencing a loss of income over the period (Figure 2.c). The gap also seems to be narrowing for the CV-up indicator (Figure 2.d), but to a lesser extent.

<figure><img src="image_2.png" /><figcaption>**Figure 2:** Income stability trends among eligible and non-eligible households</figcaption></figure>

*Source:* Author’s calculation based on the EPH microdata, 2004-2015.

*Note:* The coefficients shown correspond to the averages of the dependent variable over 2004 and 2015. Confidence intervals at 95% are shown. The poverty trends indicator measures the time spent in poverty by a household during its observation period. i.e. income per capita below $5.5/day (PPP 2011). The CV is the coefficient of variation of household income. CV-down and CV-up measure the CV of sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample comprises households with at least one minor child classified as poor or vulnerable in the first year of interview, i.e. average income per capita below $11.5/day. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. Clustered standard errors by large urban areas.
<caption>1.c) Sub-sample: upward trend in income</caption>

CV

<figure><img src="image_7.png" /></figure>

SD-APC

<figure><img src="image_8.png" /></figure>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: The coefficients shown correspond to the averages of the income stability indicators for the eligible and non-eligible groups using the survey expansion weights adjusted by kernel weights. Confidence intervals at 95% are shown. The poverty trends (PT) indicator measures the proportion of periods classified as poor for a household over its observation period, i.e. income per capita below $5.5/day (PPP 2011). The CV is the coefficient of variation of household income. The SD-APC is the standard deviation of arc percentage change of household income. The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. Standard errors are clustered at the large urban area level.

# 5. Results

## 5.1. MDD results

Table (2) presents the results of the AUH program's effect on each income stability indicator based on the MDD specification. All specifications include control variables, time and region fixed effects, and use survey weights adjusted with the kernel matching. The interaction term corresponds to the estimated impact of being eligible to the AUH on the dependent variable.

In all cases, the coefficient associated with the interaction term is always negative and strongly statistically different from zero. Results show that households eligible for the AUH experienced a 8.5 percentage point reduction in the PT indicator following the program's introduction (Column 1). This corresponds to an 17% average decrease in the proportion of periods spent in poverty for eligible households, relative to what would have occurred in the absence of the AUH. In addition, eligible households experience a decrease in income instability, as reflected by the CV indicator. After the AUH implementation, eligible households saw a 2.1 percentage points decline in the CV (Column 2), representing a 4.7% reduction in income fluctuations
A second and essential assumption of the DD strategy is that no contemporary event other than the AUH should explain any differences in outcome trends for the two groups. On this point, the literature is unanimous in stating that the AUH was the only central public policy that was implemented in Argentina in 2009 and the following years (Bertranou, 2010; Groisman et al., 2011; Garganta and Gasparini, 2015). Furthermore, possible anticipation of the program implementation is very unlikely since the AUH was not expected in the country (Maurizio and Vázquez, 2014). The AUH was notably rolled out immediately after its announcement, and covered over 3 million children in its first month. It was by far the largest program in the country regarding benefits and participants<fnref n="19" />. In the years following the AUH introduction, only the PROGESAR program was introduced in 2014, which aimed to provide additional monetary resources to households with children aged 18 to 24 enrolled in university and whose resources are below the national minimum wage. Since this financial contribution could bias estimates of the program's effect after 2014, further results exclude years after 2013, but results remain unchanged.

Equation (2) below presents a standard linear specification of the DD model corresponding to the main specification.

$$Y_i = \beta_1 D_t + \beta_2 T_i + \beta_3 (T_i * D_t) + \eta_i + \eta_t + \theta X'_i + u_i \quad (2)$$

The variable $Y_i$ corresponds to the dependent variable, corresponding to one of the indicators of income stability for a household $i$. The dummy variable $D_t$ takes the value one for the post-intervention period 2010-2015 or zero otherwise (2004-2009). $T_i$ is the treatment variable that takes the value one if a household $i$ is eligible to the AUH during its first year of interview. Region $\eta_i$ and time $\eta_t$ fixed effect are included. The set of control variables is the $X'_i$ vector measured during the first interview of household $i$. They include the age of the head of

<footnote n="19">¹⁹ See Figure A2 in Appendix to see the evolution of beneficiary households for the main social programs in Argentina from 2003 to 2013.</footnote>
compared to their initial level. The estimated effect is more pronounced when households are separated according to their income trend. For those experiencing a drop in income over the observation period, the estimated effect corresponds to a reduction of 5.1 percentage points (Column 3), representing a 12.5% decline relative to the pre-intervention level. In contrast, for households with increasing income, the effect is not statistically significant (Column 4). Similar findings emerge using the SD-APC indicator, with larger magnitudes. Estimated reductions in inter-period income volatility range from 7.7% (Column 5) to 17.6% (Column 6). These results are also robust to the use of equivalised income (Supplementary Materials Table SB1). Similarly, the SD of per capita income show similar results, but only for the sub-sample of households experiencing a drop in income (Supplementary Materials Table SB2).

<table><caption>Table 2: Matched Difference-in-difference results – Effect of AUH eligibility on income stability</caption><thead><tr><th>Dependent variables</th><th>(1)<br>PT</th><th>(2)<br>CV</th><th>(3)<br>CV<sup>D</sup></th><th>(4)<br>CV<sup>U</sup></th><th>(5)<br>SD-APC</th><th>(6)<br>SD-APC<sup>D</sup></th><th>(7)<br>SD-APC<sup>U</sup></th></tr></thead><tbody><tr><td>After*Eligible</td><td><strong>-0.0852***</strong><br>(0.0066)</td><td><strong>-0.0213***</strong><br>(0.0064)</td><td><strong>-0.0513***</strong><br>(0.0109)</td><td>-0.0066<br>(0.0117)</td><td><strong>-5.0392***</strong><br>(0.7644)</td><td><strong>-11.4036***</strong><br>(3.0767)</td><td>-1.6429<br>(1.7695)</td></tr><tr><td>Eligible</td><td><strong>0.2264***</strong><br>(0.0085)</td><td><strong>0.0935***</strong><br>(0.0081)</td><td><strong>0.0970***</strong><br>(0.0171)</td><td><strong>0.0953***</strong><br>(0.0056)</td><td><strong>14.0255***</strong><br>(0.8444)</td><td><strong>14.7735***</strong><br>(2.0763)</td><td><strong>13.8318***</strong><br>(0.7413)</td></tr><tr><td>After</td><td>0.0111<br>(0.0093)</td><td>-0.0211<br>(0.0163)</td><td>-0.0055<br>(0.0109)</td><td>-0.0291<br>(0.0205)</td><td>0.3340<br>(1.6286)</td><td>6.8297<br>(5.7102)</td><td><strong>-2.9060**</strong><br>(1.3059)</td></tr><tr><td>Controls, time and<br>regional dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>25,786</td><td>25,786</td><td>8,525</td><td>17,261</td><td>25,786</td><td>8,525</td><td>17,261</td></tr><tr><td>R-squared</td><td>0.36</td><td>0.08</td><td>0.07</td><td>0.10</td><td>0.05</td><td>0.05</td><td>0.06</td></tr><tr><td>Average</td><td>0.50</td><td>0.45</td><td>0.41</td><td>0.47</td><td>65.26</td><td>64.74</td><td>65.49</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: Matched Difference-in-difference estimates using the survey expansion weights adjusted by kernel weights. Column (1) measures the effect of the program on poverty transitions, i.e. the probability of a household falling below the poverty line ($5.5/day PPP 2011) during its observation period. Column (2) measures the effect on the coefficient of variation of per capita income, and Column (5) on the standard deviation of arc percentage change. Columns (3) and (6) restrict the sample to households with the same negative change in income between the first and second year of observations, while Columns (4) and (7) for the opposite case. The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After corresponds to the years after the AUH implementation (2010-2015). Control variables include time and region fixed effects, the number of minor and major children (and their squares), head age dummies and gender, if the head is single, household size, parents' highest level of education, and if a member has not declared one income source. The value in the last row corresponds to the average of the dependent variable for the eligible group in the pre-AUH period. Clustered standard errors (in parentheses) at the large urban area level. *** p<0.01, ** p<0.05, * p<0.1.
household and its square, its gender, the number of under-18 and over-18 children and its square, the household size, the age of the youngest child, the parents' highest level of education, dummy indicating if grandparents live in the household, if a woman heads the households, if the head is a single parent, and if the parents have multiple jobs. A final covariate identifies whether the household benefited from the PJJHD social program, as well as its interaction with the treatment variable since it targeted unemployed heads of household. Lastly, the error term $u_i$ is clustered at the large urban areas level. The DD strategy computes the changes in outcome between the control and treatment groups over time, as in Equation (3).

$$ \beta_3 = (\overline{Y}_1^T - \overline{Y}_0^T) - (\overline{Y}_1^C - \overline{Y}_0^C) \quad (3) $$

With T and C being respectively the treatment and control groups, before (0) and after (1) the AUH introduction. The treatment effect is estimated by the coefficient $\beta_3$ associated with the interaction term $(T_i * D_t)$. Then, the DD provides a consistent estimator of the impact of the AUH program on income stability. An event study regression, including leads and lags into the model as in Equation (4), is also proposed to examine at the dynamic treatment effect over time.

$$ Y_i = \eta_i + \eta_t + \sum_{\tau=-q}^{-1} \gamma_{\tau} T_{i\tau} + \sum_{\tau=0}^{m} \delta_{\tau} T_{i\tau} + \theta X'_{i} + u_i \quad (4) $$

Where the AUH implementation occurs in year 0, with $q$ leads (anticipatory effects), and $m$ lags (post-treatment effects). Year -1 is removed to avoid perfect multicollinearity and is therefore taken as the reference point,

# 4. Results

## 4.1 Main results

Table 4 presents the results of the AUH program's effect on each income stability outcome based on Equation (2). For each dependent variable, the first column (i) reports the coefficients of the baseline specification without controls, while the second column (ii) includes all controls,
## 5.2 Event study results

Instead of looking at the overall ITT effect, Figure (2) shows the dynamic effects of program eligibility on each dependent variable. The plotted coefficients represent the pre-treatment (leads) and post-treatment (lags) effects based on Equation (6) including covariates, with -1 the omitted period. These results confirm the similar evolution of the two groups prior to the AUH implementation, with most coefficients being non-significant before 2010. This pattern is also confirmed when the doubly robust estimators are used (Supplementary Materials Figure SD2). After the program's implementation, the reduction in poverty transitions and income instability is clearly visible and persist for each indicator. The magnitude of the effect is stronger for the sub-sample of households with a negative income shock (2.b), and not significant in most periods for the opposite case (2.c).

