Dos décadas de cambios en la cohesión social en América Latina (2004-2023)

Autores/as

Afiliaciones

Juan Carlos Castillo

Universidad de Chile

Centro de estudios del conflicto y cohesión social (COES)

University of Bremen

Gabriel Cortés Paredes

Centro de estudios del conflicto y cohesión social (COES)

Tomás Urzúa

Centro de estudios del conflicto y cohesión social (COES)

Julio Iturra

University of Bremen

Resumen

En un contexto regional marcado por crisis políticas, desigualdades persistentes y episodios de conflictividad social, comprender la evolución de la cohesión social es fundamental para evaluar la estabilidad democrática y la legitimidad institucional. Si bien existen numerosos estudios sobre las causas y consecuencias de la desconfianza o la polarización en América Latina, aún persiste un vacío en el análisis sistemático y longitudinal de la cohesión social como fenómeno integral. Este proyecto busca llenar ese vacío mediante el desarrollo de un conjunto de indicadores que permitan analizar con comparabilidad temporal y regional la evolución de las distintas dimensiones de la cohesión social.

Este artículo busca cubrir esas brechas proponiendo y validando un modelo de medición que permita un análisis comparativo, longitudinal y multinivel de la cohesión social en América Latina. En concreto, buscamos avanzar en: (i) una operacionalización clara y validada que integre dimensiones claves a partir de la literatura existente y los datos disponibles para la región; (ii) la estimación de trayectorias regionales y nacionales durante las últimas dos décadas; y (iii) la identificación de factores asociados a estos cambios mediante la aplicación de modelos de regresión multinivel híbridos. Con esto, se espera aportar evidencia robusta sobre los cambios en la región en las últimas dos décadas, aportando a la discusión académica y política sobre los desafíos y oportunidades de la cohesión social en América Latina.

Palabras clave

cohesión social, análisis multinivel, análisis longitudinal

Introduction

Social cohesion has emerged as a critical dimension of societal well-being and democratic stability, particularly in regions experiencing rapid social and political transformation. In Latin America, recent years have been marked by episodes of political instability, persistent inequalities and low economic growth, as well as cycles of social conflict (Salazar-Xirinachs, 2023; United Nations Development Programme, 2023). Social tensions therefore appear to have increased in the region, reflecting a lack of trust in democratic institutions and widespread discontent with corruption and inequality. In this context, social cohesion has climbed the public and academic agendas, with recent diagnoses from both international organizations and national governments warning about its strains and the challenges it poses for democratic governance and inclusive development (Castillo et al., 2022; Ministerio de Desarrollo Social y Familia, 2020; Salazar-Xirinachs, 2023; United Nations Development Programme, 2023).

Despite its widespread and everyday use in public discussion, defining social cohesion theoretically and operationally remains a challenge. The literature ranges from studies focused on one or several specific dimensions of social cohesion (Ariely, 2013; Castillo et al., 2022; Castillo et al., 2023) to efforts to synthesize the phenomenon into comprehensive indices (Delhey et al., 2018; Delhey & Dragolov, 2016; Dragolov et al., 2013; Janmaat, 2010; Langer et al., 2016). Such conceptual and methodological heterogeneity makes it difficult to compare countries and to detect transformations over time. Moreover, most of these definitions and their correspondent operationalizations have been tested mainly in European or high-income countries (Ariely, 2013; Delhey & Dragolov, 2016), with only partial references to Latin America (Janmaat, 2010). This limitation persists even though evidence suggests that national and regional differences in cultural, historical, and institutional contexts shape the cohesion of societies and the factors that determine it (Delhey & Dragolov, 2016; Janmaat, 2010). Thus, despite the widespread perception that social cohesion is under strain in Latin American societies,there is a lack of empirical evidence of differences between countries, trends over time, and the factors that explain these changes.

This article has a double aim. On the one hand, and based on previous comprehensive approaches to social cohesion, it seeks to propose and validate a measurement model that enables comparative, longitudinal, and multilevel analysis of social cohesion in Latin America. On the second hand, based on this model, we aim to advance on the estimation of regional and national trajectories over the past two decades, as well as to identufy factors associated with these changes through longitudinal multilevel regression models. This is expected to provide robust evidence on the changes in the region over the last two decades, contributing to the academic and policy discussion on the challenges and opportunities of social cohesion in Latin America.

