rafmacalaba/datause-encoder-data huggingface dataset:
choices, attitudes toward violence, and violence experience). We run all regressions for women and men separately because the majority of indicators (e.g., concerning violence and contraceptive use) are not fully comparable, i.e., the questions for men and women in the DHS survey are different.

The coefficient _β_ is of primary interest, capturing the impact of distant violent events recorded in the ACLED data. The key explanatory variable **VEdist**<sup>_g_</sup> _jt_ captures the number of violent attacks over the last five years that occurred in location _j_ and were conducted by group _g_ —i.e., either Boko Haram or Fulani. We conduct regressions looking at violent attacks conducted by each of these groups separately. To ensure that these are distant attacks, we restrict the set of locations to those that lie outside of the 50 km radius of the centroid of a household cluster. This spatial threshold is the largest within which, according to the literature (e.g., Bendavid _et al._ , 2021), nondisplaced women and children are still considered to be living dangerously close to armed conflict. We use a timeinvariant spatial-weight matrix **W** _cj_ based on an inverse function of the distance between violent events and population clusters (see e.g., Agrawal _et al._ , 2017; Kelejian _et al._ , 2013). This choice is consistent with results from alternative specifications that count all events that occur within different radii from the household cluster—i.e., 0–50 km, 51–100 km, 101–50 km, and 151–200 km—without weighting these events. In particular, we observe a dose-response relationship between the size of the spillover effects and the distance of the attacks that appears to roughly follow a linear function with a negative slope parameter, i.e., the farther the event, the smaller the impacts, in absolute terms (see Table A3 for Boko Haram violent events and Table A4 for Fulani violent events). Thus, giving higher weights to geographically close events is arguably a reasonable empirical specification. Specifically, we first calculate the distance _dcj_ from the centroid of each household cluster to the geo-location of each violent event that occurred in the past five years. Second, we restrict attacks to locations with a distance to the cluster centroid of more than 50 km. <u>1</u> Third, we weigh each event by the inverse distance, i.e., _d_<sup>_~~p~~_</sup> _cj_<sup>with</sup><sup>_p_=1.Eventually,for</sup> every household cluster, we sum all weighted events.

**C** _c_ is a vector of control variables at the cluster level. It includes characteristics that

21
OECD/DAC on the imputed multilateral contribution.<sup>11</sup> Since multilateral agencies have a wide range of lending concessionality (from IMF’s 25-30% to UN agencies’ 100%) and since it would be a very data-intensive exercise to try to obtain grant elements for each of our multilateral donors, we performed several sensitivity tests with respect to the grant equivalence of the imputed multilateral component of the aid flows, assuming that this grant element was, in turn, 50 / 65 / 80 percent.<sup>12</sup> These tests showed that final results were marginally sensitive to the share of that grant element. The total amount of grant-equivalent aid in current US$ in 2002 used for the simulation is 53.6 / 56.2 / 58.8 billion, depending on the grant element of imputed multilateral contribution.<sup>13</sup> A second measurement issue is the treatment of debt relief in the aid data. Grants for debt relief count for the whole future flow of debt service saved – in 2002, debt relief consisting of debt forgiveness grants and rescheduled debt made up almost 10% of total net ODA<sup>14</sup> ; however, in our calculations we are trying to estimate the impact of an annual flow of aid. As a result, we examined data on the annual debt service in 2001-2002 as a fraction of the stock of official debt outstanding. It turns out that this ratio is roughly 4 percent (World Bank 2005a).

> 11 OECD/DAC calculates the imputed multilateral contribution by distributing each DAC donor country’s contribution to multilateral agencies to recipient countries, using pro rata calculation of the percentage the multilateral agency gives in grants or concessional loans to each particular recipient.  These figures are then added across multilateral agencies that receive contributions from this particular DAC donor country to arrive at a donor-recipient “imputed multilateral” contribution.

> 12 Results of these sensitivity exercises are available from the authors upon request.

> 13 To make these data comparable to our base of actual GNI per capita, we deflated these aid volumes to 1995 dollars, using the same procedure as we had used for deflating GNI in Section II.

