COES & Observatory of Social Cohesion:

Updates & projections

---

Juan Carlos Castillo¹, Tomás Urzúa¹, Andreas Laffert¹ & Julio Iturra²

¹Department of Sociology, Universidad de Chile
²Bremen International Graduate School of Social Sciences

Universität Bremen

07th April, 2026

Contents

• Updates on COES & OCS projects
• ELSOC Panel Visualization
• Social Cohesion in Latin America paper
• Comparative vignette study (Julio Iturra)

Contents

• Updates on COES & OCS projects
• ELSOC Panel Visualization
• Social Cohesion in Latin America paper
• Comparative vignette study (Julio Iturra)

COES

• End of funding
• Universidad de Chile 2026 funding
• OCS projections 2026
  • improve ELSOC panel visualization
  • submit paper on social cohesion in Latin America
  • comparative vignette study paper

Latin America Visualizer (VISLATAM)

• based on LAPOP survey data
  • 20+ countries, 2004-2022 (11 waves)
  • democracy, social cohesion, trust, etc.
  • 23 countries
  • more than 400.000 cases
• link to visualizer VISLATAM

Conceptual & measurement model: horizontal

Conceptual & measurement model: vertical

Contents

• Updates on COES & OCS projects
• ELSOC Panel Visualization
• Social Cohesion in Latin America paper
• Comparative vignette study (Julio Iturra)

Good morning to everyone, I am Tomás Urzúa,

last year I was a research assistant at the Social Cohesion Observatory where one of my principles tasks was the divelopment of the Longitudinal Visualizer of Social Cohesion in Chile

Why VISELSOC?

• Visualizer for Latin America, but not something specifically focused on Chile

• ELSOC panel survey in COES

• Last year (2025) special funding (since it was the last year of COES) -> apply what we learned from the experience with VISLATAM to ELSOC data

and we had two motivations behind this project:

• F{e}rst, we had built already a visualizer for Latin America, but we didn’t have something specifically focused on Chile

• and second, in COES we had a panel survey which is ELSOC and last year we had a special funding since it was the last year of COES, so we decided to apply what we learn from the experience with VISLATAM to ELSOC data

Data

• Secondary data from Chilean Longitudinal Social Study (ELSOC)

• Panel survey that runs every year from 2016 to 2023, making it the only study of this kind in Chile and Latin America

• For the visualization, the analysis includes participants who took part in at least three waves of the study, totaling 3,666 individuals.

We used secondary data from the Chilean Longitudinal Social Study, a representative survey of Chile’s urban population that runs every year from 2016 (two thousand sixteen) to 2023 (two thousand twenty-three), making it a yiunique source of information of its kind in Chile and Latin America.

For the creation of the visualizer, only respondents who participated in at least three waves of the study were considered, resulting in a total of three thousand six hundred sixty-six individuals.

Measurement

• Measurement framework for social cohesion in Chile based on the approach proposed by Chan et al. (2006).

• The framework is based on two dimensions:

  • Horizontal: addresses the relationships between individuals and social groups

  • Vertical: captures the interactions between individuals and social institutions

The measurement with social cohesion in VISELSOC is quite similar to VISLATAM as both make the distinction between horizontal and vertical dimensions proposed by Chan et al in (2006)

But the main difference is that with ELSOC, we have more items for analyzing social cohesion than VISLATAM, and we could divelop a more comprehensive measurement framework for our data

In the case of horizontal cohesion, we can see that there are three subdimensions: public safety, territorial ties, and social networks.

Public safety consists of objective safety —such as the frequency of exposure to violent situations— and subjective safety, which refers to perceptions of safety.

Territorial ties measures people’s sense of belonging to their neighborhood and residents’ satisfaction with their local area.

Social networks are composed of three subdimensions: prosocial behavior, financial assistance, and interpersonal trust.

For vertical cohesion, we have four subdimensions. First, Trust in institutions, which measures trust in various political institutions such as the government, the political parties, and the Congress

Second, political attitudes and participation which includes political self-efficacy, political interest and political participation

Then, we have preferences for authoritarianism, which captures the extent to which individuals support authoritarian values and practices.

Finally, we have distributive justice that is composed by how unfair people consider the distribution of healthcare, education, and pensions to be

Measurement

• Differents analytical techniques for to arrive at the final version of the measurement framework.

• Sub-dimensions were calculated as average indices to facilitate the interpretation of the results.

• All the decisions behind the construction of the framework can be found in this methodological document.

We applied a several analytical tekníks for to arrive at the final version of the measurement framework such as descriptive analyses, correlation matrices, both exploratory and confirmatory factor analysis to verify that the model’s underlying structure was optimal for its application in the Chilean context.

Although we confirmed the factor structure of the data, all sub-dimensions were calculated as average indices to facilitate the interpretation of the results

and all the decisions behind the construction of the framework can be found in this

Construction

• The visualizer was completely created by the team at the Social Cohesion Observatory (OCS).

• It was built using code with Quarto and Shiny App.

