rafmacalaba/data-use-mentions-extended huggingface dataset:
risks or whether more targeted interventions are needed.

The application builds the model using 2015-2019 country-level data on food insecurity conditions available for around 80 of 144 countries classified in the World Bank’s IDA and IBRD cohorts (59 IDA, 15 Blend, 70 IBRD), together with annual covariates that capture economic and structural drivers of food insecurity that can be obtained from official public sources. The model is a local-linear regression implemented using a Cubist model that utilizes decision trees to partition the data and ensemble multiple robust linear models that can extrapolate local elasticities. The algorithm provides a novel approach to combine the flexibility of decision trees with the stable extrapolation properties of linear regression techniques. The predictions are validated against different sets of holdout data that test the model’s ability to forecast large temporal changes and fill regional gaps. Since simplicity carries a premium, the accuracy of the proposed approach is also compared to that of a linear regression. The results indicate that the linear regression is not able to cope with the heterogeneity and nonlinearity in the data. The final Cubist model, however, uses 11 easy-to-interpret indicators, to explain more than 95% of the variation in out-of-sample data.

The key predictive features are easily projected forward using available economic outlooks. The model is applied to historical economic data and the WEO
models are not capable of estimating economy-wide impacts of energy or climate change policies.

Researchers are working on linking the top-down models with bottom-up models so that they can capture technological details and at the same time produce economy-wide impacts of a policy. There are two ways to link top-down CGE models with bottom-up sectoral models. One way is hard linking of these two models which refers to directly reflecting detailed subsectors/technologies in the top-down model by disaggregating economic sectors/technologies in the social accounting matrices (SAMs), the main database for calibrating a CGE model. The resulting model is also referred to as a hybrid model (e.g., Dai et al. 2011; Dai et al. 2016; Dai and Mischke, 2014; and Schafer and Jacoby, 2006). However, the hard linkage is quite complicated due to the lack of data, and it is applicable only for limited technology, such as electricity generation. Studies such as Timilsina et al. (2018), Timilsina and Shretha (2006), Timilsina and Landis (2014) have applied this approach to model impacts of substitution of fossil fuel-based electricity generation with renewable energy-based electricity generation. The second way is understood as soft linking. It is an iterative process where the top-down model is run first to project key variables such as sectoral outputs and value-added, commodity prices and so on. These variables are then used in the bottom-up model, which then produces optimal energy supply or electricity supply systems depending upon whether the bottom-up model is an energy sector model or electricity model. The results from the bottom-up the model are then fed into the top-down model. A few rounds of iterations are carried out until both models converge. If the modeling assumptions are consistent between the two models, iteratively running the two soft-linked models can lead to a consistent equilibrium (Böhringer and Rutherford 2008, 2009). Several studies, such as Bukowski and Kowal (2010), Drouet et al. (2005), Schafer and Jacoby (2006), World Bank (
mark> <mark>Sandleris, G., Wright, M.L.J., 2014. The costs of financial crises: resource misallocation, productivity, and welfare in the 2001 Argentine crisis. Scand. J. Econ. 116,</mark> <mark>Sandleris, G., 2016. The costs of sovereign default: Theory and empirical evidence. Economia, 16(2), pp.1-27. https://ideas.repec.org/a/col/000425/014408.html</mark> <mark>Sarkees, M. R., Wayman, F., 2010. Resort to War: 1816 - 2007. Washington DC: CQ Press. Souma, W., 2001. Universal structure of the personal income distribution. Fractals, 9(04), 463-470. https://doi.org/10.1142/S0218348X01000816</mark> <mark>Standard and Poor’s, 2006. Sovereign Defaults and Rating Transition Data: 2006 Update. Standard and Poor’s.</mark> <mark>Stiglitz, J. E., Fitoussi, J. P., Durand, M., 2018. Measuring What Counts for Economic and Social Performance. OECD. https://doi.org/10.1787/9789264307292-en</mark> <mark>Strauss, J. and Duncan, T., 1998. Health, nutrition, and economic development. Journal of economic literature, 36(2), pp.766-817.</mark> <mark>https://EconPapers.repec.org/RePEc:aea:jeclit:v:36:y:1998:i:2:p:766-817</mark> 39
WPS3784 headcount index in rural Rwanda stands at 47 percent, against only 13 percent in urban areas<sup>2</sup> (Table 1). The EICV also shows that poverty varies substantially across provinces. The poverty incidence is highest in Gikongoro (57 percent) and lowest in Kigali Ville (10 percent).

