Abstract
Numerous studies have borne out the effects of cultural and music education on individuals’ well-being, considering music as a mainly systematic practice or skill or as established educational supply. However, few studies assess the impact of music programmes designed to achieve specific goals, where music is considered as a tool for social change. As a case study, we take the Medellin Music School Network (Colombia), whose education programme for music initiation has been running for 23 years. Our aim is to evaluate the economic and social impact generated by participating in this programme. We use a quasi-experimental propensity score matching technique as the evaluation method. Results show that the programme significantly reduces the probability of participants’ becoming involved in conflict, added to which they perceive a better quality of life. Students achieve better academic performance and intensify cultural consumption and participation in artistic activities. Institutional efficacy is reflected through beneficiaries expressing a positive and significant willingness to pay in order to maintain the programme. The work also aims to evidence the usefulness of the methodology for evaluating the impact of cultural policies, particularly in developing areas.
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Source: REMM, www.redmusicamedellin.org
Source: Own elaboration based on the management reports of the REMM
Source: Own elaboration
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Notes
For further details, see www.redmusicamedellin.org.
A summary of the main characteristics and content of the music education programme may be found in “Appendix 1”.
According to the programme information, the dropout rate is low and stands at less than 10% per year and is generally due to the academic workload at school or university. One reflection of the level of permanence is that 48% of the present-day teaching staff and musical instrument teachers are graduates of the programme.
The 11 integrated ensembles are made up of four symphony orchestras, three choral orchestras and four popular music groups. The presence of popular music groups together with the Colombian music school (Pedregal) enables the REMM to explore autochthonous music, which is reflected in a wide and varied repertoire, ranging from universal classical works to contemporary works composed by school conductors or other musicians in the city, the country and the region.
In accordance with the National Department of Statistics in Colombia, the socio-economic stratification of urban areas classifies the residential dwellings that receive public services into groups. It is mainly carried out in order to levy a differential charge, by different strata, for the public services provided to households, thus enabling subsidies to be allocated and contributions to be levied on these areas. This means that those who have the highest economic capacity pay more for public services and contribute so as to enable the lower strata to make their payments, following a progressive fiscal system. The classification in strata in Colombia is as follows: (1) low–low, (2) low, (3) medium–low, (4) medium, (5) medium–high and (6) high.
The advantage of PSM is that it only requires information of the outcomes after the programme (cross section data), whereas when applying DD, it is necessary to measure outcomes both before and after the programme (panel data). Moreover, PSM assumes that only observed characteristics can influence participation and outcome, whereas DD can control for unobserved factors. However, for DD to be valid, the comparison group must accurately represent the change in outcomes that would have been experienced by the treatment group in the absence of treatment (i.e. unobserved factors must be time invariant). A more robust method is “matched DD”, which combines PSM and DD (Gertler et al. 2017).
We also used a logit specification for the propensity score, and the results did not change significantly. These complementary results are available from the authors upon request. Machine learning methods may also provide an alternative nonparametric approach to propensity score estimation, but they are not suitable for small samples (Pirracchio et al. 2015).
We chose an intermediate age to subsequently check (8 years later) the effects on academic improvement and job opportunities.
Notable events were also recalled to help jog the interviewees’ memory, such as the IX South American Games or the World Cup held in South Africa that was won by Spain, with the official song sung by the Colombian singer Shakira (Waka Waka). The survey model and the primary results thereof are available upon request from the authors.
Commune 1—Popular, Commune 2—Santa Cruz, Commune 3—Manrique, Commune 4—Aranjuez, Commune 5—Castilla, Commune 6—Doce de Octubre, Commune 7—Robledo, Commune 8—Villa Hermosa, Commune 9—Buenos Aires, Commune 10—La Candelaria, Commune 11—Laureles-Estadio, Commune 12—La América, Commune 13—San Javier, Commune 14—Poblado, Commune 15—Guayabal y Commune 16—Belén.
All of the estimations performed in this study have been carried out using Stata software.
When using the two other matching techniques posited in the research (Caliper and Kernel), the goodness-of-fit tables yield similar results. These complementary results are available from the authors upon request.
Later on, in the estimations of the causal effects using the nearest neighbour and Kernel techniques, it can be seen that the sample used indeed corresponds to 176 observations (N = 176), whilst in the case of the nearest neighbour with Caliper (0.01) 13 observations are seen to lie outside the common support region, such that N = 167.
It should be pointed out that it was not possible to apply this technique to all the outcome vectors, since the variable must be known both before and after treatment. The early age at which pupils join the REMM in the baseline (on average, when aged 14) means that certain variables (related to cultural consumption, WTP, labour market, and so on) lie outside the individual’s area of decision at the time, although this not the case for the proxy of academic performance (being enrolled in the school year corresponding to their age).
References
Aerts, K., & Schmidt, T. (2008). Two for the price of one? Additionality effects of R&D subsidies: A comparison between Flanders and Germany. Research Policy, 37(5), 806–822.
Alemán, X., Duryea, S., Guerra, N., McEwan, P., Muñoz, R., Stampini, M., et al. (2017). The effects of musical training on child development: A randomized trial of El Sistema in Venezuela. Prevention Science, 18(7), 865–878.
Arias-Pérez, J. A. (2013). Gestión Humana: Actualidad y futuro en la Red de Escuelas de Música de Medellín. Trabajo para optar el título de Especialista en Gestión del Talento Humano y la Productividad. Medellín: Universidad de Antioquia.
Ateca-Amestoy, V. (2009). El capital humano como determinante del consumo cultural. Estudios de Economía Aplicada, 27(1), 87–110.
