rafmacalaba/data-use-mentions-extended huggingface dataset:
any of the first five rounds of the phone survey preceding Round 6, and 0 otherwise.

# **_(ii) Contact with Their Teacher_**

To define the contact between students and their teacher, we use the question asking whether children or else in the household were in touch with their teacher - a anyone question that was asked only in Round 1, Round 2, and Round 5 of the Nigeria COVID-19 NLPS. We generate a binary variable equal to 1 for an individual residing in a household who answered "Yes" to this question in any of the survey Rounds for which the question was asked, and 0 otherwise. Table 1 shows that 52% of students have been in contact with teachers during the period of protracted schools’ closure.

# **2.2.7 COVID-19 and the Opportunity Cost of Schooling**

Central to this study is the hypothesis that lockdown measures induced by the outbreak of COVID-19 undermine children’s school resilience. Nigeria offers an excellent context for testing this hypothesis because according to UNICEF’s 2013 estimates, in absolute value, one in every five of the world’s out-of-school children lives in Nigeria. Moreover, various factors inherent to the country’s socioeconomic fabric drive education deprivation, including geography, poverty and socio-cultural norms and practices that discourage attendance in formal education, especially for girls.<sup>17</sup> This multiplicity of potential factors raises the challenge of identifying the impact of COVID-19 on school resilience.

Descriptive statistics presented in Table 8 show the impact of COVID-19 lockdown measures on proxies/determinants of the opportunity cost of schooling to parents. We built this table using survey respondents’ answers to various questions about household socioeconomic conditions contained in the 2020 COVID-19 NLPS phone surveys. For example, in the COVID-19 NLPS round 4 conducted in August 2020, households were asked to compare their current income from various sources to their income in August 2019, and to indicate whether these incomes had increased, decreased, or remained the same.
from UN-Comtrade, accessed via the World Bank’s WITS platform. We use US import data for 1998-2008 at the HS four-digit level, including all exporting countries. In light of the high quality of US import data, we replace all missing values with zero to indicate that no trade took place for the given exporterproduct-year combination. We only include trade data for which we have corresponding refusals data, namely HS chapters 3, 7, 8, 9, 16, and 20 from which we exclude nuts and meat preparations since those products belong to other FDA industry codes not analyzed in this paper. In addition to trade data to control for imports, we source per capita GDP data in PPP terms from the World Development Indicators. Finally, we include US effectively applied import tariffs as an additional explanatory variable. These data are sourced from UNCTAD’s TRAINS database via the World Bank’s WITS platform. Since many missing values are returned, we use the world average by product-year combination when data on effectively applied import tariffs are unavailable on a bilateral basis.

# **3.3 Preliminary Analysis**

Before moving to a fully-specified econometric model, it is useful to examine some simple correlations in the data to see whether they support our three hypotheses, namely the own reputation effect, the sector reputation effect, and the neighbor reputation effect. As outlined above, we expect to see positive associations between, on the one hand, the number of refusals for a given country-product-year combination, and, on the other, the number of refusals affecting that country-product combination in the previous year (own reputation), the number of refusals affecting related products—those in the same HS2 chapter—from the same country in the previous year (sector reputation), and the number of refusals affecting the same product from related countries—the five geographically closest to the exporter—in the previous year (neighbor reputation). For ease of presentation we convert the variables to logarithms to reduce dispersion.

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of cooperative decision making is relatively well understood. In water resource management especially, where there can be so many indicators of water resource conditions and the performance of management efforts, forums for information sharing are vital to reducing information asymmetries and promoting cooperation.

Since information will not automatically be perceived the same way by all stakeholders, and the implications of information about resource conditions will differ among these groups, it is arguably as important that there also be institutionalized or other regular forums in which basin stakeholders can communicate. _All other things being equal, we expect to find successful decentralized water resource management more likely where information sharing and communication among stakeholders are more apparent_ .

<u>Mechanisms for conflict resolution</u> are needed to prevent disagreements from arising. Resource users can and will disagree about how well their interests are being represented and protected, about how well the resource management program is working and whether it is time for a change, about the distribution of benefits and costs, and manifold other issues.

The success and sustainability of decentralized resource management efforts therefore also depend on the presence of forums for addressing conflicts. _All other things being equal, we would expect successful implementation of decentralized water resource management more likely in settings where forums for conflict resolution exist_ .

# **3. The data collection and quality assurance process**

The above analytical framework was applied, using a comprehensive dataset of 83 river basin organizations. A survey instrument was developed and applied to river basins of the International Network of Basin Organizations (INBO) as well as to other river basin organizations that have been identified The instrument (Annex 4) provides the necessary data to be used in the empirical analysis.

