rafmacalaba/data-use-mentions-extended huggingface dataset:
Expectations concerning place-based climate risks influences economic geographic outcomes. For example, in the United States the risk of floods negatively affects firm entry, employment and output, and is associated with a reduction in aggregate output, of which only 20% is attributed to direct damages while the remaining 80% stems from expectation effects (Jia et al., 2022). In a recent study, Cevik and Miryugin (2022) document the impact of climate change vulnerability on corporate performance using a large panel dataset of more than 3.3 million non-financial firms from 24 developing countries over the period 1997–2019. Their results suggest that firms operating in countries with greater exposure to climate risks have difficulty in accessing debt financing even at higher interest rates, while being less productive and profitable relative to firms in countries with lower vulnerability. These effects are significantly greater for smaller firms, especially in high-risk sectors and countries, and those with weaker capacity to adapt to and mitigate the consequences of climate change. Their results are in line with previous findings suggesting the positive relationship between climate risks and cost of debt, which can result in a decline in firms’ leverage (Ginglinger and Moreau, 2019).

Such exposure to climate risks are detrimental to the firm’s financial position. For instance, while actual climate changes negatively affects firms’ stock returns, earnings and equity valuations (Venturini, 2022; Bansal et al., 2016; Addoum et al., 2021; Hugon and Law, 2019; Pankratz et al., 2019), the risks of such events may lead to a reassessment of the value of a large range of firms’ assets (plants, property, and equipment) and to increased operating costs, such as relocation costs and insurance costs, resulting in lower profits and reduced repayment capacity. Greater physical climate risk led to lower leverage in the post Paris Agreement period, owing to both demand and supply side effects. On the demand side, firm’s optimal leverage reduced due to the larger expected distress
One goal of SP is to assure some minimum standard of living in the society, as typically indicated by a poverty line. Naturally, national SP efforts depend on prevailing national poverty lines rather than international absolute lines, which aim to have common real value across countries (such as the World Bank’s $1.90 a day line). This motivates our next hypothesis:

**_Distribution 3: The relative poverty hypothesis._** _Higher national poverty lines in rich countries imply that more public SP spending is needed to reduce poverty._ In assessing the _a priori_ case for this hypothesis it must first be recognized that there are two, potentially offsetting, ways in which a higher average income will alter the cost of reducing poverty using SP spending. Poverty lines are typically relative in rich countries, meaning that they rise in proportion to the mean or median income; low- and middle-income countries, by contrast, tend to have absolute or only weakly relative poverty lines, meaning that they have an elasticity to the mean that is less than unity (Ravallion et al. 2009). On the other hand, the national poverty gap index (PG) using weakly relative poverty measures is likely to be a decreasing function of mean income.<sup>14</sup> It is then an empirical question whether the aggregate poverty gap per capita (PG times the national poverty line) is increasing or decreasing with mean income. If the (positive) elasticity of the national poverty line to mean income exceeds (in absolute value) the negative elasticity of the national poverty gap index with respect to mean income, then the cost of targeted efforts in using SP to reduce poverty will tend to be higher in rich countries.

To test this we can draw on estimates from the literature of these two elasticities. <u>Ravallion (2016b, Appendix) estimates the cross-country elasticity of the national poverty line to</u> the mean to be 0.52 (robust s.e.
Policy Research Working Paper 5546

# **Abstract**

This paper proposes a new framework to analyze aid effectiveness. Using World Bank firm survey data and OECD aid flow data, the authors analyze whether aid targets areas that firms in developing countries have identified as obstacles for their growth and whether aid actually improves firms’ perceptions of those areas. The analysis finds that aid does target the areas that firms have identified as obstacles; aid funding trade related projects is particularly effective in targeting the correct countries. For the most part, aid has a positive impact on improving firms’ perceptions, particularly in the business environment. And for each target area, smaller aid disbursements tend to be more effective at improving firm perceptions than larger disbursements.

This paper is a product of the Trade and Integration Team, Development Research Group. It is related to the Global Trade and Financial Architecture Project, under the Transparency and Competitiveness Trust Fund. 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 author may be contacted at jswilson @worldbank.org and eferro@brandeis.edu.

_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
System" (draft report prepared for Food Security Research Project in Egypt, Cairo, January 1997, p. 28).

18 Ali and Adtams, (note (3) above).

19The 1990/91 HI1ES Survey included 14,232 households; 8,352 urban and 5,880 rural households. 20 For more details on the 1997 IFPRI Egypt Integrated Household Survey, see Guarav Datt, Dean Joliffe and Manohar Sharma, "A Profile of Poverty in Egypt: 1997" (final draft report, International Food Policy Research Institute, Washington, DC, March 31, 1998).

