rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix 3a.  Variables and Definitions in the WFP Mobile Phone Survey**

|Variable|Definition|
|---|---|
|BorrowOrHelp|# ofdayshousehold using this coping strategy per week|
|Cereals|# of days household eating this food item per week|
|CSG==1|coping strategy group==poor|
|CSG==2|coping strategy group==borderline|
|CSG==3|coping strategy group==acceptable|
|Dairy|# of days household eating this food item per week|
|Eggs|# of days household eating this food item per week|
|FCG|food consumption group|
|FCS|food consumption score|
|Fruits|# of days household eating this food item per week|
|HouseType==Camp|prevalence-->where HouseHold is staying|
|HouseType==Other|prevalence-->where HouseHold is staying|
|HouseType==Own_home|prevalence-->where HouseHold is staying|
|HouseType==Public_building|prevalence-->where HouseHold is staying|
|HouseType==Rental|prevalence-->where HouseHold is staying|
|HouseType==Staying_with_someone_for_free|prevalence-->where HouseHold is staying|
|HouseType==Unfinished_building|prevalence-->where HouseHold is staying|
|IDP_YN==Y|prevalence-->household is IDP|
|LessExpensiveFood|# of days household using this coping strategy per week|
|LimitPortionSize|# of days household using this coping strategy per week|
|Meat|# of days household eating this food item per week|
|Pulses|# of days household eating this food item per week|
|rCSI|reduced coping strategy|
|ReduceNumMeals|# of days household using this coping strategy per week|
|RestrictConsumption|# of days household using this coping strategy per week|
|Veg|# of days household eating this food item per week|
|FoodAssistance_YN==Y|prevalence-->household received food asssistance in past month|

31
Table S.33: Checking on spillover effects - GPA z-scores: Case D <u>(OLS)</u>

|Dependent variables:|||GPA|z-scores|||
|---|---|---|---|---|---|---|
||Bengali|English|Math|General<br>science|Islam<br>studies|Bangladesh<br>general<br>studies|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|
|**December 2012**|||||||
|Share of treated students and their|-3.558|0.400|-1.481|-4.135|-5.630**|-3.723*|
|siblings in each school-grade|(3.030)|(3.709)|(4.182)|(4.717)|(2.669)|(1.956)<br>|
|Share of students in each school-grade that|2.176|0.156|1.149|2.460|2.406|2.073*|
|had no access to electricity at baseline|(1.624)|(2.301)|(2.156)|(2.387)|(1.753)|(1.080)|
|All products|-0.020|-0.041|-0.001|-0.031|-0.094|-0.076|
||(0.081)|(0.078)|(0.069)|(0.072)|(0.072)|(0.089)|
|One product|-0.027|-0.005|0.009|-0.007|-0.110|-0.085|
||(0.095)|(0.078)|(0.093)|(0.103)|(0.085)|(0.094)|
|R-squared|0.225|0.215|0.208|0.216|0.231|0.226|
|No. of obs.|849|849|849|849|849|849|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|
|**December 2013**|||||||
|Share of treated students and their|0.214|3.376|1.312|-0.948|-1.473|-1.442|
|siblings in each school-grade|(3.019)|(3.542)|(3.182)|(3.334)|(3.254)|(3.025)|
|Share of students in each school-grade that|-0.552|-3.034|-0.422|0.385|1.791|0.711|
|had no access to electricity at baseline|(2.004)|(2.628)|(1.705)|(1.973)|(1.878)|(2.148)|
|All products|-0.047|-0.063|-0.043|0.053|0.058|0.068|
||(0.087)|(0.064)|(0.057)|(0.084)|(0.082)|(0.069)|
|One product|0.022|-0.142*|-0.084|0.040|0.026|0.132|
||(0.088)|(0.070)|(0.072)|(0.079)|(0.081)|(0.090)|
|R-squared|0.258|0.263|0.390|0.297|0.262|0.272|
|No. of obs.|849|849|849|849|849|849|
||(3a)|(3b)|(3c)|(3d)|(3e)|(3f)|
|**December 2014**|||||||
|Share of treated students and their|-4.198***|0.829|2.445|-1.269|-3.809**|-4.219**|
|siblings in each school-grade|(1.519)|(1.410)|(1.943)|(1.772)|(1.790)|(2.031)|
|Share of students in each school-grade that|2.304**|-0.043|-0.695|0.575|2.136**|2.215*|
|had no access to electricity at baseline|(0.913)|(0.838)|(1.150)|(0.928)|(0.900)|(1.133)|
|All products|-0.077|-0.036|-0.104|-0.099|-0.051|-0.118|
||(0.082)|(0.085)|(0.087)|(0.081)|(0.089)|(0.090)|
|One product|0.062|0.041|0.033|-0.065|0.043|-0.020|
||(0.092)|(0.086)|(0.087)|(0.091)|(0.099)|(0.092)|
|R-squared|0.119|0.099|0.164|0.149|0.111|0.131|
|No. of obs.|845|845|845|845|845|845|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