> 14 As suggested by OECD/DAC, we included (negative) offsetting entries for debt relief in this calculation to avoid double-counting (DCD/DAC 2002).

10
Our results appear in Figure 10 and show that the observed change in household income when excluding income from the Auxilio Emergencial followed a U-shaped pattern. This estimate was done with a panel of households (about three-fourths of the original data) that appear in both the 2019 and 2020 PNAD-C household surveys. The largest income loss appeared in quintile 5 (19.6%), followed by quintiles 1 (17.6%) and 4 (17.4%). This is at odds with the simulation results, which show declines of about 12%-13% percent for the bottom 3 quintiles and smaller declines at the top. When including the Auxilio Emergencial as part of household income, households across the income distribution experienced smaller income losses than when excluding this social program. This highlights that the AE program covered households across the income distribution. Income declines, however, were highest for the higher quintiles of the distribution, while there was an income gain for quintile 1, highlighting that the AE program was also more targeted towards the poorest households. Since the microsimulation results did not account for this emergency transfer program (as explained in an earlier section), the results from the 2020 microdata incorporating the AE program cannot be compared with the microsimulations. The rest of the analysis thus focuses on comparing the projected and observed income and poverty changes under the assumption of excluding the AE program.

**Figure 10. Change in household per capita income between 2019 and 2020. Observed and projected values**

Projected and observed excluding AE Observed including AE <!-- Start of picture text -->
-11.9%<br>Q1 Q1 14.3%<br>-17.6%<br>-13.0%<br>Q2 Q2 -1.2%<br>-16.0%<br>-11.6%<br>Q3 Q3 -7.8%<br>-15.0%<br>-10.0%<br>Q4 Q4 -12.6%<br>-17.4%<br>-8.8%<br>Q5 Q5 -17.8%<br>-19.6%<br>Projected Observed - Without AE Observed - With AE<br><!-- End of picture text -->

Notes: Estimations obtained using the PNAD-Continua 2019 and 2020.

To understand the sources of the difference between the observed change in household per capita income and our projections, we calculate the changes in employment and the changes in labor earnings between 2019 and 2020 using the actual 2019 and 2020 household surveys. Figure 11 shows that quintile 1 suffered the largest decline in employment and quintile 5 faced the largest 24
shock. Overall, the evidence suggests that households are likely to pool some resources, along with some retention of own income by individual members. In this case, access to one‘s own income sources as well as the strength of one‘s bargaining power within the household becomes crucial to welfare outcomes.

Important gender differences also exist in the access and control over resources in Malian society. Women in Mali are highly dependent on men ─ the responsibility first of fathers, and then husbands and if widowed adult sons (with luck). They marry very young ─ legally as young as 11 with parental consent ─ and typically marry much older husbands. The 2006 DHS reveals that 46, 70 and 90 percent of women were married by ages 15, 17 and 20, respectively. More than 43 percent of Malian rural families are polygamous (DHS 2006). The groom‘s family commonly pays bride wealth and brides move to their husband‘s village and extended family structure. A woman comes to a marriage with her own limited wealth—a trousseau that typically includes bedding, cooking pots, kitchen utensils and a small wardrobe that marks her new identity as a housewife.

In rural Mali, she often has few opportunities to build on those meager belongings. Traditional inheritance rules of descent favor men through whom houses and land are passed on. Women‘s access to property is limited and achieved primarily through their husbands. For example, land use rights can only be obtained though marriage and remain contingent on marital status. In the case of divorce or widowhood, the rights are typically lost.<sup>8</sup> Using the 2006 DHS, Peterman (2010) finds that only 40 percent of widows received any assets following a husband‘s death. The majority of the assets went to the widowed wife or her children in only 29 percent of cases.

In this culture, men and their wives do not share incomes and they keep separate budgets. In many ethnic groups such as the Bambara, men are responsible for providing the house and the grain, while women hold responsibility for the ―sauce‖ to accompany the staple, and for themselves and their dependents (Wooten 2003; Madhavan 2001). Wives are also responsible for child care, all food preparation and other domestic tasks such as washing, cleaning, water and fuel collection. Furthermore, wives and other household members are expected to work on the household‘s communal land under the management of the male head ─ husbands, or the eldest

> 8 Exceptions can occur, for example when there is a son who will eventually inherit but may not be of age or is currently busy with his studies or some other activity.

