help for povimp
Hai-Anh H. Dang
Minh C. Nguyen

povimp - tool for poverty imputation

povimp depvar indepvars [if] [in] [weight]
, by(varname numeric) from(numlist max=1)
to(numlist max=1) method(string) [options]

Description

Obtaining consistent estimates on poverty over time as well as monitoring poverty trends
on a timely basis is essential for poverty reduction. However, these objectives are not readily
achieved in practice when household consumption data are neither frequently collected, nor constructed
using consistent and transparent criteria. The challenge can be broadly regarded as one involving
missing data: consumption (or income) data are available in one period but in the next period(s) are
either not available, or are not comparable. Dang, Lanjouw, and Serajuddin (2014) propose a framework
that offers poverty imputation in these settings; povimp implements their poverty imputation procedures.

This program is designed for datasets with two or more cross sectional survey round, where
consumption data in the survey round of interest are missing but the control variables are non-missing.

Options

Required

by(varname)specifies the variable that indicates the survey year (or round).

from(#)specifies the survey year (or round) that has consumption data and
that provides the underlying regression for imputation. The number for this base
survey year takes one of the values specified in the variable used in the by(varname)
option. For example, if the year variable has two values 2008 and 2010, either of
which can be specified as a number to be used.

to(#)specifies the survey year (or round) that has missing consumption data and needs
to be imputed into. The number for this survey year takes one of the values specified in the
variable used in the by(varname) option. For example, if the year variable has two values 2008
and 2010, either of which can be specified as a number to be used.

method(string)specifies the imputation method. Four methods are allowed.

normal: using the linear regression (OLS) model with the distribution of the error terms assumed to be normal.

empirical: using the linear regression (OLS) model with the empirical distribution of the error terms.

probit: using the probit regression model.

logit: using the logit regression model.

pline(varname)specifies the variable that indicates the poverty line(s).

Optional

wtstats(varname) specifies the weight variable for the summary statistics.
If left blank, unweighted estimates are provided. Note that weights should generally
be used for the summary statistics unless the data are self-weighted, but unweighted
estimates are an optional feature.

cluster(varname) specifies the cluster variable or the primary sampling unit.

strata(varname) specifies the strata variable.

decomp(relative or absolute) specifies the consumption (or income) variable as the main variable that can be used in the decomposition
of the changes in poverty between these two periods. This option is only specified when two survey
rounds with consumption data are available. If this option is used, the poverty lines from pline(varname) will be used for both periods. One can
use either suboption relative or absolute for the decomposition. The suboption relative shows the relative contributions
in percentage terms from the characteristics and estimated coefficients, while the suboption absolute shows the contributions in absolute terms instead.

wald specifies that the Wald (Chow) test result for the assumption of constant
parameters in both survey years are shown. This option is only specified when two survey
rounds with consumption data are available.

rep(#) specifies the number of simulations. We recommend using 1,000 simulations or more for
robust estimation of standard errors. If left blank, the default number of replication in Stata (50)
is used.

seed(#) specifies the random seed number that can be used for replication of results. The default seed is 1234567.

noconstant suppresses the constant term (intercept) in the model.

Saved Results

povimp returns results in e() format.
By typing return list, the following results are reported:

Scalars

e(cmdline) the code line used in the session

e(N1) estimation sample in the first period, (from)

e(N2) estimation sample in the second period, (to)

e(pov_imp) poverty rates based on imputed data

e(pov_var) variance of poverty rates based on imputed data

e(p2_p21) difference of actual poverty rate of later period (to) and imputed poverty rate

e(p21_p1) difference of imputed poverty rate and actual poverty rate of initial period (from)

e(p21_p1) difference of imputed poverty rate and actual poverty rate of initial period (from)

e(p) two-sided p-value

e(F) F statistic

e(df) test constraints degrees of freedom

e(df_r) residual degrees of freedom

Examples

1) Assume that consumption data are available in the 2008 survey round but are either missing
or not comparably constructed in the 2010 survey round. We can impute poverty rate from survey year
2008 into survey year 2010 using the normal linear regression model with the distribution of the
error terms assumed to be normal, where the poverty line in 2008 is 40.

. gen pline= 40

. povimp y x1 x2 x3, by(year) from(2008) to(2010) pline(pline) cluster(psu) strata(district) method(normal)

2) Assume that consumption data are available in two survey rounds in 2005 and 2008. To help with
model selection for imputation in a more recent year, we can decompose the change in poverty
rates between survey year 2005 and survey year 2008 and show the Wald test result for the assumption of
constant parameters in both survey years.

. gen pline= 30 if year==2005

. replace pline= 50 if year=2008

. // y is available in both 2005 and 2008

. povimp y x1 x2 x3, by(year) from(2005) to(2008) pline(pline) decomp(relative) wald cluster(psu) strata(district) method(probit)

References

Dang, Hai-Anh H., Peter F. Lanjouw, and Umar Serajuddin. (2014). “Updating Poverty Estimates
at Frequent Intervals in the Absence of Consumption Data: Methods and Illustration with Reference to a
Middle-Income Country”, World Bank Policy Research Paper # 7043. Washington DC: The World Bank.

Authors

Hai-Anh H. Dang, Economist, World Bank, USA, hdang@worldbank.org

Minh C. Nguyen, Economist, World Bank, USA, mnguyen3@worldbank.org

Thanks for citing povimp as follows

povimp is a user-written program that is freely distributed to the research community.

Please use the following citation:

Dang, Hai-Anh H. and Minh C. Nguyen. (2014). “povimp: Stata module to
impute poverty in the absence of consumption data”. World Bank, Development Research Group, Poverty and Inequality Unit and Global Poverty Practice.

Acknowledgements

We would like to thank the UK Department of International Development for funding assistance through its Strategic Research Program.
All errors and ommissions are exclusively our responsibility.