rafmacalaba/data-use-mentions-extended huggingface dataset:
6 percentage points more likely to have had a child. This represents an increase of 22% compared to the control group (statistically significant at the 5% level). Women assigned to the training in Togo were 14 percentage points more likely to have had a child posttreatment, statistically significant at the 5% level. This implies that women assigned to treatment were twice as likely to have a child under five post-treatment than control women. The impact of WALN is not statistically different from zero in either period. In Rwanda, we find a null effect similar to the null effect obtained with the continuous outcome.

# **5 Mechanisms**

We now turn to exploring the mechanisms behind the observed increases in fertility.

In contexts with no public pension system and limited public healthcare, coupled with the absence of inheritance rights for widows, women face a heightened risk of destitution upon the loss a spouse and in old age in general. This risk is particularly acute in SSA where the age gap between partners is the largest globally with an average of 8.6 years (Pew Research Center, 2019) and where women have a higher life expectancy by about 4 years (World Bank Development Indicators, 2021). The realization of the widowhood risk being near certain, women need to anticipate the economic consequences of it and develop mitigating strategies.

They may make two types of investment that function as informal pension schemes. First, they may invest in children—in either their quantity or quality—with the expectation that their offspring will contribute to their future income. Women may invest in children’s education and health, expecting that children will share their human capital returns on the labor or marriage market (Bau, 2021; Stark and Lucas, 1988). In a context characterized by low perceived returns to education (Nguyen, 2008), and children’s imperfect commitment to repay women’s investment in the future, women cannot entirely substitute quantity with quality. Women thus target a specific number of children to
of 483 observations. Where possible, data is collected on a monthly frequency and then aggregated at the annual level.

# _4.1 Data sources_

Data on bank financial indicators are gathered from their public disclosures available on the website of the National Bank of Kazakhstan (NBK). This published bank information contains monthly data on the balance-sheet information,<sup>j</sup> key items of the financial position of banks,<sup>k</sup> prudential ratios, and prudential capital adequacy.<sup>l</sup> The financial items are based on unconsolidated reporting according to the International Financial Reporting Standards (IFRS), which were introduced in Kazakhstan in 2004.

The data on bailout actions contain hand-collected information primarily from the financial reporting of the public sector institutions, depending on their participation in the bailouts. Where possible, the bailout actions have been verified through disclosures in the audited financial statements of the banks. The information on equity bailouts comes primarily from annual reports of the National Wealth Fund Samruk-Kazyna<sup>m</sup> and official stock market announcements by KASE (the stock exchange) about the terms of equity divestments of bank #24, bank #41, and bank #2. The information on subordinated bond instruments is retrieved from the audited financial reports of the National Bank of Kazakhstan<sup>n</sup> and its subsidiary—the Kazakhstan Sustainability Fund.<sup>o</sup> The data on bailout actions in the form of acquisitions of non-performing assets is retrieved from the financial reporting of the Problem Loan Fund.<sup>p</sup> The bailout actions with long-term deposits at preferential terms are also described in the audited financial statements of Samruk-Kazyna and the Problem Loan Fund.

The data on bank ownership and major corporate events consists of hand-collected annual information primarily using the disclosed notes of the audited financial statements of banks and, only in some cases supplemented with news media reports and information from other IFIs for cross-checking. The
|Per capita consumption, rural<br>15.64|16.59|19.02|
|% HH with cultivation as main income<br>0.35|0.37|0.38|
|Avg. Forest Cover, 2002-05<br>11.31|15.15|13.34|
|Avg. Night Lights<br>29.50|27.93|61.63|
|**Data from NASA FIRMS**|||
|Monthly Fires prior to MNREGA<br>3.61|3.73|7.33|
|**Data from ICRISAT, Planning Commission and 200**|**6 Input **|**Survey**|
|Avg. Per Capita GDP, Ag<br>3.70|4.22|4.90|
|Avg. Per Capita GDP<br>14.39|15.13|21.26|
|Avg. Area Planted in Sugarcane (000s HA)<br>5.86|5.71|10.36|
|Avg. Area Planted in Rice (000s HA)<br>102.43|117.19|58.77|
|Avg. Area Planted in Wheat (000s HA)<br>39.76|57.85|61.63|
|Avg. Area Planted in Other Crops (000s HA)<br>172.67|165.39|207.96|
|Share of holdings _>_4 HA<br>0.26|0.28|0.33|
|Combines in 2006 (000s)<br>1.41|1.12|1.41|

