rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10379

# **Abstract**

This paper analyzes how the intersectionality of gender, forced displacement, and collective violence shapes coping behaviors in conflict crises, paying particular attention to household composition by gender and age. Drawing on survey data from 17,951 individuals in North-east Nigeria, the analysis finds that coping behaviors at the household, adult, and child levels are interlinked and strongly shaped by compounding challenges stemming from individual gender, household forced displacement status, and local violence shocks. These challenges have significant welfare implications and create severe vulnerabilities and special needs for specific groups of households and individuals, such as rural communities affected by violence, large households with many children, female breadwinners, and displaced girls. The findings emphasize the need for and potential of concerted policy approaches that account for the intersectionality of gender, displacement, and violence in conflict settings and pay particular attention to specific types of communities, households, and individuals.

This paper is a product of the Gender Global Theme. 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://www.worldbank.org/prwp. The authors may be contacted at stojetz@isdc.org.

_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, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
itself contributed to a reduction in teacher absence of 2.4 percent. Our estimate of the change in teacher absence rate is exactly in this range, and suggests that the reduction of teacher absence we document is consistent with a proportional increase in ‘inputs’ into education, but a limited improvement in TFP in this period. We discuss the policy implications of this in the conclusion.

Finally, to interpret the cost of teacher absence to students, we note that the effective attention a student receives from a teacher can be increased both by reducing teacher absence as well as by hiring more teachers. To account for the reduced attention that students receive when teachers are absent, we define the “effective student teacher ratio (ESTR)” as: We use official DISE data on total enrollment and total number of teachers, combined

> 14The cross-sectional relationship is estimated by regressing village-level teacher absence on the log of district-level per-capita consumption (from the National Sample Survey) in the 2003 survey. Estimates without state fixed effects are larger (and equal -1.17) whereas estimates with state fixed effects are smaller but still significant (and equal to -0.63). Our default estimate is based on using state-fixed effects since cross-state variation in per-capita income is much more likely to be correlated with unmeasured governance quality. Tables are available on request.

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Alternatively, the empirical specifications demonstrate that food access was significantly worse in the northern parts of the country that are largely controlled by Houthi forces. The fact that food access tended to be worse in the north could be indicative of both the tightening of the ports and the lack of government salaries adversely affecting food security. The ports that supply the north have been subjected to more pressure than ports in regions controlled by the internationally‐recognized government, and reports suggest that government salaries have been paid less often in those same regions (e.g., WFP 2017). However, the worse food security outcomes could also be due to other conflict‐ related factors, such as poor governance and oversight given the lack of control by the internationally‐ recognized government.<sup>8</sup> However, there is further evidence that the tightening of the ports in particular had a large and immediate impact on the population. During November 2017, there was an unexpected air and sea blockade of the entire country, which dramatically worsened the disruption to the supply chains. One of the monthly WFP mobile phone surveys was being fielded as this blockade was implemented, and one can observe an immediate and significant decline in food access overnight. Given the speed with which households were affected, these results suggest that food supplies in the country are very tight and have been substantially stressed by the difficulty in importing goods over the course of the conflict.

There are two primary contributions of these results. First, the finding that violence is less correlated with levels of welfare than other conflict‐related shocks contrasts with findings in other settings where aggregate economic outcomes, firm outcomes, and individual employment outcomes are adversely affected by violence (e.g., Abadie and Gardeazaba 2003; Singh and Pedersen 2003; Guidolin and La Ferrara 2007; Collier and Duponchel 2013; Klapper et al. 2013; Ksoll et al. 2015; Amodio and Di Maio 2018; Tandon forthcoming; etc.).<sup>9</sup
rather well with the aggregate data from the World Bank. Second, in conversations with manufacturers in Rwanda, they often highlighted the troubled political situation in the two neighboring countries. Third, results in tables below show that the decline in the last two years is most pronounced for manufacturers, which are more likely to export to neighboring countries than agriculture or mining firms.

If we used a threshold that is 10 times higher, i.e. exporting 100 000 USD per year, only 71 firms in Rwanda would qualify as globally engaged by 2016. While this is lower than earlier number, it is not that much lower given the 10-fold increase in the threshold. It highlights that very few firms export more than 10,000 USD, but less than 100,000 USD. In more developed countries, we expect a power law, meaning that the number of firms that export 10 times as much would be 10 times smaller (or some other proportionate decrease). This is clearly not the case in Rwanda. Among the 101 firms that exceed the 10,000 USD threshold for exports, more than one third even exceed the 100-fold higher threshold of exporting at least 1 million USD per year, depicted by the black line in Figure 4.

