Data note for cross-country data on social cohesion and Covid-19

van Staveren, Irene; Pacheco-Miranda, Jimena; Bakshi, Sanchita Article Data note for cross-country data on social cohesion and Covid-19 Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: van Staveren, Irene; Pacheco-Miranda, Jimena; Bakshi, Sanchita (2024) : Data note for cross-country data on social cohesion and Covid-19, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 27, Iss. 1, pp. 1-12, https://doi.org/10.1080/15140326.2024.2364163 This Version is available at: https://hdl.handle.net/10419/314277 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Journal of Applied Economics ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/recs20 Data note for cross-country data on social cohesion and Covid-19 Irene van Staveren, Jimena Pacheco-Miranda & Sanchita Bakshi To cite this article: Irene van Staveren, Jimena Pacheco-Miranda & Sanchita Bakshi (2024) Data note for cross-country data on social cohesion and Covid-19, Journal of Applied Economics, 27:1, 2364163, DOI: 10.1080/15140326.2024.2364163 To link to this article: https://doi.org/10.1080/15140326.2024.2364163 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 13 Jun 2024. Submit your article to this journal Article views: 346 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 Data note for cross-country data on social cohesion and Covid-19 Irene van Staveren , Jimena Pacheco-Miranda and Sanchita Bakshi Institute of Social Studies of Erasmus University Rotterdam, The Netherlands ABSTRACT This Data Note is a brief explanation of a dataset compiled for a study on the relationship between social cohesion and Covid-19. The innovative variable in this cross-country dataset is social cohesion, which appears as a consolidated index and as two sub-indices on, respectively, the interpersonal dimension and the intergroup dimension of social cohesion. This variable is available for 187 countries. We discuss the underlying indicators, provide their sources and a link to the full dataset. In addition, we provide information about the online, freely available database called Indices of Social Development of which the social cohesion subindices make part. With our explanation of the theoretical background, substance of the indicators, and construction of the three social cohesion indices, we hope to inspire researchers to use them in their own cross-country research. ARTICLE HISTORY Received 4 December 2023 Accepted 31 May 2024 KEYWORDS Dataset; Social Cohesion; Covid-19 1. Introduction This data note presents an innovative measure for social cohesion at the country level, with data available for 187 countries. Social cohesion refers to feelings of connectedness and belonging between individuals in a society as well as to attitudes of mutual respect between different social groups in a society (Durkheim, 1997; Manca, 2014). Hence, social cohesion involves interpersonal trust, prosocial norms, tolerance and willingness to cooperate with members of other social groups (f.e. different religious, ethnic and socio-economic groups). We have developed a composite index which tries to capture as many of these intangible elements as possible. Following Christoforou and Davis (2014), we use measures of the extent, strength, duration, and underlying values of social relations. The dataset was created for three goals. First, empirical cross-country research on social cohesion uses a wide variety of measures, which are not always consistent with the (sociological) literature on social cohesion. Moreover, many economic studies tend to use similar measures for social cohesion and social capital even though these are different CONTACT Irene van Staveren [email protected] Institute of Social Studies of Erasmus University Rotterdam, The Netherlands JOURNAL OF APPLIED ECONOMICS 2024, VOL. 27, NO. 1, 2364163 https://doi.org/10.1080/15140326.2024.2364163 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. concepts. 1 We offer a new composite index for social cohesion consistent with the theoretical literature of the concept. It consists of measures for the two dimensions of social cohesion: the interpersonal dimension and the intergroup dimension. Second, the dataset was constructed to enable cross-country analysis of the relationship between social cohesion on the one hand and Covid-19 outcomes on the other hand. The research article using the dataset was published under the title ‘Surviving Together: Social Cohesion and Covid-19 Infections and Mortality Across the World’. 2 The dataset allows for replication studies. A third goal is that it expands the database Indices of Social Development, with a new index of social cohesion combining two existing indices. 3 This extension will help to make the dataset more widely known and even more useful to researchers, in particular for researchers interested in cross-country studies of social cohesion. We have made our dataset available in a publicly available repository. 4 The objective of the dataset is to enable researchers to use three composite indices of social cohesion for cross-country analyses. Researchers can also use data for all the other variables available in our dataset, which has data on Covid-19 infections and mortality, as well as a large set of economic, governance and healthcare control variables, all for the year 2020. 