rafmacalaba/data-use-mentions-extended huggingface dataset:
Financial Regulation and Development On this date, the NBE issued a directive requiring all commercial banks to hold 27% of new loan disbursements in NBE bills. Unlike many western economies, who used analogous measures to slash public debt, Ethiopia launched this regulation to promote long-term investment (IMF 2013b).

The relevant aspect of studying the so-called “27% rule” is given by the unique nature of this shock: 1) it was unexpected and was announced less than a month before implementation; 2) it caused a large mobilization in banks’ assets, as shown in the next subsection.

From a theoretical point of view, this policy can be mapped as a positive shock to the _s_<sup>_R_</sup> = _ρ_ parameter and the above conditions 1 and 2 make this ideal for our analysis.<sup>8</sup> It is also important to highlight that the NBE Bills are not a profitable investment, as they pay a fixed remuneration of 3% per year, lower than the minimum deposit rate, 5%, or the average lending interest, 12% (National Bank of Ethiopia 2012)<sup>9</sup> .

In order to test the implications of Propositions 1 and 2, we collect confidential data on the monthly balance sheet of all Ethiopian private banks between 2010 and 2013, on publicly available Annual Reports data on profits between 2008 and 2013, and we build a unique city-level map of Ethiopian branches, where for every bank we know in which cities all new branches have been opened, with respective month and year between 2000 and 2015.

Propositions 1 and 2 provide two fundamental elements to test the model: a shock to _s_<sup>_R_</sup> promotes deposit growth; and cross-sectional variation in _η_ characterizes a differential impact to the shock. Ethiopia is an exceptional context in which to test this model because as well as a large timeseries variation in _s_<sup>_R_</sup> , we find a large cross-sectional
observations are village heads in columns 1-2 and villages in columns 3-4. The dependent variable is: in column 1, a dummy equal to 1 if the village head in our survey data is a different individual from the incumbent competing in the most recent village election; in column 2, the number of years spent in office by the current village head; in column 3, the rate of bureaucratic turnover at the village level since the last election, defined as the fraction of new bureaucrats appointed to their current position since the last election; in column 4, the share of bureaucrats “reshuffled” within the village secretariat, defined as the fraction of bureaucrats who experienced a promotion, demotion, or lateral move since the last election. See Section 4 for details. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Conventional standard errors in parentheses and robust bias-corrected standard errors in brackets. Significance levels are based on the robust bias-corrected standard errors.

34
additional 288 replacement households—also from the GHS panel wave 3, visit 2—were included in the sample. Out of these replacement households, 188 could be interviewed. Therefore, altogether 1,030 households were attempted to be reached, with a final sample size of 717 completed interviews (175 interviews in the North East, 276 in the North Central, and 266 in the South South).

Detailed information on the sample and data collection procedures of the telephone survey can be found in Azad, Crawford, and Kaila (2018).

# **Table A.3. Sample sizes**

Sample size in three-wave panel

||First<br>wave|Second<br>wave|Third<br>wave|Total|
|---|---|---|---|---|
|Number of<br>|||||
|households|700|708|717|2125|
|Sample size in wave-visit-based p|anel (per rou|nd)|||
|Wave 1|Wave 2|Wave 2|Wave 3|Wave 3|
|PH|PP|PH|PP|PP<br>Total|
|Number of|||||
|households<br>700|707|709|717|717<br>3550|

vi
.

Bound, J., C. Brown, and N. Mathiowetz. 2001. “Measurement Error in Survey Data.” In _Handbook of Econometrics_ Vol. 5. ed. J. Heckman and E. Leamer. Amsterdam: North‐Holland, Elsevier Science.

Bound, John, and Gary Solon 1999. “Double trouble: on the value of twins‐based estimation of the return to schooling.” _Economics of Education Review_ 18 (2): 169–182.