21 In this paper, expenditures are used as a proxy for income. For this reason, the terms "expenditures"
**Table 1:** Summary Statistics of selected variables in ERIVaS sample

|Variable|Mean|Std.Dev|Min|Median|Max|Obs.|
|---|---|---|---|---|---|---|
|**Plot Characteristics**|||||||
|Slope (percent)|1.31|0.70|1.00|1.00|8.00|577|
|Elevation (m)|335.31|22.21|240.00|333.00|487.00|577|
|Potential Wetness Index|16.24|4.95|12.00|15.00|36.00|577|
|Plot has erosion problems<sup>_†_</sup><br>|0.20|0.40|0.00|0.00|1.00|577|
|Euclidean distance to dwelling (km)|2.11|2.01|0.00|1.66|18.39|577|
|Self-reported distance to market (km)<br>|7.55|5.40|0.00|7.00|30.00|577|
|HH owns the plot<sup>_†_</sup>|0.95|0.21|0.00|1.00|1.00|577|
|**Plot Manager Characteristics**|||||||
|Plot manager is female|0.01|0.08|0.00|0.00|1.00|577|
|Age of plot manager|52.81|14.24|15.00|53.00|97.00|577|
|Plot manager is literate|0.10|
process showing the highest engagement with civil society, women, and youth;<sup>99</sup> context indicators remained relatively poor (without change) in Mahalabiya,<sup>100</sup> and context indicators showed improvements in Yathrib<sup>101</sup> and Tal Afar.<sup>102</sup> While there are a myriad of reason why people may not report instances of violence or threats (including fear, or perceived pressure to give socially-desirable answers), and it is not possible to isolate the LPA as a source of contextual change, the available data does not suggest that the inclusion of diverse actors led to higher rates of, or safer returns.

An important contextual factor that may explain the limited influence of civil actors over the LPA process, as well as low value placed on their participation by government and tribal actors involved in the LPA negotiations, was that most LPAs were signed in an environment characterized by insecurity, a fractured security sector, and instances of revenge attacks and ongoing military operations in some areas. In this context, opportunity for community members to influence issues related to security was highly constrained, particularly high-profile issues like the return of persons with perceived ISIL affiliation. Rather, these issues are typically dealt with by a collection of security actors who often compete for dominance, alongside government officials and tribal leaders.<sup>103</sup> However, LPAs that engaged civil society and women appeared more likely to support the sustainability of IDP return, and the long-term success of the LPA, in three ways. First, the return of families suspected of ISIL affiliation took place gradually; some families with ‘lower’ levels of perceived affiliation who were less fearful of revenge attacks returned even while negotiations were ongoing, encouraged by the fact that the peace process had been initiated.<sup>104</sup> This gradual approach helped to build trust in the return process, both at the community level (that the return of
Attitudes and Policies toward Refugees: Evidence from Low- and Middle-Income Countries Cevat Giray Aksoy<sup>∗</sup> Thomas Ginn<sup>†</sup>

**Keywords: refugees, integration, social cohesion, refugee policies.**<sup>1</sup> **JEL codes: F22, J15, J24.**

> ∗European Bank for Reconstruction and Development (EBRD), King’s College London, IZA Institute of Labor Economics, ( `aksoyc@ebrd.com` )

> †Research Fellow, Center for Global Development (CGD) ( `tginn@cgdev.org` )

> 1We thank Leon Bost, Saheel Chodavadia, Franco Malpassi, Reva Resstack, and Pablo Zárate for superb research assistance. We are grateful to Christopher Blair, Guy Grossman, and Jeremy Weinstein for sharing part of the Developing World Refugee and Asylum-Seeker Policy dataset and to Edgar Scrase for sharing UNHCR’s sub-national population data. We additionally appreciate comments from four anonymous reviewers at the World Bank. This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. Financial support from the IKEA Foundation is also gratefully acknowledged. The views presented are those of the authors and not of CGD, EBRD or any other institution. All interpretations, errors, and omissions are
households to purchase more medical and nutritional inputs since health is a normal good. Secondly, the income from migration may relax liquidity constraints that prevent parents from investing in child health in the current period in order to reap the returns in the future. Moreover, the migration experience developed by parents will make it easier for their children to migrate; raising the expected adult wages of children and hence returns to investment in their health.

# **6.1 Migration and Health Knowledge**

Migration may also impact on health outcomes through non-monetary channels. One such channel is the transfer of health knowledge information. Grossman (1972) allows for education to change the efficiency of the health production process, allowing for better health production from a given input set. Glewwe (1999) studies the mechanisms through which mother’s education raises child health in Morocco and concludes that mother’s health knowledge rather than their level of schooling per se is the crucial skill for improving child health. Migrants may gain information about basic health practices while abroad and share this with family members. Such information may include better understanding of contraceptive practices, the importance of sanitation, and knowledge about diet, exercise and other lifestyle behaviors. Menjívar (2002) provides evidence that social networks among Guatemalan immigrants in the U.S. engage in regular transmission of medical knowledge, helping one another with information about treatments and health advice.

