# Unequal access to early childcare: What role do cognitive and behavioral factors play? A PRISMA systematic review

Laudine Carbuccia, Valentin Thouzeau, Carlo Barone, Coralie Chevallier

► To cite this version:

Laudine Carbuccia, Valentin Thouzeau, Carlo Barone, Coralie Chevallier. Unequal access to early childcare: What role do cognitive and behavioral factors play? A PRISMA systematic review. Mind & Society, 2025, 24, pp.587-620. 10.1007/s11299-025-00331-2. hal-05486562

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# Unequal access to early childcare: What role do cognitive and behavioral factors play? A PRISMA systematic review

Laudine Carbuccia<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID · Valentin Thouzeau<fnref n="2" /> · Carlo Barone<fnref n="1" />,<fnref n="3" /> · Coralie Chevallier<fnref n="2" />

Received: 18 December 2024 / Accepted: 9 July 2025 / Published online: 25 August 2025

© The Author(s) 2025

## Abstract

Despite well-documented inequalities in access to early childcare services for underprivileged populations, the cognitive and behavioral factors contributing to these disparities have been largely overlooked in existing frameworks. This article addresses this gap by integrating insights from behavioral economics and other behavioral sciences into the analysis of unequal access. First, we expand existing theoretical models by incorporating cognitive and behavioral factors, such as information gaps and decision-making heuristics, alongside structural barriers. Second, we evaluate the empirical relevance of this integration through a PRISMA mixed-methods systematic review of the literature. Our findings, supported by effect sizes, cross-validated impact scores, and p-curve analyses, indicate that cognitive and behavioral factors significantly contribute to access inequalities. We argue that public policies must go beyond structural solutions by redesigning the choice architecture and implementing interventions to support informed decision-making and reduce friction costs. Failure to integrate these behavioral insights risks perpetuating unequal access to high-quality childcare services.

**Keywords** Early childcare · Cognitive and behavioral factors · Behavioral economics · Non-take-up · PRISMA systematic review · Mixed-methods · p-curving · Boost

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

<footnote>Carlo Barone and Coralie Chevallier contributed equally.</footnote>

<footnote>Laudine Carbuccia
laudine.carbuccia@sciencespo.fr</footnote>

<footnote n="1">¹ Department of Sociology, CRIS, Sciences Po, 1, Place Saint Thomas, 75007 Paris, France</footnote>

<footnote n="2">² Institut Jean Nicod, Département d’études cognitives, Ecole normale supérieure, Université PSL, EHESS, CNRS, 75005 Paris, France</footnote>

<footnote n="3">³ LIEPP, Rue Saint-Guillaume, Sciences Po, 27, 75007 Paris, France</footnote>
# 1 Introduction

A growing body of literature underscores the importance of integrating behavioral insights into the design of public policies (Shafir 2013). Behavioral analysis and behavioral intervention tools have gained increased importance in designing effective policies (Thaler and Sunstein 2009). A key area where behavioral analysis is crucial from a policy perspective is the "last mile problem"—when public programs fail to reach the populations they aim to serve (Soman 2017). Research across social sciences addresses the issue of non-take-up by underprivileged populations of services that would significantly benefit them and help reduce poverty (Bargain et al. 2012; Reijnders et al. 2018; Vandoninck et al. 2018).

Throughout this article, we use the term "underprivileged" to refer to profiles identified in the literature as particularly at-risk of non-take-up (Janssens and Van Mechelen 2022; OECD 2004). Drawing from insights within this literature, and following well-established practice in systematic reviews on social inequalities (e.g., Herbaut and Geven 2019; Kulic et al. 2019; Schmutz 2024), this term encompasses a broad class of low socioeconomic status (SES) groups, such as low-educated, unemployed, working-class, low-income, immigrant, and racial minority families (see, for instance, Herbaut & Geven 2019). This literature highlights the potential of behavioral tools across policy domains such as health (Devoto et al. 2012), higher education (Bettinger et al. 2012; Herbaut and Geven 2019), and finance (Linos et al. 2020) and supports the implementation of behavioral interventions to address non-take-up (Thaler and Sunstein 2009).

Among the services susceptible to non-take-up, early childcare<fnref n="1" /> stands out as especially crucial (OECD 2018). It is well-documented that high-quality early childcare reduces early developmental inequalities because children from low socioeconomic status backgrounds and immigrant families experience the greatest gains from early childcare (Berger et al. 2020; Carbuccia et al. 2020; Ghirardi et al. 2022; Grobon 2018; Melhuish 2004; Schmutz 2024). Moreover, early childcare promotes women's employment, thereby increasing family income and reducing the motherhood penalty (d'Albis et al. 2017; Hermes et al. 2022; Kimmel 1998; Martinez and Perticara 2022; Simintzi et al. 2022; Waldfogel 2002). However, it is well-documented that underprivileged families access early childcare less frequently in developed countries (Cascio 2021; Collombet 2018; OECD 2019).

Various factors have been put forward to account for these inequalities (Archambault et al. 2019; Chaudry et al. 2010; Lazzari 2012; OECD 2001; Vandenbroeck 2013), but no systematic review has assessed their empirical relevance. Moreover, current theoretical frameworks focus almost exclusively on structural factors, such as reducing childcare fees or increasing available slots. Although these reforms are essential, they rely on economic incentives and seldom address individual cognitive and behavioral factors that may also contribute to access inequalities. Therefore,

<footnote n="1">¹ We use "early childcare" to refer to any regulated arrangement that provides education and care for children from birth to preschool age, classified into ISCED 01 (European Commission 2022). This includes daycare facilities, private childminders, and any other Early Childhood Education and Care facilities for children before preschool.</footnote>
inequalities can persist even when services are available and affordable, as identified in other areas of non-take-up (OECD 2025).

This article examines the role of demand-side factors in early childcare access inequalities, using a comprehensive framework that incorporates both supply-side and demand-side determinants. In the next section, we present the various supply-side and demand-side factors that may be at play in early childcare access, followed by a systematic review to investigate the empirical evidence on demand-side factors.

## 2 Literature review

In this section, we review the determinants of unequal access to early childcare (Archambault et al. 2019; Chaudry et al. 2010; Lazzari 2012; OECD 2001; Vandenbroeck 2013). We propose a synthetic classification organized around two broad categories: structural factors, and cognitive and behavioral factors. Before presenting the details of this classification, we clarify how we define the two concepts.

Structural factors, sometimes called supply-side (Archambault et al. 2019; Kulic et al. 2019) or ‘direct’ factors (OECD 2025), “include the characteristics of early childcare programs themselves, as well as their institutional environments (funding mechanisms, geographic location, etc.).” (Archambault et al. 2019).

Cognitive and behavioral factors, also called demand-side factors or ‘indirect factors’ (OECD 2025), refer to how families respond to these structural factors and other individual-level mechanisms that shape their intention to use early childcare and application behaviors. These factors are fundamentally linked to families’ resources (economic, social, cultural, and temporal; Kulic et al. 2019), which in turn determine the strategies they can leverage to secure a childcare spot. They include: (1) motivations to use early childcare, (2) beliefs and information level about the supply, (3) decision-making heuristics, and (4) resources and skills to deal with the administrative process. Importantly, in this review, we shifted our focus from the concept of "parental preferences" to an examination of the determinants of these preferences and of the intention to use early childcare services. Indeed, the literature highlights that the role of childcare preferences must be interpreted with great caution because preferences may be calibrated to existing constraints (Lazzari and Vanderbroeck 2012; Pungello and Kurtz-Costes 2000), and may be distorted by (socially) biased information about childcare services.

Having clarified our approach and the definitions of supply-side and demand-side factors, we will now examine the various elements underlying these two categories. In this analysis, we will primarily draw upon established theoretical frameworks concerning early childhood care inequalities, while also enriching them with supplementary insights from behavioral sciences (Archambault et al. 2019; Chaudry et al. 2010; Lazzari 2012; OECD 2001; Vandenbroeck 2013).
## 2.1 Structural factors

Affordability and accessibility are critical structural factors that can block access to early childcare. Affordability refers to the direct costs of early childcare (Workman and Jessen-Howard 2018).