Burke, K., and K. Beegle. 2004. “Why Children Aren’t Attending School: The Case of Northwestern Tanzania.” _Journal of African Economies_ 13(2): 333‐355.

Campanelli, P., J.M. Rothgeb, and E.A. Martin. 1989. _The Role of Respondent Comprehension and Interviewer Knowledge in CPS Labor Force Classification._ American Statistical Association Proceedings (Survey Research Methods Section).

Card, D. 1999. “The Causal Effect of Education on Earnings.” In _Handbook of Labor Economics_ Vol. 3A. Ed. O. Ashenfelter and D. Card, Amsterdam: North Holland, Elsevier Science Card D. 2001. “Estimating the Return to Schooling: Porgress on Some Persistent Econometric Problems.” _Econometrica_ 69(5): 1127‐1160.

Carneiro, P., J. J. Heckman, and E. Vytlacil. 2010. “Estimating marginal returns to education.” Working paper CWP29/10, Institute for Fiscal Studies, Department of Economics, University College London 22
_b. School dropout._ We referred to teacher reports to measure whether the participants dropped out and created a dummy indicator that equals 1 if the student abandoned school in 2019.<sup>34</sup> We collected dropout data from 868 students in the follow-up only.

_c. Academic performance._ We use students’ grades as a proxy for academic performance. Since we do not have a standardized test for the three countries, we use math grades because the mathematics curricula in these three countries are similar and, therefore, comparable.<sup>35</sup> In all cases, a higher value indicates better math grades. To standardize the measure of math-grade reports among countries and schools, we define the outcome as a dummy indicator that equals 1 if the student has a behavior score above the median of the school they attend.

A potential concern in using teachers’ reports to measure the main outcomes is that this reporting may be affected by teachers’ knowing which students are participating in the ASP. We argue that this occurrence is unlikely because we requested the records of all students, without informing the teachers that we were going to use only the data from students participating in the ASP.

# **B. Mechanisms**

According to the conceptual framework and existing evidence summarized in Heller et al. (2017) and Dinarte-Diaz and Egana-delSol (2023), behavioral outcomes correlate in a statistically significant way with skills such as self-control, emotion regulation, persistence, and prudence. For this reason, we collected data to measure proxies for these skills using the SoftGames (to measure socio-emotional skills) and Reactiva (to measure fluid intelligence and emotion regulation) applications.

# **Social-Emotional Skills**

SoftGames was developed and used by Danon et al. (2023). It can be played on a tablet and

> Guatemala, we asked teachers to do it for this study. As for 2023, data were collected for school reports in all three countries.

> 34We are able to
If program allocation during 1998-2000 was based entirely on 1997 characteristics as modeled here, the finding that urban communities received a longer duration of treatment would be inconsistent with the fact that average nutritional status in urban communities in 1997 was better than in rural areas. The allocation procedure may be based on the community characteristics in 1998 and 1999, or changes from 1997, which we cannot observe in the IFLS. In this sense, observable characteristics do not include enough information to conclude that the program was properly targeted, or to explain the apparent urban bias in its distribution. There may be other information that cannot be observed and controlled for with our data here that explain the apparent urban bias in duration of program exposure.<sup>19</sup> It is apparent from this exercise, however, that both observed and unobserved characteristics of communities receiving the program may have influenced program duration. For this reason, we favor models employing community fixed effects, and identify program effects from within village differences in child exposure to the program.

# **6.2. The Effects of the Supplementary Feeding Program**

Recognizing the variation in benefits and program protocols across the three targeted age groups, we perform the analysis on three separate subsamples of children based on their ages at the time of the survey, estimating PMT effects separately for children who were 6 to 12 months, 12 to 24 months and 24 to 60 months of age. In order to highlight the benefits of using the eligible life exposure measure, we first use the binary indicator of exposure to the PMT program to estimate the impact on child height for age and

> 19 Frankenberg et al (1999), for example, use IFLS2 and IFLS2+ to capture changes in people’s well-being at the beginning of the crisis and find that urban communities experienced larger increases in the proportion of households living below the poverty line.