What is social cohesion?

Social cohesion is a multifaceted construct that plays a critical role in community interactions, impacting various social and health outcomes. The conceptualization of social cohesion encompasses various dimensions, such as connectedness, shared values, and participation in collective activities. Although its theoretical roots date back to the origins of social theory — and it has widespread use in public discourse — defining social cohesion remains a difficult task (Chan et al., 2006). Situated at the intersection of theoretical and descriptive approaches that emphasize cooperation and integration, as well as normative considerations about how societies stay united in the face of crises, tensions, and transformations, social cohesion has been defined in multiple ways, reflecting diverse perspectives, scopes, and indicators (Castillo et al., 2021; Chan et al., 2006).

Social cohesion has been associated to different outcomes. Scholars as Turchi et al. (2023) articulate that social cohesion becomes paramount in emergency contexts, where interpersonal connections can provide essential support and resilience. This assertion is supported by Stafford et al., who reveal that diminished social cohesion is correlated with health detriments, including increased mortality rates and adverse mental health indicators (Stafford et al., 2003). Lê et al. (2013) et al. further corroborate this perspective, exploring how cohesive communities can mitigate mental health issues in the aftermath of traumatic events. Moreover, context plays a significant role in the assessment and impacts of social cohesion. Nazmi et al. (2010) illustrate how neighborhood characteristics influence health outcomes, suggesting that communities with stronger social interactions and solidarity can buffer against health stresses (Nazmi et al., 2010). In contrast, other research has shown that lower levels of perceived neighborhood cohesion are associated with adverse health conditions (Neergheen et al., 2019).

Most definitions of social cohesion share some common elements (Castillo et al., 2021):

• It is an attribute of the collective, not of individuals.
• It is a multidimensional construct
• It is a quality of social relations that enables the achievement of shared goals.
• Given that subjects have a common objective, they are capable of cooperating to achieve these objectives.

Therefore, a robust definition of social cohesion should be able to take the previous elements into consideration, and at the same time it should allow for a comprehensive operationalization and measurement. Taking this into consideration, in this article we will begin with the definition proposed by Chan, To and Chan (2006, p. 90), for whom 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”. From this definition, two main dimensions emerge. First, social cohesion has a horizontal dimension. By this we refer to the daily interactions among members of society. It contains subjective aspects (such as sense of belonging and interpersonal trust), as well as objective aspects, such as social networks and material conditions that facilitate or hinder these interactions. Second, social cohesion has a vertical dimension, which refers to the relations between individuals and social and political institutions. This dimension includes aspects such as trust in institutions, the legitimacy of the political system, and civic participation.

Measuring Social Cohesion

The measurement of social cohesion is equally critical and can vary considerably depending on the metrics employed. Bottoni emphasizes this by asserting that social cohesion is inherently multidimensional, incorporating structural components such as trust and social networks (Bottoni, 2016). Mair et al. and Cooper et al. both utilized factor analysis in their work to develop robust scales that effectively capture different facets of social cohesion, indicating the complex nature of social bonds within communities (Mair et al., 2009; Cooper et al., 2014). These scales assess variables like mutual responsibility and shared values, which are essential for understanding neighborhood dynamics.

A notable contribution to the framework of social cohesion measurement is provided by Delhey et al. (2023), whose work emphasizes the necessity of incorporating a broad spectrum of attributes beyond mere trust and conflict perceptions. In their review, Delhey et al. (2023) critique many existing measures for focusing narrowly on isolated components of social cohesion, which may lead to oversimplified interpretations. By highlighting social cohesion as a community asset that fosters solidarity and reduces conflict, Delhey et al. articulate its role in enhancing collective welfare and health outcomes. Moreover, their advocacy for multidimensional measurement approaches reflects an understanding that the impacts of social cohesion can vary significantly across different socio-cultural contexts. This adaptation is crucial for ensuring that social cohesion measures remain relevant and effective, addressing the criticisms of traditional models that fail to encompass the complexity of cohesive interactions.

Studying social cohesion

Social Cohesion is usually understood as a gradual phenomenon, meaning that societies can display higher or lower levels of cohesion (Delhey et al., 2023; Schiefer & van der Noll, 2017). This can be interpreted both longitudinally, that is, countries can increase or decrease their cohesion levels over time; as well as comparatively, that is, some countries can show higher levels of cohesion than others.

