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Research ArticleArticles
Open Access

Remedial Education

Evidence from a Sequence of Experiments in Colombia

Horacio Alvarez Marinelli, View ORCID ProfileSamuel Berlinski and View ORCID ProfileMatias Busso
Journal of Human Resources, January 2024, 59 (1) 141-174; DOI: https://doi.org/10.3368/jhr.0320-10801R2
Horacio Alvarez Marinelli
Horacio Alvarez Marinelli is a Senior Economist at the Education Division of the Inter-American Development Bank.
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Samuel Berlinski
Samuel Berlinski is Principal Economist at the Research Department of the Inter-American Development Bank.
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Matias Busso
Matias Busso is a Principal Economist at the Research Department of the Inter-American Development Bank.
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  • For correspondence: mbusso{at}iadb.org
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  • Figure 1
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    Figure 1

    Timeline (for Each Cohort)

    Notes: The figure shows the timeline of intervention and data collection for the three experiments implemented in 2015, 2016, and 2017.

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    Figure 2

    Eligibility Criteria

    Notes: The eligibility criteria changed between the first and the second cohort. For Cohort 1, eligible students were defined as those who performed approximately in the bottom 25 percent of the study population distribution of a composite literacy score. For Cohorts 2 and 3, eligible students were defined as those who read fewer than 60 words in the fluency of oral reading EGRA subtask. The fuzzy vertical line in the left figure represents the fact that in the case of Cohort 1, tutorials were completed to maximum capacity (of six students) with students immediately next to the 25th percentile threshold. In Cohorts 2 and 3, the eligibility criterion was strict (represented by a solid line).

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    Figure 3

    Effect of the Intervention on Literacy Score

    Notes: The solid black line shows the number of correct answers by eligible students in schools randomized to treatment. The dashed gray line shows the number of correct answers by students in schools randomized to control. Confidence intervals are at the 95 percent level. “Beg G3” refers to the measure taken in March (baseline) of third grade, and “End G3” refers to the measure taken by the end of third grade, after treatment. “Beg./Mid. G4” refers to the first measure taken in fourth grade, and “End G4” refers to the measure taken by the end of fourth grade. The vertical dotted line marks the approximate time of treatment.

  • Figure 4
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    Figure 4

    Treatment Effects by Cohort

    Notes: Each bar shows the estimated treatment effect for the aggregate literacy score for each cohort, with the corresponding 95 percent confidence interval. In addition, circles present the estimated treatment effects for each literacy subtask, estimated at each time horizon for each cohort. See Online Appendix Table A.4 for the individual estimated treatment effects.

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    Figure 5

    Attendance at Tutorials

    Notes: Each line shows the average number of days attended by students in each cohort. In each cohort, we sorted the schools from lowest to highest attendance. The line for Cohort 1 spans more schools because the sample of schools in the experiment in Cohort 1 was larger than that in Cohorts 2 and 3 (see Section II for more details). Horizontal lines show the total number of tutorials offered: 36 for students in Cohort 1 and 48 for students in Cohorts 2 and 3.

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    Figure 6

    Quantile Treatment Effects

    Notes: Each panel shows the quantile treatment effects on each outcome of interest estimated following Firpo (2007).