Ateca-Amestoy, V. (2011). Leisure and subjective well-being. In S. Cameron (Ed.), Handbook on the economics of leisure (pp. 52–76). Cheltenham: Edward Elgar.
Austin, P. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.
Backman, M., & Nilson, P. (2018). The role of cultural heritage in attracting skilled individuals. Journal of Cultural Economics, 42(1), 111–138.
Bernabé-Villodre, M. (2017). Music education as a tool to improve socio-emotional and intercultural health within adverse contexts in El Salvador. Procedia—Social and Behavioral Sciences, 237, 499–504.
Berry, C., & Glaeser, E. (2005). The divergence of human capital levels across cities. Papers in Regional Science, 84(3), 407–444.
Bille, T., & Schulze, G. (2006). Culture in urban and regional development. In V. Ginsburgh & D. Throsby (Eds.), Handbook of the economics of art and culture (pp. 1051–1099). Amsterdam: Elsevier.
Blood, A., & Zatorre, R. (2001). Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. PNAS—Proceeding of the National Academy of Sciences, 98(20), 11818–11823.
Boix, R., Hervás, J. L., & De Miguel, B. (2015). Micro-geographies of creative industries clusters in Europe: From hot spots to assemblages. Papers in Regional Science, 94(4), 753–773.
Bourdieu, P. (2000). Las formas del capital. Capital Económico, capital cultural y capital social. In P. Bourdieu (Ed.), Poder, derecho y clases sociales. Barcelona: Desclée.
Broh, B. (2002). Linking extracurricular programming to academic achievement: Who benefits and why? Sociology of Education, 75, 69–95.
Bucci, A., Sacco, P. L., & Segre, G. (2014). Smart endogenous growth: Cultural capital and the creative use of skills. International Journal of Manpower, 35(1/2), 33–55.
Cabane, C., Hille, A., & Lechner, M. (2016). Mozart or Pelé? The effects of adolescents’ participation in music and sports. Labour Economics, 41, 90–103.
Caliendo, M., & Kopeining, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Survey, 22(1), 31–72.
Cameron, S. (2016). Past, present and future: Music economics at the crossroads. Journal of Cultural Economics, 40(1), 1–12.
Chen, F., Lui, A., Andrade, H., Valle, C., & Mir, H. (2017). Criteria-referenced formative assessment in the arts. Educational Assessment, Evaluation and Accountability, 29(3), 297–314.
Coll-Martínez, E. J. (2019). Creativity and the city: Testing the attenuation of agglomeration economies in Barcelona. Journal of Cultural Economics. https://doi.org/10.1007/s10824-019-09340-9.
Conejo-Rodríguez, P. A. (2012). El valor formativo de la música para la educación en valores. DEDiCA. Revista de Educação e Humanidades, 2, 263–278.
Corrales-Herrero, H., & Rodríguez-Prado, B. (2016). La incidencia de la formación en la edad adulta en la empleabilidad de los individuos: El caso de España. El trimestre económico, 1(329), 149–183.
Cuesta, J. (2011). Music to my ears: The (many) socioeconomic benefits of music training programmes. Applied Economics Letters, 18, 915–918.
Daykin, N., de Viggiani, N., Pilkington, P., & Moriarty, Y. (2013). Music making for health, well-being and behaviour change in youth justice settings: A systematic review. Health Promotion International, 28(2), 197–210.
Dehejia, R., & Wahba, S. (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Review of Economics and Statistics, 94, 1053–1062.
Dehejia, R., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. Review of Economics and Statistics, 84(1), 151–161.
Eerola, P., & Eerola, T. (2014). Extended music education enhances the quality of school life. Music Education Research, 16, 88–104.
Egaña, P., Contreras, D., & Valenzuela, J. (2013). The effect of artistic activities on the development of cognitive and non-cognitive outcomes in Chile: The case of the Curanilahue Youth Orchestra. Santiago de Chile: Centro de Investigación Avanzada en Educación, Universidad de Chile.
Egaña, P., Contreras, D., & Valenzuela, J. (2019). The impact of art-education on human capital: An empirical assessment of a youth orchestra. International Journal of Educational Development, 71, 102105.
Espinal-Monsalve, N. E. (2013). La Disponibilidad a Pagar como una medida de la Legitimidad. El caso de la Biblioteca Pública Piloto para América Latina y los Parques Biblioteca de Medellín, Colombia (Doctoral thesis, Universidad Pablo de Olavide, España).
Falck, O., Fritsch, M., & Heblich, S. (2011). The phantom of the opera: Cultural amenities, human capital, and regional economic growth. Labour Economics, 18(6), 755–766.
Falck, O., Fritsch, M., Heblich, S., & Otto, A. (2018). Music in the air: Estimating the social return to cultural amenities. Journal of Cultural Economics, 42(3), 365–391.
Gardiner, M., Fox, A., Nowles, F., & Jeffrey, D. (1996). Learning improved by arts training. Nature, 381(6580), 284.
Gertler, P. J., Martínez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. (2017). Impact evaluation in practice (2nd ed.). Washington, DC: World Bank.
Gómez-Zapata, J. D., Espinal-Monsalve, N. E., & Herrero-Prieto, L. C. (2018). Economic valuation of museums as public club goods: Why build loyalty in cultural heritage consumption? Journal of Cultural Heritage, 30, 190–198.
Hallam, S. (2010). The power of music: Its impact on the intellectual, social and personal development of children and young people. International Journal of Music Education, 28(3), 269–289.
Hand, C. (2018). Do the arts make you happy? A quantile regression approach. Journal of Cultural Economics, 42(2), 271–286.
Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998). Characterizing selection bias using experimental data. Econometrica, 66(5), 1017–1098.
