When home became dangerous: tracking the hidden crisis of domestic violence through Paraguay’s pandemic years

Andreas Schneider

doi:10.53732/rccsociales/e8975

Authors

Andreas Schneider Independent Researcher. Asunción, Paraguay https://orcid.org/0000-0001-5630-1097

DOI:

https://doi.org/10.53732/rccsociales/e8975

Keywords:

domestic violence, lockdown, Paraguay, interrupted time series, counterfactual analysis

Abstract

This study assessed the impact of Paraguay’s COVID-19 lockdowns on domestic violence in five major cities using an interrupted time series design and ARIMA models on weekly data (2016–2021) to predict counterfactuals outcomes. Analysis of 1,565 cases from prosecutor records showed that, contrary to expectations, reported domestic violence generally declined during lockdown periods. Significant decreases were observed in three of the five cities, with Asunción showing the largest reduction. Although reported incidents increased by an average of 19% annually between 2016 and 2021, the post-pandemic growth rate (15.58%) remained below the pre-pandemic rate (22.04%). Higher temperatures were consistently associated with increased reporting, while public holidays corresponded to lower reporting levels. After lockdowns ended, domestic violence reports resumed positive growth trends, albeit at lower levels than before the pandemic. Overall, the study found no evidence of increased domestic violence during lockdowns in Paraguay. The findings suggest that lockdown measures may have limited victims’ access to reporting mechanisms and support services, highlighting the need to consider underreporting when interpreting crime data during crises.

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References

Agnew, R. (1992). Foundation for a General Strain Theory of Crime and Delinquency. Criminology, 30(1), 47–88. https://doi.org/10.1111/j.1745-9125.1992.tb01093.x

Agnew, R. (2001). Building on the Foundation of General Strain Theory: Specifying the Types of Strain Most Likely to Lead to Crime and Delinquency. Journal of Research in Crime and Delinquency, 38(4), 319–361. https://doi.org/10.1177/0022427801038004001

Allaire, J. J., Teague, C., Scheidegger, C., Xie, Y., & Dervieux, C. (2024). Quarto (Version 1.4) [Computer software]. https://doi.org/10.5281/zenodo.5960048

Bargain, O., & Aminjonov, U. (2021). Poverty and COVID-19 in Africa and Latin America. World Development, 142, 105422. https://doi.org/10.1016/j.worlddev.2021.105422

Barton, A. W., Larsen, N. B., Gong, Q., & Stanley, S. M. (2023). Family resiliency in the aftermath of COVID-19 pandemic: A latent profile analysis. Journal of Marriage and Family, 85(5), 1125–1137. https://doi.org/10.1111/jomf.12929

Béland, L.-P., Brodeur, A., Haddad, J., & Mikola, D. (2021). Determinants of family stress and domestic violence: Lessons from the COVID-19 outbreak. Canadian Public Policy. Analyse De Politiques, 47(3), 439–459. https://doi.org/10.3138/cpp.2020-119

Berniell, I., & Facchini, G. (2021). COVID-19 lockdown and domestic violence: Evidence from internet-search behavior in 11 countries. European Economic Review, 136, 103775. https://doi.org/10.1016/j.euroecorev.2021.103775

Bhalotra, S., Brito, E., Clarke, D., Larroulet, P., & Pino, F. (2026). Dynamic impacts of lockdown on domestic violence: Evidence from multiple policy shifts in Chile. The Review of Economics and Statistics, 1–29. https://doi.org/10.1162/rest_a_01412

Bradbury-Jones, C., & Isham, L. (2020). The pandemic paradox: The consequences of COVID-19 on domestic violence. Journal of Clinical Nursing, 29(13-14), 2047–2049. https://doi.org/10.1111/jocn.15296

Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., & Rubin, G. J. (2020). The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. The Lancet, 395(10227), 912–920. https://doi.org/10.1016/S0140-6736(20)30460-8

Campedelli, G. M., Aziani, A., & Favarin, S. (2021). Exploring the Immediate Effects of COVID-19 Containment Policies on Crime: An Empirical Analysis of the Short-Term Aftermath in Los Angeles. American journal of criminal justice, 46(5), 704–727. https://doi.org/10.1007/s12103-020-09578-6

Chalfin, A., Danagoulian, S., & Deza, M. (2021). COVID-19 has strengthened the relationship between alcohol consumption and domestic violence. National Bureau of Economic Research. https://doi.org/10.3386/w28523

Chandan, J. S., Thomas, T., Bradbury-Jones, C., Taylor, J., Bandyopadhyay, S., & Nirantharakumar, K. (2020). Risk of cardiometabolic disease and all-cause mortality in female survivors of domestic abuse. Journal of the American Heart Association, 9(4), e014580. https://doi.org/10.1161/JAHA.119.014580

