rafmacalaba/datause-encoder-data huggingface dataset:
**Table 10.B. Model comparison: Root mean squared error of zinc price forecasts**

(U.S. dollars per metric tonne)

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**<br>**Hor**|**izon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|213.6|...|...|...|...|...|
|Futures|3||150.5|...|...|...|...|
|Bivariate correlations|3|*||146.1|...|...|...|
|BVAR|3|*|*|*|401.9|...|...|
|OEM|3||||*|205.9|...|
|Machine learning|3||||*||202.1|
|Consensus|6|212.7|...|...|...|...|...|
|Futures|6||146|...|...|...|...|
|Bivariate correlations|6|*|*|312.8|...|...|...|
|BVAR|6|*|*||389.4|...|...|
|OEM|6||||*|190.3|...|
|Machine learning|6|||*|*||199.6|
|Consensus|9|225.9|...|...|...|...|...|
|Futures|9|*|142|...|...|...|...|
|Bivariate correlations|9||*|327|...|...|...|
|BVAR|9|*|*||394.8|...|...|
|OEM|9||||*|210.4|...|
|Machine learning|9||*||*||243.1|
|Consensus|12|254.7|...|...|...|...|...|
|Futures|12|*|141.9|...|...|...|...|
|Bivariate correlations|12||*|342.7|...|...|...|
|BVAR|12|*|*||418.5|...|...|
|OEM|12||*|||259.7|...|
|Machine learning|12||*||||341.3|
|Consensus|15|281.7|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15|*|...|...|537.9||...|
|OEM|15||...|...|*|281.6|...|
|Machine learning|15|*|...|...|||455|
|Consensus|18|308.9|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|BVAR|18|*|...|...|553.4|...|...|
|OEM|18||...|...|*|297.7|...|
|Machine learning|18|*|...|...||*|558.6|
|Consensus|21|330.1|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21|...|...|...|...|...|...|
|BVAR|21|*|...|...|492.3|...|...|
|OEM|21||...|...||303.2|...|
|Machine learning|21|*|...|...||*|633.9|
|Consensus|24|356.8|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24||...|...|425.9|...|...|
|OEM|24||...|...||290|...|
|Machine learning|24|*|...|...|*|*|686|

_Source_ : World Bank.

_Note_ : The forecast period is 2015Q1-2022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate average RMSE estimates for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to an F-test (RMSE).

51
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 5 Regarding the measure of institutional fragmentation and metropolitan coordination, Table 1 and Table 2 present a summary of the variables commonly used in empirical studies. In Table 1 we classify the variables using the five categories proposed by Hendrick and Shi (2015). Based on the conceptualization of fragmentation as those urban extents that spread out over several, and independent, administrative units, the most common variables are those that measure the number of local governments included in the urban extent (sometimes standardized by population or land area). Other measures focus on the degree of concentration, the dominance relationship between the central city and the periphery. As considered by the centralist view, the potential drawbacks of having institutional fragmentation can be mitigated through proper channels of metropolitan coordination. This coordination can be reached via institutions (governance), coordinated planning and infrastructure (land use planning and mobility), the presence of overlapping governments with special of general purpose (provision of public utilities), or can be the result of tight linkages between the administrative units, which intensifies human interactions, generates spatial dependence, and facilitates coordination (functional region). Table 2 classifies the potential variables according to those proposed channels of coordination.

The scarcity of empirical studies, in addition to the differences found when taking different approaches and using different economic performance indicators, suggests that there is a need to further test the empirical links between the institutional fragmentation of cities and their productivity/economic growth. This paper makes multiple contributions to the literature. First, and foremost, it will allow for testing of whether existing findings for the US and other selected OECD countries also carry-over to countries in LAC. Second, the paper will allow for the testing of the robustness of results to different institutional fragmentation and institutional coordination measures. Third, it will allow for an assessment of whether existing metropolitan coordination measures are effective in helping to produce better economic outcomes. Finally, contrary to existing literature, most of the analysis will be supported by spatial data that are readily available at the global scale and for developing countries. This will allow the paper to contribute to the development of a methodology that can be easily replicated for other regions of the world.
Section 5 presents the HAP monitoring methods and descriptive statistics, and an econometric analysis of sources of variation in HAP is documented in Section 6. Section 7 briefly concludes.

