rafmacalaba/datause-encoder-data huggingface dataset:
|`F-stat`|`=`|`347.46412`|
|---|---|---|
|`Alpha model diagnostics`|||
|`Number of observations`|`=`|`7564`|
|`Adjusted R-squared`|`=`|`.03563669`|
|`R-squared`|`=`|`.03703931`|
|`Root MSE`|`=`|`2.2858123`|
|`F-stat`|`=`|`26.407274`|
|`Model parameters`|||
|`Sigma ETA sq.`|`=`|`.02312296`|
|`Ratio of sigma eta sq over MSE`|`=`|`.11377818`|
|`Variance of epsilon`|`=`|`.18255558`|
|`<End of first stage>`|||

``` InitializingtheSecondStage,thismaytakeawhile...
Bootstrappeddrawingofbetasandparameters ``` ``` Numberofsimulations:100.Eachdot(.)represents10simulation(s).
12345 ..........
FinishedrunningtheSecondStage ```

# **6.4 Exporting the Simulated Vectors into Stata**

This subroutine is useful for bringing the simulated vectors into Stata. It provides the user the ability to further manipulate these vectors. Due to the unique structure of the Mata data, this command will only work with data created in the simulation stage.

The command’s structure is: ``` saedataexport,matasource(string)[numfiles(integer1)prefix(string)saveold datasave(string)] ```

- `matasource():` The `matasource` option allows users to specify the source ydump file created by the `sae simulate` routine . Because the size of the file can be quite large, it is advisable to use this with the `numfiles` option.

- `numfiles():` The `numfiles` option is to be used in conjunction with the `ydumpdta` option; it specthe number of datasets to be created from the simulations.

- `prefix():` The `prefix` option may be used to give a prefix to the simulated vectors.

- `saveold:` The `saveold` option can be specified in conjunction with the `ydumpdta` option, and makes the files readable by older versions of Stata.

- `datasave():` The `datasave` option allows users to specify a path where to save the exported data, this is recommended when using the `numfiles` option.

24
(25% in 2006).<sup>15</sup> The distinct age profiles that emerge from Table 2 suggest that young women are more likely to be affected by a husband’s absence and older women by a child’s.

# **5. Impacts of living in households with migrants**

We examine how living in a migrant household affects women’s labor force participation, time allocation and welfare. Controlling for other household and individual factors, how does life compare for non-migrant women who are left behind by household members versus those that are not? Do left behind women devote more time to working on the farm and on household chores including child and elderly care? The data allow us to explore these questions convincingly with respect to time allocation both in terms of participation in different activities and the number of hours spent in those activities. Ultimately, we would like to know how welfare is affected. But welfare is hard to measure and in common with most surveys and studies, we are unable to compute a broad individual level welfare measure. Instead, we can look at a number of aspects of welfare including leisure, a number of physical and psycho-social health measures that may be suggestive of heightened stress or undernourishment (BMI, self-reported health status, alcohol consumption and high blood pressure diagnosis) and one measure of empowerment given by whether women hold primary management responsibilities for running the farm. Davin (1999) and Zhang et al. (2004) have argued that through becoming decision-makers and managers of productive activities women who are left behind may be empowered and hence better off. Again, we are unable to judge whether such responsibilities enhance welfare, but we can examine whether such management responsibilities do increase for left-behind women.

> 15 This reflects negative age effect on the migration of children. In addition, it may reflect adult children coming back to take care of elderly parents. This explanation is consistent with the findings in Giles and Mu (2007) that adult children are less likely to migrate when elderly parents are in poor health. Note that since no information is available for children who don’t live in the surveyed households, child migration rates from the CHNS could both over- or under-estimate children’s actual migration rate.  If living with parents facilitates out-migration, then migration rates based on resident children will over-estimate the actual rate.  If co-residence instead reflects care for parents, then resident children’s migration will be an under-estimate.

14
# **3 Empirical Approach**

# **3.1 Artificial Missingness Creation**

Missing GPS-based plot areas measurements are often tied to numerous field logistics and cost constraints. The variable that underlies the overwhelming majority of missing GPS-based plot areas in household survey operations is the plot distance from the dwelling or the plot location with respect to the EA boundaries.<sup>5</sup> As noted above, the IHS3 instructed the enumerators to measure all plots within 2 hours travel time from the dwelling locations, while the ESS2 required the measurement of all plots, with the exception of those less than 40 square meters, irrespective of distance/travel time. For a more time and/or budget constrained operation, a lower threshold for GPS based land areas measurements could be enforced.

