The unequal distribution of administrative burden:
A framework and an illustrative case study for
understanding variation in people's experience
Mariana Chudnovsky | Rik Peeters
División de Administración Pública, Centro de
Investigación y Docencia Económicas (CIDE),
Mexico City, Mexico
Rik Peeters, División de Administración
Pública, Centro de Investigación y Docencia
Económicas (CIDE), Carretera México-Toluca
3655, Colonia Lomas de Santa Fe, CP 01210,
Alcaldía Cuajimalpa, Mexico City, Mexico.
Recent studies have demonstrated that administrative bur-
dens often reinforce existing social inequalities. However,
less attention has been paid to explaining which factors
cause variation in people's experience of administrative bur-
den. This article builds upon an emerging body of literature
on citizen factors to make two contributions. First, a theo-
retical framework is constructed to provide a coherent
overview of existing economic (cost–benefit analyses and
poverty costs) and behavioural explanations (human capital
and decision-making bias) for the unequal distribution of
administrative burden. Furthermore, policy feedback is
suggested as a possible intermediating variable to under-
stand variations in people's capacity and willingness to
engage in state-citizen interactions and the bigger bite of
administrative burden in low-trust contexts. Second, a
mixed method case study of non-participation in
Argentina's conditional cash transfer program is used to
illustrate the relevance of the identified explanations prior
to state-citizen interaction.
administrative burden, decision-making, human capital, policy
feedback, state-citizen interactions
Received: 25 February 2020 Revised: 6 June 2020 Accepted: 2 July 2020
Soc Policy Adm. 2020;1–16. wileyonlinelibrary.com/journal/spol © 2020 John Wiley & Sons Ltd 1
The literature on administrative burdens has, in recent years, identified how bureaucratic procedures and practices
can complicate the access to services and benefits for citizens (e.g. Burden et al., 2012; Heinrich, 2018; Moynihan,
Herd, & Harvey, 2015). The approach breaks away from rule- and organisational performance-focused red tape
studies (Heinrich, 2015) to address the way bureaucratic barriers affect citizenship, equity and democracy
(Moynihan & Herd, 2010, p.654; Nisar, 2018). Administrative burdens are both consequential and distributive
(Herd & Moynihan, 2018). An “individual's experience of policy implementation as onerous”(Burden, Canon, May-
er, & Moynihan, 2012, p. 741) can lead to learning, compliance and psychological costs (Moynihan et al., 2015) or
even to ‘administrative exclusion’(Brodkin & Majmundar, 2010) of access to rights, services and benefits—with
sometimes long-term consequences for social and economic participation (Heinrich, 2018). Furthermore, the experi-
ence of administrative burdens and their consequences are not equally distributed over the population (Heinrich &
Brill, 2015). Vulnerable target groups tend to have lower levels of take-up of social programs (Bhargava &
Manoli, 2015), show higher levels of exclusion and drop-out (Brodkin & Majmundar, 2010) and have more negative
state-citizen interactions (Barnes & Henly, 2018). Thereby, administrative burdens often reinforce existing
Most of the research has focused on demonstrating and conceptualising the various costs that citizens might
face in interactions with the state, while less attention has been paid to explaining burdens (Peeters, 2019) and the
variation in people's experience of burdens (Christensen, Aarøe, Baekgaard, Herd, & Moynihan, 2020). The aim of
this article is to contribute to our understanding of these issues. Here, an important distinction can be made between
‘state factors’and ‘citizen factors’(cf. Christensen et al., 2020; Heinrich, 2018, p. 217). State factors include adminis-
trative requirements, state capacity, policy design, frontline worker behaviour and other formal or informal elements
of policy implementation that—either deliberately or not—raise barriers for people's access to rights and services
(Herd & Moynihan, 2018; Peeters, 2019). However, these factors cannot fully explain why “why some people find
the same objective sets of rules or procedures more onerous or emotionally taxing than others”(Christensen
et al., 2020, p. 132).
This article builds upon the emerging literature on citizen factors, which are closely related to the concept of
administrative burden and takes up the call to “[investigate] empirically how reactions to burdens vary”(Christensen
et al., 2020, p. 6). More specifically, two contributions are made: (a) refining the theoretical framework on explana-
tions for the unequal distribution of administrative burdens and (b) presenting empirical evidence to demonstrate the
relevance of this framework for assessing non-participation in social programs in Latin America. The analytical frame-
work is illustrated through a case study of Argentina's conditional cash transfer program. This, in itself, is a useful
contribution as well to a body of research that builds predominantly on data from developed countries.