Denoting _y_ as the poverty status (incidence index) of observed individuals within the sample, and _X_ as a vector of major individual, household, community and regional characteristics, the probability of escaping poverty is defined by the probit model: Pr( _y_ = 1 _X_ ) = Φ ( _Xb_ ) (1) Φ is the standard cumulative normal probability distribution and _Xb_ is the probit score or index. The probit coefficient, _b_ , shows the impact of a one-unit increase in the predictor on the probability of not being poor. A one-unit increase in the predictor leads to an increase in the probit score by _b_ standard deviations. We model the probability of being out of poverty as a function of the following predictors: market access community variables (such as, access to roads, access to electricity, proximity of markets), household and individual characteristics (demographic profile, head of household education, land size, livestock, etc.), and a set of control variables such as provincial dummies.

Table 2 shows the results of the probit estimation. A key feature of these results is the central role of community variables in escaping poverty. Access to year-round roads, credit and electricity increase significantly the chances of escaping poverty. Of particular interest in the context of this paper is that all of the market access variables significantly influence the chance of escaping poverty. Households that do not have easy access to a road and/or have to use impracticable earth roads to connect to secondary or main roads have limited or no access to product and factor markets, substantially reducing the opportunities for escaping poverty. Regional disparities in the probability of being poor partly reflect this result. Residents of Kibuye,
180. An average active jobseeker submitted around 10 applications per quarter (Figure 22). The next set of research questions focuses on the behavioral patterns of jobseekers and employers in application and selection.

**Figure 22. Number of applications per vacancy and jobseeker over time**

<!-- Start of picture text --> 200 14<br>12<br>150<br>10<br>100<br>8<br># applica�ons received per vacancy 50 # applica�ons submi�ed per job seeker 6<br><!-- End of picture text --> _Source_ : Authors’ calculation using the data from Rozee.pk. _Note_ : The activeness of jobseekers is defined as that they apply for at least one job in a given quarter.

# **_Q. How fast do job postings receive applications? What types of job postings receive more applications?_**

**_A. The online job market is fast_** . **_A job posting receives nearly 100 applications on average in two weeks. Jobs that require a higher level of education, do not indicate gender preference, and are in manufacturing sector receive a greater number of applications. Job postings advertising higher salaries receive a smaller number of applications._**

- _Finding 1: Many applications were received immediately after jobs were posted._ 80% of applications were received in 14 days, and 90% in 25 days. This means that out of 180 applications to an average vacancy, more than 150 applications were received within 25 days. This shows how fast the online job market is.

- _Finding 2: Job postings that do not indicate gender preference receive more applications_ . The average number of applications is 6% and 75% fewer for, respectively, male- and female-preferred jobs than gender-free jobs. This result shows the effectiveness of signaling gender preference, particularly preference for female workers.

- _Finding 3: Jobs that require higher education receive more
opportunities (Munshi 2003). By analyzing consumption we focus on the joint and final effect of all such mechanisms.

The observed divergence between migrants and non-migrants in these data also persists within extended family networks (Beegle, De Weerdt, and Dercon 2011), which violates the full risk-sharing hypothesis (Townsend 1994), and does not support the notion that migration is the result of a household level maximization strategy (Stark and Bloom 1985; Rosenzweig and Stark 1989; Grimard 1997). It could, however, be consistent with other reciprocity-based models (e.g. limited commitment, moral hazard, or hidden income) that permit the co-existence of divergent consumption growth and risk sharing. In our empirical analysis, we find that migrants are affected by shocks to others in the network whereas non-migrants are not. Such unilateral insurance leads us to reject the reciprocity-based risk-sharing models.

