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Research Article

The Reading Gap in the Dominican Republic: Education System or Socioeconomic Vulnerability?

[version 1; peer review: awaiting peer review]
Daniel Cubilla-Bonnetier
https://orcid.org/0000-0003-0702-8981
1-3, Hugo Marte-Santana1, Jesús Andújar-Avilés4, Betty Reyes-Ramírez
https://orcid.org/0000-0002-4818-8941
4, Laura V. Sánchez-Vincitore
https://orcid.org/0000-0002-6343-1217
1
Daniel Cubilla-Bonnetier
https://orcid.org/0000-0003-0702-8981
1-3, Hugo Marte-Santana1, [...] Jesús Andújar-Avilés4, Betty Reyes-Ramírez
https://orcid.org/0000-0002-4818-8941
4, Laura V. Sánchez-Vincitore
https://orcid.org/0000-0002-6343-1217
1
PUBLISHED 22 May 2026
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS AWAITING PEER REVIEW

This article is included in the Developmental Psychology and Cognition gateway.

Abstract

Background

The Dominican Republic consistently ranks last among Latin American countries in international reading assessments, yet the relative contributions of socioeconomic conditions versus school system quality remain unclear. This study examined reading performance differences between public and private school students and explored the influence of socioeconomic factors on these outcomes.

Methods

A total of 1,168 students from second grade to the beginning of secondary school were assessed using standardized instruments evaluating psycholinguistic precursors of reading (phonological awareness, rapid automatized naming, short-term verbal memory, and alphabetic knowledge), oral language skills (vocabulary, grammatical comprehension, and oral comprehension), and reading performance (fluency and comprehension). Group differences were analyzed using t-tests and Welch’s t-tests; socioeconomic predictors were identified via linear regression; and ANCOVA was used to estimate system-level effects after controlling for socioeconomic status (SES).

Results

Significant performance differences favoring private school students were found across nearly all variables, with medium to large effect sizes, particularly in reading fluency. Family income was the most consistent predictor of reading fluency, vocabulary, and comprehension. Controlling for SES substantially reduced or eliminated most differences between systems, especially in comprehension and linguistic skills, suggesting that SES accounts for a substantial portion of the variance associated with the educational gap. However, medium effect sizes persisted in decoding fluency, indicating a possible residual impact of the educational system. At the transition between 2nd and 3rd grade, 40% of public-school students lacked functional reading ability.

Conclusions

Socioeconomic conditions account for much of the reading gap between public and private school students in the Dominican Republic, though residual system-level effects persist in decoding fluency. These findings highlight the urgent need to universalize pre-primary education, strengthen early reading instruction, and implement tiered support systems such as Response to Intervention in public schools.

Keywords

Reading Performance, Socioeconomic Status, Literacy Development, Achievement Gap, School Systems, Educational Policy, Dominican Republic, Simple View of Reading

Corresponding author: Laura V. Sánchez-Vincitore
Competing interests: No competing interests were disclosed.
Grant information: This study was funded by the Dominican Institute for the Evaluation and Research of Educational Quality (Instituto Dominicano de Evaluación e Investigación de la Calidad Educativa, IDEICE) of the Dominican Republic. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:  © 2026 Cubilla-Bonnetier D et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Cubilla-Bonnetier D, Marte-Santana H, Andújar-Avilés J et al. The Reading Gap in the Dominican Republic: Education System or Socioeconomic Vulnerability? [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:784 (https://doi.org/10.12688/f1000research.181492.1) First published: 22 May 2026, 15:784 (https://doi.org/10.12688/f1000research.181492.1) Latest published: 22 May 2026, 15:784 (https://doi.org/10.12688/f1000research.181492.1)

Introduction

Reading is one of the core learning outcomes of primary education and constitutes an essential cross-cutting instrument for accessing knowledge throughout schooling. Poor reading proficiency limits academic performance and increases the risk of dropout at higher levels (Hernández, 2011). In Spanish-speaking and Latin American contexts, evidence indicates that early lexical and decoding skills are robust predictors of reading comprehension and later academic success (Mancilla-Martínez & Lesaux, 2010; Moncada Nahuelquín et al., 2025). These findings underscore the need to focus educational efforts not only on schooling itself but also on the quality of early reading development.

Although the most relevant variable of reading performance is reading comprehension, it is also important to understand the student’s level of reading fluency, as this is related to the speed with which reading tasks can be performed and the amount of cognitive resources they require (Kahneman, 1973), and therefore may affect both academic performance and reading comprehension itself. Moreover, it is essential to assess the status of the variables that enable the development of reading comprehension. In this regard, the Simple View of Reading model (Gough & Tunmer, 1986; Hoover & Gough, 1990) posits that reading comprehension results from multiplying an individual’s oral comprehension ability by their decoding fluency, highlighting the relevance of assessing these two predictive variables. Furthermore, word decoding can be carried out through two distinct yet complementary routes: the lexical, or direct, route—where the word is identified as a whole image—and the phonological, or indirect, route—which requires segmenting the word, managing grapheme-phoneme conversions, and reconstructing the word (Coltheart et al., 2001; Coltheart & Rastle, 1994).

