rafmacalaba/data-use-mentions-extended huggingface dataset:
work because they are economic services such as fetching water and wood because these are not accessible within the household (e.g. the household has no access to piped water).

Research shows that the large female presence among economically active contributing family workers may lead to an undercounting of female LFPR if the questions are not worded carefully (DixonMueller and Anker, 1988). This is particularly a big problem for women and youth, as their tasks are more likely to be ignored by a respondent yet they are classified as employment according to the ILO-SNA definitions. Beegle, et. al. (2010) found that if economic activity and inactivity were included in the same question, as mutually exclusive alternatives, women are more likely to report not being economically active because they are engaged mainly in domestic activities. Yet after screening questions, many of these same women did report being engaged in economic activities. In the simple formulation, asking them if their main activity was work or domestic duties, they chose the vague phrase “domestic duties” because that described their life to them, even it did not describe the types of activities the survey was meant to measure.

Fox and Gaal (2008) used Tanzania Household Budget Survey data on economic activities of female heads of households to show that (i) when screening questions were not used and (ii) “homemaker” and other inactivity variables were offered as responses _together_ with activity variables in one activity question (to save space), the share of household heads in the working age population reporting that they were out of the labor force doubled. In urban areas, it nearly tripled as women heads of households chose homemaker as a status in much larger numbers. With increased incomes in urban areas, it is possible that some female heads of households withdrew from the labor force, but not this magnitude, given that they have to support children.

> 9 From the dataset collected for World Bank (2011); emphasis added.

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Systematically collecting and disseminating such data should follow best practices. Balancing reporting costs against obvious benefits, surveys could be updated to incorporate critical information missing in existing questionnaires. Closer collaboration between transport ministries or departments and statistical agencies would leverage existing knowledge base, inform data needs and capabilities, and help avoid replication of efforts. As alluded to in Table 1, governmental agencies can increase the use and benefit of their existing products by providing transparent procedures to facilitate access to micro-data and sharing aggregated statistics in their websites—and making these available in English for global reach. Moreover, low-cost improvements to existing efforts could make them more informative for measuring overland transportation costs. For example, to aide estimation from price gaps, data that is regularly collected for compiling the consumer price index (CPI) could cover additional locations so as to generate sufficient geographic and spatial variation for this methodology to be more reliable. Another consideration described above is the comparability of products for which prices are collected. To the extent possible, targeting the same brands for any given product would be a good practice for not just precisely measuring transport costs but also the CPI. Other potentially helpful data products are consistent and frequent measures of road quality and traffic volumes. Once again, such data are often collected in order to monitor maintenance needs and congestion. There are wellestablished methods to define and quantify road quality such as the International Road Roughness Index. Appropriately designing the spatial features of these collection efforts would come at minimal additional cost but increase their usefulness for the purpose of measuring and lowering overland transportation costs.

# **References**

Alder, S., Song, Z. and Zhu, Z. (2021). Unequal returns to China’s intercity road network. Working Paper.

Allen, T., and Arkolakis, C. (2014). Trade and the Topography of the Spatial Economy. The Quarterly Journal of Economics, 129(3), 1085-1140.

Allen, T., Atkin, D., Cantillo, S. and
Table 8 The reasons for migration into towns in three provinces in China:2000

||1|2|3|4|5|6|7|8|9|
|---|---|---|---|---|---|---|---|---|---|
|||||(%)||||||
|Zhejiang|54.92|3.92|3.03|6.55|5.55|5.96|14.29|2.99|2.79|
|Henan|17.79|12.31|7.17|7.33|7.68|13.70|21.96|4.53|7.54|
|Sichuan|18.71|9.90|5.06|21.30|6.20|9.47|14.44|8.47|6.43|

Sources: calculated from Tabulation on 2000 Population Census of the P. R. China. L705B. Note: 1, engage in production and business; 2. job transfer; 3, job assignment; 4,study and training; 5, resettlement; 6, marriage; 7, move with family member; 8, seeking help from relatives; 9, others.