Herrero-Prieto, L. C. (2011). La contribución de la cultura y las artes al desarrollo económico regional. Investigaciones Regionales, 19, 177–202.
Herrero-Prieto, L. C., Sanz-Lara, J. A., Bedate-Centeno, A., & del Barrio-Tellado, M. J. (2012). Who pays more for a cultural festival, tourists or locals? A certainty analysis of a contingent valuation application. International Journal of Tourism Research, 14(5), 495–512.
Herrero-Prieto, L. C., Sanz-Lara, J. A., & Devesa-Fernández, M. (2011). Measuring the economic value and social viability of a cultural festival as a tourism prototype. Tourism Economics, 17(3), 639–653.
Hetland, L. (2000). Listening to music enhances spatial-temporal reasoning: Evidence for the ‘Mozart Effect’. Journal of Aesthetic Education, 34, 105–148.
Hille, A., & Schupp, J. (2015). How learning a musical instrument affects the development of skills. Economics of Education Review, 44, 56–82.
Imbens, G., & Wooldridge, J. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86.
Jalan, J., & Ravallion, M. (2003). Estimating the benefit incidence of an antipoverty program by propensity-score matching. Journal of Business & Economic Statistics, 21(1), 19–30.
Johnson, C. M., & Memmott, J. E. (2006). Examination of relationships between participation in school music programs of differing quality and standardized test results. Journal of Research in Music Education, 54(4), 293–307.
Leuven, E. & Sianesi, B. (2003). psmatch2 version 4.0.12: Stata module to perform full Mahalanobisand propensity score matching, common support graphing, and covariate imbalance testing. Download from http://ideas.repec.org/c/boc/bocode/s432001.html. Accessed January 30, 2016.
Levinson, J. (2014). Values of music. In V. Ginsburgh & D. Throsby (Eds.), Handbook of the economics of art and culture (Vol. 2). Elsevier: North Holland.
Mellander, C., Florida, R., Rentfrow, P., & Potter, J. (2018). The geography of music preferences. Journal of Cultural Economics, 42(4), 593–618.
Michalos, A., & Kahlke, P. (2008). Impact of arts related activities on the perceived quality of life. Social Indicators Research, 89(2), 193–258.
Miksza, P., & Hime, L. (2015). Undergraduate music program alumni’s career path, retrospective institutional satisfaction, and financial status. Arts Education Policy Review, 116(4), 1–13.
Moral-Arce, I. (2014). Elección del método de evaluación cuantitativa de una política pública Buenas prácticas en América Latina y la Unión Europea. Colección Documentos de Trabajo nº 6. Madrid: Programa EUROsociAL.
Ntarangwi, M. (2015). Youth, music, and peace building. In J. Wright (Ed.), International encyclopedia of the social & behavioral sciences (2nd ed, pp. 842–848). Amsterdam: Elsevier.
Pérez-Torres, F. J. (2015). Evaluación de impacto de política pública del Programa Especial Desarraigados: una aplicación cuantitativa. Equidad & Desarrollo, 23, 77–125.
Pettit, B. (1999). Cultural capital and residential mobility: A model of impersistence in place. Poetics, 26(3), 177–199.
Pirracchio, R., Petersen, M. L., & van der Laan, M. (2015). Improving propensity score estimators’ robustness to model misspecification using super learner. American Journal of Epidemiology, 181(2), 108–119.
Rabinowitch, T., Cross, I., & Burnard, P. (2013). Long-term musical group interaction has a positive influence on empathy in children. Psychology of Music, 41(4), 484–498.
Ramos-Vargas, M. (2015). Music as a resource to develop cognition. Procedia—Social and Behavioral Sciences, 174, 2989–2994.
Red de Escuelas de Música de Medellín (REMM) (2010). Retrieved from www.redmusicamedellin.org. Accessed 8 Feb 2019.
Rentfrow, P., & Gosling, S. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236–1256.
Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Rosenbaum, P., & Rubin, D. (1985). Constructing a control group using multivariate matched sample methods that incorporate the propensity score. American Statistician, 39(1), 33–38.
Schellenberg, E. G. (2011). Examining the association between music lessons and intelligence. British Journal of Psychology, 102(3), 283–302.
Sloboda, J. (2012). La mente musical. La psicología cognitiva de la música. Madrid: Machado Nuevo Aprendizaje.
Smith, J. (2000). A critical survey of empirical methods for evaluating active labor market policies. Swiss Journal for Economics and Statistics, 136(3), 1–22.
Southgate, D., & Roscigno, V. (2009). The impact of music on childhood and adolescent achievement. Social Science Quarterly, 90(1), 4–21.
Swaminathana, S., Schellenberg, E. G., & Khalil, S. (2017). Revisiting the association between music lessons and intelligence: Training effects or music aptitude? Intelligence, 62, 119–124.
Taylor, P., Davies, L., Wells, D., Gilbertson, J. & Tayleur, W. (2015). CASE: A review of the social impacts of culture and sport. Department for Culture, Media and Sport. Retrieved from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/416279/A_review_of_the_Social_Impacts_of_Culture_and_Sport.pdf. Accessed 1 Apr 2019.
Thomas, M. (2016). Music education and its causal impact on student engagement and success. A program evaluation of little kids Rock. Working paper. Retrieved from https://www.arts.gov/sites/default/files/Research-Art-Works-MississippiState.pdf. Accessed 1 Apr 2019.
Tubadji, A. (2012). Culture-based development: Culture as an encompassing economic factor–empirical evidence for Germany. International Journal of Social Economics, 39(9), 690–703.