Despite the assumption that social cohesion levels change over time, the difficulty of conducting repeated measurements on population attitudes and behaviors hinders the longitudinal analysis of social cohesion. An important exception to this trend is the Mapping Social Cohesion Survey, conducted by the Scanlon Foundation Research Institute since 2007, which enables the construction of the Scanlon-Monash Index (SMI) of Social Cohesion. Thus, the SMI allows observing synthetically the evolution of Social Cohesion in Australia, recording a 22% decline since 2007 and significant decreases in 4 of its 5 dimensions (O’Donnell et al., 2024). Another notable example of time comparison is the Social Cohesion Radar, whose index can be reconstructed for four time periods between 1989 and 2012 (Delhey et al., 2023). However, its scope is limited to European and Western societies.

The literature on social cohesion appears to be more focused on establishing which countries show higher or lower levels of cohesion (Delhey et al., 2018; Delhey & Dragolov, 2016), proving the existence of cohesion regimes (Delhey & Dragolov, 2016; Janmaat, 2010), and determining the factors related to social cohesion (Delhey et al., 2018; Delhey & Dragolov, 2016; Green et al., 2011; Janmaat, 2010). These associated factors can be grouped into economic factors, political factors, and demographic factors.

Economic prosperity, often measured through GDP per capita, has been associated with higher levels of social cohesion. This association has been found across diverse cultural contexts (Delhey et al., 2023; Janmaat, 2010), although it appears to attenuate in some high-income Asian societies (Delhey et al., 2018). These findings align with universalist or modernist perspectives on social cohesion, which view cohesion as a phenomenon closely related to different stages of socioeconomic development (Janmaat, 2010). From these findings, we derive the first hypothesis of this article:

H1: Countries with higher economic development will exhibit greater levels of social cohesion

Conversely, economic inequality has generally been associated with lower levels of social cohesion (Delhey et al., 2018, 2023; Janmaat, 2010). Again, however, this association is attenuated in Asian societies, where moderate levels of inequality are associated with higher levels of cohesion (Delhey et al., 2018; Janmaat, 2010).

To date, there is no evidence regarding how inequality levels in Latin America relate to social cohesion. On one hand, despite being considered one of the world’s most unequal regions, Latin Americans display exceptionally high expectations of social mobility (Somma & Valenzuela, 2015), possibly due to increased educational coverage in many regional countries or the predominance of meritocratic justifications for educational and economic inequality (Castillo et al., 2024; Somma & Valenzuela, 2015). Nevertheless, demands for greater equality, redistribution, and fair treatment (Araujo et al., 2022; Cabib et al., 2025), which have translated into mass protests in several regional countries, suggest that economic inequality is a relevant factor for social cohesion in Latin America. Therefore, we propose the following hypotheses:

H2(a): Increases in a country’s economic inequality will be associated with lower levels of social cohesion

H2(b): Countries with higher levels of inequality will exhibit lower levels of social cohesion

The state’s capacity to respond effectively to population challenges, particularly for vulnerable groups, has been identified as one of the primary sources of social cohesion in diverse societies (ANDREWS & JILKE, 2015; Kustov & Pardelli, 2024; Njozela et al., 2016). In this vein, effective governance and robust institutions are typically associated with higher levels of social cohesion in contexts as diverse as Asia, Latin America, and Europe (Delhey et al., 2018; Green et al., 2011). In contrast, the relationship between political systems and democratic regimes with social cohesion appears to be more context-dependent. While in wealthy Western societies liberal democracy tends to reinforce social cohesion levels, in some Asian countries authoritarian regimes have been linked to equally high levels of cohesion (Delhey et al., 2018).