The age composition of in-migrants also seems to support for the view that migration is economically rewarding. When we tabulated in-migrants by age (figure 1), we found that most town in-migrants were in the categories of economic active groups of age between ages of 20 to 34. This inverted U-shaped relation of town in-migrants confirms the findings of other studies (Hare 1999; Zhao 1999). People in these age groups are most likely to migrate, and probability of migration decreases with age. In rural area, a piece of land that could be hold as a
# 4. What Is the Impact of Urea Consumption on Rice Production?

This section estimates the impact of fertilizer subsidies on urea consumption and the impact of urea use on rice yields. A simple micro-model using data from the 2007 household survey explores two important questions: What is the effect of the fertilizer subsidy on urea consumption, and what impact does urea usage have on rice production? The model proposed allows for diminishing returns to the use of fertilizer (by including a quadratic term for urea used), implying that there is an optimal point for urea usage beyond which yields start to decline. The quantity of urea consumed depends on: the price of urea, the price of other fertilizers (which are proxied with the price of SP36), the price of rice, and the price of rice seeds. In turn, the impact of urea on rice yields is dependent on: the quantity of urea used, the consumption of other inputs (land, labor, irrigation), and controls for soil quality (a dummy variable for Java-Bali) and human capital (in this case, the years of education and the age of the head of the household). The model to be estimated is summarized below: _ln rice_yield =_  _+_  _1 ln urea_used +_  _2 ln urea_usedsq +_  _3 ln land_sizei +_  _4 ln labor_cost +_  _5 irrigation +_  _6educ +_  _7age +_  _8Java&Bali +_  _(1)_ where _urea_used_ is the fitted value of the regression: _(2) ln urea_used=_  _+_  _1 lnprice_urea +_  _2 ln price_SP +_  _3 ln price_seeds +_  _4 lnprice_rice +_  _5 ln land_sizei +_  _6 ln labor_cost +_  _7irrigation +_  _8educ +_  _9age +_  _10Java&Bali +_  And _urea_usedsq_ is the fitted value of the regression: _(3) ln urea_usedsq=_  _+
the changing nature of innovation as countries approach the global technological frontier. We therefore control for this relationship by including GDP per capita and GDP per capita squared. We also allow for the possibility that discovery is driven in part by factor endowments by controlling for endowments as measured by historical net exports per capita in that particular commodity group (Leamer 1984). In addition, to control for differences across commodity groups and the different number of products composing each commodity cluster, we include dummy variables for each Leamer commodity group.

Finally, we control for historical discoveries by country/commodity group. This is accomplished using export data at a higher level of aggregation<sup>9</sup> , which is available for a longer time period but only identifies broad, sectoral-level discoveries rather than disaggregated product-level discoveries. This measure of historical pre-sample discoveries controls for time-invariant and unobserved characteristics driving discoveries

> 9 SITC Revision 1 at the 3 digit level. See Appendix for data and filter description.

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where yj,i,t+k is the log of sectoral output (or productivity) for industry _j_ in country _i_ at time _t+k_ ; γit are country-year effects to control for any variation that is common to all sectors of the country’s economy, including, for instance, aggregate output growth or reforms in other areas; αij are country-industry fixed effects to control for industry-specific factors, including, for instance, cross-country differences in the growth of certain sectors that could arise from differences in comparative advantages; ρjt are industry-time fixed effects to control for common factors across countries that affect specific industries; Tj,i,t<sup>O</sup> and Tj,i,t<sup>I</sup> denote output and input tariffs, respectively; and Xj,i,t is a vector of control variables, including two lags of changes in the dependent variables and output- and input-sectoral tariffs.

The output tariff, Tj,i,t<sup>O</sup> in each sector j is the 2-digit level corresponding tariff rate. Following closely Amiti and Konings (2007) and Topalova and Khandelwal (2011), input tariffs in each sector j are computed as a weighted average of output tariffs in all sectors, with weights reflecting the shares of imported inputs in each sector j’s total input cost: The underlying tariff data is obtained from World Integrated Trade Solution (WITS), while the information on the production structure is taken from OECD’s input-output tables.