Wheatley, D., & Bickerton, C. (2017). Subjective well-being and engagement in arts, culture and sport. Journal of Cultural Economics, 41(1), 23–45.
Yang, P. (2015). The impact of music on educational attainment. Journal of Cultural Economics, 39(4), 369–396.
Acknowledgements
The authors wish to thank the Medellin Music School Network (REMM) for granting access to its databases and information, which allowed the present study to be carried out. We would also like to thank the referee and participants at the 9th Spanish Workshop on Cultural Economics and Management for their comments and discussion on a preliminary version of the paper. We also wish to thank the comments and suggestions made by the anonymous referees of the Journal. The usual disclaimer applies.
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Appendices
Appendix 1: Characteristics of the REMM programme
Mission | Offering children and youngsters in the city of Medellin an education and a focus through music geared towards instilling an appreciation of life, fostering non-violent attitudes and changing young people through education, healthy coexistence and social appropriation | ||
Character | An introduction to music education programme that is extracurricular and independent from the formal education process (school or university) | ||
Publicity | General publicity for registration and activities made by the city council and REMM through information channels such as webpage, social networks, television, radio, posters, and so on. | ||
Intake | Annual, voluntary and free Age range between 7 and 24, resident in Medellin and enrolled in a formal education institution. No previous training in music is required Once registered, students take a music aptitude test to distribute them and form groups of students who have similar levels Each student must present a family member as a tutor to attend and take part in REMM activities | ||
Organisation | 27 schools: 13 bowed string instruments, 13 wind and percussion instruments, and one focusing on Colombian instruments Four levels: “seedbed”, pre-orchestra or pre-band, orchestra or band, integrated ensembles 11 integrated ensembles: four symphony orchestras, three choral orchestras, and four popular music groups Wide and varied repertoire based on universal classical works and including contemporary works composed by musicians from the city, the country and the region; this also provides for the possibility of performing autochthonous works | ||
Training | Teaching model: collective training programme in an introduction to music and instruments, music language, body expression, instrument technique, choral practice and instrument ensembles; complemented by workshops and psychosocial activities. Students choose their instrument in accordance with the school at which they enrolled Assessment: system of merit and recognition which mainly involves regular attendance and final presentation at the end of the year of a repertoire chosen together with their teachers. The children and youngsters who most stand out progress through the years until they come to form part of the integrated ensembles Intensity: group lessons, two sessions per week of approximately 2 h for the basic levels and 8 h a week for the advanced levels The members of the bands, orchestras and/or ensembles travel and perform together in various local, national and international tours | ||
Costs | All of the costs associated with the running and functioning as well as the REMM’s cultural programme are met by the Medellin city council. The activities organised as part of the programme are free for all citizens | ||
Average annual data | 5000 students 1000 new students 132 teachers Groups of 20 students 55,000 h of lessons | 4000 own instruments 10% drop out rate 4100 families involved Budget of two million US dollars 205 direct jobs | 500 concerts 45,000 spectators 108 events around the city each year One international tour |
Appendix 2: Measures and definition of variables
Variable | Type | Measure | |
|---|---|---|---|
Treatment | Participation in the REMM | Binary | = 1 If you participated in the REMM program |