Within the framework of this model, several studies have identified psycholinguistic abilities that influence and even predict decoding learning in its early stages, such as phonological awareness (PA), which is more closely associated with decoding accuracy at the onset of reading acquisition (Míguez-Álvarez et al., 2022; National Reading Panel, 2000). Phonological awareness refers to the metalinguistic ability to recognize and manipulate the sounds of a language (Bradley & Bryant, 1983). In contexts of low reading performance, PA has been shown to be consistently related to reading achievement, even up to the fourth grade (Sánchez-Vincitore et al. (2022); Cubilla-Bonnetier & Sánchez-Vincitore, 2025; Cubilla-Bonnetier et al, 2026), whereas in other contexts, its influence appears to be limited to preschool and the early years of primary education (Hogan et al., 2005). Other variables frequently mentioned include rapid automatized naming (RAN), typically associated with reading speed (Araújo et al., 2015; Lervåg & Hulme, 2009), and short-term verbal memory (Peng et al., 2018), although the influence of the latter is more controversial (Melby-Lervåg & Hulme, 2013), particularly in transparent orthographies such as Spanish (Caravolas et al., 2012). The other component of the Simple View of Reading model—the oral language comprehension factor—is associated with variables such as vocabulary and the understanding of grammatical structures (Ripoll-Salceda, 2010). Therefore, it seems relevant to assess not only reading performance variables but also those related to future reading acquisition.

In addition to psycholinguistic factors, socioeconomic status (SES) affects the quality of school learning through multiple pathways. This construct—understood as a measure of a family’s access to economic, cultural, and educational resources (Tan et al., 2024)—influences fundamental aspects of cognitive development, such as the quality of nutrition (Gómez et al., 2021), which in turn impacts academic achievement (Wang et al., 2021; Zerga et al., 2022) and, in particular, reading performance. Nutritional improvement programs for children can have a significant impact on reading comprehension levels in adulthood (Maluccio et al., 2009). Moreover, most of the mediation of the effect of socioeconomic status on academic performance appears to occur through its impact on components of executive functions, such as working memory, inhibition, cognitive flexibility, and processing speed (Ding et al., 2024; Mooney et al., 2024).

However, SES also influences reading development through factors specifically related to the acquisition of this skill. First, SES affects the home literacy environment (comprising variables such as book availability, the frequency of shared reading sessions, and literacy beliefs), which is directly associated with the quality of reading acquisition (Dong et al., 2020; Niklas & Schneider, 2017). Moreover, SES impacts the quality of the family linguistic environment (Dailey & Bergelson, 2022), which constitutes the main pre-literacy linguistic input the child receives. Thus, in addition to affecting the student’s receptive vocabulary and oral comprehension, SES also influences the development of the psycholinguistic precursors of reading described earlier—such as phonological awareness, the primary mediator of the relationship between SES and reading development (Villa et al., 2025)—as well as cognitive flexibility and working memory. The existence of an SES impact on neurocognitive mechanisms associated with reading, such as rapid automatized naming (RAN), is more controversial (Carioti et al., 2022; Denckla & Rudel, 1974; Romeo et al., 2018), which aligns with the possibility that RAN has a more stable and hereditary nature (Andreola et al., 2021).

Another factor that influences students’ reading development is the educational system in which they learn. In most countries around the world, there are general academic performance differences—and specifically in reading—between students in public and private schools, generally favoring the latter in secondary education (Cheema, 2024). Evidence is more abundant at the secondary level, largely due to the use of PISA assessments, although similar trends are observed in primary education, with the exception of the state of Uttar Pradesh in India, where reading performance is higher in the public sector (Pal, 2025). In Latin America, all countries show better reading performance in private schools (Duarte et al., 2010). After reanalyzing the results presented by these authors, we can estimate the gross gap in reading comprehension (without controlling for individual student variables) across the 14 studied countries at d = .66 in third grade and d = .58 in sixth grade—both medium-sized effects. In Panama, the gross difference reported in reading comprehension between the two systems is d = .32 (small), although for other reading performance variables such as accuracy and speed in text reading, the effects are large (d = .93 and d = .85, respectively, in fourth grade, converting r to d from Cubilla-Bonnetier et al., n.d.).

Evidence shows that a significant portion of the performance differences between students in public and private education systems can be attributed to the socioeconomic context of students’ families. When these factors are controlled in studies examining reading performance differences at the primary level, the gaps decrease—and when additional school-related variables are also controlled (such as the school’s socioeconomic level or exposure to violence), the differences even disappear in some cases (Duarte et al., 2010), although the specific reductions in effect size are not reported. At the secondary level, when socioeconomic differences between students are controlled, the Cohen’s d value decreases from .81 to .51 in Peru, from .60 to .20 in Mexico, from .90 to .46 in Argentina, from.74 to .29 in Colombia, and from 1.10 to.51 in Costa Rica (converting partial η2 to d from Cheema, 2024).

The Dominican Republic exhibits a structurally low level of reading development, as repeatedly demonstrated by results from international assessments. In the 2022 PISA test, despite a partial recovery following a decline between 2015 and 2018, the country ranked last among the 14 evaluated Latin American and Caribbean nations, with only 25% of 15-year-old students reaching level 2 of reading proficiency, compared to the OECD average of 74% (OECD, 2024). At the primary level, the 2019 ERCE assessment concluded that 73% of third-grade and 84% of sixth-grade students did not reach the minimum level of reading proficiency (UNESCO, 2025).