Tables

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    Table 1

    Balance, Attrition, and Compliance

    Cohort 1Cohort 2Cohort 3
    TCp-ValueTCp-ValueTCp-Value
    Panel A: Pre-Treatment Characteristics
    Avg. student
     Age8.828.830.718.498.370.098.408.470.31
     Proportion female0.500.480.740.470.500.420.500.520.76
     Scores
      Fluency of oral reading49.8450.730.1442.1742.120.9844.1743.420.51
      Knowledge of letter sounds14.8213.640.2012.4113.460.1819.5119.000.69
      Reading of nonwords28.8328.330.5424.2224.880.4126.6226.830.84
      Reading comprehension3.073.100.552.101.990.205.335.050.26
      Literacy score (avg. of subscores)0.590.590.900.390.390.370.490.480.55
      Addition12.1812.230.6010.159.970.5711.7811.890.67
      Subtraction9.519.210.408.127.740.269.449.160.35
      Math score (avg. of subscores)0.420.420.950.350.340.310.410.410.87
    Avg. school
     Grade 3 enrollment47.7147.700.8341.3342.880.7645.8343.450.54
     Morning0.790.720.500.740.760.800.770.700.49
     Rural0.250.200.530.120.170.570.150.170.74
     Avg. socioeconomic status (1–6)1.891.810.441.811.910.261.891.820.51
     Avg. class size23.9123.340.5623.4226.060.0924.6523.490.48
     Avg. number of classrooms in Grade 31.561.610.841.451.360.431.551.500.69
    Panel B: Tutorial Assignment
    Avg. tutorial size5.925.910.634.824.790.674.994.910.30
    SD literacy score of students in tutorial0.090.100.320.100.100.710.100.100.79
    Panel C: Compliance
    Ever attended a tutorial0.920.000.000.940.000.000.980.000.00
    Tutorial attendance (percent)0.730.000.000.900.000.000.900.000.00
    Tutorial attendance (sessions)26.200.000.0043.030.000.0042.990.000.00
    Panel D: Attrition
    End of Grade 30.060.070.190.070.090.270.090.090.95
    Beg./mid. Grade 40.540.600.250.120.160.080.100.120.40
    End Grade 40.210.170.090.140.120.500.140.180.20

    Notes: Panel A shows the average pre-treatment characteristics of eligible students. Scores for subtasks are expressed in number of correct responses (fluency of oral reading, reading of nonwords, reading comprehension, additions, subtractions). Literacy and math scores are expressed as average proportion of correct answers in each subtask. Panel B shows tutorials’ characteristics. In the case of the control group, we assigned students to hypothetical tutorials (even when no tutor was assigned to them). Panel C shows which eligible students attended the tutorials and the intensity of attendance. Panel D shows the attrition rates at different time horizons of the eligible students observed at baseline. Columns labeled “T” show the average for students in schools randomized to treatment. Columns labeled “C” show the average for students in schools randomized to control. Columns labeled “p-Value” show the p-value of a test of H0 : θ = 0 (see Equation 1). We present these statistics by cohort.

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      Table 2

      Treatment Effects on Literacy (Standardized Literacy Outcomes)

      Knowledge of Letter SoundsReading of NonwordsFluency of Oral ReadingReading ComprehensionLiteracy Score
      (1)(2)(3)(4)(5)
      Treatment × End of Grade 30.349***0.0730.157***0.0010.270***
      [0.059][0.050][0.049][0.044][0.056]
      Treatment × Beginning of Grade 40.312***0.105*0.192**0.150**0.264***
      [0.069][0.056][0.077][0.062][0.075]
      Treatment × End of Grade 40.300***0.088**0.0830.0550.152***
      [0.055][0.041][0.053][0.053][0.051]
      Observations4,9496,3626,3626,3626,362
      p-value of equal coeffs.0.7260.8810.2720.1030.065
      Baseline control mean (non-std.)15.0226.9446.323.3030.507
      Baseline control SD (non-std.)11.3610.0413.811.8770.142

      Notes: Each column shows the coefficients θh of Equation 2, that is, the estimated treatment effects at different time horizons for each outcome of interest. The row labeled “p-value of equal coeffs.” shows the p-value of a test H0 : θ1 = θ2 = θ3. Control mean and SD correspond to the number of correct answers (Columns 1–4) or the percent of correct answers (Column 5). All models include cohort, year, and strata fixed effects. Standard errors, shown in brackets, are clustered at the school level (the unit of randomization). Significance: *p < 0.10, **p < 0.05, ***p < 0.01.

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        Table 3

        Robustness—Treatment Effects at the End of Grade 3 (Standardized Outcomes)

        Knowledge of Letter SoundsReading of NonwordsFluency of Oral ReadingReading ComprehensionLiteracy Score
        (1)(2)(3)(4)(5)
        Panel A: Adding Controls
        Baseline outcome0.342***0.0730.168***−0.0020.273***
        [0.054][0.045][0.048][0.043][0.049]
        Individual- and school-level variables0.336***0.0780.167***0.0060.274***
        [0.058][0.047][0.047][0.043][0.054]
        School fixed effects0.448***0.1170.147*0.0440.307***
        [0.097][0.086][0.079][0.075][0.092]
        Panel B: Lee Bounds
        Lower bound0.328***0.0420.127***−0.086**0.234***
        [0.050][0.044][0.048][0.043][0.048]
        Upper bound0.371***0.106**0.192***0.0340.307***
        [0.049][0.050][0.053][0.056][0.054]
        Panel C: Multiple Hypothesis
        Testing treatment0.349***0.0730.157*0.0010.270***
        Adjusted p-value0.0010.5220.0870.9830.008

