rafmacalaba/data-use-mentions-extended huggingface dataset:
subsidies for energy inputs. Again the ERP with subsidies is estimated as the percent difference of the observed value added from the hypothetical: The innovation of the last equation above is that, with subsidies there are two effects: that of the tariff schedule, NTBs and output subsidies (the first term on the right hand side of the above equation) and that of the input subsidy (the second term), in the present due to energy subsidies.

As a practical matter in this present study, using information from the 2006/2007 inputoutput matrix, the ERPs with input subsidies on five energy input are estimated using adjusted, _observed_ cost shares (the agi) relative to gross revenue: The above is the “programmable” equation for practical calculations. Note that reference hypothetical value added is that of no interventions at all: the cost-share coefficients are changing due to changes in relative prices arising from both applied tariffs (and NTBs) and input subsidies. An additional note of caution is that the ERP approach does not account for possible factor substitution that could occur as the results of a different trade and subsidy regime. It is an approach based on a fixed-coefficient technology, the costs of which are adjusted by changes in tariffs of inputs and outputs. It is hard to anticipate what would be an industry’s response to new relative price situations in its choice of technology. It would be even more difficult to anticipate the final impacts of a simultaneous change in many industries (23 in the input-output matrix used below). (Some simulation approaches pretend to adjust for input substitutability via assuming predetermined values for elasticities of substitution, often the same values across heterogeneous industries.) So we interpret the results of the ERP approach as described in the above equations as first-order approximations of the impacts of interventions on the net income of individual industries and sectors.

8
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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Thanks to John Gibson, Maurice Schiff, Çaglar Özden and Alan Winters for useful comments, and to Madhur Arora, Jason Victor, and officials at the various consular departments and ministries for help in collecting the passport data.
Lall, Henderson, and Venables, 2017), and they are also far from where the poor live.

At a minimum, the findings indicate that the role of distance in migration decisions, highlighted in the earlier work on migration, continues to be important empirically, especially in more remote areas, and that it may have been neglected prematurely more generally. The paper did not try to uncover why distance plays such an important role. The aforementioned in-depth life histories with 75 respondents from the KHDS survey (Ingelaere et al. 2018) point among other things to transport costs and the liquidity constraints to overcome this, the socio-cultural affinity with the destination, and the ability to maintain ties with the home community and return home when things go wrong (safety net). Networks can reduce the deterrence of distance (especially over long distances and including deterrence related to socio-cultural affinity) (Lucas, 2001; 2015). It explains their importance in determining 17
Tumurchudur, and Shirotori (2009) therefore compute revealed factor intensities, using methodology introduced by Hausmann, Hwang, and Rodrik (2007). Building upon the concept of revealed comparative advantage developed by Balassa (1965), the idea is that a product exported mostly by countries well-endowed with a certain production factor is “revealed” to be intensive in that factor. Online Appendix A3 provides more detail.

# **3.3 Other variables**

We use export data from the UN Comtrade database. The data is reported at the level of 4 or 5 digits of the SITC product classification and comprises 1,299 different product categories. We construct a survival database focusing on the information about the starting and ending year of a trade relationship. To correct for left-censored observations (i.e., export spells that are already in place at the beginning of the sample) and to match the survival database with the period covering the stock market liberalization events, we drop all export spells that started before 1980 and after 1997. We also construct several spell characteristics using the original export data: the number of suppliers (i.e., exporting countries) of a given product to the world market (measured at the producttime level), initial export value of a given product (country-product-time level), total value of country’s exports to the rest of the world (country-time level), and number of export spells during the sample period for a given country-product pair (country-product level).

The physical and human capital intensities at the level of ISIC 3-digits industrial sectors are taken from Braun (2003). The data identifying products as homogeneous, differentiated or price-referenced in trade publications (an intermediate category) is from Rauch (1999). The annual data for GDP per capita in the purchasing power parity terms (GDP pc in PPP terms) is taken from the World Development Indicator Report 2006, and is reported in constant 2000 US dollars. The data for real exchange rate is also taken from the World
Conflicts and Negotiating with Others|
|Non-routine<br>interpersonal||Performing for or Working Directly with the Public<br>Coordinating the Work and Activities of Others<br>Developing and Building Teams<br>Training and Teaching Others<br>Guiding, Directing, and Motivating Subordinates<br>Coaching and Developing Others<br>Provide Consultation and Advice to Others<br>Performing Administrative Activities<br>|
|||Staffing Organizational Units|
|Non-routine<br>manual|Operate with heavy machines<br>Drive a car, truck or three wheeler<br>Repair electronic equipment|<br>Inspecting Equipment, Structures, or Material<br>Operating Vehicles, Mechanized Devices, or Equipment<br>Repairing and Maintaining Mechanical Equipment|
||Physically demanding work|Repairing and Maintaining Electronic Equipment|
|Routine<br>cognitive|Lack of freedom to decide in which way to work|Documenting/Recording Information|
||Frequency of repetitive tasks|Performing General Physical Activities|
|Routine<br>manual||Handling and Moving Objects<br>Controlling Machines and Processes<br>Monitor Processes, Materials, or Surroundings<br>Monitoring and Controlling Resources|