A consistent pattern for the CV and SD-APC indicators is that the estimated ITT only becomes significant from period +1 onwards. This reduced effect in period 0 may be due to households not immediately benefiting from the transfers, or potential payment delays in the first few months – even though the program rapidly reached its target number of beneficiaries. It may also indicate an adaptation phase, during which households adjust their finances and behavior in response to the new income source. Although the program was extended to pregnant women with the *Asignación Universal por Embarazo* in April 2011, this is unlikely to have affected the results in subsequent periods, as annual coverage remained limited according to the ANSES (60,000 to 80,000 beneficiaries compared to 3.5 million for the AUH).<fnref n="12" />

Overall, our results show that AUH-eligible households experienced greater income stability after the program implementation, particularly with a decrease in poverty transitions and income fluctuations. Since we are only able to measure the ITT effect, these results therefore

<footnote n="12">¹² However, it is difficult to measure any potential effect of this extension using the EPH survey, given that pregnant women are not identified and the ages of children under 1 year old are not specified precisely.</footnote>
time and regional dummies. The sample includes eligible and non-eligible households classified as poor and vulnerable in the first year of appearance in the survey. The interaction term corresponds to the estimated impact of the AUH on the dependent variable.

<table><caption>Table 4: Difference-in-difference model - Effect of the AUH on income stability</caption><thead><tr><th rowspan="2">Dependent variables</th><th colspan="2">(1)</th><th colspan="2">(2)</th><th colspan="2">(3)</th><th colspan="2">(4)</th></tr><tr><th>Poverty trends<br>(i)</th><th>Poverty trends<br>(ii)</th><th>CV<br>(i)</th><th>CV<br>(ii)</th><th>CV-down<br>(i)</th><th>CV-down<br>(ii)</th><th>CV-up<br>(i)</th><th>CV-up<br>(ii)</th></tr></thead><tbody><tr><td>After*Eligible</td><td><strong>-0.1097***</strong><br>(0.0117)</td><td><strong>-0.0630***</strong><br>(0.0166)</td><td><strong>-0.0235*</strong><br>(0.0120)</td><td><strong>-0.0420***</strong><br>(0.0109)</td><td><strong>-0.0367***</strong><br>(0.0091)</td><td><strong>-0.0655***</strong><br>(0.0075)</td><td>-0.0170<br>(0.0154)</td><td><strong>-0.0307*</strong><br>(0.0163)</td></tr><tr><td>Eligible</td><td><strong>0.2636***</strong><br>(0.0096)</td><td><strong>0.2086***</strong><br>(0.0134)</td><td><strong>0.0866***</strong><br>(0.0057)</td><td><strong>0.1021***</strong><br>(0.0073)</td><td><strong>0.0860***</strong><br>(0.0122)</td><td><strong>0.1070***</strong><br>(0.0204)</td><td><strong>0.0896***</strong><br>(0.0043)</td><td><strong>0.1024***</strong><br>(0.0040)</td></tr><tr><td>After</td><td><strong>-0.0545***</strong><br>(0.0095)</td><td>0.0133<br>(0.0112)</td><td>0.0079<br>(0.0082)</td><td>-0.0071<br>(0.0217)</td><td>0.0119<br>(0.0124)</td><td>0.0093<br>(0.0139)</td><td>0.0064<br>(0.0083)</td><td>-0.0137<br>(0.0290)</td></tr><tr><td>Controls, time and<br>regional dummies</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>R-squared</td><td>0.1354</td><td>0.2905</td><td>0.0226</td><td>0.0524</td><td>0.0197</td><td>0.0561</td><td>0.0262</td><td>0.0599</td></tr><tr><td>Observations</td><td>24,868</td><td>24,868</td><td>24,868</td><td>24,868</td><td>7,929</td><td>7,929</td><td>16,939</td><td>16,939</td></tr><tr><td>Average</td><td colspan="2">0.195</td><td colspan="2">0.365</td><td colspan="2">0.333</td><td colspan="2">0.377</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: Difference-in-difference estimates. Column (1) measures the program's effect on the time spent in poverty by a household during its observation period. i.e. income per capita below $5.5/day (PPP 2011). Column (2) measures the effect on the coefficient of variation of household income. Columns (3) and (4) on the sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample comprises households with at least one minor child and classified as poor or vulnerable in the first year of interview, i.e. average income per capita below $11.5/day (PPP 2011). Eligible variable takes the value 1 if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After variable takes the value one for the periods after the AUH implementation (2010-2015), otherwise zero (2004-2009). Control variables include household head age and squared, gender, whether single parent, number of children under and over 18 and squared, household size, parents' highest level of education, whether parents have multiple jobs, whether the household benefited from the PJJHD program during the period and its interaction with the treatment variable, and time and region fixed effects. Clustered standard errors are in brackets (by large urban areas). *** p<0.01, ** p<0.05, * p<0.1. The value in the last row corresponds to the average of the dependent variable for the control group before the AUH implementation.

In all cases, the coefficient associated with the interaction term is always negative and statistically different from zero, except for the last indicator, which is only significant at 10%. Including controls, time dummies and regional dummies does not result in any significant change in the value of the coefficients. Results for the poverty trends indicator with full controls (1) show that the AUH led to a 6.3 percentage point drop in the time spent in poverty. It corresponds to an average 16% reduction in time spent in poverty for eligible households, compared to what would have happened without the AUH implementation. Concerning the CV outcomes, the results also show a significant impact on reducing income instability. The AUH implies a 4.2 percentage points reduction in income fluctuations (2), i.e. an 9% reduction in income variability. Considering the direction of household income change over the period, the
represent a low estimate of the effect of the AUH on beneficiaries. Assuming a take-up rate of 82%, the estimated effect of the program corresponds to a reduction of roughly 20% in the proportion of periods spent in poverty, and to a reduction in income volatility – regardless of the direction of income change – ranging from 5.5% to 8% among those who actually received the transfer. The dynamic analysis also confirms that the estimated effect is persistent over time. These results echo those of Micha and Trombetta (2020) for the same AUH case, and are consistent with the literature showing the buffering effect of social safety net programs against income instability in the US (Hardy, 2017) or the UK (Avram et al., 2022), although contexts and programs differ.

<caption>Figure 2: Event study results –Effect of AUH eligibility on income stability</caption>

2.a) Total sample

PT

<figure><img src="image_9.png" /></figure>

CV

<figure><img src="image_10.png" /></figure>

SD-APC

<figure><img src="image_11.png" /></figure>
effect previously found on the CV is mainly drawn from households experiencing a drop in
income over the period. In other words, the program's stabilizing effect on household income
is most effective among households experiencing a drop in income, with a 15% mean reduction
in income fluctuations compared to the counterfactual (3). The estimated effect is weaker for
households with positive income trends, with a 6.5% reduction (4).

<figure><img src="image_3.png" /><figcaption>Figure 4: Estimated effect of the AUH on income stability over time</figcaption></figure>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: The coefficients shown correspond to the interaction terms between the treatment variable and a time dummy based on equation (4). Confidence intervals at 90% and 95% are shown. The poverty trends indicator measures the time spent in poverty by a household during its observation period. i.e. income per capita below $5.5/day (PPP 2011). The CV is the coefficient of variation of household income. CV-down and CV-up measure the CV of sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample comprises households with at least one minor child and classified as poor or vulnerable in the first year of interview, i.e. average income per capita below $11.5/day (PPP 2011). A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. Control variables include household head age and squared, gender, whether single parent, number of children under and over 18 and squared, household size, parents' highest level of education, whether parents have multiple jobs, whether the household benefited from the PJJHD program during the period and its interaction with the treatment variable, and time and region fixed effects. Clustered standard errors by large urban areas.
## 2.b) Sub-sample: downward trend in income

CV

<figure><img src="image_12.png" /></figure>

SD-APC

<figure><img src="image_13.png" /></figure>

## 2.c) Sub-sample: upward trend in income

CV

<figure><img src="image_14.png" /></figure>

SD-APC

<figure><img src="image_15.png" /></figure>

*Source:* Author's calculation based on the EPH microdata, 2004-2015.

*Note:* Matched Difference-in-difference estimates using the survey expansion weights adjusted by kernel weights. The coefficients shown correspond to the interaction terms between the treatment variable and a time dummy based on equation (6) with all controls and using the survey expansion weights adjusted by kernel weights. Standard errors are clustered at the large urban area level. Confidence intervals at 95% are shown. The poverty trends (PT) indicator measures the time spent in poverty by a household during its observation period i.e., income per capita below $5.5/day (PPP 2011). The CV is the coefficient of variation of household income. The SD-APC is the standard deviation of arc percentage change of household income. The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview.

## 5.3. Robustness tests

We present several robustness tests of the results in Supplementary Materials (Section D). First, we verify the parallel trends assumption by implementing false interventions in the years prior the AUH implementation. Overall, the coefficients related to the false interventions are mostly not significant with or without controls (Tables SD1 and SD2). Only a small divergence appears for 2007 but the effect does not persist over time.
Instead of looking at the AUH's overall effect on the pre- and post-period, Figure 4 below
shows the dynamics of the program's effect (interaction term between the treatment variable
and a time dummy) on each outcome variable. The plotted coefficients represent the pre-
treatment (leads) and post-treatment (lags) effects based on Equation (4) with full controls. The
-1 period corresponds to the year just before the program's arrival. Again, trends similarly until
the implementation of the AUH. The gap between the two groups widened from 2010 onwards
and lasted over time, even in period +4. The program consistently reduces the time spent in
poverty, ranging from -3 to -10 percentage points (Fig 4.a). Interestingly, the estimated impact
of the program is more substantial one or two periods after its introduction. One reason for this
may lie in extending the program to pregnant women from their 12th week of pregnancy until
birth in 2011. However, this hypothesis cannot be accurately verified in the data since the EPH
survey does not identify whether a woman is pregnant at the time of the survey. As shown in
the table above, the program also reduces the CV of household income, and this effect appears
stable over time (Fig 4.b). The effect is stronger among households with a negative trend in
income over the period compared to those with a positive trend. However, confidence intervals
of the estimated effect are also larger due to the smaller number of households in this situation.