2. Materials and methods The dataset contains observations at the country-level and are all secondary data and refer to a single year: 2020. The data can be divided into two categories. The first and main category consists of three measures of social cohesion and their underlying indicators. The indicators are from secondary sources and were collected by the data management team of the Indices of Social Development database (ISD), in order to initially compute two of the social cohesion indices. 5 These reflect the two theoretical dimensions of social cohesion: interpersonal cohesion and intergroup cohesion. These indices are labeled, respectively, the Interpersonal Safety and Trust Index and the Intergroup Cohesion Index. We used all indicators of these two indices to calculate a third social cohesion index: the Social Cohesion Index. We describe the three indices as follows. (1) The Interpersonal Safety and Trust Index refers to interpersonal norms of trust and security that exist to the extent that individuals in a society feel they can rely on those whom they have not met before. Where this is the case, the costs of social organisation and collective action are reduced. Where these norms do not exist or have been eroded over time, it becomes more difficult for 1 Social capital tends to be oriented toward individual or group benefits, whereas social cohesion tends to be inclusive and oriented toward the common good. 2 Pacheco-Miranda, Jimena, Sanchita Bakshi and Irene van Staveren (2023) ‘Surviving Together: Social Cohesion and Covid-19 Infections and Mortality Across the World’, Critical Public Health 33(5): 553-565. 3 The dataset Indices of Social Development (ISD) is freely available online, without registration, at https://isd.iss.nl. 4 https://datarepository.eur.nl/articles/dataset/Surviving_Together_Social_Cohesion_and_Covid-19_Infections_and_ Mortality_Across_the_World_Database/23523690 5 Since social cohesion is intangible, indicators are all proxy measures and often relate to sensitive issues such as victimization. Hence, we acknowledge that some of these measures may suffer from low reporting rates, limited capacity of institutions to register a crime, dark figure of crimes, and or heterogeneous definitions. We refer to the original sources (as provided in Table 1) for these measures for further information on the quality of these measures. 2I. VAN STAVEREN ET AL. Table 1. Indicators included in the three social cohesion indices. Social Cohesion Index No. countries Sub-Index Source 1 Societal polarization: Rating of differences of opinion on major political issues in this society 177 intergroup Varieties of Democracy database, Gothenburg University 2 Confidence in law and order: “Do you have confidence in the local police force, feel safe walking alone at night, had money or property stolen or have been assaulted or mugged?” 134 intergroup World Happiness Report-Gallup 3 Group grievance: Rating of legacy of vengeance-seeking group grievance or group paranoia 178 intergroup Fund for Peace/Fragile States Index 4 Terrorist attacks: Terrorist coded attacks based on weighted average of last four years (2016–2019) of 9 types 138 intergroup Global Terrorism Database (GTD) 5 Deaths in organized conflict (>25): Rating on deaths in organized conflict (at least 25 deaths) 38 intergroup Uppsala Conflict Data Program (UCDP) 6 Guerilla conflict instances: Log instances of guerrilla conflict per log capita 56 intergroup Cross-National Time Series Data Archive 7 Political risk: Average values of all indicators of the International Country Risk Guide 138 intergroup International Country Risk Guide 8 Internal conflict: Rating of the level of internal conflict 140 intergroup International Country Risk Guide 9 Risk of terrorism: Rating of risk of terrorism 134 intergroup IEP Global Terrorism Index 10 Riots: Log riots per log capita 133 intergroup Cross national Time Series Data Archive 11 Felt unsafe at home: Felt unsafe in home, % “never” 34 safety & trust Afrobarometer 12 Trust family: Trust your family, % “not very much” or “not at all” 73 safety & trust World Values Surveys 13 Trust people meet for the first time: Trust people you meet for the first time, % “not very much” or “not at all” 73 safety & trust World Values Surveys 14 Trust people you know personally: Trust people you know personally, % “not very much” or “not at all” 73 safety & trust World Values Surveys 15 Most people can be trusted: “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” % “yes” 18 safety & trust Latinobarometer 16 Had stuff stolen from home: Had stuff stolen from home, % “never” 34 safety & trust Afrobarometer 17 Have not been attacked: % have not been attacked 34 safety & trust Afrobarometer 18 Feel safe in their area at night: % Feel safe in their area at night 40 safety & trust European Social Survey 19 Prefers not to go out at night: % “yes” 42 safety & trust World Values Surveys 20 Theft of motorized land vehicle: Theft of motorized land vehicle, police recorded offences, p/100,000 pop. 60 safety & trust UN Office On Drugs and Crime 21 Theft: Theft, police recorded offences, per 100,000 population 74 safety & trust UN Office On Drugs and Crime 22 Sexual exploitation: Sexual exploitation per 100,000 inhabitants 44 safety & trust UN Office On Drugs and Crime 23 Crime victim: “Have you, or someone in your family, been assaulted, attacked, or