Accessibility barriers can be traced back to four main sources:

(1) Residential inequalities: the spatial proximity of early childcare facilities is more important for underprivileged families that tend to be less mobile, but these facilities are less often available in poor neighborhoods (Archambault et al. 2019; OECD 2001; Vandenbroeck 2013).

(2) Admission criteria: Early childcare services often face important shortages of available spots (Jessen-Howard et al. 2020), resulting in the allocation of available places based on admission criteria, which may be unfavorable to underprivileged families (e.g., priority to dual-earner families) (Archambault et al. 2019; Lazzari 2012; Vandenbroeck 2013).

(3) Functioning: The time schedules of early childcare facilities may not be adapted to underprivileged families (Archambault et al. 2019; Lazzari 2012; OECD 2001). For instance, in these families, parents are more likely to have atypical work schedules (Kaiser et al. 2019; Kalil et al. 2014; Presser 2005); but early childcare facilities often follow standard office hours (Lazzari and Vanderbroeck 2012; OECD 2001).

(4) Bureaucratic requirements: Applying to early childcare often requires documents, such as birth certificates or pay slips, that underprivileged families, especially migrant families, may lack (Archambault et al. 2019; Perreira et al. 2012).

## 2.2 Cognitive and behavioral factors

Beyond these structural factors, several cognitive and behavioral factors at the individual level can lower the intention of underprivileged parents to use early childcare and hinder their application. We start by reviewing the former, before turning to the latter.

### 2.2.1 Factors driving the intention to apply

#### 2.2.1.1 Households' beliefs and norms, and motivations to use early childcare
Early childcare may be perceived as less desirable among underprivileged households due to prevailing traditional norms (Leseman 2002) and divergent child-rearing beliefs (Archambault et al. 2019; Lazzari). Pietropolli & Triventi's study (2023) highlights the pivotal role of parents' beliefs in early childcare take-up. Two related factors can further influence households' motivations for engaging in early childcare and reduce the intention of underprivileged households to secure a spot (Pungello and Kurtz-Costes 1999b). First, the potential income loss associated with parental care, particularly when both parents were employed before pregnancy, alongside career-related

<figure><img src="image_3.png" /></figure>
motivations, serves as a significant determinant for early childcare use. However, these motivations are strongly correlated with mothers' socioeconomic status (Steinberg and Kleinert 2022). Second, access to full-time informal care, which can serve as a substitute for formal early childcare, is more commonly found in underprivileged communities that exhibit lower levels of economic activity (Scott et al. 2005).

### 2.2.1.2 Information gaps
Underprivileged families often have less information about the availability and actual costs of education and care facilities, as well as about application procedures. These information gaps may negatively affect their intention to use early childcare as well as their chances of getting a spot (Lazzari 2012; Vandenbroeck and Lazzari 2014). We distinguish three primary sources of these information gaps.

First, informal social networks are the primary source of information about early childcare among underprivileged populations (Chaudry et al. 2011). Therefore, parents will struggle to access relevant information if the majority of their neighbors, friends, and acquaintances do not use these services, which is more often the case among underprivileged populations. Second, underprivileged families are also less likely to access expert sources (Perreira et al. 2012) available through the Internet, newspapers, or books (Bell 2014; Radey and Randolph 2009; Rothbaum et al. 2008). The digital divide exacerbates systematic disadvantages in obtaining comprehensive, up-to-date information about early childcare options and the application process. Third, immigrant families and those with limited educational backgrounds encounter additional barriers when processing complex administrative information presented in formal language or requiring specific cultural knowledge about institutional procedures (Okbani 2024; Perreira et al. 2012).

The resulting information gaps and biases may involve misperceptions of the available supply (e.g., the characteristics of different childcare options), their costs, their accessibility (e.g., the likelihood of getting a spot), the formal procedures to apply, and the informal practices increasing the chances to get a spot (Aksztejn 2020; Barone et al. 2018; Keller 2003).

### 2.2.1.3 Decision heuristics
Decision-making also plays an important role in non-take-up dynamics. Poverty and economic insecurity may redirect parents' cognitive resources towards matters that are more pressing than decisions about early childcare (Mani et al. 2013; Mullainathan and Shafir 2013; Ridley et al. 2020). Parents may thus rely more heavily on fast and frugal decision heuristics to make decisions about childcare (Chaudry et al. 2010; Kahneman 2013; Tversky and Kahneman 1974). One such heuristic is to 'follow the crowd', that is, the prevalent practices in one's social surroundings, which provide the default option.

In addition, present bias is larger among underprivileged populations because of the life stressors associated with family insecurity and instability (Harrison et al. 2002; Ludwig et al. 2019; Pepper and Nettle 2017; Yesuf and Bluffstone 2008). Present bias refers to the propensity to give excessive weight to payoffs that are closer to the present time when considering trade-offs between two future
<table><caption>Fig. 1 Classification of the different factors underlying early childcare access (Perceived quality was not initially present in our classification (see Results))</caption><thead><tr><th>Cognitive and behavioral factors</th><th>Structural factors</th></tr></thead><tbody><tr><td><strong>Factors driving intention to apply</strong><ul><li>Level of information ➡ Perceived accessibility, affordability, quality, & application tricks</li><li><strong>Beliefs and social norms</strong></li><li><strong>Motivations</strong> to use childcare</li><li>Level of trust</li><li><strong>Decision heuristics</strong></li></ul></td><td><strong>Affordability</strong><ul><li>Fees</li><li>Subsidies</li></ul></td></tr><tr><td><strong>Factors hindering application</strong><ul><li><strong>Cognitive, social and temporal resources</strong> available</li><li><strong>Administrative and linguistic proficiency</strong></li><li><strong>Sludges and friction costs</strong></li></ul></td><td><strong>Accessibility</strong><ul><li>Proximity</li><li>Shortages (lack of places)</li><li>Admission criteria</li><li>Functioning (e.g., opening hours)</li><li>Bureaucratic requirements</li></ul></td></tr></tbody></table>

moments (Pepper and Nettle 2017). Because early childcare is more expensive than parental or informal care, and its benefits, e.g., in terms of child development or mother's employment, are delayed and uncertain (Chaudry et al. 2010; Herbaut and Geven 2019; Hunter et al. 2018; Wang and Sloan 2018), present bias may lead parents to postpone their application or to favor parental care. This cost/benefit structure applies to all parents, but it may be exacerbated for underprivileged parents who display lower familiarity with and lower trust towards public bureaucracies and institutions (Bell 2014; Fuller 2014; Galland and Lemel 2010; Korndörfer et al. 2015; Twenge et al. 2014).

### 2.2.2 Factors hindering application

Even if underprivileged parents intend to use early childcare, the overall complexity of the application process and the resulting cognitive and administrative burden can prevent them from securing a spot (Herd and Moynihan 2019; Sunstein 2019). Lower skills to navigate public bureaucracies, including language barriers for immigrant families, may further increase the time and cognitive bandwidth needed to apply, as well as the fear of stigma (Christensen et al. 2020; Vandenbroeck 2013).

We classify<fnref n="2" /> the factors that may contribute to social inequalities in access to early childcare in Fig. 1. Within cognitive and behavioral factors, we distinguish the factors influencing the intention to apply to early childcare from those hindering application, even when parents wish to apply. Within the structural factors, we differentiate between accessibility and affordability factors. It is worth noting that these four types of inequality mechanisms can interact with one another, and their

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

<footnote n="2">² This general classification synthesizes the contributions of existing frameworks to which cognitive and behavioral factors are added.</footnote>
respective relevance will vary both between and within countries depending on structural contexts (see the concluding section).

In the remainder of the article, we present the methodology and results of a PRISMA systematic review of empirical studies focusing on the determinants of unequal access to early childcare, with a focus on cognitive and behavioral factors. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a standardized methodology that provides guidelines for conducting and reporting systematic reviews and meta-analyses. This framework ensures transparency, reproducibility, and methodological rigor through a structured approach that includes: (1) systematic search strategies across multiple databases, (2) explicit inclusion and exclusion criteria, (3) standardized data extraction procedures, (4) quality assessment of included studies, and (5) transparent reporting of results using flow diagrams and checklists (Moher et al. 2009). This methodology is particularly valuable for synthesizing evidence across diverse study designs and research traditions, as is the case in early childcare research.