The analytical framework builds upon an economic and a behavioural approach in the literature on variation in
people's experiences of administrative burden. Attitudes towards government and government programs are
suggested here as an additional element to incorporate well-known mechanisms of ‘policy feedback’(Moynihan &
Soss, 2014) and account for the bigger bite of administrative burden in low-trust and developmental contexts
(Heinrich & Brill, 2015). The relevance of this framework is demonstrated through an analysis of non-participation in
Argentina's Universal Child Benefit (AUH—for Asignación Universal por Hijo). The impact of citizen factors is measured
here as the non-take-up of a benefit targeted at the most vulnerable population. Data from a government survey
and 32 interviews with public officials and eligible non-participants show that a combination of socio-economic vul-
nerability, scarcity of time and human capital, and negative attitudes towards the state explain why vulnerable people
might not reach out to a social program specifically targeted at them. Lastly, the concluding section of this article dis-
cusses the importance of taking citizen factors into account for policy design and better understanding the citizen
experience of bureaucratic encounters (Lotta & Marques, 2019; Raaphorst & Van de Walle, 2018).
2CHUDNOVSKY AND PEETERS
2|EXPLAINING THE UNEQUAL DISTRIBUTION OF ADMINISTRATIVE
2.1 |Citizen factors and administrative burden
As a starting point, two distinctions are identified to construct a framework for analysing variation in people's experi-
ence of administrative burdens. First, following Christensen et al. (2020) and Heinrich (2015, 2018; cf. Kahn, Katz, &
Gutek, 1976), a distinction can be made between governmental or state factors on the one hand and extra-
governmental or citizen factors on the other hand. State factors are elements and practices embedded in the policy
implementation (cf. Peeters, 2019), whereas citizen factors refer to characteristics of public service clients. Even
though both types of factors heavily influence each other—such as low state outreach in areas where the economi-
cally most vulnerable people live—the focus in this article is primarily on citizen factors. Second, within the category
of citizen factors, Christensen and others (2020) distinguish between economic and behavioural approaches to
explain variation in the experience of administrative burdens. The model they present is used here and expanded to
provide a more comprehensive theoretical framework, which—most importantly—adds the factor of attitudes
towards the state and the intermediary variable of policy feedback to better understand the relation between state
and citizen factors (Figure 1).
2.2 |Economic explanations
Starting with economic approaches to variation in the experience of administrative burden, citizens can make cost–
benefit analyses to assess whether a benefit or service is worth going through bureaucratic ‘ordeal mechanisms’,‘has-
sle’or ‘sludge’(Alatas et al., 2013; Sunstein, 2019; Thaler, 2018). The idea is that citizens seek to maximise utility
FIGURE 1 Explanations for the unequal distribution of administrative burden (based on Christensen et al., 2020,
p. 128) [Colour figure can be viewed at wileyonlinelibrary.com]
CHUDNOVSKY AND PEETERS 3
and, therefore, make rational trade-offs between opportunity costs and benefits (Tejerina, Ibarrarán, Benedetti, &
Buchbinder, 2014). A person's willingness to go through paperwork, waiting times or compliance criteria is relative to
the benefit this will generate. This implies that the bigger the relative benefit for a person, the more ordeal mecha-
nisms he or she is willing to endure. Following this assumption, the deliberate design of administrative burden can
function as a targeting or screening mechanism in social benefits (Nichols & Zeckhauser, 1982; Zeckhauser, 2019).
By introducing high compliance costs, citizens less needy of a financial benefit will select themselves out. This tech-
nique is often applied in mean-tested social programs (Das, Do, & Özler, 2005; Jalan & Ravallion, 2003). The idea is
to reduce errors of inclusion by making compliance costs high enough to ensure that people will only apply for a ben-
efit or stay in a program if they truly need it. Non-take-up and drop-out should, then, be a result of self-targeting by
the non-poor. The evidence of ordeal mechanisms as an efficient targeting mechanism is mixed (Christensen
et al., 2020, p. 129). There is evidence that wealthier beneficiaries indeed have higher drop-out rates (
Devoto, & Winters, 2008), however, the same goes for the poorest beneficiaries (González-Flores, Heracleous, &
Winters, 2012). Often, the most vulnerable target groups fail to overcome administrative burdens (Brodkin &
Majmundar, 2010; Deshpande & Li, 2019).
This indicates that additional factors are at play to explain variation in overcoming administrative burden beyond
mere rational cost–benefit analyses. In economic research, and especially in studies on variation in take-up of social
programs (Currie, 2006), this has been explained by looking at the relative costs of living in poverty. It is, in many ways,
expensive to be poor. Compliance costs tend to be bigger for people in precarious conditions than for people who
can fall back on financial resources as a result of, for instance, transportation costs (Tejerina et al., 2014), the relative
value of a benefit (Currie, 2006) and the financial consequences of poor judgement (Banerjee & Mullainathan, 2010;
Carvalho, Meier, & Wang, 2016). Moreover, living in precarious or marginalised conditions increases the number of
daily time-consuming challenges and practical constraints people face, including long commutes, varying and infre-
quent income, organising bigger households, dealing with unreliable basic services (such as water and electricity) and
managing tight budgets for daily expenses (Banerjee & Mullainathan, 2010; Evans & Schamberg, 2009;
Mullainathan & Shafir, 2013).