# **2. Household Air Pollution in Madagascar**

The 2010 Living Standards Measurement Survey (LSMS)<sup>12</sup> demonstrated that solid biomass remains the principal type of fuel used for cooking by more than 99% of households in Madagascar. Gathered fuel-wood is the primary source of cooking fuel for more than 77% of households, followed by charcoal (17.1%) and purchased fuel-wood (4.5%). In rural areas almost 87% of households continue to rely on gathered fuel-wood for cooking, compared with 45% in urban areas where more than 47% of households use charcoal as their principal cooking fuel, against 8.7% of rural households. Only 1.8% of urban households can afford clean fuels (LPG, electricity or kerosene) as their principal cooking fuel, and the percentage is even lower in rural areas (0.3%).

Among the health disorders associated with HAP, three figure among the top 25 causes of Years of Life Lost<sup>13</sup> (YLLs) due to premature mortality in Madagascar estimated by GBD 2010. Contributing an estimated 9.2% of YLLs, lower respiratory infections rank as the equal second cause of YLLs along with diarrheal diseases, after top-ranked malaria. IHD and COPD rank ninth and twenty-third respectively, contributing 3.0% and 0.8% of the YLLs. In 2007 the WHO estimated that more than 10,000 deaths from acute lower respiratory infections in children under five in Madagascar are attributable to solid fuel use, as well as an additional 1,400 deaths from chronic bronchitis predominantly in women over 30 years of age.<sup>14</sup> The top three causes of DALYs<sup>15</sup> in Madagascar in 2010 were malaria, diarrheal disease and lower respiratory infections.<sup>16</sup> While a range of risk factors may contribute to each of these disorders, HAP is the second leading risk factor in the burden of disease for Madagascar, accounting for some 6.7% of

> 12 Enquête Périodique auprès des Ménage (EPM) 2010, Institut National de la Statistique, Direction des Statistiques des Ménages.

> 13 Years of Life Lost (YLL) quantify premature mortality by weighting younger deaths more than older deaths (GBD 2010).

> 14 Indoor Air Pollution: National Burden of Disease Estimates, WHO 2007

> 15 Disability-Adjusted Life Years combine YLLs with Years Lived with Disability (YLDs) (GBD 2010). 16 GBD 2010

4
we aggregate the data by city to obtain the percentage of households with sanitation access in each city. We therefore consider panel data by city from 1986 to 2009. The time span includes the preprivatization period in the case of Maldonado because the two privatizations in Maldonado occurred in 1993 and 2000, as discussed above.

The ECH survey is only representative at the department level or at the city level for the largest cities in terms of population. Therefore, and to ensure a representative sample, we only retain the capital cities of the different departments and other large cities in our sample. We have a total of 35 cities, three in the treatment group (Maldonado, Pan de Azúcar, and San Carlos) and 32 in the control group (Artigas, Bella Unión, Canelones, Carmelo, Colonia, Dolores, Durazno, Florida, Fray Bentos, Lascano, Libertad, Melo, Mercedes, Minas, Montevideo, Paso de los Toros, Paysandú, Periferia Canelones, Rivera, Rocha, Rosario, Río Branco, Salto, San José de Mayo, San Ramón, Santa Lucía, Sarandí del Yí, Sarandí Grande, Tacuarembó, Tranqueras, Treinta y Tres, Trinidad, and Young).<sup>13</sup> Because some of the selected cities were not surveyed in certain years (primarily in the older edition of the ECH), we have an unbalanced panel that may imply panel attrition bias. For instance, of the 24 time periods in the treatment group, Pan de Azúcar appears 15 times, and San Carlos appears 18 times. In the control group, we have observations for Lascano in 10 of the 24 time periods; for Bella Unión, Libertad, and Rosario in 12 of the years; for Santa Lucía in 15 of the years; for Carmelo, Dolores, Paso de los Toros, Río Branco, San Ramón, Sarandí del Yí, and Sarandí Grande in 17 of the years; and for Young in 18 of the years. Therefore, we have a total of 735 observations.<sup>14</sup> Some cities began to appear in the ECH because of their rapid population growth; therefore, we checked the robustness of results to a smaller subsample in terms of the time span from 1993 to 2009.