Baseline variables (matching variables) | Gender | Binary | = 1 Man |
Age | Discrete | Age of the interviewee in 2010 | |
Educational level | |||
Secondary (6th to 9th) | Binary | School year in which the interviewee was in 2010 = 1 Secondary | |
High school (10th to 11th) | Binary | School year in which the interviewee was in 2010 = 1 High school | |
Family size | Discrete | Number of people living with the interviewee in 2010 | |
Maximum educational level of the mother | |||
Elementary school (1st to 5th) | Binary | = 1 Elementary school | |
Secondary (6th to 9th) | Binary | = 1 Secondary | |
High school (10th to 11th) | Binary | = 1 High school | |
Technical | Binary | = 1 Technical | |
Technological | Binary | = 1 Technological | |
Undergraduate (college) | Binary | = 1 Undergraduate (college) | |
Graduate | Binary | = 1 Graduate | |
Maximum educational level of the father | |||
Elementary school (1st to 5th) | Binary | = 1 Elementary school | |
Secondary (6th to 9th) | Binary | = 1 Secondary | |
High school (10th to 11th) | Binary | = 1 High school | |
Technical | Binary | = 1 Technical | |
Technological | Binary | = 1 Technological | |
Undergraduate (college) | Binary | = 1 Undergraduate (college) | |
Graduate | Binary | = 1 Graduate | |
Place of residence | |||
Commune 1—Popular | Binary | = 1 Popular | |
Commune 3—Manrique | Binary | = 1 Manrique | |
Commune 4—Aranjuez | Binary | = 1 Aranjuez | |
Commune 7—Robledo | Binary | = 1 Robledo | |
Commune 8—Villa Hermosa | Binary | = 1 Villa Hernisa | |
Commune 9—Buenos Aires | Binary | = 1 Buenos Aires | |
Commune 10—La Candelaria | Binary | = 1 La Candelaria | |
Commune 11—Laureles-Estadio | Binary | = 1 Laureles-Estadio | |
Commune 12—La América | Binary | = 1 La América | |
Commune 13—San Javier | Binary | = 1 San Javier | |
Commune 15—Guayabal | Binary | = 1 Guayabal | |
Commune 16—Belén | Binary | = 1 Belén | |
Out of Medellin | Binary | = 1 Out of Medellin | |
Outcome variables | Social efficacy | ||
Being involved in situations of conflict and/or violence | Binary | Direct question of the questionnaire = 1 “You or any of the members of your family group have been involved in a conflict situation (discussion, civil conflict, altercation, violent events, dispute, fight, among others) in the last 5 years” | |
Changing place of residence to areas of a higher stratum | Binary | = 1 If the interviewee changed their place of residence from 2010 to 2018, to an area of greater socio-economic status in the city | |
Housing conditions | Continuous | For that outcome we built an indicator which allowed us to sum up the battery of questions relating to the number of spaces (living room, bedrooms, kitchens, bathroom, among others) and utilities (such as running water, electricity, garbage collection) in a house | |
Educational and labour efficacy | |||
Average marks of the studies | Continuous | Direct question of the questionnaire “What was the average overall grade you graduated with?” | |
Finishing studies in the expected time | Binary | = 1 If you finish higher studies than the interviewee agreed, in the time established according to your age | |
Obtaining study grants | Binary | Direct question of the questionnaire = 1 If you receive a scholarship for the development of your studies | |
Professional vocation-studying arts | Binary | = 1 If the interviewee conducted or performs higher studies related to arts | |
Entry into the labour market | Binary | = 1 If the interviewee entered the labour market | |
Income above a legal monthly salary | Binary | = 1 If the interviewee receives a monthly salary higher than the regulated minimum | |
Cultural consumption | |||
Doing sports | Discrete | Direct question of the questionnaire: “Number of times you performed or attended the following activities during the last year” | |
Attending football matches | Discrete | ||
Participating in sports competitions | Discrete | ||
Cinema attendance | Discrete | ||
Theatre attendance | Discrete | ||
Going dancing | Discrete | ||
Museum attendance | Discrete | ||
Book reading | Discrete | ||
Reading newspapers | Discrete | ||
Attending classical music concerts | Discrete | ||
Attending concerts | Discrete | ||
