High-Frequency Monitoring of COVID-19 Impacts 2020
Socio-Economic/Monitoring Survey [hh/sems]
The World Bank has launched a quick-deploying high-frequency phone-monitoring survey of households to generate near real-time insights on the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based response to the crisis. At a moment when all conventional modes of data collection have had to be suspended, a phone-based rapid data collection/tracking tool can generate large payoffs by helping identify affected populations across the vast archipelago as the contagion spreads, identify with a high degree of granularity the mechanisms of socio-economic impact, identify gaps in public policy response as the Government responds, generating insight that could be useful in scaling up or redirecting resources as necessary as the affected population copes and eventually regains economic footing.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Anonymized and edited dataset for public distribution
Study updated with Round 5 data
The Indonesia COVID-19 High-Frequency survey covered the following topics:
- Roster (Rounds 1, 2, 3, 4, 5; only fully updated in R1 and R4)
- Knowledge and behavior (Rounds 1, 3)
- Employment and income loss (Rounds 1, 3, 5)
- Food security (Rounds 1, 2, 3, 4, 5)
- Access to health services (Rounds 2, 3, 4, 5)
- Digital transactions (Rounds 2, 4)
- Education (Rounds 2, 4)
- Coping mechanisms (Rounds 2, 4)
- Concerns/Subjective Welfare (Rounds 2, 4, 5)
- Safety-nets (Rounds 1, 3, 5)
Producers and sponsors
Bill and Melinda Gates Foundation
Global Financing Facility
Three stages of sampling strategies were applied. For the first stage, districts (as primary sampling unit (PSU)) were selected based on probability proportional to size (PPS) systematic sampling in each stratum, with the probability of selection was proportional to the estimated number of households based on the National Household Survey of Socio-economic (SUSENAS) 2019 data. Prior to the selection, districts were sorted by provincial code. In the second stage, villages (as secondary sampling unit (SSU)) were selected systematically in each district, with probability of selection was proportional to the estimated number of households based on the Village Potential Census (PODES) 2018 data. Prior to the selection, villages were sorted by sub-district code. In the third stage, the number of households were selected systematically in each selected village. Prior to the selection, all households were sorted by implicit stratification, that is gender and education level of the head of households. If the primary selected households could not be contacted or refused to participate in the survey, these households were replaced by households from the same area where the non-response households were located and with the same gender and level of education of households’ head, in order to maintain the same distribution and representativeness of sampled households as in the initial design.
The HiFy survey was initially designed as a 5-round panel surveys. By end of the fifth round, it is expected that the survey can maintain around 3,000 panel households. Based on the experience of phone-based, panel survey conducted previously in other study in Indonesia, the response rates were expected to be around 60 percent to 80 percent. However, learned from other similar surveys globally, response rates of phone-based survey, moreover phone-based panel survey, are generally below 50 percent. Meanwhile, in the case of the HiFy, information on some of households’ phone numbers were from about 2 years prior the survey with a potential risk that the targeted respondents might not be contactable through that provided numbers (already inactive or the targeted respondents had changed their phone numbers). With these considerations, the estimated response rate of the first survey was set at 60 percent, while the response rates of the following rounds were expected to be 80 percent. Having these assumptions and target, the first round of the survey was expected to target 5,100 households, with 8,500 households in the lists. The actual sample of households in the first round was 4,338 households, or 85 percent of the 5,100 target households. However, the response rates in the following rounds are higher than expected, making the sampled households successfully interviewed in Round 2 were 4,119 (95% of Round 1 samples), and in Rounds 3, 4, and 5 were 4,067 (94%), 3,953 (91%), and 3,686 (85%) respectively. The number of panel households up to Rounds 3, 4, and 5 are 3,981 (92%), 3,794 (87%), and 3,601 (83%), respectively.
Since the sampling design of the three surveys were not the same, calculating the weights for all households combined is complicated. As a practical alternative, household weights were first calculated independently by each initial survey, and then combined them altogether afterward. For this approach to be properly applied without potential bias, there should not be overlapped survey areas across different surveys. The household weights were calculated for both cross-section for each round and panel for all rounds of the survey. In each round of the survey, the initial sampling weight was calculated following the original sampling method of the survey from which the sampled households were drawn. A sampling weight trimming using the mean and standard deviation of the weights was then conducted to reduce weight variability. In particular, the weight trimming was applied to some outlier weights (only on small proportion of the samples), while keeping the total of the weights remain the same. Afterwards, the weights were calibrated using a raking method to ensure the total estimates of the households with respect to designated variables were comparable with the population estimates of those variables from the SUSENAS 2019. The designated variables included region (DKI Jakarta, Java Non-DKI Jakarta Urban/Rural, Outside Java Urban/Rural), gender of household’s head, and level of education of household’s head (junior secondary and lower, senior secondary, and tertiary). The comparisons of unweighted and weighted distributions between the HiFy and SUSENAS 2019 for the designated variables were presented below. Attrition The attrition occurred when respondents were not able to be interviewed, which was mostly because their phones were unreachable or unanswered. A test for whether attrition was random showed that the dropped households were not associated with key households’ characteristics, such as household head age, gender, education, region (DKI Jakarta, Java-non DKI Jakarta, and Outside Java), and wealth status. However, there was a weak association between households’ participation and the area where they reside, as households in urban area were less likely to participate in the follow-up surveys than those in rural area. The difference in participation rates between urban and rural samples was taken into account in the survey weight calculation. Therefore, for analysis requiring panel households, attrition bias is not a concern when interpreting changes between rounds.
Dates of Data Collection
Data Collection Mode
Computer Assisted Telephone Interview [cati]
Data Collection Notes
Data collection was conducted by the hired survey firm, SurveyMETER.
The questionnaire in English is provided for download under the Documentation section.
Public Use Files
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
World Bank. Indonesia - High-Frequency Monitoring of COVID-19 Impacts (HIFY) 2020. Ref. IDN_2020_HFMCI_v02_M. Downloaded from [uri] on [date]
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
World Bank 2021
DDI Document ID
Development Data Group
Documentation of the Study
Date of Metadata Production
DDI Document version
Study updated with Round 5 data