On the other hand, although the Dominican Republic is not among the countries with the highest global socioeconomic inequality—with a Gini index of 39 in 2024 (World Bank, 2024)—inequality has been increasing since 2022, particularly in urban areas (Oficina Nacional de Estadística, 2024). Moreover, the concentration of 30.5% of the nation’s wealth within the richest 1% is among the highest in the region (Alvaredo et al., 2022). The Palma ratio, which compares the income of the richest 10% to that of the poorest 40% of the population, has also risen, reaching 1.64 (Oficina Nacional de Estadística, 2024).

The purpose of this study was threefold: 1) To evaluate the difference in reading performance between both educational systems, calculating the size of the educational gap in reading; 2) To study the impact of socioeconomic variables on reading performance; and 3) To estimate the impact of the educational system on reading performance, controlling for socioeconomic index.

Methods

Participants

A total of 1,168 participants were evaluated, distributed across three grade-level groups: students at the end of 2nd grade and the beginning of 3rd grade (n = 380), at the end of 4th grade and the beginning of 5th grade (n = 408), and at the end of 6th grade and the beginning of 1st year of secondary education (n = 380). Twelve public and fifteen private schools from the metropolitan area of Santo Domingo were randomly selected for participation (see Table 1 for sample stratification). The study was approved by the Research Ethics Committee of Universidad Iberoamericana (code CEI2024–24). After the legal guardians of the participants signed the informed consent form and the participants’ assent was verified, their performance was assessed in two sessions of approximately 45 minutes each—the first including tasks of psycholinguistic precursors of reading, and the second assessing reading performance—by personnel specifically trained by the research team.

Table 1. Sample distribution by educational system, grade, and gender.

Gender2nd-3rd4th–5th6th-1stTotal
PublicPrivatePublicPrivatePublic Private
Girls 11510111710694106639
Boys 8183968910080529
Total 1961842131951941861168
380 408 380

Variables and instruments

Psycholinguistic precursors of reading

Phonological Awareness (PA): Assessed through all phonological awareness subtests of the PROLEXIA battery (Cuetos et al., 2020), including the 3 subtests for children under 7 years of age and the 5 for those over 7. The total score across the 8 tasks was retained. The ordinal alpha coefficients of the subtests range from.76 to.94, according to the authors.

Rapid Automatized Naming (RAN): Tests for naming colors, objects, numbers, and letters were constructed strictly following the gold standard procedures of Denckla & Rudel (1974).

Short-Term Verbal Memory (STVM): Operationalized through a classical digit-span test, using the Auditory Sequential Memory subtest from the Illinois Test of Psycholinguistic Abilities (ITPA) by Kirk et al. (2004). Cronbach’s alpha for the age range in the present study is between.85 and.87.

Alphabetical Knowledge (AK): The Letter Name or Sound subtest from the PROLEC-R test (Cuetos et al., 2014) was used, retaining the accuracy measure (percentage of correctly named letters).

Variables related to oral language

Vocabulary (VOC): To obtain a measure of receptive (comprehension) vocabulary, the PEABODY test (Dunn et al., 2006) was used, retaining the total score. For the age range included in the study, the test manual reports Cronbach’s alpha values between.90 and.94.

Grammatical Structure Comprehension (GSC): The CEG test (Mendoza et al., 2005) was used, with a Cronbach’s alpha of.91; the total score was retained as the measure.

Oral Comprehension (OC): The oral comprehension subtest from the PROLEC-R test (Cuetos et al., 2014) was employed.

Reading performance variables

Syllable Reading Fluency: Based on the scores obtained in the Syllable Reading subtest of the ECLEC test (Cubilla-Bonnetier & Sánchez-Vincitore, 2023), the number of syllables correctly read per minute was calculated. For the age range of this study, the subtest shows Cronbach’s alpha values between.88 and.97.

Word Reading Fluency (WR) and Pseudoword Reading Fluency (PWR): The Word Reading and Pseudoword Reading subtests from the PROLEC-R test (Cuetos et al., 2014) were used, respectively, with a derived calculation of correctly read words and pseudowords per minute. The overall PROLEC-R test has a Cronbach’s alpha of.79.

Text Reading Fluency (TR): Using the Text Comprehension subtest of the PROLEC-R, reading errors from the first three texts (first two for grades 2–3) were recorded, and the average number of correctly read words per minute was calculated. This measure is expected to be higher than WR, given the presence of functional words that children read automatically within a literary text.

Reading Comprehension: Percentage of correct answers to questions about the first two texts (for grades 2–3) or the first three texts (for grades 4–5 and 6–1st year of secondary) from the Text Comprehension subtest of the PROLEC-R.

All socioeconomic variables were collected through a family questionnaire previously used in the country, with a McDonald’s Ω of.79 (Cubilla-Bonnetier & Sánchez-Vincitore, 2025): Family income, Mother’s educational level, Father’s educational level, Internet connection at home, Number of children’s books at home, Father living with child at home, Number of people at home/number of adults ratio, and Years of pre-primary education (the different possible values of these variables are shown in Table 2).