# 3 Methods

## 3.1 Systematic search strategy

We first preregistered our data extraction protocol<fnref n="3" /> in the Open Science Framework repository. Then, following the PRISMA methodological framework, we conducted an extensive search in four databases (PsycINFO, Education Resources Information Center (ERIC), EconPapers, and Persée) and two search engines (Google Scholar and ProQuest). The search was conducted in English and French, the two languages mastered by the authors of the review, between December 2020 and February 2021. All manuscripts available online before February 2021 were considered. To ensure reproducibility, the search results were recorded in a spreadsheet available online (https://docs.google.com/spreadsheets/d/14pDgxZIo-MKjApaQRFGg9bKgLbKC5dOQ-RWg93eLpIE/edit?gid=1978866434#gid=1978866434), which included all studies screened, along with the reasons for their exclusion and the stage at which they were excluded (i.e., Screening or Eligibility).

The search algorithm was composed of one term referring to early childcare (“child-care” OR “child care” OR “daycare” OR “early childcare” OR “ECEC” OR “early childhood education and care” OR “creche” OR “nursery” OR “early childcare”) and one term referring to unequal access (“barriers” AND/OR “access” OR “enrollment” OR “use” OR “enrollment gap” OR “inequalities”). Some optional terms were also included (“experimental” OR “costs” OR “subsidies” OR “beliefs” OR “norms” OR “trust” OR “information” OR “informational” OR “geographical” OR “lack of space” OR “admission criteria” OR “culture” OR “cultural” OR “cognitive and behavioral” OR “social” OR “parental skills” OR “inequalities” OR “underprivileged” OR “persistence”).

<footnote n="3">³ Even though we did not deviate from our preregistered protocol, our terminology evolved during data collection.</footnote>
Two additional strategies were implemented: (1) forward and backward citations tracking, and (2) four experts from different disciplines (sociology, economics, demography, psychology) were contacted to provide a list of relevant references.

## 3.2 Inclusion and exclusion criteria

The population of interest consisted of underprivileged families in Western countries expecting a child or with at least one child of the age to attend early childcare. We adopted a broad definition of “underprivileged families” referring to a broad class of low socioeconomic status (SES) groups, such as low-educated, unemployed, working-class, low-income, immigrant, and racial minority families. We excluded studies dealing exclusively with other specific populations (e.g., children with disabilities) or that did not cover underprivileged families. "Early childcare" refers to any regulated arrangement that provides education and care for children from birth to preschool age, classified as ISCED 01 (European Commission 2022). This includes daycare facilities, private childminders, and other Early Childhood Education and Care facilities for children before preschool (if it exists in the country of interest). Therefore, we excluded studies dealing with access to other types of educational facilities (i.e., facilities above ISCED level 01, like preschool). Only original studies were included, as opposed to systematic reviews, meta-analyses, or other secondary syntheses of existing literature. We included studies using primary data as well as secondary analyses of pre-existing datasets. We included both published and unpublished manuscripts to limit publication bias.

Given that the field is relatively recent, we conducted a mixed-methods systematic review including all types of research methods, whether quantitative, qualitative, or mixed-methods. Qualitative methods are valuable to explore the subtle psychological nuances of households’ decision-making and behavior across different cultural, situational, and institutional contexts. This approach is especially relevant in a domain that has received relatively limited attention when compared to other instances of non-take-up.

Articles were initially assessed based on their titles and abstracts to screen out those that did not meet the above-mentioned eligibility criteria. The remaining articles were included or excluded based on a reading of the full text. If the full text was not available, we contacted the authors individually to obtain it. If we did not receive a response, the corresponding articles were excluded.

## 3.3 Data extraction procedure

The first author of this review and a colleague independently extracted data from the resulting sample of articles.<fnref n="4" /> For each article, the following information was

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

<footnote n="4">⁴ See the OSF project for data and scripts.</footnote>
extracted: title, publication date, authors' name(s), methodology (quantitative, qualitative, or mixed methods), and authors' primary discipline.<fnref n="5" />

Then, for each article in the final sample and each mention of an element relating to a factor in our classification, the following data were extracted: the countries covered; the data collection method (qualitative interviews or observations, focus groups, observational or correlational study, quasi-experimental, or randomized controlled trial); how the factor was assessed (self-reported measures, objective measures such as administrative data, or both); the type of underprivileged population being studied (defined according to income level, employment status, and education); sample size; the age range of the children. We also extracted the specific nature of the outcome (e.g., the relationship between the belief that early childcare is reserved to parents that both work and early childcare application); an impact score between -2 and 2 (see below), and when possible, whether the result was statistically significant and the associated *p* value; the type of test performed and the associated test value and standard deviation, if available.

## 3.4 Data-coding strategy

### 3.4.1 Factor coding

The factors measured in the studies we reviewed were grouped thematically by each coder through an iterative process, based on our classification (see Fig. 1). When absent from the initial classification, new factors were added. Once we had reached a stable classification, we assessed the frequency of occurrence of each factor in the literature.

### 3.4.2 Impact score

In this paper, our goal was to be as systematic as possible by including studies that tackle different research questions and employ various methodologies. In line with this goal, we relied on a score that was compatible with this mixed literature. Our primary outcome measurement to assess the relevance of each factor was an impact score ranging from -2 to 2. The impact score summarizes the strength of the evidence that a given factor has an impact on access to early childcare for underprivileged populations.<fnref n="6" /> Two coders independently assigned a score between -2 and 2 to each mention of each factor. The interpretation of the scoring scale is displayed in Table 1.

The rating correlation between the two coders was strong (correlation coefficient=0.89, IC 95% [0.85; 0.92], t=24.2; *p*<0.001; Evans (1996)), which points to a high degree of reliability. For each factor (e.g., proximity), an average impact score was computed from the individual impact scores assigned to each specific

<footnote n="5">⁵ The article's discipline was finally defined according to that of the first author, rather than that of the journal to deal with interdisciplinary journals and unpublished manuscripts.</footnote>

<footnote n="6">⁶ The scoring scale's details are available in the supplementary materials.</footnote>
<table><caption>**Table 1** Interpretation of the scoring scale of the impact score</caption><thead><tr><th>Score</th><th>Interpretation</th><th>Operationalization for quantitative evidence</th><th>Operationalization for qualitative evidence</th></tr></thead><tbody><tr><td>2</td><td>The evidence shows a clear negative effect of factor X on access to early childcare for underprivileged populations</td><td>Effect Size &gt; 0.1 and the effect is statistically significant</td><td>Recurring relationship mentioned by more than a third of underprivileged participants, but not by more well-off participants, and there is a convincing causal chain</td></tr><tr><td>1</td><td>The evidence suggests a negative effect of factor X on access to early childcare for underprivileged populations</td><td>Effect Size &lt; 0.1 and the effect is statistically significant OR Effect Size &gt; 0.1 but the effect is not statistically significant</td><td>Relationship mentioned by more than one to less than a third of the underprivileged participants, but not by more well-off participants</td></tr><tr><td>0</td><td>The evidence is inconclusive about the relevance of factor X for access to early childcare for underprivileged populations</td><td>Effect Size very close to zero effect and non-significant AND in conflict with another result in the article OR tests mentioned as non-significant but test values not reported</td><td>Results are difficult to interpret: quotes are used, but the proportions of participants mentioning this factor are not reported in the article, OR factor is mentioned by only one participant</td></tr><tr><td>-1</td><td>The evidence suggests no differential effect of factor X on access to early childcare for low-SES populations compared to more well-off populations</td><td>Effect Size is close to zero and the test is non-significant</td><td>More than one to less than a third non-underprivileged participants also mention the factor has having a negative impact on their access to early childcare OR that the factor has no impact on their access early childcare OR only one non-underprivileged and one underprivileged participants mentioned the factor as having an effect on their access to early childcare</td></tr><tr><td>-2</td><td>The evidence shows no differential effect of factor X on access to early childcare for low-SES populations compared to more well-off populations</td><td>Effect Size is zero OR negative</td><td>A similar proportion of underprivileged and non-underprivileged populations mention the factor as having a negative impact on their access to early childcare with more than one participant, OR evidence for no negative impact of the factor on access to early childcare, and there is a convincing causal chain</td></tr></tbody></table>

<figure><img src="image_6.png" /></figure>
occurrence of the factor in the literature, regardless of whether the item came from a
quantitative or qualitative article.