We match the input and output tariff rates with sectoral-level data (output, value added, employment and productivity) from the United Nations Industrial Development Organization (UNIDO) database. This database provides information for 22 manufacturing industries based on the INDSTAT2 2016, ISIC Revision 3.<sup>5</sup> To match the sectoral information in the OECD input-output table, we combine some of the sectors in the UNIDO 10
|46,729|||||
|Value added tax|59,708|||||
|Excise duties|40,952|||||

Notes:

* The original RLMS-HSE variable includes all child benefits received up to age of 1.5 years.

** The original RLMS-HSE variable includes all child benefits received by children aged 1.5+ years.

*** Only monthly cash payment is simulated; the original RLMS-HSE variable includes categorical benefits for transportation, medicines and recreation.

Source: Rosstat and RUSMOD for 2017 policy year and 2016 RLMS-HSE data Results for tax and benefit amounts are presented in the tables below. Expenditures on pensions are overestimated by 25 percent compared with external data (Table A-7). Expenditures on unemployment benefits are under-predicted by 27 percent, even though the number of recipients is 41
# 3. Statistical methods for prediction

- a. Interpretable features predict at least as well as deep learning from imagery

One aspect in which the existing literature on geospatial data fusion has diverged involves the nature of the geospatial indicators used. While several studies obtain predictions directly from imagery using deep learning techniques such as CNNs, others instead generate predictions from interpretable features such as land classification types, night-time light luminosity, building density, and so on. When using interpretable features, predictions can be obtained using linear models – which may be regularized – or a tree-based machine learning algorithm such as extreme gradient boosting. It is possible to use both, as demonstrated in Lee and Braithwaithe (2022), but using deep learning entails several additional costs. Deep learning models are complex and effectively a “black box” to users. Furthermore, they require specialized skills to understand and deploy, and thousands of training data points to perform well. On the other hand, the number of EAs in survey data are typically less than five hundred. Pre-trained CNNs can help circumvent the need for more data, but little is currently understood about how the specific nature of the architecture or pre-training affects prediction accuracy or bias.

A few existing studies shed light on the relative predictive power of deep learning and interpretable features. As noted above, Babenko et al. (2017) compare the predictive power of poverty predictions obtained from a CNN, as well as with those obtained from land classification, and both together. The correlation with the benchmark measure of truth, derived from the census, was equally correlated with the direct CNNs and land classifications, and the correlation improved moderately when both were included.

Engstrom et al. (2022) directly compare CNN-based estimates of headcount poverty rates with featurebased estimates in Sri Lanka. A variety of features were used, including roof type, shadows, cars, and road types. In that context, feature-based prediction is more accurate, with an R<sup>2</sup> of 0.61 and
Figure C4: DID estimates - for ward with strongest nighlight in 2010 in each district <!-- Start of picture text --> 1st assessment Baseline Endline<br>ULGSP<br>Control<br>Year<br>(a) Means and 95% CI (2000-2018)<br>1<br>Note: Sample size is N = 671<br>(b) difference-in-differences estimates<br>20<br>15<br>NTL<br>10<br>5<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>10<br>5<br>0<br>-5<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> _Note:_ Panel A presents mean NTLfor wards in ULGSP treatment and comparison districts. The sample includes only the ward with strongest nighlight in 2010 in each of the study districts. Nighlight data excludes pixels with NTL < 7. Panel B presents results from DiD estimates. Coefficients obtained estimating equation ntlwy = α + γw + θy +<sup>�2018</sup> y=1999<sup>βy(ULGSP</sup><sup>_∗_year)wy +Xwy +εwy.Standard errors are clustered at the LGA, the</sup> level of treatment.