A potential explanation for the rather limited number of firms that export small quantities is that fixed cost of exporting are important. This mechanism features prominently in the seminal paper of Melitz (2003) to explain export market participation. Only firms that are sufficiently productive and that can reasonably expect to export a sufficiently large amount, find it worthwhile to incur the necessary fixed outlays to enter the export market. These fixed costs can be related to transportation infrastructure, searching for potential clients, adjusting the product to foreign market, familiarizing oneself with customs procedures, etc.

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follow up. Within recontacted villages, an additional 412 households had left the village or could not otherwise be located and re-interviewed. When possible, replacement households were randomly drawn from a listing of eligible households in the villages with children aged 6-10 who had not been sampled in 2006. A total of 286 replacement households were added to the sample in 2008. Recontact rates therefore are 96% at the village level and 87% at the household level.

An extensive household questionnaire was used to collect information on household composition, assets, livestock, agricultural, shocks, food security, diet diversity, and social capital. The household questionnaire also included detailed education histories and daily activities for children age 6-14 (6-16 in 2008), and diet diversity, anthropometry, and hemoglobin assessments for children age 3-10 (3-12 in 2008). To the extent possible, the household survey modules were adapted either from the 2002-03 Lao Expenditure and Consumption Survey (LECS3, a Living Standards Measurement Survey) or from survey modules used in similar school feeding program evaluations. Food security and diet diversity questions were drawn from the FANTA Diet Diversity and Household Food Security scales (Hoddinott & Yohannes, 2002; Swindale & Bilinsky, 2005). In 2008, modules were added on child health and morbidity, parental perceptions of the school feeding programs.

In both 2006 and 2008, the initial English versions of the questionnaires were translated into Lao by the Lao National Statistics Centre staff and pretested. Revisions were made to the questionnaires based on pretesting. After final revisions of the English and Lao versions, a blind back-translation from Lao to English was completed and checked against the English version for consistency. Enumerators were recruited and trained by the National Statistics Centre and provincial and district officials, and included men and women fluent in at least one local language in addition to Lao. Two nutritionists with extensive training and fieldwork experience 15
The historical migrant networks are extensively used in the literature to estimate current levels of migration. Migrant networks, which are ties between migrants, former migrants, and non-migrants at the origin through bonds of kinship, friendship, and shared community origins, might be the most important mechanism for international migration (Massey, 1988). Sociologists and anthropologists have been studying the role of networks on migration for a long-time (Tilly and Brown, 1967; Mitchell, 1969; Choldin, 1973; Hugo, 1981) and economists have also found that networks play an important role in migration decisions (Hägerstrand, 1957; Greenwood, 1969; McKenzie and Rapoport, 2007; Woodru and Zenteno, 2007; Foged and Peri, 2013). This is because migrant networks reduce the potential hazards at both the destination and the origin and decrease the cost of relocation (Massey, 1988; McKenzie and Rapoport, 2007).

Historically, international migration networks are region specic in Nepal. For example, most of the people joining the Indian and British armies in the 1930's (and up to the present day) were Gorkhas from the Pokhara region when the recruitment started. Similarly, people from the southern plains migrated after the beginning of the extensive cultivation of tea in northern India (Seddon et al., 2001). People from Far-Western villages in Nepal tend to migrate to the same destination in India as their co-villagers (Thieme, 2006). Figure (A.3) shows the top 10 origin districts for laboremployment migration at present. These top districts, which are located mostly in South-Eastern Terai, account for 36.5% of the total labor-related migration from Nepal between 2008 and 2014.

In order to investigate the role of past community level migration on current levels of migration in the community, I calculate the shares of migrants in 2001 and 2011 to a particular destination country from a VDC using the 2001 and 2011 Nepal Censuses. Then, for each destination country, I regress the share of migrants to the destination
91 days, in the future.<sup>13</sup> A subset of these subjects were revisited some time prior to t=61 and given the opportunity to revise the allocation between t=61 and t=91. The extent of this revision is our dependent variable. We examine correlates of this revision decision corresponding to each of the four potential determinants of revision outlined above. Surveys at the baseline and revision stages measured household wealth, income, and expenditures as well as the participants’ expectations for each of these variables.