3.4.3 Effect sizes

Additionally, we computed standardized effect sizes and their corresponding confi-
dence intervals whenever possible. We performed calculations using the ESC pack-
age in R (Lüdecke 2018). We were unable to compute some effect sizes due to a lack
of reported information. Only one of the authors we contacted to retrieve missing
information responded.

3.5 Bias assessment among the included studies

One pitfall of the impact score is that it does not indicate the quality of the evidence concerning a given factor. Therefore, we complemented it with the Quality Assessment Tool, which is specifically designed for mixed-methods reviews to assess the risk of bias of individual studies<sup>7</sup> (QATSDD; Sirriyeh et al. 2012). This tool consists of 16 criteria, each scored on a scale of 0 to 3, covering key methodological dimensions including theoretical framework, research design appropriateness, data collection procedures, statistical reliability, and critical discussion of limitations. The criteria can be applied to both quantitative and qualitative studies, making it particularly suitable for a mixed-methods review (see Supplementary Materials for complete criteria list and our methodological adaptations for secondary data analyses). Studies are then compared according to the theoretical maximum score on each of these criteria (Hong 2017; Sirriyeh et al. 2012). Using a test-retest protocol on five randomly selected articles, three months apart, the intra-rater reliability of the first author was very high according to Cohen's criteria (correlation coefficient r=0.95, IC 95% [0.90; 0.98]; Cohen (1960)). When the grading of the two coders was compared, interrater reliability was also very strong (correlation coefficient r=0.83, IC 95% [0.65; 0.92]).

3.6 P-curve analysis

We performed p-curve analyses<fnref n="8" /> on the available sample of *p* values (Simonsohn et al. 2014). P-curve analysis is a statistical method designed to assess publication bias and distinguish between true effects and false-positive results in a body of literature (Simonsohn et al. 2014). The method examines the distribution of statistically significant *p* values (*p*<0.05) from a set of studies. Under the null hypothesis of no true effect, significant *p* values should be uniformly distributed between 0 and 0.05. However, when true effects exist, the distribution becomes right-skewed with more *p* values concentrated near 0. Conversely, if results are driven by p-hacking

<footnote n="7"><sup>7</sup> See the supplementary materials for further details.</footnote>

<footnote n="8">⁸ See the supplementary materials for a detailed explanation of the rationale of our analysis choice, p-curve, and the intuition behind this method.</footnote>
(selective reporting of significant results; Head et al. 2015), the distribution becomes left-skewed with an excess of *p* values just below 0.05. By analyzing this distribution pattern, p-curve analysis can determine whether observed significant results reflect genuine effects or are artifacts of publication bias and questionable research practices. This method is particularly valuable in systematic reviews where heterogeneous study designs and outcome measures may limit traditional meta-analytic approaches.

Because of an insufficient reporting of exact *p* values in the articles reviewed, p-curves could not be graphically represented. The available *p* values were thus first grouped into two broad categories: demand-side and supply-side factors. Significant *p* values were then aggregated into two groups: those below 0.01 and those between 0.01 and 0.05. One *p* value per article in each category was then randomly drawn. A Bernoulli test was performed to determine whether the proportion of *p* values below 0.01 was significantly different from 20%. Indeed, under the null hypothesis, i.e., in the absence of an actual effect, the proportion of *p* values lower than 0.01 should be equal to 20% if we expect *p* values lower than 0.05 to be randomly distributed. The random draw was then repeated 100 times, followed by the Bernoulli test. The full results and detailed procedure are presented in the supplementary materials.

We relied on p-curve analysis because it is particularly appropriate in contexts where most published results are significant and non-significant test statistics are unavailable. While we also included a funnel plot based on the available effect sizes, its interpretation is limited due to data heterogeneity and known distortions associated with standardized mean differences (Zwetsloot et al. 2017). We considered alternative methods, such as the z-curve (Bartos and Schimmack 2020). However, the limited availability of complete test statistics in our sample precluded its use (see Betthäuser et al. 2023, for an overview).

# 4 Results

## 4.1 Systematic search results

During the inclusion phase, 3720 articles were identified. Once duplicates were removed, this number was reduced to 525 studies. These 525 articles were then included or excluded based on their titles and abstracts. Following this screening phase, 452 articles were excluded, and 72 were retained for the eligibility phase.

We did not find the full manuscript of 5 of these articles. Therefore, 67 articles were fully read during the eligibility phase. At the end of this phase, 40 articles were excluded because they did not meet the eligibility criteria as detailed in Fig. 2. The remaining 27 articles were included in the systematic review. This relatively low number of eligible articles suggests that unequal access to early childcare remains a largely under-researched topic despite its widely recognized policy relevance.
<figure><img src="image_7.png" /><figcaption>Fig. 2 PRISMA flow chart</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 3 Countries covered by the empirical evidence</figcaption></figure>
<table><caption>**Table 2** The reviewed studies by publication date, country, research discipline, evidence type (Evid. Type), study design type, measurement type, number of households included in the study, operationalization of “underprivileged”, risk of bias (ROB)</caption><thead><tr><th>Reference</th><th>Date</th><th>Country</th><th>Discipline</th><th>Evid. Type</th><th>Design</th><th>Type of measure</th><th>N</th><th>Operationalization of “underprivileged”</th><th>ROB</th></tr></thead><tbody><tr><td>(1) Abrassart & Bonoli</td><td>2015</td><td>Switz</td><td>Economics</td><td>Quantitative</td><td>Quasi-experimental</td><td>Both</td><td>875</td><td>Income</td><td>64.2</td></tr><tr><td>(2) Binet</td><td>2003</td><td>Canada</td><td>Sociology</td><td>Qualitative</td><td>Interviews</td><td>Declarative</td><td>18</td><td>Income, employment status</td><td>73.8</td></tr><tr><td>(3) Busse & Gathmann</td><td>2018</td><td>Germany</td><td>Economics</td><td>Quantitative</td><td>Quasi-experimental</td><td>Both</td><td>16,827</td><td>Income; education; mari- tal status</td><td>66.7</td></tr><tr><td>(4) Farfan-Portet et al</td><td>2011</td><td>Belgium</td><td>Economics</td><td>Quantitative</td><td>Quasi-experimental</td><td>External</td><td>264,456</td><td>Income</td><td>66.7</td></tr><tr><td>(5) Fenech & Skattebol</td><td>2019</td><td>Australia</td><td>Sociology</td><td>Quantitative</td><td>Interviews</td><td>Declarative</td><td>8</td><td>Income</td><td>52.3</td></tr><tr><td>(6) Grace et al</td><td>2014</td><td>Australia</td><td>Educ. Sci</td><td>Mixed</td><td>Observ./Correlational</td><td>Declarative</td><td>101</td><td>Income, employment status</td><td>58</td></tr><tr><td>(7) Greenberg et al</td><td>2016</td><td>U.S</td><td>Economics</td><td>Qualitative</td><td>Interviews</td><td>Declarative</td><td>45</td><td>Income, migration back- ground</td><td>40.4</td></tr><tr><td>(8) Greenberg</td><td>2010</td><td>U.S</td><td>Economics</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>14,480</td><td>Income</td><td>71.4</td></tr><tr><td>(9) Herman</td><td>2017</td><td>France</td><td>Sociology</td><td>Qualitative</td><td>Interviews</td><td>Declarative</td><td>19</td><td>Employment status, poverty rate</td><td>14.2</td></tr><tr><td>(10) Holloway</td><td>1998</td><td>UK</td><td>Geography</td><td>Qualitative</td><td>Interviews</td><td>Declarative</td><td>32</td><td>Index</td><td>45.2</td></tr><tr><td>(11) Jessen et al</td><td>2020</td><td>Germany</td><td>Economics</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>62,437</td><td>Education, migration background</td><td>83.3</td></tr><tr><td>(12) Johnson et al</td><td>2017</td><td>U.S</td><td>Psychology</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>1050</td><td>Income, migration back- ground</td><td>59.5</td></tr><tr><td>(13) Kahn & Greenberg</td><td>2010</td><td>U.S</td><td>Sociology</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>7209</td><td>Migration background</td><td>45.4</td></tr><tr><td>(14) Marshall et al</td><td>2013</td><td>U.S</td><td>Educ. Sci</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>665</td><td>Income, education</td><td>59.2</td></tr><tr><td>(15) Ünver et al</td><td>2018</td><td>Europe</td><td>Economics</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>7213</td><td>Income (subjective)</td><td>69</td></tr><tr><td>(16) Pavolini & Van Lancker</td><td>2018</td><td>Europe</td><td>Pol. Sci</td><td>Quantitative</td><td>Observ./Correlational</td><td>Both</td><td>32,643</td><td>Index</td><td>73.8</td></tr><tr><td>(17) Pennerstorfer et al</td><td>2021</td><td>Austria</td><td>Pol. Sci</td><td>Quantitative</td><td>Observ./Correlational</td><td>External</td><td>814,115</td><td>Income, education, house price</td><td>71.4</td></tr></tbody></table>