18
With these considerations in mind, in this study we analyze the effect of weather shocks on children’s anthropometrics using the two most recent rounds of the Nigeria Demographic and Health Survey (DHS). For this purpose we interpolate climate data for each DHS cluster using daily weatherstation records from the national network of about 30 stations. Specifically, we analyze the impact of rainfall shocks on two anthropometric measures which capture two different dimensions of health status. We first look at weight-for-height, which is a good metric for acute malnutrition and is considered a proxy for short-run nutritional deficiency. In addition, we use height-for-age which better captures chronic malnutrition. In the case of short-run impacts we also analyze the effect that precipitation has on the incidence of child diarrhea. Diarrhea is a particularly important disease to study in this context because of its sensitivity to climatic conditions. As in previous papers, we focus on shocks that occur at the very beginning of life. We do not look at health status or any other outcome at adulthood since our data covers only infants and young children.

One of the main objectives of the study is to assess empirically the channels through which precipitation affects child nutritional status: the disease environment and the income effect. The relative importance these two effects is useful for the design of future policy interventions. We also analyze whether rainfall shocks have medium-run impacts on child health or whether they are shortlived. We do this by looking at the effect of shocks during the last three completed rainy seasons experienced since a child’s birth. We also test whether having easy access to health facilities helps reduce (magnify) the negative (positive) impacts that weather shocks may exert on children health outcomes. Regarding the gender bias discussion, we analyze the differential effect of shocks on girls and boys. Finally, we try to quantify the influence that increased weather variability –a predicted consequence of climate change- might have on child health.

The paper is structured as follows. In the next section we outline the conceptual framework that will guide
_Figure 21: Comparing envisioned education premiums estimated by ministry of finance and ministry of education officials._ <!-- Start of picture text --> University vs. secondary<br>TVET vs. secondary<br>Secondary vs. primary<br>0% 20% 40% 60% 80% 100% 120% 140% 160%<br>Expected premium between levels of education<br>MOF MOE<br><!-- End of picture text --> Source: CGD–World Bank 2022 survey for envisioned premiums.

_Why do officials tend to overvalue tertiary education and undervalue primary and TVET?_ One reason is likely that these officials themselves have completed tertiary education, and so are biased in favor of the value of tertiary education. Higher education is often associated with prestige and may command more attention than other levels of education such as primary and TVET. There is also some uncertainty on the actual returns; the available data are almost a decade old, and focus on formal employment, so likely differ from returns to education in today’s economies.

- 6.7 Gaps between expected and actual returns differ widely by gender and levels of education as well.

The gap between officials’ estimates and actual education premiums differs substantially from one country to another, by gender and level of education (Figure 22). As shown earlier, expectations surpass actual premiums at the tertiary level, for both gender and in all countries by large margins, except for the tertiary premium for males in Lao PDR. The gender divide is not uniform. In all countries apart from Indonesia, the premiums for secondary are underestimated more (or overestimated less) for females than for males. In the Philippines, the returns to secondary education for females are underestimated by large margins. The same pattern is observed at the tertiary level, where returns to tertiary are overestimated much more for males than for females in that country. But higher expectations for male education returns are not a common pattern
# 4 Moral imperatives

Having identified the many beneficial outcomes (particularly for developing countries) that can be delivered through the cascading forecasting approach at relatively low cost, WMO et al (2013) highlighted three critical factors needed to ensure sustainability:

1. GPCs are able to support the provision of relevant products on a long-term operational basis;

2. RSMCs are able to maintain an interpretation and guidance service on a similar long-term operational basis; and 3. regular capacity building and training can be maintained for new forecasters along with refresher training for experienced forecasters.

It has been highlighted that all three areas require investment, whether through domestic or international financing. In line with the global NWP focus of this research, we here further explore the social and ethical demand for global investment in ensuring GPC product delivery to LICs.