78
Fertility in low-fertility countries is generally assumed to remain below 2.1 children per woman during most of the projection period and reach 1.85 children per woman by 2045-2050. For countries where total fertility was below 1.85 children per woman in 2000-2005, it is assumed that over the first 5 or 10 years of the projection period fertility will follow the recently observed trends in each country. After that transition period, fertility is assumed to increase linearly at a rate of 0.07 children per woman per quinquennium. Thus, countries whose fertility is currently very low need not reach a level of 1.85 children per woman by 2050.

# _High-fertility assumptions_ :

Under the high variant, fertility is projected to remain 0.5 children above the fertility in the medium variant over most of the projection period. By 2045-2050, fertility in the high variant is therefore half a child higher than that of the medium variant. That is, countries reaching a total fertility of 1.85 children per woman in the medium variant have a total fertility of 2.35 children per woman in the high variant at the end of the projection period.

# _Low-fertility assumptions_ :

Under the low variant, fertility is projected to remain 0.5 children below the fertility in the medium variant over most of the projection period. By 2045-2050, fertility in the low variant is therefore half a child lower than that of the medium variant. That is, countries reaching a total fertility of 1.85 children per woman in the medium variant have a total fertility of 1.35 children per woman in the low variant at the end of the projection period.

# _Constant-fertility assumption_ :

For each country, fertility remains constant at the level estimated for 2000-2005.

# **B. Mortality assumptions: Increasing life expectancy except when affected by HIV/AIDS**

# _Normal-mortality assumption_ :

Mortality is projected on the basis of models of change of life expectancy produced by the United Nations Population Division. These models produce smaller gains the higher the life expectancy already reached. The selection of a model for each country is based on recent trends in life expectancy by sex. For countries highly affected by the HIV/AIDS epidemic, the model incorporating a slow pace of mortality decline has generally been used to project the reduction of general mortality risks not related to HIV/AIDS.

43
or public dissemination of the results at this stage. A few weeks after the release of the disappointing ENLACE results, Colima’s Ministry of Education (CMoE) began the design of PAE.

# **2.1 PAE Description**

PAE was a program designed to improve learning outcomes among the lowest performing public primary schools in Colima, which also provide education services to students from the most marginalized schools ((Mizala et al., 2007)). The program’s operational rules excluded multi-grade schools with one or two teachers and community schools managed by the _Consejo Nacional de Fomento Educativo_ (CONAFE).<sup>2</sup> Of the 477 primary schools in Colima in academic year 2008-2009, 40 were private, 39 were managed by CONAFE, 78 were one- and two-teacher schools, and 10, for a variety of reasons, did not have an ENLACE score. The group of PAE-eligible schools consisted of 310 public primary schools (see Figure 1) with a total student population of 62,366 (95.2% of the total number of students in public primary schools in Colima during the 2008-2009 school year). Between October and November 2009, the CMoE used the 2008-2009 ENLACE score data to construct its ranking of schools. School scores were a simple average of the three subject areas tested: math, Spanish and science across grades 3, 4, 5 and 6. Schools in the 35th percentile or less of the distribution of the school average test scores were automatically designated as PAE schools (see Figure AI in Annex). As shown in Figure 1, PAE included 108 of the 310 schools that belong to the potentially eligible population.<sup>3</sup> PAE schools were distributed across all ten municipalities of Colima and encompassed 1,091 teachers and 10,550 students in 2009.

Figure 2 illustrates the timeline followed by the design and implementation of Colima’s PAE. Between November and December 2009, the CMoE assigned the schools that were going to participate in PAE following the criteria described above. In January 2010, the selected schools were officially notified. In February 2010, at a teachers’ congress in Colima, the Governor launched the program and publicly disseminated the list of PAE schools. Although the assigned schools were presented as those with the lowest learning outcomes in the state, the Governor emphasized the co-responsibility behind low performance of schools and state education authorities and the importance of working together to make improvements. Between the public announcement of the program and the first follow up ENLACE test in May 2010, which the CMoE called Phase 1 or the “awareness” period, PAE schools were assigned a _technical adviser_ , part of the CMoE, who would visit the school three times a month to work with school directors and teachers on

> 2For details on CONAFE see www.conafe.gob.mx.

