Impact of ICTs-trained school teachers on educational outcomes: evidence from

Colombia.

Fabio Snchez

Tatiana Velasco

School of Economics

Universidad de los Andes

March, 27th.

2015

Since 2004, Computadores para Educar, a Colombian nationwide program that equipped

schools with computers giving access to technology to the students, has also trained

teachers in ICT usage for instruction. By 2014, over 41,000 schools of the 46,000 existing

in Colombia had received computers by the program. Meanwhile, of the 386,000 teachers

in the public school system, about 74,000 have been trained in ICTs. This research aims to

assess the effect that these ICTs-trained teachers have on dropout rates and performance on

standardized tests. Particularly, we use a unique dataset that allow us to track students and

teachers in all the public schools of the country by year and school since 2005, and we

combined these data with information about the program implementation. We find that that

greater proportion of ICTs trained teachers in a particular subject improve the performance

of students in that subject (math, chemistry, physics, etc.) and reduce dropout. To correct

for endogeneity, we use as instrumental variable the proportion of ICT-trained teachers by

subject or school level in the neighboring municipalities in t-1. Our results indicate that an

increase in one standard deviation in the proportion of ICT-trained teachers within a school

level reduces students drop out by 0.20 standard deviations. Similarly, an increase in one

standard deviation of proportion of ICT trained teachers in a particular subject increases

students performance on standardized test in that subject by 0.70 standard deviations.

These results are evidence that what matters for educational performance is not student

access to ICT and computers but rather the training of teachers in the usage of ICT in the

classroom.

JEL Classification: I21, I28

Key words: ICT, teachers training, instrumental variables, drop out, standardized test

I. MOTIVATION

Nowadays, computer-based learning programs are a popular educational

intervention around the world (MacLeod, 2008). Nevertheless the evidence about whether

they improve or not educational outcomes is rather mixed. This is an issue, because many

of these programs combine two kind of interventions at the same time: they provide ICT

equipment to students and they train teachers on how to use this equipment. As a

consequence, when evaluating the impact of this particular group of programs, researchers

have failed in differentiating which of the interventions conduces the effect found.

This has been the case of Computadores para Educar (CPE). This is a Colombian

program that has provided ICTs to public schools in all the country since 2001. Since 2004,

the program included a formal training in ICT of 150 hours to teachers in public schools

that received CPE equipment. By 2013, 74,000 teachers had received the ICT training,

which represents 23% of the public schools teachers. The CPE program has been

empirically evaluated two times. The first one by Barrera-Osorio and Linden (2009) whom

did not find a significant effect of the program on educational outcomes, and the second

one by Rodriguez, Snchez and Mrquez (2011) whom found a significant impact of CPE

for reducing dropout and for improving Saber 11 performance and access to higher

education. In any of these cases, the researchers were able to property identify the channels

that conduced to the found result. Specifically, they did not explain which component of the

program conducted the effect: the ICTs, the teachers training, or both.

Literature on educational policy interventions had provided insights about the

elements of these mixed programs that may conduced the effect. For example, MacLeod

(2008) argues that while teachers training programs seem to have attracted less attention

than computer-based programs, [] the content of the training programs seems to matter

more for changes in student performance than the structure of the program itself.

(MacLeod, 2008:5). Kennedy (1998) reviewed several papers on teachers intervention

programs and concluded that those programs whose content focused in teachers

knowledge of the subject, on the curriculum, or on how students learn the subject are the

ones with the bigger impact on educational outcomes. Thus, we hypotheses that ICT-

training for teachers in the main channel that conduces CPEs impacts, because it aims to

provide pedagogical and practical tools to improve classroom and teaching practices as

teachers appropriate ICTs.

In this paper we attempt to identify the channel that conduces to CPE impacts on

educational outcomes. For that end, we combined instrumental variables with fixed effects

models to identify the causal effect of the ICT-trained teachers, isolated of the CPE

equipments effect, We find that an increase of one standard deviation in the proportion of

ICT-trained teachers within a school conduces to a significant impact in dropout rate, grade

retention rate and performance. Particularly, it reduces dropout rate in 0.20 standard

deviations, grade retention rate in 0.76 standard deviations and increases Saber 11

performance in 0.7 standard deviations. Our preliminary robustness checks indicate that the

CPE impacts goes through teachers training and not through CPE equipment.

This paper is organized as follows. Section II. Presents the literature review that

frameworks our research question, section III. Provides a description of CPE program, with

a particular focus on the ICT training of teachers, section IV describes the empirical

strategy used for this papers, section V describes the information sources we used and how

we processed them, section VI present our results and section VII presents our preliminary

conclusions.

II. LITERATURE REVIEW

There is an extensive literature about ICT interventions both in developing and

developed countries with mixed results on students performance. However, teacher

training programs had attracted less attention than ICT interventions in developing

countries, while the evidence is mixed in developed countries (He, Leigh & MacLeod,

2008). In a meta-analysis conducted by Kennedy (1998), it is concluded that the content of

the training programs is more important than the structure of the program itself for changes

in student performance (He, Leigh & MacLeod, 2008). Thus, we divide the literature

between two big groups: those based on programs with only ICT interventions/dotation and

those based on programs that include both ICT dotation and teacher training.

In the group of programs with only ICT interventions/dotation, there is an extensive

literature with mixed results on students performance. For example, Angrist & Lavy

(2002) show, using a 2SLS estimation, that a large-scale program in Israel that provided PC

to elementary and middle schools between 1994 and 1998 had a positive effect on computer

use by the students but it had no effect for eight graders neither in math nor Hebrew, and

even a negative effect for fourth graders in math. Also, Rouse & Krueger (2004) evaluate

the short-term effect of a well-defined use of a computer program in the United States

known as Fast ForWord. They used OLS and IV estimation and found that there was no

statistically significant impact on the language and reading skills of the students.

