rafmacalaba/datause-encoder-data huggingface dataset:
almost 45% of children being stunted, i.e. their HAZ scores are under 2 standard deviations from the WHO median.<sup>16</sup> Boys are more likely to suffer from growth retardation than girls. Regional differences are also significant. While more than half of the children in the northern part of the country are stunted; only 34% of children suffer from acute growth retardation in the southern regions (see Table 2 for more details).

# Figure 3

Children also score below the international mean for WHZ, although the mode of the distribution is closer to the international standard. Acute malnutrition affects 17% of children, with no discernible difference between boys and girls. Differences in weight-for-height z-scores are also observed between the northern, central, and southern regions of Nigeria (see left panel of Table 1). While 11% of children are wasted in the relatively rich southern region, almost a quarter of them suffer acute malnutrition in the north.

# Tables 1 & 2

# **Climate and Weather Data**

In the most recent DHS surveys the location of sampled communities is geo-referenced using GPS technology.<sup>17</sup> These data allows us to construct a precise history of climate for each DHS cluster. For this purpose, we combine the geographic location of clusters with detailed weather data from a large weather station network spanning from the mid-1980s to 2008, the year of the last DHS. The location of the weather station network is displayed in Figure 4. The geographic coverage of the network is fairly good except for the central east and western part of the country.

# Figure 4

> 16 To have a sense of the magnitude of these figures, for a three years old child a 1 standard deviation would be equal to approximately 3.75 centimeters difference in height.

> 17 In order to maintain confidentiality of respondents, the DHS randomly displaces the GPS coordinates of each cluster. Rural clusters contain a minimum of 0 and a maximum of 5 kilometers of positional error (ICF Macro, 2011). Thus, even if the same clusters are sampled in two different rounds it would be impossible to identify them because they would have different geo-coordinates.

15
Appendix Table 3a. Estimates of the effect of the crisis on child anthropometrics for children under three years old in migrant households: Cross-sectional cohort analysis for alternative migrant definitions

||HAZ|Stunted = 1|WAZ|Underweight = 1|WHZ|Wasted = 1|
|---|---|---|---|---|---|---|
|_Panel A: Migration to United S_|_tates or Mexico t_|_hree of four y_|_ears_||||
|_Simple DD without controls_|||||||
|Migrant household = 1|0.477***|-0.124**|0.417***|-0.087**|0.226**|-0.013|
||(0.136)|(0.046)|(0.101)|(0.035)|(0.106)|(0.020)|
|Year 2012 = 1|-0.098|0.030|0.373***|-0.046|0.489***|-0.013|
||(0.136)|(0.032)|(0.098)|(0.029)|(0.100)|(0.013)|
|DD|-0.402*|0.086|-0.538***|0.087*|-0.329**|0.025|
||(0.209)|(0.060)|(0.166)|(0.048)|(0.163)|(0.024)|
|Constant|-2.306***|0.617***|-1.200***|0.220***|0.097|0.047***|
||(0.092)|(0.031)|(0.059)|(0.021)|(0.080)|(0.010)|
|_Cluster-level fixed effects DD w_|_ith age controls_||||||
|DD|-0.342|0.068|-0.512***|0.089*|-0.352*|0.027|
||(0.223)|(0.058)|(0.183)|(0.052)|(0.178)|(0.025)|
|_Cluster-level fixed effects DD w_|_ith age controls_|_and cluster-le_|_vel time trends_||||
|DD|-0.377|0.063|-0.568***|0.099|-0.375*|0.035|
||(0.242)|(0.063)|(0.203)|(0.061)|(0.188)|(0.029)|
|N|1,133|1,133|1,121|1,121|1,106|1,106|
|_Panel B: Migration to United S_|_tates in at least o_|_ne of four yea_|_rs_||||
|_Simple DD without controls_|||||||
|<br>Migrant household = 1|0.565***|-0.149***|0.421***|-0.093***|0.145|-0.026|
||(0.120)|(0.044)|(0.105)|(0.032)|(0.118)|(0.018)|
|Year 2012 = 1|-0.119|0.034|0.394***|-0.054*|0.532***|-0.023*|
||(0.128)|(0.034)|(0.099)|(0.028)|(0.108)|(0.014)|
|DD|-0.181|0.031|-0.497***|0.089**|-0.430**|0.053**|
||(0.212)|(0.072)|(0.162)|(0.044)|(0.167)|(0.026)|
|Constant|-2.376***|0.636***|-1.235***|0.229***|0.109|0.052***|
||(0.095)|(0.032)|(0.060)|(0.020)|(0.091)|(0.012)|
|_Cluster-level fixed effects DD w_|_ith age controls_||||||
|DD|-0.193|0.036|-0.447***|0.100**|-0.345*|0.051**|
||(0.198)|(0.062)|(0.165)|(0.047)|(0.175)|(0.024)|
|_Cluster-level fixed effects DD w_|<br>_ith age controls_|<br>_and cluster-le_|<br>_vel time trends_|<br>|||
|DD|-0.215|0.029|-0.494**|0.103*|-0.368*|0.054*|
||(0.209)|(0.068)|(0.188)|(0.053)|(0.191)|(0.029)|
|N|1,133|1,133|1,121|1,121|1,106|1,106|