<figure><img src="image_9.png" /></figure>
<table><caption>Table 2 (continued)</caption><thead><tr><th>Reference</th><th>Date</th><th>Country</th><th>Discipline</th><th>Evid. Type</th><th>Design</th><th>Type of measure</th><th>N</th><th>Operationalization of "underprivileged"</th><th>ROB</th></tr></thead><tbody><tr><td>(18) Petitclerc et al</td><td>2017</td><td>U.S.; Can.; Dutch.; Norway; UK</td><td>Educ. Sci</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>21,437</td><td>Income, education</td><td>66.6</td></tr><tr><td>(19) Rönkä et al</td><td>2019</td><td>Finland</td><td>Psychology</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>302</td><td>Income (subjective), education</td><td>42.8</td></tr><tr><td>(20) Sandstrom & Chaudry</td><td>2012</td><td>U.S</td><td>Educ. Sci</td><td>Qualitative</td><td>Observ./Correlational</td><td>Declarative</td><td>86</td><td>Income, migration background</td><td>71.4</td></tr><tr><td>(21) Sibley et al</td><td>2015</td><td>Norway</td><td>Educ. Sci</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>60,270</td><td>Income, education, and employment status</td><td>64.2</td></tr><tr><td>(22) Skattebol</td><td>2016</td><td>Australia</td><td>Sociology</td><td>Qualitative</td><td>Interviews</td><td>Declarative</td><td>131</td><td>Income</td><td>30.9</td></tr><tr><td>(23) Van Lancker & Ghysels</td><td>2016</td><td>Europe</td><td>Sociology</td><td>Quantitative</td><td>Observ./Correlational</td><td>Both</td><td>22,590</td><td>Education</td><td>76.2</td></tr><tr><td>(24) Vandenbroeck et al</td><td>2012</td><td>Belgium</td><td>Educ. Sci</td><td>Mixed</td><td>Observ./Correlational</td><td>Both</td><td>3789</td><td>Income, ethnicity</td><td>35.4</td></tr><tr><td>(25) Vandenbroeck et al</td><td>2008</td><td>Belgium</td><td>Educ. Sci</td><td>Quantitative</td><td>Observ./Correlational</td><td>Both</td><td>100</td><td>Education</td><td>40.4</td></tr><tr><td>(26) Wolf et al</td><td>2020</td><td>Europe</td><td>Educ. Sci</td><td>Quantitative</td><td>Observ./Correlational</td><td>Declarative</td><td>943</td><td>Education, employment status, and migration background</td><td>38.1</td></tr><tr><td>(27) Zangger & Widmer</td><td>2020</td><td>Switz</td><td>Economics</td><td>Quantitative</td><td>RCT</td><td>Both</td><td>540</td><td>Index</td><td>71.4</td></tr></tbody></table>
## 4.2 Individual study results

Figure 3 presents an overview of these 27 articles, of which six are qualitative and two use mixed methods. This literature is recent, as 89% of the studies were published between 2010 and 2021. Half of these studies were carried out in the U.S. or the UK (7 articles each), as illustrated in Fig. 3. As shown in Table 2, the category “underprivileged population” was mainly operationalized in terms of income (in 18 of 27 articles), educational level (10 articles), employment status (5 articles), migration background (5 articles), or a synthetic index (3 articles). A majority of the articles (16 of 27) rely on observational or correlational data, and two-thirds (18 of 27) rely on self-reported outcomes.

## 4.3 Empirical evaluation of the classification

### 4.3.1 Empirical validation of the measurements

The reproducibility of the assessments was assessed by comparing the scores assigned by the two independent coders. For the impact score, the inter-rater reliability was strong according to Cohen's criteria (correlation coefficient *r*=0.89, IC 95% [0.85; 0.92], t=24.2; *p*<0.001; Evans (1996)). For the risk-of-bias assessment tool, the inter-rater reliability of the QATSDD was also strong (correlation coefficient *r*=0.83, IC 95% [0.65; 0.92]).

### 4.3.2 Empirical classification of the factors

Table 3 presents the factors underlying unequal access to early childcare identified in the selected studies. Three factors that were initially present in our classification ended up not being covered by any existing study (decision heuristics, opportunity costs, and bureaucratic barriers).

Two factors were only supported by qualitative evidence (the role of information and access criteria). One factor that had not been included in the initial classification was added through the review process. Indeed, 29% of the articles identified perceived quality as a relevant factor contributing to access dynamics, making it the third most commonly studied factor in the demand-side category.

### 4.3.3 Empirical evaluation of the cognitive and behavioral factors

#### 4.3.3.1 Impact evaluation
The primary objective of this review was to assess the extent to which existing evidence supports the hypothesis that cognitive and behavioral factors also contribute to unequal access, and if so, to determine the mechanisms by which this occurs. Quantitative analyses reveal an average effect size of 0.22 (Hedge's *g*=0.22; SE=0.06; CI 95% [0.10; 0.35]; *p*=0.005) associated with these cognitive and behavioral factors. Incorporating qualitative evidence through impact scores corroborates the conclusion that demand-side factors significantly contribute to inequalities in early childcare access. Notably, no significant difference was found
<table><caption>**Table 3** Classification of the factors contributing to unequal access to early childcare according to their frequency in the empirical literature, whether they were only covered by qualitative evidence or not, their average impact score, and their risk of bias</caption><thead><tr><th>Factor</th><th>Underlying theoretical question</th><th>% of articles mentioning (ref.)</th><th>Only qualitative?</th><th>Impact score</th><th>Risk of bias</th></tr></thead><tbody><tr><td rowspan="10">Demand-side factors</td><td>Information</td><td>Does all accessible information inform parents' decisions?</td><td>15% (2; 5; 7; 10)</td><td>Yes</td><td>2</td><td>50.5</td></tr><tr><td>Parents' beliefs</td><td>Are parents' beliefs in accordance with early childcare use?</td><td>15% (2; 6; 12; 20)</td><td>No</td><td>1.4</td><td>67.3</td></tr><tr><td>Social norms</td><td>Are parents' norms in accordance with early childcare use?</td><td>33% (2; 5; 6; 8; 11; 16; 22; 23; 26)</td><td>No</td><td>1.3</td><td>65.5</td></tr><tr><td>Trust towards the institutions</td><td>Is parental trust towards institutions sufficient to rely on early childcare?</td><td>22% (2; 5; 6; 7; 12; 20)</td><td>No</td><td>1</td><td>59.3</td></tr><tr><td>Different motivations</td><td>Do parental motivations to use a particular type of care differ from those of the upper classes? (e.g., more informal care available, no employment)</td><td>19% (11; 12; 13; 21; 23)</td><td>No</td><td>1.9</td><td>65.9</td></tr><tr><td>Perceived accessibility</td><td>Do parents perceive early childcare as accessible?</td><td>11% (2; 7; 15)</td><td>No</td><td>1.75</td><td>60.7</td></tr><tr><td>Perceived affordability</td><td>Do parents perceive early childcare as affordable?</td><td>11% (2; 14; 22)</td><td>No</td><td>1.5</td><td>55.1</td></tr><tr><td>Perceived quality</td><td>Is the perceived quality of early childcare sufficient to use it?</td><td>14% (5; 6; 11; 23)</td><td>No</td><td>1.93</td><td>73.7</td></tr><tr><td>Decision heuristics</td><td>Do decision heuristics shape childcare decision-making?</td><td>0%</td><td>No</td><td></td><td></td></tr><tr><td>Resources and skills</td><td>Do parents have the resources and skills to sustain the application process?</td><td>44% (5; 6; 7; 10; 11; 12; 22; 24; 25; 26)</td><td>No</td><td>1.65</td><td>48.7</td></tr><tr><td>Opportunity costs</td><td>Is decision-making driven by the opportunity costs of staying at home?</td><td>0%</td><td>No</td><td></td><td></td></tr></tbody></table>
<figure><img src="image_10.png" /></figure>