Two observations must be made. First, there are far more control cities than treatment cities; hence, we also estimate our model restricting the number of controls to capital cities alone. The sanitation data for each capital city are available in every year of the full period; hence, in this instance, 12
the monsoon was shown to have a considerable effect on the health status of women and children in India (for example, Chambers 1982; Sahn 1987). The monsoon is associated with heavy rains that bring a multitude of diseases. Drinking water gets contaminated and results in a high incidence of water-borne gastro-intestinal infections (Khatiwada and Rimal, 2007) and cholera (Saraswathi and Deodhar, 1990). Excess humidity aggravates skin conditions, asthma and eclampsia (Subramaniam, 2007). Mosquito- and rat-borne diseases such as dengue (Bharaj _et al_ . 2008) and malaria (Kabanywanyi, _et al._ 2008) are transmitted faster in the wet and humid environment created by the monsoon.

The NFHS provides no data on the nutritional intake of mothers during pregnancy and of children during the first months of their lives. Also, the survey does not collect histories of illnesses during these periods. In the absence of these data, we use household wealth and the education of the mother as proxies for the nutritional status and disease environments into which the children were born. We argue that wealthier households have better means to smooth-out consumption of their children during the lean season and can obtain better health care if their children become sick. Maternal education has a strong positive impact on children’s nutrition and health outcomes via modern attitudes towards health care and reproductive behavior (Caldwell 1979; Thomas Strauss and Henriques 1991; Glewwe 1999).

To test the ‘nutrition-disease’ hypothesis, we regress WAZ and HAZ on a set of explanatory variables that include the socio-demographic characteristics of a household, a household wealth index, characteristics of the mother (including her educational attainments), location dummies, month-of-birth dummies, and interactions of the monthof-birth with wealth index _Ii_ and years of mother’s education _Ei_ . This regression can be expressed as:

> A significance of coefficients on month-wealth interactions ( **_βks_** ) or month-education interactions (<sup>**_γ_**</sup> **_ks_**<sup>) would point to the presence of seasonal differences in the effect of</sup> wealth and maternal education (as a proxy for nutrition and health care) on children’s health outcomes.

11
Importantly, the negative return on the accounts isn‟t merely an experimental gimmick. Besley (1995) documents that moneylenders in Western Africa have been successfully charging significant withdrawal fees on deposits.

**Figure 5: Deposit Accounts, by Country (average 2008-09)**

<!-- Start of picture text --> Deposit Accounts with Commercial Banks<br>(as Percentage of Population)<br>Central African Republic<br>Congo, Rep.<br>Chad<br>Madagascar<br>Comoros<br>Cameroon<br>Ethiopia<br>Gabon<br>Sierra Leone<br>Angola<br>Tanzania<br>Uganda<br>Togo<br>Rwanda<br>Lesotho<br>Ghana<br>Kenya<br>Swaziland<br>Botswana<br>Namibia<br>South Africa<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br><!-- End of picture text --> <u>Source: IMF-IFS Statistics, 2011.</u> The challenges presented by a lack of financial access get compounded for people who have present-biased preferences and, lacking any formal vehicles to save, just choose to consume it. However, in an RCT based in the Philippines, Ashraf, Karlan and Yin (2006) find that when offered savings accounts that require commitment, people with such preferences are more likely to sign up. Furthermore, Dupas and Robinson (2011b) find that providing informal savings 20
25

# **References**

- Adams, R. (2004), “Remittances and Poverty in Guatemala”, _Policy Research Working Paper_ , no. 3418, Research Program on International Migration and Development, International Trade Unit, Development Economics Research Group, World Bank.