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facilitate creation of a unique DMRS, ensuring that all participating countries benefit from the latest technological advancements; this is a move that would assist with further standardizing debt reporting.

# 6. Conclusions

The lack of uniform, comprehensive and timely public debt data poses severe challenges to the international financial architecture. In particular, inaccurate or diverging debt records across sources may hinder effective debt management, affect the quality of debt sustainability analysis and limit implementation of swift and fairly-designed debt restructurings. During the COVID-19 crisis and in its aftermath, pressing demands for debt reconciliation and restructuring processes have made the debt transparency agenda more relevant than ever.

Countries directly disclose their debt statistics, but these efforts are varied and not equally mandated at the national level. Debt offices with reporting obligations to IFIs have high stakes, and it is assumed that these obligations will generate incentives to comply with requests of indirect reporting. However, varying levels of coverage, poor debt recording and the use of different debt definitions and standards across multiple reporting channels lead to discrepancies across sources and generate the need for frequent reconciliation.

This paper contributes to the debt transparency discussion by: (i) measuring the extent of transparency in direct reporting; (ii) studying its determinants; (iii) describing the ecosystem in which direct and indirect reporting channels coexist; and (iv) providing novel estimates of data gaps.

The results indicate that debt transparency is best supported by standardized recording and reporting systems. LIDCs benefit from existing DRMSs, despite their limitations, as they are usually constrained in their ability to develop and maintain their own in-house systems. This result highlights the huge potential to increase transparency by improving current systems and tightly coordinating these efforts. The results also show that high levels of external scrutiny in the form of ratings and Eurobond issuances have a high impact on LICDs’ transparency as a result of the data disclosure and investor relations practices required by them. Finally, increasing the numbers of highly skilled staff in debt offices has also been identified as a key part of improving transparency
Country variables are drawn from widely used sources. GDP per capita and population data are taken from the World Bank’s World Development Indicators (WDI). Data on aid flows is drawn from the OECD DAC database and migration data is from UNDESA databases. A full set of the definitions and sources of variables used is given in Appendix A.

PEFA scores were obtained for 162 PEFA assessments in 118 countries over the period 2005 to 2011 from the PEFA Secretariat. The PEFA assessment comprises 71 individually assessed dimensions which together form 31 high level indicators (including assessment of donor systems, 68 and 28 respectively excluding them). The assessments are made on an ordinal basis, assigning a score of A to D based on a set of criteria, with A indicating highest performance, with an additional ‘+’ score possible for some indicators To facilitate statistical manipulation, we convert the ordinal scores to a numeric value of 1-4, with 4 being equivalent to ‘A’ and a ‘+’ score ascribed an additional 0.5. This methodology follows existing practice and PEFA Secretariat guidance (PEFA Secretariat 2009). Appendix B provides summary statistics at the PEFA assessment level.

Following Andrews (2010), we exclude from our analysis dimensions that are beyond the direct control of government. Indicators related to aggregate fiscal outturns are omitted (PI-1, PI-2, and PI-3), as are indicators related to donor practices (PI-D1, PI-D2, and PI-D3). Omission of these variables reflects our interest in the success or failure of government administrations in undertaking PFM functions, and avoids results being biased by macroeconomic factors or the different practices of development agencies operating in different countries. These omissions are especially important in any analysis of PICs, which are atypical in both their exposure to macroeconomic volatility and reliance on donor aid flows.

We carry out analysis at the most disaggregated level, using all 65 separate dimensions included in each assessment, rather than relying on the 25 high level performance indicators that aggregate some of the dimensions that have been used
To assign the PROBEMS scholarships, SEP issues one and sometimes two calls for applications per year depending on budget availability. The call for applications is usually issued in March of each year, targeting students starting or continuing EMS in the following academic year (August). Interested candidates start their application process by filling in a form capturing household income, the availability of household assets, parents’ education and basic academic information such as the school and grade of enrollment. All applicants must have a personal identifier known as the CURP, issued by the Government of Mexico.<sup>8</sup> SEP uses the CURP to identify applicants who are beneficiaries of the conditional cash transfer program (PROGRESA / Oportunidades) at the time of applying, which automatically excludes them from PROBEMS. The self-declared household income and assets information included in the on-line application format is used to rank students in terms of their likelihood of being below the “assets” poverty line. Finally, as stated by the call for applications, SEP gives priority to first-year EMS students. The likelihood of being poor, budget availability and EMS grade of enrollment determined a list of pre-selected candidates.

