rafmacalaba/data-use-mentions-extended huggingface dataset:
incorporate was the municipality of Rauco, which introduced FSD in 2004.<sup>24</sup> It is important to note that all municipalities were participating in FSD by 2006.

# II. DATA: SOURCE SAMPLE AND SUMMARY STATISTICS

To estimate the relationship between full day school reform and maternal labor supply we use data from Chile’s National Socio-Economic Characterization Survey (CASEN) for the period 1990–2011. We also use administrative data on enrollment and school compiled by the Ministry of Education for 1998–2011. CASEN is a representative survey at the national, regional, and urban-rural levels. It has been happened every two or three years since 1987 and provides important data on health, education, housing, income, labor market, and demographic variables at the household level.

This study considers women between the ages of 25 and 55 since they present the greatest likelihood of being the mothers of 8–13 year-olds. Following Gelbach (2002), we estimate separate models for a mother of an 8–13 year-old with and without younger children. Mothers who have both an 8–13 year-old and a younger child must find childcare for the younger child (even if the older child is in FSD), so throughout the article we split the samples according to whether the woman’s 8–13 year-olds is her youngest child. We also split the sample by marital status, so we can differentiate the effects of the FSD on married and single mothers. Mothers were classified as single if listed as head of household or as the primary individual in a subfamily.<sup>25</sup>

> 24. The last municipalities to join the program were Sierra Gorda, Rinconada, Isla de Pascua, Olivar, Rauco, and Penco. _Source_ : Ministry of Education 1998–2011.

> 25. We also estimate the results using the marital status reported by women. The results are quite similar and are available from the authors upon request.

12
6) of Table 7, we use the average value for a given industry-country pair.<sup>24</sup> Throughout Table 7, our main interaction term remains positive and significant. In-

> 22 We are grateful to Christian Lundblad for suggesting this approach to us.

> 23 Our approach builds upon previous literature that has used IFC data to construct liberalization intensity measures at the country level (see, e.g., Bekaert 1995; Edison and Warnock 2003; Bekaert, Harvey, and Lundbland 2005; De Jong and De Roon 2005; Bekaert et al. 2007).

> 24 We thus abstract from the time variation in the liberalization intensity due to the limited availability of publicly available IFC data at the industry-country level across the time dimension. The focus on the cross-sectional variation is also in line with previous literature using a country-level measure of liberalization intensity in the context of international trade (Manova 2008; Defever and Suedekum 2014).

24
# **_Box 1:  Structure of STPD classification of measures_**

# **_A Conditions on market entry_**

- _A1 Legal forms of entry (including foreign equity limits) A2 Quantitative measures (for firms and natural persons)_

- _A3 Conditions on licensing/investment screening/qualifications relating to market entry (firms /natural persons)_

- _A4 Other conditions on market entry_

# **_B Conditions on operations_**

- _B1 Conditions on supply of services_

- _B2 Conditions on service supplier_

- _B3 Conditions on government procurement_

- _B4 Other conditions on operations_

# **_C Measures affecting competition_**

- _C1 Conditions on conduct by firms_

- _C2 Governmental rights/prerogatives (including public ownership) C3 Other measures affecting competition_

# **_D Administrative procedures and regulatory transparency_**

- _D1 Administrative procedures_

- _D2 Regulatory transparency (including licensing)_

- _D3 Nature of regulatory authority (measures related to nature of regulator) D4 International standards_

- _D5 Other regulatory environment and administrative procedures_

# **_E Miscellaneous_**

Every individual policy measure is mapped to one category in the above taxonomy and is generally defined in a binary way (yes or no response). As an exception to this rule, by their very nature a few measures require a quantitative reply (e.g. level of foreign equity allowed, number of days required to process a licence application). Each policy category includes measures from across all modes of supply as applicable; for example, category A (conditions on market entry) will thus include the measures affecting entry through each of the modes of supply covered in the database, i.e. cross-border, commercial presence and presence of natural persons.