These findings confirm that the AUH significantly reduces poverty among economically
vulnerable households working in the informal sector. They also confirm that the AUH
stabilizes household income flows with a persistent effect over time. These results echo those
of Micha and Trombetta (2020) for the same AUH case and are in line with Hardy (2017), who
shows the buffering effect of social safety net programs in the US context. The effect is stronger
when focusing on the sub-sample of households with negative income trends over the period.
It indicates that households potentially benefiting from the AUH have a better capacity to
smooth their income losses than without the program. However, the estimated effect is smaller
for those with positive income trends.
The second exercise checks the sensitivity of the results to several econometric specifications, such as correction for non-random attrition, using only kernel weights, and a standard DD specification without matching (Table SD3). We also test alternative sample sizes (Table SD4) or slight changes in eligibility criteria (Table SD5). In all cases, our results are robust to the inclusion or exclusion of observations.

In the third exercise, we estimate our results using the Doubly Robust estimators suggested in the econometric literature on the DD methodology (Sant'Anna and Zhao, 2020; Callaway and Sant'Anna, 2021). The ITT estimates remain consistent and comparable in magnitude to our baseline approach using MDD (Tables SD6 and SD7).<fnref n="13" /> The results for the smaller sample of households with negative income changes are slightly affected for the CV indicator, but remain significant at 10% in most cases. Finally, the event study graphs (Figure SD2) provide further evidence for the validity of the parallel trends assumption.

### 5.4. Heterogeneous effects

This section explores the heterogeneous effects of the AUH implementation on the stability of eligible households' incomes according to household composition and structure. Table (3) reports the triple and double interaction term coefficients for each indicator using the MDD approach. Two main observations arise in opposite directions.

First, we find a stronger ITT effect among eligible households with at least three children (1.c) or a young child aged five or under (1.d). For these households, being eligible reduces the proportion of periods in poverty by about 18% compared to their pre-AUH level, instead of 14% for the others. The measured effect on income fluctuations is also primarily driven by eligible households with young children, regardless the direction of income change. This likely

<footnote n="13">¹³ These results were obtained using the Stata package -csdid- proposed by Rios-Avila et al. (2021).</footnote>
## 4.2 Robustness checks

### 4.2.1 False interventions

Several exercises are presented to test the robustness of the results found above. A first exercise verifies the parallel trends assumption by implementing false interventions before the AUH implementation. Table 5 below presents the estimated coefficients for each dependent variable for years in which a false program introduction is tested based on Equation (2).

<table><caption>Table 5: Robustness - Effect of false interventions on income stability</caption><thead><tr><th rowspan="2">Dependent variables<br>Year of the false intervention</th><th colspan="3">(1)<br>Poverty trends</th><th colspan="3">(2)<br>CV</th></tr><tr><th>2006<br>(i)</th><th>2007<br>(ii)</th><th>2008<br>(iii)</th><th>2006<br>(i)</th><th>2007<br>(ii)</th><th>2008<br>(iii)</th></tr></thead><tbody><tr><td>After*Eligible</td><td><strong>-0.0221***</strong><br>(0.0080)</td><td><strong>-0.0187*</strong><br>(0.0096)</td><td>-0.0084<br>(0.0176)</td><td>-0.0020<br>(0.0107)</td><td><strong>-0.0184*</strong><br>(0.0100)</td><td>-0.0072<br>(0.0215)</td></tr><tr><td>Eligible</td><td><strong>0.2222***</strong><br>(0.0142)</td><td><strong>0.2173***</strong><br>(0.0142)</td><td><strong>0.2125***</strong><br>(0.0138)</td><td><strong>0.1053***</strong><br>(0.0060)</td><td><strong>0.1098***</strong><br>(0.0074)</td><td><strong>0.1050***</strong><br>(0.0064)</td></tr><tr><td>After</td><td><strong>-0.1212***</strong><br>(0.0130)</td><td><strong>-0.0340***</strong><br>(0.0110)</td><td><strong>-0.0408***</strong><br>(0.0104)</td><td><strong>0.0289***</strong><br>(0.0093)</td><td><strong>0.0486***</strong><br>(0.0105)</td><td><strong>0.0413***</strong><br>(0.0111)</td></tr><tr><td>Controls, time and regional<br>dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>R-squared</td><td>0.3200</td><td>0.3199</td><td>0.3198</td><td>0.0534</td><td>0.0536</td><td>0.0534</td></tr><tr><td>Observations</td><td>15,471</td><td>15,471</td><td>15,471</td><td>15,471</td><td>15,471</td><td>15,471</td></tr><tr><td>Average</td><td>0.221</td><td>0.214</td><td>0.198</td><td>0.365</td><td>0.361</td><td>0.362</td></tr><tr><th rowspan="2">Dependent variables<br>Year of the false intervention</th><th colspan="3">(3)<br>CV-down</th><th colspan="3">(4)<br>CV-up</th></tr><tr><th>2006<br>(i)</th><th>2007<br>(ii)</th><th>2008<br>(iii)</th><th>2006<br>(i)</th><th>2007<br>(ii)</th><th>2008<br>(iii)</th></tr><tr><td>After*Eligible</td><td><strong>-0.0269**</strong><br>(0.0109)</td><td>-0.0239<br>(0.0148)</td><td>-0.0123<br>(0.0280)</td><td>0.0046<br>(0.0147)</td><td>-0.0174<br>(0.0132)</td><td>-0.0080<br>(0.0349)</td></tr><tr><td>Eligible</td><td><strong>0.1225***</strong><br>(0.0225)</td><td><strong>0.1167***</strong><br>(0.0242)</td><td><strong>0.1112***</strong><br>(0.0230)</td><td><strong>0.1019***</strong><br>(0.0074)</td><td><strong>0.1094***</strong><br>(0.0052)</td><td><strong>0.1048***</strong><br>(0.0046)</td></tr><tr><td>After</td><td><strong>0.0632***</strong><br>(0.0129)</td><td><strong>0.0451**</strong><br>(0.0171)</td><td>0.0374<br>(0.0242)</td><td><strong>0.0252**</strong><br>(0.0122)</td><td><strong>0.0471**</strong><br>(0.0173)</td><td><strong>0.0464***</strong><br>(0.0134)</td></tr><tr><td>Controls, time and regional<br>dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>R-squared</td><td>0.0640</td><td>0.0638</td><td>0.0635</td><td>0.0584</td><td>0.0586</td><td>0.0584</td></tr><tr><td>Observations</td><td>4,941</td><td>4,941</td><td>4,941</td><td>10,530</td><td>10,530</td><td>10,530</td></tr><tr><td>Average</td><td>0.330</td><td>0.334</td><td>0.330</td><td>0.380</td><td>0.372</td><td>0.375</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2009.

Note: Difference-in-difference estimates. Column (1) measures the program's impact on the time spent in poverty by a household during its observation period. i.e. income per capita below $5.5/day (PPP 2011). Column (2) measures the effect on the coefficient of variation of household income. Columns (3) and (4) on the sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample comprises households with at least one minor child and classified as poor or vulnerable in the first year of interview, i.e. average income per capita below $11.5/day (PPP 2011). Eligible variable takes the value 1 if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After variable takes the value 1 after the false program implementation, otherwise 0. Control variables include household head age and squared, gender, whether single parent, number of children under and over 18 and squared, household size, parents' highest level of education, whether parents have multiple jobs, whether the household benefited from the PJJHD program during the period and its interaction with the treatment variable, and time and region fixed effects. Clustered standard errors are in brackets (by large urban areas). *** p<0.01, ** p<0.05, * p<0.1. The value in the last row corresponds to the average of the dependent variable for the control group before the false implementation.
reflects that these households are relatively poorer, so the AUH represents a proportionally higher share of their total income.