# **3.1 River basin organization data collection process**

The survey instrument was developed and was pre-tested on 25 RBOs prior to being modified and finalized. The survey questions correspond to the list of the variables that were presented in the Methodology section. The survey was administered by INBO (Annex 1), although several questionnaires
for Latvia’s future competitiveness and prosperity unless leaders of Latvia’s business, government, university, and scientific communities develop and implement clear, concrete policies to address these challenges.

Successive Latvian governments have formally approved and adopted a number of policy papers including a National Concept on R&D (1998), a National Concept on Innovation (2001), the National Innovation Program (2003) and the Draft Programme Complement of the Single Programming Document for Latvia 2004-2006 (2003).<sup>2</sup> These documents clearly describe some of the most critical weaknesses and pressing challenges that Latvia

> 1 Latvia Innovation survey, chapter 3, page 17.

> 2 Priority 2 of the Single Programming Document is devoted to “Promotion of Enterprise and Innovation” and lists such objectives as creating new enterprises and increasing competitiveness of existing enterprises via transition to knowledge-intensive production and designing, producing and marketing internationally competitive products and services.

5
Too old<br>73%<br>64%<br>60%<br>48.5% No hope to find job<br>20%<br>21% Full time<br>9%<br>0% homemaker/housewife<br>Male Female 15-24 25-34 35-44 45-54 55-64<br>Overall Gender Age Group<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

The overall unemployment rate declined from 13 percent in 2009 to 11 percent in 2014, with variation by gender and location. The decrease in unemployment among the poorest quintile and rural areas 12
# Polygyny and Farm Households’ Resilience to Climate Shocks<sup>_∗_</sup>

Sylvain Dessy<sup>_†_</sup> Luca Tiberti<sup>_‡_</sup> Marco Tiberti<sup>§</sup> David Zoundi<sup>_¶_</sup>

**Keywords** : Village economies; Drought; Polygyny; Resilience strategy; child marriage; Mali

**JEL codes** : C12, D12, J12, J13, O55

> _∗_ Luca Tiberti acknowledges support from the Partnership for Economic Policy (PEP), which is financed by the International Development Research Centre (IDRC) of Canada, the Department for International Development (DFID) of the United Kingdom (UK Aid), the William and Flora Hewlett Foundation and Global Affairs Canada. David Zoundi has received financial support through the USAID Grant No. AID688-IO-11-00001 – Support to Improving the Availability, Quality and Policy Relevance of Agricultural and Rural Statistics in Mali. We thank Kathleen Beegle, Gayatri Koolwal, Aart Kraay, Heather Moylan and Alberto Zezza for valuable comments on an earlier draft. Usual disclaimers apply.

> _†_ Department of Economics, CRREP, PEP, and CREATE, Université Laval, Quebec City, QC, Canada. sdes@ecn.ulaval.ca

> _‡_ Université Laval (Canada), University of Florence (Italy), PEP and CREATE. luca.tiberti@ecn.ulaval.ca

> §Development Data Group, The World Bank, Rome, Italy. mtiberti@worldbank.org

> _¶_ Department of Economics and CRREP, Université Laval, Quebec City, QC, Canada. davidaime.zoundi.1@ulaval.ca
. Take‐up of the random assignment was 86 percent (a total of 3,854 applicants were shortlisted to participate in the random assignment by public lottery, but only 3,319 actually attended). This discrepancy may have been partly caused by the 3.5 month lag between application and selection. The take‐up at this stage did not affect the internal validity of the study because the random assignment was carried out only among those who attended the lottery. However, there are external validity considerations. For example, if those who did not attend the lottery had better networks and were therefore able to find alternative training or work opportunities, the impacts would be underestimated.

Take‐up was also high for a program of this type. Survey and administrative data indicate that around 10.0 percent of those assigned to one of the treatment groups did not take up the training, and, at most, 6.1 percent of the control individuals participated in the training sessions. To ensure robustness, estimates of the local average treatment effect were also produced to examine the impact of the treatment on the treated: those individuals whose treatment status was influenced by the random assignment, that is, the compliers (Angrist and Imbens 1995; Angrist, Imbens, and Rubin 1996). The original assignment is used as an instrumental variable for treatment, where treatment is defined as a binary variable that takes on a value of 1 if the individual attended at least 75 percent of the training and 0 otherwise. As expected based on the low levels of noncompliance with the random assignment, the results are similar to the ITT estimates. Given this outcome and the large amount of data already presented, only the ITT estimates are presented here.