Source: Authors’ elaboration based on Philippines STEP survey 2015-2016 and O*Net database.

57
or any refusal to answer are coded as missing values. To capture the overall appraisal of each respondent, it is also constructed a composite indicator of public services delivery by aggregating each respondent’s appraisal of all individual public services through polychoric correlation structure of the categorical responses (Lee et al., 1995; Holgado–Tello et al., 2008). The composite indicator is thus a reflection of how each respondent evaluates the range of public services provided by the state.

Table 1 shows the distribution of the respondents for each of these policy matters. It can be observed that less than one-third of the interviewees agreed with the statement that the government is handling very well or fairly well the living standards of the poor and creating jobs, although more than half appear to approve their respective government’s performance in improving essential health services (57.92%) and addressing educational needs (60.15%) across the whole sample (four rounds of the Afrobarometer survey).

Self-reported optimism is derived from the survey question: “Looking ahead, do you expect the following to be better or worse: Your living conditions in twelve months’ time?”. The responses range from _‘much worse’_ to _‘much better’._ We create a binary variable that equals 1 if the reply of the respondent is _‘better’_ or _‘much better’_ and 0 otherwise. As this question is not included in Round 6 of the Afrobarometer survey, the corresponding estimations thus incorporate data from Round 3 to Round 5. Table 1 shows that approximately 79% of the survey respondents expect an improvement in their living conditions in the twelve months following the survey data collection.

**Table 1: Distribution of respondents in the category fairly well and well**

||_Percentage_|_Observations_|
|---|---|---|
|_Creating jobs_|26.90|130,277|
|_Improving the living standards of the poor_|
Evidence from IDPs in Iraq suggests a causal relationship between forced displacement and poverty (Noumedem Temgoua et al., 2020). Forced displacement has also been negatively associated with consumption levels (Fiala, 2015; Ib ́a ̃nez and V ́elez, 2007), wages and employment (Schuettler and Caron, 2020), and food security (Pape and Sharma, 2019). Where conditions are favorable, those forcibly displaced can close at least part of this gap over time, as evidence on labor market outcomes from high-income countries shows (Schuettler and Caron, 2020).

# **5.2.2 Health and education for the next generation**

Various studies investigate long-term oucomes in terms of health and education for the second generation, which comprises either those who arrived at a young age as refugees or IDPs or were born to forcibly displaced parents. When examining these outcomes, studies need to take into account not only selection effects and non-random settlement, but also non-random onward and return migration. Refugee and IDP children benefit if they find themselves in a destination with access to better healthcare and other health enhancing factors. A study on the airlift of Ethiopian Jews to Israel shows that children who benefitted from earlier access to better prenatal health care in Israel had better educational and labor market outcomes later in life (Lavy et al., 2021). Poor mental health of primary caregivers due to displacement can have negative impacts on their children. A study sampling Syrian refugees in Jordan, for instance, found that more than half were likely to be depressed, and the mental health of refugee mothers was highly correlated with that of their children (Miguel et al., 2022). A randomized control trial among Rohingya mothers and their children in Bangladesh refugee camps showed that a multifaceted psycho-social intervention not only had positive impacts on the mental health of mothers but also on their children and improved child development outcomes more generally (Hussam et al., 2021).<sup>7</sup> An
over having two sons (Jayachandran (2017)). As desired and actual fertility in India are well above one, it is reasonable to assume that parents are not averse to having one girl, despite a strong desire for at least one boy. A formal test of orthogonality, where we regress expected dowry on the FG dummy and several other controls, yields an insignificant and extremely small coefficient on the FG indicator (p-value = 0.39).

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might be frozen in the future, the assignment to “Training and grant” and “Training, but no grant” was partly random. However, participants who tried to access the grant right after the training had a higher chance of receiving it than those who waited, so individual characteristics of the participants might have created some self‐selection into these two groups.

