rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Ratio of selected macroeconomic variables to GDP in Niger (Average 1995‐2010)**

|**Selected macroeconomic variables**|**Ratio to**<br>**GDP at**<br>**market**<br>**prices**|
|---|---|
|GDP (market prices)|100.0%|
|Private consumption|81.3%|
|Private investment|13.0%|
|Public investment|8.1%|
|Public consumption|9.1%|
|Traditional exports|13.8%|
|Resources exports|6.2%|
|Imports|31.5%|
|Taxes on consumption|3.2%|
|Taxes on resources|1.2%|
|Taxes imports|4.8%|
|Income taxes|2.0%|
|Gov transf to households|2.3%|

_statistics_

**Table 3: Values of external and calibrated parameters**

|**Description **|**Value**|
|---|---|
|Sensitivity of interest rate to debt|0.0045|
|World real interest rate|0.058|
|Output elasticity of public capital|0.11|
|Depreciation rate of public capital|0.1|
|Depreciation rate of physical capital|0.1|
|Weight of imports in the Armington function|0.202|
|Efficiency of government investment in public capital<br>accumulation|0.5|
|Leisure weight in the utility function|0.103|
|Persistence parameter|0.88|
|Standard deviation of shocks|0.13|
|Substitution elasticity in the Armington function|0.5|
|Substitution elasticity in the CET function|0.6|
|Intertemporal elasticity of substitution for<br>consumption|0.36|
|Frisch labour elasticity|0.25|

**Table 4: Standard deviation of selected variables**

(average of 100 simulations based on different draws of the resource price shocks)

||**All‐**<br>**consuming **|<sup>**All‐investing**</sup>|**All‐wise**|**Balanced**<br>**approach**|
|---|---|---|---|---|
|GDP at market prices|0.84|1.27|0.84|1.04|
|Consumption|0.71|0.99|0.78|0.85|
|Private investment (inc. Adjustment cost)|3.42|4.11|3.40|3.73|
|Traditional exports|1.32|1.11|1.31|1.16|
|Resource exports|14.54|14.85|14.66|14.76|
|Imports|1.33|1.66|1.35|1.44|
|Domestic absorption|0.69|1.03|0.70|0.83|
|Domestic sales|0.50|0.79|0.51|0.61|
|Output|0.74|1.16|0.74|0.94|
|Private investment (w/o Adjustment cost)|3.20|3.85|3.17|3.49|
|Private capital stock|1.72|2.04|1.67|1.85|
|Labor supply|0.47|0.56|0.47|0.52|
|Foreign savings|2.59|2.51|2.11|2.30|
|Foreign debt|19.80|19.49|16.56|17.86|
|Consumption price|1.51|1.48|1.53|1.45|
|Price of domestic good|2.29|2.23|2.31|2.19|
|Price of resource export|27.28|27.28|27.28|27.28|
|Wage rate|7.66|7.78|6.68|6.98|
|Government revenue|1.00|1.13|0.99|1.03|
|**Country**|**Baseline**<br>**Survey**|**Simulated**<br>**Years**|**Informality Definition**|
|---|---|---|---|
|Chile|2015|2017 and 2020||
|Colombia|2016|2017 – 2020||
|Costa Rica|2016|2017 – 2020|Wk h d t i kltd i|
|Dominican Republic|2017|2018 – 2020|orers wo o no receve wor-reae penson<br>insurance|
|Ecuador|2016|2017 – 2020|.|
|El Salvador|2016|2017 – 2019||
|Honduras|2016|2017 – 2019||
|Mexico|2016|2018 and 2020|Workers who do not receive work-related health insurance<br>benefits.|
|Panama|2016|2017 – 2019||
|Paraguay|2016|2017 – 2020|Workers who do not receive work-related pension|
|Peru|2016|2017 – 2020|insurance.|
|Uruguay|2016|2017 – 2020||

Source: Own elaboration based on SEDLAC Note: Simulated years correspond only to years with the availability of SEDLAC actual data for comparison of the proposed variations of the model.

