rafmacalaba/datause-encoder-data huggingface dataset:
# **Figures**

## **Figure 1: Cost-effectiveness of improving learning**

## **Additional SD per $100**

<!-- Start of picture text --> Minimum conditional cash transfers, Malawi<br>Read-a-Thon, Philippines<br>Girls merit scholarships, Kenya<br>Individually-paced computer assisted learning, India<br>Extra contract teacher + streaming, Kenya<br>Village-based schools, Afghanistan<br>Camera monitoring, India<br>Remedial education, India<br>Textbooks for top quintile, Kenya<br>0 1 2 3 4 5<br>Teacher incentives (year 2), Kenya<br>Electing school committee and linking to local<br>government, Indonesia<br>Linking school committee to local government,<br>Indonesia<br>Streaming by achievement, Kenya<br>Providing earnings information, Madagascar<br>0 50 100<br><!-- End of picture text --> Source: Data from J-PAL (2014a). Calculations by authors.

24
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- 4 -

0 Chart I PUBUC SECTOR DEFCIT 0.~~1 <!-- Start of picture text --> 30<br>2 _<br>26<br>24-<br>22<br>20-<br>16 -<br>14<br>12_<br>10 /<br>8- /<br>so at 82 83 84 85 56 57 55 89<br>Year<br>0 Egypt + Argenthia<br><!-- End of picture text -->
estimation is formulated in a similar way to the approach used in Nolenberger and Rodriguez-Planas (2011) and Muller and Wrohlich (2020): where outcome _Yidt_ can be employment status of the respondent i, or the number of women who are working in household _i_ in district _d_ , at time _t_ . α<sup>_DD_</sup> is the treatment effect of the Policy variable on the outcome. _Pol ic ydt_ is the treatment variable indicating the presence or intensity of the childcare expansion policy for district _d_ , at time _t_ . _X_<sup>_′_</sup> is a set _idt_ of all personal and household-level covariates. β _k_ is a vector of coefficients associated with the control variables. _uidt_ is the error term, representing unobserved factors that affect the outcome variable. Standard errors are clustered at the district level to account for potential correlations or heterogeneity among observations within districts. In our HBS analyses, we adopted a similar methodology, but we utilized regions instead of districts. Since we had only 14 regions, we opted not to cluster standard errors and used robust standard errors.

_Identification assumption._ The primary assumption underlying staggered difference in differences is that female labor supply would have followed a similar trajectory in both treated and control districts had the policy not been implemented. The presence of parallel trends in the pre-period provides suggestive evidence supporting this assumption.

In Figure 2, the labor force participation rates of women in treated districts are compared to those in control districts. Before the policy implementation, female labor supply was slightly higher in the control districts. However, by 2021, women in treated districts had reached the employment levels of those in non-treated districts. It is impor- tant to highlight that the Covid-19 pandemic affected employment rates for both women and men across all regions of Uzbekistan, and these rates have not fully recovered (see Appendix, Figures 5 and 6). Additionally, we excluded 7 districts and the capital city from the sample (approximately 10% of the total) and reanalyzed the data, presented in Appendix Figure 5. These 7 districts showed irregular coverage rates, with notable fluctuations after the Covid-19 shock. The capital city was excluded due to its significantly higher coverage rates, which had increased well before the policy’s implementation and differed substantially from other districts. When these districts are removed, the trend in female labor force participation remains consistent. Appendix Figure 6 also shows labor force participation rates for men with young children and women without young 10
**Figure 5: Trends in ratio of poorest quintile’s MDG indicator value to richest quintile’s value**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10<br>q1/q5 q1/q5 q1/q5 q1/q5<br>Full immunization Measles ANC4+ SBA<br>0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10<br>Earliest survey Latest survey<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Population average Population average Population average Population average<br>.2 .2 .2 .2<br>0 0 0 0<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Population average Population average Population average Population average<br>.2 .2 .2 .2<br>0 0 0 0<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Population average Population average Population average Population average<br>.2 .2 .2 .2<br>0 0 0 0<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The population rate is as a fraction, so e.g. 0.2 means a population average of 20%. This is true also for IMR and U5MR which are typically expressed per 1,000.