One explanation for this observed lack of reciprocity could be that migrants insure non-migrants in exchange for other benefits (Lucas and Stark 1985; Hoddinott 1994). These benefits could accrue to the migrant later in life and outside the purview of our survey data. We consider, but reject a number of such longer-run transactional motives for the observed unilateral insurance. The results are, however, very much in line with findings from the diverse literature on social norms (Platteau 2000; Cox and Fafchamps 2007; Burke and Young 2011), where those who move ahead remain obligated to their extended family in the home community.

Our results speak to an emerging literature that worries about home communities imposing a stifling ‘kin-tax’ on the upwardly mobile. Baland, Guirkinger, and Mali (2011) show how people take out costly loans in order to conceal their income, while Platteau (2012) sees migration as a means to escape the implied prying eyes and incessant demands of the kinship group. The kinship poverty trap model of Hoff and Sen (2006) predicts possible resistance from the home communities as they feel threatened by productive forces leaving and severing links with home to
education attainment (as measured by years of schooling). In this paper, we unbundle the human capital variable further using assessments of skills that are typically not available for a large sample of adults in developing and transition countries—cognitive skills (literacy proficiency) and a number of noncognitive or socioemotional skills. Our analyses of the data on adults aged 25-54 from comparable surveys in nine middle-income countries reveal that men and women differ not only with respect to how much schooling they have completed but also with regard to how much cognitive and noncognitive skills they possess. In addition, we note that the size and direction of these gender differences vary across our sample of countries.

Our sample countries span four world regions and vary widely with respect to their GDP per capita, average schooling levels, demographic characteristics such as fertility rate, and economic structures such as the employment shares of their informal and formal sectors and their industrial composition. Three of the countries were formerly part of the Soviet Union (FSU) and together with another former socialist country, Serbia, have education levels and employment rates that are generally more equal between men and women, as compared with other countries, reflecting past socialist policies in those countries. The other five countries are low-middle to middle-income countries in Africa, Asia and South America. The country differences are evident from the gender gaps in schooling, skills and earnings and in the relationship between earnings and the different measures of human capital.

Starting with the pooled sample of countries, we find that estimating the log-hourly earnings function separately for men and women is justified, given statistical significance tests. These gender differences arise in part because labor markets in these countries value the schooling and skills of men and women differently, a product of social norms and institutions that shape 57
This study utilizes a large N and large T panel data structure comprising 197 countries over 51 years over a period of 51 years spanning 1970-2020.<sup>31</sup> The full sample is composed of four subsamples: 28 low-income countries (LICs), 46 lower-middle-income countries (LMICs), 56 upper-middle-income countries (UMICs) and 67 high-income countries (HICs).<sup>32</sup> Table 1 provides information on the relevant data sources.

# 4. **RESULTS AND FINDINGS**

# **4.1 Descriptive statistical analysis**

Descriptive statistics reflect a high degree of variability and heterogeneity within the panel. This is in line with expectations given the variety of countries included covering different regions and income groups (see Table A3 in the Appendix). Pairwise correlations show evidence of a positive correlation between trade intensity and GDP per capita, even though the correlation appears low as reflected by a coefficient of 0.4 (see Table A1 in the Appendix). Similarly, Figure 2 shows that trade intensity is positively associated with GDP per capita. This indicates that, on average, countries that trade more tend to have higher level of GDP per capita.<sup>33</sup> While the correlation analysis provides evidence of a positive relationship between trade intensity and GDP per capita, correlation does not imply causality.