The first step in our analysis is to generate missing GPS-based plot areas in a way that would be similar to real-life field experience, or in other words, identify plots that could be deemed as “distant” in a large-scale survey operation. Towards this end, we work with two arbitrary but operationally-relevant distance thresholds that map well to the existing practices. In the first scenario, the plots are identified as distant if the georeferenced plot corner is located greater than 500 meters from the georeferenced dwelling unit of the associated household. The second scenario relies on a threshold of 1 kilometer instead.<sup>6</sup> Since survey implementers may want to get sense of the time requirements associated with visiting plot locations that

> 5 Kilic et al. (2017) report that missingness in GPS-based plot areas due to refusal or physical inaccessibility, as opposed to distance, is near-negligible in the LSMS-ISA-supported surveys in Tanzania and Uganda. In the case of Malawi IHS3 2010/11, the considerable missingness in the stated reasons for lack of GPS-based plot area measurement prevents us from reporting specific statistics on missingness due to refusal or physical inaccessibility. In the case of the more recent Malawi Integrated Household Panel Survey (IHPS) 2016, the GPS-based plot area measures that are missing due to refusal or physical inaccessibility constitute 9 percent of the overall sample of plots without GPS-based areas, and 1 percent of the overall sample of plots.

> 6 The variable underlying each distance threshold is the Euclidean (crow-fly) distance between the geo-referenced plot and dwelling location. Other geospatial measures of the plot distance to the dwelling were considered, including the estimated minimum cost distance that considers topography; the walking time associated with the minimum cost distance; and the inclination-adjusted measures of these two variables. The weighted pairwise correlation between any of the alternatives and our Euclidean distance measure is above 99 percent, and our results are robust to the use of these alternative distance measures.

12
Law 22/1999 spells out 11 obligatory functions of local governments (table 1). The revised Law 32/2004 removed the omnibus assignment of the residual functions to regional governments and stipulated 15 obligatory functions and a number of discretionary functions.

**Table 1.  Obligatory Functions of Sub-national Governments under Laws 22/1999 and 32/2004**

|Law 22/1999 functions|Law 32/2004 functions|
|---|---|
|Infrastructure (public works)|Development planning and control|
|Health|Planning, utilization, and supervision of zoning|
|Education|Public order and peace|
|Agriculture|Providing public means and facilities|
|Communication|Handling of health sector|
|Industry and trade|Education|
|Cooperatives|Social affairs|
|Land administration and zoning|Employment promotion|
|Capital investments|Facilitating the development of cooperatives and small|
|Environment|and medium-size businesses|
|Employment promotion|Environment<br>Agriculture|
||Demographics and civil registry<br>Administration affairs<br>Capital investment<br>Other mandatory affairs as instructed by the laws and<br>regulations|

_Source:_ Eckardt and Shah (2007) Decentralization policy mainly emphasized the third tier of government, because provinces were perceived as potential drivers of political disintegration. Compared with local governments, provinces have far more limited responsibilities, a fact that is also reflected in their much smaller share of expenditures. The provincial level has a double role as an autonomous regional government and as the regional representative of the national government. Provinces are mainly responsible for supervisory functions and are supposed to intervene in matters that require crossjurisdictional cooperation. Law 32/2004 explicitly strengthens the coordinating role of provincial governments as regional representatives of the central government, a step that was based on the perception that closer central supervision and oversight are needed to make decentralization work more effectively.

In practice, the distribution of specific responsibilities is regulated by a number of sectoral laws and numerous government regulations and ministerial decrees. For most sectors, responsibilities are shared among government levels, with the national government involved also in formally decentralized sectors (table 2). A national role in most of the sectors listed may be helpful, provided that it does not reinstitute central bureaucratic controls and is instead focused on providing financial and technical support and service delivery oversight. For example, equity concerns might necessitate a strong national role in financing and regulating standards in basic public services, such as education and health. At present national development (capital) expenditures represent more than 60 percent of total development spending, including significant outlays in decentralized service sectors, such as health, education, and infrastructure. This trend suggests that the implementation of decentralization is still lagging in some sectors.