<table><caption>**Table 3** (continued)</caption><thead><tr><th>Factor</th><th>Underlying theoretical question</th><th>% of articles mentioning (ref.)</th><th>Only qualitative?</th><th>Impact score</th><th>Risk of bias</th></tr></thead><tbody><tr><td rowspan="5">Supply-side factors</td><td>Proximity</td><td>Is there any early childcare structure at an acceptable distance?</td><td>19% (2; 5; 6; 11; 12; 20; 17; 20; 24; 25)</td><td>No</td><td>1.84</td><td>65.5</td></tr><tr><td>Enough places</td><td>Is the number of places sufficient to cover parents' needs?</td><td>30% (1; 2; 4; 6; 7; 11; 12; 17)</td><td>No</td><td>1.42</td><td>70.5</td></tr><tr><td>Access criteria enabling access</td><td>Do early childcare admission criteria enable access for underprivileged parents?</td><td>1% (9)</td><td>Yes</td><td>1.5</td><td>27.4</td></tr><tr><td>Adequate functioning to parents' life</td><td>Is the functioning of early childcare compatible with parents' lives? (only the impact of opening hours is studied in this category)</td><td>15% (6;11; 19; 20)</td><td>No</td><td>1.6</td><td>67.9</td></tr><tr><td>Direct costs: fees</td><td>Can parents afford the direct costs of early childcare?</td><td>44% (1; 3; 4; 5;7; 11; 13; 20; 22; 23)</td><td>No</td><td>1.29</td><td>64</td></tr><tr><td rowspan="2">Lack of subsidies</td><td>Are there subsidies available to help parents afford the costs of early childcare?</td><td>22% (8; 14; 18; 20; 23; 27)</td><td>No</td><td>2</td><td>69.5</td></tr><tr><td>Bureaucratic barriers</td><td>Are bureaucratic requirements in line with what parents can provide?</td><td>0%</td><td></td><td></td><td></td></tr></tbody></table>

The impact score is a metric that seeks to assess what a piece of evidence concludes about the differential effect of a given factor on access to early childcare for underprivileged populations compared to more well-off populations
<figure><img src="image_11.png" /><figcaption>Fig. 4 Meta-analytic estimates of the impact of parents' resources and skills on access inequalities</figcaption></figure>

in the mean impact score between cognitive and behavioral factors and structural factors ($M_{cog. and behavioral}$ = 1.54, $M_{structural}$ = 1.53; t value = -1.13; $p$ > 0.1; N = 156).<fnref n="9" /> The evidence covering cognitive and behavioral factors is of marginally lower quality than that for structural factors, but this difference is not statistically significant ($M_{QATSDD cog. and behavioral}$ = 58.8; $M_{QATSDD structural}$ = 62.2; t = -0.76; $p$ > 0.1). Consequently, studies employing diverse methodologies consistently indicate that demand-side factors, alongside structural factors, influence access to early childcare for underprivileged populations. An examination of each demand-side factor reported in the empirical literature reveals that all significantly affect early childcare access for disadvantaged families (see Table 3).

### 4.3.3.2 Evidence summary: qualitative synthesis of the literature on demand-side factors
This section summarizes the main findings of the literature review on demand-side factors. More details on each of these factors are available in the Supplementary Materials. Consistent with our classification (Fig. 1), we first present the results on factors that drive families' intention to use early childcare, and then turn to factors that hinder their application.

### 4.3.3.3 Parents' resources and skills, and information barriers
Parents' resources and skills to cope with the search process were the most frequently treated factors, and are the demand-side factor with the highest effect size (Hedge's g = 0.40; SE = 0.11; CI 95% [0.17; 0.64]; $p$ = 0.008), as shown in Fig. 4.

These resources and skills are crucial not only for sustaining the application process but also for acquiring relevant information, which is often critical to success.

<footnote n="9">⁹ The results presented in this section are based on the mean between the coding of coder 1 and the mean of coder 2. The results hold when the analyses are run independently on coder 1 data as well as on coder 2 data (see the supplementary materials for a comparison of the results obtained in each case).</footnote>
<figure><img src="image_12.png" /><figcaption>Fig. 5 Meta-analytic estimates of the impact of perceived quality on access inequalities</figcaption></figure>

Vandenbroeck et al. (2008) show that low-educated parents use internet resources less frequently to obtain information available on early childcare providers. These parents also have fewer social resources to assist them in acquiring relevant information about early childcare (Grace et al. 2014; Holloway 1998; Skattebol 2016).

Qualitative studies addressing informational barriers facilitate the identification of three distinct types of information that socially disadvantaged and immigrant families lack (Binet 2003; Fenech and Skattebol 2019; Greenberg 2016; Holloway 1998). These categories include: information regarding the value of early childcare (Fenech and Skattebol 2019; Holloway 1998), information about the various types of early childcare available (Greenberg 2016), and information on available benefits and subsidies (Binet 2003; Greenberg 2016). Consequently, low-income mothers interviewed by Binet (2004) in Quebec perceive early childcare as financially unaffordable due to a lack of information about available subsidies, which are, in fact, quite affordable in their region. Similarly, low-income families interviewed by Skattebol (2016) are unaware of the precise costs associated with securing a spot in early childcare due to administrative complexities, which engender psychological distress and frustration, thereby impeding their ability to take necessary actions. Therefore, while free access may encourage applications among low-income parents, even in countries where early childcare is universally available, these parents may remain unaware of this benefit (Binet 2003; Greenberg 2016).

Beyond perceived affordability, perceived quality, and accessibility substantially contribute to unequal access (Binet 2003; Greenberg 2016; Lancker and Ghysels 2012). Perceived accessibility was systematically lower for underprivileged parents, even in countries where access is relatively high (Ünver et al. 2018; Zangger and Widmer 2020). In particular, low perceived accessibility due to a lack of places increased reluctance to apply among underprivileged families, as obtaining a spot appeared to be unattainable (Binet 2003; Greenberg 2016). Unemployed mothers also reported the feeling of “stealing the place” from employed ones (Binet 2003). Providers interviewed by Greenberg (2016) assert that “perception can matter more

<figure><img src="image_13.png" /></figure>
than reality. Even if there are open spaces, the perception [of low-income and immi-
grant parents] is that there's not" (p. 17). In a context of limited supply, unemployed
mothers report experiencing a sense of "double illegitimacy" because they do not
work and feel they would be "stealing the place" from employed parents (Binet
2003).

Perceived quality constitutes the second category of factors identified as having a significant impact on access, although the estimates on Fig. 5 are derived from only two studies (Hedge's g = 0.20; SE = 0.05; CI 95% [0.09; 0.32]; *p* = 0.004; see Fig. 5). Three sub-dimensions of particular relevance can be identified: the perceived safety of facilities (Fenech and Skattebol 2019; Grace et al. 2014; Jessen et al. 2020); the perceived cultural sensitivity of the facilities, specifically, the extent to which they are perceived as integrating cultural and religious elements that are essential to parents (Jessen et al. 2020); and perceptions of staff characteristics (mean score: 1.67) (Grace et al. 2014; Jessen et al. 2020).

**4.3.3.4 Cognitive barriers** Furthermore, early childcare may be perceived as a lower priority by parents, not due to a lack of interest, but rather because economic and social hurdles deplete their mental resources available for addressing these issues (Binet 2003; Skattebol 2016). Additionally, family stability is regarded by the mothers interviewed by Skattebol (2016) as a prerequisite to engaging in early childcare participation.