Table 2. Frequency distribution of socioeconomic variables, by educational system.

VariableLevelPublicPrivate Hypothesis test
Family income 0–19,000 DP145.4%9.4%χ2(4, N = 1168) = 395.46, p < .001
19,000–28,000 DP27.4%12.4%
28,000–38,000 DP15.4%14.2%
38,000–68,000 DP7.6%22.5%
>68,000 DP4.1%41.6%
Mother’s educational level Incomplete primary ed.6.0%1.6%χ2(4, N = 1168) = 263.57, p < .001
Primary education8.6%0.7%
Secondary education62.5%30.6%
Bachelor’s degree21.6%49.9%
Postgraduate degree1.3%17.2%
Father’s educational level Incomplete primary ed.10.4%2.7%χ2(4, N = 1168) = 259.58, p < .001
Primary education16.3%3.4%
Secondary education59.4%37.5%
Bachelor’s degree12.3%43.5%
Postgraduate degree1.7%12.9%
Internet connection No21.4%3.4%χ2(1, N = 1168) = 85.69, p < .001
Yes78.6%96.6%
Number of children’s books at home 0–560.5%25.8%χ2(3, N = 1168) = 184.45, p < .001
6–1029.5%36.3%
11–207.5%22.7%
>202.5%15.2%
Father living with child Yes45.9%32.9%χ2(1, N = 1168) = 20.65, p < .001
No54.1%67.1%
Number of people/adults ratio Mean2.572.22t(1165) = 6.726, p < .001, d = .39
(SD)(1.02)(0.72)
Years of Pre-primary Mean1.602.25t(1166) = −7.427, p < .001, d = −.44
(SD)(1.52)(1.46)

1 DP: Dominican Pesos

Statistical analysis

To describe the differences between public and private school students in the characterization of various socioeconomic variables, the Chi-square test was used when variables were ordinal, analyzing frequency distributions with proportions, and the Student’s t-test was applied when variables were continuous, described by mean and standard deviation.

To achieve the objective of describing performance differences between public and private school systems across the various psycholinguistic, linguistic, and reading performance variables, since the grouping variable had only two categories, either the Student’s t-test or Welch’s t-test was applied as appropriate (the latter when the assumption of equal variances was violated according to the Brown-Forsythe test). The use of one test or the other can be identified by the presence of corrected degrees of freedom.

To achieve the second objective—determining which socioeconomic variables influence performance in reading fluency, vocabulary, and reading comprehension variables, and to what extent—linear regressions were employed. To verify whether belonging to specific socioeconomic groups is associated with a higher risk of low reading performance, the relative risk tool was used, calculated as: Risk of the group exposed to a condition/Risk of the rest of the sample.

Finally, to calculate the size of the educational gap in reading while controlling for the effect of socioeconomic variables, ANCOVAs were conducted (with each of the studied variables as the dependent variable, the educational system as the fixed factor, and the socioeconomic variables as covariates). All analyses were performed using SPSS (v.27.0.1.0) and JASP (v.0.18.3). This study was not preregistered.

Results

Socioeconomic characterization of the sample

As a preliminary step, students from public and private school systems were characterized according to the different socioeconomic variables, verifying the existence of differences between both groups. As shown in Table 2, significant differences were found in all variables related to the socioeconomic factor, both in the proportional differences of ordinal variables (family income, mother’s and father’s educational level, internet connection at home, number of children’s books at home, and father living with the child) and in the mean differences of continuous variables (number of people/adults ratio at home and years of pre-primary education). In all cases, public school students showed lower levels of well-being across the different variables.

Objective 1: Evaluation of the difference in reading performance between both systems (Size of the educational gap in reading).

For each of the variables representing precursor abilities of reading—whether those related to decoding (phonological awareness, RAN, short-term verbal memory, alphabetic knowledge) or purely linguistic variables (vocabulary, grammatical structure comprehension, or oral comprehension)—as well as for the reading performance variables (fluency in reading syllables, words, pseudowords, and texts, and reading comprehension), differences between public and private school students were examined. As shown in Table 3, in the psycholinguistic abilities that serve as precursors to reading (PA, RAN, and STVM), private school students show better performance (it should be noted that RAN variables are measured as total execution time, so higher values indicate poorer performance), with differences often of medium size (except in some grades for RAN abilities less associated with academic stimulation, such as color or object naming). In vocabulary, the advantage of private school students remains of medium size throughout primary school, approaching a large effect size in 4th grade.

Table 3. Descriptive statistics and effect sizes for differences in performance on psycholinguistic precursors of reading and reading performance variables between public and private school students, by grade.