        Notes: The table compiles different robustness checks for estimated treatment effects at the end of Grade 3. Panel A adds controls for the main specification. Individual-level controls include age, age-squared, gender, socioeconomic, and disability status. School-level controls include class size, school number of classes in Grade 3, rural, and morning school status. Panel B estimates Lee (2009) bounds. Panel C calculates Westfall–Young adjusted p-values to address for multiple hypotheses testing. Online Appendix Tables A.1, A.2, and A.3 present details and the same robustness for beginning and end of Grade 4. Significance: *p < 0.10, **p < 0.05, ***p < 0.01.

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          Table 4

          Treatment Effects on Other Education Outcomes

          Additions (SD)Subtractions (SD)Math Score (SD)Repeat Grade 3
          (1)(2)(3)(4)
          Treatment × End of Grade 30.0810.0290.063−0.005
          [0.053][0.054][0.054][0.013]
          Treatment × Beginning of Grade 40.104*0.1040.117*
          [0.055][0.066][0.063]
          Treatment × End of Grade 40.091**0.0640.086*
          [0.044][0.049][0.047]
          Observations6,3626,3626,3622,391
          p-value of equal coeffs.0.9300.6720.739
          Control mean (non-std.)11.498.7780.3930.129
          Control SD (non-std.)4.8034.3810.1570.335

          Notes: Each column shows the coefficients θh of Equation 2, that is, the estimated treatment effects at different time horizons for each outcome of interest. The row labeled “p-value of equal coeffs.” shows the p-value of a test H0 : θ1 = θ2 = θ3. Control mean and SD correspond to the baseline number of correct answers (Columns 1–3) or the proportion of students who repeated at the end of Grade 3 (Column 4). All models include cohort, year, and strata fixed effects. Standard errors, shown in brackets, are clustered at the school level (the unit of randomization). Significance: *p < 0.10, **p < 0.05, ***p < 0.01.

            • View popup
            Table 5

            Dose-Response Effects—Instrumental Variables Estimates, Standardized Outcomes

            Knowledge of Letter SoundsReading of NonwordsFluency of Oral ReadingReading ComprehensionLiteracy Score
            (1)(2)(3)(4)(5)
            Days in tutorial × End of Grade 30.009***0.0020.004***0.0000.007***
            [0.002][0.001][0.001][0.001][0.001]
            Days in tutorial × Beginning of Grade 40.007***0.003**0.005***0.004***0.007***
            [0.001][0.001][0.002][0.001][0.002]
            Days in tutorial × End of Grade 40.007***0.002**0.0020.0010.004***
            [0.001][0.001][0.001][0.001][0.001]
            Observations4,9496,3626,3626,3626,362
            p-value of equal coeffs.0.2790.7420.1430.0260.042
            Baseline control mean (non-std.)15.0226.9446.323.3030.507
            Baseline control SD (non-std.)11.3610.0413.811.8770.142

            Notes: Each column shows the estimates of the coefficients βh of Equation 3, that is, the estimated dose response at different time horizons for each outcome of interest. Dosage is measured by the number of days in which students attended the tutorial. The actual attendance was instrumented with the randomized treatment indicator variable. The first stage Kleibergen–Paap F-statistic is equal to 1828, and the associated p-value is zero. The row labeled “p-value of equal coeffs.” shows the p-value of a test H0 : β1 = β2 = β3. Control mean and SD correspond to the number of correct answers (Columns 1–4) or the percent of correct answers (Column 5). All models include cohort, year, and strata fixed effects. Standard errors, shown in brackets, are clustered at the school level (the unit of randomization). Significance: *p < 0.10, **p < 0.05, ***p < 0.01.