**4.3.3.5 Factors hindering applications** Finally, underprivileged families face significant hurdles or "friction costs" during the application process. Sometimes, these families may withdraw from the application process due to such friction costs, like difficulties related to completing paperwork (Grace et al. 2014; Greenberg 2016; Jessen et al. 2020; Wolf et al. 2020). When they do not withdraw, parents often tend to apply later and to fewer early childcare options (Vandenbroeck et al. 2008, 2014). Fenech examines how inclusive childcare providers must adapt to these challenges. One interviewed provider explains: "I don't introduce paperwork at all to them until they're comfortable. Then I go through it with them. You can't just go 'here's your paperwork, go and do it and bring it back to me.' You'll never see them again. So, it's a lot of huge support, like huge. [...] I've got photos of my family around, and it gives them that ability to relax a bit and know that I'm not here to judge who they are and what their family circumstances are."

**4.4 Heterogeneity of the cognitive and behavioral factors across social groups**

Having evaluated whether and how cognitive and behavioral factors constrain access for underprivileged groups, we then looked at how they may vary across underprivileged populations (i.e., immigrant families, low-income, low-educated, and unemployed families).

Immigrant families experience higher friction costs. For instance, difficulties with administrative procedures and linguistic barriers were more prevalent among
<table><caption>**Table 4** Heterogeneity of the cognitive and behavioral factors found in the literature across different types of underprivileged populations (i.e., whether they are defined in terms of migration background or socioeconomic background), +: yes/none: no difference found / -: less prevalent among immigrant families</caption><thead><tr><th>Factors</th><th>Reported items</th><th>Stronger (+) or lower (−) for immigrant populations than for other underprivileged groups?</th><th>Impact score (2)</th></tr></thead><tbody><tr><td>Factors influencing the intention to apply</td><td></td><td></td><td></td></tr><tr><td>Lack of information</td><td>Benefits of early childcare (5) (10)<br>Available early childcare structures (8) (10)<br>Existing subsidies and help (2)<br>Application subtleties (e.g., timeline, deadlines) (2) (22) (24) (25)<br>Lower motivation because at least one of the parents is unemployed (2) (11) (12) (13) (14) (15) (21) (23) (26)<br>Lower motivation because of more opportunities for informal care (10) (11)<br>"A child who is not yet old enough to go to school must stay with her parents" (2) (11)</td><td>+</td><td>0.33</td></tr><tr><td>Beliefs and norms</td><td>Lack of familiarity with early childcare in parents' culture (holding the belief about positive effects of early childcare on child development is a facilitator) (12)<br>Prefer informal childcare (11) (20)<br>Lack of legitimacy, feeling of « stealing a place » for working parents (2)<br>Lower trust level in early childcare staff to take care of one's child (5) (12) (20)</td><td>2</td><td>0.875</td></tr><tr><td>Low perceived quality</td><td>Lower trust level in state institutions (2) (7)<br>Perceived security of early childcare (5) (6) (11) (14)<br>Perceived cultural sensitivity of early childcare (11)<br>Insufficient perceived staff quality (e.g., training, involvement) in early childcare (6) (22) (23)</td><td>+</td><td>0.5<br>2<br>1.67<br>1.17</td></tr></tbody></table>

<figure><img src="image_14.png" /></figure>
<table><caption>**Table 4 (continued)**</caption><thead><tr><th>Factors</th><th>Reported items</th><th>Stronger (+) or lower (−) for immigrant populations than for other underprivileged groups?</th><th>Impact score (2)</th></tr></thead><tbody><tr><td>Factors hindering application</td><td></td><td></td><td></td></tr><tr><td>Friction costs</td><td>Friction costs due to reduced administrative proficiency and knowledge of the system (2) 5) (6, 7) (11) (22)<br>Friction costs due to reduced access to the media required to gather information and apply (5) (11) (24)<br>Friction costs due to linguistic barriers (5) (7) (12) (13) (26)</td><td>+</td><td>1.4<br>2<br>1.33</td></tr></tbody></table>
migrant populations than they were among native low-educated and low-income families (Greenberg 2016; Johnson et al. 2017; Kahn and Greenberg 2010). The importance of trust also seemed more relevant to explain lower access of immigrant families, while its significance appeared limited among native low-SES families (Grace et al. 2014; Greenberg 2016; Johnson et al. 2017). The native mothers interviewed by Binet (2003) in Canada perceived early childcare as an educational service promoting their children's development and complementing what their children could experience at home. Unfortunately, the migration background was too small and diverse to differentiate the analyses by country of origin, although Abrassart & Bonoli (2015) suggest that it may be an important distinction to make.

Conversely, the lack of traditional motivations to use early childcare, such as having more informal care options (e.g., unemployed relatives) competing with childcare facilities, is a factor that matters more for low-educated and low-income populations, and less for immigrant families that can less often rely on informal networks of support (Holloway 1998; Johnson et al. 2017). Table 4 summarizes how the importance of cognitive and behavioral factors found in the pool of articles varies across sub-groups.

## 4.5 Robustness checks

### 4.5.1 Publication bias

On the one hand, p-curve analyses concluded that the positive results are not due to chance alone or p-hacking, but reflect an actual effect. Specifically, 98% of the replications of the p-curve analyses validated the relevance of the effects of cognitive and behavioral factors. On the other hand, the funnel plot, based on the available effect sizes in Fig. 6, exhibits a certain asymmetry. However, these results are difficult to interpret due, for instance, to the considerable heterogeneity of the data and the fact that differences in standardized means have sometimes been used to calculate effect sizes, which distort the funnel plot towards the upper regions (Zwetslot et al., 2017).

<figure><img src="image_15.png" /><figcaption>Fig. 6 Funnel plot of effect sizes reported in quantitative studies</figcaption></figure>

<figure><img src="image_16.png" /></figure>
### 4.5.2 Data type

To test the sensitivity of the results to the coding process, the analyses presented in the article were reproduced independently on (1) coder 1's data, (2) coder 2's data, (3) an aggregated measure constructed from both, and (4) while excluding qualitative evidence. The results were very similar to the ones displayed above. The detailed results and additional figures are reported in the supplementary materials.

### 4.5.3 Impact score scale

Finally, we tested whether the results are sensitive to our coding scale of the impact score. Therefore, we reproduced the analyses using a coding scale that only ranged from -1 to +1 instead of -2 to +2. Again, the results were very similar to the ones displayed above (see supplementary materials).

## 5 Discussion and conclusion

Early childcare is important for promoting child development and alleviating poverty. Yet, we still know little about the barriers that hinder early childcare access for underprivileged families. In this paper, we draw on research from behavioral economics and other behavioral sciences to discuss how cognitive and behavioral factors affect unequal access to early childcare and reviewed the small but emergent literature that has developed in the last decade to fill this gap (Shafir 2013; Tversky and Kahneman 1974).

We found that cognitive and behavioral factors play a significant role in unequal access to early childcare. These findings underscore the importance of using behavioral diagnoses to address non-take-up and leveraging the full range of behavioral interventions to tackle this issue, from redesigning choice architecture to enhancing information and providing training and education, as proposed in the literature on behavioral public policy (Viale 2022).

### 5.1 Informational barriers

First, parents' informational resources and skills to navigate the enrollment process are crucial. Our study highlights that underprivileged parents often lack access to adequate information and social support, leading to late and limited applications. Interventions that enhance the salience of information—such as proactive information campaigns—are key to addressing these barriers (Archambault et al. 2019; Bennett 2012; Sandstrom et al. 2022). As identified through the inclusion of qualitative studies, these campaigns must provide clear and comprehensive details about the functioning of childcare services, their costs, eligibility criteria, quality, and, importantly, the timing and procedures for applications. Research shows that even in countries with universal childcare, underprivileged parents remain unaware of free or subsidized (Binet 2003; Grace et al. 2014). Only a few studies, all published after
the completion of the systematic search, have explicitly targeted these informational barriers, but they demonstrate the potential for significant improvements (Carbuccia et al. 2024; Hermes et al. 2021; Weixler et al. 2020).