2.3 |Behavioural explanations
In both studies on take-up and public administration literature, behavioural approaches are increasingly used to
understand human decision-making (Battaglio, Belardinelli, Bellé, & Cantarelli, 2019). They allow for the analysis of
public administration from the micro-level perspective of individual behaviour and attitudes, and attempt to grasp
the underlying psychology of individuals and groups (Grimmelikhuijsen, Jilke, Olsen, & Tummers, 2017). In this article,
first, we identify human capital as a key variable that influences people's ability to overcome administrative burdens.
Within this variable, a further distinction can be made between variance in people's cognitive resources and in
bureaucratic competence. Concerning the former, an individual's ‘executive functioning’is crucial for engaging in
“purposeful, goal-directed, and future-oriented behavior”(Suchy, 2009, p. 109). Christensen and others (2020) iden-
tify age, educational levels and mental and physical health as important factors for variance in cognitive resources.
Furthermore, living in poverty is associated with lower cognitive resources, which can elevate the stress of applying
for benefits (Baumberg, 2016) as well as increase learning costs to get information about government programs
(Chetty & Saez, 2013), understand procedural complexities (Hastings & Weinstein, 2008; Super, 2004), and deal with
language barriers or other application requirements (Watson, 2014).
A second element of human capital is people's ‘bureaucratic competence’or knowledge of how bureaucracy and
public service provision works (e.g. Bisgaard, 2020; Danet & Hartman, 1972; Smith, 1988). Gordon (1975) distin-
guishes between knowledge about the functioning of bureaucracy on the one hand and practical and communicative
skills on the other hand. Almond and Verba (1963) distinguish between cognitive and evaluative dispositions—the
former including both objective knowledge about bureaucracy and people's perception of their ability to influence it
4CHUDNOVSKY AND PEETERS
(Danet & Hartman, 1972). These various aspects of bureaucratic competence affect people's ability to interact effec-
tively with the state.
A second type of behavioural explanations concerns how a person's resource scarcity influences his or her
daily decision-making (Madrian & Shea, 2001; Mani, Mullainathan, Shafir, & Zhao, 2013). The emerging literature
on poverty and decision-making shows that a person's resource scarcity creates a bias in daily decision-making
(Bhargava & Manoli, 2015). Everyday purchasing decisions are cognitively demanding, leaving people drained of
the cognitive resources needed for other tasks or decisions (Banerjee & Duflo, 2011, pp. 68–70) and more vulner-
able to lapses of self-control (Spears, 2011). Moreover, scarcity—theexperienceof“having less than you feel you
need”(Mullainathan & Shafir, 2013, p. 4) –creates a mindset focused on immediate needs rather than future
goals. Scarcity tends to perpetuate itself, because the psychology of poverty encourages people to make short-
term decisions that do not structurally improve their financial situation (Mullainathan & Shafir, 2013, p. 14). While
everyone is to a certain extent bounded in their rationality, this mechanism explains a common bias in people's
problem-solving capacities, emotional control, social regulation and ability to resist short-sighted temptations
(Beer, 2012; Diamond, 2013). Crucially for the study of administrative burden, this also affects people's willing-
ness to apply for social programs as well as their capacity to overcome associated compliance, learning and psy-
As a further contribution to the existing theory, it is argued here that people's decision-making regarding the
state and social programs can also present a bias. Attitudes and expectations regarding the state have not yet
been explicitly used to explain the unequal distribution of administrative burden—despite research that indicates
the negative effect of waiting times (Mettler, 2002), intrusive bureaucratic procedures (Soss, 1999), arbitrary
enforcement (Heinrich, 2018) and other negative bureaucratic experiences (Moynihan & Soss, 2014) on people's
willingness to engage with the state (Bruch, Marx-Freere, & Soss, 2010) and on their “orientations toward the
institutions and policies of government”(Mettler & Soss, 2004, p. 62). As studies on public service performance
have also shown, the experienced quality of public services feeds back into assessments about the trustworthi-
ness of government in general (Berg & Johansson, 2019; Van Ryzin, 2011). People's expectations of government
performance are informed by previous interactions with the state (Kumlin, 2004), by prior beliefs regarding the
state (Baekgaard & Serritzlew, 2016) or by secondary information from experts, media and fellow citizens (Van
2.4 |Policy feedback
In terms of our theoretical model, ‘policy feedback’is introduced as a possible intermediating variable between the
outcomes of policies, services and programs on the one hand and various behavioural explanations on the other
hand. More specifically, it is assumed that previous experiences with bureaucracy or knowledge of how bureaucracy
works impact—albeit not fully explain—people's attitudes regarding the state as well as their bureaucratic compe-
tence. Even though it can be argued that policy feedback mechanisms impact an even broader range of citizen fac-
tors, including people's wellbeing, social mobility and economic status, for the purposes of this article its specific
relevance for possible future state-citizen interactions is highlighted.