Conversely, other programs had found positive effects of ICT

interventions/dotation. For example, Banarjee, Cole, Duflo & Linden (2005) use a

difference-in-difference estimation to evaluate a computer-assisted learning (CAL) program

focused on children at the bottom of the class in Vadodara, urban India. They found that the

program increased treatment math scores by 0.35 standard deviations the first year, and

0.47 the second year. In the same way, Barrow, Markman & Rouse (2008) used an IV

approximation and found positive and statistically significant effects of 0.25-0.42 standard

deviations on students performance in pre-algebra and algebra test scores of a program

known as I Can Learn in three urban districts in The United States. As well, Machin,

McNally & Silva (2007) found positive effects on English and Science test scores of

computers dotation in the United Kingdom using, as a natural experiment, a policy change

in the UK in 2001. Finally, Fuchs & Woessman (2004,) based on observational data,

concluded that once it is controlled for family background and school characteristics, there

is an inverted U-shape relationship between student achievement and computer/internet use

at school.

In the group of papers that evaluate programs that include both ICT dotation and

teacher training, there is less literature and it is also mixed. In this literature, programs were

integrated with a teacher training that sought for the appropriation of the ICT on teaching

and learning. On the one hand, Barrera-Osorio & Linden (2009) found no statistically

significant effect in students performance of random assigned program Computadores

para Educar (same program evaluated in this paper), which aimed to integrated computers

into teaching of language in public schools in Colombia. Also, Sharma (2014) evaluates the

One Laptop per Child program in Nepal that gave training to teachers about how to teach

using laptop-based materials. Sharma (2014) used a difference-in-difference estimation and

found no statistically significant impact on math and a negative impact on English.

On the other hand, Rodrguez, Snchez & Mrquez (2011) evaluate a large-scale

version of Computadores para Educar in Colombia (same program evaluated in this

paper) with an OLS and IV approximation. They found that the program reduces drop-out

rates, increases standardized students test scores and increases the probability of accessing

higher education. In the same way, He, Linden & MacLeod (2008) evaluate an Indian

PicTalk program for teaching English to children in grades 1 to 5 that include both

machine-based implementation and activities based on flash cards and teacher manuals and

training. They found positive effects of PicTalk program and that lower performing

students benefit more from interventions with activities implemented by teachers, while

higher performing students benefit more from interventions with self-paced machines only.

However, all this literature has failed to identify in a casual and empirical way the

channels through by which those effects took place. Nevertheless, some of the papers

mentioned above had some intuitive reasons about why their specific programs had no

impact. For example, Rouse & Krueger (2004) argues that the absence of impact may be

due to that teachers failed in learning how to use ICT to enhance instruction in an effective

way. Barrera-Osorio & Linden (2009) say that the CPE program failed to integrate

computers and the educational programs because the teachers did not incorporate the

computers into the curriculum. Finally, Rodrguez, Snchez & Mrquez (2011) conclude

that access to technology is effective only if it comes together with teacher's formation

processes.

This latter idea is important for two reasons. First, the literature about teacher

formation has found positive effects on student performance. For example, Banarjee et. Al

(2005) evaluates a remedial education program in urban India, in which a local young

woman (balsakhi) teaches basic skills to lagged children. They use a difference-in-

difference evaluation and find a positive impact on test scores of 0.14 standard deviations

the first year, and 0.28 standard deviations the second year.

Second, the evaluation of Computadores para Educar made in this paper identify

the channel through the program has the positive effects we observe. So, this paper is able

to identify whether the impact observed is due to computer dotation or teachers training.

III. COMPUTADORES PARA EDUCAR AND THE ICT-TRAINING COURSE

The Colombian Program Computadores Para Educar (CPE) began operating in

2000 under the leadership of the Ministry of Technologies of the Information and the

Communications - MTIC -. The main objective of CPE is to reduce the gap in access and

knowledge in ICTs of students of public schools in the country. For that end, CPE

undertakes various activities. Thus, CPE gathers and readapts laptops and desktops and

allocates the equipment to public schools1, provides ICT-training for teachers from those

schools and disposes electronic remains in order to reduce environmental impact.

By 2013, 82% of public schools in Colombia had received the equipment provided

by CPE. As shown in Figure 1 by 2005 only 9% of schools in the country had obtained

1 The schools selected must meet the following criteria: not having equipment, having equipment but out of

date and, having some equipment but not enough to cover the number of students attending to that school.

CPE equipment and by 2010 that number jumped to 45%. As a consequence, the number of

students receiving CPE equipment also increased over time. By 2005 percentage of senior

high schools students with CPE equipment was 25% and by 2010 such percentage reached

70%.

Since 2004, CPE reoriented its strategy by giving a fundamental role to the ICT-

training of teachers. Before 2004, CPE offered short instruction (about 20 hours) to

teachers and school staff on the basic usage and functions of the provided equipment.

Nonetheless, after 2004 the program authorities replaced such basic instruction for certified

training course offered to teachers of CPE schools.

The teachers ICT-training is a crucial stage of the program. It consists of a 150

hours course (120 hours of on-site classes and 30 hours of virtual classes) that provides

practical, theoretical and motivational skills thus teachers can efficiently incorporate ICTs

in their teaching practices. The course can be summarized in 3 steps: first, teachers learn

the basics of ICT-infrastructure management and usage. Once teachers finish this stage,

they certified as Digital Citizens. Second, teachers deepen their knowledge on ICTs

through theoretical and practical lectures. Here, they formulate and develop a classroom

project that incorporates ICT usage. The project must be related to the classroom

curriculum. Once the classroom project is properly formulated the course moves toward the

third stage. Here, teachers bring their project into classroom practice, receive feedback of

their students and proceed to evaluate the project. The course finish at the end of this stage

and teachers receive a certification as ICT-trained teachers. Then, they can present the

projects in regional and national meetings called Teach Digital where the best projects

are awarded.