In Latin America, states are typically characterized by lower levels of efficiency and higher corruption indices, accompanied by citizenries with low levels of trust in political institutions and lower participation compared to the developed world. However, this institutional distrust coexists with a strong sense of national identification, even more pronounced than in the developed world (Somma & Valenzuela, 2015). For Somma (2015), this apparent paradox relates to the propensity of Latin American societies toward personalist and populist leadership. In this context, consistent with what Guillermo O’Donnell described as delegative democracies (Toppi, 2018), broad sectors of the citizenry appear willing to trade liberal democratic principles in favor of authoritarian leadership when they perceive that democratic institutions fail to resolve their daily problems. This suggests that governance effectiveness—rather than political regime type—is the institutional factor that carries greater weight on social cohesion levels in Latin American societies. From this discussion, we propose the following hypotheses:

H4(a): Increases in a country’s governance levels will be associated with higher levels of social cohesion

H4(b): Countries with higher levels of governance will exhibit higher levels of social cohesion

Cultural and demographic variables, including ethnic, religious, and national identities, have demonstrated complex effects on social cohesion levels, varying substantially by context. This contextual character is particularly evident in the impact of ethnic diversity on social cohesion. On one hand, several studies have identified negative effects of ethnic diversity on indicators such as sense of belonging or the strength of neighborhood networks (Ariely, 2013; Gijsberts et al., 2011), an association that is particularly pronounced in contexts of high racial segregation, such as the United States (Meer & Tolsma, 2014). Nevertheless, other research has shown that institutional factors and the implementation of inclusive policies can moderate, or even reverse, this relationship (Delhey et al., 2018, 2023; Kustov & Pardelli, 2024; Meer & Tolsma, 2014; Reeskens & Wright, 2012).

The management of ethnic diversity in Europe and North America contrasts significantly with the Latin American reality, characterized by greater ethnic and linguistic heterogeneity. Despite episodes of conflict arising from relationships between states and indigenous peoples, Latin American countries have demonstrated strong national identities (Somma & Valenzuela, 2015). However, recent changes in migration patterns, which have favored intraregional migration in both relative and absolute terms (Stefoni, 2018), could constitute new sources of tension for social cohesion in Latin American societies. Although Latin Americans tend to display levels of ethnic-religious and national tolerance higher than those observed in several European countries (Somma & Valenzuela, 2015), cases such as Chile—in response to massive Venezuelan migration—evidence signs of deterioration in relations between migrants and non-migrants in recent years (Castillo et al., 2023). In light of this, we propose the following hypotheses:

H5(a): Increases in the proportion of migrant population in a country will be associated with decreases in social cohesion levels

H5(b): Countries with higher levels of ethnic diversity will exhibit lower levels of social cohesion

H5(c): The impact of migration on social cohesion will be smaller in countries with better governance levels

Methodology

Data

The main source of data for this study is the AmericasBarometer of the Latin American Public Opinion Project, also known as the LAPOP Survey. The survey aims to collect data on public opinion about democracy and governance in the Americas. The survey design is probabilistic and representative of the adult population in each country (LAPOP LAb, 2023).

The survey has been conducted regularly since 2004. To date, nine waves have been carried out, covering between 11 and 23 countries, with a total of over 400,000 interviews in two decades. The questionnaire is administered through face-to-face surveys, with the exception of Canada and the United States.

As a criterion, this study included only those countries in the region that had data available for the main indicators of the study at least five points in time. As summarized in Tabla 1, this study includes a total of 238,257 individuals nested in 174 country waves in 25 countries in the Americas.

Tabla 1: Data availability by waves and countries

Country	2004	2006	2008	2010	2012	2014	2016	2018	2023	Total
Argentina	0	0	1231	1235	689	1249	2816	2884	1469	11573
Belize	0	0	1096	1341	715	1380	0	0	1411	5943
Bolivia	2802	2563	2590	2603	2605	2848	2900	3060	0	21971
Brazil	0	0	1203	2112	681	1390	2906	2782	1407	12481
Chile	0	1412	1358	1723	699	1268	2900	2978	1525	13863
Colombia	1304	1268	1318	1272	624	1353	2886	1530	1400	12955
Costa Rica	1386	1422	1350	1373	674	1425	2758	2806	1447	14641
Dominican Republic	0	1312	1265	1292	656	1400	1288	1360	2974	11547
Ecuador	2644	2657	2768	2589	628	1315	1388	2934	1414	18337
El Salvador	1407	1573	1468	1500	620	1448	1450	1353	1457	12276
Guatemala	1237	1133	1157	1217	643	1309	1355	1362	1425	10838
Guyana	0	1151	2069	1267	654	1315	0	0	0	6456
Haiti	0	1363	1351	1596	845	1318	1681	0	0	8154
Honduras	1220	1366	1275	1437	717	1452	1349	1328	1385	11529
Jamaica	0	1219	1272	1297	706	1202	1180	1151	1209	9236
Mexico	1366	1351	1402	1385	697	1320	1374	1387	1545	11827
Nicaragua	1098	1491	1278	1373	1573	1402	1383	1376	0	10974
Panama	1528	1374	1405	1435	706	1414	2868	2924	1454	15108
Paraguay	0	0	1065	1300	676	1240	1170	1346	1310	8107
Peru	0	1340	1385	1382	670	1337	2478	1449	1481	11522
Trinidad & Tobago	0	0	0	0	0	0	0	0	1444	1444
Uruguay	0	1068	1359	1321	671	1342	2720	2866	1383	12730
Venezuela	0	1386	1330	1340	686	1377	2794	0	0	8913
Total	15992	26449	31995	33390	17835	31104	41644	36876	27140	262425