The meta-classification of measures described above is intended to facilitate convergence towards a single classification and quantification methodology. Therefore, the classification remains, at the measure level, fully compatible with the classification utilized in the OECD Services Trade Restrictiveness Regulatory Database and the previous classification of measures developed by the World Bank more than a decade ago.
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 23_ to official statistics for improving the frequency, timeliness, and granularity of key economic/development indicators for data-driven policy-making in Papua New Guinea and the Pacific Islands. Funding from DFAT (TF0B6892 and TF0B6579) is gratefully acknowledged.

Additional funding by the Federal Ministry for Economic Cooperation and Development (BMZ, Germany) as part of the World Bank’s Food Systems 2030 (FS2030) Multi-Donor Trust Fund program (TF073570 and TF0C0728) is gratefully acknowledged. The methodological improvements suggested in the paper have been implemented in the live version of the Real-Time Food Prices (RTFP) data set on the World Bank’s MicroDataLibrary as part of the Global Food and Nutrition Security PASA activities financed under the FS2030 grants.

The live data set can be accessed here https://doi.org/10.48529/2zh0-jf55 and here https://doi.org/10.48529/5hgz-q149. The improved speed of the new routines has allowed enabling the processing of other non-food items available in the original surveys as additional predictors.

# **Code and data availability statement**

Access to the code (Price Monitor, Version: 842) used to produce the results of this paper can be requested. Training data and updated validation statistics similar to those used in the paper, are published along with the model outputs as part of the RTFP data.
population AgGDP model as measured by the Mean Absolute Deviation (MAD) and Root Mean Square Error (RMSE).<sup>14</sup> While the spatial regression performs the best, global data requirements that allow high enough degrees of freedom is a challenge.

Given these data requirements and challenges, we compare the cross-entropy model to another spatial allocation model based on rural population at the country level. Then we extend a comparison of both models at the global level by mapping the correlation.

One advantage of the cross-entropy is the volume preserving pycnophylactic property, which ensures the sum of the gridded data is the original value and allows the possibility to include all information that is available from mixed levels of data (e.g. You, Wood, et al., 2014). However, this presents a challenge in terms of an assessment of a global model. Previous work on gridded data products includes evaluations of accuracy. Typically, studies evaluate the internal accuracy of the model exploiting multiple geographic levels of data (as mentioned above in Thomas et al., 2019). Similarly, Van Boeckel et al. (2011), who examine duck data in Thailand, conclude that input levels do matter, especially the importance of the presence of administrative level 1 data. Robinson et al. (2014) evaluate the livestock model in Brazil and find a positive association between the model accuracy and the administrative level of the training data used in the model. They also illustrate this inverse relationship of prediction accuracy and level of intensification in the case of chickens in Europe (Robinson et al., 2014). At the local cell level, previous models of land or population have compared results to independent local data (Siebert, D ̈oll, and Hoogeveen, 2002) or identified errors of omission in a gridded population model using the locations from household surveys (e.g. Tiecke et al., 2017).

Since we can not perform an evaluation of prediction accuracy for all countries, we compare
not the firm exports, province and lagged capital.<sup>8</sup> We also confirmed the robustness of our results by omitting 1996 from the regressions.

> 7 As establishments are not required to report their closure, exit is inferred from establishments ceasing to file reports to BPS.  We do not count as exits temporary lapses in reporting. Temporary exits account for 0.6% of all the data.

> 8 For firms that enter in 1996, we use data from 1997-2000 to backcast their capital stock using the same set of explanatory variables (but using leads rather than lags where appropriate).

15
2008 EDHSs .

_Education._ While one of the three opportunities for education is associated with attendance, the other two are associated with quality or performance. The indicator for attendance is school attendance for children between 9 and 15 years of age. The performance indicators include finishing primary schooling on time, at age 13-15; and finishing secondary general or technical on time, at age 19-20.

_Basic Housing Services._ Six human opportunity indicators represent adequate access to basic household services for the dwelling: water, sanitation, lighting energy source, cooking energy source, non-overcrowding, and telephone. Criteria for adequate access are: for water, connection to public network; for sanitation, connection to public network; for lighting energy source, electricity; for cooking energy source, use of gas bottles, natural gas, or electricity; and for telephone, access to a fixed or mobile telephone. Access to these services is measured for children between the ages of 0 and 17. The sixth indicator is non-overcrowding and is measured as the opportunity to live in a home that is not overcrowded for children between the ages of 0 and 5.