In order to properly augment the number of ICT-trained teachers, CPE developed a

strategy at national level that encompasses four steps. First, CPE calls for universities to bid

for carrying out the ICT courses for teachers in eight Colombian regions previously

defined2. The selected universities are hired by CPE to offer the ICT-training course for

two years. Every year, and before the beginning of the course the selected universities and

CPE design the ICT-training strategy and curriculum. Then, the second step begins and a

team of experts from the universities visit the schools and the local educational authorities

in order to recruit teachers for the ICT-training course. Every year, each university must

meet a minimum number of trained teachers on its assigned region. This minimum number

has increased every year. The third stept is the execution ICT course itself described above.

The fourth and final step is the socialization of the projects in regional and national

meetings called Teach Digital. In these meetings, the teachers that finished the ICT-

training courses present their project and experiences to their colleagues. The best project in

every region receives an award and is invited to present their findings at the Teach

Digital national meetings.

As a consequence of the teachers ICT-training implementation, the proportion of

trained teachers across municipalities has increased over time and expanded across the

national territory. Figure 2 represents the proportion of ICT-trained teachers in each

municipality in 2005, 2008, 2011 and 2013. In 2005 the proportion of them was under 20%

for most of the municipalities growing steadily over the years. By 2013, most of the

2 In 2014 the 8 regions grouped the following departments. Group 1: Atlntico, Bolvar, Crdoba, San Andrs

and Sucre; group 2: Cesar, la Guajira, Magdalena and Norte de Santander; group 3: Caldas, Quindo,

Risaralda and Valle del Cauca; group 4: Arauca, Boyac, Casanare, Santander and Vichada; group 5;

Caquet, Guaviare, Huila and Tolima; group 6: Cauca, Nario and Putumayo; group 7: Amazonas, Cundinamarca, Distrito Capital, Guaina, Meta and Vaups; and group 8: Antioquia and Choc. Each group has one university in charge of the training except group 1 that has four universities because of the number of schools, students and teachers.

municipalities had at least 20% of their teachers with ICT-training and some had reached

over 60% .

Between 2004 and 2013 74,434 teachers received the ICT-training certification

under the described scheme. This represents nearly 24% of the teachers in Colombian

public schools. As the teachers self-select or the ICT-training course the ICT-trained

teachers might be different from non-trained in their observable characteristics. Table 1

presents by trained and non-trained the teachers characteristics for selected years. As

observed, the teachers who enroll in the ICT-training are younger, and exhibit lower

education level (secondary or less and normalista).

Moreover, the proportion of ICT-trained teachers differs by education level and

subject. Figure 3 and 4 depicts the proportion of teachers by level and subject respectively.

Thus, figure 3 shows that a growing proportion of ICT-trained teachers teaches in primary

rather than in secondary school. Graph 4 indicates there is a significant variation of the

proportion of ICT-trained teachers by subject and year. Physics, math and social sciences

have the biggest proportion while philosophy has the lowest.

IV. DATA

The data used in this paper comes from four different sources, organized in two

different ways: the first one for dropout and grade retention measures and the second for

performance measures. In the following, we proceed to explain each of our sources and

then, we explain how we organized them in order to apply our proposed identification

strategy.

Data sources

Our main source is the administrative information of CPE program. CPE authorities

have meticulously gathered information of the three strategies of CPE implementation: the

gathering, readaption and allocation of laptops and desktops to public schools; the certified

ICT course to teachers; and the management of electronic remains. For this paper we

focused on the first two sources. From each one of them, we can track the year in which

each school received CPE equipment and if it have been replaced or thicken within a

certain school. We can also identify each ICT-certified teacher and the year in which that

teacher received the certification. Both information sources are available since the

beginning of each strategy: 2001 and 2004, respectively.

Second, we use the information of the Annex 6a from the Resolution 166 gathered

by the National Ministry of Education. This is administrative data available since 2005

until 2013 that yearly gathers information of every student in the public schools system.

Annex 6a provides information of students school and grade. It also provides socio-

economical information such as sex, age socioeconomic strata and mothers education.

With this information, we are able to identify whether a student dropped out or was retained

in a grade for a certain year.

Our third source of information allows us to characterize teachers. In this case, we

use the Annex 3a data also from the Resolution 166 which is available since 2008 until

2013. This source yearly gathers information of every teacher in the public schools system.

Annex 3a allows us to identify the school in which the teacher teaches each year, the level

in which she teaches and the subject she teaches. It also provides information of teachers

education level, age, sex and date of hired.

Finally, we use information of the Colombian standardized test Saber 11. This test is

compulsory for students finishing the last year of secondary education. The exam evaluates

students performance in math, language, social science, biology, chemistry, physics,

English and biology and includes an elective subject chosen by the student or the school.

Additionally, it gathers socioeconomic information of the student such as age,

socioeconomic strata and mothers education. This test also allows us to identify the

student's school. Saber 11 is available since 2000 until 2012.

Dataset for estimation of ICT teachers training impact

For dropout and grade retention estimations we used the Resolution 166 datasets for

students, teachers and CPE intervention. First, each dataset is reduced to one register by

educational level (either primary or secondary), year and school. Thus, we obtained

averages of the students and teachers characteristics, and dropout and grade retention rates.