• The visualizer’s source code is open access and can be found in our GitHub repository

• The visualizer was completely created by the team at the Social Cohesion Observatory

• also it was built using code with Quarto and Shiny App

• and its source code is open access and can be found in our GitHub repository

• I don´t know if all of you are familiar with Quarto, but Quarto is an open-source pəbliSHiNG system that allows you to create dynamic, reproducible documents, while Shiny App is a Quarto output format that enables the creation of interactive web applications.

Results VISELSOC

Now I would like to show you what we can do with VISELSOC. So, the visualizer has three types of analyses.

Longitudinal

First, we have the longitudinal section, where we can observe the subdimensions and how they change over time.

So let’s focus on trust on political institutions

To understand the plot, the x-axis shows the years as categories, and the “wai”-axis shows the number of people who rated the highest categories—that is, 4 and 5—in terms of percentage

Now, using the data provided by the graph, we can see that trust in institutions fluctuates over time, reaching its lowest point in 2019. This can be explained by the social crisis Chile experienced that year, a contentious period known as the “social uprising.”

Ranking

In the ranking section, we can select a sub-dimension and compare the indicators that make it up, so let’s continue with trust in political institutions

First, we can see that at the right bottom there are the legends of each item. So, we can see the exact percentage when we hover over a chart.

In this case, trust in the government is an indicator that has change drastically over time, reaching a high of 40% and a low of 16% the following year. On the other hand, trust in political parties is at a historic low, having peaked just 14% at 2018

Bivariate

And finally we have the bivariate section, where we can plot the available subdimensions, but with the added benefit of being able to observe differences by category.

So let’s cross trust in political institutions with education

These were just a couple of examples of what we can do with VISELSOC, and we think that it could be interesting for you in case you would like to make something similar with your panel data.

Contents

• Updates on COES & OCS projects
• ELSOC Panel Visualization
• Social Cohesion in Latin America paper
• Comparative vignette study (Julio Iturra)

Two decades of changes in social cohesion in Latin America (2004-2023)

---

Juan Carlos Castillo, Julio Iturra, Gabriel Cortés & Tomás Urzúa

Department of Sociology, Universidad de Chile

Universität Bremen

07th April, 2026

Project context

• Social Cohesion Observatory (OCS) was a project funded under Center for the Study of Conflict and Social Cohesion (COES 2013-2025)
• Launched in 2020 with the aim of contributing to the analysis of social cohesion in Chile and Latin America
• It is based on experience gained from international projects focused on conceptualizing and measuring social cohesion
• This study: based on data from VISLATAM (N = 179,377, nested within 174 country waves across 25 countries)

How are the regional and national trends over the past two decades in social cohesion in Latin America?

What are the main factors associated with these changes?

Theoretical and empirical background

• In Latin America, social cohesion has become increasingly important due to the political instability, persistent inequality, and social conflicts that have characterized recent years (Salazar-Xirinachs 2023).

Two main approaches:

• Focus on macro-institutional indicators (UNDP, CEPAL) (United Nations Development Programme 2023).

• Combining individual (micro) variables with macro variables (Ecosocial, OCS).

Point One: In recent years, various parts of the world have witnessed social processes reflecting widespread political instability stemming from social conflicts rooted in persistent inequality and corruption. In this context, it has become essential to understand these phenomena within the framework of social ties.

Point two: In the region, both individuals and research centers have focused their efforts on studying social cohesion as a phenomenon in itself, but also as a determinant in social, cultural, and political processes.

Social cohesion has been studied from an institutional perspective, oriented toward public policy approaches. Here, ECLAC emerges as the leading expert on the subject based on discussions among specialists and authorities, investigating how the implementation of such policies can address social ills related to social bonds

On the other hand, there are studies that have focused on the empirical dimension of social cohesion, attempting to operationalize the concept so that it can be measured. In this context, there is Ecosocial, a study that sought to document the state of social cohesion in seven Latin American countries.

Along these lines, there is also the Social Cohesion Observatory (which is part of COES), where social cohesion has been studied using various secondary surveys, but which has also developed its own measurement frameworks aimed at understanding social cohesion across its various dimensions.

Social cohesion

“is a state of affairs concerning both the vertical and the horizontal interactions among members of society as characterized by a set of attitudes and norms that includes trust, a sense of belonging and the willingness to participate and help, as well as their behavioural manifestations” (Chan et al., 2006, p. 290).

Chan, J., To, H.-P., & Chan, E. (2006). Reconsidering Social Cohesion: Developing a Definition and Analytical Framework for Empirical Research. Social Indicators Research, 75(2), 273–302.