_Early Childhood Development._ This group includes four opportunities that are associated with key factors of development and growth during early childhood (under age 5): access to assisted birth delivery (0-5), access to post-natal care (0-5), access to prenatal care (0-4), and access to immunization vaccines (0-4). Access to assisted birth delivery is defined as birth delivery assisted by a healthcare professional. Access to post-natal care 0-5 is defined as access to at least one prenatal checkup by a healthcare professional. Prenatal care for children under 5 is defined as access to at least one prenatal checkup by a healthcare professional. Finally, access to immunization and vaccines is defined as those children who have access to a vaccination card.

> 13 This section draws on the definitions of indicators used by Velez et al. (2010) for the case of Colombia.

> 14 For 2000, one
The poverty gap shows little variation by age, although it is above average among people aged over 65. It does not however increase systematically with the number of elderly in a household. In fact, in Hungary, it is the reverse-the poverty gap falls significantly in households with two or three elderly members. Many such households are poor, but they are not very far below the poverty line.

# **a&** _Former Soviet Union\_

Poverty is generally considered to have sharply increased in countries undergoing transition, partly because incomes are perceived to have become extremely unequally distributed, and mostly as a result of drastic declines in GDP. Indeed, a static comparison of the poverty rates for the FSU suggests that poverty is a serious problem in Russia and Estonia, and a nearly overwhelming one in the Kyrgyz Republic.

After five years of economic contraction, the poor in the FSU appear to be primarily the working poor, and especially the working poor with children. The working poor are testimony to the adjustment in wages, rather than in open unemployment, which has occurred. The myth of the pensioner--he idea that pensioners are especially vulnerable to poverty-is belied by several studies, including this one (World Bank, various poverty assessments in transition economies), although it is true that the extremely elderly (aged 75 and over) are more vulnerable to poverty. This sketch of the poverty profile seems relatively robust across equivalence scales-most of the poverty assessments cited used per capita measures, while in this study, the OECD equivalence scale is used.

41
Psacharopoulos (1994) for Sub‐ Saharan Africa. For the next four years 10.1 percent is assumed, the average for the world.  For education beyond the eighth year the OECD value, 6.8 percent is assumed.

11 Using PWT 8.1 data points to a human capital growth rate 0.5% over 2001‐2010, but this is based on a previous version of Barro and Lee data (version 1.3, which has since been updated).

> 12 Barro and Lee (2015) do not appear to capture the population loss in Syria from the conflict, as they use projections from United Nations (2013).

7
|||-0.16|-0.79|||**0.95**|
|Transfers|9.51||||||-9.51|

Figures represent a percentage of GDP at market value. Positive figures are revenues received by the column from the row. Negative figures are payments made by the column to the row. Figures in bold are taken directly from national accounts, all others are calculated using the calibration process.

tive entries are payments to factors or factor taxes. In the government’s column, positive figures are tax revenues, the negative figure is the transfer to consumers. The ‘Foreign’ column represents the purchase of exports and the sale of imports by the rest of the world, using foreign exchange.

# **3.1 Data**

The data are given in Appendix B. Country-specific data comprise values of exports, imports and investment as a percentage of GDP, tax revenues for each of the five taxes, and legal tax rates for domestic goods, capital and labor. These data were obtained from IMF country report statistical annexes, for the most recent years available. The data also include labor-output ratios for _untaxed_ , _domestic_ and _exports_ that are, by assumption, common for all countries. The labor-output ratios are the averages of ratios derived from SAMs prepared by IFPRI for Malawi 1998, South Africa 1999, Tanzania 2001, Zambia 1995, and Zimbabwe 1991.