30
# **Data and Methodology**

The main data source of our analysis is the World Bank’s _Povcal_ database (http://iresearch.worldbank.org/PovcalNet). The _Povcal_ database provides information on mean expenditures or income (in 2005 USD PPP terms) and the Lorenz curve of expenditures/income distributions for specific countries and years, both of which are estimated from nationally representative household surveys. The _Povcal_ database includes data on 131 countries. Since no parametric information is provided for Latin America, we also draw information directly from nationally representative household surveys for 18 Latin American countries.

The _Povcal_ database reports Lorenz curves using two approximation methods: the _General Quadratic_ and the _Beta_ Lorenz curve approximations. Combined with information on mean expenditures or income, these approximations allow estimating poverty headcount and gap indexes quite accurately, as well as Gini coefficients (see Datt, 1998, and the Appendix for details on all approximations used in the analysis). For each country in _Povcal_ , we use the approximation that best fits the actual poverty headcounts (see below). Additionally, for the 18 Latin American countries, we first convert income into 2005 USD PPP using conversion factors reported by the _International Comparison Program_ , and then estimate ourselves the best parametric approximation of the income distribution.

To adequately represent poverty in all the countries analyzed, we use a 2.5 dollars a day poverty line. We base our estimation for each country either on income or expenditures, as reported by _PovCal_ or the surveys (given the focus of the analysis on poverty change _within_ countries, we do not apply any form of correction for harmonizing income and expenditure data). The timeframe we consider in our simulations is circa 1995 to 2008, which allows us to capture the largest number of countries. To correct for the fact that, for some countries, the time frame slightly diverts from these two reference years, we report results in terms of average annual changes. Moreover, to achieve comparability with the Millennium Development Goals (MDG) reports, we also repeat the analysis for a subset of countries with high initial rates of extreme poverty (more than 10 percent of the population with income/expenditures below 1.25 dollars a day), and look at changes between 1990 and 2008.

In our main analysis, out of the 84 countries available in the _Povcal_ (including the Latin American ones), we exclude those that had poverty rates below 5 percent in 1995, since our approximation would capture with high errors the actual poverty changes in these countries. We also exclude Georgia from the analysis, as with poverty rates more than doubling in the period, it is a clear outlier. We are thus left with 74 countries (see Table 1). For the same reason, in the analysis focusing on the MDGs, we only 7
(Tsivanidis, 2019). I compute these times for four different modes of transportation: walking, car, traditional buses, and the subway. I add five minutes in each station when I compute times for the public transit network, and three minutes when I compute travel times for “car” to capture the time spent in the parking lot. To compute commuting and shopping iceberg costs, I take an average of these times across the different modes. I calculate a matrix across census tracts of approximately 13 million observations.

Table C7: Calibration of speeds using trip data from Google Maps

|Type|Speed<br>_Panel A: Public transit system_|
|---|---|
|Subway Lines|601.24 m/min|
|Metrobus|308.13 m/min|
|Bus|216.67 m/min|
|Walking|90.00 m/min|
||_Panel B: Types of roads for cars_|
|Autopista|752.03 m/min|
|Avenida|266.84 m/min|
|Boulevard|608.12 m/min|
|Calle|198.56 m/min|
|Callejón|69.643 m/min|
|Calzada|169.98 m/min|
|Carretera|623.38 m/min|
|Cerrada|123.39 m/min|
|Circuito|304.69 m/min|
|Corredor|160.75 m/min|
|Eje vial|273.98 m/min|
|Pasaje|240.71 m/min|
|Periférico|673.43 m/min|
|Viaducto|399.99 m/min|

_Notes:_ This table reports the calibration of speeds using trips from Google maps. The calibration uses 4,000 random trips. The information was downloaded with the command _gmapsdistance_ in R that uses the Distance Matrix Api from Google. I computed these times between 8 am - 11 am and 5 pm - 8 pm under different traffic scenarios.