## 5.2 Behavioral barriers

However, Carbuccia et al. (2024) demonstrate that information provision is insufficient to tackle access inequalities, which corroborates recent evidence that a rational choice perspective alone cannot explain access disparities (Pietropoli and Triventi 2023; Steinberg and Kleinert 2022). Behavioral and administrative barriers often prevent families from following through on decisions, even when they are well-informed. Boosting interventions that enhance parents’ practical skills, such as providing administrative support during the application process, can complement structural policies and play a crucial role in empowering families. The study by Hermes et al. (2021) in Germany is a compelling example of how a behavioral approach can effectively reduce barriers to access. Similarly to Carbuccia et al. (2024) in the French context, they found that combining informational support with administrative assistance closes the socioeconomic gap in application rates. Unlike Carbuccia et al. (2024) for the French case, this increase in application translated into closing half of the access gap. In both studies, these effects were not observed in more affluent families, highlighting the unique challenges faced by underprivileged populations and the relevance of behavioral interventions for equalizing access. Type-2 nudges, which engage reflective thinking, have been successful in other domains of parenting (Mayer et al. 2019). Interventions such as goal setting, reminders, and personalized notifications can thus help parents take action once they have made a decision. These tools are especially important in reducing procrastination and cognitive overload during the application process, especially in situations where there is a lot of administrative friction (Bettinger et al. 2012; Herbaut and Geven 2019).

## 5.3 Addressing heterogeneity

Our review also revealed significant heterogeneity in how cognitive and behavioral factors affect different underprivileged populations. While multilevel meta-regression enables the quantitative analysis of such heterogeneity, the number of studies and estimates in our sample did not permit such analyses. Instead, qualitative evidence was crucial in promoting human-centered design of public policy and uncovering heterogeneities. For example, immigrant families face specific challenges such as friction costs from language barriers and lower institutional trust. In contrast, low-educated and low-income families are more likely to have access to informal care or to be unemployed, which may divert them from using early childcare. These differences suggest the need for tailored interventions, such as providing targeted information and language support for migrant populations (Fenech and Skattebol 2019).

<figure><img src="image_17.png" /></figure>
## 5.4 A behavioral eye's view of structural policies

Ultimately, this review highlights that structural policies must be (re)designed with a behavioral lens. For instance, Van Lancker & Ghysels (2016) found that demand-side financial subsidies (e.g., tax deductions or ex-post allowances) are far less effective than supply-side subsidies (e.g., direct payments to childcare providers). Demand-side subsidies introduce additional cognitive and financial burdens, as parents must apply for them and pay fees upfront (Christensen et al. 2020; Herd and Moynihan 2019). Behavioral insights suggest that simplifying the application and payment processes could alleviate these burdens and improve take-up rates among underprivileged families. Conducting *sludge audits*—as suggested by Sunstein (2019)—could further identify areas where unnecessary frictions complicate access to childcare services.

Our study also revealed important limitations of existing evidence, which will need to be addressed in future work. First, all studies used income or employment status to categorize families as “underprivileged”. However, both measures raise concerns about reverse causality, as access to early childcare can impact income and employment status (Abrassart and Bonoli 2015; Jessen et al. 2020). Measures such as parental education or migration status are therefore better when longitudinal data cannot be collected. This is especially crucial because, with one exception, all quantitative studies are correlational in nature. Furthermore, all reviewed studies are retrospective, meaning that they were conducted after childcare choices had been made. This is problematic because access to early childcare may modify parents’ beliefs (Chaudry et al. 2010; Pungello and Kurtz-Costes 2000). Overall, future research should rely more on causal designs and on longitudinal and administrative data to reach stronger conclusions. Finally, only a small fraction of countries were represented in this review, which reduces its external validity. As the respective weight of factors can vary considerably across institutional contexts, replicating studies across various institutional contexts would allow for exploring how the relative weight of different factors varies cross-nationally (Van Lancker 2018; Van Lancker and Ghysels 2016). For instance, Ünver et al. (2018) highlighted strong variations in perceived accessibility according to the institutional characteristics of various European countries. Introducing for-profit early childcare facilities tends to decrease perceived and actual accessibility for underprivileged families (Pennerstorfer and Pennerstorfer, 2021; Ünver et al. 2018). Accessibility is higher for underprivileged families in countries where having a place is listed as an opposable right (Ünver et al. 2018; Van Lancker 2018; Van Lancker and Ghysels 2016). In terms of funding, perceived affordability for underprivileged families increases as the amount of state spending per child increases (Ünver et al. 2018; Van Lancker 2018).

This study has some limitations. While we performed reliability tests on the search algorithm and extended the scope of the systematic search as much as possible, we could not exclude potential bias introduced by the languages in which the searches were performed (French and English), by the databases chosen, or by the keywords used during the systematic search. Additionally, our geographic focus on Western countries, while methodologically justified to ensure contextual coherence, limits the generalizability of our findings (e.g., Achi et al. 2025). Finally, our
temporal search boundaries, which followed rigorous PRISMA guidelines, meant that we could not include emerging research published after the end of the systematic search. We have incorporated these developments into our broader discussion to contextualize our findings within the recent literature.

Finally, our study also revealed significant research gaps. First, the impact of decision heuristics and opportunity costs on households’ decision-making has not been studied, despite their potentially crucial role, and warrants investigation in future research. Second, accessing early childcare represents only the first step, as the potential positive effects for underprivileged families depend on sustained enrollment. However, these households are also at a higher risk of chronic absences and dropouts. Although we aimed to examine factors affecting parents’ persistence, such as parental satisfaction and sense of belonging, insufficient evidence hindered our analysis (Binet 2003; Fenech and Skattebol 2019; Greenberg 2016). This area warrants further exploration in future studies. Indeed, behavioral interventions are valuable not only for facilitating initial access but also for promoting sustained engagement with early childcare services. For example, in the context of preschool, Kalil et al. (2021) found that a behavioral text messaging intervention reduced chronic absenteeism by 20% and led to a 2.5-day increase in attendance over the treatment period. Lastly, this review highlights the significance of early childcare quality for underprivileged families, often surpassing direct costs, while information about quality is frequently private and difficult to access (Grace et al. 2014; Vandenbroeck et al. 2008). Behavioral interventions can help families select higher-quality options. For instance, the study by Dechausay and Anzelone (2016) demonstrates how targeted behavioral strategies can improve decision-making outcomes for families navigating complex systems.

Furthermore, while we advocate for System 2 nudges throughout the decision-making and application processes due to their overall higher acceptability (Sunstein 2016), we must also consider the cognitive bandwidth constraints faced by underprivileged populations during the early years. In some cases, offering too many choices may overwhelm families and reduce the effectiveness of decision-making interventions. Therefore, policymakers must strike a balance between providing choices to the underprivileged and simplifying the decision-making process, while preserving their preferences. Moreover, the persistent shortage of available childcare spots presents an additional challenge to the acceptability of interventions. If the supply of childcare remains fixed, policies aimed at reducing access inequalities may be perceived as competing with the role of early childcare in supporting the work-life balance of more affluent mothers (Hermes et al. 2024).

In conclusion, this systematic review highlights the importance of insights from behavioral sciences in addressing non-take-up dynamics, emphasizing that families’ cognitive bandwidth and decision heuristics should be central to policy design. Behavioral insights should not only be seen as complementary to existing structural policies but as integral to reshaping both the decision-making process and the structures that govern access to early childcare. By addressing the full spectrum of barriers, we can create more equitable and efficient systems that support underprivileged families in accessing and persisting with high-quality childcare services and reduce inequalities.
# 6 Data availability statement

The data that support the findings of this study are openly available in OSF at https://osf.io/68d2b/?view_only=26461ad8b4964e7c84b815486a53b5e4

# 7 Competing interests

The authors declare no competing interests.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s11299-025-00331-2.

**Acknowledgements** We acknowledge the support of the EUR FrontCog grant (ANR-17-EURE-0017) as well as the funding from the Centre de Recherche sur les Inégalités Sociales (CRIS) at Sciences Po for this research. We are grateful to Lara Narbona Sabaté for her help in reproducing the coding process and to Yannick Savina for his methodological support. CC acknowledges the support of ANR (ANR-21-CE28-0009).

**Author contributions** L.C., C.C., and C.B. contributed equally to the study design and conceptualization work. C.C. and L.C. contributed to the funding acquisition. L.C. was in charge of the creation of the theoretical classification, the review design, data collection, data analysis, and validation analysis and wrote the original draft under the supervision of C.C. and C.B..V.T. contributed to the review design and wrote the first script of the p-curve analyses. C.B. and C.C. reviewed and edited the manuscript.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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