> 3Two schools were dropped from the sample due to a mistake in their original classification as nonmulti-grade schools which later on was changed to multi-grade.

5
Policy Research Working Paper 10232

# **Abstract**

This paper assesses the vulnerability of credit guarantee schemes to the physical and transition risks related to climate change. Based on unique sectoral and spatial data from 29 European credit guarantee schemes linked to a range of vulnerability metrics, the paper identifies guarantees-at-risk, builds a transition risk score to rank sectors at risk, and conducts a stylized stress test to assess potential financial losses that credit guarantee schemes could incur under adverse climate-related scenarios. The results show that about one-third of credit guarantee schemes’ guarantee portfolios is toward sectors that have high exposure to a disorderly energy transition. European credit guarantee schemes are also exposed to a broad range of climate-related physical risks, especially wildfires, coastal floods, and river floods, with 24–31 percent of outstanding guarantees toward sectors that have elevated exposure to climate change and weather variability. Finally, for transition and physical risk scenarios, the annual expected loss on the guarantee portfolio could increase by EUR 181 million and EUR 128 million, respectively. The results suggest that credit guarantee schemes could start integrating climate-related financial risks into their risk management frameworks.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pcalice@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table A2: Food Insecurity Experience Scale (FIES) Questions in Ethiopia

||Pre-COVID-19 Surveys||COVID-19 Surveys||
|---|---|---|---|---|
|FIES|Question|Recall|Question|Recall|
|FS1|Did you worry that your household would not have<br>enough food?|7 day|Was there a time when you or any other adult<br>in your household were worried about not having<br>enough food to eat because of lack of money or<br>other resources?|30 day|
|FS2|How many days have you or someone in your<br>household had to rely on less preferred foods?|7 day|Was there a time when you, or any other adult<br>in your household, were unable to eat healthy<br>and nutritious/preferred foods because of a lack<br>of money or other resources?|30 day|
|FS3|How many days have you or someone in your<br>household had to limit the variety of foods eaten?|7 day|Was there a time when you, or any other adult<br>in your household, ate only a few kinds of foods|30 day|
|39|||because of a lack of money or other resources?||
|FS4<br>FS5<br>|How many days have you or someone in your<br>household had to limit portion size at meal-times?<br>How many days have you or someone in your<br>household had to reduce number of meals eaten<br>in a day?|7 day<br>7 day|Was there a time when you or others in your<br>household had to skip a meal because there was<br>not enough money or other resources to get food?<br>Was there a time when you or others in your<br>household ate less than you thought you should<br>because of a lack of money or other resources?|30 day<br>30 day|
|FS6<br>FS7|How many days have you had no food of any kind<br>in your household?<br>How many days have you or someone in your<br>household had to restrict consumption by adults|7 day<br>7 day|Was there a time when your household ran out of<br>food because of a lack of money or other resources?<br>Was there a time when you or others in your<br>household were hungry but did not eat because|30 day<br>30 day|
||for small children to eat?||there was not enough money or other resources<br>for food?||
|FS8|How many days have you or someone in your<br>household had to go a whole day and night with-<br>out eating anything?|7 day|Was there a time when you or others in your<br>household went without eating for a whole day<br>because of a lack of money or other resources?|30 day|
_Ingo Borchert and Mattia Di Ubaldo_ results with non-services depth included are available upon request.

We follow standard practice of estimating equation (4.1) with a non-linear Pseudo Poisson Maximum Likelihood (PPML) estimator as suggested by Silva and Tenreyro (2006). Apart from accounting for the presence of zeroes, which are rife in bilateral services trade data, this approach also handles the heteroscedasticity arising from the presence of several small countries (and flows) in our data. In order to be able to include the three sets of high-dimensional fixed effects described above, we employ the recent PPML estimator developed by Correia, Guimar ̃aes and Zylkin (2019).

Finally, we follow the recent innovation by Cameron et al. (2011) and Egger and Tarlea (2015) and base our inferences on three-way clustered standard errors, i.e. at the importer, exporter, and year levels. This strict multi-level clustering takes into account simultaneous correlations across the three main dimension of the panel (over time within countries, and across countries within time periods); these intra-cluster correlations are plausible for trade data.<sup>18</sup> This leads to dramatically more conservative results, compared to robust standard errors, and thus increases our confidence in the significance levels associated with estimated PTA<sup>19</sup>

# **4.2 Aggregation of services provisions**

A principal contribution of this paper is to quantify not only the overall impact of services PTAs but to elucidate specific trade effects that may emanate from the structure of the agreements or certain configurations of individual provisions therein. The design and

> 18We exploit two-way (importer and exporter) instead of three-way clustered standard errors in the models estimating the PTA effect on services VA. This is because the number of years is limited in the TiVA data, which would imply standard errors to be adjusted to a very small number of clusters. Furthermore, we believe that the correlation across-countries within time period is potentially less concerning than the correlation across-years within countries.