Since vulnerable people tend to have more negative bureaucratic experiences (Barnes & Henly, 2018; Soss,
Fording, & Schram, 2011), policy feedback can also help understand why they often experience higher administrative
burdens and why the bite of administrative burdens tends to be bigger in developing countries (Heinrich &
Brill, 2015). Policy feedback mechanisms ‘make citizens’(Mettler & Soss, 2004), shape citizen participation
(Campbell, 2012), and convey messages about someone's place in society and the way government works
(Wichowsky & Moynihan, 2008). Specifically for developing countries, negative feedback might also be produced by,
for instance, becoming a victim of corruption by street-level bureaucrats (Justesen & Bjørnskov, 2014) or by uncer-
tainty about whether you will get access to a public service (Auyero, 2011). Furthermore, there is often already a
CHUDNOVSKY AND PEETERS 5
structural lack of trust in government's ability to provide equal access to rights and services (Peeters et al., 2018;
Rothstein, 2013). For instance, in Argentina—where the case study presented here is set—64.3% of the population
has little or no trust in the state and 67.5% has little or no trust in national government (Latinobarómetro, 2015). This
is likely to decrease people's willingness to apply for government programs and engage in state-citizen interactions.
People in developing countries are more likely to develop or sustain psychological and motivational barriers for
engaging with the state because of their expectation that they will not be treated fairly, will face high administrative
burdens, or will not be able to get access to what they are entitled to (Peeters et al., 2018).
3|THE AUH CASE
3.1 |Case setting
The relevance of the aforementioned analytical framework is illustrated through a case study of non-take-up in
Argentina's conditional cash transfer program (CCT): the AUH. A common problem in CCTs is the dependence on
means-tested targeting to identify their target population (Robles Aguilar, 2014). Confronted with limited state
capacity, governments often rely on aggregated municipal rather than precise individual income data and place bur-
dens on citizens to prove eligibility. This generates errors of inclusion (leakage of resources towards non-target pop-
ulation) and exclusion (target population that does not receive the program) (World Bank, 2015). CCTs in Latin
America reach, on average, only 42.6% of all poor individuals in households with children—varying from 85.9% in
Uruguay to 11.0% in Paraguay (Robles, Rubio, & Stampini, 2015, p. 8). Considering these issues, the AUH adopted a
more universal coverage and aims to be a social protection program for all poor families with children under 18 years
old with an informal job (Cruces & Gasparini, 2008). As a consequence, non-participation in the AUH is relatively low
for Latin American standards, but remains considerable: 18% of all eligible people does not participate. Moreover,
the most vulnerable target groups show the highest levels of non-participation (20% of the extreme poor are not
covered). Interestingly, 57% of non-participation takes place because beneficiaries do not apply for the program
(Chudnovsky & Peeters, 2020).
The AUH program provides an excellent case for illustrating the impact of factors that “affect eligible citizens'
tendency to reach out for services and benefits from the state”(Christensen et al., 2020, p. 130). The case allows for
a focus on exactly the factors at play prior to any citizen-state interaction. First, focalization is not a major concern
and errors of exclusion are, therefore, not a convincing explanation for non-take-up by vulnerable groups. Second,
the financial benefit per child is relatively generous. In 2015, the benefit per child was 17.7% of the minimum wage,
with a maximum of five children. This, combined with the relatively low enrollment burdens, makes it unlikely that
potential beneficiaries' rational cost–benefit analyses can explain non-participation. Third, the most vulnerable target
group—that would financially benefit the most from access to the program—is overrepresented among the total pop-
ulation of eligible non-participants (Chudnovsky & Peeters, 2020). Fourth, the large number of non-participation
(57%) prior to any state-citizen interaction (Chudnovsky & Peeters, 2020) allows for an analysis of the factors that
cause non-take-up for a relatively easily obtainable benefit. And lastly, the setting in a low-trust country makes it
possible to include ‘attitudes towards the state’in the analysis.
The AUH covers around 3.6 million children, representing 28% of the population under the age of 18 in
Argentina. Eligibility for the AUH is automatically determined by government records and verified monthly by the
National Social Security Administration (ANSES—for Administración Nacional de la Seguridad Social). People qualify as
eligible if they are not formally employed (with an income less than the minimum wage) and have children younger
than 18 years old that reside in Argentina. Foreigners are eligible if they have at least 3 years of legal residence. The
government records are not free of registration errors, but these mostly consist of errors of inclusion: people with a
high income (for instance, through a pension) that are erroneously included as eligible. The system depends on the
self-declared income of citizens, which also implies a certain level of inaccuracy. There are, however, a few
6CHUDNOVSKY AND PEETERS
administrative requirements for enrollment. These may be important, since research suggests that even apparently
low burdens can already have a significant impact on take-up (Judge-Golden, Smith, Mor, & Borrero, 2019).
Eligible citizens must provide the following information:
•Children and parents must have an official identity document (DNI—for Documento Nacional de Identidad), which
is provided by the National Registry of Persons (RENAPER). Studies indicate that 1.6% of all people (or 168,000)
in urban areas between 0 and 17 do not have a DNI, with a child in the first socio-economic quartile having a 2.5
times higher probability of not having a DNI than their peers in the highest socioeconomic quartile (Tuñón,
Fourcade, González, & Reggini, 2012).
•Parents must provide family information—that is, legal evidence of marriage as well as the child's birth certificate
and the variations required for divorced parents, foreigners or beneficiaries who are not related by blood to the
•Parents must have access to the banking or post-office system to receive payments. ANSES automatically opens
a bank account or delivers to the beneficiary's closest post office.