Notes: Sample of children of indicated ages in 2008 or 2012. Migrant household in Panel A is defined as a household with at least one migrant in the U.S. or Mexico in three of all four years 2005-08 for 2008 or three of all four years 2009-12 for 2012. Migrant household in Panel B is defined as a household with at least one migrant in the U.S. in at least one year from 2005-08 for 2008 or at least one year from 2009-12 for 2012. Age controls are binary indicators for age-in-months at measurement; cluster level time trends are binary indicators for each cluster in 2012. Standard errors estimated allowing for clustering at the community level in parentheses. *** indicates significance at p<0.01, ** at p<0.05, and * at p<0.10.
people for cars, 14 people for microbus, 45 people for public bus, to 174 people by rail (given that a train pulls several cars).

**Table 1. Selected Parameter Assumptions Used in the Benchmark Simulations**

(for year 2006 or thereabouts)

|||Milea|geparam|eters|Total or|
|---|---|---|---|---|---|
||Auto|Microbus|Bus|Rail|average|
|Annual passenger miles per capita|353|444|418|130|1,344|
|Annual vehicle miles per capita|141|32|9|1|183|
|Average vehicle occupany|2.5|14|45|174|7.3|
|Vehicle miles per gallon|19.0|7.8|3.5|na|16.3|
|Passenger miles per gallon|48|110|160|na|109|
|Average vehicle speed, miles per hour|14.0|10.1|9.5|na|na|
|Average travel time cost, cents per passenger mile|5.5|7.7|8.1|na|na|
|Current fares, cents per passenger mile|na|1.5|0.8|0.9|na|
|Marginal operating cost, cents per passenger mile|na|1.5|2.6|1.8|na|
|_Marginal external costs_||||||
|Cents per vehicle mile||||||
|Congestion|9.7|29.2|48.7|0|15.1|
|Accidents|8.7|8.7|8.7|8.7|8.7|
|Local pollution and global pollution (converted to cents per vehicle mile)|4.2|10.3|0.3|0|0|
|Total|22.7|48.2|57.6|8.7|23.8|
|Cents per passenger mile--total|9.1|3.4|1.3|0|3.2|
|_Gasoline parameters_||||||
|Fuel use, gallons per capita|11.5|||||
|Current fuel subsidy, $/gal.|1.20|||||
|Retail price of fuel, $/gal.|1.60|||||
|_External cost per gallon_||||||
|Local pollution costs attributable to gasoline, cents per gallon|72|||||
|Global pollution, cents per gallon|9|||||
|total|81|||||
|_Own-price fuel elasticity_|-0.5|||||
|fraction of elasticity due to changes in auto and microbus miles|0.5|||||
|_Mileage elastcities_||||||
|own mileage elasticity wrt own fuel price or fare|-0.25|||||
|_Behavioral response coefficients_||||||
|Reduction in auto vehicle miles per tax-induced gallon reduction in gasoline|6.2|||||
|Reduction in microbus vehicle miles per tax-induced gallon reduction in gasoline|1.4|||||
|Increase in microbus vehicle miles per toll-induced reduction in auto vehicle miles|0.06|||||
|Increase in auto vehicle miles per toll-induced reduction in microbus vehicle miles|1.8|||||
|Increase in auto vehicle miles per fare-induced reduction in public bus passenger mi|0.12|||||
|Increase in auto vehicle miles per fare-induced reduction in rail passenger miles|0.09|||||

Source. See text and appendix.