> 19See Egger and Tarlea (2015) and Larch et al. (2019) for a more in-depth explanation of why three-way clustered standard errors should be used in panel-data gravity models.

23
Table D.5: Effect of Years of Schooling on Number of Children Born: Alternative Samples and Specifications - Analysis of Results from Table 2

||Non-|MTI|Nation|al Sample|Nation|al Sample|
|---|---|---|---|---|---|---|
||Region<br>Efect o|s Only<br>f FPE|Efect of F<br>(Separate|PE and MTI<br> Instruments)|Efect of F<br>(Combined|PE and MTI<br> Instrument)|
||Census|DHS|Census|DHS|Census|DHS|
||+ DHS|Only|+ DHS|Only|+ DHS|Only|
||(1)|(2)|(3)|(4)|(5)|(6)|
||||A|. Baseline|||
|�<br>Years of Schoolingizy|-0.437|-0.529|-0.273|-0.365|-0.257|-0.321|
||(0.090)|(0.165)|(0.109)|(0.147)|(0.117)|(0.156)|
||[0.000]|[0.001]|[0.012]|[0.001]|[0.028]|[0.040]|
|First Stage F-Statistic|14.796|5.93|14.09|6.91|38.28|19.56|
|N|69083|13922|205141|24898|205141|24898|
||,|,|,|,|,|,|
||||B. Coh|orts: 1968 - 1990|||
|�<br>Years of Schoolingizy|-0.422|-0.406|-0.212|-0.178|-0.198|-0.178|
||(0.092)|(0.115)|(0.101)|(0.117)|(0.109)|(0.133)|
||[0.000]|[0.000]|[0.035]|[0.131]|[0.069]|[0.181]|
|First Stage F-Statistic|13.36|7.54|14.64|20.55|38.79|27.76|
|N|101702|16481|249768|29547|249768|29547|
|�|,|,|,<br>C. Coh|,<br>orts: 1969 - 1989|,|,|
|Years of Schoolingizy|-0.429|-0.455|-0.189|-0.142|-0.188|-0.168|
||(0.095)|(0.149)|(0.106)|(0.123)|(0.113)|(0.141)|
||[0.000]|[0.002]|[0.074]|[0.250]|[0.096]|[0.235]|
|First Stage F-Statistic|14.34|6.51|13.93|11.63|41.97|26.44|
|N|95,469|15,108|235,691|27,106|235,691|27,106|
||||D. Coh|orts: 1971 - 1987|||
|�<br>Years of Schoolingizy|-0.427|-0.489|-0.359|-0.472|-0.280|-0.364|
||(0.105)|(0.184)|(0.113)|(0.147)|(0.112)|(0.150)|
||[0.000]|[0.008]|[0.001]|[0.001]|[0.013]|[0.015]|
|First Stage F-Statistic|17.21|5.44|13.46|7.53|29.36|14.09|
|N|76,674|12,299|190,989|22,023|190,989|22,023|
||||E. Coh|orts: 1972 - 1987|||
|�<br>|||||||
|Years of Schoolingizy|-0.380|-0.413|-0.326|-0.422|-0.230|-0.313|
||(0.099)|(0.174)|(0.124)|(0.175)|(0.121)|(0.157)|
||[0.000]|[0.017]|[0.009]|[0.016]|[0.049]|[0.046]|
|First Stage F-Statistic|14.94|3.99|13.39|6.56|28.81|13.31|
|N|74,694|11,967|186,219|21,390|186,219|21,390|