<table><caption>Table 3: Matched Difference-in-difference results – Heterogeneity by household characteristics</caption><thead><tr><th>Dependent variables</th><th>(1)<br>Poverty trends</th><th>(2)<br>CV</th><th>(3)<br>CV<sup>D</sup></th><th>(4)<br>CV<sup>U</sup></th><th>(5)<br>SD-APC</th><th>(6)<br>SD-APC<sup>D</sup></th><th>(7)<br>SD-APC<sup>U</sup></th></tr></thead><tbody><tr><td>(a) Woman head of hh<br>After*Treated*Woman</td><td><strong>0.0567***</strong><br>(0.0150)</td><td><strong>0.0274***</strong><br>(0.0099)</td><td><strong>0.0496***</strong><br>(0.0111)</td><td>0.0256<br>(0.0165)</td><td><strong>3.5731*</strong><br>(1.8214)</td><td>4.9458<br>(3.0486)</td><td>4.8785<br>(3.0357)</td></tr><tr><td>After*Treated</td><td><strong>-0.1089***</strong><br>(0.0081)</td><td><strong>-0.0318***</strong><br>(0.0050)</td><td><strong>-0.0678***</strong><br>(0.0099)</td><td><strong>-0.0168*</strong><br>(0.0084)</td><td><strong>-6.2670***</strong><br>(1.1106)</td><td><strong>-12.5575***</strong><br>(3.0355)</td><td><strong>-3.4896*</strong><br>(1.8770)</td></tr><tr><td>(b) Single parent hh<br>After*Treated*Single</td><td><strong>0.0332**</strong><br>(0.0158)</td><td>0.0080<br>(0.0096)</td><td>0.0127<br>(0.0270)</td><td>0.0113<br>(0.0160)</td><td>2.1533<br>(1.9246)</td><td>1.3461<br>(5.2387)</td><td>2.9547<br>(2.9354)</td></tr><tr><td>After*Treated</td><td><strong>-0.0970***</strong><br>(0.0075)</td><td><strong>-0.0251***</strong><br>(0.0062)</td><td><strong>-0.0571***</strong><br>(0.0157)</td><td>-0.0109<br>(0.0118)</td><td><strong>-5.7792***</strong><br>(0.9659)</td><td><strong>-12.0111***</strong><br>(3.4772)</td><td>-2.5301<br>(1.9018)</td></tr><tr><td>(c) ≥ 3 children<br>After*Treated*Children</td><td><strong>-0.0486***</strong><br>(0.0153)</td><td><strong>-0.0110*</strong><br>(0.0065)</td><td>0.0200<br>(0.0189)</td><td><strong>-0.0223**</strong><br>(0.0096)</td><td><strong>-4.9031*</strong><br>(2.5207)</td><td>0.4374<br>(2.9297)</td><td><strong>-5.6038**</strong><br>(2.6777)</td></tr><tr><td>After*Treated</td><td><strong>-0.0631***</strong><br>(0.0110)</td><td><strong>-0.0178**</strong><br>(0.0074)</td><td><strong>-0.0638***</strong><br>(0.0194)</td><td>0.0012<br>(0.0127)</td><td><strong>-3.1673**</strong><br>(1.4679)</td><td><strong>-12.3299***</strong><br>(4.3410)</td><td>0.4886<br>(2.6725)</td></tr><tr><td>(d) Young child ≤ 5<br>After*Treated*Young</td><td><strong>-0.0439**</strong><br>(0.0164)</td><td><strong>-0.0664*</strong><br>(0.0338)</td><td><strong>-0.0732*</strong><br>(0.0373)</td><td><strong>-0.0581*</strong><br>(0.0310)</td><td><strong>-12.1940*</strong><br>(6.2397)</td><td>-9.5129<br>(6.5134)</td><td><strong>-11.9537**</strong><br>(5.7085)</td></tr><tr><td>After*Treated</td><td><strong>-0.0610***</strong><br>(0.0112)</td><td>0.0158<br>(0.0227)</td><td>-0.0083<br>(0.0146)</td><td>0.0252<br>(0.0263)</td><td>1.7808<br>(3.6730)</td><td><strong>-5.9291**</strong><br>(2.1938)</td><td>4.8470<br>(4.6592)</td></tr><tr><td>Controls, time and<br>regional dummies</td><td></td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>25,786</td><td>25,786</td><td>8,525</td><td>17,261</td><td>25,786</td><td>8,525</td><td>17,261</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: Matched Difference-in-difference estimates using the survey expansion weights adjusted by kernel weights. Column (1) measures the effect of the program on poverty transitions, i.e. the probability of a household falling below the poverty line ($5.5/day PPP 2011) during its observation period. Column (2) measures the effect on the coefficient of variation of per capita income, and Column (5) on the standard deviation of arc percentage change. Columns (3) and (6) restrict the sample to households with the same negative change in income between the first and second year of observations, while Columns (4) and (7) for the opposite case. The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After corresponds to the years after the AUH implementation (2010-2015). Control variables include time and region fixed effects, the number of minor and major children (and their squares), head age dummies and gender, if the head is single, household size, parents' highest level of education, and if a member has not declared one income source. Clustered standard errors (in parentheses) at the large urban area level. *** p<0.01, ** p<0.05, * p<0.1.

Second, we find a significant gap in the program's effectiveness between eligible women and men heads of household (1.a). The estimated program's effect on poverty transitions is about two times lower for an eligible household headed by a woman than for a man (-10% vs -22%), and nearly cancelled out for the CV indicator. Although surprising, this may be explained by important differences in family structure: over 75% of women-headed households are single-parent families. We find similar results when specifically analysing eligible single-parent households (1.b), while the coefficients for other outcomes point in the same direction, though they are not statistically significant. This lower program's effect may reflect the lower resilience
While most coefficients are close to zero and not significant, some exhibit a negative and significant effect, particularly for the poverty trends indicator. Most of these coefficients are small (around 1 or 2 percentage points) and disappear as we approach 2009. In the early 2000s, several social programs (in particular, the PJJHD and Plan Familias programs) were implemented to reduce the high unemployment and poverty rates in the wake of the country's 2001 crisis. The effects found above certainly stem from these programs, which appeared in 2002 (PJJHD) and continued in 2005 (Plan Familias), affecting households similar to those eligible for the AUH (unemployed or non-employable people). The number of PJJHD beneficiaries gradually declined (Figure A2 in Appendix), and its effectiveness since its value was not adjusted for inflation. While the EPH provides information on whether households have benefited from the PJJHD program, this is not the case for the Plan Familias program, which may explain the slight divergence in trends between the two groups in the early period.

### 4.2.2 Sample size

The second exercise checks the sensitivity of the results at different sample sizes. Figure 5 summarizes the results for each dependent variable by plotting the program effect coefficients<fnref n="20" />. All estimations are based on Equation (2) with full controls and exhibit the 90% and 95% confidence intervals. The first part (5.a to 5.d) presents the program's effects when the upper monetary limit of the vulnerable group is successively raised to $13.5 and $15.5/day to include households just above the initial limit<fnref n="21" />. The coefficients are stable across the specifications. The program's impact on the CV declines as more affluent households are included in the sample, which is to be expected, as AUH weighs relatively less in household budgets.

<footnote n="20">²⁰ Tables with all coefficients are available in Appendix (Tables A2 to A4).</footnote>

<footnote n="21">²¹ Figure A4 in Appendix also shows further results using deciles of the household per capita income distribution instead, restricting the sample to households from deciles D2 to D5. Results remain the same.</footnote>
of these specific groups to income shocks due to limited time flexibility, the absence of a partner to share economic or caregiving responsibilities, and fewer opportunities in the labor market. These findings suggest that the program, while stabilizing, may have a limited capacity to lift these vulnerable households out of poverty.

### 5.5. Exploration of the underlying mechanisms

We finally explore the potential underlying mechanisms that may explain the observed drop in income fluctuations for the eligible group following the introduction of the AUH. Household income can be divided into labor income and non-labor income from private or public transfers. Introducing a new, regular source of income may alter the structure of both components. On the labor side, households might reorganize their work arrangements or choose to exit riskier income-generating activities. In additional analyses (Supplementary Materials Table SC1), we examine whether the AUH eligibility has led to changes in the average time worked in the household, or the volatility of household labor income. We find no significant change in the number of workers in the household, nor in the average hours worked among eligible households following the program implementation. However, we observe a small decline in the hours worked among spouses of household heads – who are typically women in the sample. This result is consistent with those of Garganta et al. (2017) regarding the adverse effect of the AUH on women's labor participation. Concerning the stability of labor income, results are inconclusive: we find no clear effect of program eligibility on both indicators. While some labor reorganization cannot be ruled out, this channel does not appear to be the main driver of the observed reduction in income instability.

On the non-labor side, regular and predictable transfers may substitute for informal private support (e.g., from family or friends) or for other risk-coping strategies such as selling assets, dissaving, or taking out loans – mechanisms that may be unreliable or insufficient in times of financial stress. The EPH data includes information on financial strategies used by households
<figure><img src="image_4.png" /><figcaption>Figure 5: Robustness - Effect of the AUH on income stability at alternative sample sizes</figcaption></figure>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: The coefficients shown correspond to the interaction term of difference-in-difference estimates based on equation (2). Confidence intervals at 90% and 95% are shown. The poverty trends indicator (a) measures the time spent in poverty by a household during its observation period, i.e. income per capita below $5.5/day (PPP 2011). The CV (b) is the coefficient of variation of household income. The CV-down and CV-up indicators (c and d) are calculated for households with a drop and increase in income respectively between the first and second year of observation. For (a) to (d): the sample comprises households with at least one minor child and classified as poor or vulnerable in the first year of interview. Model 1 corresponds to baseline definition of the vulnerable class including households with per capita income between $5.5 and $11.5 per day. Models 2 and 3 propose alternative definitions with household incomes between $5.5 and $13.5, and $5.5 and $15.5 per day, respectively. For (e) to (h): Model 1 is the baseline model comparison from the main results. A household is eligible if the child's parents are either informal workers, domestic employees, unemployed or inactive, or retired without health coverage over its first observation year. In Model 2, the sample includes all households with at least two interviews over time, instead of more than three. In Model 3, household eligibility is based on the entire observation period, not just the first year. In Model 3, the analysis period is restricted to 2004-2013 instead of 2004-2015. Model 5 drops households where at least one household member has not declared a source of income over the analysis period. Control variables include household head age and squared, gender, whether single parent, number of children under and over 18 and squared, household size, parents' highest level of education, whether parents have multiple jobs, whether the household benefited from the PJJHD program during the period and its interaction with the treatment variable, and time and region fixed effects. Clustered standard errors by large urban areas.
over the previous three months of each interview, allowing us to examine whether the AUH-
eligibility changed these behaviors. The findings provide further support for a change in
household financial behavior (Supplementary Materials Table SC2). Particularly, the share of
eligible households relying on in-kind donations from public or private sources declined by
approximately 13% following the AUH implementation. Borrowing patterns also slightly
changed: the proportion of eligible households taking out formal loans has increased marginally
(significant at 10%), and the proportion of those relying on deferred payment decreased.
However, given the relatively low proportion of households reporting the use of these lending
strategies, these results should be interpreted with caution. Nevertheless, these results suggest
that one of the mechanisms driving the reduction in income instability is the substitution of the
AUH transfers for more volatile or uncertain financial coping strategies.

6 Conclusion

This paper examines the impact of Argentina's largest cash transfer program on income stability within vulnerable households. Household income stability is assessed by looking at poverty transitions and income volatility through the CV and SD-APC indicators computed over a year and a half. We implement a quasi-experimental MDD strategy to evaluate the program's effectiveness by comparing eligible and non-eligible households. Our findings suggest that the program acts as a protective buffer against poverty transitions and income instability. The reduction in income fluctuations is particularly marked for eligible households that experienced a drop in income over the period, smoothing out income losses. We find heterogeneous effects according to household family structure with larger effects for families with young children but weaker impact on women-headed households (mainly single mothers). This last result suggests the program may not suffice to prevent short-term poverty among households facing more challenging shocks adaptations. An examination of mechanisms reveals that greater income
In the second part (5.e to 5.h), several alternative models are tested and compared to the baseline model from the main results (Model 1). Model 2 extends the sample to all households followed over time instead of using those with at least three interviews (including those only followed over six months). Model 3 restricts the sample by keeping only eligible and non-eligible households over the entire period, instead of the first year of interview. Model 4 restricts the analysis period to the years before 2014 to avoid potential bias from the PROGRESAR program implementation. Finally, Model 5 only keeps households that have declared all their income, and drops those where at least one household member has imputed a source of income over the period. Here again, the estimated program's effect remains very close to that estimated in Model 1, and coefficients vary at the margin. Including of households with a shorter follow-up period does not change the results and tends to increase the program's effects, particularly for the CV-up indicator.