Visiting heritage | Discrete | ||
Videogames | Discrete | ||
Institutional efficacy | |||
Willingness to pay | Continuous | Direct question of the questionnaire: “How much would be the voluntary monetary contribution that I would be willing to give once for a year to support the REMM” | |
Willingness to support the REMM | Binary | Direct question of the questionnaire: = 1 If “Do you think it is important that the community in general be involved with the support of the REMM through a voluntary monetary contribution that allows a greater impact on its activities” | |
Appendix 3: Descriptive statistics of the baseline and outcome variables
Sample | Complete | Treated | Controls | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
N = 180 | N = 66 | N = 114 | |||||||||||
Variable | Mean | SD | Min | Max | Mean | SD | Min | Max | Mean | SD | Min | Max | |
Treatment | Participation in the REMM | 0.36 | 0.48 | 0 | 1 | ||||||||
Baseline variables (matching variables) | Gender | 0.5 | 0.50 | 0 | 1 | 0.46 | 0.50 | 0 | 1 | 0.51 | 0.50 | 0 | 1 |
Age | 16.43 | 2.55 | 13 | 26 | 16.21 | 1.90 | 14 | 23 | 16.56 | 2.86 | 13 | 26 | |
Educational level | |||||||||||||
Secondary (6th to 9th) | 0.07 | 0.25 | 0 | 1 | 0.03 | 0.17 | 0 | 1 | 0.09 | 0.29 | 0 | 1 | |
High school (10th to 11th) | 0.12 | 0.33 | 0 | 1 | 0.12 | 0.32 | 0 | 1 | 0.13 | 0.33 | 0 | 1 | |
Family size | 4.40 | 1.41 | 1 | 11 | 4.60 | 1.49 | 2 | 10 | 4.28 | 1.35 | 1 | 11 | |
Maximum educational level of the mother | |||||||||||||
Elementary school (1st to 5th) | 0.13 | 0.34 | 0 | 1 | 0.13 | 0.34 | 0 | 1 | 0.14 | 0.34 | 0 | 1 | |
Secondary (6th to 9th) | 0.12 | 0.32 | 0 | 1 | 0.15 | 0.36 | 0 | 1 | 0.10 | 0.30 | 0 | 1 | |
High school (10th to 11th) | 0.27 | 0.44 | 0 | 1 | 0.33 | 0.47 | 0 | 1 | 0.24 | 0.43 | 0 | 1 | |
Technical | 0.16 | 0.36 | 0 | 1 | 0.15 | 0.36 | 0 | 1 | 0.16 | 0.37 | 0 | 1 | |
Technological | 0.08 | 0.28 | 0 | 1 | 0.03 | 0.17 | 0 | 1 | 0.12 | 0.32 | 0 | 1 | |
Undergraduate (college) | 0.14 | 0.35 | 0 | 1 | 0.15 | 0.36 | 0 | 1 | 0.14 | 0.34 | 0 | 1 | |
Graduate | 0.05 | 0.21 | 0 | 1 | 0.03 | 0.17 | 0 | 1 | 0.06 | 0.24 | 0 | 1 | |
Maximum educational level of the father | |||||||||||||
Elementary school (1st to 5th) | 0.20 | 0.40 | 0 | 1 | 0.28 | 0.45 | 0 | 1 | 0.14 | 0.35 | 0 | 1 | |
Secondary (6th to 9th) | 0.09 | 0.29 | 0 | 1 | 0.04 | 0.20 | 0 | 1 | 0.12 | 0.32 | 0 | 1 | |
High school (10th to 11th) | 0.27 | 0.44 | 0 | 1 | 0.30 | 0.46 | 0 | 1 | 0.25 | 0.43 | 0 | 1 | |
Technical | 0.10 | 0.30 | 0 | 1 | 0.10 | 0.31 | 0 | 1 | 0.10 | 0.30 | 0 | 1 | |
Technological | 0.07 | 0.25 | 0 | 1 | 0.03 | 0.17 | 0 | 1 | 0.09 | 0.29 | 0 | 1 | |
Undergraduate (college) | 0.17 | 0.38 | 0 | 1 | 0.16 | 0.37 | 0 | 1 | 0.18 | 0.38 | 0 | 1 | |
Graduate | 0.03 | 0.19 | 0 | 1 | 0.03 | 0.17 | 0 | 1 | 0.04 | 0.20 | 0 | 1 | |
Place of residence | |||||||||||||
Commune 1—Popular | 0.05 | 0.21 | 0 | 1 | 0.06 | 0.24 | 0 | 1 | 0.04 | 0.20 | 0 | 1 | |
Commune 3—Manrique | 0.07 | 0.26 | 0 | 1 | 0.09 | 0.28 | 0 | 1 | 0.07 | 0.25 | 0 | 1 | |
Commune 4—Aranjuez | 0.04 | 0.20 | 0 | 1 | 0.07 | 0.26 | 0 | 1 | 0.02 | 0.16 | 0 | 1 | |
Commune 7—Robledo | 0.06 | 0.24 | 0 | 1 | 0.07 | 0.26 | 0 | 1 | 0.05 | 0.22 | 0 | 1 | |
Commune 8—Villa Hermosa | 0.03 | 0.18 | 0 | 1 | 0.06 | 0.24 | 0 | 1 | 0.17 | 0.13 | 0 | 1 | |
Commune 9—Buenos Aires | 0.05 | 0.22 | 0 | 1 | 0.04 | 0.20 | 0 | 1 | 0.06 | 0.24 | 0 | 1 | |
Commune 10—La Candelaria | 0.05 | 0.22 | 0 | 1 | 0.04 | 0.20 | 0 | 1 | 0.06 | 0.24 | 0 | 1 | |
Commune 11—Laureles-Estadio | 0.05 | 0.21 | 0 | 1 | 0.06 | 0.24 | 0 | 1 | 0.04 | 0.20 | 0 | 1 | |
Commune 12—La América | 0.04 | 0.20 | 0 | 1 | 0.03 | 0.17 | 0 | 1 | 0.05 | 0.22 | 0 | 1 | |
Commune 13—San Javier | 0.03 | 0.18 | 0 | 1 | 0.04 | 0.20 | 0 | 1 | 0.02 | 0.16 | 0 | 1 | |
Commune 15—Guayabal | 0.06 | 0.25 | 0 | 1 | 0.13 | 0.34 | 0 | 1 | 0.02 | 0.16 | 0 | 1 | |
Commune 16—Belén | 0.04 | 0.20 | 0 | 1 | 0.03 | 0.17 | 0 | 1 | 0.05 | 0.22 | 0 | 1 | |
Out of Medellin | 0.27 | 0.44 | 0 | 1 | 0.18 | 0.38 | 0 | 1 | 0.32 | 0.47 | 0 | 1 | |
Outcome variables | Social efficacy | ||||||||||||
Being involved in situations of conflict and/or violence | 0.21 | 0.41 | 0 | 1 | 0.15 | 0.36 | 0 | 1 | 0.25 | 0.43 | 0 | 1 | |