For contextual data on countries, various data sources were used, including:

1. Open data from the World Bank. This contains various indicators on social and economic development for most countries in the world. The data portal is accessible at: https://datos.bancomundial.org/.

2. The World Bank’s Worldwide Governance Indicators. This is a survey of experts that collects data on various governance indicators, covering multiple countries with information updated between 1996 and 2003. The data is available at: https://www.worldbank.org/en/publication/worldwide-governance-indicators.

3. The V-Dem Dataset. It collects a multidimensional set of data that seeks to measure the quality of democracy around the world. The database is accessible through the R package vdemdata (Maerz et al., 2025).

Variables

Dependent variables

A Social Cohesion Index was constructed, comprising two dimensions which, in turn, are summary indices constructed from LAPOP indicators. The selection of indicators, sub-dimensions, and dimensions is based on previous work at the aggregate level by the Social Cohesion Observatory, accessible here: https://ocscoes.github.io/medicion-cohesion-LA/.

The Horizontal Cohesion Index consists of two sub-dimensions: Urban Safety and Interpersonal Trust. Urban Safety includes indicators of objective safety and subjective safety. Interpersonal Trust, meanwhile, is a single indicator of how trustworthy people are in general[¹].

The Vertical Cohesion Index consists of two dimensions: Trust in Institutions and Attitudes toward Democracy. Trust in Institutions includes indicators related to citizens’ trust in Congress, the judiciary, and political parties. Attitudes toward Democracy consists of two indicators on support for the democratic system and satisfaction with the functioning of democracy in one’s country[²].

The indicators were standardized so that all sub-dimensions and dimensions of the indices have a range from 0 to 10, with 0 indicating low levels of social cohesion and 10 indicating high levels of cohesion.

Independent variables

Economic, institutional, and cultural factors were included as independent variables. Recognizing the hierarchical structure of the data, predictors were considered at the individual level, at the wave-country level, and at the country level.

Individual variables

The main individual predictor used in this study is the educational level of individuals. The multiple LAPOP codes for this indicator were unified to create a variable with three categories, distinguishing between individuals with primary, secondary, and tertiary education.

In addition, gender, age, and political position were added as control variables.

Contextual variables

Economic prosperity was measured using the logarithm of GDP per capita at purchasing power parity (PPP) values. Economic inequality was measured using the Gini index. Both indicators were extracted from the World Bank database. In addition, the percentage of individuals with tertiary education in a country was included as a proxy for educational opportunities.

In terms of institutional factors, the Electoral Democracy Index, or polyarchy, was used to measure the democratic quality of countries. On the other hand, a governance index (\(\alpha\) = 0.96) calculated from the World Bank’s Worldwide Governance Indicators was included (Kaufmann & Kraay, 2024).

Cultural diversity will be measured as the percentage of the migrant population relative to the total population of the country, using data from the World Bank. Given that the series is available every five years, an annual series was constructed by imputing the intermediate years using logistic interpolation.

Method

Confirmatory factor analysis

A confirmatory factor analysis was performed to test the model constructed by the Social Cohesion Observatory (2025) and the theoretical proposal by Chan et al. (2006). As can be seen in Figura 1, social cohesion is understood as a latent construct consisting of two latent dimensions: vertical cohesion and horizontal cohesion.