Variable2°-3°4°-5°6°-1°S
Public x̄ (SD) n = 196Private x̄ (SD) n = 184 Test d Public x̄ (SD) n = 213Private x̄ (SD) n = 195 Test d Public x̄ (SD) n = 194Private x̄ (SD) n = 186 Test d
PA 40.96 (16.07)52.17 (13.80)t(375.098) = 7.571; p < .001.78 55.40 (14.82)63.55 (11.62)t(396.838) = 6.204; p < .001.61 58.28 (13.94)65.45 (11.09)t(365.707) = 5.559; p < .001.57
RAN C 61.16 (21.95)56.84 (33.30)n.s.n.s.50.08 (16.07)42.89 (8.99)t(338.584) = −5.64; p < .001−.55 42.89 (11.23)40.29 (24.93)n.s.n.s.
RAN O 73.70 (20.08)71.00 (19.27)n.s.n.s.61.13 (19.79)57.99 (13.58)n.s.n.s.54.69 (15.34)50.48 (13.72)t(376.187) = 2.824; p < .01−.29
RAN N 53.45 (25.24)41.50 (11.21)t(272.709) = −6.032; p < .001−.61 37.71 (14.61)30.69 (7.05)t(311.522) = −6.264; p < .001−.61 30.56 (8.10)28.49 (10.35)t(378) = −2.173; p < .05−.22
RAN L 59.35 (29.93)45.57 (16.87)t(311.309) = −5.57; p < .001−.57 39.90 (15.78)32.71 (10.00)t(362.479) = −5.544; p < .001−.54 33.42 (10.66)28.37 (9.75)t(378) = −4.816; p < .001−.49
STVM 3.83 (1.12)4.13 (1.25)t(378) = 2.45; p < .05.25 4.26 (1.29)4.67 (1.24)t(406) = 3.213; p < .001.32 4.48 (1.30)5.03 (1.25)t(378) = 4.226; p < .001.43
AK 74.72 (29.80)89.19 (18.01)t(323.927) = 5.766; p < .001.59 90.21 (15.21)93.80 (11.37)t(390.719) = 2.709; p < .01.27 93.51 (9.27)95.62 (11.64)t(378) = 1.962; p < .05.20
VOC 66.78 (12.62)73.57 (12.13)t(377.795) = 5.344; p < .001.55 79.98 (13.27)90.54 (14.39)t(406) = 7.716; p < .001.77 87.36 (13.91)97.43 (13.59)t(378) = 7.13; p < .001.73
GSC 58.89 (9.11)62.26 (9.09)t(378) = 3.599; p < .001.37 64.31 (7.65)65.84 (8.55)n.s.n.s.65.52 (8.18)68.01 (7.36)t(378) = 3.108; p < .01.32
OC 41.07 (23.55)46.13 (23.98)t(378) = 2.073; p < .05.21 47.89 (23.29)55.19 (24.20)t(406) = 3.106; p < .01.31 50.84 (24.31)54.97 (26.90)n.s.n.s.
SR 26.90 (26.99)50.07 (29.33)t(378) = 8.02; p < .001.82 54.07 (30.08)78.91 (29.45)t(406) = 8.417; p < .001.83 63.30 (30.76)87.14 (31.39)t(378) = 7.477; p < .001.77
WR 15.99 (19.91)33.00 (21.29)t(377) = 8.041; p < .001.83 39.23 (24.46)59.51 (24.19)t(406) = 8.413; p < .001.83 49.64 (25.39)70.65 (25.31)t(378) = 8.076; p < .001.83
PWR 12.95 (16.93)22.10 (13.98)t(377) = 5.718; p < .001.59 25.88 (14.56)40.94 (38.48)t(406) = 5.31; p < .001.53 33.03 (17.94)45.30 (20.10)t(378) = 6.28; p < .001.64
TR 36.01 (42.93)67.12 (45.54)t(378) = 6.854; p < .001.70 78.77 (46.17)115.66 (43.01)t(406) = 8.329; p < .001.83 100.32 (48.85)136.09 (42.26)t(378) = 7.619; p < .001.78
RC 26.04 (31.51)44.88 (31.16)t(378) = 5.856; p < .001.60 47.28 (30.24)61.00 (25.53)t(403.386) = 4.966; p < .001.49 52.19 (29.45)59.50 (27.33)t(378) = 2.505; p < .05.26

Regarding reading abilities, the differences in decoding skills (reading of syllables, words, pseudowords, and texts) remain consistent throughout primary school in favor of private school students, with medium to large effect sizes. In the domain of reading comprehension, the magnitude of the difference appears to decrease over the course of primary education, starting at a medium level and ending at a small one.

Additionally, at the end of 2nd grade and the beginning of 3rd grade, 13.6% of participants from the private school system were unable to read texts (and therefore could not be evaluated on this task), whereas in the public school system, this percentage rose to 39.3%.

Objective 2: Study of the impact of socioeconomic variables on reading performance

Linear regression models showed that the only variable that consistently predicted reading fluency (words correctly read per minute) across all grades was family income. In grades 2–3, paternal educational level and years of pre-primary education were also significant, though with weaker effects ( Table 4). In grades 4–5, maternal educational level emerged alongside family income, with a similar level of influence. The variance in reading fluency explained by the set of socioeconomic variables remained around 19% between grades 2–3 and 4–5 but decreased to 16.1% in grades 6–1st year of secondary education.

Table 4. Influence of socioeconomic variables on text reading fluency.

