rafmacalaba/data-use-mentions-extended huggingface dataset:
International Monetary Fund|Fiscal transparency—Report on Observation of Standards and Codes.|
|International Monetary Fund|Recent economic developments.|
|_Source:Authors’elaboration_.<br>_Note:_Ministry of Finance = Ministry of Finance; SOEs = state-owned e|nterprises.|

# Framing the scope of fiscal data

The data collection process for the fiscal component is divided into a number of parts:

- _National level._ There are two templates that support the collection of fiscal-related data at the national level. Quantitative variables related to the overall budget and basic macroeconomic indicators are collected following template 8. Template 9 should be filled in for the central government only. The best sources for this information are the ministry of finance, ministry of planning, and the budget offices of the parliament.

- _Government level._ Budgetary flows are collected for the central government and local governments separately following fiscal data template 10. This template should be filled in _separately_ for the different stages of the budget (estimates, releases, and actual expenditures) and, in the case of dual budgets, for each budget (development and recurrent). For example, in the case of Uganda‘s central government, which uses a dual budget, this template should be used six times for each year of data collected: development budget estimates, development budget releases, development budget actuals, recurrent budget estimates, recurrent budget releases, and recurrent

34
destination _j’s_ IPRs regime influence applications from country _i_ within that pair? The data indicate that, following the establishment of IPAs containing three or more core BTRIPS provisions, the average volume of patent applications between countries increases for member-to-member pairs and non-member-to-member pairs. This pattern holds also for PTAs classified in the World Bank’s database as featuring any legally enforceable IPR provision. Notably, this relationship is reversed for IPAs in which the United States or EU/EFTA is a partner.

> 11 The concordance is in Lybbert and Zolas (2014).

12
- Remediation: approximately 60 percent of the school loss will be fully remediated.

- Mitigation: approximately 40 percent of the school loss while schools are closed will be fully mitigated in High-income countries, but for the developing world, 30 percent.

## `o` Intermediate

- Remediation: approximately 30 percentage of the 70 percent school loss will be fully remediated.

- Mitigation: approximately 20 percent of the school loss while schools are closed will be fully mitigated in High-income countries, but in the developing world, 15 percent.

- Pessimistic

- Remediation: no remediation.

- Mitigation: approximately 10 percent of the school loss while schools are closed will be fully mitigated in High-income countries, but in the developing world, 7 percent.

# Data: Coverage, description, and indicators

The simulations use the same underlying data used to construct the original learning poverty measures, covering 80 percent of children in low- and middle-income countries.<sup>43</sup> These countries had at least one learning assessment at the end of primary, carried out in the past eight years, that is of sufficient quality<sup>44</sup> to be used for SDG monitoring (table 6).<sup>45</sup> The 80 percent population coverage for the global baseline value of learning poverty is much higher than the population coverage for the global monetary poverty indicator when it was first launched in the 1990s, with data going back to 1981.<sup>46</sup> The main exceptions are the nine countries for which NLA data were used. Only three could be included in the simulations—India, Pakistan and Bangladesh. The NLAs not used are for Afghanistan, Cambodia, China, Ethiopia, the Kyrgyz Republic, Malaysia, Sri Lanka, Uganda, and Vietnam. And three PASEC countries in Africa for which microdata have not yet been made available—the Democratic Republic of Congo, Madagascar, and Mali—were not included in the simulations. The main reason for not
_Figure 1. Protectionism in government procurement, participation in trade agreements, and income status_ _Notes: The “average protectionist index” is the country-specific mean of the “protectionist” dummy equal to one if they country lacks an equal treatment provisions, and provides for preferential treatment and domestic sourcing. WTO GPA and EU memberships are as of 31_<sup>_st_</sup> _December 2019. The income classification comes from the World Development Indicators of the World Bank. The bars show the averages and the capped lines their 95% confidence intervals._ _Figure 2. Protectionism in government procurement and the quality of institutions across countries_ _Notes: The protectionist indicators on the horizontal axis are defined in the paper. The “Ave. quality of institutions” index is constructed from the World Governance Indicators database as follows. For each year available in the database, we take the average of the percentile rank across the six governance indicators: voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. These numbers are then averaged across years by country. The bars show the averages and the capped lines their 95% confidence intervals._ 8
68|3.80|20.92|18.32|9.37|8.71|8.31|9.25|
|Loan-loss Provisions|2.90|1.37|0.55|3.60|1.22|0.85|3.94|2.66|2.21|6.54|2.04|1.21|
|Reserve Requirement|0.42|0.20|0.23|0.82|0.46|0.42|0.68|0.29|0.23|0.88|0.23|0.13|
|Taxes|1.62|1.59|1.55|1.53|1.62|2.36|4.40|12.52|4.21|0.55|3.28|4.56|
|Profit Margin|3.79|3.71|3.62|3.57|3.77|5.50|10.27|29.21|9.81|1.29|7.65|10.65|