Changing place of residence to areas of a higher stratum | 0.10 | 0.30 | 0 | 1 | 0.12 | 0.32 | 0 | 1 | 0.08 | 0.28 | 0 | 1 | |
Housing conditions | 1.07 | 0.12 | 0.69 | 1.57 | 1.05 | 0.13 | 0.80 | 1.57 | 1.07 | 0.12 | 0.69 | 1.38 | |
Educational and labour efficacy | |||||||||||||
Average marks of the studies | 4.15 | 0.27 | 3.5 | 4.8 | 4.2 | 0.30 | 3.5 | 4.8 | 4.12 | 0.25 | 3.5 | 4.8 | |
Finishing studies in the expected time | 0.62 | 0.48 | 0 | 1 | 0.77 | 0.42 | 0 | 1 | 0.53 | 0.50 | 0 | 1 | |
Obtaining study grants | 0.18 | 0.39 | 0 | 1 | 0.25 | 0.44 | 0 | 1 | 0.14 | 0.35 | 0 | 1 | |
Professional vocation-studying arts | 0.07 | 0.26 | 0 | 1 | 0.15 | 0.36 | 0 | 1 | 0.03 | 0.18 | 0 | 1 | |
Entry into the labour market | 0.64 | 0.48 | 0 | 1 | 0.68 | 0.46 | 0 | 1 | 0.62 | 0.48 | 0 | 1 | |
Income above a legal monthly salary | 0.35 | 0.48 | 0 | 1 | 0.39 | 0.49 | 0 | 1 | 0.33 | 0.47 | 0 | 1 | |
Cultural consumption | |||||||||||||
Doing sports | 0.41 | 0.49 | 0 | 1 | 0.33 | 0.47 | 0 | 1 | 0.45 | 0.50 | 0 | 1 | |
Attending football matches | 4.70 | 11.24 | 0 | 92 | 2.22 | 4.94 | 0 | 25 | 6.14 | 13.43 | 0 | 92 | |
Participating in sports competitions | 1.58 | 3.94 | 0 | 30 | 1.37 | 2.69 | 0 | 16 | 1.71 | 4.51 | 0 | 30 | |
Cinema attendance | 8.75 | 10.88 | 0 | 100 | 10.48 | 13.96 | 0 | 100 | 7.75 | 8.52 | 0 | 50 | |
Theatre attendance | 2.53 | 5.06 | 0 | 40 | 3.80 | 6.93 | 0 | 40 | 1.79 | 3.39 | 0 | 20 | |
Going dancing | 1.58 | 5.86 | 0 | 52 | 2.13 | 5.15 | 0 | 30 | 1.26 | 6.23 | 0 | 52 | |
Museum attendance | 2.20 | 5.69 | 0 | 50 | 2.21 | 2.70 | 0 | 10 | 2.20 | 6.86 | 0 | 50 | |
Book reading | 33.77 | 75.50 | 0 | 365 | 46.03 | 88.37 | 0 | 365 | 26.67 | 66.32 | 0 | 365 | |
Reading newspapers | 131.27 | 149.80 | 0 | 365 | 162.22 | 153.26 | 0 | 365 | 113.35 | 145.44 | 0 | 365 | |
Attending classical music concerts | 2.20 | 8.15 | 0 | 100 | 5.31 | 12.81 | 0 | 100 | 0.40 | 1.40 | 0 | 12 | |
Attending concerts | 2.16 | 3.23 | 0 | 27 | 3.31 | 4.48 | 0 | 27 | 1.49 | 1.92 | 0 | 10 | |
Visiting heritage | 2.98 | 13.64 | 0 | 150 | 1.54 | 3.10 | 0 | 20 | 3.81 | 16.95 | 0 | 150 | |
Videogames | 28.32 | 72.93 | 0 | 365 | 36.87 | 89.12 | 0 | 365 | 23.37 | 61.55 | 0 | 365 | |
Institutional efficacy | |||||||||||||
Willingness to pay | 51,811.07 | 85,081.35 | 0 | 700 | 82,749.62 | 126,179.6 | 0 | 700 | 33,899.28 | 37,760.02 | 0 | 200 | |
Willingness to support the REMM | 0.78 | 0.41 | 0 | 1 | 0.83 | 0.37 | 0 | 1 | 0.75 | 0.43 | 0 | 1 | |
Appendix 4: Diagnostic statistics for one-to-one nearest matching procedure (NN(1))
Variable | Sample | Mean | % Reduct | ||||
|---|---|---|---|---|---|---|---|
Treated | Control | % Biasb | Bias | T testa | p value | ||
Gender | Unmatched | 0.4697 | 0.5175 | − 9.5 | − 0.62 | 0.539 | |
Matched | 0.4838 | 0.4516 | 6.4 | 32.6 | − 0.36 | 0.722 | |
Age | Unmatched | 16.212 | 16.561 | − 14.4 | − 0.88 | 0.378 | |
Matched | 16.258 | 15.766 | 20.2 | − 40.8 | 1.38 | 0.171 | |
Educational level | |||||||
Secondary | Unmatched | 0.0303 | 0.0964 | − 27.3 | − 1.66 | 0.099 | |
Matched | 0.0322 | 0.0249 | 3.3 | 87.8 | 0.27 | 0.788 | |
High school | Unmatched | 0.1212 | 0.1315 | − 3.1 | − 0.20 | 0.842 | |
Matched | 0.1290 | 0.1129 | 4.8 | − 55.6 | 0.27 | 0.785 | |
Family size | Unmatched | 4.6061 | 4.2895 | 22.2 | 1.45 | 0.148 | |
Matched | 4.5968 | 4.5806 | 1.1 | 94.9 | 0.06 | 0.953 | |
Maximum educational level of the mother | |||||||
Elementary school | Unmatched | 0.1363 | 0.1403 | − 1.1 | − 0.07 | 0.941 | |
Matched | 0.1451 | 0.0806 | 18.6 | − 1518.1 | 1.13 | 0.260 | |
Secondary | Unmatched | 0.1515 | 0.1052 | 13.8 | 0.91 | 0.364 | |
Matched | 0.1290 | 0.1532 | − 7.2 | 47.7 | − 0.38 | 0.702 | |
High school | Unmatched | 0.3333 | 0.2456 | 19.3 | 1.26 | 0.208 | |
Matched | 0.3387 | 0.3951 | − 12.4 | 35.6 | − 0.65 | 0.518 | |
Technical | Unmatched | 0.1515 | 0.1666 | − 4.1 | − 0.27 | 0.791 | |
Matched | 0.1451 | 0.1129 | 8.8 | − 112.9 | 0.53 | 0.596 | |
Technological | Unmatched | 0.0303 | 0.1228 | − 35.2 | − 2.12 | 0.036 | |
Matched | 0.0322 | 0.0564 | − 9.2 | 73.8 | − 0.65 | 0.517 | |
Undergraduate | Unmatched | 0.1515 | 0.1403 | 3.1 | 0.20 | 0.838 | |
