Resilience Firm Survey 2020
The Caribbean
Antigua and Barbuda, Bahamas, Barbados...and 10 more, 2020
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Reference ID
LAC_2020_RFS_v01_M
Producer(s)
Alvina Erman, Melanie Simone Kappes, Julie Rozenberg
Metadata
Created on
Jul 11, 2022
Last modified
Jul 13, 2022
Page views
1836
Downloads
123
Identification
Survey ID Number
LAC_2020_RFS_v01_M
Title
Resilience Firm Survey 2020
Subtitle
The Caribbean
Country/Economy
| Name | Country code |
|---|---|
| Antigua and Barbuda | ATG |
| Bahamas | BHS |
| Barbados | BRB |
| Dominica | DMA |
| Dominican Republic | DOM |
| Grenada | GRD |
| Jamaica | JAM |
| St. Kitts and Nevis | KNA |
| St. Lucia | LCA |
| St. Vincent and the Grenadines | VCT |
| Sint Maarten | SXM |
| Trinidad and Tobago | TTO |
| Turks and Caicos | TCA |
Study type
Enterprise Survey [en/oth]
Series Information
The Resilience Firm Surveys (RFS) is a firm survey series designed to collect information from private businesses focusing on i) dependence on and reliability of critical and non-critical infrastructure, ii) use of suppliers and impacts of supply chain disruptions, iii) impacts of recent disasters – coping capacity and long-term effects, iv) firm level preparedness and management of shocks and interruptions caused by natural hazards. The survey is customized depending on the context, industry sector and research questions. The data can be used to explore policy-relevant research topics related to climate change adaptation, infrastructure resilience, private sector recovery, and more. explore the real costs of disasters on firms—both through direct damage to assets and operations, and the indirect costs of perpetuated economic inefficiencies and coping measures.
RFS data contain information on firm characteristics, use of suppliers, infrastructure dependency, firm experience with disasters and risk management and coping capacity. Firm characteristics include sector engagement, number of employees, number of clients, costs and sales information. Use of supplier information include location of suppliers, use of inputs, frequency of restocking, storage capacity, etc. Infrastructure dependency focuses on use of infrastructure, such as water, electricity and transport, frequency of disruptions, impacts of disruptions on demand and sales, use of backup infrastructure, etc. Firm experience with disasters captures both direct (damages to property) and indirect (infrastructure and supply chain disruptions) impacts of recent disasters, as well as coping behaviors and recovery of sales after a shock. Risk management and coping capacity capture information on use of hazard risk information, access to insurance and preparedness measures. The RFS can be customized to collect information on different sectors and type of disasters. So far, it has focused on the impacts of urban flooding (TZ) and hurricanes/storms (CAR).
The series was developed and financed by Global Facility for Disaster Reduction and Recovery (GFDRR) in collaboration with Disaster Risk Management, Resilience and Land Global Practice (GPURL), country counterparts, and selected survey firms.
RFS data contain information on firm characteristics, use of suppliers, infrastructure dependency, firm experience with disasters and risk management and coping capacity. Firm characteristics include sector engagement, number of employees, number of clients, costs and sales information. Use of supplier information include location of suppliers, use of inputs, frequency of restocking, storage capacity, etc. Infrastructure dependency focuses on use of infrastructure, such as water, electricity and transport, frequency of disruptions, impacts of disruptions on demand and sales, use of backup infrastructure, etc. Firm experience with disasters captures both direct (damages to property) and indirect (infrastructure and supply chain disruptions) impacts of recent disasters, as well as coping behaviors and recovery of sales after a shock. Risk management and coping capacity capture information on use of hazard risk information, access to insurance and preparedness measures. The RFS can be customized to collect information on different sectors and type of disasters. So far, it has focused on the impacts of urban flooding (TZ) and hurricanes/storms (CAR).
The series was developed and financed by Global Facility for Disaster Reduction and Recovery (GFDRR) in collaboration with Disaster Risk Management, Resilience and Land Global Practice (GPURL), country counterparts, and selected survey firms.
Abstract
The RFS in Caribbean was conducted in 13 countries between March and November 2020 and focused on the tourism industry and the restaurant, hotel and tour and transport companies. Due to the COVID-19 crisis, data collection was done both remotely and in-person depending on the restrictions in place and preference of respondent. The countries covered included Antigua and Barbuda, Bahamas, Barbados, Dominica, Dominican Republic, Grenada, Jamaica, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sint Maarten, Trinidad and Tobago, Turks and Caicos. The survey in the Caribbean focused on impacts of recent disasters to have affected the region, including Hurricane Irma, Hurricane Maria, Tropical Storm Dorian, etc. (see Table 2 for country and disaster list). The data collection was financed by the Global Facility for Disaster Reduction and Recovery (GFDRR) with the objective of better understanding how natural hazards – large and small, affect the tourism industry in the Caribbean. The data informed the 360° Resilience: A Guide to Prepare the Caribbean for a New Generation of Shocks (Rozenberg, et al. 2021) to make recommendations on how Caribbean countries can invest resources to strengthen resilience in the region.
This project was a collaborative effort between GFDRR and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL).
This project was a collaborative effort between GFDRR and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL).