Then, we combined the three datasets using the school id. As a result, we obtained a panel

by educational level, school and year. Thanks to the administrative information for CPE,

we can identify the exact year in which CPE entered the school and the exact year in which

each teacher was certified as ICT-trained. As result, we observed the average dropout and

grade retention by school, educational level and year between 2005 and 2013. One

important feature of this data is that we can identify whether a teacher has remained in the

school where she received the ICT-training or moved to another school, with or without

CPE equipment.

For the estimations for performance in Saber 11 we used the Resolution 166 dataset

for teachers, the dataset for Saber 11 and again, the dataset of CPE intervention. In this

case, we reduced each dataset to one register by subject area (or taught area in the case of

the dataset for teachers), year and school. Thus, we obtained averages of the students and

teachers characteristics, and average performance in each subject of the Saber 11 test. Then,

we combined the three datasets using the school id. As a result, we obtained a dataset that

allows us to track schools since 2004 until 2013.

In both cases, we made an important adjustment to the dataset for teachers. As

explained before, this dataset is available only since 2008. But, the ICT-training began in

2004 and the information of CPE allows us to identify the teachers that were trained since

that year. Thus, between 2004 and 2007, we fixed the total number of teachers in each

school level or area with the total number of teachers in 2008. Under this assumption for

the information between 2004 and 2007 is how we calculated the proportion of ICT-trained

teachers.

V. EMPIRICAL STRATEGY

Our objective is to assess the impact of ICT-training for teachers on educational

outcomes. Particularly, we focus on three major outcomes: Dropout rate, grade retention

rate and subject performance on the secondary exit test Saber 11. Thus, we reduce our

sample to the public schools that have the computers of CPE program, because we are

interested in measure the effect of teachers ICT-trained isolated of the computers

endowment. Under this scenario, our initial framework is described by the following

equation:

In equation ,

represents dropout rate, grade retention rate or performance

on Saber 11 in school , in the year and in municipality . Our interest variable is

which represents the proportion of ICT-trained teachers in school

, in the year and in municipality . Also, we control for socio-economical characteristics

of the students. Thus,

represents the average age of students in school ,

in the year and in municipality . Similarly, represent the average proportion

of women and

represents the average education level of students

mothers. Furthermore, we include fixed effects at the school level ( and year fixed effect

( to capture difference that could emerge over time and across schools. We also include

CPE years at the schools fixed effects ( to capture all non-observables

that may change along the CPE intervention. Finally,

represents the error term.

Nevertheless, this model has endogeneity issues that restrict us of obtaining

unbiased estimators. The main issue is that ICT-trained and non-trained teachers coexist

within schools. Furthermore, teachers self-enrolled in the ICT-training course which means

that ICT-training is non-random within teachers. Thus, although equation allows us to

control for many of the unobserved factors that may vary between schools, this framework

does not correct by within schools variation. For example, if there is an unobserved change

in the school curriculums that affects students performance, we would not be able to

correct for it and we could be assigning the effect of that change to the ICT-training. As a

consequence, we would be obtaining a non-causal and biased estimator.

In order to control for within school variation, we follow Brutti and Snchez (2015)

and exploit two facts observed in figures 3 and 4: first, the proportion of ICT-trained

teachers varies between levels and within schools; and second, the proportion of ICT-

trained teachers varies within taught subjects. For the latter, we take advantage of the fact

that all students take a secondary exit exam that evaluates seven different subjects. As all

schools must teach at least these subjects to secondary students and each one is taught by

different specialized teachers, we include a within schools variation that accounts for the

ICT-trained teachers proportion in each of the evaluated subjects and the Saber 11

performance in each of the taught subjects. For the between educational levels case, even

though we cannot observed the exactly same student in primary and secondary level

simultaneously, we can use the within educational level variation to correct for

unobservable school characteristics that may change between educational levels.

For that end, we propose the following estimation framework suitable in a time

panel dataset by school and educational level, and by school and taught subject.

In the equation ,

represents dropout rate, grade retention rate or

performance on Saber 11. As mentioned before, one distinctive characteristic of this

framework is that it allows for variation within school. Depending on the outcome

measured, we observe our variable either at the educational level or at the taught subject

level. Thus, if represents dropout rate or grade retention rate, we say

is either of

these outcomes in school , in the year , in municipality and in the educational level .

Here, we take two levels into account: primary and secondary education. On the other hand,

if represents the performance on Saber 11, we say

is the average performance on

Saber 11 in school in the year in municipality and in the taught subject . In this case,

we take into account the same subjects measured by the Saber 11 test: Mathematics,

Physics, Chemistry, Language, English, Social Science, Biology and Philosophy. This

framework allows us to control for unobservable characteristics that may change within

schools.

Our interest variable is

. This variable represent the ICT-

trained teachers proportion in school , in the year , in municipality and in the

educational level or taught subject In the educational level case, we calculated the

proportion of ICT-trained teachers either at primary or secondary level in relation to the

total of teachers in the respective level, for the school , to year and in municipality . In

the taught subject case, we only take into account teachers at the secondary level as they are

the potential teachers of the senior students taking the Saber 11 test. Under this assumption,

we calculated the proportion of ICT-trained teachers in each taught subject in relation to

the total number of teachers in the respective subject, in school , to year and in

municipality .

As in equation , we control for socio-economic characteristics of the students

and we allow for variation by educational level. We preserved the fixed effects proposed

for equation and we added fixed effects by level or subject ( that control for

unobservable and constant differences across them. Finally,

represents the error term.