Source: Own calculations, based on CBK data In 2007, the spreads of the government-owned banks declined steeply, yet this is certainly due to the large injection of capital into NBK, which was used to clean up its large overhang of non-performing loans. NBK is by far the largest majority government-owned bank, and thus the injection has a sizable effect on the spreads for that ownership category. Even with the injection, the spreads for government-owned banks remain substantially above those for private banks, while those for the government-influenced banks are roughly similar. And the spreads for the government-influenced banks were actually increasing from 2005 to 2007.

It is also worth noting that both types of government banks pay less for their deposits than do private banks. This suggests that depositors are certain that they will
Policy Research Working Paper 9444

# **Abstract**

Women’s property ownership matters for their well-being and agency; it can also advance economic prosperity and promote the human development of future generations. Yet, until recently, lack of data has constrained researchers from gaining a comprehensive overview of gender differences in property ownership in the developing world. Using Demographic and Health Survey data from 41 developing countries, this paper seeks to fill this gap, by investigating the extent of gender gaps in the incidence of property ownership (land and housing) and the factors associated with these gaps, focusing on the role of legal systems. The study finds that there is substantial variation in gender gaps across countries, but in almost all countries men are more likely to own property than women. Within countries, gender gaps are most pronounced for groups that are already disadvantaged, that is, the rural population and the poorest quintile. The disadvantage in property ownership experienced by women reflects a variety of factors, including discriminatory norms and laws on inheritance, property ownership, marital regimes, and protection from workplace discrimination. Countries with more gender egalitarian legal regimes generally have higher levels of property ownership by women, especially housing. These results suggest that reforms to establish a more gender-equitable legislative framework could be an important mechanism to increase women’s property ownership.

This paper is a product of the Gender Global Theme. 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 igaddis@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
# 5. Simulation Results (and comparison to data)

5.1. **PT of competitors’ RERs to simulated import and producer prices.** Estimates of exchange rate pass-through on simulated data are shown in Table 3. Using the preferred US+10 country model (Panel A), one can see that a 1% appreciation of the bilateral exchange rate raises import prices by 0.65% in the bivariate specification.<sup>22</sup> Note that bilateral pass-through is well above that estimated in the data, which is a well-known puzzle in the literature.

A 1% appreciation of competitors’ exchange rates in the model increases import prices by around 0.15% in the US+10 country specification (Table 3 column 2), showing that Testable Prediction 1 holds in the quantitative model. While this is a good deal lower than the estimate in the data of 0.35, (i) the estimates in the data might include some effect of imported intermediates, which are absent from the baseline model and (ii) estimates here are much larger than pass-through of competitors’ RERs in standard models (which is zero). As in the data, the addition of competitors’ exchange rates reduces bilateral pass-through, suggesting that measured pass-through is biased upwards (Testable Prediction 2). Quantitatively, the bias is 0.025 — a good deal smaller than in the data — mostly because pass-through of competitors’ RERs in the model is also smaller than in the data.

For US producer prices, a 1% increase in competitors’ RERs implies a 0.09% increase in simulated US producer prices (Table 3, column 4). These coefficients are very similar to those estimated in data, where a 1% increase in competitors’ exchange rates lead to a 0.11% increase in US producer prices.

In the model, pass-through of competitors’ RERs works through pass-through of foreign competitors’ prices. In the US+10 country model, a
of the comparison mean), and a negative effect on remittances of -3.74 percentage points (about 19% of the comparison mean).<sup>69</sup> Taken together, these three pieces of evidence strongly support the argument that a re-allocation of expenditures towards female goods and remittances is taking place.