3.2 |Method and data
Data was obtained from the Argentinian government's 2015 National Survey of Social Protection and Social Security
(ENAPROSS II, 2015) and from 32 interviews conducted between February and May 2019 with public officials and
citizens eligible for the AUH. The survey sample consists of more than 33,000 people and 10,000 households in five
of the country's provinces and the metropolitan area of Buenos Aires. It represents a total of 5,424,405 households
and 16,505,250 people in urban centres larger than 5,000 inhabitants—thereby not covering beneficiaries in rural
areas. The survey data regarding coverage and reasons for non-participation was obtained through a self-reported
questionnaire applied to a member of a preselected household identified as eligible by ANSES. The households are
part of a probabilistic sample based on data regarding vulnerability in Argentina's 2010 population census. The full
survey design can be accessed online at the Ministry of Labor´s website.
The interviews were semi-structured and held with 24 eligible citizens who have never applied for the AUH, five
ANSES officials, one RENAPER official, the director of Argentina's most important social organisation on citizen's
documentation (IADEPP), and the undersecretary of the Ministry of Labor in charge of conducting the ENAPROSS
survey. The eight public officials were selected because of their experience with and knowledge of the implementa-
tion of the AUH program. All these interviews were recorded and transcribed (see Data S1 for more details on inter-
viewees and interview protocol). Citizens were selected in two phases: (a) identification of several of the most
vulnerable settlements in greater Buenos Aires and (b) identification of eligible non-participants of the AUH through
information from local informants. The interviews with citizens were held in the following precarious settlements in
the province and city of Buenos Aires: Nuevo Amancay, Tigre, Pilar, La Cava and Villa 31. A snowball method was
used to select interviewees up to the point of theoretical saturation (Corbin & Strauss, 2008). In informal and familiar
settings, people were asked why they have never applied for the AUH. For privacy and security reasons, no audio
recordings could be made of these interviews. Instead, extensive notes were made during the interviews.
Data was analysed to illustrate the relevance of the aforementioned framework for explaining burdens in peo-
ple's tendency to reach out for social programs specifically targeted at them. The objective was not to analyse the
specific weight of each factor to explain non-participation in the AUH. Moreover, the analysis includes only the
group of eligible citizens that never applied for the AUH to allow a more precise focus on factors at play prior to any
state-citizen interaction. Therefore, the total survey sample of eligible non-participants for the AUH (1,058) was bro-
ken down in 232 respondents reported as drop-outs and 818 respondents that reported having never received the
benefit –the latter further broken down in 207 respondents that unsuccessfully tried to enrol in the program
CHUDNOVSKY AND PEETERS 7
(‘enrollment failure’) and 587 respondents (or 57% of the entire sample, representing over 200,000 people) that
never tried to enrol in the first place (‘no application’). This last number is corrected for people that self-reported as
erroneously targeted as eligible (because of a higher income, for instance) or as no longer eligible (because of recent
formal employment, for instance). This leaves a total of 425 respondents for which statistics on beneficiary and
household characteristics as well as closed and open answers for the reasons for non-participation were analysed.
The following citizen factors are identified in the survey and interview data:
•Indications for poverty costs: (a) survey statistics on the characteristics of eligible non-participants (income level
and levels of marginalisation to indicate economic vulnerability), (b) the closed and open answer section of the
survey where respondents indicate practical constraints (absence of one parent) and (c) interview answers on
practical constraints caused by vulnerability (living on the street, teen mothers, foreigners without documentation,
•Indications for human capital: (a) survey statistics on the characteristics of eligible non-participants (educational
level), (b) closed and open answers from the survey that indicate learning costs,
and (c) interview answers that
refer to lower social and human capital.
•Indications for decision-making bias
: (a) the closed and open answer section (attitudes and prejudices towards
the AUH and the state in general), (b) the closed and open answer section where respondents indicate scarcity-
influenced decisions, and (c) interview answers regarding a decision-making bias towards daily survival and low
expectations regarding the state.
A logit regression analysis of the ENAPROSS survey was used to identify relevant beneficiary characteristics to
explain non-take-up by eligible citizens for the AUH (dependent variable). Independent variables were selected based
on (a) survey questions that apply to the objective target group characteristics of the entire population between
0 and 17 years or to all households and (b) survey questions that indicate differing levels of vulnerability among the
entire target group. Following this strategy, three dimensions are constructed:
1. Income: three income levels are identified to measure the impact of economic vulnerability (Robles et al., 2015):
Extreme poverty: household with a Total Family Income (TFI)
smaller than the Basic Food Basket (BFB).
Moderate poverty: household with a TFI larger than the BFB but smaller than the Basic Total Basket (BTB).
Precarious income: household with a TFI between one and two BTB.
2. Scarcity: scarcity levels are measured through variables that reflect the indicators of the Argentinian index of
unsatisfied basic needs (NBI) (access to housing, sanitary conditions, access to education and economic capacity),
as developed by the Economic Commission for Latin America and the Caribbean (Feres & Mancero, 2001).