6
**Table 5. Heterogeneity for Employment and Work by Age** **_Samples Separated by Sex_**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Wo<br>|men<br>|Me<br>|n<br>|
||Employed|Working|Employed|Working|
|LOA|0.046|0.059+|0.010|0.020|
||(0.039)|(0.038)|(0.034)|(0.029)|
||[0.214]|[0.103]|[0.773]|[0.485]|
|ESR|-0.003|-0.028|-0.049|-0.050*|
||(0.039)|(0.039)|(0.036)|(0.030)|
||[0.947]|[0.453]|[0.187]|[0.095]|
|LOA x Youth|0.018|0.010|0.011|-0.031|
||(0.071)|(0.073)|(0.076)|(0.070)|
||[0.805]|[0.881]|[0.885]|[0.645]|
|ESR x Youth|0.032|0.044|0.139*|0.123*|
||(0.067)|(0.070)|(0.082)|(0.072)|
||[0.603]|[0.542]|[0.105]|[0.104]|
|LOA + LOA x Youth|0.064|0.070|0.021|-0.011|
||(0.059)|(0.061)|(0.066)|(0.062)|
||[0.265]|[0.252]|[0.740]|[0.857]|
|ESR + ESR x Youth|0.029|0.016|0.089|0.073|
||(0.057)|(0.061)|(0.072)|(0.064)|
||[0.588]|[0.786]|[0.227]|[0.273]|
|Observations|1,361|1,361|1,119|1,119|
|Outcome(Control Group)Mean|0.436|0.537|0.735|0.832|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by age for the reporting of employment or work status. We separate the samples by sex. We present the ITT estimates from Equation (2), where takes the value of 1 if the respondent is a youth between the ages of 15 to 24 years, and the total effect of each survey method. The vector iihss includes the indicator and the list of control variables selected using a double-LASSO procedure for each DD iihss XX outcome (see Table A3 in the Appendix for the selected variables). All estimations include EA (stratification iihss DD variable) fixed effects. Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1, + _p_ <0.130 32
**Table A.7:** Robustness Checks on Bureaucrat Outcomes (Table 3)

||||**Bureaucrat survey**||
|---|---|---|---|---|
||Enthusiasm|Motivation|Interacts dailyw/ citizens|Frequencyindex|
||(1)|(2)|(3)|(4)|
|1. Baseline|0.487<sup>_∗∗∗_</sup><br>(0.165)|22.868<br>(18.495)|0.197<sup>_∗∗_</sup><br>(0.102)|0.405<sup>_∗∗_</sup><br>(0.183)|
|2. No controls|0.343<sup>_∗∗_</sup><br>(0.155)|30.825<sup>_∗_</sup><br>(20.756)|0.198<sup>_∗_</sup><br>(0.111)|0.402<sup>_∗∗_</sup><br>(0.195)|
|3. Region fxed effects|0.322<sup>_∗∗_</sup><br>(0.152)|25.430<br>(18.557)|0.148<sup>_∗_</sup><br>(0.096)|0.337<sup>_∗∗_</sup><br>(0.174)|
|4. 3rd-Degree polynomial|0.705<sup>_∗∗∗_</sup><br>(0.236)|34.742<br>(26.132)|0.229<br>(0.136)|0.570<sup>_∗∗_</sup><br>(0.246)|
|5. 1/2 MSE-opt. bandwidth|0.539<sup>_∗∗_</sup><br>(0.287)|30.067<br>(28.168)|0.205<br>(0.165)|0.452<br>(0.281)|
|6. 3/4 MSE-opt. bandwidth|0.528<sup>_∗∗_</sup><br>(0.233)|27.447<br>(26.313)|0.194<br>(0.134)|0.455<br>(0.254)|
|7. 2x MSE-opt. bandwidth|0.318<sup>_∗∗∗_</sup><br>(0.156)|20.093<br>(15.826)|0.161<sup>_∗∗_</sup><br>(0.094)|0.329<sup>_∗∗∗_</sup><br>(0.169)|
|8. Excl. Suharto villages|0.384<sup>_∗∗_</sup><br>(0.157)|24.870<br>(20.579)|0.199<sup>_∗∗_</sup><br>(0.104)|0.403<sup>_∗∗_</sup><br>(0.181)|
|9. Fuzzy RD|0.710<sup>_∗∗∗_</sup><br>(0.235)|37.908<br>(25.172)|0.229<sup>_∗_</sup><br>(0.140)|0.582<sup>_∗∗∗_</sup><br>(0.249)|
|10. Excl. lame-duck|0.527<sup>_∗∗∗_</sup><br>(0.179)|22.989<br>(17.014)|0.188<sup>_∗∗_</sup><br>(0.103)|0.363<sup>_∗∗_</sup><br>(0.181)|
|Observations|1064|1062|1064|1064|
|Control mean|-0.057|100.6|0.57|0.32|
|Bandwidth size (%)|20.8|21.5|18.5|16.3|

_Notes_ : This table reports robustness checks on Table 3. The first row reports our baseline estimates. In the second row, we remove election year dummies and our control for the survey experiment treatment, which are included in our baseline estimation. In the third row, we include region fixed effects and dummies for pairs of election years (2015-2016, 2017-2018, etc.). The main regions in our sample are Java, Sulawesi, Sumatra, Kalimantan, and NTB-Bali. In the fourth row, we use a 3rd-degree polynomial to construct the point estimator. In the fifth, sixth, and seventh rows, we use a RD bandwidth equal to 1/2, 3/4, and twice the MSE-optimal bandwidth from Calonico et al. (2014), respectively. In the eighth row, we exclude villages with bureaucrats appointed during the Suharto era. In the ninth row, we report fuzzy RD estimates of _γ_ in equation (1). We use 1 (margin _jt >_ 0) from equation (1) to instrument for a dummy equal to 1 if the current village head in our survey sample is a different individual from the incumbent who competed in the last election. In the tenth row, we exclude villages where the current village head is serving in their third term. The dependent variables are identical to those in Table 3. The bottom panel reports the bandwidth size for the baseline estimation. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