14
of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old.<sup>19</sup> The Peabody Vocabulary Test is calibrated with a mean standard score of 100 and a standard deviation of 15, placing scores between 85 and 115 within the average range. This test, in its Spanish edition, has been validated for use in Colombia. Additionally, to ensure relevance and accuracy for Venezuelan nationals, we conducted a preliminary validation by administering the test to a sample of Venezuelan mothers in our study. This step confirmed that the words used in the test held consistent meanings for participants from Venezuela.

Figure B.1 depicts the distribution of PVTS scores for Venezuelan and Colombian children and adolescents in our sample. This visualization indicates that Venezuelan minors score lower on the PVTS to their Colombian across the en- consistently compared peers tire score distribution. In Table 6, we present the average disparities in percentile rank on the Peabody scale, revealing that Venezuelan children and adolescents, who are forcibly displaced, score approximately 12 p.p. lower than their Colombian counterparts. The difference is meaningful and in turn translated into Venezuelan minors falling into the a higher likelihood of having extremely low, moderately low, and low score categories. Additionally, these findings remain consistent even when adjusting for a range of control variables pertaining to the individual, their parents, and grandparents, underlining the robustness of these observations.

# **V.C Socioemotional and mental health development**

To explore the differences in mental health and socioemotional development across Colombian and Venezuelan minors we use multiple scales. For children aged 5-10 years, the Trauma Symptoms Checklist for Young Children is employed. This 90-item question-

> 19Children only respond to items within their “critical range”, determined by a lower limit called the “base item” and an upper limit called the “ceiling item”. The base item, marking the starting point, is determined by the individual’s chronological age in years (date of test administration - date of birth). Once the child correctly answers 8 consecutive questions, they reach the ”Base”. Subsequently, upon making 6 mistakes within 8 consecutive questions, the ceiling is established. The direct score is calculated as the item number where the test ends (ceiling item) minus the number of errors from the highest base to the end of the ceiling.

36
Table 6: Robustness V, Survival Sanitary Risk and Product Perishability. The dependent variable is the hazard rate of trade relationships for product k of ...rm i operating in country c to destination country j: All regressions are estimated using the Cox Proportional Hazard Model. We control for destination country, year, ...rm and exporting country x HS2 ...xed e¤ects, and allow the baseline hazard to vary across HS4 (columns 1-2) and across HS8 (columns 3-4). We control for alternative product characteristics, their perishability (perishabilityk). We interact the level of ...nancial development in country c with tthe perishability index (FDc�perishabilityk). In addition we include number of non EU partners to control for altrenative markets where exporters can sell their products. Robust standard errors clustered at (exporting country) � HS4 sector level are in parentheses. *, *** **, and denote statistical signi...cance at the 10%, 5%, and 1% levels, respectively.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|FDc x sanitary_riskk||-0.154**||-0.263**|
|||(0.073)||(0.108)|
|FDc x perishablek|-0.537|-0.321|0.703|0.978|
||(0.897)|(0.890)|(0.927)|(0.915)|
|sanitary_riskk||0.020***<br>(0.007)|||
|perishablek|0.296**|0.279**|||
||(0.135)|(0.131)|||
|initial_exportcikj|-0.094***|-0.094***|-0.089***|-0.088***|
||(0.007)|(0.007)|(0.007)|(0.007)|
|NProductsi|-0.003|-0.003|-0.004**|-0.004**|
||(0.002)|(0.002)|(0.002)|(0.002)|
|NDestinationscik|-0.057***|-0.057***|-0.056***|-0.056***|
||(0.008)|(0.008)|(0.009)|(0.009)|
|NPartn_nonEUcik|-0.168395*|-0.172531*|-0.148588|-0.150|
||(0.098)|(0.098)|(0.104)|(0.103)|
|Com_languagecj|-0.115***|-0.114***|-0.129***|-0.129***|
||(0.040)|(0.040)|(0.040)|(0.040)|
|Colonycj|-0.151*|-0.148*|-0.088|-0.083|
||(0.083)|(0.083)|(0.085)|(0.084)|
|Distancecj|0.043|0.043|0.090*|0.091*|
||(0.043)|(0.044)|(0.048)|(0.048)|
|Observations|14870|14870|14870|14870|