3. Marginalisation: a lack of maintenance of the public space (garbage removal, dirt, etc.) and public services (water,
electricity, etc.) is identified as an indicator of marginalised living conditions.
For each dimension, a separate logit analysis was performed. A more detailed overview of the variables used for
the logit regression in each dimension is provided in Data S1. Table 1 results indicate the marginal contribution of
each of the variables to a discrete change from 0 to 1.
The ENAPROSS survey contains a section on the reasons people give themselves for not participating in the
AUH. Table 2 presents the results of the closed answer categories.
8CHUDNOVSKY AND PEETERS
The category ‘other’gives respondents the opportunity to provide an open answer. The total number of
235 open answers was analysed and coded as follows (see Data S1 for more information on the coding;
TABLE 1 Results of logit regression
Dimension Independent variables
Non-take-up (dependent variable)
Average marginal effect (%) SE
Dimension 1: Income
Extreme poverty 18*** 0.06
Moderate poverty 11** 0.05
Precarious income 5 0.06
Obs: 791 Pseudo R
: 0.0275 Specificity: 100% Correctly classified: 70.92%
Dimension 2: Scarcity
Access to housing Overcrowded 1 attribute 6* 0.04
Overcrowded 2 attribute 12*** 0.04
Poor quality housing −3 0.04
Sanitary conditions Latrine 2 0.10
Access to education School assistance −10*** 0.04
Age 2*** 0.00
Economic capacity Size household −4*** 0.01
Low education 15*** 0.05
Medium education 17*** 0.05
Obs: 1,044 Pseudo R
: 0.0467 Specificity: 96.95% Correctly classified: 71.84%
Dimension 3: Marginalisation
Maintenance of public domain −12* 0.05
Obs: 767 Pseudo R
: 0.0000 Specificity: 100% Correctly classified: 70.40%
Note: The number of observations is not the same in all dimensions because the questions from which the variables have
different rates of non-response.
Source: Own estimation based on ENAPROSS II.
TABLE 2 Reasons for non-participation prior to state-citizen interaction
answer Other Total
% of sample (coefficient
Note: Two columns combine answer categories from the ENAPROSS survey. Column 2 (not eligible) includes the answers
‘beneficiary's income exceeds cap’,‘beneficiary is a freelancer’,‘beneficiary or child has a disability and is covered elsewhere’,
‘beneficiary deducts support from income tax’and ‘beneficiary is a pensioner’. Column 3 (disinterest) includes the answers
‘did not do the paperwork’and ‘disinterest’.
Source: Own elaboration based on ENAPROSS II.
CHUDNOVSKY AND PEETERS 9
In the interviews, people mentioned reasons for not having the AUH benefit. In Table 4, a summary of the rea-
sons mentioned is presented in terms of ‘poverty costs’,‘human capital’, and ‘decision-making bias’.
Several interviewees mentioned multiple reasons for not having the AUH, as summarised in Table 5.
In the following, the data is analysed in terms of the relationship between citizen factors and the experience of
administrative burden. Regarding the latter, where possible,
a distinction is made between compliance costs, learn-
ing costs and psychological costs, following the conceptualization of administrative burdens as developed by
Moynihan and others (2015).
4.2.1 |Poverty costs
The survey statistics indicate that living in poverty produces an increased burden for eligible people to apply to the
AUH. As the regression analysis shows, people living in extreme poverty have an 18% higher chance of not applying
and people living in moderate poverty an 11% higher chance as compared to the entire target population.
TABLE 3 Reasons for non-participation—open answers
Code # of answers Percentage
1. ID missing 43 18
2. Lack of time 24 10
3. Lack of information/incorrect information 32 14
4. Disinterest and prejudices 15 6
5. Father or mother is absent 34 15
6. Does not attend school 7 3
7. Problems with paperwork 9 4
8. Not eligible for AUH 39 16
9. In application procedure 6 3
10. Other 26 11
Total 235 100
Source: Own elaboration based on ENAPROSS II.
TABLE 4 Distribution of the analytical categories in the interviews (with examples)
# of interviews
the problem Examples (quotes)
Poverty costs 13 “I have no money to eat, less for the DNI”
Human capital 11 “I thought that I could not request the AUH and had not been informed
on the subject, so I do not know the necessary procedures”
Decision-making bias 12 “I spend my time helping in the community kitchen where I get food
for the children, what else can the poor ask for?”
10 CHUDNOVSKY AND PEETERS
Furthermore, living in marginalised areas (12%) and scarcity in the access to basic goods—through the indicators
‘households overcrowded’by one (6%) and two attributes (12%)—are also positively related to non-take-up. In the
open answers, single parent households (‘father or mother is absent’) emerge as another indicator that complicates
take-up because of compliance costs, since both parents' documentation needs to be presented in order to obtain
the AUH benefit. ‘Problems with paperwork’are also often related to vulnerable circumstances, such as underage
parents and foreign nationality.