Variable2°-3°4°-5°6°-1°S
F(8;379) = 12.109, p < .001 R2 Adj = .19 F(8;407) = 12.835, p < .001 R2 Adj = .189 F(8;378) = 10.064, p < .001 R2 Adj = .161
Beta std p Beta std p Beta std p
Mother’s educational level --.18<0.01--
Father’s educational level .15<0.05----
Family income .20<0.01.17<0.01.30<0.001
Number of children’s books ------
Years of pre-primary .10<0.05----
Number of people e/adults ------
Father living with child ------
Internet at home ------

The variable vocabulary was selected as representative of the oral language component. When examining the socioeconomic variables influencing vocabulary, family income once again emerged as significant across all grades. In grades 4–5, maternal educational level also became a predictor, with greater influence. In grades 6–1st year of secondary school, the number of children’s books at home and the presence of internet access at home also appeared as predictors, though with weaker effects ( Table 5). The explanatory power of socioeconomic variables for vocabulary increased steadily throughout primary school (from 12.5% in grades 2–3 to 16.9% in grades 6–1st year of secondary school).

Table 5. Influence of socioeconomic variables on vocabulary level.

Variable2°-3°4°-5°6°-1°S
F(8;379) = 7.767, p < .001 R2 Adj = .125 F(8;407) = 10.424, p < .001 R2 Adj = .156 F(8;378)=, p < .001 R2 Adj = .169
Beta std p Beta std p Beta std p
Mother’s educational level --.20<0.001
Father’s educational level --
Family income .16<0.05.17<0.01.19<0.01
Number of children’s books ----.11<0.05
Years of preschool ----
Number of people/adults ----
Father living with child ----
Internet at home ----.10<0.05

Table 6 presents the results of the linear regression with reading comprehension as the dependent variable and socioeconomic variables as independent predictors. A significant model could not be obtained for grades 6–1st year of secondary school. Once again, family income emerged as a predictor in grades 2–3 and 4–5, with maternal educational level and years of pre-primary education added as predictors of similar importance in grades 4–5.

Table 6. Influence of socioeconomic variables on reading comprehension.

Variable2°-3°4°-5°6°-1°S
F(8;379) = 9.325, p < .001 R2 Adj = .149 F(8;407) = 4.851, p < .001 R2 Adj = .07-
Beta std p Beta std p Beta std p
Mother’s educational level --.15<0.05--
Father’s educational level ------
Family income .27<0.001.14<0.05--
Number of children’s books ------
Years of preschool --.11<0.05--
Number of people/adults ------
Father living with child ------
Internet at home ------

In contrast to vocabulary, the proportion of variance in reading comprehension explained by socioeconomic variables decreases throughout primary school, starting at 14.9% and eventually becoming non-significant in grades 6–1st year of secondary school.

On the other hand, the relative risk (RR) of having low text reading fluency (1st quartile) for students belonging to the lowest income group (28% of the sample) is 2.2—that is, students from low-income families are more than twice as likely to exhibit low reading fluency. Similarly, students whose mothers have a low educational level (primary education completed or not completed) are 2.54 times more likely to have low text reading fluency. In the domain of reading comprehension, the 28% of the sample with low income has a 1.53 times higher risk of poor reading comprehension (1st quartile), while having a mother with a low educational level increases this risk by 1.54 times.

Objective 3: Estimation of the impact of the educational system on reading performance, controlling for socioeconomic index

To isolate the effect of the educational system on reading performance from that of socioeconomic conditions—given that the public system serves students from more vulnerable families ( Table 2)—an ANCOVA was conducted for each outcome variable at each grade level to calculate the performance difference between both educational systems while controlling for socioeconomic status ( Table 7). In the psycholinguistic variables that are precursors to reading, the differences between systems tended to decrease considerably (large effects became medium or small, and medium effects became small), if not disappear altogether. Overall, differences in oral language variables (vocabulary, grammatical structure comprehension, and oral comprehension) tended to disappear (except for vocabulary). Regarding reading performance, in grades 2–3, the differences in syllable and word reading became small, while differences in pseudoword and text reading, as well as in reading comprehension, disappeared. In grades 4–5 and 6–1st year of secondary school, although to a lesser extent, the magnitude of the differences decreased substantially (for example, the difference in reading comprehension in grades 6–1st year of secondary school ceased to be significant). However, medium effect sizes tended to persist in the decoding fluency variables.

Table 7. Effect of the type of education on each reading performance variable, controlling for socioeconomic variables (ANCOVA).

