Final_Maria Fernandez

The current educational system in Chile consists of three types of schools: public, subsidized or private. Education is based on a “voucher” system, where parents can choose where to place their children. Private schools charge a fee which varies from school to school. Subsidized schools receive public funding in the form of a fixed amount by student, and they can charge an additional tuition fee. Public schools are free of charge and also receive money from the central government for every student that attends that school. However, they are free of charge and are run by the local government (commune or municipality).

The Chilean educational system has proven to perpetuate the existing social differences among people, instead of overcoming them. Two standardized tests are taken each year as a tool for parents and the government to evaluate the schools. Results from one of those tests (PSU, taken to enter tertiary level education) show that students from public schools score significantly lower than those from private or subsidized schools.

Table 1. Average PSU Score by Type of School

Type of School

Average Score







Source: DEMRE, 2012.

In Chile, only a small percentage of children attend private schools (7%), while the rest of the students attend subsidized and public schools in almost equal parts. Nonetheless, those who attend private schools tend to come from the highest income families, while students in public schools have the lowest incomes (Brandt, 2010). This contributes to the inequities in the system, as poor children tend to go to the schools with the lowest performance.

Even though test scores are an important source of information, there are other aspects that must be taken into account if we want to know evaluate each school´s performance. Factors such as “family background and socioeconomic conditions, differential access to facilities and inherent ability” can also account for a large part of a student´s and a school´s performance (Suryadarma, et al, 2006: 2). This is also the case of teacher quality, which can explain the school´s educational results (Brandt, 2010).

The first layout of the presentation places the area of study—Chile—in its regional context. Chile is located in South America, in the south-west corner of the subcontinent. It has a population of approximately 17.2 million and its capital is Santiago. The government is organized as a centralized republic, with Presidents elected every four years.

The second layout introduces the political divisions of the country. Chile is organized in 15 regions, each of them dividing the country from east to west. At the same time, each region is subdivided into communes. The number of communes varies in every region, but the most densely populated areas in the center of the country are the ones with the most number of communes.

The following layout helps us understand the inequities in Chile´s educational system. Public schools get the lowest scores on standardized tests, and thus the students who attend them have a lower chance of going to the best Universities. As we have already mentioned, the majority of people who attend public schools come from the lowest-income families, so the level of education they are able to obtain is the lowest the system offers.

Therefore, the research question arises. Since resources are scarce and education seems to perpetuate social inequities, are there any areas (or hotspots) where the government should focus its attention in public education? The main focus is to find certain areas where the different variables to measure education are found, in order to determine whether there is a geographical area that requires special attention from the central government.

The following map tackles the first variable: are there enough schools in the country? Is there an area where there are not enough schools for the population? From the information in the map, we can see that there appears to be no shortage of schools in the most densely populated areas. The areas with the lightest colors are the ones with the least population, and with the least schools.

The data in this map was incorporated through a Euclidean distance raster of 10 km around the most populated areas (cities). This distance was chosen as it is a reasonable stretch to travel daily for students who attend school. Next, each school was entered into the map, since the X and Y coordinates were available (a Coordinate conversion had to be performed), and a Kernel density raster was made from the schools in the map. Both rasters were reclassified and then added intro one with Map algebra, to produce the map in the layout.

The information for the location of the schools was obtained from the Ministry of Education website, in an Excel spreadsheet. The information for the cities was obtained from the Integrated System of Territorial Information (SIIT) of Library of Congress (Chile) website, as a shapefile. In this case, there was a problem with the X and Y coordinates, as ArcMap did not place them on top of the country shapefile. After checking that the coordinates were correct (mapping them on Google Map), the problem was solved when a coordinate conversion was performed. When creating the point shapefile with the locations of the schools, ArcMap assumed they came in meter notation. After converting them to the same notation as the country shapefile, the points were located in the correct place.

The second map shows an index of the Average Scores in the standardized tests previously mentioned, at the commune level. From this map we can see that there is no clear hotspot, as the worst scores are not concentrated in any particular area. The colors are dispersed in the map, showing that if we only consider scores in standardized tests (the most common way to measure school performance), there would be no particular geographical area to focus on.

The index was created by taking an average of the tests, and then joining to the commune shapefile. The scores were divided into 5 categories, with natural breaks (Jenks): high, moderately high, medium, moderately low and low. The information for this map was obtained from the National System of Municipal Information (Chile), (Sinim) and the University of Chile, Department of Education Evaluation, Measurement and Registry (DEMRE) website. The commune shapefiles were retrieved from SIIT.