- Adams, R. (2005), “Remittances, Household Expenditure and Investment in Guatemala”, _Policy Research Working Paper_ , no. 3532, Research Program on International Migration and Development, International Trade Unit, Development Economics Research Group, World Bank.

- Adams, R. and J. Page (2003), “International Migration, Remittances and Poverty in Developing Countries”, _Policy Research Working Paper_ , no. 3179, Research Program on International Migration and Development, International Trade Unit, Development Economics Research Group, World Bank.

- Ai, Chunrong and E. C. Norton (2003), “Interaction Terms in Logit and Probit Models”, _Economic Letters_ , vol. 80, pp. 123-129.

- Amuedo-Dorantes C. and S. Puzo (2004a), “Remittances and Healthcare Access of Populations in Origin Communities: Evidence from Mexico”, mimeo.

- Amuedo-Dorantes C. and S. Puzo (2004b), “Workers’ Remittances and the Real Exchange rate: a Paradox of Gifts”, _World Development_ , vol. 32, pp. 1407-17.

- Arteta, C. Ó. (2003), “Are Financially Dollarized Countries More Prone to Costly Crises?”, _International Finance Discussion Paper,_ no. 763, Board of Governors of the Federal Reserve System.

- Calvo, Guillermo A. (2003), “Explaining Sudden Stops, Growth Collapse and BoP Crises: the Case of Distortionary Output Taxes ”, _NBER Working Paper_ no. 9864.

- Calvo, G. A., A. Izquierdo and E. Talvi (2003), “Sudden Stops, the real Exchange rate, and Fiscal Sustainability: Argentina’s Lessons”, _NBER Working Paper_ no. 9828.

- Calvo, G. A., A. Izquierdo and L.-F. Mejìa (2004), “On the Empirics of Sudden Stops: the Relevance of Balance-Sheet Effects”, _NBER Working Paper_ , no. 10520.

- Chami, R., C. Fullenkamp and S. Jahjah (2003), “Are Immigrant Remittance Flows a Source of Capital for Development?”, _IMF Working Paper_ , no. 189.

- Cox E. A. and M. Ureta (2003), “International Migration, Remittances, and Schooling: Evidence from El Salvador”, _Journal of Development Economics_ , vol. 72 (2), pp. 429-61.

- Duryea, S., E. Lopez Cordova and A. Olmedo (2005), “Migrant Remittances and Infant Mortality: Evidence from Mexico”, mimeo, IADB.

- Dustmann, C. and O. Kirchkamp (2002), “The Optimal Duration Migration and Activity Choice after Remigration”, _Journal of Development Economics_ , vol. 67 (2), pp. 351-72.
the “Quran” were also positively correlated with work stoppage, suggesting that increased religiosity may have played a role as a coping mechanism for those that stopped work in the early stages of the crisis.

# _4.2 Predicting Variation over Time during the Pandemic_

Table 4 reports the results of demeaned OLS regressions to predict income loss and working status by real-time indicators using all waves of HFPS data. Demeaning the data by region effectively controls for time-invariant characteristics of the region. The second-to-bottom row of column 1 shows that the predictive power for total income loss since the previous survey is 0.22. The explained variation by real-time indicators for the three subcategories of income loss is slightly larger than that for the total income loss, as shown in columns 2-4. The R<sup>2</sup> for farm income loss, nonfarm income loss, and wage income loss is 0.25, and 0.26, and 0.23 respectively. The last column presents the predicting power for whether the respondent was working when the HFPS survey was administered. The mean R<sup>2</sup> is 0.15. The results in Table 4 imply that the prevalence of income loss and working status during the survey period is meaningfully associated with real-time indicators during the pandemic. However, this association is weaker than the cross-sectional variation using wave 1, because it does not include any of the correlation between real-time indicators and time-invariant characteristics of the region such as the region’s level of development prior to the crisis.