To calibrate speeds for each mode and each type of road, I use random trips from Google Maps.<sup>56</sup> I downloaded 4000 random trips between 8 am-11 am, and 5 pm-8 pm using the command _gmaps_ distance in R that uses the Google Maps Distance Matrix Api. I use as an origin and destination, the closest vertex of each type of road or metro line. This tool has the feature that you can calculate times for different modes under several traffic scenarios: pessimistic, optimistic, or none and modes such as: walking, car, or the public transit network. Using this information, I calibrate speeds for each road and each line using the average time spent to

> 56I did not calculate times across census tracts using Google Maps because the network analysis toolkit is much faster, and the command gmaps distance takes a lot of time.

13
# **4 A of Global Brain Drain Indicators**

Our raw data in combination with our imputed data allow us to improve upon existing highskilled migration indicators (e.g. Docquier and Marfouk, 2006, Dumont and Lemaitre, 2004, Docquier, Lowell and Marfouk, 2009 and Dumont, Martin and Spielvogel, 2008).

Previous studies, focusing upon the subset of OECD destinations, provide cross-country data on the relative intensity of emigration (referred to as emigration rates), controlling for the population size and the skill structure in the origin country.<sup>16</sup> Such measures necessarily omit emigrants that reside in non-OECD destinations therefore, which in turn leads to biases that are especially severe for countries that send a large proportion of their emigrants to nonOECD nations.

First we define the following key variables: _Mg,s,t_<sup>_jk_:thestockofbilateralmigrantsfromcountry</sup><sup>_j_tocountry</sup><sup>_k_ofgender</sup><sup>_g_andskill</sup> _s_ at time _t_ _Ig,s,t_<sup>_i_:thestockoftotalimmigrantsoftype(</sup><sup>_g, s_)tocountry</sup><sup>_i_inyear</sup><sup>_t_</sup> _Eg,s,t_<sup>_i_:thestockoftotalemigrantsoftype(</sup><sup>_g, s_)fromcountry</sup><sup>_i_inyear</sup><sup>_t_</sup> _L_<sup>_i_</sup> _g,s,t_<sup>:the(observed)residentlaborforceoftype(</sup><sup>_g, s_)incountry</sup><sup>_i_inyear</sup><sup>_t_</sup> _Ng,s,t_<sup>_i_:thenaturallaborforceoftype(</sup><sup>_g, s_)incountry</sup><sup>_i_inyear</sup><sup>_t_.Thisisthenumberof</sup> workers from a given country _i_ regardless of their current location For each labor type, the aggregation of bilateral migration stocks yields total emigration

> 16Data on selected non-OECD destination countries were included in Docquier and Rapoport (2012) and in the latest version of the OECD database (DIOC-E).

26
# **Table A9.1. The Effect of the PMT on the Probability of Stunting Among Children 6 to 12 Months: Using Exposed Share of Eligible Life to Measure Program Exposure**

