rafmacalaba/data-use-mentions-extended huggingface dataset:
technologies to offer a TaRL approach to low-literate populations, contributing to filling the evidence gap pointed out by (GEEAP 2020) about the evidence of the impact of studies using such innovations and their cost-effectiveness.

The rest of this is as follows. Section 2 a of paper organized provides background northern where our takes Section 3 an overview of en- Nigeria, study place. provides tertainment education. Section 4 describes the interventions and explains the design rationale behind them. Section 5 describes the evaluation design and data collection and Section 6 presents results on main outcomes and mechanisms, spillover effects on other household members, treatment effects by gender, and robustness checks. This section also includes an effectiveness and cost-effectiveness analysis, comparing our learning results with those of other educational interventions. Section 7 concludes.

# **2 Study Setting**

This study took place in Kano and Jigawa states, located in the North West region of Nigeria. Based on model-based estimates, Nigeria has the third largest out-of-school population in the world and the largest in Sub-Saharan Africa with approximately 20 million children out-of-school (UNESCO 2022). This adds up to over 10 percent of the global outof-school population (Antoninis 2014; Bertoni et al. 2019). The problem is particularly severe in northern Nigeria, which accounts for a disproportionate number of out-of-school children in comparison to the south of the country. In 2018, only 51 percent of the schoolage children attended school, versus 84 percent at the national level (The World Bank 2021). Moreover, gender gaps in educational attainment are particularly salient in North West Nigeria, where 64% of 15-49-year-old women have no education in comparison to 38% of men of the same age, according to the Demographic and Health Survey 2018 (NPC and ICF 2019).

In Nigeria, schooling attendance does not necessarily translate into increased learning, and educational indicators for the north substantially lag behind the national average. According to the Reading and Access Research Activity report (2019), in northern Nigeria, less than 3 percent of second
# **5. Conclusion**

In this study, we show that wide mangrove belts in the Caribbean have the potential to mitigate the disruption of economic activity caused by hurricanes. We use remote sensing nightlight data to measure local economic activity, a damage index derived from a wind field model calibrated for the Caribbean to measure potential hurricane destruction, and the calculated cumulated width of mangrove along the closest path to the coast to measure mangrove protection. Using these data, we estimate the impact of hurricanes on economic activity under the assumption that hurricane strikes are exogenous conditional on cell and year fixed effects. We then use a binning estimator to explore whether there is a negative and plausibly nonlinear relationship between mangroves and hurricane damages.

Hurricanes have negative short-term effects on economic activity, with losses likely to be concentrated in coastal lowlands at risk of both wind and storm surge. We further show that within the coastal lowlands, the impact of hurricanes declines with mangrove width and specifically, that the effect of hurricanes in our sample is mitigated by 0.25 kilometer or more of mangrove width. We also conduct various robustness checks and rule out that these findings are driven by the physical characteristics of the location of the mangrove habitat or by the distance to the coast.

Our results contribute to highlight the importance of mangrove conservation and restoration in the Caribbean region, especially in the coastal lowlands, for protecting local economic activity against tropical cyclones and hurricanes. The evidence suggests it is the width of the mangrove belts that give the protection, reducing the flooding impacts of storm surges during major storms, which implies that largescale efforts will be required to achieve the benefits of the mangrove protection.

Finally, there are two important points to comment. First, we are likely to have underestimated the protective value of mangroves in the long run because mangrove protection may include additional benefits—such as lives saved, health outcomes, and human capital accumulation—that are not well captured by nightlight data. Second, while climate change and the resulting intensification of storms
% power to detect a 15 percentage points difference in switching rates between arms for a two-sided test of equality at the 5% significance level (effect size = 0 _._ 15 _/_<sup>_√_</sup> 0 _._ 3 _×_ 0 _._ 7 = 0 _._ 33). The same number of clusters would have achieved the same power for a smaller 10 percentage points difference (effect size = 0 _._ 10 _/_<sup>_√_</sup> 0 _._ 3 _×_ 0 _._ 7 = 0 _._ 22) with a less conservative _ρ_ = 0 _._ 09 and with higher demand leading to the discovery of 20 unsafe wells per cluster, while 51 clusters per arm and 40 unsafe wells per village would have been necessary with the more conservative _ρ_ = 0 _._ 12.

