``` zvar(varlistnumeric)yhat(varlistnumeric)yhat2(varlistnumeric)psu(varnamenumeric) matin(string)pwcensus(string)rep(integer1)seed(integer123456789)bootstrapebest colprocess(integer1)lnyaddvars(string)ydump(string)plinevar(varnamenumeric) plines(numlistsort)aggids(numlistsort)indicators(string)results(string)allmata] ``` The possible options are:

- `area():` The `area` option is necessary and specifies at which level the clustering is done, it indicates at which level the _ηc_ is obtained. The only constraint is that the variable must be numeric and should match across datasets, although it is recommended it follows a hierarchical structure similar to the one proposed by Zhao (2006).

- The hierarchical id should be of the same length for all observations for example: AAMMEEE.<sup>33</sup>

- `varest():` The `varest` option allows the user to select between _H3_ or _ELL_ methods for obtaining the variance of the decomposed first stage residuals. The selection has repercussions on the options available afterwards. For example, if the user selects _H3,_ parameters must be obtained via bootstrapping.

- `eta():` The `eta` option allows users to specify how they would like to draw _ηc_ for the different clusters in the second stage of the analysis. The available options are _normal_ and _non-normal._ If _non-normal_ is chosen empirical Bayes is not available to users.

- `epsilon():` The `epsilon` option allows users to specify how they would like to draw _εch_ for the different observations in the second stage of the analysis. The available options are _normal_ and _nonnormal._ If _non-normal_ is chosen empirical Bayes is not available to users.

- <u>`uniqid():`</u> The `uniqid` option specifies the numeric variable that indicates the unique identifiers in the source and target datasets. This is necessary to ensure replicability of the analysis.

- `vce():` The `vce` option allows users to replicate the variance covariance matrix from the _OLS_ in the PovMap 2.5 software. The default option is the variance covariance matrix from the PovMap software ( `ell` ), the user may specify robust or clustered variance covariance matrix to replicate the results from the `regress` command.<sup>34</sup>

- <u>`zvar():`</u> The `zvar` option is necessary for specifying the alpha model, the user must place the independent variables of the alpha model under the option.

- `yhat():` The `yhat` option is also a part of the alpha model. Variables listed here will be interacted ˆ

- with the predicted _y_ = _Xβ_ from the OLS model.

- `yhat2():` The `yhat2` option is also a part of the alpha model. Variables listed here will be interacted with the predicted _y_ ˆ<sup>2</sup> = ( _Xβ_ )<sup>2</sup> from the OLS model.

> 33In the case this were done for a specific country, AA stands for the highest aggregation level, MM stands for the second highest aggregation level, and EEE, stands for the lowest aggregation level.

> 34The variance covariance matrix presented by the PovMap software is not standard in the literature. The variance covariance matrix presented by the PovMap software is equal to _σ_<sup>2 �</sup> ( _X_<sup>_′_</sup> _WX_ )<sup>_−_1 �</sup> _X_<sup>_′_</sup> _W_<sup>2</sup> _X_<sup>�</sup> ( _X_<sup>_′_</sup> _WX_ )<sup>_−_1�</sup> where _σ_<sup>2</sup> is an estimate of Var( _wchuch_ ). It is easy to see that the weights are included twice in the variance covariance estimator, which makes it non-standard.

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