A.52
**<u>Table A.7: Relation between expenditure growth, poverty gap, and inequality</u>**

|||Dependent variable|is the growth of pe|r capita expenditure|(Log Yt– Log Yt-1|)|
|---|---|---|---|---|---|---|
|Exlanator variables||FE|||GMM||
|py|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged log of per capita expenditure|-0.123***|-0.813***|-0.909***|-0.119***|-0.252**|-0.856***|
||(0.009)|(0.062)|(0.065)|(0.013)|(0.102)|(0.134)|
|Lagged Gini index|-0.531|0.141|0.210|-0.711**|-0.573|-0.103|
||(0.351)|(0.149)|(0.160)|(0.338)|(0.395)|(0.276)|
|Lagged poverty gap|-0.080|-0.165|-0.638***|0.105|-0.351|-2.192***|
||(0.260)|(0.183)|(0.209)|(0.254)|(0.611)|(0.554)|
|Lagged log of other State spending|||0.002**|||0.002|
||||(0.001)|||(0.002)|
|Lagged log of investment spending|||0.003|||0.011|
||||(0.005)|||(0.008)|
|Lagged log of population density|||-0.092|||0.039**|
||||(0.068)|||(0.015)|
|Lagged share of urban population|||-0.001|||0.004***|
||||(0.001)|||(0.001)|
|Lagged share of population with high-<br>|||0.030|||0.018|
|school diploma|||(0.266)|||(0.178)|
|Lagged share of ethnic minority<br>|||0.002***|||0.001*|
|population|||(0.000)|||(0.000)|
|Lagged share of wage income|||0.258***|||0.379***|
||||(0.094)|||(0.107)|
|Lagged share of non-farm income|||0.144|||0.184*|
||||(0.102)|||(0.109)|
|Constant|1.396***|6.613***|7.100***|1.413***|2.367***|6.864***|
||(0.124)|(0.494)|(0.507)|(0.076)|(0.773)|(1.008)|
|Year FE|No|Yes|Yes||||
|Province FE|Yes|Yes|Yes||||
|Observations|567|567|567|567|567|567|
|R2/ Chi2|0.091|0.894|0.904|2234|4069|7018|
|Number of provinces|63|63|63|63|63|63|
|Arellano-Bond test for AR(1)||||-7.365|-6.458|-6.183|
|Arellano-Bond test for AR(2)||||2.939|4.202|3.671|
|Sargan test||||311.1|136.2|115.4|

Note: i) Authors’ estimation from VHLSSs using province FE model (columns 1 to 3) and GMM model (columns 4 to 6), ii) Robust standard errors in parentheses clustered at the province level, iii)*** p<0.01, ** p<0.05, * p<0.1.

46
One cause of heteroskedasticity in OLS models can be a violation of the assumption that the mean of the error term is identical and equal to zero across all observations in the sample. We investigated this possibility by estimating a generalized least squares (GLS) model with random effects. The means of the error terms in this model were allowed to differ across bilateral flows: ε _ijt_ = μ _ij_ + η _ijt_ .

μ _ij_ is the random effect for imports by country _i_ from country _j_ —the mean of the error term for that particular bilateral flow—and η _ijt_ is an error term that satisfies the standard OLS assumptions of being independently and identically distributed with a mean of zero. We used a Breusch-Pagan Lagrangian multiplier test to determine whether the random effects were statistically significant. We found that they were.

Coefficient estimates from the random effects model are unbiased and more efficient than OLS estimates as long as the random effects are not correlated with the other explanatory variables in the model. If the random effects are correlated with the other explanatory variables, however, then the coefficient estimates are biased, which is a more serious problem than inefficiency. Results of a Hausman test indicated that these correlations were indeed statistically significant, so we rejected the random effects estimates.

An alternative model that generates unbiased, though inefficient, coefficient estimates is a GLS model that includes fixed effects instead of random effects. We estimated this model by allowing the intercept in the Equation (6), β0, to differ across the bilateral flows (i.e., β0 _ij_ ). We used an _F_ test to determine whether the fixed effects were jointly different from zero. We found that they were.

Our preferred econometric results are therefore the ones from the GLS model with fixed effects. We show these results in Table 7. Appendix 8 contains results for the other, intermediate models.

# **5.3 Results**

The sample in the econometric analysis included all bilateral trade flows in the EFI Database during 1982-97 that satisfied two conditions: (i) either the importer or the exporter was a European country, and (ii) reported imports and reported exports were each at least 100 cubic meters. The sample period, 1982-97, was determined primarily by the availability of the Knack and Keefer governance variables. As Table 7 indicates, there are more observations in the sample for NC sawnwood: 6,140 trade flows between 1,058 pairs of countries,<sup>12</sup> vs. 4,786 trade flows between 781 pairs of countries for C sawnwood.

The results provide evidence that all three hypothesized components of discrepancies in reported trade statistics—measurement error, discrepancies in actual trade flows, and intentional underreporting—play a role in explaining the discrepancies in the sample. One of the four measurement error variables is significantly different from zero at a 5-percent level or better for

> 12 Direction of trade affects the definition of a pair of trading countries. For example, the fixed effect for Romanian exports to Germany is different from the fixed effect for German exports to Romania.

17