To assess the degree of endogenous selection into the “training but no grant” and “training and grant” groups, we examine the balance on covariates between these two groups using the baseline data. We find that older, married participants with larger families were more likely to access the grants (see Table 3). In addition, participants who received the grant were more likely to already own a business and hold a bank account, reported higher consumption levels and reached higher ranks in our literacy

> 8 A further balance test between attritors and non‐attritors from the overall study showed some systematic differences (Appendix table A1.2): at the 1 percent significance level attritors were less likely to hold a formal bank account at baseline and had a lower educational level, and at the 5 percent significance level attritors had a lower food consumption, less formal and more informal debt, and came from smaller households with fewer children. We control for these differences as additional covariates in our regressions.

> 9 Attending the training program was a pre‐requisite to accessing the grant. Part of the training included financial literacy around opening and using the bank accounts, so only those participating had bank accounts opened for them.

10
- 14 -

In our context, whether an individual had a fever may be particularly dependent on household sanitary conditions and hygiene practices, including availability of piped water and flush toilets and application of mosquito nets. These are included in _W_ in the equation. The transport connectivity variables _Z_ can also potentially be included. However, the ex post test statistics indicate that they are not statistically relevant to this sample selection. That is, whether people have a fever or not does not depend on transport connectivity. See more details in the following section.

The second empirical challenge is that there might be unobserved household or individual characteristics, which may lead to omitted variable bias. For example, those who live in warm and humid areas may be more exposed to higher risk of mosquito-borne diseases, such as malaria, than those who live in dry and cool areas. Recall that our data are two crosssectional data sets. Thus, it is impossible to control for these unobservables at the individual or household level. To address this problem, the district fixed effects are used, taking advantage of available spatial information of households in the DHS data. This may not be able to remove unobservables completely but can control for unobserved characteristics to a large extent. The number of district dummy variables that can be included is about 120, depending on specification. Statistically, the hypothesis that there are no district fixed effects can be easily rejected, as will be seen in the following sections.
rather surprisingly suggest an increment of about 10 percentage points during the conflict period compared with the 2010 rate of 26%.<sup>36</sup> We build in this increment over 2010‐2015, applying the same enrollment rate for men and women, and keep it unchanged thereafter. Primary school duration is assumed to be 6 years (6‐11 years), secondary school, 6 years (12‐17 years), and university 4 years (18‐21 years). In Appendix 3, we provide further details on how we arrive at the average years of schooling in 2018.

By using the above methodology, we constrain changes to the national average of schooling years to arise from shifts in the distribution of the total population by age and gender, and in enrollment ratios. Because we use past composite values of average years of schooling, we do not consider changes in completion rates (that is, we are assuming completion rates are unchanged). This approach also does not consider other types of heterogeneity in educational attainment, for instance, that may depend on the socioeconomic status or geographical origination of migrants and conflict victims. Verme et al. (2016), focusing on the profile of refugees in Jordan and Lebanon, find that Syrian refugees in fact tend to have slightly lower levels of educational attainment than pre‐conflict Syrians.<sup>37</sup> To obtain human capital growth, we continue to assume the same calculations as in PWT 9 for the returns to education. Our calculations for the central projection using UN population statistics suggest that average years of schooling would have declined by 1.467 years for the population aged 15‐64 with human capital contracting by an annual average growth of ‐2.59% over 2011‐2015 and ‐ 0.56% over 2016‐2018 respectively. When we consider the additional decline of 1.8 million in the Syrian population discussed in the previous section, average years of schooling declines only marginally more, that
# **1 Introduction**

It is widely recognized that climate change will disproportionately affect poorer countries (Mendelsohn, Dinar, & Williams, 2006; Tol, 2009; Bathiany, Dakos, Scheffer, & Lenton, 2018; Dell, Jones, & Olken, 2008), and poorer individuals within countries (Skoufias, Rabassa, & Olivieri, 2011; Hertel, 2016; Dennig, Budolfson, Fleurbaey, Siebert, & Socolow, 2015). Here, we provide a new assessment of the future impacts of climate change on extreme poverty by 2030, updating previous work (Rozenberg & Hallegatte, 2015; Hallegatte & Rozenberg, 2017) using the most recent available household surveys from the World Bank’s Global Monitoring Database. This assessment is done at the household level, using a bottom-up approach to explore the compounding effects of future socioeconomic development and changing climatic and environmental conditions.

Because of the uncertainty in future socioeconomic change and future climate change impacts (see Table 1), we use an exploratory modeling approach to create tens of thousands of scenarios for the next 10 years. The main idea behind this approach is to understand the consequences of various combinations of uncertainties to the system of interest (Bankes, Walker, & Kwakkel, 2013; Kwakkel & Haasnoot, 2019). The approach is mainly used for exploration, rather than prediction. Here, we do not provide a “best-guess” for the future impacts of climate change on poverty, but instead provide a range of possible impacts, and explore the influence of various relevant drivers and uncertainties. These factors include population growth, education, structural economic shifts, food prices, and health issues, among others.