As discussed in the Appendix, we measure health knowledge by the first principal component of a set of questions asking mothers whether they can name on their own or with help ten different contraceptive methods. This provides a measure of health knowledge which clearly represents fertility knowledge, and may be broadly associated with more general health knowledge on the part of the mother. For example, using data from the Mexican Health and Migration Survey (HMS) taken in eleven communities in the state of San Luis Potosi, we find a strong and significant correlation between our measure of health knowledge and whether the mother has a good explanation for the causes
|67.04|(16.09, 17.44)|0.51|0.252|
|A great extent|259|16.90|67.60|(15.90, 17.90)|0.45|0.536|
|**School Enrollment (36-72 mont**|**hs) (REF= O**|**ut of school)**|||||
|Out of School|621|12.58|50.32|(11.99, 13.16)|0.30||
|Enrolled in ECE|508|19.79|79.16|(19.25, 20.33)|0.27|<0.001|
|Enrolled in Primary|193|21.85|87.40|(21.21,22.50)|0.33|<0.001|

_Note: All estimates weighted. Psychosocial stimulation ranges from 0 to 14. Parental distress sum score ranges from 0 to 5. The age parents believe children should begin school is open ended and represents child year of age. Linearized standard error is based on the weighted sum score variation._

# Risk Factor Prevalence

Risk factor prevalence is similar in households with children 0-35 months and those with children 36-72 months. The three most prevalent risk factors in all households were: experiencing some level of food insecurity, living in a rural area, and parental distress (Figures 2 & 3). Households with children aged 0-35 months, compared to 36-72 months, experienced much higher levels of parental distress (57 percent to 53 percent, respectively) and less psychosocial stimulation (12 percent compared to 3 percent experience no psychosocial stimulation, respectively). Households with children 36-72 months experienced slightly higher levels of food insecurity (61 percent to 60 percent, respectively), lived in rural areas at higher rates (66 percent to 61 percent, respectively
trade in a particular sector is based on scale economies and/or market structure.<sup>21</sup> By engaging in IIT, a country can reduce the number of similar goods it produces and benefit from scale economies. Higher IIT ratios suggest that these sources of gains are being exploited. The IIT index measures the degree of overlap between imports and exports in the same commodity category, with a value of 1 indicating pure intra-industry trade and a value of 0 indicating pure inter-industry trade.<sup>22</sup>

|**Tabl**<br>|**e 3: Trade Comp**<br>|**lementar**<br>|**ity Index **<br>|**(TCI) at 6-digit H**<br>|**S**<br>|
|---|---|---|---|---|---|
|**Reporter**<br>**(Exporter)**|**Partner**<br>**(Importer)**|**TCI**|**(%)**|**Number of Com**<br>**Prod**|**monly Traded**<br>**ucts**|
|||(2002)|(2007)|(2002)|(2007)|
|Bangladesh|India|42.0|39.3|1,057|1,752|
|India|Bangladesh|27.1|32.2|4,256|4,270|

_Source_ : Calculated based on UN COMTRADE.

|**Table **|**4: Intra-Industry Trade (IIT) Index* in 2007: Common Set of **|**Products at 6**|**-digit HS**|
the recovery to equilibrium is rapid (see review by Blattman and Miguel, 2010). Correlated with war exposure, the literature has extensively documented long-run eects of exposures to stress on mental health (Persson and Rossin-Slater, 2018), height and diseases (Bozzoli et al., 2009), birth weight (Camacho, 2008; Quintana-Domeque and Ródenas-Serrano, 2017), and education and socioeconomic status (Almond, 2006). Previous research on conict has mostly focused on human capital accumulation during or shortly after conict (Akresh and De Walque, 2008; Bundervoet et al., 2009; Valente, 2014; Akresh et al., 2014; Pivovarova and Swee, 2015). A few recent studies have focused on the long-term impact of conict on human capital accumulation, with some nding no eect (Miguel and Roland, 2011) and others (Akresh et al., 2012; León, 2012; Justino et al., 2014; Palmer et al., 2016; Akbulut-Yuksel, 2017) nding signicant negative impacts. Domingues and Barre (2013) nds that the exposure to Mozambican civil war during childhood had adverse eect on women's adult height-for-age z-score.