The empirical strategy presented in equation allow us to correct an important

part of endogeneity issues. But, we need to take into account other sources of endogeneity

such as self-regression. It is possible that, even after controlling for within school variation,

the proportion of ICT-trained teachers is highly correlated with the previous performance

by educational level and by taught subjects. In order to check if that is the case, we proceed

to present a set of endogeneity exercises that confirm that self-regression is an issue in the

equation (2). Specifically, Figure 5 and 6 present the correlation between dropout rate and

grade retention rate by educational level before CPE program entered to the school, with

the proportion of ICT-trained teachers by level. On one hand, there is a correlation between

the proportion of ICT-trained teachers by educational level and the previous to CPE

dropout rate. Particularly, the school levels with the lower dropout rates have a higher

proportion of ICT-trained teachers. Similarly, the school levels with higher grade retention

rate have a higher proportion of ICT-trained teachers. This issue is supported by the

correlations between the mentioned variables presented in table 1. Except for the

correlation between previous to CPE dropout rate in primary and proportion of trained

teachers in that level, it is observed that all correlations are statistically significant and with

a high magnitude t-statistic. Figure 6 confirms that the auto-regression issue also exists for

the Saber 11 estimation. Lower performance in a subject before CPE is correlated with a

higher proportion of ICT-trained teachers. The results in table 2 confirm the direction of the

correlation for all subjects except Social Science and Philosophy were the t-statistic is non-

significant.

In order to correct for the auto-regression and other endogeneity issues that are not

being taken into account at this point, we look for a source of exogenous variation in the

program design that allows us to explain the proportion of ICT-trained teachers and that is

not correlated with our outcomes. Here, we recall the ICT-training strategy description of

the section III. Once the universities that will offer the course in each region are selected,

they hire a team of consultants that must look for teachers to enroll in the course. Every

year, a number of enrolled teachers must be met and this number has increased over time.

Additional to figure 3 and 4 that exhibit how the number of trained teachers increases every

year, figure 2 describes how the ICT-training course has expanded geographically along

municipalities and years since 2005. Thus, we attempted to exploit that particularity of the

program. Specifically, we calculated the average years of experience as ICT-trained

teachers of the teachers in the neighbor municipalities by educational level and by taught

area till the previous year. We prefer this variable instead of the proportion of ICT-trained

teachers for two reasons: first, it captures the geographical expansion of the training as well

as the increased in the time of implementation; second it also captures increases in the

proportion of ICT-trained teachers, which allow us to control for possible peer-pressure to

enroll in the ICT course.

The average years of experience as ICT-trained teachers of the teachers in the

neighbor municipalities by educational level and by taught area till the previous year can be

a good instrumental variable if it accomplishes two minimum criteria: the instrument is

correlated with our variable of interest and it is not related with the outcome. Figures 7 and

8 indicate that the first criteria is met, because the correlation between the suggested

instruments and the endogenous variable is positive. Furthermore, the estimations presented

in the results section display the F statistics of the first stage estimation which is highly

significant. The second criterion is the exclusion restriction. Nevertheless, in the estimation

framework that we suggest, it is not feasible that the proportion of ICT-trained teachers in

the neighbor municipalities by level or subject till the previous year may explain a within

school difference either in dropout or grade retention rate, or Saber 11 performance by

subject.

As a result, our identification strategy can be summarized in the equations and

. The former, represents the first stage of our estimation. Thus,

represents the average years of experience as ICT-

trained teachers of the teachers in the neighbor municipalities by educational level

and by taught area till the previous year . Equation represents the second stage

estimation with the same controls described for equation .

VI. RESULTS

Effects of ICT-training on performance on Saber 11

First, we present the results of ICT-trained teachers on performance on Saber 11.

Thus, we estimated the equation (2) and (3) and present the results in table 4. The

coefficients must be interpreted as the average effect of ICT-trained teachers on each

subject within school. Our OLS results suggest that the ICT-training has no significant

impact on students performance. But, once we correct the endogeneity issues with the

instrumental variable, ICT-trained teachers affect students performance on Saber 11

positively and significantly. In order to facilitate the interpretation, we present the mean

and standard deviation of the outcome and of the Proportion if ICT-teachers. Thus, we

deduct that an increased in one standard deviation in the proportion of ICT-trained teacher

in a certain taught subject within a school increases students performance in that subject by

0.82 standard deviations.

As observed, the IV coefficient exhibits an important increased compared to the

OLS coefficient. This is due to the negative biased estimator which underestimates the

effect of ICT-training. The biased can be explained by the negative correlation between the

omitted variables and the proportion of ICT-trained teachers and the positive correlation

between the omitted variables and the outcome variable. The figure 7 and the table 3

illustrate the first part of this argument. Specifically, they represent the correlation between

the proportion of ICT-trained teachers by subject, with the average performance in Saber 11

by subject of the school previous to CPE. Even though this correlation represent only one

possible endogeneity problem self-regression -, it clearly illustrates the existence of sub-

estimation problems with the OLS model.

Dropout and grade retention

Tables 5 and 6 present the results of dropout and grade retention estimates of the

OLS and IV models. In this case, the estimated coefficients must be interpreted as the

average effect of ICT-trained teachers in a certain educational level and within schools.

Again, we use the information of the average and standard deviation of the variables in

order to simplify the interpretation. Thus, with the OLS coefficient we estimate that an

increase of one standard deviation in the proportion of ICT-trained teachers within a school

level, reduces the dropout rate in 0.013 standard deviation. But, when estimated using the

IV strategy, this effect increases to 0.20 standard deviations. This is also the case for the

grade retention estimates presented in table 6. While with the OLS we predict that an

increase of one standard deviation in the proportion of ICT-teachers within a school level

reduces the grade retention rate by 0.03 standard deviations, the effect when we estimate

the IV model is 0.71 standard deviations.