6
- 15 -

Table 3: THE RISE AND FALL OF SEATRADE

|l<br>.Category<br>l~~<br>~<br>~ ~~~~~~~~"<br>l|Voluim<br>(average ann<br>18-<br>1990<br>~~~~~~~~~|e**Changes**<br>ual percentages)<br>IM9- 2000<br>~~ACTUALS<br>PROJECTIONS|
|---|---|---|
|KEY DRY BULK COMMODITIES<br>(of which)|2.8|1.6|
|Iron Ore|2.1|1.2|
|Steam Coal|4.3|_6.5_|
|Coking Coal|2.6|2.2|
|Grain|3.3|-1.7|
|Bauxite|_5.0_|0.7|
|Phosphate Rock|- 2.4|-2.0|
|Fertilizers|4.3|2.3|
|PETROLEUM<br>(of which)|5.6|1.7|
|Crude Oil|6.1|1.2|
|Products|3.1|_3.5_|
|**MINOR**BULKS<br>(of which)|1.5|1.6|
|Agricultural Products|2.0|1.3|
|Forestry Products|2.6|2.1|
|Ores nd Minerals|1.9|1.6|
|Steel Products,<br>including Scrap|0.7|1.8|
|ALL**DRY BULKS**|2.4|1.7|
|ALL LIQUID BULKS|5.7|1.7|
|ALL**BULKS COMBINED**|4.0|1.7|
|**GENERAL CARGO**|**4.7**|_5.5_|
|_imt_<br>E0Es<br>w|"|"|

Source: For Actuals: Drewry. _'Trade and Shipping Statisics"._ Various issues. London For Projections: Peters, Hans J. 1991. _'The Prospeds of Seabome Bulk Trades - A Reassessment"._ Mimeo. World Bank, Washington, D.C.
opportunities between polygynous and monogamous households living in the same village — (Dessy et al. 2021).

# **5. Conclusion**

Climate change is the defining challenge of our time, and its impacts are far-reaching and complex. As we design policies to address this challenge, it is essential that we understand the ways in which climate change affects different groups in society. There is growing evidence that climate change has a gendered impact, with women and girls disproportionately affected by extreme weather events and other climate-related phenomena. Understanding these impacts is essential if we are to design policies that are effective, equitable, and sustainable.

The largest differences in the gendered impacts of climate change come from the social responses to weather shocks. In many parts of the world, girls and women are more likely to be discriminated against from an early age, leading to reduced opportunities for education and employment. Women are more likely to withdraw from agricultural work and struggle to find alternative sources of income after weather shocks. Boys may also be taken out of school to work, while men may be forced to migrate in search of new employment opportunities. These social responses to weather shocks could ultimately result in widening the gender gaps.

To design policies that effectively address the gendered impacts of climate change, we need a better understanding of the social mechanisms through which men and women are affected. This requires investment in research that looks at the intersection of climate change, gender, and social inequality. It also requires policies that are designed with a gender perspective, taking into account the unique challenges faced by men and women in different contexts. For this, there is a critical need for improved collection and analysis of gender-disaggregated data. As the COVID-19 pandemic has shown, pervasive gaps in sex-disaggregated data caused knowledge of the gender impacts of the pandemic to be incomplete (World Bank 2021). The lack of gender-disaggregated data makes it difficult to fully understand and address the ways in which men and women are affected differently by climate change. By improving the systematic collection and analysis of gender-disaggregated data, policymakers and researchers can gain a more nuanced understanding of the gendered impacts of climate change, which can inform the development of more effective and equitable policies and interventions.

18
not conditional on use (e.g. table 1) and translates to between 133kg and 140kg/ha of fertilizer<sup>15</sup> . This is almost identical with Sheahan and Barrett (2014) who find unconditional fertilizer use for Nigeria to be 130kg/ha using the 2010/11 data. As Table 2 suggests, the average nitrogen application per hectare (as well as most input use variables) among maize farmers is relatively consistent across years lending credibility to the data. Maize prices vary widely across the different states of Nigeria; likely reflecting state level differences such as proximity to the port (for fertilizer) and local consumption and production of maize. Average state level prices generally have a standard deviation less than 15.