Various program design features may explain the high overall take‐up, the low drop‐out rates, and the differential take‐up across treatment groups among youth who were part of the random assignment. First, the program required applicants to prepare a short business proposal, which
to how this might be achieved. First, LiTS2 data show utilization of public facilities in transition countries to be generally higher than in the western comparators; this suggests that further analysis may shed more light on the potential for efficiency and quality improvements, and the extent to which better demand management practices could free up public resources to improve quality of services. Second, the prevalence of unofficial payments in transition countries is higher than in western Europe, and is an important factor in explaining dissatisfaction with public service delivery.<sup>8</sup> Lastly, the data show that mechanisms for grievance redress in the transition regions are still relatively underdeveloped in comparison to those in the comparator countries, and should be strengthened to help provide citizen feedback to policymakers on the main problems faced when interacting with public service providers.

> 8 The inverse correlation between satisfaction on the one hand and usage rates and perceived prevalence of unofficial payments is confirmed by an Ordinary Least Squares ( _OLS)_ regression of country-level average satisfaction rates for each of the eight public services covered in the LiTS2 questionnaire. This uses as explanatory variables (i) average usage rates (that is, the percentage of respondents in the country that use that particular public service) and (ii) unofficial payments prevalence rates (the percentage who say that unofficial payments are usually or always needed for that particular service). The derived coefficients for the two explanatory variables in the regression are statistically significant at the 1 percent level.

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the recall period. These regression results are reported in Appendix Table 2. Across the outcomes considered here, we find two consistent patterns. First there is a small drop in the size of the recall impact and in its interaction with PCE, suggesting that part of the heterogeneous impacts could indeed arise from differences in education. However, for all our outcome variables, the qualitative (and quantitative) finding that recall impacts are higher for the poor remains robust to the inclusion of the additional interaction. Second, for all specifications, the interaction with education is in the expected direction—the impact of the recall period is lower for more educated households—but the effect sizes are small and generally lack precision (except for the labor impact of acute illness, which is significant at the 90 percent level of confidence).

# **Conclusion**

Common sources of information concerning health conditions—the National Sample Surveys in India and the Living Standards Measurement Surveys of the World Bank—use between 2 weeks and a month as a standard recall period for illnesses. Ordinarily, we assume that decisions about recall length don‘t bias results. In this paper, we find that they do; the choice of recall period has substantive implications. With shortened 17
activities. This shows: (i) the majority of clubs provided life skills training; (ii) vocational skills training took off after travel quarantines were lifted in January 2015 (these trainings are delivered by professionals, not club mentors). Microfinance began to be disbursed from April 2015. Panel C shows club membership: the median club has 30 members, implying membership rates of around 31% given the number of women aged 12 to 25 identified in the baseline census. Finally, Panel D shows the ratio of club attendance, based on an unannounced spot check in May 2015, to club membership. On any given day, ELA clubs can have many non-members present and participating in activities. All women can take advantage of the public good benefit ELA clubs provide in terms of a space in which they can socialize with other young women, and spend time away from men.

# **4.3 Pre-Crisis Characteristics**

## **4.3.1 Villages**

Panel A of Table 1 shows that treatment and control villages are well balanced on their size, mean poverty score across households, exposure to NGOs, religion, location and proximity to primary health units (PHUs) and secondary schools. Beyond the effective implementation of the program described above, this evidence suggests there was high fidelity with treatment-control randomization protocols.<sup>25</sup> The remainder of the Table shows data from the village leader survey, conducted during the crisis. Panel B highlights the stigma associated with pregnant girls: there is near universal agreement among elders with the statement that “girls who are visibly pregnant have a bad influence on their non-pregnant peers.” In control villages, only 12% of elders agree with statements that pregnant girls should be allowed to continue their education, or to take formal exams.

Panel C shows further details on village impacts and policy responses during the crisis. Few villages were quarantined (6%), and nearly all were visited by contact tracer teams. There are no
# The guidelines suggest the following<sup>10</sup> :

_General principles._ As indicated above the guidance on expenditure allocation has emphasized the principle of public economics i.e. to identify interventions that address market failures and inefficiencies.<sup>11</sup> Existing guidelines call for an assessment of the proper role of government, by clarifying private-public roles and evaluating market failure. Whether an expenditure is warranted should be determined by whether it would address a market failure or achieve an equity objective. There is typically an appraisal of the relative efficiency of various tools, government interventions, and expenditure policy options, for achieving particular objectives. This suggests distinguishing between financing a good or service and provision of that good or service.