Following the methodology presented in the previous section, estimation weights are adjusted using neutral distribution for accounting only for the growth in the total population between the baseline survey and the simulated year. The different components of non-labor income follow the methodology described above. However, to facilitate comparisons, international remittances are modeled in all cases using a neutral distribution of the MPOs growth rate for inflows between the base and the simulated years, as follows: In addition, given that there were several changes in social programs for 2020 due to the COVID-19 pandemic and the lack of macroeconomic projections for these programs, it was necessary to assess the models' goodness of fit using a distribution that excludes those programs. Not all countries included questions in 2020 in their surveys to collect information about the public transfer programs' beneficiaries and amounts. Yet, for the countries where the programs applied during 2020 due to the COVID-19 pandemic were included in the survey and are identifiable, the programs' transferred amount was excluded, and income was re-estimated. This new income is used as the actual distributional estimations. However, households might as well adjust their consumption patterns in response to changes in total household income through changes in their labor market behavior. Hence this new income vector excluding the benefits received from programs, might overestimate the impact of the public transfers programs. Nonetheless, mobility restrictions imposed during the COVID-19 pandemic suggest that this kind of adjustment is limited and that no changes in households' behavior are a reasonable assumption. Table 2 presents the countries and the excluded programs.

14
# **Table 15. First Stage Regressions Time Use**

**Dependent Variable:  Covariate Violent Shock**

||**Household head**<br>**and spouse**|**Female**|**Male**|
|---|---|---|---|
|Land Gini|2.8470***<br>(0.9983)|2.9010***<br>(0.9762)|2.7939***<br>(1.0292)|
|_Dumm_y Native Populations in Colonial Times (1535-1540)|-0.2574**<br>(0.1262)|-0.2588**<br>(0.1276)|-0.2541**<br>(0.1257)|
||0.0104|0.0135|0.0073|
|Rural District Price Index|(0.0316)|(0.0322)|(0.0313)|
|Livestock Income as Share of Total Agricultural Income|-0.1108**|-0.1210**|-0.1005*|
||(0.0521)|(0.0520)|(0.0530)|
|_Dummy_Participation in Goverment Program Familias in|0.0300|0.0274|0.0337|
|Acción (1 if has acces to the program; 0 otherwise)|(0.0242)|(0.0244)|(0.0252)|
|Dil W|-0.0000|-0.0000|-0.0000|
|ay age|(0.0000)|(0.0000)|(0.0000)|
|_Dummy_Production Problems due to Land Quality (1|-0.0831|-0.0797|-0.0870|
|Problems in the Rural District, 0 otherwise)|(0.0739)|(0.0737)|(0.0747)|
|Plot Size at Household Creation (stadandarized b reion)|0.0054|0.0072|0.0043|
|y g|(0.0090)|(0.0100)|(0.0095)|
|A|0.0016**|0.0017*|0.0016*|
|ge|(0.0008)|(0.0009)|(0.0009)|
||0.0007|0.0005|0.0011|
|Education (years)|(0.0029)|(0.0035)|(0.0032)|
|_Dummy_Female Household Head (1 female head of|-0.0549**|-0.0619**|-0.0438|
|<br>household , 0 otherwise)|(0.0268)|(0.0253)|(0.0480)|
||0.0075|0.0161|-0.0024|
|_Dummy_Wealth Index Quintile 2|(0.0310)|(0.0316)|(0.0322)|
||0.0181|0.0159|0.0192|
|_Dummy_Wealth Index Quintile 3|(0.0366)|(0.0372)|(0.0374)|
||0.0117|0.0244|-0.0021|
|_Dummy_Wealth Index Quintile 4|(0.0374)|(0.0390)|(0.0375)|
||0.0178|0.0123|0.0212|
|_Dummy_Wealth Index Quintile 5|00403|00434|00394|
||(.)|(.)|(.)|
|Nb f hild d 5 f|0.0124|0.0169|0.0068|
|umer o cren uner years o age|(0.0148)|(0.0153)|(0.0167)|
|Number of children between 5 and 18 ears of ae|0.0001|0.0022|-0.0024|
|y g|(0.0079)|(0.0082)|(0.0081)|
|Number of members 65 or older|0.0078|0.0219|-0.0058|
||(0.0178)|(0.0173)|(0.0209)|
||-00091|-00091|-00085|
|Number of members of extended family|.<br>(0.0071)|.<br>(0.0084)|.<br>(0.0073)|
|Constant|-2.4136|-2.7853|-2.0453|
||(3.1609)|(3.2285)|(3.1129)|
|Observations|6,057|3,076|2,981|
|R-squared|0.1924|0.1925|0.1940|
|F-Test(Kleibergen-Paap)|8.934|9.239|8.457|
|Cluster by community||||
|Regional Fixed Effects||||
|*** p<0.01, ** p<0.05, * p<0.1||||

Source: CLS-WILL: First wave – household questionnaire, Instituto Geográfico Agustín Codazzi (IGAC) and Fernández (2010).