### 4.2.3 Matching

The third exercise combines the DD strategy with a kernel-based matching adapted for repeated cross-section data<fnref n="22" /> (Heckman et al., 1998; Blundell and Dias, 2009). As the sample of households changes over the survey years due to the data's rotating structure, the DD framework with repeated cross-section data may suffer from compositional change for the control and treated groups over time. The combination of matching and DD methods can control for differences in the composition of the two groups before and after the treatment (Fernández and Villar, 2017). The matching procedure uses the same control variables as the DD framework. More details on the matching process and quality are provided in Appendix (Figures A5 to A7).

<footnote n="22">²² The -diff- Stata package is used to implement the kernel-based matching estimator (Villa, 2016), as well as the -psmatch2 - package to create the matching quality graphs (Leuven and Sianesi, 2003).</footnote>
stability stems from substituting the AUH for other non-work income sources, such as informal
sources from family or friends.

Several limitations warrant consideration. First, our methodology comparing eligible and non-
eligible households does not identify the duration of assistance, masking differences between
short- and long-term beneficiaries. Some households also remain excluded from the AUH,
notably due to the conditionalities and design chosen (Pautassi et al., 2013). Secondly, data
frequency only allows biannual measurement of income viability, preventing analysis of
potential important monthly fluctuations, which can be significant for informal workers.
Additionally, lack of information on household savings or consumption restricts the scope of
the analysis to disposable income. Exploring the program's effects on other economic or
psychological dimensions using both quantitative or qualitative materials could offer valuable
insights, particularly in relation to gender.

Nevertheless, we believe the evidence presented here is relevant for public policy,
demonstrating benefits of extending access to social protection to households temporarily or at
risk of poverty beyond the most precarious. It also highlights the importance of considering
income dynamics in public policy evaluation, which deserves greater attention.

**Declaration of Competing Interest**

The author declares having no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Data Availability**

The author has made his main codes and the final database available on his personal GitHub.
[https://github.com/secarrere/](https://github.com/secarrere/). The raw EPH data are freely available on the INDEC website at
the following address: [https://www.indec.gob.ar/indec/web/Institucional-Indec-BasesDeDatos](https://www.indec.gob.ar/indec/web/Institucional-Indec-BasesDeDatos).
<table><caption>Table 6: Robustness Matched DD - Effect of the AUH on income stability</caption><thead><tr><th rowspan="3">Dependent variables</th><th colspan="2">(1)</th><th colspan="2">(2)</th><th colspan="2">(3)</th><th colspan="2">(4)</th></tr><tr><th colspan="2">Poverty trends</th><th colspan="2">CV</th><th colspan="2">CV-down</th><th colspan="2">CV-up</th></tr><tr><th>(i)</th><th>(ii)</th><th>(i)</th><th>(ii)</th><th>(i)</th><th>(ii)</th><th>(i)</th><th>(ii)</th></tr></thead><tbody><tr><td>After*Eligible</td><td><strong>-0.0806***</strong><br>(0.0126)</td><td><strong>-0.0739***</strong><br>(0.0117)</td><td><strong>-0.0337***</strong><br>(0.0085)</td><td><strong>-0.0484***</strong><br>(0.0087)</td><td>-0.0227<br>(0.0137)</td><td><strong>-0.0428***</strong><br>(0.0115)</td><td><strong>-0.0380***</strong><br>(0.0103)</td><td><strong>-0.0510***</strong><br>(0.0114)</td></tr><tr><td>Eligible</td><td><strong>0.2295***</strong><br>(0.0108)</td><td><strong>0.2269***</strong><br>(0.0120)</td><td><strong>0.0757***</strong><br>(0.0073)</td><td><strong>0.0877***</strong><br>(0.0068)</td><td><strong>0.0543***</strong><br>(0.0107)</td><td><strong>0.0683***</strong><br>(0.0112)</td><td><strong>0.0864***</strong><br>(0.0079)</td><td><strong>0.0986***</strong><br>(0.0069)</td></tr><tr><td>After</td><td><strong>-0.1048***</strong><br>(0.0134)</td><td>0.0161<br>(0.0137)</td><td>0.0125<br>(0.0078)</td><td>0.0109<br>(0.0116)</td><td>0.0010<br>(0.0138)</td><td>-0.0107<br>(0.0157)</td><td><strong>0.0165*</strong><br>(0.0089)</td><td>0.0221<br>(0.0157)</td></tr><tr><td>Controls, time and<br>regional dummies</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>R-squared</td><td>0.1316</td><td>0.2954</td><td>0.0188</td><td>0.0444</td><td>0.0103</td><td>0.0413</td><td>0.0246</td><td>0.0536</td></tr><tr><td>Observations</td><td>24,840</td><td>24,840</td><td>24,840</td><td>24,840</td><td>7,917</td><td>7,917</td><td>16,923</td><td>16,923</td></tr><tr><td>Average</td><td colspan="2">0.256</td><td colspan="2">0.358</td><td colspan="2">0.341</td><td colspan="2">0.366</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: Matched difference-in-difference estimates. Column (1) measures the program's effect on the time spent in poverty by a household during its observation period. i.e. income per capita below $5.5/day (PPP 2011). Column (2) measures the effect on the coefficient of variation of household income. Columns (3) and (4) on the sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample comprises households with at least one minor child and classified as poor or vulnerable in the first year of interview, i.e. average income per capita below $11.5/day (PPP 2011). Eligible variable takes the value 1 if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After variable takes the value one for the periods after the AUH implementation (2010-2015), otherwise zero (2004-2009). Variables used for the matching include household head age, gender, whether single parent, number of children under and over 18, household size, parents' highest level of education, whether parents have multiple jobs, whether the household benefited from the PJJHD program. Control variables are the same as those used for the matching plus an interaction term between the treatment variable and the PJJHD variable, as well as time and region fixed effects. Clustered standard errors are in brackets (by large urban areas). *** p<0.01, ** p<0.05, * p<0.1. The value in the last row corresponds to the average of the dependent variable for the control group before the AUH implementation.

Table 6 shows the results of the benchmark exercise. Again, similar results are found for each dependent variables, with and without controls. The magnitude of the impact on the different dependent variable is very similar to the DD model without matching, except for the CV-up indicator. The estimated effect is stronger and roughly equal to that found for the CV-down. All these results point in the same direction and demonstrate the program's reductive impact on poverty and income stability.

## 4.4 Heterogeneous effects

This section explores the potential heterogeneous effects of the program. The AUH effect can have a different impact on household income stability, depending on household characteristics.

Table 7 reports the triple and double interaction term coefficients for each income stability indicator. It can be noted that the overall effect of the program (double interaction term) remains significant and stable according to the different specifications, except for the CV-up indicator.
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Most heterogeneous program effects concern the poverty trends indicator (1), while those concerning the CV indicators are mostly zero.

<table><caption>Table 7: Heterogeneity Triple DD model - Effect of the AUH on income stability</caption><thead><tr><th>Dependent variables</th><th>(1)<br>Poverty trends</th><th>(2)<br>CV</th><th>(3)<br>CV-down</th><th>(4)<br>CV-up</th></tr></thead><tbody><tr><td>(a) Woman head of household<br>After*Treated*Woman</td><td><strong>0.1036***</strong><br>(0.0193)</td><td><strong>0.0504***</strong><br>(0.0153)</td><td><strong>0.0710***</strong><br>(0.0167)</td><td><strong>0.0408*</strong><br>(0.0207)</td></tr><tr><td>After*Treated</td><td><strong>-0.0987***</strong><br>(0.0171)</td><td><strong>-0.0574***</strong><br>(0.0090)</td><td><strong>-0.0839***</strong><br>(0.0085)</td><td><strong>-0.0445***</strong><br>(0.0123)</td></tr><tr><td>(b) Single parent household<br>After*Treated*Single</td><td><strong>0.0727***</strong><br>(0.0161)</td><td>0.0195<br>(0.0124)</td><td>0.0174<br>(0.0208)</td><td>0.0245<br>(0.0149)</td></tr><tr><td>After*Treated</td><td><strong>-0.0879***</strong><br>(0.0154)</td><td><strong>-0.0507***</strong><br>(0.0106)</td><td><strong>-0.0739***</strong><br>(0.0067)</td><td><strong>-0.0405***</strong><br>(0.0148)</td></tr><tr><td>(c) Three or more children<br>After*Treated*Children</td><td><strong>-0.0579***</strong><br>(0.0138)</td><td>-0.0177<br>(0.0207)</td><td>0.0279<br>(0.0316)</td><td>-0.0407<br>(0.0242)</td></tr><tr><td>After*Treated</td><td><strong>-0.0471**</strong><br>(0.0200)</td><td><strong>-0.0352*</strong><br>(0.0203)</td><td><strong>-0.0795***</strong><br>(0.0181)</td><td>-0.0148<br>(0.0267)</td></tr><tr><td>(d) Young child in the household (≤ 5 y.o)<br>After*Treated*Young</td><td><strong>-0.0488***</strong><br>(0.0135)</td><td><strong>-0.0542*</strong><br>(0.0307)</td><td><strong>-0.0444**</strong><br>(0.0166)</td><td>-0.0567<br>(0.0383)</td></tr><tr><td>After*Treated</td><td><strong>-0.0398**</strong><br>(0.0177)</td><td>-0.0135<br>(0.0262)</td><td><strong>-0.0407***</strong><br>(0.0103)</td><td>-0.0018<br>(0.0348)</td></tr><tr><td>(e) Grand-parents in the household<br>After*Treated*Grandparents</td><td><strong>0.0453**</strong><br>(0.0217)</td><td>0.0096<br>(0.0165)</td><td>0.0080<br>(0.0246)</td><td>0.0141<br>(0.0164)</td></tr><tr><td>After*Treated</td><td><strong>-0.0679***</strong><br>(0.0185)</td><td><strong>-0.0436***</strong><br>(0.0127)</td><td><strong>-0.0676***</strong><br>(0.0084)</td><td><strong>-0.0326*</strong><br>(0.0185)</td></tr><tr><td>Controls, time and regional dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>24,868</td><td>24,868</td><td>7,929</td><td>16,939</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: Estimation of a triple difference-in-difference model. Column (1) measures the program's effect on the probability of a household falling below the poverty line during its observation period. i.e. income per capita below $5.5/day (PPP 2011). Column (2) measures the effect on the coefficient of variation of household income. Columns (3) and (4) on the sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample comprises households with at least one minor child and classified as poor or vulnerable in the first year of interview, i.e. average income per capita below $11.5/day (PPP 2011). Eligible variable takes the value 1 if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After variable takes the value one for the periods after the AUH implementation (2010-2015), otherwise zero (2004-2009). Control variables include household head age and squared, gender, whether single parent, number of children under and over 18 and squared, household size, parents' highest level of education, whether parents have multiple jobs, whether the household benefited from the PJJHD program during the period and its interaction with the treatment variable, and time and region fixed effects. Clustered standard errors are in brackets (by large urban areas). *** p<0.01, ** p<0.05, * p<0.1.