The signs of the statistically significant variables generally make intuitive sense when predicting variation over time. Increased Google searches for “Milk” and “Bible” are a reaction to negative shocks and are therefore positively correlated with income loss and negatively correlated with current work. Google searches for “Gym” are positively associated with total income declines, suggesting that exercise may be a source of stress relief when income declines. Searches for “Jobs” are positively correlated with farm income declines and wage income declines. Google searches for “Free meal” are positively correlated with the prevalence of income declines and “Cheap food” is negatively correlated with employment. Searches for “online school” are negatively associated with the prevalence of non-farm and wage income declines, suggesting that households in harder hit areas are less able to invest in online schooling.

Since an individual’s income might rise and fall along with changes in infection risk as well as stringency measures over time during the pandemic, we also investigate how well real-time 19
supervisors about the presence of a new reminder in SIRE and were invited to enter the online portal each time. The platform was also used to capture baseline and follow-up information through online questionnaires, which complemented baseline administrative information available at the beginning of the school year and a follow-up survey implemented on paper after the school year came to an end.

# 4. Experimental design and data

## Eligibility

As mentioned above, there are areas in Guatemala where children just do not have reasonable access to secondary schools. Given its features, ENTRE is targeted at schools and students where the supply-side constraints are less likely to be binding. Therefore, to minimize the role of these constraints, the universe of eligible schools was restricted to public schools in the top 30% of municipalities in terms of supply of secondary schools per student. In addition, within those municipalities, the very small schools likely to be located in remote rural areas (the schools belonging to the bottom 15% in terms of school size) were excluded from eligibility, as these schools tend to face binding local secondary school supply limitations. Finally, to target schools that could benefit more from the type of information provided by the program, we dropped schools that—according to our predictive model—had zero students at-risk of dropping out. As a result, the universe of eligible schools totals 6,037 public primary schools.<sup>6</sup>

## Experimental design and research questions

The program was designed as a multi-arm randomized experiment. Randomization was used to create four experimental groups of 1,000 schools each (three treatment arms and one control arm). Namely, from the 6,037 eligible schools, a simple random sample of 4,000 was drawn and then each was randomly assigned to one of the four experimental groups. The schools in the first treatment group receive component 1 (the half-day training and the user-friendly guidance manual, as well as a letter from the Minister of Education) which is aimed at improving knowledge on simple evidence-based methods to help students make the transition to lower secondary school –

> 6 In total, there are approximately 15,930 public and 2,699 private primary schools in Guatemala.

13
Figure 14: Size of formal and informal establishments by age cohort: Manufacturing <!-- Start of picture text --> Burkina Faso (2015) Cameroon (2008)<br>Informal Formal Informal Formal<br>Ghana (2013) Rwanda (2013)<br>Informal Formal Informal Formal<br>Mean employment size relative to entrants<br>Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years<br>Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years<br>50<br>40<br>30<br>20<br>10<br>0<br>15<br>6<br>4 10<br>2 5<br>0 0<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the selected countries; see section 3. _Note:_ The reference is the total employment for each age cohort and formality status. The figure is constructed based on data for establishments in the manufacturing sector. Establishments with missing employment or age data and state-owned are excluded.

Figures 10–13 illustrate growth differentials between formal and informal establishments by plotting the employment distribution at different age cohorts. Among formal establishments, the employment shares of larger class sizes continuously increase when looking at older age cohorts. These establishments tend to increase in size with additional years of market operation even if they face challenges in the business environment that stifle their growth. As for the informal establishments, the smallest size class has the highest employment shares and displays only a slight decline with older age cohorts. We also note the presence of a few informal establishments that are large or have been in operation for several years.

Similarly, figure 14 depicts the size of incumbents relative to new informal establishments. Here, we also see that incumbents experience size growth with age, but this tendency is most common among formal establishments rather than informal ones. Note that this pattern holds to a lesser extent in Burkina Faso.

27