Lastly, the interview data confirms the increased experience of burdens for the most vulnerable target group.
Interviewees often mention compliance costs regarding the need to present the national identity document (DNI) in
order to access the AUH. Take, for instance, Blanca. She is 17 years old, has two children and is pregnant. She needs
the AUH benefit because she lives on the streets. However, she does not have an identity document (DNI). Milagros
is 16 years old and faces a similar problem. Her mother abandoned her when she told her she was pregnant. She
lives on the streets and neither she nor her two children have the DNI.
4.2.2 |Human capital
Survey statistics on educational levels indicate that levels of human capital impact non-take-up as well. Both low
(15%) and medium (17%) levels of education by the head of the family significantly increase the chance of non-take-
up. In the open answers, lack of information or incorrect information about the AUH can be associated with elevated
learning costs. Answers here include ‘does not know about program’,‘does not know how to do procedure’, and
‘thought they were not eligible’. In the interviews, several citizens also expressed the increased compliance and
learning costs they face because of lower human capital levels. For instance, Sabrina is 21 years old and lives with
her grandmother. She has three children and never applied for the AUH because she does not know how to read or
write and, therefore, cannot fill up the forms to obtain the benefit. Elsewhere, José, 28 years old, wrongly believes
that he needs to pay for obtaining the national identity document (DNI): “I don't have a (credit) card to pay, I lost
mine and the mother left us […]. I have no money to eat, less for the DNI”.
TABLE 5 Number of analytical categories in the interviews (with examples)
# of interviews
one or more
problems Examples (quotes)
14 Poverty costs (7) Human capital (4) Decision-making bias (3)
“My document is expired,
and I have not yet
renewed it since I do
not have the money to
“I do not know how to
read, and we live from
grandma's pension. She
says we do not need
“Wherever I go, they
already look at you
with contempt, so
they do not give you
8“They say that I am not in the system [to get the DNI], but since I do not know
how to read or write we cannot do anything […]. I went to social assistance,
from there they sent me to the psychologist, they think that because you are
poor you are crazy”
(human capital and decision-making bias)
2“They make you go from here to there too many times and for everything you
have to travel and we do not even have money to buy the public transport card.
We deal with what we have, it is easier to live like this than to beg them”
(poverty costs, human capital and decision-making bias)
CHUDNOVSKY AND PEETERS 11
4.2.3 |Decision-making bias
In the survey's open answers, a lack of time is often associated with a focus on daily survival, as evidenced in answers
such as ‘busy with other things’and ‘does not have time to apply’–revealing increased compliance costs because of a
decision-making bias. Moreover, children are sometimes taken out of school to help with the family economy (‘does not
attend school’), which is supported by the survey statistics where school assistance shows a 10% lesser chance of non-
take-up. Lastly, the closed answer categories ‘disinterest’(16%) and ‘does not know’(18%) might also indicate a
decision-making bias. Even though this remains open for interpretation, the answers at least suggest that the AUH ben-
efit is not something people have given much thought nor plays a large role in their decision-making process. In the
interviews, several eligible non-participants explain how their daily struggle for survival impacts their decisions and per-
ception of compliance costs. For instance, Rosa and Julio, both 47 years old and with nine children, are Bolivians and
never applied for their DNI. They lost their documents and never tried to solve this situation.
Disinterest in obtaining the AUH benefit can also indicate a sign of negative attitudes towards the state and gov-
ernment programs. In the open answer category ‘disinterest and prejudices’answers include ‘does not like bureaucratic
procedures’,‘never applied’and ‘no interest’. In the interviews, public officials confirm the existence of negative atti-
tudes towards the state. According to the undersecretary in charge of ENAPROSS, there is fear of contact with the
state among the poorest. They are afraid that the state ‘will take something away’from them and are afraid of punish-
ment for not having a DNI or a permanent address. The police ‘represents’the face of the state for most of the vulnera-
ble population, which further generates stigma (and fear) towards the state. Negative attitudes were also mentioned in
the interviews at the ANSES central offices and with eligible non-participants, who tend to see interactions with the
state as a problem rather than a solution. Consistent with the notion that citizen factors already exist prior to state-citi-
zen interactions (Christensen et al., 2020, p. 130), these attitudes reduce people's willingness to apply for the AUH,
regardless of how high or low the administrative burdens in the actual application procedure might be.
Many people in Nuevo Amancay, Tigre, Pilar, Villa 31 and La Cava live under conditions of extreme poverty.
Most neighbourhoods do not have drainage, nor paved roads. They are not connected to the public electricity net-
work and there is no garbage collection. Every time it rains heavily, their streets and houses are flooded. Houses are
made of precarious materials, many of them without a proper roof, with clay floors and without adequate bathrooms.
These are marginal neighbourhoods in which the houses are built with waste materials that their owners put
together from the street and where people live with few economic resources. Many people depend on community
kitchens. For these people, the state is ‘far away’–both geographically and metaphorically. For instance, Dionisia,
39 years old and with five children, relies on her neighbours instead of on the state. She helps in the community
kitchen, where she obtains food for herself and her children. She says she would rather ask local politicians for a job
when they visit the neighbourhood than to go to an ANSES office: ‘when we go there, they treat us as indigents’.
