Poverty Impact Analysis: Approaches and Methods - Chapter 5 ppsx

CHAPTER 5

Identifying Poverty Predictors Using

Household Living Standards Surveys in

Viet Nam

Linh Nguyen

Introduction

Poverty predictor modeling (PPM) based on a regression-type analysis of

household income and expenditure and other variables (predictors) from

household surveys of living standards, has been receiving more attention

from researchers and practitioners. This interest comes from the fact that

PPM provides an easy and low-cost way to collect baseline and follow-up

poverty measures for monitoring progress and evaluating the poverty impact

of development projects and policies. But while PPM is popular, the reliability

of this methodology has yet to be checked.

In Viet Nam, there have been a number of efforts to develop and use

poverty predictor models for poverty mapping (Minot 1998, Minot and

Baulch 2002 and 2003, MOLISA 2005). These studies were mostly intended

for use in poverty targeting and budget transfers. There has been no effort,

however, to apply the approach to ex-ante poverty estimates of participatory

assessments of various policies. Moreover, there has been no attempt to use

data sets of the subsequent comparable household surveys to assess how

good the predictors really are.

The approach presented in this study is an attempt to develop a practical

alternative to the time-consuming and expensive collection of income and

expenditure data for assessing poverty at local levels. In Phase 1 of the study,

data from 2002 living standards surveys of Viet Nam’s General Statistical

Offi ce were used to examine the relationship between poverty and a

household’s characteristics using a multiple regression modeling technique.

This technique detects variables or predictors that have correlated effects

on a household’s living standards and, consequently, its poverty status. In

Phase 2, signifi cant predictors were tested using a 1997/98 living standards

survey to check the consistency and stability of the models across time.

In Phase 3, another regression modeling procedure was implemented for

two provinces in the North Central Coast subregion to further test the

methodology and to check whether the poverty predictors would be different

Application of Tools to Identify the Poor

128 Identifying Poverty Predictors Using Household Living Standards Surveys in Viet Nam

at more a disaggregated level. Finally, in Phase 4, reliable and easy-to-collect

poverty predictors within the regression model were used to generate a short

questionnaire1 for frequent implementation or for data collection at local

levels.2

Data and Methods

Data

For Phases 1 and 2, the work uses the 1997/98 Viet Nam Living Standard

Survey (VLSS) and the 2002 Viet Nam Household Living Standard Survey

(VHLSS), both implemented by the General Statistical Offi ce. These surveys

provide data on income, expenditure, and other characteristics of households

such as demography, education, health, assets, housing, etc. They are

fairly well-organized, have high-quality data, and can be a good source of

information for poverty analysis and assessment at the national and even at

the provincial levels.

The 2002 VHLSS data were crucial to this work. The information was

used to derive the basic poverty predictor model and to test the stability of

the model. The survey had a general sample size of 75,000 households and

collected information about household living standards and basic communal

socioeconomic conditions including income and expenditures. Income data

came from all 75,000 households, but expenditure data were from only

30,000 households.

The total sample used in the study was composed of 29,510 households.

For comparison, the sample was split into urban and rural data sets. There

were 22,601 rural households in the sample, while the rest were urban. To test

the stability of the model across the whole data set, the rural and urban data

sets were further split into a learning data set and a validation data set. This

was done by randomly drawing a subsample of 50 percent of the total sample

as the learning data set for both rural and urban areas. The other 50 percent

subsample was used as the validation data set. The learning and validation

data sets had to be very similar to each other to ensure the comparability of

the two models’ statistics. Summary statistics of the 2002 VHLSS rural data

set are presented in Table 5.1.

1 The questionnaire used in the pilot survey can be downloaded at http://www.adb.org/

Statistics/reta_6073.asp.

2 Aside from predictors, some questions were also included in the questionnaire to create

variables for specific studies relating to poverty.

Poverty Impact Analysis: Tools and Applications

Chapter 5 129

Method for Phase 1

The Model. The ultimate goal

of this study was to build a good

regression model to examine the

relationship between household

expenditure and household

characteristics using the 2002 VHLSS. Multiple regression modeling was the

method employed in the study in the following form:

Dependent Variable = ȕ0 + (Independent Variablei x ȕi

) + ei

The dependent variable was the household’s annual expenditure per capita

or one of its transformations, rather than income as a measure of household

living standards, to ensure international comparability.3 The right-hand

side variables were household characteristics from survey data, also called

poverty predictors. The model’s parameters were as follows: ȕ0 was the

model intercept or constant, while ȕi

were respective regression coeffi cients.

Finally, ei

were random errors that included effects of all variables on the

dependent variable other than the ones explicitly considered in the model.

The commonly used method, weighted least squares, was used in this

study to estimate model parameters (ȕ0 and ȕi

) by minimizing the sum of

random errors ei

across households using the sampling weight. It worked

by incorporating extra nonnegative constants or weights associated with

each data point into the fi tting criterion. The size of the weight indicated the

precision of the information contained in the associated observation.

Optimizing the weighted fi tting criterion to fi nd the parameter estimates

allowed the use of weights to determine the contribution of each observation

to the fi nal parameter estimates. It was important to note that the weight for

each observation was given relative to the weights of the other observations;

so different sets of absolute weights could have identical effects.4

A model-building procedure was implemented on the learning data set

until a satisfactory model of poverty predictors was achieved. Next, the

predictor variables were created based on the validation data set, which was

in turn used as a basis for creating the poverty predictor model. Finally, the

statistics of the two models for the learning and validation data sets were

compared. If these statistics were similar, then the model was considered

3 Income is usually more underestimated than expenditure in household surveys, which

is another reason for using expenditure in the model.

4 See http://www.itl.nist.gov/div898/handbook/pmd/section1/pmd143.htm.

Table 5.1 Summary Statistics of the 2002

Viet Nam Household Living Standard Survey

of Rural Area

Variable Samples Mean Standard Deviation

Learning 11,299 2,838.758 1,672.116

Validation 11,302 2,842.604 1,633.516

Source: Author’s calculation.