Given that the available funding allowed the inclusion of a larger number of villages, it was possible to include the additional experimental arm C, for which however sample size was dictated by budget constraints rather than power calculations.

The assignment to treatment arms was done by the principal investigators using random assignment, using the statistical software Stata, after stratification. Strata were determined by whether the share of unsafe wells in the BAMWSP testing campaign carried out years earlier was below or above

> 7The experimental design also included two exploratory arms, with only six villages each, where tests were sold individually either at a village-level price of BDT45 or BDT90, but with payment required only in case of ‘good news’, that is, in case of arsenic level no higher than 50 ppb. The inclusion of these proof-of-concept conditional sales was motivated by the aversion expressed by several members of focus groups to the idea of ‘paying for bad news’. Sales conditional on the results may have thus increased demand (a prediction strongly supported by the observed purchase rates), although the conditional payment
(Kanter et al., 2019; Singh et al., 2018).<sup>21</sup> With increased availability and affordability, data from satellite sensors such as the European Space Agency’s SENTINEL-2 Multi Spectral Imager and the series of sensors from Planet’s Dove satellites are being explored to bring satellite monitoring to the individual-field level. Recent research has shown that data from low-cost satellite sensors can help predict crop yields at the individual-field scale, which can serve as crucial information for farmers to inform their agriculturalmanagement practices and help them make decisions about where and when to apply fertilizer (Jain et al., 2019). Building the capacity of local agricultural-extension officers to appreciate the usefulness of these datasets and apply them in practice will be critical (Dash, 2019).

Other initiatives and technologies, such as nitrogen-fixing bacteria, efficiency enhancing fertilizers or fertilizer deep placement that targets fertilizer right at the source also show promise. At the same time, even as countries focus attention on best management practices on-farm, there is an increasing need to extend attention to off-farm actors across the agricultural value chain that are capable of influencing farmlevel nitrogen management (Kanter et al., 2020a).

For example, since 2015, the Government of India has made efforts to improve nitrogen use efficiency in agriculture and has mandated manufactures of urea, a type of nitrogen fertilizer that is commonly applied in India, to produce neem-coated urea. Neem-coating helps to reduce leakages by making it more difficult for black marketers to divert urea to industrial consumers (Government of India, 2016). At the same time, it has the potential to benefit farmers. Since neem acts as a nitrification inhibitor, it allows a more gradual release of nitrogen into the soil, thereby improving nitrogen use efficiency. Neem, however, is one of many possible compounds and the responses of different crops under different conditions can be highly variable (Searchinger et al., 2020).

Another technique developed by the International Fertilizer Development Center is using fertilizer deep placement
# **c. Generosity (benefits as % of MYPP)**

# **d. Inequality, poverty, and poverty gap impact**

<!-- Start of picture text --> 50.0 Marginal Contributions to<br>0.01<br>Inequality<br>Change in Poverty Gap 1.39<br>Marginal Contributions to<br>0.40<br>Poverty<br>0.5 0.0 0.0 0.1<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**In the context of evaluating the impact of both Monthly Social Allowance (MSA) and heating allowances, a substantial portion of households in the lowest income brackets, particularly the first and second deciles, would benefit from at least one of these transfers (figure 13, panel a)** . Surprisingly, even households in the fourth decile would receive support. For those in the first decile, the level of generosity would be notably high, reaching approximately 28% of their income (figure 13, panel c). However, while the assistance provided to the first decile is substantial, the overall effect on inequality at the societal level remains relatively unchanged. This observation implies that although MSA plays a crucial role in directing assistance to the most financially vulnerable, the scale of its impact on broader income inequality remains limited.