As for the Saber 11 performance estimates, we argue that the change in the size of

the coefficient is due to the endogeneity issues corrected with the instrumental variable. For

example, for the grade retention estimates, we find evidence of sub estimation when using

the OLS model. As exhibit by figure 7 and table 2, the correlation between the grade

retention rate previous to CPE and the proportion of ICT teachers is positive. Intuitively,

we also say there is a positive correlation between the grade retention rate previous to CPE

and the proportion of ICT-trained teachers. Thus, these endogeneity problems are highly

corrected by our instrumental variable.

CPE equipment effect versus ICT-trained teachers effect

In this paper we also attempt to measure if the CPE effect found in the paper of

Rodrguez & Snchez (2015) is due to the effect of ICT-teachers training. Thus, we

replicated in our data the identification strategy they proposed in the most recent version of

their paper and estimate the following equation.

Equation (5) follows the same structure of the equation (1). Thus, instead of

measuring a within school effect, we measure a between schools effect. In this case, our

explicative variable is the number of years that CPE equipment has been on the school.

Particularly, we apply this equation only to the school that eventually receive the CPE

intervention but that do not have ICT-trained teachers. As a consequence, we can ensure

that the observed effect we might find is only due to CPE equipment and not due to CPE

teachers.

As explained by Rodriguez and Snchez (2015), this strategy has endogeneity issues

that need to be attempted. For that end, they proposed to use the percentage of schools in

municipality m where school s is located that has been served by CPE k+1 years. This

strategy explodes the variation observed in the expansion strategy of CPE, particularly of

the intervention of equipment endowments. For our purposes we slightly modify the

instrument to the proportion of schools in neighbor municipalities m-1 to school s location

that has been served by CPE k+1 years. This is the particular value that the instrument in

equation (3)

will take in our estimations.

Table 7 presents the result of the equation (5). For the performance and grade

retention estimates, we observed that CPE has no significant impact these outcomes. In the

dropout case, we observe a slightly reduction of the dropout rate of 0.05 standard deviations

in the OLS model and of 0.02 standard deviations in the IV model.

VII. PRELIMINAR CONCLUSIONS

Since 2001, Computadores para Educar has served to the students of Colombian

public schools with ICT equipment. Even more, it has trained teacher in ICT to ensure that

the new infrastructure is properly integrated into the classroom activities. With this model,

CPE has proven to be effective in reducing dropout rate and school performance.

Nevertheless, we do not have evidence of which of the described aspects explain the

program impact: the ICT equipment, or the ICT-training for teacher, or both. In this paper

we attempt solve this question by using fixed effect techniques and instrumental variables.

This is an important question for the literature about educational policy intervention.

As explained by Kennedy (1998) and MacLeon (2008), there is evidence that indicates the

effectiveness of interventions that affect the classroom practices and teachers practices for

improving educational outcomes. As they discuss, these type of interventions have proven

to be more effective for improving educational outcomes than those that merely affect

classroom resources.

Our hypothesis is that CPE improves educational outcomes through teachers

training and not through ICT equipment. Our first estimations have provided us with

evidence that our hypothesis is not rejected. Particularly, we find that increasing by one

standard deviation the proportion of ICT-trained teachers by educational level reduces

dropout by 0.20 standard deviations and reduces grade retention by 0.76 standard

deviations. Even more, improving the proportion of ICT-trained teachers by taught subject,

increases the average performance by subject in Saber 11 test by 0.7 standard deviations.

Conversely, the estimations of the effect of ICT equipment on the students of the

schools that do not have trained teachers, do not exhibit a consistent effect on educational

outcomes. The estimation presented in table 7 indicate that CPE equipment has a small

effect on dropout reduction of 0,003 standard deviations, but it does not have a significant

impact on grade retention or Saber 11 performance.

Our estimations require further analysis. Particularly, we attempt to explore three

additional issues with our data. First, it is possible that our instrumental variable presents

additional endogeneity through a spatial correlation. Thus, we need to take into account

concepts and techniques of spatial econometrics in order to correct this issue. Second, we

must identify possible spillover effects of the ICT-training within school and between

subjects or educational levels. Third, we must look for non-linearities in the found effect.

VIII. REFERENCES

Angrist, Joshua and Victor Lavy. 2002. New evidence on classroom computers and

pupil learning. The Economic Journal 112 (October): 735-765.

Banarjee, Abhijit V., Shawn Cole, Esther Duflo and Leigh Linden. 2007.

Remedying Education: Evidence from Two Randomized Experiments in India. The

Quearterly Journal of Economics 122, no. 3: 1235-1264.

Barrera-Osorio, Felipe and Leigh L. Linden. 2009. The Use and Misuse of

Computers in Education: Evidence from a Randomized Experiment in Colombia. Working

Paper no. 4836, World Bank Policy Research Working Papers.

Barrow, Lisa, Lisa Markam and Cecilia E. Rouse. 2008. Technologys Edge: The

Educational Benefits of Computer-aided Instruction. Working Paper no. 14240, National

Bureau of Economic Research, Cambridge, MA.

Fuchs, Thomas and Ludger Woessmann. 2004. Computer and student learning:

Bivariate and multivariate evidence on the availability and use of computers at home and at

school. Working Paper no. 1321, CESifo.

He, Fang, Leigh L. Linden and Margaret MacLeod. 2008. How to Teach English in

India: Testing the Relative Productivity of Instruction Methods within the Pratham English

Language Education Program. Working Paper.

Kennedy, Mary. 1998. Form and Substance in Inservice Teacher Education.

Research Monograph no. 13. National Institute for Science Education.

Machin, Stephen, Sandra McNally and Olmo Silva. 2007. New technology in

schools: Is there a payoff? The Economic Journal 117 (July): 1145-1167.