Data for tax revenue from sales of _domestic_ are derived from tax revenues from domestic VATs. In the absence of a VAT, general sales tax revenues are used. Corporate income tax revenues are interpreted as tax revenues from _formal capital_ . Personal income tax revenues are equated with revenues from _formal labor_ . Data for tax revenues from _exports_ and _imports_ are taken directly from the national 9
OZIER - REPLICATION REDUX – p. 18 of 41 not come into existence until 2000 (ISRCTN 2018). Now, of course, many in the discipline of economics have started considering whether, when, and how it makes sense to register experiments and to specify analytical designs in advance (Olken 2015, Coffman and Niederle 2015, Anderson and Magruder 2017, Fafchamps and Labonne 2017).<sup>16</sup> Another reflection on scientific norms: unlike in an efficacy trial of a new medication, it is difficult to imagine how double‐blinding would be possible for many experiments in the social sciences. While it may be possible to obscure the treatment status of study participants so that field data collectors may be blind to it in some cases, treatment and comparison groups generally know what they are receiving. Yet a third epistemological issue: in the social sciences, studies regularly sample from a large population of interest, making CONSORT‐compliant diagrams—a standard for communicating randomized trials in medicine—more complicated to depict (Schultz, et al. 2010).

The back‐and‐forth between the replication authors and the study authors, while sometimes reading as a tense disagreement, is for the most part a polite and gracious scholarly exchange.<sup>17</sup> Aiken, Davey, and colleagues thank Miguel, Kremer, and colleagues for their openness with data and assistance with aspects of the replication; Hicks, Miguel, and Kremer, in turn, thank the replication team for identifying issues in the analysis that could be resolved in the public replication data. So why the apparent conflict of the “worm wars?” Below, I go into some detail describing the two replications, their findings, and how those findings were communicated.

# 3.1. Pure (verification) replication

The replication report’s abstract makes the pattern of results sound complicated and nuanced: _“We_ _noted various discrepancies between the published results and those from this reanalysis. These ranged_ _in importance
of Economics and Statistics_ , 96, 729–744.

- Felbermayr, G., I. Heiland, and E. Yalcin (2012): “Mitigating Liquidity Constraints: Public Export Credit Guarantees in Germany,” CESifo Working Paper Series 3908, CESifo.

- Felbermayr, G. J. and E. Yalcin (2013): “Export Credit Guarantees and Export Performance: An Empirical Analysis for Germany,” _The World Economy_ , 36, 967–999.

- Fernandes, A. M., C. Freund, and M. D. Pierola (2016): “Exporter behavior, country size and stage of development: Evidence from the Exporter Dynamics Database,” _Journal of Development Economics_ , 119, 121–137.

- Fleisig, H. and C. Hill (1984): “The Benefits and Costs of Official Export Credit Programs,” in _The Structure and Evolution of Recent U.S. Trade Policy_ , ed. by R. E. Baldwin and A. O. Kruger, Chicago, IL: University of Chicago Press, 321–358.

- G ̈org, H., M. Henry, and E. Strobl (2008): “Grant Support and Exporting Activity,” _The Review of Economics and Statistics_ , 90, 168–174.

- Greenaway, D., A. Guariglia, and R. Kneller (2007): “Financial factors and exporting decisions,” _Journal of International Economics_ , 73, 377–395.

- Halpern, L., M. Koren, and A. Szeidl (2015): “Imported Inputs and Productivity,” _American Economic Review_ , 105, 3660–3703.

28
model was trained on night-time lights, accuracy declined precipitously when the model attempted to predict within the lower portion of the per capita consumption distribution. In the African countries considered, most poor households live in dark rural areas, and a model trained to predict only nighttime lights cannot distinguish welfare levels among them.

Babenko et al. (2017) improved upon this method by using daytime imagery to train a CNN model directly using survey data on per capita income. They trained the CNN model to predict the share of the population in extreme and moderate poverty in different Area Geo Estadistica Basicas (AGEBs) – small areas analogous to a census block -- based on per capita income data collected in the 2014 MCS-ENIGH household survey. The prediction of AGEB-level poverty rates achieved an R<sup>2</sup> of 0.47 when compared with survey estimates from withheld AGEBs. Interestingly, this is within the range reported Jean et al. (2016) for per capita consumption, despite the difference in welfare measure (income vs. consumption) and country context. A prediction model based solely on land-cover classification predicted equally well, however, and models that used both predicted poverty (from the CNN) and the land-cover classification achieved an R<sup>2</sup> of 0.57. This suggests that the CNN-based estimates of poverty in this case did not capture all of the imagery features correlated with average household per capita income. We further touch on the differences between interpretable and CNN-derived features below.