In addition, we implemented a complementary test of hyperbolic discounting models in our experimental context: average revisions toward sooner should be larger the shorter the time lag between the revision decision and the first disbursement date (t=61). We randomized the number of days prior to t=61 when each experimental subject had to make the revision decision.<sup>14</sup> We find that initial allocations usually adhere to the law of demand; individuals allocated more income to later periods when offered higher rates of return to waiting. We interpret this to indicate that experimental subjects mostly understood the choices they were making and made them in a way that economic theory has some hope of explaining. We also find that “static” preference reversals are frequent, but only slightly more likely to be “present”-biased (as opposed to “future”-biased). This finding largely contrasts other studies using the multiple price list (MPL) method, but is consistent with Andreoni and Sprenger’s (2011) findings that UC San Diego students exhibit no present-bias on average.

Turning to revision behavior, we find that revisions are common, often substantial in size, and shift money both sooner and later. We find support for “present”-biased preferences as a driver of revisions: subjects shift more money toward sooner when: (1) their initial allocations are “present”-biased, and (2) the time lag to disbursement is
and fertility are other major threats to the identication strategy. The conict intensity variable is dened at the level of an individual's village of residence at the time of the survey. Systematic sorting by economic and physical status between the stayers at high conict villages and movers from the high conict to low conict villages is of concern, which will lead to overestimation of the impact of conict. Unfortunately, I do not observe women's village of birth. However, the 2016 NDHS asked each individual how long she has been residing at the place where she was surveyed and if not her entire life, the name of the district from which she migrated. Twenty-six percent of the women in the sample are living in a dierent district. Although the survey did not collect the information, marriage is the most likely reason for women's migration, as most women in Nepal move to their husband's home permanently from their maternal home, which is also likely to be their place of birth.

I dene conict at the level of women's districts of birth (district they moved from) and estimate the same specication as Equation (7) except with district of birth xed eects and region of birth trends and present the results in Table 6. Columns 1 to 3 examine the presences of dierences in migration patterns between the control and treated cohorts. The dierence-in-dierences across the specications are zero, suggesting no selective migration. Columns 4 to 9 re-estimate the main results from Table 4. The dierences-in-dierences estimates, although reduced in magnitude, are statistically signicant and in the same direction as in the main specication for age cohort 0 to 3. Villages within district are highly diverse and dening conict at the district level takes away that variation. Additionally, I limit the sample to women living in the village where they were surveyed their entire life and estimate Equation (7)
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# **_5.1 Local linearity of learning gains_**

In the initial example presented in this paper, _LAYS_ is calculated using scores of 8<sup>th</sup> - graders. When we apply the relative learning in 8<sup>th</sup> grade to different average numbers of years of schooling, ranging from about 6 to 14 years of schooling, we are implicitly assuming that this learning ratio across countries remains constant—that each year of schooling is worth the same amount, in terms of learning, in any given country over that range (even as the learning rate differs across countries). While this assumption will not literally be true, we can test to see whether it makes sense as a rough estimate of cross-country differences. As the following sections will show, the evidence suggests that it does.

# _ASER data from India_

One way to investigate learning trajectories is through an assessment that tests the same content across multiple grades. Tests such as PISA and TIMSS are tailored to each grade and age in which they are administered and are normed at the relevant level. This approach might produce linearity by test construction or scaling, even if the underlying learning trajectory on a constant measure would not be linear. To get around this problem, we analyze learning data from India collected for the Annual Status of Education Report (ASER), for which the NGO Pratham administers the same exact test to students from ages 5 to 16 across Grades 1 to 12 (ASER Centre 2017). The ASER data enable us to assess the rate of learning with a stable, comparable metric across grades and over time. To allow us to map out the specific trajectory for learning in school, we restrict our sample to school-going children.<sup>18</sup> Figure 5.1 shows that students learn to divide along an S-shaped learning trajectory, with a locally linear interval from Grades 6 to 10. Figure 5.2 shows how often this locally linear interval appears at
condominium buildings and grounds is still in many cases undertaken by public companies at below-cost payments.

**Table 4. Ownership of Apartments and Buildings in Studied Countries as % of Housing**

|||**Stock**<br>|||||
|---|---|---|---|---|---|---|
|**% of housing stock***|**Armenia**|**Lithuania**|**Poland**|**Romania**|**Russia**|**Serbia**|
|Apartment units|45.2|62.0|71.2|42.8|71.4|29.9|
|in public ownership|5.8|3.8|13.8|1.3|31.4|2.3|
|inprivate ownership|39.4|58.2|57.4|41.5|40.0|27.6|
|in buildings with<br>condominiums|5.9|8.7|5.8|20.7|5.0|n/a|
|_in buildings with whole_<br>_ownership structure*_<br>_in other buildings_|<5%<br>>33%|<5%<br>>50%|~60-65<br><5%|<5%<br>>20%|<15%<br>>50%|<5%<br>>25%|
|in buildings with scattered<br>public-private ownership|>5%|>10%|almost<br>nil|14.5|36.0|>5%|

_* Estimated_ _Sources_ : Apartment sector size: National Statistical Offices. Ownership status: LSMS, HBS, and Lithuania Household
below a particular income threshold. Accordingly, we simulate the poverty impact of a program that targets the poorest income quintile, and compare the results with a program that targets the population below 2.5 USD per day.