**Conversely, the impact on poverty levels is more pronounced than alternative scenarios (figure 13, panel d). The
UNESCO 2010 _Education for All: Global Monitoring Report_ , the Quantitative Impact of Armed Conflict on Education UNICEF, 2003, "Côte d'Ivoire sub-Regional Crisis Donor Update," 15 September, available on http://reliefweb.int/node/117935 UNOCHA, 2003a, "Fighting Near the Liberian Capital Drives Thousands into Bush," February 8, available on http://reliefweb.int/node/119067 UNOCHA, 2004, "Fighting in Côte d'Ivoire Jeopardizes Humanitarian Aid," November 4, available on http://reliefweb.int/node/157760 UNOCHA, 2006a, "Côte d'Ivoire: Five Dead in Clashes with UN Peacekeepers in _Wild West_ ," January 18, available on www.irinnews.org/report.asp?ReportID=51196 UNOCHA, 2006b, "Côte d'Ivoire: UN Staff Being Evacuated as Sanctions Loom," January 27, available on http://www.irinnews.org/printreport.aspx?reportid=57960 World Bank 2010. Country brief: Côte d'Ivoire. World Bank, January 20. <u>http://go.worldbank.org/SN2JJ08PI0</u> Zhao, Z. 2000. Data Issues of Using Matching Methods to Estimate Treatment Effects: An Illustration with NSW Data Set," Working Paper, China Centre for Economic Research.

28
large LAC economies between 2017 and 2019 was around 29 percent, compared to the 23 percent average for all EMDEs.

# 7.3 Regional investment needs

Investment needs in the region remain signi fi cant, both regarding gross fi xed capital formation (GFCF) for services like transportation and digital connectivity and, beyond GFCF, regarding investment in human capital formation through improved healthcare and education. Low-quality infrastructure, re fl ecting historically low investment, weighs on regional productivity and economic growth. Thus, infrastructure bottlenecks may be a key factor limiting agglomeration-related productivity gains that might otherwise be expected to accrue from the region’s high levels of urbanization (Gómez-Lobo et al. 2022). High income and wealth inequality between and within countries contributes to highly variable performance on health and education indicators. Even the region’s richer countries have pockets of signi fi cant need, despite higher spending on human development than in other EMDEs (World Bank 2022f). LAC economies could bene fi t substantially from a global green transition, but realizing this potential will require greater investment in enabling industries, backed by conducive policy frameworks. More generally, the increase in labor productivity needed across LAC to raise living standards is likely to be achieved only through higher investment growth, including in the private sector.

Infrastructure. Surveys indicate that mediocre infrastructure is a key constraint holding back LAC’s development. In 2017, the average economy in LAC ranked 79th out of 136 countries on infrastructure quality, marginally better than the EMDE average but well below the averages of East Asia and the Paci fi c (EAP), Europe and Central Asia (ECA), and the Middle East and North Africa (MNA; World Economic Forum 2018). It has been estimated that meeting the infrastructure-related sustainable development goals (SDGs) will require infrastructure investment in LAC of around 4.5 percent of GDP annually at least ( fi gure 4.12; World Bank 2019c). Extrapolating from data from 2008-15, roughly 70 percent of such needed infrastructure investment (over
categories: starters (ages 16–29), young families (ages 30–44), mature families (ages 45–59), and empty nesters (ages 60 and over). Four categories of dwelling status of the household were coded based on the household surveys: owned by the household (1), rented from the state (2), private rentals (3), and free or other (4). For each survey, the four categories were assigned based on the following information (Table 2).

