usaid pat description

8/7/2019 USAID PAT Description

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USAID Poverty Assessment Tools (PATs)

WHAT are the USAID Poverty Assessment Tools?

USAID Poverty Assessment Tools (PATs) include two components: short, country-specific questionnaires designed to assess the prevalence of extreme

poverty; and data entry templates to process and store the poverty data and estimate the percentage of

extreme poverty in a given population.Although developed to meet a Congressional reporting requirement for USAID implementingpartners, the PATs can be used by anyone wishing to measure the prevalence of poverty within a

particular population in relevant countries. The tools: are derived from a nationally representative database for each country; collect information on client households, with questions ranging from education to

housing conditions to asset ownership; have 10-25 questions per survey; and take around 15-20 minutes to implement, on average.

Accompanied by a customized data entry template, implementation guide and training materials,PATs provide practitioners with an easy-to-use tool to estimate the poverty outreach of theirprograms.

WHO should implement a Poverty Assessment Tool?

Any organization wanting to know the percentage of very poor people in a given population canuse a PAT to determine its poverty outreach. The country-specific PAT surveys, data entrytemplates, implementation manual, training materials and background documents are allavailable at no cost at the project website: www.povertytools.org. PATs are being used ascomponents of microfinance evaluations and Social Performance Monitoring, among otherapplications.

If you are a USAID implementing partner and are unsure whether or not you are required toimplement, please contact the PAT Help Desk at [email protected]

WHERE are USAID tools available?Twenty-nine country-specific tools to date have been certified by USAID.

Current tools as of October 2009 include: Albania, Azerbaijan, Bangladesh, Bolivia,Bosnia and Herzegovina, Cambodia, Colombia, East Timor, Ethiopia, Ghana, Guatemala,Haiti, India, Indonesia, Jamaica, Kazakhstan, Kosovo, Madagascar, Malawi, Mexico,Nepal, Paraguay, Peru, Philippines, Serbia, Tajikistan, Uganda, Vietnam and West Bank.

Tools currently being developed are listed on www.povertytools.org.


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Developing USAID Poverty Assessment Tools: Notes on

Methodology

Overall approach to the tool development

The approach used to develop a new poverty assessment tool is built on the lessons learned andmethods refined during the original USAID/IRIS project, Developing Poverty Assessment Tools(September 2003 to October 2006). In the initial phase of the project, the IRIS Center analyzeddata from existing national household surveys and from surveys it conducted itself in countrieswhere survey data were not available. The aim was to identify household indicators most closelyassociated with a household being very poor in terms of per-capita expenditures or income.IRIS used statistical methods to identify the 15 indicators that most closely track the per-capitaexpenditures or income of each household, as revealed by the household survey data. Inaddition, IRIS compared the performance of 8 different statistical methods in quantifying thestatistical links between these 15 indicators and household expenditures/income; the accuracy of

each method was assessed using criteria developed especially for this project. In this manner,IRIS identified the best-performing set of indicators (with associated weights) and statisticalmethod for identifying the poverty status of households in each country. Statistical testing foraccuracy was carried out for twelve countries in total.

In addition, the indicators that appeared among the best 15 in at least one of the twelvecountries were included in the next part of the project: testing for practicality. USAID selectedseventeen MFIs or microenterprise organizations to conduct field tests of practicality. Eachquestion was rated as to whether the respondent found it to be sensitive, difficult, or that it wasperceived that she falsified her answer. The lessons learned from the practicality testing wereused to remove impractical indicators from consideration for the final poverty assessment tools.

The end result of this development process was a country-specific poverty assessment tool thatestimatesrather than directly measureshousehold per capita consumption expenditure (orincome) or the probability that a household is very poor based on a short set of practicalindicators. Each country tool is incorporated into a data entry template that allows amicroenterprise practitioner to easily enter and store the responses of its sampled clients toindicator questions and will also estimate the percentage of that practitioners client householdswho are very poor or poor.

In October 2006, USAID contracted the IRIS Center to build on the statistical methods andpracticality information generated during the original project to develop poverty assessment tools

for use by microenterprise practitioners in additional countries. Tools developed in this nextphase featured a second poverty line to measure the prevalence of poor households in additionto the very poor. For $1/day PPP tools, this was the $2/day PPP; for median poverty tools, thepoor threshold was be the national poverty line. The international poverty line was recalculatedrecently to incorporate new PPP rates and is now $1.25 a day in 2005 PPP terms. All PATswhere the international poverty line is binding will be updated to the $1.25/day in 2005 PPPvalue.


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Process used to select included indicators

Suitable household surveys, such as the LSMS, typically include variables related to education,housing characteristics, consumer durables, agricultural assets, illness and disability, andemployment. Generally, depending on the country, 80 to 120 indicators from these categories

are considered.

The MAXR procedure in SAS was used to select the best poverty indicators (for variables foundto be practical) from the pool of potential indicators in an automated manner. MAXR iscommonly used to narrow a large pool of possible indicators into a more limited, yet statisticallypowerful set of indicators. The MAXR technique seeks to maximize explained variance (i.e., R2)by adding one variable at a time (per step) to the regression model, and then considering allcombinations among pairs of regressors to move from one step to the next. Thus, the MAXRtechnique allows us to identify the best model containing 15 variables (not including controlvariables for household size, age of the household head, and location). These 15 variables arethen reviewed to double-check the practicality of the source questions.

The MAXR procedure yielded the best 15 variables for the OLS model (also used for theQuantile model) and another set of best 15 variables for the Linear Probability model (also usedfor the Probit model). The final set of indicators and their weights, therefore depended onselecting one of these four statistical modelsOLS, Quantile, Linear Probability, or Probitasthe best model.1 This selection of the best model was based on the Balance Poverty AccuracyCriterion (BPAC) and the Poverty Incidence Error (PIE), along with practicality considerations.2

Attaching weights to the selected indicators

The weights attached to the indicators in a PAT are the estimated regression coefficients found inthe best model, as discussed above. In constructing the PAT for each country, these weights areinserted into the back-end analysis program of the EPI template used to calculate the incidenceof extreme poverty among each implementing organizations clients.

1 The set of indicators and their weights also depended on the selection of a 1-step or 2-step statisticalmodel.2 For a detailed discussion of these accuracy criteria, see Note on Assessment and Improvement of ToolAccuracy at http://www.povertytools.org/other_documents/AssessingImproving_Accuracy.pdf

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