victim of crime in the last 12 months?” % “yes” 18 safety & trust Latinobarometer (Continued) JOURNAL OF APPLIED ECONOMICS 3 Table 1. (Continued). Social Cohesion Index No. countries Sub-Index Source 24 Freq. alcohol on streets: Frequency of alcohol consumed on the streets in your neighbourhood, % “very frequently” and “quite frequently” 42 safety & trust World Values Surveys 25 Freq. drug sale in neighbourhood: Frequency of drug sale in streets in your neighborhood, % “very frequently” and “quite frequently” 42 safety & trust World Values Surveys 26 Freq. robbery in neighbourhood: Frequency of robberies in your neighbourhood, % “very frequently” and “quite frequently” 42 safety & trust World Values Surveys 27 Victim crime in neighbourhood: Have you been victim of any crime in the past 12 months, % “yes” 43 safety & trust Americas Barometer 28 Victim of attempted murder: Have you been victim of attempted murder in the past 12 months, % “yes” 5 safety & trust UN Office On Drugs and Crime 29 Kidnapping Rate: Have you been victim of attempted kidnapping in the past 12 months, % “yes” 65 safety & trust UN Office On Drugs and Crime 30 Homicide rate: homicide rate 183 safety & trust UN Crime and Justice Information Network 31 Satisfied freedom to choose in life: Are you satisfied with your freedom to choose what you do with your life? % “yes” 144 safety & trust World Happiness Report-Gallup 32 Trust neighbourhood: Trust your neighbourhood, % “not very much” or “not at all” 73 safety & trust World Values Surveys Source: Indices of Social Development, two web pages: (1) https://isd.iss.nl/home/intergroup-cohesion/ and (2) https://isd.iss.nl/home/interpersonal-safety-and-trust/. 4I. VAN STAVEREN ET AL. individuals to form group associations, undertake an enterprise, and live safely and securely. (2) The Intergroup Cohesion Index refers to relations of cooperation and respect between identity groups in a society. Where this cooperation breaks down, there is the potential for polarization and conflictual acts such as ethnically or religiously motivated killing, targeted assassination and kidnapping, acts of terror such as public bombings or shootings, or riots involving grievous bodily harm to citizens, with concomitant effects upon countries’ development. (3) The Social Cohesion Index combines the two dimensions of social cohesion, namely, the interpersonal dimension and the intergroup dimension. It is the most comprehensive measure of social cohesion in the dataset. The social cohesion data are available for a large number of countries: (1) Intergroup Cohesion Index: 10 indicators;168 countries (2) Interpersonal Safety and Trust Index: 22 indicators;160 countries (3) Social Cohesion Index: 32 indicators;187 countries The reason why the number of cases for the combined index is higher than that of either of the two underlying indices, is the nonparametric aggregation methodology that is used for constructing the indices. First, a principal component analysis was performed for the selection of indicators per index. Second, a percentile matching method was used, resulting in a ranking of all countries in an index, ranging between zero and one (Foa & Tanner, 2010). The ranking is based on a relatively large set of indicators, but data does not need to be available on every single indicator for every country. In other words, if an index has a total set of 15 indicators, some countries may have data for 5, others for 8, and a few for 10 indicators. Moreover, for each indicator, data tends to be available for different countries. For example, Afro Barometer has data only for African countries while the European Value Survey has data only for Europe. A minimum of three data points is required for a country to appear in the ranking. 6 The measurement of all variables is rescaled to a range of 0–1. The matching percentiles method starts with a random master variable and assigns the values of the master variable to the country ranking in the next variable. This is repeated until all indicators have been matched in this manner. Then an average is calculated per country for the results of the matching. Finally, a Monte Carlo simulation is applied 1,000 times and the final country scores per index are an average of these 1,000 results. Although the dataset is only for the year 2020, data for the two sub-indices of social cohesion are freely available online from the ISD database for the period 1990–2020, on a five-year basis. In the Appendix A, we provide the Stata code for researchers who may want to calculate the index for other years. Moreover, researchers are welcome to approach one of the authors for further information. 6 This may seem to limit the reliability of an index, or at least for the value of a country with only three data points. However, the matching percentiles method has been tested with different thresholds, and it was found that a minimum of three data points gives a relatively reliable value (Foa & Tanner, 2010). Moreover, we are transparent about this by providing the standard error for each country value in every index, which is automatically included when one downloads data from the ISD online database. JOURNAL OF APPLIED ECONOMICS 5 The data reported has values between 0 and 1 and include not only the index value but also reports the standard error for each country. Moreover, the dataset includes all the underlying indicators per index. The indicators for the three social cohesion indices are listed in Table 1. The second category of variables in the dataset consists of data from freely available online secondary sources without any form of data transformation. They include Covid-19 data (infection rate, death rate and excess deaths), economic data (GDP per capita and Gini index), governance data (democracy index, public services index, government performance index and corruption index), and health care data (health expenditure as % of GDP, hospital beds per 1,000, percentage over 65 years, universal health coverage). The details of these variables are described in Table 2. 