It would be difficult to justify the responsibility of higher income countries to ensure the delivery of the full suite of weather and climate forecasting data to LICs as a "public service" without discussing the ethics behind it. However, the subject of ethics tends to make many stakeholders uncomfortable. After all, is not everyone in the world entitled to the greatest “pursuit of happiness”? And aren’t developed countries who claim to be moral ascendants of modern civilization responsible for sharing information that could benefit the greatest number of people?

These questions are of course debated in many contexts, but the emphasis on ethical principles is nonetheless important when dealing with a service that provides such a significant global value. Additionally, as demonstrated in Section 5 on financing, many social issues that currently necessitate development aid can benefit from improved weather and climate forecasting. The justification behind providing the full suite of weather and climate information should therefore go beyond merely comparing costs with benefits, or identifying feasible or sustainable sources of funding for these services.

In the next 15 years, the services provided by weather and climate services will play an ever increasing role, with GDP projected to double and a billion more people moving to cities (New Climate
Center-West regions are currently not available.<sup>17</sup> We provide further details on the geographical and temporal coverage of the dataset in Table 1. Starting with geographical coverage, our budget execution tables (commitment, verification, and payment) are available for all seven states. The procurement data are less comprehensive: the dataset currently includes no procurement data for SP, and the data for PB and PE include information on tenders and participants, but not on the more disaggregated level of items. In terms of temporal coverage, most of our budget execution data starts in the earlyto mid-2000s, with the exception of PE (2012), PR (2013), and MG (2014), and currently runs until 2021. Once again, data on procurement is less comprehensive and, with the exception of CE (2009-2021), starts in the mid-2010s.

In Table 2 and Table 3, we provide simple descriptive statistics from our public procurement and budget execution datasets, respectively. For procurement, across the six states covered, we observe over 2.4 million unique tenders and almost 800,000 unique suppliers. Since we observe microdata on each tender and often items, we are able to compute statistics such as the share of tenders that are deserted and/or unsuccessful (e.g., no bidders in a competitive tender) and the average number of items listed in a tender. We can also document, for example, that across states approximately 30%-40% of tenders are not competitive auctions – meaning they are directly awarded to suppliers by means other than an auction – but in terms of total purchase value these non-competitive tenders always represent less than 20% of total amounts. This is consistent with the fact that, similar to other countries, Brazilian law allows small purchases to be performed without competitive auctions (Fazio, 2022). We can also compute measures of competitiveness in tenders, such as the number of participants per tender - the average fluctuates between two and four across
**Figure 3.7: Time to Obtain Tuberculosis Treatment in a Public Health Facility (calendar days)**

<!-- Start of picture text --> Time to obtain tuberculosis testing and treatment in public<br>health facility<br>6<br>5<br>5<br>4 4 4<br>4<br>3 3 3<br>3<br>2<br>2<br>1<br>0<br>Tuberculosis testing Tuberculosis treatment<br>Chile Ghana India Vietnam<br>Calendar days<br><!-- End of picture text --> _Source: Living Life data_

# **3.5. Access to Education**

Education can contribute to an individual’s long-term social and financial opportunities. Inclusive education systems are the foundations of more equitable societies (OECD 2008). _Living Life’s_ Access to Education focus area aims to understand bureaucratic challenges for accessing each level of education including enrollment procedures, requirements, and costs.

Access to Education data found that primary school enrollment is free and compulsory in all surveyed economies. Secondary school enrollment is free of charge in all economies except Vietnam. Public tertiary education is only available for a fee in all economies. While in India and Vietnam students are assigned to specific primary and secondary schools based on their residential address, this criterion is not imposed in Chile and Ghana, where children can be enrolled in any public primary or secondary school.