Notes: Columns 1-3 report the mean of the named variable by district according to the MNREGA phase of that district. Data from the 2001 Census, the 2011 Census, the 2012 SECC and the VCF data come from the SHRUG dataset
**Figure 5.  Share of youths aged 15-18 considering migrating, by sex and current activity.**

**Figure 6. Share of youths aged 19-25 considering migrating, by sex and current activity.**

<!-- Start of picture text --> Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>100% 100%<br>87%<br>90% 90%<br>80% 80%<br>71%<br>70% 69% 64% 64% 67% 71% 67% 70% 60% 62%<br>60% 60% 57% 58%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Male Female Male Female Male Female Male Female Male Female Male Female<br>NEETs Currently at school Working NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity. Source: Own calculations based on Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024.

<mark>Regarding migration destinations, our findings show that</mark> **<mark>both youths aged 15-18 and 19-25 prefer relocating to urban areas, such as capital cities and towns, or other countries rather than rural areas.</mark>** <mark>This preference is particularly evident among the youngest youths,
**Table 3. C Hurricane Irma in Antigua and Barbuda (September 2017): Deviations of macro-fiscal outcomes from projections made immediately prior to the disaster (percent of GDP)**

||**Antigua**<br>**Barbuda**|**and**|**Other**<br>**SIDS**|**Caribbean**|
|---|---|---|---|---|
||2017/18|2018/19|2017/18|2018/19|
|GDP growth|4.1|3.7|-0.3|-0.3|
|Govt Revenue|-2.1|-2.8|-1.3|-0.8|
|Govt Expenditure|0.9|1.3|-1.6|-0.9|
|Net Lending|-2.9|-4.0|0.2|0.1|
|Gross Public Debt|0.9|-2.2|-4.4|-3.7|

_The figures in the table show the difference between the outcome for each variable and the projection made just before the disaster. Net lending is equivalent to the overall fiscal balance._ <u>Source: WEO database</u> Table 3.B shows the impact of Hurricane Maria, which struck Dominica in September 2017 and which caused the largest amount of damages—as a share of GDP (279 percent)—by any tropical cyclone in the Caribbean or Oceania in the last three decades. Compared to pre-disaster projections, the impact on GDP was slightly negative in the first 12 months but positive in the second. There was a huge increase in government expenditure in both periods, which when combined, amounted to almost 50 percent of GDP. Government revenues also increased, but only by about half
Tables 5 and 6 present results of regressions between child health outcomes and flaring, after various characteristics of the parents and household (and also of the child, in Table 6) are controlled for. The cluster-level results in Table 5 use as control variables: the cluster averages for wealth quintile and the average person per room, proportion of households in the cluster with poor wall and poor roof, and the proportion of mothers who read a newspaper (as a proxy for access to information).<sup>13</sup> After controlling for the cluster level characteristics, we find a positive association between flaring exposure and incidence rates of all diseases considered except diarrhoea. Our results are in line with evidence from Gobo et al. (2009) where analysis of medical records showed a greater frequency of disease types such as asthma, cough, breathing difficulty, eye/skin irritation in areas with a long history of gas flaring compared to areas with no flaring. Our results also imply a varying relationship between wealth and incidence of diseases. Richer clusters on average have significant less fever and diarrhoea, while cough and respiratory symptom incidence show no similar association, suggesting a lesser role for income/wealth as a mitigating factor for respiratory diseases in this context. Aside from flaring exposure, we find overcrowding significantly associated with cough and respiratory symptoms but not with fever and diarrhoea.