> 2A common approach in the literature is to exploit conict variation at a broader regional level, which has the potential to misclassify one's exposure to conict and induce measurement errors. For instance, using detailed GPS data on distance between survey villages and conict sites, Akresh et al. (2014) show that substantial number of households in Eritrea were misclassied as being in non-conict region in the Akresh et al. (2012) paper that used less precise regional conict data and there are signicant dierences in the estimated eects of the Eritrea-Ethiopia conict between the two measures of the conict: eects are 87-188% larger using the GPS based measure than the regional based measure of the conict.

> 3Previous studies of Nepal
<sup>13</sup> The government consumption de...nitions follow Perotti (2005a). Thus, government consumption spending is de...ned as gov-

> 11See Perotti (2005a, 2007).

12See Romp and de Haan (2007) for an in-depth survey on public capital and economic growth. 13Much of the recent literature, to capture the stance of ...scal policy, have utilised government consumption spending as de...ned as current real terms spending on goods and services. This is because such a measure contains little automatic cyclical component, thus helping to identify government spending shifts over and beyond those changes caused as a natural consequence of the business cycle. Furthermore, government purchases have no direct link to productivity, thus, limiting the number of channels through which government spending in‡uences the real economy. This measure of ...scal policy is also less likely to be in‡uenced by the automatic stabilisers channels, which operate primarily through tax revenue and transfer payments. Moreover, in economic downturns, it is politically easier to cut capital expenditure (public investment) than government consumption of goods and services, such as health care. For these reasons, the baseline model in this paper includes government consumption spending.

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Developing countries that hope to transform their economies structurally by anchoring themselves to GVCs are often disappointed by the quality of jobs offshored. The so-called ‘smile curve’ logic asserts that developing countries are mainly being offshored low value added per worker jobs while those with high intellectual and value added content are retained in the industrialized and advanced countries.<sup>5</sup> Thus, there is a rising concern among developing nations that not only are the ‘good jobs’ retained in advanced economies while the ‘bad jobs’ are shuffled to developing countries, but also that this trend is deepening (Baldwin et al., 2014). For example, in the well documented case study for Apple iPod, of a total value of USD 150, only USD 4 can be attributed to local producers in China while the rest of the value accrues to the United States, Japan and the Republic of Korea (Dedrick et al. 2010). Recent work using the OECD Trade in Value Added (TiVA) database suggests that 67% of VA trade accrues to OECD countries while other countries contribute minimally (11% Asian newly industrialized countries such as Singapore; Hong Kong SAR, China; Taiwan, China; Korea; Malaysia; Thailand; the Philippines; 9% for China; 5% for the remaining BRICS countries and only 8% for all other developing economies covered by the database) (e.g. Banga, 2013).

Baldwin (2012) summarizes the location decision of firms in a supply chain context as a trade-off between dispersion (e.g. direct factor costs such as wages) and agglomeration forces (e.g. ‘separation’ costs such as transmission and transport costs). Clustering is a natural outcome in certain sectors and stages such as fashion clothing that involves local or technological spillovers while in others where such benefits are limited, a geographical separation of stages is observed. Dispersion is generally explained by the relatively higher wages of low-skill labor in the ‘headquarter’ economies (e
of informality. Out of 517 firms in the full sample, 163 firms or 32 percent are formal according to this definition. As shown in Table 1, it is more common for firms to register locally: out of 517 firms in the full sample, 158 firms (31%) are registered locally while only 59 (11%) are registered at the central or national level. In terms of dual registration, out of these 59 firms that have registered centrally, 90 percent (53) have also registered locally, but out of the 158 firms that have registered locally, only 34 percent (53) registered also centrally.<sup>8</sup> While most of the paper focuses on this type of informality, section 3.4 briefly discusses the intensive margin of informality, namely informality of employment.

**Table 1: Distribution of firm (in)formality variables**

|||Registere<br>|d atregistr<br>|argeneral?<br>|
|---|---|---|---|---|
|||No|Yes|Total|
|Registered|No|353|6|359|
|locally?|Yes|105|53|158|
||Total|458|59|517|

Note: Unweighted data. The questions are: “Is your business registered with any local authority / municipality?” and “Is your business registered with the country revenue authority (or national registrar general)?”

# **_3.2 Formal versus informal firms and performance variables: Descriptive statistics_**

An initial question is whether most informal firms are associated with lower performance outcomes than formal firms. The main performance variables are labor productivity, sales, employment, and exporting. Labor productivity is measured as value added (total sales minus raw materials & intermediate inputs plus water & electricity used in production) divided by the total number of full-time working people including owners.
any of these events in the last year.<sup>9</sup>

> 7 On issues of measurement of intra-family violence and perception of security, see World Bank, 2008

> 8 D‘Costa, 2004

> 9 Sonalde Desai (personal communication, 2008) based on NCAER-University of Maryland Human Development Survey 2005.  Note that in the India survey the questions were regarding actual experience of respondent while in

6