**Figure 2** . The relationship between trade intensity and growth

<!-- Start of picture text --> A- Trade (% of GDP) - 1970-2020 B-Adjusted trade (% of GDP) - 1970-2020<br>12 12<br>CYM CYM<br>11109876 BRAUSASDNARGJPNINDBGDAFGCHNPAKNGACOLPYFUGARWAETHAUSCOMTURNPLHTIPERBFANERTZAMMRIRNMDGURYMEXECUITACMRESPGRCZAFGTMEGYFRACAFGNBIDNVENBENMLICUBSLESENGBRRUSKENCHLSYRNZLBOLUZBGHAGINNCLYEMDEUDZATCDCODCANMARPHLDOMZWEALBPRTCIVLAOFINSLVKORISRROULKAPRYZMBSWEGMBLBYIRQNORERINICGUMSRMOZCRIBMUISLSAUMRTWSLARPSEPOLSSDDNKAUTKAZXKXTONHRV B TUNBTNTGOGEOTHAAZENJAMUKR M GABBIHQATTKMHNDAGOBRBCPVGRLBLZVUTMKDOMNBHSDMANAMBGRPNGKWTSURFSMCHEKGZBWAFROLVAPRIMNEBRNKIRMNGSLBTJKKHMTLSFJICOGVNMMDAGNQCZEMUSMYSJORNLDCYPLTUBLRPANMHLSVNLSOBELMNPSYCHUNSWZIRLSVKESTAREMACABWNRUCUWBHRGUYATGASMMDVSXMMLTLUXHKGDJISMRSGPVIR 11109876 COMNRUGNBKIRTONMHLFSMWSMDMAVUTBTNGMBPYFRWAERICAFCPVTLSSLBSLEHTIBLZBFAGRLASMATGNERMNPSYCBENGUMFRONPLNCLLAOMLIUGABMUMDTCDSURSXMXKXLSOAFMNEALBMRTARMGINTJKSDNMDVTGOTZAKGZ KHMPSEBRBCUWFJI G ETHMDAABWMOZNICSENZMBSWZSSDZWEGUYURYBOLCMRMKDYEMCODGEOMNGMMRSLVNAMGHABIHBWABGDUZBGTMSMRBHSISLPRYCOGMUSTKMKENPNGECUHNDGABDJIJGNQBRNCRILBNDOMPAKLKA A CIVSRBPERLVAMZECOLARGCYPJORTUNHRVNGALBYESTMARMACOMNSYRMLTEGYCUBAGOLTUNZLPANBGRCHLKAZBHRROUDZAGRCBLRSVNQATBRAVIRPHLVENIRQPRTTURUKRISRZAFIRNKWTPRIFINIDNINDAUSAUTSVKVNMHUNNORCZEMEXDNKPOLTHALUXESPSWERUSSAUKORMYSJPNIRLCHNITAARECANCHEFRAGBRUSABELNLDDEUHKGSGP<br>5 BDI SOM 5 BDI SOM<br>4 4<br>3 4 4 5 5 6 6 -8 -6 -4 -2 0 2 4<br>Log of Av. Trade (% GDP) (1970-2020) Log of Av
9 (GIS, LIS and CMS) is evident. In the case of Gansu, the rise in coverage between 2000 and 2004 seems to be due to an expansion of insurance coverage among children for which the contributions (and presumably the benefits) are fairly low. Considerable provincial variation in insurance coverage is evident in the H8BS, especially in the CMS which was operating in only two provinces in 1998.

As covariates, we include health status, per capita household income, schooling, and in the case of the cross-section age, gender, ethnicity, marital status and the number of children aged 5 or less. Two of the surveys contain self-assessed health (in the GSCF, the assessment was actually done by the woman of the household), which at least in industrialized countries has been found to be a good predictor of mortality and the onset of disability (cf. Idler and Benyamini 1997). In the case of the H8BS, no self-assessment of health is available, and we used instead a chronic illness dummy. We have measured schooling by years of schooling in the CHNS and the GSCF, but in the H8BS have left education coded as levels of attainment. In each of the panel models, we include wave fixed effects as well as individual-specific fixed effects.

# IV. **RESULTS**

The basic CHNS results are reported in Table 4, the results for the Gansu panel in Table 6, and the results for the Health VIII baseline survey in Table 7. Additional results for the CNHS are reported in Table 5.