3. Conclusion The dataset that we present here was tailor-made for our research on the effect of social cohesion on the differences in Covid-19 infections and death rates between countries across the world in 2020. As such, it is limited to the variables that were used in the Table 2. Data from secondary sources. Variable Description Source Covid cases per 10,000 Cases: Counts include confirmed and probable (where reported) Covid-19 Data Repository CSSE, Johns Hopkins University Covid deaths per 10,000 Deaths: Counts include confirmed and probable (where reported) Covid-19 Data Repository CSSE, Johns Hopkins University Excess deaths Excess mortality estimates associated with Covid-19 WHO GDP per capita GDP per capita is gross domestic product divided by midyear population. Data are in constant 2015 U.S. dollars World Bank Hospital beds per thousand The number of hospital beds available per every 1,000 inhabitants in a population World Bank Percentage over 65 Population aged 65 and above as a percentage of the total population World Bank Health expenditure % GDP Level of current health expenditure expressed as a percentage of GDP World Bank Universal health coverage All people have access to the full range of quality health services they need, when and where they need them, without financial hardship, % WHO Gini index The extent to which the distribution of income among individuals or households within an economy deviates from a perfectly equal distribution World Bank Developing countries Countries with low and lower middle incomes World Bank Democracy index Democracy ratings Varieties of Democracy database, Gothenburg University Public services index The presence of basic state functions that serve the people Fund for Peace/Fragile States Index Governance performance index Perceptions of the quality of public services, the civil service and the degree of independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies World Bank Corruption index Perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. World Bank 6I. VAN STAVEREN ET AL. published article. In addition, the data is cross-section only. However, the many control variables that the dataset contains also allows its use for the analysis of social cohesion in relation to other public health outcomes, for example, the spread of Dengue or Ebola, or for inequality of health care access, by substituting the dependent variable for an outcome variable of one’s choice. Moreover, we have provided the Code to calculate the Social Cohesion Index for earlier years with data of the two sub-indices that are freely available in the online ISD database. 7 At the same time, the innovative and key explanatory variable, the Social Cohesion Index, may be used for the analysis of a wide variety of research topics and could address very different research questions than those related to public health. In particular, we have clearly distinguished the concept and measurement of social cohesion from that of social capital. For this reason, our Social Cohesion Index may be used for applied research on effects of social cohesion instead of social capital. Examples of other research topics in which the index could be used as an independent variable in cross-country analysis range from inequality, human development and sustainability to innovation and economic growth. These are all topics in which social cohesion may play a role because of its concern with trust, tolerance, solidarity and cooperation. We hope that researchers will feel inspired and encouraged to use our cross-country index of social cohesion in their own research. Disclosure statement No potential conflict of interest was reported by the authors. Notes on contributors Irene van Staveren is professor of Pluralist Development Economics at the Institute of Social Studies of Erasmus University Rotterdam. Jimena Pacheco-Miranda is PhD researcher at the Institute of Social Studies of Erasmus University Rotterdam. Sanchita Bakshi is PhD researcher at the Institute of Social Studies of Erasmus University Rotterdam. ORCID Irene van Staveren http://orcid.org/0000-0001-7548-6795 Jimena Pacheco-Miranda http://orcid.org/0000-0002-4768-0918 Data availability statement The dataset is called Surviving Together: Social Cohesion and Covid-19 Infections and Mortality Across the World Dataset. It is available in the following repository: https://datarepository.eur.nl/ articles/dataset/Surviving_Together_Social_Cohesion_and_Covid-19_Infections_and_Mortality_ 7 We plan to publish the data for the Social Cohesion Index for all years (1990–2020) before the end of 2024 on the website of the ISD database (https://isd.iss.nl/). JOURNAL OF APPLIED ECONOMICS 7