Figura 1: Conceptual Framework for Social Cohesion

Multilevel analysis

Given the hierarchical structure of the data, hybrid multilevel regression models were estimated. This technique allows individual-level data to be used to decompose country-level effects into their components between countries (between effects) and within a country over time (within effects) (Fairbrother, 2014; Schmidt-Catran & Fairbrother, 2016). The models were estimated using the R package lme4 (Bates et al., 2015).

The proposed model could be formally expressed as:

\[ y_{jti} = \beta_{0}(t) + \beta_{1}X_{jti} + \gamma_{we}(Z_{jt}-\bar{Z}_{j}) + \gamma_{be}\bar{Z}_{j} + v_j + u_{jt} + e_{jti} \] The model integrates three levels with individuals \(i\) nested in waves-countries \(t\) nested in countries \(j\). \(X_{jti}\) represents individual-level variables, while \(Z_{jt}\) are contextual variables at the wave-country level. Given that \(Z_{jt}\) contains variance at both level 2 and level 3, it was broken down into the average of the variable across all its waves (\(\bar{Z}_{j}\)) and the intra-country deviation in a given wave (\(Z_{jt}-\bar{Z}_{j}\)). Thus, \(\gamma_{we}\) represents the within effect, that is, the effect of change in a country over time, while \(\gamma_{be}\) represents the between effect, that is, the structural differences between countries. In addition, \(\beta_{0}(t)\) controls for changes over time not explained by the model. Finally, \(v_j\), \(u_{jt}\) and \(e_{jti}\) represent the errors at the country, wave-country, and individual levels.

Resultados

Análisis Factorial Confirmatorio

Figura 2: Análisis Factorial Confirmatorio Modelo de Medición de Cohesión Social

En Figura 2 se presentan los resultados del análisis factorial confirmatorio hecho a partir del modelo de medición propuesto. En primer lugar, se observa que los indicadores presentan cargas factoriales moderadas, las cuales van del 0.45 al 0.6 dependiendo del caso, lo que sugiere que los indicadores reflejan parcialmente las dimensiones latentes. Los índices de ajuste son de buena calidad, apuntando a una fiabilidad del constructo. Ahora, dado que el modelo cuenta con un solo grado de libertad, las medidas de ajuste global deben interpretarse con precaución. En suma, el modelo ofrece un ajuste aceptable a nivel identificacional, pero la validez de los factores son limitadas, lo que podría solucionarse aumentando el número o la calidad de los indicadores en futuras mediciones.

Descriptives

Figura 3: Evolución dimensiones Cohesión Social (2004-2022)

Figura 3 shows the averages for each year for the different types of cohesion. First, it can be seen that horizontal cohesion maintained an upward trend between 2004 and 2012, except for the decline in 2010. After this, horizontal cohesion faced a sustained decline until 2018, before rebounding and even exceeding its 2004 level. Vertical cohesion shows a similar pattern of variation to horizontal cohesion, with a persistent rise over time until 2012, when it suffered a sharp decline at the regional level. This decline continued until 2016, when the trend reversed, with an increase in vertical cohesion until the last year recorded. Although the latest measurement of vertical cohesion shows higher levels than at the beginning, its scores are generally considerably low when compared to horizontal cohesion, differing by approximately 1.5 points in all waves. In summary, Latin America has consistently shown greater horizontal cohesion than vertical cohesion from 2004 to 2022¹.

Relationship between and within country level factors with social cohesión

Figura 4: Country-level association of macro-level factors and social cohesion. Red line represents the association including Canada and United States.