**Table 2: Descriptive statistics for key study variables**

|**Variables**|**2**|**010**|**2**|**012**|
|---|---|---|---|---|
||**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|
|Household adult equivalency units (units)|5.237|2.699|5.522|2.883|
|Area planted (hectares)|1.543|1.728|1.322|1.342|
|Nitrogen applied (kilograms per hectare)|38.08|73.62|36.62|68.66|
|Seeding rate (kilograms per hectare)|19.52|30.21|18.56|24.15|
|Organic Fertilizer (1/0)|0.0190|0.136|0.0173|0.130|
|Farmer purchased seed (1/0)|0.229|0.421|0.159|0.365|
|Male plot manager (1/0)|0.845|0.362|0.832|0.374|
|Mechanization (1/0)|0.0664|0.249|0.0331|0.179|
|Animal traction use (1/0)|0.277|0.448|0.268|0.443|
|Irrigation (1/0)|0.0227|0.149|0.0165|0.128|
|Agro chemical use (1/0)|0.424|0.494|0.423|0.494|
|Distance to central market (kilometers)|73.08|40.35|70.19|43.04|
|Topographic wetness index (units)|14.42|2.710|14.28|2.501|
|Slope (percent)|3.582|2.578|3.421|2.483|
|Annual Mean Temperature (<sup>0</sup>C*10)|257.9|13.76|259.7|12.63|
|Annual Precipitation (mm)|1,335|454.0|1,363|501.2|
|Any household member could sell land (1/0)|0.757|0.429|0.780|0.415|
|Plot elevation (meters)|380.0|255.7|400.1|291.7|
|No other crop planted (1/0)|0.207|0.405|0.159|0.366|
|One other crops planted (1/0)|0.402|0.491|0.356|0.479|
|Two other crops planted(1/0)|0.239|0.427|0.264|0.441|
|Three or more other crops planted(1/0)|0.153|0.360|0.221|0.415|

15 This translates to about 150kg of fertilizer per hectare assuming the nutrient content of NPK fertilizer is 27:13:13 while that of Urea is 46% of the fertilizer quantity.

14
instruments (variables that plausibly affect only corruption, but not confidence in institutions, and vice versa) is very difficult to implement since it is hard to make a compelling case for the requisite exclusion restrictions.

In particular, we find it hard to make a convincing case that there are variables in the GWP that predict corruption at the individual level but do not have predictive power for confidence in institutions that we could then use as instruments for corruption. To illustrate why we think this approach is not promising, consider the identifying assumptions implicit in the few papers that have considered this reverse causation problem. Cho and Kirwin (2007) make the identifying assumption that variables such as respondents‟ overall trust in others, and their perceptions of the political influence of ethnic groups, matter only for corruption and has no direct effect on confidence in institutions (see their Table 1). Lavallee, Razafindrakoto, and Robaud (2008) claim with little justification that a dummy variable indicating that the respondent is head of the household, and a variable capturing the respondent‟s views on the acceptability of paying a bribe, matter only for corruption and have no direct effect on confidence. We do not find such exclusion restrictions to be convincing. One might easily imagine that any of these variables are directly correlated with confidence in public institutions: for example respondents‟ might believe that paying a bribe is acceptable precisely because they have no confidence in public institutions. It is also striking that in both papers, the instrumented estimates of the effects of corruption on confidence are vastly _larger_ in absolute value than the uninstrumented estimates, while the feedback problem these authors seek to correct would suggest that the true effects of corruption on confidence should be much _smaller_ in absolute value than the corresponding OLS estimates (see columns (1) and (2) of Table 1 in Cho and Kirwin (2007) and Table 4 in Lavalee, Razafindrakoto and Robaud (2008)). These counterintuitive results likely signal nothing more than a failure of the exclusion restrictions required to justify the instrumental variables estimator.<sup>13 14</sup> In contrast, we have consistently found that the magnitude of the

> 13 Lavalee, Razafindrakoto and Robaud (2008) claim support for their identification strategy in the fact that tests of overidentifying restrictions fail to reject the null of instrument validity.  Here they fall into the (unfortunately common) pitfall of failing to realize that such tests are valid _only if at least one instrument is indeed valid_ .  We think it is very difficult to make such a case in this context.