5|DISCUSSION AND CONCLUSION
Administrative burdens are an “individual's experience of policy implementation as onerous”(Burden et al., 2012,
p. 741), but the jury is still out on what exactly explains the higher burdens (Moynihan et al., 2015), higher levels of
‘administrative exclusion’(Brodkin & Majmundar, 2010) and lower levels of take-up (Bhargava & Manoli, 2015)
among vulnerable target groups of government programs. What determines the experience of a burden cannot solely
be attributed to state factors, such as the design characteristics of government programs and bureaucratic proce-
dures, but also depends on citizen factors that shape people's capacity and willingness to engage in bureaucratic
encounters and apply for social programs. The analysis and findings presented here make two contributions.
First, it builds upon existing contributions (Christensen et al., 2020; Heinrich, 2018) to refine the theoretical frame-
work on explanations for the unequal distribution of administrative burdens. A distinction is made between economic
and behavioural explanations. The former includes rational trade-offs regarding the cost–benefit of going through
12 CHUDNOVSKY AND PEETERS
ordeal mechanisms as well as the relatively higher costs of living in economic vulnerability. The behavioural approach,
by contrast, looks at the psychology of decision-making and at variations in human capital. Furthermore, policy feedback
is suggested as a possible intermediating variable between policy outcomes on the one hand and people's willingness to
interact with the state and apply for government programs on the other hand. This also helps understand the bigger bite
of administrative burden in low-trust and developmental contexts (Heinrich & Brill, 2015) and is consistent with the
notion that citizen factors exist prior to any state-citizen interaction (Christensen et al., 2020, p. 130).
Second, the findings of a case study of non-take-up in Argentina's conditional cash transfer program illustrate
the relevance of the theoretical framework. Data from a government survey and from interviews with civil servants
and eligible non-participants demonstrate that citizen factors “affect eligible citizens' tendency to reach out for ser-
vices and benefits from the state”(Christensen et al., 2020, p. 130). Evidence for both economic and behavioural
explanations was found, which suggests the importance of incorporating multiple factors in the analysis of variance
in people's willingness to apply and their capacity to overcome administrative burdens. More research is needed to
measure the relative impact of each individual factor in different contexts (which the data presented here does not
allow for). Furthermore, future studies should also focus on the interplay between state factors and citizen factors
and on policy feedback mechanisms to improve our understanding of the causes of the unequal distribution of
Lastly, “investigating empirically how reactions to burdens vary”(Christensen et al., 2020, p. 132) not only helps
to explain the distributive nature of administrative burden (Herd & Moynihan, 2018), but can also lead to relevant
insights for policy makers. Even though citizen factors already exist prior to any state-citizen interaction, improving
bureaucratic encounters (Lotta & Marques, 2019; Raaphorst & Van de Walle, 2018) can have a significant impact on
take-up of social programs. Understanding the role of the citizen in bureaucratic encounters and how seemingly low
burdens can have a disproportionately negative effect on vulnerable target groups can be incorporated in audits that
look at the citizen experience of government programs and bureaucratic procedures (cf. Sunstein, 2019). Subse-
quently, actions can be taken to mitigate the impact of citizen factors on program take-up—both at the level of policy
design and at the level of state-citizen interactions. Exclusion from benefits has both immediate and long-term
effects on social mobility and intergenerational poverty (Heinrich, 2018). Improving the experience of state-citizen
interactions is, therefore, a key democratic concern.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
Mariana Chudnovsky https://orcid.org/0000-0002-3966-4731
Rik Peeters https://orcid.org/0000-0002-9013-6192
Although learning costs are, in principle, not limited to people with lower social and human capital, the survey analysis sug-
gest that a lack of knowledge about the AUH program is more prevalent among the most vulnerable target groups. The
AUH is well-known in Argentina and has a simple application procedure, which is also reflected in the relatively high levels
of coverage as compared to similar programs in the region.
Note that the intermediating variable in the theoretical framework “policy feedback”was not directly tested. This remains
a theoretical assumption in our framework which future research will need to confirm or disconfirm. However, the evi-
dence presented here for negative attitudes towards the state as a contributing factor to non-take-up presents a strong
indication of policy feedback mechanisms at play.
The economic rational choice explanation is not taken into account because the combination of relatively low enrollment
burdens, high financial benefit and an overrepresentation of exclusion by the most vulnerable target group is assumed to
exclude this explanation.
CHUDNOVSKY AND PEETERS 13
The Total Family Income (TFI) is calculated after deduction of transfers received from the AUH. This allows us to estimate
the actual level of vulnerability pre-transfer for each family.
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be directly linked to an experience of compliance, learning and psychological costs. This is consistent with the notion that
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How to cite this article: Chudnovsky M, Peeters R. The unequal distribution of administrative burden: A
framework and an illustrative case study for understanding variation in people's experience of burdens. Soc
Policy Adm. 2020;1–16. https://doi.org/10.1111/spol.12639
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