27
**Table 7.  Estimates of model (8) with the Economic Freedom Index as the dependent variable**

|**Variable**|**Equa**|**tion(8)**|
|---|---|---|
|Dependent var.: Economic Freedom Index|**Coeff.**|**Robust s.e.**|
|∑���<br>�<br>A�<br>�.�= # policy actions raised to γ = 0.0, cumulated|0.994|0.248***|
|Cumulated length of engagement|‐0.0487|0.0132***|
|GDPper capita at PPP|0.000102|0.000134|
|Aid/GDP|‐19.0|5.77***|
|Political Rights|‐0.205|0.304|
|Country&year fixed effects|Yes||
|N|2518||
|R‐squared,within|0.217||
|Number of countries|154||

Note: * = significance level of 10% or less; ** = 5% or less; *** = 1% or less. Standard errors are adjusted for 145 clusters.

The key element in this estimation is that cumulated policy actions (raised to the power 0) have a positive and statistically significant impact on the quality of government policy as measured by the Economic Freedom Index. The coefficient on the cumulated length of the engagement is negative and statistically significant. These findings buttress the earlier findings using the CPIA as the dependent variable. GDP has the expected sign (positive) but is insignificant. Aid/GDP is negative this time, and significant.

# **6. Robustness tests**

In this section we present the results from three robustness tests: quantile regression, a Tobit model and split samples.<sup>17</sup> The first deploys a 50% quantile regression (or Least Absolute Deviations regression) which allows for contamination, viz. the possibility that some elements of the data come from a different distribution than that of the bulk of the data. Under these circumstances, OLS does not deliver consistent estimates. Methods have been developed to cope with _single_ outliers, e.g. Cook’s statistic, but this would be inadequate in our case – a large data set with several RHS variables – since there may be clusters of observations in n‐tuple space which might not be detected by Cook’s statistic, but which could greatly distort the coefficient estimates. A more rigorous way of coping with such situations of potential contamination is to use robust regression approaches. LAD or 50% quantile regression eliminates the distorting effect of y‐outliers. The results are presented in Table 8.<sup>18</sup> 17 Attempts have been made to implement a more general correction for endogeneity by lagging the variable of interest. However, given the cumulated nature of the error term, this strategy is not feasible. Future work may aim to address potential endogeneity bias more explicitly.

> 18 The STATA module QRPD was used, which is adapted for panel regression.

19
# **3 The Modeling Framework**

The model consists of a sequentially dynamic CGE model that is linked to a microsimulation. The microsimulation takes the changes in factor and goods prices as given; hence, there is no feedback between these two parts of the model. We consider this framework particularly suited for the questions at hand, as the CGE model captures some of the main features of structural change and the relative price changes accompanying them. The microsimulation then allows for a detailed empirical assessment of the household responses to these changes. One of the major advantages of the microsimulation is that its unit of observation is the individual rather than the household, thereby offering a much richer representation of distributional dynamics.

# **3.1 The Macro Model**

A 1997 Social Accounting Matrix (SAM) has been used as the initial benchmark equilibrium for the CGE model. This SAM has been assembled from various sources incorporating data from the 1997 Input Output table, information from the SAM assembled by Harrison, Rutherford, Tarr, and Gurgel (2003), and the 1997 and 2001 PNAD household surveys. For the purposes of this model the full SAM – which includes 41 sectors, 41 commodities, 12 factors (skilled and unskilled labor by gender and by farm and non-farm occupation, agricultural and non-agricultural capital, land and natural resources), an aggregate household account, and other accounts (government, savings and investment, and rest of the world)<sup>8</sup> – has been aggregated to a smaller size and it comprises the accounts shown in Table 1.