              • View popup
              Table 6

              Targeting (Nonstandardized Outcomes)

              NonwordsAdditionSubtractionIndex
              (1)(2)(3)(4)
              Cohort 2−4.024***−2.151***−1.425***−2.533***
              [0.718][0.296][0.267][0.348]
              Cohort 3−1.848***−0.375−0.057−0.760**
              [0.704][0.291][0.282][0.355]
              Observations2,6102,6102,6102,610
              p-value of equal coeffs.0.0030.0000.0000.000
              Cohort 1 control mean28.3312.239.21216.59
              Cohort 1 control SD10.515.0954.7404.766

              Notes: Each column shows an OLS estimate of a model in which the dependent variable is an outcome measured at baseline (measured by the number of correct answers in that subtask), and the independent variables are dichotomous variables indicating that the students belong to Cohort 2 or Cohort 3. The index shown in Column 4 is a simple average of the scores in the three subtasks shown in Columns 1–3. All models include cohort, year, and strata fixed effects. Standard errors, shown in brackets, are clustered at the school level (the unit of randomization). Significance: *p < 0.10, **p < 0.05, ***p < 0.01.

                • View popup
                Table 7

                Treatment Effect Heterogeneity (Standardized Outcomes)

                Knowledge of Letter SoundsReading of NonwordsFluency of Oral ReadingReading ComprehensionLiteracy Score
                (1)(2)(3)(4)(5)
                Panel A: Size of Tutorial Group
                1–5 students0.330***0.0020.063−0.0580.177***
                [0.064][0.054][0.059][0.065][0.059]
                6 students0.370***0.152**0.254***0.0540.363***
                [0.082][0.059][0.062][0.044][0.071]
                p-value of equal coeffs.0.6790.0180.0250.1530.025
                Panel B: Peers’ Initial Ability
                High0.366***0.0540.090−0.0650.230***
                [0.066][0.063][0.064][0.057][0.068]
                Low0.334***0.0960.221***0.0560.305***
                [0.071][0.065][0.064][0.052][0.070]
                p-value of equal coeffs.0.6950.6230.1590.0950.402
                Panel C: Homogeneity of Tutorial Group
                SD below median0.413***0.097*0.202***0.0250.327***
                [0.067][0.058][0.060][0.052][0.065]
                SD above median0.283***0.0530.109*−0.0340.207***
                [0.068][0.058][0.057][0.053][0.063]
                p-value of equal coeffs.0.0920.5110.2370.3850.107
                Panel D: Tutor’s Previous Experience
                No0.468***0.0310.201***0.0410.313***
                [0.101][0.064][0.060][0.050][0.076]
                Yes0.245**0.252***0.471***0.0980.458***
                [0.095][0.080][0.102][0.099][0.095]
                p-value of equal coeffs.0.0660.0170.0180.5780.161

                Notes: Each panel shows estimates of θ1 of Equation 2 estimated separately for two different groups of treated students where the comparison is against all the students in the control group. The row “p-value of equal coeffs.” shows the p-value of a Chow test of equality of coefficients in the different samples. See text for the description of each dimension of heterogeneity that is explored in the table. All models include cohort, year, and strata fixed effects. Standard errors, shown in brackets, are clustered at the school level (the unit of randomization). Significance: *p < 0.10, **p < 0.05, ***p < 0.01.

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                Remedial Education
                Horacio Alvarez Marinelli, Samuel Berlinski, Matias Busso
                Journal of Human Resources Jan 2024, 59 (1) 141-174; DOI: 10.3368/jhr.0320-10801R2

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                Remedial Education
                Horacio Alvarez Marinelli, Samuel Berlinski, Matias Busso
                Journal of Human Resources Jan 2024, 59 (1) 141-174; DOI: 10.3368/jhr.0320-10801R2
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                • Article
                  • Abstract
                  • I. Introduction
                  • II. Intervention
                  • III. Research Design
                  • IV. The Causal Impact of Remediation in Small-Group Tutorials
                  • V. Sequential Experimental Results
                  • VI. Cost-Effectiveness
                  • VII. Conclusion
                  • Acknowledgments
                  • Footnotes
                  • References
                • Figures & Data
                • Supplemental
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