Differences and changes in country level factors and social cohesion

Tabla 2: Multilevel regression for individual level factors and social cohesion

	Horizontal		Vertical
	Model 1	Model 2	Model 3	Model 4
(Intercept)	6.205***	6.240***	5.254***	5.029***
	(0.115)	(0.114)	(0.125)	(0.123)
Income decile	0.027***	0.026***	0.004*	0.002
	(0.002)	(0.002)	(0.002)	(0.002)
Education (ref.=No formal schooling)
Primary	0.202***	0.196***	0.132***	0.122***
	(0.013)	(0.013)	(0.010)	(0.010)
Tertiary	0.099***	0.102***	0.003	0.002
	(0.013)	(0.013)	(0.010)	(0.010)
Political position (ref.=Undefined)
Left		-0.162***		0.073***
		(0.017)		(0.013)
Center		-0.052**		0.193***
		(0.017)		(0.013)
Right		0.041*		0.568***
		(0.017)		(0.013)
Time	-0.051***	-0.048***	-0.100***	-0.098***
	(0.011)	(0.011)	(0.015)	(0.015)
Male (ref.=Female)	0.202***	0.204***	0.044***	0.033***
	(0.010)	(0.010)	(0.008)	(0.008)
Age	0.098***	0.095***	-0.007	-0.012*
	(0.006)	(0.006)	(0.005)	(0.005)
Age2	0.048***	0.046***	0.094***	0.090***
	(0.004)	(0.004)	(0.003)	(0.003)
Controls	Yes	Yes	Yes	Yes
BIC	958966.852	958777.616	849552.485	846499.344
Num. obs.	213130	213130	213130	213130
Num. Country-wave	161	161	161	161
Num. Country	22	22	22	22
Var: Country-wave (Intercept)	0.095	0.093	0.195	0.187
Var: Country (Intercept)	0.199	0.194	0.170	0.167
Var: Residual	5.016	5.010	2.999	2.956
***p < 0.001; **p < 0.01; *p < 0.05

Tabla 3: Multilevel regression for country level factors and social cohesion

	Horizontal			Vertical
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Time	-0.086**	-0.094**	-0.105***	-0.180***	-0.208***	-0.214***
	(0.031)	(0.031)	(0.030)	(0.040)	(0.038)	(0.038)
Gini (BE)	-1.811	-1.791	-1.767	-3.757*	-3.691*	-3.718*
	(2.320)	(1.959)	(2.054)	(1.901)	(1.637)	(1.697)
Gini (WE)	-3.401*	-3.538*	-3.503*	-6.648**	-5.916**	-5.876**
	(1.581)	(1.600)	(1.556)	(2.164)	(2.029)	(2.015)
GDP (BE)	0.198	0.161	0.120	0.258	0.223	0.207
	(0.176)	(0.189)	(0.229)	(0.149)	(0.165)	(0.194)
GDP (WE)	0.171	0.197	0.546	0.401	0.468	0.730*
	(0.277)	(0.276)	(0.294)	(0.368)	(0.342)	(0.372)
Democracy (BE)		-3.079*	-2.972*		-2.111*	-2.058
		(1.206)	(1.263)		(1.015)	(1.050)
Democracy (WE)		-0.830	-0.631		-3.792***	-3.632***
		(0.599)	(0.586)		(0.762)	(0.762)
Governace (BE)		0.995**	0.973**		0.756**	0.744**
		(0.319)	(0.334)		(0.267)	(0.277)
Governace (WE)		-0.085	-0.210		0.743*	0.640*
		(0.251)	(0.248)		(0.320)	(0.322)
Migration (BE)			0.019			0.007
			(0.050)			(0.041)
Migration (WE)			-0.111**			-0.090
			(0.038)			(0.050)
AIC	958631.141	958627.080	958631.660	846342.480	846318.283	846327.710
BIC	958815.995	958853.013	958878.132	846527.334	846544.215	846574.182
Log Likelihood	-479297.571	-479291.540	-479291.830	-423153.240	-423137.141	-423139.855
Num. obs.	213130	213130	213130	213130	213130	213130
Num. groups: country_wave	161	161	161	161	161	161
Num. groups: pais	22	22	22	22	22	22
Var: country_wave (Intercept)	0.091	0.090	0.085	0.178	0.151	0.149
Var: pais (Intercept)	0.202	0.136	0.148	0.121	0.084	0.090
Var: Residual	5.010	5.010	5.010	2.956	2.956	2.956
***p < 0.001; **p < 0.01; *p < 0.05

Interactions

Así…

• todas las iteraciones la mismo tiempo y ver que pasa
• si alguna de las interacciones no es ninguna de las dos cohesiones
  • para las interaccines que son son significativas dejarlas, las otras no

Table 6…

Tabla 4: Multilevel regression for macro factors and general social cohesion

Tabla 5

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Notas

1. It should be noted that social cohesion (green line) is the average of vertical and horizontal cohesion, which is why its interpretation is not detailed.