Data and code for performing quantitative analyses for "Resilience of garment workers' Families and economic hardship during the COVID-19 Pandemic in Indonesia"
Information on related paper
Title: Understanding the Resilience of garment workers' Families through a mixed-methods Approach: Surviving the economic hardship during the COVID-19 Pandemic in Indonesia
Authors: Andhika Aji Baskoro, Puguh Prasetyoputra, Luh Kitty Katherina, Ari Purwanto Sarwo Prasojo, Ardanareswari Ayu Pitaloka
Authors affiliation: Research Center for Population, National Research and Innovation Agency (BRIN), Jl. Jend. Gatot Subroto Kav. 10, Jakarta Selatan 12710, Indonesia
Articles' DOI: https://doi.org/10.1007/s11205-023-03277-5
Abstract: The Covid-19 pandemic has exerted enormous economic stressors on garment workers in the form of income decline, furlough, and layoffs, affecting their families. However, research on family resilience among garment workers is limited, particularly in Indonesia. This study examines the factors associated with the resilience of garment workers’ families. We used a complementary mixed-methods approach to analyze data from the 2021 Family and Community Resilience Survey. To enrich the study, we also performed 23 in-depth interviews and two focus group discussions in Bogor and Bandung Regencies. We assess family resilience as their current status in resolving their most disruptive stressor. We fitted a multinomial logistic regression model and assessed the relative variable importance, with socio-economic characteristics, social assistance, and family organizational factors as groups of explanatory variables. Less than half of the families (46.67%) overcame their most significant stressor. Regression analysis shows that wealth index, cash assistance, and role in the family are the three most contributing variables. Qualitative results underscore the importance of economic resources or access to cash assistance during the Covid-19 pandemic. However, reliance on Emok Bank or other informal lenders can create new stressors due to their high-interest rates. This option is common among garment workers, who usually cannot access the government’s assistance as many are migrants. The study emphasizes the need to strengthen formal social protection systems, especially for vulnerable populations like garment workers, to protect them from future crises.
List of files in this repository:
| Filename | Description |
|---|---|
| Readme.md | Information about this repository. |
| data-&-code-for-resilience-garment-workers-families-economic-hardship-covid19-pandemic-indonesia.Rproj | .Rproj file, open project in RStudio. |
| data\ | Folder with data for modeling multinomial logistic regression in .rds format. |
| figures\ | Folder which contain plotted figures in .png format. |
| R\ | Folder with r syntax to perform the analysis. |
| rds\ | Folder with multinomial logitic regression object in .rds format. |
| tables\ | Folder with exported model estimation output in .xlsx format. |
> sessionInfo()
R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=Indonesian_Indonesia.utf8 LC_CTYPE=Indonesian_Indonesia.utf8
[3] LC_MONETARY=Indonesian_Indonesia.utf8 LC_NUMERIC=C
[5] LC_TIME=Indonesian_Indonesia.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] here_1.0.1 xlsx_0.6.5 modelsummary_1.4.1
[4] brglm2_0.9 lubridate_1.9.2 forcats_1.0.0
[7] stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1
[10] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[13] ggplot2_3.4.2 tidyverse_2.0.0 haven_2.5.2
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.39 rJava_1.0-6
[4] lattice_0.20-45 parameters_0.21.0 colorspace_2.1-0
[7] vctrs_0.6.2 generics_0.1.3 htmltools_0.5.5
[10] enrichwith_0.3.1 utf8_1.2.3 rlang_1.1.1
[13] pillar_1.9.0 glue_1.6.2 withr_2.5.0
[16] effectsize_0.8.3 emmeans_1.8.5 lifecycle_1.0.3
[19] munsell_0.5.0 gtable_0.3.3 bayestestR_0.13.1
[22] mvtnorm_1.1-3 htmlwidgets_1.6.2 coda_0.19-4
[25] knitr_1.42 tzdb_0.3.0 fastmap_1.1.1
[28] pscl_1.5.5.1 datawizard_0.7.1 fansi_1.0.4
[31] xlsxjars_0.6.1 xtable_1.8-4 scales_1.2.1
[34] backports_1.4.1 DT_0.27 checkmate_2.2.0
[37] hms_1.1.3 digest_0.6.31 stringi_1.7.12
[40] insight_0.19.1 numDeriv_2016.8-1.1 grid_4.2.1
[43] rprojroot_2.0.3 cli_3.6.1 tools_4.2.1
[46] magrittr_2.0.3 pkgconfig_2.0.3 MASS_7.3-57
[49] Matrix_1.5-4 estimability_1.4.1 timechange_0.2.0
[52] rstudioapi_0.14 R6_2.5.1 tables_0.9.17
[55] nnet_7.3-18 compiler_4.2.1