||Poole<br>|d Linear<br>|Linear<br>|Probability<br>|
|---|---|---|---|---|
||Pro|ability|w/Com|munityFEs|
|Year (2000=1)|-0.043|-0.049|-0.054|-0.049|
||[0.046]|[0.046]|[0.100]|[0.103]|
|_Exposed Share_*Year|0.005|0.010|-0.062|-0.081|
||[0.051]|[0.052]|[0.130]|[0.133]|
|Gender (boy=1)|-0.028|-0.029|0.024|0.026|
||[0.033]|[0.034]|[0.054]|[0.057]|
|Age|0.024**|0.024**|0.019|0.020|
||[0.010]|[0.010]|[0.017]|[0.017]|
|Mother education (years)|0.002|0.002|0.010|0.010|
||[0.004]|[0.004]|[0.007]|[0.007]|
|Father education (years)|-0.000|-0.000|-0.001|-0.001|
||[0.004]|[0.004]|[0.006]|[0.006]|
|Mother height (cm)|-0.011***|-0.011***|-0.012**|-0.011**|
||[0.003]|[0.003]|[0.005]|[0.005]|
|Father height (cm)|-0.014***|-0.014***|-0.021***|-0.020***|
||[0.003]|[0.003]|[0.005]|[0.006]|
|Head of HH (male=1)|0.016|0.014|0.001|-0.004|
||[0.060]|[0.060]|[0.115]|[0.118]|
|Number of children 5-14 yo in HH|0.010|0.012|0.009|0.008|
||[0.017]|[0.017]|[0.026]|[0.026]|
|HH owns private toilet (Yes=1)|-0.045|-0.039|-0.086|-0.086|
||[0.037]|[0.037]|[0.058]|[0.059]|
|HH has sanitation (Yes=1)|0.015|0.007|0.012|-0.013|
||[0.046]|[0.053]|[0.108]|[0.129]|
|Type of community (urban=1), C|<br>-|<br>-0.031|<br>-|<br>-0.025|
|||[0.046]||[0.129]|
|Distance to district capital (km), C|-|0.000|-|0.000|
|||[0.001]||[0.002]|
|Distance to bus station (km), C|-|-0.004|-|-0.001|
|||[0.003]||[0.006]|
|Land w/ technical irrigation (%), C|-|-0.058|-|-0.296|
|||[0.091]||[0.220]|
|Comm. has asphalt road (Yes=1), C|-|0.032|-|0.013|
|||[0.045]||[0.115]|
|C h Y=1 C||0039||0032|
|omm. as sewerage system (es),|-|-.|-|.|
|||[0.038]||[0.097]|
|Comm. has piped water (Yes=1), C|-|<br>0.036|-|<br>0.016|
|||[0.041]||[0.091]|
|Constant|3.886***|3.906***|5.185***|4.960***|
||[0.664]|[0.670]|[1.078]|[1.141]|
|R-squared|0.09|0.10|0.52|0.53|

Notes: _Exposed Share_ is the share of a child’s eligible life exposed to the program and is calculated as described on page 11. n is 525. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

46
Remittances offer one medium for risk sharing between households. Table 1 provides a summary of the average remittance flows over the past 12 months in 2010 between the migrant households and households living in or near their baseline villages. While non-migrant households were net receivers of remittances, Table 1 shows that transfers flow both ways. This could lead one to think – mistakenly as the analysis below reveals – that these are relationships of reciprocal risk sharing. The data in Table 1 are self-reported and it is interesting to note that migrants claim to send more home than non-migrants acknowledge. A similar discrepancy does not exist in migrant-migrant or stayer-stayer dyads<sup>14</sup> .

# _[Table 1 here]_

Table 2 provides an overview of the reasons for leaving the baseline village. More than one-third of the female respondents but none of the male respondents cited marriage as the reason for migrating, which is what one would expect in a culture with patrilocal marriages. Less than 15 percent of the female respondents reported that they left because of work. In contrast, almost 45 percent of the male migrants reported to have moved because they had found work or went looking for work.

# _[Table 2 here]_

The consumption data originate from extensive food and non-food consumption modules in the survey, carefully designed to maintain comparability across survey rounds and controlling for seasonality. The aggregates are temporally and spatially deflated using data from a price questionnaire included in the survey. Consumption is expressed in annual per capita terms using 2010 Tanzanian shillings.<sup>15</sup> Table 3 provides the summary of the consumption and poverty developments of the panel respondents with respect to their 2010 location. On average, consumption levels in the sample almost doubled over 19 years. Individuals who stayed in their community saw their consumption increase by more than 40 percent. Consumption growth for migrants was much higher: those who left Kagera saw their consumption nearly triple over the same two decades. The poverty statistics tell the same story:

> 14 By dyad we refer to a pair of households.

> 15 Using adult equivalent units as the denominator instead of household size produces almost identical results across all specifications.

11
we excluded all points within 10m of that point (to avoid selecting multiple points too close together). Enumerators visited each of these points, and identified when a point fell on cultivable land.<sup>17</sup> With the help of a key informant (often the village leader), they then recorded the name of the cultivator of the plot, their contact information, as well as a sufficiently detailed description of the plot. These listed cultivators form our main household survey sample. For each cultivator, one of their identified plots was randomly selected, which we refer to as the _sample plot_ .