Rodrguez, Snchez & Mrquez (2011) Impacto del Programa Computadores para

Educar en la desercin estudiantil, el logro escolar y el ingreso a la educacin superior.

Documento CEDE No. 15

Rouse, Cecilia E. and Alan B. Krueger. 2004. Putting computerized instruction to

the test: a randomized evaluation of a scientifically based reading program. Economics of

Education Review 23: 323-338.

Sharma, Uttam. 2014. Can Computers Increase Human Capital in Developing

Countries? An Evaluation of Nepals One Laptop per Child Program. Agricultural and

Applied Economics Associations 2014 Annual Meeting, Minneapolis, MN, July 27-29,

2014.

Figure 1. Accumulated Number of Schools with CPE equipment by year

Source: Resolution 166. Ministry of Education

Figure 2. Percentage of ICT-trained teachers by municipality

Source: Resolution 166. Ministry of Education

4,168 5,567 8,242

11,329 15,287

20,859

25,262 28,696

37,396

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

2005 2006 2007 2008 2009 2010 2011 2012 2013

Figure 3. Proportion of ICT-trained teachers by level

Source: Resolution 166. Ministry of Education

Figure 4. Proportion of ICT-trained teachers proportion by subject

Source: Resolution 166. Ministry of Education

0

0,05

0,1

0,15

0,2

0,25

0,3

2005 2006 2007 2008 2009 2010 2011 2012 2013

ICT-

trai

ne

d t

eac

he

rs

Year

Primary Secondary

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

2004 2005 2006 2007 2008 2009 2010 2011 2012

Biology Social Science Language English

Math Chemistry Physics Philosophy

Table 1. Teachers characteristics

2005 2008 2011 2013

Training Training Training Training

No Yes

t-

statistic No Yes

t-

statistic No Yes

t-

statistic No Yes

t-

statistic

Primary Age 43.43 41.29 *** 44.65 43.11 *** 45.35 45.01 *** 46.12 45.79 ***

Woman 0.77 0.75 *** 0.76 0.75 *** 0.75 0.73 *** 0.76 0.75 ***

Teacher's educative level

Secondary or less 0.19 0.18 *** 0.18 0.17 *** 0.26 0.31 *** 0.07 0.07 **

Normalista 0.09 0.11 *** 0.11 0.13 *** 0.09 0.14 *** 0.13 0.12 ***

Bachelor 0.56 0.56

0.57 0.56 ** 0.54 0.47 *** 0.56 0.52 ***

Graduate education 0.16 0.15 ** 0.14 0.14

0.11 0.11

0.24 0.29 ***

Teacher's experience

Years 15.22 13.33 *** 15.47 14.44 *** 15.42 15.62 ** 15.45 16.49 ***

Years since ICT-training

0.11

0.90

2.65

4.46

Change of school

Change of school between

2009-2013

0.23 0.23 ** 0.36 0.38 ***

Change of school after ICT-

training between 2009-2013

0.15

0.29

N 90,853 30,705 108,193 35,088 93,656 32,678 105,558 38,466

Secondary Age 44.51 41.00 *** 45.34 42.55 *** 45.50 44.48 *** 45.91 45.87

Woman 0.55 0.58 *** 0.54 0.57 *** 0.52 0.54 *** 0.53 0.56 ***

Teacher's educative level

Secondary or less 0.13 0.15 *** 0.13 0.14 ** 0.13 0.18 *** 0.02 0.03 ***

Normalista 0.03 0.04 *** 0.03 0.05 *** 0.02 0.04 *** 0.01 0.02 ***

Bachelor 0.61 0.62

0.65 0.65

0.69 0.64 *** 0.67 0.64 ***

Graduate education 0.23 0.19 *** 0.19 0.16 *** 0.16 0.14 *** 0.30 0.31 ***

Teacher's experience

Years 14.87 11.46 *** 14.60 12.14 *** 13.94 13.26 *** 15.06 14.44 ***

Years since ICT-training 0.00 0.12

0.88

2.78

4.65

Change of school

Change of school between

2009-2013

0.21 0.22

0.35 0.37 ***

Change of school after ICT-

training between 2009-2013

0.15

0.30

N 90,252 13,073 112,133 15,870 106,250 14,549 122,390 17,096

Figure 5. Endogeneity test for Dropout rate and grade retention rate by educational

level

Figure 6. Endogeneity test for Saber 11 score by subject

0.2

.4.6

Pro

p.

of

ICT

tra

ined

te

ach

er

by le

ve

l

0 .1 .2 .3 .4Dropout rate by level

Endogeneity graph Dropout

0.2

.4.6

.8

Pro

p.

of

ICT

tra

ined

te

ach

er

by le

ve

l

0 .1 .2 .3retention rate by level

Endogeneity graph retention

.2.4

.6.8

Pro

p.

of

ICT

tra

ined

te

ach

er

by d

iscip

lin

e

38 40 42 44 46 48Saber 11 score by discipline before CPE

Endogeneity graph Saber 11

Figure 7. Correlation between instrumental and instrumented variable by

educational level

Figure 8. Correlation between instrumental and instrumented variable by taught

subject

0.1

.2.3

.4

Pro

p.

of

ICT

tra

ined

te

ach

er

by a

rea

0 .5 1 1.5 2Theacher's experience in neighbour municipalities t-1

Instrumental variables Dropout

0

.05

.1.1

5.2

Pro

p.

of

ICT

tra

ined

te

ach

er

by d

iscip

lin

e

0 .2 .4 .6 .8Theacher's experience in neighbour municipalities t-1

Instrumental variables Saber 11

Table 2. Endogeneity test. Correlation between the proportion of trained teachers

and previous dropout rate and grade retention rate by educational level

(1) (2) (3)