Two main results stand out. First, the AUH's effect on poverty reduction is more effective among households with 3 or more children (1.c), and those with a young child under 6 (1.d). In both cases, the program reduces the time spent in poverty by around 20% compared to the counterfactual, instead of 10% for the others. Similarly, the impact on the CV-down indicator is two times more efficient in households with a young child (3.d) compared to those with older children (-20% vs -10.5). Given that these households are poorer on average in the sample, the
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AUH represents a proportionally higher share of their total income. Similarly, as households with young children have high expenses (nursery, specific food, and products for infants, this additional source of income helps mitigate the impact of adverse income shocks on household income. Secondly, there is a significant gap in the program's effectiveness between men and women heads of household (1.a). This may be explained by the fact that the family structure of female heads of household differs significantly from that of men since most of them are single (88%). A similar result is found when looking at the differentiated effectiveness of the program on single heads of household (1.b). It appears that AUH does not prevent these households from falling into poverty or significantly affect their income fluctuations. Finally, the program's impact is also weaker in households with grandparents (1.e). As most of these individuals are outside the labor market, adapting to an income shock may be more challenging, and any additional healthcare expenses for dependent parents could be a limiting factor in poverty reduction for these households.

# 5 Summary and concluding remarks

This paper evaluates the impact of the AUH – the largest Argentina’s non-contributory program – on income stability within economically vulnerable households over the 2004-2015 period. The AUH aims to extend social coverage to children in poor and vulnerable households. A quasi-experimental DD method is employed to assess the intention-to-treat by comparing eligible and non-eligible but similar households. Income stability is evaluated by looking at poverty transitions, i.e. the time spent in poverty by a household during its observation period, and the CV in income. Three main results stand out.

First, the AUH significantly reduces the time spent in poverty for eligible households by 16% compared to the situation without the program implementation. This protective effect,
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stemming from a direct income boost, enables eligible and low-income households to avoid slipping back into poverty and reduce income swings that would lead to a return to poverty.

Second, the program effectively stabilizes income streams, particularly among households whose income declined over the period, with an average 15% reduction in the CV for eligible households, still compared to the situation without the AUH implementation. It confirms the program's role in mitigating income losses through a predictable and consistent income source throughout the year.

Third, the program affects eligible households heterogeneously depending on their family structure. The program's effect on poverty is higher for households with a young child and larger family sizes (more than two children), potentially stemming from their relatively poorer economic conditions in the sample. Conversely, the reducing effects on poverty and income-stabilizing effects are considerably reduced in woman-headed households, the latter being overwhelmingly single women with children parents. The presence of grandparents in the household also tends to reduce the poverty-reducing effect of the program. It suggests that the program is insufficient to stabilize these households' economic situation, whose adaptation to a shock is more challenging (lack of flexibility in work, higher economic burden).

However, several limitations warrant consideration. First, while the results show that the AUH helps reduce household income fluctuations, comparing eligible and non-eligible households fails to elucidate the duration of a household's program benefit. Antipoverty policies can inadvertently increase income instability, particularly for households teetering on the edge of eligibility or household members intermittently engaged in the formal sector (Wolf et al., 2014). Although the AUH has been promoted as universal, many households are still excluded from the program because of administrative barriers, geographical remoteness, lack of documentation, or processing of files (Pautassi et al., 2013). Similarly, the low frequency of household interviews per year makes it difficult to assess the program's impact on intra-year
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Gennetian, L.A., Wolf, S., Hill, H.D., & Morris, P.A. (2015). Intrag year household income dynamics and adolescent school behavior. *Demography*, 52, 455–483.
https://doi.org/10.1007/s13524-015-0370-9

Hardy, B.L. (2017). Income instability and the response of the safety net. *Contemporary Economic Policy*, 35, 312–330. https://doi.org/10.1111/coep.12187

Haushofer, J., & Shapiro, J. (2016). The short-term impact of unconditional cash transfers to the poor: experimental evidence from Kenya. *The Quarterly Journal of Economics*, 131, 1973–2042. https://doi.org/10.1093/qje/qjw025

Heckman, J.J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. *The Review of Economic Studies*, 65, 261–294. https://doi.org/10.1111/1467-937X.00044
household income fluctuation (two interviews at most). Second, the EPH survey does not allow the examination of the underlying mechanisms driving the decline or increase in income and the specific shocks experienced by the household. Exploring potential changes in consumption or saving habits among program beneficiaries could offer valuable insights.

In conclusion, the results found in this paper are relevant from a public policy perspective and show the benefits of extending access to social protection for households that are not only poor but also at high risk of poverty, whether due to family situations or precarious employment status. However, the proliferation of non-contributory CT programs alongside formal contributory systems in the LA region exacerbates the divide between the two systems, diverging from the universal ambit of family policies. It is needed to design more cohesive, unified, and sustainable social protection systems that cover risks common to all citizens, irrespective of their labor status, aligning with a universal social protection system.
Hill, H.D., Morris, P., Gennetian, L.A., Wolf, S., & Tubbs, C. (2013). The consequences of income instability for children’s well-being. *Child Development Perspectives, 7*, 85–90.

https://doi.org/10.1111/cdep.12018

Hill, H.D., Romich, J., Mattingly, M.J., Shamsuddin, S., & Wething, H. (2017). An Introduction to Household Economic Instability and Social Policy. *Social Service Review, 91*, 371–389.

https://doi.org/10.1086/694110

Hill, H.D. (2021). Family income level, variability, and trend as predictors of child achievement and behavior. *Demography, 58*, 1499–1524. https://doi.org/10.1215/00703370-9357529

ILO (2024). World Social Protection Report 2024–26: Universal social protection for climate action and a just transition. International Labour Office: Geneva, Switzerland.

https://doi.org/10.54394/ZMDK5543

Jolliffe, D., & Prydz, E.B. (2016). Estimating international poverty lines from comparable national thresholds. *The Journal of Economic Inequality, 14*, 185–198.

https://doi.org/10.1007/s10888-016-9327-5

Lenis, D., Nguyen, T.Q., Dong, N., & Stuart, E.A. (2019). It’s all about balance: propensity score matching in the context of complex survey data. *Biostatistics, 20*, 147–163.

https://doi.org/10.1093/biostatistics/kxx063

Leuven, E., & Sianesi, B. (2003). psmatch2: Stata Module to Perform Full Mahalanobis and Propensity Score Matching, Common Support Graphing, and Covariate Imbalance Testing.

*Statistical Software Components* S432001, Boston College Department of Economics, revised 01 Feb 2018. Chestnut Hill, MA.

Micha, A., & Trombetta, M. (2020). Movilidad de ingresos en hogares vulnerables. *Desarrollo Económico, 60*, 118–152. https://www.jstor.org/stable/26976101
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# Appendix

<figure><img src="image_16.png" /><figcaption>Figure A1: Evolution of Argentina's main social programs (2003-2015)</figcaption></figure>

Source: Data come from the Abiertos Asignaciones Universales (ANSES) for the AUH and from Fenwick (2013) for the *Plan Familias/PJJHD* programs.

Note: PJJHD = *Plan Jefes y Jefas de Hogares Desocupados*; AUH = *Asignacion Universal por Hijo*. The AUH was implemented in November 2009.
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# Supplementary Materials

## A) Descriptive statistics:

<figure><img src="image_17.png" /><figcaption>Figure SA1: EPH data structure</figcaption></figure>

*Source:* Author's elaboration.

*Note:* Each line represents a household's complete follow-up in the EPH survey from entry (period 1) to exit (period 4). The stack of lines A to D depicts the complete tracking for Year 1 to Year 2.
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Papadopoulos, T., Velázquez Leyer, R. (2016). Two Decades of Social Investment in Latin America: Outcomes, Shortcomings and Achievements of Conditional Cash Transfers. *Social Policy & Society* 15, 435-449. https://doi.org/10.1017/S1474746416000117

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<caption>**Figure SA2 : Evolution of income structure by AUH eligibility status**</caption>

<figure><img src="image_18.png" /><figcaption>1.b) Share of labor income</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>1.c) Share of non-labor income – private source</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>1.d) Share of non-labor income – public source</figcaption></figure>

<figure><img src="image_21.png" /></figure>

*Source:* Author’s calculation based on the EPH microdata, 2004-2015.