# **2.2.2 Survey**

Our baseline survey covered 1,695 spatially sampled cultivators in August - October 2015. The survey includes detailed agricultural production data (season-by-season) for seasons 2014 Dry through 2015 Rainy 2 (June 2014 - May 2015). The dates of this survey and follow up surveys, along with the agricultural seasons they cover, are presented in Figure 1. Details of the construction of key variables we use for the analysis are presented in Appendix A. As mentioned above, this is not a “true” baseline as some farmers had already gained access to irrigation in 2014 Dry. However, relatively small parts of the site had access to irrigation at this point; in Section 3.2.1 we highlight that 2014 Dry adoption of irrigation is less than 25% of adoption in subsequent dry seasons, and in Section 3.1.1 we show balance across the command area boundary in household and plot characteristics. A panel of plot-level production and input data are maintained for two plots, which were mapped using GPS devices for precise location and area measurement. The two plots on which panel data is collected represent the primary data for analysis; they include the sample plot (described above) and the farmer’s next most important plot (defined at baseline; we refer to this as the “most important plot” or “MIP”). We also collected data on household characteristics, labor force behavior, and a short consumption and food security module. In analysis, we will focus on the sample plots to learn about the effects of the irrigation itself, and the most important plot to learn about how the presence of irrigation on the sample plot impacts households’ productive decisions on their other plots.

Three follow up household surveys were conducted in May - June 2017, November

> below in Section 6. Additionally, only two of the three sites have a viable boundary of cultivable land both just inside and just outside the command area; we use only these sites for our analysis of the impacts of access to irrigation in Section 3 and Section 5.

> 17This was to discard forest, swamps, thick bushes, bodies of water, or other terrain which would make cultivation impossible.

11
# **Public Expenditure Following Disasters**<sup>**1**</sup>

David Bevan & Samantha Cook

**JEL codes:** H12, H43, H81, H84

**Keywords:** Public Expenditure, Natural Disasters, Sovereign Disaster Financing

> 1 This research has been completed as part of a series of papers developed under the Sovereign Disaster Risk Financing and Insurance Impact Appraisal project. The project is led by the World Bank Disaster Risk Financing and Insurance Program and funded through a cooperation between the UK Department for International Development (DFID) and the Global Facility for Disaster Reduction and Recovery (GFDRR).
Figure 11. Effect of institutional environment on accountability in Bolivia <!-- Start of picture text --> 9<br>accou itability = 1.043 IE - 1.0788<br>R2 = 0.741<br>*audit<br>t central bank<br>7 . ;~~~~~~~~~~~~~~~ tele DM-regu atien<br>6<br>5 +7f~~~~~saci t labor<br>* customs<br>5 6 7 8 9<br>institutitonal environment<br><!-- End of picture text --> 57
<!-- Start of picture text --> 208. acl<br>18%<br>hasea,<br>12%<br>10% —— Domestici creditto<br>8% private sector (%4<br>BM GDP)<br>— Domestic credit<br>aa2% to<br>Df ott publicGOP} sector (%<br>ce) sv 7<br><!-- End of picture text --> <!-- Start of picture text --> Credit to the private sector (% GDP)<br>Narnibia<br>Kenya<br>Burundi<br>GhanaBani<br>enn<br>MozambiqueRwanda<br>TanzaniaZambia<br>UgandaMalawi<br>a 20 40 60 80<br><!-- End of picture text --> <!-- Start of picture text --> Nominal Spread (2006)<br>Malawi<br>Zambia<br>Uganda<br>Kenya<br>Mozambique<br>Botswana<br>Ghana<br>Narnibia<br>Swaziland<br>South Africa<br>n in an An<br><!-- End of picture text -->
Figure 12. Effect of institutional environment on employee morale in Bolivia <!-- Start of picture text --> 8<br>mnwale = 0.6631 IE + 1.5892<br>R2 = 0.7606<br>biking-regula on<br>7<br>. ine / t + c~~~~~~~~~~~~~~rentral bank<br>4 tax _m-regulation */e telec<br>institutiomsl envion<br>/ + Santa Cnuz<br>5 6 a 7 8i 9orm<br>Chuguisa L<br>4 .... ....<br>5 6 7 8 9<br>institutional environment<br><!-- End of picture text --> 58