47
accounting for time-specific effects (with the inclusion of period-specific dummies), equation (7) could be rewritten as<sup>11</sup> : or This differencing, however, introduces a new bias since the error term (ε _it -_ ε _it-1)_ is correlated with the lagged dependent variable ( _git-1 -git-2)_ . Under the assumptions that the error term ε is not serially correlated, Arellano and Bond (1991) propose a two-step generalized methods of moments (GMM) estimator using as moment conditions:

> and if the explanatory variables _x_ are predetermined but not strictly exogenous:

In the first step, the error terms are assumed to be independent and homoskedastic across countries and time. In the second step, the residuals obtained in the first step are used to construct a consistent estimate of the covariance matrix, and thus relax the assumptions of independence and homoskedasticity. Using the moment conditions in (16) and (17) and denoting θ=[αβ’]’, the GMM estimator of θand corresponding covariance matrix Σθ are given by: where _~~x~~_ =[g _it-1 x_ ], _Z_ is the matrix of instruments, and Ω<sup>ˆ</sup> is a consistent estimate of the covariance matrix of the moment conditions constructed with the residuals of the first step regression.

The consistency of the GMM estimator above depends on the validity of the assumption that the error terms do not exhibit serial correlation as well as on the validity of the instruments. We present two specification tests to address these issues. The first is the Sargan test of overidentifying restrictions. The second test examines whether the error term ε is not serially correlated, which in turn would imply that the difference error in (14) does not present second-order serial correlation.

We rely on two main sources of data. Inequality data come from Dollar and Kraay’s database on inequality. This database expands the inequality data used in their 2002 paper and contains 953 observations of the Gini coefficient for 137 countries. We acknowledge that the Gini coefficient is less than perfect and that other measures, such as the income

> 11 Clearly, equation (6) would also be subject to a similar transformation.

-10-
- 28 -

Table 4. Infant Mortality (1985), Female Education and Program Variables

|Indqendent Variables|(1)<br>Enrollme|Dependent<br>nts 1970|Variable, IMR 19<br>(2)<br>Enrollment|85<br>s 1975|
|---|---|---|---|---|
||Coefficient|t-ratio|Coefficient|t-ratio|
|Constant|85.246|7.474***|98.849|7.561'**|
|Female Secondary<br>Gross Enrollment Rate, 1970|-3.309|-2.373**|||
|Female Secondary<br>Gross Enrollment Rate squared|0.020|0.946|||
|Male Secondary|||||
|<br>Gross Enrollment Rate, 1970|1.609|1.718|.||
|Male Secondary<br>Gross Enrollment Rate squared|-0.01|-0.494|||
|Female Secondary<br>Gross Enrllmcnt Rate, 1975|||_-3.055_|-3.720***|
|Female Secondary<br>GrossEnrollmentRate squared|.___._||0.014|1.237|
|Male Secondary|||||
|<br>Gross Enrollment Rate, 1975|||0.710|1.113|
|Male Secondary<br>Gross Enrollment Rate squared|||0.01|0.910|
|Family planning services|1.049|1.047|-0.187|-1.682*|
|Population per physician, 1984|0.001|2.433**|0.001|3.795'St|
|Urban population as % of total population|0.660|1.681w|0.389|1.571|
|GDP per capita, 1985|-0.018|-2.466**|-0.010|-3.872***|
|Africa Regional Dummy|8.540|0.604|10.470|1.532|
|Asia Regional Dummy|3.252|0.229|9.299|1.118|
|LAC Regional Dummy|20.611|1.080|19.094|2.315**|
|Adjusted R<sup>2</sup>||_.75_|.7|3|
|n=||64|6|5|

Absolute values of t-ratios, where m, *, and * indicate significance levels of 10, 5, and I percent respectively.