Newhouse et al. (2022) use the CNN poverty estimates and land cover classification from Babenko et al. (2017) as inputs into Empirical Best Predictor (EBP) models to predict poverty at the municipality level. The EBP model provides a simple framework for combining the two features in a linear mixed model, in addition to offering a well-established parametric bootstrap method for estimating uncertainty (Gonzalez-Manteiga et al, 2008). Official estimates for municipalities developed by the government based on the household-level intercensus were used
the 2006 and 2008 Comprehensive Local Government Performance Assessment System (CLoGPAS).

The NPS surveys are designed to be nationally representative. The country was stratified by local council areas. Enumeration areas (EA) were then selected in each local council according to the size of the population living in that local council area, with over-sampling of small local council areas.<sup>1</sup> An EA is roughly equivalent in size to a village in rural areas (80 to 100 households) and a neighborhood in urban areas (100 to 120 households). Within each enumeration area, ten households were selected from the 2004 census books and surveyed. Surveyors interviewed an adult member of the household, usually the head or spouse, alternating between interviewing male and female respondents.<sup>2</sup> In each of the three rounds, 2005, 2007 and 2008, about 6,300 household members were interviewed. The three 1In rural areas, an EA is about equal to a medium sized village or a small village and surrounding hamlets. There are about 9,600 EAs in Sierra Leone, with an average population of 483. Sixty-three percent of rural EAs contain only one locality, or village, and 90 percent consist of three localities or less (Glennerster, Miguel and Rothenberg, 2010, pp. 6-7).

> 2In 2008, the final sample was 47 percent female and 53 percent male. The 2007 survey shows equivalent figures.

8
# **H RD Plot with Local-linear Fitted Polynomial**

Figure H.1. Socioeconomic Well-being – Fuzzy RD Plots with Local-linear Fitted Polynomial <!-- Start of picture text --> Well-being (Index) Consumption per Capita (log)<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8th 2018)<br>Sample average within bin Polynomial fit of order 1 Sample average within bin Polynomial fit of order 1<br>Labor Income (log) Employed<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1 Sample average within bin Polynomial fit of order 1<br>.6 1.6<br>.5 1.55<br>.4 1.5<br>.3 1.45<br>.2 1.4<br>Well-being (Index) .1<br>1.35<br>0 Consumption per Capita (log)<br>1.3<br>-.1 1.25<br>.5 .75<br>.7<br>.45<br>.65<br>.6<br>.4<br>Employed .55<br>Labor Income (log) .35 .5<br>.45<br>.3 .4<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD
0.66)_|
|Grievances handled throuh formal sstem|092|091|092|093|-001|002|
|g y|.<br>_(0.02)_|.<br>_(0.02)_|.<br>_(0.03)_|.<br>_(0.02)_|.<br>_(0.77)_|.<br>_(0.64)_|

_Note:_ This table presents group averages and differences in means for GOs matched across the two waves of the panel data. Columns (1)-(4) present average and s.d. for each variable described for ULGSP LGAs and comparator, for each of the survey waves. Columns (5)-(6) present differences in means for the specific groups and the p-value of a t-test for equality of means (clustering at LGA-level).

49
very low trade diversion compensated by the trade creation related to the agreement. On the other hand, joining the CPTPP along with other major Southeast Asian economies would generate a positive economic impact for Indonesia with an expected 0.58% increase in GDP.

These macro effects translate into reductions of poverty, albeit the growth incidence curves (along the income distribution) have important differences between the agreements. The EU-IDN CEPA yields the highest expected growth of income relative to the other PTAs at every point of the income distribution. However, the size of the gains grows monotonically with income for the EU-

> 3 See: https://www.cnbc.com/2018/04/18/indonesia-is-on-path-to-joining-the-trans-pacific-partnership-financeminister-says.html.

3