20
# Appendices

|Appendix Table 1: Constraints and Interventions<br>in theBank's<br>Higher<br>EducationProjects.........................<br>.<br>_50_|
|---|
|Appendix Table 2: Results of Bank-Assisted Interventions to Improve<br>Femiale Higher Education ...................................<br>_55_|
|Appendix Table 3: Recent and Ongoing Bank-Assisted<br>Interventions to Improve Female Access to Higher Education .............<br>62|
|**List**of Tables|
|Table 1: Review of Bank-Assisted Higher Education Projects<br>.<br>..................<br>6|
|Table 2: Qualitative and Quantitative Treatment of<br>Gender-Specific Interventions ................................<br>31|
|Table 3: Characteristics of Countries and Promising Approaches:<br>A Typology from Bank Experience ...........................<br>41|
|List<br>of Figres|
|Figurela:HigherEducation Projects and Project Investment: 1972-1981 ....<br>...<br>...<br>. 8|
|Figure lb: Higher Education Projects and Project Investment: 1982-1992 ....<br>.......<br>8|
|Figure 2a: Projects Acknowledging Gender as an Issue and Project Investment: 1972-1981 9|
|Figure 2b: Projects Acknowledging Gender as an Issue and Project Investment: 1982-1992 9|
|Figure 3a: Projects with Interventions and Project Investnent: 1972-1981 ....<br>......<br>11|
|Figure 3b: Projects with Interventions and Project Investment: 1982-1981 ....<br>......<br>11|
|Figure 4: Specific Interventions by Type .............................<br>_20_|
the AS1 group, and had issued title certificates for 1,481 (covering 410 hectares). The remaining 815 unfinished cases were to be completed by the Lands Commission of Ghana. In addition to this, plans for 5,729 land parcels, covering 5,039 hectares had been produced.

The nature of the project and the interventions which accompanied this pilot program were aimed at some of the structural barriers to land registration. The fact that the program created a new registration district and an office with modern equipment for land data collection, processing and storage removes some of the bottlenecks associated with previous attempts. The program also created incentive structures to nudge officials to maintain a constant workflow. Finally, the program facilitated negotiations with chiefs and family heads who hold allodial titles to land in order to ensure consent for the issuance of titles.

# **4.2 Evaluation Design**

We build our analysis on three survey waves conducted in 20 communities that are located around a main road that forms a closed circuit. This main road geographically divides many of the sample communities into two halves. This circuit also corresponds to the boundary of the Land Titling Program. Households within the loop were eligible to participate in the pilot while households outside of the loop were not eligible in the first phase of the program which spans our entire evaluation period. The physical demarcation of the road dividing communities into two forms the basis for our empirical evaluation strategy.<sup>7</sup> More precisely, households located within the loop of the road and not more than 200 meters away from the road are considered as the treatment group in our study. On the other side of the main road, i.e. outside of the loop and outside of the pilot area, we sampled households such that they form two types of control groups: a short-term control group and a long-term control group. At the time of project initiation, it was anticipated that households just outside the loop would be the most likely future recipients of the land registration assistance (although that never materialized). We refer to households outside of the loop but within 200 meters of the road as the short-term control group.<sup>8</sup> Households outside the loop and located more than 500 meters away from the road constitute the long-term control group. For this evaluation we will exclusively focus on a comparison of the treatment group with the short-term control group. The locations of sample household around the road used to distinguish between treatment and control households are also shown in Appendix Figure A1.

> 7 Investigations at the start of our evaluation indicated that this division of communities was unintentional, the road was chosen since it gave a clear boundary. 8 The sample was designed to include every household within 200 meters of the boundary on either side. The 200 meter range was chosen to give us our desired sample size based on the number of households estimated from satellite photographs.

12