45
# Neither by Land nor by Sea: The Rise of Electronic Remittances during COVID-19<sup>*</sup>

Lelys Dinarte-Diaz<sup>†</sup> David Jaume<sup>‡</sup> Eduardo Medina-Cortina<sup>§</sup> Hernan Winkler<sup>¶</sup>

**Keywords:** COVID-19, remittances, financial inclusion, international migration, Mexico. **JEL Classification:** F24, F22, I18, G50

> *We are very grateful for discussions and comments from Kathleen Beegle, Mirian Bruhn, Michael Clemens, Deon Filmer, John Giles, David McKenzie, Caglar Ozden, Berk Ozler, Bob Rijkers, the team of the World Bank Country Office in Mexico, and participants at IPA Remittance Flows and the COVID19 Pandemic Webinar, Australia National University Public Webinar, UNCDF Seminar, and 5th Annual IZA/World Bank/NJD/UNU-WIDER Jobs and Development Conference. We also thank Carolina Zaragoza Flores at Mexico’s Ministry of Foreign Affairs and Rodrigo Garibay Aymes at the Department of Consular Affairs for generously assisting us with the _matricula consular_ database. Maria Montoya-Aguirre provided superb research assistance. The contents of this study are the responsibility of the authors and do not necessarily reflect the views of their employers, funding agencies, or governments.

> †The World Bank. Email: ldinartediaz@worldbank.org

> ‡Central Bank of Mexico. Email: djaumep@banxico.org.mx

> §University of Illinois at Urbana-Champaign. Email: emm5@illinois.edu

> ¶Amazon. Email: hernanwinkler@gmail.com
# 4.1. Data

The main databases used in this section are the household surveys. Given constant improvements and changes to the surveys (e.g., changes in questions, sample, and sampling methods, etc.), and lack of comparability across countries, we use the harmonized household surveys developed by the World Bank, named SEDLAC (the Socio‐Economic Database for Latin America and the Caribbean). SEDLAC database has harmonized socioeconomic household survey information for 24 countries in Latin America, from 1990 onwards.

In the case of Central America, SEDLAC provides information from 2000 particularly on employment, income, and expenditure. However, surveys are not available or comparable in all years. Since 2000, the available and comparable surveys are as follows: Honduras (2001 – 2013), El Salvador (2004 – 2012), Guatemala (2000, 2006, and 2011), Costa Rica (2001 – 2013), Nicaragua (2005 and 2009), and Panama (2008 – 2013).<sup>23</sup> This data limitation shapes the empirical approach adopted in section 4.2.

Our unit of analysis is a sub‐national region. SEDLAC makes available information that is representative at a sub‐national level based on the sampling design of each country. Based on this criteria, and prioritizing the comparability over time (i.e., the lowest sub‐national level that is covered in all available years), we selected 27 sub‐national regions for the six countries in Central America. The average number of (sub‐ national) regions is 5 per country.

There are four main dependent variables at the household level: per capita labor income, per capita total income, poverty (less than US$ 4 per day) and extreme poverty (less than US$ 2.5 per day). Descriptive statistics are shown in Table 4. Guatemala, Honduras and Nicaragua are the poorest countries in the region with more than 50 percent population living under poverty on average during the last decade. Poverty and extreme poverty has been substantially reduced in Panama, Costa Rica, El Salvador, and Nicaragua. El Salvador shows the strongest extreme poverty reduction from 22 percent in 2004, down to 14.7 percent in 2012. However, in the case of Guatemala and Honduras, poverty has increased marginally. Total income and labor income follows the same pattern as poverty, increasing in Panama and Costa Rica, but decreasing in Honduras and Guatemala.