Variable2°-3°4°-5°6°-1°S
ANOVA pbonf d d (wc1)ANCOVA pbonf d d (wc1)ANCOVA pbonf d d (wc1)
PA F(1,372) = 13.388<.001.49 .78F(1,400) = 6.452.011.32 .61n.s.n.s.n.s..57
RAN C n.s.n.s.n.s.n.s.F(1,400) = 5.308.022−.29 −.55n.s.n.s.n.s.n.s.
RAN O n.s.n.s.n.s.n.s.n.s.n.s.n.s.n.s.n.s.n.s.n.s.−.29
RAN N F(1,372) = 9.935.002−.42 −.61F(1,400) = 12.557<.001−.44 −.61n.s.n.s.n.s.−.22
RAN L F(1,372) = 7.016.008−.35 −.57F(1,400) = 5.650.018−30 −.54n.s.n.s.n.s.−.49
STVM n.s.n.s.n.s..25n.s.n.s.n.s..32F(1,372) = 6.026.015.33 .43
AK F(1, 372) = 4.345.038.28 .59n.s.n.s.n.s..27n.s.n.s.n.s..20
VOC n.s.n.s.n.s..55F(1,400) = 10.518.001.41 .77F(1,372) = 4.270.039.28 .73
GSC n.s.n.s.n.s..37n.s.n.s.n.s.n.s.n.s.n.s.n.s..32
OC n.s.n.s.n.s..21n.s.n.s.n.s..31n.s.n.s.n.s.n.s.
SR F(1, 372) = 7.926.005.38 .82F(1,400) = 31.487<.001.70 .83F(1,372) = 14.817<.001.51 .77
WR F(1, 371) = 5.080.025.30 .83F(1,400) = 16.109<.001.50 .83F(1,372) = 13.232<.001.49 .83
PWR n.s.n.s.n.s..59F(1,400) = 12.166<.001.44 .53F(1,372) = 14.008<.001.50 .64
TR n.s.n.s.n.s..70F(1,400) = 11.483<.001.42 .83F(1,372) = 8.373.004.39 .78
RC n.s.n.s.n.s..60F(1,400) = 5.841.016.30 .49n.s.n.s.n.s..26

1 Cohen’s d without controlling SES variables, taken from Table 3.

Discussion

The results revealed significant performance differences between students from public and private schools across nearly all evaluated variables. Overall, students from the private sector demonstrated higher performance in psycholinguistic, linguistic, and reading skills, with medium to large effect sizes—particularly in reading fluency. In the public system, 4 out of 10 children assessed were unable to read texts by the end of 2nd grade or the beginning of 3rd grade, a concerning proportion that is three times higher than that of the private system. Linear regression analyses showed that family income was the most consistent predictor of reading performance, vocabulary, and reading comprehension, although other factors, such as parental educational level, also showed specific influences depending on grade level. Children from low-income families and those whose mothers have a low educational level are more than twice as likely to exhibit low reading fluency.

When controlling for socioeconomic variables, much of the difference between educational systems decreased or disappeared—especially in reading comprehension and linguistic abilities—suggesting that socioeconomic context accounts for a substantial portion of the variance associated with the gap between public and private systems, primarily linked to structural inequalities (OECD, 2024; Acevedo et al., 2023). Nevertheless, some differences persisted, particularly in measures of reading fluency, indicating a possible residual effect of the educational system on the development of certain reading skills.

It is noteworthy that the differences in reading performance in grades 2–3, once SES is controlled, are small or even nonexistent for certain variables. This suggests that, at this grade level, the cross-sectional comparison does not yet reveal the substantial differences observed at higher grades. However, differences are already present in the main predictors of future reading performance: PA and AK (predictors of reading accuracy) and RAN for numbers and letters (predictors of reading speed). This may help explain, at least partially, the larger differences observed in reading performance at grades 4 and 5 in this cross-sectional sample, since although in countries with higher levels of educational development PA and AK are no longer predictors of future reading performance by grades 2 or 3, in this type of context the dependence on PA remains strong, even up to grade 4 (Sánchez-Vincitore et al., 2022; Cubilla-Bonnetier & Sánchez-Vincitore, 2025).

The effect sizes of the raw differences in reading comprehension—d = .49 in grades 3–4 and d = .26 in grades 6–1st year of secondary—are smaller than those reported by Duarte et al. (2010), although the time elapsed may affect the comparability of these values. Furthermore, when controlling for socioeconomic variables, the effect of the type of education on reading performance appears to increase from grade 2 to grade 4, and then decrease again by grade 6 (in the case of reading comprehension, it is significant only in grade 4). This may reflect the cumulative effect of a school system that fails to compensate for structural interfamily differences, while the slight decline in the difference observed in grade 6 may be due to the fact that socioeconomically vulnerable students tend to be those with the lowest academic performance. This could result in a lag among these students, potentially leading to voluntary or involuntary dropout from the educational system—although these hypotheses should be confirmed by studies specifically designed for that purpose (perhaps longitudinal in nature).

The proportion by which the gap in the different reading performance variables decreases when controlling for SES is consistent with what has been reported in secondary education through PISA assessments, since in most Latin American countries the gap is reduced by approximately half (Cheema, 2024).

Once the effect of SES is excluded, differences in receptive vocabulary levels are found in grades 4–5 and 6–1st year of secondary, but not in grades 2–3. Regardless of the factors explaining the reading gap between systems, it is important to remember that reading is one of the main mechanisms for lexical acquisition once it becomes functional. Therefore, lower reading performance may account for reduced vocabulary growth in the medium term. It would be advisable to specifically study the impact of this phenomenon.

Additionally, this study may contribute to the debate on the more “biological” or innate nature of RAN. The fact that differences in RAN performance between educational systems persist after controlling for SES could contradict the innatist hypothesis (Andreola et al., 2021; Carioti et al., 2022). However, it should be noted that the only differences that remain are found in alphanumeric RAN variables, which are based on the processing of learned tokens (numbers and letters) and are therefore more specifically related to instruction and potentially more strongly associated with the quality and quantity of stimulation received at school.