Table 1: List of uncertain factors driving socioeconomic development and poverty and considered in this analysis.

|Category|Parameter|
|---|---|
||Population growth<br>Age structure|
|Socioeconomic -<br>SSP|Average productivity growth<br>Education attainment<br>Labor participation<br
business-to-business connectivity, e-commerce and e-government systems, and other ICT services. Indeed, Senegal is well positioned to become a regional hub for 3G and 4G services.

# **4.1.EXPORT COMPETITIVENESS**

Historically, tourism and ICT have been the key services exports in Senegal. While export statistics suggest competitive performance of the ICT services sector, the tourism sector has lost competitiveness. It has been noted that Senegal was only second to Kenya on the African continent as a tourism destination during the 1980s. Currently, many countries in western and eastern Africa have surpassed the two countries in terms of contribution of tourism to exports. The challenges faced by tourism in Senegal include beach erosions, lack of product diversification, lack of effective strategy, and dependence on a narrow consumer market.

A deliberate strategy to diversify services export beyond tourism and ICT is needed. Financial services, health and education services are gaining steam in terms of contributions to GDP growth (see figure 4). Meanwhile, these three sectors contribute substantially to the low ranking of Senegal in the Global Competitiveness Report (GCR) 2014-2015. In other words, the sectors hold potential contributions to export growth and competitiveness if appropriate strategies are implemented to sustain their growth. These sectors should be listed as priority.

It is essential that Senegal seeks to expand its markets, one of the binding limitations also identified in the GCR. A narrow market limits competitiveness by limiting the possibilities for “learning-tocompete” by firms that would otherwise be essential in scenarios of enlarging markets. Although these limitations may be associated with membership in an economic and monetary union, a growing list of emerging markets around the world provide potential markets for Senegal’s services.

To begin, Senegal can capitalize on its unique position in West Africa to tap into rapidly expanding regional markets. Senegal is the institutional and financial center of WAEMU and home to several major research institutions, all of which can be leveraged to promote innovations in financial, education and health services. Regional demand for education services is increasing
Finally, we explore whether perceptions of safety and mobility could be associated with conflict. While this information was not collected in 2014, in Table 6 we report the results of a crosssectional regression analysis that considers a safety index (higher values indicate not feeling safe) and a mobility index (higher values indicate more mobility). There is a clear and significant pattern – women living in districts with medium intensity conflict report feeling more safe and more mobile compared to low intensity districts. However, this result is reversed for women living in strong intensity districts, who report feeling less safe and less mobile during the day (but not at night). These results indicate that there could be changes to the community and neighborhood due to conflict that could be indirectly associated with women’s role in decisionmaking. It could also explain why the female decision-making index is not significantly affected in strong intensity districts, since safety and mobility are diminished.

_Table 6: Mechanisms: Cross-sectional analysis from YHDS 2021_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Sa|fety|Mob|ility|
||At day|At night|Going out to<br>market at day|Going out to<br>work at day|
|Medium|-0.057**|-1.090***|0.794***|0.343***|
||(0.024)|(0.035)|(0.017)|(0.023)|
|Strong|0.718***|0.513***|-0.071***|0.036**|
||(0.015)|(0.031)|(0.013)|(0.017)|
|PSU dummies|Yes
Argentina<br>United States<br>Turkey<br>Philippines<br>Pakistan<br>New Zealand<br>Maldives<br>Malaysia<br>Ireland<br>Indonesia<br>Iceland<br>Hungary<br>Guyana<br>Guernsey<br>Gibraltar<br>Gambia<br>El Salvador<br>China<br>Cayman Islands<br>Bangladesh<br>Armenia<br>Hong Kong, China<br>Korea, Rep.<br>Jersey<br>Tanzania<br>South Africa<br>Norway<br>Malawi<br>Liechtenstein<br>Isle of Man<br>Greece<br>Botswana<br>Bosnia and Herzegovina<br>Belgium<br>Belarus<br>Austria<br>Qatar<br>Croatia<br>-4 -2 0 2 4 6 8<br><!-- End of picture text --> 6 This is based on a survey of bank borrowers on the extent to which they had to pay a bribe to get a bank loan. Since in this effort we controlled for economy-wide corruption, it is not the case that our results reflect countries stepping up supervision in response to greater corruption.