**Table 2. Survey Specific Definitions of Dwelling Status**

|**Code**|**Romania**|**Serbia**|**Armenia**|**Poland**|**Russia**|
|---|---|---|---|---|---|
|1=Own|Personal<br>property|Owner|Owned|Ownership of house; ownership<br>of apartment in municipal<br>building or an apartment<br>building, membership in<br>cooperative, ownership;<br>membership in cooperative,<br>tenancy|Your household<br>(privatized, bought)|
|2=State<br>rental|Leased by<br>state|Protected<br>tenant|State or<br>municipality<br>rented|Rental of apartment - regulated<br>rent; social security apartment|Government or<br>municipal<br>authorities|
|3=Private<br>rental|Leased by<br>landlord|Paying guest,<br>rents out<br>whole<br>apartment|Private person<br>rented|Rental of apartment - free<br>market rent|Leased from a<br>private person,<br>private legal entity|
|4=Free or<br>other|
<br>Bank Staff Calculation. Bars (or lines) show simple group averages. Here EMDEs and EAP averges do not include Indonesia.<br>The shaded area is capturing the COVID-19 years.<br>2000 2003 2006 2009 2012 2015 2018 2021<br><!-- End of picture text -->

# _Employment informality_

22. **Informal employment in Indonesia has also declined but remained higher than the average levels in other EMDEs and the EAP region.** Using self-employment as a proxy for informal employment, Indonesia has experienced a steady decline in informal employment since the 1990s (figure 3). However, there seems to be a reversal starting in 2016. A reversal that has been accelerated by the COVID-19 shock. Indeed, self-employment as a share of total employment increased from 49.9 percent in 2019 to 53.4 percent during the pandemic in 2020. Despite the historical decline, the level of employment informality in Indonesia is consistently higher than the level in the EAP region and other EMDEs. Using data on informal employment provided by ILO - a broader measure than self-employment - four out of five workers are informally employed in Indonesia, notably higher than the average informal employment rate in EAP or EMDEs (i.e., about 56 percent of total employment).

> 6 As shown in section 2, Output informality is proxied by MIMIC-based estimates or LFS estimates as elaborated in the previous section. The model-based MIMIC estimates are used mainly in cross-country comparisons, while the LFS estimates are used in the within-country comparison.

9
<br>48.5 44.8 1.0 0.9 1.3 1.3 1.5<br>38.7 40.4 41.2<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>90.0 5.0<br>80.0 4.0<br>70.0 3.0<br>(Millions)<br>60.0 2.0<br>Ninis not searching for work (% relevant Ninis population) Ninis not searching for work<br>50.0 1.0<br>90.0<br>8.0<br>80.0<br>6.0<br>70.0<br>4.0<br>60.0 (Millions)<br>50.0 2.0<br>Ninis not searching for work (% relevant Ninis population) Ninis not searching for work<br>40.0 0.0<br><!-- End of picture text --> Panel D: Number of ninis not searching for work in LAC by gender (19-24 years) Source: authors’ own computation with data from CEDLAS and The World Bank (2015) _Household status:_ Figure 12 and Figure 13 present the breakdown of ninis who decide to start their own households (defined as those ninis who are either a household head, partner of a household head, or married or in a civil partnership), both overall and with children. In both cases, the patterns are similar for both age groups, with a much greater percentage of female ninis starting households and starting households with children. While for 19 to 24 year-olds the percentage of ninis—be they male or female—starting their own households with or without children has risen over the past 18 19
states increases protection; protection decreases in farm income and as prices improve; imported commodities receive greater price supports; the greater the share of output exported, the greater the amount of price support. All these coefficients support some political economy model, though the precise connection is not made formally clear and the connection of the variables to any underlying theory is tenuous. Regardless, the collective evidence in favor of the influence of special interest is impressive.<sup>9</sup> The next two sections contain the empirical contribution of this paper. Data on the structure of lobbying by agricultural political action committees (PACs) is merged with data on agricultural protection in order investigate evidence of direct association between the two. The models discussed above all indicate that lobbying matters, whether lobbying involves money in exchange for favorable policy or whether it involves provision of information that is mutually beneficial to the lobbyist and policymaker. The detailed graphical analysis of the structure of lobbying spending undertaken in the next section does inform the debate about whether lobbying in the US agricultural sector is informational or of a quid-pro-quo nature. The formal econometric analysis that follows, however, does not distinguish between the two types of lobbying. Rather, it is motivated by the need to make the association between lobbying and protection explicit.