For child anthropometrics, we find significant positive associations between gas flaring and rates of stunting, wasting, and under-weight. These effects are larger than what is reported in Table 3, with no control variables included. Across these three anthropometric indicators, a standard deviation higher volume of gas flaring is associated with up to a 0.1 standard deviation higher rate of stunting, wasting or under-weight. However, the data reveal no significant association between flaring exposure and child mortality outcomes.

Our results at the individual level (in Table 6) are consistent with the cluster level findings. We find a positive association between flaring exposure and incidence of all the diseases
so this is an ideal candidate for BMA. We consider below two datasets which have been used in many studies on this topic.

# The FLS data

The first dataset we use contains _k_ = 41 potential regressors for modelling average per capita growth over the period 1960-1992 for a sample of _n_ = 72 countries. It was used in Fern ́andez _et al._ (2001b) (FLS), which presents more details.

First, we consider the analysis of the 50 subsamples which each consist of 62 observations. In line with the higher model size penalty implicit in the benchmark Beta prior with _c_ = 1 and the case with fixed _g_ = _k_<sup>2</sup> , we note that marginal posterior inclusion probabilities of the regressors tend to be (sometimes much) smaller for these cases. In keeping with the higher _g_ values induced by these priors, the posterior mean of _βj_ for model _Mj_ will be larger in absolute value (less shrinkage to the prior mean of zero), but this will typically be counteracted by more mass on smaller models. The resulting marginal posterior means of the regression coefficients can be smaller or larger than those for the other priors on _g_ . For the other priors posterior inclusion probabilities and posterior inference on the regression coefficients are quite similar, even though the posterior inference on _g_ can be somewhat different. Figure 6 presents boxplots over the 50 subsamples for the posterior medians of _g_ (on a log scale), the number of visited models and the posterior mean model size. As with the simulated data, posterior medians of _g_ are smallest for the hyper- _g_ priors and largest for the benchmark prior with _c_ = 1 and the case with fixed _g_ = _k_<sup>2</sup> . This leads to most models visited in the chain for the hyper- _g_ priors and least models in the chain for the benchmark with _c_ = 1 and _g_ = _k_<sup>2</sup> cases. Posterior model sizes are lower
# **6. Poverty and the sectoral structure of net domestic product**

It is also of interest to know how the relationship between poverty and the sectoral structure of national output has evolved over time. By decomposing the overall growth rate of net domestic product (NDP) into its components we can investigate how the composition of NDP growth has mattered to poverty reduction. The national accounts do not of course have an “urban-rural” breakdown, so this dimension is now latent.

_k_ We divide NDP per capita,<sup>_y_</sup> _t_<sup>, into</sup><sup>_k_sources as</sup> _yt_   _yit_ . Taking the differences over _i_  1 time, we can write the growth rate of NDP as the share-weighted sum of the growth rates across _k_ the _k_ sources, i.e.,  ln _yt_   _sit_  1<sup></sup> ln _yit_ where _sit_  _yit_ / _yt_ is the share of NDP due to the _i_ ’th _i_  1 source. (This uses the approximation that  _yt_ / _yt_  1   ln _yt_ .) Given that the NDP data are annual (unlike the NSS data used in the last section to study the urban-rural pattern of growth), we can now test for a one-year lag in the effect of growth. We allow for sectorally-differentiated impact of growth by estimating a regression equation of the following form: Here, _i_ =1,2,3 denotes the primary, secondary and tertiary sector respectively, and _X_ denotes controls (discussed further below). In the special case in which  0 _i_   0 and  1 _i_   1 for _i_ =1,2,3, the above equation collapses to a simple regression of the rate of poverty reduction on NDP growth and its lag (  ln<sup>_y_</sup> _t_ and  ln<sup>_y_</sup> _t_ 
Policy Research Working Paper 7749