*Note*: The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. Clustered standard errors at the large urban area level.
UNICEF, ANSES, Ministerio de Desarrollo Social (2017). Análisis y propuestas de mejora para ampliar la Asignación Universal por Hijo. Buenos Aires, Argentina : UNICEF.

https://www.unicef.org/argentina/informes/an%C3%A1lisis-y-propuestas-de-mejora-para-ampliar-la-asignaci%C3%B3n-universal-por-hijo

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<table><caption>Table SA1 Characteristics of AUH beneficiaries (ENGHo, 2012)</caption><thead><tr><th></th><th>Head of household (%)</th><th>Spouse (%)</th></tr></thead><tbody><tr><td><strong>Household level</strong></td><td></td><td></td></tr><tr><td>Household size</td><td>4.94</td><td></td></tr><tr><td>Income per capita ($US)</td><td>$10.97/day</td><td></td></tr><tr><td><strong>Parents level</strong></td><td></td><td></td></tr><tr><td>Sex</td><td></td><td></td></tr><tr><td>Men</td><td>65.7</td><td>15.5</td></tr><tr><td>Women</td><td>34.3</td><td>84.5</td></tr><tr><td>Age</td><td>41.2</td><td>36.6</td></tr><tr><td><strong>Age composition</strong></td><td></td><td></td></tr><tr><td>&lt;19</td><td>0.0</td><td>1.0</td></tr><tr><td>19-25</td><td>7.2</td><td>13.6</td></tr><tr><td>26-34</td><td>27.3</td><td>34.2</td></tr><tr><td>35-64</td><td>60.3</td><td>49.1</td></tr><tr><td>&gt;64</td><td>5.2</td><td>2.1</td></tr><tr><td><strong>Education</strong></td><td></td><td></td></tr><tr><td>Primary Incomplete</td><td>18.1</td><td>15.3</td></tr><tr><td>Primary complete</td><td>29.9</td><td>32.1</td></tr><tr><td>Secondary incomplete</td><td>25.2</td><td>24.9</td></tr><tr><td>Secondary complete</td><td>19.8</td><td>21.0</td></tr><tr><td>University incomplete</td><td>3.6</td><td>4.7</td></tr><tr><td>University complete</td><td>2.2</td><td>1.9</td></tr><tr><td>Others</td><td>1.2</td><td>0.2</td></tr><tr><td><strong>Labor status</strong></td><td></td><td></td></tr><tr><td>Employee</td><td>54.6</td><td>30.7</td></tr><tr><td>Self-employed</td><td>21.9</td><td>12.7</td></tr><tr><td>Employer</td><td>1.7</td><td>0.3</td></tr><tr><td>Family worker</td><td>0.0</td><td>0.2</td></tr><tr><td>Inactive/unemployed</td><td>21.7</td><td>56.1</td></tr><tr><td>Observations</td><td>2806</td><td>1970</td></tr></tbody></table>

Source: Author's elaboration based on ENGHo (2012)

Note: Income values are deflated in 2018 pesos and converted in $US (PPP 2011). The factor conversion from the World Bank is 14.23.
# Appendix

<figure><img src="image_5.png" /><figcaption>Figure A1: Structure of the EPH panel data</figcaption></figure>

Source: Author's adaptation of the EPH methodology (INDEC, 2003).

<figure><img src="image_6.png" /><figcaption>Figure A2: Evolution of the main social programs in Argentina (2003-2013)</figcaption></figure>

Source: Data come from the Abiertos Asignaciones Universales (ANSES) for the AUH and from Fenwick (2013) for the PF (Plan Familias)/PJJHD programs

Note: PJJHD = Plan Jefes y Jefas de Hogares Desocupados; AUH = Asignacion Universal por Hijo. The AUH was implemented in November 2009.
## B) Additional results:

<table><caption>Table SB1: Matched Difference-in-Difference results – Equivalised Income</caption><thead><tr><th>Dependent variables</th><th>(1)<br>EQ CV</th><th>(2)<br>EQ CV<sup>D</sup></th><th>(3)<br>EQ CV<sup>U</sup></th><th>(4)<br>EQ SD-APC</th><th>(5)<br>EQ SD-APC<sup>D</sup></th><th>(6)<br>EQ SD-APC<sup>U</sup></th></tr></thead><tbody><tr><td>After*Eligible</td><td><strong>-0.0191***</strong><br>(0.0052)</td><td><strong>-0.0479***</strong><br>(0.0120)</td><td>-0.0047<br>(0.0112)</td><td><strong>-4.8636***</strong><br>(0.7758)</td><td><strong>-11.4319***</strong><br>(3.2739)</td><td>-1.1957<br>(2.0364)</td></tr><tr><td>Eligible</td><td><strong>0.0816***</strong><br>(0.0069)</td><td><strong>0.0867***</strong><br>(0.0158)</td><td><strong>0.0827***</strong><br>(0.0051)</td><td><strong>13.8787***</strong><br>(0.8433)</td><td><strong>14.9238***</strong><br>(2.0120)</td><td><strong>13.5671***</strong><br>(0.7659)</td></tr><tr><td>After</td><td>-0.0189<br>(0.0139)</td><td>-0.0078<br>(0.0092)</td><td>-0.0242<br>(0.0182)</td><td>0.1448<br>(1.5242)</td><td>5.4158<br>(4.9924)</td><td><strong>-2.5164*</strong><br>(1.3573)</td></tr><tr><td>Controls, time and<br>regional dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>25,786</td><td>8,766</td><td>17,020</td><td>25,786</td><td>8,766</td><td>17,020</td></tr><tr><td>R-squared</td><td>0.08</td><td>0.07</td><td>0.10</td><td>0.05</td><td>0.05</td><td>0.06</td></tr><tr><td>Average</td><td>0.39</td><td>0.35</td><td>0.41</td><td>64.94</td><td>64.48</td><td>65.16</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: Matched Difference-in-difference estimates using the survey expansion weights adjusted by kernel weights. Column (1) measures the effect on the coefficient of variation of equivalised income, and Column (4) on the standard deviation of arc percentage change. Columns (2) and (5) restrict the sample to households with the same negative change in income between the first and second year of observations, while Columns (3) and (6) for the opposite case. The OECD equivalence scale is used (1 for the first adult, 0.5 for each additional member older than 14, and 0.3 for children under 14). The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After corresponds to the years after the AUH implementation (2010-2015). Control variables include time and region fixed effects, the number of minor and major children (and their squares), head age dummies and gender, if the head is single, household size, parents' highest level of education, and if a member has not declared one income source. Clustered standard errors at the large urban area level (in parentheses). The value in the last row corresponds to the average of the dependent variable for the eligible group in the pre-AUH period. *** p<0.01, ** p<0.05, * p<0.1.
<figure><img src="image_7.png" /><figcaption>Figure A3: Evolution in the structure of household income among eligible and non-eligible households</figcaption></figure>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: The sample is composed of households with at least one minor child and classified as poor or vulnerable in the first year of appearance in the survey, i.e. income per capita below $11.5/day (PPP 2011). A household is eligible for the program if a child has parents who are either working in the informal sector, domestic employees, unemployed or inactive, or retired without health coverage for the first observation year. Income from labor includes retirement pensions. Confidence intervals at 95% are shown.
<table><caption>Table SB2: Matched Difference-in-Difference results – SD and SD Equivalised Income</caption><thead><tr><th>Dependent variables</th><th>(1)<br>SD</th><th>(2)<br>SD<sup>D</sup></th><th>(3)<br>SD<sup>U</sup></th><th>(4)<br>EQ SD</th><th>(5)<br>EQ SD<sup>D</sup></th><th>(6)<br>EQ SD<sup>U</sup></th></tr></thead><tbody><tr><td>After*Eligible</td><td>3.8267<br>(3.4912)</td><td><strong>-4.7112**</strong><br>(2.1198)</td><td>7.7185<br>(5.8053)</td><td>6.0587<br>(5.2820)</td><td><strong>-11.2814**</strong><br>(4.5575)</td><td>13.8716<br>(9.7277)</td></tr><tr><td>Eligible</td><td><strong>-6.9815***</strong><br>(2.3060)</td><td>-0.4340<br>(1.8590)</td><td><strong>-7.8249**</strong><br>(3.0145)</td><td><strong>-14.0164***</strong><br>(4.3682)</td><td>-0.3094<br>(3.7443)</td><td><strong>-16.0547***</strong><br>(5.6676)</td></tr><tr><td>After</td><td>-9.1590<br>(8.4733)</td><td>4.5819<br>(2.7046)</td><td>-16.5243<br>(9.9616)</td><td>-18.8376<br>(14.6110)</td><td>7.2543<br>(5.9031)</td><td><strong>-32.9074*</strong><br>(17.5659)</td></tr><tr><td>Controls, time and<br>regional dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>25,786</td><td>8,525</td><td>17,261</td><td>25,786</td><td>8,766</td><td>17,020</td></tr><tr><td>R-squared</td><td>0.13</td><td>0.19</td><td>0.14</td><td>0.12</td><td>0.18</td><td>0.13</td></tr><tr><td>Average</td><td>87.79</td><td>53.59</td><td>103.37</td><td>157.93</td><td>102.13</td><td>184.44</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: Matched Difference-in-difference estimates using the survey expansion weights adjusted by kernel weights. Column (1) measures the effect on the standard deviation of per capita income, and Column (4) on the standard deviation of equivalised income. Columns (2) and (5) restrict the sample to households with the same negative change in income between the first and second year of observations, while Columns (3) and (6) for the opposite case. The OECD equivalence scale is used (1 for the first adult, 0.5 for each additional member older than 14, and 0.3 for children under 14). The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After corresponds to the years after the AUH implementation (2010-2015). Control variables include the number of minor and major children (and their squares), head age dummies and gender, if the head is single, household size, parents' highest level of education, and if a member has not declared one income source. Clustered standard errors at the large urban area level (in parentheses). The value in the last row corresponds to the average of the dependent variable for the eligible group in the pre-AUH period. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table A1: Characteristics of the AUH beneficiaries (ENGHo, 2012)</caption><thead><tr><th colspan="2">Household level</th><th colspan="2">4.94<br/>$10.97/day</th></tr><tr><th colspan="2">Parents level</th><th>Head of household (%)</th><th>Spouse (%)</th></tr></thead><tbody><tr><td rowspan="2">Sex</td><td>Men</td><td>65.7</td><td>15.5</td></tr><tr><td>Women</td><td>34.3</td><td>84.5</td></tr><tr><td rowspan="5">Age (years)</td><td>&lt;19</td><td>41.2</td><td>36.6</td></tr><tr><td>19-25</td><td>0.0</td><td>1.0</td></tr><tr><td>26-34</td><td>7.2</td><td>13.6</td></tr><tr><td>35-64</td><td>27.3</td><td>34.2</td></tr><tr><td>&gt;64</td><td>60.3</td><td>49.1</td></tr><tr><td rowspan="7">Age composition</td><td>&lt;19</td><td>5.2</td><td>2.1</td></tr><tr><td>19-25</td><td>18.1</td><td>15.3</td></tr><tr><td>26-34</td><td>29.9</td><td>32.1</td></tr><tr><td>35-64</td><td>25.2</td><td>24.9</td></tr><tr><td>&gt;64</td><td>19.8</td><td>21.0</td></tr><tr><td>University incomplete</td><td>3.6</td><td>4.7</td></tr><tr><td>University complete</td><td>2.2</td><td>1.9</td></tr><tr><td rowspan="5">Labor status</td><td>Others</td><td>1.2</td><td>0.2</td></tr><tr><td>Employee</td><td>54.6</td><td>30.7</td></tr><tr><td>Self-employed</td><td>21.9</td><td>12.7</td></tr><tr><td>Employer</td><td>1.7</td><td>0.3</td></tr><tr><td>Family worker</td><td>0.0</td><td>0.2</td></tr><tr><td></td><td>Inactive/unemployed</td><td>21.7</td><td>56.1</td></tr><tr><td colspan="2">Observations</td><td>2806</td><td>1970</td></tr></tbody></table>

Source: Authors’ elaboration based on ENGHo (2012)

Note: Income values are deflated in 2018 pesos and converted in $US (PPP 2011) adapted for 2018. The factor conversion from the World Bank is 14.23.

