This section discusses the methodological implications of using Survey of Well-being via Instant and Frequent Tracking (SWIFT) surveys as a data source for estimating poverty. Building on insights from the previous section, the applicability of SWIFT data for generating poverty maps is discussed in this section.

Definition

The World Bank Group’s SWIFT is a rapid assessment tool that estimates household income or expenditures to measure household poverty. SWIFT does not collect direct income or consumption data; instead, it collects poverty correlates such as household size, ownership of assets, or education levels, and then converts them to poverty statistics using estimation models. These poverty correlates are collected through a customized questionnaire consisting of 10–15 questions, which typically takes approximately five minutes to administer (hence, “swift”).

SWIFT survey data are collected through computer-assisted personal interviews, enabling data to be collected using tablets or smartphones and uploaded to a data cloud, making them accessible in real time. Analysts can then download the data and convert them into poverty and distributional statistics.

Data Sources

To derive the survey questionnaires, the SWIFT team develops a model based on household survey data. To ensure optimal results, there should be at least two rounds of highly comparable household survey data (such as the LSMS). These data sets should be no more than five years apart, and at least one of them should be no more than three years old.

Given that these data requirements are not always satisfied, some of the requirements can be relaxed in some cases. First, if the latest survey was carried out within the previous two years or is in progress, the SWIFT team can produce models using only the latest survey data, assuming that consumption patterns did not change significantly since the data were collected. Second, if the latest survey is older than five years, but there is a survey in progress, the SWIFT team can create a questionnaire to include variables that are likely to be in models that will be developed from the new survey.

Based on the model trained on the available survey data, the SWIFT team creates a questionnaire and collects data on poverty correlates, such as household size, ownership of assets, education levels, employment status, and so on, to estimate household income or expenditures. There are several versions of the SWIFT survey, including the classic SWIFT 1.0, which is described here; the SWIFT Plus, which can be used in locations experiencing economic shocks; the SWIFT-COVID19, which is specific to the COVID-19 situation; and the SWIFT 2.0, which can be used when there are no reliable or recent data for the location of interest. SWIFT surveys can also be included in any household survey to incorporate a poverty lens.

Since the SWIFT program launched in 2014, more than 100 SWIFT surveys have been or are being conducted in over 50 countries. However, unlike LSMS data, which are available online in the World Bank Microdata Library, SWIFT survey data are not accessible for public use at this time.

Methods

SWIFT relies on the availability of both consumption and nonconsumption data collected through a national household survey. The SWIFT survey model is derived by imputing consumption data based on the consumption data available in the household survey and collecting specific nonconsumption data through a custom questionnaire.

To derive a stable model, a cross-validation exercise is first conducted. The relevant household survey data are split randomly into 10 subsamples (or folds). Nine of these folds are used for training the model, and the remaining fold is used for testing. A model is estimated from nine folds by running a stepwise ordinary least square (OLS) regression, and the performance of the model is evaluated in the remaining fold. Because the remaining fold was not included when the model was trained, no performance indicators in the remaining fold are subject to the problem of overfitting.

This cross-validation exercise is intended to determine the optimal threshold of the p-value for the OLS regression equation. After the selection of the optimal p-value, OLS is applied to the full sample of data to estimate a model. Once the model for estimating household consumption is complete, the next step is to develop the questionnaire to collect nonconsumption data. It is critical that at this stage researchers consider the survey sampling design, as this highly influences the sampling precision of the survey. Finally, poverty rates are estimated using the multiple imputation method. The accuracy of SWIFT estimations relies on strong underlying models, which in turn rely on the quality and accuracy of the underlying large data sets used when designing the SWIFT survey.

Applicability Considerations

The use and accessibility of SWIFT surveys for poverty estimates is somewhat limited, given the data requirements. For a SWIFT survey, national-level data must be available to identify the poverty correlates the survey will measure. Recent, high-quality data may not be available for all countries.

Further, the SWIFT method is designed to provide poverty estimates for a specific geographic area or target group, such as national poverty estimates or poverty estimates for participants of a particular program. Since these poverty estimates cannot be disaggregated below the target level of the model, SWIFT survey estimates cannot (at this time) be used to develop poverty maps. However, given the method’s light touch and relatively low cost, SWIFT poverty estimates could be useful for assessing poverty in the context of evaluations. Large-scale surveys and censuses are elaborate exercises that require significant resources; the SWIFT method offers an efficient way to obtain poverty estimates in certain contexts.

However, because SWIFT aims to produce models specific to areas and populations in which projects are being implemented, the method is well suited to measure the impact of specific interventions on the income levels of target beneficiaries. Furthermore, given its relatively low cost, SWIFT could be implemented at times to better understand changes in poverty rates. Both of these features make the data well suited to the generation of granular poverty maps in various geographic contexts.

The Bank Group’s SWIFT team provides support to teams interested in using SWIFT surveys in their studies.1 This support may enhance opportunities to use this method. Given their limited scope, SWIFT surveys cost less than US$100,000 per country to implement and are substantially cheaper than longer survey exercises. SWIFT survey results can be interpreted by applying standard analytical techniques.

Example: Estimating Poverty Rates in Uganda Using SWIFT

Heitmann and Buri (2019) used results from a SWIFT survey in conjunction with CDR data to estimate poverty rates in Uganda as part of a larger study on using satellite imagery to estimate poverty at neighborhood levels. The survey focused on Northern Uganda, covering 9,037 households in the Karamoja, Mid-North, West Nile, and Adjumani administrative areas. The location of each household surveyed was geolocated using GPS. The researchers aimed to identify correspondence between CDR and household survey data by matching phone numbers across the two data sources to explore additional methods to predict poverty through CDR data.

The survey responses did not overlap with the CDR data well because of the randomized design of the survey. Of the 9,037 households surveyed, only 222 were also present in the CDR data. The researchers therefore did not have sufficient observations to draw meaningful prediction models from this exercise. They instead used household information aggregated by cell-tower catchment area to estimate poverty rates. Even so, these models had an extremely low explanatory power, with an R2 of 0.01.

The researchers concluded that in such cases, research teams should consider conducting a light-touch baseline survey to understand the general market share of cell phone usage and then design a survey that over-samples in a statically controllable manner to achieve sufficient overlap between survey and CDR data sets.