We discuss first the effects of the covariates. In all models, (increasingly) poor health significantly raises the probability of an individual experiencing ‘high’ or ‘catastrophic’ health payments. The probability of ‘high’ out-of-pocket payments varies somewhat with per capita
# **Religious Terrorism, Forced Migration, and Women’s Empowerment: Evidence from the Boko Haram Insurgency**<sup>∗</sup>

Paola Elice<sup>1</sup> , Fernanda Martínez Flores<sup>2</sup> , and Arndt R. Reichert<sup>3</sup>

> 3 _Leibniz University of Hannover, The World Bank, RWI_

_JEL Codes:_ D74, J22, O12, O15, R23 _Keywords:_ Boko Haram, Fulani, conflict, forced displacement, employment, fertility, social norms, gender norms ∗This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership.” The program is funded by the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO) and is managed by the World Bank Group. The program was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The aim of the program is to expand global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. The work was initiated by the authors while Arndt Reichert was an economist at the Development Impact Evaluation Department of the World Bank. This work does not necessarily reflect the views of FCDO, the World Bank Group, or UNHCR. We thank Julia Bredtmann, Georgia Christina Kosmidou Bradley, Catherine Marie Rose Defontaine, Tilman Brück, and Barbora Sedova for their comments. We also thank Henry Kwenin and David Musombi for guidance on the use of International Organization for Migration data. We also thank the participants of the 2022 German Development Economics Conference, internal seminars of the RWI - Leibniz Institute for Economic Research, the University of Duisburg-Essen research seminar, the Leibniz University Hannover Brown Bag seminar, and the International Security and Development Center’s New Methods for Impact Evaluations in Conflict and Humanitarian Emergency Settings workshop for helpful comments. Lesman Ghazaryan and Duaa Kattana provided excellent research assistance. Computational reproducibility was verified by Development Impact Evaluation Analytics.
, as the citizenry can exert some pressure on the executive, thus enhancing public accountability and ultimately achieving better quality services. This has been possible in a context less differences in the levels of opting out for private schools between the poorer and the middle class, compared to the wider gaps observed in the adoption of private solutions in the electricity sector and the health sector.

One of the main caveats of this paper is that we have not been able to test whether there are differences in civil participation and political activism across socioeconomic classes or income quintiles, due to lack of reliable income variables in opinion surveys. Still, we believe that the discussion embedded in this country case may be an interesting starting point for a

[30]
very much a work in progress<sup>5</sup> ). On the other hand, of the seven cases with some measurable policy impact, three followed losses in court: in these cases, the political authorities acceded to the petitioners‘ requests (at least in part) despite losing in court.

By far the most consequential litigation stream was a series of three cases involving judicial review of government funding for K-12 education, which contributed to a significant increase in funding for education in Indonesia. In the _Judicial Review of the 2005 State Budget Law_ , and in two subsequent challenges on the same grounds, the Constitutional Court ordered the government to comply with a constitutional requirement that specified that the government devote 20% of its expenditures to education. These rulings contributed to an increase in education‘s share of the budget from 7% to nearly 12% in the next few years (and eventually 20%, once the definition of the numerator changed). Gauri and Brinks estimated, conservatively, that at least 750,000 students received significantly better schooling as a result of more financing (out of some 50 million students enrolled in primary and secondary education).

In Indonesia, the poorest are underrepresented in public primary education because middle class families commonly use public schools while many of the poorest families are not enrolled at all. Still, these middle class families are not rich by global standards: World Bank figures show that approximately _half_ of Indonesians consumed less than US$2/day in 2007.<sup>6</sup> Adding money to the public school budget might draw more unenrolled, lower income families into the system. Still, we estimate the beneficiary mix as if the additional money did not affect the income distribution among public school students. Using data from the government and the World Bank, we estimate that 36.3% of these students were from the lowest two income

> 5 See for instance http://www.ilo.org/public/english/region
overall debt maturities increased or decreased for emerging market firms since the GFC would require data on bank loan maturities, which are not typically available across economies.

> 5 The COVID-19 pandemic could have exacerbated problems with debt rollovers. Aggregate estimates for all emerging economies (excluding China) show that, assuming these economies lost market access in 2020, they might not be able to rollover as much as $1.6 trillion of their external public and private debt maturing in 2020. This value represents 28 percent of the stock of external debt in emerging economies (excluding China) in 2018 (Bolton et al., 2020).

18