**Figure 9: Relative targeting (children only)**

<!-- Start of picture text --> 80%<br>Categorical<br>60% Targeted bottom 20%<br>Targeted Extreme Poor<br>40%<br>20%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases The poverty impact of targeting the poorest quintile varies dramatically according to the poverty headcount and gap of each country. In Peru and Ecuador, where extreme poverty incidence among children is close to 20 percent, the two approaches are equivalent. In cases where poverty incidence is lower than 20 percent, a relative targeting would still reach all poor, but will dilute the average transfer due to the inclusion of non-poor beneficiaries. The resulting lower average transfer would imply that some of the poor will not be able to be lifted out of poverty. The lowest poverty impact in targeting the poorest quintile is however in countries with widespread pockets of poverty exceeding 20 percent, and in countries that have large poverty gaps, such as Nicaragua, Guatemala and Bolivia. In these cases, targeting the bottom quintile misses the beneficiaries who remain close to the poverty line, hence the transfers lift very few (or no) beneficiaries out of poverty. At the same time, however, the transfers reach more generously the poorest beneficiaries that remain far from the poverty line. It is not possible to assess, from a welfare perspective, which modality delivers higher welfare. The exercise shows however that changing the targeting modality can affect 17
at the “pre-analytical” stage, and the reproducibility of the analysis. This, in turn, has farreaching implications for the consistency of welfare comparisons: when the methodology for identifying outliers differs, the consistency of both inter-temporal and international and intra-country geographic comparisons is at risk (Ravallion 1994; Atkinson 2019: ch. 4). Our preliminary empirical exploration, based on a selection of data sets drawn from the RuLIS project, provides a tentative assessment of the effect that lack of harmonization in dealing with outliers can have on welfare comparisons.

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roof. When enumerators and manuals are different for different surveys, there can be systematic differences to the same question asked.

Table 10 presents the statistical tests of the common variables between the two data sets (SWIFT and MCNA). All the common variables between the surveys are significantly different. The average household size in the SWIFT survey for the north is about 7, which is significantly greater (by one member) compared to the MCNA data. Similarly, there is significant difference in housing type, source of drinking water and household employment rate between the two surveys. As a result, the PPMT score in the MCNA data is slightly higher than in the SWIFT data. This implies that the PPMT would suggest an average person from the MCNA data is richer compared to an average person from the SWIFT data, which would directly impact the eligibility overlap using a common cut-off threshold.

_Table 10: Summary statistics of key variables by MCNA and SWIFT survey_

||**Survey**|||
|---|---|---|---|
|Variables|**MCNA**|**SWIFT**|**Difference**|
|Home: house or flat|0.905|0.986|-0.082***|
||[0.294]|[0.117]|(0.012)|
|Household size|5.946|6.993|-1.048***|
||[2.801]|[3.274]|(0.273)|
|Employment rate (emp/hhsize)|0.180|0.209|-0.029***|
||[0.179]|[0.144]|(0.011)|
|Water: public network tap|0.809|0.949|-0.141***|
||[0.
range from 1000 to 2000; b. estimated by expert; c. data from field survey based on Hainan project; super critical fluid process is expensive on capital and operation costs compared with conventional acid/basic catalyzed process according to Hainan project; d. data from Wu et al.(2008).

# **3.3.3 Challenges**

The feedstock cost again is one of the major challenges to the development of biodiesel. For example, jatropha has a gestation lag of 3-4 years and the amount of capital required is substantial (Pradip, et al 2010), particularly when it has be grown in marginal lands. Although about 0.2 million hectares of planting base have been built in China, only 40 tonnes of jatropha-derived oil has been produced so far (Wang, 2010). Farmers do not plant jatropha unless they see explicit return from it. According to the case study in Panzhihua, the capital cost of planting base is about 4500-6000 Yuan/ha. (Xin & Zhang, 2011). Due to high risk, the actual development of Jatropha industry in Panzhihua shows sharp contrast to initial expectation (Liu et al., 2010).

# **3.4 Second-generation biofuels**

# **3.4.1 Resource potential**

The cellulosic resource for 2<sup>nd</sup> generation biofuels includes agriculture and forest residues (Table 9).

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