However, when analyzing the quality of teachers, we can clearly identify two hotspots in the country. Since the Ministry of Education states that a Basic or Insufficient evaluation is a fail grade, in those areas we can see that there is a high and moderately high concentration of teachers who do not have the necessary skills to be teaching their students.

In order to create this map, it was necessary to come up with the ratio of teachers who received an evaluation of Basic or Insufficient. This information was available, by school, in the Ministry of Education website. Then, only the schools where over 80% of the teachers received the lowest scores were put in the map (again with X, Y coordinates and Coordinate conversion). A Kernel density raster was performed on all the points, and this was later classified with natural breaks (Jens), to obtain the areas with the highest concentration of low-skilled teachers.

The next layout shows the concentration of schools where over 80% of the teacher´s skills were evaluated as basic or insufficient. The Chilean Ministry of Education considers that teachers who obtain either of these two scores are not qualified to be teaching in public schools. When analyzing the map we can see that there is clearly a concentration of schools where teachers are not qualified enough. We can expect that the quality of education they are imparting to the students will be very low.

To build this map a teacher quality index was built. The first step was to take a ratio of the teachers with insufficient or basic evaluation, to the total teachers. Only schools where over 80% of the teachers received the lowest scores were kept in the table. Each school was placed on the map (with X, Y coordinates and coordinate conversion). A kernel density raster was then performed in order to determine if there was a concentration of pootly evaluated teachers in any particular region of the country. This index was divided into 5 categories, sorted by natural breaks (Jenks).

The following layout shows the concentration of poor students in schools. The map shows that there is a clear hotspot where most of the poor students are concentrated. The Chilean government refers to these students as “vulnerable”, since their families have very low incomes, they live in precarious conditions, their parents may be missing, etc. The government divides the “vulnerable” students into 3 groups, according to their level of “vulnerability”. We can see that clearly there is an area of the country where more if these students reside, and when comparing this to our previous map, the location of the hotspot is almost the same.

To map the poverty information, a “vulnerability· index had to be built. The first step was to take a ratio of the poor students (2 priority levels of “vulnerability”) to the total students. Any school with over 75% of its students in either one of the two higher “vulnerability” groups was placed on the map (with X, Y coordinates and Coordinate conversion). A kernel density raster was then performed in order to determine if there was a concentration of poor students in any particular region of the country. This index was divided into 5 categories, sorted by natural breaks (Jenks).

The final analysis maps the communed with the lowest scored in an education index. As we have seen before, parent´s education is an important factor that determined the child´s academic performance. In this case, we can see that again there is a hotspot where the low and very low cases reside. The shaded area in the center of the country closely resembles the hotspots we had seen in the previous maps.

The education index was obtained at the commune level, for each commune where information was available. Since we could not obtain data on the years of education for each student or school, we used the education attainment level of the commune as a proxy. Also, the poverty index and median income level were added to the index, and they too serve as proxies, since we assumed people with lower educational attainment obtained lower-skilled jobs and received lower wages. Each one of these variables was joined to the commune shapefile table, and they were added to create a new index with the filed calculator. The education attainment variable was weighed at 50% and the other poverty-related variables were weighed at 25% each. The information for educational attainment was obtained from Sinim, and the information for income and povery level was obtained from the Ministry of Social Development website.

The final layout shows the schools that are located in the hotspot. The findings from the research show that these schools are the ones where the variables that determine education performance are lowest. Even though it is not clear from the test scores, this is the area where the poorest students live, where their parents have lower income and educational level, and they also have the worst teachers. It is therefore these two regions where the central government should place its attention, because these are the children who are in the most disadvantaged position with respect to the rest of the country.

GIS was very useful for this project because it allowed to visualize where the students with the most pressing educational need live in Chile. Another type of analysis would not have enabled us to see so clearly where the hotspot was, since it would not have showed this in a visual way.



Brandt, Nicola. (2010). Chile: Climbing on Giants´ Shoulders: Better Schools for all Chilean Children. Organisation for Economic Co-operation and Development (OCDE).
Integrated System of Territorial Information (SIIT), Library of Congress Chile,
National Statistics Institute Chile (INE),
La Tercera,
Ministry of Education Chile (Mineduc),
National System of Municipal Information Chile (Sinim),
Suryadarma, Daniel, et al. (2006). Improving Student Performance in Public Primary Schools in Developing Countries. Evidence from Indonesia. World Bank Research Paper.
The Clinic,
University of Chile, Department of Education Evaluation, Measurement and Registry (DEMRE),
University of Chile, Department of Education Evaluation, Measurement and Registry (DEMRE). (2012). Compendio Estadístico Proceso de Admisión Año Académico 2012. Santiago, University of Chile.

World Bank, Data Catalog,



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