> 23 For further details, please refer to the World Bank’s Equity Lab website:

> <u>http://globalpractices.worldbank.org/teamsites/Poverty/LACDataLab/Pages/TeamHome.aspx</u>

15
such as GDP and other national accounts variables, replacing all indicators based on the 2005 ICP round. The PPP conversion factors have also historically been used by the World Bank for constructing estimates of global poverty. In principle, the approach used by the World Bank to estimate global poverty is to estimate a fixed benchmark (or poverty line) denominated in PPP US dollars and then convert this value into local currencies based on the PPP conversion factor to ensure that the value of the poverty line represents approximately the same standard of living across the world.<sup>2</sup> In essence, the PPP conversion factors are the exchange rates used to keep the real value of the poverty line the same across all countries.<sup>3</sup> PPP data though are only collected sporadically and with changes to methodologies, which can result in jarring changes to the estimated relative cost of living across countries with the release of new PPP data.<sup>4</sup> Between 2005 and 2011 there were no new PPP data collected for developing countries, but many global indicators are nonetheless reported annually in PPP terms. One method for handling the interim years followed by the World Development Indicators is to estimate extrapolated PPP conversion factors by the relative rates of inflation between the United States (U.S.) and the local country. Global poverty estimates do not directly use the extrapolated PPP estimates, but follow an approach that is conceptually equivalent to using the extrapolations. Specifically, the current value of consumption in local currency is brought back or forward to the

> 2 For a more detailed description of how the poverty line is estimated, see World Bank (2014b). The PPP conversion factors used for poverty estimates are those estimated for household final consumption expenditure. The data is available from ICP at http://icp.worldbank.org/icp/GlobalResult.aspx and through the WDI.

> 3 And, in contrast to using market currency exchange rates, Deaton (2005) notes that “Making comparisons in PPP units corrects, or at least diminishes, the gross understatement of living standards in poor countries relative to rich ...” (p. 2)

> 4 The ICP was started in 1968 as a collaborative effort of the United Nations Statistical Division (UNSD) and the University of Pennsylvania and the first round of ICP data were collected in 1970, covering 10 countries. Subsequent rounds of the ICP occurred on an occasional basis. Between 1970 and 2013, there have been eight rounds of ICP data collection with the coverage of countries increasing from 10 countries to 199 countries in the most recent 2011 round of data collection. For more discussion of the history of the ICP, see http://go.worldbank.org/WLPETUYSO0 or World Bank (2014b).

3
is the impact of completing training for an individual who takes up training when they are selected in the course lottery, and does not take it up otherwise. A concern with this estimation is the possibility that simply being selected for a course may affect employment outcomes, even if individuals do not take the course or drop out after only a few days. For example, individuals (or employers) may take selection for the course as a signal of quality, which may give selected applicants more confidence approaching employers or employers a spur to hire these individuals. Showing up and finding out that you don’t like the course may cause reluctant job-seekers to try harder to look for work. Due to these concerns, we focus on the ITT estimates for most of our analysis, and just report the LATE estimates for our overall employment outcomes.

The primary outcomes of interest relate to employment. We consider a variety of employment measures which aim to measure whether individuals are employed at all, as well as how much they are working, how much they earn from this work, and the quality and formality of this employment. Appendix A explains how our key variables were constructed. We pre-specified these outcomes and how they would be measured in a pre-analysis plan (Casey et al, 2012) that was archived on February 6, 2012, before any follow-up survey data was received.<sup>13</sup> To control further for multiple hypothesis testing among employment outcomes, we follow Kling et al. (2007) in estimating a mean treatment effect on an aggregated employment index. This first transforms each employment outcome by subtracting the mean and dividing by the standard deviation, and then takes an average across outcomes.

In addition to estimating the overall impact of training, we are interested in exploring the heterogeneity of impacts to help understand whether certain types of courses offer larger impacts, or certain types of people benefit more from training. Our pre-analysis plan specified dimensions of heterogeneity of interest. To estimate heterogeneity with respect to characteristic _X_ , we estimate Outcomei = α+ βAssignedtoTraining+ γAssignedtoTraining∗X+ ρX+ ∑457s=1 θsδi,s + εi (3) Recently Fink et al. (2012) have criticized randomized experiments looking for heterogeneity in treatment effects for not controlling adequately for multiple hypothesis testing. They recommend the use of the Benjamini and Hochberg (1995) approach which holds constant the

> 13 The pre-analysis plan was archived and time-stamped by the J-PAL Hypothesis Registry, and is available at http://www.povertyactionlab.org/sites/default/files/documents/ISKURIE_AnalysisPlan_v4.pdf.

13
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37
ing the costs of implementing each method and the number of attempts per completed survey (3.69 CATI vs. 2.9 WhatsApp), the direct cost for each completed survey by CATI is double that of each completed survey using WhatsApp (US$ 11.40 vs. US$ 6.20). However, when we adjust the cost by probability of completion (0.72 vs. 0.3), the relationship flips and the cost of CATI becomes 25 percent lower (US$ 16 vs. US$ 21.20).