All levels Primary Secondary

Dropout rate before intervention* -0.484*** 0.023 -0.327***

(-25.430) (0.783) (-10.561)

Observations 31,113 20,248 10,865

R-squared 0.026 0.012 0.032

Grade retention rate before intervention~ 0.549*** 0.240*** -0.448***

(18.788) (6.682) (-6.790)

Observations 23,712 15,346 8,366

R-squared 0.024 0.017 0.025

Years Fixed Effects Yes Yes Yes

Robust t-statistics in parentheses

*** p

35

Table 3. Endogeneity test. Correlation between the proportion of trained teachers and previous Saber 11 performance by subject

(1) (2) (3) (4) (5) (6) (7) (8) (9)

All subjects Biology

Social

Science Language English Math Chemistry Physics Philosophy

Saber 11 score before CPE

intervention* -0.019*** -0.017*** -0.002 -0.020*** -0.043*** -0.030*** -0.018*** -0.041*** -0.007

(-19.828) (-5.382) (-1.028) (-6.980) (-11.256) (-12.691) (-4.502) (-10.747) (-0.912)

Observations 22,513 2,929 4,450 3,232 2,189 3,660 1,675 3,866 512

R-squared 0.058 0.064 0.021 0.059 0.160 0.109 0.085 0.094 0.049

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

Robust t-statistics in parentheses

*** p

36

Table 4. Estimation of the effect of ICT-trained teachers on Saber 11 performance by

subject

(1) (2) (3)

OLS IV First Stage

Proportion of ICT trained teachers by subject -0.011 11.433***

(0.026) (0.650)

Average years of experience of teachers ICT-trained in

neighbouring municipalities at t-1 by subject 0.161***

(0.007)

Saber 11 score before CPE intervention*

Kleibergen-Paap rk Wald F- statistic

528.487

Average score in Saber 11 test by subject 43,06

Standar deviation [2.68]

Average proportion of ICT trained teachers by subject 0.05

Standar deviation [0.18]

Observations 237,718 237,718 237,718

R-squared 0.359 -0.364 0.059

Number of schools 4,888 4,888 4,888

Students' characteristics Yes Yes Yes

Year fixed effect Yes Yes Yes

Years of CPE in the school fixed effect Yes Yes Yes

School fixed effect Yes Yes Yes

Discipline fixed effect Yes Yes Yes

Robust standard errors in parentheses

*** p

37

Table 5. Estimation of the effect of ICT-trained teachers on dropout rate performance by

level

(1) (2) (3)

OLS IV First Stage

Proportion of ICT trained teachers by level -0.006*** -0.091***

(0.002) (0.022)

Average years of experience of teachers ICT-trained in

neighbouring municipalities at t-1 by discipline

0.188***

(0.012)

Average dropout rate 0.13

Standard deviation 0.09

Average proportion of ICT trained teachers by level 0.10

Standar deviation 0.20

Kleibergen-Paap rk Wald F- statistic

262.89

Observations 125,436 125,400 125,400

R-squared 0.316 0.294 0.271

Number of Schools 11,034 10,998 10,998

Year Fixed Effect Yes Yes Yes

Years of CPE in the School Fixed Effect Yes Yes Yes

School Fixed Effect Yes Yes Yes

Educative level Fixed Effect Yes Yes Yes

Robust standard errors in parentheses

*** p

38

Table 6. Estimation of the effect of ICT-trained teachers on grade retention rate

performance by level

(1) (2) (3)

OLS IV First Stage

Proportion of ICT trained teachers by level -0.022*** -0.519***

(0.002) (0.032)

Average years of experience of teachers ICT-trained in

neighbouring municipalities at t-1 by level

0.242***

(0.014)

Average grade retention rate 0.10

Standard deviation 0.11

Average proportion of ICT trained teachers by level 0.13

Standar deviation 0.15

Kleibergen-Paap rk Wald F- statistic

313.06

Observations 127.436 127.356 127.436

R-squared 0.099 -1,287 0.256

Number of Schools 11.263 11.183 11.263

Year Fixed Effect Yes Yes Yes

Years of CPE in the School Fixed Effect Yes Yes Yes

School Fixed Effect Yes Yes Yes

Educative level Fixed Effect Yes Yes Yes

Robust standard errors in parentheses

*** p

39

Table 7. Estimation of the effect of Computers for Teaching on Educational outcomes. Only Schools with computers and without ICT-trained teachers

Saber 11 Dropout rate Grade retention rate

(1) (2) (3) (4) (5) (6)

OLS IV OLS IV OLS IV

Years of CPE intervention 0.025 0.035 -0.002*** -0.001** -0.000 0.001

(0.021) (0.021) (0.001) (0.001) (0.001) (0.001)

First Stage

Years of CPE intervention at the neighbour

mun

-0.323

0.028***

-0.275

0.002

0.001

(0.001)***

Kleibergen-Paap rk Wald F- statistic

1.50E+04

7.50E+04

7.50E+04

Average score in Saber 11 test by subject 43.25 0.13 0.10

Standar deviation 2.56 0.09 0.08

Average year of CPE intervention 1.28 1.25 1.53

Standar deviation 2.24 2.31 2.61

Observations 15,669 15,669 43,910 43,910 35.394 35.394

R-squared 0.003 0.002 0.174 0.174 0.046 0.046

Number of Schools 3,018 3,018 8,647 8,647 7.778 7.778

Year fixed effect Yes Yes Yes Yes Yes Yes

School Fixed effect Yes Yes Yes Yes Yes Yes

Robust standard errors in parentheses

*** p