Matched | 0.1612 | 0.1612 | 0.0 | 100.0 | 0.00 | 1.000 | |
Graduate | Unmatched | 0.0303 | 0.0614 | − 14.8 | − 0.92 | 0.359 | |
Matched | 0.0322 | 0.0249 | 3.8 | 74.1 | 0.27 | 0.788 | |
Maximum educational level of father | |||||||
Elementary school | Unmatched | 0.2878 | 0.1912 | 33.8 | 2.26 | 0.025 | |
Matched | 0.2741 | 0.1935 | 19.7 | 41.9 | 1.06 | 0.293 | |
Secondary | Unmatched | 0.0454 | 0.1228 | − 28.0 | − 1.71 | 0.088 | |
Matched | 0.0483 | 0.0806 | − 11.7 | 58.3 | − 0.73 | 0.469 | |
High school | Unmatched | 0.3030 | 0.2543 | 10.8 | 0.70 | 0.483 | |
Matched | 0.2903 | 0.3306 | − 9.0 | 17.1 | − 0.48 | 0.631 | |
Technical | Unmatched | 0.1060 | 0.1052 | 0.3 | 0.02 | 0.987 | |
Matched | 0.1129 | 0.1693 | − 18.3 | − 6979.0 | − 0.90 | 0.371 | |
Technological | Unmatched | 0.0303 | 0.0964 | − 27.3 | − 1.66 | 0.099 | |
Matched | 0.0322 | 0.0161 | 6.6 | 75.6 | 0.58 | 0.563 | |
Undergraduate | Unmatched | 0.1666 | 0.1842 | − 4.6 | − 0.30 | 0.768 | |
Matched | 0.1774 | 0.0967 | 21.1 | − 359.7 | 1.30 | 0.195 | |
Graduate | Unmatched | 0.0303 | 0.0438 | − 7.1 | − 0.45 | 0.652 | |
Matched | 0.3226 | 0.4839 | − 8.5 | − 19.0 | − 0.45 | 0.651 | |
Place of residence | |||||||
Commune 1—Popular | Unmatched | 0.0606 | 0.0438 | 7.5 | 0.49 | 0.622 | |
Matched | 0.0645 | 0.0887 | − 10.8 | − 44.5 | − 0.50 | 0.616 | |
Commune 3—Manrique | Unmatched | 0.09091 | 0.0701 | 7.6 | 0.50 | 0.619 | |
Matched | 0.0967 | 0.0645 | 11.8 | − 55.6 | 0.66 | 0.513 | |
Commune 4—Aranjuez | Unmatched | 0.0757 | 0.0263 | 22.5 | 1.55 | 0.122 | |
Matched | 0.0806 | 0.0645 | 7.3 | 67.4 | 0.34 | 0.732 | |
Commune 7—Robledo | Unmatched | 0.0757 | 0.0526 | 9.4 | 0.62 | 0.535 | |
Matched | 0.0806 | 0.1048 | − 9.8 | − 4.6 | − 0.46 | 0.646 | |
Commune 8—Villa Hermosa | Unmatched | 0.0606 | 0.0175 | 22.2 | 1.55 | 0.122 | |
Matched | 0.0483 | 0.0645 | − 8.3 | 62.5 | − 0.39 | 0.700 | |
Commune 9—Buenos Aires | Unmatched | 0.0454 | 0.0614 | − 7.1 | − 0.45 | 0.655 | |
Matched | 0.0483 | 0.0564 | − 3.6 | 49.4 | − 0.20 | 0.842 | |
Commune 10—La Candelaria | Unmatched | 0.0454 | 0.0614 | − 7.1 | − 0.45 | 0.655 | |
Matched | 0.0483 | 0.0564 | − 3.6 | 49.4 | − 0.20 | 0.842 | |
Commune 11—Laureles-Estadio | Unmatched | 0.0606 | 0.0438 | 7.5 | 0.49 | 0.622 | |
Matched | 0.6452 | 0.0725 | − 3.6 | 51.8 | − 0.18 | 0.860 | |
Commune 12—La América | Unmatched | 0.0303 | 0.0526 | − 11.2 | − 0.70 | 0.486 | |
Matched | 0.0322 | 0.0322 | 0.0 | 100.0 | − 0.00 | 1.000 | |
Commune 13—San Javier | Unmatched | 0.0454 | 0.0263 | 10.2 | 0.69 | 0.493 | |
Matched | 0.0483 | 0.0241 | 12.9 | − 26.4 | 0.72 | 0.475 | |
Commune 15—Guayabal | Unmatched | 0.1363 | 0.0263 | 40.8 | 2.90 | 0.004 | |
Matched | 0.0967 | 0.0806 | 6.0 | 85.3 | 0.31 | 0.755 | |
Commune 16—Belén | Unmatched | 0.0303 | 0.0563 | − 11.2 | − 0.70 | 0.486 | |
Matched | 0.0322 | 0.1613 | 8.1 | 27.8 | 0.58 | 0.563 | |
Out of Medellin | Unmatched | 0.1818 | 0.3245 | − 33.1 | − 2.09 | 0.038 | |
Matched | 0.1935 | 0.2338 | − 9.3 | 71.8 | − 0.54 | 0.588 | |
Appendix 5: Aggregated diagnostic statistics for matching procedures
Pseudo R2a | LR testb | p > χ2b | Mean biasc | Median biasc | Out of common supportd | |
|---|---|---|---|---|---|---|
Unmatched sample | 0.197 | 46.70 | 0.045 | 14.9 | 11.0 | |
Matched sample | ||||||
NN(1) | 0.075 | 12.88 | 0.999 | 8.9 | 8.4 | 4 |
NN(1) with Caliper | 0.107 | 15.66 | 0.993 | 10.2 | 7.6 | 13 |
Kernel | 0.042 | 7.30 | 1.000 | 6.8 | 5.9 | 4 |
Appendix 6: Average treatment effects (matched differences in differences)
Finishing studies within the expected time | Treated | Controls | ATT | T stat | |
|---|---|---|---|---|---|
NN(1) | N = 176 | − 0.04838 | − 0.3225 | 0.2741 | 2.21** |
NN(1) with Caliper | N = 167 | − 0.0566 | − 0.3396 | 0.2830 | 2.15** |
Kernel | N = 176 | − 0.0483 | − 0.2845 | 0.2361 | 2.49** |
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Gómez-Zapata, J.D., Herrero-Prieto, L.C. & Rodríguez-Prado, B. Does music soothe the soul? Evaluating the impact of a music education programme in Medellin, Colombia. J Cult Econ 45, 63–104 (2021). https://doi.org/10.1007/s10824-020-09387-z
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DOI: https://doi.org/10.1007/s10824-020-09387-z