When a candidate is pre-selected–usually one month after the call for applications is closed, he or she is notified by email with instructions on how to complete the application process. The next step is to enroll in EMS or continue to the second or third grade, and present, at the school, all the relevant documentation to validate the information uploaded in the program’s portal. School directors receive the list of pre-selected candidates via email and are responsible for validating the information provided by the candidates through the program’s portal. In particular, the school director has to validate that the student is indeed enrolled in his or her school, that the student has no subject areas of previous semesters below the passing mark, and validate the GPA as declared by the student. Once the school director validates the information, the candidates receive a second email notifying them that
without replacement”, that target conscientiousness. Phone calls were also conducted in Wolof.

A mid-line survey was conducted in January 2020, and an end-line survey was conducted by phone in May 2020. Our sample started with the 386 individuals interviewed at baseline. At mid-line, 344 answered our survey (10.9% attrition), while at end-line we were able to reach 371 respondents (3.1% attrition).<sup>17</sup> Despite the fact that this light touch intervention has a relatively low “dose”, it is expected that the repeated reminders function as triggers that initiate the conscientiousness states in the daily life of the workers and instigate change processes. Skill change can be best elicited through repeating behaviors that differ from typical, trait-like behavior (Allemand and Flückiger, 2017; Roberts et al., 2017; Wrzus and Roberts, 2017). The accumulation of

> an effect.

> 15Full details of the content of the training session are provided in Appendix A.

> 16The consultant has previously led several training programs in Senegal in personal development, conflict management, judicial defense and youth support.

> 17 We lose a further three observations due to missing data on earnings. Appendix Table B.1 compares baseline characteristics of attrition and non-attrition groups. We do not find any major systematic differences between these groups and any statistically significant differences disappear after controlling for multiple hypothesis testing. We also do not find a statistically significant difference in attrition between the treatment and control groups.

7
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 31 Hill, D., Bowman, M.J. and Khunda, J.S. (2012) Storm surge barriers to protect New York City. Against the Deluge. Papers from a conference held in Brooklyn, New York, 30-31 March 2009. American Society of Civil Engineers.

Hinkel, J., Lincke, D., Vafeidis, A.T., Perrette, M., Nicholls, R.J., Tol, R.S.J., Marzeion, B., Fettweis, X., Ionescu, C., Levermann, A. (2014) Coastal flood damage and adaptation cost under 21st century sea-level rise. Proceedings of the National Academy of Sciences 111, 3292–-3297. doi:10.1073/pnas.1222469111 Hinkel, J., Aerts, J.C.J.H., Brown, S., Jiménez, J.A., Lincke, D., Nicholls, R.J., Scussolini, P., Sanchez-Arcilla, A., Vafeidis, A., Addo, K.A. (2018). The ability of societies to adapt to 21st century sea-level rise. Nature Climate Change, accepted.

Hoozemans, F.M.J., Marchand, M., Pennekamp, H.A. (1993) Sea level rise: A global vulnerability assessment. Vulnerability assessments for population, coastal wetlands and rice production on a global scale. (Delft Hydraulics / Rijkswaterstaat, The Hague).

IIASA (2016) SSP Public Database, Version 1. Available at: https://tntcat.iiasa.ac.at/SspDb Jonkman, S.N., Hillen, M.M., Nicholls, R.J., Kanning, W., van Ledden, M. (2013) Costs of Adapting Coastal Defences to Sea-Level Rise— New Estimates and Their Implications. Journal of