<figure><img src="image_8.png" /><figcaption>Figure A4: Effect of the AUH on income stability using deciles of household per capita income distribution</figcaption></figure>

○ Model 1: ≤ D2

○ Model 2: ≤ D3

○ Model 3: ≤ D4

○ Model 4: ≤ D5

Source: Author’s calculation based on the EPH microdata, 2004-2009.

Note: Coefficients shown correspond to the interaction term of difference-in-difference estimates based on equation (2). Confidence intervals at 90% and 95% are shown. The poverty trends indicator measures the probability of a household falling below the poverty line during its observation period. i.e. income per capita below $5.5/day (PPP 2011). The CV is the coefficient of variation of household income. CV-down and CV-up measures the CV of sub-samples of households with the same change in income between the first and second year of interview (negative or positive). The sample is composed of households with minor child belonging to deciles D2 to D5 of the per capita household income distribution in their first year of interview. Households are eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. Control variables include household head age and squared, gender, whether single parent, number of children under and over 18 and squared, household size, parents' highest level of education, whether parents have multiple jobs, whether the household benefited from the PJJHD program during the period and its interaction with the treatment variable, and time and region fixed effects. Clustered standard errors by large urban areas.
### C) Mechanisms:

<table><caption>Table SC1: Matched Difference-in-Difference results – Labor supply and income volatility</caption><thead><tr><th>Dependent variables</th><th>(1)<br>CV labor</th><th>(2)<br>SD-APC<br>labor</th><th>(3)<br>Hours<br>worked</th><th>(4)<br>Hours<br>worked<br>– Head</th><th>(5)<br>Hours<br>worked<br>– Spouse</th><th>(6)<br>Number of<br>workers</th></tr></thead><tbody><tr><td>After*Eligible</td><td>0.0099<br>(0.0143)</td><td><strong>-2.5541*</strong><br>(1.4472)</td><td>0.0030<br>(0.8183)</td><td>0.3175<br>(0.4947)</td><td><strong>-1.0643*</strong><br>(0.6120)</td><td>-0.0236<br>(0.0416)</td></tr><tr><td>Eligible</td><td><strong>0.1250***</strong><br>(0.0089)</td><td><strong>20.0975***</strong><br>(1.1802)</td><td><strong>-4.0618***</strong><br>(0.3781)</td><td><strong>-2.8169***</strong><br>(0.5664)</td><td><strong>1.2583***</strong><br>(0.3169)</td><td><strong>0.0665**</strong><br>(0.0301)</td></tr><tr><td>After</td><td>-0.0041<br>(0.0215)</td><td>5.0958**<br>(2.1464)</td><td>-0.9466<br>(0.7558)</td><td><strong>-2.4758***</strong><br>(0.5475)</td><td>-0.2930<br>(0.3751)</td><td><strong>-0.0818**</strong><br>(0.0395)</td></tr><tr><td>Controls, time and<br>regional dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>25,125</td><td>25,125</td><td>25,786</td><td>25,786</td><td>18,952</td><td>25,786</td></tr><tr><td>R-squared</td><td>0.10</td><td>0.07</td><td>0.10</td><td>0.36</td><td>0.33</td><td>0.42</td></tr><tr><td>Average</td><td>0.59</td><td>84.17</td><td>30.78</td><td>29.59</td><td>8.70</td><td>1.53</td></tr></tbody></table>

*Source:* Author’s calculation based on the EPH microdata, 2004-2015.

*Note:* Matched Difference-in-difference estimates using the survey expansion weights adjusted by kernel weights. Column (2) measures the effect of the program on the coefficient of variation of per capita income from labor sources, and Column (2) on the standard deviation of arc percentage change. Columns (3) measures the average weakly hours worked in the household over the period, and Columns (4) and (5) for the head of household and the spouse, respectively. Column (6) measures the average number of workers in the household over the period. The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After corresponds to the years after the AUH implementation (2010-2015). Control variables include time and region fixed effects, the number of minor and major children (and their squares), head age dummies and gender, if the head is single, household size, parents' highest level of education, and if a member has not declared one income source. Clustered standard errors at the large urban area level (in parentheses). The value in the last row corresponds to the average of the dependent variable for the eligible group in the pre-AUH period. *** p<0.01, ** p<0.05, * p<0.1.
Matching procedure and quality

Since households in treated and control groups cannot be followed over the pre- and post-
periods, three sets of weights are calculated independently according to the calculated
propensity score (two sets of weights in the pre- and post-periods for the control group and one
in the pre- period for the treated group). The variables used for the matching are the number of
under-18 and over-18 children and its square, the household size, age of the youngest child, the
parents' highest level of education, dummy indicating if grandparents live in the household, if
a woman heads the households, if a single parent heads the household, if the parents have
multiple jobs, and the age and age squared of the head.

Figures A5 to A7 illustrate the quality of matching. Figure A5 and A6 show the distribution of the propensity score before and after matching, as well as the propensity score distribution by group between the post-treated group and the three other control groups (pre-treated, pre-control and post-control) to visually check the overlap of the region of common support. In each case, there is a wide common support the two groups with similar propensity score distributions. Figure A7 demonstrates that the matching clearly reduces standardized bias across covariates compared to the unmatched situation. Eligible and non-eligible households are more similar in terms of observable characteristics than in the unmatched model (apart from the age of the head of household, which differs slightly).
<table><caption>Table SC2: Matched Difference-in-Difference results – Household financial behavior</caption><thead><tr><th>Dependent variables</th><th>(1)<br>In-kind donations<br>(church, state,<br>association)</th><th>(2)<br>In-kind donations<br>(family, friends,<br>neighbors)</th><th>(3)<br>Monetary<br>donations (friends,<br>neighbors)</th><th>(4)<br>Drawing on<br>savings</th><th>(5)<br>Informal<br>loans</th><th>(6)<br>Sales of<br>assets</th><th>(7)<br>Formal<br>loans</th><th>(8)<br>Credit card,<br>deferred payment</th></tr></thead><tbody><tr><td>After*Eligible</td><td><strong>-0.0311***</strong><br>(0.0070)</td><td><strong>-0.0170**</strong><br>(0.0078)</td><td>-0.0076<br>(0.0061)</td><td>0.0071<br>(0.0068)</td><td>-0.0061<br>(0.0073)</td><td>0.0064<br>(0.0040)</td><td><strong>0.0150*</strong><br>(0.0074)</td><td><strong>-0.0373**</strong><br>(0.0157)</td></tr><tr><td>Eligible</td><td><strong>0.0622***</strong><br>(0.0067)</td><td><strong>0.0484***</strong><br>(0.0045)</td><td><strong>0.0490***</strong><br>(0.0030)</td><td><strong>-0.0142***</strong><br>(0.0050)</td><td><strong>0.0257***</strong><br>(0.0043)</td><td><strong>0.0184***</strong><br>(0.0047)</td><td><strong>-0.0625***</strong><br>(0.0052)</td><td><strong>-0.0945***</strong><br>(0.0152)</td></tr><tr><td>After</td><td><strong>-0.0239**</strong><br>(0.0108)</td><td>0.0047<br>(0.0130)</td><td>-0.0035<br>(0.0138)</td><td><strong>-0.0535**</strong><br>(0.0251)</td><td><strong>-0.0193*</strong><br>(0.0105)</td><td><strong>-0.0174***</strong><br>(0.0049)</td><td><strong>-0.0293**</strong><br>(0.0138)</td><td><strong>-0.0224**</strong><br>(0.0105)</td></tr><tr><td>Controls, time and regional<br>dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>25,786</td><td>25,786</td><td>25,786</td><td>25,786</td><td>25,786</td><td>25,786</td><td>25,786</td><td>25,786</td></tr><tr><td>R-squared</td><td>0.18</td><td>0.09</td><td>0.18</td><td>0.07</td><td>0.06</td><td>0.03</td><td>0.07</td><td>0.13</td></tr><tr><td>Average</td><td>0.24</td><td>0.14</td><td>0.13</td><td>0.16</td><td>0.18</td><td>0.10</td><td>0.04</td><td>0.25</td></tr></tbody></table>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Note: Matched Difference-in-difference estimates using the survey expansion weights adjusted by kernel weights. Each dependent variable represents the probability that a household uses the financial strategy during its observation period. Columns (1) and (2) respectively measure the effect of the program on the probability of relying on in-kind donations from the public and private sectors; Column (3) on monetary donations from family and community (remittances); Column (4) on the probability of drawing on savings; Column (5) on taking out an informal loan (from a third party); Column (6) on the probability of selling assets; Column (7) on taking out a formal loan (from a banking institution); and Column (8) on the use of credit cards and installment payments. The sample comprises households with at least one minor child, and belonging to the first three deciles of the national per capita income distribution in the first year of observation. A household is eligible if the parents work in the informal sector, are domestic employees, are inactive, unemployed, or retired without health coverage, during their first year of interview. After corresponds to the years after the AUH implementation (2010-2015). Control variables include time and region fixed effects, the number of minor and major children (and their squares), head age dummies and gender, if the head is single, household size, parents' highest level of education, and if a member has not declared one income source. Clustered standard errors at the large urban area level (in parentheses). The value in the last row corresponds to the average of the dependent variable for the eligible group in the pre-AUH period. *** p<0.01, ** p<0.05, * p<0.1.
Figure A5: Propensity score by group and common support

(a) Treated in post-period vs Control in pre-period

(b) Treated in post-period vs Control in post-period

<figure><img src="image_9.png" /></figure>

(c) Treated in post-period vs Control in pre-period

<figure><img src="image_10.png" /></figure>

Source: Author's calculation based on the EPH microdata, 2004-2015.

Figure A6: Propensity score before and after matching

<figure><img src="image_11.png" /></figure>

Source: Author's calculation based on the EPH microdata, 2004-2015.