There is some evidence that the promotion effect operates by improving the selected women’s attitudes and beliefs about themselves. Table 5 shows the results for several different measures of attitudes and beliefs, as well as an overall index in column (1).<sup>70</sup> I find positive point estimates for women’s confidence, their involvement in negotiation, gender attitudes and their aspirations, though none of these are individually significant. The summary index of all these outcomes is positive and significant at the 10% significance level. While this is not conclusive evidence, these results are consistent with the leadership position improving the selected women’s self-beliefs and attitudes.

> 69Note that 47% of the selected women living in one-person households have been officially promoted with an income increase at the time of the household surveys, so that this lack of effect cannot be attributed to a lack of income increase.

> 70All variables are defined in Table C.4 in the Online Appendix.

26
company is ISO-certified and has set procedures for budgeting operating and capital expenditures, as well as for other processes. KPLC has set internal auditing procedures with adequate reporting and supervision mechanisms. Because it is a

> 17 Data for utility governance indicators were collected in 2015. Given the dynamic nature of governance frameworks, specific conditions may have changed since then.

> 18 For a more detailed look at the indices used in this section, see the Annex.

> 19 The requirements of the sector, diversity of skills, academic qualifications, gender, age, and experience are among the factors to be taken into consideration when constituting the board.

31
<sup>25</sup> While differences in methods of elicitation exist across research and data collection efforts, head-to-head within-survey experiments to compare methods for measuring quantity of input application does not seem to have been undertaken as yet. Within-survey experiments about mineral fertilizer and pesticide quantity application could center on recall periods or applications over different units (by plot vs by crop, for example) compared against a benchmark of farmer diaries or weekly calls to assess week-by week quantities applied, also with a view to best addressing measurement issues associated with nonstandard reporting units. Similar within-survey experiments about agricultural non-labor input timing could be conducted. We have encountered no research empirically exploring methods for measuring the quantity of fertilizer or pesticide applied using within-survey variation in recall periods or input application diaries.

# **VIII. Conclusions**

Agricultural input quality in Sub-Saharan Africa is beginning to receive needed attention from researchers and policy makers; however, the focus so far has been on urea fertilizer nutrient quality and on glyphosate herbicide. Results in this review document that urea quality does meet listed specifications, but that nutrient shortages can characterize other market-available fertilizers,

> 25Recent work by Mueller et al. (2022) assesses recall bias in reported pesticide use, disaggregated by active ingredient, over a two-year period, finding that reported prevalence rates (irrespective of quantity applied) of most of the main pesticides increased after a two-year recall period relative to the baseline.

21
# **Violence and Newborn Health: Estimates for Colombia**

Laura Rodríguez<sup>1</sup>

**JEL codes** : I10, J13, J16

**<mark>Keywords</mark>** <mark>: Violence; Pregnancy; Birthweight; Newborn health.</mark>

1 <u>lrodrigueztak@worldbank.org. World Bank, Poverty and Equity Global Practice. The work for this paper was</u> conducted while at the Department of Economics of The University of Manchester. The author is grateful for financial support from the Department of Development and Alumni Relations and the Department of Economics at the University of Manchester. The findings and conclusions in this paper are those of the author and do not represent the views of The University of Manchester nor of the World Bank and its affiliated organizations. The author is grateful to Katsushi Imai, Kunal Sen, Martin Foureaux-Koppensteiner and Peter Backus for the support, discussions and suggestions, as well as to participants at seminars at the University of Manchester, the Nordic Conference of Development Economics 2018, LACEA-LAMES 2018, Universidad EAFIT, Universidad del Rosario, iHEA 2019 and ESPE Annual Conference 2019, whose questions and comments were particularly insightful and helped to improve the paper. Special gratitude is due to Andrés Suárez for his time and willingness to discuss the CNMH’s conflict data, and to the CNMH for making it available. Additional thanks are due to William Wiseman and Jose Antonio Cuesta for their comments. All errors are my own.
# **ONLINE APPENDIX**