## **3.5.1. Procedures and requirements**

The minimum requirement to be enrolled into primary education in all four economies is the attainment of the age established by legislation to start compulsory basic education (Table 3.5). In order to move to secondary education students must have completed primary school as proven by relevant records or certificates. Enrollment into tertiary education is conditional upon the successful completion of secondary education as proven by relevant examinations or certificates.

**
estimates using household survey data only. Czajka (2017) used tax and survey data from Côte d’Ivoire to implement a similar correction- he adjusted the survey data incomes in the formal private sector in each percentile by the ratio of the tax to survey ratio of mean incomes in each percentile. This adjustment increased the share of the top 1 percent by nearly 50 percent.

Van der Weide et al. (2018) examine inequality in urban Egypt and are concerned with the under capturing of top incomes in household surveys. They note that in most developing countries tax data, even in tabulated form, are not available. The authors also highlight that tax evasion is common in LMICs, as is a large informal sector, which limits the usefulness of tax data even when it does exist. To solve the lack of administrative data in Egypt, Van der Weide et al. (2018) use data on urban house prices, obtained from a private company, to improve estimates of top incomes. There are several steps undertaken by Van der Weide et al. (2018) to use house prices to replace top incomes, not all of which seem reliable. One obvious issue is the lack of house prices in the surveys to estimate a relationship between household income and house prices, which would then be applied to the admin data on house prices. The lack of house prices means the authors estimate the relationship between household incomes and housing rents, which are imputed for owner occupied housing. There is also no detail provided on how rents are imputed. Despite these issues, this is an example of a method that can be used when tax data are not available. The impact of the adjustments using house price data to impute incomes is very substantial- the Gini rises by around 30% after the replacement of top incomes.

# Reweighting and replacement

The third solution to missing top incomes that uses household and administrative data is a combination of the previous two methods. Blanchet et al. (2022) focus on reweighting to
equilibrium simulation with post-simulation analysis of household impacts.” This approach is best characterized by its two-step nature, whereby the general equilibrium model is first simulated to get commodity and factor price changes. These are then fed into a micro-simulation framework that calculates the effects on individual, or highly disaggregated representative households. Various poverty measures can be applied to assess the distributional effects of shocks.<sup>9</sup> An obvious limitation of post-simulation analysis, at least in the view of general equilibrium practitioners, is that the reactions of households to commodity and factor price changes in the post-simulation analysis are not transmitted back to the general equilibrium model. Although this absence of feedback is not satisfactory from a theoretical point of view, the resulting measurement error is likely to be small. This is due to the fact that: (a) spending patterns tend to be a function of household income per capita, so the transfer of income between two households with similar income levels (e.g., rural non-farm labor and agricultural labor) has little effect on aggregate demand, and (b) the change in spending patterns as households become wealthier is a gradual one, and the change in the overall income _distribution_ stemming from trade policy changes is also modest (recall the results from Cogneau and Robilliard above). Therefore, the change in aggregate demand due to changes in the distribution of income across strata and across income levels is likely to be very small. So ignoring the impact of such changes on equilibrium prices is most likely not very damaging.<sup>10</sup> The simplest micro-macro syntheses are really just an extension of the partial equilibrium, cost-of-living studies to include a full vector of commodity prices as well as factor prices. Chen and Ravallion’s (2003) study of the poverty implications of China’s WTO accession offers a good example of this approach. In this work, the authors combine disaggregated household survey data with trade 9 A few studies in this general category are: Robilliard, Bourguignon
effective control, the firm is not in reality a female-headed firm. Furthermore, the previous literature using the firm sample of several African countries in the WBES data has documented that using the definition of women managers tends to better capture firms truly led by women than that of women’s participation in firm ownership (Aterido and Hallward-Driemeier, 2011). We thus conservatively define female-headed firms. We rely on two variables in the WBES data set to capture aspects of female leadership: women’s participation in ownership (“whether women have some ownership of the firm”), and

> 20 More information on the SIGI can be found here: https://www.genderindex.org/.

16