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Firm level
Coverage
Geographic Coverage
Antigua and Barbuda, Bahamas, Barbados, Dominica, Dominican Republic, Grenada, Jamaica, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sint Maarten, Trinidad and Tobago, Turks and Caicos.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Alvina Erman | World Bank |
| Melanie Simone Kappes | World Bank |
| Julie Rozenberg | World Bank |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| Global Facility for Disaster Risk Reduction | GFDRR |
Sampling
Sampling Procedure
The sample was drawn to achieve representativeness at the country level as well as the regional level. In the Dominican Republic, sampling was done in a way to also achieve representativeness in 4 provinces in the country. Since there was no comprehensive list of firms operating in the tourism industry readily available to sample from, the firm hired to collect data created a sampling frame from scratch by contacting relevant organizations and websites. To be able to say something about different sectors within the tourism industry, the sampling was stratified by three sectors, including hotels and accommodation, restaurants and bars, and a third sector including rental, taxi and tour companies, attractions and souvenir shops (referred to in this note as hotel, restaurant and tour/transport sectors). The sample selection was then completed in one stage in which firms were selected by using a systematic random sampling method from each stratum.
Once the firm is selected for inclusion in the survey, every effort was made to interview the firm. The survey response rate was low due to the COVID pandemic, and replacements were done. Replacements were drawn from the same stratum. Due to restrictions in some countries, firms were not reachable, even after several attempts and replacements had been done. To compensate for low response rate in some countries, the sample size in other countries was increased. As a result, The Bahamas and Turks and Caicos have lower than expected sample size so caution should be applied when interpreting country level results from these two countries. See Technical Note for more detail on composition of final sample.
The final sample contains a total of 1413 firms across the 13 countries. Dominican Republic has the largest number of observations because the objective of sampling was also to achieve province level representativeness, in addition to country level representativeness, in 4 providences that rely heavily on tourism.
To make the survey estimates representative of the population, it is necessary to apply weights to selected firms during analysis. Regional weights (weight) are applied to statistics representing regional values while country weights (weight_i) are applied to all country level statistics.
Once the firm is selected for inclusion in the survey, every effort was made to interview the firm. The survey response rate was low due to the COVID pandemic, and replacements were done. Replacements were drawn from the same stratum. Due to restrictions in some countries, firms were not reachable, even after several attempts and replacements had been done. To compensate for low response rate in some countries, the sample size in other countries was increased. As a result, The Bahamas and Turks and Caicos have lower than expected sample size so caution should be applied when interpreting country level results from these two countries. See Technical Note for more detail on composition of final sample.
The final sample contains a total of 1413 firms across the 13 countries. Dominican Republic has the largest number of observations because the objective of sampling was also to achieve province level representativeness, in addition to country level representativeness, in 4 providences that rely heavily on tourism.
To make the survey estimates representative of the population, it is necessary to apply weights to selected firms during analysis. Regional weights (weight) are applied to statistics representing regional values while country weights (weight_i) are applied to all country level statistics.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2020-03 | 2020-11 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Supervision
The World Bank
Data Collection Notes
Data collection was carried out both in-person and remotely due to restrictions put in place due to the COVID-19 crisis.
Data Collectors
| Name | Abbreviation |
|---|---|
| UDA Consulting | UDA |
Questionnaires
Questionnaires
• Respondent characteristics
• Firm characteristics
• Clients
• Infrastructure dependence and disruptions
o Water
o Electricity
o Communication (phone and internet)
o Road and boat
• Suppliers
• Disaster preparedness
• Impacts of recent disasters (see Table 2)
• Impacts of disease outbreaks (Zika and COVID-19)
• Financial accounts
• Firm characteristics
• Clients
• Infrastructure dependence and disruptions
o Water
o Electricity
o Communication (phone and internet)
o Road and boat
• Suppliers
• Disaster preparedness
• Impacts of recent disasters (see Table 2)
• Impacts of disease outbreaks (Zika and COVID-19)
• Financial accounts
Data Processing
Data Editing
The following data editing was done for anonymization purpose:
• Precise location data, such as GPS coordinates, and subnational administrative divisions (admin 1) were dropped
• Identifying and contact information, such as firm name, respondent’s name, supplier names, phone number and email contact, were dropped
• Number of fulltime workers above 100 was recoded to “above 100 fulltime workers” to mitigate re-identification of the largest firms.
See technical note for more details on anonymization.
• Precise location data, such as GPS coordinates, and subnational administrative divisions (admin 1) were dropped
• Identifying and contact information, such as firm name, respondent’s name, supplier names, phone number and email contact, were dropped
• Number of fulltime workers above 100 was recoded to “above 100 fulltime workers” to mitigate re-identification of the largest firms.
See technical note for more details on anonymization.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Alvina Erman | GFDRR | aerman@worldbank.org |
Confidentiality
Confidentiality has been ensured through a process of anonymization (see Technical Document for details).
Access conditions
Data is accessible for licensed users only and further dissemination of data is not allowed.
Citation requirements
The World Bank. Resilience-Firm Survey (RFS) 2020, The Caribbean. Ref: LAC_2020_RFS_v01_M. Dataset downloaded from microdata.worldbank.org on [date].
Disclaimer and copyrights
Disclaimer
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.
Metadata production
DDI Document ID
DDI_LAC_2020_RFS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
Date of Metadata Production
2022-07-11
DDI Document version
Version 01 (July 2022)