_Menu of technical tools._ For evaluating expenditure options, guidelines provide a menu of available technical tools for steering policy choices, including cost-effectiveness analysis, multi-criteria analysis, service delivery surveys, expenditure tracking, and social cost-benefit analysis. To the extent that the guidelines discuss the equity objective it is to refer to established methodologies such as benefit incidence analysis.

What is the consensus on the usefulness of quantitative techniques for guiding resource allocation? Some guidelines are less confident about the value of existing empirical tools (for example, World Bank, 1993). Some sectors lend themselves more easily to economic cost-benefit analysis. For others, the criteria are less well defined. In theory the allocation of resources across programs should be based on the highest returns. In practice, a full economic analysis will not be possible except for a few sectors. Even within sectors that lend themselves to quantitative analysis, existing data may be inadequate for conducting a proper analysis. In this case, some guidelines suggest alternative resources for evaluating expenditure options, including the existing body of experience on how programs work in a country and experiences in other countries with similar characteristics. In practice, it may be optimistic to think that there exists a body of empirical work that quantifies all the critical parameters necessary for allocation decisions
, and units of measurement are disparate across goods. But imports can be recovered by dividing the import-to-output ratio by imports (from UNCTAD, see below) in order to recover value of production. Where imports are zero this method does not allow measurement of production, and those observations have to be dropped. Fortunately, imports are strictly zero for only few goods.

21 Agricultural protection may be measured using ad valorem tariffs as well. Model (3) was also estimated using four tariffs measures are available. Three measures are from the UNCTAD TRAINS database at the 6-digit HS levels for 1993, 1996, and 1999. They are an ad valorem measure, a specific tariff measure, and a tariff coverage measure. The fourth measure is an ad valorem tariff measure from the International Trade Commission (ITC) database at the 8-digit HS level for 19972001. Import-weighted averages of these tariffs were used to concord to the 4- and 5-digit HS level of the PSD goods at which the analysis is conducted. While specific tariffs are well explained by the model and reported in the tables, the ad valorem tariff data are not well explained by the variables included. This is probably because tariffs have been determined multilaterally and are not appropriate for testing models of unilateral protection. The tariff results are available from the author.

22 From the raw data on the UNCTAD TRAINS database, Haveman has constructed binary indicators of NTMs at the 6 digit HS level. These are used in the present analysis. The TRAINS database does have some unresolved issues, some of which are corrected in more recent versions, but recent versions have only recent-dated NTM data.

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shows a high degree of spatial correlation, underscoring the need to accommodate this feature in the econometric analysis. Lightning strikes are least prevalent in the south, which is covered by the Sahara desert, and most prevalent in the Chaambi mountains close to the Algerian border and in the Cape Bon peninsula located in the North East. A comparison of the spatial variations in lightning (Figure 1c) and in internet access (Figure 1b) points to a high correlation.

**Other data** We complement census data with delegation-level nighttime light data (Li et al., 2020) for the 1995-2014 period, which we use as a proxy for income. Information on

> 5We only consider metrics for the male population where possible, since the sample of recruits comprises men only. In practice, this distinction makes little difference to our results. The correlation between male internet access rate and overall internet access rate is _ρ_ = _._ 94.

9
When such proxy indicators are used for targeting instead of explicit poverty estimates, there is mis-targeting both due to the targeting errors described above and due to problems with the proxy welfare index at the community level.<sup>3</sup> In recent years there has been growing experience with the development of “poverty maps” that explicitly estimate consumption or income-based welfare outcomes at the local level.<sup>4</sup> This approach involves imputing consumption or income at the unit-record level into the population census based on a set of regression models estimated from household survey data. The imputed household-level income or consumption estimates are then aggregated into welfare indices (including but not confined to poverty measures) at different levels of geographic aggregation. While there is inevitably a degree of statistical error associated with these welfare indices, this uncertainty can be quantified, and it is not uncommon to find that the method produces reliable estimates of poverty for communities comprised of only 1,0005,000 households on average.<sup>5</sup> This paper asks to what extent the high degree of spatial disaggregation offered by such poverty maps in three different countries can help to improve targeting schemes aimed at reducing poverty. In this sense, the paper closely builds on the earlier analysis in Ravallion (1993) who finds that spatial disaggregation to the broad regional level in Indonesia – the lowest level at which household survey data provide reliable estimates of poverty – improves targeting but only to a modest extent. As in Ravallion (1993), we consider the distribution of a hypothetical budget to a country’s population, assuming that we have no information about the poverty status of this population other than the geographic location of residence and the level

> another in terms of the degree of inequality they exhibit and that there should be no presumption that inequality is less severe in poor communities.

> 3 For an example of the latter, see Hentschel et al. (2000).

> 4  Hentschel et al, 2000, and Elbers