# **A Additional Results**

Table A1: Provider Beliefs in the Control Group: Health Worker Post-Intervention Survey

||(1)<br>N|(2)<br>Mean|(3)<br>SD|
|---|---|---|---|
|_Panel A. Malaria Prevalence and Pressure_||||
|Malaria Prevalence: General Population|43|0.277|0.226|
|Malaria Prevalence: CSCom Patients|43|0.443|0.200|
|Feels Pressure from Patients to Prescribe Unnecessary Medication|43|0.512|0.506|
|Feels Pressure: Antimalarials|22|0.682|0.477|
|_Panel B. Confdence in RDT_||||
|Uses RDT to Diagnose Malaria|43|0.977|0.152|
|Positive out of 100: Simple Malaria|43|55.953|32.969|
|Patients with Positive RDT out of 100 with Severe Malaria|43|88.884|16.449|
|Would Treat Relative with Negative RDT|43|0.465|0.505|
|_Panel C. Confdence in Microscopy Test_||||
|Uses Microscopy to Diagnose Malaria|43|0.698|0.465|
|Patients with Positive Microscopy Test out of 100 with Simple Malaria|43|83.977|21.381|
|Patients with Positive Microscopy Test out of 100 with Severe Malaria|43|95.442|8.525|
|Would Treat Relative with Negative Microscopy Test|43|0.326|0.474|

Notes: Results from post-intervention health worker survey. Sample limited to clinics that received the basic provider training. Malaria prevalence refers to prevalence estimated by the health worker. A health worker is coded as feeling pressure to prescribe if s/he answers yes to the
- Checchi, D., Fiorio, C. V. and Leonardi, M. (2013), ‘Intergenerational persistence of educational attainment in Italy’, _Economics Letters_ **118** (1), 229–232.

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Heckman, J (1976), “ The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models”, Chapter in Sanford Berg Ed. _Annals of Economic and Social Measurement_ , Volume 5, number 4.

Hertz Tom, T. Jayasundera, P. Piraino, S. Selcuk, N. Smith and A. Veraschagina (2007). “The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends. The B.E. Journal of Economic Analysis and Policy (Advances), 7(2), Article 10.

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Figure B.2: Missing commitment identifiers <!-- Start of picture text --> CE MG<br>0.0003 0.04<br>0.02<br>0.0002<br>0.00<br>0.0001 0.02<br>0.04<br>0.0000<br>2009 2011 2013 2015 2017 2019 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>PB PR<br>0.30<br>0.04<br>0.25<br>0.02<br>0.20<br>0.15 0.00<br>0.10 0.02<br>0.05<br>0.04<br>0.00<br>2009 2011 2013 2015 2017 2019 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>RS SP<br>0.8 5<br>0.6 4<br>0.4 3<br>0.2 2<br>1<br>0.0<br>2010 2012 2014 2016 2018 2020 2008 2010 2012 2014 2016 2018 2020<br>Year Year<br>Null Commitments (%) Null Total Committed (%)<br>%<br>%<br>%<br><!-- End of picture text -->

**Notes:** This figure presents the percentage of observations by year for which we were unable to build a unique identifier (id_empenho_bd) in the _commitment_ table.

49
systemic differences were discovered during the analysis. We carry out a more nuanced comparison between the type of information each data source provides, as well as the magnitude of these differences and their causes in Appendix II. Due to the granularity of SDI, which includes information on source and quality of electricity, as well as differences in definitions and on expected accuracy from direct observations versus small-area estimations, this paper favors the use of SDI data on access to electricity. Nonetheless, we note that the GEP data might still provide useful information for other studies when no first-hand data is available, particularly when using electrification as a control that might also capture other socioeconomic variables.

# 4. Results

This section presents and discusses the main results for each quality indicator. All specifications include controls at the facility level and county fixed effects.

## 4.1. Provider's Knowledge

Diagnostic errors are a source of avoidable illness and death, now more than ever since many previously incurable conditions now have a treatment (Khullar, Jha, & Jena, 2015). We explore here two indicators for diagnostic accuracy, one related to children and adults' illnesses and another one focusing on children and pregnant women-related ailments. As shown in Table 2, access to a stable electricity connection is positively related to diagnostic accuracy at the facility level, while longer distance to the nearest at least fair quality road is negatively related with this measure of provider's knowledge.

Column 1 in Table 2 displays the results for general cases (diarrhea with dehydration, pneumonia, diabetes, and tuberculosis). Here, access to electricity service is related to an increase of 4.20 percentage points in the diagnostic accuracy compared to facilities without access to electricity. Even though unstable access to electricity is also positively related to diagnostic accuracy, the relationship is not significant. Column 2 shows diagnostic accuracy for childhood and pregnant women cases (diarrhea with dehydration, pneumonia, postpartum hemorrhage, and neonatal asphyxia); the results follow the same logic, with a positive and