# **Abstract**

Using unique household level data collected in northern Mali in August and September 2015, this paper explores whether the content of the 2015 Peace Accord reflects the concerns and priorities of the citizens, local leaders, and refugees, or whether it is an exclusive agreement between elites who fail to represent the interests of the population at large. The study finds that local leaders’ opinions are appropriate proxies for the citizens they represent, and most (but not all) of the measures stipulated in the Peace Accord align. The greatest divergence between citizen preferences and actions specified in the Peace Accord is the emphasis in the latter on road infrastructure. When different activities are costed, this disconnect becomes very apparent. The bulk of funds (45 percent) are apportioned to infrastructure construction, while the typical respondent prefers job creation; the poorest prefer investments in social services. However, most of infrastructure spending has been earmarked for the medium term. Short-term priorities reflect the preferences of the population in northern Mali much better. It is concluded that although the Peace Accord had all the elements to be an exclusive elite pact, its priorities largely reflect those of the population, granting it much higher probability of success.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at jhoogeveen@worldbank.org and jlendorfer@gmail.com.

_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 be cited accordingly. The findings, interpretations,
Figure 10: Rural Poverty in Sudan by State, 2009 and 2014/15

### (a) Rural Poverty in Sudan, 2014/15

<!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

- (b) Rural Poverty Change by State, 2009 to 2014/15

<!-- Start of picture text --> 5%<br>-5%<br>-15%<br>-25%<br>-35%<br>% Δ<br><!-- End of picture text -->

### _Source_ : Authors’ calculations using NBHS 2009 and NHBPS 2014/15.

_Note:_ Because the states in Kordofan and Darfur were split from larger states in 2014/15, we use the poverty rate corresponding to the parent state for the initial poverty statistic for those states.

# Incomes, Consumption, and Assets in Rural Sudan

## Incomes

The average rural household earns at least one-third of its total income from agriculture, while this is only 5 percent in urban Sudan (Figure 11). This is not surprising given that the majority of rural households depend on agriculture as a source of livelihood with little diversification into the nonagricultural sector. The dependence on agriculture is predominant among the rural poor, with at least 39 percent of the total income of the rural poor accruing from agriculture compared to 31 percent for the rural non-poor. Importantly, the actual reliance on agriculture is likely to be higher than these numbers suggest, as income from wages is often tied to agricultural employment. The surveys do not make it possible to accurately differentiate the source of wage employment.

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# _A subjective but structured process_

Subjective assessments have been used in the economic literature to complement more objective approaches. For example, in measuring individual well‐being a subjective assessment has been adopted on the grounds that everybody has his or her own views on what a good life looks like, and what makes a good life may touch on dimensions for which no reliable indicator is available (Frey and Stutzer 2002; Veenhoven 2004). Deciding whether a specific place is a city poses challenges similar to those posed by measuring well‐being.

For a subjective assessment to be thorough, researchers should ideally visit each city, town, and village in the sample and “see” how they look like. However, the time and cost implications of visiting 1,277 locations scattered across a large country are dissuasive. Building on the literature, we adopt a second‐ best approach, which is to rely on high‐quality, open‐source images by Google Earth.

Google Earth is an integrated collection of processed satellite, aerial, street view, and 3‐D images. A zoom‐ in option is available, and ground views and different time views are also available whenever possible. This combination of perspectives enables a layperson to judge the characteristics of a specific location. No special training in remote sensing is needed.

We overlaid the digitized boundary of each sample unit on top of Google Earth images and conducted a subjective assessment of the images falling within the corresponding boundary. For the vast majority of units, we rely on images dated as having been taken in 2010–12 to be consistent with the population data from the 2011 Census of India. However, when the images for these three years are of poor quality, we complement them with images from other years.