_<u>Table 1: CGE Model accounts</u>_

**<u>Model Sectors</u>** 1 CerealGrains 7 OilMinerals 13 MachineryEquipment 2 OilSeeds 8 LightManufacturing 14 OtherServices 3 RawSugar 9 AgriIndustriesExp 15 Construction 4 OthCrops 10 WoodProductsPaper 16 TradeCommunication 5 Livestock 11 ChemicalsOilPr 17 PublicServices 6 RawAnimalProducts 12 MetalMineralProducts **<u>Factors of Production</u>** 18 Land 20 Capital 22 Non agriculture Unskil. Lab 19 Natural Resources 21 Skilled Labor 23 Agriculture Unskil. Lab **<u>Other Accounts</u>** 24 Production Taxes 28 Direct Taxes 31 Investment-Savings 25 Indirect Taxes 29 Households 32 Variation of Stocks 26 Tariffs 30 Government 33 Rest of the World 27 Export Taxes

> 8 See annex for a full list of accounts.

8
|First Broad-Based Growth Development PolicyLoan|Neeta G. Sirur|
|---|---|
|First Broad-Based Growth Development PolicyLoan|Neeta G. Sirur|
|China HFC-23 Emissions Reduction and Sustainable Development|BeJuha Antti Kalevi Seppala|
|China HFC-23 Emissions Reduction and Sustainable Development|BeJuha Antti Kalevi Seppala|
|MDTF for National Sudan Reconstruction and Development|Mosllem Ahmed Alamir|
|MDTF for National Sudan Reconstruction and Development|Mosllem Ahmed Alamir|
|MDTF for the Reconstruction and Development of South Sudan|Bolormaa Amgaabazar|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|ST LUCIA WATER SUPPLY INFRASTRUCTURE IMPROVEMENT|Patricia Lopez Martinez|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|BiodiversityConservation in Productive ForestryLandscapes|Peter Jipp|
|Debt Relief Credit Under the Enhanced HIPC Initiative|Ulrich Lachler|
|Debt Relief Credit Under the Enhanced HIPC Initiative|Ulrich Lachler|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|CommunityDevelopment Fund|Endashaw Tadesse Gossa|
|Highway2|Nijat Valiyev|
significantly – 50% from its peak (US$100/metric ton in 2018) – at the beginning of the pandemic. However, the price rebounded and continued to rise above its 2018 level, reaching US$160/metric ton. Coal prices were at their bottom when COVID cases, deaths, and stringency were at their highest in the first wave of COVID-19. The price then recovered and remained high during and even after the second wave of the pandemic. Again, visual inspection suggests that the price of coal responds to the stringency of public health measures. The story is somewhat different for the price of natural gas, whose path in the United States seems to have diverged from that in Europe and does not necessarily co-vary with COVID-19 cases or stringency.

# **3. Methods**

Our first goal is to establish whether COVID-19 severity affected total electricity generation and, if so, by how much. We take advantage of our panel dataset, which covers selected countries for two years before and almost two years into the pandemic, and fit the regression: i 1C C i 1 i 3 i 4<sup>C</sup> i i i i i i i (1) EE = ββ CC + ββ CC + ββ HHC<sup>+ ββCC+ ααHH+ δδ+ θθ</sup> + ηη + εε where _i_ denotes the country, _t_ the date, _COVID_ measures COVID severity, _C_ is generation capacity, _HDD_ and _CDD_ are the heating and cooling degree days, respectively, and _H_ is a holiday dummy.

Equation (1) contains a rich set of fixed effects (country-by-year, country-by-month, and country-by-day-of-the-week). It controls for installed capacity in an effort to capture the underlying generation patterns. We note, however, that daily data capacity has only very limited variation over time, and thus it tends to act almost like a country fixed effect. The interpretation of Equation (1) is straightforward: Essentially, we wish to see whether COVID-19 severity has any additional explanatory power once we control for the other determinants of generation.

One challenge is that both electricity generation and COVID-19 severity are seasonal. Electricity demand and supply are influenced by heating and cooling needs and activities, as well as weather, all of which are seasonal in nature. One common observation is that COVID-19 cases tend to rise during the fall and winter when school resumes and most activities are moved indoors. Different variations of the virus also tend to come in waves. We take advantage of the variation across countries in the timing of these waves and in the mitigation or prevention response by governments to identify the effect of COVID.

10