# **References**

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- Beegle, K., De Weerdt, J., Friedman, J., and Gibson, J. (2012). Methods of household consumption measurement through surveys: Experimental results from tanzania. _Journal of development Economics_ , 98(1):3–18.

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23
# **Figure 1: Round 1 and 2 means in the RCBP and non-RCBP kebeles**

<!-- Start of picture text --> Tried new agricultural activities in the Received advice from peer (=1)<br>past year (=1)<br>RCBP Non RCBP<br>RCBP Non RCBP<br>0.7<br>0.54 0.6<br>0.52 0.5<br>0.4<br>0.5<br>0.3<br>0.48<br>0.2<br>0.46 0.1<br>0.44 0<br>Round 1 Round 2 Round 1 Round 2<br>Contact with extension officer was Received advice from an NGO (=1)<br>regular (=1)<br>RCBP Non RCBP<br>RCBP Non RCBP<br>0.1<br>1.2<br>0.08<br>1<br>0.8 0.06<br>0.6<br>0.04<br>0.4<br>0.02<br>0.2<br>0 0<br>Round 1 Round 2 Round 1 Round 2<br><!-- End of picture text --> Note: Means are calculated over the entire sample of respondents to the extension module, which was administered to the household head and another household member of the opposite sex (the spouse in male-headed households, another male in farmer in female-headed households).

24
Niger. The type of treatment sought clearly depends on country characteristics and policies. For example, 29 percent of cases of fever in children resulted in a visit to a lower-level public facility in Eastern and Southern Africa but this masks a range of 16 percent in Malawi to 41 percent in Zambia. The data are consistent in showing a very small degree of treatment or advice sought from traditional healers: on average 1.2 percent of cases in Western and Central Africa and 1.6 percent of cases in Eastern and Southern Africa.

**Table 10. Treatment/Advice sought as a result of a child under 3 having a fever in the past two weeks**

||_No_<br>_Treatment/ _<br>_Advice_|_Public,_<br>_higher_<br>_level_|_Public,_<br>_lowerlevel_|_Private,_<br>_medical_|_Private,_<br>_commercial_|_Traditional_|_Total_|
|---|---|---|---|---|---|---|---|
|_WesternandCentral_|_Africa_|||||||
|BurkinaFaso1992/3|76.6|1.7|16.5|1.7|0.0|3.5|100|
|C6ted'Ivoire1994/5|15.0|26.8|32.1|17.5|_5.4_|3.3|100|
|Cameroon1991|52.1|8.1|19.2|14.5|2.3|3.8|100|
|Ghana1993|31.3|22.3|15.6|12.5|14.3|4.1|100|
|Niger1992|74.4|0.1|10.7|8.7|3.8|2.4|100|
|Nigeria1999|38.8|13.1|14.2|11.5|22.2|0.3|100|
|Senegal1992/3|60.7|4.0|25.7|4.8|2.1|2.6|100|
|Total|43.0|12.2|15.7|10.9|17.0|1.2|100|
|_EasternandSouthern_|_Africa_|||||||
|Kenya1998|23.4|12.2|24.7|22.5|16.5|0.8|100|
|Madagascar1992|49.4|13.1|18.5|13.7|2.6|2.7|100|
|Malawi1996|31.1|3.1|16.0|17.2|31.1|_1.5_|100|
|Rwanda1992|54.6|5.4|26.3|7.5|3.0|3.2|100|
|Tanzania1991/2|35.7|10.7|40.2|9.1|2.6|1.7|100|
|Zambia1992|22.1|9.4|40.9|19.1|6.2|2.3|100|
|Zimbabwe1999|35.2|6.2|31.2|15.2|12.0|0.3|100|
|Total|33.2|9.5|29.2|15.6|10.9|1.6|100|

_Source:_ Author's calculations from pooled and weighted DHS data. The classification is: "higher-level public" is government hospital, "lower-level public" is government health center, government health post, mobile clinic, community health worker, "private medical" is private hospital/clinic, private doctor, private mobile clinic, and "private commercial" is pharmacy or shop. In some countries additional options have been mapped to this classification. The "no modern sector" category includes no treatment or "professional" advice and advice from friends or family.

_21_