Another relevant finding is that public school students begin primary education with a lower average number of years of pre-primary education. Therefore, in addition to the effects of family socioeconomic factors—which influence the quality and quantity of linguistic stimulation and the availability of reading materials and shared reading sessions at home—there is also reduced school-based stimulation. This combination limits the potential to compensate for such adverse factors and may contribute to perpetuating the educational gap. In this regard, the country’s public education system, both at the pre-primary and primary levels, does not appear to be fulfilling its expected role as a compensator for preexisting differences related to students’ material and familial stimulation conditions, thereby hindering its function as a social elevator.

Limitations

Several methodological limitations should be acknowledged. First, this study is cross-sectional: comparisons across grade bands reflect different cohorts rather than the same students followed over time. Accordingly, language describing how gaps ‘develop’ or ‘increase’ across grades should be understood as descriptive of cross-sectional patterns rather than as developmental trajectories. Second, the public–private comparison is observational, since it is impossible to randomly assign students to school type. Although ANCOVA was used to adjust for measured SES variables, this approach assumes that the included covariates adequately capture the selection process—a strong assumption that cannot be fully verified. Unmeasured confounders may remain, and the adjusted differences between systems should therefore be interpreted with caution rather than as estimates of causal system effects. Third, the SES variables were entered as separate predictors in the regression models. While this approach offers broad conceptual coverage, multicollinearity among these indicators is plausible and may affect the stability of individual coefficients; results should therefore be interpreted at the level of the overall model rather than individual predictors. Finally, the persistence of medium effect sizes in decoding fluency after SES adjustment is interpreted as a possible residual system effect; however, without direct measures of instructional quality or quantity, this interpretation remains speculative and should be treated as a hypothesis for future research. Regarding generalizability, although a random sample of schools was used, all were located in the metropolitan area of Santo Domingo, which represents approximately 35% of the country’s population. Future research should include representative samples from other regions of the country.

Notwithstanding these limitations, the strength of this study lies in examining a theoretically differentiated set of reading components, and not only a global reading outcome. This allows for a more granular analysis of how different aspects of reading development are differentially sensitive to SES conditions versus schooling context. Prior work has established that SES-related gaps in reading are not uniform across skills (Hoff, 2013), in the sense that language-based outcomes such as vocabulary tend to be strongly tied to family linguistic environment, while decoding-related skills may reflect instructional exposure more directly. The present study tests whether this differential sensitivity is observable in a low-resource Latin American context where both SES inequality and instructional quality variation are pronounced.

Recommendations

One of the key findings of this study is that students in the public education system in the Dominican Republic begin primary school with less time spent in pre-primary education compared to those in the private system. The years of pre-primary education serve, among other purposes, to foster language development and stimulate the foundational skills required for reading acquisition. It is necessary to continue the ongoing national effort to universalize pre-primary education, as it is essential for building the foundation of future reading learning. Additionally, public early education should strengthen the stimulation of the essential skills underlying decoding and reading comprehension that were found to be underdeveloped in 2nd grade in the present study: phonological awareness, alphabetic knowledge, vocabulary, and grammatical structure comprehension. Although no significant differences were found between systems in the latter two skills once SES was controlled, schools should play a compensatory role in addressing disparities created by students’ contextual backgrounds.

Furthermore, it is essential to address the educational emergency represented by the fact that 2 out of every 5 students in public schools lack functional reading ability at the transition between 2nd and 3rd grade. This situation, in addition to requiring systematic and early detection systems for low decoding levels (before comprehension difficulties become evident), demands the implementation of early and progressive support mechanisms with increasing levels of individualized attention—such as those proposed by the Response to Intervention (RtI) model—which has accumulated extensive evidence regarding its effectiveness (Burns et al., 2005). It should be noted that these recommendations are grounded in the broader empirical literature on early literacy intervention and are consistent with the patterns observed in the present data; they are not directly derived from the correlational analyses reported here, which do not permit causal inference.

Ethical considerations

This study was approved by the Research Ethics Committee of Universidad Iberoamericana (approval code CEI2024–24). Informed consent was obtained from the legal guardians of all participants, and the assent of participants was verified prior to assessment. Participation was voluntary, and all data were treated confidentially.

Declaration of AI-assisted technologies

During the preparation of this work the authors used ChatGPT-5 and Claude Sonnet 4.6. to ensure the quality of the English writing. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Data availability

The data underlying the results reported in this article are available at: OSF. Database for The Reading Gap in the Dominican Republic: Education System or Socioeconomic Vulnerability? https://osf.io/rk4p3 (Cubilla et al., 2026).

This project contains the following underlying data:

Reading Gap in Dominican Republic.sav (Participant-level dataset. Each row represents one participant (N = 1,168). Contains scores on all study variables — psycholinguistic precursors of reading, oral language measures, and reading performance outcomes — along with sociodemographic and socioeconomic information.)

Data are available under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).

The screening instrument used in this study is not currently available as open access. Researchers interested in accessing the materials may contact [email protected] to discuss terms of use.

Reporting guidelines

This observational cross-sectional study was reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.

Acknowledgements

The authors wish to thank Dr. Ginia Montes de Oca for her support of the project, and Nahia Castro and Verónica Rodríguez, as well as all those who participated in data collection.

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