Dimensions of social cohesion

Methodological document: Social Cohesion in Latin America

Associated factors

Individual level:

• Education

Country level

• Economic development
• Governance
• Economic inequality
• Migrant population

(Somma y Valenzuela 2015; Janmaat 2010; Delhey et al. 2018; Delhey, Dragolov, y Boehnke 2023; Castillo et al. 2023)

Hypotheses

\(H_{1}\): Individuals with higher levels of education will exhibit higher levels of social cohesion

\(H_{2}\): Countries with higher levels of inequality will exhibit lower levels of social cohesion

\(H_{3}\): Countries with higher levels of economic development will exhibit higher levels of social cohesion

\(H_{4}\): Countries with higher levels of governance will exhibit higher levels of social cohesion

\(H_{5}\): Increases in the proportion of the migrant population in a country will be associated with decreases in levels of social cohesion

Both the absolute value of the hypothesis’s directionality and the change over time (increase/decrease) must be explained

Methodology

Data

• The data comes primarily from the Latin American Public Opinion Project.

• Some suplements from Latinobarómetro and the World Values Survey

• Sample of N = 179,377 individuals across 174 country waves in 25 countries, covering the period from 2004 to 2023.

Variables

• Dependent variables: vertical cohesion index and horizontal cohesion index.

• Individual independent variables: educational level.
• Contextual independent variables: GDP per capita, Gini Index, Electoral Democracy Index (VDEM), Governance Index (WB), migration rate.

The measurement framework for Latin America consists of a vertical dimension and a horizontal dimension.

The vertical dimension considers trust in institutions, measured by the degree of trust in the judiciary, Congress, and political parties. Attitudes toward democracy assess how much someone supports democracy and how satisfied they are with it. Finally, distributive justice is measured by asking how fair the distribution of income in the country is perceived to be.

Horizontal cohesion is measured by public safety, which addresses subjective safety—that is, the perception of safety—and objective safety, measured by whether or not the respondent has been a victim of crime. Additionally, interpersonal trust is understood as the level of trust people have in their neighbors.

Methods

• CFA to validate the conceptual model

• Hybrid multilevel regression models (Schmidt-Catran y Fairbrother 2016):
• \(y_{jti} = \beta_{0}(t) + \beta_{1}X_{jti} + \gamma_{be}\bar{Z}_{j} + \gamma_{we}(Z_{jt}-\bar{Z}_{j}) + v_j + u_{jt} + e_{jti}\)

Results

CFA

Social cohesion over time

Migration

Horizontal

Vertical

Individual level

Macro level

Discussion

• Decrease of social cohesion overall

• Inequality is related negatively with social cohesion

• Positive effect of governance

• Negative electoral democracy?

• Negative migration?

Para cerrar, solo volver a recalcar la relevancia de este estudio, que con sus limitaciones y con lo que falta por hacer ya muestran resultados prometedores. Hay pocos estudios que se enfocan en comprender la cohesión social desde una perspectiva longitudinal y multinivel, menos para América Latina, y creemos que los hallazgos que esta investigación pueda entregar son importante para aportar, desde los datos, a la discusión académica y política de la cohesión social.

Contents

• Updates on COES & OCS projects
• ELSOC Panel Visualization
• Social Cohesion in Latin America paper
• Comparative vignette study (Julio Iturra)

References

Castillo, Juan Carlos, Macarena Bonhomme, Daniel Miranda, y Julio Iturra. 2023. «Social Cohesion and Attitudinal Changes toward Migration: A Longitudinal Perspective amid the COVID-19 Pandemic». Frontiers in Sociology 7 (enero): 1009567. https://doi.org/10.3389/fsoc.2022.1009567.

Delhey, Jan, Klaus Boehnke, Georgi Dragolov, Zsófia S. Ignácz, Mandi Larsen, Jan Lorenz, y Michael Koch. 2018. «Social Cohesion and Its Correlates: A Comparison of Western and Asian Societies». Comparative Sociology 17 (3-4): 426-55. https://doi.org/10.1163/15691330-12341468.

Delhey, Jan, Georgi Dragolov, y Klaus Boehnke. 2023. «Social Cohesion in International Comparison: A Review of Key Measures and Findings». KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie 75 (S1): 95-120. https://doi.org/10.1007/s11577-023-00891-6.

Janmaat, Jan Germen. 2010. «Social Cohesion as a Real-life Phenomenon: Assessing the Explanatory Power of the Universalist and Particularist Perspectives». Social Indicators Research 100 (1): 61-83. https://doi.org/10.1007/s11205-010-9604-9.

Salazar-Xirinachs, José Manuel. 2023. «Repensar, reimaginar, transformar: los “qué” y los “cómo” para avanzar hacia un modelo de desarrollo más productivo, inclusivo y sostenible». Revista de la CEPAL 2023 (141): 11-43. https://doi.org/10.18356/16820908-2023-141-2.

Schmidt-Catran, Alexander W., y Malcolm Fairbrother. 2016. «The Random Effects in Multilevel Models: Getting Them Wrong and Getting Them Right». European Sociological Review 32 (1): 23-38. https://doi.org/10.1093/esr/jcv090.

Somma, Nicolás M, y Eduardo Valenzuela. 2015. «Las Paradojas de La Cohesión Social En América Latina». Revista del CLAD Reforma y Democracia, n.º 61: 43-74.

United Nations Development Programme. 2023. Trapped: High Inequality and Low Growth in Latin America and the Caribbean: Regional Human Development Report 2021. United Nations. https://doi.org/10.18356/9789210057844.