The sample of cities, towns, and villages was randomly split into three subsets, which were in turn allocated to three research analysts. All research analysts relied on the same decision tree to independently classify the units allocated to them as urban or rural
<!-- Start of picture text --> Product level trade data Labor force survey<br>(2010-2020) (2010-2020)<br>1% wo<br>mo o:<br>Cy es<br>Se £§<br>“ee 8, ge<br>G&S &<br>Industry and individual level<br>merged data (using 2 digit<br>ISIC level)<br>* The industry data is panel from 2010to 2020<br>* The worker data is cross-sectional panel as the LFS don't survey the sample person over time.<br>* We also have firmlevel survey data but were not able to use it<br><!-- End of picture text -->
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**Figure 10. European Bank Equities and Profitability**

<!-- Start of picture text --> a. Stock Market Indexes b. Return on Equity<br>120 12<br>Jan 2011– May 2012 2007–10<br>10.0 9.9<br>110 10<br>Euro Stoxx 50<br>8<br>100 Euro Stoxx 600 Banks 6.6<br>6<br>90 3.9<br>4<br>1.9<br>80 2<br>1.0<br>70 0<br>-2<br>60<br>-2.8<br>-4<br>50<br>-6<br>-5.4<br>2007 2008 2009 2010<br>Index Date = All Banks Large Banks<br>Percentage<br><!-- End of picture text --> Source: Panel a, Bloomberg; Panel b, ECB Consolidated Banking Data.

# **III. THE SCOPE OF DELEVERAGING AND ASSOCIATED RISKS**

**15. Information as of early 2012 suggests that many banks have been selling or scaling down their businesses within or across borders** . In particular, banks have been adjusting their business strategies to focus on ―core‖ markets of strategic importance, and abandoning activities and locations that require higher capital or that do not offer profitable opportunities going forward: for example, because of adverse regulatory regimes. Appendix table A.3 lists recent actions undertaken by many banks.

**16. Data also point to a marked deterioration in credit conditions across the Euro Area in late 2011, with some turnaround in the first quarter of 2012** . Driven by more prudent risk management practices, European banks have been tightening lending standards since fall 2011 (figure 11). The ECB‘s lending surveys (ECB 2012a, 2012b) show that
cost 582 of delaying each sector in our numerical experiments by five and ten years in Figure VII. We find that 583 our earlier arguments are well-supported: energy is consistently the most expensive sector to delay. 584 In computing the correlation of each sectoral characteristic and relative aggregate policy costs (see 585 Table IV), we find that the annual emissions rate is consistently the most correlated sectoral parameter 586 with changes in relative policy cost increases, followed by the marginal investment cost and the capital 587 depreciation rate.<sup>23</sup> These results would suggest that policymakers using delay to assuage PECs should

> 21One possible exception to this conclusion could be policies which are administratively burdensome and do not lead to tangible emissions reductions. In this case one would have the worst of both worlds: nontrivial costs and a reduced remaining carbon budget, which would demand an accelerated and costly policy schedule later to remain compliant with the emissions budget.

> 22This point would suggest that the approach taken by the EU in their establishment of ETS2 is prudent from an economic and political perspective.

> 23We note that there is significant anti-covariance between emissions rates and marginal investment costs in the IPCC AR6 data (Intergovernmental Panel on Climate Change, 2022b); energy and forestry are at once the cheapest sectors and the most emissions intensive, while waste has the lowest emissions rate and the highest marginal investment cost. This happenstantial attribute of the data does skew our results somewhat; for example, excluding energy, forestry, and waste results in unclear correlation structures between additional policy costs and sectoral characteristics. However it would be difficult to claim a relationship with only four sectors is at all statistically significant. We therefore rely on the intuition established above in our analysis: the higher the emissions rate, the more of a distortion in the optimal allocation of emissions across ETSs, causing higher additional policy costs relative to the optimal. Note this statement is

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