*! version 1.0.0 11nov2017

Title

help for sae World Bank/Poverty and Equity GP - GSG1

[SAE] intro -- Introduction to sae - small area estimation packages

[Suggestion: Read
[SAE] intro substantive
first.]

Description

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| The sae suite of commands deals with small area estimation, |
| abbreviated as sae data. To become familiar with sae |
| as quickly as possible, do the following |
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| 1. See A simple example under Remarks below. |
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| 2. If you have data that require simulating, see |
| [SAE] sae data |
| [SAE] sae simulate |
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| 3. Alternatively, if you have already simulated data, see |
| [SAE] sae proc |
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| 4. To fit your model, see |
| [SAE] sae model |
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To perform data related functins such as to import the target dataset to a more manageable
format for the simulations; to export the resulting simulations to a dataset of the
user’s preference; and to compare the variables between the sources of data.

sae data import import sae data into Mata data file
sae data export export sae Mata data file to Stata data

To perform first stage estimation on sae data. This routine is for obtaining the GLS estimates of the first stage. The
sub-routines, sae model lmm (linear mixed model) and sae model povmap (povmap) are used interchangeably

sae model lmm model first stage estimations for the linear mixed models

To perform second stage estimation on sae data. This routine and sub-routine obtains the GLS estimates of the first
stage, and goes on to perform the Monte Carlo simulations

sae sim lmm simulate based on first stage models for the linear mixed models

The stats and inds sub-routines are useful for processing Mata formatted
simulation output and producing indicators with new thresholds or weights, as well as profiling.

sae proc stats produce the statistics based on simulated data
sae proc ind prodiuce Poverty and Inequality indictors based on simulated data

Remarks

Remarks are presented under the following headings:

A simple example
Suggested reading order

References

Bedi, T., A. Coudouel, and K. Simler (2007). More than a pretty picture: using poverty maps to design
better policies and interventions. World Bank Publications.

Demombynes, G., C. Elbers, J. O. Lanjouw, and P. Lanjouw (2008). How good is a map? putting small
area estimation to the test. Rivista Internazionale di Scienze Sociali, 465–494.

Elbers, C., J. O. Lanjouw, and P. Lanjouw (2002). Micro-level estimation of welfare. 2911.

Elbers, C., J. O. Lanjouw, and P. Lanjouw (2003). Micro–level estimation of poverty and inequality. Econometrica
71 (1), 355–364.

Foster, J., J. Greer, and E. Thorbecke (1984). A class of decomposable poverty measures. Econometrica:
Journal of the Econometric Society, 761–766.

Harvey, A. C. (1976). Estimating regression models with multiplicative heteroscedasticity. Econometrica:
Journal of the Econometric Society, 461–465.

Haslett, S., M. Isidro, and G. Jones (2010). Comparison of survey regression techniques in the context of
small area estimation of poverty. Survey Methodology 36 (2), 157–170.

Henderson, C. R. (1953). Estimation of variance and covariance components. Biometrics 9 (2), 226–252.

Huang, R. and M. Hidiroglou (2003). Design consistent estimators for a mixed linear model on survey data.

Proceedings of the Survey Research Methods Section, American Statistical Association (2003), 1897–1904.

Molina, I. and J. Rao (2010). Small area estimation of poverty indicators. Canadian Journal of Statistics
38 (3), 369–385.

Rao, J. N. and I. Molina (2015). Small area estimation. John Wiley & Sons.

Tarozzi, A. and A. Deaton (2009). Using census and survey data to estimate poverty and inequality for
small areas. The review of economics and statistics 91 (4), 773–792.

Van der Weide, R. (2014). GLS estimation and empirical bayes prediction for linear mixed models with
heteroskedasticity and sampling weights: a background study for the povmap project. World Bank Policy
Research Working Paper (7028).

Zhao, Q. (2006). User manual for povmap. World Bank. http://siteresources.worldbank.org/INTPGI/
Resources/342674-1092157888460/Zhao_ManualPovMap.pdf .

Authors

Minh Cong Nguyen, mnguyen3@worldbank.org

Paul Andres Corral Rodas, pcorralrodas@worldbank.org

Joao Pedro Azevedo, jazevedo@worldbank.org

Qinghua Zhao, qzhao@worldbank.org

World Bank

Thanks for citing sae as follows

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

Please use the following citation:

World Bank. (2017). “Small Area Estimation: An extended ELL approach.”. World Bank.

Acknowledgements

We would like to thank all the users for their comments during the initial development of the codes.
All errors and ommissions are exclusively our responsibility.