# **3. Graphical Analysis of PAC Contributions 1991-2000**

# _A. Data_

Raw lobbying data for the five congressional election cycles (for the 103<sup>rd</sup> through 106<sup>th</sup> Congress) between 1991-2000 were downloaded from the Federal Election Commission (FEC) website (www.fec.org). The data are in three relational data files: candidate information files (CN), PAC committee information files (CM), and files containing transactions between PACs and candidates (PAS). For each election cycle, aggregate contributions by each PAC to every candidate were computed from the PAS files, and then

> 9   While the three sets of models described up to
. The _Market sophistication_ pillar reflects the underlying market conditions that are relevant for business operation and innovation activities; it includes availability of credit; conditions to support investment and access to international markets. The _Business sophistication_ pillar measures the extent to which firms are conducive to innovation activity; it measures the employment of qualified workers by firms, the presence of innovation linkages and public/private academic partnerships in the economy, and the capacity of firms in the country to absorb knowledge. From the output side, the results of innovative activities within the economy are captured by two main pillars. The _Knowledge and technology outputs_ pillar comprises the traditional fruits of innovation, and includes measures of knowledge creation, impact and diffusion. Finally, the “ _Creative outputs_ ” pillar captures the extent to which the country makes use of intangible assets, the country’s capacity to produce creative goods and services and online creativity.<sup>4</sup>

> 2 See Global Innovation Index (2017) for further details.

> 3 See Global Innovation Index (2017), Annex 1 for further methodological details.

> 4 It is worth highlighting the connections between the extended approach of national innovation system displayed in Figure 2 and the measurement scheme applied by the GII data set, displayed in Figure 4. The supply and demand dimensions of innovation as well as the markets where knowledge accumulation takes place – highlighted in Figure 2 - are somehow captured across the innovation enabling pillars displayed in Figure 4. For instance, the set of external factors that influences the firm’s incentives to innovate highlighted in Figure 2 (macro context, the competitive structure and trade regime) are somehow captured by the _Market sophistication_ enabling pillar in Figure 4. The same happens with the supply of human capital highlighted as one of the supply side factors in Figure 2, which is captured by the _Human capital and research_ pillar in Figure 4.

5
**Figure 15: Indicators related to social capital and collective action in the Dominican Republic**

||Rank (out<br>of 24)|DR (0-100<br>scale)|Regional<br>Average|
|---|---|---|---|
|Interpersonal Trust|15|57.7|59.6|
|**Civilparticipation**||||
|Religious Groups|4|60.5|43.1|
|Parents' Associations|6|27.1|22|
|Committees for CommunityImprovement|1|25|14.1|
|Professional Associations|8|8.3|7.5|
|Women's Associations|4|13.3|8.7|
|**Political activism**||||
|Protest Participation|19|5.4|8.0|
|Electoral Participation|12|76.1|73.4|
|Interest inpolitics|5|44.5|37.8|
|Percentage Attemptingto Persuade Others|10|33.5|31.5|
|Percentage Who Worked For Candidates|1|19.9|11|
|**Localgovernment**||||
|Attended a Municipal Meeting|1|27.3|11.2|
|Demand-Makingon Municipal Government|8|15.1|12|
|Satisfaction with Local Government Services<br>Source: Americas Barometer 2010,Seligson and Smith,2010|20|48.8|51.8|

Source: Americas Barometer 2010, Seligson and Smith, 2010 Red: indicator for the DR below regional average; green, above regional average As we have seen, the case of the Dominican Republic is paradoxical. On the one hand, Dominican population registers a relatively high level of associative participation and approval for democracy compared to other countries in Latin America and the Caribbean (Espinal and Morgan