**Figure 8: Variations across countries in trends in relative and absolute wealth-related inequality in MDG indicators**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI<br>Concentration index Absolute concentration index<br>.03 .04 .01 .02<br>.02 .03.02 0 .010<br>.010 .010 -.01 -.01-.02<br>-.01 -.02<br>-.01 -.02 -.03<br>-.02 -.03 -.03 -.04<br>Change in conc. index p.a. -.03 Change in conc. index p.a. -.04-.05 Change in conc. index p.a. -.04 Change in conc. index p.a. -.05-.06<br>-.04 -.06 -.05 -.07<br>.04 .015 .02 .03<br>.03 .01 .01<br>.02 .005 0 .02<br>.01 0 -.01 .01<br>0 -.005-.01 -.02-.03 0<br>-.01<br>-.015 -.04 -.01<br>Change in conc. index p.a. -.02 Change in conc. index p.a. -.02 Change in conc. index p.a. -.05 Change in conc. index p.a.<br>-.03 -.02<br>-.025 -.06<br>-.04 -.03 -.07 -.03<br>.1 .01 .005 .1<br>.08.06 .0050 0-.005-.01 .08.06.04<br>.04 -.005 .02<br>.02 -.01 -.015-.02 0<br>Change in conc. index p.a. 0 Change in conc. index p.a. -.015 Change in conc. index p.a. -.025 Change in conc. index p.a. -.02-.04<br>-.02 -.02 -.03 -.06<br>-.04 -.025 -.035 -.08<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The vertical axis measures the annual average change in the index, not the percentage change.

33
|||**Increase in Import Value**|**Country with Largest Increase**<br>**Country withllar**|**gest Decrease in**|
|---|---|---|---|---|
|**Product**|<br>**Product Description**|**(US$, thousands)**|**in Import Value**<br>**Import**|**Value**|
|392690|<br>Articles of plastics & articles of other materials of headings 39.01 to 39.14, n.e.s. in Ch 39|-1,768|Russian Federation<br>Germany||
||||Change in import value<br>933|-920|
||||Import value in 2008<br>14,930|19,540|
||||% Change in import value<br>6.25|-4.71|
|841950|<br>Heat exchange units, whether/not electrically heated|-1,527|Russian Federation<br>Italy||
||||Change in import value<br>1,674|-1,266|
||||Import value in 2008<br>31,870|13,760|
||||% Change in import value<br>5.25|-9.20|
|20321|Carcasses/half-carcasses of swine, frozen|-1,424|Russian Federation<br>Poland||
||||Change in import value<br>10|-706|
||||Import value in 2008<br>84|11,585|
||||% Change in import value<br>11.93|-6.09|
|870331|<br>Vehicles principally designed for the transport of persons (excl. of 87.02 & 8703.10-8703.24), with C-I|-1,254|Russian Federation<br>Spain||
||internal combustion piston engine (diesel/semi-diesel), of a cylinder capacity not >1500cc||Change in import value<br>1|-404|
||||Import value in 2008<br>6|3,098|
||||% Change in import value<br>15.87|-13.04|
|210390|<br>Sauces & preparations therefor, n.e.s.; mixed condiments & mixed seasonings, n.e.s.|-1,214|Russian Federation<br>Germany||
||||Change in import value<br>1,581|-722|
||||Import value in 2008<br>19,653|3,667|
||||% Change in import value<br>8.04|-19.69|
|20319|Meat of swine (excl. carcasses/half-carcasses/hams/shoulders & cuts thereof), fresh/chilled|-1,039|N/A<br>Netherlands||
||||Change in import value|-548|
||||Import value in 2008|7,800|
||||% Change in import value|-7.03|
|851762|<br>Machines for the reception, conversion & transmission/regeneration of voice, images/other data, incl.|-959|Russian Federation<br>China||
||switching & routing apparatus||Change in import value<br>91|-580|
||||Import value in 2008<br>2,669|42,620|
||||% Change in import value<br>3.41|-1.36|
|20329|Meat of swine (excl. carcasses/half-carcasses/hams/shoulders & cuts thereof ), frozen|-952|Russian Federation<br>Germany||
||||Change in import value<br>7|-387|
||||Import value in 2008<br>160|18,767|
||||% Change in import value<br>4.36|-2.06|
|300490|<br>Medicaments (excluding goods of heading 30.02/30.05/30.06/3004.10-3004.50) consisting of mixed/unmixed|-898|Ukraine<br>Germany||
||products for therapeutic/prophylactic uses, put up in measured doses (including those in the form of<br>||Change in import value<br>149|-213|
||transdermal administration systems)/in forms/packi||Import value in 2008<br>15,402|42,036|
||||% Change in import value<br>0.97|-0.51|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 49