Cohen, L. E., & Felson, M. (1979). Social Change and Crime Rate Trends: A Routine Activity Approach. American Sociological Review, 44(4), 588–608. https://doi.org/10.2307/2094589

Cohn, E. G. (1993). The prediction of police calls for service: The influence of weather and temporal variables on rape and domestic violence. Journal of Environmental Psychology, 13(1), 71–83. https://doi.org/10.1016/S0272-4944(05)80216-6

Field, S. (1992). The effect of temperature on crime. The British Journal of Criminology, 32(3), 340–351. https://doi.org/10.1093/oxfordjournals.bjc.a048222

Gibbs, A., Dunkle, K., Ramsoomar, L., Willan, S., Jama Shai, N., Chatterji, S., Naved, R., & Jewkes, R. (2020). New learnings on drivers of men’s physical and/or sexual violence against their female partners, and women’s experiences of this, and the implications for prevention interventions. Global Health Action, 13(1), https://doi.org/10.1080/16549716.2020.1739845

Halford, E., Dixon, A., Farrell, G., Malleson, N., & Tilley, N. (2020). Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime. Crime Science, 9(1), 11. https://doi.org/10.1186/s40163-020-00121-w

Hoeboer, C. M., Kitselaar, W. M., Henrich, J. F., Miedzobrodzka, E. J., Wohlstetter, B., Giebels, E., Meynen, G., Kruisbergen, E. W., Kempes, M., Olff, M., & Kogel, C. H. de. (2024). The Impact of COVID-19 on Crime: A Systematic Review. American Journal of Criminal Justice, 49(2), 274–303. https://doi.org/10.1007/s12103-023-09746-4

Instituto Nacional de Estadística. (2023). Resultados preliminares del censo 2022. Instituto Nacional de Estadística. https://www.ine.gov.py/censo2022/documentos/Revista_Censo_2022.pdf

John, N., Casey, S. E., Carino, G., & McGovern, T. (2020). Lessons Never Learned: Crisis and gender-based violence. Developing World Bioethics, 20(2), 65–68. https://doi.org/10.1111/dewb.12261

Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomization is not an option: interrupted time series analysis. BMJ, 350, h2750. https://doi.org/10.1136/bmj.h2750

Mazzoleni Insfrán, J. (2021). Salud Pública en tiempos de COVID-19 en Paraguay, marzo 2020/2021. Revista de salud pública del Paraguay, 11(1), 1–7. https://doi.org/10.18004/rspp.2021.junio.1

Mohler, G., Bertozzi, A. L., Carter, J., Short, M. B., Sledge, D., Tita, G. E., Uchida, C. D., & Brantingham, P. J. (2020). Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis. Journal of Criminal Justice, 68, 101692. https://doi.org/10.1016/j.jcrimjus.2020.101692

Morgan, A., & Boxall, H. (2020). Social isolation, time spent at home, financial stress and domestic violence during the COVID-19 pandemic. Australian Institute of Criminology. https://doi.org/10.52922/ti04855

Perez-Vincent, S. M., & Carreras, E. (2022). Domestic violence reporting during the COVID-19 pandemic: evidence from Latin America. Review of Economics of the Household, 20(3), 799–830. https://doi.org/10.1007/s11150-022-09607-9

Piquero, A. R., Jennings, W. G., Jemison, E., Kaukinen, C., & Knaul, F. M. (2021). Domestic violence during the COVID-19 pandemic - evidence from a systematic review and meta-analysis. Journal of Criminal Justice, 74, 1–10. https://doi.org/10.1016/j.jcrimjus.2021.101806

R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Reluga, T. C. (2010). Game Theory of Social Distancing in Response to an Epidemic. PLOS Computational Biology, 6(5), e1000793. https://doi.org/10.1371/journal.pcbi.1000793

Schneider, A. (2024). El crimen callejero durante el COVID-19: una comparación retrospectiva de dos capitales latinoamericanas. Revista Paraguay desde las Ciencias Sociales, 14, 73–91. https://publicaciones.sociales.uba.ar/index.php/revistaparaguay/article/view/9800

Schneider, W. J. (2024). Apaquarto [Computer software]. https://github.com/wjschne/apaquarto

Visual Crossing. (2024). Weather Data & Weather API. Corporation, V. C. https://www.visualcrossing.com/

Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299–309. https://doi.org/10.1046/j.1365-2710.2002.00430.x

World Health Organization. (2023). UN News. https://news.un.org/en/story/2023/05/1136367